THE IMPACT OF THE PERCEPTION OF THE WORK …

i

THE IMPACT OF THE PERCEPTION

OF THE WORK ENVIRONMENT

ON EMPLOYEE TURNOVER INTENTION A quantitative study investigating the effects of

the perception of elements of the work environment

on employee’s intention to leave their organization

Wageningen University and Research

Student R.J.J. (Rob) de Leeuw

Student number 950712508070

Study program Management, Economics and Consumer studies

Specialization Management studies

Thesis code BMO-80436

Supervisor Dr. H.B. Kok

First examiner Dr. H.B. Kok

Second examiner Maria Annosi

Date of submission 22 November 2020

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ABSTRACT

Objectives The objectives of this study are to provide theoretical insights into the relationship

between the perception of elements of the physical work environment and

employees’ turnover intention and give recommendations about the design of

elements of the work environment to decrease employee turnover intention.

Methods This study uses a mixed method design. Starting with a literature study, four

theoretical sub-research questions are answered. Based on this theoretical

framework the conceptual framework is developed and multiple hypotheses are

formulated. Thereafter, the three empirical sub-research questions are answered.

Structural Equation Modelling (SEM) is used to test the hypotheses. This analysis

was conducted in R studio, using a sample size of 192 employees active in the

financial sector in the Netherlands.

Results The factor analysis results in seven factors from the perceived work environment:

(1) layout, (2) air quality, (3), lighting, (4) cleanliness, (5) equipment, (6) furniture

and (7) signs. The SEM analysis shows that both job satisfaction ( = -.409), p <

.01) and affective commitment ( = -.303, p < .01) have a significant negative

relationship with turnover intention. No significant direct relationships are found

between the perception of the defined elements of the work environment and

turnover intention. The mediation analysis shows that job satisfaction mediates the

relationship between turnover intention and both the perception of the layout ( =

-.320, p < .01) and the equipment ( = -.367, p < .01). Furthermore, affective

commitment mediates the relationship between the perception of equipment and

turnover intention ( = -.252, p < .05).

Conclusions Concluding, the current study shows a relationship between both job satisfaction

and turnover intention, and affective commitment and turnover intention.

Additionally, job satisfaction is found to mediate the relationship between the

perception of the layout and turnover intention, as well as between the perception

of the equipment and turnover intention. The relationship between the perception

of the equipment and turnover intention is also mediated by affective commitment.

Nevertheless, no evidence is found for any direct relationships between the

perception of elements of the work environment and employee turnover intention.

Besides the perception of the layout and the equipment, other elements of the work

environment do not have a significant relationship with one of the two mediators

and thus no other mediating effects are found. Therefore, more research is needed

to find more underlying factors in organizations that determine job satisfaction,

affective commitment and turnover intention.

Keywords: Facility design, turnover intention, perception, physical work environment.

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PREFACE

This report is the result of my master thesis project, which is part of the Master of Science degree

in Management, Economics and Consumer studies at the Wageningen University.

Finishing this report means finalizing my student life and the start of a new phase! The past six years I was able to delve into a broad range of topics at several universities. I had the honour to spent five months in Canada during my minor in 2017 and finished my first master’s degree in Tilburg in 2019. The past year I have dived into the depths of the physical work environment and employee behaviour and attitudes. I have spent weeks learning how to code in R and understanding the ins and outs of structural equation modelling. At times, it was a mental struggle, but I kept challenging myself and kept working. However, the fact that you are currently reading this marks the end of this journey. I am looking forward starting a new phase in life and start working. It is time to bring my developed skills and knowledge into practice, and I am very excited to say that I may start as trainee at FrieslandCampina in Leeuwarden. A huge opportunity and a very challenging next step in my life!

Via this way I would like to express my gratitude to my supervisor Herman Kok. Herman, you provided me with the necessary feedback, but at the same time also gave me the freedom and time to find solutions on my own. This was a big part of my learning process during my thesis. Subsequently, I would like to thank Maria Annosi for her feedback. Especially at the start and the end you provided me with some substantive feedback on my work. Lastly, I would like to thank my parents. Especially the last couple of months were quite intense, when the three of us were working from home. But your support and the creative breaks between my writing kept me going till the end. Thank you! For the readers of this report, I hope you find it interesting and enjoy reading it. Rob de Leeuw Wageningen, November 2020

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CONTENTS

ABSTRACT ........................................................................................................................ ii

PREFACE .......................................................................................................................... iii

CONTENTS ....................................................................................................................... iv

LIST OF TABLES & FIGURES ......................................................................................... vi

LIST OF ABBREVIATIONS ............................................................................................. vii

1 INTRODUCTION ............................................................................................................. 1

1.1 PROBLEM STATEMENT ............................................................................................ 2

1.2 RESEARCH QUESTIONS .......................................................................................... 4

1.3 RESEARCH FRAMEWORK ....................................................................................... 4

1.4 RESEARCH OUTLINE ............................................................................................... 5

2 THEORY ON WORK ENVIRONMENT & EMPLOYEE TURNOVER INTENTION........ 6

2.1 WORK ENVIRONMENT ............................................................................................. 6

2.2 EMPLOYEE TURNOVER INTENTION .................................................................... 15

2.3 WORK ENVIRONMENT AND EMPLOYEE TURNOVER INTENTION.................... 17

2.4 CONCEPTUAL FRAMEWORK AND HYPOTHESES .............................................. 21

3 METHODS ..................................................................................................................... 24

3.1 LITERATURE STUDY .............................................................................................. 24

3.2 CONSTRUCTS ......................................................................................................... 24

3.3 QUESTIONNAIRE .................................................................................................... 26

3.4 SAMPLE AND SETTING .......................................................................................... 27

3.5 DATA ANALYSIS ...................................................................................................... 28

4 RESULTS ...................................................................................................................... 30

4.1 CHARACTERISTICS OF RESPONDENTS ............................................................. 30

4.2 PRELIMINARY DATA ANALYSIS ............................................................................ 31

4.3 DESCRIPTIVE STATISTICS .................................................................................... 31

4.4 EXPLORATORY FACTOR ANALYSIS .................................................................... 37

4.5 CONFIRMATORY FACTOR ANALYSIS .................................................................. 39

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4.6 STRUCTURAL EQUATION MODEL ........................................................................ 42

4.7 HYPOTHESES TESTING ......................................................................................... 44

5 CONCLUSION & DISCUSSION ................................................................................... 49

5.1 CONCLUSION .......................................................................................................... 49

5.2 DISCUSSION ............................................................................................................ 50

5.3 LIMITATIONS ............................................................................................................ 53

5.4 IMPLICATIONS & RECOMMENDATIONS............................................................... 54

REFERENCES ................................................................................................................. 56

APPENDICES ................................................................................................................... 68

APPENDIX 1 – Research framework ...................................................................................... 68

APPENDIX 2 – Questionnaire ................................................................................................. 69

APPENDIX 3 – Original constructs and their source............................................................... 82

APPENDIX 4 – Additional output Chapter 4 ............................................................................ 84

APPENDIX 5 – R coding ......................................................................................................... 91

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LIST OF TABLES & FIGURES

Table 1 Types of workplaces and their description

Table 2 Coding of the demographic variables

Table 3 Characteristics and demographics of the respondents

Table 4 Means, standard deviations, and minimum- and maximum scores for all the items

Table 5 Means, standard deviations and correlations of variables

Table 6 KMO and Bartlett’s test

Table 7 Factor analysis: measurement model

Table 8 Total variance explained: measurement model

Table 9 Fit measures

Table 10 Validity and reliability measures

Table 11 Fit measures structural model

Table 12 Percentage of variance explained in structural model

Table 13 Results hypotheses 1-6

Table 14 Results hypothesis 7 and 8

Table 15 Job satisfaction as mediator between perception of layout and turnover intention

Table 16 Job satisfaction as mediator between perception of equipment and turnover

intention

Table 17 Affective commitment as mediator between perception of equipment and

turnover intention

Table 18 Job satisfaction as mediator: 9a, 10a, 11a1, 11a2 12a, 13a1, 13a2 and 14a

Table 19 Affective commitment as mediator: 9b, 10b, 11b1, 11b2 12b, 13b1, 13b2 and 14b

Table 20 Included constructs and their source

Table 21 Correlation table, top left

Table 22 Correlation table, bottom left

Table 23 Correlation table, bottom right

Table 24 EFA: Perception of elements of the work environment

Table 25 EFA: Job satisfaction, Affective commitment and Turnover intention

Table 26 Correlation table latent variables

Table 27 Test for discriminant validity (AVE > squared correlation of latent variables)

Table 28 Relationship perception of the work environment and affective commitment

Table 29 Relationship perception of the work environment and job satisfaction

Figure 1 Research framework

Figure 2 Three environmental dimensions and the discussed underlying elements

Figure 3 Conceptual framework

Figure 4 Hypothesized model

Figure 5 Measurement model

Figure 6 Results structural equation model

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LIST OF ABBREVIATIONS

AVE Average Variance Extracted

CFA Confirmatory Factor Analysis

CFI Comparative Fit Index

CI Confidence Interval

CR Composite Reliability

EFA Explanatory Factor Analysis

GFI Goodness of Fit Index

HBO Hoger Beroepsonderwijs

MBO Middelbaar Beroepsonderwijs

PCA Principal Component Analysis

RMSEA Root Mean Square Error of Approximation

SEM Structural Equation Model

SRMR Standardized Root Mean Square Residual

TLI Tucker-Lewis Index

WO Wetenschappelijk Onderwijs

1

1

1 INTRODUCTION

"Clients do not come first. Employees come first.

If you take care of your employees, they will take care of the clients."

–

Richard Branson

(founder of the Virgin Group)

The fast-changing economy, increased competition, and growing pressure on organizations to

perform leads to growing importance among companies to develop employee retention strategies

(Cloutier, Felusiak & Pemberton-Jones, 2015). Retaining employees, and thus human capital, can

give organizations a significant competitive advantage (Ramlall, 2003). When an employee

voluntarily leaves, the organization unintentionally loses human capital — skills and knowledge —

which has been directly linked with the loss of competitive advantage (Hatch & Dyer, 2004; Luthans

& Youssef, 2004).

However, the impact of employee turnover on organizations even extends beyond losing human

capital and competitive advantage. Many studies link employee turnover to several direct and

indirect costs. For example, increased recruitment and training costs, lower levels of job

satisfaction, employee morale and even customers’ perception of service quality of the organization

(Cheng & Brown, 1998; Clark-Rayner & Hartcourt, 2000; Cho, Johanson, & Guchait, 2009; Memon,

Salleh, Baharom, & Harun, 2014). Additionally, Dess and Shaw (2001) find that employee turnover

puts more pressure on workforce planning and results in a drain on management time.

Employees switching jobs is often due to dissatisfaction with wages (Borghans & Golsteyn, 2011)

or they want to increase their human capital by gathering more knowledge at another company,

because the more knowledge they possess, the more they will get paid (Gius, 2003). Many past

studies on turnover intention, therefore, focused on the effects of compensations and rewards,

training and development (Samuel & Chipunza, 2009; Das & Baruah, 2013), autonomy and

empowerment (Mehta, Kurbetti & Dhankar, 2014). On the other hand, according to Vischer (2008),

the sense of belonging, which is measured through the appropriation of space (Davis & Altman,

1976), needs further study because of its essential link with the turnover intention. A sense of

belonging (appropriation) is identified as an outcome measure of environmental studies

(Sundstrom & Sundstrom, 1986). If employees have the feeling that they do not belong to an

organization, their intention to leave the organization (i.e., turnover intention) will increase. The

turnover intention, being ‘the conscious and deliberate wilfulness to leave the organization’ (Tett &

Meyer, 1993, p. 262), proves to be one of the best predictors of actual turnover (Griffeth, Hom &

Gaertner, 2000; Van Dick et al., 2004; Trimble, 2006). This link can be explained by the Theory of

Reasoned Action as described by Ajzen and Madden (1986). According to this theory, behavioural

intention determines an individual’s behaviour. The more an individual shows a particular intention

CHAPTER 1 – INTRODUCTION

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to a specific behaviour, the more likely they are to act on this behaviour. Interestingly, multiple

studies show that employee behaviour is affected by the work environment (Peters and O’Connor,

1980; Blumberg and Pringle, 1982; Peters, O’Connor, & Eulberg, 1985; Olson and Borman, 1989;

Carnevale, 1992; Bitner, 1992; Niles and Harris-Bowlsbey, 2002; Kyriakidou and Ozbilgin, 2004).

For example, in the study of Carnevale (1992) is explained how the work environment is perceived

and result in a sense of the place. This sense of a place, the perception of the environment, is

related to individual attitudes and behaviours. These include employee satisfaction and

performance (Carnevale, 1992). Another example is the study of Bitner (1992). In her study, she

explains how the perception of the environment functions as a stimulus that results in internal

responses. These internal responses of the employee can be cognitive, emotional and

physiological and in turn result in an external response in the form of behaviour. This can be

approach or avoidance behaviour towards the environment (Bitner, 1992; Mehrabian & Russell,

1974). The study of Bitner (1992) is based on previous findings from the study of Mehrabian and

Russell (1974). In their study, they focus on the emotional internal response as a result of the

stimulus; the environment. Similarly, they conclude that this internal response can result in

approach or avoidance behaviour. This behaviour represents the desire among employees (not) to

stay longer in a particular environment (Bitner, 1992; Mehrabian and Russell, 1974). Additionally,

employees can feel (dis)satisfied with the environment and feel (not) affiliated to the environment

in which they work (Bitner, 1992). However, these studies do not focus on the long run. It is not

said that this avoidance behaviour, as a result of an internal response to an environment, results

in turnover intention and actual turnover. However, the study of Carnevale (1992) suggest that

satisfaction with the physical work environment is associated with turnover intention.

Overall, a better understanding of how the work environment affects employees can help managers

to make well-informed decisions when (re-) designing the work environment (Kok, Mobach, &

Omta, 2015). Additionally, having a better understanding of how elements of the work environment

affect turnover intention allows organizations to improve elements in their work environment and

reduce the direct and indirect costs associated with employee turnover.

1.1 PROBLEM STATEMENT

The consequences of employee turnover can be far-reaching. Not only is employee turnover

related to many direct costs, but also many indirect costs. An example would be the loss of human

capital, to which the loss of competitive advantage is linked (Hatch & Dyer, 2004; Luthans &

Youssef, 2004). But also, the costs related to recruitment, selection and training of new employees

are tremendous (Chang, Wang & Huang, 2013). Yet, the number of empirical studies that examine

the causes and antecedents of turnover intention are limited (Mor Barak, Nissly & Levin, 2001).

Understanding the causes and antecedents of turnover intention is relevant because it will help

employers to reduce turnover rates. This knowledge helps the employers to focus on effective

factors that they can control to reduce turnover intentions and actual turnover as a consequence.

These factors can be for example the nature of the work process, but can also be the worker

characteristics, or the work environment (Mor Barak, Nissly & Levin, 2001; Blankertz & Robinson,

1997). In their review and meta-analysis, Mor Barak, Nissly and Levin (2001) distinguish three

major categories of turnover antecedents that emerge from empirical studies. These three

categories are demographic factors, professional perceptions, and organizational conditions. The

demographic factors can be both work-related (e.g., tenure) and personal (e.g., age or gender).

Professional perceptions include for example job satisfaction and organizational affective

commitment (Tett & Meyer, 1993). Lastly, the organizational conditions are for example the level

of social support, organizational hierarchy, but also physical comfort. Physical comfort is the

perceived comfort of the ‘built’ elements of the work environment (Mor Barak, Nissly & Levin, 2001).


3

A very similar categorization was given by Steil, Floriani and Bello (2019) in their systematic review

on antecedents of turnover intention. Similarly, as Mor Barak, Nissly and Levin (2001), they

distinguish three categories of antecedents. Namely, personal, occupational and environmental

antecedents.

This last group, the work environment, including physical comfort, has been studied to a certain

extent during the past decades. Most of these studies focused on how the perception of the work

environment can affect the behaviour of its occupants (e.g., Bitner, 1992; Mehrabian and Russell,

1974; Carnevale, 1992; Carlopio, 1996). For example, in the study of Bitner (1992) explains how

the perception of an environment can result in avoidance behaviour among employees. However,

the environment Bitner studied is the service environment (e.g., a store) and not an office

environment, and avoidance behaviour towards the work environment and not necessarily towards

the job itself. Additionally, various studies concentrated on the physical work environment and their

relationship with various attitudes as the outcome variable. For example, employee job satisfaction

(Moos, 1994; Carlopio, 1996), employee productivity (Clements-Croome & Baizhan, 2000; Sarode

& Shirsath, 2014) and employee commitment (McGuire & McLaren, 2009; Carlopio, 1996).

However, these studies do not test for the direct effect of the work environment on turnover

intention. The few studies that did examine the direct relationship between work environment and

turnover intention did not examine the effects of individual elements of the physical environment,

but instead focused on the work environment as a whole and even included elements of the non-

physical environment in their work environment construct (e.g., Santoni & Harahap, 2018;

Kurniawaty, Ramly & Ramlawati, 2019). Additionally, Carlopio (1996) postulates that elements of

the physical work environment might have certain effects on employee behaviour and attitudes,

including job satisfaction and employee turnover intention. Furthermore, Ashkanasy, Ayoko and

Jehn (2014) proposed a new framework based on their literature about the physical work

environment and its effect on employee behaviour and attitudes. This framework proposes a link

between the physical work environment and turnover intention. Whereas, they have reasons to

suggest that elements of the physical work environment result in affective reactions that then lead

to judgment-driven behaviours. As an example of judgment-driven behaviour, they give actual

turnover. They suggest that future research should focus on these effects of elements of the

physical work environment on employee behaviour and attitudes, including turnover intention.

Based on these findings, it would be interesting to study these relationships, to see whether the

perception of elements of the physical work environment affects an employees’ intention to leave

their organization, or in other words their turnover intention. In this study, it is important to take into

account previous antecedents of turnover intention, including the demographic factors and

professional perceptions such as job satisfaction and affective commitment, as described in the

meta-review of Mor Barak, Nissly and Levin (2001).

The primary purpose of this study is to provide clarification on this topic by examining the

relationship between the perception of elements of the work environment and three employee

outcome variables: organizational affective commitment, job satisfaction, and turnover intention,

while controlling for several demographic variables. Exploring what perceptible elements of the

physical work environment affect employee turnover intention will not only fill this knowledge gap

but will also help organizations to circumvent the negative effects that are related to employee

turnover. Thus, the managerial relevance of examining the effects of the perception of elements of

the work environment on turnover intention, is that it will help managers to introduce strategies

concerning the work environment that reduce turnover intention. An improved work environment

will help to retain valuable employees and result in less direct and indirect costs related to employee

turnover.


4

The objectives of this study are twofold:

1. Provide theoretical insights into the relationship between the perception of elements of

the work environment and employees’ turnover intention;

2. Give recommendations about the design of elements of the work environment to

decrease employee turnover intention.

1.2 RESEARCH QUESTIONS

The central research question is derived from the research objective mentioned before. The central research question of the study is:

To what extent is the perception of elements of the work environment related to employee

turnover intention?

Several sub-research questions are formulated to answer this central research question. A

distinction is made between theoretical- and empirical research questions.

Theoretical research questions:

1. What are the elements of the work environment that can be perceived, and how can these

elements be operationalized?

2. What defines turnover intention and how can it be measured?

3. Which elements of the work environment can affect employee turnover intention?

4. What are, apart from elements of the work environment, additional aspects that may spark

employee turnover intention?

Empirical research questions:

5. How do employees evaluate different elements of their work environment?

6. How do employees evaluate their turnover intention?

7. What are the effects of the perception of elements of the work environment on employee

turnover intention?

1.3 RESEARCH FRAMEWORK

Figure 1 represents the research framework giving a clear overview of all steps taken in this study

to achieve the research objectives.

The first part of this research consists of the theoretical phase, during which the relevant literature

is discussed. This phase focuses on answering the theoretical research questions as formulated in

paragraph 1.2. The conceptual framework presented at the end of this phase provides insight into

how different types of objective data can be collected. It gives an overview of the included

constructs and how these constructs can be related. Based on this, several hypotheses are

developed and then tested in the second part of this study.

The empirical phase consists of the data collection using a questionnaire. This data is used to

answer empirical research questions. Finally, the analytical phase presents the findings and

provides conclusions and recommendations based on theoretical and empirical research. Several

findings, limitations and recommendations are discussed at the end of this study.


5

Figure 1

Research framework

1.4 RESEARCH OUTLINE

The remainder of the study consists of four parts. Chapter two discusses the work environment and

employee turnover intention. It ends with the hypotheses and conceptual framework. The third

chapter describes the research methodology and its research design. It furthermore explains the

sample and setting, measurement, and analysis. The fourth chapter presents the results of the

study, after which chapter five contains the conclusion and discussion.

6

2

2 THEORY ON WORK ENVIRONMENT & EMPLOYEE TURNOVER INTENTION

By reducing employee turnover, organizations can save a lot of direct and indirect costs (Memon

et al., 2014). Improving the work environment may be an effective way to reduce employee turnover

intention since the work environment affects employee behaviour (Bitner, 1992). Therefore, this

literature study investigates how the perception of elements of the work environment affects

behaviour and how this may affect turnover intentions. The outcomes of this study indicate how the

perception of elements of the work environment and turnover intention are related and results in

the conceptual framework.

The first paragraph elaborates on the work environment and discusses the relevant literature. The

second paragraph discusses the theory on employee turnover intention, and the third paragraph

discusses the relation between both concepts. These three paragraphs answer the first four sub-

research questions. Finally, in the last paragraph, the conceptual framework is presented and

explained.

2.1 WORK ENVIRONMENT

The past century there has been a shift in the economy from manufacturing sectors working from

factories, towards more service and knowledge-based industries based in office environments (Van

Meel, 2000; Haynes, 2008). Whereas a century ago most employees worked in factories,

nowadays most employees spend their time working in an office environment. Formerly, people

saw the work environment as a passive setting for work, but nowadays people acknowledge that

the office environment can be a tool- and active support for employees to get their job done

(Newsham, 1997; Vischer, 2008).

Until recently, most office environments were designed according to a 19th century model of work.

