Regulating and facilitating: the role of emotional intelligence in maintaining and using positive affect for creativity.

Journal of Applied Psychology 2015, Vol. 100, No. 3, 917–934

© 2014 American Psychological Association 0021-9010/15/$12.00 http://dx.doi.org/10.1037/a0038452

Regulating and Facilitating: The Role of Emotional Intelligence in Maintaining and Using Positive Affect for Creativity Michael R. Parke, Myeong-Gu Seo, and Elad N. Sherf

This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

University of Maryland Although past research has identified the effects of emotional intelligence on numerous employee outcomes, the relationship between emotional intelligence and creativity has not been well established. We draw upon affective information processing theory to explain how two facets of emotional intelligence— emotion regulation and emotion facilitation—shape employee creativity. Specifically, we propose that emotion regulation ability enables employees to maintain higher positive affect (PA) when faced with unique knowledge processing requirements, while emotion facilitation ability enables employees to use their PA to enhance their creativity. We find support for our hypotheses using a multimethod (ability test, experience sampling, survey) and multisource (archival, self-reported, supervisor-reported) research design of early career managers across a wide range of jobs. Keywords: emotional intelligence, creativity, positive affect, knowledge processing requirements

Organizations are affectively charged places (Weiss & Cropanzano, 1996), and the evidence that affect1 influences important employee behaviors and outcomes is pervasive (Brief & Weiss, 2002; Elfenbein, 2007). Given this, employees with the ability to effectively manage their emotions as well as intentionally harness and use emotions and emotional information (i.e., emotional intelligence; Mayer, Caruso, & Salovey, 1999; Salovey & Mayer, 1990) should have more beneficial outcomes than those who lack such abilities. A growing body of literature on emotional intelligence at work supports this claim (Côté & Miners, 2006; Farh, Seo, & Tesluk, 2012; Grant, 2013; Joseph & Newman, 2010; Kluemper, DeGroot, & Choi, 2013; Rubin, Munz, & Bommer, 2005). However, when the outcome of interest is employee creativity— the production of new and useful ideas (Amabile, 1996)—there is a lack of theory and empirical evidence linking emotional intelligence ability to it (e.g., Joseph & Newman, 2010). Although studies have investigated the potential interpersonal role of emotional intelligence and creativity, such as how leaders impact follower creativity (Castro, Gomes, & de Sousa, 2012; Zhou & George, 2003) or how team emotional intelligence helps facilitate team creativity (Barczak, Lassk, & Mulki, 2010), little attention has been given to the individual role emotional intelligence ability2 (henceforth EI) plays in influencing employee creativity. This gap

likely results from initial theoretical consensus that EI and creativity are unrelated. For example, in their review of the EI literature, Mayer, Roberts, and Barsade (2008) conclude that “EI also may exhibit relations with social intelligence, but apparently not with creativity” (p. 519). The logic underlying this consensus is that because EI captures abilities in consensual or convergent thinking to produce normative solutions to social and emotional situations and because creativity represents the ability to formulate novel and divergent ideas, then these constructs capture different cognitive abilities that do not relate (Ivcevic, Brackett, & Mayer, 2007; Zenasni & Lubart, 2009). We propose this conclusion is premature. This is because its logic is rooted in cognitive explanations of the link between EI and creativity (e.g., differences in processing information) and largely overlooks the affective mechanisms linking these constructs. Given that a substantial amount of research focuses on affect (e.g., positive affect) as an antecedent to creativity (Amabile, Barsade, Mueller, & Staw, 2005; Baas, De Dreu, & Nijstad, 2008) and that EI directly pertains to the management and use of emotions (Mayer et al., 2008), we would expect EI to relate to creativity through its effects on affect. Yet, theory that specifies the affective-based role EI plays in shaping employee creativity is lacking. To address this theoretical and practical gap, we draw upon affective information processing (AIP) theory (Gohm & Clore,

This article was published Online First December 22, 2014. Michael R. Parke, Myeong-Gu Seo, and Elad N. Sherf, Robert H. Smith School of Business, University of Maryland. We thank Dr. Subra Tangirala and Dr. Crystal Farh for their helpful feedback and suggestions during the development of this article. We also give special thanks to Dr. Paul Tesluk and Dr. Sirkwoo Jin for their assistance and collaboration in collecting the data. Correspondence concerning this article should be addressed to Michael R. Parke, Smith School of Business, University of Maryland, Van Munching Hall, College Park, Maryland, 20742. E-mail: mparke@rhsmith .umd.edu

1 Because our theory does not depend on differences in affective constructs, we use the words affect, emotions, and mood interchangeably. Although these are all related, typically affect is used as an umbrella term capturing emotions and moods, emotions are more short-lived states that are tied to particular events, and moods are more prolonged and diffused states (Barsade & Gibson, 2007; Brief & Weiss, 2002). 2 In our theory, we focus on ability-based emotional intelligence, which represents a theoretically and empirically valid conceptualization of emotional intelligence, as opposed to mixed-models or trait approaches that combine characteristics of emotional abilities along with trait dispositions (Côté, 2014; Joseph et al., 2014; Mayer et al., 2008).

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2000, 2002). AIP theory accounts for the full range of emotional experiences and proposes that individual differences exist in (a) how people process and respond to emotion-eliciting events (e.g., work requirements) and (b) how people use emotions as information to enhance decision making and thinking (Gohm & Clore, 2000, 2002). Using AIP as our theoretical guide, and in alignment with EI scholars’ advocacy of a facet approach (Elfenbein, 2007; Farh et al., 2012; Kluemper et al., 2013), we propose that EI should not relate to employee creativity either directly or as the overall gestalt of interrelated emotional abilities, but through the moderating roles of two specific EI abilities (i.e., facets), namely, the regulation and use of affect (see Figure 1). First, we propose that EI enables employees to respond more positively to work requirements through emotion regulation ability—the ability to manage emotions of the self and others (Mayer et al., 2008). This helps employees to sustain higher levels of positive affect (PA), or “a state of high energy, full concentration, and pleasurable engagement” (Watson, Clark, & Tellegen, 1988, p. 1063), which likely impacts their creativity. We focus on PA as the key emotional mechanism because it not only has been identified as a likely antecedent to creativity across many studies and contexts (Baas et al., 2008; Hennessey & Amabile, 2010), but it is also a highly beneficial and desired employee state for organizations (Quinn, Spreitzer, & Lam, 2012). As for work characteristics, we focus on the dual-mode of knowledge processing requirements (Morgeson & Humphrey, 2006). These are creative processing requirements, or the extent to which a job incumbent is expected to engage in the creative process or produce creative outcomes (Morgeson & Humphrey, 2006; Shalley, Gilson, & Blum, 2000) and information processing requirements – “the degree to which a job requires attending to and processing data or other information” (Morgeson & Humphrey, 2006, p. 1323). Often referred to as exploration and exploitation in strategic research (March, 1991; Mom, Van Den Bosch, & Volberda, 2007), these work characteristics reflect the knowledge processing that is core to creativity (Amabile, 1996): creative processing involves search, play, exper-

imentation, and generation (exploration), while information processing encompasses monitoring, integration, and selection of information (exploitation) in order to solve work problems and issues. Integrating AIP theory with these key cognitive processing requirements of work (Humphrey, Nahrgang, & Morgeson, 2007; Shalley et al., 2000), we propose that information processing requirements can create demands that generate fatigue and lower PA for employees who lack emotion regulation ability. In contrast, we suggest that jobs with low creative processing requirements can foster a more boring and less stimulating environment that can lead to lower PA for employees who lack emotion regulation ability. For each knowledge processing requirement, emotion regulation ability enables employees to respond more positively by using effective affect management strategies (Côté, 2014; Gross & John, 2003; Mayer et al., 2008) in order to maintain higher PA, which may ultimately enhance their creativity. Second, we propose that EI enables employees to more effectively use their PA for creativity through emotion facilitation ability—the ability to use affective states to enhance thinking and decision making (Mayer et al., 2008). Employees with higher emotion facilitation know how to capitalize on the functional effects of PA such as engaging in brainstorming when in an enthusiastic or excited mood. As a result, the relationship between PA and creativity is likely positive and stronger when employees have higher levels of emotion facilitation ability. By investigating EI’s role in maintaining and using PA for employee creativity, our study makes several theoretical contributions to knowledge about EI, creativity, work design, and AIP. As our primary contribution, we elucidate the affective mechanisms and contextual factors that explain how, why, and when the two EI facets of emotion regulation and emotion facilitation impact employee creativity. Second, we explain how emotion regulation ability enables employees to generate and maintain PA under unique knowledge processing requirements. This not only extends research on emotion regulation ability, which has primarily focused on the role of emotion regulation in jobs with emotional

Employee Emotional Intelligence Emotion Regulation

Emotion Facilitation

Information Processing Requirements Employee Positive Affect Creative Processing Requirements

Figure 1. Theoretical model.

