Predictors of Employee Turnover in the Dutch Fashion Retail:

The Role of Work Overload, Emotional Labour, Emotional

Exhaustion and Multi-faceted Job Satisfaction

Author: Lonneke Schaap

Student ID: 10380965

Supervisor: Daphne Dekker

Submission Date: 15-08-2014

Paper Type: Master Thesis – Final version

University: University of Amsterdam

Course: Business Studies

Track : Leadership & Management

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ABSTRACT

Purpose – The purpose of this study is to examine the predictors of employee turnover in the

retail fashion industry. The research model analyzes the role of work overload, emotional

labour, emotional exhaustion and multi-faceted job satisfaction in predicting employee

turnover.

Design/methodology/approach – Data was collected by the use of a self-administered

questionnaire. 103 employees working in the fashion retail industry participated in the study.

Data was analyzed with IBM SPSS statistics 22.

Findings – Both work overload and emotional labour positively influence emotional

exhaustion. In turn, emotional exhaustion is negatively related to six dimensions of multi-

faceted job satisfaction; job satisfaction with overall job, co-workers, supervision, policy, pay

and customers. Only two dimensions are negatively related to employee turnover; job

satisfaction with overall job and job satisfaction with promotion. Only job satisfaction with

overall job mediates the relation between emotional exhaustion and employee turnover.

Practical implications – Both job satisfaction with overall job and job satisfaction with

promotion are negatively related to employee turnover. In order to decrease employee

turnover, management of fashion retail organizations should consider ways to increase job

satisfaction with overall job and job satisfaction with promotion. The model shows that an

increase of job satisfaction with overall job can be achieved by lowering emotional

exhaustion, work overload and emotional labour.

Originality/value – This study fills the research gap of employee turnover in the fashion

retail industry. A comprehensive model is used including industry-specific characteristics

such as work overload, emotional labour and emotional exhaustion. The overall model has

never been studied before. This is also the first time that the construct of multi-faceted job

satisfaction is examined in a fashion retail context specifically.

Keywords - Work Overload, Emotional Labour, Emotional Exhaustion, Multi-faceted Job

Satisfaction, Turnover Intentions, Fashion, Retail

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TABLE OF CONTENTS

1. Introduction 7

1.1. Introduction 7

1.2. The Dutch Fashion Retail Industry 10

1.3. Theoretical and Practical Relevance 10

2. Literature Review 12

2.1. Work Overload 12

2.2. Emotional Labour 13

2.3. Antecedents of Emotional Exhaustion 15

2.4. Antecedents of Multi-faceted Job Satisfaction 17

2.5. Antecedents of Employee Turnover 21

2.6. Research Model and Hypotheses 23

3. Methodology 24

3.1. Research Design 24

3.2. Questionnaire 24

3.3. Research Sample 25

3.4. Data Collection 25

3.5. Measures 26

3.6. Control Variables 27

3.7. Data Analysis 28

4. Results 31

4.1. Data Cleaning 31

4.1.1. Coding Variables and Recoding Counter-indicative Items 31

4.1.2. Missing Values 31

4.1.3. Detecting Outliers 32

4.1.4. Confirmatory Factor Analysis (CFA) 32

4.1.5. Computing Reliability 32

4.1.6. Computing Scale Means 33

4.1.7. Checking Normality and Distribution of Data 33

4.2. Testing Hypotheses 35

4.2.1. The Influence of Work Overload and Emotional Labour on Emotional 35

Exhaustion

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4.2.2. The Relation between Emotional Exhaustion and Multi-faceted Job 36

Satisfaction

4.2.3. Emotional exhaustion as Mediator between Work Overload and 39

Multi-faceted Job Satisfaction

4.2.4. Emotional Exhaustion as Mediator between Emotional Labour and 42

Multi-faceted Job Satisfaction

4.2.5. The Relation between Multi-faceted Job Satisfaction and 45

Turnover Intentions

4.2.6. Multi-faceted Job Satisfaction as Mediator between 46

Emotional Exhaustion and Turnover Intentions

4.3. Research Model with Results 47

5. Discussion 48

5.1. Conclusion 48

5.2. Discussion 49

5.3. Theoretical Implications 51

5.4. Practical Implications 51

5.5. Limitations and Future Research 52

6. References 54

7. Appendices 61

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LIST OF FIGURES AND TABLES

Figure 1: Statistics on Employees in the Dutch Retail Industry in 2010 11

Figure 2: Research Model 11

Figure 3: Mediation Model 30

Figure 4: Research Model with Results 47

Table 1: Means, Standard Deviations, Correlations and Reliability Coefficients 34

Table 2: Kurtosis, Skewness and the Kolmogorov-Smirnov Test 35

Table 3: Summary of Multiple Regression Analysis for Emotional Exhaustion 36

Table 4: Summary of Linear Regression Analyses for Multi-faceted Job Satisfaction 38

Table 5: Path Coefficients and Confidence Intervals; Emotional Exhaustion 41

as a Mediator between Work Overload and Multi-faceted Job Satisfaction

Table 6: Path Coefficients and Confidence Intervals; Emotional Exhaustion 44

as a Mediator between Emotional Labour and Multi-faceted Job Satisfaction

Table 7: Summary of Multiple Regression Analyses for Turnover Intentions 45

Table 8: Path Coefficients and Confidence Intervals; Multi-faceted Job Satisfaction 46

as a Mediator between Emotional Exhaustion and Turnover Intentions

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1. INTRODUCTION

1.1. Introduction

Imagine that you are in charge of a large fashion retail company which is in need for many

front-line employees on a regular basis. However, employee turnover rate appears to be high

and each time a new employee is hired, this person or another quits within a short period of

time. As one might picture, this situation is rather problematic and leads to numerous

organizational disadvantages. The overall subject of employee turnover is not a novel one but

has been of great interest over the last decades and has proven to be detrimental for

companies (Regts & Molleman, 2013). In general, prior literature supports this viewpoint

(Argote & Epple, 1990; Shaw, Gupta & Delery, 2005; Siebert & Zubanov, 2009) and

numerous organizational disadvantages of employee turnover are found such as an incline in

productivity (Batt, 2002; Huselid, 1995), future revenue growth (Baron, Hannan & Burton,

2001) and profitability (Glebbeek & Bax, 2004; Ton & Huckman, 2008). Additional

organizational disadvantages of high employee turnover are a decrease in customer service

(Kacmar, Andrews, Van Rooy, Steilberg & Cerrone, 2006) and customer satisfaction

(Morrow & McElroy, 2007).

Most research explains the negative relation between employee turnover and

organizational performance on the basis of the social and human capital theory, as these two

theories have received most attention in prior literature (Park & Shaw, 2013). The human

capital theory assumes that employees who have experience are of more value to a company

because they possess the needed skills and knowledge. When these employees decide quit, it

is harmful to the organization because it loses employees with experience who contribute to

the human capital of the firm. It takes time to substitute these employees and to reach similar

levels of knowledge. The replacement of personnel also adds costs in terms of recruiting,

selecting and training new employees. The social capital theory argues that turnover has a

negative effect on organizational performance because when an employee decides to leave, he

or she disturbs the organization’s social capital which has to do with social relations between

employees such as trust and collective goal orientation. Moreover, socialization costs are

involved when new employees enter an organization.

Originally, labour turnover can be divided into either voluntary or involuntary

turnover (Morrell, Loan-Clarke & Wilkinson, 2001; Price, 1977). “An instance of voluntary

turnover, or a quit, reflects an employee's decision to leave an organization, whereas an

instance of involuntary turnover, or a discharge, reflects an employer's decision to terminate

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the employment relationship” (Shaw, Delery, Jenkins & Gupta, 1998, p. 511). Because this

study aims to predict the antecedents of why employees decide to quit, this paper refers to the

concept of voluntary employee turnover when using the definition of employee turnover.

Voluntary turnover is often unexpected and hard to manage for organizations (Shaw et al.

1998).

Much attention has been paid in prior literature to the subject of employee turnover in

general but less is known about employee turnover in the retail industry. Rhoads, Swinyard,

Geurts and Price (2002) argue that having and retaining a good workforce is a major

requirement for being successful as a retail company. By maintaining competent employees,

recruitment and training costs can be reduced and more value can be offered to customers.

This results in benefits while gaining competitive advantage over other market players. It is

therefore important to decrease employee turnover in the retail industry (Kim, Knight &

Crutsinger, 2009).

Although a number of studies investigated employee turnover in the retail industry,

there is still much to discover (Booth & Hamer, 2007). Even more absent is a comprehensive

model on the most important predictors of employee turnover in the fashion retail industry

specifically. Research on this subject is lacking in this specific branch. This study tries to gain

more insight into the predictors of employee turnover in the fashion retail industry and will

thereby contribute to the lack of research in this specific area. Due to this lack of knowledge,

this study is mainly based on arguments from research conducted in the overall retail industry

since the fashion retail is presented as part of the overall retail industry (Peek & Veghel,

2011). It is therefore assumed that there are many similarities between the fashion retail

industry and the overall retail industry. Figure 1 illustrates this in terms of statistics.

Multiple factors influence the decision of employees to leave an organization in the

fashion retail industry. Prior literature claims that job satisfaction is one of the most important

drivers behind employee turnover in general (Jaramillo, Mulki & Solomon, 2006). This also

accounts for the retail industry (Booth & Hamer, 2007; Henrie, 2004). In order to draw an as

complete model as possible, this study focuses on the concept of multi-faceted job satisfaction

instead of single-faceted job satisfaction as key antecedent of employee turnover. Multi-

faceted job satisfaction has mainly been used in business-to-business context and so far, only

Chung, Rutherford and Park (2012) explored the concept of multi-faceted job satisfaction in

the retail industry. They argue that future research is needed to test the effects of multi-faceted

job satisfaction on outcomes such as employee turnover. Therefore this study includes multi-

faceted job satisfaction as major predictor of employee turnover.

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Multi-faceted job satisfaction in turn, is found to be predicted by emotional exhaustion

in both a sales and retail environment (Chung et al., 2012; Rutherford, Boles, Hamwi,

Madupalli & Rutherford, 2009). Broadbridge (1999) argues that jobs in retail have become

more stressful over time due to organizational changes which are forced by the multiplicity

and complexity of retail organizations. These changes can be assigned to technological,

environmental and market issues, such as online shopping, customer needs and

internationalization. Since jobs in the retail industry are related to long, unsocial hours;

psychical endeavor and routine work, one can imagine that emotional exhaustion plays

especially in this particular context an important role (Broadbridge, 1999). Therefore

emotional exhaustion is assumed to be a major predictor of multi-faceted job satisfaction.

In turn, this study argues that work overload and emotional labour are again major

stressors of emotional exhaustion and are specifically present in the retail sector. Broadbridge

(2002) found that jobs in retail are considered to be stressful among the majority and that this

is partly caused by work overload. Another industry specific characteristic is emotional

labour. The display of emotions is particularly important in the service industry in which

emotional labour is part of front-line employees’ jobs (Ashforth & Humphrey, 1993). In this

study, comparison between front-line employees from the service industry and the retail

industry is often made since both can be considered as service jobs which require ‘person to

person’ interaction and ‘soft skills’ (Warhurst & Nickson, 2007). According to Warhurst and

Nickson (2007), most research on front-line employees from both the retail and service

industry is concerned with emotional labour. However, the impact of emotions in the service

industry is not a well-understood area yet (Hennig-Thurau, Groth, Paul & Gremler, 2006).

Limited research on emotional labour in the workplace has been conducted (Schaubroeck &

Jones, 2000).

In sum, this study examines the antecedents of employee turnover in the fashion retail

industry. Specific industry characteristics are included in the research model and the role of

work overload, emotional labour, emotional exhaustion and multi-faceted job satisfaction in

predicting employee turnover is analyzed. A questionnaire will be distributed among a sample

of Dutch front-line employees in the fashion retail branch. The following research question

will be addressed: “What is the role of work overload, emotional labour, emotional

exhaustion and multi-faceted job satisfaction in predicting employee turnover?” See figure 2

for the conceptual model of this research.

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1.2. The Dutch Fashion Retail Industry

The Dutch retail industry is strongly diversified since there are 45 subsectors including

fashion (Peek & Veghel, 2011). According to het Hoofdbedrijfschap Detailhandel (hbd, 2011)

in 2011, a total number of 680.200 people were employed in the Dutch retail sector. The retail

sector is one of the largest commercial sectors and 10% of all jobs in the Netherlands can be

found in the retail industry (hbd, 2011). Both absenteeism and labour turnover are found to be

high in the retail industry (Broadbridge, 1999; Rhoades & Eisenberger, 2002). According to

Eurostat data, in 2006, just over 60% of the Dutch retail workforce stayed in their current job

for less than five years and 27% of annual leavers have been counted in the Netherlands.

In 2009, consumers spend 14.6 billion Euros on fashion in the Netherlands

(Fashionunited, 2009). A total of 76.800 paid jobs in fashion stores have been counted in the

Netherlands in 2013 (hbd, 2013). This number does not only include sales assistants, but

covers all positions in a store. Most of the jobs in fashion stores are part-time. 35% contains

of jobs less than 16 hours whereas approximately 19% of the fashion stores contains full-time

jobs (38 hours or more). The remaining part works somewhere in between part-time and full-

time. 86% of all employees in fashion stores are female; this is slightly more than the overall

retail industry (hbd, 2010). See figure 1.

