SPECIAL ISSUE PAPER

Team Spirit Makes the Difference: The Interactive Effects of Team Work Engagement and Organizational Constraints during a Military Operation on Psychological Outcomes Afterwards S. M. Boermans1*†, W. Kamphuis2, R. Delahaij2, C. van den Berg3 & M. C. Euwema1 1

Research Group Organizational and Occupational Psychology and Professional Learning, University of Leuven, Leuven, Belgium Department of Human Behaviour & Organisational Innovations, TNO, Soesterberg 3 Ministry of Defence Netherlands, Behavioural Sciences, Utrecht, The Netherlands 2

Abstract This article prospectively explores the effects of collective team work engagement and organizational constraints during military deployment on individual-level psychological outcomes afterwards. Participants were 971 Dutch peacekeepers within 93 teams who were deployed between the end of 2008 and beginning of 2010, for an average of 4 months, in the International Security Assistance Force. Surveys were administered 2 months into deployment and 6 months afterwards. Multi-level regression analyses demonstrated that team work engagement during deployment moderated the relation between organizational constraints and post-deployment fatigue symptoms. Team members reported less fatigue symptoms after deployment if they were part of highly engaged teams during deployment, particularly when concerns about organizational constraints during deployment were high. In contrast, low team work engagement was related to more fatigue symptoms, particularly when concerns about organizational constraints were high. Contrary to expectations, no effects for team work engagement or organizational constraints were found for post-traumatic growth. The present study highlights that investing in team work engagement is important for those working in highly demanding jobs. Copyright © 2014 John Wiley & Sons, Ltd. Received 8 October 2013; Revised 20 October 2014; Accepted 21 October 2014 Keywords team work engagement; organizational constraints; longitudinal research; fatigue symptoms; post-traumatic growth *Correspondence Sylvie Boermans, Research Group Organizational and Occupational Psychology and Professional Learning, University of Leuven, Tiensestraat 102, B-3000, Leuven, Belgium. † Email: [email protected] Published online in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/smi.2621

Introduction High-reliability organizations (HROs) such as emergency, police, military, function in hazardous, fast-paced, and hyper dynamic environments, where reliable performance is crucial as errors can have devastating consequences. In order to achieve high reliability performance, HROs are typically characterized by a high degree of reciprocal interdependency among units and levels, complex technologies, a strong chain of command, and strict rules and procedures concerning response options (Roberts & Rousseau, 1989; Weick, Sutcliffe, & Obstfeld, 2008). However, such organizational constraints appears to be an important stressor among HRO professionals as it limits their capacity to control situational demands (e.g. Boermans, Kamphuis, Delahaij, Korteling, & Euwema, 2013; Gaines & Jermier, 1983; Hoge et al., 2004; Schwarz, 2005). It 386

therefore seems important to understand what enables them to effectively deal with organizational constraints while working in high-risk environments. In the present article, we elaborate on the relationship between organizational constraints, team-level work engagement and long-term individual-level outcomes in a military setting. On the basis of the job demand-control (JDC) model (Karasek, 1979), we propose that the relationship between organizational constraints during deployment and post-deployment psychological functioning will vary as a function of team work engagement. We focus on two indicators of psychological functioning, fatigue symptoms and post-traumatic growth (PTG), as these indicators are often used in clinical practice within the military in several countries (see for an overview Hughes, Kinder, & Stress and Health 30: 386–396 (2014) © 2014 John Wiley & Sons, Ltd.

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Cooper, 2012). In what follows, we firstly elaborate on the theoretical underpinnings of the JDC model. We then present an empirical study to test our hypotheses.

The job demand-control model with the high reliability context Organizational constraints The JDC model (Karasek, 1979) coupled with the expanded JDC-support model (Johnson & Hall, 1988) represents one of the most fundamental and most cited theories in the research area of occupational stress. Also within high-risk contexts, the models have been used to predict occupational stress criteria such as psychological strain (Bliese & Castro, 2000) and psychosomatic health (Aasa, Brulin, Angquist, & BarneKow-Bergkviat, 2005; de Jonge, Dollard, Dormann, Le Blanc, & Houtman, 2000). Although the JDC model has been conceptualized and applied in many different ways, central to the model is the inclusion of three components, namely job demands, job control and psychological strain (Karasek, 1979). Job demands are ‘psychological stressors involved in accomplishing the workload, stressors related to unexpected tasks, and stressors of job-related personal conflict’ (Karasek, 1979, p. 291). Job control, or job decision latitude, refers to the authority to make decisions at work and the possibility of developing competence. Psychological strain is defined as symptoms of mental strain that result in poor psychological and physiological wellbeing (Karasek, 1979). For the most part, job demands have been measured by scales such as workload, time pressure or role conflict (Karasek & Theorell, 1990; van der Doef & Maes, 1999). However, these factors might not serve as the best indicator for the job demand-component of the model across all contexts or occupations. Snyder, Krauss, Chen, Finlinson, and Huang (2008) demonstrated that within the occupational safety context, organizational constraints were most relevant. Organizational or situational constraints are defined as factors in the work environment that negatively impact performance and are beyond the employee’s control (Peters & O’Connor, 1980; Spector & Jex, 1988; Villanova & Roman, 1993) and, by definition, prevent employees from performing optimally (Britt, McKibben, Greene-Shortridge, Odle-Dusseau, & Herleman, 2012; De Jonge et al., 2000). Organizational constraints also appear to be a significant job demand for those working in other HROs. Strict rules among frontline policing personnel for example were related to higher levels of emotional exhaustion, as compared with support personnel within the same department (Gaines & Jermier, 1983). Peacekeepers in the present study, who were deployed as part of the international safety and security mission to Afghanistan, also reported being most worried about constrained response options, even more so than physical workload, Stress and Health 30: 386–396 (2014) © 2014 John Wiley & Sons, Ltd.

