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Daily Micro-Breaks and Job Performance: General Work Engagement as a Cross-Level Moderator

Sooyeol Kim School of Labor and Employment Relations University of Illinois at Urbana-Champaign Champaign, IL 61820 Telephone: 703-999-9969 e-mail: [email protected] YoungAh Park, Ph.D. School of Labor and Employment Relations University of Illinois at Urbana-Champaign 504 East Armory Avenue Champaign, IL, 61820 Tel: (217) 333-1482 [email protected] Lucille Headrick School of Labor and Employment Relations University of Illinois at Urbana-Champaign 504 East Armory Avenue Champaign, IL, 61820 Tel: (217) 333-1482 [email protected] Author Note:

We acknowledge the presentation of an earlier version of this study at the 2015 Academy of Management annual conference in Atlanta, Georgia and the publication of this study at the 2015 Academy of Management Proceeding.

©American Psychological Association. This paper is not the copy of record and may not exactly replicate the authoritative document published in the APA journal. The final article is available at http://dx.doi.org/10.1037/apl0000308 Citation: Kim, S., Park, Y., & Headrick, L. (2018, March 29). Daily Micro-Breaks and Job Performance: General Work Engagement as a Cross-Level Moderator. Journal of Applied Psychology. Advance online publication. http://dx.doi.org/10.1037/apl0000308

Running Head: MICRO-BREAKS AND JOB PERFORMANCE Abstract Despite the growing research on work recovery and its well-being outcomes, surprisingly little attention has been paid to at-work recovery and its job performance outcomes. The current study extends the work recovery literature by examining day-level relationships between prototypical micro-breaks and job performance as mediated by state positive affect. Furthermore, general work engagement is tested as a cross-level moderator weakening the indirect effects of microbreaks on job performance via positive affect. Using multi-source experience sampling method, the authors collected two daily surveys from 71 call center employees and obtained objective records of daily sales performance for two consecutive weeks (n = 632). Multilevel path analysis results showed that relaxation, socialization, and cognitive micro-breaks were related to increased positive affect at work which, in turn, predicted greater sales performance. However, breaks for nutrition-intake (having snacks and drinks) did not show significant effects. Importantly, micro-breaks had significant indirect effects on job performance via positive affect only for workers who had lower general work engagement, whereas the indirect effects did not exist for workers who had higher general work engagement. Furthermore, Bayesian multilevel analyses confirmed the results. Theoretical and practical implications, limitations, and future research directions are discussed. Keywords: micro-breaks, job performance, positive affect, recovery, resources, work engagement

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Daily Micro-Breaks and Job Performance: General Work Engagement as a Cross-Level Moderator “All work and no play makes Jack a dull boy” −A proverb− The popular media warns that working individuals should take breaks or risk becoming “worn-out” or “dulled.” The Cable News Network has recently featured on several business cases that integrate respite activities into the workplace (e.g., foosball, yoga, snack bars, naps) as some organizations believe that fun and relaxing activities between tasks increase employee morale and promote productivity (Rodriguez, 2013). Conversely, some organizations and managers may consider such activities to be counterproductive and disdain breaks as ways to “goof off” on company time. In light of the contrasting views on respite activities within the workplace, can organizational psychology provide any insight into whether respite activities at work are beneficial for job performance outcomes? Researchers have studied recovery processes that allow workers to unwind and repair negative consequences of continual work (see, Demerouti, Bakker, Geurts, & Taris, 2009, for a review). The recovery literature recommends micro-breaks as energy management strategies for sustaining employee resources throughout a workday (Fritz, Lam, & Spreitzer, 2011; Hunter & Wu, 2016; Trougakos & Hideg, 2009; Zacher, Brailsford, & Parker, 2014). A recent study also showed that on days when office workers took frequent afternoon micro-breaks, they reported substantially reduced effects of work demands on negative affect at the end of the workday (Kim, Park, & Niu, 2017). Despite increasing evidence that micro-breaks have salutary effects on well-being and strain, the literature offers little evidence and insights as to whether and how micro-breaks promote employee job performance (Trougakos & Hideg, 2009). Moreover,

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scholarly knowledge is lacking regarding whether micro-breaks benefit job performance for all employees or certain employees only. In this study, we aim to better explain the relationship between at-work recovery and its job performance outcome. Examining the link between micro-breaks and performance will advance recovery theory and help organizations recognize potential benefits of respite activities at work. Figure 1 illustrates our conceptual model to examine day-level relationships between prototypical micro-breaks—relaxation, nutrition-intake, socialization, and cognitive—and job performance outcomes, as well as determine a mechanism underlying the relationships. Another important aim is to test general work engagement as an individual difference factor that crossmoderates the intermediary mechanism in predicting job performance. Our theoretical model and empirical test contribute to the recovery literature in three key ways. First, research has shown that recovery outside work relates to the next day’s positive work-related outcomes, such as proactive behaviors and task performance (e.g., Binnewies, Sonnentag, Mojza, 2009; Sonnentag, 2003; ten Brummelhuis & Bakker, 2012). However, we have scarce evidence regarding whether at-work recovery through micro-breaks generates similar positive work outcomes. This little attention is surprising in that employees spend most of their time at work. Also, the ultimate purpose of recovery is to promote job performance as well as well-being (Sonnentag & Fritz, 2007). Therefore, testing job performance as an outcome of micro-breaks is highly warranted. In doing so, we obtained objective records of daily sales performance to remove self-report biases and minimize common method variance concerns (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). Also, importantly, because most recovery research assessed its outcomes with self-report measures, it remains difficult to determine whether recovery positively influences employees’ substantive work outcomes above and

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beyond their perceptual outcomes (Sonnentag, Venz, & Casper, 2017, for a review). Accordingly, we studied call center employees and collected their daily sales performance records to test whether micro-breaks increase their important resource (i.e., state positive affect) which then translates into tangible, core performance outcomes (i.e., daily sales in call center business). We further confirmed our results with Bayesian multilevel analyses to provide rigorous evidence. Second, whereas previous research has found beneficial effects of work breaks on lower strain symptoms and higher positive affective displays (e.g., Hunter & Wu, 2016; Trougakos, Beal, Green, & Weiss, 2008), no study has investigated within-person mechanisms by which micro-breaks lead to job performance outcome. It is not clear which underlying psychological experiences explain the links between break activities and performance (Trougakos & Hideg, 2009). Specifically, combining resource-based theories with empirical research on at-work recovery, we conceptualize micro-breaks as resource-replenishing events that aid job performance because they increase employee resource levels, such as positive affect, especially for employees who primarily interact with customers on a daily basis. Furthermore, given that affective states can shape job attitudes and drive important work behaviors (Beal, Weiss, Barros, & MacDermid, 2005; Weiss & Cropanzano, 1996), it is important to test increased positive affect as a linking mechanism. Thus, our theoretical reasoning and empirical test of the psychological mechanism improve understandings of exact channels through which microbreaks lead to desirable performance results. Third, empirical research on moderators of work break effects is rare (for an exception, see Trougakos, Hideg, Cheng, & Beal, 2014) and virtually nonexistent for performance outcomes. Considering the lack of knowledge about whether or not processes of micro-breaks