Employee Creativity


EMOTIONAL INTELLIGENCE & CREATIVITY

labor demands (Joseph & Newman, 2010; Kluemper et al., 2013), but it also nuances research on work design by elucidating the differential and affective-based effects of information processing and creative processing requirements on employee PA, directly, and on creativity, indirectly. Third, we nuance the relationship between PA and creativity, suggesting that emotional capabilities should be considered when studying affect as an antecedent to creativity—a perspective that has largely been overlooked in existing research (Hennessey & Amabile, 2010). Finally, by showing how two EI dimensions play unique roles in different stages of affective information processing, we extend research on AIP and EI which mostly focuses on one stage of the emotional experience (Elfenbein & Ambady, 2002; Farh et al., 2012; Feldman, 1995; Gohm & Clore, 2002). We begin with a brief overview of AIP theory and how it guides our theoretical model. Following, we present the unique challenges posed by information processing and low creative processing requirements on employee’s PA. We then discuss how emotion regulation ability can help employees maintain higher PA in these environments. Finally, we propose how emotion facilitation ability can enable employees to utilize PA to enhance their creativity.

Theory and Hypotheses Affective Information Processing Theory AIP theory (Gohm & Clore, 2000, 2002) establishes that individual differences exist in emotional processing. Specifically, the theory proposes that each stage of emotional experience—from the emotion-eliciting stimuli, to individuals’ emotional reactions, to emotional influences on behavior—is (a) influenced by individual differences, and (b) functional or dysfunctional results occur depending on these individual differences. For example, in studying how affect influences decision making, AIP research shows that whether or not individuals make effective decisions depends on how they experience and regulate their emotions (e.g., Do people experience intense emotions?) and how they consider and use emotional information (e.g., What do people do with their emotions?; Seo & Barrett, 2007). AIP theory suggests that employees with higher abilities in regulating their emotions should have more conducive affect at work and those who utilize emotional information should be better decision makers and thinkers. The EI facets of emotion regulation and emotion facilitation reflect these abilities, respectively. Extrapolating these arguments to our current study, we propose that the knowledge requirements of information processing and creative processing will affect employees’ PA depending on their emotion regulation ability. Furthermore, we suggest that employees’ PA will enhance creativity depending on their emotion facilitation ability.

Emotion Regulation: Maintaining PA in the Face of Knowledge Requirements Information processing requirements and emotion regulation ability. Information processing requirements represent the extent to which employees must focus on and manage information to accomplish their work (Humphrey et al., 2007). Jobs with high information processing demands require employees to maintain

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focused concentration in attending to, accumulating, and integrating large amounts of data, information, and knowledge to solve problems (Humphrey et al., 2007; Wood, 1986). In addition, this cognitive demand necessitates that employees (a) continuously incorporate new information into existing knowledge structures and (b) use multiple informational cues in making uncertain judgments (Wood, 1986). Although information processing requirements can have positive effects on motivation and overall job satisfaction (e.g., Humphrey et al., 2007), the demands that this knowledge characteristic poses on employees can also have emotional and well-being costs (Martin & Wall, 1989; Wall, Jackson, & Mullarkey, 1995). This is especially true for knowledge workers and managers (the focus of our study) which, of the entire population of occupations, are at the moderate to high range of information processing requirements (Johns, 2006; Morgeson & Humphrey, 2006). We propose that information processing requirements are negatively associated with PA of knowledge workers because the high cognitive processing demands likely leave employees feeling fatigued (i.e., low PA). Research shows that prolonged and sustained cognitive processing—which are characteristic of jobs with high information processing requirements— consistently lead to feelings of fatigue (for a summary, see Ackerman & Kanfer, 2009). Related, high information processing requirements necessitate more decision making and exploiting of knowledge to solve problems, which may also leads employees to experience lower PA as past research shows that decision fatigue often results from continuously making choices (Vohs et al., 2008). Finally, indirect empirical evidence shows that challenge demands, a broader and related construct to information processing requirements, can lead to fatigue, lower engagement, or exhaustion (LePine, LePine, & Jackson, 2004; Sonnentag, Binnewies, & Mojza, 2010; Sonnentag & Zijlstra, 2006)—all of which reflect low levels of PA. Hypothesis 1: Information processing requirements negatively relate to knowledge workers’ PA. However, as AIP theory states, not all individuals affectively respond to demands in the same way. We propose that whether or not knowledge workers maintain higher levels of PA in the face of information processing requirements depends on their emotion regulation ability. Emotion regulation refers to the thoughts or behaviors individuals enact to influence “which emotions they have, when they have them, and how they experience and express these emotions” (Gross, 1998a, p. 275). Emotion regulation as an ability refers to the knowledge and appropriate use of effective strategies to control and alter emotion experiences and expressions in the self and others (Côté, 2014). Many of these strategies were first identified in the stress and coping literature (e.g., Folkman, Lazarus, Dunkel-Schetter, DeLongis, & Gruen, 1986) and subsequent research on emotion regulation further specified different types of emotion regulation strategies and how they are used to respond to a wide variety of emotions besides stress (Folkman & Moskowitz, 2004; Gross, 1998b). EI and AIP theory extended this stream of knowledge by suggesting that individuals differ in their ability to select and implement optimal or effective regulation strategies when faced with emotional problems or issues (Côté, 2014; Salovey, Bedell, Detweiler, & Mayer, 1999).


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In general, emotion regulation strategies vary in their effectiveness (Webb, Miles, & Sheeran, 2012). Effective in this sense means achieving the desired emotional state, which typically refers to a neutral experience or PA (Gross & John, 2003; Gross & Thompson, 2007). The more effective strategies often occur prior to the full-blown emotional responses (to use a metaphor, this is when the water is heating up before it boils) in which individuals change their situation or cognitively change the meaning of the situation in order to alter their emotional state (Gross, 1998a; Gross & Thompson, 2007). For example, when individuals use cognitive change to regulate emotions (i.e., cognitive reappraisal, construal, or reframing)— changing the appraisal of a situation in order to “alter its emotional significance, either by changing how one thinks about the situation or about one’s capacity to manage the demands it poses” (Gross & Thompson, 2007, p. 20)—this usually leads to the experience of more PA (Gross, 2013). In contrast, when individuals use suppression to regulate emotions— hiding or holding back emotion expression—this usually leads to less PA (Gross, 2013). In addition, effectiveness in managing emotions is largely achieved when the implemented strategy aligns with the specific situation requirements (Barrett & Gross, 2001; Côté, 2014). For instance, situation change, which involves situation selection, or “taking actions that make it more (or less) likely that one will end up in a situation one expects will give rise to desirable (or undesirable) emotions” (Gross & Thompson, 2007, p. 14), and situation modification, or directly changing “the external features of a situation in a way that will alter one’s emotional response to that situation” (Gross, Sheppes, & Urry, 2011, p. 768) are generally effective, but not in every situation. For example, a study examining college students use of situation change strategies before and after an exam shows that these strategies helped students increase their PA prior to the exam (hope and eagerness), but had no effect on their PA after the exam (Folkman & Lazarus, 1985). Based on this research, and consistent with AIP theory, we propose that knowledge workers with high emotion regulation ability use effective emotion regulation strategies as well as appropriately select strategies given the situational constraints to manage the demands associated with information processing requirements, which likely results in higher levels of PA. In contrast, employees with low emotion regulation ability are more likely to ineffectively handle these demands (i.e., not engage in emotional regulation activities or use less effective strategies), therefore resulting in lower levels of PA. In other words, the proposed negative effect of information processing requirements on PA likely only occurs for employees with low emotion regulation ability. We support these claims with the following rationale. First, because individuals with high emotion regulation ability know and use more effective strategies to manage their emotions (Côté, 2014), they will likely counteract the emotional costs associated with demands from information processing requirements to maintain higher PA. We propose that they achieve this using the emotion regulation strategies referenced above. For example, using situation change in jobs with high information processing requirements, knowledge workers with high emotion regulation ability might structure their work days in a way that is conducive to maintaining higher PA (or avoiding drops in PA). These employees might take advantage of within-day work breaks to re-

