Emotional Labor Predicts Service Performance Depending on Activation and Inhibition Regulatory Fit

JOMXXX10.1177/0149206316672530Journal of ManagementChi and Grandey / Emotional Labor Predicts Service Performance research-article6725302016

Journal of Management Vol. 45 No. 2, February 2019 673–700 DOI: https://doi.org/10.1177/014920631667253010.1177/0149206316672530 © The Author(s) 2016 Article reuse guidelines: sagepub.com/journals-permissions

Emotional Labor Predicts Service Performance Depending on Activation and Inhibition Regulatory Fit

Nai-Wen Chi National Sun Yat-Sen University Alicia A. Grandey Pennsylvania State University

When service providers regulate their moods and expressions (i.e., deep acting and surface acting), are they better performers? Drawing on the framework of activation-inhibition regulatory systems and regulatory fit, we propose (a) that deep acting represents an activation-oriented regulation strategy and surface acting, an inhibition-oriented regulation strategy; (b) that these strategies have separate pathways to desirable performance (i.e., affective delivery) and counterproductive performance (i.e., service sabotage), respectively; and (c) that performance is optimized when momentary regulation strategies are aligned with activation- and inhibition-oriented traits. Empirically, across two studies, we employ a multilevel approach (i.e., within- and between-person), a multisource approach (i.e., self, coworker, customer), and a multicontext approach (i.e., banks and restaurants) to test regulatory fit as applied to emotional labor. In two studies, we support separate activation and inhibition pathways, plus regulatory fit, in that deep acting is beneficial to affective delivery for those higher in two activation traits—namely, extraversion and openness—and that surface acting predicts service sabotage for those lower in an inhibition trait: conscientiousness. We empirically rule out mood as the explanation for these effects, propose future research to apply regulatory fit to other outcomes and contexts, and suggest practical implications for services.

Keywords: emotional labor; service performance; affective delivery; service sabotage; Big Five personality traits; moods

Acknowledgments: This article was accepted under the editorship of Patrick M. Wright. The authors contributed equally to this paper. The current study was supported by the National Science Council of Taiwan (Grant NSC 100- 2410-H-110-003). We thank Robert Melloy for his review of a draft of this paper and the action editor Daniel Beal and two anonymous reviewers for constructive comments and suggestions. Corresponding author: Nai-Wen Chi, Institution of Human Resource Management, National Sun Yat-Sen University, 70 Lienhai Rd., Kaohsiung 80424, Taiwan.

E-mail: [email protected], [email protected]

673 674 Journal of Management / February 2019

Service providers know that they must exude a friendly and welcoming demeanor, though at times they may feel irritated or bored. In such cases, emotional labor is key to achieving performance goals. They may try to improve their moods by reappraising situations or fake their expressions by hiding their true feelings—two emotion regulation strategies known as deep acting and surface acting, respectively (Hochschild, 1983). These strategies require effort that can be costly to the employee; moreover, whether and when these strategies are actually effective for interpersonal performance is unclear. Only a handful of studies test whether these emotion regulation strategies predict service performance, with weak or mixed effects, and almost none consider the possibility that they could predict more counterproductive behaviors (Hüsheger & Schewe, 2011; Mesmer-Magnus, DeChurch, & Wax, 2012; Yam, Fehr, Keng-Highberger, Klotz, & Reynolds, 2016). We propose conceptual and methodological explanations for these weak effects and clarify when emotional labor strategies are linked to performance. Conceptually, we propose that the effectiveness of surface acting and deep acting are conditional on personality traits—specifically, some people are better at enacting the strategies than others. Specifically, service providers may report using surface and deep acting to a similar extent, but they might differ in their capacity to translate that emotion regulation strategy into observable performance behaviors. Such a capacity could be indicated by personality traits, as practiced tendencies that are known to predict emotion regulation strategies at the person level (Diefendorff, Croyle, & Gosserand, 2005; Kammeyer-Mueller et al., 2013); however, few studies have assessed whether traits moderate their performance effectiveness in the moment (e.g., Chi, Grandey, Diamond, & Krimmel, 2011). Exploring interactive effects of traits may help to untangle the weak relationships of emotional labor with performance. Unfortunately, no existing theoretical frameworks exist to explain how personality intersects with emotional labor to predict performance (Dahling & Johnson, 2013); we propose a new framework to understand when emotion regulation strategies harm or benefit performance. There are also methodological explanations for the weak effects between emotional labor and performance that we aim to address. First, much prior research on emotional labor effectiveness is not at an appropriate level of analysis for the phenomenon. A person-level approach ignores the momentary and transient nature of emotion regulation (Beal, Trougakos, Weiss, & Green, 2006; Scott & Barnes, 2011), which may be more or less effective in an acute versus chronic event. Second, prior studies do not always take into account the effects of felt moods on emotion regulation (Gabriel & Diefendorff, 2015; Totterdell & Holman, 2003) and service outcomes (Beal et al., 2006; Miner & Glomb, 2010); thus, it is unclear if surface acting and deep acting uniquely predict performance or if relationships are due to shared affective influences. Third, existing research on emotional labor and performance focuses on productive behaviors (e.g., Bono & Vey, 2005), but emotion regulation strategies may also make counterproductive behaviors more likely (e.g., avoiding the customer), result- ing in weak overall effectiveness. Thus, we believe that, to determine the effectiveness of emotional labor, researchers must examine within-person variations of emotional labor strategies on productive and counterproductive performance, beyond effects of momentary moods. In the current article, we address these theoretical and methodological issues and uniquely contribute to the literature in several ways. First, we draw on established theories of affect Chi, Grandey / Emotional Labor Predicts Service Performance 675 and self-regulation to argue that both emotional labor strategies and personality traits can be conceptualized as activation- and inhibition-oriented self-regulation (see Elliot & Thrash, 2002, 2010; Lanaj, Chang, & Johnson, 2012). We then apply regulatory fit theory (Higgins, 2000) to suggest that the match of the momentary regulatory strategy and chronic motivational orientation of the employee (e.g., activation or inhibition oriented traits) enhances performance, separate from the effects of felt moods (Aaker & Lee, 2006; Cesario, Higgins, & Scholer, 2008; Johnson, Smith, Wallace, Hill, & Baron, 2015). By doing so, we contribute to theory by proposing an integrated and unique way of thinking about what deep acting and surface acting represent, how and why they are related to service performance, and what traits serve as the boundary conditions to those relationships (Whetten, 1989). Second, we use research methods that address prior limitations and provide a robust empirical contribution. We do this by (a) studying emotional labor–performance relationships within person (at the daily level and the transaction level) to be more consistent with the transient nature of emotion regulation, (b) testing relationships with affective delivery and service sabotage to assess differential outcomes by strategy, and (c) controlling for the effect of felt moods on performance to permit inferences about the unique effect of emotion regulation. Finally, we conduct a constructive replication (Lykken, 1968) and overcome limitations about the generalizability of our findings by conducting two studies that vary in service context (banks and restaurants), timing of the assessments (day and encounter levels), and source of data (coworkers, self, and customers). Such an empirical approach permits us to be more confident in forming conclusions about our theoretical model.

