the role of affect and leadership during ... - Wiley Online Library

PERSONNEL PSYCHOLOGY 2012, 65, 121–165

THE ROLE OF AFFECT AND LEADERSHIP DURING ORGANIZATIONAL CHANGE MYEONG-GU SEO Department of Management and Organization University of Maryland M. SUSAN TAYLOR Department of Management and Organization University of Maryland N. SHARON HILL Department of Management The George Washington University XIAOMENG ZHANG Kogod School of Business American University PAUL E. TESLUK School of Management University at Buffalo NATALIA M. LORINKOVA Department of Management and IS Wayne State University

Based on multilevel data collected at 2 points in time, we examine the role of employees’ affective experiences in shaping their commitment and behavioral responses to both the initial (Time 1) and later (Time 2) phases of organizational change (12 months later). We also test the cross-level effect of workgroup managers’ transformational leadership on their employees’ responses to change. We find strong support for predicted longitudinal relationships between employees’ affective experiences and their commitment and behavioral responses to change. In particular, employees’ positive and negative affect (NA) at Time 1 significantly predict both their commitment to change and the 3 dimensions (supportive, resistant, and creative) of behavioral responses at Time 2. Further, the effects of NA directly influence employee change commitment and behaviors at Time 2, whereas the long-term effects of positive affect occur both directly and indirectly through commitment to change at Time 1. Finally, our results support the hypothesized role of workgroup managers’ transformational leadership in shaping employees’ affective reactions and commitment to change at the initial This research was supported by National Science Foundation (#0452984) and the Smith Technology Integration Initiative Grant to Myeong-Gu Seo, Susan Taylor, and Paul Tesluk. Correspondence and requests for reprints should be addressed to Myeong-Gu Seo, Department of Management and Organization, University of Maryland, College Park, MD 20742-1815; [email protected]. C 2012 Wiley Periodicals, Inc.

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PERSONNEL PSYCHOLOGY phase of change and thereby, their subsequent behavioral responses in the later phase. We discuss the implications for theory and practice in organizational change.

During the last 2 decades, a variety of forces in the external environment including enhanced competition, customer dissatisfaction, reduced revenues, among other factors, have increased the frequency of change in all types of organizations, including those in the private, public/governmental, and nonprofit sectors (Attaran, 2004). However, despite the high frequency with which organizations undertake change, more often than not these efforts fail to deliver the desired results on a variety of outcomes, including cost reduction, employee attitudes and productivity, and revenue growth (Attaran, 2004; Marks, 2006; Paper & Chang, 2005). Although many factors undoubtedly contribute to failed organizational change efforts, scholars and practitioners increasingly point to the important role of the “human element.” In particular, they emphasize employees’ lack of commitment to enacting significant and enduring changes in work behaviors (cf., Beer, Eisenstat, & Spector, 1990). Research from a number of different sources suggests that the intense negative emotions experienced by employees are at the heart of employees’ low commitment to change (e.g., Bartunek, 1984; Buono & Bowditch, 1989; Fugate, Kinicki, & Prussia, 2008; Kiefer, 2005). Yet, surprisingly little work has examined the direct role of employee emotions in determining their commitment to change, even though logic and indirect findings clearly suggest a linkage between these two variables. Rather, researchers examining emotions in the context of organizational change have typically assessed them indirectly (cf., Mossholder, Settoon, Armenakis, & Harris, 2000), using general emotion-laden constructs, such as unfairness, job security, resistance to change (cf., Kiefer, 2005), and/or cynicism (Dean, Brandes, & Dharwadkar, 1998; Reichers, Wanous, & Austin, 1997), rather than direct assessments of employees’ emotional states. Moreover, although employees’ emotional experiences during organizational change are relatively transient in nature (Weiss & Cropanzano, 1996), prior research has not considered whether the impact of employees’ short-lived emotions on their commitment and behavioral reactions to change is necessarily short term. There is indirect evidence that the outcome of intense emotional experiences during organizational change can persist for a long period of time, even years after the event. For example, negative emotions can cause the development of posttraumatic stress (e.g., Bacharach & Bamberger, 2007; Flannery, 1999). Further, positive emotions can result in the accumulation of enduring personal (physical, intellectual, social, and psychological) resources for effective coping and personal well-being (e.g., Fredrickson, 2001; Fredrickson, Cohn, Coffey,

