T. E. EIKEN
1and P. Ø. SAKSVIK
21 Department of Psychology, Norwegian University of Science and Technology 2 Department of Psychology, Norwegian University of Science and Technology
Introduction
During the last few decades there has been a drastic increase in the number of organizations utilizing technol- ogy to cope with constant challenges. Research has shown contradictory findings regarding the consequences of the technology for employees` levels of stress (e.g. Korunka, Weiss & Karetta, 1993; Agervold, 1987). Two types of tech- nology will be examined in this study. While computer technology implies personal computers, machine technology includes equipment directly involved in the production process (including assembly lines).
The study is based on the demand-control-support model (Karasek & Theorell, 1990). According to this model, stress is mainly dependent on demands (e.g. workload, work pace) and degree of control (decision authority and skill discretion). While control is the primary risk factor for job-related stress, high demands are supposedly only a risk fac- tor when control is low. In our study of technology and stress, we therefore find it interesting not only to focus on the direct effects, but also the technologies indirect influence on stress through its effects on employees` levels of demands and control. Earlier studies focusing on the consequences of technology for factors like demands, power distribution and job content are inconsistent (centralization versus decentralization, degrading versus upgrading) (e.g. Dopson & Stewart, 1993; Buchanan & Boddy, 1982). Context variables like organizational size, variability in the environment and the preferences of the leader most likely contribute to the effect in each situation.
Social support, the third dimension in this model, is said to be one of the most important factors in stress re- duction. It is generally suggested that social support can work to reduce stress in three ways (e.g. House, 1981): di- rectly- by fulfilling human needs, indirectly- by reducing the stressor (in this case demands), and at last through the buffering effect. The buffering effect claims that social support modifies the relationship between the stressor and strain and is only effective when the stressors are present (in this case when the demands are high). Although the effects of social support in reducing stress have been confirmed empirically, the concept has often been criticized. Researchers have failed to agree on a common definition of social support, and are often not adequately explicit in the source or the kind of support they relate to in their studies. Many researchers seem to equalize social support with social interaction (e.g. Johnson & Hall, 1988) and even operationalize interpersonal conflicts as negative social support (Karasek & Theo- rell, 1990). A consequence of this is that other dimensions of social interactions have been more or less neglected.
This study has two primary goals. The first is to study the relationship between computer and machine tech- nology and stress. To achieve this, we will test for direct effects, indirect effects (through demands and control), and interaction effects (in this case between technology and organizational size to get an idea of the role of context vari- ables). The second goal is to expand the concept of social relations within stress research by identifying other possible types of social relations other than social support that may be important for stress. We will also test for direct effects of these relations on stress.
Method
The research is based on a questionnaire survey of employees in the Norwegian food and beverage industry. The questionnaires were distributed and collected by local labor inspectors from The Norwegian Labor Inspection who had received training in the standardized procedures. This resulted in a representative sample of 1343 employees which amounted to a mean response rate of 58%.
In this study we used questions from nine scales in the questionnaire (in addition to standard demographic variables) to measure the relevant predictors, moderators and outcomes.
Demands and Control were measured by, or based on, items from the Job Content Questionnaire (JCQ) (Karasek, 1985)
and a short version of the Quality of employment survey (QES) (Theorell et al., 1991). Five items (e.g. work pace, deadlines, constant attention) constitute the Demands dimension (α=.74), and five items (e.g. decision authority, possi- bilities to learn) constitute the Control dimension (α=.76).
Computer technology was measured with a single item on how often the employee is dependent upon using a computer. Machine technology was similarly measured with a single item on how often the employee spends work time in an
assembly line or in a situation that to a large degree is controlled by a machine. Response alternatives were, respec- tively, a 7-point scale of response alternatives ranging from “never” to “all the time”, and a 5-point scale from “very seldom” to “very often”.
Social relations among colleagues were measured by items in five scales in the questionnaire (based on JCQ (Karasek,
1985), LOQ (Watkins & Marsick, 1997), SSB (1993), QPS-Nordic (Lindström et al., 1997), and James` psychometric model (e.g. James & McIntyre, 1996)). A factor analysis of the items resulted in five dimensions of social relations: Social support (9 items, α=.87), Respect and development (6 items, α=.76), Group pressure (8 items, α=.74), Harass- ment (3 items, α=.64), and Bullying (2 items, α=.72).
Social relations with supervisor were measured by items in six scales (based on The Managerial Practices Survey (Yukl
& Lepsinger, 1990) in addition to the ones mentioned for social relations among colleagues). A factor analysis identi- fied four dimensions: Supervisor socioemotional support (5 items, α=.76), Supervisor instrumental support (11 items, α=.93), Supervisor informational support (4 items, α=.84), and Supervisor delegation (2 items, α=.85).
