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BURNOUT, TYPE A PERSONALITY AND LOCUS OF CONTROL IN UNIVERSITY

STUDENTS

by

CANDICE JENNIFER JONES

Minor Dissertation

Submitted in partial fulfilment of the requirements for the degree

of

Magister Commercii

in

Industrial Psychology

Faculty of Management

UNIVERSITY OF JOHANNESBURG

Supervisor: Dr Brandon Morgan

2016

I

DECLARATION OF ADHERENCE: ETHICS IN RESEARCH

STATEMENT: I Candice Jennifer Jones certify that the minor dissertation submitted by me for the degree Master’s of Commerce (Industrial Psychology) at the University of Johannesburg, apart from the help recognised, is my independent work and has not been submitted by me for a degree at another university. C. J. JONES

II

ACKNOWLEDGEMENTS

I would like to express my sincere gratitude and appreciation to the following individuals

who have made the completion of this research study possible.

First and foremost, to God, I am eternally grateful for the many blessings, guidance and

wisdom that you continue to provide for me. Thank you for giving me the strength and ability

to exceed beyond what I thought was possible, and for helping me to overcome the many

obstacles along my journey towards becoming a professional Industrial Psychologist.

Everything that I have achieved and accomplished is because of you. All the glory to you,

Lord Jesus.

Dr Morgan, my supervisor, for your knowledge and continuous guidance, support,

dedication, patience, understanding nature and hard work. I feel blessed to have had you as a

supervisor. I admire your expertise in your field of work and your work ethic. Thank you for

developing my research skills and for driving high quality work. I am truly grateful for the

opportunity to have worked with you, and thankful to you, as this dissertation would not have

been possible without you.

To the students who participated in my research study, an acknowledgement and grateful

thank you, for your time, effort and willingness to contribute towards my study.

Thank you to Professor Schaufeli, Professor Bryant and Dr Levenson for granting me

permission to use your research instruments in my study, without these instruments I would

not have been able to complete my research study.

To Ms Saccaggi, I am truly grateful for your assistance towards ensuring the high quality of

my research study.

I would also like to thank the Pay It Forward Trust Fund, for blessing me with the

opportunity to fulfil my calling.

Lastly, to my family Ralph, Jenny and Ryan Jones; partner, Tristan Keeley; and friend,

Stacey Fellingham - I am forever grateful for your prayers, support, encouragement, and

III

continuous love. Thank you for always believing in me and for pushing me forward in hard

times.

IV

ABSTRACT

Burnout in university students has far reaching negative outcomes for both the student and

educational institution. The development of burnout is related to both environmental and

individual characteristics. The focus of this study is on the relationship between burnout and

two individual characteristics, namely global Type A personality and locus of control.

Theoretically both of these characteristics are expected to be related to burnout. Limited

research has, however, investigated the relationship among these two individual

characteristics and burnout with students in the context of South Africa. In order to determine

the relationship between burnout, Type A personality and locus of control, three scales (The

Maslach Burnout Inventory – Student Survey, Student Jenkins Activity Survey, and

Levenson’s Internal, Powerful Others, and Chance Locus of Control scale) were administered

to 387 university students enrolled at a South African local university.

The relationships between the variables were analysed using Pearson’s correlation

coefficients and multiple regression. The results demonstrated that locus of control had a

stronger relationship with burnout than did global Type A personality in university students.

In particular, statistically significant negative relationships were found between global Type

A personality and two of the burnout dimensions: cynicism and professional inefficacy. In

the presence of all the independent variables, global Type A personality was found to only

predict cynicism. Furthermore, the unique contribution of global Type A personality to

cynicism was small. Internal locus of control was found to demonstrate statistically

significant negative relationships with emotional exhaustion, cynicism, and professional

inefficacy. Powerful Others and Chance locus of control each demonstrated positive

relationships with the three burnout dimensions. Dominance analysis indicated that internal

locus of control (Internality) and Powerful Others demonstrated the largest relative

importance for predicting burnout in university students.

As a whole the results support extant literature and show that the association among

individual characteristics, namely global Type A personality and locus of control, and

burnout in the student context, is similar to the relationship that exists in the organisational

context. The current study’s findings indicate the importance of researching burnout in the

student context. The study also demonstrated that the individual factors of global Type A

V

personality and locus of control are related to student burnout. Implications for research and

practice are presented.

Key words: student, burnout, personality, Type A, locus of control

     

TABLE OF CONTENTS

Page

DECLARATION OF ADHERENCE: ETHICS IN RESEARCH I

ACKNOWLEDGEMENTS II

ABSTRACT IV

LIST OF TABLES

VI

CHAPTER 1: INTRODUCTION AND OVERVIEW OF THE STUDY

1.1 Rationale and problem statement 1

1.2 Research aims 5

1.3 Definition of key terms 5

1.3.1 Burnout 5

1.3.2 Type A personality 6

1.3.3 Locus of control 6

1.4 Content overview of the succeeding chapters

7

CHAPTER 2: LITERATURE REVIEW

2.1 Introduction 8

2.2 The conceptualisation of burnout 8

2.2.1 The structure of burnout 8

2.2.2 The third dimension of burnout 9

2.2.3 Alternative conceptualisations of burnout 10

2.3 Burnout in university students 11

2.4 Process model of burnout 12

2.4.1 Environmental factors as antecedents of burnout 13

2.4.2 Development of burnout according to the JD-R model 15

2.4.3 Individual factors as antecedents of burnout 17

2.4.4 Costs of burnout 17

2.5 Type A personality and locus of control 18

2.5.1 Type A personality 18

2.5.1.1 Dimensionality of Type A personality 19

     

2.5.2 Type A personality in the organisational context 19

2.5.2.1 Positive work-related outcomes 19

2.5.2.2 Negative work-related outcomes 20

2.5.3 Type A personality in the university setting 21

2.5.3.1 Positive student outcomes 21

2.5.3.2 Negative student outcomes 21

2.5.4 Relationship between Type A personality and burnout 21

2.5.5 Locus of control 22

2.5.5.1 Dimensionality of locus of control 23

2.5.6 Locus of control in the organisational context 24

2.5.6.1 Positive work-related outcomes 24

2.5.6.2 Negative work-related outcomes 25

2.5.7 Locus of control in the university setting 26

2.5.7.1 Positive student outcomes 26

2.5.7.2 Negative student outcomes 26

2.5.8 Relationship between locus of control and burnout 27

2.5.9 Summary and content overview of the succeeding chapter

27

CHAPTER 3: METHOD

3.1 Introduction 29

3.2 Research design 29

3.3 Sample 29

3.4 Research procedure 32

3.5 Instruments 32

3.5.1 The Maslach Burnout Inventory – Student Survey (MBI-SS) 32

3.5.2 The short form of the Student Jenkins Activity Survey (SJAS) 33

3.5.3 The Internal, Powerful Others, and Chance (IPC) Locus of Control scale 34

3.6 Statistical analysis 35

3.7 Ethical considerations 37

3.8 Content overview of the succeeding chapter

37

     

CHAPTER 4: RESULTS

4.1 Introduction 38

4.2 Descriptive statistics 38

4.3 Reliability 39

4.4 Correlation coefficients 40

4.5 Multiple regression 41

4.5.1 Multiple regression for emotional exhaustion 41

4.5.2 Multiple regression for cynicism 42

4.5.3 Multiple regression for professional inefficacy 43

4.6 Content overview of the succeeding chapter 44

CHAPTER 5: DISCUSSION AND CONCLUSION

5.1 Introduction 45

5.2 Aims/objectives 45

5.3 Relationship between burnout and Type A personality 45

5.4 Relationship between burnout and locus of control 47

5.5 Implications, limitations, and recommendations 50

5.5.1 Theoretical and practical implications 50

5.5.2 Limitations and recommendations 51

5.6 Conclusion

53

REFERENCES

54

APPENDICES

Appendix A: Descriptive statistics of the items of each scale 93

Appendix B: Density plots, and beanplots of the MBI-SS, global Type A

personality, and Levenson’s IPC locus of control scales

95

Appendix C: Scatterplots and Loess non-parametric regression lines of the

variables in the study

99

Appendix D: Dominance analysis tables 100

VI

LIST OF TABLES

Table Page

Table 3.1: Demographic Characteristics of Participants 31

Table 4.1: Descriptive Statistics of Type A Personality, Locus of Control and

Burnout

39

Table 4.2: Reliability Coefficients of the MBI-SS, SJAS, and Levenson’s IPC

Locus of Control Scale

39

Table 4.3: Pearson and Spearman Rho Correlation Coefficients for the Summated

Scale Scores

41

Table 4.4: Multiple Regression for Emotional Exhaustion 42

Table 4.5: Multiple Regression for Cynicism 43

Table 4.6: Multiple Regression for Professional Inefficacy 44

  1  

CHAPTER 1

INTRODUCTION AND OVERVIEW OF THE STUDY

1.1 Rationale and problem statement

Studying at university is a stressful and potentially overwhelming life stage for many students

(Bresó, Salanova, & Schaufeli, 2007; Stoliker & Lafreniere, 2015). Due to the stressors,

demands, and challenges associated with studying, it is not uncommon for students to

experience impaired performance (see Andrews & Wilding, 2004; Dyrbye, Thomas, &

Shanafelt, 2005), various mental health problems (see Aldiabat, Matani, & le Navenec,

2014), and more specifically, to prematurely end their studies (see Bennett, 2003; Berge &

Huang, 2004; Pillay & Ngcobo, 2010; Stoliker & Lafreniere, 2015). In South Africa

approximately 51% of university students fail to graduate (South African Institute of Race

Relations, 2015). Stress and burnout, as well as their associated negative consequences, could

potentially contribute to this high dropout rate (Deary, Watson, & Hogston, 2003; Dyrbye et

al., 2010; Moneta, 2011). University students must successfully cope with several stress-

inducing and demanding experiences, such as having to deal with copious amounts of

university work (e.g., assignments and examinations), novel challenges, possible fear of

failure, having to manage academic pressure, and having to develop new social support

networks (Jacobs & Dodd, 2003; Lin & Huang, 2012; Pillay & Ngcobo, 2010; Stoliker &

Lafreniere, 2015). These experiences may lead to burnout if a student is unable to manage

them successfully (Bakker, 2015; Jacobs & Dodd, 2003; Lin & Huang, 2012).

Although it has long been recognised that university students can suffer from burnout

(Neumann, Finlay-Neumann, & Reichel, 1990; Pines, Aronson, & Kafry, 1981) most burnout

research has been found to focus on the workplace (Lin & Huang, 2012; Schaufeli &

Enzmann, 1998). This research has provided much knowledge regarding the negative effects

of burnout for employees. For example, burnt-out employees have been shown to develop

various withdrawal behaviours, including intent to permanently retire from the organisation

and/or engaging in absenteeism (Jackson & Schuler, 1983; Maslach, Schaufeli, & Leiter,

2001), experience mental and physical health problems (Leiter & Maslach, 2001), have

negative attitudes toward the organisation, job, and/or clients (Leiter & Maslach, 2001), be

less committed to the organisation (Leiter & Maslach, 1988) and be less productive (Maslach

et al., 2001). Such research has also provided a wealth of information on strategies for

  2  

preventing burnout (Awa, Plaumann, & Walter, 2010; Halbesleben & Buckley, 2004;

Jackson & Schuler, 1983; Maslach & Goldberg, 1998; Maslach et al., 2001). Research on

burnout in students has only recently appeared in earnest in academic publications (Duru,

Duru, & Balkis, 2014; Hu & Schaufeli, 2009; Lian, Sun, Ji, Li, & Peng, 2014; Moore &

Loosemore, 2014; Schaufeli, Martínez, Pinto, Salanova, & Bakker, 2002; Stoeber, Childs,

Hayward, & Feast, 2011; Stoliker & Lafreniere, 2015).

The relative lack of research on student burnout is problematic because university students

engage in “structured coercive activities” (e.g., participating in various course projects; Bresó

et al., 2007, p. 462) that are similar to the types of tasks found in the working world (Bresó et

al., 2007; Noushad, 2008; Schaufeli & Taris, 2005). The academic environment can be

viewed as a microcosm of the working environment (Bowles & Gintis, 1999; Lounsbury,

Gibson, Sundstrom, Wilburn, & Loveland, 2004; Munson & Rubenstein, 1992) and burnout

may have similar negative consequences/outcomes for students as have been found for

employees in the workplace. For example, burnout among students is associated with reduced

academic performance (Neumann et al., 1990; Schaufeli et al., 2002), reduced commitment to

the university and studies (Law, 2007; Neumann et al., 1990; Schaufeli et al., 2002),

increased intention to drop-out of higher education (Dyrbye et al., 2010), exhaustion (Law,

2007), and reduced overall health and well-being (Yueh-Tzu, 2009).

Given the negative consequences associated with student burnout, it is important that the

antecedents of burnout in the academic context be investigated and preventative measures be

implemented to counteract burnout. Research on the antecedents of burnout in the

organisational context (Maslach et al., 2001; Schaufeli & Buunk, 2003) has generally found

that burnout is caused by both environmental and individual factors (Schaufeli & Enzmann,

1998; Maslach et al., 2001). Environmental factors include various job, occupational, and

organisational characteristics that are linked to the development of burnout, such as work

overload, time pressure, role conflict, and lack of social support from supervisors and co-

workers (Malsach et al., 2001). It is found that occupations with high demands and low

resources are linked to the development of burnout (Demerouti, Bakker, Nachreiner, &

Schaufeli, 2001; Rothmann & Joubert, 2007). Individual factors refer to unique individual

attributes/resources that could potentially lead to, or buffer, the development of burnout.

These include demographic factors (such as gender and age), different coping styles,

personality traits (such as Neuroticism), and attitudes toward work (Maslach et al., 2001;

  3  

Schaufeli & Enzmann, 1998). These factors are also related to the development of burnout in

students (Mokgele & Rothmann, 2014). For example, research has found that younger

working individuals are more prone to experience burnout early in their careers and tend to

experience higher burnout levels than do working individuals who are older than 30 or 40

years of age (Maslach et al., 2001).

This study focuses on individual factors (i.e., individual differences) related to the

development of burnout. While it is well recognised that burnout is a social phenomenon and

related to environmental factors (i.e., demands and resources) and that such factors are linked

to the development of burnout, some individuals have a greater likelihood of developing

burnout than others (cf. Maslach et al., 2001; Taris, Houtman, & Schaufeli, 2013). The notion

of transitioning from school to the university context is considered to be a major life decision

and phase for most young adults (Stoliker & Lafreniere, 2015). Individual characteristics

may, in turn, play a role in how they are able to manage the various academic demands and

experiences associated with the university context (Xanthopoulou, Bakker, Demerouti, &

Schaufeli, 2007). In particular, young adults are not passive recipients of the environment,

but rather bring with them unique qualities, such as personality characteristics and work-

related attitudes into the university setting (Maslach et al., 2001). These characteristics may

serve as personal resources allowing students to better deal with the stressors and demands of

university (Bakker & Demerouti, 2014; Xanthopoulou et al., 2007) or they may result in self-

created demands in the university environment that can lead to burnout (Hallberg, Johansson,

& Schaufeli, 2007). There is strong evidence that personality and self-evaluations are

associated with a variety of desirable and undesirable outcomes (Judge, van Vianen, & de

Pater, 2004).

