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BRANDED MARKETING EVENTS: THE INFLUENCE OF EVENT
EXPERIENCE ON CUSTOMER ENGAGEMENT
A thesis submitted in fulfilment
of the requirement for the degree of
DOCTOR OF PHILOSOPHY
By
Teagan Lynette Altschwager, B.Com. (Hons)
School of Marketing and Management
Adelaide Business School
University of Adelaide
November 2014
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ABSTRACT
This thesis investigates the role of branded marketing events (BMEs) in facilitating
customer engagement. As business environments become more dynamic and interactive,
customers are seeking participation through unique experiences with brands (Vivek,
Beatty, and Morgan 2012), and hence customer engagement has emerged as an important
concept in academe. Previous research has demonstrated that customer engagement is
highly impactful in enhancing customer-brand relationships (Brodie, Hollebeek, Jurić, and
Ilić 2011a), however, there has been little research exploring the antecedents that facilitate
customer engagement. This thesis proposes that BMEs can be used as strategic tools to
facilitate engagement with an event, with engagement transferred to the brand and
ultimately resulting in enhanced behavioural intention of loyalty.
A quantitative online survey was conducted in the South Australian wine industry to
investigate how experiential components of a BME contribute to both customer event
engagement and customer brand engagement. The impact of BME experiences on
behavioural intention of loyalty, and the moderation effect of experiential needs are
examined through structural equation modelling.
Results indicate that customer event engagement has a mediating effect on the relationship
between BME experiences and customer brand engagement. Sensorial, relational and
pragmatic experiences are found to only impact customer event engagement, while
cognitive experience has a direct impact on customer brand engagement. This highlights
that the heightened state of engagement can transfer between focal objects; from the event
to the brand. This provides further insight into the BME’s impact on customer brand
engagement and behavioural intention of loyalty. In addition, support is found for the
inclusion of a social dimension of customer brand engagement, and partial support for a
social dimension of customer event engagement. Therefore, results of this thesis suggest
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that BME experiences facilitate customer engagement and subsequent behavioural
intention of loyalty.
The moderating influences of the individual’s experiential needs, namely need for
cognition, need for affect, and novelty-seeking needs are also examined. There is evidence
that attendees with a strong need for cognition engage more strongly with relational BME
experiences, while attendees with low need for cognition engage more strongly with
sensorial BME experiences. However, few moderating effects are identified overall.
This research empirically demonstrates the strong and positive relationship between BMEs
and customer engagement, and advocates the use of BMEs as an effective brand-building
activity. This thesis contributes to the knowledge of customer engagement through
identifying engagement transfer between two focal engagement objects, and provides
support for the inclusion of a social engagement dimension. The findings provide support
for the BME activities that managers undertake with the intention of facilitating customer
engagement and providing brand-related outcomes through such endeavours.
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TABLE OF CONTENTS
ABSTRACT ............................................................................................................................ I
TABLE OF CONTENTS ..................................................................................................... III
LIST OF FIGURES ........................................................................................................... VII
LIST OF TABLES ............................................................................................................ VIII
DECLARATION .................................................................................................................. X
PUBLICATIONS ................................................................................................................. XI
ACKNOWLEDGMENTS ................................................................................................. XII
KEY TERMS, DEFINITIONS AND ABBREVIATIONS .............................................. XIV
CHAPTER 1: INTRODUCTION .................................................................................... 1
1.1 Background to the Research ........................................................................................ 1
1.2 Research Problem and Propositions ............................................................................ 2
1.3 Justification for the Research ....................................................................................... 3
1.4 Research Context ......................................................................................................... 5
1.5 Research Method ......................................................................................................... 6
1.6 Delimitation and Scope of the Thesis .......................................................................... 7
1.7 Outline of the Thesis .................................................................................................... 9
1.8 Chapter 1 Summary ................................................................................................... 10
CHAPTER 2: LITERATURE REVIEW ....................................................................... 11
2.1 Chapter 2 Introduction ............................................................................................... 11
2.2 Customer Engagement ............................................................................................... 14
2.2.1 Theoretical Foundations of Customer Engagement ............................................ 14
2.2.2 Customer Engagement Conceptualisation .......................................................... 15
2.2.2.1 Different Perspectives of Customer Engagement ........................................ 18
2.2.2.2 Definition of Customer Engagement ........................................................... 19
2.2.2.3 Dimensions of Customer Engagement ........................................................ 20
2.2.2.4 What Customer Engagement is not: Related Concepts ............................... 22
2.2.2.5 Antecedents and Outcomes of Customer Engagement ................................ 26
2.3 Branded Marketing Events ........................................................................................ 28
2.3.1 Marketing Events ................................................................................................ 28
2.3.1.1 Defining Marketing Events .......................................................................... 30
2.3.2 Conceptualisation of Marketing Events .............................................................. 32
2.3.2.1 Branded Marketing Events - a Definition .................................................... 33
2.3.2.2 Investigating a Broader Conceptualisation of BMEs: Customer Experience ................................................................................................................................. 35
2.3.3 Customer Experience .......................................................................................... 36
2.3.4 Components of Experience within a BME ......................................................... 37
2.3.5 Outcomes of BMEs experiences ......................................................................... 39
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2.4 Customer Engagement and BMEs: A Conceptual Framework ................................. 41
2.4.1.1 The role of Social Engagement within Customer Engagement ................... 44
2.4.2 Relationships between Experiential Components of a BME and Customer Engagement ................................................................................................................. 47
2.4.2.1 Cognitive Experience ................................................................................... 50
2.4.2.2 Emotional Experience .................................................................................. 52
2.4.2.3 Sensorial Experience .................................................................................... 53
2.4.2.4 Pragmatic Experience .................................................................................. 55
2.4.2.5 Relational Experience .................................................................................. 56
2.4.3 The Interplay between Customer Engagement Objects ...................................... 59
2.4.3.1 Customer Engagement to Behavioural Intention of Loyalty ....................... 62
2.5 Experiential Needs ..................................................................................................... 66
2.5.1 Conceptualising Experiential Needs ................................................................... 66
2.6 Hypotheses Summary ................................................................................................ 74
2.7 Conceptual Framework .............................................................................................. 76
2.8 Chapter 2 Summary ................................................................................................... 78
CHAPTER 3: RESEARCH METHOD ......................................................................... 79
3.1 Chapter 3 Introduction ............................................................................................... 79
3.2 Research Design ........................................................................................................ 80
3.3 Unit of analysis .......................................................................................................... 81
3.4 Data Collection Method ............................................................................................. 82
3.4.1 Measurement Instrument .................................................................................... 82
3.4.2 Operationalistion of the Theoretical Constructs ................................................. 83
3.4.3 Measurement Scales ........................................................................................... 84
3.4.4 Questionnaire Design .......................................................................................... 92
3.4.4.1 Scaling ......................................................................................................... 93
3.4.4.2 Questionnaire Content ................................................................................. 94
3.4.4.3 Questionnaire Structure and Sequencing ..................................................... 94
3.4.5 Ethics and Information Confidentiality .............................................................. 97
3.4.6 Data Coding and Editing ..................................................................................... 97
3.5 Pre-Test: University of Adelaide Orientation Week .................................................. 98
3.5.1 Overview ............................................................................................................. 98
3.5.2 Subjects ............................................................................................................... 98
3.5.3 Sample and Respondent Profile .......................................................................... 99
3.5.4 Data Collection Procedure ................................................................................ 100
3.5.5 Pre-test Data Analysis ....................................................................................... 100
3.6 Main Study: South Australian Wine Industry .......................................................... 103
3.6.1 Overview ........................................................................................................... 103
3.6.2 Subjects ............................................................................................................. 103
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3.6.3 Selection of Participating Wineries .................................................................. 103
3.6.4 Selection of Individual Respondents ................................................................ 104
3.6.5 Respondent Profiles .......................................................................................... 105
3.6.6 Data Collection Procedure ................................................................................ 106
3.7 Preliminary Analysis ................................................................................................ 108
3.7.1 Data Cleaning ................................................................................................... 108
3.7.2 Non-Response Bias ........................................................................................... 109
3.7.3 Construct Validity ............................................................................................. 111
3.7.3.1 Convergent Validity ................................................................................... 112
3.7.3.2 Discriminant Validity and Reliability Testing ........................................... 126
3.7.4 Testing for Common Method Bias ................................................................... 131
3.8 Chapter 3 Summary ................................................................................................. 132
CHAPTER 4: RESULTS ............................................................................................. 133
4.1 Chapter 4 Introduction ............................................................................................. 133
4.2 Social Engagement as an Independent Engagement Dimension ............................. 134
4.2.1 Convergent Validity of Social Engagement Dimensions ................................. 134
4.2.2 Discriminant Validity and Reliability Testing of Social Event Engagement ... 137
4.2.3 Discriminant Validity and Reliability Testing of Social Brand Engagement ... 140
4.2.4 Discriminant Validity of Social Constructs ...................................................... 143
4.2.5 Structural Model of Customer Event Engagement ........................................... 146
4.2.6 Structural Model of Customer Brand Engagement ........................................... 149
4.2.7 Discussion of Hypothesis 1............................................................................... 151
4.3 Path Model Analysis using Structural Equation Modelling .................................... 153
4.3.1 Path Model Analysis ......................................................................................... 153
4.3.2 Calculation of Composite Variables ................................................................. 154
4.4 Evaluating Path Models ........................................................................................... 158
4.4.1 Model Specification .......................................................................................... 158
4.4.2 Model Identification ......................................................................................... 158
4.4.3 Model Estimation .............................................................................................. 159
4.4.4 Model Re-specification ..................................................................................... 161
4.4.5 Discussion of Hypothesis 2............................................................................... 166
4.4.6 Discussion of Hypothesis 3............................................................................... 172
4.4.7 Discussion of Hypothesis 4............................................................................... 173
4.5 The Moderation Effect of Experiential Needs ......................................................... 176
4.5.1 Method for Multi-group Analysis ..................................................................... 176
4.5.2 Need for Cognition ........................................................................................... 178
4.5.3 Need for Affect ................................................................................................. 180
4.5.4 Novelty-Seeking Needs .................................................................................... 183
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4.5.5 Discussion of Hypothesis 5............................................................................... 186
4.6 Chapter 4 Summary ................................................................................................. 189
CHAPTER 5: DISCUSSION AND CONCLUSION .................................................. 191
5.1 Chapter 5 Introduction ............................................................................................. 191
5.2 Summary of Findings ............................................................................................... 192
5.2.1 The Role of Social Engagement ....................................................................... 192
5.2.2 Experiential Components of A BME that Facilitate Customer Engagement ... 193
5.2.3 Engagement Transfer from Event to Brand ...................................................... 195
5.2.4 The Impact of Customer Engagement on Behavioural Intention of Loyalty ... 196
5.2.5 How the Individual’s Experiential Needs Moderate Event Engagement ......... 197
5.2.6 Updated Study Framework ............................................................................... 198
5.3 Contributions to the Academic Discipline ............................................................... 199
5.4 Managerial Implications .......................................................................................... 203
5.5 Limitations of the Research ..................................................................................... 205
5.6 Directions for Future Research ................................................................................ 207
5.7 Concluding Thoughts ............................................................................................... 210
APPENDICES ................................................................................................................... 212
REFERENCES .................................................................................................................. 220
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LIST OF FIGURES
Figure 1-1: Theoretical Framework ....................................................................................... 5 Figure 2-1: Conceptual Framework ..................................................................................... 76 Figure 3-1: Measurement Model - Cognitive Experience ................................................. 114 Figure 3-2: Measurement Model - Emotional Experience ................................................ 115 Figure 3-3: Measurement Model - Sensorial Experience .................................................. 116 Figure 3-4: Measurement Model - Pragmatic Experience ................................................. 116 Figure 3-5: Measurement Model - Relational Experience ................................................. 117 Figure 3-6: Measurement Model - Event Attention ........................................................... 117 Figure 3-7: Measurement Model - Brand Attention .......................................................... 118 Figure 3-8: Measurement Model - Event Identification .................................................... 119 Figure 3-9: Measurement Model - Brand Identification .................................................... 120 Figure 3-10: Measurement Model - Event Enthusiasm ..................................................... 120 Figure 3-11: Measurement Model - Brand Enthusiasm ..................................................... 121 Figure 3-12: Measurement Model - Event Absorption ...................................................... 121 Figure 3-13: Measurement Model - Brand Absorption ..................................................... 122 Figure 3-14: Measurement Model - Event Interaction ...................................................... 122 Figure 3-15: Measurement Model - Brand Interaction ...................................................... 123 Figure 3-16: Measurement Model - Behavioural Intention of Loyalty ............................. 123 Figure 3-17: Measurement Model - Novelty-Seeking ....................................................... 124 Figure 3-18: Measurement Model - Need for Affect ......................................................... 124 Figure 3-19: Measurement Model - Need for Cognition ................................................... 125 Figure 4-1: Measurement Model – Social Event Engagement .......................................... 135 Figure 4-2: Measurement Model - Social Brand Engagement .......................................... 136 Figure 4-3: Measurement Model - Event Engagement ...................................................... 137 Figure 4-4: Measurement Model - Brand Engagement ..................................................... 140 Figure 4-5: Measurement Model - Social Constructs ........................................................ 144 Figure 4-6: Measurement Model - Customer Event Engagement ..................................... 147 Figure 4-7: Measurement Model - Customer Brand Engagement ..................................... 150 Figure 4-8: Identified Path Model ..................................................................................... 160 Figure 4-9: Re-Specified Path Model ................................................................................ 164 Figure 4-10: Path for Low Need for Cognition ................................................................. 178 Figure 4-11: Path Model for High Need for Cognition ..................................................... 179 Figure 4-12: Path Model for Low Need for Affect ............................................................ 181 Figure 4-13: Path Model for High Need for Affect ........................................................... 181 Figure 4-14: Path Model for Low Novelty-Seeking Needs ............................................... 183 Figure 4-15: Path Model for High Novelty-Seeking Needs .............................................. 184 Figure 5-1: Updated Study Framework ............................................................................. 199
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LIST OF TABLES
Table 2-1: Overview of Customer Engagement Literature ................................................. 16 Table 2-2 Antecedents and Outcomes of Customer Engagement ....................................... 27 Table 2-3: Key Features of Marketing Events ..................................................................... 28 Table 2-4: Summary of Marketing Event Definitions ........................................................ 30 Table 2-5: Experiential Components within a BME ........................................................... 38 Table 2-6: Summary of Hypotheses .................................................................................... 74 Table 3-1: Measurement of Constructs ................................................................................ 90 Table 3-2: O-Week Events Profile ....................................................................................... 99 Table 3-3: Winery Participants .......................................................................................... 104 Table 3-4: Respondent Profile for Number of Responses and Age ................................... 105 Table 3-5: Summary of Data Collection Procedures ......................................................... 107 Table 3-6: Testing for Non-Response Bias ........................................................................ 110 Table 3-7: Summary of Indices Used to Assess Model Fit ............................................... 113 Table 3-8: Goodness of Fit Indices for Cognitive Experience .......................................... 114 Table 3-9: Goodness of Fit Indices for Emotional Experience ......................................... 115 Table 3-10: Goodness of Fit Indices for Sensorial Experience ......................................... 116 Table 3-11: Goodness of Fit Indices for Pragmatic Experience ........................................ 116 Table 3-12: Goodness of Fit Indices for Relational Experience ........................................ 117 Table 3-13: Goodness of Fit Indices for Event Attention .................................................. 117 Table 3-14: Goodness of Fit Indices for Brand Attention ................................................. 118 Table 3-15: Goodness of Fit Indices for Event Identification ........................................... 119 Table 3-16: Goodness of Fit Indices for Brand Identification ........................................... 120 Table 3-17: Goodness of Fit Indices for Event Enthusiasm .............................................. 120 Table 3-18: Goodness of Fit Indices for Brand Enthusiasm .............................................. 121 Table 3-19: Goodness of Fit Indices for Event Absorption ............................................... 121 Table 3-20: Goodness of Fit Indices for Brand Absorption .............................................. 122 Table 3-21: Goodness of Fit Indices for Event Interaction ............................................... 122 Table 3-22: Goodness of Fit Indices for Brand Interaction ............................................... 123 Table 3-23: Goodness of Fit Indices for Behavioural Intention of Loyalty ...................... 123 Table 3-24: Goodness of Fit Indices for Novelty-Seeking ................................................ 124 Table 3-25: Goodness of Fit Indices for Need for Affect .................................................. 124 Table 3-26: Goodness of Fit Indices for Need for Cognition ............................................ 125 Table 3-27: Reliability and Validity Indices ...................................................................... 127 Table 3-28: Reliability and Validity of Measurement Model ............................................ 129 Table 3-29: Common Method Bias - Goodness of Fit Indices .......................................... 131 Table 4-1: Social Engagement Items Included and Excluded From Model ...................... 134 Table 4-2: Goodness of Fit Indices - Social Event Engagement ....................................... 135 Table 4-3: Goodness of Fit Indices - Social Brand Engagement ....................................... 136
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Table 4-4: Goodness of Fit Indices - Event Engagement .................................................. 137 Table 4-5: Reliability and Validity – Event Engagement .................................................. 139 Table 4-6: Goodness of Fit Indices - Brand Engagement .................................................. 141 Table 4-7: Reliability and Validity – Brand Engagement ................................................. 142 Table 4-8: Goodness of Fit Indices - Social Constructs .................................................... 144 Table 4-9: Reliability and Validity – Social Constructs .................................................... 145 Table 4-10: Goodness of Fit Indices – Event Engagement ............................................... 148 Table 4-11: Goodness of Fit Indices – Brand Engagement ............................................... 150 Table 4-12: Summary of Hypothesis 1 .............................................................................. 151 Table 4-13: Factor Loadings and Error Variances for Composite Variables .................... 157 Table 4-14: Goodness of Fit Indices for Identified Path Model ........................................ 160 Table 4-15: Regression Weights – Original Path Model ................................................... 161 Table 4-16: Goodness of Fit Indices for Re-Specified Path Model ................................... 164 Table 4-17: Regression Weights: - Re-Specified Path Model ........................................... 165 Table 4-18: Summary of Hypothesis 2 .............................................................................. 166 Table 4-19: Summary of Hypothesis 3 .............................................................................. 172 Table 4-20: Summary of Hypothesis 4 .............................................................................. 173 Table 4-21: Experiential Needs Groups - Value Classification ......................................... 177 Table 4-22: Nested Model Comparisons and Goodness of Fit Indices - Need For Cognition ...... 179 Table 4-23: Need for Cognition ......................................................................................... 180 Table 4-24: Nested Model Comparisons and Goodness of Fit Indices - Need for Affect ........... 182 Table 4-25: Need for Affect ............................................................................................... 182 Table 4-26: Nested Model Comparisons and Goodness of Fit Indices - Novelty-Seeking Needs .... 184 Table 4-27: Novelty-Seeking Needs .................................................................................. 185 Table 4-28: Summary of Hypothesis 5 .............................................................................. 186
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DECLARATION
I certify that this work contains no material which has been accepted
for the award of any other degree or diploma in my name, in any
university or other tertiary institution and, to the best of my
knowledge and belief, contains no material previously published or
written by another person, except where due reference has been made
in the text. In addition, I certify that no part of this work will, in the
future, be used in a submission in my name, for any other degree or
diploma in any university or other tertiary institution without the prior
approval of the University of Adelaide and where applicable, any
partner institution responsible for the joint-award of this degree.
I give consent to this copy of my thesis when deposited in the
University Library, being made available for loan and photocopying,
subject to the provisions of the Copyright Act 1968.
The author acknowledges that copyright of published works
contained within this thesis resides with the copyright holder(s) of
those works.
I also give permission for the digital version of my thesis to be made
available on the web, via the University’s digital research repository,
the Library Search and also through web search engines, unless
permission has been granted by the University to restrict access for a
period of time.
Signed: ___________________________________
Date: November 28, 2014
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PUBLICATIONS
The following publications are based upon the research presented in this thesis, and may
contain results and materials presented herein.
Altschwager, T., Goodman, S., Conduit, J., Habel, C. “Branded Marketing Events: a Proposed ‘Experiential Needs’ based Conceptual Framework” (forthcoming) Event Management: an International Journal (accepted May 2014)
Altschwager, T., Conduit, J., Bouzdine-Chameeva, T., Goodman, S. “A comparison of Wine Event Experiences in France and Australia” (under review) Journal of Travel Research
Altschwager, T., Conduit, J., Goodman, S. (2014) “Dinner or Music: Which Events Enhance Customer Brand Engagement?” Australian and New Zealand Marketing Academy Conference, Brisbane Australia
Altschwager, T., Conduit, J., Bouzdine-Chameeva, T., Goodman, S. (2014) “Customer Engagement: a Comparison between Australian and French Wine Events” International Conference of the Academy of Wine Business Research, Geisenheim, Germany * Best paper award
Altschwager, T., Conduit, J., Goodman, S. (2014) “Wine events: a way to engage customers?” Wine and Viticulture Journal (forthcoming November-December issue)
Altschwager, T., Conduit, J., and Goodman, S. (2013) “Facilitating Engagement by Aligning Brand Marketing Events and Customer Experimental Needs” Australian and New Zealand Marketing Academy Conference, Auckland New Zealand
Altschwager, T., Conduit, J., Goodman, S. (2013) “Branded Marketing Events: Facilitating Customer Engagement” International Conference of the Academy of Wine Business Research, St Catharines Canada
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ACKNOWLEDGMENTS
My PhD journey would not have been possible without the support of many people.
First, I wish to thank my supervisors, Dr. Steve Goodman and Dr. Jodie Conduit for their
support throughout my PhD. Steve’s assistance with approaching industry partners,
research partners, which led to invaluable collaborations with Professor Tatiana Bouzdine-
Chameeva, was greatly appreciated.
Jodie, where do I even start! Thank you for everything. I feel truly honoured to be your
first PhD student to completion; you are an exceptional supervisor and a wonderful person.
I can never thank you enough for all of your guidance throughout this process.
The people I’ve met throughout this PhD have become my dear friends - thank you all for
being such wonderful support. Zubair Ali Shahid, Rebecca Dolan, Joanne Ho, Hande
Akman and Ervin Sim, your encouragement and friendship have meant the world to me.
Special thanks must also be given to Cibo, for being my second home.
I acknowledge the support of the University of Adelaide, which provided the scholarship
and funding that enabled me to complete my research and attend conferences. I also
acknowledge Ray Adam for his valuable help in proof-reading the abstract, introduction,
literature review and discussion chapters of this thesis.
Last, but by no means least, thank you to my beautiful family. Thank you for believing in
me, even at times when I didn’t believe in myself. To Grace, Sonya, Ruby and Arch for
being my support crew. And finally, to Michael. You are brilliant, and I could not have
done this without you.
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For Grace
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KEY TERMS, DEFINITIONS AND ABBREVIATIONS
The definitions of selected terms are listed to provide clarity and to set certain
terminologies for the context in which they were utilised in this thesis;
Associative network theory: theory that customers retain information about events in
memory; through a process of ‘spreading activation’ certain sets of event-related
information can trigger thoughts about related information, in this case linked
information about the host brand (Smith 2004).
Behavioural intention of loyalty (BIL): word-of-mouth and purchase intention (Zeithaml,
Berry, and Parasuraman 1996).
Branded marketing events (BMEs) : a brand-initiated experience that serves as a
platform for customers to interact with the brand and other actors (definition in this
thesis, page 35).
Customer engagement: “a psychological state that occurs by virtue of interactive, co-
creative customer experiences with a focal agent/object (e.g., a brand) in focal
service relationships” (Brodie et al. 2011a, pg 260). Focal objects investigated in
this thesis are; the event (referred to as customer event engagement) and the brand
(referred to as customer brand engagement).
Customer experience: a customer-centric concept, and encompasses all interactions and
experiences between a customer and brand (Gentile, Spiller, and Noci 2007),
including those outside of regular consumption activity.
Engagement dimensions: This thesis follows a five-dimensional view of customer
engagement; attention, enthusiasm, interaction, identification and absorption (So,
King, and Sparks 2012). (Definitions of each engagement dimension are provided
on pages 22 to 24).
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Event experience: The broader construct of brand experience is considered to encapsulate
event experiences (Brakus, Schmitt, and Zarantonello 2009). Brand experience
includes numerous brand-related stimuli, including branding, communications, as
well as the environments and situations in which the brand is experienced,
including events (Brakus et al. 2009). This thesis focuses on brand-related stimuli
from a BME; therefore, the researcher refers to this as event experience instead of
brand experience.
Experiential components of a BME: the components of a BME utilised in this thesis are
cognitive, emotional, sensorial, pragmatic and relational (Gentile et al. 2007)
Experiential needs: the need that an individual seeks to fulfil through experiences
Experiential needs investigated in this thesis are need for cognition, need for affect
and novelty-seeking needs (definitions of each type of experiential need are
provided on pages 69 to 72).
Optimum stimulation level (OSL) theory: theory that individuals seek out stimulation
from particular environments in order to achieve satisfaction (Steenkamp and
Baumgartner 1992).
Service dominant (S-D) logic: describes the shift in marketing over the past several
decades to a new marketing philosophy that considers “the exchange of intangibles,
specialized skills and knowledge, and processes (doing things for and with)”, with a
view to develop a more comprehensive and inclusive perspective of marketing
thought (Vargo and Lusch 2004, pg 3).
Social engagement: the customer’s heightened level of interest regarding the focus of
engagement (i.e. the event or the brand) based on personal exchanges with other
actors (definition in this thesis, page 46). Focal objects investigated in this thesis
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are; the event (referred to as social event engagement) and the brand (referred to as
social brand engagement).
Social exchange theory: argues that customers engage in activities that provide emotional
rewards including social approval and human contact (Abdul-Ghani, Hyde, and
Marshall 2011).
Theory of consumption values: states that various consumption values perceived by the
customer are focal in explaining consumer choice with regards to purchase (or not
purchase) as well as brand selection (Sheth, Newman, and Gross 1991).
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Introduction Chapter
CHAPTER 1: INTRODUCTION
1.1 Background to the Research
In recent years, business environments have become more dynamic and interactive, with
customers seeking participation through unique experiences with the offerings and
activities of the organisation (Vivek et al. 2012). Customer engagement has emerged as an
important construct that facilitates customer-brand interaction and relationships (Brodie et
al. 2011a), customer loyalty (Bowden 2009) and contributes to a firm’s financial value
(Bijmolt, Leeflang, Block, Eisenbeiss, Hardie, Lemmens, and Saffert 2010; Kumar, Aksoy,
Donkers, Venkatesan, Wiesel, and Tillmanns 2010). As academic interest in customer
engagement continues to grow, research is broadening to explore various methods of
facilitating engagement, both customer- and brand-initiated (Vivek et al. 2012). However,
despite the interest in customer engagement there have been few contributions that focus
on the strategic drivers that facilitate customer engagement.
The focus of this thesis is to investigate branded marketing events (BMEs); a brand-
initiated experience that serves as a platform for customers to interact with the brand and
other actors. Founded in marketing events and customer experience literature, a BME is
interactive in nature, unique to the individual, highly experiential and essentially brings the
brand to life (Wohlfeil and Whelan 2006). The characteristics of these events are
conducive to engendering a psychological state of customer engagement with the brand
(Brodie, Ilic, Juric, and Hollebeek 2011b), and are expected to enhance behavioural
intention of loyalty toward the brand (So et al. 2012).
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Introduction Chapter
1.2 Research Problem and Propositions
The central purpose of this thesis is to explore how BME experiences facilitate customer
engagement. The primary research problem addressed in this thesis is:
How do BME experiences impact customer engagement, and what impact does
this have on behavioural intention of loyalty? How does the individual’s
experiential needs moderate a BME’s impact on customer engagement?
This research problem is further articulated in this thesis through the examination of
customer engagement, marketing events and customer experience literature. It is argued
that conceptually, customers engage with the event itself and through this engagement with
the event their engagement transfers to the brand. An individual’s experiential needs
moderate this process in that the fulfilment of experiential needs strengthens the
relationship between BME experiential components and customer event engagement. In
developing the conceptual framework (Figure 1-1, and is discussed in Chapter 2), five
research questions are identified. These research questions are established in Chapter 2 and
frame the development of hypotheses which are empirically investigated in Chapter 4. The
five research questions addressed in this thesis are:
SUMMARY OF RESEARCH QUESTIONS: 1. What is the role of social engagement within the overall customer engagement construct?
2. How do the experiential components of a BME facilitate customer engagement?
3. What is the relationship between the engagement with two focal objects; customer event
engagement and customer brand engagement?
4. What impact does customer engagement have on behavioural intention of loyalty?
5. Does an individual's experiential needs moderate the relationship between BME experiences and
customer event engagement?
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Introduction Chapter
1.3 Justification for the Research
Service-dominant (S-D) logic has changed the way marketing researchers think and
approach interactions with customers (Vargo and Lusch 2004). Customers are no longer
satisfied with passive communication with brands; they want to interact, customise, and
drive their own brand experiences (Vivek et al. 2012). It is the brand’s duty to provide a
platform for interaction and customer engagement to occur. A branded marketing event is
an effective means of facilitating customer engagement as it is completely interactive and
subjective in nature, whereby the perceived value is determined by the individual. Previous
marketing event studies have investigated event effectiveness (Weihe, Mau, and Silberer
2006; Whelan and Wohlfeil 2006); however, research in this area is yet to investigate
ability of events to facilitate customer engagement.
This thesis contributes to the overall understanding of customer engagement and the
interplay between engagement objects (the event and the brand). A general lack of
empirical quantitative enquiry into customer engagement is observed in Table 2-1 (Chapter
2). Further quantitative research, including this thesis, is required to contribute to the
empirical justification of customer engagement operationalisation and its brand-related
outcomes. Research in customer engagement, as a relatively new body of literature, is
starting to build with regards to understanding the various antecedents and outcomes of
customer engagement (Fehrer, Woratschek, and Germelmann 2013); however, research
does not yet investigate the strategic use of events to drive customer engagement.
This thesis makes several key contributions to theory and literature. First, it contributes to
the understanding of facilitating customer engagement through the effective platform of
BMEs. Various components of a BME experience are identified from customer experience
literature (Gentile et al. 2007), and used to investigate the relationship between BME
experience and customer engagement. These relationships are supported by social
exchange theory (Möller 2013; Saks 2006), as resources are contributed by the brand and 3 | P a g e
Introduction Chapter
the customer for mutual benefit; the brand provides a platform (BMEs) through which
unique and memorable experiences occur, and the customer in turn contributes through
their engagement with the event and with the brand. Social exchange theory also supports
the existence of a social dimension of customer engagement (Abdul-Ghani et al. 2011),
which is also explored in this thesis.
Second, this thesis explores the interplay between two engagement objects; customer event
engagement and customer brand engagement. Using associative network theory (Smith
2004), this thesis postulates that customer engagement experienced with the event can also
replicate onto the associated brand, thus facilitating customer brand engagement. Extant
literature on customer engagement to date does not explore multiple focal engagement
objects simultaneously. However, this relationship is important to understand as it is
becoming more common for brands to initiate experiences with customers that extend
beyond normal service interactions (for example, BMEs); in this situation it is unclear
whether the brand receives any benefit for undertaking such interactions.
S-D logic recognises that value is uniquely created and determined by the individual
(Vargo and Lusch 2008). Therefore, an understanding of the individuals’ needs within the
scope of BMEs is important to gauge a comprehensive understanding of how customer
engagement is facilitated during BMEs. Specifically, the moderation of experiential needs
in the relationship between BME experiential components and customer event engagement
is examined. Optimum stimulation level theory (Steenkamp and Baumgartner 1992) guides
the hypotheses presented in this thesis, arguing that the relationship between customer
event engagement and BME experiences is enhanced when the individual’s experiential
needs are satisfied during the BME. Customer engagement literature recognises that the
individuals’ needs can moderate the level of customer engagement facilitated (Brodie et al.
2011a), but currently has not explored this moderation effect.
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Introduction Chapter
The theoretical framework in Figure 1-1 highlights the focal constructs of interest and
presents them together to depict their relationships. A more detailed explanation and
theoretical justification for each of the hypothesised relationships is discussed in Chapter 2
and is presented in a conceptual framework, Figure 2-1.
FIGURE 1-1: THEORETICAL FRAMEWORK
1.4 Research Context
The research context of this thesis is wine-related BMEs held in the South Australian wine
industry. The Australian wine industry is dominated by a small number of large wine
brands, with over two thousand small wineries competing for the remaining market
(Winetitles 2013). Customer engagement through event experiences is therefore an
important strategy for wine brands to differentiate and create closer connections with their
customers.
Wine marketing studies recognise the impact of events and customer experience in the
wine industry (Hoffman, Beverland, and Rasmussen 2001; Pikkemaat, Peters, Boksberger,
and Secco 2009). Wine brands already host events and implement other activities to create
customer experience and engage customers (Barth 2007). However, the wine industry
context receives little attention in customer engagement literature (Hollebeek 2010).
Research has indicated that the individual’s hedonic attitudes tend to impact their
attendance of special events more so than utilitarian drivers (Gursoy, Spangenberg, and
Rutherford 2006). Wine consumption is considered a primarily hedonic experience
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Introduction Chapter
(O'Neill and Charters 2000), related with leisurely activity and lifestyle (Jingxue,
Morrison, Cai, and Linton 2008) and therefore is deemed an appropriate context in which
to conduct the study for this thesis.
Research on similar hedonic experiences (e.g. in tourism) have started to recognise and
adopt customer engagement strategies (So, King, Sparks, and Wang 2014). Therefore, the
wine industry provides a context in which a diverse range of events are available for
investigation in this thesis, where unique and memorable customer experiences are
anticipated, and where the individual’s experiential needs are expected to moderate the
impact of these experiences on customer engagement.
1.5 Research Method
This section provides an overview of the research method adopted in this thesis. A detailed
description and justification of the procedures is discussed in Chapter 3.
A deductive, quantitative research approach is taken in this thesis to investigate causal
relationships between theoretically developed constructs. An online questionnaire is
developed and pre-tested in the University sector and the main study is conducted in the
South Australian wine industry. The researcher attended many of the events to seek the
participation of event attendees; willing participants provided their email address and were
sent the online questionnaire. For a small number of events the participating wineries
emailed the survey link to attendees on the researcher’s behalf.
Existing scales are selected to capture each of the constructs within the conceptual model,
and minor modifications made to wording when required to ensure their applicability to the
context. Items are assessed in the pre-test analysis for construct validity and reliability;
poor-fitting items are excluded from the main study to shorten the survey length.
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Introduction Chapter
Data analysis is conducted using SPSS 21 and AMOS 21, and structural equation
modelling (SEM) employed. Each latent construct and associated measured variables are
tested for construct validity and reliability using various measurement models before
structural models and path models are assessed. Composite measures are calculated to
enable complex models to be investigated with sample size constraints. Multi-group
analysis is conducted to identify the effects of moderators on each of the paths within the
model.
1.6 Delimitation and Scope of the Thesis
The managerial implications, limitations and future directions topics are discussed in
Chapter 5, and attest that the relationships identified in this thesis cannot be generalised
beyond the scope of this thesis. Further examination of the relationships among the key
constructs is recommended in various industry settings.
The scope of this thesis is limited to wine-related BMEs held in the South Australian wine
industry. The findings of this thesis are relevant for related fields, particularly where the
host brand or industry is primarily hedonic. However, further research is required to
extrapolate these relationships in the context of unrelated fields, for example for utilitarian
brands and products.
Data collection for this thesis was limited to South Australia. A broader national study
would have captured a greater variety of wine regions. However, while wine regions in
Australia carry some importance, the customer has been found to associate predominantly
with the individual wine brand (Rasmussen and Lockshin 1999). Therefore, data collection in
the main South Australian wine regions are not believed to be of detriment to the
generalisability of the findings in reflecting the broader Australian population.
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In addition, the location-based boundaries imposed on this research mean that cultural
influences cannot be identified. This is particularly relevant to the wine industry, where
consumer wine culture is anticipated to impact customer behaviours, outcomes and
perceptions related to the wine industry (Overby, Gardial, and Woodruff 2004). Therefore,
research replicating these relationships is advised in the specific country of interest to
account for cultural differences, particularly in ‘old world’ wine countries (e.g. France) as
Australia is considered a ‘new world’ wine country (O'Neill, Palmer, and Charters 2002).
Furthermore, this thesis specifically investigates customer experience and engagement
within the platform of the event, and does not account for the various experiences and
interactions between the customer and brand beyond the BME. There is confusion in the
use of the term ‘experience’; experience can describe knowledge or expertise in retrospect
(for example I have experience in this topic) whereas an experience refers to living
through, undertaking or facing a specific event (Palmer 2010). The parameters of ‘the
experience’ in this thesis reflect the duration of the event; however, it is recognised that a
customer’s overall ‘experience’ (from a cumulative perspective) with a brand can extend
beyond this one event experience and cannot be controlled.
Finally, although other notions such as customer orientation, customer management and
relationship marketing are relevant and often drawn on in discussing customer
engagement, they are emergent from the perspective of Goods-Dominant logic; S-D logic
has progressed these ideas with a greater emphasis on the customer’s central role in value
creation (Vargo and Lusch 2008). The various theoretical frameworks grounding customer
engagement is a likely cause of the varying perspectives that researchers in the area have
on the customer engagement construct; in particular the various definitions of customer
engagement and perspectives of customer engagement as either a behaviour or a
psychological mindset. Therefore, this thesis has set a demarcation of the research area and
follows the S-D logic perspective throughout.
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1.7 Outline of the Thesis
The structure of this thesis follows the five chapter approach in Perry (1998); each chapter
is briefly outlined below.
CHAPTER 1 provides an introduction and background to this thesis. This chapter briefly
describes the research problem and subsequent propositions, justification for the research,
and outlines the scope and delimitations of this thesis.
CHAPTER 2 reviews the literature regarding customer engagement, marketing events and
customer experience, and outlines the theoretical underpinning of the relevant research
propositions and hypotheses. The emergence of customer engagement and its theoretical
foundation in S-D logic is discussed. A review of marketing events and customer
experience literature is undertaken to demonstrate the applicability of BMEs in facilitating
customer engagement and to outline the experiential components of a BME. The
behavioural intention of loyalty toward the brand is discussed and its outcomes from
customer event engagement and customer brand engagement explored. Finally, this chapter
discusses the moderator variables that potentially influence the relationships between the
experiential components of BMEs and customer event engagement. Chapter 2 concludes
with a summary and justification of hypotheses and a conceptual framework that is
empirically tested in the following chapters.
CHAPTER 3 describes the research method used to establish the relationships among the
key constructs. Details concerning sample size, data collection procedures, and
questionnaire design are presented and justified. This chapter has a focus on the
measurement constructs, and provides information regarding the operationalisation of the
constructs. The pre-test data analysis is described and changes to the final questionnaire are
identified. This chapter concludes with an examination of the construct validity and
reliability of the measures used in the main study.
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Introduction Chapter
CHAPTER 4 addresses the main propositions and hypotheses of this thesis. Social
customer engagement constructs are examined with reference to the event and the brand,
and are tested for their applicability as an additional independent dimension of customer
engagement. Path model analysis investigates the influence of the BME experiential
component antecedents to customer engagement, the relationship between customer event
engagement and customer brand engagement, and the outcome of behavioural intention of
loyalty. Finally, this chapter demonstrates the multi-group analysis used to investigate the
moderation of experiential needs in the relationship between the experiential components
of a BME and customer event engagement.
CHAPTER 5 integrates the key findings from the literature review and results chapters. It
identifies the contributions to academic knowledge and managerial implications. This
chapter concludes with the study limitations and directions for future research.
1.8 Chapter 1 Summary
This chapter laid the foundations for this thesis. It introduced the research problem,
research questions and hypotheses. Then the research was justified, the method was briefly
described and justified, the thesis was outlined, and the delimitations and scope were
given. On these foundations, the thesis can proceed with a detailed description of the
research. The next chapter presents a summary of extant literature, mainly in the research
areas of customer experience, marketing events and customer experience.
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CHAPTER 2: LITERATURE REVIEW
2.1 Chapter 2 Introduction
Engagement refers to a psychological mindset that occurs through interactive experiences
between engagement subjects and objects (Brodie et al. 2011a; Hollebeek 2011a).