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Pek, & Finkel, 2008). Yet, few scholars have investigated the dynamic unfolding of emotional influence during organizational change over time. Therefore, the first objective of our study is to examine both the shortand long-term influence of employee affect, or experienced emotion, on their commitment and behavioral chain of reactions. Furthermore, our current understanding of employee behavioral responses to change and their implications for change success is highly simplistic and largely captured in a single dimension that is best termed “supportiveness of change.” This does not sufficiently explain the complex activities that employees generally undertake during organizational change (cf., Herscovitch & Meyer, 2002). We note, for example, that employees also exhibit various forms of resistant behaviors that interfere with successful change (Miles, 2010; Reger, Gustafson, Demarie, & Mullane, 1994). Such resistance is not necessarily, however, the same as lack of supportiveness. Further, successful implementation of organizational change requires a high degree of employee creativity in order to fundamentally change old ways of behaving and develop new approaches. This type of employee creativity also appears distinct from supportiveness and is likely to serve different but important functions such as developing innovative work practices that accomplish the change program’s goals (cf., Amabile & Conti, 1999; Probst, Stewart, Gruys, & Tierney, 2007). Yet, few scholars have simultaneously investigated multiple dimensions of employee behavioral responses to organizational change, including the supportiveness, resistance, and creativeness dimensions. Therefore, the second objective of this paper is to propose and empirically examine the psychological processes that emerge over time as employees experience a range of positive and negative emotions during organizational change, develop both affective and normative commitment to the change in response to this affect, and then enact supportive, resistant, and creative change behaviors. We argue that it is critically important for organizational leaders to understand these processes in order to effectively manage the change. Finally, growing recognition of important employee affective and behavioral reactions to organizational change raises questions about what constitutes effective leadership of change. Here, we refer to leadership that recognizes and manages employee emotional experiences. Although senior-level leaders have received the most attention in the planned change literature to date (Kanter, Stein, & Jick, 1992; Kotter, 1995; Nadler & Tushman, 1995), the conceptual and inductive work by Huy (1999, 2002) has identified workgroup managers’ leadership to be an important determinant of the effectiveness of their own and their employees’ emotional responses to organizational change. Yet, there have been no empirical tests of his model nor has Huy’s inductive framework been integrated with

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existing leadership theories in order to provide a coherent body of research incorporating both the organizational change and leadership literatures (Herold, Fedor, Caldwell, & Liu, 2008). To address this gap, we study the cross-level relationships between workgroup managers’ transformational leadership and related employee emotional reactions to change. We focus on transformational leadership because it is particularly relevant to the context of organizational change (Bass & Riggio, 2006), has been found to predict followers’ affective commitment to change (Herold et al., 2008), and has been identified in the leadership behaviors of first-line and middle-level managers, as well as in executive leaders (Bass, 1990). This paper proceeds as follows. Based on affective events theory (Weiss & Cropanzano, 1996), we first develop a longitudinal, conceptual framework that predicts both the short- and long-term consequences of employees’ affective experiences on multiple dimensions of their commitment and behavioral reactions to organizational change. Then, we incorporate the transformational leadership of employees’ immediate managers as a cross-level antecedent of employees’ affective experiences during organizational change. We test this multilevel, dynamic conceptual framework in a longitudinal field study of a large government agency that had recently begun to implement an organization-wide change. Prior research has rarely examined such dynamism in organizational change; hence, this study provides important implications for better understanding and managing the complexity of organizational change.

The Nature and Consequences of Affective Experience During Organizational Change

According to affective events theory (Weiss & Cropanzano, 1996), various events occur in organizations that have immediate affective consequences (called affective events) for their employees. Thus, from the perspective of this theory, organizational change can be understood as a series of affective events that trigger a range of intense and enduring emotional reactions from employees (e.g., Bartunek, 1984; Fugate et al., 2008; Huy, 2002; Kiefer, 2005). These emotional reactions, in turn, influence employee behavior either directly or indirectly through the formation of more stable psychological states (Weiss & Cropanzano, 1996). Therefore, based on affective events theory, an important step in understanding employees’ responses during organizational change is to study the nature of their affective experiences during the change. The organizational change literature provides rich evidence that employees’ affective reactions during change represent two core dimensions of affective experience, commonly known to emotion researchers as


