University of Ghana http://ugspace.ug.edu.gh UNIVERSITY OF GHANA LEGON DEPARTMENT OF PSYCHOLOGY COMMUTING STRESS, PERSONALITY AND WORKER WELL- BEING: A STUDY AMONG HAULAGE DRIVERS IN TEMA BY JAMES HENRY DODOO (10599392) THIS THESIS IS SUBMITTED TO THE UNIVERSITY OF GHANA, LEGON IN PARTIAL FULFILLMENT OF THE REQUIREMENT FOR THE AWARD OF MPHIL PSYCHOLOGY DEGREE JULY, 2018 i University of Ghana http://ugspace.ug.edu.gh DECLARATION I hereby declare that this thesis is the result of my own research work and that no part of it has been presented for another degree by anyone for any academic award in this university or elsewhere. All references used in the work have been fully acknowledged. I bear sole responsibility for any shortcomings. …………………………………… ……………………….. James Henry Dodoo Date (10599392) I hereby declare that the preparation and presentation of this thesis was supervised in accordance with the guidelines on the supervision of thesis laid down by the University of Ghana. …………………………………… Date:….../….…../.2018. DR. INUSAH ABDUL- NASIRU (Principal Supervisor) …………………………..……….. Date: …….../….…../.2018. DR. ENOCH TEYE- KWADJO (Second Supervisor) ii University of Ghana http://ugspace.ug.edu.gh DEDICATION This work is dedicated to my awesome parents. Thank you for believing in me. iii University of Ghana http://ugspace.ug.edu.gh ACKNOWLEDGEMENT First of all I am most grateful to God for granting me grace throughout this work. His grace and mercies endure forever. Selah. I also appreciate the efforts and contributions of my supervisors, who through their unwavering efforts have guided this write up to a successful end. To them I say “May God richly bless and keep you. I am enamoured by the efforts of my family, whose ceaseless prayers and provision of resources have sponsored me from day one till now. Words cannot describe how much I appreciate you (Mr James Henry Dodoo, Mrs Victoria Dodoo, Emmanuel Dodoo, Jemima Dodoo, Josephine Dodoo, Joana Degbator, and Esther Biney). I am very grateful to Doctor Mawunyo Ladson for helping me throughout my data collection in Tema. God has your name in His book of remembrance, for all your support throughout this journey. Not forgetting the brilliant efforts of Titus Adusu, who was an anchor during the data collection stage as well. Not forgetting the terrific three, my group study (Emmanuel, Sandra and Eileen) for all their support I am very thankful for the cooperation I got from the Union executives of Haulage drivers Ghana and companies such as, Total Ghana, Shell Ghana, Engen Ghana, Batco oil company, Kodson Plus company limited, Kiteko, J.K Horgle, James K. Ahiadome, Accra Plains Depot, Bon Aqua, Mobile Water Company limited and Unilever Ghana. May your businesses forever flourish for the glory of God and for the betterment of mankind. Last but not the least; my sincere gratitude goes to Clement Twumasi for his assistance in uplifting words of encouragement and thorough contribution throughout the process. May God open pathways wider than doors for you, greener pastures await. iv University of Ghana http://ugspace.ug.edu.gh TABLE OF CONTENTS DECLARATION ..................................................................................................................................... i DEDICATION ....................................................................................................................................... iii ACKNOWLEDGEMENT ..................................................................................................................... iv TABLE OF CONTENTS ........................................................................................................................ v LIST OF FIGURES .............................................................................................................................. vii LIST OF TABLES ............................................................................................................................... viii CHAPTER ONE ..................................................................................................................................... 1 INTRODUCTION .................................................................................................................................. 1 1.1 Background of the study ............................................................................................................... 1 1.1.1 Commuting stress ................................................................................................................... 2 1.1.2 Commuting Distance............................................................................................................ 4 1.1.3 Personality ............................................................................................................................ 4 1.1.4 Worker Well-being .............................................................................................................. 6 1.1.5 Demographic factors .............................................................................................................. 7 1.2 Problem Statement ...................................................................................................................... 10 1.3 Aims and Objectives of the Study............................................................................................... 11 1.4 Relevance of the Study ............................................................................................................... 12 CHAPTER TWO ................................................................................................................................ 13 LITERATURE REVIEW .................................................................................................................. 13 2.1 Theoretical Framework ............................................................................................................... 13 2.1.1 Selye’s Model of Stress ........................................................................................................ 13 2.1.2 Transactional Stress Model ............................................................................................... 16 2.1.3 The Conservation of Resources (COR) Model ................................................................ 17 2.2 Review of Literature ................................................................................................................... 22 2.3 Hypotheses .................................................................................................................................. 33 2.4 Conceptual framework ................................................................................................................ 34 CHAPTER THREE .............................................................................................................................. 36 METHODOLOGY ............................................................................................................................... 36 v University of Ghana http://ugspace.ug.edu.gh 3.1 Population ................................................................................................................................... 36 3.2 Sample and Sampling Technique ................................................................................................ 36 3.3 Research Design .......................................................................................................................... 40 3.4 Data Collection Questionnaires .................................................................................................. 40 3.4 1 Commuting stress ................................................................................................................. 41 3.4.2 Personality ............................................................................................................................ 42 3.4.3 Worker Well-being .............................................................................................................. 42 3.5 Pilot Study ................................................................................................................................... 43 3.6 Procedure .................................................................................................................................... 43 3.7 Ethical considerations ................................................................................................................. 44 CHAPTER FOUR ................................................................................................................................. 45 RESULTS ............................................................................................................................................. 45 4.1 Preliminary Analysis ................................................................................................................... 45 4.1.1 Analytical Strategy ............................................................................................................... 45 4.1.2 Exploratory Factor Analysis ................................................................................................ 46 4.1.3 Descriptive Statistics of Key Study Variables and Test of Normality ................................. 46 4.1.4 Pairwise Correlations among demographic factors and key study variables ....................... 47 4.1.5 Procedure for moderation..................................................................................................... 48 4.2 Main Analysis ............................................................................................................................. 49 4.2.1 Observed Model ................................................................................................................... 57 4.2.2 Comparing Worker Well-being and Commuting Stress averagely across significant demographic moderators ............................................................................................................... 57 4.3 Summary of Major Research Findings ....................................................................................... 61 CHAPTER FIVE .................................................................................................................................. 63 DISCUSSION ....................................................................................................................................... 63 5.1 Main Findings ............................................................................................................................. 63 5.1.1 Commuting Stress and Worker Well-being ......................................................................... 63 5.1.2 Moderation Effects of Demographic factors and Personality facets on the relationship of Commuting Stress and Worker Well-being .................................................................................. 64 5.2 Limitations of the study .............................................................................................................. 68 5.3 Recommendations for future research ........................................................................................ 68 5.4 Practical Implications and Conclusion ........................................................................................ 69 REFERENCES ..................................................................................................................................... 72 APPENDICES ...................................................................................................................................... 87 vi University of Ghana http://ugspace.ug.edu.gh LIST OF FIGURES Figure 1 Process through which stress affects an individual ……………………………..…16 Figure 2 Conceptual framework of commuting stress, personality and worker well- being………………………………………………………………………………………….31 Figure 3 Moderation Plot for Educational Status among Haulage drivers…………………..50 Figure 4 Moderation Graph for Age among Haulage drivers………………………...……...51 Figure 5 Moderation Graph for Religion among Haulage drivers……………………...……52 Figure 6 Moderation Graph for Conscientiousness among Haulage drivers………………...53 Figure 7 Observed Model………………………………………………………………...…..54 vii University of Ghana http://ugspace.ug.edu.gh LIST OF TABLES Table 1 Frequency Distribution of Demographic Characteristics of Haulage drivers……….36 Table 2 Summary Measures and Reliability Indices of key study variables…………………43 Table 3 Correlation Matrix of Key Variables of the study………………………………………………………………………………………….44 Table 4 Hierarchical Multiple Regression Analysis of the association between Commuting Stress and Worker Well-being by controlling demographic factors and Personality facets………………………………………………………………………………………….47 Table 5 Examining the potentially significant moderators by Hayes’s Process Macro……...59 Table 6 Summary of the 3-Way MANOVA between-Subjects Effects of Age, Educational status and Religion on Worker Well-being and Commuting Stress………………………...55 Table 7 Descriptive Summary of Commuting Stress and Worker Well-being across potential demographic moderators..…………………………………………………………...……….57 Table 8 Summary of Means and Standard deviations for Age and Educational status on Worker Well-being and Commuting Stress……………………………………….…………58 viii University of Ghana http://ugspace.ug.edu.gh ABSTRACT Though commuting to and from work has some health benefits, depending on one’s mode of commute, commuters often face the risk of contracting health issues. Commuting to and from work has posed health problems among the worker population in many countries, including Ghana. Despite the fact that there is a lot of research on commuting, there are few studies which report on commuting stress among haulage drivers. This study focused on commercial drivers, specifically the haulage driver community. It also explored the relationship between commuting stress, personality and worker well-being by using a non-experimental cross sectional survey design. Haulage drivers (N = 211) were recruited using different sampling methods such as convenience, purposive and snowball techniques so as to maximize response rate. The measurement questionnaires included the driver stress scale, personality scale and the Ryff well-being scale. Study findings revealed that commuting stress significantly decreased worker well-being of haulage drivers, after controlling for the effects of both demographic factors and personality facets. Educational status, age and religion moderated the relationship between commuting stress and worker well-being. However, only conscientiousness had significant moderating effect on the relationship between commuting stress and worker well- being. The moderation analysis also revealed that educational status, age and levels of conscientiousness significantly increased the relationship between commuting stress and worker well-being. It is recommended that future research should use a mixed methods research design to help explain how commuting stress affect worker well-being among the haulage commuting. It is also recommended that future research explores other haulage industries like, waste, ore, sand and the like. The implications of these results are discussed. ix University of Ghana http://ugspace.ug.edu.gh CHAPTER ONE INTRODUCTION 1.1 Background of the study Over the past decades, stress has become an issue that has been widely discussed in studies and in everyday life (Schneiderman, Ironson, & Siegel, 2005). One aspect of stress that is gaining more research and public attention is the stress resulting from commuting from one place to another (Sposato, Röderer, & Cervinka, 2012). The distance between one’s residence and workplace makes daily travel necessary, and has been reported to influence perceived stress (Barling, Kelloway, & Frone, 2004). Congested roads and public transport commuting have been reported as major causes of commuting stress, as these factors have a great influence on the welfare of employees (Amposah-Tawiah, Annor, & Beckham, 2016; Hennessy, 2008; Hilbrecht, Smale, & Mock, 2014). The psychological and physical effects of commuting stress on the health and psychological well-being of individuals is well documented (Novaco & Gonzalez, 2009; Wener, Evans, & Boately 2005). The extent of its implications on employee welfare is difficult to estimate, although literature suggests that it is immense (Olsson, Garling, Ettema, Friman, & Fujii, 2013). Gulian, Matthews, Glendon, Davies, and Debney (1989) estimated that almost 18 percent of GDP in developing countries is lost annually as a result of the consequences associated with stress resulting from illness, absence, staff turnover and medical expenses. Although the effects of stress is well investigated in most aspects of human life and among many population groups such as among nurses, physicians, and banking staff, there appears to be little interest in trying to understand commuting stress among drivers, especially haulage drivers, particularly in Ghana. Therefore, stress relating to the commuting behaviour of haulage drivers in general is not well understood due to the paucity of research (Gulian et al., 1989). 1 University of Ghana http://ugspace.ug.edu.gh Commuting behaviour of haulage drivers usually involves the transportation of goods by road and rail from the station to a designated area. Some of these goods may include ore, coal, water, waste, oil, gas, agricultural products and others. These goods are often transported in large quantities and sometimes over long distances. Commuting comes at a cost, these costs could either be psychological and/or social. One of such costs is commuting stress. It is often attributed to unpredictability and a sense of loss of control when driving. Also, haulage drivers who experience commuting stress are more likely to be involved in motor vehicle accidents which threaten the well-being of both the driver and the society at large (Matthews, Dorn, & Glendon, 1991). 1.1.1 Commuting stress In this study, commuting stress has been operationalized as the stress ensuing from commuting in a haulage vehicle from a loading station to a designated area. Commuting stress, in most other studies has been conceptualized as the stress one encounters in commuting from home to work and vice versa (Amponsah-Tawiah, Annor, & Arthur, 2016; Holland, 2016; Koslowsky, 1997; Mattisson, Jakobsson, Håkansson, & Cromley, 2016; Novaco & Collier, 1994). The disparity between the definitions is because haulage drivers are the focus of this study. Commuting stress has been found to be one of life’s least enjoyable activities and has been labelled as "stress, that doesn’t pay” (Stutzer, & Frey, 2008, p. 339). Longer hours of commuting are associated with more stress, giving rise to lower levels of driver well-being (Novaco & Gonzalez, 2009). As reported by Costal, Pickup, and Di Martino (1988), long commutes are bad for both people's health and productivity because of the challenges presented by the various modes of transportation by way of vehicular vibrations, noise, overcrowding and microclimatic conditions. These have been reported to cause sleep problems, psychosomatic complaints and difficulties with family and social life. They also suggest that absenteeism becomes a side effect, especially among females, which in turn affects overall 2 University of Ghana http://ugspace.ug.edu.gh productivity. However according to Novaco and Gonzalez (2009), long commutes cannot be fully responsible for stress, hence the emergence of “impedance” concept. Impedance has been described as the preventing of movement in fulfilling an objective. Impedance theories conceptualize commuting stress to capture the frustrating tensions that come about as a result of road congestion. These tensions interfere with set objectives, and cause unpleasantness, thereby reducing performance and satisfaction (Novaco & Gonzalez, 2009). According to Novaco and Gonzalez (2009), one of the greatest forms of impedance results from travelling long distances in a slow manner; on the other hand, the least level of impedance comes about as a result of travelling a short distance in a little time. They also revealed the need for the concept of impedance to be investigated through a quasi-experimental design with variable measures on physiology, task performance, mood states, subjective distress, and physical health problems. These measures involved questionnaires and travel logs which had to be repetitively tested every day of the week at the company site. A study of commuters by Koslowsky, Aizer, and Krausz (1996), defined commute impedance as having three-level physical facets and found that it was significantly related to subjective stress. In other studies, travel impedance was defined by two dimensions, the physical and perceptual (Novaco, Kliewer, & Broquet, 1991). Physical impedance was objectively measured with distance, time and any other factors that contributed to traffic congestion. The time of the day also played a significant role in congestion. Perceptual impedance was defined as an individual’s subjective view of what makes commuting strenuous. From these two dimensions, it is important to note that commutes that have high physical impedance may not necessarily have high subjective impedance and vice versa. For instance, an individual who experiences high physical impedance in commuting may report low subjective impedance whilst others who may experience low physical impedance may report high subjective impedance. According to Novaco, Stokols, and Milanesi (1990), other factors often play a role in determining ones subjective impedance level, some of which include health problems, 3 University of Ghana http://ugspace.ug.edu.gh residential satisfaction and job satisfaction. An individual’s personality and experience at home and work may have an impact on his or her commuting experience. The term “inter-domain transfer effects” was coined as a result of home and/or work experiences having an impact on ones commuting experience. Findings also suggest that high level commute impedance was associated with lower levels of perceived control. Early research identifies control and predictability as major factors in stress (Bollini, Walker, Hamann, & Kestler, 2004; Frazier, Steward, & Mortensen, 2004; Troup & Dewe, 2002). According to Kluger (1998), these variables are also key in studying commuting stress. Koslowsky (1997) posits that predictability serves as consolation for one’s inability to control his or her environment. Predictability is, however, not always assured. Sometimes distances which should take less time to cover, end up becoming lengthy depending on impedance levels at that particular time. Unpredictability is one of the causes for the high subjective impedance reports by most commuters (Koslowsky, 1997). Control can be defined as one’s ability to exercise full regulatory authority over the internal environment of a vehicle and choose routes he or she wishes to take to work (Schaeffer, Street, Singer & Baum, 1988). Commuting has been reported to be more stressful when there is less control over traffic congestion, time pressure, and the environment within the vehicle (Lyons & Chatterjee, 2008). 