dissertation chauner | supervisor: prof. dr. torsten ...file/... · technology-derived services...

Technology-Derived ServicesConception, Acceptance, and Business Impact

D I S S E R T A T I O N

of the University of St.Gallen,

School of Management,

Economics, Law, Social Sciences

and International Affairs

to obtain the title of

Doctor of Philosophy in Management

submitted by

Christian Hauner

from

Germany

Approved on the application of

Prof. Dr. Torsten Tomczak

and

Prof. Dr. Andreas Herrmann

Dissertation no. 4404

Rosch-Buch, Schesslitz 2015

The University of St.Gallen, School of Management, Economics, Law, Social Sciences and In-

ternational Affairs hereby consents to the printing of the present dissertation, without hereby

expressing any opinion on the views herein expressed.

St.Gallen, May 19, 2015

The President:

Prof. Dr. Thomas Bieger

Fur meine Familie

Maci, Sonja, Helmut, Nici, Alex, Gitta, Karl und Alex

Vorwort

Die Promotion an der Universitat St.Gallen war fur mich eine ganz besondere Erfahrung. Die

vorliegende Dissertation ist das Ergebnis dieser unglaublich spannenden und lehrreichen Zeit.

Gerne mochte ich denjenigen Menschen danken, die mich hierbei begleitet und unterstutzt

haben.

In erster Linie mochte ich meinem Doktorvater, Prof. Dr. Torsten Tomczak danken. Seine

personliche und fachliche Unterstutzung haben das Gelingen dieser Dissertation uberhaupt erst

moglich gemacht. Weiterhin mochte ich meinem Ko-Referenten Prof. Dr. Andreas Herrmann

danken, der mich als akademischer Mentor beim Entstehen dieser Arbeit intensiv begleitet hat.

Danken mochte ich auch meinen Kollegen an der Forschungsstelle fur Customer Insight: Dr.

Lucas Beck, Dr. des Emanuel de Bellis, Dennis Esch, Dr. Christian Hildebrand, Dr. Marcel

Mazur, Jessica Mueller-Stewens und Maik Walter. Ganz besonderen Dank mochte ich in diesem

Zusammenhang Dr. Tobias Schlager aussprechen, der stets ein offenes Ohr fur mich hatte und

mir jederzeit mit Rat und Tat zur Seite stand.

Außerordentlicher Dank gilt einem ganz besonderen Menschen: Meiner Freundin, Marcella

Grohmann. Sie ist immer fur mich da und gibt mir immer Ruckhalt, besonders in schwierigen

Zeiten. Ich bin sehr froh, diesen einzigartigen Menschen gefunden zu haben.

Der großte Dank gilt meiner Mutter, Sonja Hauner, meinem Vater, Helmut Hauner, und meiner

Schwester, Nicole Hauner. Ihr unerschutterlicher Glaube in meine Fahigkeiten, ihre grenzen-

lose Unterstutzung und ihre unermessliche Liebe haben mir immer wieder Kraft gegeben, meine

Ziele zu verfolgen und meine Traume zu verwirklichen. Ich bin sehr stolz, diese besonderen

Menschen als Eltern und Schwester zu haben.

Abstract

This thesis introduces a new type of services, Technology-Derived Services (TDS). TDS are in-

telligent products with an innate IT-based capability to autonomously operate and are therefore

providing value directly to the customer without any necessary interaction during the value cre-

ation process of its user or its manufacturer. From a customer’s point of view, two distinct fields

of application arise: employing TDS in an object-related or in a person-related context. Despite

their rapid growth on the market, TDS have not drawn academia’s attention yet. To address

this issue, this thesis comes up with a new acceptance model for TDS, which was derived from

literature reviews on technology and service acceptance. Results of two studies give empirical

evidence for the validity and reliability of new measurements, namely anticipated temporal dis-

charge, perceived service safety, and perceived controllability as well as the nomologic validity

of the proposed acceptance model for TDS. When TDS are applied in an object-related context,

customers’ attitude towards using the TDS is positively influenced by their anticipated temporal

discharge and their perceived controllability. In contrast, when TDS are applied in a person-

related context, the positive effect of perceived controllability on customers’ attitude towards

using TDS is mediated by perceived service safety. Anticipated temporal discharge has also a

positive effect on customers’ attitude towards using TDS in a person-related context. Study 3

examines customers’ evaluation of TDS in comparison to conventional products and services.

The results show that TDS are generally perceived as providing lower quality than conventional

services and products. Nevertheless, customers are aware of the advantage of TDS, namely

time saving, as they anticipate the same temporal discharge for TDS compared to conventional

services and higher temporal discharge compared to conventional products. Finally, results also

reveal that customers’ attitude towards TDS is positively influenced by anticipated quality and

anticipated temporal discharge. To sum up, the author contributes to research by conceptualiz-

ing TDS, establishing a new acceptance model for TDS, and providing a theoretical framework

to compare customers’ evaluations of TDS with conventional products and services. Finally,

the thesis comes up with first managerial implications for TDS and provides a future research

agenda in the field of TDS.

Zusammenfassung

Die vorliegende Arbeit befasst sich mit einer neuen Art von Services, den Technology-Derived

Services (TDS). TDS basieren auf Produkten, die aufgrund von IT-basierten Funktionalitaten

in der Lage sind, autonom zu operieren und somit dem Kunden direkten Nutzen stiften. Dabei

ist wahrend der Leistungserbringung keine Interaktion des Kunden oder des Herstellers mehr

notwendig. Aus Kundensicht konnen TDS in objekt-bezogenen oder in personen-bezogenen

Anwendungen eingesetzt werden. Entgegen des immer schneller werdenden Marktwachstums,

wurden TDS in der bisherigen Forschung noch nicht naher untersucht. Um diese Lucke zu

schliessen, wird ein Akzeptanzmodell fur TDS, basierend auf bisherigen Erkenntnissen zur

Technologie- und Service-Akzeptanz, erarbeitet. Die Ergebnisse zweier Studien bestatigen die

Validitat und Reliabilitat der neu eingefuhrten Skalen erwartete Zeitersparnis, wahrgenommene

Kontrollierbarkeit und wahrgenommene Service-Sicherheit und die nomologische Validitat des

abgeleiteten Akzeptanzmodells fur TDS. Bei objekt-bezogenen Anwendungen wird die Ein-

stellung gegenuber der Nutzung von TDS positiv von der erwarteten Zeitersparnis und der

wahrgenommenen Kontrollierbarkeit beeinflusst. Im Gegensatz dazu wird bei personen- be-

zogenen Anwendungen der positive Einfluss von wahrgenommener Kontrollierbarkeit auf die

Einstellung gegenuber der Nutzung von TDS durch die wahrgenommene Service-Sicherheit

mediiert. Die erwartete Zeitersparnis hat auch bei personen-bezogenen Anwendungen einen

positiven Einfluss auf die Einstellung gegenuber der Nutzung von TDS. In einer weiteren Studie

werden TDS mit bestehenden, konventionellen Produkten und Services verglichen. Die Ergeb-

nisse der Studie zeigen dabei, dass Konsumenten die Qualitat von TDS im Vergleich zu konven-

tionellen Produkten und Services generell negativer bewerten. Die erwartete Zeitersparnis wird

von Kunden bei TDS und konventionellen Services als gleichwertig angesehen. Im Vergleich

zu konventionellen Produkten nehmen Kunden eine signifikant hohere Zeitersparnis bei TDS

wahr. Die Einstellung der Kunden gegenuber TDS wird positiv von der erwarteten Zeiterspar-

nis sowie der wahrgenommenen Qualitat beeinflusst. Die vorliegende Arbeit leistet mit einem

neuen Akzeptanzmodell fur TDS, einem neuen theoretischen Rahmen zur Vergleichbarkeit von

TDS mit konventionellen Services und Produkten sowie einer Research-Agenda einen wis-

senschaftlichen Beitrag und leitet entsprechende Handlungsempfehlungen fur die Praxis ab.

Contents

Contents

List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . III

List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V

List of Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VI

1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 Problem Setting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 Research Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.3 Structure of the Dissertation. . . . . . . . . . . . . . . . . . . . . . . . 3

2 Technology-Derived Services: Blurring the distinction between products andservices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52.1 Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.1.1 Product Types . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.1.2 Intelligent, autonomous operating products: Services included . . . . . 8

2.2 Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.2.1 Service transition: The Next Level . . . . . . . . . . . . . . . . . 10

2.2.2 Technology-Derived Services: A New Type of Service Innovation and

Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3 Advancing towards a New Service Classification . . . . . . . . . . . . . . . . 133.1 Service-Provision: Assisted or Derived . . . . . . . . . . . . . . . . . . . 13

3.2 Service-Relatedness: Human or Object . . . . . . . . . . . . . . . . . . . 15

4 Literature Review on Technology and Service Acceptance . . . . . . . . . . . 174.1 Technology Acceptance . . . . . . . . . . . . . . . . . . . . . . . . . 17

4.1.1 Models for Technology Acceptance . . . . . . . . . . . . . . . . . 17

4.1.2 Shortcomings in the light of Technology-Derived Services . . . . . . . 20

I

Contents

4.2 Service Acceptance . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

4.2.1 Evaluation of Services . . . . . . . . . . . . . . . . . . . . . . . 21

4.2.2 Drawbacks of Evaluation for Technology-Derived Services. . . . . . . 23

5 Assessing the Acceptance of Technology-Derived Services . . . . . . . . . . . 245.1 Conceptualizing the Acceptance Model for Technology-Derived Services. . . . 24

5.1.1 Attitude towards using the TDS . . . . . . . . . . . . . . . . . . . 25

5.1.2 Anticipated Temporal Discharge . . . . . . . . . . . . . . . . . . 25

5.1.3 Perceived Service Safety . . . . . . . . . . . . . . . . . . . . . . 26

5.1.4 Perceived Controllability . . . . . . . . . . . . . . . . . . . . . . 28

5.2 Empirical Evaluation of the Acceptance Model for Technology-Derived Ser-

vices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

5.2.1 Study 1: New Construct Validation . . . . . . . . . . . . . . . . . 31

5.2.1.1 Data Collection and Measurement . . . . . . . . . . . . . . 31

5.2.1.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

5.2.1.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

5.2.2 Study 2: Validation of the Acceptance Model for TDS . . . . . . . . . 40

5.2.2.1 Data Collection and Measurement . . . . . . . . . . . . . . 40

5.2.2.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

5.2.2.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

6 Business Impact of Technology-Derived Services . . . . . . . . . . . . . . . . 496.1 Theoretical Background and Hypotheses . . . . . . . . . . . . . . . . . . 49

6.2 Study 3: Effects of TDS on classic product and service businesses . . . . . . . 52

6.2.1 Design, Procedure, and Data Collection . . . . . . . . . . . . . . . 52

6.2.2 Measures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

6.2.3 Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

6.2.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

7 Overall Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 697.1 Theoretical Implications . . . . . . . . . . . . . . . . . . . . . . . . . 70

7.2 Managerial Implications . . . . . . . . . . . . . . . . . . . . . . . . . 73

7.3 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

7.4 Future Research Agenda . . . . . . . . . . . . . . . . . . . . . . . . . 77

8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

II

List of Tables

List of Tables

5.1 Anticipated Temporal Discharge . . . . . . . . . . . . . . . . . . . . . . . . . 26

5.2 Perceived Service Safety . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

5.3 Perceived Controllability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

5.4 Rotated Factor Loadings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

5.5 Final item set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

5.6 Unidimensionality for all scales . . . . . . . . . . . . . . . . . . . . . . . . . 36

5.7 Convergent Validity and Reliability Assessment Study 1 . . . . . . . . . . . . 37

5.8 Correlation Matrix for Discriminant Validity Assessment Study 2 . . . . . . . . 37

5.9 Convergent Validity and Reliability Assessment Study 2 . . . . . . . . . . . . 43

5.10 Correlation Matrix for Discriminant Validity Assessment Study 2 . . . . . . . . 44

5.11 Measurement invariance tests Study 2 . . . . . . . . . . . . . . . . . . . . . . 44

5.12 Structural Models Estimation Study 2 . . . . . . . . . . . . . . . . . . . . . . 45

III

List of Figures

List of Figures

1.1 Structure of the thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

3.1 Expansion of technology’s role in customer interaction (Own representation

based on Froehle & Roth, 2004) . . . . . . . . . . . . . . . . . . . . . . . . . 14

3.2 New Service-Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

4.1 Technology Acceptance Model 3 (Own representation based on Venkatesh &

Bala, 2008) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

4.2 Unified Theory of Acceptance and Use of Technology 2 (Own representation

based on Venkatesh, Thong, & Xu, 2012) . . . . . . . . . . . . . . . . . . . . 20

5.1 Acceptance Model for Technology-Derived Services . . . . . . . . . . . . . . 30

5.2 Scree plot for the initial item set . . . . . . . . . . . . . . . . . . . . . . . . . 35

5.3 Stimuli: Object related TDS . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

5.4 Stimuli: Person related TDS . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

5.5 Results of the Multi-group Structural Equation Model (SEM) . . . . . . . . . . 46

6.1 Stimuli: TDS as product . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

6.2 Stimuli: TDS as a service . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

6.3 Stimuli: Advanced product as product . . . . . . . . . . . . . . . . . . . . . . 55

6.4 Stimuli: Advanced product as a service . . . . . . . . . . . . . . . . . . . . . 55

6.5 Manipulation check type of offer (detailed) . . . . . . . . . . . . . . . . . . . 57

6.6 Manipulation check type of object . . . . . . . . . . . . . . . . . . . . . . . . 58

6.7 Manipulation check type of object . . . . . . . . . . . . . . . . . . . . . . . . 58

6.8 Interaction plot for anticipated temporal discharge . . . . . . . . . . . . . . . . 59

6.9 Diagnosis plot for analysis of covariance (DV: anticipated temporal discharge) . 59

6.10 Main effect of object type on anticipated quality . . . . . . . . . . . . . . . . . 61

6.11 Diagnosis plot for analysis of covariance (DV: anticipated quality) . . . . . . . 61

6.12 Results of the moderated mediation analysis Study 3 . . . . . . . . . . . . . . 63

IV

List of Figures

6.13 Interaction effect of type and offer on consumers’ attitude towards the offer . . 64

6.14 Diagnosis plot for analysis of covariance (DV: attitude towards the offer) . . . . 64

6.15 Main effect of type of offer on relative willingness to pay . . . . . . . . . . . . 65

6.16 Diagnosis plot for analysis of covariance (DV: relative willingness to pay) . . . 65

V

List of Abbreviations

List of Abbreviations

ATM Automated Teller Machine

B2B Business-to-Business

B2C Business-to-Consumers

CFA Confirmatory Factor Analysis

CMV Common Method Variance

EFA Exploratory Factor Analysis

FA Factor Analysis

High-Tech High-Technology

IT information technology

R&D Research & Development

SDL Service-Dominant Logic

SEM Structural Equation Model

SST Self-Service Technologies

TAM Technology Acceptance Model

TDS Technology-Derived Services

TRA Theory of Reasoned Action

UTAUT Unified Theory of Acceptance and Use of Technology

WOM Word of Mouth

VI

1 Introduction

1Introduction

There are an endless number of things to

discover about robotics. A lot of it is just

too fantastic for people to believe.

Daniel H. Wilson

1.1 Problem Setting

Recently, so-called intelligent products have entered the market place. These products are

characterized by their capability to autonomously and directly deliver value to the customer.

Equipped with latest information technology (IT), they are capable of collecting, processing,

and producing information. Therefore, intelligent products can operate autonomously and in-

dependently (S. A. Rijsdijk & Hultink, 2009) and “overtake daily activities and concerns from

their user” (S. Rijsdijk, Hultkin, & Diamantopoulos, 2007, p.342). Thus, facilitated by techno-

logical sophistication, their core benefit is to provide value for customers autonomously.

Just to give some examples of already available intelligent products, one could think of

Husqvarna’s autonomous mowers called Automowers or Samsung’s vacuum-robots. Going one

step ahead of time, Research & Development (R&D) departments of Mercedes-Benz, BMW,

and AUDI have first trucks and cars capable of autonomously finding their way from place A

to destination B. Going even further, Honda recently presented their latest version of ASIMO,

a humanoid robot which could have major impacts for example in the health-care system in

the future. These examples also demonstrate that depending on the application context, the

intelligent products either do not involve humans or do involve humans in their provision of

value.

Furthermore, the projected business impact of these new technologies is nothing short of be-

ing one of the eight trillion-dollar trends of the future (Harris, Schwedel, & Kim, 2011). Buera

1

1 Introduction

and Kaboski (2012) found that the excessive growth-rate in the service sector, rising from 50%

in 1950s close to 80% in the 21st century, has been propelled by more specialized and therefore

higher-skilled and higher paid workers. As the workers then were faced with their respective

opportunity costs for specific tasks, they decided to use more and more services. However,

the above mentioned new technologies could have a significant impact on less complex ser-

vices. That is, human service employees in this service industries could possibly be replaced

by technology. Hence, industrialization of services could take place in the future (Gautschi &

Ravichandran, 2006).

However, despite any experts’ appraisals, it is customers’ acceptance that determines whether

a new technology turns into a success-story or turns out to be a white elephant. The same applies

for autonomously operating intelligent products, even though their major advantage is glaring,

namely time saving. Therefore, this thesis aims at giving a new classification for this new and

emerging type of services derived by technologies and providing first insights on customers’

acceptance of this new type of services.

1.2 Research Questions

This thesis aims to gain insights into customers’ acceptance of intelligent products capable of

providing this new type of service and its potential business impact. Therefore, the presented

thesis (a) introduces this new type of services, (b) derives a new acceptance model based on

newly established constructs for this new type of services and (c) assesses customers’ perception

of these intelligent products capable of providing this new type of services in the context of

classic products and classic services. Thus, the author strives to give answers on the following

research questions:

Research Question 1:

What are the key characteristics of this new type of services that differ from existing

services?


What dimensions influence customers’ acceptance of these new services, and how

does the application context of these new services (person-related vs. object-related)

affect customers’ acceptance?


What characteristics of this new type of services determine its market success, com-

pared to its conventional counterparts?

2

1 Introduction

1.3 Structure of the Dissertation

The remainder of this thesis is structured as the following: Chapter 2 introduces the new type

of services which is the central aspect of this thesis. Thereby, the underlying products are dis-

cussed, followed by a review of the current service domain and the introduction of the new

service type. Based on that, chapter 3 comes up with a new service classification. To gain first

insights on customers’ acceptance of this new service types, chapter 4 draws on both streams

of literature, namely technology acceptance and service acceptance. Building on these find-

ings, a new acceptance model is proposed and empirically validated in chapter 5. To shed first

light on the business impact of this new type of services, chapter 6 employs an experimental

design. Finally, chapter 7 derives theoretical and managerial implications from the results pre-

sented in the previous chapters and provides a research agenda on this topic. Chapter 8 provides

a general conclusion of customers’ acceptance of this new type of services and its impact on

the established products and service businesses. Figure 1.1 provides an overview of all chapters.

3

1 Introduction

1. Introduction Problem Setting and Research Questions

2. Technology-Derived Services Blurring the distinction between products and services

3. Advancing towards a New Service Classification Service-Provision (Assisted or Derived) and Service-Relatedness (Human or Object)

4. Literature Review on Technology and Service Acceptance Models for Technology Acceptance and Evaluation of Services

7. Overall Discussion Theoretical and Managerial Implications, Limitations, and Future Research Agenda

5. Assessing the Acceptance of Technology-Derived Services

6. Business Impact of Technology-Derived Services

Study 1 (EFA & CFA)

New Construct

Validation

Study 2 (SEM)

Validation of the

Acceptance Model for TDS

Discussion Discussion

Study 3 (Experiment)

Impact of TDS on classic product and

service businesses

8. Conclusion

What dimensions influence customers’ acceptance of these new services, and how does the application context of these new services affect customers’ acceptance?

What characteristics of this new type of services determine its market success, compared to its conventional counterparts?

Figure 1.1: Structure of the thesis

4

2 Technology-Derived Services: Blurring the distinction between products and services

2

Technology-Derived Services:Blurring the distinction betweenproducts and services

Mercedes Benz’s Mercedes me, BMW’s Connected Drive, or Audi’s Audi connect are just

some recent examples, where manufacturing companies extend their product range by addi-

tional services. This phenomenon is known as servitization (Vandermerwe & Rada, 1988) and

describes that classic industrial companies provide services in addition to their product range.

At the same time, service industries try to increase their service productivity by the application

of high-tech instead of employees (R. Rust & Huang, 2012). To give some examples, think

of a self-check in kiosks at airports, online-banking applications, or apple pay. At the 2015’s

international consumer electronics show, members of the board of management of Daimler AG

were autonomously chauffeured to the event by Mercedes’s concept car F015. To that point, the

following questions arise: Is this intelligent product a mean for some taxi companies to increase

their productivity, because the self-driving F015 does not require a human chauffeur, or is this

intelligent product meant as an additional service to the basic car? In short: This intelligent

product seems to vanish the apparent distinction between services and products.

2.1 Products

Typically, when consumers talk about products, the correct term is goods, manufactured goods,

or physical goods (e.g. R. T. Rust & Huang, 2014). As the mean of products is to fulfill

customers’ needs, products in general comprise goods and services (Kotler, Keller, & Blieml,

2007). However, products in the marketing context are exclusively associated with physical ob-

jects, which can be purchased (Homburg & Krohmer, 2006). This terminology is also echoed

5


in the literature on “new product development” (e.g., Olson, Walker, & Ruekert, 1995) or on

“product innovation” (e.g. De Luca & Atuahene-Gima, 2007). Moreover, products are typically

described by attributes like design (e.g. Bloch, 2011; Landwehr, Wentzel, & Herrmann, 2013;

Talke, Salomo, Wieringa, & Lutz, 2009), quality (e.g. Golder, Mitra, & Moorman, 2012),

and innovativeness, for example in terms of product originality (e.g. Moldovan, Goldenberg, &

Chattopadhyay, 2011), product usefulness (e.g. Dahl, Chattopadhyay, & Gorn, 1999; Henard

& Szymanski, 2001), or product superiority (e.g. S. Rijsdijk, Langerak, & Hultkin, 2011).

Especially in the product innovation context, research is about High-Technology (High-Tech)

products, which are in general distinct from other durable goods in terms of production (Mel-

drum, 1995), the requirement of science and technology (Rubera & Kirca, 2012), and most

importantly their impact on consumer choice decisions (Erden, Keane, Oncu, & Strebel, 2005).

