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Role Overload and Underload in Relation to Occupational Stress and Health

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Most research on work stress has focused on the concept of role overload, or too many job demands, as opposed to role underload, or too few job demands. Therefore, the present study was carried out to determine if different levels of job demands may be differentially associated with job-related stress as well as with various health outcomes. Specifically, in the present study, we used data from the Eurobarometer Survey on Working Conditions (n = 16,000) to investigate whether role overload and underload resulted in different negative health outcomes. We also examined to see whether different job characteristics, such as having control of your work schedule, differentially buffered the effects of role demands on work stress for workers experiencing role overload, role underload, or neither (i.e. matched). Results indicated that respondents reporting role overload had the highest level of all 16 negative health outcomes, with the role underload group being the next highest and the matched group being the lowest. In addition, a series of hierarchical logistic regression analyses showed that the control variable of time buffered stress for the role matched and role underload groups, while both time and autonomy buffered stress for the role overload group. The implications of the results for both theory and practice are discussed. Copyright © 2009 John Wiley & Sons, Ltd.
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Stress and Health 26: 99–111 (2010) © 2009 John Wiley & Sons, Ltd. 99
RESEARCH ARTICLE
Role Overload and Underload in Relation to
Occupational Stress and Health
Kenneth S. Shultz1*, Mo Wang2 & Deborah A. Olson3
1Department of Psychology, California State University, San Bernardino, CA, USA
2Department of Psychology, University of Maryland, College Park, MD, USA
3Department of Business Management and Leadership, University of La Verne, La Verne, CA, USA
Summary
Most research on work stress has focused on the concept of role overload, or too many job demands, as opposed
to role underload, or too few job demands. Therefore, the present study was carried out to determine if different
levels of job demands may be differentially associated with job-related stress as well as with various health outcomes.
Specifi cally, in the present study, we used data from the Eurobarometer Survey on Working Conditions (n = 16,000)
to investigate whether role overload and underload resulted in different negative health outcomes. We also exam-
ined to see whether different job characteristics, such as having control of your work schedule, differentially buffered
the effects of role demands on work stress for workers experiencing role overload, role underload, or neither (i.e.
matched). Results indicated that respondents reporting role overload had the highest level of all 16 negative health
outcomes, with the role underload group being the next highest and the matched group being the lowest. In addi-
tion, a series of hierarchical logistic regression analyses showed that the control variable of time buffered stress for
the role matched and role underload groups, while both time and autonomy buffered stress for the role overload
group. The implications of the results for both theory and practice are discussed. Copyright © 2009 John Wiley &
Sons, Ltd.
Received 23 March 2009; Accepted 22 June 2009; Revised 1 July 2009
Keywords
job stress; role overload; occupational health
*Correspondence
Kenneth S. Shultz, Department of Psychology, California State University, San Bernardino, 5500 University Parkway, San Bernardino, CA
92407, USA.
Email: kshultz@csusb.edu
Published online 6 July 2009 in Wiley InterScience (www.interscience.wiley.com) DOI: 10.1002/smi.1268
studies using medical records and occupational classifi -
cation data, has demonstrated that a high level of work
demand combined with a lack of control over one’s
work environment is related to negative health out-
comes (e.g. an elevated risk of coronary heart disease;
Ganster, Fox, & Dwyer, 2001; Ganster & Murphy, 2000;
Wegman & McGee, 2004). However, as de Lange, Taris,
Kompier, Houtman and Bongers (2003) and Taris
A prominent way in which work may affect the health
and well-being of workers is explained by the job
demand–control model of work stress (Karasek, 1979;
1989). The demand–control model purports that work
placing high demands on workers while allowing them
little personal control will lead to adverse health conse-
quences. In the last decade, research at the individual
level of analysis, as well as large-scale epidemiological
Role Overload and Underload K. S. Shultz, M. Wang and D. A. Olson
100 Stress and Health 26: 99–111 (2010) © 2009 John Wiley & Sons, Ltd.
(2006) recently pointed out, most studies support the
main effects of demand and control, but not the interac-
tion between demand and control, on stress outcomes.
This may be due to the existence of potential modera-
tors, such as individual characteristics (Shultz, Wang,
Crimmins & Fisher, In Press; Meier, Semmer, Elfering,
& Jacobshagen, 2008) and situational factors (Van der
Doef & Maes, 1999). So far, few studies have explored
these possible boundary conditions and more research
on this topic is certainly warranted. Thus, in the present
study, we examined the situational factor of role over-
load versus role underload as a possible moderator of
the demands–control model.
In Karasek’s (1979; also see Karasek & Theorell,
1990) original conceptualization of job demands, he
focused specifi cally on the psychological work demands
or mental workload of the job, including time demands,
problem-solving demands and monitoring demands.
However, as Beehr, Glaser, Canali and Wallwey (2001)
have noted, many of the studies testing Karasek’s
demand–control model have not adhered to his origi-
nal conceptualization of job demands as mental work-
load but more as physical demands and organizational
constraints. Therefore, in the present study, we adhered
more strictly to Karasek’s original conceptualization of
job demands as mental workload.
Similarly, control at work has been defi ned in two
ways in the literature: (1) ‘job decision latitude’, which
includes both personal discretion and job skill level;
and (2) more narrowly, in terms of being able to infl u-
ence the work environment in a way that one may be
able to infl uence the outcomes (Ganster & Murphy,
2000). In the present study, we were able to tap into
both defi nitions of job controls as outlined earlier.
The person–environment (P–E) fi t theory is ubiqui-
tous in organizational psychology. Its application can
be found in areas as diverse as job satisfaction (Dawis
& Lofquist, 1984), job design (Hackman & Oldham,
1980), employee selection (Schneider & Schmitt, 1992)
and career choice (Holland, 1985), to name just a few.
The P–E fi t approach is also prominent in the organi-
zational stress literature, in that it places emphasis on
the interaction and congruence between the person and
their environment. As noted by Edwards and Harrison
(1993, p. 628), ‘. . . the central hypothesis of P–E fi t
theory is that misfi t between the person and the envi-
ronment leads to psychological, physiological, and
behavioral strains, which ultimately increase morbidity
and mortality’.
