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The Impact of Stressful Commuting on Subsequent Performance During Low Versus High Frustration Tasks

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Abstract

The present study examined the possible spillover effect of driver stress on performance in the work/school environment. Following their regular commute to school, 74 participants completed the State Driver Stress questionnaire in the parking lot and then reported to the laboratory. They were then randomly assigned (unknowingly) to either a “difficult” or “easy” task group and given a series of visual motor tasks which involved geometric shapes that were to be traced with a pencil. They were told to complete as many different puzzles in five minutes as possible. However, the “difficult” group was actually given an insolvable puzzle which they would spend the entire five minutes attempting to solve (to create task frustration). The “easy” group were given simple solvable puzzles (and could complete several puzzles in five minutes). Afterwards, all participants were given the same “moderate” level puzzle to complete and told that their performance was being recorded on that single puzzle. The time needed to complete the moderate task was predicted by state drivers stress X assigned task and state driver stress X mood after the first task. Those who report a more stressful commute took longer to complete the second task but predominantly when experiencing the difficult task prior to testing. Similarly, a more stressful commute led to greater time to complete the task among those with a more negative mood prior to the moderate task. This confirms that a stressful commute can spill over to impact performance in subsequent environments.
In: Advances in Psychology Research, Volume 54ISBN 978-1-60456-129-6
Editor: Alexandra M. Columbus, pp. 263-272 © 2007 Nova Science Publishers, Inc.
Chapt e r 9
The Impact of Str ess f u l Com m ut i n g on
Subs e q ue n t Perfor m a n ce During Low
Versu s High Frus t r a ti o n Tasks
Dwight A. Hennessy* and Robert Jakubowski**
*Department of Psychology
Buffalo State College, Buffalo, NY
**Department of Psychology
Colorado State University, Fort Collins, CO
Ab st r a c t
The present study examined the possible spillover effect of driver stress on
performance in the work/school environment. Following their regular commute to
school, 74 participants completed the State Driver Stress questionnaire in the parking
lot and then reported to the laboratory. They were then randomly assigned
(unknowingly) to either a “difficult” or “easy” task group and given a series of visual
motor tasks which involved geometric shapes that were to be traced with a pencil. They
were told to complete as many different puzzles in five minutes as possible. However,
the “difficult” group was actually given an insolvable puzzle which they would spend
the entire five minutes attempting to solve (to create task frustration). The “easy” group
were given simple solvable puzzles (and could complete several puzzles in five
minutes). Afterwards, all participants were given the same “moderate” level puzzle to
complete and told that their performance was being recorded on that single puzzle. The
time needed to complete the moderate task was predicted by state drivers stress X
assigned task and state driver stress X mood after the first task. Those who report a
more stressful commute took longer to complete the second task but predominantly
when experiencing the difficult task prior to testing. Similarly, a more stressful
commute led to greater time to complete the task among those with a more negative
mood prior to the moderate task. This confirms that a stressful commute can spill over
to impact performance in subsequent environments.
In tr o d u c t i o n
Dwight A. Hennessy and Robert Jakubowski
Research has previously established that unresolved stressors or hassles from one domain
can persist, even when no longer in conscious awareness, and add to the pressures of
subsequent life contexts (Kohn and Macdonald, 1992; Lazarus, 1981; Taylor, 1991). These
“after-effects” can continue to do psychological and physiological damage, and may intensify
over time as they accumulate with other previously unresolved stress reactions (Glass and
Singer, 1972). This “spillover effect has been found between the home and workplace
environments. For example, workplace stress can lead to elevated stress, conflict at home, and
social withdrawal (Koizumi, Sugawara, and Kitamura, 2001; Repetti, 1989; Thompson, Kirk-
Brown, and Brown, 2001) while stress from the home has been found to magnify job overload,
distress, occupational stress, and arguments/conflicts in the workplace (Barnett, Marshall, and
Pleck, 1992; Behson, 2002; Bolger, DeLongis, Kessler, and Wethington, 1989; Jenkins, 1996;
Jones and Fletcher, 1996; Leiter and Durup, 1996).
