Injustice hurts,literally: The role of sleep and emotional exhaustion in the relationship between organizational justice and musculoskeletal disorders

Abstract

The physical health consequences of perceived injustice at work are an important yet underexplored area of research. Using the job-stress recovery literature as an overarching framework, we argued that incomplete recovery because of sleep disorders and subsequent emotional exhaustion is a possible underlying mechanism through which organizational justice relates to employee musculoskeletal disorders (MSD). Using both self-administered questionnaires and medical examination to assess MSD, we tested our argument in two studies. Based on a randomly selected sample of employees from a variety of organizations, Study 1 found organizational justice to be negatively related to MSD through diminished sleep-related disorders. Using a sample of employees in nursing homes for the elderly, Study 2 extended these results by showing that the organizational justice–MSD relationship is sequentially mediated by sleep disorders and emotional exhaustion.

Keywords

emotional exhaustion MSD musculoskeletal disorders organizational justice sleep

There is substantial evidence that unfair treatment in the workplace leads to strong psychological and physiological reactions among employees (Robbins et al., 2012; Yang et al., 2014). The association between fairness perceptions and employee physical health has so far received limited attention, and the mechanisms through which fairness perceptions affect physical health are not adequately understood (Greenberg, 2010; Robbins et al., 2012). In this study, we explore the mechanisms by which workplace fairness affects musculoskeletal disorders (MSD).

MSD are one of the most significant and costly global health problems that affect the working population. According to the World Health Organization (WHO), MSD refer to health problems of the locomotor apparatus (that is, of muscles, tendons, skeleton, cartilage, ligaments and nerves) and include all forms of ill-health ranging from minor, transitory disorders to irreversible, disabling injuries. Across Europe, the average proportion of persons reporting MSD as their most serious work-related health problem was 54% in 2005; MSD then constituted 38% of total occupational diseases (European Agency for Safety and Health at Work, 2010). MSD are the greatest cause of total lost work days in the United States as in most other Organisation for Economic Co-operation and Development (OECD) countries (Nelson et al., 2005; OECD, 2010). MSD are also a matter of growing concern in developing countries as urbanization and rapid industrialization continue (e.g. Louw et al., 2007).

A common belief is that MSD are primarily caused by physical occupational factors, and research has indeed repeatedly demonstrated the adverse effects of biomechanical factors (e.g. excessive repetition, awkward postures, heavy lifting) on MSD (da Costa and Vieira, 2010). In comparison, less attention has been given to the role of psychosocial work characteristics in the prevalence of MSD even though a growing body of evidence indicates significant associations (e.g. Herin et al., 2012; Roquelaure et al., 2009). These studies have established that psychosocial work stressors such as highly monotonous work, high job demands, low job control, high job strain, low supervisor support and high job insecurity increase the risk of developing musculoskeletal symptoms (Lang et al., 2012). Only a handful of studies have explored the organizational justice–MSD relationship. For example, Pekkarinen et al. (2013) investigated whether distributive justice moderates the effects of job demands on MSD. Heponiemi et al. (2013) examined whether organizational justice moderates the association of shift work and employment type with patient-related stress, stress symptoms and MSD. However, none of these studies has explored the underlying mechanisms by which justice at work produces its effects on MSD.

To address this gap, we draw upon the job-stress recovery literature (Sonnentag et al., 2009) to develop a model that links fairness perceptions to employee MSD through sleep disorders and subsequent emotional exhaustion. The recovery approach assumes that the process of psychophysiological unwinding after exposure to job stressors is crucial for employees’ mental and physical health (Geurts and Sonnentag, 2006). Sleep represents a vital period for individuals to unwind from work, replenish resources and recover from both mental and physical fatigue (Åkerstedt et al., 2009). There is accumulating evidence indicating that sleeping poorly increases the risk of emotional exhaustion (Jansson-Fröjmark and Lindblom, 2010; Söderström et al., 2012) and MSD (Canivet et al., 2008; Rasmussen-Barr et al., 2014), and that unfair treatment affects sleep quality (Elovainio et al., 2009; Greenberg, 2006; Hietapakka et al., 2013). Integrating this research with a job-stress recovery perspective, we posit that incomplete recovery because of sleep disorders and subsequent emotional exhaustion is a possible underlying mechanism through which organizational (in)justice relates to employee MSD.

Theoretical background and hypotheses

Organizational justice refers to the fairness of the treatment employees experience from their organizations and their agents (Greenberg, 1993). Employees’ perceptions of justice can be evaluated along several dimensions (Colquitt, 2001), including distributive justice (i.e. the perceived fairness of decision outcomes), procedural justice (i.e. the perceived fairness of decision-making processes) and interpersonal justice (the fairness of interpersonal treatment during decision-making procedures), which are sometimes divided into interpersonal justice (i.e. perceptions of whether one is treated with respect and dignity during the implementation of a procedure) and informational justice (i.e. perceptions of the adequacy and timeliness of explanations provided by authority figures).

The scarce studies that have, to date, evaluated the association between organizational justice and MSD have focused on one of several of these justice facets (distributive in Pekkarinen et al., 2013; procedural and interactional combined in Heponiemi et al., 2013). Here, we adopt a broadly defined justice concept that reflects an overall sense of fairness (Ambrose and Schminke, 2009). This approach is consistent with previous conceptualization and operationalization of organizational justice (e.g. Zhang et al., 2014) as well as with meta-analytic findings, suggesting that support for the differential effects of justice facets is mixed (Ambrose et al., 2007; Robbins et al., 2012). This holistic view of organizational justice is also in line with Robbins et al.’s (2012: 249) recommendation to ‘examine profiles of unfairness rather than solitary facets when studying important outcomes such as employee health’ as ‘focusing on one facet or some subset of facets may substantially reduce the predictive validity of the results’.