Emotional Labor and Service Performance The two primary emotion labor strategies used by service employees when interacting with customers are known as deep acting and surface acting (Hochschild, 1983). Deep acting indicates the extent that the employee is trying to improve one’s mood to appear genuine to customers, whereas surface acting indicates the extent that one is hiding felt emotions and pretending when interacting with customers. These emotion regulation strategies are performed to meet the display rules and cope with dissonant emotions (Diefendorff et al., 2005), but their relationship with service performance is unclear. Service performance involves many types of skills and behaviors (e.g., efficiency, product knowledge), but the central component is interpersonal behaviors (Parasuraman, Zeithaml, & Berry, 1988). In this article, we focus on two main forms of interpersonal performance that represent productive and counterproductive behaviors: affective delivery and service sabotage. Affective delivery refers to the extent that employees’ emotional displays meet or exceed the expected service norms (Tsai & Huang, 2002), also called emotional performance (Bono & Vey, 2005). For the majority of service jobs, affective delivery is the extent that customers perceive friendliness, interest, and concern from the employee (Beal et al., 2006; Grandey, 2003; Groth, Hennig-Thurau, & Walsh, 2009), which predicts customers’ satisfaction with service and return intentions (Pugh, 2001; Tsai & Huang, 2002). There is also a need to expand the performance criterion to see whether emotion regulation strategies result in counterproductive behaviors (Grandey & Gabriel, 2015). Service sabotage is a type of counterproductive behavior where “an employee intentionally harms the legitimate interests of a customer” (M. Wang, Liao, Zhan, & Shi, 2011, p. 312), such as making mistakes and slowing 676 Journal of Management / February 2019 one’s response. Service sabotage is costly in terms of customer dissatisfaction and com- plaints (Harris & Reynolds, 2003). Within-person assessments are necessary to know whether performance is improved or harmed at the times that certain emotion regulation strategies are used. For affective delivery, the few within-person studies found weak to modest positive effects for deep acting and null or weak negative effects for surface acting (e.g., Beal et al., 2006; Chi et al., 2011; Gabriel & Diefendorff, 2015; Maneotis, Grandey, & Krauss, 2014). For service sabotage, no known studies have tested within-person effects of emotion regulation strategies to counterproductive service behaviors, and person-level studies found only indirect or weak effects of surface acting on counterproductive behaviors (Grandey, 2003; M. Wang et al., 2011; Yam et al., 2016). Our model (see Figure 1) tries to explain these mixed and null effects by arguing that (a) deep acting and surface acting are momentary regulatory orientations with separate pathways to affective delivery and service sabotage and (b) prediction is improved by assessing regulatory fit with the employees’ motivational orientation (e.g., traits).

Activation- and Inhibition-Oriented Emotion Regulation Strategies and Performance A fundamental distinction for explaining human motivation is the existence of two distinct regulatory systems that permit the attainment of goals: an activation system (i.e., approach-oriented, growth, and nurturance motives) and an inhibition system (i.e., avoid- oriented, control, and security motives; Elliot & Thrash, 2010). The approach/activation and avoid/inhibition distinction continues to be examined and applied to the study of personality, emotions, and self-regulation (Elliot, 2006) and more recently to workplace outcomes (Johnson et al., 2015; Lanaj et al., 2012). At its most basic, approach motivations are “the energization of behavior by, or the direction of behavior toward, positive stimuli,” whereas avoidance motivations are “the energization of behavior by, or the direction of behavior away from, negative stimuli” (Elliot, 2006, p. 112). Activation-oriented traits and strategies are associated with positive affect and seeking desired behavior; inhibition-oriented traits and strategies are associated with negative affect and avoiding undesirable behaviors. Deep acting and surface acting seem to indicate two distinct regulatory orientations toward the same goal of expressing emotions as part of the work role (Dahling & Johnson, 2013). Deep acting is about activating cognitions (e.g., using imagination, focusing on positive thoughts, or changing perspectives) to improve mood and follow the rules by appearing genuine with customers (Hochschild, 1983). As such, deep acting seems to be an activation- oriented strategy. In contrast, the focus of surface acting is to hide or fake one’s feelings and control one’s personal impulses, avoiding negative displays that would violate the display rules. Thus, surface acting seems to be consistent with an inhibition-oriented strategy. In fact, an activation-inhibition view of deep and surface acting helps to explain recent person-level evidence (Kammayer-Mueller et al., 2013). The results of meta-analysis supported a separate pathways model such that (a) positive affectivity and display rules about expressing positive affectivity predicted deep acting, which predicted performance (activation oriented), and (b) negative affectivity and display rules about suppressing negative affectivity predicted surface acting, which predicted exhaustion (inhibition oriented). We make similar within-person predictions based on the activation-inhibition pathway. Chi, Grandey / Emotional Labor Predicts Service Performance 677

Figure 1 A Multilevel Model of Emotional Labor and Service Performance

Note: Above the dotted line shows between-person variables (Level 2), and below the line are within-person variables and relationships (Level 1). Dark gray boxes indicate inhibition-oriented constructs; light gray boxes are activation-oriented constructs. Thin gray arrows indicate controlled effects in the model; wide arrows are predicted effects. H1-H4 = Hypotheses 1-4.

Activation Path: Deep Acting and Affective Delivery We expect that at times one is using activation-oriented strategies (deep acting), which are rated as providing higher affective delivery and requiring the activation of positive expressions (see top dark line in Figure 1). Deep acting is a strategy that activates cognition and strives to create positive internal feelings and genuine expressive and behavioral cues, which are linked to higher ratings of affective delivery (Grandey, Fisk, Mattila, Jansen, & Sideman, 2005; Henning-Thurau, Groth, Paul, & Gremler, 2006). In contrast, inhibition-oriented strategies (surface acting) are not expected to clearly benefit or harm affective delivery. Focusing on inhibiting negative displays and one’s impulses results in leakage and ambiguous positive expressions (Grandey et al., 2005) that are not reliably interpreted or evaluated (Groth et al., 2009). No significant relationship is expected between surface acting and affective delivery. Notably, activation orientation is also linked to positive affect (Lanaj et al., 2012). People in more positive affective states are more likely to use activation-oriented strategies (i.e., deep act) and engage in activation-oriented behavior such as affective delivery (Beal et al., 2006; Diefendorff, Erickson, Grandey, & Dahling, 2011; Gabriel & Diefendorff, 2015; 678 Journal of Management / February 2019

Judge, Woolf, & Hurst, 2009). However, regulatory strategies are expected to predict goal- directed behavior beyond felt mood (Cesario et al., 2008), and thus we test if deep acting predicts ratings of affective delivery beyond positive mood:

Hypothesis 1: Within-person variations in deep acting are positively related to affective delivery beyond positive mood.

Inhibition Path: Surface Acting and Service Sabotage Surface acting is a strategy that focuses on inhibiting or suppressing one’s own impulses, and service sabotage is a behavior that is more likely when one lacks the ability to inhibit or practice self-control (M. Wang et al., 2011). When employees regulate by suppressing expressions, they deplete regulatory resources (Diestel, Rivkin, & Schmidt, 2015; Hülsheger, Lang, & Maier, 2010), and this increases the likelihood of later unethical and counterproductive actions (Baumeister, Vohs, & Tice, 2007; Christian & Ellis, 2011). In experiments, suppressing emotions in one’s work role predicted more interpersonal avoidance and less task persistence—avoidant behaviors that are counterproductive to the work role (Wallace, Edwards, Shull, & Finch, 2009). We expect that at times people use surface acting: This inhibitory focus on emotional displays means lowered capacity to inhibit counterproductive service behaviors, a relationship that has not been assessed with within-person methods (see lower dark line in Figure 1). Deep acting is not expected to predict counterproductive behavior, given that activation- oriented strategies are less relevant for behaviors that require inhibition. Activating positive thoughts and emotions for deep acting may require some regulatory resources, but the activation gains (e.g., positive mood and social feedback) result in no relationship with depletion (Brotheridge & Lee, 2003; Huang, Chiarburu, Zhang, Li, & Grandey, 2015; Scott & Barnes, 2011) and no effect on counterproductive behaviors, such as task errors, poor decisions, and reduced persistence (Goldberg & Grandey, 2007; Wallace et al., 2009). We do not predict this direct effect, but we do test it to confirm that deep acting does not affect sabotage. Inhibition-oriented strategies tend to be associated with negative affect; in fact, negative affect is linked to both surface acting (Gabriel & Diefendorff, 2015; Rupp & Spencer, 2006) and service sabotage (Chi, Tsai, & Tseng, 2013; M. Wang et al., 2011). Given the theoretical framework of regulatory strategies, we expect that the inhibition-orientated strategy predicts service sabotage through depletion, not negative moods (Cesario et al., 2008). To our knowledge, no known studies have assessed whether using certain emotional labor strategies are linked sabotage, while controlling for mood. Accordingly, we predict the following:

Hypothesis 2: Within-person variations in surface acting are positively related to service sabotage beyond felt mood.