cover and sustain higher levels of PA and avoid fatigue (Trougakos, Beal, Green, & Weiss, 2008; Trougakos & Hideg, 2009). Second, because it is likely that individuals high on emotion regulation ability use cognitive reappraisal more often (McRae, Jacobs, Ray, John, & Gross, 2012; Thory, 2013), they may draw upon this strategy when faced with high information processing requirements in order to appraise them in a more positive fashion. For example, an employee could frame this demanding situation as an opportunity to challenge him or herself to grow and to prove he or she can handle the demands, which would result in maintaining higher levels of PA. Not only do laboratory studies show that such cognitive reframing of demanding activities can help maintain or increase PA for participants (McRae, Ciesielski, & Gross, 2012; Miu & Cris¸an, 2011), but initial field research suggests that these types of strategies are actually used by employees at work (Diefendorff, Richard, & Yang, 2008; Thory, 2013). Finally, given that employees with high emotion regulation ability are more likely to choose and implement appropriate emotion regulation strategies given the situational constraints (Côté, 2014), this should also help them maintain higher PA when faced with different instances of information processing requirements. In contrast, we propose that knowledge workers with low emotion regulation ability are unlikely to use effective emotion regulation strategies or will choose inappropriate strategies given the situation to cope with the demands posed by information processing requirements, thereby resulting in lower levels of PA. This leads to the following hypothesis: Hypothesis 2: Emotion regulation ability moderates the relationship between information processing requirements and PA such that the relationship is negative and stronger when employees have low levels of emotion regulation ability. Creative processing requirements and emotion regulation ability. The complementary knowledge requirement to information processing is creative processing requirements, which refers to how much the job requires employees to engage in the creative process or produce creative outcomes (Shalley et al., 2000). Jobs with high creative processing requirements are associated with increased exploring, experimenting, and generating ideas by employees in order to solve problems (Amabile, Conti, Coon, Lazenby, & Herron, 1996). Because creative processing requirements establish explicit instructions (Amabile et al., 1996) or creative goals (Gilson & Shalley, 2004), employees are more likely to engage in these creative behaviors. Research shows that this type of problem solving is inherently enjoyable as exploring possibilities and generating ideas more likely leads to the experience of enthusiasm, excitement, and/or pleasurable engagement (i.e., higher PA; Amabile et al., 2005; Csikszentmihalyi, 1996). In contrast, jobs with low creative processing requirements put less emphasis (or no emphasis) on exploring, experimenting, and generating new ideas. Because of this, employees are less likely to engage in creative problem solving and experience the enthusiasm or excitement as a byproduct. Instead, these types of environments may evoke boredom or lack of interest (i.e., low PA). Therefore, we propose that creative processing requirements lead to higher PA among knowledge workers. Hypothesis 3: Creative processing requirements positively relate to knowledge workers’ PA.



Given that creative processing requirements likely increase employee PA, it is the lack of creative processing that potentially poses a threat to employees’ positive emotions and moods. In the face of such environments, we again expect employees’ emotion regulation ability to play an important role in maintaining higher levels of PA. Consistent with AIP theory, we propose that the negative effects of low creative processing requirements on knowledge workers’ PA depend on individual differences in emotion regulation ability. In the face of low creative processing, employees with high emotion regulation ability likely regulate their responses to the boredom or lack of interest that this knowledge characteristic can elicit by using effective emotion regulation strategies. For example, these employees might utilize situation selection by incorporating more creative behaviors (e.g., exploration and experimentation) in their tasks not formally prescribed by their role, which leads to higher levels of PA. Research suggests that when individuals face boring or uninteresting tasks and activities, they often use strategies to change their situation to make it more interesting if they have the requisite abilities to do so (Sansone, Thoman, & Smith, 2010). For example, research found that individuals may add variety or alter the procedures to a boring task when such a strategy was relevant and available to them (Sansone, Weir, Harpster, & Morgan, 1992). In addition, Diefendorff et al. (2008, p. 506) found that individuals use emotion regulation strategies such as keeping “myself busy working on other things” and doing “something enjoyable to improve my mood” when facing boredom. Given that emotion regulation represents a key ability to using such effective strategies to alter emotions, employees high (low) on this ability will likely (likely not) utilize these strategies (e.g., situation selection) to alter their more boring tasks in order to maintain interest and PA. In addition, knowledge workers with high emotion regulation ability could cognitively construe their work differently to make it more intrinsically motivating (e.g., “why is this important?”), which also leads to higher levels of PA (Beal & Ghandour, 2011; Grant & Berry, 2011). Research shows that such reframing or redefining of tasks can maintain interest and PA when the requirements of the work do not naturally facilitate it (Sansone et al., 2010). Finally, employees with higher emotion regulation ability likely choose and implement appropriate emotion regulation strategies that fit the different instances of low creative processing requirements (Côté, 2014), which leads to higher levels of PA. In contrast, employees low on emotion regulation ability are less likely to draw upon these strategies or use inappropriate strategies given the situation, which, in the case of low creative processing requirements, could result in lower levels of PA. Thus, in jobs with abundant creative processing requirements, employees may naturally experience a high level of PA and their EI becomes less important. However, when jobs lack such requirements, employees’ emotion regulation ability might be essential for maintaining PA. Therefore, we propose: Hypothesis 4: Emotion regulation ability moderates the relationship between creative processing requirements and PA such that the relationship is positive and stronger when employees have low levels of emotion regulation ability. Up until this point, we have used AIP theory to explain the first stage of emotional experience, proposing that emotion regulation

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ability is the critical individual difference to how employees maintain higher levels of PA in jobs with high information processing and low creative processing requirements. Next, we turn to the later stage of emotional experience and AIP predictions to explain how the EI facet of emotion facilitation ability enables employees to use their PA for creativity.

Emotion Facilitation: Using PA for Creativity A significant body of research has investigated PA as an antecedent to creativity (Baas et al., 2008; Hennessey & Amabile, 2010). This research suggests two theoretical mechanisms responsible for the link between PA and creativity. The first is cognitive in which PA increases cognitive flexibility by broadening and expanding the scope of attention and cognitive elements, which enables the variation and connections necessary for creative insight (Amabile et al., 2005). This argument is heavily supported by empirical evidence (Amabile et al., 2005; Baas et al., 2008; Isen, Daubman, & Nowicki, 1987; Isen & Daubman, 1984; Isen, Johnson, Mertz, & Robinson, 1985). The second mechanism is motivational, referring to how long employees persist in developing their creative ideas. Some scholars suggest that PA not only motivates employees to approach their tasks (e.g., take on the challenge of creativity), but it also “enhances psychological engagement and builds energy for sustaining effort” (Grant & Berry, 2011, p. 74). In other words, PA motivates and sustains persistence in coming up with and developing new and useful ideas (Fredrickson, 1998; Seo, Barrett, & Bartunek, 2004; Shalley, Zhou, & Oldham, 2004). In contrast, other scholars (George & Zhou, 2002, 2007; George, 2007) argue that PA may in fact reduce persistence of developing solutions to problems. They draw upon mood-as-information theory (Martin, Ward, Achee, & Wyer, 1993; Schwarz, 1990) to suggest that PA, “which signals that everything is going well, might not always propel people to put forth high levels of effort to find new and better ways of doing things” (George & Zhou, 2002, p. 687). In other words, employees interpret their PA as feedback that no further development is needed for their work. The above discussion on the motivational mechanism suggests that there are competing affectiveinformation processes of PA, which can have functionally opposite effects on creativity. We propose that the ultimate motivational effect of PA on creativity depends on how the affectiveinformation processes are handled by employees. Given the positive cognitive and motivational effects of PA on creativity, we propose that employees who are better able to take advantage of their PA will likely exhibit higher levels of creativity. To make this claim, we draw upon the AIP perspective to propose that emotion facilitation ability enables knowledge workers to use their PA to enhance their creativity. As mentioned previously, AIP theory suggests that differences in outcomes of emotional experiences, such as decision making and effort, are due to differences in how individuals use their emotions (Gohm & Clore, 2000, 2002). Emotion facilitation— defined as the ability to use affect to enhance decision making and thinking— captures these differences (Mayer et al., 2008). It encompasses such abilities as allowing emotions to direct attention and effort (Mayer et al., 1999, 2008), identifying feelings and their effects during decision making (Seo & Barrett, 2007), or knowing which emotional states are conducive for specific tasks (Mayer et al., 2008). Thus, we propose that