Stress was measured by a translated version of Cooper’s Job Stress Scale (Cooper, 1981). The scale consisted of 22
items with response alternatives on a 6-point scale from “no stress” to “very much stress”.
The data analyses in this study were carried out using SPSS 10.0 and LISREL (Jöreskog & Sörbom, 1993). Factor analyses were carried out to identify the dimensions in social relations. Main effects of all the predictors on stress were then computed in using blockwise multiple regression analyses. Factorial ANOVA (General Linear Model) were used to find interaction effects, and indirect effects were uncovered by a Structural equation model (SEM).
Results
Factor analyses with orthogonal rotation were carried out to determine the underpinning structures in the sur- veys` statements and questions concerning relations with colleagues and supervisors. This resulted in five types of posi- tive and negative social relations among colleagues: Social support, Respect and development, Group pressure, Har- assment, and Bullying. The Social support dimension resembles Karasek and Theorell`s socioemotional social support, while Respect and development reflects social interaction which contributes to self-evaluation, learning, and develop- ment.
The factor analysis of relations with supervisors revealed four types of positive social relations: Instrumental support, Informational support, Socioemotional support, and Delegation. The first three types of relations resemble types of social support identified earlier by among others Karasek and Theorell (1990) and House (1981), hence the names. Delegation reflects employees` perception of whether they are given the opportunity to make their own deci- sions relevant to carrying out their tasks.
Main effects
The results of the multiple regression analysis are shown in Table 1. The total model explained 41% of the variance in stress. All steps in the model contributed to a significant increase in the variance explained.
Table 1. Main effects of demographic variables, demands, control, and social relations on employees` perceived levels
of stress.
Stress
Variable Beta t Adj. R²
Gender -.04 -1.607 Age .00 - .044 Years of education .09 3.585*** Organizational size -.08 -3.405*** Block 1 .014*** Computer technology .08 2.990** Machine technology -.01 - .192 Block 2 .008** Demands .31 13.208*** Control .17 5.528*** Block 3 .158*** Social support -.11 -3.804***
Respect and development -.07 -1.774
Group pressure .16 5.892***
Harassment .08 3.488***
Bullying .12 4.650***
Block 4 .216***
Supervisor instrumental support -.05 -1.121 Supervisor informational support .03 .765 Supervisor socioemotional support -.20 -5.357***
Supervisor delegating -.01 - .390
Block 5 .015***
Sum R² adj., block 1-5 .411
F 51.503***
* : p < .05 **: p <= .01 ***: p<= .001
Demands stand out as the variable with the highest t-value; high demands cause a large increase in employees` perceived levels of stress. The most surprising finding is that high control also results in a significant increase in stress.
The two types of technology showed practically no direct effects on stress, but as we will see later, these effects varied to some degree with organizational size.
Supervisor socioemotional support stands out as the most crucial variable in the model for the reduction of stress, and is the only type of relation with supervisors that is of significance for stress. Among social relations with colleagues, so- cial support is the only type of relation that contributes to reduction in stress. The negative relations proved to be the most crucial, with group pressure being the second strongest predictor for increased stress overall.
Interaction effects
Using a factorial ANOVA (General Linear Model), we tested for five interaction effects: a) Demands X Con- trol, b) Organizational size X Computer technology, c) Organizational size X Machine technology, d) Demands X So- cial support, and e) Demands X Supervisor socioemotional support. Each variable was divided into two categories equivalent to a “high” and “low” level, with the exception of Organizational size which was categorized as: “small” (below 50 employees), “medium” (50-100 employees), and “large” (over 100 employees).
While no interaction effect was found between demands and control, the other four interactions proved to be significant.
Organizational size Large Medium Small Stress 50 48 46 44 42 40 Computer tech. Seldom Often Organizational size Large Medium Small Stress 52 50 48 46 44 42 40 Machine tech. Seldom Often a) b)
Figure 1a and b. Graphs of the interaction effects between computer and machine technology and organizational size
The interaction effect between computer technology and organizational size is shown graphically in Figure 1a: F(4.01); p < .05. A post hoc LSD test showed that there exist no significant differences in stress between employees that work with computer technology often in organizations of different sizes. But both the graph and the LSD test confirm that working with computer technology increases stress in small and large organizations, but has no significant effect on stress levels in medium-sized organizations.