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positive affect and negative affect (Watson & Tellegen, 1985) or more recently, as positive and negative activation (Watson, Wiese, Vaidya, & Tellegen, 1999). For example, qualitative reports from employees regarding their experiences during organizational change frequently mention that thinking about the effects the change might bring to their jobs and careers elicits feelings of high negative affect, such as anxiety, stress, and anger (e.g., Kiefer, 2005; Jick, 1990; Schweiger & Denisi, 1991). Researchers have also found that employees often experience depression, sadness, and fatigue (the bipolar opposite of high positive affect experiences such as excitement or enthusiasm: Watson, Clark, & Tellegen, 1988; Watson & Tellegen, 1985) when they realize that they have no control over the changes that are occurring in their work. Employees experience similar feelings when they must cope with seemingly impossible workloads resulting from a loss of staff at a time when the organization must maintain existing value-added activities while also implementing new ones that further the success of the change (e.g., Buono & Bowditch, 1989; Kanter, 1984). In response to the barrage of factors that negatively impact employee emotions during organizational change, scholars have recommended that managers in organizations undergoing change generate high levels of positive affect in the form of employee excitement and enthusiasm (e.g., Cooperrider, 1990; Cooperrider & Srivastva, 1987; Huy, 2002; Watkins & Mohr, 2001). For example, they may vividly portray a highly desirable future state or vision of the organization associated with successful change implementation. Finally, affective events theory (Weiss & Cropanzano, 1996) also predicts that positive and negative affect will have both immediate (shortterm) and enduring (long-term) consequences. By integrating affective events theory and organizational change research, we build a theoretical model (Figure 1) that links the dynamic, long-term consequences of employees’ affective experiences to their commitment and behavioral responses during organizational change. This extends existing emotion research that has mainly focused on the immediate consequences of affective experiences (for reviews see Barsade & Gibson, 2007; Weiss & Cropanzano, 1996). As summarized in Figure 1, we develop hypotheses regarding the long-term relationships between employees’ affective experience at the initial phase of change (Time 1) and their commitment to organizational change (both affective and normative forms) and behavioral responses (supportive, resistant, and creative dimensions) in the later phase of change (Time 2). In addition, we develop hypotheses related to the indirect mechanisms through which these long-term relationships are mediated by their initial levels of affective and normative commitment to change (Time 1).

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Figure 1: Conceptual Model.

- Affective - Normative T1

Commitment to Change

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- Supportive - Resistant - Creative T2

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Employees’ Affective Experiences and Responses to Change

In this section, we build hypotheses linking employees’ affective experiences in the initial phase of change to their short-term (Time 1) and long-term (Time 2) commitment to change and their long-term behavioral responses to change. Affective Experience and Commitment to Change

Commitment to change refers to the degree of an individual’s willingness and desire to support the change (Herscovitch & Meyer, 2002). In this study, we consider two distinct dimensions of commitment to change that Herscovitch and Meyer (2002) found to predict discretionary behavioral outcomes:1 affective and normative commitment to change. Affective commitment to change is a desire to support the change based on a belief in its inherent benefits (Meyer & Allen, 1997); normative commitment to change is a feeling of obligation to support the change that results from a sense of needing to reciprocate positive treatment received from the organization (Meyer & Allen, 1997) and/or a sense of moral duty rooted in loyalty to the organization (Meyer & Parfyonova, 2009). We argue that employees’ early affective experiences during organizational change impact both affective and normative commitment to change both in the early and later phases of the change. We start by discussing the shortterm impact of employees’ affective experience on their commitment to change. Short-Term Relationship Between Affect and Commitment to Change

We predict that employees’ initial affective experiences during the change will influence their early commitment to change. This may occur through two mechanisms. The first mechanism is described by the feeling as information (Schwarz, 1990; Schwarz & Clore, 2003), more recently termed the risk-as-feelings hypotheses (Loewenstein, Weber, Hsee, & Welch, 2001). These both suggest a direct infusion effect in which individuals’ positive or negative affect provides immediate evaluative information, in this case about the change, including how it is being managed and how employees feel about their treatment from the organization during the change. With regard to positive affect, employees’ positive feelings 1 According to Herscovitch and Meyer (2002), there is another component of commitment to change, “continuance commitment,” which is rooted in perceived negative consequences of noncompliance to change. We did not include this particular type of commitment in this study because we did not expect it to be related to the discretionary behavioral responses to change (Herscovitch & Meyer, 2002) that we predict in this paper.