Within this design, the focus was on performing rather than thinking, and the work environment was

often very uniform, and the occupation was standardized (Vischer, 2008). However, the

developments in information technology during the last decades allowed employees to work

anytime and anywhere (Van Meel, 2000; Vischer, 2008). This development resulted in new ways

of working, which enabled employees to choose where and when they want to work, depending on

the task they have at hand (Dooley, 2017).

Nowadays, the office environment is not conceptualized as a passive setting, but instead as a tool

to help employees getting their work done and offering employees active support in their work

(Newsham, 1997). This shift resulted in a growing interest in how employees behave as a function

of their work environment, which resulted in multiple studies recognizing employee behaviour is

CHAPTER 2 – THEORY ON WORK ENVIRONMENT & EMPLOYEE TURNOVER INTENTION

7

impacted by elements of the work environment (Peters and O’Connor, 1980; Peters et al., 1985;

Olson and Borman, 1989; Kyriakidou and Ozbilgin, 2004).

2.1.1 Types of work environment

In the past, multiple studies tried to categorize the workplace based on characteristics of the

environment. This categorization is often based on the functional and architectural characteristics

of the workplace. The layout is a common characteristic on which workplaces are distinguished.

One of the older categorizations is that of Ahlin and Westlander (1991) who identified three types

of workplaces: (1) cell-office; (2) combi-office; and (3) open-plan office. Van Meel (2000) uses a

very similar categorization. He distinguished four types of workplaces in his study. Similar to Ahlin

and Westlander, he identified the cell-, combi-, and open-plan office. However, he also added the

category ‘landscape office’, which are large open areas with an arbitrary layout of the furniture in

the large open area. This categorization differs from the open-plan office, which he described as

an open space in which many people can work in a very structured layout of the desks.

In their pervasive study, Danielsson and Bodin (2008) divided the workplace over seven categories.

Table 1 contains a description of these categories.

Table 1

Types of workplaces and their description

Type of workplace Description

Cell-office Single person office

Shared-room office 2-3 persons sharing an office

Open-plan offices

Small open-plan office 4-9 persons sharing an office

Medium open-plan office 10-24 persons sharing an office

Large open-plan office 24+ persons sharing an office

Flex-office No individual workstation. Often open plan, but not a defining feature.

Combi-office No strict spatial definition. Sharing facilities.

This categorization is based on the degree of openness of the workplace, the number of persons

in the room, and whether employees have their workspace (Danielsson & Bodin, 2008). However,

more elements distinguish work environments from each other. Elements of the physical

environment that have proven to affect its occupants. For example, ambient conditions or the signs

in the work environment (Bitner, 1992; Elsbach & Pratt, 2007; Al Horr et al., 2016).

Before these elements are discussed in more detail, it is necessary to have a clear understanding

of what is understood as the physical work environment.


8

2.1.2 Physical work environment

Important to note is the difference made between the physical environment and other work

environments such as the social- or the purely natural environment (Elsbach & Pratt, 2007). Where

the social environment is described as the social structures and norms in the workplace, and the

natural environment as the surroundings entirely constructed by nature, the physical environment

consists of all the material objects and stimuli that employees encounter and interact with

(Carnevale, 1992; Sundstrom, Bell, Busby, & Asmus, 1996). These objects and stimuli can be

arranged in many ways, resulting in the different types of workplaces as described above (e.g.,

cell-office and open-space office plans). Examples of these objects and stimuli are the equipment,

furnishing, layout and ambient conditions such as air quality or noise (Elsbach & Pratt, 2007).

Organizations often combine more than one type of workplace and the workplaces, and the

elements of these, are subject to change (Vischer, 2007). These changes in physical environment

range from small-scale (e.g., adding new furniture) to large-scale (e.g., moving to a new building).

While each design element has its positive and negative implications, managers often need to

make trade-offs, in the design of the physical work environment, between the different objects and

their arrangements, to come to the best physical work environment for each situation. This trade-

off also depends on the financial resources available and a good understanding of the needs and

wishes of the employees concerning the design elements of the workplace (Elsbach & Pratt, 2007).

2.1.3 Design elements of workplace

Elsbach and Pratt (2007, p.184) distinguish four design elements of the physical environment.

These four elements are: “(1) enclosures and barriers in workspaces; (2) adjustable work

arrangements, equipment, and furnishings; (3) personalisation of workspaces, including the display

of well-known symbols; and (4) nature-like ambient surroundings, including natural light, presence

of plants, wood interiors, views of nature, and natural aromas.” Whereas Elsbach and Pratt

distinguish four design elements of the physical environment, Al Horr et al. (2016) identify eight

physical environmental factors that affect occupants in their extensive literature review that includes

over 300 papers. The eight factors that they identify are (1) Indoor air quality; (2) Thermal comfort;

(3) Lighting and daylighting; (4) Noise and acoustics; (5) Office layout; (6) Biophilia and views; (7)

Look and feel; and (8) Location and amenities. In their review, Al Horr et al. (2016) emphasize that

these eight physical factors do interact with each other and have some crossover. One of the

examples they give is the interaction between daylighting and the thermal state of the office.

Referring to Lyons, Arasteh and Huizenga (2000), they explain how windows absorb and transfer

a significant amount of solar radiation into the indoor environment. The main difference between

the study of Elsbach and Pratt (2007) and Al Horr et al. (2016) is that Al Horr and colleagues split

the 4th design element of Elsbach and Pratt, the nature-like ambient surroundings, in multiple

smaller design elements. In their study, Al Horr and colleagues identify the elements ‘indoor air

quality’, ‘thermal comfort’, ‘lighting and daylighting’, ‘noise and acoustics’, and ‘biophilia and views’,

which are compromised in the element ‘nature-like ambient surroundings’ by Elsbach and Pratt.

As explained by Al Horr et al. (2016), these elements of the physical environment interact with each

other. Bitner (1992) explains something similar in her study. She describes how people respond to

their environments holistically. Based on the total configuration of all discrete stimuli, people

respond to the environment. These stimuli come from the different dimensions of the environment

that they perceive. Bitner (1992) identifies three different environmental dimensions, namely: (1)

Ambient conditions; (2) Spatial layout and functionality; and (3) Signs, symbols and artefacts.

These three ways of categorizing the physical environment by Elsbach and Pratt, Al Horr et al., and

Bitner, have many similarities. Each of the eight categories defined by Al Horr and colleagues can

be placed under one of the dimensions as defined by Bitner. Furthermore, the four categories


9

Elsbach and Pratt distinguish, are very similar to those of Bitner. The main difference between both

categorizations is that Elsbach and Pratt split the spatial layout and functionality dimensions in

“enclosures and barriers in workplaces” and “adjustable work arrangements, equipment, and

furnishing”.

As these three studies made clear, there is a complex mix of physical environmental elements that

can affect its occupants’ behaviour. However, all three studies show very similar categorizations,

that are all covered within the three dimensions of Bitner (1992): ambient conditions, spatial layout

and functionality, and signs, symbols and artefacts. This thesis continuous using Bitner’s (1992)

definition of the environmental dimensions of the physical work environment. The next paragraph

explains these three dimensions in more detail and discusses the elements within these dimensions

that can be perceived by the employees. These paragraphs explain how the work environment can

function a stimulus that triggers an individual's internal response, which subsequentially results in

a behavioural response.

2.1.4 Servicescape model of Bitner (1992)

Bitner (1992) defines three environmental dimensions in her servicescape model. These

dimensions, and the elements they contain, together result in the perceived environment and form

the stimulus. The three environmental dimensions are (1) ambient conditions, (2) spatial layout and

functionality, and (3) signs, symbols and artefacts. The second and third dimensions are commonly

referred to as ‘interior layout and design’ (Brauer, 1992), or as Bitner (1992) labels them ‘the built

environment’. This built environment can be controlled to a large extent by management, in contrast

to the ambient conditions, which are more difficult to control (Wakefield & Blodgett, 1996).

The first dimension, the ambient conditions, are those factors of the environment that affect the five

senses and include lighting, temperature, air quality, scent, colour and noise (Bitner, 1992).

According to Bitner, multiple studies showed that these conditions could either enhance or decline

the performance of occupants within this physical environment. Spatial layout of the physical

environment is described by Bitner (1992, p. 66) as “the ways in which machinery, equipment, and

furnishings are arranged, the size and shape of those items, and the spatial relationships among

them”. How well these items can facilitate performance and goal accomplishment is referred to by

the term ‘functionality’. Finally, the last environmental dimension is signs, symbols and artefacts,

which is described by Bitner (1992) as items in the physical office environment that are particularly

important for forming the first impression and give occupants implicit cues about the norms and

rules for behaviour in the place. These items function as signals that communicate about the place

to its users (Bitner, 1992). According to the literature review of Mari and Poggesi (2013), that

includes 92 classical studies on the servicescape, most of the articles about the servicescape focus

on the first two dimensions of Bitner (1992): the ambient conditions and spatial layout and

functionality. The signs, symbols and artefacts dimensions often get less attention in the literature.

The next section will discuss the elements included in these dimensions in more detail, to get a

better idea of what can be perceived in the environment.

2.1.5 Ambient conditions

As explained in the previous paragraph, ambient conditions are elements of the environment that

affect the five senses and include lighting, temperature, air quality, scent, colour, sound and

biophilia (Bitner, 1992). The next paragraphs will give a short description of these elements and

their effects on the occupants of the work environment.


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Lighting

A distinction can be made between two types of lighting, namely daylight and artificial light. Both

have an effect on employees’ satisfaction and productivity in an office environment (Alrubaih et al.,

2013; Sivaji, Shopian, Nor, Chuan, & Bahri, 2013; Al Horr et al., 2016). Humans need light in their

life, as it is a regulator of their performance and their physiology (Aries, 2005; Al Horr et al., 2016).

Office employees are dependent on the available daylight or artificial lighting, while they spend

most of their time indoors. Previous studies found that organizations that focus on the amount of

daylight in their offices and optimize these amounts will have higher employee productivity and less

absenteeism (Browning & Romm, 1995; Fay, Rea & Figueiro, 2002). Additionally, the amount of

daylight is related to the thermal state of the office. Windows absorb solar radiation into the office,

which affects the thermal state of the office (Al Horr et al., 2016). This change in the thermal state

of the office affects the employees’ perception of indoor air quality as well. As De Dear and Brager

(2002) found, the decrease in temperature in the office results in a better perceived indoor air

quality.

Temperature

Multiple studies found how temperature affects work performance (e.g., Seppänen & Fisk, 2006;

Wargocki et al., 2008). These studies showed how the performance of employees decreases when

the temperature is above 23-24 °C. The ambient temperature of the work environment has a

significant role in defining the thermal comfort of the employees in this environment (Al Horr et al.,

2016). However, it is essential to note that the optimum temperature for optimal productivity differs

per tasks and function (Huizenga, Abbaszadeh, Zagreus, & Arens, 2006; Tanabe, Nishihara, &

Haneda, 2007).

The temperature can also affect employees in a purely physiological way (Bitner, 1992). For

example, when it is too cold, people start to shiver, or when it is too hot, they start to perspire.

These physical responses may cause dissatisfaction with a particular environment (Bitner, 1992).

These effects of temperature and the resulting thermal comfort on occupant’s satisfaction and

productivity is supported by multiple studies (e.g., Tanabe et al., 2007, Djongyang, Tchinda &

Njomo, 2010, Lan, Wargocki & Lian, 2011)

Indoor air quality

Indoor air quality has an impact on both the productivity of the employees and their satisfaction. An

improved air quality will increase the productivity and performance of the employees (Wyon, 2004;

Al Horr et al., 2016). Additionally, an improved indoor air quality results in improved health of the

employees and increased job satisfaction (Wargocki, Wyon, Baik, Clausen, & Fanger, 1999;

Lagercrantz et al., 2000; Wargocki et al., 2008). Furthermore, Milton, Glencross and Walters (2000)

found in their study that the indoor air quality has a significant effect on the short-term sick leave.

Worse indoor air quality will result in more short-term sick leaves.

Scent / Odour

The indoor air quality can also affect the scent within the building. Too little fresh air can make the

room smell stuffy. The scent within an environment can affect its occupant’s health and mood

(Diego et al., 1998; Nicell, 2009), as well as their performance (Basevitch et al., 2011). For example,

Schweitzer, Gilpin and Frampton (2004) found that smelly scents can stimulate stress, whereas a

pleasant scent can reduce blood pressure. Thus, the scent within an environment can affect

occupants both physiologically as psychologically.


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Noise and Acoustics

Noise can be defined as a sound that is perceived unpleasant by the users and is a significant

source of dissatisfaction with the office environment (Bitner, 1992; Sundstrom, Town, Rice, Osborn,

& Brill, 1994; Al Horr et al., 2016). Increased noise levels result in reduced performance according

to the findings of Witterseh, Wyon and Clausen (2004). Noise or bad acoustics in an office

environment also affects the productivity and job satisfaction of employees (Sundstrom et al., 1994;

Banbury and Berry, 2005). For employees to work efficiently, a certain degree of noise control is

required. The design of the office environment should take into account this noise control, while

bad acoustics results in inefficient employees and employees that are dissatisfied with their work

environment (Balazova, Clausen, Rindel, Poulsen, & Wyon, 2008; Frontczak et al., 2012).

Colours

The colours used in an environment affects the emotional state of the people in it. Different colours

result in different psychological responses (Mahnke, 1996). For example, cold colours (blue, green

and purple) have a calming effect, while warm colours (yellow, orange and red) have an arousing

effect (Ou, Luo, Woodcock, & Wright, 2004; Küller, Ballal, Laike, Mikellides, & Tonello, 2006). Thus,

the colours used in an environment have a psychological effect on the people in this environment.

Additionally, the colour schemes used in an office environment can affect employee performance

and productivity (Öztürk, Yilmazer, & Ural, 2012). The right colour will bring the employee in the

right mood which will encourage productivity and will result in better-performing employees

(Kamaruzzaman & Zawawi 2010; Kamarulzaman, Saleh, Hashim, Hashim, & Abdul-Ghani, 2011).

Biophilia

Biophilia is the tendency of humans to seek connection with nature and other forms of life (Wilson,

1984). Humans are highly responsive to forms of nature and its processes and patterns (Nabhan,

St Antoine, Kellert, & Wilson, 1993). Multiple studies found that bringing greenery or natural

elements inside an office has a positive effect on the productivity and satisfaction of employees

(Heerwagen & Orians, 1986; Grinde & Patil, 2009; Heerwagen, 2009). Additionally, indoor greenery

is found to be negatively related with the stress of the occupants (Al Horr et al., 2016) and it

improves the indoor air quality (Lohr, Pearson-Mims, & Goodwin, 1996). Even the passive viewing

of natural elements through windows has a positive effect on the productivity of occupants of the

room (Al Horr, 2016) and results in reduced stress levels (Chang & Chen, 2005). Furthermore,

Elzeyadi (2011) found that the presence of biophilia in offices reduces absenteeism rates of

employees. Overall, the inclusion of biophilia in office environments has a positive effect.

2.1.6 Spatial layout and functionality

The spatial layout and functionality dimensions exist of fixed and semi-fixed elements, while the

dimension ‘ambient conditions’ exists of mostly non-fixed elements (Rapoport, 1982). The fixed

and semi-fixed elements of the work environment that are included in this study are spatial layout

and functionality, equipment and furniture. In addition to the spatial layout and functionality, privacy

will be discussed. Because privacy is seen as a direct consequence of the spatial layout.

Layout & Functionality

The layout of a building refers to how all the objects in the environment are placed. These objects

include the furnishing and equipment. Important factors are the size and shape of these objects,

but also the location in the environment is important (Bitner, 1992). The functionality of these

objects is determined by how well these objects facilitate the performance and achievement of

goals (Bitner, 1992). When a layout is sufficient and functioning, it will provide convenience and will


12

make the use of the environment more pleasurable (Wakefield & Blodgett, 1996; Siu, Wan & Dong,

2012). On the other hand, if the layout does not match the work process, it can negatively affect

the occupant’s level of well-being. For example, when occupants have to fulfil a complex task, they

will be dissatisfied with a layout that causes distraction from other occupants (Al Horr et al., 2016).

Furthermore, Preiser, White and Rabinowitz (1988) found that the spatial layout affects occupant’s

behaviour. A good layout will allow occupants to access all spaces conveniently and will result in

satisfaction with the environment (Mustafa, 2017).

Privacy

Closely related to the layout of an office environment is the privacy that comes with a particular

layout. When the office layout causes too many people to be in the same space, a sense of

crowding can arise (Dilani, 2008). In her research, Aubert-Gamet (1997) showed that layout is

strongly related to the sense of crowding and thus with the perceived privacy.

Marquardt, Veitch and Charles (2002, p. 8) define privacy as “the degree to which one’s social

interactions are regulated”. Privacy is subdivided into several types, including visual privacy, sound

privacy (O’Neill & Carayon, 1993; Kim & de Dear, 2012) and privacy from distractions (O’Neill,

1994; Marquardt, Veitch and Charles, 2002).

As described in paragraph 2.1, several office types can be distinguished (Danielsson & Bodin,

2009). They describe in their study how the level of privacy decreases as the openness of the office

increases. The more open the office, the more people are in the same space, the easier it becomes

to get disrupted visually or auditorily. Most privacy is perceived in the cell-offices, while the levels

of distraction and interruptions are the lowest in these types of offices. The level of satisfaction with

the amount of privacy depends on the task at hand (Al Horr et al., 2016).

Enclosures & Barriers

Closely related to the spatial layout and the privacy of the work environment, are the enclosures

and barriers in the physical work environment. Examples of closures and barriers are hallways,

doors, walls, partitions, cubicles or other things that “buffer workers from each other and from

ambient disturbances” (Elsbach & Pratt, 2007, p. 4). The differences between the office types as

described in paragraph 2.1 depend to a large extent on the closures and barriers in the physical

work environment. For example, the open plan-office has only a few closures and barriers, whereas

the cell-office has many (Maher & von Hippel, 2005). When the physical work environment has

fewer closures and barriers, the privacy ratings will be lower (Oldham & Rotchford, 1983) and the

overall satisfaction with the physical environment will be lower (Brill, 1984). Oldham, Kulik and

Stepina (1991) found that a high degree of enclosures and barriers increases the satisfaction and

performance on simple tasks. However, it reduces the satisfaction and performance of those

employees working on complex tasks. On the other hand, Sundstrom, Burt and Kamp (1980) found

that employees prefer the privacy that comes with more enclosures when working on complex and

routine tasks.

Equipment & Furniture

As Bitner (1992) describes, the equipment must facilitate performance and the accomplishment of

goals. Besides its need for task performance, equipment and furniture are one of the main points

of interaction between employees and their physical environment (Carlopio, 1996). Furniture and

equipment are closely linked to the layout of the building. The layout of equipment and furniture in

the work environment can have a major impact on the occupant’s ability to achieve their goals and

complete their tasks (Bitner, 1992).

Ellickson and Logsdon (2002) found in their study a significant relationship between the perception

of equipment and overall job satisfaction. Additionally, Carlopio and Gardner (1992) found that


13

furniture that can be adapted to individual physical characteristics, often called ergonomic furniture,

is perceived as more pleasurable and result in improved satisfaction with the work environment.

O’Neill’s (1994) research shows very similar results. He describes in his study how the adjustability

of all kinds of furniture, to meet the physical needs of its users, results in increased environmental

satisfaction. For example, the perceived comfort of a chair is increased if it is adjustable (O’Neill,

1994). The study of Makhbul (2013) shows that poor ergonomic furniture has its consequences. In

his study, he found that workplaces with poor ergonomic furniture lowers the productivity of its

occupants and also has a significant effect on their well-being.

In general, the quality of furniture and equipment in offices have a positive effect on the perceived

comfort of, and general satisfaction with, the physical office environment (Marquardt, Veitch &

Charles, 2002).

2.1.7 Signs, Symbols and Artefacts

The third environmental dimension that is described by Bitner (1992) are the signs, symbols and

artefacts in the physical office environment. As she describes in her paper, these items are

essential for forming the first impression and giving occupants implicit cues about the norms and

rules of the environment. These items consist mainly of semi-fixed elements.

According to Bitner (1992), signs and symbols encompass explicit and implicit elements. These

elements are used to communicate information to the employees and directing their behaviour.

Additionally, these elements can be used to communicate the organizational culture to the

employees (Siu, Wan & Don, 2012). Signs are the most direct way to communicate the meaning of

a place and conveying the norms and expected behaviour to employees (Bitner, 1992).

Another important element for communicating the organizational culture are artefacts. Artefacts are

those items in an environment that communicate about the place and its culture to its users (Bitner,

1992). Examples of artefacts are the presence of artworks, personal objects of the employees, the

quality of the materials used for construction, ceremonies, dress codes, and the language used.

Bang (1995) identifies four different categories of artefacts, namely: (1) behavioural expressions;

(2) verbal expressions; (3) material expressions; and (4) structural expressions. Bjerke, Ind and De

Paoli (2007) mention in their paper that there are potential relations between artefacts that express

the organizational culture and the motivation and satisfaction of employees. Additionally, Gagliardi,

Clegg, Hardy and Nord (1999) claim that artefacts impact the behaviour of employees.

2.1.8 Operationalization of the perception of the work environment

Based on the framework of Bitner (1992), the last paragraphs described three dimensions that can

be distinguished in the work environment and the elements within each dimension. An overview of

these three dimensions and all the underlying elements that have been discussed in this chapter

can be found in Figure 2. As Bitner (1992) explains in her study, it is the subjective and holistic

perception of these elements of the work environment that result in an internal response within the

employee. This holistic perception is based on a fusion of all aspects of the environment. However,

each perception may have different outcomes on the employees (Bitner, 1992). Perception is “the

process of experiencing organized and interpreted information extracted from sensations” (Jacobs,

2006, p. 122). This organized information from the environment enters the human body through

the five senses and helps us to understand and interact with the environment. More specifically,

environmental perception is often defined as “awareness of, or feelings about, the environment,

and the act of apprehending the environment by the senses” (Zube, 1984, p. 7). Information about

how employees perceive their work environment can inform managers about the employees’

concerns about the environment and gives information about probable responses to environmental

conditions (Zube, 1984).