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are applicable to all workers, Trougakos and Hideg (2009) and Sonnentag et al. (2017) have called for research on moderators that potentially influence the relationship between resources gained from breaks and job performance. As such, we propose that employees who have a stable characteristic of high work engagement—a general tendency to experience work in active and energetic ways across situations (Schaufeli, Bakker, & Salanova, 2006)—have less need for frequent micro-breaks to increase their transient positive affect, possibly because they already have a significant and stable resource reservoir. As moderation tests are necessary for enriching theories, our study contributes to the recovery literature and its theory development. Theoretical Foundations and Hypotheses Development The Concept of Micro-Breaks and Theories Explaining Their Recovery Effects Micro-break activities are short, informal respite activities taken voluntarily between tasks (Kim et al., 2017; Trougakos & Hideg, 2009). This concept of micro-breaks is distinguished from other institutionalized breaks, such as lunch or formally scheduled breaks. As individuals take micro-breaks as needed amid task activities, they are less structured compared to formally prescheduled breaks. Although there is no established standard regarding the length of micro-breaks, they generally last anywhere from a few seconds to several minutes. Most important, employees pursue micro-break activities at their own discretion. If breaks are taken otherwise (e.g., unwillingly due to others’ requests or at inopportune moments), they can be experienced as work interruptions causing distractions, backlogs, and frustrations (Jett & George, 2003). The voluntary nature of micro-breaks also aligns with the literature on off-job recovery indicating the importance of autonomy during respites (Sonnentag & Fritz, 2007). For example, positive relationships between evening leisure activities and next-morning recovery state were more pronounced among employees with greater intrinsic motivation for the activities (ten

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Brummelhuis & Trougakos, 2014). Additionally, Trougakos et al. (2014) found that employee autonomy over how to use lunch breaks determined whether the breaks had beneficial or harmful effects on strain. Thus, self-initiated, voluntary micro-breaks indicate that employees choose most optimal timing and preferred activities for their momentary respites and accommodate their idiosyncratic recovery needs and daily rhythms (Kühnel, Zacher, de Bloom, & Bledow, 2017). Next, we briefly introduce three theories that commonly explain recovery processes in the literature and explain how our conceptual model of micro-breaks sits within the theoretical frameworks: Conservation of resources theory (COR; Hobfoll, 1989), ego-depletion theory (Baumeister, Bratslavsky, Muraven, & Tice, 1998; Baumeister, Muraven, & Tice, 2000), and effort-recovery model (Meijman & Mulder, 1998). First, COR theory assumes that individuals have limited resources (e.g., energy) necessary to address various demands in their life (Hobfoll, 1989). Resource depletion causes strain and poor functioning, so individuals try to conserve resources and avoid resource losses. In the workplace, employees use their personal resources to deal with work demands and job stressors and, as such, their resources become depleted over the course of a workday. This resource perspective suggests that employees cannot continue work efforts indefinitely throughout the workday but they need to replenish drained resources. In that sense, employees can take micro-breaks and engage in their choice of respite activities to restore depleted resources. Similarly, ego-depletion theory is based on the resource scarcity perspective: a central, psychological resource (ego) determines individuals’ self-regulation capacity, but it is finite and drains easily if used continuously (Baumeister et al., 1998, 2000). Self-regulatory resources are particularly important for service employees as emotion regulation is essential for their quality service work (Grandey, 2000). For example, call center employees often must suppress negative

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emotions and display positive emotions if they are to meet their customer service needs and sales goals (Grandey, Dickter, & Sin, 2004). Thus, call center work is commonly considered a high self-control context in which self-regulatory resource depletion can make it difficult to control response tendencies and amplify positive emotions while concentrating on tasks at hand (e.g., following call scripts, answering customer questions, searching for best customer options); as a result, job performance may deteriorate. Nevertheless, ego-depletion theory suggests that selfregulatory resources can be renewed after sufficient rest (Muraven & Baumeister, 2000). In addition, studies based on this theory points to positive affect as an important central resource that counteracts ego depletion and facilitates behavioral performance. For example, Tice, Baumeister, Shmueli, and Muraven (2007) found that self-regulatory task performance improved when positive affect was induced by experimental conditions, such as watching a comedy video which is similar to micro-break activity. A field study applying the theory also found that respite activities enhance positive affect felt and displayed at work that is needed for performing service tasks (i.e., cheerleading instruction; Trougakos et al., 2008). Effort-recovery model (Meijman & Mulder, 1998) further highlights that taking timely respites is important to recover diminished self-regulatory resources and even generate resource surpluses. The model posits that individuals should rest momentarily to allow their functional systems (e.g., emotional, cognitive) to recuperate from accumulated load reactions of continuous working, such as fatigue; however, when respite opportunities are delayed, the load reactions persist to the extent that it becomes more difficult to return to one’s baseline functioning. In this regard, self-initiated micro-breaks can provide the necessary disengagement from work just in time of needs. Accordingly, work breaks were found to be related to increased well-being and

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decreased strain (Hunter & Wu, 2016; Kim et al., 2017; Kühnel et al., 2017; Trougakos et al., 2008; Zacher et al., 2014). Building on these theoretical frameworks and empirical findings, we contend that microbreak activities will be positively linked to job performance through increased positive affect as a resource. We test daily fluctuating positive affect—feeling active, confident, interested, concentrating, enthusiastic, and happy—as a linking mechanism in that positive affect is an integral part of self-regulatory resources and task accomplishment for emotional laborers (Beal et al., 2005). In particular, we investigate four prototypical micro-breaks (i.e., relaxation, nutritionintake, socialization, and cognitive activities), considering that recovery effects may hinge on the exact nature of the activities employees engage in during breaks. In the next section, we first hypothesize how each category of micro-breaks increases positive affect. Then, drawing on the episodic model of affective influences on job performance by Beal et al. (2005), we theorize the intermediary role of positive affect in connecting micro-breaks and job performance. Last, we turn to general work engagement as a moderator in the indirect effect of micro-breaks on job performance. Relaxation Activities Relaxation is to momentarily relieve psychological and physical tension from continuous work and further prevent its short-term accumulations throughout a workday. Common relaxation activities include taking a short nap or walk, meditating, daydreaming, and stretching, all of which are characterized by low effort or effortless activities (e.g., Sianoja, Syrek, de Bloom, Korpela, & Kinnunen, 2017; Trougakos & Hideg, 2009). Effort-recovery model explains that relaxation activities help individuals’ physical and psychological systems return to pre-stress levels (Meijman & Mulder, 1998). As such, relaxation is considered one of the core recovery

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experiences for retreating physically and psychologically to restore depleted resources (Sonnentag & Fritz, 2007). Likewise, previous studies found that short relaxation at work (e.g., stretching, napping) is associated with lower physical and mental fatigue and higher positive emotions (Henning, Jacques, Kissel, Sullivan, & Alteras-Webb, 1997; Trougakos et al., 2008). Also, on days when employees had more relaxation activities, they reported much lower impacts of work demands on affective distress (Kim et al., 2017) and higher vitality, such as feeling energetic and cheerful, at work (Zacher et al., 2014). Furthermore, an experimental study found that individuals who appraised their micro-breaks as relaxing reported more feelings of vigor (Bennett, 2015). Moreover, short relaxation activities (i.e., park walks, soundscape of nature, relaxation exercises) not only reduced employees’ daily fatigue but also led to more positive states, such as feelings of vigor, enjoyment, and concentration (Sianoja et al., 2017; Steidle, Gonzalez-Morales, Hoppe, Michel, & O’shea, 2017). In summary, these combined findings suggest that relaxation micro-breaks provide optimal conditions for releasing any negative load reactions of continuous working and resource recovery, thereby increasing positive and pleasant states for the next task episodes. Hypothesis 1a: Daily relaxation micro-breaks will be positively related to increased positive affect at work. Nutrition-Intake Activities Nutrition-intake activities refer to snacking and drinking at work. Human physiology runs on nutrients like a fuel (e.g., water, minerals, protein, glucose), so getting adequate supplies of nutrients is essential for human energy and daily functioning (Renner, Sproesser, Strohbach, & Schupp, 2012). For example, glucose (blood sugar) is one of the major sources of nutritional energy and, as such, individuals who have higher glucose levels tend to show less negative