1.3. Theoretical and Practical Relevance

As described earlier, there is a lack of research and theories in the area of employee turnover

in the fashion retail industry. However, the contribution of this study is not only relevant for

theoretical purposes but also for practical ones. The size of the retail industry in relation with

the commercial and economic interests, the height of the employee turnover rate and the

aforementioned disadvantages of employee turnover indicate the importance of the topic. It is

therefore crucial to understand the main drivers behind employee turnover in the fashion retail

sector. The results of this study can be helpful for organizations in order to reduce their

employee turnover rates and increase performance. For example, if employee turnover

appears to be a result of low job satisfaction, management of an organization can make

decisions on how to improve job satisfaction among front-line employees. Improved job

satisfaction can for example be achieved by giving employees more responsibility and

autonomy, stimulating variety in skills and enhancing interpersonal relationships (Harter,

Schmidt & Hayes, 2002).

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Figure 1: Statistics on Employees in the Dutch Retail Industry in 2010

Fashion Stores Total Retail

Gender

Male 14% 37%

Female 86% 63%

Age

till 17 years 5% 11%

17 to 20 years 19% 24%

21 to 25 years 16% 14%

26 to 35 years 17% 16%

36 to 45 years 19% 17%

46 to 55 years 15% 12%

56 years and older 9% 6%

Ethnicity

Natives 80% 83%

Western Immigrants 9% 7%

Non-Western Immigrants 11% 10%

HBD en ITS, 2010

Figure 2: Research Model

Work Overload

Emotional

Labour

Emotional

Exhaustion

Multi-faceted Job

Satisfaction

- Overall-job

- Co-workers

- Supervision

- Policy

- Pay

- Promotion

- Customers

Turnover

Intentions

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2. LITERATURE REVIEW

This chapter will provide an overview of the relevant literature in the research area of this

study. Furthermore, hypotheses will be formed. First, the theoretical background of work

overload and emotional labour will be discussed. Second, the literature on emotional

exhaustion will be explained in relation to work overload and emotional labour. Third, the

concept of multi-faceted job satisfaction will be elaborated in relation to emotional

exhaustion. Last, the theoretical background on employee turnover and its predictors will be

defined.

2.1. Work Overload

Based on Rizzo, House and Lirtzman (1970), Bolino and Tumely (2005) state that “Role

overload describes situations in which employees feel that there are too many responsibilities

or activities expected of them in light of the time available, their abilities, and other

constraints”, p.741. This definition is used throughout this paper to describe the definition of

“work overload”. It should be noticed that “role overload” is replaced by the term “work

overload”. Much research on work overload has been conducted in general; especially the

impact of work overload on the outcomes of sales people (Jones, Chonko, Rangarajan &

Roberts, 2007). However, there is a lack of research on work overload in the (fashion) retail

industry specifically.

In general, work (over)load has increased over the past decades. This is partly due to

organizational restructuring and a larger focus on productivity improvements, resulting in

more responsibilities for employees. Besides, companies focus more and more on maximizing

their profits and revenues which means that cost-cutting appears wherever possible, including

lay-offs. This in turn leads to fewer employees per organization who remain stuck with too

much work (Mulki, Lassk & Jaramillo, 2008). This increase in work overload is found to be

harmful to the well-being of employees. Physical and mental health decrease when employees

experience more stress (Jones et al., 2007). Also found is that work overload leads to a

decrease in organizational commitment and higher levels of absenteeism because of sickness.

This leads again to a decline in the overall profitability of an organization (Duxbury &

Higgins, 2001). A study by Broadbridge (1999) in the retail sector supports this and claims

that outcomes of pressures on the work floor can have a negative impact on an employee’s

well-being. Therefore work overload is assumed to be a major job stressor in this study.

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2.2. Emotional Labour

Morris and Feldman (1996) state that organizations progressively want to influence the kind

of behavior and image their employees transcend to clients. According to Cho, Rutherford and

Park (2013) it is important that employees successfully control their emotions, not only for

themselves but also for customers. As a result, organizations have increasingly been

developing specific guidelines which prescribe desired emotions to be expressed by

employees, called display rules. Ekman (1973) specified display rules as standards of

behavior which prescribe the appropriate emotions in specific situations and how these

emotions should be displayed.

The display of emotions is particularly important in the service industry in which

emotional labour is part of front-line employees’ jobs. There are a few reasons for this

(Ashforth & Humphrey, 1993). First, front-line employees carry out and promote the

organization to customers directly because they operate in boundary spanning roles. Second,

front-line employees are often involved in face-to-face interactions with customers in which

front-line employees have direct contact with them. Third, the presence of customers can

cause uncertain situations in which the quality of the service encounter might fluctuate. Last,

it is difficult for customers to judge the level of service quality, because the provided services

are often hard to ‘grasp’ and are intangible.

These aspects emphasize the importance of service front-line employees because the

behavior heavily influences how customers perceive the overall quality of the organization.

Front-line employees have to welcome their customers in a positive manner and during the

interaction, positive emotions will be transferred to customers (Cho et al., 2013). Since the

behavior of front-line employees plays an important role in the organization’s performance,

they are expected to behave according to an organization’s display rules. According to

Grandey, Fisk, Mattila, Jansen and Sideman (2005), a widespread display rule is the “service-

with-a-smile” rule which means that employees should always express indefectible positive

emotions when interacting with customers. Display rules thus emphasize the “superficial”

side of emotions; these emotions which are direct observable. Deeper emotions or actual

feelings are not taken into account (Ashforth & Humphrey, 1993).

Researchers all agree with the underlying assumption that emotional labour is about

the regulation of emotions to ensure that these are in line with the organizational display rules

(Goffman, 1959). However, there are different theoretical approaches concerning emotional

labour and there is no clear consensus about its specific construct (Glomb & Tews, 2004).

According to Morris and Feldman (1996) emotional labour is defined as “…the effort,

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planning, and control needed to express organizationally desired emotion during interpersonal

transactions”, p. 987. They further argue that as long as the expressed emotions are in line

with the organizational display rules, it creates emotional labour. This is supported by

Brotheridge and Lee (2003). According to Ashforth and Humphrey (1993), emotional labour

is the “act of expressing socially desirable emotions”, p. 88-89. No matter if internal feelings

are in line with these desirable emotions. These researchers thus argue that emotional labour

involves both genuine and in-genuine feelings.

However, Mann (1999a) describes emotional labour as: “The state that exists when

there is a discrepancy between the emotional demeanor that an individual displays because it

is considered appropriate, and the emotions that are genuinely felt but that would be

inappropriate to display”, p. 353. This definition is closely related to emotional dissonance.

Although researchers agree that dissonance is included in the concept of emotional labour,

there is no agreement on whether emotional dissonance is a necessary condition for emotional

labour (Glomb & Tews, 2004). Nevertheless, this paper follows the reasoning of Mann

(1999a). Thereby arguing that emotional dissonance is a requirement for emotional labour to

exist and that emotional labour is solely present when employees fake or oppress certain

feelings whereby genuinely felt emotions are not taken into account. This definition is chosen,

because employees have to make more effort when organizational display rules are

incongruent with their genuine feelings (Ashforth & Humphrey, 1993; Morris & Feldman,

1996). This has proven to lead to negative outcomes such as job-related stress (Adelmann,

1995) and emotional exhaustion (Morris & Feldman, 1997) and fits the conceptual model of

this study best.

It is thus argued that emotional labour is concerned with the discrepancy between an

employee’s true feelings and the expressed feelings which are desired by the organization.

“The process of emotional labour itself typically involves two common processes:

suppressing the negative emotions that one is feeling and faking positive emotions that one is

not feeling”, p. 470 (Glomb & Tews, 2004 in Sliter, Jex, Wolford & McInnerney, 2010). Prior

literature has argued that employees in the service industry are often involved in both

processes. This means that they welcome customers in a friendly manner when actually not

feeling well and they hide their irritation when customers behave impolite (Grandey, Fisk &

Steiner, 2005). Engaging in the two processes of emotional labour; suppressing negative

emotions and faking positive emotions, has direct implications for an employee’s well-being

(Grandey, 2003).

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2.3. Antecedents of Emotional Exhaustion

Emotional exhaustion is the most prominent dimension of job burnout, among

depersonalization and personal accomplishment (Maslach & Jackson, 1981). Based on these

authors, Rutherford et al. (2009) define burnout as “… a psychological syndrome or condition

that manifests in reactions to chronic stress experienced by people who provide services”,

p.1147). Also based on Maslach and Jackson (1981), Chung et al. (2012) define emotional

exhaustion as “…the feeling of emotional overextension and exhaustion attributable to one’s

work, p.703. Emotional exhaustion is known to be as a type of stress that is caused by

stressors at the work floor (Cropanzano, Rupp & Byrne, 2003) and is the most researched

dimension of job burnout in a sales environment (Rutherford et al., 2009). According to

Singh, Goolsby and Rhoads (1994), emotional exhaustion is more likely to arise at employees

who are involved in boundary spanning roles. Front-line employees engage in boundary-

spanning roles because they are always in between the customer and the employer. There are

three reasons for this.

First, they are responsible for representing the store and the image of the store. Second,

their task is to guarantee service quality and communicate improvements internally in order to

meet the demands of the customer better. Last, the level of quality and customer satisfaction

relies for a big part on the behavior of front-line employees (Bettencourt & Brown, 2003).

Service employees are in a position which requires them to engage in boundary-spanning

activities and it is therefore presumed that they are more likely to experience role stress

(Kahn, Wolfe, Quinn & Snoek, 1964; Singh, 1993). Cooper and Baglioni (1988) tried to

distinguish the level of stress among different occupations and found that jobs in the retail are

exposed to an above average level of stress. Broadbridge (1999) found that positions in the

retail industry are considered to be stressful. As such, emotional exhaustion is particularly

present within the service and retail industry, since employees are often in direct contact with

their customers, who expect them to live up to high demands. This makes them more

vulnerable to emotional exhaustion (Cordes & Dougherty, 1993; Rutherford et al., 2009).

It is important to understand the antecedents of emotional exhaustion since emotional

exhaustion leads to important organizational outcomes, such as job satisfaction,

organizational commitment and turnover intentions (Babakus, Cravens, Johnston & Moncrief,

1996). According to Cho et al. (2013) the emotional understanding of employees also affects

the way customers perceive levels of service quality, satisfaction and loyalty. Robinson and

Griffiths (2005) found that work overload is most often mentioned as the main source of job

related stress. This is supported by other research. For example, Janssen, De Jonge and

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Bakker (1999) conducted a study among nurses and found that burnout is mainly caused by

both workload and limited social support. Furthermore they state that stress is a result of

resources which are threatened by demands (e.g. workload or role stress). Another meta-

analysis conducted by Lee and Ashforth (1996) claims that there is a relationship between

work overload and limited support on one side and emotional exhaustion on the other.

However, these outcomes concern organizations in general and are not specifically focused on

the retail industry.

Broadbridge (2002) however, did focus on the retail industry and found that work

overload is one of the key stressors for employees. Also applicable to the retail environment,

Firth, Mellor, Moore and Loquet (2004) found that certain stressors, among which work

overload, have a direct influence on stressful feelings and they further argue that work

overload has a direct relation with stress feelings and job satisfaction. Since work overload is

a potential job stressor, it is argued that there exists a positive relation between stress related

feelings and emotional exhaustion. So when an employee experiences more work overload, it

is likely that an employee’s emotional exhaustion increases. Therefore the following is

hypothesized:

H1. Work overload is positively related to an employee’s emotional exhaustion.

Further argued is that emotional labour is an important antecedent of emotional exhaustion.

As stated, front-line employees are more prone to emotional exhaustion due to their

participation in boundary spanning roles. Sales persons need to express positive emotions and

hide negative emotions in order to accomplish customer satisfaction and customer loyalty

(Lam, Kraus & Ahearne, 2010). This refers to the concept of emotional labour: a discrepancy

between real feelings and feelings which need to be expressed by the organization. Emotional

dissonance, which is in this case closely related to emotional labour, is even larger in

organizations where employees have face-to-face contact with customers all the time because

they always have to live up to the organizational display rule, even though this is not in line

with their genuine feelings (Morris & Feldman, 1996).

Hochschild (1979, 1983) argues that complying with organizational display rules

might cause harmful psychological effects among front-line employees. This is supported by

other researchers. The engagement in organizational display rules has been found to be linked

to stress-related physiological arousal (Gross & Levenson, 1997) and job strain which

concerns bad work attitudes and burnout (Brotheridge & Grandey, 2002).

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In general, employees have to make more effort when organizational display rules are

incongruent with their genuine feelings (Ashforth & Humphrey, 1993; Morris & Feldman,

1996). The negative effects of emotional labour can be explained by the Conservation Of

Resources (COR) theory by Hobfoll and Freedy’s (1993). This theory argues that people,

when possible, try to preserve their resources. When they engage in fake emotions or when

they suppress their emotions, their resources cannot be guarded which leads to emotional

exhaustion (Sliter et al., 2010). Grandey, Fisk and Steiner (2005) also argue that faking and

oppressing feelings drain personal resources and causes job stress for front-line employees.