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high levels of emotion facilitation ability can enable employees to utilize their PA for creativity, and as a result, PA is associated with higher creative performance for these employees. In contrast, we propose that employees low on emotion facilitation ability are less able to capitalize on their PA for creativity, which likely results in lower creative performance. Our reasons for these claims are twofold. First, in terms of the cognitive mechanism, we propose that individuals with high emotion facilitation ability might engage in strategies that facilitate or strengthen the positive cognitive effects of PA on creativity. Because PA is cognitively conducive for creativity (Amabile et al., 2005; Baas et al., 2008), we suggest that when individuals with high emotion facilitation ability experience PA, they will likely know to focus their efforts on the more creative aspects of their work. For example, a scholar with high emotion facilitation ability who is excited or enthusiastic might choose to work on brainstorming for a new article as opposed to cleaning a dataset. Related, employees with high emotion facilitation ability might recognize the moments of PA as ideal to engage in creative action. Thus, given the natural fluctuations of PA over the course of a day (Weiss, Nicholas, & Daus, 1999), employees with high facilitation ability may synchronize their creative activities to their emotional rhythms. Finally, because individuals with high emotion facilitation know how to include or exclude emotions in thought when solving problems (Mayer et al., 2008), these employees could allow themselves to be enthused and excited when developing ideas instead of letting their PA dwindle. In contrast, individuals low on emotion facilitation ability are unlikely to realize how their PA could enhance their effectiveness at developing creative ideas, and therefore, they are less likely to engage in strategies that enable them to capitalize on PA’s functional effects. Second, in terms of the motivational mechanism, we propose that employees with high emotion facilitation ability correctly use their PA (Gohm & Clore, 2000, 2002). As mentioned earlier, employees with high facilitation ability use emotions to direct attention and effort (Mayer et al., 1999, 2008). Thus, they should use their PA to persist in developing their creativity. In other words, when experiencing PA, employees with high emotion facilitation will not abandon their efforts toward generating solutions to organizational problems, but instead, use their mood to sustain and continue to develop their ideas (Grant & Berry, 2011). In contrast, employees who misattribute their PA as a signal that “all is well and no further development is needed” are likely employees with low emotion facilitation ability because they fail to use PA to enhance their problem-solving or thinking (Gohm & Clore, 2000). In sum, we expect a stronger positive relationship between PA and creativity at higher levels of emotion facilitation. As for the relationship between PA and creativity for employees with low emotion facilitation ability, this is less clear. This is because we lack theory and empirical evidence to claim which mechanism of the PA-creativity relationship— cognitive or motivational—is more important. Thus, we formally hypothesize for high levels of emotion facilitation ability, but approach the PA-creativity relationship at low levels of emotion facilitation ability in a more exploratory fashion. Hypothesis 5: Emotion facilitation ability moderates the relationship between PA and creativity such that the relationship

is positive and stronger when employees have high levels of emotion facilitation ability.

Method Sample and Procedure To test our model, we recruited 177 early career professionals from a part-time Master of Business Administration program of a large university in the Mid-Atlantic United States. The study was part of a 360 leadership development assignment in which participants received detailed reports on various measures they and their supervisors completed. The sample represented full-time knowledge workers from a variety of industries and roles, which enabled us to generalize our findings about the relationships in our model to a range of organizational settings. Industries most commonly represented in the final sample were financial services (20.9%), consulting (16.3%), defense contractor (14.0%), and high tech (9.3%). We obtained data for this study in two phases of collection. In the first phase we used an experience sampling procedure (Barrett & Barrett, 2001) to assess participants’ affective experience over time. We elected to measure PA this way because experience sampling provides a more robust and precise measure of average emotional experiences at work rather than asking participants to report their general level of affect (Fisher, 2000; Weiss et al., 1999). For 25 work days, participants received an e-mail randomly sent during the hours of 9:00 a.m. and 3:00 p.m. directing them to an online survey. This survey inquired about their current emotional experiences (see details below under Measures section). In the second phase (approximately 15–18 days after the experience sampling procedure began), participants completed an EI test and a survey that contained measures that served as control variables. Finally, in this same phase, participants solicited at least one supervisor (a total of 264 supervisors) to complete a questionnaire regarding their creativity. The questionnaire emphasized that feedback will be used for developmental purposes, which has been found to be of higher quality than feedback used for salary and bonus allocation purposes (Greguras, Robie, Schleicher, & Goff, 2003). For our analysis, we retained focal participants who: (a) completed the EI test, (b) had ratings from at least one immediate supervisor that was highly familiar with the focal participant’s work, and (c) replied to the daily affect survey at least 12 days of the 25 sampled. The last criterion was used to ensure we received an adequate representation of affective experience. Twelve days is consistent with the length of time that scholars recommend using experience sampling methods to capture a generalizable representation of employees’ work experiences (e.g., work affect; Ilies, Wilson, & Wagner, 2009; Scott, Barnes, & Wagner, 2012). Our final sample consisted of 129 focal participants (response rate ⫽ 73%) and 172 matched supervisors (mean number of supervisors per participants was 1.45, SD ⫽ .59). Average age was 28.71 years (SD ⫽ 4.64), and the sample was 70.5% male and 58.1% Caucasian. To check for possible nonresponse biases, we tested for differences between respondents and nonrespondents on demographic (age, gender, and ethnicity), tenure, and job characteristics data. Analysis indicated no significant differences between groups on any of these variables. Given these results and our relatively


high response rate, we had no reason to believe that nonrespondents differed from respondents in a systematic manner.


Measures Emotion regulation and emotion facilitation. We assessed emotion regulation ability and emotion facilitation ability using the Mayer–Salovey–Caruso Emotional Intelligence Test (MSCEIT) Version 2.0 (Mayer, Salovey, & Caruso, 2002), a 141-item computer-based test composed of tasks assessing emotional abilities, which provides scores for overall EI and each of the four facets. The MSCEIT is one of the most widely used and validated tools to assess overall EI and its facets (Mayer et al., 2008). Not only is it consistently reliable (test-retest and internal consistency), but it also shows strong evidence for discriminant and criterion validity (Brackett & Mayer, 2003; Brackett, Rivers, Shiffman, Lerner, & Salovey, 2006; Côté & Miners, 2006; Mayer, Salovey, Caruso, & Sitarenios, 2003). Furthermore, the MSCEIT shows relatively strong factor structure in which its task items map onto their respective theoretical constructs (Mayer et al., 2008). This is important given our focus on specific EI facets. Overall EI and subdimension scores ranged from 0 to 180 and were derived from a copyrighted scoring system provided by the test publisher. Given that the test creators strongly advise against using Cronbach’s alpha due to the heterogeneity of task items that measure facet scores, we followed their recommendations (Mayer et al., 2003) to calculate split-half reliabilities for internal consistencies of emotion regulation and emotion facilitation: .59 and .74, respectively.3 Although the emotion regulation internal consistency was slightly low, it falls within range of reported reliabilities (Côté, Decelles, McCarthy, Van Kleef, & Hideg, 2011; Côté, Lopes, Salovey, & Miners, 2010; Farh et al., 2012). Information processing and creative processing requirements. To generate a score for information processing and creative processing, we used the Occupational Information Network (OⴱNET) database (Peterson, Mumford, Borman, Jeanneret, & Fleishman, 1999). The OⴱNET was created by the U.S. Department of Labor in order to (a) replace the outdated Dictionary of Occupational Titles and (b) systematically organize all occupations in the U.S. to enable cross-job comparisons on the knowledge, skills, tasks, and job characteristics. OⴱNET is a highly recommended source of data (Morgeson & Dierdoff, 2011) and is increasingly being used in organizational studies (Côté & Miners, 2006; Glomb, Kammeyer-Mueller, & Rotundo, 2004; Tierney & Farmer, 2011). In addition to the richness of its data, we used OⴱNET variables rather than employee perceptions for two reasons. First, more objective measures capture both the direct effects (e.g., automatic and implicit processing; Barrett & Gross, 2001; LeDoux, 1995) and indirect effects (mediated by appraisals of the environment; Roseman, Spindel, & Jose, 1990) on employees’ affect. In contrast, perceptual measures of the work environment would largely limit the contextual effects on emotions to those that are mediated through perceptions and appraisals. Second, perceptual measures might confound with employees’ EI to handle the two types of contexts we are investigating. For example, Farh et al. (2012) found that EI is negatively related to perceptions of managerial work demands, r ⫽ ⫺.21, p ⬍ .01 and Dong, Seo, and Bartol (2014) found that EI is negatively related to perceptions of devel-