Figure 1b shows the interaction effect between machine technology and organizational size: F(3.82), p < .05. In this case, the post hoc LSD test confirms the tendency apparent in the graph that working with machines is by far, most stressful in medium-sized organizations. As the graph indicates, work with machine technology causes a slight increase in stress in small organizations and a considerable increase in stress in medium organizations, but has no effect on stress in large organizations.
a) Demands High Low Stress 70 60 50 40 30 Social support Low High b) Demands High Low Stress 70 60 50 40 30 Superv. supp. Low High Figure 2a and b: Graphs of the interaction effects between a) Demands and social support and b) Demands and super-
Control Social support Computer technology Supervisor soc.em. support Demands Stress 0.13 0.16 -0.05 -0.07 0.23 0.35 0.16 -0.17 -0.33 -0.21 0.08 -0.32 0.14 0.25 R ² = 0.36
Figure 2a and 2b show the interaction effects between Demands and Social support among colleagues (F(4.59); p < .05) and between demands and supervisor socioemotional support (F(7.92); p < 0.5) respectively. As both graphs show, the effect of Demands is clearly dependent on the level of support. At high levels of support (either kind), both high and low levels of Demands cause less stress than they do at low levels of support.
The graphs and post hoc LSD tests also give partial support to the so-called buffering effect. Support from both col- leagues and supervisors are more effective in reducing stress when the demands are high rather than low. In other words, the moderating effect of social support on the relationship between demands and stress is stronger when de- mands are high. Nevertheless, the results indicate that both types of support moderate the effect of demands on stress at
both levels of demands, and this is inconsistent with the buffering effect, which claims that social support reduces stress only when demands are high.
Indirect effects
Using LISREL, we studied the indirect effects of technology and social support on stress (using Supervisor socioemo- tional support as social support from supervisors) in a structural equation model (SEM). A chi-square of .26 df = 1, p = .61) indicates that the model is a good fit to the sample data. (It is important to be aware of the fact that models with only one degree of freedom don’t necessarily have to fit the data very well to achieve acceptable goodness of fit scores, but this will have little effect on the regression coefficients in the model that are our primary focus.)
Figure 3. Direct and indirect effects between technology, social support, demands, control and stress.
The SEM in Figure 3 shows that both computer and machine technology contributed to an increase in stress by increasing Demands. At the same time, computer technology increases control, which in this population results in an additional increase in stress. Machine technology on the other hand, causes a considerable reduction in employee con- trol and in that way contributes to reduce stress.
Social support and supervisor socioemotional support show virtually no effect on demands, contrary to the theory of social support’s indirect effect on stress. However, support from both colleagues and supervisors bring about an in- crease in employees` perceived control, and hence (under these conditions) an increase in stress.
Finally, the SEM shows that control also has an indirect effect on stress: control reduces demands. So although control contributes to increase stress directly, it at the same time reduces stress by decreasing demands.
Machine technology
Discussion
The findings in this study contradict one of the most basic assumptions in the demand-control model. Control turned out to be one of the factors that contributed the most to increased stress in this population. Researchers have earlier suggested that social class (e.g. Karasek & Theorell, 1990) and personality and contextual aspects (Kaminski, 1993) can influence whether or not employees actually want control. On the other hand, for these employees, Karasek`s concept of control as a combination of decision authority and skill discretion may be completely irrelevant. Perhaps they experience more control through the influence of their union representatives, or from the predictability in their work, than from individual control over tasks.
This study also showed that the effects of technology on stress are far more complex than earlier studies have acknowledged by only studying the direct effect. The technologies showed practically no direct effect on stress, but this varied to some degree with organizational size (showing the importance of context variables). At the same time, both computer and machine technology strongly influenced stress indirectly, by altering the employees` perceived levels of demands and control.
Social support from both colleagues and supervisors operated to reduce stress directly. Both types of support also showed weak tendencies to reduce stress the most when the demands were high (the buffering effect). Other forms of social relations with colleagues and supervisors were identified as well, and strong relationships with stress were found for several of them. Negative relations among colleagues (e.g. group pressure and harassment) proved to be the most crucial types of relations for perceived stress. This indicates the need for expanding our understanding of social relations in future stress research.
Conclusion
A better understanding of the effects of technology on employees and organization require the identification of the context variables that determine which of the effects that will occur in different situations. We also present two suggestions for improvements in the demand-control-support model. The results indicate the need for a more occupa- tion-dependent control concept that to a higher degree will capture the essence of what the employees themselves actu- ally perceive as control. Finally, the model needs to include negative social relations as own types of relations, instead of viewing such relations merely as negative social support.
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