2010 employees’ propensity for emotional contagion as a...
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University of BoltonUBIR: University of Bolton Institutional Repository
Bolton Business School: Dissertations Bolton Business School
2010
Employees’ propensity for emotional contagion asa mediating variable between job satisfaction andperceptions of customers’ mood: a comparativecase study.Maria Rita Rueff Negrão Mendonça LopesUniversity of Bolton, [email protected]
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Digital Commons CitationRueff Negrão Mendonça Lopes, Maria Rita. "Employees’ propensity for emotional contagion as a mediating variable between jobsatisfaction and perceptions of customers’ mood: a comparative case study.." (2010). Bolton Business School: Dissertations. Paper 5.http://digitalcommons.bolton.ac.uk/bbs_dissertations/5
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Employees’ propensity for emotional contagion as a mediating
variable between job satisfaction and perceptions of customers’
mood: a comparative case study
Maria Rita Rueff Negrão Mendonça Lopes
Project submitted in part fulfilment of the Masters of Art Human Resource
Management
Bolton Business School
The University of Bolton – June 2010
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Employees’ propensity for emotional contagion as a mediating variable between
job satisfaction and perceptions of customers’ mood: a comparative case study
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Table of Contents
Page
Table of Contents 3
1. Abstract 4
2. Acknowledgments 5
3. Introduction 6
4 literature Review 8
4.1 Job satisfaction 8
4.2 Customer Interaction 12
4.3 Mood 16
4.4 Emotional Contagion 20
4.4.1 Emotions… Where do they come from? 20
4.4.2 Emotional transference, convergence or contagion 22
5 Conceptual Framework 26
6 Methodology 27
6.1 Hypothesis and analysis model 28
6.2 Methods 29
6.2.1 Socio-demographic data 31 6.2.2 Age 31
6.2.3 Gender 32
6.2.4 Working hours 33
6.2.5 Type of contact with customers 34
6.2.6 Work Experience 34
6.2.7 Job satisfaction 35
6.2.8 Emotional contagion 37
6.2.9 Perception of customers’ mood 37
7. Ethics 39
8. Organisational background and sample 42
8.1 Organisation 1 42
8.2 Organisation 2 42
8.3 Sample description 43
9. Findings and Analysis 45
9.1 Descriptive Statistics 46
9.1.1 Emotional Contagion 47
9.1.2 Job Satisfaction 48
10. Conclusions and recommendations 62
11. Recommendations 67
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1. Abstract
Past investigations have demonstrated the important role of emotions in the
workplace. The present research focuses on frontline employees and on how
the relation between their perceptions of customers’ mood and job satisfaction
can be moderated by their levels of emotional contagion. A comparative case
study was conducted with two conditions of service encounters: face-to-face
and telephone. The results show that for both conditions there is a positive and
significant correlation between perceptions of customers’ mood and job
satisfaction. Only for the face to face group this relation was moderated by
employees’ propensity for emotional contagion. The author suggests that where
all the sources of emotional contagion are present (proxemics, kinesics, and
paralinguistic) the emotional contagion scale created by Doherty (1997) could
and should be used as a recruitment tool. For the job environments where
employees are not exposed to the visual sources, more research is still needed
to understand how the contagion process takes place.
Keywords: emotional contagion, job satisfaction, front line employees, customers, mood.
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2. Acknowledgments
I dedicate this work to my supervisor and lecturers for the priceless stock of
knowledge, dedication and incommensurable support – I owe you what I know;
to my family and friends for the courage, confidence, values and strong belief in
me – I owe you what I am; to my manager and work colleagues for the
unendingly understanding, flexibility and investment in my personal
development; to all the participants of the study and particularly to Jon Simpson
for making this research possible; and at last to my friend and partner Alberto
Caio for the extraordinary presence in my life, without which this Master would
not be possible.
Thank you all, thank you very much.
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3. Introduction
Particularly directed to customer service organisations, this research will
explore the role of employees’ propensity for emotional contagion on overall job
satisfaction, when such employees are permanently exposed to customers’
moods that, according to Dallimore et al. (2007), may vary in both valence
(positive or negative), and arousal (high or low). In the current scenario of
expansion of the service economy (Grandey et al., 2004), Groth (2005) defends
that it is extremely important to explore the dynamics associated with the role of
public facing employees.
From a Human Resource Management (HRM) perspective, to scrutinize the
dimensions of such roles can be extremely helpful for the strategic design of
employees’ development techniques and effective recruitment tools.
The relevance of understanding the requirements to be a successful frontline
employee is undeniable, as the quality of service encounters determines
customer satisfaction and consequent organisational success (Specht et al.,
2007). Therefore, the present study focuses on the influence of employees’
perception of customers’ mood on their job satisfaction, arguing that such
perception will impact on the way such workers form their attitudinal and
cognitive schemas regarding the way they feel about their job. Moreover, the
author suggests that this relation is moderated by an individual characteristic,
the propensity for emotional contagion. In order to test such hypotheses a
comparative study was conducted in two organisations. In one of them the
interaction with customers occurs face to face, whereas in the other this contact
is established via telephone. In the first part, the literature review and
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conceptual framework are presented by critically describing the more sonant
theories and relevant academic findings on the subjects. The conceptual
framework will briefly introduce the reader to the research model and underlying
hypothesis. Following this, the methodology will be disclosed, including
important factors such as the measurement instruments’ validity and reliability.
Finally, a findings and analysis chapter will expose the results and a discussion
of these findings, followed by a conclusion and recommendations chapter,
where a summary of the principal arguments as well as recommendations for
future research will be developed.
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4. Literature Review
4.1 Job satisfaction
Job satisfaction is a construct that has been part of the academic literature for a
long time, without a consensus being reached on its definition (Falkenburg and
Schyns, 2007).
Locke (1976, p. 1300) described it as “a pleasurable or positive emotional state
resulting from the appraisal of one’s job or job experiences”. More recently,
Alqashan and Alzubi (2009) illustrate it as the rewarding nature of a person’s
work. Being either related to work experiences or with the nature of the work
itself, the role of job satisfaction as an organisational competitive asset is one of
unquestionable value and increasing interest, and this is deservedly so given
its, albeit indirect, relation with organisational productivity (Wright and Davis,
2003).
Early studies on the matter tried to relate job satisfaction with a vast array of
variables. Staw et al. (1986), for example, argued that it was connected with
early experiences in life. Their research, however, had several reliability issues
such as the failure to control situational variants, the very small sample size and
also the modest correlations that jeopardised the generalisability of the findings.
Furthermore, in 1989, Arvey et al. studied two monozygotic twins who had
always lived apart from each other. The researchers affirmed that job
satisfaction had a genetic source but many scholars refuted the validity of such
arguments (see, e.g., Cropanzano and James, 1990), based mainly on the
methodology used by the authors to draw such conclusion.
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More related with organisational studies, recent evidence has supported the
relation between job satisfaction and organisational commitment (Testa 2001)
and turnover (Udechukwu, 2009). Hence, the costs of continuous recruitment
and training can be reduced when the levels of job satisfaction are high, as
withdrawal or counterproductive behaviours will be reduced (Slattery and
Selvarajan, 2005).
Many authors have sustained that job satisfaction is related to organisational
citizenship behaviour (Organ et al., 2006; Podsakoff et al., 2000), intra
organisational behaviour (Bradford et al., 2009) and performance (Bowling,
2007). Although the causal relationship between performance and job
satisfaction has been empirically investigated since the 1920’s Hawthorne
Studies (Boshoff and Allen, 2000), doubts still remained about the nature of this
relationship (see, for example Cranny et al., 1992). As Steers stated (1981, p.
309) ‘(…) the fact that workers are satisfied does not mean that they will
produce more, only that they are satisfied’.
While it was widely believed that satisfied workers would perform well, it was
later demonstrated that this relation was stronger in the opposite direction, i.e.,
those who perform well are more satisfied with their job (Boshoff and Allen,
2000).
Regarding frontline employees, job satisfaction is often depicted as a result of
motivation that is closely linked with the individuals’ innate will to help others
(Pandey and Stazyk, 2008). Although many researchers have supported this
conception (see, e.g., Park and Rainey, 2007, 2008; Steijn, 2008; Taylor, 2008)
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a panoply of other variables can increase or reduce job satisfaction (see, e.g.
Vandenabeele, 2009) and being a satisfied frontline employee does not
necessarily imply a vocation to serve as such roles imply different tasks and
responsibilities.
Nevertheless, the revenues of job satisfaction are increasingly obtaining more
and better recognition: a happy and committed work force represents human
capital (Starks and Brooks, 2009), the stock of knowledge, skills and abilities
that the individual possesses and that contribute enormously for the distinctive
core competencies that allow a firm to perform better than its competitors
(Harrison and Kessels, 2004; Lawler, 2008).
Furthermore, scholars identify extrinsic and controllable variables that are
directly related with job satisfaction, inter alia, long working hours (Burke and
Fiksenbaum, 2008; De Raeve et al., 2007; Dembe et al., 2005; van der Hulst,
2003), training (Sahinidis and Bouris, 2008), on-line training (Sitzmann et al.,
2006), payment (Carr et al., 1996; Gupta and Shaw, 1998), enhancement of
employees’ voice (Batt et al., 2002; Hodson, 2002; Kato and Morishima, 2002),
leadership style, organisational culture, organisational conditions (Mor Barak, et
al., 2001), and work environment (Wright and Davis, 2003).
The manipulation of the mentioned variables to increase job satisfaction is a
strategic via for achieving organisational goals that include the amplification of
employee’s positive outcomes (Cameron et al., 2003), augment of productivity
(Guthrie, 2001), expansion of corporate financial performance (Carpenter et al.
2001) and retention (Guthrie, 2001).
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However, in the current economic scenario it is not always easy to assure that
job satisfaction is present in employees. In a reality of crisis, one of the most
common ways for organisations to cut on costs is by sacking its workers (Nixon
et al., 2004). For the remaining employees this fact represents a breach of the
psychological contract, the quid pro quo of all working relations (Hui et al., 2004;
Smithson and Lewis, 2000). The consequences of job insecurity, which has
been unsurprisingly found to be negatively related with job satisfaction (Sverke
et al., 2002), performance (Judge et al., 2001), and customer service (Kotler
and Keller, 2006), may even produce damaging effects on workers’ citizenship
behaviours (Feather and Rauter, 2004; Wong et al., 2005), attitudes (Sverke et
al., 2002), health and safety acquiescence (Probst and Brubaker, 2001),
counterproductive conducts (Harrison et al., 2006), and health (Ferrie et al.,
2005).
Additionally, researchers started to explore intrinsic sources from where job
satisfaction could arise; employees’ personal characteristics as personal
growth, personal development, life / work balance, and personal relationships
(Matheny, 2008) were also identified as tightly linked with job satisfaction.
The investigation of the influence of personality traits on job satisfaction is also
a significant part of the literature (e.g. Judge et al., 2002; Lounsbury et al.,
2003). Boudreau et al (2001) found that agreeableness, conscientiousness and
neuroticism are negatively correlated with the construct, whereas extraversion
is positively associated. In an experience with 496 workers, Seibert and Kramer
(2001) also describe a negative relation between neuroticism and job
satisfaction and a positive one between job satisfaction and extraversion.
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Judge et al. (1995) characterize job satisfaction as an intrinsic sine qua non
dimension of career success.
Although many scholars have tried to define affective traits and experiences as
predictors of various organisational outcomes (for a meta-analysis see, e.g.
Meyer et al., 2002), the exploration of the emotional variables of job satisfaction
is still scarce and, according to Seo et al. (2004, p. 424): “Understandings of
work (…) that fail to consider human emotion are incomplete”.
