A Naturalistic Multilevel Framework for Studying ...

APPLIED PSYCHOLOGY: AN INTERNATIONAL REVIEW, 2016, 65 (2), 223–258 doi: 10.1111/apps.12069

A Naturalistic Multilevel Framework for Studying Transient and Chronic Effects of Psychosocial Work Stressors on Employee Health and Well-Being Remus Ilies*, Sherry S.Y. Aw, and Vivien K.G. Lim National University of Singapore, Singapore Research in work and organisational health psychology (WOHP) has traditionally employed methodologies targeted at examining between-individual associations of psychosocial stressors, psychological strain, health, and wellbeing. Recently, however, there has been a shift towards more ecologically valid assessments of these classes of constructs, i.e. assessing them as and when they occur, often involving multiple assessments of the individual within a day. Known as Ecological Momentary Assessment (EMA), studies employing this methodology enable examinations of within-individual (daily) fluctuations in well-being as a result of work stressors and other environmental factors, as well as investigations of person–environment interactions. In addition, the study of employee health and well-being can benefit from the application of new and exciting technologies for measurement, such as smartphones and wearable devices for the tracking of physiological well-being indicators. Drawing on the Allostatic Load Model as an integrative framework, the current article aims to organise previous EMA research efforts in the field of WOHP, provide an overview of methodological tools that can be used in EMA research, and provide guidelines for analyzing EMA data. Finally, we conclude by discussing opportunities and challenges in the use of EMA in WOHP.

INTRODUCTION Naturalistic studies of stress, health, and well-being involve measuring the constructs that are examined within the environment where they occur, such as measuring psychosocial work stressors on the job, in order to provide ecologically valid measures of these constructs. Assessment methods for such naturalistic studies have been developed mainly for examining constructs that exhibit a relatively high degree of variability across time (e.g. workload, affect, blood * Address for correspondence: Remus Ilies, Department of Management and Organization, National University of Singapore, Singapore. Email: [email protected] C 2016 International Association of Applied Psychology. V

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pressure, etc.) and typically involve multiple momentary or daily measurements. These methods, variously called Ecological Momentary Assessment (EMA; Beal & Weiss, 2003), Experience-Sampling Methodology (Alliger & Williams, 1993; Csikszentmihalyi & Larson, 1987), Ambulatory Assessment (Trull & Ebner-Priemer, 2014) or Diary Methods (e.g. Bolger, Davis, & Rafaeli, 2003), have long been used in the study of emotions in daily life (e.g. Flugel, 1925). Furthermore, such methods have been used for at least three decades in the study of daily stress (Caspi, Bolger, & Eckenrode, 1987; Eckenrode, 1984; Stone & Neale, 1984). Recent technological and methodological developments have led to a dramatic increase in the utility and use of such naturalistic methods in work and organisational psychology (Beal & Weiss, 2003; Dimotakis, Ilies, & Judge, 2013; Ilies, Schwind, & Heller, 2007a). Furthermore, EMA methods involving electronic assessment devices (e.g. smartphones, blood pressure or sleep monitors, etc.) can now be applied to the study of a much wider range of phenomena than ever before, with some researchers claiming that the use of such devices (smartphones) for research “could revolutionize all fields of psychology and other behavioral sciences” (Miller, 2012, p. 221). In this article, we are concerned with naturalistic studies in work and organisational health psychology (WOHP); more specifically, with the application of EMA methods in studying the influence of psychosocial work stressors on employee psychological and physiological health, as well as in the identification of potential interventions, programs, and strategies for minimising this influence. The main purpose of this article is to provide an integrated multilevel model of the stressful effects of work on employee stress and health that can be used (a) to organise previous research efforts on this topic and (b) to design new empirical studies to advance theory and research in this area, along with describing the methodological tools that are needed to test propositions derived from this model. Towards accomplishing this goal, the article proceeds as follows. First, we provide a selective review of EMA research in WOHP, with a particular focus on studies examining employee work stress and its outcomes (e.g. burnout, health indicators). However, this is not to say that outcomes signaling employee thriving and flourishing at work (e.g. flow, work engagement) are unimportant in the study of employee well-being and WOHP, as such positive outcomes are indeed essential for the long-term well-being of organisations and employees alike. Rather, because stress is prevalent in today's workplace, we focus on understanding short-term fluctuations in stress indicators, and the processes linking these transient fluctuations to chronic psychological and physiological outcomes as to inform theory and practice with respect to potential ways for minimising the negative effects of stress. We believe that this selective focus on stress and physiological measures aligns with one of the main contributions of this review, which centers on the application of new technologies for collecting EMA data. For more comprehensive reviews of EMA in the broader C 2016 International Association of Applied Psychology. V

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organisational behavior context, we point interested readers to recent works by Beal (2015), Ohly, Sonnentag, Niessen, and Zapf (2010), and Sonnentag (2015). Second, we describe the Allostatic Load (AL) model of stress (e.g. McEwen, 2007) and outline how this theoretical model can be used as a general framework for studying the effects of work stress on employee health and well-being. We suggest that the AL model provides WOHP researchers with a framework for integrating the within-individual studies described in our review with the more traditional between-individual (cross-sectional and longitudinal) studies, to better conceptualize how transient stressors may relate to chronic outcomes. Third, we review EMA methodology and propose specific, new methodological approaches for collecting EMA data, made possible by recent technological advances. In relation to this, we provide some guidelines, suggestions, and resources for analyzing EMA data (e.g. multilevel modeling) that are appropriate for testing phenomena within this framework that would be useful to researchers new to conducting EMA research. Finally, we discuss opportunities and challenges in applying this theoretical framework and associated methodologies to the study of employee health and well-being, and we then provide suggestions for future applications and research questions in the area.

STUDIES ON WORK STRESS Over the past few decades, research on work stress has continued to attract a great amount of research attention due to its near ubiquitous nature, as well as associated healthcare costs (Ganster, Fox, & Dwyer, 2001; Manning, Jackson, & Fusilier, 1996). As testament to the importance of understanding stress and its outcomes, numerous theories and models have been proposed to explain how and why psychosocial stressors can impair our well-being. For instance, some of the more prominent theories on stress that have evolved over the years and have been applied to work settings include Lazarus! (1966) transactional model of stress, job demands-control theory and job demands-resources theory (Demerouti, Bakker, Nachreiner, & Schaufeli, 2001; Karasek, 1979), conservation of resources theory (Hobfoll, 1989), and the effort–reward imbalance model (Siegrist, 2002). A common thread across these different theories concerns the role of the individual!s cognitive appraisals in the stress process (Ganster & Rosen, 2013). When faced with a potential stressor in the environment, individuals appraise the severity of the threat along with their ability (or available resources) to cope with the threat (Lazarus, 1993). This, in turn, influences their behavioral and emotional responses, and subsequent experience of stress (and decrease in wellbeing). As such, a definition of stress that has been commonly adopted in the literature takes into account the interaction between environmental forces and the individual!s behavioral and psychological responses to these challenges (Beehr & Newman, 1978). Collectively, however, these theories fall short in detailing how these psychological and behavioral responses to work stressors eventually C 2016 International Association of Applied Psychology. V

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relate to health and well-being (Ganster & Rosen, 2013). Such an understanding of the intervening mechanisms is important as it helps shed light on the mechanisms in which transient indicators of stress can have cumulative effects over time, leading to chronic physical and mental health outcomes. Existing studies in the vein of these traditional stress theories have found that workplace stressors or strains are associated with a variety of negative outcomes such as physical illness and cardiovascular disease (Bartholomew, Ntoumanis, Cuevas, & Lonsdale, 2014; Johnson & Hall, 1988), absenteeism (Bakker, Demerouti, de Boer, & Schaufeli, 2003), and lower psychological well-being in the forms of burnout, psychosomatic complaints, and job dissatisfaction (de Jonge, Bosma, Peter, & Siegrist, 2000; Hakanen, Bakker, & Schaufeli, 2006). A common criticism leveled against these studies is their use of a cross-sectional design, which limits our ability to draw causal inferences with respect to the relationships between job stressors, strain, and employee health and well-being (Ilies et al., 2007a). Furthermore, studies that employed longitudinal designs focused largely on between-individual associations of job stressors, physical and psychological health (e.g. de Jonge, Dormann, Janssen, Dollard, Landeweerd, & Nijhuis, 2001; Moen, Kelly, & Lam, 2013; Roelen, van Rhenen, Schaufeli, van der Klink, Magerøy, Moen, Bjorvatn, & Pallesen, 2014; von Thiele Schwarz, 2011). For instance, Moen and colleagues (2013) drew upon the job demandscontrol model and found that time-related job demands at work were associated with poorer psychological well-being and health measured six months later. Similarly, in a sample of nurses, Roelen and colleagues (2014) found that psychological job demands were related to increased sickness and absences at a one-year follow-up. While these studies are valuable in informing us of the possible long-term consequences of work stress, between-individual designs cannot capture the influences of transient (situational) stressors on employee wellbeing. Also, when subjective assessments are used in these designs, the measured scores suffer from retrospective memory biases (Beal & Weiss, 2003). Finally, studies examining between-individual associations cannot examine the psychological processes that reflect day-to-day experiences and explain the links between discrete stressors and health and well-being outcomes that exhibit substantive fluctuations over time (e.g. affective states, heart rate, etc.). To address these gaps, a stream of research examining work stress models at the intraindividual level has recently emerged (e.g. Bono, Glomb, Shen, Kim, & Koch, 2013; Ilies, Dimotakis, & de Pater, 2010a; Ilies, Dimotakis, & Watson, 2010b; Jacobs, Myin-Germeys, Derom, Delespaul, van Os, & Nicolson, 2007). We thus reviewed articles in this area that have been published in the past halfdecade (2010–15), from various journals including the Academy of Management Journal, Journal of Applied Psychology, Journal of Occupational and Organizational Psychology, Applied Psychology: An International Review, and Journal of Occupational Health Psychology (see Table 1), and describe some of these C 2016 International Association of Applied Psychology. V

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Ilies, Johnson, Judge, & Keeney (2011a)

Dimotakis, Scott, & Koopman (2011)

Ilies, Dimotakis, & de Pater (2010a)

N 5 60 employees; 380 observations Four measurement points for 10 consecutive work days. Signalcontingent sampling N 5 49 employees; 1,270 observations Three surveys for 10 consecutive days.