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role conflict or time-related issues (Boermans et al., 2013). Finally, 62% of US peacekeepers who were deployed to Iraq reported being in threatening situations where they were unable to respond offensively because of the constrained rules of engagement (Hoge et al., 2004). On the basis of these findings, we use organizational constraints during deployment as our job demand-component of the JDC model. Team work engagement In his original specification of JDC model, Karasek (1979) argued that high job demands are not harmful in themselves, but only when accompanied by low decision latitude or control. Even though this contention has inspired a great deal of research effort, this assumption has also been criticized for being too simplistic in that it falls short to include other factors that may be related to psychological strain. One factor that has repeatedly proven its value for buffering the negative effect of high job demands is a supportive environment. In a later version of the JDC model, the JDC-support model, social support was therefore included into the model (Johnson & Hall, 1988). Given the fact that teamwork is an essential component of HROs (Baker, Day, & Salas, 2006), we focus on the buffering potential of the team. Researchers have elaborated on a number of concepts to think about social supportive environment, including different sources of support (i.e. supervisor, colleagues, family and spouse) or different kinds of support (i.e. instrumental, informational or emotional). We focus on another social support resource, i.e. team work engagement. Team work engagement can best be considered as an indication of team well-being (Peterson, Park, & Sweeney, 2008) that is defined by team optimism and shared perceptions of vigour and dedication (see for a review Costa, Passos, & Bakker, 2012). In short, team vigour refers to high collective levels of energy and willingness to invest effort in work. Team dedication describes the collective involvement in work and a shared sense of significance, enthusiasm and pride while doing so (see for a review Costa et al., 2012). Team work engagement can be distinguished from related construct such as cohesion, team potency or collective efficacy as the latter three are first and foremost cognitive in nature (i.e. they are defined by beliefs), whereas team work engagement specifically comprises an affective dimension (Bledow, Schmitt, Frese, & Kühnel, 2011; Costa, Passos, & Bakker, 2014). Indeed, Costa et al. (2014) recently demonstrated that team work engagement was distinct from team potency and collective efficacy. Torrente, Salanova, Llorens, and Schaufeli (2012) also found discriminant validity of the construct. They demonstrated that a supportive team climate, coordination and teamwork act as antecedents for the emergence of team work engagement, which in turn was related to team performance. One apparent way through which team work engagement may buffer against high job demands related 387

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to high-risk environments is by helping individuals to reappraise demands as a challenge, rather than becoming frustrated by them. High levels of collective enthusiasm and dedication may facilitate collective and individual coping skills that enable team members to effectively deal with concerns about organizational constraints. Such teams may find ways to work around the constraints or be more acceptant of them. For instance, shared experiences of difficulties, encouragement and good humour are likely to relieve tension from the stifling and disconcerting effects of organizational constraints, while maintaining meaningful goal strivings and well-being (Mouthaan, Euwema, & Weerts, 2005; Romero & Pescosolido, 2008). Also, fraternal comradeships and interpersonal trust allow members to maintain a sense of psychological safety despite continuous operational demands (Brewer & Kramer, 1986; Haslam, Jetten, Postmes, & Haslam, 2009). A drawback of the existing body of knowledge on the relationship between social resources, job demands and post-deployment psychological strain is that most studies are based on retrospective designs. When using such an approach, it remains unclear whether qualitative teammember relationship actually intervene early on in the sense-making process in response to operational stressors (i.e. during deployment) or whether it can be explained by cognitive reappraisal processes after deployment (Metts, Sprecher, & Cupach, 1991). In addition, most research has focused on relationships at the same level of analyses. That is, research on team functioning has mainly paid attention to team-level attributes, and individual functioning has mainly focused on individual-level attributes. Relatively few studies have investigated cross-level relationships (exceptions include Chen, Kanfer, DeShon, Mathieu, & Kozlowski, 2009; Chen & Bliese, 2002). The present study seeks to advance current insights by prospectively examining the buffering potential of team work engagement. We do so by taking a multilevel perspective. More specifically, we expect that high levels of team work engagement will ameliorate the exhausting effect of disconcerting organizational constraints during deployment on individual-level fatigue symptoms after deployment. H1a: High levels of team work engagement will buffer the negative effect from collective concerns about organizational constraints during deployment on fatigue symptoms after deployment. Conversely, poor interpersonal interactions have been negatively related to individual psychological functioning. Solomon, Mikulincer, and Hobfoll (1986), found that peacekeepers who had poor relations with their buddies reported greater feelings of loneliness and increased the likelihood of combat stress reactions during deployment. Low team work engagement may therefore actually intensify the depleting effects of organizational constraints on fatigue symptoms. For instance, low levels 388