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There are several ways of measuring perception. A good way to measure the perception of the

environment is by using self-reported measures such as a Likert Scale (Brown, 2011). Using a

Likert scale, one is able to measures the different perceived environmental qualities and results of

individual respondents

Figure 2

Three environmental dimensions (Bitner, 1992) and the discussed underlying elements

This study focuses on the perceptible elements of the work environment. Some elements, such as

gasses and infrasound are imperceptible. These imperceptible elements of the environment are

not included in this study. The perceptible elements of an environment can be divided into three

dimensions (Bitner, 1992). Several studies used these three dimensions as a starting point for

building their construct measuring the work environment (e.g., Wakefield & Blodgett, 1996). This

construct has shown to be reliable in several studies (e.g., Han & Ryu, 2009; Siu, Wan & Dong,

2012). This construct exists of six scales which contains a total of 30 elements of the work

environment. These items are measured on a five-point Likert scale, ranging from strongly disagree

(1) to, strongly agree (5). The six scales are layout, space, ambient conditions, the functionality of

equipment and furniture, cleanliness, and signs. The scales layout, space, and functionality refer

to the spatial layout and functionality dimensions. The ambient conditions scale covers the

dimension that is named likewise, and the cleanliness is included in this dimension as well. Finally,

the signs scale is related to the signs, symbols and artefacts dimension. Together these six scales

give a comprehensive view of how the ‘built physical work environment’ is perceived. The model

does not contain the whole perceptible environment, whereas this would result in a construct that

was too large. A selection was made to keep the length of the survey short to minimize the response

bias caused by boredom and monotony (Schmitt, Ford & Stults, 1986), but still covers elements

within all three dimensions.


15

2.1.9 Conclusion – Paragraph 2.1

As described in the previous paragraphs, the primary purpose of a work environment is to support

occupants to get their work done, perform and be productive, to achieve the organizational goals

(Newsham, 1997; Vischer, 2008). Multiple studies have shown that the work environment can be

an efficient tool to steer employee behaviour (Peters and O’Connor, 1980; Peters et al., 1985;

Olson and Borman, 1989; Kyriakidou and Ozbilgin, 2004). Both Bitner (1992) and Mehrabian and

Russel (1974) explain in their studies how the behaviour of occupants can be influenced by the

work environment. They explain how the perception of elements of the environment can influence

several internal responses that affect the occupant’s behaviour and attitude.

Multiple studies divide the work environment into different categories based on the number of

employees in a room and whether employees have their workspace or not. A conventional

categorization is that of Danielsson and Bodin (2008). In their study, they divide the work

environment into seven different types of offices. However, besides this categorization based on

the number of persons in a room, more elements distinguish work environments. Elements of the

environment that have proven to affect its occupants (Bitner, 1992; Elsbach & Pratt, 2007; Al Horr

et al., 2016). As Rapoport (1982) explains, these elements can be fixed, semi-fixed or non-fixed.

Bitner (1992) divides these elements into three environmental dimensions, namely: (1) ambient

conditions, (2) spatial layout and functionality, and (3) signs, symbols and artefacts. Examples of

ambient conditions are the lighting, temperature, air quality, scent, colours and noise in the physical

office environment. When considering the spatial layout and functionality, which consists of fixed-

and semi-fixed elements, interesting factors are the layout itself and the privacy that comes with it.

The layout and associated privacy also depend on the number of walls and barriers. Additionally,

biophilia is an interesting element in the work environment that has its effects on employees.

Finally, the signs, symbols and artefacts play an important role in communicating the organizational

values to employees and steering their behaviour (Gagliardi, Clegg, Hardy & Nord, 1999; Bjerke,

Ind & De Paoli, 2007; Siu, Wan & Don, 2012).

These different elements of the environment can be perceived via the five senses. Perception is

described as “the process of experiencing organized and interpreted information extracted from

sensations” (Jacobs, 2006, p. 122). This organized information helps us to understand and interact

with the environment. Our environmental perceptions are our feelings about the environment and

our understanding of the environment via our senses (Zube, 1984). A good way of measuring

perception is by the use of self-reported scales, for example Likert scales (Brown, 2011). Using a

Likert scale, the employees in the environment can rate the different elements of their environment.

There are a lot of elements of the work environment that can be perceived and that have shown to

affect its occupant. These elements can be placed under one of the dimensions as described by

Bitner (1992). An existing construct measuring the perception of elements of an environment, and

that has shown to be reliable, is the construct of Wakefield and Blodgett (1996). This construct

covers the three dimensions (Bitner, 1992) of the environment that can be perceived, but at the

same time is not too long, to prevent bias caused by boredom and monotony (Schmitt, Ford &

Stults, 1986).

2.2 EMPLOYEE TURNOVER INTENTION

Intentions are often the best predictors for actual behaviour (Igbaria & Greenhaus, 1992). Multiple

studies found that the same applies to turnover intention, which has shown to be one of the best

predictors of actual turnover (Griffeth, Hom & Gaertner, 2000; Van Dick et al., 2004; Trimble, 2006).

Organizations that know the causes of turnover intentions can prevent this intention to become

reality (Campion, 1991).


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Many researchers studied the concept of turnover intention, resulting in a lot of different, but very

similar, definitions of the concept. For example, Watrous, Huffman and Pritchard (2006) define

turnover intention as the thoughts of employees to voluntarily quit their job. Bothma and Roodt

(2013, p. 2) define the intention to leave the organization as ‘... an individual’s behavioural intention

or conation to leave the employ of the organization.’ Another very similar, and often used definition

of turnover intention is the definition of Tett and Meyer (1993, p. 262): ‘… the conscious and

deliberate wilfulness to leave the organization’. This last definition of Tett and Meyer (1993) is the

definition used in this study.

In the literature, several categories of turnover are distinguished. The categorization that is often

made consists of three types, namely: a) involuntary and voluntary turnover, b) functional and

dysfunctional turnover, and c) controllable and uncontrollable turnover (Mathis & Jackson, 2011).

The distinction between voluntary and involuntary turnover is based on whether the employee

decides for himself to leave (voluntary) or if he is dismissed (involuntary) by the company (Stumpf

& Dawley, 1981). In other words, voluntary turnover is the employee’s decision to leave the

organization, whereas involuntary turnover is the decision of the employer to end the employment

(Shaw, Delery, Jenkins & Gupta, 1998). The distinction between functional and dysfunctional

turnover depends on whether the employee leaving the organization is beneficial for the employer

or not. Dysfunctional turnover is when the employee wants to leave the organization while the

employer wants to retain the employee. Functional turnover is when the employee wants to leave

the organization, and the employer does not care whether the employee stays or not (Dalton,

Krackhardt & Porter, 1981). The third distinction is between controllable and uncontrollable

turnover, or in other words, avoidable and unavoidable turnover (Abelson, 1987). Controllable

turnover is the case of employee turnover that could have been avoided, for example, through a

raise or a promotion. On the other hand, uncontrollable turnover are these cases in which the

organization could not have done anything to avoid the turnover: the turnover was unavoidable for

example when the employee quits, following a husband who needs to move (Dalton et al., 1981;

Abelson, 1987).

Companies are deliberately trying to avoid voluntary and dysfunctional turnover because of the

substantial cost that is related to these types of employee turnover. These include the costs of loss

of productivity, recruitment, training, loss of continuity, and many more (Koh & Goh, 1995). In order

to reduce these costs related to employee turnover, the right retention strategies should be adopted

that reduce employee turnover intention (Das & Baruah, 2013). Employee retention strategies are

crucial for organizations, while it allows organizations to develop interventions that can reduce

turnover intention and actual turnover, and subsequently prevent the direct and indirect costs

related to turnover (Nedd, 2006). Osteraker (1999) divides employee retention strategies into three

dimensions; the social-, mental-, and physical dimension. The social dimension suggests that

employees are affected by the interaction with other people, both external and internal. The mental

dimension refers to the characteristics of the work, and the physical dimensions are the working

conditions and the pay, both of which can affect employee retention (Osteraker, 1999).

2.2.1 Measure turnover intention

Actual turnover can be easily measured by counting the historical turnover data; however, the

turnover intention is harder to measure. In order to measure the turnover intention, a scale is

required that measures employees’ intentions. Often the intention to leave the organization is

sparked by employee job attitude combined with job alternatives (Mitchell et al., 2001). Traditional

studies about turnover intention, therefore, often tested the attitudinal constructs job satisfaction

and organizational affective commitment (Griffeth & Hom, 1995). These two constructs will be

elaborated on at the end of this chapter. Nevertheless, some studies include a construct that


17

directly measures employee turnover intention. For example, the study of Mitchell and colleagues

(2001) or the study of Hom, Griffeth and Sellaro (1984). Both measured employee turnover

intention with three items. The construct used in both studies is an adapted version of the original

construct of Mobley, Horner and Hollingsworth (1978). Earlier studies on the turnover intention

often relied on one-item measures, which showed to be less reliable (Miller, Katerberg & Hulin,

1979; Mobley 1982). The aggregation of multiple items helps to cancel out a part of the random

error around an individual’s true score. However, it does add to the length of the questionnaire.

Although, with three items instead of one, this is not a constraint. These three items are often

phrased as follows (Mobley et al., 1978):

(1) “I often think of leaving the organization”.

(2) “I intend to look for a new job within the next year”; and

(3) “If I could choose again, I would not work for this organization”.

These - or very similar - items are often used to measure employee turnover intention and show

high internal consistency and is considered reliable (Mitchell et al., 2001).

2.2.2 Conclusion – Paragraph 2.2

As is described at the start of these paragraphs about turnover intention, intentions have shown to

be a good predictor for actual behaviour, including the intention to leave an organization as the

predictor of actual turnover (Griffeth, Hom & Gaertner, 2000). Many researchers studied the

concept of turnover intention. The definition that is often used, and which is used in this study, is

the definition of Tett and Meyer, who define the intention to leave the organization (i.e., turnover

intention) as ‘the conscious and deliberate wilfulness to leave the organization’ (Tett & Meyer, 1993,

p. 262). Several categorizations are made within the broader concept of turnover intention. Often

the distinction is made between:

(1) involuntary and voluntary turnover;

(2) functional and dysfunctional turnover; and

(3) controllable and uncontrollable turnover (Mathis & Jackson, 2011).

Organizations focus on the controllable, voluntary and dysfunctional turnover. These are the cases

of turnover that they can control and during which an employee decides to leave that will harm the

functioning of the organization. In order to reduce employee turnover, retention strategies are

essential for organizations. The reasons why employees stay might not be the exact reverse of the

reasons why they leave, but they are related. The right interventions, for example, in the work

environment, can reduce employees’ turnover intention (Nedd, 2006; Reitz & Anderson, 2011).

Lately, most studies measure turnover intention, using a self-reporting measure that consists of

multiple items. The three items to measure turnover intention, as phrased by Mobley and

colleagues (1978), have been used a lot and has proven to be a reliable scale and will therefore

be used in this study (Mitchell et al., 2001).

2.3 WORK ENVIRONMENT AND EMPLOYEE TURNOVER INTENTION

Most of the studies about the work environment and its effects on turnover intention focus on the

non-physical (or non-built) work environment. Examples of these are studies focusing on the effects

of the organizational structure and climate (Hong & Kaur, 2008), the reward system (Jauhar, Ting,

Rahim & Fareen, 2017), the managerial style (Dixon & Hart, 2010) or the human resource practices


18

on turnover intention (Garcia-Chas, Neira-Fontela & Castro-Casal, 2014). Additionally, a lot of

research about the work environment and turnover intention focuses on nurses, health- and social

workers (Flinkman, Leino-Kilpi & Salanterä, 2010; Chan et al., 2013). There are some studies that

study the effect of the built or physical work environment on employee turnover intention. Most of

these studies test for the effects of the work environment as a whole and also include elements of

the non-physical work environment in their work environment construct (e.g., Kurniawaty, Ramly &

Ramlawati, 2019; Santoni & Harahap, 2018).

An interesting study about the physical work environment and turnover intention is the study of the

American Society of Interior Designers (ASID) in which 663 participants were given carte blanche

at the start to make a list of those factors that affect their decision to accept or leave a job. In total,

21 per cent of the participants stated the physical workplace as the number one factor. When the

participants were explicitly asked whether the physical environment in which they work would affect

their decisions to leave, a total of 51 per cent of the participants said it would (Earle, 2003).

Another example of a study that examined the effect of the physical work environment on employee

turnover intention is the study of Kurniawaty, Ramly and Ramlawati (2019). In their study, they use

100 employees from a Malaysian bank to test the relationships between the work environment and

intention to leave the organization. Additionally, they also test the mediating effect of job satisfaction

in this relation. Based on their regression analysis, they conclude that there is a significant negative

relationship between work environment and turnover intention ( = -.177, p < .01). These findings

suggest that those employees who are more positive about their work environment as a whole are

less likely to leave their organization. Furthermore, they found a significant positive relationship

between work environment and job satisfaction ( = .216 p < .01), and a significant negative

relationship between job satisfaction and turnover intention ( = -.195, p < .01). These findings

suggest that those employees who are more satisfied are less likely to leave their organization.

Included in their construct for work environment are ambient conditions such as lighting, air

circulation and temperature, but also available equipment and furniture is incorporated in their work

environment construct (Kurniawaty, Ramly & Ramlawati, 2019). Besides these elements from the

physical work environment, they also included several non-physical elements in their work

environment construct. Examples of these elements are good relationships with superiors and co-

workers.

In 1994, Montgomery, Heubach, Weimer and Heerwagen did a pre-post study of a laboratory that

was renovated. They were able to track the turnover rates the year before and the year after the

renovation. The renovation included the heating, ventilation and air conditioning system, the spatial

layout and aesthetic upgrades. The study showed significant differences in turnover rate pre and

post-renovation. The turnover rate decreased by 60% in the year after the renovation compared

with the years before the renovation (Heerwagen, 2000).

Another study that examined the effect of the work environment on both job satisfaction and

intention to leave the organization, is the study of Santoni and Harahap (2018). They gathered data

among 260 employees working in the plastic industry of household appliances in Jakarta. The

results of their analysis show a significant negative relationship between the work environment and

turnover intention ( = -.220, p < .01). Included in their work environment construct were the layout,

space provided, lighting, air conditions, furniture and equipment. However, there were also several

non-physical work environment elements included in the work environment construct. For example,

the organizational values and work security (Santoni & Harahap, 2018).

As far as known, no studies examined the effects of signs, symbols and artefacts in the physical

work environment on turnover intention. Despite the fact the important role signs, symbols and

artefacts in communicating organizational values and steering employee behaviour and attitudes

(Bitner, 1992; Siu, Wan & Don, 2012). This propound lack of research is in line with the conclusion


19

of Mari and Poggesi (2013) in their literature review. They conclude that the effects on behaviour

and attitudes of the third dimension of Bitner (1992), the signs, symbols and artefacts, is less

studied than the other two dimensions. However, as Bitner (1992) explains, it forms an important

dimension of the work environment and affects occupant’s behaviour. Therefore, it is included in

this study.

Concerning cleanliness of the work environment, Vos et al., (2018) conclude in their literature

review about perceived cleanliness, that cleanliness is a requirement for a good physical working

condition, among other conditions such as lighting, adequate equipment and furniture. They

describe the perceived cleanliness of an environment as an important ambient condition. Overall,

they conclude that the perceived cleanliness has a noteworthy influence on the behaviour of the

occupant of the environment. However, they do encourage more research on perceived cleanliness

and its effect on occupants. As far as known, no studies examined the direct effect of perceived

cleanliness on employee turnover intention. Despite this, there is general agreement about the

importance of cleanliness of an environment (Vos et al., 2018). This becomes clear in the studies

that examined the relationship between cleanliness and employee commitment (e.g., Hanaysha,

2016; Aydogdu & Asikgil, 2011; Applebaum, 2008) and employee job satisfaction (e.g.,

Ikartrinasari, Prayogo, Ariyanti, 2018; Balouch & Hassan, 2014; Ayim Gyekye, 2005). One study

was found that had cleanliness included in their physical work environment construct. This study of

Ikatrinasari, Prayogo and Ariyanti (2018) found a significant positive relationship between

perceived work environment and job satisfaction = .461 p < .01). More interestingly, they found a

significant correlation between the indicator for cleanliness and intention to leave the organization.

This suggests an important relationship between the cleanliness of the environment and turnover

intention.

2.3.1 Other factors that spark turnover intention

Besides elements of the physical work environment, several other factors can spark employee

turnover intention. In their literature review, Das and Baruah (2013) give a whole list of factors that

are related to turnover intention. They conclude that this list can be reduced to two crucial factors,

namely the lack of affective commitment and job satisfaction. Both factors are the result of the list

of underlying factors (Mobley et al., 1979). This list contains factors such as compensation, rewards

and recognition, the opportunity for growth, work environment, participation in decision-making,

work-life balance, work environment, training and development, leadership, and job security (Das

& Baruah, 2013; Boxall, Macky & Rasmussen, 2003). For example, when the compensation level

is perceived as too low, the employee tends to be less satisfied and committed. Similar relations

are found between compensation level and affective organizational commitment (Vandenberghe &

Tremblay, 2008). In their study, they also found a significant negative relationship between both

commitment and turnover intention, and job satisfaction and turnover intention. In their review

paper, Strachota and colleagues (2003) conclude that job dissatisfaction is cited as a major cause

of employee turnover intention. Other reviews of the literature on the relationship between job

satisfaction and turnover intention consistently report a negative relationship between both (Tett &

Meyer, 1993; Hellman, 1997; Porter & Steers, 1973; Mobley, Griffeth, Hand & Meglino, 1979;

Coomber & Barriball, 2007). Similarly, multiple studies report a negative relationship between

affective commitment and turnover intention (Tett & Meyer, 1993; Aydogdu & Asikgil, 2011; Chang,

1999; Lew, 2009).

Interestingly, several studies show relationships between the physical work environment and

employee job satisfaction and affective commitment. For example, Danielsson and Bodin (2008)

studied the differences in job satisfaction based on office type. Another example is the study of


20

Leder et al. (2016). In this study, the effects of the built office environment on employee satisfaction

was analysed. This analysis showed that several elements of the work environment have a

significant positive effect on employee satisfaction. A final example of a study that examines the

relationship between the physical work environment and job satisfaction is the study of Al Horr and

colleagues (2016). In their extensive literature review, they explain that the findings of multiple

surveys showed that employees are dissatisfied with the ‘open-plan’ office. Reasons for this

dissatisfaction are distractions, noise, and lack of privacy that comes with this work environment

design and the corresponding layout.

There are also several studies that examined the effects of the physical work environment on

commitment. For example, McGuire and McLaren (2009) confirm in their study the significant

positive relationship between the physical work environment and employee commitment (β = 0.53,

p < 0.01). Previous studies of for example Weiss (1999) and Wise (1987) had similar results.

Overall, providing adequate facilities to employees will result in greater employee commitment,

including affective commitment (McGuire & McLaren, 2009).

Besides job satisfaction and affective commitment, several demographic variables that have shown

to covary with turnover intention. These variables are the employee’s gender, age and

organizational tenure (Arnold & Feldman, 1982; Cotton and Tuttle, 1986; Gregersen and Black,

1992; Lee et al., 2004; Mitchell et al., 2001). For example, the findings of Arnold and Feldman

(1992) suggest that employees that work longer for an organization (high tenure) have a lower

turnover intention. Additionally, they found a negative relationship between age and turnover

intention, suggesting that the older an employee gets, the less intended they are to leave the

organization. Similar results were found in the study of Mobley et al. 1979. Arnold and Feldman

(1982) also found a significant correlation between gender and turnover intention. Their findings

suggest that females are more likely to leave an organization than males. Similar results were found

by Marsh and Mannari (1977). Furthermore, Cotton and Tuttle (1978) mention in their meta-

analysis and review about employee turnover literature that the educational level correlates with

turnover intention. Their meta-analysis produces highly significant results (p < .005), indicating a

positive correlation between educational level and turnover intention. Suggesting that higher

educated employees have a higher intention to leave their organizations.

2.3.2 Conclusion – paragraph 2.3

Multiple studies examined the effects of the work environment on employee turnover intentions.

Most of these studies focused on the non-physical work environment and focused for example on

the effects of organizational structure and climate (Hong & Kaur, 2008), the reward system (Jauhar,

Ting, Rahim & Fareen, 2017), the managerial style (Dixon & Hart, 2010) or the human resource

practices on turnover intention (Garcia-Chas, Neira-Fontela & Castro-Casal, 2014). However, there

are a few studies that examined the effects of the physical work environment on turnover intention.

Findings of these studies suggest effects of the layout (Montgomery et al., 1994; Santoni &

Harahap, 2018), space (Santoni & Harahap, 2018), furniture and equipment (Kurniawaty, Ramly &

Ramlawati, 2019), cleanliness (Ikatrinasari, Prayogo & Ariyanti, 2018) and ambient conditions

(Kurniawaty, Ramly & Ramlawati, 2019; Montgomery et al., 1994; Santoni & Harahap, 2018) on

employee turnover intention.

So far, no academics incorporated signs in their research on the effects of the perceived work

environment on employee turnover intention. Despite the considered importance of signs in

steering employee behaviour and attitudes (Siu, Wan & Don, 2012). According to Bitner (1992),

the signs, symbols and artefacts form an important dimension in the perception of an environment

that cannot be ignored in examining the effects of the perceived environment on its occupant’s

behaviour and attitudes. Therefore, this study not only focuses on the layout, space, ambient


21

conditions, functionality of equipment and furniture, and cleanliness. But also includes the

perception of the signs in the office environment.

Besides the physical work environment, there are several other factors that might spark employee

turnover intention. Das and Baruah (2013) conclude that this list can be brought down to two crucial

factors that comprise most of the underlying factors. These two factors are employee job

satisfaction and organizational affective commitment. Both job satisfaction and affective

commitment have consistently shown to be negatively related to employee turnover intention

(Aydogdu & Asikgil, 2011). The work environment is one of the underlying factors of both employee

job satisfaction and affective commitment (Das & Baruah, 2013). Several studies on the physical

work environment found significant positive relationships between the physical work environment

and job satisfaction (e.g., Leder et al., 2016). Similarly, several studies found a significant

relationship between the physical work environment and affective commitment (e.g., McGuire &

McLaren, 2009).

