ArticlePDF Available

Steering towards happiness: An experience sampling study on the determinants of happiness of truck drivers

October 2019
Transportation Research Part A Policy and Practice 128(1):131-148

DOI:10.1016/j.tra.2019.07.017

License
CC BY-NC-ND 4.0

Authors:

Erasmus University Rotterdam

The relatively low levels of employee well-being reported among truck drivers directly relate to some of the key challenges faced in the transportation industry, including high turnover of staff and difficulties attracting people to the profession. Drawing on the job demands-resources model, this study addresses this problem by examining how various state-like and trait-like job demands and resources relate to truck drivers’ momentary happiness at work. Using an experience sampling study comprising 82 Dutch truck drivers, truck drivers were found to be happier during off-job activities and non-work-related job activities, such as breaks, than during work-related job activities. Furthermore, this study shows that road congestion aggravates the inverse relationship between work-related job activities and momentary happiness. Social support of colleagues and flexible work hours alleviate this relationship. These findings provide valuable information to the industry about the road to happiness for truck drivers.

Variable overview.

…

Within-subjects fixed-effects model linking trait job demands and resources during work activities to momentary happiness at work.

…

Figures - uploaded by Indy Wijngaards

Content may be subject to copyright.

Content uploaded by Indy Wijngaards

Content may be subject to copyright.

Contents lists available at ScienceDirect

Transportation Research Part A

journal homepage: www.elsevier.com/locate/tra

Steering towards happiness: An experience sampling study on the

determinants of happiness of truck drivers

Indy Wijngaards

a,b,⁎

, Martijn Hendriks

, Martijn J. Burger

a,c

Erasmus Happiness Economics Research Organization, Erasmus University Rotterdam, Rotterdam, the Netherlands

Erasmus School of Health & Policy Management, Erasmus University Rotterdam, Rotterdam, the Netherlands

Erasmus School of Economics and Tinbergen Institute, Erasmus University Rotterdam, Rotterdam, the Netherlands

ARTICLE INFO

Keywords:

Momentary happiness

Truck drivers

Job demands-resources model

Experience sampling method

ABSTRACT

The relatively low levels of employee well-being reported among truck drivers directly relate to

some of the key challenges faced in the transportation industry, including high turnover of staﬀ

and diﬃculties attracting people to the profession. Drawing on the job demands-resources model,

this study addresses this problem by examining how various state-like and trait-like job demands

and resources relate to truck drivers’ momentary happiness at work. Using an experience sam-

pling study comprising 82 Dutch truck drivers, truck drivers were found to be happier during oﬀ-

job activities and non-work-related job activities, such as breaks, than during work-related job

activities. Furthermore, this study shows that road congestion aggravates the inverse relationship

between work-related job activities and momentary happiness. Social support of colleagues and

ﬂexible work hours alleviate this relationship. These ﬁndings provide valuable information to the

industry about the road to happiness for truck drivers.

1. Introduction

Truck drivers continue to play an integral part within the world economy. However, due to high job demands, truck drivers are at

high risk for anxiety (Apostolopoulos et al., 2016; De Croon et al., 2004), depression (Da Silva-Júnior et al., 2009), and fatigue

(Boyce, 2016). More generally, data on subjective well-being (SWB) show that employees working in the transportation sector score

well below average on life satisfaction and job satisfaction (De Neve and Ward, 2017). The struggles of truck drivers are powerfully

illustrated by an interview subject of Apostolopoulos et al. (2016): “It’s rough and rugged … it’s hard and it’s stressful. You know,

maybe that’s why I turn to drugs, I don’t know. It’s not the type of life I really want to live but, you know, it gives me what I need to

maintain my family and to maintain me and my lifestyle” (p. 55).

Low levels of well-being among truck drivers can have various adverse eﬀects, including lower work productivity (Stewart et al.,

2003), poor health outcomes (Apostolopoulos et al., 2013) and reduced personal and public safety (Apostolopoulos et al., 2016). In

addition, due to the prospect of low truck driver happiness, logistics companies have diﬃculties attracting new talent. These adverse

eﬀects are particularly pertinent because many Western countries currently face a shortage of transportation workers because of a

combination of high voluntary turnover (Prockl et al., 2017; Staats et al., 2017), a rapidly aging workforce (American Trucking

Association, 2018), and diﬃculties ﬁnding young, capable drivers (Rauwald and Schmidt, 2012; Schulz et al., 2014). For instance, the

United States currently faces a shortage of 50,000 drivers, a ﬁgure that could increase to 174,000 drivers by 2026 (American

https://doi.org/10.1016/j.tra.2019.07.017

Received 28 January 2019; Received in revised form 3 June 2019; Accepted 25 July 2019

⁎

Corresponding author at: Erasmus Happiness Economics Research Organization, Erasmus University Rotterdam, P.O. Box 1738, 3000 DR,

Rotterdam, the Netherlands.

E-mail address: wijngaards@ese.eur.nl (I. Wijngaards).

Transportation Research Part A 128 (2019) 131–148

Available online 09 August 2019

(http://creativecommons.org/licenses/BY-NC-ND/4.0/).

Trucking Association, 2017). Similarly, it is expected that in Germany, 40% of truck drivers on the road today will retire in the next

ten years, leading to a large shortage of drivers (Weiss, 2013). As noted by Suzuki et al. (2009) and Fournier et al. (2012), these low

retention rates and labor shortages turn out to be a very costly issue for transportation companies.

To adequately address these issues faced by the transportation industry, a better understanding of the subjective experience of its

employees is crucial (Schulz et al., 2014). Various studies have addressed truck drivers’ job satisfaction and its antecedents (e.g., De

Croon et al., 2002; Johnson et al., 2011; McElroy et al., 1993; Prockl et al., 2017), but to the best of the authors’ knowledge, no

studies have investigated truck drivers’ momentary happiness and its antecedents. This gap in the literature is deemed signiﬁcant,

because the former, exclusive focus on job satisfaction is unwarranted. First, although overall job satisfaction provides some useful

information regarding truck drivers’ overall happiness at work, studies focusing exclusively on statically measured job satisfaction

ignore much of the variation in happiness that takes place over the course of a day (Fisher, 2000; Ilies and Judge, 2002), and they fail

to examine the eﬀects of speciﬁc (work) events (Miner et al., 2005). Second, past studies’ close attention to job satisfaction may be

disproportionate because it constitutes only one dimension of subjective well-being (Diener, 1984; Diener et al., 1999), and because

aﬀective states (e.g., moods and emotions) are better predictors of certain work outcomes than evaluative states (e.g., job satisfaction,

Bakker and Oerlemans, 2011; Van Katwyk et al., 2000).

Thus, the current study contributes to the existing literature in several ways. First, this study intends to build on current studies of

the well-being of truck drivers by focusing on the momentary happiness (i.e., aﬀective and transient feelings) of truck drivers.

Speciﬁcally, this study examines the diﬀerences in momentary happiness during various job and oﬀ-job activities and assesses the

impact of occupation-relevant job characteristics on the momentary happiness of truck drivers. The consideration of oﬀ-job activities

is important, as happiness during oﬀ-job activities can spill over into the work context and vice versa (Ten Brummelhuis and Bakker,

2012), and plays an important role in a person’s general happiness set-point (Diener et al., 2006). The ﬁndings of this study can oﬀer

logistics companies insights about what makes truck drivers happy and unhappy, and this information can be used to improve the

well-being of truck drivers and address commonplace problems in the transportation sector, such as diﬃculties in attracting new staﬀ

and employee turnover.

Second, this study tests the core proposition of the job demands-resources (JD-R) model (Demerouti et al., 2001)—that job

demands (e.g., long working hours and job insecurity) and job resources (e.g., social support and job variety) are, respectively,

negative and positive determinants of well-being in every occupation—by examining how momentary happiness relates to several

core job demands and resources of truck drivers. Additionally, to the best of the authors’ knowledge, this study is the ﬁrst to

investigate not only how truck drivers’ happiness relates to relatively stable trait job demands and resources (e.g., job insecurity and

pay) but also the role of various highly ﬂuctuating state job demands and resources (e.g., road congestion and road quality during a

speciﬁc trip or work day). State job demands are important because they can play a signiﬁcant mediating role in the relationship

between trait job demands and resources and trait well-being (Schaufeli and Van Rhenen, 2006) and may have particularly strong

and unique eﬀects on transient feelings of happiness.

Third, this study oﬀers a methodological contribution to the transportation literature by capturing momentary happiness and

state-like job characteristics using an experience sampling method measure (ESM, Csikszentmihalyi and Hunter, 2003), a method that

asks respondents to report on their moods and time spending several times per day, thereby explicitly incorporating the dynamic

aspect of day-to-day happiness and activities (Scollon et al., 2009). This type of multiple-moment assessment method reduces

memory bias, relies less on global heuristics, increases ecological validity, controls for the top-down eﬀect in the assessment of SWB,

and allows for a better view on the situational circumstances that inﬂuence an experience (Kahneman et al., 2004; Scollon et al.,

2009).

In summary, this study intends to answer the following three research questions:

Q1: How do the momentary happiness levels of truck drivers diﬀer across job and oﬀ-job activities?

Q2: How do trait-like job demands and job resources moderate the relationship between truck-drivers’ work-related job activities

and their momentary happiness?

Q3: How do state-like job demands and job resources moderate the relationship between truck driving and momentary happiness?

The remainder of this paper is structured as follows. First, a conceptualization of SWB is provided, and multiple sets of hypotheses

are presented. Next, the study’s sample and research procedure, survey instruments, and approach to statistical analyses are dis-

cussed, followed by a presentation of the research ﬁndings. Finally, a discussion of the research ﬁndings and conclusions are oﬀered.

2. Theoretical background

2.1. Subjective well-being

The concept of SWB concerns the appreciation of one’s personal condition and comprises aﬀective experiences (i.e., moods,

emotions, aﬀectivity) and cognitive comparisons (Diener, 1984; e.g., life satisfaction, Diener et al., 1999; Veenhoven, 2000).

SWB

comprises context-free states (e.g., life satisfaction or general mood) as well as context-speciﬁc states (e.g., job satisfaction and job

SWB should therefore be considered a general concept or ﬁeld of study rather than a metric in and of itself that can be operationalized by

aggregating construct scores (Diener et al., 1999).

I. Wijngaards, et al. Transportation Research Part A 128 (2019) 131–148

132

aﬀect, Taris and Schaufeli, 2015). This study focuses on context-free states by considering truck drivers’ moods: “diﬀuse aﬀect states,

characterized by a relative enduring predominance of certain types of subjective feelings that aﬀect the experience and behavior of a

person” (Scherer, 2005, p. 705). These aﬀective states are also often characterized as momentary happiness (e.g., Bryson and

MacKerron, 2017; Csikszentmihalyi and Hunter, 2003; Howell et al., 2011). Momentary happiness encapsulates various positive (e.g.,

joyful, engaged) and negative states (e.g., stressed, angry) (Bakker and Oerlemans, 2011). Although SWB constructs generally show

signiﬁcant intercorrelations (Bowling et al., 2010; Krueger and Schkade, 2008), the correlations between aﬀective SWB and cognitive

SWB and between context-free and context-speciﬁc states are only modest. For example, the relationship of aﬀectivity with job

satisfaction (Bowling et al., 2010), job facet satisfaction (Bowling et al., 2008) and life satisfaction (Kahneman and Deaton, 2010) are

typically less than 0.4.

2.2. Activities and momentary happiness

The truck driving occupation is characterized by high job demands (De Croon et al., 2004), including frequently working

overtime, and low task variety, among other demands, as well as a lack of recovery opportunities (Chen and Xie, 2014; Morrow and

Crum, 2004; for evidence from the Netherlands, see Van Zenderen et al., 2017). Both can be expected to negatively aﬀect happiness,

possibly leading to momentary happiness levels below a driver’s happiness set-point (Kuykendall et al., 2015). Their combined

negative eﬀect may surpass their individual negative eﬀects, because recovery in the form of leisure activities plays an important role

in mitigating the eﬀects of job demands on job stress (Sonnentag and Fritz, 2015) and happiness more generally (Kuykendall et al.,

2015), and vice versa for a lack of recovery. Many theories have been proposed that underlie this notion (for an extensive reviews, see

Newman et al., 2014; Sonnentag and Fritz, 2015). A prominent example in this regard is the conservation of resources (COR) theory

(Hobfoll, 1989), which proposes that individuals build resources (e.g., energy, concentration, motivation) during leisure activities

that, in turn, can be used at work. Another example is activity theory (Havighurst, 1963), which argues that happiness is increased by

engagement in meaningful and social leisure activities outside work, such as meeting others and doing volunteer work. Accordingly,

in most occupations, people generally feel happier during leisure activities than during work activities (Bryson and MacKerron,

2017), and we believe truck drivers are no exception given the relatively high job demands and lack of recovery opportunities in this

occupation. For these reasons, the following hypothesis is posed:

H1a. Oﬀ-job activities are associated with higher momentary happiness than job activities among truck drivers.