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of team work engagement may comprise team abilities to effectively deal with situational demands when faced with an acute situation, making them vulnerable for the development of psychological strain. H1b: Low levels of team work engagement will intensify the negative effect from collective concerns about organizational constraints during deployment on fatigue symptoms after deployment. Post-traumatic growth Most of the research on the JDC model and demandscontrol-support model has focused on psychological strain. However, during the past two decades, it has come into attention that dealing with stressful events should not be considered as exclusively negative but should also be seen as a catalyst for personal development (Aldwin, 1994). Research among emergency ambulance personnel for instance demonstrated positive post-trauma changes as the result of exposure to occupational hazards (Paton, 2005; Pietrantoni & Prati, 2008; Shakespeare-Finch, 2003). Positive outcomes included positive feelings about helping others, finding meaning from dealing with difficulties, making friends for life, contributing to the work setting, fulfilling one’s potential and the overall pleasure derived from being able to do one’s work well (Mouthaan et al., 2005; Newby et al., 2005; Parmak, Mylle, & Euwema, 2010; Schok, Kleber, Elands, & Weerts, 2008). Today, there are a variety of theories concerning how people benefit from adversity, most often referred to as post-traumatic growth, stress-related growth, benefit finding or adversarial growth (Zoellner & Maercker, 2006). In their theory of PTG, Tedeschi and Calhoun (2004) are very clear in stating that ‘posttraumatic growth is not simply a return to baseline—it is an experience of improvement that for some persons is deeply profound’ (p. 4). Examples of positive psychological change are an increased appreciation of life, setting of new life priorities, a sense of increased personal strength, identification of new possibilities, improved closeness of intimate relationships or positive spiritual change (Tedeschi & Calhoun, 2004). Theoretically, people are most likely to be motivated to search for new meanings and directions in their life when they attach enduring significance to an ‘extremely stressful experience’ (Zoellner & Maercker, 2006). Even though organizational constraints in itself might not be considered as such an experience, we highlight that the peacekeeping operation in the present study was a highly challenging operation in which peacekeepers were confronted with direct fire attacks and continuous threat of Improvised Explosive Devices. Under such conditions, Rules of Engagement, that continuously change during military operations, or poor supply of materials may enhance feelings of powerlessness. As such, it stands to reason that organizational constraints during deployment meets the condition of an extremely stressful experience. Stress and Health 30: 386–396 (2014) © 2014 John Wiley & Sons, Ltd.

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Although research has argued for the importance of investigating positive and negative outcomes as a result of exposure to demands, few studies have investigated this in a prospective way. An exception is a study by Britt et al. (2007) in which they demonstrated that soldiers who were enthousiastic about their work during deployment reported more benefits from the experience 6 months after deployment such as deriving a sense of significance from it. Conversely, soldiers who felt depressed during deployment reported more post-traumatic stress symptoms afterwards and felt that the deployment experience put too much of a strain on them and their families. Building on the Britt et al. (2007) work, we seek to determine whether team work engagement is able to transform the effects of collective concerns about organizational constraints during deployment into positive personal changes afterwards. That is, positively engaged military teams may enable their members to constructively deal with organizational constraints, despite continuous risk of (personal) safety. As a result, members may feel proud or contented that their team was able to overcome such obstacles and develop personal competencies by constructively dealing with it. H2a: High team work engagement boosts PTG after deployment, especially when collective concerns about organizational constraints during deployment are high. Conversely, low team work engagement might make it more difficult for team members to construe positive meaning or personal significance from the deployment experiences, especially when teams are concerned about organizational constraints. Low engaged military teams may comprise individual coping strategies and sense of teambelonging individual coping strategies and performance, which in turn may leave peacekeepers feeling isolated and frustrated about the deployment, rather than making friends for life or an increased sense of personal strength. H2b: Low team work engagement mitigates PTG after deployment, especially when collective concerns about organizational constraints during deployment are high.