Fisher (2002) stated that employees with higher levels of job satisfaction are
more likely to experience positive emotions and good moods at work. However,
the influence of customer’s mood in this relation was not considered, even if
some of the research participants worked directly with customers.
Therefore this research focus is the investigation of a still virgin field: the
mediating role of employees’ propensity for emotional contagion between job
satisfaction and exposure to consumer’s mood.
4.2 Customer interaction
The interaction with customers goes beyond an automatic response to
immediate requests. It involves a range of normative behaviours expected from
employees that will increase customers’ attitudes towards the organisation
(Dallimore et al., 2007). Pugh (2001) points that customers request more than
just a product and that one of the expectations from service interactions is an
emotional input. An employee needs to acquire both cognitive and affective
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elements to respond to this demand, as customers’ expectations are to be felt
and simultaneously understood (Luong, 2005).
Employees’ behaviours during service interactions have been shown to have an
impact on customers’ perceptions (Dallimore et al., 2007; Gabbott and Hogg,
2001; Sundaram and Webster, 2000) often referred to as ‘employee affective
delivery’ (Tsai and Huang, 2002). Such delivery arises from a range of
normative behaviours that include, e.g., smiling, thanking and greeting
(Dallimore et al., 2007), and have been shown to exert influence in areas such
as satisfaction appraisals (Menon and Dube, 2000; Smith and Bolton, 2002),
consumers’ intentions to return (Tsai and Huang, 2002) and even on
consumers’ mood (Pugh, 2001). Understanding the dynamics of affective
deliveries is valuable for organisations (Diefendorff and Richard, 2003) as they
can serve as differentiators and provide intangible outcomes such as word of
mouth recommendations and loyalty (Luong, 2005). However, this relation does
not exist in a vacuum and other external variables have been identified in the
literature to promote such outcomes, inter alia, the space design and ambience
(e.g. Cameron et al., 2003; Lee and Dubinsky, 2003).
Diefendorff and Richard (2003) indicate that the form of affection expected from
the worker varies according to his / her position. From frontline employees,
research sustains that the most ubiquitous expected behaviour is friendliness
(Luong, 2005). The non verbal components of such behaviours consist of three
categories: proxemic, i.e., the physical distance between individuals;
paralinguistics, i.e., the features of spoken communication that do not engage
words (e.g., fluency, tone, vocal pitches), and kinesics, also known as body
language (Gabbott and Hogg, 2001; Hess et al., 2000; Sundaram and Webster,
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2000). But the causal relation between employees’ affective delivery and
organisational outcomes is not mechanical; customers are not a tabula rasa and
their own affective state (mood) can influence the way how such deliveries are
perceived (Tsai and Huang, 2002). Moreover, frontline employees ‘(…) and
customers, by definition, come from different organizations with different
expectations and backgrounds’ (Verbeke and Bagozzi, 2002, p. 731), therefore
to achieve a tuning between both is a complex process of co-coordinated
properties that include behavioural, emotional and physiological components
(Keltner and Anderson, 2000).
It is not always easy for employees to display positive emotions during service
interactions (Grandey et al., 2004), as customers do not always act in neutral
and functional ways (e.g. Ringberg et al., 2007).
Consumers’ attitudes are not a new subject in organisational, marketing, and
finance related literature, where the emergence of terms such as consumer
value (Gale, 1994; Kotler, et al., 1999; Sweeney and Soutar, 2001; Woodruff,
1997), led to an exhaustive instrumental research to anticipate and manipulate
the subsequent organisational gains (Lin et al., 2009).
Predicting customers’ loyalty, satisfaction, buying trends, negotiation and buying
styles (see, e.g., Arnett, 2009; Johansson, 2006) is a major concern to scholars
and practitioners. A consequence of this interest is the uncountable amount of
published customer focused questionnaires, models and theories (e.g. Bradford
et al., 2009; Howcroft et al., 2007; Nagle and Holden, 2002; Paridon and
Carraher, 2009; Walters, 2007).
However, the relationship between organisation and customer is dyadic,
dynamic and reciprocal, and should not be superficially or instrumentally
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described. The impact of the customers in organisations goes beyond what one
can read on the balance sheet. Customers may affect an organisation’s
reputation, image, trust, and ultimately, its employees.
Consciousness about customers’ inappropriate behaviour began to arise in the
psychology and marketing fields of knowledge (e.g. Fullerton and Punj, 2004),
where concerns as compulsive and addictive buying behaviour (see, for
example, Budden and Griffin, 1996; Elliot et al., 1996; Hassey and Smith,
1996), kleptomania (Fullerton, 2007), fraudulent complaining (Reynolds and
Harris, 2005) and shop lifting (Phillips et al., 2005; Reynolds and Harris, 2006)
were leading the research directions.
Customers’ misbehaviour and improper conduct is receiving nowadays more of
the HRM attention, either in academic (Grandey et al, 2004), and practitioner-
oriented research (e.g. Dube, 2003), and the reasons are plausible: the British
Retail Consortium (BRC, 2009, in press) annually reports the escalating
occurrences of clients’ misbehaviour and its consequences on employees’
physical and emotional well-being; also a study conducted by the Union of
Shop, Distributive and Allied Workers (USDAW, 2004) revealed that frontline
employees are constantly exposed to customers’ negative behaviours.
HRM literature regards the effects of workplace negative experiences (e.g.
Glomb and Liao, 2003), on workers’ emotional regulation (Grandey et al, 2004),
employment intentions (Canton et al, 2009), performance, turnover (Witt et al.,
2004; Wu and Hu, 2009), motivation and job satisfaction (Elias, 2004).
This research proposes that customers’ mood can be an independent variable
of job satisfaction, with more or less strength depending on the employees’
propensity for emotional contagion.
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4.3 Mood
Mood is often described as an affective state that varies in valence (positive and
negative) and arousal (high or low) and that is connected with both cognitive
and behavioural processes (e.g., Dallimore et al., 2007; Forgas and Vargas,
2000; George and Zhou, 2007). The subject began to hold academic interest
particularly in the fields of psychology and psychiatry (e.g. Joormann et al.
2007; Kovacs et al., 2009). Laboratory experiences have been conducted to
infer what constitutes each of the mood’s valences: dimensions of a positive
mood are, inter alia, divergent thinking, straightforward schemas and heuristics,
fluid categorization and ideation (e.g., Clore at al., 2001; Fredrickson, 2001;
Kaufmann, 2003). Negative mood is depicted as promoting a more analytical
and systematic approach, with greater attention to detail and less preoccupation
with existing scripts and schemas (e.g. Kaufmann, 2003).
The study of mood is not recent and researchers have tried to investigate this
affective state resorting to multiple strategies in laboratorial studies. Some of
the most common Mood Induction Procedures (MIP) on participants use music
that can be depicted as depressing, such as the ‘Opera 131 for Strings’ by
Beethoven (Gilboa-Schechtman et al., 2000) and ‘Russia Under the Mongolian
Yoke’ by Prokofiev (Gemar et al., 2001; Kelvin et al., 1999), and neutral or
cheerful music like the ‘Waltzes Nos. XI and XII’ by Chopin (Startup and Davey,
2001).
Other common procedure is the Velten MIP (Velten, 1968), which consists of a
set of mood related statements that the participant is required to read and to
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experience the mood accordingly with the one inherent to each statement.
Velten’s MIP continues to be widely used and improved (see for example, David
et al., 2006; Wagner et al., 2009) and has been defined as extremely effective
for depressive mood induction (Gerrards-Hesse et al., 1994). Other strategies
include stories, autobiographic evocations, photographs and environment
manipulation (Haaga et al., 2002; Ingram and Ritter, 2000; Richards and Gross,
2000; Startup and Davey, 2001). Recently, Goritz and Moser (2006) tried to
verify the feasibility of such mood induction strategies through the web. They
found that both the Velten method and photographs were efficient in inducting
negative moods but were ineffective in producing positive moods when applied
virtually.
An interesting experiment realised by Petrides and Furnham (2003) found that
MIP is also related to Emotional Intelligence (EI) traits, i.e., individuals with high
levels of EI are more sensitive to MIP and are also faster to identify other
people’s moods through their facial expressions.
A research conducted by Gohm (2003) found that high levels of EI are related
with effective mood regulation processes. Mood has also been shown to
influence information processing, i.e., positive moods increase top down
information processing and assimilation, whereas negative moods favour this
processing in a more accommodative and externally centred style (Bless,
2001). However, as negative moods increase suspicion about the information to
be processed (Forgas and East, 2008) they also reduce processing biases and
judgemental errors and augment accuracy in, for example, observer’s
recollections (Forgas, 1998; Forgas et al., 2005). Nevertheless, some
researchers argue that it is obsolete to observe the effects of positive or
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negative moods separately; these authorities defend that one should consider
the interactive effects of both valences (e.g. George and Zhou, 2007; Schwarz,
2002; Schwarz and Clore, 2003). This school of thought postulates that, for
example, the impact of a bad mood in processing a determinate experience
cannot be studied in isolation; previous positive moods can determine how a
person deals with a negative one, therefore they are interactive and mutually
dependent. The ‘mood-as-information’ theory goes further and states that
moods are unconscious informative systems that provide a person with the
necessary information to form a cognitive and behavioural reaction to it (see, for
example, Forgas and Vargas, 2000; Schwarz, 2002; Schwarz and Clore, 2003).
However, can a mood not be in the extreme pole of this process, i.e., be itself
the response or consequence of a cognitive \ behavioural reaction?
Moreover, a vast number of studies tried to relate memory and mood (e.g.,
Besser et al., 2008), supporting that depressed individuals tend to recall more
negative stimuli than individuals in a neutral or positive mood (see, e.g. Gilboa
et al., 1997; Matt et al., 1992). However, the results are not unswerving in the
literature (e.g. Bradley et al., 1994; Ilsley et al., 1995) as they do not
consistently demonstrate the effect of mood in explicit memory. Furthermore, as
memory consists of a multi-dimensional set of processes and components, it
remains indecisive which of those are influenced by depressive states,
jeopardising the validity of such studies (Direnfeld and Roberts, 2005).
Mood research has recently started to amplify its scope and extent, acquiring a
great amount of attention from organisational scholars (e.g. Brief and Weiss,
2002; Fisher, 2002; Rhoades et al., 2001). Organisational investigations have
found relations between positive mood and chore performance (e.g., Erez and
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Isen, 2002; Totterdell, 2000), assisting behaviours (Fisher, 2002; Lyubomirsky
et al., 2005), in-role performance (Eisenberger et tal., 2001), and perception of
personal achievements (Thoresen et al., 2003), although the causality of such
relations remains somehow ambiguous.
On the other hand, some researchers support that negative moods tend to
optimise creativity and improve problem solving, as subjects under this affective
state tend to defy the status quo and to look for alternative solutions (see, for
example, Gasper, 2003; George and Zhou, 2002; Kauffman and Vosbur, 1997).
Scholars defend that mood exerts influence in important areas like risk taking
decisions (Clark et al. 2001; Yuen and Lee, 2002), creativity (Davis, 2009;
Kaufmann, 2003), scepticism and detection of deception (Forgas and East,
2008, a; 2008, b), information processing (Bless and Fiedler, 2006),
metacognitive experiences (Efklides and Petkaki, 2005), verbal fluency (Clark et
al., 2001), social influence strategies (Forgas, 2007), and comparative
judgements (Qiu and Yeung, 2008).
In the work context, researchers found relations between mood and
performance (Chavez and Mendez, 2008; Coulson et al., 2008), organisational
spontaneity (George and Brief, 1992), prosocial behaviour at work (George,
1991), employee withdrawal behaviour (Andrew et al., 2005), and creativity in
the workplace (George and Zhou, 2007).