The most frequently researched individual factor in burnout is personality (Schaufeli &

Enzmann, 1998) because personality is considered essential to individual perceptions of

stress (Saksvik & Hetland, 2011) and the way in which individuals manage stressful

situations (Maslach et al., 2001). Predominantly research has investigated the association

between burnout and the Big Five personality traits (Alarcon, Eschleman, & Bowling, 2009;

Anvari, Kalali, & Gholipour, 2011; Armon, Shirom, & Melamed, 2012; Bakker, van der Zee,

Lewig, & Dollard, 2006) and lower level personality variables, including Type A personality

(Alarcon et al., 2009; Utami & Nahartyo, 2013), locus of control (Gan, Shang, & Zhang,

2007; Hsieh & Wang, 2012), hardiness (Alarcon et al., 2009; Lo Bue, Taverniers, Mylle, &

  4  

Euwema, 2013), achievement motivation (Hsu, Chen, Yu, & Lou, 2010), and self-esteem

(Alarcon et al., 2009; Blom, 2012).

In the South African context the association between burnout and the Big Five personality

traits has been investigated in the student population, yielding similar results to research

conducted in the workplace (Morgan & de Bruin, 2010). The relationship between burnout

and Type A personality, or burnout and locus of control, in university students in South

Africa has not previously been investigated. Type A personality and locus of control have

been shown to explain an individual’s approach to work and study, especially in terms of

behaviours and attitudes (Anderson & Hamilton, 2005; Arslan & Akin, 2014; Baka & Derbis,

2012; Elias & Farag, 2011; Ng, Sorensen, & Eby, 2006; Spector, 1982; Watson,

Minzenmayer, & Bowler, 2006). In the organisational context, Type A personality and locus

of control correlate with organisational outcomes, including job performance (Anderson &

Schneier, 1978; Barling & Charbonneau, 1992), job satisfaction (Hanif & Sultan, 2011),

turnover (Lewin & Sager, 2010; Rasch & Harrell, 1989), job stressors (Spector & O’Connell,

1994), and burnout (Hallberg et al., 2007; Maslach et al., 2001). In the student population,

individuals with Type A personality display higher rates of depression and social monitoring,

lower levels of social desirability and communal orientation, and a decrease in individual task

performance across time (Watson et al., 2006). Students possessing an external locus of

control often experience more trait anxiety and are less able to cope with this anxiety than are

students who possess an internal locus of control (Arslan, Dilmac, & Hamarta, 2009).

These variables are also related to burnout in the organisational context (Alarcon et al., 2009;

Idemudia, Jegede, Madu, & Arowolo, 2000; Kalbers & Fogarty, 2005) with research

indicating that global Type A personality and locus of control may account for as much as

18% (Jamal & Baba, 2001) to 62% (Zafar, Zahra, & Zia, 2014) of the variance in burnout.

The relationship between Type A personality, locus of control, and burnout in the university

context is relatively unknown, although it is expected that they will demonstrate similar

relationships as in the organisational context. The aim of this study is thus to investigate the

relationship between burnout and these two personality variables in university students in

South Africa, thereby adding to the knowledge concerning student burnout in South Africa.

The results of this research may also be used to inform possible preventative strategies to

reduce the development of burnout in students. For example, there is evidence that Type A

behaviour patterns (Nunes, Frank, & Kornfeld, 1987) and locus of control (Hans, 2000) may

  5  

be modified through interventions. Preventative strategies may assist in reducing the

relatively high dropout rate of university students in the context of South Africa. Although

this study focuses on burnout in the student context, the results may also assist in

understanding personality dimensions that potentially impact on burnout of employees in the

organisational context.

1.2 Research aims

The aims of this study are to investigate (a) the relationship between burnout and global Type

A personality and (b) the relationship between burnout and locus of control in university

students. The specific objectives of this study are:

1. To investigate the relationship between global Type A personality and emotional

exhaustion;

2. To investigate the relationship between global Type A personality and cynicism;

3. To investigate the relationship between global Type A personality and professional

inefficacy;

4. To investigate the relationship between locus of control (Internal, Chance, Powerful

Others) and emotional exhaustion;

5. To investigate the relationship between locus of control (Internal, Chance, Powerful

Others) and cynicism;

6. To investigate the relationship between locus of control (Internal, Chance, Powerful

Others) and professional inefficacy.

1.3 Definition of key terms

In the paragraphs that follow an overview of the key terms used throughout this study is

provided.

1.3.1 Burnout

There are many burnout definitions in the literature. Schaufeli and Enzmann (1998)

suggested that burnout consists of both state and process characteristics. They defined

burnout as an enduring negative mind-set that normal individuals possess with regards to

  6  

their work, which is mainly modelled by exhaustion and coupled with the characteristics of

decreased motivation, distress, reduced effectiveness, and negative attitudes and behaviours

in the workplace. This definition implies that burnout is a progressive process of gradual

decline (Schaufeli & Buunk, 2003) in an individual’s energy, motivation, effectiveness,

attitudes, and behaviour that develops over a period of time (Schaufeli & Buunk, 2003;

Schaufeli & Enzmann, 1998).

1.3.2 Type A personality

Initially Type A personality was conceptualised in the medical field by Friedman and

Rosenman (1959), and refers to an individual “aggressively involved in a chronic, incessant

struggle to achieve more and more in less and less time, and, if required to do so, against the

opposing efforts of other things or other persons” (Friedman & Rosenman, 1974, p. 84).

Other personality types have also been identified in the literature and include Type B

(Friedman & Rosenman, 1974; Spence, Helmreich, & Pred, 1987), Type C (Eysenck, 1994;

Themoshok, 1990), and Type D personality (Denollet & Sys, 1996). Type B personality is

seen as the antithesis of Type A personality (Friedman & Rosenman, 1974) while Type C and

Type D personality are both concerned with negative affect (Denollet & Sys, 1996; Eysenck,

1994). In this study the focus is on Type A personality because it is theoretically related to

stress and burnout (i.e., trying to get more and more done despite workplace/university

demands).

1.3.3 Locus of control

Historically, locus of control was introduced by Rotter (1966). According to Rotter (1954,

1975) behaviour potential is a function of expectancies, reinforcement values, and the

situation in which the person finds him/her-self. In this way, the likelihood for a behaviour to

manifest is reliant on the expected value of reinforcement for the individual (Rotter, 1975).

Rotter (1954, 1975) distinguished between generalised expectancies and specific

expectancies. Locus of control is based on generalised expectancies and can be defined as

“the degree to which persons expect that a reinforcement or an outcome of their behavior is

contingent on their own behavior or personal characteristics [internal locus of control] versus

the degree to which persons expect that the reinforcement or outcome is a function of chance,

  7  

luck, or fate, is under the control of powerful others, or is simply unpredictable [external

locus of control]” (Rotter, 1990, p. 56).

1.4 Content overview of the succeeding chapters

A review of the literature focusing on burnout, Type A personality, and locus of control is

provided in Chapter 2. Literature is reviewed concerning the structure of burnout, and

burnout in the student context. Type A personality and locus of control, and the association

between these variables and burnout is also discussed. In chapter 3 the research method is

presented. This includes a discussion of the sampling approach, research process, instruments

used to collect the data, statistical analyses employed, and ethical considerations. Chapter 4

provides the results that were obtained. In Chapter 5 a discussion of these results and a

conclusion are provided.

  8  

CHAPTER 2

LITERATURE REVIEW

2.1 Introduction

In this chapter the existing literature concerning burnout, Type A personality, and locus of

control is reviewed. The chapter starts with a presentation of the structure of burnout and a

discussion of burnout in university students. A process model of burnout is then presented.

This is followed by an investigation of Type A personality, and the relationship between

burnout and Type A personality. Locus of control and the relationship between burnout and

locus of control are then presented.

2.2 The conceptualisation of burnout

In this section the conceptualisation of burnout is discussed. The discussion focuses on the

structure of burnout and in particular on the role of the third factor in burnout. Lastly,

alternative structural models of burnout are presented.

2.2.1 The structure of burnout

Burnout was historically conceived as possessing three inter-related dimensions: (a)

emotional exhaustion, (b) depersonalisation, and (c) reduced personal accomplishment

(Maslach & Jackson, 1981; Maslach, Jackson, & Leiter, 1996). This conceptualisation was

operationalised as the Maslach Burnout Inventory (MBI; Maslach & Jackson, 1981), which is

one of the most frequently employed burnout measures globally (Maslach et al., 2001). In the

MBI, emotional exhaustion is defined as the depletion of emotional resources and/or energy

due to interaction/contact with clients/patients. Depersonalisation is defined as distancing

oneself from and depersonalising clients/patients. Reduced personal accomplishment is

regarded as possessing negative self-evaluations with respect to one’s competence at work

(Bresó et al., 2007; Maslach et al., 2001; Maslach et al., 1996; Maslach & Jackson, 1981).

Although these definitions were initially specific to the helping professions burnout has

subsequently been found to occur in occupations outside of the helping professions (Maslach

et al., 2001). The Maslach Burnout Inventory General Survey (MBI-GS; Schaufeli, Leiter,

  9  

Maslach, & Jackson, 1996) was established to enable the measurement of burnout in different

work settings. The three burnout dimensions in the MBI were extended and relabelled in the

MBI-GS as emotional exhaustion, cynicism, and reduced professional efficacy (Schaufeli et

al., 1996). Emotional exhaustion, in the MBI-GS, is the burnout component perceived to

resemble stress, and refers to emotional strain and/or limited emotional resources. Cynicism

refers to intentionally distancing oneself both cognitively and emotionally from work. Lastly,

reduced professional efficacy is defined as having experiences of being incompetent at one’s

place of work (Maslach et al., 2001; Maslach et al., 1996; Schaufeli et al., 1996).

2.2.2 The third dimension of burnout

The role of reduced personal accomplishment/efficacy in burnout has been critiqued. Some

researchers argue that emotional exhaustion and depersonalisation/cynicism are the most

important burnout dimensions rather than emotional exhaustion, depersonalisation/cynicism,

and reduced personal accomplishment/efficacy (Schaufeli & Bakker, 2004; Schaufeli &

Taris, 2005). Various authors have provided several critiques against the inclusion of reduced

personal accomplishment/efficacy as a burnout dimension (Bresó et al., 2007; Schaufeli &

Salanova, 2007). Firstly, they argued that reduced personal accomplishment/efficacy shares

more variance with personality than with burnout. More specifically, reduced personal

accomplishment/efficacy embodies a quality of self-efficacy and demonstrates stronger

relationships with use of control and self-appraisal of performance than with psychological

strain (Bresó et al., 2007; Cordes & Dougherty, 1993; Lee & Ashforth, 1990; Qiao &

Schaufeli, 2011). It also relates more strongly to engagement (the antithesis of burnout) than

to burnout (Maslach & Leiter, 1997; Schaufeli et al., 2002).

Reduced personal accomplishment/efficacy also demonstrates small correlations with

emotional exhaustion and depersonalisation/cynicism (Bresó et al., 2007). For example, Lee

and Ashforth (1990, 1993, 1996) found that emotional exhaustion and depersonalisation have

weak/small to moderate correlations with personal accomplishment. Taris, Le Blanc,

Schaufeli, and Schreurs (2005) and Kitaoka-Higashiguchi et al. (2004) also found emotional

exhaustion and depersonalisation/cynicism to have a stronger correlation with each other than

they do with reduced personal accomplishment/efficacy. Lastly, it appears that low scores on

professional efficacy are not always related to individuals who are burnt-out. That is, people

who report burnout generally score high on emotional exhaustion and

  10  

depersonalisation/cynicism, but it is not necessarily the case that such individuals will appear

low on personal accomplishment/efficacy1 (Bresó et al., 2007). Therefore it may be likely

that reduced personal accomplishment/efficacy is not part of burnout but a related yet distinct

construct (Bresó et al., 2007).

Due to the problems associated with the reduced personal accomplishment/efficacy

dimension, some authors have argued that an inefficacy scale be employed to measure

burnout rather than an efficacy scale (Bresó et al., 2007; Morgan, de Bruin, & de Bruin,

2014; Qiao & Schaufeli, 2011; Schaufeli & Salanova, 2007). The inefficacy scale is the

antithesis of efficacy and uses negatively phrased items rather than positively phrased items

(Bresó et al., 2007; Schaufeli & Salanova, 2007). Such a conceptualisation accords with the

negatively phrased items of the emotional exhaustion and depersonalisation/cynicism scales

(i.e., all scales are scored in the same direction). Research has found that inefficacy beliefs

have a stronger correlation with both the exhaustion and cynicism dimensions than do

efficacy beliefs (Bresó et al., 2007; Morgan et al., 2014; Schaufeli & Salanova, 2007).

Morgan et al. (2014) also found that the inefficacy scale demonstrated greater overall

saturation with a general burnout factor. This indicates that professional inefficacy shares

more variance with burnout than does professional efficacy. There is thus strong evidence

supporting the utilisation of an inefficacy scale instead of the efficacy scale in measuring

burnout. This study thus uses an inefficacy scale to measure burnout, and refers to

professional inefficacy rather than professional efficacy as the third burnout dimension.

Professional inefficacy in this study refers to feeling incompetent, or having feelings of

failure at work/university (Schaufeli et al., 2002).

2.2.3 Alternative conceptualisations of burnout

Although the three-factor structure in the MBI remains popular, there are different

conceptualisations of the structure of burnout. For example, some authors have argued that

burnout should be a unidimensional construct consisting of work-related exhaustion (Shirom,

1989, 2003), whereas others have argued that burnout is best operationalised using a four-

dimensional model that includes emotional exhaustion, depersonalisation, cynicism, and

reduced personal accomplishment/efficacy (Salanova, Llorens, García-Renedo, Burriel, &

                                                                                                               1 Personal accomplishment/efficacy is scored in the opposite direction to exhaustion and depersonalisation.

  11  

Bresó, 2005). Alternative conceptualisations of exhaustion have also been proposed

(Demerouti, Bakker, Vardakou, & Kantas, 2003; Kalliath, O’Driscoll, Gillespie, & Bluedorn,

2000; Kristensen, Borritz, Villadsen, & Christensen, 2005). For example, Shirom (1989) and

Shirom and Melamed (2006) expanded the exhaustion dimension of burnout to include three

types of exhaustion (emotional, physical, and cognitive). Demerouti (1999) and Demerouti et

al. (2003) similarly expanded the exhaustion dimension to include physical, affective, and

cognitive features of exhaustion. However, most burnout research makes use of the two- or

three-factor model measured by the MBI.

