PREPARING FOR THE WORKDAY:

THE EFFECTS OF PRE-WORK STRATEGIES ON PSYCHOLOGICAL

ENGAGEMENT AND WELL-BEING

A Dissertation

Presented to

The Graduate Faculty of the University of Akron

In Partial Fulfillment

of the Requirements for the Degree

Doctor of Philosophy

August, 2019 PREPARING FOR THE WORKDAY:

THE EFFECTS OF PRE-WORK STRATEGIES ON PSYCHOLOGICAL

ENGAGEMENT AND WELL-BEING

Megan Nolan

Dissertation

Approved: Accepted:

Adviser Department Chair Dr. James Diefendorff Dr. Paul Levy

Committee Member Interim Dean of the College Dr. Dennis Doverspike Dr. Linda Subich

Committee Member Dean of the Graduate School Dr. Paul Levy Dr. Chand Midha

Committee Member Date Dr. Erin Makarius

Committee Member Dr. Amanda Thayer ABSTRACT

Recent research on reattachment (i.e., rebuilding a mental connection to work before starting work) has begun to provide evidence that individuals use specific strategies to facilitate the reconnection between life domains. The current study argues that reattachment is just one of several “pre-work” strategies that individuals can adopt to ease the transition between home and work domains and enhance their daily experiences.

Pre-work is defined as active daily preparation for a given workday in which individuals bring their attention back to work, mobilize their energy, and/or reflect on the reasons they work. In addition to reattachment, individuals may use energy mobilization strategies to increase their sense of feeling energized and positive about work or positive reflection strategies to increase their sense of feeling autonomously motivated and emotionally connected with their work. The current study developed a psychometrically sound pre-work scale to accurately and reliably assess three distinct pre-work strategies and found support for a three-factor structure. In a second study, experience sampling methods were employed to examine how cognitive, physical, and emotional engagement translate pre-work strategies into satisfaction and emotional exhaustion during the day.

Additionally, two variables—employee resilience and perceived supervisor support— were examined as cross‐level moderators of these relations. Daily-survey data was collected from 114 employees (total of 936 days) and analyzed with multilevel path analysis. Results suggest that day-level cognitive reattachment predicted cognitive and physical engagement, energy mobilization predicted cognitive engagement, and positive- reflection predicted emotional engagement. Cognitive engagement, in turn, predicted emotional exhaustion, and emotional engagement predicted both job satisfaction and

iii emotional exhaustion. Furthermore, the relation between positive reflection and emotional engagement was strengthened at high levels of perceived supervisor support.

The current study highlights the important role of a variety of pre-work strategies that employees can adopt to ease the transition between life domains and enhance their daily experiences.

iv ACKNOWLEDGEMENTS

I’d like to acknowledge the individuals who have played a critical role in my academic achievement. To my adviser, Jim Diefendorff, I cannot thank you enough for your continued guidance and mentorship. I respect and admire your expertise, work ethic, and kindness, and I am grateful to have had the opportunity to learn from you. Thank you to each of my committee members for your helpful comments and feedback. Thank you to my colleagues in Jim’s lab for assisting on this project and to Allison Gabriel for providing statistical guidance. Thank you also to Alicia Grandey for sparking my interest in I-O psychology and encouraging me to pursue a graduate degree.

To my Team, thank you for making our graduate experience collaborative and fun. Thank you to my friends both inside and outside of Akron (I am lucky that there are too many of you to name individually) for keeping me smiling. Thank you to Mike

Leider for your support, love, and friendship. It’s comforting to know I can always rely on you, and I look forward to sharing many more “de-stress” walks, cups of coffee, and laughs together. Finally, I would like to acknowledge with gratitude the unwavering support and love of my parents, Tim and Terri Nolan, and siblings, Tim and Shannon.

Thank you for instilling in me the importance of family, friendship, hard work, and fun.

Without you, none of this would have been possible.

v TABLE OF CONTENTS

List of Tables ...... ix

List of Figures ...... xi

CHAPTER I. STATEMENT OF THE PROBLEM ...... 1

II. LITERATURE REVIEW ...... 11

Engagement...... 11

Recovery as an Antecedent of Engagement ...... 14

Recovery During Work ...... 16

Recovery After Work ...... 18

Review of ‘Resource Building Strategies’ ...... 25

Coping ...... 25

Emotion Regulation ...... 32

Meaning Making ...... 34

Summary of Resource Building Strategies ...... 38

Mentally Preparing for Work: A Pre-Work Framework ...... 39

Cognitive Reattachment ...... 44

Energy Mobilization ...... 46

Positive Reflection ...... 47

Well-Being: Job Satisfaction and Emotional Exhaustion ...... 49

Moderating Variables...... 52

Supplemental Predictions...... 55

III. METHODOLOGY ...... 57

STUDY 1: MEASURE DEVELOPMENT ...... 57 vi Information Gathering ...... 57

Item Generation ...... 58

Sample and Procedure...... 60

Measures ...... 61

Results ...... 63

STUDY 2: MAIN STUDY ...... 74

Participants and Procedure ...... 74

Person-Level Measures ...... 79

ESM Measures ...... 80

ESM Control Variables ...... 82

Preliminary Analyses...... 83

Confirmatory Factor Analyses...... 85

Analytic Strategy ...... 89

IV. RESULTS ...... 90

Partitioning of Variance Components ...... 90

Analytic Approach A: Separation of Measures ...... 97

Analytic Approach B: Engagement throughout the Day ...... 107

V. DISCUSSION ...... 114

Methodological Contributions ...... 114

Theoretical Contributions ...... 115

Limitations and Future Directions ...... 124

Practical Implications...... 128

Conclusion ...... 129

vii REFERENCES ...... 130

APPENDICIES

Appendix A Pre-Work Factor Analytic Study ...... 163

Appendix B Main Study: Person-Level Survey...... 167

Appendix C Main Study: Event-Level Surveys ...... 169

Appendix D Call Center Sample: Informed Consent Form ...... 173

Appendix E Call Center Sample: Participant Communications ...... 175

Appendix F Social Media Sample: Informed Consent Form...... 182

Appendix G Social Media Sample: Participant Communications ...... 183

viii LIST OF TABLES

Table 1. Pre-Work VS. Related Constructs ...... 43

Table 2. Descriptive Statistics for All Pre-Work Items...... 64

Table 3. EFA Fit Indices including All Items...... 66

Table 4. CFA Fit Indices including all items...... 66

Table 5. CFA to Explore Model Fit...... 69

Table 6. CFA (N=314) on Final Pre-Work Scale: Items, Means, Standard Deviations,

Cronbach's alphas, and Factor Loadings of the 14-item Pre-Work...... 69

Table 7. Factor Intercorrelations on 14-item Pre-Work Scale...... 70

Table 8. CFA (N=314): Items, Means, Standard Deviations, Cronbach's alphas, and

Factor Loadings of the 9-item Pre-Work ESM Scale...... 72

Table 9. Correlations between Pre-Work Scales and Outcomes...... 73

Table 10. Study 2 Means, Standard Deviations, and Mean Differences for Main

Variables...... 84

Table 11. Multilevel CFA Results ...... 88

Table 12. Percentage of within‐individual variance among daily variables...... 91

Table 13. Summary of Hypotheses...... 92

Table 14. Reliabilities, descriptive statistics and correlations for all study variables...... 95

Table 15. Analytic Approach A, Separation of Measures: Testing Main, Mediation, and

Moderation Effects...... 99

Table 16. Summary of Hypotheses for Analytic Approach A: Separation of Measures 100

Table 17. Wald Tests for Supplemental Hypothesis...... 107

ix Table 18. Analytic Approach B, Engagement throughout the Day: Testing Main,

Mediation and Moderation Effects ...... 109

Table 19. Summary of Hypotheses for Initial (Analytic Approach B: Engagement throughout the Day) Models ...... 110

x LIST OF FIGURES

Figure 1. Theoretical Model with Hypotheses ...... 10

Figure 2. Multilevel Path Analysis for Analytic Approach A: Separation of Measures,

Model 1...... 98

Figure 3. Multilevel path analysis for Analytic Approach A: Separation of Measures,

Model 2...... 104

Figure 4. Interaction between Positive Reflection and PSS...... 105

xi CHAPTER I

STATEMENT OF THE PROBLEM

Employees experience a multitude of work demands over the course of a day.

Demands (i.e., physical, psychological, social, or organizational features of the job that require continued effort; Demerouti, Bakker, Nachreiner, & Schaufeli, 2001) may result in increased stress levels, exhaustion, and burnout (e.g., Demerouti et al., 2001; Meijman

& Mulder, 1998), which is problematic for both employees and organizations (e.g., Elkin

& Rosch, 1990; Van der Hek & Plomp, 1997). However, employees can draw from an arsenal of coping strategies to manage demands. For example, employees can engage in recovery activities during and after the workday to help them manage accumulated stress and replenish depleted resources (e.g., Meijman & Mulder, 1998). Recovery from work is associated with positive employee outcomes like engagement (e.g., Sonnentag, 2003), which in turn, is linked to an array of beneficial outcomes, such as increased levels of performance (e.g., Xanthopoulou et al., 2009), proactivity (Sonnentag, 2003), positive affect at home (Culbertson, Mills, & Fullagar, 2012), morning positive activation (a state of high positive affect and high arousal that is characterized by a state of feeling active, strong, and delighted; Sonnentag, Binnewies, & Mojza, 2008), and lower levels of fatigue

(Sonnentag, Mojza, Demerouti, & Bakker, 2012).

Recovery research has primarily focused on processes that repair the negative effects that strain has on individuals when a stressor is absent (Demerouti, Bakker,

Geurts, & Taris, T, 2009), ultimately returning an individual’s functioning to its level before the stressor(s) was encountered (Sonnentag & Natter, 2004). As such, recovery researchers tend to focus on the processes that occur when the stressor is no longer

1 present, such as during work breaks or at the end of the day after a person leaves the workplace (Sonnentag & Geurts, 2009). In contrast, there is less research that has examined processes that occur before work begins and impact how individuals experience stressors during the workday. Although there has been a considerable amount of research focused on how athletes mentally and physically prepare for high pressure games or physical performances (see Cotterill, 2010 for overview of pre-performance routines), there is a gap in research about how non-athlete employees prepare for a typical work day. Research in this area has examined how an individual’s start-of- workday mood and well-being impact both their experiences and affect throughout the day. For example, employees’ start-of-workday mood was found to impact their perceptions of work events and affect following events throughout a workday (Rothbard

& Wilk, 2011). Similarly, individuals’ emotional exhaustion prior to work was found to impact their postwork emotional exhaustion (Kammeyer-Mueller, Simon, & Judge,

2016). Evidently, employees’ start-of-day state matters. Yet, little is known about the ways in which employees can proactively impact their start-of-day state and how these efforts may alter their experience of work events throughout the day. Just as it is beneficial for individuals to disconnect from work to reduce strain and replenish energy

(e.g., Meijman & Mulder, 1998; Sonnentag, 2003), it may be beneficial for them to actively reconnect with work.

Sonnentag and Kuhnel (2016) provided initial support for the idea that employees have cognitive “reconnection” experiences at the work-home interface that impact their engagement levels at work. They introduced the concept of “reattachment,” which involves mentally reconnecting to work before actually starting the workday. From a

2 boundary-management perspective (Ashforth et al., 2000), reattachment involves mentally crossing the boundary between the non-work and work domains, before immersing oneself completely into one’s work (Sonnentag & Kuhnel, 2016). For example, an employee may reattach by mentally simulating the workday and thinking of the task that he or she needs to complete (Sonnentag & Kuhnel, 2016). Using an experience sampling study, they showed that psychological detachment from work during leisure time (i.e., after the work day has ended) and reattachment before the work day begins both positively predict daily engagement. Building on this initial study,

Sonnentag, Eck, Fritz, and Kuhnel (2019) identified specific mechanisms that link reattachment in the morning to engagement later in the day. Using a daily diary study, they found that reattachment lead to cognitive (anticipated task focus) and affective processes (activated positive affect), as well as processes related to the mobilization of job resources (i.e., social support and job control), which in turn predicted engagement during the day.

Although reattachment sheds light on how individuals cognitively reconnect with work, this may be only one of several critical processes that help people cross the boundary between the nonwork and work domain. Ashforth and colleagues (2000) suggested that psychological preparation for reentry into one’s work role likely involves a combination of attention and arousal. They explain that individuals must adopt the appropriate cognitive frame (e.g., I’m entering my “employee” role), along with the appropriate arousal state (Ashforth et al., 2000). Reattachment as defined by Sonnentag and Kuhnel (2016) primarily captures the attentional component of psychological preparation, but does not explicitly tap into motivational processes that may facilitate

3 how individuals transition from non-work to work domains. For instance, individuals may attempt to motivate themselves for work using several strategies. They may try to increase their readiness to work by “pumping/psyching themselves up” (Hochschild,

1979), thinking about their goals (e.g., Locke & Latham, 2002), or reflecting on prosocial reasons why they work (Britt, Adler, & Bartone, 2001). Thus, employees may use additional strategies to reconnect with work that impact daily engagement and well-being outcomes that are not currently well understood or captured in the current conceptualization of reattachment. Thus, there is a need to better understand how individuals make the transition from non-work to work from both a cognitive and motivational perspective.

Furthermore, the current study addresses several limitations of previous research on reattachment (Sonnentag & Kuhnel, 2016; Sonnentag et al., 2019). Following recommendations by Byrne and colleagues (2016), the current study will use the Job

Engagement Scale (JES) to measure engagement unlike previous studies which have used the Utrecht Work Engagement Scale (UWES). Compared to the UWES, the dimensions of the JES exhibit clearer differences from each other and less overlap with associated job attitudes (Byrne et al., 2016). In addition to examining the relationships between pre- work strategies and engagement, the current study will also examine the relationships between pre-work and employee emotional exhaustion and job satisfaction. Furthermore, satisfaction and emotional exhaustion variables will be assessed at three time points per day, allowing changes in well-being to be modeled. Lastly, the current study controls for daily negative work reflection to rule out the possibility that the benefit of pre-work on engagement merely reflects the absence of negative work reflection (Meier et al., 2016).

4 CURRENT STUDY

The current study explores the strategies employees may use to cross the boundary from non-work to work, preparing them to enter their work role. Just as there are several experiences that facilitate recovery from work (i.e., detachment, relaxation, mastery, control, and community experiences; Mojza, Lorenz, Sonnentag, & Binnewies,

2010), there may be a variety of ‘pre-work’ strategies that facilitate reconnection with work. Extending research on reattachment (Sonnentag & Kuhnel, 2016; Sonnentag et al,

2019), the current study develops a “pre-work” framework that specifies the strategies people use to re-engage with work. Research spanning several literatures within the organizational psychology domain sheds light on how individuals may prepare for the workday. In addition to the recovery literature (e.g., Sonnentag, Venz, & Casper, 2017;

Parke et al., 2017; Zacher, Brailsford, & Parker, 2014), the current study examines the broader literature on stress and coping (e.g., Bliese, Edwards, & Sonnentag, 2017;

Folkman & Lazarus, 1985), emotion regulation (e.g. Tice & Bratslavsky, 2000), and positive organizational behavior (e.g., Luthans, 2002a) to inform the current conceptualization of pre-work.

The current study breaks down pre-work into three primary strategies: cognitive reattachment, energy mobilization, and positive reflection. Overall, these strategies capture the cognitive and motivational processes individuals use to re-engage with work.

Furthermore, the current study proposes that individuals that engage in these pre-work strategies will likely experience adaptive well-being outcomes. These relationships are important to explore given the significant impact well-being has on individual’s work and nonwork lives (Harter, Schmidt, & Keyes, 2002). The three pre-work strategies are

5 predicted to impact levels of job satisfaction and emotional exhaustion, two key well- being outcomes in the I-O and occupational stress literature (Örtqvist & Wincent, 2010), through their effects on engagement. Job satisfaction refers to the evaluative judgment of a person’s work situation (Weiss, 2002), whereas emotional exhaustion, considered the main component of job burnout, is a state of depletion and fatigue (Maslach & Jackson,

1981; Wright & Cropanzano, 1998). Positive states of well-being at work are related to healthier employee outcomes, such as physical (Hilleras, Jorm, Herlitz, & Winblad,

1998) and mental health (Kohn & Schooler, 1982). Positive well-being is also associated with healthier organizations, as it is positively related to organizational citizenship behaviors (e.g., Bornam, Penner, Allen, & Motowidlo, 2001) and negatively related to turnover and absenteeism (e.g., Spector, 1997).

Specifically, pre-work strategies are expected to relate to job satisfaction and emotional exhaustion through their differential effects on cognitive, physical, and emotional engagement (Kahn, 1990) as depicted in Figure 1. Although all three pre-work strategies may relate to each component of engagement, the strongest relationships are expected for the following: pre-work that cognitively reconnects employees back to work

(i.e., cognitive reattachment) will most strongly relate to the cognitive dimension of engagement. Pre-work that makes individuals feel motivated to work by increasing positive affect (i.e., energy mobilization) will most strongly relate to the energetic dimension of engagement (i.e., physical engagement). Finally, pre-work that increases perceptions of meaningful work and the feeling of being autonomously motivated (i.e., positive reflection) will most strongly relate to emotional engagement.

6 Furthermore, this study aims to construct a comprehensive pre-work framework by examining how specific resources may shape the effects of pre-work on engagement.

The current study examines resilience, an important individual difference in the capacity to recover from stress and adversity (Block & Kremen, 1996; Smith et al., 2008) and perceived supervisor support (PSS), which refers to employees’ general views regarding the degree to which supervisors value their contributions and care about their well-being

(Kottke & Sharafinski, 1988) as boundary conditions for the relations between pre-work and engagement. Resilience can be conceptualized as a personal resource that promotes effective coping and adaptive behaviors, as well as decreases negative reactions to stressful events (Rutter, 1987). PSS, on the other hand, can be conceptualized as a critical job resource as it may be essential to energizing and motivating employees and can impact their capacity for managing stress (Karasek, Triantis, & Chaudhry, 1982).

Although the nature of “resources” can be difficult to conceptualize and define

(Hobfoll, 2018), Hobfoll (1988, 1998) provided several criteria for what constitutes a resource. Specifically, he stated that personal, material, energy, and condition resources of interest were those that are central to major goal attainment or survival, common across large groups of individuals, and their effects are context dependent, such that in one context a resource might be salient and positive and in another might be salient but negative. Furthermore, Resource Caravans Theory (Hobfoll, 2011) suggests that resources will interact with one another. As such, it is important to examine how resources stemming from various sources, including personal strategies, individual differences, and environmental factors, interact to influence outcomes. Also, according to

Hofoll (2011), resources tend to occur in clusters or “resource caravans.” In other words,

7 resources, such as being high in resilience or having a supportive supervisor, tend to spawn other resources or prolong their effects (Hobfoll, 2011). Based on this theory, those who are high in resilience or perceive their supervisors as supportive may be more capable of acquiring resource gains through pre-work. Thus, the effects of pre-work on engagement are predicted to be stronger for (a) those that are high in resilience or (b) perceive their supervisor as supportive.

The current study contributes to the existing literature in multiple ways. The most noteworthy contribution is that it extends the literature on reattachment by going beyond primarily cognitive strategies and incorporating motivational processes, ultimately shedding light on the multitude of ways employees proactively reconnect with work daily. Second, it introduces proactive strategies that help build personal resources before stressors of the work day are encountered, which has largely been overlooked, as the majority of research has focused on recovery strategies aimed at replenishing resources once they’ve been depleted (Meijman & Mulder, 1998). Third, it examines how variability in daily pre-work may affect important outcomes, such as daily engagement, job satisfaction and emotional exhaustion. Fourth, it examines factors that may impact the relationships between pre-work and engagement, such as the role of resilience and perceived supervisor support. Lastly, it uses two approaches to model the hypothesized and non-hypothesized relations, each with distinct advantages.

The first approach, “Analytic Approach A: Separation of Measures,” will examine engagement assessed mid-shift as the mediator in the model. An advantage of this approach is that the predictor (pre-work strategy), mediator (mid-shift engagement), and outcome (end-of-shift satisfaction and exhaustion) variables in the model are

8 assessed at three separate time points, providing a rigorous test of the model from an analytical perspective. In contrast, the second approach, “Analytic Approach B:

Engagement throughout the Day,” will examine engagement averaged across mid- and end-shift as the mediator in the model. This approach arguably better matches the hypotheses from a conceptual perspective since the hypotheses in the current study suggest that pre-work will benefit individuals throughout the day, not during the first half of the day only. As such, both approaches will be used to examine the relationships between variables.

Overall, examining the relationships between pre-work strategies, engagement, and well-being (job satisfaction and emotional exhaustion), as well as the moderating roles of resilience and PSS may yield results that can better inform future research and practice to improve the employee experience.

9 Figure 1. Theoretical Model with Hypotheses

10 CHAPTER II

LITERATURE REVIEW

Engagement

People spend a considerable amount of time at work throughout their lives. Not surprisingly, during this time many individuals want to feel enthusiastic and motivated, and experience their work as pleasant and meaningful. Kahn (1990) captured these ideas in his concept of engagement. According to Kahn, engagement is a motivational concept that involves simultaneously investing cognitive, physical, and emotional energies into the work role (Rich, Lepine, & Crawford, 2010). In his view, engagement represents the investment of multiple dimensions of the self – cognitive, physical, and emotional – so that the experience is holistic and simultaneous (Kahn, 1992; Rich et al., 2010).

Although there are two main approaches to engagement in the literature, the current study adopts the approach introduced by Kahn (1990), which conceptualizes engagement as “harnessing of the organizational members” selves in their work roles (p.

694). This approach conceptualizes engagement as the simultaneous investment of an individual’s cognitive, physical, and emotional energy in work performance. The second approach conceptualizes engagement as the opposite of burnout (Maslach & Leiter, 1997) and defines it as a “pervasive affective-cognitive state” (p. 74) composed of three dimensions: vigor, dedication, and absorption (Schaufeli, Salanova, Gonzalez-Roma, &

Bakker, 2002). Although the “opposite of burnout” latter approach is most often used by researchers to define engagement, Kahn’s (1990) conceptualization is considered one of strongest (Byrne et al., 2016). In the current study, engagement is conceptualized using

Kahn’s (1990) approach for three main reasons. First, his approach is grounded in strong

11 theory. Second, Byrne and colleagues (2016) found that the subscales of the job engagement scale (JES), which is based on Kahn’s definition of engagement, exhibited clearer differences from each other than the UWES scale by Schaufeli et al. (2002), which is based on the “opposite of burnout” approach. Furthermore, the JES scale exhibited less overlap with associated job attitudes compared to the UWES, leading them to recommend that researchers use the JES over the UWES (Byrne et al., 2016). Third, the JES has accumulated sound validity evidence for its structure and use (e.g., Alfes,

Shantz, Truss, & Soane, 2013; Chen, Yen, & Tsai, 2014; He, Zhu, & Zheng, 2014; Rich et al., 2010; Shuck, Twyford, Reio, & Shuck, 2014).

Kahn (1990) posited that engaged individuals experience a connection with their work on a cognitive, physical, and emotional level. Individuals exhibit cognitive engagement when they are cognitively vigilant, deliberately thinking and tracking information, actively problem solving, and being focused during role performance (Kahn,

1990; Rich et al., 2010). In contrast, physical engagement captures the physical energy individuals exert on the job to accomplish their roles (Kular et al., 2008), and is characterized by exerting energy, effort, and vigor on one’s job (Kahn, 1990; Rich et al.,

2010). Lastly, emotional engagement is characterized by empathic connection to others and expression of excitement in the work role (Kahn, 1990). Emotionally engaged individuals feel emotionally connected to and dedicated to their work and to others in the service of their work (Rich et al., 2010).

Although much of the research on engagement has conceptualized it as a relatively pervasive affective-cognitive state (Schaufeli et al., 2002) that varies between persons (e.g., Schaufeli et al., 2002; Schaufeli & Salanova, 2007), Kahn (1990)

12

hypothesized that engagement varies both between- and within-individuals. Whereas enduring or lasting engagement refers to the extent to which employees feel engaged in in relation to their work in general over a period of time, daily engagement refers to a transient state that exists in each moment and fluctuates over short periods of time within the same individual (e.g., day to day or hour to hour; Sonnentag et al., 2010). It captures how employees experience their work moment to moment; as something they are fully concentrated on (cognitive component), as energizing (physical component), and as significant and meaningful (emotional component; Bakker, 2014; Bakker, Schaufeli,

Leiter, & Taris, 2008). In support of Kahn’s (1990) idea that engagement varies within individuals, Sonnentag (2003) argued and demonstrated that levels of engagement vary within the employees day-to-day. Furthermore, she showed that this variation was related to changes in day-level recovery resulting from leisure time activities. In another study, it was found that on the week-level, 47% of the total variance in engagement was within- persons (Bakker & Bal, 2010). Consistent with this finding, daily diary studies have demonstrated that across different occupational settings, within-person fluctuations account for 42% of total variance in engagement (Xanthopoulou & Bakker, 2013). In sum, research findings support both within- and between-person fluctuations in engagement.

In addition to investigating the state versus trait-like nature of engagement, scholars have also examined the relationship between engagement and various outcomes.

The evidence overwhelmingly suggests that engagement is associated with beneficial outcomes at both the employee and organizational level. For instance, engaged employees exhibit better physical and mental health (Leijten et al., 2015; Reis, Hoppe, &

13

Schröder, 2015). Evidence also suggests that on days when employees exhibit higher engagement, they experience lower levels of fatigue during the workday (Sonnentag,

Mojza, Demerouti, & Bakker, 2012) and higher levels of positive affect at home

(Culbertson et al., 2012). Besides being a positive experience for employees, engagement predicts important organizational outcomes, including employee task and contextual performance (Christian, Garza, & Slaughter, 2011), proactive behavior (Sonnentag,

2003), and daily financial returns (Xanthopoulou, Bakker, Demerouti, & Schaufeli,

2009). In sum, the construct of engagement has been associated with an array of positive outcomes at both the individual- and organization-level and thus, is of great interest to researchers and practitioners alike. To better understand the construct of engagement and potentially find ways to increase employees’ levels of it, researchers have also studied its antecedents, one of which is recovery from work.

Recovery as an Antecedent of Engagement

Although stable factors such as job characteristics (Hackman & Oldham, 1980), charismatic leadership (Bass & Avolio, 1990), and personality (Macey & Schneider,

2008) impact the psychological experience of engagement, there are also day-to-day factors, such as recovery experiences that occur during work and after work (free time), that can protect individuals from strain and contribute to them being more or less engaged on a momentary basis (e.g., Gorgievski & Hobfoll, 2008; Hobfoll, 1998; Sonnentag,

2001; 2003; Sonnentag, Binnewies, et al., 2010). The effort-recovery model (Meijman &

Mulder, 1998) suggests that individuals experience work demands that can cause strain accumulation. Furthermore, this strain will eventually lead to reduced well-being if not reversed through recovery (Meijman & Mulder, 1998). Recovery strategies help people

14

unwind and restore resources after their strain levels have increased (Craig & Cooper,

1992) and can occur on-the-job during work breaks (e.g. Trougakos & Hideg, 2009;

Fritz et al., 2011), as well as after work during one’s free time (e.g., Sonnentag & Fritz,

2007). Recovery that occurs either during or after work ensures that employees have sufficient energetic and self-regulatory resources, which later helps them be engaged with work, that is to direct their attention to work, be immersed in their job, and feel energetic while working (Kuhnel, Zacher, DeBloom, & Bledow, 2016). However, without sufficient recovery the effects of stress may accumulate (Meijman & Mulder, 1998).