Though the scope of mood’s research was enlarged, one needs to comply with
specific work characteristics where moods are stronger in intensity and broader
in sources of provenience, particularly in customer service organisations, where
more organisational actors are involved (Sy et al, 2005).
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As ‘when a customer interacts with an organization during service delivery, their
exchange can similarly be considered a social exchange’ (Groth, 2005, p. 13),
consumers’ mood can and should not be overlooked as a source of emotional
contagion.
4.4 Emotional Contagion
4.4.1. Emotions… where do they come from?
The physical biases of emotional and social processes have gained an
increasing amount of interest in literature over the last decades (Olson et al.,
2007). It is extremely difficult to explore such processes, particularly because
such investigations are based on animal experiences and on the study of the
abnormal, i.e., only cases with a different \ damaged brain function can provide
clues of what the ‘normal’ functions are. The neurological investigation path
indicates that emotions are tightly linked with the amygdala and the prefrontal
cortex, although more recent experiments support that the temporal pole (TP),
i.e., the paralimbic region between both, is perhaps the centre of the emotional
and social neuronal connections (Kondo et al., 2005; Mesulam, 2000; Olson et
al., 2007).
Female macaques with surgical disruptions of the TP present anti-social
behaviour, disinterest in their peers and often a violent conduct (see, for
example, Franzen and Myers, 1973; Kling et al., 1993). In humans, it has been
found that atrophy in this area leads to personality alterations, depression,
apathy and to inappropriate social behaviour (Bozeat et al., 2000; Gorno-
Tempini et al., 2004; Mychack et al., 2001; Thompson et al., 2003).
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Furthermore, TP damage can cause vacillating and unstable moods like
irritation and anxiety (Glosser et al., 2000), and psychiatric conditions, inter alia,
bipolar disorder, schizophrenia and mania (Brooks and Hoblyn, 2005; Carran et
al., 2003; Crespo-Facorro et al., 2004; Kasai et al., 2003; Murai and Fujimoto,
2003). The Theory of Mind, which can be described as the ‘(…) ability to infer
desires, intentions, or beliefs in others’ (Olson et al., 2007, p. 1723), including
their actions and emotions, is also intrinsically related to the TP function.
Laboratorial studies detected TP activation in subjects when they were asked to
formulate moral resolutions (Moll et al., 2002; Heekeren et al., 2003) to identify
deception on others (Grezes et al., 2004), and also to detect other people’s
emotional state (Carr et al., 2003; Vollm et al., 2006).
Moriguchi et al. (2006) found that TP is also correlated with ‘personal distress
scores’, an instrument to measure the extent to which one perceives other
people negative moods. Although these results lead many researchers to
believe that TP is part of the emotional brain (e.g. Frith and Frith, 2003), other
schools of knowledge argue that TP does influence memory instead of
emotions and that is the reason why emotional perceptions and behaviours are
affected when the TP is damaged (e.g., Shaw et al., 2004). But to support this
belief is to say that all our interpretation of the external world has been learned,
stored and codified, while there is scientific support that the emotional function
is visceral and responds to perceptual stimuli that goes beyond what we learned
and memorised (Kondo et al., 2005).
Researchers found that TP is activated when one faces a vast array of external
emotions such as sadness (Eugene et al., 2003; Levesque et al., 2003), anger
(Damasio et al., 2000), anxiety (Kimbrell et al., 1999), disgust and fear (Phillips
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et al., 1998). Literature also states that TP is related to positive emotions like
maternal instinct (Ranote et al., 2004), sexual arousal (Beauregard, 2001) and
humour (Mobbs et al., 2003). Therefore we may argue that it is in the TP that a
grand part of the emotional exchange processes happens.
4.4.2 Emotional transference, convergence or contagion
It is undeniable that other people’s characteristics have an effect on our
everyday lives, after all, the human being is a social animal, subject to socially
induced affect (McIntosh et al., 1994) and the sources of emotional influence
can be more difficult to measure or observe than the behaviours of the ones
around us. Even other people’s mood can have an impact on the way we
experience the world (Kelly, 2004), and particularly in a workplace context, they
can affect employees’ welfare, emotional stability and subsequent job
satisfaction (Fredrickson, 2003).
Kelly (2004, p. 99), describes emotional contagion as ‘the process whereby the
moods (…) of those around us influence our own emotional state’. Goleman
(2000, p. 164) compares this phenomena to ‘some kind of social virus’, a part of
the unseen human interaction economy that is often unnoticed.
Although this social phenomenon was firstly studied following a ‘group mind’
perspective and, moreover, in the context of pathological masses behaviours
(Le Bon, 1986), it was later described as an automatic and involuntary converge
of emotions (Hatfield et al., 1994), with the function of what Kelly (2004) calls
social adaptation. According to Barsade (2002) emotional contagion is a
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process with two phases, where at first people inadvertently copy other people’s
external signals of mood (e.g., tone of voice, facial expression, posture) and, at
a second stage, this involuntary afferent mimicry tends to persist, leading ‘one
to experience the moods that are associated with those behaviours’ (Sy et al,
2005, p. 296). Neumman and Strack (2000) conducted a research in which
participants were asked to hear a philosophical manuscript recitation in two
different vocal tones, happy and sad. The authors found that even the exposure
to someone’s tone of voice can activate according behaviours on the hearer,
although many authors support that it takes more sources of mood for effective
emotional convergence (see, for example, Barsade, 2002; Gabbott and Hogg,
2001).
Appraisal theory states that emotions are involuntary and unconscious (see, for
example, Hatfield et al., 1994) and that one’s emotional state is continuously
being influenced by the feedback or activation from other people behavioural
mimicry. The resultant emotions will then lead to a postevaluative judgement,
i.e., an appraisal or evaluation of what was felt, called the ‘meaning analysis’
(Dallimore et al., 2007).
Researchers have tried to identify demographic differences on the propensity
for emotional contagion but with inconclusive results so far (e.g. McColl-
Kennedy et al., 2003; Hesse et al., 1999). Mattila et al., (2003) found that the
employee’s gender can have an impact on consumers’ satisfaction when
negative affective states are displayed. Goos and Silverman (2002) found that
angry expressions are more easily recognisable on males. Simpson and Stroh
(2004), however, defend that this happens not due to some genetic attribute but
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because males are less worried with complying with social rules associated with
femininity, like the oppression of negative emotions. Males also appear to have
less capability to distinguish angry facial expressions (Doherty et al., 1995) but
express anger more markedly (Kring and Emmons, 1990).
Other authors defend that it is the affiliative motivation, i.e., the longing to
interact with others that determine how susceptible one is to catch other
people’s emotions (e.g. Chartrand et al., 2005; Lakin and Chartrand, 2003;
Sinclair and Huntsinger, 2006)
Doherty (1997) asserts that it is the personal characteristics that determine
one’s susceptibility for emotional contagion, and designs the Emotional
Contagion Scale (ECS), which is a ‘measure of individual differences in
susceptibility to catching the emotions of others’ (Doherty, 1997, p. 149) with a
high reliability (Cronbach’s α = 0.90).
In the organisational context, emotional contagion is part of the academic
literature since the early 30’s, when the subject started to acquire relevant
academic interest, particularly in the United States (see, for example, Fisher
and Hanna, 1931; Kornhauser and Sharp, 1932; Hersey, 1932; Hoppock, 1935;
Roethlisberger and Dickson, 1939).
Recent research on the impact of emotions in the workplace has explored the
effect of leader’s mood on employees (e.g., Lipman-Blumen, 2005; Tepper,
2007; Tepper et al., 2006; Wu, 2008), like abusive supervision (Tepper, 2000,
2007) and toxic leadership (Lipman-Blumen, 2005), underlining the damaging
effects that negative emotions cause, inter alia, burn-out, family work conflict,
emotional exhaustion, negative affect and employee resistance (see, e.g., Duffy
et al., 2002; Tepper et al., 2004, 2006, 2008; Zellars et al. 2002). Scholars also
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focused on employees’ emotional impact on customers (e.g. Deane, 2007;
Hennig-Thurau, 2004; Pugh, 2001), reinforcing the motto ‘service with a smile’
(Dallimore et al., 2007). One may argue that indeed, ‘service encounters are
breeding grounds for emotion’ (Grace, 2007, p.271) and research has identified
emotions like irritation, disappointment and pleasure as a heavy component of
service satisfaction (van Dolen et al., 2001) and both valences of mood as part
of service encounter appraisal (Mattila and Enz, 2002).
From the frontline employees’ perspective, it is obvious that they are subject to
this contagion on a daily basis, where the possible sources of such influence
are various (e.g. Dallimore et al., 2007). Academics have shown that customers’
negative emotional displays have an impact in employees, and can lead to
absenteeism (Grandey et al., 2004) and stress (Dorman and Zapf, 2004).
Scholars defend that the reason for customers’ negative emotional displays are
tied with a need for social support or anxiety reduction (e.g. Schoefer and
Diamantopoulos, 2008). Nyer (2000) stated that negative behaviours are
displayed as a strategy to vent unhappiness whereas Nyer and Gopinath (2005)
affirm that emotional release is the true motivation for unpleasant vocal
behaviour. Apart from what causes such behaviours, it is not surprising that
their consequences are not positive for the receivers.
Despite the quantity of research regarding the correlations between negative
work experiences and job satisfaction (e.g. Liu et al., 2004; Schneider et al.,
2005), there is a gap in the literature concerning the mediating role of
employees’ susceptibility for emotional contagion between these two variables.
This gap aroused the curiosity of the author; as each person is a different
reality, it is interesting to explore how a singular personal dimension as the
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propensity for emotional contagion can moderate the impact of customers’
mood on job satisfaction. To tackle the influence of measurable personal
characteristics may help to improve HRM requisites for selection and strategies
for practice.
5. Conceptual Framework
Following the above mentioned, the author draws the hypothesis of a linear
relation between the perception of customers’ mood and employees’ job
satisfaction, in the sense that higher or lower levels of one variable will be
associated with respective levels of the other. This correlation will be moderated
by employees’ propensity for emotional contagion. The author hypothesises that
employees’ perception of customers’ mood will evoke a congruent mood state
in these workers through the process of emotional contagion. This contagion
will have an impact on employees’ cognitive and behavioural processes, as
individuals tend to evaluate and judge the environment according to the way
they feel it. Consequently, job satisfaction will be highly affected by this cycle as
ultimately the extent to which how satisfied employees are with their job relies
on their informative, cognitive, and behavioural schemas.
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6. Methodology
6.1 Hypothesis and analysis model
The present research explores a new moderator of job satisfaction in front line
employees, for which the main task is to interact with customers: their
propensity for emotional contagion. As previously mentioned in the literature
review, satisfaction may arise from emotional experiences at work. Moreover, it
was also demonstrated that human beings tend to be emotionally influenced by
the emotional displays of other people. It was supported that all types of service
Sender Customer
Affective
states
Receiver Service Provider
Changed emotional
state
Convergence with sender’s
affective
Emotional Contagion Process
Stage 1
Receiver mimics sender through congruent facial / vocal
/ body displays
Stage 2
Receiver afferent feedback resulting in corresponding
affective state
Impact on receivers’ job satisfaction
Mood as information: impact on receiver’s
cognitive and behavioural schemas / processes.
Environment (work) appraisal according to the most prevalent
mood
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interaction are a form of social exchange. Therefore the following hypotheses
are formulated:
Hypothesis 1: Customers’ mood and job satisfaction are positively and
significantly related, in the sense that positive perceptions of customers’ mood
are associated with higher levels of job satisfaction.
