Perceived prosocial impact, perceived situational ...

Research Article

Journal of Organizational Behavior, J. Organiz. Behav. 36, 806–824 (2015) Published online 10 April 2015 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/job.2005

Perceived prosocial impact, perceived situational constraints, and proactive work behavior: Looking at two distinct affective pathways SABINE SONNENTAG* AND ANITA STARZYK Department of Psychology, School of Social Sciences, University of Mannheim, Germany

Summary

This paper examines the role of affect as a linking mechanism between experiences at work (perceived prosocial impact and situational constraints) and two distinct components of proactive work behavior (issue identification and implementation). Based on a dual-tuning perspective, we argue that both positive affect and negative affect can be beneficial for proactive work behavior. Multi-level path analysis using daily-survey data from 153 employees showed that perceived prosocial impact predicted positive affect and that situational constraints as a typical hindrance stressor predicted negative affect. Negative affect, in turn, predicted issue identification, and positive affect predicted implementation. Overall, our study suggests that both positive and negative affects can be valuable in the organizational context by contributing to distinct components of proactive behavior. Copyright © 2015 John Wiley & Sons, Ltd. Keywords: proactive behavior; affect; perceived prosocial impact; situational constraints; diary study

Introduction Employee affect at work influences behavior in organizations (Barsade & Gibson, 2007; Brief & Weiss, 2002) and is particularly important for proactive work behavior (i.e., employees’ efforts to change the internal organizational work environment; Parker & Collins, 2010). Specifically, when employees experience positive affect and feel energetic, they come up with ideas about how to improve work procedures and they take actions to initiate change (Binnewies, Sonnentag, & Mojza, 2009; Fritz & Sonnentag, 2009). Thus, positive affect is crucial for proactive work behavior. But what about negative affect? Research has shown that not only positive affect, but also situational constraints (i.e., hassles due to poor equipment or interruptions) and other job stressors predict proactive work behavior (Fay & Sonnentag, 2002). From a perspective on workplace affect, this finding is puzzling because when employees experience situational constraints, they predominantly react with negative affect (Baethge & Rigotti, 2013; Zohar, Tzischinski, & Epstein, 2003). Could it be that not only positive affect but also negative affect stimulates proactive work behavior? The role of negative affect, however, has rarely been addressed in empirical studies on proactive work behavior (for an exception, see den Hartog & Belschak, 2007), although researchers have acknowledged that negative affect can have an impact on behaviors that are beneficial for the organization (DeDreu, Baas, & Nijstad, 2008; George, 2011). In this paper, we draw on the mood-as-information model (Schwarz & Clore, 1983) and the dual-tuning perspective (George & Zhou, 2007; George, 2011) and propose that proactive work behavior gains from both positive and negative affect: When people experience positive affect, they attend to a broad range of stimuli, are willing to take risks, think more broadly, and develop more positive expectancies (Erez & Isen, 2002; Schwarz, 1990). When experiencing negative affect, people tend to perceive the current situation as problematic; they seek information *Correspondence to: Sabine Sonnentag, Department of Psychology, University of Mannheim, Schloss Ehrenhof Ost, D-68131 Mannheim, Germany. E-mail: [email protected]

Copyright © 2015 John Wiley & Sons, Ltd.

Received 29 January 2014 Revised 22 January 2015, Accepted 05 February 2015

AFFECTIVE PATHWAYS TO PROACTIVE WORK BEHAVIOR

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and engage in causal analytical reasoning (Schwarz, 1990). Importantly, proactive work behavior requires both: To become proactive, employees must notice things in the work environment that need to be improved (stimulated by negative affect), and they must think broadly and expect positive outcomes (stimulated by positive affect). We address these different pathways to proactive work behavior by examining both positive affect and negative affect as predictors of proactive work behavior. To gain a deeper understanding of the antecedents of proactive work behavior and of the everyday work context in which it occurs, it is necessary to look beyond affective states as its immediate precursors. Affective states are not arbitrary fluctuations of affect but are rooted in experiences and events happening at work (Weiss & Beal, 2005; Weiss & Cropanzano, 1996). Job stressors are “prime examples” (Rodell & Judge, 2009, p. 1439) of such everyday experiences that lead to affective reactions (Daniels, Harris, & Briner, 2004), particularly because they can undermine goal-directed behavior (Frese & Zapf, 1994). Of notable relevance for stimulating negative affective states are stressors that signal obstacles during goal pursuit and that hinder goal progress and performance (Zohar, 1999; Zohar et al., 2003). For instance, when an employee witnesses a breakdown of the high-quality printer that he needs to finalize the presentation material that is due in 1 hour, it is not unlikely that his negative affect will increase and he will feel irritated. Unlike quantitative demands and other challenge stressors that sometimes stimulate positive affective states (Petrou, Demerouti, Peeters, Schaufeli, & Hetland, 2012; Rodell & Judge, 2009), stressors that inhibit goal process and performance elicit negative affective states (Rodell & Judge, 2009; Zohar, 1999). Therefore, we focus on the experience of situational constraints (i.e., encountering circumstances that hinder successful performance; Peters & O’Connor, 1980) as predictor of negative affect. Fortunately, experiences at work are not only negative and stressful. Researchers have described the experience of helping others and making a prosocial impact as an antipode to stressors (Eisenberger, 2013; Midlarsky, 1991). Empirical studies have shown that when employees help others and experience a positive impact on them, their own positive affect increases (Glomb, Bhave, Miner, & Wall, 2011; Sonnentag & Grant, 2012). Specifically, in our study, we look at perceived prosocial impact (i.e., “the subjective experience of benefitting others”; Grant & Campbell, 2007, p. 667)—an experience that has been shown to be particularly effective in influencing affect and well-being (Aknin, Dunn, Whillans, Grant, & Norton, 2013)—and examine it as a predictor of positive affect. In this study, we examine proactive work behavior and its antecedents at a within-person level, using daily-survey data. Previous research has been dominated by a between-person approach, examining individual-difference variables and rather stable workplace factors as predictors of proactive work behavior (for meta-analyses, cf. Fuller & Marler, 2009; Thomas, Whitman, & Viswesvaran, 2010; Tornau & Frese, 2013). Albeit providing useful insights, this between-person perspective neglects that proactive work behavior substantially fluctuates within persons (Binnewies et al., 2009; Sonnentag, 2003): On one day, a person might spend a lot of time thinking about a problem that needs to be resolved by proactive action, whereas on another day, the same person might refrain from any proactive thoughts or actions. Our within-person analysis goes beyond the previous between-person approach and will help to better understand the dynamic nature of proactive work behavior and the situational and affective processes involved. Taken together, we aim at answering three main questions with our study: (i) How are day-specific positive and negative experiences at work (i.e., perceived prosocial impact and situational constraints) related to positive and negative affect? (ii) How are day-specific positive affect and negative affect related to day-specific proactive work behavior? And (iii) can a positive affective pathway be differentiated from a negative affective pathway when predicting day-specific proactive work behavior? To gain a more detailed view of proactive work behavior, we differentiate between two components: identifying issues for becoming proactive (i.e., issue identification) and actually engaging in a proactive act (i.e., implementation of proactive action). Our study seeks to make several contributions. First, it provides a deeper insight into the everyday experiences at work that drive proactive work behavior. With its focus on day-level processes and the inclusion of both positive and negative affect, our study contributes to a better understanding about when employees become proactive and why. Specifically, by including negative affect as a possible positive predictor of proactive work behavior, it challenges the dominant view in the organizational literature that positive affective states predict positive outcomes and negative affective states predict negative outcomes (Dalal, Lam, Weiss, Welch, & Hulin, 2009). By differentiating Copyright © 2015 John Wiley & Sons, Ltd.