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the well-being of their followers. The second survey is administered 6 months after deployment and focuses on a range of mental and physical health issues. This survey was used to signal problems and offer help to employees who struggle with deployment-related health problems. On average, the interval between the assessments during and after deployment is 8 months. Analyses were conducted on data that were collected during the last period of the ISAF mission (end of 2008 to the beginning of 2010). The original sample during deployment consisted of 2914 participants nested within 210 teams, which was representative of the population of peacekeepers participating in the mission; over 90% completed the survey. The original sample after deployment consisted of 1151 participants nested within 118 teams, with a response rate of 56.57%. The discrepancy between response rates at both assessment times can be explained by the fact that during deployment, assessment is organized through the chain of command, in which the survey is scheduled into a team’s daily activities. After deployment however, surveys are sent to employees by mail which is well known to cause a large non-response rate. The 56.57% response is comparable with rates reported elsewhere (Baruch & Holtom, 2008). The initial combined dataset (i.e. during and after deployment) consisted of a sample of 1342 respondents. As the primary focus in the present study concerns the predictive value of a team-level variable (team work engagement), we excluded teams with less than five respondents during deployment (excluded 109 participants nested within 34 teams). The smallest team consisted of five members, and the largest team consisted of 58 members [M = 25.28, standard deviaton (SD) = 13.37]. We excluded participants with missing values on either the independent or independent variables (n = 262). The final sample consisted of 971 participants nested within 93 teams. Importantly, teams were fixed, meaning that teams were formed before deployment. There were a few incidences where a team member had to depart its team beforehand; however, team members were not replaced with new members. As such, we can be assured data during deployment were correctly linked to its individual members after deployment. The majority of participants were male (93%), and the average age was 29.27 years (SD = 8.72).

Method Sample and procedure Between 2006 and 2011, the Netherlands Armed Forces participated in the International Security Assistance Force (ISAF), led by the NATO. As part of a standard research program within the Netherlands Armed Forces, paper-and-pencil surveys are administered to all deployed employees during and after deployment. The first survey is administered halfway through a 4month deployment and assesses the most significant job demands, job resources and engagement. This survey was used to provide leaders in-team feedback on Stress and Health 30: 386–396 (2014) © 2014 John Wiley & Sons, Ltd.

Measures Team work engagement To assess team-level work engagement, we adapted the vigour and dedication scales from the Utrecht Work Engagement Scale (Schaufeli, Bakker, & Salanova, 2006) according to the referent-shift composition model (Chan, 1998; Klein, Conn, Smith, & Sorra, 2001). In the referent-shift composition model, the basic content of the original constructs remains unchanged, but the referent of the content changes from the individual to the team. In this way, it is possible to assess the 389

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agreement of the team members on team work engagement. To keep the time to fill in the survey manageable for the participants, the scale was reduced to a total of five items. Example items are ‘The members of my team work with enthusiasm’ and ‘The members of my team are proud of the work they do’ (Van Boxmeer, Verwijs, Euwema, & Dalenberg, 2010). A mean score was calculated from the five items. Cronbach’s α was 0.92. Items were scored on a seven-point Likert-type scale ranging from 0 (never) to 6 (strongly agree). ICC1 and ICC2 were calculated (Klein et al., 2001). Values greater than 0.12 for ICC1 indicate an adequate level of within-unit agreement (James, 1982). For the ICC2, values greater than 0.60 support aggregation (Glick, 1985). ICC1 was 20.04% and ICC2 was 79%, which justifies aggregation. Organizational constraints On the basis of the Bartone, Adler, and Vaitkus (1998) framework, five items were constructed that are considered to be highly characteristic for this specific military deployment (van Boxmeer et al., 2007b; Boermans et al., 2013). Participants were asked to indicate how demanding the items were. Items were ‘ROE constraints on response options’, ‘Unclear Rules of Engagement’, ‘Unclear command structure’, ‘Unresponsive supply chain’ and ‘Unfair differences between teams’. Cronbach’s α was 0.74. Items were scored on a seven-point Likert-type scale 0 (barely demanding) to 6 (extremely demanding). The theoretical underpinnings for aggregation perceptions regarding demands from job constraints to the team level as that if members share a sufficient amount of variance in their perceptions, and then the common (or objective) aspect of this lies in the job itself (Semmer, Zapf, & Greif, 1996). ICC1 was 17.43% and ICC2 was 77.75%, which justifies aggregating scores to the team level. Fatigue symptoms The 20-item Checklist Individual Strength (CIS) questionnaire (Beurkens et al., 2000) was used to assess fatigue. To keep the time to fill in the survey manageable for the participants, three items were selected to asses fatigue symptoms. The items that were used are ‘I feel tired’, ‘I don’t do much during the day’ and ‘I feel weak’. Items were scored on a seven-point Likert-type scale ranging from 1 (strongly disagree) to 7 (strongly agree). Cronbach’s α was 0.84. Post-traumatic growth The 21-item Post-Traumatic Growth Inventory (PTGI) was used to assess PTG (Tedeschi & Calhoun, 1996). The PTGI originally consists of five scales. In order to keep the time to fill in the survey manageable for the participants, five items were selected that have been found to be most discriminant for its scale (Tedeschi & Calhoun, 1996) and translated. Example items are ‘I know better than before that I can effectively deal with difficulties’, ‘I have more appreciation for life’ and ‘I have a greater sense of relatedness to 390