Whereas truck drivers mostly engage in work-related job activities during work time (e.g., driving, deliveries, and pick-ups), they

also engage in some non-work-related job activities (typically eating and resting breaks). COR theory as well as the eﬀort-recovery

model (Meijman and Mulder, 1998) predict that the buﬀering eﬀect of engaging in recovery activities such as breaks also holds for

recovery activities during the work day (Hunter and Wu, 2016). Empirical studies conﬁrm that lunch breaks (Hunter and Wu, 2016;

Trougakos et al., 2014) and microbreaks (Kim et al., 2017) can help people to recover from daily stressors (e.g., by satisfying the basic

need to interact with other people). Some studies suggest that these theories could also apply to the truck driving occupation, as

breaks reduce fatigue and crash risks (Chen and Xie, 2014) and improve overall occupational health (Apostolopoulos et al., 2012). As

such, it is expected that non-work-related job activities (i.e., breaks) trigger greater momentary happiness than work-related job

activities. Therefore, the following hypothesis is posed:

H1b: Non-work-related job activities are associated with higher momentary happiness than work-related job activities among

truck drivers.

2.3. Job characteristics and momentary happiness

This study draws on the JD-R model (Bakker et al., 2004; Bakker and Demerouti, 2017; Demerouti et al., 2001) to further expand

upon hypothesis 1a. The negative relationship between work-related job activities and momentary happiness is likely to be dependent

on the favorability of truck drivers’ job characteristics.

The JD-R model posits that every job characteristic can be classiﬁed into two general categories: job demands and job resources

(Schaufeli and Taris, 2014). Job demands refer to “those physical, social, or organizational aspects of the job that require sustained

physical or mental eﬀort and are therefore associated with certain physiological and psychological costs (e.g., exhaustion)”

(Demerouti et al., 2001, p. 501). Job resources can be deﬁned as “those physical, psychological, social or organizational aspects of the

job that may do any of the following: be functional in achieving work goals, reduce job demands at the associated physiological and

psychological costs or stimulate personal growth and development” (Demerouti et al., 2001, p. 501). This theoretical model posits

that both job demands and job resources work as proximal determinants of various aspects of employee well-being. Although the

traditional focus is on motivational states (e.g., work engagement) and health states (e.g., stress, burnout), the model can also be

applied to aﬀective feelings of (un)happiness (Bakker and Oerlemans, 2011). Typically, job demands negatively aﬀect employee well-

being, and job resources positively aﬀect employee well-being. In turn, employee well-being determines organizational outcome

variables such as productivity, absenteeism, and turnover (Bakker and Demerouti, 2007; for empirical evidence, see Crawford et al.,

2010). Because this study is interested in the relationship between activities and momentary happiness, it does not follow the

tradition of examining the direct impact of job demands and resources on well-being but instead looks into their alleviating or

aggravating potentials in the hypothesized negative relationship between work-related job activities and momentary happiness.

I. Wijngaards, et al. Transportation Research Part A 128 (2019) 131–148

133

The JD-R model is a ﬂexible model, as at its core lies the proposition that while there may be occupation-speciﬁc job demands and

resources, their general relationships with well-being are relevant across all sectors and occupations (Korunka et al., 2009; Van

Droogenbroeck and Spruyt, 2016). It is, however, vital to select job demands and resources that are relevant or speciﬁc to the

occupation because their exact manifestation can be highly dependent on the occupational setting (e.g., De Croon et al., 2002).

Furthermore, JD-R theory distinguishes between state-like and trait-like job demands, job resources, and well-being (Bakker,

2015). States mirror a person’s feelings about the environment (e.g., job demands and resources) and the self (e.g., well-being) at

particular moments in time and are considered to be highly ﬂuctuating (Kühnel et al., 2012; Xanthopoulou et al., 2008). In contrast,

traits are regarded as individual dispositions or global experiences that remain relatively stable over time (Bakker, 2015). This

distinction is important because stable long-term eﬀects and transient short-term eﬀects can have divergent determinants and

consequences due to diﬀerences in their phenomenological nature. For instance, trip duration can be both state-like (e.g., making a

longer trip than usual) and trait-like (e.g., making long trips on a daily basis), and these may have unique eﬀects on momentary

happiness.

The current study focuses on a selection of state-like and trait-like job demands and job resources that are relevant for the truck

driving occupation. This selection was made by reviewing the truck driving literature. In addition, we collaborated with the Dutch

Sector Institute of Transportation and Logistics (in Dutch: Sectorinstituut Transport en Logistiek) to discover which job charactersitics

are particularly salient to Dutch truck drivers. However, given the large number of potentially relevant job demands and resources,

this selection is inevitably incomplete.

2.3.1. Trait job demands

This study focuses on three trait job demands: the frequency of working overtime, job insecurity, and average trip duration.

The high frequency of working overtime, often associated with long driving hours and extreme workloads, is a straining job

demand for truck drivers (Morrow and Crum, 2004; for evidence from the Netherlands, see Boeijinga et al., 2017). Working overtime

can interfere with truck drivers’ ability to balance their work and private lives (Berg et al., 2003), hamper people’s ability to recover

from work (Beckers et al., 2008), and disturb their sleeping rhythms (Kanazawa et al., 2006), which can in turn reduce their well-

being (Beckers et al., 2008) and result in chronic fatigue (Hege et al., 2015).

Job insecurity functions as another job demand for truck drivers; in recent years, an increasing number of Dutch truck drivers

have started working under temporary employment contracts (Wagenaar, 2018). Although truck drivers are generally in high de-

mand (Johnson et al., 2011), job insecurity can foster feelings of powerlessness and uncontrollability (De Witte, 1999), which in turn

lead to increased work stress (De Witte, 1999), lower job satisfaction (De Cuyper and De Witte, 2006), and lower life satisfaction

(Silla et al., 2009).

Truck drivers’ average trip duration is a likely occupation-speciﬁc job demand. Even though many just-in-time deliveries in a

working day can be stressful (Kemp et al., 2013), it is expected that, compared to short-haul truck drivers, truck drivers who have

longer average trip durations tend to experience resource depletion by increased feelings of social isolation and monotonous work as

well as work overload and work-family conﬂicts (Apostolopoulos et al., 2013; Crizzle et al., 2017). In line with the above argu-

mentations, the following hypotheses are posited:

H2a. Frequently working overtime aggravates the negative relationship between work-related job activities and truck drivers'

momentary happiness.

H2b. Job insecurity aggravates the negative relationship between work-related job activities and momentary happiness.

H2c. A long average trip duration aggravates the negative relationship between work-related job activities and momentary

happiness.

2.3.2. Trait job resources

This study examines the role of four resources that are relevant for the truck driving occupation: pay, colleague support, ﬂexibility

of work hours, and task variety. Pay, or income more generally, tends to have a positive relationship with emotional well-being for

people with relatively low or modest incomes, such as most truck drivers (Kahneman and Deaton, 2010). This is illustrated by the fact

that truck drivers have indicated that better salary is the most important factor for changing jobs (Van Zenderen et al., 2017). The

positive eﬀect of receiving relatively high pay may be reinforced by the controversy surrounding the pay of Dutch truck drivers.

Dutch employers have followed the trend of employing an increasingly large number of truckers from low-income European Union

countries (Hilal, 2008; Pijpers, 2010) who have started participating in the international transportation market as an excuse to

underpay Dutch truck drivers (Cremers, 2014). The eﬀort-reward imbalance model (Siegrist and Peter, 1996; Van Vegchel et al.,

2005) predicts that employee perceptions of being insuﬃciently rewarded based on one’s eﬀorts reduces employee well-being. The

salience of pay unfairness in the truck driving setting might make pay a particularly important determinant of truck drivers’ well-

being.

The individualistic nature of the truck driving occupation could cause truck drivers to feel socially isolated and experience limited

social support (Crizzle et al., 2017; Orris et al., 1997), resulting in mental health complaints (Kemp et al., 2013; Shattell et al., 2010).

Social support works as a resource for truck drivers (Van Zenderen et al., 2017), as it satisﬁes individuals’ desire for relatedness (e.g.,

pleasant social interactions with colleagues), facilitates coping (e.g., blowing oﬀ steam after a stressful situation) and can be used to

decrease workload (e.g., a colleague taking over a ride, Bakker and Demerouti, 2007).

Furthermore, truck drivers regularly cope with tight and sometimes unrealistic schedules (Apostolopoulos et al., 2016; Hege et al.,

2015) and extended periods away from home (Shattell et al., 2010). This lack of ﬂexibility in work schedules is likely to diminish

I. Wijngaards, et al. Transportation Research Part A 128 (2019) 131–148

134

truck drivers’ sense of autonomy (Thompson and Prottas, 2006) and consequent well-being (Bakker and Demerouti, 2007). Ac-

cordingly, it is expected that ﬂexible work hours would help alleviate truck driver stress, as they facilitate the reduction of role

conﬂicts and work-life conﬂict (Rau and Hyland, 2002).

The task variety available to truck drivers is generally considered low, as truck drivers often engage in driving for long periods of

time (Shattell et al., 2010). This monotonous driving could diminish the meaningfulness of the job (Hackman and Oldham, 1974) and

increase feelings of boredom (Parker et al., 2008).

Hence, following the assumption that job resources have a positive eﬀect on happiness, the following hypotheses are posed:

H3a. High pay alleviates the negative relationship between work-related job activities and momentary happiness.

H3b. Social support of colleagues alleviates the negative relationship between work-related job activities and momentary hap-

piness.

H3c. Having ﬂexible work hours alleviates the negative relationship between work-related job activities and momentary hap-

piness.

H3d. Task variety alleviates the negative relationship between work-related job activities and momentary happiness.

2.3.3. State job demands

Thus far, this study has hypothesized the moderating eﬀect of job demands and resources in the relationship between work-

related job activities and momentary happiness. However, it is essential also to examine job demands and resources that are spe-

ciﬁcally relevant during individual job activities—in particular, those related to the main task of truck drivers: driving a truck. One

prominent source of job demands relevant to this activity are environmental conditions (Crizzle et al., 2017; Shattell et al., 2010), and

we will focus here on two such environmental conditions: road congestion and poor road conditions.

Road congestion functions as a job demand (Rowden et al., 2011; Shattell et al., 2010), as it often result in negative emotions

(Eckenrode, 1984; Hennessy and Wiesenthal, 1999; Rowden et al., 2011) such as frustration and aggression (Shinar and Compton,

2004). Moreover, busy roads force truck drivers to deplete energy resources to concentrate on the road (Shattell et al., 2010).

Poor road conditions may also act as a job demand (Shattell et al., 2010), although the road quality in the Netherlands is generally

good (Bruntlett and Bruntlett, 2018). For example, driving on roads with many potholes results in increased levels of whole-body

vibration, in turn causing discomfort and, if suﬃciently continuous, pain (Bovenzi, 2009). Driving on poorly lit roads make truck

drivers drowsy and pressure them to pay extra attention.

Following this argumentation, two hypotheses are put forward:

H4a. Road congestion aggravates the negative relationship between truck driving and momentary happiness.

H4b. Poor road conditions aggravate the negative relationship between truck driving and momentary happiness.

2.3.4. State job resources

Social support can also be viewed as state job resource. Truck drivers typically spend most of their working hours on the road

without any physical company and lack the opportunity to virtually connect. Situations in which drivers have passengers in the truck

provide a valuable opportunity for social support and distraction. For instance, when a driver has passengers with whom he or she

can interact, he or she will be “more occupied with something” (Smith, 1981) and distracted from the “boring road” (Ettema et al.,

2012). Some evidence from commuting studies suggests that the negative emotions resulting from job stressors can be attenuated by

the presence of passengers (Ettema et al., 2012; Lancée et al., 2017). In a study among truck drivers, Hatami et al. (2019) have shown

that having a codriver decreases feelings of stress and loneliness, thereby increasing SWB. As such, the following hypothesis is

posited:

H5. Having passengers alleviates the negative relationship between truck driving and momentary happiness.

In Fig. 1, a conceptual model is presented that summarizes all hypotheses. Hypothesis 1a represents the top arrow, and hypothesis

1b represents bottom arrow. Hypotheses 2 to 5 concern the arrow in the middle. Hypotheses 2 and 3 involve the moderating eﬀect of

trait job demands and trait job resources on the relationship between work-related job activities and momentary happiness. Hy-

potheses 4 and 5 summarize the argumentation about the moderating eﬀect of state job demands and resources in the relationship

between work-related job activities and momentary happiness.

3. Methods

3.1. Procedure and sample

The data collection was conducted by a Dutch academic research institute in collaboration with the Dutch Sector Institute of

Transportation and Logistics from February to December 2016. Transportation workers were recruited via the Sector Institute’s

newspaper, digital newsletter, and website. To incentivize participation, it was announced on these platforms that three randomly

selected survey respondents would win a power bank, which is a portable battery that can charge USB-connected devices, such as

smartphones and tablets. This convenience sampling procedure resulted in 339 national and international truck drivers participating

in a one-time survey asking about trait-like work characteristics and their demographic characteristics.