Activation- and Inhibition-Oriented Regulatory Fit and Performance In organizational sciences, the Big Five taxonomy (i.e., extraversion, agreeableness, neuroticism/emotional stability, conscientiousness, and openness to experience) is dominant as the most comprehensive but parsimonious approach for studying personality (McCrae & John, 1992). In emotional labor research, neuroticism predicts surface acting, and agreeableness Chi, Grandey / Emotional Labor Predicts Service Performance 679 predicts deep acting, with extraversion showing some mixed effects (Bono & Vey, 2007; Diefendorff et al., 2005). Moreover, some Big Five traits are found to be predictors of service performance. Service providers higher in extraversion tend to be better at affective delivery and service performance (Chi et al., 2011; Liao & Chuang, 2004), and those higher in conscientiousness are less likely to engage in counterproductive performance (Harris & Ogbonna, 2009). Emotional labor–performance linkages are moderated by specific traits (i.e., extraversion), but these do not systematically consider all five traits (e.g., Chi et al., 2011; Judge et al., 2009). A comprehensive theory is needed to explain why and how the five personality traits might moderate the links of emotion regulation and service performance. We suggest that regulatory fit (Higgins, 2000) can (1) explain and account for direct effects of Big Five traits on regulation strategies and performance and (2) explain how and why traits interact with strategies to predict these two types of performance in the most parsimonious way. First, according to regulatory fit theory, traits represent chronic motivational tendencies such that they exist at the person level, and people are likely to choose regulation strategies at the event level based on those tendencies (Johnson et al., 2015). In fact, Big Five traits can be grouped into activation and inhibition focused, based on neurologic and behavioral evidence for tendencies toward these motivational processes in pursuing goals (Elliot & Thrash, 2002, 2010; Lanaj et al., 2012; Manczak, Zapata-Gietl, & McAdams, 2014). Activation- oriented motivational tendencies include extraversion, agreeableness, and openness to experience, which represent seeking growth and development through social interactions and activities, such as the arts and intellectual pursuits (Manczak et al., 2014). Inhibition-oriented motivational tendencies include conscientiousness and emotional stability, which share the tendency toward self-control over behavior, emotions, and cognitions (Barrick & Mount, 2000; John & Gross, 2007; McCrae & Löckenhoff, 2010). Moreover, meta-analytic evidence supports that an activation-focused trait (agreeableness) is the best predictor of deep acting (both activation oriented; rho = .29, SD = .00) and an inhibition-focused trait (emotional stability) is the best predictor of surface acting (both inhibition oriented; rho = .31, SD = .15) (Mesmer-Magnus et al., 2012). Thus, the multilevel view of activation and inhibition tendencies explains why certain traits tend to predict surface and deep acting. At the same time, these modest effects show that a person does not always act, in the moment, in a way congruent with his or her chronic motivational orientation (i.e., activation or inhibition oriented). Second, the alignment between chronic tendencies and strategies should enhance performance. Regulatory fit theory posits that there is “increased motivational intensity when there is a match between the manner in which a person pursues a goal and his or her goal orientation” (Aaker & Lee, 2006, p. 15). While those with higher activation/inhibition-oriented traits should be more likely to use activation/inhibition-oriented strategies, those strategies work better when used by people who have congruent traits. When momentary regulatory strategies match the stable goal tendencies, there is more motivational strength and more self-regulatory resources available for the performance when compared with a misfit (Aaker & Lee, 2006; Johnson et al., 2015). We develop these predictions below.

Activation-Related Regulatory Fit and Affective Delivery According to regulatory fit theory, activation-oriented regulatory strategies (i.e., deep acting) performed by service providers who are high in activation-related traits (i.e., extraversion, openness, and agreeableness) are more likely to experience positive motivational energy toward 680 Journal of Management / February 2019 the goal of service performance. Extraversion represents behavioral activation (e.g., assertive- ness, enthusiasm, sensation seeking; Quilty, DeYoung, Oakman, & Bagby, 2014); openness represents more cognitive activation (e.g., abstract thinking, intellectual pursuits, aesthetic/arts seeking; DeYoung, Peterson, & Higgins, 2005); and agreeableness represents social-relational activation by prioritizing others over the self (e.g., helping, nurturing relationships, coopera- tion; Lanaj et al., 2012; McCrae & Löckenhoff, 2010; Meier, Robinson, & Wilkowski, 2006). When people high in these activation-oriented tendencies perform deep acting, the alignment “feels right” (Johnson et al., 2015, p. 1506) such that they should be more able to experience motivational energy and activate positive expressions. Specifically, people higher in extraversion or agreeableness are motivated to understand others’ feelings and behaviors to either dominate/lead or cooperate/help, respectively. This suggests practiced skill at perspective taking and social connections such that deep acting is likely to translate into positive expressions to others (Gross, 1998; Rupp, McCance, Spencer, & Sonntag, 2008). People higher in openness to experience are comfortable with abstract ideas and the arts, both of which may enable deep-acting strategies to be done effectively through imagination, a focus on positive thoughts, or dramatic skill (McCrae & Löckenhoff, 2010). Overall, these three chronic activation-oriented traits involve enhanced skill in trans- lating moods into positive expressions easily recognized by others, due to practiced social interactions (extraversion), exposure to abstract ideas and theatre (openness), and tendency to seek social harmony (agreeableness; DeYoung et al., 2005; John & Gross, 2007). People lower in these activation tendencies should be less capable at generating thoughts and expressions necessary to make deep acting translate into observable positive expressions—thus,

Hypothesis 3: Within-person variations in deep acting are more strongly related to affective delivery when used by people with higher activation-oriented motivational tendencies (i.e., extraversion, openness, and agreeableness) than by those lower in activation-related traits.

Inhibition-Related Regulatory Fit and Service Sabotage Inhibition-related traits refer to the general tendency for impulse inhibition and self-control to achieve goals (Elliot & Thrash, 2010; Knyazev & Slobodskaya, 2006). Specifically, conscientiousness refers to the tendency to interpose thought between impulses and action, thus avoiding goal failure and unethical choices through self-control (McCrae & Löckenhoff, 2010). Emotional stability is linked to regulating emotions and controlling irrational thoughts, while those low in this tend to be depressed due to a lack of emotional control (Barrick & Mount, 2000; John & Gross, 2007). Thus, inhibition-related traits suggest greater capacity for controlling impulses and emotions. Self-control strategies require more conscious effort for those with lower inhibition- related traits, whereas those who are higher in the traits engage in inhibitory regulation strategies more automatically (Mauss, Bunge, & Gross, 2007). Given that surface acting involves suppressing one’s true feelings and impulses with customers, it is more likely to be depleting for those lower in these inhibition-oriented traits. Service providers who are higher in conscientiousness should find that suppressing their negative expressions is congruent with their rule-abiding tendencies and should thus be less depleted (Aaker & Lee, 2006). Similarly, service providers higher in emotional stability are practiced at self-control of moods such that suppressing and inhibiting emotions may be less effortful. As such, people lower in Chi, Grandey / Emotional Labor Predicts Service Performance 681 self-control tendencies (low conscientiousness or emotional stability) should be more depleted from surface acting and more likely to act unethically or in a deviant way than those who are higher in these tendencies (Yam et al., 2016). To our knowledge, there is no research testing how emotional labor strategies and personality traits together are related to counterproductive behavior. According to regulatory fit, we expect that surface acting, an inhibitory-oriented regulatory strategy, should result in less counterproductive behavior (service sabotage) for individuals with congruent inhibition- related traits as compared with those low in these traits—thus,

Hypothesis 4: Within-person variations in surface acting are more weakly related to service sabotage when used by people with high inhibition-oriented motivational tendencies (i.e., conscientiousness and emotional stability) than by those low in inhibition-related traits.

Study 1 Method Sample and Procedures We recruited 55 bank tellers and their coworkers from 20 branches of one Taiwan bank to participate in a 2-week study with support from the bank executives. They completed an initial survey of demographic information and personality traits. Bank tellers then provided their email addresses and the email address of one coworker who had the regular opportunity to observe the teller interacting with customers (Grandey, 2003). Both employee and coworker then participated in an experience sampling method (Beal & Weiss, 2003) in which they received a survey by email at the end of each of 10 workdays. We received 397 matched surveys out of a possible 550 (72% response rate). The participants were mostly female (83%), were 24 to 45 years old (M = 28.7, SD = 4.60), and had about 5 years of tenure (M = 5.30, SD = 5.12).

