STRESS REDUCTION INTERVENTION RELATED TO WORK ISSUES-Research Paper

International Journal of Stress Management Employee to Leader Crossover of Workload and Physical Strain Shani Pindek, Lorenzo Lucianetti, Stacey R. Kessler, and Paul E. Spector Online First Publication, August 13, 2020. http://dx.doi.org/10.1037/str0000211

CITATION Pindek, S., Lucianetti, L., Kessler, S. R., & Spector, P. E. (2020, August 13). Employee to Leader Crossover of Workload and Physical Strain. International Journal of Stress Management. Advance online publication. http://dx.doi.org/10.1037/str0000211

Employee to Leader Crossover of Workload and Physical Strain

Shani Pindek University of Haifa

Lorenzo Lucianetti University of Chieti and Pescara

Stacey R. Kessler Kennesaw State University

Paul E. Spector University of South Florida

Most stress research focuses on the effect of workplace stressors on frontline employees. However, less research has focused on how employees affect leaders’ stress. The purpose of this study is to highlight an important yet neglected type of stressor crossover: from employees to their leader. The current study used multisource data (i.e., subordinate and leader) from 588 employees working in 164 groups in Italy. Results indicated that subordinates’ workloads were associated with their leaders’ workload both directly and indirectly via subordinates’ physical strain. The leaders’ workload then related to their own physical strain. This study has implications for understanding the stressful role of managing subordinates and can inform practitioners interested in improving leaders’ well-being. This is one of the first studies to address how subordinates’ stressors contributes to their leaders’ stressors, and the use of a multisource multilevel design helps overcome concerns of common method bias and increases our confidence in the results.

Keywords: work stress, multilevel modeling, workload, physical strain, leadership

Work stress negatively affects employees’ psychological and physical health as well as work attitudes and performance (Ganster & Schaubroeck, 1991; Lepine, Podsakoff, & Lepine, 2005; Nixon, Mazzola, Bauer, Krueger, & Spector, 2011; Spector, Chen, & O’Connell, 2000). Such impacts have significant financial costs for organizations and society at large. For example, work-related depression, a known consequence of chronic work stress, is esti- mated to cost European employers €617 billion annually (Matrix, 2013). These findings indicate that work stress is a significant problem despite commissions, policies, and treaties aimed at pro- moting employee wellness (Eurofound & EU-OSHA, 2014).

Although much of the work stress literature focuses on frontline employees (i.e., employees with a nonmanagerial role), there is a limited body of research that acknowledges the unique stress experienced by leaders/managers. The majority of leader research examines various sources of stressors inherent in the role of leader or manager (Cubitt & Burt, 2002; Poirel, Lapointe, & Yvon, 2012; Wong, DeSanctis, & Staudenmayer, 2007; Zwingmann, Wolf, &

Richter, 2016). A report from the Center for Creative Leadership, however, indicates that subordinates have a substantial impact on their leaders’ stress experiences (Campbell, Baltes, Martin, & Meddings, 2007). That is, 75% of the leaders surveyed reported that having a leadership role increased their stress level and that subordinates contribute to the leaders’ stress just as much as their own leader, peers, and customers (Campbell et al., 2007). Ironi- cally, there is ample research examining how leaders negatively affect subordinates’ stress and well-being (see review in Skakon, Nielsen, Borg, & Guzman, 2010), but very few studies have focused on how subordinates affect their leader’s stress (Westman & Etzion, 1999; Wirtz, Rigotti, Otto, & Loeb, 2017).

To focus on leaders’ stress, we examine whether stress crosses over between individuals who belong to the same work team (Westman, Bakker, Roziner, & Sonnentag, 2011), and specifically between the subordinate and the leader. We test a model by which a subordinate stressor and strain cross over to the supervisor in the form of the same stressor and strain. We base our theorizing on the job demands-resources theory/model (JD-R; Bakker & Demerouti, 2007, 2017). The JD-R model separates working conditions into job demands (i.e., “physical, psychological, social, or organiza- tional aspects of the job that require sustained physical and/or psychological effort and are therefore associated with certain phys- iological and/or psychological costs”) and job resources (i.e., “physical, psychological, social, or organizational aspects of the job that are functional in achieving work goals, reduce job de- mands and the associated physiological and psychological costs, or stimulate personal growth, learning, and development” (Bakker & Demerouti, 2017, p. 274). Based upon work by Wirtz et al. (2017), we argue that the resource role (Bakker & Demerouti, 2007) a leader plays for his or her subordinates constitutes a job demand

X Shani Pindek, Department of Human Services, University of Haifa; Lorenzo Lucianetti, Department of Management and Business Adminis- tration, University of Chieti and Pescara; Stacey R. Kessler, Michael A. Leven School of Management, Entrepreneurship and Hospitality Coles College of Business, Kennesaw State University; Paul E. Spector, School of Information Systems and Management, University of South Florida.

An earlier version of this article was presented at Israeli Organizational Behavior Conference, Tel-Aviv, Israel, 2018.

Correspondence concerning this article should be addressed to Shani Pindek, Department of Human Services, University of Haifa, Abba Khoushy Avenue 199, Haifa 3498838, Israel. E-mail: [email protected]

T hi

s do

cu m

en t

is co

py ri

gh te

d by

th e

A m

er ic

an P

sy ch

ol og

ic al

A ss

oc ia

ti on

or on

e of

it s

al li

ed pu

bl is

he rs

. T

hi s

ar ti

cl e

is in

te nd

ed so

le ly

fo r

th e

pe rs

on al

us e

of th

e in

di vi

du al

us er

an d

is no

t to

be di

ss em

in at

ed br

oa dl

y.

International Journal of Stress Management © 2020 American Psychological Association 2020, Vol. 3, No. 999, 000 ISSN: 1072-5245 http://dx.doi.org/10.1037/str0000211

1

for that leader. In other words, in acting as a resource for subor- dinates, particularly when these subordinates experience high de- mands, the leader needs to exert additional mental energy and effort to support his or her subordinates. For example, a leader might support subordinates by showing them how to do a task more efficiently or by providing emotional support for subordi- nates who are coping with high demands. These additional efforts on the part of the leader constitute an increase in the leader’s workload. In this way, increased demands placed on the subordi- nates may cross over in the form of increased demands for the leader.