J. Organiz. Behav. 36, 806–824 (2015) DOI: 10.1002/job

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between two aspects of proactive work behavior (issue identification vs. implementation), our study will help to understand why the benefits derived from negative affect might have been overlooked in the past. Second, our study extends research on perceived prosocial impact and on situational constraints. So far, research has mainly looked at the benefits of perceived prosocial impact for in-role performance and well-being (Grant, 2008; Grant & Campbell, 2007). By linking perceived prosocial impact to positive affect and positive affect to proactive work behavior, our study highlights the relevance of perceived prosocial impact for a broader set of organizational outcomes beyond its immediate effect on in-role performance. With respect to situational constraints, earlier research has provided some evidence that job stressors are related to proactive work behavior (Fay & Sonnentag, 2002; Fritz & Sonnentag, 2009). However, the underlying mechanism remained unexplored. Our study sheds light on affect as a possible underlying process. Third, we connect research on the predictors of affective states at work with literature on the consequences of these states, demonstrating that affect that may stimulate proactive work behavior is rooted in both positive and negative experiences people have at work. Thereby, our study illustrates that affect that motivates work behavior does not emerge randomly but develops as a reaction to the ongoing experiences at work (Weiss & Rupp, 2011). With this approach, we contribute to a better understanding of how experiences, affect, and behavior are intertwined at work and how even negative experiences might contribute to behaviors that ultimately benefit the organization.

Proactive Work Behavior Parker and Collins (2010, p. 636) characterized proactive work behavior as “taking control of, and bringing about change within, the internal organizational environment.” A prototypical example of this type of proactive work behavior is “taking charge” (Morrison & Phelps, 1999) that refers to individual employees’ “voluntary and constructive efforts … to effect organizationally functional change with respect to how work is executed within the contexts of their jobs, work units, or organizations” (p. 403). This type of proactive work behavior aims at improving the immediate and larger work environment by changing work methods and procedures. Typically, bringing about change in one’s work situation may not be accomplished with a single action but requires a complex set of behaviors such as identifying an issue where improvement is needed, deciding about what to do, and then actually doing something (Bindl, Parker, Totterdell, & Hagger-Johnson, 2012; Frese & Fay, 2001; Grant & Ashford, 2008). In this paper, we focus on two distinct aspects of proactive work behavior: issue identification and implementation of the proactive action. Issue identification takes place during the anticipation phase of proactive work behavior (Grant & Ashford, 2008). It implies to recognize problems in the work situation that need to be changed, to identify issues where action is needed, and to imagine how things would look like if problems and difficulties would be resolved. Implementation implies actually engaging in proactive action. Whereas issue identification mainly refers to the anticipation of action, implementation means doing something in order to resolve the problems and to improve the situation.

Affect at Work Employees’ affective states at work play a core role for different kinds of behaviors, including in-role performance (Tsai, Chen, & Liu, 2007), creativity (Amabile, Barsade, Mueller, & Staw, 2005), organizational citizenship behavior (Ilies, Scott, & Judge, 2006), and proactive work behavior (Fritz & Sonnentag, 2009). Basically, affective states differ with respect to their valance and their degree of activation (Russell, 1980). Positive and negative affective states associated with a high degree of activation—as opposed to low-activation states—are particularly important in influencing behavior at work (Warr, Bindl, Parker, & Inceoglu, 2014). Positive activated affect—positive affect Copyright © 2015 John Wiley & Sons, Ltd.



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hereafter—refers to states of excitement, energy, alertness, and determination (Watson, 1988); negative activated affect—negative affect hereafter—refers to states of distress, fear, or anger (Watson, 1988). Importantly, positive affect and negative affect are independent dimensions of affect and are only weakly correlated, even at the day level: For instance, Watson and Clark (1997) found correlations between positive affect and negative affect of r = !.06 and r = !.05 when asking participants about their mood in the “moment” and “today,” respectively. This independence of positive affect and negative affect implies that during a day at work, people can experience high levels of positive and high levels of negative affect within short periods of time.