I. Wijngaards, et al. Transportation Research Part A 128 (2019) 131–148

135

After this one-time survey, 82 truck drivers voluntarily participated in a follow-up ESM study.

The goal of the ESM study was to

capture state-like variables and momentary happiness. After stating their agreement to participate in this follow-up study, partici-

pants were informed on how to download the ESM application onto their mobile phones. When they had downloaded the application,

participants were provided a tutorial with instructions on how to use the application in order to maximize the quality and quantity of

responses. Next, in line with common practice in ESM research (Fisher and To, 2012; Larson and Csikszentmihalyi, 2014), re-

spondents received four notiﬁcations each day asking them to indicate (i) how they were feeling, (ii) what they were doing, (iii) and

who was with them in the past hour. The notiﬁcations were distributed throughout daytime, covering the entire waking day. Two

consecutive signals were always more than an hour apart. Out of safety considerations, the truck drivers were instructed to answer

this question when they were oﬀ the road (e.g., on a break or at a drop-oﬀ or pick-up location).

The total number of observations in

the utilized ESM dataset was 4175, and the median number of responses was 30. The data were fully anonymized and treated

conﬁdentially.

The table in Appendix A summarizes the demographic composition and well-being of the sample of ESM respondents and

compares these to the Dutch truck driver population and the attrition sample (i.e., those who participated in the initial study but not

in the follow-up ESM study). The sample of ESM participants was generally representative in terms of demographic composition and

well-being of the attrition sample and general population, with some exceptions (see Appendix A). For reasons of anonymity, re-

spondents were not asked to indicate for which company they worked. However, because the Sector Institute is a cooperative of the

main employers’ associations and employees’ organizations in the Dutch transportation and logistics sector, the survey respondents

likely worked for a great variety of companies in the transportation and logistics sector.

3.2. Measures

Trait-like variables were measured using survey instruments in a cross-sectional survey, because they were expected to be rather

stable over time. Because of their transient nature, state-like variables were measured through survey instruments in the ESM pro-

cedure. The items were presented in Dutch. A list of all the current study’s variables and the number of observations per category is

provided in Table 1.

Except for momentary happiness, all scales were collapsed into fewer categories based on the logical ordering of answer cate-

gories (e.g., merging “Strongly agree” and “Agree” into “Agree”). Most variables were measured on ordinal Likert-scales. Likert scales

are commonly treated as interval variables when survey data is normally distributed and a linear associations is expected (MacCallum

et al., 2002). Yet, because Shapiro-Wilk’s test of normality showed that all variables followed a non-normal distribution (p< 0.05),

variables had to be treated as ordinal (Agresti, 2018). In addition, we did not necessarily expect linear associations; for instance, a

lack of task variety can be expected to relate to lower momentary happiness, but too much task variety can become overwhelming

and reduce momentary happiness. Finally, since the limited number of observations and infrequent use of some categories, collapsing

the scales into fewer categories was deemed necessary to have acceptable levels of statistical power and avoid type 1 errors. The

scales of other categorical variables were collapsed for the same reason.

Job activities

Momentary happiness

during work-related job

activities

Work-related job activities

Trait job resources

- Pay

- Job variety

- Social support

- Flexible work hours

Trait job demands

- Average trip duration

- Frequently working

overtime

- Job insecurity

Off-job activities

State job resources

- Passengers

State job demands

- Road congestion

- Road condition

Momentary happiness

during off-job activities

Non-work-related job activities

Momentary happiness

during non-work related job

activities

Fig. 1. Conceptual model.

A threshold of ﬁve ESM observations was adopted, as some participants participated just once or twice.

For this reason, we were unable to collect data on true momentary activities, feelings, or company (e.g., “What are you doing right now?”) and

had to prompt with a question that allowed more ﬂexibility.

I. Wijngaards, et al. Transportation Research Part A 128 (2019) 131–148

136

3.2.1. State-like variables

The considered state-like variables can be classiﬁed into four groups: state-like employee well-being, activity, state job demands,

and state job resources.

3.2.1.1. State-like happiness. Momentary happiness was assessed with a single-item question: “How happy did you feel in the last

hour?” Responses were rated on an 11-point Likert scale ranging from 0 (“Very unhappy”) to 10 (“Very happy”).

3.2.1.2. Activity. Respondents were asked to report what they had been primarily doing in the last hour. They ﬁrst had to select

whether they were engaged in a job or an oﬀ-job activity. For job activities, they could subsequently select one of the following

activities: driving, eating, delivery and pick-up, rest/relaxation, administrative task, logistics task, or other. The categories eating and

rest/relaxation were combined into a category of non-work-related job activities. For oﬀ-job activities, subjects could choose one of

the following activities: sleeping, taking care of oneself, taking care of another person, travelling, studying, doing household tasks,

eating, communicating with another person, relaxing, watching TV or using a computer, working out, engaging in outdoor activity,

or other.

3.2.1.3. State job demands. When respondents answered “driving” as their activity, they were asked how busy the road was

(1 = “Very unbusy”, 2 = “Reasonably unbusy”, 3 = “Reasonably busy”, 4 = “Very busy”) and what the quality of the road was

(1 = “Very bad”, 2 = “Reasonably bad”, 3 = “Reasonably good”, 4 = “Very good”). The 4-point Likert scales were dichotomized by

combining the lowest two scores and the highest two scores (e.g., 0 = “No road congestion”, 1 = “Road congestion”).

3.2.1.4. State job resources. As a follow-up to the activity question, respondents were asked if they were alone or with colleagues,

customers or friends. This variable was dichotomized to having passengers or not while driving (0 = “No”, 1 = “Yes”).

Table 1

Variable overview.

Variable category Variable Categories N

observations/

/Mean (SD)

State-like variables Momentary happiness

0 (“Very unhappy”) to 10 (“Very happy”) 7.45 (1.42)

Activity *–

Passengers

Passengers 45

No passengers 916

Road congestion

Road congestion 257

No road congestion 435

Road quality

Good road quality 891

Poor road quality 72

Road familiarity

High road familiarity 928

Low road familiarity 34

Trait-like variables Working overtime

Once or multiple times a week 47

Less than once or multiple times a week 35

Job insecurity

Few worries 60

Some worries 10

Many worries 12

Average trip duration

3 h or less 48

Longer than 3 h 34

Pay

€1800 or less 17

€1801 or more 65

Colleague support

Disagree 15

Neutral 9

Agree 59

Flexible work hours

Disagree 36

Neutral 14

Agree 32

Task variety

Disagree 9

Neutral 17

Agree 56

Note. SD = Standard deviation.

N= 4175.

N= 962 (number of driving episodes).

N= 82.

* The observations per category can be found in Fig. 2.

The most frequent passengers were colleagues (26 observations), followed by customers (9 observations), friends/acquaintances (5 observations)

and other (5 observations).

I. Wijngaards, et al. Transportation Research Part A 128 (2019) 131–148

137

3.2.2. Trait-like variables

The considered trait-like variables can also be classiﬁed into four groups: trait job demands and trait job resources.

3.2.2.1. Trait job demands. Working overtime was measured with the item “How often do you have to work overtime for your job?”

This categorical variable was dichotomized to working overtime one or multiple times per week or not (0 = “No”, 1 =“Yes”) in order

to have two categories of approximately the same sample size. Following the same rationale, average trip duration assessed using the

question “In general, how long does an average trip from your pick-up location to your drop-oﬀ location take you?” was

dichotomized (0 = “3h or less”, 1 = “longer than 3h”). Job insecurity was measured with the item “To what extent do you worry

about the possibility of losing your job?” with answer categories on a 7-point Likert scale (1 = “No worrying at all” to 7 = “Worrying

a lot”). The variable was recoded into three categories (1 = “Few worries”, 2 = “Some worries”, 3 = “Many worries”) by combining

the lowest three scores and the highest three scores.

3.2.2.2. Trait job resources. Pay was assessed with the item “To what category does your net monthly income belong?” For the

analysis, this categorical variable was dichotomized to create two approximately equally large groups (0 = €1800 or less, 1 = €1801

or more). Colleague support was assessed with the item “Do you have the feeling that you can count on the support and help of your

colleagues?” Flexibility in work hours was assessed with “To what extent do you have the feeling that you have ﬂexibility in

determining your work hours?” Task variety was measured by the question “Do you have enough variation in your work?” These

three questions had answer categories on a 7-point Likert scale (1 = “Totally disagree” to 7 = “Totally agree”). The variables were

recoded into three categories (1 = “Disagree”, 2 = “Neutral”, 3 = “Agree”) by combining the lowest three scores and the highest

three scores.

3.3. Statistical analyses

Within-subject ﬁxed-eﬀects regressions were performed to test the hypotheses. The results were stratiﬁed into three parts. First,

the authors provided an overview of momentary happiness during various activities using descriptive statistics and three ﬁxed-eﬀects

models. A major advantage of ﬁxed-eﬀects models is the exclusion of top-down eﬀects of a person’s general well-being on momentary

happiness (Bryson and MacKerron, 2017; Lancée et al., 2017; Morris and Guerra, 2015). In other words, individual ﬁxed eﬀects

control for individual-speciﬁc characteristics that remain constant over time, including people’s baseline or reference happiness level.

This distortion is caused by the reciprocal relationship between state-like and trait-like SWB constructs. As an explanation, mo-

mentary happiness adds to general well-being (bottom-up eﬀect), whereas general well-being also aﬀects momentary happiness

during diﬀerent activities (top-down eﬀect; Headey et al., 1991; for evidence from transportation research, see De Vos, 2019).

Since

the authors are most interested in the types of activities that aﬀect the momentary happiness of truck drivers, it is important to

account for this top-down eﬀect.

All ﬁxed-eﬀects models were estimated using individual-clustered robust standard errors. The ﬁrst model concerned the diﬀer-

ence between momentary happiness at work and oﬀ work. The second model distinguished between momentary happiness during

work-related job activities, non-work-related job activities, other job activities and oﬀ-job activities. The last model was identical to

the second model, but it instead estimated the impact of speciﬁc work-related job activities (i.e., driving, pick-up/drop-oﬀ, logistical

tasks and administrative tasks).

Subsequently, several ﬁxed-eﬀects models were estimated to explore what trait-like job characteristics moderate the relationship

between work-related job activities and momentary happiness. In particular, these models were used to investigate whether speciﬁc

trait job demands and job resources increase or decrease the diﬀerence in momentary happiness between work-related job activities

and oﬀ-job activities. These models controlled for speciﬁc job and oﬀ-job activities (driving, relaxing, etc.) to capture variation in

momentary happiness caused by these speciﬁc activities. Oﬀ-job activities were included as a reference category to eliminate the

possible confounding eﬀect of between-person diﬀerences in aﬀective disposition, which became relevant when trait job char-

acteristics were introduced to the model.

Notably, job demands and resources could theoretically also inﬂuence happiness levels during oﬀ-job activities and could thus

bias the ﬁxed-eﬀect model estimates. To test this, a between-subject linear regression that assessed the inﬂuence of diﬀerent job

characteristics on an individual’s average happiness during oﬀ-job activities was conducted. As shown in Appendix C, the results of

this linear regression model indicate that job characteristics play basically no signiﬁcant role in predicting momentary happiness

during oﬀ-job activities. Therefore, the presented diﬀerences between job and oﬀ-job activities can be interpreted as the inﬂuence of

job characteristics on momentary happiness during work-related job activities.

Additionally, four ﬁxed-eﬀects models were estimated to assess the extent to which state job demands and resources experienced

while driving have the potential to decrease or increase the diﬀerence in momentary happiness between driving and oﬀ-job activities.

For all models, this study controlled for time of day (i.e., morning, afternoon, evening, night) and day of the week to capture

common daily and weekly happiness patterns that are unrelated to speciﬁc activities or job characteristics.

This assumption was veriﬁed, as Pearson correlation analyses examining the relationships between momentary happiness, job satisfaction, life

satisfaction, self-reported health and stress at work suggested that trait-like and state-like well-being constructs are highly related. The results can be

found in Appendix B.

I. Wijngaards, et al. Transportation Research Part A 128 (2019) 131–148

138

4. Results

This section begins with an overview of some descriptive statistics about momentary happiness at work and momentary happiness

oﬀ work. Then, the results of the ﬁxed-eﬀects models are presented and the antecedents of momentary happiness at work are

discussed.

4.1. Descriptive statistics

The average momentary happiness score of truck drivers was 7.45 (SD =1.42). The average momentary happiness score for oﬀ-

job activities (M = 7.69, SD = 1.36) was greater than the average momentary happiness score for job activities (M = 7.24,

SD = 1.43). A between-subjects t-test showed that the diﬀerence in average momentary happiness during oﬀ-job activities and job

activities was statistically signiﬁcant, t(4172) = 10.46, p< 0.001.

Zooming in on the more speciﬁc activities, as visualized in Fig. 2, some activities were associated with higher levels of happiness

than others. In terms of job activities, truck drivers reported higher momentary happiness while driving than during other work-

related job tasks, though they did not perceive it to be as pleasant as non-work-related job activities (i.e., eating and relaxing). With

respect to leisure activities, truck drivers appeared to be happiest while relaxing or during active leisure activities, particularly

working out and outdoor activities.