Measures Following Brislin (1980), we employed the standard translation and back-translation procedures to translate the original version of the questionnaire into Chinese.

Employee personality traits. Employee traits were measured in the initial survey through Goldberg’s (1999) International Personality Item Pool, with 10 items per trait (1 = strongly disagree, 5 = strongly agree): extraversion (e.g., “I feel comfortable around people,” α = .82), emotional stability (e.g., “I get stressed out easily,” reversed, α = .81), openness to experience (e.g., “I am quick to understand things,” α = .85), conscientiousness (e.g., “I pay attention to details,” α = .86), and agreeableness (e.g., “I take time out for others,” α = .89).

Daily emotion regulation. The two emotion regulation strategies were measured with Grandey’s (2003) scale based on Brotheridge and Lee (2003). Bank tellers rated how often each day (1 = never to 5 = always) they engaged in surface acting (5 items, α = .94; e.g., “I just pretend to have the emotions I needed to display to the customer”) and deep acting (3 items, α = .82; e.g., “I try to actually experience the emotions I must show to the customer”). 682 Journal of Management / February 2019

Daily affective delivery. The six-item Affective Delivery Scale was used to measure coworkers’ ratings of bank tellers’ daily service delivery (Grandey, 2003). Coworkers were asked to indicate whether the service workers exhibited the described behaviors while interacting with customers that day. Sample items include “This bank teller treats customers with courtesy, respect, and politeness” and “This bank teller shows friendliness and warmth to customers.” Responses were made on a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree, α = .92).

Daily service sabotage. We used five items from Chi et al. (2013) and Harris and Ogbonna (2006) to measure service sabotage in the face-to-face service interactions: “intentionally hurrying customers when you want to,” “ignore service rules to make things easier for you,” “intentionally slow down service when you want to,” “behaving negatively towards customers,” and “mistreating customers deliberately.” Respondents indicated the extent that they engaged in these behaviors each day (1 = never, 5 = always; α = .92). We obtained self- ratings since sabotage behavior by a bank teller is likely to be covert (e.g., slowing down, giving less money).

Control variables. Since our focal relationships about emotion regulation and performance were at the day level, it was important to control for daily moods to rule out this alternative explanation for relationships. At the end of each workday, employees responded to eight mood items (To, Fisher, Ashkanasy, & Rowe, 2012) on a 5-point scale (1 = not at all, 5 = extremely): four items for positive moods (e.g., “excited” and “enthusiastic”; α = .91) and four items for negative moods (e.g., “anxious” and “upset”; α = .85). We also included gender (1 = male, 2 = female) and employee tenure as control variables, both of which are linked to the emotional labor strategies and performance (e.g., Scott & Barnes, 2011).

Validity of Measurement Model We test the fit of our proposed 6-factor model (i.e., surface acting, deep acting, employee positive mood, employee negative mood, self-rated service sabotage, and coworker-rated affective delivery) with the item responses from employees and coworkers. The results showed that the proposed 6-factor measurement model provided a fairly good fit to the data (χ2 [309] = 1,613.70, comparative fit index [CFI] = .91, incremental fit index [IFI] = .91, standardized root mean square residual [SRMR] = .06) and a better fit than a 4-factor model (i.e., surface acting and negative mood, deep acting and positive mood, self-rated service sabotage, and coworker-rated affective delivery; Δχ2[9] = 1,168.32, p < .01). As further evidence that the responses were not primarily based on self-reporting biases, the confidence intervals of plus or minus two standard errors around the correlations among the factors did not include 1.0 (Anderson & Gerbing, 1988). Thus, we proceeded to test our model predictions.

Analysis and Results Table 1 presents the means, standard deviations, reliabilities, and between- and within- person correlations among the study variables. ** * * ** ** ** ** 13 .35 .41 .35 .28 .44 .45 .16 .30 (.89) −.01 −.06 −.06 −.14 * ** ** 12 .21 .18 .59 .26 .11 .07 .49 .15 (.86) −.14 −.18 −.18 ** * * * 11 .19 .33 .35 .32 .01 .28 (.85) −.12 −.07 −.01 −.02 ** ** .16 .18 .06 .40 10 (.81) −.05 −.04 −.25 −.38 −.24 ** * ** * 9 .06 .04 .28 .44 .28 .34 (.82) −.05 −.11 ** 8 .22 .24 .09 — −.01 −.02 −.51 −.25 * * 7 .04 .09 — −.27 −.33 −.08 −.22 ** * ** 6 .60 .30 .18 .64 (.92) −.12 * 5 .11 (.92) −.13 −.03 −.17 −.12 ** ** ** 4 .68 .21 .13 .48 Table 1 (.85) −.16 ** ** 3 .08 .17 .12 .38 (.91) −.04 * ** ** 2 .27 .05 .07 .32 .13 (.82) ** ** ** ** 1 .56 .56 .15 (.94) −.00 −.15 SD 0.95 0.59 0.86 0.76 1.03 1.01 0.86 0.81 0.98 0.86 0.50 0.37 Descriptives and Bivariate Correlations Among Study 1 Variables Among Study 1 Descriptives and Bivariate Correlations 61.49 M 2.20 1.46 2.57 1.69 5.04 5.48 5.28 5.89 6.15 3.33 3.86 1.84 63.50 1. Surface acting 6. Service sabotage 3. Positive mood 4. Negative mood 9. Extraversion 2. Deep acting 5. Affective delivery 7. Gender 8. Tenure, months

10. Emotional stability 11. Openness to experience 12. Conscientiousness 13. Agreeableness Note: Cronbach’s alpha coefficients are presented in parentheses on the main diagonal. Coefficients above diagonal between-person correlations based 55 bank tellers. Coefficients below the main diagonal are within-person correlations based on 397 daily surveys across * p < .05 (two-tailed). ** p < .01 (two-tailed).

683 684 Journal of Management / February 2019

Multilevel Model Estimation and Analytic Strategy Before testing the hypotheses, we examine whether significant between-person variances exist for the Level 1 variables. The null model specified emotional labor strategies, affective delivery, and service sabotage as the outcome variables and included no predictors at either Level 1 or Level 2 to examine the between-person variance. The results show that the intra- class correlation (ICC1) values ranged from .48 to .71 for the Level 1 study variables (F = 11.29~27.48, all p < .01). These values suggest significant between- and within-person variances among the Level 1 variables. For analyses on cross-level interaction, the Level 1 predictors (i.e., surface acting and deep acting) were group mean centered, and we grand mean centered the Level 2 predictors (Enders & Tofighi, 2007). We estimated our hypothesized multilevel model (see Figure 1) using Mplus 6.0 (Muthén & Muthén, 2010). We employed the Two-Level Random Model of Mplus to test for the cross-level moderating effects of personality traits on the relationships between emotional labor strategies and service performance, and we were able to model the cross-level main effects of gender, tenure, and personality traits on emotion regulation and service performance. In fact, when modeling the Level 2 predictors on Level 1 emotion regulation, we found that deep acting was significantly predicted by agreeableness (unstandardized coefficient = .29, SE = .11, p < .05) and surface acting was significantly predicted by extraversion (unstandardized coefficient = .32, SE = .13, p < .05); none of the other traits nor gender or tenure had significant effects. At Level 1 (i.e., within-person level), we specified the emotional regulation–service performance slopes to be random. In addition, service workers’ daily positive and negative moods were included as control variables as within-person variables. At Level 2 (i.e., between-person level), we specified cross-level main effects of gender, tenure, and all five personality traits on service performance; then, the cross-level interaction term of activation-oriented traits and deep acting on affective delivery; and, finally, the interaction term of inhibition-oriented traits and surface acting on service sabotage. If our proposal that deep acting and surface acting are activation- and inhibition-oriented strategies, is incorrect, respectively, then we should find that the activation- and inhibition-oriented personality traits are equally likely to work (or not) on either emotional labor strategy. Thus, we also included nonpredicted interaction terms of (a) activation-oriented traits and surface acting on affective delivery and (b) inhibition-oriented traits and deep acting on sabotage, as a stronger test of our predictions.