Present study
The major purpose of the present study was to examine
the infl uence of specifi c job demands, job controls and
P–E fi t characteristics (i.e. role overload and role under-
load) on physical health and psychological stress out-
comes. Prior research based on the job demand–control
model of occupational stress has indicated that placing
high demands on workers while allowing them little
personal control will lead to adverse health conse-
quences. However, low demand situations are not pre-
dicted to increase work-related stress. Other research in
the area of work stress, however, has indicated that
some aspects of work may be perceived as stressful by
employees when present in excess but also when there
is a less than an optimal level (see Kahn & Byosiere,
1992; Sulsky & Smith, 2005). In other words, research-
ers have suggested that there is a curvilinear relation-
ship between work characteristics and strains (Edwards,
1996). For example, having too much work to do (role
overload) is generally considered stressful, but not
having enough work to do (role underload) may be
perceived as stressful as well and lead to boredom stress
(Fisher, 1993; Parasuraman & Purohit, 2000). In addi-
tion, overload and underload may lead to different
health outcomes and stress reactions. This proposed
curvilinear relationship is somewhat analogous to
Warr’s (1987) vitamin model, which proposed a non-
linear relationship between work characteristics and a
variety of mental health outcomes.1
Research questions
The following research questions were addressed in the
present study:
Research question 1: What are the differential health
outcomes associated with role underload versus role over-
load? For example, does role underload tend to lead to
chronic degenerative diseases such as cardiovascular
and pulmonary disorders, whereas role overload leads
to overuse injuries such as back and foot pain, as sug-
gested by Ilmarinen (1994)? That is, we need to look at
‘imbalance’, not just overload, as both can lead to
health-related problems. The P–E fi t theory discussed
earlier, for example, is predicated on the need to
examine for such imbalance (French, Caplan, &
Harrison, 1982; Parasuraman & Purohit, 2000).
1 We thank one of the anonymous reviewers for pointing out this
important similarity.
Stress and Health 26: 99–111 (2010) © 2009 John Wiley & Sons, Ltd. 101
K. S. Shultz, M. Wang and D. A. Olson Role Overload and Underload
Research question 2: Does the demand–control model
of work stress apply to both overload and underload condi-
tions or just to overload conditions? Karasek’s (1979) con-
ceptualization of the demand–control model would seem
to indicate that only those experiencing overload should
benefi t from the stress-buffering effects of various job
controls. However, the P–E fi t model would predict that
any imbalance (overload or underload) should lead to
experienced strains, and thus job-related controls should
have the potential to buffer the effects of job demands for
workers who experience underload, as well as overload,
conditions.
Method
Data
Data from the Second European Survey on Working
Conditions (Eurobarometer 44.2) was used in the
present study. This Eurobarometer data set is part of a
larger series of surveys dating back to 1970 that are
conducted by EOS-Gallup Europe and sponsored by
the Commission of the European Community in order
to examine a wide variety of cross-national and cross-
contemporaneous social science research issues. The
Eurobarometer collected data from a representative
sample that included approximately 16,000 working
adults in 15 western European Countries, with roughly
1,000 participants coming from each country (see Reif
& Malier, 1996, for more details). Participants in the
study ranged from age 18 to 83 (mean = 38.9, standard
deviation = 11.73). Men comprised 57 per cent of the
sample. The most prominent occupations reported
included: craft and related trade workers (17 per cent),
clerks (15 per cent), service and sales workers (13 per
cent), and technicians (12 per cent). Approximately 70
per cent of workers were employed in the private sector.
Unfortunately, no other relevant demographic data
were provided on this sample.
We chose this data set for a variety of reasons. Firstly,
the Second European Survey on Working Conditions
contains two types of job demands proposed by Karasek
(1979). In addition, information on three aspects of
perceived control exercised by the worker was also col-
lected via the survey. Finally, attempts to uncover inter-
action effects using hierarchical moderated logistic
regression require large sample sizes in order to obtain
suffi cient statistical power. As a result, the use of the
large-scale Eurobarometer data set provided us with the
opportunity to conduct a powerful test of the moderat-
ing effects of the demand–control model, as well as the
P–E fi t model, of work stress across overload, matched
and underload groups, while also controlling a sizeable
number of demographic variables.
Measures
Demographic variables
The demographic variables of gender, years on main
job, hours worked per week, employment sector, job
category and supervisory responsibilities were included
in the present study. Gender was coded 0 for women
and 1 for men. Employment sector was coded 1 for
public sector and 2 for private sector. Job category was
coded 0 for white-collar workers (i.e. legislators and
managers, professional, technicians, clerks, service and
sales workers) and 1 for blue-collar workers (i.e. agri-
cultural and fi shery workers, craft and related trade
workers, plant and machine operators, elementary
occupations, and armed forces). Supervisory responsi-
bility was coded 0 for no supervisory responsibilities
and 1 for any supervisory responsibilities. Each of these
variables has been shown to be related to work stress in
past research (Beehr, 1995; Sulsky & Smith, 2005).
Therefore, it was imperative to control for these demo-
graphic variables before testing the demand–control
model and how it may differentially apply to those
workers experiencing role overload, underload or
balance in order to rule out these common alternative
explanations for perceived job stress.
Job demands
Karasek and Theorell (1990) focus on mental
demands of tasks when defi ning job demands in their
demand–control theory. Thus, Beehr et al. (2001) sug-
gested that demands in demand–control theory would
include three types of job demands: time demands,
monitoring demands and problem-solving demands.