2.1.1 Product Types

Following Han, Chung, and Sohn (2009), with the accelerated ascent of technological innova-

tions, two basic types of products are fostered: convergent and dedicated products. While con-

vergent products are primarily concerned with customers’ convenience by providing as many

functions as possible with one product, dedicated products address consumers’ interest in pure

performance by focusing on one core feature (Han et al., 2009). To illustrate this differentia-

tion, one may think of Apple’s MacBook, which covers a wide range of functions like making

video-calls, watching movies, playing videogames, or conducting research. In contrary, Miele’s

vacuum cleaner is solely designed to maximize its cleaning-power, and thus optimized for its

core function.

Having a closer look on convergent products, Gill (2008) points out that the basic mean of

a convergent product is either utilitarian or hedonic (Voss, Spangenberg, & Grohmann, 2003).

Furthermore, it turned out that adding hedonic, incongruent features to an utilitarian base-

product results in higher customer satisfaction compared to utilitarian add-on features. But

when the base-product is hedonic in nature, adding congruent, i.e. hedonic features results in

higher customer satisfaction in comparison to utilitarian add-on features (Gill, 2008). In line

with this stream of literature, Bertini, Ofek, and Ariely (2009) found that alignable add-on fea-

tures, notwithstanding whether these add-on features are hedonic or utilitarian in nature, lead

to decreased value perception of the base product. In contrast, non-alignable add-on features

increase the perceived value of the base product. These effects were moderated by the amount

of relevant information on the product at question and customers’ product expectation Bertini

et al. (2009). That is, product information on the one hand diminishes the aforementioned main

effects. On the other hand, alignable attributes which meet customers expectation significantly

6


decrease their product evaluation, whereas unexpected non-alignable attributes significantly in-

crease customers’ product evaluation (Bertini et al., 2009).

However, there seems to be a threshold for convergent products with respect to a reason-

able number of functional means. As Thompson, Hamilton, and Rust (2005) argue, consumers

are more attracted by a highly functional product prior to its first use, while consumers’ con-

cern during and after initial usage of the product shifts towards ease of use, which is lower by

nature for high-complex compared to simple convergent products. Correspondingly, customer

satisfaction is highest for products with a moderate number of capabilities (Thompson et al.,

2005). Nevertheless, several research investigations confirmed that consumers still have great

difficulties in properly estimating their real usage behavior before the purchase and therefore

prefer high-capable convergent products (e.g. Thompson et al., 2005). An easy remedy for this

paradox was suggested by Goodman and Irmark (2013), namely usage estimation prior to the

product purchase. Throughout five studies the authors showed that when participants had to

indicate their usage of each provided feature within the next week, they changed their purchase

preference to moderate-complex products, stated higher satisfaction as well as recommendation

intention (Goodman & Irmark, 2013). Although, a different approach by Thompson and Nor-

ton (2011) suggests that customers choosing high-complex products also gain social utility as

they are perceived to be more innovative. In line with Thompson et al. (2005) their findings

reversed when customers’ anticipated usage was asked for.

To shed light on the antecedents of these high-capable products, Lukas, Whitwell, and Heide

(2013) investigated the impact of organizational culture on the so-called “product capability

overprovision” (Lukas et al., 2013, p.1) in a Business-to-Business (B2B) context. Drawing on

the idea of the Competing Values Framework (Quinn & Rohrbaugh, 1983), the authors found

that two of the four originally proposed values, namely adhocracy, clan, hierarchy, and market

(Deshpande, Farley, & Webster Jr., 1993), contribute to the above mentioned discrepancy of

required and provided product capabilities. In particular, adhocracy and market cultures turned

out to have significant impact on firms’ overshooting of customer demands.

Besides this company based differentiation of product types, current literature also focuses

on customer’s perspective of product types. In specific, the technological identity of an object

arises from the assignment of agentive functions, which “are imposed on entities in pursuit of

the practical interests of” (Faulkner, 2009, p.443) customers. Accordingly, two distinct types of

assignments are present in our daily lives: different functional assignments to the same techni-

cal object and so-called nested assignments. Giving an example for the former type, one could

think of using a pan for cooking or as a drum. Nested assignments are characterized by starting

in a broad product category, which, in turn, is narrowed down by successively specializing the

7


technological object (Faulkner, 2009). To give an example, one could think of garden tools

as the product category, lawn mowers as a sub-category and finally Husqvarna’s Automower

as a special product within the sub-category. Further, Choi and Fishbach (2011) found that

customers often differentiate for themselves, whether a choice is instrumental or experiential

in nature. Thereby, the differentiation between these two choice types is that for the latter one

the customer has no specific goal in mind, whereas instrumental choices are due to meet prior

known requirements for a specific task (Choi & Fishbach, 2011).

2.1.2 Intelligent, autonomous operating products: Services included

Product innovations occur in three different stages of extant, namely incremental innovations,

really new products, or radical innovations (Garcia & Calantone, 2002). While incremental

innovations are described by targeting existing customers with enhanced product capabilities

based on established technologies, really new products either aim at new customer segments, or

are built on new technologies. In the case of radical innovations, a company tries to attract new

customers with the introduction of new technologies (Garcia & Calantone, 2002). Regardless

of the specific type of an innovation, successful firms have to focus on innovations, “the pro-

cess of bringing new products and services to market” (Hauser, Tellis, & Griffin, 2006, p.687).

That is, because firms’ long-term value is largely dependent on new product introductions, in

specific customers’ expeditious acceptance of newly launched products (Pauwels, Silva-Risso,

& Hanssens, 2004). Hauser et al. (2006) identified several fields of innovation management,

namely customer response to innovation, organizations and innovation, strategic market entry,

prescriptions for product development, and outcomes from innovation. Accordingly, the central

focus of this dissertation is on customer response to innovations.

Being more precise, the focus of this thesis is on autonomous operating products. These

products represent the top range of so-called intelligent products (e.g. S. Rijsdijk et al., 2007).

Given their ability of collecting, processing, and producing information, these products are able

to operate autonomously, and independently (S. A. Rijsdijk & Hultink, 2009). Corresponding

to these capabilities, prior literature suggests several dimensions for a products’ intelligence,

which are autonomy, adaptability, reactivity, multi-functionality, ability to cooperate, humanlike

interaction, and personality (S. A. Rijsdijk & Hultink, 2009). Accordingly, depending on the

implemented IT-based capabilities, intelligent products either provide assistance to customers

or “overtake daily activities and concerns from their user” fully autonomously (S. Rijsdijk et al.,

2007, p.342). Especially the latter ones are characterized by being capable of delivering value

8


autonomously and directly to the customer. The author refers to this specific type of services as

Technology-Derived Services (TDS). For the sake of simplicity, the author uses the term TDS

as a abbreviation for intelligent products capable of providing TDS mostly in the remainder in

the thesis.

As TDS are able to provide direct value to their owners, they perfectly match the latest

definition of services (R. T. Rust & Huang, 2014), notwithstanding they are physical goods.

Therefore, the service strategy of service transformation (Huang & Rust, 2013), which states

that services become more and more product-like as a consequence of the wide IT application to

enhance service-productivity, is just one side of a coin. The other side of the coin are intelligent,

autonomously operating products, heavily equipped with latest IT and therefore capable of

directly deliver value to consumers. Nevertheless, both the product evolution and the service

evolution directly contribute to the fact “that [IT] eventually blurs the distinction between goods

and services” (Huang & Rust, 2013, p.257).

The emergence of these TDS also aligns with recent work of Cusumano, Kahl, and Suarez

(2015) on product-related service strategies, which basically differentiates between smoothing,

adapting, and substituting services. In specific, TDS suit the classification of adapting services,

which are characterized as “significantly expand the functionality of a product or help the cus-

tomer develop significant new uses or adapt the product to novel conditions” (Cusumano et al.,

2015, p.563).

2.2 Services

“Service is any direct provision or co-creation of value between a provider and a customer”

(R. T. Rust & Huang, 2014, p.2). Thereby, most services have personal intense, jointly pro-

duction of value by user and provider, and intangibility in common (Anderson, Fornell, & Rust,

1997). However, while the latter two aspects still hold, the typical personal intense, which

allows for customized services at the expense of delivering constant quality (Anderson et al.,

1997), has changed over the last decade. As service businesses strive for higher productiv-

ity, technologies in the form of automated systems are nowadays often used instead of labor-

based services (R. Rust & Huang, 2012). Prominent examples of these automated systems

are telephone customer services, or self-services, like self-check-ins on airports or Automated

Teller Machine (ATM)s (e.g. Meuter, Bitner, Ostrom, & Brown, 2005). Additionally, the in-

creased use of automated-systems also results in lowered heterogeneity of the service provision

that “some services can be very homogeneous.” (Correa, Ellram, Scavarda, & Cooper, 2007,

p.450). Hence, the mutual dependence of services and High-Tech products increases, empha-

9


sizing Greenfield’s (2002) notion that “no services can be produced without a prior investment

in capital goods” (Greenfield, 2002, p.20).

2.2.1 Service transition: The Next Level

With the beginning of servitization, i.e. companies from both services and goods industries

start providing bundles of products and services (Vandermerwe & Rada, 1988), the idea came

up that “services dominate this era” (Vandermerwe & Rada, 1988, p.316). A broad stream

of literature evolved on either service transitions, where services are provided in addition to

an existing product range, (Fang, Palmatier, & Steenkamp, 2008; Lusch, Vargo, & O’Brien,

2007; Ulaga & Reinartz, 2011) or pricing of these hybrid offerings(Guiltinan, 1987; Hanson

& Martin, 1990). Having in mind the aforementioned research of Gill (2008) and Bertini et

al. (2009), investigating the effects of product features and add-on features on products’ market

success, servitization appears to be an analogous phenomenon, except that services and no

physical goods are added to a base product.

Going even further, Vargo and Lusch (2004) came up with the paradigm of a Service-

Dominant Logic (SDL), which underlies the rational of “value in use” (Vargo & Lusch, 2004,

p.7). Hence, value is always a co-creation attained by customers, (Lusch & Vargo, 2006) using

any provided operand resource, for example a laptop or a simple knife (e.g. Barrutia & Gilsanz,

2012).

However, drawing on the idea of value in use, there seems to be a new type of products

where the consistent understanding of the aforementioned value creation has some shortcom-

ings. Specifically, TDS (see chapter 2.1.2) are capable of providing value without any necessary

interaction of the customer. Following the fundamentals of servitization and service transition,

one could conclude that this new type of products has the so-called additional services incor-

porated and consequently the capability to provide total solutions fully autonomously. Thus,

solely the product creates value for the customer independently from any external factors.

Moreover, thinking about prominent drawbacks of services, namely inseparability, intangi-

bility, perishability (e.g. Correa et al., 2007; Gadrey, 2000), with the ascent of TDS services

become tangible and storable, as the autonomous operating product is a physical object. With

respect to the inseparability of services, a more complex approach is necessary in the context of

TDS. The basic meaning of inseparability states that production and consumption of services

take place at the same time (Berry, Seiders, & Grewal, 2002). This fact is also implied in the

SDL, where “the consumer is always involved in the production of value” (Vargo & Lusch,

2004, p.11) and therefore is seen as a co-producer (Prahalad & Ramaswamy, 2000).

However, this strict definition of service inseparability is at question when following Keh and

10


Pang (2010), who argue that services can in fact be separated in either time or spatial distance or

in both dimensions. To give an example, one could think of a gardener taking care of your lawn

while you are at work. Further, this service separation has significant impacts on customers’

perception of service convenience and risk perception (Keh & Pang, 2010). Moreover, the eval-

uation of services often depends not merely on the output, but on the process itself, irrespective

whether the customer is involved in the process or is not (Gronroos, 1998). This understanding

of a multi-stage service evaluation is also echoed in more recent literature (Golder, Mitra, &

Moorman, 2012). In the light of TDS, services can be provided in both presence or absence

of the customer, even though the customer is never involved in the process. To conclude, servi-

tization has changed in such way that some firms nowadays not only provide product-related

additional services, like it is the case for BMW’s Connected Drive. With the accelerated advent

of TDS today’s manufacturing companies also provide product-innate services, e.g. intelligent,

autonomous lawn mowers.

Adapting on the distinction of relieving and enabling services processes (Normann, 2001),

TDS are able to relief their customers from certain tasks, for example trimming the lawn or

driving the car. This is also the case for classic services, where a gardener takes care of trim-

ming the lawn or a chauffeur drives her or his passengers to a given destination. Therefore,

TDS enable their users to focus on other tasks, while executing the primary task, trimming the

lawn or navigating the car (Lusch, Vargo, & Tanniru, 2010). In contrast, add-on services are

mostly concerned to increase customers’ task performance (Lusch et al., 2010). To give an

example, one could think of BMW’s Connected Drive feature Real Time Traffic Information,

which allows its users to avoid traffic jams and therefore exaggerate users’ trip efficiency.

2.2.2 Technology-Derived Services: A New Type of Service Innovationand Definition

Service Innovation TDS

To that extant one could question, whether these new TDS meet the requirements of a ser-

vice innovation. In general, a service innovation is “a new or enhanced intangible offering

that involves the firm’s performance of a task/ activity intended to benefit customers” (Dotzel,

Shankar, & Berry, 2013, p.259). Examples of recent literature on service innovations are dif-

ferentiated in internet-enabled versus people-enabled service innovations (Dotzel et al., 2013).

In contrast, TDS are enabled by intelligent products, built to autonomously perform tasks which

generate value for consumers. Thus, the author first concludes that TDS fulfill the prerequisites

of service innovations, although based on intelligent products. Second, he expands the present

11


differentiation of service innovations by adding TDS.

Definition of TDS

Summing up the previously outlined new and distinct characteristics of TDS, one can conclude

that TDS are intelligent products with an innate IT-based capability to autonomously operate

and therefore providing value directly to the customer without any necessary interaction during

the value creation process of its user or its manufacturer. Furthermore, as TDS are tangible

products, transfer of ownership can take place in contrary to classic services.

12

3 Advancing towards a New Service Classification

3Advancing towards a New Service Classifi-cation

A broad stream of literature has addressed various aspects of services within the last decades

(e.g. Berry et al., 2002; Hui, Thakor, & Gill, 1998; Leclerc, Schmitt, & Dube, 1995; Parasur-

aman, Zeithaml, & Berry, 1988; Wentzel, Tomczak, & Henkel, 2014). Especially technology-

assisted services like Self-Service Technologies (SST) or remote-services have raised the atten-

tion of academics since the last decade (e.g. Buttgen, Schumann, & Ates, 2012; Meuter et al.,

2005; R. Rust & Huang, 2012; R. T. Rust & Huang, 2014; Wunderlich, von Wangenheim, &

Bitner, 2012). However, the considerable body of literature on services has not taken TDS and

their unique characteristics for customers into account. Therefore, this thesis provides a new

service classification based on service-provision and service-relatedness.

3.1 Service-Provision: Assisted or Derived

In the light of remote-services, Paluch and Blut (2013) clearly point out that literature already

addressed the issue of new service provisions (e.g. Moeller, 2008). Nevertheless, research has

not considered customers’ perceptions of the latest service provision, TDS, yet. Consequently,

the currently used classifications fall short in capturing TDS correctly, as they lack of either an

appropriate technological dimension or a technological dimension at all.

Moreover, TDS expand the proposed five fundamental forms of customers’ interaction with

technologies during the service process, presented by Froehle and Roth (2004). Intelligent

products capable of providing TDS operate autonomously, hence they do not require any inter-

action of the customer or the provider during the service process. Consequently, TDS represent

a sixth form of interaction in the field of technology infused services: NO customer or provider

interaction. Figure 3.1 illustrates the expanded form of technology infused service provision

(Froehle & Roth, 2004).

13


Technology

Service Customer

Service Rep

Technology

Service Customer

Service Rep

Technology

Service Customer

Service Rep

Modes of “Face-to-Face“

Customer Contact

A. Technology-Free Customer Contact

B. Technology-Assisted Customer Contact

C. Technology-Facilitated Customer Contact

Technology

Service Customer

Service Rep

Technology

Service Customer

Service Rep

D. Technology-Mediated Customer Contact

E. Technology-Generated Customer Contact

(Self-Service)

Modes of “Face-to-Screen“

Customer Contact

Mode of “NO“

Customer Contact

Technology

Service Customer

Service Rep

F. Technology-Derived Services No Customer Contact

Figure 3.1: Expansion of technology’s role in customer interaction (Own representation based on

Froehle & Roth, 2004)

14


The most mature and most extensively investigated form of service provision are technology-

assisted services. Thereby, technology-assisted services range from relying hardly on technical

equipment up to the extant that services heavily rely on technologies (e.g. Bolton & Saxena-

Iyver, 2009; R. Rust & Huang, 2012). In addition, so-called SST have attracted researchers’

attention during the last decade (eg. Collier & Kimes, 2012; Meuter et al., 2005; Meuter,

Ostrom, Roundtree, & Bitner, 2000). Indicative for SST is that the customer her-/ or himself

is part of the service process and therefore is coincident with the value in use paradigm. As

a consequence, Wunderlich et al. (2012), who are also investigating remote-services, derived

a differentiation of services by focusing on the activities of a user and a provider during the

service-process. Moreover, as technological improvements accelerate these days, particularly

in the field of IT, it seems reasonable to differentiate services on the degree to which they are

enabled by technology or on “the extent to which technology is utilized in the creation and

delivery of the service” (Bolton & Saxena-Iyver, 2009, p.91). That is, service classification

can also be attained based on the technological sophistication and the activity of both user and

provider within the service process. However, as already outlined above, the author argues

that the technological differentiation should be refined to cover the full range of nowadays

services. In specific, with the advent of TDS, theory does not provide a clear categorization

for this new type of services, despite their major market impact in the future (Harris et al.,

2011). Accordingly, the author suggests a new dimension, namely service provision, which

discriminates services as the following: Technology-Assisted Services and Technology-Derived

Services. Thereby, the technology-assisted services still require human interactions, provided

by either the customer her-/ or himself or the service providing company. As an example,

one could think of getting a haircut or using an ATM. In contrast, TDS operate completely

autonomous, except for initializing or very rarely applied maintenance, like it is the case for

Husqvarna’s Automower.

3.2 Service-Relatedness: Human or Object

Besides this refinement of the technological dimension of service types there is yet another

dimension, which may be crucial for customer’s service acceptance. Drawing on Lovelock

and Gummerson (2004), services can be differentiated whether physical acts are exerted on

customer’s bodies or on owned objects. For illustration of that distinction, one could of cleaning

windows of a skyscraper or mowing the lawn of a customer versus getting a fitness check-up, a

taxi-ride or a surgery. Although Keh and Pang (2010) refer to this service distinction, no deeper

investigation on customers’ acceptance of different types of service provisions for object-related

15


versus person-related services was carried out. Consequently, the author address this gap by

specifying this differentiation of service types as service relatedness. That is, if services are

exercised on customers’ bodies it is referred to as person-related services. In contrast, if services

are exerted on physical objects, these are object-related services. In combination with the above

described refinement, the author provides a new service-classification based on the dimensions

service provision and service relatedness, see figure 3.2.

New$Service$Classifica/on$

techno

logy*assisted/

services/

techno

logy*derived/

services/

person*related/object*related/

Assistance)

Experience)

service'relatedness''

service'provision'

…focus/of/the/disserta8on/

Figure 3.2: New Service-Classification

16

4 Literature Review on Technology and Service Acceptance

4Literature Review onTechnology and Service Acceptance

As the previous chapters show, TDS, for instance self-driving cars, can be classified either as

products or as services. To account for both types of offerings, the author provides a literature

review on technology acceptance and service acceptance. In doing so, the author identifies

possible deficiencies of the existing acceptance literature with regard to customers’ acceptance

of intelligent products capable of providing TDS.

4.1 Technology Acceptance

This section gives an overview of the latest theoretical insights regarding the acceptance of

innovations from a customer’s point of view. In accordance, the most reasonable and established

conceptual models from both services and products marketing are briefly introduced, drawbacks

in the light of TDS are outlined, and suitable predictors for the adoption of TDS are identified.

4.1.1 Models for Technology Acceptance

Two of the most recent models, which aim at giving a holistic understanding of customers’

adoption of technology are the Technology Acceptance Model (TAM) 3 of Venkatesh and Bala

(2008) as well as the Unified Theory of Acceptance and Use of Technology (UTAUT) 2 pro-

posed by Venkatesh, Thong, and Xu (2012).

TAM The idea of TAM 3 is to better understand IT adoption in an organizational context

(Venkatesh & Bala, 2008). The TAM 3 is the latest version of the fundamental TAM, which was

officially introduced by F. Davis (1989). The TAM is a specific form of the more generic Theory

of Reasoned Action (TRA) (Fishbein & Ajzen, 1975) and aimed at predicting customers’ use

17


of IT devices (F. Davis, Bagozzi, & Warshaw, 1989). The core of the model is still the same for

the TAM 3, namely the effect of perceived ease of use and perceived usefulness on behavioral

intention (Venkatesh & Bala, 2008). Thereby, the construct perceived ease of use tries to

measure “the degree to which a person believes that using a particular system would be free of

effort” (F. Davis, 1989, p.320), whereas the construct perceived usefulness aims at measuring

“the degree to which a person believes that using a particular system would enhance his or her

job performance” (F. Davis, 1989, p.320).

Additionally, Venkatesh (2000) incorporated Behavioral Decision Theory into the TAM by

including the anchor and adjustment heuristic. In specific, the anchors for perceived ease of use

were computer self-efficacy, perception of external control, computer anxiety, and computer

playfulness (Venkatesh, 2000). The constructs associated with adjustments were perceived

enjoyment as well as objective usability (Venkatesh, 2000). Therefore, this extended model

was able to account for the effects of customers’ individual differences regarding general beliefs

and experiences on perceived ease of use (Venkatesh, 2000).

As the TAM was introduced to better understand the adoption of IT in an organizational con-

text, the latest version.TAM 3, also includes profession related constructs, namely image, job

relevance, output quality, and result demonstrability (Venkatesh & Bala, 2008). Furthermore,

as already proposed in the TAM 2 (Venkatesh & Davis, 2000), the TAM3 also accounts for the

effects of subjective norm, i.e. a “person’s perception that most people who are important to

him think he should or should not perform the behavior in question” (Fishbein & Ajzen, 1975,

p.320). Figure 4.1 shows the proposed research model of TAM 3 with its core constructs and

their relations.

UTAUT Based on a comparative analysis of eight technology acceptance models, Venkatesh,

Morris, Davis, and Davis (2003) came up with the UTAUT. Thereby, they identified and com-

bined the most influential predictors for employees’ behavioral intention and use behavior in

one model, the UTAUT. In addition, Venkatesh et al. (2003) included four moderators in the

model, age, gender, experience, and voluntariness of use. In specific, the empirical evaluation

of the UTAUT gave evidence that both age and gender moderate the effects of performance

expectancy, effort expectancy, and social influence on behavioral intention, whereas only age

moderates the effect of facilitating conditions on use behavior. Further, consumers’ experience

was shown to moderate the effect of effort expectancy and social influence on the behavioral

intention as well as the effect of facilitating conditions on use behavior. Finally, the empiri-

cal validation of the UTAUT revealed that voluntariness of use moderates the effect of social

influence on behavioral intention (Venkatesh et al., 2003).