2.3 Burnout in university students

In addition to the organisational context burnout also occurs in the student population (Bresó

et al., 2007; Jacobs & Dodd, 2003; Law, 2010; Morgan & de Bruin, 2010; Noushad, 2008;

Schaufeli et al., 2002). Research has found that burnout occurs among a wide range of

student sample groups from diverse countries including Australia (Lingard, Yip, Rowlinson,

& Kvan, 2007; Robins, Roberts, & Sarris, 2015), Canada (Stoliker & Lafreniere, 2015),

China (Gan et al., 2007; Lingard et al., 2007), the Netherlands (Bresó et al., 2007; Schaufeli

et al., 2002), Finland (Meriläinen, 2014), Iran (Charkhabi, Abarghuei, & Hayati, 2013), the

United States of America (USA; Jacobs & Dodd, 2003), Portugal (Schaufeli et al., 2002),

South Africa (Kotzé & Kleynhans, 2013; Morgan & de Bruin, 2010; Olwage & Mostert,

2014; Van Rooyen, 2008), Spain (Bresó et al., 2007; Schaufeli et al., 2002), Taiwan (Lin &

Huang, 2012, 2014), and Turkey (Duru et al., 2014).

In the student population, the definition of burnout is associated with the previously

discussed burnout factor structure (emotional exhaustion, cynicism, and inefficacy). In the

student population burnout is thus defined as “feeling exhausted because of study demands,

having a cynical and detached attitude toward one’s study, and feeling incompetent as a

student” (Schaufeli et al., 2002, p. 465). Burnout has negative consequences for students. For

example, Stolkier and Lafreniere (2015) found that students’ engagement with their studies,

their academic performance, and their views regarding stress are negatively affected by

experiences of burnout. Kotzé and Kleynhans (2013) investigated a South African student

sample and found that scores low on cynicism were associated with higher scores on

performance. In addition, Lin and Huang (2012) found that loneliness among students is

related to burnout.

  12  

There is evidence that students who possess high levels of emotional exhaustion also have

high levels of cynicism (Duru et al., 2014; Wei, Wang, & Macdonald, 2015). It is therefore

probable that students who perceive that they have limited energy and resources leading to

feelings of emotional exhaustion, also express attitudes of apathy and feelings of withdrawal

from their studies (Duru et al., 2014). High levels of cynicism have been associated with

negative outcomes for students. For example, high scores on cynicism are correlated with low

scores on academic achievement, and feelings of being both incapable and less academically

successful (Duru et al., 2014). Support for these findings are provided by Schaufeli et al.

(2002), who reported a negative association among cynicism and student academic

performance. Pisarik (2009) demonstrated that students who experience a lack of intent or

purpose as to why they attend their university (also referred to as expressing amotivation) are

at a higher risk of experiencing greater emotional exhaustion and cynicism, accompanied by

decreases in experiences of professional efficacy, than are students possessing more intrinsic

motivation.

2.4 Process model of burnout

There are various theories and models in the literature that account for the development and

outcomes of burnout. Each theoretical approach differs in the degree to which it focuses on

certain factors in the development of burnout (Bakker & Demerouti, 2014; Schaufeli &

Enzmann, 1998). However, Schaufeli and Enzmann (1998) cautioned that many of these

theories have received limited empirical support. Early theories and models of burnout tended

to ignore the rapidly changing nature of jobs (Bakker & Demerouti, 2014). In this dissertation

a modified process model of burnout (see Maslach et al., 1996) and the Job Demands-

Resources (JD-R) model (Demerouti et al., 2001) are used to position Type A personality and

locus of control in the development of burnout. Type A personality and locus of control are

reviewed in detail in section 2.5. The process model (Maslach et al., 1996) focuses on job

demands and limited resources as central factors in the development of burnout. The model

also highlights the costs of burnout to the individual. The JD-R model extends this process

model (Schaufeli & Taris, 2014) by focusing on demands, resources, the interaction between

the two, and job crafting in developing both burnout and engagement (Tims, Bakker, &

Derks, 2013). Mokgele and Rothmann (2014) recently extended the JD-R model to the

student population through the development of the Study Demands-Resources (SD-R) model.

This model consists of study demands and study resources, and assumes that both will play

  13  

an important role in developing burnout and engagement among students (Mokgele &

Rothmann, 2014).

The process model modified by Morgan (2008) and used in this study includes individual

factors as causative/preventative factors in burnout in addition to demands and resources.

This is because it has been found that numerous individual characteristics are associated with

burnout (Maslach et al., 2001). Individual factors (such as personality) may lead to/prevent

burnout or interact (moderate/mediate) with demands and resources (environmental factors)

in the development of burnout (cf. Bakker et al., 2010; Xanthopoulou et al., 2007). This

accords with a within context theoretical framework (Maslach et al., 2001). The modified

process model highlights (a) the importance of individual factors as a separate (but

interrelated) component in the development of burnout and, (b) the costs of burnout

associated with the organisation in addition to the cost for the individual. As such, the

modified process model focuses on environmental/organisational factors in the JD-R model

(Demerouti et al., 2001), individual factors, such as, demographic variables, work-attitudes,

personality characteristics, and different coping styles (Maslach et al., 2001; Schaufeli &

Enzmann, 1998), as well as the costs of burnout to the individual and organisation (Maslach

et al., 2001; Morgan, 2008). Each of these components is briefly discussed below.

2.4.1 Environmental factors as antecedents of burnout

Environmental factors are subsumed within the JD-R model. This model was established with

the assumption that burnout develops in the presence of high job demands and low job

resources, regardless of occupation type (Demerouti et al., 2001; Schaufeli & Taris, 2014).

The social, physical, and/or organisational elements associated with an occupation that

require enduring physical and/or mental effort from an individual are job demands

(Demerouti et al., 2001). Job demands include, for example, intense work pressure, work

overload, time pressure, emotional demands, and the physical working environment (Bakker,

Demerouti, & Verbeke, 2004; Demerouti et al., 2001). Various demands have been found to

be related to burnout across different organisational settings (see Schaufeli & Taris, 2014, for

an overview of these demands).

The SD-R model was developed to better comprehend the impact that study characteristics

have on students’ health and well-being (Mokgele & Rothmann, 2014). Study demands in the

  14  

SD-R model are academic circumstances that are likely to manifest stress responses from

students when these circumstances overpower their own capabilities and limitations (i.e.,

when demands exceed resources) (Demerouti et al., 2001; Mokgele & Rothmann, 2014).

Study demands include having to behave in a studious manner (Dahlin, Fjell, & Runeson,

2010; Rigg, Day, & Adler, 2013), subjective experiences of workload (Jacobs & Dodd, 2003;

Kember, 2004), and reaching time limits to accomplish academic goals/demands (Dong Hun,

Sunwoo, & Sichang, 2005; Kember, 2004; Van Rooyen, 2008). Numerous other stressors

may also constitute or relate to study demands, such as student grades and student

competition, concerns about one’s career and future success, financial pressures, the physical

learning environment, physical distance from place of residence to the university, and limited

recreational and social activities (Dong Hun et al., 2005; Van Rooyen, 2008).

The social, physical, psychological, and/or organisational elements associated with an

occupation that are considered to, “(a) be functional in achieving work goals; (b) reduce job

demands and the associated physiological and psychological costs; [and] (c) stimulate

personal growth and development” are regarded as job resources (Demerouti et al., 2001, p.

501). They occur at four different levels: (a) the organisational level, with regards to career

opportunities and job security; (b) the social and interpersonal relationship level, in terms of

receiving support from one’s supervisor and colleagues; (c) the work organisation level, in

terms of involvement in decision making and role clarity; and (d) the task level, in terms of

autonomy, task variety and feedback (Bakker et al., 2004). Various other resources are also

related to engagement across different organisational settings (see Schaufeli & Taris, 2014,

for an overviews of these resources).

In the university setting study resources are regarded as factors that tend to enable students to

be engaged with their studies, influencing their energy and/or motivation (Demerouti &

Bakker, 2011; Mokgele & Rothmann, 2014). These resources have an interaction effect and

reduce burnout when study demands are high (Demerouti & Bakker, 2011; Mokgele &

Rothmann, 2014). Examples of study resources provided by Mokgele and Rothmann (2014)

include peer social support, the nature of assignments/tasks (including personal growth,

feedback and autonomy), and fostering good relationships with lecturers/academic staff.

  15  

2.4.2 Development of burnout according to the JD-R model

The JD-R model posits that burnout occurs in two-phases (Demerouti et al., 2001). The first

phase occurs when job demands lead to persistent overextending, which results in

exhaustion/burnout if the individual does not have an opportunity to recover from the

overextension (Demerouti et al., 2001). This is acknowledged as the energetic (Demerouti et

al., 2001) or health impairment process (Schaufeli & Bakker, 2004; Schaufeli & Taris, 2014).

The second phase occurs when a lack of job resources tends to prevent one’s ability to meet

job demands, leading to withdrawal behaviours (Demerouti et al., 2001). Such withdrawal

behaviour can result in disengagement from one’s working duties (Demerouti et al., 2001)

and is therefore referred to as the motivational process (Schaufeli & Bakker, 2004). Lastly,

there is an interplay among demands and resources that takes place, in that resources have the

ability to mitigate the impact that demands may have. In other words, the association between

job demands and exhaustion is mediated/moderated by resources (Bakker et al., 2004;

Demerouti et al., 2001). For example, a person may not necessarily develop exhaustion or

burnout if he/she has high demands and high resources.

Job demands can be divided into challenge and hindrance demands (LePine, Podsakoff, &

LePine, 2005; Tadić, Bakker, & Oerlemans, 2015). Challenge demands require both energy

and effort from people, but can lead to goal accomplishment, learning, and development

when managed (LePine et al., 2005; Tadić et al., 2015). In contrast, hindrance demands

represent working conditions that do not provide potential for development (LePine et al.,

2005; Tadić et al., 2015). While both demands demonstrate a positive relationship with

burnout, challenge demands promote engagement and demonstrate a smaller relationship

with burnout (Bakker & Sanz-Vergel, 2013; Crawford, LePine, & Rich, 2010; Tadić et al.,

2015), whereas hindrance demands are negatively associated with engagement and have a

stronger positive association with burnout (Crawford et al., 2010; Tadić et al., 2015). There is

evidence that job resources promote individual well-being in situations of high challenge

demands, but not in circumstances of high hindrance demands (Tadić et al., 2015). Job

crafting has recently been added to the JD-R model (Demerouti, Bakker, & Halbesleben,

2015). More specifically, there is evidence that the seeking resources and reducing demands

components of job crafting are related to resources and demands (Demerouti et al., 2015).

  16  

The JD-R model has received extensive research support in the organisational context (see

Schaufeli & Taris, 2014, for an overview) both internationally (cf. Bakker, Demrouti, &

Euwema, 2005; Bakker, Demerouti, & Schaufeli, 2003; Fernet, Austin, & Vallernand, 2012;

Llorens, Bakker, Schaufeli, & Salanova, 2006; Robins et al., 2015; Schaufeli & Bakker,

2004) and in South Africa (Fourie, Rothmann, & van de Vijver, 2008; Janse van Rensburg,

Boonzaier, & Boonzaier, 2013; Rothmann & Jordaan, 2006; Rothmann & Joubert, 2007). In

the South African context, Fourie et al. (2008) found that when job demands were high and

job resources were low, they tended to demonstrate a strong link to the development of

burnout among non-professional counsellors. Rothmann and Joubert (2007) similarly found

that exhaustion (burnout) was predicted when job demands were high and job resources were

low. In turn, engagement was predicted in the presence of high job resources, such as social

support, among managerial staff (Rothmann & Joubert, 2007). In the academic staff context,

Rothmann and Jordaan (2006) showed that job resources predicted a substantial amount of

variance in engagement. In particular, job resources were found to explain 26% to 38% of the

variance in vigour and dedication, respectively (Rothmann & Jordaan, 2006). Janse van

Rensburg et al. (2013) found similar results in a sample of call-centre agents.

In the student context Mokgele and Rothmann (2014) found that high study demands and a

lack of study resources were positively associated with burnout. In addition, Jacobs and Dodd

(2003) demonstrated that lower levels of burnout were linked to social support. Olwage and

Mostert (2014) similarly found that parental support had a negative correlation with burnout.

There is also evidence of an interaction between individual factors and demands/resources in

the development of burnout in students. Research found, for example, that subjective

responses to the amount of work and a negative temperament toward studies were associated

with burnout (Jacobs & Dodd, 2003). Meriläinen (2014) found that negative teaching-

learning experiences affected students’ self-efficacy and expectations to succeed at

university. These in turn were associated with decreased achievement motivation and higher

levels of exhaustion and cynicism. Indeed, Mokgele and Rothmann (2014) found that

possessing a supportive relationship with one’s lecturers had the greatest impact on both a

student’s energy as well as motivation.

  17  

2.4.3 Individual factors as antecedents of burnout

Individual factors refer to the unique individual attributes that could protect an individual

from or lead to the development of burnout (Maslach et al., 2001), or alternatively serve as

personal demands/resources (Robins et al., 2015). Personal resources, which are positive self-

evaluations (Bakker & Demerouti, 2014), have been found to interact with job demands and

resources in predicting both exhaustion as well as engagement (Xanthopoulou et al., 2007).

Individual factors include demographic variables (such as age, gender, and marital status),

different coping styles (such as problem focused coping, confrontational coping and

escape/avoidance coping), attitudes toward work, and personality characteristics (Maslach et

al., 2001; Schaufeli & Enzmann, 1998; Storm & Rothmann, 2003). Most research has

focused on the Big five personality traits (Bakker et al., 2006; Morgan & de Bruin, 2010),

and to a lesser extent hardiness (Alarcon et al., 2009; Lo Bue et al., 2013), locus of control

(Alarcon et al., 2009; Gan et al., 2007), and Type A personality (Alarcon et al., 2009). For

example, in the student population Morgan and de Bruin (2010) demonstrated that

Neuroticism, Extroversion, and Conscientiousness were strongly linked to each of the three

burnout factors, and were able to explain approximately .11% to 11.76% of the variance in

the three burnout factors. Neuroticism is particularly strongly related to the development of

burnout (Taris et al., 2013). Type A personality and locus of control are discussed in section

2.5 of this research study.

2.4.4 Costs of burnout

There are numerous consequences associated with burnout for the individual as well as for

the organisation/university (Cordes & Dougherty, 1993). For the individual, burnout has been

linked to various physical illnesses, personal dysfunction, reduced self-esteem, anxiety,

depression (Maslach et al., 2001), substance abuse (Maslach & Goldberg, 1998),

headaches/migraines, and sleep problems (Kahill, 1988). Burnout is also related to negative

attitudes towards the self (Kahill, 1988) and has a negative impact on a person’s family and

peers. For example, Jackson and Maslach (1982) demonstrated that police officers who

scored high on burnout tended to be less involved with friends and family. People who score

high on burnout also engage in more conflicts with their work colleagues (Maslach et al.,

2001). Burnout is linked to similar outcomes in the student context, including somatic

complaints such as headaches/migraines, and physical illness (Mokgele & Rothmann, 2014).

  18  

At the interface between the individual and organisation, burnout has led employees to have

negative attitudes toward their job and employers (Kahill, 1988), withdrawal, higher

turnover, absenteeism, and dissatisfaction with the job (Bakker et al., 2003; Huynh,

Xanthopoulou, & Winefield, 2014; Maslach & Goldberg, 1998). Burnout at work is also

related to reduced performance and productivity (Maslach et al., 2001). A longitudinal study

conducted by De Beer, Pienaar, and Rothmann (2016) found that burnout functions as a

mediator in the association between work overload and psychological ill-health symptoms.