Both time demands and problem-solving demands
were assessed in the Eurobarometer data set. Tight
deadlines (two items, r = 0.67) were measured with the
items, ‘Does your main paid job involve . . .’: (1)
working at very high speed; and (2) working to tight
deadlines. The response scale was 1 = all the time, 2 =
almost all the time, 3 = around three-fourths of the
time, 4 = around half the time, 5 = around one-fourth
of the time, 6 = almost never and 7 = never. The two
items were averaged to obtain a score on this scale. This
Role Overload and Underload K. S. Shultz, M. Wang and D. A. Olson
102 Stress and Health 26: 99–111 (2010) © 2009 John Wiley & Sons, Ltd.
scale was reverse-scored so that a higher score repre-
sents tighter time deadlines.
For problem solving (four items, α = 0.61), the item
stem read, ‘Generally, does your main paid job involve,
or not, . . . ?’: (1) solving unforeseen problems on your
own; (2) complex tasks; (3) learning new things; and
(4) deciding, possibly with colleagues, on departmental
issues such as the division of tasks, staff replacements,
production objectives, timetables, etc. A ‘yes’ (coded as
1), ‘no’ (coded as 0) and ‘DK’ (do not know, coded as
missing) response format was used. The four items
were summed to obtain a score on this scale, with
higher scores representing higher problem solving.
Job controls
The three variables of fl exibility in scheduling (three
items, α = 0.69), time to get the job completed (one
item) and perceptions of autonomy (three items, α =
0.78) were included in the current study as job control
measures. The item stem for the three schedule fl exibil-
ity items read, ‘For each of the following statements,
please answer yes or no’: (1) you can take your break
when you wish; (2) you are free to decide when to take
holidays or days off; and (3) you have a fi xed start and
nish time every day (reverse-scored). Item responses
were coded as 1 for ‘yes’ and 0 for ‘no’. Items were
summed to obtain a score on this scale, with higher
scores representing higher schedule fl exibility. The time
to get the job completed item used the same item stem
as the fl exibility in scheduling items and asked, ‘You
have enough time to get the job completed’ (yes or no).
The stem for the three perceptions of autonomy items
read, ‘Are you able, or not, to choose or change . . . ?’:
(1) your order of tasks; (2) your methods of work; and
(3) your speed or rate of work. Again a yes or no
response format was used, and items were summed to
obtain a score on this scale, with higher scores repre-
senting more autonomy.
Role overload and underload
The item stem for the question on role demands
read, ‘How well do you think your skills matched the
demands imposed on you by your job?’: (1) the demands
are too high (designated as role overload, n = 1,132 or
7 per cent); (2) they match (designated as matched or
balance, n = 13,177 or 82.5 per cent); (3) the demands
are too low (designated as role underload, n = 1,383 or
8.6 per cent); and (4) DK (do not know—set to missing,
n = 288 or 1.8 per cent).
Work stress
The question ‘Does your work affect your health, or
not? If yes, how does it affect your health?’ was asked of
participants. For the latter part of the question, 16
aspects of health were presented for participants to
endorse. The two most frequently occurring health
problems endorsed were backaches (30 per cent) and
stress (28 per cent). All other aspects of health were
reported by 20 per cent or less of participants, with 10
aspects having an endorsement rate of less than 10 per
cent. Also, while other items on the scale (e.g. back-
aches, anxiety, sleeping problems and irritability) may
be indicative of psychological distress, they were not
direct assessments of stress. In addition, given that most
of these additional items have a much lower base rate
than the stress item, including those in the logistic
regression analysis that would have led to statistical
diffi culties in identifying moderation effects in the
demand–control model. Therefore, only the response
to the stress item was used in the current study. For
workers who endorsed ‘stress’ as the response to the
question, their responses were coded as ‘1’. For workers
who did not endorse ‘stress’ as the response to the ques-
tion, their responses were coded as ‘0’.
Analyses
Chi-square analyses were used to determine if there
were signifi cant differences in the health outcomes
associated with role overload and role underload
(addressing research question 1). In addition, three
separate hierarchical logistic regressions (one with
those indicating overload, one with those indicating
underload and one indicating with those indicating
matched or balance were conducted for the stress
response in order to examine whether or not there are
differences in how the demand–control model of work
stress applies to workers experiencing different levels of
role-load (addressing research question 2). We used
logistic regression because of the dichotomous response
format of the perceived work stress measure employed
in this study.
For each of the three role load groups of workers, the
hierarchical logistic regression analysis included three
steps. In the fi rst step, the demographic variables out-
lined earlier were entered into the equation. In the
second step, the two psychological demand variables
(i.e. time deadlines and problem-solving requirements)
and three psychological control variables (i.e. time to
Stress and Health 26: 99–111 (2010) © 2009 John Wiley & Sons, Ltd. 103
K. S. Shultz, M. Wang and D. A. Olson Role Overload and Underload
complete tasks, autonomy and schedule fl exibility) were
entered into the equation to estimate their main effects.
Finally, in step three, the six interaction terms for the
two demands by three controls were entered into the
regression equation. At each step, the coeffi cients and
odds ratios were calculated for each individual predictor
variable and/or interaction term, as were the step and
model chi-square, as well as pseudo R2 effect size values.
All of the control and demand variables were standard-
ized or centred (i.e. converted to deviation scores from
their respective grand means) within role load category
before creating the interaction terms and entering the
variables into the regression equations, in order to
reduce non-essential multicollinearity and improve
interpretability (Cohen, Cohen, West, & Aiken, 2003).
Results
Descriptive statistics (test of research
question 1)
The participants were asked to what extent they believed
their work affected their health. Table I shows per cent
frequency data for 16 different health conditions across
the three role demand categories. The matched group
was most likely to agree with the statement that ‘No, it
(my work) does not affect my health’ (46 per cent), with
the role underload group agreeing at 38 per cent and
the role overload group at half the rate of the matched
group at only 23 per cent. For all the specifi c health
conditions, the role overload group reported the highest
percentage frequencies. Conversely, the matched group
reported the lowest frequencies, while the role under-
load group was somewhere in between the two other
groups. The three groups differed signifi cantly on all 16
health conditions, with the phi coeffi cient effect size
estimates ranging from 0.050 (for skin problems) to
0.136 (for stress). These would be considered relatively
small effect sizes, where the typical interpretation
is 0.10 small but not trivial, 0.30 moderate and 0.50
large effect estimates (Cohen, 1988). In fact, the role
overload group was two to three times more likely to
report each of the 16 health problems compared with
the matched group. For approximately half of the 16
health factors, the role underload group is about
Table I. Percentage of participants experiencing matched, role overload and role underload reporting their work affects specifi c
health outcomes
Does your work affect your health, or not?