Using a sample of mostly manufacturing employees, we propose a multilevel indirect effects model whereby the subordinates’ demands and their resulting strain both serve as additional job demands for the leader, which in turn, contributes to increased leader strain. As a result, the current study makes the following contributions. First, little research exists on the conceptualization of subordinates as a workplace stressor for the leader, despite the importance of this source of stress (Campbell et al., 2007). This is likely because occupational health research tends to focus on the effect of stressors on frontline (nonmanagerial) employees (Skakon et al., 2010). However, leaders are also employees, and their well-being should not be forgotten. A second study contri- bution is the novel use of the JD-R theory. Instead of taking the traditional approach of conceptualizing leaders’ support as a sub- ordinate’s resource, we view this process through the lens of the leader, conceptualizing subordinates’ needs as a demand on the leader (because the leader’s job includes supporting the employee, and therefore when the employees’ needs increase, the leader has increased demands to support those employees). This novel im- plementation of JD-R theory to the subordinate–leader interaction will enhance our limited understanding of leaders’ sources of stress (i.e., stressors).

Theoretical Background

Work Stress Crossover

The concept of crossover, defined as the interpersonal process by which stressors or strains experienced by one individual affect the experience of stressors or strains by another individual within the same social environment (Westman, 2001; Westman et al., 2011), has been examined extensively in the work environment. For instance, researchers have found that strains can be shared between coworkers and that strain (i.e., burnout) can cross over from one coworker to another (Semmer, Zapf, & Greif, 1996; Totterdell, Hershcovis, Niven, Reich, & Stride, 2012). Crossover between leaders and subordinates has been studied as well, and much of it has focused on how the leader affects the subordinates’ stressors and strains. This is because leaders play an active role in shaping their subordinates’ job demands (Brown & Benson, 2005) and can craft their subordinates’ work conditions in a way that helps some aspects of employee well-being, such as work engage- ment (Vincent-Höper, Muser, & Janneck, 2012). Although the leaders’ actions can help alleviate subordinates’ stressors and/or strains (e.g., transformational leadership behaviors; Diebig, Bor- mann, & Rowold, 2017), they can also be an inherent source of stress for their subordinates (e.g., abusive supervision; Wu & Hu, 2009). The review by Skakon et al. (2010) found that leaders’

strain crosses over to subordinates and that some leader behaviors can increase subordinates stressors.

One exception to the commonly studied direction of crossover is the study by Wirtz et al. (2017) that suggested that the crossover of strain from subordinates to their leaders could be the result of a crossover of stressors. They found that strains such as work (dis- )engagement and emotional exhaustion crossed over from subor- dinates to their leaders. The crossover of these stress variables between individuals can be explained by using mechanisms such as social exchange (Bakker & Schaufeli, 2000) and crossover of psychological states (Bakker, Westman, & van Emmerik, 2009), which reflect an empathic mechanism.

The crossover model (Westman, 2001), although originally con- ceptualized to examine the transference of stressors and strains between the work and home domains, offers up several mecha- nisms relevant to the work domain. These include a direct em- pathic crossover of strain mentioned earlier, an effect of the shared environment (i.e., shared stressors), and an indirect process via coping, support, and social undermining. However, these mecha- nisms can take a different form when applied to the transference of stressors and strains between subordinates and their leaders. For example, subordinates’ needs can act as an added demand on the leader. When subordinates are experiencing higher demands, their needs are likely greater, and this increased need is expected to create additional demands on their leaders. This impact that one employee’s demands can have on another employee’s demands is a type of crossover of stress, and there are several possible mech- anisms by which it can occur, as discussed next.

Potential Subordinate–Supervisor Crossover Mechanisms

Wirtz et al. (2017) discussed how emotionally exhausted sub- ordinates required additional effort on the part of their leaders, who need to uplift their subordinates’ spirits. This added demand de- pletes the leader’s own resources, thus contributing to an increase in the leaders’ emotional exhaustion. Building on these ideas, we focus on the leaders’ job demands or workload (i.e., the perceived amount of work in terms of volume and pace; Spector & Jex, 1998) as a stressor because handling subordinates’ problems is typically part of the leader’s job. We argue that when subordinates experi- ence increased job demands (i.e., have a high workload), the leader’s workload will also increase for several reasons.

First, a good leader is responsive to his or her subordinates, assisting them in handling the requirements of their jobs, perhaps to the extent that they take on some of their subordinates tasks when the workload is too high, or provide guidance in how to complete tasks more efficiently, as a means of instrumental sup- port (Dormann & Zapf, 1999). In this sense, environmental con- ditions such as workload are shared between employees and their leader. Second, a high workload for the employees can produce increased demands for the leader who must be sure all works are accomplished. This again contributes to a direct increase in lead- ers’ workload as a result of the higher workload experienced by his or her employees. Third, the emotional support that leaders pro- vide their employees who are experiencing higher workloads rep- resents another potential demand that increases leaders’ work- loads.

T hi

s do

cu m

en t

is co

py ri

gh te

d by

th e

A m

er ic

an P

sy ch

ol og

ic al

A ss

oc ia

ti on

or on

e of

it s

al li

ed pu

bl is

he rs

. T

hi s

ar ti

cl e

is in

te nd

ed so

le ly

fo r

th e

pe rs

on al

us e

of th

e in

di vi

du al

us er

an d

is no

t to

be di

ss em

in at

ed br

oa dl

y.

2 PINDEK, LUCIANETTI, KESSLER, AND SPECTOR

In fact, in addition to their own workload, leaders reported that the need to develop others and manage underperforming subordi- nates were their main work stressors (Campbell et al., 2007). Therefore, when subordinates experience a greater workload, the effects are expected to cross over in the form of an increase in workload for the leader. This occurs because the leader needs to extend additional resources to provide support to the subordinate so that the work is completed, and these added tasks increase the leader’s own workload.

Hypothesis 1: The subordinates’ workload is directly and positively associated with their leader’s workload.

Another potential source of workload for the leader, beyond the subordinates’ workload, is the subordinates’ well-being. Accord- ing to the JD-R theory, workload is a job demand which results in decrements to well-being (strain). In this study, we apply that process to both subordinates and leaders. Therefore, both subor- dinates and leaders who experience increased demands (and more specifically, workload), are expected to experience increased strains. Workload has been meta-analytically linked to a broad array of strains (Bowling, Alarcon, Bragg, & Hartman, 2015). In this study, we focus on physical strains, as they are a prevalent response to stressors, including workload (Nixon et al., 2011). Physical strain is particularly relevant for our framework, the JD-R, because it is a strain response on one hand, but also con- stitutes a loss of resources (physical well-being) that makes it harder to meet the demands of the job for the focal employee (see loss spirals of resources; Demerouti, Bakker, & Bulters, 2004). Physical strain is also particularly relevant to employees who work in more physically demanding jobs such as those in the manufac- turing sector as studied here. This is because their physical symp- toms likely make it harder for them to complete job tasks and are an important antecedent of sickness absenteeism (Väänänen et al., 2003).