High demands coupled with low recovery can result in long-term negative outcomes, such as reduced well-being and employee engagement and more health problems (e.g.,

Sonnentag & Fritz, 2015). There are two main perspectives regarding how recovery occurs and resources are replenished. The first perspective, based on ego depletion theory

(Muraen & Baumeister, 2000) and Conservation of Resources theory (Hobfall, 1989), assumes that resources are limited and that work depletes resources (e.g., Trougakos &

Hideg, 2009; Quinn, Spreitzer, & Lam, 2009). As such, employees must detach from work to manage or replenish their resources. In other words, strain will be reversed and systems will return to their pre-stressor level during times of “nonwork” or leisure and are essential for physical and mental health (Craig & Cooper, 1992). In contrast, the second perspective, based on broaden and build theory (Fredrickson, 2001) and self- determination theory (SDT; Ryan & Deci, 2000), assumes that resources can be replenished through a variety of activities, including pleasant activities that are intrinsically motivating or activities that satisfy one’s basic psychological needs of relatedness, competence, and autonomy (Gagne & Deci, 2005). According to this

15

perspective, it may be unnecessary for employees to psychologically detach from work to replenish. Rather engaging with work, itself, can help replenish employee’s resources. In the subsequent section I review recovery experiences that occur. Many studies have examined how individuals replenish depleted resources during breaks at work

(Trougakos, Beal, Green, & Weiss, 2008) and during free time after work (e.g.,

Sonnentag, 2001; Sonnentag & Zijlstra, 2006).

Recovery During Work

During the workday people may take short respites or longer scheduled breaks

(i.e., lunch break). Fritz and colleague (2011) defined “energy management strategies” as activities that employees deliberately engage in during work to keep their energy levels high (Fritz et al., 2011). These strategies target one’s subjective sense of well-being at work and impact employee engagement. Fritz and colleagues (2011) classified energy management strategies used during the workday into two types of strategies: micro- breaks and work-related strategies. Micro-breaks allow for momentary detachment from work and subsequent energy recovery through the brief respite. Respite activities may include taking a bathroom break, getting coffee, or eating a snack. This is consistent with the “resources are limited” perspective because it assumes people need to stop working to replenish energy (e.g., Trougakos & Hideg, 2009; Quinn et al., 2009). The other types of strategies are work-related and include changing work tasks, thinking about the meaning of work, or helping a co-worker. They involve switching up how one approaches work.

Unlike micro-breaks, these strategies are consistent with the perspective that resources, like energy, can be generated by engaging in pleasant activities (Fredrickson, 2001) or by doing work that satisfies basic psychological needs (Ryan & Deci, 2000). Research

16 suggests that shorter breaks during the workday (i.e., micro-breaks) can improve engagement (Wendsche, Lohmann-Haislah, & Wegge, 2016; Zacher, Brailsford, &

Parker, 2014), particularly during the afternoon (Kühnel, Zacher, de Bloom, & Bledow,

2017). However, it is still unclear which micro-break activities and experiences are most beneficial. While many individuals engage in micro-breaks, some research suggests that work-related breaks are more restorative compared to micro-breaks. In their cross- sectional study, Fritz and colleagues (2011) examined the micro-breaks used by knowledge workers. They found that many commonly used micro-breaks, such as listening to music or checking in with a friend, were positively associated with fatigue and negatively associated with vitality, which suggests that employees may seek out micro-break strategies when they are already fatigued (Fritz et al., 2011). Interestingly, they found that the best breaks for increasing energy levels (vitality) were work-related and included learning, creating relationships, and meaning making at work. Yet, not all work-related breaks were helpful. Some strategies, such as switching to another task, had no impact on energy levels.

Other research found that micro-breaks were associated with better physical outcomes for employees working on repetitive office-tasks, such as the experience of less musculoskeletal discomfort and strain (e.g., Fisher, Andres, Airth, & Smith, 1993;

Henning, Jacques, Kissel, Sullivan, & Alteras-Webb, 1997; McLean, Tingley, Scott, &

Rickards, 2001). In general, the evidence suggests that breaks help reduce fatigue effects and increase engagement and productivity.

In addition to short breaks, it is common for employees to take longer, planned breaks throughout the day, such as lunch breaks. In exploring the possible benefits of

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lunch breaks over one-year, researchers found recovery processes occurring during lunch breaks have both short- and long-term effects (Sianoja, Kinnunen, de Bloom, Korpela, &

Geurts, 2016). Other research suggests that what people do during lunch breaks impacts post-break behavior and experiences. For example, in a study on cheerleading instructors, respite breaks used for napping, relaxing, and socializing were associated with positive post-break emotions and positive affective display, whereas chore breaks (i.e., running errands, practicing material, and preparing for upcoming sessions) were associated with negative post-break emotions (Trougakos, Beal, Green, and Weiss, 2008). In a second study with administrative employees, social and work-related activities during lunch breaks were found to positively relate to end-of-workday fatigue, particularly when employees felt that they had little control over the activities they pursued (Trougakos,

Hideg, Cheng, and Beal, 2014). Another study found that when employees were assigned to either a muscle relaxation lunch break or a social “small-talk” lunch break, only those in the relaxation group experienced reduced strain after lunch (Krajewski, Wieland, &

Sauerland, 2010). This finding provides further support that social lunch break activities coupled with low autonomy are not very effective in reducing strain. In sum, it appears that feeling low control over one’s work break hampers recovery.

Recovery After Work

Employees may also feel more engaged and enjoy higher levels of well-being and performance when they recover from work during non-work or free time (Meijman &

Mulder, 1998). Although research has examined recovery that occurs during the evening

(e.g., Sonnentag, 2001), weekends (e.g., Fritz & Sonnentag, 2005; Fritz, Sonnentag,

Spector, & McInroe, 2010), and even vacations (e.g., Fritz & Sonnentag, 2006; Westman

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& Eden, 1997; de Bloom et al., 2009), much of the work (and my review of it) focuses on end-of-day activities. Recovery experiences after work refer to the psychological mechanisms that explain how specific leisure activities help restore people’s health and well-being after a stressful work day (Sonnentag & Fritz, 2007) and are thought to help

“undo” strain reactions caused by work. Sonnentag and Fritz (2007) argued that the underlying psychological experiences necessary for recovery may vary little across persons even though the specific activities individuals experience as recovering may differ. For example, one person might recover from job stress by taking a leisurely walk while another reads a book, yet the underlying process (e.g., relaxation) is the same

(Sonnentag & Fritz, 2007). Thus, they went beyond specific activities to examine the underlying experiences of recovery to better understand the psychological processes leading to recovery.

Five underlying psychological experiences associated with recovery have been identified in the literature (Fritz & Sonnentag, 2005; Sonnentag & Fritz, 2007). These include psychological detachment from work (i.e., not thinking about work during non- work time), mastery (i.e., facing a positive challenge), relaxation (i.e., having a low activation level), control (i.e., feeling of control over non-work time), and community

(i.e., opportunities for social contact and connectedness; Fritz & Sonnentag, 2005).

Empirical studies focusing on between-person differences of these recovery experiences suggest that those who score high on each experience enhanced well-being. In regard to within-person differences, studies have shown that employees’ evening recovery experiences are associated with positive mood states the next morning (van Wijhe,

Peeters, Schaufeli, & Ouweneel, 2013), although the majority of this research has

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examined detachment, with relatively few studies examining the other four recovery experiences (i.e., mastery, control, relaxation, community; Sonnentag et al., 2017).

Psychological detachment tends to be the most commonly studied recovery experience and refers to an “individual’s sense of being away from the work situation”

(Etzion et al., 1998, p. 579). Detached individuals are not occupied with job-related duties and tend to mentally disengage themselves from work (Sonnentag & Bayer, 2005).

When individuals continue to think about their jobs after work (i.e., they do not mentally detach) the same internal resources called upon during work are used without any break

(Sonnentag & Fritz, 2007), and full recovery is unlikely to occur.

Studies that used daily surveys have found that employees’ evening detachment impact their psychological experiences the next day (e.g., Sonnentag et al., 2017; van

Wijhe, Peeters, Schaufeli, & Ouweneel, 2013; Sanz-Vergel, Demerouti, Bakker, &

Moreno-Jiménez, 2011). For instance, evening detachment was related to favorable mood states the following morning (van Wijhe, et al., 2013). Studies on this issue have shown that, on evenings when employees detach from their jobs, they experience lower levels of work-family conflict (Sanz-Vergel, et al., 2011) and better sleep quality (Clinton,

Conway, & Sturges, 2017; Hülsheger et al., 2014). In addition, at bedtime they experience lower levels of negative affect (Sonnentag & Binnewies, 2013) and higher levels of vigor (Demerouti et al., 2012). Furthermore, the next morning, employees feel more recovered (Volman et al., 2013) and vigorous (Clinton et al., 2017; ten

Brummelhuis & Bakker, 2012), and experience lower negative affect, and fatigue

(Sonnentag et al., 2008).

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Detachment during the evening has also been found to buffer the association between negative affect experienced at work and next morning negative affect

(Sonnentag & Binnewies, 2013). Similarly, it has also been found to buffer the association between end-of-work affective and physical distress and next morning affective and physical distress (Park, Fritz, & Jex, 2015). In sum, these findings suggest that psychological detachment from work is crucial after a difficult day (Sonnentag et al.,

2017).

Another underlying psychological experience of recovery is mastery. Mastery experiences are conceptualized as learning opportunities and challenges that may occur during certain leisure time activities (Sonnentag & Fritz, 2007). These opportunities and challenges may include engaging in new or difficult physical activities (e.g., cycling, marathon training, rock climbing) or intellectually stimulating activities (e.g., learning a new language). There are two primary reasons why mastery activities positively relate to recovery (Sonnentag & Fritz, 2007). First, mastery activities generate new resources, such as a sense of self-efficacy (Bandura, 1997) and new skills/abilities (Hobfoll, 1989).

Second, stimulating activities, which are often accompanied by mastery experiences, enhance positive mood and well-being (Totterdell & Parkinson, 1999). At the person level, having mastery experiences during non-work time is related to diminished psychological distress and physical complaints (Shimazu et al., 2012), reduced exhaustion and need for recovery (Siltaloppi et al., 2009), and increased vigor at work (de

Bloom et al., 2015; Kinnunen, Mauno, & Siltaloppi, 2010). These experiences are also negatively associated with work family conflict (Molino, Cortese, Bakker, Ghislieri,

2015) and positively associated to life satisfaction (Lee, Choo, & Hyun, 2016; Park &

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Fritz, 2015). At the day level, a study found that employees experience higher levels of positive affect after evenings with high levels of mastery experiences (Sonnentag et al.,

2008).

Relaxation, another one of the five underlying psychological experiences of recovery, is often associated with leisure activities. It is characterized by a state of increased positive affect and low activation (Stone, Kennedy-Moore, & Neale, 1995).

Individuals often expect to experience relaxation when engaging in non-challenging activities that require few social, intellectual, or physical demands. Some individuals may engage in activities explicitly aimed to relax the body and mind such as muscle relaxation

(Jacobson, 1938) or meditation (Grossman, Niemann, Schmidt, & Walach, 2004).

Relaxation may also result from engaging in other activities such as listening to music

(Pelletier, 2004) or going for a walk in a pleasant natural environment (Hartig, Evans,

Jamner, Davis, & Ga¨rling, 2003).

At the person level, employees who relax during nonwork time report experiencing less psychological distress and physical complaints (Shimazu et al., 2012) along with better subjective health (de Bloom, Kinnunen, & Korpela, 2015). At the day level, research suggests that evening relaxation is associated with serenity (Sonnentag et al., 2008) and vigor (ten Brummelhuis & Bakker, 2012) the next morning.

The fourth psychological experience, control, refers to the extent to which an individual can decide which activity to pursue during leisure time, as well as how, when, and where to pursue it (Sonnentag & Fritz, 2007). The experience of control during leisure time may increase self-efficacy and feelings of competence, which in turn satisfies the need for control and promotes well-being (Sonnentag & Fritz, 2007). Thus, control in

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the evening may be conceptualized as an external resource that enhances recovery from work during non-work time (Sonnentag & Fritz, 2007). In addition, it provides the individual the opportunity to choose preferred leisure activities, which may facilitate the recovery process. At the person level, employees who experience control during non- work time report lower levels of psychological distress, physical complaints (Shimazu et al., 2012), need for recovery (Siltaloppi et al., 2009), and work family conflict (Molino et al., 2014). They also report better subjective health (de Bloom et al., 2015) and higher levels of life satisfaction (Lee et al., 2016). Research focused at the day-level suggests that after evenings with high levels of control, employees experience a more positive mood, characterized by positive valence, energetic arousal, and calmness (Dettmers,

Vahle-Hinz, Bamberg, Friedrich, & Keller, 2016).

The final psychological recovery experience, community, captures the extent to which a recovery activity offers chances to build relationships (Fritz & Sonnentag, 2005) and connect with others (Sonnentag & Fritz, 2007 as cited in Mojza et al., 2010), such as pursuing activities with people one likes to be around (Fritz & Sonnentag, 2005).

Recovery may occur as a result of community experiences because such experiences are associated with increased social resources and social support (Fritz & Sonnentag, 2005).

For example, feeling connected to others was related to daily well-being (Reis, Sheldon,

Gable, Roscoe, & Ryan, 2000; Sheldon, Ryan, & Reis, 1996). Despite the seeming importance of community experiences, little research has examined it, perhaps because the recovery experience measure developed by Sonnentag and Fritz (2007) only captured the first four recovery experiences (detachment, mastery, relaxation, and control), not community experiences.

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As evident from the review above, a large body of research has explored the relationships between recovery experiences and well-being indicators and has found that recovery is positively associated with well-being. However, by focusing solely on breaks from work (during or after the workday), recovery researchers may have overlooked other processes that impact how employees experience work. Thus, employees may be able to actively shape their daily level of engagement not only through recovery, but through the adoption of “pre-work” strategies that reconnect them to work.

I conceptualize pre-work as daily strategies employees may use to prepare themselves for reentry into their work role. Consistent with the boundary management perspective (Ashforth et al. 2000), these strategies enact psychological mechanisms that refocus individuals’ attention on work and/or prepare them emotionally and energetically for work, easing the transition from the non-work to work domain. Theoretically, pre- work strategies impact individual’s pre-work psychological state, which affects how they experience work and how they perceive and respond to work demands (Meijman &

Mulder, 1998; ten Brummelhuis & Bakker, 2012). Yet we know very little about ways in which individuals may mentally prepare for work. Like recovery experiences (Sonnentag

& Fritz, 2007), pre-work helps individuals manage resources. However, pre-work is proactive in that it helps to build personal resources before stressors of the workday are encountered, unlike recovery which is aimed at replenishing resources once they have been depleted (Meijman & Mulder, 1998). In some ways, pre-work strategies and recovery activities can be viewed as proactive versus reactive attempts to manage one’s resources. Just as reactive medical care attempts to resolve symptoms once the disease has already occurred (Gillies, Baird, & Gilles, 1995; Hood & Flores, 2012), recovery

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attempts to reduce stress from work once it has been experienced. Proactive medical care, on the other hand, provides the opportunity for disease prevention (e.g., through screenings, risk assessments, encouraging a healthy lifestyle; Gillies et al., 1995; Hood &

Flores, 2012), like how pre-work may provide individuals the opportunity to maximize personal resources and minimize stress accumulation.

Review of ‘Resource Building Strategies’

Existing research related to recovery, coping, emotion regulation and positive organizational behavior sheds light on how individuals can prospectively prepare for and build resources before the workday. To identify pre-work strategies, I not only draw from the recovery literature (e.g., Sonnentag, Venz, & Casper, 2017; Parke et al., 2018;

Zacher, Brailsford, & Parker, 2014), as previously reviewed, but from the stress/coping

(e.g., Bliese, Edwards, & Sonnentag, 2017; Folkman & Lazarus, 1985), emotional regulation (e.g., Tice & Bratslavsky, 2000), and positive organizational behavior (e.g.,

Luthans, 2002a) literatures. In the following sections I review concepts from these literatures that are used to inform my pre-work framework.

The review begins with a general description of coping, followed by a description of the three main types of coping: future-oriented (including daily planning and reattachment strategies); emotion-focused; and meaning-making (self-affirmation and positive work reflection).

Coping

Coping is defined as thoughts and behaviors that individuals use to manage the demands from stressful circumstances (Lazarus & Folkman, 1984; Folkman & Lazarus,

1980). Rather than being passive respondents to work-related demands (Lazarus, 1991),

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employees may use a variety of behavioral and cognitive strategies to alter, re-evaluate, or avoid stressful circumstances (Parkes, 1994). For instance, to manage a stressor, an individual may think about it in a way that reduces its impact, seek social support, or avoid exposure to the stressful situation to name only a few strategies (Dewe et al., 2010).

Lazarus and Folkman’s (1984) coping theory suggests that coping is a process that occurs in the context of a situation that is appraised as personally significant and as exceeding the individual's resources for coping. After the coping process is started, individuals may use a variety of coping strategies. One common distinction in this area of research is between problem-focused coping and emotion-focused coping (Folkman &

Lazarus, 1980). Problem-focused coping tends to occur when people feel that something constructive can be done about the stressor and may include gathering information, making decisions, or planning how to deal with the stressor (Folkman & Lazarus, 1985).

In contrast, emotion-focused coping is used to reduce the negative emotions associated with the problem and may include engaging in distracting activities, using alcohol or drugs, or seeking emotional support (Folkman & Lazarus, 1985). Although the problem- and emotion-focused distinction is a commonly used, researchers have since identified a different type of coping, namely meaning-focused coping, which occurs when individuals adopt cognitive strategies to manage the meaning of a situation (Park & Folkman, 1997).

This occurs when individuals attempt to modify the meaning of a stressful situation by drawing on goals, beliefs, and values, especially in cases of enduring stress that can’t be altered through problem-focused coping (Park & Folkman, 1997).

Coping is a complex process impacted by the environment and by individual differences related to how people appraise stress and their resources for coping (Folkman

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& Moskowitz, 2004). Because of this complexity, strategies are not inherently good or bad in terms of coping effectiveness (Lazarus & Folkman, 1984). Rather, the adaptive qualities of coping processes depend on the stressful context in which they occur (Britt,

Crane, Hodson, & Adler, 2015; Mattlin et al., 1990). Folkman and Moskowitz (2004) have argued that problem-focused coping strategies are more effective when the demand is controllable to some extent, whereas emotion-focused strategies are best when the stressor encountered is largely uncontrollable. Research suggests that certain types of escapist coping strategies, such as wishful thinking, alcohol use, and denial regarding the situation are consistently related to poorer mental health outcomes (Folkman &

Moskowitz, 2004).

Most studies focus on how people cope with events that occurred in the past or that are occurring in the present even though the concept of threat (i.e., anticipated harm or loss) is central to coping. As such, a relatively unexplored idea in the coping literature is related to the ways people cope in advance to prevent or mitigate the impact of events that are potential stressors. This type of coping is elaborated on in the subsequent sections.

Future-Oriented Coping. Relatively little work has explored the process that occurs when individuals attempt to cope with potential future stressors. Aspinwall and

Taylor (1997) use the term “proactive coping” to refer to responses to potential stressors.

Their model defines five aspects of the proactive coping process: (a) the importance of building resources, including time, money, and social support, that can be used to prevent or counter future net losses (see also Hobfoll, 1989), (b) recognition of potential stressors,

27 (c) initial appraisals of potential stressors, (d) preliminary coping efforts, (e) and the seeking and use of feedback about the success of one’s efforts (Aspinwall, 2003).

Aspinwall and Taylor (1997) viewed proactive coping as almost always being problem-focused (i.e., active) since it requires putting in effort to prevent or modify a stressor before it occurs. They note that although emotion-focused coping strategies related to emotional regulation, such as positive reappraisal, may be effective for coping with astressor (e.g., Dunkel-Schetter, Feinstein, Taylor, & Falke, 1992), such strategies will be unhelpful during the proactive stage because they inhibit active resolution of a problem. In addition to resources (e.g., time, money, support), stable individual differences, like optimism and control beliefs, are thought to influence whether one will engage in proactive coping.

Research by Schwarzer and Knoll (2003) has also focused on how individuals may cope with potential stressors. They distinguished between specific types of future- oriented coping strategies, namely, anticipatory, preventive, and proactive coping.

According to them, anticipatory coping refers to efforts to deal with a critical event that is certain or fairly certain to occur in the near future (e.g., preparing for a scheduled presentation); preventive coping foreshadows an uncertain threat potential in the distant future (e.g., enrolling in a skill development workshop to prevent being seen as incompetent at work); and proactive coping, which occurs when individuals appraise upcoming situations as challenges that may create opportunities for mastery, growth or gain (Folkman & Lazarus, 1985). When individuals foresee opportunities for mastery, growth or gain, as is the case with proactive coping as defined by Schwarzer and Knoll

(2003), they are likely to approach the situation enthusiastically and feel like they are

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moving toward positively valued goals (Folkman & Moskowitz, 2004). Thus, according to Schwarzer and Knoll (2003), proactive coping differs from anticipatory and preventive coping in that it is approach-oriented and focused on positive growth.

Although the idea that individuals use strategies to cope with potential stressors was introduced over twenty years ago (Aspinwall & Taylor, 1997), research on this anticipatory phase of the stress-coping process is lacking in the job stress literature

(Brosschot, Gerin, & Thayer, 2006; Meurs & Perrewé, 2011). An exception to this, however, is research on daily planning and reattachment, both of which explore how anticipatory processes may play a part in the job stress process (Pereira & Elfering, 2014;

Rook & Zijlstra, 2006).

Daily Planning. Employees may prepare for work by thinking about work, anticipating problems, developing plans, and addressing obstacles (Hoc, 1988). Planning is the process of mentally simulating potential courses of action with the goal of identifying the best strategy for converting an individual’s resources into actions for goal attainment (Austin & Vancouver, 1996; Hayes-Roth & Hayes-Roth, 1978) and may be conceptualized as a form of anticipatory coping as defined Schwarzer and Knoll (2003).

Planning can make problematic situations easier to understand and allow one to identify and anticipate potential obstacles and difficulties (Hoc, 1988). With regards to stressors, planning involves anticipating the nature of problems and developing strategies for handling the problem. Although planning is often problem-focused, it differs from problem-focused coping because it involves mentally simulating future situations and developing strategies for handling future problems, rather than actively addressing currently experienced problems. In other words, planning can be thought of as occurring

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during the appraisal stage, whereas problem-focused coping occurs during the actual coping phase (Carver et al., 1989).

To plan for work, employees think about what they want to achieve while at work

(i.e., goal setting) and/or how they will achieve it (i.e., goal striving). Thus, goal setting and simulated goal striving can be important aspects of planning. In the goal-setting literature, a goal is the aim of an action, usually to attain a certain standard of proficiency within a specified time limit (Ryan, 1970). It is well established that goals direct attention, effort, and persistence toward goal-relevant activities and away from goal irrelevant activities (Locke & Latham, 1990). In addition, goals often lead to the adoption of task-relevant knowledge and strategies, especially under more complex task conditions

(Wood & Locke, 1990). Specifically, goal setting research has found that when confronted with task goals, people automatically use the knowledge and skills they have already acquired that are relevant to goal attainment. If goal attainment requires the use of skills that are not automatized, people draw from a collection of skills that they have used previously in similar or related contexts and then apply them to the present situation.

If individuals have not previously encountered the task associated with the goal, they will develop strategies that enable them to attain their goals (Smith, Locke, & Barry, 1990). In sum, explicit planning is a natural product of having goals, especially when situations are novel and/or complex.

Motivation involves both goal-setting (establishment of standards) and goal- striving (pursuing standards). Planning is often thought to support goal-setting processes, but it can also be used to bolster goal-striving (Diefendorff & Lord, 2003). The effects that planning has on individual performance have been mixed (Claessens et al., 2004;

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Frese et al., 2007; Macan, 1994; Sitzmann & Johnson, 2012), For example, planning alone increased performance in some studies (Claessens, Van Eerde, Rutte, & Roe, 2004;

Frese et al., 2007), but not in others (Macan, 1994; Sitzmann & Johnson, 2012). To bring clarity to the issue, Parke and colleagues (2018) showed that the mixed findings regarding the benefits of planning may be due to researchers overlooking distinct forms of planning. They investigated why and when two distinct types of daily planning influence performance in an organizational context. They discovered that time management planning (TMP; employees create task lists, prioritize task lists) and contingency planning (CP; a type of implementation intention where employees anticipate and plan for possible interruptions in their work) are both positively related to performance. However, they found that TMP’s positive effects on performance, but not

CP’s, are weaker when employees have many interruptions in their work. This finding suggests that CP may be more effective when employees experience high levels of interruptions. Although these finding shed light on the effectiveness of task-related planning, the researchers did not consider other ways in which individuals may prepare themselves for the workday. For instance, they may motivate themselves not only through work-related planning, but also by engaging in non-work-related activities (e.g., listening to upbeat music) or by reflecting on the positive impact and meaningfulness of their work.

Reattachment. Employees may also “reattach” with work to prepare themselves for the day. As previously mentioned, Sonnentag and Kühnel (2016) coined the term

“reattachment”, which they defined as the “process of rebuilding a mental connection with one’s work” (Sonnentag & Kühnel, 2016, p. 379) after a nonwork period. They

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describe reattachment as “mentally crossing the boundary between the nonwork and work domain, before immersing oneself fully into one’s work again” (Sonnentag & Kuhnel,

2016, p. 381) and creating an anticipatory “mental contact” with one’s upcoming workday, thereby bringing one’s attention from non-work issues back to one’s work. For instance, they describe how an individual might mentally simulate the workday and think of the tasks one needs to accomplish or about the people one will be meeting during the day. Reattachment can imply some deliberate planning or even ruminative thoughts, but neither planning nor rumination are necessary parts of reattachment. Using a daily diary study, Sonnentag and Kuhnel (2016) found that evening psychological detachment and morning reattachment positively predicted work engagement throughout the day, and the association between reattachment and work engagement was stronger in the morning than in the afternoon. Building on this initial reattachment study, Sonnentag and colleagues

(2019) identified several mechanisms linking morning reattachment to engagement during the day. Using a daily diary study, they found that reattachment lead to cognitive

(anticipated task focus) and affective processes (activated positive affect), as well as processes related to the mobilization of job resources (i.e., social support and job control), which in turn predicted engagement during the day.

Emotion Regulation

In addition to adopting future-oriented strategies like planning and reattaching, employees may also regulate their emotions in attempts to cope with extant stressors. A great deal of work has focused on how emotions impact behaviors (e.g., Frijda, 1986;

Sudakov, Ganten & Nikolov, 1989). Emotions are evoked either by perceived events, or by thoughts of past or future events, and it is well documented that emotions (arousal and

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tone) have an impact on information processing. Because of their influence on information processing, emotions evoked during work are likely to affect the performance process (Frijda, 1986). Emotion regulation has been conceptualized as a

“process by which individuals influence which emotions they have, when they have them, and how they experience and express these emotions” (p. 275). Emotion regulation can be thought of in terms of monitoring what one is currently feeling and expressing relative to standards for what one should feel or express (Tice & Bratslavsky, 2000).

Monitoring focuses on understanding one’s present emotions (Tice & Bratslavsky, 2000), whereas the standards represent the types of emotions that are either desirable (i.e., hedonistic motive) or useful for achieving some other objective (i.e., instrumental motive;

Tamir, Mitchell, & Gross, 2008).

Successful emotion regulation requires a repeated evaluation of one’s emotions relative to the desired emotions (Baumeister et al., 1994). Once a discrepancy between felt and desired emotions is detected, individuals can employ a variety of regulation or coping strategies to align felt affect with what is needed or desired. Emotion regulation is aimed at reducing or altering one’s emotional reactions or the distress one experiences in response to a stressor and include changing how one thinks or feels about stressor(s).