18


Experience Voluntariness

Subjective Norm

Image

Job Relevance

Output Quality

Result Demonstrability

Computer Self-efficacy

Computer Playfulness

Computer Anxiety

Perceptions of External Control

Objective Usability

Perceived Enjoyment

Anchor

Adjustment

Perceived Usefulness

Behavioral Intention Use Behavior

Perceived Ease of Use

Technology Acceptance Model (TAM)

Figure 4.1: Technology Acceptance Model 3 (Own representation based on Venkatesh & Bala, 2008)

To further refine the UTAUT especially for the “consumer use context” (Venkatesh et al.,

2012, p.158), Venkatesh et al. (2012) included three additional constructs in the model, namely

hedonic motivation, price value, and habit, while omitting the originally included moderator

voluntariness of use. The authors referred to this new model as UTAUT 2 (see Figure 4.2).

Prior literature defined the dimension hedonic in various contexts (e.g. Arnold & Reynolds,

2003; Gill, 2008; Yim, Chan, & Lam, 2012) as “resulting from sensations derived from the

experience of using products” (Voss et al., 2003, p. 310). Accordingly, Nysveen, Pedersen, and

Thorbjønsen (2005) found that perceived enjoyment, one facet of hedonic values (Yim et al.,

2012), has greater impact on experiential behavioral intentions than on goal-directed behavioral

intentions. As the UTAUT 2 aims at better understanding technology acceptance in consumers’

daily lives, Venkatesh et al. (2012) incorporated this construct into their original UTAUT model

to cover “the fun a consumer has in using the product” (Arnold & Reynolds, 2003, p.78).

As the UTAUT 2 shifts from an organizational context to a consumer context, price value

plays an important role, because the customer has to pay the price for a service or a product him-

or herself. Drawing on Zeithaml (1988), consumers perceive value as the “overall assessment

of the utility of a product based on perceptions of what is received and what is given” (Zeithaml,

1988, p.14). Recent literature also shows that the two distinct consumer perceptions of price,

19


Performance Expectancy 1

Effort Expectancy 2

Hedonic Motivation

Social Influence 3

Facilitating Conditions 4

Price Value

Habit

Behavioral Intention Use Behavior

Age Gender Experience

Notes: 1.  Moderated by age and gender 2.  Moderated by age, gender, and

experience. 3.  Moderated by age, gender, and

experience. 4.  Effect on use behavior is

moderated by age and experience. 5.  New relationships are shown as

darker lines.

Figure 4.2: Unified Theory of Acceptance and Use of Technology 2 (Own representation based on

Venkatesh, Thong, & Xu, 2012)

either as a quality indicator or as a sacrifice (Leavitt, 1954), depends on temporal construal

such that consumers’ focus is more on sacrifice when the purchase is imminent (Bornemann

& Homburg, 2011). Accordingly, the construct price value adds an important and realistic

determinant of consumers’ decision making to the UTAUT.

Finally, literature has shown that habitual activities, characterized as the predicted future

behavior based on previous behavior (Venkatesh, Morris, & Ackerman, 2000), significantly

influence consumers’ prospective use of technology (Kim & Malhotra, 2005).

4.1.2 Shortcomings in the light of Technology-Derived Services

To sum it up, literature on behavioral intention and usage intention of new technologies pro-

vides a thorough understanding of the relevant constructs for customers’ technology acceptance.

Nevertheless, most of researchers’ effort was dedicated to gain insights into IT acceptance in

an organizational context, except Venkatesh et al.’s (2012) adoption of consumer specific con-

structs within the UTAUT. However, with the presence of TDS, the author proposes that there

are distinct constructs, which are more suitable to explain customers’ acceptance of this new

services as the above described models emerged in the light of IT acceptance. This proposition

20


also aligns with Schepers and Wetzels (2007) finding that the predictive power of the constructs

included in their model is significantly dependent on the technology at question. This point will

be further developed in Chapter 5.1.

4.2 Service Acceptance

4.2.1 Evaluation of Services

Literature has focused on several constructs in the context of service evaluation. Prior work in

academia revealed that service quality, service value, and service satisfaction have significant

direct and indirect effects on behavioral intention (Barrutia & Gilsanz, 2012; Brady et al.,

2005; Cronin Jr., Brady, & Hult, 2000), which is strong indicator for Word of Mouth (WOM),

customer loyalty, cross-selling, and ultimately price-premium acceptance (Zeithaml, Berry, &

Parasuraman, 1996).

Service Quality

Especially service quality was and still is of major interest in academia (eg. Bolton & Drew,

1991; Brady & Cronin Jr., 2001; Golder et al., 2012; Parasuraman et al., 1988; Sivakumar,

Li, & Dong, 2014; Zeithaml et al., 1996). In general, service quality can either be described as

consumers’ reconciliation of expected service performance and actual performance (Bolton &

Drew, 1991) or understood as a higher-order formative construct based on the latent constructs

interaction quality, physical environment quality, and outcome quality, which are reflected in a

total of nine variables (Brady & Cronin Jr., 2001).

In addition, Sivakumar, Li, and Dong (2014) explored how the occurrence of service failure

effects customers’ perception of service quality depending on different temporal dimensions.

As nowadays competitive business environment is often associated with cost-cuttings and is

therefore more prone to service failures, the more important it is for service companies to con-

trol the occurrence of service failures and the corresponding exaggerated customer satisfaction

(Sivakumar et al., 2014).

Moreover, since the increasing use of technology for service provision to encounter the chal-

lenge of service productivity (R. Rust & Huang, 2012), literature has also addressed the ques-

tion how this exaggerating implementation of technology within the service process affects

consumers’ perception of service quality and therefore consumers’ behavioral intention (Dab-

holkar, 1996).

21


Service Value

Besides the service quality, customers also make judgments on the service value, i.e. their

consideration of gains and sacrifices arising from the service at question (Zeithaml, 1988).

Mathwick, Mahotra, and Rogdon (2001) referred to service value also as “consumer return on

invest” (Mathwick et al., 2001, p.41). Thereby, the investment is not limited to monetary as-

pects, as prior literature already states (eg. Zeithaml, 1988), but also encompasses temporal and

effort aspects (Berry et al., 2002). In addition, prior research also proposed a hierarchical order

of values, such that value is derived by goals and purposes, situational usage consequences, or

attributes and performance of a product (Woodruff, 1997).

Since the introduction of the SDL by Vargo and Lusch (2004), literature on service value also

investigated how to increase perceived value given that the customer is an operand resource

in the value creation process. Therefore, current research identified a key-driver of customer

value, namely self-efficacy (van Beuningen, de Ruyter, & Wetzels, 2011). In specific, it turned

out that exaggerated customer self-efficacy, caused by the service, positively contributes the

perceived service value. Furthermore, Barrutia and Gilsanz (2012) give empirical evidence,

that value perception is influenced in a positive way by both perceived service quality and

customer experience, whereas the corresponding interaction effect has a negative impact on

value perception. Having in mind that customer self-efficacy results out of gained experiences

(Gist & Mitchell, 1992), these findings confirmed the ones of van Beuningen et al. (2011) and

indicate that quality perception is even a stronger predictor for value perception.

Service Satisfaction

Following Woodruff (1997), customers’ service satisfaction contributes to “the feelings in re-

sponse to evaluations of one ore more use experiences” (Woodruff, 1997, p.143). Thereby,

evaluation means customers’ comparison of the actual outcome and their expected outcome of

an employed service (Dotzel et al., 2013). Further, Lam, Shankar, and Murthy (2004) point

out that satisfaction has either a specific mean for a single experience or an overall long-term

character.

Especially for understanding customers’ acceptance of TDS, literature already provides em-

pirical evidence that satisfaction with a technology-assisted service can be increased for service

processes which take less time and are more personalized (Collier & Kimes, 2012). Further,

whenever customers are part of the value creation in a service context, recent literature also

stresses the necessity of hedonic aspects during the service process as a means to exaggerate

service satisfaction (Yim et al., 2012).

22


Behavioral Intention

Although the aforementioned constructs were conceptualized, operationalized, and empirically

investigated in a service context, the main goal was the same as it was in a technology accep-

tance context: deepen the understanding of behavioral intentions of customers, namely WOM,

loyalty, cross-selling, and price-premium. This fact is also echoed in most recent literature,

which suggests a so-called value-satisfaction-behavioral intentions chain in the service context

(Barrutia & Gilsanz, 2012).

4.2.2 Drawbacks of Evaluation for Technology-Derived Services

As literature on service acceptance aims at identifying reasonable ways how to influence cus-

tomers’ behavioral and usage behavior, it has to take technology-assisted services and their

distinct characteristics more and more into account. Nevertheless, it still lacks to address the

emerging market of TDS. Being more precise, since TDS are not dependent on any customer

interaction, i.e. the consumer is no longer seen as an operand resource as it was the case in

the light of the SDL paradigm, research on customers’ contribution to service evaluation falls

short although technology specific constructs were already considered (e.g. Barrutia & Gilsanz,

2012; Buttgen et al., 2012; Collier & Kimes, 2012; Meuter et al., 2005; van Beuningen et

al., 2011).

In addition, the above mentioned constructs service quality, service value, and service sat-

isfaction arise in three distinct temporal stages, i.e. during the pre-purchase phase, the service

encounter, and the post-purchase phase (e.g. Bradley & Sparks, 2002). Since this paper strives

to shed light on customers’ acceptance of TDS, the author focuses on the pre-purchase phase.

During this phase, customers are typically concerned with their perception of risks associated

with the service and the corresponding service attributes, e.g. their perceived service quality.

While literature already provides several findings for various service attributes like social utility

(e.g. Nysveen et al., 2005) or convenience (e.g. Collier & Kimes, 2012), conceptual models on

behavioral intentions in the service context still lack to incorporate risk perception adequately,

although recent literature clearly points at this issue (Wunderlich et al., 2012).

23

5 Assessing the Acceptance of Technology-Derived Services

5Assessing the Acceptance ofTechnology-Derived Services

By recapping both the shortcomings of technology acceptance models and the drawbacks of

service acceptance research, it turned out that literature either misses to account for TDS spe-

cific characteristics or has neglected this new type of services at all. To address this gap, the

author provides a new acceptance model for TDS in the Business-to-Consumers (B2C) con-

text. Thereby, new TDS specific constructs are derived and their relations with respect to the

acceptance of TDS are hypothesized.

5.1 Conceptualizing the Acceptance Model for

Technology-Derived Services

Since literature has not addressed TDS, yet they are in the market for a while, it is no sur-

prise that acceptance models for technology and services still lack of appropriate predictors.

To resolve this gap, the following section derives new constructs, which are suitable for cap-

turing the new and distinct characteristics of TDS. In specific, the author proposes that an-ticipated temporal discharge, perceived controllability, and perceived service safety have

significant effects on customers’ acceptance of TDS. To assess customers’ acceptance of TDS

in the model, the author incorporates attitude towards using the TDS, as this is a reliable in-

dication of customers’ future intentions (e.g. Nysveen et al., 2005). Finally, the relations of the

aforementioned constructs within the acceptance model for TDS are hypothesized.

24


5.1.1 Attitude towards using the TDS

The model comprises consumers’ attitude towards making use of a TDS as an indicator of the

acceptance of TDS. This aligns with both research streams on technology and service accep-

tance (e.g. Curran, Meuter, & Surprenant, 2003; Dabholkar & Bagozzi, 2002; F. Davis, 1989;

Nysveen et al., 2005; Wunderlich et al., 2012).

Despite the broad body of literature on technology and service acceptance, there is still a

lack of accurate constructs to fully understand the acceptance of TDS. Therefore, the author

introduces three new predictors in the following sections.

5.1.2 Anticipated Temporal Discharge

Since the distinction of TDS is to provide direct value to customers without any additional

operand resource, the major advantage of these products is obvious: saving time. When tak-

ing a closer look on crucial constructs of previous acceptance models, namely performance

expectancy and perceived usefulness (e.g. Venkatesh & Bala, 2008; Venkatesh et al., 2012),

some of the empirically tested items are closely linked to time saving. In specific, participants

were asked to indicate whether the technology at hand increases their performance and pro-

ductivity (Venkatesh et al., 2012) or help them to accomplish a task more quickly (Venkatesh

& Bala, 2008). However, none of the related constructs clearly accounts for perceived time

saving.

This is in line with Kleijnen, de Ruyter, and Wetzels (2007) finding that time utilities directly

contribute to consumers’ value perception of services, as time is perishable and not storable for

later use (Okada & Hoch, 2004). Therefore, time is “it is the scarce resource” (Leclerc et al.,

1995, p.110). Moreover, as consumers strive to spend more and more time in the so-called

third space, that is time spent in leisure or retail environment (Hourahine & Howard, 2004),

value perception of time-saving products will increase in the future. Accordingly, services

allowing for polychronic use of time, i.e. carrying out different tasks at the same time (Cotte,

Ratneshwar, & Mick, 2004; Feldman & Hornik, 1981), will be favored by customers more

and more. Therefore, a new construct is proposed to properly address the unique time-saving

capability of TDS, namely anticipated temporal discharge.

In line with the aforementioned reasoning, the author proposes the following hypothesis:

H1: Anticipated temporal discharge of TDS has a positive effect on consumers’

attention towards using the TDS.

25


The author suggest the following items to represent the perceived temporal discharge con-

struct (see table 5.1):

Table 5.1: Anticipated Temporal Discharge

New Con-struct

Items Explanation

Anticipated

Temporal

Discharge

Using the system increases my overall produc-

tivity

The degree to which an

individual attributes time

savings to the use of TDS

Using TDS allows me to reallocate my time to

other activities

Using TDS saves me time

Using TDS let me focus on other tasks

I find TDS effective*(* indicates that the item is adopted from Venkatesh, Morris, Davis, and Davis (2003))

5.1.3 Perceived Service Safety

As prior literature demonstrates, services in general are perceived to be riskier than products

(Mitchell & Greatorex, 1993; Murray & Schlacter, 1990). Therefore, the author proposes

that perceived service safety, which is a positive manifestation of perceived physical risk, is a

relevant determinant of customers’ acceptance of TDS.

In general, consumers’ risk perception comprises different dimensions of risk, which con-

tribute to the overall risk perception. In specific, the overall risk perception is defined by social

risk, psychological risk, performance risk, financial risk, and physical risk, which was sug-

gested and empirically tested by Jacoby and Kaplan (1972). Further, Kaplan, Szybillo, and

Jacoby (1974) showed that the overall risk perception of customers is best predicted by their

indicated performance risk, whereas physical risk has the least predictive power for the overall

risk perception. Drawing on these findings, Brooker (1984) added time-loss as a new dimen-

sion of consumers’ risk perception to the originally proposed five dimensions. The results were

twofold as they revealed that the newly included dimension time-loss has the second highest

loading on consumers’ overall risk perception.They also confirmed that performance risk is the

strongest predictor for the overall risk perception. Rethinking the established constructs per-

formance expectancy (Venkatesh et al., 2003) and perceived usefulness (Venkatesh & Bala,

2008), it is worth noting that these two constructs reflect the aforementioned dimensions of

26


risk, i.e. performance risk and time-loss. Nevertheless, the authors termed the corresponding

items as gains. It is worth noting that Venkatesh et al. (2003) and Venkatesh and Bala (2008)

also use the aforementioned dimensions of risk, i.e. time-loss and performance risk, but frame

it positively as performance expectancy and perceived usefulness.

However, with regard to TDS it is at question, whether these findings still hold. In specific,

the author doubts that physical risk still has the lowest predictive power for the overall risk

perception, which was the case in prior research (Brooker, 1984). Especially in a person-related

context (e.g. an autonomous driving car) the author proposes that the physical dimension of

perceived risk gains significant attention. This is substantiated by prior literature, which states

that physical risk perception is significantly higher for services compared to products in any

case (Murray & Schlacter, 1990). To adequately capture this issue in the acceptance model for

TDS, the author suggests the new construct of perceived service safety, representing an reversed

equivalent to physical risk (cf. Venkatesh et al., 2003). Since a myriad of research shows that

risk perceptions have a significant effect on customers’ attitudinal and behavioral responses

(e.g. De Ruyter, Wetzels, & Kleijnen, 2001; Im, Kim, & Han, 2008; Meuter et al., 2005), the

author proposes for TDS applied in a person-related context that:

H2: When TDS are applied in a person-related context, perceived service safety has

a positive effect on consumers’ attitude towards using the TDS.

The following items are proposed to reflect customers’ perceived service safety (see table

5.2):

Table 5.2: Perceived Service Safety

New Con-struct

Items Explanation

Perceived

Service Safety

The TDS gives me the feeling of being safe The degree to which an

individual perceives the

service process to be safe

with respect to potential

physical damages

Using TDS increases my safety

I would recommend TDS for safety reasons

I have no safety concerns using the TDS

I am relaxed while making use of TDS(items are author’s own proposition)

27


5.1.4 Perceived Controllability

In general, customers’ control over any process and the respective outcomes can be distin-

guished into their perception of control and their motivation for control (Burger, 1984). Thereby,

the latter one is associated with customers’ desire of control over a process (e.g. Law, Logan, &

Baron, 1994; Wortman & Brehm, 1975), whereas perception of control is linked to Rotter’s

(1966) locus of control. In the light of the acceptance of TDS the author focuses on customers’

perception of control.

Drawing on the idea of internal and external control over outcomes (Rotter, 1966), Bradley

and Sparks (2002) conceptualized the so-called service locus of control, which is primarily

concerned with consumers’ expectation (Shapiro Jr., Schwartz, & Astin, 1996) of control over

a service. Thereby, locus of control focuses on events in the future rather than on retrospective

causalities (Bradley & Sparks, 2002). In accordance, recent literature clearly points out the

importance of customers’ perception of control over the service process in technology-assisted

service contexts (Wunderlich et al., 2012). More precise, it turns out that an interview-partner

stresses that “control plays a very important role.” (Wunderlich et al., 2012, p.11). In line

with that, Meuter, Ostrom, Bitner, and Roundtree (2003) give empirical evidence that perceived

control over the service process leads to increased service quality perceptions. These findings

were also echoed by Collier and Sherrell (2010).

Intelligent products capable of delivering TDS are designed to operate without any required

customer-interaction during the service process. That is, the customer is no longer an interactive

part in the value-creation process. However, this fact results in a decrease of customers’ belief

to be able to control the service process (e.g. Shapiro Jr. et al., 1996). As an example, one

might think of an automated lawn mower which operates even when its owner is not at home.

Especially when TDS can be provided spatially distant to their users, that is when TDS are

object-related, perceived controllability has a positive effect on customers’ attitude towards

using the TDS. Therefore, the author proposes the following hypothesis:

H3a: When TDS are applied in an object-related context, perceived controllability

has a direct positive effect on customers’ attitude towards using the TDS.

In case of person-related TDS, where no spatial separation is possible, the author follows an-

other reasoning. Prior research has shown that a lack of process transparency over an operating

service encounter in persona leads to exaggerated risk perceptions (Wunderlich et al., 2012).

Since in case of a TDS the service provider’s employee is totally replaced by an intelligent prod-

uct providing the TDS, customer’s perceived lack of process transparency, i.e. controllability

28


of the service process, might be increased. In line with that, the author hypothesizes that per-

ceived controllability has a positive effect on perceived service safety. Drawing on the previous

hypothesis H2, stating that in case of a person-related TDS perceived service safety has a direct

effect on customer’s attitude towards using the TDS, the author proposes a mediated effect of

perceived controllability on attitude towards using the TDS:

H3b: When TDS are applied in a person-related context, the positive effect of per-

ceived controllability on customers’ attitude towards using the TDS is mediated by

consumers’ perceived service safety.

The following items are proposed to represent the construct of controllability (see table 5.3):

Table 5.3: Perceived Controllability

AdaptedConstruct

Items Explanation

Perceived

Controllability

The TDS gives me the feeling of being in con-

trol*

The degree to which anindividual believes tocommand and exert powerover the process of a TDS(Collier & Sherrell, 2010)

The TDS allows the customer to be in charge*

While employing TDS, I feel decisive*

This TDS gives me more control over the ser-

vice process*(* indicates that the item is adapted from Collier and Sherrell (2010))

Based on the proposed predictors above and their hypothesized effects on consumers’ atti-

tude towards using a TDS, figure 5.1 presents the acceptance model of TDS.

29


Anticipated Tem

poral D

ischarge

Perceived C

ontrollability

Perceived Service Safety

Attitude tow

ards using the TD

S

Application C

ontext: Person-related / O

bject-related

bold: new predictors

dotted: moderator

H1 :$+$$

H2 :$+$$

H3a :$+$$

H3b :$+$$

(+)$

(,)$

Figure5.1:A

cceptanceM

odelforTechnology-Derived

Services

30


5.2 Empirical Evaluation of the

Acceptance Model for Technology-Derived Services

To test the hypothesized acceptance model for TDS, which also includes the validation of the

newly proposed and adapted constructs, two surveys were carried out. Study 1 covers the

empirical evaluation of the new constructs, which comprises several types of a Factor Analysis

(FA) on the proposed items. Study 2 is employed to assess the empirical validation of the

proposed acceptance model for TDS. As the model contains a mediation path, the applied SEM

assures for highest estimation performance compared to regression based mediation analyses

(e.g. Iacobucci, Saldanha, & Deng, 2007; Zhao, Lynch Jr., & Chen, 2010). All analyses were

run in the lavaan! (lavaan!) package (version 0.5-16) for R (version 3.1.1).

5.2.1 Study 1: New Construct Validation

The purpose of study 1 is to gain confidence in the empirical validity of the newly proposed

constructs presented in the light of the acceptance model for TDS. Therefore, a Confirmatory

Factor Analysis (CFA) was carried out, which is the most appropriate empirical analysis for

testing a hypothesized item-factor structure (e.g. Russell, 2002). As prior academic work

demonstrates, engaging an Exploratory Factor Analysis (EFA) prior to the CFA is a reasonable

way to identify improper items of new or adapted constructs (Sethi & Iqbal, 2008).

5.2.1.1 Data Collection and Measurement

Data Collection

Participants for study 1 were required via Amazon Mechanical Turk (mturk). As prior litera-

ture already stated, mturk is a reasonable source for collecting data in a scientific environment

(e.g. Berinsky, Huber, & Lenz, 2012; Buhrmester, Kwang, & Gosling, 2011), which is also

echoed in its wide usage in recent academic work (e.g. D. Davis & Herr, 2014; Giebelhausen,

Robinson, Siriani, & Brady, 2014; Goodman & Irmark, 2013; May & Monga, 2014).