Notwithstanding the importance of the home environment in an individual’s total life
space, there is comparatively little information on the potential impact of other domains on
workplace reactions, such as daily commuting. Driver stress is an important social issue
because it has been linked to a number of negative outcomes, such as increased blood pressure
and heart rate (Novaco, Stokols, Campbell, and Stokols, 1979; Schaeffer, Street, Singer, and
Baum, 1988), negative mood (Gulian, Debney, Glendon, Davies and Matthews, 1989a),
emotional arousal (Hennessy and Wiesenthal, 1997), poor concentration levels (Matthews,
Dorn, and Glondon, 1991), driving errors, lapses, violations (Westerman and Haigney, 2000),
traffic offences (Matthews, Tsuda, Xin, and Ozeki, 1999), traffic collisions (Legree, Heffner,
Psotka, Martin, and Medsker, 2003; Selzer and Vinokur, 1974), and aggression (Hennessy and
Wiesenthal, 1999). Most regular commuters report facing numerous frustrations or irritations
that can lead to driver stress (Gulian, Matthews, Glendon, Davies, and Debney, 1989b;
Novaco, Stokols, and Milanesi, 1990). In fact, a recent workplace survey found that traffic
congestion was the most commonly reported source of daily stress among U.K. employees
(BBC News, 2000). This issue is particularly pressing considering that 60% of those over the
age of 16 in the United States are part of the labor force, of which 88% commute to work via
personal vehicle (U.S. Census, 2000).
Recent research has begun to examine the impact of driver stress on attitudes and
behaviors in ensuing environments. Schaeffer et al. (1988) found elevated systolic and diastolic
blood pressure and greater errors on proof reading task following a more “impeded” commute.
White and Rotton (1998) assigned participants to drive their vehicle between two designated
locations or ride a bus the same route and compared both groups to a non-driving control. They
found lower frustration tolerance (giving up more quickly) when completing unsolvable puzzles
among those that drove or rode the bus compared to the control. Similarly, Van Rooy (2006)
measured mood while participants drove a predetermined route and examined its impact on
subsequent employee evaluations. He found more negative evaluations following longer
commuting times and higher traffic congestion. Finally, Hennessy (in press) found elevated
workplace aggression (obstructionism and workplace hostility) following a more stressful
commute leading to that work day. Taken together, these studies confirm that workplace
attitudes, behavior, and performance can be influenced by a previous commute. However, the
arousal and affect instigated by a stressful commute appear to dissipate quickly despite the fact
that behavioral effects are present following stressful commutes (Hennessy, in press; Van
Rooy, 2006; Van Rooy and Rotton, 2003; White and Rotton, 1998). Hennessy (in press)
argued that unresolved stress from the traffic environment likely continues to influence and
intensify subsequent workplace reactions to potential stressors, even though their immediate
The Impact of Stressful Commuting on Subsequent Performance…
emotional effects may become indistinguishable in conscious awareness. As new tasks demand
attention in the non-driving environment, the affective arousal of traffic stressors may be
supplanted by the more immediate demands within the subsequent environment. In this respect,
the nature of succeeding tasks likely influences the outcome of spillover effects. Hence, the
present study was designed to examine the interactive influence of driver stress and subsequent
task demands on performance. State driver stress was expected to decrease performance on
subsequent tasks, but would be exaggerated by task difficulty (state driver stress X task
difficulty) or subsequent mood (state driver stress X mood).
M et h o d
Participants
Participants (50 women, 24 men) were recruited through course advertisement and word of
mouth. Compensation included 1% extra credit and entry into a draw to win a $25 gift
certificate. Their ages ranged from 18 41 years with an average of 22.53 (SD = 4.43).
Women ranged from 18 – 36 years with an average of 21.75 (SD = 2.57) and men ranged from
18 41 years with an average of 23.53 (SD = 5.21). Their grade distribution included 38
seniors (women = 20, men = 18), 14 juniors (women = 6, men = 8), 6 sophomores (women = 5,
men = 1), and 16 freshman (women = 12, men = 4). Their daily commute to school ranged
from 7 60 minutes with an average of 22.19 (SD = 9.78). Women ranged from 7 60
minutes with an average of 22.73 (SD = 11.08) and men ranged from 10 – 40 minutes with an
average of 21.50 (SD = 7.92).
Measures
State Driver Stress was measured using a variation of the Driving Behaviour Inventory—
General (DBI-Gen, Gulian et al., 1989b). The DBI-Gen consists of 16 items designed to
measure a susceptibility to stress while driving. Each item represents a situation that is often
experienced by highly stressed drivers. Consistent with Hennessy and Wiesenthal (1997), items
were revised to represent state experiences from their current commute. Participants were
asked to rate how much they agree with each item, using a 0 5 Likert scale (0 = “totally
disagree” to 5 = “totally agree”). Hennessy and Wiesenthal (1997) have found this revisions to
show high internal reliability in both low (alpha = .92) and high (alpha = .90) congestion.