Engagement is a term commonly used in industry and academe, and it is investigated in
various research areas including sociology, political science, psychology, and
organisational behaviour (Brodie et al. 2011a). However, this concept has only recently
been adopted in the marketing discipline (Vivek et al. 2012). “Customer Engagement” as a
concept has emerged as a popular research area due to the changing perspective of
customer-company relationships, as evidenced by the shift first to relationship marketing,
and more recently to service-dominant (S-D) logic. Focal to the S-D logic perspective is
the notion that marketing is customer-centric; this extends beyond customer orientation to
include collaboration, learning, and adapting to each customer and their dynamic needs
(Vargo and Lusch 2004). This perspective provides insights about the customer not
previously recognised, namely that there are far greater outcomes for companies who do
not just communicate ‘one-way’ to customers, but instead communicate interactively with
customers and recognise that those customers uniquely perceive value (Vargo and Lusch
2008).
For the past decade, the concept of customer engagement has been a key research priority
of the Marketing Science Institute; first appearing in the 2006-2008 Research Priorities to
establish a greater understanding of engagement (MSI 2006), it was again listed in 2010-
2012 to encourage further conceptual development (MSI 2010). The 2014-2016 report
places “the understanding of customers and customer experience” as a Tier 1 Research
Priority, calling for further conceptual development, measurement, and a broader
investigation of the various marketing activities that may create engagement (MSI 2014).
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Customer engagement has also strongly emerged as a concept of practitioner interest and
this is reflected in the abundance of engagement articles in Harvard Business Review (e.g.
Koehn 2011; Wang 2012), the Gallup Business Journal (e.g. O'Boyle and Fleming 2014;
Sorenson and Adkins 2014) and the Economist (e.g. Voyles 2007), as well as industry
conferences (e.g. the Annual Summit on Customer Engagement 2014). In recent years,
business environments have become more dynamic and interactive, with customers
seeking participation through unique experiences with the offerings and activities of the
organisation (Vivek et al. 2012).
This literature review focuses on the conceptual domain of customer engagement and
demonstrates the ability of customer experience to facilitate customer engagement.
Research to date has predominantly focused on creating a common understanding of the
definition of customer engagement, dimensions that capture the construct, and how to
distinguish customer engagement from related concepts, particularly involvement (Brodie
et al. 2011b). As common conceptualisations emerge, new research needs to shift its focus
to empirical enquiry and further understanding of how customer engagement is facilitated
through different platforms and in different contexts. This thesis explores customer
engagement from a strategic standpoint, identifies the brand-provided resources within an
event experience and investigates which experiences customers choose to engage with.
Therefore, the literature on customer experience and marketing events is consulted to
examine in detail the components of experience that facilitate customer engagement.
Current literature considers customer engagement with a focal object, but there is little
acknowledgement of the multiple factors within a service system with which a customer
engages. This thesis considers customer engagement with both the event and the brand,
and argues that the focal direction of the engagement can project from the event to the
brand using associative network theory. In addition, this thesis contributes to the debate of
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whether social engagement is an independent dimension of customer engagement with
reference to social exchange theory.
Finally, with the understanding that customers uniquely create and interpret value, this
thesis investigates the role that an individual’s experiential needs play in the facilitation of
customer engagement within an event experience. The moderating effect of the consumers’
experiential needs are based on MacInnis and Jaworski’s (1989) categorisation of
consumer needs, as well as related theories including exploratory consumer behaviour.
Customers attending an event are likely to possess varying levels of cognitive needs, a
desire for novelty-seeking or excitement, and/or a need for affect. These needs are likely to
influence how the event is perceived and whether customer engagement is facilitated.
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2.2 Customer Engagement
Engagement is a multidisciplinary concept reflecting a variety of contexts including
employee engagement (Kahn 1990; Saks 2006), student engagement (Kahu 2013; Skinner,
Wellborn, and Connell 1990) and community engagement (Algesheimer, Dholakia, and
Herrmann 2005; Keener 1999). While there are some consistencies in engagement
conceptualisation across the various academic disciplines, for example the existence of
cognitive, emotional and behavioural dimensions of engagement (Kahn 1990; Kahu 2013;
Saks 2006), there are also considerable differences such as focal subjects (‘who’ is
engaging) and objects (with ‘what’ is the subject engaging) (Hollebeek 2011a). An
overview of engagement conceptualisations and studies in the academic disciplines of
sociology, political science, psychology, educational psychology, and organisational
behaviour can be found in Brodie et al. (2011a). While research is gaining momentum in
this area, customer engagement within marketing academe is still a relatively recent
concept (Abdul-Ghani et al. 2011).
2.2.1 Theoretical Foundations of Customer Engagement
The literature on customer engagement in marketing has emerged from the current focus
on service-dominant (S-D) logic. Vargo and Lusch’s (2004, pg 2) seminal paper on S-D
logic describes the shift in marketing over the past several decades from a goods-based
view to a new marketing philosophy that considers “the exchange of intangibles,
specialized skills and knowledge, and processes (doing things for and with)”, with a view
to develop a more comprehensive and inclusive perspective of marketing thought. This
reorientation has implications for how marketers perceive and approach the customer,
exchange processes and markets (Vargo and Lusch 2004). Compared to the more narrow
focus of the ‘goods dominant’ perspective, in which one-way, mass communication was
considered an effective way to ‘market to’ customers (Vargo and Lusch 2004), S-D logic
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considers a more inclusive perspective, with emphasis on the interactive nature of
experiences and co-creative environments (Brodie et al. 2011b). From this perspective, the
brand does not merely provide value, but instead value is unique to each individual and
created with the consumer (Vargo and Lusch 2004).
The S-D logic provides a number of insights relevant to the understanding of customer
engagement. First, customers are co-creators of value (Vargo and Lusch 2014). They do
not passively receive information and value from organisations, but instead create value
that is unique and determined individually (Vargo and Lusch 2014). Therefore, interaction
is a prevalent and necessary construct in customer-company relationships, as customer
interaction will allow them to create value. As a result, companies recognise that rather
than a focus on delivering value, they must focus on providing a platform and resources for
the customer to interact and create value (Vargo and Lusch 2008). The event experience is
the resource provided by the brand that customers draw from to facilitate engagement. This
thesis takes the perspective of S-D logic and considers customer engagement facilitated
during provider-initiated events through this theoretical lens.
2.2.2 Customer Engagement Conceptualisation
Research on customer engagement emphasises its conceptual infancy with regards to
theoretical development (Brodie et al. 2011b) and encourages researchers to focus on the
development of conceptual understanding and identifying the characteristics of the
engagement construct (MSI 2010). While it is not the primary objective of this thesis to
contribute to overall construct definition and development, the fact that the literature on
customer engagement remains focused on the conceptual boundaries of the construct
necessitates a comprehensive overview to establish the theoretical position of this thesis
before introducing contextual elements of the research. Table 2-1 provides a summary of
definitions of customer engagement and related engagement subjects from the literature.
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TABLE 2-1: OVERVIEW OF CUSTOMER ENGAGEMENT LITERATURE
Reference Definition Perspective Dimensions Method
(Higgins and Scholer 2009, pg 102)
“Engagement is a state of being involved, occupied, fully absorbed, or engrossed in something—sustained attention”
Psychological Not stated Conceptual
(Bijmolt et al. 2010, pg 341)
“The behavioral manifestation from a customer toward a brand or a firm which goes beyond purchase behavior”
Behavioural Word of Mouth, Co-creation, Complaining Behaviour
Conceptual
(Hollebeek 2010, pg 3)
“the level of a consumer’s cognitive, emotional and behaviourally‐based motivation in brand interactions”
Psychological Cognitive, Emotional, Behavioural
Conceptual
(Mollen and Wilson 2010, pg 12)
“a cognitive and affective commitment to an active relationship with the brand as personified by the website or other computer-mediated entities designed to communicate brand value”
Psychological Cognitive, Affective
Conceptual
(van Doorn, Lemon, Mittal, Nass, Pick, Pirner, and Verhoef 2010, pg 254)
“a customer’s behavioural manifestations that have a brand or firm focus, beyond purchase, resulting from motivational drivers”
Behavioural Valence, Form/modality, Scope, Nature of impact, Customer goals
Conceptual
(Abdul-Ghani et al. 2011, pg 1060).
“a consumer’s ongoing attention to an object of consumption such as a website or brand”
Psychological Utilitarian, Hedonic, Social
Empirical Qualitative
(Brodie et al. 2011a, pg 260)
“A psychological state that occurs by virtue of interactive, co-creative customer experiences with a focal agent/object (e.g., a brand) in focal service relationships”
Psychological Cognitive, Emotional, Behavioural
Conceptual
(Brodie et al. 2011b, pg 3)
“Consumer engagement in a virtual brand community involves specific interactive experiences between consumers and the brand, and/or other members of the community. Consumer engagement is a context-dependent, psychological state characterized by fluctuating intensity levels that occur within dynamic, iterative engagement processes”
Psychological Cognitive, Emotional, Behavioural
Empirical Qualitative
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Reference Definition Perspective Dimensions Method
(Hollebeek 2011b, pg 555)
“the level of a customer’s cognitive, emotional and behavioral investment in specific brand interactions”
Psychological Cognitive, Emotional, Behavioural
Empirical Qualitative
(Hollebeek 2011a, pg 790)
“the level of an individual customer’s motivational, brand-related and context-dependent state of mind characterised by specific levels of cognitive, emotional and behavioural activity in direct brand interactions”
Psychological Cognitive, Emotional, Behavioural
Conceptual
(Gambetti, Graffigna, and Biraghi 2012, pg 659)
“A dynamic and process-based concept evolving in intensity on the basis of the brand capability of increasingly intercepting consumers’ desires and expectations using all possible physical and virtual touchpoints between brand and consumers. CBE appears as an overarching marketing concept encapsulating different consumer decision-making dimensions, from brand preference to brand purchase”
Psychological Experiential, Social
Empirical Qualitative
(So et al. 2012) Refers to Brodie et al. (2011a) customer engagement definition
Psychological Identification, Attention, Enthusiasm, Absorption, Interaction
Empirical Quantitative
(Vivek et al. 2012, pg 127)
“the intensity of an individual’s participation in and connection with an organization’s offerings and/ or organizational activities, which either the customer or the organization initiate”
Psychological Cognitive, Emotional, Behavioural, Social
Empirical Qualitative
(Calder, Isaac, and Malthouse 2013, pg 4)
“a psychological state that occurs by virtue of interactive, co-creative customer experiences with a focal agent/object (e.g. a brand) … under a specific set of context-dependent conditions … and exists as a dynamic, iterative process”
Psychological Social, Intrinsic enjoyment, Utilitarian, Identity, Civic
Empirical Quantitative
(Hollebeek, Glynn, and Brodie 2014, pg 1)
“A consumer's positively valenced brand-related cognitive, emotional and behavioral activity during or related to focal consumer/brand interactions”
Psychological Cognitive, Emotional, Behavioural
Empirical Qualitative and Quantitative
(Taheri, Jafari, and O'Gorman 2014, pg 321)
“involvement with and commitment to a consumption experience”
Psychological Cognitive, Emotional, Behavioural
Empirical Quantitative
(So et al. 2014, pg 2)
“A customer’s personal connection to a brand as manifested in cognitive, affective, and behavioural responses outside of the purchase”
Psychological Identification, Attention, Enthusiasm, Absorption, Interaction
Empirical Quantitative
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2.2.2.1 Different Perspectives of Customer Engagement
The overview of customer engagement and other relevant engagement subject studies
listed in Table 2-1 shows an inconsistency in the conceptualisation of engagement,
particularly with reference to distinguishing engagement from involvement (Higgins and
Scholer 2009) or attention (Abdul-Ghani et al. 2011). This necessitates an emphasis
regarding the definition and conceptualisation of customer engagement in order to clarify
its meaning in this thesis.
Two perspectives of customer engagement conceptualisation have emerged from recent
studies and are highlighted in Table 2-1; customer engagement as a behaviour (Bijmolt et
al. 2010; van Doorn et al. 2010; Verhoef, Reinartz, and Krafft 2010), and customer
engagement as a psychological mindset. The latter is multidimensional in nature and
includes a behavioural dimension (Brodie et al. 2011a; Calder et al. 2013; Gambetti et al.
2012; Hollebeek 2011a; So et al. 2012; Vivek et al. 2012). While the behavioural
dimension is pivotal to the overall customer engagement construct and is the most easily
observable manifestation of engagement for researchers, this dimension in isolation does
not entirely explain whether the customer is truly engaged (So et al. 2012). The
behavioural dimension of customer engagement is only one element of engagement and
does not explain the intention or motivation causing the behaviour (So et al. 2012), making
engagement indistinguishable from constructs such as participation (Brodie et al. 2011a).
The notion of customer engagement as a psychological mindset considered from a
multidimensional perspective allows this thesis to capture the complexity of the construct
(So et al. 2012) and therefore is utilised to ensure a comprehensive depiction of customer
engagement.
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2.2.2.2 Definition of Customer Engagement
This thesis follows the definition provided in Brodie et al.’s (2011a, pg 260) seminal
customer engagement paper, in which customer engagement is defined as “a psychological
state that occurs by virtue of interactive, co-creative customer experiences with a focal
agent/object (e.g., a brand) in focal service relationships”. This particular
conceptualisation is adopted in this thesis as it clearly identifies the interactive nature of
experiences, which distinguishes it from related concepts, including involvement, and
places an emphasis on the customer’s central role in the creation of the experience to
facilitate engagement (Brodie et al. 2011a). In addition, this definition follows the
perspective of customer engagement as a multidimensional psychological mindset (Brodie
et al. 2011a), the approach adopted in this thesis.
A key element of the customer engagement definition is that engagement emerges “from
two-way interactions between relevant engagement subject(s) and object(s)” (Hollebeek
2011a, pg 787). The engagement subject refers to the person who facilitates the
engagement, i.e. customers, while the engagement object identifies to what the person’s
engagement is directed, i.e. the brand (Hollebeek 2011a). Customer engagement has been
described with regards to various engagement objects, for example media, advertising,
entertainment or brands (Abdul-Ghani et al. 2011; Vivek et al. 2012). This thesis extends
current literature as it considers how customer engagement is facilitated with an event and
the associated brand hosting the event (from here referred to as the host brand). This
contribution is further discussed in section 2.4.3.
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2.2.2.3 Dimensions of Customer Engagement
When considering customer engagement from a psychological mindset perspective, it has
been generally conceptualised to incorporate three dimensions; cognitive, emotional and
behavioural engagement (Brodie et al. 2011a; Hollebeek 2011a; Taheri et al. 2014). So, et
al. (2012; 2014) extend this view and propose a five-dimensional conceptualisation;
attention, enthusiasm, interaction, identification and absorption. These elements capture
the commonly accepted tri-dimensional perspective of engagement with the attention
(cognitive), enthusiasm (emotional), and interaction (behavioural) elements, however it
extends this conceptualisation with an additional two dimensions; identification and
absorption. This thesis implements the broader five-dimension conceptualisation of
customer engagement provided by So et al. (2012; 2014) as it is consistent with, and
extends, the discussion of customer engagement. The context in which the So et al. (2012)
measure has been utilised, engagement with tourism brands is closely aligned with the
context of this thesis.
Attention represents a “consumer’s attentiveness and focus on the brand” (So et al. 2012,
pg 6). This definition is consistent with cognitive engagement, or ‘immersion’, which is the
extent of a consumer’s “brand-related concentration in particular brand interactions”
(Hollebeek 2011b, pg 566) as well as ‘vigour’ or the willingness to invest effort into an
activity (Salanova, Agut, and Peiró 2005). It represents the concentration or cognitive
resources a consumer commits in their interactions with the event or brand (Hollebeek
2011b). Customers with high levels of attention or cognitive engagement have a strong
focus on information related to the event and brand (So et al. 2012).
Enthusiasm “represents an individual’s strong level of excitement and interest regarding
the focus of engagement, such as a brand” (So et al. 2012, pg 5; Vivek et al. 2012). This
definition is consistent with emotional engagement, using the terms ‘passion’ and positive
affection to reflect the extent of a consumer’s “positive brand-related affect in particular
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brand interactions” (Hollebeek 2011b, pg 567). Customers with strong enthusiasm
experience great excitement and emotional connection to the event and brand (So et al.
2012).
Interaction represents the “behavioral manifestation of a consumer’s relationship with the
brand beyond traditional consumptive behavior” (So et al. 2012, pg 7). This is consistent
with behavioural engagement, or ‘activation’, and reflects the extent of a consumer’s
“energy, effort and/or time spent on a brand in particular brand interactions” (Hollebeek
2011b, pg 569). Interaction or behavioural engagement encompasses the active
participation in event or brand-related activities (Mollen and Wilson 2010; So et al. 2012).
Identification is introduced by So et al. (2012), and while they acknowledge that it is
absent from customer engagement research to date, they draw from employee engagement
to argue its applicability to the customer engagement space. Grounded in Social Identity
Theory, identification explains the relationship between customers and specific brands; if
an association with the brand can provide the consumer with a means of conveying self-
expression or self-definition, this will enhance the relationship between the consumer and
the brand (So et al. 2012). Identification in this context is defined as “an individual’s
perceived oneness with or belongingness to an organization, and at the brand level,
identification occurs when the consumer sees his or her self-image as overlapping the
brand’s image” (So et al. 2012, pg 7). This construct is also consistent with Sprott, Czellar
& Spangenberg’s (2009) brand engagement in self-concept construct. Customers with high
levels of identification interact in an event and with a brand, as doing so enhances their
self-image or provides a means of self-expression.
Absorption is a “pleasant state in which the customer is fully concentrated, happy, and
deeply engrossed while playing his role, and an absorbed customer interacting with the
brand or other customers perceives time as passing quickly” (So et al. 2012, pg 6). The
term is often associated with the concept of flow, an optimal experience, and is described
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using terms such as engrossment, intrinsic enjoyment, or effortless concentration
(Csikzentmihaly 1990). The concept of flow is also discussed in the marketing events
literature (Drengner, Gaus, and Jahn 2008), and is often associated with emotional
elements of the experience. As this thesis investigates customer engagement during events,
the inclusion of absorption as an engagement dimension is appropriate for this context.
Absorption is considered in this thesis as an extremely high level of enthusiasm or
emotional engagement, containing the additional characteristics of engrossment in the
experience and losing sense of time (So et al. 2012; Vivek et al. 2012).
2.2.2.4 What Customer Engagement is not: Related Concepts
Important to the conceptualisation of customer engagement is to establish its unique
qualities that distinguish it from related concepts. This has been strongly emphasised in
previous research, with a particular focus on differentiating engagement from involvement
(Brodie et al. 2011b). This need has arisen from various misconceptions of engagement.
For example, Abdul-Ghani et al. (2011) demonstrate confusion of involvement and
engagement, using the term involvement as the cognitive motivation towards a product
category, versus engagement as the affective or emotional associations with a market
offering. This perspective is inconsistent with other engagement research, which argues
that the engagement construct includes cognitive, emotional and behavioural elements
(Brodie et al. 2011a). Therefore, this literature review provides an overview of the
conceptual differences to distinguish customer engagement from a number of related
concepts. First, customer engagement is differentiated from related but clearly distinct
constructs including involvement, brand experience and satisfaction. Second, overlapping
constructs which contribute to the understanding of engagement but are not exhaustive,
including participation, interactivity, commitment, flow, identification and loyalty are
addressed.
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The difference between involvement and engagement is argued extensively throughout
customer engagement literature (Bowden 2009; Brodie et al. 2011b; Brodie et al. 2011a;
Mollen and Wilson 2010). The primary characteristic of engagement distinguishing it from
involvement is the emphasis on interaction and therefore the inclusion of a behavioural
dimension of engagement (Brodie et al. 2011b; Hollebeek 2010; Hollebeek 2011a).
Customer engagement is more extensive than involvement; while involvement considers
the relevance or interest a consumer may possess with regards to a brand or other focal
object, engagement requires interaction between the engagement object and subject
(Brodie et al. 2011b).
Commitment is often described as an emotional attachment associated with attitude; “a
customer is considered to be committed when his or her values, self-image, and attitudes
are strongly linked to a specific choice alternative” (Bowden 2009, pg 70). While
commitment encompasses the ‘psychological state’ of engagement, it does not capture the
interactive or actionable/behavioural dimension of engagement. It is also a resultant
concept, and has been recognised as an outcome of engagement (Brodie et al. 2011b).
Brand experience is an internal and behavioural response from an individual resulting
from brand-related stimuli (Brakus et al. 2009). Brand experience does not presume a
motivational state, which distinguishes it from customer engagement (Hollebeek 2011a); it
can include experiences in which the consumer shows little interest or connection with the
brand (Brakus et al. 2009). This thesis takes the perspective that a brand experience is a
context through which customer engagement can be facilitated, but it does not capture the
psychological state that encompasses customer engagement.
Satisfaction is another construct that is often confused with engagement; it is however
distinguishable in that satisfaction is an evaluation process (Calder et al. 2013).
Engagement captures the active interactions and heightened psychological state within a
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particular moment; satisfaction is considered an evaluative or reflective outcome of that
moment (Calder et al. 2013).
Loyalty is a commitment to an organisation exhibited through consistent repeat purchases
despite adverse situational influences or efforts from competitors (Oliver 1999). Loyalty is
considered an outcome that is partially driven or resulting from engagement, and reflects
an enduring outcome rather than a heightened and momentary psychological state
(Bowden 2009; Hollebeek 2011a).
The next series of concepts are considered to partially represent engagement or have
overlapping qualities, however are not exhaustive of engagement. These concepts are
useful in contributing to the overall understanding of engagement, however customer
engagement is considered to be a broader and more encapsulating construct that recognises
and considers the complex and multifaceted nature of the experience.
Flow has been previously described as a distinct construct to engagement (Mollen and
Wilson 2010), however the five-dimensional perspective of customer engagement adopted
and empirically supported by So et al. (2012) incorporates the dimension of ‘absorption’,
which captures a psychological state similar to the notion of flow. While Mollen and
Wilson (2010) claim that flow is passive, and therefore distinct from engagement, it is
argued in this thesis that a state of flow is a highly active construct, to such an extent that
customers lose sense of everything else outside of that experience. Discussion of a flow
state uses words including ‘engrossed’, and a state of ‘optimal experience’ (So et al. 2012).
Customers in a state of flow display complete concentration, a feeling that the activity they
are participating in is all-encompassing; it is a highly enjoyable psychological state in
which the consumer feels engrossed in the activity causing them to lose sense of time,
however still feel in control of the activity (Drengner et al. 2008). This thesis takes the
perspective that flow is an active consumer psychological state and hence contributes to
the overall customer engagement construct. While the concept of flow is a heightened state
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and is associated with emotional elements of the experience (Drengner et al. 2008), it is not
an absolute indicator of customer engagement. It can, however, be included as a key
element of the construct.
While the previous concepts encapsulate the psychological aspects of engagement without
reference to active participation, the constructs of interactivity and participation
demonstrate this overlap in reverse, capturing the behavioural manifestations without
consideration of the purpose or intent behind those actions. Mollen and Wilson (2010, pg
10) argue that “engagement differs from simple interactivity because it must include
creative, purposeful activity”. Interactivity has therefore been considered an antecedent to
engagement (Hollebeek 2011a), or one dimension of the engagement construct (So et al.
2012). The perspective taken in this thesis is that interactivity is one dimension of
customer engagement; interaction is a focal element of engagement, as evidenced by the
consistent reference to interaction in customer engagement definitions (Brodie et al. 2011a;
Calder et al. 2013; Hollebeek et al. 2014; So et al. 2012). ‘Interaction’ is commonly used
as the distinguishing characteristic between engagement and other related concepts,
including involvement (Brodie et al. 2011b; Hollebeek 2010; Hollebeek 2011a). However,
customer engagement is a multidimensional construct (Brodie et al. 2011a; So et al. 2012)
and must therefore consider interaction as one dimension to be utilised in combination with
other dimensions to gain a comprehensive view of engagement.
Participation is a similar construct to interactivity and is defined as “the degree to which
customers produce and deliver service” (Brodie et al. 2011a, pg 261). Both definitions
imply action between the customer and the company, but do not capture the intention or
motivation driving these actions, and hence do not encapsulate the full notion of customer
engagement.
In summary, numerous variables have been confused with customer engagement due to the
relatively underdeveloped literature investigating it. Many of these concepts have now
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been distinguished from customer engagement, but careful delineation is still required from
researchers to ensure accurate and consistent understanding. One cause of confusion is the
misconception of customer engagement relative to its antecedents and outcomes. Common
antecedents and outcomes identified in customer engagement literature are now
highlighted.
2.2.2.5 Antecedents and Outcomes of Customer Engagement
As research on customer engagement expands, an emphasis on further conceptual
development including the investigation of the various antecedents that create engagement
is required (MSI 2014). Therefore, it is necessary to understand the various antecedents
and outcomes of customer engagement identified in extant literature, and contribute to the
understanding of engagement by investigating currently unexplored variables that are
likely to drive customer engagement.
Numerous antecedents and outcomes of customer engagement have been proposed in
previous research. Fehrer et al. (2013) provide an overview of the various antecedents and
outcomes through a systematic review of customer engagement literature. A summary of
antecedents and outcomes of customer engagement are shown in Table 2-2. Constructs are
categorised into three main groups; (i) identified antecedents of customer engagement
(identification, identity, hedonism), (ii) identified outcomes of customer engagement
(loyalty, customer value, word of mouth, product innovation), and (iii) constructs that have
been considered as either antecedents or outcomes, depending on the study context
(participation, satisfaction, trust, involvement, commitment and interaction) (Fehrer et al.
2013). This overview highlights that there is ambiguity in the customer engagement
literature with regards to whether constructs are considered antecedents or outcomes. In
addition, the overview demonstrates a narrow focus of constructs that largely capture
‘personal states of being’; very few papers investigate the strategic facilitation of customer
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engagement. This is an important connection to make in customer engagement research, as
new platforms or activities that facilitate customer engagement require further exploration.
TABLE 2-2: ANTECEDENTS AND OUTCOMES OF CUSTOMER ENGAGEMENT
Antecedents
Cus
tom
er E
ngag
emen
t
Outcomes
Identification Loyalty
Identity Customer value
Hedonism Word of mouth
Product innovation
Satisfaction Satisfaction
Trust Trust
Commitment Commitment
Involvement Involvement
Interaction Interaction
Table based on Fehrer et al. (2013)
Few studies have investigated customer experience as an antecedent of customer
engagement. So et al. (2012) consider brand experience as an outcome of customer
engagement; however, these authors adopt a different perspective of the term experience.
Experience can be described as knowledge or expertise in retrospect (for example, I have
experience in this topic) whereas ‘an experience’ refers to living through, undertaking or
facing a specific event (Palmer 2010). This thesis investigates ‘an experience’, specifically
a provider-initiated event in which the components of experiences are resources provided
by the organisation with which customers can interact, create their own unique value, and
facilitate engagement. A key contribution of this thesis is the investigation of customer
experience created through an engagement platform of BMEs and to identify that this
experience drives customer engagement. The next section explores the literature areas of
marketing events and customer experience to further understand how events drive
customer engagement.
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2.3 Branded Marketing Events
2.3.1 Marketing Events
A marketing event is an innovative tool for creating a customer experience, which has
recently received increased attention from researchers (Drengner et al. 2008; Wood 2009).
Marketing events seek to elicit active interaction between the consumer and the brand
using an experiential approach (Wood 2009). Adopting this view and working to facilitate
positive customer experiences, an environment is created that is conducive to
communicating high volumes of marketing content or messages with the objective of
eliciting favourable consumer responses (Schmitt 1999). Marketing events elicit active
engagement between the customer and the organisation due to their interactive and
experiential nature, and are argued to have a far greater effectiveness than traditional
marketing (Holbrook and Hirschman 1982). Wohlfeil and Whelan (2006) identify four key
features of a marketing event, outlined in Table 2-3.
TABLE 2-3: KEY FEATURES OF MARKETING EVENTS
Experience-orientation As a marketing event is personally experienced by those who attend, the ‘media experience’ is a lot stronger than only passively receiving information
Interactivity Participants can interact both with other participants, as well as brand representatives
Self-initiation Marketing events have the intention of influencing customers on an emotional level, and thus can affect the emotional associations the consumer has with the brand
Dramaturgy An event acts as a ‘dramatization’ of the company’s brand image; it brings the brand image to life
Source: (Wohlfeil and Whelan 2006, pp 645-646)
The key features of marketing events, in particular interactivity and self-initiation parallel
central elements of S-D logic and support the proposition that a marketing event can elicit
customer engagement, as the individual drives active experiences (Brodie et al. 2011a). It
is commonly understood in this literature space that the consumer drives the event
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experience and has control over whether they chose to interact during an event and the
nature of that interaction (Close, Finney, Lacey, and Sneath 2006). Organisations provide
the event platform to initiate active customer engagement, with the objective of creating
value, developing an emotional connection and encouraging loyalty (Crowther 2011).
Despite the recognition of ‘customer-initiated experiences’ echoed in marketing events
studies, little research in this area explicitly refers to the customer engagement literature. A
number of marketing events papers incorporate the word engagement in the title (e.g.
Close et al. 2006; Whelan and Wohlfeil 2006); however these articles do not define or
conceptualise the engagement construct. It is the intent of this thesis to further develop the
marketing events literature and bridge the gap in knowledge between event experience and
customer engagement.
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2.3.1.1 Defining Marketing Events
Table 2-4 provides a summary of definitions provided in the literature of marketing events.
While the applicability of marketing events within customer engagement literature is
evident, there is still a disconnect in terms of the language used or emphasis given to
particular elements of the marketing event experience.
TABLE 2-4: SUMMARY OF MARKETING EVENT DEFINITIONS
Reference Definition
(Gupta 2003) “occurrences designed to communicate particular messages to target audiences”
(Sneath, Finney, and Close 2005, pg 374)
“A variety of activities including ‘the marketing of events and marketing with events’. The marketing of an event is not related to sponsorship, whereas marketing with events entails the promotion of sponsors through the sponsorship vehicle”
(Close et al. 2006)
“the practice of promoting the interests of an organization and its brands by associating the organization with a specific activity”
(Weihe et al. 2006, pg 202)
“Marketing-events are used as a channel to communicate a brand and as a platform for a unique presentation of a brand”
(Whelan and Wohlfeil 2006, pg 327)
“Brand-related hyperrealities whereby the brand message is turned into a ‘real-lived’ multisensual brand experience, resulting in a strengthened emotional attachment to the brand. Event-marketing also facilitates voluntary dialogue and interaction between highly targeted participants”
(Drengner et al. 2008, pg 138)
“a communication tool whose purpose is to disseminate a company’s marketing messages by involving the target groups in experiential activities”
(Getz 2008, pg 404)
“a spatial-temporal phenomenon, and each is unique because of interactions among the setting, people, and management systems – including design elements and the program”
(Wood 2009, pg 248)
“any event that helps market a product/service, idea place or person; any event that communicates with a target audience; any event which has the potential to communicate”
(Crowther 2010, pg 371)
“A grouping that comprises a wide and rich variety of event types, termed ‘marketing event platforms’.... Each individual occasion is expressed as an ‘episode’, with organisations likely to engage in a number of 'marketing event episodes' over a given time period to achieve different objectives”
(Leischnig, Schwertfeger, and Geigenmueller 2011, pg 621)
“A communication instrument whose purpose is to promote the interests of a company and its brands by associating the company with a specific activity. Events are characterized by three aspects: (1) events are typically offered on a discrete or intermittent basis; (2) events allow companies face-to-face contact with their target audience by actively engaging customers with the company and the brand; (3) events are primarily based on entertainment and thus creative exciting and pleasant experiences”
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Three points of interest regarding marketing event definitions emerge from Table 2-4.
First, there is confusion in the literature between marketing events and sponsorship (Sneath
et al. 2005). While sponsorship is often considered to be a type of marketing event, their
inclusion is debated on the basis that these events exist for some other purpose but are used
later for marketing, and sponsorship agreements generally lack control over event
operations (Wood 2009). Second, these definitions indicate the high level of interaction
that occurs through the platform of marketing events (Getz 2008; Whelan and Wohlfeil
2006). As a result, each event is unique due to the interactions of the customers with each
other and with the event (Getz 2008). Marketing events therefore have the capacity to
facilitate customer engagement, as interaction and unique experience are central elements
of customer engagement (Brodie et al. 2011a). Third, a dated lexicon is generally used in
marketing event definitions reflecting a goods-dominant logic as opposed to S-D logic. For
example, terms including a communication tool/instrument (Drengner et al. 2008;
Leischnig et al. 2011) that promotes/communicates to customers (Close et al. 2006; Gupta
2003) imply one-way communication to customers, which is conflict to the unique
interactions emphasised in S-D logic (Vargo and Lusch 2004).
In the next section, these points are used to discuss the conceptualisation and redefinition
of a branded marketing event.
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2.3.2 Conceptualisation of Marketing Events
There is considerable debate among scholars regarding the conceptualisation and definition
of marketing events (Close et al. 2006; Drengner et al. 2008; Wohlfeil and Whelan 2006;
Wood 2009). A sponsorship agreement entitles the sponsoring brand to associate with an
event (Drengner et al. 2008); the sponsorship event is not created for the purpose of
communicating brand information or facilitation of customer-brand interaction. Due to this
lack of emphasis and the focus on the event itself, not the sponsor, customers at
sponsorship events may not interact with the brand in the event space (Drengner et al.
2008). In addition, within a sponsorship agreement the sponsoring brand generally lacks
control over event operations and distribution of brand-related information (Drengner et al.
2008; Mau, Weihe, and Silberer 2006). Multiple brands can sponsor the same event to the
detriment of each sponsor as the event becomes cluttered with conflicting brand messages
(Wood 2009). This results in a reduced ability for the event to translate to brand-related
outcomes for the sponsoring brand.
Despite similarities to sponsorship events, marketing events are created specifically for the
purpose of marketing a brand (Wood 2009). This is a major benefit of a marketing event,
as the brand is the central focus and the event can be tailored to emphasise brand-related
information and encourage customer-brand interaction (Wood 2009). The host brand of a
marketing event maintains control over the marketing dialogue and event operations. The
differing attributes and subsequent brand outcomes are demonstrated in Mau et al. (2006),
who conclude that sponsored events and marketing events are not only different activities,
but that marketing events have the ability to be more effective in influencing customer
attitudes. The literature is clear that sponsorship and marketing events are separate
activities; sponsorship should not be considered within the definition of marketing events
(Wood 2009).
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2.3.2.1 Branded Marketing Events - a Definition
The term branded marketing event is introduced in this thesis and defined as follows:
A branded marketing event (BME) is a brand-initiated experience that serves as a
platform for customers to interact with the brand and other actors.
The explicit purpose of a BME is to create a unique brand-related experience with the
customer. The term ‘branded marketing event’ more clearly positions an event as a
branding activity; it is brand-centric, with the intention of eliciting brand-related outcomes
such as customer brand engagement. This definition builds on marketing events literature
(Drengner et al. 2008; Whelan and Wohlfeil 2006) but also captures the nature of dynamic
interactions, and hence recognises the principles of S-D logic (Vargo and Lusch 2008) and
customer engagement (Brodie et al. 2011a).
Where marketing events were previously discussed as a “communication tool whose
purpose is to disseminate a company’s marketing messages by involving the target groups
in experiential activities” (Drengner et al. 2008, pg 138, emphasis added), the definition
proposed in this thesis removes words that imply a Goods-Dominant logic; e.g.
communication tool (see definitions from Gupta 2003; Leischnig et al. 2011; Weihe et al.
2006 in Table 2-4), promote or disseminate (see Close et al. 2006; Drengner et al. 2008;
Leischnig et al. 2011 in Table 2-4), and involving (see Drengner et al. 2008 Table 2-4).
The emphasis on BMEs as a platform for customer-initiated experiences creates a stronger
alignment of the proposed definition to the S-D logic (Vargo and Lusch 2008). The
proposed definition also maintains a strong reference to interactions within the experience.
Interaction is recognised in marketing events definitions (see Getz 2008; Whelan and
Wohlfeil 2006 in Table 2-4) and is a central characteristic of customer engagement (Brodie
et al. 2011a).
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The broader construct of brand experience is considered to encapsulate event experiences
(Brakus et al. 2009). Brand experience includes numerous brand-related stimuli, including
branding, communications, and the environments and situations in which the brand is
experienced, including events (Brakus et al. 2009). This thesis focuses on brand-related
stimuli from a BME; therefore, the researcher refers to this as event experience instead of
brand experience.
The proposed definition identifies that BMEs are a platform through which the brand and
customer can interact. This platform contains multiple touch points where the customer can
interact with the brand, specific event activities and/or other customers attending the event.
While this thesis does not examine customer engagement with each of these individual
touch points, it is recognised that overall customer event engagement is an aggregate of
numerous touch points.
Finally, as the term ‘branded marketing event’ is used in this thesis, it is acknowledged
that extensive literature exists around brands and branding. A brand is defined as “an
identifiable product, service, person or place augmented in such a way that the buyer or
user perceives relevant unique added values which match their needs more closely” (De
Chernatony and Dall’Olmo Riley 1998, pg 424). This research domain is broad in scope,
with various approaches including brand equity (Keller and Lehmann 2006), brand value
(Kamakura and Russell 1993), brand performance (Harris and De Chernatony 2001), brand
salience (Romaniuk and Sharp 2004) and brand perceptions (Romaniuk and Sharp 2003).
There are often conflicting perspectives within this body of literature; for example Sharp
and Sharp (1997) argue that brand loyalty has minimal impact on repeat purchase, while
Chaudhuri and Holbrook (2001) emphasise the considerable brand impacts resulting from
brand loyalty.
However, this literature area tends towards a goods-dominant logic perspective, in that
companies develop a brand proposition and communicate this to customers; this is
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considered a dated and narrow-focused approach (Vargo and Lusch 2004). Researchers are
encouraged to ‘break free from the industrial age paradigm of branding’ and consider an
expanded view of brand-customer relationships, where brands connect with and are shaped
by customers (Christodoulides 2008). This thesis adopts this S-D logic perspective. It
recognises that customers do not passively receive information, but instead have an
individualised perception of ‘value’, including a unique and individually determined
perception of a brand (Vargo and Lusch 2014). Therefore, while the branding literature
body is acknowledged, it is not a central focus of this thesis. Instead, this thesis builds from
customer engagement, marketing events and customer experience literature, where direct
applicability to S-D logic is apparent.
2.3.2.2 Investigating a Broader Conceptualisation of BMEs: Customer Experience
Marketing events literature has taken a narrow approach in its identification of different
event types. Studies have typically highlighted the ability of events to have entertainment
or educational value (see Leischnig et al. 2011 Table 2-4). This dichotomy is based on the
notion that events target attendees on an emotional level, while at the same time engage
and interact with the consumer, creating the ability to strongly communicate brand-related
information (Drengner et al. 2008). Empirically, studies have taken a focus predominantly
on entertainment events, however researchers have identified the need for various ‘types’
of events to be considered (Leischnig et al. 2011; Packer and Ballantyne 2004; Whelan and
Wohlfeil 2006). This thesis consults customer experience literature to inform a broader
range of event types that may facilitate customer engagement.
Many marketing events studies refer to the customer experience literature in developing
their understanding of event experiences and types (Crowther 2010; Leischnig et al. 2011;
Whelan and Wohlfeil 2006; Wohlfeil and Whelan 2006; Wood 2009). For example,
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marketing events that recognise the importance of customer experience. As marketing
events are highly experiential in nature, it is paramount that BMEs are informed from
customer experience in order to ensure their effectiveness (Crowther 2010). This thesis
explores the broader conceptualisation offered in customer experience literature to
establish a more complete and exhaustive view of BME experiences.
2.3.3 Customer Experience
The emergence of customer experience has seen increased popularity as researchers and
practitioners seek alternative media that recognise consumers’ needs for novelty and
individualism (Schmitt 1999). Customer experience is a customer-centric concept, and
encompasses all interactions and experiences between a customer and a brand (Gentile et
al. 2007), including those outside of regular consumption activity. This concept is a
depiction of the view of experience taken in this thesis, as it takes a customer-centric
approach and captures the broad set of experiences that BMEs contain.