One general strategy is positive reappraisal, which is aimed at reframing a situation to see it in a positive light and may include benefit reminding (e.g., Affleck & Tennen, 1996), downward social comparisons (e.g., Wills, 1981; Wood, 1989), or perspective-taking

(Gross, 1998). For instance, employees may reappraise a stressful situation as a learning opportunity (i.e., benefit reminding; Carver & Connor-Smith, 2010; Gross, 2013) or think about how others have experienced much worse situations (i.e., downward social

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comparison). Other strategies include using humor, relaxation techniques, or prayer to alter how one feels (Moran & Massam, 1997; Martin, 2001). Changing one’s felt emotions has been found to be an effective strategy, especially when individuals do not have the ability to change the work situation or alter the source of stress (Aryee et al.,

1999; Pearlin and Schooler, 1978).

As noted above, individuals may engage in emotion regulation for different reasons. Generally, the literature has found that individuals regulate their affect for hedonic reasons (e.g., to simply feel good) or instrumental reasons (i.e., to achieve some higher-level goal or objective). Most of the time, emotion regulation at work can be linked to some instrumental objective, such as enabling better concentration on a task or better serving a customer. For instance, research on instrumental affect regulation has shown that if individuals believe a certain emotion (e.g., anger) will enhance their performance on a task, they will engage in activities that are likely to increase that emotion prior to completing the task; this is true even if the emotion is negative and presumably makes the person feel worse (Tamir, Bigman, Rhodes, Salerno, & Schreier,

2015).

Meaning Making

It is a “fundamental human motive” to want to derive meaning from events (Britt,

Adler, & Bartone, 2001, p. 54) including work. Meaningful work refers to work that employees believe is significant in that it serves an important purpose (Pratt & Ashforth,

2003; Rosso, Dekas, & Wrzesniewski, 2010). Various factors influence whether individuals experience their work as meaningful, including job characteristics (Hackman

& Oldham, 1980; Kahn, 1990), individual traits (Lips-Wiersma, 2002; Wrzesniewski et

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al., 1997), social interactions on the job (Robertson, 2013; Wellman & Spreitzer, 2011), and person-job fit (Kahn, 1990; Shamir, 1991). Furthermore, it is thought to arise when people have a clear understanding of their abilities and what is expected of them, know that their effort has a clear purpose within an organization, and when that effort benefits other people (Steger & Dik, 2009; Steger & Dik, 2010).

The original job characteristics model (Hackman & Oldham, 1980) assumed that managers and human resource departments were responsible for creating meaningful work environments and employees were largely reactive to external circumstances.

However, since then researchers have argued and shown that, regardless of the external circumstances, employees can play an active role in enhancing their perceptions of meaningful work (Bakker & Demerouti, 2017). Specifically, they can influence the meaningfulness of their work by adopting specific strategies (e.g., Demerouti, Bakker,

Nachreiner, & Schaufeli, 2001), including self-affirmation and positive work reflection.

For example, individuals may think about their reasons for performing their work (e.g., my job allows me the opportunity to support my family). Because of this reflection, individuals are more likely to perceive their work as meaningful, feel a sense of ownership over the work (Hackman & Oldhman, 1980), and become self-determined

(Deci & Ryan, 1985; Ryan & Deci, 2000). When individuals become self-determined they engage in their work for identified reasons (e.g., a sense of personal importance or valuing, consistent with other personally important values) that are fully endorsed by the self, as opposed to reasons that feel pressured or coerced (Deci & Ryan, 1985; Ryan &

Deci, 2000).

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Self-Affirmation. According to self-affirmation theory, people are driven to protect their self-integrity (Sherman & Cohen, 2006), or their global sense of personal adequacy (i.e., “I am a good, moral person”; Steel, 1988). Steele (1988) suggested that the self is made up of different domains: roles (responsibilities a person has, such as being a friend or professional), values (aspirations people live in accordance with, like treating others with kindness), and belief systems (ideologies to which a person ascribes, such as religious beliefs). Employees may forge connections between these different aspects of the self and their work activities by (a) “self-affirming” or thinking about their roles, values and/or beliefs and by (b) connecting aspects of the self to their work. Self- affirmation may enable employees to make stronger connections between the content of their work and personal values and strengths. After these connections are formed, employees should perceive greater meaning and purpose in their work (Dik & Duffy,

2009) and become self-determined as previously described (Deci & Ryan, 1995). Many researchers believe that a perceived fit between an individual’s self and his/her role (i.e., work role fit; Kristof, 1996) will lead to an experienced sense of meaning due to the ability of the individual to more freely express his/her values and beliefs (Brief & Nord,

1990; Shamir, 1991).

In prior work, researchers have experimentally induced participants to self-affirm by having them write about core personal values. These induced self-affirmations generally lead to positive outcomes, such as reductions in prejudice (Fein & Spencer,

1997; Zarate & Garza, 2002, chose fewer downward social comparisons (e.g., Spencer,

Fein, & Lomore, 2001), and made fewer external attributions for others’ behaviors (Liu

& Steele, 1986). In addition, they tend to lead to positive outcomes, including lower

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cortisol responses to stress (Creswell, Welch, & Taylor, 2005) and health promoting behaviors (Epton et al., 2014). Similarly, Emanuel, Howell, Taber, Ferrer, Klein, &

Harris (2018) found that spontaneous self-affirmation was associated with better mental and physical well-being, including greater happiness, hopefulness, optimism, personal health efficacy, and subjective health, as well as less sadness. In line with these findings,

I suspect that engaging in self-affirmation before work may lead individuals to see their work as more important and valuable, which can lead to a greater desire to perform it.

Positive Work Reflection. Positive work reflection is a form of recollection that refers to contemplating the positive aspects of one’s job during non-work time and remembering positive events encountered during the working day (Fritz & Sonnentag,

2005). Positive work reflection may include thoughts about pleasurable events such as successful task accomplishment and supportive work relationships. It does not need to be an in-depth elaboration of the job’s positive features but can be a consideration of positive events and experiences on the job (Lazarus & Folkman, 1984). Positive work reflection can be seen as a job-specific form of savoring, or reminiscing about positive events in ways that amplify, prolong, or rekindle the feelings that the events elicited

(Bryant, 1989).

Although not many studies have investigated positive work reflection (exceptions are Daniel & Sonnentag, 2014; Fritz & Sonnentag, 2005, 2006; Meier, Cho, & Dumani,

2016), the limited research on this topic has found that it is beneficial for well-being.

Further, the beneficial effects are incremental to that of psychological detachment and the absence of negative work reflection (Meier et al., 2016). Another study found that day-

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level positive work reflection was related to day-level fluctuations in high activation positive mood (e.g., enthusiasm and joviality; Sonnentag & Grant, 2012).

When employees positively reflect on their work they may experience gratitude.

The construct of gratitude is a fundamental variable in the positive psychology literature

(Seligman and Csikszentmihalyi, 2000; Seligman, 2002). Gratitude has been conceptualized as an emotion, attitude, moral virtue, habit, personality trait, and coping response (Emmons & McCullough, 2003). In addition, it is considered a fundamental individual resource (Emmons and Shelton, 2002; Snyder, Lopen, & Teramoto, 2010) and an important human strength that contributes to both subjective happiness (Emmons and

Crumpler, 2000; McCullough, Emmons, & Tsang, 2002; Peterson and Seligman, 2004) and the appreciation of life's simple pleasures (Watkins et al., 2003). For example, caregivers of people with AIDS were better able to get through their days when they felt thankful for friendship during a social gathering or appreciated a beautiful sunset

(Folkman & Moskowitz, 2000). Furthermore, gratitude appears to be a promising individual strength in the organizational context (Fehr et al., 2017). Recent work found that it is related to well-being (Emmons, 2003), positive relationships and social support at work (Hu & Kaplan, 2015), prosocial organizational behaviors (Michie, 2009; Grant and Gino, 2010), organizational citizenship behaviors, teamwork, and altruism (Dik et al.,

2015), as well as efficiency, success, productivity, and job performance (Emmons, 2003;

Grant and Wrzesniewski, 2010). Reflecting on the positive aspects of one’s work may foster gratitude, ultimately increasing employees’ appreciation of and perceptions of meaning in their work.

Summary of Resource Building Strategies

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Arguably all the reviewed strategies can be conceptualized as ways to cope with demands and can be used in an anticipatory way. Coping is defined as thoughts and behaviors that people use to manage the demands of stressful situations (Lazarus &

Folkman, 1984) and can be categorized into problem-, emotion-, and meaning-focused strategies. Particularly relevant to pre-work is the idea that people may use future oriented coping strategies (i.e., anticipatory, preventive, and proactive; Aspinwall &

Taylor, 1997; Schwarzer and Knoll, 2003). Planning is a strategy used during the anticipatory phase of coping when employees think about what they want to achieve while at work (i.e., goal setting) and/or how they will achieve it (i.e., goal striving) whereas reattachment is the cognitive process of mentally reconnecting with work before beginning the workday (Sonnentag & Kuhnel, 2016). Conceptually planning and reattachment (as defined by Sonnentag and Kuhnel, 2016) overlap because individuals can reattach before work through planning, though reattachment is not limited to planning and may include other strategies such as mental simulation or ruminative thoughts. It’s likely that people not only cognitive reconnect with work, but reconnect in other ways as well. In addition to more cognitive based strategies, individuals may use strategies that impact their arousal and affective state. For instance, they may use emotion regulation strategies; such as positive reappraisal to put themselves in a good mood or instrumental affect regulation to improve performance. In addition, employees may use specific meaningfulness based strategies (e.g., cognitive crafting, positive work reflection) before work to facilitate a positive reconnection with work.

Mentally Preparing for Work: A Pre-Work Framework

The focus of the proposed investigation is on pre-work strategies employees use

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to facilitate the psychological transition from their non-work to work roles. Pre-work is defined as active daily preparation for a given workday in which individuals bring their attention back to work, mobilize their energy, and/or reflect on the reasons they work.

The purpose of pre-work is to proactively build or protect resources (i.e., energies; ten

Brummelhuis & Bakker, 2012) that can be used by workers during the day. It is a multidimensional construct comprised of three primary strategies: cognitive reattachment

(enhances cognitive reconnection), energy mobilization (enhances positive affect), and positive reflection (enhances perceived meaningfulness). These three strategies enact cognitive, affective, and motivational processes of reconnecting with work after a period of nonwork, and thus proactively build or protect resources to be used during the workday. To reconnect with work, I theorize that employees can think about their workday (reattachment), engage in behaviors that help them feel motivated and positive about work (energy mobilization), as well as reflect on and infuse meaning into their work (positive reflection).

Individuals may adopt idiosyncratic methods for engaging in pre-work, though they are likely to serve the same objective. That is, the specific activities or thoughts that individuals use to cognitively reattach, mobilize their energy, and reflect positively about work may differ, but the effects of being ready for work and being more likely to feel engaged are similar. For example, results from one person might mobilize energy by exercising whereas another person may listen to upbeat music. Although the activities are

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different, both are energy mobilization strategies that result in high activation positive affect.

In addition to what individuals can do, it is also important to consider when and where pre-work occurs. I expect that pre-work can occur either at home before starting work, during the commute to work, or during the first few minutes after arriving at work.

Furthermore, the transition between home and work roles (i.e., pre-work) can become routinized over time. Ashforth and colleagues (2000) draw on scripts and schemas to describe how this occurs. A script or event schema is a cognitive structure that specifies the typical sequence of behaviors and events in each goal-oriented situation or process

(Fiske & Taylor, 1991; Gioia & Poole, 1984). For example, the transition from home (non- work domain) to work (work domain) might involve being awakened by the alarm clock, making coffee, going for a run, and setting goals for the day. Thus, transition scripts organize transition tasks in a temporal flow, which guides the individual and provides a sense of predictability and control (Lord & Foti, 1986). A transition script develops as the individual continues to engage in relatively consistent tasks (Poole, Gray, & Gioia, 1990).

Tasks become cognitively linked into sub-routines (waking up, making coffee, going for a run) and sub-routines become linked into higher-order routines (the home-work transition or pre-work routine). Thus, the greater one's experience with the transition, the more elaborate the script and the less difficult the role transition (Sims & Lorenzi, 1992). In time, the transition process is likely to become relatively automatic or "mindless" (Ashforth &

Fried, 1988; Langer, 1989).

In developing my conceptualization of pre-work, I consider how different strategies link to outcomes through distinct mechanisms. Although I predict that all three pre-work

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strategies will relate to all three components of engagement, I expect that cognitive reattachment, energy mobilization, and positive reflection strategies will relate most strongly to cognitive, physical, and emotional engagement respectively. Engagement, in turn, is predicted to impact well-being outcomes. As previously stated, I view pre-work strategies as activities that undo or counteract the effects of disengagement. Just as it is beneficial for employees to disengage from work by having recovery experiences during non-work time, it may be beneficial for them to re-engage work by having pre-work experiences in the transition from non-work to work.

It is also important to distinguish pre-work from other related concepts like start- of-workday mood (Rothbard & Wilk, 2011), job crafting (Wrzesniewski and Dutton,

2001), and mindfulness (Bishop et al., 2004). Start-of-workday mood is an affective frame that colors how people view and feel about their daily workplace experiences. The authors describe it as something individuals experience as a result of external events (e.g., a sick child requires attention) or ambient affect (e.g., waking up “on the wrong side of the bed;”

Rothbard & Wilk, 2011). The authors do not describe employees as having an active role in shaping their pre-work state. In contrast, pre-work captures strategies that individuals use to intentionally influence their state.

Pre-work is also distinct from job crafting, which refers to proactive changes employees make in their work tasks (task crafting), the type of relationships engaged in at work (relationship crafting; frequency and duration of social interaction with clients, colleagues, and providers), and in the appraisal of their work (cognitive crafting; referring to the subjective meaning ascribed to the work). Although both pre-work and job crafting are proactive, job crafting tends to occur during the workday and results in actual changes

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to the job. An exception to this is cognitive crafting, which could be used as a pre-work positive reflection strategy to increase the meaningfulness of work. However, this captures only one of several pre-work positive reflection strategies.

Finally, pre-work is also distinct from mindfulness. Critical to the definition of mindfulness is the self-regulation of attention so that it is maintained on immediate experience, thereby allowing for increased recognition of mental events in the present moment (Bishop et al., 2004). The second component involves adopting an orientation towards one’s present experiences that is characterized by curiosity, openness, and acceptance (Bishop et al., 2004, p. 232). Thus, mindfulness involves being focused on the present moment and the acceptance of one’s current state. In contrast, pre-work is focused on the goal of “reconnection with work,” not on the present moment. When using pre-work strategies, individuals are attempting to put themselves in the right mental or motivational state that will aid in their reconnection with work. Thus, individuals are not accepting their current state as is, but attempting to change it so that it better serves the purpose of facilitating reconnection with work. Table 1 summarizes the ways in which pre-work is distinct from other related constructs.

Table 1. Pre-Work VS. Related Constructs

Construct Pre-work Recovery Spillover Job Crafting Mindfulness (Affect/Emo

Exhaustion) Timing Before demands After demands Both before and During work day During work begin occur after Reaction Proactive Reactive Reactive Proactive Reactive

Type of Future Work Disconnecting Continued Current work Current work Connection from work Connection

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In the following sections, I further define each pre-work strategy and describe predictions for how they relate to engagement and well-being.

Cognitive Reattachment

Building on the work of Sonnentag and Kuhnel (2016), I define cognitive reattachment as an individual’s sense of mentally reconnecting with the work situation by bringing their attention back to work and/or planning for the upcoming day. Just as detached individuals disengage themselves mentally from work by not thinking about job- related topics (Sonnentag & Bayer, 2005), individuals reattach themselves back to work by thinking about job related topics. Job-related topics individuals may think about prior to work may include general characteristics of their jobs, the individuals they interact with on the job (co-workers, supervisors, clients/customers), or routine job tasks. People experience reattachment when they cognitively reconnect with work (i.e., tune back into work) and/or consider what events may occur. Thus, it may involve mental simulation (i.e., imagining how real or hypothetical events are going to take place in the future at work;

Taylor & Pham, 1996; Taylor & Schneider, 1989) or planning. Individuals may plan by thinking about when and where they will complete their work. For example, they may make physical or mental “to do” lists. In addition, planning may take the form of proactive coping (i.e., planning for upcoming work-related challenges; Scwarzer & Knoll, 2003) and/or preventive coping (i.e., planning for potential threats; Scwarzer & Knoll, 2003).

Although reattachment is primarily cognitive in nature, it may impact affect in a variety of ways. Thinking about one’s day may produce affect that is positive (e.g., an individual feels excited about an upcoming client meeting), neutral (e.g., an individual feels

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indifferent about a meeting), or negative (e.g., an individual feels anxious about a meeting;

Sonnentag & Kuhnel, 2016). In this way, the cognitive process of engaging in reattachment might have an indirect effect on a person’s affective state, which can shape their energy for the day. When reattachment produces positive affect, individuals may need to do relatively little in the way of energy mobilization. However, when reattachment produces neutral or negative affect, individuals may attempt to engage in pre-work activities that counteract this affect (and the resulting low energy). I discuss the nature of these energy mobilization strategies in more detail below.

Specifically, it is predicted that individuals will experience higher levels of cognitive engagement on days when they reattach to work because it promotes greater attentiveness and increased focus (Kahn, 1990). By mentally reconnecting with work, employees direct their cognitions and attention back to work, which reduces the cognitive availability of off-task cognitions. By proactively thinking about work-related topics, such as upcoming tasks before beginning to work, individuals may experience increased task attentional pull (Beal et al., 2005). As such, when the individual arrives to work he/she can direct more attention towards the tasks at hand (Beal et al., 2005). In the current study, it’s hypothesized that reattachment will predict the cognitive dimension of the JES (Rich et al.,

2010), which reflects cognitive vigilance, mental connection, focus, and absorption during role performance (Kahn, 1990; Rich et al., 2010).

Hypothesis 1: At the within-person level of analysis, pre-work reattachment is

positively related to cognitive engagement during the day.

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Energy Mobilization

The second dimension of pre-work, energy mobilization, is defined as an individual’s sense of feeling energized and positive about work. This strategy captures the idea that employees may engage in behaviors prior to work that enhance their energetic motivation to work. It is helpful to think about energy mobilization activities as being on a continuum ranging from general activities that may seem unrelated to work, such as exercise, listening to music, or speaking with a friend, to specific activities directed at work, like goal setting. Some of these activities, such as exercising and talking with friends, have previously been conceptualized as recovery activities (e.g., ten

Brummelhuis & Bakker, 2012) because they provide opportunities to switch off one's attention from work-related matters and stop work-related intrusive thoughts (Cropley &

Millward, 2009; Kaplan, 1995). However, these same activities may also be used by individuals before the start of a workday to mobilize their energy and increase their positive affect leading them to feel excited about the upcoming day.

Additionally, it is well established that individuals may motivate themselves by setting goals (Locke & Latham, 2002), which direct attention, effort, and persistence toward goal-relevant activities and away from goal irrelevant activities (Locke & Latham,

1990). Thus, there are a variety of activities that individuals may engage in to “pump themselves up” for work. Although the focus of these activities may or may not be on work, the effects of these activities foster energy and positive affect in preparation for the upcoming workday.

Theoretically, the aforementioned mobilization activities counteract the recovery experience of relaxation. Recall that relaxation involves “deactivating” the focus on work

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from an affective or energetic (versus cognitive) perspective. In contrast, energy mobilizations strategies involve “reactivating” one for the workday from an affective or energetic perspective. I predict that energy mobilization will lead to higher levels of physical engagement (Kahn 1990) throughout the day. People exhibit physical engagement when they become physically invested, exert effort, and persist on tasks over time. Activities like goal setting can increase energy, persistence on tasks (Locke &

Latham, 2002), and effort exerted to attain goals (Katz & Kahn, 1978). As such, I predict that:

Hypothesis 2: At the within-person level of analysis, pre-work energy

mobilization is positively related to physical engagement during the day.

Positive Reflection

The third strategy, positive reflection, is defined as an individual’s sense of feeling autonomously motivated (Ryan & Deci, 2000) and emotionally connected with their work (Rich et al., 2010) as a result of infusing meaning into their work. For instance, employees may infuse meaning into their work by reflecting on why their work matters and the reasons why they work (e.g., Wrzesniewski et al., 1997; Deci & Ryan,

2005). Employees may consider the prosocial implications of their work and anticipate ways in which their work will positively impact co-workers, clients, or society (e.g.,

Grant, 2007; 2008). In addition, they may reflect on the important reasons why they work, such as to support loved ones (i.e., family motivation; Menges, Tussing, Wihler, &

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Grant, 2017) or to achieve a personally valued goal that aligns with their identity (values, beliefs). These strategies, in turn, lead individuals to view their work as meaningful.

Positive reflection leads to a motivational state, but not from an energetic perspective. Although it may not increase individuals’ energy, attention, or persistence, it does shift them to a more autonomous motivational state (i.e., identified) rather than controlled (Ryan & Deci, 2000). By reflecting on the reasons why one works (i.e., impact it has, alignment with value), people may feel more connected to others (co-workers, clients, society) or to the work itself. As such, positive reflection helps individuals get in contact with their basic values and needs (Shapiro, Carlson, Astin, & Freedman, 2006) and promotes self-determined behavior (Glomb et al., 2011). There are a variety of tactics individuals may use to positively reflect and increase the meaningfulness of their work before beginning work. For example, individuals may engage in meaning making through cognitive crafting strategies (Wrzesniewski & Dutton, 2001). Specifically, employees may infuse value into seemingly small or insignificant work tasks that often are ignored or unappreciated by society or by one’s organization (Wrzesniewski & Dutton, 2001).

For example, employees working in a call center may think about the thousands of lives they will save by calling people to schedule appointments to donate blood. Individuals may also enhance their perceptions of meaningful work by thinking about how their work aligns with their personal short- or long-term goals (Wrzesniewski et al., 1997). For instance, a server waiting tables in a restaurant may remind herself that the job is helping her afford the cost of school and reach her goal of obtaining a college degree before starting her shift. Another employee may think about how the job is providing him with a means to financially support his loved ones, which aligns with his value of working hard

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to support family (Menges et al., 2017). Thus, individuals can increase their perceptions of meaningful work by reflecting on how their work helps them achieve personally important goals.

It’s expected that individuals will feel greater emotional connection to their work and to others in the service of their work (Kahn, 1990) on days when they positively reflect. Thinking about the autonomous and prosocial reasons for pursuing work activities can enhance the meaningfulness of work (Grant, 2008). As such, this should lead individuals to feel more proud, excited and enthusiastic about their work. After connecting work to autonomous and/or prosocial goals, the purpose of work becomes more salient and individuals feel more emotionally connected to it.

Hypothesis 3: At the within-person level of analysis, pre-work positive reflection

is positively related to emotional engagement during the day.

Well-Being: Job Satisfaction and Emotional Exhaustion

It’s predicted that the use of pre-work strategies will lead to higher end-of-work day well-being (job satisfaction and emotional exhaustion), through greater engagement during the day. Job satisfaction refers to the evaluative judgment of a person’s work situation (Weiss, 2002). It can be conceptualized as a facet of well-being that is characterized by high levels of pleasure and low levels of activation unlike engagement, which is perceived as a more active state of well-being (Bakker and Oerlemans, 2011).

Emotional exhaustion, on the other hand, is a state of depletion and fatigue that is considered the main component of job burnout (Maslach & Jackson, 1981; Wright &

Cropanzano, 1998) and occurs when the emotional demands exceed what an individual can afford during interpersonal interactions at work (Maslach, Schaufeli, & Leiter, 2001).

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Both job satisfaction and exhaustion relate to important employee and organizational outcomes such as life satisfaction (Campbell et al., 1976), physical health (Hilleras et al.,

1998), and mental health (Kohn & Schooler, 1982), as well as task and contextual performance, turnover, and absenteeism (Cropanzano, Rupp, & Byrne, 2003;

Halbesleben & Bowler, 2007; Harrison, Newman, & Roth, 2006; Ybema, Smulders, &

Bongers, 2010).

Recall that cognitive reattachment is predicted to lead to cognitive engagement.

Cognitive engagement results in the active thinking and tracking of information, mentally connecting disconnected puzzle pieces in problem solving, and feeling focused and absorbed during role performance (Kahn, 1990; Rich et al., 2010). Cognitively engaged individuals should feel engrossed in their work and happy about the quality of their work and the progress they’ve made. As such, they are more likely to feel satisfied. In addition, if they are engaged and absorbed in their work tasks they are less likely feel emotionally exhausted by their work (Veenhoven, 1996).

Hypothesis 4: At the within-person level of analysis, cognitive engagement during

the day a) is positively associated with change in job satisfaction and b)

negatively associated with change in emotional exhaustion.

Recall that energy mobilization not only leads individuals to feel motivated to work, but also increases positive affect. Thus, when individuals are physically engaged they should also experience positive affect. Research suggests that state affect or mood impacts well-being outcomes like job satisfaction (Fishbein & Ajzen, 1975) as both mood and job satisfaction are psychological states that are influenced by short-term variability

(Ilies & Judge, 2002). Fredrickson’s (1998, 2002) ‘‘broaden-and-build’’ theory of

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positive emotions also suggests that positive affect will lead to positive well-being.

Research with the broaden-and-build theory showed that momentary experiences of positive emotions, such as those that should be experienced when feeling physically engaged during work, can build enduring psychological resources and trigger upward spirals toward emotional well-being.

Hypothesis 5: At the within-person level of analysis, physical engagement is a)

positively associated with change in job satisfaction and b) negatively associated

with change in emotional exhaustion.

Lastly, emotional engagement is characterized by empathic connection to others and expression of excitement in the work role (Kahn, 1990). Emotionally engaged individuals feel like their work is meaningful; they are emotionally connected to and dedicated to their work and to others in the service of their work (Rich et al., 2010), satisfying their basic human needs of autonomy and relatedness (Deci & Ryan, 2000).

Autonomous self-regulation, in turn, perseveres vitality and energy (Ryan & Deci, 2008) and is linked to job satisfaction (Bono & Judge, 2003; George & Jones, 1996; Judge,

Bono, Erez, & Locke, 2005). As such, I predict that

Hypothesis 6: At the within-person level of analysis, emotional engagement a) is

positively associated with change in job satisfaction and b) negatively associated

change in emotional exhaustion.

Recall that pre-work strategies facilitate reconnection and re-engagement with work. Re- engagement with work, in turn, is expected to impact employee job satisfaction and emotional exhaustion. As such, I predict that

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Hypothesis 7: Pre-work has indirect effects on job satisfaction through

engagement. Specifically, a) reattachment positively impacts change in job

satisfaction through cognitive engagement, b) energy mobilization positively

impacts change in job satisfaction through physical engagement, and c) positive

reflection positively impacts change in job satisfaction through emotional

engagement.

Hypothesis 8: Pre-work has indirect effects on emotional exhaustion through

engagement. Specifically, a) reattachment negatively impacts change in emotional

exhaustion through cognitive engagement, b) energy mobilization negatively

impacts change in emotional exhaustion through physical engagement, and c)

positive reflection negatively impacts change in emotional exhaustion through

emotional engagement.

Moderating Variables

Trait resilience. I examine trait resilience as a boundary condition to better understand whether pre-work benefits some workers more than others. According to job demands-resource theory, personal resources, such as psychological resilience, have a direct positive effect on engagement and are expected to buffer the undesirable impact of job demands on strain (Bakker & Demerouti, 2017). Psychological resilience is viewed as an individual difference in the capacity to bounce back or recover from stress and adversity (Block & Kremen, 1996; Smith et al., 2008). Researchers view resilience as an important resource reservoir that helps individuals manage situations experienced in life

(e.g., Block & Kremen, 1996; Taylor, Kemeny, Reed, Bower, & Gruenwalkd, 2000).