However, the impact of emotions in the service industry is not a well-understood area yet

(Hennig-Thurau et al., 2006). Nevertheless, based on the COR theory, the following is

hypothesized:

H2. Emotional labour is positively related to an employee’s emotional exhaustion.

2.4. Antecedents of Multi-faceted Job Satisfaction

Job satisfaction can be described as “a pleasurable or positive emotional state resulting from

the appraisal of one’s job or job experiences” (Locke, 1976, p. 1304). This overall

understanding of job satisfaction using global measurements has helped in the exploration of

the antecedents of job satisfaction (Arnold, Flaherty, Voss & Mowen, 2009; Babin & Boles,

1996). However, there has been critique on global measures of job satisfaction because they

leave out measures of individual aspects (Churchill, Ford & Walker, 1974). Widely held

literature views job satisfaction as a universal and single-layered dimension, while it is often

approached as both a precursor and consequence (Rutherford et al., 2009), as a respond to

this, several multi-faceted job satisfaction scales have been designed.

Wood, Chonko and Hunt (1986) use a four dimension scale, including satisfaction on

pay, closure, information and variety. Smith, Kendall and Hulin (1969) designed a five

dimension scale which covers satisfaction with pay, opportunities for promotion, supervision,

type of work and coworkers on the job. Churchill et al. (1974) developed a seven dimension

scale with 95 items named INDSALES, covering satisfaction with overall job, co-workers,

supervision, policy, pay, promotion and customers. Similar to Chung et al. (2012), this study

uses the reduced 28-item INDSALES scale (Comer, Machleit & Lagace, 1989). This scale is

chosen because it explains more facets than the other scales and is therefore more likely to

obtain richer information. Moreover, the scale has been ultimately developed to be used in a

sales environment and is therefore applicable to this study. Besides, the scale simultaneously

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measures individual facets of job satisfaction as well as global job satisfaction (satisfaction

with overall job), thereby taking advantages from both measures.

The study of Rutherford et al. (2009) is one of the few which focuses on the link

between emotional exhaustion and multi-faceted job satisfaction. They found that emotional

exhaustion is related to five of the seven dimension of multi-faceted job satisfaction, namely

satisfaction with: overall job, supervision, policy, pay and promotion. No significant

relationship was found between emotional exhaustion and satisfaction with co-workers and

customers. However, they conducted their study in a sales environment and not specifically in

a retail context. Another interesting study explored the link between emotional exhaustion and

multi-faceted job satisfaction particularly in the retail context (Chung et al., 2012). Results

indicate that emotional exhaustion is one of the most prominent predictors of multi-faceted

job satisfaction. They found that emotional exhaustion is significantly and negatively related

to job satisfaction with: overall job, co-workers, supervision, pay and promotion. They did not

find any relation between emotional exhaustion and job satisfaction with policy and

customers.

These studies on multi-faceted job satisfaction show different outcomes and in general,

not much research has been conducted yet on the effect of emotional exhaustion in relation to

multi-faceted job satisfaction, but rather in relation to global job satisfaction (Rutherford et

al., 2009). The rest of this paragraph will build theoretical support for the relations between

emotional exhaustion and each of the seven dimensions of job satisfaction.

Contrasting results have been discussed in prior research about the relation between

emotional exhaustion and global job satisfaction. A number of studies did not find any

relationship, assuming that emotional exhaustion has no effect on job satisfaction (Boles,

Johnston & Hair, 1997). However, in general, it is widely acknowledged that burnout is

negatively related to job satisfaction and as described earlier, emotional exhaustion is a part of

burnout (Maslach, 1981; Singh et al., 1994). Singh et al. (1994) argue that in contrast to role

stressors, burnout is always destructive and has a negative, linear curve with various job

outcomes such as job satisfaction. The negative relation is based on two theoretical

arguments. ‘First, because psychological burnout is the outcome of an appraisal process by

which an individual evaluates the demands vis-a-vis his or her re-sources, it is posited that the

outcome of this appraisal should affect an individual's psychological well-being on the job,

including job satisfaction. Second, because both are affective responses, it is hypothesized

that burnout feelings should be related to job satisfaction’ (Singh et al., 1994, p.561). The

negative link has found support in prior literature in financial firms and service organizations

19

(Jaramillo et al., 2006), the retail banks sector (Karatepe & Tetinkus, 2006) and in a sales

environment (Babakus et al., 1996). A recent study in the retail industry of Cho et al. (2013)

also found support for the negative link between emotional exhaustion and job satisfaction.

However, their study is conducted in Asia and might therefore not be generalized.

Furthermore, it is expected that emotional exhaustion is negatively related to job

satisfaction with co-workers and customers, although this latter construct has not found full

support in any of the aforementioned studies on multi-faceted job satisfaction. However,

Leiter and Maslach (1988) argue that emotional exhaustion is closely related to interpersonal

relationships and that it correlates high with being in contact with other people. When

someone is emotionally exhausted, a person’s resources are sooner depleted. It is therefore

expected that emotional exhaustion sooner leads to negative experiences with co-workers and

customers and in turn, reduces job satisfaction on these dimensions.

Also, a negative link between emotional exhaustion and job satisfaction with

supervision and policy is assumed. Employees prefer to work for an organization in which

managers respond to their needs (Booth & Hamer, 2007). Following this reasoning, a possible

explanation for the negative relation between emotional exhaustion and job satisfaction with

supervision and policy could be that an employee, who feels emotionally exhausted, has not

many resources left and has more difficulties coping with job-related tasks or activities. This

person might be sooner depleted and irritated, requesting for more flexibility. When the policy

or supervision of a company is not able to respond to this, an employee might develop a more

negative view of the organization and thereby reducing his or her job satisfaction concerning

these two facets.

Moreover, emotional exhaustion is expected to have a negative relation with job

satisfaction with pay and promotion. This can be explained by the social exchange theory

(Hatfield & Sprecher, 1984), assuming that people always strive for equity in an employee-

organization ratio. Emotionally exhausted employees have probably put much energy and

effort in the organization. This in turn results in a demand for equivalent rewards from the

organization in order to maintain a feeling of equity. These rewards might for example be

promotion or pay (Van Dierendonck, Schaufeli & Buunk, 1996). When the organization is not

able to provide higher rewards, an employee’s job satisfaction with pay and promotion will

decrease.

Based on the aforementioned theories, this study argues that emotional exhaustion has

a negative impact on all dimensions of multi-faceted job satisfaction. This leads to the

following hypothesis:

20

H3. Emotional exhaustion is negatively related to an employee’s multi-faceted job

satisfaction (including 1.Overall job, 2. Co-workers, 3. Supervision, 4. Policy,

5. Pay, 6. Promotion and 7. Customers).

Some researchers have found a direct effect of certain job stressors on global job satisfaction

(Wunder & Dougherty, 1982; Currivan, 2000). In a retail context, Firth & al. (2004) found

that workload directly relates to feelings of stress and overall job satisfaction. However, this

study expects that emotional exhaustion acts as a mediator in the negative relation between

work overload on multi-faceted job satisfaction and emotional labour on multi-faceted job

satisfaction. Both work overload and emotional labour are expected to result in higher levels

of emotional exhaustion. Experiencing emotional exhaustion makes it more likely that the

perception of an employee on the different dimensions of job satisfaction changes, rather than

work overload or emotional labour on their own. This is supported by Singh et al. (1994),

arguing that burnout (among which is emotional exhaustion) is not a job stressor in itself, but

rather a result of multiple role stressors. The study recognizes that there is in general a

significant and direct effect of role stressors (e.g. conflict, overload) on important

organizational outcomes, such as job satisfaction. However, these outcomes do not reflect

enough strength and consistency to be fully supported. Further argued is that burnout is a far

more prominent predictor of organizational outcomes than role stressor(s). Based on this

theory and the overall lack of understanding about the antecedents of multi-faceted job

satisfaction in a retail environment (Chung et al., 2012), the following hypotheses are

assumed:

H4. The negative relation between work overload and multi-faceted job satisfaction

(including 1.Overall job, 2. Co-workers, 3. Supervision, 4. Policy, 5. Pay, 6.

Promotion and 7. Customers) is mediated by an employee’s emotional

exhaustion.

H5. The negative relation between emotional labour and multi-faceted job

satisfaction (including 1.Overall job, 2. Co-workers, 3. Supervision, 4. Policy,

5. Pay, 6. Promotion and 7. Customers) is mediated by an employee’s

emotional exhaustion.

21

2.5. Antecedents of Employee Turnover

Prior research has shown that turnover intention is the best predictor of actual turnover (Regts

& Molleman, 2012). “Turnover intentions can be defined as an employees’ state of mind to

leave an organization” (Singh, Verbeke & Rhoads, 1996 in Alexandrov, Babakus and Yavas,

2007, p.357). In this study, voluntary employee turnover is measured in terms of turnover

intentions. Sager’s (1991) has shown that turnover intentions provide enough validity and

makes an effective distinction between those who leave and those who stay.

Multiple factors influence employee turnover in general. For example, a meta-analysis

of Hom and Griffeth (1995) found that employees leave an organization when they become

dissatisfied and lose their organizational commitment. Hom and Kinicki (2001) build further

on this and state that there are three main reasons for employee turnover: the external business

environment, the personal element and the job satisfaction element. According to Chang et al.

(2013) the main antecedents of turnover intention in previous studies are: job autonomy, fair

reward, job satisfaction, organizational commitment, tenure, social support, and demographic

variables. Other studies have investigated employee turnover in a retail context specifically.

One interesting study is conducted in by Henrie (2004) who found that key arguments for

employee turnover are: too little working hours, bad payments, no career opportunities,

overwork, unsocial work hours, bad training, poor staff facilities, being afraid of redundancy

and staff views were not heard. However, this study took place in the UK and might not be

generalized. Alexandrov et al. (2007) explored the effects of psychological climate on

turnover in a retail environment and argue that this results in affective responses as job

satisfaction and affective organizational commitment and in turn influences employees’

turnover intentions.

As illustrated, there are numerous predictors of employee turnover in the retail context

which poses a limitation for identifying a comprehensive model which covers all its

antecedents. However, almost all of the aforementioned studies include job satisfaction and as

an important predictor of employee turnover. This is supported by other authors, stating that

dominant models in literature focus on job satisfaction as one of the main drivers of labour

turnover in organizations (Henrie, 2004; Jaramillo et al., 2006; March & Simon, 1958). Based

on this, job satisfaction is considered as the major predictor of employee turnover. In order to

capture as much information as possible, the concept of multi-faceted job satisfaction is used

instead of global job satisfaction.

The relation between job satisfaction and turnover intentions of salespersons has been

widely examined (Ladik, Marshall, Lassk & Moncrief, 2002) and several studies (Boles,

22

Johnston & Hair, 1997; Jaramillo et al., 2006) found a negative relation between general job

satisfaction and turnover intentions. However, there is only limited research on the relation

between multiple facets of job satisfaction and the intention to leave (Rutherford et al., 2009).

Rutherford et al. (2009) only found a significant link between job satisfaction with overall job

and job satisfaction with promotion on the propensity to leave, whereas the other dimensions

did not strongly enough relate to turnover intentions. However, this study has been conducted

in a sales environment whereas findings in a retail context might differ. Based on previous

results in the literature and the relative lack of knowledge on the relationship between multi-

faceted job satisfaction and turnover intentions, this paper argues that all dimensions of job

satisfaction are negatively related to employee turnover intentions. Therefore, the following is

proposed:

H6. Multi-faceted job satisfaction (including 1.Overall job, 2. Co-workers, 3.

Supervision, 4. Policy, 5. Pay, 6. Promotion and 7. Customers) is negatively

related to an employee’s turnover intentions.

Given that emotional exhaustion is expected to predict multi-faceted job satisfaction and

multi-faceted job satisfaction predicts in turn employee turnover intentions, it is assumed that

multi-faceted job satisfaction mediates the relationship between emotional exhaustion and

employee turnover intentions. However, there are mixed findings on the mediating role of job

satisfaction in relation to emotional exhaustion and turnover intentions (Rutherford et al.,

2009). Boles et al. (1997) argue that emotional exhaustion is directly related to turnover

intentions; they did not find support for the mediating role of job satisfaction. O’Driscoll and

Beehr (1994) found the opposite; job satisfaction mediates the effects of uncertainty and role

stressors on turnover intentions and strain. However, both studies address global job

satisfaction instead of multi-faceted job satisfaction. Rutherford et al. (2009) found that multi-

faceted job satisfaction mediates the relation between emotional exhaustion and the

propensity to leave. Given these mixed findings and the lack of previous research on multi-

faceted job satisfaction as a mediator, the following hypotheses will be tested:

H7. The positive relation between emotional exhaustion and employee turnover

intentions is mediated by an employee’s multi-faceted job satisfaction

(including 1.Overall job, 2. Co-workers, 3. Supervision, 4. Policy, 5. Pay, 6.

Promotion and 7. Customers).

23

2.6. Research Model and Hypotheses

Work Overload

Emotional

Labour

Emotional

Exhaustion

Multi-faceted Job

Satisfaction

- Overall-job

- Co-workers

- Supervision

- Policy

- Pay

- Promotion

- Customers

Turnover

Intentions

H2

H1

H3 H6

H4 + H5

H7

H1. Work overload is positively related to an employee’s emotional exhaustion.

H2. Emotional labour is positively related to an employee’s emotional exhaustion.