There is some confusion around the use of ‘experience’ as a verb or as a noun (Palmer
2010). This thesis adopts the perspective of experience as a noun; “a process of undergoing
and living through an event” (Palmer 2010, pg 197). This perspective aligns with Pine and
Gilmore’s (1998) popular conceptualisation of experience; a memorable event that a brand
creates using their goods and services as central elements of that experience to engage
customers. This conceptualisation also makes the clear connection between events and
customer experience. It is widely recognised that an event falls within the plethora of
customer experiences (Gentile et al. 2007; Oh, Fiore, and Jeoung 2007; Schmitt 1999;
Yuan and Wu 2008), and as such, customer experience measures are used to capture a
BME experience. BMEs are conceptualised in this thesis as the platform in which customer
engagement occurs, and event experiences occur within this platform through various
customer interactions.
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2.3.4 Components of Experience within a BME
Customer experience has been referred to as encompassing all interactions and experiences
between a customer and a brand (Gentile et al. 2007) including those outside of regular
consumption activity. This conceptualisation indicates that customer experience is a very
broad concept, capturing a wide diversity of interactions, and therefore requires a method
of classifying this diversity and simplifying an otherwise complex construct. While ‘BME
experiences’ are identified as a specific type or subset of experience to be explored in this
thesis, the diversity of experiences remains. As identified in the previous discussion of
marketing events, events are provided by the host brand and are tailored to suit the brand’s
objectives (Wood 2009), demonstrating the highly unique nature of events. Therefore,
identifying specific experiential components of a BME is important in gaining a better
understanding of how BMEs facilitate customer engagement.
Customer experience studies have commonly developed typologies of experiential
components that capture the totality of the experience. The experiential components
utilised in this thesis are Cognitive, Emotional, Sensorial, Pragmatic and Relational
(Gentile et al. 2007) and reflect the diversity and unique nature of experiences.
There are many commonly recognised dimensions of a customer experience, including the
sensorial, emotional and cognitive experience components (Brakus et al. 2009; Chang and
Chieng 2006; Gentile et al. 2007; Sahin, Zehir, and Kitapçı 2011; Schmitt 1999; Tynan and
McKechnie 2009; Yuan and Wu 2008). Additional experiential elements, including
pragmatic, lifestyle and relational aspects of the experience are also often proposed and
evaluated in academic studies (Chang and Chieng 2006; Sahin et al. 2011; Tynan and
McKechnie 2009). The conceptualisation of Gentile et al. (2007) takes the broadest
perspective of these studies and encompasses all of the commonly identified experiential
components proposed in this literature space; Cognitive, Emotional, Sensorial, Pragmatic,
Relational and Lifestyle. This perspective of experience is considered robust in terms of
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covering the social/relational elements (Chang and Chieng 2006; Schmitt 1999; Tynan and
McKechnie 2009) as well as the behavioural or ‘pragmatic’ components (Brakus et al.
2009; Chang and Chieng 2006; Sahin et al. 2011; Schmitt 1999). Given the diverse nature
of BMEs due to the varying objectives of the host brand and the unique interactions
created during events (Wood 2009), the components are considered applicable across
various types of experiences and therefore they are utilised within the context of BMEs in
this thesis.
Gentile et al. (2007) also include a ‘lifestyle’ component of experience, which is not
present in many studies (Brakus et al. 2009; Schmitt 1999; Yuan and Wu 2008) and is not
listed explicitly in the study by Chang and Chieng (2006), but was included as a survey
item within their measurement of pragmatic experience. Due to the ambiguity of the
lifestyle component, commonly excluded or embedded within other experiential
components in previous studies, it has not been incorporated in this thesis. Table 2-5
outlines the definitions of each experiential component.
TABLE 2-5: EXPERIENTIAL COMPONENTS WITHIN A BME
Sensorial Experiences that aim to provide positive sensory stimulation, addressing sight, hearing, touch, taste and/or smell
Emotional Experiences that evoke an affective response or relation (with a company, brand or products), by targeting moods, feelings and/or emotions
Cognitive Experiences that stimulate thought or conscious mental processes
Pragmatic Experiences that involve physical action – “the practical act of doing something”
Relational Experiences that provide social context and relationships with others
Source: Definitions adapted from Gentile et al. (2007)
An additional reason for utilising this conceptualisation of experiential components is that
Gentile et al. (2007) apply a conceptual lens consistent with the S-D logic perspective
taken in this thesis. Gentile et al. (2007) recognise that customers actively create their
experience, rather than passively receive the experience from a company. BMEs may
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comprise one, several or all of these components (Gentile et al. 2007) and in providing the
platform through which customers undertake these experiences, BMEs can facilitate
customer engagement.
2.3.5 Outcomes of BMEs experiences
Events are described as a ‘hyperreality’, a highly engaging and memorable experience that
is shaped to communicate a particular message about the brand (Whelan and Wohlfeil
2006). Events have the ability to build strong customer relationships and create an
association between the brand and the qualities of the event (Wood 2009). BMEs can
provide numerous benefits for brands; they can enhance awareness and familiarity of the
brand, create strong, positive brand images, influence consumer attitudes, and create
emotional brand attachment (Whelan and Wohlfeil 2006). Previous marketing events
studies have identified numerous outcome variables, most commonly event satisfaction
(Leischnig et al. 2011), influencing brand image (Drengner et al. 2008), creating positive
brand opinion (Close et al. 2006) and influencing customer attitudes (Martensen,
Gronholdt, Bendtsen, and Jensen 2007; Sneath et al. 2005). In addition, marketing events
can result in enhanced attitudes towards the brand (Leischnig et al. 2011; Weihe et al.
2006) and purchase intention (Close et al. 2006; Martensen et al. 2007; Whelan and
Wohlfeil 2006). However, customer engagement as an outcome of a BME experience has
not received explicit attention.
It is likely that customer engagement was facilitated during the interactions and
experiences of these events, and contributes to the relationships identified in these previous
studies (e.g. events leading to purchase intention) without specific inclusion in their
research. Introducing customer engagement into this framework can therefore provide a
more comprehensive view of how BMEs facilitate engagement. In addition, customer
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while others do not; customer engagement must be facilitated in order for brand-related
outcomes to occur, which implies that engagement is a mediating variable within this
relationship.
This thesis identifies the pivotal relationship between BME experience and customer
engagement. Due to the highly personalised and interactive experience that both the
customer and the host brand uniquely create, this marketing approach has the ability to
create strong connections and facilitate engagement (Crowther 2010). Therefore, this thesis
posits that various components of experience can facilitate customer engagement. Brands
provide the resources that drive various experiences to occur through the BME platform;
the customer then contributes their own resources as they interact and create value through
this unique experience. It is therefore recognised in this thesis that BMEs are a suitable
platform in which to facilitate customer engagement; utilising the concepts from the
literature bodies of marketing events and customer experience informs further development
and understanding of how customer engagement is driven through the context of BMEs.
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2.4 Customer Engagement and BMEs: A Conceptual Framework
The central purpose of this thesis is to explore how BME experiences facilitate customer
engagement. Therefore, the following section draws from marketing event, customer
experience and customer engagement literature to outline the sources of similarity between
the constructs, and provide insight into how various BME experiences drive customer
engagement. The complementarity of these constructs is assessed with reference to various
principles of the S-D logic; specifically, the common perception taken in these literature
bodies that customers are drivers of their own unique experience (Calder et al. 2013; Close
et al. 2006) and their common emphasis on interaction (Brodie et al. 2011a; Whelan and
Wohlfeil 2006). Although S-D logic has been discussed as the theoretical underpinning to
customer engagement (Brodie et al. 2011a), the following discussion highlights its
relevance to the customer experience and marketing event literature and hence provide a
framework for understanding the relationships between BMEs and customer engagement.
Customer engagement research commonly refers to experiences as an element of
facilitating engagement (Brodie and Hollebeek 2011; Gambetti et al. 2012; Mollen and
Wilson 2010). Vivek et al. (2012) introduce a classification of customer engagement foci,
where provider-initiated activities, including events, are identified as one method of
facilitating engagement. This trend is mirrored in the customer experience literature, as
experiences with a brand are described as unique and personal, and can result in a
motivational state of engagement (Calder et al. 2013; Gentile et al. 2007).
Marketing events literature commonly refers to the ability of events to create unique
experiences, and uses customer experience literature to inform this element of events
(Crowther 2010; Leischnig et al. 2011; Whelan and Wohlfeil 2006; Wohlfeil and Whelan
2006; Wood 2009). Marketing events studies have also used the term engagement,
however do not conceptualise or explore engagement (Close et al. 2006; Whelan and
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Wohlfeil 2006). This thesis further integrates these constructs and subsequently informs a
strategic approach of using BME experiences to drive customer engagement.
The complementarity of marketing events, customer experience and customer engagement
is evident through their applicability to S-D logic. First, S-D logic reiterates that customers
are the drivers of value creation (Vargo and Lusch 2008) and the company only provides
value propositions or platforms through which customers interact and create their own
value. This message is echoed in customer experience literature; companies provide the
context for the experience to occur that enables the customer to create their own unique
experiences (Gentile et al. 2007; Zomerdijk and Voss 2010). Customer experience
literature also recognises that events are highly subjective in nature (Brakus et al. 2009)
and therefore the individual perceives and determines the value in the experience. Research
in marketing events has also made the connection with S-D logic (Crowther 2010;
Crowther and Donlan 2011), particularly emphasising that customers drive their own
unique experiences (Wohlfeil and Whelan 2006). A central element of customer
engagement is the interaction that occurs to create a heightened psychological state, and
thus is inherently subjective to the individual (Brodie et al. 2011a). A BME is therefore
considered an effective means of facilitating customer engagement, as it is interactive and
subjective in nature, whereby the individual determines and perceives value. This
experience, personal and unique to the individual, leads to engagement (Gentile et al.
2007). A BME should elicit strong customer brand engagement, the mechanics of which
are the focus of this thesis.
Second, S-D logic recognises that customers are not passive in their contact with firms;
rather they create value through extensive interaction to shape their brand experiences
(Vargo and Lusch 2004). Marketing events literature has primarily overlooked the active
participation of customers in the communication of the marketing message (Drengner et al.
2008). However, it is this aspect of the experience that makes BMEs a highly effective
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means of eliciting customer engagement (Brodie et al. 2011a; Vargo and Lusch 2004).
Participants interact extensively with other participants and brand representatives during an
event experience (Wohlfeil and Whelan 2006). S-D logic is used in customer experience
literature to explain the interactions occurring during an experience (Crowther 2010).
Customer experience is argued to occur through interaction, and relies on the consumer
driving and uniquely creating their desired experience (Tynan and McKechnie 2009).
Interaction is a focal element of customer engagement, and is the primary quality
distinguishing it from related concepts such as involvement (Brodie et al. 2011a;
Hollebeek 2011a). Therefore, the interactive elements of a BME create an environment
conducive to customer engagement (Vargo and Lusch 2004; Vivek et al. 2012; Wohlfeil
and Whelan 2006). Specifically, the BME is the platform through which customer
engagement is facilitated as customers interact and create their own unique and valuable
experiences (Zomerdijk and Voss 2010). Through high levels of interaction within this
platform, customers create a BME experience that is of most value to them, and therefore
gain the most value from the experience (Prahalad and Ramaswamy 2004).
In summary, discussion in the literature of marketing events, customer experience and
customer engagement have commonalities with reference to their applicability to S-D
logic. Studies that comprehensively bring these literature bodies together are scarce.
Therefore, this thesis investigates BMEs as a brand-initiated engagement platform, which
encompasses activities beyond the normal offering of the organisation. This thesis
contributes to the knowledge of customer engagement through investigating how BMEs,
comprised of various experiential components, facilitate customer engagement.
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2.4.1.1 The role of Social Engagement within Customer Engagement
Social engagement refers to a dimension of engagement based on personal exchanges with
other actors; however, its presence and conceptualisation within the overall customer
engagement construct is often debated in the customer engagement literature (Calder,
Malthouse, and Schaedel 2009; Gambetti et al. 2012; Sawhney, Verona, and Prandelli
2005; Vivek et al. 2012). While the three dimensional perspective (cognitive, emotional,
and behavioural) of customer engagement is widely accepted (Hollebeek 2011b), various
customer engagement studies have included a social dimension of engagement (Vivek et
al. 2012), social-interactive engagement (Calder et al. 2009), or social elements of
engagement (Gambetti et al. 2012; Sawhney et al. 2005). Others have a strong social focus
throughout their discussion of customer engagement (Abdul-Ghani et al. 2011;
Algesheimer et al. 2005), but do not explicitly indicate social engagement as an
independent engagement dimension (Brodie et al. 2011b). This thesis argues that the
impact of social influences within the BME experience is substantial, and includes social
engagement as a dimension of the customer engagement construct.
Social exchange theory argues that customers engage in activities that provide emotional
rewards including social approval and human contact (Abdul-Ghani et al. 2011).
Customers interactively establish value with organisations and, given the opportunity, will
engage in practices to create value (Schau, Muñiz Jr, and Arnould 2009). Practices dictate
what is necessary for engaging social actors in a meaningful way within a particular setting
(Schau et al. 2009). Organisations should encourage customers to interact in order to drive
engagement with the brand (Schau et al. 2009). Therefore, including a social dimension of
engagement as separate and unique to the other dimensions of engagement is an important
development, as it captures the heightened psychological state of the customer during their
unique and meaningful interactions with other actors either in context of, or directly
towards, the brand.
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A definition of social engagement is the customer’s heightened level of interest regarding
the focus of engagement (i.e. the event or the brand) based on personal exchanges with
other actors. In this regard social engagement is an important addition as it takes a holistic
perspective and captures interactions beyond the control of the brand, but which also
contribute to the overall facilitation of customer engagement (Verhoef, Lemon,
Parasuraman, Roggeveen, Tsiros, and Schlesinger 2009). The inclusion of a social
dimension of engagement broadens the perspective of interactions from customer-brand to
include customer-customer in the context of brands (Kozinets 2014).
The definition of social engagement constructed for this thesis is consistent with So et al.’s
(2012) definitions of customer engagement dimensions (see section 2.2.2.3). A comparison
to So et al.’s (2012) enthusiasm definition is used identify the similarity in structure;
enthusiasm “represents an individual’s strong level of excitement and interest regarding the
focus of engagement, such as a brand” (So et al. 2012, pg 5). The social engagement
definition follows the same structure of identifying the engagement subject (the customer),
the engagement state (heightened level of interest), the engagement object (regarding the
focus of engagement i.e. the event or the brand) and an outline of the social nature of the
engagement (based on personal exchanges with other actors). A recent conceptual paper by
Kozinets (2014, pg 10) is the first to provide an in-depth conceptualisation and definition
for social brand engagement; “meaningful connection, creation and communication
between one consumer and one or more other consumers, using brands”. The Kozinets
(2014) definition is consistent with the social engagement definition constructed for this
thesis. The next section discusses customer social engagement with reference to the event
and the brand.
Research Question: What is the role of social engagement within the overall customer engagement construct?
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Social event engagement is defined as the customer’s heightened level of interest
regarding the event based on personal exchanges with other customers. This form of
engagement occurs when the customer has a personal exchange with other customers
within the context of, or with reference to, the event.
Customer engagement literature has conceptually acknowledged that social engagement
can occur with a provider-initiated activity or event (Kozinets 2014; Vivek et al. 2012). A
range of service encounters, including BMEs, are experienced either intentionally with
others, or in the presence of other customers (Tombs and McColl-Kennedy 2010; Zhang,
Beatty, and Mothersbaugh 2010). This has become considerably more important given the
current marketing trend of brands creating memorable experiences for their customers
(Zhang et al. 2010). Customers are constantly interacting both with brand representatives
and other customers in attendance of the event (Drengner et al. 2008). Therefore, whether a
person attends an event with others (friends, family) or interacts directly or indirectly with
others attendees unknown to the consumer but present at the event, personal exchanges are
abundant. The personal exchanges pertaining to the event can contribute to the customer’s
heightened psychological state with reference to the event, and hence build their level of
customer event engagement. As a result of these insights, the following hypothesis is
proposed;
Hypothesis 1a: Social event engagement is a dimension of customer event
engagement.
Social brand engagement is defined as the customer’s heightened level of interest
regarding the brand based on personal exchanges with other customers. This form of
engagement occurs when the customer has a personal exchange with other customers about
or with reference to the brand. This conversation builds the customer’s interest in the brand
due to the brand-related information exchanged between the customers. The host brand is
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not directly in control of this social exchange, however can offer opportunities for
customers to interact (i.e. during a BME) and hence facilitate social brand engagement.
A number of the studies with a focus on social engagement are set in an online context
(e.g. Calder et al. 2009), which is considered to have a high level of customer-to-customer
as well as customer-to brand interactions within the virtual space (Abdul-Ghani et al. 2011;
2011b). This level of social interaction is equally applicable to BMEs, as they are
fundamentally a social activity (Kozinets 2014). The interactions with other customers
within the same experience can have a considerable impact on either enhancing or
damaging that customer’s experience with the associated brand (Zhang et al. 2010).
Therefore, social brand engagement is an important consideration within a BME due to the
large opportunity for personal exchanges between customers to occur, and for the brand-
related discussion within this exchange to contribute to the customer brand engagement
construct. Therefore, the following hypothesis is proposed:
Hypothesis 1b: Social brand engagement is a dimension of customer brand
engagement.
2.4.2 Relationships between Experiential Components of a BME and Customer
Engagement
The marketing events literature provides a sound argument and theoretical justification for
events to facilitate customer engagement through their mutual applicability to S-D logic.
However, little research has embraced the engagement literature to provide an
understanding of how marketing events engage customers. A BME is an effective means of
creating meaningful experiences as it is interactive and subjective in nature, leading to
customer engagement (Brodie et al. 2011a; Calder et al. 2013).
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While social exchange theory (Möller 2013; Saks 2006) was discussed in the previous
section (2.4.1.1) in the context social engagement, it can also be used to explain the
relationship between BME experiences and customer engagement on a broader level. This
theory explores reciprocity between the brand and the customer, in which both parties
actively contribute to the relationship in order to receive mutual benefits or avoid potential
risks (Cropanzano and Mitchell 2005; Möller 2013). While social exchange theory has
been previously discussed in the context of employee engagement (Saks 2006), its
applicability extends to customer engagement insofar as it highlights the contribution of
resources from both the brand and the customer.
Stronger engagement results from both parties contributing resources, interacting and
abiding by the implied ‘rules of exchange’ (Saks 2006). The actions and reactions from
both parties over time are a process of building a mutually beneficial relationship, for
example increased customer trust and commitment (Cropanzano and Mitchell 2005). The
conclusion drawn from this theory is that extensive contribution from the consumer (i.e.
their engagement) and the brand (in this context the provision of a BME) will result in
mutually beneficial outcomes.
The brand resources within this exchange are the provision of various components of
experience through the BME platform. Marketing events studies have identified categories
of events; however, as previously discussed this categorisation is relatively narrow in its
focus. As a result, customer experience literature is examined to find a more
comprehensive conceptualisation of experience. This thesis investigates five experiential
components; Cognitive, Emotional, Sensorial, Pragmatic, and Relational (Gentile et al.
2007).
Research Question: How do the experiential components of a BME facilitate customer engagement?
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The following section identifies the ability for each experiential component to contribute to
customer engagement. Each experiential component is discussed, and the resulting
customer event engagement and customer brand engagement outcomes are proposed. The
connections are proposed by identifying the brand-provided resources within the exchange
(BME experiential component) and the logical corresponding resources provided by the
customer (dimensions of customer engagement) (Saks 2006).
The hypotheses identify the general relationship anticipated between the experiential
component, customer event engagement and customer brand engagement. It is
acknowledged that specific experiential components may contribute to individual customer
engagement dimensions (e.g. attention, absorption, immersion) as opposed to customer
engagement in a general sense; however, this is beyond the scope of this thesis. Therefore,
customer engagement outcomes are considered from an overall perspective in this thesis,
not on particular engagement dimensions, as each should contribute to and enhance each
other and result in a general level of customer engagement (Brodie et al. 2011a). Future
research should investigate the more particular relationships concerning the dimensions of
engagement, and this is discussed in Chapter 5.
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2.4.2.1 Cognitive Experience
A cognitive experience is one that requires the participant to actively think, or aims to
stimulate thought or conscious mental processes in a particular area of interest or
knowledge (Gentile et al. 2007). In the context of the wine industry a wine education event
is an example of a strong cognitive experience. For a cognitive experience, the brand-
provided resources encompass information designed to make the customer think and
possess intellectual appeal; cognitive experiences therefore appeal to customers through
stimulating intrigue (Schmitt 1999). For example, wine education sessions involve
teaching the attendee about various topics including the winery’s history, blends or the
process of wine production.
In order for customer engagement to occur, the customer must also provide resources
within the BME and contribute to the exchange (Saks 2006). Customers are anticipated to
elicit event attention, displaying attentiveness during the event (So et al. 2012; Tynan and
McKechnie 2009) and a heightened level of concentration in the event interaction
(Hollebeek 2011b) in response to the information and cognitive stimulation provided
during the BME. Customer event engagement therefore occurs when the customer elicits a
willingness to invest mental effort into the cognitive experience (Salanova et al. 2005).
Learning through a cognitive experience requires active participation of the customer (Pine
and Gilmore 1998), for example thinking through ideas and asking questions during a wine
education session or participating in demonstrations that reflect a discussion topic such as
the fermentation process (Charters and Ali-Knight 2000; So et al. 2012). Customers could
therefore engage through event interaction, actively participating in the event experience
by discussing topics with winemakers or other customers, or participating in
demonstrations (So et al. 2012).
In addition, customers could respond to the brand-provided resources during cognitive
BME experiences with event enthusiasm (So et al. 2012). If the customer possesses a
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strong interest in wine, they would find the experience of learning about wine enjoyable
(Packer and Ballantyne 2004), eliciting a heightened level of affect in response to the
information provided during the BME (Hollebeek 2011b).
The brand-provided resources in a cognitive experience also have the capacity to facilitate
customer brand engagement if the resources within the BME experience are brand-centric.
Wine education sessions are likely to focus on brand-related information, for example the
discussion is likely to be about the wine brand’s varietals or how their particular wine is
produced (Charters and Ali-Knight 2000).
Cognitive experiences that involve thinking and mental processes directly related to the
brand (e.g. discussing the different processes of producing wine, different varietals) change
the customer’s perception of the brand and its products (Yuan and Wu 2008). Education
events are described as eliciting customer interaction, however the customer’s focus is not
necessarily on the event but rather the content shared during the event (Pine and Gilmore
1998). Customers can also respond to a cognitive experience with enhanced identification
with reference to the brand (So et al. 2012); the customer feels that the brand is a means of
self-expression (Sprott et al. 2009). For example, a wine connoisseur, having experienced a
cognitive event and learning more about a particular wine brand, would feel that their
knowledge related to that wine brand contributes to their wine lifestyle, hence causing
them to feel strong brand identification (So et al. 2012).
Therefore the following hypotheses are proposed;
Hypothesis 2a: Cognitive event experience contributes to customer event engagement
Hypothesis 2b: Cognitive event experience contributes to customer brand engagement
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2.4.2.2 Emotional Experience
An emotional experience is one that evokes an affective response and targets moods,
feelings and/or emotions (Gentile et al. 2007). An example of an emotional experience in
the wine industry is a winery picnic, where attendees are encouraged to relax and enjoy
live music. For an emotional experience, the brand-provided resources include the source
of entertainment designed to generate customer enjoyment (Tynan and McKechnie 2009)
and provide emotional value (Yuan and Wu 2008). Emotional experiences therefore appeal
to the customer’s emotions by stimulating their excitement, joy and interest in the activity
(Schmitt 1999; So et al. 2012). For example, winery picnics or music events are run to
elicit relaxation, leisure and enjoyment (Jingxue et al. 2008).
The anticipated customer resources contributed to this exchange include event enthusiasm,
displaying positive affect during the event (Hollebeek 2011b) and a heightened level of
excitement and interest regarding the event interaction in response to the source of
entertainment provided during the BME (So et al. 2012; Vivek et al. 2012). Customer
event engagement therefore occurs when the customer elicits enthusiasm and willingness
to further interact in the event activities (So et al. 2012). Depending on the intensity of the
experience, the customer can also elicit event absorption due to the strong emotional
qualities the event provides (Drengner et al. 2008; So et al. 2012). For example, during a
music event at a winery, the customer may feel such excitement and enthrallment in the
music that they experience a state of flow, becoming completely engrossed in the
experience and lose sense of time (Csikzentmihaly 1990; Vivek et al. 2012).
The brand-provided resources in an emotional experience also have the capacity to
facilitate customer brand engagement if the resources within the BME experience are
brand-centric. Winery picnic events can have an emphasis on the brand, for example
serving the brand’s wines and providing tastings during the event. Emotional experiences
can also reflect or embody the image of the brand (Drengner et al. 2008), leading to
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customer brand engagement; for example, a music event at a winery could emphasise the
brand’s youthful image by showcasing new bands with a younger following, or project an
image of elegance or sophistication by playing jazz or opera. If the brand’s image is
evident in the emotional experience, the customer’s heightened interactions during the
BME can create a closer connection to the brand (Wohlfeil and Whelan 2006) and event
enthusiasm (So et al. 2012). Customers can also respond to an emotional experience with
enhanced identification with reference to the brand (So et al. 2012) if the customer feels
that the brand’s image is consistent with their own self-image.
Therefore the following hypotheses are proposed;
Hypothesis 2c: Emotional event experience contributes to customer event engagement
Hypothesis 2d: Emotional event experience contributes to customer brand engagement
2.4.2.3 Sensorial Experience
A sensorial experience is one that provides positive sensory stimulation, addressing sight,
hearing, touch, taste and/or smell (Gentile et al. 2007). A wine industry example of a
sensorial experience is a wine tasting or wine and food pairing event.
For a sensorial experience, the brand-provided resources encompass sources of sight,
sound, scent, taste, and touch (Yuan and Wu 2008) designed to provide sensory meaning
and stimulation (Gentile et al. 2007; Schmitt 1999). The customer-provided resources in
this exchange include event interaction, displaying participation in event activities (Mollen
and Wilson 2010) and a heightened level of energy toward the event interaction in
response to the sensorial elements provided during the BME (Hollebeek 2011b). The focal
element of a sensorial experience is sensory stimulation (Gentile et al. 2007); therefore the
experience inherently requires active participation from the customer (they must taste,
touch, smell something).
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In addition, sensorial experiences appeal to customers through aesthetics or excitement
(Schmitt 1999) stimulating event enthusiasm (So et al. 2012). For example, wine
consumption is strongly related to leisure activity and aesthetic consumption (Charters and
Pettigrew 2005), in particular wine and food which trigger the human senses of taste
(Gentile et al. 2007). Event enthusiasm is facilitated by a sensorial experience when the
customer elicits feelings of pleasure, happiness, or a positive mood towards the event
(Hollebeek 2011b).
The brand-provided resources in a sensorial experience also have the capacity to facilitate
customer brand engagement if the resources within the BME experience are brand-centric.
For example, customers pay attention to brand-related information during a wine tasting
event (So et al. 2012), as tasting the wines or pairing wines with food provides sensory-
related information (e.g. what varietals of the brand’s wine the customer enjoys, or the type
of food that matches with particular wine products). Sensorial experience is therefore
posited to contribute to customer brand engagement as the customer gives their attention in
order to acquire new brand knowledge through learning wine tastes and smells, or
recognising the taste of appropriate food and wine pairings (Schmitt 1999). For the wine
connoisseur, the sensorial experience becomes a means through which customers learn
about the brand and gain knowledge in an area of interest to them (Charters and Ali-Knight
2000).
Therefore the following hypotheses are proposed;
Hypothesis 2e: Sensorial event experience contributes to customer event engagement
Hypothesis 2f: Sensorial event experience contributes to customer brand engagement
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2.4.2.4 Pragmatic Experience
A pragmatic experience requires physical behaviours or actions from the customer (Gentile
et al. 2007). This is likened to ‘escapist experiences’ conceptualised by Pine and Gilmore
(1998), and involves a high level of customer immersion in the activity. In the context of
the wine industry a grape-stomp or a wine-blending event is an example of a pragmatic
experience.
For a pragmatic experience, the brand-provided resources encompass physical activities
designed to stimulate active customer participation (Mollen and Wilson 2010). For
example, wine-blending events involve extensive customer participation in the wine
production processes. The customer-provided resources in this exchange are anticipated to
include event interaction (Pine and Gilmore 1998), displaying considerable effort and
energy elicited during the event (Hollebeek 2011b) and a heightened level of behaviour in
the event interaction in response to the physical elements provided during the pragmatic
BME experience (So et al. 2012). Customer event engagement therefore occurs when the
customer elicits a willingness to participate and elicit energy into the pragmatic experience
(So et al. 2012).
Customers could also respond to the brand-provided resources during pragmatic BME
experiences with event attention (Pine and Gilmore 1998; So et al. 2012). If the customer
has a strong wine involvement, they would find the experience of participating in wine-
making engaging and elicit a heightened level of interest and focus in order to attain new
brand-related information (Hollebeek 2011b), eliciting a heightened level of event
attention in response to the physical activity provided during the BME (So et al. 2012).
In addition, customers could elicit a heightened level of excitement and interest in response
to the pragmatic experience (Vivek et al. 2012) if they experience pleasure or novelty in
participating in a pragmatic experience. For example, customers participating in a grape-
stomp or creating their own wine blend find great enjoyment, excitement, and novelty in
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the experience, resulting in a heightened state of positive affect and interest in the BME
(Hollebeek 2011b; Vivek et al. 2012). A highly unique, novel or exciting pragmatic BME
experience could also elicit event absorption in which the customer experiences a
heightened state of flow, engrossment and complete concentration and happiness during
the activity (Csikzentmihaly 1990; So et al. 2012).
The brand-provided resources in a pragmatic experience also have the capacity to facilitate
customer brand engagement if the resources within the BME experience are brand-centric.
A wine-blending event is brand-centric, as the customer participates in the production
process of making their own unique wine blend based on the existing varietals on offer at
the winery. Pragmatic experience is therefore posited to contribute to customer brand
engagement as the customer gives their attention (So et al. 2012) in order to acquire new
brand knowledge through learning about the wine product process and blending of wine
varietals (Schmitt 1999).
Therefore the following hypotheses are proposed;
Hypothesis 2g: Pragmatic event experience contributes to customer event engagement
Hypothesis 2h: Pragmatic event experience contributes to customer brand engagement
2.4.2.5 Relational Experience
A relational experience is one that emphasises the social context and relationships with
others (Gentile et al. 2007). Events are typically public with many people in attendance,
and so the entire experience occurs within a social context (Zhang et al. 2010). Wine
dinner events or meeting the winemaker are examples of relational BME experiences in the
wine industry.
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For a relational experience, the brand-provided resources encompass activities designed to
be experienced together with other people (Gentile et al. 2007); relational experiences
therefore appeal to customers through social event engagement, a heightened state of
interest regarding the event based on these personal exchanges with other customers
(discussed in section 2.4.1.1) (Kozinets 2014; Vivek et al. 2012). For example, wine dinner
events involve large groups of people, either known or unknown to each other,
participating in a food and wine degustation; the customers are seated together, and
encouraged to mingle and socially interact during the dinner. These interactions can induce
a heightened sense of connectedness with other customers in the context of the event
(Kozinets 2014), providing social value (Tynan and McKechnie 2009).
In addition, customers could respond to the brand-provided resources during relational
BME experiences with event enthusiasm (So et al. 2012). Interactions with winemakers or
other attendees during a relational experience are intended to be enjoyable, exciting
experiences due to the aesthetics and leisure associated with wine activities (Jingxue et al.
2008). Therefore, the customer would elicit a heightened state of excitement and emotional
connection to the event (So et al. 2012) in response to an experience that connects the
customer to a broader social context (Schmitt 1999).
The brand-provided resources in a relational experience also have the capacity to facilitate
customer brand engagement if the resources within the BME experience are brand-centric.
Wine dinner events and ‘meet the winemaker’ events are likely to include brand-related
information, for example the winemaker talking about their experiences working at the
winery (Charters and Ali-Knight 2000). Therefore, while the brand-provided resources are
the activities that connect people (Schmitt 1999), the exchanges that occur during the
relational experience are cognitive in nature and specific to the brand (Yuan and Wu
2008). The customer therefore provides their focus and concentration to brand-related
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information relayed by the winemaker or other attendees, creating a heightened state of
brand attention (So et al. 2012).
In addition, a relational experience can also provide a means of affirming the customer’s
identity and sense of belongingness as the experience connects the customers to
likeminded others in a social group (e.g. the ‘wine’ lifestyle) (Gentile et al. 2007). For
example, a wine dinner places the customer in a situation with other likeminded customers;
their shared interest in the wine brand reinforces the customer’s self-image and need to be
positively perceived by others (Schmitt 1999; So et al. 2012). Therefore, the customer
responds to the relational experience with a heightened sense of self-image and connection
toward the brand (So et al. 2012), as the brand is a focal element of the experience.
Therefore, the following hypotheses are proposed;
Hypothesis 2i: Relational event experience contributes to customer event engagement
Hypothesis 2j: Relational event experience contributes to customer brand engagement
While the connections between the various components of event experience are proposed
to contribute to customer event engagement and customer brand engagement, it remains
unclear what relationship exists between the two customer engagement constructs. The
following section investigates the interplay between the two engagement objects.
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2.4.3 The Interplay between Customer Engagement Objects
Organisations host BMEs to achieve brand-related outcomes; to create a closer connection
with their customers and interact in a unique way to create value (Wood 2009). While it is
established that provider-initiated activities are a means of facilitating customer
engagement (Vivek et al. 2012), a BME experience is beyond the normal interactions with
a brand. It is therefore important to investigate if and how the connection is made between
the event experience and the host brand, as this impacts whether organisations receive any
benefit in hosting BMEs.
Customer engagement recognises that either the customer or the provider is the initiator of
engagement (Vivek et al. 2012) and highlights the need for an interaction between a focal
object and the customer (Brodie et al. 2011a; Hollebeek et al. 2014). In this regard,
customer event engagement occurs from the interactions during the BME (Vivek et al.
2012). Although Vivek et al. (2012) recognise that providers may initiate activities or
events to engage customers with the event, they do not explain how the nature of
interaction with the event facilitates customer brand engagement.
The literature on customer engagement identifies that an interaction must occur between
the ‘engagement subject’ (e.g. the customer) and an ‘engagement object’ (e.g. a brand)
(Hollebeek 2011a), however, extant studies do not explore the relationship between
multiple engagement objects. Brodie et al. (2011b) identifies that customers can engage
with numerous engagement objects but these objects were in reference to ‘themes’ of
discussion with members of an online community. These multiple objects were not
described as having a causal relationship, but rather as specific ‘topics of interest’ in which
a community member may engage. Brodie et al. (2011b) also mentioned a possible
relationship between the online community engagement objects (themes), however this
was not explored.
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It is proposed in this thesis that the engagement subject, the customer, will interact and
create value during an event experience and therefore will elicit customer event
engagement. However, with an understanding that ‘brand experience’ encompasses a broad
range of brand-related stimuli, including environments in which the brand is present (e.g.
events) (Brakus et al. 2009), it is also posited that a relationship exists between customer
event engagement and customer brand engagement. The relationship between customer
event engagement and customer brand engagement is not explored in the customer
engagement literature, making it a main contribution of this thesis.
There are a number of marketing events studies that investigate brand-related outcomes
resulting from events (Crowther 2011; Martensen et al. 2007; Weihe et al. 2006; Whelan
and Wohlfeil 2006). Marketing events are expected to create customer engagement with
the brand due to the high level of brand information and brand-related experiences within
the event (Whelan and Wohlfeil 2006). In addition, positive brand outcomes arise when the
event experience accurately depicts brand personality or desired brand images and values
(Crowther 2011). Therefore, a relationship between BMEs and host brand outcomes is
recognised in marketing events literature that is not explained in customer engagement
literature. This thesis contributes to the engagement literature by exploring customer event
engagement and its relationship with, and ability to further facilitate, customer brand
engagement.
Associative network theory (ANT) is the underpinning of this phenomenon. ANT is
founded in psychology from similar theoretical domains including ‘spreading activation
theory in memory’ (Anderson 1983), and ‘semantic processing’ (Collins and Loftus 1975).
Research Question: What is the relationship between the engagement with two focal objects; customer event engagement and customer brand engagement?
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ANT provides an understanding of how memory operates and specifically the mental
process of connecting pieces of information in memory (Smith 2004). When two pieces of
information are received simultaneously they can be mentally matched-together and
produce transfer; the associations of the first piece of information are replicated with the
second (Smith 2004). Memory contains ‘nodes’, or individual pieces of information, that
are triggered or called to mind through a process of activation (Smith 2004). When a
person has an experience that triggers multiple nodes simultaneously, connections can be
formed in memory which causes information of one concept to activate information of
another; this is referred to as ‘spreading activation’ (Smith 2004).
ANT has been used to understand various phenomena in the field of marketing; for
example celebrity endorsement, brand extensions (Keller and Aaker 1992) and sponsorship
(Smith 2004). In a sponsorship context, customers retain information about events in
memory; through a process of ‘spreading activation’ certain sets of event-related
information can trigger thoughts about related information, in this case linked information
about the host brand (Smith 2004). Particularly within sponsorship literature, ANT is
described with reference to the concept of brand image transfer. Brand image transfer
describes a process whereby customers initially have specific associations towards an
event. When a brand is presented with this event and a perceived connection between the
two is established, the associations with the event can become linked and projected onto
the brand (Gwinner 1997; Gwinner and Eaton 1999). Brand image transfer is commonly
used in marketing events literature to describe common associations or attitudes placed
upon an event and host brand (Martensen et al. 2007).
This thesis extends ANT into the context of BMEs and customer engagement. A recent
sponsorship paper has taken an initial step in bringing ANT into the customer engagement
domain, finding that more interactive or engaging brands benefit from enhanced brand
recall compared to passive brands (Pokrywczynski and Brinker 2014). A particularly
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strong connection is anticipated for BMEs as these events are branded and tailored
specifically to relate to the host brand. As BMEs are proposed to facilitate high levels of
customer engagement, the benefit to the brand in hosting a BME is expected to increase.
This thesis further extends ANT into customer engagement literature and proposes that
spreading activation can also occur between engagement objects; from event engagement
to brand engagement. The customer first engages with the event and due to the strong
connection between the event and the brand, this state of engagement can also project onto
the brand. This relationship is yet to be investigated in customer engagement literature, and
hence is a contribution of this thesis. Therefore, the following hypothesis is proposed:
Hypothesis 3: There is a positive relationship between customer event engagement
and customer brand engagement
2.4.3.1 Customer Engagement to Behavioural Intention of Loyalty
Research on customer engagement has investigated various outcomes, including
satisfaction, loyalty, commitment and trust (Brodie et al. 2011b), however, relatively less
attention has been given to purchase intention outcomes; for example ‘brand usage intent’
(Hollebeek et al. 2014) and behavioural intention of loyalty (So et al. 2012). Given that
organising a BME is a considerable investment for the host brand, it is important to
determine the brand-related outcomes. Particularly for engagement activities that do not
necessarily stimulate an immediate monetary outcome for the brand, such as a BME, it is
important to investigate the anticipated brand-related outcomes resulting from customer
engagement. Evidence of brand benefits would further support BMEs as an antecedent of
customer engagement and an effective brand-building activity.
This thesis investigates behavioural intention of loyalty (BIL) toward the brand as an
indication of consumption behaviour (Zeithaml et al. 1996). Originally investigated as an
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outcome of service quality (Zeithaml et al. 1996), the BIL construct has been widely
utilised to reflect the outcomes resulting from relationship marketing (Hennig-Thurau,
Gwinner, and Gremler 2002) as well as engagement (So et al. 2012). Zeithaml et al. (1996)
conceptualise four categories of BIL; word-of-mouth, purchase intention, price sensitivity
and complaining behaviour. Service quality must surpass a certain satisfaction threshold in
order to impact BIL (Zeithaml et al. 1996); this is consistent with the idea of engagement
and BMEs in that extraordinary experiences or a heightened psychological state are
required to have a strong brand impact.
This thesis employs only two of the BIL categories proposed in Zeithaml et al. (1996);
word-of-mouth and purchase intention. The decision to adopt only two dimensions was
based on a number of considerations. First, results from Zeithaml et al.’s (1996) study
indicated ‘loyalty’ as the largest BIL factor, encompassing favourable behavioural
intention items including positive word of mouth/willingness to recommend, reporting the
brand as their first choice to buy and do business with in the future. Second, word-of-
mouth and purchase intention are already identified outcomes in customer engagement,
marketing events and customer experience literature; employing consistent outcome
variables in this thesis provides empirical support for these existing assertions in the
literature. Finally, as BMEs extend beyond normal customer-brand interactions (Vivek et
al. 2012) and do not necessarily involve a monetary transaction, it was debated whether the
constructs of price sensitivity and complaining behaviour maintained strong applicability
in this context.