That is, overtime it appears that work overload leads to burnout, and burnout, in turn, leads to

psychological ill-health symptoms (De Beer et al., 2016). In the context of students there is

evidence that burnout influences students’ perceptions of and commitment to the university

(Neumann et al., 1990), intentions to drop out (Law, 2007), and leads to a reduction in

academic performance (Schaufeli et al., 2002). Burnout therefore plays an important role in

students’ university effectiveness (Neumann et al., 1990).

2.5 Type A personality and locus of control

In this section Type A personality and locus of control are discussed, followed by an

investigation of the relationships between these variables and burnout in the organisational

and academic context.

2.5.1 Type A personality

The concept of Type A personality was identified when Friedman and Rosenman (1959)

observed that patients with coronary heart disease (CHD) appeared to be in a chronic struggle

to obtain goals, and displayed behaviours that indicated that they were in conflict with time

and with others around them. They suggested that individuals displaying this behaviour

pattern, which they labelled as Type A, were more likely to develop coronary heart disease

and other psychosomatic symptoms (Friedman & Rosenman, 1959, 1974). In their

conceptualisation, the behavioural dispositions exhibited by the Type A individual are

aggressiveness, impatience, hostility, ambitiousness, competitive achievement striving, and

time urgency (Friedman & Rosenman, 1974; Hallberg et al., 2007; Rosenman, 1978). People

scoring high on Type A personality live fast paced lives where they work under constant time

pressure, tend to push themselves and their ambitions to extreme levels, take on new and

different challenges, place excessive demands on themselves, increase the difficulty of their

  19  

goals, are often impatient, and tend to be excessively involved in their work and work roles

(Baka & Derbis, 2012; Mahajan & Rastogi, 2011; Rayburn & Rayburn, 1996). They also

tend to value and/or enjoy responsibility, challenges, and recognition (Burke & Weir, 1980).

2.5.1.1 Dimensionality of Type A personality

Type A personality was historically conceived as a global construct associated with the

development of coronary heart disease (Friedman & Rosenman, 1959). However, Type A

personality is also multidimensional (Barling & Boswell, 1995; Dembroski & Costa, 1987)

and some authors have therefore argued against using a global Type A personality score for

predicting behavioural outcomes (cf. Day & Jreige, 2002). Various underlying dimensions

have been found for Type A personality. These include general speed, eating fast, impatience,

competitiveness, and anger (Edwards, Baglioni, & Cooper, 1990). There is evidence that

Type A personality may consist of two overall dimensions, namely, the achievement striving

dimension and the impatience-irritability dimension (Day & Jreige, 2002; Spence et al.,

1987). The dimension that involves a hard driving orientation, in which individuals are active

and serious about their work is referred to as achievement striving (Spence et al., 1987). The

impatience-irritability dimension includes behaviours such as anger, time urgency and

intolerance (Spence et al., 1987). Each of these dimensions appears to be related to different

outcomes (Barling & Charbonneau, 1992; Bluen, Barling, & Burns, 1990; Hallberg et al.,

2007; Spence et al., 1987).

2.5.2 Type A personality in the organisational context

Type A personality has enjoyed a wealth of research in the organisational context. In the

paragraphs that follow, this research is briefly summarised.

2.5.2.1 Positive work-related outcomes

A variety of positive outcomes are associated with Type A personality. According to

Friedman (1969) people who score high on global Type A personality possess specific work-

related attributes, such as achievement striving, increased personal effort, competitiveness,

and ambition, that promote their work performance. Individuals who score high on global

Type A personality and percieved control are, for example, found to perform well at work

  20  

(Lee, Ashford, & Bobko, 1990). In the South African context Bluen et al. (1990)

demonstrated that the achievement striving dimension of Type A personality was positively

correlated with sales performance in insurance brokers. Type A personality therefore appears

to be correlated with high levels of success in the organisational context (Aziz & Vallejo,

2007). People who score high on global Type A personality display many favourable

working behaviours which tend to be acknowledged and rewarded at work (Day & Jreige,

2002). Indeed, there is evidence that they tend to be in occupational positions with high levels

of responsibility (Byrne & Reinhart, 1989; Chesney & Rosenman, 1980).

2.5.2.2 Negative work-related outcomes

Despite the many positive outcomes associated with the general Type A personality, there are

also certain negative work-related consequences associated with it. For example, global Type

A personality is associated with various negative workplace behaviours such as irritability,

abrasiveness, egocentric tendencies, poor listening skills, and poor interpersonal relationships

(Burke & Weir, 1980; Ganster, Sime, & Mayes, 1989). Individuals who score high on global

Type A personality also tend to enage more frequently in different types of workplace

aggression (Baron, Neuman, & Geddes, 1999). They are thus more likely to become involved

in workplace conflict and show aggressive behaviours (Baron, 1989; Baron et al., 1999;

Evans, Palsane, & Carrere, 1987).

Individuals scoring high on global Type A personality also appear to work in contexts

characterised by large workloads, strong competition, pressurised deadlines, conflicting

demands, and high performance standards (Burke & Deszca, 1982; Ganster et al., 1989;

Howard, Cunningham, & Rechnitzer, 1977). In addition, they generally work longer hours

than people who score lower on global Type A personality (Chesney & Rosenman, 1980;

Howard et al., 1977) and score higher on scales measuring workaholism (Erden, Toplu, &

Yaşhoğlu, 2013). People who score high on global Type A personality (Evans et al., 1987)

and Type A’s impatience-irritability dimension, also tend to perceive higher levels of stress

(Day & Jreige, 2002). Type A’s impatience-irritability dimension is also related to depression

(Bluen et al., 1990). Unsurprisingly, evidence shows that global Type A personality is linked

to various health related problems, such as cardiovascular disease (Schaubroeck, Ganster, &

Kemmerer, 1994).

  21  

2.5.3 Type A personality in the university setting

Research on Type A personality in university students has yielded similar results to those

found in the organisational context. In this section the positive and negative consequences

associated with Type A personality in university students are presented.

2.5.3.1 Positive student outcomes

Compared to students who do not score high on global Type A personality students who

score high on global Type A personality tend to be motivated to work harder on their studies

(Mahajan & Rastogi, 2011), be more involved with their studies, spend more time in lectures

(Waldron et al., 1980), adopt a hard-driving approach toward their university tasks (Burnam,

Pennebaker, & Glass, 1975), and express greater certainty of being able to achieve their goals

(Tang & Liu, 1989). They also tend to obtain good academic results (Glass, 1977; Northam

& Bluen, 1994; Waldron et al., 1980) and be more productive than students who score higher

on Type B personality (Tang, 1987).

2.5.3.2 Negative student outcomes

There are also negative outcomes associated with Type A personality in the university

context. Research shows that students who score high on global Type A personality tend to

feel overwhelmed by their studies (Mahajan & Rastogi, 2011). As with people in the

organisational setting, these students also tend to experience elevated levels of aggression

(Innamorati & Pompili, 2006) and have less social support than students who score lower on

general Type A personality (Mahajan & Rastogi, 2011). Furthermore, the anger and

competitiveness dimensions of Type A personality are both positively related to depression

(Northam & Bluen, 1994). Type A’s impatience-irritability dimension has demonstrated a

positive association with poor health (Northam & Bluen, 1994).

2.5.4 Relationship between Type A personality and burnout

Given the literature discussed above it is clear that there are many attitudinal and behavioural

characteristics that relate Type A personality to burnout (Alarcon et al., 2009; Hallberg et al.,

2007; Maslach et al., 2001; Nowack, 1987). For example, people who score high on Type A

  22  

personality are likely to view their working context in a negative way (Kirmeyer, 1988), and

may tend to draw negative reactions from those around them (Spector & O’Connell, 1994).

These individuals are also more likely to possess a tendency to control their own work roles

and duties in ways that inevitably lead to the development of various types of stressors

(Caplan & Jones, 1975). These, in turn, are linked to the development of burnout (Schaufeli

& Enzmann, 1998).

For example, a study on a sample of teachers found that global Type A personality was

positively correlated with a total burnout score comprising of the emotional exhaustion,

depersonalisation and lack of personal accomplishment dimensions (Jamal & Baba, 2001). In

relation to the burnout dimensions, Jiang, Yan, and Danhu (2004) demonstrated that Type A

personality had a positive correlation with emotional exhaustion and depersonalisation in

medical staff. López et al. (2008) demonstrated similar findings. There is also evidence that

global Type A personality has a positive correlation with personal accomplishment (Fuslier

& Manning, 2005; López et al., 2008). A meta-analysis on personality and burnout conducted

by Alarcon et al. (2009), for instance, showed global Type A personality to have a positive

correlation with only the dimension of personal accomplishment. Hallberg et al. (2007)

investigated the association among burnout, global Type A, and the achievement striving and

irritability impatience dimensions. They found that a positive relationship existed between

global Type A personality and emotional exhaustion. However, when looking at the sub-

dimensions it appears that it is Type A’s irritability dimension rather than Type A’s

achievement striving dimension that is correlated with burnout. It also appears that the

achievement striving component has a negative relationship with emotional exhaustion and

cynicism. They thus argued that the dimension of achievement striving is a positive aspect of

Type A personality that leads to engagement while irritability is a more harmful component

that leads to burnout.

2.5.5 Locus of control

As mentioned in Chapter 1, locus of control is based on generalised expectancies of the cause

of an outcome (Rotter, 1954). In this way locus of control is concerned with future behaviour

(rather than past behaviour) and the belief that a particular behaviour can influence the nature

of reinforcement (Furnham, 2008; Furnham & Steele, 1993). It is thus concerned with

whether or not a person claims that a particular reinforcement occurs (causal relationship)

  23  

because of his/her own behaviour or because of some external force (Rotter, 1966). Simply

stated, “[l]ocus of control is … a belief [expectancy] that a response will, or will not,

influence the attainment of reinforcement” (Furnham & Steele, 1993, p. 444). Although locus

of control is conceptually split into an internal and external locus of control continuum, the

dimensionality of locus of control remains open to debate (Furnham, 2008).

2.5.5.1 Dimensionality of locus of control

Historically locus of control was identified as being unidimensional and normally distributed

in the population (Rotter, 1966). However, research has generally failed to support this

conceptualisation. There is evidence for a two-factor model (Parkes, 1985; Presson, Clark, &

Benassi, 1997), a three-factor model (Smith, Trompenaars, & Dugan, 1995), a four-factor

model (Collins, 1974), and a bifactor model consisting of several sub-factors (Duffy, Shiflett,

& Downey, 1977). The external locus of control factor has generally been subdivided into

more than one factor (Furnham & Steele, 1993). The most popular conceptualisation of

external locus of control is that of Levenson (1973, 1981), who created a multidimensional

model of locus of control consisting of Internality, Chance, and Powerful Others. Internality

refers to the degree of control that an individual believes he/she has over his/her own life

(Levenson, 1981). According to Levenson (1973) external locus of control can be separated

into two dimensions comprising of a chance/luck (an unordered world) and an ordered world

that is controlled by powerful others. In other words, Chance locus of control refers to the

degree of control that an individual believes is due to chance/luck (Levenson, 1981).

Furthermore, Powerful Others locus of control refers to the degree of control that an

individual believes is due to those who have power over him/herself (Levenson, 1981). Thus,

in contrast to Rotter (1966), Levenson (1981) argued that a person can score high on both

internal and external locus of control, although this is unlikely. Definitions of Levenson’s

dimensions are provided in section 3.5.3.

Much supporting evidence is provided to the tripartite model of locus of control. For

example, literature has demonstrated that internal locus of control (Internality) is uncorrelated

with Chance and Powerful Others and that Chance and Powerful Others are only moderately

correlated (De las Cuevas, Peñate, Betancort, & Cabrera, 2015; Levenson, 1973; Ross, Ross,

Short, & Cataldo, 2015). Exploratory and confirmatory factor analyses also suggest that there

are three factors underlying locus of control, which are substantially linked to Levenson’s

  24  

tripartite model (Bright, Kane, Marsh, & Bishop, 2013; De las Cuevas et al., 2015; Guan et

al., 2013). Some authors have argued that Chance and Powerful others are related to different

behaviours and have different predictive outcomes, thereby further reinforcing the notion that

they are two separate but interrelated dimensions (Brosschot, Gebhardt, & Godaert, 1994).

Given this body of research this study uses the multidimensional model proposed by

Levenson (1981) rather than the unidimensional model of Rotter (1966).

2.5.6 Locus of control in the organisational context

Locus of control is regarded as an important variable in understanding individual behaviour

in the workplace (Spector, 1982). In particular, research has demonstrated that locus of

control is linked to several work-related outcomes. A brief overview of this research is

presented in the paragraphs that follow.

2.5.6.1 Positive work-related outcomes

Internal locus of control is associated with various positive outcomes in the organisational

context. Individuals who score high on internal locus of control, for example, experience

greater well-being at work (Meier, Semmer, Elfering, & Jacobshagen, 2008). They also

attribute work outcomes to their own ability and effort (Lonergan & Maher, 2000), whereas

those scoring high on external locus of control often attribute work outcomes as being due to

chance (Spector, 1988). People possessing an internal locus of control are therefore able to

modify their behaviours in accordance with the outcome that they experience, whereas those

who score lower on internal locus of control tend to not do so (Rotter, 1966). Unsurprisingly,

high scores on internal locus of control are linked to goal and task orientated behaviours, high

job motivation, good performance (Spector, 1982), and high levels of job satisfaction

(Tillman, Smith, & Tillman, 2010). Research using Levenson’s (1981) tripartite model with a

sample of teachers showed internal locus of control (Internality) to be positively correlated

with self-esteem, achievement motivation, use of active coping strategies, and pleasantness of

life events (Brosschot et al., 1994).

  25  

2.5.6.2 Negative work-related outcomes

Locus of control is also associated with a number of negative work-related outcomes. In

particular, locus of control is strongly related to stress (Jagannathan, Thampi, & Anshu, 2013;

Spector & O’Connell, 1994). For example, perceived control is related to emotional stress

and work stressors (Hanif & Sultan, 2011; Spector, 1986; Spector, Cooper, & Aguilar-Vafaie,

2002; Spector & O’Connell, 1994). People who score higher on external locus of control

often experience occupations with high demands as more strenuous (Botha & Pienaar, 2006;

Spector, 1988) than do people who score higher on internal locus of control (Spector, 1982;

Spector & O’Connell, 1994). Possessing higher stress levels is associated with a greater

chance of experiencing depression (Gray-Stanley et al., 2010; Meier et al., 2008). High

scores on external locus of control are also associated with counterproductive work

behaviours (Arya & Khandelwal, 2013; Sprung & Jex, 2012), perceived lack of control over

job outcomes, and helplessness (Sprung & Jex, 2012). Furthermore, research conducted using

Levenson’s (1981) tripartite model found that Chance and Powerful Others had a positive

correlation with neuroticism, social inadequacy and hostility, and a negative relationship with

self-esteem (Brosschot et al., 1994). Powerful Others and Chance were also positively

associated with avoidant coping strategies and intensity of daily hassles, and Powerful Others

has a negative association with seeking social support (Brosschot et al., 1994).