A dual-affective pathway model to proactive work behavior Carver and Scheier (1998) described affect as both a reaction to goal-directed behavior (e.g., negative affect arises when goal-directed activity is disrupted) and information that stimulates future behavior. Accordingly, we look at positive affect and negative affect as predictors of proactive work behavior as well as consequences of previous experiences at work. We propose a dual-pathway model to proactive work behavior, comprising a positive affective pathway—nurtured by perceived prosocial impact—that fosters issue identification and implementation and a negative affective pathway—elicited by the experience of situational constraints—that supports issue identification. We will start with the positive affective pathway, first describing the link between positive affect and proactive work behavior, and then discussing perceived prosocial impact as a predictor of positive affect; subsequently, we turn to the negative affective pathway. Figure 1 displays our conceptual model. The positive affective pathway: Positive affect and proactive work behavior Positive affect is crucial for proactive work behavior. According to Parker, Bindl, and Strauss (2010), the “energized to motivation” (p. 838) resulting from positive affect with a high activation level is the core affective antecedent of proactive work behavior. Positive affect not only stimulates cognitions and behaviors that facilitate proactive work behavior, but it also provides the energy necessary for engaging and persisting in proactive work behavior. Fredrickson (1998) argued that positive emotions broaden people’s “momentary thought–action repertoire” (p. 304). When being in a positive affective state, people think and act more broadly. For instance, they have more flexible and creative thoughts (Isen, Daubmann, & Nowicki, 1987; Murray, Sujan, Hirt, & Sujan, 1990), show more approach behavior (Watson, Wiese, Vaidya, & Tellegen, 1999), and have a broader variety of behaviors (Fredrickson, 1998). Importantly, positive affect stimulates positive expectancies (Erez & Isen, 2002; Seo, Bartunek, & Barrett, 2010), the setting of higher goals (Ilies & Judge, 2005), increased effort (Seo et al., 2010), and higher levels of persistence (Tsai et al., 2007). Positive affect should benefit both aspects of proactive work behavior (issue identification and implementation). With respect to issue identification, thinking broadly and flexibly about a situation helps in identifying

Figure 1. Conceptual model at the within-person level. Paths for time index and for lagged effects from the previous afternoon are omitted for the sake of clarity. Solid lines refer to hypothesized relationships. Dashed lines refer to relationships involving control variables. (M) = Morning. (A) = Afternoon Copyright © 2015 John Wiley & Sons, Ltd.


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opportunities for improvement and in imagining how an improved situation could look like. Positive expectancies are important in order to believe that addressing the issues will result in a better future situation. With respect to implementation, the informational value of positive affect is important: As proposed in the dual-tuning (George, 2011) and the mood-as-information models (Schwarz & Clore, 1983), positive affect signals that things are on a good track and will lead to positive outcomes. This information should help to get started with any proactive action, and it should fuel effort and persistence during the implementation process. Seeing the situation in a positive light, along with effort and persistence, is crucial because obstacles may occur during the proactive process (Frese, Fay, Hilburger, Leng, & Tag, 1997) and because proactive action might not always be welcome (Grant, Parker, & Collins, 2009). Studies have provided rather consistent empirical support for the proposition that positive affect is related to proactive behavior at work in general, both at the person level (den Hartog & Belschak, 2007) and at the day level (Fay & Sonnentag, 2012; Fritz & Sonnentag, 2009). In addition, Bindl et al. (2012) reported positive (activated) affect to be positively related to issue identification and implementation (called “envisioning” and “enacting” by Bindl et al., 2012). Taken together, we propose positive affect to predict proactive work behavior: Hypothesis 1a: Positive affect will be positively related to issue identification. Hypothesis 1b: Positive affect will be positively related to implementation. The positive affective pathway: Perceived prosocial impact and positive affect Grant (2008, p. 110) characterized perceived prosocial impact as “the degree to which employees feel that their actions benefit other people.” It refers to employees’ feeling that what they do has a positive effect on others and can “make a difference” (Grant, 2008, p. 110). Perceived prosocial impact may result from completing job tasks that offer the opportunity to benefit others as well as from deliberate efforts of helping others. It is not the mere act of completing a prosocial job task or helping another person that constitutes perceived prosocial impact; it is the understanding that others benefit from what one is doing (Grant, 2008). First evidence about the relevance of perceived prosocial impact comes from cross-sectional research that showed positive associations between perceived prosocial impact and positive states such as job satisfaction (Grant & Campbell, 2007), work engagement (Freeney & Fellenz, 2013), and happiness (Moynihan, DeLeire, & Enami, in press). Perceived prosocial impact does not only differ between persons but also fluctuates within persons from day to day (Sonnentag & Grant, 2012). In many jobs, some days will offer the opportunity to help others, and other days will not. Even when having the opportunity to help others, employees may not engage in helping every day, and they may not feel that they have had a positive impact on others—even when they have helped (e.g., because they may not receive feedback on every day). We suggest that perceiving prosocial impact during the working day will increase positive affect throughout the day. Research has shown that on days when people help others or engage in a broader set of eudaimonic behaviors (i.e., behaviors that contribute to a meaningful life as opposed to purely hedonic behaviors), they experience higher levels of subjective well-being (including positive affect, vitality, and life satisfaction) and need satisfaction than on days when they engage less in helping or other eudaimonic behaviors (Steger, Kashdan, & Oishi, 2008; Weinstein & Ryan, 2010). With respect to helping behaviors in a job context, Glomb et al. (2011) showed that acts of altruism predicted an increase in positive affect on the same day. Possibly, the effect of helping and perceived prosocial impact on the satisfaction of basic human needs (particularly the need for relatedness and competence; Deci & Ryan, 2012) accounts for our hypothesized beneficial effects of perceived prosocial impact on positive affect. For instance, using daily-survey data, Weinstein and Ryan (2010) have shown that helping others was related to need satisfaction. In another day-level study, Sonnentag and Grant (2012) demonstrated that perceived prosocial impact at work predicted increased feelings of competence. Fulfillment of the needs for relatedness and competence, in turn, has been shown to predict positive affective states at the day level (Reis, Sheldon, Gable, Roscoe, & Ryan, 2000; Sheldon, Ryan, & Reis, 1996). Based on this line of reasoning, we propose Copyright © 2015 John Wiley & Sons, Ltd.