The theory of consumption values (Sheth et al. 1991) provides a general framework to
explain consumption behaviour; the theory states that various consumption values
perceived by the customer are focal in explaining consumer choice with regards to
purchase (or not purchase) as well as brand selection (Sheth et al. 1991). This theory is
consistent with S-D logic and customer engagement in that uniquely created and
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individually perceived value is focal to the interactions between customers and brands
(Brodie et al. 2011a; Vargo and Lusch 2004). This theory has been applied to understand
experiences, and supports the link between customer engagement and brand-related
outcomes (Vivek et al. 2012); therefore, it provides insight into the relationship between
customer engagement (with the event and the brand) and behavioural intention of loyalty
investigated in this thesis.
The BIL dimensions of word-of-mouth and purchase intention have specifically been
utilised in So et al. (2012), however with reference to a different engagement object
(tourism brands). This thesis extends the research on BIL resulting from customer
engagement as it explores the impact from both an event and brand perspective. These
individual relationships are discussed in the following section.
The main objective of a company in hosting a marketing event is to lead to brand-related
outcomes, for example increased brand purchase intention (Crowther 2010; Drengner et al.
2008; Leischnig et al. 2011; Martensen et al. 2007) and word of mouth (Crowther 2011;
Gupta 2003; Wood 2009). Similarly, customer experiences have been found to impact on
loyalty intentions and customer satisfaction (Klaus and Maklan 2013) as well as word of
mouth (Grewal, Levy, and Kumar 2009) and purchase intentions (Palmer 2010). The
relationship between customer event engagement and BIL is consistent with the extant
literature as it captures brand word-of-mouth and purchase intention (Zeithaml et al. 1996).
A BME experience that facilitates customer event engagement can have positive outcomes
for the brand because the unique customer-brand interactions during the BME allow the
customer to construct relevant meanings about the brand, leading to loyalty (Crowther and
Donlan 2011). In summary, these studies support the presence of a direct relationship
Research Question: What impact does customer engagement have on behavioural intention of loyalty?
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between customer event engagement and behavioural intention of loyalty toward the brand.
Therefore the following hypothesis is proposed:
Hypothesis 4a: There is a positive relationship between customer event engagement
and behavioural intention of loyalty
The notion that customer brand engagement enhances a customer’s predisposition towards
a brand and hence increases behavioural intention of loyalty has been espoused in recent
literature (Bowden 2009; Hollebeek 2011a). The heightened psychological state and
interactions that occur between customer and the brand is thought to predispose the
customer to future purchases (So et al. 2012). This relationship is also supported is
customer experience literature; the attitudes formed from a brand experience are expected
to predict brand purchase intention (Zarantonello and Schmitt 2010). Despite these
previous assertions that customer brand engagement facilitates behavioural intention of
loyalty (So et al. 2012), there is little empirical examination of this relationship. Therefore
the following hypothesis is proposed:
Hypothesis 4b: There is a positive relationship between customer brand engagement
and behavioural intention of loyalty
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2.5 Experiential Needs
BMEs can facilitate customer engagement as they are unique customer-driven experiences,
creating significant customer value (Brodie et al. 2011a; Calder et al. 2013). Consistent
with this view, this thesis explores how individuals with varying experiential needs engage
with the event through different BME experiential components (cognitive, emotional,
sensorial, pragmatic and relational). It is expected that the fulfilment of experiential needs
will strengthen the relationship between BME experiential components and customer event
engagement.
2.5.1 Conceptualising Experiential Needs
Consumer needs are defined as requirements for something essential or desirable that is
lacking, and are categorised as utilitarian, means-to-an-end based needs, and expressive,
based on internal aspirations to fulfil a social or aesthetic need (MacInnis and Jaworski
1989). Within the category of expressive needs are experiential needs (MacInnis and
Jaworski 1989), the need that an individual seeks to fulfil through experiences based
broadly on their need for sensory or cognitive stimulation. People possessing a need for
sensory stimulation seek experiences that are novel, exciting and entertaining. They seek
variety, risk taking and adventure (Baumgartner and Steenkamp 1996; Orth and Bourrain
2005). Consumers with cognitive stimulation needs find motivation in information-seeking
or curiosity; these people seek experiences that aid them in acquiring or enhancing their
knowledge in a particular area of interest (Baumgartner and Steenkamp 1996; Orth and
Bourrain 2005). MacInnis and Jaworski’s (1989) experiential needs have conceptual
similarity to exploratory consumer behaviour, which suggests that individuals engage in
activities for intrinsic pleasure gained from a unique experience rather than for extrinsic
outcomes (Baumgartner and Steenkamp 1996). These behaviours are motivated by a desire
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for experiences that provide excitement, novelty, variety, or satisfy curiosity (Baumgartner
and Steenkamp 1996).
The influence of experiential needs is explained with optimum stimulation level (OSL)
theory; individuals seek out stimulation from particular environments in order to achieve
satisfaction (Steenkamp and Baumgartner 1992). Individuals differ in their perceived level
of ‘ideal’ stimulation, and will engage in exploratory behaviour in order to achieve their
ideal level of stimulation (Orth and Bourrain 2005; Steenkamp and Baumgartner 1992).
The role of experiential needs within this theory is in the delineation of the types of
stimulation and level of stimulation sought by the individual, as this will determine the
extent to which the individual engages in exploratory behaviour (Steenkamp and
Baumgartner 1992). Therefore, the individual’s experiential needs are an important factor
in explaining behaviour as it moderates the level of engagement facilitated in particular
environments (Steenkamp and Baumgartner 1992).
OSL theory is beneficial in the understanding of individual variations of facilitated
customer event engagement from particular BME experiences (Steenkamp and
Baumgartner 1992). The experiential needs of the individual have a moderating effect as
they influence the perceived relevance and attention given to certain stimuli, in this case
BME experiences (MacInnis and Jaworski 1989). The specific experiential needs
investigated in this thesis include need for cognition, need for affect and novelty-seeking
needs. These experiential needs were considered relevant to this thesis as each reflects key
motivations of customers identified in marketing events literature (Crompton and McKay
1997; Csikszentmihalyi 2000; Leischnig et al. 2011; Whelan and Wohlfeil 2006; Wohlfeil
and Whelan 2006). In addition, these experiential needs were consistently identified in
various consumer needs studies (Calder et al. 2009; MacInnis and Jaworski 1989; Orth and
Bourrain 2005; Steenkamp and Baumgartner 1992; Wilson 1997). It is expected that each
of these constructs influences the types of BMEs in which customer’s find value, and
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hence moderates the ability for event experiences to facilitate customer engagement. The
impact of consumer needs on customer engagement has not been empirically tested,
making this another contribution of this thesis.
While the potential moderating effect of a customer’s needs on customer engagement has
been noted in previous customer engagement research, little is known about the value of
this influence (Hollebeek 2011a). Individual-specific variables, for example the customer’s
need for cognition, are identified as potential moderators of customer engagement, as this
reflects the central themes of customer engagement being contextual in nature and driven
by the individual (Brodie et al. 2011a). As the experience during a BME is customer-
driven (Wohlfeil and Whelan 2006), it follows that their personal interests or needs will
influence their propensity to interact and engage with the various experiential components
of BMEs. It is expected that individuals who possess a strong experiential need (for
cognition, affect, or novelty-seeking) are more likely to elicit event engagement from
particular experiential components that align with or fulfil those needs (Higgins and
Scholer 2009). A greater understanding of the role of the individual’s experiential needs
will provide insight into why some BMEs are more effective at building customer
engagement with some groups of customers than with others (Crompton and McKay
1997).
Experiential needs delineate the various experiences individuals seek (Laurent and
Kapferer 1985; MacInnis and Jaworski 1989; Park and Young 1986). The experiential
needs of the individual impacts the perceived relevance and attention given to certain
experiences (MacInnis and Jaworski 1989), and therefore moderates the strength of the
relationship between particular BME experiences and customer event engagement
Research Question: Does an individual's experiential needs moderate the relationship between BME experiences and customer event engagement?
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(Steenkamp and Baumgartner 1992). The moderating effect of experiential needs has not
been investigated in customer engagement literature, and therefore is a contribution of this
thesis.
The broad framing of the following moderation hypotheses has been used because the
literature at this stage does not offer insight into particular moderator effects. Without a
clear theoretical foundation for such hypotheses, it was decided that an exploratory
approach be taken for this particular research question.
Need for cognition is broadly described as “the desire to be informed” (Wilson 1997, pg
553). Individuals with a high need for cognition find learning and acquiring new
knowledge enjoyable, and find great benefit in experiences that satisfy their curiosity about
topics of interest (Calder et al. 2009). Therefore, individuals who have a high need for
cognition are anticipated to engage in curiosity motivated behaviours (Steenkamp and
Baumgartner 1992), and find greater relevance in BME experiences that satisfy their need
for further learning, self-education or general curiosity (Calder et al. 2009). Following the
idea that ‘thinking is fun’ (Hallahan 2009), these individuals are more likely to interact
during cognitive BME experiences that are thought provoking, informative or educational,
which will facilitate customer engagement during the event. Need for cognition is
highlighted as a potential moderator of customer engagement, however has not been
investigated empirically (Brodie et al. 2011a).
Individuals with a high need for cognition are expected to elicit a stronger relationship
between cognitive experiences and customer event engagement. A cognitive experience
requires the participant to think and sparks interest through exchanging information and
knowledge (Gentile et al. 2007); individuals with a high need for cognition have a drive to
be informed, learn and acquire new knowledge (Calder et al. 2009), and therefore a
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cognitive experience is expected to satisfy their need for cognition (Packer and Ballantyne
2004).
Individuals with a high need for cognition could also facilitate a stronger level of customer
event engagement from pragmatic or relational BME experiences; for example, a relational
experience involving a wine-maker could involve sharing of information, or a pragmatic
experience such as the customer creating their own wine blend could teach them about
wine production. The individual’s need for learning and acquiring new knowledge is
satisfied from the information they receive through these relational and pragmatic
experiences (Calder et al. 2009), thus strengthening the relationship between these BME
experiences and customer event engagement.
Individuals with a low need for cognition are expected to display a weaker relationship
between cognitive BME experience and customer event engagement. These individuals do
not have a desire to learn and acquire new knowledge, and therefore will not elicit the
same level of interest or perceived the same relevance in experiences requiring them to
think, learn and exchange information (Gentile et al. 2007), and therefore the relationship
between cognitive experience and customer event engagement is reduced.
Therefore the following hypothesis is proposed:
Hypothesis 5a: An individual’s need for cognition from an experience will moderate
the relationship between the BME experience and customer event engagement
Need for affect refers to an individual’s motivation to “approach or avoid situations and
activities that are emotion inducing for themselves and others” (Maio and Esses 2001, pg
585). People who possess a strong need for affect actively seek emotional experiences and
stimuli in various aspects of their lives (Sojka and Giese 1997). Therefore, customers
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attending a BME who have a high need for affect are expected to engage to a greater extent
in emotionally-driven experiences that provide entertainment, escape, aesthetic enjoyment
and/or emotional release (Calder et al. 2009).
Individuals with a high need for affect are anticipated to have a stronger relationship
between emotional experiences and customer event engagement. A central focus of
emotional experiences is evoking an affective response or positive mood in the individual
(Gentile et al. 2007), for example a winery picnic. For an individual with a high need for
affect, this type of event would fulfil their need for an emotionally-driven experience
providing entertainment, enjoyment and escape (Calder et al. 2009; Maio and Esses 2001)
and therefore facilitate a stronger relationship between emotional BME experience and
customer event engagement.
Individuals with a high need for affect could also exhibit a stronger relationship between
sensorial experiences and customer event engagement. A sensorial experience provides
sensory stimulation, for example wine and food pairing, and wine is often considered a
form of aesthetic consumption (Charters and Pettigrew 2005). This experience could fulfil
the individual’s high need for affect through providing aesthetic enjoyment, positive mood
and entertainment (Sojka and Giese 1997), therefore strengthening the relationship
between sensorial experience and customer event engagement.
Individuals with a low need for affect are expected to demonstrate a weaker relationship
between emotional BME experience and customer event engagement. These individuals do
not actively seek emotional experiences (Sojka and Giese 1997), and while they may find
an emotional experience pleasant, it does not specifically fulfil any desired need they
possess. Therefore, an emotional experience would have less ability to elicit a heightened
state as the customer does not perceive unique value from the experience (Brodie et al.
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2011a), and the relationship between emotional experience and customer event
engagement would be weaker for individuals with low need for affect.
Therefore the following hypothesis is proposed:
Hypothesis 5b: An individual’s need for affect from an experience will moderate the
relationship between the BME experience and customer event engagement
Novelty-seeking is “the desire of the individual to seek out novel stimuli” (Hirschman
1980). People with a high need for novelty-seeking have a desire for experiences that
provide a change from routine, and include excitement, surprise and adventure (Lee and
Crompton 1992). They are variety seekers (Steenkamp and Baumgartner 1992) and
consider new experiences as a means of escape or to alleviate boredom (Lee and Crompton
1992). Therefore customers with a high novelty-seeking need are anticipated to find
enjoyment in any BME experience that is different to their usual experiences in everyday
life, providing variety and/or novelty.
Individuals with a high novelty-seeking need are anticipated to display a stronger
relationship between pragmatic and sensorial experiences and customer event engagement.
Pragmatic experiences involve physical activities or actions, and are likened to escapist
experiences (Gentile et al. 2007; Pine and Gilmore 1998), while sensorial experiences
provide sensory stimulation (Gentile et al. 2007). These experiences (for example a grape
stomp, or a wine and food pairing) are expected to involve activities that extend beyond the
individual’s normal day-to-day lives, and therefore fulfil the need for variety, novelty and
change from routine sought by individuals with a high novelty-seeking need (Lee and
Crompton 1992).
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These individuals will perceive a high level of value and fulfilment in pragmatic and
sensorial experiences (Steenkamp and Baumgartner 1992), and therefore strengthen the
relationships between pragmatic and sensorial experiences and customer event
engagement.
Individuals with low novelty-seeking needs are expected to elicit a weaker relationship
between pragmatic and sensorial experiences and customer event engagement. These
individuals are referred to as ‘novelty avoiding’ (Lee and Crompton 1992), and prefer
familiar or planned experiences. Therefore, pragmatic and sensorial BME components
which contain unfamiliar experiences extending beyond the individual’s normal activities
may be perceived by novelty avoiders as undesirable (Lee and Crompton 1992) or lacking
any perceived value. Therefore, a weaker relationship occurs between pragmatic and
sensorial experience and customer event engagement for individuals with low novelty-
seeking needs.
Therefore the following hypothesis is proposed:
Hypothesis 5c: An individual’s novelty-seeking needs from an experience will moderate
the relationship between the BME experience and customer event engagement
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2.6 Hypotheses Summary
In summary, this thesis explores how experiential components of BMEs facilitate customer
engagement with the event and with the brand, and investigates the behavioural intention
of loyalty that result. The moderating effect of the individual’s experiential needs in the
relationship between BME experiential components and customer event engagement is
also investigated.
The hypotheses presented in this chapter are summarised in the following Table 2-6:
TABLE 2-6: SUMMARY OF HYPOTHESES
H# Hypothesis
1a Social event engagement is a dimension of customer event engagement
1b Social brand engagement is a dimension of customer brand engagement
2a Cognitive event experience contributes to customer event engagement
2b Cognitive event experience contributes to customer brand engagement
2c Emotional event experience contributes to customer event engagement
2d Emotional event experience contributes to customer brand engagement
2e Sensorial event experience contributes to customer event engagement
2f Sensorial event experience contributes to customer brand engagement
2g Pragmatic event experience contributes to customer event engagement
2h Pragmatic event experience contributes to customer brand engagement
2i Relational event experience contributes to customer event engagement
2j Relational event experience contributes to customer brand engagement
3 There is a positive relationship between customer event engagement and customer brand engagement
4a There is a positive relationship between customer event engagement and behavioural intention of loyalty
4b There is a positive relationship between customer brand engagement and behavioural intention of loyalty
5a An individual’s need for cognition from an experience will moderate the relationship between the BME experience and customer event engagement
5b An individual’s need for affect from an experience will moderate the relationship between the BME experience and customer event engagement
5c An individual’s novelty-seeking needs from an experience will moderate the relationship between the BME experience and customer event engagement
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These hypotheses capture how BME experiences facilitate customer event engagement and
customer brand engagement; the role of social engagement within the broader customer
engagement construct; the interplay between engagement objects (the event and the brand);
the behavioural intention of loyalty outcomes resulting from customer engagement at
BMEs; and the moderation effects of experiential needs (need for cognition, need for affect
and novelty-seeking needs) on the relationship between BME experiences and customer
event engagement. These relationships are presented in the following conceptual
framework.
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2.7 Conceptual Framework
The hypotheses are summarised in a conceptual framework (Figure 2-1). This illustrates
the ability for a BME, comprised of various experiential components, to facilitate customer
engagement with the event and with the brand. The individual’s experiential needs are
proposed to moderate the process of facilitating customer engagement, as fulfilment of
experiential needs is expected strengthen the relationship between BME experiential
components and customer event engagement. Customer event engagement is anticipated to
project onto the host brand, and lead to increased behavioural intention of loyalty.
FIGURE 2-1: CONCEPTUAL FRAMEWORK
There are three main theoretical contributions to this thesis. First, social exchange theory is
used to identify the resources provided by the organisation (i.e. the experiential
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components of the BME platform) and the reciprocated heightened psychological state of
engagement elicited by the customer as a result. This relationship has received little
academic attention, and contributes to knowledge by predicting the ability of a BME to
facilitate customer engagement. Second, the expected relationship between engagement
objects, namely the event and the brand, is explored. Associative network theory is used to
inform the interplay between engagement objects, specifically that engagement with the
event will be projected onto the brand. Third, it is posited that an individual’s experiential
needs will moderate the relationship between BME experiences and customer event
engagement. Optimum stimulation level theory provides insight into the various types of
stimulation individuals seek from particular environments, suggesting that the relationship
between BME experiences and customer event engagement is strengthened when an
individual’s experiential needs are fulfilled.
In addition, this thesis explores the role of a social engagement dimension within the
broader construct of customer engagement, which has been the focus of significant debate
in customer engagement research to date. The findings of this study will enable
practitioners to strategically design BMEs to more effectively engage their customers.
Managers can tailor their events to facilitate specific engagement outcomes and may
provide particular experiences that fulfil customer needs sought from an event.
Finally, this thesis contributes to customer engagement literature as it confirms and extends
previous research positing that customer engagement leads to BIL (So et al. 2012). This
relationship is supported by the theory of consumption values (Sheth et al. 1991), and
extends current knowledge through investigating the impact of the customer engagement
of two focal objects, the event and the brand, on BIL.
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2.8 Chapter 2 Summary
This chapter provided an overview of extant literature to inform this thesis and identified
contributions of the study. First, customer engagement was introduced as the central
concept, with theoretical grounding in service-dominant logic. A conceptual understanding
and viewpoint of the researcher was provided regarding customer engagement
perspectives, definition of the construct, dimensions of customer engagement, related
concepts, antecedents and outcomes.
Branded marketing events, based on marketing events and customer experience literature,
was explained as the context and platform through which this thesis will investigate
customer engagement. As a method of facilitating customer engagement, it is important to
have an understanding of how organisations create a platform for customers to engage with
the organisation.
It was then posited that initial engagement with the BME would also trigger engagement
with the host brand. From this interplay between engagement objects it is expected that the
customer will elicit increased behavioural intention of loyalty.
Finally, the individual’s experiential needs were explored as a moderator in facilitating
customer engagement. It is anticipated that a customer is more willing to engage with
events that fulfil their need for cognition, need for affect and novelty-seeking.
Hypotheses were introduced and a conceptual framework outlined to explain the direction
of this thesis. The next chapter will explicate the data collection procedures and research
design approach taken in this thesis.
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CHAPTER 3: RESEARCH METHOD
3.1 Chapter 3 Introduction
This chapter outlines the methodological approach used to collect data and test the
hypotheses presented in this thesis. First, the research design and unit of analysis adopted
is outlined. A description of the data collection method follows, with a focus on the
measurement instrument, and questionnaire design. Important considerations regarding the
operationalisation of constructs and measurement scales are discussed. Next, the pre-test
study including sample, data collection methods and procedures, and pre-test data analysis
are described. The main study follows, with an outline of participating winery and
individual respondent selection, respondent profiles and data collection methods.
Following data cleaning, the analyses undertaken to determine the reliability and validity
of the measures are presented.
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3.2 Research Design
A deductive research approach was taken to provide empirical evidence for proposed
relationships and causality between constructs (Neuman 2006). The research questions
established in the literature review were a result of identified gaps in theory; more
specifically gaps linking the constructs of BME experiential components, customer event
engagement, customer brand engagement, behavioural intention of loyalty, and moderation
effects of experiential needs. Therefore the approach of this thesis was to examine causal
relationships between constructs (Neuman 2006).
This thesis adopted a quantitative research approach, implementing a cross-sectional study
to obtain data at one point in time (post-event) by individuals who participated only once
in the study (Neuman 2006). A questionnaire was developed and distributed in two study
stages. Existing measures were used to represent each of the constructs, with minor
adaptation to suit the research contexts. A pre-test was conducted in the University sector.
This preliminary study served as a pilot study, and was used to conduct confirmatory factor
analysis and statistical testing of constructs. The questionnaire was modified on the basis
of these findings before the commencement of the next study. The main study was
conducted in the South Australian wine sector, from the McLaren Vale, Adelaide Hills and
Barossa Valley wine regions.
This thesis employs causal research utilising structural equation modelling (from here
referred to as SEM) to verify predictions with the data. SEM is a widely used and accepted
analysis technique of complete models (Kline 2011) in a variety of disciplines, and
particularly for those researching in the social sciences (Hooper, Coughlan, and Mullen
2008). SEM is also a widely used technique in leading marketing journals (Martínez-
López, Gázquez-Abad, and Sousa 2013). SEM takes a confirmatory approach to testing a
range of structural relationships simultaneously and, unlike other multivariate methods, can
estimate and correct for measurement error (Byrne 2001). In addition, this thesis includes 80 | P a g e
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latent (unobservable) and measured variables; SEM has the ability to incorporate both
types of variables (Byrne 2001).
SEM analysis is an effective tool for theory testing and developing marketing models
(Steenkamp and Baumgartner 2000). This approach was chosen because engagement has
only recently been adopted in marketing literature (Vivek et al. 2012). Research on
customer engagement generally lacks empirical quantitative enquiry (see Table 2-1 in
Chapter 2) (Brodie et al. 2011b). As common conceptualisations of customer engagement
emerge, research must focus on empirically measuring the impact of customer engagement
and exploring a wider range of potential antecedents and outcomes to gain a
comprehensive understanding of the construct. Therefore, this thesis conducts quantitative
research to investigate customer engagement, and contributes to the empirical justification
of its operationalisation, antecedents (experiential components of BMEs) and outcomes
(behavioural intention of loyalty).
3.3 Unit of analysis
As this thesis endeavours to understand the impact of event experiences on the individual
customer, the unit of analysis is the individual who attended the event. This distinction is
key in framing the entire thesis with regards to preferred respondents, sample size, and
frame of reference taken in the survey design (Neuman 2006). This unit of analysis is also
consistent with the theoretical underpinning of this thesis; the S-D logic, as it regards
customer-brand experiences as interactive and individually driven and interpreted (Vargo
and Lusch 2008). In addition, it is recognised that each experiential component of the BME
is not mutually exclusive; one event may include a number of different experiential
components, and again this perception is derived from the individual (Gentile et al. 2007).
Therefore, in conducting this thesis, it was paramount that the perceived experience and
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3.4 Data Collection Method
3.4.1 Measurement Instrument
This thesis used self-administered questionnaires, predominantly through online surveys
administered using Qualtrics software. An online survey was deemed the most appropriate
for this thesis for a number of reasons. First, online surveys are a more time and cost
efficient data collection method (Denissen, Neumann, and Zalk 2010); once the survey is
developed, it is easy to distribute to a large number of people. Furthermore, large samples
can be obtained in a short amount of time, and with little manual work needed from the
researcher (Denissen et al. 2010). This was particularly important for this thesis as both the
pre-test and main study were conducted at various events. An online data collection
method enabled easy replication of the survey for different events, timely delivery of the
survey to respondents immediately following the event, and the elimination of transcription
errors, all of which save time in data preparation for analysis (Fricker Jr and Schonlau
2010).
Common limitations of online surveys, for example the perception of the survey being
‘spam’ leading to low response rate and problems with sample coverage (Denissen et al.
2010; Fricker Jr and Schonlau 2010; Vicente and Reis), is mitigated by the researcher
attending the majority of the events to obtain emails addresses from attendees. The
researcher was given the opportunity to explain the nature of the study, ask for consent
from the respondents, and provide information regarding survey distribution and incentives
to participate. This also ensured that surveys were only being sent to relevant respondents;
those who attended the specific events.
Self-administered questionnaires were sent to respondents immediately following the
event; the data collection period was two weeks following the event, which allowed for
follow-up reminder emails to those who had not started the survey, and those who had only
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partially completed the survey. The decision to apply a self-administered questionnaire
design after the event experience was made for a number of reasons. The surveys were
completed at the respondent’s convenience (Neuman 2006) and therefore did not interrupt
their event experience. This was also at the request of the host brands, who wanted
minimal disruption to attendees during the event. In addition, this method ensured the
researcher did not influence the results through social desirability bias (Neuman 2006).
Similar approaches of making contact with participants during an event and requesting
they complete the questionnaire post-event are common in a number of marketing events
studies (e.g. Crompton and McKay 1997; Lee, Sandler, and Shani 1997).
3.4.2 Operationalistion of the Theoretical Constructs
This section introduces the measures used to operationalise each construct from the
literature. Existing measures were used for all constructs; often there were a variety of
alternative measures within each body of literature, therefore the most appropriate and
comprehensive measurement scale was selected. Minor modifications were made to
existing scales to create linguistic style consistency and ensure their applicability to the
new research context (Brakus et al. 2009), while still maintaining their original meaning. A
number of existing scales, for example experiential components, had been previously
designed for studies that focused on the customer’s experience with a product (Gentile et
al. 2007) or with reference to experiential product-centric brands (Brakus et al. 2009).
However, as this thesis explored experience and engagement at events they were adapted
to reflect this characteristic.
The survey was adapted for two different contexts; a pre-test held in the University sector
(a student-sample) followed by the main study held in the South Australian Wine sector.
Student samples are often argued to lack generalisability; however, when they encompass
an applicable population of interest or share a theoretically relevant commonality student
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samples are considered appropriate (Randall and Gibson 1990). The focus of this thesis
was to capture the experiences and perceptions of event attendees, and as the student
sample had all attended a University event, they were deemed an appropriate study sample.
3.4.3 Measurement Scales
The main constructs of importance for this survey were experiential components; event
engagement; brand engagement; behavioural intention of loyalty; and experiential needs
(need for cognition, need for affect and novelty-seeking needs). Theoretical constructs
were operationalised for this questionnaire using established measures from literature
(Brakus et al. 2009; Calder et al. 2009; Chang and Chieng 2006; Hallahan 2009; Lee and
Crompton 1992; Maio and Esses 2001; So et al. 2012; Sojka and Giese 1997; Sweeney and
Soutar 2001).
An objective indication of experiential components was not of concern, rather it was
important to capture the attendee’s individual perception of the event experience to
determine whether engagement was achieved, and whether the type of experience aligned
with that individual’s experiential needs. A close examination of the literature suggests that
the main components of an experience are cognitive, emotional, sensory, pragmatic and
relational (Gentile et al. 2007). Many studies used various sub-sets and combinations of
these components, commonly reflecting the sensorial, emotional and cognitive components
of experience (Brakus et al. 2009; Chang and Chieng 2006; Gentile et al. 2007; Sahin et al.
2011; Schmitt 1999; Tynan and McKechnie 2009; Yuan and Wu 2008). However, it was
determined that Gentile et al.’s (2007) conceptualisation was robust and comprehensive,
with attention also given to social/relational elements (Chang and Chieng 2006; Schmitt
1999; Tynan and McKechnie 2009) as well as behavioural or ‘pragmatic’ components
(Brakus et al. 2009; Chang and Chieng 2006; Sahin et al. 2011; Schmitt 1999).
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While Gentile et al.’s (2007) conceptualisation of experiential components was
implemented in this thesis, their study context was highly product-centric and their
measurement items were difficult to replicate in the context of this thesis. Chang and
Chieng’s (2006) measures were used instead, as their study followed a similar
conceptualisation of experiential components to Gentile et al. (2007) and was conducted in
coffee stores, hence their items were easier to apply to the context of this thesis with only
minor rewording (from ‘coffee store’ to ‘event experience’). Chang and Chieng’s (2006)
experience measure only includes three items per experiential component. Three items
were reported as having item to total coefficient values below 0.5 in one study context,
which suggests that these items did not significantly contribute to the reliability of the
construct (Chang and Chieng 2006). Each of the experience measures were therefore
supplemented with additional items to account for any item quality issues, as a minimum
of three items per construct are required to run a congeneric measurement model (Hair,
Black, Babin, and Anderson 2012). The cognitive, sensorial, emotional and pragmatic
experiences were supplemented with items from Brakus et al. (2009), and relational
experience was supplemented with items from Sweeney and Soutar (2001).
Customer engagement is an emergent literature area; research to date has focused
predominantly on conceptual development (Brodie et al. 2011a). Measures capturing
elements of customer engagement are limited, for example ‘brand engagement in self-
concept’ (Sprott et al. 2009) and Calder, Isaac, and Malthouse’s (2013) measures for
engagement. Neither of these measures reflect the commonly accepted dimensions of
customer engagement (Brodie et al. 2011a) and is not consistent with the conceptualisation
of engagement taken in this thesis.
The five dimension conceptualisation of customer engagement from So et al. (2012) and
their subsequent measures were found most applicable to this thesis; these measures
capture the commonly accepted three dimensions of customer engagement (Brodie et al.
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2011a), while extending this view to investigate a broader and more comprehensive idea of
customer engagement. In addition, these measures were used in a hospitality and tourism
context of service encounters (So et al. 2012); therefore the measures were easily
replicable to fit the context of this thesis.
A major contribution of this research is the investigation of engagement from both an event
and brand perspective, to explore the relationship between two engagement objects in the
one study. Gwinner and Eaton’s (1999) study on brand image transfer explored this
process by replicating the same set of items, first phrased to describe the particular events
and then reworded to describe the brand. Image transfer was confirmed when very similar
responses were indicated for event image and brand image (Gwinner and Eaton 1999).
Drengner et al. (2008) used the same technique, replicating the same items to identify
image transfer from the event to the brand. Therefore, for this thesis the same engagement
measures were used twice; first to reflect engagement during an event, and then reworded
to capture engagement with the brand. This required careful consideration of the rewording
of items so that the ‘event engagement’ items consistently referred to the ‘event’ or implied
engagement within the event experience, while the ‘brand engagement’ items were clearly
distinguished to capture engagement occurring with the brand (Brakus et al. 2009).
Respondent instructions were also included at the beginning of the customer event
engagement and customer brand engagement items to ensure that participants understood
and differentiated between the repeated questions (Hair, Lukas, Miller, Bush, and Ortinau
2008).
There is significant debate within the engagement literature of the existence and
conceptualisation of social engagement; some discuss social engagement as a separate
dimension (Calder et al. 2009; Vivek et al. 2012), while others maintain that social
engagement is a subset within affective engagement (Brodie et al. 2011a). Measurement
items from Calder et al. (2009) were included to capture social event engagement and
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social brand engagement. While social engagement has been introduced in other literature
areas, for example social psychology (Achterberg, Pot, Kerkstra, Ooms, Muller, and Ribbe
2003; Glass, De Leon, Bassuk, and Berkman 2006; Huo, Binning, and Molina 2009),
Calder et al. (2009) investigated customer engagement arising from experiences, and was
therefore utilised in this thesis. Calder et al. (2009) provide a different conceptualisation of
customer engagement which includes a social-interactive engagement element;
‘community’ and ‘participation and socialising’ are identified as the two dimensions of
social-interactive engagement (Calder et al. 2009). The majority of items for community
and participation and socialising were used in this thesis; however, three items were highly
specific to the online context investigated in Calder et al.’s (2009, pg 325) study and were
not applicable to the BME context, for example, “I often feel guilty about the amount of
time I spend on this site socializing”.
The ‘behavioural intention of loyalty’ measure (So et al. 2012), encompassing word of
mouth and purchase intention was utilised in this thesis; these four key items implemented
in So et al.’s (2012) study is based on Zeithaml et al. (1996), an extensively cited and
replicated measure. The shortened version of this measurement scale from So et al. (2012)
was considered appropriate for this thesis due to the conceptual relevance of the included
constructs (word of mouth and purchase intention) with regards to customer engagement
literature; the additional constructs captured in Zeithaml et al.’s (1996) scale (price
sensitivity and complaining behaviour) were not consistent with expected customer
engagement outcomes (Fehrer et al. 2013) and hence were not included in this thesis.
The individual’s experiential needs were reported using well-established and highly
replicated measures, mainly from psychology literature. A consolidated version of the need
for cognition scale (Cacioppo, Petty, and Kao 1984) from Hallahan (2009) was used in this
thesis.
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Hallahan’s (2009) study investigated need for cognition as a moderator between the level
of thinking and attention a customer gives to information in advertising. The role of need
for cognition in Hallahan’s (2009) study is consistent with how the construct is considered
in this thesis; need for cognition is an individual differences variable (in this thesis,
experiential need) which moderates the amount of thinking (in this thesis, event attention)
a customer elicits towards particular information (in this thesis, cognitive BME
experience). Hallahan’s (2009) results indicated that, of the completed need for cognition
scale, one factor consisting of nine items emerged as the predominant factor influencing
the customer’s level of thinking and attention of advertising information; those items were
therefore implemented in this thesis.
Elements of the novelty-seeking scale from Lee and Crompton (1992) were implemented
as the paper was conducted in a similar study context (tourism) and has been shown to be
robust. This paper investigated novelty from multiple dimensions; thrill, change from
routine, boredom alleviation and surprise (Lee and Crompton 1992). However, to reduce
the 21 item scale, only the change of routine dimension was implemented as these items
were perceived to have the most applicability to the context of this thesis. In addition, only
four of the change of routine items (of nine) were used, as the remaining items were not
easily transferable to this study context (for example ‘I want to experience customs and
cultures different from those in my own environment on vacation’; ‘I like to travel to
adventurous places’), and also reported comparatively lower factor score weights
compared to the items implemented (Lee and Crompton 1992).
Finally, the individual’s need for affect was reported using measures from Sojka and Giese
(1997) and Maio and Esses (2001). There are varying perspectives and numerous scales to
measure affect (Sojka and Giese 1997); for this thesis items were selected that were
perceived to address situations or experiences where need for affect influenced the
individual’s perceptions (e.g. when I recall a situation, I usually recall the emotional
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aspects of the situation). Items with less applicability to the context of this thesis were not
included in the questionnaire (e.g. I’m good at empathising with other people’s problems; I
enjoy trying to explain my feelings – even if it’s only to myself) (Sojka and Giese 1997).
The four selected items were supplemented with an additional two items from Maio and
Esses (2001) that also made reference to emotional experiences or situations (I feel that I
need to experience strong emotions regularly; I approach situations in which I expect to
experience strong emotions).
In summary, the previous discussion highlights the process taken to select robust measures
to represent each of the constructs identified in this thesis, while also attempting to shorten
the survey length to avoid confusion or frustration among the participants (Hair et al.
2012). Each of the constructs and their related measures are described in the following
Table 3-1. Some measurement items were slightly modified to suit the context of the study;
this is a common method to achieve strong relevance and applicability of the measures
(e.g. Algesheimer et al. 2005; Hightower, Brady, and Baker 2002).