In light of the aforementioned research it is possible for one to view internal locus of control

as positive and external locus of control as negative. Furnham and Steele (1993) cautioned

against adopting this “good guy-bad guy dichotomy” (Rotter, 1975, p. 60). They argued, for

example, that people who score high on internal locus of control may experience more

negative consequences to their self-esteem following a failure than individuals scoring high

on external locus of control. Furthermore, due to their general view that people are

responsible for their own outcomes, those scoring high on internal locus of control may not

be particularly supportive of others. In this way, both benefits and hindrances are associated

with an internal and external locus of control.

  26  

2.5.7 Locus of control in the university setting

Several studies have been conducted on locus of control in the university setting. In this

section the positive and negative outcomes associated with locus of control in university

students are discussed.

2.5.7.1. Positive student outcomes

In the student context, research has found that students displaying an internal locus of control

have greater levels of academic achievement than students displaying an external locus of

control (Findley & Cooper, 1983). These students also tend to make greater use of

metacognition (Arslan & Akin, 2014). Use of metacognition is associated with enhanced

learning outcomes (Jacobs & Paris, 1987). Using Levenson’s model, Prociuk and Breen

(1975) found that students possessing an internal locus of control (Internality) tended to be

more academically successful than students with high scores on Chance and/or Powerful

Others. Students possessing an internal locus of control also tend to exert more effort (Arslan

& Alkin, 2014), have greater motivation in their studies (Anderson & Hamilton, 2005),

experience greater subjective well-being (Dave, Tripathi, Singh, & Udainiya, 2011), express

a more positive self-concept, and tend to view themselves as being more efficient in the

university setting, in comparison to students possessing an external locus of control (Sagone

& de Caroli, 2014).

2.5.7.2 Negative student outcomes

Locus of control is associated with various negative outcomes in the university context. For

example, students who score high on external locus of control often claim that their

behaviours are as a result of chance and therefore experience stress, poor health, and

decreased self-efficacy (De Carvalho, Gadzella, Henley, & Ball, 2009; Roddenberry & Renk,

2010). There is also a positive relationship between belief in Powerful Others and Chance

and severe stress (De Carvalho et al., 2009). Belief in Chance, in particular, is correlated with

high levels of stress (De Carvalho et al., 2009). There is further evidence that high scores on

external locus of control, Chance, and/or Powerful others are correlated with depression

(Brosschot et al., 1994; Zawawi & Hamaideh, 2009), sleeping difficulties (Brosschot et al.,

1994), and poor financial management (Britt, Cumbie, & Bell, 2013).

  27  

2.5.8 Relationship between locus of control and burnout

Much research attention has focused on the association between burnout and locus of control

in the organisational context (cf. De Hoogh & den Hartog, 2009; Maslach et al., 2001;

Schaufeli & Enzmann, 1998; Wang, Bowling, & Eschleman, 2010). Research suggests that

individuals who score higher on external locus of control have greater burnout levels than do

individuals who score higher on internal locus of control (Maslach et al., 2001; Schaufeli &

Enzmann, 1998). For example, Alarcon et al. (2009) found that internal locus of control was

negatively correlated with the dimensions of emotional exhaustion and depersonalisation, and

was positively correlated with personal accomplishment. Hamwi, Rutherford, Boles, and

Madupalli (2014) found external locus of control to be indirectly and positively correlated

with emotional exhaustion. Jiang et al. (2004) found that locus of control was positively

correlated with the dimensions of emotional exhaustion and depersonalisation, and negatively

correlated with personal accomplishment. Research using Levenon’s (1973) tripartite model

showed Internality to be negatively correlated with each of the three dimensions of burnout

(using lack of personal accomplishment rather than personal accomplishment as the third

dimension) and that Chance and Powerful Others are positively correlated with all three

burnout dimensions (Nejad, Esmaili, & Jenaabadi, 2014).

Several studies examined the association among locus of control and burnout in the student

context (Gan et al., 2007; Kalantarkousheh, Araqi, Zamanipour, & Fandokht, 2013; Li, Song

& Guo, 2009). Gan et al. (2007) showed locus of control to be a significant predictor of all

three factors (exhaustion, cynicism, and professional efficacy) of student burnout, while

Kalantarkousheh et al. (2013) demonstrated a positive correlation between external locus of

control and a total burnout score consisting of the three burnout dimensions. Li et al. (2009)

similarly found locus of control to be correlated with student burnout and found that locus of

control mediates the relationship between academic stress and burnout. However, there is

also evidence that internal locus of control has a negligible relationship with burnout in

students (Kalantarkousheh et al., 2013).

2.5.9 Summary and content overview of the succeeding chapter

It appears that burnout, consisting of emotional exhaustion, depersonalisation/cynicism, and

inefficacy, is caused by both environmental factors (demands and resources) and individual

  28  

characteristics. In particular, research has demonstrated the existing association between the

personality variables of Type A personality and locus of control with burnout. Global Type A

personality has been found to demonstrate a positive association with each burnout

dimension: emotional exhaustion, depersonalisation/cynicism and personal accomplishment.

In turn, locus of control has demonstrated a positive relationship with two burnout

dimensions, namely, emotional exhaustion and depersonalisation, and has furthermore shown

a negative correlation with personal accomplishment as the third burnout dimension. More

specifically research has shown that internal locus of control has a negative association with

emotional exhaustion, depersonalisation, and lack of personal accomplishment. Research

conducted on external locus of control and burnout has demonstrated that there is a positive

relationship between possessing an external locus of control and experiencing emotional

exhaustion, cynicism, and inefficacy. Research has also shown that Powerful Others and

Chance are positively related to each of the three dimensions of burnout: emotional

exhaustion, cynicism, and lack of personal accomplishment. The succeeding chapter presents

the research method.

  29  

CHAPTER 3

METHOD

3.1 Introduction

The research method is discussed in this chapter. It begins with an overview of the research

design and a description of the research sample, leading into a discussion of the

procedure/process and instruments employed. A discussion of the data analysis conducted is

then presented, as well as a discussuion of the ethical procedures adhered to in the execution

of the study.

3.2 Research design

A quantitative research approach was used in this study. This approach involves the

measurement of variables for individual participants in order to obtain numerical scores,

which are then interpreted through descriptive and inferential statistical analysis (Gravetter &

Forzano, 2012). Both a non-experimental cross-sectional survey design and correlational

research was employed (Wilson & MacLean, 2011). This design is concerned with the

measurement of naturally occurring variables at one point in time. The correlational research

approach enabled the identification and description of patterns of relationships between

variables (Gravetter & Forzano, 2012).

3.3 Sample

The study used a sample of South African university students (n = 387). Participants were

sourced from a university in the Gauteng province and accessed using convenience sampling,

a non-probability sampling technique, in which the researcher is unaware as to what the

probability of obtaining a particular individual will be (Gravetter & Forzano, 2012). In order

to enhance the generalisability of the results two strategies proposed by Gravetter and

Forzano (2012) were implemented. Firstly, attempts were made to ascertain that the sample

was reasonably representative of the population of students. As such, students were obtained

from different faculties, ethnic groups, languages, genders, and years of study. Secondly, a

well-presented description of both how the sample was obtained and the demographics of the

participants is provided. This allows other researchers to examine the limits of

  30  

generalisability in this study. Students who indicated that they had a full-time job were

removed from the sample in order to prevent potential confounding of student burnout and

work related burnout. The composition of the sample group is provided below.

The participants mean age was 20.59 years (Mdn = 20.00, SD = 2.43). Table 3.1 depicts

further demographic characteristics of the participants. Table 3.1 shows that there was a

greater amount of women (n = 247, 63.7%) than men (n = 140, 36.1%) in the sample. Most

of the participants identified themselves as belonging to the Black African ethnic group (n =

331, 85.3%) with the remainder identifying with the Coloured/Mixed Ethnicity (n = 13,

3.4%), Indian/Asian (n = 13, 3.4%), White (n = 6, 1.5%), and other (n = 24, 6.2%) ethnic

groups. The most spoken home language was isiZulu (n = 97, 25%). Other home languages

indicated by the participants were Afrikaans (n = 17, 4.4%), English (n = 46, 11.9%),

isiNdebele (n = 9, 2.3%), isiXhosa (n = 26, 6.7%), Northern Sotho (n = 40, 10.3%), Sesotho

(n = 31, 8%), Setswana (n = 44, 11.3%), siSwati (n = 27, 7%), Tshivenda (n = 27, 7%),

Xitsonga (n = 18, 4.6%) and other (n = 6, 1.5%). As suggested by the relatively young mean

age of the sample, most of the participants were registered in their first (n = 187, 48.2%) or

second (n = 118, 30.4%) year of study. The remainder were registered in their third (n = 60,

15.5%), fourth (n = 10, 2.6%), honours (n = 2, 0.5%), or Masters/Doctorate (n = 10, 2.6%)

year. For the most part the participants were registered in the Faculty of Management (n =

218, 56.2%) or the Faculty of Education (n = 125, 32.2%). The remainder were registered in

the Faculties of Economic and Financial Science (n = 2, 0.5%), Humanities (n = 23, 5.9%),

Law (n = 3, 0.8%), or Science (n = 17, 4.4%).

  31  

Table 3.1

Demographic Characteristics of Participants (n = 387)

Item Characteristics Frequency Percentage

Gender Male 140 36.1

Female 247 63.7

Ethnic Group Black African 331 85.3

Coloured/Mixed 13 3.4

Indian/Asian 13 3.4

White 6 1.5

Other 24 6.2

Home Language isiZulu 97 25

Afrikaans 17 4.4

English 46 11.9

isiNdebele 9 2.3

isiXhosa 26 6.7

Northern Sotho 40 10.3

Sesotho 31 8

Setswana 44 11.3

siSwati 27 7

Tshivenda 27 7

Xitsonga 18 4.6

Other 6 1.5

Year of Study First Year 187 48.2

Second Year 118 30.4

Third Year 60 15.5

Fourth Year 10 2.6

Honours 2 0.5

Masters/Doctorate 10 2.6

Faculty of Registration Management 218 56.2

Education 125 32.2

Economic and Financial Science 2 0.5

Humanities 23 5.9

Law 3 0.8

Science 17 4.4

  32  

3.4 Research procedure

The students were asked to complete a biographical questionnaire, the Maslach Burnout

Inventory–Student Survey (MBI-SS; Schaufeli et al., 2002), the short form of the Student

Jenkins Activity Survey (SJAS; Yarnold, Bryant, & Grimm, 1987), and the Internal,

Powerful Others, and Chance Locus of Control Scale (Levenson, 1981). All instruments were

administered to students during normal lecture classes and times, with permission from

course lectures. During administration students were made aware of the purpose and the

nature of the study’s investigation, and were informed that participating in the research was

voluntary (see section 3.7.). Students were allowed to ask questions regarding the research

and were provided with the researcher’s contact details and the contact details of the

supervisor. All the questionnaires were completed during the lecture classes under normal

testing conditions and the students appeared to complete the questionnaires in a serious

manner. The completion of the questionnaires was overseen by the researcher and various

lecturers. Questionnaires were collected directly from the students once completed. The data

was then captured and analysed by the researcher and supervisor.

3.5 Instruments

A biographical questionnaire and three psychometric instruments were used. Each of the

psychometric instruments is discussed below.

3.5.1 The Maslach Burnout Inventory – Student Survey (MBI-SS)

The MBI-SS (Schaufeli et al., 2002) is based on the Maslach Burnout Inventory–General

Survey (Schaufeli et al., 1996), developed especially for the student context. It is a self-report

questionnaire including 15 items designed to measure three burnout dimensions: (a)

emotional exhaustion, measured with five items; (b) cynicism, measured with four items; and

(c) professional efficacy, measured with six items. In this study the professional inefficacy

instead of the professional efficacy scale is used (cf. Morgan et al., 2014). The professional

inefficacy scale consists of six items (Bresó et al., 2007; Schaufeli & Salanova, 2007). All

items in the MBI-SS are scored on a 7-point frequency rating scale [0 (never) to 6 (always)].

Burnout is indicated by scores that are high on emotional exhaustion, cynicism, and

professional inefficacy (Schaufeli et al., 2002; Hu & Schaufeli, 2009).

  33  

Several studies have been conducted in the South African context on the reliability and

validity of the MBI-SS (Morgan et al., 2014; Mostert, Pienaar, Gauche, & Jackson, 2007;

Pienaar & Sieberhagen, 2005). Pienaar and Sieberhagen (2005) reported Cronbach alpha

coefficients of .79 for exhaustion and .73 for cynicism. Mostert et al. (2007) reported

Cronbach alpha coefficients of .74 for exhaustion and .68 for cynicism. Morgan and de Bruin

(2010) found Cronbach reliability alphas higher than those in the previous studies, .87 for

emotional exhaustion, .88 for cynicism, and .81 for professional inefficacy. Morgan et al.

(2014) reported Cronbach reliabilities of .87 for emotional exhaustion, .86 for cynicism, and

.81 for professional inefficacy. Suitable construct validity has also been demonstrated via

factor analysis (Morgan, 2008; Morgan et al., 2014). Morgan (2008), using exploratory factor

analysis, demonstrated support for the three-factor burnout structure and found that only one

item from the depersonalisation dimension cross-loaded onto the professional efficacy scale.

Morgan et al. (2014) similarly demonstrated support for the three factors in burnout using a

bifactor exploratory factor analysis, and demonstrated support for the use of the inefficacy

scale instead of the efficacy scale. An overview of the reliability coefficients obtained in this

study is provided in Table 2 in section 4.3.

3.5.2 The short form of the Student Jenkins Activity Survey (SJAS)

The short form of the Student Jenkins Activity Survey (Yarnold et al., 1987) is a self-report

questionnaire consisting of 21 multiple-choice items adapted from Glass’s (1977) original

long form (44 multiple-choice items) of the Student Jenkins Activity Survey, which in turn

was based on the adult version of the Jenkins Activity Survey (AJAS; Jenkins, Zyzanski, &

Rosenman, 1979). The items in the SJAS are dichotomous (0 or 1) in nature. The SJAS

measures three dimensions of Type A personality (hard-driving/competitive, rapid eating,

and rapid speaking). The hard-driving/competitive dimension consists of 11 items while the

rapid eating and rapid speaking dimensions each consist of two items. Having two items per

dimension is not optimal for analysis, particularly when these items are dichotomous.

Unsurprisingly the two dimensions had poor internal consistency2. This study’s focus is on

global Type A personality thereby indicating that it is inappropriate to only use the hard-

driving/competitive dimension (because hard-driving/competitive is only one dimension of

Type A). Therefore, in this study the total SJAS score (general Type A factor) was                                                                                                                2 Hard-Driving/Competitive = .61 (95% CI = .54 - .68), Rapid Eating = .53 (95% CI = .36 - .69), and Rapid Speaking = .52 (95% CI = .35 - . 69).

  34  

interpreted to provide a global Type A personality score3 (Bryant & Yarnold, 1995). A

weighting of 0 is allocated to Type B responses and a weighting of 1 is allocated to Type A

responses. Using this weighting, a score ranging from 0 to 21 is obtained. Students can be

classified as Type A or Type B using either a median split or a mean and standard deviation

split (Bryant & Yarnold, 1995; Yarnold et al., 1987). In this study the total raw score was

used rather than creating a split because a split can lead to loss of information. Each student

therefore obtained a Type A raw score ranging from 0 to 21.