There are two main theoretical perspectives that have been mobilized to understand the organizational justice–MSD relationship. The first is commonly known as the ‘stressor–strain’ perspective; as in most theoretical models describing the relationship between psychosocial work factors and MSD, injustice at work is seen as a job stressor that over time indirectly leads to MSD by eliciting negative emotional states, unhealthy behaviors, and pathophysiological changes (Ganster and Rosen, 2013; Robbins et al., 2012). There is some indirect evidence supporting this explanation. For example, perceptions of organizational injustice have been related to burnout symptoms (Robbins et al., 2012) and burnout symptoms to increased risk of MSD (Melamed, 2009). A second perspective borrows from the job demands–resources model (Demerouti et al., 2001) to position organizational justice as a resource that can decrease MSD or moderate the associations between job demands and MSD. In support of this argument, Pekkarinen et al. (2013) found that distributive justice was both directly related to lower MSD and helped to protect against the detrimental effects of mental workload on MSD. In a similar vein, Heponiemi et al. (2013) found that organizational justice – measured with a combination of procedural and interactional justice item – was associated with lower levels of MSD and mitigated musculoskeletal symptoms among permanent (vs temporary) employees.

Although each of these perspectives provides an important foundation, when taken in isolation they do not fully explain the theoretical linkages between injustice and MSD. In particular, the stressor–strain explanation provides limited insight into the specific physiological processes that may connect injustice to MSD (Ganster and Rosen, 2013), and the job demands–resources explanation provides limited insight into the psychological mechanisms that may be involved in this relationship (Schaufeli and Taris, 2014). This leads us to consider a third approach that draws upon the job-stress recovery literature and bridges the two perspectives described above. According to this integrative approach, injustice at work is a stressor that could contribute to a lack of rest and recovery, which, in turn, could contribute to the development of muscular pain. Recovery from work can be defined as ‘the process of reducing or eliminating physical and psychological strain symptoms that have been caused by job demands and stressful events at work’ (Sonnentag and Fritz, 2015: 72). Two resource-related theories underline the importance of the recovery process on physical health: the Conservation of Resources (COR) theory and the Effort–Recovery (ER) theory. In short, the COR model posits that ‘people strive to retain, project, and build resources [which include objects, personal characteristics, conditions, and energies] and that what is threatening to them is the potential or actual loss of these valued resources’ (Hobfoll, 1989: 513). The ER model (Meijman and Mulder, 1998) posits that effort spent responding to work demands has both physical and psychological costs. Recovery after work is then necessary to repair the negative strain effects and achieve goals during the workday. As ten Brummelhuis and Bakker (2012) summarized, both theories suggest that recovery can occur: (i) when stressors cease and employees refrain from using the same types of personal resources as used at work; and (ii) when employees can reload their depleted personal resources after work. If recovery is hindered, stress-induced physiological and psychological changes can persist and contribute over time to the development of MSD (Lundberg, 2002).

Among the many factors that can facilitate or impede recovery (see Demerouti et al., 2009 for a review), sleep occupies a central position as it can be conceptualized as both a recovery process and a recovery outcome (Sonnentag and Geurts, 2009). Sleep fulfils the need for recovery associated with exposure to work stressors (Sluiter et al., 2003) and helps employees to renew personal resources in their daily lives (Nägel and Sonnentag, 2013). As Åkerstedt et al. summarize (2009: 234), ‘sleep will not only reset alertness, mood, and performance capacity to normal levels after a night of sleep. It will also regenerate the central nervous system, the metabolic system, the endocrine system, and the immune system’. Given the key role of sleep in recovery, we hypothesize that sleep disorders might constitute an intermediate link in the mechanisms leading from organizational justice to MSD.

Sleep disorders include a wide range of problems including those producing difficulty in initiating or maintaining sleep, or excessive sleepiness (American Academy of Sleep Medicine, 2014). Previous research has shown that perceptions of organizational justice are associated with sleep problems (Elovainio et al., 2003; Greenberg, 2006) and are predictors of sleep disturbances over time (Elovainio et al., 2009). Indirect evidence suggests that prolonged stress (as indicated by self-reported negative emotions as well as quantitative markers of cardiac dysregulation) is a mechanism through which perceived organizational injustice affects sleep disorders (Elovainio et al., 2006; Hietapakka et al., 2013). There is also indirect evidence suggesting that it is not the stress experience itself that disturbs sleep but rather rumination and worries about future injustice experiences (Harvey et al., 2005) that are also consequences of unfair treatment at work (Bies and Tripp, 2002; Rodell and Colquitt, 2009). As Eib et al. (2015) argued, ‘injustice at work elicits a prolonged mental representation of the stressor – i.e. employees who experience injustice at their workplace tend to not stop thinking about work’; this inability to withdraw from work has been shown to impair sleep (Kudielka et al., 2004).

Moreover, prospective studies suggest that sleep problems worsen the long-term prognosis of existing musculoskeletal chronic pain and increase the long-term prognosis of musculoskeletal pain among pain-free individuals (Finan et al., 2013; Lusa et al., 2015; Rasmussen-Barr et al., 2014). Considering these findings through the lens of recovery, we expect organizational justice to be linked to MSD by the intermediate pathway of sleep disorders, which have the potential to impede employees both from distancing mentally from work and from reloading their depleted personal resources.

In support of our prediction, there is also physiological evidence, much of which is grounded in the allostatic load model (McEwen, 1998). The term allostatic load refers to ‘the cost of chronic exposure to fluctuating or heightened neural or neuroendocrine response resulting from repeated or chronic environmental challenge that an individual reacts to as being particularly stressful’ (McEwen and Stellar, 1993: 2093). From this perspective, pathologies can result when hormones such as cortisol are chronically elevated and when functional load reactions are not followed by psychophysiological unwinding. Perceptions of injustice have been shown to affect cortisol activity within the hypothalamic-pituitary-adrenal (HPA) axis (Yang et al., 2014) – a major component of the hormonal system involved in the regulation of the stress response. HPA axis hyperactivity (including alterations in the rhythm of cortisol production and alterations of the immune/inflammation system) can have many negative effects on sleep and, in turn, sleep disturbances can exacerbate HPA axis dysfunction (Buckley and Schatzberg, 2005). Evidence suggests that MSD may be the consequence of a dysfunctional HPA (Generaal et al., 2014), which prevents muscle relaxation and healing (Lundberg, 2002). In addition, disturbed sleep has been found to increase musculoskeletal pain sensitivity and thus the clinical expression of MSD (Finan et al., 2013). It should be noted, however, that the neurophysiological mechanisms underlying the sleep disorders–MSD relationship have yet to be clearly determined.

