Stress and Counterproductive Work Behavior: Multiple ... - CiteSeerX

Journal of Occupational Health Psychology 2009, Vol. 14, No. 3, 257–271

© 2009 American Psychological Association 1076-8998/09/$12.00 DOI: 10.1037/a0014951

Stress and Counterproductive Work Behavior: Multiple Relationships Between Demands, Control, and Soldier Indiscipline Over Time Jennifer S. Tucker, Robert R. Sinclair, and Cynthia D. Mohr

Amy B. Adler, Jeffrey L. Thomas, and Angela D. Salvi

Portland State University

Walter Reed Army Institute of Research

Cognitive Resource Theory (CRT) suggests that under high levels of stress, employees are more prone to committing indiscipline. As few studies have examined this relationship over time, the authors conducted a six-wave longitudinal study examining the relationship of soldiers’ indiscipline with work demands and control. The study included archival data collected quarterly over 2 years from 1,701 soldiers representing 10 units in garrison (Germany and Italy), in training rotations (Grafenwoehr, Germany), and on peacekeeping deployments (Kosovo, Kuwait). No main effects were found for work overload, and the findings for the moderating effects of control were contradictory. Within each time point, as work overload increased, soldiers who felt less control committed more indiscipline, supporting CRT. Over time, however, as work overload increased, soldiers who perceived less control 6 months earlier committed less indiscipline. Additionally, the authors found reverse causal effects for control such that prior perceptions of a lack of control were associated with indiscipline and prior incidents of indiscipline with less control. Keywords: work stress, job control, indiscipline, counterproductive work behavior, longitudinal research

Jennifer S. Tucker, Robert R. Sinclair, and Cynthia D. Mohr, Department of Psychology, Portland State University; Amy B. Adler and Angela D. Salvi, U.S. Army Medical Research Unit-Europe, Jeffrey L. Thomas, Division of Psychiatry and Neuroscience, Department of Military Psychiatry, Walter Reed Army Institute of Research. We would like to acknowledge COL Carl A. Castro, PhD, as the principal investigator for the WRAIR protocol that the data for this paper were collected under. This research was conducted in partial fulfillment of Jennifer S. Tucker’s dissertation at Portland State University. We thank Wayne W. Wakeland, PhD, Talya N. Bauer, PhD, and Alan Cabelly, PhD, for assistance related to the preparation of this article. An earlier version of this paper was presented at the annual meeting of the Society for Industrial and Organizational Psychology, Dallas, TX, 2006. The views expressed in this paper are those of the authors and do not necessarily represent the official policy or position of the Department of Defense, the U.S. Army Medical Research and Materiel Command, or the U.S. Army Research Institute. Research findings described in this paper were collected under WRAIR Research Protocol #700 entitled, “A Human Dimensions Assessment of the Impact of OPTEMPO on the Forward-Deployed Soldier.” Correspondence concerning this article should be addressed to Jennifer S. Tucker, U.S. Army Research Institute, P.O. Box 52086, Fort Benning, GA, 31995-2086. E-mail: jennifer.s [email protected]

Increasingly frequent deployments and personnel shortages force United States military personnel to work longer and harder than in the recent past. For example, Reed and Segal (2000) cited a 1996 U.S. Department of the Army report indicating that although defense spending (before the war in Iraq) and military force structure had been reduced by approximately 38% and 35%, respectively, the use of military force had increased by almost 300%. Prior concerns about operations tempo (OPTEMPO), the “rate of military actions or missions” (Castro & Adler, 1999, p. 87), have been intensified by the Iraq war. In fact, compared to soldiers who have served only one Iraq deployment, soldiers serving on multiple deployments report significantly higher levels of acute stress (Office of the Surgeon General, 2006). Therefore, increased OPTEMPO represents a critical influence on work overload. Military deployments involve many stressors including isolation, ambiguity, danger, powerlessness, boredom, and work overload (Bartone, 2006) coupled with increased autonomy and decisionmaking discretion (Adkinson, 2000). One of the many concerns about such demanding work environments is that they may be conducive to counterproductive work behavior (CWB), typically

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defined as intentional employee behaviors that are contrary to organizational goals (Sackett & Devore, 2001). By undermining self-regulatory processes, stress is a proximal antecedent to CWB, weakening and eventually overriding the cognitive controls that prevent CWB (Boye & Jones, 1997). In the military context, most CWBs concern Campbell and colleagues’ (1990, 1993) Maintaining Personal Discipline and Physical Fitness/Military Bearing dimensions. Personal Discipline involves adherence to Army regulations and traditions, self-control, and high standards of conduct. Physical Fitness and Military Bearing involves maintaining an appropriate military appearance and staying in good physical condition. Together, these two forms of behavior reflect maintaining discipline; a failure to do so is sometimes referred to as indiscipline. Although the CWB literature provides a theoretical basis for hypotheses about indiscipline, indiscipline is a broad construct that includes other forms of counterproductive behavior that undermine unit effectiveness in addition to intentional acts. These behaviors may be unintentional (e.g., accidents) or may not be considered CWB in civilian organizations, such as violations of regulations (e.g., absenteeism, drug use, physical fitness; cf. MacDonough, 1991; Trlek, 2003). Thus, in the military context, indiscipline ranges from minor offenses, such as poor task performance, to serious offenses, such as criminal acts. Despite their wide differences in content, these forms of indiscipline share the common themes that they are conceptually related to self-regulation and soldiers who engage in them face disciplinary action.

Self-Regulation, Stress, and Indiscipline Although the idea that stressors influence indiscipline is certainly not new (cf. Mack, Shannon, Quick, & Quick, 1998), it has received relatively little attention from occupational health psychologists. Cognitive Resource Theory (CRT) provides a useful framework to study the stress-indiscipline link (cf., Probst & Brubaker, 2001). CRT describes how employees use self-regulatory processes to allocate cognitive resources to on- and off-task behaviors (Kanfer & Ackerman, 1989). Under stress, individuals experience a loss of self-regulatory capacity and are unable to persist on subsequent tasks (Baumeister, 2001). Thus, as work demands increase, employees have fewer resources to devote to performance or to refrain from indiscipline.