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Hypothesis 2: Perceived prosocial impact will be positively related to positive affect. By linking Hypotheses 1a and 1b with Hypothesis 2, we propose an indirect effect from perceived prosocial impact to issue identification and implementation via positive affect. Hypothesis 3: Perceived prosocial impact is indirectly related to (a) issue identification and (b) implementation via positive affect. The negative affective pathway: Negative affect and proactive work behavior Whereas most research on affect and proactivity has focused on the role of positive affect (Bindl & Parker, 2012), only a few studies have looked at negative affect (e.g., den Hartog & Belschak, 2007). This is an important oversight because negative affect might have a stimulating effect on proactive work behavior as well. According to the dualtuning model (George & Zhou, 2007; George, 2011), negative affective states are a unique source of information and elicit cognitive processes that are highly relevant for effective functioning (Schwarz & Clore, 1983). Specifically, negative affective states signal the individual that the current situation is problematic, that positive outcomes are at risk, and that negative outcomes are likely; thereby, negative affect motivates to change the current situation (Schwarz, 1990). Thus, negative affect indicates a need to take action. So far, empirical evidence on the association between negative affect and proactive work behavior is inconsistent. For instance, some studies looking at trait negative affect found evidence for a positive relationship between negative affect and proactive behavior at work (Den Hartog & Belschak, 2007, Study 1; Fay & Sonnentag, 2012), whereas other studies did not find such a relationship (Den Hartog & Belschak, 2007, Study 2; Foo, Uy, & Baron, 2009). Similarly, findings for state negative affect are mixed as well (Fay & Sonnentag, 2012; Foo et al., 2009). Importantly, all these studies did not distinguish between issue identification and implementation of proactive action. We suggest that the picture becomes clearer when differentiating between issue identification and implementation. Issue identification should benefit from negative affect (in addition to the impact of positive affect), whereas for implementation, negative affect should not be particularly helpful. We propose that the specific informational value associated with negative affect is important for issue identification. Experiencing negative affect signals the employee that the work situation is not optimal and that things need to be changed. Moreover, because of moodcongruent information processing (Bower, 1981), negative aspects of the work situation become more salient when being in a negative affective state. Therefore, when being in a negative affective state, an employee should identify more issues in the work situation that need improvement. When it comes to implementation, however, the role of negative affective states is less clear: On the one hand, being in a negative affective state during the implementation phase might be beneficial up to a certain point, because a negative affective state fuels the belief that something is wrong with the situation and therefore something needs to be done (George & Zhou, 2007; Schwarz, 1990). On the other hand, however, being in a negative affective state will hinder positive outcome expectancies, and effort as well as persistence will not be at the optimal levels (Seo, Barrett, & Bartunek, 2004). We therefore do not formulate an explicit hypothesis about the relationship between negative affect and implementation; we will test this relationship in an exploratory manner. Building on our reasoning of the benefit of negative affect for issue identification, we propose Hypothesis 4: Negative affect will be positively related to issue identification. The negative affective pathway: Perceived situational constraints and negative affect Situational constraints are “aspects of a work setting which inhibit persons from using their abilities or expressing their motivation effectively at work” (Peters, Chassie, Lindholm, O’Connor, & Kline, 1982, p. 9). Typical examples include lack of information, problems with tools and equipment, and problems with materials and supply (Peters & Copyright © 2015 John Wiley & Sons, Ltd.


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O’Connor, 1980; Spector & Jex, 1998). Such features of a work situation can be subsumed under the broader concept of “hindrance stressors” (LePine, Podsakoff, & LePine, 2005). Importantly, although workplaces differ in the overall amount of situational constraints employees are encountering, situational constraints and perceptions of them fluctuate substantially from day to day (Sonnentag & Jelden, 2009; Tuckey, Searle, Boyd, Winefield, & Winefield, in press): On some days, many constraints and hassles hamper task accomplishment, whereas on other days, no constraints or hassles occur, enabling a smooth work process. For instance, in an administrative job context, employees may experience such constraints when they have to accomplish specialized tasks for which timely information is not reliably available on all days. Because situational constraints have a negative impact on task accomplishment and thereby impede goal attainment at work (Peters & O’Connor, 1980), an employee’s negative affect increases when encountering situational constraints. According to the transactional stress model, people appraise events with respect to their goals, including goal relevance and potential harm or threat for goal attainment (Lazarus & Folkman, 1984). If during this appraisal process they perceive any harm or threat of goal attainment, the affective reaction will be negative. Similarly, cybernetic theory has argued that during goal pursuit, people compare the actual rate of goal progress with the expected rate of progress (i.e., the “standard”; Carver & Scheier, 1990). If the actual rate of progress is lower than the standard, negative affect will occur. In a work situation, an employee will consider his or her average rate of goal progress and average level of situational constraints as “the standard.” When on one day, however, rate of goal progress is lower—for instance because of perceiving more situational constraints—negative affect will increase. Empirically, it has been shown that situational constraints are related to poor job performance (Gilboa, Shirom, Fried, & Cooper, 2008), emphasizing our argument that they will be perceived as a threat to goal attainment at work. Day-level studies, in addition, have shown that situational constraints such as perceived hassles (Zohar, 1999) and perceived goal-disruptive events (Zohar et al., 2003) occurring on specific days are related to negative affect on that day. Based on this reasoning and earlier findings on situational constraints, we propose Hypothesis 5: Perceived situational constraints will be positively related to negative affect. By linking Hypothesis 4 with Hypothesis 5, we propose an indirect effect of situational constraints on issue identification via negative affect. Hypothesis 6: Situational constraints are indirectly related to issue identification via negative affect.

Method Procedure and sample To recruit study participants, we contacted public and private organizations operating in different industries within Germany. After management expressed the organization’s willingness to participate in the study, we used organizations’ mailing lists and/or intranet to inform white-collar employees about the study procedure and to invite full-time employees to take part in the study. To encourage participation, we offered feedback about the study results as well as an attractive lottery prize (one travel voucher worth 800 Euros). Participants were asked to complete an online survey first and then two short surveys per day, over the period of two regular workweeks. The online survey assessed demographic and background data. Daily surveys were implemented on personal digital assistants using isurvey software and had to be answered before lunchtime and before the end of the working day. A research assistant met with each participant, mostly face-to-face, Copyright © 2015 John Wiley & Sons, Ltd.



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and provided a short training in using the digital assistant. Personalized alarms were programmed on the devices to remind participants to complete the daily surveys. We collected a total of 1259 morning surveys and a total of 1176 afternoon surveys from 165 persons, for an overall response rate of 75.5 percent (i.e., 2435 out of 3224 possible responses; the total number of possible responses was 3224 instead of 165 × 5 × 2 = 3300 because of public holidays during the data collection period). Excluding surveys that were completed at wrong times (e.g., morning surveys completed in the afternoon) and matching morning and afternoon survey from the same days resulted in a total of 1005 days from 159 persons. To be able to control for affect and proactive behavior on the previous day, we matched daily-survey data from consecutive workdays, resulting in a final data set of 844 days from 153 persons. In this final sample of 153 participants (48 percent female), mean age was 38.2 years (SD = 9.4) and mean professional tenure was 14.2 years (SD = 9.3). About a third of the participants (35 percent) held a leadership position. Mean contract working time was 38.8 hours per week (SD = 4.3), and mean actual working time was 43.5 hours per week (SD = 7.3). In terms of industry types, most participants worked in the private (48.4 percent) and the public (24.4 percent) service sectors. In terms of occupations, 49.7 percent of the participants held administrative jobs, 17.1 percent had service jobs, 13.7 percent worked as managers or consultants, 11.2 percent had IT or other professional jobs, and 8.7 percent had other jobs.