However, these physical symptoms, when experienced by sub- ordinates, potentially play an additional role in the experiences of the leader. The subordinates’ reduced physical well-being acts as yet another demand on the leader, as the leader is now required to invest additional resources both to complete the tasks and to alleviate the damage to subordinates’ well-being.

Hypothesis 2: The subordinates’ physical strain is directly and positively associated with the leader’s workload.

In addition to the direct effect of subordinate physical strain on the leader’s workload, subordinate workload, sometimes referred to as time pressure, can cause employees to behave less safely and result in more injuries (Zohar, 2002). It is thought that the in- creased pace and shortened breaks that characterize a high work- load prevent recovery and lead to physical symptoms. These physical symptoms cause some employees to work less or be absent, increasing the workload of the remaining employees in the group even further (Koukoulaki, 2014). Therefore, physical symp- toms are a particularly relevant strain to our framework. The increased physical strain on employees creates additional demands on the other group members as well as their leader. Therefore, we hypothesize that in addition to a direct crossover of workload from subordinates to their leaders, there would be an indirect crossover via subordinates’ physical strain. We therefore hypothesize this indirect effect, as well as the complete a mediation chain from subordinate stressor to strain to supervisor stressor to strain. An overview of the study’s hypotheses is presented in Figure 1.

Hypothesis 3: There is an indirect effect of subordinates’ workload on leader’s workload via subordinates’ physical strain.

Hypothesis 4: There is an indirect effect of subordinates’ workload on leader’s physical strain via subordinates’ physi- cal strain on leader’s workload (subordinates’ workload leads to subordinates’ physical strain, which leads to leader’s work- load, which leads to leader’s physical strain).

Method

Participants and Procedure

Researchers visited 55 different organizations in Italy and in- vited groups of employees and their leaders to complete a ques- tionnaire as part of a larger study. Complete data were received from 606 employees working in 173 work groups. Each work- group participated in its entirety (i.e., for every group, data were

Leader

H1

H2

Subordinate Subordinates’

Workload

Subordinates’

Physical

Symptoms

Leaders’

Workload

Leaders’

Physical

Symptoms

H3

H4

Subordinate

Figure 1. Study model overview. The dashed lines indicate indirect effects.

T hi

s do

cu m

en t

is co

py ri

gh te

d by

th e

A m

er ic

an P

sy ch

ol og

ic al

A ss

oc ia

ti on

or on

e of

it s

al li

ed pu

bl is

he rs

. T

hi s

ar ti

cl e

is in

te nd

ed so

le ly

fo r

th e

pe rs

on al

us e

of th

e in

di vi

du al

us er

an d

is no

t to

be di

ss em

in at

ed br

oa dl

y.

3SUBORDINATE STRESS TRANSFERS TO LEADERS

collected from all group members, as well as the leader, a first line manager), and each employee belonged only to one workgroup. However, within each organization, team participation was volun- tary and not all teams chose to participate. The matching of employees and the leader with their group was done using a numerical identifier that enabled anonymity in the data file.

The organizations that took part in this study were from a wide range of industries, including many manufacturing organizations (of materials, machinery, vehicles, foods/beverages), and several wholesale, logistics and construction organizations. Therefore, this sample can be considered to be comprised of more physically demanding jobs.

Consistent with other multilevel studies (Farh & Chen, 2014; Zhou, Wang, Chen, & Shi, 2012), we set a minimum of three employees per work group, resulting in the exclusion of nine groups. Therefore, the sample consisted of 586 employees working in 164 groups, and their 164 leaders (a total of 750 respondents). The mean age of participants was 38.4 (SD � 9.9), and the sample was disproportionately male (68.2%). Participants had worked in their current group for an average of 8.4 years (SD � 6.4) and had been employed in their organizations for an average of 11.4 years (SD � 7.9). The group leaders were slightly older (M � 40.5, SD � 9.1) and mostly male (81.4%). They also had a slightly higher average organizational tenure (M � 12.8, SD � 7.7) and group tenure (M � 9.8, SD � 6.9). The number of employees per group ranged between three and 20 members (not including the leader) with an average group size of 3.9.

Measures

All survey items were translated from English into Italian by one translator and then back-translated into English by a second translator. The few resulting discrepancies were discussed and adjustments were made. The surveys included demographic items, workload, and physical strain and were identical for both leaders and employees.

Workload. Spector and Jex’s (1998) five-item quantitative workload inventory was used to assess employees’ and leaders’ workload. Scale responses ranged from 1 (less than once per month or never) to 5 (several times per day). Higher scores indicate higher levels of workload. A sample item is “How often do you have to do more work than you can do well?” The alpha for both the employees and the leaders is 0.89.

Physical strain. Spector and Jex’s (1998) 13-item physical symptoms inventory was used to assess employees’ and leaders’ physical strain. Scale responses ranged from 1 (not at all) to 5 (several times per day). A sample item is “Over the past month, how often have you experienced a headache?” Higher scores indicate higher levels of strain. The alpha for the employees is 0.80, and for the leaders is 0.74.

Confirmatory Factor Analysis

Before testing out model, we used Mplus 7.4 (Muthén & Muthén, 1998 –2012) to run a multilevel confirmatory factor anal- ysis (MCFA) with a robust maximum likelihood estimator (MLR) to ensure the dimensionality and discriminant validity of the study variables. Given the relatively large number of items (Hurley et al., 1997) and deviations from normality (Williams & O’Boyle, 2008),

both of which negatively affect overall fit indices, we created three parcels from the 13 physical strain items, as recommended by Williams and O’Boyle (2008). We used these parcels instead of single items in the following series of MCFAs. We then ran the MCFA with six factors: two factors at the within level (subordi- nates’ workload and physical strain) and four factors at the be- tween level (leaders’ workload and physical strain and subordi- nates’ workload and physical strain). Results indicated adequate fit, �(117)2 � 252.46, p � .001, root mean square error of approx- imation (RMSEA) � .04; comparative fit index (CFI) � .93; Tucker–Lewis Index (TLI) � .91; standardized root mean square residual (SRMRWithin) � 0.05; SRMRBetween � .08. Moreover, the six-factor model had significantly better fit (using the Satorra- Bentler scaled ��2 test which is appropriate for MLR models; S-B��(6)2 � 114.70, p � .001), than a three-factor model, with one factor at the within level and two factors (one for each source) at the between level, which did not fit the data well, �(123)2 � 540.50, p � .001, RMSEA � .08; CFI � .78; TLI � .74; SRMRWithin � 0.11; SRMRBetween � .14.