H3. Emotional exhaustion is negatively related to an employee’s multi-faceted job

satisfaction (including 1.Overall job, 2. Co-workers, 3. Supervision, 4. Policy, 5. Pay,

6. Promotion and 7. Customers).

H4. The negative relation between work overload and multi-faceted job satisfaction

(including 1.Overall job, 2. Co-workers, 3. Supervision, 4. Policy, 5. Pay, 6.

Promotion and 7. Customers) is mediated by an employee’s emotional exhaustion.

H5. The negative relation between emotional labour and multi-faceted job satisfaction

(including 1.Overall job, 2. Co-workers, 3. Supervision, 4. Policy, 5. Pay, 6.

Promotion and 7. Customers) is mediated by an employee’s emotional exhaustion.

H6. Multi-faceted job satisfaction (including 1.Overall job, 2. Co-workers, 3. Supervision,

4. Policy, 5. Pay, 6. Promotion and 7. Customers) is negatively related to an

employee’s turnover intentions.

H7. The positive relation between emotional exhaustion and employee turnover intentions

is mediated by an employee’s multi-faceted job satisfaction (including 1.Overall job,

2. Co-workers, 3. Supervision, 4. Policy, 5. Pay, 6. Promotion and 7. Customers).

24

3. METHODOLOGY

3.1. Research Design

This study enhances a deductive and explanatory approach, because preconceived hypotheses

based on existing theory are used while explaining relations between variables (Lewis &

Saunders, 2012). In order to collect the data, a quantitative approach was chosen which

allows for reaching a high number of responses. This was done through the use of a self-

administered, internet-mediated questionnaire. This approach is efficient and simultaneously

keeps costs low (Lewis & Saunders, 2012). Moreover, data was collected through the

distribution of this self-administered questionnaire by delivery and collection. An advantage

of this latter approach is that it is more personal and a decent response is often given (Lewis

& Saunders, 2012). The study is cross-sectional which means that data is collected at one

point in time instead of a longitudinal study, measuring multiple points in time. Even though

results of the latter design appear to be more reliable, a cross-sectional design was chosen in

this study due to restrictions of costs and time.

3.2. Questionnaire

The developed questionnaire consists of closed questions in which a likert scale of 1 (e.g.

strongly disagree) to 5 (e.g. strongly agree) is used. Closed questions are often recommended

in this type of study since this increases reliability (Lewis & Saunders, 2012). Besides, control

variables such as age, gender and level of education are adopted in the questionnaire. The

questionnaire is based on existing measures in the literature of which the language of

instruction is English. Since the questionnaire was distributed in the Netherlands, most

respondents were expected to be Dutch speaking natives. Probably, a Dutch questionnaire will

therefore be easier accessible to people and increase the response rate. Moreover, a Dutch

questionnaire will increase the validity of the answers since familiarity with the language

increases understanding (Lewis & Saunders, 2012). Therefore, the questionnaire was

translated into Dutch with the “back and forward method”, recommended by Field (2005).

Following, the questionnaire was evaluated on clarity and possible errors by two different

people, after which improvements were implemented. Last, an introduction was written with

clear instructions. The introduction explained the purpose of the research and the time it takes

to fill out the survey. It also stated that anonymity was guaranteed; results would be treated in

a strictly confidential manner and participation was voluntary. Furthermore, contact

information was provided in case respondents had any questions or remarks. Also, a word of

25

thanks was given. In order to maximize response rates, a gift card of €50, - was allotted

among all participants. These last steps all contribute to conducting research in an ethical and

responsible manner.

3.3. Research Sample

The sample consists of 103 front-line employees working in fashion retail stores in the

Netherlands. 76.7% is female and 23.3% is male. This is congruent with statistics on gender

of hbd (figure 2) showing that more females than males are working in this industry. The age

of the participants is mostly between 17 and 35 years old (92.4%), representing relative

young employees. 31.1% works full-time whereas 68.9% works part-time. This is also in line

with statistics from hbd, showing that more employees work part-time than full-time. The

tenure in job ranges from 5 days to 15 years, and the average tenure is 3 years. The level of

education is respectively 19.4% for secondary or high school, 34% for middle level applied

education (MBO), 43.7% higher education or bachelor (HBO) and 1.9% a university or

master’s degree (WO). The nationality is predominantly Dutch (87.4%) which is also

congruent with statistics of hbd. Since this study is specifically focused on the fashion retail

industry, only front-line employees working in fashion stores are included in this research.

Data was collected by using non-probability sampling methods. This forms a limitation,

because it lowers theoretical value and generalizability (Lewis & Saunders, 2012). However,

this method is chosen because there was no sampling frame on front-line employees in the

fashion retail industry available.

3.4. Data Collection

The questionnaire was developed and distributed with Qualtrics (www.qualtrics.com). In

order to reach the right focus group, a digital link was posted and distributed through social

media channels such as Facebook. Everyone in the researchers’ network was requested to

further distribute or share the link within their own network. Furthermore, possible

respondents working in a fashion store were approached via a private message or email in

which the link of the survey was pasted. Besides, an email with the link was distributed

among students of the Amsterdam Fashion Institute in order to increase the response rate.

According to Lewis and Saunders (2012) it is beneficial to distribute digital questionnaires

because one is able to reach a high number of participants in a time and cost efficient

manner. Besides, this strategy enhances feelings of being ‘anonymous’ which results in less

social desirable answers and interviewer bias. Furthermore, questionnaires were printed and

26

personally distributed among fashion stores. After, these results were uploaded by hand into

the online program of Qualtrics.

3.5. Measures

The independent variable is work overload which is measured according to the modified role

overload scale, using 3 items (“I never seem to have enough time to get everything done at

work”; α = .82) of Bolino and Turnley (2005). This is measured on a five-point Likert-type

scale with anchors 1 = Strongly Disagree and 5 = Strongly Agree. Based on Rizzo et al.

(1970), Bolino & Tumely (2005) state that “Role overload describes situations in which

employees feel that there are too many responsibilities or activities expected of them in light

of the time available, their abilities, and other constraints”, p.741.

Emotional labour is defined as “the state that exists when there is a discrepancy

between the emotional demeanor that an individual displays because it is considered

appropriate, and the emotions that are genuinely felt but that would be inappropriate to

display” (Mann, 1999a, p. 353). This variable is measured by the Response-Focused

Emotion Regulation Scale from Grandey, Fisk and Steiner (2005) (“I fake a good

mood”). The measurement scale from 1 = Never to 5 = Always. A principal-components

analysis showed that it met traditional critera (eigenvalues 1.0). α = .89 for the U.S.

sample and α = .83 for the French sample. Items 1, 2, 3, 5 and 7 are from the Surface

Acting scale used in Grandey (2003) and items 4 and 6 are taken from the 3-item

Surface Acting scale created by Brotheridge and Lee (2003).

Emotional exhaustion is measured in this study by 4 items from Kreitner and Kinicki

(1992) (“I feel emotionally drained from my work”; α = .80). Items are measured on a five-

point scale, anchored with 1 = never and 5 = always and measured is how often one feels

emotionally overextended and exhausted by one’s work. This construct is an adaptation of

the Maslach Burnout Inventory (MBI) which originally consists of 9 items.

Multi-faceted job satisfaction consists of seven dimensions (including 1.Overall job,

2. Co-workers, 3. Supervision, 4. Policy, 5. Pay, 6. Promotion and 7. Customers). It is

measured with the reduced 28-item INDSALES scale by Comer, Machleit and Lagace

(1989). It measures the level of agreement for each statement (“My job is satisfying”; α =

.89) with anchors of 1 = strongly disagree and 5 = strongly agree. In measuring

satisfaction with supervision, “supervisor” is replaced by “manager” since this definition is

practically more relevant.

27

Intention to leave is the dependent variable and is based on the 4-item scale of

Bluedorn (1982). The scale assesses the chance of quitting the job during the next 3 months,

6 months, next year and next 2 years (“How would you rate your chances of quitting this job

in the next 3 months”; α = .84). It is anchored by 1 = very low and 5 = very high.

3.6. Control Variables

Control variables are included in the study to control the relations between predictor and

outcome variables, thereby reducing unintended effects and improving results (Field, 2005).

In this study, the following control variables are included: age, gender, tenure in job, number

of hours working, level of education, nationality and organizational service climate.

Age appears to have influence on the research model of this study. According to

Bedeian, Ferris and Kacmar (1992), older employees possess more self-confidence than

younger employees and are therefore more likely to indicate a higher degree of job

satisfaction. Besides, younger employees generally switch more often between jobs because

they are still full of ambition and are less committed to one specific organization (Bedeian,

Pizzolatto, Long & Griffeth, 1991). Moreover, one can imagine that older employees in the

fashion retail have more difficulty with switching from one organization to another.

Therefore, older employees are more likely to stay for a longer period at the same company

than younger employees.

Gender is incorporated in the analysis since prior literature has proven that there are

fundamental differences between male and female in the way they behave and respond to

work-related matters. In retail positions, women might experience more intervention between

work and private life than men (Babin & Boles, 1998). It therefore appears that men and

women respond differently to stress related issues and job satisfaction (Chung et al., 2012).

Tenure in job is also controlled for. The longer an employee works in an

organization, the less likely this person will leave the company because organizational

benefits are more likely to arise (e.g. promotion, status) (Hellman, 1997). Also, employees

who are new to the company go through a process of socialization first. This might affect

their thoughts about their job (Hofstede, 1980).

The number of hours also influences the research model. It appears that employees

who work part-time experience lower levels of stress than employees who have a full-time

job. Besides, part-time workers are more satisfied with their job and are less prone to quit

(Wotruba, 1990). Number of hours is measured by asking respondents if they work part-time

or full-time.

28

Level of education is also included as control variable. It appears that the level of education

has an influence on the working experience of employees (Bedeian et al., 1992), which again

influences important variables in the research model of this study. One might also imagine

that someone who is highly educated might have different ambitions than working in a

fashion retail store for which an educational background is not required. This again

influences an employees’ motivation and also variables such as job satisfaction and turnover

intentions.

Nationality is another control variable. Each nationality has a different cultural

background and this background influences perceptions and behaviors (Hofstede, 1980). As

one can imagine, these perceptions and behaviors might in turn influence job related stress

factors, job satisfaction and turnover intentions.

The last variable which is controlled for is service climate. “Service climate refers to

employees’ shared perceptions of the practices, procedures, and behaviors that are rewarded,

supported and expected by the organization with regard to customer service and customer

service quality” (Schneider, White & Paul, 1998 in Salanova, Agut & Peiro, 2005, p. 1217).

Service climate appears to influence employee attitudes, perceptions and organizational

citizenship behavior (Walumbwa, Hartnell & Oke, 2010) and thus influences the research

model of this study. Service climate is measured with a reduced version (4 items) of the 7-

item Global Service Climate Scale (Schneider et al., 1998) (“Employees in our organization

have knowledge of the job and the skills to deliver superior quality work and service”; α=.84)

Answers range from 1 = strongly disagree to 5 = strongly agree.

3.7. Data Analysis

Data analysis of this study can roughly be divided into two steps. First, data was explored and

basic assumptions were checked, also referred to as ‘data cleaning’. These steps are needed to

prepare the data for the second step of the data analysis; testing hypotheses. In order to

perform the data analysis, IBM SPSS statistics 22 was used. In order to perform the first step,

all data was coded and variables were created. Next, data was checked on counter-indicative

items and missing values. Furthermore, the overall distribution and normality of the data was

explored. After, a Confirmatory Factor Analysis (CFA) was performed. CFA focuses on the

latent structure of a measurement instrument. Since this is not possible in SPSS, SmartPLS

(Ringle, Wende, & Will 2005) is used as software to conduct CFA. “CFA verifies the number

of underlying dimensions of the instrument (factors) and the pattern of item-factor

relationships (factor loadings). CFA also assists in the determination of how a test should be

29

scored” (Brown, 2012, p.3). Last, scale reliability was assessed and scale means were

computed.

After this first step, hypotheses were tested. Throughout all tests, a significance level

of p = .05 was maintained. Hypotheses 1 and 2 were simultaneously tested with a multiple

regression analysis. Multiple regression analysis tests the effect of multiple predictor variables

on one outcome variable (Field, 2005). This also accounts for hypothesis 6, which tests the

relation of multi-faceted job satisfaction on turnover intentions. Multi-faceted job satisfaction

contains of multiple variables and thus multiple predictors. There is one outcome variable

only, turnover intentions, and therefore multiple regression analysis was also used to test this

hypothesis.

Hypothesis 3 predicts the relation between emotional exhaustion and the different

dimensions of multi-faceted job satisfaction. In this case, it is not possible to conduct a

multiple regression analysis, because there is only one predictor variable and multiple

outcome variables. Therefore simple linear regression analysis was used seven times to test

the relations between emotional exhaustion and the seven dimensions of multi-faceted job

satisfaction. Although better programs exist instead of SPSS to perform this analysis with

(e.g. LISREL 8 by Joreskog & Sorbom, 1993), this study chooses for regression analyses in

SPSS over other methods or programs due to the time and scope of this research. Besides, the

model is very complex and there is a lack of research-based knowledge on how to perform

the analysis from and to the different dimensions of multi-faceted job satisfaction

(Rutherford et al., 2009).

