Does recovery buffer against emotional labor outcomes at work? Analyzing age differences care professionals

1 Does recovery buffer against emotional labor in terms of motivational outcomes at work? Analyzing age differences among Finnish health care professionals Saija Mauno1, 2, Mervi Ruokolainen1, Jessica De Bloom1,3, & Ulla Kinnunen1; 1 University of Tampere, School of Social Sciences and Humanities (psychology), Finland 2 University of Jyväskylä, Department of Psychology, Finland 3 University of Tampere, Institute for Advanced Social Research, Finland Keywords: emotional labor, health care, recovery from work, age differences, work motivation Address correspondence to: Saija Mauno, Dr. Prof. University of Jyväskylä, Finland Department of Psychology, Post Box 35 40014 University of Jyväskylä, Finland e-mail to:[email protected] Acknowledgements: The study was supported by the Foundation for Municipal Development, Finland 2 ABSTRACT Introduction: Emotional labor (EL) is a common stressor in health care work and factors buffering its negative outcomes deserve more attention. More information is also needed on whether the effects of EL and its buffers are age-specific as health care workers are aging. Aim: We examined recovery from work as a stress buffer between EL and motivational outcomes, i.e., job satisfaction, dedication to work, organizational citizenship behavior, aged-specifically. We also examined whether the effects of EL and recovery showed age-specific variations in relation to the outcomes. The age groups were moreover compared in key constructs. Method: The study was conducted among Finnish nurses and physicians (n = 4, 311) representing four different age groups. Cross-sectional data were collected by survey in 2014. Analyses were conducted age-specifically. Results: The regression analyses showed that early middle-aged employees (35-44 years old) benefited more than the other age groups from good recovery if experiencing high EL in terms of higher job satisfaction, dedication to work, and organizational citizenship behavior. Particularly harmful EL was in early middle-age by explaining lower job satisfaction and dedication to work at this time of life. Good recovery predicted positively all outcomes across the age groups, thus these linkages showed no age-specific variation. Mean comparison analyses revealed that the youngest age group (18-34 years) reported the greatest EL. Conclusions: Early middle-age seems to be a vulnerable time of life for experiencing EL as stressful. Good recovery from work constitutes one critical buffering resource for this age group. 3 Does recovery buffer against emotional labor in terms of motivational outcomes at work? Analyzing age differences among Finnish health care professionals 1. Introduction and aims The aging population impacts on today’s health care sector by depleting the workforce of healthcare professionals (), and by increasing the number of people needing medical care (see e.g., Cooper, Getzen, McKee, & Laud, 2002; Oulton, 2006; Parker & Thorslund, 2007). This puts health care professionals under pressure by forcing them to work faster, increasing their already high work stress and impairing their work ability. Due to a labor shortage in health care, there is an interest in retaining health care professionals in the workforce as long as possible. For these reasons, it is crucial to understand, particularly in health care work, which factors buffer against the negative effects of work stress and whether these factors show age-specific variation. The present study focuses on emotional labor (EL) as a specific work stressor. EL is encountered also in caring work where the work is characterized by human interaction. EL is a welldocumented work stressor contributing to negative health and well-being outcomes (Hüelsheger & Schewe, 2011), signifying that we urgently need more information on factors that might buffer against EL. In this study, we investigate perceived recovery from work as a potential buffering factor between EL and three specific work-related motivational outcomes, namely job satisfaction, dedication to work, and organizational citizenship behavior (OCB) (for specific definitions, see measures). The specific focus will be on age differences in these relationships as these differences have received surprisingly little attention in EL and recovery research. The study was conducted among Finnish health care professionals (n = 4, 311) representing four different age groups. 