Homogeneity of Subordinate Variables

To justify modeling the subordinate variables (workload and physical strain) at the between level, we calculated both intraclass correlations, or ICC(1), and median rwg(j) values. The ICC(1) level for physical strain was .13, indicating that 13% of the variance in subordinate physical strain resides between groups. The ICC(2) was .31; all ICC(2) values were calculated using the first three group members in each group given the large variations in group size. The median rwg(j) value was .92, indicating strong agreement (LeBreton & Senter, 2008) and far exceeding the rec- ommended .70 threshold (Glick, 1985). For workload, the ICC(1) level was .50, indicating that 50% of the variance in subordinate workload resides between groups. The ICC(2) was .75. The me- dian rwg(j) value was .83, which is again considered strong agree- ment. These results, together with the nested structure of the data, indicate that multilevel modeling is the appropriate approach for analyzing these data, as ignoring the nesting of the data would violate assumptions of independence of the observations in the sample. Moreover, using the multilevel modeling approach is a more reliable way of estimating group level effects than using the group-average effects approach (Lüdtke et al., 2008).

Analytic Approach

All hypotheses were tested simultaneously as part of a single model. Mplus 7.4 (Muthén & Muthén, 1998 –2012) was used to analyze a fixed effects multilevel model with a robust MLR. We chose a fixed effects model because our model does not include moderators of the within-level relationships and focuses mainly on between-level relationships. Leader variables were only modeled at the between level and were grand-mean centered. Subordinate variables were modeled at both the within and between levels, a procedure that creates latent within-level and between-level vari- ables, whereby the within level variable is group-mean centered. We also grand-mean centered the between-level subordinate vari- ables (Enders & Tofighi, 2007). We used a two-tailed threshold of .05 for significance tests. The model consisted of the paths de- picted in Figure 1, as well as confidence intervals (CIs) for the

T hi

s do

cu m

en t

is co

py ri

gh te

d by

th e

A m

er ic

an P

sy ch

ol og

ic al

A ss

oc ia

ti on

or on

e of

it s

al li

ed pu

bl is

he rs

. T

hi s

ar ti

cl e

is in

te nd

ed so

le ly

fo r

th e

pe rs

on al

us e

of th

e in

di vi

du al

us er

an d

is no

t to

be di

ss em

in at

ed br

oa dl

y.

4 PINDEK, LUCIANETTI, KESSLER, AND SPECTOR

indirect effect of subordinate workload to leader workload via subordinate physical strain, and the complete indirect effect (the previously described indirect effect that also leads to leader’s physical strain). Path estimates from this model were used to test the hypotheses. In addition, the inclusion of control variables (i.e., regressing the physical strain variables on subordinates’ and lead- ers’ age, gender, and team size) did not change the pattern of significance of the results, and none of these control variables were significantly associated with the physical strain variables. We therefore report results without the inclusion of these control variables.1

Results

Descriptive statistics and correlations are available in Table 1. Fit indices indicated strong support for the study model, �(2)2 � 2.18, p � .34, RMSEA � .01; CFI � .999; TLI � .995; SRMRWithin � 0.00; SRMRBetween � .06. All hypotheses were tested using parameter estimates obtained from this model (Figure 2). Hypotheses 1 and 2 were supported as both subordinates’ workload (standardized b � .54, p � .01) and subordinates’ physical strain (standardized b � .31, p � .05) were positively related to the leaders’ workload. In addition, Hypothesis 3 was supported as the indirect effect linking subordinates’ workload to leader’s workload via subordinates’ strain was significant (unstan- dardized b � .23, p � .05, 95% CI [.01, .45]). However, Hypoth- esis 4 was not supported, as the complete indirect effect (subor- dinates’ workload to subordinates’ physical strain to leader’s workload to leader’s physical strain) was not significant (unstan- dardized b � .03, p � .11, 95% CI [�.01, .07]). We further examined the indirect path linking subordinates’ physical strain to leader’s physical strain via the leader’s workload, and it was not significant (unstandardized b � .13, p � .11, 95% CI [�.03, .29]). Finally, there were positive relationships between workload and physical strain for both the subordinates, within level (b � .33, p � .01) between level (b � .51, p � .01), and the leaders (b � .18, p � .01).

Tests of Alternative Models

The cross-sectional nature of the data does not allow for a test of the directionality of the effects. However, we are able to discuss the fit of the data to models that propose different direction of effects. In the first alternative model, we reversed the order be-

tween leader and subordinate. In this model, leader workload relates to subordinate workload both directly and indirectly, via leader physical symptoms. Subordinate workload is then related to subordinate physical symptoms. This model had a somewhat worse fit to the data, �(2)2 � 7.53, p � .05, RMSEA � .07; CFI � .958; TLI � .852; SRMRWithin � 0.01; SRMRBetween � .10, since the �2 test is significant and the TLI value is lower than the accepted norms. It also has a nonsignificant path (leader symptoms do not relate to subordinate workload, unstandardized b � �.01, p � .85). We therefore conclude that this model is a less accurate representation of the data.

In the second alternative model, we retained the original order between subordinate and leader, but reversed the order of the stressor (workload) and strain (physical symptoms). This model did not fit the data, �(2)2 � 64.25, p � .001, RMSEA � .23; CFI � .522; TLI � .000; SRMRWithin � 0.00; SRMRBetween � .27. Note that the calculation of the TLI index resulted in a negative value, and was therefore set to zero, according to recommendations (Kenny, 2014). We therefore conclude that this second alternative model is not an appropriate description of the data and that our hypothesized model presented the best fit to the data. Complete results for the two alternative models can be obtained from Shani Pindek.

Discussion

Much of the research at the intersection of the work stress and the leadership literatures focuses on the leader as a source of subordinates’ stress. However, little research has focused on the leaders’ stressors and strains, and their subordinates’ role in exac- erbating them. This is surprising given the important role that leaders play in their organizations. Therefore, the goal of the current study was to examine work stressors and strains from the leader’s perspective. To do so, we united two theoretical mecha- nisms, the JD-R model (Bakker & Demerouti, 2007) and the crossover model (Westman, 2001) to test a multisource indirect effects model. Model results suggested that, in line with extant literature (Nixon et al., 2011), workload was associated with physical strain for both employees and leaders. Although most stress studies use employee strains as the ultimate outcome vari- able of interest, we examined whether the employee’s stressor/ strain process contributed to the leader’s stress. Results were consistent with a crossover process whereby employees’ stressor/ strain triggered their leader’s stress process by increasing the leader’s workload, although results pertaining to the crossover to the leader’s physical strain were less consistent. We did not find support for a crossover of physical strain from employees to their leader via an increased workload for the leader (but the correlation between employee physical strain and their leader’s physical strain was small and significant). Possible alternative pathways for the crossover of strain are discussed in the study limitations section.