Based on the above arguments, we thus propose:

Hypothesis 1: Organizational justice is negatively related to musculoskeletal disorders through diminished sleep-related disorders.

To further substantiate our claim that sleep recovery plays a key role in the organizational justice–MSD relationship, we also predict that the adverse effects of sleep disorders are transmitted through emotional exhaustion. Emotional exhaustion is a core dimension of job burnout (Maslach et al., 2001). It refers to chronic feelings of being overextended and depleted of one’s emotional and physical resources (Maslach et al., 2008). Sleep disturbances attributable to injustice perceptions seem to play an essential role in emotional exhaustion development. Prospective studies (e.g. Jansson-Fröjmark and Lindblom, 2010; Söderström et al., 2012) have shown that insufficient sleep is related to high exhaustion levels (rather than the reverse). A plausible mechanism for this is that the fatigue-inducing effects of sleep disturbance reduce resources for coping with stress, thereby setting off a spiral of resource losses (Hobfoll, 1989). This spiral may, in the long run, lead to emotional exhaustion.

Moreover, accumulated evidence suggests that emotional exhaustion has a negative impact on certain aspects of physical health (Shirom, 2010). In particular, emotional exhaustion was found to be positively associated with musculoskeletal pain in a variety of occupational groups (Langballe et al., 2009) and also to predict the onset and maintenance of MSD (Grossi et al., 2009; Melamed, 2009). Although the physiological mechanisms linking emotional exhaustion and the development of MSD remain to be clarified (see Melamed, 2009: 318), the mediating mechanisms linking sleep disorders and MSD (e.g. HPA axis dysfunction) may also be applicable to the exhaustion–MSD relationship. As exhaustion is a strong indicator of fatigue accumulation – and encompasses the fatigue effects of sleep disturbance – we expect exhaustion to carry the sleep disorders effects to MSD.

Hence, sleep disorders appear to increase employees’ emotional exhaustion, which, in turn, increases the risk of developing of MSD. To summarize our reasoning, we propose the following hypothesis:

Hypothesis 2: Organizational justice is negatively related to musculoskeletal disorders through diminished sleep-related disorders and emotional exhaustion linked in series.

Below we report two studies in which we tested the above hypotheses. Study 1 was conducted as an initial examination of Hypothesis 1. Study 2 was carried out to extend the results of Study 1 by testing Hypothesis 2.

Study 1 method

Sample and procedure

Two hundred and sixty-six participants were randomly selected from employees working for a variety of organizations located in the south-west region of France, and undergoing a regularly scheduled annual health examination between February and May 2013. Five occupational health nurses and two occupational physicians were involved in data collection. Random sampling was ensured through a fixed sampling rate of the participants based on the order in which they were scheduled for health examination. This sampling procedure yielded a heterogeneous sample of employees from different industry sectors, employer sizes, and socio-economic and cultural backgrounds. The main socio-demographic characteristics of the participants are summarized in Table 1. Participants were asked to fill in a questionnaire that included measures of organizational justice, sleep disorders, and other job-related and demographic variables. Immediately after completing the questionnaire, participants underwent a standardized clinical examination, performed by a nurse or physician. In order to increase the consistency of the examination procedure and to enhance the quality of data collection, the nurses and physicians were trained by one of the study investigators, who is an occupational physician. They were also instructed not to read the individuals’ responses to the self-report attitudinal questionnaire.

Table 1.

Descriptive statistics and correlations among Study 1 variables.

		Mean	SD	1	2	3	4	5	6	7	8	9	10	11	12
1	CD-Upper-extremity MSD (0 = no, 1 = yes)	.18	.38	–
2	SR-Upper-extremity MSD (0 = no, 1 = yes)	.31	.46	.67**	–
3	SR-Lower-extremity MSD (0 = no, 1 = yes)	.33	.47	.22**	.37**	–
4	Sleep disorders (0–3)	1.09	.72	.22**	.26**	.16**	(.68)
5	Overall organizational justice (1–5)	3.71	.87	−.27**	−.26**	−.09	−.18**	(.85)
6	Age (years)	40.68	10.86	.02	.06	.19**	.21**	.01	–
7	Gender (0 = male, 1 = female)	.54	.50	.23**	.15*	.04	.11	−.07	.12	–
8	Education (1–4)	1.79	.84	.02	−.02	−.09	.02	−.15*	−.23**	.05	–
9	Organizational tenure (years)	10.97	8.85	−.06	−.01	.03	.10	.05	.54**	−.02	−.12*	–
10	Job tenure (years)	8.71	7.82	−.02	.00	.04	.04	.07	.48**	−.01	−.20**	.81**	–
11	Psychological job demand (1–5)	3.09	.76	.01	.07	.01	.03	−.06	−.04	.08	.18**	.04	.00	(.72)
12	Physical job demand (1–5)	3.02	1.25	.06	.18**	.10	−.05	−.01	.00	−.04	−.37**	−.09	.01	.15*	(.87)
13	Job type: Clerk, service and sales worker (0 = no, 1 = yes)	.63	.48	.15*	.11	−.04	−.04	−.09	−.02	.33**	−.10	−.03	−.02	−.06	.15*
14	Job type: Blue-collar (0 = no, 1 = yes)	.14	.35	−.08	−.04	.01	.00	.03	.11	−.16**	−.30**	.04	.11	−.23**	.12
15	Job type: Technician (0 = no, 1 = yes)	.15	.36	−.12	−.08	.01	.00	.07	−.15*	−.23**	.21**	−.07	−.08	.14*	−.16**
16	Job type: Manager/professional (0 = no, 1 = yes)	.08	.26	−.02	−.04	.04	.07	.03	.10	−.08	.29**	.10	.00	.21**	−.21**

N = 266. SD = standard deviation; CD = clinically-detected; MSD = musculoskeletal disorders; SR = self-reported. Pearson product moment correlations are reported for pairs of continuous variables, Spearman rank correlations are reported for pairs of continuous and dichotomous variables, and Phi correlations are reported for pairs of dichotomous variables. Internal consistency values (Cronbach’s alphas) appear across the diagonal in parentheses. Education was coded: 1 = less than high school degree, 2 = high school degree, 3 = bachelor’s degree and 4 = master’s degree.

p < .05.

p < .01.