A total of 210 participants (108 men) completed the survey with an average age of 34 years

(agemin=19 years; agemax=72 years). The educational level of the sample is as following: 55,8%

of the participants have earned a bachelor’s degree or higher, 26,2% have graduated form col-

lege and 18% were in comprehensive school or have no graduation.

Measurement

After a short welcome and introduction phase, participants were randomly assigned to one

31


of three innovations, which were subsequently evaluated in the remainder of the study. The

three innovations were an autonomous vacuum cleaner, an app-controlled remote-parking of a

vehicle, and an autonomous driving car. To ensure that participants properly understood the

presented innovations, each innovation was provided with a short video-clip showing its func-

tionality in addition to a picture of the innovation and a short description containing key facts

of the innovation. Since the study aims to provide empirical validation of the new constructs

employed in the acceptance model for TDS rather than carrying out an experiment, possible

confounding effects caused by the video-clips were negligible.

Participants’ evaluation of the innovations were measured by using the newly proposed con-

structs, which were introduced in section 5.1, namely anticipated temporal discharge, perceived

controllability, and perceived service safety. All items were measured on a seven-point Likert

scale. The respective anchors were ”fully disagree” and ”fully agree”.

32


5.2.1.2 Results

Prior to the CFA, an EFA is applied to identify possible improper items, which were proposed

for the new and adapted constructs. Thereby, both types of EFA principal axis analysis and

principal component analysis are used.

EFA

Principal component analysis derives the factor extraction by analyzing a correlation matrix,

as the communalities for all items are constrained to be 1.0, i.e. it is deemed that all variance

is captured by the extracted factors. In contrast, the principal axis analysis extracts the factors

based on a covariance-matrix, since the communalities are not constrained. Thus, the starting

values for the communalities are estimated by using the squared multiple correlation between

one item and the other items in the measurement-set. Therefore, the factor loadings are typically

lower for principal axis analysis than for principal component analysis by definition, because

the communalities are higher for the latter ones (Russell, 2002).

The appropriateness of the data for conducting a FA was also verified. In specific, the

Kaiser-Meyer-Olkin (KMO) value (0.89) indicates a very good distinction and reliability of

the extracted factors (Field, Miles, & Field, 2012).

Results from both the principal component analysis, and the principal axis analysis confirm

the proposed item-factor structure, whereas the factor-loadings of some items where not satis-

fying (see table 5.4 for details). The proposed number of factors represented by the new and

adapted items was also verified by applying a Screeplot analysis, see figure 5.2. Besides the vi-

sual representation, figure 5.2 contains graphical illustrations of additional numerical analyses,

which all suggest an optimal number of three factors.

Based on the presented results of the FA, the following items are dismissed for the remain-

ing analyses and will not be used in further studies: PSS4, PSS5, and PC3. Although the factor

loading for ATD5 is above the threshold of .7 in principal component analysis, the more de-

manding principal axis analysis rejects item ATD5. Thus, item ATD5 will also be removed for

further analyses. Table 5.5 summarizes the final items used in the remainder of this thesis.

Finally, comparing the values of the correlation matrices’ determinants of the initial item set

(4.61 ∗ 10−7) with the final item set (2.15 ∗ 10−5) reveals that the latter one also satisfies the

advised threshold (> 1.0 ∗ 10−5) for excluding multicollinearity (Joireman, Shaffer, Balliet, &

Strathman, 2012).

CFA

The final item set was further analyzed by applying a CFA. First, to confirm the unidimen-

33


Table 5.4: Rotated Factor Loadings

PCA PAA

Items ATD PC PSS ATD PC PSS

ATD1 0.84 0.29 0.25 0.82 0.29 0.26

ATD2 0.90 0.24 0.18 0.90 0.23 0.19

ATD3 0.84 0.27 0.18 0.81 0.28 0.20

ATD4 0.91 0.20 0.16 0.90 0.19 0.17

ATD5 0.72 0.21 0.39 0.67 0.24 0.37

PSS1 0.30 0.29 0.83 0.31 0.30 0.80

PSS2 0.23 0.23 0.88 0.24 0.25 0.83

PSS3 0.21 0.27 0.88 0.22 0.28 0.85

PSS4 0.43 0.57 0.11 0.41 0.47 0.19

PSS5 0.62 0.41 0.42 0.59 0.4 0.42

PC1 0.19 0.89 0.24 0.21 0.88 0.24

PC2 0.24 0.86 0.25 0.26 0.84 0.25

PC3 0.33 0.66 0.41 0.35 0.62 0.39

PC4 0.24 0.85 0.24 0.26 0.82 0.25

Note. N=210; Factor loadings for both analyses after factor-rotation (varimax)

sionality of each construct demonstrated in the EFA, three single-factor models were analyzed.

The respective fit statistics are listed in table 5.6 and verify the unidimensionality of the new

constructs.

Finally, standardized parameter estimates, average variance extracted (AVE), composite re-

liability, and discriminant validity were also analyzed by using a CFA containing all ten items.

Table 5.7 shows the standardized loadings for the items, the composite reliability and the AVE

for the constructs. All factor loadings are significant and range from 0.859 (lowest) to 0.964

(highest). Since each construct’s CR value is > 0.7, construct reliability is given (cf. Bagozzi

& Yi, 1988). The recommended threshold value of > 0.5 for the AVE by Fornell and Larcker

(1981) was also exceeded by all factors. Hence, the analysis confirmed the proposed convergent

validity for all three constructs.

34


2 4 6 8 10

01

23

45

6

Scree Plot

Components

Eigenvalues

Eigenvalues (>mean = 3 )Parallel Analysis (n = 3 )Optimal Coordinates (n = 3 )Acceleration Factor (n = 1 )

(OC)

(AF)

Figure 5.2: Scree plot for the initial item set

Table 5.5: Final item set

Items

ATD1 Using the system increases my overall productivity

ATD2 Using the system allows me to reallocate my time to other activities

ATD3 Using the system saves me time

ATD4 Using the system let me focus on other tasks

PSS1 The TDS gives me the feeling of being safe

PSS2 Using TDS increases my safety

PSS3 I would recommend TDS for safety reasons

PC1 The TDS gives me the feeling of being in control

PC2 The TDS allows the customer to be in charge

PC4 This TDS gives me more control over the service process

Discriminant validity was assessed by the most restrictive test, i.e. a construct’s AVE has

to be always higher than its squared correlation with the other factors in the respective setting

(Barrutia & Gilsanz, 2012). Results in table 5.8 back up discriminant validity for all three new

35


Table 5.6: Unidimensionality for all scales

# Items χ2 d.f. p RMSEA GFI NNFI CFI

Anticipated temporal dis-

charge

4 0.09 2 0.98 0.00 1.00 1.00 1.00

Perceived service safety 3 0.05 1 0.83 0.00 1.00 1.00 1.00

Perceived controllability 3 2.97 1 0.08 0.07 0.99 0.98 0.99Note. RMSEA = root mean square error of approximation; GFI = Goodness of Fit Index; NNFI =

non-normed fit index; CFI = comparative fit index; As a model with a single-factor and three indicators

is just identified, i.e. model’s df =0, the loadings of the latter two indicators of the affected constructs are

constrained to be equal (cf. S. Rijsdijk et al., 2007)

constructs.

In terms of model fit, all fit indexes satisfied the proposed thresholds (L. Hu & Bentler,

1999), suggesting a good model fit. In specific, χ2(32) = 38.793, comparative fit index (CFI) =

0.996, Tucker-Lewis index (TLI) = 0.995, and root mean square error of approximation (RM-

SEA) = 0.032. See also table 5.7.

36


Table 5.7: Convergent Validity and Reliability Assessment Study 1

Construct & Item St. Loading CR AVE

Anticipated temporal discharge 0.956 0.845

TDS increases my overall productivity 0.905∗∗∗

TDS allows me to reallocate my time to other activities 0.964∗∗∗

TDS saves me time 0.859∗∗∗

TDS let me focus on other tasks 0.945∗∗∗

Perceived service safety 0.933 0.823

The TDS gives me the feeling of being safe 0.904∗∗∗

Using TDS increases my safety 0.903∗∗∗

I would recommend TDS for safety reasons 0.916∗∗∗

Perceived controllability 0.940 0.838

The TDS gives me the feeling of being in control 0.931∗∗∗

The TDS allows the customer to be in charge 0.934∗∗∗

This TDS gives me more control over the service process 0.881∗∗∗

Note. Standard. Loading = standardized loading; CR = construct reliability; AVE = average variance

extracted; CFI = Comparative Fit Index; RMSEA = root mean squared error of approximations; SRMR

= standardized root mean squared residuals; TLI = Tucker-Lewis Index;

Model fit indexes (Robust): χ2 = 38.793; df = 32; CFI = 0.996; TLI = 0.995; RMSEA = 0.032; SRMR

= 0.033; ∗∗∗p < 0.01.

Table 5.8: Correlation Matrix for Discriminant Validity Assessment Study 2

ATD PSS PC

ATD 0.845 0.47 0.34

PSS 0.69 0.823 0.51

PC 0.58 0.71 0.838Note. Correlations between constructs are below the diagonal. Shared variances between each and other

constructs (squared correlations) in the model are above the diagonal. The diagonal shows constructs’

AVEs.

37


5.2.1.3 Discussion

As of now, research can not provide academics and managers alike with suitable constructs to

measure the key dimensions regarding the emerging phenomenon of intelligent products capa-

ble of delivering TDS, like for instance self-driving cars, or automated lawn mowers. Therefore,

study 1 provides an empirical evidence for the reliability and validity of the newly proposed

constructs, namely anticipated temporal discharge, perceived service safety, and perceived con-

trollability. Accordingly, theoretical and managerial implications are provided in the following.

Theoretical Contribution

Building on a review of two literature streams, regarding technology acceptance and service

acceptance, the author identifies a lack of constructs that are suitable to describe customers’

acceptance of intelligent products capable of providing TDS. To resolve this issue, the author

proposes three new constructs, which reflect the key characteristics of TDS: anticipated tempo-

ral discharge, perceived service safety, and perceived controllability.

First, prior literature provided no appropriate construct to measure customers’ anticipated

temporal discharge, that is customers’ expectancy of time saving when making use of a TDS.

Since time saving is the key benefit of TDS for customers, e.g. getting your lawn trimmed

while being at work or preparing a presentation while getting chauffeured to your next meeting

in your self-driving car, a construct accounting for that fact was necessary. Therefore the author

proposes a five-item scale to operationalize customers’ anticipated temporal discharge. After

a scale-purification, the author demonstrated that the remaining four items meet all required

conditions to be considered as valid and reliable. Hence, the author contributes to the literature

of technology and service acceptance by adding a new distinct scale to measure the degree to

which an individual attributes time savings to the use of a TDS.

Second, despite the notion of physical risk in prior literature (e.g. Jacoby & Kaplan, 1972;

Kaplan et al., 1974), none of the most recent academic works on technology acceptance or ser-

vice acceptance incorporated this construct in their research models (e.g. Barrutia & Gilsanz,

2012; Venkatesh & Bala, 2008; Venkatesh et al., 2012; Wunderlich et al., 2012). Given

the fact that TDS are also applied in a person-related context, like it is the case for a customer

getting chauffeured in a self-driving car, the above mentioned research models fall short of ad-

dressing customers’ perceived service safety. To meet this issue, the author proposes a five-item

scale to measure this construct. Scale-purification suggested to dismiss two items. The remain-

ing three items gave proof to be valid and reliable. Thus, by providing an empirically tested

38


scale to account for the degree to which an individual perceives the service process to be safe

with respect to potential physical damages the author contributes to the existing literature on

technology acceptance and service acceptance.

Third, the author adapts on an existing scale to provide an accurate measure for customers’

perceived controllability of a TDS (Collier & Sherrell, 2010). The scale is meant to cap-

ture customers’ perceived controllability of the service process when applying a TDS. The

aforementioned adapted four-item scale (cf. Collier & Sherrell, 2010) was administered to ex-

ploratory factor analyses for purification. The remaining three items proved to be valid and

reliable. Therefore, the author contributes to the existing literature on technology acceptance

and service acceptance by offering an optimized construct that helps to assess the degree to

which an individual believes to command and exert power over the process of a TDS (cf. Col-

lier & Sherrell, 2010).

Managerial Implication

Since more and more companies provide intelligent products capable of delivering TDS, it is

about time to provide managers with appropriate measurements for customers’ evaluation of

their offerings. Given these newly proposed constructs, managers now have scales at hand to

assess customers’ evaluation of the unique characteristics of TDS. Their application seems par-

ticularly useful for areas like new product development and market research. In the latter field

of application it can help to anticipate customers’ needs in order to exaggerate evaluations of

the unique characteristics of TDS. Within the new product development, the proposed scales

can help to identify an optimal design of intelligent products capable of providing TDS with

respect to customers’ evaluation.

Limitations and Future Research Directions

The author derived the proposed items for all three constructs based on a literature review (e.g.

Voss et al., 2003) and gave empirical proof for their validity and reliability (e.g. Batra, Ahuvia,

& Bagozzi, 2012). Nevertheless, the present study has some shortcomings. Recent academic

work states that large numbers of participants are not a necessity to perform structural equation

models (Iacobucci, 2010). However, this only holds true in case of established constructs, used

in a structural equations model (cf. Bearden, Sharma, & Teel, 1982). As the goal of this study

was to establish empirical validity and reliability, the sample size of 210 participants may be a

limitation of this study. Further research could apply the proposed scales on a wider range of

TDS to assess criterion validity of each of the new scales.

39


5.2.2 Study 2: Validation of the Acceptance Model for TDS

In Study 2 the nomological validity of the core acceptance model for TDS is tested. There-

fore, the hypothesized paths are evaluated by applying a SEM. In specific, after evaluating the

measurement model via a CFA, the structural model is then assessed by running a SEM.

5.2.2.1 Data Collection and Measurement

Data Collection

Participants for study 2 were required by an online panel (mturk). For issues concerning the

adequacy of online panels for data collection please revise chapter 5.2.1.1.

A total of 347 participants (171 men) completed the survey with an average age of 36 years

(agemin=18 years; agemax=74 years). The educational level of the sample is as following: as

49% of the participants have earned a bachelor’s degree or higher, 28% have graduated form

college and 23% were in comprehensive school or have no graduation.

Measurement

Participants were randomly assigned to one of two innovations after a short welcome page. The

two innovations were an autonomous vacuum cleaner, and an autonomous driving car. Each

innovation was presented with a picture of the innovation and a description of the innovation’s

function. Because the two innovations represent both types of applications for TDS, that is an

object related TDS (vacuum cleaner) and a person related TDS (car) context, it is an optimal

design to empirically test validity of the acceptance model for TDS. Figures figure 5.3 and 5.4

show both stimuli.

Participants’ evaluation of the innovation was measured by using the newly proposed con-

structs, which were introduced in section 5.1, namely anticipated temporal discharge, perceived

controllability, and perceived service safety. All items were measured on a seven-point Lik-

ert scale. The respective anchors were ”fully disagree” and ”fully agree”. Participants’ atti-

tude towards using the TDS was assessed with four 7-point bi-polar items proposed by Dab-

holkar and Bagozzi (2002), namely bad/good, unpleasant/pleasant, harmful/beneficial, unfa-

vorable/favorable.

5.2.2.2 Results

This section captures the empirical validation of both the newly proposed constructs as well as

the hypothesized acceptance model for TDS. Whereas the employed measurement model eval-

40


Figure 5.3: Stimuli: Object related TDS

Figure 5.4: Stimuli: Person related TDS

41


uates the factor structure of the constructs by running a CFA, the subsequent SEM is applied

for testing the nomological validity of the acceptance model for TDS. All analyses are run in

the lavaan package (version 0.5-16) for R (version 3.1.1).

Measurement Model

Table 5.9 contains the results of the CFA. Thereby, the standardized loadings clearly indi-

cate unidimensionality of the constructs, as all loadings are significant (std.loadingmin = .850).

For each construct the corresponding composite reliability (CRmin = .939) exceeds the recom-

mended threshold of .7 (Bagozzi & Yi, 1988) and the average variance extracted (AV Emin =

.798) satisfies the condition for convergent validity established by Fornell and Larcker (1981).

Discriminant validity was assessed by the most restrictive test, i.e. a construct’s AVE has to be

higher than its squared correlation with the other factors in the respective setting (e.g. Barrutia

& Gilsanz, 2012; Fornell & Larcker, 1981). Results in table 5.10 back up discriminant validity

for all three new constructs. Finally, measurement invariance was assessed. Following Cheung

and Rensvold (1999), data satisfies the conditions for metric invariance, because there are no

significant differences between the configural model and the restricted model, where loadings

are constrained to be equal across groups. This is the prerequisite for comparing regression

slopes in a multi-group analysis.(cf. Chen, 2007). Corresponding results are presented in table

5.11.

Structural Model

Table 5.12 shows the results of the multiple-group analysis of the structural model and the

respective fit indexes. To account for the small sample size, fit indexes were derived by applying

the Satorra-Bentler scaling corrections (e.g. L. Hu & Bentler, 1999; Satorra & Bentler, 1994).

All indexes give proof of a reasonable model fit (e.g. Bagozzi & Yi, 1988, 2012; L. Hu &

Bentler, 1999; Iacobucci, 2010). That is, χ2144 = 207.440, CFI = 0.984, TLI = 0.979, RMSEA

= 0.050, and SRMR = 0.040. Hence, all indexes are below the recommended thresholds or

exceed the desired fit indexes. Figure 5.5 visualizes the results of the multi-group analysis.

To test the hypotheses, a multiple-group analysis was carried out, since it is capable of

accounting for moderating effects of categorical variables (e.g. Bagozzi & Yi, 2012). The

corresponding results confirm all of the proposed hypotheses (see table 5.12). Particularly,

the conditional indirect effect of participants’ perceived controllability on participants’ attitude

towards using the TDS is significant for person-related TDS only (H3b). Consequently, the

effect of participants’ perceived service safety on attitude towards using the TDS was only true

in case of a person-related application of the TDS, i.e. H2 was confirmed. In contrast, the direct

42


Table 5.9: Convergent Validity and Reliability Assessment Study 2

Construct & Item St. Loading CR AVE

Attitude towards using the innovation 0.940 0.798

bad / good 0.921∗∗∗

unpleasant / pleasant 0.850∗∗∗

harmful / beneficial 0.876∗∗∗

unfavorable / favorable 0.924∗∗∗

Anticipated temporal discharge 0.961 0.860

TDS increases my overall productivity 0.926∗∗∗

TDS allows me to reallocate my time to other activities 0.944∗∗∗

TDS saves me time 0.897∗∗∗

TDS let me focus on other tasks 0.941∗∗∗

Perceived service safety 0.946 0.853

The TDS gives me the feeling of being safe 0.904∗∗∗

Using TDS increases my safety 0.938∗∗∗

I would recommend TDS for safety reasons 0.928∗∗∗

Perceived controllability 0.939 0.853

The TDS gives me the feeling of being in control 0.908∗∗∗

The TDS allows the customer to be in charge 0.950∗∗∗

This TDS gives me more control over the service process 0.884∗∗∗





= 0.038; ∗∗∗p < 0.01.

effect of perceived control on attitude towards using the TDS is valid for object-related TDS

(H3a) only.

Following Paternoster, Brame, Mazerolle, and Piquero (1998) the proposed moderation ef-

fects of application type on perceived control and on perceived service safety were confirmed

by applying the subsequent equation, z = b1−b2√(SE2

b1+SE2

b2). Here, the indexes represent the differ-

43


Table 5.10: Correlation Matrix for Discriminant Validity Assessment Study 2

ATD PSS PC ATI

ATD .860 0.211 0.408 0.407

PSS 0.459 .853 0.467 0.387

PC 0.639 0.684 .836 0.354

ATI 0.638 0.622 0.595 .798Note. Correlations between constructs are below the diagonal. Shared variances between each and other

constructs (squared correlations) in the model are above the diagonal. The diagonal shows constructs’

AVEs.

Table 5.11: Measurement invariance tests Study 2

χ2 df p CFI RMSEA BIC

Model 1: configural invariance

263.145 142 .000 .978 .070 13732.983

Model 2: weak invariance (equal loadings across groups)

275.578 152 .000 .977 .068 13686.923

Model 1 compared to Model 2

∆χ2 ∆ df ∆ p ∆ CFI

12.432 10.000 .257 .000Note.

ent slopes and the respective standard errors for the two groups, i.e. object-related context (b1)

and person-related context (b2). Results revealed that the slopes for the two groups were sig-

nificantly different for both perceived control, t(343) = 2.869, p < 0.01, and perceived service

safety, t(343) = 7.448, p < 0.01. Hence, hypotheses H3a and H3b are supported.

Finally, effects of the Common Method Variance (CMV), first discussed by D. Campbell and

Fiske (1959), are addressed. CMV deals with the problem of measuring independent constructs

and dependent constructs with the same method, here the same questionnaire (cf. Podsakoff,

MacKenzie, & Podsakoff, 2012). A post-hoc test following Haman’s single factor test (e.g.

Noordhoff, Kyriakopoulos, Moorman, Pauwels, & Dellaert, 2011) indicates that there could

possibly be some effect of CMV, as the proportion of the explained variance by the single fac-

tor is at the threshold of .5, although no majority of the variance is explained by a single factor

44


and this parsimonious model is more likely to reveal CMV (Chang, Van Witteloostuijn, & Eden,

2010).

Table 5.12: Structural Models Estimation Study 2

Hypothesis & Effects Core Model

Person Related Context Object Related Context

Estimate Est. / SE p Value Estimate Est. / SE p Value

Direct Effects

ATD →ATI

.318∗∗∗ 5.916 .000 .433∗∗∗ 5.343 .000

PC→ PSS .769∗∗∗ 9.774 .000 .581∗∗∗ 7.195 .000

PSS →ATI

.558∗∗∗ 8.848 .000 −.023 −.489 .625

PC→ ATI −.068 −.968 .333 .195∗∗∗ 3.368 .001

Indirect Effect

PC→ ATI .429∗∗∗ 6.246 .000 −.013 −.488 .625

R2ATI .681 .558

PSS .478 .340





= 0.040.

45


Anticipated Temporal Discharge

Perceived Controllability


Attitude towards using the TDS

.318***&&

.558***&

(.068&n.s.&

.769***&&

Person-related

Object-related

Anticipated Temporal Discharge

Perceived Controllability


Attitude towards using the TDS

.433***%%

&.023%n.s.%

.195***%

.581***%%

Figure 5.5: Results of the Multi-group SEM

46


5.2.2.3 Discussion


The goal of study 2 was to provide empirical evidence for the nomological validity of the pro-

posed acceptance model for TDS. Since the corresponding results confirmed the proposed

hypotheses, the nomological validity of the proposed acceptance model is approved. Addition-

ally, the results gave further evidence for the empirical validity and reliability of the proposed

scales anticipated temporal discharge, perceived service safety, and perceived controllability.