Hypothesis 1a: The relation between customers’ mood and job satisfaction is
moderated by employees’ propensity for emotional contagion.
6.2 Methods
As this research is explanatory, the relationship between variables is tested
resorting to self-administered questionnaires (Saunders et al., 2007). The use of
questionnaires has several advantages, particularly in cross-sectional studies:
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the collection of data is quick, economic, standardised, easy and faster to
analyse and independent of the observer (Gill and Johnson, 2005). The
questionnaires were administered by the researcher and, according to
Saunders et al (2009), the researcher’s presence increases the likelihood of
response rates. In the moment of data collection both confidentiality and
anonymity were assured to participants. As in all research methods, there are
disadvantages (Gray, 2009). To avoid contamination of responses, the
respondents were asked to answer privately and were previously informed that
there was no ‘right or wrong’ answer.
The questionnaires had attached a question answering instruction page. The
use of four different questionnaires had the critical purpose of increasing the
research comprehensiveness and to achieve triangulation.
The option for quantitative research is related to the nature of the study itself.
Deductive approach is tightly linked with the natural sciences. This research
aims to test an a priori theory concerning the relation between variables and, as
a result, allow them to be controlled (Collis and Hussey, 2003). By proposing
that job satisfaction can be affected by the perception of customers’ mood the
author formulates a hypothesis based on the presupposed correlation between
a dependent variable (job satisfaction) and an independent variable (customers’
mood). Furthermore, there is also the postulation that employees’ propensity for
emotional contagion represents a third variable that will moderate the above
mentioned relationship. This constitutes the leitmotiv of a deductive approach –
the formulation, operationalisation and testing of an a priori theory. According
to Torraco (2002, p.356), the deductive research should ‘serve technical /
utilitarian interests for relating means and ends’. The final objective is the
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recommendation of a new tool for the recruitment process of frontline
employees (the emotional contagion scale) if H1a is supported.
Additionally, this research being cross-sectional, the time horizon is congruent
with the chosen nomothetic approach, as ‘deductive research can be quicker to
complete’ (Saunders et al., 2007, p. 121).
The use of structured questionnaires, however, presents limitations. First, the
researcher only gets answers to the questions that are asked (Johnson and
Harris, 2002). Although this might seem obvious, there are factors that can
threat the validity of the research, as the negligence of unconsidered variables
can cause obsolete conclusions or the formulation of superfluous relations.
By undertaking a comprehensive and in-depth review of the relevant published
works, the researcher can more assertively identify the confounding impact of
extraneous variables which will consequently be considered and introduced in
the questionnaires. Being the time horizon of a greater extension and the
research design would include open interviews in order to assess possible
overlooked variables.
Also the issue of replicability is intrinsically associated with the scientific
method. The use of questionnaires represents replicable means of
measurement, i.e., third parties may reproduce the study resorting to the same
methodology, at other point of time. Results from different studies may be more
easily compared in quantitative research, as the collection and interpretation of
data is less dependent on the researcher’s analytical skills. Reliability is of vital
importance in the measurement of hypothetical constructs (Miles and Banyard,
2007). Reliability has two different meanings and ways of testing, Temporal
stability and Internal Consistency. Temporal stability means the consistency of
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responses over time to a single instrument. Deary et al. (2004), for example,
administered an intelligence test to the same sample when participants were 11
years old and later when the subjects were 67, finding a correlation of 0.66
among the measures, which suggests that intelligence is constant over time.
This is also called test-retest reliability. However, as this is a cross sectional
research, test-retest is not a suitable way to measure the reliability of the
measures due to time constraints. As so, the author resorts to the internal
consistency method by calculating the Cronbach’s alpha, which can be simply
described as ‘(…) an estimate of the correlation between the true score and the
measured score’ (Miles and Banyard, 2007, p. 281), or the extent to each all the
questionnaire’s items are measuring the same construct. Cronbach’s alpha was
calculated for all instruments and only questionnaires with a Cronbach’s alpha
superior to 0.7 were used (ranging from zero to unit) as almost all scholars
agree that the ideal alpha value should never be below this score (see, for
example, Clark-Carter, 2004; Kline, 2000).
6.2.1 Socio-demographic data
The first questionnaire addresses respondents’ socio-demographic data. This
type of data is often present in work related research (see, for example, Griffeth
et al., 2000; Sturman, 2003).
6.2.2 Age
Age will be considered in the research regarding the fact that previous studies
support its importance when tackling organisational phenomena. Thornton and
Dumke (2005) concluded from their meta-analysis that older workers possess
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greater problem-solving and decision making skills. Sturman (2003) and Ng and
Feldman (2005) explored the relation between age and performance. Shirom et
al (2008) concluded that age was negatively associated with absenteeism. As
age seems to influence employees’ attitudes towards work, it is treated as a
control variable in this research.
6.2.3 Gender
Gender is one of the most common control variables in work literature and
research (Nelson and Burke, 2002). Its moderator influence has been explored
in a vast array of relations, inter alia, between performance and work family
conflict (Byron, 2005), procrastination (Steel, 2007), sexual arrestment (Willness
et al., 2007) and organisational commitment (Wright and Bonett, 2002). Tamres
et al. (2002) theorise that women are more able to use different coping
strategies when dealing with stressors. Recent studies found that women
experience more stressors than men (e.g., Rodriguez et al., 2001; Vagg et al.,
2002) and the role congruity theory defends that women interpret a male
dominated work environment as a stressor (Eagly and Karau, 2002; Taylor,
2006). Moreover, recent research supports that gender stereotyping still exists
in organisations, and that men are more likely to receive positive performance
evaluations (Goldman et al., 2006; Lyness and Heilman, 2006). The biosocial
theory that supports gender disparity as one of the main sources of human
behaviour (Wood and Eagly, 2002) states that such differences are a
combination of physical specifications and the economical and structural
features of societies. Regarding the amount of evidence that gender
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differences may indeed influence research findings, this variable is also
considered in the questionnaire.
6.2.4 Working hours
Working hours is a crescent topic in literature particularly in recent years, where
the number of part-time workers has increased heavily (Clinebell and Clinebell,
2007). Nevertheless, there is an ambiguity in research findings. Regarding job
satisfaction, there are authors who defend a null relation between this variable
and working hours (e.g., Krausz et al., 2000), whereas other scholars like
Conway and Briner (2002) report mixed results. The relation between working
hours and organisational commitment is also dubious, as some authors find no
significant differences between part-time and full-time employees (Krausz et al.,
2000), whereas Jacobsen (2000), for example, reports that part-time workers
enjoy higher levels of organisational commitment. Also turnover intentions were
associated with working hours and while Krausz et al. (2000) defend that there
is no relation between these variables, Jacobsen (2000) argues that part-time
workers have more turnover intents and McBey and Karakowsky (2000) depict
working hours as an important predictor variable for turnover. Moreover, where
some research links part-time work with inferior levels of participation
(Jacobsen, 2000) and job autonomy (Harley and Whitehouse, 2001), authors
like Thorsteinson (2003) underline that working hours have no influence in such
fields. Such discrepancies may be related with a variety of causes such as the
methodology, sample, or reliability of the measurement instruments in the supra
mentioned investigations. Nevertheless, giving the contradictory nature of the
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results, working hours are considered in this research as nominal variables:
part-time / full-time.
6.2.5 Type of contact with customers
The most prevalent type of contact with customer (face to face or telephone) is
a control variable for various reasons. First, based on the emotional contagion
literature, the author presupposes that employees who deal personally with
customers are exposed to more sources of contagion, i.e., body language, tone
of voice and speech. However, the two very distinct organisational scenarios
require attention to the nature of the jobs. The respondents that represent the
face to face category work in a job centre and deal with people that necessitate
their help, i.e., unemployed individuals that require the interaction with these
participants. The telephone category consists of sales people that contact
potential customers who are not always keen to get their phone calls. These
employees work under pressure, targets and constant negative responses from
the people they contact, which will naturally increase their levels of work related
stress (Deane, 2007; Singh, 2000; Taylor et al, 2002;). So, although these
employees are only exposed to two sources of emotional contagion (tone of
voice and speech), the nature of their function justifies the exploration of this
variable, which is also treated as nominal.
6.2.6. Work experience
According to the literature, work experience is one of the predictors of the work
environment quality (Babin and Boles, 1996). Scholars like Rhoades et al.
(2002) argue that it is concurrent with job satisfaction and Knight et al (2006)
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defend that it is tightly linked with career intention, which arises from the
emotional experiences of the job. As one may assume that experience leads to
a greater neutrality and knowledge about how to manage customer interaction,
this variable is also considered ranging from ‘less than one year’ to ‘more than 9
years’.
An employee who has worked for a long time with customers will presumably
possess greater ‘personal defence’ strategies to deal with adverse situations.
Although there are no two identical situations, experience equals learning in
almost all areas of life and particularly in a work context, it gives the preparation
to more clearly evaluate each situation and behave accordingly.
6.2.7 Job satisfaction
To measure this construct, the author resorts to a well known and often used
set of 3 items, with a Cronbach’s α = 0.87, taken from the Michigan
Organizational Assessment Questionnaire (Cook et al, 1981): ‘All in all, I am
satisfied with my job’, ‘In general, I don’t like my job’ (reversed) and ‘In general I
like working here’.
However, the literature about job satisfaction, the dependant variable (DV),
often relates it with five dimensions: organisational commitment (see, for
example, Griffin et al., 2010; Markovits et al., 2007), job involvement (Moynihan
and Pandey, 2007; Reid et al., 2008), job routineness (Wright and Davis, 2003;
Yoo and Brooks, 2005), self-efficacy (Molinari and Monserud, 2009; Wang and
Netemeyer, 2002) and role clarity (Male and Male, 2003, Whitaker et al., 2007).
As so, these dimensions were also measured resorting to other authors’ work.
Organisational commitment was measured with Meyer’s et al. (1993, p. 544) set
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of three items, with a Cronbach’s α = 0.80: ‘I would not leave my organization
right now because I have a sense of obligation to the people in it’, ‘this
organization deserves my loyalty’ and ‘I owe a great deal to my organization’.
From Saleh and Hosek (1976, p. 218) the author took the items for job
involvement: ‘the major satisfaction in my life comes from my job’, ‘the most
important things I do are involved with my job’ and ‘I enjoy my work more than
anything else I do’. The Cronbach’s α = 0.76.
Job routineness and self-efficacy were measured with single items taken from
Moynihan and Pandey (2007), which are respectively: ‘people here do the same
job in the same way every day’ and ‘I can successfully perform any task
assigned to me on my current job’. Finally, role clarity was measured with three
items adapted from Rizzo et al (1970): ‘my job has clear, planned goals and
objectives’, ‘I feel certain about how much authority I have’, ‘I know exactly what
is expected of me’, Cronbach’s α = 0.81. In total, the job satisfaction
questionnaire consisted of 14 items measured with a 6 point Lickert scale,
ranging from ‘1 - strongly disagree’ to ‘6 - strongly agree’. The choice of a 6
point scale was to strategically avoid central tendency. A pilot test with this
combination of items was realised and the reliability was extremely satisfactory
(Cronbach’s α = 0.915). The difference of the alpha’s value if some of the items
were deleted was not relevant, therefore the job satisfaction questionnaire
maintained the 14 initial items for data collection (refer to appendix 1).