A central finding that supports our crossover argument is the high correlation between subordinate’s workload and the leader’s

1 Although not hypothesized, we also examined possible effects of workload dispersion (SD of workload levels within each group). Subordi- nate workload dispersion did add incrementally over average workload level in predicting leader workload. This dispersion was not included in any of the reported results.

Table 1 Descriptive Statistics and Correlations Between Study Variables

1 2 3 4

1. Leaders’ workload — 2. leaders’ strain 0.18� — 3. subordinates’ workload 0.61�� 0.11 — 0.33��

4. subordinates’ strain 0.32�� 0.16� 0.38�� — Within level M 14.79 18.60 Within level SD 5.26 5.07 Group level M 14.88 18.60 15.43 17.62 Group level SD 4.40 3.30 5.57 4.24

Note. Within correlations (N � 586 employees) are above the diagonal, and between (group) level correlations (N � 164) are below the diagonal. � p � .05. �� p � .01.

T hi

s do

cu m

en t

is co

py ri

gh te

d by

th e

A m

er ic

an P

sy ch

ol og

ic al

A ss

oc ia

ti on

or on

e of

it s

al li

ed pu

bl is

he rs

. T

hi s

ar ti

cl e

is in

te nd

ed so

le ly

fo r

th e

pe rs

on al

us e

of th

e in

di vi

du al

us er

an d

is no

t to

be di

ss em

in at

ed br

oa dl

y.

5SUBORDINATE STRESS TRANSFERS TO LEADERS

workload (r � .61). The finding lends support for the crossover process within a workplace stress framework. Using this lens, we contend that there are many possible pathways by which a stressor, namely, workload, crosses over between subordinate and leader. In the current study, a direct crossover of workload (possibly ex- plained by leaders taking on some of their subordinates’ tasks, or an increased demand for support) and an indirect crossover of workload via increased subordinate physical strain (which repre- sents a further increased demand on the leader, because when subordinates experience physical strain, the leader steps in and takes on additional tasks to alleviate the subordinate’s workload or provides other types of support) were both supported by the data. It is important to note that the nature of our sample, which includes many employees who engage in manual labor and their first line supervisors (who are able to lend assistance with task completion when the need arises), make the use of a physical strain variable very appropriate as an additional indirect crossover mechanism. This indirect effect also demonstrates that there is more to the shared workload than simply the shared environment, and that workload does indeed cross over. That is, the direct relationship between the subordinates’ and leader’s workload can be attributed, at least in part, to a level of workload that is assigned to the group and is shared between all members of the group as well as the leader. However, the indirect effect via subordinate strain, that is significant beyond the direct effect, supports a crossover of de- mands from the subordinate to the leader.

Another interesting finding is the high ICC(1) level for work- load, which is an indication that workgroups, at least in the current sample, are rather homogeneous in terms of workload (Savels- bergh, Gevers, van der Heijden, & Poell, 2012). The high ICC(1) level in conjunction with the high cross-source correlation indi- cates that this homogeneity extends to the leader as well as being shared by group members.

Theoretical and Practical Implications

The current study united two theoretical perspectives, the JD-R model (Bakker & Demerouti, 2007) and the crossover model (Westman, 2001). According to the JD-R model (Bakker & De- merouti, 2007), job demands are linked to strain outcomes. In the current study, we found empirical support for this process for both subordinates and leaders. That is, high workload was associated with higher levels of physical strain. Then, using the crossover

model (Westman, 2001), we argued for a crossover effect between subordinates and their leaders. Not only did job demands impact each individual (i.e., the leader and subordinate separately), but the subordinates’ process crossed over to affect the leader.

On the practical side, study results point to the need for leaders to manage not only their own stress, but to some extent, their subordinate’s stress. More specifically, our findings suggest that attention needs to be given to the specific stressors that are en- demic to leadership roles in helping subordinates cope with their work demands and associated strains. A better understanding of the stressor–strain interplay across employees in different levels of their organizations, showing how the effects can flow upward and downward, will enable us to devise strategies that leaders can use to cope with their own stressors as they assist subordinates in coping with theirs.

Strengths, Limitations, and Future Direction

A notable strength of the current study is the use of a multi- source, multilevel design, which helps overcome concerns of com- mon method variance (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003) and increases our confidence in the results. Although lon- gitudinal designs often provide no better solution to common method variance problems than do cross-sectional designs, the use of multisource data is a recommended remedy (Spector, 2019).

However, the cross-sectional nature of the data is a significant limitation because the data cannot provide a conclusion regarding the directionality of the relationships. Although we argued that crossover occurred from subordinates to leaders, it is also likely that crossover occurs from leaders to subordinates (i.e., Wirtz et al., 2017), even though alternative models did not fit the data as well as our hypothesized model. However, the goal of the current study was to question the traditional ways of thinking about this chain of events as a unidirectional process whereby leaders impact subordinates (either by providing resources or by creating de- mands for them), but not the other way around. In this study, we demonstrated that it is plausible that subordinates’ stressors also cross over to affect leaders. Given the dynamic interpersonal nature of the leader/subordinate relationship, it is likely that this is a bidirectional process with crossover occurring in both directions at different periods of time. In addition, external events likely impact the workload of both supervisors and subordinates, thus driving at least some of the homogeneity in workload. Future

Leader

.18**

.54**

.31*

.51** Subordinate

.33**

Subordinates’

Workload

Subordinates’

Physical

Symptoms

Leaders’

Workload

Leaders’

Physical

Symptoms

Figure 2. Study model results. For subordinate level data, the path coefficient for the within-level relationship is presented below the arrow and the path coefficient for the between-level relationship is presented above the arrow. The leader variables are only modeled at the between level given that they are inherently group level variables (i.e., one leader per work group). All path coefficient estimates are standardized. � p � .05. �� p � .01.

T hi

s do

cu m

en t

is co

py ri

gh te

d by

th e

A m

er ic

an P

sy ch

ol og

ic al

A ss

oc ia

ti on

or on

e of

it s

al li

ed pu

bl is

he rs

. T

hi s

ar ti

cl e

is in

te nd

ed so

le ly

fo r

th e

pe rs

on al

us e

of th

e in

di vi

du al

us er

an d

is no

t to

be di

ss em

in at

ed br

oa dl

y.