1.1.2 Commuting Distance For the purpose of this study commuting distance has been defined as the distance covered by the haulage driver when commuting in a haulage vehicle from the station to the assigned destination(s). 1.1.3 Personality Personality stems from the Latin word “persona”. It refers to our outwardly visible characteristics, observable by all. Personality can be described as the impression others have 4 University of Ghana http://ugspace.ug.edu.gh of us, thus what others view of us actually is. It has also been defined as a set of features that make a person peculiar (Weinberg & Gould, 1999). Schultz and Schultz (2016) define personality as an internally and externally unique, relatively lasting aspect of a person’s character that influences his or her behaviour in different situations. Allport (1937) defined personality as ‘the dynamic organization within the individual of those psychophysical systems that determine his unique adjustments to the environment’ (p.48). McAdams and Pals (2006) assert that “Personality is an individual’s unique variation on the general evolutionary design for human nature, expressed as a developing pattern of dispositional traits, characteristic adaptations, and integrative life stories complexly and differentially situated in culture” (McAdams & Pals 2006, p. 212). Early studies in psychology focused on the study of consciousness, behaviour and the unconscious mind (Schultz & Schultz, 2016). The topic of personality only became a formalized study under the American Psychological Association in the late 1930’s. In psychology, personality has been studied from different perspectives. Thørrisen (2013) argues that there are four central perspectives. These are the psychodynamic, situational, interactional and trait perspectives. The psychodynamic approach theorizes human behaviour as largely a result of unconscious desires. The situational perspective on the other hand emphasizes the importance of environmental factors in the creation of one’s personality. The interactional stance emphasizes the interaction between the environment and the individual (Thørrisen, 2013). The trait position, however, asserts that the identification and organization of traits on which people differ is key in defining personality. McCrae and Costa (2003) explain personality trait as a consistent array of feelings, views and behaviour an individual adheres to. Personality traits often reveal an individuals’ distinct pattern of behaviour in a variety of situations. One who possesses a calm personality trait, for instance, may be predisposed to experiencing unique mood states which, in turn, affect emotional and behavioural responses 5 University of Ghana http://ugspace.ug.edu.gh (Ge et al., 2014). According to literature personality trait is one of the most prominent individual factors to consider in studying driving behaviour, driving styles, attitudes towards driving safety, perceived risk (Tao, Zhang, & Qu, 2017). The Big Five Factor Model is most popular among personality trait studies (Ge et al., 2014). This model consists of Openness, Conscientiousness, Extraversion, Agreeableness and Neuroticism. In brief openness simply means an individual with this trait is often open to experiences, such individuals may be adventurous, curious, and appreciative of nature and art. On the other hand, one low in openness would exhibit the opposite. Individuals who possess a strong sense of responsibility, loyalty, discipline and often happen to be high achievers may be said to be high in conscientiousness. The measure of an individual’s kindness, politeness and warmth can be termed as agreeableness. An individual high on neuroticism may easily slip into depressive states and easily find fault with others. Extroversive individuals on the other hand are sociable, chatty, enjoy the company of others, whilst introverts are exactly the opposite (Matthews, Deary, & Whiteman, 2003). Research shows that personality contributes up to 50% variance in subjective well-being (Howell, Ksendzova, Nestingen, Yerahian & Iyer 2017; Schimmack, Radhakrishnan, Oishi, Dzokoto, & Ahadi, 2002; Vittersø, & Nilsen, 2002) 1.1.4 Worker Well-being Scholars such as Dodge, Daly, Huyton, and Sanders (2012) have observed that, well- being research is advancing steadily. Necku (2015) defines psychological well-being as a good or satisfactory condition of existence which is characterized by health, happiness, and prosperity. He states that psychological well-being is a state in which a mentally fit person possesses a number of positive mental health qualities which includes an active adjustment to the environment and unity of personality. According to Arthur and Reber (2001), psychological well-being means functioning at a high level of behavioural and emotional adjustment and adaptiveness, and not just the mere absence of illness. 6 University of Ghana http://ugspace.ug.edu.gh Employee well-being is a state in which the employee fulfils his or her own potential, can cope with the normal stresses of life, can work productively and fruitfully, and is able to make a contribution to his or her community (WHO, 2011). According to Stutzer and Frey (2008), subjective well-being is higher life satisfaction and lower mental distress. In this study, worker well-being has been defined as the overall physical and mental health of the haulage driver. 1.1.5 Demographic factors It is important to note that this study also incorporated demographic data as were found relevant to the variables in this study, they include, age, marital status, driving experience, religion and education. Age: Studies on the relationship between stress and age are critical in understanding the stress experience and coping mechanisms individuals go through. Often, this is because stress and coping are subjectively measured (Aldwin, Sutton, Chiara, & Iip, 1996). Older people could differ from young people in what they consider as stressful and the amount of stress experienced depending on the type of measure used. According to Aldwin (1991), there have been some nuances in reports on age and stress. Some researchers report either a decrease with age, increase with age or no significant difference, often depending on one’s coping approach. Comparatively, the elderly report lesser stressful life events than the young (Paykel, 1983). In support of Paykel’s (1983), Aldwin (1990) believes that the reason behind the heightened stressful life experience of the young is because they are faced with more novel experiences as compared to the elderly, some of which may include new jobs, marriage, having children, and the like. However, he posits that the elderly may also go through problems like retirement, health issues, death of close friends and children leaving home. On the other hand, Teri and Lewinsohn (1982) assert that older people may be on the same wavelength in reportage of life events, though types of events may not be the same. 7 University of Ghana http://ugspace.ug.edu.gh In a study on daily stress, personality and age on negative affect, one perspective on age and stress revealed that reactivity to stress decreases at an older age, thereby lessening its impact, whilst the other indicates that older people high in neuroticism have a heightened sense of reactivity thereby making them prone to higher stress levels. (Mroczek, & Almeida, 2004). Studies on wellbeing and age have also proved that life does not necessarily get worse with age. Reports rather suggest that, most elderly people were more satisfied with their lives, though they did not report high levels of happiness (Campbell et al., 1976). A study by Brown et al.(2017) revealed that younger drivers exhibited relatively more reckless and risky driving as compared to older drivers. Other studies support these findings (Constantinou, Panayiotou, Konstantinou, Loutsiou-ladd, & Kapardis, 2011; Hatfield & Fernandes, 2009; Scott-parker & Weston, 2017) Inferring from the above studies it is imperative that the age related nuances be studied thoroughly. Marital status: According to most studies on marital status, unmarried people have relatively higher stress levels compared to married couples (Diener, Gohm, Suh, & Oishi, 2000; Waite & Gallagher, 2000; McPherson, Smith-Lovin & Brashears, 2006). However it is important to note that there are external and internal factors that can pose a threat to marriage life satisfaction (Woszidlo & Segrin, 2013). These may include work stress spill over, family interference in the marriage, poor communication among couples and the like. Marital status may well be an interesting demographic factor to explore. There have been many reports on the wellbeing of married people over the unmarried, marriage has been reported to lengthen life and foster emotional soundness (Coombs & Coombs, 1991; Shapiro & Keyes, 2008). However stressors in marriage come about as a result of becoming parents. Parenting stress in relation to marriage stress, can be attributed to a lot of factors, some of which include problems in parent-family functioning and less than ideal parent-child interaction (Rapport, Chung, Shore, Denney, & Isaacs, 2000). Reports suggest that caretaking 8 University of Ghana http://ugspace.ug.edu.gh issues like feeding, excessive crying and child illness can be attributed to parenting stress (Beebe, Casey, & Pinto-Martin, 1993; Hagekull & Dahl, 1987; Östberg & Hagekull 2000). Infant difficultness and parenting stress has been found to be often due to sleeping challenges faced by young children (Thunström 1999). A study on early food refusal indicated that children who persistently refuse food in the early years of life may show other signs of childhood difficulties, thereby spurring negative perceptions of parenting (Lindberg, Bohlin, Hagekull, & Thunström, 1994). Story and Bradbury (2004) suggest that marriage stress is often due to environmental pressures, which influence the rates of marriages and divorce. They report that these go as far as to determine interactions and satisfaction in marriage. Undermining the effects of stress on marriages can have serious repercussions by way of employing inappropriate interventions in dealing with issues that arise between the couple (Story & Bradbury, 2004). Driving Experience: Research suggests that inexperienced drivers have the most abrasive accident record in driving. This has been attributed to cognition rather than performance errors (Mueller & Trick, 2012). Performance errors may include inappropriate vehicular control, panic, and freezing whilst cognitive errors involve the lack of ability to focus or sustain attention, inappropriate visual search strategies, failure to recognize and predict impending danger, poor decision making, selecting inappropriate speeds for certain situations and careless road space management (De Craen, Twisk, Hagenzieker, Elffers, & Brookhuis, 2011). Reports also suggest that most inexperienced drivers overestimate their own abilities; complacency has often been the cause of damage caused by most novice drivers (De Craen et al., 2011) Religion: Religion has often been reported as a variable which mitigates the effects of stress on an individual thereby promoting psychological wellbeing (Ano & Vasconcelles, 2005). Greater religiosity has been known to predict less substance abuse, antisocial behaviour, 9 University of Ghana http://ugspace.ug.edu.gh depression, and suicidality (Fabricatore & Handal, 2004). Reports also suggest that the tendency of an individual to use religion as a coping mechanism may be especially high if the individual is facing disability, illness and in worst case scenarios, death. An article cited in Graham, Furr, and Flowers (2001) by Spilka, Shaver and Kirkpatrick (1985) suggest three ways through which religion helps individuals to cope with stress: (i) it makes for meaningful life (ii) it offers the individual a greater sense of control over his or her life situations (iii) increases self-esteem. Educational level : A study conducted by Lunau, Siegrist, Dragano, and Wahrendorf (2015) found that educational level and work stress were inversely related in that, individuals with higher levels of education had lower levels of stress. Also, Avendano, Jürges, and Mackenbach (2009) found that mortality rates were relatively higher among European individuals who had low level of education as compared to those of higher socio economic status. On the other hand, Wang (2005) indicated that low educational levels cannot be said to have direct link with depression due to lack of biological evidence. However, he posits that individuals with low educational level are more likely to find themselves in stressful environments which may in turn have a negative effect on their overall health status. 1.2 Problem Statement Spending long hours in traffic commuting, can pose a serious threat to the health and well-being of a driver. Amponsah-Tawiah, Annor, and Beckham (2016) along with other studies (Feng & Boyle, 2014; Novaco & Gonzalez, 2009b; Sposato & Cervinka, 2011; Urhonen, Lie, & Aamodt, 2016), reported that long hours of commuting is associated with health problems and this has an adverse effect on personal relationships and productivity at work. Long commuting hours were reported to come with negative consequences such as high blood pressure, musculoskeletal disorders, increased anger, resentment at work, increased 10 University of Ghana http://ugspace.ug.edu.gh absenteeism, delays and failure to concentrate and perform (Eriksson, Friman, & Garling, 2013). Commuting stress among haulage drivers has a significant negative impact on their physical, emotional and behavioural well-being and this can go a long way to negatively affect the organizations in which these drivers work. According to the International Labour Organization (ILO), the estimated cost of stress to business productivity is nerve racking. They report that, the estimated cost of work-related depression in Europe is €617 billion per year. This cost comprises of the expenses to employers of absenteeism and presenteeism (€272 billion), loss of productivity (€242 billion), healthcare costs (€63 billion) and social welfare costs by way of disability benefit payments (€39 billion) (Hassard, Teoh, Cox, Dewe, Cosmar, Gründler and Van den Broek, 2014). Though many studies have reported on commuting, few studies have been conducted on commuting stress among haulage drivers (Häkkänen, & Summala, 2001). Existing studies on commuting often focus on commuting patterns and commuting modes. Target populations in the literature often involve employees commuting from work to home and vice versa, these commuters include private vehicle, bicycle, and bus users. In Ghana only few studies have been conducted on commuting stress (see, for example, Amponsah-Tawiah et al., 2016) among various categories of employees excluding commercial drivers. This study focuses on commercial drivers, specifically the haulage driver community. Thus this study seeks to bridge this gap by further exploring the associations between commuting stress, personality and worker well-being among haulage drivers within the Tema Industrial Area of Ghana. 1.3 Aims and Objectives of the Study The central aim of this study is to explore the relationship between commuting stress personality and worker well-being among haulage drivers in Tema, Ghana. 11 University of Ghana http://ugspace.ug.edu.gh Main Objectives i. Determine whether commuting stress would significantly predict worker well-being over and above the effects of demographic factors of the haulage drivers. ii. Determine whether commuting stress would significantly predict worker well-being by controlling for the facets of personality among the haulage drivers. Auxiliary Objectives i. Examine the variables that moderate the relationship between commuting stress and worker well-being. ii. Compare worker well-being and commuting stress across significant demographic moderators. 1.4 Relevance of the Study The purpose of this study is to explore the relationship between commuting stress, personality and worker well-being among haulage drivers in Ghana. By understanding the relationship between commuting stress and worker well-being, this study would suggest practical recommendations that would help stakeholders within the haulage industry to devise means that will boost driver well-being and productivity. This study would also propose principles which will help haulage drivers to properly manage their workload, time, pressure and commute stress in order to reduce errors in the execution of their duties. Finally, this study would contribute to the body of knowledge on commuting stress and worker well-being among drivers in developing countries like Ghana. 12 University of Ghana http://ugspace.ug.edu.gh CHAPTER TWO LITERATURE REVIEW In this chapter, theories that attempt to explain commuting stress would be expounded in the Theoretical Framework. The section would also critically review the literature on variables of interest in an attempt to reveal the extent of work undertaken in this field indicating the grey areas that this study seeks to explore. 2.1 Theoretical Framework 2.1.1 Selye’s Model of Stress Hans Selye (1956) is often referred to as the originator of the modern stress theory (Capel & Gurnsey, 1987). The earliest efforts to clarify the process of stress related illnesses were given by Selye (1976). He believed that individuals experienced three stages of response when they encountered stressful situations. The three stages were referred to as GAS or the Generalized Adaptation Syndrome. 1. Alarm Reaction: In the first stage, resistance to the stressor decreases. It is then followed by a process whereby the individual ‘s defence mechanisms become more active. 2. Resistance Stage: This stage epitomizes maximum adaptation, whereby the individual is accustomed to the stressor and should ideally represent a return to equilibrium for the individual. If the stress continues and defence mechanisms do not work, the individual moves to the third stage. 3. Exhaustion: – At this point the individuals coping mechanisms breakdown, leading to exhaustion. 13 University of Ghana http://ugspace.ug.edu.gh Evidently, according to Hans Seyles model of stress, in an event where a haulage driver is caught up in unforeseen traffic congestion, especially when he or she has to deliver some cargo to a specific destination, he or she can be said to be experiencing the alarm stage of this stress theory. In the second stage the haulage driver may display a heightened level of immunity towards the stressor. According to the theory, the driver may be experiencing exhaustion when he finds that the delay may be protracted and hence may affect other delivery deadlines, this in effect could cause anxiety, frustration and other stress related symptoms that may be hazardous to his health. It is interesting to note that the earlier work of Greenwood (1979) reports that stress responses manifest in different forms and do not always follow a predefined design. When the design digresses from its custom significantly, the result manifests in sicknesses. Selye‘s GAS model, therefore explains that the resistance stage is cut short if the stressors are severe and last for longer periods of time. He also believed that ailments could result. These ailments may include high blood pressure and gastrointestinal issues (Urhonen, Lie, & Aamodt, 2016). In an attempt to adapt to environmental stressors, the human body encounters some undesired results. These inappropriate responses are side effects of adaptation (Greenwood, 1979). On the other hand, when an individual is able to successfully accommodate a stressor he or she attains new levels of homeostasis. It is important to note that diseases manifest only if an individual is not able to cope with the stressor. GAS is fundamentally a resistance mechanism of the human body, in which the body reacts to stimuli which pose a threat to one’s equilibrium or normalcy. Brannon and Feist, (1997) believe that Selye‘s views were overly centred on the physiological aspects of stress because of the use of animals in his research. They point out that he neglected aspects of thought processing such as perception and interpretation of stressful experiences, which are unique to humans. Stress can be positively and negatively 14 University of Ghana http://ugspace.ug.edu.gh valued. It yields positive outcomes when there is the potential of gaining from the stressful situation. Stress can be good (Eustress), and on the other hand it can be bad. Robbins (2013) posits that competitive workplace stress does not necessarily impede progress or hinder goal achievement. Earlier studies propose that, competitive stress has lesser negative consequences as compared to stressors that hindrance stress. Stress is often associated with means and demands. Means are things within an individual’s control that can be used to resolve the demands. Demands are responsibilities, pressures, obligations, and even uncertainties that individuals face in the workplace. Figure one displays the process through which stress affects an individual from the stage of potential sources of stress to consequences of stress. Figure 1. Model of stress. Adapted from “Organizational Behavior” by Robbins (2013, p.597) 15 University of Ghana http://ugspace.ug.edu.gh Seyles theory on stress was one of the earliest, it paved the way for other stress theories. During his tests with rats he was able to prove that the three stages of stress were empirical and thus triggered some physiological responses. One of the weaknesses of the GAS stress theory, according to Krohne (2002), is that the concept of stress became very similar to many concepts thereby causes it to loss its scientific relevance. Some of these terms include “anxiety, conflict and threat”. He also believed that Seyle failed to take into consideration any coping mechanism that could potentially mediate the relationship between stress and the outcome. 