Hypotheses 4, 5 and 7 all focus on mediating effects. These mediating effects were

tested in SPSS with the process script created by Hayes (2012) in which a bootstrap

confidence level of 95% was maintained throughout the analyses. Simple mediation was used

by means of process 4, focusing only on one mediating variable. An example of a simple

mediation model is showed in figure 3. The relation between the independent (X) variable

and the mediator (M) is called a. The path between M and the dependent variable (Y),

controlled for X, is named b. The indirect effect is the product of a and b (ab). The direct

relation between X and Y is expressed by c’. The total effect is the sum of the direct and

indirect effect: c = c’ + ab (Preacher & Hayes, 2008).

30

Figure 3: Mediation Model

Preacher and Hayes (2008)

31

4. RESULTS

This chapter explains the findings of the data analysis. First, data cleaning and the associated

results will be discussed. Second, hypotheses will be tested and the outcomes will be

described. The variables used in the data analysis are work overload, emotional labour,

emotional exhaustion, multi-faceted job satisfaction (including 1.Overall job, 2. Co-workers,

3. Supervision, 4. Policy, 5. Pay, 6. Promotion and 7. Customers) and turnover intentions. The

control variables included are age, gender, level of education, tenure in job, hours in job and

service climate. Nationality has not been included since this variable contained more missing

values than all other variables and this would decrease the reliability of the study (Field,

2005). IBM SPSS statistics 22 is used for all analyses, except for the CFA, which is analyzed

with SmartPLS (Ringle, Wende, and Will 2005).

4.1. Data Cleaning

4.1.1. Coding Variables and Recoding Counter-indicative Items

The first step in the analysis was to code all items. After, counter-indicative items were re-

coded. Coding scales were reversed by using the button ‘Recode into Same Variables’ in

SPSS.

4.1.2. Missing Values

Missing values were replaced by using a HotDeck imputation which is, according to Myers

(2011), a valid and simple method to perform. A frequency table was produced in which

missing values were shown with the frequencies option. In total, 10 items contained missing

values. First it was tested if the missing values were less than 10% compared to the total

sample, otherwise it is not possible to run a HotDeck imputation (Myers, 2011). This was the

case for all variables which contained missing values.

In order to check corresponding deck variables, a correlation test was done with the

correlate-bivariate option. Important is that deck variables have almost no missing data and

show discrete values. Also, deck variables should be related to the variable in which data are

missing but the relation between these should not be too important for the outcomes of the

research (Myers, 2011). Considering this, deck variables were chosen on basis of the highest

(Pearson) correlation. After running the HotDeck macro, frequencies options was used again

to check whether all missing values were corrected.

32

4.1.3. Detecting Outliers

Furthermore, univariate outliers were checked. In order to do this, variables were transformed

into Z-scores. Z-scores with a value >|3| indicate outliers. In total, there were 4 outliers which

surpassed the value of |3|. However, these outliers are considered as legitimate since the

related values are still within the measurement scale. The outliers in this case are probably not

representing an error or mistake, but truly deviate from the overall opinion given on those

four variables. In this case, it is better to keep the outliers because it better represents the

population as a whole (Orr, Sackett & DuBois, 1991).

4.1.4. Confirmatory Factor Analysis (CFA)

After, CFA was performed to measure the construct validity of the existing measurement

instruments. To ensure indicator reliability, the correlation between a latent variable and its

indicator should be higher than .70, because the latent variable should explain enough (>50%)

of the variance of its indicator (Henseler, Ringle & Sinkovics, 2009). This is supported by

Hair, Ringle and Sarstedt (2011) stating that the absolute standardized loading should be

higher than .70. Indicators with factor loadings lower than .40 should be removed from the

analysis and indicators with a factor loading between .40 and .70 are advised to be removed

from analysis only when scale reliability significantly increases to ensure indicator reliability

(from lower than .70 to .70 or higher). In order to guarantee convergent validity, the average

variance extracted (AVE) must be more than .50. Furthermore, the factor loading with the

latent construct is supposed to be higher than the associated cross loadings in order to ensure

discriminant validity. Following these guidelines, only two indicators were removed from the

analysis: one item of job satisfaction with promotion and one item of job satisfaction with

pay. The item of job satisfaction with promotion showed a factor loading of .440 and when

removing this item, overall scale reliability improved from α=.678 to α=.745. The item of job

satisfaction with pay was .373 and was therefore immediately removed from further analysis.

All other indicators complied with the guidelines described by Hair, Ringle and Sarstedt

(2011) and were therefore kept in further analyses.

4.1.5. Computing Reliability

The next step was to check the scale reliability for all variables. This comes down to

measuring the internal consistency. In general, Cronbach’s alpha (α) should be .70 or higher

(Field, 2005; Nunnally & Bernstein, 1994). This was the case for almost all variables (work

overload, α=.902; emotional labour, α=.845; emotional exhaustion, α=.817; satisfaction with

33

overall job, α=.823; satisfaction with co-workers, α =.755, satisfaction with supervision,

α=.865; satisfaction with policy α=.756; satisfaction with promotion α=.745, satisfaction with

customers, α=.818; turnover intentions, α=.915). Only one variables represented α<.70, which

is satisfaction with pay, α=.632. However, only values below .60 indicate a serious lack of

reliability (Hair, Ringle & Sarstedt, 2011) and therefore this variable is kept in the analysis.

Corrected item-total correlation was for all items higher than .30 which is sufficient.

4.1.6. Computing Scale Means

Based on the CFA and scale reliability, variable means were computed in order to create a

‘total variable’ under which all constructs of one variable were collected. This was necessary

to proceed with the following steps. Table 1 presents means, standard deviations, correlations

and reliability coefficients (cronbach’s alpha) for all variables used in this study.

4.1.7. Checking Normality and Distribution of Data

The data should have a normal distribution, because most sophisticated analyses (e.g.

regression analysis) are based on this (Field, 2005). Data was explored by using frequency

distributions, histograms, the Kolmogorov-Smirnov Test, and QQ plots. The histogram should

present a bell-shape when data is normally distributed (Field, 2005). Visually, data looked

close to normal.

However, the Kolmogorov-Smirnov test showed other results. Most test values were

significant (p<.005) which indicates values of kurtosis and skewness. Kurtosis is related to the

extent to which data is distributed in a more peaked shape or a more flattened shape. Positive

kurtosis means that data is distributed around the tails of a bell-shape (a flat shape) whereas

negative kurtosis means that data is closely centered around the mean value (a peak shape)

(Field, 2005). Skewness refers to the symmetry of the bell-shape. When data is positively

skewed it means that data is mainly centered on the left side of the mean with extreme values

on the right side. A negative skewness indicates the opposite; data is mainly centered around

the right side with extreme values on the left. Values of kurtosis and skewness > |1| indicate

extreme values (Field, 2005). Table 2 provides an overview of values for kurtosis, skewness

and the Kolmogorov-Smirnov test. As shown, data is not normally distributed. However,

correction was not needed in this case.

Table 1: Means, Standard Deviations, Correlations and Reliability Coefficients

Variable Mean SD 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Gender 1.77 0.42 1.00

Age 25.42 8.00 .009 1.00

Tenure in job 2.95 3.07 -.037 .466** 1.00

Hours 1.69 0.47 .126 -.062 -.013 1.00

Education level 4.31 0.84 -.125 .064 .099 -.152 1.00

Service Climate 3.65 0.62 -.126 .098 -.018 -.151 -.170 1.00

Work overload 2.58 0.96 .076 .088 .073 -.254** .181 -.115 .902

Emotional labour 2.43 0.70 -.115 -.207* -.052 .037 .041 -.076 .143 .845

Emotional exhaustion 2.37 0.76 .085 -.165 .023 .055 .143 -.123 .516** .462** .817

Job satisfaction with overall job 3.24 0.72 .153 .212* -.047 -.060 -.117 .293** -.056 -.464** -.319** .823

Job satisfaction with co-workers 4.10 0.57 -.162 .072 .008 -.061 -.079 .241* -.208* -.247* -.289** .215* .755

Job satisfaction with supervision 3.60 0.91 .010 -.104 -.091 .062 -.157 .243* -.331** -.152 -.281** .210* .336** .865

Job satisfaction with policy 3.24 0.73 .002 .050 -.104 -.072 -.128 .323** -.269** -.348** -.373** .386** .314** .388** .756

Job satisfaction with pay 3.29 0.75 .018 -.149 -.110 .127 .131 .172 -.320** -.067 -.160 .094 .043 .221* .234* .625

Job satisfaction with promotion 2.85 0.88 .071 -.158 -.152 -.093 -.126 .242* .091 -.149 -.021 .285** .107 .231* .267** .131 .745

Job satisfaction with customers 3.35 0.64 -.231* -.047 -.078 -.054 .089 .190 -.254** -.103 -.247* .207* .090 -.112 .000 .224* .011 .818

Turnover intentions 2.72 1.24 .108 -.401** -.218* .141 .185 -.192 .117 .288** .305** -.455** -.266** -.189 -.271** .006 -.236* -.090 .915

*. Correlation is significant at the 0.05 level (2-tailed).

**. Correlation is significant at the 0.01 level (2-tailed).

35

Table 2: Kurtosis, Skewness and the Kolmogorov-Smirnov Test

Variables Skewness Kurtosis

Kolmogorov-

Smirnov

Work overload .689 -.051 .192**

Emotional labour -.085 -.957 .104

Emotional exhaustion .094 -.605 .084

Job satisfaction with overall job -.185 -.168 .146**

Job satisfaction with co-workers -.444 .431 .185**

Job satisfaction with supervision -.432 -.121 .119**

Job satisfaction with policy .205 -.417 .125**

Job satisfaction with pay -.072 -.223 .095

Job satisfaction with promotion .263 -.471 .129**

Job satisfaction with customers -.523 .808 .119**

Turnover intentions .223 -1.03 .109*

**. Significant at the .001 level (2-tailed).

*. Significant at the .005 level (2-tailed).

4.2. Testing Hypotheses

This subchapter will test the hypotheses. Throughout all statistical analyses, control variables

are included and a confidence level of 95% is maintained.

4.2.1. The Influence of Work Overload and Emotional Labour on Emotional Exhaustion

First, hypotheses 1 and 2 were tested. Hypothesis 1 predicts the relation between work

overload and emotional exhaustion. Hypothesis 2 focuses on the relation between emotional

labour and emotional exhaustion. Since work overload and emotional labour are both

predictor variables of emotional exhaustion, multiple regression analysis is used.

After conducting multiple regression analysis in SPSS, it appears that the control

variables on their own explain 7.9% of the variance in emotional exhaustion. Adding work

overload and emotional labour results in 47.5% of the explanation of the variance in

emotional labour. This means that both work overload and emotional labour explain (47.5% -

7.9%) 39.6% of the variance in emotional labour. Further tested was whether the model in

general presents a significant model. This was done by an ANOVA test which “tests whether

the model is significantly better at predicting the outcome than using the mean as a ‘best

guess’” (Field, 2005, p.189). It appeared that the model only representing control variables

36

was insignificant since (F=1.355; p=.241). However, the overall regression model was

significantly useful (F=10.533; p<.001). To test whether this distribution was also significant,

the value of the t-test was checked. “If the t-test associated with the b-value is significant then

the predictor is making a significant contribution to the model” (Field, 2005, p.193). In this

case, work overload has the strongest effect on emotional exhaustion (B=.496), compared to

emotional labour (B=.362). Both work overload and emotional labour have a significant effect

on emotional exhaustion. The effect of work overload on emotional exhaustion was positively

significant (β=.390; t=6.093; p<.001) and the effect of emotional labour on emotional

exhaustion was also positive significant (β=.389; t=4.589; p<.001). Both Hypothesis 1 and 2

are supported.

Table 3: Summary of Multiple Regression Analysis for Emotional Exhaustion

Variables B β t Sig.

Work overload 0.496** .390** 6.093 .000

Emotional labour 0.362** .389** 4.589 .000

**. Significant at the 0.01 level (2-tailed).

*. Significant at the 0.05 level (2-tailed).

4.2.2. The Relation between Emotional Exhaustion and Multi-faceted Job Satisfaction

Hypothesis 3 predicts the relation between emotional exhaustion and the different dimensions

of multi-faceted job satisfaction. First, the relation between emotional exhaustion and job

satisfaction with overall job was tested. The control variables explained 17.5% of the variance

in job satisfaction with overall job and this model appeared to be significant (F=3.366;

p=.005). The overall model explained 24.1% of the variance in job satisfaction with overall

job and was also significant (F=4.273; p<.001). Emotional exhaustion on its own thus

explained (24.1% - 17.5%) 6.6% of the variance in job satisfaction with overall job. The

contribution also showed to be significant (β=-.256; t=-2.861; p=.005). It can therefore be

stated that emotional exhaustion has a significant negative effect on job satisfaction with

overall job.

Second, the relation between emotional exhaustion and satisfaction with co-workers

was examined. The control variables on their own explained 7.9% of the variance in job

satisfaction with co-workers. When emotional exhaustion was added, the variance increased

to 13.2%. This means that emotional exhaustion on its own explains (13.2% - 7.9%) 5.3% of

the variance in job satisfaction with co-workers. The model with only control variables was

37

not significantly useful (F=1.350; p=.243) neither was the entire regression model, including

emotional exhaustion (F=2.038; p=.058). This is probably caused by the control variables

since the t-test of emotional exhaustion appeared to be significant (β=-.178; t=-2.401;

p=.018). It can therefore be stated that the relation between emotional exhaustion and job

satisfaction with co-workers was significantly negative.