6 PINDEK, LUCIANETTI, KESSLER, AND SPECTOR

quantitative studies could be designed in an attempt to estimate the extent of crossover in each direction, and this is also a ripe area for qualitative research. Qualitative research could be used to provide insight regarding the specific circumstances under which each direction of crossover is more likely to occur.

A second limitation is that our study is based on a positive view of leadership, grounded in positive psychology (Seligman & Csik- szentmihalyi, 2000). In other words, our approach inherently as- sumed that leaders were fulfilling their role obligations that in- clude maintaining their subordinates’ well-being and appropriate working conditions. However, leadership style (functional vs. dys- functional) could act as a moderator. For example, passive/ avoidant (laissez-faire leadership; Hinkin & Schriesheim, 2008; Skogstad, Einarsen, Torsheim, Aasland, & Hetland, 2007) leaders would likely be far less affected by a subordinate’s stressors given how uninvolved they are. These laissez-faire leaders would likely not assume a compensatory role for their subordinates. However, transformational leaders who demonstrate individual consideration for their subordinates would likely be more affected by their subordinates’ stress. In support of these ideas, main effects be- tween leaders’ leadership styles and their strain levels support the idea that being more involved with one’s subordinates (i.e., trans- formational leaders) is associated with higher subsequent levels of emotional exhaustion (Zwingmann et al., 2016). Future studies could examine the leadership style or the quality of leader– member exchange as moderators of the crossover of workload (directly and via subordinate strain) from subordinates to their leader. Another avenue for future research is examining a com- pensatory role that subordinates can assume. This compensatory role can be manifested either as compensating for the subordi- nate’s own reduced ability to handle his or her workload or by compensating for a team-member who is experiencing a workload that is too high.

Although not tested in this study, there is another path by which workload can potentially cross over between individuals holding various roles in the organization (and therefore also from employ- ees to leaders). In line with emotional contagion theory (Hatfield, Cacioppo, & Rapson, 1994), empirical research has found support for workgroup members influencing each other’s emotions and attitudes, as witnessing stressful emotions is sufficient to increase an individual’s experience of similar emotions. For example, in a diary study of health care workers, Totterdell et al. (2012) found that employees felt emotionally drained after witnessing unpleas- ant interactions both directly and indirectly (hearsay). Burnout is another stress related variable that has been shown to cross over between members of a workgroup (e.g., crossover of burnout between intensive care unit nurses; Bakker, Le Blanc, & Schaufeli, 2005). Therefore, it is possible that our strain variable crosses over, and affects perceived workload levels. Our design is not suited to tease apart our suggested mechanisms for the crossover of stres- sors/strains from these other contagion-oriented mechanisms. However, this disentanglement is important because it has the potential to lead to different interventions, and therefore future studies should conduct comparative tests of these possible mech- anisms.

In addition to other mechanisms that can explain the pathways modeled in the current study, there are other possible pathways that were not tested and modeled. For example, we consider the indirect effects of subordinate workload via subordinate physical

strain on the leader, but the interactive effect between subordinate workload and physical strain can also be examined, because when physical strain it may be that subordinate workload more readily translates into leader workload than when physical strain is low. Alternatively, subordinate workload may cross over to the leader via subordinate misbehavior or reduced motivation and perfor- mance. These situations whereby subordinates’ increased work- load results in a decrease in their performance output, would likely have adverse effects on leaders’ workload, similar to the ones shown in the current study. These too are promising avenues for future research.

Finally, there are limitations to the generalizability of the re- sults. Our results are based on specific industries and may not generalize to all employees. Furthermore, most of our groups were small (only six groups that had more than five team members), and we cannot know if these results replicate with larger teams where perhaps leaders are not expected to compensate for their subordi- nates when they are not meeting their demands (although control- ling for group size did not affect our results, so the effects of group size might not be large).

Conclusions

The results of the current study suggest the need to examine a forgotten segment of the workforce when studying workplace stress, that of the leader. Typically, researchers examine the leader as either a source of stress (Che, Zhou, Kessler, & Spector, 2017) or as a resource (Bakker & Demerouti, 2007) for employees. However, the stressors associated with the leader’s role has not received enough attention (Wirtz et al., 2017). The current article points to the subordinates as an important source of demands on the leader, affecting their well-being.

References

Bakker, A. B., & Demerouti, E. (2007). The job demands-resources model: State of the art. Journal of Managerial Psychology, 22, 309 –328. http://dx.doi.org/10.1108/02683940710733115

Bakker, A. B., & Demerouti, E. (2017). Job demands-resources theory: Taking stock and looking forward. Journal of Occupational Health Psychology, 22, 273–285. http://dx.doi.org/10.1037/ocp0000056

Bakker, A. B., Le Blanc, P. M., & Schaufeli, W. B. (2005). Burnout contagion among intensive care nurses. Journal of Advanced Nursing, 51, 276 –287. http://dx.doi.org/10.1111/j.1365-2648.2005.03494.x

Bakker, A. B., & Schaufeli, W. B. (2000). Burnout contagion processes among teachers. Journal of Applied Social Psychology, 30, 2289 –2308. http://dx.doi.org/10.1111/j.1559-1816.2000.tb02437.x

Bakker, A. B., Westman, M., & van Emmerik, I. J. H. (2009). Advance- ments in crossover theory. Journal of Managerial Psychology, 24, 206 –219. http://dx.doi.org/10.1108/02683940910939304

Bowling, N. A., Alarcon, G. M., Bragg, C. B., & Hartman, M. J. (2015). A meta-analytic examination of the potential correlates and conse- quences of workload. Work and Stress, 29, 95–113. http://dx.doi.org/10 .1080/02678373.2015.1033037

Brown, M., & Benson, J. (2005). Managing to overload? Work overload and performance appraisal processes. Group and Organization Manage- ment, 30, 99 –124. http://dx.doi.org/10.1177/1059601104269117

Campbell, M., Baltes, J. I., Martin, A., & Meddings, K. (2007). The stress of leadership: Center for Creative Leadership. Retrieved from https:// www.ccl.org/wp-content/uploads/2015/04/StressofLeadership.pdf

Che, X. X., Zhou, Z. E., Kessler, S. R., & Spector, P. E. (2017). Stressors beget stressors: The effect of passive leadership on employee health

T hi

s do

cu m

en t

is co

py ri

gh te

d by

th e

A m

er ic

an P

sy ch

ol og

ic al

A ss

oc ia

ti on

or on

e of

it s

al li

ed pu

bl is

he rs

. T

hi s

ar ti

cl e

is in

te nd

ed so

le ly

fo r

th e

pe rs

on al

us e

of th

e in

di vi

du al

us er

an d

is no

t to

be di

ss em

in at

ed br

oa dl

y.