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emotions and more helping behaviors because of high self-regulatory resources (Gailliot et al., 2007). In addition, some nonessential nutrients from foods and beverages can influence emotional and mental states. For example, caffeine is a mild stimulant boosting feelings of alertness, activeness, and energy (Hausser, Schlemmer, Kaiser, Kalis, & Mojzisch, 2014). Recent studies have discovered beneficial effects of caffeine intake, given the common coffee and tea breaks at work. Specifically, caffeine consumption attenuated sleep deprivation effects on feelings of energy depletion (Welsh, Ellis, Christian, & Mai, 2014) and minimized adverse effects of daily work demands on end-of-work negative affect (Kim et al., 2017). Although most employees take regular lunches, they may still feel hungry or thirsty as they continuously use nutritional energy to exert self-control efforts for job tasks throughout the work day. Indeed, employees tend to eat more snacks on days when they want to reduce feelings of frustration and fatigue (i.e., high affect-regulation motive) and thereby boost their energy at work (Sonnentag, Pundt, & Venz, 2016). In short, brief breaks for snacks and beverages between tasks may help employees avoid discomforting physiological experiences and further increase mental alertness and energy. Thus, we expect that nutrition-intake breaks will increase positive affect at work (e.g., feeling active and concentrating, etc.). Hypothesis 1b: Daily nutrition-intake micro-breaks will be positively related to increased positive affect at work. Socialization Activities Off-job recovery research suggests that one way to assist recovery is through social activities in which employees interact with others, garner social support, and psychologically detach from work-related thoughts (Sonnentag, 2001; ten Brummelhuis & Bakker, 2012). In this study, we contend that nonwork-related social contacts and interactions at work can boost

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employees’ positive affect. The concept of relational energy (Owens, Baker, Sumpter, & Cameron, 2016) explains that interpersonal interactions at work give employees a heightened level of psychological resourcefulness that can enhance their capacity to do work. That is, employees can be energized by and even proactively seek social interactions to increase their energy at work. Moreover, employees who draw more relational energy from social interactions tend to be highly engaged in their job and more productive (Owens et al., 2016). Trougakos et al. (2014) also found that daily lunch break socialization reduced end-of-work fatigue when employees had high autonomy for their lunch break (i.e., deciding what they want to do during the break). Similarly, Kim et al. (2017) demonstrated that voluntary, nonwork social activities weakened the effects of daily work demands on subsequent negative affect at work. Moreover, Zacher et al. (2014) showed that daily micro-breaks including social contacts and interactions (i.e., talking about common interests like sports or hobbies, checking in with a friend and family member) increased feelings of vitality at work, whereas work-related break activities (e.g., seeking feedback) failed to do so. Taken together, we argue that social interactions and contacts should be free from work-related aspects to ensure optimal recovery effects of micro-breaks. Thus, we expect that voluntary, nonwork-related social activities during micro-breaks will increase positive affect. Hypothesis 1c: Daily socialization micro-breaks will be positively related to increased positive affect at work. Cognitive Activities Cognitive micro-break activities refer to any activities that facilitate a mental break from work demands although they may still require some cognitive attention and effort. Examples include casual reading and browsing the Internet for entertainment or personal learning. Most

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important is that cognitive distraction momentarily shifts employees’ attention from high selfcontrol demands toward chosen activities for entertainment or casual learning. The off-job recovery literature has suggested that psychological detachment from work is critical for replenishing energy and affective resources (see Sonnentag & Fritz, 2015, for a review). In addition, doing preferred activities during breaks increases personal resources at work such as energy, motivation, and concentration (Hunter & Wu, 2016). However, contrary to the hypothesized recovery effects of cognitive activities at work, Kim et al. (2017) found that voluntarily chosen cognitive breaks aggravated employee distress. As a post-hoc explanation, they attributed the unexpected finding to their failure in distinguishing pleasurable activities from personal chores and duties (e.g., online banking). Accordingly, we expect that pleasurable or personally meaningful cognitive breaks will provide temporary escape from work demands and momentarily improve affective state, as reflected in our cognitive activity items. Thus, we hypothesize the following. Hypothesis 1d: Daily cognitive micro-breaks will be positively related to increased positive affect at work. Indirect Effects of Micro-Break Activities on Job Performance via Positive Affective Resource Broaden-and-build theory (Fredrickson, 1998, 2001) posits that individuals’ positive emotions from positive episodes can widen momentary thought−action repertoires, thereby increasing performance in various contexts. That is, momentary positive experiences generate positive affect which, then, leads individuals to seek underexplored paths of thoughts and actions rather than typical, automatic behavioral options. This proposition is congruent with the episodic model of affective influences on performance (Beal et al., 2005) as the model theorizes that

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affective state influences cognition and behavioral styles that are conducive to effective task achievement. In other words, the model posits that affective states can not only influence one’s attentional regulation for task conducts but also directly impact on task approaches and momentary response tendencies in doing job tasks (Beal et al., 2005). This combined theoretical perspective suggests that positive affect accruing from micro breaks will influence job performance. More specifically, in the context of call center jobs, we argue that positive affective state from micro-breaks may lead to better sales performance for the following reasons. First, call center employees interact daily with multiple customers. Their task episodes tend to begin and end with each call they make. In emotionally draining jobs, especially when employees faced with difficulties and frustrations, they tend to perceive more difficulty in displaying positive emotions because of greater emotional dissonance between their true feelings and outward emotional expressions (Beal, Trougakos, Weiss, & Green, 2006). However, shortly after taking micro-breaks, the resultant positive affect may help employees reappraise and reframe task situations more confidently and positively, thereby increasing work motivation. Indeed, lab experiments showed that positive affect improved task performance through enhanced beliefs that task effort would result in good performance (Erez & Isen, 2002). Similarly, a field study using insurance sales agents found that positive affect predicted task performance by boosting motivational components such as task self-efficacy and persistency (Tsai, Chen, & Liu, 2007). Thus, we expect that positive affect gained from between-task micro-breaks (e.g., feeling confident, enthusiastic, active, etc.) will shape more positive work attitudes and increase task motivation momentarily, facilitating job performance.

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Second, the performance model of affective influences (Beal et al., 2005) views that performance during an episode is a joint function of resource level and resource allocation. That is, job performance is a result of a dynamic process in which individuals control and allocate various resources to task activities. The model further suggests that state positive affect may allow employees to better address emotional labor while simultaneously staying focused on cognitive processing. For example, even during difficult calls, employees may use their state positive affect to please dissatisfied customers while cognitively searching for the best services and products. That is, employees in positive affective state may easily alter emotion-regulation strategies without being distracted from finding the best customer options. In other words, this example indicates that positive affect helps employees allocate attentional resources to information-processing as well as address emotional tasks during the sales calls. Accordingly, when call center workers were in a pleasant and positive affective state, they solved customer problems more efficiently during calls (Miner & Glomb, 2010) and showed more verbal fluency in unscripted interactions with customers (“no dead air time”), an important call-quality metric (Rothbard & Wilk, 2011). Also, bank tellers’ positive emotion displays were associated with customers’ positive perception of received services (Pugh, 2001). Likewise, positive emotion in sales clerks was found to relate to their customers’ actual product purchase (Tsai, 2001). Taken all together, we argue that positive affect derived from micro-breaks will function as a self-regulatory resource allowing employees to maintain their work motivation, better handle difficult calls, and display positive emotions in order to meet their service and sales goals. Hypothesis 2: There will be indirect relationships between micro-breaks—(a) relaxation, (b) nutrition-intake, (c) socialization, and (d) cognitive activities—and job performance through increased positive affect, such that on days when employees take more micro-