N 5 67 employees; 1,937 observations Four measurement points a day for 10 consecutive work days. Signal-contingent sampling N 5 49 employees; 735 observations Three surveys for 5 consecutive work days. Interval-contingent sampling N 5 64 employees; 354 observations Five measurement points for 10 consecutive work days. Signalcontingent sampling

Ilies, Dimotakis, & Watson (2010b)

Sanz-Vergel, Demerouti, Moreno-Jimenez, & Mayo (2010)

Sample and design

Author (Year)

Self-report: Negative affect, via web-based survey

Self-report: Negative affect, via PDAs

Self-report: affective distress and workload via PDAs Physiological indicators: blood pressure via OMRON HEM-637 automated monitors

Self-report: Negative affect, via personal digital assistant (PDA) Physiological indicators: Heart rate and blood pressure via OMRON HEM-637 automated monitors Self-report: Exhaustion (Maslach Burnout Inventory), via diary booklet

Measure of stress and health

Workload was positively associated with affective distress and blood pressure within-individuals Moderating effect of job control and organisational support across levels Daily negative interactions at work were associated with higher negative affect, which in turn was negatively related to employee job satisfaction Individual differences in agreeableness and social support moderated the relationship between the experience of interpersonal conflict and negative affect, such

Work pressure positively related to experience of exhaustion

Employee negative affect is related to increased blood pressure and heart rate within-individuals

Main findings

TABLE 1 EMA Studies Examining Physiological and Psychological Stress Indicators


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N 5 120 employees; 696 observations Three surveys for 5 consecutive days; Interval-contingent sampling N 5 90 interior architects; 326 observations Two measurement points for 5 consecutive work days. Interval-contingent sampling

N 5 98 employees; 446–482 observations Two surveys daily over 5 consecutive days

N 5 68 employees; 878 observations

Van Hooff, Geurts, Beckers, & Kompier (2011)

Volmer, Binnewies, Sonnentag, & Niessen (2012)

Daniels, Wimalasiri, Beesley, & Cheyne (2012)

Binnewies & W€ ornlein (2011)

Sample and design

Author (Year)


Self-report: Anxious affect, via PDAs

Self-report: Negative affect mood, negative work reflection, via pen-and-paper questionnaires

Self-report: Negative affect, time pressure and situational constraints, via online web survey

Self-report: Fatigue using the Dutch Profile of Mood States questionnaire, via pen-and-paper diaries


TABLE 1 Continued

that the relationship was stronger for more agreeable employees and those with less social support Experienced pleasure during work and off-work was negatively related to fatigue; unpleasant and effortful work activities was positively related to fatigue Individual differences in job control moderated the relationship between day-level negative affect and creativity, such that for persons with lower levels of job control, daily negative affect was negatively related to daily creativity Daily social conflicts with customers at work were positively related to negative affect, and in turn negative work reflection after work Within-individuals, beliefs about the adverse impact of problemsolving demands at one time

Main findings

228 ILIES ET AL.


N 5 133 employees; 582–619 observations Four daily surveys over 5 work days, signalcontingent sampling

N 5 85 working college students; 330 observations Two daily surveys over five work days

Barnes, Ghumman, & Scott, Study 2 (2013)

Four daily surveys over 5 work days, signalcontingent sampling N 5 37; 1,004 observations (495 unpleasant, 509 pleasant interactions) Event-contingent sampling— participants to answer survey after witnessing a positive or negative interaction between coworkers N 5 64 administrative employees; 364 observations Two daily surveys over four work days, interval-contingent sampling

Sample and design

Baethge & Rigotti (2013)

Schmitt, Zacher, & Frese (2012)

Totterdell, Hershcovis, Niven, Reich, & Stride (2012)

Author (Year)

Self-report: Sleep quantity using Pittsburgh Sleep Diary, via web survey

Self-report: Mental demands, time pressure, irritation, via handheld computers

Self-report: Fatigue, using the Profile of Mood States, via online web survey

Self-report: Negative mood, emotional exhaustion, via pen-andpaper booklets


TABLE 1 Continued

Employees! daily use of selectionoptimisation-compensation strategies buffered the negative relationship between problemsolving demands and fatigue experiences Workflow interruptions had negative effects on individual performance, forgetfulness, and irritation. Relationships were partially mediated by mental demands and time pressure Daily variance in sleep over the week predicted daily variance in OCB towards both individuals and organisations, mediated by job satisfaction

Negative mood after witnessing unpleasant interactions was in turn related to greater emotional exhaustion

point was related to anxious affect at the next time point

Main findings


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Wang, Liu, Liao, Gong, Kammeyer-Mueller, & Shi (2013)

Igic, Ryser, & Elfering (2013)

Sonnentag & Binnewies (2013)

N 5 61 employees

Bono, Glomb, Shen, Kim, & Koch (2013)

N 5 149 employees Two daily surveys over 10 consecutive work days, interval-contingent sampling

Two measurement a day over 14 days, interval-contingent sampling

N 5 39 employees; 512 observations

Three surveys daily over 15 days, blood pressure measured in the morning, afternoon, and evening; signal-contingent sampling N 5 96 employees; 289 matched data sets Three surveys daily over 5 consecutive working days; interval-contingent sampling

Sample and design

Author (Year)

Self-report: Daily job control, time pressure, social stressors, biomechanical work strain Physiological indicators: Physical activities via accelerometrybased device (SenseWear armband); Body height and spinal shrinkage via stadiometer Self-report: Rumination at night, negative mood in the morning, via paper-and-pencil surveys

Self-report: Negative affect, via handheld computers

Self-report: Perceived stress, physical symptoms, via PDAs and phone interviews Physiological indicators: Blood pressure, via ambulatory blood pressure monitor


TABLE 1 Continued

Customer mistreatment increased negative rumination at night, and in turn negative mood the following morning. Relationships moderated by differences in service rule commitment and perceived organisational support

Spillover of negative affect from work to home was attenuated by psychological detachment from work; spillover of negative affect at work to negative affect the next morning was attenuated by psychological detachment and sleep quality Spinal disks shrank more during workdays than non-workdays, lower levels of daily job control predicted greater spinal shrinkage

Momentary, lagged, daily, and dayto-evening analyses showed that positive work events and positive reflection are associated with reduced stress and improved health

Main findings

230 ILIES ET AL.


N 5 131 employees, 988–1,707 measurements for morning, afternoon, and evening surveys Three surveys daily over 14 consecutive days; interval-contingent sampling

N 5 58 employees; 2,313 observations Four measurements daily over 10 consecutive working days; interval-contingent sampling

N 5 219 employees; 1,095 observations Two daily surveys over 5 work days, interval-contingent sampling N 5 126 employees; 580 days Two surveys daily over 5 consecutive days; interval-contingent sampling N 5 121 employees, 591–599 observations each day Three measurements daily over 5 consecutive working days; interval-contingent sampling

Meier, Gross, Spector, & Semmer (2013)

Shockley & Allen (2013)

H€ ulsheger, Alberts, Feinholdt, & Lang, Study 1 (2013)

H€ ulsheger, Lang, Depenbrock, Fehrmann, Zijlstra, & Alberts (2014)

Feuerhahn, Sonnentag, & Woll (2014)

Sample and design

Author (Year)

Self-report: sleep quality, via penand-paper booklets

Self-report: Negative affect, via pen-and-paper booklet

Self-report: emotional exhaustion, via pen-and-paper booklet

Physiological Indicators: Blood pressure and heart rate, via OMRON HEM-673 wristwatch monitor

Self-report: Angry mood and somatic complaints, via pen-andpaper surveys


TABLE 1 Continued

Day-level results showed that mindfulness during work was related to subsequent sleep quality, mediated by psychological detachment from work

Exercise activities after work were not related to negative affect in the evening

Daily relationship conflict was positively related to angry mood, but not somatic complaints. Task conflict moderated relationship between relationship conflict, angry mood and somatic complaints the next day Episodes of work-to-family interference was related to increase in heart rate but not blood pressure; Relationship between family-to-work interference and blood pressure is moderated by perceptions of family-supportive supervision Mindfulness negatively related to emotional exhaustion at both within- and between-person levels, mediated by surface acting

Main findings


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N 5 78 bus drivers; 356–644 responses for each survey Three surveys each day, over 2 weeks N 5 60, 521 measurement points Three times a day over 10 working days; interval-contingent sampling

Wagner, Barnes, & Scott (2014)


Diestel, Rivkin, & Schmidt (2015)

Nicholson & Griffin (2015)

H€ ulsheger, Feinholdt, & N€ ubold (2015)

N 5 140, 836–1,314 daily observations Randomised field experiment with EMA three times a day over 10 workdays N 5 175 employees, 1,321 day-level observations Two surveys a day over 5 consecutive work days Study 1 N 5 63, 617 measurement points

N 5 61 teachers, 915 data points Twice a day over 15 days

Garrick, Mak, Cathcart, Winwood, Bakker, & Lushington (2014)

Rivkin, Diestel, & Schmidt (2015)

Sample and design

Author (Year)

Self-report: ego depletion, need for recovery

Self-report: well-being, via online web survey

Self-report: Sleep quality and duration, via pen-and-paper diary booklets

Self-report: ego depletion, need for recovery

Self-report: state anxiety, work-tofamily conflict, emotional exhaustion, and insomnia, via online web survey

Self-report: Emotional exhaustion and depersonalisation, via online web survey


TABLE 1 Continued

Day-specific emotional dissonance had negative effects on ego depletion and need for recovery

Psychosocial safety climates (PSC) moderated relationships between job demands and fatigue; PSC also has main effect on fatigue Daily surface acting was related to greater emotional exhaustion, work-to-family conflict, and insomnia, mediated by state anxiety On days with higher self-control demands, employees with higher affective commitment reported lower levels of ego depletion and need for recovery in the evening Mindfulness training intervention increased daily levels of mindfulness and improved sleep quality and duration over the study period Day-level incivility was negatively related to well-being and recovery the following morning

Main findings

232 ILIES ET AL.

N 5 76 employees Two surveys a day over 10 consecutive work days

N 5 80 dual-earner couples (160 participants), 800 matched occasions Two surveys every day for 5 consecutive work days; intervalcontingent sampling

Sanz-Vergel, Rodr"ıguezMu~ noz, & Nielsen (2015)

Study 2 N 5 108, 1,073 measurement points Three times a day over 10 consecutive work days N 5 94 employees, 1,424 observations Two surveys every day for 10 consecutive work days

Sample and design

Zhou, Yan, Che, & Meier (2015)

Trougakos, Beal, Cheng, Hideg, & Zweig (2015)

Author (Year)


Self-report: Family–work conflict, interpersonal conflict at work and at home, via pen-and-paper booklets

Self-report: Negative affect, via pen-and-paper booklet

Self-report: Emotional exhaustion, via online web survey


TABLE 1 Continued

Surface acting indirectly related to OCBs targeted at individuals, mediated by experience of exhaustion. Relationships were exacerbated by chronic exhaustion Daily workplace incivility was positively related to negative affect at the end of the day; exacerbated for individuals with low emotional stability, high hostile attribution, external locus of control, with low chronic workload, and more chaotic organisational constraints Family-work conflict predicted interpersonal conflict at work, and in turn conflicts with the partner at home; exacerbated by neuroticism

in the evening; these relationships were moderated by sleep quality and trait self-control