7SUBORDINATE STRESS TRANSFERS TO LEADERS

through workload and work–family conflict. Work and Stress, 31, 338 – 354. http://dx.doi.org/10.1080/02678373.2017.1317881

Cubitt, S., & Burt, C. (2002). Leadership style, loneliness and occupational stress in New Zealand primary school principals. New Zealand Journal of Educational Studies, 37, 159 –169.

Demerouti, E., Bakker, A. B., & Bulters, A. J. (2004). The loss spiral of work pressure, work– home interference and exhaustion: Reciprocal relations in a three-wave study. Journal of Vocational Behavior, 64, 131–149. http://dx.doi.org/10.1016/S0001-8791(03)00030-7

Diebig, M., Bormann, K. C., & Rowold, J. (2017). Day-level transforma- tional leadership and followers’ daily level of stress: A moderated mediation model of team cooperation, role conflict, and type of com- munication. European Journal of Work and Organizational Psychology, 26, 234 –249. http://dx.doi.org/10.1080/1359432X.2016.1250741

Dormann, C., & Zapf, D. (1999). Social support, social stressors at work, and depressive symptoms: Testing for main and moderating effects with structural equations in a three-wave longitudinal study. Journal of Ap- plied Psychology, 84, 874 – 884. http://dx.doi.org/10.1037/0021-9010.84 .6.874

Enders, C. K., & Tofighi, D. (2007). Centering predictor variables in cross-sectional multilevel models: A new look at an old issue. Psycho- logical Methods, 12, 121–138. http://dx.doi.org/10.1037/1082-989X.12 .2.121

Eurofound & EU-OSHA. (2014). Psychosocial risks in Europe: Prevalence and strategies for prevention. Retrieved from http://eurofound.europa.eu/ publications/report/2014/eu-member-states/working-conditions/ psychosocial-risks-in-europe-prevalence-and-strategies-for-prevention

Farh, C. I. C., & Chen, Z. (2014). Beyond the individual victim: Multilevel consequences of abusive supervision in teams. Journal of Applied Psy- chology, 99, 1074 –1095. http://dx.doi.org/10.1037/a0037636

Ganster, D. C., & Schaubroeck, J. (1991). Work stress and employee health. Journal of Management, 17, 235–271. http://dx.doi.org/10.1177/ 014920639101700202

Glick, W. H. (1985). Conceptualizing and measuring organizational and psychological climate: Pitfalls in multilevel research. The Academy of Management Review, 10, 601– 616. http://dx.doi.org/10.5465/amr.1985 .4279045

Hatfield, E., Cacioppo, J. T., & Rapson, R. L. (1994). Emotional conta- gion. Cambridge, United Kingdom: Cambridge University Press.

Hinkin, T. R., & Schriesheim, C. A. (2008). An examination of “nonlead- ership”: From laissez-faire leadership to leader reward omission and punishment omission. Journal of Applied Psychology, 93, 1234 –1248. http://dx.doi.org/10.1037/a0012875

Hurley, A. E., Scandura, T. A., Schriesheim, C. A., Brannick, M. T., Seers, A., Vandenberg, R. J., & Williams, L. J. (1997). Exploratory and confirmatory factor analysis: Guidelines, issues, and alternatives. Jour- nal of Organizational Behavior, 18, 667– 683. http://dx.doi.org/10.1002/ (SICI)1099-1379(199711)18:6�667::AID-JOB874�3.0.CO;2-T

Kenny, D. A. (2014). Measuring model fit. Retrieved from http:// davidakenny.net/cm/fit.htm

Koukoulaki, T. (2014). The impact of lean production on musculoskeletal and psychosocial risks: An examination of sociotechnical trends over 20 years. Applied Ergonomics, 45, 198 –212. http://dx.doi.org/10.1016/j .apergo.2013.07.018

LeBreton, J. M., & Senter, J. L. (2008). Answers to 20 Questions about interrater reliability and interrater agreement. Organizational Research Methods, 11, 815– 852. http://dx.doi.org/10.1177/1094428106296642

Lepine, J. A., Podsakoff, N. P., & Lepine, M. A. (2005). A meta-analytic test of the challenge stressor– hindrance stressor framework: An expla- nation for inconsistent relationships among stressors and performance. Academy of Management Journal, 48, 764 –775. http://dx.doi.org/10 .5465/amj.2005.18803921

Lüdtke, O., Marsh, H. W., Robitzsch, A., Trautwein, U., Asparouhov, T., & Muthén, B. (2008). The multilevel latent covariate model: A new,

more reliable approach to group-level effects in contextual studies. Psychological Methods, 13, 203–229. http://dx.doi.org/10.1037/ a0012869

Matrix. (2013, May). Economic analysis of workplace mental health pro- motion and mental disorder prevention programmes and of their poten- tial contribution to EU health, social and economic policy objectives, Executive Agency for Health and Consumers, Specific Request EAHC/ 2011/Health/19 for the implementation of Framework Contract EAHC/ 2010/Health/01 Lot 2. Retrieved from http://ec.europa.eu/health/ mental_health/docs/matrix_economic_analysis_mh_promotion_en.pdf

Muthén, L. K., & Muthén, B. O. (1998 –2012). Mplus user’s guide (7th ed.). Los Angeles, CA: Author.