2.1.2 Transactional Stress Model The Transactional Model of stress aids in evaluating the methods through which individuals cope with stressful events (Lazarus & Cohen, 1977). Stressful experiences take on an interactional value between the person and his or her environment. Transactions are often based on the impact that the external stressor has. The extent of impact is aggravated and or mitigated based on the individual’s appraisal of the stressor. Social and cultural resources also help the individual in coping with stressors (Lazarus & Cohen, 1977). On encountering a stressor, the individual appraises it as an impending threat. According to the transactional model of stress, primary appraisal is said to have occurred when there is an evaluation of a stressor as a potential threat to one’s well-being. It usually involves one’s perception of an event as either stressful or positive. Some questions that an individual may consider during this phase may include “Is this going to affect my performance negatively or positively?”, “Am I going to be able to be successful with these new changes?”. With this type of subjective questioning the individual is able to evaluate the extent to which a situation is a threat or not. In the second phase of this model, the individual assesses his or her ability to cope with the stressor in question (Lazarus & Cohen, 1977). Basically secondary appraisals just help one 16 University of Ghana http://ugspace.ug.edu.gh weigh in on options he or she has in order to deal with the stressful situation. Persons who have many options to deal with the stressor will be able to cope better with the threat and vice versa. For instance, if a situation is labelled as a threat one may question himself or herself,” What can I do about this?” Lazarus and Cohen (1977) assert that individuals often evaluate situations to find out whether their stress levels are manageable, and if not, what measures to put in place in order to manage those situations. This model also points out that individuals differ in their evaluation of the stressful and not stressful, meaning that what is stressful to one individual may not necessarily be stressful to another. Larazus and Cohen’s stress model states that both personal and environmental factors are responsible for stress. It’s also unique in its focus on cognitive appraisal in the evaluation of stress. According to the transactional model one’s appraisal of a situation is weightier than the situation itself. This theory takes a subjective stance on stress appraisal, suggesting individual differences in stress and coping. In relation to this study a haulage driver’s appraisal of a road situation would determine his ability to cope. This theory hypothesizes that personality differences would play a vital role in one’s appraisal of a stressor. A haulage driver who encounters a situation on the road may have to assess his ability or inability to cope with the stressor. He does this by checking his alternatives or options and/or resource (cultural and social) reserve. Hobfoll (1989) criticizes the transactional stress model for being overly centred on perception, with no means to measure or test individual appraisal or vulnerability to stressors, because this theory does not clearly state the true measure of one’s stress level. 2.1.3 The Conservation of Resources (COR) Model The Conservation of Resources (COR) Model was propounded by Hobfoll (1989). According to this model, people always attempt to secure and retain resources. These resources may include objects like food, water, clothing, home etc. Resources may also include individual 17 University of Ghana http://ugspace.ug.edu.gh issues such as self-esteem, conditions such as maintaining relationships and energies like time, money and knowledge. Ito and Brotheridge (2003) assert that literature supports findings about how an individual’s ability or inability to cope with a stressor is highly dependent on how he or she gains, protects and uses the resources he or she has. Hobfoll (1989) believes that stress comes about as a result of the threat to loss of resources and/or the loss of resource. He also believes that stress can result out of one’s inability to gain resources after making a significant investment of his or her own resource reserve. The COR theory helps explain commute stress because it asserts that haulage drivers who feel their resources are being threatened by a road situation may resort to use up their resource reserve in order to counter the impending situation. According to Hobfoll (1989), loss of resources is significantly more damaging to the individual as compared to gain of a relatively significant amount of resource, hence individuals strive to have more resource reserve. From the theory one can infer that individuals with more resource may be able to withstand situations that impede one’s ability to gain resource over individuals with less resource. This study would focus mainly on the COR theory of stress. The main objective of this study was to determine whether commuting stress would significantly predict worker well- being of haulage drivers. Consistent with the COR theory, differences among haulage drivers may be attributable to one’s resource mass. In that, drivers with more resource mass may be able to better cope with commuting stress. For example, drivers with knowledge of many routes may be able to reach his or her destination earlier than others who may be handicapped in that respect. Also drivers with more efficient vehicles may have a higher wellbeing as compared to others who may not have that resource at their disposal. These nuances may determine the extent of the effect of commuting stress on well-being. The personality of a haulage driver could be very influential in determining the effect of a stressor on his or her well-being. Hobfoll’s theory suggests that one’s personality could 18 University of Ghana http://ugspace.ug.edu.gh be a potential resource which could either foster or hinder his or her well-being. For example, one who possesses a conscientious personality may be able to better cope with traffic congestion and impedance over a neurotic one. Well-being could be affected, either negatively or positively based on the personality type of the driver. Psychological Well-Being theory According to Diener (1984), in earlier psychological studies, well-being had often been sidelined in studies. The focus was illnesses, unhappiness and suffering. He believed that psychological studies often sought to resolve the negative issues hence little attention was paid to positive functioning. However, Ryff (1989) argues that studies on psychological well-being have often overlooked critical factors of positive functioning. According to her theory of psychological well-being certain essential features, such as self - acceptance, positive relations with others, autonomy, environmental mastery, purpose in life, personal growth are worth considering. Self – acceptance - This can be defined as a key element of one’s well-being as it is characteristic of self – actualization, optimum functioning and maturity. The individual appreciates him or herself and accepts full responsibility for both positive and negative occurrences that may have happened in the past. Positive relations with others – This also fosters psychological wellbeing. Earlier theories underline the benefit of good relationships. Ones capability to have positive affection towards another contributes substantially to his or her psychological well-being. Often individuals who self- actualize possess strong affections, they have deeper friendships and show much more empathy for others. The ability to relate warmly with others also shows maturity .Theories on adult development also stress on close relationships and offering counselling to others. Due to 19 University of Ghana http://ugspace.ug.edu.gh the points stated above importance of positive relations with others must not be undermined in studying well-being across all stages in life. Autonomy – In the literature it is important to note that no small emphasis is laid on self determination, independence and self regulation. It is characteristic of self actualizers to be autonomous and resistant to phenomena such as group think (act that involves conformity to group norms at the expense of progressive, innovative thinking) and enculturation (learning cultures and values of a group so as to conform to their standards) . This individual does not seek to please the crowd but rather places value through subjective standards (Ryff , 1989). Environmental mastery – This characteristic of psychological wellbeing encompasses the individual’s ability to harness conditions in his or her environment to suit his or her tastes. One who is able to exert him or herself over his or her environment can be termed as being mature. One major characteristic of life span development is the ability of the individual to be able to influence and regulate complicated environment. Advancement here is measured by the individual’s use of mental and physical ability to innovate, create and imprint on his or her environment in a substantive manner. Positive aging is also represented by the optimal use of resources available to the individual in his or her environment. Purpose in life – According to Ryff (1989) the belief and feeling that there’s meaning and purpose to life is key for positive functioning. When an individual has a keen sense of direction in life, understanding of purpose and acts intentionally towards the fulfillment of goals, he or she can be said to be driven by purpose. This purposeful drive is a sign of maturity, and one who possesses these characteristics can be said to be living a meaningful life. Personal growth – the ability of an individual to progress from one stage of psychological prowess and skill to another is also characteristic of positive functioning. The ingrained motivation to advance from one level of skill to another demands that the individual be open 20 University of Ghana http://ugspace.ug.edu.gh to new experiences so as to have such goals materialized. This continual gradual growth makes him or her more versatile and gives rise to problem solving capacity. Life span studies also focus on progressive ability to handle issues that arise at each stage of growth. In relation to this study, Ryffs (1989) wellbeing theory due to its encompassing nature of key elements of well-being studies and psychological functioning, worker well-being would be measured in terms of , environmental mastery, purpose in life, personal growth, autonomy, positive relation with others and self-acceptance. The Big five-factor theory This study would draw theoretical underpinnings from the big five-factor model of personality. This model is most popular among driving studies (Ge et al., 2014). It consists of five factor traits namely openness, conscientiousness, extraversion, agreeableness and neuroticism. Openness - An individual with this trait is often open to experiences, such individuals may be adventurous, curious, and appreciative of nature and art. On the other hand, one low in openness may exhibit shyness and fear. Conscientiousness - Conscientious individuals possess a strong sense of responsibility, loyalty and discipline. They are usually optimistic and high achieving. Agreeableness - The measure of an individual’s kindness, politeness and warmth can be termed as agreeableness. Neuroticism - An individual high on neuroticism may easily slip into depressive states and easily find fault with others. 21 University of Ghana http://ugspace.ug.edu.gh Extraversion - Extroversive individuals on the other hand are sociable, chatty, enjoy the company of others, whilst introverts are exactly the opposite (Matthews, Deary, & Whiteman, 2003). 2.2 Review of Literature A study by Amponsah-Tawiah et al. (2016), used structural equation modelling to test whether commuting stress would have an effect on the job satisfaction of an employee and their turnover intentions. Results revealed that commuting stress was positively related to burnout and turnover intention. This study also revealed that commuting stress had no direct impact on job satisfaction but indirectly affected turnover intentions through burnout. They also found no differences among men and women. Unlike Amponsah-Tawiah et al. (2016), this study would explore the relationship between commuting stress, personality and worker well- being by using Hierarchical regression, while controlling for personality facets and demographic factors. Their study was also unable to examine potential factors that could impact commuting stress. This study also seeks to critically explore potential variables (personality facets and demographics), that may influence the relationship between commuting stress and worker well-being. Burch (2015) examined the effect of daily commuting stress and end-of-day job strain on the safety of an individual’s commute through the experience of work-related rumination. This study sampled participants who worked full time and commuted to work by private vehicle on a daily basis. Results showed that commuting stress influenced safety behaviours during commute. Job strain was also found to affect commuting safety behaviour, though being partially mediated by work-related affective rumination. He also found that work–related affective rumination worsened the impact of commuting stress on commuting safety behaviour. This study’s findings reveal that a spill-over can occur between one’s attitude towards work and commuting experience, thereby impacting safety behaviour outside the workplace. 22 University of Ghana http://ugspace.ug.edu.gh Furthermore, the scales adapted in this study were dichotomous (yes/no), thereby limiting responses. In a Dutch study to find out whether commuting time had an impact on labour supply, Gimenez-nadal and Molina (2014) found that both men and women spent a maximum of 3.22 hours commuting per day. Results also suggest that commuting bears a significant positive relationship with work duration within specific time-space constraints but later negatively affect physical competencies. However, in a study conducted in London by Cassidy (1992) , longer-distance commuters reported less positive and more negative experience of commuting, they did not perceive themselves as any more stressed than any other group. The study revealed that there were no differences in work stress. Long distance commuters were also reported to have scored higher in achievement motivation and job commitment. They also exhibited a more positive and confident problem-solving style and a more positive attitude to leisure. It is important to note that most studies that explore long distance commuting are often conducted outside Sub-Saharan Africa, this study would explore the Ghanaian experience of haulage commuting. Sposato and Cervinka (2011) investigated the impact of certain factors on commuting stress, they also sought to find out if these variables had any relationships. These factors included control, impedance and duration of commute and predictability. Results revealed that control was the most potent predictor of commute stress. The reason behind this was that, duration of commute was found to interact with control, which grew in a recurring manner overtime. They also found that control had a significant effect on predictability. In contrast to Sposato and Cervinka's (2011) focus, this study would examine the impact of personality and demographic dynamics on commuting stress and worker well-being. Martin and Licheron (2014) conducted a study to evaluate the commuting paradox in job well- being. The study took on a cross sectional survey design and employed a large sample size of 16,994, made up of both men and women. Results of the study established that 23 University of Ghana http://ugspace.ug.edu.gh commuting is a burdensome task that eats into valuable time and causes stress and fatigue. These results also suggest that increased travel time may well have a negative effect on home- work commuting. Private car or motorcycle users were also found to experience higher commute stress than public transport users. Furthermore, commuters who had worse commute were found to be less satisfied and more stressed at work than their colleagues. Many studies often consider commuting on “home to work” basis; conversely, this study seeks to examine commuting as one’s occupation or career. In an attempt to assess the relationship between commute mode, neighbourhood public transport connectivity and subjective wellbeing, Chng, White, Abraham, and Skippon (2016) sampled data on 3,630 commuters. The results showed that only walking commutes were associated with satisfaction. However, results also suggest that higher life satisfaction in walking did not produce the same results in individuals with lower mental distress. It was found that though public transportation connectivity was associated with lower levels of mental distress, train users with better connectivity had higher levels of mental distress. Connectivity was also found to have no relationship with one’s choice of public transport, rather public transportation was patronised by young commuters who often engage in long travels and have less or no offspring and personal vehicle. A cross-sectional study by Nomoto, Hara, and Kikuchi (2015) to investigate the effects of long-time commuting and long-hour working on lifestyle (physical exercise, sleep, breakfast, alcohol intake, smoking and mental health) found that longer-time commuters were more likely to sleep less, exercise less and work less longer than short-time commuters. Results also showed that long-hour workers were more likely to experience shorter commutes, frequently work on holidays, smoke more and have higher stress coping capability. It can be inferred from this study that, productivity ratings lean towards individuals who experience shorter commutes. 