Specifically, resilient individuals are more likely to proactively prepare for hardships and

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minimize the impact of stressful events on themselves by using their psychological resources effectively (Fredrickson, Cohn, Coffey, Pek, & Finkel, 2008). They are also more likely to adapt to negative situations because they tend to seek out the positive in situations, search for creative solutions to difficult challenges, and focus on recovering losses they encounter (Bonanno, 2004; Tugade & Fredrickson, 2007). The literature has documented several beneficial effects of resilience at work. For example, in a study of more than 1,000 employees working in a variety of industries, higher resilience was related to greater job satisfaction, work happiness, and organizational commitment

(Youssef & Luthans, 2007). Research findings indicate that resilience impacts commitment to change by helping individuals to experience and capitalize on positive affect (Shin, Taylor, & Seo, 2012). Furthermore, according to Hobfoll (2011), resources tend to occur in clusters or “resource caravans”. Those who are high on resilience may be more capable of acquiring resource gains through pre-work. In other words, resources, like resilience, tend to generate or may even prolong the effects of other resources

(Hobfoll, 2011) like pre-work.

Since psychological resilience promotes effective coping and adaptive behaviors, as well as minimizes negative reactions to stressful events (Rutter, 1987), the effects of pre-work may be compounded for resilient individuals (Tugade & Fredrickson, 2004).

Thus, they may experience even higher levels of engagement during work on days they use pre-work strategies. In contrast, individuals low in resilience are less likely to bounce back from negative experiences and may benefit to a lesser extent by engaging in pre- work activities. Therefore, it may be particularly beneficial for high resilient individuals to use pre-work. Thus, pre-work may be a particularly valuable tool in producing

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desirable workplace experiences and outcomes for those who are high in resilience. As such, the effects of pre-work on engagement will be stronger for those high in resilience than for those low in resilience. More specifically,

Hypothesis 9: Psychological resilience moderates the within-person effects of

pre-work strategies on workday engagement, such that the effects of a)

reattachment on cognitive engagement, b) energy mobilization on physical

engagement, and c) positive reflection on emotional engagement are stronger for

individuals high in resilience compared to individuals low in resilience.

Perceived Supervisor Support. In addition to individual differences, characteristics of the work environment, such as perceived supervisor support (PSS) may influence the links of pre-work strategies with engagement. PSS, refers to employees’ general views regarding the degree to which supervisors value their contributions and care about their well-being (Kottke & Sharafinski, 1988). Employees’ PSS may be essential to energizing and motivating them and can impact their capacity for managing stress (Karasek, Triantis, & Chaudhry, 1982). According to the Job Characteristics

Theory, supervisor support provides tangible, psychological, and emotional resources to employees that influence the psychological state of engagement (Kossek et al., 2011). In support of this, empirical studies have shown that supervisor support is a strong correlate of engagement (Bakker & Demerouti, 2007; Bakker, Demerouti, & Euwema, 2005;

James et al., 2011; Richman et al., 2008; Salanova et al., 2005). Social support has also been identified as a job resource at the interpersonal and social relations level in the Job

Demands-Resource (JD-R) model (Bakker & Demerouti, 2007). Research on the JD-R

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model has found that social support from one’s supervisor and coworkers is related to a number of positive work outcomes and negatively related to disengagement and burnout

(Bakker et al., 2004; Demerouti, Bakker, Nachreiner, & Schaufeli, 2001; Schaufeli &

Bakker, 2004). Supervisor support also buffers the negative effects of job demands

(Bakker et al., 2007).

From these perspectives, perceived supervisor support is essential to energizing and motivating workers to excel (Bakker & Demerouti, 2007), and like pre-work, impacts an individual’s capacity for managing stress (Karasek, Triantis, & Chaudhry, 1982).

Furthermore, according to Hobfoll (2011), resources tend to occur in clusters or “resource caravans”. Those who perceive their supervisors as supportive may be more capable of acquiring resource gains through pre-work. In other words, resources, such as a supportive supervisor, tend to generate or may even prolong the effects of other resources

(Hobfoll, 2011) like pre-work. Therefore, the effects of pre-work on engagement may be weaker when individuals do not perceive their supervisors as supportive. In sum, I expect pre-work to interact with perceptions of supervisor support to influence engagement. The beneficial effects of pre-work on engagement may be limited when employees do not perceive their supervisor as supportive. Thus, I predict the following:

Hypothesis 10: Supervisor support moderates the within-person effects of pre-

work strategies on engagement, such that the effects of a) reattachment on

cognitive engagement, b) energy mobilization on physical engagement, and c)

positive reflection on emotional engagement are stronger for individuals who

perceive their supervisor as supportive.

Supplemental Predictions

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In addition to examining the links between pre-work, engagement, and well- being, the current study will also explore the duration of the effects of pre-work on engagement and well-being. Research suggests that work demands consume a person’s energy resources (e.g., ten Brummelhuis & Bakker. 2012) and can lead to fatigue over the course of the day (van Hoof, Geurts, Kompier, & Taris, 2007). This decrease in energy is reflected in a decrease in engagement over time, with engagement being higher earlier in the day.

Similarly, I predict that pre-work will have a stronger relationship with engagement earlier in the day compared to later in the day. That is, it will predict engagement in the first half of the day more accurately than it will predict engagement in the second half of the day. The reasons for this are that (a) individuals will experience a decline in the benefits of pre-work at different rates (with more individuals seeing reduced benefits as the day goes on) and (b) there will be more intervening events that can override the effects of pre-work (either making things worse and nullifying the benefits or making things better and sustaining engagement longer than the sole benefits of pre-work would suggest). Consistent with this rational, Sonnentag and Kuhnel (2016) showed that reattachment was related to engagement throughout the day, but the relationship was stronger in the morning than in the afternoon. In attempt to build on this finding, I will examine how pre-work strategies (cognitive reattachment, energy mobilization, positive reflection) relate to specific components of engagement both earlier in the day and later in the day.

Supplementary Hypothesis 1: The effects of pre-work on engagement will be

stronger in the first half of the day compared to the second half of the day.

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CHAPTER III

METHODOLOGY

STUDY 1: MEASURE DEVELOPMENT

To develop a reliable and valid measure of pre-work, three major steps that are critical to scale development were followed (Spector, 1992; Hinkin, 1995; 1998): (1) information gathering, (2) item development, and (3) confirmatory factor analysis (CFA) and validation.

Information Gathering

A combination of deductive and inductive methods were used to generate items to assess pre-work. Deductive scale development requires a thorough understanding of the construct to be measured and is a common approach used to develop new scales (Hinkin,

1998). In the current study, items were developed in a deductive manner based on the literature review, dimension definitions, and pre-existing scales.

Inductive methods, on the other hand, base item development on qualitative information regarding the construct of interest obtained from the opinions of individuals, such as through focus groups (Hinkin, 1995). This approach ensures that items are based on the actual experiences of employees. For the inductive approach, six 1.5-hour focus groups comprised of three to six call center representatives employed at a call center were conducted (Nolan, Diefendorff, & Detorakis, 2018). Two of the six groups were comprised of high performers, two groups were comprised of medium performers, and two groups were comprised of low performers that the organization identified based on organizational performance data. Questions focused on the sources of positivity and

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negativity employees experienced at work and the ways in which they prepare for, respond to, and recover from stressors.

Of interest to the current study were the strategies employees reported using prior to beginning the workday to reconnect with work. Specifically, participants were asked,

“Before coming into work, do you do anything physically/mentally to get ready for the day?” (Nolan et al., 2018). Results of this qualitative study provide preliminary support that employees do use strategies prior to the start of their workday that impact their readiness to work (Nolan et al., 2018). For example, three of the six focus groups reported mentally preparing for the day in some way that resulted in high activation and high positive affect. Specifically, one participant stated, “I sit in my car and listen to

Aerosmith or Prince to get myself psyched up to come in to work,” as doing so helped him to have the “right mindset and not take rude customers personal.”

Item Generation

The pre-work scale items were intended to reflect the three dimensions of pre- work (cognitive reattachment, energy mobilization, and positive reflection). To ensure that items were clear and concise, guidelines provided by Hinkin (1995; 1998) were followed, such as avoiding double barreled items, reverse-scored items, and jargon. A pool of 23 statements (items) was generated to reflect the pre-work conceptualizations

(cognitive reattachment, energy mobilization, and positive reflection). Although the majority of the items were original items (14/23), nine items were used “as is” or modified from previously published sources.

To measure cognitive reattachment, four items from Sonnentag and Kuhnel’s

(2016) reattachment measure (items 1 through 4 in Table 2) were included. To develop

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item 5 (“I thought about what might happen during work.”), the following item from

Sonnentag and Kuhnel (2016) was modified: “I thought about what I will encounter at my work today.” Finally, items 6 through 8 (Table 2) were original items. To assess energy mobilization, all seven items (items 9 through 15 in Table 2) were original items developed for the current study. Finally, of the eight items used to assess positive reflection, five were modified from other measures. Item 16 (“I thought about how my work positively affects the lives of other people [e.g., my family/clients/customers]”) and item 17 (“…I reflected on how my work makes a difference in the lives of others [e.g., my family/clients/customers]) are both based on the following task significance item from Morgeson and Humphrey (2006): “The results of my work are likely to significantly affect the lives of other people.” Item 18 (“I considered how my work positively impacts society”) and item 22 (“…I thought about how my job adds purpose to my life”) were developed by significantly modifying the following items from Slemp and Vella-

Brodrick’s (2013) cognitive crafting measure: “Remind yourself of the importance of your work for the broader community” and “Think about how your job gives your life purpose” respectively. Finally, item 19 (“I reflected on how my work allows me to provide for/support my family”) was inspired by two items from Menges and colleague’s

(2017)’s family motivation measure (“I care about supporting my family” and “My family benefits from my job”). The remaining three items (items 20, 21, and 23) were original items developed for the study.

Before I collected data on the pre-work scale, I had multiple discussions about the proposed definitions of pre-work and the constructed items with an I-O Psychology faculty member at a state university whose primary area of research is organizational

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psychology, as well as two advanced graduate students working on their PhD in I/O

Psychology (1 female, 1 male). They reviewed the 23 items for clarity and fit with the respective dimension. The scale consisted of eight items for cognitive reattachment, seven items for energy mobilization, and eight items for positive reflection. All items were answered using a 5-point agreement scale (1=strongly disagree, 5=strongly agree).

Sample and Procedure

Amazon’s Mechanical Turk (MTurk) was used to recruit workers to complete my survey. I required that MTurk workers have a HIT approval rate greater than 90 percent, be at least 18 years of age and live in the United States. Participants were not permitted to complete the survey if they did not meet these requirements.

Of the 561 participants who completed the survey, 115 participants were excluded because they did not accurately respond to two out of two attentional check items that instructed them to select a specific response answer (e.g., “…to check that you are reading this item, please select “Agree”). Another nine participants were removed because they completed the survey in under 4 minutes (240 seconds). Such a short completion time suggests they were not carefully responding to items. Although MTurk workers were required to live in the United States, the IP addresses for 49 participants were from outside of the United State. Thus, those participants were removed from analyses. Finally, only participants who indicated that they had gone to work the prior day (i.e., “yesterday”) were included because it is important that participants can accurately recall what they did prior to work and how they felt during and after work.

After dropping 74 participants who indicated they did not work the previous day, the sample was comprised of a total of 314 participants (51% male; 49% female). For CFA,

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a minimum sample size between 200 and 400 has been recommended for multivariate normal data (Jackson, 2001; Hoelter, 1983). For nonnormal data, a minimum sample size of 250 has been recommended (Hu & Bentler, 1999); Yu & Muthén, 2002). In addition, as the number of items increases, it may be necessary to increase the number of respondents. With a total of 23 items, data should be collected from at least 300 participants to exceed a 5:1 ratio of cases to free parameters (Taneka, 1987). As such, the sample size of 314 is adequate.

Participants worked on average 39.28 hours per week (SD= 9.20). Most of the sample were White (78%), 7.6% were Black, 6.4% were Hispanic/Latino, and 6.4% were

Asian or Island Pacifier. The largest proportion of participants fell between the ages of 25 and 34 (46.2%), followed by the 35-44 years old age range (20.7%).

Measures

Pre-Work Scale. The 23 pre-work items were presented by dimension, as grouping items helps reduce cognitive load and improves the reliability of scales

(Harrison & McLaughlin, 1996). Prior to completing the pre-work scale participants were explicitly instructed to think about the last time they worked. They were asked to report what time they started to work that day, what time they finished work that day, and to list what activities they did before work. These questions were asked in attempt to jog participants’ memories about the specific day in question (Kahneman, Kreuger, &

Schkade, 2004).

Specific instructions for the pre-work scale were as follows: “The following statements are about what you did this morning before work or during the first few minutes of work today. Please read each statement carefully and use the scale to indicate

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your level of agreement and disagreement with each statement. Before I started work today (i.e., while at home, during the commute, or first few minutes of work)”.

In addition to the pre-work scale, participants completed several other measures so that criterion-related validity of the pre-work scale could be assessed. Except for the pre-work scale, these measures have been used previously and are well established.

Participants rated their engagement in pre-work strategies, as well as their job engagement, job satisfaction, and emotional exhaustion using a five-point Likert scale that ranged from "strongly disagree" (1) to "strongly agree" (5). The complete survey can be found in Appendix A.

Job Engagement Scale. A total of 18 items were used to assess job engagement

(Rich et al., 2010; α = .95), with six items assessing each of the three dimensions of job engagement: cognitive engagement (α = .92), physical engagement (α = .90), and emotional engagement (α = .93). A cognitive engagement item is “I concentrated on my job,” a physical engagement item is, “I exerted a lot of energy on my job,” and an emotional engagement item is “I was excited about my job.”

Emotional Exhaustion. Three items were used to assess emotional exhaustion

(Wharton, 1993; α = .92), including “I felt emotionally drained,” “I felt used up,” and “I felt burned out.”

Job Satisfaction scale. Three items were used to assess job satisfaction

(Cammann, Fichman, Jenkins, & Klesh, 1979; α = .85), including “I was satisfied with my job,” “I liked working here,” and “I didn't like my job.”

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Results

The factor structure of the measure was assessed by conducting an exploratory and confirmatory factor analysis (EFA and CFA). EFA was used to reduce the set of observed variables to a smaller, more parsimonious set of variables (Hinkin, 1998).

Second, a CFA was conducted to assess the quality of the factor structure by statistically testing the significance of the overall model (e.g., distinction among scales), as well as the relationships among items and scales. CFA is appropriate when there is an a priori hypothesized structure that is thought to be present in the data (Fabrigar et. al, 1999;

McDonald & Marsh, 1990; Nunnally & Bernstein, 1994).

The normality of the pre-work items was assessed to determine whether using robust maximum likelihood (MLR) as the estimator for the EFA and CFA analyses was appropriate. This estimation provides the same parameter estimates as ML, but both the model chi-square and standard errors of the parameter estimates are corrected for non- normality in large samples (Brown, 2015). One way to assess normality is to divide the skewness and kurtosis statistic by their standard errors. Concern arises when this value is greater than z = +3.29 (p < .001, two-tailed test; Tabachnick & Fidell, 2007). Based on these criteria, the skewness of ten items and the kurtosis of 14 items deviated from normality. Another method used to assess normality is to examine the absolute skew and kurtosis values (values over 1.00 suggest non-normality). Using this criterion, one item had an absolute skew value over 1.0 (1.05), and 12 items had absolute kurtosis values over 1.0, but all under 1.20. Based on skew and kurtosis statistics, there was some evidence of multivariate non-normality in the data. As a result, the MLR estimator was used, which should lead to results that are robust to any effects of non-normality (Muthén & Muthén,

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1998-2007). The Kaiser-Meyer-Olkin measure of sampling adequacy (KMO-test) was also

examined to determine if the data was suited for factor analysis. The statistic is a measure

of the proportion of variance among variables that might be common variance. KMO

values between 0.8 and 1 indicate the sampling is adequate (Cerny & Kaiser, 1977). The

KMO was .94, suggesting the data is suited for factor analysis. Descriptive statistics for all

23 items can be found in Table 2.

Table 2. Descriptive Statistics for All Pre-Work Items. Item N M SD Var 1. I thought about what I will encounter at my work. 314 3.54 1.07 1.15 2. I mentally tuned into my work. 313 3.45 1.16 1.35 3. I prepared mentally for it. 314 3.54 1.14 1.30 4. I considered the upcoming workday. 313 3.75 .99 .98 5. I thought about what might happen during work. 313 3.47 1.17 1.38 6. I created a (mental or physical) "to do" list for the day. 314 3.60 1.16 1.34 7. I put myself in the right mindset for my job. 314 3.65 1.00 .99 8. I visualized when and where I would get my work done. 314 3.34 1.19 1.41 9. I made sure I was in a positive state that would help me do 313 3.52 1.09 1.19 my work well. 10. I pumped myself up for work. 314 3.08 1.22 1.49 11. I told myself that today would be a good day. 313 3.28 1.21 1.47 12. I gave myself a pep talk. 313 2.75 1.24 1.54 13. I motivated myself by thinking about my goals for the day. 312 3.44 1.11 1.22 14. I got myself excited for the day by reflecting on what I can 313 3.15 1.19 1.43 accomplish. 15. I energized myself by thinking about how I can improve 312 3.14 1.23 1.52 my performance. 16. I thought about how my work positively affects the lives of 314 3.03 1.26 1.60 other people (e.g., my family/clients/customers). 17. I reflected on how my work makes a difference in the lives 312 2.96 1.23 1.51 of others (e.g., my family/clients/customers). 18. I considered how my work positively impacts society. 313 2.82 1.26 1.58 19. I reflected on how my work allows me to provide 314 3.44 1.16 1.34 for/support my family. 20. I thought about how my work is an important part of who I 314 3.07 1.22 1.49 am. 21. I thought about how my work aligns with my values and 314 2.95 1.24 1.53 beliefs. 22. I thought about how my job adds purpose to my life. 312 3.05 1.26 1.58 23. I considered how my work adds meaning to my life. 312 3.01 1.27 1.62

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Exploratory factor analysis

To examine whether the pre-work scale followed the three dimensions of pre- work, the factor structure of the scale was examined. The 23 items were submitted to an exploratory factor analysis (EFA) using the MLR estimation and an oblique rotation

(Geomin; default in Mplus) using Mplus Version 7.3 software (Muthén & Muthén,

2014). Multiple criteria for determining the number of factors to retain were used (Ford,

MacCallum, & Tait, 1986; Kim & Mueller, 1978; Stevens, 1992). The eigenvalue >1 rule suggested a three-factor structure (Floyd & Widaman, 1995). The eigenvalues for the first five extracted factors were "1 = 11.57, "2 = 2.21, "3 = 1.19, "4 = .95, and "5 = .81. In contrast, both the parallel analysis (PA; Horn, 1965) and scree plot suggested two factors.

In addition, I examined fit indices, including the Tucker–Lewis Index (TLI), the

Comparative Fit Index (CFI; Bentler, 1990), the Root Mean Square Error of

Approximation (RMSEA; Browne & Cudek, 1993), and Standardized Root Mean Square

Residual (SRMR). TLI and CFI values greater than .95 represent acceptable fit (Hu &

Bentler, 1999). SRMR values less than .08 and RMSEA values less than .06–.08 indicate acceptable fit (Browne & Cudeck, 1993; Hu & Bentler, 1999). Fit indices suggest that the four-factor solution fits the data best (see Table 3). Interestingly, with the four-factor solution the Positive Reflection factor was split. Items related to prosocial reflection (16,

17, 18) loaded on their own factor. Based on these results, I further examined the 3- and

4-factor structures using confirmatory factor analyses (CFA).

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Table 3. EFA Fit Indices including All Items. Model χ2 df Root Mean Comparative Tucker- SRMR Square Error Fit Index Lewis of Index Approximation 1 factor 1136.42 230 .11 .74 .71 .09

2 factor 647.53 208 .08 .87 .85 .05

3 factor 485.27 187 .07 .91 .88 .04 4 factor 279.28 167 .05 .97 .95 .03

Confirmatory factor analysis

To assess the structure of the pre-work scale, I specified a series of models and

tested them using confirmatory factor analysis (CFA). To assess model fit, the following

indices were used: TLI, CFI, RMSEA, and SRMR. Once again, the four-factor model

appeared to fit the data best based on fit statistics and the chi-square difference test.

Nevertheless, the CFI and TLI indices did not quite reach acceptable fit as they were

below .95 (See Table 4).

Table 4. CFA Fit Indices including all items. Model χ2 df Model ∆χ2 ∆df RMSE CFI TLI SRMR Comparison A

M1: 750.84* 229 .09 .85 .84 .07 Two factors M2: 595.42* 227 M2 vs. M3 155.42* 2 .07 .89 .88 .06 Three factors M3: 449.79* 224 M3 vs. M4 145.63* 3 .06 .94 .93 .05 Four factors Note. CFI = Comparative Fit Index; TLI = Tucker-Lewis Index

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In attempt to identify problematic items, I removed those that did not load strongly on to one factor. Although guidelines for item removal vary within the literature

(Tabachnick & Fidell, 2001), some researchers have suggested that an item is a good identifier of the factor if the loading is .70 or higher (Garson, 2010) and does not significantly cross load on another factor greater than .32 (Costello & Osborne, 2005;

Tabachnick & Fidell, 2001). My aim was to identify a simple factor structure whereby each factor is represented by several items that each load strongly on that factor only

(Pett et al., 2003; Tabachnick & Fidell, 2001). Practically, “several items” is generally considered to be at least three to five items with strong loadings (Guadagnoli & Velicer,

1988). Thus, I removed items that had factor loadings below .70. or modification indices for cross loadings above .32.

After examining the 4-factor CFA solution with all 23 items, I removed items based on the aforementioned criteria. Specifically, I removed items 6, 12, and 19 due to factor loadings below 0.70 and items 8, 9, and 18 due to modification indices for cross loadings above 0.32. With the removal of item 18, the fourth “pro-social” factor only contained two items, suggesting that it may be a weak and unstable factor (Costello &

Osborne, 2005). Next, I explored the 3-factor solution (see Table 5). After examining the

3-factor solution with all 23 items, I removed items 6, 12, 17, and 19 because they had loadings below .70 and items 7, 8, 9 due to modification indices for cross loadings above

0.32. After this first step, I re-ran the CFA with the remaining 16 items and reexamined item loadings and cross loadings. See “Model 2” in Table 4 for results. As a second step, item 16 was removed due to a factor loading less than .70. I re-ran the CFA with the remaining 15 items (see “Model 3” in Table 4 for results). All items loaded on their

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respective factors above .70 and did not have modification indices for cross loadings above .32. However, as a third and final step I chose to remove item 23 due to its redundancy with item 22. It had a large modification index with item 22 (~46).

Furthermore, removal of item 23 had less of an effect on the reliability of the subscale.

The fit indices of the final factor structure consisting of five cognitive reattachment items, five energy mobilization items and four positive reflection items indicates strong fit with the data (see model 4 “14-item Final Scale” in Table 5 for results). Analysis of internal consistency revealed that the alphas for each scale were above the typical .70 cutoff

(Nunnally, 1978). The cognitive reattachment, energy mobilization, and positive reflection had alphas of .89, .89, and .90, respectively. The overall alpha of the scale was

.93 (see Table 6).

Factor inter-item correlations can be found in Table 7. The strong interrelationships among the three pre-work dimensions (average r = .68) suggested a commonality indicative of a higher-order factor (Kline, 2005; Law et al., 1998).

Accordingly, I specified an additional model in which I loaded the three first-order pre- work dimensions onto a second-order pre-work dimension. Because the number of estimated endogenous relationships and degrees of freedom in this model were the same as those for the model with three correlated pre-work dimensions, the fit statistics of the second-order model indicated exactly the same good fit with the data.

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Table 5. CFA to Explore Model Fit. Model χ2 df RMSEA CFI TLI SRMR

1: 23-item scale 595.42* 227 .07 .89 .88 .06

2: 16-item scale 236.34* 101 .07 .94 .93 .05

3: 15-item scale 174.63* 87 .06 .96 .95 .05 4: 14-item Final Scale 133.44* 74 .05 .97 .96 .04 Note. Model 1 contains all of the items. Model 2 removed items 6, 12, 17, 19, 7, 8, 9. Model 3 contains 15 items (removed item 16). Final Scale contains 14 items (removed item 23).

Table 6. CFA (N=314) on Final Pre-Work Scale: Items, Means, Standard Deviations, Cronbach's alphas, and Factor Loadings of the 14-item Pre-Work. Original Factor # Item N M SD α 1 2 3 Cognitive Reattachment .89 1 I thought about what I will 314 3.54 1.15 .75 encounter at my work. 2 I mentally tuned into my work. 313 3.45 1.35 .79 3 I prepared mentally for it. 314 3.54 1.29 .80 4 I considered the upcoming 313 3.75 .98 .79 workday. 5 I thought about what might happen 313 3.47 1.37 .81 during work. Energy Mobilization .89 10 I pumped myself up for work. 314 3.08 1.49 .76 11 I told myself that today would be a 313 3.28 1.46 .74 good day. 13 I motivated myself by thinking 312 3.44 1.22 .80 about my goals for the day. 14 I got myself excited for the day by 313 3.15 1.42 .81 reflecting on what I can accomplish. 15 I energized myself by thinking 312 3.14 1.52 .85 about how I can improve my performance.

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Table 6 (continued). Positive Reflection .90 18 I considered how my work 313 2.82 1.57 0.73 positively impacts society. 20 I thought about how my work 314 3.07 1.49 0.87 is an important part of who I am. 21 I thought about how my work 314 2.95 1.53 0.85 aligns with my values and beliefs. 22 I thought about how my job 312 3.05 1.58 0.88 adds purpose to my life. Total Scale .93

Table 7. Factor Intercorrelations on 14-item Pre-Work Scale. F1: CR F2: EM F3: PR

F1 -- F2 .74 --

F3 .56 .75 --

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Reducing Scale for Experience Sampling Study

The 14-item pre-work scale was reduced to 9-items (3 items per dimension) so that it was suitable for the current experience sampling study. To reduce items on each dimension, I considered the psychometric properties of the scale and the theoretical definition of the construct. For cognitive reattachment, I retained the following items because they had the highest factor loadings and the biggest impact on the Cronbach alpha: “I mentally tuned into my work,” “I prepared mentally for it,” and “I thought about what might happen during work.” For the energy mobilization dimension, those items with the highest factor loadings were retained, including “I motivated myself by thinking about my goals for the day,” “I got myself excited for the day by reflecting on what I can accomplish,” and “I energized myself by thinking about how I can improve my performance.” Finally, for the Positive Reflection dimension I retained items “I thought about how my work aligns with my values and beliefs” and “I thought about how my job adds purpose to my life” because they had relatively high variances (compared to item 20 which was removed). Finally, it was important to retain item 18 (“I considered how my work positively impacts society”) from a content perspective since it is the only item related to prosocial reflection. The 9-item ESM pre-work measure has excellent model fit

(휒2 (24) = 23.95, CFI = 1.00, TLI = 1.00, RMSEA = .00, SRMR = .02) and strong reliability (α = .90; Table 8).

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Table 8. CFA (N=314): Items, Means, Standard Deviations, Cronbach's alphas, and Factor Loadings of the 9-item Pre-Work ESM Scale. Factor # Item N M SD α 1 2 3 Cognitive Reattachment .84 2 I mentally tuned into my 313 3.45 1.35 .83 work. 3 I prepared mentally for it. 314 3.54 1.29 .84 5 I thought about what might 313 3.47 1.37 .72 happen during work. Energy Mobilization .87 13 I motivated myself by 312 3.44 1.22 .81 thinking about my goals for the day. 14 I got myself excited for the 313 3.15 1.42 .81 day by reflecting on what I can accomplish. 15 I energized myself by 312 3.14 1.52 .86 thinking about how I can improve my performance. Positive Reflection .86 18 I considered how my work 313 2.82 1.57 .75 positively impacts society. 21 I thought about how my 314 2.95 1.53 .84 work aligns with my values and beliefs. 22 I thought about how my job 312 3.05 1.58 .88 adds purpose to my life. Total Scale .90

Evidence of Validity

As a first step toward demonstrating criterion-related validity, I examined how the pre-work scale relates to other measures (Table 9). As expected, both the pre-work scale

(total score) and the three dimensions of pre-work positively correlated with the Job

Engagement Scale and sub dimensions (Rich et al., 2010) and job satisfaction (Cammann et al., 1979). Only the Energy Mobilization subscale negatively related to emotional exhaustion. The other pre-work subscales and overall scale were unrelated to exhaustion.