Hypothesis Testing The multilevel modeling results are presented in Table 2. We estimated the random effects of all Level 1 paths and summarized the significance of these random effects in Table 2. Model 1 shows the coefficients for the daily (i.e., mood, emotion regulation) and person- level (gender, tenure, personality) main effects and then the cross-level moderating effects. As shown in Table 2 (Model 1), deep acting had a positive effect on affective delivery (γ = .16, p < .01) beyond mood and the other controls, whereas surface acting was not a significant predictor beyond the other variables. As shown in Model 3, within-person variation in surface acting was positively associated with service sabotage (γ = .21, p < .01) beyond daily mood and the other controls, and deep acting was not a significant predictor beyond the other variables. Hypotheses 1 and 2 were supported. Chi, Grandey / Emotional Labor Predicts Service Performance 685

Table 2 Study 1 Results of Multilevel Modeling on Affective Delivery and Service Sabotage

Affective delivery Service sabotage

Independent variable Model 1 Model 2 Model 3 Model 4

Level 1: Day level Intercepta,b 3.86** (.05) 3.85** (.05) 1.51** (.05) 1.50** (.06) Positive mood 0.08* (.03) 0.07* (.03) 0.03 (.05) 0.04 (.03) Negative mood −0.05 (.04) −0.03 (.04) 0.11* (.05) 0.10* (.04) SAb −0.03 (.04) −0.03 (.04) 0.21** (.04) 0.20** (.03) DAa,b 0.16** (.03) 0.16** (.03) 0.04 (.03) 0.03 (.03) Level 2: Person level Gender 0.05 (.19) 0.04 (.18) −0.20 (.21) −0.20 (.19) Tenure 0.01** (.00) 0.01** (.00) −0.01 (.00) −0.00 (.00) Activation: Extraversion 0.12* (.05) 0.11* (.05) 0.05 (.07) 0.04 (.06) Activation: Openness −0.01 (.09) −0.01 (.09) 0.11 (.08) 0.12 (.10) Activation: Agreeableness 0.04 (.07) 0.02 (.08) −0.13 (.09) −0.14 (.08) Inhibition: Conscientiousness −0.01 (.08) −0.04 (.09) −0.19* (.09) −0.19* (.09) Inhibition: Emotional stability 0.07 (.05) 0.05 (.06) −0.19* (.07) −0.20* (.07) Cross-level interactions Activation-oriented regulatory fit Extraversion × DA 0.07* (.03) Openness × DA 0.09* (.04) Agreeableness × DA 0.06 (.06) Inhibition-oriented regulatory fit Conscientiousness × SA 0.14* (.07) Emotional Stability × SA 0.04 (.09) Nonpredicted interaction effects Extraversion × SA 0.06 (.05) Openness × SA 0.06 (.06) Agreeableness × SA 0.05 (.05) Conscientiousness × DA 0.04 (.07) Emotional Stability × DA 0.07 (.06)

Note: Level 1, n = 394; Level 2, n = 55. Model controls for cross-level effects of traits on emotional labor. Unstandardized coefficients are shown in each equation. Standard errors in parentheses. Italics indicated nonpredicted interaction effects; omitting them does not change the results. When we added all possible trait–emotional labor interactions in the models, the shown results did not change, and no nonproposed interactive effects were significant. DA = deep acting; SA = surface acting. aA significant random effect for the affective delivery model. bA significant random effect for the service sabotage model. +p < .10 (two-tailed). *p < .05 (two-tailed). **p < .01 (two-tailed).

Hypothesis 3 stated that activation-related traits (extraversion, openness, and agreeableness) enhance the effect of deep acting on affective delivery. These three cross-level interaction effects were added to Model 2. As the coefficients in Table 2 show, both extraversion and openness to experience moderated the relationship between deep acting and affective delivery (γ = .07 and .09, p’s < .05), whereas agreeableness did not moderate this relationship (γ 686 Journal of Management / February 2019

Figure 2 The Cross-Level Interaction of Level 2 Extraversion on Level 1 Deep Acting–Affective Delivery Relationship in Study 1

= .06, p > .10). To clarify the form of interactions (Aiken & West, 1991), we plotted the deep acting–affective delivery relationship under high (1 SD above the mean) and low (1 SD below the mean) levels of extraversion (see Figure 2) and openness to experience (see Figure 4). The results show that deep acting was positively related to coworker-rated affective delivery when bank tellers were high on extraversion and openness to experience (γ = .23 and .21, p’s < .01) whereas the positive relationship was neutralized for more introverted and low- openness employees (γ = .06 and .08, p’s > .10). Thus, Hypothesis 3 was partially supported. Furthermore, the activation traits did not interact with surface acting to improve the prediction of affective delivery (γ = −.06 ~ −.05, all p’s > .05), suggesting that activation-oriented traits moderate only the activation pathway rather than the inhibition one. Hypothesis 4 proposed that inhibition-related traits (conscientiousness and emotional stability) moderated the effect of surface acting on service sabotage. The results of these cross-level effects are presented in Model 4 of Table 2. The effect of surface acting on sabotage was moderated by conscientiousness (γ = −.14, p < .05), but emotional stability did not have significant moderating effects (γ = −.04, p > .10). To clarify the form of the significant interaction (Aiken & West, 1991), we plotted the slope of surface acting on sabotage with conscientiousness at one standard deviation above and below the mean (see Figure 6). The positive relationship between surface acting and sabotage was strongly positive for bank tellers low on conscientiousness (γ = .31, p < .01) but neutralized for highly conscientious employees (γ = .06, p > .10). Therefore, Hypothesis 4 was partially supported. The nonpredicted effects for the interaction of inhibition-oriented traits and deep acting did not improve prediction of sabotage (γ = .04 and .07, all p’s > .05).1

Discussion Overall, we find support for an activation/inhibition view of deep acting and surface acting, based on their separate effects on affective delivery and service sabotage, respectively. On days when employees were activating cognitions to feel and express positive moods (i.e., deep acting), coworkers are more likely to rate them as interacting well with customers (i.e., affective delivery). Daily surface acting neither improved nor harmed that day’s affective Chi, Grandey / Emotional Labor Predicts Service Performance 687 delivery, confirming that inhibiting felt emotions creates ambiguous cues. Surface acting that day did, however, harm performance in that they were more likely to report sabotaging performance, suggesting an inability to control negative impulses and poor decisions. For both these relationships, an alternative explanation is that the effects are due to felt positive or negative moods. However, our analyses support that the specific regulation strategy affects these outcomes beyond the felt mood, consistent with the activation/inhibition theoretical perspective (Cesario et al., 2008). In other words, deep acting activates positive thoughts and feelings, and this strategy predicts positive affective delivery beyond positive moods; surface acting inhibits impulses and negative expressions, and this strategy predicts deviant behavior beyond negative moods. Moreover, these emotion regulation strategies are better at predicting service performance when there is regulatory fit with chronic traits (Johnson et al., 2015). Employees high in two activation-related traits (i.e., extraversion and openness) were able to translate the deep acting strategy to observed positive affective delivery; employees with low levels of these traits were unable to use deep acting to transform moods and expressions effectively. Higher conscientiousness—a tendency to control impulses to achieve goals—enabled employees to use surface acting without acting deviantly with sabotage, but surface acting was still predictive of sabotage for employees who do not practice self-control as readily. However, in contrast to our predictions, agreeableness (an activation-oriented trait) did not moderate the deep acting–affective delivery relationship, and emotional stability (an inhibition-oriented trait) did not moderate the surface acting-sabotage relationship. There are methodological explanations for why we may not have found support for these two predictions, which we aim to address in Study 2. First, we relied on daily assessments that may not best capture the effect of momentary strategies on performance, since employees vary in their use of strategies at a momentary level (Gabriel & Diefendorff, 2015). This is important conceptually as well as methodologically; deep acting with a specific customer should predict affective delivery with that customer, if the person has activation traits that enable the strategy to work well. Similarly, surface acting in an encounter is likely to predict subsequent depletion and impaired performance directed at the same target, unless inhibition traits are high. Thus, in Study 2, we test for these same interaction effects, where perhaps agreeableness (as an activation tendency about relationships) and emotional stability (as an inhibition tendency about emotions) are more likely to emerge. Another limitation/explanation for our lack of predicted interaction effects is the source of our performance measures. In Study 1, employees rated service sabotage, and although self- ratings and other ratings of counterproductive behavior tend to be positively and moderately correlated (Berry, Carpenter, & Barratt, 2012), social desirability bias may affect their responding. Also in Study 1, coworkers rated affective delivery, but these ratings may be due to liking or social desirability, rather than observing the behavior consistently (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). Thus, rater biases could create a strong direct effect, thereby making it statistically difficult to find interaction effects even if they are truly there. In Study 2, we test whether our proposed effects are confirmed, using customer perceptions of performance. Finally, it is possible that our effects cannot be replicated or that they may take on a different pattern with another sample. Bank teller interactions with customers tend to be fairly brief and standardized, potentially limiting the need for emotional regulation and variability in affective delivery. Service sabotage is rare and particularly unethical in a financial transaction, potentially constraining this outcome of interest. In Study 2, we test the model with 688 Journal of Management / February 2019 more prototypical service workers (restaurant servers), who have longer interactions that occur both on the floor and off the floor, thus permitting more variation in affective delivery and service sabotage as well as the emotion regulation strategies.