If yes, how does it affect your health?
Per cent experiencing condition
χ2
(df = 2)*
Phi
coeffi cient
Matched
(n = 13,177)
Role overload
(n = 1,132)
Role underload
(n = 1,383)
No, it does not affect my health. 236.93 0.123 46.0 23.0 38.0
Yes, it does affect my health.
Ear problems 69.73 0.067 6.3 13.0 7.5
Eye problems 54.15 0.059 8.6 15.0 11.0
Skin problems 38.66 0.050 5.6 9.8 7.7
Backache 169.45 0.104 28.0 46.0 34.0
Headaches 154.27 0.099 13.0 25.0 18.0
Stomach ache 56.85 0.060 4.1 8.9 5.4
Muscular pains in arms or legs 91.51 0.076 17.0 28.0 22.0
Respiratory diffi culties 49.56 0.056 3.8 8.0 5.5
Stress 291.16 0.136 27.0 51.0 28.0
Overall fatigue 150.74 0.098 18.0 33.0 23.0
Sleeping problems 147.81 0.097 5.9 15.0 8.8
Allergies 35.29 0.047 3.6 6.7 5.4
Heart disease 24.75 0.040 1.1 2.7 1.2
Anxiety 148.91 0.097 6.3 16.0 8.7
Irritability 177.10 0.106 9.4 21.0 15.0
Personal problems 119.56 0.087 2.5 7.6 5.6
Other (spontaneous) 1.550.016 1.3 1.8 1.4
My work improves my health (spontaneous) 0.4150.005 1.1 1.1 1.3
Do not know 0.5330.006 1.2 1.3 1.0
* Pearson chi-square signifi cant at p < 0.001 unless otherwise designated.
Non-signifi cant.
Role Overload and Underload K. S. Shultz, M. Wang and D. A. Olson
104 Stress and Health 26: 99–111 (2010) © 2009 John Wiley & Sons, Ltd.
midway between the reported frequencies for the
matched and role overload groups. Most striking is the
fact that the role underload group is no more likely to
report health problems labelled as ‘stress’ and ‘heart
disease’ than the matched group. This is counter to
what Ilmarinen (1994) suggested (as noted earlier)
and only provides partial support for the P–E fi t
perspective.
Hierarchical logistic regression (test of
research question 2)
Table II reports the results of the hierarchical logistic
regression analysis for the physical health outcome of
stress for the matched group. This regression analysis
shows that in step one, the fi ve demographic variables
of gender, job category, employment sector, years on
main job and hours worked per week signifi cantly
predicted the health outcome of stress—χ2 (6, n = 13,
177) = 237.46, p < 0.01, Nagelkerke pseudo R2 = 0.031.
For block (or model) 2, the two job demands and
three job controls as a group signifi cantly predicted job
stress above and beyond the demographic variables—
step χ2 (5, n = 13,177) = 832.20, p < 0.01, Nagelkerke
pseudo R2 = 0.133. Thus, pseudo R2 more than qua-
drupled, increasing by 0.102 when the fi ve additional
variables were added to the prediction equation. In
addition, both job demands of deadlines and problem
solving and the control variable of time were signifi cant
individual predictors (see Table II).
In the fi nal block (or model 3), the six interaction
terms combined signifi cantly increased the prediction
of job stress—step χ 2 (6, n = 13,177) = 37.74, p < 0.05,
Nagelkerke pseudo R2 = 0.138. However, the pseudo R2
value only increased slightly by 0.005. In addition, the
deadline by time and problem solving by time interac-
tion terms were signifi cant individual predictors. Fol-
lowing Cohen et al.’s (2003) procedure, these interaction
effects are plotted in Figures 1 and 2, respectively. Spe-
cifi cally, the positive relationships between the deadline
demands/problem-solving demands and the probabil-
ity of perceiving work stress were less pronounced for
workers who had enough time to complete their tasks
than for those who did not have enough time to com-
plete their tasks.
Table III reports the results of the hierarchical logis-
tic regression analysis for the physical health outcome
of stress for the overload group. This regression analysis
shows that in step one, the six demographic variables
together signifi cantly predicted job stress—χ2 (6, n =
1,132) = 19.27, p < 0.01, Nagelkerke pseudo R2 = 0.027.
In addition, the average hours worked per week and
supervisory responsibilities also were signifi cant indi-
vidual predictors.
For block (or model) 2, the two job demands and
three job controls as a group signifi cantly predicted job
stress above and beyond the demographic variables—
step χ2 (5, n = 1,132) = 40.42, p < 0.01, Nagelkerke
pseudo R2 = 0.081. Thus, pseudo R2 tripled, increasing
by 0.054 when the fi ve additional variables were added
to the prediction equation. In addition, the job demand
of problem solving and the control variables of time
and autonomy were signifi cant individual predictors
(see Table III).
In the fi nal block (or model 3), the six interaction
terms combined did not signifi cantly increase the pre-
diction of job stress—step χ 2 (6, n = 1,132) = 8.06, p >
0.05, Nagelkerke pseudo R2 = 0.092. However, the
pseudo R2 value did increase slightly by 0.011 and the
deadline by time interaction term was a signifi cant indi-
vidual predictor. This interaction is plotted in Figure 3.
Specifi cally, there was a positive relationship between
the deadline demands and the probability of perceiving
work stress for workers who did not have enough time
to complete their tasks, while the same relationship was
negligible or even negative for those who had enough
time to complete their tasks.
Table IV reports the results of the hierarchical logis-
tic regression analysis for the physical health outcome
of stress for the underload group. This regression analy-
sis shows that in step one, the six demographic variables
together signifi cantly predicted job stress—χ2 (6, n =
1,383) = 29.00, p < 0.01, Nagelkerke pseudo R2 = 0.038.