Nixon, A. E., Mazzola, J. J., Bauer, J., Krueger, J. R., & Spector, P. E. (2011). Can work make you sick? A meta-analysis of the relationships between job stressors and physical symptoms. Work and Stress, 25, 1–22. http://dx.doi.org/10.1080/02678373.2011.569175

Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88, 879 –903. http://dx.doi.org/10.1037/0021-9010.88.5.879

Poirel, E., Lapointe, P., & Yvon, F. (2012). Coping with administrative constraints by Quebec school principals. Canadian Journal of School Psychology, 27, 302–318. http://dx.doi.org/10.1177/0829573512461131

Savelsbergh, C., Gevers, J., van der Heijden, B. I. J. M., & Poell, R. F. (2012). Team role stress: Relationships with team learning and perfor- mance in project teams. Group and Organization Management, 37, 67–100. http://dx.doi.org/10.1177/1059601111431977

Seligman, M. E., & Csikszentmihalyi, M. (2000). Positive psychology. An introduction. American Psychologist, 55, 5–14. http://dx.doi.org/10 .1037/0003-066X.55.1.5

Semmer, N. K., Zapf, D., & Greif, S. (1996). ‘Shared job strain’: A new approach for assessing the validity of job stress measurements. Journal of Occupational and Organizational Psychology, 69, 293–310. http://dx .doi.org/10.1111/j.2044-8325.1996.tb00616.x

Skakon, J., Nielsen, K., Borg, V., & Guzman, J. (2010). Are leaders’ well-being, behaviours and style associated with the affective well-being of their employees? A systematic review of three decades of research. Work and Stress, 24, 107–139. http://dx.doi.org/10.1080/02678373.2010 .495262

Skogstad, A., Einarsen, S., Torsheim, T., Aasland, M. S., & Hetland, H. (2007). The destructiveness of laissez-faire leadership behavior. Journal of Occupational Health Psychology, 12, 80 –92. http://dx.doi.org/10 .1037/1076-8998.12.1.80

Spector, P. E. (2019). Do not cross me: Optimizing the use of cross- sectional designs. Journal of Business and Psychology, 34, 125–137. http://dx.doi.org/10.1007/s10869-018-09613-8

Spector, P. E., Chen, P. Y., & O’Connell, B. J. (2000). A longitudinal study of relations between job stressors and job strains while controlling for prior negative affectivity and strains. Journal of Applied Psychology, 85, 211–218. http://dx.doi.org/10.1037/0021-9010.85.2.211

Spector, P. E., & Jex, S. M. (1998). Development of four self-report measures of job stressors and strain: Interpersonal Conflict at Work Scale, Organizational Constraints Scale, Quantitative Workload Inven- tory, and Physical Symptoms Inventory. Journal of Occupational Health Psychology, 3, 356 –367. http://dx.doi.org/10.1037/1076-8998.3.4.356

Totterdell, P., Hershcovis, M. S., Niven, K., Reich, T. C., & Stride, C. (2012). Can employees be emotionally drained by witnessing unpleasant interactions between coworkers? A diary study of induced emotion regulation. Work and Stress, 26, 112–129. http://dx.doi.org/10.1080/ 02678373.2012.681153

Väänänen, A., Toppinen-Tanner, S., Kalimo, R., Mutanen, P., Vahtera, J., & Peiró, J. M. (2003). Job characteristics, physical and psychological symptoms, and social support as antecedents of sickness absence among men and women in the private industrial sector. Social Science and

T hi

s do

cu m

en t

is co

py ri

gh te

d by

th e

A m

er ic

an P

sy ch

ol og

ic al

A ss

oc ia

ti on

or on

e of

it s

al li

ed pu

bl is

he rs

. T

hi s

ar ti

cl e

is in

te nd

ed so

le ly

fo r

th e

pe rs

on al

us e

of th

e in

di vi

du al

us er

an d

is no

t to

be di

ss em

in at

ed br

oa dl

y.

8 PINDEK, LUCIANETTI, KESSLER, AND SPECTOR

Medicine, 57, 807– 824. http://dx.doi.org/10.1016/S0277-9536 (02)00450-1

Vincent-Höper, S., Muser, C., & Janneck, M. (2012). Transformational leadership, work engagement, and occupational success. The Career Development International, 17, 663– 682. http://dx.doi.org/10.1108/ 13620431211283805

Westman, M. (2001). Stress and strain crossover. Human Relations, 54, 717–751. http://dx.doi.org/10.1177/0018726701546002

Westman, M., Bakker, A. B., Roziner, I., & Sonnentag, S. (2011). Cross- over of job demands and emotional exhaustion within teams: A longi- tudinal multilevel study. Anxiety, Stress, and Coping, 24, 561–577. http://dx.doi.org/10.1080/10615806.2011.558191

Westman, M., & Etzion, D. (1999). The crossover of strain from school principals to teachers and vice versa. Journal of Occupational Health Psychology, 4, 269 –278. http://dx.doi.org/10.1037/1076-8998.4.3.269

Williams, L. J., & O’Boyle, E. H., Jr. (2008). Measurement models for linking latent variables and indicators: A review of human resource management research using parcels. Human Resource Management Re- view, 18, 233–242. http://dx.doi.org/10.1016/j.hrmr.2008.07.002

Wirtz, N., Rigotti, T., Otto, K., & Loeb, C. (2017). What about the leader? Crossover of emotional exhaustion and work engagement from follow- ers to leaders. Journal of Occupational Health Psychology, 22, 86 –97. http://dx.doi.org/10.1037/ocp0000024

Wong, S.-S., DeSanctis, G., & Staudenmayer, N. (2007). The relationship between task interdependency and role stress: A revisit of the job demands– control model. Journal of Management Studies, 44, 284 –303. http://dx.doi.org/10.1111/j.1467-6486.2007.00689.x

Wu, T.-Y., & Hu, C. (2009). Abusive supervision and employee emotional exhaustion: Dispositional antecedents and boundaries. Group and Or- ganization Management, 34, 143–169. http://dx.doi.org/10.1177/ 1059601108331217

Zhou, L., Wang, M., Chen, G., & Shi, J. (2012). Supervisors’ upward exchange relationships and subordinate outcomes: Testing the multilevel mediation role of empowerment. Journal of Applied Psychology, 97, 668 – 680. http://dx.doi.org/10.1037/a0026305

Zohar, D. (2002). Modifying supervisory practices to improve subunit safety: A leadership-based intervention model. Journal of Applied Psy- chology, 87, 156 –163. http://dx.doi.org/10.1037/0021-9010.87.1.156

Zwingmann, I., Wolf, S., & Richter, P. (2016). Every light has its shadow: A longitudinal study of transformational leadership and leaders’ emo- tional exhaustion. Journal of Applied Social Psychology, 46, 19 –33. http://dx.doi.org/10.1111/jasp.12352

Received June 19, 2019 Revision received January 30, 2020

Accepted July 8, 2020 �

T hi

s do

cu m

en t

is co

py ri

gh te

d by

th e

A m

er ic

an P

sy ch

ol og

ic al

A ss

oc ia

ti on

or on

e of

it s

al li

ed pu

bl is

he rs

. T

hi s

ar ti

cl e

is in

te nd

ed so

le ly

fo r

th e

pe rs

on al

us e

of th

e in

di vi

du al

us er

an d

is no

t to

be di

ss em

in at

ed br

oa dl

y.

9SUBORDINATE STRESS TRANSFERS TO LEADERS