After, the relation between emotional exhaustion and job satisfaction with supervision

was analyzed. Regression analysis showed that control variables on their own explained 9.5%

of the variance in job satisfaction with supervision. When emotional exhaustion was added,

the model explained 16.6% of the variance in job satisfaction with supervision. Emotional

exhaustion on its own thus explained (16.6% - 9.5%) 7.1% of the variance in job satisfaction

with supervision. The model with only control variables was insignificant (F=1.660; p=.139),

whereas the total model was (F=2.675; p=.014). The contribution of emotional exhaustion

was also significant negative (β=-.334; t=-2.834; p=.006). It can therefore be stated that the

negative relation between emotional exhaustion and job satisfaction with supervision found

significant support.

Moreover, the relation between emotional exhaustion and job satisfaction with policy

was examined. Control variables explained 12.5% of the variance in job satisfaction and when

emotional exhaustion was added, the model explained 22.9% of het variance. In total,

emotional exhaustion explained (22.9%-12.5%) 10.4%. The model with only control variables

was significant (F=2.258; p=.044) as was the model when emotional exhaustion was added

(F=3.996; p=.001). Furthermore, the relation also appeared significant since (β=-.327; t=-

3.571; p=.001). This means that the relation between emotional exhaustion and job

satisfaction with policy is significant negative.

After, the relation of emotional exhaustion on job satisfaction with pay was tested. The

control variables explained 12.5% of the variance in job satisfaction with pay. The model with

emotional exhaustion included explained 16.8% of the variance which means that emotional

exhaustion on its own explain (16.8% - 12.5%) = 4.3% of the variance in job satisfaction with

pay. The model with only control variables appears to be significantly useful (F=2.265;

p=.044) as well as the model when emotional exhaustion was added (F=2.720; p=.013). The

contribution of emotional exhaustion was negatively significant (β=-.217; t=-2.213; p=.029) it

can therefore be stated that the negative relation between emotional exhaustion and job

satisfaction with pay found support.

This does not account for the relation between emotional exhaustion and job

satisfaction with promotion. The control variables on their own explained 11.8% of the

38

variance in job satisfaction with promotion. When emotional exhaustion was added to the

model, the same percentage of 11.8% explained the variance in job satisfaction with

promotion. Therefore it can be stated that there is no difference in variance explained when

emotional exhaustion is included. Both models are not significantly useful, the model with

only control variables (F=2.119; p=.058) and overall model (F=1.799; p=.096). The

contribution of emotional exhaustion to job satisfaction with promotion was also insignificant

(β=-.013; t=-.114; p=.909) and therefore the negative relation between emotional exhaustion

and job satisfaction with promotion is not supported.

In the last simple linear regression analysis, the effect of emotional exhaustion on job

satisfaction with customers was tested. Control variables on their own explained 9.5% of the

variance in job satisfaction with customers. When emotional exhaustion was added, the total

model explained 15.1% which means that emotional exhaustion on its own explained (15.1%

– 9.5%) 5.6% of the variance in job satisfaction with customers. The model of only control

variables was not significant (F=1.669; p=.137) whereas the total model was (F=2.394;

p=.027). The contribution was also significant (β=-.209; t=-2.489; p=.015) which means that

there is a significant negative relation between emotional exhaustion and job satisfaction with

customers.

In sum, emotional exhaustion is significant negatively related to an employee’s multi-

faceted job satisfaction (including, 1. Overall job, 2. Co-workers, 3. Supervision, 4. Policy, 5.

Pay, 7. Customers); partially supporting hypothesis 3.

Table 4: Summary of Linear Regression Analyses for Multi-faceted Job Satisfaction

Variables β t Sig.

Emotional exhaustion - Overall job -.256** -2.861 .005

Emotional exhaustion - Co-workers -.178* -2.401 .018

Emotional exhaustion - Supervision -.334** -2.834 .006

Emotional exhaustion - Policy -.327** -3.571 .001

Emotional exhaustion - Pay -.217* -2.213 .029

Emotional exhaustion - Promotion -.013 -0.114 .909

Emotional exhaustion - Customers -.209* -2.489 .015

**. Significant at the 0.01 level (2-tailed).

*. Significant at the 0.05 level (2-tailed).

39

4.2.3. Emotional Exhaustion as Mediator between Work Overload and Multi-faceted Job

Satisfaction

The mediating effect of emotional exhaustion between work overload and multi-faceted job

satisfaction was examined. According to Hayes (2009) all individual paths in a mediation

model should be tested to define if M acts as a mediator, regardless of their statistical

significance. The significance or non significance of the individual paths is not related to

whether the indirect effect is statistically significant, even if X and Y are not associated.

Therefore, all variables in the mediation model of hypothesis 4 were tested. Initially, there are

two independent variables (work overload and emotional labour) predicting multi-faceted job

satisfaction through emotional exhaustion. Therefore, emotional labour was controlled for and

was included in the analysis together with the control variables (Hayes, 2013). An effect is

significant when the 95% confidence interval does not include zero and an effect is non-

significant when the interval does include zero (Hayes, 2013).

First, the mediating effect of emotional exhaustion between work overload and job

satisfaction with overall job was tested. The total effect of the mediation was positive, but

non-significant (β=.014, LLCI: -.1232, ULCI: .1516). The direct effect of work overload on

job satisfaction with overall job was positive, but also non-significant (β=.076, LLCI=-.0859,

ULCI=.2374). The indirect effect of work overload on job satisfaction with overall job also

appeared to be negative and insignificant (β=-.062, LLCI=-.1733, ULCI=.0149).

Second, emotional exhaustion was tested as a mediator between work overload and job

satisfaction with co-workers. The total effect was insignificant (β=-.085, LLCI=-.2049,

ULCI=.0349) as well as the direct effect (β=-.052, LLCI=-.1944, ULCI=-.0897) and the

indirect effect (β=-.033, LLCI=-.1202, ULCI=.0589).

After, the mediating effect of emotional exhaustion between work overload and job

satisfaction with supervision was tested. The total effect was significant (β=-.251, LLCI=-

.4418, ULCI=-.0603). The direct effect of work overload on job satisfaction with supervision

appeared to be non significant (β=-.182; LLCI=-.4075, ULCI=.0430). The indirect effect of

work overload on job satisfaction with supervision also appeared to be non-significant (β=-

.069, LLCI=-.1900, ULCI=.0360). Therefore emotional exhaustion does not mediate the

relation between work overload and job satisfaction with supervision. It might be somewhat

remarkable that the total effect appeared to be significant while the indirect effect and direct

effect were not. However, this happens very often and a detectable total effect is not a

requirement for an indirect effect to exist. Many different ‘paths’ are involved in mediation

40

analysis and it might happen for example, that different paths point towards different direction

in which a positive sign cancel out a negative sign (Hayes, 2009).

Next, the mediating role of emotional exhaustion was tested between work overload

and job satisfaction with policy. The total effect was significant (β=-.160, LLCI: -.3059,

ULCI: -.0140). However, both direct and indirect effects were non-significant. The outcome

of the direct effect was (β=-.099, LLCI: -.2712, ULCI: .0728) and the outcome of the indirect

effect was (β=-.061, LLCI: -.1577, ULCI: .0293). In this case, emotional exhaustion does not

mediate the relation between work overload and job satisfaction with policy.

After, the mediating effect between work overload and job satisfaction with promotion

was examined. The total effect was insignificant (β=-.154, LLCI: -.0323, ULCI: .3405). Both

direct and indirect effects were also non-significant. The outcome of the direct effect was

(β=.167 LLCI: -.0546, ULCI: .3888) and the outcome of the indirect effect was (β=-.013,

LLCI: -.1349, ULCI: .1007).

Furthermore, the mediating effect between work overload and job satisfaction with

pay was examined. The total effect appeared significant (β=-.237, LLCI: -.3926, ULCI: -

.0808) as well as the direct effect (β=-.213, LLCI: -.3982, ULCI: -.0279). However, the

indirect effect was non-significant (β=-.024, LLCI: -.1346, ULCI: .0697). This means that

emotional exhaustion is not mediating the relation between work overload and job satisfaction

with pay. However, there is a direct relation between work overload and job satisfaction with

pay.

Last, the mediating role of emotional exhaustion in the relation between work

overload and job satisfaction with customers was tested. The total effect was significant (β=-

.159, LLCI: -.2957, ULCI: -.0231). The direct effect of work overload on job satisfaction with

customers was non-significant (β=-.116, LLCI: -.2771, ULCI: .0453). The indirect also

appeared to be non-significant (β=-.044, LLCI: -.1247, ULCI: .0343). Emotional exhaustion

does not mediate the relation between work overload and the different dimensions of job

satisfaction. Therefore, hypothesis 4 is not supported.

41

Table 5: Path Coefficients and Confidence Intervals; Emotional Exhaustion as a Mediator

between Work Overload and Multi-faceted Job Satisfaction

Variables β LLCI ULCI

Work overload to job satisfaction with overall job

Total effect (c1) .014 -.1232 .1516

Direct effect (c'1) .076 -.0859 .2374

Indirect effect (a1b1) -.062 -.1733 .0149

Work overload to job satisfaction with co-workers

Total effect (c1) -.085 -.2049 .0349

Direct effect (c'1) -.052 -.1944 .0897

Indirect effect (a1b1) -.033 -.1202 .0589

Work overload to job satisfaction with supervision

Total effect (c1) -.251* -.4418 -.0603

Direct effect (c'1) -.182 -.4075 .0430

Indirect effect (a1b1) -.069 -.1900 .0360

Work overload to job satisfaction with policy

Total effect (c1) -.160* -.3059 -.0140

Direct effect (c'1) -.099 -.2712 .0728

Indirect effect (a1b1) -.061 -.1577 .0293

Work overload to job satisfaction with pay

Total effect (c1) -.237** -.3926 -.0808

Direct effect (c'1) -.213* -.3982 -.0279

Indirect effect (a1b1) -.024 -.1346 .0697

Work overload to job satisfaction with promotion

Total effect (c1) .154 -.0323 .3405

Direct effect (c'1) .167 -.0546 .3888

Indirect effect (a1b1) -.013 -.1349 .1007

Work overload to job satisfaction with customers

Total effect (c1) -.159* -.2957 -.0231

Direct effect (c'1) -.116 -.2771 .0453

Indirect effect (a1b1) -.044 -.1247 .0343

**. Significant at the 0.01 level (2-tailed).

*. Significant at the 0.05 level (2-tailed).

42

4.2.4. Emotional Exhaustion as Mediator between Emotional Labour and Multi-faceted Job

Satisfaction

Hypothesis 5 predicts that emotional exhaustion acts as a mediator between emotional labour

and multi-faceted job satisfaction. The analysis has been controlled for the control variables

and work overload.

First the mediating role of emotional exhaustion was tested in the relation between

emotional labour and job satisfaction with overall job. The total effect was significant (β=-

.414, LLCI: -.5964, ULCI: -.2324). The direct effect also appeared to be significant (β=-.353,

LLCI: -.5535, ULCI: -.1525) but the indirect effect was not (β=-.061 LLCI: -.1630, ULCI:

.0211). Therefore, emotional exhaustion is no mediator between emotional labour and job

satisfaction with overall job. However, there appears to be a direct significant effect from

emotional labour to job satisfaction with overall job.

Second, the mediating effect of emotional exhaustion between emotional labour and

job satisfaction with co-workers was tested. The total effect was significant (β=-.172, LLCI: -

.3307, ULCI: -.0131). The direct effect was non-significant (β=-.139, LLCI: -.3155, ULCI:

.0368) as well as the indirect effect (β=-.033, LLCI: -.1427, ULCI: .0559) indicating that

emotional exhaustion does not mediate the relation between emotional labour and job

satisfaction with co-workers.

After, emotional exhaustion was tested as a mediator in the relation between emotional

labour and job satisfaction with supervision. The total effect was non-significant (β=-.146,

LLCI: -.3990, ULCI: .1062). The direct effect of emotional labour on job satisfaction with

supervision also appeared to be non-significant (β=-.078, LLCI: -.3571, ULCI: .2016). This

insignificance also accounts for the indirect effect (β=-.069, LLCI: -.2580, ULCI: .0260).

After, the mediating role of emotional exhaustion between emotional labour and job

satisfaction with policy was examined. The total effect was significant (β=-.304, LLCI: -

.4977, ULCI: -.1110). The direct effect of emotional exhaustion on job satisfaction with

policy also appeared to be significant (β=-.244, LLCI: -.4570, ULCI: -.0304). The indirect

relation however turned out to be non significant (β=-.061, LLCI: -.1660, ULCI: .0193). This

means that emotional exhaustion does not mediate the relation between emotional labour and

job satisfaction with policy, but emotional labour has a direct significant effect on job

satisfaction with policy.

Next, emotional exhaustion was examined as mediator in the link between emotional

labour and job satisfaction with pay. The total effect was non-significant (β=-.049, LLCI: -

43

.2559, ULCI: .1571), as well as the direct effect (β=-.026, LLCI: -.2554, ULCI: .2039) and the

indirect effect (β=-.024, LLCI: -.1433, ULCI: .0729).