Main findings


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articles in greater detail below. Again, our focus in this review concerns outcomes related to stress rather than positive well-being indicators (e.g. some studies may have examined both positive and negative affect, but we reference only the findings related to negative affect). The studies in our review adopted EMA approaches, with repeated measures of individuals! daily psychological or physiological indicators such as affect, fatigue, heart rate, cortisol levels, and blood pressure, alongside their experiences of job demands and other work experiences or events. Rather than single moment snapshots when respondents are asked to recollect feelings or experiences, as in cross-sectional studies, or the bigger picture outlook of employee well-being associated with longitudinal research, these EMA studies are rich in detail—allowing for the tracking of real-time experiences, and the assessment of meaningful patterns in within-person fluctuations of well-being states and their antecedents (Beal & Weiss, 2003; Ilies et al., 2007a). Because EMA studies linking work stressors to strain and well-being within individuals examine outcomes that vary considerably across time (e.g. from day to day or within days), such studies typically focus on the temporary activation of the biological stress response, indicated, for example, by increases in momentary or daily cardiovascular (e.g. blood pressure) or adrenocortical stress response (e.g. salivary cortisol) measures. Such temporary activation of the biological stress response actually prepares the individual to cope with the stressor and is in essence an adaptive response (McEwen, 2005). However, repeated or prolonged exposure to stressors and the associated activation of the biological stress response can lead to chronic stress and stress-related health disorders (Jacobs et al., 2007; McEwen, 2005). It follows that EMA studies examining within-individual linkages among stressors and indicators of biological stress response activation allow for a better understanding of micro-level causal mechanisms by which work stressors may influence well-being. Such studies may also use objective and subjective measures concurrently in assessing stress responses and well-being states. For example, the study by Ilies et al. (2010a) drew upon the job demands-control model and showed that on days in which employees faced heavier workloads, they experienced greater burnout and strain later in the day. Moreover, these relationships were mediated by affective distress and, to a lesser extent, by systolic blood pressure during the workday. Noteworthy in the Ilies et al. (2010a) study are the small within-individual correlations of blood pressure to psychological indicators of distress and strain (.08–.22) and the very small (and not statistically significant) betweenindividual correlations among the average scores on these variables. This pattern of results suggests that (a) physiological measures such as blood pressure might be rather poor indicators of distress responses and strain and (b) that the psychobiological processes linking work stressors to physiological indicators of health and well-being operate differently at the within- and C 2016 International Association of Applied Psychology. V


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between-individual levels of conceptualisation and analysis. We return to each of these issues later in the article. In another study, Bono and colleagues (2013) tracked participants! daily events, stress, and health over a period of three weeks. Integrating conservation of resources theory and affective events theory, the authors proposed that environmental and psychosocial stressors in the form of negative workplace events and family-to-work conflict would deplete employees! resources and result in higher levels of self-reported stress, health complaints, and ambulatory blood pressure. In contrast, positive events at work and reflecting on positive work events were reasoned to build employee resources through the fulfillment of psychological needs. Indeed, the study mostly supported those hypotheses; however, the only physiological measure of well-being in the study—blood pressure—showed negligible correlations with the hypothesised predictors (lower than .05) and the regression analyses predicting blood pressure showed the weakest results among all the outcomes. Outside of the workplace (and outside of our review), Daly and colleagues measured participants! ambulatory heart rate throughout the day and used the day reconstruction method to capture episodic information and emotional states associated with specific episodes (Daly, Delaney, Doran, Harmon, & MacLachlan, 2010). Their results showed that negative affect was associated with increased ambulatory heart rate, and this relationship remained significant when a number of contextual (e.g. exercising, resting) and person-based variables (e.g. personality, body mass index) were controlled for. In this study, positive affect was not related to heart rate. The authors suggested that the observed pattern of results occurred because the negative affect associated with the felt inability to cope with stressors had a stronger and more prolonged negative effect on participants. In their study examining affect, blood pressure, and heart rate, Ilies et al. (2010b) proposed that both positive and negative affect would relate to heart rate (because both affect dimensions reflect activation), but only negative affect should be related to blood pressure (because negative but not positive affect is related, conceptually, to the threat/distress response system and blood pressure has been shown to be a correlate of these responses). The findings fully supported this proposed pattern of relationships. Data for this study were collected from full-time employees at work who responded to four daily surveys measuring affect at work on personal digital assistant devices, and also measured their blood pressure during each of the measurement occasions using automated cardiovascular monitors, over a two-week period. EMA stress research has also examined salivary cortisol as it indicates adrenocortical stress responses. Jacobs et al. (2007) examined within-individual associations among daily stressors, mood states, and salivary cortisol in a sample of 556 participants over five consecutive days. These participants were signaled via a wristwatch ten times daily to complete paper-and-pencil assessments and collect saliva samples within 15 minutes of being signaled. Results C 2016 International Association of Applied Psychology. V

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showed that momentary negative but not positive affect was related to momentary cortisol, and negative affect also mediated the observed relationship between momentary stress reports and cortisol levels. These findings show that even minor daily stress events are associated with both emotional and neuroendocrine effects, and the authors suggest that daily affective stress reactivity (individual tendencies to experience stress-related negative emotions) might predict future health outcomes. Together, these EMA studies have not only helped to increase our understanding of the relationships between work stressors and employees! stress responses, but also highlight the need for researchers to undertake closer examination of the relationships between the different physiological and psychological stress indicators (e.g. heart rate and affect).

THE ALLOSTATIC LOAD MODEL OF STRESS Although the EMA studies described in the previous section appear to examine starkly different health indicators (e.g. momentary or daily mood, blood pressure, and heart rate) when compared to those examined in the betweensubjects studies mentioned above (e.g. incidence of cardiovascular disease, depression, and work absences), these two foci offer us complementary yet causally related perspectives on the outcomes of work stress. The Allostatic Load (AL) Model (McEwen, 2007; McEwen & Stellar, 1993) provides a useful organising framework for (a) the integration of the multilevel mechanisms that produce these transient and chronic stress outcomes, and (b) the examination of the linkages among these outcomes across varying time periods over which the outcomes are manifested and across different levels of conceptualisation and analysis, through its distinction between primary, secondary, and tertiary allostatic processes. Allostasis refers to the adjustment of physiological systems (e.g. cardiovascular and endocrine systems) in response to environmental stressors. When these physiological systems are overloaded by repeated or prolonged exposure to stress, they become dysregulated and can lead to adverse psychological and physical outcomes. In the AL model of stress, physiological reactions are triggered when the individual perceives or anticipates a potential stressor in the environment (Ganster & Rosen, 2013). The encounter of real or imagined stressors first triggers primary physiological mediators aimed at helping the individual cope with the threat (e.g. increased cardiovascular activation and cortisol release). Transient increases in these primary physiological mediators in response to daily stressors are illustrated in the EMA studies that we reviewed above (e.g. Ilies et al., 2010a; Jacobs et al., 2007). But this is only the first step in understanding how work stressors may influence employees! health and well-being. C 2016 International Association of Applied Psychology. V


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According to the AL model of stress, it is the chronic activation of these primary mechanisms that disrupts homeostatic systems by over- or underproducing hormones, and hence alters established set points for the immune, cardiovascular, and metabolic systems (Ganster & Rosen, 2013). These alterations in the systems! set points are manifested through changes in secondary physiological mediators that include increases in resting blood pressure, blood sugar and cholesterol levels, as well as poorer immune system functioning. These secondary mediators are best thought of as reflecting somewhat stable (at least over medium-range time periods) between-individual differences, as opposed to the transient, state-like nature of the primary mediators. Finally, prolonged or repeated changes in secondary set points can eventually lead to tertiary allostatic processes, which can include cardiovascular disease, diabetes, depression, and even death (Ganster & Rosen, 2013; McEwen, 2007). Applying this framework to the existing WOHP literature in general and to the study of work stressors in particular, the health and well-being indicators examined in previous intraindividual studies of job demands and stressors can be viewed as reflecting the primary AL process. This is because negative emotions, cortisol, and momentary blood pressure are considered initial (primary) mediators in the AL model while the outcomes examined in betweenindividual studies (e.g. cardiovascular disease and depression) are tertiary health outcomes. However, research investigating secondary AL processes and outcomes in the context of work stress is lacking. There has also been little research linking parameters of primary AL processes (e.g. affective, cardiovascular, or adrenocortical reactivity to daily work stressors) to secondary AL mediators or tertiary end outcomes. We view this as an area with tremendous potential for future contributions in the study of the effects of psychosocial work stressors; we explain how such research can be conducted in the remainder of the manuscript, and we give suggestions for future research linking EMA studies to chronic health and well-being outcomes in the concluding sections of the article.

ECOLOGICAL MOMENTARY ASSESSMENT METHODS EMA studies generally aim to examine momentary variations in individuals! experiences and psychological states as they go about their daily routines in a way that preserves ecological validity (Csikszentmihalyi & Larson, 1987; Ohly et al., 2010). The resultant data are then used to identify meaningful patterns and dynamics in individuals! subjective experiences and their outcomes. In perhaps the earliest review of EMA studies, Csikszentmihalyi and Larson (1987) compared EMA and time budget diary studies to establish the reliability and validity of EMA data. The early studies (e.g. Csikszentmihalyi, Larson, & Prescott, 1977) employed paper-and-pencil designs, where participants were required to carry a booklet of surveys along with an electronic pager (or C 2016 International Association of Applied Psychology. V

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“beeper”) that would signal participants at several times during the day to complete the survey. On the basis of the studies in their review, Csikszentmihalyi and Larson (1987) concluded that EMA was able to provide accurate and reliable estimates of participants! activity frequencies and psychological states across time, by reducing individual recall biases, and also demonstrated convergent and discriminant validity across different situations, personalities, and groups. Since these early studies, organisational researchers have gradually recognised the value of EMA methodology. For instance, Beal and Weiss (2003) wrote one of the first review articles targeted at management research, noting that although EMA techniques “have been in use in other areas of the social and medical sciences for many years, organizational researchers have not taken advantage of these techniques” (Beal & Weiss, 2003, p. 440). The authors extol the advantages of EMA methods and provide some suggestions as to how EMA can be an effective methodology in organisational research, such as by examining day-to-day fluctuations in affect, personality, and performance, and they contend that EMA techniques allow for a more in-depth examination of the psychological mechanisms underlying various relationships among such fluctuating constructs. Heeding this call, there has indeed been a proliferation of workplace EMA studies in recent years, prompting a review by Ohly and colleagues discussing the state of the art and future potential of organisational EMA research (Ohly et al., 2010). Their review of extant EMA studies focuses on three broad categories of research questions that have been examined thus far: (1) how one variable (e.g. fatigue) fluctuates over time, (2) relationships between transient states (e.g. relationships between affect and helping behaviors), and (3) how stable personality or environmental attributes influence short-term experiences and behaviors (e.g. how personality traits such as agreeableness influence helping behaviors). EMA researchers often distinguish between three different sampling schedules, event-contingent, signal-contingent, and interval-contingent sampling, which determines when and the number of times participants respond to the survey (Alliger & Williams, 1993; Wheeler & Reis, 1991). The type of sampling schedule used would depend on the research question of interest. An example of a signalcontingent method is the study by Snir and Zohar (2008), where participants were signaled on a pre-programmed wristwatch at four random times a day to complete a pen-and-paper survey regarding their work hours, activities, social environment, and affective states. Signal-contingent sampling allows the researcher to obtain a representative estimate of the participant!s typical activities, thoughts, and feelings within a day, and is used to avoid possible sampling biases. Interval-contingent sampling is used when the research question requires the assessment at specific times of the day. For instance, one study examined how recovery activities and experiences in the evening would influence morning C 2016 International Association of Applied Psychology. V