Yarnold, Mueser, Grau, and Grimm (1986) reported that across the SJAS’s hard-

driving/competitive and speed/impatience dimensions, internal consistency coefficients

tended to be moderate across various demographic groups, ranging from .57 to .72. The alpha

coefficients for the total SJAS scale ranged between .40 and .62 (Yarnold et al., 1986). Other

research has found higher alpha coefficients for the total SJAS scale. For example, Bryant

and Yarnold (1995) reported a Cronbach alpha coefficient of .87 for the total SJAS scale. The

Yarnold et al. (1986) study of university students of various demographic groups (ethnicity

and sex) demonstrated a high level of reliability for the SJAS across two different time

periods, i.e., two weeks and three months, with test-retest reliability coefficients ranging from

.70 to .96. These findings suggest that the SJAS demonstrates satisfactory internal

consistency and high test-retest reliability. Various studies were conducted on different factor

structures of the SJAS. Evidence has been found for both a unidimensional structure

consisting of a total score as well as for a multidimensional structure (Bryant & Yarnold,

1995). An overview of the reliability coefficients obtained in this study is provided in Table 2

in section 4.3.

3.5.3 The Internal, Powerful Others, and Chance (IPC) Locus of Control Scale

The Internal, Powerful Others, and Chance Locus of Control Scale (IPC; Levenson, 1981) is

a self-report measure consisting of three subscales (Internal, Powerful Others, and Chance),

each containing eight items. Items are scored using a 6-point Likert format, that ranges from

-3 to 3. Rasch analysis indicated that the 6-point rating scale was not optimal for each scale

(Morgan & de Bruin, manuscript in preparation). It was thus collapsed to a 4-point rating

category for each scale that ranges from 1 to 4 (Linacre, 2015).                                                                                                                3 However, separate correlation coefficients for each of the scales are provided in the results section to determine if each of the dimensions has a different relationship with the burnout dimensions.    

  35  

The Internality scale assesses the degree to which one claims to be in control of his/her life

outcomes. This scale is therefore akin to internal locus of control. The Powerful Others and

Chance scales represent an external orientation to the world rather than an internal

orientation. The Powerful Others scale is concerned with the perception that the world is

ordered but that it is controlled by powerful others. The Chance scale involves the belief that

the world is unordered and that chance, fate, or luck determine outcomes. Scoring high on

any of the subscales are indicative of the tendency to believe in a particular source of control.

For example, a low score on the Internality scale does not imply that a person believes in

chance as determining outcomes, but rather that the world is unordered and that he/she cannot

control outcomes (Levenson, 1973, 1981).

Internal consistency coefficients across the three scale scores tend to be relatively satisfactory

(see Levenson, 1981, for an overview). Split-half reliabilities of .72 for Internality, .65 for

Powerful Others, and .71 for Chance have been reported (Walkey, 1979). A study conducted

with university students in the United States of America found one-week test-retest

reliabilities of .64 for Internality, .74 for Powerful Others, and .78 for Chance (Long,

Williams, Gaynor, & Clark, 1988). A study conducted by Stewart (2012) on a student sample

reported similar test-retest coefficients of .60 for Internality, .79 for Powerful Others, and .80

for Chance. Various factor analytic studies have demonstrated evidence for a three-factor

locus of control model (Bright et al., 2013; De las Cuevas et al., 2015; Levenson, 1973;

Walkely, 1979) supporting the construct validity of the instrument. An overview of the

reliability coefficients obtained in this study is provided in Table 2 in section 4.3.

3.6 Statistical analysis

Descriptive and inferential statistics were utilised to analyse the data. Reliability was

investigated using Cronbach’s alpha (Cronbach, 1951), robust Cronbach’s alpha (Christmann

& Van Aelst, 2006), Guttman’s Lambda 2 (Guttman, 1945) and Gadermann, Guhn and

Zumbo’s (2012) ordinal reliability analysis. The psych (Revelle, 2014) package version 1.5.8

in R (R Core Team, 2014) version 3.2.1 was used to conduct the reliability analyses. The

relationships between the variables were determined using Pearson’s correlation coefficient.

The effect sizes of the correlations were interpreted using Cohen’s (1988) guidelines [.10 =

small, .30 = moderate, and ≥ .50 = large].

  36  

In order to determine the effects of both Type A personality and locus of control (Internality,

Powerful Others, and Chance) as predictors of emotional exhaustion, cynicism, and

professional inefficacy, multiple regression was employed (Cohen, Cohen, West, & Aiken,

2013). Prior to the regression analyses the assumptions of regression were investigated. More

specifically, assumptions regarding univariate and multivariate outliers, linearity, normally

distributed residuals, multicollinearity, and homoscedasticity of residuals were investigated

(Cohen et al., 2013; Tabachnick & Fidell, 2007). These assumptions were investigated using

the mvtoulier package (Filzmoser & Gschwandter, 2015) version 2.0.6 and car package (Fox

& Weisberg, 2011) version 2.1-0 in R (R Core Team, 2014).

The R2 and adjusted R2 values were utilised to indicate the amount of variance in the

dependent variable explained by the independent variables (Baguley, 2012). Following the

guidelines of Yin and Fan (2001), the Claudy 3 formula (Claudy, 1978) was used to calculate

the adjusted R2. This was done using the yhat package (Nimon, Oswald, & Roberts, 2013)

version 2.0-0 in R (R Core Team, 2014). Robust standard errors were used due to the

presence of minor heteroscedasticity of residuals for the cynicism and professional inefficacy

regression analyses (Hayes & Cai, 2007). More specifically, the HC4 estimator was used

(Cribari-Neto, 2004). It was necessary to use robust estimators to reduce biased parameter

estimates (Hayes & Cai, 2007). The sandwich package (Zeileis, 2004) version 2.3-3 in R (R

Core Team, 2014) was used to implement the HC4 estimator. Cohen's ƒ2 was used to indicate

the global effect size of each regression analysis (Cohen, 1988). It was calculated using the

Effect Size Calculator for Multiple Regression software (Soper, 2015). The criteria of .02 as

small, .15 as medium and .35 as large were used to interpret the effect size (Cohen, 1988).

The unique variance contributed by each independent variable in the regression analysis was

determined using squared semi-partial correlations (Tabachnick & Fidell, 2007).

Lastly, dominance analysis was implemented in order to be able to ascertain the relative

importance of each of the predictors in the regression model (Azen & Budescu, 2003;

Budescu, 1993). Dominance analysis is a technique used to determine the relative importance

of each predictor in the regression model (i.e., which predictors in the p set of predictors are

the most useful predictors in the model). In the context of dominance analysis, a predictor is

considered to be more important than another predictor when it is the strongest predictor

amongst all possible competing predictors in all possible subset models (Azen & Budescu,

2003). The original formulation of dominance analysis only investigated complete dominance

  37  

(Budescu, 1993), which occurs when the variance contribution of a predictor variable is

greater than all other predictors in all possible subset models. However, complete dominance

is a stringent target that is seldom met (Azen & Budescu, 2003; Budescu, 1993). Azen and

Budescu (2003) therefore stipulated two different dominance levels, namely conditional

dominance and general dominance. Conditional dominance occurs when the average

contribution made by a predictor in each subset model is greater than the other predictors in

that subset model. General dominance uses the average increase in variance for all predictors

across all possible subset models (Azen & Budescu, 2003). Dominance analysis was

conducted using the yhat package (Nimon et al., 2013) in R (R Core Team, 2014).

3.7 Ethical considerations

Ethical clearance for the study was attained from the Department of Industrial Psychology

and People Management Research Ethics Committee, and organisational approval was

obtained to administer the questionnaires to the students. Student participants were provided

with an information sheet and an informed consent form. The information sheet made the

participants aware of the nature, purpose, expectations, and conditions of participation. The

participants were further informed of and provided with the contact details for their local

university counselling center, should they have felt that they required counselling support

(this service is offered at no cost to students). The participants were also told that all their

information would remain anonymous and confidential (i.e., participants had anonymity and

confidentiality) and that they had the right to refuse participation. One course lecturer offered

students a minor increase in their course results should they voluntarily complete the

questionnaires. This was an agreement made between the lecturer and her students and was

independent of the research agreement for this study. The data was only used in this study if

written informed consent was given by the participant to use his/her data. All data was stored

in a password protected document that can only be accessed by the researcher and supervisor.

Permission was obtained from the authors of the MBI-SS, SJAS, and IPC questionnaires to

use them in this study.

3.8 Content overview of the succeeding chapter

The next chapter presents the results. This includes a presentation of descriptive and

inferential statistics from the data.

  38  

CHAPTER 4

RESULTS

4.1 Introduction

The results are presented in this chapter. It starts by presenting the descriptive statistics of the

scales. Reliability and correlation coefficients are then presented. Lastly, the multiple

regression analyses are provided.

4.2 Descriptive statistics

Descriptive statistics for the scales are provided in Table 4.1 (descriptive statistics for all the

items of all the scales are provided in Appendix A). The items all demonstrated acceptable

standard errors indicating an accurate degree of measurement (Nunnally & Bernstein, 1994).

For the most part the items demonstrated statistically significant skewness and kurtosis

values (p < .05). Table 4.1 indicates the means, standard deviations, medians, skewness,

kurtosis, and standard error of means for each of the scales in this study. The MBI-SS scales

and the global Type A personality scale also demonstrated statistically significant skewness.

Inspection of QQ plots density plots, and beanplots indicated that the three MBI-SS scales

deviated from normality (see Appendix B). The global Type A personality scale and the locus

of control scales were mostly normally distributed. The mean scores for the three burnout

dimensions were 11.81 for emotional exhaustion, 5.30 for cynicism, and 8.71 for professional

inefficacy. The mean score for global Type A personality was 7.50, and for the three locus of

control scales the mean scores were 23.74 for Internal, 17.76 for Powerful Others, and 19.84

for Chance. As a whole the participants demonstrated low burnout and high internal locus of

control.

  39  

Table 4.1

Descriptive Statistics of Type A Personality, Locus of Control and Burnout

Variable Mean SD Median Skewness Kurtosis SE

Emotional Exhaustion 11.81 5.65 11.0 .63 .28 .29

Cynicism 5.30 4.67 4.0 1.13 1.34 .24

Professional Inefficacy 8.71 5.79 8.0 .69 .03 .29

Global Type A 7.50 3.15 7.0 .33 -.12 .16

Internal 23.74 3.79 24.0 -.20 -.26 .19

Powerful Others 17.76 4.32 18.0 .09 -.47 .22

Chance 19.84 4.30 2.0 -.02 -.07 .22

Note. SD = standard deviation, SE = standard error of mean.

4.3 Reliability

The estimated reliability coefficients for each of the scales are provided in Table 4.2. The

reliability coefficients of the MBI-SS scales and Powerful Others scale were mostly

satisfactory, while the reliability coefficients for the global Type A personality, Internal, and

Chance scales were only marginally satisfactory. The ordinal alpha coefficients painted a

more satisfactory picture. Given the ordinal nature of the data the ordinal alpha coefficients

may be better indicators of the reliability of the scales.

Table 4.2

Reliability Coefficients of the MBI-SS, SJAS, and Levenson’s IPC Locus of Control Scale

Scale Cronbach’s α Robust α Ordinal α λ2

Emotional Exhaustion .78 (.72 - .85) .79 .80 .78

Cynicism .78 (.71 - .86) .79 .83 .79

Professional Inefficacy .69 (.62 - .76) .71 .76 .70

Global Type A .60 (.54 - .66) .60 .74 .62

Internal .60 (.52 - .68) .60 .69 .61

Powerful Others .71 (.65 - .77) .72 .75 .71

Chance .61 (.54 - .69) .63 .67 .62

Note. 95% confidence intervals for α in parentheses. λ2 = Guttman’s lambda 2. α of

original Internality scale prior to Rasch analysis = .55. α of original Powerful Others scale

prior to Rasch analysis = .72. α of original Chance scale prior to Rasch analysis = .65.

  40  

4.4 Correlation coefficients

Pearson correlation coefficients between the scales are presented in Table 4.3. As mentioned

in the method section the SJAS subscale scores were also calculated to determine if there are

differences between them. Inspection of the scatterplots and Loess non-parametric regression

lines (Cleveland, 1979) indicated that the relationships between the variables were mostly

linear (see Appendix C). Table 4.3 indicates that there was a moderate to large effect size

between the correlation coefficients of the MBI-SS scale scores. Furthermore, global Type A

personality was found to demonstrate a small statistically significant negative relationship

with cynicism (r = -.152; p < .01) and professional inefficacy (r = -.160; p < .01), and a non-

significant negative relationship with emotional exhaustion (r = -.069; p = .176). Internal

locus of control had a small statistically significant negative relationship with emotional

exhaustion (r = -.220; p < .001), cynicism (r = -.196; p < .001), and professional inefficacy (r

= -.189; p < .001). Powerful Others demonstrated a small statistically significant positive

relationship with emotional exhaustion (r = .162; p < .01) and cynicism (r = .233; p < .001),

and a medium statistically significant positive relationship with professional inefficacy (r =

.356; p < .001). Chance locus of control demonstrated a small statistically significant positive

relationship with emotional exhaustion (r = .168; p < .001), cynicism (r = .212; p < .001), and

professional inefficacy (r = .247; p < .001). Only the Internality scale of Levenson’s IPC

locus of control scales demonstrated a statistically significant correlation with global Type A

personality. It is important to note that the correlation coefficients reported here may be

smaller than the true correlations due to the unreliability of the measures (i.e., attenuation).

It is interesting to note that the hard-driving/competitive scale of the SJAS demonstrated

negative correlations with the three burnout dimensions while the rapid eating and rapid

speaking scales demonstrated positive correlations with the three burnout dimensions.

Although there was a negligible correlation between the observed rapid eating and rapid

speaking scores, this may not reflect the true correlation between the variables due to the

unreliability of the scale scores and restricted variance (Kline, 2005). As noted in section

3.5.2, the rapid eating and rapid speaking scales consisted of too few items and their

reliability was too low to be meaningfully used as independent variables. Therefore, in the

regression analyses that follow, global Type A personality was used as the independent

variable.

  41  

Table 4.3

Pearson and Spearman Rho Correlation Coefficients for the Summated Scale Scores Scales 1 2 3 4 5 6 7 8 9 10

1 Emotional

Exhaustion .

2 Cynicism .478***†† .

3 Professional

Inefficacy .399***††

.535***††† .

4 Global Type A -.069 -.152**† -.160**†

.

5 Hard-driving/

competitive -.167**†

-.267***† -.274***† . .

6 Rapid Eating .165**† .132**†

.139**† . .021 .

7 Rapid Speaking .166**† .142**†

.148**† . -.025 .103*† .

8 Internal -.220***† -.196***†

-.189***† .237***†

.284***† -.071 .044 .

9 Powerful

Others .162**†

.233***† .356***††

-.069 -.152**† .147**† .062 -

.010 .

10 Chance .168***† .212***†

.247***† -.074 -.105*† .074 .023 .017 .555***†††

.

Note. *** p < .001. ** p < .01. * p < .05. † Practically significant correlation r ≥ .10. †† Practically significant correlation r ≥ .30.

††† Practically significant correlation r ≥ .50.