Besides the work environment, job satisfaction and affective commitment, academics found several

demographic factors that covary with turnover intention. The most important ones are gender, age,

organizational tenure and educational level (Cotton and Tuttle, 1986; Arnold & Feldman, 1982).

2.4 CONCEPTUAL FRAMEWORK AND HYPOTHESES

Many researchers try to find the essential determinants of employee turnover intention. Knowledge

about these determinants helps them to develop managerial implications that helps them to lower

employee turnover intention, resulting in fewer problems and costs related to high turnover rates

(Arshadi & Shahbazi, 2013).

As explained before, there are several elements of the work environment that can be perceived

and have effects on occupant behaviour and attitudes (Bitner, 1992). As explained in paragraph

2.3, a few studies suggest relationships between the work environment and employee turnover

intention. Findings of these studies suggest effects of the layout (Montgomery et al., 1994; Santoni

& Harahap, 2018), space (Santoni & Harahap, 2018), the functionality of furniture and equipment

(Kurniawaty, Ramly & Ramlawati, 2019), cleanliness (Ikatrinasari, Prayogo & Ariyanti, 2018) and

ambient conditions (Kurniawaty, Ramly & Ramlawati, 2019; Montgomery et al., 1994; Santoni &

Harahap, 2018) on employee turnover intention. Based on these findings the following hypotheses

are formulated, suggesting that the more positive the employee’s perception of the element of the

work environment, the less they are intended to leave their organization.

1. Employee’s perception of the layout of their work environment is negatively related to

their turnover intention.

2. Employee’s perception of the space of their work environment is negatively related to


3. Employee’s perception of the functionality of equipment and furniture of their work

environment is negatively related to their turnover intention.

4. Employee’s perception of the cleanliness of their work environment is negatively related

to their turnover intention.

5. Employee’s perception of the ambient conditions of their work environment is negatively

related to their turnover intention.


22

6. Employee’s perception of the signs of their work environment is negatively related to


There are several factors besides the work environment that can spark an employee turnover

intention. These factors can be brought down to two crucial factors that have consequently shown

to have a significant negative relationship with turnover intention. These factors are job satisfaction

and affective commitment (Das & Baruah, 2013; Tett & Meyer, 1993; Aydogdu & Asikgil, 2011).

Both factors have shown to be the consequence of several underlying factors such as

compensation, rewards and recognition, the opportunity for growth, work environment, participation

in decision-making, work-life balance, work environment, training and development, leadership,

and job security (Mobley et al. 1979). Validating the relationship between turnover intention and

both job satisfaction and affective commitment is an important step in our model that is presented

at the end of this paragraph. Therefore, the following hypotheses are formulated:

7. Job satisfaction is negatively related to employee turnover intention

8. Affective commitment is negatively related to employee turnover intention

Besides the hypothesized direct effect of the perception of elements of the work environment on

turnover intention, there is also a hypothesized indirect effect through the mediating variables job

satisfaction and affective commitment. Both factors are expected to be negatively related to

turnover intention. Additionally, based on findings in previous studies, it can be expected that the

perceptions of several elements of the work environment are positively related to job satisfaction

and affective commitment (e.g., McGuire & McLaren, 2009; Leder et al., 2016). These relationships

can be explained by the framework of Bitner (1992). In her study, she explains how the perception

of an environment results in a response within the occupants of the environment. This internal

response can be affective and attitudinal and can include feelings of satisfaction and commitment

(Mehrabian & Russell, 1974). This internal response results in a behavioural response of the

occupant (Bitner, 1992; Mehrabian & Russell, 1974). Interestingly, the internal responses such as

job satisfaction and affective commitment have been linked to turnover intention (Aydogdu &

Asikgil, 2011). Therefore, it is hypothesized that the relationship between the perception of the

previously defined elements of the work environment and turnover intention is mediated by both

(a) job satisfaction and (b) affective commitment. This led to the following hypotheses:

9a. Job satisfaction mediates the relationship between employee’s perception of the layout

and their turnover intention.

9b. Affective commitment mediates the relationship between employee’s perception of the

layout and their turnover intention.

10a. Job satisfaction mediates the relationship between employee’s perception of space


10b. Affective commitment mediates the relationship between employee’s perception of

space and their turnover intention.

11a. Job satisfaction mediates the relationship between employee’s perception of the

functionality of equipment and furniture and their turnover intention.


the functionality of equipment and furniture and their turnover intention.


23


cleanliness and their turnover intention.


the cleanliness and their turnover intention.


ambient conditions and their turnover intention.


the ambient conditions and their turnover intention.

14a. Job satisfaction mediates the relationship between employee’s perception of the signs



the signs and their turnover intention.

Literature has indicated that the demographic variables age, gender, organizational tenure and

educational level can covary with turnover intention (Arnold & Feldman, 1982; Cotton & Tuttle,

1978). Therefore, these variables are included in the conceptual framework.

Based on the theoretical framework presented in the previous paragraphs, the conceptual

framework is designed as presented in Figure 3. The conceptual model shows the hypothesized

relationships between perceptible elements of the work environment – the independent variables

– and employee intention to leave the organization, the dependent variable. Additionally, the

conceptual model includes job satisfaction and affective commitment as mediating variables,

because of its hypothesized relationship with both the perception of elements of the work

environment and employee turnover intention.

Figure 3

Conceptual framework

24

3

3 METHODS

This study conducts quantitative research. This quantitative data is gathered using a questionnaire.

In this chapter is explained how the questionnaire is designed; how the data is gathered; and how

this data is analysed. First, a short description of the process behind the literature findings is given,

where after the constructs and the questionnaire are discussed. After that, the sampling method

and the data analysis is explained. This chapter also sheds a light on the reliability and validity of

this study, the used methods and included constructs. A complete overview of the research

framework can be found in Appendix 1.

3.1 LITERATURE STUDY

The main focus of the literature study is to describe the relationship between the perception of

elements of the work environment and an employee turnover intention to answer the theoretical

research questions. Before the relationship between both is studied, the two topics are discussed

separately. Literature for both topics is gathered using the search engines of Scopus and Google

Scholar. To optimize the searches, Boolean operators (AND, OR), double quotations marks (“...”)

and wildcards (? and *) are used. Additionally, the papers need to be in English and be peer-

reviewed. The snowballing method is used to find additional relevant articles. Relevant references

that are included in the found articles are also included in this study. The literature study results in

a theoretical framework and several hypotheses that form the basis of the quantitative part of this

study.

3.2 CONSTRUCTS

A questionnaire is developed based on the identified constructs during the literature study. This

questionnaire gathers data about how employees perceive their work environment, their attitude

towards their job and their behavioural intentions. The constructs that are included in the

questionnaire are job satisfaction, affective commitment, intention to leave the organization, and

the perception of the work environment. The next couple of paragraphs gives a more detailed

overview of the included constructs. The original constructs can be found in Table 20, Appendix 3.

3.2.1 Job Satisfaction

The five items measuring job satisfaction are based on the job satisfaction scale developed by

Brayfield and Rothe (1951). Multiple scholars have confirmed the validity and reliability of this scale

(e.g., Lambert, Hogan and Griffin, 2007; Hochwarter et al., 2003; Ilies and Judge, 2002). These

five items are scored on a Likert scale consisting of five categories of disagreement-agreement

(Brayfield and Rothe, 1951). A higher score indicates higher job satisfaction.

CHAPTER 3 – METHODS

25

3.2.2 Affective Commitment

The included items measuring affective commitment are based on the three-component model of

organizational commitment of Allen and Meyer (1990). This construct exists of a total of 24 items

and is divided into three parts: normative-, affective- and continuance commitment. Each part

consists of eight items that can be scored on a five-point Likert scale from strongly disagree (1) to,

strongly agree (5). A higher score indicates stronger employee commitment.

This paper specifically focuses on the affective commitment of employees because this component

can be seen as the core of organizational commitment (Mercurio, 2015; Samudi, Slambolchi, &

Mobarakabadi, 2016). Moreover, multiple studies have shown that this component predicts

employee behaviour better than the two other components (Meyer, Paunonen, Gellatly, Goffin, &

Jackson, 1989; Tett & Meyer, 1993). Affective commitment is defined as “the degree to which an

individual is psychologically attached to an employing organization through feelings such as loyalty,

affection, warmth, belongingness, fondness, pleasure, and so on” (Jaros, Jermier, Koehler &

Sincich, 1993 p. 954). It describes the employees’ emotional bond to the organization (Chang,

1999) and their desire to stay with the organization (Allen and Meyer, 1990).

The reliability and validity of the organizational commitment scale of Allen and Meyer (1990) has

been confirmed by many scholars (e.g., Kehoe & Wright, 2010; Meyer, Stanley, Herscovitch, &

Topolnytsky, 2002; Hackett, Bycio & Hausdorf, 1994)

3.2.3 Turnover Intention

The turnover intention scale of Mitchell et al. (2001) exists of three items that are used to measure

the intention to leave the organization. These three items are scored on a five-point Likert scale

from strongly disagree (1) to, strongly agree (5). This scale was validated by several scholars (e.g.,

Takawira, Coetzee & Schreuder, 2014; Halbesleben & Wheeler, 2008). An example of a statement

from the turnover intention scale is ‘Do you intend to leave the organization in the next 12 months?’’.

3.2.4 Perception of Work Environment

The 29 items measuring the perception of the work environment are adapted from Wakefield and

Blodgett (1996). This construct exists of six parts: layout (Lay), space (Spa), ambient (Amb), the

functionality of equipment and furniture (Fun), cleanliness (Cle), and signs (Sig). One single item

is added to measure the holistic perception of the work environment. All items are measured on a

five-point Likert scale from strongly disagree (1) to, strongly agree (5). Similar constructs for

measuring the perception of the work environment as the ones used in the study of Wakefield and

Blodgett (1996) has shown excellent reliability and validity (e.g., Wakefield & Baker, 1998; Han &

Ryu, 2009; Siu, Wan & Dong, 2012).

3.2.5 Demographic variables

Several items measuring demographic variables are included because scholars found that these

can covary with employee turnover intention. These variables are gender, age and organizational

tenure (Cotton and Tuttle, 1986; Gregersen and Black, 1992; Lee et al., 2004; Mitchell et al., 2001).

Additionally, Arnold and Feldman (1982) observed a relationship between the level of education

and employee turnover. Therefore, a question regarding the respondents’ educational level is also

included in the questionnaire.

The coding of the Demographic variables can be found in Table 2. For the variables age, tenure,

hours and education, a higher score indicates more years, more time or higher education.


26

Table 2

Coding of the demographic variables

Demographic variable Answer Code

Gender Male 1 Female

2

Age 0-17 years 1 18-29 years 2 30-39 years 3 40-49 years 4 50-59 years 5 60+ years

6

Highest Education Level Lagere school 1 Middelbare school 2 MBO 3 HBO 4 WO

5

Weekly Hours 0-8 hours 1 9-16 hours 2 17-27 hours 3 25-32 hours 4 33-40 hours 5 40+ hours

6

Organizational Tenure 0-5 years 1 6-10 years 2 11-15 years 3 16-20 years 4 21-25 years 5 25+ years 6

3.3 QUESTIONNAIRE

Data describing employees’ perception of elements of their work environment and their turnover

intention is needed to test the previously described hypotheses. A questionnaire is conducted to

collect this data. The constructs and items in the questionnaire are based on the theoretical

framework, as presented in chapter 2 and explained in paragraph 3.2. The questionnaire consists

of two main parts. The first part consists of items measuring turnover intention, perception of the

work environment, employee satisfaction and employee affective commitment. The second part

consists of demographic questions, designed to gather information about the gender, age,

educational level and organizational tenure of the respondent. All the items in the first part are

measured on a five-point Likert scale, ranging from “1 = strongly disagree; 2 = disagree; 3 = neutral;

4 = agree; 5 = strongly agree”.

The questionnaire is accessible online via a link for all employees of the participating organizations.

All respondents get the same questions, and all items appear in the same order for each

respondent. All questions in the questionnaire are mandatory. However, the respondent is able to

stop the questionnaire at any time, resulting in an incomplete questionnaire. Only completed


27

questionnaires are considered valid and are included in the study. Based on the pilot study, it is

expected that employees are able to complete the questionnaire within ten minutes.

3.3.1 Pilot questionnaire

Before the questionnaire was distributed, it was first tested during a pilot among six participants to

make sure it was free of errors and that the questions were interpreted correctly. Additionally, the

pilot gave an idea of how long the actual survey takes. Some questions were rephrased a bit if they

were considered as unclear. Furthermore, the order of the different topics was changed. In the

original questionnaire, the respondents needed to answer the demographic questions first. In the

final questionnaire, these demographic questions are answered at the end. This decision aimed to

make people more honest in giving their opinion and intention when they have not yet shared their

personal details.

Finally, the facility manager of one of the locations had some remarks. These remarks focused on

the information at the start of the questionnaire. Based on these remarks, the privacy statement

was rewritten to suit the wishes of the participating companies better. Additionally, the introduction

of the questionnaire was rewritten to give respondents a better idea of what to expect.

3.3.2 Final questionnaire

The final questionnaire exists of 54 items divided over three broad categories: 32 items about the

work environment, 16 items about the work itself and finally six demographic items. The full

questionnaire can be found in Appendix 2. The average time to complete the questionnaire was 7

minutes and 52 seconds. Furthermore, respondents could send remarks via the mail. Only one

respondent sent an email, which was about the exclusion of questions regarding “office gardens”.

3.4 SAMPLE AND SETTING

People from the same sector - the financial sector - and working in the same work environment -

same office location - were targeted using purposive sampling. A total of seven local organizations

active in the financial sector were approached simultaneously. The contact person, often the local

director or facility manager, were asked whether they were willing to distribute the questionnaire

among their employees. The targeted organizations were found within the researchers’ network

and consisted of local banking and accounting offices. Of the seven targeted offices, a total of four

were willing to participate.

The targeted population existed of approximately 675 employees working at four different locations

of companies active in the financial sector. A total of 242 employees started the questionnaire

between the 2nd and 29th of January 2020. This number resulted in an initial response rate of

around 36%. Of these 242 questionnaires started, a total of 215 questionnaires were completed

(88,84%). In the biggest office (A) approximately 485 employees are working, of whom 162 finished

the questionnaire (33,40%). In the second office (B), a total of 68 employees are working. Of these

68 employees, 30 employees completed the questionnaire (44,12%). At the third office (C), a total

of 55 employees are working of whom 17 completed the questionnaire (30.91%). The fourth office

(D) has a total of 63 employees, of whom six completed the questionnaire (9,52%). Besides the

fourth office, these response rates can be considered as normal if we compare them to the findings

of Baruch and Holtom (2008). They analyse 1607 published studies in their review and found an

average response rate of 35.7% with a standard deviation of 18.8% for studies that utilize data

collected from organizations.


28

3.5 DATA ANALYSIS

Before the data is analysed, all incomplete data is omitted from the dataset. The gathered

quantitative data is analysed using the Lavaan package in R Studio. Structural Equation Modelling

(SEM) is used to analyse structural relationships between the measured variable ‘turnover

intention’ and latent (unobserved) variables that are inferred from the observed variables. The

coding used in R can be found in Appendix 5. SEM is the technique of choice to obtain significant

information about the determinants for the participants’ perception (Hair, Sarstedt, Hopkins &

Kuppelwieser, 2014). This study uses SEM to find the latent variables within the work environment

that affect the employee’s perception of the environment. SEM enables the simultaneous

estimation of a system of structural equations, the goodness of fit, and the total effects (Hair,

Anderson, Tatham & Black, 1998). Therefore, SEM is seen as an excellent method for testing

causal models.

Following the process described by Anderson and Gerbing in 1988, an analysis plan is designed

to test the hypothesis. The model that is tested consists, like most behavioural research, of

variables that are not directly measurable, also known as latent variables (Churchill, 1979; Hair et

al., 1998). According to Hox, Moerbeek and van de Schoot (2017), SEM is the best-suited model

for analysing a model that contains latent variables. The SEM analysis consists of two steps, two

separate models: the measurement model and the structural model.

The first step is the measurement model. The items included in the questionnaire are analysed to

assess the construct reliability and measurement validity of the model. Explanatory- and

Confirmatory Factor Analyses (EFA & CFA) are used to put the observed independent variables

into factors (Hair et al., 1998). It is assumed within the factor analysis that a set of underlying latent

variables can explain the covariances between the set of observed variables. During the EFA, there

is no hypothesis about the relationship between the latent variables and observed variables and

the number of underlying latent variables. The model is arbitrary: all variables will load on all factors.

Additionally, for each factor, one loading is fixed to one, to give the latent variable an interpretable

scale in both analyses.

EFA is conducted, using a principal component analysis to see whether the included items fall

under the predefined constructs that are based on previous studies. As the name already says, the

EFA is exclusively exploratory, which means that there is no prior specification of the number of

factors. A maximum likelihood estimation is used to specify the number of underlying factors and

to test the goodness of fit. Items that have a corrected item-total correlation of less than .5 are

eliminated from the scales (Koufteros, 1999). Additionally, a Cronbach alpha higher than .7 for

established scales and .6 for new scales is desired (Churchill, 1979). However, according to

Anderson and Gerbing (1988), EFA does not result in an exact test of un-dimensionality.

Additionally, the EFA does not give an explicit test statistic for the assessment of the discriminant

and convergent validity. This is given in the CFA.

In SEM, the hypothesized model is specified before the analysis is started, based on previous

findings and theories. After the model is defined, the factor loadings and (co)variances are

estimated. A statistical chi-square test is conducted to test how well the hypothesized model fits

the data. If the chi-square is significant, the model is rejected, and a better model needs to be

found. This is done by removing paths and parameters from the model.

The confirmatory analysis is done after the exploratory analysis. This analysis involves the

specification and estimation of the parameters in the hypothesized model of the factor structure. A

set of latent variables (factors) will account for the covariances among the set of observed variables

in this model (Koufteros, 1999). The path diagram of this model is presented in a figure in the results

chapter. The observed variables in this model are represented by squares and the latent variables


29

by circles. A causal effect of the latent variable on the observed variable is represented by an arrow

from a latent variable to an observed variable.

Multiple fit indices will be used to assess the model fit of the CFA and SEM. These include root

mean square error of approximation (RMSEA), Bentler-Bonett Normed fit index (NFI), comparative

fit index (CFI), and Chi-square.

Additionally, mediation analysis is conducted to test for indirect effects of the perception of elements

of the work environment on turnover intention. This analysis tests whether the effect of the

independent variable on the dependent variable is mediated by another variable. The mediators in

this study are job satisfaction and affective commitment. The mediation analysis consists of several

steps. First, the total direct effect is measured. This is the direct effect of the independent variable

on the dependent variable. Without any interference of the mediator. The relation between both

variables does not necessarily have to be significant to continue with the next step (Bollen, 1989;

Hayes, 2017). Second, the effect of the independent variable on the mediating variable is tested.

This effect needs to be significant to establish mediation. Third, the combined effect of the

independent variable and the mediating variable on the dependent variable is tested. This step

confirms whether the mediator has a significant effect on the dependent variable or not, while

controlling for the independent variable. To be in place as a mediator, the mediator must explain

more of the variance in the dependent variable than the independent variable does. If the

independent variable during this third step is not significant, but the mediating variable is, there is

complete mediation. There is an incomplete mediation when both the effect of the independent

variable and the mediating variable on the dependent variable are significant. In this case, there is

another effect of the independent variable on the dependent variable, which does not go through

the included mediator. During these steps bootstrapping is used to become more confident of the

findings. The model is bootstrapped 1.000 times. I.e. The model is recalculated 1.000 times, using

randomly drawn subsamples from the data (Rucker, Preacher, Tormala & Petty, 2011; Rosseel,

2012). The R coding used for different models can be found in Appendix 5.

30

4

4 RESULTS

This chapter presents the results of this study and starts with the respondents’ characteristics, the

preliminary data analysis and the descriptive statistics. Hereafter, the exploratory and confirmatory

factor analyses are presented. Then the structural equation model is conducted, and finally, the

hypotheses are tested.

4.1 CHARACTERISTICS OF RESPONDENTS

In total, 215 employees from four offices and two organizations completed the whole questionnaire.

The characteristics of these respondents can be found in Table 3. The number of males and

females participating were almost equally split, with a total of 108 males and 107 females. Most of

the respondents were working in the large open-plan office, namely a total of 122 (56,7%). Most of

the respondents are between 40-49 years old (37,2%). However, also a large share of the

respondents is aged between 30-39 years and 50-59 years. Furthermore, most respondents work

five days per week, and almost all respondents work at least four days, or as measured between

25-32 or 33-40 hours. Additionally, a total of 125 respondents finished an HBO study and another

34 finished a university degree. Finally, most of the respondents, a total of 51 (23,7%), worked for

more than 25 years for the same organization. On the other hand, a total of 45 employees (20,9%)

just started working for their organization.

Table 3

Characteristics and demographics of the respondents

Questions/options Number of responses Percentage

Office Type (n=215) Cell-Office (1 person) 4 1,9% Shared-room office (2-3 persons) 3 1,4% Small open-plan office (4-9 persons) 16 7,4% Medium open-plan office (10-24 persons) 35 16,3% Large open-plan office (24+ persons) 122 56,7% Flex office 31 14,4% Combi office 4 1,9% Office location (n=215) Location A 162 75,4% Location B 30 14,0% Location C 17 7,9% Location D 6 2,8% Gender (n=215) Male 108 50,2% Female 107 49,8%

CHAPTER 4 – RESULTS

31

Age (n=215) 18-29 years 22 10,2% 30-39 years 44 20,5% 40-49 years 80 37,2% 50-59 years 58 27,0% 60+ years 11 5,1% Educational level Lagere school 0 0,0% Middelbare school 8 3,7% MBO 48 22,3% HBO 125 58,2% WO 34 15,8% Working hours 0-8 hours 0 0,0% 9-16 hours 4 1,9% 17-24 hours 8 3,7% 25-32 hours 52 24,2% 33-40 hours 126 58,6% 40+ hours 25 11,6% Organizational tenure 0-5 years 45 20,9% 6-10 years 30 14,0% 11-15 years 31 14,4% 16-20 years 30 14,0% 21-25 years 28 13,0% 25+ years 51 23,7%

4.2 PRELIMINARY DATA ANALYSIS

Data were prepared and screened using the procedures as recommended by Kline (1997). For

those questionnaires that missed a single answer variable mean imputation was used.