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TABLE 3-1: MEASUREMENT OF CONSTRUCTS Scale (Reference) Item Final Measure (modified for this thesis) Cognitive Experience (Chang and Chieng 2006)
EXP_COG1 This event tried to intrigue me EXP_COG2 This event tried to stimulate my curiosity EXP_COG3 This event appealed to my creative thinking
Emotional Experience (items 1, 2 and 3 from Chang and Chieng 2006) (items 4, 5 and 6 from Brakus et al. 2009)
EXP_EMO1 This event tried to put me in a certain mood EXP_EMO2 This event tried to be emotional EXP_EMO3 This event tried to arouse feelings in me EXP_EMO4 This event induced feelings and sentiments EXP_EMO5 I had strong emotions at this event EXP_EMO6* This event was an emotional event
Sensorial Experience (Chang and Chieng 2006)
EXP_SEN1 This event was focused on creating a sensory experience EXP_SEN2 This event tried to excite my senses EXP_SEN3 This event provided sensory enjoyment
Pragmatic Experience (items 1, 2, and 3 from Chang and Chieng 2006) (items 4, 5 and 6 from Brakus et al. 2009)
EXP_PRAG1* This event tried to remind me of activities I can do EXP_PRAG2* This event got me to think about my behaviour EXP_PRAG3* This event made me think about my lifestyle EXP_PRAG4 I engaged in physical actions and behaviours when I attended
this event EXP_PRAG5 This event was action oriented EXP_PRAG6 This event involved physical experiences
Relational Experience (Sweeney and Soutar 2001)
EXP_REL1* Attending this event helped me to feel accepted EXP_REL2 Attending this event improved the way I am perceived EXP_REL3 Attending this event made a good impression on other people EXP_REL4 Attending this event gave me social approval EXP_REL5 Attending this event created a favourable perception of me
among other people EXP_REL6* This event had a positive social image
Event Attention (So et al. 2012)
ATT_EVENT1* I liked learning about this event ATT_EVENT2 I paid a lot of attention at this event ATT_EVENT3 Anything related to this event grabbed my attention ATT_EVENT4 I concentrated a lot during this event
Event Identification (So et al. 2012)
ID_EVENT1 When someone criticises this event, it feels like a personal insult ID_EVENT2 When I talk about this event, I usually say ‘we’ rather than
‘they’ ID_EVENT3 This event’s successes are my successes ID_EVENT4 When someone praises this event, it feels like a personal
compliment Event Enthusiasm (So et al. 2012)
ENTH_EVENT1* I was enthusiastic about this event ENTH_EVENT2 I was heavily into this event ENTH_EVENT3 I was passionate about this event ENTH_EVENT4 I felt excited about this event ENTH_EVENT5 I loved this event
Event Absorption (So et al. 2012)
AB_EVENT1* When I was taking part in this event, I forgot everything else around me
AB_EVENT2 Time flew when I was taking part in this event AB_EVENT3* When I was taking part in this event, I got carried away AB_EVENT4* When I was taking part in this event, it was difficult to detach
myself AB_EVENT5 When I was taking part in this event, I was immersed AB_EVENT6 When I was taking part in this event intensely, I felt happy
Event Interaction (So et al. 2012)
INT_EVENT1 I liked getting involved in discussions at this event INT_EVENT2 I enjoyed interacting with like-minded others at this event INT_EVENT3 I liked actively participating in discussions at this event INT_EVENT4 I thoroughly enjoyed exchanging ideas with other people during
this event INT_EVENT5* I frequently participated in activities during this event
* item was removed during construct analysis and pre-testing
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Scale (Reference) Item Final Measure (modified for this thesis) Social Event Engagement (Calder et al. 2009)
SOC_EVENT1* This event did a good job of getting its attendees to contribute or provide feedback
SOC_EVENT2* I liked meeting other people who attended this event SOC_EVENT3 I’ve gotten interested in things I otherwise wouldn’t have
because of other attendees at this event SOC_EVENT4 I was as interested in input from other attendees as I was in
information provided by organisers during this event SOC_EVENT5 A big reason I liked this event is what I got from other attendees SOC_EVENT6* I did quite a bit of socialising at this event SOC_EVENT7* I contributed to conversations at this event
Brand Attention (So et al. 2012)
ATT_BRAND1* I like learning about X Winery ATT_BRAND2 I pay a lot of attention to anything about X Winery ATT_BRAND3 Anything related to X Winery grabs my attention ATT_BRAND4 I concentrate a lot on X Winery
Brand Identification (So et al. 2012)
ID_BRAND1 When someone criticises X Winery, it feels like a personal insult ID_BRAND2 When I talk about X Winery, I usually say ‘we’ rather than
‘they’ ID_BRAND3 X Winery’s successes are my successes ID_BRAND4 When someone praises X Winery, it feels like a personal
compliment Brand Enthusiasm (So et al. 2012)
ENTH_BRAND1* I am enthusiastic about X Winery ENTH_BRAND2 I am heavily into X Winery ENTH_BRAND3 I am passionate about X Winery ENTH_BRAND4 I feel excited about X Winery ENTH_BRAND5 I love X Winery
Brand Absorption (So et al. 2012)
AB_BRAND1* When I am interacting with X Winery, I forget everything else around me
AB_BRAND2 Time flies when I am interacting with X Winery AB_BRAND3* When I am interacting with X Winery, I get carried away AB_BRAND4 When interacting with X Winery, it is difficult to detach myself AB_BRAND5 In my interaction with X Winery, I am immersed AB_BRAND6 When interacting with X Winery intensely, I feel happy
Brand Interaction (So et al. 2012)
INT_BRAND1 I like to get involved in discussions about X Winery INT_BRAND2 I enjoy interacting with like-minded others about X Winery INT_BRAND3 I like actively participating in discussions about X Winery INT_BRAND4 I thoroughly enjoy exchanging ideas with other people about X
Winery INT_BRAND5* I frequently participate in activities related to X Winery
Social Brand Engagement (Calder et al. 2009)
SOC_BRAND1* X Winery does a good job of getting people to contribute or provide feedback
SOC_BRAND2* I like meeting other people who enjoy X SOC_BRAND3 I’ve gotten interested in things I otherwise wouldn’t have
because of other X Winery consumers SOC_BRAND4 I’m as interested in what others think about X Winery as I am in
more formal information about X SOC_BRAND5 A big reason I like X Winery is what I get from talking to others
about the wine SOC_BRAND6* I do quite a bit of socialising that relates to X Winery SOC_BRAND7* I contribute to conversations about X Winery
Behavioural intention of loyalty (So et al. 2012)
BIL_BRAND1 I would say positive things about X Winery to other people BIL_BRAND2 I would recommend X Winery to someone who seeks my advice BIL_BRAND3 I would encourage friends and relatives to purchase X Wines BIL_BRAND4 I would purchase X wines in the future
* item was removed during construct analysis and pre-testing
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Scale (Reference) Item Final Measure (modified for this thesis) Novelty-seeking needs (Lee and Crompton 1992)
NEED_NOV1 I like to find myself at destinations where I can explore new things
NEED_NOV2 I want to experience new and different things NEED_NOV3 I enjoy the change of environment which allows me to experience
something new NEED_NOV4* My ideal experience involves looking at things I have not seen
before Need for affect (items 1, 2, 3 and 4 from Sojka and Giese 1997) (items 5 and 6 from Maio and Esses 2001)
NEED_AFF1* I'm more of a "feeler" than a "thinker" NEED_AFF2* When I recall a situation, I usually recall the emotional aspects of
the situation NEED_AFF3* I prefer a task that is emotional and important to a task that is
intellectual and important NEED_AFF4 Emotion excites me NEED_AFF5 I feel that I need to experience strong emotions regularly NEED_AFF6 I approach situations in which I expect to experience strong
emotions Need for cognition (Hallahan 2009)
R_NEED_COG1* Thinking is not my idea of fun R_NEED_COG2 I would rather do something that requires little thought than
something that is sure to challenge my thinking abilities R_NEED_COG3 I try to anticipate and avoid situations where there is a likely
chance I will have to think in depth about something R_NEED_COG4 I only think as hard as I have to R_NEED_COG5 Learning new ways to think doesn't excite me very much R_NEED_COG6* It’s enough for me that something gets the job done: I don’t care
how or why it works R_NEED_COG7 I prefer to think about small, daily projects to long-term ones R_NEED_COG8 I like tasks that require little thought once I have learned them R_NEED_COG9* I feel relief rather than satisfaction after completing a task that
required a lot of mental effort * item was removed during construct analysis and pre-testing
3.4.4 Questionnaire Design
This section outlines the format of the questionnaire and the theoretical basis for its
development. The questionnaire is included in Appendix A-1. The questions were designed
to be easily understood by participants without assistance from the researcher. For this
reason, several measurement items were slightly modified to enhance understanding in the
context in which they were being completed. Detailed explanations and examples were
also included to accompany the questions and ensure understanding from the participants.
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3.4.4.1 Scaling
All constructs were measured using likert type scales, with seven response options. Likert
style questions are relatively easy to construct and administer and are readily understood
by respondents, making them particularly suitable for a self-administered questionnaire
(Hair et al. 2008). All established measures utilised in this thesis had been previously
implemented using a likert scale structure (Brakus et al. 2009; Chang and Chieng 2006;
Hallahan 2009; Lee and Crompton 1992; Maio and Esses 2001; So et al. 2012; Sojka and
Giese 1997; Sweeney and Soutar 2001).
Seven point response formats are commonly used in well-developed measures as to ensure
sufficient scale variance, which is particularly important for conducting SEM (Noar 2003).
Scales that extend beyond seven points will provide little benefit for analysis or scale
variance (Noar 2003). A seven point scale was also common in the established measures
implemented in this thesis (Brakus et al. 2009; So et al. 2012; Sweeney and Soutar 2001).
Therefore, all constructs in this thesis were measured using a seven point likert scale
ranging from (1) strongly disagree, (4) neither agree nor disagree, to (7) strongly agree.
The inclusion of a neutral response choice in scale development is often debated (Hair et
al. 2008; Neuman 2006). A forced-choice scale, one that does not have a neutral response
option, may result in lower-quality data as some respondents may lack knowledge or
experience of a given topic and are unable to accurately respond to the question (Hair et al.
2008). However, a free-choice scale, one that includes a neutral response option, may
provide an easy option for respondents who are indecisive or do not want to reveal their
true feelings (Hair et al. 2008). Although there are arguments for and against providing an
odd number of response options (Neuman 2006), this thesis provided a neutral midpoint (4
– neither agree nor disagree) to allow for those who were genuinely undecided, as opposed
to forcing an opinion.
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3.4.4.2 Questionnaire Content
The questionnaire was designed to capture the perceived experiences of the participant
during the BME and its ability to facilitate customer event engagement and customer brand
engagement. The questionnaire also created a profile of the respondent’s experiential needs
with regards to cognition, affect, and novelty-seeking. There were no qualifying questions
throughout the questionnaire; all respondents were asked to complete all sections.
Respondent instructions were provided extensively throughout to ensure the respondent
had a high level of understanding (Hair et al. 2008). This is particularly important for
online surveys as they are self-administered and provide no way for respondents to ask
questions or clarify the requirements of the survey (Hair et al. 2008). The sequence in
which these concepts were presented to the respondents is outlined in the following
section.
3.4.4.3 Questionnaire Structure and Sequencing
Introduction – The introduction screen included a brief outline of the research project and
a summary of questionnaire content (Hair et al. 2008), and a description of the opportunity
to win an incentive to increase the respondent’s motivation to complete the survey
(Denissen et al. 2010; Hair et al. 2008). Participants were instructed that questions pertain
to one specific event, not numerous events they may have attended. Included in the
introduction were explicit directions instructing respondents to answer all questions even if
some appeared similar or abstract. This was emphasised, as multi-item measures were used
for each construct; repetition may cause confusion for the participant (Hair et al. 2008). A
force-response logic was used on all questions with the online medium, to ensure
respondents did not skip over particular items. Screen breaks were also placed in the
questionnaire to limit the number of questions per screen; similar-sounding questions were
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not on the same page, reducing the level of confusion for the participant (Toepoel, Das,
and Soest 2009).
Event experiences – measures representing each of the five components of experience
were presented first to establish the perceived type of BME experience encountered by the
respondent. Specific instructions preceded each section, and examples of ‘experience’
types were given to ensure items were understood. For example, the cognitive experience
items were introduced by explaining that this type of experience includes any components
of the event that made the participant think, intrigued them, or made them want to seek
further information.
Customer event engagement – items for each of the five engagement dimensions
(attention, absorption, enthusiasm, interaction, identification) were then presented to
capture the respondent’s level of engagement during the event experience. It was reiterated
throughout this section that the items related to the event in question, not about the
participant’s perceptions of this style of event in general, or previous events held by the
brand. The respondent instructions indicated that the questions referred to their interactions
and behaviours during the event, and were asked to answer the questions only about the
event in question.
Customer brand engagement - This section was sequenced to follow event engagement
measures as both originated from the same measure and therefore had very similar
wording. It was important to present the event-related questions first to establish event
engagement before investigating any engagement with the brand. In this section the
questions were framed towards the brand as opposed to the event experience – respondents
were notified that the questions would appear similar but that it was part of the design.
Behavioural intentions of loyalty – measures representing the respondent’s behavioural
intention of loyalty, encompassing word of mouth and brand purchase intention were
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presented in this section. These items were the final brand-specific questions of the survey,
and were placed after the customer brand engagement measures while the reported
recollections of the brand were still in the participant’s mind and therefore easy to answer.
Experiential needs – measures capturing the respondent’s levels of need for cognition,
need for affect and need for novelty were included to determine the respondent’s levels of
experiential needs. This section required participants to answer questions directly related to
their own personal needs and predispositions to particular kinds of activities/experiences. It
was considered appropriate to have these questions follow the event and brand constructs
as to not distract the respondent from thinking about their event experience. These
questions are also very specific to the individual, and more personal and self-reflective in
nature; therefore these items were placed towards the end of the survey to indicate a
‘transition phase’ (Hair et al. 2008). This communicated to the respondent that the nature
of the questions were about to change from brand focused to more individual-specific
information (Hair et al. 2008).
Demographics – questions of age and nationality were included at the end of the
questionnaire. This section was included last as personal information can often be
considered threatening or uncomfortable by respondents and creates a perception of non-
anonymity (Hair et al. 2008). Events that ran on multiple days also included a question
asking them to specify the specific date/event they attended. Participants could choose if
they wanted to enter the prize draw by leaving their name and email address at the end of
the questionnaire; this was placed at the end of the survey to ensure only respondents who
completed the survey could enter the draw.
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3.4.5 Ethics and Information Confidentiality
This thesis had ethics approval granted by the University of Adelaide Human Research
Ethics Committee (Appendix A-2). Participants were assured confidentiality of their
responses and personal information in the introduction to the survey. The nature and
purpose of the research was expressed to participants before asking for their consent to
participate; it was emphasised that the study was undertaken by a PhD candidate at
University of Adelaide to assure participants that the research was not a commercial
venture, nor would their information be given to any third party. The participants were
advised that their information (email address) would only be used to the purpose of
contacting them with the survey and to notify them if they won the completion incentive.
3.4.6 Data Coding and Editing
The data received was prepared through Qualtrics online survey software – question items
were automatically coded, and a response ID assigned to each participant. These codes
were later modified for readability. Non-responses were automatically coded in SPSS as a
full stop (Pallant 2010). A number of items were negatively worded in the original survey;
these variables were reverse-coded using SPSS21 before data analysis commenced.
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3.5 Pre-Test: University of Adelaide Orientation Week
3.5.1 Overview
The main objective of the pre-test was to determine the length of time taken to complete
the survey online and to run preliminary factor analysis to assess the validity of the
selected measures. In particular, experiential components were measured using items from
Chang and Chieng (2006) and Brakus et al. (2009); factor analysis would identify
unnecessary items that could be excluded from the main study. The pre-test was conducted
in the University sector, the details of which are described in this section.
3.5.2 Subjects
The University of Adelaide Orientation Week (from here referred to as ‘O-Week’) was
considered an appropriate platform from which to run a pre-test of the study and validate
the measurement instrument. Students attended a wide variety of events including
information sessions, University support lectures, social events, barbeques, which allowed
the researcher to investigate a range of experiences.
University of Adelaide students who attended an event during O-Week was determined as
the sampling frame as it allowed a large number of individuals attending a range of
different O-Week events to be surveyed. These students were from various disciplines,
were commencing or continuing students, and studying at an undergraduate or
postgraduate level. Respondents from a number of different events were approached (see
Table 3-2). Equivalent questionnaires were distributed to all respondents; participants were
asked to indicate the event they attended at the beginning of the questionnaire.
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3.5.3 Sample and Respondent Profile
A purposive or judgment sampling approach was used, whereby the researcher uses
subjective information, in this case location/attendance of an event, to identify the sample
for inclusion in the study (Neuman 2006). The researcher attended preliminary lectures,
information sessions, and the Student barbeque event and distributed fliers that outlined the
study and contained the online survey URL. The qualifying dimension relevant to survey
participants was attendance to an O-Week event, and these participants were approached
with survey fliers during an event, thus already achieving that qualifier.
The researcher attended 10 events held at O-Week which ran over five days, and obtained
a total of 223 responses. A limitation of this pre-test is the likelihood of students attending
more than one event, and this distorting their responses. To mitigate this, the participant
was instructed at the beginning of the survey to identify one event they had attended and to
answer all survey questions with reference to that event. They were reminded of this
throughout the survey. Also, respondents were asked to indicate the total number of O-
Week events they had attended. The following Table 3-2 indicates the types of events
attended by students, and the number of responses specifically on each event type.
TABLE 3-2: O-WEEK EVENTS PROFILE
Event Number of Respondents % of Total Responses
Adelaide University Union activities 7 3
Tours 10 5
Hub Day Out barbeque* 90 40
Welcome Sessions (faculty/school) 12 5
Doing Uni information session 9 4
Marketing introduction lecture* 25 11
Information /help sessions 8 4
Reality Bites 36 16
O'meet 3 2
Trial Lectures 23 10
Total 223 100%
* indicates events in which surveys were distributed in person
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3.5.4 Data Collection Procedure
Data was collected both online (after the event) and with a paper survey (during the event).
Both approaches have been implemented in previous event studies, e.g. post-event
(Crompton and McKay 1997; Lee et al. 1997) and during the event (Close et al. 2006;
Leischnig et al. 2011).
Online surveys were preferred due to increased convenience for the participant who could
complete the survey in their own time (Neuman 2006), and easy replication of the survey
for different events (Fricker Jr and Schonlau 2010). In addition, this data collection
procedure was often at the request of the O-Week organisers, as there was available little
time in the sessions to complete the questionnaire.
Self-complete paper surveys were also distributed to students to complete during the Hub
Day Out barbeque and at the end of the marketing introduction session (see Table 3-2).
The barbeque was an informal event running for two hours where students had enough
time to participate in the survey; the marketing introduction session finished ten minutes
early and willing participants were asked to stay and complete the survey sheet before
leaving the lecture.
The questionnaire was expected to take approximately fifteen minutes to complete online,
and an incentive of one iPad Mini was provided to encourage participation (Denissen et al.
2010; Hair et al. 2008). All participants who completed the questionnaire went into the
draw to win the prize.
3.5.5 Pre-test Data Analysis
A major finding of the pre-test was that the survey was too long. It is recommended that
online surveys take no longer than 15 minutes, as lengthier surveys may result in a
decreased completion rate and compromise response validity (Rea and Parker 2012).
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However, Qualtrics survey software reported a duration trimmed mean of 31 minutes for
participants to complete the online survey (a trimmed mean factors in respondents who had
left and re-entered the survey). Approximately 64 participants exited the survey part way
through, an approximate completion rate of 78% (223 completed responses from 287).
Therefore, serious consideration was given to removing unnecessary items during
preliminary analysis.
Factor Analysis was conducted to establish validity and reliability of the measures included
in the pre-test survey. The pre-test included multiple measures for each experiential
component to ensure each construct was adequately captured; measures from Chang and
Chieng (2006) and Brakus et al. (2009) were used to investigate cognitive, emotional,
sensorial and pragmatic experience, while measures from Chang and Chieng (2006) and
Sweeney and Soutar (2001) explored relational experience (as Brakus et al. 2009 did not
measure relational experience).
Testing of constructs indicated that the additional items from Brakus et al. (2009) enhanced
the reliability of the emotional and pragmatic experiential constructs, and therefore all
items were included in the main study (see Appendix A-3). However, the Brakus et al.
(2009) measures did not enhance the reliability of the cognitive and sensorial measures,
and therefore only the Chang and Chieng (2006) items were included in the main study.
The relational experience items from Sweeney and Soutar (2001) achieved strong
reliability; inclusion of the two items from Chang and Chieng (2006) did not increase the
reliability of the construct and were therefore removed from the main study. A list of the
items changes in the pre-study versus the main study, and a summary of reliability values
are provided in Appendix A-3.
As previously discussed in Chapter 2, the conceptualisation of engagement presented by
So et al. (2012) identifies a five dimension construct; however, this thesis had originally
followed a three dimension conceptualisation, consistent with extant literature on customer
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engagement (Brodie et al. 2011a). The pre-test survey only included items that aligned
with the three predominant dimensions of engagement; So et al.’s (2012) attention and
enthusiasm measures were used in the pre-test to capture cognitive and emotional
dimensions of engagement, while participation measures (Chan, Yim, and Lam 2010) were
used to investigate behavioural engagement. However, upon further reflection and
exploration of the customer engagement literature, the researcher decided that the
expanded five dimension view of customer engagement had great applicability to the event
context, and provided an additional element to the study; to question and investigate the
dimensionality of customer engagement. This five dimension framework still included the
commonly used three dimensions of engagement, with an additional two elements; this
meant that the dimensionality of engagement could be compared in further research. The
researcher also decided that for completeness of replication of So et al.’s (2012)
dimensions of engagement, the participation measures would be replaced with So et al.’s
(2012) interaction measures. In brief, the researcher recognised these oversights after the
pre-test, and decided that the benefit of making these changes to the main study
outweighed the limitations of having a consistent survey in the pre-test and main study.
Calder et al.’s (2009) social engagement measures, and the need for cognition, novelty-
seeking and need for affect measures all achieved reliability and remained in the survey for
the main study.
Finally, word of mouth was identified in customer engagement literature as a potential
outcome of engagement (Fehrer et al. 2013; Vivek et al. 2012); three word of mouth items
from So et al.’s (2012) behavioural intention of loyalty construct was used in the pre-test.
The fourth item in the BIL measure, purchase intention, was omitted as it did not logically
fit the context of the pre-test; i.e. ‘I would purchase the University of Adelaide in the
future’. However, for completeness of measure replication, the purchase intention item was
included in the main study (‘I would purchase wine X in the future’).
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3.6 Main Study: South Australian Wine Industry
3.6.1 Overview
The main study of this thesis was conducted at wine-related events in the South Australian
wine industry. Ten events from six wineries were used to collect data, with 274 complete
responses obtained. The objective of the main study was to assess the proposed
relationships and hypotheses explicated in Chapter 2. The details of the main study,
including the identification and selection of participating wineries and events, individual
respondent selection and profile, and data collection procedure are described in the
following section.
3.6.2 Subjects
The target population of the main study was Australian wine customers; the study subjects
were recent attendees of selected wine events held in the main South Australian wine
regions surrounding Adelaide; McLaren Vale, the Adelaide Hills and the Barossa Valley.
A focus on individual attendees allowed data to reflect the individual’s perspective of the
event experience, which is consistent with the theoretical underpinning of S-D logic
followed in this thesis (as previously discussed in section 3.3).
3.6.3 Selection of Participating Wineries
A purposive sampling technique (Neuman 2006) was used to identify appropriate wineries
to take part in this research. Wineries that had recently conducted or were about to conduct
a relevant BME for their customers were sought. Research was conducted online to
identify wine events held in the wine regions surrounding Adelaide within the data
collection period (June to December 2013). Event information was obtained from winery
websites and wine region tourism websites (e.g. http://mclarenvale.info,
http://www.barossa.com, http://www.adelaidehillswine.com.au).
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Wineries were approached across a range of winery sizes and a known range of events.
The researcher contacted the marketing manager or director of each winery (via email) and
explained the nature of the study, and the participation requirements and outcomes for the
winery. Participating wineries were given a report on the nature of customer engagement
and perceptions of the experience specific to their event. Six of the thirteen approached
wineries agreed to take part in the study.
Respondents from ten different wine events (from six wineries) were contacted, providing
a broad range of particular types of event experiences. To examine the major constructs of
this thesis, it was important that the participating wineries were hosting their own BMEs
for customers. These events needed to be distinguished from joint events, sponsored
events, or community based events, to ensure emphasis was placed on the individual wine
brand during the event (Wood 2009). It was also important that the organisations included
in the study covered the comprehensive range of experiential components conceptualised
in the study (i.e. cognitive, emotional, sensorial, pragmatic and relational; Gentile et al.
2007). The participating wineries and events are outlined in Table 3-3.
TABLE 3-3: WINERY PARTICIPANTS
Wine brand Events held Location Winery size*
Brand A 3 Concerts at Winery Adelaide hills 500-999
Brand B Lunch and Dinner with the winemaker events McLaren Vale 250-499
Brand C Picnic at the winery (& live music) Barossa 100-249
Brand D Silent Auction with food and wine Currency Creek 20-49
Brand E Dinner with the winemakers McLaren Vale 2500-4999
Brand F New wine launch (with lunch) McLaren Vale 500-999
*winery size by tonnage crushed (Winetitles 2013)
3.6.4 Selection of Individual Respondents
A purposive sampling approach was used to identify individual respondents (Neuman
2006). The researcher attended various wine events run by the participating wine brands
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and approached all attendees to ask if they would be willing to participate in the study.
Willing participants provided their email to the researcher, with a link to the online survey
provided the following day. A self-administered survey minimised the impact of the
researcher on the respondent, for example social desirability bias (Neuman 2006).
The qualifying dimension relevant to survey participants was that the participant had
attended a branded-wine event; as these participants were approached during a given event,
this qualifier was already met. For multi-day events, participants were asked to identify the
particular event date they had attended at the beginning of the questionnaire, and were
instructed to answer all questions with reference to that specific event (not previous wine
events they may have attended).
3.6.5 Respondent Profiles
The following Table 3-4 outlines the characteristics of individual respondents from each
event compared to the characteristics of the overall sample, based on number of responses
and age distribution. Country of residence was also investigated, however only 13
responses from the total response set of 274 resided in an interactional country (Austria 1;
China 1; Ireland 1; New Zealand 1; United Kingdom 8; United States 1).
TABLE 3-4: RESPONDENT PROFILE FOR NUMBER OF RESPONSES AND AGE
Event Winery n
Age distribution Wine Involvement Consumption Frequency
18-33 34-49 50+ M SD Var 1* 2* 3* 4* 5* Concert M Brand A 50 21 22 7 5.26 1.06 1.13 9 23 7 6 5 Concert J Brand A 50 28 19 3 5.62 1.17 1.37 6 19 16 7 2 Concert T Brand A 12 0 10 2 5.44 0.92 0.85 3 6 1 2 0 Dinner C Brand B 17 3 6 8 5.55 1.38 1.91 6 7 2 1 1 Lunch P Brand B 58 8 13 37 5.51 1.18 1.40 24 26 6 1 1 Lunch T Brand B 13 3 2 8 5.42 1.03 1.05 2 10 0 1 0 Picnic W Brand C 26 6 15 5 5.15 0.94 0.88 0 13 8 4 1 Auction C Brand D 20 2 7 11 3.79 1.43 2.06 1 6 3 4 6 Dinner B Brand E 26 1 10 15 5.85 0.86 0.74 12 12 0 2 0 Lunch H Brand F 2 0 0 2 6.07 0.76 0.58 1 1 0 0 0
Total 274 72 104 98 5.36 1.21 1.46 64 123 43 28 16 * Consumption frequency values: 1 = almost everyday
2 = 2-3 times a week 3 = once a week
4 = 2-3 times per month 5 = once or less per month
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All age brackets were adequately represented (see Table 3-4), with minor variations in
respondent age for certain events; e.g. winery concerts. This may reflect the target market
of the concert’s performer or the nature of the event itself.
An additional characteristic of relevance to this context is wine involvement and wine
consumption frequency. Table 3-4 outlines wine involvement means and standard
deviations from respondents from each event, as well as for the overall sample.
3.6.6 Data Collection Procedure
Attendees were approached during the event to seek their participation in the study. If they
agreed to participate, they were asked to provide their email so a survey link could be sent
to them after the event. An incentive of a chance to win a wine-related prize was provided
to increase participation rate (Denissen et al. 2010; Hair et al. 2008). The incentive was
negotiated with the winery, and was a wine-brand related prize; for example a bottle of
Shiraz Cabernet (for Auction C) and a two pack of La Biondina and il Briconne
Sangiovese (Lunch P).
The survey was emailed to participants within a day of the wine event. A second reminder
email was distributed a week later to those who had not yet participated, and a final email
was distributed a few days later indicating that the survey would close by the end of the
week. The survey included wine brand logos and event photos in order to present a
professional image, and to promote the collaboration of the winery. Online surveys were
selected as an appropriate form of questionnaire administration due to it being less
intrusive than asking attendees to participate during the event. The participating wineries
insisted that attendees be disturbed as little as possible during the event; as well as this,
participation rate was expected to be considerably higher if the participant did not have to
complete the survey during the event. In addition, this protocol was followed to ensure the
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researcher did not influence the results through social desirability bias (Neuman 2006).
The data collection period was the two weeks immediately after the event.
In a small number of cases the wineries handled distribution of surveys on the researcher’s
behalf to avoid any negative customer perceptions resulting from their email information
being given to a third party. Therefore, the procedures undertaken in the collection of the
completed questionnaires varied slightly. This may have impacted on the relative response
rate of the respective events, but not on the nature of the responses; as indicated in Table 3-
5, the response rates for events in which the researcher was present (between 44% and
60%) were considerably higher than the events in which the researcher was absent
(between 11% and 40%). An approximate number of attendees are also listed in Table 3-5;
the researcher did not have access to the specific number of attendees of all events,
however could deduce an approximate value from email lists or a count of attendees at one
point during an event. Due to the public, non-ticketed nature of some events, the total
number of attendees was not known even to the host.
TABLE 3-5: SUMMARY OF DATA COLLECTION PROCEDURES Brand Event Approximate
number of attendees
Researcher present
Incentive Number of Respondents
Response Rate
Brand A Concert M at Winery 265 No No 50 20%
Brand A Concert J at Winery 294 No No 50 17%
Brand A Concert T at Winery 79 No No 12 16%
Brand B Lunch C with the winemakers
50 Yes Yes 17 53%
Brand B Dinner P with the winemakers
150 Yes Yes 58 60%
Brand B Lunch T with the winemakers
80 Yes Yes 13 44%
Brand C Picnic W at the winery (& live music)
70 Yes No 26 55%
Brand D Silent Auction C with food and wine
100 Yes yes 20 54%
Brand E Dinner B with the winemakers
240 No Yes 26 11%
Brand F New wine launch H (with lunch)
30 No No 2 40%
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3.7 Preliminary Analysis
3.7.1 Data Cleaning
A number of data cleaning and quality control methods were conducted to identify
potentially random or inattentive responses to ensure the quality of the data included in
analysis (Meade and Craig 2012). This is a particularly important process for online
surveys due to factors including anonymity and lack of environmental control with
unknown distractions and divided attention influencing the respondent (Meade and Craig
2012).
First, all incomplete data was identified. The researcher excluded responses with more than
10% of items missing (Hair et al. 2012). None of the incomplete responses met the
minimum missing values rule, with a total of 86 incomplete responses deleted. All of the
remaining responses had complete data.
Survey duration was then examined, as a considerably short response time can indicate
lack of thought and attention given to survey items, while a considerably long response
time can indicate inattention or distractions experienced by the respondent (Meade and
Craig 2012). Responses falling in the 15% (less than 10 minutes) and 85% (39.5 minutes
and greater) percentiles were flagged for further quality control testing.
Data was then assessed for response patterns or responses with minimal variance, with 20+
responses in a row flagged for further quality tests; only three responses were flagged
based on this criterion (Malhotra 2009; Meade and Craig 2012).
An additional quality control technique is to include self-reported survey attention or
interest questions, which provides respondents with the opportunity to provide feedback on
their boredom or decreased attention by the end of the survey (Meade and Craig 2012).
While this approach was not taken in this survey, there was a free response comments
section at the end of each survey; responses that included comments were checked for
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evidence of respondents’ frustrations, reported boredom or difficulties with the
questionnaire.
As a result of data quality testing, three responses were deleted as the researcher could
conclusively state that these responses were of poor quality:
Respondent A – high number of repeated responses (response = 4, neutral position on the
seven-point likert scale), and fell within the 15% percentile on survey duration.
Respondent B – very high number of repeated responses (all responses either 7 or 1,
endpoints of the seven-point likert scale).
Respondent C – commented that they may have misinterpreted the questions.
3.7.2 Non-Response Bias
Non-response bias testing was conducted to ensure there were no considerable differences
in responses between early and late respondents (Armstrong and Overton 1977). The early
respondents group included participants who completed the survey within a day of the
initial email invitation (n=117, 43% of respondents); the late respondents group included
those who completed the survey after receiving two reminder emails (n=30, 11% of
respondents). An independent sample t-test was conducted to assess the difference between
early and late respondents, and Levene’s test for equality of variances was observed to
indicate whether equal variances were assumed or not assumed, and reported accordingly
(Coakes, Steed, and Ong 2010). Relational experience was the only construct indicating a
significant difference between early and late respondents (see Table 3-6), indicating that a
problem of non-response bias was unlikely.
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TABLE 3-6: TESTING FOR NON-RESPONSE BIAS
Construct Group Statistics Independent Samples Test
Response Group Mean Sig (2-tailed) Cognitive Experience Early 4.77 0.80 Late 4.70 Emotional Experience
Early 5.11 0.09 Late 4.68 Sensory Experience
Early 5.60 0.98 Late 5.59 Pragmatic Experience
Early 4.30 0.58 Late 4.14 Relational Experience
Early 4.10 0.02 Late 3.52 Event Attention
Early 4.98 0.62 Late 4.87 Event Identification
Early 3.38 0.23 Late 3.01 Event Enthusiasm
Early 5.26 0.32 Late 5.02 Event Absorption
Early 5.14 0.14 Late 4.77 Event Interaction
Early 5.13 0.29 Late 5.37 Social Event Engagement
Early 3.74 0.53 Late 3.56 Brand Attention
Early 5.03 0.67 Late 5.14 Brand Identification
Early 2.94 0.28 Late 2.61 Brand Enthusiasm
Early 4.36 0.62 Late 4.51 Brand Absorption
Early 3.94 0.15 Late 4.35 Brand Interaction
Early 4.45 0.26 Late 4.73 Social Brand Engagement
Early 3.46 0.50 Late 3.64 BIL
Early 6.01 0.93 Late 5.99 Novelty-seeking
Early 6.00 0.07 Late 6.31 Need for Affect
Early 4.29 0.16 Late 4.65 Need for Cognition
Early 5.34 0.37 Late 5.61
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3.7.3 Construct Validity
This thesis employs SEM, a multivariate technique that simultaneously assesses numerous
dependence relationships (Hair et al. 2012). This technique is valuable for this thesis
because measured variables are used to observe latent constructs, and because customer
engagement is considered a dependent variable resulting from various BME experiences
and simultaneously an independent variable in a subsequent dependence relationship with
behavioural intention of loyalty. SEM analysis allows the testing of all of these
relationships in one technique (Hair et al. 2012).
A latent construct refers to a construct that cannot be directly measured, but rather is
represented by multiple measured variables (questionnaire items) (Hair et al. 2012). Latent
constructs are beneficial are they are a more comprehensive approach of exploring a
complex construct; a number of measures are used to reflect the latent construct, and are
then tested for their contribution to that concept (construct validity) and only that concept
(discriminant validity) (Hair et al. 2012). In addition, SEM can estimate and account for
measurement error, which is the degree to which measured variables do not describe the
latent construct (Hair et al. 2012).
The first step of SEM is to define individual constructs through the investigation of
construct validity, which is the assessment of measurement variables and the extent to
which they represent the intended latent construct (Hair et al. 2012). This process assures
the quality of each construct implemented in the measurement model and subsequent path
diagram analysis (Hair et al. 2012). Construct validity is assessed through convergent
validity and discriminant validity testing (Hair et al. 2012).
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3.7.3.1 Convergent Validity
One-factor congeneric measurement models were tested using AMOS 21 prior to the
evaluation of a comprehensive measurement model. The purpose of this test is to assess
convergent validity; that the measured variables predicted to contribute to each latent
construct share a high level of common variance (Hair et al. 2012). In addition, one-factor
congeneric measurement models produce factor score weights which are necessary to
calculate composite measures (Rowe 2002); the justification and process of calculating
composite measures for path model analysis is discussed in Chapter 4.
It is recommended that a range of fit indices are considered for the analysis of models, to
account for any effects based on data non-normality, sample size, or model complexity
(Hair et al. 2012; Lei and Lomax 2005). There are a wide variety of fit indices available for
reporting, but as a general guide the range of indices selected should include; χ² value and
associated df; one absolute fit index (GFI, RMSEA); one goodness-of-fit index (GFI, CFI,
TLI); one incremental fit index (NFI, CRI, TLI); and one badness-of-fit index (RMSEA)
(Hair et al. 2012).
The only statistically based measure of model fit is the Chi-Square (Hair et al. 2012). If the
required non-significance is met (p-value > 0.05), it indicates that the proposed theory fits
reality (Hair et al. 2012). However, chi-square and its significance value are reported with
the knowledge that this value becomes less meaningful for more complex models with a
large number of observed variables tested (Hair et al. 2012). The Normed Chi-Square
reflects the Chi-Square adjusted by the degrees of freedom; the acceptable levels shown in
Table 3-7 include values between 1 and 3, with values below 1 representing an overfit of
the model and values over 3 indicating poor model fit (Hair et al. 2012).
Goodness-of-fit index (GFI) was one of the first fit indices to attempt to have less
sensitivity to sample size, with values greater than 0.90 supporting model fit (Hair et al.
2012). Normed Fit Index (NFI) is an incremental fit index, and reflects a ratio of the
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difference in chi-square of the model versus the null model (Hair et al. 2012). Values
nearing 1 indicate model fit. The Tucker-Lewis Index (TLI) compares the chi-square of the
specified model to the null model (Hair et al. 2012). In addition, TLI is not normed which
means that values can exceed 1 or fall below 0; a TLI value near 1 suggests good model fit
(Hair et al. 2012). Comparative Fit Index (CFI) is a similar index, however is normed and
therefore ranges between 0 and 1; values above 0.90 supporting model fit (Hair et al.
2012).
The Root Mean-Square Error of Approximation (RMSEA) is a badness-of-fit index, and is
commonly reported with chi-square as it attempts to correct the shortfalls associated with
chi-square, namely sample size and model complexity (Hair et al. 2012). A low RMSEA
value indicates model fit; < 0.05 indicates best fit, however 0.05-0.08 is acceptable (Hair et
al. 2012).
In summary, this thesis reports the principal goodness-of-fit index (χ²/df), Goodness-of-fit
index (GFI), Root mean square error of approximation (RMSEA) and Normed Fit Index
(NFI), Tucker Lewis Index (TLI) and Comparative Fit Index (CFI) (Hair et al. 2012;
Hooper et al. 2008; Martínez-López et al. 2013). Each construct model in this thesis was
tested against these fit indices and their related cut-off values as shown in Table 3-7:
TABLE 3-7: SUMMARY OF INDICES USED TO ASSESS MODEL FIT Name Abbreviation Type Acceptable level
Chi-Square χ² Model fit p> 0.05
Normed Chi-Square χ²/df Absolute Fit Model Parsimony
1.0 < χ²/df > 3.0
Goodness-of-Fit GFI Absolute fit GFI > 0.90
Normed Fit Index NFI Incremental Fit NFI > 0.95
Tucker-Lewis Index TLI Incremental Fit TLI > 0.95
Comparative Fit Index CFI Incremental Fit CFI > 0.95
Root Mean-Square Error of Approximation
RMSEA Absolute Fit <0.05, 0.05–0.08 acceptable
Sources: (Hair et al. 2012; Hooper et al. 2008; Kline 2011; Martínez-López et al. 2013)
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The following Figures 3-1 to 3-19 report the one-factor congeneric measurement models
and their corresponding fit indices for each construct investigated in this thesis. Factor
loadings between each measured variable and their latent variables were also observed,
with values above .07 indicating ideal convergent validity (Kline 2011) and values above
0.50 indicating acceptable convergent validity (Hair et al. 2012).
FIGURE 3-1: MEASUREMENT MODEL - COGNITIVE EXPERIENCE
TABLE 3-8: GOODNESS OF FIT INDICES FOR COGNITIVE EXPERIENCE χ² df χ²/df p GFI NFI TLI CFI RMSEA
0.82 1 0.82 .37 0.99 0.99 1.00 1.00 0.00
The one-factor congeneric measurement model for cognitive experience achieved
satisfactory goodness-of-fit with respect to the fit indices described in Table 3-7. Items 1
and 2 met the ideal threshold value of >0.70 supporting convergent validity of the model
(Kline 2011). Item 3 (COG_EXP3=.68) achieved an acceptable factor loading of above
0.50 (Hair et al. 2012). While item 3 did not reach the ideal threshold value, its inclusion
provided an additional theoretical nuance to the construct and was therefore kept in the
model.
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FIGURE 3-2: MEASUREMENT MODEL - EMOTIONAL EXPERIENCE
TABLE 3-9: GOODNESS OF FIT INDICES FOR EMOTIONAL EXPERIENCE χ² df χ²/df p GFI NFI TLI CFI RMSEA
7.50 4 1.88 0.11 0.99 0.99 0.99 0.99 0.06
The one-factor congeneric measurement model for emotional experience achieved
satisfactory goodness-of-fit with respect to the fit indices described in Table 3-7. Four
variables achieved the ideal factor loadings threshold of > 0.70, indicating convergent
validity (Kline 2011). Item 5 (EXP_EMO5 = 0.68) achieved an acceptable factor loading
of above 0.50 (Hair et al. 2012). While item 5 did not reach the ideal threshold value, it
was maintained in the model due to its conceptual relevance.
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FIGURE 3-3: MEASUREMENT MODEL - SENSORIAL EXPERIENCE
TABLE 3-10: GOODNESS OF FIT INDICES FOR SENSORIAL EXPERIENCE χ² df χ²/df p GFI NFI TLI CFI RMSEA
3.30 1 3.30 0.07 0.99 0.99 0.99 0.99 0.09
The one-factor congeneric measurement model for sensorial experience achieved
satisfactory goodness-of-fit with respect to the fit indices described in Table 3-7. All
variables achieved factor loadings > 0.70, indicating convergent validity (Kline 2011).
FIGURE 3-4: MEASUREMENT MODEL - PRAGMATIC EXPERIENCE
TABLE 3-11: GOODNESS OF FIT INDICES FOR PRAGMATIC EXPERIENCE χ² df χ²/df p GFI NFI TLI CFI RMSEA
0.74 1 0.74 0.39 0.99 0.99 1.00 1.00 0.00
The one-factor congeneric measurement model for pragmatic experience achieved
satisfactory goodness-of-fit with respect to the fit indices described in Table 3-7. All
variables achieved factor loadings > 0.70, indicating convergent validity (Kline 2011).
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FIGURE 3-5: MEASUREMENT MODEL - RELATIONAL EXPERIENCE
TABLE 3-12: GOODNESS OF FIT INDICES FOR RELATIONAL EXPERIENCE χ² df χ²/df p GFI NFI TLI CFI RMSEA
4.21 2 2.11 0.12 0.99 0.99 0.99 0.99 0.06
The one-factor congeneric measurement model for relational experience achieved
satisfactory goodness-of-fit with respect to the fit indices described in Table 3-7. All
variables achieved factor loadings > 0.70, indicating convergent validity (Kline 2011).
FIGURE 3-6: MEASUREMENT MODEL - EVENT ATTENTION
TABLE 3-13: GOODNESS OF FIT INDICES FOR EVENT ATTENTION χ² df χ²/df p GFI NFI TLI CFI RMSEA
1.93 1 1.93 0.17 0.99 0.99 0.99 0.99 0.06
The one-factor congeneric measurement model for event attention achieved satisfactory
goodness-of-fit with respect to the fit indices described in Table 3-7. All variables achieved
factor loadings > 0.70, indicating convergent validity (Kline 2011).