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opmental challenges, r ⫽ ⫺.17, p ⬍ .05. Our theory proposes that employees with high emotion regulation ability modify their situations or the meaning of their situations to maintain higher PA. If, for example, an employee in a job with higher information processing requirements constantly reframes this situation as an opportunity to grow, this employee is likely to perceive these challenge demands as lower than an employee who does not reframe them and constantly feels fatigued or burned out. Thus, using a more objective measure (e.g., OⴱNET) can provide a cleaner test of our theory. Participants reported their job titles in our survey. Two coders then independently matched occupation titles from our sample with OⴱNET job titles. The original coding matched for 92% of the sample, and the discrepancies were resolved for the final 8%. All OⴱNET variables range from 0 to 100 with higher scores indicating the more important or more characteristic the specific dimension is of the particular job.4 We operationalized information processing requirements using the OⴱNET Conventional variable, which is derived from Holland’s classification of job environments (Holland, 1997). Of Holland’s six classifications of work environments, the conventional dimension represents the extent of routineness and standardization of information processing. Specifically, conventional occupations are characterized by orderliness, routine, conformity, and working to meet predictable demands or specified standards (Gottfredson & Richards, 1999). We reversed the measure so that a high score refers to high information processing. From our sample, example jobs with high information processing requirements include industrial-organizational psychologists, microbiologists, and electronics engineers. Jobs with low information processing include administrative assistants, treasurers, and controllers. For creative processing requirements, we used the OⴱNET variable: “Job requires creativity and alternative thinking to develop new ideas for and answers to work-related problems.” Advertising managers, manufacturing engineers, and industrial engineers were at the high end of creative processing, while jobs with low creative processing were credit analysts, computer user support specialists, and pharmacy technicians. PA. Drawing on prior conceptualizations of the core affective structure by Russell and Barrett (1999), we selected the two prototypical PA items: “excited” and “enthusiastic.” For 25 workdays, an e-mail was randomly sent to participants during work 3 For comparison, we also calculated Cronbach’s alpha. These were approximately the same as the split-half reliabilities: emotion regulation ⫽ .57 and emotion facilitation ⫽ .67. 4 To further validate our OⴱNET measures, we collected additional data on Amazon Mechanical Turk to ensure that the more objective measures of the work context related to employee perceptions of these variables. Two-hundred working participants self-reported the level of information processing (Wall et al., 1995) and creative processing (Gilson & Shalley, 2004) of their jobs along with their job title. Using the same matching procedure as described above, we obtained participants’ OⴱNET scores of information processing and creativity processing requirement. These significantly correlated with the respective self-report measures of information processing (r ⫽ .15, p ⬍ .05) and creative processing (r ⫽ .27, p ⬍ .05). Both of these fall within the range of reported correlations between objective and subjective measures of the work context found in past studies, which range from .08 to .40 (Gerhart, 1988; Judge et al., 2000; Spector & Jex, 1991).


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hours directing them to an online survey. Participants were instructed to respond to the survey within 2 hours of the e-mail. Daily responses that occurred outside of this 2-hr window were discarded. Participants used a 5-point scale (1 ⫽ not at all to 5 ⫽ extremely so) to indicate to what extent each of the words described their current emotional experience (“How are you feeling right now?”). We aggregated experience-level data to the person level to calculate an average PA (i.e., on average, how much a person experienced PA at work). This approach aligns with previous research measuring workplace mood that investigated between-person relationships (Dong et al., 2014; Weiss et al., 1999). Cronbach’s alpha was .78. Creativity. Creativity was assessed by employees’ supervisor(s) using a 13-item creative performance measure (Zhou & George, 2001) on a five-point scale (1 ⫽ strongly disagree to 5 ⫽ strongly agree). A sample item was: “This person comes up with new and practical ideas to improve performance.” Cronbach’s alpha was .96. For participants who had more than one supervisor rating, there was a strong level of agreement between supervisors regarding levels of creativity (average rwg ⫽ .92), with 93% of estimates above .80 and an ICC(2) value of .96. This provided sufficient justification for aggregation (LeBreton & Senter, 2008). Controls. Because our focus is on specifying EI’s role in shaping employee creativity, we needed to control for possible confounds or alternative explanations. First, consistent with past research on creativity (Bledow, Rosing, & Frese, 2013; Grant & Berry, 2011; Shalley et al., 2000), we controlled for gender and job tenure. Strong gender differences exist in EI ability (Joseph & Newman, 2010) and emotional experiences (e.g., Fujita, Diener, & Sandvik, 1991; Scott & Barnes, 2011), and other research shows potential gender differences in creativity (Hennessey & Amabile, 2010). We also controlled for job tenure because “research shows that extant experience in a particular field is necessary for creative success” (Tierney & Farmer, 2002, p. 1138). Second, of the Big Five personality traits, openness to experience is the trait most linked to creativity (Shalley et al., 2004) and EI (Mayer et al., 2008). We controlled for this personality trait using Goldberg’s (1999) scale (Cronbach’s alpha was .79). Third, an alternative explanation for emotion regulation abilities helping employees maintain higher PA is that instead, employees differ in their tendency to experience PA (i.e., experiencing PA is part of employees’ personality). Extraversion represents the “basic dimensions of emotional temperament that broadly reflect individual differences in the propensity to experience . . . positive affect” (Watson & Clark, 1992, p. 446) and past research finds a strong and positive relationship between the two. Thus, we controlled for extraversion using the same Goldberg (1999) scale to rule out this possibility (Cronbach’s alpha was .88). Fourth, to show that the predicted effects of emotion regulation on employee PA and the effects of PA on creativity occur over and above the effects of negative affective states, which can be related to these three variables (Barrett & Russell, 1998; George & Zhou, 2002; Mayer et al., 2008), we controlled for average experience of negative affective states using five items (angry, nervous, tired, unhappy, depressed, disappointed; Cronbach’s alpha was .76). Finally, given our focus on knowledge processing requirements, we wanted to control for other key contextual variables that might influence PA and creativity. We controlled for leader-member exchange (LMX; Liden & Maslyn, 1998; Cronbach’s alpha was .90) and support for

creativity (Madjar, Oldham, & Pratt, 2002; Cronbach’s alpha was .80), which have both been linked to PA and creativity in past research (Epitropaki & Martin, 2005; Gerstner & Day, 1997; Liao, Liu, & Loi, 2010; Madjar et al., 2002; Tierney, Farmer, & Graen, 1999).

Analytical Strategy We analyzed the data in a single-level5 regression path analysis using a maximum likelihood estimator in Mplus 7.0 (Muthén & Muthén, 2010). This approach provided the means to simultaneously examine the individual relationships in our model as well as integrative tests of indirect and moderated effects using a bootstrapping methodology (Edwards & Lambert, 2007). To test our moderation hypotheses, we followed recommendations by Aiken and West (1991) for both the ordering of the models and conducting simple slope tests to facilitate interpretation of our results. All independent variables (including controls) were mean centered.

Results Descriptive statistics, internal reliability coefficients, and correlations among the variables used in this study are displayed in Table 1.

Regression Analysis Table 2 presents the results from our regression analysis. We first regressed PA on all of our control variables (Table 2, Model 1). Next, we entered the two independent variables (Table 2, Model 2). As expected, both information and creative processing requirements were significantly related to PA in the hypothesized direction. That is, information processing negatively related to employee PA (␤ ⫽ ⫺.22, p ⫽ .03), whereas creative processing positively related to employee PA (␤ ⫽ .27, p ⫽ .01). Thus, Hypotheses 1 and 3 were supported. Hypothesis 2 stated that emotion regulation ability moderates the relationship between information processing requirements and PA such that the relationship is negative and stronger when employees have low levels of emotion regulation ability. Results indicated that information processing requirements significantly interacted with emotion regulation ability to predict PA (␤ ⫽ .28, p ⫽ .001, Table 2, Model 3). Simple slopes demonstrated that information processing requirements had a strong negative effect on PA at low levels (␤ ⫽ ⫺.54, p ⬍ .001, ⫺1 SD) but did not relate at high levels (␤ ⫽ .10, ns, ⫹1 SD) of emotion regulation 5 Given that individuals were clustered into job classifications and that information and creative processing variables contain no within job variance (e.g., all financial analysts jobs receive the same score for information and creative processing), there is some ambiguity in our study to whether or not the knowledge process effects are at the job or individual level. To investigate this issue, we examined ICCs for the other substantive measures. Results indicated inadequate support for job-based nesting in our study. That is, the ICC(1) values were not distinct from zero for all variables except for PA. Although PA’s ICC(1) reached acceptable levels (.21), the ICC(2) value was very low (.32), indicating low group (job) mean reliability. Based on these results, it is highly likely that the effects in our model are occurring primarily at the individual level and not at the job-level. Consequently, we considered all variables as individual level characteristics.

(.96) (.78) .10 — ⫺.09 ⫺.01 — .59ⴱⴱ .16 ⫺.01 (.59) .14 ⫺.01 .24ⴱⴱ ⫺.03 (.74) .25ⴱⴱ .10 .00 .01 ⫺.02 (.80) .05 .08 .05 ⫺.09 .10 .09 (.90) .50ⴱⴱ .14 .18ⴱ .13 ⫺.01 .17 ⫺.02 (.76) ⫺.19ⴱ ⫺.18ⴱ ⫺.19ⴱ .14 ⫺.06 ⫺.07 .00 .07 Note. N ⫽ 129. Internal consistency reliabilities appear in parentheses along the diagonal. a Dummy coded: 0 ⫽ male, 1 ⫽ female. ⴱ p ⬍ .05. ⴱⴱ p ⬍ .01.

(.88) .36ⴱⴱ ⫺.16 .17 .17 .01 .10 .11 .08 .29ⴱⴱ .04 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13.