Questionnaires which had more missing answers were deleted from the dataset and excluded from

the analysis. Furthermore, univariate outliers were identified as those with standardized scores

more than three standard deviations from the mean. Additionally, multivariate outliers were

identified using Mahalanobis distance (Hair, Anderson, Tatham & Black, 1998). A total of 22

univariate outliers and one multivariate outlier were found and deleted. The remaining sample used

for analysis numbered 192 cases.

4.3 DESCRIPTIVE STATISTICS

A total of 192 complete questionnaires were included in this study. These 192 respondents

answered a total of 29 items related to the six predefined elements of their work environment. They

scored these items on a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree). Additionally,

they also completed a total of 16 items considering their affective commitment, job satisfaction and

turnover intention. The mean scores of all these items are presented in the fourth column of Table

4. This gives a good overview of how the employees evaluate the perception of different elements

of their work environment and how they evaluate their turnover intention.

Overall, the items for perceived layout and cleanliness of the office environments were scored the

highest. Primarily, the accessibility of the toilets due to the layout (Lay3) and the perceived

cleanliness of the canteen (Cle2) was scored high (the exact description of the items can be found

in Appendix 2). The mean scores for the intention to leave the organization items were low,


32

indicating that on average, the respondents did not consider leaving their organization. The three

highest scores were given to the items ‘I find my work pleasant’ (Sat1), ‘Time flies at work’ (Sat2),

and ‘The layout of our office environment makes it easy to access the toilets’ (Lay3). Suggesting

high levels of job satisfaction and a positive perception of the layout. The scores for the other items

measuring the perception of the layout are also relatively high.

The three lowest scores were given to the three items measuring intention to leave the organization

(Int1, Int2 and Int3). Suggesting an overall low turnover intention among the respondents. The three

items measuring the perception of the work environment with the lowest scores are ‘The amount

of plants in my office environment is pleasant’ (Amb7), ‘I have enough privacy at my workplace’

(Spa2), and ‘The temperature in my work environment is comfortable’ (Amb1). These low scores

suggest some issues with the ambient conditions, as well as with the privacy due to the perception

of the space within the office environment.

Looking at the average evaluation of the different elements of the work environment, the employees

evaluate the perception of the layout of their work environment the highest (M = 4.16, SD = .60).

Thereafter, the perceived cleanliness is evaluated the highest with a mean of 4.05 and a standard

deviation of .57. Overall, the perception of the ambient conditions had the lowest scores (M = 3.05,

SD = .97). The perception of the signs was evaluated slightly better (M = 3.16, SD = .89). The two

other perceived elements of the work environment, ‘space’ and ‘functionality of equipment and

furniture’ were evaluated above average with mean scores of 3.48 (SD = .70) and 3.51 (SD = .95)

respectively.

Overall, the employees evaluated their intention to leave their organization relatively low (M = 2.06,

SD = .93). All three the items measuring the intention to leave the organization were scored very

similarly, with mean scores of 2.02, 2.16 and 2.02. The second item, ‘feel to leave the organization

within 12 months’ was evaluated slightly higher, however, this is only a minor difference.

Table 4

Means, standard deviations, and minimum- and maximum scores for all the items (N=192)

Group

Item

code Measure Mean SD Min Max

Ambient

conditions Amb1 Temperature 2.70 1.14 1 5

Amb2 Air quality 2.85 1.04 1 5

Amb3 Scent 3.39 .73 1 5

Amb4 Music/Sounds 2.77 1.02 1 5

Amb5 Daylight 3.48 .92 1 5

Amb6 Artificial light 3.51 .85 1 5

Amb7 Biophilia 2.65 1.07 1 5

Average 3.05 .97

Cleanliness

Cle1 Toilets 3.94 .75 2 5

Cle2 Canteen 4.11 .47 3 5

Cle3 Stair- and hallways 4.08 .48 3 5

Cle4 Overall 4.05 .59 3 5


33

Average 4.05 .57

Affective

Commitment Com1 Stay with organization 3.58 .88 2 5

Com2 Talk about organization

with others 3.58 .74 1 5

Com3 Share the organization’s

problems 2.77 .87 2 5

Com4 Potential feeling of

attachment 2.76 .88 1 5

Com5 Feel part of the family 3.35 .91 1 5

Com6 Emotional attachment 3.51 .89 1 5

Com7 Personal meaning 3.35 .79 2 5

Com8 Feelings of attachment 3.66 .83 1 5

Average 3.32 .85

Functionality

of equipment and Fun1 Availability of equipment 4.08 .63 2 5

furniture Fun2 Advancement of equipment 3.48 .90 1 5

Fun3 Quality of equipment 3.65 .89 1 5

Fun4 Comfort of furniture 3.14 1.16 1 5

Fun5 Quality of furniture 3.22 1.16 1 5

Fun6 Arrangement of furniture 3.50 .98 1 5

Average 3.51 .95

Intention to Leave

the organization

Int1 Plan to leave within 12

months 2.02 .92 1 5

Int2 Feel to leave within 12

months 2.14 .99 1 5

Int3 Will to leave organization

within 12 months 2.02 .87 1 5

Average 2.06 .93

Layout

Lay1 Reach the reception 4.20 .64 2 5

Lay2 Reach the meeting rooms 4.07 .66 2 5

Lay3 Reach the toilets 4.32 .52 3 5

Lay4 Reach the canteen 4.05 .67 2 5

Lay5 Move through building 4.17 .52 3 5

Average 4.16 .60

Job Satisfaction

Sat1 Pleasantness of work 4.24 .66 2 5

Sat2 Felt duration of the workday 4.34 .55 3 5


34

Sat3 Satisfaction with job 3.94 .67 2 5

Sat4 Enthusiasm 4.09 .56 2 5

Sat5 Satisfaction with work 4.00 .64 2 5

Average 4.12 .62

Signs

Sig1 Quantity 3.16 .90 1 5

Sig2 Visibility 3.12 .89 1 5

Sig3 Understandability 3.23 .86 1 5

Sig4 Usefulness 3.12 .89 1 5

Average 3.16 .89

Space

Spa1 Personal space to work 4.06 .67 2 5

Spa2 Privacy at workspace 2.68 1.06 1 5

Spa3 Close to relevant

colleagues 3.90 .76 2 5

Spa4 Not too close to others 3.27 .31 1 5

Average 3.48 .70

Table 5 show the correlations for the variables and constructs that were included in this study (see

Appendix 4, table 21, 22 and 23 for full correlation tables). The table shows the following significant

correlations between turnover intention and some independent variables:

- Turnover intention and perception of the space, -.148, p < .05; - Turnover intention and job satisfaction, r = -.401, p < .01; - Turnover intention and affective commitment, r = -.457, p < .01.

None of the control variables showed any correlation with turnover intention. However, there were

several significant correlations between the different perceived elements of the work environment.

Additionally, all the perceived elements of the work environment showed a significant correlation

with job satisfaction (p < .01). Furthermore, both the perception of space and the perceived

functionality of the furniture and equipment are significantly correlated to affective commitment (p

< .01). Additionally, the perceived cleanliness is significantly correlated to affective commitment at

p < .05. The effects size of these significant correlations can be considered as medium because

the correlation coefficient is around .30 (Field, 2013).

Other interesting significant correlations are between gender and both perceived ambient

conditions and perceived functionality of equipment and furniture. The significant negative

correlation between gender and perception of ambient conditions (r = -.263, p < .01) suggest that

overall female score their perception of the ambient conditions lower than males. Similarly, the

significant negative correlation between gender and perceived functionality of equipment and

furniture (r = -.201, p < .01) suggest that females are likely to have a lower perception of the

functionality of equipment and furniture. A significant correlation with a small effect size (Field,

2013) exists between the control variable organizational tenure and the perception of the signs (r

= .144, p < .05). This positive correlation suggests that people who work longer for an organization

have a more positive perception of the signs.


35

Table 5

Means, standard deviations and correlations of variables

Mean Std. Dev.

1 2 3 4 5 6 7 8 9 10 11 12 13 14

1 Layout 4.16 0.49

2 Space 3.48 0.57 .226**

3 Ambient 3.05 0.61 .205** .481**

4 Functionality 3.51 0.72 .327** .449** .479**

5 Cleanliness 4.05 0.47 .278** .276** .268** .379**

6 Signs 3.16 0.86 .164* .329** .302** .219** .175*

7 Satisfaction 4.12 0.47 .301** .260** .231** .304** .256** .209**

8 Commitment 3.32 0.55 0.10 .226** 0.13 .228** .177* 0.02 .434**

9 Intention 2.06 0.89 -0.04 -.184* 0.00 -0.02 0.01 -0.01 -.401** -.457**

10 Gender 1.48 0.51 0.01 -0.01 -.263** -.201** -0.10 -0.02 -0.08 -0.04 0.01

11 Age 3.98 1.06 -0.04 -0.12 0.07 0.00 -0.09 0.06 0.01 0.04 0.00 0.03

12 Education 3.85 0.73 0.09 -0.01 0.11 0.11 0.02 0.02 0.04 0.03 -0.12 -.258** -0.10

13 Hours 4.73 0.78 0.03 -0.08 -0.06 -0.03 0.02 0.02 0.08 0.02 -0.06 -.478** -0.06 .263**

14 Tenure 3.52 1.88 0.02 -0.03 -0.01 -0.06 -0.03 .144* 0.09 0.13 -0.05 .240** .596** -.247** -.193**

Notes: N=192. ** Correlation significant at the 0.01 level (2-tailed). * Correlation significant at the 0.05 level (2-tailed).


36

Figure 4

Hypothesized model


37

4.4 EXPLORATORY FACTOR ANALYSIS

The hypothesized model, as can be seen in Figure 4, consist of nine latent (unobserved) variables.

These variables are labelled as Layout (Lay), Space (Spa), Ambient conditions (Amb), Functionality

of equipment and furniture (Fun), Cleanliness (Cle), Signs (Sig), Job Satisfaction (Sat), Affective

Commitment (Com), and Turnover Intention (Int). The first six variables are exogenous

(independent), and the last three are endogenous (dependent). The model consists of four

constructs, namely the perception of the work environment, job satisfaction, affective commitment

and intention to leave the organization. These four constructs were based on previous research to

enhance validity. Principal component analysis (PCA) is conducted with orthogonal rotations

(varimax) on the 16 items for job satisfaction, affective commitment, and turnover intention, and for

the 29 items measuring the perception of the work environment (Appendix 4, Table 24 and 25).

The Kaiser-Meyer-Olkin measure verifies the sampling adequacy for the analysis, KMO = .80

(‘good/great’ according to Field, 2013). Bartlett’s test of sphericity χ² (595) = 4731.38, p < .001,

indicated that correlations between items is sufficiently large for PCA (Table 6).

Table 6

KMO and Bartlett’s Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy.

0.80

Bartlett’s Test of Sphericity

Approx. Chi-Square 4731.38 df 595 Sig. 0.00

The maximum likelihood factor analysis with a cut-off point of .50 and the Kaiser’s criterion of

eigenvalues greater than 1 (see Field, 2013) yielded a ten-factor solution as the best fit for the data,

accounting for 77.89% of the variance. Some items were excluded pairwise to purify the scales.

Items were excluded if their loadings were too low or if they were not loading on a single specific

factor (Koufteros, 1999). The results of this EFA are presented in Table 7 and 8.

The original factor ‘ambient conditions’ was split in two different factors: ‘indoor air quality’ and ‘air

temperature’ loaded on one factor, which can be called ‘indoor air’, with an eigenvalue of 1.12,

accounting for 3,38% of the variance. The item ‘scent’ was deleted to purify the newly created

factor. Furthermore, the items ‘artificial lighting’ and ‘natural lighting’ loaded on another factor with

an eigenvalue of 1.00, accounting for 3.01% of the variance. This factor is labelled as ‘lighting’. The

hypothesized factor ‘functionality of equipment and furniture’ was also split into two factors. The

items ‘availability of equipment’, ‘advancement of equipment’, and ‘quality of equipment’ loaded on

one factor with an eigenvalue of 1.48, accounting for 4.50% of the variance. This factor can be

labelled as ‘equipment’. The items ‘comfort of furniture’, ‘quality of furniture’, and ‘arrangement of

furniture’ loaded on another factor with an eigenvalue of 1.83, accounting for 5.53% of the variance.

This factor is labelled as ‘furniture’. The two items, ‘privacy at workspace’ and ‘not too close to

others’, that were supposed to measure the perception of the space also loaded on this newly

created factor ‘furniture’. However, after inspection of both items, it was decided that both did not

measure the same underlying factor as the three previously mentioned items. Thus, these two

items were excluded from further analysis.


38

Table 7

Factor Analysis: Measurement Model

Factor

Dimension Items

1 2 3 4 5 6 7 8 9 10

Pleasantness of work

0.579

Job

satisfaction

Enthusiasm

0.844

Satisfaction with work

0.846

Feel part of the ‘family’

0.747

Affective

commitment

Emotional attachment

0.847

Personal meaning

0.717

Feelings of attachment

0.667

Plan to leave

0.943

Intention to

leave the

organization

Feel to leave

0.902

Will to leave

0.942

Reach reception

0.775

Layout

Reach meeting rooms

0.796

Reach toilets

0.763

Reach canteen

0.780

Move through building

0.848

Air temperature

0.861

Air quality Air quality

0.869

Daylight

0.880

Lighting Artificial light

0.868

Availability of equipment

0.831

Equipment Advancement

0.765

Quality of equipment

0.747

Comfort of furniture

0.820

Furniture

Quality of furniture

0.832

Arrangement of furniture

0.772

Canteen

0.842

Cleanliness

Stair- and hallways

0.879

Overall

0.783

Quantity 0.954

Signs

Visibility 0.964

Understandability 0.943

Usefulness

0.966

Extraction Method: Principal Component Analysis. 10 components extracted.


39

Table 8

Total Variance Explained: Measurement Model

4.5 CONFIRMATORY FACTOR ANALYSIS

A measurement model was estimated, using the maximum likelihood estimation method. This

estimation was done to refine all measures for the structural model. This measurement model

consists of the ten factors and 32 items, which resulted from the EFA (Figure 5). The results of this

factor analysis showed excellent fit to the data (χ2 = 545,17 df = 419 p < .001, χ2 /df = 1,301, root-

mean-square error of approximation [RMSEA] = .040, comparative fit index [CFI] = .971, goodness

of fit index [GFI] = .989, standardized root-mean-square residual [SRMR] = .048, Tucker-Lewis

Index [TLI] = .965). All of these fit measures satisfy the fit thresholds as defined by Schreiber and

colleagues (2006) as can be seen in Table 9.

Table 9

Fit measures

Fit measure The general rule for acceptable data (Schreiber et al., 2006) Outcome CFA

χ2 /df ≤ 2 1,301 RMSEA < .06 .040 SRMR ≤ .08 .048 TLI ≥ .95 .965 GFI ≥ .95 .989 CFI ≥ .95 .971

Initial Eigenvalues

Extraction Sums of Squared

Loadings

Rotation Sums of Squared

Loadings

Component Total

% of

Variance

Cumul

ative

%

Total

% of

Variance

Cumul

ative

%

Total

% of

Variance

Cumul

ative

%

1 7.14 21.65 21.65

7.14 21.65 21.65

3.85 11.68 11.68

2 4.06 12.31 33.96

4.06 12.31 33.96

3.43 10.38 22.06

3 3.22 9.74 43.70

3.22 9.74 43.70

2.87 8.70 30.76

4 2.54 7.69 51.39

2.54 7.69 51.39

2.84 8.62 39.38

5 2.02 6.11 57.50

2.02 6.11 57.50

2.54 7.69 47.07

6 1.83 5.53 63.03

1.83 5.53 63.03

2.45 7.41 54.48

7 1.48 4.50 67.53

1.48 4.50 67.53

2.20 6.66 61.14

8 1.31 3.97 71.50

1.31 3.97 71.50

2.07 6.27 67.41

9 1.12 3.38 74.88

1.12 3.38 74.88

1.78 5.41 72.82

10 1.00 3.01 77.89

1.00 3.01 77.89

1.67 5.07 77.89

11 0.74 2.25 80.14

Extraction Method: Principal Component Analysis


40

Figure 5

Measurement model


41

For the included factors, the Cronbach’s alpha estimates were between .776 and .979 and can be

considered as reliable (Table 10). A factor is considered as reliable as there Cronbach’s Alpha

score is above .70 (Nunnally, 1978).

Furthermore, the validity of the construct was tested using the factors loadings within the

constructs, average variance extracted (AVE) and the correlation between the constructs. The

outcomes of these tests can be found in Table 10. As can be seen in this table, all standardized

factors loadings were reasonably high, ranging from .579 to .966. These loadings indicate that the

constructs have convergent validity (Anderson & Gerbing, 1984). Additionally, AVE for every factor

exceeds the cut-off of .50, as suggested by Hair et al. (1998). Moreover, all AVE values were higher

than the squared correlation between constructs (Appendix 4, Table 26 and 27), indicating

adequate discriminant validity for all factors.

Regarding the construct validity, the composite reliability (CR) was calculated for each construct.

The CR scores for each construct exceeded the desired threshold of .70 (Bagozzi and Yi, 1988).

Therefore, construct validity is assured.

Table 10

Validity and reliability measures

Factor Item code

Item total

correlation

Standardized factor

loadings AVE CR

Cronbach's

Alpha

Layout

Lay1 0.647 0.775 0.629 0.868 0.865

Lay2 0.691 0.796

Lay3 0.675 0.763

Lay4 0.674 0.780

Lay5 0.805 0.848

Indoor Air Amb1 0.731 0.861 0.748 0.809 0.843

Amb2 0.731 0.869

Equipment

Fun1 0.586 0.831 0.611 0.776 0.806

Fun2 0.729 0.765

Fun3 0.696 0.747

Lighting Amb5 0.636 0.880 0.764 0.774 0.776

Amb6 0.636 0.868

Furniture

Fun4 0.822 0.820 0.654 0.831 0.866

Fun5 0.844 0.832

Fun6 0.594 0.772

Cleanliness

Cle2 0.700 0.842 0.698 0.836 0.841

Cle3 0.779 0.879

Cle4 0.666 0.783

Signs

Sig1 0.936 0.954 0.916 0.977 0.979

Sig2 0.965 0.964

Sig3 0.925 0.944

Sig4 0.961 0.966

Job

satisfaction

Sat1 0.499 0.579 0.588 0.752 0.794

Sat4 0.679 0.844

Sat5 0.760 0.846


42

4.6 STRUCTURAL EQUATION MODEL

Structural equation modelling was conducted using the maximum likelihood estimation method to

assess the proposed conceptual model. The outcomes of this test revealed that the model fits the

data reasonably well (χ2 = 532,69 df = 425, p < .001, χ2 /df = 1.253, RMSEA = .036, CFI = .975,

GFI = .990, SRMR = .036, TLI = .971). All of these fit measures satisfy the fit thresholds as defined

by Schreiber and colleagues (2006) as can be seen in Table 11.

Table 11

Fit measures structural model

Fit measure

The general rule for acceptable data

(Schreiber et al., 2006)

Outcome CFA

χ2 /df ≤ 2 1,253 RMSEA < .06 .036 SRMR ≤ .08 .052 TLI ≥ .95 .971 GFI ≥ .95 .990 CFI ≥ .95 .975

This proposed model was compared with an alternative model. In this alternative model, the direct

relationships between the factors concerning the work environment and turnover intention were

deleted. The results showed that this model also fits the data reasonably well (χ2 = 545,66 df =

432, p < .001, χ2 /df = 1.263, RMSEA = .037, CFI = .974, GFI = .990, SRMR = .058, TLI = .970).

This model did not show any significant differences with the hypothesized structural model ((∆χ2 =

12.77, ∆df = 7, p > .05). Thus, deleting these direct paths from the six factors of the work

environment to employee turnover intention did not significantly change the model fit. Therefore,

the proposed model was kept for further analyses.

An SEM analysis was conduct based on the proposed model to test the hypotheses. The parameter

estimates between the variables in the model were estimated, and step by step, all insignificant

paths (p>.05) were eliminated. During each step, the least significant path was omitted. Eventually,

this resulted in the deletion of 11 paths. The model fit of this most parsimonious model was: χ2 =

565,67 df = 436, p < .001, χ2 /df = 1.297, RMSEA = .039, CFI = .970, GFI = .990, SRMR = .065,

TLI = .966. Figure 6 shows this parsimonious model with all the included significant standardized

regression coefficients.

Affective

commitment

Com5 0.565 0.747 0.559 0.757 0.779

Com6 0.654 0.847

Com7 0.603 0.717

Com8 0.524 0.667

Turnover

intention

Int1 0.940 0.943 0.863 0.949 0.960

Int2 0.898 0.902

Int3 0.917 0.942


43

Figure 6

Results Structural Equation Model

Note: the figure shows a significant positive relation between the perception of Equipment and Intention to Leave. This relation contradicts with the

hypothesized negative relation between both constructs.

** p < .01


44

Table 12

Percentage of variance explained in structural model

Variable R2

Turnover intention .246 Job Satisfaction .173 Affective Commitment .104

Overall, the model portrayed in Figure 6 explains 24.6 percent in variance in employee turnover

intention, 17.3 percent of variance in job satisfaction, and 10.4 percent of variance in affective

commitment (Table 12).

4.7 HYPOTHESES TESTING

The formulated hypotheses were tested based on the proposed structural model (Figure 5, p.

40). Some initial hypotheses needed to be deleted or reformulated and split after the factor

analysis. Hypotheses 2, 10a and 10b, which hypothesized that the perception of space was

related to turnover intention (2) and that this relation is mediated by job satisfaction (10a) and

affective commitment (10b), were deleted. Because the factor perception of space was not

included as a factor in the proposed structural model.