Work Overload Recent examples of indiscipline from the Iraq War offer important illustrations of the potentially severe effects of work overload on soldiers’ indiscipline. For example, the forces at the Abu Ghraib Detention Facility experienced chronic physical danger and were both understaffed and underresourced (Taguba, 2004). Thus, Bartone (2004) concluded that work overload most likely contributed to the abusive behavior that took place. Additional evidence supports linkages between work overload and the specific forms of indiscipline in our study including accidents (Cannon-Bowers & Salas, 1998), absenteeism (Bakker, Demerouti, de Boer, & Schaufeli, 2003; Dwyer & Ganster, 1991), aggression (Chen & Spector, 1992), and substance abuse (Bray, Fairbank, & Marsden, 1999; Moisan et al., 1999; Storr, Trinkoff, & Anthony, 1999; Trinkoff, Zhou, Storr, & Soeken, 2000). Although little research has explored work characteristics as predictors of exercise or rifle marksmanship performance, some evidence suggests potential links. For example, Payne, Jones, and Harris (2002) investigated the effects of workplace demands on individuals’ exercise behavior. They found that employees in high-strain jobs exercised less than those in low-strain jobs and that employees who intended to exercise but did not had higher work demands and lower exercise self-efficacy than those who actually did exercise. As the effects of high workload on performance may be stronger for tasks that require sustained attention (Huey & Wickens, 1993), work overload also should be negatively associated with performance on the Army’s Basic Rifle Marksmanship qualification exam. Passing the qualification exam reflects vigilance processes such as locating, marking, prioritizing, and determining the range to combat targets (cf. U.S. Department of the Army, 2003). To perform discrete control tasks, such as target acquisition, individuals make tradeoffs between the speed and accuracy of movements; known as Fitts’ Law, faster movements are made with less accuracy and more precise movements are made more slowly (Huey & Wickens, 1993). Successful performance depends on the individual’s correct assessment of whether speed or accuracy is most critical, and soldiers’ ability to respond to changing priorities is influenced by the total demands of current tasks, resource requirements, instructions, feedback, and training (Huey & Wickens, 1993). Thus, stress caused from work overload may affect exam performance if soldiers have

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insufficient resources to focus on marksmanship tasks. The preceding review suggests that work overload affects indiscipline through mediating self-regulatory mechanisms. For the present study, we expected that work stress would place increased demands on soldiers as they perform their primary job duties. As soldiers devote more self-regulatory resources to cope with stressors, they have fewer resources to maintain a high level of military discipline, professionalism, and performance. They may feel cognitively and emotionally overwhelmed and lack the self control to appropriately respond to demanding situations, leading to indiscipline.

Job Control Job control reflects the degree to which employees feel that they have autonomy over how they complete their job tasks or the degree to which they can participate in organizational decision-making processes (Jex, 1998). Low perceived control directly creates stress and influences employees’ reactions to their work environments (Spector, 1998). Job control also moderates stressor-performance relationships such that stress leads to more harmful outcomes when people believe they lack control over the situation (Jex, 1998). Thus, control should have both main effects on indiscipline and should buffer the work overload-indiscipline relationship. Although little research has examined the controlindiscipline link, Fox, Spector, and Miles (2001) found that perceived autonomy predicted organizational forms of CWB, such as work avoidance and speaking negatively about the organization, but not personal forms, such as insulting or refusing to help a coworker. Spector and Fox (2002) explained that this finding may be expected since autonomy is associated more with control over the work itself. Additional evidence for the buffering effects of control on the demands-indiscipline relationship is provided by studies showing that the combination of high job demands and low control is associated with behaviors such as increased drug use (Moisan et al., 1999; Storr et al., 1999) and absenteeism (Dwyer & Ganster, 1991) as well as less frequent physical exercise (Payne et al., 2002). Taken as a whole, this research suggests higher rates of indiscipline when people experience the combination of high task demands and low control.

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The Present Study Cross-sectional studies of stress cannot capture how stress-indiscipline relationships unfold over time and, thus, may miss important aspects of these relationships. However, few studies have examined stress-CWB relationships over time (Spector & Fox, 2005), and some research suggests potential ambiguity in the causal direction of these relationships (Penney & Spector, 2005; Spector & Fox, 2005). Longitudinal studies that permit stronger causal inferences are a needed contribution to this literature. Based on the preceding review, we expected work overload and control effects on indiscipline both within the same quarter (concurrent effects; a period of 3 months) and the following quarter (lagged effects; a period of 6 months). Hypothesis 1 (concurrent effects): Indiscipline is associated with increased work overload and decreased control within each time point. Hypothesis 2 (concurrent effects): Control moderates (attenuates) the work overload-indiscipline relationship within each time point such that soldiers who have more control are expected to commit less indiscipline under conditions of high workload than are soldiers who experience less control. Hypothesis 3 (lagged effects): Work overload experienced during one quarter is associated with increased indiscipline the following quarter. Hypothesis 4 (lagged effects): Control experienced during one quarter is associated with decreased indiscipline the following quarter. Hypothesis 5 (lagged effects): Control moderates (attenuates) the lagged work overloadindiscipline relation such that soldiers who have more control during one quarter are expected to commit less indiscipline the following quarter under conditions of high workload than are soldiers who experience less control. Sonnentag and Frese (2003), in a review of longitudinal studies of the stress-response process published from 1981 to 2000, found evidence of reverse effects between stressors and strains, that is, strain as a causal antecedent of stressors. We investigated similar effects in the present study. For example,

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soldiers who commit indiscipline may be confined to quarters or given less discretion over their job duties and thus experience less control. Soldiers who commit indiscipline also may experience increased workloads that reflect extra duties assigned as part of their units’ disciplinary response, as well as corrective physical fitness and marksmanship training or mandatory drug or alcohol treatment. Hypothesis 6: Indiscipline is associated with increased work overload and decreased control the following quarter.