72 Table 9. Correlations between Pre-Work Scales and Outcomes.

Variable Alpha Mean SD Pre-Work Cognitive Energy Positive Scale (14 Reattachment Mobilization Reflection items) Job Engagement Scale .95 3.78 .75 .53** .42** .53** .41** (18 items) Cognitive Engagement .92 3.88 .81 .39** .32** .41** .27** (6 items) Physical Engagement .90 3.83 .79 .41** .35** .41** .29** (6 items) Emotional Engagement .93 3.62 .93 .58** .43** .56** .50** (6 items) Emotional Exhaustion .92 2.87 1.17 -.02 .05 -.11* .01 (3 items) Job Satisfaction (3 items) .85 3.22 .50 .39** .29** .36** .35** Note. *p ≤ .05, **p ≤ .01.

73 STUDY 2: MAIN STUDY

The main study used experience sampling methodology (ESM), a type of data collection in which participants respond to repeated assessments at moments over time.

This methodology was adopted because it allows for the investigation of within-person processes occurring in real-life situations.

Participants and Procedure

Data were collected from a total of 114 participants. In determining the number of participants needed, both generalizability and statistical power were considered. To be able to make generalizable conclusions about experiences across days and persons based on statistically significant study findings, a large sample and many days per participants are needed. However, when many daily surveys are required participant fatigue and decreased motivation to continue become issues that may lead individuals to decide not to participate in the study, drop out early, or skip surveys over time (Ohly, Sonnentag,

Niessen, & Zapf, 2010). For the current study, I aimed to have at least 100 participants complete three surveys per day for a total of 10 working days. This provides a large sample size (the minimum level 1 N of 100 x 3 days x 3 surveys = 900; maximum of

N=3,000) with enough power to test both within-person hypotheses and changes in variables and cross-level interaction hypotheses.

Participants were compensated on a sliding scale to enhance their willingness to complete as many surveys as possible. Participants could earn up to $50 based on their level of participation. The initial person-level survey was worth $5 and then a sliding scale was used for the daily surveys that rewarded at least 3 complete days and

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incentivized up to 10 complete days, such that 3 complete days of surveys = $9; 4 complete days = $12; 5 complete days = $20, 6 complete days of surveys = $25; 7 complete days = $30; 8 complete days of surveys = $35; 9 complete days of surveys =

$40; and 10 complete days of surveys = $45. Participants were recruited from two primary sources: a call center organization in North East Ohio (hereafter referred to as

“Call Corp”) as well as through social media sites, including LinkedIn, Reddit, Twitter, and Facebook. Participants from Call Corp were recruited during two organization-wide meetings. At these meetings, employees were provided with information about the data collection process, the time commitment of participating, and how they would be compensated. Interested employees were then invited to sign up to attend an orientation meeting. Approximately five orientation meetings were held. During the orientation meeting, more detailed information about the data collection process was provided, including approximately when the surveys would signal each day (i.e., time of day) and the timeframe for completing each survey. In addition, each participant self-generated a meaningful unique identification number that was used to link participant data across the study. Participants reviewed the primary consent form and were asked to read and sign a hardcopy of the form (Appendix D).

Participants recruited through social media sites were required to be at least 18 years old and work at least 30 hours per week in the United States in a job where they interact with individuals outside of their organization (e.g. customers, patients, clients, students or some other members of the public). For example, occupations that interact with the public include, but are not limited to nurses/doctors, secretaries, teachers, customer service representatives (retail, food service), and lawyers to name a

75 few. Individuals who were interested in and eligible for my study were then directed to a

“recruitment” form where they were provided with detailed information about the study.

They also responded to eligibility questions (e.g., age, weekly work hours), provided information about their work schedule, and created their unique ID code (see Appendix

G).

Data collection occurred in two phases. After attending the orientation meeting

(for Call Corp participants) or completing the initial recruitment form (for social media participants), participants were e-mailed detailed instructions about the data collection process, the importance of providing accurate and complete data, and reminded of the pay schedule. This e-mail also contained a link to an online survey consisting of person- level measures (i.e., resilience, perceptions of supervisor support and demographics). On the first page of the survey, participants were prompted to enter their unique ID and read the informed consent form (see Appendix E and G). Participants were required to complete this survey to be eligible to participate in the event-level surveys.

During the second phase, participants were asked to complete three daily surveys per day for up to 10 work days. Although participants were encouraged to complete all the surveys within a 14-day window if possible, the survey window was extended as needed to accommodate approximately 57 participants for various reasons, including scheduling errors (i.e., the participant did not receive a survey at the correct time), scheduling miscommunications (i.e., participant’s work schedules changed), or absences from work due to illness or holidays. On average participants completed surveys within a

15-day window. Participants completed surveys at three time points: when they (a) first arrived at work, (b) during their lunch break (or other mid-day break), and (c) at the end

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of the workday, and each survey took approximately 5 minutes to complete. On average, participants completed surveys for 8 days (8 days X 3 surveys/day = 24 surveys). Daily pre-work, morning recovery, and negative reflection were assessed when participants first arrived to work; engagement was assessed during their mid-day break and at the end of the workday; job satisfaction and emotional exhaustion were assessed at all three time points.

Surveys were collected through Qualtrics survey platform. This platform allows researchers to specify when surveys are administered to participants via e-mail.

Participants received links to the start-, mid-, and end-of-shift surveys via email at the same time each day. They could complete the surveys online via their work computer or mobile phone. Qualtrics also permits the researcher to set an expiration time on the survey. By requiring participants to complete each survey within an allotted time after it has been signaled, it ensures that the participant’s current experiences are being captured and that participants are not waiting to complete all three surveys at the end of the day before leaving work. In addition, the surveys are time-stamped so as to further verify the surveys were completed during the required window of time.

Throughout the data collection process, participants were contacted to a) ensure they were not experiencing problems with the surveys, b) develop rapport with them and keep them motivated, and c) remind them of the importance of accurate and frequent responding. The first communication was made on the second day of data collection.

Participants received an e-mail asking how the first day went and if they encountered any problems. The second communication occurred on the sixth day of data collection via e- mail. Participants were reminded and encouraged to respond to surveys as frequently and

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accurately as possible. The third e-mail occurred on approximately the eighth day of the data collection again via e-mail.

Following completion of the data collection, Qualtrics data was downloaded onto a computer to determine the number of event-level surveys each of the participants completed. After each participant’s response rate was determined, they were compensated with either a check (for the Call Corp sample) or an Amazon.com gift card

(for the social media sample). Initially, 100 Call Corp participants completed the one- time survey (55% of the total employees working at the organization). Among the 100 participants who completed the one-time survey, 93 (93%) began to participate in the daily surveys. However, data was retained for only those employees who completed the start-, mid-, and end-shift surveys for at least three workdays. The reason for this is that a minimum of three days are needed to model day-level (start of shift → mid-shift → end- of-shift) within-person relationships appropriately (Beal, Trougakos, Weiss, & Dalal,

2013; Singer & Willet, 2003). The final Call Corp sample consisted of 64 participants

(508 complete days) after removing those who participated in fewer than three complete days (508 complete days X 3 surveys/day = 1524 complete surveys). On average, participants in this sample were 33.14 years old (SD = 12.09), with 84.38% identifying as female (12.5% male; 1.56% transgender) and 79.69% being white (18.75% Black).

Regarding the social media sample, 92 participants completed the one-time survey. Seventy-five of these individuals (82%) began to participate in the daily surveys, with 50 of these participants completing at least three full days of surveys (428 complete days X 3 surveys/day = 1284 complete surveys). On average, participants were 31.72 years old (SD = 8.32), with 76% identifying as female (24% male) and 88% being white

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(4% Black). Participants were employed in a variety of industries: 24% worked in educational services; 22% in health care; 8% in retail; 6% in administrative support; 6% in professional, scientific, & technical; 4% in construction; and 4% in finances. Each of the following industries employed 2% of the sample: arts/entertainment/recreation, food services, information technology, public administration, social assistance, utilities, and wholesale trade. Finally, 12% worked in “other”.

The final sample (combining the call center and social media samples) of 114 participants provided 936 full days of surveys (or 2808 event surveys) out of a possible

1140 days (3420 event surveys; 82% completion rate). On average, participants were

32.53 years old (SD = 10.53), with 79.6% identifying as female (19.8% male; .6% transgender), and 83.33% being white (13.16% Black). The suitability of combining the samples is discussed in the upcoming section titled “preliminary analyses.”

Person-Level Measures

Resilience. Employee’s’ resilience was measured using 14-items from Block and

Kremen’s Resilience measure (1996; Appendix B). An example item is “I am generous with my friends.” Responses were given on a 4-point rating scale ranging from 1 (does not apply at all) to 4 (applies very strongly). Block and Kremen (1996) reported the alpha coefficient reliability of the ER89 to be .76, and Letzring et al. (2005) reported a reliability coefficient of .72. In the present study, the reliability coefficient was .83.

Block and Kremen (1996) did not suggest a factor structure for this measure in the original study, so it was assumed to be unidimensional. However, since then, there has been debate over the factor structure of the scale with some arguing that it is multi- dimensional, comprised of either two- or three- factors (Farkas & Orosz, 2015).

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Perceived Supervisor Support. Employees’ perceptions of supervisor support was measured using 8-items (Eisenberger et al., 2002; Appendix B). An example item is

“My supervisor strongly considers my goals and values.” Responses were given on a 5- point rating scale ranging from 1 (strongly disagree) to 5 (strongly agree). Eisenberger and colleagues (2002) reported the alpha coefficient reliability of the PSS to be .88. The coefficient alpha for the scale in the current sample was .83.

ESM Measures

In accordance with recommendations from Uy, Foo, and Aguinis (2010) and Beal

(2015), reduced versions of established measures were used to reduce participant fatigue

(e.g., Christensen, Barret, Bliss‐Moreau, Lebo, & Kaschub, 2003). Participants responded to each item on a scale ranging from 1 (strongly disagree) to 5 (strongly agree). Using the same response scale may help to minimize participant burden associated with taking the surveys repeatedly (e.g., Beal, 2015). The alphas reported in the subsequent section were calculated on within-person centered data.

Pre-Work. Pre-work strategies were measured with a 9-item scale developed for the current study (Appendix C). Three items assessed each dimension of pre-work: cognitive reattachment, energy mobilization, and positive reflection. Employees were presented with the sentence stem, “Before I started work (i.e., while at home, during the commute, or first few minutes of work)....” and then asked to indicate the degree to which they agreed with a list of nine statements. A sample cognitive reattachment item is “…I mentally tuned into my work.” A sample energy mobilization item is, “I motivated myself by thinking about my goals for the day,” and a sample positive reflection item is,

“…I considered how my work positively impacts society.” For cognitive reattachment,

80 energy mobilization, and positive reflection, the average within-person reliabilities were

.75, .79, and .79 respectively.

Engagement. Engagement was assessed using nine items from the Job

Engagement Scale (JES; Rich et al., 2010; Appendix C). The three items retained for each dimension (cognitive, physical, and emotion) were chosen based on factor loadings

(Rich et al., 2010) and content, such that items that clearly differentiate between engagement dimensions were retained. For instance, emotional engagement items related to excitement and enthusiasm are somewhat difficult to differentiate from physical engagement. As such, emotional engagement items that more closely align with autonomous motivation (i.e., interest and pride in work) were chosen. The within-person reliabilities for cognitive engagement assessed mid-day and afternoon (during each day) were .88 and .87 respectively. The within-person reliabilities for physical engagement assessed mid-day and afternoon were .70 and .76 respectively. Finally, the within-person reliabilities for emotional engagement assessed mid-day and afternoon were both .78.

Emotional Exhaustion. Three items were used to assess emotional exhaustion in the moment (Wharton, 1993; Appendix C) that were applicable to the study of event- level emotional exhaustion (for a similar three-item version of Wharton’s [1993] measure, see: Bennett, Gabriel, Calderwood, Dahling, & Trougakos, 2016; Diefendorff,

Gabriel, Nolan, & Yang, 2019). Participants were presented with the stem, “Right now...” followed by the following items: “…I feel emotionally drained,” “…I feel used up,” and

“…I feel burned out.” The within-person reliabilities for emotional exhaustion at the start-, mid-, and end of-shift were .87, .84, and .86 respectively.

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Job Satisfaction. Two items from the Cammann, Fichman, Jenkins, and Klesh,

(1979) measure of job satisfaction were used (Appendix C). Again, participants were presented with the stem, “Right now...” followed by the following items: “…I am satisfied with my job,” and “… I like working here.” These two items have been used in past research to examine event-level job satisfaction (see Benedetti, Diefendorff, Gabriel,

& Chandler, 2015). The within-person reliabilities for job satisfaction at the start-, mid-, and end-of-shift were .67, .72, and .76 respectively.

ESM Control Variables

Negative Work Reflection. Negative work reflection was assessed using two items adapted from Sonnentag and Fritz (2006). Sonnentag and Fritz (2006) assessed negative work reflection during vacation. In the current study items were adjusted to reflect daily negative work reflection prior to starting work (instead of during vacation).

Specifically, participants were presented with the item stem, “BEFORE I started work

(i.e., while at home, during the commute, or first few minutes of work)....” followed by the following items: “… I could not stop thinking about negative aspects of my job” adapted from the item “During vacation, I considered the negative aspects of my job,”

(Sonnentag & Fritz,2006) and “… I could not stop thinking about bad work experiences” adapted from the item “During vacation, I noticed what is negative about my work”

(Sonnentag & Fritz, 2006). The within-person reliability for this scale was .84.

Morning State Recovery Level. Morning recovery was assessed using four items from Sonnentag and Kruel (2006). This measure assesses a person's momentary state of being recovered. Participants were presented with the item stem, “When I woke up this morning…” followed by the following items: “…I felt well rested,” “…I felt physically

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recovered,” “…I felt mentally recovered,” and “…I was full of new energy.” In a previous study that used these same four items, Sonnentag, Mojza, Demerouti, & Bakker

(2012) reported the Cronbach's alpha ranged between .89 and .91 (M = .90) for their four days of data collection. The within-person reliability for the current study was .91.

Preliminary Analyses. Before testing hypotheses, I examined several issues to understand the suitability of combining the two samples. First, I examined whether there were differences on any study variables across the two samples. As shown in Table 10, although Call Corp participants reported higher levels of resilience and physical engagement (end-of-shift) than the social media sample, the Call Corp employees reported lower levels of morning recovery and higher levels of negative reflection, engaged in lower levels of cognitive reattachment and energy mobilization, and reported being less emotionally engaged (mid- and end-of-shift) compared to the social media sample. The Call Corp sample also reported lower levels of satisfaction (start-, mid-, and end-of-shift) and higher levels of emotional exhaustion (start-, mid-, and end-of-shift) throughout the days. The samples did not differ on positive reflection, cognitive engagement (mid- and end-of-shift) or physical engagement (mid-shift). In sum, Call

Corp participants reported having more negative daily experiences than the social media participants overall.

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Table 10. Study 2 Means, Standard Deviations, and Mean Differences for Main Variables.

Call Corp Social Mean Differences Media

Variable N M (SD) N M (SD) t (df)

Resilience 64 3.65 (.56) 50 3.46 (.60) 4.91 (932)**

PSS 64 3.56 (.81) 50 3.68 (.81) -2.19 (902.01)*

Morning 508 3.14(1.14) 427 3.71 (1.05) -7.88 (926.18)** Recovery Cognitive 508 3.24 (.93) 428 3.68 (.86) -7.5 (934)** Reattachment Energy 507 2.98(1.05) 428 3.19 (.98) -3.17 (933)** Mobilization Positive 507 2.90 (1.01) 428 2.92 (1.09) -.28 (880.07) Reflection Negative 508 2.60 (1.17) 428 1.89 (1.01) 10.05 (933.53)** Reflection Cognitive 508 4.01 (.85) 427 4.04 (.80) -.69 (920.04) engage mid Physical engage 508 3.96 (.87) 427 3.91 (.75) 1.01 (933) mid Emotional 508 3.32 (1.02) 427 3.57 (.94) -3.78 (925.10)** engage mid Cognitive 508 4.01 (.89) 427 3.98 (.77) .55 (933) engage end Physical engage 508 3.98 (.93) 427 3.84 (.77) 2.37 (932.63)* end Emotional 508 3.28 (1.06) 427 3.58 (.93) -4.53 (931.57)** engage end Satisfaction start 508 3.78 (1.02) 427 4.04 (.93) -4.09 (927.12)**

Satisfaction mid 508 3.75 (1.04) 426 4.04 (.96) -4.41 (923.84)**

Satisfaction end 508 3.76 (2.07) 427 3.97 (.97) -3.22 (928.67)** Emotional Exh 508 2.94 (1.33) 427 1.94 (1.15) 12.30 (932.40)** start Emotional Exh 508 3.07 (1.33) 426 2.01 (1.20) 12.74 (927.25)** mid Emotional Exh 508 3.23 (1.24) 427 2.22 (1.24) 11.79 (933)** end Note. PSS = Perceived Supervisor Support. Engage = Engagement. Exh = Exhaustion. N for Resilience and PSS is at the person-level. For all other variables, N is at the day- level. **p<.001, *p<.05.

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To control for sample differences, I created a dichotomous variable indicating sample source and used it as a person-level covariate in predicting the dependent variables (job satisfaction and emotional exhaustion assessed at the end-of-shift). It is important to point out that, theoretically, the focus of the current study is on within- person relationships and analytically the data are within-person centered, thereby removing between-person variance. Because the relations examined are relative to each person’s individual mean, the influence of between-person variables, including the sample source are effectively controlled for in the analyses. Nevertheless, the data source

(Call Corp or social media) was still modeled as a between-person covariate.

Confirmatory Factor Analyses. A set of multilevel confirmatory factor analyses

(CFA; Dyer, Hanges, & Hall, 2005) using Mplus 8.2 (Muthén & Muthén, 2012) confirmed that the variables assessed in the daily surveys are distinct constructs (Table

11).

First, a CFA was performed on the pre-work data only (combining both samples) to confirm that a three-factor model (cognitive reattachment, energy mobilization, positive reflection) fit better than one- or two-factor model. A three-factor model with all items loading on their respective factors showed good model fit (휒2 (48) = 189.66, CFI =

.97, TLI = .95, RMSEA = .06, SRMRwithin = .04; SRMRbetween = .05) and fit the data better than the one-factor model and various two-factor models (See Table 11). The chi- square difference test for comparing nested models showed that the three-factor model fits better than the one-factor model, ∆휒2 (6) = 745.96, p < .05, and all possible two- factor models (all ∆휒2 (4) are greater than the critical value of 11.143, with p = .05).

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In addition, a CFA was performed on the pre-work data for each sample separately to ensure that three-factor solution fit best for both samples (Table 11). A three-factor model with all items loading on their respective factors showed good model fit for the Call Corp sample (휒2 (48) = 146.07, CFI = .96, TLI = .94, RMSEA = .06,

SRMRwithin = .04; SRMRbetween = .04), as well as the Social Media sample (휒2 (48) =

122.48, CFI = .96, TLI = .94, RMSEA = .06, SRMRwithin = .04; SRMRbetween = .09).

Furthermore, the chi-square difference test for comparing nested models showed that the three-factor model fits better than all possible two-factor models for both samples (all

∆휒2 (4) are greater than the critical value of 11.143, with p = .05).

Next, scales that were administered the same number of times within a given day were included in the same CFA model. Thus, pre-work, morning recovery and negative reflection, all of which were measured once per day during the start-of-shift survey, were included in the same model. A separate multilevel CFA was conducted on engagement, which was measured twice/day. A final CFA was conducted on job satisfaction and emotional exhaustion, which were assessed three times per day.

For the CFA on measures assessed once/day, a five-factor model (3 pre-work dimensions, recovery, negative work reflection) with all items loading on their respective

2 factors (휒 (160) = 435.00, CFI = .97, TLI = .96, RMSEA = .04, SRMRwithin = .03;

SRMRbetween = .07) showed excellent model fit and fit the data better than the various 4- factor models (∆휒2 (8) > 17.54, p < .05) and three-factor models (∆휒2 (14) > 26.12, p <

.05).

For the CFA on engagement, which was the only variable assessed twice/day, the three-factor model fit best (∆휒2 (6) = 1848.26, p < .05). For the CFA on the variables

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assessed three times/day (job satisfaction and emotional exhaustion), the two-factor model fit best (∆휒2 (2) = 938.88, p <.05). Lastly, I aggregated all within person scales to the ‘day level’ and conducted one multilevel CFA, which included all items. More specifically, I averaged across the job satisfaction items (assessed three times per day), the emotional exhaustion items (assessed three times per day), and engagement items

(assessed two times per day). I then combined these scales with the pre-work, morning recovery, and negative reflection scales (each assessed one/day). The multilevel CFA with 10 factors (cognitive reattachment, energy mobilization, positive reflection, morning recovery, negative reflection, cognitive engagement, physical engagement, emotional engagement, job satisfaction, and emotional exhaustion) showed acceptable model fit (휒2

(664) = 1424.75, CFI = .96, TLI = .95, RMSEA = .04, SRMRwithin = .03; SRMRbetween =

.07) and fit the data better than the CFA with 9 factors (emotional exhaustion and satisfaction combined): 휒2 (682) = 1885.47, CFI = .92, TLI = .90, RMSEA = .04,

SRMRwithin = .04; SRMRbetween = .12; ∆휒2 (18) = 460.72, p < .05. Finally, a CFA was conducted on the between-person variables: perceived supervisor support and resilience.

The two-factor model fit the data better than a one-factor model: ∆휒2 (1) = 163.45, p <

.05.

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Table 11. Multilevel CFA Results Model 휒2 df ∆χ2 ∆df CFI TLI RMSEA SRMR SRMR within between Pre-Work Only (both samples) 3-factor 189.66 48 .97 .95 .06 .04 .05 2-factor (CR & EM combine) 518.39 52 328.73 4 .89 .84 .10 .06 .10 2-factor (CR & PR combine) 769.35 52 579.69 4 .82 .75 .12 .08 .19 2-factor (EM & PR combine) 537.75 52 348.09 4 .88 .83 .10 .06 .07 Pre-Work Only (Call Corp)

3-factor 146.07 48 .96 .94 .06 .04 .04 2-factor (CR & EM combine) 366.08 52 220.01 4 .87 .82 .11 .06 .10 2-factor (CR & PR combine) 408.18 52 262.11 4 .85 .80 .12 .07 .10 2-factor (EM & PR combine) 223.08 52 77.01 4 .93 .90 .08 .05 .04 Pre-Work Only (Social Media Only) 3-factor 122.48 48 .96 .94 .06 .04 .09 2-factor (CR & EM combine) 219.42 52 96.94 4 .90 .87 .09 .06 .11 2-factor (CR & PR combine) 399.79 52 277.31 4 .80 .72 .13 .09 .29 2-factor (EM & PR combine) 321.22 52 198.74 4 .85 .79 .11 .07 .22 Scales 1/day (Pre-Work, Morning Recovery & Negative Reflection) 5 factor 435.00 160 .97 .96 .04 .03 .07 4 factor (CR & EM combine) 779.89 168 344.89 8 .93 .91 .06 .05 .09 4 factor (EM & PR combine) 797.15 168 362.15 8 .92 .90 .06 .04 .09 Scales 2/day (Engagement Scales): 3-factor 221.51 48 .98 .97 .04 .03 .06 2-factor (CE & PE combine) 705.69 52 484.18 4 .93 .90 .08 .04 .06 2-factor (CE & EE combine) 1651.11 52 1429.60 4 .82 .75 .13 .11 .15 2-factor (PE & EE combine) 1511.64 52 1290.16 4 .83 .77 .12 .09 .18 Scales 3/day (Sat & Exh) 1-factor (Sat & Exh combine) 950.61 10 .84 .68 .18 .12 .12 2-factor 11.74 8 938.87 2 1.00 1.00 .01 .004 .02 Note. CR = Cognitive Reattachment Pre-Work. EM = Energy Mobilization Pre-Work. PR = Positive Reflection Pre-Work. PW = Pre-Work. MR = Morning Recovery. NR = Negative Work Reflection. CE = Cognitive Engagement. PE = Physical Engagement. EE = Emotional Engagement. Sat = Job Satisfaction. Exh = Emotional Exhaustion.

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Analytic Strategy

For hypothesis testing, multilevel path analysis in Mplus 8.2 was used (Muthén &

Muthén, 2012). This is appropriate since the data has a multilevel structure (days nested within people). I first estimated a model (M1) with random slopes to test the main and mediation effects (cross-level moderators were not included in M1). I then estimated a second model (M2) that included perceived supervisor support and resilience (Level 2 variables) as a predictor of within-person random slopes of pre-work strategies and engagement (Preacher, Zhang, & Zyphur, 2016). Within-individual predictors and controls were within-person centered, as doing so removes variance attributable to the person-level of analysis. In other words, person-level factors, such as individual differences, cannot influence the relations estimated amongst within-person constructs

(e.g., Scott & Barnes, 2011). Within-person predictors were modeled using random slopes. Level-2 (between-person) control and moderating variables were grand-mean centered (Bliese, 2000; Enders & Tofighi, 2007; Gabriel et al., 2018). Finally, to reduce model complexity, the effects of control variables and direct effects were modeled with fixed slopes (e.g., Wang, Liao, Zhan, & Shi, 2011; Wang et al., 2013; Gabriel et al.,

2018).

In accordance with recommendations from Preacher, Zyphur, and Zhang (2010), mediation and moderated-mediation hypotheses were tested using a parametric bootstrap procedure. For mediation, indirect effects were calculated using a Monte Carlo procedure with 20,000 replications to create bias-corrected confidence intervals (CIs) for each indirect effect (e.g., Selig & Preacher, 2008) in R. For moderated-mediation, indirect effects at conditional (±1 SD) values of the moderators were calculated and used a Monte

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Carlo procedure with 20,000 replications to create bias-corrected confidence intervals

(CIs) for each indirect effect (e.g., Selig & Preacher, 2008).

CHAPTER IV

RESULTS

Partitioning of Variance Components

As a first step in my multilevel analyses, I investigated whether systematic within- and between-individual variance existed in the ESM (i.e., Level-1) variables (pre- work, morning recovery, negative work reflection, engagement, emotional exhaustion, job satisfaction) by estimating a null model for each variable. The null model partitions the total variance of a variable into within- and between-individual components, and the intercept for each null model represents the average level of that variable across individuals. If no within-individual variance exists in the criterion variables, then multilevel analyses will not be appropriate because there will be only between-individual variance to explain. Table 12 provides estimates of the within-individual variance in the daily measures. Importantly, results of null models revealed that all variables had a large proportion of within-person variances (ranging from 27% to 58%), suggesting that multilevel analyses are appropriate.

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Table 12. Percentage of within‐individual variance among daily variables. Measurement Construct Within- Between- % of Within-individual Occasion individual individual variance variance (e2) variance (r2) Start-of-Shift Cognitive Reattachment .49 .36 57.65% Energy Mobilization .44 .58 43.14% Positive Reflection .48 .57 45.71% Morning recovery .66 .60 52.38% Negative work reflection .51 .77 39.84% Satisfaction .26 .72 26.53% Emotional exhaustion .53 1.27 29.44% Mid-Shift Cognitive Engagement .33 .34 49.25% Physical Engagement .30 .35 46.15% Emotional Engagement .41 .56 42.27% Satisfaction .31 .73 29.81% Emotional exhaustion .53 1.36 28.04% End-of-Shift Cognitive Engagement .36 .35 50.70% Physical Engagement .33 .39 45.83% Emotional Engagement .40 .62 39.22% Satisfaction .34 .73 31.78% Emotional exhaustion .64 1.33 32.49% Note: The percentage of variance within-individuals was calculated as (e2/(e2 + r2).