Measures

We used well-established measures that had been previously translated and validated in French. Descriptive statistics and Cronbach’s alpha reliabilities are presented in Table 1. Unless otherwise noted, responses were provided on five-point Likert scales ranging from strongly disagree (1) to strongly agree (5).

Organizational justice

Perceptions of organizational justice were assessed with an abridged 15-item version of Colquitt’s (2001) 20-item measure, previously translated and validated in French. This measure assessed employees’ perceptions of distributive (four items, e.g. ‘The rewards I receive are justified given my performance’), procedural (four items, e.g. ‘In my organization, procedures are based on accurate information’), interpersonal (three items, e.g. ‘My supervisor treats me with respect’), and informational justice (four items, e.g. ‘My supervisor communicates details in a timely manner’). The results of a confirmatory factor analysis (CFA) supported a four-factor structure: χ² (84) = 206.36, p < .01, comparative fit index (CFI) = .98, non-normed fit index (NNFI) = .98, root mean square error of approximation (RMSEA) = .074, standardized root mean square residual (SRMR) = .036. However, because we did not hypothesize differential relations with any of the four scales, and consistent with other studies (e.g. Zhang et al., 2014), we specified overall organizational justice as a latent construct, using the four justice dimensions as indicators (Colquitt and Rodell, 2015: 192–193). To test whether this was statistically appropriate, we conducted another CFA in which the 15 items were loaded onto their respective first-order factors, and where these first-order factors were loaded onto an overall second-order factor. Results indicated that this model had an acceptable fit with the data (χ² [86] = 255.64, p < .01, CFI = .98, NNFI = .97, RMSEA = .086, SRMR = .066), with standardized factor loadings of the four dimensions on the overall justice factor ranging from .72 to .91 (p < .01). We thus proceeded to test the study hypotheses using this combined organizational justice factor (Cronbach’s alpha = .85). Supplementary analyses treating procedural, distributive, informational, and interpersonal justice as separate dimensions are provided in online-only Appendix D. (http://hum.sagepub.com/supplemental)

Sleep disorders

We measured sleep disorders with a five-item scale from Marquié and Foret (1999). Employees were asked to rate on a four-point scale (0 = never, 1 = seldom, 2 = sometimes, 3 = often) the frequency in the last month of five sleep difficulty symptoms, namely: (1) difficulty falling asleep, (2) difficulty maintaining sleep, (3) difficulty getting back to sleep, (4) premature awakening and (5) hypnotic medication use (α = .68).

Musculoskeletal disorders

We constructed three measures of MSD: one for the lower extremity of the body and two for the upper extremity. The nurses and physicians involved in data collection were trained by one of the study investigators to perform a standardized physical examination based on an international protocol (SALTSA consensus) for the evaluation of work-related upper-extremity MSD (Sluiter et al., 2001; See also Roquelaure et al., 2006). Using a French version of the Nordic questionnaire (Kuorinka et al., 1987), they began the examination by asking the employee about lower-extremity pain (in lower back, thigh/hip, knee, or ankle/feet) and upper-extremity pain (in the neck, shoulder, elbow, wrist/hand, or upper back) in the preceding 12 months and in the past seven days. Employees who reported lower-extremity pain in the last seven days were identified as having self-reported lower-extremity MSD (32.7% of the sample). As there is little evidence regarding the reliability and validity of diagnostic physical examination tests for the lower extremity, no physical examination was performed to further substantiate the information retrieved by the Nordic questionnaire.

The employees who reported upper-extremity pain were identified as having self-reported upper-extremity MSD (30.8% of the sample). If upper-extremity pain occurred during the past 12 months, a physical examination was performed by the nurse/physician using a standardized clinical procedure. The physical examination, which consisted of inspection, palpation, passive movements, resisted movements and a variety of maneuvers, allowed the detection of six upper-extremity MSD: radiating neck complaints, rotator cuff syndrome, lateral epicondylitis, medial epicondylitis, de Quervain’s disease and carpal tunnel syndrome. The physical examination was considered as positive if any of the six principal upper-extremity disorders was present. Employees were identified as having clinically-detected upper-extremity MSD (17.6% of the sample) on the basis of symptoms reported on the date of the examination or for at least four days in the preceding week and physical examination findings.

In sum, the MSD measures were modeled as binary variables (0 = absence of MSD; 1 = presence of at least one MSD). The case definition of MSD used in this study was based on symptoms only (‘self-reported MSD’), whether or not physical examination signs were positive. For complementary analyses, a stricter definition of upper-extremity MSD based on both symptoms and physical signs (‘clinically-detected MSD’) was used. The prevalence rates of MSD by body region are summarized in Online-only Appendix A.

Control variables

In studies of psychosocial variables in relation to MSD, the interpretation of the results may depend upon whether other potential confounding variables were controlled for in the analyses. This concern can be illustrated by Davis and Heaney’s (2000) review, which showed that controlling for physical demands in the relationship between psychosocial work factors and low back pain resulted in a 20% increase in null results as compared with the unadjusted analyses. Accordingly, we controlled for several demographic and work-related variables that have proven to increase or decrease the risk of MSD (Lang et al., 2012; Malchaire et al., 2001; Pekkarinen et al., 2013; Roquelaure et al., 2009): age, gender, educational level, occupational category, organizational tenure, job tenure, and psychological and physical job demands. Psychological and physical job demands were measured with six items (e.g. ‘My job requires long periods of intense concentration on the task’; α = .72) and five items (e.g. ‘My job requires lots of physical effort’; α = .87), respectively, derived from the French version of the Karasek Job Content Questionnaire (Karasek et al., 1998).

Confirmatory factor analyses

We tested and compared the fit of a series of nested CFA models to examine the discriminant validity of the four latent constructs used in this study (i.e. organizational justice, sleep disorders, psychological job demand and physical job demand). For organizational justice, we used scale scores of the four dimensions as observed indicators. For all other study constructs, individual items were used. The hypothesized four-factor measurement model fitted the data acceptably (χ² [164] = 386.70, p < .01, CFI = .91, NNFI = .89, RMSEA = .072, SRMR = .067) and significantly better than any of the alternative measurement models (p Δχ² < .01). Overall, these results provide evidence that our constructs possess adequate discriminant validity for use in hypotheses testing.