Method Participants We used an archival sample from a larger study examining the effects of OPTEMPO conducted by researchers from the U.S. Army Medical Research Unit-Europe. Portions of this data set not concerning our central research questions appear in Castro and Alder (2005) and Tucker, Sinclair, Mohr, Adler, Thomas, and Salvi (2008). Our study used a portion of these data collected from surveys distributed quarterly to soldiers over 2 years (June 1999 through June 2001) and objective data from unit records. Although there was some variability across the quarters regarding when the surveys were administered (i.e., it was not exactly 3 months from survey to survey), we did not estimate growth curves, for which unequal time lags would raise particularly serious concerns. The soldiers were from 10 companies representing units in garrison (Germany and Italy), in training rotations (Grafenwoehr, Germany), and on peacekeeping deployments (Kosovo, Kuwait). The data set included 1,701 active Army personnel who completed at least one survey across six time points (Time 1 ⫽ 685, T2 ⫽ 670; T3 ⫽ 642; T4 ⫽ 604; T5 ⫽ 686, T6 ⫽ 599). Fifty-three percent of the participants were junior-enlisted (military Grades E1 through E4), 39% were noncommissioned officers, 6% were commissioned officers, and 2% were warrant officers. Further, 54% were White, 22% Black, 13% Hispanic, 3% Asian, and 7% other; 50% were married, 40% single, and 10% either separated or divorced. The mean age was 26.13 years; the soldiers averaged 5.76 years of military service, and most (85%) were male. These demographic characteristics are fairly typical of current active duty Army personnel (Office of Army Demographics, n.d.).

Measures Work overload. We assessed work overload with a three-item measure of quantitative work overload adapted from Cammann, Fichman, Jenkins, and Klesh’s (1983) Role Overload Scale (see Jex, Bliese, Buzzell, & Primeau, 2001 and Tucker, Sinclair, & Thomas, 2005 for military applications of this scale; alphas: T1 ⫽ .84, T2 ⫽ .83, T3 ⫽ .84, T4 ⫽ .83, T5 ⫽ .86, T6 ⫽ .89). Participants responded to the items using a 5-point agreement scale (1 ⫽ strongly disagree to 5 ⫽ strongly agree). Job control. We assessed control with a threeitem autonomy measure adapted from the Job Diagnostic Survey General Satisfaction Scale (Hackman & Oldham, 1975). Responses were made on a 5-point agreement scale (alphas: T1 ⫽ .75, T2 ⫽ .79, T3 ⫽ .78, T4 ⫽ .82, T5 ⫽ .82, T6 ⫽ .82). Indiscipline. We used objective unit data to assess several forms of indiscipline, including failed drug tests, physical fitness tests, and weapons qualifications tests; incidents of absence without leave (AWOL); accidents; Provost Marshall Incidents (PMIs; incidents involving the violation of civilian law); and Uniform Code of Military Justice violations (UCMJ; incidents involving the violation of military law). Of the 545 soldiers who committed indiscipline during any of the six time periods, only 111 (20.4%) were multiple offenders over time, and 81 (15%) repeated the same behavior, whereas only 30 (6%) had multiple incidents of different offenses. The most common repeated incidents were failed physical fitness and weapons qualification tests, which are among the less severe forms of indiscipline we studied. Few soldiers committed multiple offenses within each time period; no more than eight participants committed more than one offense during any 3-month time period (T1 ⫽ 3; T2 ⫽ 5; T3 ⫽ 8; T4 ⫽ 1; T5 ⫽ 1; T6 ⫽ 2). Several of these cases were people who appeared to have received multiple punishments for the same offense rather than being multiple offenders. We considered whether we needed to weight the incidents in terms of their severity or other characteristics. However, preliminary analyses indicated a very low base rate for behaviors judged as more severe by four military SMEs (e.g., UCMJ violations, AWOL; Table 1). This suggested that using a weighted scale or conducting separate analyses of serious incidents was neither necessary nor appropriate. Thus, all soldiers who committed at least one incident of indiscipline within a time period were coded as one; all other soldiers were coded as zero

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Table 1 Frequency of Indiscipline Behaviors Over Time Indiscipline behavior

T1

T2

T3

T4

T5

T6

Failed physical fitness tests Failed weapons qualifications PMIs UCMJ violations: Company Grade Article 15 UCMJ violations: Drug offense UCMJ violations: Field Grade Article 15 Accidents UCMJ violations: General misconduct AWOL UCMJ violations: Alcohol offense

36 20 22 9 3 2 3 1 0 1

26 38 14 7 6 4 4 1 2 1

33 13 20 15 6 13 2 3 0 1

53 13 6 5 1 3 0 0 1 0

32 3 7 6 7 2 8 2 3 1

26 9 20 13 0 3 0 2 1 2

Note. PMI ⫽ Provost Marshall Incidents; UCMJ ⫽ Uniform Code of Military Justice; AWOL ⫽ absent without leave. An Article 15 of the UCMJ is considered nonjudicial punishment. Company Grade Article 15s are administered by company grade commanders (Captains) resulting in lesser punishment; Field Grade Article 15s are administered by field-grade commanders (Major or above) resulting in greater punishment.

(indiscipline T1 ⫽ 98; T2 ⫽ 103; T3 ⫽ 108; T4 ⫽ 84; T5 ⫽ 73; T6 ⫽ 79). Control variables. Based on a literature review and preliminary correlational analyses, we used the following controls in all of the analyses: deployment tempo, gender, tobacco and alcohol use, physical exercise, age, and rank. Deployment tempo is an OPTEMPO measure reflecting the rate at which soldiers deploy during their enlistments; the total number of deployments is divided by years of military service (Castro & Adler, 1999). We imputed the value .29 for soldiers with less than a year of service (a retired Army Major indicated that new enlisted personnel train for approximately 14 weeks, which is 3.5 months out of a year or .29). For tobacco use, participants were asked to provide the average number of times per day they used tobacco in the past week. For alcohol use, participants were asked how many alcoholic drinks they had in the past week. For physical exercise, participants were asked how many times they exercised for 30 min or more during the past week.