4.5 Multiple regression

Multiple regression analyses conducted to investigate the predictive effects of global Type A

personality and locus of control on the three burnout dimensions and burnout are presented in

Tables 4.4 to 4.6.

4.5.1 Multiple regression for emotional exhaustion

Multiple regression was utilised to investigate the ability of scores on global Type A

personality, Internal, Powerful Others, and Chance to predict scores on emotional exhaustion

(see Table 4.4). The independent variables were jointly able to explain around 8.4% of the

variance in emotional exhaustion [R2 = .084 (.032 - .136), F(4,383) = 8.779, p < .001, f =

.092]. Inspection of the confidence intervals for the independent variables indicated that

internal locus of control (Internality) (B = -.329, p < .001) was the only statistically

  42  

significant predictor4 at the 95% confidence level while controlling for all other variables in

the model. Dominance analysis indicated that internal locus of control (Internality) had

complete dominance over the three other variables5. The Internality scale also made the

largest unique contribution to the model (sr2 = .046).

Table 4.4

Multiple Regression for Emotional Exhaustion Coefficient B SE B β t p Part GD

Intercept 14.346 (9.495 — 19.198) 2.467 5.815 < .001

Global Type A -.002 (-.189 — .185) .095 -.001 -.022 .983 -.001 .002

Internal -.329 (-.480 — -.177) .077 -.221 -4.265 < .001 -.214 .046

Powerful Others .122 (-.028 — .273) .077 .094 1.595 .112 .078 .016

Chance .157 (.000 — .314) .080 .120 1.966 .050 .099 .019

R2 = .084

Adjusted R2 = .075

Residual SE = 5.431

Note. B = unstandardised beta. 95% confidence intervals in parentheses. β = standardised beta. Part = semi-

partial correlation coefficient. GD = general dominance.

4.5.2 Multiple regression for cynicism

Multiple regression was utilised to investigate the ability of the scores on global Type A

personality, Internal, Powerful Others, and Chance to predict scores on cynicism (see Table

4.5). The independent variables were jointly found to explain around 11% of the variance in

cynicism [R2 = .110 (.052 - .168), F(4,383) = 11.870, p < .001, f = .124]. Inspection of the

confidence intervals for the independent variables indicated that all of the independent

variables were statistically significant at the 95% confidence level while controlling for all

other variables in the model. Dominance analysis indicated that Powerful Others had

complete dominance over Chance, but not Internality. Powerful Others also did not

demonstrate conditional dominance over the other variables. No one variable had complete or

conditional dominance over all the variables. Inspection of the semi-partial correlation

coefficients indicated that internal locus of control (Internality) (sr2 = .029) and Powerful

Others (sr2 = .017) made the largest unique contribution to the model.

                                                                                                               4 That is, the confidence interval values did not intersect with a value of 0 rounded off to three decimal places. 5  Dominances  tables  are  provided  in  Appendix  D.  

  43  

Table 4.5

Multiple Regression for Cynicism Coefficient B SE B Β t p Part GD

Intercept 5.808 (1.750 — 9.866) 2.064 2.814 .005

Global Type A -.135 (-.270 — -.001) .068 -.091 -1.985 .048 -.088 .015

Internal -.215 (-.353 — -.078) .070 -.175 -3.083 .002 -.170 .033

Powerful Others .171 (.038 — .304) .067 .158 2.536 .012 .131 .035

Chance .130 (.002 — .258) .065 .120 2.001 .046 .100 .027

R2 = .110

Adjusted R2 = .102

Residual SE = 4.430 Note. B = unstandardised beta. 95% confidence intervals in parentheses. β = standardised beta. Part = semi-

partial correlation coefficient. GD = general dominance weights.

4.5.3 Multiple regression for professional inefficacy

Multiple regression was utilised to investigate the ability of scores on global Type A

personality, Internal, Powerful Others, and Chance to predict scores on professional

inefficacy (see Table 4.6). The independent variables were jointly found to explain around

17% of the variance in professional inefficacy [R2 = .174 (.106 - .241), F(4,383) = 20.140, p

< .001, f = .210]. Inspection of the confidence intervals for the independent variables

indicated that internal locus of control (Internality) (B = -.252, p = .003) and Powerful Others

(B = .413, p < .001) were statistically significant at the 95% confidence level while

controlling for all other variables in the model. Dominance analysis indicated that Powerful

Others had complete dominance over all the other variables. Inspection of the semi-partial

correlation coefficients indicated that Powerful Others (sr2 = .065) made the largest unique

contribution to the model.

  44  

Table 4.6

Multiple Regression for Professional Inefficacy Coefficient B SE B β t p Part GD

Intercept 6.741 (1.830 — 11.652) 2.498 2.699 .007

Global Type A -.174 (-.351 — .004) .090 -.094 -1.927 .055 -.091 .016

Internal -.252 (-.419 — -.085) .085 -.165 -2.973 .003 -.160 .031

Powerful Others .413 (.253 — .572) .081 .308 5.080 < .001 .256 .095

Chance .098 (-.065 — .261) .083 .073 1.178 .239 .060 .032

R2 = .174

Adjusted R2 = .166

Residual SE = 5.294

Note. B = unstandardised beta. 95% confidence intervals in parentheses. β = standardized beta. Part = semi-

partial correlation coefficient. Superscript for independent variables is ordering of relative importance based on

general dominance. GD = general dominance weights.

4.6 Content overview of the succeeding chapter

Chapter 5 provides an investigation and discussion of the results obtained from the data. This

is followed by the implications, limitations, recommendations and conclusion of the study.

  45  

CHAPTER 5

DISCUSSION AND CONCLUSION

5.1 Introduction

The results are discussed in this chapter. The aims and objectives of this study are outlined,

leading into an interpretation and discussion of the findings in relation to the literature on

burnout, Type A personality, and locus of control. The implications for research and practice,

together with the limitations and recommendations of the study are also provided. This is

followed by the conclusion.

5.2 Aims/objectives

The aims of this study were twofold, namely, to investigate the relationship between (a)

burnout and global Type A personality and (b) burnout and locus of control in university

students. The specific objectives were to investigate the relationships between these

personality variables and the three burnout dimensions.

5.3 Relationship between burnout and Type A personality

The results indicated small statistically significant negative correlations between global Type

A personality and two of the burnout dimensions (cynicism and professional inefficacy). The

correlation between global Type A personality and emotional exhaustion was non-significant.

These results suggest that the higher a student scores on global Type A personality the lower

he/she scores on cynicism and professional inefficacy. In other words, students who possess a

Type A personality are less inclined to experience detached attitudes and feelings of

incompetence towards their studies. When controlling for locus of control, global Type A

personality only correlated significantly with cynicism. This implies that locus of control is

potentially a better burnout predictor in students than is global Type A personality. However,

the unique contribution of global Type A personality to cynicism was small. Dominance

analysis also indicated that its relative importance was low. The results further indicated that

global Type A personality and internal locus of control were positively correlated and there

was a negligible correlation between global Type A personality, Powerful Others, and

Chance locus of control. Thus, students who score high on global Type A personality also

  46  

tend to believe that they can control the outcomes of their behaviours. As a whole there is

insufficient evidence that global Type A personality has a meaningful relationship with

burnout in university students.

The results obtained in this study appear to be somewhat contradictory to the literature, which

has generally found that global Type A personality is positively related to emotional

exhaustion (Jiang et al., 2004) and cynicism/depersonalisation (Jiang et al., 2004; López et

al., 2008). However, it supports the finding that there is a positive association among global

Type A personality and personal accomplishment (i.e., the opposite of inefficacy) (Alarcon et

al., 2009; Fusilier & Manning, 2005; López, et al., 2008). The contradictory nature of the

findings can be explained by the underlying dimensions of Type A personality. The hard-

driving/competitive dimension had a negative relationship with the three burnout dimensions

while the rapid eating and rapid speaking dimensions were found to be positively associated

with the three dimensions of burnout. This is similar to findings reported by Hallberg et al.

(2007), who found that the achievement striving dimension of Type A personality, which is

similar to the hard-driving/competitive dimension in the current study, had a non-significant

negative relationship with emotional exhaustion and cynicism, and a significant positive

relationship with work engagement. In their study the impatience/irritability dimension of

Type A personality had a statistically significant positive relationship with emotional

exhaustion and cynicism, and a statistically significant negative relationship with work

engagement (Hallberg et al., 2007). As such, the current study suggests that Type A

individuals in the student context may be more likely to develop burnout due to impatient

(rapid eating and rapid speaking) behaviours and that the hard-driving/competitive

behaviours are more likely preventative of burnout. The results therefore suggest that Type A

personality has the same relationship with burnout in students as it has with burnout in the

organisational context.

There are a variety of reasons why Type A personality, particularly the hard-

driving/competitive dimension, may be negatively related to cynicism and professional

inefficacy in students. For example, Type A students are involved, motivated and hard-

driving with regards to their studies, and often spend additional time in the lecture classroom

(Burnam et al., 1975; Mahajan & Rastogi, 2011; Waldron et al., 1980). These Type A

characteristics reflect how highly invested Type A students are in their studies and may, as a

result, prevent feelings of cynicism. Type A individuals also have high regard for

  47  

achievement striving/meeting their high performance standards and for receiving

recognition/rewards (Day & Jreige, 2002; Friedman, 1969; Hallberg et al., 2007). The student

environment provides students with immediate performance feedback and recognition for

good academic work and grades. Such affirmation and recognition, which is highly valuable

to Type A students (Burke & Weir, 1980), may encourage them to feel more motivated,

competent and invested in their studies.

According to the JD-R model and the SD-R model low resources can hinder an individual’s

ability to match the high demands of their work, resulting in experiences of withdrawal

(Demerouti et al., 2001). In turn, high resources can mitigate the influence of high demands

(Bakker et al., 2004; Demerouti et al., 2001). The student context provides an environment

where students have high availability and access to various resources, including peer and

lecture support, counselling and career services, various student facilities, tutorial sessions,

student materials (library access, book stores, printing and internet access), and financial aid

(e.g., student finance). It is possible that students who score high on the achievement

dimension of Type A personality are more likely to make the most use out of these resources

because of their achievement striving characteristics and desires. It is also possible that Type

A students are more likely to view their study demands as challenges rather than as

hinderances, due to their achievement striving/hardriving approach towards their studies.

That is, they may view demands as potential ways to develop themselves and reach their

goals (Tadić et al., 2015). As a result, Type A students may experience increasing levels of

engagement with their studies, in addition to feelings of accomplishment and competence as

students (Crawford et al., 2010).

5.4 Relationship between burnout and locus of control

The correlation coefficients obtained indicated that internal locus of control had a small

negative relationship with emotional exhaustion, cynicism, and professional inefficacy.

Powerful Others had a small positive relationship with emotional exhaustion and cynicism,

and a medium positive relationship with professional inefficacy. Chance was found to

demonstrate a small positive association with each of the three dimensions of burnout. These

results suggest that the higher an individual scores on internal locus of control, the lower

he/she scores on emotional exhaustion, cynicism, and professional inefficacy. In other words,

students who believe that they are able to control outcomes are less likely to experience

  48  

emotional exhaustion, to display detached attitudes towards their studies, and/or to express

feelings of incompetence or inefficiency. Conversely, the higher that a student scores on

external locus of control (i.e., Powerful Others and Chance), the higher he/she scores on each

of the three burnout dimensions. Students who believe that outcomes are controlled by

powerful others or by chance, fate, and/or luck, are thus more likely to experience emotional

exhaustion, cynicism, and professional inefficacy. In the regression analyses, dominance

analysis indicated that internal locus of control (Internality) and Powerful Others were the

most important predictors of burnout. In particular, internal locus of control (Internality) was

the most important predictor of emotional exhaustion. Powerful Others and internal locus of

control (Internality) were both the most important predictors of cynicism, whereas Powerful

Others was the most important predictor of professional inefficacy.

The findings obtained in this study are supported by previous research conducted in the

organisational context, which indicates that internal locus of control has a negative

correlation with emotional exhaustion, cynicism/depersonalisation (Alarcon et al., 2009;

Nejad et al., 2014), and is positively correlated with personal accomplishment (Alarcon et al.,

2009). Research also indicates that Powerful Others and Chance are positively related to the

three burnout dimensions when lack of personal accomplishment is viewed as the third

dimension (Nejad et al., 2014). As such, it appears that the relationship between locus of

control and burnout is similar for individuals in the student context as for individuals in the

working world. Indeed, in the student context Kalantarkousheh et al. (2013) demonstrated a

positive association between external locus of control and a total burnout score consisting of

academic exhaustion, disinterestedness, and inefficiency.

There are a variety of characteristics associated with internal locus of control that may lead to

a reduction in burnout. For example, students who possess an internal locus of control are

often highly motivated and put a lot of effort into their studies (Anderson & Hamilton, 2005;

Arslan & Alkin, 2014). These students may therefore be extremely involved, invested, and/or

engaged in their studies. In light of their involvement, these students may experience work

with high demands as being less strenuous than do individuals with an external locus of

control (Spector, 1982; Spector & O’Connell, 1994). This may be due to students’ belief that

they can control their own outcomes and therefore they are likely to view their study

demands as challenges (LePine et al., 2005; Tadić et al., 2015) that they can manage,

manipulate, learn and develop from and inevitably overcome and achieve the outcomes they

  49  

desire. As previously mentioned, challenge demands tend to have a positive link with

engagement, and a positive association with burnout that is weaker than the relationship

between hinderance demands and burnout (Bakker & Sanz-Vergel, 2013; Crawford et al.,

2010; Tadić et al., 2015). As such, students who display an internal locus of control may

experience increased engagement with studies. These characteristics of possessing an internal

locus of control may prevent these students from becoming exhausted and disengaged, and

from experiencing feelings of incompetence towards their studies.

In turn, students who possess an external locus of control (i.e., Powerful Others and/or

Chance) and believe that either powerful others or chance, luck, or fate control their

outcomes, may be more likely to view their study demands as being hindrances (LePine et

al., 2005; Tadić et al., 2015) as they do not believe that they have control over their own

study outcomes. As such, students possessing an external locus of control may view their

study demands as uncontrollable, thereby experiencing study demands as more strenuous. As

such, students with an external locus of control may have a greater likelihood than students

with an internal locus of control to experience feelings of burnout.

Students possessing an internal locus of control may also have more personal resources that

buffer their study demands and that help them to influence their own outcomes and to meet

their high work demands. For instance, individuals with an internal locus of control have

been found to make more use of both active coping strategies (Brosschot et al., 1994) and

metacognition (Arslan & Akin, 2014). Use of active coping strategies and metacognition may

provide students with the ability to proactively meet and overcome their high study demands,

and in turn enable the fostering of enhanced learning outcomes (Jacobs & Paris, 1987). These

behaviours may also prevent students from experiencing burnout through fostering of

rewards. Students who score higher on internal locus of control may be more likely to modify

their own behaviours, as they believe that they can control their own outcomes (Rotter,

1966). As such, these individuals may be better able to effectively manage their academic

lives. For example, students who have an internal locus of control may be more likely to

modify ineffective behaviours that are leading to undesirable outcomes, such as seeking

additional assistance from lecturers or attending extra tutoring sessions to improve poor

academic performance due to lack of personal effort towards one’s studies or as a result from

being absent from lecture classes.