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recovery (Sonnentag, Binnewies, & Mojza, 2008). Participants in this study were sampled at specific times in the day (before sleep and upon waking) and asked to record their activities and subjective perceptions of well-being and sleep quality on a handheld computer. Interval-sampling is generally used when the researchers are interested in examining specific daily periods, such as the beginning or end of the workday, where participants are able to recollect events that have happened just prior to the sampling interval (e.g. assessing the previous night!s sleep quality upon waking, as in the Sonnentag et al. (2008) study). Finally, event-contingent sampling is used when the research interest is about a specific event (e.g. thoughts about a customer service interaction), rather than in capturing the typical workday. Grebner, Elfering, Semmer, Kaiser-Probst, and Schlapbach (2004) examined the effects of stressful situations at work, and instructed participants to complete an experience-sampling survey regarding their perceptions, thoughts, and feelings whenever they encountered a stressful situation. In these research questions, obtaining a representative sample of the participant!s day is not the focus, as in signal-contingent sampling, but rather the participant!s immediate reactions to a specific event. In our review of EMA methods in WOHP research, we have detailed, for each of the studies covered, the number of participants recruited, the number of times participants were sampled in each study (and the corresponding number of observations when this information was provided), and sampling strategy, as well as the measures of stress used (both physiological and psychological indicators). These studies include traditional EMA methods, as well as some newer methods and technologies (as we detail in the next section). We hope that this review can serve as a guide and starting point for WOHP researchers who are thinking of conducting research using EMA methodology. Again, we note that our review selectively focuses on stress outcomes in WOHP, and we refer readers to other recent reviews of EMA methods and well-being in organisational behavior (see Beal, 2015; Ohly et al., 2010; Sonnentag, 2015). In addition, in the Appendix, we provide a list of several resources that could be useful for setting up EMA studies.

TECHNOLOGICAL ADVANCES IN EMA RESEARCH More central to the goals of the current paper, recent technological advances have birthed promising new ways for conducting EMA studies in organisational health psychology. The advent of the smartphone, greater Internet connectivity, and other portable devices equipped with biosensors have provided psychologists with the capabilities to capture an even richer set of momentary psychological and physiological variables (Houtveen & de Geus, 2009; Kuntsche & Labhart, 2013; Miller, 2012). In particular, Miller (2012) and C 2016 International Association of Applied Psychology. V

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Kuntsche and Labhart (2013) discuss how smartphones “offer huge potential to gather precise, objective, sustained, and ecologically valid data” (Miller, 2012, p. 221), and put forth several reasons why psychologists should recognise and make use of smartphones as a valuable research tool. First, smartphones have become a ubiquitous device in everyday life, with an estimated 1.6 billion smartphone users across the globe in 2014. This number is expected to exceed 2.5 billion by 2018 (eMarketer, 2014). This allows researchers tremendous potential in reaching out to a large number of participants with minimal interference to their daily routines, as respondents are able to complete EMA surveys directly from their smartphones. This is in contrast to earlier technologies such as personal digital assistants or even pen-and-paper booklets which required participants to carry around additional material for the purposes of the study and run the risk of participants forgetting to bring or misplacing the surveys. Compared to these more traditional methods, smartphones also have the added advantage of verifying compliance, as participants! responses are automatically time-stamped. Second, smartphones are increasingly equipped with various technologies that are relevant for EMA studies, including GPS to track location and movement patterns, accelerometers to assess activity levels (e.g. number of steps walked), large data storage capabilities and Internet connectivity, giving researchers greater flexibility in signaling, collecting, and recording of data in real time. Integrated connectivity technologies (e.g. Bluetooth) also provide the ability to connect to peripheral devices such as heart rate monitors and electroencephalogram headsets to assess physiological and even neurological indicators. Kuntsche and Labhart (2013) provide an overview of data collection methods in smartphones, categorising features into automated versus manual data collection. For instance, the GPS feature allows for the automatic capture of participants! location and movement patterns throughout the day with minimal effort from participants. Participants! current mood states and perceptions can be captured by asking them to actively provide the information on a smartphone application or survey loaded on the smartphone!s web browser. Indeed, a variety of psychological applications have been developed and tested for research purposes (e.g. Froehlich, Chen, Consolvo, Harrison, & Landay, 2007; Killingsworth & Gilbert, 2010), although their usage is still uncommon in WOHP research. Other commercial services are also available (see Appendix), claiming to include features such as GPS tracking and customising surveys for participants either through online web browsers or mobile applications. As mentioned, many smartphones now come with the ability to communicate with peripheral devices, a feature that makes them well suited for use in WOHP research. Biosensors have been developed that are portable, noninvasive, and lightweight, some of which can even be worn with minimal interference to participants! routines (e.g. “smart watches”, such as the Apple Watch, or activity trackers worn around the wrist that automatically log indicators C 2016 International Association of Applied Psychology. V


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such as sleep patterns, number of steps taken a day, distance traveled, and heart rate). These devices are often compatible with dedicated health applications in smartphones (e.g. the Health application in Apple!s iPhones), that when paired, enable not only the simultaneous recording of physiological indicators alongside participants! survey responses, but also allow for the continuous tracking of variables such as heart rate and blood pressure throughout the day to obtain data on individual averages and circadian rhythms, as well as identifying meaningful patterns (Houtveen & de Geus, 2009; Kaplan & Stone, 2013). Indeed, Kaplan and Stone (2013) provide arguments for extended ambulatory blood pressure and cardiovascular monitoring, as it allows for the detection of deviations from regular circadian patterns, and can be a better predictor of adverse cardiovascular events than assessments at a single time point (Ishikawa, Ishikawa, Edmonson, Pickering, & Schwarz, 2011; Kaplan & Stone, 2013). Furthermore, as these biological indicators are often dynamic in response to the environment, their measurements throughout the day can allow researchers to examine the effects of particular situations on blood pressure or heart rate. These data can in turn be easily synchronised to the smartphone via a dedicated health tracking application, or stored in the Internet. At the time that we write this article, Apple has announced the release of the Research Kit for iPhone, which will enable iPhone users to participate in medical research with the data being sent directly to the researchers. With over 700 million iPhones sold by Apple, the potential sampling frame for research is enormous, and we certainly predict that in addition to medical research, WOHP researchers would also take advantage of this new opportunity to design EMA studies to be run on the iPhone. Applied to the WOHP setting and the Allostatic Load Model, a researcher interested, for example, in examining the effects of social interactions at work on after-work fatigue can have participants wear a heart rate monitor throughout the workday, and instruct them to fill in an experience sampling survey on their smartphones after certain interactions (e.g. those that are particularly positive or negative), regarding the quality of the interaction, their emotions during the interaction, and evaluations of the interaction. Based on the timestamps and participants! ratings of the interaction, researchers can then assess the effects of cognitions and emotions on the primary physiological mediator of cardiovascular activity, and its subsequent effects on fatigue at the end of the workday. In this respect, EMA studies can provide researchers with rich and dynamic data that can be used to assess both primary and secondary mediators. For primary mediators, examining variability of the individual!s immediate responses to negative interactions or stressors (e.g. cortisol release, heart rate) would be indicative of their physiological reactivity on a day-to-day basis. For instance, the individual!s heart rate may remain elevated for a prolonged amount of time after a negative interaction, which could be suggestive of difficulties in C 2016 International Association of Applied Psychology. V

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recovering from stress and returning to homeostatic set points, as in the AL model (e.g. Roy, Steptoe, & Kirschbaum, 1998). Further, one could also aggregate participants! daily data across the study period to examine averages (as an indicator of prolonged stress) or deviations (as an indicator of general physiological reactivity) to assess secondary mediators. For example, higher average heart rate can indicate prolonged activation of the stress response and hence allostatic overload, while higher resting blood pressure or salivary cortisol upon awakening, an example of altered set points, is a secondary mediator linked to cardiovascular disease and hypertension (Seeman & McEwen, 1996). Within-individual designs can also inform us as to individuals! variability in heart rate across days or weeks and can also serve as a gauge of one!s cardiovascular reactivity to stress—research has demonstrated that higher reactivity to stress is associated with higher susceptibility to illnesses (Cacioppo, 1998). As another example, let us imagine that a WOHP researcher is interested in examining the detrimental effects of interpersonal conflict, a work stressor, on employees! health, following Ilies, Johnson, Judge, and Keeney (2011a) who have examined the transient (within-individual) effect of interpersonal conflict on negative affect and also investigated whether this effect is moderated by both situational and personality factors. In this hypothetical follow-up study, the researchers not only include negative affect as a daily outcome (like Ilies et al.), but also examine the effect of conflict on an allostatic primary mediator outcome—cortisol level. Interpersonal conflict (and negative affect) can be measured daily (perhaps several times per day) at work using a simple survey delivered on participants! smartphone, heart rate (to be used as a control variable in within-individual analyses) can be tracked via a smartwatch that transmits the data to participants! phones, and cortisol levels can be recorded daily using cotton swabs that participants put in test tubes that are then collected by the researchers and sent for analysis (e.g. Jacobs et al., 2007). Also, demographic and personality variables would be collected on a one-time survey administered before the start of the daily assessments.