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breaks, they are more likely to gain positive affect and thereby achieve higher performance. General Work-Engagement as a Cross-Level Moderator Work engagement is defined as one’s inclination to view work in a positive, fulfilling way that is characterized by vigor, dedication, and absorption (Schaufeli et al., 2006). The concept essentially captures the extent that individuals experience their work as stimulating and energetic (vigor), meaningful and significant (dedication), and interesting and captivating (absorption). Although work engagement levels can fluctuate daily, some individuals are more engaged than others across situations, a tendency known as general work engagement (Breevaart, Bakker, Demerouti, & Hetland, 2012)—hereinafter work engagement in short. This construct is about dedicating the “full self” and being highly motivated for work, and thus differs from other traditional job attitudes such as job satisfaction and organizational commitment (see, Christian, Garza, & Slaughter, 2011, for a review). A corollary in COR theory (Hobfoll, 2001) is that: “those with greater resources are less vulnerable to resource loss and more capable of orchestrating resource gain. Conversely, those with fewer resources are more vulnerable to resource loss and less capable to resource gain” (pp. 349). Although work engagement has not been examined as a cross-level moderator of the affect−performance link within individuals, the past literature suggests that work engagement is an important motivational resource for employee performance. In a cross-sectional study of hotel workers and their customers (Salanova, Agut, & Peiro, 2005), employees’ general work engagement was related to higher service climate which was, in turn, linked to employee performance and then customer loyalty. Also, employees’ work engagement predicted subsequent job performance as assessed by their supervisors and coworkers (Halbesleben &

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Wheeler, 2008). A meta-analytic study also confirmed that work engagement explains incremental variance in task performance above and beyond other job attitudes (Christian, Garza, & Slaughter, 2011). Thus, work engagement represents a relatively stable resource that may reduce one’s sensitivity to the effects of fluctuating positive affect on job performance. More specifically, task performance during an episode is a joint function of resource level and resource allocation (Beal et al., 2005). As such, employees with high work engagement may be able to offset temporary losses of affective resources by drawing from larger resource reservoirs. That is, even with dwindling transient affective resources, they still remain motivated to pursue persistent task efforts and effectively allocate other necessary resources. In addition, highly engaged employees tend to go beyond their roles to help achieve goals of coworkers and their organization, further suggesting that highly engaged individuals are better able to “free up” their personal resources (Christian et al., 2011). Moreover, general work engagement is driven by both job resources (e.g., social support, feedback) and personal resources (e.g., resiliency, positive self-evaluations) (Bakker, 2011; Christian et al., 2011). This may indicate that individuals with high work engagement have a “caravan of resources” for sustaining work motivation and enhancing performance; therefore, they are less affected by transient, volatile resources (Hobfoll, 2001). Translating the conceptual and theoretical reasoning into the current context, we hypothesize that positive affect gained from micro-breaks will have a weaker relation with job performance especially for those who have high work engagement but will have a stronger relation for those who have low work engagement. Hypothesis 3: Work engagement at the between-person level will moderate (reduce) the day-level relationship between positive affect and sales performance, such that

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employees with high (low) work engagement will be less (more) likely to be influenced by positive affect during the workday. Further, our combined hypotheses (H1−H3) imply that the strength of the indirect relationships between micro-breaks and performance outcomes via positive affect may differ by the levels of work engagement. That is, high work engagement makes employees less sensitive and vulnerable to state positive affect from micro-breaks for their performance benefits, such that the indirect effects of micro-breaks on job performance will be weaker for employees with high work engagement but will be stronger for those with low work engagement. Hypothesis 4: The day-level indirect effects of (a) relaxation, (b) nutrition-intake, (c) socialization, and (d) cognitive micro-breaks on performance outcomes via positive affect will be weaker (stronger) for employees who have higher (lower) levels of work engagement. Method Sample and Procedure The current study is the first publication based on data collected as part of larger study entitled, “Employees’ Work and Nonwork Experiences in Telemarketing Job.” IRB approval was granted by Kansas State University for protocol number 7120. Participants included 71 telemarketers at call centers in Korea. Their primary job is to call customers and persuade them to purchase services or products (O*Net, 2015). The participants sold insurance, credit cards, cable and Internet, and landline and mobile phone services. Consequently, they often engage in emotional labor: they must suppress their true feelings, display positive emotions, answer customers’ questions, and solicit sales (Grandey, 2003). Industries represented were finance (49%) or telecommunications (51%). The two groups showed no significant differences

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regarding age, job tenure, weekly work hours, and work engagement (p-values = .29 − .82). Most participants were women (79%), reflecting the female-dominated call center field in Korea (Korea Labor and Information Service, 2012). The final sample averaged 37.03 years-old (SD = 7.58), organizational tenure of 4.15 years (SD = 3.19), and 39.45 hours worked per week (SD = 6.42). Participants were recruited through several online community websites for call center workers in Korea. With permission from the websites’ administrators, the recruitment message was posted for about 4 weeks, explaining the two-phase online study procedure (an initial survey and two daily surveys for 10 workdays), compensation for participation (a $30 value mobile gift card), and eligibility for participation. To participate, they had to work full-time at call centers with a regular work schedule (9 a.m. to 6 p.m.) in accordance with regular working hours in Korea, and fixed lunch hours (no shift and telecommuting workers). The initial survey link was distributed to 165 participants who expressed interest via email. Completion of the initial survey indicated their consent to participate in daily surveys and provide their objective performance report retrieved from their organizations’ sales database (i.e., daily sales performance records during the participation period). Their organizations used archival sales records to calculate incentive payments so employees were allowed to track and retrieve their sales records freely on their work computer 1. A total of 105 out of 165 individuals completed the initial survey (64%), reporting demographic information and their work engagement. They also self-generated ID codes and used throughout the study to allow us to match responses across measurement occasions.

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Before this study, we conducted a pilot focus group interview with call center workers in Korea. We learned that they would be able to retrieve their individual sales performance report from their work computer which was connected to their company’s sales management system.

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About one week after the initial survey, they started answering two short daily surveys for 10 consecutive workdays: one in the morning (Time 1) to assess their morning positive affect (control variable) and another at the end of each workday before leaving their call center (Time 2) to measure their day-level workload (control variable), engagement in micro-break activities, and positive affect at work. As expected in daily diary studies, 34 participants (n = 7) skipped some of the daily surveys (e.g., completing only morning or end-of-work survey), or failed to complete any daily surveys after the first phase (n = 27). They were removed, leaving 71 of 105 individuals (68%). No significant differences were observed between the initial sample and the final sample regarding age, sex, education level, organizational and job tenure, average work hours per week, and work engagement (p-values = .22 − .73). On average, they completed the morning survey at 9:22 a.m. (SD = 0.34), and the end-of-work survey at 6:21 p.m. (SD = 0.78). In addition, each Friday, participants emailed us the week’s sales report along with their ID code. The sales reports included successful sales transactions and amount of gross sales in Korean Won for each day during the entire week. By matching these performance records with daily survey responses, we obtained 632 day-level data points of 710 points possible (71 participants x 10 workdays), yielding a response rate of 89%. Day-Level Measures The measures were provided in Korean. To ensure accurate translation of English-based measures, we used a translation-back translation procedure with two independent bilingual translators (Brislin, 1970). All scales were slightly adapted to suit the daily measurement context, except for the micro-break activities measure. Micro-break activities (Time 2). Table 1 displays the nine items of micro-break activities and their categories. We measured micro-break activities by slightly adapting the items