  50  

The results suggest that students who perceive their external environment as predictable yet

influenced by powerful others tend to score high on emotional exhaustion, cynicism, and

professional inefficacy. Dominance analysis also indicated that Powerful Others had general

dominance over the other variables for cynicism and professional inefficacy. The general

dominance weight was similar to internal locus of control (Internality) and Chance for

cynicism, but was much larger than the other independent variables for professional

inefficacy. This suggests that students who believe that the world is ordered but is controlled

by powerful others are much more likely to experience a feeling of inefficacy. It is possible

that students who score high on Powerful Others experience negative and detached attitudes

towards their studies because they feel that other people (such as lecturers) influence the

outcome of their studies. These students may feel that there is no point in putting extra effort

into their studies as the grade they receive will not only be based on their abilities but on

whether or not their effort is recognised by powerful others. These feelings may lead to a

sense of inefficacy at university.

Students who score high on Powerful Others and Chance may also not make use of study

resources because they feel that they are not in control of the environment, and as such they

may experience feelings of helplessness (Sprung & Jex, 2012). These individuals are also

likely to make use of avoidant coping strategies (Brosschot et al., 1994), which may lead to

withdrawal behaviours (Demerouti et al., 2001). For instance, research has shown that

individuals scoring high on Powerful Others and Chance locus of control tend to be

associated with social inadequacy and those only scoring high on Powerful Others tend to

seek less social support (Brosschot et al., 1994). Reduced social support (reduced resources)

is associated with the development of burnout (Maslach et al., 2001).

5.5 Implications, limitations, and recommendations

In the paragraphs that follow the implications, limitations, and recommendations for this

study are presented.

5.5.1 Theoretical and practical implications

This study demonstrated that global Type A personality, as measured with the short form of

the SJAS, may act as a protective factor in the prevention of experiences of burnout among

  51  

students. However, as Hallberg et al. (2007) found, this study also indicates that the

dimensions underlying global Type A personality have different relationships with burnout.

This study has also contributed to theory on student burnout and locus of control in the South

African context by using a tripartite model of burnout rather than Rotter’s (1966)

unidimensional model. In particular, the results indicate that a belief in control over outcomes

may be a protective factor against the development of burnout. This study has not only shown

that burnout occurs in university students but that there are individual factors that are related

to burnout. Thus, this study has demonstrated that it is not only the environment that is

important in the development of burnout in the student context. As such, it may be useful to

investigate the role of individual characteristics as personal resources or factors that lead to

demands, in the JD-R/SD-R model.

The findings suggest that preventative measures for preventing burnout in students may be

enhanced by providing students with information on potential risk factors for the

development of burnout, such as scoring low on global Type A personality (as measured with

the short form of the SJAS) and internal locus of control, and having a high score on external

locus of control. This may assist students in raising their awareness of whether they are

predisposed to burnout. There may also be merit in reducing the harmful characteristics of

Type A personality, such as irritable and intolerant behaviours, and increasing the beneficial

aspects of possessing an internal locus of control, such as motivation, effort and use of study

resources. These characteristics will help students take control of their environment.

Furthermore, there may be value in addressing the sense of powerlessness in students who

possess an external locus of control, which may lead to experiences of burnout, by making

these students understand that they can control the outcomes of their studies.

5.5.2 Limitations and recommendations

This study is subject to several limitations that necessitate caution in interpretation of the

results. The sampling strategy in this study is one potential limitation that can reduce the

external validity of the study, in that a convenience sample was used. While the students were

obtained from diverse faculties, genders, languages, ethnicities and years of study, the results

are not automatically generalisable to all university students in South Africa. Indeed, the

majority of participants were obtained from the faculties of Management and Education. It is

therefore recommended that future studies investigate other universities in Gauteng, focusing

  52  

on a wide range of faculties, and obtaining a larger and more evenly distributed sample in

terms of ethnicity, gender, and year of study. This could be done in collaboration with

university wellness programmes, whereby the research obtained may assist with and provide

further insight into developing more targeted approaches for burnout interventions for

students.

The relatively low reliability indices obtained in this study for the IPC locus of control scales

and the short form of the SJAS need to be taken into account when investigating the

relationships between the variables. Low reliability may mask the true relationship between

the variables due to the presence of error variance. Future research should consider using

alternative measures of Type A personality (for example, the Framingham Type A Scale

(FTAS; Haynes, Levine, Scotch, Feinleib, & Kannel, 1978), which globally measures the

Type A personality dimensions of time urgency, impatience, competitiveness, dominance,

and need to excel) or alternatively devise measures for Type A personality and locus of

control for the South African context. Developing new instruments allows for an in-depth

investigation of error and validity, and may therefore allow for better representation of the

relationship between these variables and burnout. The two statistical techniques used in this

study (i.e., correlation and regression) do not separate true (common) variance from error

variance. The results may, therefore, be influenced by unwanted measurement error.

Although the results of this study are still viable it may be useful to use structural equation

modelling in future studies because this technique is able to model true score variance and

error variance separately. In this study the relatively small sample size in relation to the

number of model parameters made the application of structural equation modelling difficult

when applying appropriate estimators for categorical data (such as weighted least square

mean and variance adjusted estimation). While creating parcels may be a viable strategy,

there is some contention around using parcels (Marsh, Lüdtke, Nagengast, Morin, & von

Davier, 2013).

There may also be merit in using alternative measures of Type A personality because the

short form of the SJAS only measures three different dimensions of Type A personality

(hard-driving/competitive, rapid eating, and rapid speaking). As mentioned in section 2.5.1.1

Type A personality has several underlying dimensions. The results of this study may

therefore only be generalisable to Type A personality as measured by the short form of the

SJAS. Future research should consider alternative dimensions of Type A personality. In

  53  

particular, it may be useful to use a measure that adequately separates achievement striving

and impatience-irritability. The two impatience/irritability scales of the short form of the

SJAS consisted of two items each, which made their use in the regression analysis

problematic due to their poor reliability. Although this study found that global Type A

personality generally had a negligible relationship with burnout, there appears to be merit in

using the different dimensions of Type A personality rather than global Type A personality as

independent variables (cf. Day & Jreige, 2002).

Lastly, given the findings in this study and the relative lack of research in the South African

context into the individual factors influencing the development of student burnout, it is

recommended that more research be carried out to investigate the relationship between Type

A personality, locus of control and burnout. Such studies may further enhance the

understanding of the role of individual factors in the development of burnout. Research into

the relationship between other personality variables, such as hardiness, and core self-

evaluative traits also needs to be conducted, as this study has demonstrated that personality

variables are linked to the development of burnout. This will allow for better informed

interventions and will better enable the prevention of burnout in university students. There

may also be merit in investigating the mediating/moderating effects of individual factors in

the JD-R/SD-R model to better understand the relationship between demands, resources, and

burnout.

5.6 Conclusion

The purpose of this study was to investigate the relationship between burnout and two

individual difference personality variables (Type A personality and locus of control) in South

African university students. It is one of the first studies to investigate the relationships

between these variables in South African university students. In this study, university

students (n = 387) from a local university were sampled. As a whole the results indicate that

global Type A personality has a negligible relationship with burnout while locus of control is

demonstrated as a stronger burnout predictor. These results have implications for theory and

practice, demonstrating that individual characteristics are indeed related to burnout. In

conclusion, this study’s findings hold importance for furthering future burnout studies and

have contributed valuable knowledge and insight into the growing literature on student

burnout in the South African context.

  54  

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Appendix A

Descriptive statistics of the items of each scale

Table A1

Item Level and Total Score Descriptive Statistics

Items Mean SD Median Skewness Kurtosis SE

M1 2.68 1.36 3.0 0.15 0.05 0.07 M2 2.60 1.69 3.0 0.22 -0.74 0.09 M3 2.65 1.68 3.0 0.45 -0.59 0.09 M4 1.84 1.48 1.0 0.78 0.05 0.08 M5 2.04 1.50 2.0 0.60 -0.05 0.08 M6 1.03 1.40 0.0 1.53 1.92 0.07 M7 1.37 1.49 1.0 1.07 0.51 0.08 M8 1.92 1.69 2.0 0.54 -0.69 0.09 M9 0.98 1.43 0.0 1.55 1.85 0.07 M10 1.54 1.27 1.0 0.66 -0.04 0.06 M11 2.18 1.76 2.0 0.45 -0.75 0.09 M12 1.13 1.60 0.0 1.37 0.87 0.08 M13 1.15 1.55 1.0 1.48 1.45 0.08 M14 0.92 1.42 0.0 1.75 2.60 0.07 M15 1.79 1.59 1.0 0.75 -0.02 0.08 J1 0.51 0.50 1.0 -0.03 -2.00 0.03 J2 0.24 0.43 0.0 1.21 -0.54 0.02 J3 0.39 0.49 0.0 0.46 -1.79 0.02 J4 0.13 0.34 0.0 2.17 2.71 0.02 J5 0.37 0.48 0.0 0.52 -1.74 0.02 J6 0.24 0.43 0.0 1.21 -0.54 0.02 J7 0.27 0.44 0.0 1.06 -0.88 0.02 J8 0.54 0.50 1.0 -0.17 -1.98 0.03 J9 0.60 0.49 1.0 -0.41 -1.84 0.02 J10 0.60 0.49 1.0 -0.39 -1.85 0.02 J11 0.15 0.36 0.0 1.93 1.73 0.02 J12 0.36 0.48 0.0 0.57 -1.68 0.02 J13 0.61 0.49 1.0 -0.47 -1.78 0.02 J14 0.12 0.32 0.0 2.39 3.72 0.02 J15 0.34 0.47 0.0 0.67 -1.56 0.02

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J16 0.16 0.37 0.0 1.80 1.24 0.02 J17 0.09 0.29 0.0 2.84 6.11 0.01 J18 0.53 0.50 1.0 -0.10 -1.99 0.03 J19 0.31 0.46 0.0 0.81 -1.35 0.02 J20 0.49 0.50 0.0 0.05 -2.00 0.03 J21 0.45 0.50 0.0 0.20 -1.97 0.03 L1 3.21 0.84 3.0 -0.72 -0.42 0.04 L2 2.13 0.86 2.0 0.20 -0.84 0.04

L3 2.30 1.00 2 0.21 -1.05 0.05

L4 1.98 1.05 2.0 0.73 -0.74 0.05 L5 3.21 0.89 3.0 -0.92 0.00 0.05 L6 2.48 0.94 3.0 -0.08 -0.91 0.05

L7 2.03 0.90 2.0 0.51 -0.60 0.05

L8 2.55 0.92 3 -0.23 -0.80 0.05

L9 2.62 1.17 3.0 -0.15 -1.46 0.06

L10 3.07 0.81 3.0 -0.70 0.14 0.04

L11 1.86 0.89 2 0.67 -0.53 0.05

L12 1.99 0.93 2.0 0.56 -0.66 0.05

L13 2.18 0.93 2 0.21 -0.97 0.05

L14 2.34 0.96 2.0 0.08 -0.99 0.05

L15 2.09 0.99 2 0.35 -1.06 0.05

L16 2.15 0.96 2.0 0.37 -0.85 0.05

L17 1.89 0.96 2 0.71 -0.63 0.05

L18 2.52 1.06 2.0 0.03 -1.24 0.05 L19 3.12 0.88 3.0 -0.64 -0.51 0.05 L20 2.55 0.91 3 -0.19 -0.77 0.05

L21 3.49 0.77 4.0 -1.37 1.00 0.04 L22 2.32 0.96 2 0.08 -1.00 0.05

L23 3.60 0.64 4.0 -1.68 2.93 0.03

L24 3.64 1.67 4.0 -0.17 -1.13 0.08

Note. M1 – M5 = emotional exhaustion, M6 – M9 = cynicism, M10 – M15 = professional

inefficacy, J1 – J21 = Student Jenkins Activity Survey, L1 – L24 = Internal, Powerful Others,

Chance. SD = standard deviation, SE = standard error.  

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Appendix B

Density plots, and beanplots of the MBI-SS, global Type A personality, and Levenson’s

IPC locus of control scales

Figure B1: Density plot, and beanplot of emotional exhaustion

Figure B2: Density plot, and beanplot of cynicism

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Figure B3: Density plot, and beanplot of professional inefficacy

Figure B4: Density plot, and beanplot of the global Type A personality scale

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Figure B5: Density plot, and beanplot of Internality

Figure B6: Density plot, and beanplot of Powerful Others

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Figure B7: Density plot, and beanplot of Chance

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Appendix C

Scatterplots and Loess non-parametric regression lines of the variables in the study

Figure C1: Scatterplots and Loess non-parametric regression lines of the MBI-SS, global

Type A personality, and Levenson’s IPC locus of control scales

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Appendix D

Dominance analysis tables

Table D1

Complete, Conditional and General Dominance Weights for Emotional Exhaustion

Dominance Subset Size Model R2 Type A Internal PO Chance

Complete 1 .005 . .044 .025 .027

1 .048 .000 . .026 .029

1 .026 .003 .048 . .009

1 .028 .003 .050 .007 .

2 .049 . . .025 .029

2 .030 . .044 . .008

2 .074 .000 . . .010

2 .031 . .046 .007 .

2 .078 .000 . .006 .

2 .035 .003 .049 . .

3 .074 . . . .010

3 .078 . . .006 .

3 .038 . .046 . .

3 .084 .000 . . .

4 .084 . . . .

Conditional 0 .005 .048 .026 .028

1 .002 .047 .019 .022

2 .001 .047 .013 .016

3 .000 .046 .006 .010

General .002 .047 .016 .019

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Table D2

Complete, Conditional and General Dominance Weights for Cynicism

Dominance Subset Size Model R2 Type A Internal PO Chance

Complete 1 .023 . .027 .050 .040

1 .038 .012 . .053 .046

1 .054 .019 .037 . .010

1 .045 .019 .040 .019 .

2 .050 . . .050 .043

2 .073 . .028 . .009

2 .092 .009 . . .011

2 .064 . .029 .018 .

2 .085 .008 . .018 .

2 .064 .018 .039 . .

3 .100 . . . .010

3 .093 . . .017 .

3 .082 . .029 . .

3 .103 .008 . . .

4 .110 . . . .

Conditional 0 .023 .038 .054 .045

1 .016 .035 .041 .032

2 .012 .032 .029 .021

3 .008 .029 .017 .010

General .015 .033 .035 .027

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Table D3

Complete, Conditional and General Dominance Weights for Professional Inefficacy

Dominance Subset Size Model R2 Type A Internal PO Chance

Complete 1 .026 . .024 .120 .056

1 .036 .014 . .125 .063

1 .127 .018 .035 . .004

1 .061 .020 .037 .069 .

2 .050 . . .120 .059

2 .145 . .025 . .003

2 .161 .009 . . .004

2 .081 . .027 .067 .

2 .099 .010 . .067 .

2 .130 .018 .035 . .

3 .170 . . . .004

3 .108 . . .065 .

3 .148 . .026 . .

3 .165 .008 . . .

4 .174 . . . .

Conditional 0 .026 .036 .127 .061

1 .018 .032 .105 .041

2 .012 .029 .085 .022

3 .008 .026 .065 .004

General .016 .031 .095 .032