Missing Data Issues and Analyses There was very little missing data within each case, as soldiers tended to provide complete data. Therefore, we replaced item-level missing data with the mean of the individual’s responses to the rest of the scale. However, many soldiers did not complete a survey at every time point (461 participants completed one survey, 391 completed two, 310 completed three, 218 completed four, 113 completed five, and 46 completed all six surveys). There are many

possible combinations of missing data across the six time points. For example, one soldier could have completed a survey at T2 and T4, whereas another may have responded at T3 and T6. However, the general pattern of missing data is illustrated by examining the 685 soldiers who completed the T1 survey, 433 of whom also completed T2 surveys, 260 completed T1 through T3, 144 completed T1 through T4, 80 completed T1 through T5, and 46 completed all six surveys. A series of MANOVAs and chisquares indicated no significant differences between responders and nonresponders on the mean levels of or covariation among the study variables, suggesting that any systematic differences between responders and nonresponders are inconsequential to the analyses of interest. Because of the multilevel structure of the data and the unbalanced nature of the variables (i.e., soldiers having different numbers of surveys), we tested our hypotheses with a hierarchical linear modeling software program (HLM 6.02 software package; Raudenbush, Bryk, & Congdon, 2005). HLM assumes data are missing at random and is quite robust to missing data, making use of the entire dataset, even when only one observation is present (Raudenbush & Bryk, 2002); cases providing fewer observations are weighted less than those providing more observations (Snijders & Bosker, 1999). Thus, compared to other types of longitudinal analyses (e.g., structural equation modeling), our data pose less of a problem for multilevel analyses (Snijders & Bosker, 1999). Because indiscipline had a binary distribution, a simple log transformation would not yield an approximately normal distribution. Therefore, we conducted

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the nonlinear multilevel regression analyses described below using a binomial (Bernoulli) sampling model with a logit link function (Raudenbush & Bryk, 2002, p. 457). We estimated two sets of equations: a within-person model (Level 1) and a between-person model (Level 2) with all variables centered in all analyses. We interpreted the results for the population-average models with robust standard errors (Raudenbush & Bryk, 2002, pp. 303–304). Because of power considerations, researchers using multilevel models have reported findings using a more liberal significance level (i.e., p ⬍ .10; Bliese & Britt, 2001). Thus, we report findings at the p ⬍ .10 and .05 significance levels for the multilevel regression analyses. In longitudinal analyses, failure to account for autocorrelation leads to underestimated standard errors and inflated t values (Bliese & Ployhart, 2002). We controlled for these effects by entering five orthogonal dummy variables for the six time points as fixed effects in the concurrent regression models (Hox, 2002). The inclusion of the lagged indiscipline variables accounted for the autoregressive nature of the data (Cohen, Cohen, West, & Aiken, 2003), precluding the need for the dummy variables.

Results The between-person correlations across the time points indicated that, as expected, earlier reports of control were negatively associated with later incidents of indiscipline for six time points (r ⫽ ⫺.09 to ⫺.13, p ⬍ .05; Table 2). Unexpectedly, earlier incidents of indiscipline were negatively related to work overload for three time points (r ⫽ ⫺.08 to ⫺.14, p ⬍ .05). Interestingly, work overload was not associated with control within each time period, but was related to control over time. For example, T1 work overload was related to T3 and T4 control (r ⫽ ⫺.11 and ⫺.14, p ⬍ .05, respectively). Although indiscipline was related across all of the time points, the strongest associations were found between the current time point and the first subsequent time point. For example, T1 indiscipline was more strongly related to T2 indiscipline (r ⫽ .27, p ⬍ .01) than to T5 indiscipline (r ⫽ .09, p ⬍ .05). The control variables were related to each other and to other study variables as expected.

Concurrent Effects The results for the concurrent within-person relationships (see Table 3) revealed that only the inter-

action of work overload and control (b ⫽ ⫺.09, p ⬍ .05) was related to indiscipline. Thus, Hypothesis 1 was not supported. We interpreted the interaction by plotting the work overload-indiscipline relationship for soldiers with high and low control defined as 1 standard deviation (SD) above and below the mean, respectively (Aiken & West, 1991). We obtained the within-person SDs by estimating null models (a model with a random intercept and no predictors) with work overload and control as the outcomes. Figure 1 shows that as work overload increased, soldiers who felt less control at work committed more indiscipline, supporting Hypothesis 2. To examine the simple slopes for the overload-control interaction, we performed significance tests of the work overload-indiscipline relationships by recentering control so that 0 represented either high or low control. The tests of the simple slopes showed that soldiers who experienced less control were more likely to commit indiscipline (b ⫽ .10, p ⬍ .05); there was no such relationship for soldiers who experienced more control (b ⫽ ⫺.01, p ⫽ .82, ns).

Lagged Effects With six of waves of data, it is possible to estimate models with five time lags; however, preliminary analyses indicated insufficient degrees of freedom to estimate models with third-, fourth-, and fifth-order time lags. Therefore, Table 4 shows the results for the final model with first- and second-order time lags such that work overload and control during one quarter predict indiscipline either the following quarter (4 to 6 months later; first-order time lag) or two quarters later (7 to 9 months later; second-order time lag). The results for a model including only the first-order lags indicated that the predictors did not vary across individuals so we fixed all the slopes in the final model. The results demonstrated that control at the first-order time lag (b ⫽ ⫺.48, p ⬍ .05) was a significant predictor after controlling for the indiscipline autocorrelation (see Table 4), providing support for Hypothesis 4. To interpret these findings and generate an index of effect size we exponentiated the partial slope for control. For each unit increase in prior reports of control at the first-order time lag, the odds of soldiers committing indiscipline decreased by 38% (100[e(-.48) – 1]). The interaction of work overload and control at the second-order time lag also was a significant predictor of indiscipline (b ⫽ .26, p ⬍ .05). However, Figure 2 shows that as work overload increased, soldiers who felt less control 9 months previously committed less

STRESS AND CWB

indiscipline, which was unexpected. Tests of the simple slopes showed that soldiers who experienced less control were less likely to commit indiscipline (b ⫽ ⫺.28, p ⬍ .10); for soldiers who experienced more control, there was no such relationship (b ⫽ .03, p ⫽ .84, ns). Thus, Hypothesis 5 was not supported. Further, soldiers who committed indiscipline were likely to be repeat offenders (see Table 4). After controlling for work overload and control, indiscipline at both the first-order (b ⫽ 2.93, p ⬍ .05) and second-order time lags (b ⫽ .80, p ⬍ .05) predicted current indiscipline. Exponentiation of these partial slopes indicates that prior incidents of indiscipline increased the odds of soldiers committing current indiscipline by a factor of 18.73 (6-month lag) and 2.23 (9-month lag).