Scoring consisted of the mean of the 16 items, with higher scores indicating greater state driver
stress.
Ten items from the Stress Arousal Checklist (Mackay, Cox, Burrows, and Lazzerini,
1978) were used in order to measure mood after the first assigned task. Half of the items
indicated positive mood (relaxed, contented, peaceful, comfortable, and calm) and the other
half indicated negative mood (tense, bothered, nervous, uneasy, and distressed). All items were
reworded in the present tense in order to represent “state” measures of mood (for example, "I
am feeling tense" rather than simply "Tense"). All responses were placed on a 0-5 Likert scale
(0 = “strongly disagree” to 5 = “strongly agree”) indicating the extent each item pertained to
their current state. For scoring purposes, the positive mood items were reverse keyed. Scoring
consisted of calculating the mean of the 10 responses.
9
Dwight A. Hennessy and Robert Jakubowski
Procedure
The researchers initially met with participants to gain informed consent and to fully explain
the research procedure. During this meeting, participants were provided a sealed envelope
containing the state driver stress questions which were completed when they arrived on campus
on the day of their target commute (in their vehicles immediately after parking). Immediately
following its completion, they reported to the laboratory to complete the second portion of the
research. They were randomly assigned (unknowingly) to either an easy task group or a
difficult task group. For both groups, the participant was seated at a table with a series of 5X4
inch open faced booklets. Each booklet was comprised of 25 sheets with the same geometric
figure on each sheet, while each booklet represented a unique geometric figure. They were
informed that their task was to begin with the first booklet and trace over the geometric figure
completely without lifting their pen or crossing a line (all participants were initially shown an
example of a successful trace and what would constitute an error). If they made an error, they
were to begin again on the next page of that booklet and continue that process until they
completed that shape successfully. When a shape was completed, they would then move on to
another booklet (i.e. a new shape) and continue the process until their five minute time limit
was up. Their stated assignment was to successfully complete as many shapes as possible in the
allotted time.
For those in the easy task group, the shapes were all solvable with minimal effort.
However, for those in the difficult group, the shapes were all unsolvable. As a result, they
would remain on the first shape for the entire duration of the allotted time. This procedure was
used to create a task frustration due to the inability to complete an assigned task. Following the
five minute time limit, all participants were asked to complete a measure of mood. They were
then given a final shape and asked to complete it as quickly as possible within a five minute
time span. This shape was a moderate level of difficulty (which could be completed) and was
the same for all participants. The purpose of this last moderately difficult shape was to provide
a common task during which performance could be measured (the difficult group could not
complete their initial shape, so performance was not comparable to the easy group). The time to
complete the task was taken as the measure of performance. While the number of shapes
attempted could be used as an indication of performance, they were not considered in the
present study because it was too heavily confounded with the fact that those in the difficult
group would be more prone to give up, give less effort, or show greater helplessness which
would lead to fewer attempts and the appearance of fewer errors. This is based on White and
Rotton (1998) who found that participants tended to show greater frustration tolerance by
giving up on insolvable tasks. Finally, participants were debriefed about the true nature of the
study and were then asked not to divulge the nature of the deception to other potential
participants in an attempt to minimize diffusion of treatment.
Resul t s
Both state driver stress and mood after task questionnaire showed high internal reliability
(alphas = .93 and .94 respectively). The average level of state drivers stress was 2.49 (SD = .
67) and the average mood after the first task was 3.95 (SD = 2.20). The time to complete the
The Impact of Stressful Commuting on Subsequent Performance…
moderate task successfully was taken as the dependent measures of performance. The average
time to complete the moderate task was 161.53 seconds (SD = 96.41). A partial factorial
ANOVA was calculated using gender (women vs. men), assigned task (easy vs. difficult),
mood after the first task (median split low vs. high), and state driver stress (median split low
vs. high) as IV, along with all 2 way interactions. As can be seen in Table 1, time to complete
the second moderate task was predicted by the interactions of state drivers stress X assigned
task and state driver stress X mood after the first task. Specifically, those who reported a more
stressful commute took longer to complete the second task but predominantly when
subsequently experiencing difficult tasks (see Figure 1). Similarly, a more stressful commute
led to greater time to complete the second task among those with a more negative mood
following their initial task (see Figure 2).