4.2. Activities and momentary happiness

As shown in Table 2, the results from the within-subject analyses show that truck drivers were happier oﬀ work than they were at

work, providing support for hypothesis 1a. Furthermore, the lower happiness during job activities was driven by work-related job

activities as opposed to non-work-related job activities, supporting hypothesis 1b. Model 3 showed that truck drivers were parti-

cularly unhappy during logistical tasks and delivery/pick-up tasks, and Wald tests conﬁrmed that truck drivers were signiﬁcantly

happier while driving than during delivery/pick-up tasks (χ

= 12.63, p< 0.001) and logistical tasks (χ

= 6.57, p< 0.05).

4.3. Job activities, trait job demands and trait job resources

As exhibited in Table 3, the within-subjects analyses showed that none of the considered trait-like job demands (i.e., working

overtime, job insecurity, and average trip duration) aggravated the relationship between work-related job activities and momentary

happiness. As such, hypothesis 2 was not supported. With respect to trait-like job resources, colleague support and ﬂexible working

hours alleviated this relationship, whereas pay and job variety did not. Although the results suggested a moderate interaction eﬀect of

job variety, there is too much uncertainty about the true value of the parameter estimate. As a consequence, hypotheses 3b and 3c

were supported, while hypothesis 3a and 3d were not supported

518

487

143

39 48

101

127

266

116

140

6.0

6.5

7.0

7.5

8.0

8.5

9.0

Driving

Delivery/pick−up

Eating and relaxing

Administrative task

Logistical task

Other

Sleep

Personal care

Care for other person

Travelling

Household

Eating

Communicating with someone

Relaxing

TV/computer

Working out

Outdoor activity

Unclassified

Activity

Momentary happiness

Activity group

Job activities

Off−job activities

Fig. 2. Bar chart depicting average momentary happiness per activity (including 95% conﬁdence intervals and total observations). Note. Unadjusted

means reported with 95% conﬁdence intervals, depicted on the top of the bars. Number of observations depicted above the bars. Activity categories

with fewer than 20 observations were omitted from this plot.

I. Wijngaards, et al. Transportation Research Part A 128 (2019) 131–148

139

4.4. Driving, state job demands and state job resources

The results of the last within-subjects ﬁxed-eﬀect models are displayed in Table 4. The data showed that only road congestion

functioned as job demand in the prediction of truck drivers’ momentary happiness. Quality of the road did not have a signiﬁcant

moderating eﬀect on the relationship between truck driving and momentary happiness. Accordingly, hypothesis 4a was supported,

whereas hypothesis 4b was not supported. Hypothesis 5 was not supported, as having passengers did not aggravate the negative

relationship between work-related job activities and momentary happiness.

5. Discussion

In Table 5, the outcomes of the hypothesis testing are presented. The results of the present study indicate that truck drivers are

happier during oﬀ-work activities than during job activities. Moreover, truck drivers reported more momentary happiness during

non-work-related job activities (e.g., breaks) than during work-related job activities (e.g., driving, administrative tasks). One job

demand, road congestion, was found to aggravate the relationship between work-related job activities (i.e., truck driving) and

momentary happiness. The other considered job demands—frequently working overtime, job insecurity, average trip duration, and

poor road quality—did not function as signiﬁcant moderators, although here as well all of the coeﬃcients besides that of working

overtime were in the expected direction. Two job resources—social support of colleagues and task variety—were found to alleviate

the negative relationship between work-related job activities and momentary happiness. The other considered job resources—pay,

task variety, and having company while driving—did not signiﬁcantly moderate this relationship, although the coeﬃcients were in

the expected direction. Given the limited sample size, the present study’s results should be interpreted as showing which job

characteristics and activities aﬀect momentary happiness most strongly, but insigniﬁcant relationships do not necessarily imply that

those job characteristics are irrelevant to momentary happiness. An additional factor that should be taken into consideration is the

variation within these job demands and job resources. For instance, although all passenger types were categorized into one category

for reasons of sample size, there may be variation in how diﬀerent passengers inﬂuence the happiness of truck drivers. For instance, it

is theoretically plausible that the presence of a colleague would evoke more enjoyable conversation and social support than the

presence of a customer or supervisor.

The present study found mixed evidence for the robustness of the JD-R model in a truck driving occupation. More speciﬁcally, half

of the hypotheses about trait job resources were supported by the data. Conversely, although road congestion turned out to be an

important moderator in the relationship between truck driving and momentary happiness, no other signiﬁcant interaction eﬀects of

job demands were found. This ﬁnding demonstrates the importance selecting job aspects that are relevant for the target population

(Bakker and Demerouti, 2007; De Croon et al., 2004). To illustrate, the insigniﬁcant interaction of working overtime frequently may

have been explained by the fact that the truck driving profession is typically characterized by long and overtime working hours

(Beckers et al., 2008), as corroborated in the present study’s data, i.e., only 17% of truck drivers never work overtime. Because truck

drivers are apparently used to working overtime, they most likely have accepted this job stressor and have adjusted to the situation,

which in turn could have reduce the stressor’s negative impact on their happiness (Diener et al., 2006, p. 200; Ritter et al., 2016).

5.1. Limitations and future research directions

The present study’s limitations regarding the (i) selection of variables, (ii) validity of the measures, and (iii) generalizability of the

sample can be addressed in future research.

First, this study considered only a limited selection of job demands and resources that are potentially relevant to the truck driving

Table 2

Within-subjects ﬁxed-eﬀects model linking activity classes and speciﬁc activities to momentary happiness levels.

Variable (1) (2) (3)

Oﬀ-job activities Reference Reference Reference

Job activities −0.409*** (0.064)

Work-related job activities −0.393*** (0.060)

Driving −0.281*** (0.059)

Delivery/Pickup −0.517*** (0.075)

Administrative task −0.280 (0.186)

Logistical task −0.621*** (0.145)

Non-work-related job activities −0.071 (0.083) −0.072 (0.082)

Other job activities −0.968*** (0.234) −0.968*** (0.234)

Controls for the time of day and day of the week Yes Yes Yes

Within R

0.061 0.072 0.078

ICC 0.515 0.517 0.519

Total Nof respondents 82 82 82

Observations 4175 4175 4175

Note. *** = p< 0.001; ** = p< 0.01; * = p< 0.05;

†

=p< 0.10; t= Time; R

= Explained variance; ICC = Interclass correlation;

N= Sample size.

I. Wijngaards, et al. Transportation Research Part A 128 (2019) 131–148

140

Table 3

Within-subjects ﬁxed-eﬀects model linking trait job demands and resources during work activities to momentary happiness at work.

Variable (1) (2) (3) (4) (5) (6) (7)

Oﬀ-job activities Reference Reference Reference Reference Reference Reference Reference

Work-related job activities

−0.392*** (0.092) −0.354*** (0.073) −0.386*** (0.066) −0.430*** (0.096) −0.700*** (0.144) −0.513*** (0.096) −0.782*** (0.222)

Work-related job activities * Working overtime: one or multiple

times a week

−0.003 (0.123)

Work-related job activities * Job insecurity: some worries

0.130 (0.240)

Work-related job activities * Job insecurity: many worries

−0.281 (0.189)

Work-related job activities * Average trip duration: longer than 3

hours

−0.019 (0.130)

Work-related job activities * Monthly net income €1801 or more

0.047 (0.120)

Work-related job activities * Support of colleagues: neutral

−0.168 (0.262)

Work-related job activities * Support of colleagues: agree

0.445** (0.152)

Work-related job activities * Flexible work hours: neutral

0.052 (0.122)

Work-related job activities * Flexible work hours: agree

0.260** (0.124)

Work-related job activities * Task variety: neutral

0.456 (0.280)

Work-related job activities * Task variety: agree

0.415

†

(0.231)

Controls for time of day and day of week Yes Yes Yes Yes Yes Yes Yes

Controls for all speciﬁc activities

Yes Yes Yes Yes Yes Yes Yes

Within R

0.072 0.075 0.072 0.072 0.082 0.075 0.075

ICC 0.517 0.515 0.516 0.517 0.519 0.513 0.513

Total Nof respondents 82 82 82 82 82 82 82

Observations 4175 4175 4175 4175 4175 4175 4175

Note. *** = p< 0.001; ** = p< 0.01; * = p< 0.05;

†

=p< 0.10; R

= Explained variance; ICC = Interclass correlation; N= Sample size.

= Work-related job activities include driving, pick-up/drop-oﬀ, administrative tasks, and logistical tasks.

= Reference category is working overtime a few times a month or less.

= Reference category is “few worries”.

= Reference category is average trip duration of 3 h or less.

= Reference category is monthly net income €1800 or less.

= Reference category is “disagree”.

= Dummies for all speciﬁc oﬀ-job and job activities were included.

I. Wijngaards, et al. Transportation Research Part A 128 (2019) 131–148

141

occupation. Other job demands and resources could also play an important role in predicting truck driver well-being during work

activities, and job demands and resources may interact with each other in inﬂuencing well-being (Bakker and Demerouti, 2007). For

instance, the negative eﬀect of road congestion on well-being may be aggravated when truck drivers deal with very tight schedules or

buﬀered when they have the autonomy to plan their own routes. Valuable future research directions would be the consideration of a

complementary or larger set of job demands and resources, an explicit test of interactions between speciﬁc job demands and job

resources, as well as testing considering other well-being variables, such as state work engagement, momentary fatigue, and stress.

Second, all survey measures in this study were single-item measures. Single-item momentary happiness measures are considered

valid (Tadić et al., 2013), and the use of too many items in an ESM study is even undesirable (Scollon et al., 2009); however, the

measurement of the trait-like variables in particular could have been better if multiple-item survey measures had been used. For

instance, the Work Design Questionnaire (Morgeson and Humphrey, 2006) could function as means to more reliably and validly

measure task variety, social support, and working overtime. In addition, most measures used in this study were subjective in nature.

As objective data and subjective evaluations of a phenomenon (e.g., heavy traﬃc) are often complementary (Jahedi and Méndez,

2014), future researchers are encouraged to triangulate subjective and objective measures. For instance, administrative records (e.g.,

when and where truck drivers often work) combined with open-source traﬃc data can be used as objective indicators of road

familiarity, road congestion, and road quality. Even more ambitiously, behavioral and physiological data generated by sensors (e.g., a

smart watch, cameras in trucks) could help researchers measure the interactions between employee well-being and driving behavior

(for an example, see Lee et al., 2015). One speciﬁc issue regarding the reporting of activities was that respondents were asked about

their primary activity in the past hour. However, drivers may engage in several activities within an hour, and the duration of the

activity may not always have the strongest eﬀect on their happiness.

Third, the study’s sample is subject to limitations. Although the sample was reasonably representative of the Dutch truck driving

population in terms of demographic characteristics and well-being, the generalizability of the results to truck drivers in other

countries remains an open question and merits attention in future research. In addition, as discussed, the limited sample size sets the

bar high for ﬁnding supporting evidence for the hypotheses.

Table 4

Within-subjects ﬁxed-eﬀects model linking state job demands and resources during driving to momentary happiness at work.

Variable (1) (2) (4)

Oﬀ-job activities Reference Reference Reference

Driving −0.235** (0.073) −0.511*** (0.179) −0.289*** (0.059)

Driving * Road congestion

−0.169* (0.082)

Driving * Poor road quality

−0.246 (0.162)

Driving * Passengers

0.157 (0.132)

Controls for time of day and day of week

No Yes Yes

Control for other job activities

Yes Yes Yes

0.079 0.079 0.078

ICC 0.519 0.518 0.518

Total Nof respondents 82 82 82

Observations 4175 4175 4175

Note. *** = p< 0.001; ** = p< 0.01; * = p< 0.05;

†

=p< 0.10; R

= Explained variance; ICC = Interclass correlation; N= Sample size.

= Reference category is “No road congestion”.

= Reference category is “Good road condition”.

= Reference category is “Being alone”.

= Because road congestion is heavily dependent on time of the day and day of the week, these were not included as controls.

= These activities include pick-up/drop-oﬀ, administrative tasks, logistical tasks, non-work-related job activities (i.e., eating, relaxing), and

other job activities

Table 5

An overview of the present study’s research ﬁndings.

Hypothesis Status

H1a. Oﬀ-job activities are associated with more momentary happiness states than job activities among truck drivers. Supported

H1b: Non-work-related job activities are associated with more momentary happiness than non-work-related job activities among truck

drivers.