1.1. Defining EL as a stressor and recovery as a stress buffer According to Hochschild (1983), EL refers to a required expression of appropriate emotions during inter-personal face-to-face or voice-to-voice transactions. More specific definitions have subsequently been proposed (Zapf, 2002; Zapf, Vogt, Seifert, Mertini, & Isic, 1999). In line with these definitions, EL refers here to “the effort, planning, and control needed to express organizationally-desired emotions during interpersonal transactions” (Morris & Feldman, 1996, p. 987). Many occupations and organizations have certain rules regarding the display of emotions, specifying which emotions may be expressed in interpersonal transactions between employees and clients / customers / patients. For 4 example, health care employees, the target group of the present study, are expected to express sympathy, sensitivity, and overall friendly and caring emotional behavior while interacting with patients (Zapf, Seifert, Schmutte, Mertini, & Holz, 2001, Zapf, Semmer, & Johnson, 2014). However, displaying these emotions may become problematic when they do not match with an employees’ true emotions. Faking or expressing insincere emotions may cause feelings of emotional dissonance, and dealing with these feelings of dissonance may cause stress and result in an impairement of health and well-being as has been documented in various empirical studies (see e.g., Hüelsheger & Schewe, 2011; Zapf et al., 1999, 2014). However, it should be pointed out that EL does not necessarily imply negative outcomes if an employee has adequate coping resources, protecting him/her against the negative effects of EL (see e.g., Bechtoldt, Rohrmann, De Pater, & Swody, 2011; Pugh, Groth, & Hennig-Thurau, 2011). Coping resources include various contextual (e.g., informal and formal support available) and individual (e.g., skills and personality factors) resources. The ability to recover from job stress, which we focus on as an individual coping asset, can be regarded as a form of successful adaptation and coping in the job stress process. Specifically, in line with the stress buffer model (Cohen & Wills, 1985), we conceptualize recovery as a salutary stress buffering resource that is assumed to mitigate the negative effects of job stressors (here EL) on various outcomes. Recovery is seen as a process that allows individuals to replenish their psychobiological resources, constituting one key factor in promoting long-term work performance and well-being at work, and in retaining employees in the work force for longer (see e.g., Fritz, Sonnentag, Spector, & McInroe, 2010; Sonnentag & Fritz, 2015). Specifically, recovery refers to how much an individual feels physically and mentally refreshed during off-job time (Binnewies, Sonnentag, & Mojza, 2009). In the present study, recovery is defined as a state of feeling recovered from work during leisure time. The stress buffering role of recovery, i.e., whether it mitigates the detrimental effects of stressors on wellbeing/health/performance, has been examined only in a few studies. In these studies the focus has been on recovery experiences, e.g., detaching mentally from work, and feeling relaxed during free time (see e.g., Kinnunen, Mauno, & Siltaloppi, 2010; Moreno-Jimenez et al., 2009). Overall these studies have found that recovery experiences buffer against the negative effects of job stressors (e.g., time demands, work-family conflict, and job insecurity) on employee well-being. Accordingly, successful recovery during free time seems to be beneficial to employees’ well-being under stressful job conditions. Despite these valuable insights, these earlier studies have concentrated on stressors other than EL, 5 which we focus on here. Consequently, it is important to examine whether recovery from work during off-job time is also a beneficial resource against EL. 1.2. The role of age differences in the stress buffer process The present study set out from the assumption that stress buffering effects may not be the same for everyone, meaning that some individuals may benefit more than others from certain buffering resources due to individual differences. Indeed, earlier research findings on stress and coping suggest that one important individual difference determining coping effectiveness and overall adaptation in life adversities is chronological age (e.g., Aldwin, 1991; Ben-Zur, 2002; Diehl, Coyle, & Labouvie-Vief, 1996). Consequently, age very likely also moderates the relationship between work stressors and outcomes, implying that the effects of work stressors on outcomes tend to be stronger/weaker at a certain age (see e.g., Johnson, Holdsworth, Hoel, & Zapf, 2013; Mauno, Kinnunen, & Ruokolainen, 2013). However, we do not yet know whether this age-specific hypothesis also concerns EL as earlier studies have examined other work stressors. Viewed theoretically, both life management and coping theories propose that human adaptation to stress and life adversities may vary with age also concerning coping with work stress. For example, socio-emotional selectivity theory (Carstensen, Fung, & Charles, 2003) argues that positive emotions, emotion control and regulation tend to improve with increasing age because older individuals are more prone to see their future as limited and are less concerned about it (see also Aldwin, 1991; Diehl et al., 1996). On the other hand, as individuals are aging they may also suffer from some losses in physiological and cognitive capacity and skills (e.g., Barness-Farrell, Rumery, & Swody, 2002), implying poorer resilience and adaptation to stress. Thus, there seem to be mixed views on how age(ing) is related to human stress, coping and adaptation. Empirical findings concerning age-specific relationships between stress, coping, and adaptation have also yielded mixed results: some studies suggest that older