Furthermore, hypotheses 3, 11a and 11b, which were related to the perception of ambient

conditions, were split into six new hypotheses (3a, 3b, 11a1, 11a2, 11b1 and 11b2). Hypotheses

3a, 11a1 and 11b1 hypothesize that there is a negative relation between the perception of the

indoor air and turnover intention (3a), and that this relation is mediated by job satisfaction (11a1)

and affective commitment (11b1). Whereas, hypothesis 3b, 11a2 and 11b2 hypothesize that

there is a relation between the perceived lighting and turnover intention (3b), and that this

relation is mediated by job satisfaction (11a2) and affective commitment (11b2).

Similarly, the three hypotheses (5, 13a and 13b) related to the perceived functionality of

equipment and furniture were split into six new hypotheses (5a, 5b, 13a1, 13a2, 13b1 and 13b2).

Hypotheses 5a, 13a1 and 13b1 hypothesize that there is a negative relation between the

perception of equipment and turnover intention (5a), and that this relation is mediated by job

satisfaction (13a1) and affective commitment (13b1). Whereas, hypothesis 5b, 13a2 and 13b2

hypothesize that there is a relation between the perception of furniture and turnover intention

(5b), and that this relation is mediated by job satisfaction (13a2) and affective commitment

(13b2).

The parameter estimates, which were assessed using the maximum likelihood estimation

method, can be found in Table 13 and 14. These tables also indicate whether the associated

hypotheses are supported. The results of the structural equation model, with the significant

standardized path coefficients, are presented in Figure 6 on the previous page.


45

Table 13

Results hypotheses 1-6

Hypothesis Hypothesized path z p Result

1 Perception of layout

→ Turnover intention .025 .257 .797

Not supported

2 Perception of space

→ Turnover intention - - - -

3a Perception of indoor air

→ Turnover intention (.011) (.096) .924

Not supported

3b Perception of Lighting


Not supported

4 Perception of cleanliness

→ Turnover intention .150 1.754 .079

Not supported

5a Perception of equipment

→ Turnover intention .258 2.718 .007

Not supported

5b Perception of furniture


Not supported

6 Perception of signs

→ Affective Commitment .000 .002 .999

Not supported

Table 14

Results hypothesis 7 and 8


7 Job satisfaction

→ Turnover intention (.409) (4.605) .000 Supported

8 Affective commitment

→ Intention to Leave (.303) (3.207) .001 Supported

Hypothesis 7 was supported, indicating that employee turnover intention was a negative

function of job satisfaction. The relationship between job satisfaction and turnover intention was

negative and significant ( = -.409, p < .01). Additionally, hypothesis 8 was supported, indicating

that turnover intention was a negative function of affective commitment. There is a significant

negative relationship between affective commitment and turnover intention ( = -.303, p < .01)

4.7.1 Mediation analysis

A mediation analysis is conducted to test the formulated mediation hypotheses. This analysis

tests whether job satisfaction and/or affective commitment mediates the relationship between

the perception of the different defined elements of the work environment and employee turnover

intention. As the structural equation model in Figure 6 (p.43) shows, there are two perceived

elements of the work environment that have a significant relationship with one or more of the

potential mediators. The perception of equipment is significantly related to both job satisfaction

( = .301, p < .01) and affective commitment ( = .398, p < .05). Additionally, the perception of

the layout shows a significant relation to job satisfaction ( = .274, p < .01). These are the paths

labelled with a1, a2 and a3 in Table 15, 16 and 17. These paths need to be significant in order

to have a potential mediating effect. All the other perceived elements of the work environment

are excluded from this mediation analysis, whereas they did not have a significant relationship

with one or more of the mediators (Appendix 4, Table 28 and 29).


46

Table 15

Job satisfaction as mediator between perception of the layout and turnover intention

Relation Label se z p CI. Lower

CI. Upper

Layout →

Job Satisfaction a1 .274 .086 3.190 .001 .124 .467

Job Satisfaction →

Turnover intention b1 (1.167) .230 (5.083) .000 (1.679) (.763)

Layout →

Turnover intention c1 .251 .189 1.328 .184 (.089) .624

Layout →


Turnover intention

a1*b1 (.320) .101 (3.174) .002 (.549) (.151)

Layout +


Turnover intention

Total (.069) .177 (.390) .697 (.416) .282

Table 16

Job satisfaction as mediator between perception of equipment and turnover intention


CI. Upper

Equipment →

Job satisfaction a2 .301 .109 2.756 .006 .125 .562

Job satisfaction →

Intention b2 (1.218) .216 (5.650) .000 (1.680) (.827)

Equipment →

Turnover intention c2 .426 .223 1.912 .056 .076 .967

Equipment →


Turnover intention

a2*b2 (.367) .140 (2.625) .009 (.725) (.155)

Equipment +


Turnover intention

Total .059 .211 .282 .778 (.317) .492

Table 17

Affective commitment as mediator between perception of equipment and turnover intention


CI. Upper

Equipment →

Affective commitment a3 .398 .160 2.480 .013 .113 .742

Affective commitment

→ Turnover intention b3 (.633) .145 (4.359) .000 (.918) (.381)

Equipment →

Turnover intention c3 .293 .218 1.342 .180 (.066) .779

Equipment →

Affective commitment

→ Turnover intention

a3*b3 (.252) .115 (2.193) .028 (.524) (.073)

Equipment + Affective commitment


Total .041 .214 .191 .848 (.335) .499


47

Layout did not show a significant direct relation to turnover intention ( = .189, p = .184). On

the other hand, equipment did show a significant direct relation to turnover intention ( = .426,

p < .01). This direct path is labelled as ‘c’ in the Tables 15 and 17. This direct effect does not

necessarily have to be significant to have a mediation effect. The third step in mediation

analysis consists of the combined effect of the independent variable and mediating variable on

the dependent variable. This resulted in a significant combined effect of layout and job

satisfaction on turnover intention ( = -.320, p < .01). This effect is labelled as a1*b1 in Table

15. Similarly, the combined effect of equipment and affective commitment on turnover intention,

which is labelled as a2*b2, is significant ( = -.320, p < .01). Furthermore, a significant combined

effect of equipment and affective commitment on turnover intention was found ( = -.252, p <

.05). As can be seen in Table 17, this path is labelled a3*b3.

To summarize, the effect of perception of equipment on employee turnover intention was

completely mediated via both job satisfaction (hypothesis 9a) and affective commitment

(hypothesis 13a1). As can be seen in Table 18 and 19, the regression coefficient between both

mediators and employee turnover intention was significant. The indirect effect via job

satisfaction was -.367 and the indirect via affective commitment was -.252. The significance of

this indirect effect was tested using bootstrapping procedures. Unstandardized indirect effects

were computed for each 1.000 bootstrapped samples. The 95% confidence interval was

computed by determining the indirect effects at the 2.5th and 97.5th percentiles. This resulted

for the indirect effect of perception of equipment on turnover intention, via job satisfaction, in a

confidence interval (ci) that ranged from -.725 and .155. This indirect effect of = -.367 was

significant (p < .01). Similarly, the indirect effect of perception of equipment on turnover intention

via affective commitment was found to be significant ( = -.252, p < 0.05). Finally, the perception

of the layout had a significant indirect effect on turnover intention via the mediating variable job

satisfaction ( = -.320, p < 0.01). This is also a complete mediation, because there is no

significant relation between the perception of the layout and turnover intention, but there is a

significant indirect effect via job satisfaction. This result supports hypothesis 13b1. These

findings are summarized in Table 18 and 19. These tables also indicate whether the associated

hypotheses are supported.

Table 18

Job satisfaction as mediator: hypotheses 9a, 10a, 11a1, 11a2 12a, 13a1, 13a2 and 14a


9a Perception of layout + Job satisfaction


10a Perception of space + Job satisfaction


11a1 Perception of indoor air + Job satisfaction

→ Turnover intention (.066) (1.493) .136

Not supported

11a2 Perception of Lighting + Job satisfaction


Not supported

12a Perception of cleanliness + Job satisfaction


Not supported

13a1 Perception of equipment + Job satisfaction


13a2 Perception of furniture + Job satisfaction

(.078) (1.851) .064 Not supported


48


14a Perception of signs + Job satisfaction

→ Affective Commitment (.035) (1.134) .257

Not supported

Table 19

Affective commitment as mediator: hypotheses 9b, 10b, 11b1, 11b2 12b, 13b1, 13b2 and 14b


9b Perception of layout + Affective commitment


Not supported

10b Perception of space + Affective commitment


11b1 Perception of indoor air + Affective commitment


Not supported

11b2 Perception of Lighting + Affective commitment


Not supported

12b Perception of cleanliness + Affective commitment


Not supported

13b1 Perception of equipment + Affective commitment


13b2 Perception of furniture + Affective commitment


Not supported

14b Perception of signs + Affective commitment


Not supported

Besides hypothesis 7 and 8, the three other hypotheses that were supported are hypothesis

9a, 13a1 and 13b1. Hypothesis 7 concerned the mediating effect of job satisfaction in the

relationship between the perception of layout and turnover intention. The mediation analysis

did confirm this hypothesized mediating effect of job satisfaction ( = -.320, p < .01). Similarly,

hypothesis 13a1 is confirmed: job satisfaction mediates the relationship between perception of

the equipment and turnover intention ( = -.367, p < .01). Finally, affective commitment

mediates the relationship between the perception of the equipment and turnover intention ( =

-.252, p < .01). Based on this finding, hypothesis 13b1 is confirmed.

The other hypotheses were all rejected. No significant mediation effects were found between

the perception of the other defined elements of the work environment and turnover intention.

The results did show a significant relation between the perceived functionality of the equipment

and turnover intention ( = .208, p < .01), however, this relation was hypothesized (5a) to be

negative but turned out to be positive. Therefore, this hypothesis is not supported.

49

5

5 CONCLUSION & DISCUSSION

This chapter starts with the conclusion, whereafter in the second part the results are discussed.

In this discussion, the findings are discussed and are compared with the literature. Additionally,

I will reflect on some notable results. Thereafter, the limitations of this study are presented.

Finally, some recommendations and implications are given.

5.1 CONCLUSION

This study aims to shed a light on the relationship between the perception of elements of the

work environment and employee turnover intention to provide theoretical insights and give

recommendations. To get a clear and structured solution to this objective, seven sub-research

questions are formulated, which are answered in the theoretical and empirical parts of this

study. Using the findings presented in the theoretical framework and the results of the empirical

research, the next paragraph answers the main research question. The main research question

is:

To what extent is the perception of elements of the work environment related to

employee turnover intention?

To answer this question, this study looked into the direct and indirect effects of the perception

of different elements of the work environment on employee turnover intention. Based on the

results, there are no direct effects of the perception of the different elements of the work

environment and turnover intention. Hence, no support was found for the hypotheses 1-6.

Thus, it can be concluded that employee turnover intention is not directly related to the

perception of the defined elements of the work environment, including the perception of the

layout, indoor air, lighting, cleanliness, equipment, furniture, and signs.

In line with previous studies, and the related hypotheses in this study, job satisfaction fully

mediates the relationship between the perception of the layout and turnover intention ( = -

.320, p < .01). This supports hypothesis 9a. Subsequently, job satisfaction also fully mediates

the relationship between the perception of the equipment and turnover intention ( = -.367, p <

.01). Thereby supporting hypothesis 13a1. Furthermore, hypothesis 13b1 is supported. The

relationship between the perception of the equipment and turnover intention is found to be

mediated by employee affective commitment ( = -.252, p < .01).

These findings suggest that the perception of elements of the work environment does not have

any direct relationship to employee turnover intention. Or in other words, altering the elements

of the work environment that are included in this study will not likely result in other levels of

turnover intention. However, the perception of the equipment and the layout do have a positive

effect on employee job satisfaction, and higher levels of job satisfaction results in a lower

turnover intention. A positive perception of the equipment will also result in higher levels of

affective commitment, which results in a lower turnover intention. Therefore, it would be

interesting for organizations to focus on the layout of their offices and the equipment they

provide their employees. A layout that makes it easy for employees to get to the places where

CHAPTER 5 – CONCLUSION & DISCUSSION

50

they need to be, will be perceived as more positive. Additionally, the availability, quality and

advancement of the equipment are important for a more positive perception of the equipment.

In conclusion, this study explored the relationships between the perception of several elements

of the work environment and employee turnover intention. It gained and understanding of this

relationship and added to the literature. Based on the findings, it can be concluded that the

equipment in the office environment plays an important role. A more positive perception of the

equipment results in higher levels of employee job satisfaction and affective commitment. The

results of this study do confirm the previously found negative relationship between both job

satisfaction and turnover intention, and affective commitment and turnover intention. In other

words, higher levels of employee job satisfaction and affective commitment result in a lower

employee turnover intention. Additionally, the results show a mediating effect of job satisfaction

in the relationship between both the perception of the layout and turnover intention, and the

perception of the equipment and turnover intention. These findings suggest that both the

perception of the layout and the equipment have a significant positive effect on job satisfaction

and in turn job satisfaction has a significant negative effect on turnover intention. Additionally,

affective commitment mediates the relationship between the perception of the equipment and

turnover intention. Overall, the equipment can be considered as a very important element in

the work environment because of its positive effects on both employee job satisfaction and

affective commitment. Resulting in lower employee turnover intention via these two mediators.

5.2 DISCUSSION

The purpose of this discussion is to evaluate the research process and the results of this study

in light of existing literature. Additionally, some personal findings and ideas are put forward. The

main objectives of this explorative study were to provide empirical insights into the relationship

between the perception of elements of the work environment and employee turnover intention

and to give recommendations on the design of elements of the work environment to decrease


To reach these objectives, this study investigated the relationships between the perception of

several elements of the work environment and job satisfaction, affective commitment, and

turnover intention. More specifically, based on the theoretical part it was hypothesized that a

more positive perception of the layout, space, ambient conditions, cleanliness, signs, or the

functionality of the equipment and furniture, result in a lower turnover intention. Additionally, it

was expected that this relationship between the perception of elements of the work environment

and turnover intention was mediated by job satisfaction and affective commitment. For

example, employees that perceive their equipment as more positive will have higher job

satisfaction, resulting in a lower turnover intention.

However, the results show that the effects of the perception of the elements of the work

environment on employee turnover intention are small and insignificant. Based on the stimuli-

organism-response model of Mehrabian and Russell (1974) it was expected that the perceptible

elements of the environment can function as a stimulus, resulting in a behavioural response via

an internal emotional response, including certain intentions. That intention determines

behaviour is explained by the Theory of reasoned action (Ajzen & Madden, 1986). However,

none of the perceived elements showed to have a significant effect on turnover intention in this

study. This might be caused by the fact that this study focuses on the perception of individual

elements of the work environment instead of the work environment as a whole. As Bitner (1992)

explains in her study, it is the holistic perception of the environment that functions as the stimuli

that result in an internal and behavioural response. Several studies found that the perception

of the work environment affects employee turnover intention (e.g., Santoni & Harahap, 2018;

Kurniawaty, Ramly & Ramlawati, 2019). These studies combined the perception of multiple

elements of the work environment in one construct and some studies even included non-


51

physical elements of the work environment in their work environment construct. These studies

showed that the combined perception of multiple elements in one factor affects employee

turnover intention. As explained, this explorative study tested for the effect of individual

elements, to get to the root causes of turnover intention caused by the physical work

environment, based on the stimuli-organism-response model (Mehrabian & Russell, 1974). But

based on the results, it can be concluded that none of the elements included in this studied has

a significant direct effect on turnover intention.

A contribution of this study is that it confirms the previously found negative relationships

between both job satisfaction and turnover intention, and affective commitment and turnover

intention. In other words, employees who have higher job satisfaction or affective commitment

will have a lower turnover intention. These findings are in line with the findings of Aydogdu and

Asikgil (2011), and once again emphasize the importance of both job satisfaction and affective

commitment in relationship to employee turnover intention.

Other notable findings are the mediating effects of both job satisfaction and affective

commitment in the relationship between the perception of the equipment and turnover intention.

Additionally, job satisfaction mediates the relationship between the perception of the layout and

employee turnover intention. These findings tie well with previous studies wherein the

perception of elements of the work environment has been related to employee job satisfaction

and affective commitment (e.g., McGuire & McLaren, 2009; Leder et al., 2016) and the

expected relationship between these two variables and turnover intention (Aydogdu & Asikgil,

2011). However, when comparing our results to those of older studies, it must be pointed out

that in older studies there was not tested for mediating effects. Both described relationships

were found in separate studies. This study combines both previously found relationships and

the results provide evidence for these hypothesized mediating effects.

Nevertheless, mediating effects were only found in these three relationships. The perception of

the other elements of the work environment did not have an indirect effect via one of the two

included mediating variables: job satisfaction and affective commitment. Although the

employees are less likely to leave the organization when they have higher levels of job

satisfaction and affective commitment. It could therefore be argued that there are other factors

than the physical work environment that result in higher levels of job satisfaction and affective

commitment. Examples that are given in other studies are organizational culture (Lund, 2003),

salary (Parvin & Kabir, 2011) and managerial style (Lok & Crawford, 2004). Further research

should investigate if any other factors function as a mediator in the relationship between the

perception of the work environment and turnover intention. One could think of other emotional

internal organisms as a response to the environmental stimuli as mediating variables. One

could speculate that, for example, employee wellbeing or organizational identification can

mediate the relationship between the perception of elements of the work environment and


Another remarkable finding is that perception of the equipment is positively related to turnover

intention. This finding suggests that when employees perceive the equipment as more positive,

they have a higher turnover intention. Based on the literature framework, it was hypothesized

that a more positive perception of the equipment would result in a lower turnover intention

(Kurniawaty, Ramly & Ramlawati, 2019). However, the results show exactly the opposite. A

potential explanation for this finding could be that employees who perceive the equipment as

more positive might feel that they lose autonomy in their job. Whereas a positive evaluation of

the equipment indicates that there is a diverse range of advanced equipment that is of high

quality. Employees might get the feeling that the equipment is starting to do their work, resulting

in a loss of autonomy. Brey argued in his study in 1999 that new technologies in the work

environment can threaten employee autonomy. This feeling of loss of autonomy has been


52

linked to turnover intention in several studies (e.g., Kim & Stoner, 2008; Dysvik & Kuvaas, 2013;

Galletta, Portoghese & Battistelli, 2011). This study did not include feeling of autonomy as a

variable. But for future studies, this would be an interesting variable to include and see whether

similar results are found.

On the other hand, this study also showed the importance of the availability, advancement and

quality of the equipment. The perception of the equipment showed to be positively related to

both employee job satisfaction and affective commitment. This is in line with the findings of

previous studies. As van Meel (2000) and Vischer (2008) explain in their study, the

developments in information technology, resulting in better equipment, allowed employees to

work anytime and anywhere. Equipment that facilitates performance and goal accomplishment

will be perceived as more positive. Employees can feel supported in their work by the

functionality of their equipment (Bitner, 1992). These findings suggest a fine line considering

the perception of the equipment. Equipment that is perceived as positive results in higher levels

of job satisfaction and affective commitment, which will lead to lower turnover intention. But

equipment that is perceived as positive is probably taking away job autonomy, resulting in

higher turnover intention. Future studies should confirm whether job autonomy mediates this

relationship between the perception of the equipment and turnover intention.

This suggestion of adding job autonomy to the model is in line with another outcome that

deserves some attention. The outcome of concern is the percentage of variance explained in

turnover intention by the structural model (around 25 percent). This score suggests that other

factors affect the amount of turnover intention that the employees reported. As was mentioned

before, several studies investigated the antecedents of turnover intention. These studies found

how several demographic aspects such as age, tenure, gender and educational level influence

employee turnover intention (Arnold & Feldman, 1982). But besides the demographic factors,

which are included in this study, researchers found several other factors that influence

employee turnover intention. Examples of these factors are the organizational structure and

climate (Hong & Kaur, 2008), the reward system (Jauhar, Ting, Rahim & Fareen, 2017), the

managerial style (Dixon & Hart, 2010) or the human resource practices (Garcia-Chas, Neira-

Fontela & Castro-Casal, 2014). Based on the study of Das and Baruah (2013), job satisfaction

and affective commitment were considered as the most important factors influencing turnover

intention and therefore included in this study. But as the total variance explained suggests,

there are other factors that explain the variation in turnover intention. This study did not include

any other factors, whereas this would result in many constructs about which data needed to be

collected with the questionnaire. A selection was made to keep the length of the questionnaire

short to minimize the response bias caused by boredom and monotony (Schmitt, Ford & Stults,

1986). This selection included the perceptible elements of the work environment and the

mediating variables that were considered the most important based on the literature framework.

Another interesting note to make is that this study again did not find any significant relationship

between the perception of the signs and either one of the outcome variables. Previous studies

already mentioned that the effects of signs are minimal. Most of the studies focus on the other

two dimensions of Bitner (1992). Signs were included in this study as an element, despite these

suggestions of the limited effect, because it is considered as an important part of the work

environment according to Bitner (1992). Once again, no significant effect of the perception of

signs on emotional- and behavioural outcome variables was found. Based on this finding man

can question the importance of signs in a work environment because it remains unclear to what

extent it affects employees.

Finally, it is interesting to note that none of the included control variables had a significant

relationship with turnover intention. Based on previous studies, it was expected that females

would score higher on turnover intention than males. This expectation was based on the meta-


53

review of Arnold and Feldman (1982). The results of this study however showed that there was

no difference in turnover intention between males and females. Both groups were well

represented in the sample. The review of Arnold and Feldman might be based on outdated

results. Future studies should point out whether the difference in turnover intention between

males and females have diminished. Additionally, age, educational level and organizational

tenure did not have any significant effect on turnover intention. Concerning the educational

level, the current sample might not have a sufficient spread. Most of the employees included in

the sample have the same educational level. Additionally, it could be that the control variables

age and organizational tenure are of less importance in the financial sector as a predictor of

turnover intention. But the insignificance of these control variables does not necessarily impact

the conclusions based the test for the relationship between the perception of elements of the

work environment and employee turnover intention.

5.3 LIMITATIONS

A first limitation of this study is that there is not tested if there are any differences in perception

between the four different offices that participated in this study. However, most of the

employees (80.4%) works in an open-plan type of office, in which most of the environmental

elements will be, to a certain extent, the same.

Another potential limitation could be that personality traits of the respondents are not included

in the analysis. As the study of Jeswani and Dave (2012) showed, some personality traits can

have a significant impact on the intention to leave the organization. Similar results were found

by Zimmerman (2008). This effect is not controlled for in this study and might be interesting to

include in further study. However, the control variables that were included in the study did not

show any significant relationship with the dependent variable, although the fact that this was

expected. Including personality traits as control variable does not necessarily have to result in

a significant effect.