The author presents the first acceptance model for TDS that comprises the new TDS specific

constructs. Since current technology acceptance models and service acceptance models have

drawbacks with respect to the new characteristics of TDS (see chapter 4), the author provides

an initial solution to resolve this issue. Although the proposed model for the acceptance of TDS

focused only on the respective key dimensions, namely anticipated temporal discharge, per-

ceived service safety, and perceived controllability, the model explains a reasonable proportion

of participants’ attitude towards using the presented TDS. In the person-related condition (the

autonomous driving car) the model accounted for 68.1% of the variance of participants’ attitude

towards using the TDS and in the object-related condition (the autonomous vacuum cleaner) the

model explained 55.8% of the variance of participants’ attitude towards using the TDS.


In combination with the newly proposed constructs, managers now are provided with a tool

for a holistic assessment of customers’ acceptance of any TDS, as the model can be applied to

evaluate customers’ acceptance of TDS in both a person-related context and an object-related

context.

The model allows managers not only to determine which design characteristics of a TDS in-

fluence anticipated temporal discharge, perceived service safety, and perceived controllability,

but also to assess the overall impact of a design change on customers’ attitude towards using

the TDS.

Limitations and Future Research Directions

The presented acceptance model for TDS strives for parsimony. Although the model achieved

good explanatory power, the author still proposes to identify and incorporate further constructs.

Drawing on the beginnings of the TAM (F. Davis, 1989) and its evolution until the latest ver-

sion, the so-called TAM 3 (Venkatesh & Bala, 2008), the author stresses to identify further

constructs either by revising prior literature or by conducting qualitative research to uncover

further drivers for the acceptance of TDS. The author also proposes that implementing indi-

47


vidual predispositions as moderators into the model could further reveal interesting insights

on customers’ acceptance of TDS (e.g. Devaraj, Easley, & Crant, 2008). In specific, future

research could investigate how individuals’ regulatory focus (Higgins, 1997) or motivation

for control (Burger, 1984; Wortman & Brehm, 1975) moderates the proposed effects of the

acceptance model for TDS.

48

6 Business Impact of Technology-Derived Services

6Business Impact ofTechnology-Derived Services

Besides the proposed Acceptance Model for TDS, which was introduced in chapter 5, the author

strives to provide first insights on how this new type of services could possibly affect established

business models in the following. The hypothesized effects are examined in an experimental

setting provided in this chapter.

6.1 Theoretical Background and Hypotheses

Advancements in technology have not only lead to more standardized, technology-based ser-

vices (e.g. R. Rust & Huang, 2012), but also to more autonomous, smart products (e.g. S. Rijs-

dijk et al., 2007; S. A. Rijsdijk & Hultink, 2009). These products deliver services to their users

without further active input and hence increasingly blur the distinction between products and

services (Huang & Rust, 2013). While much research has been conducted to understand how

individuals perceive and respond to an increasing technology-based service delivery (Meuter et

al., 2005, 2000), much less research attention has been paid to individuals’ attitude and inten-

tion towards increasingly autonomous products providing TDS. However, as firms have started

to introduce intelligent products capable of delivering TDS which affect consumers’ daily lives

(e.g., autonomous lawn mowers, vacuum cleaners, parking assistance), it is important to under-

stand if, why and when consumers would prefer such TDS over their conventional counterparts.

TDS are characterized by their autonomous completion of a task. In comparison to conven-

tional products, intelligent products providing TDS do not need any further input from the user

other than the push of a power button. Everything else, like the mowing of a lawn or vacuuming

of an apartment, is left up to the engaged intelligent product. Hence, users’ required efforts to

accomplish a task are lowered, i.e. they gain more free time, which is measured by anticipated

49


temporal discharge. Likewise, users gain more free time when signing up for a conventional

service, such as getting their apartment vacuumed or their lawn trimmed by a housekeeper or a

gardener. Hence, users are likely to experience freed up time in both cases, TDS and conven-

tional services. Thus, the author proposes the following hypotheses:

H4a: Customers’ anticipated temporal discharge is higher for an intelligent product

delivering TDS compared to a conventional product.

H4b: Customers’ anticipated temporal discharge of an intelligent product deliver-

ing TDS is equivalent to a conventional service.

This gain of leisure time, however, takes its toll. TDS will gain more and more importance

in the future, but first academics and managers alike have to come up with solutions for current

challenges, which are hypothesized below. Unlike in the case of conventional products that rely

on users’ input when completing a task (e.g. Lusch & Vargo, 2006; Vargo & Lusch, 2004), an

intelligent product providing TDS does no longer require user’s input for the service process,

given its autonomy. However, prior research has shown that individuals generally overestimate

the quality of their own work or perceive outcomes better, the more self they are able to put into

the process. This effect has been termed as the IKEA effect (Mochon, Norton, & Ariely, 2012),

or the I-made-it-myself effect (e.g. Troye & Supphellen, 2012). Transferring these findings to

the realm of TDS, one can propose that consumers may anticipate minor process quality of

intelligent products delivering TDS compared to conventional products due to the lack of own

input. Hence, the author proposes the following hypothesis:

H5a: Customers’ anticipated process quality of intelligent products capable of pro-

viding TDS is lower than for conventional products.

Prior academic work has shown that service providers can increase their efficiency by im-

plementing more automation to their service processes at the cost of lower perceived process

quality (e.g. Anderson et al., 1997). In line with that, R. Rust and Huang (2012) clearly stress

the point that “at a given level of technology, less labor intensity in service decreases service

quality”(p. 49). Therefore, one can conclude that customers may anticipate a lowered process

quality for TDS in comparison to a regular service delivery, since an intelligent product capable

of delivering TDS embodies a fully automated service representative. Thus, the author proposes

the following hypothesis:

H5b: Customers’ anticipated process quality is lower for TDS than for conventional

services.

50


Drawing on Cronin Jr and Taylor’s (1992) reasoning that service quality in the end is an

attitude, the author conceptualizes the effect of object type () and anticipated temporal discharge

on customers’ attitude towards the TDS as the following:

Customers’ perceived service quality has been conceptualized as a multi-dimensional con-

struct in various academic works (cf. Brady & Cronin Jr., 2001). Nevertheless, these concepts

have in common that service quality is basically determined by two factors, process quality and

outcome quality (e.g. Barrutia & Gilsanz, 2012; Surprenant & Solomon, 1987). Whereas the

latter one addresses the general objective of subscribing to a service agreement (e.g. getting

the lawn trimmed and therefore freeing up time) the former one is linked to the degree of how

well the service was provided (i.e., how well is the lawn trimmed) (cf. Sivakumar et al., 2014;

Smith, Bolton, & Wagner, 1999). Adapting on the service dominant logic and its paradigm of

value in use (Vargo & Lusch, 2004), users also have a focal objective in mind when buying

a product and are also concerned with product’s ability to increase their process quality (e.g.

F. Davis, 1989; Venkatesh & Bala, 2008; Venkatesh et al., 2012). Hence, it seems reasonable

to apply the aforementioned dual evaluation process of services also in a product context.

Since users’ main objective is freeing up time for other activities when leaving work for

the intelligent product delivering TDS, customers’ outcome quality is determined by their an-

ticipated temporal discharge. Likewise, process quality is determined on how well the user

“expects” (Dabholkar, 1996) the intelligent product delivering the TDS to fulfill the assigned

task. In other words, process quality is represented by anticipated quality of the delivered TDS.

Therefore, the author proposes:

H6: The effect of type of object (classic product vs. intelligent product capable

of delivering TDS) on customers’ attitude towards the object is mediated by cus-

tomers’ anticipated quality.

As already hypothesized that customers’ anticipated temporal discharge of an intelligent

product capable of delivering TDS is moderated by the offer type (product vs. service), the

author proposes the following hypothesis:

H7a: In a product context, the effect of type of object (classic product vs. intel-

ligent product capable of delivering TDS) on customers’ attitude is mediated by

anticipated temporal discharge.

H7b: In a service context, the effect of type of object (classic product vs. intelli-

gent product capable of delivering TDS) on customers’ attitude is not mediated by

anticipated temporal discharge.

51


6.2 Study 3: Effects of TDS on classic product and service

businesses

6.2.1 Design, Procedure, and Data Collection

Study Design and Procedure

Study 3 used a 2 (type of offer: product vs. service) x 2 (object type: classic product vs. in-

telligent product capable of delivering TDS) between-subjects design to test hypotheses H4a,

through H7b. After a short welcome page, an instructional manipulation check (cf. Oppen-

heimer, Meyvis, & Davidenko, 2009), and having answered questions concerning their predis-

positions, participants were randomly assigned to one of the four conditions. Each condition

was edited like a consumer report, presenting the stimulus with a picture and a description of the

core features as well as the respective order process. Once participants have read the consumer

report, they rated their attitude towards the described stimuli. They also evaluated the stimulus

with respect to the newly proposed scale anticipated temporal discharge (see chapter 5.1). Af-

terwards, they filled out the manipulation checks. Since the stimuli contained no momentarily

priming effects, there is no concern about the position of the manipulation checks after the core

dependent variables (cf. Fuchs, Schreier, & van Osselaer, 2015; Goodman & Irmark, 2013).

Finally, participants’ demographics were recorded.

Data Collection

Participants for study 3 were recruited by an online panel (mturk). For issues concerning the

adequacy of online panels for data collection, please revise chapter 5.2.1.1. A total of 163

participants (57 men) completed the survey with an average age of 37 years (agemin=18 years;

agemax=71 years). The educational level of the sample is as following: 42% of the participants

have earned a bachelor’s degree or higher, 34% have graduated form college and 24% were in

comprehensive school or have no graduation.

6.2.2 Measures

Independent Variables

Both manipulations type of offer, namely type of offer (product vs. service) and object type

(classic vs. intelligent product capable of delivering TDS), were incorporated in a fictitious con-

sumer report. Figures 6.1 - 6.4 show the corresponding original stimuli. Offer type was firstly

manipulated using the headline, i.e. “Smart House-Keeping Products for you!” vs. “Smart

52


House-Keeping Services for you!”. Secondly, offers’ specifications and the according order

processes were respectively adjusted whether it is about a service or a product. Finally, the type

of offer (service vs. product) was written in bold fonts at the beginning of the specifications and

the beginning of the order process to positively influence participants’ process fluency of the

stimulus (cf. Novemsky, Dhar, Schwarz, & Simonson, 2007; Shen, Jiang, & Adaval, 2010;

Tsai & McGill, 2011).

Object type was manipulated by a visual representation of the vacuum cleaner, i.e. a picture

of a regular vacuum cleaner for the “classic” condition and a self-made digital mock of an au-

tonomous vacuum cleaner for the “TDS” condition. In addition, the description part of the con-

sumer report clearly pointed out that the offer is about a “cutting edge vacuum cleaner” (classic

condition) versus a “cutting edge autonomous vacuum cleaner” (TDS condition). Lastly, object

type was also manipulated at the beginning of the order process. That is, the offer was written

in bold fonts, “IT Clean’s Smart Vacuum Cleaner” for the classic condition and “IT Clean’s

Autonomous Vacuum Cleaner” for the TDS condition.

Dependent Variables

Anticipated temporal discharge was assessed by applying the newly proposed four items, see

chapter 5.1 (α = .96). Anticipated quality was assessed on a 7-point bipolar scale, adapted from

the scale suggested by Suri and Monroe (2003) (α = .91). Participants’ attitude towards the

offer was assessed with five bi-polar items on 7-point scales. The item pairs were bad/good, un-

pleasant/pleasant, harmful/beneficial, unfavorable/favorable, unfair/fair (α = .90) (cf. Bansal,

Taylor, & James, 2005; M. C. Campbell, 2007; Dabholkar & Bagozzi, 2002; Sinclair &

Irani, 2005). To allow for a comparable measurement of participants’ willingness to pay due

to the fact that the conditions comprise products and services, the relative willingness to pay

was recorded by a single-item scale. In specific, participants were asked “How much more

or less are you willing to pay for IT Clean’s smart/ autonomous vacuum cleaner // IT Clean’s

autonomous/ smart house-keeping service compared to a conventional vacuum cleaner/ house-

keeping service”. The scale had a range from -100% to +100%.

Manipulation Checks

To check for manipulation of type of offer, participants answered a single-item. They were

asked to indicate how they perceived the offer (“For me, IT Clean is more of a. . . product (1)

- service (7)”). Manipulation of object type (classic vs. intelligent product capable of deliver-

ing TDS) was assessed by asking two items. Item one was adapted from the autonomy scale

53


Figure 6.1: Stimuli: TDS as product

Figure 6.2: Stimuli: TDS as a service

presented by S. Rijsdijk et al. (2007) (“IT Clean does things by itself.”). Item two asked for

participants perception of required human interaction during the operating process of IT Clean

54


Figure 6.3: Stimuli: Advanced product as product

Figure 6.4: Stimuli: Advanced product as a service

on a bi-polar scale (“I would say that IT Clean. . . requires human interaction while operating(1)

- does not require human interaction while operating (7)”).

55


Covariates

The questionnaire incorporated extraneous variables to account for the respective variance. Fol-

lowing Parasuraman (2000), the author recorded consumers’ innovativeness, which is defined

as a predisposition to be interested in and to seek for new products or brands (e.g. Goldsmith

& Hofacker, 1991; Steenkamp, ter Hofstede, & Wedel, 1999). Participants’ innovativeness

was assessed with the scale presented by Bruner II and Kumar (2007) (α = .95). As the re-

search object deals with high-technology, it seems reasonable to incorporate participants’ age

as a covariate in the analysis (e.g. Ma, Yang, & Mourali, 2014). Finally, participants’ prior

experiences with offers like the presented ones were assessed with one item (“Have you had

already experience with a smart product (offer type product)/ smart service (offer type service)

like IT Clean within the last three years?”).

56


6.2.3 Results

Manipulation ChecksThe expected manipulation of service vs. product was approved, as participants in the service

condition systematically rated the offer on a product (1) to service (7) scale higher than did par-

ticipants in the product condition, MProduct = 2.61,MService = 4.64,F(1,161) = 49.2, p < .001.

The sequentially rejective Bonferroni test (cf. Holm, 1979) also confirmed, that participants in

the service condition significantly perceived the intelligent product capable of providing TDS

more as a service than did participants in the product condition (MProduct = 3.00,MService =

3.99, t(81) =−2.11, p < .05).

Also, the manipulation of classic vs. intelligent product capable of providing TDS was con-

firmed, since participants in the classic condition significantly rated the object’s ability to do

things by itself lower than did participants in the intelligence condition (MClassic = 4.03,MIntelligence =

5.88,F(1,161) = 62.5, p< .001). Finally, participants perceived the objects’ capability to oper-

ate without human interaction systematically higher than did participants in the classic condition

(MClassic = 3.08,MIntelligence = 5.52,F(1,161) = 85.4, p < .001). Results of the manipulation

checks are presented in figures 6.5 - 6.7.

Product Service

Type

For m

e IT

Cle

an is

mor

e of

a p

rodu

ct (1

) - s

ervi

ce (7

)

12

34

56

7

ClassicIntelligent

Figure 6.5: Manipulation check type of offer (detailed)

57


Classic Intelligent

Type

It do

es th

ings

by

itsel

f

12

34

56

7

Figure 6.6: Manipulation check type of object

Classic Intelligent

Type

Ope

rate

s w

ithou

t hum

an in

tera

ctio

n

12

34

56

7Figure 6.7: Manipulation check type of object

58


Hypothesis testing

Anticipated temporal discharge was analyzed by a 2 x 2 analysis of covariance (ANCOVA).

Participants’ previous experience with smart products/ services had an insignificant effect on an-

ticipated temporal discharge. In contrast, participants’ innovativeness (F(1,155) = 7.7.39, p <

.01) and age (F(1,155) = 10.33, p < .01) had a significant effect on anticipated temporal

discharge. Object type had a marginal significant main effect on anticipated temporal dis-

charge, MClassic = 4.85,MIntelligence = 5.26,F(1,155) = 3.82, p = .0525). The main effect

of offer had a significant effect on anticipated temporal discharge, MProduct = 4.76,MService =

5.36,F(1,155) = 7.46, p < .01. Further, analysis revealed a significant interaction effect of ob-

ject type (classic vs. intelligent product capable of delivering TDS) and type of offer (product vs.

service), MClassProd = 4.28,MIntelProd = 5.26,MClassServ = 5.47,MIntelServ = 5.26,F(1,155) =

10.26, p < .01 on participants’ anticipated temporal discharge, see figure 6.8.

Product Service

Type

Ant

icip

ated

tem

pora

l dis

char

ge

12

34

56

7

ClassicIntelligent

Figure 6.8: Interaction plot for anticipated

temporal discharge

3.0 3.5 4.0 4.5 5.0 5.5 6.0

-4-2

02

Fitted values

Residuals

Residuals vs Fitted

72

43291

-2 -1 0 1 2

-3-2

-10

12

Theoretical Quantiles

Sta

ndar

dize

d re

sidu

als

Normal Q-Q

72

432258

3.0 3.5 4.0 4.5 5.0 5.5 6.0

0.0

0.5

1.0

1.5

Fitted values

Standardized residuals

Scale-Location72

432258

0.00 0.04 0.08 0.12

-4-3

-2-1

01

2

Leverage

Sta

ndar

dize

d re

sidu

als

Cook's distance

Residuals vs Leverage

72

432258

Figure 6.9: Diagnosis plot for analysis of

covariance (DV: anticipated temporal

discharge)

Diagnostic plots indicate that anticipated temporal discharge does not follow normality (see

figure 6.9). To resolve this issue, the Box-Andersen Test was applied on the factorial model only

(cf. Box & Andersen, 1955). Results confirmed that type of offer has a significant effect on par-

ticipants’ anticipated temporal discharge, F(1,159) = 6.73, p < 0.01. The significant interac-

tion effect of object type and type of offer was also confirmed, F(1,159) = 7.01, p < 0.01. Ad-

ditional tests for homogeneity of variance affirmed the visual tendency that variances are equal

59


across groups (FProductType(1,161) = 2.21, p = .14;FApplicationContext(1,161) = 1.89, p = .17).

To account for independence of covariates and the treatment groups (cf. Miller & Chap-

man, 2001), analyses of variance demonstrated that participants’ age and innovativeness did

not significantly differ between groups (age: MClassProd = 36.1,MIntelProd = 39.0,MClassServ =

34.8,MIntelServ = 37.6,F(3,158) = 0.9, p= .44; innovativeness: MClassProd = 4.14,MIntelProd =

3.49,MClassServ = 3.75,MIntelServ = 3.64, F(3,158) = 1.08, p = .36). A χ2 test also confirmed

that participants’ prior experience with intelligent products capable of delivering TDS, χ2(3) =

4.13, p = 0.248 did not significantly differ between groups.

Finally, testing for homogeneity of regression slopes (e.g. Alexander & DeShon, 1994)

it shows that none of the additionally included interaction terms had a significant effect on

participants’ anticipated temporal discharge. Therefore, homogeneity of regression slopes is

confirmed. Hence, hypotheses H4a and H4b are confirmed.

To analyze the effect of object type and type of offer on participants’ anticipated process

quality a 2 x 2 analysis of covariance was employed. None of the covariates, age, prior ex-

perience, and user innovativeness had a significant effect on anticipated quality. Importantly,

object type had a significant main effect on anticipated quality, MClassic = 5.46,MIntelligence =

4.81,F(1,155) = 11.32, p < .001 (see figure 6.10). The main effect of type of offer had no sig-

nificant effect on anticipated quality, MProduct = 5.21,MService = 5.05,F(1,155)= 0.56, p= .46.

The analysis also revealed an insignificant interaction effect of object type (classic vs. intelli-

gent product capable of providing TDS) and type of offer (product vs. service), F(1,155) =

.32, p = .57, on participants’ anticipated outcome quality.

The respective diagnostic plots indicate that normality is satisfied, but homogeneity of vari-

ance may not be fulfilled (see figure 6.11). Additional tests for homogeneity of variance af-

firmed the visual tendency that variances are not equal across groups. In specific, Levene’s

Test for homogeneity of variance shows that the variance between the object type conditions is

not equal, FOb jectType(1,161) = 3.95, p < .05, whereas conditions of type of offer have equal

variances, FTypeo f O f f er(1,161) = 1.89, p = .17. To account for this issue, the Brown-Forsythe

test statistic is applied (cf. Brown & Forsythe, 1974). Results confirmed the significant main

effect of object type on anticipated quality (F1,159 = 4.63, p < .05). An additionally carried

out robustified F-Test further confirmed the significant main effect of object type on anticipated

quality (F1,161 = 9.25, p < .05).

Homogeneity of regression slopes (e.g. Alexander & DeShon, 1994) was approved, since

60


Product Service

Type

Ant

icip

ated

Out

com

e Q

ualit

y

12

34

56

7

ClassicIntelligent

Figure 6.10: Main effect of object type on

anticipated quality

4.4 4.6 4.8 5.0 5.2 5.4 5.6 5.8

-3-2

-10

12

3

Fitted values

Residuals

Residuals vs Fitted

4469429

-2 -1 0 1 2

-2-1

01

2


Sta

ndar

dize

d re

sidu

als

Normal Q-Q

4462994

4.4 4.6 4.8 5.0 5.2 5.4 5.6 5.8

0.0

0.5

1.0

1.5

Fitted valuesStandardized residuals

Scale-Location446

29 94

0.00 0.04 0.08 0.12

-3-2

-10

12

Leverage

Sta

ndar

dize

d re

sidu

als

Cook's distance


65440

29


covariance (DV: anticipated quality)

none of the additionally included interaction terms had a significant effect on participants’ an-

ticipated temporal discharge. Thus, hypotheses H5a and H5b are accepted.

61


To test hypotheses H6, H7a, and H7b, a moderated mediation analysis was carried out (cf.

Muller, Judd, & Yzerbyt, 2005; Preacher, Rucker, & Hayes, 2007). Model 7 of the PROCESS

macro is most appropriate, as it allows for a multiple mediation with moderated effects from

the experimental stimuli on the mediators (Hayes, 2012). Thereby, three multiple regression

models were applied, that is two mediator models and the dependent variable model. The first

mediator model analyzed the effect of object type, type of offer, and the interaction effect of

object type x type of offer on anticipated process quality. As expected, only object type had a

significant effect on anticipated process quality (b = −.544, t = −2.029, p < .05). The second

mediator model analyzed the effect of object type, type of offer, and the interaction effect object

type x type of offer on anticipated temporal discharge. Results confirmed that the interaction

has a significant effect on anticipated temporal discharge, b = −1.202, t = −2.645, p < .01.