Measures We assessed our study variables with the morning and afternoon surveys. Specifically, we measured perceived prosocial impact and perceived situational constraints, along with a range of control variables, in the morning, and positive affect, negative affect, and proactive work behavior in the afternoon. Table 1 displays the descriptives for all study variables. Core study variables We measured perceived prosocial impact with three items developed by Grant (2008), adapted for day-specific assessment. A sample item is “Today, I felt that my work makes a positive difference in other people’s lives.” Participants were instructed to respond to the items with respect to the “past hours since started work in the morning.” Cronbach’s alpha computed separately for the various days of data collection ranged between .93 and .97 (M = 0.95). We measured perceived situational constraints with three items from Semmer (1984; Zapf, 1993), adapted for day-specific measurement. A sample item is “Today I had to work with materials and information that were incomplete and outdated.” Cronbach’s alpha ranged between .66 and .86 (M = 0.80). We assessed positive affect and negative affect with items from the Positive and Negative Affect Schedule (PANAS) that capture the high-activation aspect of affect (Watson, Clark, & Tellegen, 1988). To keep the surveys reasonably short, we followed the approach taken in earlier research (Amabile et al., 2005; Sonnentag, Binnewies, & Mojza, 2008) and assessed positive and negative affects with reduced sets of items. Specifically, we measured positive affect with six items (e.g., “active,” “interested,” “excited,” “strong,” “inspired,” and “alert”) and negative affect with five items (e.g., “distressed,” “upset,” “irritable,” “nervous,” and “jittery”). Participants responded to the items with respect to how they felt “this afternoon at work.” Cronbach’s alpha ranged between .80 and .85 (M = 0.83) for positive affect and between .71 and .86 (M = 0.80) for negative affect. We assessed issue identification and implementation as two major aspects of proactive work behavior. We measured issue identification in the afternoon with four items adapted from Morrison and Phelps’s (1999) taking-charge measure, instructing participants to respond with respect to their behavior “in the past hours since noontime.” A sample item is “During the past hours, I thought it would be good to institute new work methods that are more effective for our department or the organization.” Cronbach’s alpha ranged between .90 and .97 (M = 0.94). Similarly, we measured implementation in the afternoon with four adapted items from Morrison and Phelps (1999). A sample item is “During the past hours, I undertook concrete steps to institute new work Copyright © 2015 John Wiley & Sons, Ltd.


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1 Morning perceived prosocial impact 2 Morning perceived situational constraints 3 Morning positive affect 4 Morning negative affect 5 Morning issue identification 6 Morning implementation 7 Afternoon positive affect 8 Afternoon negative affect 9 Afternoon issue identification 10 Afternoon implementation

M

SD

M

SD

ICC

1

2.80 1.43 3.28 1.43 1.86 1.50 3.13 1.47 1.75 1.46

0.98 0.60 0.58 0.44 0.89 0.64 0.59 0.49 0.87 0.66

2.83 1.41 3.28 1.40 1.82 1.48 3.13 1.44 1.72 1.45

1.16 0.71 0.68 0.57 1.07 0.84 0.69 0.60 1.01 0.81

0.65 0.58 0.57 0.45 0.54 0.43 0.60 0.45 0.59 0.52

!.08 .48 !.21 .07 .15 .46 !.16 .09 .10

2

3

4

5

6

7

8

9

10

!.05

.42 !.07

!.20 .26 !.32

.13 .31 .09 .26

.42 .12 !.22 .83 .36 .13

.16 .14 .15 .11 .61

.63 .06 .39 .87 .55

.36 !.04 .71 !.20 .08 .17

!.11 .30 !.24 .61 .24 .09 !.23

.11 .35 .06 .20 .66 .47 .07 .27

.42 .21

.10 .16 .11 .07 .46 .60 .15 .12 .65

.70

!.06 .33 .48 .20 .03 .38 .54 .25

!.35 .01 .12 .85 !.33 .01 .04

.17 .16 .60 .81

!.18 .09 .17

Note: Means and standard deviations at the between-person level are displayed in columns 1 and 2; means and standard deviations at the within-person level are displayed in columns 3 and 4. Correlations below the diagonal are between-person correlations (N = 153), with correlations of r ≥ |.21| being significant at p < .01 and correlations of r ≥ |.16| being significant at p < .05. Correlations above the diagonal are within-person correlations (844 days, i.e., 844 mornings and 844 afternoons) with correlations of r ≥ |.09| being significant at p < .01 and correlations of r ≥ |.07| being significant at p < .05. ICC = percentage of variance between persons (ICC = variance between persons / (variance between persons + varriance within persons)).



Table 1. Means, standard deviations, and zero-order correlations.



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methods that are more effective for our department or the organization.” Cronbach’s alpha ranged between .86 and .96 (M = 0.91). Control variables To conduct a strong test of our hypotheses when predicting affect and proactive behavior in the afternoon, we controlled for affect and proactive behavior in the morning. We assessed positive affect and negative affect in the morning with the same sets of items as done in the afternoon. Cronbach’s alpha ranged between .80 and .86 (M = 0.83) for positive affect and between .76 and .87 (M = 0.82) for negative affect. Similarly, we assessed issue identification and implementation with the same sets of items as in the afternoon, instructing participants to report how they felt “this morning, at work.” Cronbach’s alpha ranged between .90 and .97 (M = 0.94) for issue identification and between .86 and .96 (M = 0.91) for implementation. Construct validity We tested the construct validity of all measures with two sets of multi-level confirmatory factor analyses, using Mplus 6.1, one for the morning data and one for the afternoon data. For the morning data, we compared a six-factor model (perceived prosocial impact, perceived situational constraints, positive affect, negative affect, issue identification, and implementation) with all items loading on their respective factors with alternative models. The six-factor model had a good fit, χ 2 = 491.029, df = 260, CFI = 0.946, RMSEA = 0.033, and fit the data better than alternative models (Table 2). Similarly, for the afternoon data, a four-factor model (positive affect, negative affect, issue identification, and implementation) with all items loading on their respective factors had a reasonable fit, χ 2 = 329.771, df = 146, CFI = 0.926, RMSEA = 0.039, and fit the data better than alternative models (Table 2). Overall, these confirmatory factor analyses show that all measures refer to distinct constructs.