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FIGURE 3-7: MEASUREMENT MODEL - BRAND ATTENTION
TABLE 3-14: GOODNESS OF FIT INDICES FOR BRAND ATTENTION χ² df χ²/df p GFI NFI TLI CFI RMSEA
0.68 1 0.68 0.41 0.99 0.99 1.00 1.00 0.00
The one-factor congeneric measurement model for brand attention achieved satisfactory
goodness-of-fit with respect to the fit indices described in Table 3-7. All variables achieved
factor loadings > 0.70, indicating convergent validity (Kline 2011).
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FIGURE 3-8: MEASUREMENT MODEL - EVENT IDENTIFICATION
TABLE 3-15: GOODNESS OF FIT INDICES FOR EVENT IDENTIFICATION χ² df χ²/df p GFI NFI TLI CFI RMSEA
4.35 2 2.18 0.11 0.99 0.99 0.99 0.99 0.07
The one-factor congeneric measurement model for event identification achieved
satisfactory goodness-of-fit with respect to the fit indices described in Table 3-7. All
variables achieved ideal factor loadings of > 0.70, indicating convergent validity (Kline
2011) with the exception of item 2 (ID_EVENT2= 0.66) which still achieved an acceptable
factor loading of > 0.50 (Hair et al. 2012). Item 5 also remained in the model because it
provided an additional theoretical nuance to the construct.
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FIGURE 3-9: MEASUREMENT MODEL - BRAND IDENTIFICATION
TABLE 3-16: GOODNESS OF FIT INDICES FOR BRAND IDENTIFICATION χ² df χ²/df p GFI NFI TLI CFI RMSEA
0.97 1 0.97 0.32 0.99 0.99 1.00 1.00 0.00
The one-factor congeneric measurement model for brand identification achieved
satisfactory goodness-of-fit with respect to the fit indices described in Table 3-7. All
variables achieved factor loadings > 0.70, indicating convergent validity (Kline 2011)
FIGURE 3-10: MEASUREMENT MODEL - EVENT ENTHUSIASM
TABLE 3-17: GOODNESS OF FIT INDICES FOR EVENT ENTHUSIASM χ² df χ²/df p GFI NFI TLI CFI RMSEA
0.91 1 0.91 0.34 0.99 0.99 1.00 1.00 0.00
The one-factor congeneric measurement model for event enthusiasm achieved satisfactory
goodness-of-fit with respect to the fit indices described in Table 3-7. All variables achieved
factor loadings > 0.70, indicating convergent validity (Kline 2011). 120 | P a g e
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FIGURE 3-11: MEASUREMENT MODEL - BRAND ENTHUSIASM
TABLE 3-18: GOODNESS OF FIT INDICES FOR BRAND ENTHUSIASM χ² df χ²/df p GFI NFI TLI CFI RMSEA
2.66 1 2.66 0.10 0.99 0.99 0.99 0.99 0.08
The one-factor congeneric measurement model for brand enthusiasm achieved satisfactory
goodness-of-fit with respect to the fit indices described in Table 3-7. All variables achieved
factor loadings > 0.70, indicating convergent validity (Kline 2011).
FIGURE 3-12: MEASUREMENT MODEL - EVENT ABSORPTION
TABLE 3-19: GOODNESS OF FIT INDICES FOR EVENT ABSORPTION χ² df χ²/df p GFI NFI TLI CFI RMSEA
1.21 1 1.21 0.27 0.99 0.99 0.99 0.99 0.03
The one-factor congeneric measurement model for event absorption achieved satisfactory
goodness-of-fit with respect to the fit indices described in Table 3-7. All variables achieved
factor loadings > 0.70, indicating convergent validity (Kline 2011).
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FIGURE 3-13: MEASUREMENT MODEL - BRAND ABSORPTION
TABLE 3-20: GOODNESS OF FIT INDICES FOR BRAND ABSORPTION χ² df χ²/df p GFI NFI TLI CFI RMSEA
2.20 1 2.20 0.14 0.99 0.99 0.99 0.99 0.07
The one-factor congeneric measurement model for brand absorption achieved satisfactory
goodness-of-fit with respect to the fit indices described in Table 3-7. All variables achieved
factor loadings > 0.70, indicating convergent validity (Kline 2011).
FIGURE 3-14: MEASUREMENT MODEL - EVENT INTERACTION
TABLE 3-21: GOODNESS OF FIT INDICES FOR EVENT INTERACTION χ² df χ²/df p GFI NFI TLI* CFI RMSEA
.037 1 .037 .848 1.00 1.00 1.01 1.00 .00
The one-factor congeneric measurement model for event interaction achieved satisfactory
goodness-of-fit with respect to the fit indices described in Table 3-7. All variables achieved
factor loadings > 0.70, indicating convergent validity (Kline 2011).
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FIGURE 3-15: MEASUREMENT MODEL - BRAND INTERACTION
TABLE 3-22: GOODNESS OF FIT INDICES FOR BRAND INTERACTION χ² df χ²/df p GFI NFI TLI CFI RMSEA
0.47 1 0.47 0.49 0.99 1.00 1.00 1.00 0.00
The one-factor congeneric measurement model for brand interaction achieved satisfactory
goodness-of-fit with respect to the fit indices described in Table 3-7. All variables achieved
factor loadings > 0.70, indicating convergent validity (Kline 2011).
FIGURE 3-16: MEASUREMENT MODEL - BEHAVIOURAL INTENTION OF LOYALTY
TABLE 3-23: GOODNESS OF FIT INDICES FOR BEHAVIOURAL INTENTION OF LOYALTY χ² df χ²/df p GFI NFI TLI CFI RMSEA
0.87 1 0.87 0.35 0.99 0.99 1.00 1.00 .00
The one-factor congeneric measurement model for behavioural intention of loyalty
achieved satisfactory goodness-of-fit with respect to the fit indices described in Table 3-7.
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FIGURE 3-17: MEASUREMENT MODEL - NOVELTY-SEEKING
TABLE 3-24: GOODNESS OF FIT INDICES FOR NOVELTY-SEEKING χ² df χ²/df p GFI NFI TLI CFI RMSEA
0.91 1 0.91 0.34 0.99 0.99 1.00 1.00 0.00
The one-factor congeneric measurement model for novelty-seeking achieved satisfactory
goodness-of-fit with respect to the fit indices described in Table 3-7. All variables achieved
factor loadings > 0.70, indicating convergent validity (Kline 2011).
FIGURE 3-18: MEASUREMENT MODEL - NEED FOR AFFECT
TABLE 3-25: GOODNESS OF FIT INDICES FOR NEED FOR AFFECT χ² df χ²/df p GFI NFI TLI CFI RMSEA
2.92 1 2.92 0.09 0.99 0.99 0.99 0.99 0.08
The one-factor congeneric measurement model for need for affect achieved satisfactory
goodness-of-fit with respect to the fit indices described in Table 3-7. All variables achieved
factor loadings > 0.70, indicating convergent validity (Kline 2011).
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FIGURE 3-19: MEASUREMENT MODEL - NEED FOR COGNITION
TABLE 3-26: GOODNESS OF FIT INDICES FOR NEED FOR COGNITION χ² df χ²/df p GFI NFI TLI CFI RMSEA
13.4 9 1.49 0.15 0.98 0.99 0.99 0.99 0.04
The one-factor congeneric measurement model for need for cognition achieved satisfactory
goodness-of-fit with respect to the fit indices described in Table 3-7. All variables achieved
factor loadings > 0.70, indicating convergent validity (Kline 2011).
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3.7.3.2 Discriminant Validity and Reliability Testing
Following the assessment of one-factor congeneric measurement models, an overall
measurement model was analysed. This measurement model comprising all latent
constructs and their respective measured variables enables discriminant validity testing and
confirms (along with convergent validity) construct validity (Hair et al. 2012).
Measurement model testing is the first of the two-step SEM process, in which a
comprehensive confirmatory factor analysis (CFA) is conducted to evaluate measurement
model fit and construct validity (Hair et al. 2012). The second step is assessment of the
structural model, in which hypotheses are tested and significance of relationships are
assessed (Hair et al. 2012). This second step is taken only after CFA has confirmed
construct validity and is described in Chapter 4; the first step is crucial in assuring the
quality of the measures used to calculate the structural model outcomes (Hair et al. 2012).
Discriminant validity ensures that the measured variables used to capture a particular latent
construct are contributing distinctly to that construct, and tests the extent to which each
construct correlates with other constructs; it measures whether a construct is truly distinct
from others (Hair et al. 2012). The average variance extracted (AVE) estimate is used to
measure discriminant validity, and should achieve a greater value than the squared
correlation estimate (HSC) which would indicate that the latent construct explains the
variance in its associated measured variables more than other constructs in the model (Hair
et al. 2012).
In addition, reliability is investigated as it is a necessary element of validity testing; it
assesses the extent to which measured variables are internally consistent (Hair et al. 2012)
and free from random measurement error (Kline 2011). Cronbach’s coefficient alpha is the
most widely used measurement of reliability, however is criticised as construct weights are
constrained to be equal and therefore underestimates reliability (Peterson and Kim 2013).
The construct reliability measure is often used with SEM and allows construct weights to
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vary (Peterson and Kim 2013) and therefore is also included to determine reliability (Hair
et al. 2012). The key validity and reliability indices and analysis thresholds are summarised
in the following Table 3-27:
TABLE 3-27: RELIABILITY AND VALIDITY INDICES
Indicator Threshold Reference
Construct reliability > 0.7 (Fornell and Larcker 1981; Hair et al. 2012)
Cronbach’s alpha >0.7 (De Vaus 2002)
Average Variance Extracted
> 0.5 to achieve convergent validity Must exceed the square of the correlations between constructs (HSC) to achieve discriminant validity
(Fornell and Larcker 1981; Hair et al. 2012)
The results of the overall measurement model are presented in Table 3-28. Results indicate
Cronbach’s alphas of at least 0.80 (De Vaus 2002) and high construct reliability scores all
exceeding the threshold of 0.7 (Fornell and Larcker 1981). Convergent validity is
confirmed with all average variance extracted values exceeding 0.5 (Hair et al. 2012) and
discriminant validity is examined with all average variance extracted scores exceeding the
square of the correlations between constructs (Hair et al. 2012).
The event absorption construct was the only variable to not achieve discriminant validity
(Table 3-28), as it was found to have high correlation with event enthusiasm. However, the
event absorption construct achieved reliability and convergent validity (De Vaus 2002;
Hair et al. 2012). Conceptually, event absorption (likened to flow) and event enthusiasm
(emotional engagement) have been identified as having similarities with their reference
and applicability to emotional experience (Drengner et al. 2008). Enthusiasm refers to a
heightened level of excitement and interest (So et al. 2012; Vivek et al. 2012), while
absorption is a heightened state of complete concentration, intrinsic enjoyment and deep
engrossment (Csikzentmihaly 1990; So et al. 2012). While the constructs share common
traits of happiness and positive emotion, the two remain distinct constructs; absorption is
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an extremely high level of enthusiasm, containing the additional characteristics of
engrossment in the experience and losing sense of time (So et al. 2012; Vivek et al. 2012).
Marketing events are identified as highly experience-oriented, interactive, emotional, and a
dramaturgy of the brand (Wohlfeil and Whelan 2006); these characteristics highlight the
applicability of both enthusiasm and absorption to be facilitated during BMEs. Therefore,
as event absorption and event enthusiasm are conceptually related but distinct and are both
applicable to BMEs, it is argued that each remain in the model as separate constructs.
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TABLE 3-28: RELIABILITY AND VALIDITY OF MEASUREMENT MODEL
Scale Item L C CA CR AVE HSC
Cognitive Experience EXP_COG1 0.88 0.78 0.85 0.86 0.67 0.32 EXP_COG2 0.87 0.76 EXP_COG3 0.69 0.48
Emotional Experience
EXP_EMO1 0.74 0.55 0.89 0.89 0.62 0.47 EXP_EMO2 0.79 0.63 EXP_EMO3 0.87 0.76 EXP_EMO4 0.80 0.64 EXP_EMO5 0.73 0.53
Sensorial Experience EXP_SEN1 0.92 0.84 0.94 0.94 0.83 0.47 EXP_SEN2 0.95 0.90 EXP_SEN3 0.87 0.75
Pragmatic Experience EXP_PRAG4 0.80 0.65 0.84 0.84 0.64 0.27 EXP_PRAG5 0.81 0.65 EXP_PRAG6 0.79 0.62
Relational Experience
EXP_REL2 0.87 0.75 0.96 0.96 0.86 0.19 EXP_REL3 0.93 0.87 EXP_REL4 0.97 0.94 EXP_REL5 0.93 0.86
Event Attention ATT_EVENT2 0.80 0.64 0.84 0.84 0.64 0.60 ATT_EVENT3 0.84 0.71 ATT_EVENT4 0.76 0.57
Event Identification
ID_EVENT1 0.77 0.60 0.88 0.89 0.67 0.23 ID_EVENT2 0.66 0.44 ID_EVENT3 0.90 0.80
ID_EVENT4 0.92 0.84
Event Enthusiasm
ENTH_EVENT2 0.91 0.83 0.91 0.92 0.74 0.71 ENTH_EVENT3 0.90 0.81 ENTH_EVENT4 0.84 0.70 ENTH_EVENT5 0.78 0.61
Event Absorption
AB_EVENT2 0.75 0.57 0.84 0.84 0.64 0.71 AB_EVENT5 0.78 0.61
AB_EVENT6 0.86 0.74
Event Interaction
INT_EVENT1 0.87 0.76 0.95 0.95 0.82 0.38 INT_EVENT2 0.87 0.76 INT_EVENT3 0.96 0.91 INT_EVENT4 0.93 0.86
Social Event Engagement
SOC_EVENT3 0.76 0.57 0.85 0.85 0.65 0.38 SOC_EVENT4 0.85 0.71
SOC_EVENT5 0.81 0.66
L = loadings C = correlations
CA = cronbach’s alpha CR = construct reliability
AVE = average variance extracted HSC = highest squared correlation
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Scale Item L C CA CR VE HSC
Brand Attention
ATT_BRAND2 0.94 0.87 0.94 0.94 0.85 0.64 ATT_BRAND3 0.94 0.89
ATT_BRAND4 0.88 0.77
Brand Identification
ID_BRAND1 0.85 0.72 0.92 0.92 0.75 0.49 ID_BRAND2 0.78 0.61 ID_BRAND3 0.88 0.78
ID_BRAND4 0.94 0.88
Brand Enthusiasm
ENTH_BRAND2 0.95 0.91 0.96 0.96 0.86 0.64 ENTH_BRAND3 0.96 0.93 ENTH_BRAND4 0.93 0.87
ENTH_BRAND5 0.86 0.73
Brand Absorption
AB_BRAND2 0.90 0.8 0.90 0.91 0.76 0.63 AB_BRAND5 0.88 0.77
AB_BRAND6 0.85 0.71
Brand Interaction
INT_BRAND1 0.89 0.80 0.97 0.97 0.87 0.59 INT_BRAND2 0.95 0.91 INT_BRAND3 0.97 0.94
INT_BRAND4 0.93 0.86
Social Brand Engagement
SOC_BRAND3 0.85 0.72 0.88 0.88 0.70 0.44 SOC_BRAND4 0.85 0.72
SOC_BRAND5 0.82 0.67
Novelty-seeking
NEED_NOV1 0.90 0.81 0.95 0.95 0.86 0.28 NEED_NOV2 0.93 0.86
NEED_NOV3 0.95 0.91
Need for Affect
NEED_AFF4 0.85 0.73 0.91 0.91 0.78 0.16 NEED_AFF5 0.88 0.77
NEED_AFF6 0.91 0.83
Cognitive Needs
R_NEED_COG2 0.80 0.63 0.91 0.91 0.63 0.13 R_NEED_COG3 0.87 0.75 R_NEED_COG4 0.82 0.67 R_NEED_COG5 0.79 0.63 R_NEED_COG7 0.74 0.55
R_NEED_COG8 0.76 0.57
Behavioural Intention of Loyalty
BIL_BRAND1 .951 .905 0.97 0.96 0.87 0.42 BIL_BRAND2 .988 .976
BIL_BRAND3 .911 .830
BIL_BRAND4 .871 .759
L = loadings C = correlations
CA = cronbach’s alpha CR = construct reliability
VE = average variance extracted HSC = highest squared correlation
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3.7.4 Testing for Common Method Bias
The final test run to confirm quality of the construct was to check for common method
bias. This method considers biases respondents have towards answering questions with
relation to various factors, including social desirability bias, scale formats, and item
context effects (e.g. scale length or context-inducted mood) (Podsakoff, MacKenzie, Lee,
and Podsakoff 2003). All final items in the study were tested using the Harman’s single-
factor technique, to check that variance cannot be account for by a single general factor
(Podsakoff et al. 2003). Results indicated extremely poor fit for when all survey items were
considered part of one general factor, confirming that this survey does not have problems
with common method bias (Podsakoff et al. 2003).
TABLE 3-29: COMMON METHOD BIAS - GOODNESS OF FIT INDICES χ² df χ²/df p GFI NFI TLI CFI RMSEA
19197.01 3485 5.51 0.00 0.26 0.28 0.31 0.32 0.13
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3.8 Chapter 3 Summary
This chapter described the process undertaken to collect data for this thesis. A deductive,
quantitative research design was employed in this thesis to investigate causal relationships
through SEM. The unit of analysis was the individual; specifically, the event attendee.
A pre-test in the University sector revealed that the initial survey was too long and
assessed the selected measurement items. A number of event experience items were
removed, and customer engagement and behavioural intention of loyalty items were
replaced as a result of the preliminary analysis.
The main study was conducted in the South Australian wine industry and included six
participating wineries hosting ten wine-related BMEs. Respondents were primarily
approached during each event and invited to participate in the study, providing their email
address for the online survey to be sent to them after the event.
Preliminary analysis was also described in this chapter. First, a number of data cleaning
and quality control tests were conducted, resulting in three responses being removed from
the sample collected for the main data analysis. One-factor congeneric measurement
models were run to assess the model fit of each construct. Reliability and validity was
confirmed for the majority of constructs.
The next chapter describes the main data analysis conducted in this thesis, and investigates
the proposed thesis hypotheses identified in Chapter 2.
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CHAPTER 4: RESULTS
4.1 Chapter 4 Introduction
This chapter outlines each step of data analysis to address the research questions described
in Chapter 2. Data was analysed with SPSS21 and AMOS21 applying structural equation
modelling (SEM). The first section investigates the proposition that social engagement is a
dimension of customer engagement.
Section two introduces the identified path model reflecting the conceptual model and
hypothesised relationships for this thesis. The section begins with a discussion of
composite variables, their applicability to path model analysis and the process of
composite variable calculation. The steps for SEM analysis are then discussed which
includes model specification, identification, estimation and re-specification. Hypotheses
regarding the impact of components of BME experience on customer event engagement
and customer brand engagement, the relationship between the two customer engagement
constructs, and the outcome of behavioural intention of loyalty are then discussed with
reference to the final re-specified model.
Section three utilises multi-group analysis to investigate the moderation effect of
experiential needs. The model was tested comparing ‘low’ and ‘high’ groups of 1) need for
cognition, 2) need for affect and 3) novelty-seeking needs. An exploratory approach was
taken in this analysis step, as the investigation of moderating variables is scarce in
customer engagement literature. While results are described and briefly discussed in this
chapter, a detailed discussion of findings and their theoretical and managerial implications
is provided in Chapter 5.
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4.2 Social Engagement as an Independent Engagement Dimension
In Chapter 3, So et al.’s (2012) five-dimension conceptualisation of customer engagement
was tested for statistical fit from both a brand and event perspective. However, social
engagement has been conceptualised diversely in customer engagement studies; as a social
dimension of engagement (Vivek et al. 2012), social-interactive engagement (Calder et al.
2009), social elements of engagement (Gambetti et al. 2012; Sawhney et al. 2005), or a
strong focus throughout their discussion (Abdul-Ghani et al. 2011; Algesheimer et al.
2005); however, there is no consistency in the literature on the presence or role of social
engagement. Therefore, this thesis aims to provide some clarity to this debate and
investigates social engagement as an independent dimension of customer engagement.
4.2.1 Convergent Validity of Social Engagement Dimensions
The original social engagement construct utilised in this thesis consisted of seven items
from Calder et al. (2009), however the social event engagement and social brand
engagement models were reduced to three items for the purpose of parsimony.
TABLE 4-1: SOCIAL ENGAGEMENT ITEMS INCLUDED AND EXCLUDED FROM MODEL
Item no: Included Excluded
SOC_EVENT1 This event did a good job of getting its attendees to contribute or provide feedback
SOC_EVENT2 I liked meeting other people who attended this event
SOC_EVENT3 I’ve gotten interested in things I otherwise wouldn’t have because of other attendees at this event
SOC_EVENT4 I was as interested in input from other attendees as I was in information provided by organisers during this event
SOC_EVENT5 A big reason I liked this event is what I got from other attendees
SOC_EVENT6 I did quite a bit of socialising at this event
SOC_EVENT7 I contributed to conversations at this event
*the same items were replicated for social brand engagement; substitute the word ‘event’ for ‘brand’
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The three included items reflect the interactions the customer has with other actors with
respect to the event or brand; in particular the information and value received from other
attendees. In comparison, the excluded items had a common focus on the individual’s
reported contribution to social interactions. The included items share a common trait of
emphasising the customer’s level of interest and value from social reactions, whereas the
excluded items focus on the nature of the interaction. While the excluded items referred to
social interactions, they do not refer to a heightened state of interest.
The process of reaching the final model involved running the original model with all seven
items and identifying those reporting low factor loading values (Kline 2011). The
following Figures 4-1 and 4-2 report the final one-factor congeneric measurement models
for social event engagement and social brand engagement.
FIGURE 4-1: MEASUREMENT MODEL – SOCIAL EVENT ENGAGEMENT
TABLE 4-2: GOODNESS OF FIT INDICES - SOCIAL EVENT ENGAGEMENT χ² df χ²/df p GFI NFI TLI CFI RMSEA
3.2 1 3.2 0.07 0.99 0.99 0.98 0.99 0.09
The social event engagement congeneric model achieved acceptable fit with the data,
although χ²/df > 3 extends beyond the threshold value required and RMSEA is above the
<0.08 (Hair et al. 2012). This model was deemed acceptable as factor loadings for each
indicator were above 0.70 which indicates convergent validity (Kline 2011). As this model
only included three indicators, it is a just-identified model; therefore, an equality constraint
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was imposed on the error terms of SOC_EVENT3 and SOC_EVENT4 in order to
sufficiently identify the model (Kline 2011).
The assertions regarding these final items were further supported as the social brand
engagement model implemented the same three items to achieve model fit (Figure 4-2 and
Table 4-3). The items capture the impact of social interactions regarding the brand in
representing social brand engagement.
FIGURE 4-2: MEASUREMENT MODEL - SOCIAL BRAND ENGAGEMENT
TABLE 4-3: GOODNESS OF FIT INDICES - SOCIAL BRAND ENGAGEMENT χ² df χ²/df p GFI NFI TLI CFI RMSEA
0.77 1 0.77 0.38 0.99 0.99 1.00 1.00 0.00
The social brand engagement congeneric model achieved fit with the data, although χ²/df
<1 suggests model overfit (Hair et al. 2012). Factor loadings for each measured variable
were above 0.70 confirming convergent validity (Kline 2011). This model is a just-
identified model, therefore an equality constraint was imposed on the error terms of
SOC_BRAND3 and SOC_BRAND4 to identify the model (Kline 2011).
In summary, the one-factor congeneric measurement models indicated that social event
engagement and social brand engagement achieved convergent validity. The next section
describes the discriminant validity and reliability testing of customer event engagement
and customer brand engagement with the inclusion of their respective social engagement
dimensions.
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4.2.2 Discriminant Validity and Reliability Testing of Social Event Engagement
Following the testing of one-factor congeneric models for social event engagement and
social brand engagement, the social event engagement construct was included in a
confirmatory factor analysis model to investigate the discriminant validity of the construct.
FIGURE 4-3: MEASUREMENT MODEL - EVENT ENGAGEMENT
TABLE 4-4: GOODNESS OF FIT INDICES - EVENT ENGAGEMENT
χ² df χ²/df p GFI NFI TLI CFI RMSEA
411.87 174 2.37 0.00 0.87 0.91 0.94 0.95 0.07
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The event engagement measurement model (Figure 4-3 and Table 4-4) achieved
satisfactory fit with the data. A number of fit indices were below the desired threshold
values (p<0.05, GFI<0.90, NFI<0.95, TLI<0.94), however when model complexity
increases the original GOF thresholds become unrealistic (Hair et al. 2012). All factor
loadings met the ideal threshold value of 0.70 (Table 4-3) supporting convergent validity
of the model (Kline 2011) with the exception of ID_EVENT2 = 0.66 which still achieved
an acceptable factor loading of above 0.50 (Hair et al. 2012).
The social event engagement construct demonstrated sound reliability and discriminant
validity (Table 4-5). Cronbach’s alpha and construct reliability threshold values were
achieved for all event engagement dimensions, indicating internal consistency (CA > 0.80
and CR > 0.7). Convergent validity was confirmed (AVE > 0.5) and discriminant validity
was achieved as the average variance extracted (AVE) exceeded the highest squared
correlation (HSC), demonstrating that social event engagement is a robust construct, and
distinct from the other customer event engagement dimensions (Hair et al. 2012). The
event absorption construct AVE value did not exceed HSC; however, as discussed in
Chapter 3 (section 3.7.3.2), event absorption and event enthusiasm have similarities with
their mutual reference to emotional drivers of engagement (Drengner et al. 2008).
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TABLE 4-5: RELIABILITY AND VALIDITY – EVENT ENGAGEMENT Scale Item L C CA CR AVE HSC
Event Attention
ATT_EVENT2 0.81 0.66 0.84 0.84 0.64 0.59
ATT_EVENT3 0.82 0.68
ATT_EVENT4 0.78 0.60
Event Identification
ID_EVENT1 0.77 0.59 0.88 0.89 0.67 0.23
ID_EVENT2 0.66 0.44
ID_EVENT3 0.90 0.81
ID_EVENT4 0.92 0.84
Event Enthusiasm
ENTH_EVENT2 0.92 0.85 0.91 0.92 0.73 0.68
ENTH_EVENT3 0.91 0.82
ENTH_EVENT4 0.84 0.70
ENTH_EVENT5 0.76 0.57
Event Absorption
AB_EVENT2 0.74 0.55 0.84 0.84 0.64 0.68
AB_EVENT5 0.85 0.72
AB_EVENT6 0.81 0.66
Event Interaction
INT_EVENT1 0.87 0.76 0.95 0.95 0.82 0.37
INT_EVENT2 0.87 0.76
INT_EVENT3 0.96 0.92
INT_EVENT4 0.93 0.86
Social Event Engagement
SOC_EVENT3 0.74 0.55 0.85 0.85 0.65 0.37
SOC_EVENT4 0.87 0.76
SOC_EVENT5 0.80 0.64
L = loadings C = correlations
CA = cronbach’s alpha CR = construct reliability
AVE = average variance extracted HSC = highest squared correlation
In summary, the social event engagement construct achieved satisfactory model fit, both as
a stand-alone construct as evidenced in the one-factor congeneric measurement model, and
within the broader measurement model of event engagement. Construct validity and
reliability were achieved with the exception of event absorption; however, this construct
remained in the model due to the conceptual recognition that it is closely related and yet
distinct from event enthusiasm (discussion in section 3.7.3.2).
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4.2.3 Discriminant Validity and Reliability Testing of Social Brand Engagement
The tests for discriminant validity and reliability were replicated for the social brand
engagement construct. A measurement model of brand engagement was analysed to ensure
social brand engagement was a discriminant and reliable construct (Hair et al. 2012).
FIGURE 4-4: MEASUREMENT MODEL - BRAND ENGAGEMENT
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TABLE 4-6: GOODNESS OF FIT INDICES - BRAND ENGAGEMENT χ² df χ²/df p GFI NFI TLI CFI RMSEA
340.18 172 1.98 0.00 0.90 0.95 0.97 0.97 0.06
The brand engagement measurement model (Figure 4-4) achieved reasonable fit with the
data. While the significance value was not achieved (p <0.05), it is recognised that the
significance value becomes less indicative of model fit when model complexity increases,
particularly for a smaller sample size (Hair et al. 2012). The remaining fit indices suggest
model fit and all factor loadings were greater than 0.70, indicating convergent validity
(Kline 2011).
The results of this model provide initial support for social brand engagement as an
independent dimension of brand engagement. Additional testing from this model examined
the discriminant validity and reliability of the constructs, and is outlined in Table 4-7.
The results from the brand engagement measurement model (Table 4-7) indicate that social
brand engagement achieves discriminant validity and reliability as an independent
dimension of the brand engagement construct; cronbach’s alpha is above 0.70 (De Vaus
2002) indicating reliability of social brand engagement, and a construct reliability (CR)
value above 0.7 indicating internal consistency (Hair et al. 2012). Convergent validity is
confirmed with the average variance extracted (AVE) exceeding 0.5 and discriminant
validity was achieved as the AVE score exceeded the square of the correlations between
constructs (HSC) (Hair et al. 2012).
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TABLE 4-7: RELIABILITY AND VALIDITY – BRAND ENGAGEMENT Scale Item L C CA CR AVE HSC
Brand Attention
ATT_BRAND2 0.93 0.87 0.94 0.94 0.85 0.63
ATT_BRAND3 0.95 0.90
ATT_BRAND4 0.88 0.77
Brand Identification
ID_BRAND1 0.84 0.71 0.92 0.92 0.74 0.43
ID_BRAND2 0.76 0.57
ID_BRAND3 0.88 0.77
ID_BRAND4 0.95 0.90
Brand Enthusiasm
ENTH_BRAND2 0.96 0.92 0.96 0.96 0.85 0.63
ENTH_BRAND3 0.97 0.95
ENTH_BRAND4 0.92 0.85
ENTH_BRAND5 0.83 0.70
Brand Absorption
AB_BRAND2 0.89 0.8 0.90 0.91 0.76 0.62
AB_BRAND5 0.87 0.75
AB_BRAND6 0.86 0.73
Brand Interaction
INT_BRAND1 0.88 0.77 0.97 0.96 0.87 0.59
INT_BRAND2 0.94 0.89
INT_BRAND3 0.98 0.96
INT_BRAND4 0.93 0.86
Social Brand Engagement
SOC_BRAND3 0.84 0.71 0.88 0.88 0.70 0.44
SOC_BRAND4 0.86 0.73
SOC_BRAND5 0.82 0.67
L = loadings C = correlations
CA = cronbach’s alpha CR = construct reliability
AVE = average variance extracted HSC = highest squared correlation
In summary, the social brand engagement construct achieved good model fit, both as a
stand-along construct as evidenced in the one-factor congeneric measurement model, and
within the broader measurement model of brand engagement. Construct validity and
reliability were achieved. Therefore, the social brand engagement construct is a worthwhile
addition to the brand engagement construct and is discriminant from other brand
engagement dimensions. The final test for discriminant validity and reliability was
conducted with social event engagement, social brand engagement and relational
experience components as they share a common focus on social elements.
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4.2.4 Discriminant Validity of Social Constructs
Various social constructs were utilised in this thesis; each is conceptually unique and
distinct, however share a common focus on social elements. Therefore, it was important to
demonstrate the empirical uniqueness of the relational experience, social event engagement
and social brand engagement constructs.
A relational experience is one that emphasises the social context and relationships with
others (Gentile et al. 2007).
Social event engagement is the customer’s heightened level of interest regarding the event
based on personal exchanges with other customers, and occurs when the customer has a
personal exchange with other customers about or with reference to the event.
Social brand engagement is the customer’s heightened level of interest regarding the brand
based on personal exchanges with other customers, and occurs when the customer has a
personal exchange with other customers about or with reference to the brand.
Conceptually these constructs differ as relational experience reflects the ‘nature’ of the
experience in which social interactions are abundant; it does not encompass any motivation
or psychological state derived from that experience. In contrast, social engagement
encompasses the heightened psychological state of the customer based on personal
exchanges with others, which can either reflect on the event or the brand.
A measurement model (Figure 4-5) containing the relational experience, social event
engagement and social brand engagement constructs was assessed for discriminant
validity.
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FIGURE 4-5: MEASUREMENT MODEL - SOCIAL CONSTRUCTS
TABLE 4-8: GOODNESS OF FIT INDICES - SOCIAL CONSTRUCTS
χ² df χ²/df p GFI NFI TLI CFI RMSEA
89.80 32 2.81 0.00 0.94 0.96 0.96 0.97 0.08
The social constructs model achieved fit with the data. Significance was not achieved;
however, model fit was still concluded as significance value becomes less indicative of
model fit of complex models, particularly with a smaller sample size (Hair et al. 2012). In
addition, standardised factor loadings for each indicator were above 0.70 confirming
convergent validity (Kline 2011).
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TABLE 4-9: RELIABILITY AND VALIDITY – SOCIAL CONSTRUCTS Scale Item L C CA CR AVE HSC
Relational Experience
EXP_REL2 0.87 0.75 0.96 0.96 0.86 0.17
EXP_REL3 0.93 0.87
EXP_REL4 0.97 0.94
EXP_REL5 0.93 0.86
Social Event Engagement
SOC_EVENT3 0.77 0.60 0.85 0.85 0.65 0.27
SOC_EVENT4 0.84 0.71
SOC_EVENT5 0.80 0.64
Social Brand Engagement
SOC_BRAND3 0.90 0.81 0.88 0.88 0.70 0.27
SOC_BRAND4 0.83 0.69
SOC_BRAND5 0.78 0.60
L = loadings C = correlations
CA = cronbach’s alpha CR = construct reliability
AVE = average variance extracted HSC = highest squared correlation
Table 4-9 highlights that social brand engagement, social event engagement and relational
experience are empirically distinct constructs. The social brand engagement and social
event engagement constructs, although having a covariance of 0.52 (Figure 4-5) achieved
discriminant validity (AVE > HSC) (Hair et al. 2012). Furthermore, although social brand
engagement and social event engagement latent constructs indicated strong covariance
with relational experience (0.31 and 0.41 respectively) discriminant validity was also
achieved (AVE > HSC) (Hair et al. 2012).
Reliability and internal consistency was achieved for each construct (CA > 0.80 and CR >
0.7 respectively), and convergent validity (AVE > 0.5) (Hair et al. 2012). These results
indicate that social event engagement, social brand engagement and relational experience
have achieved construct validity. Support is provided for each construct as empirically and
conceptually distinct; the following structural models and path models include these
constructs with the knowledge that each is accurate and distinct.
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4.2.5 Structural Model of Customer Event Engagement
Measurement models and structural models are similar with regards to the latent constructs
and measured variables included the analysis, however measurement models identify
covariances between latent constructs and assess discriminant validity and reliability (Hair
et al. 2012). A structural model instead includes paths from each latent variable to a
higher-order latent construct, which allows the model to estimate the strength and
significance of the impact each latent variable (e.g. social engagement) has on the higher-
order construct (e.g. customer engagement) (Hair et al. 2012).
The purpose of running these models was solely to assess how well a six dimension model
of customer engagement (event and brand) including social engagement fitted the data;
therefore, a confirmatory modelling strategy was employed (Hair et al. 2012). This strategy
is the most direct utilisation of SEM in which the conceptual model, founded in the
relevant literature, is assessed for model fit without investigation of competing models
(Hair et al. 2012).
A structural model of event engagement dimensions including attention, enthusiasm,
absorption, identification, interaction and social event engagement leading to a higher
order latent construct of customer event engagement was assessed. The findings of this
structural model would provide support that social event engagement is an important
dimension that significantly contributes to the higher-order construct of customer event
engagement.
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FIGURE 4-6: MEASUREMENT MODEL - CUSTOMER EVENT ENGAGEMENT
* Covariance between z25 and z35 = .57 not shown on diagram.
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The event engagement structural model (Figure 4-6 and Table 4-10) revealed questionable
fit with the data. Due to the complexity of the model and the relatively small sample size, it
is unrealistic to expect fit indices to reach their original thresholds (Hair et al. 2012).
However, low standardised factor loadings were found for event interaction (0.36) and
social event engagement (0.31) which are less than commonly accepted levels of 0.7 or
0.50 to confirm convergent validity (Hair et al. 2012; Kline 2011). Event identification
(0.51) was also lower than the ideal 0.70 value, however was greater than 0.5 and therefore
acceptable (Hair et al. 2012).
To achieve reasonable model fit, an error covariance was placed between event interaction
and social event engagement. While it is recognised that event interaction and social event
engagement have common traits, they are conceptually distinct constructs. Interaction
encompasses the energy or effort the customer exerts in order to actively participate in
event activities (So et al. 2012). Social event engagement is the customer’s heightened
level of interest regarding the event based on personal exchanges with other customers, and
occurs when the customer has a personal exchange with other customers with reference to
the event.
While the error covariance may suggest that the social event engagement and event
interaction constructs are distinct from the other customer event engagement dimensions,
the two constructs were maintained in further analysis for a number of reasons. First, the
purpose of hypothesis 1 was to explore whether social engagement can be considered an
element of customer engagement; the results provide partial support for this hypothesis.
Second, the social event engagement construct achieved discriminant validity, reliability,
and convergent validity as reported in sections 4.2.1 and 4.2.2. Third, support is found for
TABLE 4-10: GOODNESS OF FIT INDICES – EVENT ENGAGEMENT χ² df χ²/df p GFI NFI TLI CFI RMSEA
463.07 182 2.54 0.00 0.85 0.90 0.93 0.94 0.08
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the inclusion of social brand engagement (discussed in section 4.2.6); social event
engagement and event interactions should remain for the purpose of complete replication
of the customer engagement measures with regards to the brand and the event. It is
recognised that this is a limitation of the research, and is discussed in Chapter 5.
4.2.6 Structural Model of Customer Brand Engagement
The structural model process was replicated for brand engagement in which the dimensions
of attention, enthusiasm, absorption, identification, interaction and social brand
engagement were assessed for their relationship with the higher-order latent construct of
customer brand engagement.
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FIGURE 4-7: MEASUREMENT MODEL - CUSTOMER BRAND ENGAGEMENT
TABLE 4-11: GOODNESS OF FIT INDICES – BRAND ENGAGEMENT χ² df χ²/df p GFI NFI TLI CFI RMSEA
441.75 183 2.41 0.00 0.87 0.93 0.96 0.96 0.07
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The brand engagement structural model (Figure 4-7 and Table 4-11) achieved acceptable
fit with the data. Standardised factor loadings for each indicator were above 0.70
confirming convergent validity (Kline 2011) with the exception of brand identification
(0.64) which achieved an acceptable (>0.50) although not ideal value (Hair et al. 2012).
The findings of the structural model support the proposition that social brand engagement
is a significant dimension of customer brand engagement, with a standardised factor
loading of 0.76 (Figure 4-7). The personal exchanges between customers have a strong
impact on the brand, as brand-related discussion can impact the overall interest the
customer has in the brand. Social brand engagement is therefore an important dimension
that significantly contributes to the higher-order construct of customer brand engagement.
4.2.7 Discussion of Hypothesis 1
TABLE 4-12: SUMMARY OF HYPOTHESIS 1 H# Hypothesis Supported/ Not Supported
1a Social event engagement is a dimension of customer event engagement
Partially Supported
1b Social brand engagement is a dimension of customer brand engagement
Supported
H1a: Social event engagement is a dimension of customer event engagement
Convergent validity, discriminant validity and reliability of each of the customer event
engagement dimensions were confirmed; attention, enthusiasm, absorption, identification,
interaction and social event engagement. These results support social event engagement as
distinct from the other dimensions of customer event engagement. However, the structural
model of customer event engagement only achieved satisfactory fit. A covariance was
placed on the error terms of social engagement and interaction dimensions in the event
engagement construct model. The social event engagement and event interaction constructs
remained in the model despite their potential distinction from the other customer event
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engagement dimensions. This structure of customer engagement has been used by Calder
et al. (2009), who identify two broad engagement constructs; personal engagement and
social-interactive engagement elements (Calder et al. 2009). This finding is discussed
further in Chapter 5, directions for future research. Therefore, H1a was partially
supported.
H1b: Social brand engagement is a dimension of customer brand engagement
A measurement model confirmed the discriminant validity and reliability of each of the
customer brand engagement dimensions; attention, enthusiasm, absorption, identification,
interaction and social brand engagement. The findings confirm that social brand
engagement holds as a unique dimensions and not a subset of an existing element of brand
engagement. The second-step involved structural model testing, in which social brand
engagement was found to significantly contribute to the higher-order construct of customer
brand engagement. This structural model achieved fit, and therefore H1b was supported.