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6.2.8. Emotional contagion
This construct was measured with Doherty’s (1997, p. 134) emotional contagion
scale, which can be described as ‘(…) a short, reliable, unidimensional measure
of individual differences in susceptibility to emotional contagion’. This scale
consists of 15 items that are measured with 4 frequency of occurrence
indicators (1 never, 2 rarely, 3 often, 4 always). The choice of this scale lies
mainly in two factors: its high reliability (Cronbach’s α = 0.90) and its simplicity.
The 15 items are grouped in five sub groups or dimensions of emotion:
Happiness (items 1, 2, and 11); Love (items 6, 9 and 12); Fear (items 8, 13 and
15); Anger (items 5, 7 and10) and Sadness (items 1, 4 and 14). The questions
regard an individual’s more customary reactions to day-to-day situations, for
example ‘being with a happy person picks me up when I’m feeling down’
(happiness item), ‘I love when the one I love holds me close’ (love item),
‘listening to the shrill screams of a terrified child in a dentist’s waiting room
makes me feel nervous’ (fear item), ‘it irritates me to be around angry people’
(anger item), and ‘I get filled with sorrow when people talk about the death of
their loved ones’ (sadness item). The use of a simple language and common
situations as examples for attitude measurement makes this instrument very
attractive and easy to both understand and to administer.
6.2.9 Perception of customers’ mood
As far as the author’s knowledge is concerned, there is no published instrument
to measure this construct. Given this absence in the available literature, the
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author designed an instrument which consists of a 7 items questionnaire
comprising 3 dimensions:
1- the employees’ attitudes regarding the customer: ‘customers are always
polite’, customers are always in a good mood’, and ‘customers are always
friendly’, 2 - the employees’ appraisal about service encounters: ‘service
interactions are always smooth and easy’ and ‘I find it very pleasant to interact
with customers’, and 3 - the employees evaluation regarding the effect that
customers have in their life ‘customers have a positive effect on how I feel about
myself’ and ‘customers make me feel proud about my work’.
This combination of different dimensions increases content validity, i.e. ‘the
degree to which a measure covers the full range of behaviour of the ability
being measured’ (Clark-Carter, 2004, p. 31). Concurrent validity implies the
comparison of a new measure’s validity against the validity of a pre-existent
measure. As a measure for employees’ perception of customers’ mood was not
found by the researcher, it was not possible to measure concurrent validity. A
pilot test was conducted with 10 participants who work directly with customers,
all at the University of Bolton. Four participants work in the reception of the
university’s Sports Centre, two participants work in the cafeteria, three are
librarians and one works in financial services. The pilot questionnaire allowed
the author to infer the facial validity of the measure, which means the extent to
which participants understand the instrument in use. For this purpose, the
respondents were asked about the perceptibility of the measure and the
attractiveness of the questionnaire layout. The entire sample stated that the
questionnaire was fairly easy and simple to both understand and fill.
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The reliability of the measurement instrument was assessed resorting to
Cronbach’s alpha (refer to appendix 2). Initially the alpha was 0.67. The author
made the statistical analysis ‘Cronbach’s alpha if item deleted’, which revealed
that if the item ‘customers have a positive effect on how I feel about myself’ was
deleted, the alpha value would increase to 0.72. Therefore this item was deleted
and the instrument reliability was guaranteed.
The items were measured with a Lickert’s scale of agreement, ranging from 1
‘Strongly disagree’, to 6 ‘Strongly agree’. Once again, the absence of a central
point was strategic in order to avoid central tendency.
The final questionnaire had a total of 4 pages (see appendix 3) which,
according to Saunders (2007), is within the ideal number of pages (range from 1
to 8). The rationale of this ideal number lies in not exhausting the respondents.
According to the author, after the eighth page responses will start to be
inattentive and automatic which does not provide the researcher with valuable
information. The first page was an informed consent letter, explaining briefly
that the research was part of a Masters programme and that the data was
totally anonymous and confidential. The researcher’s contact information was
also provided for any possible question or clarification participants might need.
7. Ethics
The importance of ethics in research started to congregate attention from all
fields of knowledge after the Nuremberg Trials, where atrocities in concentration
camps, allegedly in the name of scientific development, were documented
(Lincon and Cannella, 2009). Nowadays it is a major concern to conduct all
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types of research following the established ethical principles, that are guided by
the doctrines of primum non nocere, or nonmaleficence (cause no harm) and
preferably doing good (Saunders, 2009).
Regarding the University of Bolton’s Code of Ethical Standards in Research
Involving Human Participants (2006), and also Saunders et al. (2009)
recommendations about ethics on all research phases, the following measures
were applied before, during, and after the research process:
Formulation of the research topic: the topic was created under the approval of a
tutor, and only after her agreement the research design was formulated. The
topic was devised with the main preoccupation of causing no harm to
participants.
Research Design and Gaining Access: Organisations’ members were contacted
when it was convenient to them. Participants were informed about the research
topic, objectives, methods, and of their right to withdrawal from the study at any
time. If any modification was to be done in the future, both parts agreed that
their participation will be renegotiated. The right to quality research was
discussed, regarding participants’ and researcher’s time limitations and the best
strategies to overcome such boundaries. Anonymity, confidentiality, and privacy
were assured.
Data collection: The questionnaires were anonymous. Participants’ names were
numerically codified. When requested, participants had prior access to the
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questionnaires. Participants were also informed of their right to decline
responding to any question (Easterby-Smith et al., 2002).
Questionnaires had an information sheet containing the objective of the study,
the participants’ rights, the researcher’s name and contacts, and the contact
details of the Department’s Research Ethics Officer so that participants were
able to report any events that might violate their integrity and welfare. Voluntary
informed consent, in writing, was not asked to participants as their anonymity
could be threatened. Participants’ deception is not applicable in this research.
Data processing and storage: the researcher complied with the Data Protection
Act (1998) and the Data Protection Policy (University of Bolton, 2006), with the
following precautions:
Participants’ confidentiality and anonymity was maintained at all times and by all
means; data from questionnaires was kept on a password protected file, and
only one person beside the researcher knew the password. Participants’ names
and related data were numerically codified. Physical records were kept locked
and would be destroyed as soon as possible. Participants were offered access
to the research results. Researcher’s conclusions will be showed to interested
participants before the dissertation’s submission (Informant verification).
The Research Ethics Check List – Form RE1 was filled, signed, and attached to
the research proposal.
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8. Organisational background and sample
8.1 Organisation 1
This organisation is a job centre located in the North West of England.
Participants’ main task is to advise and follow the progress on unemployed
individuals who seek their professional directions. Participants stated that the
job climate has seen visible alterations since the present crisis begun. Where
previously their main clients were younger people of lower educational levels
and poor socio-economic origins, nowadays there is no way to describe a
standard job seeker. From teachers to consultants and from all age groups,
unemployed people are a layer of society that is growing in both number and
diversity. Everyday participants have to deal with opposite situations and
individuals in really despair. ‘It can be destroying sometimes’, a participant
stated, ‘people are starting to lose hope, particularly when you look around and
realise that even education is not enough anymore to guarantee you a better
life’. But even for respondents this economical climate is starting to show an
impact. Days after the questionnaires’ collection there was a two day strike
following the increasing numbers of redundancies and also low salaries.
8.2 Organisation 2
This organisation is a multinational company with offices all around the globe. It
operates as a business intelligence provider, from whom other companies can
purchase a vast array of services that go from private consultancy to reports
and statistics on various business areas and locations. The sample for this
research works in the Manchester’s office and its main task is to contact
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companies offering these services. Participants interact with customers by
telephone trying to sell products and services with a target of £30.000 worth of
sales each month. ‘It’s almost impossible you know’ a participant confessed, ‘I
mean, you never know but I’ve been working here for almost six month and my
best achievement was £26.000. This is crazy, honestly, I never go home on
time to have tea with my wife and the pressure is just incredible…’.
Furthermore, the person on the other side of the line is not always polite or
welcoming to their calls. It is not unusual for employees to be mistreated by
those potential clients and to juggle that fact with constant pressure from their
supervisors to achieve such high targets in this climate of recession is not easy.
8.3 Sample description
A total of 80 questionnaires were distributed and 59 were returned completed.
In organisation 1, 25 respondents filled the questionnaires, 76% (N= 19) were
females and 24% (N=6) were males. The most prevalent age group among this
sample ranged from 25 to 45, representing a total of 48% of the sample (N=12),
followed by 32% in the age group of 46 to 55 (N=8). The number of staff
working part-time and full-time was almost similar (13 and 12, respectively).
In organisation 2, 34 questionnaires were collected. 62% of respondents were
female and 38% were male. 68% of participants (N=23) belonged to the age
group situated between 25 and 35 years old and 88% work full-time (N=30).
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Sample Size
Age
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45
Years of experience in the role
9. Findings and Analysis
Before starting the data analysis and hypothesis testing, the reliability of the two
scales adapted from other authors was once more verified with the research
data. The Emotional Contagion Scale maintained its high reliability formerly
reported by Doherty (1997). In the present research the Cronbach’s alpha is still
very satisfactory (α= .834). To delete an item would not result in a significant
reliability rising. The item that could have more impact in the alpha value if
deleted (Item 5 ‘I clench my jaws and my shoulders get tight when I see the
Deleted: ¶
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angry faces on the news’) would not bring a noteworthy difference in terms of
reliability (difference = 0.006) and therefore was retained (refer to appendix 4).
In the Job Satisfaction Scale reliability analysis results were different (refer to
appendix 5). If the item 10 (‘People here do the same job every day),
correspondent to job routineness was deleted, the alpha value would increase
to .914. Moreover, this item was negatively correlated with the others.
Consequently it was deleted and not considered as an indicator of the variable
in study.
Furthermore, in order to obtain a single variable from the retained items, each
variable was computed by calculating the mean of its constituent items.
Consequently, three new singe variables were formed (‘Perception of
Customers’ Mood’, ‘Emotional Contagion’ and ‘Job Satisfaction’) and data was
ready for the analysis process.
9.1 Descriptive Statistics
Perception of Customers’ mood
As presented in the graphics infra, it is clear that the perception of customers’
mood is more positive amongst the second group (telephone), with around 30%
of the subjects rating customers’ mood between = 3.5 and = 4.5 (ranging
from one to six). From the group of people who deals with customers face to
face, 20% rates it in = 2.83 and 16% in = 3.33. Although for both groups the
perception of customers mood was low, it was definitely lower for the first one.
[Type text]
47
Employees’ perception of customer mood
9.1.1 Emotional Contagion
The results of the emotional contagion scale were higher for the second group.
The data is based on the mean of the scale items, ranging from 1 to 4. As
demonstrated in the graphics below, 56% of the first group scored between =
2.40 to = 2.53, 24% scored between = 0.87 and = 2.13, and 20% between
= 2.60 and = 3.27. In the second group the scores are considerably higher
and also more dispersed, with 35.2% scoring from = 2.60 to = 2.80, 14.7%
scoring = 2.87 and 26.5% scoring between = 2.87 and = 3.53.
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48
Emotional Contagion
9.1.2 Job Satisfaction
In a scale from 1 to 6, 48% of the first group scored between = 3.31 and =
3.62. 17.7% of the second group scored higher, from = 3.85 to = 4.08.
Although 29.4% of the second group had a job satisfaction score <= 3
compared to the 24% of the first group, the higher score in the face to face
assemblage was = 4 (4%) whereas 35.1% of the telephone group scored
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49
1.00
2.08
2.54
2.92
3.00
3.08
3.15
3.31
3.38
3.46
3.54
3.62
3.69
3.77
3.85
4.00
Job Satisfaction
4
6
8
10
12
14
16
Perc
ent
Face to Face
1.00
1.62
1.77
2.69
2.77
2.85
2.92
3.00
3.23
3.38
3.46
3.54
3.62
3.85
4.08
4.15
4.31
4.62
5.00
5.08
5.15
5.23
Job Satisfaction
2
4
6
8
10
12
Perc
ent
Telephone
from = 4 to = 5.23. The scores were higher and more dispersed among the
participants from organisation 2.