Supported

H2a. Having to work overtime frequently aggravates the negative relationship between work-related job activities and momentary happiness. Not supported

H2b. High job insecurity aggravates the negative relationship between work-related job activities and momentary happiness. Not supported

H2c. Having a long average trip duration aggravates the negative relationship between work-related job activities and momentary happiness. Not supported

H3a. High pay alleviates the negative relationship between work-related job activities and momentary happiness. Not supported

H3b. Social support of colleagues alleviates the negative relationship between work-related job activities and momentary happiness. Supported

H3c. Having ﬂexible work hours alleviates the negative relationship between work-related job activities and momentary happiness. Supported

H3d. Task variety alleviates the negative relationship between work-related job activities and momentary happiness. Not supported

H4a. Road congestion alleviates the negative relationship between truck driving and momentary happiness. Supported

H4b. Poor road conditions alleviate the negative relationship between truck driving and momentary happiness. Not supported

H5. Having passengers alleviates the negative relationship between truck driving and momentary happiness. Not supported

I. Wijngaards, et al. Transportation Research Part A 128 (2019) 131–148

142

5.2. Practical recommendations

Within the aforementioned limitations, this study oﬀers practitioners several interesting guidelines for policy making. In line with

research that evidenced the importance of high-quality work breaks (Hunter and Wu, 2016; Trougakos et al., 2014), the higher

momentary happiness during work breaks than during work-related job activities underscore that the happiness of truck drivers

beneﬁts from the facilitation of suﬃcient relaxing breaks by transportation companies. The ﬁnding that busy roads take a toll on the

momentary happiness of truck drivers provide an argument for the prioritization of investments in clever scheduling of deliveries to

avoid extremely busy roads, for example, during rush hour (Kok et al., 2012). As indicated by Johnson et al. (2011), adequate on-the-

road training could also make a diﬀerence in making truck drivers feel more conﬁdent and capable on busier roads. Furthermore, the

results show that practitioners can positively inﬂuence truck driver happiness by facilitating colleague support and making sure that

truck drivers have some ﬂexibility in their scheduling. For example, truck driving companies can create a platform for colleague

support by organizing team events and creating social media groups (Kemp et al., 2013; Williams et al., 2011). On a more general

level, truck driving companies are advised to pay more attention to the well-being of truck drivers (Boyce, 2016), in turn reducing

their turnover intentions (Kemp et al., 2013). This can be done by starting a dialogue and asking truck drivers for feedback about

their jobs, their experiences and, more generally, their lives as a whole (Kemp et al., 2013).

6. Concluding remarks

Transportation companies are at a turning point. With a growing shortage of truck drivers and considerable turnover rates, there

is a strong incentive to invest in promoting the well-being of truck drivers a priority. Although measuring work stress, fatigue and

other health-related constructs functions as a starting point, it is pivotal to adopt a more comprehensive approach to truck driver

well-being. In particular, by tracking the momentary happiness of truck drivers over time, transportation companies can better

understand when, with whom, and why truck drivers are happy. In this regard, this study shows that social support from colleagues

and a ﬂexible work schedule are pivotal job resources for feeling happy at work, while road congestion is a particularly important

factor that impairs truck drivers’ happiness at work.

Acknowledgement

I, Indy Wijngaards, received support from the Netherlands Organisation for Scientiﬁc Research (NWO), grant number

652.001.003. See: https://www.nwo.nl/. The funders had no role in study design, data collection and analysis, decision to publish, or

preparation of the manuscript. I would like to have this information in the paper.

Appendix A

See Table A.1.

Table A.1

Comparison of reduced sample (N= 82), attrition sample (N= 257) and representative sample (N= 76,537).

Variable Category ESM sample Attrition sample Representative sample

Demographics

Gender Male 96.3% 98.1% 91.0%

Female 3.7% 1.9% 9.0%

Age Mean 45.67 52.9

44.0

SD 12.37 9.3 –

Contract status Temporary 15.8% 20.6% 8.0%

Permanent 81.7% 75.5% 83.0%

Self-employed 1.2% 0.8% 4.0%

Employment agency 1.2% 3.1% 5.0%

Relationship status No partner 23.2% 13.4%

–

Partner 76.8% 66.9% –

Children No children 40.2% 33.1% –

Children 59.8% 66.9% –

Education level Primary or secondary school 52.2% 58.4% 59.0%

Professional or higher education 48.8% 41.6% 41.0%

Driver type National driver 61.0% 71.2% –

(continued on next page)

I. Wijngaards, et al. Transportation Research Part A 128 (2019) 131–148

143

Appendix B

See Table B.1.

Appendix C

See Table C.1.

Table A.1 (continued)

Variable Category ESM sample Attrition sample Representative sample

International driver 39.0% 28.8% –

Personal income (net/monthly) ≤ €1800 20.7% 26.8% –

≥ €1801 79.3% 73.2% –

Subjective well-being

Life satisfaction Mean 7.35 7.44 7.57

SD 1.12 1.33 –

Trait happiness Mean 5.30 5.22 –

SD 1.19 1.18 –

Stressful feelings at work Mean 3.54 3.74 –

SD 1.97 1.82 –

Notes. – = No data available;

= The ESM and attrition samples were compared using chi square-tests (for categorical variables) and independent t-tests (for continuous

variables). Signiﬁcant diﬀerences at the 5% signiﬁcance level were found for age and relationship status.

= Data of a representative sample of the truck driver population in the Netherlands are based on research by the Dutch Sector Institute of

Transportation and Logistics (Van Zenderen and Sombekke, 2016)

= While standard deviations were not available and statistical comparisons of means were not possible, it seems that the ESM’s distributions of

education, gender and contract status diverged from the representative sample. Average age and mean life satisfaction in the two samples seemed to

correspond; SD = Standard deviation.

Table B.1

Between-subject bivariate Pearson correlations between well-being variables (N= 82).

1. 2. 3. 4. 5.

1. Momentary happiness –

2. Job satisfaction 0.46*** –

3. Life satisfaction 0.66*** 0.38*** –

4. Self-reported health 0.30*** 0.21

†

0.21

†

–

5. Feelings of stress at work −0.39*** −0.13 −0.44*** −0.19

†

–

Note. *** = p< 0.001; ** = p< 0.01; * = p< 0.05;

†

=p< 0.10; N= Sample size. As commonly done in studies assessing happiness (Cheung

and Lucas, 2014; Wanous et al., 1997) and health states (Macias et al., 2015), all measures were single-item. The questions had answer categories

ranging on a 7-point Likert scale (e.g., 1 = “Never” to 7= “Very often”, and 1 =“Very dissatisﬁed” to 7 = “Very satisﬁed”). Job satisfaction was

assessed with the question “How satisﬁed are you with your current job?”. Life satisfaction was assessed with the question “Taking all into con-

sideration, how satisﬁed are you with your life?”. Self-reported health was assessed with the question “In general, how is your health?”. Feelings of

stress at work were assessed with the question “In the last 4 weeks, how often did you experience feelings of stress during work?”.

I. Wijngaards, et al. Transportation Research Part A 128 (2019) 131–148

144

Table C.1

Between-subject linear regression model on the relationship between average momentary happiness during oﬀ-job activities and job characteristics.

Variable (1) (2) (3) (4) (5) (6) (7)

Intercept 7.423*** (0.537) 7.259*** (0.518) 7.337*** (0.517) 7.392*** (0.566) 7.384*** (0.602) 7.237*** (0.544) 6.591*** (0.659)

Education level −0.028 (0.255) 0.061 (0.262) −0.018 (0.255) −0.027 (0.256) −0.020 (0.256) 0.017 (0.261) −0.120 (0.254)

Age −0.007 (0.010) 0.007 (0.010) 0.007 (0.010) 0.007 (0.010) 0.007 (0.010) 0.007 (0.010) 0.012 (0.010)

Working overtime: one or multiple times a week

−0.133 (0.255)

Job insecurity: some worries

0.495 (0.397)

Job insecurity: many worries

−0.115 (0.369)

Average duration trip: more than 3 hours

0.057 (0.261)

Monthly net income €1801 or more

−0.064 (0.313)

Support of colleagues: neutral

−0.310 (0.503)

Support of colleagues: agree

0.008 (0.333)

Flexible work hours: neutral

0.260 (0.369)

Flexible work hours: agree

0.072 (0.281)

Task variety: neutral

1.072* (0.471)

Task variety: agree

0.522 (0.401)

0.011 0.030 0.008 0.008 0.015 0.013 0.075

Total Nof respondents 81 81 81 81 81 81 81

Note. *** = p< 0.001; ** = p< 0.01; * = p< 0.05;

†

=p< 0.10; t= time; R

= Explained variance; N= Sample size.

To ensure that the nature of the diﬀerent oﬀ-job activities did not bias the results of this analysis, the dependent variable, average momentary happiness during oﬀ-job activities, was corrected for the

more speciﬁc oﬀ-job activities (based on a ﬁxed eﬀects regression in which momentary happiness scores were regressed on dummies for all speciﬁc oﬀ-job activities). One person was excluded from the

analysis (N= 81 instead of N= 82), as this person did not provide any ESM-responses during oﬀ-job activities.

= Reference category is working overtime a few times a month or less.

= Reference category is ‘few worries’;

= Reference category is average trip duration of 3 h or less;

= Reference category is monthly net income €1800 or less.

= Reference category is ‘disagree’.

I. Wijngaards, et al. Transportation Research Part A 128 (2019) 131–148

145

Appendix D. Supplementary material

Supplementary data to this article can be found online at https://doi.org/10.1016/j.tra.2019.07.017.

References

Agresti, A., 2018. An introduction to categorical data analysis. Wiley, New York, NY, US.

American Trucking Association, 2018. Turnover Rate at Large Truckload Carriers Rises in First Quarter. Accelerating Churn Rate Indicates Continued Tightness in

Driver Market. [WWW Document]. URL http://www.trucking.org/article/Turnover-Rate-at-Large-Truckload-Carriers-Rises-in-First-Quarter.

American Trucking Association, 2017. ATA Truck Driver Shortage Report.

Apostolopoulos, Y., Shattell, M.M., Sönmez, S., Strack, R., Haldeman, L., Jones, V., 2012. Active living in the trucking sector: environmental barriers and health

promotion strategies. J. Phys. Activity Health 9, 259–269.

Apostolopoulos, Y., Sönmez, S., Hege, A., Lemke, M., 2016. Work strain, social isolation and mental health of long-haul truckers. Occup. Therapy Mental Health 32,

50–69.

Apostolopoulos, Y., Sönmez, S., Shattell, M.M., Gonzales, C., Fehrenbacher, C., 2013. Health survey of US long-haul truck drivers: work environment, physical health,

and healthcare access. Work 46, 113–123.

Bakker, A.B., 2015. Towards a multilevel approach of employee well-being. Eur. J. Work Organiz. Psychol. 24, 839–843.

Bakker, A.B., Demerouti, E., 2017. Job demands–resources theory: Taking stock and looking forward. J. Occup. Health Psychol. 22, 273.

Bakker, A.B., Demerouti, E., 2007. The job demands-resources model: State of the art. J. Manage. Psychol. 22, 309–328.

Bakker, A.B., Demerouti, E., Verbeke, W., 2004. Using the Job Demands-Resources model to predict burnout and performance. Hum. Resour. Manage. 43, 83–104.

Bakker, A.B., Oerlemans, W., 2011. Subjective well-being in organizations. In: Cameron, K.S., Spreitzer, G.M. (Eds.), The Oxford Handbook of Positive Organizational

Scholarship. Oxford University Press, New York, NY, US, pp. 178–189.

Beckers, D.G., van der Linden, D., Smulders, P.G., Kompier, M.A., Taris, T.W., Geurts, S.A., 2008. Voluntary or involuntary? Control over overtime and rewards for

overtime in relation to fatigue and work satisfaction. Work Stress 22, 33–50.

Berg, P., Kalleberg, A.L., Appelbaum, E., 2003. Balancing work and family: The role of high-commitment environments. Indus. Relat.: J. Econ. Soc. 42, 168–188.

Boeijinga, A., Hoeken, H., Sanders, J., 2017. An analysis of health promotion materials for Dutch truck drivers: Oﬀ target and too complex? Work 56, 539–549.

Bovenzi, M., 2009. Metrics of whole-body vibration and exposure–response relationship for low back pain in professional drivers: a prospective cohort study. Int. Arch.

Occup. Environ. Health 82, 893–917.

Bowling, N.A., Eschleman, K.J., Wang, Q., 2010. A meta-analytic examination of the relationship between job satisfaction and subjective well-being. J. Occup. Organiz.

Psychol. 83, 915–934.

Bowling, N.A., Hendricks, E.A., Wagner, S.H., 2008. Positive and negative aﬀectivity and facet satisfaction: A meta-analysis. J. Bus. Psychol. 23, 115–125.

Boyce, W.S., 2016. Does truck driver health and wellness deserve more attention? J. Transp. Health 3, 124–128.

Bruntlett, M., Bruntlett, C., 2018. Building the cycling city: the Dutch blueprint for urban vitality. Island Press, Washington, DC, US.

Bryson, A., MacKerron, G., 2017. Are you happy while you work? Econ. J. 127, 106–125.

Chen, C., Xie, Y., 2014. The impacts of multiple rest-break periods on commercial truck driver’s crash risk. J. Saf. Res. 48, 87–93.