others’. Items were scored on a seven-point Likerttype scale ranging from 1 (strongly disagree) to 7 (strongly agree). Cronbach’s α was 0.83. Location Because the data were collected at three different locations in Afghanistan, we dummy coded the location. The majority of respondents were stationed in Tarin Kowt (n = 683; 70.3%), and in Deh Rawod (n = 227; 23.4%). A minority was stationed in Kandahar (n = 61; 6.4%). Rank Data were obtained from peacekeepers in the rank categories private (n = 275; 28.5%), non-commissioned officers (NCO; n = 226; 23.4%) and commissioned officers (n = 463; 48%). Seven participants did not report their rank. Perceived threat To rule out the possibility that current fatigue symptoms or PTG was influenced by individuals’ retrospective perceptions of threat during deployment, we entered self-reported perceived threat measured after deployment as a control variable. A scale with five items was specifically designed for the present study. Example items are ‘having felt powerless during deployment’ and ‘having felt threatened during deployment’. Items were scored on a five-point Likert-type scale ranging from 0 (never) to 5 (always). Cronbach’s α was 0.84.

Data analyses Firstly, a common factor analysis principal axis factoring (PAF) with Promax rotation supported the distinctiveness of the constructs used in the present study; Barlett’s test was significant (χ 2(78) = 5408.10, p < 0.0001), and Kaiser-Meyer-Olkin (KMO) was 0.80. Table I shows the means, standard deviations and the correlations between the study variables. All three components had eigenvalues over Kaiser’s criterion of 1 and in combination explained 55.38% of the variance. The dimensionality of team work engagement and organizational constraints during deployment has been confirmed and described elsewhere (van Boxmeer et al., 2007b; Boermans et al., 2013; Boermans, van Boxmeer, van Gelooven, & Euwema, unpublished manuscript). Next, we examined whether the nested structure of the data necessitated multi-level analyses, because ignoring the nested structure may produce unreliable standard errors and result in misspecification of models (Hox, 2002; Snijders & Bosker, 1999). Therefore, we tested the null hypothesis that there are no group differences on the dependent variables. That is, that the true between-group variance is zero (Snijders & Bosker, 1999). The F-value for group effects derived from two separate analyses of variance for both dependent variables were significant: for fatigue symptoms, F(92, 878) = 1.34, p < 0.05; for PTG, F(92, 878) = 1.58, p < 0.05, confirming the appropriateness of multi-level analyses. Stress and Health 30: 386–396 (2014) © 2014 John Wiley & Sons, Ltd.

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Table I. Means, standard deviation and correlations of individual-level (n = 971) and team-level variables (n = 93)

Team-level variables during deployment 1. Organizational constraints 2. Team work engagement Individual-level variables post-deployment 3. Perceived threat 4. Fatigue symptoms 5. Post-traumatic growth

M

SD

1

1.39 4.28

0.51 0.52

— 0.15**

2.43 2.24 3.14

0.71 1.48 1.17

0.31** 0.04 0.11*

2

3

4

5

— 0.19** 0.31**

— 0.07*

—

— 0.21** 0.14** 0.12**

Note: Team-level variables represent aggregated scores of team members that were assessed 2 months into deployment; individual-level variables were assessed 6 months after deployment. SD: standard deviation. *p < 0.05; **p < 0.01.

Four competing models were compared. Firstly, the intercept-only model estimates the variability of the intercept of the dependent variable across all groups. This model contains no explanatory variables and therefore serves as the baseline model. In the second model, we entered the control variables. The third model, team work engagement and organizational constraints, was entered as main effects into the model. To determine whether organizational constraints had differential effects for teams with high or low team work engagement, we included the interaction term between these variables in the final model. We used the maximum likelihood method for fitting the models and compared the 2 log likelihood ratio (or deviance) between the models to determine improvement of model fit to the data. On the basis of the recommendations made by Enders and Tofighi (2007), the continuous predictor variables were grand-mean centred. Centering is used to establish a meaningful zero point for the intercept and reduces multicollinearity among the variables.