Table 1. ANOVA Model for Simulator Lane Violations
df MS F Eta2
Gender 1 328.92 0.04 .001
Task Difficulty 1 28052.32 3.75 .053
Mood After Task 1 124661.86 16.66* .199
State Driver Stress 1 27064.76 3.61 .051
Gender X Task 1 3062.94 0.40 .006
Gender X Mood 1 4248.89 0.56 .008
Gender X Driver Stress 1 1264.49 0.16 .003
Task X Mood 1 7232.04 0.96 .014
Task X Driver Stress 1 41460.41 5.54* .076
Mood X Driver Stress 1 64421.52 8.61* .114
Error 67 7480.66
Note: * p<.05.
11
Dwight A. Hennessy and Robert Jakubowski
Figure 1. Performance on Moderate Task As A Function of State Driver Stress X Task Difficulty.
Figure 2. Performance on Moderate Task As A Function of State Driver Stress X Mood After Task.
Conc l usi o n
The findings in this chapter demonstrate that spillover does occur from the traffic
environment to school or work environments. However, the scope of this spillover is likely
dependent on the nature of the subsequent work itself. Stress experienced while driving did
decrease performance on a subsequent task, but this was moderated by the difficulty of tasks
performed just prior to testing. This confirms the assertion by Hennessy (in press) that the
emotional arousal experienced during a stressful commute likely persists unconsciously and
exacerbates the psychological and cognitive demands of stressors in subsequent work/school
environments. It is this accumulation of hassles over time that is the crux of the spillover effect.
While its impact might not be direct or evident, driver stress could potentially decrease
cognitive energy and perception of ensuing tasks that are, in their own rights, demanding on the
individual. Non-job hassles have been previously found to increase subsequent exhaustion and
decrease effort (Fritz and Sonnentag, 2005, 2006), increase cognitive burnout (Kohan and
Mazmanian, 2003), decrease performance on memory tasks that require higher order
processing and executive control (vonDras, Powless, Olson, Wheeler, and Snudden, 2005) and
to slow short term memory processing (Brand, Hanson, and Godaert, 2000). In this respect,
driver stress may influence the cognitive processes involved in effort, attention, information
processing, and appraisal that are essential to effective coping in the post-driving environment
(see Matthews, 2002; Matthews et al., 1991). It is possible that the efforts to deal with traffic
stress may exhaust coping resources needed to deal with subsequent task related stressors in the
work/school environment, while simultaneously priming individuals to construe such
constraints themselves as more demanding and stressful.
Post commute performance was also slower among those who experienced a more negative
mood state during testing regardless of the difficulty of the tasks. Numerous s tudies have
The Impact of Stressful Commuting on Subsequent Performance…
shown daily stress to be associated with a more negative mood state (Affleck, Tennen, Urrows,
and Higgins, 1994; Almeida, Wethington, and Kessler, 2002; DeLongis, Folkman, and
Lazarus, 1988; van Eck, Nicolson, and Berkhof 1998), which can potentially increase anxiety
and arousal (Jones and Fletcher 1996; van Eck et al., 1998). Further, a more intense negative
mood can lead to increased adverse reactions to daily hassles (Ciarrochi and Anderson, 2002)
while negative mood states can influences how information is evaluated in a given
environment, particularly by reducing processing capacity (Ellis and Ashbrook, 1988; Forgas,
1998; Yook and Albert, 1999). Hence, it is possible that participants that experienced negative
mood during their work tasks in the present study may have experienced the unresolved hassles
more intensely, ultimately heightening the impact of the task by decreasing attention resource,
cognitive focus, and persistence on the task. It is important to highlight that, for some, even
simple tasks may be perceived as stressful or anxiety provoking, which may have increased
evaluation apprehension, narrowed the focus of attention, or decreased problem solving in the
present study. This has significant implications for work and school environments in that a
large portion of these populations rely on roadway commuting and these contexts contain a
wide variety of challenges that can decrease overall mood states (e.g. conflicts with others,
workload demands, deadlines, long hours). Future research should look to determine ways for
individuals and corporations to reduce the stressful experience of commuting prior to the onset
of work or school.