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The table above supports that the mentioned means’ differences between the
two groups on the three variables are statistically significant and therefore, the
previously proposed comparative case study analysis is pertinent. There is a
significant difference between groups in the variable job satisfaction (t = -1.542,
df = 57, p = .0645), Emotional Contagion (t = -3.38, df = 57, p = 0.005), and
Employee’s Perception of Customers’ mood (t = -2.81, df = 57, p = 0.0035).
Hypothesis 1: ‘Customers’ mood and job satisfaction are positively and
significantly related, in the sense that positive perceptions of customers’ mood
are associated with higher levels of job satisfaction’.
In order to test hypothesis 1 the author started to explore the existence of a
significant correlation between the independent and the criterion variables.
Being both treated as scales, the author resorts to the statistical parametric test
Pearson Product-Moment Correlation Coefficient, commonly referred to as
PMCC or Pearson’s r.
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1.00 2.00 3.00 4.00 5.00
Employee's perception of customer mood
0
2
4
6
8
10
Fre
qu
en
cy
Mean = 3.1582Std. Dev. = 0.82084N = 59
Employee's perception of customer mood
1.00 2.00 3.00 4.00 5.00 6.00
Job Satisfaction
0
5
10
15
20
Fre
qu
en
cy
Mean = 3.4524Std. Dev. = 0.87823N = 59
Job Satisfaction
There are 3 requirements that should not be violated to test a statistical
correlation with this tool:
1- Both variables should be scales. When variables were computed, they
were treated as scales
2- Neither distribution should be highly skewed. The two histograms below
show that neither variable’s distribution is highly skewed
3- The scatter plot must not depict a curvilinear relation between the
variables. The scatter plot below also shows a non curvilinear relation
between variables.
Histograms
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1.00 2.00 3.00 4.00 5.00 6.00
Job Satisfaction
1.00
2.00
3.00
4.00
5.00
Em
ploy
ee's
per
cept
ion
of c
usto
mer
moo
d
Scatter plot
Being the requirements fulfilled, the Pearson’s correlation was calculated (refer
to appendix 6). The results show that there is a significant positive correlation
between employees’ perception of customers’ mood and job satisfaction for the
first group (r = .546, n = 25, p < 0.0005, two-tailed), and an even stronger
positive correlation for the second group (r = .718, n = 34, p < 0.0005, two-
tailed).
Although hypothesis 1 was apparently supported by the results, a deeper
analysis regarding the control variables reveals some interesting results: age
and gender showed no major statistical influence in this relation but in both
groups, for employees who work in a part-time regime, the relation between
their perception of customers’ mood and job satisfaction has no statistical
significance.
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A more profound analysis was performed in order to explore more dimensions
of the correlation between the independent variable (IV) and the dependent
variable (DV). The linear regression test allows sharpening how much the
criterion variable (job satisfaction) is explained by the independent variable
(perception of customers’ mood), to quantify the quality of the model to explain
the linearity of the relation between the variables, and to infer the accuracy of
the model to explain the linearity of the relation between the variables in the
universe.
The referred model also comprises a set of assumptions that were not violated.
Such assumptions are described in detail on appendix 7.
For both groups’ analysis none assumption was violated and the linear
regression was effectuated to investigate the validity and robustness of H1
(refer to appendix 8 for group 1 and to appendix 9 for group 2). The table below
shows that, on average, the first group has a negative perception of customers’
mood ( = 2.83) whereas the second group has a more positive, although not
very high, perception of customers’ mood ( = 3.4). Both the face to face and
the telephone groups have positive (although median) levels of job satisfaction
( = 3.25, = 3.60, respectively).
Employee's
perception of
customer mood Job Satisfaction
Mean Mean
Face to Face 2.83 3.25 Most prevalent type of
contact with customers Telephone 3.40 3.60
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In both analysis the proposed model regarding a correlation between the two
variables has shown to be positively significant (Face to Face: F(1,23) = 9.766, p
= 0.05; Telephone: F (1,32) = 34.078, p = 0.000), i.e., to be valid in terms of its
generalisation to the population. For the face to face condition, the perception of
customers’ mood explains 29.8% of the variance in job satisfaction. This means
that job satisfaction as a whole depends on their perception of customers’ mood
in 29.8%. The telephone groups’ perception of customers’ mood explains a very
strong and high percentage of 51.6% of their job satisfaction. Moreover, for
each unit that is changed in the IV, 0.587 of the DV changes for the face to face
group and 0.813 changes for the telephone group. These are very high values
that prove the influence of customers’ mood in the job satisfaction for both
conditions. Therefore we accept H1.
For the telephone group the success of their job mostly depends on the
relations they are able to create with customers (for posterior sales). As so, if
the customers’ mood is negative, the impact on possible sales will be vast as
customers’ bad moods are typically a sign of a not willingness to do business,
although this might occasionally not happen. Furthermore, for these participants
all that the job comprises is talking to customers and do sales. For reaching the
targets at the end of the month these employees need to establish cordial
relations and sell services and products. Despite few and punctual interactions
with managers and co-workers, potential customers are the only source of
interaction they have during their working hours. Therefore, negative customer
moods result in lower levels of job satisfaction and, in parallel, positive
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perceptions of customers’ moods result in higher levels of job satisfaction for
this group.
For the face to face participants, customers’ mood has a lower percentage of
impact on employees’ job satisfaction. However, 29.8% of variance on the DV
explained by the IV is still a strong value that should not be disregarded. For
this group, the nature of the job is totally different. Dealing with negative moods
is probably a constant of participants’ day to day interactions. This type of
‘customers’ are actually unemployed people looking for help. Unemployment is
a condition that does not necessarily augment or contribute to positive moods.
Moreover, this group of people not only work with customers’ by advising,
interviewing and diagnosing further opportunities for CV enhancement, they
also work with institutions and potential employers, trying to create new
partnerships for the job centre. Clerical and administrative functions are also a
percentage of their tasks, therefore the contact with customers has less impact
on their job satisfaction as dealing with customers is not all this group does in
the workplace.
For both groups H1 was accepted; the perception of customers’ mood was
shown to have a strong impact on job satisfaction. This finding is consistent with
the mood-as-information theory which defends that moods will influence
cognitive and behavioural processes trough their informative effects (e.g.
Schwarz and Clore, 2003). As so, people tend to recall the moods associated
with experiences, contexts and situations in order to gather information that
permits the formulation of cognitive and behavioural schemas. A positive
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perception of customers’ mood results in a more positive cognitive appraisal of
the job itself increasing job satisfaction, whereas a more negative discernment
of such moods will create a lower evaluation of the job.
However, another basic premise of this theory, as previously described in the
theoretical background, is that one present affective state will evoke an
evaluation about the whole environment. People in a good mood or surrounded
by good moods tend therefore to evaluate more positively the environment they
are in. This phenomenon may present a limitation for this research as
participants evaluate contexts according to their affective states, even if such
affective states are not directly related with the research questions.
Consequently, a person who is experiencing a bad mood that might have
uncountable sources of provenience will tend to more negatively rate their
perceptions of customers’ mood and job satisfaction, in all the likelihood of an
individual in a good mood will tend to be more benevolent in his / her
appraisals. Moreover, as many authors affirm (see, for example, George and
Zhou, 2007; Watson, 2000) these affective states that may vary in both valence
(positive or negative), and arousal (high or low) do not occur in parallel and one
can experience negative and positive moods alternately. In fact: ‘In the course
of the day, a few days or a week, to the extent that someone experiences both
kinds of moods at different times, he or she will also experience differential
cognitive tuning effects’ (George and Zhou, 2007, p. 606). As so, the responses
from participants could have been influenced by the cognitive impact of certain
and punctual affective states. This factor could have been avoided with a test
retest.
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This finding is also congruent with the previously described conceptions that
workplace negative experiences are related with job satisfaction (e.g., Elias,
2004) and deepens them by focusing on a particular workplace experience,
which is the perception of customers’ mood.
Hypothesis 1a: The relation between customers’ mood and job satisfaction is
moderated by employees’ propensity for emotional contagion.
In order to test H1a the author resorted to the statistical test Multiple Linear
regression. To avoid technical collinearity, i.e. a high correlation between the
predictors (perception of customers’ mood and propensity for emotional
contagion), the author centred the variables by subtracting to each variable the
value of its mean. The term of interaction was consequently obtained by
multiplying the centred variables (IV and moderator).
As it can be noted on the table infra, for the telephone group the moderator role
of employees’ propensity for emotional contagion was not statistically significant
(F(3,30) = 11.825; p=.000) between their perception of customers mood and job
satisfaction (sig.<0.05).
ANOVAb
Model Sum of Squares df Mean Square F Sig.
Regression 18,065 3 6,022 11,825 ,000a
Residual 15,277 30 ,509
1
Total 33,343 33
a. Predictors: (Constant), pmc_emscentred, ECSCentrada, PCMCentred
b. Dependent Variable: Job Satisfaction
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Although it was previously shown that customers’ mood have a big impact on
their job satisfaction, this relation is not moderated by the phenomenon of
emotional contagion. This fact can be explained by several reasons:
These workers are not exposed to all the sources of emotional contagion. They
do not see the customer, the body language, the facial expression. Although the
research conducted by Neumann and Strack (2000) sustains that vocal tones
can also result on a behavioural feedback from the recipient, who will
consequently activate corresponding moods, the present investigation supports
the theory that more sources are needed for the emotional contagion process.
This finding is consistent with the supra mentioned non verbal components of
emotional contagion (proxemic, paralinguistic and kinesics) to which this group
of participants is not exposed (Gabbott and Hogg, 2001; Hess et al., 2000;
Sundaram and Webster, 2000).
Moreover, for these individuals, the customer is a mean to reach an end (sales
and targets). They do not necessarily relate to the customers’ negative / positive
moods and are neither aware of the reasons for that mood. In fact, when
contacted by a sales person, a person can display a certain type of mood (e.g.,
politeness versus arrogance) for no particular reason, and sales people tend to
know that – as they are contacted by other sales people as well.
In the face to face group, oppositely, customers’ negative moods can be easily
understood and comprehended by the employee, and to be sympathetic with
the customer is a sine qua non condition for successfully performing their job. In
the telephone group, sympathy with the customers’ mood is not a determinant
for sales. The duration of the contacts are also smaller than in the face to face
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group, where an employee can follow a customers’ situation and progress for
more than six months. This factor can also explain why there is no emotional
contagion involved between IV and DV for the telephone group, even though
many authors describe the emotional contagion process as automatic and
involuntary (see, for example, Doherty, 1997). As so, the not statistically
significance of employees’ propensity for emotional contagion as a moderator in
the telephone group is easily comprehended and supported by previous
research.
However, for the face to face group, the levels of propensity for emotional
contagion showed to have a big impact on the linear relation between the IV
and the DV (see tables below). For each unit of the moderator, the variance
between DV and IV changes in parallel 0,954, an unquestionable high value
highly close to the unit. We can also note that emotional contagion, alone, has
no principal effect on job satisfaction (p =0.248, p>0.05), whereas the
interaction of the IV and the moderator has a principal and strong effect (p
=0.02, p<0.05).
The moderator role is positively significant and can be generalised to the
universe (F (3,21 )= 6,220; p = 0.003 (p<0,05)). This model has also proved to be
strongly significant, as the interaction of the variables ‘employees’ propensity for
emotional contagion’ and ‘perception of customers’ mood’ explains per se
39.5% of the variation on the levels of job satisfaction. This is a very high result
in social sciences statistical research. We accept H1a for the face to face group.