Analytical approach

We tested our hypotheses using path analytic procedures and the bootstrapping approach described by Preacher and Hayes (2008). Accordingly, we estimated the indirect effect of organizational justice on MSD using unstandardized coefficients and utilized bootstrapping procedures with 10,000 re-samples to place 95% confidence intervals (CIs) around the estimates of the indirect effect. Bootstrapping provides evidence of mediation if the bias-corrected 95% CI excludes zero for the indirect effect (that is, if the indirect effect is statistically significant). Mediational analyses were performed using the SPSS Process macro (Hayes, 2013: Model 4), where organizational justice was the predictor, sleep disorders was the mediator, and MSD were the outcomes. All analyses included control variables.

Study 1 results

Descriptive statistics and correlations among Study 1 variables are shown in Table 1. It is noteworthy that organizational justice exhibited significant negative correlations with sleep disorders (r = −.18, p < .01) and clinically-detected and self-reported upper-extremity MSD (r = −.27 and −.26, respectively, p < .01) but was unrelated to self-reported lower-extremity MSD (r = −.09, p > .05). Also of interest, sleep disorders were associated with increased clinically-detected and self-reported upper-extremity MSD (r = .22 and .26, respectively, p < .01) and self-reported lower-extremity MSD (r = .16, p < .01).

Using the SPSS Process macro, three separate analyses (one for each type of MSD) were run to test the proposed indirect effects of organizational justice on MSD through sleep disorders. Each included a series of ordinary least squares and logistic regressions (depending on the nature of the dependent variable), which are reported in online-only Appendices B1–B4.

As shown in Table 2, bootstrapping analyses indicated a statistically significant indirect effect of organizational justice on clinically-detected (−.12, 95% CI = [−.28, −.02]) and self-reported upper-extremity MSD (−.12, 95% CI = [−.26, −.03]), and a marginally significant indirect effect on self-reported lower-extremity MSD (−.05, 90% CI = [−.13, −.00]). Overall, the results of Study 1 provide initial evidence for our first hypothesis, which predicted that organizational justice is negatively related to MSD through diminished sleep-related disorders. In the next study, we sought to extend these results by testing Hypothesis 2, which proposed that the negative relationship between organizational justice and MSD is transmitted sequentially through sleep disorders and emotional exhaustion (in that order).

Table 2.

Results of indirect effects tests.

	Study 1			Study 2
	CD-UEMSD	SR-UEMSD	SR-LEMSD	CD-UEMSD	SR-UEMSD	SR-LEMSD
Direct effect of organizational justice	−.89**	−.66**	−.28†	−.23	−.13	−.04
Direct effect of organizational justice	[–1.32, −.46]	[–1.01, −.30]	[−.60, .05]	[−.78, .32]	[−.57, .32]	[−.50, .41]
Total indirect effect	−.12*	−.12*	−.05†	−.36*	−.24*	−.22*
	[−.28, −.02]	[−.26, −.03]	[−.15, .01]	[−.69, −.08]	[−.47, −.04]	[−.47, −.01]
Indirect effect of justice through sleep disorders only	−.12*	−.12*	−.05†	−.06	−.06	−.01
Indirect effect of justice through sleep disorders only	[−.28, −.02]	[−.26, −.03]	[−.15, .01]	[−.22, .02]	[−.19, .01]	[−.10, .07]
Indirect effect of justice through emotional exhaustion only				−.27*	−.16†	−.19*
				[−.55, −.03]	[−.37, .01]	[−.43, −.00]
Indirect effect of justice through sleep disorders and emotional exhaustion in serial				−.03*	−.02*	−.02*
				[−.09, −.00]	[−.06, −.00]	[−.07, −.00]

CD = clinically-detected; SR = self-reported; MSD = musculoskeletal disorders; UE = upper extremity; LE = lower extremity. Unstandardized bootstrapped estimates are reported. 95% confidence intervals are reported in brackets. The bias-corrected 95% bootstrap confidence intervals were estimated using the SPSS Process macro (version 2.13: Hayes, 2013). Bootstrap N = 10,000. All analyses include control variables.

†

p < .10.

p < .05.

p < .01.

Study 2 method

Sample and procedure

We obtained the agreement of five French nursing homes for elderly people to conduct a study about their employees’ work attitudes and well-being. As in Study 1, prospective participants were asked to fill in a questionnaire, and to subsequently undergo a standardized clinical examination performed by an experienced registered nurse who was recruited and trained by the research team to collect data. A final sample of 223 employees volunteered to take part in this study, yielding a 79% response rate. The sample was mainly female (86.5%), and nearly half of the sample (45.3%) was involved in the daily care of elderly people (i.e. physical care, medical support and social activities). The main socio-demographic characteristics of the participants are summarized in Table 3.

Table 3.

Descriptive statistics and correlations among Study 2 variables.