Also, object type (b = .989, t = 3.083, p < .01) and type of offer (b = 1.194, t = 3.695, p < .01)

have a significant effect on anticipated temporal discharge. Finally, the dependent variable

model regressed participants’ attitude towards the offer on anticipated temporal discharge, an-

ticipated process quality, customers’ age and customers’ innovativeness. As proposed, par-

ticipants’ attitude towards the offer is predicted by anticipated process quality (b = .743, t =

19.597, p < .01) and anticipated temporal discharge (b = .107, t = 3.371, p < .01). In con-

trast, product type (b = −.046, t = −.216, p = .594) and both covariates participants’ age

(b = .003, t = .758, p = .450) and participants’ innovativeness (b = .040, t = 1.679, p = .095)

have no significant effect on participants’ attitude towards the offer. Notably, the achieved

model fit is good , since R2 = .81. Further, the model employed a bootstrap with 10.000 draws

for the analysis of the conditional indirect effects. As hypothesized, the effect of product type

(classic vs. intelligent product capable of providing TDS) on participants’ attitude towards the

offer was moderated by anticipated process quality in both product context and service con-

text (95%CIProduct : −.778,−.018;95%CIService : −.951,−.125). The according value for the

index of moderated mediation further revealed that there is no significant difference between

the indirect effect in a product context compared to a service context (95%CI : −.688, .410).

Thus, hypothesis H6 is accepted. Anticipated temporal discharge mediated the effect of ob-

ject type (classic vs. intelligent product capable of providing TDS) on attitude only in the

product condition (95%CI : .030, .228). Contrary, in the service condition the effect of object

type on attitude towards the offer mediated by anticipated temporal discharge is not signifi-

cant (95%CI : −.113, .033). The results of the so-called index of moderated mediation post

procedure affirmed that the indirect effect of product type on participants’ attitude is signifi-

cantly different from each other (95%CI : −.300,−.026). Hence, hypotheses H7a and H7b are

accepted. Figure 6.12 summarizes the results of the process analysis.

62


Object'type'

(classic'vs.'intelligent)'

Applica5

on'con

text'

(produ

ct'vs.'se

rvice)'

An5cipated

'tempo

ral'

discharge'

An5cipated

'quality'

A>tude

'towards'th

e'off

er'

.990**

prod

uct'

.743*** '

G.212

'n.s. se

rvice''

G.544

* '

.107*** '

.046'n.s.' '

Figu

re6.

12:R

esul

tsof

the

mod

erat

edm

edia

tion

anal

ysis

Stud

y3

63


Further Results

Besides the results of the moderated mediation, the author further conducted a 2 x 2 AN-

COVA to analyze participants’ attitude towards the offer. None of the covariates had a signif-

icant effect on consumers’ attitude towards the offer, but users’ innovativeness (F(1,155) =

7.12, p < .01). Type of object had a significant main effect on attitude towards the offer,

MClassic = 5.41,MIntelligence = 4.99,F(1,155) = 6.35, p < .0128. However, the main effect of

offer was insignificant, MProduct = 5.29, MService = 5.10, F(1,155) = 1.22, p > .27. Further,

it revealed a significant interaction effect of type (classic vs intelligent) and offer (product vs

service), MClassProd = 5.36,MIntelProd = 5.22,MClassServ = 5.45,MIntelServ = 4.77,F(1,155) =

3.93, p < .05 on participants’ attitude, see figure 6.13.

Product Service

Type

Attitude

12

34

56

7

ClassicIntelligent

Figure 6.13: Interaction effect of type and offer

on consumers’ attitude towards the

offer

4.5 5.0 5.5 6.0

-3-2

-10

12

Fitted values

Residuals

Residuals vs Fitted

39073 278

-2 -1 0 1 2

-2-1

01

2

Theoretical QuantilesS

tand

ardi

zed

resi

dual

s

Normal Q-Q

39073278

4.5 5.0 5.5 6.0

0.0

0.5

1.0

1.5

Fitted values


Scale-Location39073 278

0.00 0.04 0.08 0.12

-3-2

-10

12

Leverage

Sta

ndar

dize

d re

sidu

als

Cook's distance


390

65

217


covariance (DV: attitude towards the

offer)

The respective diagnostic plots indicate that normality is satisfied, but homogeneity of vari-

ance may not be fulfilled (see figure 6.14). Additional tests for homogeneity of variance af-

firmed the visual tendency that variances are not equal across groups. In specific, Levene’s

Test for homogeneity of variance shows that the variance between the object type conditions

is not equal, FOb jectType(1,161) = 5.8, p < .05, whereas conditions of type of offer have equal

variances, FTypeo f O f f er(1,161) = 3.76, p > .05. To account for this issue, the Brown-Forsythe

test statistic is applied (cf. Brown & Forsythe, 1974). Results did not confirm the significant

main effect of object type on anticipated quality (F1,159 = .34, p > .05). An additionally carried

out robustified F-Test revealed a marginal significant main effect of object type on anticipated

quality (F1,161 = 3.57, p < .1) as well as a marginal significant interaction effect of type of offer

64


and object type (F1,161 = 3.26, p < .1).

Finally, to gain first exploratory insights into customers’ willingness to pay in the light of

TDS, the author conducted a 2 (classic vs. intelligent product capable of providing TDS)

x 2 (product vs. service) ANCOVA. Results show a significant main effect of type of of-

fer, MProduct = 25.13%, MService = −5.19%,F(1,155) = 27.77, p < .001 (see figure 6.15).

In contrast, object type (MClassic = 13.61%, MIntelligent = 6.63%,F(1,155) = 1.43, p > .23)

and the interaction effect of type of offer and object type (MClassProd = 29.52%, MIntelProd =

20.52%,MClassServ = −3.97%,MIntelServ = −6.27%,F(1,155) = 0.23, p > .63) had insignifi-

cant effects on participants’ relative willingness to pay. Also, none of the covariates age, prior

experience, and innovativeness had a significant effect on relative willingness to pay.

Product Service

Type

Rel

ativ

e W

illin

gnes

s to

Pay

-10

010

2030

Figure 6.15: Main effect of type of offer on

relative willingness to pay

-10 0 10 20 30 40

-100

-50

050

100

Fitted values

Residuals

Residuals vs Fitted

16529

181

-2 -1 0 1 2-3

-2-1

01

23


Sta

ndar

dize

d re

sidu

als

Normal Q-Q

16529

181

-10 0 10 20 30 40

0.0

0.5

1.0

1.5

Fitted values


Scale-Location16529181

0.00 0.04 0.08 0.12

-3-2

-10

12

3

Leverage

Sta

ndar

dize

d re

sidu

als

Cook's distance


210

29165


covariance (DV: relative willingness

to pay)

Diagnostic plots indicate that not all model premises are satisfied. That is, figure 6.16 re-

veals that relative willingness to pay does not follow normality. To resolve this issue, the Box-

Andersen Test was applied on the factorial model only (cf. Box & Andersen, 1955). Results

confirmed that type of offer has a significant effect on participants’ relative willingness to pay,

F(1,160) = 27.46, p < 0.01.

Homogeneity of regression slopes (e.g. Alexander & DeShon, 1994) was approved, because

none of the additionally included interaction terms had a significant effect on participants’ rel-

ative willingness to pay.

65


6.2.4 Discussion


As the name implies, intelligent products capable of providing TDS “blur[s] the distinction

between goods and services” (p.257 Huang & Rust, 2013). Thus, these intelligent products can

either be sold as common products or can be offered as a new type of services.

Therefore, study 3 provides first insights into customers’ evaluation of intelligent products

capable of providing TDS compared to classic products and classic services. As hypothesized,

the positive effect of anticipated temporal discharge was seen as a gain in the product condition

only. In the service condition, participants evaluated anticipated temporal discharge for the

classic condition and the TDS condition equally.

The hypothesized negative effect of intelligent products delivering TDS on customers’ per-

ceived quality was confirmed for both the product condition and the service condition. Both

the mediated effect of type of object (classic vs. intelligent product capable of delivering TDS)

by anticipated quality on customers’ attitude towards the offer and the conditional indirect ef-

fect of type of object (classic vs. intelligent product capable of delivering TDS) by anticipated

temporal discharge on customers’ attitude towards the offer were confirmed.

Notably, the manipulation check of offer type (product vs. service) revealed that the intel-

ligent product capable of delivering TDS somehow underlines the notion of Huang and Rust

(2013). That is, although the manipulation of offer type (product vs. service) was confirmed,

participants in the product condition perceived the intelligent product significantly higher as a

service than did participants rating the classic product. The exact opposite is true for the service

condition, where participants perceived the intelligent product capable of delivering TDS more

as a product compared to participants rating their perception towards a classic service.


Results of the study have several implications for practitioners. It turned out that the focal ad-

vantage of intelligent products capable of providing TDS, namely time saving, is rewarded in

a product context only. Therefore, intelligent products capable of providing TDS may be more

successfully promoted in a product context than in a service context. Further, the study reveals

that customers’ evaluation of anticipated process quality of an intelligent product capable of

providing TDS is systematically rated lower than for their classic counterparts, irrespectively

whether the offer type is a service or a product. Hence, managers should derive solutions how to

exaggerate customers’ anticipated process quality of intelligent products capable of TDS. This

issue is even more serious from a service business perspective. The study reveals that in case of

66


a service context customers’ attitude towards the offer is primarily determined by their antici-

pated quality. Thus, when managers are thinking about replacing human service employees by

intelligent products capable of providing TDS, e.g. replacing some gardeners by autonomous

lawn mowers, in order to increase their productivity (R. Rust & Huang, 2012), customers’ low-

ered anticipated quality should be resolved first.

Limitations and Further Research Directions

The current study may suffer from several limitations. The presented stimuli, classic and au-

tonomous vacuum cleaners, are already available in the market for a while, hence these products

are common for participants. Therefore, it is at question whether the presented findings are also

applicable to intelligent products capable of providing TDS, which will be launched in the fu-

ture. Further, the study examines customers’ evaluation of TDS in an object-related context.

Given that TDS are also applied in a person-related context, like in the case of an self-driving

car, the author proposes to verify the findings presented in this study in this context.

Further research could also investigate how customers’ perception of services is affected,

when autonomous products are not used for private use but applied by an external party con-

tracted to deliver the task at hand. Given that intelligent products capable of delivering TDS

are taking over its owner’s effort to complete a task, one could ask how this affects customers’

perception of fairness when a third party provides a TDS. The question could be, how does the

fact that an intelligent product capable of delivering TDS lowers the service provider’s effort

affect customers’ perception of this service compared to a conventional provided service? For

instance, customers preference for an autonomous lawn mower may be lower when it does not

free up the customers’ own labor, but that of a gardener.

Additionally, intelligent products cover a wide range of tasks which can either be object-

related, e.g. vacuuming the floor, or person-related, such as the parking of a car. Especially

in the latter application context, the aspect of control becomes critical when considering con-

sumers’ acceptance of and preference for intelligent products. Given the users’ personal in-

volvement, person-related tasks may pose a high (perceived) personal (physical) risk and may

hence be considered rather critical. More precisely, consumers would probably expect a higher

quality when using conventional products by themselves and should not be willing to give up

their own control when tasks are critical to the self.

Furthermore, literature on SST proposes that customers always attribute service outcomes

internally, notwithstanding they were successful or not, because consumers can improve their

67


self-efficacy in the light of SST (Duval & Silvia, 2002). Since there is no customer input

required in the case of TDS, an investigation on how service failures are attributed and an

exploration of possible resolutions of this issue would be of great value for companies.

68

7 Overall Discussion

7Overall Discussion

First and foremost the thesis introduces a new type of services, TDS. TDS are provided by

intelligent products, which operate completely autonomously and therefore do not require any

human interaction for the service provision. Further, these new services can be applied in

an object-related context, e.g. for cleaning up an apartment or for trimming the lawn, and

in a person-related context, e.g. getting chauffeured by google’s self-driving cabs. However,

these two distinct application-contexts differ with regard to customers’ acceptance of this ser-

vice type. As an example, one could think of applying an automated lawn mower and getting

chauffeured with a self-driving car like the F015 from Mercedes-Benz. In the former setting,

customers are primarily concerned how much time they can save when applying such an au-

tonomous lawn mower. In contrast, when customers are being chauffeured by self-driving cars

in the near future, their primary concern might be their perceived service safety of that specific

TDS, because they are directly involved in the process in case of an accident. The arising the-

oretical and managerial implication as well as the limitations of this thesis are discussed in the

following. Finally, a research agenda is provided, suggesting promising future research direc-

tions in the light of TDS.

69


7.1 Theoretical Implications

Despite their existence on the market, academia has still not addressed TDS. Although prior

literature has already investigated the underlying intelligent products (e.g. S. Rijsdijk et al.,

2007), yet from a strict product view. Since intelligent, autonomous products operate without

any required interaction of humans, they have the capability of directly providing value to their

users, i.e. providing TDS. Hence, the author provides an initial definition and conceptualiza-

tion of TDS, as the following: TDS are intelligent products with an innate IT-based capability

to autonomously operate and are therefore providing value directly to the customer without any

necessary interaction during the value creation process of its user or its manufacturer. Further,

the author suggests a new service classification, whereupon services are distinguished by their

field of application, namely person-related and object-related, and the degree of technical infu-

sion (Froehle & Roth, 2004). Therefore, the thesis contributes to research by conceptualizing

this new type of services and providing a new service classification. In doing so, the author

contributes to the “vitality of the marketing discipline” (MacInnis, 2011, p.136).

Prior research investigated customers’ acceptance of products within the literature stream

of technology acceptance (e.g. F. Davis, 1989; Venkatesh et al., 2012). At the same time,

academics explored customers’ acceptance of various kinds of services, like SST (Meuter et

al., 2005), technology-infused services (e.g. Giebelhausen et al., 2014), or electronic services

(Barrutia & Gilsanz, 2012). However, none of these two literature streams, technology and

service acceptance, has realized the accelerated rise of intelligent products capable of deliver-

ing TDS. Given that TDS do not require any human interaction during the service process and

are applied in object-related or person-related contexts, the established constructs and models

for customer acceptance of technologies or services do not accurately capture the key drivers

of customers’ acceptance for TDS, namely anticipated temporal discharge, perceived service

safety, and perceived controllability. To address this issue, the thesis derives new measure-

ments for all three key drivers of customers’ acceptance of TDS. Thus, the author contributes

to research by providing first empirically tested measurements to assess customers’ anticipated

temporal discharge, perceived service safety, and perceived controllability of TDS.

Based on the new measurements, the author hypothesizes a new acceptance model for TDS,

which also takes the diverse application contexts of TDS (i.e. object-related or person-related)

into account. This very parsimonious model already explains about 60 percent of the variance

of customers’ attitude towards using a TDS in an object-related context and even about 70

70


percent of the variance of customers’ attitude towards using a TDS in an person-related con-

text. Accordingly, the author suggests that the derived new measurements represent the key

dimensions for the acceptance of TDS, namely time saving, controllability, and physical risk

perception. Although the model explains a majority of customers’ acceptance of TDS, there

is still room for improvement. In specific, implementing customers’ individual predispositions

as moderators in the model could reveal further insights into their acceptance of TDS. This

newly proposed model for the acceptance of TDS provides researchers with the first appropri-

ate framework to understand customers’ acceptance of TDS as well as with a promising starting

point for further investigations in the light of TDS. Compared to the prominent, existing frame-

works regarding the assessment of customers’ acceptance of technology, i.e. the TAM and the

UTAUT, the proposed acceptance model for TDS is not meant to be applied in an IT-context.

On the contrary: it is the first framework to investigate customers’ acceptance of intelligent

products capable of providing TDS that exert physical acts on either objects (object-related) or

persons (person-related).

Additionally, this thesis also reveals that the mechanism of customers’ acceptance of TDS

differs depending on the application context. When TDS are applied in a person-related con-

text, for example a self-driving car, customers’ acceptance is positively influenced by their

anticipated temporal discharge. Furthermore, the positive effect of perceived controllability on

customers’ acceptance is mediated by their perceived service safety. This indirect effect further

highlights the importance of the new dimension perceived service safety, which is not included

in existing frameworks, to explain technology acceptance of customers. In contrast, when TDS

are applied in an object-related context, anticipated temporal discharge and perceived controlla-

bility have a positive, direct effect on customers’ acceptance of TDS, whereas perceived service

safety does not affect customers’ acceptance of TDS. As a consequence, the model proofs to be

suitable to assess customers’ acceptance of TDS, notwithstanding their application context, i.e.

person-related or object-related.

Moreover, the author sheds first light on customers’ evaluation of TDS compared to conven-

tional products and conventional services in an object-related context. When TDS are compared

to the latter ones, the findings show that customers anticipate the same temporal discharge, i.e.

time saving, as for conventional services. However, customers anticipate a lower quality for

TDS compared to conventional services. This is also true when TDS are compared to classic

products. Nevertheless, customers anticipate higher temporal discharge for TDS than for classic

products.

71


Furthermore, drawing on the paradigm of SDL and prior research on service quality, the au-

thor provides a theoretical framework, which allows for comparing TDS in the product domain

as well as in the service domain. In specific, the framework states that the evaluation of a prod-

uct or a service with respect to its value in use is determined by customers’ anticipated temporal

discharge, i.e. time saving, and anticipated quality, i.e. process quality. Corresponding findings

indicate that both determinants have a positive effect on customers’ attitude towards the prod-

uct or service at question. Considering the fact that TDS are associated with lower anticipated

process quality and that customers’ anticipated temporal discharge is beneficial in a product

comparison only, customers’ attitude towards TDS is lower in a service comparison and about

equal in a product comparison.

To sum up, the thesis contributes to research by expanding the existing literature on cus-

tomers’ acceptance of technology and services by the acceptance model for TDS and its as-

sociated measurements. Further, it draws attention on the difference between person-related

and object-related applications of TDS. Finally, it provides a theoretical framework to assess

customers’ evaluation of TDS in comparison to conventional products and services. Thus, the

findings open up new and promising alleys for future research, which are further discussed in

chapter 7.4.

72


7.2 Managerial Implications

Google’s self-driving cab, Mercedes’s self-driving prototype F015, introduced at the interna-

tional consumer electronics show in Las Vegas 2015, or Audi’s RS7 piloted driving concept

provide evidence that companies worldwide are working on intelligent products capable of pro-

viding TDS. The increasing number of TDS, also in the form of automated lawn mowers or

automated vacuum cleaners, underlines the importance of this new type of services. Therefore,

this thesis provides first guidelines for managers how to increase customers’ acceptance of TDS

and which steps have to be taken in order to surpass customers’ evaluation of classic products

and services.

Since anticipated temporal discharge, i.e. time saving, is the focal advantage of TDS irre-

spectively their application context (person-related or object-related), managers are well advised

to clearly promote this point. This could be achieved in several ways. One option could be to

point at the absolute time “saved”, when making use of a TDS. Another strategy could be to

draw comparisons like trimming the lawn or reading a book while getting the lawn trimmed

by TDS. Addressing customers’ opportunity costs, when performing the task for themselves,

could also benefit customers’ attention regarding anticipated temporal discharge.

Managers should also thoroughly consider how they can increase customers’ perceived con-

trollability of a TDS. Especially in a person-related application, this is a key issue, since per-

ceived controllability positively influences customers’ perceived service safety. To create the

impression of controllability, managers have several options at hand. Product design is one of

them. Thereby, managers could decide to provide TDS with characteristic control features as

known from conventional products or service. To give an example, one could think of a steering

wheel and pedals in a self-driving car. Another approach could be to inform customers across

multiple communication channels, how the service process works in detail when employing a

TDS. Thereby, companies offer process transparency to their customers.

Perceived service safety is particularly important for person-related usage of TDS. As al-

ready outlined, increased perceived controllability leads to exaggerated perceptions of service

safety. Therefore, managers should strongly draw their attention to strategies aiming at increas-

ing customers’ perception of the controllability of their TDS. Aside from that, managers should

also promote free product trials to their customers, accompanied by qualified employees ex-

plaining the function of their TDS. Managers could also think of initializing an international

73


standard, which independently monitors and evaluates the capabilities of their TDS. To give an

example of such a standard, one could think of the European New Car Assignment Programme

(ENCAP) applied in the automotive industry.

As the acceptance model for TDS shows, the above mentioned key drivers play together in

a holistic mechanism. Therefore, managers should strive for an optimal adaption of measures

to enhance all three key drivers in the light of person-related TDS applications and optimally

coordinate actions to increase customers’ perceived controllability and anticipated temporal dis-

charge in the case of object-related applications of TDS.

Given the acceptance model for TDS and its measurements for the key drivers of customers’

acceptance of TDS, managers now have a first tool at hand that they can use in their R&D

departments to identify promising product designs with regard to increased customer accep-

tance of their TDS. Thereby, the main criteria managers should focus on are perceived process

quality, perceived controllability, and perceived service safety. The former one is especially

important in case of object-related applications of TDS, as today’s customers still doubt the

process quality of intelligent products capable of providing TDS.

Besides the internal approach to identify reasonable solutions to enhance customers’ ac-

ceptance of TDS, managers should also be aware of a new promising external approach. As

intelligent products capable of providing TDS generate massive data, this “database” of actual

usage could also reveal further insights. This idea somehow follows the reasoning of the so-

called Internet of Things. At this point it is worth noting that although the customer is taken out

of the service process, companies might get more information about their usage and possible

issues regarding TDS in the field than ever before. To give an example, one could think of

TESLA’s electric car Model S, which is regularly provided with software-updates by TESLA

and transfers usage data back to the company. Despite the fact that there is no customer in-

teraction necessary, the company has the opportunity to implement little design changes of an

assistant system and gets almost immediate feedback of its customers in the form of real usage

data to evaluate the effectiveness of their update. Hence, managers are well advised to consider

both internal and external approaches to enhance customers’ acceptance of TDS in the future.

From a more general perspective, managers should also question, how the new phenomenon

of TDS could possibly affect current business models. To give an example, one could think of

self-driving taxis. Once customers’ acceptance of using this type of TDS is well established, it

74


is at question, how this affects the car-sharing market or the conventional taxi market. Further-

more, service companies working in a field with lower task complexity, for instance trimming

the lawn or vacuuming the gym, could possibly think of employing intelligent products capable

of providing TDS in addition to their human workforce. However, replacing human workforce

by TDS nowadays might be a wrong decision, as customers anticipate a lower process quality

for TDS than for conventional services.