ANALYSIS OF EMA DATA Data collected using EMA are nested within at least two levels (i.e. measurement occasions nested within people) and cannot be analyzed with simple regression analyses or other techniques based on regression (e.g. path analysis). Rather, such data must be analyzed with multilevel techniques that account for the non-independence of the scores at the lower level(s) (e.g. repeated measures scores provided by the same participant are not independent) and give adequate options for disentangling effects manifested at the different levels of the data structure. There are many statistical packages available for conducting C 2016 International Association of Applied Psychology. V


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multilevel analyses (e.g. HLM, Raudenbush & Bryk, 2002; M-Plus, Muth"en & Muth"en, 2010), with the most commonly used program being HLM. Below we provide an illustration of the principles of multilevel data analysis as they apply to EMA data, using the hypothetical study example that we described above in the section on assessment methodology. Multilevel modeling of EMA data requires the simultaneous estimation of regression models at (at least) two distinct levels of analysis. At the first level of analysis (e.g. within-individual), the scores for the outcomes of interest (e.g. primary AL mediators) are regressed on the within-person scores for the hypothesised predictors (e.g. daily stressors). Outcomes and predictors in this instance commonly represent momentary or daily scores. The equations below show regressions for predicting salivary cortisol scores (CORT) with interpersonal conflict (CONFLICT) at the first level of analysis. Level-1 regression:

CORTij 5b0j 1b1j ðCONFLICTÞ 1 rij

Level-2 regression:

b0j 5c00 1 U0j b1j 5c10 1 U1j

Mixed-level regression: CORTij 5c00 1 c10 ðCONFLICTÞ 1 U0j 1 U1j ðCONFLICTÞ 1 rij In these equations, c00 represents the mean (pooled) intercept, and c10 represents the mean (pooled) slope. The level-1 residual variance is given by Var (rij), the variance in the individuals! intercepts is given by Var (U0j), and the variance in their slopes is given by Var (U1j). Assuming that the researchers are interested in estimating the withinindividual effect of interpersonal conflict on cortisol (not confounded by between-individual differences), the predictor scores must be centered relative to each individual!s average score (i.e. at level 1, the model regresses individuals! cortisol scores on their deviations of conflict scores around each individual!s own mean), rather than centering these scores relative to the grand mean or leaving them uncentered which are the other options in multilevel analyses like those described here.1 This procedure is akin to the one described in the Ilies, Johnson, Judge, and Keeney (2011a) study, with respect to the effect of interpersonal conflict on negative affect. Of note here is that in the mixed-level regression, the residual term (U0j 1 U1j(CONFLICT) 1 rij) is not independent of the predictor scores, which is why this type of nested data should not be analyzed with regular (one-level) least-squared regression.

1 For extensive discussions of centering in multilevel models see Enders and Tofighi (2007), or Hofmann, Griffin, and Gavin (2000), among others. C 2016 International Association of Applied Psychology. V

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When one wants to estimate the effects of a level-2 variable (e.g. personality or BMI; Daly et al., 2010; Ilies et al., 2011a) on level-1 parameters (e.g. b0j or b1j), the level-2 variable, agreeableness (AGREE) in this example (see Ilies et al.) is entered in the level-2 equations: Level-1:

CORTij 5b0j 1 b1j ðCONFLICTÞ 1 rij

Level-2:

b0j 5c00 1 c01 ðAGREEÞ 1 U0j

b1j 5c10 1 c11 ðAGREEÞ 1 U1j

Mixed-level regression: CORTij 5c00 1c01 ðAGREEÞ 1 c10 ðCONFLICTÞ 1 c11 ðAGREEÞðCONFLICTÞ 1 U0j 1 U1j ðCONFLICTÞ 1 rij

In these equations, c01 represents the effect of the level-2 predictor on the intercept of the level-1 variable, in essence, the between-individual main effect of agreeableness on average cortisol. As can best be seen in the mixed-level equation, c11 represents the effects of the level-2 predictor—agreeableness—on individuals! characteristics slope for own relationship among interpersonal conflict and cortisol. That represents a cross-level moderating effect, like the one found by Ilies et al. (2011a) where agreeableness exacerbated the effect of interpersonal conflict on negative affect. Multilevel analyses can also test within-level moderating effects, like the buffering effect of daily social support (a level-1 variable) on the interpersonal conflict-to-negative affect effect found by Ilies et al. Multilevel analyses like those that we illustrate here can also provide tests for mediated or indirect effects, such as the within-individual mediating effect of negative affect in explaining the relationships among daily stress and cortisol documented by Jacobs et al. (2007). Conceptually, testing mediation in multilevel modeling is no different from testing it in single-level analyses. For example, in the study example that we used to illustrate analyses of EMA data, the researcher can introduce individuals! momentary negative affect scores as an additional within-individual predictor of cortisol (in the level-1 equation, see above) and examine whether negative affect significantly predicts cortisol and whether the effect of interpersonal conflict on cortisol that was previously estimated (before the inclusion of negative affect as a predictor) disappears or is substantially diminished in magnitude (assuming that interpersonal conflict and negative affect are also related; see Baron & Kenny, 1986). In addition, researchers can also test more complex moderated-mediation models in multilevel analyses; the technical details for conducting such tests (and also for the latest methodology in testing indirect effects in multilevel models) are beyond the scope of this article, and interested readers should consult recent methodological treatises on these topics (e.g. Bauer, Preacher, & Gil, 2006; Dimotakis C 2016 International Association of Applied Psychology. V


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et al., 2013; Kenny, Korchmaros, & Bolger, 2003; MacKinnon, Lockwood, Hoffman, West, & Sheets, 2002; Tofighi & MacKinnon, 2011). Finally, analyses of multilevel EMA data can not only test whether personlevel constructs predict patterns of within-individual relationships (e.g. main effects or cross-level interactions; as explained above), but can also (a) examine whether some pooled level-1 estimates, which represent parameters of primary AL functioning, predict person-level (secondary or tertiary AL mediators or outcomes), or (b) provide other primary AL functioning parameters to be used in subsequent analyses predicting longer term health and well-being consequences. For example, individuals! characteristic level-1 slopes for predicting cortisol with interpersonal conflict (indicating each individual!s adrenocortical stress reactivity) can be used to predict between-individual differences in the set points of their metabolic system (e.g. cholesterol or average blood glucose levels), thus linking parameters of primary AL functioning to secondary mediators (see Ganster & Rosen, 2013).

CHALLENGES AND OPPORTUNITIES EMA research is not without challenges. First, EMA studies involve intensive data collection with daily surveys (sometimes several times daily) for multiple days, thus requiring a substantial amount of time from participants. This requirement makes it difficult to find participants (and typically implies that participants have to be well compensated). Therefore, EMA sample sizes are generally small. Although the fact that EMA data consist of multiple data points for each participant, thus increasing statistical power for withinindividual analyses, given the small effects sizes for predicting physiological measures (e.g. blood pressure; Ilies et al., 2010a), power can be a limiting issue in this type of research. Also, small sample sizes imply low power for detecting between-individual or cross-level effects (Snijders & Bosker, 1993), which would make the detection of mechanisms linking primary AL functioning to secondary mediators and tertiary outcomes problematic (perhaps explaining the paucity of such research). Also, compliance with the research protocol can be an issue in EMA studies, especially those that involve complicated measurements such as salivary cortisol sampling (see Jacobs, Nicolson, Derom, Delespaul, van Os, & Myin-Germeys, 2005), although we believe that such measurements will become easier to comply with in the future as testing technology advances, similar to other physiological measures (e.g. glucose meters that provide immediate testing and upload data to smartphones via Bluetooth). Due to the heavy burden on participants in completing EMA studies, these challenges regarding compliance and participation (or dropout) rates should not be taken lightly. While monetary incentives (or lotteries) have most often been used as motivators for participation and compliance, some researchers C 2016 International Association of Applied Psychology. V

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have argued against the use of monetary incentives as motivators as they can compromise the quality of the data (e.g. faking responses; Green, Rafaeli, Bolger, Shrout, & Reis, 2006). They suggest that communicating with participants the importance of timely responses, letting participants know that their compliance would be evaluated, as well as highlighting the benefits of participation to participants and their organisation (e.g. through interventions or highlighting the practical implications of the results for participants! performance, well-being, and long-term health) would lead to greater satisfaction and motivation to comply with the study. An additional challenge with regard to incorporating new technologies (e.g. smartphones and activity trackers or smart watches) in EMA research concerns participants! privacy. For instance, such devices with built-in GPS functions may automatically record participants! locations throughout the day and without their knowledge or consent (Miller, 2012), while the collection of physiological measures throughout the day and other health data may be considered by some to be invasive. This may raise additional concerns for institutional review boards (IRB), and possibly affect participant sampling, as individuals who are greatly concerned about privacy are unlikely to participate in EMA studies. While we acknowledge that there is no easy way around these privacy issues, it is important to be upfront about these issues both to the IRB as well as potential participants, in terms of the types of data that would be recorded by these devices or EMA smartphone applications, how their data would be used in the research, and to possibly include additional safeguards in the research design to ensure that identifiable information (e.g. names, contact numbers, email addresses) are recorded separately from participant response logs (see Trull, 2015, for additional information on ethical issues in EMA studies). The challenges associated with conducting intensive EMA studies notwithstanding, we believe the application of EMA methods to WOHP, particularly within an AL framework, offers great promise. Perhaps the greatest opportunity for WOHP research using naturalistic assessment methodologies lies in examining cross-level linkages (in essence linking transient stress processes to chronic outcomes) that connect employees! primary AL functioning to the set points of secondary processes (e.g. resting heart rate, cholesterol levels) and to tertiary (long-term) health and well-being outcomes (e.g. cardiovascular disease, depression) made possible by the application of the three-level AL stress model (see Ganster & Rosen, 2013) to WOHP and to the study of the effects of psychosocial work stressors. Studies that are designed within this integrated AL framework, coupled with the available assessment technologies, can address issues that not only involve the cross-level linkages, but also with respect to the potential efficacy of mechanisms (e.g. organisational interventions) aimed at reducing the negative effects of work stressors on employee health and well-being, which is a primary goal of WOHP. C 2016 International Association of Applied Psychology. V