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Tests of Hypotheses

A summary of hypotheses is provided in Table 13.

Table 13. Summary of Hypotheses. At the within-person level of analysis… H1 …pre-work reattachment is positively related to cognitive engagement during the day. H2 …pre-work energy mobilization is positively related to physical engagement during the day. H3 …pre-work positive reflection is positively related to emotional engagement during the day. H4 At the within-person level of analysis cognitive engagement during the day is… A …positively associated with change in job satisfaction. B …negatively associated with change in emotional exhaustion. H5 At the within-person level of analysis physical engagement is… A …positively associated with change in job satisfaction B …negatively associated with change in emotional exhaustion. H6 At the within-person level of analysis emotional engagement is… A …positively associated with change in job satisfaction B …negatively associated change in emotional exhaustion. H7 Pre-work has indirect effects on job satisfaction through engagement. Specifically… A …reattachment positively impacts change in job satisfaction through cognitive engagement B …energy mobilization positively impacts change in job satisfaction through physical engagement C …positive reflection positively impacts change in job satisfaction through emotional engagement.

H8 Pre-work has indirect effects on emotional exhaustion through engagement. Specifically… A …reattachment negatively impacts change in emotional exhaustion through cognitive engagement B …energy mobilization negatively impacts change in emotional exhaustion through physical engagement C …positive reflection negatively impacts change in emotional exhaustion through emotional engagement.

H9 Psychological resilience moderates the within-person effects of pre-work strategies on workday engagement, such that the effects of… A …reattachment on cognitive engagement B …energy mobilization on physical engagement

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C …positive reflection on emotional engagement are stronger for individuals high in resilience compared to individuals low in resilience. H10 Supervisor support moderates the within-person effects of pre-work strategies on engagement, such that the effects of… a …reattachment on cognitive engagement b …energy mobilization on physical engagement c …positive reflection on emotional engagement are stronger for individuals who perceive their supervisor as supportive. Supp The effects of pre-work on engagement will be stronger in the first half of the Hyp day compared to the second half of the day.

Means, standard deviations, and inter-correlations among study variables are presented in Table 14. Within-person correlations between pre-work strategies ranged from r = .43 to .58; between-person correlations between pre-work strategies ranged from r = .51 to .78, suggesting that the pre-work strategies are related, yet distinct constructs.

Researchers have argued and shown that a correlation higher than .30 represents related constructs and a correlation of .80 or higher between two constructs suggests redundancy

(Brown, 2006; Kline, 2005). Interestingly, negative work reflection was positively related to cognitive reattachment (r = .15, p < .01) and positive reflection (r = .09, p < .01) at the within-person level, suggesting that individuals may simultaneously use pre-work strategies and negatively reflect on work before beginning their workdays. In other words, individuals had days where they generally think more about work prior to their shift, with those cognitions covering a variety of content and being both positive and negative.

Within-person correlations between start-of-shift cognitive reattachment and mid- shift cognitive engagement (r = .17, p < .001), start-of-shift energy mobilization and mid- shift physical engagement (r = .08, p = .014), and start-of-shift positive reflection and

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mid-shift emotional engagement (r = .16, p < .001) were all significant, providing preliminary support for Hypotheses 1 through 3. The majority of within-person correlations between the mid-shift engagement dimensions and end-of-shift exhaustion and satisfaction were significant. Specifically, within-person correlations between cognitive engagement (mid-shift) and job satisfaction assessed at the end of shift (r = .22, p < .001), as well as cognitive engagement (mid-shift) and emotional exhaustion at the end of shift (r = -.16, p < .001) were significant, providing preliminary support for hypothesis 4a and 4b. Although the within-person correlation between physical engagement (mid-shift) and emotional exhaustion assessed at the end of shift was not significant (r = -.04, ns), the correlation between and physical engagement and job satisfaction (r = .18, p < .001) was significant, providing preliminary support for hypothesis 5a, but not 5b. Finally, within-person correlations between emotional engagement (mid-shift) and job satisfaction assessed at the end of shift (r = .30, p < .001) and emotional engagement (mid-shift) and exhaustion assessed at the end of shift (r = -

.21, p < .05) were significant, providing preliminary support for hypothesis 6a and 6b.

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Table 14. Reliabilities, descriptive statistics and correlations for all study variables. α M SD 1 2 3 4 5 6 7 8 9 10 1. Res .83 3.58 .57 -- 2. PSS .83 3.66 .81 .20* -- 3. Morning .91 3.40 1.13 .32** .06 -- .24** .29** .29** -.12** .17** .08* .11** Rec 4. Cog .75 3.44 .92 0.14 .05 .30** -- .58** .43** .15** .17** .07* .08* Reattach 5.Energy .79 3.08 1.02 .36** .06 .47** .67** -- .58** .04 .20** .08* .14** Mob 6. Pos Ref .79 2.91 1.04 .28** .08 .29** .51** .78** -- .09** .14** .03 .16** 7. Neg .84 2.28 1.16 -.05 -.03 -.52** .06 -.11 .02 -- -.01 -.01 -.02 Reflect 8. Cog Eng .88 4.02 .83 .21* -.01 .32** .45** .40** .36** -.17 -- .67** .38** mid 9. Phys Eng .70 3.94 .82 .12 -.02 .25** .45** .40** .37** -.10 .83** -- .35** mid 10. Emo Eng .78 3.43 .99 .27** .03 .45** .20* .50** .60** -.32** .50** .44** -- mid 11.Cog Eng .87 4.00 .84 .19* -.05 .27** .44** .43** .40** -.14 .87** .77** .44** end 12. Phys Eng .76 3.91 .86 .12 -.10 .20* .42** .39** .35** -.06 .76** .88** .35** end 13. Emo Eng .78 3.42 1.01 .29** .06 .44** .22* .50** .58** -.32** .48** .44** .93** end 14. Sat start .67 3.90 .99 .26** .10 .47** 0.18 .34** .43** -.45** .43** .36** .75** 15. Sat mid .72 3.88 1.01 .24** .07 .46** 0.15 .34** .43** -.47** .46** .37** .81** 16. Sat end .76 3.85 1.03 .19* .06 .46** 0.12 .29** .37** -.48** .42** .35** .73** 17. Emo Exh .87 2.48 1.34 -.18 -.05 -.68** -.17 -.27** -.16 .69** -.26** -.10 -.40** start 18. Emo Exh .84 2.59 1.37 -.17 -.09 -.67** -.19* -.30** -.18 .67** -.28** -.10 -.45** mid 19. Emo Exh .86 2.77 1.40 -.17 -.16 -.65** -.17 -.30** -.19* .65** -.28** -.13 -.45** end

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Table 14 (continued). α M SD 11 12 13 14 15 16 17 18 19 1. Res .83 3.58 .57 2. PSS .83 3.66 .81 3. Morning Rec .91 3.40 1.13 .08* .05 .06 .33** .14** .11** -.37** -.22** -.15** 4. Cog Reattach .75 3.44 .92 .15** .16** .08* .18** .12** .10** -.09** .01 -.06 5.Energy Mob .79 3.08 1.02 .14** .12** .09** .27** .14** .15** -.13** -.08* -.07* 6. Pos Ref .79 2.91 1.04 .10** .08* .10** .27** .22** .19** -.12** -.12** -.08* 7. Neg Reflect .84 2.28 1.16 -.04 -.07* -.11** -.12** -.11** -.09** .23** .14** .09** 8. Cog Eng mid .88 4.02 .83 .33** .27** .19** .12** .26** .22** -.15** -.16** -.16** 9. Phys Eng mid .70 3.94 .82 .28** .28** .18** .10** .20** .18** -.09** -.02 -.04 10. Emo Eng mid .78 3.43 .99 .16** .13** .31** .21** .45** .30** -.13** -.31** -.21** 11.Cog Eng end .87 4.00 .84 -- .71** .40** .11** .10** .22** -.03 -.07* -0.04 12. Phys Eng end .76 3.91 .86 .85** -- .41** .09** .07* .19** -.04 -.04 -.002 13. Emo Eng end .78 3.42 1.01 .49** .43** -- .14** .23** .43** -.02 -.18** -.25** 14. Sat start .67 3.90 .99 .37** .26** .73** -- .34** .23** -.38** -.21** -.18** 15. Sat mid .72 3.88 1.01 .40** .28** .78** .93** -- .42** -.18** -.33** -.26** 16. Sat end .76 3.85 1.03 .40** .29** .76** .92** .95** -- -.11** -.20** -.29** 17. Emo Exh start .87 2.48 1.34 -.21* -.03 -.38** -.52** -.51** -.53** -- .33** .18** 18. Emo Exh mid .84 2.59 1.37 -.23* -.03 -.42** -.52** -.55** -.56** .96** -- .39** 19. Emo Exh end .86 2.77 1.40 -.22* -.02 -.42** -.52** -.55** -.57** .94** .97** -- Note. Both between-individual and within-individual correlations for Variable 3 to Variable 22 are reported. Correlations below the diagonal are based on the means aggregated from daily scores (n=114 participants). Correlations above the diagonal are the within-person correlations (n=936; total number of days). *p< .05*. **p<.01.

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Analytic Approach A: Separation of Measures

For “Analytic Approach A: Separation of Measures”, hypothesized and non- hypothesized relations between pre-work strategies (assessed at the start-of-shift) and the engagement dimensions (assessed mid-shift) as well as change in job satisfaction and emotional exhaustion from mid-shift to end-of-shift were modeled. Results from my multilevel path analysis for Analytic Approach A (Model 1) are depicted in Figure 2.

Table 15 reports results of both Analytic Approach A, Model 1 (M1) and Analytic

Approach B, Model 2 (M2). See Table 16 for a summary of hypotheses and findings.

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Figure 2. Multilevel Path Analysis for Analytic Approach A: Separation of Measures, Model 1.

Note. Hypothesized effects are bolded. Significant, non-hypothesized effects are not bolded.

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Table 15. Analytic Approach A, Separation of Measures: Testing Main, Mediation, and Moderation Effects. Cognitive Physical Emotional Engagement Job Satisfaction Emotional Exhaustion (end-shift) Engagement Engagement (mid-shift) (end-shift) (mid-shift) (mid-shift) M1 M2 M1 M2 M1 M2 M1 M2 M1 M2 Variables Est. SE Est SE Est SE Est SE Est SE Est SE Est SE Est SE Est SE Est SE

Start-Shift Cog. Reattach (CR) .05 .04 .04 .04 .03 .04 .02 .04 -.03 .04 -.02 .04 -.02 .03 -.02 .03 -.05 .04 -.05 .04 Energy Mob. (EM) .10* .04 .11** .04 .05 .04 .06 .04 .05 .05 .06 .05 .03 .04 .04 .04 .01 .06 .01 .06 Pos. Reflection (PR) -.001 .04 -.01 .04 -.04 .04 -.04 .04 .11* .05 .10* .05 .06 .04 .06 .04 .01 .04 .01 .04

Morn. Recovery .08* .03 .08* .03 .04 .03 .04 .03 .04 .03 .04 .03

Neg. Reflection -.02 .03 -.02 .04 -.01 .04 -.01 .04 -.03 .04 -.03 .04

Mid-Shift

Job Satisfaction .30** .06 .33 .06

Emo Exhaustion .37** .05 .37 .04

Cognitive Engage .06 .05 -.19** .06

Physical Engage .06 .04 .15* .06

Emotional Engage .15** .04 -.13* .05

Variables at the Person Level

Source .14 .15 -.06 .15 -.87** .20 -.71** .21

PSS -.04 .08 -.04 .08 -.04 .09

Resilience (Res) .18 .11 .10 .11 .26* .11 Cross-level interactions CR X PSS -.03 .05 -.01 .04 .02 .05 CR X RES -.11 .07 -.03 .06 .04 .08 EM X PSS -.04 .05 .004 .05 -.09 .05 EM X RES -.03 .09 .02 .08 -.03 .09 PR X PSS .10 .05 .03 .05 .15* .06 PR X RES -.004 .07 -.04 .07 -.04 .08 Note. Cog. Reattach = Cognitive Reattachment. Energy Mob. = Energy Mobilization; Pos. Reflection = Positive Reflection. Engage = Engagement. Emo. Exhaustion = Emotional Exhaustion. *p < .05, **p < .01. Analyses were run with and without the covariates (morning recovery and negative reflection) in the model to ensure that their inclusion were not driving significant results. The same pattern of results emerged with one exception: when covariates were removed from the model, Positive Reflection and PSS interacted to predict Cognitive Engagement (mid-shift).

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Table 16. Summary of Hypotheses for Analytic Approach A: Separation of Measures Findings At the within-person level of analysis… H1 …pre-work reattachment is positively related to cognitive engagement during the day. Not supported

H2 …pre-work energy mobilization is positively related to physical engagement during the day. Not supported

H3 …pre-work positive reflection is positively related to emotional engagement during the day. Supported

H4 At the within-person level of analysis cognitive engagement during the day is… a …positively associated with change in job satisfaction. Not supported b …negatively associated with change in emotional exhaustion. Supported H5 At the within-person level of analysis physical engagement is… a …positively associated with change in job satisfaction. Not supported b …negatively associated with change in emotional exhaustion. Not Supporteda

H6 At the within-person level of analysis emotional engagement is… a …positively associated with change in job satisfaction Supported b …negatively associated change in emotional exhaustion. Supported H7 Pre-work has indirect effects on job satisfaction through engagement. Specifically… a …reattachment positively impacts change in job satisfaction through cognitive engagement Not supported b …energy mobilization positively impacts change in job satisfaction through physical engagement Not supported c …positive reflection positively impacts change in job satisfaction through emotional engagement. Supported H8 Pre-work has indirect effects on emotional exhaustion through engagement. Specifically… a …reattachment negatively impacts change in emotional exhaustion through cognitive engagement Not supported b …energy mobilization negatively impacts change in emotional exhaustion through physical engagement Not supported c …positive reflection negatively impacts change in emotional exhaustion through emotional engagement. Supported H9 Psychological resilience moderates the within-person effects of pre-work strategies on workday engagement, such that the effects of… a …reattachment on cognitive engagement Not supported b …energy mobilization on physical engagement Not supported c …positive reflection on emotional engagement are stronger for individuals high in resilience compared to individuals low Not supported in resilience. H10 Supervisor support moderates the within-person effects of pre-work strategies on engagement, such that the effects of... a …reattachment on cognitive engagement Not supported b …energy mobilization on physical engagement Not supported c …positive reflection on emotional engagement are stronger for individuals who perceive their supervisor as supportive. Supported Supp The effects of pre-work on engagement will be stronger in the first half of the day compared to the second half of the day. Hyp a Physical engagement was significantly positively associated with emotional exhaustion

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Results showed that cognitive reattachment was unrelated to cognitive engagement (γ = .05, ns) and energy mobilization was unrelated to physical engagement

(γ = .05, ns), failing to provide support for Hypotheses 1 and 2, respectively. Start-of- shift, positive reflection, however, was positively related to mid-shift emotional engagement (γ = .11, p < .05), providing support for Hypothesis 3. Of the six non- hypothesized paths, only one was significant; specifically, start-of-shift energy mobilization was positively related to mid-shift cognitive engagement (γ = .10, p = .01).

Recall that all but one correlation between pre-work strategies and mid-shift engagement were significant (except for positive reflection and physical engagement), but many of these effects became nonsignificant in the full model. To investigate potential reasons for this change, I examine some simpler models. As an example, when non-hypothesized links between pre-work and engagement were removed from the model (i.e., I only modeled cognitive reattachment predicting cognitive engagement; energy mobilization predicting physical engagement; and positive reflection predicting emotional engagement), morning cognitive reattachment became a significant predictor of cognitive engagement (γ = .07, p = .04), in support of H1. Further investigation showed that this link became non-significant when energy mobilization was added as a predictor of cognitive engagement. Energy mobilization was not a significant predictor of physical engagement in this model, suggesting that Hypothesis 2 did not receive support even in the simpler model, though it approached significance (p = .07) when morning recovery was removed as a control.

As an indicator of effect size, I calculated the pseudo r2 values for different parts

2 2 of my models. The formula is – 휎̂ (Model A)− 휎̂ (Model B) – and it reflects the reduction in 휎̂ 2 (Model A)

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the variance in a variable when predictors are added to the model compared to the null model with no predictors (Rosenthal & Rosnow, 1984; Singer & Willett, 2003). The within-person pseudo r2 for mid-day cognitive engagement for the covariates (morning recovery and negative reflection) was 2.92%; adding the pre-work scales accounted for an additional 11.71% of the within-person variance in mid-day cognitive engagement.

The within-person pseudo r2 for mid-shift physical engagement in a model with just the covariates was .66%. The incremental within-person pseudo r2 for physical engagement when pre-work was added to the model was 2.31%. The within-person pseudo r2 for mid- day emotional engagement in a model with only covariates was 1.69%. Adding the pre- work scales accounted for an additional 8.35% of the within-person variance.

Cognitive engagement (mid-shift) was unrelated to changes in job satisfaction from mid-shift to end-shift (γ = 0.06, ns) and was negatively related to changes in emotional exhaustion from mid-shift to end-shift (γ = -.19, p = .001), providing support for hypothesis 4b, but not hypothesis 4a. Mid-shift physical engagement was unrelated to satisfaction (H5a; γ = .06, ns) and was unexpectedly positively related to exhaustion

(H5b; γ = .19, p = .01), as H5b posited that physical engagement would be negatively related to exhaustion. As such, neither hypotheses 5a or 5b were supported. The positive relationship between physical engagement and exhaustion appears to be the result of a suppressor effect as the correlation between physical engagement and emotional exhaustion was nonsignificant. After investigating this effect further, I found that the coefficient between physical engagement and exhaustion became positive and significant when cognitive engagement is added as a simultaneous predictor. Finally, emotional engagement was positively related to satisfaction (γ = .15, p < .001) and negatively

102 related to emotional exhaustion (γ = -.13, p = .01) as predicted by hypotheses 6a and 6b respectively.

It was also predicted that start-of-shift pre-work strategies would have indirect effects on end-of-shift job satisfaction and emotional exhaustion through engagement during the day. Mediation effects were tested with a Monte Carlo simulation with 20,000 replications using the online software R. Specifically, cognitive reattachment was predicted to positively impact change in job satisfaction through cognitive engagement

(H7a) and negatively impact change in emotional exhaustion through cognitive engagement (H8a), both of which were not supported. Energy mobilization was predicted to positively impact change in job satisfaction through physical engagement (H7b) and negatively impact change in emotional exhaustion through physical engagement (H8b), neither of which were supported. Finally, positive reflection was predicted to positively impact job satisfaction through emotional engagement (H7c), as well as negatively impact change in emotional exhaustion through emotional engagement (H8c). The mediation test based on a Monte Carlo simulation with 20,000 replications indicated that the indirect effect of positive reflection on job satisfaction through emotional engagement was positive and significant (γ = .02, 95% CIs [.003, .04]), providing support for H7c.

The indirect effect of positive reflection on emotional exhaustion through emotional engagement was negative and significant (γ = -.015, 95% CIs [-.03389, -.001314]), providing support for H8c. Finally, although not hypothesized, the indirect effect of energy mobilization on emotional exhaustion through cognitive engagement was negative and significant (γ =-.019, 95% CIs [-.04, -.003]).

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Psychological resilience (hypothesis 9) and perceived supervisor support

(hypothesis 10) were predicted to strengthen the effects of pre-work strategies on engagement. Specifically, the relations between reattachment on cognitive engagement

(H9a and H10a), energy mobilization on physical engagement (H9 and H10b), and positive reflection on emotional engagement (H9c and H10c) were expected to be stronger for individuals high in resilience (compared to individuals low in resilience) and for those who perceive their supervisor as supportive. Results from my multilevel path analysis for M2 is depicted in Figure 3.

Figure 3. Multilevel path analysis for Analytic Approach A: Separation of Measures, Model 2.

Results showed that PSS was positively related to the within-person random slope of positive reflection predicting emotional engagement (γ = .15, p = .016), providing

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support for H10c. The pseudo r2 for PSS in predicting the within-person variance in the slopes between positive reflection in predicting emotional engagement was .35 (i.e., .35% of the variance in slopes was accounted for by PSS). To graph the interaction (see Figure

4), I modeled the significant interaction between positive reflection and emotional engagement (mid-shift) at three conditional values of the moderator (i.e., the mean and one SD above and below the mean) controlling for morning recovery and negative reflection using R (Aguinas, Gottfredson, & Culpepper, 2013).

Figure 4. Interaction between Positive Reflection and PSS.

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Simple slope analyses (Preacher, Curran, & Bauer, 2006) showed that positive reflection and emotional engagement had a stronger relationship when PSS was high

(simple slope = .22, p < .01) versus low (simple slope = -.02, p = .82), and the simple slopes were significantly different (difference = .24, p = .016). These results provide support for H10c only.

I further tested the indirect effect of positive reflection on job satisfaction and emotional exhaustion via emotional engagement with the moderation of PSS on the relation between positive reflection and emotional engagement. Results showed that the indirect effect was positive and significant for job satisfaction when PSS is high (γ = .02,

95% CI [.002, .04]), but not significant when PSS is low (γ = -.002, 95% CI [-.02, .01]).

The difference between these two conditional indirect effects was significant (γ = -.02,

95% CIs [-.05, -.001]), suggesting that PSS strengthens the indirect effect of positive reflection on job satisfaction via emotional engagement. Results showed that the indirect effect was not significant for emotional exhaustion when PSS is high (γ = -.03, 95% CI [-

.06, .001]) or when PSS is low (γ = .002, 95% CI [-.02, .02]).

Finally, to test the supplemental hypothesis, which predicted that the effects of pre-work on engagement will be stronger in the first half of the day compared to the second half of the day, I utilized the “model test” command in Mplus, which computes

Wald tests assessing the equality of each path. The results of the Wald tests indicate whether the relationship between pre-work and engagement (mid-shift) is equal to the relationship between pre-work and engagement (end-of-shift). In contrast to my prediction, the effects of pre-work on mid- and end-of-shift engagement were not

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significantly different (see Table 17). In other words, pre-work was not a stronger predictor of engagement at mid-shift compared to end-of-shift.

Table 17. Wald Tests for Supplemental Hypothesis. Wald Test Hypothesis Chi-square Probability Finding The effects of pre-work on engagement will be stronger in the first half of the day (mid- shit) compared to the second half of the day (end-of-shift) Cognitive Reattachment  Cognitive .14 .71 Not supported engagement Cognitive Reattachment  Physical 2.22 .14 Not supported engagement Cognitive Reattachment  Emotional 1.15 .28 Not supported engagement Energy Mobilization  Cognitive .89 .35 Not supported engagement Energy Mobilization  Physical engagement .35 .55 Not supported Energy Mobilization  Emotional .69 .41 Not supported engagement Positive Reflection  Cognitive engagement .06 .80 Not supported Positive Reflection  Physical engagement .32 .57 Not supported Positive Reflection  Emotional 1.56 .21 Not supported engagement

Analytic Approach B: Engagement throughout the Day

Hypotheses were also tested by modeling how pre-work strategies related to engagement throughout the day by averaging mid-shift and end-of-shift engagement.

Given that my hypotheses focused on engagement during the workday, this assessment arguably better reflects the idea that pre-work may benefit individuals throughout the day. Furthermore, I did not find support for the supplemental prediction that pre-work would have stronger effects early in the day compared to late in the day, which also provides justification for averaging the mid- and end-shift engagement assessments. As

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such, I modeled the pre-work variables as independent variables, engagement variables

(averaged across mid- and end-of-shift) as mediators, and end-of-day job satisfaction and exhaustion as the dependent variables, controlling for start-of-day job satisfaction and emotional exhaustion (instead of just mid-day satisfaction and exhaustion).

Hypothesized and non-hypothesized relations between pre-work strategies

(assessed at the start-of-shift) and engagement dimensions (averaged across the mid- and end-of-shift) as well as change in job satisfaction and emotional exhaustion from start-of- shift to end-of-shift were modeled. Table 18 reports results and Table 19 provides a summary of hypotheses and findings.

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Table 18. Analytic Approach B, Engagement throughout the Day: Testing Main, Mediation and Moderation Effects

Cognitive Engagement Physical Engagement Emotional Engagement Job Satisfaction Emotional Exhaustion (averaged) (averaged) (averaged) (end-shift) (end-shift) M1 M2 M1 M2 M1 M2 M1 M2 M1 M2 Variables Est. SE Est SE Est SE Est SE Est SE Est SE Est SE Est SE Est SE Est SE

Start-Shift Cog. Reattachment (CR) .09* .04 .08* .03 .08* .04 .07* .03 .01 .04 .01 .04 -.01 .03 -.01 .03 -.02 .05 -.02 .05 Energy Mob. (EM) .07* .03 .08* .03 .04 .03 .04 .04 .03 .03 .03 .03 .004 .05 .01 .05 .01 .06 .002 .06 Pos. Reflection (PR) .004 .03 .01 .03 -.02 .03 -.02 .03 .09** .04 .09* .04 .07 .05 .08 .05 .001 .05 .002 .05 Morn. Recovery .05* .02 .05* .02 .02 .02 .02 .02 .02 .02 .02 .02 Neg. Reflection -.03 .04 -.04 .04 -.05 .04 -.05 .04 -.07* .04 -.07 .04 Job Satisfaction .10* .05 .11* .05 Emotional Exhaustion .16** .04 .16** .04 Mid- & End-of-Shift Averaged Cognitive Engagement .11 .09 -.22* .09 Physical Engagement -.002 .06 .37** .09 Emotional Engagement .49** .07 -.48** .07 Variables at the Person Level Source .05 .11 -.10 .13 -.78** .27 -.70** .19 PSS -.06 .08 -.07 .08 -.03 .09 Resilience (Res) .20 .11 .13 .11 .38* .12 Cross-level interactions CR X PSS -.05 .04 -.03 .04 .02 .03 CR X RES -.06 .06 -.04 .06 .04 .06 EM X PSS .01 .04 .01 .04 -.03 .04 EM X RES -.08 .05 -.04 .05 -.08 .06 PR X PSS .04 .04 .03 .04 .08 .05 PR X RES .03 .05 .01 .06 .03 .06 Note. *p < .05, **p < .01 Analyses were run with and without the covariates (morning recovery and negative reflection) in the model to ensure that their inclusion did not drive significant results. The same pattern of results emerged.