Study 2 Method Sample and Procedures Data were collected from 61 first-line servers at 13 restaurants that were matched in terms of location (a southern city in Taiwan), restaurant type (family restaurant), and price (around U.S. $10 per person per meal). Five research assistants visited each restaurant several times to recruit servers to complete a personality assessment before their shifts, during which we obtained mood, emotion regulation, and performance data for five service encounters for each service provider. After the bill was paid, research assistants asked one customer at the table to complete a survey about the server’s performance. To reduce potential issues regarding social desirability (Podsakoff & Organ, 1986), the assistants assured servers that their responses would be kept anonymous and used for research purposes only, and customer surveys were returned directly to the research assistants when completed. We obtained data from 305 service encounters from the 61 servers. Servers were likely to be female (77%); their ages ranged from 18 to 42 years (M = 24.54, SD = 5.60); and they had tenure for an average of 1.42 years (SD = 1.62). Customers were also more likely to be female (73%), and each table had an average of 2.43 customers (SD = 1.29).

Measures Person-level measures are the same as Study 1, and alpha coefficients are shown in Table 3. Encounter-level measures (i.e., emotional labor and customer-rated performance) were based on the same measures in Study 1 but were modified to say “during this service encounter,” and the performance items were modified to take the customer’s perspective. We again controlled for employees’ gender and tenure, as well as their positive and negative mood during each service encounter. We also controlled for other situational factors unique to the interaction context: store busyness during the encounter (1 = no other customers, 5 = all tables occupied and a waiting line; M = 2.55, SD = 0.97), which can influence service employees’ displayed emotions and perceptions of performance (Grandey et al., 2005; Pugh, 2001; Sutton & Rafaeli, 1988), and customer gender, which may influence ratings of interpersonal performance (Mattila, Grandey, & Fisk, 2003; Pugh, 2001; Tsai & Huang, 2002).

Validity of Measurement Model We performed a within-person confirmatory factor analysis with the items reported by both sources. The results showed that the proposed 6-factor model provided a better fit to the data (χ2 [309] = 1,305.81, CFI = .92, IFI = .92, SRMR = .05) than a 2-factor model by rating source (e.g., employee and customer rated; Δχ2 = 3,479.53, Δdf = 14, p < .01; CFI = .74, IFI = .74, SRMR = .16). Finally, confidence intervals of plus or minus two standard errors around the within-person correlations among these factors did not include 1.0, supporting the dis- criminant validity among the study variables (Anderson & Gerbing, 1988). * ** ** ** * * ** * 15 .16 .25 .38 .01 .02 .29 .36 .22 .27 (.82) −.23 −.06 −.25 −.47 −.48 * * ** ** * ** 14 .27 .13 .06 .19 .38 .30 .20 .20 .40 (.75) −.09 −.33 −.06 −.25 ** 13 .01 .03 .15 .17 .21 .34 .20 .01 (.81) −.03 −.22 −.01 −.15 * .10 .14 .04 .13 12 (.81) -.01 −.10 −.05 −.16 −.27 −.07 −.06 .13 .02 .22 .21 .04 .01 .12 .14 .11 11 (.76) −.03 .06 .01 .16 — .07 .07 10 −.15 −.02 −.03 −.16 * 9 .02 .06 — −.26 −.13 −.15 −.05 −.07 −.04 ** ** ** * ** 8 .53 .36 (.94) −.13 −.02 −.61 −.72 −.27 ** ** ** * * 7 .20 .25 .71 (.95) −.03 −.50 −.36 −.31 ** * ** * 6 .01 .30 .30 (.82) −.09 −.12 −.26 −.48 ** ** * * 5 .22 .04 .18 .17 Table 3 (.91) −.26 −.17 −.14 ** ** ** 4 .08 .01 .34 .16 (.81) −.15 −.08 −.20 ** ** ** ** * 3 .10 .22 .23 .02 (.92) −.24 −.14 −.42 2 — .11 .11 .01 .02 −.03 −.09 −.02 .03 — .07 1 .06 −.11 −.10 −.04 −.01 Descriptives and Bivariate Correlations Among Study 2 Variables Among Study 2 Descriptives and Bivariate Correlations 0.97 0.67 0.47 0.59 0.67 0.37 0.66 0.70 0.71 0.79 0.46 1.01 1.01 0.64 SD 21.08 M 2.55 3.03 1.21 4.25 1.63 1.72 3.49 3.58 3.63 3.89 1.70 2.31 3.10 3.81 18.49 2. Store busyness 6. Negative mood 7. Affective delivery 8. Service sabotage 9. Server gender 4. Deep acting 1. Customer gender 3. Surface acting 5. Positive mood

12. Emotional stability 10. Tenure, months Note: Cronbach’s alpha coefficients are presented in parentheses on the main diagonal. Coefficients above diagonal between-person correlations based 61 restaurant servers. Coefficients below the main diagonal are within-person correlations based on 305 service encounters across * p < .05 (two-tailed). ** p < .01 (two-tailed). 11. Extraversion 14. Conscientiousness 15. Agreeableness

13. Openness to experience

689 690 Journal of Management / February 2019

Analysis and Results Table 3 presents the descriptives and correlations among the study variables at within- and between-persons levels of analysis. We followed the same approach as in Study 1. ICC1 values ranged from .15 to .67 for our Level 1 variables (F = 1.96~11.32, all p’s < .01), supporting that there are significant between- and within-person variances of the Level 1 variables. None of the Level 2 effects of gender, tenure, and five personality traits on Level 1 emotional labor strategies were significant. Only Level 2 conscientiousness predicted Level 1 deep acting (unstandardized coefficient = .32, SE = .11), and this cross-level effect is taken into account in our multilevel tests of hypotheses. The random effects of all Level 1 paths are estimated in the models.