In addition, the average hours worked per week was a
signifi cant individual predictor.
For block (or model) 2, the two job demands and
three job controls as a group signifi cantly predicted job
stress above and beyond the demographic variables—
step χ2 (5, n = 1,383) = 76.30, p < 0.01, Nagelkerke
pseudo R2 = 0.132. Thus, pseudo R2 more than tripled,
increasing by 0.094 when the fi ve additional variables
were added to the prediction equation. In addition, the
job demand of deadlines and the control variable of
time were signifi cant individual predictors (see Table
IV).
In the fi nal block (or model 3), the six interaction
terms combined did not signifi cantly increase the pre-
diction of job stress—step χ2 (6, n = 1,383) = 5.38,
Stress and Health 26: 99–111 (2010) © 2009 John Wiley & Sons, Ltd. 105
K. S. Shultz, M. Wang and D. A. Olson Role Overload and Underload
Table II. Estimated coeffi cients, odds ratios and confi dence intervals (CI) for odds ratios for perceived stress in logistic regression for
respondents who perceived a match of individual skills and job demands
n = 13,177 χ2 (df) 2 log
likelihood
Nagel kerke
R2
Perceived Stress
Coeffi cient
estimates
Standard
Error
Odds
ratio
95 per cent CI
for odds ratio
Step 1 Block 237.46 (6)**
Model 237.46 (6)** 12,809.44 0.031
Demographic variables
Gender0.145** 0.050 0.865 0.784–0.955
Job category0.275 ** 0.053 0.759 0.685–0.842
Employment sector§0.245** 0.052 0.783 0.707–0.866
Average years in job 0.006** 0.002 1.006 1.002–1.010
Average hours worked
per week
0.018** 0.002 1.018 1.014–1.022
Supervisory duties0.009 0.053 0.991 0.884–1.099
Step 2 Block 832.20 (5)**
Model 1,069.66 (11)** 11,977.23 0.133
Demands
Deadlines 0.234** 0.013 1.264 1.233–1.296
Problem solving 0.130** 0.023 1.139 1.089–1.191
Controls
Time 0.754** 0.053 0.470 0.417–0.531
Auton omy 0.009 0.071 0.991 0.862–1.140
Schedule exibility 0.051 0.079 0.950 0.814–1.109
Step 3 Block 37.74 (6)**
Model 1,107.40 (17)** 11,939.49 0.138
Interactions
Deadline by time 0.046** 0.011 0.955 0.934–0.975
Deadlines by autonomy 0.037 0.029 1.038 0.981–1.098
Deadlines by schedule
exibility
0.009 0.037 1.009 0.938–1.085
Problem solving by time 0.178** 0.050 0.837 0.758–0.924
Problem solving by
autonomy
0.014 0.056 1.014 0.908–1.132
Problem solving by
schedule exibility
0.207 0.067 0.813 0.713–0.926
Intercept 1.229** 0.111 0.293
* p < 0.05; ** p < 0.01.
0 = women, 1 = men.
0 = white collar, 1 = blue collar.
§ 1 = public sector, 2 = private sector.
0 = no supervisory responsibility; 1 = some supervisory responsibility.
Figure 1 Deadline (demand) by time
(control) interaction on work stress in
matched workers
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
hgiHwoL Deadline Demand
Probability of feeling stressed
Do not have enough time
Have enough time
Role Overload and Underload K. S. Shultz, M. Wang and D. A. Olson
106 Stress and Health 26: 99–111 (2010) © 2009 John Wiley & Sons, Ltd.
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
hgiHwoL Deadline Demand
Probability of feeling stressed
Do not have enough time
Have enough time
Figure 2 Deadline (demand) by time (control) interaction on work stress in overload workers
Table III. Estimated coeffi cients, odds ratios and confi dence intervals (CI) for odds ratios for perceived stress in logistic regression for
respondents who perceive an overload of individual skills and job demands
n = 1,132 χ2 (df) 2 log
likelihood
Nagel kerke
R2
Perceived stress
Coeffi cient
estimates SE
Odds
ratio
95 per cent CI
for odds ratio
Step 1 Block 19.27 (6)**
Model 19.27 (6)** 1,293.98 0.027
Demographic variables
Gender0.215 0.149 0.806 0.602–1.081
Job category0.169 0.156 0.844 0.622–1.146
Employment sector§0.021 0.158 0.980 0.718–1.336
Average years in job 0.008 0.007 1.008 0.995–1.022
Average hours worked per week 0.012* 0.006 1.012 1.001–1.024
Supervisory duties0.329* 0.156 1.389 1.022–1.887
Step 2 Block 40.42 (5)**
Model 59.69 (11)** 1,253.57 0.081
Demands
Deadlines 0.065 0.038 1.067 0.990–1.150
Problem solving 0.164* 0.063 1.178 1.040–1.333
Controls
Time 0.518** 0.142 0.596 0.451–0.788
Auton omy 0.421* 0.200 0.656 0.443–0.972
Schedule exibility 0.266 0.239 0.767 0.480–1.225
Step 3 Block 8.06 (6)
Model 67.75 (17)** 1,245.51 0.092
Interactions
Deadline by time 0.182* 0.074 0.833 0.721–0.953
Deadlines by autonomy 0.047 0.098 0.954 0.787–1.155
Deadlines by schedule fl exibility 0.059 0.117 1.061 0.843–1.335
Problem solving by time 0.112 0.112 0.884 0.718–1.113
Problem solving by autonomy 0.093 0.151 0.911 0.678–1.225
Problem solving by schedule
exibility
0.190 0.189 1.209 0.835–1.751
Intercept 0.483 0.319 0.617
* p < 0.05; ** p < 0.01.
0 = women, 1 = men.
0 = white collar, 1 = blue collar.
§ 1 = public sector, 2 = private sector.
0 = no supervisory responsibility; 1 = some supervisory responsibility.