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Table 19. Summary of Hypotheses for Initial (Analytic Approach B: Engagement throughout the Day) Models Analytic Analytic Approach A Approach B At the within-person level of analysis… H1 …pre-work reattachment is positively related to cognitive engagement during the day. Not supported Supported

H2 …pre-work energy mobilization is positively related to physical engagement during the day. Not supporteda Not supporteda

H3 …pre-work positive reflection is positively related to emotional engagement during the day. Supported Supported

H4 At the within-person level of analysis cognitive engagement during the day is… a …positively associated with change in job satisfaction. Not supported Not supported b …negatively associated with change in emotional exhaustion. Supported Supported H5 At the within-person level of analysis physical engagement is… a …positively associated with change in job satisfaction. Not supported Not supported b …negatively associated with change in emotional exhaustion. Not Supportedb Not Supportedb H6 At the within-person level of analysis emotional engagement is… a …positively associated with change in job satisfaction Supported Supported b …negatively associated change in emotional exhaustion. Supported Supported H7 Pre-work has indirect effects on job satisfaction through engagement. Specifically… a …reattachment positively impacts change in job satisfaction through cognitive engagement Not supported Not supported b …energy mobilization positively impacts change in job satisfaction through physical engagement Not supported Not supported c …positive reflection positively impacts change in job satisfaction through emotional engagement. Supported Supported H8 Pre-work has indirect effects on emotional exhaustion through engagement. Specifically… a …reattachment negatively impacts change in emotional exhaustion through cognitive engagement Not supported Supported b …energy mobilization negatively impacts change in emotional exhaustion through physical engagement Not supported Not supported c …positive reflection negatively impacts change in emotional exhaustion through emotional engagement. Supported Supported H9 Psychological resilience moderates the within-person effects of pre-work strategies on workday engagement, such that the effects of… a …reattachment on cognitive engagement Not supported Not supported b …energy mobilization on physical engagement Not supported Not supported c …positive reflection on emotional engagement are stronger for individuals high in resilience compared to individuals low in Not supported Not supported resilience. H10 Supervisor support moderates the within-person effects of pre-work strategies on engagement, such that the effects of... a …reattachment on cognitive engagement Not supported Not supported b …energy mobilization on physical engagement Not supported Not supported c …positive reflection on emotional engagement are stronger for individuals who perceive their supervisor as supportive. Supported Not supported

Supp The effects of pre-work on engagement will be stronger in the first half of the day compared to the second half of the day. Not supported Not applicable Hyp aEnergy mobilization is significantly positively related to cognitive engagement b Physical engagement is significantly positively associated with emotional exhaustion

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Results showed that cognitive reattachment was positively related to cognitive engagement (γ = .09, p = .02), providing support for Hypothesis 1, whereas in the initial model tested cognitive reattachment was unrelated to cognitive engagement. Energy mobilization was unrelated to physical engagement (γ = .04, ns), failing to provide support for Hypothesis 2 (consistent with the initial model). Positive reflection was positively related to emotional engagement (γ = .09, p < .05), providing support for

Hypothesis 3 (again, consistent with the initial model). Of the six non-hypothesized paths, two paths were significant; specifically, start-of-shift cognitive reattachment was positively related to physical engagement (γ = .08, p = .02), and energy mobilization was positively related to cognitive engagement (γ = .07, p = .03), whereas in the initial model tested, only energy mobilization was positively related to mid-shift cognitive engagement.

Cognitive engagement was unrelated to change in job satisfaction from start-of- shift to end-shift (γ = 0.11, ns) and was negatively related to change in emotional exhaustion from start-of-shift to end-shift (γ = -.22, p < .05), providing support for hypothesis 4b, but not hypothesis 4a. Physical engagement was unrelated to satisfaction

(H5a; γ = -.002, ns) and was unexpectedly positively related to exhaustion (γ = .37, p <

.001), as H5b predicted it would be negatively related to exhaustion. As such, neither hypotheses 5a or 5b were supported. Finally, emotional engagement was positively related to satisfaction (γ = .49, p < .001) and negatively related to emotional exhaustion

(γ = -.48, p < .001) as predicted by hypotheses 6a and 6b respectively.

It was also predicted that start-of-shift pre-work strategies would have indirect effects on change in job satisfaction and emotional exhaustion from start- to end-of-shift

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through engagement during the day. Mediation effects were tested with a Monte Carlo simulation with 20,000 replications using the online software R. Specifically, cognitive reattachment was predicted to positively impact change in job satisfaction through cognitive engagement (H7a) and negatively impact change in emotional exhaustion through cognitive engagement (H8a). Although cognitive reattachment did not have an indirect effect on change in job satisfaction through cognitive engagement failing to support H7a, cognitive reattachment did negatively impact change in exhaustion through cognitive engagement (γ = -0.019, 95% CI [-.05, -.0006]) in support of H8a. Energy mobilization was predicted to positively impact change in job satisfaction through physical engagement (H7b) and negatively impact change in emotional exhaustion through physical engagement (H8b), neither of which were supported. Finally, positive reflection was predicted to positively impact change in job satisfaction through emotional engagement (H7c), which was supported (γ = 0.05, 95% CI [.0108, .084]), as well as negatively impact change in emotional exhaustion through emotional engagement (H8c), which was also supported (γ = -.05, 95% CI [-.084, -.0104]).

Non-hypothesized indirect effects that were significant included the following: cognitive reattachment positively predicted exhaustion through physical engagement (γ =

0.03, 95% CI [.003, .07]), and energy mobilization negatively predicted exhaustion through cognitive engagement (γ = -0.02, 95% CI [-.04, -.000046]).

Psychological resilience (hypothesis 9) and perceived supervisor support

(hypothesis 10) were predicted to strengthen the effects of pre-work strategies on engagement. Specifically, the relations between reattachment on cognitive engagement

(H9a and H10a), energy mobilization on physical engagement (H9 and H10b), and

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positive reflection on emotional engagement (H9c and H10c) were expected to be stronger for individuals high in resilience (compared to individuals low in resilience) and for those who perceive their supervisor as supportive. None of these results were significant, so no support was found for H9 or H10.

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CHAPTER V

DISCUSSION

Recent research has begun to provide evidence that individuals use specific strategies to facilitate the reconnection between the non-work and work domains.

Building on recent work that examined the role of reattachment on engagement during the day (Sonnentag & Kuhnel, 2016; Sonnentag et al., 2019), the current study identified and investigated distinct pre-work strategies employees use to facilitate the psychological transition from their non-work to work roles. Pre-work was defined in this paper as active daily preparation for a given workday in which individuals bring their attention back to work, mobilize their energy, and/or reflect on the reasons they work.

Methodological Contributions

In terms of methodological contributions to the literature, first and foremost, the current study developed a psychometrically sound pre-work scale to accurately and reliably assess three distinct pre-work strategies. Secondly, experience sampling methods were employed to examine how within-person variability in pre-work strategies– relates to engagement, emotional exhaustion, and job satisfaction throughout the day. Results revealed that pre-work strategies add incremental prediction in engagement beyond other established recovery and morning-based predictors and that these strategies relate to end- of-shift satisfaction and exhaustion through workday engagement. In addition, this study replicated links of daily work engagement with daily well-being and satisfaction. Below,

I highlight specific theoretical contributions.

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Theoretical Contributions

The results of the current study theoretically advance the energy management literatures in several ways. This was the first study to examine distinct “pre-work” strategies that capture the cognitive and motivational processes that aid individuals in crossing the boundary from non-work to work, whereas previous research primarily focused on the cognitive processes during this transition between domains (Sonnentag &

Kuhnel, 2016; Sonnentag et al., 2019).

Psychological preparation for reentry into one’s work role likely involves a combination of attention and arousal. Ashforth and colleagues (2000) explained that individuals must adopt the appropriate cognitive frame (e.g., I’m entering my “employee” role), along with the appropriate arousal state (Ashforth et al., 2000). Sonnentag and

Kuhnel (2016) captured the attentional component of psychological preparation when they introduced reattachment, but ignored other motivational processes that may facilitate how individuals transition from non-work to work domains. As such, little is known about other potentially viable strategies employees can use to ease the transition between home and work, contributing to positive experiences during the day. Thus, the primary contribution of the current study was to shed light on the ways individuals make the transition from non-work to work from a motivational perspective.

Drawing from several literatures in organizational psychology, three primary pre- work strategies were identified, namely cognitive reattachment, energy mobilization, and positive reflection. These strategies were theorized to proactively build or protect resources to be used during the workday. Consistent with Sonnentag and Kuhnel’s definition, the current study defined cognitive reattachment as an individual’s sense of

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mentally reconnecting with work by bringing their attention back to work, planning for the upcoming day, and/or considering what events may occur. Thus, it may involve mental simulation (i.e., imagining how events are going to take place; Taylor & Pham,

1996; Taylor & Schneider, 1989) or planning when and where they will complete their work. Although primarily cognitive in nature, it may lead to positive affect, neutral affect, or negative affect, which can shape individuals’ energy for the day. When reattachment leads to neutral or negative affect, individuals may engage in pre-work energy mobilization strategies that counteract this affect and the resulting low energy, which is a home-to-work transition strategy, that up until this point, has been overlooked.

Energy mobilization strategies enhance individuals’ energetic motivation to work by increasing their sense of feeling energized and positive about work. Energy mobilization activities range from general activities that may seem unrelated to work

(e.g., exercise, listening to music, speaking with a friend) to specific activities directed at work, like goal setting. Previous research on reattachment (Sonnentag & Kuhnel, 2016;

Sonnentag et al., 2019) did not identify a set of strategies that increase positive valence and arousal. Furthermore, no distinction between cognitive reattachment (thinking about the workday) and goal setting was made. Rather Sonnentag and colleagues (2019) stated that “reattachment to work triggers work-related goals and during reattachment one thinks about what will happen during the specific day” (page 7). They viewed reattachment as a first step toward goal achievement. Nevertheless, although reattachment may lead to goal setting or the two may co-occur, the current study viewed the two strategies as distinct because reattachment does not imply a specific affective tone (Sonnentag & Kuhnel, 2016), whereas goal setting does. As such, the current study

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conceptualized goal setting as an energy mobilization strategy because it increases positive affect and arousal, unlike cognitive reattachment, which may or may not lead to positive affect.

The final pre-work strategy, positive reflection, is an individual’s sense of feeling autonomously motivated (Ryan & Deci, 2000) and emotionally connected with work

(Rich et al., 2010) as a result of infusing meaning into their work. They may infuse meaning into their work by reflecting on factors such as (a) why their work matters, (b) valued reasons they work (e.g., Wrzesniewski et al., 1997; Deci & Ryan, 2005), or (c) the prosocial implications of their work. Positive reflection leads to a motivational state, but not from an energetic perspective like energy mobilization. Although it may not increase individuals’ energy, attention, or persistence, it does shift them to a more autonomous motivational state (i.e., identified) rather than controlled (Ryan & Deci, 2000). By reflecting on the reasons why one works (i.e., impact it has, alignment with value), people may feel more connected to others (co-workers, clients, society) or to the work itself.

Results of the scale-development pilot study showed that pre-work is multi- dimensional, comprised of three-factors and that individuals do, in fact, utilize pre-work strategies prior to the start of their work shifts. As such, employees have several strategies to choose from to boost work engagement through their own behaviors. Not only may they reconnect with work by thinking about their workday and what they need to accomplish (reattachment) as previously demonstrated (Sonnentag & Kuhnel, 2016;

Sonnentag et al., 2019), but they also may engage in behaviors that help them feel

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energized and positive about work (energy mobilization) and/or reflect on and infuse meaning into their work (positive reflection).

The current study showed that the degree of pre-work strategies adopted prior to work fluctuates from day-to-day, just as Sonnentag and colleagues (2019) found that reattachment fluctuates daily. This lends further support for the notion that employees’ boundary crossing between the nonwork and work domain is not a stable factor and it is important to examine fluctuations in these strategies over the course of time.

Furthermore, the extent to which employees used pre-work strategies on a given day was related to job satisfaction and employee exhaustion during the workday through engagement. Importantly, the current study was the first to examine how pre-work related to cognitive, physical, and emotional dimensions of engagement as conceptualized by

Khan (1990), heeding calls to examine engagement using the Job Engagement Scale

(JES; Klein et al., 2006) in research contexts, as opposed to the more commonly used

UWES scale (Schaufeli et al., 2002).

It is well established that recovery experiences are critical for individuals’ well- being, and the current study is the first to suggest that distinct pre-work experiences may be just as important. Like recovery experiences (Sonnentag & Fritz, 2007), pre-work helps individuals manage resources. However, unlike recovery experiences, pre-work is proactive; it helps to build personal resources before stressors of the workday are encountered. In the current study, individuals were more likely to enact pre-work strategies on days when they reported experiencing higher levels of recovery prior to their work shift. The state of being recovered captures energetic resources in a particular moment (Debus et al., 2014), so it makes sense that the enactment of pre-work strategies

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requires some level of energy. Pre-work predicted engagement during the day above and beyond morning state of recovery, demonstrating that the process of proactively building resources is critical for employees to maximize personal resources and minimize stress accumulation during the day.

In addition, the current study is the first to provide evidence that the benefits of pre-work on engagement do not merely reflect the absence of negative work reflection

(Meier et al., 2016). Interestingly, negative work reflection was positively related to cognitive reattachment and positive reflection at the within-person level, suggesting that individuals appear to have days where they generally think more about work prior to their shift, with those cognitions covering a variety of content and being both positive and negative. Although this experience prior to the start of the workday may pose work- related demands on the individual that absorb resources (Fritz & Sonnentag, 2006), pre- work predicted engagement during the day.

In sum, the current study provides evidence that pre-work provides beneficial effects above and beyond feeling recovered prior to starting work and these beneficial effects are not a function of the absence of negative reflection. Together, the three pre- work strategies accounted for additional within-person variance in mid-day cognitive engagement (within-person pseudo r2 = 11.71%), physical engagement (within-person pseudo r2 = 2.31%) and emotional engagement (within-person pseudo r2 = 8.35%) above and beyond morning recovery and negative reflection. The current study was also the first, to my knowledge, to examine the relationship between pre-work strategies and changes in job satisfaction and emotional exhaustion, two important well-being

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indicators, during the day. In the subsequent paragraphs I describe substantive findings related to each pre-work strategy.

Cognitive Reattachment. Cognitive reattachment predicted mid-shift cognitive engagement only when the non-hypothesized relationship between energy mobilization and cognitive engagement was removed from the full model. Thus, cognitive reattachment lead to mid-shift cognitive engagement, but did not predict cognitive engagement above and beyond energy mobilization. When the mid- and end-shift engagement assessments were averaged, however, cognitive reattachment predicted both cognitive engagement and physical engagement during the day. Cognitive reattachment, which involves an explicit focus on work itself (i.e., thinking about and planning for the upcoming day), not only brought employees’ attention back to work (cognitive engagement) as predicted, but also lead them to be physically invested and exert effort on tasks (physical engagement) during the day, suggesting that it a useful strategy individuals can employ to ease the transition from non-work to work. In terms of indirect effects, cognitive reattachment negatively impacted change in exhaustion through average cognitive engagement (as predicted). Finally, neither resilience nor perceived supervisor support moderated the relationship between cognitive reattachment (mid-shift only or averaged) and engagement, which is discussed in “Limitations and Future

Directions” section.

Energy Mobilization. Energy mobilization, which increases motivation from an energetic sense, was unrelated to physical engagement (mid-shift only or averaged).

Recall, however, that cognitive reattachment unexpectedly predicted physical engagement during the day. In trying to make sense of this finding, it’s possible that for

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pre-work strategies to relate to physical engagement there must be an explicit focus on the work itself, as is the case with cognitive reattachment. Energy mobilization activities may or may not be focused on work, which may explain the null finding between that strategy and physical engagement.

Although not hypothesized, energy mobilization was predictive of cognitive engagement (mid-shift only and averaged), which reflects cognitive vigilance, mental connection, focus, and absorption during role performance (Kahn, 1990; Rich et al.,

2010), suggesting it is an effective strategy to use to transition from home to work. The goal setting aspect of energy mobilization may have driven this relationship given that goal-setting directs attention effort, and persistence toward goal-relevant activities (Locke

& Latham, 1990). Furthermore, the indirect effect of energy mobilization on emotional exhaustion through cognitive engagement was negative and significant. Finally, neither resilience nor perceived supervisor support moderated the relationship between energy mobilization and engagement (mid-shift only or averaged), as was the case with cognitive reattachment.

Positive Reflection. Positive reflection predicted emotional engagement (i.e., feeling proud, interested, excited and enthusiastic about work; Klein, 1990; Rich et al.,

2010) and emotional engagement predicted both job satisfaction and emotional exhaustion, lending support for the notion that positive reflection pre-work is an effective strategy employees can use to transition from home to work. Furthermore, the indirect effects were significant: positive reflection positively impacted change in job satisfaction through emotional engagement and negatively impacted change in emotional exhaustion through emotional engagement. It’s noteworthy that this was the only strategy to impact

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both exhaustion and job satisfaction, whereas the previously discussed strategies impacted emotional exhaustion only. Positive reflection may be a particularly effective at driving positive attitudes (and not solely preventing negative experiences) because it connects work to autonomous and/or prosocial goals, satisfying basic human needs

(Shapiro et al., 2006) and promoting self-determined behavior (Glomb et al., 2011).

Furthermore, perceived supervisor support strengthened the relationship between pre- work positive reflection and emotional engagement. Specifically, at higher levels of perceived supervisor support, the relationship between positive reflection and emotional engagement was stronger. This finding provides support for the Resources Caravan

Theory (Hobfoll, 2011), which posits that resources tend to occur in clusters or “resource caravans.” In the current study, those who perceive their supervisors as supportive appear to be more capable of acquiring resource gains through pre-work positive reflection. In the subsequent section, I elaborate on why this may be the case in the “Limitations and

Future Directions” section.

Engagement to Well-Being Indicators. Engagement was associated with indicators of well-being, consistent with past studies (e.g., Crawford, LePine, & Rich,

2010; Leijten, van den Heuvel, van der Beek, Ybema, Robroek, & Burdorf, 2015).

Specifically, cognitive engagement (mid-shift only and averaged) was negatively related to changes in emotional exhaustion, but was unrelated to job satisfaction. Physical engagement was unrelated to satisfaction and unexpectedly exhibited a positive relationship to exhaustion because of a suppressor effect. Emotional engagement was positively associated with job satisfaction and negatively associated with exhaustion.

Although the null findings were unexpected, it should be noted that relatively few ESM

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studies have assessed engagement using the JES (Rich et al., 2010) as the majority have used the UWES scale (Schaufeli et al., 2002). Future research may find that specific engagement dimensions differentially relate to well-being indicators depending on the physical, cognitive, and emotional demands of the job. For example, perhaps physical engagement has stronger relations with well-being indicators in certain work contexts, such as those that require high levels of physical effort. Similarly, the relations between cognitive engagement and well-being may depend on cognitive demands of the job.

Although not the focus of the current study, the results of Study 1 (Scale

Development Study) suggested that each pre-work strategy most strongly related to emotional engagement at the between-person level of analysis. This was also true in

Study 2 (the “Main Study’) for energy mobilization and positive reflection, but not for cognitive reattachment, as between-person cognitive reattachment had stronger relations with cognitive and physical engagement compared to emotional engagement. Perhaps those who on typically engage in energy mobilization and positive reflection are more likely to experience interest and excitement in their work (i.e., emotional engagement) because of the motivational nature of these two strategies. Recall energy mobilization is theorized to be motivational from an energetic sense, whereas positive reflection is motivational from an autonomous motivation sense. Thus, energy mobilization and positive reflection seem to translate into high levels of emotional engagement at the between-person level of analysis because of the role of arousal and motivation. Although not the focus of the current study, more research is needed at the between-person level of analysis to understand the effects that pre-work has on engagement and other outcomes.

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Limitations and Future Directions

The current study, of course, is not without limitations. One potential limitation is that the data came from two separate samples. Although I collected data from call center representatives within one organization, which is arguably a strength of the current study,

I was unable to reach the desired sample size. As such, I expanded my sample to include participants recruited from various social media websites. Ideally, all participants would have originated from the same sample. With that said, this weakness is arguably less of an issue because the primary focus of the current study is on within-person relationships.

The sample was also fairly limited in scope in that it was predominantly female.

However, the sample can also be considered a strength as it is comprised, in part, by employees working in a high stress/low control environment (Karasek, 1979), which is a population often overlooked in organizational behavior research.

Another limitation of the current study is related to when variables were assessed.

Recovery was assessed in the morning only; future studies should capture broader recovery experiences ideally in the evening or prior to bed time to assess them closer to the time they occur in real life. In addition, pre-work was only assessed at the start-of- shift. Although the current study posited that pre-work occurs either at home before starting work, during the commute to work, or during the first few minutes after arriving at work, it’s also possible that a) employees enact specific pre-work’ strategies during the evening before work and b) employees are more or less likely to use certain pre-work strategies depending on the time of day. Furthermore, does the effectiveness of specific strategies depend on the time of day they are being enacted? Does the use of pre-work strategies at night interfere with or enhance the beneficial effects of recovering from

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work and does this depend on the specific strategy used? In other words, it’s possible that the use of pre-work interferes with psychological detachment from work and hinders effective recovery. Alternatively, it’s possible that by engaging in both recovery and pre- work the night prior to work, employees experience enhanced morning recovery and well-being, because of feeling both rested and prepared for the day upon waking up.

Exploring these questions will help us further define and understand the boundaries of pre-work.

The current study posited that although the specific activities or thoughts that individuals use to cognitively reattach, mobilize their energy, and reflect positively about work may differ, the effects are similar. Nevertheless, it may be worthwhile for future research to explore the specific methods or activities individuals use through qualitative work and whether their effects are similar or not. In addition to better understanding what individuals do to reconnect with work, research should examine when and where pre- work occurs.

The current study focused on consequences of pre-work strategies and found that cognitive reattachment and energy mobilization were effective at preventing exhaustion, but were unrelated to satisfaction. Positive reflection, in contrast, was effective at both preventing exhaustion and fostering satisfaction, suggesting it may be the most effective strategy. Research can build on this initial work by examining additional consequences of pre-work, such as performance, which may yield results that can better inform future research and practice.

In addition, future research should examine the antecedents or predictors of pre- work as opposed to solely focusing on consequences. Perhaps people with fewer

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demands or responsibilities before work (e.g., not having childcare responsibilities) as well as more resources (e.g., time, energy) are more likely to engage in pre-work. In addition, individuals in highly segmented roles (i.e., inflexible role boundaries; high contrast in work and non-work roles; Ashforth et al., 2000) may be more likely to use pre-work to minimize the difficulty or effort required to become psychologically and physically disengaged from one role and re-engaged in another role (Burr, 1972). Gender may be another interesting antecedent to explore. Future research should examine the extent to which males and females engage in different pre-work strategies.

In addition to studying antecedents of pre-work, future research should explore other moderating variables in the pre-work framework. In the current study, most of the interaction effects were non-significant. The lack of significant findings may be explained, in part, by previous research, which has suggested that an interaction effect is more likely to occur if resources and demands match regarding domain (i.e., cognitive, emotional, and physical; Cohen & Wills, 1985; de Jonge & Dormann, 2003, 2006;

Feuerhahn et al., 2013). Although I did not assess demands in the present study, the same logic may apply to interactions between resources. In other words, an interaction effect may be more likely to occur when resources match regarding domain. For example, in the current study, resilience did not moderate the relationship between cognitive reattachment and cognitive engagement. Perhaps a significant interaction would have emerged if individual difference variable that match the domain of cognitive reattachment (i.e., cognitive in nature), such as general intelligence, were chosen. Other research has found that whether or not a moderation effect appears may depend on the source of support. For instance, Albar Marín and García-Ramírez (2005) found that only

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supervisor and family support moderated the effect of job stress on emotional exhaustion in a sample of nurses. It is possible that other sources of social support (co-worker or family) might be more helpful in strengthening the relationship between pre-work strategies and engagement. Autonomy is another variable that may impact the relationship between pre-work and outcomes. Just as previous research has found that autonomy impacts the effectiveness of social lunch break activities on outcomes

(Trougakos et al., 2014), it may also impact the effectiveness of pre-work and should be explored in future research. Finally, it may be worthwhile to examine whether shift-work impacts the pre-work strategies employees adopt and/or the effectiveness of strategies.

The current study used a longitudinal design that captures daily well-being at multiple time points within a day over the course of many work days, however, I did not control for the number of days individuals participated in the surveys, which could potentially impact the results. Future research should model the role of time potentially using advanced methods, such as growth curve analysis.

Furthermore, an experimental design is needed to isolate the effects of specific pre-work strategies on daily well-being and performance. As such, future research may wish to combine an experimental and experience sampling design to isolate the effects of specific pre-work strategies on well-being and performance outcomes over the course of a day. Such an experimental/ESM study design will shed light on the effectiveness of various ‘pre-work’ strategies employees may use to reconnect with work. It’s possible that small interventions, such as training employees to use specific pre-work strategies, improves daily well-being and performance.

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Although the primary focus of the current study was on strategies employees may enact before the work day begins to help them manage demands, employees may use additional strategies during or after difficult situations (and days) to help them manage stress. Future research should examine the ways in which these various coping approaches (and timeframes) may combine to lead to benefit or harm for individuals. For instance, it may be that one needs to only engage in one of these coping strategies to be happy and healthy. Perhaps recovery after work is less important for individuals who adopt pre-work strategies before work, effective emotion regulation strategies during interpersonal interactions, and/or use effective coping strategies throughout the workday.

As such, mastery of one process at one point in time may compensate for deficiency in another at a later point in time. Alternatively, various energy management strategies may be positively related, such that individuals use them at similar levels. That is, if an employee tends to reattach before work, he/she may be more likely to regulate their emotions during interactions, take breaks throughout the day, and engage in recovery experiences after work. Or, perhaps, various energy management strategies are unrelated.

Given these possibilities, future research should begin to view processes from these distinct content areas as parts of a larger process, rather than as separate processes.

Practical Implications

The current study suggests that employees are more engaged and less exhausted on days when they report using pre-work strategies prior to work, and they are more satisfied on days when they enact positive reflection pre-work. As such, employees should be made aware of the power of pre-work and encouraged to incorporate pre-work strategies into their daily routines. Furthermore, it may be worthwhile for organizations to

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provide employees with time at the beginning of their shifts to engage in pre-work strategies. Encouraging employees to spend the first few minutes of their shifts thinking about positive aspects of work and their goals may boost employee engagement and well- being during the day. Having this time built in to the workday may be particularly helpful for less proactive employees or for those with hectic or busy home lives (e.g., child care responsibilities) who find it difficult to engage in pre-work prior to starting their shift.

Furthermore, giving employees time to reconnect with work at work will likely convey the message that the organization and supervisors care about their employees’ well-being and may increase employees’ perceptions of support. This, in turn, may strengthen the effects of positive reflection on emotional engagement, helping employees feel more positive and interested in work.

Conclusion

The current study shed light on the multiple pre-work strategies employees may adopt to ease the transition from non-work to work. Employees should be informed about their own power and role in shaping their daily experiences and well-being and may wish to incorporate these relatively low-effort pre-work strategies into their routines prior to work. Positive reflection may be a particularly useful strategy for employees to adopt as it was the only strategy that impacted all three daily experiences — emotional engagement, satisfaction, and exhaustion. Furthermore, positive states of well-being at work can impact the employer’s bottom line as well. As such, organizations may wish to consider ways in which they can facilitate employees’ daily transitions back to work.

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Appendix A

Pre-Work Factor Analytic Study

1. Did you work this week? Yes/No 2. When was the last time you worked? Today, Yesterday, two days ago, three days ago, four days ago, five days ago, six days ago, seven days ago, over seven days ago 3. Think about the last time that you worked. a. When did you start work on that day? b. When did you end work on that day? c. What did you do before work (list activities)?

Pre-Work Measure

Instructions: Please answer the following questions about today or the last day that you worked.

1 = Strongly disagree 2 = Somewhat disagree 3 = Neither agree nor disagree 4 = Somewhat agree 5 = Strongly agree

BEFORE I started work (i.e., while at home, during the commute, or first few minutes of work)....

Cognitive reattachment 1. …I thought about what I will encounter at my work. 2. …I mentally tuned into my work. 3. …I prepared mentally for it. 4. …I considered the upcoming workday. 5. …I thought about what might happen during work. 6. …I created a (mental or physical) “to do” list for the day. 7. …I put myself in the right mindset for my job. 8. …I visualized when and where I would get my work done.

Energy Mobilization

1. …I made sure I was in a positive state that would help me do my work well. 2. …I “pumped myself up” for work. 3. …I told myself that today would be a good day. 4. …I gave myself a pep talk

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5. …I motivated myself by thinking about my goals for the day. 6. …I got myself excited for the day by reflecting on what I can accomplish. 7. …I energized myself by thinking about how I can improve my performance.

Positive Reflection

1. …I thought about how my work positively affects the lives of other people (e.g., my family/clients/customers). 2. …I reflected on how my work makes a difference in the lives of others (e.g., my family/clients/customers). 3. …I considered how my work positively impacts society. 4. …I reflected on how my work allows me to provide for/support my family. 5. …I thought about how my work is an important part of who I am. 6. …I thought about how my work aligns with my values and beliefs. 7. …I thought about how my job adds purpose to my life. 8. …I considered how my work adds meaning to my life.

Negative Work Reflection BEFORE I started work (i.e., while at home, during the commute, or first few minutes of work)....