Hypothesis Testing The results of multilevel modeling testing our predictions are presented in Table 4. At the encounter level, deep acting positively related to customer-rated affective delivery (γ = .16, p < .01) beyond employee mood (see Model 1, Table 4). As expected, encounter-level surface acting is positively related to customer-rated service sabotage (γ = .12, p < .01) beyond the effect of employee mood. Hypotheses 1 and 2 were supported. As shown in Table 4 (see Model 2), both extraversion and openness positively and significantly moderated the relationship between deep acting and affective delivery (γ = .20 and .10, all p’s < .05), consistent with Hypothesis 3. Again, the forms of interactions are consistent with expectations: Deep acting was positively related to customer-rated affective delivery when servers were high on extraversion and openness (γ = .26 and .20, all p’s < .05; see Figures 3 and 5), while this relationship was neutralized when servers were low on extraversion and openness (γ = .02 and .08, all p’s > .10). However, agreeableness did not moderate the relationship between deep acting and affective delivery. Thus, Hypothesis 3 is only partially supported, but more important, these findings are consistent with Study 1. Next we tested Hypothesis 4, with inhibition-related traits as moderators of the surface acting–service sabotage relationship (see Model 4, Table 4). Figure 7 shows that the positive relationship between surface acting and customer-rated service sabotage was neutralized when servers’ conscientiousness was high (γ = .01, p > .10) whereas this relationship was positive when servers were low in conscientiousness (γ = .16, p < .05). As in Study 1, emotional stability did not moderate this relationship such that Hypothesis 4 was only partially supported.2

Discussion We tested our model to be specifically focused on the activation and inhibition pathways. To rule out methodological explanations of the results in Study 1, we obtained (a) new data from a prototypical emotional labor job with longer and more variable interactions (i.e., restaurant servers); (b) transaction-level data that permit us to capture how mood, emotion regulation, and performance are linked at the time that they occur; and (c) customer ratings as the focal perceiver of interest for service performance outcomes. With all these changes to the methods of Study 2, our results are nearly identical to Study 1. Thus, Study 2 provides a constructive replication of the findings in Study 1 (Lykken, 1968). Our encounter-level study supports the following: (a) that deep acting can be conceptualized as an activation strategy that is effective for desired, goal-directed behavior in that interaction; (b) that surface acting is an inhibition strategy most relevant for predicting undesired, Chi, Grandey / Emotional Labor Predicts Service Performance 691

Table 4 Study 2 Results of Multilevel Modeling on Affective Delivery and Service Sabotage

Affective delivery Service sabotage

Independent variable Model 1 Model 2 Model 3 Model 4

Level 1: Encounter level Intercepta,b 4.10** (.13) 4.08** (.13) 1.77** (.16) 1.76** (.15) Customer gender 0.09 (.08) 0.09 (.08) −0.08 (.09) −0.06 (.09) Store busyness 0.00 (.00) 0.00 (.00) −0.00 (.00) −0.00 (.00) Positive mood 0.01 (.04) 0.02 (.04) −0.05 (.05) −0.03 (.05) Negative mood −0.02 (.07) −0.02 (.08) 0.08 (.07) 0.07 (.09) SAb −0.14* (.06) −0.12* (.06) 0.12** (.04) 0.09* (.04) DAa,b 0.16** (.05) 0.15** (.05) −0.20** (.08) −0.18* (.09) Level 2: Person level Gender −0.15 (.11) −0.14 (.11) −0.06 (.14) −0.06 (.14) Tenure −0.00 (.00) −0.00 (.00) −0.00 (.00) −0.00 (.00) Activation: Extraversion 0.04 (.07) 0.04 (.07) 0.04 (.08) 0.03 (.06) Activation: Openness −0.03 (.07) −0.03 (.07) 0.04 (.06) 0.04 (.06) Activation: Agreeableness 0.17** (.06) 0.17** (.06) −0.27** (.05) −0.25** (.05) Inhibition: Conscientiousness 0.17* (.09) 0.16* (.08) −0.10 (.07) −0.09 (.06) Inhibition: Emotional stability 0.02 (.08) 0.02 (.08) −0.07 (.07) −0.08 (.09) Cross-level interactions Activation-oriented regulatory fit Extraversion × DA 0.20** (.07) Openness × DA 0.10* (.03) Agreeableness × DA 0.04 (.12) Inhibition-oriented regulatory fit Conscientiousness × SA −0.11* (.04) Emotional Stability × SA −0.05 (.06)

Note: Level 1, n = 305; Level 2, n = 61. Model controls for cross-level effects of traits on emotional labor. Unstandardized coefficients are shown in each equation. Standard errors in parentheses. When we added all possible trait–emotional labor interactions in the models, the shown results did not change and no nonproposed interactive effects were significant. DA = deep acting; SA = surface acting. aA significant random effect for the affective delivery model. bA significant random effect for the service sabotage model. *p < .05 (two-tailed). **p < .01 (two-tailed). counterproductive behavior in the same interaction; and (c) that these effects exist beyond felt mood during that encounter. Moreover, we find support for regulatory fit between strategies and traits: Deep acting generates observable positive expressions only for people with high levels of activation traits (i.e., extraversion and openness), and surface acting impairs the capacity to inhibit undesired impulses only for people low in an inhibition trait (i.e., conscientiousness). We again did not find any support for agreeableness and emotional stability as moderators of emotion regulation strategies on performance, despite our different methods. Our Level 2 sample size (number of employees) is small, perhaps contributing to our difficulty in finding some of the cross-level moderating effects, in that they may be smaller than the effects that we do find. Although this is counter to our regulatory fit predictions, the consistent finding 692 Journal of Management / February 2019

Figure 3 The Cross-Level Interaction of Level 2 Extraversion on Level 1 Deep Acting–Affective Delivery Relationship in Study 2

Figure 4 The Cross-Level Interaction of Level 2 Openness to Experience on Level 1 Deep Acting–Affective Delivery Relationship in Study 1

Figure 5 The Cross-Level Interaction of Level 2 Openness to Experience on Level 1 Deep Acting–Affective Delivery Relationship in Study 2 Chi, Grandey / Emotional Labor Predicts Service Performance 693

Figure 6 The Cross-Level Interaction of Level 2 Conscientiousness on Level 1 Surface Acting–Service Sabotage Relationship in Study 1

Figure 7 The Cross-Level Interaction of Level 2 Conscientiousness on Level 1 Surface Acting–Service Sabotage Relationship in Study 2

across the two studies leads us to believe that this is a substantive and interesting lack of effect. We discuss some possible explanations, along with implications and next steps in general discussion.

General Discussion Service employees cannot always feel positively and meet the “service with a smile” expectations; they must regulate their emotions or expressions in an effortful way. Our research shows that service workers’ felt states are less critical for performance than how they are regulating their emotions and expressions, particularly for customers’ reactions. Yet, the employee’s emotion regulation strategies do not always translate into desirable service performance: How employees regulate their emotions and dispositional tendencies jointly determine effectiveness. Across two studies, we find support for a comprehensive, multilevel model of stable personality traits influencing the covariation of emotional labor strategies and service performance over time in ways that explain the distinct but weak pathways from 694 Journal of Management / February 2019 emotion regulation strategies to performance (Hülsheger et al., 2010; Kammayer-Muller et al., 2013).

Theoretical Implications In recent years, researchers have called for a comprehensive model to (a) explain why emotional labor strategies are differentially related to performance and (b) incorporate personality traits in the process (e.g., Bono & Vey, 2007; Chi et al., 2011; Dahling & Johnson, 2013; Judge et al., 2009; Grandey & Gabriel, 2015). We develop a new theoretical model that meets these two needs. First, we apply the activation-inhibition view of motivation to emotional labor, which explains (a) the associations of deep acting with positive moods and activated expressive behaviors and (b) the association of surface acting with negative moods and depletion from inhibition (Hülsheger et al., 2010; Hülsheger & Schewe, 2011). Moreover, this theoretical view can explain why the effect of surface and deep acting is not due to changes in mood; although positive and negative moods are related to activation and inhibition, respectively, the mecha- nism by which the strategies work is motivational energy, not the valence of one’s feelings. A separate pathway (activation-inhibition) approach presents a different explanation for distinct effects, beyond the idea that surface acting is always “bad” whereas deep acting is always “good.” If surface acting represented bad intentions and behavior, it would undermine affective delivery. Instead, surface acting is neither good nor bad for affective delivery (and is only “bad” for service sabotage for employees low in conscientiousness). This is perhaps odd: Why would someone engage in surface acting if it has no effect on emotional performance? We suggest that surface acting is effective at avoiding negative displays but creates ambiguity that depends on the perceiver’s motivation to see, or ability to detect, authenticity (Groth et al., 2009). Second, by integrating emotional labor with regulatory fit theory, we show that even using deep acting is not always “good”—one must be high in extraversion or openness to see the benefits. Regulatory fit theory suggests that when the motivational tendency is congruent with the regulatory strategy in the moment, motivational energy is enhanced, and performance is improved (Lanaj et al., 2012). Regulatory fit has been discussed in marketing litera- tures, as improving the actor’s mood and behavioral intentions toward products (Higgins, 2000), but this is a new approach to understanding emotional labor and personality (see also Dahling & Johnson, 2013). We draw on existing research arguing that personality traits can be conceptualized as activation- or inhibition-oriented motivational tendencies (Elliot & Thrash, 2010; Knyazev & Slobodskaya, 2006), and we account for prior findings that traits predict person-level emotion regulation (e.g., Mesmer-Magnus et al., 2012), but testing how relationships change within person remains an area of needed research. Activation-related traits (extraversion and openness) enable one to translate deep-acting strategies (e.g., cognitions such as reappraisal, perspective taking, positive refocusing) into expressive cues that are viewed positively, and an inhibition-related trait (conscientiousness) enables one to surface act (e.g., behavioral inhibition of one’s true emotions and impulses with others) without depleting the person to the point of deviating from rules.