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of common respite activities that Kim et al. (2017) developed and fully tested according to the literature on micro-breaks at work (Fritz et al., 2011; Trougakos & Hideg, 2009). Specifically, we added a small phrase (“for entertainment”) at the end of their cognitive activity items to clearly indicate the pleasurable nature of the activities. This adapted measure asked respondents to recall their short, informal respites taken voluntarily during their work day and then rate how often they engaged in those break activities (1 = never to 5 = very frequently): two items each for relaxation, nutrition-intake, and cognitive activities, and three items for social activities. We then computed an average score within each of the categories to produce four micro-break variables. We did not calculate the coefficient alphas as these activities are not caused by a latent construct, and each category of break activities is defined by the combination of its measures (i.e., formative measure; Diamantopoulos & Siguaw, 2006). Positive affect (Time 2). Positive affect was assessed with positive emotion scales from the Positive and Negative Affective Schedule (PANAS; Watson, Clark, & Tellegen, 1988). To minimize participants’ burden, we used six shortened descriptors (i.e., happy, enthusiastic, active, concentrating, confident, interested) as common in diary studies. Participants indicated how extensively they had felt the positive emotions during their workday (1 = none to 5 = to a great extent). The average Cronbach’s alpha across observations was .91. Job performance. To operationalize job performance, we used daily sales performance records in Korean Won which indicate gross sales for each work day. Considering that the call center employees’ gross sales greatly varied across the industries due to the different unit prices of the products and services they sold, we standardized daily sales performance scores, using the individuals’ respective average gross sales of the two weeks during the study period 2. Therefore,

2

We also used individuals’ average gross sales for the month to calculate standardized performance scores but the significance of our results did not differ.

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negative values represent below-average performance in two working weeks, while positive values indicate above-average performance. Control variables. We measured morning positive affect as a control variable (Time 1) because baseline affective state could influence subsequent affect and work approaches during the workday. We used the same PANAS affective descriptors and response options to ask participants to rate how extensively they felt the emotions in the morning. The average Cronbach’s alpha across observations was .90. In addition, we measured day-specific workload as another control variable because it could influence affective state at work (Ilies et al., 2007) and opportunities to interact with customers on the phone. This was assessed at the end of the workday with a short three-item Quantitative Workload Inventory (Spector & Jex, 1998), on a 5point rating format (1 = strongly disagree to 5 = strongly agree). Example item was “Today, I had a lot of work to do.” The average Cronbach’s alpha across observations was .89. Person-Level Measure General work engagement. This was assessed in the initial survey with the short nineitem Utrecht Work Engagement Scale (Schaufeli et al., 2006) on a 5-point rating format (1 = strongly disagree to 5 = strongly agree). Participants rated how well each item described them in general at work. Example items included: “At my work, I feel bursting with energy in general” and “I am enthusiastic about my job, in general.” The Cronbach’s alpha was .89. Analytic Approach We used multilevel path analysis to test the hypothesized model shown in Figure 1 with Mplus 6.12 (Muthén & Muthén, 2007). With this approach, we were able to accommodate the multilevel structure of the data (i.e., daily responses nested in individuals) and simultaneously estimate the path coefficients for the hypothesized relationships. In our model, as control

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variables, morning positive affect and workload were specified to have fixed effects on positive affect (Time 2) as well as on sales performance. We specified the Level-1 (i.e., intraindividual level) fixed effects of four micro-break activities on positive affect, and the Level-1 random effect of positive affect on sales performance. In addition, we specified the direct fixed effects of the micro-break activities on sales performance. To facilitate the interpretation of the findings, all Level-1 predictors (i.e., workload, morning positive affect, micro-break activities) were person-mean centered to obtain unbiased estimates of the intraindividual-level relationships (Enders & Tofighi, 2007). The Level-2 variable, work engagement, was grand-mean centered. Variance partitioning showed that within-person fluctuations explained a significant amount of the variance in the mediator and outcome variable: 84.1% for positive affect during the workday and 73.4% for sales performance. Also, substantial variability was due to within-person fluctuations in micro-breaks (75.3% for relaxation, 74% for nutrition-intake, 86.7% for socialization, and 55.1% for cognitive activities). Thus, the multilevel-modeling approach was appropriate to test the hypotheses. Mediation hypotheses were tested via Monte Carlo simulation procedures using the open-source software R, found at http://www.quantpsy.org/medmc/medmc111.htm (Bauer, Preacher, & Gil, 2006; Preacher & Selig, 2010). Furthermore, we ran Bayesian multilevel analysis to confirm our findings and respond to recent calls to deal with traditional null hypotheses significance testing issues (Andraszewicz et al., 2015; McKee & Miller, 2015). Critics have objected to the traditional significance testing because p-values and statistical significance can be misinterpreted, and model comparison processes are lacking in regression analyses (e.g., Johnson, 2013; Sellke, Bayarri, & Berger, 2001). Bayesian analysis is an alternative to overcome these issues because it depends less on p-

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values and significance and provides more flexibility for modeling complex error structures (Andraszewicz et al., 2015; Gelman, 2003). Results Preliminary Analysis Table 2 presents means, standard deviations, and intercorrelations of the study variables. As expected, at the within-person level, the four micro-breaks were positively related with positive affect at Time 2 (rs = .17 to .24, ps < .001). Positive affect at Time 2 was also positively related with sales performance (r = .29, p < .001). Four types of micro-breaks were also moderately correlated with each other (rs = .19 to .57, ps < .01), except the relationship between relaxation and cognitive activities (r = .08, p = .055) and relaxation and nutrition-intake activities (r = -.02, p = .664). In addition, we checked whether there were any time trends in our mediator and outcome variables because they were measured repeatedly over two weeks. We entered the linear time trend variable in each regression model for positive affect and performance outcomes but found no significant linear time trends in positive affect (γ = .01, p = .999) and sales performance (γ = .001, p = .999). Hypothesis Testing Table 3 presents the results from the multilevel path analysis that estimated all the path coefficients simultaneously. Relaxation (γ = .13, p < .001), social (γ = .14, p < .001), and cognitive micro-break activities (γ = .13, p < .01) were positively related to increased positive affect when morning baseline affect and workload were controlled for; thus, H1a, H1c, and H1d were supported, respectively. However, the effect of nutrition-intake activities on positive affect was not significant (γ = .04, p = .333), failing to support H1b. In Table 3, positive affect was

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positively related to sales job performance (γ = .41, p < .001), after controlling for morning affect and workload. Increased positive affect was further hypothesized to connect the relationship between employees’ break activities and job performance (H2a – H2d). Testing a series of indirect effects based on 20,000 Monte Carlo replications showed that the indirect effect of relaxation activities on sales performance via positive affect was .05 with 95% bias-corrected bootstrap confidence interval (CI) from 0.02 to 0.09. As the CI did not include zero, H2a was supported. However, nutrition-intake activities did not show a significant indirect effect on sales performance as the CI included zero (95% CI [-0.03, 0.07]). Thus, H2b was not supported. The indirect effect of social activities on sales performance via positive affect was .06 (95% CI [0.02, 0.10]), supporting H2c. Cognitive activities indirectly affected sales performance via positive affect (.05, 95% CI [0.02, 0.08]), so H2d was supported. Note that there were no significant direct relationships between the micro-breaks and sales performance. Nonetheless, the effects of relaxation, socialization, and cognitive micro-breaks on performance were fully mediated by positive affect. We tested a cross-level moderation effect of work engagement on the within-person relationship between positive affect and sales performance. In Table 3, results showed that work engagement was negatively associated with the random slope between positive affect and sales amount (γ = -.15, p < .05). Following Preacher, Curran, and Bauer (2006), we conducted simple slope tests in multi-level modeling to confirm the nature of the interaction effects. As Figure 2 shows, the interaction pattern was observed for sales performance outcome: the positive withinperson link between positive affect and sales performance existed only for those who had low