24 University of Ghana http://ugspace.ug.edu.gh On the other hand, Ohta, Mizoue, Mishima, and Ikeda (2007) found that healthy lifestyles such as eating breakfast regularly, sleeping adequately, exercising and refraining from smoking habits, as well as active coping resulted in lower psychological distress among workers. Similarly in a study on commute time, time spent in activities beneficial to well-being, and the relationship to self-assessed well-being, Hilbrecht, Smale, and Mock (2014) found that long commute had an adverse impact on life satisfaction and caused an increased sense of time pressure. Wener and Evans (2007) measured differences between car and train commuters regarding physical activity. They found that train commuters walked an average of 30% more steps per day. They also found that train commuters walked for a period of 10 minutes or more while traveling significantly more often, and were 4 times more likely to walk 10,000 steps per day than car commuters. Their report revealed that transportation means had a significant effect on the volume of physical actions commuters accrue on the usual work day. It is important to note that planned or coordinated exercise programs were exempted from this study. Whereas Wener and Evans (2007) measured differences between car and train commuters this studies would centre on the effect of commuting stress on worker well-being In a similar study, Ohta, Mizoue, Mishima, and Ikeda (2007) suggest that leisure time exercises and walking or cycling to work has a positive effect on the mental health of men. Furthermore, in a study on commute well-being, Smith (2017) found that individuals who rode bicycles and walked to work were happier with their commutes since they were somewhat unaffected by traffic congestion as compared to individuals who patronize buses and drive private vehicles. Sandow (2014) focused on the social implications of long-distance commuting on commuters and their spouses. She sought to find out the extent to which long-distance commuting increased the chances that couples will divorce. Results suggest that, separation rates are higher among couples who commute long distances unlike couples who do not 25 University of Ghana http://ugspace.ug.edu.gh commute long distances. According to this study, men’s chances of separating from their spouses come about as a result of commuting only temporarily, whilst women’s chances heighten when long distance commuting seems permanent. The effects of commuting on relationships were also reported to vary depending on residential context. In a study which examined the effects of commuting stress on commuters’ individual life and work domains, Mahmudin (2012) sampled 660 commuters through a questionnaire survey. The results revealed that longer commutes had a significantly greater impact on commuting stress. Their study also found that the stress associated with commuting affected how individuals who went on commutes reported somatic symptoms of disease health and discontentment with their commuting experience. According to this study, commuting stress is a major factor in one’s intention to quit his or her job. In a study to understand the impact of commuting duration on the psychological health of men and women, Roberts, Hodgson, and Dolan (2011) used information from the British Household Panel Survey in a fixed impacts system that incorporates factors known to decide mental wellbeing and other factors which may give remuneration to driving, for example, salary, work fulfilment and lodging quality. Results disclose that, even after these factors are controlled for, driving has a negative impact on the mental soundness of ladies, yet not men. Clarifications for this distinction were investigated and no proof was discovered. This means that it is not necessarily because of females’ shorter working hours or weaker work related position. However, females were found to be more sensitive to commuting duration, and this had an effect on their primary obligations regarding everyday family assignments like taking care of children and managing house chores. Similarly, Feng and Boyle (2014) studied to find out whether long journeys to work are negatively associated with commuters’ mental health. Reports indicate that long journeys to work are indeed associated with a higher risk of poor mental health for women but not for men. Novaco et al. (1991) also reported that women who commuted on high physical impedance 26 University of Ghana http://ugspace.ug.edu.gh routes often had the most negative effects. Novaco and Collier (1994) examined commuting stress in automobile travel with a large representative sample of 2,591. They found that commuting stress was significantly associated with distance and duration of the commute, controlling for age and income. According to them, long distance was defined as above 20 miles. Results also showed that gender moderated the relationship between commute stress and distance, as such; women suffer stress spill-overs to work and home. Results from this study also suggest that ride-sharing (carpooling) lessens the effect of commuting stress on drivers. Whilst most studies encompass work-home commute, this study would focus on haulage vehicle commuting and how this is likely to impact well-being. Comparably, Páez and Whalen (2010) conducted a study on how university students were affected by socio-demographic and attitudinal variables, with regards to an increase or decrease in their desire for daily commute. Results indicate that active travellers (individuals who walk or cycle) tend to be less dissatisfied with their commute, followed by individuals who travel in personal vehicles and transit users. The study also reports that attitudinal responses play a major role in determining a student’s desire to travel more or less, these variables had to do with the social environment preference of the individual. Some of these preferences include, availability of local activities, quality of facilities, productive use of the commute, and the intrinsic value found in the commute travel. We can infer from these two studies that social environment helps mitigate the effects of stress. In a study to explore the relative effects of built environment, travel attitudes, and travel characteristics on commute satisfaction, Ye and Titheridge (2017) found that commuting characteristics, such as mode choice, congestion, and the type of services one experiences during transit have a direct influence on commute satisfaction. Attitude was found to have both a direct and indirect effect on commute satisfaction, whilst built environment was proven to have indirect effects through influencing commuting characteristics. 27 University of Ghana http://ugspace.ug.edu.gh Lyons and Chatterjee (2008) explored the different economic, health and social impact of commuting on individuals. They also sought to tease out the significance of commuting for individuals in the society. The paper also reviewed the commute experience, attitudes towards commute and the use of time during the journey. Results suggest that there are social, economic and financial benefits from an improved travel experience for individuals whose commutes cover long journeys. They also propose that, improving the quality of the travel experience may contribute to the trend towards long-distance commuting. In a study that examined whether relaxation, detachment, mastery and stressful delays caused by commuting from work to home affected employees’ recovery status, van Hooff (2015) employed a multilevel analyses which revealed that relaxation was positively related to employees recovery status whilst stressful delays were negatively related to recovery. Daily job demands were also expected to moderate these effects, however for detachment, similar relations were found but only on days with high job demands. Mastery was not related to employees’ recovery status. One shortfall of van Hooff's (2015) study is that most participants were highly educated thus limiting possible generalization. In contrast most of this study’s participants had only basic education. In another study, Wener and Evans (2011) provide a cross-sectional comparison of car and train commuters with several stress indicators, including statistical controls for group characteristics. They explored potential underlying psychological processes (i.e., control, effort, predictability) to help explain stress differences related to commuting mode. Findings indicate that there were statistically significant differences for perceived commuting stress and mood. Also car commuters showed significantly higher levels of reported stress and, more negative mood. Mediational analyses indicated that effort and predictability are to a large extent responsible for the stress associated with car commuting. In a similar study, Brutus, Javadian, and Panaccio (2017) investigated the impact of various commuting modes on stress 28 University of Ghana http://ugspace.ug.edu.gh and mood upon arrival at work. They found that comparatively cyclists often experience lower levels of early stress. In a study to explore methods through which users of various commute modes (walkers, bicyclists, drivers, and transport clients) assessed their travel to work, Lajeunesse (2010), surveyed commute preferences among different groups. This was done with respect to contrasts for dispositional mindfulness and time luxuriousness (the recognition that one has adequate time to take part in pleasurable, significant activity). He additionally investigated the immediate and oblique connection amongst care and drive related attunement (how much commuters are happy with their work travel and find it tranquil), how time affluence, commute related pressure, and competence mediate this relationship. This study considered 786 college representatives about their (a) relative degrees of capability, stress, and attunement with regards to the work drive; (b) view of time affluence over the earlier month; and (c) levels of dispositional care. Results uncovered that bus users, walkers and bicyclists revealed fundamentally less stress than drivers. Walkers and bicyclists detailed more noteworthy positive trip based effect than drivers and bus users. Moreover, walkers and bus users held a generally more prominent view of time affluence than drivers. In effect, Lajeunesse (2010), proposes that these discoveries recommend with a specific end goal to urge people to take part in dynamic transportation, as it may demonstrate beneficial means to improve singular level time affluence and a sense of ability to use non-mechanized modes of commute. Urhonen, Lie, and Aamodt(2016) studied the relationship between commuting and subjective health complaints, using data from a web-based questionnaire. In a sample of 2,126 railway employees, 644 (30.3%) had long commute times. According to reports from the study, individuals who commuted 60 minutes or more each day were characterized by significantly higher numbers and degrees of subjective health complaints compared with their peers with short commutes. Also employees with long commutes reported more complaints than those with short commutes. Findings also suggest that there were significant associations between 29 University of Ghana http://ugspace.ug.edu.gh employees with long commutes and the number and degree of incidences of self-reported musculoskeletal pain, pseudo-neurologic complaints, and gastrointestinal problems. From their findings, Urhonen, Lie, and Aamodt (2016) likewise suggest that commuters who had long commutes for over 10 years report more gastrointestinal and musculoskeletal protests than those with long drives for less than 2 years, additionally, workers with long drives invested less energy with their families and recreation exercises contrasted those with short drives. Mattisson et al. (2016) conducted a longitudinal study to compare the difference between ones level of stress and individual characteristics among 30-60 minutes vehicle commuters across different locations in Sweden, Scania. They observed that spatial heterogeneity in stress levels varied over the differences in locations and time. The local differences in stress levels among participants were partly explained by socio-demographic characteristics. Findings also suggest that stressed commuters in highly stressed areas in the year 2000, were most likely to keep to their mode of commute and time over those who were not. They were also more likely to have the same workplace and residential location after ten years. Croon, Blonk, Zwart, Broersen, and Croon (2014) studied the effects of job control, quantitative workload, and two occupation specific job demands (physical demands and supervisor demands) on fatigue and job dissatisfaction. The study was done among Dutch lorry drivers in an attempt to build upon Karacek’s Job Demands and Control (JD-C) Model. They found that the consideration of physical and supervisor requests in the JD-C display clarified a lot of difference in weariness (3%) and work disappointment (7%) far beyond job control and quantitative workload. Besides, as per Karasek's connection theory, job control cushioned the positive connection amongst workload and employment disappointment. In a study to evaluate lorry driver work stress by measuring adrenaline and noradrenaline excreted in the urine, and to find out the factors which are responsible for excretion rates of catecholamine’s, Van der Beek, Meijman, Frings-Dresen, Kuiper, and Kuiper 30 University of Ghana http://ugspace.ug.edu.gh (1995) found that certain indicators reveal that work stress of lorry drivers was on the high side. They also found that at the end of the work day, full recovery did not actually take place. Findings also posit that working day excretion rates of adrenaline and specifically noradrenaline were high among professional drivers. Furthermore, excretion rates of adrenaline on the working days were related to psychosomatic complaints and not to psychosocial job strain. According to this study an imperative explanatory variable was the excretion rate of noradrenaline during the physical work (loading and unloading). Commuting can be regarded as a burden. However, compensation has aided in mitigating the effects of burdensome commuting (Rana & Munir, 2011). They also studied whether burdens of commuting are indeed fully compensated by certain factors. They found an association between commuting and personal well-being after controlling for a range of individual characteristics. Aberg and Rimmo (1998) revealed that commuting stress had a negative impact on the wellbeing of employees. The negative implications are perceived as a challenge to both bosses and employees. This suggests that, females, youth, shift, low maintenance, and non-white- collar employees will be more likely to have high-strain roles. Those with such roles view their work to be physically requesting and less fulfilling. Low individual livelihoods and low levels of education were also connected with higher commuting pressure. Barling, Kelloway and Frone (2004) showed that commute stress was of grave significance. They concluded that organizations should consider the problem of work and commuting stress by fully investigating the stress contributing factors through learning and awareness. In this regard the employees should be given regular training for developing strong emotional competencies which will ultimately help them to boost up their performance and combat stress in proactive ways. Khan, Yusoff, and Khan (2014) found that a negative relationship exists between job stress, commuting and performance, whereas a strong positive relationship was found between emotional intelligence and job performance. The findings of study show that employers of 31 University of Ghana http://ugspace.ug.edu.gh construction workers in Pakistan should focus not only on identifying the stress factors but should also try to manage their emotional competences through a conducive work environment (Barling, Kelloway, & Frone, 2004). In this way they can deal with the problem of job stress and boost up their job performance. According to Gulian et al. (1989) a negative correlation exists between job stress and job performances. This suggested that job stress significantly reduced the performance of individuals. Employees whose nature of work involved long commuting hours throughout the day, reported less positive and more negative experience of commuting. They also did not perceive themselves as any more stressed than any other group. Neither did they report any difference in work stress. They did however; score higher on achievement motivation and job commitment. They also exhibited a more positive and confident problem-solving style and a more positive attitude to leisure (Gulian et al., 1989). This suggests that they had developed positive ways of coping with the lengthy commuting. Drivers tended to feel more in control and often found a long uninterrupted drive relaxing. Train travellers are able to engage in positive activities, such as reading or catching up on work, during a long uninterrupted train journey. However, there is a suggestion that the picture may hide possible longer-term ill- effects. Longer distance commuters, despite the aforementioned positive factors, reported significantly less time spent at home, socializing and engaging in active leisure (Rana & Munir, 2011). The effect of a disturbance of the balance between the various life domains indicate more time spent at home, but higher levels of home stress. These effects cannot be easily interpreted hence one must be cautious about drawing strong conclusions. Most studies on commuting focus on trips to and from work in commercial and private vehicles. Other studies in this area explore differences among the various modes of commuting, eg. cycling, using public and private transportation. In line with the literature, most studies reveal the adverse effects of commuting stress on wellbeing. 32 University of Ghana http://ugspace.ug.edu.gh Unlike most of the studies done in the area of commuting, this study would narrow in on the commuting experience of haulage drivers, their personality, their extent of stress and how they are able to cope with the resulting stress from work trips. This study would also measure their worker well-being. 2.3 Hypotheses Hypothesis 1: Commuting stress would negatively predict worker well-being, after controlling for both demographic factors and the facets of personality among haulage drivers (Feng & Boyle, 2014; Novaco & Gonzalez, 2009b; Sposato & Cervinka, 2011; Urhonen, Lie, & Aamodt, 2016). Hypothesis 2: Demographic factors would significantly moderate the relationship between Commuting Stress and Worker Well-being. Hypothesis 3: The facets of personality would significantly moderate the relationship between Commuting Stress and Worker Well-being (Tao, Zhang, & Qu, 2017). 33 University of Ghana http://ugspace.ug.edu.gh 2.4 Conceptual framework MODERATOR PERSONALITY TYPES 1. OPENNESS 2. CONSCIENTIOUSNESS 3. EXTRAVERSION 4. AGREEABLENESS 5. NEUROTICISM INDEPENDENT DEPENDENT VARIABLE VARIABLE COMMUTING WORKER- WELLBEING STRESS DEMOGRAPHIC CHARACTERISTICS 1. AGE 2. MARITAL STATUS 3. DRIVING EXPERIENCE 4. RELIGION 5. EDUCATION LEVEL 6. COMMUTING DISTANCE FIGURE 2: CONCEPTUAL FRAMEWORK OF COMMUTING STRESS, PERSONALITY AND WORKER WELL-BEING Narrative on Conceptual Framework of Commuting Stress, Personality and Worker well-being This study used the conceptual framework in Figure 2, detailing the relationship among commuting stress, personality and worker well-being. In the framework, personality is a moderating variable, expected to influence the relationship between commuting stress and worker well-being. Commuting stress is the independent variable for this study, whilst worker well-being will be the outcome variable. Personality as used in this study is measured with levels such as, openness, extraversion, neuroticism, conscientiousness and agreeableness. Demographic variables include marital status, number of children, education level, driving experience and religion of haulage drivers. These variables are anticipated to be confounding variables because; they may have bilateral influences on commuting stress and worker well- being 34 University of Ghana http://ugspace.ug.edu.gh Operational Definitions In relation to this study the following terms have been operationalized; Commuting stress: This has been operationalized as the stress ensuing from commuting in a haulage vehicle from a loading station to a designated area. Haulage Drivers: Professional individuals who drive haulage vehicles in order to transport goods by road from the station to a designated area. Some of these goods may include ore, coal, water, waste, oil, gas, agricultural products and others. Worker wellbeing: In this study, worker well-being has been defined as the overall physical and mental health of the haulage driver. Personality: This study adapts the trait theory of personality by McCrae and Costa (2003), that states that personality trait as a consistent array of feelings, views and behaviour an individual adheres to. 35 University of Ghana http://ugspace.ug.edu.gh CHAPTER THREE METHODOLOGY This chapter details the practical methods that served as a guide in the conduct of this study. Firstly, the main population and sample size would be discussed. This would include a comprehensive description of the features of the sample, comprising of the criterion for their inclusion as well as the techniques used in the sampling. The research design used for the study was thoroughly discussed, in terms of the research questionnaires employed in the collection of data, pilot study, procedure and the data collection strategies. Ethical procedures that guided data collection was also discussed. 3.1 Population This study population included selected haulage drivers in Tema Industrial Area. This area is known to have many haulage drivers in the country. Inclusion criteria: Only haulage drivers with loading stations within Tema Industrial Area were included in this study. Exclusion criteria: Haulage drivers with loading stations outside Tema Industrial Area were excluded from this study. Haulage drivers within the afore-mentioned areas who did not consent to the study were also excluded. 3.2 Sample and Sampling Technique To ensure that accurate and reliable information on commute Stress, personality and worker well-being was obtained, this study adopted a purposive, convenience and snowball sampling techniques. A non-probability purposive sampling technique or judgement sampling technique was employed because of the definite criteria of the sample population (Singleton & 36 University of Ghana http://ugspace.ug.edu.gh Straits, 2005). According to Singleton and Straits (2005), random assignment of individual subjects would be impossible due to the fact that this kind of sampling is most suitable for intact groups. Convenience Haphazard or Accidental sampling method served the purpose of enabling accessibility to research participants (Etikan, Musa, & Alkassim, 2016). This sampling technique was employed because it fosters accessibility, addresses the issue of geographical proximity, targets willing and available individuals, and is relatively fast and cost effective (Necku, 2015). The Snowballing technique fostered quick responses from participants since they directed the researcher to other prospective participants thus increasing the rate of responses till the target sample size (N = 213) was reached. Though this kind of sampling is often for rare populations (Handcock & Gile, 2011), it helped solve the ambiguity in locating actual drivers over mates. Since most stations were widely populated by both of them. Haulage drivers cooperated by pointing the researchers to other colleagues after filling their questionnaires. An introductory letter and a copy of the questionnaire were submitted to the various companies. Others were convinced via a face to face dialogue on the details of the study and upon seeing a student’s identification card, before granting permission to engage their haulage drivers. Upon summarizing the details and significance of the study most drivers agreed to take part in the study by appending their signature to the questionnaire. Cochran's (1977) sample size formula was used in calculating the required sample size to be used for this study. Thus, the minimum sample size was calculated using the formula, z 2 n   /2  p(1 p) 2 ; where z /2 = the critical value at 5% alpha level usually set at 95%; p=  response rate of 90.3% from a previous study by Amponsah-Tawiah, Annor, and Arthur (2016);  = level of error expected which is 5%). This yielded a minimum sample size of 135. 