Results Correlations and descriptive results Table I provides the zero-order correlations and summary statistics for team-level variables during deployment and individual-level variables after deployment. The weak correlation between fatigue symptoms and PTG indicates that these variables represent distinct constructs. The strongest relations for fatigue symptoms and PTG after deployment were retrospective perceptions of perceived threat. Organizational constraints and team work engagement during deployment were positively related to perceived threat afterwards. However, team work engagement was negatively related to fatigue symptoms, whereas the weakest correlation was found between organizational constraints and fatigue symptoms. Fatigue symptoms Table II provides the results for fatigue symptoms 2 months after deployment. The intercept-only model Stress and Health 30: 386–396 (2014) © 2014 John Wiley & Sons, Ltd.

showed that 3.64% of the variance in fatigue symptoms was explained by group membership. Entering the control variables into model 2 better fitted the data as compared with the intercept-only model; χ 2(3) = 83.39, p < 0.0001. Interestingly, the amount of explained variance by group membership in model 2 increased to 4.84%, suggesting that the control variables suppressed the effects of group membership on fatigue symptoms. This also implies that there are still other factors that explain between-group differences. The third model, including team work engagement and organizational constraints, improved model fit to the data, χ 2(2) = 22.68, p < 0.0001, and yielded a main effect for team work engagement, F(1, 57.57) = 20.73, p < 0.0001. The effect of organizational constraints was not significant (p = 0.09). In addition, the effect of group membership became non-significant, indicating that it was fully accounted for by team engagement and shared perceptions of organizational constraints. Finally, adding the interaction term into model 4 significantly improved model fit; χ 2(1) = 5.38, p < 0.05. There was a main effect for perceived threat, F(1, 872.99) = 62.68, p < 0.0001, and for team engagement, F(1, 58.71) = 25.01, p < 0.0001. That is, peacekeepers who were member of low engaged teams during deployment and peacekeepers who looked back on the deployment as a threatening experience reported more fatigue symptoms as compared with those who were member of highly engaged teams during deployment or did not perceive it as specifically threatening afterwards. Most relevant here, the interaction effect between team work engagement and organizational constraints was also significant, F(1, 87.45) = 5.55, p < 0.05. As can be seen from Figure 1, team work engagement moderated the effect of organizational constraints on fatigue symptoms. Organizational constraints during deployment were not directly related to post-deployment fatigue symptoms. However, under conditions of high organizational constraints, peacekeepers who were part of high engaged teams reported the lowest level of fatigue symptoms after deployment, whereas low levels of 391

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Table II. MLM for individual-level fatigue symptoms 6 months after deployment (n = 971) and team-level variables (n = 93)

Fixed effects Intercept Location Kandahar Tarin Kowt Deh Rawod Rank Private NCO CO Perceived threatb Organizational constraintsc Team work engagementc Team work engagement × Organizational constraints Random effects 2 σ individual level 2 σ group level 2 log likelihood

Model 1 intercept-only

Model 2

Β

Β

SE

2.23**

0.06

2.10** 0.08*

0.09 0.05 3504.83

Model 3 SE

Β

Model 4 SE

Β

SE

2.22**

0.14

2.18**

0.12

2.15**

0.12

0.47 0.05 0a

0.27 0.14 —

0.13 0.12 —

0.25 0.12 —

0.24 0.20 —

0.24 0.13 —

0.20 0.09 0a 0.48**

0.12 0.12 — 0.07

0.12 0.04 — 0.54** 0.20 0.51**

0.12 0.12 — 0.07 0.11 0.11

0.14 0.04 — 0.54** 0.08 0.53** 0.64*

0.12 0.13 — 0.07 0.12 0.11 0.27

1.96** 0.09 0.10* 0.04 3421.44

1.96** 0.09 0.04 0.03 3398.76

1.96** 0.09 0.03 0.03 3393.38

MLM: ; SE: standard error; NCO: non-commissioned officers; CO: commissioned officers. a Reference category. b Represents an individual-level variable. c Represents a team-level variable. *p < 0.05; **p < 0.01.

Figure 1 Moderating effect of team work engagement on the relationship between lack of job control during deployment and postdeployment fatigue symptoms

collective engagement predicted the highest level of fatigue symptoms after deployment. None of the models indicated that location or rank had an effect on postdeployment fatigue symptoms. Post-traumatic growth Table III provides the results for PTG. The interceptonly model showed that 5.45% of the variance in PTG was explained by group membership. Including the 392

control variables into model 2 improved the model fit, as compared with the intercept-only model; χ 2(3) = 109.02, p < 0.0001. The effect of group membership on PTG remained the same. Contrary to our hypothesis, including team work engagement and organizational constraints in the third model did not improve fit to the data; χ 2(2) = 1.18, ns. The interaction term between team work engagement and organizational constraints was also non-significant (Table III). We therefore only discuss the second model that included the control Stress and Health 30: 386–396 (2014) © 2014 John Wiley & Sons, Ltd.