		Mean	SD	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16
1	CD-Upper-extremity MSD (0 = no, 1 = yes)	.26	.44	–
2	SR-Upper-extremity MSD (0 = no, 1 = yes)	.45	.50	.52**	–
3	SR-Lower-extremity MSD (0 = no, 1 = yes)	.39	.49	.13	.10	–
4	Emotional exhaustion (1–5)	2.63	.90	.23**	.23**	.19**	(.83)
5	Sleep disorders (0–3)	1.29	.71	.17*	.17*	.09	.27**	(.69)
6	Overall organizational justice (1–5)	3.41	.73	−.20**	−.14*	−.09	−.38**	−.14*	(.74)
7	Age (years)	43.00	11.14	.29**	.07	.00	−.13	.10	−.07	–
8	Gender (0 = male, 1 = female)	.87	.34	.15*	.14*	.02	.09	.12	−.08	.17*	–
9	Education (1–4)	1.89	.91	−.03	.07	.01	.04	−.06	.01	−.13	.07	–
10	Organizational tenure (years)	8.81	8.94	.17*	.09	.13*	−.07	.09	−.07	.46**	.07	−.19**	–
11	Job tenure (years)	6.64	7.68	.18**	.11	.05	−.06	.00	−.12	.39**	.05	−.11	.79**	–
12	Psychological job demand (1–5)	2.53	.86	.12	.14*	.03	.24**	.08	−.10	−.13	.20**	.03	−.12	−.10	(.82)
13	Physical job demand (1–5)	3.53	1.18	.14*	.13	.03	.21**	.09	−.13*	−.02	.05	−.32**	−.13*	−.15*	.22**	(.86)
14	Smoking (0 = no, 1 = yes)	.37	.48	.05	.02	.05	.09	.01	.00	−.14*	.06	−.18**	−.17*	−.11	.06	.05	–
15	Participation in sports/exercise (0–3)	.78	.96	.09	.08	.07	−.12	−.04	.00	−.01	−.16*	.20**	.05	.03	.01	−.08	−.18**	–
16	Body mass index (kg/m²)	24.48	4.36	.10	−.03	.03	−.01	.06	−.13	.16*	−.06	−.12	.13	.13*	−.03	.12	−.18**	−.08	–
17	Job type: Nurse/assistant nurse (0 = no, 1 = yes)	.45	.50	.03	.05	.06	.05	.11	−.05	−.13	.15*	.20**	−.22**	−.18**	.40**	.14*	.07	.05	−.04
18	Job type: Housekeeping worker (0 = no, 1 = yes)	.25	.43	−.07	.02	−.10	−.05	.04	.09	.18**	.14*	−.41**	.09	.11	−.13*	.22**	.05	−.18**	−.04
19	Job type: Supervisory/managerial employee (0 = no, 1 = yes)	.07	.25	.04	.05	−.03	.01	−.09	.01	−.06	−.10	.37**	.05	.00	.01	−.27**	−.06	.25**	−.01
20	Job type: Other (0 = no, 1 = yes)	.23	.42	.01	−.10	.05	−.02	−.12	−.04	.01	−.25**	−.03	.14*	.11	−.34**	−.24**	−.11	−.02	.09

N = 223. SD = standard deviation; CD = clinically-detected; SR = self-reported. Pearson product moment correlations are reported for pairs of continuous variables, Spearman rank correlations are reported for pairs of continuous and dichotomous variables, and Phi correlations are reported for pairs of dichotomous variables. Internal consistency values (Cronbach’s alphas) appear across the diagonal in parentheses. Education was coded: 1 = less than high school degree, 2 = high school degree, 3 = bachelor’s degree and 4= master’s degree. The ‘other’ job-type category includes administrative staff, kitchen staff, and maintenance personnel.

p < .05.

p < .01.

Measures

As in Study 1, we used well-established measures that had been previously translated and validated in French. Descriptive statistics and Cronbach’s alpha reliabilities are presented in Table 3. Unless otherwise noted, responses were provided on five-point Likert scales ranging from strongly disagree (1) to strongly agree (5).

Musculoskeletal disorders

We used the same procedure as in Study 1 to assess the prevalence of MSD among participants: 38.6% of the employees were identified as having self-reported lower-extremity MSD, 44.8% as having self-reported upper-extremity, and 26.4% as having clinically-detected upper-extremity MSD. The prevalence rates of MSD by body region are summarized in online-only Appendix A.

Organizational justice

We assessed perceptions of organizational justice with the same 15-item scale as in Study 1. Overall, the results of a CFA supported a four-factor structure: χ² (84) = 242.78, p < .01, CFI = .96, NNFI = .94, RMSEA = .092, SRMR = .071. The fit of the second-order model, that is, four first-order factors plus one second-order factor, was less acceptable (χ² [86] = 278.08, p < .01, CFI = .95, NNFI = .93, RMSEA = .100, SRMR = .099) even if the justice dimensions loaded on the overall organizational justice factor to a statistically significant degree (loadings ranging from .35 to .93, p < .01). As none of our hypotheses referenced specific justice dimensions or unique aspects of the justice dimensions, we proceeded to test them using this combined organizational justice factor (α = .74). However, we also examined our hypotheses treating procedural, distributive, informational and interpersonal justice as separate dimensions, to check whether the aggregation to latent justice did not collapse across important nuances (see Online-only Appendix D).

Sleep disorders

We used the same five-item measure as in Study 1 to assess sleep disorders (α = .69).

Emotional exhaustion

We assessed emotional exhaustion using a five-item version of the Maslach Burnout Inventory (MBI – General Survey: Maslach et al., 2008). A sample item includes, ‘I feel fatigued when I get up in the morning and have to face another day on the job’ (α = .83).

Control variables

We controlled for the same potential confounding factors as in Study 1. Physical job demand was measured with the same five-item scale as in Study 1 (α = .86). Psychological job demand was measured with seven items derived from the Nursing Stress Scale (Gray-Toft and Anderson, 1981). Items were anchored from 1 (never) to 5 (very often) and inquired about the psychological demands of work in terms of exposure to death and dying, inadequate preparation, and interactions with patients and family (α = .82).

We also controlled for three additional potential confounding factors that are associated with an increased risk of MSD, namely smoking behavior (Palmer et al., 2003), physical inactivity (Holth et al., 2008) and high body mass index (BMI: Viester et al., 2013). Smoking behavior was coded as 0 = non-smoker and 1 = current smoker. Physical activity was measured by asking employees how frequently, on average, they took physical exercise. Responses were scored on a four-point scale (0 = never or very rarely, 1 = less than once per week, 2 = 1–2 days per week, 3 = 3 or more days per week). Employees’ height and weight were measured by the nurse and BMI was computed as weight (kg)/height (m²). Subsequently, BMI was classified into four categories: underweight (BMI < 18.5 kg/m²; 4.9 % of the sample), normal weight (BMI = 18.5–24.9 kg/m²; 60.1% of the sample), overweight (BMI = 25–29.9 kg/m²; 23.3 % of the sample) and obese (BMI ⩾ 30 kg/m²; 11.7% of the sample).

Confirmatory factor analyses

We followed the same CFA procedure as in Study 1 to examine the discriminant validity of the five latent constructs used in this study (i.e. organizational justice, sleep disorders, emotional exhaustion, psychological job demand and physical job demand). The hypothesized measurement model fitted the data satisfactorily (χ² [289] = 510.84, p < .01, CFI = .93, NNFI = .92, RMSEA = .059, SRMR = .071) and significantly better than any of the alternative measurement models (p Δχ² < .01). Overall, these results provide evidence that our constructs possess adequate discriminant validity for use in hypothesis testing.