37 University of Ghana http://ugspace.ug.edu.gh However, 25% of additional sample was considered to make up for non-response as well as missing questionnaires. Hence, a total of 213 haulage drivers were considered as the study participants from Tema Industrial area. Out of this sample 211 were able to completely fill out the questionnaire whilst 2 had to be excused due to “duty calls”. Due to the fact that questionnaires were researcher assisted, response rate was high, out of 213, 211 were able to fully complete the questionnaire, and thus 99.1% of the participants completed the questionnaires. The “Section A” segment consisted of demographic detail. Sex distribution was excluded from this study because the sample population consisted of 211 (100%) males. Age on the other hand ranged from 20 – 57 which were placed into different range groups (20-30, 31-40,41- 50,51 and above. Most drivers (85%) were between the age ranges of 31-40 years, on the other hand, the results revealed that drivers ranging from 20-30 were the fewest (10%). An item on educational status revealed that most drivers’ (92.4%) had gone through basic education as compared to those who had no basic education (7.6%). Marital status revealed that unmarried drivers consisted of about 19% whereas those who were married were 81%. With religion 157 drivers were Christian whilst 54 were Muslim. Most of the haulage drivers had experience the haulage industry for over 10 years (64.9%), whilst drivers with less than 10 years’ experience constituted (35.1%). Demographics also reveal that most drivers (7.1%) usually travelled less than 10000km, individuals who travelled more than 10000 km constituted 92.9% of the responses. There were load variations in this study, in that most drivers transported different products. Some of these products included Oils (76.8%) (Petrol, Diesel Gas, and Aviation Fuel), Water (12.3%) Unilever Products (8.1%) and Others (Torch battery, Rubber, Cement, Fish (2.8%). These demographic details are elaborated in Table 1 38 University of Ghana http://ugspace.ug.edu.gh Table 1: Frequency Distribution of Demographic Characteristics of Haulage drivers (N=211) Demographic Variables N Percentages (%) Age of respondents Between 20 and 30 years 21 10.0 Between 31 and 40 years 85 40.3 Between 41 and 50 years 63 29.9 51 years and above 42 19.9 Educational status No formal education 16 7.6 Formal education 195 92.4 Marital Status Single 40 19.0 Married 171 81.0 Religion Christianity 157 74.4 Islamic 54 25.6 Number of years of driving 10 years and below 74 35.1 Above 10 years 137 64.9 Distance covered 10000km and below 196 92.9 Above 10000km 15 7.1 Type of load carried Oil (Lubricants, Aviation, etc.) 162 76.8 Water 26 12.3 Unilever Products 17 8.1 Others (Cement, Torch battery and Rubbers) 6 2.8 The participants of this study were mostly Drivers with stations situated in Tema, most of who were employed with subsidiaries of Tema Oil Refinery. Other companies like J.K Horgle,, James K. Ahiadome, Bon Aqua, Mobile Water Company limited, Shell Ghana and Unilever were also included in this study. Drivers around the harbour were also contacted. These variations were considered in order to gain a wide-ranging set of responses from the sample group. 39 University of Ghana http://ugspace.ug.edu.gh 3.3 Research Design This research used a cross sectional survey research design. This design was used so as to meet time constraints, and to study a wide range of driver characteristics. The survey administration was researcher-assisted due to the anticipated language disparities and educational level among the target population. One research assistant was trained on how details had to be collected to assist with the administration of the study questionnaires. 3.4 Data Collection Questionnaires This quantitative study employed questionnaires in the collection of data for analysis. The questionnaire had 67 items, divided into four sections. It had a combination of five different scales that measured the key variables of interest and demographics. The first section of the questionnaire was made up of questions that sought to obtain demographic details. The item on age was blank, in that it gave the participants the opportunity to fill in their raw ages after which categories were formed per the range of age groups for analysis. Age ranges included four categories: 20 – 30 years, 31-40years, 41- 50years, and 51 and above These categories were chosen to conform to previous similar studies such as Amponsah-Tawiah et al. (2016) and Martinussen, Hakamies-Blomqvist, Møller, Özkan, and Lajunen, (2013). In this study there was no item on gender because all the participants were male. Educational status was also measured per two categories, the formally educated (Primary, JSS/O-Level, SHS/A-level, Technical/Vocational, and Form 4) and those who had no formal education. An item on marital status was also included, participants had to state whether they were married or single. Religious affiliation was also measured on three categories, these include, Christianity, Islam and Traditional beliefs. Years of haulage driving experience was also sought out. Driving distance covered by a driver per trip was also measured. It was measured from the average speed and time used by haulage drivers from the 40 University of Ghana http://ugspace.ug.edu.gh station to their respective assigned destinations. Thus, commuting distance travelled by the drivers was estimated using the relation; D V T (1) Where D (measured in km) is the estimated commuting distance V (Measured in km/h) is the average speed used by a driver for each trip and T (Measured in hours) is the average time spent or used in driving from the station to assigned destinations. Finally, an item on the type of load the haulage driver transports was included in the demographic details. 3.4 1 Commuting stress This study employed a revised version of the Driving Behaviour Inventory- General (DBI – Gen) driver stress scale (Gulian et al. 1989) by Hennessy and Wiesenthal (1997) to measure commuting stress. This scale has been shown to be able to ascertain whether some particular driver traits make him or her more susceptible to driver stress. The DBI- Gen has 16 items, some of which include items such as “When I get irritated I drive aggressively’, `Trying but failing to overtake frustrates me’, or `I get annoyed by driving behind other vehicles. Items on this scale were answered on five-point Likert scales ranging from 1 (strongly disagree) to 5 (strongly agree). The DBI-Gen has been reported to be a suitable measure of driver stress (Matthews et al. 1991; Glendon, Dorn, Matthews, Gulian, Davies, & Debney,1993; Hennessy & Wiesenthal, 1997). The state driver stress inventory (SDSI) was also included in measuring commuting stress. Items on this scale were 10 in number. This section was to assess the situation specific driver state and to find out if these states could trigger driver stress. This scale was designed by Mackay et al. (1978), and is labelled as the Stress Arousal Checklist. Driver states under the 41 University of Ghana http://ugspace.ug.edu.gh stress arousal Checklist were as follows “relaxed, contented, peaceful, comfortable, calm, tense, bothered, nervous, uneasy and distressed” Subjective impedance, time urgency and perceived control are also captured in this questionnaire. These items appeared as follows “Traffic conditions are congested”. “I am in a hurry”, “I am concerned about getting to my destination on time” and “I do not have a flexible time schedule”,“I feel I do not have control over this driving situation”. In all there were about 26 items on this scale. 3.4.2 Personality To test for personality type, the Big five inventory (BFI) by John and Srivastava (1999), abbreviated to 10 items by Rammstedt, & John (2007) was used. Though this scale was narrowed down to 10 items, it was recommended in scenarios where there were time constraints. It is also reported to offer an adequate assessment of personality based on validity and reliability testing (Rammstedt, & John, 2007). According to Saucier (1994), few item scales have been reported to serve as a means of eliminating item redundancy and therefore often have an effect on fatigue, frustration and boredom that results from answering cloned items. Some of the items on this measure include “I see myself to be someone who is reserved”, I see myself to be generally trusting. Items on this scale were answered on five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). 3.4.3 Worker Well-being Ryff’s (1995) scale of Well-being was used to measure worker wellbeing. Measurement was made on the composite score of six dimensions, namely, autonomy, self-acceptance, purpose in life, positive relations, personal growth and environmental mastery. Some items on the scale are as follows “in many ways I am disappointed with my achievements” (self- 42 University of Ghana http://ugspace.ug.edu.gh acceptance), “maintaining close relationships have been difficult and frustrating for me”(positive relations), “I tend to be influenced by people with strong opinions”(autonomy), “ In general I feel I am in charge of the situation in which I live”(environmental mastery), “I think it is important to have new experiences that challenge how you think about yourself and the world” (personal growth), “I live one day at a time and don’t really think about the future” (purpose in life). Items on this scale were answered on a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree), however seven items on this scale were reverse keyed. 3.5 Pilot Study The questionnaires were piloted before actual data collection began. Even though standardised scales with highly acceptable reliability and validity were used to collect data, it was important to pilot the questionnaires for a variety of reasons. Reliability is defined as the stability or consistency of the scale (Gravetter, & Wallnau, 2006). According to Gravetter and Wallnau (2006) when the same people are measured at the same or similar conditions, a reliable measure should produce the same or similar results. The pilot study therefore helped the researcher determine reliabilities and ecological validity of the selected questionnaires. Furthermore, the pilot study was done to check for clarity of items on the scales. This was to deal with the possibility of unfamiliar, vague, or ambiguous items in the Ghanaian setting, resulting in the difficulty of understanding and interpretation. Results for these tests can be seen in Table 2 in the results session. 3.6 Procedure Ethical clearance was obtained from the Ethics Committee for Humanities (ECH), University of Ghana, Legon. One research assistant was recruited to assist in the data collection 43 University of Ghana http://ugspace.ug.edu.gh and was given training on the administration of the questionnaires. The groups and companies which met the inclusion criteria were identified and approached by the researcher and his assistant. Details about the study were thoroughly explained orally by both the researcher and the research assistant to prospective participants and those who were willing to take part were asked to consent by either their signature or abbreviation. After this, the questionnaires were given to the participants to fill. However, because some respondents were not fluent in English, the questions were translated and ticked based on their responses during the data collection. Those who could not complete the questionnaire due to time constrains were free to opt out, their responses were therefore not included in the analysis. The data collection lasted for 4 weeks. The completed questionnaires were then sorted out for analysis. 3.7 Ethical considerations Ethical clearance was sought from the Humanities Ethics Committee of the University of Ghana before data collection commenced. All participants of this study were informed about the terms and conditions of their participation in the study. This was made known to them explicitly during the recruitment stage. All participants granted the researcher permission to conduct the study. For the sake of confidentiality, participants were advised not to write their names on the questionnaire. This study involved one research assistant who was trained and advised to adhere diligently to all ethical procedures. 44 University of Ghana http://ugspace.ug.edu.gh CHAPTER FOUR RESULTS This chapter presents the results of the data analyses with summaries on the various hypotheses tested using tables. The IBM SPSS version 22 was used for the validation of scales, descriptive summaries and data analysis. The major statistical tests employed in testing the hypotheses are also discussed. The analysis involved two sections; preliminary and hypothesis testing. Detailed tables and graphs are used to present findings on various tests with their relative interpretation. 4.1 Preliminary Analysis The preliminary analyses comprised of exploratory factor analyses of the scales used, reliability of scales, normality of study variables, descriptive statistics, and bivariate correlation among the variables. 4.1.1 Analytical Strategy Exploratory factor analysis (EFA) was used to examine the questionnaires. The factor analysis was used to determine the validity of the scales in the settings of haulage drivers. This was done by examining the amount of variance each item on the scales Moreover; items which contributed very low variance (communality) to the overall variations were deleted based on the loadings of the estimated factor solution. Thus, the EFA help to determine whether the items for each scale would yield factor loadings that could be considered as part of a single construct. 45 University of Ghana http://ugspace.ug.edu.gh 4.1.2 Exploratory Factor Analysis EFA was conducted on the items by employing Principal Component Analysis with Varimax rotation based on the correlation between the factors (required components). The rationale for the use of this technique was to examine all the scales for unidimensionality. Kaiser-Meyer-Olkin (KMO) sampling adequacy and the Bartlett’s test of sphericity were used to assess factorability. Items that satisfied the loading criteria of (≥. 40) were retained and those that failed were deleted. 4.1.3 Descriptive Statistics of Key Study Variables and Test of Normality Descriptive summary of demographic variables as well as normality tests of main variables measured on continuous scale (interval scale) were conducted. The normality assumption for parametric tests was achieved in accordance with the acceptable range of ±2 proposed by Tabachnick and Fidell (2001). The scores of each scale used ranged between 1 and 5. Table 2 below presents results of the descriptive summary and normality tests of key study variables. Table 2: Summary Measures and Reliability Indices of key study variables Variables Mean SD Skewness Kurtosis Alpha(α) Min Max Personality Facets Extraversion 2.81 1.17 0.12 -0.63 0.293 1.00 5.00 Agreeableness 3.12 1.14 -0.03 -0.57 0.557 1.00 5.00 Conscientiousness 4.60 0.72 -1.56 1.01 0.505 1.21 5.00 Neuroticism 1.69 0.83 0.81 -0.56 0.359 1.00 4.50 Openness 2.94 0.74 -0.43 1.63 0.533 1.00 5.00 Commuting Stress 1.83 0.51 -1.79 1.43 0.803 1.12 4.50 Worker Well-being 4.05 0.41 1.68 1.09 0.732 1.45 4.71 It can be observed from Table 2 that, the average score for conscientiousness (Mean=4.60, SD=0.72) among all the participants was relatively higher than the mean scores of the other facets of personality. Thus, the haulage drivers on the average had higher scores for 46 University of Ghana http://ugspace.ug.edu.gh conscientiousness as opposed to extraversion (Mean=2.81, SD=1.17), agreeableness (Mean=3.12, SD=1.14), neuroticism (Mean=1.69, SD=0.83) and openness (Mean=2.94, SD=0.74). Moreover, the average score for Commuting Stress among the haulage drivers were 1.83 (SD=0.51) out of a maximum score of 5. Also, it was found out that the expected score of Worker Well-being among drivers was 4.05 (SD=0.41). 4.1.4 Pairwise Correlations among demographic factors and key study variables In order to examine linear relationship among the underlying variables to be used in the regression analyses, pairwise correlation was carried out using the Pearson correlation coefficient. The correlations among predictor variables were relatively low (r < 0.6); suggesting that multicollinearity would not exist among the predictors. The results of the bivariate correlations are summarized in Table 3. Table 3: Correlation Matrix of Key Variables of the study Variables 1 2 3 4 5 6 7 8 9 10 11 12 1 Age 2 Educational status -0.20 3 Marital Status 0.43*** -0.05 4 Religion -0.10 0.09 -0.08 5 Driving Experience 0.53*** -0.02 0.38*** -0.001 6 Distance covered 0.12 0.01 0.04 0.18 0.13 7 Extraversion 0.06 -0.05 0.01 0.04 0.12 0.12 8 Agreeableness -0.10 0.08 -0.07 0.12 -0.01 -0.10 -0.08 9 Conscientiousness -0.01 -0.001 -0.07 0.10 0.002 0.001 -0.01 0.05 10 Neuroticism 0.21*** 0.08 -0.19** 0.07 -0.21** 0.003 -0.03 -0.03 -0.10 11 Openness 0.16* -0.10 0.10 -0.08 0.07 0.02 0.12 -0.03 -0.06 -0.09 12 Commuting Stress -0.10 -0.07 -0.09 0.04 -0.09 -0.005 0.04 0.19** -0.19** 0.13 -0.10 13 Worker Well-being 0.12 0.21** 0.08 -0.12 0.08 -0.01 0.01 -0.02 0.32*** -0.17* 0.07 -0.36*** *p<0.05, **p<0.01, ***p<0.001 From Table 3, none of the demographic characteristics of respondents had significant relationship with Commuting Stress. However, among the personality facets, conscientiousness (r = -0.19, p<0.01) and agreeableness (r = 0.19, p<0.01) had significant 47 University of Ghana http://ugspace.ug.edu.gh association with Commuting Stress. Both correlations were relatively low. However, there was a negatively low correlation between conscientiousness and Commuting stress. This suggested that, an increase in a driver’s scores of conscientiousness caused a decrease in the scores of Commuting Stress and vice versa. Results also reveal that, an increase in scores of agreeableness of drivers increased that of Commuting Stress and vice versa. For correlation between Worker Well-being and other predictors, it was found that educational status (r = 0.21, p<0.01), conscientiousness (r = 0.32, p<0.01), neuroticism (r = -0.17, p<0.05) and Commuting Stress (r = -0.36, p<0.001) were significant predictors that could influence the Worker Well- being of haulage drivers. 4.1.5 Procedure for moderation The procedures proposed during moderation analysis according to Aiken and West (1991) are that; the independent and moderator variables must be centred or standardized by linearly transforming the variables (finding difference between each score and the mean value for the underlying variable) so as to eliminate multi-collinearity (high correlation between the potential moderating variable and the underlying independent variable). The interaction term (product term) is created by multiplying the centred independent variable and potential moderator. However, for socio-demographic factors which are dummy coded, centring is not needed. Then, in hierarchical regression model, both the predictor and the moderator variable must be specified in block 1; after which the interaction term (interaction effect) is added to the previous model in block 2. The significance in the change of R2 as well as the interaction term must be checked. Consequently, if both are significant, then moderation is said to have occurred. However, if the predictor and moderator are not significant with the interaction term added, then complete moderation is said to have occurred. When both the predictor and moderator with interaction term added are significant, then moderation has occurred; however, the main effects are also significant. Hayes (2012) proposed an algorithm known as “Hayes 48 University of Ghana http://ugspace.ug.edu.gh Process Macro” to explore moderation effects by implementing the aforementioned procedures directly. Thus, the “Hayes Process Macro” was used explore potential moderators identified from the previous hypothesis tested. 4.2 Main Analysis Four hypotheses were proposed in this study. Hypotheses 1 was tested using Hierarchical Multiple regression. Hypotheses 2 and 3 were tested using Hayes Process Macro; whereas hypothesis 4 was tested by employing a 3-Way Multivariate Analysis of Variance (3- Way MANOVA) based on the number (three) of demographic factors with potential moderation effects from the previous tests. Hypothesis One H1: Commuting Stress would negatively predict Worker Well-being by controlling for both demographic factors (age, marital status, driving experience, religion, and educational level) and the facets of personality (conscientiousness, agreeableness, neuroticism, openness, extraversion) among haulage drivers. Table 4 presents results of the test using a 3-step Hierarchical Multiple regression. 49 University of Ghana http://ugspace.ug.edu.gh Table 4: Hierarchical Multiple Regression Analysis of the association between Commuting Stress and Worker Well-being by controlling demographic factors and Personality facets Unstandardized Standardized Variables Coefficient Coefficient P F R2 ∆R2 B S E Beta (β) Step 1 0.005 3.18** 0.086 Constant 3.28 0.277 Age 0.06 0.038 0.14 Educational status 0.38 0.106 0.25*** Marital Status 0.02 0.080 0.02 Religion -0.12 0.065 -0.13 Number of years of driving 0.01 0.070 0.01 Distance covered -0.12 0.110 -0.01 Step 2 0.000 5.15*** 0.222 0.136 Constant 2.39 0.355 Age 0.06 0.036 0.12* Educational status 0.41 0.100 0.26*** Marital Status 0.03 0.075 0.03 Religion -0.14 0.061 -0.15* Number of years of driving -0.02 0.066 -0.02 Distance covered -0.01 0.104 -0.01 Extraversion 0.01 0.023 0.01 Agreeableness -0.10 0.023 -0.03 Conscientiousness 0.20 0.037 0.34*** Neuroticism -0.06 0.033 -0.12 Openness 0 . 