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Table III. MLM for individual-level post-traumatic growth 6 months after deployment (n = 971) and team-level variables (n = 93) Model 1 intercept-only Β Fixed effects Intercept Location Kandahar Tarin Kowt Deh Rawod Rank Private NCO CO Perceived threatb Organizational constraintsc Team work engagementc Team work engagement × Organizational constraints Random effects 2 σ individual level 2 σ group level 2 log likelihood

3.17**

1.29** 0.07* 3054.15

SE

0.05

0.06 0.03

Model 2 Β

Model 3 SE

Β

Model 4 SE

Β

SE

3.13**

0.09

3.13**

0.09

3.13**

0.09

0.55** 0.11 0a

0.16 0.09 —

0.51** 0.13 —

0.17 0.09 —

0.51** 0.13 —

0.17 0.09 —

0.28** 0.20* 0a 0.48**

0.09 0.09 — 0.05

0.31** 0.21* — 0.48** 0.07 0.06

0.09 0.09 — 0.05 0.08 0.08

0.31** 0.21* — 0.48** 0.07 0.06 0.00

0.09 0.09 — 0.05 0.09 0.08 0.20

1.20** 0.06 0.00 0.02 2911.45

1.20** 0.06 0.00 0.01 2910.27

1.20** 0.06 0.00 0.01 2910.27

Note: Models 3 and 4 did not improve model fit compared with model 2. MLM: multilevel model; SE: standard error; NCO: non-commissioned officers; CO: commissioned officers. a Reference category. b Represents an individual-level variable. c Represents a team-level variable. *p < 0.05; **p < 0.01.

variables. This model yielded a main effect for perceived threat, F(1, 624.63) = 92.30, p < 0.0001, which indicated that when looking back on deployment, peacekeepers who looked back on the deployment as a threatening experience reported more PTG as compared with those who did not look back on it as specifically threatening. Contrary to the findings on fatigue symptoms, location and rank demonstrated a main effect on PTG, F(2, 61.11) = 5.70, p < 0.01 and F(2, 663.07) = 6.15, p < 0.01, resp. Peacekeepers who were stationed in Kandahar reported less PTG from the deployment as compared with those who were stationed in Tarin Kowt or Deh Rawod, and those in the rank of private and non-commissioned officers reported more PTG, as compared with commissioned officers.

Discussion The present study prospectively explored the role of team work engagement as a key moderator for the relationship between organizational constraints and individual psychological functioning. In line with our hypothesis, results confirmed that team work engagement, as an indicator of positive team well-being, transformed the effect of organizational constraints during deployment on individual-level fatigue symptoms 6 months after deployment. Team members were less fatigued 6 months Stress and Health 30: 386–396 (2014) © 2014 John Wiley & Sons, Ltd.

after deployment if they were part of highly engaged teams during deployment, particularly when concerns about organizational constraints was high. In contrast, team members who were part of lowly engaged teams during deployment reported the highest level of fatigue symptoms afterwards, particularly when concerns about organizational constraints was high. No effects were found for PTG. Somewhat surprisingly, there was no main effect of concerns about organizational constraints during deployment on individual-level fatigue symptoms afterwards. This may seem inconsistent with the general assumption that organizational constraints are directly related to psychological strain. However, it is in line with Karasek’s (1979) contention that job demands in itself are not harmful. Only when employees have little decision latitude or a qualitatively poor social environment will job demands have their exhausting effects, which is exactly what we found. Organizational constraints only had their depleting effects when soldiers were part of lowly engaged teams. A related explanation for the lack of a main effect can be found in our operationalization of organizational constraints. We took team’s shared level of concerns about organizational constraints because shared perceptions are considered to be a more ‘objective’ measure of job demands (Semmer et al., 1996). In keeping with the idea 393

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that ‘meaning’ is socially constructed rather than an objective or merely an intra-personal process (e.g. Berger & Luckmann, 1966; Searle, 1995), the level of team work engagement in the present study appeared to provide the lens through which concerns about organizational constraints during deployment gained its significance in the aftermath of it. Unfortunately, owing to restrictions of the database, we were not able to link the data at the individual level. We sought to overcome this obstacle by controlling for hindsight bias of perceived threat, which is an important predictor for poor psychological adjustment. Even after controlling for perceived threat, the interaction between team work engagement and organizational constraints remained significant. However, in order to fully demonstrate cross-level interaction effects of teamlevel variables (i.e. team work engagement and collective concerns about organizational constraints) on individual-level outcomes, individual-level variables (i.e. personal work engagement and individual concerns about organizational constraints) should have been controlled for. Contrary to expectations, team work engagement and organizational constraints were not predictive for PTG, nor was there an interaction effect. We argued that the combination organizational constraints in a high-risk environment could be a positive catalyst for change, but only when team work engagement is high. Results clearly did not support this reasoning. Even though concerns about organizational constraints in the context of a high-risk environment is highly disconcerting, our results suggest that organizational constraints do not meet the condition of a extremely stressful experience nor is it a catalyst for personal growth. Even though PTG has been conceptualized as a positive outcome following trauma, the evidence supporting this link is mixed. Research has found a relationship between PTG and post-traumatic stress symptoms in which survivors of traumatic events who developed post-traumatic stress disorder also reported PTG as a consequence of the trauma (Frazier, Conlon, & Glaser, 2001; Park, Cohen, & Murch, 1996). Some scholars have argued that this may indicate coping efforts in which PTG is a form a self-enhancing appraisals or positive illusions aimed at restoring shattered beliefs or a personal sense of psychological safety (e.g. Taylor & Armor, 1996). Many theorists acknowledge that PTG can be both, coping style and coping outcome (e.g. Affleck & Tennen, 1996; Tedeschi & Calhoun, 2004; Maercker & Zoellner, 2004). However, this two-component model of PTG makes it difficult to determine to what extent the present study measured process or outcomes. For instance, our results showed a strong relationship between retrospective perceptions of threat and PTG, suggesting that some peacekeepers in the 394