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Model Summaryb
Model R R Square Adjusted R Square
Std. Error of the
Estimate Durbin-Watson
1 ,686a ,470 ,395 ,49202 1,963
a. Predictors: (Constant), PCMCentrado_EMCCentrado, EMCCentred,
PerceptionCustomersMoodcentrado
b. Dependent Variable: Job Satisfaction
ANOVAb
Model
Sum of
Squares df
Mean
Squar
e F Sig.
Regression 4,517 3 1,506 6,220 ,003a
Residual 5,084 21 ,242
Total 9,601 24
coefficientsa
Unstand.
Coefficient
s
Stand.
Coefficient
s Correlations
Collinearity
Statistics
Model B
Std.
Erro
r Beta t Sig.
Zero
-
orde
r
Partia
l Part
Toleranc
e VIF
(Constant) 3,267 ,099
33,11
4
,00
0
EMCCentrado ,253 ,213 ,193 1,189 ,24
8
,082 ,251 ,18
9
,962 1,03
9
PerceptionCustomersMoodcentr
ed
,406 ,187 ,377 2,167 ,04
2
,546 ,427 ,34
4
,832 1,20
2
1
PCMCentrado_EMCCentred ,954 ,381 ,444 2,507 ,02
0
,561 ,480 ,39
8
,805 1,24
3
a. Dependent Variable: Job Satisfaction
b. Dependent Variable: Job Satisfaction
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Furthermore, the graphic below presents some interesting facts related to H1a.
We can see that when levels of job satisfaction are negative (blue and green
lines), there is not a linear relation between the IV and the DV. However, when
emotional contagion levels are positive (yellow line), an increase in the IV will
be accompanied by an augment in the DV. The relation between both variables
is therefore amplified in terms of intensity by the moderator effects of
employees’ propensity for emotional contagion. This does not suggest or prove
a causality effect (which was not the purpose of the present investigation), but
one may firmly state that the relation between predictor and criterion is definitely
influenced by the moderator variable. Furthermore, an interesting detail is that
the impact of the moderator arises in both positive and negative levels of
perception of customers’ mood / job satisfaction. This suggests that not only
employees who have a negative perception of customers will have a lower job
satisfaction, but also that employees’ with better perceptions of customers’
mood will have higher levels of job satisfaction.
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10. Conclusion
The role of emotions, personality and affective traits at work has proved in
previous research to have an impact on work dynamics. In this investigation
emotional economy in a work scenario was studied with a focus on the dyadic
interaction between employees and customers. Previous academic works were
therefore extended and this field of knowledge enriched by the following means:
1) the specific influence of the perception of customers’ mood on job
satisfaction was investigated; 2) a reconciliation of different theoretical
backgrounds regarding the sources of emotional contagion was tackled by
resorting to two different work contexts where the interaction with customers
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(sources of contagion) occurs in dissimilar ways (face to face and telephone); 3)
addition to a developing volume of literature by exploring the moderator role of
emotional contagion between frontline employees’ perception of customers’
mood and their job satisfaction; 4) addition to a growing body of literature
regarding job attitudes of part-time workers 5) practical applications by the final
consideration and exploration of an additional tool to be used on the selection of
service employees (the emotional contagion scale).
The results support that indeed emotional experiences at work have an impact
on job satisfaction. Being job satisfaction related to so many dimensions of
work, inter alia, performance (Bowling, 2007), organisational commitment
(Testa, 2001), intra organisational behaviour (Bradford et al., 2009), and
organisational citizenship behaviour (Organ et al., 2006; Podsakoff et al., 2000),
it is of determinant importance to explore its dynamics and dimensions. The
author found that, independently of the type of interaction, customers’ mood has
an impact on job satisfaction by explaining a high percentage of its variance for
both groups. This finding suggests that how an employee perceives customers
will influence how he / she perceives and evaluates the job itself. It is obviously
difficult (if not impossible) for an organisation to change the way customers treat
employees. However, some strategies can be implemented to smooth the
strength of this external influence. Training and a culture of support could and
should be part of every job that involves contact with customers. Training for
such employees should not be limited to topics like customer service. As this
research shows, a good work environment requires a healthy management of
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emotions therefore training should also tackle vital areas for staff welfare like
emotional intelligence and resilience.
Interestingly, the results show that the correlation between customers’ mood
and job satisfaction is not significant amongst part-time workers. One may
argue that for this group of workers, matters of organisational involvement,
purposes of being employed, and even the time of exposure to consumers’
mood may explain the absence of a relation between the two variables.
However, the results could have been better tackled if the volatile nature of part-
time positions was explored because these ‘employees vary widely in terms of
the individual characteristics they possess, the job duties they are responsible’
and even ‘different life situations and preferences may lead an individual to
seek out part-time employment’ (Maynard et al., 2005, p. 146).
Furthermore, previous research has identified other facts that moderated the
impact of mood on organisations. George and Zouh (2007), for example,
showed that the impact of affective states on creativity is moderated by
supervisor developmental feedback, interactional justice, and trust whereas
Tsai et al (2007) found that the relation between positive moods and task
performance is moderated by helping and support (for and from others), self-
efficacy and task persistence. One may argue that, for both groups, not only
emotional contagion should have been studied but other variables like
organisational culture, leadership, training and team support could have also
been taken into account as possible moderators. However, this study being
cross sectional with a constrained time frame, there was no possibility to include
further variables in the equation. An amplification of the used research methods
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65
would have also enriched the investigation. Open interviews, for example,
would have allowed the collection of different points of view and experiences
that could and certainly would have unfolded a series of new research
questions and hypothesis.
We found that, for the telephone group, emotional contagion was not a
statistically significant moderator between the perception of customers’ mood
and job satisfaction. This is an interesting finding which is consistent with most
of the theories on the contagion process, which trumpet the necessity of visual
contact for the emotional transference to happen. Nevertheless, it seems that
this is also a field that needs enlargement in terms of research. Do not blind
people, for example, have the propensity to catch the moods of others in the
same way that employees interacting through telephone should have? Then
why do the results indicate that emotional contagion is not a moderator for
group 2? Are there other parts of the brain rather than the temporal pole that
are activated when the contagion process occurs only through vocal tones?
Further non participant observation and laboratorial investigation need to take
place to achieve a greater understanding of this social and neuropsychological
phenomenon.
Contrastingly, for the face to face group results show that emotional contagion
moderates the linear relation between job satisfaction and perception of
customers’ mood, these variables being either positive or negative. In some
work contexts where the customer relations are not always so positive (e.g.,
sales, police) lower levels of emotional contagion can be useful to both protect
the employee from the surrounding negativity and to protect the customer from
a ‘negatively contaminated’ employee. For employees who work in predictable
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66
positive scenarios of customer interaction, higher levels of emotional contagion
can be positive for their job satisfaction as the feedback loop will result in
positive judgments about the environment.
Emotional contagion, therefore, is not necessarily an undesirable personal
characteristic of front line employees, as it can increase job satisfaction levels
when the surrounding moods are constructive. In this study, the evaluations of
customers’ mood were somehow low for both groups as both work
circumstances tend not to be joyful ones. The analysis of another case study
where the work environment and principally the customers’ mood were
predictably positive could yield interesting results.
Moreover, and as it was previously mentioned, people responses tend to be
influenced by their present affective state (mood as information theory). This
research highly relied on participants’ self evaluations which could be
contaminated by their present moods. Feasible ways to avoid this common
method possible bias are, for example, to resort to other sources of
measurement and responses or to perform the same tests at different points of
time (test retest) (Podsakoff et al., 2003).
The author considers the findings of the present study as valuable and
somehow a starting point for additional research- particularly by teasing the
established theories of emotional contagion regarding its sources of
provenience. This is still an infant field of research, as in fact the inclusion of
overall emotional variables in work related academic investigation started to
gain more interest only about two decades ago, with the work of Daniel
Goleman on emotional intelligence (see, for example, Goleman, 2000). The
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67
present work shows that, indeed, each individual is different and although the
reality might be presented similarly for all employees, it is their personal
characteristics (particularly focusing on their levels of emotional contagion) that
will dictate the way he / she behaves, perceives and evaluates the environment.
The author also believes that the utility of the emotional contagion scale as a
selection tool was proved, considering the above mentioned results. Subject to
the job’s nature and specifications, human resource professionals and front line
managers could discuss and agree to what levels of propensity for contagion
are ideal for each job position and use this scale as one more selection tool
because, in terms of selection methods, melius est abundare quam deficere.
11. Recommendations
Based of the above said, prolific directions for future research would include the
assessment of the variables in studies resorting to more than one type of
measurement instrument. Open interviews, for example, although threatened by
the possibility of subjectivity, often unclose a vast array of new research topics,
questions and hypothesis.
The replication of this study in work environments where both the valence and
arousal of customers’ mood were predictably higher could lead to some
interesting and complimentary results.
Additionally, and for the practical purposes of the use of the emotional
contagion scale, it would definitely enrich organisational knowledge to address
the following topics:
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68
Is the propensity for emotional contagion a trait (fixed, permanent and
immutable over time) or is it a state (that varies in different scenarios or from
specific situations)?
The first question could be explored with a longitudinal study by applying the
scale in different points of time and the second one with laboratorial
experiments where the independent variables that could cause, mediate, and
moderate the levels of emotional contagion were controlled by the researcher.
The same question is extendable to the perception of customers’ mood. How
does an employee join different experiences with different customers to form a
single and general opinion? Is it dependant on the latest interactions or do
employees congregate all experiences and form one opinion that is consistent
over time to avoid cognitive dissonances?
Moreover, the exploration of the contagion process without visual contact would
open doors in this field of research in the same way that the inclusion of other
moderators in the explored relation would permit a better understanding of the
phenomenon.
Furthermore, and due to the increasing reliance on teams, it would be
interesting to explore how positive and negative moods amongst team members
complement each other and may influence the relation between perception of
customers’ mood and job satisfaction.
As this study was cross-sectional, one cannot unambiguously trumpet causality
between the dependant and independent variables. This can be addressed in
further studies with a longer time horizon.
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69
The enlargement of the number of participants would benefit matters of
generalisability and validity.
Despite the now exposed, the author stalwartly believes that this study is a
starting point for future research that, by addressing the recommendations and
limitations notwithstanding, could hopefully add to the existing knowledge on the
field and amplify the practical applications of academic research.
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70
Appendix 1 Reliability statistics for the Job Satisfaction questionnaire
Reliability Statistics
Cronbach's Alpha
Cronbach's Alpha
Based on Standardized Items
N of Item
s
.915 .912 14
Item-Total Statistics
Scale Mean if Item Deleted
Scale Variance if
Item Deleted
Corrected Item-Total Correlation
Squared Multiple
Correlation
Cronbach's Alpha if Item
Deleted
Job sat1
47.10 159.211 .812 . .901
Job sat 2
47.20 158.178 .774 . .903
Job sat 3
47.00 158.889 .810 . .901
Job inv1
48.20 171.511 .651 . .908
Job inv 2
49.10 172.100 .846 . .905
Job inv 3
48.90 168.767 .761 . .905
Org com 1
47.70 150.233 .882 . .898
Org com 2
48.00 159.556 .887 . .899
Org com 3
48.00 164.667 .783 . .903
Job rout
47.70 196.456 -.130 . .933
Self effic
46.80 193.733 -.052 . .927
Role clar1
47.10 162.989 .707 . .906
Role clar2
47.10 178.544 .300 . .921
Role clar3
47.00 154.444 .851 . .899
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Appendix 2 Reliability statistics for the Perception of Customers’ Mood questionnaire
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72
Appendix 3 Final Questionnaire
The following questions have multiple choice answers.