Analytical approach

As in Study 1, we tested for mediation using the Process macro for SPSS (Hayes, 2013), except that we employed Model 6, which estimates the conditional indirect effects of a single independent variable on an outcome variable through two mediator variables linked in series. This procedure allowed us to determine whether sleep disorders and emotional exhaustion mediate the organizational justice–MSD relationship, both uniquely and in sequence.

Study 2 results

Descriptive statistics and correlations among Study 2 variables are shown in Table 3. Of interest is that organizational justice exhibited significant negative correlations with sleep disorders (r = −.14, p < .05), emotional exhaustion (r = −.38, p < .01), and clinically-detected and self-reported upper-extremity MSD (r = −.20, p < .01 and r = −.14, p < .05, respectively) but was unrelated to self-reported lower-extremity MSD (r = −.09, p > .05). Also of interest, both sleep disorders and emotional exhaustion were associated with increased clinically-detected MSD (r = .17, p < .05 and r = .23, p < .01, respectively) and self-reported upper-extremity MSD (r = .17, p < .05 and r = .23, p < .01, respectively); emotional exhaustion was associated with increased self-reported lower-extremity MSD (r = .19, p <.01) but sleep disorders were not (r = .09, p > .05).

Bootstrap results for indirect effects are presented in Table 2. Results of the series of ordinary least squares and logistic regressions generated by the SPSS Process macro are reported in online-only Appendices C1–C4. As shown in Table 2, bootstrapping analyses indicated a statistically significant total indirect effect of organizational justice on clinically-detected upper-extremity MSD (−.36, 95% CI = [−.69, −.08]), self-reported upper-extremity MSD (−.24, 95% CI = [−.47, −.04]) and self-reported lower-extremity MSD (−.22, 95% CI = [−.47, −.01]), respectively. This total indirect effect could be broken down into three unique effects via sleep disorders and emotional exhaustion independently and also in series. The indirect effect via sleep disorders and emotional exhaustion in sequence (−.03, −.02, −.02 for clinically-detected upper-extremity MSD, self-reported upper-extremity MSD, and self-reported lower-extremity MSD, respectively) was found to be statistically significant at the 95% confidence level, thus lending support to Hypothesis 2. Furthermore, Table 2 indicates that the unique indirect effect via emotional exhaustion is statistically significant, whereas the unique indirect effect via sleep disorders is not. These additional results are discussed below.

Discussion

Drawing on the job-stress recovery literature, the present research advances our understanding of the physical health consequences of perceived injustice at work. Using a randomly selected sample of employees from a variety of organizations, Study 1 found organizational justice to be negatively related to MSD through diminished sleep-related disorders. Using a sample of employees working in nursing homes for the elderly, Study 2 extended these results by showing that the organizational justice–MSD relationship is mediated by sleep disorders and emotional exhaustion linked in series. Below, we outline possible implications of these results for theory and practice.

Theoretical and practical implications

This research makes several contributions. First, our research contributes to the literature on organizational justice. We help to provide a more complete picture of the underlying processes that account for the relationship between perceived injustice and MSD. Although several explanations have been proposed to understand this relationship, further theoretical development has been called for (Greenberg, 2010; Robbins et al., 2012). Our research complements the stressor–strain and job demands–resources perspectives by proposing hindrance of recovery processes as a likely explanation of the health consequences of unfairness at work. Furthermore, although experiencing injustice at work is a primary source of stress, there are surprisingly few studies that have investigated the physical health consequences of perceived unfairness. Our study belongs to the very few that have examined empirically the association between organizational justice and MSD. To the best of our knowledge, it is also the first to show that organization justice is related not only to self-reported complaints of musculoskeletal pain but also to clinical screening. As Robbins et al. pointed out (2012: 250), ‘research examining the impact of unfairness on objective measures of employee health is sorely lacking’. We thus contribute to the important issue of whether injustice at work has a tangible impact on employees’ health status or whether it simply impacts the way in which employees perceive their health.

Second, our focus on sleep disorders contributes to the growing research on the workplace effects of sleep. Although sleep is considered as a key process for recovering from job stress, research examining the role of sleep in the relationship between job stressors and physical health is surprisingly scarce. The present study is one of very few to position sleep as an important mediating mechanism that explains how organizational justice influences MSD among employees (Canivet et al., 2008; Rasmussen-Barr et al., 2014). Management and applied psychology scholars’ interest in the topic of sleep has undoubtedly intensified during the past decade, but there is still a dearth of research (Barnes, 2012). As this topic has been investigated mainly by researchers in the fields of medicine, physiology, and ergonomics, there is a need for more managerially relevant knowledge on the consequences of sleep. The evidence is accumulating regarding the adverse effects of sleeping poorly on organizationally relevant outcomes such as job satisfaction, organizational citizenship behavior, or deviant behavior (e.g. Barnes et al., 2013; Christian and Ellis, 2011). We further this trend by adding more empirical evidence concerning MSD.

Third, our research also contributes to the job-stress recovery literature by calling attention to the role of employees’ fair treatment for recovery processes and by extending the limited knowledge on outcomes of impaired recovery processes. Only recently has recovery research considered the critical role of poor recovery in the transition between work-related stress reactions and physical health (Geurts and Sonnentag, 2006). Our study helps to provide a more complete picture of incomplete recovery antecedents and consequences.

Our research also has implications for employers interested in understanding and managing employee MSD. Strategies to reduce MSD risk (e.g. providing posture education or assistive devices, redesigning tasks or workstations) are primarily targeted at physical hazards despite evidence suggesting that a narrow focus on the physical aspects of work is not optimal, and may even be ineffective in certain situations (Oakman and Chan, 2015). The findings from this study indicate that increased employee MSD may be a function of employees’ perceptions of unfair treatment and not just an indicator of their poor posture or ill-designed workstation. Organizations’ policies and procedures aimed at reducing MSD risk should thus include techniques for enhancing perceptions of organizational justice in the workplace. These techniques may include job rotation plans to avoid uneven distributions of work or training supervisory staff and team members in interpersonal skills and participative decision-making, as such justice-based interventions have demonstrated their efficacy (Greenberg, 2010).