Explaining Null Interaction Effects and Future Directions Overall, regulatory fit as a theoretical frame provides specific multilevel predictions about which traits matter for which emotion regulation strategy. The evidence suggested that the Chi, Grandey / Emotional Labor Predicts Service Performance 695 model works well with emotional labor, with a few notable exceptions. We did not find the predicted effects for the activation trait agreeableness and the inhibitory trait emotional stability. A post hoc explanation for this null finding is that the match needs to be even more specific than what we have proposed, in terms of both the regulatory strategy and the outcome. First, perhaps agreeableness and emotional stability would work better for different regulatory strategies. Deep acting is a strategy for regulating cognitions and expressions to activate friendly expressions, and it is aided by openness and extraversion. Agreeableness may be more helpful for other emotion regulation strategies, such as amplifying expressed com- passion, an expectation typical in health care. In the other case, surface acting is about following organizational rules about avoiding negative displays, which is aided by conscientiousness. Emotional stability may reduce problems created by other, more intrapsychic regulation strategies, such as ignoring or avoiding thinking about negative feelings. Second, perhaps our outcomes of affective delivery and service sabotage were ill matched to agreeableness and emotional stability. Agreeableness is associated with prosocial motives and voluntary helping (Meier et al., 2006); engaging in deep acting for people higher in these traits may energize them to engage in extra-role behaviors (e.g., customer-directed citizen- ship behavior) rather than in-role behaviors (i.e., affective delivery prescribed by display rules). In the other case, emotional stability is a general tendency regarding the inhibition of negative emotions not specific to work rules or roles (John & Gross, 2007); engaging in surface acting for people lower in this trait may more likely result in exhaustion rather than the emergence of interpersonal work behaviors, as it does for conscientiousness. Future research can test these specific extensions of regulatory fit by adding other regulatory strategies and intrapsychic outcomes (i.e., exhaustion) to our model. As another potential explanation, it is interesting that agreeableness and emotional stability are the traits that tend to be differential and strong predictors of emotion regulation and service behaviors in between-person studies (Chi et al., 2013; Diefendorff et al., 2005; Kammeyer-Mueller et al., 2013; Liao & Chuang, 2004). Instead of moderating the effectiveness of emotion regulation on service outcomes, agreeableness seems to activate motivation for deep acting and positive service behaviors (Diefendorff et al., 2005; Liao & Chuang, 2004), whereas emotional stability seems to inhibit the need for surface acting and negative service behaviors (Chi et al., 2013; Diefendorff et al., 2005). Our data partially confirm this: Agreeableness significantly predicts daily deep acting in Study 1 and has positive coefficients with deep acting (though nonsignificant) in Study 2. Notably, we cannot adequately test the possibility of direct effects due to our small person-level samples, which reduce our statistical power to find such effects. However, such a proposal is consistent with prior person-level findings (Diefendorff, et al., 2005; Mesmer-Magnus et al., 2012) and worth pursuing in further research.

General Limitations Although we propose mechanisms of our direct and moderating effects—and results are generally consistent with these proposals—we did not directly test the mechanisms. Regulatory fit would suggest that motivational energy is enhanced when there is congruence, which enables deep acting to translate to observed displays and surface acting to be less depleting. Thus, while we do not test the mechanisms, our results are consistent with the theoretical ideas. We encourage future researchers to incorporate expressive cues (e.g., 696 Journal of Management / February 2019 display intensity, authenticity; Grandey et al., 2005) and regulatory capacity (e.g., atten- tional, motivational; Diestel et al., 2015) as an extension of our model and to test the moderated mediation effects with traits. We obtained data each day or from encounters over a brief period; however, emotions and their regulation vary even during a single encounter (Gabriel & Diefendorff, 2015). As such, future researchers mechanisms may want a more fine-grained assessment of mechanisms. Moreover, employees’ emotional regulation and service behaviors might exhibit cyclical pat- terns with feedback loops (e.g., customer reactions affect future regulation) in ways that cannot be effectively modeled without additional data points (Beal et al., 2006; Beal & Weiss, 2003). Finally, although we show generalizability of our model to two different service contexts, we cannot be certain if our results would transfer to other service contexts. Service contexts vary in their familiarity between the employee and the customer, which changes the value and impact of emotional labor strategies (K. L. Wang & Groth, 2014) and may also increase the importance of certain traits that are about relational harmony (e.g., agreeableness). In addition, our service contexts were both located in Taiwan, and it is possible that different norms for emotional expression change the skill and depletion of regulation (Butler, Lee, & Gross, 2009). Thus, next steps include testing our model in other service and cultural contexts.

Practical Implications To increase the benefits associated with affective delivery (e.g., increasing customers’ willingness to return and recommend; Tsai & Huang, 2002) and reduce the costs associated with service sabotage (e.g., reducing customer satisfaction and firm financial performance; Harris & Ogbonna, 2009), service organizations should socialize and train service workers to have an activation orientation. Activation-oriented strategies can be encouraged by motivational practices in organizations and are related to other beneficial work outcomes. For exam- ple, organizations can emphasize activation-oriented display rules (e.g., “Be excited and enthusiastic during service interactions”) rather than inhibition-oriented display rules (e.g., “Don’t let customers know you are down”), which affect emotion regulation strategies (Diefendorff et al., 2005). Furthermore, display rules are more likely to enhance service performance in autonomy-supportive workplaces (Christoforou & Ashforth, 2015) such that employees feel that they have the choice to engage in emotion regulation to improve their interactions. In addition, our results support the importance of considering the fit of socialization and selection practices (human resource “bundles”). Many emotional labor papers conclude with encouraging human resource managers to use training for deep acting (e.g., Grandey, 2000); yet, our research suggests that deep-acting training will not be effective if the person is low in activation-related traits (extraversion and openness). Furthermore, surface acting may be a necessary first response to rude or frustrating customers that reduces showing negative emotions (Rupp & Spencer, 2006); controlling impulses in such interactions is easier for those higher in conscientiousness. Therefore, service organizations are encouraged to use selection tools to assess applicants’ extraversion, openness, and conscientiousness via personality tests or situational interviews, as well as training and reinforcement of applicants’ Chi, Grandey / Emotional Labor Predicts Service Performance 697 emotional labor strategies with work sampling or situational judgment interviews (Gatewood, Feild, & Barrick, 2011). Finally, we found that emotion regulation varies greatly from day to day—even encounter to encounter within a day—and that these activation- and inhibition-oriented regulatory strategies are more important for variations in performance than the employee’s positive or negative mood. By ensuring that employees have regulatory fit between traits and strategies, they can engage in interpersonal behaviors that enhance customer satisfaction and thus return business and profits.

Notes 1. To confirm our proposed theoretical model, we also tested nonpredicted effects: (a) activation-related regulatory fit (interaction between activation traits and deep acting) on inhibition-related service sabotage and (b) inhibition-related regulatory fit (interaction between inhibition traits and surface acting) on activation-related affective delivery. The results showed that none of these regulatory fit interactions predicted alternative outcomes (γ = −.06 ~ .05, ns). 2. We confirmed our model by testing nonpredicted effects. We did not find support for five nonpredicted interactions—(a) surface acting on affective delivery moderated by activation traits and (b) deep acting on sabotage moderated by inhibition traits; γ = −.04 ~ .07, ns). As in Study 1, activation regulatory fit did not predict service sabotage, and inhibition regulatory fit did not predict affective delivery (γ = −.08 ~ .09, ns).

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