Fut u re Consi d er at io ns
While the present study did demonstrate that the spillover of hassles from the traffic
work/school environment is dependent on individual and task variables in the subsequent
environment, performance was only measured on a single time focused task. The task that was
used stressed time based outcomes with no emphasis on accuracy or minimizing errors, which
prohibited the analysis of error based outcomes due to the fact that it was impossible to prevent
a speed-accuracy trade off and that individuals given unsolvable tasks in the past typically
show reduced effort and frustration tolerance (giving up on tasks more easily) (see White and
Rotton, 1998). Despite the fact that speed of performance is a crucial aspect to many tasks and
occupations, future research needs to examine more accuracy based performance.
It is also important to note that performance was measured at a single time period
following the morning commute by no more than one hour. Performance is a cumulative
process that depends on a number of factors, including arousal, motivation, and skill. Outcomes
from one single task in a five minute time span may be somewhat restricted and not indicative
of more global performance or ability. Further, restricting testing so close to the morning
commute may limit generalizability to the extent that driver stress may be more “fresh”
compared to later in the day. Similarly, state sources of driver stress are often transient and
reactions can vary temporally and, as such, may not be tapped within a single commute.
However, the present study did find differences in performance in expected directions despite
the fact that participants did not know which interactive commute or task difficulty groups they
represented. None the less, future research may benefit from a more prolonged measurement
period in which participants are tested on more than one occasion across the span of the entire
work day.
There are also numerous personal and organizational factors that may impact the
experience of driver stress, workplace performance, and the experience of hassles that were not
13
Dwight A. Hennessy and Robert Jakubowski
included in the present study. Future research should examine a broader mix of potential
stressors from within the traffic context (time urgency, perceived control, traffic conditions),
workplace (e.g. differences in job type, conflict, time pressures), the home environment (family
conflict, time schedules, social support), and the individual (trait stress susceptibility, life
events, rumination).
Finally, there is evidence that the work – home spillover process is different than the home
work process, and that spillover can also lead to positive outcomes (Grzywacz and Marks,
2000). Adding the traffic environment as in intermediate step between home and work, should
necessitate the need for greater understanding of the whole process including factors from all
three contexts and in both morning and evening commutes. Hence, future research should
investigate how interpretations and actions in the traffic environment are impacted by positive
and negative experiences, and how this environment alters thoughts, feelings, and actions in the
subsequent home domain.
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... When stress related to the commute to work is followed by a negative experience (task frustration), performance slowed down significantly in a group of 74 participants. The State Driver Stress measure was used (Hennessy & Jakubowski, 2007). Stress during way to work has shown to cause workplace aggression among males (Hennessy, 2008). ...
Thesis
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Past studies in environmental psychology (Mehrabian & Russell, 1974) and retailing (Donovan & Rossiter, 1982) have suggested a causal relationship between the physical and social characteristics of retail environments and the behaviour and evaluation of consumers. Environmental cues emitted by elements of the servicescape (“the objective physical factors that can be controlled by the firm to enhance (or constrain) employee and customer actions” Bitner, 1992, p. 65) influence emotional states which, in turn, can lead to changes in the behaviour. More pleasant emotions make people “approach” the environment (e.g., stay for longer, interact with others), less pleasant emotions lead to “avoidance” (e.g., stay for a shorter time, less likely to interact with others, etc.). This dissertation investigates if this relationship applies to airport retail environments. First, the author conducted a very detailed review of the relevant literature followed by a qualitative study involving semi structured interviews with senior managers representing ten of the largest 50 European airports. Grounded in the gained scientific as well as managerial insight, a conceptual model is proposed which assumes that physical elements of an airport store (eg., lighting, design, architecture, temperature), the behaviour and appearance of staff, as well as perceived time pressure influence the emotions of passengers as they are browsing in the shop. Focusing on emotions pleasure and stress, the author hypothises that both determine the likelihood that customers ‘approach’ or ‘avoid’ the store, and their opinion of the shopping experience and the shop itself on exit. In order to test the model, hypotheses were developed and data obtained by conducting a large scale survey near the main duty free store of Budapest airport in 2013. Using the quota sampling method, a convenient sample (N=948) was taken by intercepting departing passengers when entering the airport, and then conducting face-to-face interviews at store exit. Emotions were measured using a relatively recent instrument, which employs hold-out pictures showing facial expressions of natural basic emotional states (happiness, fear, surprise, anger) instead of verbal scales, in order to reduce an expected bias due to language and cultural differences.
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