Reverse Causal Effects As shown in Table 5, soldiers who committed indiscipline the previous quarter reported less control the following quarter (b ⫽ ⫺.10, p ⬍ .10), providing partial support for Hypothesis 6.

Discussion A self-regulatory perspective on indiscipline suggests that stressors undermine soldiers’ abilities to maintain a high level of discipline and performance. Consistent with the perspective that indiscipline is a distal outcome of the stress response, the lack of concurrent findings for the main effects of stress suggests that a period of 3 months may be too short for individuals to exhibit maladaptive coping responses resulting in indiscipline. Alternately, some forms of indiscipline may be the result of “hot feelings,” such as a sense of injustice or intense interpersonal conflict, and may subside quickly, making a 3-month time period too long for such analyses. Future research is needed to determine whether performance decrements occur in less than a 3-month time period as researchers have found that stress negatively affects some psychosocial outcomes in less than 3 months (De Lange, Taris, Kompier, Houtman, & Bongers, 2003). These possibilities illustrate the continued need for research to identify the appropriate time frame for longitudinal stress research. Ideally, such studies would allow researchers to specify appropriate time frames for a given set of predictors and criteria and generate predictions about expected patterns of findings given a particular time frame and set of variables. Given that this ideal may be unrealistic, we call for continued accumulation of

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findings concerning stress-outcome relationships across different time frames. Overall, the general lack of effects for work overload was somewhat surprising, especially given prior research linking work overload to a host of psychosocial outcomes. It could be that work overload affects other outcomes not included in this study. For example, past military research has linked higher work overload with lower well-being, attachment, and readiness outcomes and higher depression (Tucker et al., 2005). Similarly, research with civilian samples has found that work overload is positively related to psychological strains such as anxiety and frustration (Spector & Jex, 1998). Future research could investigate other types of indiscipline that may have stronger associations with work overload. For instance, employees who feel overloaded may feel justified in being tardy or stealing from the organization. On the other hand, deployed soldiers often have a strong sense of purpose to their missions, such as in the case of peacekeepers delivering desperately needed support to civilian populations beset by civil conflict. Thus, when deployments are intensely stressful, the sense of purpose associated with the mission may help buffer soldiers from the negative effects of work overload. Although military deployments involve quite unique work in some respects, members of civilian organizations may experience similar psychological effects when critical and engaging events take place in the organization, such as the debut of an important new product. During these events, employees may feel more committed to the organization and avoid committing indiscipline. Our findings reinforce prior literature suggesting the importance of control as a resource for helping employees cope with work demands and maintain acceptable performance levels. Frese (1989) suggested that employees who have greater control may influence the design of their jobs, thus minimizing stressors. However, our results indicated that the control effects did not persist for more than one time lag (approximately 6 months). It is important to note that we were only able to test these relationships out to two time lags and other patterns of control effects are possible. For example, the effects may be present at the first time lag, absent at the second time lag, and present at subsequent time lags. These results, along with findings showing control effects at a 1-year time lag (De Lange, Taris, Kompier, Houtman, & Bongers, 2004), highlight a need for theoretical developments and supporting empirical data concerning

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Table 2 Means, Standard Deviations, Internal Consistency Estimates, and Intercorrelations Among the Study Variables Scale Average control variables 1. Age 2. Gender 3. Rank 4. Alcohol use 5. Tobacco use 6. Physical exercise 7. Deployment tempo Predictors 8. Work overload T1 9. Control T1 10. Work overload T2 11. Control T2 12. Work overload T3 13. Control T3 14. Work overload T4 15. Control T4 16. Work overload T5 17. Control T5 18. Work overload T6 19. Control T6 Outcomes 20. Indiscipline T1 21. Indiscipline T2 22. Indiscipline T3 23. Indiscipline T4 24. Indiscipline T5 25. Indiscipline T6 Scale Predictors 9. Control T1 10. Work overload T2 11. Control T2 12. Work overload T3 13. Control T3 14. Work overload T4 15. Control T4 16. Work overload T5 17. Control T5 18. Work overload T6 19. Control T6 Outcomes 20. Indiscipline T1 21. Indiscipline T2 22. Indiscipline T3 23. Indiscipline T4 24. Indiscipline T5 25. Indiscipline T6

Mean

SD

1. 2. Age Gender

25.93 5.90 (—) .85 .36 .07ⴱ 15.10 3.36 .54ⴱ 5.17 8.69 ⫺.16ⴱ 6.54 11.19 ⫺.09ⴱ 3.93 1.66 ⫺.06ⴱ .23 .67 ⫺.03 2.99 3.59 3.04 3.50 3.02 3.57 3.01 3.55 3.07 3.51 3.02 3.54

.89 .05 .79 .27ⴱ .84 .06 .81 .31ⴱ .89 .07 .82 .22ⴱ .85 .09ⴱ .83 .21ⴱ .86 ⫺.04 .83 .23ⴱ .86 .09ⴱ .82 .18ⴱ

.07 .08 .08 .06 .05 .06 9. C T1

.26 .27 .27 .23 .22 .24

⫺.10ⴱ ⫺.08ⴱ ⫺.14ⴱ ⫺.08ⴱ ⫺.11ⴱ ⫺.07ⴱ

3. Rank

4. 5. 6. 7. 8. Alcohol Tobacco Exercise Deploy WO T1

(—) .07ⴱ .19ⴱ .15ⴱ .06ⴱ .10ⴱ

(—) ⫺.11ⴱ ⫺.13ⴱ ⫺.08ⴱ ⫺.02

(—) .27ⴱ .02 .09ⴱ

(—) ⫺.03 .03

(—) ⫺.00

(—)