Please chose the one that best describes your situation
(one answer per question)
1. Gender
Male
Female
2. Your age
18-24
25-35
36-45
45-55
56-65
More than 65
3. Working Hours
Part-Time
Full-Time
4. Most prevalent type of contact with Customers
Face to face
By telephone
By E-mail
5. For how long have you been performing your actual
job?
Less than 1 year
1-4 years
5-8 years
More than 9 years
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To what extent do you agree with each of the following
affirmations?
1. How do you perceive your customers? Please indicate below
how much you do agree with each of the following
affirmations
Strongly
disagree Disagree
Often
disagree
Often
agree Agree
Strongly
agree
Service interactions
are always
smooth and easy
I find it very
pleasant to
interact with customers
Customers are
always polite
Customers are
always in a
good mood
Customers are always
friendly
Customers make me feel proud about
my work
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How often do the following statements apply to you?
Never Rarely Often Always
If Someone I'm talking with begins
to cry , I get teary-eyed Being with a happy person picks
me up when I'm feeling down When someone smiles warmly at me, I smile back and feel warm
inside
I get filled with sorrow when
people talk about the death of their loved ones
I clench my jaws and my shoulders
get tight when I see the angry
faces on the news
When I look into the eyes of the
one I love, my mind is filled with thoughts of romance
It irritates me to be around angry people Watching the fearful faces of victims on the news makes me try to imagine how they might be
feeling
I melt when the one I love holds me close I tense when overhearing an angry quarrel Being around happy people fills my mind with happy thoughts I sense my body responding when the one I love touches me I notice myself getting tense when I’m around people who are
stressed out
I cry at sad movies
Listening to the shrill screams of a
terrified child in a dentist’s waiting
room makes me feel nervous
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To what extent do you agree with each of the following
affirmations?
Strongly
disagree Disagree
Often
disagree
Often
agree Agree
Strongly
agree
All in all, I am
satisfied with my job
In general, I don’t
like my job In general I like working here The most important things I do are
involved with my
job
I enjoy my work
more than anything else I do
The major
satisfaction in my life comes from my
job
This organization
deserves my loyalty
I would not leave my organization
right now because I have a sense of
obligation to the people in it
I owe a great deal
to my organization
I can successfully perform any task
assigned to me on
my current job
My job has clear,
planned goals and objectives
I feel certain about
how much authority
I have
I know exactly what is expected of me
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Appendix 4 Reliability statistics for the Emotional Contagion Scale
Reliability Statistics for the Emotional Contagion Scale
Cronbach's Alpha N of Items
.834 15
Scale Mean if Item Deleted
Scale Variance if
Item Deleted
Corrected Item-Total Correlation
Cronbach's Alpha if Item
Deleted
Emotional Contagion 1 37.12 39.865 .195 .839
Emotional Contagion 2 36.47 37.012 .476 .823
Emotional Contagion 3 36.24 38.977 .377 .828
Emotional Contagion 4 36.36 37.337 .430 .826
Emotional Contagion 5 36.98 40.189 .164 .840
Emotional Contagion 6 36.63 37.721 .390 .828
Emotional Contagion 7 36.51 35.668 .563 .817
Emotional Contagion 8 36.63 34.755 .638 .811
Emotional Contagion 15 37.07 36.823 .454 .824
Emotional Contagion 12 36.53 36.219 .570 .817
Emotional Contagion 13 36.63 36.548 .573 .817
Emotional Contagion 14 36.59 37.590 .438 .825
Emotional Contagion 11 36.37 35.962 .572 .816
Emotional Contagion 9 36.54 36.804 .468 .823
Emotional Contagion 10 36.66 37.193 .494 .822
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Appendix 5 Reliability statistics for the Job Satisfaction questionnaire
Cronbach's Alpha N of Items
.897 14
Item-Total Statistics
Scale Mean if Item Deleted
Scale Variance if
Item Deleted
Corrected Item-Total Correlation
Cronbach's Alpha if Item
Deleted
Job Satisfaction 1 44.69 109.595 .698 .885
Job Satisfaction 2 44.27 114.684 .554 .892
Job Satisfaction 3 44.31 109.629 .786 .882
Job Satisfaction 4 45.14 114.740 .608 .889
Job Satisfaction 5 45.66 120.193 .449 .895
Job Satisfaction 6 45.46 116.701 .589 .890
Job Satisfaction 7 44.66 110.538 .757 .883
Job Satisfaction 8 44.93 110.719 .637 .888
Job Satisfaction 9 44.86 114.533 .684 .887
Job Satisfaction 10 44.88 130.348 -.016 .914
Job Satisfaction 11 44.39 113.552 .534 .893
Job Satisfaction 12 44.53 110.702 .704 .885
Job Satisfaction 13 44.54 114.770 .565 .891
Job Satisfaction 14 44.54 109.046 .726 .884
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Appendix 6 Pearson Correlation
** Correlation is significant at the 0.01 level (2-tailed).
Most prevalent type of contact with customers
Employee's perception of
customer mood
Job Satisfaction
Face to Face Employee's perception of customer mood
Pearson Correlation 1 .546(**)
Sig. (2-tailed) . .005
N 25 25
Job Satisfaction Pearson Correlation .546(**) 1
Sig. (2-tailed) .005 .
N 25 25
Telephone Employee's perception of customer mood
Pearson Correlation 1 .718(**)
Sig. (2-tailed) . .000
N 34 34
Job Satisfaction Pearson Correlation .718(**) 1
Sig. (2-tailed) .000 .
N 34 34
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79
Appendix 7 Assumptions of the linear regression
1 – The relationship between DV and VI must be linear. This can be confirmed
in the dispersion diagram.
2 - The residuals cannot not be autocorrelated. This assumption can be tested
with the Durbin-Watson test, in which the values range from 0 to 4. Values close
to 0 mean a positive autocorrelation, whereas values close to 4 indicate a
negative autocorrelation. The ideal value must be close to 2.
3 – Homoscedasticity refers to the constant variance of the residuals. This is an
extremely important assumption, as its violation might invalidate all the
statistical analysis. It is desirable that the distribution of the errors’ standard
deviation of the dependent variable is independent of the value of the
independent variable. This assumption can be tested resorting to the
scatterplot.
4 – Normality of the errors’ distribution. This can be verified by the histogram,
which should not be highly skewed.
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80
Appendix 8 Linear Regression, Face to face group
Descriptive Statistics
Mean Std. Deviation N
Job Satisfaction 3,2492 ,63249 25
Employee's perception of
customer mood
2,8267 ,58823 25
Correlations
Job Satisfaction
Employee's
perception of
customer mood
Job Satisfaction 1,000 ,546 Pearson Correlation
Employee's perception of
customer mood
,546 1,000
Job Satisfaction . ,002 Sig. (1-tailed)
Employee's perception of
customer mood
,002 .
Job Satisfaction 25 25 N
Employee's perception of
customer mood
25 25
Variables Entered/Removedb
Model Variables Entered Variables Removed Method
dimension0
1 Employee's perception of
customer mooda
. Enter
a. All requested variables entered.
b. Dependent Variable: Job Satisfaction
Model Summaryb
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81
Variables Entered/Removedb
Model Variables Entered Variables Removed Method
dimension0
1 Employee's perception of
customer mooda
. Enter
a. All requested variables entered.
Model
R R Square Adjusted R Square
Std. Error of the
Estimate Durbin-Watson
dimension0
1 .546a .298 .268 .54131 1.915
a. Predictors: (Constant), Employee's perception of customer mood
b. Dependent Variable: Job Satisfaction
ANOVAb
Model Sum of Squares df Mean Square F Sig.
Regressio
n
2.862 1 2.862 9.766 .005a
Residual 6.739 23 .293
1
Total 9.601 24
a. Predictors: (Constant), Employee's perception of customer mood
b. Dependent Variable: Job Satisfaction
Coefficientsa
Unstandardized
Coefficients
Standardized
Coefficients Correlations
Collinearity
Statistics Model
B
Std.
Error Beta t Sig.
Zero-
order Partial Part Tolerance VIF
(Constant) 1.590 .542 2.934 .007 1
Employee's
perception
of customer
mood
.587 .188 .546 3.125 .005 .546 .546 .546 1.000 1.000
a. Dependent Variable: Job Satisfaction
Collinearity Diagnosticsa
Variance Proportions Model Dimension
Eigenvalue Condition Index (Constant)
Employee's
perception of
customer mood
1 1.980 1.000 .01 .01
1
2 .020 9.910 .99 .99
a. Dependent Variable: Job Satisfaction
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82
Residuals Statisticsa
Minimum Maximum Mean Std. Deviation N
Predicted Value 2,2748 3,7423 3,2492 ,34530 25
Residual -1,27478 ,88652 ,00000 ,52991 25
Std. Predicted Value -2,822 1,428 ,000 1,000 25
Std. Residual -2,355 1,638 ,000 ,979 25
a. Dependent Variable: Job Satisfaction
[Type text]
83
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84
Appendix 9 Linear Regression, Telephone group
Descriptive Statistics
Mean Std. Deviation N
Job Satisfaction 3,6018 1,00518 34
Employee's perception of
customer mood
3,4020 ,88774 34
Correlations
Job Satisfaction
Employee's
perception of
customer mood
Job Satisfaction 1,000 ,718 Pearson Correlation
Employee's perception of
customer mood
,718 1,000
Job Satisfaction . ,000 Sig. (1-tailed)
Employee's perception of
customer mood
,000 .
Job Satisfaction 34 34 N
Employee's perception of
customer mood
34 34
Variables Entered/Removedb
Model Variables Entered
Variables
Removed Method
1 Employee's
perception of
customer mooda
. Enter
a. All requested variables entered.
b. Dependent Variable: Job Satisfaction
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85
Model Summaryb
Model R R Square Adjusted R Square
Std. Error of the
Estimate Durbin-Watson
1 ,718a ,516 ,501 ,71035 1,820
a. Predictors: (Constant), Employee's perception of customer mood
b. Dependent Variable: Job Satisfaction
ANOVAb
Model Sum of Squares df Mean Square F Sig.
Regression 17,196 1 17,196 34,078 ,000a
Residual 16,147 32 ,505
1
Total 33,343 33
a. Predictors: (Constant), Employee's perception of customer mood
b. Dependent Variable: Job Satisfaction
Coefficientsa
Unstandardized
Coefficients
Standardized
Coefficients Correlations
Collinearity
Statistics
Model B
Std.
Error Beta t Sig.
Zero-
order Partial Part Tolerance VIF
(Constant) ,836 ,489 1,708 ,097 1
Employee's
perception of
customer mood
,813 ,139 ,718 5,838 ,000 ,718 ,718 ,718 1,000 1,000
a. Dependent Variable: Job Satisfaction
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86
Collinearity Diagnosticsa
Variance Proportions
Model Dimension Eigenvalue Condition Index (Constant)
Employee's
perception of
customer mood
1 1,969 1,000 ,02 ,02 1
2 ,031 7,906 ,98 ,98
a. Dependent Variable: Job Satisfaction
Residuals Statisticsa
Minimum Maximum Mean Std. Deviation N
Predicted Value 1,7842 4,7657 3,6018 ,72186 34
Residual -1,38853 1,74700 ,00000 ,69951 34
Std. Predicted Value -2,518 1,612 ,000 1,000 34
Std. Residual -1,955 2,459 ,000 ,985 34
a. Dependent Variable: Job Satisfaction
[Type text]
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88
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