Job tenure Gendera Extraversion Openness to experience Negative affective states LMX Support for creativity Emotion facilitation Emotion regulation Creative processing req. Information processing req. Positive affect Creativity

2.22 0.29 3.44 3.78 0.99 4.17 3.66 97.48 95.31 68.42 31.28 2.08 4.07

2.18 0.46 0.73 0.56 0.53 0.70 0.67 11.71 7.45 8.74 19.14 0.87 0.66

— .09 .12 .08 .13 .03 .07 .02 .09 ⫺.05 .03 .02 .07

— ⫺.01 ⫺.12 ⫺.03 .16 .12 .19ⴱ .16 .07 .10 ⫺.12 ⫺.19ⴱ

(.79) ⫺.30ⴱⴱ .06 .06 ⫺.01 ⫺.04 .13 .05 .14 ⫺.08

9 8 7 6 5 4 3 2 1 SD M Variable

Table 1 Means, Standard Deviations, and Correlations Among all Variables


10

11

12

13


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ability. As illustrated in Figure 2, employees with high emotion regulation ability maintained higher PA when information processing requirements increased from low to high, but employees with low levels of emotion regulation ability suffered a loss in PA. Thus, Hypothesis 2 was supported. Hypothesis 4 stated that emotion regulation ability moderates the relationship between creative processing requirements and PA such that the relationship is positive and stronger when employees have low levels of emotion regulation ability. Results indicated that creative processing significantly interacted with emotion regulation ability to predict PA (␤ ⫽ ⫺.28, p ⫽ .001, Table 2, Model 3). Simple slopes demonstrated that creative processing requirements positively related to PA at low levels (␤ ⫽ .58, p ⬍ .001, ⫺1 SD), but did not relate at high levels (␤ ⫽ ⫺.04, ns, ⫹1 SD) of emotion regulation ability. As illustrated in Figure 3, emotion regulation ability helped maintain employees’ PA in low creative processing requirements. When creative processing was high, employees experienced relatively high levels of PA regardless of their level of emotion regulation ability. However, employees with high emotion regulation ability experienced substantially higher PA than low emotion regulation ability employees when creative processing requirements changed from high to low. Hence, Hypothesis 4 was supported.6 Hypothesis 5 stated that emotion facilitation ability will moderate the relationship between PA and creativity such that PA will relate more positively to creativity at high levels of emotion facilitation ability. Before testing this interaction, we ran the main effects model. PA’s main effect was positive, but insignificant (␤ ⫽ .09, ns, Table 2, Model 5). Next, including the interaction term indicated that emotion facilitation ability interacted with PA to significantly predict creativity (␤ ⫽ .24, p ⫽ .005, Table 2, Model 6). Figure 4 represents this interaction. Simple slopes indicated that PA had a strong, positive relationship with creativity at high levels of emotion facilitation ability (␤ ⫽ .34, p ⫽ .02, ⫹1 SD) and a negative, but insignificant, relationship with creativity at low levels of emotion facilitation ability (␤ ⫽ ⫺.24, ns, ⫺1 SD). Thus, the results support Hypothesis 5.

Auxiliary Analysis We performed a set of auxiliary analyses. First, an important assumption we made is that specific facets of EI (emotion regulation and facilitation), instead of the overall level of EI or other facets (emotion perception and understanding), are main drivers of the interaction effects predicted in the study. We ensured the robustness of our facet predictions and results in two ways. First, we tested whether participants’ overall EI scores could substitute for emotion regulation and facilitation ability. The results of the overall EI analysis show that none of these interactions were statistically significant. Second, we included all EI facet interactions together (e.g., Farh et al., 2012) to ensure that emotion regulation ability and emotion facilitation ability mattered most (and over and above the other facets) for PA and creativity, 6 Because the first stage of our model had two interactions terms and because information processing and creative processing requirements were highly correlated, we also tested the first stage moderation hypotheses separately. While the regression weights decreased, both remained significant at p ⬍ .05.


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Job tenure Gendera Extraversion Openness to experience Negative affective states LMX Support for creativity Information processing req. Creative processing req. Emotion regulation Emotion facilitation Info. processing ⴱ Emotion regulation Creative processing ⴱ Emotion regulation Positive affect Positive affect ⴱ Emotion facilitation R2 ⌬R2

Positive affect (Model 1)



Creativity (Model 4)



⫺.01 ⫺.10 .26ⴱⴱ .10 .09 .11 .04

.01 ⫺.12 .24ⴱⴱ .07 .04 .06 .01 ⴚ.22ⴱ .27ⴱⴱ .14 ⫺.02

.00 ⫺.12 .21ⴱ .05 .01 .03 .05 ⫺.22ⴱ .27ⴱⴱ .19ⴱ ⫺.05 .28ⴱⴱ ⴚ.28ⴱⴱ

.08 ⫺.22ⴱ .06 ⫺.14 .04 ⫺.06 .13 .02 .02

.08 ⫺.21ⴱ .05 ⫺.15 .05 ⫺.06 .13 .04 .00 ⫺.05 .03

.08 ⫺.24ⴱⴱ .04 ⫺.15 .06 ⫺.03 .09 .02 .01 .00 .03 .08 ⫺.09 .05 .24ⴱⴱ .14ⴱ .06e

.09 .13ⴱ

.20ⴱⴱ .07b

.29ⴱⴱ .09c

.08

.08 .00d

Note. N ⫽ 129; Standardized regression weights provided; All variables (except dependent variables) were mean centered. a Dummy coded: 0 ⫽ male, 1 ⫽ female. b, c, d, e ⌬R2 values are with respect to the incremental variance over Model 1, Model 2, Model 4, and Model 5, respectively. ⴱ p ⬍ .05. ⴱⴱ p ⬍ .01.

respectively. The first stage results show that only emotion regulation ability significantly interacts with the knowledge requirement variables to predict PA, whereas none of the other facetcontext interactions were significant. As for the second stage prediction, the interaction between emotion facilitation ability and PA is the only facet interaction term that approaches significance (␤ ⫽ .18; p ⫽ .057). This marginally significant result is most likely due to low power given that emotion facilitation and PA interaction term is significant without the inclusion of these additional variables. Third, although our model focused on individual differences in EI influencing the maintenance and use of the average levels of PA, it might also impact the variance of PA. For example, information and creative processing requirements— due to the unpredictability in the former and the variety in the latter (Amabile, 1996; Wood, 1986)—might lead to more fluctuations in PA (i.e.,

higher variance). Emotion regulation might neutralize these effects given that employees with higher emotion regulation have more stability and control over their emotions (Gross & Thompson, 2007; Joseph & Newman, 2010). In addition, some research suggests that fluctuations in emotions (e.g., a change from negative to positive) might matter more for creativity than average levels of PA (Bledow et al., 2013). Therefore, the variance in PA might predict creativity, or alternatively, the interaction between the mean and the variance in PA might predict creativity. Finally, emotion facilitation might interact with the variance in PA to predict creativity. Because we captured PA over time, we could test for these effects. The results indicated that all relationships were insignificant except that creative processing positively predicted the variance of PA (␤ ⫽ .25; p ⬍ .01). Overall, this analysis suggests that the mean of PA, and not its variance, is driving the relationships in our theoretical model.

3.5

3.5

3

3

2.5

Low Emotion Regulation Ability

2

High Emotion Regulation Ability

2.5

Low Emotion Regulation Ability

2

High Emotion Regulation Ability

1.5

1.5 1

Positive Affect

Positive Affect


Table 2 Results of Regression Analysis

1

Low

High

Information Processing Requirements

Figure 2. The interactive effects of information processing requirements and emotion regulation ability on positive affect.

Low

High

Creative Processing Requirements

Figure 3. The interactive effects of creative processing requirements and emotion regulation ability on positive affect.


creative processing had the same pattern of results: at low emotion regulation ability and high emotion facilitation ability, low creative processing had a negative indirect effect on creativity (␤ ⫽ ⫺.18, p ⬍ .05, ⫺1 SD), but this indirect effect was attenuated at high levels of emotion regulation ability keeping emotion facilitation ability high (␤ ⫽ .01, ns, ⫹1 SD). All other pathways were insignificant. These results connect the two stages that were analyzed in Hypothesis 1–5. They show that the interactive effects of information and low creative processing requirements with emotion regulation on PA carry through to creativity depending on the level of emotion facilitation ability. Consistent with our theorizing, individual differences in EI influence each stage of the emotional experience, which ultimately influences creativity.

4.9 4.7

Creativity

4.5 Low Emotion Facilitation Ability

4.3 4.1

High Emotion Facilitation Ability

3.9 3.7 3.5


Low

927

High

Positive Affect

Figure 4. The interactive effects of positive affect and emotion facilitation ability on creativity.

Discussion Our article aimed to theoretically explain the role EI plays in employee creativity. Using AIP theory (Gohm & Clore, 2000, 2002) and a multimethod and multisource research design, we made progress toward this aim. Our results provide initial evidence to the claims that emotion regulation ability helps employees to maintain higher PA in jobs with high information processing or low creative processing requirements, and emotion facilitation ability helps employees to translate their PA into higher creativity. The results of our study contribute to research on emotional intelligence, creativity, work design, and the AIP perspective.