.13ⴱ ⫺.03 .07 ⫺.02 .07 .11ⴱ .13ⴱ .03 .03 ⫺.06 .10ⴱ ⫺.11ⴱ

.15ⴱ .17ⴱ .07 .24ⴱ .18ⴱ .15ⴱ .16ⴱ .18ⴱ .11ⴱ .19ⴱ .08 .17ⴱ

.07 ⫺.07 .05ⴱ ⫺.09 .04 ⫺.00 .03 ⫺.04 .00 ⫺.08ⴱ ⫺.00 ⫺.05

.03 ⫺.04 .04 ⫺.11ⴱ .01 ⫺.04 .08 ⫺.00 .01 ⫺.08ⴱ ⫺.01 ⫺.09ⴱ

⫺.17ⴱ .10ⴱ ⫺.01 .09ⴱ ⫺.05 .04 ⫺.06 .08 ⫺.04 .04 .01 .00

⫺.01 .04 .08 ⫺.03 .03 ⫺.01 .02 .05 ⫺.01 ⫺.05 ⫺.01 ⫺.06

(.84) ⫺.02 .32ⴱ ⫺.08 .28ⴱ ⫺.11ⴱ .34ⴱ ⫺.14ⴱ .35ⴱ ⫺.06 .34ⴱ ⫺.05

.02 .03 .03 .01 .00 .02

.01 ⫺.01 ⫺.02 ⫺.02 ⫺.03 ⫺.03

⫺.02 ⫺.04 ⫺.03 ⫺.07 ⫺.03 ⫺.06

.04 ⫺.03 ⫺.04 ⫺.01 .03 ⫺.02

⫺.11ⴱ ⫺.10ⴱ ⫺.17ⴱ ⫺.14ⴱ ⫺.17ⴱ ⫺.14ⴱ

.05 .06 .06 .04 .05 .02

.06 .07ⴱ .05 .06ⴱ .07ⴱ .02

10. 11. 12. 13. 14. 15. 16. 17. 18. 19. WO T2 C T2 WO T3 C T3 WO T4 C T4 WO T5 C T5 WO T6 C T6

(.75) ⫺.06 .47ⴱ ⫺.03 .35ⴱ .04 .49ⴱ ⫺.00 .37ⴱ ⫺.19ⴱ .35ⴱ

(.83) ⫺.08ⴱ .44ⴱ ⫺.14ⴱ .32ⴱ ⫺.12ⴱ .33ⴱ ⫺.10 .36ⴱ ⫺.05

(.79) ⫺.09 .46ⴱ ⫺.12 .33ⴱ ⫺.06 .37ⴱ .01 .36ⴱ

(.84) ⫺.04 .54ⴱ ⫺.13 .38ⴱ ⫺.08 .27ⴱ ⫺.09

(.78) ⫺.04 .46ⴱ .03 .42ⴱ .04 .35ⴱ

(.83) .03 .45ⴱ ⫺.06 .39ⴱ ⫺.11

(.82) .09 .47ⴱ ⫺.07 .36ⴱ

(.86) .04 .31ⴱ ⫺.15ⴱ

(.82) ⫺.01 .43ⴱ

⫺.07 ⫺.07 ⫺.00 ⫺.06 ⫺.12ⴱ ⫺.04

.04 .02 .04 ⫺.06 ⫺.01 ⫺.07

⫺.04 ⫺.01 ⫺.05 ⫺.09ⴱ ⫺.11ⴱ ⫺.09ⴱ

⫺.03 ⫺.03 .03 .01 .07 ⫺.03

⫺.05 ⫺.02 ⫺.01 ⫺.02 ⫺.05 ⫺.03

⫺.14ⴱ ⫺.12ⴱ ⫺.02 .00 .02 ⫺.07

⫺.07 ⫺.01 ⫺.03 ⫺.09ⴱ ⫺.13ⴱ ⫺.12ⴱ

⫺.02 .03 ⫺.01 ⫺.08ⴱ ⫺.07 ⫺.06

⫺.01 .06 ⫺.04 ⫺.08ⴱ ⫺.13ⴱ ⫺.06

Scale

20. I T1

21. I T2

22. I T3

Outcomes 20. Indiscipline T1 21. Indiscipline T2 22. Indiscipline T3

(—) .56ⴱ .27ⴱ

(—) .38ⴱ

(—)

23. I T4

24. I T5

(.89) .00 ⫺.03 .01 ⫺.03 ⫺.04 ⫺.03 ⫺.08

(.82) .06 .09 .04 .03 ⫺.10ⴱ ⫺.04 25. I T6

(table continues)

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Table 2 (Continued) Scale 23. Indiscipline T4 24. Indiscipline T5 25. Indiscipline T6

20. I T1

21. I T2

22. I T3

23. I T4

24. I T5

25. I T6

.19ⴱ .09ⴱ .16ⴱ

.25ⴱ .16ⴱ .18ⴱ

.43ⴱ .25ⴱ .24ⴱ

(—) .35ⴱ .35ⴱ

(—) .54ⴱ

(—)

Note. Coefficient alpha internal consistencies are located on the diagonal in parentheses. Spearman’s rho was computed for associations between all the variables and indiscipline behaviors as well as between all the variables and gender. Pearson correlations were computed for all other associations. Pairwise n ⫽ 151–1,696. Gender: males ⫽ 1, females ⫽ 0. Deployment tempo ⫽ number of deployments/number years in service. Rank: 11–19 ⫽ Enlisted, 21–29 ⫽ Commissioned Officers, 30 –35 ⫽ Warrant Officers. WO ⫽ work overload; C ⫽ control; I ⫽ indiscipline; T ⫽ time. ⴱ p ⬍ .05, at least.