0 4 0.036 0.07 Step 3 0.000 6.80*** 0.293 0.071 Constant 2.99 0.365 Age 0.05 0.035 0.12* Educational status 0.36 0.096 0.23*** Marital Status 0.02 0.072 0.02 Religion -0.13 0.059 -0.14* Number of years of driving -0.03 0.063 -0.04 Distance covered -0.01 0.099 -0.01 Extraversion 0.01 0.022 0.03 Agreeableness 0.01 0.023 0.03 Conscientiousness 0.17 0.036 0.29*** Neuroticism -0.04 0.031 -0.09 Openness 0.03 0.034 0.05 Commuting Stress -0.23 0.051 -0.28*** Adjusted R2= 0.059, 0.179, 0.250 for steps 1, 2 and 3; N=211; *p<0.05, **p<0.01, ***p<0.001 50 University of Ghana http://ugspace.ug.edu.gh From Table 4, educational status was the only demographic factor that was significant in predicting Worker Well-being by accounting for 8.6% of the variation in Worker Well-being of drivers at the first step (F = 3.18, p<0.001) of the regression model. Introduction of the personality facets significantly explained additional variance of 13.6% in Worker Well-being at the second stage of the model (F = 5.15, p<0.001). However, educational status (β = 0.26, p<0.001), age (β=0.12, p<0.05), religion (β = -0.15, p<0.05) as well as conscientiousness (β = 0.34, p<0.001) were significant at the second step of the regression model. Thus, only conscientiousness as a personality facet was significant. Finally, the inclusion of Commuting Stress (β = -0.28, p<0.001) at the third step of the model (F = 6.80, p<0.001) further accounted for additional 7.1% of the variance in Worker Well-being after controlling for the effects of both demographic factors and personality facets of haulage drivers. Thus, Commuting Stress was negatively related to Worker Well-being after controlling for both the effects of demographic factors and the personality facets. This finding was similar to the results from the correlation analysis where there was a negative association between Commuting Stress and Worker Well-being (r = -0.36, p<0.001). Consequently, among all intervening variables in this model, educational status, age, religion and conscientiousness had significant effect on the relationship between Commuting Stress and Worker Well-being of haulage drivers. Therefore, the first hypothesis was supported. Hypotheses Two and Three H2: Demographic factors (age, marital status, driving experience, religion, and educational level) would significantly moderate the relationship between Commuting Stress and Worker Well-being. H3: Personality traits (conscientiousness, agreeableness, neuroticism, openness, extraversion) would significantly moderate would the relationship between Commuting Stress and Worker Well-being. 51 University of Ghana http://ugspace.ug.edu.gh Table 5 presents results of the moderation analyses. Table 5: Examining the potentially significant moderators by Hayes’s Process Macro (2012) Worker Well-being Β R2 ∆R2 T p Model 1 Constant 6.05 0.252 0.085*** 12.92 0.000 Commuting Stress -1.33*** -6.00 0.000 Educational status -0.84** -3.34 0.001 Educational status × Commuting Stress 0.58*** 4.86 0.000 Model 2 Constant 5.19 0.183 0.043** 20.71 0.000 Commuting Stress -0.67*** -5.29 0.000 Age -0.26** -2.73 0.007 Age × Commuting Stress 0.16** 3.32 0.001 Model 3 Constant 4.00 0.172 0.029** 13.79 0.000 Commuting Stress 0.10ns 0.65 0.515 Religion 0.42* 2.07 0.040 Religion × Commuting Stress -0.28** -2.68 0.008 Model 4 Constant 6.09 0.325 0.130*** 15.09 0.000 Commuting Stress -1.36*** -7.50 0.000 Conscientiousness -0.39*** -4.28 0.000 Conscientiousness ×Commuting Stress 0.27*** 6.32 0.000 N=211; *p<0.05, **p<0.01, ***p<0.001, ns=not significant Moderation effect of Education status In Table 5, it can be observed from Hayes’s Process (first model) that commuting Stress (β = -1.33, p<0.001) and educational status (β = -0.84, p<0.01) with the interaction term (β = 0.58, p<0.001) are significant. In addition, there was significant change (8.5%) in the percentage of variance in worker well-being upon including predictor (commuting stress), educational status (moderator) as well as the interaction term. Since commuting stress and 52 University of Ghana http://ugspace.ug.edu.gh educational status with the interaction term were both significant, moderation occurred but the main effects were also significant. Therefore, educational status moderated the relationship between commuting stress and worker well-being. Additionally, commuting stress of drivers was categorized into low (1 standard deviation from the mean), average (2 standard deviations from the mean) and high (3 standard deviations from the mean) based in the number of standard deviations from the mean. Figure 3 shows the moderation graph of educational status of the haulage drivers. 4.5 4 3.5 3 2.5 2 No formal education 1.5 Formal education 1 0.5 0 Low Stress Average Stress High Stress Commuting Stress Figure 3: Moderation Plot for Educational Status among Haulage drivers Moderation effect of Age (from Table 5) In Figure 3 it can be observed from second model of the moderation test that Commuting Stress (β = -0.67, p<0.001) and age (β = -0.26, p<0.01) with the interaction or product term (β = 0.16, p<0.01) are significant. This therefore suggested that both the main and interaction effect were significant. Thus, age is a significant moderator between Commuting Stress and Worker Well-being of haulage drivers. Figure 4 shows the moderation graph across age groups of the haulage drivers. 53 Worker Well-being University of Ghana http://ugspace.ug.edu.gh 4.3 4.2 4.1 4 20-30years 3.9 31-40years 41-50years 3.8 3.7 3.6 Low Stress Average Stress High Stress Commuting Stress Figure 4: Moderation Graph for Age among Haulage drivers Moderation effect of Religion (from Table 5) Also, it can be realized from the third model of the moderation test that the main effect of religion (β = 0.42, p<0.05) is significant; however, that of Commuting Stress was not significant (at 5% alpha level) with the interaction term. The interaction effect on the contrary, was significant (β = -0.28, p<0.01) with additional variance of 2.9% (∆R2=0.029, p<0.01) in Worker Well-being accounted by the model. Thus, the main effect of Commuting Stress was not significant as opposed to that of the religion of drivers with interaction effect. The moderation graph for religion is shown by Figure 5. 54 Worker Well-being University of Ghana http://ugspace.ug.edu.gh 4.3 4.2 4.1 4 3.9 Christianity 3.8 Islamic 3.7 3.6 3.5 Low Stress Average Stress High Stress Commuting Stress Figure 5: Moderation Graph for Religion among Haulage drivers. Moderation effect of Conscientiousness (from Table 5) Finally, the fourth model from the moderation test revealed that Commuting Stress (β = -1.36, p<0.001) and conscientiousness (β = -0.39, p<0.001) with the interaction term (β = 0.27, p<0.001) are significant as opposed to the other personality facets, which had no moderation effect. Approximately 13.0% additional variance (∆R2=0.130, p<0.001) in Worker Well-being was accounted for by the regression model. Based on the number of standard deviations from the mean after centering the variables, the levels of conscientiousness of haulage drivers were categorized into low (1 SD), average (2 SD) and high (3 SD). Figure 6 shows a moderation graph for conscientiousness as a personality facet. 55 Worker Well-being University of Ghana http://ugspace.ug.edu.gh 4.2 4.1 4 3.9 Low Conscientiousness Average Conscientiousness 3.8 High Conscientiousness 3.7 3.6 Low Stress Average Stress High Stress Commuting Stress Figure 6: Moderation Graph for Conscientiousness among Haulage drivers Deductions from the moderation analyses Hence, it was deduced from the various moderation tests using Hayes’s process macro (2012) that only educational status, age and religion were the demographic factors that moderated the relationship between Commuting Stress and Worker Well-being significantly. Also, among the personality facets (extraversion, agreeableness, neuroticism and openness), only conscientiousness had significant moderating effect on the association between Commuting Stress and Worker Well-being. In light of the findings from the hierarchical regression as well as the various tests of moderation, the new model of the study is presented by Figure 7. 56 Worker Well-being University of Ghana http://ugspace.ug.edu.gh 4.2.1 Observed Model PERSONALITY CONSCIENTIOUSNESS β = 0.34 INDEPENDENT DEPENDENT VARIABLE VARIABLE C O M M U T I N G β = -0.28 WORKERWELL- STRESS BEING D E M O G RAPHIC CHARACTERISTICS AGE, β = 0.12 EDUCATION, β = 0.26 RELIGION, β = -0.15 Figure 7: Observed Model 4.2.2 Comparing Worker Well-being and Commuting Stress averagely across significant demographic moderators The final objective of the study was to compare worker well-being and commuting stress averagely across demographic factors with significant moderating effects respectively (educational status, age and religion). This test would be imperative since the moderation graphs do not statistically justify that both commuting stress and worker well-being are significantly different across the moderators. Thus, meaningful conclusions on mean differences can only be made through inferential statistics as opposed to the descriptive statistics as presented by the moderation graph. Hence, a 3-Way MANOVA was used to justify 57 University of Ghana http://ugspace.ug.edu.gh significant mean differences among the demographic moderators. This was because; only three demographic factors (educational status, religion and age) were confirmed to significantly moderate the relationship between Commuting Stress and Worker Well-being from the moderation analyses. In addition, a 3-Way MANOVA was used since two variables measured on continuous scale (commuting stress and worker well-being) were being compared across three categorical variables (educational status, religion and age). The final hypothesis for the study was stated as; Hypothesis Four (Auxiliary) H4: There would be significant difference in Worker Well-being and Commuting stress by demographic moderators. Table 6 presents results of the test. Table 6: Summary of the 3-Way MANOVA between-Subjects Effects of Age, Educational status and Religion on Worker Well-being and Commuting Stress Dependent Type III Sum Mean Source Variable of Squares Df Square F P Corrected Well-being 9.42 12 0.79 5.94 0.000 Model Stress 8.23 12 0.69 2.85 0.001 Intercept Well-being 397.98 1 397.98 3012.42 0.000 Stress 133.03 1 133.03 552.91 0.000 Age Well-being 4.06*** 3 1.35 10.25 0.000 Stress 3.25** 3 1.08 4.51 0.004 Educational Well-being 2.94*** 1 2.94 22.23 0.000 Status Stress 2.44** 1 2.44 10.14 0.002 Religion Well-being 0.01ns 1 0.01 0.01 0.916 Stress 0.03ns 1 0.03 0.12 0.730 Age × Well-being 2.18** 3 0.73 5.50 0.001 Education Stress 2.52* 3 0.84 3.49 0.017 Age × Well-being 0.57ns 3 0.19 1.44 0.233 Religion Stress 0.70ns 3 0.24 0.98 0.405 Education Well-being 0.06ns 1 0.06 0.44 0.510 ×Religion Stress 0.01ns 1 0.01 0.02 0.882 N=211; *p<0.05, **p<0.01, ***p<0.001, ns=not significant 58 University of Ghana http://ugspace.ug.edu.gh It can be observed from table 6 that there was significant mean difference in both Commuting Stress and Worker Well-being across age and educational status. However, the mean difference in both Commuting Stress and Worker Well-being were not significant across religion. In addition, there was significant interaction effect between age and educational status of haulage drivers with respect to both Commuting Stress (F=5.50, p<0.001) and Worker Well-being (F = 3.42, p<0.05). However, there was no interaction effect between age and religion as well as educational status and religion for both Commuting Stress and Worker Well-being on the average. Thus, Commuting Stress and Worker Well-being of haulage drivers were averagely influenced significantly by age and educational status. Table 7 presents a descriptive summary of both Commuting Stress and Worker Well-being scores across the significant demographic moderators. Table 8 on the other hand, summarizes the means and standard deviations of Commuting Stress and Worker Well-being across age and educational status with significant interaction effect. 59 University of Ghana http://ugspace.ug.edu.gh Table 7: Descriptive Summary of Commuting Stress and Worker Well-being across potential demographic moderators Variables Mean SD Min Max Commuting Stress Age 20-30years 2.00 0.84 1.15 4.50 31-40years 1.82 0.42 1.12 2.77 41-50years 1.80 0.54 1.12 4.30 Above 50 years 1.82 0.45 1.15 3.00 Educational status No formal education 1.96 0.86 1.23 4.50 Formal education 1.82 0.48 1.12 4.30 Religion Christianity 1.82 0.48 1.12 4.30 Islamic 1.87 0.62 1.15 4.50 Worker Well-being Age 20-30years 3.77 0.73 1.58 4.41 31-40years 4.08 0.31 3.18 4.65 41-50years 4.11 0.28 3.53 4.71 Above 50 years 4.05 0.50 1.45 4.59 Educational status No formal education 3.76 0.89 1.45 4.41 Formal education 4.08 0.34 2.13 4.71 Religion Christianity 4.09 0.36 1.45 4.71 Islamic 3.97 0.52 1.58 4.65 60 University of Ghana http://ugspace.ug.edu.gh Table 8: Summary of Means and Standard deviations for Age and Educational status on Worker Well-being and Commuting Stress Educational status Age Mean SD Commuting Stress No formal education 20-30years 4.50 0.01 31-40years 1.60 0.03 41-50years 1.72 0.65 Above 50 years 1.94 0.56 Formal education 20-30years 1.88 0.63 31-40years 1.83 0.42 41-50years 1.81 0.53 Above 50 years 1.80 0.44 Worker Well-being No formal education 20-30years 1.58 0.02 31-40years 4.03 0.04 41-50years 4.12 0.24 Above 50 years 3.59 1.07 Formal education 20-30years 3.88 0.55 31-40years 4.09 0.31 41-50years 4.11 0.28 Above 50 years 4.13 0.29 4.3 Summary of Major Research Findings Four different hypotheses were formulated and tested so as to achieve the objectives of the study. Major findings made from the study are as follows:  The correlation analysis brought to light that none of the demographic factors (age, educational status, marital status, religion, years of driving and distance covered) had significant relationship with Commuting Stress. However, only educational status had significant relationship with Worker Well-being of haulage drivers.  In addition, the correlation analysis further revealed that among the personality domains (extraversion, agreeableness, conscientiousness, neuroticism and openness), only agreeableness and conscientiousness had significant association with Commuting 61 University of Ghana http://ugspace.ug.edu.gh Stress; whereas, only conscientiousness and neuroticism had significant relationship with Worker Well-being.  Commuting Stress significantly decreased Worker Well-being of haulage drivers after controlling for the effects of both demographic factors and personality facets.  The moderation analyses revealed that educational status, age and religion were the demographic factors that moderated the relationship between Commuting Stress and Worker Well-being significantly; whereas, only conscientiousness as a personality facet had significant moderating effect on the relationship between Commuting Stress and Worker Well-being. The interaction effects from the, moderation analysis also revealed that educational status (0.58, p<0.001), age (0.16, p<0.01) and levels of conscientiousness (0.27, p<0.001) significantly increased the relationship between commuting stress and worker well-being as opposed to religion which had negative impact on the relationship.  The Worker Well-being and Commuting Stress levels among the haulage drivers were averagely different across both age and educational status. Both age and educational status had significant interaction effects on Commuting Stress and Worker Well-being on the average. 62 University of Ghana http://ugspace.ug.edu.gh CHAPTER FIVE DISCUSSION In this chapter, findings of the study are discussed in relation to main objectives and previous research conducted on the study variables. Limitations of the study, as well as implications and conclusion would sum up this study’s purpose. 5.1 Main Findings 5.1.1 Commuting Stress and Worker Well-being This study primarily sought to determine whether commuting stress would significantly predict worker well-being over and above the effects of demographic factors of the haulage drivers. This was done by controlling for the facets of personality and demographic details among the haulage drivers. The results of the analysis revealed that commuting stress significantly decreased worker well-being of haulage drivers after controlling for the effects of both demographic factors and personality facets. This implies that there was an inverse relationship between commuting stress and worker well-being, in that, an increase in commuting stress caused a decrease in worker well-being and vice versa. Similarly, Desmond and Matthews (2009) found that haulage drivers experienced relatively heightened stress and fatigue in their line of duty as compared to other professional drivers. They suggested that the prolonged drive induced increased fatigue and reduced energy, also, distress and worry were reported to increase cognitive interference. Contrary to findings by Desmond and Matthews (2009), Jensen et al. (2008) suggest that haulage drivers experience of locomotor diseases was relatively less than that of other professional drivers. In a study by Solomon, Doucette, Garland, and McGinn (2004), long-distance truck drivers were reported to suffer from and were at an increased risk of numerous medical ailments, due to the nature of their profession. Overall, this resulted in poor well-being as a result of poor access to health 63 University of Ghana http://ugspace.ug.edu.gh care services such as health insurance and regular medical check-ups. In Ghana Amponsah- Tawiah et al. (2016) revealed that Commuting Stress could lead to turnover intention through personal burnout. This finding proposed that employees who experienced high commuting stress were more likely to consider leaving their jobs, and to an extent commuting stress was responsible through the eliciting of burnout symptoms leading to loss of personal resources such as time and energy, which could have been invested in other life domains. These findings fall within the framework of the COR theory. Equally, findings from Novaco, Stokols, and Milanesi (1990b) implied that both commuters and work organizations incurred hidden costs associated with high-impedance commuting. These costs were said to reflect in illness-related absence from work, disability claims, and reduced levels of employee productivity and morale. Commuting stress was reported to be associated with decreased well-being (i.e. lower health status and higher depression) for all respondents, persons who reported traffic stress and who lived in neighbourhoods with a high vehicular burden have significantly lower well-being than those living in areas with lower vehicular burdens. That is, the effect of traffic stress on health is worse for persons living in environments with more vehicle use, compared to those living in areas with less vehicle use. 5.1.2 Moderation Effects of Demographic factors and Personality facets on the relationship of Commuting Stress and Worker Well-being The plot in Figure 3 reveals that among drivers with high stress levels, those with formal education tend to have better Worker Well-being than those with no formal education. On the contrary, drivers with low stress levels had relatively the same level of Worker Wellbeing for both formally educated and those with no formal education. In addition, Figure 3 shows graphically that increase in Commuting Stress averagely decreases the Worker Well-being for both haulage drivers who had formal and informal education respectively. This finding agrees 64 University of Ghana http://ugspace.ug.edu.gh with that from the various hypotheses tested and correlation analysis where Commuting Stress was negatively related with Worker Well-being. It can be deduced from figure 4 that for drivers with high Commuting Stress, those aged 41-50 years had higher Worker Well-being than those aged 20-30 years and 31-40 years respectively. However, among drivers with low Commuting Stress, those age 20-30 years had higher Worker Well-being than those aged 31-40 years and 41-50 years. This confirms why age was negatively associated with Worker Well-being (β = -0.26, p<0.01). In addition, additional 4.3% of the variance in Worker Well-being (∆R2=0.043, p<0.01) was accounted for by the inclusion of the moderator (age), predictor (Commuting Stress) and interaction term. Hence, age of drivers moderated the association between Commuting Stress and Worker Well- being significantly. From Figure 5, it can be observed that Christian drivers with high levels of Commuting Stress had relatively higher Worker Well-being than Islamic drivers with high levels Commuting Stress. However, Islamic drivers with low levels of Commuting Stress had higher Worker Well-being comparatively than Christian drivers with low levels of Commuting Stress. Consequently, religion had a moderating effect on the relationship between Commuting Stress and Worker Well-being among haulage drivers. Figure 6 reveals that among drivers with high Commuting stress levels, those with high levels of conscientiousness had relatively better Worker Well-being as compared to those with low and average levels of conscientiousness respectively. On the other hand, among haulage drivers with low levels of Commuting Stress, those with low levels of conscientiousness relatively had better Worker Well-being than those with average and high levels of conscientiousness respectively. Thus, conscientiousness of haulage drivers had significant moderating effect on the relationship between Commuting Stress and Worker Well-being. 