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For example, we know that day-to-day variations in workload (Ilies, Schwind, Wagner, Johnson, DeRue, & Ilgen, 2007b), interpersonal conflict (Ilies et al., 2011a) or other stressors (e.g. social stress; Jacobs et al., 2007) are associated with variations in primary mediators such as negative affect (Ilies et al., 2007b), emotional exhaustion (Ilies, Huth, Ryan, & Dimotakis, 2015) or cortisol levels (Jacobs et al., 2007). While emotional constructs such as daily negative affect and emotional exhaustion are important outcomes in themselves, future research should examine whether day-to-day variability in negative affect, emotional exhaustion, or cortisol levels induced by job demands, have longer-term effects on secondary mediators and perhaps on tertiary end outcomes. If that proves to be the case (through perhaps what Ganster and Rosen call dysregulations of diurnal patterns) then interventions aimed at reducing the amplitude of such fluctuations in primary mediators (i.e. reducing emotional reactivity via mindfulness or meditation exercises; van Hooff & Baas, 2013) may be effective in preventing chronic effects on health and wellbeing. Of course, studies adopting such a multilevel framework would face additional challenges in determining appropriate time lags between measurements (such as in relating primary AL mediators with secondary mediators, or secondary mediators with tertiary mediators), as well as having to span substantial time periods in order to examine the relationships between these constructs, which can place additional burden on participants (and researchers!). Determining appropriate measurement periods for secondary mediators can be difficult. While primary mediators are best assessed on a daily level, and tertiary outcomes can be assessed after the long term (e.g. several years), secondary mediators manifest themselves in a more fluid manner and they can be assessed using a wide range of timeframes. Different studies examining secondary mediators adopted timeframes that ranged from several weeks to several years. For instance, one study found that male participants! serum cholesterol levels (a secondary mediator indicative of allostatic overload and linked to cardiovascular disease) measured every two weeks varied significantly in response to work stressors on a cyclical basis (Friedman, Rosenman, & Carroll, 1958). Studies using rats too showed that prolonged stress over the course of three weeks could result in significant changes to their brains! neural structure and growth of new neurons (e.g. Magarinos, McEwen, Flugge, & Fuchs, 1996). However, other research examining immune system and cardiovascular functioning adopted timeframes of 18 months to two years (e.g. Shirom, Toker, Berliner, & Shapira, 2008; Xie, Schaubroeck, & Lam, 2008). Multiple research studies testing the propositions of the AL model are thus necessary in order for us to better understand how the different levels of primary, secondary, and tertiary indicators relate to one another, and how the experience of stress influences each level. C 2016 International Association of Applied Psychology. V

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More broadly, an additional question for future WOHP research could be to examine whether interventions at the daily level (e.g. micro-breaks during the workday, Trougakos, Beal, Green & Weiss, 2008; psychological recovery at home, Sonnentag et al., 2008; von Thiele Schwarz, 2011; work–family interpersonal capitalisation, Ilies, Keeney, & Scott, 2011b) can alleviate the negative effects of stress and prevent secondary AL process dysregulation. Specifically, researchers could combine EMA methodology with experimental manipulations in the field, measuring participants! stress perceptions and heart rate activity on a daily basis, and comparing the treatment group (e.g. participants who were tasked to engage in capitalisation with their spouses) with a control group. Such a design would test whether participants in the control group evidence higher average levels of heart rate activity compared to the treatment group—as social support and inclusion have been found to modulate responses to stressors, increasing the threshold at which the stress response is activated, i.e. reducing reactivity to stressors (Bovard, 1985). One final suggestion would be to embed within-individual EMA designs in longitudinal studies to provide us with a more nuanced understanding of the different pathways through which social and psychological work stressors interact with physiological indicators (Piazza, Almeida, Dmitrieva, & Klein, 2010). Participants! daily data could first be collected for two weeks, with an additional two weeks at a six-month follow-up, and possibly a third wave (of two weeks) six months after that. Such a design would allow researchers to examine, within-individuals, how participants! physiological set points (secondary mediators) have changed over the study period in response to stress.2 From a practical (managerial) perspective, we should note that primary AL adaptation and adjustment processes are affected by individual behaviors and lifestyles (smoking, physical activity, alcohol intake, and diet). Organisations must recognise this and implement programs and interventions aimed at fortifying employees such that they become more resilient to job demands in the initial adaptation process. The challenge is for organisations to play a more active role and invest in intervention efforts aimed at adjusting employees! lifestyle and consequently their health and well-being because what happens outside work (e.g. employees! eating, drinking, smoking behaviors, or their exercise habits) can affect how individuals respond to changes in work conditions and stress. Organisational support, especially from supervisors, has also been shown to reduce the effects of work-related factors on indicators of distress (Ilies et al., 2010a; Ng & Feldman, 2013) and managers should thus be cognisant of these protective effects of support. Finally, organisations can also focus on positive work experiences, not only on preventing the negative effects of excessive job demands or other work 2

We thank an anonymous reviewer for this suggestion.



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stressors. For instance, research could examine possible interactions between work stressors (the current focus of our review) and positive work experiences (e.g. positive affect, state work engagement) in influencing employee outcomes and behaviors. In this respect, employees should be encouraged to capitalise on the positive aspects of their work (Ilies, Keeney, & Goh, 2015; Ilies et al., 2011b), and human resource departments should implement “countervailing interventions” (see Kelloway, Hurrell, & Day, 2008) that may not directly prevent risks or health problems but increase the positive experience of work (rather than decreasing the negative aspects), and hence buffer any negative effects of work stress on well-being. Towards that end, organisations can redesign work to provide opportunities for training, development, and self-efficacy to increase employees! psychological resources, thus counterbalancing the effects of job stressors and perhaps sever the links between the daily processes of dealing with job demands and the secondary AL mediators that can lead to poor health and decreased well-being.

REFERENCES Alliger, G.M., & Williams, K.J. (1993). Using signal-contingent experience sampling methodology to study work in the field: A discussion and illustration examining task perceptions and mood. Personnel Psychology, 46, 525–549. Baethge, A., & Rigotti, T. (2013). Interruptions to workflow: Their relationship with irritation and satisfaction with performance, and the mediating roles of time pressure and mental demands. Work & Stress, 27(1), 43–63. Bakker, A.B., Demerouti, E., de Boer, E., & Schaufeli, W.B. (2003). Job demands and job resources as predictors of absence duration and frequency. Journal of Vocational Behavior, 62, 341–356. Barnes, C.M., Ghumman, S., & Scott, B.A. (2013). Sleep and organizational citizenship behavior: The mediating role of job satisfaction. Journal of Occupational Health Psychology, 18(1), 16–26. Baron, R.M., & Kenny, D.A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173–1182. Bartholomew, K.J., Ntoumanis, N., Cuevas, R., & Lonsdale, C. (2014). Job pressure and ill-health in physical education teachers: The mediating role of psychological need thwarting. Teaching and Teacher Education, 37, 101–107. Bauer, D.J., Preacher, K.J., & Gil, K.M. (2006). Conceptualizing and testing random indirect effects and moderated mediation in multilevel models: New procedures and recommendations. Psychological Methods, 11, 142–163. Beal, D.J. (2015). ESM 2.0: State of the art and future potential of experience sampling methods in organizational research. Annual Review of Organizational Psychology and Organizational Behavior, 2(2), 383–407. Beal, D.J., & Weiss, H.M. (2003). Methods of ecological momentary assessment in organizational research. Organizational Research Methods, 6, 440–464. C 2016 International Association of Applied Psychology. V

250

ILIES ET AL.

Beehr, T.A., & Newman, J.E. (1978). Job stress, employee health, and organizational effectiveness: A facet analysis, model, and literature review. Personnel Psychology, 31, 665–699. Binnewies, C., & W€ ornlein, S.C. (2011). What makes a creative day? A diary study on the interplay between affect, job stressors, and job control. Journal of Organizational Behavior, 32, 589–607. Bolger, N., Davis, A., & Rafaeli, E. (2003). Diary methods: Capturing life as it is lived. Annual Review of Psychology, 54, 579–616. Bono, J.E., Glomb, T.M., Shen, W., Kim, E., & Koch, A.J. (2013). Building positive resources: Effects of positive events and positive reflection on work stress and health. Academy of Management Journal, 56, 1601–1627. Bovard, E.W. (1985). Brain mechanisms in effects of social support on viability. In R.B. Williams Jr. (Ed.), Perspectives on behavioral medicine, Vol. 2. Neuroendocrine control and behavior (pp. 103–129). Orlando, FL: Academic Press. Cacioppo, J.T. (1998). Somatic responses to psychological stress: The reactivity hypothesis. In M. Sabourin, F. Craik, & M. Robert (Eds.), Advances in psychological science (Vol. 2, pp. 87–112). Hove, East Sussex: Psychology Press. Caspi, A., Bolger, N., & Eckenrode, J. (1987). Linking person and context in the daily stress process. Journal of Personality and Social Psychology, 52, 184–195. Csikszentmihalyi, M., & Larson, R. (1987). Validity and reliability of the experiencesampling method. Journal of Nervous & Mental Disease, 175, 526–537. Csikszentmihalyi, M., Larson, R., & Prescott, S. (1977). The ecology of adolescent activity and experience. Journal of Youth and Adolescence, 6, 281–294. Daly, M., Delaney, L., Doran, P.P., Harmon, C., & MacLachlan, M. (2010). Naturalistic monitoring of the affect-heart rate relationship: A day reconstruction study. Health Psychology, 29, 186–195. Daniels, K., Wimalasiri, V., Beesley, N., & Cheyne, A. (2012). Affective well-being and within-day beliefs about job demands! influence on work performance: An experience sampling study. Journal of Occupational and Organizational Psychology, 85, 666–674. de Jonge, J., Bosma, H., Peter, R., & Siegrist, J. (2000). Job strain, effort–reward imbalance and employee well-being: A large-scale cross-sectional study. Social Science & Medicine, 50, 1317–1327. de Jonge, J., Dormann, C., Janssen, P.P.M., Dollard, M.F., Landeweerd, J.A., & Nijhuis, F.J.N. (2001). Testing reciprocal relationships between job characteristics and psychological well-being: A cross-lagged structural equation model. Journal of Occupational and Organizational Psychology, 74, 29–46. Demerouti, E., Bakker, A.B., Nachreiner, F., & Schaufeli, W.B. (2001). The job demands-resources model of burnout. Journal of Applied Psychology, 86, 499–512. Diestel, S., Rivkin, W., & Schmidt, K.-H. (2015). Sleep quality and self-control capacity as protective resources in the daily emotional labor process. Journal of Applied Psychology, 100(3), 809–827. Dimotakis, N., Ilies, R., & Judge, T.A. (2013). Experience sampling methodology. In J.M. Cortina & R.S. Landis (Eds.), Modern research methods for the study of behavior in organizations (pp. 319–348). New York: Routledge. Eckenrode, J. (1984). Impact of chronic and acute stressors on daily reports of mood. Journal of Personality and Social Psychology, 46, 907–918. C 2016 International Association of Applied Psychology. V