people tend to cope less successfully with stressors than younger people, whereas other studies show the opposite (see e.g., Aldwin, 1991; Mauno et al., 2013; Whitty, 2010). One recent study in a work context conducted by Johnson et al. (2013) showed that older employees were more effective in using certain adaptive coping strategies (e.g., humor, downplaying) in the presence of high level of social stress at work (caused by customers) examining emotional exhaustion as a criterion for coping effectiveness. In general, older employees also exhibited better emotion regulation coping in this study. However, the authors conclude that the 6 relationship between age, job stress, and coping is complex and that more research is needed especially as the working population is aging rapidly. Altogether, systematic research on age differences in coping with work stress is rare. In addition, none of these studies has examined whether recovery, as a coping resource, buffers the relationship between work stressors and employee outcomes differently across different age groups. Overall age differences in recovery have rarely been studied, yet a few of the existing studies suggest that need for recovery increases as age increases (Kiss, De Meester, & Brackman, 2008; Mohren, Jansen, & Kant, 2010). The reason for this increasing need for recovery may due to a decrease in work capacity among ageing workers that will result in an increasing need for recovery if the workload remains the same. For instance, it has been shown that employees aged over 45 years scored higher on need for recovery than did younger employees (< 45 years) (e.g., Kiss et al., 2008; Mohren et al., 2010). However, these earlier studies have not examined whether the effects of recovery on employee outcomes vary by age or whether recovery is more beneficial at certain age if work stress is high. These issues will be scrutinized here. 1.3. Study objectives This study has three main objectives. First, we investigate whether the relationships between EL and work-related outcomes (job satisfaction, dedication at work, OCB) vary by age. Second, we examine whether the relationships between recovery from work and work-related outcomes show age-specific variation. The two first objectives mean that we examine whether age acts as a moderator between EL and the outcomes (the first objective), and between recovery and the outcomes (the second objective). Third, and most importantly, we also explore whether recovery from work is a more beneficial coping resource in the presence of a high level of EL among younger vs. older employees (i.e., an examination of EL x recovery interaction effect across the age groups). This third objective is expected to show whether there are age differences in the buffering effects of recovery in the association between EL and the outcomes. Specifically, we will compare four age groups in this study: 18-34 years, 35-44 years, 45-54 years, and 55-65 years old. 7 2. Methods 2.1. Participants The data used in this study were collected among Finnish nurses and physicians in 2014. Three health care labor unions (FMA: the Finnish Medical Association, TEHY: the Union of Health and Social Care Professionals and SuPer: The Finnish Union of Practical Nurses), to which the great majority of Finnish physicians and nurses belong, invited their members to participate in our survey. In the FMA (the trade union representing physicians) the invitation was sent via e-mail to 3,000 in TEHY to 10,000 and in SuPer to 6,963 (the trade unions representing nurses) randomly sampled members’ work e-mail addresses. A total of 4, 311 physicians (n = 604) and nurses (n = 3707) working in the health care sector responded to our electronic questionnaire. The overall response rate was low, only 20.4%. The invitation message did not reach all potential respondents, as many e-mail addresses were not valid/in use. Therefore the response rate given is only a rough (conservative) estimate. However, it is noteworthy that the membership rates of Finnish trade unions are generally high, ranging from 60 to 80 %, suggesting that our respondents do indeed represent the target population (physicians, nurses) relatively well despite a low response rate. Indeed, despite the relatively low response rate, the respondents represented the target population reasonably well in terms of gender and age (see more in Saarinen et al., 2015). The vast majority of the participants were female: 94% of nurses and 67% of physicians. The mean age of nurses was 47.7 years (SD = 10.8) and physicians 46.5 years (SD = 11.2). Of the nurses 6 per cent were working in managerial positions and of the physicians 27 per cent. All participants were categorized into the following four age groups: 18-34 (Group 1, n = 1,184), 35-44 (Group 2, n = 991), 45-54 (Group 3, n = 1,133) and 55-65 (Group 4, n = 783) years old. All analyses were performed on the basis of these four age groups. 