Cheung, F., Lucas, R.E., 2014. Assessing the validity of single-item life satisfaction measures: Results from three large samples. Qual. Life Res. 23, 2809–2818.

Crawford, E.R., LePine, J.A., Rich, B.L., 2010. Linking job demands and resources to employee engagement and burnout: a theoretical extension and meta-analytic test.

J. Appl. Psychol. 95, 834–848.

Cremers, J., 2014. Schijnconstructies in het internationaal wegtransport. Zeggenschap. Tijdschrift over arbeidsverhoudingen 25, 32–34.

Crizzle, A.M., Bigelow, P., Adams, D., Gooderham, S., Myers, A.M., Thiﬀault, P., 2017. Health and wellness of long-haul truck and bus drivers: A systematic literature

review and directions for future research. J. Transp. Health 7, 90–109.

Csikszentmihalyi, M., Hunter, J., 2003. Happiness in everyday life: The uses of experience sampling. J. Happiness Stud. 4, 185–199.

Da Silva-Júnior, F.P., de Pinho, R.S.N., de Mello, M.T., de Bruin, V.M.S., De Bruin, P.F.C., 2009. Risk factors for depression in truck drivers. Soc. Psychiatry Psychiatr.

Epidemiol. 44, 125–129.

De Croon, E.M., Blonk, R.W., De Zwart, B.C., Frings-Dresen, M.H., Broersen, J.P., 2002. Job stress, fatigue, and job dissatisfaction in Dutch lorry drivers: towards an

occupation speciﬁc model of job demands and control. Occup. Environ. Med. 59, 356–361.

De Croon, E.M., Sluiter, J.K., Blonk, R.W., Broersen, J.P., Frings-Dresen, M.H., 2004. Stressful work, psychological job strain, and turnover: a 2-year prospective cohort

study of truck drivers. J. Appl. Psychol. 89, 442–452.

De Cuyper, N., De Witte, H., 2006. The impact of job insecurity and contract type on attitudes, well-being and behavioural reports: a psychological contract per-

spective. J. Occup. Organiz. Psychol. 79, 395–409.

De Neve, J.E., Ward, G., 2017. Happiness at Work (No. CEP Discussion Paper No 1474).

De Vos, J., 2019. Analysing the eﬀect of trip satisfaction on satisfaction with the leisure activity at the destination of the trip, in relationship with life satisfaction.

Transportation 46, 1–23.

De Witte, H.D., 1999. Job insecurity and psychological well-being: Review of the literature and exploration of some unresolved issues. Eur. J. Work Organiz. Psychol.

8, 155–177.

Demerouti, E., Bakker, A.B., Nachreiner, F., Schaufeli, W.B., 2001. The job demands-resources model of burnout. J. Appl. Psychol. 86, 499–512.

Diener, E., 1984. Subjective well-being. Psychol. Bull. 95, 542–575.

Diener, E., Lucas, R.E., Scollon, C.N., 2006. Beyond the hedonic treadmill: Revising the adaptation theory of well-being. Am. Psychol. 61, 305–314.

Diener, E., Suh, E.M., Lucas, R.E., Smith, H.L., 1999. Subjective well-being: Three decades of progress. Psychol. Bull. 125, 276–302.

Eckenrode, J., 1984. Impact of chronic and acute stressors on daily reports of mood. J. Pers. Soc. Psychol. 46, 907.

Ettema, D., Friman, M., Gärling, T., Olsson, L.E., Fujii, S., 2012. How in-vehicle activities aﬀect work commuters’ satisfaction with public transport. J. Transp. Geogr.

24, 215–222.

Fisher, C.D., 2000. Mood and emotions while working: missing pieces of job satisfaction? J. Organiz. Behav. 21, 185–202.

Fisher, C.D., To, M.L., 2012. Using experience sampling methodology in organizational behavior. J. Organiz. Behav. 33, 865–877.

Fournier, P.-S., Lamontagne, S., Gagnon, J., 2012. Interactions between dispatchers and truck drivers in a high turnover context. Relat. Indus./Indus. Relat. 67,

263–282.

Hackman, J.R., Oldham, G.R., 1974. The job diagnostic survey: An instrument for the diagnosis of jobs and the evaluation of job redesign projects. Yale University,

Department of Administrative Sciences, New Haven, CT, US.

Hatami, A., Vosoughi, S., Hosseini, A.F., Ebrahimi, H., 2019. Eﬀect of co-driver on job content and depression of truck drivers. Saf. Health Work 10 (1), 75–79.

Havighurst, R.J., 1963. Successful aging. Processes Aging: Soc. Psychol. Perspect. 1, 299–320.

Headey, B., Veenhoven, R., Wearing, A., 1991. Top-down versus bottom-up theories of subjective well-being. Soc. Indic. Res. 24, 81–100.

Hege, A., Perko, M., Johnson, A., Yu, C.H., Sönmez, S., Apostolopoulos, Y., 2015. Surveying the impact of work hours and schedules on commercial motor vehicle

driver sleep. Saf. Health Work 6, 104–113.

Hennessy, D.A., Wiesenthal, D.L., 1999. Traﬃc congestion, driver stress, and driver aggression. Aggressive Behavior: Oﬃcial. J. Int. Soc. Res. Aggress. 25, 409–423.

Hilal, N., 2008. Unintended eﬀects of deregulation in the European Union: The case of road freight transport. Sociologie du travail 50, e19–e29.

Hobfoll, S.E., 1989. Conservation of resources: A new attempt at conceptualizing stress. Am. Psychol. 44, 513.

I. Wijngaards, et al. Transportation Research Part A 128 (2019) 131–148

146

Howell, R.T., Chenot, D., Hill, G., Howell, C.J., 2011. Momentary happiness: The role of psychological need satisfaction. J. Happiness Stud. 12, 1–15.

Hunter, E.M., Wu, C., 2016. Give me a better break: Choosing workday break activities to maximize resource recovery. J. Appl. Psychol. 101, 302.

Ilies, R., Judge, T.A., 2002. Understanding the dynamic relationships among personality, mood, and job satisfaction: A ﬁeld experience sampling study. Organ. Behav.

Hum. Decis. Process. 89, 1119–1139.

Jahedi, S., Méndez, F., 2014. On the advantages and disadvantages of subjective measures. J. Econ. Behav. Organ. 98, 97–114.

Johnson, J.C., Bristow, D.N., McClure, D.J., Schneider, K.C., 2011. Determinants of job satisfaction among long-distance truck drivers: An interview study in the United

States. Int. J. Manage. 28, 203–217.

Kahneman, D., Deaton, A., 2010. High income improves evaluation of life but not emotional well-being. In: Proceedings of the National Academy of Sciences, pp.

16489–16493.

Kahneman, D., Krueger, A.B., Schkade, D.A., Schwarz, N., Stone, A.A., 2004. A survey method for characterizing daily life experience: The day reconstruction method.

Science. 306, 1776–1780.

Kanazawa, H., Suzuki, M., Onoda, T., Yokozawa, N., 2006. Excess workload and sleep-related symptoms among commercial long-haul truck drivers. Sleep Biol.

Rhythms 4, 121–128.

Kemp, E., Kopp, S.W., Kemp Jr., E.C., 2013. Take this job and shove it: Examining the inﬂuence of role stressors and emotional exhaustion on organizational

commitment and identiﬁcation in professional truck drivers. J. Busin. Logist. 34, 33–45.

Kim, S., Park, Y., Niu, Q., 2017. Micro-break activities at work to recover from daily work demands. J. Organiz. Behav. 38, 28–44.

Kok, A.L., Hans, E.W., Schutten, J.M., 2012. Vehicle routing under time-dependent travel times: the impact of congestion avoidance. Comput. Oper. Res. 39, 910–918.

Korunka, C., Kubicek, B., Schaufeli, W.B., Hoonakker, P., 2009. Work engagement and burnout: Testing the robustness of the Job Demands-Resources model. J.

Positive Psychol. 4, 243–255.

Krueger, A.B., Schkade, D.A., 2008. The reliability of subjective well-being measures. J. Publ. Econ. 92, 1833–1845.

Kühnel, J., Sonnentag, S., Bledow, R., 2012. Resources and time pressure as day-level antecedents of work engagement. J. Occup. Organiz. Psychol. 85, 181–198.

Kuykendall, L., Tay, L., Ng, V., 2015. Leisure engagement and subjective well-being: A meta-analysis. Psychol. Bull. 141, 364–403.

Lancée, S., Veenhoven, R., Burger, M., 2017. Mood during commute in the Netherlands: What way of travel feels best for what kind of people? Transport. Res. Part A:

Policy Pract. 104, 195–208.

Larson, R., Csikszentmihalyi, M., 2014. The experience sampling method. In: Flow and the Foundations of Positive Psychology. Springer, pp. 21–34.

Lee, B.G., Lee, B.L., Chung, W.Y., 2015. Smartwatch-based driver alertness monitoring with wearable motion and physiological sensor. In: Engineering in Medicine and

Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE. IEEE, pp. 6126–6129.

MacCallum, R.C., Zhang, S., Preacher, K.J., Rucker, D.D., 2002. On the practice of dichotomization of quantitative variables. Psychol. Methods 7, 19.

Macias, C., Gold, P.B., Öngür, D., Cohen, B.M., Panch, T., 2015. Are single-item global ratings useful for assessing health status? J. Clin. Psychol. Med. Settings 22,

251–264.

McElroy, J.C., Rodriguez, J.M., Griﬃn, G.C., Morrow, P.C., Wilson, M.G., 1993. Career stage, time spent on the road, and truckload driver attitudes. Transportation

Journal 33, 5–14.

Meijman, T.F., Mulder, G., 1998. Psychological aspects of workload. Handbook of Work and Organizational Psychology. Vol. 2.

Miner, A., Glomb, T., Hulin, C., 2005. Experience sampling mood and its correlates at work. J. Occup. Organiz. Psychol. 78, 171–193.

Morgeson, F.P., Humphrey, S.E., 2006. The Work Design Questionnaire (WDQ): developing and validating a comprehensive measure for assessing job design and the

nature of work. J. Appl. Psychol. 91, 1321.

Morris, E.A., Guerra, E., 2015. Mood and mode: does how we travel aﬀect how we feel? Transportation 42, 25–43.

Morrow, P.C., Crum, M.R., 2004. Antecedents of fatigue, close calls, and crashes among commercial motor-vehicle drivers. J. Saf. Res. 35, 59–69.

Newman, D.B., Tay, L., Diener, E., 2014. Leisure and subjective well-being: A model of psychological mechanisms as mediating factors. J. Happiness Stud. 15,

555–578.

Orris, P., Hartman, D.E., Strauss, P., Anderson, R.J., Collins, J., Knopp, C., Xu, Y., Melius, J., 1997. Stress among package truck drivers. Am. J. Ind. Med. 31, 202–210.

Parker, S.K., Ohly, S., Kanfer, R., Chen, G., Pritchard, R.D., 2008. Designing motivating jobs: An expanded framework for linking work characteristics and motivation.

In: Kanfer, R., Chen, G., Pritchard, R.D. (Eds.), The Organizational Frontiers Series: Vol. 27. Work Motivation: Past, Present, and Future. Routledge/Taylor &

Francis Group, New York, NY, US, pp. 233–284.

Pijpers, R., 2010. International employment agencies and migrant ﬂexiwork in an enlarged European Union. J. Ethnic Migrat. Stud. 36, 1079–1097.

Prockl, G., Teller, C., Kotzab, H., Angell, R., 2017. Antecedents of truck drivers’ job satisfaction and retention proneness. J. Busin. Logist. 38, 184–196.

Rau, B.L., Hyland, M.A.M., 2002. Role conﬂict and ﬂexible work arrangements: The eﬀects on applicant attraction. Pers. Psychol. 55, 111–136.

Rauwald, C., Schmidt, N., 2012. Wanted in Europe: more truck drivers. Wall Street J.

Ritter, K.J., Matthews, R.A., Ford, M.T., Henderson, A.A., 2016. Understanding role stressors and job satisfaction over time using adaptation theory. J. Appl. Psychol.

101, 1655–1669.

Rowden, P., Matthews, G., Watson, B., Biggs, H., 2011. The relative impact of work-related stress, life stress and driving environment stress on driving outcomes.

Accid. Anal. Prev. 43, 1332–1340.

Schaufeli, W.B., Taris, T.W., 2014. A critical review of the job demands-resources model: implications for improving work and health. In: Bauer, G., Hämmig, O. (Eds.),

Bridging Occupational, Organizational and Public Health. Springer, Dordrecht, NL, pp. 43–68.

Schaufeli, W.B., Van Rhenen, W., 2006. Over de rol van positieve en negatieve emoties bij het welbevinden van managers: Een studie met de Job-related Aﬀective

Well-being Scale (JAWS)[About the role of positive and negative emotions in managers’ well-being: A study using the Job-related Aﬀective Well-being Scale

(JAWS)]. Gedrag Organisatie 19, 223–244.

Scherer, K.R., 2005. What are emotions? And how can they be measured? Soc. Sci. Informat. 44, 695–729.