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levels of work engagement (γ = .61, SE = .07, p < .001) but not for those who had high levels of work engagement (γ = .31, SE = .16, p = .06). Thus, H3 was supported. Last, we tested whether estimated indirect effects of micro-breaks on sales performance via positive affect differed at the lower (-1 SD) and higher (+1 SD) level of work engagement. Relaxation activities had a significant indirect effect of .17 (p < .05) under the low work engagement level, versus .01 (p = .743) under the high work engagement level. Moreover, the indirect effects were significantly different between the two conditions: -.15 (95% CI [-.302, -.002]). We did not test the moderated indirect effect of nutrition-intake activities as H1b and H2b were not supported. Social activities had a significant indirect effect of .16 (p < .001) under the low work engagement level, versus .01 (p = .801) under the high work engagement level. The two conditions had significantly different indirect effects: -.15 (95% CI [-.276, -.015]). Last, cognitive activities also had a significant indirect effect of .18 (p < .01) under the low work engagement level but not under the high work engagement level (.04, p = .460). The indirect effects between the two conditions were significantly different: -.14 (95% CI [-.273, -.014]). Therefore, work engagement significantly moderated the indirect relationships between the three micro-breaks and job performance via positive affect, supporting H4a, H4c, and H4d, respectively. Following suggestions by Preacher and Kelley (2011), we calculated kappasquared (𝜅𝜅 2), the proportion of the maximum possible indirect effects, to evaluate effect sizes of P

indirect effects. The 𝜅𝜅 2 values were .04, .043, and .037, respectively for relaxation, social, and P

cognitive activities’ indirect effect on performance through positive affect. The range of these effect sizes are between small (.01) and medium (.09) size (Cohen, 1988; Preacher & Kelley,

2011). However, the 𝜅𝜅 2 value for nutrition-intake’s indirect effect was .009 in accordance with P

its nonsignificant indirect effect.

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Bayesian Multilevel Analyses to Confirm the Results For Bayesian multilevel analysis, we used uninformative (objective) prior information (Kruschke, Aguinis, & Joo, 2012) with the current data to produce a posterior distribution of parameter estimates. For the Bayesian inference, we used Markov Chain Monte Carlo simulation using the open-source software R. This method iteratively draw samples from a set of conditional distributions to create the posterior density of interest (Martin, Quinn, & Park, 2011). Following Yuan and MacKinnon (2009), we ran the analysis with two chains iterating 10,000 times with 500 burn-in iterations, and both chains converged for all estimated parameters. Table 4 details the results for indirect effects of the four micro-breaks on sales performance via positive affect. Specifically, relaxation activities had an indirect effect of .15 (95% CI [0.08, 0.21]) for sales job performance. Socialization activities had an indirect effect of .12 (95% CI [0.07, 0.20]) while cognitive activities had an indirect effect of .13 (95% CI [0.05, 0.21]). However, nutritionintake activities did not show a significant indirect effect on job performance as shown in the previous analyses. General work engagement was also confirmed to moderate the link between positive affect and job performance: -.15 (95% CI [-0.24, -0.08]). In addition, our Bayesian multilevel analysis results found the moderated indirect effect of relaxation (-.02, 95% CI [-0.03, -0.01]), social (-.02, 95% CI [-0.04, -0.01]), and cognitive (-.02, 95% CI [-0.03, -0.01]) activities on sales performance through positive affect. However, the moderated indirect effect of nutrition-intake activities was not supported (.001, 95% CI [-0.02, 0.01]). These Bayesian results indicate that the indirect effects of relaxation, social, and cognitive activities will be reduced by .02 when the score of general work engagement increases by one unit. Thus, the results from Bayesian analyses indicated robustness in our initial results. Supplementary Analyses

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While a priori theory of affective influences on job performance (Beal et al., 2005) and its empirical literature suggest a path direction from state affect to job performance (Shockley, Ispas, Rossi, & Levine, 2012), it may be possible that preceding episodes of successful sales increase subsequent positive affect during the workday. Thus, we further explored our partial data given that 85% of our final sample (n = 60 out of 71 participants) provided time information of their sales transactions, so we were able to separate their sales performance in the morning (before lunch hour) and afternoon (after lunch hour). This allowed us to test a possible role of morning performance in increasing positive affect during the workday (536 day-level data points out of a possible total of 600). The multilevel path analysis showed that morning sales performance did not predict increased positive affect during the workday (γ = .12, p = .221), controlling for morning positive affect (γ = 0.58, p = .255) and day-specific workload (γ = -.075, p = .314). Also, without the control variables, the path from morning performance to positive affect still remained nonsignificant. Discussion The purpose of this study was to determine whether and how micro-breaks are positively linked to daily job performance outcomes within call center employees. Another important goal was to test whether general work engagement moderates the daily affective process of microbreaks involving job performance. Our results from both traditional and Bayesian multilevel analyses show that relaxation, socialization, and cognitive activities were positively related to increased positive affect leading to greater sales performance, after controlling for daily workload and baseline morning affect. We also find that positive affect fully mediates the daylevel relationships between the three micro-breaks and sales performance. Micro-breaks for nutrition-intake, however, do not predict positive affect and job performance. Furthermore,

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micro-breaks have varying indirect effects depending on the levels of work engagement between persons. Specifically, for employees who have high work engagement, micro-breaks have no significant day-level indirect effects on sales performance via positive affect, but employees who have low work engagement show significant indirect effects. Theoretical Implications Our findings offer important implications to the work recovery literature, which has predominantly investigated off-job recovery experiences and their positive outcomes, but has underexplored on-the-job recovery phenomena and work-relevant outcomes (Trougakos & Hideg, 2009). Considering that employees spend a significant amount of time in their work places, we examined micro-breaks as a way to recover momentary affective resources for call center employees’ job performance. In particular, to maximize more prescriptive knowledge about on-the-job recovery, we considered the specific content of the breaks. That is, when employees voluntarily engage in respite activities that facilitate relaxation and socialization, they can relieve themselves from work demands and increase their affective resources. Also, importantly, our results showed that cognitive activities can still boost positive affect when the activities had personal entertainment and learning purposes. Thus, our findings support the theorized recovery effect of specific micro-breaks as providing important resources (Baumeister et al., 1998; Hobfoll, 1989; Meijman & Mulder, 1998). On the other hand, nutrition-intake activities did not have a significant indirect effect on job performance via positive affect. As a post-hoc explanation, it may be plausible that employees enjoy snacks and coffee while interacting with others or watching a fun video footage. Indeed, the within-person correlations showed that on days when individuals had more nutrition-intake activities, they were also likely to have socialization (r = .32, p < .001) and

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cognitive activities at work (r = .57, p < .001). In addition, when we ran another path model including nutrition-intake breaks as a sole predictor, nutrition-intake significantly increased positive affect (γ = .14, p < .001) which, in turn, led to higher sales performance (γ = .50, p < .001). Furthermore, considering the short-term effects of caffeine in boosting one’s energy (cf. Kim et al., 2017; Welsh et al., 2014), we differentiated consumption of caffeine vs. snacks and noncaffeinated beverage as separate predictors in exploratory analyses. Caffeine-intake seemed to provide a quick boost in positive affect (γ = .10, p = .041), whereas snacking and having noncaffeine drinks did not show the effects (γ = .04, p = .309). Nevertheless, the significant effect of caffeine-intake disappeared when all types of micro-breaks were included in the analysis. These patterns suggest that the effects of nutrition-intake might have been masked by other break activities due to their concurrent occurrences. Thus, it may be premature to draw a definitive conclusion that nutrition-intake activities do not have recovery effects. Moreover, the notion of limited self-regulatory resources has been central to the recovery literature, but no study has actually tested specific resources as a mechanism involved in daily micro-breaks and job performance outcomes (Trougakos & Hideg, 2009). Although Hunter and Wu (2016) tested combined personal resources (energy, motivation, concentration) as a linking mechanism between preferred break activities and somatic symptoms (e.g., headaches, eye strain), it remained unclear whether micro-breaks can be connected to performance outcomes via increased resources. Building on the major theoretical frameworks in recovery, we further drew upon the performance model of affective influences (Beal et al., 2005) to theorize why state positive affect from micro-break activities translates into substantive job performance outcomes. This supported the theoretical notion that positive affect is a key self-regulatory resource especially for employees who often need emotion regulation to achieve service and sales goals.