Finally, our theory and results suggest that information processing and low creative processing requirements may reduce creativity for employees low on emotion regulation ability via their PA. To test these indirect effects, we followed procedures outlined by Mathieu and Taylor (2006) and Edwards and Lambert (2007). We used a bootstrapping procedure in Mplus 7.0 (Muthén & Muthén, 2010) involving 1,000 data draws with the use of linear regression with maximum likelihood estimates to confirm the existence of indirect effects. Furthermore, we varied high and low levels of emotion regulation ability and emotion facilitation ability. The results of this analysis are reported in Table 3. Results showed that information processing and low creative processing requirements indirectly influenced creativity (via PA) in a manner consistent with our above results. That is, information processing had a negative indirect effect on creativity when emotion regulation ability was low and emotion facilitation ability was high (␤ ⫽ ⫺.17, p ⬍ .05, ⫺1 SD), but this indirect negative effect was attenuated at high levels of emotion regulation ability keeping emotion facilitation ability high (␤ ⫽ .03, ns, ⫹1 SD). Low

Theoretical Contributions Establishing EI’s role in facilitating employee creativity. Although much research has shown EI’s positive effects for workplace outcomes (Farh et al., 2012; Joseph & Newman, 2010; Kafetsios & Zampetakis, 2008; Rubin et al., 2005; Seo & Barrett, 2007), past work has largely overlooked EI’s role with employee creativity. Some initial studies have examined the interpersonal role EI plays in fostering creativity such as how leaders with high EI can increase the creativity of their followers (Castro et al., 2012;

Table 3 Indirect and Conditional Indirect Effects of Information Processing and Low Creative Processing Requirements on Creativity Via PA Indirect effects via PA

Information processing requirements Unconditional High ERA, High EFA Low ERA, High EFA High ERA, Low EFA Low ERA, Low EFA Low creative processing requirements Unconditional High ERA, High EFA Low ERA, High EFA High ERA, Low EFA Low ERA, Low EFA

Estimate

Bootstrapped 95% CI

Statistical significance

⫺.01 .03 ⫺.17 ⫺.02 .12

[⫺.06, .04] [⫺.05, .12] [⫺.33, ⫺.01] [⫺.10, .05] [⫺.04, .28]

n.s. n.s. p ⬍ .05 n.s. n.s.

⫺.01 .01 ⫺.18 ⫺.01 .13

[⫺.07, .05] [⫺.07, .09] [⫺.36, ⫺.01] [⫺.08, .06] [⫺.05, .32]

n.s. n.s. p ⬍ .05 n.s. n.s.

Note. N ⫽ 129; ERA ⫽ emotion regulation ability; EFA ⫽ emotion facilitation ability. Standardized estimates reported. Table entries were computed in MPLUS 7.0 using linear regression with maximum likelihood estimator. The bootstrapping confidence intervals were derived using 1,000 data draws.


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Rego, Sousa, Pina, Correia, & Saur-Amaral, 2007; Zhou & George, 2003) or how teams composed of members high in EI have higher team creativity (Barczak et al., 2010). However, these studies focus on social facilitation mechanisms, such as trust or higher quality relationships, to explain the interpersonal effects EI has on creativity. In addition, a few articles have used trait or mixed-models of emotional intelligence to suggest that it may lead to creativity (Harris, Reiter-Palmon, & Kaufman, 2013; Wolfradt, Felfe, & Köster, 2002), but these conceptualizations of EI lack theoretical and empirical validity as they overlap with a number of important predictors of employee outcomes (e.g., self-efficacy, personality, general mental ability; Côté, 2014; Joseph, Jin, Newman, & O’Boyle, 2014). In contrast, our study examines abilitybased EI and explores the self-regulatory mechanisms of how EI helps employees maintain and use PA for creativity. We extend existing studies that focused on the cognitive mechanisms between EI and creativity (Ivcevic et al., 2007; Zenasni & Lubart, 2009) by incorporating AIP theory to demonstrate EI’s affective-based role. Establishing how EI facets help maintain (emotion regulation) and use PA (emotion facilitation) for creativity suggests that EI may play other important moderating roles in processing and translating emotional experience to workplace outcomes (Côté, 2014). Managing emotions with different knowledge requirements. Our results also highlight the important role of emotion regulation ability in enabling employees to respond positively to knowledge processing requirements. First, we nuance theory on work design by explicating the affective-based and differential effects of information processing and creative processing requirements on employee PA. Our results show that information processing and creative processing requirements negatively and positively relate to PA, respectively. Past research has largely focused on the motivational or cognitive effects of these work characteristics and has assumed that they are relatively the same when it comes to influencing employee outcomes. For example, research that examined the relationship between job complexity and creativity uses theoretical arguments that incorporate both information processing and creative processing as a part of job complexity (e.g., Oldham & Cummings, 1996; Tierney & Farmer, 2002). In addition, when combined and studied as challenge demands, these work requirements have been proposed and found to have both positive and negative effects on motivation and affective outcomes (Crawford et al., 2010; LePine et al., 2005; Sonnentag et al., 2010; Sonnentag & Zijlstra, 2006). Differing from these approaches, we build from recent advancements in work design that has decomposed larger, global constructs into narrower facets and suggests that while these smaller facets are often highly related, they can have substantially different effects on employee outcomes (Humphrey et al., 2007). This approach enabled us to explicate the unique effects these two knowledge processing variables have on proximal antecedents of creativity (e.g., PA), which is especially important given that the two knowledge requirements are highly correlated (r ⫽ .59). We further extend these insights by showing that individual differences in emotion regulation ability differentially interact with information and creative processing requirements to predict the level of PA employees experience at work. Although knowledge workers low in emotion regulation ability are more at the mercy of their environments, individuals high in emotion regulation ability are more even-keeled and exert more control of their emotional states regardless of the contextual factors. Employees with high

emotion regulation ability likely maintain higher PA by using effective emotion regulation strategies to either (a) respond positively to demands of their work in high information processing requirements or (b) make their work more interesting and enjoyable in jobs with low creative processing. Our findings provide initial evidence of these mechanisms and are consistent with other scholar’s claims that emotion regulation ability is “the tool through which we create and maintain positive affective states” (Joseph & Newman, 2010, p. 56). Furthermore, our study shows emotion regulation ability’s positive effect on emotions in work environments beyond those characterized by emotion labor demands, which has been the focal point in extant literature (Joseph & Newman, 2010; Kluemper et al., 2013). Our contributions are also important given that PA is an indicator of well-being (Diener, 2000) and positively impacts many beneficial outcomes in organizations beyond creativity, such as citizenship behavior (George, 1991) and performance (Tsai, Chen, & Liu, 2007). In sum, we highlight the theoretical benefits of examining the narrower facets of work characteristics, their affective mechanisms, and how emotion regulation ability is an important individual difference for scholars and practitioners to consider when making predictions of how work requirements affect employee emotions and downstream outcomes. Utilizing PA for creativity. In identifying EI’s second role in employee creativity, we provide a new perspective to studying the affect-creativity relationship. Specifically, we find evidence that employees with high emotion facilitation ability use their PA to enhance the positive effects of PA on creativity, while individuals low on this ability fail to do so. This suggests that how employees use their affect may matter in addition to which moods and emotions they experience. Much of the work that investigates affect as an antecedent of creativity takes the latter approach by focusing on the specific moods or emotions individuals experience (Baas et al., 2008; George, 2007; Hennessey & Amabile, 2010). For example, some work suggests that emotional ambivalence (“the simultaneous experience of positive and negative emotions”) leads to creativity (Fong, 2006, p. 1016). In a more dynamic approach, Bledow et al. (2013) suggest that the affective shift from negative to positive affect begets creativity. Finally, other perspectives propose that the activation and valence dimensions of affect uniquely contribute to creativity (Baas et al., 2008; To, Fisher, Ashkanasy, & Rowe, 2012). Our study suggests an alternative perspective to this stream of research: in addition to investigating which moods or emotions people experience and when they experience them, scholars should also consider how individuals use their affect for creativity. In our study, we used AIP theory to take such an approach and demonstrate that emotion facilitation may be a key ability for using PA for creative outcomes. This is especially true because at low levels of emotion facilitation ability, PA negatively related to creativity (albeit this relationship was insignificant). EI facets play unique roles in influencing emotions and their outcomes. Finally, our study contributes to the AIP perspective by demonstrating how two EI dimensions play unique roles at different stages of affective information processing. AIP and EI research has largely focused on either antecedent-affect relationships or affect-outcome relationships (e.g., Elfenbein & Ambady, 2002; Farh et al., 2012; Feldman, 1995; Gohm & Clore, 2002). However, because EI influences both how emotions are experi-



enced and how the experienced emotions lead to work outcomes (Elfenbein, 2007), we believe it is important for research to investigate the effects of EI on these two emotional processes in a comprehensive way. For example, in our study, we expected that emotion regulation could independently and indirectly enhance creativity by enabling employees to maintain higher PA; however, because the relationship in our sample between PA and creativity is less strong (which we believe is driven by the moderator of emotion facilitation), our empirical results suggest that emotion facilitation is needed to transmit the first stage effects of emotion regulation ability onto creativity. In addition, much of EI research has focused on overall EI (and not facet) predictions (Mayer et al., 2008). Considering that our study found moderation effects for the facets of EI and not overall EI scores, research should continue to build theory and test facet predictions as it may reveal further insights (Elfenbein, 2007). Our study contributes toward these ends by showing how EI facets served as moderators that influenced both antecedent-emotion and emotion-outcome relationships.