Schulz, S.A., Luthans, K.W., Messersmith, J.G., 2014. Psychological capital: A new tool for driver retention. Int. J. Phys. Distrib. Logist. Manage. 44, 621–634.

Scollon, C.N., Prieto, C.K., Diener, E., 2009. Experience sampling: Promises and pitfalls, strength and weaknesses. In: Diener, E. (Ed.), Assessing well-being. Springer,

Dordrecht, NL, pp. 157–180.

Shattell, M., Apostolopoulos, Y., Sönmez, S., Griﬃn, M., 2010. Occupational stressors and the mental health of truckers. Issues Mental Health Nurs. 31, 561–568.

Shinar, D., Compton, R., 2004. Aggressive driving: an observational study of driver, vehicle, and situational variables. Accid. Anal. Prev. 36, 429–437.

Siegrist, J., Peter, R., 1996. Measuring eﬀort-reward imbalance at work: guidelines. Heinrich Heine University, Düsseldorf, DE.

Silla, I., De Cuyper, N., Gracia, F.J., Peiró, J.M., De Witte, H., 2009. Job insecurity and well-being: Moderation by employability. J. Happiness Stud. 10, 739.

Smith, R.P., 1981. Boredom: A review. Hum. Factors 23, 329–340.

Sonnentag, S., Fritz, C., 2015. Recovery from job stress: The stressor-detachment model as an integrative framework. J. Organiz. Behav. 36, S72–S103.

Staats, U., Lohaus, D., Christmann, A., Woitschek, M., 2017. Fighting against a shortage of truck drivers in logistics: Measures that employers can take to promote

drivers’ work ability and health. Work 1–15.

Stewart, W.F., Ricci, J.A., Chee, E., Hahn, S.R., Morganstein, D., 2003. Cost of lost productive work time among US workers with depression. JAMA 289, 3135–3144.

Suzuki, Y., Crum, M.R., Pautsch, G.R., 2009. Predicting truck driver turnover. Transport. Res. Part E: Logist. Transport. Rev. 45, 538–550.

Tadić, M., Bakker, A.B., Oerlemans, W.G., 2013. Work happiness among teachers: A day reconstruction study on the role of self-concordance. J. Sch. Psychol. 51,

735–750.

Taris, T.W., Schaufeli, W., 2015. Individual well-being and performance at work: A conceptual and theoretical overview. In: Van Veldhoven, M., Peccei, R. (Eds.), Well-

Being and Performance at Work: The Role of Context. Psychology Press, London, UK, pp. 24–43.

Ten Brummelhuis, L.L., Bakker, A.B., 2012. A resource perspective on the work–home interface: The work–home resources model. Am. Psychol. 67, 545.

Thompson, C.A., Prottas, D.J., 2006. Relationships among organizational family support, job autonomy, perceived control, and employee well-being. J. Occup.

Organiz. Psychol. 11, 100–118.

Trougakos, J.P., Hideg, I., Cheng, B.H., Beal, D.J., 2014. Lunch breaks unpacked: The role of autonomy as a moderator of recovery during lunch. Acad. Manag. J. 57,

I. Wijngaards, et al. Transportation Research Part A 128 (2019) 131–148

147

405–421.

Van Droogenbroeck, F., Spruyt, B., 2016. I ain’t gonna make it. Comparing job demands-Resources and attrition intention between senior teachers and senior

employees of six other occupational categories in Flanders. Int. J. Aging Hum. Dev. 83, 128–155.

Van Katwyk, P.T., Fox, S., Spector, P.E., Kelloway, E.K., 2000. Using the Job-Related Aﬀective Well-Being Scale (JAWS) to investigate aﬀective responses to work

stressors. J. Occup. Health Psychol. 5, 219.

Van Vegchel, N., De Jonge, J., Bosma, H., Schaufeli, W., 2005. Reviewing the eﬀort–reward imbalance model: drawing up the balance of 45 empirical studies. Soc. Sci.

Med. 60, 1117–1131.

Van Zenderen, K., Sombekke, E., 2016. Arbeidsmarktrapportage beroepsgoederenvervoer over de weg en logistiek 2016: Instroom en inzetbaarheid.

Van Zenderen, K., Sombekke, E., Van de Pol, G., 2017. Arbeidsmarktrapportage beroepsgoederenvervoer over de weg en logistiek 2017: Instroom en inzetbaarheid.

Veenhoven, R., 2000. The four qualities of life: Ordering concepts and measures of the good life. J. Happiness Stud. 1, 1–39.

Wagenaar, S., 2018. Kansen voor vrachtwagen-chauﬀeurs.

Wanous, J.P., Reichers, A.E., Hudy, M.J., 1997. Overall job satisfaction: how good are single-item measures? J. Appl. Psychol. 82, 247–252.

Weiss, R., 2013. Germany wants more truck drivers. [WWW Document]. URL https://www.bloomberg.com/news/articles/2013-08-29/germany-wants-more-truck-

drivers.

Williams, Z., Garver, M.S., Stephen Taylor, G., 2011. Understanding truck driver need-based segments: creating a strategy for retention. J. Busin. Logist. 32, 194–208.

Xanthopoulou, D., Bakker, A.B., Heuven, E., Demerouti, E., Schaufeli, W.B., 2008. Working in the sky: A diary study on work engagement among ﬂight attendants. J.

Occup. Health Psychol. 13, 345–356.

I. Wijngaards, et al. Transportation Research Part A 128 (2019) 131–148

148

Enhancing Policy Generation with GraphRAG and YouTube Data: A Logistics Case Study

Article

Full-text available

Mar 2025

Graph-based retrieval-augmented generation (GraphRAG) represents an innovative advancement in natural language processing, leveraging the power of large language models (LLMs) for complex tasks such as policy generation. This research presents a GraphRAG model trained on YouTube data containing keywords related to logistics issues to generate policy proposals addressing these challenges. The collected data include both video subtitles and user comments, which are used to fine-tune the GraphRAG model. To evaluate the effectiveness of this approach, the performance of the proposed model is compared to a standard generative pre-trained transformer (GPT) model. The results show that the GraphRAG model outperforms the GPT model in most prompts, highlighting its potential to generate more accurate and contextually relevant policy recommendations. This study not only contributes to the evolving field of LLM-based natural language processing (NLP) applications but also explores new methods for improving model efficiency and scalability in real-world domains like logistics policy making.

Dark Triad and Resilience to Personal Loss: Evidence from the 2023 Türkiye-Syria Earthquakes

Article

Full-text available

Mar 2025
OMEGA-J DEATH DYING

Can narcissism help foster resiliency against adversity? In this study, we used Turkish panel data, to examine whether narcissism can buffer the negative impact of personal loss on change in subjective well-being in the wake of the Türkiye-Syria earthquakes of 2023. Results show that the adaptation to personal loss was stronger for individuals high on narcissism. At the same time, we found no evidence that individuals high on Machiavellianism or psychopathy – other Dark Triad traits - were better able to adapt to personal loss.

Staying in a Stressful Job? The Role of Job Embeddedness for Truck Drivers’ Well-Being and Turnover Intentions

Article

Full-text available

Feb 2025
J BUS PSYCHOL

In this study, we examine the role of job embeddedness in a stressful blue-collar job. Building upon previous research and COR theory, we examined whether being embedded in the organization and community prevents workers from being exhausted and intending to quit, even when work stress (time pressure, situational constraints, and electronic surveillance) is high. Based on a sample of 97 local truck drivers of nine organizations who participated in a survey administered through telephone interviews, the results confirmed the predicted relationship between being embedded in the organization and intentions to quit one’s job. Additionally, we found that being embedded in the community, but not in the organization, moderated the stressor-outcome relationships: Higher time pressure was related to more turnover intentions when drivers were less embedded in the community. However, more situational constraints were related to intentions to quit when drivers were more embedded in the community. Consistent with this finding, these drivers also felt more exhausted when they experienced more situational constraints. Thus, our study demonstrated that the role of community embeddedness varies depending on the stressor. Theoretical and practical implications are discussed.

Risk of crashes among self-employed truck drivers: Prevalence evaluation using fatigue data and machine learning prediction models

Article

Full-text available

Nov 2024
J SAFETY RES

Introduction: Transportation companies have increasingly shifted their workforce from permanent to outsourced roles, a trend that has consequences for self-employed truck drivers. This transition leads to extended working hours, resulting in fatigue and an increased risk of crashes. The present study investigates the factors contributing to fatigue and impairment in truck driving performance while developing a machine learning-based model for predicting the risk of traffic crashes. Method: To achieve this, a comprehensive questionnaire was designed, covering various aspects of the participants' sociodemographic characteristics, health, sleep, and working conditions. The questionnaire was administered to 363 self-employed truck drivers operating in the State of São Paulo, Brazil. Approximately 63% of the participants were smokers, while 17.56% reported drinking alcohol more than four times a week, and also admitted to being involved in at least one crash in the last three years. Fifty percent of the respondents reported consuming drugs (such as amphetamines, marijuana, or cocaine). Results: The surveyed individuals declared driving for approximately 14.62 h (SD = 1.97) before they felt fatigued, with an average of approximately 5.92 h of sleep in the last 24 h (SD = 0.96). Truck drivers unanimously agreed that waiting times for truck loading/unloading significantly impact the duration of their working day and rest time. The study employed eight machine learning algorithms to estimate the likelihood of truck drivers being involved in crashes, achieving accuracy rates ranging between 78% and 85%. Conclusions: These results validated the construction of accurate machine learning-derived models. Practical Applications: These findings can inform policies and practices aimed at enhancing the safety and well-being of self-employed truck drivers and the broader public.

Promoting well-being through happiness at work: a systematic literature review and future research agenda

Article

Full-text available

Jul 2024
MANAGE DECIS

Purpose Our study aims to understand what is known about happiness at work (HAW) in terms of publication, citations, dimensions and characteristics, as well as how knowledge about HAW is generated regarding theoretical frameworks, context and methods. Additionally, it explores future directions for HAW research. Design/methodology/approach This paper conducts a systematic literature review of 56 empirical articles published between 2000 and 2022 to comprehensively explore HAW. It examines publication trends, citation patterns, dimensions, characteristics, theoretical frameworks, contextual factors and research methodologies employed in HAW studies. Findings Our findings suggest that while HAW research has gained momentum, there is still a need for exploration, particularly in developing countries. Various theoretical frameworks such as the job demand-resources model, social exchange theory and broaden-and-build theory are identified, with suggestions for the adoption of less popular theories like the positive emotion, engagement, relationships, meaning and accomplishment (PERMA) model and flow theory for future investigations. The review contributes to workplace happiness literature by offering a comprehensive analysis spanning two decades and provides valuable insights for guiding future research toward exploring factors influencing employee well-being. Originality/value Our article offers a structured analysis of HAW literature, emphasizing the necessity for more extensive research, especially in developing nations. It provides valuable insights into the theories and dimensions associated with HAW, guiding future research and assisting organizations in formulating strategies to enhance employee happiness and overall well-being.

Exploring the challenges faced by Dutch truck drivers in the era of technological advancement

Article

Full-text available

Apr 2024

Introduction Despite their important role in the economy, truck drivers face several challenges, including adapting to advancing technology. The current study investigated the occupational experiences of Dutch truck drivers to detect common patterns. Methods A questionnaire was distributed to professional drivers in order to collect data on public image, traffic safety, work pressure, transport crime, driver shortage, and sector improvements. Results The findings based on 3,708 respondents revealed a general dissatisfaction with the image of the industry and reluctance to recommend the profession. A factor analysis of the questionnaire items identified two primary factors: ‘Work Pressure’, more common among national drivers, and ‘Safety & Security Concerns’, more common among international drivers. A ChatGPT-assisted analysis of textbox comments indicated that vehicle technology received mixed feedback, with praise for safety and fuel-efficiency improvements, but concerns about reliability and intrusiveness. Discussion In conclusion, Dutch professional truck drivers indicate a need for industry improvements. While the work pressure for truck drivers in general may not be high relative to certain other occupational groups, truck drivers appear to face a deficit of support and respect.

Uluslararası Ağır Vasıta Sürücüsü Olma Üzerine Bir Olgubilimi Araştırması

Chapter

Oct 2023

Cut the scrap? the impact of truck age on driver retention, driving safety, and driving productivity

Article

Full-text available

Oct 2024

In many countries, vehicle replacement policies are implemented to reduce the average age of the vehicles on the road. Through these policies, policymakers typically aim to reduce emissions and to stimulate demand for automobiles through vehicle renewal. Not much is known however, about the more detailed operational consequences of vehicle age in truck transportation. In this study, we empirically address this issue by analyzing data obtained from 27 thousand trips made by 916 drivers in 355 unique trucks, over a period of 346 days. Using this data, we test the relationship between truck age and driver retention, productivity, and unsafe driving behavior. Our results demonstrate that truck age significantly impacts driver turnover, with every additional year of truck age relating to an approximate 5% higher risk of leaving the job at a given point in time. Contrarily, drivers with older trucks drove significantly less volatile, which suggests that they drive more safely. The results illustrate that vehicle age influences a broader spectrum of outcome measures than typically considered when assessing the expected impact of replacement policies. Policymakers and trucking companies can use these results to make more informed decisions about vehicle replacement, thereby also addressing the widespread issue of driver shortage.