Table 2. Results of multi-level confirmatory factor analysis. χ2

df

SCF

CFI

TLI

RMSEA

S–B χ 2

df

p

Morning survey Model 1M: Six-factor model Model 2M: Best fitting five-factor model Model 3M: Best fitting four-factor model Model 4M: Best fitting three-factor model Model 5M: Best fitting two-factor model Model 6M: One-factor model

491.029 1030.471 1509.860 2006.944 2860.655 2981.790

260 265 269 272 274 275

1.649 1.697 1.651 1.654 1.712 1.739

0.946 0.822 0.711 0.596 0.398 0.370

0.938 0.798 0.678 0.554 0.340 0.312

0.033 0.059 0.074 0.087 0.106 0.109

223.766 984.154 1423.264 1417.995 1325.947

5 9 12 14 15

.001 .001 .001 .001 .001

Afternoon survey Model 1A: Four-factor model Model 2A: Best fitting three-factor model Model 3A: Best fitting two-factor model Model 4A: One-factor model

329.771 660.706 1051.607 1539.349

146 149 151 152

1.793 1.929 1.906 2.010

0.926 0.793 0.635 0.438

0.913 0.762 0.587 0.368

0.039 0.064 0.084 0.104

79.933 271.498 343.399

3 5 6

.001 .001 .001

Note: SCF = scale correction factor; CFI = comparative fit index; TLI = Tucker–Lewis index; RMSEA = root mean square error of approximation. 2 2 S–B χ = Satorra–Bentler χ referring to the comparison with the six-factor model (for morning survey) and the four-factor model (for afternoon survey), respectively. Model 2M: issue identification and implementation loading on one common factor. Model 3M: issue identification and implementation loading on one common factor and positive affect and perceived prosocial impact loading on one common factor. Model 4M: issue identification and implementation loading on first, positive affect, negative affect, and perceived prosocial impact loading on second, and perceived situational constraints loading on third factor. Model 5M: issue identification, implementation, positive affect, and negative affect loading on one factor, and perceived prosocial impact and perceived situational constraints loading on second factor. Model 2A: issue identification and implementation loading on one common factor. Model 3A: issue identification and implementation loading on one and positive affect and negative affect loading on the other factor.



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Results Preliminary analysis Before testing our hypotheses, we analyzed the within-person and between-person variance components of our study variables. Specifically, we computed the intraclass correlations (ICCs) based on unconditional random coefficient models. As shown in Table 1, ICCs ranged between .43 and .65, indicating that all variables had substantial within-person and between-person variance, justifying our multi-level approach to data analysis.

Hypothesis testing We tested our hypotheses in one overall two-level path model in which we specified paths at the day level (i.e., within person) and at the person level (i.e., between person), using Mplus 6.1. This approach enabled us to capture within-person and between-person effects and to test all hypotheses simultaneously in one overall model. Although our hypotheses referred to within-person processes only, we included the between-person part in our model to gain a more complete picture of the affective pathways to proactive behavior. As control variables, we included the respective morning scores when predicting afternoon positive affect, afternoon negative affect, afternoon issue identification, and afternoon implementation. In addition, we specified a path from morning issue identification to afternoon implementation and allowed afternoon issue identification and afternoon implementation to correlate. To account for serial dependence and possible time trends in our data, we also controlled for the outcome variable in the previous afternoon (day-1) and for a time index representing the day of data collection (cf. To, Fisher, Ashkanasy, & Rowe, 2012, for a similar approach). This model resulted in a good overall fit, χ 2 = 116.55, df = 48, CFI = 0.97, RMSEA = 0.041. Table 3 shows the parameter estimates of the multi-level path model. On the withinperson level, perceived prosocial impact was positively related to positive affect in the afternoon. Because we controlled for positive affect in the morning, the significant relationship between perceived prosocial impact and positive affect in the afternoon implies a change in positive affect from morning to afternoon. On days when employees perceived to have prosocial impact, they were more likely to experience an increase in positive affect over the course of the day. This finding supports Hypothesis 2. Within-person analysis further showed that afternoon positive affect was positively related to implementation in the afternoon, even when controlling for implementation in the morning. On days when employees experienced positive affect in the afternoon, they were more likely to show increased implementation from morning to afternoon. Positive affect did not predict a change in issue identification from morning to afternoon. Thus, data provided support for Hypothesis 1b but not for Hypothesis 1a. To test Hypothesis 3, we examined the indirect effect from perceived prosocial impact on implementation via positive affect, using a Monte Carlo simulation procedure with 20 000 replications, implemented in R (Selig & Preacher, 2008). The indirect effect of perceived prosocial impact on implementation via afternoon positive affect was 0.004, with a 95% bias-corrected bootstrap CI of [!0.0000, 0.0092], just missing the conventional significance level. The 90% bias-corrected bootstrap CI of [0.0003, 0.0080] did not include zero. Taken together, data tended to support Hypothesis 3b but not Hypothesis 3a. With respect to the negative affective pathway, the within-person relationship between perceived situational constraints in the morning and negative affect in the afternoon was significant. On days when employees experienced situational constraints in the morning, their negative affect increased from morning to the afternoon. This finding provides support for Hypothesis 5. Afternoon negative affect predicted an increase in issue identification from morning to afternoon, supporting Hypothesis 4. Afternoon negative affect did not predict any change in implementation. The indirect effect of situational constraints on issue identification via afternoon negative affect was 0.025, with a 95% bias-corrected bootstrap CI of [0.0040, 0.0549]. Thus, Hypothesis 6 received support. Copyright © 2015 John Wiley & Sons, Ltd.