Practical Implications Our study offers several practical considerations. First, we offer managers an important variable to consider when hiring knowledge workers to produce creativity in jobs with high information processing or low creative processing requirements. Because EI tests exist (MacCann & Roberts, 2008; Mayer et al., 2003), organizations could include this ability test along with other personality measures for screening purposes. Second, managers possess a trainable way to increase employees’ abilities at managing their emotional states and responses to work requirements. Not only can EI be learned and developed (Mayer et al., 2008), but laboratory research and initial field studies shows that cognitive reappraisal interventions can be effective in altering emotional experiences (Gross, 1998a; Magen & Gross, 2007; Thory, 2013). Thus, managers can implement training programs aimed at increasing individuals’ EI or help them practice emotion regulation strategies such as situation selection or cognitive reappraisal. This would be especially useful for managers that need a way to help employees cope and succeed in environments with high information processing or low creative processing requirements when they cannot alter the characteristics of the job. Third, we believe our study contributes a more practical application of the mood effects on creativity. Given the number of exogenous factors that influence moods (Elfenbein, 2007), focusing on trying to stimulate or manipulate specific employees mood at work (or the timing of such moods) to enhance creativity (or other organizational outcomes) may prove unrealistically challenging. Instead, managers and employees can increase agency of employees over their moods and resultant outcomes by encouraging awareness and training of how to use moods to influence thinking and problem solving (e.g., Seo & Barrett, 2007). That way, the complication of inducing certain moods can be avoided, and instead, more parsimonious advice can be given: employees should learn how to use moods to enhance their effectiveness (Mayer et al., 2008). Organizations can promote greater awareness of the emotions and moods people experience at work and how these influence employee effectiveness. Bringing company attention to employee affect will provide greater motivation for em-

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ployees to attend to and use affect in organizations, which will likely lead to improved outcomes such as creativity.

Limitations and Future Research Our results should be qualified given certain limitations of our study. First, the causal nature of PA and creativity cannot be determined due to the cross-sectional design. Past research shows that affect and creativity may be reciprocally related (Amabile et al., 2005). Future research should take a longitudinal or experimental approach to studying the relationships among work requirements, EI, affect, and creativity. Second, our sample size is modest and is from more knowledge intensive occupations. Given the possibility that jobs with relatively lower knowledge requirements such as blue collar work or other occupations (e.g., security guards, toll booth operators) may exhibit a different relationship between the work context and PA (Johns, 2006), future studies should theoretically and empirically explore such differences. Third, the reliabilities of our EI facet measures are modest. Although lower reliability should reduce the chances of finding an effect, and we found robust moderation effects, future research should further investigate the reliability of specific EI facets (Côté, 2014). Fourth, although we ruled out two alternative explanations driving the effects of our endogenous variables by controlling for leadership (LMX) and organizational support (support for creativity), and we attempted to maintain theoretical focus with our selection of the dual-mode of knowledge processing requirements, there are other work characteristics beyond information and creative processing requirements that could likely interact with EI to influence PA and creativity. One such promising direction might be to investigate the difference between the effects of challenge (e.g., workload and time pressure) and hindrance work demands (e.g., organizational politics; Cavanaugh, Boswell, Roehling, & Boudreau, 2000; Crawford et al., 2010) on employee PA and creativity. Theoretically, we might expect challenge demands to increase creativity directly (Shalley et al., 2004), yet reduce creativity indirectly through lower PA (e.g., fatigue or exhaustion). As for hindrance demands, this should lead to negative effects on creativity and PA. Yet, what is more interesting is how EI might moderate these effects through affecting the underlying appraisal processes and their affective outcomes (e.g., Dong et al., 2014). Future research can investigate such questions. Fifth, we relied on several untested theoretical assumptions. We suggested that people with higher emotion regulation ability generally use more effective emotion regulation strategies as well as select and implement appropriate strategies given situational constraints. In addition, we argued that people with high emotion facilitation ability more likely align their PA with creative behavior and correctly use their PA to persist on developing ideas. Although these arguments align precisely with AIP predictions (Gohm & Clore, 2000), EI theory (Côté, 2014; Mayer et al., 2008), and with what the MSCEIT captures in test questions, the assumption is still that people who have EI knowledge actually use it. Despite the theoretical rationale, these predictions remain untested by our study as well as the majority of existing EI research. It is possible that EI abilities do not necessarily translate into the use of EI strategies or that employees are limited to use such EI strategies given constraints of the job (e.g., employees cannot change aspects


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of their work). Thus, future research should empirically examine these predictions by capturing both EI ability and EI behaviors to examine the underlying theoretical processes in our hypothesized model. Another assumption involves how existing research theoretically combines the cognitive mechanism (e.g., enhanced cognitive flexibility) and the motivational mechanism (e.g., increased persistence in developing ideas) in explaining how PA impacts creativity (e.g., Baas et al., 2008; Grant & Berry, 2011). However, research, including our study, has yet to tease apart these underlying mechanisms. Future research that can isolate these effects would not only help determine their relative predictive validity, but can also reveal insights when testing them under moderating conditions. Sixth, while we believe our use of the OⴱNET variables is a strength of our study, it warrants further discussion on two issues. The first is whether or not the use of perceptual measures of knowledge processing requirements would alter the relationships in our theoretical model. For example, most research shows that more objective measures of context produce weaker relationships with outcomes than perceptual measures of context (e.g., Melamed, Ben-Avi, Luz, & Green, 1995; Morgeson & Humphrey, 2006; Spector & Jex, 1991). Although we attempted to supplement this issue by collecting additional data to further validate our use of OⴱNET measures (see Footnote 4), a stronger approach for future research is to include both objective and perceptual measures of work characteristics to simultaneously test the relationships in the model (e.g., Judge et al., 2000). As others have noted, employing objective and perceptual measures can help rule out whether the effects occur directly or are mediated through perceptions of the work context (Brannick, Chan, Conway, Lance, & Spector, 2010; Liu, Spector, & Jex, 2005; Spector, 1994). Furthermore, the level of creative processing requirements may impact supervisors’ evaluation of employee creativity. Thus, future research may want to capture supervisors’ rating of creative requirements in order to control for this relationship. The second issue involves levels of analysis. OⴱNET variables are job-based, which means employees are nested within job classifications. Although our model variables showed weak nesting effects and job-level reliabilities (see Footnote 5), indicating that our model was highly likely to be occurring at the individual level, we could not tease apart and test an unconflated model where the relative strength of these relationships across levels is examined simultaneously. Future studies on work characteristics with appropriate between-job variance and reliabilities can test which level their effects reside and/or how the relationships compare across levels (Chen, Bliese, & Mathieu, 2005; Dierdorff & Morgeson, 2013). Despite these limitations, our use of a research design that incorporated a heterogeneous sample, an experience sampling measure of employee PA, an ability test of EI facets, globally measured contextual variables, supervisor-rated creativity, and controls for alternative explanations, gives us greater confidence in the validity of our results. Our article shows that, by regulating and facilitating affect, employees with EI abilities can maintain higher PA and use it to enhance their creativity at work. Given that employee creativity is a critical determinant of an organization’s growth and survival (George, 2007), we suggest that scholars and practitioners alike should consider EI as

an important factor in determining creative outcomes in the workplace.

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Received November 25, 2013 Revision received October 21, 2014 Accepted October 24, 2014 䡲

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The Positive Effects of Trait Emotional Intelligence during a Performance Review Discussion - A Psychophysiological Study.

Coronary Heart Disease and Emotional Intelligence.

Editorial: Emotional Intelligence and Cognitive Abilities.

Intelligence and Creativity in Problem Solving: The Importance of Test Features in Cognition Research.

Validation of Emotional Intelligence Measure (MIE) for the Portuguese population.

Emotional intelligence predicts individual differences in proneness for flow among musicians: the role of control and distributed attention.

Beliefs and environmental behavior: the moderating effect of emotional intelligence.