Stress and Health 26: 99–111 (2010) © 2009 John Wiley & Sons, Ltd. 107
K. S. Shultz, M. Wang and D. A. Olson Role Overload and Underload
p > 0.05, Nagelkerke pseudo R2 = 0.139. However, the
pseudo R2 value did increase slightly by 0.007. None of
the individual interaction terms were signifi cant.
Discussion
Karasek’s (1979) demand–control model has received
some support in terms of how workers can use job
controls to buffer the effects of excessive job demands
that result in experienced psychological stress. However,
previous research has not examined possible differences
in how the demand–control model might apply differ-
ently based on one’s perceived match between their
skills and the job demands (i.e. matched, overload and
underload). Previous tests of Karasek’s model have
used predominantly only those workers experiencing
too many demands. The results of the present study
support, in part, the notion that too many, as well as
too few, work demands can be associated with higher
levels of work related illnesses (as shown in Table I).
This is consistent with prior occupational stress research
that has suggested that there is a curvilinear relation-
ship between workload and perceived strains (Edwards,
1996; Kahn & Byosiere, 1992).
We also sought to extend prior research by examin-
ing whether each of these conditions (i.e. role overload,
Table IV. Estimated coeffi cients, odds ratios and confi dence intervals (CI) for odds ratios for perceived stress in logistic regression for
those respondents who perceive an underload of individual skills and job demands
n = 1,383 χ2 (df) 2 log
likelihood
Nagel kerke
R2
Perceived Stress
Coeffi cient
estimates SE
Odds
ratio
95 per cent CI
for odds ratio
Step 1 Block 29.00 (6)**
Model 29.00 (6)** 1,278.74 0.038
Demographic variables
Gender0.224 0.115 0.799 0.590–1.082
Job category0.013 0.158 0.988 0.724–1.347
Employment sector§0.261 0.162 0.770 0.561–1.058
Average years in job 0.005 0.008 0.995 0.979–1.011
Average hours worked per week 0.021** 0.006 1.021 1.010–1.033
Supervisory duties0.318 0.180 1.374 0.965–1.956
Step 2 Block 76.30 (5)**
Model 105.30 (11)** 1,202.45 0.132
Demands
Deadlines 0.200** 0.039 1.221 1.132–1.318
Problem solving 0.120 0.065 1.127 0.992–1.281
Controls
Time 0.703** 0.190 0.495 0.341–0.718
Auton omy 0.172 0.210 0.842 0.558–1.271
Schedule exibility 0.158 0.251 0.854 0.814–1.109
Step 3 Block 5.38 (6)
Model 110.68 (17)** 1,197.06 0.139
Interactions
Deadline by time 0.081 0.086 1.084 0.915–1.283
Deadlines by autonomy 0.053 0.097 0.948 0.785–1.146
Deadlines by schedule fl exibility 0.030 0.123 1.030 0.810–1.312
Problem solving by time 0.227 0.126 0.797 0.622–1.021
Problem solving by autonomy 0.092 0.160 0.912 0.667–1.249
Problem solving by schedule
exibility
0.076 0.191 0.927 0.638–1.348
Intercept 1.216** 0.344 0.296
* p < 0.05; ** p < 0.01.
0 = women, 1 = men.
0 = white collar, 1 = blue collar.
§ 1 = public sector, 2 = private sector.
0 = no supervisory responsibility; 1 = some supervisory responsibility.SSS
Role Overload and Underload K. S. Shultz, M. Wang and D. A. Olson
108 Stress and Health 26: 99–111 (2010) © 2009 John Wiley & Sons, Ltd.
role underload and matched) were associated with dif-
ferent types of health outcomes. However, we found
that role overload was associated with higher reported
frequencies for all health-related outcomes, both psy-
chological and physical in nature. Some authors (e.g.
Ilmarinen, 1994) have hypothesized that role overload
should be associated with overuse physical health out-
comes such as back pain. This was clearly the case here
with those individuals indicating role overload having
twice the rate of backache problems as those with a
match between their work demands and skills. Unfor-
tunately, this fi nding of nearly ‘double the effect’ for
role overload respondents was true for almost all cate-
gories. Hence, backaches do not appear to be a unique
instance.
With regard to role underload, the anticipated higher
rates of psychological effects (e.g. sleeping problems,
irritability, personal problems and anxiety) did gener-
ally occur in comparison to the matched group but not
when compared to the role overload group. Thus, based
on results of these data, it appears that role overload is
more detrimental to both one’s physical and psycho-
logical health than role underload. However, as
Parasuraman and Purohit (2000) found, in certain clas-
sifi cations of workers at least (e.g. orchestra musicians),
both role overload and underload can infl uence psy-
chological health and well-being.
Our second research question concerned the gener-
alizability of the demand–control model of stress to
instances of role underload. In the present research, we
found that overload, underload and matched workers
reported somewhat different effects of demands and
controls (i.e. main effects) from their respective jobs
after controlling for numerous demographic variables
such as gender, job type, employment sector, hours
worked per week, years on the job and supervisory
responsibilities. Specifi cally, for the matched group,
both demands (deadlines and problem solving) and the
time control variable resulted in signifi cant main
effects. However, for the overload group, only the
problem-solving demand, and time and autonomy
control main effects were signifi cant, whereas for the
underload group, only the deadline demand variable
and time control variables were signifi cant main effects.
Thus, having enough time to get the job done (time)
was the only common signifi cant factor across all three
groups.
The interaction effects between demands and con-
trols were also somewhat different for the three groups.