251

Enders, C.K., & Tofighi, D. (2007). Centering predictor variables in cross-sectional multilevel models: A new look at an old issue. Psychological Methods, 12, 121–138. eMarketer. (2014). 2 billion consumers worldwide to get smart(phones) in 2016. Retrieved from: http://www.emarketer.com/Article/2-Billion-Consumers-Worldwide-Smartphones-by-2016/1011694 on 1 March 2015. Feuerhahn, N., Sonnentag, S., & Woll, A. (2014). Exercise after work, psychological mediators, and affect: A day-level study. European Journal of Work and Organizational Psychology, 23(1), 62–79. Flugel, J.C. (1925). A quantitative study of feeling and emotion in everyday life. British Journal of Psychology, 9, 318–355. Friedman, M., Rosenman, R.H., & Carroll, V. (1958). Changes in serum cholesterol and blood clotting time in men subjected to cyclic variation of occupational stress. Circulation, 17, 852–861. Froehlich, J., Chen, M.Y., Consolvo, S., Harrison, B., & Landay, J. (2007). MyExperience: A system for in situ tracing and capturing of user feedback on mobile phones. In Proceedings of Mobi-Sys, 2007 (pp. 57–70). New York: ACM. Ganster, D.C., Fox, M.L., & Dwyer, D.J. (2001). Explaining employees! health care costs: A prospective examination of stressful job demands, personal control, and physiological reactivity. Journal of Applied Psychology, 86, 954–964. Ganster, D.C., & Rosen, C.C. (2013). Work stress and employee health: A multidisciplinary review. Journal of Management, 39, 1085–1122. Garrick, A., Mak, A.S., Cathcart, S., Winwood, P.C., Bakker, A.B., & Lushington, K. (2014). Psychosocial safety climate moderating the effects of daily job demands and recovery on fatigue and work engagement. Journal of Occupational and Organizational Psychology, 87, 694–714. Grebner, S., Elfering, A., Semmer, N.K., Kaiser-Probst, C., & Schlapbach, M. (2004). Stressful situations at work and in private life among young workers: An event sampling approach. Social Indicators Research, 67, 11–49. Green, A.S., Rafaeli, E., Bolger, N., Shrout, P.E., & Reis, H.T. (2006). Paper or plastic? Data equivalence in paper and electronic diaries. Psychological Methods, 11(1), 87–105. Hakanen, J.J., Bakker, A.B., & Schaufeli, W.B. (2006). Burnout and work engagement among school teachers. Journal of School Psychology, 43, 495–513. Hobfoll, S.E. (1989). Conservation of resources: A new attempt at conceptualizing stress. American Psychologist, 44, 513–524. Hofmann, D.A., Griffin, M.A., & Gavin, M.B. (2000). The application of hierarchical linear modeling to organizational research. In K. Klein & S. Kozlowski (Eds.), Multilevel theory, research, and methods in organizations: Foundations, extensions, and new directions (pp. 467–511). San Francisco, CA: Jossey-Bass. Houtveen, J.H., & de Geus, E.J.C. (2009). Noninvasive psychophysiological ambulatory recordings: Study design and data analysis strategies. European Psychologist, 14, 132–141. H€ ulsheger, U.R., Alberts, H.J.E.M., Feinholdt, A., & Lang, J.W.B. (2013). Benefits of mindfulness at work: The role of mindfulness in emotion regulation, emotional exhaustion, and job satisfaction. Journal of Applied Psychology, 98(2), 310–325. C 2016 International Association of Applied Psychology. V

252

ILIES ET AL.

H€ ulsheger, U.R., Feinholdt, A., & N€ ubold, A. (2015). A low-dose mindfulness intervention and recovery from work: Effects on psychological detachment, sleep quality, and sleep duration. Journal of Occupational and Organizational Psychology, 88, 464–489. H€ ulsheger, U.R., Lang, J.W.B., Depenbrock, F., Fehrmann, C., Zijlstra, F.R.H., & Alberts, H.J.E.M. (2014). The power of presence: The role of mindfulness at work for daily levels and change trajectories of psychological detachment and sleep quality. Journal of Applied Psychology, 99(6), 1113–1128. Igic, I., Ryser, S., & Elfering, A. (2013). Does daily work stress make you shorter? An ambulatory field study of daily work stressors, job control, and spinal shrinkage. Journal of Occupational Health Psychology, 18(4), 469–480. Ilies, R., Dimotakis, N., & de Pater, I.E. (2010a). Psychological and physiological reactions to high workload: Implications for well-being. Personnel Psychology, 63, 407–436. Ilies, R., Dimotakis, N., & Watson, D. (2010b). Mood, blood pressure, and heart rate at work: An experience sampling study. Journal of Occupational Health Psychology, 15, 120–130. Ilies, R., Huth, M.L., Ryan, A.M., & Dimotakis, N. (2015). Explaining the link between workload and work–family conflict: Physical, cognitive and emotional fatigue. Journal of Educational Psychology, 107, 1136–1149. Ilies, R., Johnson, M., Judge, T.A., & Keeney, J. (2011a). A within-individual study of interpersonal conflict as a work stressor: Dispositional and situational moderators. Journal of Organizational Behavior, 32, 44–64. Ilies, R., Keeney, J., & Goh, Z.W. (2015). Capitalising on positive work events by sharing them at home. Applied Psychology: An International Review, 64(3), 578– 598. doi: 10.1111/apps Ilies, R., Keeney, J., & Scott, B.A. (2011b). Work–family interpersonal capitalization: Sharing positive work events at home. Organizational Behavior and Human Decision Processes, 114, 115–126. Ilies, R., Schwind, K.M., & Heller, D. (2007a). Employee well-being: A multi-level model linking work and non-work domains. European Journal of Work and Organizational Psychology, 16, 326–341. Ilies, R., Schwind, K.M., Wagner, D.T., Johnson, M., DeRue, D.S., & Ilgen, D.R. (2007b). When can employees have a family life? The effects of daily workload and affect on work–family conflict and social activities at home. Journal of Applied Psychology, 92, 1368–1379. Ilies, R., Wilson, K.S., & Wagner, D.T. (2009). The spillover of job satisfaction onto employees! family lives: The facilitating role of work–family integration. Academy of Management Journal, 52, 87–102. Ishikawa, J., Ishikawa, Y., Edmondson, D., Pickering, T.G., & Schwartz, J.E. (2011). Age and the difference between awake ambulatory blood pressure and office blood pressure: A meta-analysis. Blood Pressure Monitoring, 16, 159–167. Jacobs, N., Myin-Germeys, I., Derom, C., Delespaul, P., van Os, J., & Nicolson, N.A. (2007). A momentary assessment study of the relationship between affective and adrenocortical stress responses in daily life. Biological Psychology, 74, 60–66. C 2016 International Association of Applied Psychology. V


253

Jacobs, N., Nicolson, N.A., Derom, C., Delespaul, P., van Os, J., & Myin-Germeys, I. (2005). Electronic monitoring of salivary cortisol sampling compliance in daily life. Life Sciences, 76, 2431–2443. Johnson, J.V., & Hall, E.M. (1988). Job strain, work place social support, and cardiovascular disease: A cross-sectional study of a random sample of the Swedish working population. American Journal of Public Health, 78, 1336–1342. Kaplan, R.M., & Stone, A.A. (2013). Bringing the laboratory and clinic to the community: Mobile technologies for health promotion and disease prevention. Annual Review of Psychology, 64, 471–498. Karasek, R.A. (1979). Job demands, job decision latitude, and mental strain: Implications for job redesign. Administrative Science Quarterly, 24, 285–308. Kelloway, E.K., Hurrell, J.J., & Day, A. (2008). Workplace interventions for occupational stress. In K. N€aswall, J. Hellgren, & M. Sverke (Eds.), The individual in the changing working life (pp. 419–441). Cambridge: Cambridge University Press. Kenny, D.A., Korchmaros, J.D., & Bolger, N. (2003). Lower level mediation in multilevel models. Psychological Methods, 8, 115–128. Killingsworth, M.A., & Gilbert, D.T. (2010). A wandering mind is an unhappy mind. Science, 330, 932. Kuntsche, E., & Labhart, F. (2013). Using personal cell phones for ecological momentary assessment: An overview of current developments. European Psychologist, 18, 3–11. Larson, R., & Csikszentmihalyi, M. (1983). The experience sampling method. In H.T. Reis (Ed.), New directions for methodology of social and behavioral sciences (Vol. 15, pp. 41–56). San Francisco, CA: Jossey-Bass. Lazarus, R.S. (1966). Psychological stress and the coping process. New York: McGraw-Hill. Lazarus, R.S. (1993). From psychological stress to the emotions: A history of changing outlooks. Annual Review of Psychology, 44, 1–21. McEwen, B.S. (2005). Glucocorticoids, depression, and mood disorders: Structural remodeling in the brain. Metabolism, 54, 20–23. McEwen, B.S. (2007). Physiology and neurobiology of stress and adaptation: Central role of the brain. Physiological Review, 87, 873–904. McEwen, B.S., & Stellar, E. (1993). Stress and the individual: Mechanisms leading to disease. Archives of Internal Medicine, 153, 2093–2101. MacKinnon, D.P., Lockwood, C.M., Hoffman, J.M., West, S.G., & Sheets, V. (2002). A comparison of methods to test mediation and other intervening variable effects. Psychological Methods, 7, 83–104. Magarinos, A.M., McEwen, B.S., Flugge, G., & Fuchs, E. (1996). Chronic psychosocial stress causes apical dendritic atrophy of hippocampal CA3 pyramidal neurons in subordinate tree shrews. Journal of Neuroscience, 16, 3534–3540. Manning, M.R., Jackson, C.N., & Fusilier, M.R. (1996). Occupational stress, social support, and the costs of health care. Academy of Management Journal, 39, 738–750. Meier, L.L., Gross, S., Spector, P.E., & Semmer, N.K. (2013). Relationship and task conflict at work: Interactive short-term effects on angry mood and somatic complaints. Journal of Occupational Health Psychology, 18(2), 144–156. Miller, G. (2012). The smartphone psychology manifesto. Perspectives on Psychological Science, 7, 221–237. C 2016 International Association of Applied Psychology. V

254

ILIES ET AL.