65 University of Ghana http://ugspace.ug.edu.gh The study further explored the moderation effects of demographic factors and personality facets. Moderation effects were examined so as to identify the existence of interaction effects as well as its effects on the direction or magnitude of the relationship between Commuting Stress and Worker-Wellbeing. The moderation analyses revealed that educational status, age and religion were the demographic factors that moderated the relationship between Commuting Stress and Worker Well-being significantly; whereas, only conscientiousness as a personality facet had significant moderating effect on the relationship between Commuting Stress and Worker Well-being. The interaction effects from the moderation analysis also revealed that educational status (0.58, p<0.001), age (0.16, p<0.01) and levels of conscientiousness (0.27, p<0.001) significantly increased the relationship between commuting stress and worker well-being as opposed to religion which had negative impact on the relationship. In this study educational status was measured categorically, thus participants were either formally educated or informally educated. Educated individuals ranged from basic/primary education to tertiary and above. Informally educated participants had never been to school. It is crucial to note that most of the haulage drivers had basic level education. In the literature very few studies report on the educational status of drivers, however in a study by O'Toole (1990), risk of mortality was higher for men with poorer education. According to them the underlining reason for this is risky driving. Similarly, Nafukho and Hinton (2003) found that educational level had an impact driver accident. Although these studies do not speak directly to commuting stress and well-being, it is important to note that the inability to address stressors adequately can lead to accidents (Lazarus,1966) which in turn affect one’s well-being. This study’s results reveal that age significantly moderated the relationship between commuting stress and worker well-being. Older drivers had a comparative advantage of higher Worker Well-being over younger drivers after commuting. It can also be inferred that commuting stress affected young drivers to a greater extent.; Some reasons for this have been 66 University of Ghana http://ugspace.ug.edu.gh explained in the literature. According to Reason et al. (1990) young drivers often drive in a risky and aggressive manner. They over estimate their skills and under-estimate road dangers (Fisher et al. 2002). Shope (2006) reports that teenagers often have the highest crash rate per miles driven. Neurological data also suggest that major responsibilities of the brain such as inhibition, reasoning and decision making are not fully developed till after age 25. To an extent these reasons explain why younger drivers often jeopardize their well-being by adapting risky driving patterns. In a study on age disparities on health difficulties and attitudes towards driving on self- regulation, Conlon, Rahaley, and Davis (2017) found that, contrary to this study’s findings on worker well-being, older drivers reported poorer general health, poorer vision, reduced strength and flexibility for safe driving, more cognitive difficulties, more negative attitudes toward driving, greater negative feedback, reduced driving exposure and poorer driving confidence as compared to younger drivers. This variation in results can be attributed to the vast difference between the age groups under study, baby boomer drivers (48-67) and older drivers (67-91). Most studies on religion have reported religion to have a significant positive relationship with well-being (Siegel, Anderman, & Schrimshaw, 2001; Ano, & Vasconcelles, 2005). However, results from this study suggest that religion had a negative effect on the relationship between commuting stress and well-being of the drivers. In this study the religious component was based on two categories, Christianity and Islam, study participants ticked accordingly. This negative influence was experienced more by Muslim’s than Christian’s drivers. Literature supports these findings to an extent. Reports suggest that religion could be somewhat hypercritical, alienating and exclusive (Williams & Sternthal, 2007). Also, though religious membership may have some benefits like, social networking, these relations sometimes turn out to be a source of stress (Chatters 2000). For example, failure to uphold group norms could lead to open criticism by members and leaders alike. Religion could also 67 University of Ghana http://ugspace.ug.edu.gh cause individuals to feel guilty when they fail to live up to standards held by the group, this in turn could lead to low levels of wellbeing (Trenholm, Trent, & Compton,1998) Conscientiousness had a positive effect on the relationship between commuting stress and worker well-being, this is consistent with other driver oriented literature (Starkey and Isler 2016; Jovanović,, Lipovac, Stanojević, & Stanojević 2011; Brandau, Daghofer, Hofmann, & Spitzer 2011; Arthur,& Graziano 1996; Arthur & Doverspike 2001; Seibokaite & Endriulaitiene 2012; Bogg & Roberts, 2004) . One of the reasons for the positive effect of conscientiousness on well-being is that, individuals who possess this trait in relatively high levels are more likely to be able to function efficiently and achieve their goals. Thus their ability to accomplish set goals leads to greater well-being (Hayes & Joseph, 2003). 5.2 Limitations of the study A few limitations to this study must be noted. Firstly, the study was mainly based on self- report data, therefore no observational or interview data was gathered and responses might thus be socially desirable. Due to the non-probability method of sampling in this study, findings cannot be generalized to other haulage drivers. Language disparities could also be a major reason for non-response rate and probably poor answering of some items on the questionnaire. 5.3 Recommendations for future research Due to time constraints the study did not explore the qualitative aspect of the haulage drivers’ experience in Ghana. Thus, future research is therefore encouraged to explore the in- depth essence of the haulage experience by adopting qualitative means. Also, other haulage industries in some future studies can be investigated, such as garbage collection, ore, and other cargo transportation companies. 68 University of Ghana http://ugspace.ug.edu.gh 5.4 Practical Implications and Conclusion This study found that Commuting Stress significantly decreased Worker Well-being of haulage drivers. In order to mitigate the effects of commuting stress on the drivers it is important for stakeholders in this industry to draw journey plans that are realistic and without overly strict terms. This would help curb the urge of drivers to reach and deliver to their various destinations on time. Moderation analyses revealed that educational status, age and religion were the demographic factors that moderated the relationship between Commuting Stress and Worker Well-being significantly; whereas, only conscientiousness as a personality facet had significant moderating effect on the relationship between Commuting Stress and Worker Well-being. Haulage industries must therefore invest on educating their drivers in both basic and driver centric knowledge. As seen in this study, education had a significant positive effect on worker well-being. Young drivers must be closely coached so as to get them to adapt safe driving styles and not be a danger to both themselves and other road users. This is because; the study revealed that drivers within the younger age group (20-30 years) were found to have relatively lower well-being with an increase in their stress levels. Thus, these drivers require adequate stress management training so as to improve upon their well-being and lower their stress levels. The study brought to light that religion had a negative impact on the relationship between Worker Well-being and Commuting Stress of the haulage drivers. This suggests the need for religious leaders to put measures in place by organizing forums on managing ones’ level of stress so as to improve upon their well-being; especially among members who drive haulage vehicles. These forms of encouragement would inspire and promote wellbeing of the congregation and more specifically, drivers. 69 University of Ghana http://ugspace.ug.edu.gh Conscientiousness has been reported to have a significant positive effect on wellbeing. Thus drivers must be thought to build up this trait which is rooted critical thinking so as to always adapt a problem solving approach to every issue on the road. Haulage industries can invest in professionals who would consistently and regularly focus on enhancing conscientiousness into their drivers, as this would trim down accident involvement and boost wellbeing. In conclusion, this study’s main aim was to explore the relationship between commuting stress, personality and worker well-being among haulage drivers in Ghana in Tema. The moderating roles of conscientiousness, age, educational status and religion on the relationship between commuting stress and worker well-being were also established. The study variables were tested using self-report measures among a sample of 211 haulage drivers in Tema. Three main hypotheses served as guides for this study. As hypothesised, there was a significant relationship between commuting stress and worker well-being. Findings from this study revealed an inverse relationship among the two variables, such that an increase in commuting stress caused a decrease in worker well-being and vice versa. Furthermore, findings from the study brought to light that age, educational level, religion and conscientiousness moderated the relationship between commuting stress and worker well- being. Thus, young drivers had higher commuting stress levels which in turn affected their well-being negatively, also educated drivers experienced less commuting stress levels as compared to uneducated drivers. According to the findings of this study religion had a negative effect on the relationship between commuting stress and worker well-being, whilst conscientiousness had a positive effect on the relationship the main variables. In Ghana, the transportation of good and services are very critical to economic growth, it is therefore necessary that problems arising from commuting stress and its effect on worker well-being be thoroughly addressed. It is expected that these contributions would be valuable 70 University of Ghana http://ugspace.ug.edu.gh to the existing literature on organisational behaviour. Moreover, it is the hope of the researcher that this study would spur on interest in prospective research to engage in further studies in the informal sector. Based on this study, authorities such as the Ministry of roads and transport, Motor Transport and Traffic Directorate (MTTU) and Union executives of haulage drivers, Ghana, can help formulate and enact policies that would positively catapult the well-being of haulage drivers in Ghana as this is crucial to nationwide development. 71 University of Ghana http://ugspace.ug.edu.gh REFERENCES Aberg, L., & Rimmo, P. A. (1998). Dimensions of aberrant driver behaviour. Ergonomics, 41(1), 39-56. Aiken, L.S., & West, S.G. (1991). Multiple regression: Testing and interpreting interactions. Newbury Park, CA: Sage Aldwin, C. M. (1990). The elders life stress inventory: Egocentric and nonegocentric stress. Stress and coping in later-life families, 49-69. Aldwin, C. M. (1991). Does age affect the stress and coping process? Implications of age differences in perceived control. Journal of Gerontology, 46(4), 174-180. Aldwin, C. M., Sutton, K. J., Chiara, G., & Iip, A. S. (1996). Age Differences in Stress , Coping , and Appraisal : Findings From the Normative Aging Study, 5(4), 179–188. Allport, G. W. (1937). Personality: A psychological interpretation. Amponsah-Tawiah, K., Annor, F., & Arthur, B. G. (2016). Linking commuting stress to job satisfaction and turnover intention: The mediating role of burnout. Journal of Workplace sBehavioral Health. https://doi.org/10.1080/15555240.2016.1159518 Anthony N. Fabricatore , Paul J. Handal, D. M. R. & F. H. G. (2004). Stress, Religion, and Mental Health: Religious Coping in Mediating and Moderating Roles. International Journal for the Psychology of Religion, 14(2), 109–123. https://doi.org/10.1207/s15327582ijpr1402 Ano, G. G., & Vasconcelles, E. B. (2005). Religious coping and psychological adjustment to stress: A meta‐analysis. Journal of clinical psychology, 61(4), 461-480. Arthur Jr, W., & Doverspike, D. (2001). Predicting motor vehicle crash involvement from a personality measure and a driving knowledge test. Journal of Prevention & Intervention in the Community, 22(1), 35-42. Arthur Jr, W., & Graziano, W. G. (1996). The five‐factor model, conscientiousness, and 72 University of Ghana http://ugspace.ug.edu.gh driving accident involvement. Journal of personality, 64(3), 593-618. Arthur, S. R., & Reber, E. (2001). The Penguin dictionary of psychology. Penguin Books Limited. Avendano, M., Jürges, H., & Mackenbach, J. P. (2009). Educational level and changes in health across Europe: Longitudinal results from share. Journal of European Social Policy, 19(4), 301–316. https://doi.org/10.1177/1350506809341512 Barling, J., Kelloway, E. K., & Frone, M. R. (Eds.). (2004). Handbook of work stress. Sage publications Beebe, S. A., Casey, R., & Pinto-Martin, J. (1993). Association of reported infant crying and maternal parenting stress. Clinical Pediatrics, 32(1), 15-19. Bogg, T., & Roberts, B. W. (2004). Conscientiousness and health-related behaviors: a meta- analysis of the leading behavioral contributors to mortality. Psychological bulletin, 130(6), 887. Bollini, A. M., Walker, E. F., Hamann, S., & Kestler, L. (2004). The influence of perceived control and locus of control on the cortisol and subjective responses to stress. Biological psychology, 67(3), 245-260. Brandau, H., Daghofer, F., Hofmann, M., & Spitzer, P. (2011). Personality subtypes of young moped drivers, their relationship to risk-taking behavior and involvement in road crashes in an Austrian sample. Accident Analysis & Prevention, 43(5), 1713-1719. Brannon, L., & Feist, J. (1997). Living with chronic illness. Health Psychology, 265-300. Brown, T. G., Claude, M., Eldeb, M., Tremblay, J., Vingilis, E., Nadeau, L., … Bechara, A. (2017). The effect of age on the personality and cognitive characteristics of three distinct risky driving offender groups. Personality and Individual Differences, 113, 48–56. https://doi.org/10.1016/j.paid.2017.03.007 73 University of Ghana http://ugspace.ug.edu.gh Brutus, S., Javadian, R., & Panaccio, A. J. (2017). Cycling, car, or public transit: a study of stress and mood upon arrival at work. International Journal of Workplace Health Management. https://doi.org/10.1108/IJWHM-10-2015-0059 Capel, l., & Gurnsey, J. (1987). Managing stress. Constable & Company Limited. Cassidy, T. (1992). Commuting‐related Stress: Consequences and Implications. Employee Counselling Today, 4(2), 15–21. https://doi.org/10.1108/13665629210013465 Chatters, L. M. (2000). Religion and health: Public health research and practice. Annual review of public health, 21(1), 335-367. Chng, S., White, M., Abraham, C., & Skippon, S. (2016). Commuting and wellbeing in London: The roles of commute mode and local public transport connectivity. Preventive Medicine. https://doi.org/10.1016/j.ypmed.2016.04.014 Lazarus, R. S., & Cohen, J. B. (1977). Environmental stress. In Human behavior and environment (pp. 89-127). Springer, Boston, MA. Lazarus, R. S. (1966). Psychological stress and the coping process. Cochran, W. G. (1977). Sampling Techniques: 3d Ed. New York: Wiley. Conlon, E. G., Rahaley, N., & Davis, J. (2017). The influence of age-related health difficulties and attitudes toward driving on driving self-regulation in the baby boomer and older adult generations. Accident Analysis and Prevention, 102, 12–22. https://doi.org/10.1016/j.aap.2017.02.010 Constantinou, E., Panayiotou, G., Konstantinou, N., Loutsiou-ladd, A., & Kapardis, A. (2011). Risky and aggressive driving in young adults : Personality matters. Accident Analysis and Prevention, 43(4), 1323–1331. https://doi.org/10.1016/j.aap.2011.02.002 Coombs, R. H., & Coombs, R. H. (1991). Marital Status and Personal Well-Being : A Literature Review Published by : National Council on Family Relations Stable URL : 74 University of Ghana http://ugspace.ug.edu.gh http://www.jstor.org/stable/585665 Linked references are available on JSTOR for this article : Marital Status and Personal Well-B, 40(1), 97–102. Costal, G., Pickup, L., & Di Martino, V. (1988). Commuting—a further stress factor for working people: evidence from the European Community. International archives of occupational and environmental health, 60(5), 377-385. Croon, A. E. M. De, Blonk, R. W. B., Zwart, B. C. H. De, Broersen, J., & Croon, E. M. De. (2014). Job Stress , Fatigue , and Job Dissatisfaction in Dutch Lorry Drivers : Towards an Occupation in Dutch lorry Job stress , fatigue , and job dissatisfaction drivers : towards an occupation specific model of job demands and control. De Craen, S., Twisk, D. A. M., Hagenzieker, M. P., Elffers, H., & Brookhuis, K. A. (2011). Do young novice drivers overestimate their driving skills more than experienced drivers? Different methods lead to different conclusions. Accident Analysis and Prevention, 43(5), 1660–1665. https://doi.org/10.1016/j.aap.2011.03.024 Desmond, P. A., & Matthews, G. (2009). Individual differences in stress and fatigue in two field studies of driving. Transportation Research Part F: Traffic Psychology and Behaviour, 12(4), 265–276. https://doi.org/10.1016/j.trf.2008.12.006 Diener, E., Gohm, C. L., Suh, E., & Oishi, S. (2000). Similarity of the relations between marital status and subjective well-being across cultures. Journal of Cross-cultural Psychology, 31(4), 419-436. Dodge, R., Daly, A., Huyton, J., & Sanders, L. (2012). The challenge of defining wellbeing. International Journal of Wellbeing. https://doi.org/10.5502/ijw.v2i3.4 Eriksson, L., Friman, M., & Gärling, T. (2013). Perceived attributes of bus and car mediating satisfaction with the work commute. Transportation Research Part A: Policy and Practice, 47, 87-96. 75 University of Ghana http://ugspace.ug.edu.gh Etikan, I., Musa, S. A., & Alkassim, R. S. (2016). Comparison of convenience sampling and purposive sampling. American Journal of Theoretical and Applied Statistics, 5(1), 1-4. Feng, Z., & Boyle, P. (2014). Do Long Journeys to Work Have Adverse Effects on Mental Health? Environment and Behavior, 46(5), 609–625. https://doi.org/10.1177/0013916512472053 Fisher, D. L., Laurie, N. E., Glaser, R., Connerney, K., Pollatsek, A., Duffy, S. A., & Brock, J. (2002). Use of a fixed-base driving simulator to evaluate the effects of experience and PC-based risk awareness training on drivers' decisions. Human factors, 44(2), 287-302. Frazier, P., Steward, J., & Mortensen, H. (2004). Perceived control and adjustment to trauma: A comparison across events. Journal of Social and Clinical Psychology, 23(3), 303-324. Ge, Y., Qu, W., Jiang, C., Du, F., Sun, X., & Zhang, K. (2014). The effect of stress and personality on dangerous driving behavior among Chinese drivers. Accident Analysis and Prevention, 73, 34–40. https://doi.org/10.1016/j.aap.2014.07.024 Gimenez-nadal, J. I., & Molina, J. A. (2014). Commuting Time and Labour Supply in the Netherlands A Time Use Study. Journal of Transport Economics and Policy, 48(3), 409–426. Graham, S., Furr, S., Flowers, C., & Burke, M. T. (2001). Research and theory religion and spirituality in coping with stress. Counseling and Values, 46(1), 2-13. Gravetter, F. J., & Wallnau, L. B. (2006). Statistics for the behavioral sciences(7th ed.). Belmont, CA: Thomson Wadsworth Greenwood, J. W. (1979). Managing executive stress: A systems approach. John Wiley & Sons. Gulian, E., Matthews, G., Glendon, A. I., Davies, D. R., & Debney, L. M. (1989). Dimensions of driver stress. Ergonomics, 32(6), 585-602. 76 University of Ghana http://ugspace.ug.edu.gh Häkkänen, H., & Summala, H. (2001). Fatal traffic accidents among trailer truck drivers and accident causes as viewed by other truck drivers. Accident Analysis & Prevention, 33(2), 187-196. Hagekull, B., & Dahl, M. (1987). Infants with and without feeding difficulties: maternal experiences. International Journal of Eating Disorders, 6(1), 83-98. Handcock, M. S., & Gile, K. J. (2011). Comment: On the concept of snowball sampling. Sociological Methodology, 41(1), 367-371. Hassard, J., Teoh, K., Cox, T., Dewe, P., Cosmar, M., Gründler, R., ... & Van den Broek, K. (2014). Calculating the cost of work-related stress and psychosocial risks. Hatfield, J., & Fernandes, R. (2009). The role of risk-propensity in the risky driving of younger drivers, 41, 25–35. https://doi.org/10.1016/j.aap.2008.08.023 Hayes, A. F. (2012). PROCESS [Macro]. The Ohio State University. Hayes, N., & Joseph, S. (2003). Big 5 correlates of three measures of subjective well-being. Personality and Individual differences, 34(4), 723-727. Hennessy, D. A., & Wiesenthal, D. L. (1997). The relationship between traffic congestion, driver stress and direct versus indirect coping behaviours. Ergonomics, 40(3), 348–361. https://doi.org/10.1080/001401397188198 Hilbrecht, M., Smale, B., & Mock, S. E. (2014). Highway to health? Commute time and well- being among Canadian adults. World Leisure Journal, 56(2), 151–163. https://doi.org/10.1080/16078055.2014.903723 Hobfoll, S. E. (1989). Conservation of resources: A new attempt at conceptualizing stress. American psychologist, 44(3), 513. Holland, D. M. (2016). Cost of Commuting: A Review of Determinants, Outcomes, and Theory of Commuting-Related Stress. University Honors Theses. 