Conductor de transporte de carga en México

Article

Full-text available

Jun 2024

El objetivo de esta investigación es contrastar las prioridades de las características del perfil laboral del transportista entre conductores (hombres y mujeres) y representantes de empresas en México a través de un método que plantea dos aspectos que consisten de un estudio bibliométrico y un análisis estadístico con el fin de tener compatibilidad de intereses. Se realizó una encuesta a conductores de transporte de carga y representantes de empresas en México dedicadas a este giro. Se realizó ANOVA de una vía y se validó estadísticamente con la prueba de Kruskal-Wallis; y las características consideradas en el perfil se clasificaron en orden descendente según las estadísticas. Los resultados son que las características de estabilidad emocional, permanencia familiar, experiencia previa de conducción y nivel educativo entre conductores y representantes de la empresa son estadísticamente diferentes en sus prioridades. La originalidad de los hallazgos permite implementar mecanismos de gestión para entender y cubrir la demanda de conductores en México, mientras que la limitación fue la muestra tomada por la disponibilidad de conductores y representantes de las empresas.

Job satisfaction and perceived workload in the context of personality typology among subway train drivers

Article

Full-text available

Nov 2023

Job satisfaction is a highly relevant topic across all sectors of the national economy worldwide. A contented employee significantly enhances a company’s performance compared to a dissatisfied one. Simultaneously, a contented employee increases the human capital value of the company, which has a substantial impact on its overall value. However, employee satisfaction is not a static state; it can be influenced by various factors, one of which is perceived workload resulting from the nature of the job. The aim of this article is to investigate whether there is a demonstrable impact of perceived workload on employee satisfaction. This relationship is examined within the context of subway train drivers in the Czech Republic. In exploring this relationship, we also delve into the psychological factor of whether there is a connection between perceived workload and the satisfaction of subway train drivers based on their personality types according to the MBTI test. These dependencies are assessed through correlation analysis using a comprehensive dataset obtained. In the course of the research, data were collected from the entire basic sample of respondents, namely from 654 subway train drivers. The results confirmed the existing influence of perceived workload on employee satisfaction and, concurrently, the influence of perceived workload on employee satisfaction based on the personality typology determined by the MBTI test. These findings are undoubtedly beneficial for the management of transportation companies, particularly those involved in passenger transportation, specifically in the provision of subway services.

Give Me a Better Break: Choosing Workday Break Activities to Maximize Resource Recovery

Article

Full-text available

Aug 2015

Surprisingly little research investigates employee breaks at work, and even less research provides prescriptive suggestions for better workday breaks in terms of when, where, and how break activities are most beneficial. Based on the effort–recovery model and using experience sampling methodology, we examined the characteristics of employee workday breaks with 95 employees across 5 workdays. In addition, we examined resources as a mediator between break characteristics and well-being. Multilevel analysis results indicated that activities that were preferred and earlier in the work shift related to more resource recovery following the break. We also found that resources mediated the influence of preferred break activities and time of break on health symptoms and that resource recovery benefited person-level outcomes of emotional exhaustion, job satisfaction, and organizational citizenship behavior. Finally, break length interacted with the number of breaks per day such that longer breaks and frequent short breaks were associated with more resources than infrequent short breaks.

Analysing the effect of trip satisfaction on satisfaction with the leisure activity at the destination of the trip, in relationship with life satisfaction

Article

Full-text available

Jun 2019
TRANSPORTATION

Jonas De Vos

Previous studies have indicated that positive (or negative) experiences of activity episodes are likely to correlate with positive (or negative) evaluations of a persons’ life. An accumulation of short-term experiences can positively or negatively affect life satisfaction, while it is also plausible that this long-term satisfaction affects emotions experienced during an activity. In this study we analyse how (1) satisfaction with a trip towards the most recent leisure activity, (2) satisfaction with that activity and (3) life satisfaction are correlated with each other, by executing a structural equation modelling approach. Results of this study—using data from a cross-sectional survey of 1213 respondents residing in the city of Ghent (Belgium)—suggest that life satisfaction has an important effect on both travel satisfaction and activity satisfaction. On the other hand, there seems to be a stronger effect from activity satisfaction on life satisfaction than from travel satisfaction on life satisfaction, suggesting that travel satisfaction mainly has an indirect effect on life satisfaction, through participation in—and satisfaction with—leisure activities.

Effect of Co-Driver on Job Content and Depression of Truck Drivers

Article

Full-text available

Jun 2018

Background Since the presence of a co-driver can be considered as a companion, partner, or friend for a driver through eliminating driver's loneliness, it plays a significant role in health and safety of drivers. The objective of this study was to investigate the effect of co-drivers on depression and occupational stress on male truck drivers. Methods This study was an interventional case-control study. Seventy truck drivers were selected and divided into two groups: case (33 truck drivers with co-drivers) and control (37 truck drivers without co-drivers). Two Goldberg depression inventories (for evaluating driver's depression) and the Karasek job content questionnaire (for evaluating driver's job stress) were used to collect data which were completed by interview. Results The results showed that job content values for the case group were higher in all dimensions except job nature. The comparison of the percentages showed significant difference between two groups. Depression rate in drivers with co-driver is truly less than depression rate in drivers without co-driver. There was significant positive relationship between dimensions of job content and depression rate. Conclusion According to the results of this study, it can be claimed that a co-driver decreases stress and loneliness of drivers, as well as increases work performance and job satisfaction, and, in turn, leads to a decrease in job-related depression.

Mood during commute in the Netherlands

Article

Full-text available

May 2017
TRANSPORT RES A-POL

Question How happy we are depends partly on how we live our life and part of our way of life is the commute between home and work. In this context we are faced with the question of how much time spent on commuting is optimal happiness wise, and what means of transportation. Since our personal experience is limited, it is helpful to draw on the experience of other people, of people like us in particular. Earlier research Several cross-sectional studies have found lower subjective wellbeing among long-distance commuters and among users of public transportation. Yet these differences could be due to selection effects, such as unhappy people ending up more often in distant jobs without having a car. Still another limitation is that earlier research has focused on the average effect of commuting, rather than specifying what is optimal for whom. Method Data of the Dutch ‘GeluksWijzer’ (Happiness Indicator) study were analyzed, in the context of which 5000 participants recorded both what they had done in the previous day and how happy they had felt during these activities. This data allows comparison between how the same person feels at home and during their commute. The number of participants is large enough to allow a split-up between different kinds of people, in particular among the many well-educated women who participated in this study. Results People feel typically less well when commuting than at home, and this negative difference is largest when commuting using public transportation and smallest when commuting by bike. It is not per se the commuting time that depresses mood, but specific combinations of commuting time and commuting mode. Increasing commuting times can even lead to an uplift of mood when the commute is by bike or foot. Split-up by different kinds of people shows considerable differences, especially with regard to the different modes and company when travelling. Optimal ways of commuting for different kinds of people are presented in a summary table, from which individuals can read what will fit them best. The differences illustrate that research focusing on averages will not help individuals to make a more informed choice with respect to commuting mode.

Mood and emotions while working: missing pieces of job satisfaction?

Article

Mar 2000
J ORGAN BEHAV

Cynthia D. Fisher

Job satisfaction is often described as an affective response to one's job, but is usually measured largely as a cognitive evaluation of job features. This paper explores several hypothesized relationships between real time affect while working and standard measures of job satisfaction. Experience sampling methodology was used to obtain up to 50 reports of immediate mood and emotions from 121 employed persons over a two week period. As expected, real time affect is related to overall satisfaction but is not identical to satisfaction. Moment to moment affect is more strongly related to a faces measure of satisfaction than to more verbal measures of satisfaction. Positive and negative emotions both make unique contributions to predicting overall satisfaction, and affect accounts for variance in overall satisfaction above and beyond facet satisfactions. Frequency of net positive emotion is a stronger predictor of overall satisfaction than is intensity of positive emotion. It is concluded that affect while working is a missing piece of overall job attitude, as well as a phenomenon worthy of investigation in its own right. Implications for further research and for improving the conceptualization and measurement of job satisfaction are discussed. Copyright © 2000 John Wiley & Sons, Ltd.

Are You Happy While You Work?

Article

Jan 2016

Using a new data source permitting individuals to record their wellbeing via a smartphone, we explore within-person variance in individuals' wellbeing measured momentarily at random points in time. We find paid work is ranked lower than any of the other 39 activities individuals can report engaging in, with the exception of being sick in bed. Precisely how unhappy one is while working varies significantly with where you work; whether you are combining work with other activities; whether you are alone or with others; and the time of day or night you are working.

Fighting against a shortage of truck drivers in logistics: Measures that employers can take to promote drivers’ work ability and health

Article

Oct 2017
Work

Background: For several years, the transportation industry has been concerned about a severe shortage of professional truck drivers. Studies investigating the reasons found that poor working conditions and stresses and strains resulting from physiological and psychological job demands have had a negative impact on drivers' health and ability to work. Nevertheless, until now, most employers have refrained from offering measures to support the work ability and well-being of drivers, mainly due to financial pressures in the industry. Objective: The present study was aimed at designing adequate and affordable measures to support drivers' health. Method: With reference to the Work Ability Index and the house of work ability (Ilmarinen & Tuomi, 2004), 56 truck drivers participated in guided interviews about their working conditions and health-related problems as well as their attitudes, experiences, and desires with respect to being offered supportive measures by their employers. Results: The measures derived are specific and realizable and expected to be widely accepted by professional drivers. They are designed to elicit a positive attitude in the drivers toward exercising and to help them overcome related psychological barriers. Conclusion: The implementation of the recommended measures can be expected to support drivers' work ability and help reduce the frictional costs of their employers.

Health and wellness of long-haul truck and bus drivers: A systematic literature review and directions for future research

Article

Jul 2017

Objectives The purpose was to provide a comprehensive review of the literature related to the health and wellness of truck (long and short-haul) and bus drivers in Canada and the USA. Methods The following databases were searched: Medline (Pubmed), Cumulative Index to Nursing and Allied Health Literature (CINAHL), PsycINFO and Scopus, as well as the grey literature using a combination of key words (e.g. truck or bus drivers, accidents, health, wellness, road safety). Only English articles published between 2000 and 2016 were included. The search yielded 33 peer reviewed articles and 9 reports relevant to the health and wellness of CMV drivers. Results The findings show that long-haul truck drivers have multiple risk factors (i.e., smoking, obesity, hypertension, poor diet, lack of exercise, stress and sleep) that can lead to various medical conditions (i.e., cardiovascular disorders, diabetes) and adverse events (i.e., crashes). Several medical conditions including sleep apnea (and fatigue more generally), obesity and cardiovascular disorders are all associated with increased crash risk. There was little information on bus drivers or short-haul truck drivers, however, the available information would suggest they are also exposed to negative work and driver environments leading to the development of risk factors associated with medical conditions. Conclusions Further research is needed to characterize the work environment and lifestyle practices (particularly sleep, smoking, diet and exercise) of truck and bus drivers to understand the interactions between various risk factors and health outcomes. Obtaining baseline information, including national prevalence rates of health issues, is vitally important for public health, regulatory organizations, and industry to coordinate prevention efforts.

An analysis of health promotion materials for Dutch truck drivers: Off target and too complex?

Article

Feb 2017

Background: Despite various health promotion initiatives, unfavorable figures regarding Dutch truck drivers' eating behaviors, exercise behaviors, and absenteeism have not improved. Objective: The aim was to obtain a better understanding of the low level of effectiveness of current health interventions for Dutch truck drivers by examining to what extent these are tailored to the target group's particular mindset (focus of content) and health literacy skills (presentation of content). Methods: The article analyzes 21 health promotion materials for Dutch truck drivers using a two-step approach: (a) an analysis of the materials' focus, guided by the Health Action Process Approach; and (b) an argumentation analysis, guided by pragma-dialectics. Results: The corpus analysis revealed: (a) a predominant focus on the motivation phase; and (b) in line with the aim of motivating the target group, a consistent use of pragmatic arguments, which were typically presented in an implicit way. Conclusions: The results indicate that existing health promotion materials for Dutch truck drivers are not sufficiently tailored to the target group's mindset and health literacy skills. Recommendations are offered to develop more tailored/effective health interventions targeting this high-risk, underserved occupational group.

Individual well-being and performance at work: A conceptual and theoretical overview

Article

Jan 2015

Steering towards happiness: An experience sampling study on the determinants of happiness of truck drivers

Abstract and Figures

Recommended publications

Research on Impact Property of 40Cr Steel under Different Tempering Conditions

Safety Behaviour of Heavy Truck Drivers in International Transport

Health and Wellness Programs for Commercial Motor-Vehicle Drivers

Truck Drivers Use of the Internet: A Mobile Health Lifeline

Health promotion in the trucking setting: Understanding Dutch truck drivers' road to healthy lifesty...