Moen, P., Kelly, E.L., & Lam, J. (2013). Healthy work revisited: Do changes in time strain predict well-being? Journal of Occupational Health Psychology, 19, 157–172. Muth"en, L.K., & Muth"en, B.O. (2010). Mplus user!s guide, 6th edn. Los Angeles, CA: Muth"en & Muth"en. Ng, T.W.H., & Feldman, D.C. (2013). The effects of organisational embeddedness on insomnia. Applied Psychology: An International Review, 62, 330–357. Nicholson, T., & Griffin, B. (2015). Here today but not gone tomorrow: Incivility affects after-work and next-day recovery. Journal of Occupational Health Psychology, 20(2), 218–225. Ohly, S., Sonnentag, S., Niessen, C., & Zapf, D. (2010). Diary studies in organizational research: An introduction and some practical recommendations. Journal of Personnel Psychology, 9, 79–93. Piazza, J.R., Almeida, D.M., Dmitrieva, N.O., & Klein, L.C. (2010). Frontiers in the use of biomarkers of health in research on stress and aging. Journal of Gerontology: Psychological Sciences, 65B(5), 513–525. Raudenbush, S.W., & Bryk, A.S. (2002). Hierarchical linear models: Applications and data analysis methods. Newbury Park, CA: Sage Publications. Rivikin, W., Diestel, S., & Schmidt, K.-H. (2015). Affective commitment as a moderator of the adverse relationships between day-specific self-control demands and psychological well-being. Journal of Vocational Behavior, 88, 185–194. Roelen, C., van Rhenen, W., Schaufeli, W., van der Klink, J., Magerøy, N., Moen, B., Bjorvatn, B., & Pallesen, S. (2014). Mental and physical health-related functioning mediates between psychological job demands and sickness absence among nurses. Journal of Advanced Nursing, 70, 1780–1792. Roy, M.P., Steptoe, A., & Kirschbaum, C. (1998). Life events and social support as moderators of individual differences in cardiovascular and cortisol reactivity. Journal of Personality and Social Psychology, 75(5), 1273–1281. Sanz-Vergel, A.I., Rodr"ıguez-Mu~ noz, A., & Nielsen, K. (2015). The thin line between work and home: The spillover and crossover of daily conflicts. Journal of Occupational and Organizational Psychology, 88, 1–18. Schmitt, A., Zacher, H., & Frese, M. (2012). The buffering effect of selection, optimization, and compensation strategy use on the relationship between problem solving demands and occupational well-being: A daily diary study. Journal of Occupational Health Psychology, 17(2), 139–149. Seeman, T., & McEwen, B.S. (1996). Impact of social environmental characteristics on neuroendocrine regulation. Psychosomatic Medicine, 58(5), 459–471. Shirom, A., Toker, S., Berliner, S., & Shapira, I. (2008). The Job Demand-ControlSupport Model and stress-related low-grade inflammatory responses among healthy employees: A longitudinal study. Work & Stress, 22(2), 138–152. Shockley, K.M., & Allen, T.D. (2013). Episodic work–family conflict, cardiovascular indicators, and social support: An experience sampling approach. Journal of Occupational Health Psychology, 18(3), 262–275. Siegrist, J. (2002). Effort–reward imbalance at work and health. In P.L. Perrew"e & D.C. Ganster (Eds.), Research in occupational stress and well-being. Volume 2. Historical and current perspectives on stress and health (pp. 261–291). Amsterdam: Elsevier/JAI. C 2016 International Association of Applied Psychology. V


255

Snijders, T.A.B., & Bosker, R.J. (1993). Standard errors and sample sizes for twolevel research. Journal of Educational Statistics, 18, 237–259. Snir, R., & Zohar, D. (2008). Workaholism as discretionary time investment at work: An experience-sampling study. Applied Psychology: An International Review, 57, 109–127. Sonnentag, S. (2015). Dynamics of well-being. Annual Review of Organizational Psychology and Organizational Behavior, 2(1), 261–293. Sonnentag, S., & Binnewies, C. (2013). Daily affect spillover from work to home: Detachment from work and sleep as moderators. Journal of Vocational Behavior, 83, 198–208. Sonnentag, S., Binnewies, C., & Mojza, E. J. (2008). “Did you have a nice evening?” A day-level study on recovery experiences, sleep and affect. Journal of Applied Psychology, 93, 674–684. Stone, A.A., & Neale, J.M. (1984). The effects of severe daily events on mood. Journal of Personality and Social Psychology, 46, 137–144. Tofighi, D., & MacKinnon, D.P. (2011). RMediation: An R package for mediation analysis confidence intervals. Behavior Research Methods, 43, 692–700. 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 & Stress, 26(2), 112– 129. Trougakos, J.P., Beal, D.J., Cheng, B.H., Hideg, I., & Zweig, D. (2015). Too drained to help: A resource depletion perspective on daily interpersonal citizenship behaviors. Journal of Applied Psychology, 100(1), 227–236. Trougakos, J.P., Beal, D.J., Green, S.G., & Weiss, H.M. (2008). Making the break count: An episodic examination of recovery activities, emotional experiences, and positive affective displays. Academy of Management Journal, 51(1), 131– 146. Trull, T.J. (2015). Ethical issues in researching daily life. Monitor on Psychology, 46(4), 70. Trull, T.J., & Ebner-Priemer, U. (2014). The role of ambulatory assessment in psychological science. Current Directions in Psychological Science, 23, 466–470. van Hooff, M.L.M., & Baas, M. (2013). Recovering by means of meditation: The role of recovery experiences and intrinsic motivation. Applied Psychology: An International Review, 62, 185–210. Volmer, J., Binnewies, C., Sonnentag, S., & Niessen, C. (2012). Do social conflicts with customers at work encroach upon our private lives? A diary study. Journal of Occupational Health Psychology, 17(3), 304–315. von Thiele Schwarz, U. (2011). Inability to withdraw from work as related to poor next-day recovery and fatigue among women. Applied Psychology: An International Review, 60, 377–396. Wagner, D.T., Barnes, C.M., & Scott, B.A. (2014). Driving it home: How workplace emotional labor harms employee home life. Personnel Psychology, 67, 487–516. Wang, M., Liu, S., Liao, H., & Gong, Y. (2013). Can!t get it out of my mind: Employee rumination after customer mistreatment and negative mood in the next morning. Journal of Applied Psychology, 98(6), 989–1004. C 2016 International Association of Applied Psychology. V

256

ILIES ET AL.

Wheeler, L., & Reis, H.T. (1991). Self-recording of everyday life events: Origins, types, and uses. Journal of Personality, 59, 339–354. Xie, J.L., Schaubroeck, J., & Lam, S.S.K. (2008). Theories of job stress and the role of traditional values: A longitudinal study in China. Journal of Applied Psychology, 93(4), 831–848. Zhou, Z.E., Yan, Y., Che, X.X., & Meier, L.L. (2015). Effect of workplace incivility on end-of-work negative affect: Examining individual and organizational moderators in a daily diary study. Journal of Occupational Health Psychology, 20(1), 117–130.

APPENDIX: EMA RESOURCES 1. Commercial Software for EMA Studies3 MetricWire (metricwire.com) ESm.iRxReminder.com EmotionSense (emotionsense.org) movisensXS (xs.movisens.com) myexperience (myexperience.sourceforge.net) ESM Capture (esmcapture.com) Life Data (lifedatacorp.com) Mobile EMA (mobileema.com) AndWellness: An open mobile system for activity and experience sampling. Hicks, J., Ramanathan, N., Kim, D., Monibi, M., Selsky, J., Hansen, M., & Estrin, D. In Wireless Health 2010 (pp. 34–43). ACM. 2. Recommended Texts on Conducting EMA Studies Alliger, G.M., & Williams, K.J. (1993). Using signal-contingent experience sampling methodology to study work in the field: A discussion and illustration examining task perceptions and mood. Personnel Psychology, 46, 525–549. Beal, D.J. (2015). ESM 2.0: State of the art and future potential of experience sampling methods in organizational research. Annual Review of Organizational Psychology and Organizational Behavior, 2, 383–407. Beal, D.J., & Weiss, H.M. (2003). Methods of ecological momentary assessment in organizational research. Organizational Research Methods, 6, 440–464. 3 This Appendix serves as a listing of known service providers for EMA research. The authors do not claim to have used these providers in their own research, do not recommend specific commercial service providers over others, and do not have any affiliations to these providers. C 2016 International Association of Applied Psychology. V


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Bolger, N., Davis, A., & Rafaeli, E. (2003). Diary methods: Capturing life as it is lived. Annual Review of Psychology, 54, 579–616. Fisher, C.D., & To, M.L. (2012). Using experience sampling methodology in organizational behavior. Journal of Organizational Behavior, 33, 865–877. Froehlich, J., Chen, M.Y., Consolvo, S., Harrison, B., & Landay, J. (2007). MyExperience: A system for in situ tracing and capturing of user feedback on mobile phones. In Proceedings of Mobi-Sys, 2007 (pp. 57–70). New York: ACM. Houtveen, J.H., & de Geus, E.J.C. (2009). Noninvasive psychophysiological ambulatory recordings: Study design and data analysis strategies. European Psychologist, 14, 132–141. Kaplan, R.M., & Stone, A.A. (2013). Bringing the laboratory and clinic to the community: Mobile technologies for health promotion and disease prevention. Annual Review of Psychology, 64, 471–498. Kuntsche, E., & Labhart, F. (2013). Using personal cell phones for ecological momentary assessment: An overview of current developments. European Psychologist, 18, 3–11. Larson, R., & Csikszentmihalyi, M. (1983). The experience sampling method. In H.T. Reis (Ed.), New directions for methodology of social and behavioral sciences (Vol. 15, pp. 41–56). San Francisco, CA: Jossey-Bass. Ohly, S., Sonnentag, S., Niessen, C., & Zapf, D. (2010). Diary studies in organizational research: An introduction and some practical recommendations. Journal of Personnel Psychology, 9, 79–93. Sonnentag, S. (2015). Dynamics of well-being. Annual Review of Organizational Psychology and Organizational Behavior, 2(1), 261–293. 3. Recommended Texts on Analyzing Multilevel EMA Data Aiken, L.S., & West, S.G. (1991). Multiple regression: Testing and interpreting interactions. Newbury Park, CA and London: Sage Bauer, D.J., Preacher, K.J., & Gil, K.M. (2006). Conceptualizing and testing random indirect effects and moderated mediation in multilevel models: New procedures and recommendations. Psychological Methods, 11, 142– 163. Bryk, A.S., & Raudenbush, S.W. (1992). Hierarchical linear models: Applications and data analysis methods. Thousand Oaks, CA: Sage. Dimotakis, N., Ilies, R., & Judge, T.A. (2013). Experience sampling methodology. In J.M. Cortina & R.S. Landis (Eds.), Modern research methods for the study of behavior in organizations (pp. 319–348). New York: Routledge. Enders, C.K., & Tofighi, D. (2007). Centering predictor variables in crosssectional multilevel models: A new look at an old issue. Psychological Methods, 12, 121–138. Hofmann, D.A., Griffin, M.A., & Gavin, M.B. (2000). The application of hierarchical linear modeling to organizational research. In K. Klein & S. C 2016 International Association of Applied Psychology. V

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Kozlowski (Eds.), Multilevel theory, research, and methods in organizations: Foundations, extensions, and new directions (pp. 467–511). San Francisco, CA: Jossey-Bass. Kenny, D.A., Korchmaros, J.D., & Bolger, N. (2003). Lower level mediation in multilevel models. Psychological Methods, 8, 115–128. MacKinnon, D.P., Lockwood, C.M., Hoffman, J.M., West, S.G., & Sheets, V. (2002). A comparison of methods to test mediation and other intervening variable effects. Psychological Methods, 7, 83–104. Muth"en, L.K., & Muth"en, B.O. (2010). Mplus user!s guide, 6th edn. Los Angeles, CA: Muth"en & Muth"en. Raudenbush, S.W., & Bryk, A.S. (2002). Hierarchical linear models: Applications and data analysis methods. Newbury Park, CA: Sage Publications. Tofighi, D., & MacKinnon, D.P. (2011). RMediation: An R package for mediation analysis confidence intervals. Behavior Research Methods, 43, 692–700.


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