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Of important note, both our study and Hunter and Wu (2016) found full mediation effects in which break activities were not directly linked to the outcome variables but only through increased personal resources. The consistent indirect effects in our study further corroborate the theoretically driven role of micro-breaks in temporarily halting resource expenditure and replenishing important resources for positive work outcomes. Thus, we advance theoretical and empirical knowledge by showing that recovery activities (i.e., micro-breaks) influence performance through an enhanced resource. Moreover, our findings of the objective performance outcomes expand a recent study’s result that short, self-initiated breaks at work were related to employees’ positive approaches to their job with more vigor, dedication, and enthusiasm (Kühnel et al., 2017). Last, it may be too simplistic to assume that work breaks aid momentary recovery for all employees without considering individual or situational differences (Trougakos et al., 2014; Sonnetag et al., 2017). When it comes to job performance, some individuals may be more motivated to perform well, regardless of their fluctuating resource levels (Trougakos & Hideg, 2009). In accordance, we found that work engagement moderated the indirect effects of microbreaks on performance via positive affect, supporting the contention that not all individuals are sensitized to transient affective resource for their job performance. That is, relaxation, socialization, and cognitive micro-breaks are most beneficial for employees in low work engagement in that they depend more on state positive affect due to their lack of work motivation in general. In contrast, fluctuating positive affect has much less influence on employees in high work engagement as they can remain highly engaged in their job tasks, regardless of situations. In other words, those who have high work engagement may not need frequent micro-breaks to replenish affective resources for job performance. Therefore, our findings add to the performance

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model of affective influences (Beal et al., 2005) by showing that the within-person variability of transient affect−performance linkage is contingent upon the important individual difference factor, particularly for tasks that require high self-regulations. Additionally, our findings support COR theory (Hobfoll, 2002): a stable resource reservoir (general work engagement) increases resistance to the ebb and flow of transient resources (state positive affect). In short, our study contributed to identifying general work engagement as a boundary condition for the performance benefits of micro-breaks via positive affect. Limitations and Future Research Directions Overall, we used more rigorous methodological approaches: two daily surveys for ten consecutive days, objective performance records, and confirmation through Bayesian multilevel analysis that lessens concerns for traditional null hypothesis testing issues (Andraszewicz et al., 2015; McKee & Miller, 2015). However, we must acknowledge a few limitations for future research directions. First, the same measurement points for the predictor and mediator limited causal inferences in our study, although we controlled for morning affect and workload to rule out the possibility that they drive the hypothesized relationships. Nevertheless, for better causal inferences, future studies should separate the time points for the predictor and mediator when possible. In addition, although objective sales records were used to show that micro-breaks matters for tangible job outcomes beyond the perceptual outcomes in the literature, our study cannot determine the exact causal direction from work-day positive affect and performance outcomes due to the inherent limitations of correlational design and daily aggregated sales data. Thus, to empirically determine the causal directions, we strongly recommend that future studies should confirm our findings using laboratory or field experiments in which researchers can

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manipulate micro-breaks and induce positive affect and then examine various aspects of job performance including quality of handling service and sales calls beyond quantitative metrics. Third, although we found relaxation, socialization, and cognitive micro-breaks to have positive effects independent of each other, nutrition-intake activities did not significantly increase positive affect. Our current design cannot disentangle possible patterns of co-occurring activities during micro-break episodes. Thus, future research should measure micro-break activities at an episodic level, using alternative methods, such as event-contingent diary in which participants record any event of micro-breaks in detail every time it occurs (Wheeler & Reis, 1991). Also, the event-level approach may better reflect post-break affective changes, their influences on performance-related behaviors, and more nuanced break activities, such as habitual versus purposeful snacking at work (cf. Sonnentag et al., 2016). Next, our sample included only call center employees, perhaps restricting generalizability of the current findings. Future studies should examine whether micro-breaks bring similar performance benefits across different jobs and industries. In particular, given the fully mediated effects via positive affect in our study, future research should confirm whether the indirect effects also replicate in different job settings other than customer service and sales jobs. For example, the impact of positive affective state will be beneficial only to the extent that the affect matches the task requirements, in that positive affect might not be so helpful when job tasks require a narrower set of response tendencies and attentional focus (Beal et al., 2005). In addition, whereas we investigated only nonwork-related social activities as breaks in this study, employees in other job contexts might enjoy work-related social activities. For example, a researcher may take a small break from his or her manuscript-writing task and have a casual conversation with colleagues about new research ideas. This break activity may still boost

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the person’s positive affect. Perhaps, some specific job or performance contexts determine the potential, salutary effects of work-related activities during breaks. Thus, future research needs to explore when and why the context of work provides energizing experiences during breaks. Another important note is that we tested general work engagement as the only cross-level moderator, so future research should search for other situational or contextual factors that may moderate micro-breaks’ recovery effects or their indirect effects on performance outcomes. For example, some organizations and work groups may emphasize performance and competition too much, underestimating values of well-being and social interactions (e.g., Hammer, Saksvik, Nytro, Torvatn, & Bayazit, 2004). Under such workplace norms, employees might experience adverse effects of micro-breaks as they may feel guilty about nonwork respite activities at work. In fact, an organizational survey has shown that about 20% of respondent employees said they did not step away from their workspaces due to guilt (Staples Advantage, 2014). Future research should go beyond our study to explore micro-break timing. For example, micro-breaks later in the day were found to be less effective than breaks before work shifts for replenishing resources (Hunter & Wu, 2016), whereas another study found that afternoon microbreaks boosted daily work engagement while breaks in the morning failed to do so (Kühnel et al., 2017). Although micro-breaks’ exact timings, durations, and specific activities may be difficult to track in real time, researchers might try a day reconstruction method (see Diener & Tay, 2014, for a review) in which participants systematically parse their previous day into major activities and then rate their experiences and time spent on each activity (e.g., Oerlemans & Bakker, 2014). In addition, micro-breaks might have an optimal duration for the best performance outcomes; too much might be counterproductive. Thus, future research should explore micro-breaks’ durations,