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4.3 Path Model Analysis using Structural Equation Modelling
4.3.1 Path Model Analysis
Path analysis refers to a SEM approach that estimates the strength and significance of
relationship between constructs, assessed through the strength of paths illustrated in the
path diagram (Hair et al. 2012). A path diagram is a visual representation of the complete
conceptual model and includes the complete set of hypothesised relationships among the
included constructs (Hair et al. 2012).
Path model analysis was used to investigate hypotheses H2, H3 and H4;
H2a-j: Cognitive, emotional, sensorial, pragmatic and relational event experiences contribute to
customer event engagement and customer brand engagement
H3: There is a positive relationship between customer event engagement and customer brand
engagement
H4a-b:There is a positive relationship between customer event engagement (and customer brand
engagement) and behavioural intention of loyalty
Each of these hypotheses are assessed using the complete path model as this allows the
nature of the relationships among the antecedents (BME experiential components), event
engagement, brand engagement and behavioural intention of loyalty to be explored
simultaneously and their combined effects considered (Hair et al. 2012). In this regard,
path analysis is a comprehensive method that captures the relationships among
independent variables and considers the direct and indirect effects on the dependent
variables (Kline 2011). The relationships between experiential components and customer
engagement (H2a-j), in particular, benefit from path model analysis as each experiential
component is assessed for its contribution to event engagement and brand engagement, and
test the relationship between event engagement and brand engagement. Overall, this
represents two relationships between experience and brand engagement; directly and
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indirectly through event engagement. The latter is referred to as an indirect effect (Kline
2011).
SEM requires a large sample size to ensure statistical stability. A rule of thumb is that the
ratio of sample size to the number of model parameters should be at least 5:1, preferably
10:1 (Hair et al. 2012; Kline 2011). An even greater ratio is required in situations where
data does not conform to assumptions of multivariate normality (Hair et al. 2012), as is
common in research practice (Byrne 2001). It is recommended that sample sizes of above
200 be implemented to ensure accuracy (Hair et al. 2012). The achieved sample size of
274, although an acceptable size for simple models (e.g. one-factor congeneric
measurement models), was not deemed sufficient for the analysis of the proposed complex
models if latent and observed variables were included (Rowe 2002). Therefore, composite
variables were calculated based on the results of the one-factor congeneric measurement
model, and the path model constructed using these composite measures.
4.3.2 Calculation of Composite Variables
Composite variables have commonly been calculated as a means of data reduction (Rowe
2002), and enable a more accurate evaluation of complex models despite a limited sample
size. The use of composite variables includes firstly creating the variables using factor
score weights through AMOS 21 (Rowe 2002). Second, the factor loading and error
variance value for each composite variable is computed to remove additional complexity
from the overall model, hence providing greater stability and accuracy of the path model
results. The resulting composite variables and their respective factor loadings and error
variances are implemented in the path analysis model.
Factor score weights were derived from each one-factor congeneric measurement model
and were used to calculate the composite variables (Rowe 2002) once each construct had
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satisfied construct validity and reliability. The values estimated from a single one-factor
congeneric measurement model are likely to be more stable than values desired from an
overall measurement model as the model is far less complex; this is particularly important
when using a relatively small sample size. This ensures reliability and validity of the
composite variables (Rowe 2002). Fitting a one-factor congeneric measurement model
allows for differences in the degree to which each individual measure contributes to the
overall composite scale, thus providing a more realistic representation of the data (Rowe
2002). In addition, fitting a one-factor congeneric measurement model takes into account
the measurement error associated with the measurement of the indicator variables.
The factor score weights were transferred into EXCEL 2010 and used to calculate a sum of
weights for each construct. The factor score weight for each item were divided by the sum
of weights calculated for the respective construct to produce a proportionally weighted
scale score for each item (Rowe 2002). The final composite scores were then computed in
SPSS 21.
Path models can investigate the relationships amongst the latent variables underlying these
composite scales rather than the original observed variables. An extra step was also taken
to further reduce the amount of paths to be estimated, hence decreasing the complexity of
the model and creating greater stability in the path model results; this involved computing
the factor loadings and error variances for each composite variable and including this
information in the path model (Politis 2001). This approach is beneficial as the structural
parameter estimates are more stable; instead of using a large number of raw indicator
variables to measure the latent constructs in the full model, a much smaller number of
composite indicators are computed. Each latent variable was therefore measured by a
single composite variable in which the composite scale variance, standard deviation and
reliability were used to fix the composite variable factor loading and measurement error
variance (Politis 2001). These values were calculated from the standard deviation of the
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composite variable and the reliability of the composite variable, which is calculated using
coefficient H (Hancock and Mueller 2001; Mueller and Hancock 2008).
Cronbach’s alpha is criticised as being an under-estimate of the reliability for congeneric
measures. Conversely, coefficient H is a ‘maximised’ reliability indicator and is considered
a better estimate of composite reliability if one-factor congeneric measurement models
have been used to develop the composite variable (Mueller and Hancock 2008). Therefore,
coefficient H values were calculated for each composite variable as an indicator of
composite reliability.
The final step, reported in Table 4-13, is calculating the factor loading (λ = sx*√ rx) and
error variance (Ɵ = sx2 [1-rx]) values for each composite variable (Politis 2001). The
resultant values are built into the path model to account for the known amount of error
associated with the measurement of each latent variable (Politis 2001).
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TABLE 4-13: FACTOR LOADINGS AND ERROR VARIANCES FOR COMPOSITE VARIABLES Name of Latent Variable Standard
Deviation of Composite (sx)
Reliability of Composite (rx)
Factor Loading (λ) (sx*√ rx)
Error Variance (Ɵ) (sx
2 [1-rx])
Cognitive experience 1.23 0.89 1.16 0.17
Emotional experience 1.20 0.91 1.15 0.13
Sensory experience 1.27 0.94 1.23 0.09
Pragmatic experience 1.36 0.84 1.25 0.30
Relational experience 1.37 0.97 1.34 0.06
Event attention 1.26 0.85 1.16 0.24
Event identification 1.52 0.92 1.46 0.18
Event enthusiasm 1.30 0.94 1.25 0.11
Event absorption 1.22 0.85 1.12 0.22
Event interaction 1.26 0.97 1.24 0.06
Social event engagement 1.40 0.85 1.29 0.30
Brand attention 1.35 0.95 1.32 0.09
Brand identification 1.50 0.96 1.46 0.10
Brand enthusiasm 1.53 0.98 1.51 0.06
Brand absorption 1.42 0.91 1.36 0.18
Brand interaction 1.22 0.98 1.21 0.04
Social brand engagement 1.28 0.88 1.21 0.19
Behavioural intention of loyalty 1.12 0.98 1.11 0.02
Novelty-seeking 0.86 0.95 0.84 0.04
Need for affect 1.29 0.91 1.23 0.14
Need for cognition 1.20 0.92 1.15 0.12
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4.4 Evaluating Path Models
The process of path model analysis follows the following basic steps of SEM; specify the
model, evaluate model identification, estimate the model and re-specify the model (Kline
2011). These steps are common and followed in most analyses (Kline 2011). This thesis
describes each step in the following section.
4.4.1 Model Specification
Model specification involves the diagrammatic representation of proposed relationships
between constructs of interest which enables testing of hypotheses (Kline 2011). The
specified model utilised in this thesis is theory-driven and reflects the conceptual model
introduced in Chapter 2. It is important that the model is grounded in theory because SEM
is a confirmatory technique; the program cannot suggest path relationships but rather
assesses the accuracy of predicted relationships against the data (Hair et al. 2012).
Therefore, theoretical relationships established in the literature are used to specify
relationships between constructs and establish causation (Hair et al. 2012).
4.4.2 Model Identification
Model identification refers to the ability for the program to allow a unique estimate of all
model parameters; if the requirements of identification are not met, the model is ‘not
identified’ and analysis cannot be conducted or output is meaningless (Kline 2011). Two
requirements must be met in order for the path model to be identified; first, the number of
observations must equal or be greater than the number of free model parameters; second,
all latent variables must have an assigned scale (Kline 2011). The specified model created
in this thesis met the requirements of model identification.
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4.4.3 Model Estimation
Model estimation includes evaluating model fit and interpreting the parameter estimates
(Kline 2011). As previously discussed in Chapter 3, a variety of fit indices were utilised in
this thesis to assess how well the data fit the theoretical model; the principal goodness-of-
fit index (χ²/df), Goodness-of-fit index (GFI), Root mean square error of approximation
(RMSEA), Normed Fit Index (NFI), Tucker Lewis Index (TLI) and Comparative Fit Index
(CFI) (Hair et al. 2012). The fit indices and their respective threshold values are outlined in
Table 3-7.
The estimation technique utilised throughout this thesis is maximum likelihood estimation
as it is a flexible approach, has proven robust even when the data does not meet normality
assumptions, and therefore is the most widely used approach and the default estimation
technique in most SEM programs including AMOS21 (Hair et al. 2012).
Parameter estimates are then examined to assess each of the relationships proposed in the
model. Parameter estimates must be statistically significant and in the predicted direction;
standardised loading estimates are reported in Table 4-15 (Hair et al. 2012).
Figure 4-8 shows the identified path model, consisting of the composite variables
representing the five experiential components, event engagement, brand engagement and
behavioural intention of loyalty.
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FIGURE 4-8: IDENTIFIED PATH MODEL
TABLE 4-14: GOODNESS OF FIT INDICES FOR IDENTIFIED PATH MODEL χ² df χ²/df p GFI NFI TLI CFI RMSEA
541.00 118 4.60 0.00 0.80 0.81 0.80 0.84 0.12
Results from the identified path model indicate poor fit with the data (χ²/df =4.60, p=0.00,
GFI=0.80, NFI=0.81, TLI=0.80, CFI =0.84, RMSEA =0.12), with no fit indices reaching
their respective thresholds as outlined in Table 3-7. It is very common for specified models
to not achieve fit (Kline 2011); a poor fitting model indicates that some hypotheses
reflected by the paths may not have empirical support, or a relationship exists between
constructs or error variables that have not yet been accounted for in the model. Parameter
estimates are observed to identify insignificant and problematic paths contributing to poor
model fit.
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TABLE 4-15: REGRESSION WEIGHTS – ORIGINAL PATH MODEL Estimate S.E. C.R. p
EVENT_ENGAGEMENTEMOTIONAL_EXP .155 .073 2.115 .034
EVENT_ENGAGEMENTSENSORIAL_EXP .229 .066 3.488 ***
EVENT_ENGAGEMENTCOGNITIVE_EXP .052 .061 .851 .395
EVENT_ENGAGEMENTRELATIONAL_EXP .254 .050 5.062 ***
EVENT_ENGAGEMENTPRAGMATIC_EXP .223 .060 3.736 ***
BRAND_ENGAGEMENTCOGNITIVE_EXP .328 .079 4.132 ***
BRAND_ENGAGEMENTEVENT_ENGAGEMENT .991 .137 7.238 ***
BRAND_ENGAGEMENTEMOTIONAL_EXP -.181 .094 -1.930 .054
BRAND_ENGAGEMENTSENSORIAL_EXP .017 .087 .199 .842
BRAND_ENGAGEMENTPRAGMATIC_EXP -.268 .081 -3.293 ***
BRAND_ENGAGEMENTRELATIONAL_EXP -.065 .069 -.935 .350
BEH_INT_LOYALTY BRAND_ENGAGEMENT .532 .067 7.916 ***
BEH_INT_LOYALTY EVENT_ENGAGEMENT .120 .083 1.441 .149
*** indicates p-value significantly different from zero at the 0.001 level (two-tailed)
Regression weights output (Table 4-15) shows a number of insignificant paths; cognitive
and emotional experiences did not have significant relationships with event engagement;
emotional, sensorial and relational experiences did not have significant relationships with
brand engagement; finally, the relationship between event engagement and behavioural
intention of loyalty was also insignificant. This led to model re-specification, which is
described in the following section.
4.4.4 Model Re-specification
Model re-specification is the process of adding or deleting paths in order to achieve a more
parsimonious model (Hair et al. 2012; Kline 2011). However, manipulations to the model
should not occur without justification; re-specification should adhere to the same principles
followed in model specification (Kline 2011). The re-specified model must also achieve
the aforementioned requirements of model identification (Kline 2011).
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First, insignificant paths were eliminated (Hair et al. 2012). A number of insignificant
paths between emotional, sensorial and relational experience leading to brand engagement
were removed. This is conceptually justified as components of BME experience engage
customers initially with the event, which then translates into brand engagement through the
process of engagement transfer. An insignificant path between emotional experience and
event engagement was also removed; therefore, the emotional experience construct was
removed entirely as no paths (hypothesised relationships) remained. This finding is
unexpected as the emotional elements of an event is strongly emphasised in marketing
events literature (Leischnig et al. 2011; Packer and Ballantyne 2004; Whelan and Wohlfeil
2006). Conceptual justification for the removal of this construct is in detailed the following
section 4.4.5.
In addition, the path between cognitive experience and event engagement was removed;
the remaining significant path indicated a direct relationship between cognitive experience
and brand engagement. Events with a strong cognitive experience are likely to be highly
brand-centric, with discussion and information based on the brand itself. Further
justification is given in section 4.4.5.
Finally, the insignificant path between event engagement and behavioural intention of
loyalty was removed. This finding supports the notion of engagement transfer, as it
demonstrates how brand outcomes do not occur directly from event engagement; brand
engagement must be facilitated through its relationship with event engagement in order for
behavioural intention of loyalty to result.
Following the elimination of insignificant paths, modification indices (MI) were identified
in AMOS 21 (Hair et al. 2012). Modification indices estimate the level of model
improvement achieved if an additional path was entered into the model, with values of 4.0
or greater suggesting significant model improvement (Hair et al. 2012). Expected
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parameter changes are also consulted as it indicates the estimated positive or negative
change for the parameter in the model (Byrne 2001).
A modification made to the model was to place covariances on the error terms for each
event and brand engagement counterpart. The nature of the measurement items for event
and brand engagement was described in Chapter 3; the same items were replicated for both
engagement objects with rewording to reflect either ‘event’ or ‘brand’. This technique has
been implemented in previous studies (Drengner et al. 2008; Gwinner and Eaton 1999),
with similar responses giving support of image transfer. However, repeated measures can
experience issues of auto-correlation error and therefore error covariances were placed
between each event and brand engagement dimension to account for this error (Kline
2011); i.e. between event attention and brand attention; event and brand identification;
event and brand enthusiasm; event and brand absorption; event and brand interaction;
event and brand social engagement.
The re-specified model, accounting for each of the identified modifications, is now
discussed.
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FIGURE 4-9: RE-SPECIFIED PATH MODEL
* Covariances between e6 and e12 = .24; e7 and e13 = .61; e8 and e14 = .17; e9 and e15 = .14; e10 and e16 = .24; e11 and e17 = .27 not shown on diagram.
TABLE 4-16: GOODNESS OF FIT INDICES FOR RE-SPECIFIED PATH MODEL χ² df χ²/df p GFI NFI TLI CFI RMSEA
268.45 104 2.58 0.00 0.89 0.90 0.91 0.93 0.08
Reasonable model fit is established for the re-specified path model (χ²/df =2.58, p=0.00,
GFI=0.89, NFI=0.90, TLI=0.91, CFI =0.93, RMSEA =0.08), based on the threshold values
of fit indices. Although the p-value was not significant and a number of fit indices only
came close to their threshold values, the values obtained were deemed sufficient due to the
complexity of the model which can weaken the overall fit and therefore cause the original
fit thresholds to be unattainable (Hair et al. 2012). In addition, through assessment of
parameter estimates (Table 4-17) it is found that all included paths achieved significance.
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In brief, the conceptual model was re-specified based on statistical and theoretical
considerations with the aim of achieving a more parsimonious model. Insignificant paths
were removed and covariances were placed between a number of error terms.
Rationalisation of each of these modifications was provided and led to a re-specified model
which achieved reasonable model fit.
The following section details the implications of the path model findings for hypotheses 2,
3 and 4 introduced in Chapter 2.
TABLE 4-17: REGRESSION WEIGHTS: - RE-SPECIFIED PATH MODEL
Estimate S.E. C.R. p
EVENT_ENG SENSORIAL_EXPERIENCE .330 .055 5.964 ***
EVENT_ENG PRAGMATIC_EXPERIENCE .282 .059 4.808 ***
EVENT_ENG RELATIONAL_EXPERIENCE .259 .050 5.195 ***
BRAND_ENG EVENT_ENGAGEMENT .844 .103 8.175 ***
BRAND_ENG COGNITIVE_EXPERIENCE .297 .070 4.238 ***
BRAND_ENG PRAGMATIC_EXPERIENCE -.279 .079 -3.512 ***
BEH_INT_LOYALTY BRAND_ENGAGEMENT .604 .048 12.534 ***
*** indicates p-value significantly different from zero at the 0.001 level (two-tailed)
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4.4.5 Discussion of Hypothesis 2
The set of hypotheses 2a-2j address the research question of whether experiential
components of a BME facilitate customer event engagement and customer brand
engagement. It was proposed that due to the nature of BMEs with regards to their high
level of interaction, and the unique and customer-driven experience created, that BMEs
could facilitate customer engagement (Crowther and Donlan 2011). Support of these
hypotheses would advocate the use of BMEs as a strategic driver of customer engagement.
The re-specified path model (Figure 4-9) indicated significant relationships between
sensorial, pragmatic and relational BME experiences and event engagement, while
cognitive experience directly impacted brand engagement. Pragmatic experience had a
direct negative relationship with customer brand engagement, but this will be argued to be
due to a suppressor effect. The hypotheses and associated outcome obtained from data
analysis are outlined in Table 4-18. Each hypothesis is then briefly addressed in the
following paragraphs. Emphasis is given to results which do not support the hypotheses
developed in Chapter 2, while supported hypotheses have already been described and will
be the focus of discussion in Chapter 5.
TABLE 4-18: SUMMARY OF HYPOTHESIS 2
H# Hypothesis Supported/Not Supported
2a Cognitive event experience contributes to customer event engagement Not supported
2b Cognitive event experience contributes to customer brand engagement Supported
2c Emotional event experience contributes to customer event engagement Not Supported
2d Emotional event experience contributes to customer brand engagement Not Supported
2e Sensorial event experience contributes to customer event engagement Supported
2f Sensorial event experience contributes to customer brand engagement Not Supported
2g Pragmatic event experience contributes to customer event engagement Supported
2h Pragmatic event experience contributes to customer brand engagement Not Supported
2i Relational event experience contributes to customer event engagement Supported
2j Relational event experience contributes to customer brand engagement Not Supported
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H2a: Cognitive event experience contributes to customer event engagement
The results revealed an insignificant relationship between cognitive experience and event
engagement. Cognitive experiences include education or information sessions in which the
attendee is provided with brand information to increase their brand-related knowledge in
an area of interest (Gentile et al. 2007). Education events are described as eliciting
customer interaction, however the customer’s focus is not necessarily on the event but
rather the content shared during the event (Pine and Gilmore 1998).The experience sparks
attention and interest towards the brand specifically, and not with the event activity. The
event is brand-centric, with discussions and information focused specifically on the brand;
therefore, the focal object of engagement is the brand, not the event. Consequently, H2a
was not supported.
H2b: Cognitive event experience contributes to customer brand engagement
A significant direct relationship was found between cognitive experience and brand
engagement. This was the only significant direct relationship between a BME experience
and brand engagement; the other BME experiences impacted on event engagement, and
therefore indirectly influenced brand engagement. As previously discussed, the brand-
centric nature of a cognitive experience provides the rationale for a strong direct impact on
brand engagement (Pine and Gilmore 1998). Cognitive experiences involve thinking and
mental processes regarding the brand specifically, which impacts the customer’s
perception of the brand (Yuan and Wu 2008). Therefore the cognitive experience affects
engagement toward the brand, as opposed to being transferred from the event through to
the brand. H2b was supported.
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H2c: Emotional event experience contributes to customer event engagement
The results showed that the relationship between emotional experience and event
engagement was insignificant. This finding is unexpected as the emotional elements of an
event is strongly emphasised in marketing events literature (Leischnig et al. 2011; Packer
and Ballantyne 2004; Whelan and Wohlfeil 2006). However, this finding is explained as
emotional experiences may create pleasant experiences or feelings of excitement but not
necessarily evoke the level of interaction or psychological state with regards to the event
required to achieve engagement (Pine and Gilmore 1998); customers can enjoy an
emotional BME experience while remaining passive in that experience. Therefore, H2c
was not supported.
H2d: Emotional event experience contributes to customer brand engagement
The relationship between emotional experience and brand engagement was insignificant,
H2d was not supported; this finding led to the removal of the emotional experience
construct from the re-specified model as neither of the hypotheses regarding emotional
experience were supported. The brand-provided resources in an emotional experience
include the source of entertainment designed to generate customer enjoyment (Tynan and
McKechnie 2009). However, this experience may not provide sufficient emotional value
(Yuan and Wu 2008) that would encourage customers to contribute to the exchange with
their engagement. This is an important finding of this thesis, and is discussed with
reference to managerial implications in Chapter 5.
H2e: Sensorial event experience contributes to customer event engagement
The parameter estimate highlighting the relationship between sensorial experience and
event engagement was significant, leading to the support of H2e. The brand-provided
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resources in a sensorial experience, including sources of sight, sound, scent, taste, and
touch (Yuan and Wu 2008) provide sensory meaning and stimulation (Gentile et al. 2007;
Schmitt 1999), and facilitate customer event engagement.
H2f: Sensorial event experience contributes to customer brand engagement
The relationship between sensorial experience and brand engagement was insignificant,
emphasising that sensorial experiences only have an indirect impact on brand engagement
through the event engagement construct. H2f was not supported. The focal element of a
sensorial experience is sensory stimulation (Gentile et al. 2007); therefore the experience
inherently requires active participation from the customer in the BME activity. Customer
engagement occurs with the event as the focus of the participation is on the event activity,
not the brand.
H2g: Pragmatic event experience contributes to customer event engagement
The results showed that pragmatic experience had a significant relationship with event
engagement, giving support to H2g. A pragmatic experience encompasses physical
activities designed to stimulate active customer participation (Mollen and Wilson 2010),
and drives customer event engagement.
H2h: Pragmatic event experience contributes to customer brand engagement
Although a significant relationship was indicated between pragmatic experience and brand
engagement, the parameter estimate reported a strong negative relationship. This finding
contradicts the hypothesised relationship established from literature, and is in conflict with
the other paths identified in the model, for example pragmatic experience has a negative
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effect on brand engagement, however brand engagement maintains a significant and
positive relationship with BIL.
It is argued that this construct has experienced a suppression effect (Kline 2011).
Suppressor variables are relatively common in SEM, however are often met with problems
of interpretation by researchers (Maassen and Bakker 2001). Although statistically the p-
value is significant (>0.05), a suppressor variable does not have a relationship with the
dependent variable, but instead has correlations with one or more independent variables.
The commonalities between the suppressor and other independent variables are irrelevant
to the identified dependent variable (Maassen and Bakker 2001). Therefore, the suppressor
variable (pragmatic experience) enhances the predictive power of the related independent
variables (other experiential component constructs); however, as the suppressor variable
and other independent variables are positively correlated with regards to an aspect
irrelevant to the dependent variable (brand engagement), the relationship in the path model
between the suppressor (pragmatic experience) and the dependent variable (brand
engagement) becomes negative.
Conceptually, this phenomenon illustrates that pragmatic experience has relationships with
the other components of experience; cognitive, sensorial and relational. Together these
constructs have strong predictive power on ‘something’ other than brand engagement; this
could reflect their combined impact on event engagement, or it could refer to something
not captured in the path model. As identified in the conceptual discussion of pragmatic
experience (Section 2.4.2.4, Chapter 2), pragmatic experiences can vary greatly and
include cognitive or emotional elements, depending on the activity (Pine and Gilmore
1998). Therefore, the customer may not perceive the experience as purely ‘pragmatic’, but
rather as pragmatic and emotional (e.g. a grape stomp) or pragmatic and cognitive (e.g.
creating your own wine blend). The combined effects of these perceived experiences
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contribute to the path model but do not reflect a relationship between pragmatic experience
and brand engagement, hence the suppression effect resulting in a negative path estimate.
This path was maintained in the model as it achieved significance and therefore has
predictive power, however as a suppressor variable this relationship should not be
interpreted as pragmatic experiences having a negative impact on brand engagement
(Maassen and Bakker 2001). Instead, it is recognised that the pragmatic experience
construct contributes to the other independent variables (BME experiences) and together
explain relationships in the model. Therefore, H2h is not supported.
H2i: Relational event experience contributes to customer event engagement
The relationship between relational experience and event engagement was significant, and
therefore H2i was supported. The interactions during a relational event induce a heightened
sense of connectedness with other customers in the context of the event (Kozinets 2014),
providing social value (Tynan and McKechnie 2009).
H2j: Relational event experience contributes to customer brand engagement
The results did not support the relationship between relational experience and brand
engagement. Relational experiences only contribute to brand engagement indirectly
through event engagement which reported a significant relationship. Therefore, H2j was
not supported.
For a relational experience, the brand-provided resources encompass activities designed to
be experienced together with other people (Gentile et al. 2007). Therefore, because the
other attendees and relational activity in which they interact are focal to the experience,
relational experience facilitates customer event engagement, not customer brand
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Overall, the findings are in support of BME experiences facilitating customer event
engagement. The sensorial, pragmatic and relational experiences during a BME facilitate a
heightened psychological state in the customer, resulting in engagement with the event;
however, these experiences do not have a significant relationship with customer brand
engagement. Cognitive experience facilitates customer brand engagement, but does not
have a significant relationship with customer event engagement. This finding reflects the
highly brand-centric nature of cognitive experiences. The next section discusses hypothesis
3; the relationship between customer event engagement and customer brand engagement.
4.4.6 Discussion of Hypothesis 3
The following section addresses the research question of the relationship between two
focal engagement objects; specifically does customer event engagement further facilitate
customer brand engagement. The re-specified path model (Figure 4-9) indicated a
significant relationship between the event engagement and brand engagement constructs,
supporting hypothesis 3.
TABLE 4-19: SUMMARY OF HYPOTHESIS 3
H# Hypothesis Supported/ Not Supported
3 There is a positive relationship between customer event engagement and customer brand engagement Supported
H3: There is a positive relationship between customer event engagement and
customer brand engagement
A strong and significant relationship was evident between customer event engagement and
customer brand engagement, and H3 was supported. The customer interacts and creates
value during an event experience and therefore elicits customer event engagement. Then,
due to the strong connection between the event and the brand, this state of engagement also
projects onto the brand (Smith 2004).
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Additionally, the results support a notion of engagement transfer. It was found that BME
experiences had a significant relationship with customer event engagement, however,
largely not with customer brand engagement (with the exception of cognitive experience).
It could therefore be argued that customer engagement does not generally occur directly
with the brand, but rather customer event engagement is facilitated as a result of the BME
experience, which is then transferred onto customer brand engagement.
4.4.7 Discussion of Hypothesis 4
The following discussion provides insight into the research question on outcomes of brand
engagement; namely, whether brand engagement leads to increased behavioural intention
of loyalty. The re-specified path model (Figure 4-9) indicated a significant relationship
between brand engagement and brand behavioural intention of loyalty. The relationship
between event engagement and behavioural intention of loyalty was insignificant.
TABLE 4-20: SUMMARY OF HYPOTHESIS 4 H# Hypothesis Supported/ Not Supported
4a There is a positive relationship between customer event engagement and behavioural intention of loyalty Not supported
4b There is a positive relationship between customer brand engagement and behavioural intention of loyalty Supported
H4a: There is a positive relationship between customer event engagement and
behavioural intention of loyalty
The direct relationship between event engagement and behavioural intention of loyalty was
not significant, and therefore H4a was not supported. Although BME experiences
generally had a relationship with customer event engagement, not customer brand
engagement, the customer event engagement construct did not contribute to behavioural
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intention of loyalty. Marketing event research that take an associative network theory
perspective advocate that brand-related outcomes are a direct result of brand attitudes, not
event attitudes (Martensen et al. 2007). These brand attitudes are the drivers of brand-
related outcomes (Martensen et al. 2007).
H4b: There is a positive relationship between customer brand engagement and
behavioural intention of loyalty
The re-specified path model showed a strong and significant relationship between brand
engagement and behavioural intention of loyalty, in support of H4b. The heightened
psychological state that occurs between customer and the brand impacts the customers
future purchase intentions and word of mouth, two key dimensions of BIL (Sheth et al.
1991; So et al. 2012).This supports the notion that customer brand engagement must be
facilitated in order for brand-outcomes to occur, as customer brand engagement is the only
significant and direct impact on behavioural intention of loyalty.
These findings provide support for a notion of engagement transfer. BME experiences
generally have a relationship with event engagement, not brand engagement. However,
event engagement does not directly contribute to behavioural intention of loyalty, which
indicates that an additional construct (brand engagement) must provide a mediation effect
between event engagement (an outcome of BME experiences) and behavioural intention of
loyalty toward the brand. This relationship is consistent with the process of spreading
activation within associative network theory (Smith 2004). Customer event engagement is
first facilitated, which is then projected onto customer brand engagement. The results
justify the inclusion of both customer event engagement and customer brand engagement
constructs; BME experiences generally result in customer event engagement (Vivek et al.
2012), while only customer brand engagement results in behavioural intention of loyalty
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engagement (Smith 2004). Therefore, strong support is found for the transfer of the
engagement effect from the focal object of the event to the brand as the focal object.
Following model re-specification, multi-group path analysis was conducted to test whether
model parameters vary between high and low experiential needs samples. The results are
outlined in the following section.
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4.5 The Moderation Effect of Experiential Needs
4.5.1 Method for Multi-group Analysis
The previous analysis focused on the thesis sample in its entirety, which included
respondents who are likely to exhibit different experiential needs. Chapter 2 identified the
potential for an individual’s experiential needs to moderate the extent to which particular
experiential components effect event engagement (MacInnis and Jaworski 1989;
Steenkamp and Baumgartner 1992 ; Wilson 1997). This research question of moderation is
investigated in this section.
This thesis utilised multi-group analysis to consider moderation effects. This particular
analysis technique allowed the calculation of moderation in the complete path model and
the investigation of its effects on individual paths, providing a holistic view of the
moderation impact (Kline 2011). Multi-group analysis was deemed valuable to identify
whether the final model replicated well for each sub-sample, or whether different impacts
were found. Using the group analysis feature in AMOS 21, parameters were constrained
and the model-re-estimated. Nested model comparisons tests (Chi-Square difference test)
assessed whether values of the model parameters varied across groups (Kline 2011;
Vandenberg and Lance 2000). Nested models involved the estimation and comparison of
two different models. First, a baseline model was calculated by simultaneously estimating
the final generic path model across both groups (Byrne 2001). Structural regression
weights were then constrained and set equal across the groups, followed by a re-estimation
of the model. To show the existence of moderators, the analysis must show that the
equality constraint is not reasonable, and that the two parameters significantly differ from
each other (Byrne 2001). Comparison of models was conducted using chi-square change
and significance values (Byrne 2001), with a p-value < .05 indicating that the constrained
model has significantly worse fit, and therefore the unconstrained model can be assumed as
correct.
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High and low groups were created by calculating the mean value of each construct,
splitting the sample into high and low groups, and removing the mean value to two decimal
places. These values are outlined in Table 4-21.
TABLE 4-21: EXPERIENTIAL NEEDS GROUPS - VALUE CLASSIFICATION Cognition 5.37 n Affect 4.26 n Novelty 6.05 n
Lower group (<) 5.36 127 Lower group (<) 4.25 129 Lower group (<) 6.04 163
higher group (>) 5.38 146 higher group (>) 4.27 143 higher group (>) 6.06 111
The high and low groups for each experiential need were then categorised within the path
model, which allowed the output to reflect the entire sample, the ‘low’ needs group and the
‘high’ needs group. The results for each experiential need are shown on a replicated path
model; one indicating the path estimates and relationships present for the high needs group
and the other reflecting the results from the low needs group.
Due to the complexity of the model, and the limited sample size per group, it was deemed
appropriate to further create composite measures for both brand engagement and event
engagement as a means of data reduction (Rowe 2002). This would allow more accurate
observation of differences in structural weights (i.e. paths within the model).
The results of the chi-square difference test (∆χ²) were then presented, measuring
invariance of the unconstrained and constrained models (Vandenberg and Lance 2000).
This test observed the change in goodness-of-fit when cross-group constraints were
imposed upon the model (Cheung and Rensvold 2002). In addition, the critical ratios for
differences between parameters was reported as this clearly indicates the specific
relationships of significant difference between high needs and low needs groups.
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4.5.2 Need for Cognition
The following path models demonstrate the differences in standardised estimates when the
path model is calculated using only the low need for cognition group (Figure 4-10) and the
high need for cognition group (Figure 4-11).
FIGURE 4-10: PATH FOR LOW NEED FOR COGNITION
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FIGURE 4-11: PATH MODEL FOR HIGH NEED FOR COGNITION
The chi-square difference test (∆χ²) (Vandenberg and Lance 2000) indicated that the
unconstrained and constrained models were significantly different (p=0.01) and the
equality constraint is not reasonable; therefore, high and low need for cognition groups
differ from each other.
TABLE 4-22: NESTED MODEL COMPARISONS AND GOODNESS OF FIT INDICES - NEED FOR COGNITION
χ² ∆χ² df ∆ df χ²/df p GFI NFI TLI CFI RMSEA
Unconstrained Model 30.82 - 16 - 1.93 0.01 0.97 0.96 0.95 0.98 0.06
Structural Weights Equal Model 50.60 19.73 23 7 2.20 0.00 0.95 0.93 0.93 0.96 0.07
Significance of nested model comparison, assuming unconstrained model to be correct: p = 0.01
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TABLE 4-23: NEED FOR COGNITION Low needs High needs Critical ratios a
BILBRAND_ENGAGEMENT 0.74 0.71 -0.91
BRAND_ENGPRAG_EXP -0.28 -0.28 0.04
EVENT_ENGSENS_EXP 0.55 0.20 -2.80*
EVENT_ENGPRAG_EXP 0.19 0.37 1.31
EVENT_ENGREL_EXP 0.08 0.42 3.24*
BRAND_ENGEVENT_ENG 0.79 0.91 1.02
BRAND_ENGCOG_EXP 0.23 0.16 -0.60 a Critical Ratios for Differences between Parameters (Unconstrained) * significant difference (>1.96)
An examination of the individual hypothesised relationships (Table 4-23) showed that the
relationship between sensorial experience and event engagement was significantly stronger
for individuals with low need for cognition (0.55) than high need for cognition (0.20). In
addition, the relationship between relational experience and event engagement was
significantly stronger for individuals with high need for cognition (0.42) than low need for
cognition (0.08). The remaining five relationships did not achieve a significant critical ratio
value (<1.96), indicating that high and low need for cognition groups do not differ in their
engagement resulting from pragmatic and cognitive experiences. Conceptual support for
these findings is provided in the discussion of hypotheses section.
4.5.3 Need for Affect
The multi-group path analysis was then replicated to investigate the moderation effect of
need for affect. The path models were then tested for differences in standardised estimates
between the low need for affect group (Figure 4-12) and the high need for affect group
(Figure 4-13).
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FIGURE 4-12: PATH MODEL FOR LOW NEED FOR AFFECT
FIGURE 4-13: PATH MODEL FOR HIGH NEED FOR AFFECT
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The chi-square difference test (∆χ²) (Vandenberg and Lance 2000) indicated that the
unconstrained and constrained models were not significantly different (p=0.67) and the
equality constraint is reasonable; therefore, high and low need for affect groups do not
differ from each other.
TABLE 4-25: NEED FOR AFFECT Low needs High needs Critical ratios a
BIL BRAND_ENGAGEMENT 0.70 0.72 0.412
BRAND ENG PRAG_EXP -0.35 -0.22 0.949
EVENT_ENG SENS_EXP 0.31 0.47 0.743
EVENT_ENG PRAG_EXP 0.32 0.17 -0.947
EVENT_ENG REL_EXP 0.37 0.20 -1.35
BRAND_ENGEVENT_ENG 0.82 0.84 -0.452
BRAND_ENG COG_EXP 0.23 0.18 -0.812 a Critical Ratios for Differences between Parameters (Unconstrained) * significant (>1.96)
An examination of the individual hypothesised relationships confirmed that there were no
significant path differences for high need for affect versus low need for affect groups, as all
critical ratio values were below 1.96. Therefore, there is no difference between individuals
with high and low need for affect with regards to the influence of BME experiences on
event engagement. Implications of this result are considered in the discussion of
hypotheses section.
TABLE 4-24: NESTED MODEL COMPARISONS AND GOODNESS OF FIT INDICES - NEED FOR AFFECT
χ² ∆χ² df ∆ df χ²/df p GFI NFI TLI CFI RMSEA
Unconstrained Model 35.42 - 16 - 2.21 0.00 0.97 0.95 0.92 0.97 0.07
Structural Weights Equal Model 40.33 4.91 23 7 1.75 0.01 0.96 0.94 0.95 0.97 0.05
Significance of nested model comparison, assuming unconstrained model to be correct: p = 0.67
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4.5.4 Novelty-Seeking Needs
The final multi-group path analysis tested moderation by novelty-seeking needs. The
following path models outline the differences in standardised estimates between the low
novelty-seeking needs group (Figure 4-14) and the high novelty-seeking needs group
(Figure 4-15).
FIGURE 4-14: PATH MODEL FOR LOW NOVELTY-SEEKING NEEDS
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FIGURE 4-15: PATH MODEL FOR HIGH NOVELTY-SEEKING NEEDS
The chi-square difference test (∆χ²) (Vandenberg and Lance 2000) revealed that the
unconstrained and constrained models were not significantly different (p=0.16) and the
equality constraint is reasonable; therefore, high and low novelty-seeking needs groups do
not differ from each other.
TABLE 4-26: NESTED MODEL COMPARISONS AND GOODNESS OF FIT INDICES - NOVELTY-SEEKING NEEDS
χ² ∆χ² df ∆ df χ²/df p GFI NFI TLI CFI RMSEA
Unconstrained Model 32.25 - 16 - 2.02 0.01 0.97 0.95 0.93 0.98 0.06
Structural Weights Equal Model 42.86 10.62 23 7 1.86 0.01 0.96 0.94 0.94 0.97 0.06
Significance of nested model comparison, assuming unconstrained model to be correct: p = 0.16
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TABLE 4-27: NOVELTY-SEEKING NEEDS Low needs High needs Critical ratios a
BIL BRAND_ENG 0.73 0.69 -1.36
BRAND_ENG PRAG_EXP -0.30 -0.25 0.38
EVENT_ENG SENS_EXP 0.37 0.27 -0.38
EVENT_ENG PRAG_EXP 0.29 0.31 0.04
EVENT_ENG REL_EXP 0.18 0.40 2.02
BRAND_ENGEVENT_ENG 0.80 0.90 1.46
BRAND_ENG COG_EXP 0.22 0.16 -0.45 a Critical Ratios for Differences between Parameters (Unconstrained) * Significance (>1.96)
This finding of invariance was further confirmed from the individual hypothesised
relationships outlined in Table 4-27; no significant path differences were present for high
novelty-seeking needs versus low novelty-seeking needs groups. The discussion of
hypotheses section describes this outcome in greater detail.
In summary, there was little evidence to suggest that the individual’s experiential needs
moderated the relationship between BME experiential components and customer event
engagement. Significant difference was found for need for cognition; however, the
moderation only impacted two relationships. The next section provides a discussion of
hypotheses.
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4.5.5 Discussion of Hypothesis 5
TABLE 4-28: SUMMARY OF HYPOTHESIS 5
H# Hypothesis Supported/ Not Supported
5a An individual’s need for cognition from an experience will moderate the relationship between the BME experience and customer event engagement Partially Supported
5b An individual’s need for affect from an experience will moderate the relationship between the BME experience and customer event engagement Not Supported
5c An individual’s novelty-seeking needs from an experience will moderate the relationship between the BME experience and customer event engagement Not Supported
An exploratory approach was taken to investigate the moderation effect of experiential
needs in the relationship between BME experiences and customer event engagement. The
current literature does not offer insight into the effect of individual needs in the
hypothesised relationships, and so an overall moderation effect was explored. Multi-group
analysis was employed to test for moderating effects between high and low experiential
needs groups based on need for cognition, need for affect and novelty-seeking needs. The
overall moderation of the model (chi-square difference test) and the identification of
individual relationships were assessed.