The matched group had two signifi cant interaction
terms (deadline by time and problem solving by time),
the overload group had one signifi cant interaction term
(deadline by time), while for the underload group,
none of the interaction terms were signifi cant. Specifi -
cally, matched and overload workers who reported
having enough time to complete their work were actu-
ally less likely to report stress even with high deadline
demands than those who reported not having enough
time to complete their work (see Figures 1 and 2,
respectively). In addition, having enough time to com-
plete their work also buffered stress when there were
high problem-solving demands for the matched group
(see Figure 3). Thus, those workers experiencing a
match appear to be particularly in need of job controls
0.00%
10.00%
20.00%
30.00%
40.00%
hgiHwoL Problem Solving Demand
Probability of feeling stressed
Do not have enough time
Have enough time
Figure 3 Problem solving (demand) by time (control) interaction on work stress in matched workers
Stress and Health 26: 99–111 (2010) © 2009 John Wiley & Sons, Ltd. 109
K. S. Shultz, M. Wang and D. A. Olson Role Overload and Underload
in order to reduce the likelihood of experiencing stress
resulting from job demands such as stringent deadlines
and heavy problem solving-focused work.
The current fi ndings shed light in terms of reconcil-
ing previous inconsistent fi ndings in the demand–
control model literature. Specifi cally, we found that the
signifi cant demands differed for the overload (problem
solving), underload (deadlines) and matched (both
deadlines and problem solving) groups. In addition,
while for all three groups time was a signifi cant job
control variable, autonomy was also a signifi cant job
control variable but for only the overload group. This
may be because high levels of job skill requirement (i.e.
overload) usually involve higher levels of cognitive
functioning such as reasoning and decision making. In
this case, the freedom to make decisions may be the
most important and relevant job control that would
help the worker deal with the job demands. The current
ndings in the underload group are consistent with
Taris’ (2006) notion that most previous studies support
the main effects of demand and control but not the
interaction between demand and control on stress out-
comes. Overall, the present fi ndings suggest that per-
ceived skills match may be one of the potential
moderators that set boundaries for the demand–control
model because the basic premise of the demand–control
model (i.e. the stress buffering effect of job controls) is
more likely to be observed in workers who are experi-
encing either a matched or role overload. However, it
should be noted that the effect size estimates for the
effects reported here were all relatively small, thus sug-
gesting that other variables (as yet unidentifi ed in this
study) contribute to the experience of occupational
stress.
Limitations and future research
There are several important limitations to the current
study. Firstly, we only used self-report measures in the
present research. Therefore, common method bias may
infl uence our current results. Using a self-report
measure to assess perceived work stress may also lead
to a possible issue with drawing causal conclusions—
respondents may overestimate the impact of their work
on their stress experience or report something as due
to work that may be due to other factors. Nevertheless,
based on Lazarus’ (1966) well-cited work regarding the
cognitive appraisal of stress and the notion that whether
a particular stimulus is perceived as a stressor varies
across individuals and that perception is in the eye of
the beholder, it makes sense to ask individuals directly
about their perceived level of work stress. However,
future studies may want to include both self-report and
biological measures (e.g. cortisol level) to achieve reli-
able assessment of perceived work stress.
A second limitation is related to the archival nature
of the data used in the current study. Because we used
measures that were devised for a different research
purpose, some measures for the current variables may
not seem ideal, especially the single-item measures used
for time to get the job done and perceived work stress.
The reliability and variance of these constructs would
have increased if multiple indicators or established scale
measures were available from this archival data set.
Thus, relationships related to these variables might well
be underestimated in the current study because of to
measurement errors. Future studies may test these rela-
tionships via well-established scales instead of single-
item measures to provide more accurate estimates.
However, it should be noted that while the use of
archival data does impose some clear limitations to the
interpretations of our results, the extremely large and
diverse sample used here is a clear strength of this study.
Another strength was the ability to test multiple
demands and controls, and their interactions, while
controlling for numerous demographic variables,
which would not be possible in most primary study
data sets with much smaller sample sizes.
Finally, the Eurobarometer archival data set used a
cross-sectional design. As a result, we cannot rule out
that the observed differences between various job
demand groups in the use of various job controls to
buffer the stress associated with various job demands
are a result of cohort or period effect (Barnes-Farrell,
2005; Shultz & Adams, 2007). That is, different groups
from the mid-1990s may have been using different
coping strategies to deal with work stress as a result of
the period of time the data were collected or as a result
of cohort differences in how the various groups of
workers of that era dealt with job stress. In addition,
cross-sectional data do not allow for causal inferences
the way that longitudinal designs would. Therefore,
future studies would need to use a cross-sequential-
type design and include measures that detect cohort
differences in stress appraisal and coping strategies in
order to tease apart various effects such as cohort and
period effects, as well as allow for more causal
inferences.
Role Overload and Underload K. S. Shultz, M. Wang and D. A. Olson
110 Stress and Health 26: 99–111 (2010) © 2009 John Wiley & Sons, Ltd.
Conclusion
In summary, it was clear that both role overload and
role underload can result in higher reported frequen-
cies of numerous negative health-related outcomes,
although it appears role overload is more detrimental
than role underload. In addition, the current study sug-
gested that skill match imposes an important boundary
condition for applying the demand–control model. The
buffering effect of job control on perceived work stress
caused by job demands was found to be more salient
for the matched and overload groups than for the
underload group. This fi nding helps reconcile the pre-
vious inconsistent fi ndings in the demand–control
model literature by providing a plausible explanation
of the lack of support for the demand–control
interaction.
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... Nevertheless, this was supported by the study done by Newton et al. [66]. It is found out that employees who have high emotional intelligence suffer from stress due to role underload where the role overload and role underload were identified as a stressor in the study conducted by Shultz et al. [67]. They explained that the less emotionally intelligent employees perceived role underload as an advantage rather than a disadvantage. ...
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This study investigates the impact of emotional intelligence on the job performance of the banking employees in Sri Lanka with the mediation impact of occupational stress. The quantitative approach uses the confirmatory survey method, and it was verified. The data has been analyzed by using Structural Equation Modelling (SEM). The confirmatory factor analysis results, RAMSEA=0.089, CFI=0.920, and χ2/df, =3.437, show that the proposed conceptual model fit. The findings reveal that emotional intelligence has a direct effect of 0.385 on job performance and an indirect effect of 0.023 through the mediatory path of occupational stress, resulting in a total effect of 0.408, which is significant (p<0.05). Therefore partial mediation can be observed. Even though higher emotional intelligence leads to higher job performance, this positive impact is lowered due to the partial mediation effect of occupational stress. Therefore, it can be stated that to improve the banking employees' performance, and emotional intelligence can be used as a human resource management strategy.