Next to that, the result might be biased to some extent due to the fact that the respondents

have given the socially desired answers. Especially, for the questions about their intention to

leave, but also, about their commitment and satisfaction. Various steps have been taken to

minimize this bias. Anonymous processing of the results was promised to all respondents at

the start of the questionnaire. Additionally, the more personal demographic questions were

asked at the end of the questionnaire, after the respondents had already filled in the questions

about their intention to leave the organization.

A fourth potential limitation might be that a single questionnaire containing self-reported

measures is used to gather the data. This might result in a distortion in the tested relationship

among the constructs by the effect of common method bias (Spector et al., 2019). Harman’s

single factor score was conducted to test whether common method bias was a concern in this

study. In this test, all items that measure the latent variables are loaded on a single factor.

Common method bias does not affect the data if the total explained variance of this one factor

is less than 50% (Podsakoff, MacKenzie, Podsakoff, 2012). The results of this test show that

the items measuring the latent variables in this study explain 19.45% of the total variance.

Therefore, common method bias is not a concern in this study. However, future studies could

also focus on testing the model using other sources of information and focus on more objective

behavioural outcomes, such as actual turnover levels.

Fifth, this study used a questionnaire which was distributed among local offices of firms active

in the financial sector, to circumvent any potential differences between sectors. One limitation

of this method however is that it might impact the generalizability of this study, because it is

uncertain whether the findings hold for other sectors. Future studies could strengthen the

findings in this study by addressing a larger sample. This would result in a more reliable and

stable view. Additionally, to make the findings more generalizable, the sample could exist of

organizations from multiple sectors. This would help to filter out potential extraneous variables

that have an effect on employee turnover intention.


54

A final limitation of this study also concerns the generalizability. The sampling method used for

this study was convenience sampling. This resulted in an unequal distribution of the

respondents among educational level and office types. The sample used in this study over-

represents the open-plan type of office and employees with HBO as their highest educational

level. Interesting would be to see whether the results are the same for lower educated

employees or in other office types.

5.4 IMPLICATIONS & RECOMMENDATIONS

Based on the findings of the literature study and the results of the empirical study several

managerial implications and recommendations for further research can be given.

First, as far as the researcher knows, the effects of the perception of individual elements of the

work on employee turnover intention have been barely researched. Most studies researched

the relationship between the perception of the work environment as a whole and employee

turnover intention. These studies also included elements of the non-physical work environment

in their work environment construct. Therefore, this study provides new and additional insights

into the field of research related to the physical work environment. Overall, this study provides

evidence for the relationship between the perception of the equipment and turnover intention

via both mediating variables job satisfaction and affective commitment. In contrast to the study

of Kurniawaty, Ramly and Ramlawati (2019) that measure the effect of the work environment

as a whole on turnover intention and include the perception of the equipment, among others,

in a single factor. Therefore, this study strengthens the body of evidence of the relationship

between perception of the equipment and turnover intention. Similarly, this study provides

evidence that the relationship between the perception of the layout and turnover intention is

completely mediated via job satisfaction. Previous studies did not assess this isolated effect of

the perception of the layout but instead included it in one factor named ‘work environment’

(Santoni & Harahap, 2018). These findings suggest that the equipment and layout play an

important role within the work environment if we look at its relationship to turnover intention.

Managers that want to decrease the turnover intention among their employees by adapting the

physical work environment should therefore focus on both the equipment and layout of their

office environment. Finally, in line with previous findings (see Aydogdu & Asikgil, 2011), this

study provides evidence for the negative relationship between job satisfaction and turnover

intention, and affective commitment and turnover intention. Stressing the importance of high

levels of job satisfaction and affective commitment, whereas it results in lower employee

turnover intention. For managers this means that, concerning the physical work environment,

they should focus on the equipment and the layout because of its positive relationship with job

satisfaction and affective commitment. But as is suggested in the discussion, managers should

be aware that these two elements in combination with job satisfaction and turnover intention

only explain around 25 percent of the variance in turnover intention.

Secondly, an interesting trade-off that should be made when deciding whether changes in the

work environment help lower the turnover intention is between the costs and the benefits.

Organizations are deliberately trying to avoid turnover intentions because of the direct and

indirect costs related to employee turnover (Koh & Goh, 1995). However, changes in the layout

of the work environment can be quite rigorous and costly. If changes in the layout of the current

environment are not possible, it would require a new office to create an environment in which

the layout is perceived as more positive. Questionably remains whether the benefits of these

change outweigh the related costs. Besides the costs of turnover intention, there are a lot of

other factors that should be included in the decisions about whether to redesign the

environment. These can include the effects on performance, productivity, wellbeing, and

amount of sick leave. This cost-benefit analysis would be an interesting topic for further study.


55

Furthermore, further research is required to see whether similar results can be found for

employees working in other sectors than the financial sector. Additionally, it would be interesting

to see whether there are any differences between office types. In the literature part a light was

shed on potential differences between office types, but it was outside the scope of this study to

test for these differences in the empirical part of this study.

Additionally, an interesting focus for further study would be to investigate whether there are any

other variables that mediate the relation between the perception of the work environment and

employee turnover intention. This study focused on two other factors that spark turnover

intention, but there are several other factors that might spark turnover intention. Factors that

can be mediators in the relationship between the perception of elements of the work

environment and turnover intention. For example, salary (Nawaz & Pangil, 2016), autonomy

(Kim & Stoner, 2008) or leadership style (Liu et al., 2013).

Finally, interesting to mention is that all the data was collected before the start of the Covid-19

pandemic. So, during the data collection all the participant were still working mostly in their

offices. The whole “working from home” situation was not yet a thing. This new work situation

might impact the perception of employees of the work environment. For example, most

employees will be more critical towards the indoor air quality in their offices and also the

perception of layout might have been changed with all the routing. Time will tell whether these

changes are just temporarily or will remain.

However, in these times of working from home, organizations can break with ways of working

from the past that were suboptimal. Organizations have the time to rethink and redesign their

office environments based on facts. A return to a well-designed office environment can result

in more satisfied and committed employees that are less likely to leave the organization. The

results of this study can be a first step in the right direction; however, I am well aware that there

is still a lot of elements of the work environment that can be further studied. One of the

respondents mentioned in the comment section, she was very dissatisfied with their open-plan

office (“kantoortuin”). The working from home during the Covid-19 pandemic refuelled this

public discussion about the future of the open plan office (e.g., NRC, 2020; AD, 2020).

Additionally, newspapers are reporting increasing complaints about the lack of ergonomic

workstations at home, resulting in more back pains (Trouw, 2020). Right now, we have to decide

for the future of the design of our office environments and relevant knowledge about the effects

of elements of the work environment is crucial for these design decisions. Because one thing

is certain: the physical environment impacts its occupants!

56

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68

APPENDICES

APPENDIX 1 – Research framework

APPENDICES

69

APPENDIX 2 – Questionnaire

_________________________________________________________________________________

Start of Block: Introductie

Geachte deelnemer,

Graag nodig ik u uit om deel te nemen aan mijn afstudeeronderzoek. Dit onderzoek richt zich op het

effect van uw werkomgeving op uw tevredenheid en intentie om werkzaam te blijven voor uw

werkgever.

Dit onderzoek bestaat uit 4 korte onderdelen en duurt ongeveer 10 minuten. De vragen gaan over uw

primaire werkgever en hebben betrekking op uw eigen werkplek(ken).

Leest u alstublieft de hiernavolgende instructies voordat u de vragenlijst invult:

1. U wordt verzocht de vragen zélf, dus zonder overleg met anderen, in te vullen.

2. De vragenlijst vraagt naar uw mening, u kunt dus nooit een fout antwoord geven!

3. Sta niet te lang stil staan bij de vragen, maar kruis het antwoord aan dat het eerst in u opkomt.

4. Sommige vragen worden nogmaals op een andere manier gesteld om de betrouwbaarheid van

de vragenlijst te verhogen.

5. Het invullen van deze vragenlijst zal ongeveer 10 minuten in beslag nemen.

Anonimiteit

Uw deelname is geheel anoniem en, door de software die ik gebruik, ook niet te herleiden naar

individuen. De geaggregeerde data zal enkel worden gebruikt voor mijn onderzoek. Los van dat de

gegevens anoniem zijn, ga ik vanzelfsprekend vertrouwelijk om met de gegenereerde data.

Toestemmingsverklaring

Met het geven van uw toestemming verklaart u deze persoonsgegevens vrijwillig te hebben verstrekt.

U heeft het recht om de gegeven toestemming ook weer in te trekken. De door u verstrekte

persoonsgegevens zullen uitsluitend voor het doel worden gebruikt waarvoor u deze heeft verstrekt. U

heeft het recht op inzage, verwijdering, correctie of beperking van de verwerking van

persoonsgegevens, alsmede het recht om bezwaar te maken en het recht op

gegevensoverdraagbaarheid. Https://www.wur.nl/nl/over-wageningen/integriteit-en-privacy.htm

Let op: er is geen sprake van een absoluut recht voor betrokkenen. Dat zal telkens per situatie en

ingeroepen recht moeten worden afgewogen. Wanneer de verwerking noodzakelijk is voor de

uitvoering van een wettelijke taak of algemeen belang (bijvoorbeeld: archivering in algemeen belang,

wetenschappelijk of historisch onderzoek of statistiek) dan bestaat het recht om niet genoemd te

willen worden niet.

Door rechtsonder op “→” te drukken gaat u akkoord met de hierboven genoemde

voorwaarden.

Alvast hartelijk bedankt voor uw medewerking aan het onderzoek!

Met vriendelijke groet,

Rob de Leeuw

[email protected]

Mijn onderzoek voer ik uit onder supervisie van de leerstoelgroep 'Business Management &

Organization' aan de Wageningen Universiteit. Indien u een klacht heeft, kunt u deze indienen bij

WUR via [email protected]. Ook kunt u een klacht indienen bij de Autoriteit Persoonsgegevens. Meer

APPENDICES

70

informatie kunt u vinden op www.autoriteitpersoonsgegevens.nl. Heeft u vragen, dan kunt u terecht bij

de Functionaris Gegevensbescherming van WUR via [email protected].

Meer informatie over de verwerking van persoonsgegevens kunt u vinden op www.wur.nl/privacy

End of Block: Introductie

Start of Block: Werkomgeving

Office type

Onderstaand vindt u de beschrijving van 7 verschillende type werkplekken. Geeft u aan op welk type

werkplek u het meest werkzaam bent.

o Een gesloten ruimte waar ik alleen werk

o Een gesloten ruimte waar ik met 1 of 2 andere collega's werk

o Een open werkplek met 4 tot 9 mensen in één ruimte

o Een open werkplek met 10 tot 24 mensen in één ruimte

o Een open werkplek met meer dan 24 mensen in één ruimte

o Ik heb geen vaste werkplek, want ik werk op verschillende werkplekken (flexplek)

o Ik ben voornamelijk werkzaam buiten mijn eigen werkplek (projectruimten, vergaderzalen, klant bezoeken)

APPENDICES

71

Layout

De volgende stellingen hebben betrekking op uw werkplek. Hierbij kunt u aangeven in welke mate u

het hiermee eens dan wel oneens bent.

Sterk mee oneens (1)

Oneens (2) Neutraal (3) Eens (4) Sterk mee eens (5)

De indeling van ons gebouw maakt het

gemakkelijk de receptie te bereiken

o o o o o


gemakkelijk om de

vergaderruimtes te bereiken

o o o o o


gemakkelijk de toiletten te bereiken

o o o o o


gemakkelijk om de kantine te

bereiken

o o o o o

Over het geheel genomen maakt de indeling van

ons gebouw maakt het

gemakkelijk om te komen waar

u wilt

o o o o o

APPENDICES

72

Space



Sterk mee

oneens Oneens Neutraal Eens

Sterk mee eens

Ik heb voldoende

ruimte op mijn werkplek

o o o o o Ik heb

voldoende privacy op mijn

werkplek o o o o o

Ik zit dicht bij de collega's met wie ik

moet samenwerken

o o o o o

Ik zit niet te dicht op mijn

collega's o o o o o

APPENDICES

73

Ambient



Sterk mee


Sterk mee eens

De temperatuur op uw werkplek is comfortabel o o o o o

De luchtkwaliteit op uw werkplek is goed o o o o o De geur in ons

gebouw is aangenaam o o o o o De

achtergrondmuziek/het achtergrondgeluid is

passend o o o o o

De hoeveelheid daglicht is aangenaam o o o o o

De hoeveelheid kunstlicht is aangenaam o o o o o

De hoeveelheid (kamer)planten is

prettig o o o o o

APPENDICES

74

Functionality



Sterk mee


Sterk mee eens

De apparatuur die ik nodig

heb is beschikbaar

op mijn werkplek

o o o o o

De apparatuur die

beschikbaar is op mijn

werkplek is geavanceerd

o o o o o

De apparatuur die

beschikbaar is op mijn

werkplek is van kwaliteit

o o o o o

Het meubilair op mijn

werkplek is comfortabel

o o o o o Het meubilair

op mijn werkplek is van kwaliteit

o o o o o Het meubilair

op mijn werkplek is

logisch ingedeeld

o o o o o

APPENDICES

75

Cleanliness



Sterk mee


Sterk mee eens

Mijn kantoor onderhoudt

schone toiletten

o o o o o Mijn kantoor onderhoudt een schone

kantine o o o o o

Mijn kantoor onderhoudt

schone gangen,

trappen en uitgangen

o o o o o

Over het algemeen is mijn kantoor

schoon o o o o o

APPENDICES

76

Signs



Sterk mee


Sterk mee eens

Er is voldoende

bewegwijzering in ons kantoor

aanwezig

o o o o o

De bewegwijzering

is duidelijk zichtbaar

o o o o o De

bewegwijzering is begrijpelijk o o o o o

De bewegwijzering

maakt het makkelijk alles

te vinden

o o o o o

Holistic

De volgende stelling heeft betrekking op uw werkplek. Hierbij kunt u aangeven in welke mate u het

hiermee eens dan wel oneens bent.

Sterk mee


Sterk mee eens

Over het geheel ben ik tevreden met mijn werkplek

o o o o o

End of Block: Werkomgeving

Start of Block: Uw werk

APPENDICES

77

Satisfaction

De volgende stellingen hebben betrekking op uw ervaringen op het werk en uw gevoelens bij uw

werk. Hierbij kunt u aangeven in welke mate u het hiermee eens dan wel oneens bent.

Sterk mee


Sterk mee eens

Ik vind mijn werk nogal

onaangenaam o o o o o Elke dag op

werk lijkt alsof het nooit eindigt

o o o o o Ik ben redelijk tevreden met mijn huidige

baan o o o o o

De meeste dagen ben ik enthousiast

over mijn werk o o o o o

Ik vind echt plezier in mijn

werk o o o o o

APPENDICES

78

Commitment



Sterk mee


Sterk mee eens

Ik zou graag de rest van mijn

carrière bij deze organisatie

willen doorbrengen

o o o o o

Ik vind het leuk om mijn

organisatie te bespreken met

mensen van buitenaf

o o o o o

Ik heb echt het gevoel dat de

problemen van deze organisatie van mijzelf zijn

o o o o o

Ik denk dat ik gemakkelijk net zo gehecht zou kunnen raken

aan een andere organisatie als

ik aan deze organisatie ben

o o o o o

Ik voel me niet als "deel van de familie" binnen mijn organisatie

o o o o o Ik voel me niet

"emotioneel verbonden" met deze organisatie

o o o o o Deze

organisatie heeft veel

persoonlijke betekenis voor

mij

o o o o o

Ik voel me niet sterk verbonden

met mijn organisatie

o o o o o

APPENDICES

79

Intention



Sterk mee


Sterk mee eens

Ik ben van plan de

organisatie in de

komende 12 maanden te

verlaten

o o o o o

Ik heb het gevoel dat ik de komende 12 maanden

de organisatie wil verlaten

o o o o o

Ik zal de organisatie

in de komende 12

maanden verlaten

o o o o o

End of Block: Uw werk

Start of Block: Algemene informatie

A1 Wat is uw geslacht?

o Man

o Vrouw

o Anders

APPENDICES

80

A2 Hoe oud bent u?

o 0-17 jaar

o 18-29 jaar

o 30-39 jaar

o 40-49 jaar

o 50-59 jaar

o 60 jaar of ouder

A3 Wat is uw hoogst voltooide opleiding?

o Lagere school

o Middelbare school

o Middelbaar Beroeps Onderwijs (MBO)

o Hoger Beroeps Onderwijs (HBO)

o Wetenschappelijk Onderwijs (WO)

o Anders, namelijk: ________________________________________________

A5 Hoeveel uur per week werkt u gemiddeld voor uw bedrijf?

o 0-8 uur

o 9-16 uur

o 17-24 uur

o 25-32 uur

o 33-40 uur

o 40+ uur

APPENDICES

81

A5 Hoeveel jaar bent u al werkzaam voor uw bedrijf?

o 0-5 jaar

o 6-10 jaar

o 11-15 jaar

o 16-20 jaar

o 21-25 jaar

o 25+ jaar

A6 Wat is de naam van uw bedrijf en in welke plaats bevindt uw kantoor zich?

________________________________________________________________

End of Block: Algemene informatie

Start of Block: Afsluiting

Slot

Dit wat het einde van dit onderzoek. Dank u wel voor uw deelname!

Mocht u nog open of aanmerkingen hebben of nog een vraag hebben over het doel van mijn

onderzoek, dan kunt u mailen naar: [email protected]

Met vriendelijke groet,

Rob de Leeuw

End of Block: Afsluiting

APPENDICES

82

APPENDIX 3 – Original constructs and their source

Table 20

Included constructs and their source

Construct Category Item

Source

Job Satisfaction (JS) JS1: I consider my job as rather unpleasant JS2: Each day of work seems like it never ends JS3: I feel fairly satisfied with my present job JS4: Most days I am enthusiastic about my work JS5: I find real enjoyment in my work

Brayfield and Rothe (1951)

Organizational Commitment (OC) Affective commitment OC1: I would be very happy to spend the rest of my career with this

organization OC2: I enjoy discussing my organization with people outside it OC3: I really feel as if this organization’s problems are my own OC4: I think that I could easily become as attached to another organization

as I am to this one (R) OC5: I do not feel like ‘part of the family’ at my organization (R) OC6: I do not feel ‘emotionally attached’ to this organization (R) OC7: This organization has a great deal of personal meaning for me OC8: I do not feel a strong sense of belonging to my organization (R)

Allen & Meyer (1990)

Intention to Leave (IL) IL1: Do you intend to leave the organization in the next 12 months? IL2: How strongly do you feel about leaving the organization within the next

12 months? IL3: How likely is it that you will leave the organization in the next 12

months?

Mitchell et al. (2001)

Perception of Work Environment (PWE) Layout LAY1 The facility layout makes it easy to access the reception LAY2 The facility layout makes it easy to access the meeting rooms LAY3 The facility layout makes it easy to access the toilets LAY4 The facility layout makes it easy to access the canteen LAY5 Overall, the facility layout makes it easy to get to where you want to Space SPA1 I have enough space in my workplace SPA2 I have sufficient privacy in my workplace SPA3 I am close to the colleagues I have to work with SPA4 I am not too close to my colleagues. Ambient AMB1 The temperature in the facility is comfortable AMB2 The air quality in the facility is good AMB3 The background music/sound is appropriate AMB4 The odour in the facility is pleasant AMB5 The lighting in the facility is adequate AMB6 The lighting in the facility is easy on eye AMB7 Overall, the ambient condition of the facility makes it comfortable to

Wakefield and Blodgett (1996)

APPENDICES

83

stay inside Functionality FUN1 Electronic equipment that you need is available in the facility FUN2 Electronic equipment offered by this facility is technologically

advanced FUN3 The facility offers high performing electronic equipment FUN4 The furniture in my facility is comfortable FUN5 The furniture in my facility is of quality FUN6 The furniture in my facility is logically arranged Cleanliness CLE1 The facility maintains clean restrooms CLE2 The facility maintains clean lunch areas CLE3 The facility maintains clean walkways and exits CLE4 Overall, the facility is kept clean Signs and symbols SS1 There is sufficient signage in the facility SS2 The signage in the facility is large enough to be seen SS3 The signage in the facility is easy to be understood SS4 The signage in the facility makes it easy to find your way Holistic environment H1 Overall, I am satisfied with my work environment

APPENDICES

84

APPENDIX 4 – Additional output Chapter 4

Table 21

Correlation table, top left

Lay1 Lay2 Lay3 Lay4 Lay5 Spa1 Spa2 Spa3 Spa4 Amb1 Amb2 Amb3 Amb4 Amb5 Amb6 Amb7 Fun1 Fun2 Fun3 Fun4 Fun5 Fun6

Lay1 1.00

Lay2 .578** 1.00

Lay3 .517** .520** 1.00

Lay4 .488** .538** .556** 1.00

Lay5 .590** .659** .671** .682** 1.00

Spa1 .302** .252** .292** .214** .347** 1.00

Spa2 0.09 .144* 0.00 0.07 0.05 .210** 1.00

Spa3 -0.01 -0.02 0.01 0.09 0.08 0.05 0.01 1.00

Spa4 0.13 .192** 0.11 0.07 0.13 .442** .508** -0.08 1.00

Amb1 0.08 0.04 0.10 0.01 .142* .338** .212** 0.01 .299** 1.00

Amb2 .148* 0.14 .167* 0.07 .205** .223** .176* 0.01 .286** .731** 1.00

Amb3 0.10 0.12 0.14 0.06 0.13 .221** .284** 0.05 .323** .459** .557** 1.00

Amb4 0.09 .148* .150* 0.01 .145* 0.05 .385** 0.06 .267** 0.11 0.14 .312** 1.00

Amb5 .176* 0.04 0.10 .166* .145* .184* 0.13 0.08 .189** .194** .148* .172* .181* 1.00

Amb6 .169* 0.09 0.14 .178* .204** .260** 0.14 0.10 .253** .258** .255** .318** .216** .636** 1.00