Managers could supplement these efforts by helping employees to manage adverse reactions to perceptions of unfair treatment (e.g. training in methods of relaxing, managing conflicts with superiors). Specifically, our findings suggest that promoting employee sleep quality is particularly worthwhile. To accomplish this, employers would be well advised to enhance organizational and supervisory support for work–life balance (Berkman et al., 2010) and create boundaries around work-relevant information and communication technology use while at home (Barber and Jenkins, 2014). They could also offer recovery training programs to improve employee sleep quality (Hahn et al., 2011).

Strengths, limitations and directions for future research

This research has a number of strengths and limitations that should be considered when interpreting the results. In terms of strengths, our study used both self-administered questionnaires and clinical examination to evaluate MSD. Questionnaires are frequently used to assess the prevalence of musculoskeletal symptoms in working populations, but their validity, compared with the more objective method of physical examination, has been questioned (Lenderink et al., 2012). Although some studies showed that the questionnaire approach gives a fairly good estimate of MSD prevalence (e.g. Descatha et al., 2007), several other studies reported higher prevalence for questionnaires compared with physical examination (e.g. Zetterberg et al., 1997). Our findings are particularly robust as they were obtained using both self- and other-reported measures. Another strength is that we demonstrated the robustness of the hypothesized relationships across two different samples, controlling for many potential confounders.

This study also has limitations that suggest directions for future research. First, statements of causal relationships in this study are solely based on past theoretical and empirical evidence as our cross-sectional design precludes us from establishing the direction of causality. Although prospective and experimental studies offer strong support for the causal interpretation that we adopted, it is also possible that sleep disorders affect both justice and pain perceptions. Second, we used self-reported and concurrently collected measures for some of our key study constructs, which may have led to common-method inflations of effects. These concerns were alleviated by discriminant validity analyses showing that the constructs used in this study were empirically distinct. Moreover, organizational justice and emotional exhaustion address internal psychological states that can be appropriately assessed using self-report measures (Ganster, 2008). Nevertheless, it would have been preferable to include an objective measure of sleep disorders (e.g. actigraphy, medical records), because subjective ratings of sleep quality do not necessarily reflect objective measures, and may lead to overestimated associations between sleep disorders and health outcomes (Jackowska et al., 2011). In order to address the first two limitations and provide further valuable insights into the hypothesized mediating mechanisms involved in the relationship between justice and MSD, future research should therefore use multisource, prospective and experimental designs.

A third limitation is that we focused on a single – albeit crucial – recovery process (i.e. sleep), ignoring many others (Sonnentag and Fritz, 2015) such as off-job activities (e.g. physical activities) or recovery experiences (e.g. relaxation). Although our analyses revealed a significant indirect effect of justice on MSD through sleep disorders and emotional exhaustion linked in series, they also showed that the unique indirect effect of justice through emotional exhaustion was large and statistically significant. This suggests that perceptions of organizational injustice may inhibit other recovery processes associated with reduced MSD. Thus, a fruitful avenue of research would be to determine both which other recovery processes are impacted by organizational justice and which other impaired recovery processes predict MSD occurrence.

Fourth, it should be noted that our MSD measures do not include pain intensity. Although research suggests that complex MSD case definitions (e.g. involving pain intensity) yield similar associations with occupational risk factors to those using simpler definitions (Palmer et al., 2012), future research could use measures that include the intensity of pain in order to help in prognosis of MSD. A fifth limitation is that we were not able to collect physiological indicators of recovery (e.g. decline in cortisol across the day). This hampered our ability to provide a more stringent test of our theoretical arguments. An exciting avenue for future research would be to consider both physiological and psychological recovery indicators (Sonnentag and Geurts, 2009). A final limitation of our research is that our justice scale only asked about the degree of justice rule adherence, whereas our argument focuses on injustice reactions. Because recent work suggests that reactions to justice and injustice may differ (Colquitt et al., 2015), future research could gain from using justice measures that sample both justice rule adherence and justice rule violation.

In conclusion, our research offers an alternative way of looking at the relationship between organizational justice and health disorders by showing that incomplete recovery because of sleep disorders and subsequent emotional exhaustion is a possible underlying mechanism through which (in)justice relates to employee MSD. We hope the present results, the above limitations, and the many interesting and unanswered questions will stimulate research in this area. Fairness is important in the workplace: ‘injustice is not just a judgment, it is also an emotional and physiological experience’ (Bies and Tripp, 2002: 211) and, in fact, injustice hurts.

Footnotes

Acknowledgements

We express our gratitude to our Associate Editor Catherine Connelly and three anonymous reviewers for their invaluable comments and guidance throughout the review processes. We also thank Steven Grover for his insights on earlier versions of this manuscript.

Funding

This research was partly supported by a grant from the Conseil Régional Midi-Pyrénées.

Supplemental material

The online appendices are available at .

Caroline Manville is an associate professor of Organizational Behavior and Human Resource Management at Toulouse School of Management, University of Toulouse 1 Capitole, France. Dr Manville’s research interests include trust violation and repair, organizational justice and well-being at work. Her research has been published in scholarly journals such as Human Relations, European Management Journal and Relations Industrielles/Industrial Relations. [Email: caroline.manville@ut-capitole.fr]

Assâad El Akremi is a professor of Management and Organizational Behavior at Toulouse School of Management, University of Toulouse 1 Capitole, France. In addition to organizational justice and work stress, his current research interests include social exchange, social identity, corporate social responsibility and research methods. His work has been published in Strategic Management Journal, Journal of Organizational Behavior, Journal of Management and Human Relations, among others. [Email: assaad.el-akremi@ut-capitole.fr]

Michel Niezborala is an occupational health physician and epidemiologist. He is currently working for the French Ministry of Labour. He is involved in territorial animation of occupational health policy. His main areas of interest are psychosocial risks and the perceived arduousness of work. His research has been published in scholarly journals such as Pharmacoepidemiology and Drug Safety, Occupational and Environmental Medicine, Applied Ergonomics and Addictive Behaviors. [Email: michel.niezborala@gmail.com]

Karim Mignonac is a professor of Organizational Behavior and Human Resource Management at Toulouse School of Management, University of Toulouse 1 Capitole, France. His research interests include workplace commitment/identification, volunteering and franchising. His work has been published in Strategic Management Journal, Journal of Organizational Behavior, Human Relations, Human Resource Management and Journal of Vocational Behavior, among others. [Email: karim.mignonac@ut-capitole.fr]

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