... 7 Moreover, occupational stress results from a variety of job-related conditions such as too many obligations, too little authority, too much or too little workload, lack of support, role conflict and role ambiguity, and role overload. [8][9][10][11] Role theory, particularly a subset of role theory called role strain, was used to guide this study. It is important to note that this particular study did not explicitly test role theory; rather, role theory was used to explain how an individual will act or is expected to interact within occupational roles and work status. ...
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The roles and responsibilities for program directors of professional educational programs are numerous. The purpose of this study was to investigate how multiplicity of roles and responsibilities influence occupational stress. Role theory was the theoretical framework to organize the research and to investigate the influence of multiple roles and responsibilities on occupational stress of Athletic Training Program Directors (ATPDs). This mixed methods study investigated which occupational roles and responsibilities contributed to the greatest amount of occupational stress for ATPDs. All ATPDs from the Commission on Accreditation for Athletic Training Education (CAATE) accredited programs were invited to participate in an online survey to investigate personal and program characteristics and to determine which occupational roles and responsibilities produce the most occupational stress. Eighty-three ATPDs participated in this study, and the stress levels in the various occupational roles and responsibilities were measured, summed, and averaged. The results indicated that accreditation was the most stressful occupational role and responsibility category whereas service was the least stressful occupational role and responsibility category. This study presents information which heightens awareness of occupational stress experienced by ATPDs and contributes to the understanding of the multifaceted ATPD position.
Chapter
In diesem Kapitel lernen Sie verschiedene Methoden zur Stressbewältigung kennen. Nicht jede Methode ist für jede Situation oder für jeden Menschen in gleicher Weise geeignet. Man kann die Methoden ordnen von eher einfach lernbaren Methoden wie Ablenkung bis hin zu so umfassenden Ansätzen wie einer Umstellung des gesamten Lebensstils. Im Idealfall beherrscht man viele unterschiedliche Methoden zur Stressbewältigung, weil es so wahrscheinlicher wird, in einer konkreten Situation eine hilfreiche Methode zur Hand zu haben. Je wirksamer eine Methode ist, desto mehr Zeit und Energie muss man in der Regel investieren, um sie zu lernen oder umzusetzen. So sind beispielsweise eine Einstellungsänderung oder die Umstellung des Lebensstils in vielen Fällen sehr wirksam, können aber nicht so einfach umgesetzt werden wie beispielsweise eine gezielte Ablenkung. Man kann die Methoden der Stressbewältigung nach verschiedenen Kriterien einteilen, beispielsweise in problemfokussierte oder emotionsfokussierte Methoden oder wie nachfolgend sortiert von kurzfristiger bis langfristiger Wirkung.
Chapter
Digital Twin (DT) impacts significantly to both industries and research. It has emerged as a promising technology enabling us to add value to our lives and society. DT enables us to virtualize any physical systems and observe real-time dynamics of their status, processes, and functions by using the data obtained from the physical counterpart. This paper attempts to explore a new direction to enhance cyber resilience in the perspective of cybersecurity and Digital Twins. We enumerate definitions of the Digital Twin concept to introduce readers to this disruptive concept. We then explore the existing literature to develop a holistic analysis of the DT’s integration into cybersecurity. Our research questions develop a novel roadmap for a promising direction of research, which is worth exploring in the future and is validated by an extensive and systematic survey of recent works. Our research has aimed to properly illustrate the current research state in this area and can benefit both community and industry to further the integration of Digital Twins into Cybersecurity.
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Building upon conservation of resources theory and affective event theory, this study broadens the organizational citizenship behavior (OCB) literature by investigating the effects of two forms of OCB on employees' positive emotion, perceived role overload, and quality of work-life (QWL). Based on data collected from 321 hotel employees in China, the study findings indicate that engaging in organizational citizenship behavior toward the organization (OCBO) generates positive emotions, consequently affecting employees' QWL. Similarly, performing organizational citizenship behavior toward individuals in the organization (OCBI) has a positive impact on employees' positive emotions. Moreover, the results reveal a negative relationship between OCBO, and employees perceived role overload, suggesting that employees may achieve resource acquisition and alleviate role overload through OCB. The study findings make valuable theoretical contributions and provide meaningful empirical implications for hospitality organizations.
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Mirroring a worldwide phenomenon in industrialized nations, the U.S. is experiencing a change in its demographic structure known as population aging. Concern about the aging population tends to focus on the adequacy of Medicare and Social Security, retirement of older Americans, and the need to identify policies, programs, and strategies that address the health and safety needs of older workers. Older workers differ from their younger counterparts in a variety of physical, psychological, and social factors. Evaluating the extent, causes, and effects of these factors and improving the research and data systems necessary to address the health and safety needs of older workers may significantly impact both their ability to remain in the workforce and their well being in retirement. Health and Safety Needs of Older Workers provides an image of what is currently known about the health and safety needs of older workers and the research needed to encourage social polices that guarantee older workers a meaningful share of the nation's work opportunities. © 2004 by the National Academy of Sciences. All rights reserved.
Article
The Demand-Control model of occupational stress posits an interaction between job demands and job control predicting psychological strain, but previous research has found such an interaction only rarely or inconsistently. Such research, however, has often failed to measure either demands or strain faithfully to the model's constructs, or has simply failed to test for a statistical interaction. The present study corrected these shortcomings by going back to basics. Using a sample of 115 employees in a manufacturing company, it operationalized the variables more consistently with their original conceptualizations. However, when the hypothesized Demand-Control interaction was then tested, it still failed. Outcomes other than psychological strain (e.g. job dissatisfaction) were related negatively rather than positively to demands. This highlights the difference between psychological strain and dissatisfaction and casts doubt on models positing dissatisfaction as an intervening variable between stressors and strains.