Amb7 0.06 0.09 -0.01 0.07 0.02 0.13 .277** 0.05 .246** .211** .345** .371** .231** .208** .278** 1.00

Fun1 0.12 .266** .307** .164* .327** .237** 0.03 0.05 0.08 .165* .155* .184* 0.10 0.07 0.11 0.01 1.00

Fun2 .196** .309** .196** 0.14 .293** .223** .190** 0.02 .181* .154* .203** .237** .261** 0.06 0.12 .166* .562** 1.00

Fun3 .158* .229** .218** .150* .267** .261** .182* 0.05 .195** .273** .232** .219** .220** 0.08 .160* 0.14 .515** .690** 1.00

Fun4 .244** .249** .186** 0.14 .238** .419** .288** -0.10 .404** .406** .397** .337** .182* .157* .224** .243** .278** .422** .428** 1.00

Fun5 .196** .291** .189** .162* .243** .381** .284** -0.09 .384** .418** .409** .358** .150* .142* .190** .232** .271** .432** .497** .879** 1.00

Fun6 0.07 .160* .170* 0.12 .160* .379** .302** 0.07 .456** .312** .298** .371** .238** .204** .221** .294** .149* .308** .328** .562** .590** 1.00

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

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

APPENDICES

85

Table 22

Correlation table, bottom left

Lay1 Lay2 Lay3 Lay4 Lay5 Spa1 Spa2 Spa3 Spa4 Amb1 Amb2 Amb3 Amb4 Amb5 Amb6 Amb7 Fun1 Fun2 Fun3 Fun4 Fun5 Fun6

Cle1 0.11 0.07 .223** .151* .147* .330** .176* -0.06 .338** .202** .192** 0.13 0.10 0.09 0.11 0.08 .199** .198** .277** .312** .312** .291**

Cle2 0.13 .197** .346** .350** .265** .313** -0.03 -0.04 0.11 .145* .155* .160* -0.02 0.10 0.09 -0.04 .290** .142* .198** .231** .284** .230**

Cle3 0.13 .164* .269** .245** .193** .307** 0.00 -0.06 .171* .199** .203** .189** -0.05 0.12 0.13 0.07 .236** .160* 0.12 .250** .286** .200**

Cle4 .252** 0.07 .241** .166* .196** .349** 0.12 -0.06 .290** .318** .329** .225** 0.03 .170* .182* .184* 0.13 0.10 .161* .265** .284** .277**

Sig1 0.07 .178* 0.05 0.10 .190** .150* .263** 0.04 .259** .186** .144* .267** .200** .175* .142* .145* 0.06 .166* 0.10 .185* .210** .168*

Sig2 0.14 .201** 0.06 0.13 .204** .154* .334** 0.04 .276** .201** .150* .249** .231** .172* 0.13 .176* 0.08 .169* 0.11 .216** .203** 0.13

Sig3 0.06 .166* 0.09 0.09 .179* 0.14 .283** 0.06 .291** .201** .157* .269** .258** .161* 0.10 .157* 0.12 .157* .148* .192** .200** .171*

Sig4 0.08 .192** 0.07 0.11 .226** .146* .287** 0.06 .264** .178* .151* .255** .274** .169* 0.14 .168* 0.09 .180* 0.12 .176* .187** .163*

Sat1 .172* .144* .205** .163* .277** .252** 0.08 0.02 .169* .195** .161* 0.11 0.07 .146* .220** -0.01 .245** .254** .223** .223** .247** .187**

Sat2 .238** .197** .276** .183* .289** .259** .171* -0.04 .236** .193** 0.12 .212** 0.10 .180* .285** 0.05 .225** 0.14 .172* .220** .220** .225**

Sat3 0.09 0.12 0.08 0.12 0.13 0.04 0.14 .152* -0.01 0.03 0.05 .185* 0.11 -0.01 0.02 0.07 .159* .159* .159* -0.01 0.02 .243**

Sat4 .170* .184* .191** .184* .237** 0.11 .243** 0.13 0.06 0.14 0.08 0.12 0.12 0.09 .158* 0.13 .144* .154* .189** 0.07 0.11 .159*

Sat5 .230** .225** .158* .256** .253** .171* .240** 0.06 0.10 0.12 0.06 0.11 .168* 0.09 0.13 0.08 .221** .246** .257** .148* .184* .201**

Com1 0.07 .203** 0.11 0.08 .171* .200** .175* .163* 0.11 0.12 0.07 0.10 .156* 0.04 .142* 0.14 0.10 0.11 0.11 0.09 .147* .204**

Com2 0.06 .199** 0.08 0.03 0.14 0.09 .222** 0.08 0.10 0.12 0.14 0.10 0.13 0.03 0.04 0.07 0.06 0.07 .220** .149* .243** 0.14

Com3 -0.04 -0.05 -0.04 -0.13 -0.12 0.01 0.14 -0.02 .176* 0.11 .142* 0.13 0.10 -0.01 0.00 .162* -0.04 0.10 0.11 .162* .207** .216**

Com4 -0.01 -0.03 -0.08 -0.01 -0.06 0.03 0.04 0.07 0.03 -0.06 -0.03 -0.04 0.02 -0.02 0.00 0.09 -0.09 -0.07 -0.06 -0.03 0.03 -0.02

Com5 0.12 0.08 .163* 0.02 0.09 0.10 0.07 0.03 0.03 0.00 -0.01 -0.01 0.12 -0.08 -0.05 -0.04 .199** 0.12 0.13 0.08 0.10 0.04

Com6 0.11 0.04 .146* 0.01 0.10 .179* 0.03 -0.03 0.05 0.05 0.01 0.05 0.14 -0.01 0.08 -0.09 .200** 0.09 .207** 0.12 .197** 0.07

Com7 0.06 0.10 0.10 0.04 0.04 0.05 0.14 0.08 0.13 0.08 0.05 .159* .299** -0.03 0.01 0.09 0.08 0.08 0.12 0.12 .174* .143*

Com8 0.01 0.07 0.10 0.06 0.08 0.12 .198** 0.06 0.12 0.02 -0.02 .175* 0.12 -0.09 0.02 0.01 0.11 0.11 .143* 0.13 .186** 0.14

Int1 0.03 -0.07 -0.01 -0.01 -0.03 -0.14 -.150* -0.04 -0.13 -0.03 0.08 0.04 -.146* 0.07 0.02 0.02 -0.07 0.07 0.02 -0.01 -0.05 -0.08

Int2 -0.01 -0.01 0.01 -0.03 -0.02 -0.08 -.178* -0.08 -0.09 -0.01 0.08 0.07 -0.14 0.04 0.03 -0.01 -0.03 0.07 0.01 0.01 -0.01 -0.08

Int3 -0.05 -0.08 -0.04 -0.06 -0.08 -.146* -0.14 -0.01 -.143* -0.07 0.03 0.01 -0.08 0.07 0.01 -0.03 -0.04 0.09 0.00 -0.02 -0.06 -0.07



APPENDICES

86

Table 23

Correlation table, bottom right

Cle1 Cle2 Cle3 Cle4 Sig1 Sig2 Sig3 Sig4 Sat1 Sat2 Sat3 Sat4 Sat5 Com1 Com2 Com3 Com4 Com5 Com6 Com7 Com8 Int1 Int2 Int3

Cle1 1.00

Cle2 .486** 1.00

Cle3 .449** .724** 1.00

Cle4 .553** .570** .665** 1.00

Sig1 0.11 .145* 0.12 0.13 1.00

Sig2 0.12 .168* 0.13 .178* .932** 1.00

Sig3 0.14 .206** 0.12 .173* .879** .921** 1.00

Sig4 0.11 .155* 0.13 .148* .928** .951** .916** 1.00

Sat1 .273** .268** .249** .200** .238** .227** .250** .226** 1.00

Sat2 .189** .296** .247** .160* .157* .162* .161* .148* .684** 1.00

Sat3 -0.03 .171* .160* 0.05 0.13 0.12 0.13 0.14 .220** .267** 1.00

Sat4 0.10 0.12 0.09 0.04 0.09 0.12 0.11 0.09 .412** .482** .488** 1.00

Sat5 .198** .229** .203** 0.11 .146* .174* 0.13 0.14 .522** .538** .475** .778** 1.00

Com1 0.05 0.04 0.06 -0.06 0.10 0.09 0.10 0.09 .256** .334** .232** .416** .446** 1.00

Com2 .174* 0.11 0.03 0.03 0.06 0.05 0.07 0.07 .230** .282** .151* .395** .387** .367** 1.00

Com3 .230** 0.07 0.03 0.09 0.00 0.03 -0.01 0.03 0.05 0.11 0.09 0.12 0.11 .194** .297** 1.00

Com4 0.04 -0.04 -0.03 -0.02 -0.07 -0.05 -0.11 -0.11 0.05 .160* 0.07 .171* .204** .340** .229** .256** 1.00

Com5 .215** .190** 0.14 0.13 -0.07 -0.07 -0.06 -0.07 .201** .284** 0.05 .352** .334** .276** .235** 0.14 .268** 1.00

Com6 .219** .175* 0.11 0.08 -0.04 -0.05 -0.04 -0.04 .256** .241** 0.01 .257** .305** .257** .322** .207** .304** .623** 1.00

Com7 .240** 0.10 0.05 0.02 0.10 0.09 0.09 0.07 0.14 .214** 0.10 .310** .344** .428** .393** .312** .440** .435** .491** 1.00

Com8 .145* 0.13 0.08 -0.03 0.06 0.02 0.03 0.04 .186** .256** .161* .222** .245** .331** .352** .232** .330** .357** .483** .415** 1.00

Int1 -0.03 -0.01 0.06 0.06 -0.02 -0.01 -0.01 0.01

-

.208**

-

.370** -.177*

-

.342**

-

.376**

-

.508**

-

.257** -0.06

-

.350**

-

.254**

-

.246**

-

.302**

-

.327** 1.00

Int2 -0.03 0.02 0.09 0.03 -0.02 -0.03 -0.03 -0.01

-

.250**

-

.365**

-

.192**

-

.390**

-

.412**

-

.484**

-

.278** -0.07

-

.369**

-

.267**

-

.240**

-

.310**

-

.292** .895** 1.00

Int3 -0.05 -0.02 0.05 -0.02 0.00 -0.01 0.01 0.03

-

.219**

-

.378** -0.14

-

.328**

-

.349**

-

.467**

-

.263** -0.04

-

.342**

-

.215**

-

.218**

-

.295**

-

.337** .922** .865** 1.00



APPENDICES

87

Table 24

EFA: Perception of elements of the work environment

Item

code Items

Factor Dimension

1 2 3 4 5 6 7

Lay1 Reach reception

0.784

Layout

Lay2 Reach meeting rooms

0.796

Lay3 Reach toilets

0.757

Lay4 Reach canteen

0.778

Lay5 Move through building

0.854

Amb1 Air temperature

0.820

Ambient

conditions

Amb2 Air quality

0.883

Amb3 Scent

0.645

Amb4 Music/Sounds

Amb5 Daylight

0.773

Amb6 Artificial light

0.773

Amb7 Biophilia

Fun1 Availability of equipment

0.763

Functionality

of equipment

and furniture

Fun2 Advancement of equipment

0.823

Fun3 Quality of equipment

0.802

Fun4 Comfort of furniture

0.557

Fun5 Quality of furniture

0.522

Fun6 Arrangement of furniture

0.595

Cle1 Toilets

0.689

Cleanliness

Cle2 Canteen

0.822

Cle3 Stair- and hallways

0.838

Cle4 Overall

0.787

Sig1 Quantity 0.939

Signs Sig2 Visibility 0.955

Sig3 Understandability 0.936

Sig4 Usefulness 0.957

APPENDICES

88

Spa1 Personal space to work

Space

Spa2 Privacy at workspace

0.709

Spa3 Close to relevant colleagues

Spa4 Not too close to others

0.741

Extraction Method: Principal Component Analysis.

Rotation Method: Varimax with Kaiser Normalization.

A Rotation converged in 7 iterations.

Table 25

EFA: Job satisfaction, Affective commitment and Turnover intention

Item

code Items

Factor

Dimension 1 2 3

Sat1 Pleasantness of work

0.736

Job

satisfaction

Sat2 Felt duration of the workday

0.716

Sat3 Satisfaction with job

0.625

Sat4 Enthusiasm

0.782

Sat5 Satisfaction with work

0.813

Com1 Stay with organization

Com2 Talk about organization with others

0.508

Affective

commitment

Com3 Share the organization’s problems

0.531

Com4 Potential feeling of attachment

0.534

Com5 Feel part of the family

0.658

Com6 Emotional attachment

0.766

Com7 Personal meaning

0.743

Com8 Feelings of attachment

0.630

Int1 Plan to leave within 12 months 0.935

Intention to

leave the

organization

Int2 Feel to leave within 12 months 0.900

Int3 Will to leave within 12 months 0.929

Extraction Method: Principal Component Analysis.

Rotation Method: Varimax with Kaiser Normalization

A Rotation converged in 5 iterations.

APPENDICES

89

Table 26

Correlation table latent variables

1. 2. 3. 4. 5. 6. 7. 8. 9. 10.

1. Layout 1 2. Indoor Air 0.10 1 3. Lighting 0.14 0.27** 1 4. Equipment 0.25** 0.24** 0.15* 1 5. Furniture 0.27** 0.43** 0.26** 0.43** 1 6. Cleanliness 0.19** 0.26** 0.2** 0.21** 0.35** 1 7. Signs 0.19** 0.17* 0.22** 0.2** 0.22** 0.15* 1 8. Job satisfaction 0.29** 0.08 0.15* 0.27** 0.23** 0.21** 0.18** 1 9. Affective commitment 0.07 0.05 -0.04 0.19** 0.16* 0.10 -0.02 0.31* 1 10. Turnover intention -0.07 -0.04 0.05 0.00 -0.06 0.02 0.01 -0.37** -0.36** 1


* Correlation significant at the 0.05 level (2-tailed

Table 27

Test for discriminant validity (AVE > squared correlation of latent variables)

1. 2. 3. 4. 5. 6. 7. 8. 9. 10. AVE

1. Layout 1 0.629

2. Indoor air 0.010 1 0.748

3. Lighting 0.020 0.073 1 0.611

4. Equipment 0.063 0.058 0.023 1 0.764

5. Furniture 0.073 0.185 0.068 0.185 1 0.654

6. Cleanliness 0.036 0.068 0.040 0.044 0.123 1 0.698

7. Signs 0.036 0.029 0.048 0.040 0.048 0.023 1 0.916

8. Job satisfaction 0.084 0.006 0.023 0.073 0.053 0.044 0.032 1 .0588

9. Affective commitment 0.005 0.003 0.002 0.036 0.026 0.010 0.000 0.096 1 0.559

10. Turnover intention 0.005 0.002 0.003 0.000 0.004 0.000 0.000 0.137 0.130 1 0.863

90

Table 28

Relations between perception of elements of the work environment and affective commitment

Hypothesized path Coefficient (std. all) Z-value P(>|Z|) Mediation step 1

Layout

→ Affective commitment .038 .381 .703 Not supported

Space

→ Affective commitment - - - -

Indoor air

→ Affective Commitment (.079) (.798) .425 Not supported

Lighting

→ Affective commitment (.019) (.189) .850 Not supported

Cleanliness

→ Affective commitment .140 1.534 .125 Not supported

Equipment

→ Affective commitment .273 2.952 .003 Supported

Furniture

→ Affective Commitment .152 1.241 .215 Not supported

Signs

→ Affective Commitment (.096) (1.117) .264 Not supported

Table 29

Relations between perception of elements of the work environment and job satisfaction

Path Coefficient (std. all) Z value P(>|Z|) Mediation step 1

Layout

→ Job satisfaction .228 2.512 .012 Supported

Space

→ Job satisfaction - - - -

Indoor air

→ Job satisfaction (.067) (.672) .501 Not supported

Lighting

→ Job satisfaction .055 .625 .532 Not supported

Cleanliness

→ Job satisfaction .130 1.485 .137 Not supported

Equipment

→ Job satisfaction .278 2.951 .003 Supported

Furniture

→ Job satisfaction (.050) (.432) .665 Not supported

Signs

→ Job satisfaction .081 1.022 .307 Not supported

APPENDICES

91

APPENDIX 5 – R coding

Measurement model library(lavaan) library(haven) library(dplyr) Thesis_data <- read_sav("/Users/robdeleeuw/Desktop/DATA Master thesis/Final Final dataset.sav") ### model description Sem.Model <- ' Layout=~Lay1+Lay2+Lay3+Lay4+Lay5 IndoorAir=~Amb1+Amb2 Lighting=~Amb5+Amb6 Equipment=~Fun1+Fun2+Fun3 Furniture=~Fun4+Fun5+Fun6 Cleanliness=~Cle2+Cle3+Cle4 Signs=~Sig1+Sig2+Sig3+Sig4 Satisfaction=~Sat1+Sat4+Sat5 Commitment=~Com5+Com6+Com7+Com8 Intention=~Int1+Int2+Int3 Lay1 ~~ Sig2 ### structural paths Intention ~ Age + Education + Gender + Hours + Tenure + Commitment + Satisfaction + Layout + IndoorAir + Lighting + Equipment + Furniture + Cleanliness + Signs Satisfaction ~ Layout + IndoorAir + Lighting + Equipment + Furniture + Cleanliness + Signs Commitment ~ Layout + IndoorAir + Lighting + Equipment + Furniture + Cleanliness + Signs ' fit <- sem(Sem.Model,data=Thesis_data,std.lv=TRUE, missing="fiml") inspect(fit,'r2') fitmeasures(fit,c("gfi","agfi","nfi","cfi","rmsea","srmr","tli")) summary(fit, standardized=TRUE) coef(fit) parameterestimates(fit) fitted(fit) Structural model library(lavaan) library(haven) library(dplyr) Thesis_data <- read_sav("/Users/robdeleeuw/Desktop/DATA Master thesis/Final Final dataset.sav") ### model description Sem.Model <- ' Layout=~Lay1+Lay2+Lay3+Lay4+Lay5 IndoorAir=~Amb1+Amb2 Lighting=~Amb5+Amb6 Equipment=~Fun1+Fun2+Fun3 Furniture=~Fun4+Fun5+Fun6 Cleanliness=~Cle2+Cle3+Cle4 Signs=~Sig1+Sig2+Sig3+Sig4 Satisfaction=~Sat1+Sat4+Sat5 Commitment=~Com5+Com6+Com7+Com8 Intention=~Int1+Int2+Int3 Lay1 ~~ Sig2

APPENDICES

92

### structural paths Intention ~ Commitment + Satisfaction + Equipment Satisfaction ~Layout + Equipment Commitment ~ Equipment ' fit <- sem(Sem.Model,data=Thesis_data,std.lv=TRUE, missing="fiml") inspect(fit,'r2') fitmeasures(fit,c("gfi","agfi","nfi","cfi","rmsea","srmr","tli")) summary(fit, standardized=TRUE) coef(fit) parameterestimates(fit) fitted(fit) Structural model – including insignificant paths of control variables library(lavaan) library(haven) library(dplyr) Thesis_data <- read_sav("/Users/robdeleeuw/Desktop/DATA Master thesis/Final Final dataset.sav") ### model description Sem.Model <- ' Layout=~Lay1+Lay2+Lay3+Lay4+Lay5 IndoorAir=~Amb1+Amb2 Lighting=~Amb5+Amb6 Equipment=~Fun1+Fun2+Fun3 Furniture=~Fun4+Fun5+Fun6 Cleanliness=~Cle2+Cle3+Cle4 Signs=~Sig1+Sig2+Sig3+Sig4 Satisfaction=~Sat1+Sat4+Sat5 Commitment=~Com5+Com6+Com7+Com8 Intention=~Int1+Int2+Int3 Lay1 ~~ Sig2 ### structural paths Intention ~ Age + Education + Gender + Hours + Tenure + Commitment + Satisfaction Satisfaction ~ Layout + Equipment Commitment ~ Equipment ' fit <- sem(Sem.Model,data=Thesis_data,std.lv=TRUE, missing="fiml") inspect(fit,'r2') fitmeasures(fit,c("gfi","agfi","nfi","cfi","rmsea","srmr","tli")) summary(fit, standardized=TRUE) coef(fit) parameterestimates(fit) fitted(fit)

APPENDICES

93

Table 6

Significant paths structural model, including insignificant control variables

Regression Estimate () Z-value p (<.05)

Intention ~ Age .011 .121 .904 Education (.197) (1.785) .074 Gender (.054) (.306) .760 Hours (.024) (.214) .831 Tenure (.009) (.174) .862 Commitment (.303) (3.168) .002* Satisfaction (.410) (4.571) .000* Equipment .258 2.718 .007* Satisfaction ~ Layout .228 2.509 .012* Equipment .278 2.951 .003* Commitment ~ Equipment .273 2.965 .003*

Mediation analysis – test for indirect effects library(knitr) library(lavaan) library(psych) library(MBESS) Thesis_data <- read_sav("/Users/robdeleeuw/Desktop/DATA Master thesis/Final Final dataset.sav") model <- ' # Latent variables Layout=~Lay1+Lay2+Lay3+Lay4+Lay5 IndoorAir=~Amb1+Amb2 Lighting=~Amb5+Amb6 Equipment=~Fun1+Fun2+Fun3 Furniture=~Fun4+Fun5+Fun6 Cleanliness=~Cle2+Cle3+Cle4 Signs=~Sig1+Sig2+Sig3+Sig4 Satisfaction=~Sat1+Sat4+Sat5 Commitment=~Com5+Com6+Com7+Com8 Intention=~Int1+Int2+Int3 # Covariance Lay1 ~~ Sig2 Satisfaction ~~ Commitment' # outcome model Intention ~ c*IndependentVariable+ b*MediatingVariable + other independent variable(s) + control variables # mediator model MediatingVariable ~ a*IndependentVariable + other independent variable(s) # indirect effect IndirectEffect := a*b # total effect total := c + a*b

APPENDICES

94

fit <- sem(model, data=Thesis_data, se = "bootstrap", bootstrap = 1000) summary(fit, fit.measures=TRUE, standardize=TRUE, rsquare=TRUE) parameterestimates(fit, boot.ci.type = "bca.simple", standardized = TRUE) %>% kable()

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