Finally, managers from manufacturing companies should also consider their current product

range, when their intelligent products capable of providing TDS are applied in a person-related

context. As customers’ time is freed up during the service process, managers may want to pro-

vide their customers with further services or product features during that phase. As an example,

one could think of a person being chauffeured from home to work in her or his self-driving

car. For that case, managers should identify, which features or services might be of great value

for their customers. Therefore, the author points out some avenues for future research within

chapter 7.4.

75


7.3 Limitations

Throughout the thesis, the author conducted several studies to empirically verify the proposed

measurements and the derived hypotheses according to the acceptance model of TDS and the

theoretical framework for the comparison of TDS with conventional products and services. Spe-

cific limitations for each of these studies were already given in chapters 5.2.1.3 (study 1), 5.2.2.3

(study 2), and 6.2.4 (study 3). Nevertheless, the author provides further general limitations in

the following.

As all three studies were conducted in an online setting, the findings may fall short with

respect to eternal validity. Therefore, future research could carry out a field study, like it could

possibly be done in the previous describe case of TESLA (see chapter 7.2), in order to give

evidence for the external validity of the presented findings.

The focal objective of intelligent products capable of providing TDS is to replace customers’

required interaction to fulfill a specific task. Nevertheless, situations may arise in which cus-

tomers’ goals are more satisfied by conventional products or conventional services.The question

is, are consumers striving to experience aesthetic or sensory pleasure, joy, and fun (Hirschman

& Holbrook, 1982) or it they are they focusing on auxiliary benefits to accomplish functional

and practical tasks (Strahilevitz & Myers, 1998). These two distinct types of goals, namely he-

donic and utilitarian (Wertenbroch & Dhar, 2000), will also affect customers’ attitudes towards

autonomous products.

As outlined in the introduction, this new type of services is supposed to have an impact on

less skill-demanding service sectors. Therefore, drawing on the service pyramid of Parasur-

aman (e.g. 2000) even more services shift from a employee-customer relationship towards a

technology-customer relationship. Hence, the author suggests that TDS will also have major

impacts in the context of B2B. Especially the close connection with the customer might be

heavily reduced due to the lack of personal stuff. In addition, R. T. Rust and Huang (2014)

mention that the classic understanding of customer-company exchange will change, such that

it becomes more dynamic. Thereby, TDS could make a remarkable contribution, as they could

provide data on customers’ real usage-patterns. However, this could not be investigated in the

present thesis.

76


7.4 Future Research Agenda

This thesis provides first academic work on the phenomenon of intelligent products capable of

providing TDS in the light of customers’ acceptance and evaluation in comparison to conven-

tional products and services. Nevertheless, there is still plenty of room for further research.

Especially in the light of TDS, it seems reasonable not to consider only product specifications

but also to consider consumers’ personal characteristics as means of predictors for innovation

adoption in future research (e.g. Billeter, Kalra, & Loewenstein, 2011).

Initial Propositions

Current findings provide evidence that consumers’ self construal effects their adoption behavior

for really new products (Ma, Yang, & Mourali, 2014). In specific, really new products are

more likely to be adopted by consumers high in self construal, whereas consumers low in self

construal prefer incremental new products. Nevertheless, these effects are dependent on product

specific cues like degree of popularity or scarcity, causing the main effect to be altered or even

to be reversed (Ma et al., 2014). Furthermore, literature further investigated the effects of

consumers’ personal status on their adoption behavior of innovations (Y. Hu & Van den Bulte,

2014). Thereby, it turned out that middle status consumers are most likely to adopt innovations

earlier than both low and high status consumers but in each case without being prone to social

cues (Y. Hu & Van den Bulte, 2014). Nevertheless, this only holds true for products which are

perceived being capable to enhance consumers’ current social status. Given that TDS can be

seen as really new products, the author suggests that both social status and self-construal jointly

affect customers’ acceptance of TDS.

Proposition P1: Customers of middle (low or high) social status, adopt TDS earlier

(later) when they are high (low) in self-construal.

When thinking of van Osselaer and Janiszewski’s (2012) consumer choice model, one could

ask if there are goal-related situations in which consumers benefit from TDS more or less. In

specific, Alderson’s (1957) idea of consummatory and instrumental motivated consumption be-

havior states that customers’ instrumentally driven purchase decisions are primarily concerned

with the auxiliary benefits of a product for a given task. This aligns with Choi and Fishbach’s

(2011) idea of instrumental or experiential consumer choices. Since the major distinction of

TDS to any other services is the autonomous completion of tasks, instrumentally motivated

customers are proposed to have a greater tendency towards the acceptance of TDS than experi-

ential motivated customers. Therefore, the author proposes:

77


Proposition P2: The more instrumentally (experientially) motivated customers are,

the more (less) favorable consumers evaluate TDS.

Moreover, literature states that perceived capability is the main predictor for customers’

product evaluation before the initial usage, whereas most attention is paid to perceived usabil-

ity after customers’ first application of the product (Thompson et al., 2005). According to

Hamilton and Thompson (2007), the change in evaluation priorities is due to different mental

construals evoked by direct or indirect experience with the product. That is, the more concrete a

customer’s idea of a product and therefore the lower the construal level, the more the customer

is concerned with the product’s ease of use (Hamilton & Thompson, 2007). To conclude, the

capabilities of TDS are limited these days, as most of them are dedicated products like a lawn

mower. At the same time, customers’ convenience in terms of time saving is largely increased,

Therefore, the author proposes the following:

Proposition P3: Consumers will have a more (less) favorable perception of TDS,

when these are represented in more (less) detail, as customers’ attention is drawn

from capability to usability (usability to capability).

Besides the empirical evidence of risk perception and its dimensions, literature also provides

a broad body of research on how to handle risk perception in the context of technology accep-

tance. To begin, it is notable to point out Cohen’s (2002) concern “that consumers may not

adequately comprehend the benefits and risks of using such highly technical products” (Cohen,

2002, p.172). At first sight, it seems obvious how to deal with benefits and risks, i.e. gains

and losses, when thinking about Tversky and Kahnemann’s (1981) so-called “Asian disease

problem”. They show, that customers become more risk seeking when choices are framed as

losses, while customers exhibit a clear tendency towards risk aversion when choices are framed

as gains. Although this experiment deals with customers’ lives, participants where not really

anxious as it was just a study. However, when customers perceive a certain risk that their health

could actually be threatened by consuming TDS and therefore give rise to customers’ anxiety

of using this technology, research has shown that a combination of perceived risk and fear leads

to exaggerated pessimistic risk estimates (Lerner & Keltner, 2001).

First results from previous research on how to deal with consumers’ risk perception in a

communications context state that framing losses in that context leads to a general increase in

risk aversion, whereas concentrating on gains leads to risk aversion against threats which are

constantly present (Cox, Cox, & Zimet, 2006).

78


Therefore it seems reasonable to conclude:

Proposition P4: Information on potential risk arising from TDS negatively corre-

lates with consumers’ perceived service safety.

Further, Fischer, Volckner, and Sattler (2010) point out that a brand’s central function is

consumers’ risk reduction prior to a purchase decision. Specifically, consumers try to compen-

sate lacking post-usage information by relying on long-term reputation of the brand at question

(Fischer et al., 2010). Since TDS are not common yet, the author hypothesizes that the brand

of a company, which offers TDS, effects consumers’ perceived service safety. That is:

Proposition P5: TDS provided by companies with a strong brand will be evaluated

higher in terms of perceived service safety than TDS provided by companies with a

weaker brand.

In addition, Gurhan-Canli and Batra (2004) emphasized that it is firms’ innovativeness and

trustworthiness that has a positive effect on high-risk consumer decisions. Therefore, the author

further proposes:

Proposition P5a: TDS provided by companies with high reputation regarding inno-

vation and high trustworthiness are evaluated higher than provided by companies

that are low in both dimensions.

Since new products often involve not only a single company producing all the required com-

ponents but a whole supplier network, as it is for instance the case in the automotive industry,

it is questionable whether to make use of the so-called ingredient branding strategy (Kaushik

& Keller, 2002). Thereby, the rational behind is to decide if a newly introduced product fea-

ture should be co- or self-branded. Giving an example, one could think of General Motors and

AUDI as two well known car manufacturers. However, these two companies significantly dif-

fer in terms of liability, especially due to recent product-recalls of General Motors. Therefore,

in the light of TDS it is at question which of the two previously mentioned strategies is more

reasonable, i.e. co- or self-branding of an automated highway-pilot. Drawing on Gurhan-Canli

and Batra’s (2004) notion that a company’s trustworthiness positively contributes to consumers’

decisions in a high-risk context, the author proposes:

Proposition P6: In the case of high (low) brand image, self-branding (co-branding)

of product features enabling the base product to provide TDS is positively corre-

lated with customers’ acceptance of this TDS.

79


Future Research Direction

Besides this communicative approach to decrease technological risk perception, Fagan, Neill,

and Wooldridge (2003) found that experience with new technologies, e.g. computers, consid-

erably alleviates usage anxiety. On a more individual level, it also turned out that consumers’

willingness to try out latest technological applications is a significant determinant of reduced

technological anxiety (Thatcher & Perrewe, 2002).

In line with the above mentioned questions arising with respect to customers’ perceived ser-

vice safety, academia could further investigate, whether customers have different perceptions of

service safety in the case that they use their own car capable of autonomously driving compared

to the case that they use a self-driving taxi.

Furthermore, future research could also address the question, how task complexity, e.g. trim-

ming the lawn or navigating a car, and environmental complexity, for instance one’s garden or

downtown Manhattan, influence customers’ perceived service safety and perceived controlla-

bility.

80

8 Conclusion

8Conclusion

With the advent of intelligent products like autonomous lawn mowers or self-driving cars, a

new kind of services has emerged in the marketplace, TDS. TDS are intelligent products with

an innate IT-based capability to autonomously operate and are therefore providing value di-

rectly to the customer without any necessary interaction during the value creation process of

its user or its manufacturer. Despite the broad stream of literature on technology acceptance

and service acceptance, research has not addressed the phenomenon of TDS yet. Therefore,

the author provides new measurements to capture the key dimensions influencing customers’

acceptance of TDS, namely anticipated temporal discharge, perceived controllability, and per-

ceived service safety. Results of study 1 confirmed the validity and reliability of the proposed

measurements. Based on these measurements, the author hypothesized a new acceptance model

for TDS. Results of Study 2 verified the proposed acceptance model. In specific, when TDS

are applied in an object-related context, like it is the case for an autonomous lawn mower, an-

ticipated temporal discharge and perceived controllability have a positive effect on customers’

attitude towards using the TDS. When TDS are applied in a person-related context, anticipated

temporal discharge has a positive effect on customers’ attitude towards using the TDS, whereas

the positive effect of perceived controllability on customers’ attitude towards using the TDS

is mediated by perceived service safety. Building on a newly proposed theoretical framework,

which allows for comparing TDS with conventional products and services, study 3 shows that

customers’ attitude towards TDS is determined by customers’ anticipated temporal discharge

and anticipated quality. In the case of a product comparison, TDS are associated with higher

anticipated temporal discharge and lower anticipated quality than a conventional product. In the

case of a service comparison, customers anticipate the same temporal discharge for TDS as for

conventional services, while their anticipated quality is lower than for conventional services.

Drawing on these results, implications for practitioners as well as for academics are derived.

Although this thesis gives first insights into customers’ acceptance of TDS and their evaluation

compared to conventional products and services, avenues for future research are pointed out.

81

References

References

Alderson, W. (1957). Marketing behavior and executive action. Homewood, IL: Irwin.

Alexander, R. A., & DeShon, R. P. (1994). Effect of error variance heterogeneity on the power

of tests for regression slope differences. Psychological Bulletin, 115(2), 308.

Anderson, E., Fornell, C., & Rust, R. (1997). Customer satisfaction, productivity, and prof-

itability: Differences between goods and services. Marketing Science, 16(2), 129-145.

Arnold, M., & Reynolds, K. (2003). Hedonic shopping motivations. Journal of Retailing,

79(2), 77-95.

Bagozzi, R., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the

Academy of Marketing Science, 16(1), 74-94.

Bagozzi, R., & Yi, Y. (2012). Specification, evaluation, and interpretation of structural equation

models. Journal of the Academy of Marketing Science, 40(1), 8-34.

Bansal, H. S., Taylor, S. F., & James, Y. S. (2005). “Migrating” to new service providers: To-

ward a unifying framework of consumers’ switching behaviors. Journal of the Academy

of Marketing Science, 33(1), 96–115.

Barrutia, J., & Gilsanz, A. (2012). Electronic service quality and value: Do consumer

knowledge-related resources matter? Journal of Service Research, 16(2), 231-246.

Batra, R., Ahuvia, A., & Bagozzi, R. P. (2012). Brand love. Journal of Marketing, 76(2), 1–16.

Bearden, W. O., Sharma, S., & Teel, J. E. (1982). Sample size effects on chi square and other

statistics used in evaluating causal models. Journal of Marketing Research, 425–430.

Berinsky, A., Huber, G., & Lenz, G. (2012). Evaluating online labor markets for experimental

research: Amazon.com’s mechanical turk. Political Analysis, 20(3), 351-368.

Berry, L., Seiders, K., & Grewal, D. (2002). Understanding service convenience. Journal of

Marketing, 66(7), 1-17.

Bertini, M., Ofek, E., & Ariely, D. (2009). The impact of add-on features on consumer product

evaluations. Journal of Consumer Research, 36(1), 17-28.

Billeter, D., Kalra, A., & Loewenstein, G. (2011). Underpredicting learning after initial expe-

rience with a product. Journal of Consumer Research, 37(5), 723-736.

Bloch, P. (2011). Product design and marketing: Reflections after fifteen years. Journal of

82

References

Product Innovation Management, 28(3), 378-380.

Bolton, R. N., & Drew, J. (1991). A multistage model of customers’ assessments of service

quality and value. Journal of Consumer Research, 17(4), 375-384.

Bolton, R. N., & Saxena-Iyver, S. (2009). Interactive services: A framework, synthesis and

research directions. Journal of Interactive Marketing, 23(1), 91-104.

Bornemann, T., & Homburg, C. (2011). Psychological distance and the dual role of price.

Journal of Consumer Research, 38(3), 490-504.

Box, G. E., & Andersen, S. L. (1955). Permutation theory in the derivation of robust criteria

and the study of departures from assumption. Journal of the Royal Statistical Society.

Series B (Methodological), 1–34.

Bradley, G., & Sparks, B. (2002). Service locus of control: Its conceptualization and measure-

ment. Journal of Service Research, 4(4), 312-324.

Brady, M., & Cronin Jr., J. (2001). Some new thoughts on conceptualizing perceived service

quality: A hierarchial approach. Journal of Marketing, 65(3), 34-49.

Brady, M., Knight, G., Cronin Jr., J., Tomas, G., Hult, M., & Keillor, B. (2005). Removing the

contextual lens: A multinational, multi-setting comparison of service evaluation models.

Journal of Retailing, 81(3), 215-230.

Brooker, G. (1984). An assessment of an expanded measure of perceived risk. Advances in

Consumer Research, 11(1), 439-441.

Brown, M. B., & Forsythe, A. B. (1974). Robust tests for the equality of variances. Journal of

the American Statistical Association, 69(346), 364–367.

Bruner II, G., & Kumar, A. (2007). Gadget lovers. Journal of the Academy of Marketing

Science, 35(3), 329-339.

Buera, F., & Kaboski, J. (2012). The rise of the service economy. American Economic Review,

102(6), 2540-2569.

Buhrmester, M., Kwang, T., & Gosling, D. (2011). Amazon’s mechanical turk: A new source

of inexpensive, yet high-quality, data? Perspectives on Psychological Science, 6(1), 3-5.

Burger, J. (1984). Desire for control, locus of control, and proneness to depression. Journal of

Personality, 52(1), 71-89.

Buttgen, M., Schumann, J., & Ates, Z. (2012). Service locus of control and customer copro-

duction: The role of prior service experience and organizational socialization. Journal of

Service Research, 15(2), 166-181.

Campbell, D., & Fiske, D. (1959). Convergent and discriminant validation by the multitrait-

multimethod matrix. Psychological Bulletin, 56(2), 81-105.

Campbell, M. C. (2007). “Says who?!” how the source of price information and affect influence

83

References

perceived price (un) fairness. Journal of Marketing Research, 44(2), 261–271.

Chang, S.-J., Van Witteloostuijn, A., & Eden, L. (2010). From the editors: Common method

variance in international business research. Journal of International Business Studies,

41(2), 178–184.

Chen, F. F. (2007). Sensitivity of goodness of fit indexes to lack of measurement invariance.

Structural Equation Modeling, 14(3), 464–504.

Cheung, G., & Rensvold, R. (1999). Testing factorial invariance across groups: A reconceptu-

alization and proposed new method. Journal of Management, 25(1), 1-27.

Choi, J., & Fishbach, A. (2011). Choice as an end versus a means. Journal of Marketing

Research, 48(3), 544-554.

Cohen, J. (2002). Introductory comments: Direct-to-consumer prescription drug advertising:

Evaluating regulatory policy in the united states and new zealand. Journal of Public

Policy & Marketing, 21(2), 172-173.

Collier, J., & Kimes, S. (2012). Only if it is convenient: Understanding how convenience

influences self-service technology evaluation. Journal of Service Research, 16(1), 39-

51.

Collier, J., & Sherrell, D. (2010). Examining the influence of control and convenience in a

self-service setting. Journal of the Academy of Marketing Science, 38(4), 490-509.

Correa, H., Ellram, L., Scavarda, A., & Cooper, M. (2007). An operations management view of

the serivces and goods offering mix. International Journal of Operations & Production

Management, 27(5), 444-463.

Cotte, J., Ratneshwar, S., & Mick, D. (2004). The time of their lives: Phenomenological

and metaphorical characteristics of consumer timestyles. Journal of Consumer Research,

31(2), 333-345.

Cox, A., Cox, D., & Zimet, G. (2006). Understanding consumer responses to product risk

information. Journal of Marketing, 70(1), 79-91.

Cronin Jr., J., Brady, M., & Hult, G. (2000). Assessing the effects of quality, value, and

customer satisfaction on consumer behavioral intentions in service environments. Journal

of Retailing, 76(2), 193-218.

Cronin Jr, J. J., & Taylor, S. A. (1992). Measuring service quality: a reexamination and

extension. The journal of marketing, 55–68.

Curran, J. M., Meuter, M. L., & Surprenant, C. F. (2003). Intentions to use self-service tech-

nologies: A confluence of multiple attitudes. Journal of Service Research, 5(3), 209–224.

Cusumano, M., Kahl, S., & Suarez, F. (2015). Services, industry evolution, and the competitive

strategies of product firms. Strategic Management Journal, 36(4), 559-575.

84

References

Dabholkar, P. A. (1996). Consumer evaluations of new technology-based self-service options:

an investigation of alternative models of service quality. International Journal of research

in Marketing, 13(1), 29–51.

Dabholkar, P. A., & Bagozzi, R. P. (2002). An attitudinal model of technology-based self-

service: moderating effects of consumer traits and situational factors. Journal of the

Academy of Marketing Science, 30(3), 184–201.

Dahl, D., Chattopadhyay, A., & Gorn, G. (1999). The use of visual mental imagery in new

product design. Journal of Marketing Research, 36(1), 18-28.

Davis, D., & Herr, P. M. (2014). From bye to buy: Homophones as a phonological route to

priming. Journal of Consumer Research, 40(6), 1063-1077.

Davis, F. (1989). Perceived usefulness, perceived ease of use, and user acceptance of informa-

tion technology. MIS Quarterly, 13(3), 319-340.

Davis, F., Bagozzi, R., & Warshaw, P. (1989). User acceptance of computer technology: A

comparision of two theoretical models. Management Science, 35(8), 982-1003.

De Luca, L., & Atuahene-Gima, K. (2007). Market knowledge dimensions and cross-functional

collaboration: Examining the different routs to product innovation performance. Journal

of Marketing, 71(1), 95-112.

De Ruyter, K., Wetzels, M., & Kleijnen, M. (2001). Customer adoption of e-service: an

experimental study. International Journal of Service Industry Management, 12(2), 184–

207.

Deshpande, R., Farley, J., & Webster Jr., F. (1993). Corporate culture, customer orientation, and

innovativeness in japanese firms: A quadrad analysis. Journal of Marketing, 57, 23-27.

Devaraj, S., Easley, R., & Crant, J. (2008). How does personality matter? relating the five-factor

model to technology acceptance and use. Information Systems Research, 19(1), 93-105.

Dotzel, T., Shankar, V., & Berry, L. (2013). Service innovativeness and firm value. Journal of

Marketing Research, 50(2), 259-276.

Duval, T., & Silvia, P. (2002). Self-awareness, probability of improvements, and the self-serving

bias. Journal of Personality and Social Psychology, 82(1), 49-61.

Erden, T., Keane, M., Oncu, T., & Strebel, J. (2005). Learning about computers: An analysis of

information search and technology choice. Quantitative Marketing and Economics, 3(3),

207-246.

Fagan, M., Neill, S., & Wooldridge, B. (2003). An empirical investigation into the relation-

ship between computer self-efficacy, anxiety, experience, support and usage. Journal of

Computer Information Systems, 44(2), 95-104.

Fang, E., Palmatier, R., & Steenkamp, J.-B. E. (2008). Effect of service transition strategies on

85

References

firm value. Journal of Marketing, 72(5), 1-14.

Faulkner, J., P. anf Runde. (2009). On the identity of technological objects and user innovations

in function. Academy of Management Review, 34(3), 442-462.

Feldman, L., & Hornik, J. (1981). The use of time: An integrated conceptual model. Journal

of Consumer Research, 7(4), 407-419.

Field, A., Miles, J., & Field, Z. (2012). Discovering statistics using r. London: Sage Publica-

tions.

Fischer, M., Volckner, F., & Sattler, H. (2010). How important are brands? a cross-category,

cross-country study. Journal of Marketing Research, 47(5), 823-839.

Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to

theory and research. MA: Addison-Wesley.

Fornell, C., & Larcker, D. (1981). Evaluating structural equation models with unobservable

variables and measurement error. Journal of Marketing Research, 18(1), 39-50.

Froehle, C. M., & Roth, A. V. (2004). New measurement scales for evaluating perceptions of the

technology-mediated customer service experience. Journal of Operations Management,

22(1), 1–21.

Fuchs, C., Schreier, M., & van Osselaer, S. (2015). The handmade effect: What’s love got to

do with it? Journal of Marketing, 79(2), in press.

Gadrey, J. (2000). The characterization of goods and services: An alternative approach. Review

of Income and Wealth, 46(3), 369-387.

Garcia, R., & Calantone, R. (2002). A critical look at technological innovation typology and

innovativeness terminology: a literature review. Journal of Product Innovation Manage-

ment, 19(2), 110-132.

Gautschi, D., & Ravichandran, T. (2006). Industrialization of services: an agenda for a scientific

management approach to services. In Conference on service sciences, management, and

engineering. Palisades, NY.