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timings, and their recovery effects on various outcomes, both over the course of a workday and greater periods of time. Last, we recommend that future studies investigate antecedents to micro-breaks and their specific content. For example, Bowling, Beehr, and Swader (2005) found that extraverted individuals engage in more frequent and positive social interactions at work. Also, a few studies using a latent profile analysis approach found that work and personal factors (e.g., role ambiguity, job control, supervisor support) are associated with certain profiles of post-work recovery experiences (Bennett, Gabriel, Calderwood, Dabling, & Trougakos, 2016; Kinnunen et al., 2016). Other studies have recently suggested that organizational climates (e.g., eating and exercise climate) may influence employees’ snacking at work and health behaviors in general (Sonnentag et al., 2016; Sonnentag & Pundt, 2016). Therefore, future research may find it fruitful to examine various personality, work, and organizational factors as potential predictors of micro-breaks and specific activities chosen. Practical Implications Our findings have important implications for organizational and managerial practices. Organizations should educate their employees and managers in the values of micro-breaks between task episodes for enhancing job performance, so that self-initiated micro-breaks are not frowned upon. Considering that employees spend most of their lifetime working, they should strategically construct their workday activities to allow brief moments for recovery, possibly using a time-tracking app. In addition, organizations may redesign their call centers to be more conducive to micro-break activities. For example, areas might be designated for specific relaxation activities such as meditation or listening to music. Indeed, an intervention study found that call center employees reported reduced physical and psychological strain after using a silent

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break room with daybeds for practicing progressive muscle relaxation (Krajewski, Wieland, & Sauerland, 2010). Organizational wellness training might include instructions for effective desk stretching between calls and encouraging social interactions during breaks. In call centers, employees are often separated by partitions which might constrain positive social interactions, so easily accessible areas for breaks would help. For cognitive activities, we recommend giving employees free access to the Internet, books, magazines, and newspapers, so that employees can take a mental break from work demands. As our results showed that micro-breaks have conditional indirect effects on performance via positive affect, managers should be aware that employees may vary in their need for microbreaks depending on their general work engagement levels. Especially after difficult calls, those who have low work engagement may need small breaks to boost positive affect and prevent further loss of affective resources. Relatedly, we found that general work engagement positively predicted an average level of job performance across days (i.e., direct effect of general work engagement on the intercept of daily job performance). Thus, in addition to encouraging microbreaks on a need basis, we recommend that managers should help employees increase work engagement to improve their performance, recognizing that day-to-day engagement experiences may develop into work engagement over time (Bakker, 2011). Also, considering that both job and personal resources drive the general work engagement levels, managers may try to provide workers with necessary job resources (e.g., autonomy, performance feedback, social support, coaching), as well as training to build personal resources such as self-efficacy and optimism (Bakker, 2011).

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MICRO-BREAKS AND JOB PERFORMANCE Table 1 Common Micro-Break Activities Category Relaxation activities

Examples • Stretching, walking around the office, or relaxing briefly • Daydreaming, gazing out the office windows, taking a quick nap, or any other psychological relaxation

Nutrition-intake activities

• Drinking caffeinated beverages (e.g., energy drinks, coffee, black or green tea) • Snacking (e.g., cookies) or drinking non-caffeinated beverages (e.g., juice, water, vitamin water)

Social activities

• Chatting with coworkers on non-work related topics • Texting, using instant messenger, or calling to friends or family members • Checking personal SNS (e.g., Facebook, Twitter, or personal blogs)

Cognitive activities

• Reading books, newspapers, or magazines for personal learning or entertainment. • Surfing the Web for entertainment (e.g., watching short video clips, playing a game)

48

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Table 2 Means, Standard Deviations, and Intercorrelations among Study Variables M

SDa

SDb

1. General work engagement (Level 2)

3.16



1.00

2. Morning positive affect (Control)

2.84

0.72

0.30



3. Workload (Control)

3.00

0.68

0.29



-.04

4. Relaxation activities

2.58

0.88

0.50



-.04

-.07

5. Nutrition-intake activities

2.70

0.86

0.50



.05

-.05

-.02

6. Social activities

2.68

0.92

0.44



-.06

-.06

.19***

.32***

7. Cognitive activities

2.88

0.82

0.59



.04

-.07

.08

.57***

.29**

8. Positive affect

2.88

0.66

0.34



.12**

-.03

.21***

.17***

.24***

.23***

9. Sales performance

0.00

1.07

0.51



.22***

.10**

-.05

.07

.07

.01

Variable

1

2

3

4

5

6

7

8

9

.18

.07

-.08

.08

.14

.02

.08

.16

-.19

-.03

.19

.03

.22

.24*

.16

.02

-.11

.06

-.15

-.02

.05

-.11

.31**

.14

.24*

-.17

.40**

.68***

.19

.02

.33**

.18

.06

.32**

-.10 .22

.29***

Note. All variables are within persons, except general work engagement. Correlations below the diagonal represent within-person correlations (n = 632). Correlations above the diagonal represent between-person correlations (n = 71). To calculate between-person correlations, we averaged withinperson scores across days. a = within-person, b = between-person. Sales performance was standardized based on individual mean scores during the two weeks. *p < .05, **p < .01; ***p < .001.

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Table 3 Unstandardized Coefficients of the Multilevel Model Positive Affect

Intercept General work engagementa Control: Morning positive affectb Control: Workloadb Relaxation activitiesb Nutrition-intake activitiesb Socialization activitiesb Cognitive activitiesb Positive affect (PA)b PA x General work engagementc

Sales Performance

Estimate 2.88

S.E. 0.04

Est./SE 72.57***

0.11 0.002 0.13 0.04 0.14 0.13

0.04 0.04 0.03 0.04 0.03 0.05

3.24** 0.06 4.04*** 0.97 4.46*** 2.77**

Estimate -1.18 0.52 0.37 0.21 -0.02 0.04 0.04 0.08 0.41 - 0.15

S.E. 0.21 0.18 0.06 0.06 0.05 0.06 0.05 0.07 0.07 0.06

Est./SE -5.63*** 2.93** 6.59*** 3.49*** -0.34 0.62 0.84 1.11 5.88*** -2.59*

Level 1 n = 632, Level 2 n = 71. a between-person variable; b within-persons variables; c cross-level interaction term; S.E. = standard error. All results came from a path model that included all variables: controls, predictors, mediator, moderator, and interaction term. *p < .05, **p < .01; ***p < .001.

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Table 4 Results of Bayesian Multilevel Analysis Sales Performance Estimate

S.E.

2.5% of CI

97.5% of CI

Indirect effects of relaxation activities 0.15 0.01 0.08 0.21 Indirect effects of nutrition-intake activities 0.04 0.01 -0.03 0.08 Indirect effects of social activities 0.12 0.01 0.07 0.20 Indirect effects of cognitive activities 0.13 0.01 0.05 0.21 Positive affect (PA) 0.89 0.09 0.69 1.24 General work engagement 0.40 0.06 0.20 0.68 PA x Work engagement -0.15 0.02 -0.24 -0.08 Conditional indirect effects of relaxation activities -0.02 0.01 -0.03 -0.01 Conditional indirect effects of nutrition-intake activities 0.001 0.01 -0.02 0.01 Conditional indirect effects of social activities -0.02 0.01 -0.04 -0.01 Conditional indirect effects of cognitive activities -0.02 0.01 -0.03 -0.01 Note. Level 1 n = 632, Level 2 n = 71. SE = standard error. PA = positive affect. The conditional indirect effects indicate that as the score of moderator, general work engagement, increases by one unit, each indirect effect decreases, except nutrition-intake breaks.

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52

General work engagement Between-Individual (Level 2) -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Within-Individual (Level 1) Day-specific relaxation activities + Day-specific nutrition-intake activities

+ +

Day-specific social activities

Day-specific positive affect

Day-specific sales performance +

+ Day-specific cognitive activities

Figure 1. Conceptual Model Note. Control variables’ paths were not shown in the figure for simplicity but were included in the analysis (i.e., day-specific morning positive affect and workload predicting the mediator and outcome variable).

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53

Sales Performance

2

1

0

Low General Work Engagement

-1

High General Work Engagement

-2

-3 Low Positive Affect

High Positive Affective

Figure 2. Cross-Level Moderation Effect of General Work Engagement in Predicting Daily Sales Performance