Table 3 Multilevel Regression Results for the Concurrent Work Overload- and Control-Indiscipline Relationships Indiscipline Relationship (n ⫽ 1,625) Level-1 model: Main effects Work overload Control Level 1 model with the interaction Work Overload ⫻ Control Level-2 model: Control variables Age Gender Rank Alcohol use Tobacco use Physical exercise Deployment tempo

b

SE

t

.03 ⫺.04

.03 .04

.75 ⫺1.11

⫺.09ⴱ

.04

⫺2.47

⫺.02ⴱ ⫺.04 ⫺.10ⴱ .02ⴱ .00 .03 ⫺.06

.01 .11 .02 .01 .00 .02 .04

⫺2.82 ⫺.36 ⫺6.40 3.38 .68 1.17 ⫺1.51

Note. The concurrent analyses controlled for autocorrelation effects. Two different models were estimated to determine the main effects of work overload and control and the interaction effect of work overload and control on indiscipline; b values (unstandardized regression coefficients) represent the test of the average work overloadindiscipline, control-indiscipline, and Work Overload ⫻ Control-Indiscipline relationships against zero. All slopes were random. ⴱ p ⬍ .05, at least.

flect the organization’s response to indiscipline as soldiers who committed indiscipline had much less control over their job duties in subsequent quarters. This finding highlights the potential value of longitudinal designs and demonstrates that models of the stress-outcome relationship that rely on crosssectional designs oversimplify what may often be a complex dynamic process between stressors and responses to stress. Prior reviews provide only modest support for the methodological effects of work overload and control on individual outcomes (De Lange et al., 2003; Sonnentag & Frese, 2003; van der Doef & Maes, 1998, 1999). van der Doef and Maes (1999) recommended that researchers include measures of both workplace demands and control at each time point to determine whether the weak effects in prior studies reflect changes in work characteristics over time. By following these recommendations, we found that control moderated the concurrent and lagged effects of work overload on indiscipline, although not always in the expected pattern of effects.

Low Control

Indiscipline (logit link)

the appropriate timeframe(s) for when control effects are likely to occur and for how long they are likely to persist. Better predictions as to the long-term effects of interventions aimed at increasing control at work also would provide practitioners with critical information for cost/benefit analyses. The reverse causal effects indicate that soldiers who committed indiscipline during one quarter reported less control the following quarter. These findings partially supported our predictions and may re-

High Control

0.14 0.13 0.12 0.11 Low

High

Work Overload

Figure 1. Plot of the interaction between concurrent work overload and control predicting indiscipline.

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Table 4 Multilevel Regression Results for the Autoregressive Analyses of Work Overload, Control, and Indiscipline Indiscipline Autocorrelations (n ⫽ 1,625) Level-1 model Indiscipline lag 1 Indiscipline lag 2 Work overload lag 1 Work overload lag 2 Control lag 1 Control lag 2 Level-1 model with the interaction Work Overload ⫻ Control lag 1 Work Overload ⫻ Control lag 2 Level-2 model: Control variables Age Gender Rank Alcohol use Tobacco use Physical exercise Deployment tempo

b

SE

t

2.93ⴱ .80ⴱ .21 ⫺.10 ⫺.48ⴱ .19

.32 .33 .15 .15 .16 .19

9.02 2.44 1.38 ⫺.69 ⫺3.04 1.01

⫺.03 .26ⴱ

.11 .12

⫺.26 2.15

.04 ⫺.19 ⫺.13 .01 ⫺.00 ⫺.07 .57†

.03 .38 .10 .02 .01 .10 .32

1.18 ⫺.50 ⫺1.29 .84 ⫺.17 ⫺.74 1.79

Note. The models tested the within-person lagged effects of work overload and control on indiscipline after controlling for the indiscipline autocorrelation for T1–T6. Lag 1 and lag 2 refer to first-order (4 to 6 months later) and second-order (7 to 9 months later) time lags. Different models were estimated to determine the main effects of work overload and control and the interaction effects of work overload and control on indiscipline; b values (unstandardized regression coefficients) represent the test of the average indiscipline-indiscipline, work overload-indiscipline, control-indiscipline, and Work Overload ⫻ Control-Indiscipline relationships against zero. All slopes were fixed. ⴱ p ⬍ .05, at least. † p ⬍ .10.

The concurrent interactive effects supported the CRT approach, as soldiers who experienced more work overload and less control over their job duties committed more indiscipline within each time point. Interestingly, the moderating effects of control changed over time such that soldiers who perceived less control 6 to 9 months earlier committed less

indiscipline as their work overload increased. They may have had access to other resources, such as a supportive supervisor or a hardy personality, which helped them refrain from indiscipline under high workloads. On the other hand, our findings may not be surprising given the military nature of the sample; people who join the Army may prefer more structured work

Low Control

Indiscipline (logit link)

High Control

0.05 0.04 0.03 0.02 Low

High Work Overload

Figure 2. Plot of the interaction between the second-order lagged work overload and control variables predicting indiscipline.

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267

Table 5 Multilevel Regression Results for Lagged Indiscipline-Work Overload and Indiscipline-Control (Reverse Causal) Relationships Work overload Relationship (n ⫽ 1,625) Level-1 stress model Work overload lag 1 Indiscipline lag 1 Level-2 Model: Control variables Age Gender Rank Alcohol use Tobacco use Physical exercise Deployment tempo Level-1 stress model Control lag 1 Indiscipline lag 1 Level-2 model: Control variables Age Gender Rank Alcohol use Tobacco use Physical exercise Deployment tempo

Control

b

SE

t

.31ⴱ ⫺.04

.02 .06

12.68 ⫺.69

⫺.00 .13ⴱ .03ⴱ .00 .00 .01 ⫺.04

.00 .06 .01 .00 .00 .01 .06

⫺.43 2.25 3.54 .87 .38 .68 ⫺.71

b

SE

t

.41ⴱ ⫺.10†

.02 .06

16.56 ⫺1.67

.01ⴱ .01 .01ⴱ ⫺.01ⴱ ⫺.00 .04 .01

.00 .05 .01 .00 .00 .01 .05

3.83 .28 2.12 ⫺2.15 ⫺.58 3.01 .15

Note. The models tested the within-person reverse causal relationships for T1-T6. Lag 1 refers to a first-order time lag (4 to 6 months later); b values (unstandardized regression coefficients) represent the test of the average lagged work overload-work overload, indiscipline-work overload, control-control, and indiscipline-control relationships against zero. The Level-1 indiscipline, work overload, and control slopes were allowed to vary across the individuals. ⴱ p ⬍ .05, at least. † p ⬍ .10.

and supervision and perform quite well having to adhere to external rules and regulations. Further, as this rigid work environment is stable across time, it is very possible that the effects from experiencing less control would persist over time. One explanation for why the interactive effects for control changed over time is that the work environments for the soldiers changed during the data collection period. That is, the level of control soldiers had over their work performance may have increased as they were transferred to training rotations and then deployed. The deployed work environment often affords soldiers with more control over their work than when they are at their home stations. Thus, it is likely that as the soldiers’ work environments changed so did their perceptions of control. Soldiers with greater control may have had more flexibility in prioritizing the heavy demands caused by the increased OPTEMPO, leading to better management of valuable resources, such as time, energy, and motivation, and the ability to maintain effective performance.