S. M. Boermans et al.

present study were still actively trying to make sense of difficult deployment experiences. Interestingly, results demonstrated that deployment location and peacekeepers’ rank were particular important for predicting PTG, whereas these variables were not related to fatigue symptoms. That is, being stationed in Tarin Kowt or Deh Rawod was related to higher levels PTG, as compared with being stationed in Kandahar. Also those in the rank of private and non-commissioned officers reported higher levels PTG, as compared with commissioned officers. A potential explanation concerns the extent to which military personnel actually work in the frontline of the operational theatre. Tarin Kowt and Deh Rawod were located in the frontline of the dynamic and turbulent operational theatre, whereas Kandahar concerned the headquarters and airfield of the Dutch army and was located in a more stable area. As such, those stationed at Kandahar were less likely to be exposed to potential operational hazards. Similarly, private or non-commissioned officers perform their tasks in the frontline of the operational theatre, whereas higher-level commanders often perform their coordinating tasks from a larger distance to the ‘frontline’. Although taking these context-specific variables into account may limit generalizability of our research findings, the results highlight that context does affects the occurrence and meaning of organizational behaviour and employee well-being as well as functional relationships between variables (Johns, 2006) and should therefore be incorporated in organizational research. In modern military operations, military and policing roles often blur, and soldiers have to be able to integrate two seemingly competing roles: that of a peacekeeper with the classic role of a warrior (Broesder, Vogelaar, Euwema, & Op den Buijs, 2009). Although military organizations recognize the role of peacekeeper, soldiers’ ability to execute both roles successively or even simultaneously has not been sufficiently incorporated in pre-deployment training (Blackstone, 2005; Broesder, 2008; Jamison, 2004). Consider for instance, witnessing human rights violations towards civilians or children while Rules of Engagement prohibit peacekeeping soldiers from taking action, even though they have the skills to intervene. To avoid performance breakdown, it is crucial to understand how teams cope with uncertainties pertaining to organizational constraints, warrior-peacekeepers identity, and situational uncertainty. The results are limited by error variance associated with response bias and other sources that undoubtedly account for a portion of the covariance observed among these constructs. Although the confirmatory factor analysis, within-team consensus and betweenteam differences provide support for our measures of Stress and Health 30: 386–396 (2014) © 2014 John Wiley & Sons, Ltd.

S. M. Boermans et al.

Team Spirits under Pressure

team work engagement and demands from organizational constraints, response bias may explain a portion of the covariance shared with the case workers’ perceptions. The methodologies we used were designed in part to control for this threat. Firstly, the simultaneous analysis of team work engagement and demands from organizational constraints captures the unique variance explained in the outcome by each predictor over and above the other in the model. As some portion of the response bias and other sources of common method error variance is shared across these measures, the unique variance in fatigue symptoms and PTG explained by each predictor is less likely to include that error variance. Secondly, the multi-level analytic approach is designed to control for the aggregation bias and underestimated standard errors in clustered data that inflate the risk of type I errors. Although these methods do not eliminate the problems posed by common method error variance, they reduce the risks of type I errors created by a common method error. The unique context of deployment raises the question of whether the results will generalize to other HROs and beyond. Even though these findings may not automatically generalize to traditional occupations, the impact of organizational constraints has also been

found in other HROs. (e.g. Boermans, Kamphuis, Delahaij, Korteling, & Euwema, 2013; Gaines & Jermier, 1983; Hoge et al., 2004; Schwarz, 2005). As such, the present results have bearings for other HROs as well. Our findings demonstrate that team work engagement during deployment functions as a ‘force multiplier’ in the aftermath of deployment. High levels of collective enthusiasm and dedication are a crucial buffer against job demands, whereas poor levels of team work engagement actually constitute a risk factor for psychological adaptation. The present study highlights that team work engagement is a crucial pathway for preventing organizational constraints from getting their well-known exhausting effects. Insights into the contingencies for building and sustaining good team work engagement could result in more efficient and effective interventions that boost team work engagement, which in turn enables its members to manage difficulties during deployment and quickly recover afterwards.

Conflict of interest The authors have declared that they have no conflict of interest.

Blackstone, R.C. (2005). Somalia: Soldiers in SOSO.

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