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Table 3. Unstandardized coefficients from multi-level path analysis. Estimate

SE

Estimate

z

Predicting afternoon positive affect Between level Intercept Positive affect in the morning Negative affect in the morning Perceived prosocial impact Perceived situational constraints Residual variance Within level Intercept Time index Lagged affect (previous afternoon) Positive affect in the morning Negative affect in the morning Perceived prosocial impact Perceived situational constraints Residual variance

z

Predicting afternoon negative affect

0.012 0.823 – 0.044 – 0.095

0.025 0.076 – 0.031 – 0.024

0.476 10.892*** – 1.394 – 3.867***

0.074 – 0.865 – 0.103 0.074

0.015 – 0.061 – 0.053 0.015

0.168 – 14.254*** – 1.932 4.915***

0.700

0.124 0.006 0.041 0.038 – 0.019 – 0.017

5.656*** 0.301 !3.795*** 18.017*** – 2.212* – 13.593***

0.480 !0.010 !0.117 – 0.598 – 0.125 0.216

0.080 0.006 0.043 – 0.054 – 0.047 0.022

5.989*** !1.600 !2.747** – 11.003*** – 2.670** 9.634***

!0.155 0.689 – 0.043 – 0.225

Predicting afternoon issue identification Between level Intercept Issue identification in the morning Implementation in the morning Positive affect in the afternoon Negative affect in the afternoon Residual variance Within level Intercept Time index Lagged proactive behavior (previous afternoon) Issue identification in the morning Implementation in the morning Positive affect in the afternoon Negative affect in the afternoon Residual variance

SE

Predicting afternoon implementation

1.711 0.804 – 0.078 0.195 0.178

0.033 0.052 – 0.060 0.105 0.029

51.473 15.361*** – 1.312 1.860 6.202***

1.450 0.043 0.707 0.069 0.138 0.221

0.037 0.058 0.072 0.054 0.101 0.102

38.968 0.740 9.836*** 1.282 1.365 2.167*

0.172 !0.005 !0.153 0.605 – 0.061 0.201 0.557

0.216 0.010 0.045 0.046 – 0.048 0.069 0.052

0.796 !0.473 !3.392 13.255*** – 1.262 2.919** 10.645***

0.123 0.014 !0.072 0.137 0.424 0.083 0.075 0.410

0.165 0.009 0.040 0.046 0.056 0.039 0.049 0.048

0.746 1.612 !1.772 2.999** 7.528*** 2.108* 1.512 8.552***

Note: Estimates are unstandardized, resulting from an overall analysis including the prediction of affect and proactive work behavior in one model. Between-level predictor and control variables were centered at the grand mean. *p < .05; **p < .01; ***p < .001.

At the between-person level, only the stabilities between morning variables and the respective afternoon variables were significant.

Additional analyses One might argue that also reverse processes occur with morning proactive behavior predicting afternoon affect. To test this idea, we included additional paths into the hypothesized model (i.e., in addition to all the other predictor and control variables). Specifically, we added paths from morning issue identification and morning implementation to afternoon positive affect and afternoon negative affect. Overall, this model had a good fit, χ 2 = 107.52, df = 40, Copyright © 2015 John Wiley & Sons, Ltd.


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CFI = 0.966, RMSEA = 0.045, but it was not superior to the hypothesized model, Satorra–Bentler scaled χ 2 difference = 10.52, Δdf = 8, ns. The within-person path from morning implementation to afternoon positive affect was significant (β = 0.059; SE = 0.029; p < .05), while all other within-person and between-person paths from morning proactive behavior to afternoon affect were not. Importantly, all significant paths identified when testing our hypotheses remained significant. Because employees were nested within organizations, we also ran three-level models with organization as the Level-3 variable, person as Level-2 variable, and day as Level-1 variable. Unconditional random coefficient models showed that for all study variables, organization-level variance was very small (not exceeding 3.1 percent of the overall variance). Results from hypotheses testing remained unchanged when running a three-level model.

Discussion Our study demonstrated that perceived prosocial impact predicts an increase in positive affect from morning to afternoon. Positive affect in turn is related to the implementation component of proactive work behavior. Moreover, we found that experiencing situational constraints at work predicts an increase in negative affect later on the working day. Negative affect, in turn, predicts issue identification as one important aspect of proactive work behavior. Interestingly, positive affect only predicts implementation—but not issue identification, and negative affect predicts issue identification—but not implementation. This overall pattern of findings suggests distinct affective pathways that link everyday experiences at work to the proactive process: Negative, stressful experiences such as situational constraints increase negative affect that in turn triggers the identification of problems and other issues that could be addressed by a proactive action. Negative affect, however, does not help in actually showing any implementation behavior. More positive and energizing experiences such as perceived prosocial impact, however, increase positive affect that in turn stimulates actual implementation behavior. Thus, our study shows that proactive work behavior as a whole can benefit from both positive affect and negative affect. By separately addressing issue identification and implementation, our study highlights the specific and distinct contributions of negative and positive affect: Negative affect helps to identify the issues for proactive action, whereas positive affect is needed for the actual implementation. These findings are particularly noteworthy because issue identification and implementation are positively correlated: Positive affect and negative affect predict unique components of proactive work behavior that when taken together converge in a proactive process that brings about change in the organization. Whereas most previous research on affect and proactive work behavior focused on positive affect, our study demonstrates that negative affect plays a beneficial role for proactivity, particularly because it points employees’ attention to issues that need improvement. According to the dual-tuning perspective and the mood-as-information model, negative affect signals that things are not running in an optimal way and that a change is needed. Thereby, negative affect can contribute to positive organizational outcomes. Nevertheless, there is broad evidence that negative affect also predicts less desirable outcomes (Dalal et al., 2009; Scott & Barnes, 2011). Therefore, it is an important task for future research to identify day-specific and person-level factors that may influence the effect of negative affect: When is negative affect used to initiate constructive outcomes (e.g., by engaging in issue identification) and when is it used in a more destructive way (e.g., by engaging in counterproductive work behavior such as work withdrawal)? We did not find support for our hypothesis that also positive affect predicts issue identification. Possibly, divergent thinking and positive expectations usually associated with positive affect (Erez & Isen, 2002; Isen et al., 1987) are not useful for identifying issues that need improvement, for instance, because the positive affective valence signals that things are not problematic enough and do not need immediate attention. Positive affect, however, becomes relevant for implementation when high levels of energy and persistence as well as positive expectations are needed. Copyright © 2015 John Wiley & Sons, Ltd.