Moreover, the mediating effect of emotional exhaustion was examined between

emotional labour and job satisfaction with promotion. The total effect was non-significant

(β=-.235, LLCI: -.4824, ULCI: .0115), as well as the direct effect (β=-.222, LLCI: -.4974,

ULCI: .0525) and the indirect effect (β=-.013, LLCI: -.1365, ULCI: .1051).

Last, the mediating effect of emotional exhaustion was analyzed between emotional

labour and job satisfaction with customers. The total effect appeared to be insignificant (β=-

.087, LLCI: -.2675, ULCI: .0936). The direct effect was also insignificant (β=-.044, LLCI: -

.2435, ULCI: .1564) as well as the indirect effect (β=-.043, LLCI: -.1440, ULCI: .0284).

Thus, emotional exhaustion is not a mediator in the relation between emotional labour

and multi-faceted job satisfaction and therefore hypothesis 5 is not supported.

44

Table 6: Path Coefficients and Confidence Intervals; Emotional Exhaustion as a Mediator

between Emotional Labour and Multi-faceted Job Satisfaction

Variables β LLCI ULCI

Emotional labour to job satisfaction with overall job

Total effect (c1) -.414** -.5964 -.2324

Direct effect (c'1) -.353** -.5535 -.1525

Indirect effect (a1b1) -.061 -.1630 .0211

Emotional labour to job satisfaction with co-workers

Total effect (c1) -.172* -.3307 -.0131

Direct effect (c'1) -.139 -.3155 .0368

Indirect effect (a1b1) -.033 -.1427 .0559

Emotional labour to job satisfaction with supervision

Total effect (c1) -.146 -.3990 .1062

Direct effect (c'1) -.078 -.3571 .2016

Indirect effect (a1b1) -.067 -.2580 .0260

Emotional labour to job satisfaction with policy

Total effect (c1) -.304** -.4977 -.111

Direct effect (c'1) -.244* -.457 -.0304

Indirect effect (a1b1) -.061 -.166 .0193

Emotional labour to job satisfaction with pay

Total effect (c1) -.049 -.2559 .1571

Direct effect (c'1) -.026 -.2554 .2039

Indirect effect (a1b1) -.0236 -.1433 .0729

Emotional labour to job satisfaction with promotion

Total effect (c1) -.235 -.4824 .0115

Direct effect (c'1) -.222 -.4974 .0525

Indirect effect (a1b1) -.013 -.1365 .1051

Emotional labour to job satisfaction with customers

Total effect (c1) -.087 -.2675 .0936

Direct effect (c'1) -.044 -.2435 .1564

Indirect effect (a1b1) -.043 -.1440 .0284

**. Significant at the 0.01 level (2-tailed).

*. Significant at the 0.05 level (2-tailed).

45

4.2.5. The Relation between Multi-faceted Job Satisfaction and Turnover Intentions

The effect of multi-faceted job satisfaction on turnover intentions was analyzed by multiple

regression analysis. In this regression, all dimensions of multi-faceted job satisfaction were

included. The control variables on their own explained 25.1% of the variance in turnover

intentions. Adding all dimensions of multi-faceted job satisfaction resulted in 44.4%

explanation of the variance in turnover intentions, which means that all dimensions together

explain (44.4% - 25.1%) 19.3% of the variance in turnover intentions. The model with only

control variables was significantly useful (F=5.306, p<.001) as well as the total model

(F=5.407, p<.001). Job satisfaction with overall job had the highest standardized coefficient

(B=-.306) and second was job satisfaction with promotion (B=-.181). Only these two

dimensions of multi-faceted job satisfaction appeared to be significantly and negatively

related to turnover intentions; job satisfaction with overall job (t=-3,103 p=.003) and job

satisfaction with promotion (t=-2.038, p=.045).

The other five dimensions were negatively related to turnover intentions, but these

relations were not significant. Job satisfaction with co-workers (B=-.088, t=-.987, p=.326),

job satisfaction with supervision (B=-.088, t=-.927, p=.356), job satisfaction with policy (B=-

.036, t=-.371, p=.711), job satisfaction with pay (B=-.026, t=-.282, p=.779) and job

satisfaction with customers (B=-.040, t=-.438, p=.663). Hypothesis 6 predicts that multi-

faceted job satisfaction is negatively related to an employee’s turnover intentions. This

appears to be true for only two dimensions of multi-faceted job satisfaction: job satisfaction

with overall job and job satisfaction with promotion. Hypothesis 6 is therefore partially

supported.

Table 7: Summary of Multiple Regression Analyses for Turnover Intentions

Variables B β t Sig.

Overall job - turnover intentions -.306** -.529** -3,103 .003

Co-workers - turnover intentions -.088 -.196 -0.987 .326

Supervision - turnover intentions -.088 -.121 -0.927 .356

Policy - turnover intentions -.036 -.062 -0.371 .711

Pay - turnover intentions -.026 -.042 -0.282 .779

Promotion - turnover intentions -.181* -.257* -2,038 .045

Customers - turnover intentions -.040 -.078 -0.438 .663

**. Significant at the 0.01 level (2-tailed).

*. Significant at the 0.05 level (2-tailed).

46

4.2.6. Multi-faceted Job Satisfaction as Mediator between Emotional Exhaustion and

Turnover Intentions

The mediating effect of multi-faceted job satisfaction was tested on the relation between

emotional exhaustion and turnover intentions. According to Hayes (2009) all individual paths,

whether significant or not, should be included in the mediator model and thus in the analyses.

Therefore all dimensions of job satisfaction were included in the mediator analysis. The

process script created by Hayes (2012) allows multiple mediators whereby all dimensions of

multi-faceted job satisfaction can be simultaneously tested as mediator between emotional

exhaustion and turnover intentions. As the results showed, the total effect of the model was

positive and significant at the .05 level (β=.332, LLCI: .0347, ULCI: .6293). However, the

direct effect of emotional exhaustion on turnover intentions appeared non-significant (β=.010,

LLCI: -.2201, ULCI: .4190) whereas the indirect effect appears to be significant only for job

satisfaction with overall job (β=.132, LLCI: .0342, ULCI: .2879). The indirect effect of the

other dimensions appeared to be insignificant (see table 8 for results). Hypothesis 7 is

therefore partially supported. The positive relation between emotional exhaustion and

employee turnover intentions is mediated by an employee’s multi-faceted job satisfaction

(including 1.Overall job).

Table 8: Path Coefficients and Confidence Intervals; Multi-faceted Job Satisfaction as a

Mediator between Emotional Exhaustion and Turnover Intentions

Variables β LLCI ULCI

Job satisfaction as overall job as mediator

Indirect effect (a1b1) .132* .0342 .2879

Job satisfaction with co-workers as mediator

Indirect effect (a1b1) .033 -.0416 .1694

Job satisfaction with supervision as mediator

Indirect effect (a1b1) .035 -.0448 .1483

Job satisfaction with policy as mediator

Indirect effect (a1b1) .012 -.0851 .1142

Job satisfaction with pay as mediator

Indirect effect (a1b1) .007 -.0772 .0822

Job satisfaction with promotion as mediator

Indirect effect (a1b1) .004 -.0579 .0684

Job satisfaction with customers as mediator

Indirect effect (a1b1) .010 -.0946 .1160

**. Significant at the 0.01 level (2-tailed).

*. Significant at the 0.05 level (2-tailed).

47

4.3. Research Model with Results

Figure 4: Research Model with Results

**. Significant at the 0.01 level (2-tailed).

*. Significant at the 0.05 level (2-tailed).

48

5. DISCUSSION

5.1. Conclusion

This chapter discusses all findings and provides an answer to the research question of this

study: “What is the role of work overload, emotional labour, emotional exhaustion and multi-

faceted job satisfaction in predicting employee turnover?” It is found that when work

overload increases, an employee’s feeling of being emotional exhausted will also increase.

This is congruent with prior literature, arguing that an overload of work is related to stress and

thereby harming an employees’ (Firth et al., 2004). Emotional labour also appears to be

positively related to emotional exhaustion. In this paper, emotional labour is defined as the

discrepancy between an employee’s genuine feelings and expressed feelings. This discrepancy

has proven to result in job-related feelings of stress and negatively influences an employees’

well-being. This can be explained by the Conservation Of Resources (COR) theory by

Hobfoll and Freedy’s (1993). This theory argues that trying to overcome this discrepancy

depletes an employee’s resources which in turn lead to emotional exhaustion.

As expected, emotional exhaustion is negatively related to job satisfaction with overall

job, co-workers, supervision, policy, pay and customers. The relation between emotional

exhaustion and job satisfaction with promotion is negative, but not significant. Further,

emotional exhaustion appears not to be mediator in the relation between work overload and

multi-faceted job satisfaction and emotional labour and multi-faceted job satisfaction.

However, work overload appears to have a direct negative effect on job satisfaction with pay.

It also appears that emotional labour is directly and negatively related to job satisfaction with

overall job and job satisfaction with policy.

Furthermore, only two dimensions of multi-faceted job satisfaction are negatively

related to an employee’s turnover intentions; job satisfaction with overall job and job

satisfaction with promotion. The other five dimensions turned out to be negative but

insignificant related to turnover intentions. These findings are congruent with the study of

Rutherford et al. (2009). The negative link between job satisfaction with overall job and

turnover intentions has found broad support in existing literature and can be explained by the

idea that employees who are in general unsatisfied with their job, search for another job,

resulting in higher turnover. The negative link between job satisfaction with promotion and

turnover intentions can be explained by the idea that when employees are satisfied with their

career prospects, they are more willing to stay at an organization because there are many

49

challenges and opportunities, guaranteeing a long-term future at the organization (Booth &

Hamer, 2007). Moreover, the positive relation between emotional exhaustion and turnover

intentions is mediated by only one dimension of multi-faceted job satisfaction; job satisfaction

with overall job.

An answer to the research question can now be provided. Both work overload and

emotional labour are positively related to emotional exhaustion. Emotional exhaustion is in

turn negatively related to six dimensions of multi-faceted job satisfaction: overall job, co-

workers, supervision, policy, pay and customers. Emotional exhaustion does not mediate the

negative relation between work overload and the different dimensions of multi-faceted job

satisfaction and neither does it mediate the negative relation between emotional labour and

multi-faceted job satisfaction. However, work overload appears to have a direct negative

effect on job satisfaction with pay and emotional labour is directly negatively related to job

satisfaction with overall job and job satisfaction with policy. Job satisfaction with overall job

and promotion are in turn related to employee turnover intentions and job satisfaction with

overall job shows to be a mediator between emotional exhaustion and turnover intentions.

5.2. Discussion

Not all hypotheses are fully supported and therefore some unexpected findings will be

discussed throughout this paragraph. Hypothesis 3 predicts that emotional exhaustion is

negatively related to all seven dimensions of multi-faceted job satisfaction. However,

emotional exhaustion appeared to be negative, but insignificant to job satisfaction with

promotion. This is contrasting to findings of previous studies (Chung et al., 2012; Rutherford

et al., 2009) and to the social exchange theory (Hatfield & Sprecher, 1984), assuming that

emotional exhaustion is significant negatively related to job satisfaction with promotion.

However, not much research has been conducted yet on the relation between multi-faceted job

satisfaction and employee turnover. A possible alternative explanation for the insignificant

relation between emotional exhaustion and job satisfaction with promotion might be that

employees who are emotionally exhausted do not feel the need to grow further and take on

more challenges, because they simply lack the energy. Promotion opportunities might not be

of much interest to employees who feel emotionally exhausted and therefore the relation

between emotional exhaustion and job satisfaction with promotion turns out to be

insignificant. In addition, moderators might be involved such as an employee’s ambition to

grow. Employees who do not have the ambition to grow within the company might be less

50

concerned about future career opportunities and therefore the role of job satisfaction with

promotion is undermined, regardless their level of emotional exhaustion.

Surprisingly, emotional exhaustion does not mediate the relation between work

overload and multi-faceted job satisfaction as predicted by hypothesis 4. Neither does it

mediate the relation between emotional labour and multi-faceted job satisfaction, as assumed

by hypothesis 5. It is therefore argued that additional work-related characteristics and

emotions, beyond this research model, play a role in these mediating effects. For example,

Chung et al. (2012) examined more antecedents of multi-faceted job satisfaction in the retail

industry such as role ambiguity, role conflict, work-family conflict and family-work conflict.

These additional job stressors might explain the insignificant mediating role of emotional

exhaustion in this study.

Appearing from the mediation tests is that work overload is directly negative related to

job satisfaction with pay. This is contrasting to Singh (1994), arguing that emotional

exhaustion is a far more prominent predictor of organizational outcomes than role stressor(s).

This unexpected finding can also be explained by the social exchange theory (Hatfield &

Sprecher, 1984). When an employee experiences work overload, he or she is making effort to

cope with this work overload. According to the social exchange theory, he or she wants to get

compensated for this. One way of getting compensation is to receive higher rewards in the

form of pay. However, if the company does not increase pay rates, an employee might

experience less job satisfaction with pay.

Furthermore, emotional labour is directly negative related to job satisfaction with

overall job and job satisfaction with policy. Apparently, the negative outcomes of the

discrepancy between genuine and expressed feelings of an employee directly effects job

satisfaction on these two dimensions. This direct negative effect of emotional labour on

overall job satisfaction has found support in earlier literature (Hochschild, 1983; Morris &

Feldman, 1996).