77 University of Ghana http://ugspace.ug.edu.gh https://doi.org/10.15760/honors.263 Howell, R. T., Ksendzova, M., Nestingen, E., Yerahian, C., & Iyer, R. (2017). Your personality on a good day: How trait and state personality predict daily well- being. Journal of Research in Personality, 69, 250-263. Ito, J. K., & Brotheridge, C. M. (2003). Resources, coping strategies, and emotional exhaustion: A conservation of resources perspective. Journal of Vocational Behavior, 63(3), 490-509. Jensen, A., Kaerlev, L., Tüchsen, F., Hannerz, H., Dahl, S., Nielsen, P. S., & Olsen, J. (2008). Locomotor diseases among male long-haul truck drivers and other professional drivers. International Archives of Occupational and Environmental Health, 81(7), 821–827. https://doi.org/10.1007/s00420-007-0270-4 Jovanović, D., Lipovac, K., Stanojević, P., & Stanojević, D. (2011). The effects of personality traits on driving-related anger and aggressive behaviour in traffic among Serbian drivers. Transportation research part F: traffic psychology and behaviour, 14(1), 43-53. Khan, F., Yusoff, R. M., & Khan, A. (2014). Job demands, burnout and resources in teaching a conceptual review. World Applied Sciences Journal, 30(1), 20-28. Kluger, A. N. (1998). Commute variability and strain. Journal of Organizational Behavior: The International Journal of Industrial, Occupational and Organizational Psychology and Behavior, 19(2), 147-165. Koslowsky, M., Aizer, A., & Krausz, M. (1996). Stressor and personal variables in the commuting experience. International Journal of Manpower, 17(3), 4-14. Koslowsky, M. (1997). Commuting stress: Problems of definition and variable identification. Applied Psychology, 46(2), 153–173. https://doi.org/10.1111/j.1464- 78 University of Ghana http://ugspace.ug.edu.gh 0597.1997.tb01222.x Krohne, H. W. (2002). Stress and coping theories. International Encyclopedia of the Social Behavioral Sceinces, 22, 15163-15170. Lajeunesse, S. M. (2010). Mindfulness, time affluence, and affective appraisals of the journey to work: an exploration of relationships. Lindberg, L., Bohlin, G., Hagekull, B., & Thunström, M. (1994). Early food refusal: Infant and family characteristics. Infant Mental Health Journal, 15(3), 262-277. Lunau, T., Siegrist, J., Dragano, N., & Wahrendorf, M. (2015). The association between education and work stress: does the policy context matter?. PloS one, 10(3), e0121573. Lyons, G., & Chatterjee, K. (2008). A human perspective on the daily commute: Costs, benefits and trade-offs. Transport Reviews, 28(2), 181–198. https://doi.org/10.1080/01441640701559484 Mahmudin, N. D. M. (2012). Transfer effects and permeable boundaries : An empirical study of the effects of commuting stress on employees ’ work and life. Southeast Asia Psychology Joutnal, 1, 1–9. Martin, L., & Licheron, J. (2014). Commuting and well-being at work : An empirical analysis in the cross-border region of Luxembourg, 1–23. Martinussen, L. M., Hakamies-Blomqvist, L., Møller, M., Özkan, T., & Lajunen, T. (2013). Age, gender, mileage and the DBQ: The validity of the Driver Behavior Questionnaire in different driver groups. Accident Analysis & Prevention, 52, 228-236. Matthews, G., Dorn, L., & Ian Glendon, A. (1991). Personality correlates of driver stress. Personality and Individual Differences. https://doi.org/10.1016/0191-8869(91)90248-A Matthews, G., Deary, I. J., & Whiteman, M. C. (2003). Personality traits. Cambridge University Press. 79 University of Ghana http://ugspace.ug.edu.gh Mattisson, K., Jakobsson, K., Håkansson, C., & Cromley, E. (2016). Spatial heterogeneity in repeated measures of perceived stress among car commuters in Scania, Sweden. International Journal of Health Geographics, 15(1), 1–14. https://doi.org/10.1186/s12942-016-0054-8 McPherson, M., Smith-Lovin, L., & Brashears, M. E. (2006). Social isolation in America: Changes in core discussion networks over two decades. American sociological review, 71(3), 353-375. Mroczek, D. K., & Almeida, D. M. (2004). The effect of daily stress, personality, and age on daily negative affect. Journal of personality, 72(2), 355-378. Mueller, A. S., & Trick, L. M. (2012). Driving in fog: The effects of driving experience and visibility on speed compensation and hazard avoidance. Accident Analysis & Prevention, 48, 472-479. Nafukho, F. M., & Hinton, B. E. (2003). Determining the relationship between drivers' level of education, training, working conditions, and job performance in Kenya. Human Resource Development Quarterly, 14(3), 265-283. Necku, C. S. (2015). The relationship between military-civilian transition, psychological well-being and social adjustment among retired military personnel in ghana (Doctoral dissertation, University of Ghana). Nomoto, M., Hara, A., & Kikuchi, K. (2015). Effects of Long-Time Commuting and Long- Hour Working on Lifestyle and Mental Health Among School Teachers in Tokyo, Japan. Journal of Human Ergology, 44(1), 1–9. Novaco, R. W., & Collier, C. (1994). Commuting stress, ridesharing, and gender: Analyses from the 1993 state of the commute study in southern california. University of California Transportation Center. Retrieved from https://escholarship.org/uc/item/5fs1d377 80 University of Ghana http://ugspace.ug.edu.gh Novaco, R. W., & Gonzalez, O. I. (2009a). Commuting and well-being. In Technology and Psychological Well-being (pp. 174–205). Cambridge University Press. https://doi.org/10.1017/CBO9780511635373.008 Novaco, R. W., & Gonzalez, O. I. (2009b). Commuting and well-being. In Technology and Psychological Well-being (pp. 174–205). https://doi.org/10.1017/CBO9780511635373.008 Novaco, R. W., Kliewer, W., & Broquet, A. (1991). Home environmental consequences of commute travel impedance. American Journal of Community Psychology, 19(6), 881– 909. https://doi.org/10.1007/BF00937890 Novaco, R. W., Stokols, D., & Milanesi, L. (1990a). Objective and subjective dimensions of travel impedance as determinants of commuting stress. American Journal of Community Psychology, 18(2), 231–257. https://doi.org/10.1007/BF00931303 Novaco, R. W., Stokols, D., & Milanesi, L. (1990b). Objective and subjective dimensions of travel impedance as determinants of commuting stress. American Journal of Community Psychology. https://doi.org/10.1007/BF00931303 Ohta, M., Mizoue, T., Mishima, N., & Ikeda, M. (2007). Effect of the physical activities in leisure time and commuting to work on mental health. Journal of Occupational Health, 49(1), 46–52. https://doi.org/10.1539/joh.49.46 Olsson, L. E., Gärling, T., Ettema, D., Friman, M., & Fujii, S. (2013). Happiness and satisfaction with work commute. Social indicators research, 111(1), 255-263. Östberg, M., & Hagekull, B. (2000). A structural modeling approach to the understanding of parenting stress. Journal of clinical child psychology, 29(4), 615-625. O'Toole, B. I. (1990). Intelligence and behaviour and motor vehicle accident mortality. Accident Analysis & Prevention, 22(3), 211-221. 81 University of Ghana http://ugspace.ug.edu.gh Páez, A., & Whalen, K. (2010). Enjoyment of commute: A comparison of different transportation modes. Transportation Research Part A: Policy and Practice, 44(7), 537– 549. https://doi.org/10.1016/j.tra.2010.04.003 Rammstedt, B., & John, O. P. (2007). Measuring personality in one minute or less: A 10-item short version of the Big Five Inventory in English and German. Journal of research in Personality, 41(1), 203-212. Rana, B., & Munir, K. (2011). Impact of stressors on the performance of employees. Rapport, M. D., Chung, K.-M., Shore, G., Denney, C. B., & Isaacs, P. (2000). Understanding of Parenting Stress. Journal of Clinical Child Psychology, 29(4), 555–568. https://doi.org/10.1207/S15374424JCCP2904 Reason, J., Manstead, A., Stradling, S., Baxter, J., & Campbell, K. (1990). Errors and violations on the roads: a real distinction?. Ergonomics, 33(10-11), 1315-1332. Robbins, S. (2013). Organizational Behavior. Zhurnal Eksperimental’noi i Teoreticheskoi Fiziki. https://doi.org/10.12737/4477 Roberts, J., Hodgson, R., & Dolan, P. (2011). “It’s driving her mad”: Gender differences in the effects of commuting on psychological health. Journal of Health Economics, 30(5), 1064–1076. https://doi.org/10.1016/j.jhealeco.2011.07.006 Ryff, C. D., & Keyes, C. L. M. (1995). The structure of psychological well-being revisited. Journal of personality and social psychology, 69(4), 719. Sandow, E. (2014). Til Work Do Us Part: The Social Fallacy of Long-distance Commuting. Urban Studies, 51(3), 526–543. https://doi.org/10.1177/0042098013498280 Saucier, G. (1994). Mini-Markers: A brief version of Goldberg's unipolar Big-Five markers. Journal of personality assessment, 63(3), 506-516. Schaeffer, M. H., Street, S. W., Singer, J. E., & Baum, A. (1988). Effects of Control on the 82 University of Ghana http://ugspace.ug.edu.gh Stress Reactions of Commuters 1. Journal of Applied Social Psychology, 18(11), 944- 957. Schimmack, U., Radhakrishnan, P., Oishi, S., Dzokoto, V., & Ahadi, S. (2002). Culture, personality, and subjective well-being: Integrating process models of life satisfaction. Journal of personality and social psychology, 82(4), 582. Schneiderman, N., Ironson, G., & Siegel, S. D. (2005). Stress and health: psychological, behavioral, and biological determinants. Annu. Rev. Clin. Psychol., 1, 607-628. Schultz, D. P., & Schultz, S. E. (2016). Theories of Personality. American Sociological Review (Vol. 22). https://doi.org/10.2307/2089174 Scott-parker, B., & Weston, L. (2017). Sensitivity to reward and risky driving , risky decision making , and risky health behaviour : A literature review. Transportation Research Part F: Psychology and Behaviour, 49, 93–109. https://doi.org/10.1016/j.trf.2017.05.008 Seibokaite, L., & Endriulaitiene, A. (2012). The role of personality traits, work motivation and organizational safety climate in risky occupational performance of professional drivers. Baltic Journal of Management, 7(1), 103-118. Seyle, H. (1956). The stress of life Seyle, H. (1976). Stresses of life. Shope, J. T. (2006). Influences on youthful driving behavior and their potential for guiding interventions to reduce crashes. Injury Prevention, 12(suppl 1), i9-i14. Singleton, R. A., Straits, B. C., & Straits, M. M. (2005). Approaches to Social Sciences. Siegel, K., Anderman, S. J., & Schrimshaw, E. W. (2001). Religion and coping with health- related stress. Psychology and Health, 16(6), 631-653. Smith, O. (2017). Commute well-being differences by mode: Evidence from Portland, 83 University of Ghana http://ugspace.ug.edu.gh Oregon, USA. Journal of Transport and Health, 4, 246–254. https://doi.org/10.1016/j.jth.2016.08.005 Solomon, A. J., Doucette, J. T., Garland, E., & McGinn, T. (2004). Healthcare and the long haul: Long distance truck drivers--a medically underserved population. American Journal of Industrial Medicine, 46(5), 463–471. https://doi.org/10.1002/ajim.20072. Spilka, B., Shaver, P., & Kirkpatrick, L. A. (1985). A general attribution theory for the psychology of religion. Journal for the scientific study of religion, 1-20. Sposato, R. G., & Cervinka, R. (2011). Commuting Stress and How New Information Technologies Could Help. Stress: The International Journal on the Biology of Stress, 7(1979), 2011–2011. John, O. P., & Srivastava, S. (1999). The Big Five trait taxonomy: History, measurement, and theoretical perspectives. Handbook of personality: Theory and research, 2(1999), 102- 138. Starkey, N. J., & Isler, R. B. (2016). The role of executive function, personality and attitudes to risks in explaining self-reported driving behaviour in adolescent and adult male drivers. Transportation research part F: traffic psychology and behaviour, 38, 127-136. Story, L. B., & Bradbury, T. N. (2004). Understanding marriage and stress: Essential questions and challenges. Clinical Psychology Review, 23(8), 1139–1162. https://doi.org/10.1016/j.cpr.2003.10.002 Tabachnick, B. G., & Fidell, L. S. (2001). Computer-assisted research design and analysis (Vol. 748). Boston: Allyn and Bacon. Tao, D., Zhang, R., & Qu, X. (2017). The role of personality traits and driving experience in self-reported risky driving behaviors and accident risk among Chinese drivers. Accident Analysis and Prevention, 99, 228–235. https://doi.org/10.1016/j.aap.2016.12.009 84 University of Ghana http://ugspace.ug.edu.gh Thørrisen, M. M. (2013). Personality and Driving Behavior. Master Thesis in Health- and Social Psychology Faculty, (December 2013). Thunström, M. (1999). Severe sleep problems among infants in a normal population in Sweden: prevalence, severity and correlates. Acta paediatrica, 88(12), 1356-1363. Trenholm, P., Trent, J., & Compton, W. C. (1998). Negative religious conflict as a predictor of panic disorder. Journal of Clinical Psychology, 54(1), 59-65. Troup, C., & Dewe, P. (2002). Exploring the nature of control and its role in the appraisal of workplace stress. Work & Stress, 16(4), 335-355. Urhonen, T., Lie, A., & Aamodt, G. (2016). Associations between long commutes and subjective health complaints among railway workers in Norway. Preventive Medicine Reports, 4, 490–495. https://doi.org/10.1016/j.pmedr.2016.09.001 Van der Beek, A. J., Meijman, T. F., Frings-Dresen, M. H. W., Kuiper, J. I., & Kuiper, S. (1995). Lorry drivers’ work stress evaluated by catecholamines excreted in urine. Occupational and Environmental Medicine, 52(7), 464–469. https://doi.org/10.1136/oem.52.7.464 van Hooff, M. L. M. (2015). The daily commute from work to home: examining employees’ experiences in relation to their recovery status. Stress and Health : Journal of the International Society for the Investigation of Stress, 31(2), 124–137. https://doi.org/10.1002/smi.2534 Vittersø, J., & Nilsen, F. (2002). The conceptual and relational structure of subjective well- being, neuroticism, and extraversion: Once again, neuroticism is the important predictor of happiness. Social Indicators Research, 57(1), 89-118. Waite, L., & Gallagher, M. (2000). The case for marriage: Why married people are healthier, happier, and better-off financially. Westminster, MD: Broadway Books. 85 University of Ghana http://ugspace.ug.edu.gh Wang, J. (2005). Work stress as a risk factor for major depressive episode (s). Psychological medicine, 35(6), 865-871. Weinberg, R. S., & Gould, D. (1999). Personality and sport. Foundations of sport and exercise psychology, 25-46. Wener, R. E., & Evans, G. W. (2011). Comparing stress of car and train commuters. Transportation Research Part F: Traffic Psychology and Behaviour. https://doi.org/10.1016/j.trf.2010.11.008 Wener, R. E., & Evans, G. W. (2007). Levels of Physical Activity in Car and Mass Transit Commuting. Environment and Behavior, 39(1), 62–74. Wener, R., Evans, G., & Boately, P. (2005). Commuting stress: Psychophysiological effects of a trip and spillover into the workplace. Transportation Research Record: Journal of the Transportation Research Board, (1924), 112-117. Williams, D. R., & Sternthal, M. J. (2007). Spirituality, religion and health: evidence and research directions. Medical journal of Australia, 186(10), S47. Woszidlo, A., & Segrin, C. (2013). Direct and Indirect Effects of Newlywed Couples’ Neuroticism and Stressful Events on Marital Satisfaction Through Mutual Problem Solving. Marriage and Family Review, 49(6), 520–545. https://doi.org/10.1080/01494929.2013.772933 Ye, R., & Titheridge, H. (2017). Satisfaction with the commute: The role of travel mode choice, built environment and attitudes. Transportation Research Part D: Transport and Environment, 52, 535–547. https://doi.org/10.1016/j.trd.2016.06.011 86 University of Ghana http://ugspace.ug.edu.gh APPENDICES APPENDIX 1: QUESTIONNAIRE SECTION A: ABOUT YOURSELF (DEMOGRAPHIC DATA) Please TICK or provide the information that best describe you or your team. 1. What is your sex? Female Male 2. Age 3. Highest level of education? No formal education Primary JSS/O-level SHS/ A-level Technical and vocational Tertiary Others, Specify ……………………. 4. Marital status? Married Single 5. Religion Christianity Islam Traditional Please specify…………………………………. Driving Details 6. Please specify your number of years of driving experience………………………………. 7. What’s your minimum driving speed (km/h) in travelling from the station to your destination? …………………………………… 8. What’s your maximum driving speed (km/h) in travelling from the station to your destination? …………………………………… 87 University of Ghana http://ugspace.ug.edu.gh 9. What’s the minimum amount of time you spend on the road doing deliveries? ……………………. 10. What’s the maximum amount of time you spend on the road doing deliveries? ……………………. SECTION B How well do the following statements apply to you when you are driving? Please circle or tick the one that applies to you. Responses to each item is measured on a 5-point scale with scale point anchors labelled: 1=Strongly Disagree (SD) 2= Disagree a little (D) 3= Neither Agree nor Disagree (NA/D) 4= Agree a little (A) 5= Strongly Agree Item Strongly Disagree Neither Agree a Strongly Disagree a little agree little Agree nor disagree 1. I am in a hurry. 1 2 3 4 5 2. I feel I do not have control of this driving situation. 1 2 3 4 5 3. I am concerned about getting to my destination on time. 1 2 3 4 5 4. Traffic conditions are congested. 1 2 3 4 5 5. I do not have a flexible time schedule. 1 2 3 4 5 6. I am annoyed by driving 1 2 3 4 5 behind other vehicles 88 University of Ghana http://ugspace.ug.edu.gh 7. Trying but failing to overtake 1 2 3 4 5 is bothering me 8. Trying but failing to overtake is frustrating me. 1 2 3 4 5 Item Strongly Disagree Neither Agree a Strongly Disagree a little agree little Agree nor disagree 9. I am not patient during this rush hour. 1 2 3 4 5 10. Because I am irritated I am driving aggressively. 1 2 3 4 5 11. I do mind being overtaken 1 2 3 4 5 12. I am feeling aggressive. 1 2 3 4 5 13. I am feeling frustrated. 1 2 3 4 5 14. I am losing my temper when other drivers are doing silly 1 2 3 4 5 things 15. I am feeling tense when 1 2 3 4 5 overtaking other vehicles 16. I am feeling satisfied when overtaking other vehicles. 1 2 3 4 5 17. I am feeling tense 1 2 3 4 5 89 University of Ghana http://ugspace.ug.edu.gh 18. I am feeling uneasy 1 2 3 4 5 19. I feel nervous. 1 2 3 4 5 Item Strongly Disagree Neither Agree a Strongly Disagree a little agree little Agree nor disagree 20. I am feeling bothered. 1 2 3 4 5 21. I am feeling distressed. 1 2 3 4 5 22. I am not feeling peaceful. 1 2 3 4 5 23. I am not feeling relaxed. 1 2 3 4 5 24. I am not feeling contented. 1 2 3 4 5 25. I am not feeling comfortable. 1 2 3 4 5 26. I am feeling calm. 1 2 3 4 5 Commuting distance 27. How many trips do you make a day? Specify………………………………………….. 28. What is the average speed used per trip? 90 University of Ghana http://ugspace.ug.edu.gh  Trip 1, specify………………………………………….  Trip 2, specify………………………………………….  Others, specify………………………………………… 29. What is the average time used per trip?  Trip 1, specify …………………………………………...  Trip 2, specify …………………………………………...  Others, specify ………………………………………….. 91 University of Ghana http://ugspace.ug.edu.gh SECTION C How well do the following statements apply to you? Please circle or tick the one that applies to you. Responses to each item are measured on a 5-point scale with scale point anchors 1=Strongly Disagree 2=Disagree 3=Neutral 4 =Agree 5 = Strongly Agree Strongly Strongly Item Disagree Neutral Agree Disagree Agree 1.I tend to be influenced by people 1 2 3 4 5 with strong opinions 2. In general, I feel I am in charge of 1 2 3 4 5 the situation in which I live 3. In many ways, I feel disappointed 1 2 3 4 5 about my achievements in life. 4. I think it is important to have new experiences that challenge how you 1 2 3 4 5 think about yourself and the world. 92 University of Ghana http://ugspace.ug.edu.gh 5. Maintaining close relationships have been difficult for me and 1 2 3 4 5 frustrating for me . Strongly Strongly Item Disagree Neutral Agree Disagree Agree SECTION D 6. I live life one day at a time and How well do the following statements descri1b e your Per2s onality? P3le ase circ4le the one 5th at apdpoline’st troe aylloyu .t hink about the future. Responses to each item is measured on a 5-point scale with scale point anchors labelled: 17=. SWtrhoenng Ily l oDoiks aagt rtehee (sStoDr)y of 2m=y D liifsea,g ree a little (D) 3=I aNmei pthleear sAedg rweeit nho hro Dwi stahginreges (hNavAe/ D) 14 = Agree a2 l ittle (A) 3 5=4 Strongly5 Agree turned out. ITEM Disagree Disagree Neither Agree Strongly 8. I sometimes feel as if I’ve done aSltl rongly a little Agree nor a Agree 1 2 3 4 5 Disagree little there is to do in life. I see myself to be someone who; …9…. I. ihs arvees ecrovnefdi dence in my own 1 2 3 4 5 o pinions, even if they are contrary to 1 2 3 4 5 …….is generally trusting 1 2 3 4 5 the general consensus. ……. tends to be lazy 1 2 3 4 5 10. I have not experienced many w…ar.…m iasn rde ltaruxsetdin, gh arnedlaletiso nstsrheispss with 1 1 2 2 3 3 44 55 well others. 1…1 .T…heh adse mfeawn darst iosft iecv ienrtyedreasyt sl ife get 1 2 3 4 5 1 2 3 4 5 me down. …….is outgoing, sociable 1 2 3 4 5 93 University of Ghana http://ugspace.ug.edu.gh ……. tends to find fault with 1 2 3 4 5 others 12. For me, life has been a 1 2 3 4 5 ……. does a thorough job 1 2 3 4 5 continuous process of learning, changing, and growth. ……. gets nervous easily 1 2 3 4 5 Strongly Stro ngly Item Disagree Neutral Agree ……. has an active imagination 1 2 3 4 5 Disagree Agree 13. People would describe me as a APgiPvEinNg DpIeXrs o2n :, wETillHinIgC tAo Lsh AarPeP mRyO VAL L1E TTER 2 3 4 5 time with others 14. I gave up trying to make big improvements or changes in my life a 1 2 3 4 5 long time ago. 15. Some people wander aimlessly through life, but I am not one of 1 2 3 4 5 them. 16. I like most aspects of my 1 2 3 4 5 personality. 17. I judge myself by what I think is 1 2 3 4 5 important, not by what others think. 94 University of Ghana http://ugspace.ug.edu.gh 95 University of Ghana http://ugspace.ug.edu.gh 96