Giebelhausen, M., Robinson, S., Siriani, N., & Brady, M. (2014). Touch versus tech: When

technology functions as a barrier or a benefit to service encounters. Journal of Marketing,

78(4), 113-124.

Gill, T. (2008). Convergent products: What functionalities add more value to the base? Journal

of Marketing, 72(2), 46-62.

Gist, M., & Mitchell, T. (1992). Self-efficacy: A theoretical analysis of its determinants and

malleability. Academy of Management Review, 17(2), 183-211.

Golder, N., Mitra, D., & Moorman, C. (2012). What is quality? an integrative framework of

process and states. Journal of Marketing, 76(4), 1-23.

86

References

Goldsmith, R., & Hofacker, C. (1991). Measuring consumer innovativeness. Journal of the

Academy of Marketing Science, 19(3), 209-221.

Goodman, J., & Irmark, C. (2013). Having versus consuming: Failure to estimate usage

frequency makes consumers prefer multifeature products. Journal of Marketing Research,

50(1), 44-54.

Greenfield, H. (2002). A note on the goods/services dichotomy. The Service Industries Journal,

22(4), 19-21.

Gronroos, C. (1998). Marketing services: the case of a missing product. Journal of Business &

Industrial Marketing, 13(4/5), 322-338.

Guiltinan, J. (1987). The price bundeling of services: A normative framework. Journal of

Marketing, 51, 74-85.

Gurhan-Canli, Z., & Batra, R. (2004). When corporate image affects product evaluations: The

moderating role of perceived risk. Journal of Marketing Research, 41(2), 197-205.

Hamilton, R., & Thompson, D. (2007). Is there a substitute for direct experience? comparing

consumers’ preferences after direct and indirect product experience. Journal of Consumer

Research, 34(4), 546-555.

Han, J., Chung, S., & Sohn, Y. (2009). Technologically convergence: When do consumers

prefer convergent products to dedicated products? Journal of Marketing, 73(9), 97-108.

Hanson, W., & Martin, R. (1990). Optimal bundle pricing. Management Science, 36(2), 155-

174.

Harris, K., Schwedel, A., & Kim, A. (2011). The great eight. trillion-dollar growth trends to

2020. New York and Dallas: Bain & Company.

Hauser, J., Tellis, G., & Griffin, A. (2006). Research on innovation: A review and agenda for

marketing science. Marketing Science, 25(6), 687-717.

Hayes, A. F. (2012). Process: A versatile computational tool for observed variable mediation,

moderation, and conditional process modeling.

Henard, D., & Szymanski, D. (2001). Why some new products are more successful than others.

Journal of Marketing Research, 38(3), 362-375.

Higgins, E. T. (1997). Beyond pleasure and pain. American psychologist, 52(12), 1280.

Hirschman, E. C., & Holbrook, M. B. (1982). Hedonic consumption: emerging concepts,

methods and propositions. The Journal of Marketing, 92–101.

Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian journal

of statistics, 65–70.

Homburg, C., & Krohmer, H. (2006). Marketingmanagement. Wiesbaden: Gabler.

Hourahine, B., & Howard, M. (2004). Money on the move: Opportunities for financial service

87

References

providers in the ‘third space’. Journal of Financial Services Marketing, 9(1), 57-67.

Hu, L., & Bentler, M. (1999). Cutoff criteria for fit indexes in covariance structure analysis:

Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidis-

ciplinary Journal, 6(1), 1-55.

Hu, Y., & Van den Bulte, C. (2014). Nonmonotonic status effects in new product adoption.

Marketing Science, 33(4), 509-533.

Huang, M.-H., & Rust, R. (2013). It-related service: A multidisciplinary perspective. Journal

of Service Research, 16(3), 251-258.

Hui, M., Thakor, M., & Gill, R. (1998). The effect of delay type and service stage on consumers’

reactions to waiting. Journal of Consumer Research, 24(4), 469-480.

Iacobucci, D. (2010). Structural equations modeling: Fit indices, sample size and advanced

topics. Journal of Consumer Psychology, 20(1), 90-98.

Iacobucci, D., Saldanha, N., & Deng, X. (2007). A meditation on mediation: Evidence that

structural equations models perform better than regressions. Journal of Consumer Psy-

chology, 17(2), 140-154.

Im, I., Kim, Y., & Han, H.-J. (2008). The effects of perceived risk and technology type on

users’ acceptance of technologies. Information & Management, 45(1), 1–9.

Jacoby, J., & Kaplan, L. (1972). The components of perceived risk. Advances in Consumer

Research, 3(3), 382-383.

Joireman, J., Shaffer, M., Balliet, D., & Strathman, A. (2012). Promotion orientation explains

why future-oriented people exercise and eat healthy: Evidence from the two-factor con-

sideration of future consequences-14 scale. Personality and Social Psychology Bulletin,

38(10), 1272-1287.

Kaplan, L., Szybillo, G., & Jacoby, J. (1974). Components of perceived risk in product pur-

chase: A cross-validation. Journal of Applied Psychology, 59(3), 287-291.

Kaushik, K., & Keller, K. (2002). The effects of ingredient branding strategies on host brand

extendibility. Journal of Marketing, 66(1), 73-93.

Keh, H., & Pang, J. (2010). Customer reactions to service separation. Journal of Marketing,

74(2), 55-70.

Kim, S., & Malhotra, N. (2005). A longitudinal model of continued is use: An integrative view

of four mechanisms underlying postadoption phenomena. Management Science, 51(5),

741-755.

Kleijnen, M., de Ruyter, K., & Wetzels, M. (2007). An assessment of value creation in mobile

service delivery and the moderating role of time consciousness. Journal of Retailing,

83(1), 33-46.

88

References

Kotler, P., Keller, K., & Blieml, F. (2007). Marketing management. Munich: Pearson Studium.

Lam, S., Shankar, V., & Murthy, M. (2004). Customer value, satisfaction, loyalty, and swithcing

costs: An illustration from a business-to-business service ntext. Journal of the Academy

of Marketing Science, 32(3), 292-311.

Landwehr, J. R., Wentzel, D., & Herrmann, A. (2013). Product design for the long run: con-

sumer responses to typical and atypical designs at different stages of exposure. Journal

of Marketing, 77(5), 92–107.

Law, A., Logan, H., & Baron, R. (1994). Desire for control, felt control, and stress inoculation

training during dental treatment. Journal of Personality and Social Psychology, 67(5),

926-936.

Leavitt, H. (1954). A note on some experimental findings about the meaning of price. The

Journal of Business, 27(3), 205-210.

Leclerc, F., Schmitt, B., & Dube, L. (1995). Waiting time and decision making: Is time like

money. Journal of Consumer Research, 22(1), 110-119.

Lerner, J., & Keltner, D. (2001). Fear, anger, and risk. Journal of Personality and Social

Psychology, 81(1), 146-159.

Lovelock, C., & Gummerson, E. (2004). Whither services marketing?: In search of a new

paradigm and fresh perspectives. Journal of Service Research, 7(1), 20-41.

Lukas, B., Whitwell, G., & Heide, J. (2013). Why do customers get more than they need? how

organizational culture shapes product capability decisions. Journal of Marketing, 77(1),

1-12.

Lusch, R., & Vargo, S. (2006). Service-dominant logic: reactions, reflections and refinements.

Marketing Theory, 6(3), 281-288.

Lusch, R., Vargo, S., & O’Brien, M. (2007). Competing through service: Insights from service-

dominant logic. Journal of Retailing, 83(1), 5-18.

Lusch, R., Vargo, S., & Tanniru, M. (2010). Service, value networks and learning. Journal of

the Academy of Marketing Science, 38(1), 19-31.

Ma, Z., Yang, Z., & Mourali, M. (2014). Consumer adoption of new products: Independent

versus interdependent self-perspectives. Journal of Marketing, 78(2), 101-117.

MacInnis, D. J. (2011). A framework for conceptual contributions in marketing. Journal of

Marketing, 75(4), 136–154.

Mathwick, C., Mahotra, N., & Rogdon, E. (2001). Experiental value: conceptualization, mea-

surement and application in the catalog and internet shopping environment. Journal of

Retailing, 77(1), 39-56.

May, F., & Monga, A. (2014). When time has a will of its own, the powerless don’t have

89

References

the will to wait: Antropomorphism of time can decrease patience. Journal of Consumer

Research, 40(5), 924-942.

Meldrum, M. (1995). Marketing high-tech products: the emerging themes. European Journal

of Marketing, 29(10), 45-48.

Meuter, M., Bitner, M., Ostrom, A., & Brown, S. (2005). Choosing among alternative service

delivery modes: An investigation of customer trial of self-service technology. Journal of

Marketing, 69(2), 61-83.

Meuter, M., Ostrom, A., Bitner, M., & Roundtree, R. (2003). The influence of technology

anxiety on consumer use and experiences with self-services technologies. Journal of

Business Research, 56(11), 899-906.

Meuter, M., Ostrom, A., Roundtree, R., & Bitner, M. (2000). Self-service technologies: Un-

derstanding customer satisfaction with technology-based service encounters. Journal of

Marketing, 64(3), 50-64.

Miller, G. A., & Chapman, J. P. (2001). Misunderstanding analysis of covariance. Journal of

abnormal psychology, 110(1), 40.

Mitchell, V.-W., & Greatorex, M. (1993). Risk perception and reduction in the purchase of

consumer services. The Service Industries Journal, 13(4), 179-200.

Mochon, D., Norton, M. I., & Ariely, D. (2012). Bolstering and restoring feelings of compe-

tence via the ikea effect. International Journal of Research in Marketing, 29(4), 363–369.

Moeller, S. (2008). Customer integration - a key to an implementation perspective of service

provision. Journal of Service Research, 11(2), 197-210.

Moldovan, S., Goldenberg, J., & Chattopadhyay, A. (2011). The different roles of product orig-

inality and usefulness in generating word-of-mouth. International Journal of Research in

Marketing, 28(2), 109-119.

Muller, D., Judd, C. M., & Yzerbyt, V. Y. (2005). When moderation is mediated and mediation

is moderated. Journal of personality and social psychology, 89(6), 852.

Murray, K., & Schlacter, J. (1990). The impact of services versus goods on consumers’ as-

sessment of perceived risk and variability. Journal of the Academy of Marketing Science,

18(1), 51-65.

Noordhoff, C., Kyriakopoulos, K., Moorman, C., Pauwels, P., & Dellaert, B. (2011). The

bright side and dark side of embedded ties in business-to-business innovation. Journal of

Marketing, 75(5), 34–52.

Normann, R. (2001). Reframing business: when the map changes the landscape. Chichester:

Wiley.

Novemsky, N., Dhar, R., Schwarz, N., & Simonson, I. (2007). Preference fluency in choice.

90

References

Journal of Marketing Research, 44(3), 347–356.

Nysveen, H., Pedersen, P., & Thorbjønsen, H. (2005). Intentions to use mobile services:

Antecedetns and cross-service comparison. Journal of the Academy of Marketing Science,

33(3), 330-346.

Okada, E., & Hoch, S. (2004). Spending time versus spending money. Journal of Consumer

Research, 31(2), 313-323.

Olson, E., Walker, O., & Ruekert, R. (1995). Organizing for effective new product development:

The moderating role of product innovativeness. Journal of Marketing, 59(1), 48-62.

Oppenheimer, D. M., Meyvis, T., & Davidenko, N. (2009). Instructional manipulation checks:

Detecting satisficing to increase statistical power. Journal of Experimental Social Psy-

chology, 45(4), 867–872.

Paluch, S., & Blut, M. (2013). Service separation and customer satisfaction: Assessing the

service separation/customer integration paradox. Journal of Service Research, 16(3),

415-427.

Parasuraman, A. (2000). Technology readiness index (tri): A multiple-item scale to measure

readiness to embrace new technologies. Journal of Service Research, 2(4), 307-320.

Parasuraman, A., Zeithaml, V., & Berry, L. (1988). Servqual: A multiple-item scale for mea-

suring consumer perceptions of service quality. Journal of Retailing, 64(1), 12-40.

Paternoster, R., Brame, R., Mazerolle, P., & Piquero, A. (1998). Using the correct statistical

test for the equality of regression coefficients. Criminology, 36(4), 859–866.

Pauwels, K., Silva-Risso, S., J. Srinivasan, & Hanssens, D. (2004). New products, sales promo-

tions, and firm value: The case of the automobile industry. Journal of Marketing, 68(4),

142-156.

Podsakoff, P., MacKenzie, S., & Podsakoff, N. (2012). Sources of method bias in social science

and recommendations on how to control it. Annual Review of Psychology, 63, 539-569.

Prahalad, C., & Ramaswamy, V. (2000). Co-opting customer competence. Harvard Business

Review, 78(1), 79-87.

Preacher, K. J., Rucker, D. D., & Hayes, A. F. (2007). Addressing moderated mediation

hypotheses: Theory, methods, and prescriptions. Multivariate behavioral research, 42(1),

185–227.

Quinn, R., & Rohrbaugh, J. (1983). A spatial model of effectiveness criteria: Towards a

competing values approach to organizational analysis. Management Science, 29(3), 363-

377.

Rijsdijk, S., Hultkin, E., & Diamantopoulos, A. (2007). Product intelligence: its conceptual-

ization, measurement and impact on consumer satisfaction. Journal of the Academy of

91

References

Marketing Science, 35(3), 340-356.

Rijsdijk, S., Langerak, F., & Hultkin, E. (2011). Understanding a two-sided coin: Antecedents

and consequences of a decomposed product advantage. Journal of Product Innovation

Management, 28(1), 33-47.

Rijsdijk, S. A., & Hultink, E. J. (2009). How today’s consumers perceive tomorrow’s smart

products*. Journal of Product Innovation Management, 26(1), 24–42.

Rotter, J. (1966). Generalized expectancies for internal versus external control of reinforcement.

Psychological Monographs: General and Applied, 80(1), 1-28.

Rubera, G., & Kirca, A. (2012). Firm innovativeness and its performance outcomes: A meta-

analytic review and theoretical integration. Journal of Marketing, 76(3), 130-147.

Russell, D. (2002). In search of underlying dimensions: The use (and abuse) of factor analysis

in personality and social psychology bulletin. Personality and Social Psychology Bulletin,

28(12), 1629-1646.

Rust, R., & Huang, M.-H. (2012). Optimizing service productivity. Journal of Marketing,

76(2), 47-66.

Rust, R. T., & Huang, M.-H. (2014). The service revolution and the transformation of marketing

science. Marketing Science, 33(2), 206–221.

Satorra, A., & Bentler, P. M. (1994). Latent variabes analysis: Applications for developmental

research. In A. von Eye & C. Clogg (Eds.), (p. 399-419). Thousand Oaks, CA: Sage

Publications.

Schepers, J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance model: In-

vestigationg subjecitve norm and moderation effects. Information & Management, 44(1),

90-103.

Sethi, R., & Iqbal, Z. (2008). Stage-gate controls, learning failure, and adverse effect on novel

new products. Journal of Marketing, 72(1), 118-134.

Shapiro Jr., D., Schwartz, C., & Astin, J. (1996). Controlling ourselves, controlling our world:

Psychology’s role in understanding positive and negative consequences of seeking and

gaining control. American Psychologist, 51(12), 1213-1230.

Shen, H., Jiang, Y., & Adaval, R. (2010). Contrast and assimilation effects of processing

fluency. Journal of Consumer Research, 36(5), 876–889.

Sinclair, J., & Irani, T. (2005). Advocacy advertising for biotechnology: The effect of public

accountability on corporate trust and attitude toward the ad. Journal of Advertising, 34(3),

59–73.

Sivakumar, K., Li, M., & Dong, B. (2014). Service quality: The impact of frequency, timing,

proximity, and sequence of failures and delights. Journal of Marketing, 78(1), 41-58.

92

References

Smith, A. K., Bolton, R. N., & Wagner, J. (1999). A model of customer satisfaction with service

encounters involving failure and recovery. Journal of marketing research, 36, 356–372.

Steenkamp, J.-B. E., ter Hofstede, F., & Wedel, M. (1999). A cross-national investigation into

the individual and national cultural antecedents of consumer innovativeness. Journal of

Marketing, 63(2), 55-69.

Strahilevitz, M. A., & Myers, J. (1998). Donations to charity as purchase incentives: How

well they work may depend on what you are trying to sell. Journal of consumer research,

24(4), 434.

Suri, R., & Monroe, K. B. (2003). The effects of time constraints on consumers’ judgments of

prices and products. Journal of consumer research, 30(1), 92–104.

Surprenant, C. F., & Solomon, M. R. (1987). Predictability and personalization in the service

encounter. the Journal of Marketing, 51(2), 86–96.

Talke, K., Salomo, S., Wieringa, J., & Lutz, A. (2009). What about design newness? investigat-

ing the relevance of a neglected dimension of product innovativeness. Journal of Product

Innovation Management, 26(6), 601-615.

Thatcher, J., & Perrewe, P. (2002). An empirical examination of individual traits as antecedents

to computer anxiety and computer self-efficacy. MIS Quarterly, 26(4), 381-396.

Thompson, D., Hamilton, R., & Rust, R. (2005). Feature fatigue: When product capabilities

become too much of a good thing. Journal of Marketing Research, 42(4), 431-442.

Thompson, D., & Norton, M. (2011). The social utility of feature creep. Journal of Marketing

Research, 48(3), 555-565.

Troye, S. V., & Supphellen, M. (2012). Consumer participation in coproduction:“i made it

myself” effects on consumers’ sensory perceptions and evaluations of outcome and input

product. Journal of Marketing, 76(2), 33–46.

Tsai, C. I., & McGill, A. L. (2011). No pain, no gain? how fluency and construal level affect

consumer confidence. Journal of Consumer Research, 37(5), 807–821.

Tversky, A., & Kahnemann, D. (1981). The framing of decisions and the psychology of choice.

Science, 211(4481), 453-458.

Ulaga, W., & Reinartz, W. (2011). Hybrid offerings: How manufacturing firms combine goods

and services successfully. Journal of Marketing, 75(6), 5-23.

van Beuningen, J., de Ruyter, K., & Wetzels, M. (2011). The power of self-efficacy change

during service provision: Making your customers feel better about themselves pays off.

Journal of Service Research, 14(1), 108-125.

Vandermerwe, S., & Rada, J. (1988). Servitization of business: Adding value by adding ser-

vices. European Management Journal, 6(4), 314-324.

93

References

van Osselaer, S., & Janiszewski, C. (2012). A goal-based model of product evaluation and

choice. Journal of Consumer Research, 39(2), 260-292.

Vargo, S., & Lusch, R. (2004). Evolving to a new dominant logic for marketing. Journal of

Marketing, 68(1), 1-17.

Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic

motivation, and emotion into the technology acceptance model. Information Systems

Research, 11(4), 342-365.

Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on

interventions. Decision Science, 39(2), 273-315.

Venkatesh, V., & Davis, F. (2000). A theoretical extension of the technology acceptance model:

Four longitudinal field studies. Management Science, 46(2), 186-204.

Venkatesh, V., Morris, M., & Ackerman, P. (2000). A longitudinal field investigation of gender

differences in individual technology adoption decision-making processes. Organizational

Behavior and Human Decision Processes, 83(1), 33-60.

Venkatesh, V., Morris, M., Davis, G., & Davis, F. (2003). User acceptance of information

technology: Toward a unified view. MIS Quarterly, 27(3), 425-478.

Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information

technology: Extending the unified theory of acceptance and use of technology. MIS

Quarterly, 36(1), 157-178.

Voss, K., Spangenberg, E., & Grohmann, B. (2003). Measuring the hedonic and utilitarian

dimensions of consumer attitude. Journal of Marketing Research, 40(3), 310-320.

Wentzel, D., Tomczak, T., & Henkel, S. (2014). Can friends also become customers? the impact

of employee referral programs on referral likelihood. Journal of Service Research, 17(2),

119–133.

Wertenbroch, K., & Dhar, R. (2000). Consumer choice between hedonic and utilitarian goods.

Journal of Marketing Research, 37(1), 60-71.

Woodruff, R. (1997). Customer value: The next source for competitive advantage. Journal of

the Academy of Marketing Science, 25(2), 139-153.

Wortman, G., & Brehm, J. (1975). Responses to uncontrollable outcomes: An integration of

reactance theory and the lerned helplesness model. In L. Berkowitz (Ed.), Advances in

experimental social psychology (Vol. 8). New York: Academic Press.

Wunderlich, N., von Wangenheim, F., & Bitner, M. (2012). High tech and high touch: A frame-

work for understanding user attitudes and behaviors related to smart interactive services.

Journal of Service Research, 16(1), 3-20.

Yim, C., Chan, K., & Lam, S. S. (2012). Do customers and employees enjoy service par-

94

References

ticipation? synergistic effects of self- and other-efficacy. Journal of Marketing, 76(6),

121-140.

Zeithaml, V. (1988). Consumer perceptions of price, quality, and value: A means-end model

and synthesis of evidence. Journal of Marketing, 52(3), 2-22.

Zeithaml, V., Berry, L., & Parasuraman, A. (1996). The behavioral consequences of service

quality. Journal of Marketing, 60(2), 31-46.

Zhao, X., Lynch Jr., J., & Chen, Q. (2010). Reconsidering baron and kenny: Myths and truths

about mediation analysis. Journal of Consumer Research, 37(2), 197-206.

95

Curriculum Vitae

Curriculum Vitae

Name Christian Hauner

Date of Birth 21st of February 1985 in Landshut, Germany

Education2012 - 2015 University of St.Gallen, Switzerland

Doctoral Candidate in Business Administration

2012 University of Michigan, USA

Summer School in Quantitative Research Methods

2009 - 2012 Technische Universitat Munchen, Germany

Master Studies in Industrial Engineering

2005 - 2009 University of applied Sciences Landshut, Germany

Diploma Studies in Mechanical Engineering

2005 Hans-Leinberger-Gymansium Landshut, Germany

Abitur

Working Experience2012 - 2015 Center for Customer Insight, University of St.Gallen, Switzerland

Research Associate and Project Leader

2011 Technische Universitat Munchen, Germany

Student assistant at the Department of Service and Technology Marketing

and at the Department of Automotive Engineering

2009 BMW Group, Munich, Germany

Diploma thesis

2008 - 2009 BMW Manufacturing Co., LLC, Spartanburg, USA

Internship

2008 Audi AG, Ingolstadt, Germany

Working student


Working student


Internship

96

Curriculum Vitae

2006 E.ON Bayern AG, Landshut, Germany

Working Student

2005 Fa. Franz Ostermaier, Landshut, Germany

Internship

97

dissertation chauner | supervisor: prof. dr. torsten ...file/... · technology-derived services...

Documents