2.2. Measures The present study examined EL as a specific stressor related to human factors at work. EL has been regarded as a multidimensional concept (Zapf et al., 1999, 2014). Here we used a two-dimensional operationalization of the phenomenon, focusing on two sub-scales, i.e. emotional dissonance and emotional sensitivity requirements at work. We selected these two dimensions of EL as in a large validation study (Zapf et al., 1999) they showed the most consistent relationships with several psychological outcomes also examined in the present study in a work context. This two-dimensional 8 scale has also been used in earlier Finnish studies (Mauno, Ruokolainen, Kinnunen, & De Bloom, 2015), showing a significant relationship with motivational work-related outcomes (work engagement). Specifically, emotional dissonance refers to an employee’s perception of a dissonance (or discrepancy) between real/true and expressed emotions, whereas sensitivity requirements refers to the degree to which an employee needs to be sensitive to the feelings of patients/customers (Zapf et al., 1999). Emotional dissonance (3 items; e.g., “Display emotions which do not correspond to inner feelings”, “Requirements to suppress feelings”) and sensitivity requirements (3 items; e.g., “Important to know what a patient feels”, “Requirements to be sensitive to the feelings of customers”) were scored on a scale ranging from 1 (rarely, never) to 5 (very often, always). The Cronbach’s alpha for the EL scale (consisting the items of emotional dissonance and sensitivity requirements) was 0.61. An employee’s overall state of perceived recovery from work was examined as a buffering resource (moderator) in the present study. Specifically, recovery was measured with a single question about an individual’s evaluation of his or her recovery status (“How well do you generally feel you recover from the strain caused by your job after the working day”), ranging from 1 (not at all) to 10 (completely). This question has been used earlier in Finnish studies and it has been shown to have good criterion validity, that is, to correlate, for example, with high need for recovery after work (see e.g., Kauppinen et al., 2012; Kinnunen, Siltaloppi, & Sonnentag, 2011). International studies have also successfully used a similar one-item-based assessment of recovery from work (Niks, Gevers, De Jonge, & Houtman, 2016; Windwood, Winefield, Dawson, & Lushington, 2005). The present study focuses on motivational (job satisfaction, dedication to work) and performance-related (OCB) outcomes in the work context. Job satisfaction refers to a pleasurable or positive emotional state resulting from the appraisal of one’s job or job experiences (see Locke, 1976) and it was measured with one item (“How satisfied do you generally feel about your current job”) using a response scale from 1 (totally dissatisfied) to 10 (totally satisfied). It has been shown that job satisfaction can be measured reliably using one-item-based assessment (Wanous, Reichers, & Hurdy, 1997). Dedication to work refers to a strong psychological involvement in one’s work, combined with a sense of significance, enthusiasm, inspiration, pride, and challenge (Schaufeli, Salanova, GonzalezRoma, & Bakker, 2002). We measured dedication with a sub-scale from the Utrecht Work Engagement Scale (3 items; “I am proud of the work that I do”) (Schaufeli et al., 2002). This particular sub-scale, with other sub-scales of work engagement, has been validated in Finland among various occupational 9 groups (Seppälä et al., 2009). The responses were given on a scale from 0 (never) to 6 (always, daily). The Cronbach’s alpha for the dedication scale was 0.88. OCB refers to an employee’s extra-role performance at work, implying that (s)he is willing to do more at work than what is formally required in his/her job description, and that doing more is also voluntary (see Organ, 1988). We measured OCB on a scale developed by Lee and Allen (2002) and this particular scale proved to be a valid and reliable assessment of OCB in this original study. The version used in the present study included two sub-dimensions: OCB towards colleagues (e.g., “I am glad to help my colleagues with their job-related problems”) and OCB towards one´s organization (e.g., “I express new ideas to improve our organization”). Cronbach’s alpha for the overall OCB scale was 0.74. Correlations between the study variables are presented in Table 1. Age and EL showed a significant negative correlation (r = -.07, p <.001): younger employees reported higher EL. Recovery from work was negatively related to EL (r = -.27, p <.001): the higher the EL, the poorer was recovery from work. EL showed robust negative correlations with job satisfaction (r = -.28, p <.001) and dedication at work (r = -.21, p <.001): higher EL associated with lower job satisfaction and impaired dedication at work. However, EL was not significantly related to OCB (r = .02, ns). Recovery from work was significantly and robustly associated with all three outcomes: job satisfaction (r = .55, p <.001), dedication at work (r = .43, p <.001), and OCB (r = .19, p <.001). ----------Insert Table 1 about here------- 2.3. Statistical analyses We started with an examination of age differences on the key variables (EL, recovery, job satisfaction, dedication, OCB). Specifically, we compared group means in these variables across four age groups (Group 1 = 18-34, Group 2 = 35-44, Group 3 = 45-54, Group 4 = 55-65 years old) with ANCOVAs. Covariates included occupational group (nurses/physicians), gender (1 = male, 2 = female) and supervisory / managerial position (1 = no, 2 = yes) as basic demographic factors. They were available in both samples of nurses and physicians. Paired comparisons between the four age groups were performed, using Bonferroni corrections to counteract the problem of multiple comparisons. Direct (EL, recovery) and moderator relationships (EL × recovery) in explaining work-related outcomes (job satisfaction, dedication, and OCB) were examined with moderated hierarchical regression analysis, which is the most common method for examining moderator effects using multiple 16 4.5. Conclusions The present study is the first study to ascertain the age-specific relationships between EL, recovery from work, and work-related outcomes, showing that these associations may partly be age-specific. We found that particularly early middle-age (35-44 years) seems to be a vulnerable time of life to experience EL as stressful. Good recovery from work might constitute one critical buffering resource for this age group. Early middle-aged employees might benefit from activities and strategies conducive to good recovery from work stress (e.g., psychological detachment from work during free time) especially if they experience interpersonal stress (EL) at work. 4.6. Highlights - Emotional labor is a common job stressor among health care professionals and therefore more research evidence is needed concerning its buffers. - Age-specific knowledge regarding the buffers of different job stressors is needed as health care professionals in industrialized countries are aging. - The results showed that recovery from work buffering against emotional labor was age-specific: among early middle-aged health care professionals good off-job recovery served as a stress buffer against impaired job satisfaction, dedication to work, and organizational citizenship behavior, if employees experienced high emotional labor. - Good recovery from work should be encouraged, particularly among early middle-aged health care professionals as a stress relieving coping resource. - Psychological detachment from work while not working is one of the best off-job recovery strategies and should be encouraged among health care professionals. References Aldwin, C. M. (1991). 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Taris (Eds.), An introduction to contemporary work psychology (pp. 144-169). WileyBlackwell: Chichester, UK. Zapf, D., Vogt, C., Seifert, C., Mertini, H., & Isic, A. (1999). Emotion work as a source of stress: The concept and development of an instrument. European Journal of Work and Organizational Psychology, 8, 371-400. Table 1. Correlations (Pearson) between the study variables. Measures 1 2 3 4 5 6 7 8 9 1. Age (in years) - 2. Occupational group (physicians/nurses) .04* - 3. Gender (women/men) -.02 -.32*** - 4. Supervisor position (no/yes) .13*** -.26*** .15*** - 5. Emotional labor (EL) -.07*** .04* -.04** -.05*** - 6. Recovery from work (RE) .01 -.06*** .05*** .06*** -.27*** - 7. EL x RE .01 -.02 .03 -.01 -.19*** .18*** - 8. Job satisfaction .12*** .04** -.01 .08*** -.28*** .55*** .14*** - 9. Dedication to work .06*** -.01 -.03* .04* -.21*** .43*** .11*** .68*** - 10. OCB .02 .02 -.01 .20*** .02 .19*** -.01 .26*** .36*** * p ≤ .05, ** p ≤ .01, *** p ≤ .001. Table 2. Results of regression analyses explaining job satisfaction, dedication to work, and OCB across four age groups. Predictors Job Satisfaction Dedication to work Org. Citizenship Behavior / OCB Age groups (years) 18-34 35-44 45-54 55-65 18-34 35-44 45-54 55-65 18-34 35-44 45-54 55-65 n (in each age group) 1044 850 961 641 1050 853 966 647 1050 853 965 648 Step 1: controls ∆R2 .005 .014 .009 .017 .003 .009 .010 .009 .055 .025 .072 .055 Step 2 β: EL -.140 -.390 -.280 -.290 -.094 -.310 -.240 -.160 .030 -.020 .030 .010 ∆R2 .021 .153 .077 .082 .009 .092 .058 .027 .001 .000 .001 .000 Step 3 β: RE .530 .560 .490 .520 .360 .450 .390 .402 .150 .200 .200 .210 ∆R2 .260 .279 .226 .244 .121 .184 .141 .147 .022 .035 .037 .042 Step 4 β: EL× RE -.001 .110 .001 -.013 .021 .100 .050 -.050 -.050 .080 -.030 -.060 ∆R2 .000 .011 .000 .000 .000 .009 .002 .002 .002 .005 .001 .004 Note. EL = emotional labor; RE = recovery from work; ∆R2 = change in explanation rate in each step. Underlined standardized β-coefficients/∆R2 p ≤ .05. Bolded standardized β-coefficients/∆R2 p ≤ .001. For Step 1 concerning control variables (physicians/nurses, gender, supervisory position) only ∆R2 are shown. Figure 1. Recovery from work as a buffer between EL and job satisfaction: employees aged 35-44. Figure 2. Recovery from work as a buffer between EL and dedication to work: employees aged 35-44. Figure 3. Recovery from work as a buffer between EL and OCB: employees aged 35-44. 6 6,5 7 7,5 8 8,5 9 9,5 10 Low emotional labour High emotional labour Job satisfaction Low recovery High recovery 2 2,5 3 3,5 4 4,5 5 5,5 6 Low emotional labour High emotional labour Dedication Low recovery High recovery 2 2,5 3 3,5 4 4,5 5 Low emotional labour High emotional labour OCB Low recovery High recovery