H5a: An individual’s need for cognition from an experience will moderate the
relationship between the BME experience and customer event engagement
Partial supported was found for a moderating effect of the individual’s need for cognition
on the relationships between BME experiences and customer event engagement. While the
chi-square difference test indicated model variance, only two paths were significantly
impacted by need for cognition. Therefore, H5a was partially supported.
Sensorial experience had a stronger impact on event engagement for individuals with low
need for cognition, indicating that individuals with a desire to be informed did not engage
strongly with events providing sensory stimulation (Wilson 1997). Sensorial experience
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did not fulfil the need for information and knowledge sought from the individual, and
therefore event engagement was not achieved (Gentile et al. 2007).
Relational experience had a stronger impact on event engagement for individuals with high
need for cognition, indicating that individuals found relevance and fulfilment of cognitive
needs from experiences with a focus on social interaction (Calder et al. 2009). Relational
experience could involve discussions with the winemaker or other knowledgeable
individuals (Charters and Ali-Knight 2000), and these interactions could provide insight or
knowledge and therefore fulfil the individual’s need for information.
H5b: An individual’s need for affect from an experience will moderate the
relationship between the BME experience and customer event engagement
H5c: An individual’s novelty-seeking needs from an experience will moderate the
relationship between the BME experience and customer event engagement
Results from the multi-group analysis did not reveal a significant moderating effect of the
individual’s need for affect nor novelty-seeking needs in the relationships between BME
experiences and customer event engagement. Therefore, H5b and H5c were not supported.
While the results may accurately reflect that no moderation effect exists for need for affect
or novelty-seeking needs, these findings could also indicate a limitation of the study. The
influence of the individual’s experiential needs could occur during their decision making
process of whether to attend the event. Therefore, individuals attending BMEs are more
likely to already find interest in the activity. Further discussion of this finding is reported in
Chapter 5.
The effects of experiential needs could have occurred during the individual’s decision
making process of whether to attend the event, therefore limiting the attendees (and survey
respondents) to those who already anticipated a fulfilment of their needs through 187 | P a g e
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attendance of that particular event. According to optimum stimulation level (OSL) theory,
individuals seek out stimulation from particular environments in order to achieve
satisfaction; individuals engage in exploratory behaviour to achieve their ideal level of
stimulation (Steenkamp and Baumgartner 1992). Therefore, individuals in attendance of an
event would have sought that particular experience as they believed it would fulfil their
experiential needs and achieve their desired level of stimulation. This explanation provides
support for the impact of experiential needs, albeit not captured within the parameters of
this thesis. This limitation also provides an avenue for future research, and is discussed in
Chapter 5.
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4.6 Chapter 4 Summary
The results of the quantitative research for this thesis were detailed in this chapter, and
were outlined in three sections. First, reliability and validity testing was run to investigate
the robustness of a social dimension of engagement (Hypotheses 1a and 1b). It was
concluded that the social brand engagement was a unique and independent dimension
within the customer brand engagement model, and was therefore included for the
remaining analysis. Social event engagement was also implemented throughout the
analysis, despite some weaknesses observed in the customer event engagement structural
model.
In section two, path model analysis was conducted which captured the hypothesised
relationships of interest in this thesis. The impact of BME experiential components on
customer engagement with the event and the brand (Hypotheses 2a-j), the existence of
engagement transfer reflected in the relationship between customer event engagement and
customer brand engagement (Hypothesis 3), and the behavioural intention of loyalty
toward the brand resulting from customer engagement with the brand (Hypotheses 4a and
4b) were all investigated.
The justification for the calculation of composite variables was made, and the process of
path model analysis was outlined. With the aim of achieving a higher level of parsimony,
the specified model was re-specified based on empirical and theoretical considerations.
The re-specified model achieved sound model fit, and goodness of fit indices and
individual paths were presented. The findings indicated support for BME experiences
leading to customer event engagement, with the exception of cognitive experience which
had a direct relationship with brand engagement. There was support for the existence of the
notion of engagement transfer. A significant relationship was found between event
engagement and brand engagement, BME experiences mainly led to event engagement and
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behavioural intention of loyalty. Therefore, because customer event engagement mediates
the relationship between event experience and customer brand engagement, it is argued
that the focus of the engagement transfers from the event to the brand.
Finally, a multi-group analysis was conducted to compare high and low experiential needs
groups based on need for cognition, need for affect and novelty-seeking needs. The results
indicated that generally a moderation effect was not present. Need for cognition was found
to have a partial moderation effect, with only two relationships impacted by moderation.
The following Chapter 5 is the discussion and conclusion of this thesis. The findings and
their implications for theory and practice are discussed, as well as managerial implications,
limitations and directions for future research.
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CHAPTER 5: DISCUSSION AND CONCLUSION
5.1 Chapter 5 Introduction
The primary objective of this thesis was to explore the role of branded marketing event
(BME) experiences in the facilitation of customer engagement. It was proposed that the
sensorial, emotional, cognitive, pragmatic and relational components of a BME experience
would facilitate customer event engagement and customer brand engagement. Experiential
needs (need for cognition, need for affect and novelty-seeking needs) were posited to
moderate the extent to which BME experiences facilitate customer event engagement. It
was also proposed that the BME experience would directly and indirectly impact customer
brand engagement through customer event engagement. Finally, behavioural intention of
loyalty (BIL) was hypothesised as being the result of customer engagement. A conceptual
model was developed which drew from the literature on customer engagement, marketing
events and customer experience. This conceptual model was tested empirically and support
was found for the majority of the hypotheses, however some distinct and interesting
divergent results were also found. The analysis for five key research questions and
associated hypotheses were presented in Chapter 4.
This chapter identifies and summarises the main findings and conclusions of this thesis.
The theoretical contributions to academic knowledge arising from this thesis are
highlighted. The practical applications of these results are then outlined in a discussion of
the managerial implications. Finally, the chapter concludes with the limitations of the
research and directions for future research.
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5.2 Summary of Findings
5.2.1 The Role of Social Engagement
The findings in chapter 4 provide partial support for a social dimension within customer
event engagement (H1a), and support for a social dimension within customer brand
engagement (H1b). The inclusion of a social dimension of engagement as advocated in
Vivek et al. (2012) and Calder et al. (2009) is consistent with the results of this thesis.
Social brand engagement fits within the customer brand engagement measurement model,
holds as a distinct construct and not a subset of another engagement dimension, and
significantly contributes to the higher order construct of total customer brand engagement.
Social brand engagement has gained traction in recent customer engagement research, as
scholars are recognising the importance of an expanded view of customer engagement
(Kozinets 2014).
Social event engagement, however, is only partially supported as a dimension of customer
event engagement. The structural model for customer event engagement reveals a
covariance between social event engagement and event interaction constructs. Social and
interactive engagement have a strong connection as social engagement inherently contains
interactions with others (Calder et al. 2009; Kozinets 2014). Therefore, social event
engagement is an important inclusion of customer event engagement due to the high level
of customer-to-customer interactions during a BME (Wohlfeil and Whelan 2006).
Calder et al.’s (2009) conceptualisation of engagement identifies personal engagement and
social-interactive engagement elements (Calder et al. 2009). These elements comprise
various dimensions, for example social-interactive engagement includes ‘community’ and
‘participation and socialising’ dimensions (Calder et al. 2009). Although this
conceptualisation is not widely adopted in customer engagement literature to date, the
results from this thesis are consistent with Calder et al. (2009), and is an avenue for future
research.
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5.2.2 Experiential Components of A BME that Facilitate Customer Engagement
Customer experience and marketing event literature advocate the ability for various
experiences to contribute to either the event (Drengner et al. 2008; Pine and Gilmore 1998)
or the brand (Brakus et al. 2009; Whelan and Wohlfeil 2006); therefore, hypotheses were
developed to capture the impact that each experiential component had on both customer
event engagement and customer brand engagement. The findings indicate that sensorial,
pragmatic and relational components of experience contribute to customer event
engagement (H2e, H2g and H2i) but not directly to customer brand engagement (H2f, H2h
and H2j). In addition, the findings reveal that cognitive experiences do not have a
significant impact on customer event engagement (H2a), but rather have a strong direct
relationship with customer brand engagement (H2b).
The cognitive component of a BME experiences encompasses learning, cognitive
processing and experiences providing mental stimulation for the participant (Gentile et al.
2007). BMEs are a brand-initiated activity, and therefore it is expected that a cognitive
experience would relate directly to the brand (Pine and Gilmore 1998). A common
example of a cognitive BME experience is a wine education session. Within this
experience, attendees are provided information about the wine brand including the
different wine varietals on offer and appropriate wine and food pairing. Therefore, it is
plausible that a cognitive BME experience would not necessarily facilitate customer event
engagement; i.e. encouraging feelings of wanting to learn more about the event,
contributing to discussions and paying attention to information regarding the event, but
instead would facilitate customer brand engagement (Pine and Gilmore 1998). Attendees
of a cognitive BME experience gain a higher propensity to seek brand information, learn
more about the brand, and facilitate discussions regarding the brand, hence exhibiting
customer brand engagement (Hollebeek 2011b; Salanova et al. 2005; So et al. 2012).
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Findings indicate that the emotional component of experience does not facilitate
engagement with the event or the brand. This is an unexpected and important finding as the
emotional nature of event experience is a strong focus of marketing event literature
(Leischnig et al. 2011; Packer and Ballantyne 2004; Whelan and Wohlfeil 2006). While
emotional experiences create pleasant experiences or feelings of excitement, the intensity
or value from this experience is not enough to facilitate customer engagement with the
event or brand (Pine and Gilmore 1998). From a social exchange theory perspective, the
resources contributed by the brand are not perceived as enough value to warrant the
customer’s contribution of their engagement (Cropanzano and Mitchell 2005).
Results of this thesis show that the sensorial, pragmatic and relational experiential
components of BMEs facilitate customer event engagement. A sensorial experience
provides sensory meaning and stimulation for customers (Schmitt 1999), for example a
wine and food pairing event, resulting in the customer’s participation and heightened
interaction in event activities (Mollen and Wilson 2010). Focal to a sensory experience is
sensory stimulation (Gentile et al. 2007), which inherently requires customer interaction
with the activity; for example tasting the wine and food and identifying wine fragrances.
Wine has a strong connection with aesthetic consumption (Charters and Pettigrew 2005),
and therefore wine events with a sensorial focus were predicted heighten the customer’s
excitement and event enthusiasm through aesthetics (Schmitt 1999; So et al. 2012).
A pragmatic experience requiring physical behaviours or actions from the customer
(Gentile et al. 2007) is also expected to stimulate customer event engagement through the
active customer participation inherent in the experience (Mollen and Wilson 2010). A
wine-blending event requires the customer to participate in the wine production process;
their engagement is apparent from their considerable interaction effort elicited toward the
activity (Hollebeek 2011b), and their heightened level of attention given to the activity
(Pine and Gilmore 1998).
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A relational experience, for example wine dinner events or meeting the winemaker, is
designed to be experienced with other people (Gentile et al. 2007). Relational experiences
therefore appeal to customers through social event engagement and event enthusiasm
(Kozinets 2014; Vivek et al. 2012), and facilitate customer event engagement.
5.2.3 Engagement Transfer from Event to Brand
This thesis supports the existence of a mediated relationship between BME experiences
and customer brand engagement through customer event engagement (H3). The findings
are consistent with associative network theory in that event related information is projected
onto the brand (Smith 2004). The findings demonstrate that the engagement experienced
with reference to the event is also replicated onto the brand, suggesting that event
engagement transferred to brand engagement.
The results of this thesis suggest that overall customer event engagement has a strong
mediating effect, with BME experiences facilitating customer brand engagement
predominantly through customer event engagement. The impact of sensorial, pragmatic
and relational experiential components on customer brand engagement is fully mediated
through customer event engagement, while cognitive experience has a direct relationship
with customer brand engagement (H2b). The researcher considers this a process of
‘engagement transfer’, where the psychological state facilitated towards the event has a
flow on effect and facilitates engagement with the host brand.
Finally, the notion of engagement transfer is further relevant, because although customer
brand engagement has a significant relationship with BIL (H4b), there is not a significant
direct relationship between customer event engagement and BIL (H4b). Therefore, for BIL
to occur, the BME must facilitate customer brand engagement. BME experiences have a
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strong mediating effect on customer brand engagement only through customer event
engagement, highlighting the importance of engagement transfer in this process.
5.2.4 The Impact of Customer Engagement on Behavioural Intention of Loyalty
There are mixed views and a lack of clarity in the marketing event and engagement
literature regarding whether BIL results directly from customer event engagement
(Crowther 2010; Drengner et al. 2008; Leischnig et al. 2011), or customer brand
engagement (Bowden 2009; Hollebeek 2011a). Customer engagement literature
predominantly focuses on the brand as the focal object of the engagement, leading to
brand-related outcomes (e.g. Gambetti et al. 2012; Hollebeek et al. 2014; So et al. 2012;
Wirtz, Den Ambtman, Bloemer, Horváth, Ramaseshan, Van De Klundert, Canli, and
Kandampully 2013). However, these studies are not conducted in the context of BMEs and
do not investigate the interplay between two engagement objects. An expanded view of
associative network theory provides insight into the relationships between two engagement
objects, and their brand-related outcomes (Smith 2004). Associative network theory
suggests that a direct relationship exists between the brand-related associations and BIL
outcomes (Smith 2004). This means that the associations the customer has with the event
are not the direct cause of BIL; these associations must replicate onto the brand, and it is
the brand associations originating from the event experience that have an effect on BIL
(Smith 2004). Marketing event research that takes a brand image transfer perspective also
advocates that brand-related outcomes are a direct result of brand attitudes, not event
attitudes (Martensen et al. 2007). Through brand image transfer (Gwinner and Eaton 1999)
the event attitudes are transferred onto the brand. These brand attitudes are the drivers of
brand-related outcomes (Martensen et al. 2007).
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5.2.5 How the Individual’s Experiential Needs Moderate Event Engagement
Findings of the multi-group analysis reveal that only need for cognition (H5a) has a
significant moderating influence between BME experience and customer event
engagement. Individuals with high need for cognition find relational experiences more
impactful in facilitating customer event engagement; this moderation effect is justified
because the opportunity to discuss and share information with likeminded others can fulfil
the individual’s need for cognition (Calder et al. 2009; Steenkamp and Baumgartner 1992).
The individual’s knowledge is reinforced or highlighted through their sharing of wine
knowledge with others. Relational experiences provide these individuals with a platform
for showcasing their knowledge to others, sharing opinions and ideas about wine, and
learning more about wine from other knowledgeable individuals, hence encouraging
customer event engagement.
Individuals with high need for cognition are less likely to facilitate event engagement
during sensorial BME experiences; sensorial experiences provide sensory stimulation,
which does not fulfil the individual’s desire to be informed or gain information and
knowledge on an area of interest (Gentile et al. 2007; Wilson 1997). While an individual
with high need for cognition would still find pleasure in a wine-related sensorial
experience as it is consistent with their wine lifestyle (Charters and Ali-Knight 2000), their
level of engagement is likely to be weaker than if they had experienced an event that
satisfied their need for learning and general curiosity (Calder et al. 2009).
These moderation effects are consistent with optimum stimulation level (OSL) theory
which has been used to explain exploratory consumer behaviour (Steenkamp and
Baumgartner 1992). OSL theory states that customers actively seek out environments that
provide their desired level of stimulation; the level of stimulation and type of stimulation is
derived from the customer’s inherent needs (Steenkamp and Baumgartner 1992). The
individual’s needs influence the perceived relevance of event experience and can result in
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heightened engagement when the experience fulfils their needs (MacInnis and Jaworski
1989).
The impact of cognitive and pragmatic BME experiences on event engagement does not
significantly differ for individuals with high need for cognition versus those with low need
for cognition. It was suggested in Chapter 4 that limitations of the research may have
impacted these findings, and are discussed in the limitations of the research section. There
is little evidence to suggest that the individual’s need for affect (H5b) or novelty-seeking
needs (H5c) moderate any of the relationships between BME experiences and customer
event engagement.
5.2.6 Updated Study Framework
The findings from Chapter 4 led to an updated study framework as outlined in Figure 5-1.
A number of modifications have been included in the study framework, highlighting key
findings of this thesis.
First, the updated framework captures the sensorial, relational and pragmatic BME
experiential components leading to customer event engagement (H2e, H2g and H2i), and
cognitive experience leading directly to customer brand engagement (H2b). Emotional
experience has been removed from the framework entirely. This indicates that experiential
components may have a different impact on customer engagement and subsequent BIL
depending on the focal object of engagement. Sensorial, relational and pragmatic BME
experiences require customer engagement with the event as the focal object; customer
event engagement will then replicate onto the brand and contribute to BIL. Conversely,
cognitive BME experiences require direct customer engagement with the brand in order to
lead to BIL outcomes. This particular finding has important managerial implications,
suggesting that a BME with a strong cognitive focus will have strong direct brand
implications in terms of facilitating engagement and BIL outcomes.
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The updated study framework also captures the process of engagement transfer from
customer event engagement to customer brand engagement (H3), and the outcome of BIL
resulting from customer brand engagement (H4b). While conceptual arguments could be
established from the literature (Section 2.4.3.1, Chapter 2) for customer event engagement
and customer brand engagement to both directly impact BIL, the results of this thesis
favour the notion of engagement transfer. The relationship between customer event
engagement and BIL was fully mediated through customer brand engagement; a significant
direct relationship was not found.
FIGURE 5-1: UPDATED STUDY FRAMEWORK
5.3 Contributions to the Academic Discipline
The main contribution of this thesis is the establishment of an empirical relationship
between BME experiences, customer event engagement and customer brand engagement.
An association between BMEs and customer engagement is discussed in previous literature
with little empirical support (Calder et al. 2009; Vivek et al. 2012). This thesis
demonstrates that the sensorial, pragmatic and relational components of BME experiences
facilitate customer event engagement, and that cognitive experiences facilitate customer
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brand engagement. The sensorial, pragmatic and relational components of a BME
experience indirectly facilitate customer brand engagement, fully mediated through
customer event engagement. This establishes an empirical association between BME
experiences and customer engagement on both an event and brand level, which is
supported by social exchange theory (Cropanzano and Mitchell 2005).
This thesis provides quantitative evidence to contribute to the empirical justification of
customer engagement operationalisation and its brand-related outcomes, namely BIL. This
thesis undertook empirical quantitative enquiry on the customer engagement construct, as
the nature of customer engagement research is still heavily conceptual (discussed in Table
2-1, Chapter 2) (Brodie et al. 2011b). This thesis implements the measures of engagement
provided by So et al. (2012), and extends the construct with the inclusion of a social
engagement dimension. Support is found for the operationalisation of the customer brand
engagement measurement model in the context of BMEs.
Establishing this connection between BME experiences and customer engagement
provides further support for the integration of the bodies of literature pertaining to
marketing events, customer experience and customer engagement, all grounded in S-D
logic. This thesis empirically examines BMEs as comprising various customer experience
components; sensorial, emotional, cognitive, pragmatic and relational (Gentile et al. 2007).
Various studies in customer experience have examined different experiential components;
however, they are often inconsistent with regards to the dimensionality of customer
experience (Brakus et al. 2009; Chang and Chieng 2006; Gentile et al. 2007; Sahin et al.
2011; Schmitt 1999; Tynan and McKechnie 2009; Yuan and Wu 2008). In addition, often
research focused on the customer’s experience with a product (Gentile et al. 2007) or
experiential product-centric brands (Brakus et al. 2009). Never before has this
conceptualisation of experiential components been used to investigate drivers of customer
engagement, and therefore is a central contribution of this thesis. The measures from
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Chang and Chieng (2006) were implemented in this thesis, and extend the applicability of
this scale to customer engagement and events literature.
Previous research in customer engagement has primarily focused on the brand as the focal
object of the engagement (e.g. Fehrer et al. 2013; Gambetti et al. 2012; Hollebeek et al.
2014; So et al. 2012; Wirtz et al. 2013). Although Vivek et al. (2012) classify various foci
of customer engagement and propose that customers engage with provider-initiated
activities (i.e. BMEs), customer event engagement has not been examined empirically.
This thesis was the first to examine customer engagement with two focal objects in one
study, and investigate the relationship between engagement objects (Brodie et al. 2011b).
Customer event engagement and customer brand engagement were both explored as
outcomes of a BME experience. The interplay between customer event engagement and
customer brand engagement was also explored.
The identification of engagement transfer from customer event engagement to customer
brand engagement provides support for associative network theory (Smith 2004) in
explaining how customer engagement is facilitated through BME experiences. This thesis
is the first to extend associative network theory in the area of customer engagement and
advocates the investigation of multiple engagement objects and their interplay in reaching
brand outcomes.
This thesis extends the knowledge of the relationship between customer engagement and
BIL; while BIL has been identified as an outcome of engagement (So et al. 2012), this
thesis is the first to explore BIL as an outcome of customer event engagement and
customer brand engagement. Confusion exists in the marketing event and customer
engagement literature regarding whether brand-related outcomes result from event
engagement or brand engagement (Bowden 2009; Crowther 2010; Drengner et al. 2008;
Hollebeek 2011a; Leischnig et al. 2011). This thesis provides clarity on this issue by
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engagement on BIL. The results suggest that customer event engagement does not have a
direct relationship with BIL, but instead contributes to customer brand engagement.
Customer brand engagement has a direct and significant relationship with BIL.
This thesis provides empirical evidence to support the inclusion of a social dimension
within customer brand engagement, and partial support for social engagement as part of the
customer event engagement construct. There is considerable debate in customer
engagement literature regarding the presence and role of a social element of engagement
(Calder et al. 2009; Gambetti et al. 2012; Sawhney et al. 2005; Vivek et al. 2012). This
finding of this thesis are consistent with social exchange theory (Abdul-Ghani et al. 2011)
and demonstrates that customers can achieve a heightened psychological state of social
connection and human contact with reference to the brand (and similarly, the event).
The antecedent of BME experiences contributing to customer engagement, and outcomes
of customer engagement were also empirically investigated. Therefore, the nomological
network of customer engagement was further identified (Brodie et al. 2011a). Further
support was found for customer brand engagement leading to BIL (So et al. 2012). This
thesis further contributed to the understanding of outcomes of customer engagement as it
investigates the impact of both customer event engagement and customer brand
engagement on BIL.
The conceptualisation and outcomes of customer engagement have been the central focus
of customer engagement literature to date (Brodie et al. 2011b). This thesis is the first
study to examine the antecedent of BME experiences contributing to customer
engagement, and the role of moderating variables impacting this relationship.
The findings from this thesis have broader application to related disciplines given the
multidisciplinary nature of engagement including sociology, political science, psychology,
educational psychology, and organisational behaviour (Brodie et al. 2011a). Student
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of how experiences facilitate engagement, the relationship between multiple engagement
objects, and further insights provided in this thesis. In addition, the insights on event
experience would have great value in retail, service and tourism industries, where special
events and memorable consumer experiences play a key role in differentiation.
5.4 Managerial Implications
As the respondents of this thesis were drawn from attendees of South Australian winery-
hosted events, the results will have use for managers in similar settings. While implications
can be drawn for managers in different environmental settings, further investigation should
occur before the results are generalised.
For managers and event organisers, a notable finding to emerge from this thesis is the
importance of structuring BMEs to facilitate customer engagement for the event and the
brand. The findings show that relational, pragmatic and sensorial experiences have a direct
relationship with event engagement, while cognitive experience has a direct relationship
with brand engagement. This means that practitioners should consider designing BMEs
with a cognitive focus to engage customers with the brand specifically. Wine education
seminars with a direct focus on the wine brand (rather than a ‘generic’ wine education
session) will have a direct impact on customer brand engagement, and result in BIL.
However, managers and event organisers are advised that if a cognitive experience is
implemented, emphasis on the brand is pivotal. Cognitive experience does not contribute to
customer event engagement, and therefore the event must contain brand-related
information and stimulate customer interest to acquire brand-related knowledge in order to
achieve customer brand engagement.
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Alternatively, practitioners can implement events focusing on relational, pragmatic and/or
sensorial experiences, and these BMEs should focus on the event experience as the focal
engagement object, not the brand. Meet the wine maker events (relational), wine-blending
sessions (pragmatic) or wine and food pairing (sensorial) should be designed to allow
attendees to interact, become excited and feel socially or emotionally connected with the
event experience. As customer event engagement has a strong relationship with customer
brand engagement, it is inferred that these experiential components also have an indirect
impact on BIL. Therefore, the relational, pragmatic and sensorial experiences should aim
to facilitate engagement with the event specifically, because through engagement transfer,
this event engagement will also impact brand engagement and subsequent behavioural
intention of loyalty.
All event experiences should have some reference or focus on the brand, as it is brand
engagement that leads to behavioural intention of loyalty toward the brand. The fact that
only customer brand engagement and not customer event engagement leads to BIL
reiterates the need for managers to have a branded marketing event mindset. It is through
engagement transfer from event to brand that brand-related outcomes occur; therefore, the
brand must have central focus when planning events.
In addition, as experiential needs do not have a major impact on the types of experiences
leading to customer event engagement, managers are advised to plan BMEs that include a
combination of sensorial, pragmatic and relational experiences as customers generally
exhibit customer event engagement as a result.
Emotional experience was the only experiential component that did not have a significant
relationship with customer engagement. This finding is particularly relevant for managers,
as many marketing event studies emphasise the emotional elements of the experience as
having an impact on brand outcomes (Leischnig et al. 2011). While emotional experiences
create pleasant experiences or feelings of excitement, the intensity or value from this
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experience is not enough to facilitate customer engagement with the event or brand (Pine
and Gilmore 1998). Creating an emotional experience is a common strategy for many
event organisers, and is particularly emphasised in the wine industry, as wine lifestyle is
often associated with leisure, hedonism and aesthetic consumption (Charters and Pettigrew
2005; O'Neill and Charters 2000). It is recommended that marketing managers should not
invest on predominantly emotional experiences as they are not found to have a significant
relationship with customer engagement. If an emotional experience approach is taken by
the brand, it is recommended that this style of experience is implemented in combination
with other experiential components (e.g. cognitive, pragmatic, sensorial, and/or relational),
as these BME components have a significant impact on customer engagement. This finding
is based on respondents from a range of wine-events, and therefore has generalisability
within the wine industry. However, replication of the study is required to determine
whether this finding applies to different contexts or products/services.
5.5 Limitations of the Research
This section addresses some of the limitations of this thesis. From these limitations,
opportunities for further research are identified and examined.
The results of this thesis do not examine the change in BIL as an outcome of the BME
experience, but rather establish associations between the two constructs. A pre- and post-
event survey would have provided more insight into the level of BIL felt by the customer
before experiencing the BME as well as after. In addition, a longitudinal research design
would have provided a more comprehensive view of the development of customer
engagement pre- and post-event, and captured the impact of multiple moments of
engagement over time (Brodie et al. 2011a). It has been suggested that customer
engagement is cyclical, and that reciprocal effects between antecedents and outcomes
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could be present (Fehrer et al. 2013; Hollebeek et al. 2014). However, this was not
captured in this thesis.
In addition, it is recognised that the individual’s experiential needs could impact their
decision to attend a BME, which could explain the limited impact experiential needs had
on the relationship between BME experiences and customer event engagement. It is
probable that those who attended (and who participate in the survey) were limited to
attendees who already found interest in the activity. This provides an explanation for
experiential needs not demonstrating a moderating effect through multi-group analysis.
The individual’s experiential needs would have impacted their decision to attend the event,
and therefore the respondents captured in this thesis study would more generally find value
or fulfilment of their needs with the event they have chosen to attend.
A number of method limitations are present in this thesis. First, there is a lack of
consistency between the pre-tested items and those used in the main study. Upon further
reflection and exploration of the customer engagement literature, the researcher decided
that So et al.’s (2012) expanded five dimension view of customer engagement had great
applicability to the event context, and allowed the investigation of the dimensionality of
customer engagement. The researcher also decided that for completeness of replication of
So et al.’s (2012) dimensions of engagement, the participation measures would be replaced
with So et al.’s (2012) interaction measures. The researcher recognised these oversights
after the pre-test, however decided that the insights gained from changing a number of the
items in the main study would outweigh the limitations of having a consistent survey in the
pre-test and main study. Second, a relatively small sample size and participation of a small
number of South Australian wineries meant that event type or brand bias could not be
examined individually. A larger scale study with a greater diversity of events and larger
response rates for each individual event is required in order to control for any biases that
may influence the results.
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Discussion and Conclusion Chapter
It was acknowledged in Chapter 2 that specific experiential components may contribute to
individual customer engagement dimensions (e.g. attention, absorption, immersion) as
opposed to customer engagement in a general sense. Instead, customer engagement
outcomes were considered from an overall perspective with support from customer
engagement literature that each engagement dimension should contribute to and enhance
the others, and result in a general level of customer engagement (Brodie et al. 2011a). This
is a limitation of this thesis, as the relationships between BME experiential components
were not investigated with reference to each customer engagement dimension, and
therefore do not provide a complete understanding of how customer engagement is
facilitated.
Furthermore, this thesis considers the impact of each experiential on customer event
engagement and customer brand engagement; however, it is also acknowledged that an
event can include multiple or all experiential components, and this was not accounted for
in the analysis. ‘Complex’ experiences, containing multiple experiential components, could
facilitate a different level of customer engagement than experiences reflecting a single
experiential component (Gentile et al. 2007; Pine and Gilmore 1998); however, it was not
explored in this thesis.
5.6 Directions for Future Research
This section highlights areas for future research. The discussion focuses on opportunities
that emerge from the limitations of this thesis and future research directions identified from
Chapter 2 and 3.
In examining the role of social engagement within customer event engagement and
customer brand engagement, this thesis took the perspective that social engagement would
have equal applicability for both engagement objects. However, there is evidence in this
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Discussion and Conclusion Chapter
thesis to suggest that social engagement has a different effect within customer event
engagement versus customer brand engagement. In particular, the social event engagement
and event interaction elements had a strong connection that indicated they may be distinct
from the other customer engagement dimensions. In the BME context, which is inherently
social, respondents may find less disparity between a heightened state encompassing the
participation in activities (interaction) and a heightened state of connectedness with others
as part of their engagement (social). Future research should further investigate different
perspectives on the dimensionality of engagement; two dimensions of engagement (Calder
et al. 2009), three dimensions of engagement (Brodie et al. 2011a) or five dimensions of
engagement (So et al. 2012).
As identified in the limitations of the research, a single survey implemented shortly after a
BME has limited ability to capture drivers to attend the BME, identify pre-event customer
brand engagement or BIL, or examine the extended effects of customer brand engagement
after the event. Different research/survey methods could provide a more comprehensive
view of the impact of customer engagement. Two such examples include pre- and post-
event surveys and longitudinal research. A longitudinal approach could explore the
endurance of customer engagement over time. Previous research has recognised the
potential for customer engagement to have enduring qualities, as well as the ability over
time to exhibit varying levels of engagement intensity (Brodie et al. 2011b). However,
little research to date has explored in detail the customer engagement construct over time.
Future research featuring a longitudinal design should investigate the enduring brand-
related outcomes sustained from a BME experience.
In addition, future research could implement an experimental design to more exhaustively
capture varying experiential needs and test the moderating impact on customer engagement
when these individuals are exposed to experiences that do not satisfy their experiential
needs. Similar manipulations could explore a more exhaustive combination of single or
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Discussion and Conclusion Chapter
multiple experiential components to further investigate their impact on customer event
engagement and customer brand engagement.
Qualitative research approaches implemented instead of, or in addition to, a quantitative
research approach provide a different direction for future research. In situ approaches such
as a diary study would provide insight into the unique, personal and momentary
experiences of BME attendees. Interviews or focus groups could further investigate the
impact of various experiential components, the perceived level of customer event
engagement and customer brand engagement facilitated, as well as exploring the
experiential needs of the individual that may drive attendance to a BME. A qualitative
approach provides the researcher with the opportunity to discover additional factors
impacting an individual’s BME experience that may have otherwise gone unnoticed or not
captured in a survey. For example, an attendee may feel a sense of engagement with an
additional object other than the event or the brand (perhaps with the service staff, the wine
region, or the band/musician playing during the BME).
Another avenue for future research is further exploration of the different elements of
customer engagement to identify their individual impact on BIL and similar brand-related
outcomes. The concept of engagement intensity, and the ability for individual engagement
dimension to contribute to overall engagement intensity is acknowledged (Brodie et al.
2011b) but not investigated. The interplay between engagement dimensions has the ability
to generate various levels of engagement intensity, and single engagement dimensions may
have the ability to influence the other engagement dimensions (Brodie et al. 2011b). For
example, emotional engagement may facilitate enhanced levels of cognitive and/or
behavioural engagement (Brodie et al. 2011b). In addition, future research could integrate
BME experiential components within this investigation to determine which components of
experience lead to which dimensions of customer engagement and subsequently result in
BIL or related brand-outcomes. The individual’s experience and perception of experiential
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Discussion and Conclusion Chapter
components are unique, and so the same event may be perceived as including different
components of experience and facilitating different dimensions of engagement. An added
complexity to this process is the ability of events to comprise a number of different
experience components, and thus encourage a variety of event engagement dimensions to
unfold.
Finally, future research needs to extend into different contexts in which customer
engagement may occur. This thesis focused on the wine industry; an offline and hedonic
context. Similar studies should investigate utilitarian contexts, different industries and
various products/services to demonstrate applicability and generalisability of the customer
engagement construct. For example, trade-show and industry events are a common form of
business-to-business interactions (Herbig, O'Hara, and Palumbo 1994); however,
engagement is not well understood outside of the typical customer-brand or customer-
customer interactions.
5.7 Concluding Thoughts
As a result of this thesis there is further knowledge of how brands can strategically
facilitate customer engagement. Greater insight into the nature of customer engagement,
namely its dimensions (social engagement), antecedents (BME experience) and moderators
(experiential needs) has been achieved.
Incorporating the research areas of customer engagement, marketing events and customer
experience, consistent in their applicability to S-D logic (Vargo and Lusch 2014), has
allowed a more strategic investigation of customer engagement. The findings from this
thesis have provided a framework for understanding the experiential components of BMEs
(Gentile et al. 2007) and their impact on customer event engagement, customer brand
engagement and BIL (So et al. 2012). This thesis provides support for social exchange
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Discussion and Conclusion Chapter
theory (Cropanzano and Mitchell 2005; Saks 2006) as a predictor of the resources
contributed in the form of BME experiences from the brand and customer engagement
contributed by the individual in explaining the reciprocal interactions leading to mutual
outcomes for the customer and the brand.
The process of engagement transfer is a central contribution of this thesis, justifying how a
BME, an activity extending beyond the normal customer-brand interactions (Vivek et al.
2012), has the capacity to lead to BIL for the host brand. Engagement facilitated with the
event, which facilitates engagement with the brand, demonstrates that BMEs are a unique
brand-building activity founded in associative network theory (Smith 2004).
While customer engagement literature has for a short time reached an understanding of
customer engagement conceptualisation, this thesis extends these ideas through
quantitative empirical enquiry and explores under-researched antecedents, outcomes and
moderators of the customer engagement process. The development of effective BME
experiences requires a thorough investigation of customer experiences, and the individual’s
experiential needs that influence their ability or willingness to engage with the event.
Continual development regarding the dimensions, platforms and antecedents that facilitate
customer engagement is essential for the development of customer engagement research as
a prominent and impactful research area of marketing, and ensuring its broad applicability
and managerial benefits.
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APPENDICES
APPENDIX A-1: QUESTIONNAIRE
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APPENDIX A-2: ETHICS APPROVAL
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APPENDIX A-3: ADDITITONAL ANALYSIS FROM PRE-TEST
GOODNESS OF FIT INDICES FOR ALL ITEMS – PRE- TEST Construct χ² df χ²/df GFI NFI CFI RMSEA Cognitive experience 3.31 1 3.31 0.99 0.99 0.99 0.10 Emotional experience 11.80 6 1.97 0.98 0.97 0.99 0.07 Sensorial experience 1.62 1 1.62 0.99 0.99 0.99 0.05 Pragmatic experience 0.68 1 0.68 0.99 0.99 1.00 0.00 Relational experience 6.31 7 0.90 0.99 0.99 1.00 0.00 Cognitive event engagement 2.71 2 1.35 0.99 0.99 0.99 0.04 Emotional event engagement 1.15 1 1.15 0.99 0.99 1.00 0.03 Event participation 6.85 3 2.28 0.99 0.99 0.99 0.08 Social event engagement 11.46 11 1.04 0.99 0.99 0.99 0.01 Cognitive brand engagement 0.85 1 0.85 0.99 0.99 1.00 0.00 Emotional brand engagement 1.64 1 1.64 0.99 0.99 0.99 0.05 Brand participation 3.83 3 1.28 0.99 0.99 0.99 0.04 Social brand engagement 15.37 11 1.40 0.98 0.98 0.99 0.04 Need for cognition 34.53 24 1.44 0.97 0.96 0.99 0.04 Need for affect 9.74 6 1.62 0.99 0.99 0.99 0.05 Novelty-seeking 1.04 1 1.04 0.99 0.99 1.00 0.01 Word of mouth 0.74 1 0.74 0.99 0.99 1.00 0.00
SUMMARY OF ITEM CHANGES IN PRE-TEST VERSUS MAIN STUDY Pre-test Main Study Cognitive Experience Chang and Chieng (2006)
This event tried to intrigue me This event tried to intrigue me This event tried to stimulate my curiosity This event tried to stimulate my curiosity This event appealed to my creative thinking This event appealed to my creative thinking
Brakus et al. (2009)
I engaged in a lot of thinking when I attended this event
-
This event did not make me think - This event stimulated my curiosity and problem solving
-
Reliability: The 3 Chang and Chieng (2006) items received a Cronbach’s alpha of 0.81. When the 3 Brakus et al. (2009) items were added to the reliability test, Cronbach’s alpha was 0.82. Therefore, Chang and Chieng’s (2006) items were used in the main study to achieve parsimony. Sensorial Experience Chang and Chieng (2006)
This event was focused on creating a sensory experience
This event was focused on creating a sensory experience
This event did not try to engage my senses - This event tried to excite my senses This event tried to excite my senses This event provided sensory enjoyment This event provided sensory enjoyment
Brakus et al. (2009)
This event made a strong impression on my visual sense or other senses
-
I found this event interesting in a sensory way
-
This event did not appeal to my senses - Reliability: The 4 Chang and Chieng (2006) items received a Cronbach’s alpha of 0.85. Strong reliability was already achieved with the 4 Chang and Chieng (2006) items, therefore there was little benefit in including the Brakus et al. (2009) items. The 4 Chang and Chieng (2006) items were then assessed through a one-factor congeneric measurement model; poor inter-item correlation was found for item 2, and the model achieved fit when this item was removed. Therefore the main study included items 1, 3, and 4 from Chang and Chieng (2006).
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Relational Experience Chang and Chieng (2006)
This event offered me a sense of group belonging
-
I could relate to other people through this event
-
Sweeney and Soutar (2001)
Attending this event helped me to feel accepted
Attending this event helped me to feel accepted
Attending this event improved the way I am perceived
Attending this event improved the way I am perceived
Attending this event made a good impression on other people
Attending this event made a good impression on other people
Attending this event gave me social approval
Attending this event gave me social approval
Attending this event created a favourable perception of me among other people
Attending this event created a favourable perception of me among other people
This event had a positive social image This event had a positive social image Reliability: The 6 Sweeney and Soutar (2001) items received a Cronbach’s alpha of 0.91, while the Chang and Chieng (2006) items received a Cronbach’s alpha of 0.79. In the interest of parsimony only the 6 Sweeney and Soutar (2001) items were used in the main study. Pragmatic Experience – no changes from pre-test to main study Emotional Experience – no changes from pre-test to main study PARTICIPATION MEASURES USED IN PRE-TEST Chan, Yim, and Lam (2010)
I spent a lot of time sharing information about my needs and opinions with organisers during this event I put a lot of effort into expressing my personal needs to organisers during this event I provided suggestions to organisers for improving the event I had a high level of participation in this event I was very much involved in deciding how this event should be provided
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