Five dimensions of multi-faceted job satisfaction were not significantly negative

related to turnover intentions (co-workers, supervision, policy, pay, customers). As Rutherford

(2009) argues, there are few studies on the relation between multi-faceted job satisfaction and

turnover intentions. Apparently, these dimensions are not strong enough related to turnover

intentions. Moderators might be involved which are not covered in the current study. These

dimensions could also be linked to other important organizational outcomes such as

51

organizational commitment of organizational citizenship behavior (OCB), thereby indirectly

influencing turnover intentions. This might explain the insignificant findings.

5.3. Theoretical Implications

This study contributes to the existing knowledge on predictors of employee turnover in the

fashion retail industry. As described, there is an overall lack of research on employee turnover

in this specific branch. This study fills this gap by providing a comprehensive research model,

covering industry-specific characteristics such as work overload, emotional labour and

emotional exhaustion. In general, prior studies have examined overall job satisfaction as main

predictor of employee turnover (Jaramillo et al., 2006), also in the retail industry (Booth &

Hamer, 2007; Henrie, 2004) and research has mainly focused on the link between emotional

exhaustion and overall job satisfaction (Babakus et al., 1996).

This is the first time the construct of multi-faceted job satisfaction has been conducted

in a fashion retail context, although this has been investigated before in the overall retail

industry (Chung et al., 2012). The multi-faceted job satisfaction construct can be

recommended over the global job satisfaction construct in future research. The multi-faceted

job satisfaction construct is able to explain relations between different variables better,

thereby providing more details and drawing a more complete picture. Although parts of this

model have been conducted in prior research (Chung et al., 2012; Rutherford et al., 2009), the

overall model has never been studied before. Therefore the study provides an interesting

addition to the existing literature.

5.4. Practical Implications

This study has some important practical implications. Reducing employee turnover is linked

to numerous organizational advantages, improves organizational performance and results in

competitive advantage (Kim et al., 2009). Therefore, the results of this study can be helpful

for fashion retail organizations. This study confirms that work overload, emotional labour,

emotional exhaustion and some dimensions of job satisfaction play an important role in the

prediction of employee turnover and shows both direct and indirect ways to decrease

employee turnover.

Both job satisfaction with overall job and job satisfaction with promotion appear to

have a direct negative relation with employee turnover. In order to decrease employee

52

turnover, management of fashion retail organizations should consider ways to increase job

satisfaction with overall job and job satisfaction with promotion.

Prior literature argues that improved job satisfaction with overall job can be achieved

by giving employees more responsibility and autonomy, stimulating variety in skills and

enhancing interpersonal relationships (Harter et al., 2002). This study shows that a decrease of

work overload and emotional labour results in less emotional exhaustion and more job

satisfaction on multiple dimensions, among which is job satisfaction with overall job. Job

satisfaction with overall job mediates the positive relation between emotional exhaustion and

employee turnover.

It can therefore be argued that both work overload and emotional labour are stepping

stones in the conceptual model. Management should therefore consider the amount of work

overload employees need to perform. For instance, work overload can be reduced by creating

a supportive climate in which employees help each other out and share the work load (House,

1981). A way to reduce the level of emotional labour is to provide social support and establish

rewarding relationships. This helps to replenish an employee’s resources (Brotheridge & Lee,

2002). The decrease of these two job stressors results in a decrease of emotional exhaustion.

In turn, a lower level of emotional exhaustion increases job satisfaction on multiple

dimensions, among job satisfaction with overall job.

An increase of job satisfaction with promotion cannot be explained by the research

model of this study. However, prior literature argues that maintaining a professional climate

has a positive impact on job satisfaction with promotion. Companies should therefore have

clear guidelines on ethics and maintain widespread company rules; this enhances perceptions

of fairness and increases job satisfaction (Deshpande, 1996).

5.5. Limitations and Future Research

Even though this study has important theoretical and practical implications, some limitations

and suggestions for future research need to be addressed. First of all, the data is not normally

distributed which means that some variables show high values of kurtosis or skewness. This

poses limitations concerning the reliability of the data since regression analyses initially

require a normal distribution. The-non parametric distribution might have implications for the

findings of this study (significance, correlations), resulting in slightly different outcomes.

Future research might focus on replicating this study using suitable programs, other than

53

SPSS, for analyzing non-parametric data with (e.g. “partial least squares structural equation

modeling” with SmartPLS by Ringle, Wende, & Will 2005).

Another limitation concerning the data analysis is the way in which the relation

between emotional exhaustion and the seven dimensions of multi-faceted job satisfaction is

tested. SPSS does not allow one to perform a regression analysis with multiple dependent

variables. Therefore, regression analysis is done by performing a simple linear regression

analysis seven times. However, the different dimensions of multi-faceted job satisfaction

might also correlate with each other but this has now been excluded from the analysis.

Therefore, results might be slightly different by using different analytical tools. Although this

choice is well-considered, see 3.7., future research could use a different program for analysis

which allows one to include multiple outcome variables in one regression analysis (e.g.

LISREL 8 by Joreskog & Sorbom, 1993).

Future research could also examine an employee’s ambition to grow as a possible

moderator between emotional exhaustion and job satisfaction with promotion. Besides,

additional work-related characteristics and emotions, such as role ambiguity, could be added

in future research as possible mediators.

Future research could also investigate the role of moderators and mediators (e.g.

commitment or OCB) in the relation between multi-faceted job satisfaction and employee

turnover. This might help in drawing an as complete model as possible and to obtain a better

understanding of the different facets of multi-faceted job satisfaction and employee turnover.

Overall, future research could help in getting a better understanding of the predictors of

employee turnover in the fashion retail industry.

54

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7. APPENDICES

7.1. Questionnaire

The questionnaire has been distributed in both Dutch and English. This appendix only shows

the English version.

Dear sales assistant,

I am working on my master graduation project at the University of Amsterdam (UvA) in which I conduct

research on the job satisfaction of sales assistants in the Dutch retail fashion industry. The goal of the research is

to get more insight in how sales assistants experience their job in this specific industry. Therefore I need many

participants and I would like to ask you if you can fill out the survey attached. Your help is very much

appreciated!

All participants will remain completely anonymous and the results are strictly confidential. The outcomes will

only be used for study purposes. You do not have to fill out your name or other sensitive data. Filling out this

survey will take you less than 10 minutes.

By participating you have a chance to win a 50 Euro gift card from H&M. At the end of the survey you can

chose to participate in the lottery by filling in your email address. Again: respondents will remain completely

anonymous and your potential email address cannot be linked to the survey you have filled out.

Please click on the link below to participate in the survey:

https://nlpsych.qualtrics.com/SE/?SID=SV_72srrjqoR5LtmSx

For any further questions you can send an email to: lonneke.l.schaap@gmail.com.

Thank you very much and goodluck!

Lonneke Schaap

When you click on the link:

Thank you for filling out this survey!

Please select right above in the screen the language you are most comfortable with.

By participating you have a chance to win a 50 Euro gift card from H&M.

At the end of the survey you can chose to participate in the lottery by filling in your email address. Again:

respondents will remain completely anonymous and your potential email address cannot be linked to the survey

you have filled out. During the survey, you will be provided with statements. Please click with each statement on

the answer which suits you best. Only your own opinion is important; not the opinion of others. It is important

that you fill out all the questions, even though they might be difficult and keep in mind that there are no wrong or

good answers.

For any further questions you can send an email to: lonneke.l.schaap@gmail.com.

Thank you very much and goodluck!

Lonneke Schaap

62

The following statements are related to how satisfied you are with your job. We would like to remind you again

that all answers given will be completely anonymous. Please indicate how much you agree with the statements.

Satisfaction with overall job

Strongly

Disagree (1)

Disagree (2) Neutral (3) Agree (4) Strongly Agree

(5)

My work gives a

sense of

accomplishment.

(1)

My job is

exciting. (2)

My work is

satisfying. (3)

I’m really doing

something

worthwhile in

my job. (4)

Satisfaction with management

Strongly

Disagree (1)

Disagree (2) Neutral (3) Agree (4) Strongly Agree

(5)

My manager

really tries to

get our ideas

about things. (1)

My manager

has always been

fair in dealings

with me. (2)

My manager

gives us credit

and praise for

work well done.

(3)

My manager

lives up to

his/her

promises. (4)

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Satisfaction with company policy and support

Strongly

Disagree (1)

Disagree (2) Neutral (3) Agree (4) Strongly Agree

(5)

Management is

progressive. (1)

Top

management

really knows its

job. (2)

This company

operates

efficiently and

smoothly. (3)

I receive good

support from the

headquarters.

(4)

Satisfaction with promotion

Strongly

Disagree (1)

Disagree (2) Neutral (3) Agree (4) Strongly Agree

(5)

The company

has an unfair

promotion

policy. (1)

My

opportunities for

advancement

are limited. (2)

There are plenty

of good jobs

here at the

company for

those who want

to get ahead. (3)

I have a good

chance for

promotion. (4)

64

Satisfaction with pay

Strongly

Disagree (1)

Disagree (2) Neutral (3) Agree (4) Strongly Agree

(5)

My pay is low

in comparison

with what others

get for similar

work in other

companies. (1)

In my opinion,

the pay here is

lower than in

other

companies. (2)

I’m paid fairly

compared with

other employees

in this company.

(3)

My income is

adequate for

normal

expenses. (4)

Satisfaction with customers

Strongly

Disagree (1)

Disagree (2) Neutral (3) Agree (4) Strongly Agree

(5)

My customers

live up to their

promises. (1)

My customers

are trustworthy.

(2)

My customers

are loyal. (3)

My customers

are

understanding.

(4)

65

Satisfaction with co-workers

Strongly

Disagree (1)

Disagree (2) Neutral (3) Agree (4) Strongly Agree

(5)

My fellow

workers are

selfish. (1)

My fellow

workers are

pleasant. (2)

The people I

work with are

very friendly.

(3)

The people I

work with help

each other out

when someone

falls behind or

gets in a tight

spot. (4)

66

The following statements are statements about behaviors and feelings related to your job. Please fill out the

answer which suits you best.

How much do you agree with the following statements?

Strongly

Disagree (1)

Disagree (2) Neutral (3) Agree (4) Strongly Agree

(5)

The amount of

work I am

expected to do

is too big. (1)

I never seem to

have enough

time to get

everything done

at work. (2)

It often seems

like I have too

much work for

one person to

do. (3)

67

When interacting with the public (customers, clients), how often do you actually do the following behaviors

during a typical work day?

Never (1) Rarely (2) Sometimes (3) Most of the

Time (4)

Always (5)

I fake a good

mood. (1)

I put on a show

or performance.

(2)

I just pretend to

have the

emotions I need

to display for my

job. (3)

I hide my true

feelings about

situations. (4)

I put on an act in

order to deal

with

customers/clients

in an appropriate

way. (5)

I resist

expressing my

true feelings. (6)

I put on a mask

in order to

display the

emotions I

needed to for my

job. (7)

68

How often do you actually experience the following feelings?

Never (1) Rarely (2) Sometimes (3) Most of the

Time (4)

Always (5)

I feel

emotionally

drained from

my work. (1)

I feel used up at

the end of the

workday. (2)

I feel fatigued

when I get up in

the morning and

have to face

another day on

the job. (3)

I feel burned out

from my work.

(4)

How would you rate your chances of:

Very low (1) Low (2) Average (3) High (4) Very high (5)

Quitting this job

in the next 3

months. (1)

Quitting this job

in the next 6

months. (2)

Quitting in this

job in the next

year. (3)

Quitting this job

in the next 2

years. (4)

69

The following questions are related to the brand or organization you work for and are supposed to give insight

into the service climate. Again: answers will remain completely anonymous and strictly confidential; the

outcomes are only used for study purposes.

1. Which brand/organization are you working for?

……………………………………………………………………………………………………………………….

2. How much do you agree with the following statements?

Strongly

Disagree (1)

Disagree (2) Neither Agree

nor Disagree (3)

Agree (4) Strongly Agree

(5)

Sales assistants

in our

organization

have knowledge

of the job and

the skills to

deliver superior

quality work

and service. (1)

Sales assistants

receive

recognition and

rewards for the

delivery of

superior work

and service. (2)

The overall

quality of

service provided

by our

organization to

customers is

excellent. (3)

Sales assistants

are provided

with tools,

technology, and

other resources

to support the

delivery of

quality work

and service. (4)

70

Please fill out the following information.

3. What is your gender?

Male (1)

Female (2)

4. What is your age in years? (Fill out numbers)

……………………………………………………………………………………………………………………….

5. What is your nationality?(Multiple choice: 193 options)

6. How long have you been in this job?(Years)

……………………………………………………………………………………………………………………….

7. Do you work full-time or part-time?

Full-time (1)

Part-time (2)

8. What is the highest level of education you have received?

None (1)

Primary or elementary school (2)

Secondary or high school (3)

Middle level applied education (MBO) (4)

Higher education or bachelor (HBO) (5)

University or Master's degree (WO) (6)

Prefer not to say (7)

9. If you want to participate in the lottery and make a chance to win a 50 Euros gift card of H&M, please fill out

your email address below. Again, your email address will not be linked to this survey.

……………………………………………………………………………………………………………………….

Thank you very much for filling out this survey!