Thus, although we share Taris’ (2006) skepticism about the existence of a broadly generalizable demand-control interaction, our study demonstrates that control effects may be more complex than originally theorized, highlighting the continued need for longitudinal research in this area. Frese and Zapf (1988) suggested that the stress response for a single stressor over time could reflect combinations of five different trajectories. For instance, coping strategies that initially reduce the stressor effects may be ineffective in the long term, leading to increased effects over time. The combination of these processes would best be depicted by a wave-shaped curve capturing health status over time (Frese & Zapf, 1988). As very little is known regarding the exact timeframe in which a stressor affects health or when adaptation might occur (Frese & Zapf, 1988), one fruitful area for future research would be continued investigation of the multiple possible effects of stressors over time to determine whether the results generalize across performance criteria, research contexts, and research designs.

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Other Research Directions Subsequent studies could employ CRT to test the self-regulation processes affecting indiscipline. Roe (1999) proposed five self-regulation mechanisms related to performance: action, energetic, emotional, vitality, and self-image. Factors affecting action preparation (e.g., analysis and planning) and action execution (e.g., executing and controlling) could be assessed to determine whether employees have the necessary personal (e.g., skills) or environmental resources (e.g., supplies) to maintain an appropriate level of discipline. Further, emotionally aroused workers may reduce preparatory activity (e.g., problem solving) and take shortcuts during action execution (Roe, 1999). Researchers could investigate which emotion regulation strategies are more effective in coping with stress and ultimately preventing indiscipline. Past research has established that personality traits affect the propensity to commit indiscipline (Sackett & Devore, 2001; Sinclair & Tucker, 2006). Although indiscipline was collapsed into one outcome for this study, future research could examine specific traitindiscipline relationships. For example, selfregulatory traits, such as self-control (cf. Baumeister, 2001) might be more strongly associated with what Spector, Fox, Penney, Bruursema, Goh, and Kessler (2006) describe as hostile CWB rather than instrumental CWB. Similarly, Bartone (2004) in reference to the Abu Ghraib abuse posited, “it may be that persons high in Agreeableness (including Trust and Altruism) make more compassionate and effective prison guards, less likely to engage in prisoner abuse” (p. 13).

Methodological Issues and Limitations Objective data have certain psychometric advantages in that they eliminate some potential concerns with subjective interpretations of events as well as some concerns about participant recall. However, indiscipline may be underreported for several reasons, including organizational culture influences in which certain types of indiscipline may not be regarded as a serious problem, social norms that discourage participants from reporting, and motives of unit leaders who seek to maintain a good public image for their units. Underreporting of indiscipline leads to suppressed variability and the resulting underestimation of potential stress effects. Supplementing objective data with other data sources may prove beneficial, if not essential, for indiscipline research.

For example, peer ratings or self-reports might be better data for low intensity indiscipline that is less likely to be formally reported. A second issue related to the nature of our indiscipline measure concerns the nature of the incidents included in the measure. Some behaviors considered to be punishable offenses by the Army, such as failing physical fitness or weapons tests, are not defined as indiscipline by most civilian organizations. Including these incidents in our measure increases the potential for inter- and intraindividual variability and thus for stronger stressor effects than might be found in a civilian setting. Although such differences raise generalizability concerns, the incidents of interest are critical concerns to the military, particularly in a deployed environment. In any case, investigation of intraorganizational variability and generalizability of effects remains an important question for further research. Another potential concern with our study is that our measures concerned a series of 3-month time intervals. Within each interval, it is possible that soldiers committed indiscipline before completing our surveys. Soldiers who committed indiscipline before responding to our surveys may have received resources by their leaders to cope with any work stress causing the poor performance. For example, soldiers who failed a physical fitness test may have been given time during normal duty hours to work on their fitness. This could conceivably result in a negative relationship between indiscipline and stress within a time period. Alternatively, soldiers who committed indiscipline may have received punishments from the organization before completing the stress assessments. To the extent that these punishments were stressful, this would produce a positive relationship between indiscipline and stress. These possibilities illustrate some of the challenges of linking self-reported and objective data and demonstrate the need for ever greater precision in specifying temporal aspects of stress-related processes.

Practical Implications Our findings indicated that, relative to periods of less control, intervals after a period of enhanced control were associated with a 38% lower likelihood of indiscipline. The authority structure of the military may make participatory interventions impractical, particularly for deployed soldiers. However, assuming our findings generalize to the civilian context, they suggest another potential benefit of control-focused interventions. Managers could

STRESS AND CWB

increase control by implementing participative decision-making strategies in which those who are responsible for executing the decisions provide input into their formulation (Jex, 1998). Although we did not test for escalating patterns of indiscipline, the findings suggest that soldiers who committed indiscipline during one quarter were more likely to commit indiscipline the following quarter. This could be an important finding for managers looking for ways to prevent negative outcomes for their organizations. For example, employees who are poor performers or commit drug-related offenses could eventually cause work accidents that are both costly and harmful. These possibilities suggest the value of zero-tolerance policies for indiscipline that require offenders to receive additional training or rehabilitation. Similarly, the consistency of indiscipline over time could imply a dispositional basis to indiscipline that can be addressed in selection systems (Sackett & DeVore, 2001). Clearly, leaders should consider the nature, severity, and duration of the offense when deciding on the appropriate response to indiscipline; however, they may want to regard minor incidents of indiscipline as potential warning signs rather than isolated incidents.

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Received January 3, 2008 Revision received October 14, 2008 Accepted November 3, 2008 y