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It is important to note that we have examined high activated positive and high activated negative affects. Thus, it might be the activation component of positive affect and negative affect that accounts for the positive relationships with proactive work behavior. The findings might be different for non-activated positive affect and non-activated negative affect. For instance, when being relaxed and calm, one might not have sufficient energy to pursue the implementation of proactive action; being fatigued or depressed might not help in identifying issues for proactive action because during these deactivated states, employees are reluctant to spend further effort at work beyond the immediate task requirements (Hockey, 1997; Trougakos, Beal, Cheng, Hideg, & Zweig, 2015). Therefore, it seems that it is the high activation level that is particularly important for moving the proactive process forward. Our findings are also interesting in the light of the Affective Shift Model: Bledow and his co-workers argued that negative affect stimulates work engagement and creativity when it is followed by positive affect (Bledow, Schmitt, Frese, & Kühnel, 2011; Bledow, Rosing, & Frese, 2013). Thus, a specific combination of negative and positive affect seems to be particularly important. Instead of directly looking at the temporal order of negative and positive affect, our study suggests that negative affect is beneficial for an early phase in the action process (i.e., issue identification), whereas positive affect is important for a later phase (i.e., implementation). Based on our findings, one might speculate that negative affect can be an important “stepping stone” so that positive affect can unfold its full potential. The additional analysis of reverse causal processes showed that implementation of proactive work behavior predicts positive affect, suggesting that proactive behavior in itself may be a positive experience. Possibly, positive affect that results from implementing proactive action triggers future proactive behavior, pointing to a selfreinforcing process. Importantly, taking this reciprocal path between implementation and positive affect into account did not alter the other significant results. Findings at the between-person level differ largely from the findings at the within-person level. For instance, at the between-person level, neither perceived prosocial impact nor perceived situational constraints were related to an increase in positive or negative affect from morning to afternoon. Thus, differences between persons in perceived prosocial impact or perceived situational constraints cannot explain why some persons experience a change in positive or negative affect over the course of the day, while others do not. Possibly, personality factors or allocations of work tasks over the day are more important for between-person differences in affect change during a workday. Overall, this distinct pattern of findings at the between-person level highlights the importance of differentiating between within-person and between-person relationships (Dalal, Bhave, & Fiset, 2014; Fisher & To, 2012).

Limitations and research implications Our study has some limitations. First, with respect to positive and negative experiences as predictors of affect, we focused on perceived prosocial impact and perceived situational constraints. Perceived prosocial impact and perceived situational constraints are exemplary positive and negative experiences, and they indeed predicted change in positive affect and negative affect, respectively, over the course of the day. Our study, however, does not provide any information if other experiences at work play a similar role for positive and negative affect. Future research may want to use a broader set of experiences as predictors of affective states and proactive work behavior. Second, we deliberately chose a daily-diary design to capture short-term fluctuations in proactive work behavior. Although this design provides new insights in the dynamics of experiences at work, affect, and proactive work behavior over the course of the day, it does not allow any conclusions about longer-term processes. For instance, one single experience of a situational constraint alone may not yet stimulate proactive work behavior; it might need a series of situational constraints experienced over some time until an employee is convinced that something needs to be done. Similarly, the benefit of perceived prosocial impact may spill over into the next day. Our design cannot capture these processes. Future studies are needed that will address both short-term and longer-term processes and allow for the analysis of more complex processes such the accumulation of positive and negative experiences. Third, we assessed our data with self-report measures that may be associated with some concerns, including common method variance (Podsakoff, MacKenzie, & Podsakoff, 2012). To mitigate these concerns, we followed Copyright © 2015 John Wiley & Sons, Ltd.


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the advice of Conway and Lance (2010) and conducted confirmatory factor analyses to demonstrate construct validity. Moreover, by using a daily-survey design and a multi-level analysis in which we differentiated between a between-person and a within-person part, we removed all between-person sources of inflated relationships (e.g., trait negative affectivity and social desirability) from the within-person part of our model. Finally, most of our study constructs (subjective experiences and affect) prototypically ask for self-report measures; identifying issues that may need proactive action is mainly a cognitive process that is difficult to observe from the outside. Taken together, we believe that our self-report measures are not a major threat for the validity of our findings. Finally, the reliability of the situational constraints measure was comparably low on one day, possibly because employees might have experienced one specific hassle on this day. Future research might want to identify specific constraints that are particularly stressful and increase negative affect. Despite the low reliability on one day, overall, the mean scale reliability across all days of data collection was still acceptable.

Practical implications Perceived prosocial impact was related to an increase in positive affect that in turn predicted proactive work behavior. Thus, with respect to practical implications, enhancing the experience of benefitting others at work might stimulate proactive behavior. This could be done by job-design efforts so that the job provides more opportunities for making a difference (Grant, 2007) and by enabling employees to have meaningful contact with the beneficiaries of their work (Grant, 2008). Moreover, specific interventions might aim at increasing employees’ perception of having an impact. This goal could be achieved by encouraging employees to reflect at the end of each working day about how their work has helped others or how it may benefit others in the future (cf. Seligman, Steen, Park, & Peterson, 2005). Our study suggests that implementation of proactive work behavior benefits from positive affect. Besides prosocial impact, positive affect may also result from other experiences at work such as confidence to accomplish an important task (Fisher, Minbashian, Beckmann, & Wood, 2013), positive feedback about one’s own performance (Ilies & Judge, 2005), or even short energizing breaks (Fritz, Lam, & Spreitzer, 2011). Therefore, when planning their workdays, employees and their managers might deliberately schedule tasks and activities that have the potential to stimulate positive affect. Negative affect predicted issue identification as one important component of proactive work behavior. However, we are reluctant to recommend fostering negative affect because it is associated with less desirable outcomes as well (Dalal et al., 2009). Maybe, when recognizing that an employee is in a state of high negative affect, managers may want to start a conversation that aims at identifying issues in the work situation that need improvement. Thereby, they might learn about some issues they otherwise would have missed. In conclusion, our study identified positive affect and negative affect as the linking mechanism between everyday work experiences and proactive work behavior. Importantly, positive affect and negative affect stimulate distinct components of proactive work behavior, highlighting one of the core ideas of the dual-tuning perspective that both positive and negative affects can be functional and adaptive and that they may stimulate behaviors that are valued within organizations (George, 2011).

Acknowledgements This research was funded by a grant from the German Research Foundation (DFG, So 295/7-1) that is gratefully acknowledged. We thank Ann-Kathrin Dekker, Sebastian Gaber, Clara Roeingh, Lena Röser, Lisa Rostock, and Paula Schabel for their help in data collection, and Doris Fay and Tina Urbach for helpful comments on an earlier version of this paper. Copyright © 2015 John Wiley & Sons, Ltd.



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