Instructions: Please rate the degree to which you experienced the following this morning.

1. … I could not stop thinking about negative aspects of my job. 2. … I could not stop thinking about bad work experiences.

Job Engagement Scale (Rich, Lepine, Crawford, 2010).

Instructions: Think about the last time you went to work. Please indicate the degree to which the following statements describe your experience ON THAT DAY.

1 = Strongly disagree 2 = Somewhat disagree 3 = Neither agree nor disagree 4 = Somewhat agree 5 = Strongly agree

DURING the workday

1. ...I worked with intensity on my job 2. …I exerted my full effort to my job 3. …I devoted a lot of energy to my job 4. …I tried my hardest to perform well on my job 5. …I strived as hard as I could to complete my job 6. …I exerted a lot of energy on my job. 7. …I felt enthusiastic in my job

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8. …I felt energetic at my job 9. …I was interested in my job 10. …I felt proud of my job 11. …I felt positive about my job 12. …I was excited about my job 13. …My mind was focused on my job 14. …I paid a lot of attention to my job 15. …I focused a great deal of attention on my job 16. …I was absorbed by my job 17. …I concentrated on my job 18. …I devoted a lot of attention to my job Emotional Exhaustion Scale (Wharton, 1993).

Instructions: Please indicate the degree to which the following statements describe your experience at the end of today's workday or at the end of the last day you worked.

1 = Strongly disagree 2 = Somewhat disagree 3 = Neither agree nor disagree 4 = Somewhat agree 5 = Strongly agree

At the END of the day...

1. ...I felt emotionally drained. 2. ...I felt used up. 3. ...I felt burned out.

Job Satisfaction (Cammann, Fichman, Jenkins, & Klesh, 1979).

Instructions: Please indicate the degree to which the following statements describe your experience at the end of today's workday or at the end of the last day you worked.

Scale: 5 point scale with 1 = Strongly Disagree; 2 = Somewhat Disagree; 3 = Neither

Agree/Disagree; 4 = Somewhat Agree; 5 = Strongly Agree

At the END of the day...

1. …I was satisfied with my job. 2. …I didn’t like my job. 3. … I liked working here.

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Demographics

1. Please indicate your race. a. White/Caucasian, Black/African-American, Hispanic or Latino, Asian or Pacific Islander, Native American or American Indian, Prefer not to say, Other 2. Please indicate your gender. a. Male, female, Non-binary/third gender, Prefer not to say, Prefer to self describe 3. Please indicate your job title. 4. Please indicate your organizational tenure in years (i.e. how long have you been at your current organization). 5. Do you work in customer service? Yes/no/prefer to describe 6. Does your job require face-to-face customer interaction? Yes/no/prefer to describe 7. Please briefly list a few (3 to 5) of your primary job duties.

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Appendix B

Main Study: Person-Level Survey

Resilience (Block & Kremen, 1996).

Scale: 1 = Does not apply at all; 2 = Applies Slightly; 3 = Applies Somewhat; 4 =

Applies Very Strongly

Instructions: Please read the below statements about yourself and indicate how well it applies to you by selecting 1 (it does not apply at) to 4 (applies very strongly). Let me know how true the following characteristics are as they apply to you generally:

Characteristics about you:

1. I am generous with my friends. 2. I quickly get over and recover from being startled. 3. I enjoy dealing with new and unusual situations. 4. I usually succeed in making a favorable impression on people. 5. I enjoy trying new foods I have never tasted before. 6. I am regarded as a very energetic person. 7. I like to take different paths to familiar places. 8. I am more curious than most people. 9. Most of the people I meet are likable. 10. I usually think carefully about something before acting. 11. I like to do new and different things. 12. My daily life is full of things that keep me interested. 13. I would be willing to describe myself as a pretty “strong” personality. 14. I get over my anger at someone reasonably quickly.

Perceived Supervisor Support (Eisenberger, R., Stinglhamber, F., Vandenberghe, C.,

Sucharski, I. L., & Rhoade, L., 2002).

Scale: 1-5 scale ranging from 1=Strongly Disagree to 5= Strongly Agree

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Instructions: The following items describe your own feelings about your supervisor. Read each item carefully and indicate the degree of your agreement or disagreement with each item.

1. My supervisor strongly considers my goals and values. 2. Help is available from my supervisor when I have a problem. 3. My supervisor really cares about my well-being. 4. My supervisor would forgive an honest mistake on my part. 5. My supervisor is willing to help me when I need a special favor. 6. My supervisor cares about my opinions. 7. If given the opportunity, my supervisor would take advantage of me. 8. My supervisor shows very little concern for me.

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Appendix C

Main Study: Event-Level Surveys

START-OF-SHIFT SURVEY:

Recovery Level in the Morning (Sonnentag & Kruel, 2006; Sonnentag et al., 2012).

Instructions: The following statements are about how recovered you felt when you woke up this morning. Please read each statement carefully and use the scale to indicate your level of agreement and disagreement with each statement.

Scale: 5-point Likert items (1 = strongly disagree, 2 = disagree, 3 = neither agree or disagree, 4 = agree, 5 = strongly agree

The scale refers to how recovered a person feels in the morning.

1. When I woke up this morning, I felt well rested. 2. When I woke up this morning, I felt physically recovered. 3. When I woke up this morning, I felt mentally recovered. 4. When I woke up this morning, I was full of new energy. Pre-Work Measure

Instructions: Please answer the following questions about your time before work that day.

Scale: 5 point scale with 1 = Strongly disagree; 2 = Somewhat disagree; 3 = Neither agree nor disagree; 4 = Somewhat agree; 5 = Strongly agree

BEFORE I started work (i.e., while at home, during the commute, or first few minutes of work)....

Cognitive reattachment I mentally tuned into my work I prepared mentally for it. I thought about what might happen during work.

Energy Mobilization I motivated myself by thinking about my goals for the day. I got myself excited for the day by reflecting on what I can accomplish. I energized myself by thinking about how I can improve my performance.

Positive Reflection I considered how my work positively impacts society. I thought about how my work aligns with my values and beliefs.

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I thought about how my job adds purpose to my life.

Negative Work Reflection (Fritz & Sonnentag, 2006).

Scale: 5 point scale with 1 = Strongly disagree; 2 = Somewhat disagree; 3 = Neither agree nor disagree; 4 = Somewhat agree; 5 = Strongly agree

BEFORE I started work (i.e., while at home, during the commute, or first few minutes of work)....

1. …I considered the negative aspects of my job. 2. …I thought about what I do not like about my job.

Job Satisfaction (Cammann, C., Fichman, M., Jenkins, D., & Klesh, J., 1979).

Scale: 5 point scale with 1 = Strongly disagree; 2 = Somewhat disagree; 3 = Neither agree nor disagree; 4 = Somewhat agree; 5 = Strongly agree

Instructions: The following statements are about how you feel about work RIGHT NOW. Please read each carefully and respond to the degree to which you agree with the following questions.

1. Right now, I am satisfied with my job. 2. Right now, I like working here.

Emotional Exhaustion (Wharton, 1993).

Scale: 5 point scale with 1 = Strongly disagree; 2 = Somewhat disagree; 3 = Neither agree nor disagree; 4 = Somewhat agree; 5 = Strongly agree

1. I feel emotionally drained. 2. I feel used up. 3. I feel burned out.

Closing Screen: Thank you for completing your start-of-shift survey! By clicking “submit” below your responses will be submitted.

Your next daily survey will be sent via email today at INESRT TIME. You will have until INSERT TIME to complete the mid-shift survey.

If you have any questions, please contact: Megan Nolan The University of Akron [email protected] xxx-xxx-xxxx

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MID- AND END-OF-SHIFT SURVEYS:

Work Engagement (Rich, Lepine, & Crawford, 2010).

Scale: 5 point scale with 1 = Strongly disagree; 2 = Somewhat disagree; 3 = Neither agree nor disagree; 4 = Somewhat agree; 5 = Strongly agree

Instructions: The following statements are about how you feel at work in this moment. Please read each statement carefully and use the following scale to indicate your level of agreement and disagreement with each statement.

Since completing the previous survey... Cognitive Engagement 1. …my mind has been focused on my job.

2. …I paid a lot of attention to my job.

3. …I focused a great deal of attention on my job.

Physical engagement 1. …I exerted my full effort on my job.

2. …I tried my hardest to perform well on the job.

3. …I strived as hard as I can to complete my job.

Emotional engagement 1. … I was interested in my job.

2. …I felt proud of my job.

3. …I felt enthusiastic about my job.

Job Satisfaction (Cammann, C., Fichman, M., Jenkins, D., & Klesh, J., 1979).

Scale: 5 point scale with 1 = Strongly disagree; 2 = Somewhat disagree; 3 = Neither agree nor disagree; 4 = Somewhat agree; 5 = Strongly agree

Instructions: The following statements are about how you feel about work RIGHT NOW. Please read each carefully and respond to the degree to which you agree with the following questions.

1. Right now, I am satisfied with my job.

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2. Right now, I like working here.

Emotional Exhaustion (Wharton, 1993).

Scale: 5 point scale with 1 = Strongly disagree; 2 = Somewhat disagree; 3 = Neither agree nor disagree; 4 = Somewhat agree; 5 = Strongly agree

1. I feel emotionally drained.

2. I feel used up.

3. I feel burned out.

Closing Screen: Thank you for completing your mid-shift survey! By clicking “submit” below your responses will be submitted.

Your next daily survey will be sent via email today at INESRT TIME. You will have until INSERT TIME to complete the end-of-shift survey.

If you have any questions, please contact: Megan Nolan The University of Akron [email protected] xxx-xxx-xxxx

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Appendix D

Call Center Sample: Informed Consent Form

PROJECT TITLE: Experiences of Call Center Employees

RESEARCH PURPOSE AND PROCEDURES: You are invited to participate in a three-part study aimed at understanding emotional processes, well-being, and effectiveness in the call center profession conducted by Dr. James Diefendorff, a Professor of Industrial/Organizational Psychology at The University of Akron, and Dr. John Trougakos, an Associate Professor of Management at The University of Toronto. In Part 1 you will be asked to complete a one-time survey assessing your traits, work perceptions, and job attitudes. If you complete Part 1 you will be eligible to participate in Part 2. In Part 2 you will be asked to complete daily questionnaires that focus on your daily stressors, coping strategies, and job attitudes. Finally, in Part 3 you will be asked to listen to between one and three recorded calls and provide dynamic continuous ratings of your felt affect and coping response during the course of those calls. In addition, you will be asked to complete individually-administered assessments of emotion detection ability. Unique participant generated identification codes will be used to match surveys across measurement occasions and will also be used to match surveys with company records of performance, call-level metrics, and turnover. Once data are matched, identifying information will be removed from the dataset. At no time will the company or their representatives be permitted access to any individual survey information.

TIME COMMITMENT: Part 1 will take approximately 45-60 minutes. Part 2 will occur over a 14 day time period. Participants will be asked to complete 3 five-minute surveys each day they work at Call Corp in that time window. Part 3 will take approximately 60 minutes and will occur at Call Corp at a schedule time of your convenience (e.g., before your work shift).

EXCLUSION: Individuals must be 18 years of age or older and employed at Call Corp to participate.

BENEFITS, RISKS, AND DISCOMFORTS: The benefit of participating in the study is to gain more insight about the experiences of call center employees. The risks and discomforts are minimal.

COMPENSATION: You can earn up to $5 for completing phase 1, up to $45 for completing all surveys in phase 2, and $10 for phase 3.

CONSENT: I have been fully informed of the above-described procedure with its possible benefits and risks. I also understand that my responses will be maintained in a confidential manner by the researcher. I voluntarily give my permission for my participation in this study. I know that the investigator will be available to answer any questions I may have. If, at any time, I feel my questions have not been adequately

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answered, I know that I can contact the primary researcher (James Diefendorff, [email protected]) or the IRB with questions about the rights of research subjects (xxx-xxx-xxxx). I understand that I am free to withdraw this consent and discontinue participation in this project at any time without penalty. I am also aware that I may keep a copy of this consent for my records.

I, the undersigned, agree to participate in this study.

______Participant Signature Date

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Appendix E

Call Center Sample: Participant Communications

Recruitment Information Sheet

Experiences of Call Center Employees

Thank you for your interest in the Experiences of Call Center Employees study being conducted by a research team from The University of Akron lead by Dr. James Diefendorff, a Professor of Industrial-Organizational Psychology.

The purpose of this study is to understand the work experiences of call center employees at Call Corp. By participating you will be contributing to research that is aimed at improving the emotional and psychological well-being of call center workers. To show our appreciation, you can earn up to $50 by completing all three phases of this research, including a Phase I survey, Phase II daily surveys administered over two weeks, and a Phase III individual session measuring emotional experiences during specific customer calls. In order to complete Phases II and/or III, you must first complete Phase I.

Your participation in all three Phases is voluntary. If you are interested in participating you will be asked to fill out the recruitment form today and we will contact you with more information about completing the three phases.

1. Phase I: Complete an initial survey that will take approximately 45 minutes to complete. This survey will be e-mailed to you, and you must complete it before INSERT DATE to be eligible for the study. You can earn $5 for completing this survey.

2. Phase II: Complete three surveys a day for up to 10 workdays. These surveys will take about 5 minutes to complete and will be sent to your e-mail at specific times throughout the day (at the beginning of your shift, mid-shift, and towards the end of your shift). You will have a 1.5 hour window of time to complete the survey once the email link is sent. All surveys and reminders will be delivered via e-mail sent from Megan Nolan via Qualtrics, an online survey platform. You can earn up to $45 for completing these surveys.

3. Phase III: Involves listening to between one and three of your recorded calls and providing ratings of how you felt and how you managed your emotions during the call. In addition, you will be asked to complete individually-administered assessments of emotion detection ability. This portion will take about 60 minutes to complete and will be scheduled at a time that is convenient for you, after Phase II is completed. You can earn $10 for completing this phase.

The daily surveys will be administered during the following workweeks:

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• Dates x – x • Dates x – x

The continuous ratings of calls and assessments of emotion detection ability will be scheduled individually with participants once the daily surveys are complete.

Researchers from The University of Akron will be in touch with you over the course of the study to answer any questions you may have and to remind you to complete the surveys.

You can earn up to $50 based on your participation. You will earn $5 for completing the one-time person level survey. In addition, you can earn up to $45 for completing the event level surveys, such that 3 complete days of surveys = $9; 4 complete days = $12; 5 complete days = $20, 6 complete days of surveys = $25; 7 complete days = $30; 8 complete days of surveys = $35; 9 complete days of surveys = $40; and 10 complete days of surveys = $45. As a note, “complete days” means that all three surveys for a given workday (i.e., a day you were working at Call Corp) were completed. Missed surveys, unfortunately, cannot be made up due to the nature of the study. Finally, you can earn up to $10 if you participating in the continuous ratings of calls and emotion detection assessments.

To participate in this study, please fill out the recruitment form today. Check your e-mail and be sure to complete the mandatory opt-in survey before INSERT START DATE.

Space in the study is limited, and participants for the study will be taken on a first come, first serve basis.

All information collected will be kept confidential. Only the research team will have access to the data and NO INDIVIDUAL RESPONSES WILL BE SHARED WITH ANYONE AT CALL CORP. Please contact James Diefendorff ([email protected]) or Megan Nolan ([email protected]) with any questions or concerns about the study. The Institutional Review Board (IRB) at The University of Akron has approved this study.

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Experiences of Call Center Employees: Participant Recruitment Form Name:

E-mail:

Cell Phone Number:

Can you access e-mail on your cell phone? Please circle Yes or No

What shift do you typically work (morning/evening)?

How many hours long is your shift typically?

How many hours do you work per week on average?

As part of this study, the investigators need to link your responses across time. To do this, you will create your own unique code. This will allow the investigators to link your responses over time and pay you for your participation in the various parts of the study.

Your code will be 5 characters long. • The first character will be the first letter of your own middle name. • The second character will be the first letter of the month that you were born. • The third character will be the last digit of your phone number. • The fourth character will be the first letter of your own street name. • The fifth character will be the first letter of your mother's first name As an example, let’s say Sally Marie Thompson is participating in this project. She was born on February 20, 1984. Her phone number is 610-987-3454, and she lives on Upland Road. Her mother’s name is Linda Edward Thompson. Sally’s identification code would be MF4UL.

1. What is the first letter of your own middle name (if none write X)? 2. What is the first letter of the month that you were born? 3. What is the last digit of your phone number? 4. What is the first letter of your own street name? 5. What is the first letter of your mother's first name?

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Experiences of Call Center Employees - Part 1 and Information E-mail Hello,

Thank you for your interest in the Experiences of Call Center Employees study! A copy of the IRB approving this study is included at the end of this e-mail for your records.

First, you will be asked to complete a one-time online survey assessing your personal traits, work perceptions, and attitudes, which will take approximately 45 minutes to complete. You will earn $5 for completing this survey and be eligible to participate in the daily survey portion of the study.

• The one-time survey must be completed before INSERT DATE. • Follow this link to the one-time survey: o Survey Link

After successful completion of the one-time online survey (Phase I), you will be eligible to participate in a daily survey study (Phase II) where you will be asked to complete three daily surveys per working day (i.e., only on days that you are working at Call Corp) for fourteen days, as well as an individual call-level emotional experience session (Phase III). Phase II surveys will assess your daily perceptions of stressors, coping, and well-being and each will take 5 minutes to complete. The Phase III emotional experience session will be individually administered and schedule at a time of your convenience.

You can earn up to $50 based on your participation.

You will earn $5 for completing the one-time person level survey.

In addition, you can earn up to $45 for completing the event level surveys, such that: • 3 complete days (3 surveys/day for 3 days) = $9 • 4 complete days (3 surveys/day for 4 days) = $12 • 5 complete days (3 surveys/day for 5 days) = $20 • 6 complete days (3 surveys/day for 6 days) = $25 • 7 complete days (3 surveys/day for 7 days) = $30 • 8 complete days (3 surveys/day for 8 days) = $35 • 9 complete days (3 surveys/day for 9 days) = $40 • 10 complete days (3 surveys/day for 10 days) = $45. Missed surveys, unfortunately, cannot be made up due to the nature of the study. Also, incomplete days or fewer than 3 complete days does not permit proper analysis of the data and will not result in payment.

Finally, you can earn up to $10 if you participating in the continuous ratings of calls and emotion detection assessments.

Approximately 3-4 weeks after the study, we will bring your check to Call Corp.

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Please contact Megan Nolan ([email protected]) with any questions.

Thank you! Dr. James Diefendorff [email protected]

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Experiences of Call Center Employees – Confirmation and Part 2 Information E-mail

Hello,

Thank you for completing Part 1 (the one-time survey)! This e-mail confirms that you are enrolled to complete the daily surveys that will occur on the following days:

• INSERT “WEEK DAY”, “MONTH” “DATE”

As a reminder, throughout the duration of this study, you will receive three surveys per day via e-mail; the survey links will be sent to the e-mail that you are receiving this message age. The first email will be sent by INSERT TIME (to be completed by INSERT TIME), the second by TIME (completed by INSERT TIME), and the third by INSERT TIME (to be completed by INSERT TIME).

As a reminder, you can earn up to $45 by participating in this portion of the study, such that 3 complete days of surveys = $9; 4 complete days = $12; 5 complete days = $20, 6 complete days of surveys = $25; 7 complete days = $30; 8 complete days of surveys = $35; 9 complete days of surveys = $40; and 10 complete days of surveys = $45. As a note, “complete days” means that all three surveys for a given workday (i.e., a day you were working at Call Corp) were completed. Missed surveys, unfortunately, cannot be made up due to the nature of the study.

Please contact Megan Nolan ([email protected]) with any questions.

Thank you! Dr. James Diefendorff [email protected]

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Sample E-mail Participants will receive Containing Daily Survey Links Hello,

Thank you for signing up to participate in our study! Your answers are incredibly value, and we appreciate your time.

This e-mail contains the link to your morning survey below: you have until 10:30am today to complete it.

As a reminder, throughout the duration of this study, you will receive three surveys per day via e-mail.

You will have an opportunity to earn up to $45 by participating in this portion of the study, such that 3 complete days of surveys = $9; 4 complete days = $12; 5 complete days = $20, 6 complete days of surveys = $25; 7 complete days = $30; 8 complete days of surveys = $35; 9 complete days of surveys = $40; and 10 complete days of surveys = $45. As a note, “complete days” means that all three surveys for a given workday (i.e., a day you were working at Call Corp) were completed. Missed surveys, unfortunately, cannot be made up due to the nature of the study.

Follow this link to the survey between ? and 10:30 a.m. today: Survey Link

Or copy and paste the URL below into your Internet browser: URL Link

Please contact Megan Nolan ([email protected]) with any questions.

Thank you! Dr. James Diefendorff [email protected]

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Appendix F

Social Media Sample: Informed Consent Form

Informed Consent Form: Online Survey and Daily Questionnaires

PROJECT TITLE: Experiences of Employees

RESEARCH PURPOSE AND PROCEDURES: You are invited to participate in a two-part study aimed at understanding emotional processes, well-being, and effectiveness conducted by Dr. James Diefendorff, a Professor of Industrial/Organizational Psychology at The University of Akron, and Dr. John Trougakos, an Associate Professor of Management at The University of Toronto. In Part 1 you will be asked to complete a one- time survey assessing your traits, work perceptions, and job attitudes. If you complete Part 1 you will be eligible to participate in Part 2. In Part 2 you will be asked to complete daily questionnaires that focus on your daily stressors, coping strategies, and job attitudes. Strict confidentiality will be maintained.

TIME COMMITMENT: Part 1 will take approximately 45-60 minutes. Part 2 will occur over a three-week time period. Participants will be asked to complete 3 five-minute surveys each day they work in that time window.

EXCLUSION: Individuals must be 18 years of age or older, work at least 30 hours per week, and interact with the public in some capacity.

BENEFITS, RISKS, AND DISCOMFORTS: The benefit of participating in the study is to gain more insight about the experiences of employees. The risks and discomforts are minimal.

COMPENSATION: You can earn up to $5 for completing phase 1, up to $45 for completing all surveys in phase 2. CONSENT: I have been fully informed of the above-described procedure with its possible benefits and risks. I also understand that my responses will be maintained in a confidential manner by the researcher. I voluntarily give my permission for my participation in this study. I know that the investigator will be available to answer any questions I may have. If, at any time, I feel my questions have not been adequately answered, I know that I can contact the primary researcher (James Diefendorff, [email protected]) or the IRB with questions about the rights of research subjects (xxx-xxx-xxxx). I understand that I am free to withdraw this consent and discontinue participation in this project at any time without penalty. I am also aware that I may keep a copy of this consent for my records. By typing your name in the box below and pressing “>> Next” you provide your electronic signature and voluntarily agree to participate in this research study. First Name Last Name

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Appendix G

Social Media Sample: Participant Communications

Recruitment Information Sheet – Students will distribute

Experiences of Employees

I am currently recruiting for a research study focusing on employees’ experiences and well-being at work. By participating, you can earn up to $50 on an Amazon Gift Card and I will receive extra credit in class!

You will be asked to complete a brief form about your work schedule and a 45- minute survey. You will then be emailed a brief, 5-minute survey three times a day for 15 consecutive work days (X - X, X - X, and X - X). You will be emailed at specific times corresponding to the start of your shift, middle of your shift, and the end of your shift.

Your participation will assist in contributing to research that may provide insight into employees’ daily work experiences.

To qualify for the study, you must be at least 18 years old and work at least 30 hours per week. In addition, your job must require you to interact with individuals outside of your organization – customers, the public, government, and other external sources either in person, in writing, or by telephone or email. For example, occupations that interact with the public include but are not limited to nurses/doctors, secretaries, teachers, customer service representatives (retail, food service), and lawyers to name a few.

To participate in this study, please write your name and email below:

Name: ______

E-mail: ______

The researchers will e-mail you be emailed a 45-minute opt-in survey Space in the study is limited, and participants for the study will be taken on a first come, first serve basis. If interested, please sign up as soon as possible!

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Information Form to Post on Social Media

I am currently recruiting for a study focusing on employees’ experiences and well-being at work lead by James Diefendorff, a Professor of Industrial-Organizational (I-O) Psychology, and myself (Megan Nolan), a PhD student at The University of Akron. Broadly speaking, our goal is to understand the types of stressors employees experience at work and identify ways to improve their emotional and psychological well- being. To do so, we need to learn more about you and your experiences. Maximum participation in this study will earn you a $50 Amazon gift card and minimum participation will earn you a $14 Amazon gift card!

By participating in this study, you will be asked to complete a 5-minute recruitment form today. You will then be emailed an enrollment survey that takes 45 minutes to complete and will earn you $5. Once this is complete, you will be enrolled in the second phase of the study where you will be asked to complete three short surveys per working day for up to ten days during the following timeframe: Nov. 12th – Nov. 30th. These surveys take about 5 to 7 minutes to complete. Minimum participation involves having three complete days of responses and will earn you $9 more dollars. Complete 10 days of these short surveys to earn $45 more dollars for a grand total of $50.

To participate in this research, you must be at least 18 years old, work at least 30 hours per week in the United States in a job where you interact with individuals outside of your organization (e.g. customers, patients, clients, students or some other members of the public). For example, occupations that interact with the public include but are not limited to nurses/doctors, secretaries, teachers, customer service representatives (retail, food service), and lawyers to name a few.

To participate in this study, please click on the link below. If interested, please sign up as soon as possible!

To sign up, click here.

Please contact me (Megan Nolan, [email protected]) with any questions or concerns about the study. And, if you know of someone who is eligible for the study and would be interested in participating, please feel free to forward the above link along!

Thank you for your participation and support!

An Institutional Review Board responsible for human subjects research at The University of Akron reviewed this research project and found it to be acceptable, according to applicable state and federal regulations and University policies designed to protect the rights and welfare of participants in research.

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Experiences of Employees – Confirmation and Part 2 Information E-mail

Hello,

Thank you for completing Part 1 (the one-time survey)! This e-mail confirms that you are enrolled to complete the daily surveys that will occur on the following days:

• INSERT “WEEK DAY”, “MONTH” “DATE”

As a reminder, throughout the duration of this study, you will receive three surveys per day via e-mail; the survey links will be sent to the e-mail that you are receiving this message age. The first email will be sent by INSERT TIME (to be completed by INSERT TIME), the second by TIME (completed by INSERT TIME), and the third by INSERT TIME (to be completed by INSERT TIME).

As a reminder, you can earn up to $45 by participating in this portion of the study, such that 3 complete days of surveys = $9; 4 complete days = $12; 5 complete days = $20, 6 complete days of surveys = $25; 7 complete days = $30; 8 complete days of surveys = $35; 9 complete days of surveys = $40; and 10 complete days of surveys = $45. As a note, “complete days” means that all three surveys for a given workday (i.e., a day you were working at Call Corp) were completed. Missed surveys, unfortunately, cannot be made up due to the nature of the study.

Please contact Megan Nolan ([email protected]) with any questions.

Thank you! Dr. James Diefendorff [email protected]

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Hello,

Thank you for signing up to participate in our study! Your answers are incredibly valuable, and we appreciate your time.

This e-mail contains the link to your morning survey below: you have until 10:30am today to complete it.

As a reminder, throughout the duration of this study, you will receive three surveys per day via e-mail.

You will have an opportunity to earn up to $45 by participating in this portion of the study, such that 3 complete days of surveys = $9; 4 complete days = $12; 5 complete days = $20, 6 complete days of surveys = $25; 7 complete days = $30; 8 complete days of surveys = $35; 9 complete days of surveys = $40; and 10 complete days of surveys = $45. As a note, “complete days” means that all three surveys for a given workday (i.e., a day you were working at Call Corp) were completed. Missed surveys, unfortunately, cannot be made up due to the nature of the study.

Follow this link to the survey: Survey Link

Or copy and paste the URL below into your Internet browser: URL Link

Please contact Megan Nolan ([email protected]) with any questions.

Thank you! Dr. James Diefendorff [email protected]

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