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serve as positive evaluative information about the change (outcomes) that should promote a desire to support it. Similarly, with regard to normative commitment, positive affect provides positive evaluative information about how employees are being treated during the change and how the change is being managed that will likely strengthen their felt obligation to support the change. On the other hand, those who feel more negative affect are likely to show less commitment to change because their negative feelings serve as negative evaluative information about the change that directly undermines the desire and felt obligation to support it. We argue that these effects of direct affective infusion are likely to be short term in nature because most affective experiences in organizations are relatively short lived (Barsade & Gibson, 2007). Therefore, we expect that the affect infusion effects will occur primarily at the moment that positive or negative feelings are experienced. The second mechanism, mood congruence recall effect, describes the individual tendency to recall materials from memory that are consistent with one’s affective state at the time of recall (e.g., Meyer, Gayle, Meeham, & Harman, 1990). Based on this body of research, employees experiencing more positive affect are likely to develop stronger affective and normative commitment to change by recalling more positive information about both the change and the treatment from the organization during the change, respectively, from their memories in forming their evaluative judgment about the change, whereas employees experiencing more negative affect are likely to display less commitment to change due to the greater amount of negative information about the change retrieved from their memories. Similar to the direct affective infusion effect described above, this memory effect also occurs in the short term while people are experiencing positive or negative feelings. Based on these arguments, we hypothesize: Hypothesis 1a: During the initial phase of change (Time 1), employees’ experience of positive affect will be positively related to their affective (Hypothesis 1a1) and normative (Hypothesis 1a2) commitment to change. Hypothesis 1b: During the initial phase of change (Time 1), employees’ experience of negative affect will be negatively related to their affective (Hypothesis 1b1) and normative (Hypothesis 1b2) commitment to change Long-Term Relationship Between Affect and Commitment to Change

Research has shown that once developed, early levels of commitment tend to persist over time (Bateman & Strasser, 1984; Meyer, Hecht, Gill, & Toplonytsky, 2010; Porter, Steers, Mowday, & Boulian, 1974). Hence,


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we expect that affective experiences, both positive and negative, during the initial phase of change will be positively related to affective and normative commitment in the later phase of change through their relationship to Time 1 commitment to change. However, we predict that the relationship between positive affect and Time 2 commitment to change will be fully mediated by Time 1 commitment to change, whereas in the case of negative affect, this long-term relationship will be only partially mediated by Time 1 commitment. To explain these differential predictions for the long-term relationship between positive and negative affect and commitment to change we refer again to the memory retrieval effect described earlier. Related to this effect, a growing body of research suggests that positive and negative emotional experiences are themselves stored as emotional memories, which are continuously retrieved as salient information to form evaluative judgments (for reviews see Porter & Peace, 2007; Sotgiu & Mormont, 2008). However, recent studies show that positive emotional memories deteriorate more rapidly over time in terms of consistency and quality relative to negative emotional (traumatic) experiences that remain virtually unchanged for several months to several years (e.g., Porter & Peace, 2007). Because positive emotional memories tend to deteriorate quickly, we expect that the relationship between positive affect at Time 1 and commitment to change at Time 2 will operate mainly through Time 1 commitment to change. In other words, the long-term relationship between positive affect and commitment to change will be fully mediated by employees’ early levels of commitment to change. On the other hand, given the more long-term enduring effect of negative affect on memories, we expect a partial mediation effect, in which in addition to influencing employees’ initial commitment to change that in turn is carried over to the later phase of change, negative affect also directly and negatively affects their commitment to change in the later phase of change. Hypothesis 2a: Employees’ experience of positive affect during the initial phase of change (Time 1) will be positively related to their affective (Hypothesis 2a1) and normative (Hypothesis 2a2) commitment to change in the later phase of change (Time 2); these relationships will be fully mediated by their affective (Hypothesis 2a3) and normative (Hypothesis 2a4) commitment to change in the initial phase of change (Time 1), respectively. Hypothesis 2b: Employees’ experience of negative affect during the initial phase of change (Time 1) will be negatively related to their affective (Hypothesis 2b1) and normative (Hypothesis 2b2) commitment to change in the later

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phase of change (Time 2); these relationships will be partially mediated by their affective (Hypothesis 2b3) and normative (Hypothesis 2b4) commitment to change in the initial phase of change (Time 1), respectively. Commitment to Change and Multiple Dimensions of Behavioral Responses to Change

In this section, we develop hypotheses linking employees’ early affective and normative commitment to change (Time 1) to their behavioral responses later in the change (Time 2). In addition, we extend the work of Herscovitch and Meyer (2002) by moving beyond a focus on employees’ behavioral support for the change to examine two additional behavioral responses: behavioral resistance to change and creative behavior for change. We develop hypotheses related to each of these three behavioral responses in turn. First, behavioral support for change refers to the degree to which employees engage in behaviors that demonstrate support for a change, for example, by going along with the spirit of the change and by being prepared to make modest sacrifices or even go above and beyond what is formally required to ensure the success of the change. Conceptually, those who are affectively committed to change because they see its inherent benefits are likely to engage in supportive behaviors that advance the change effort. A similar argument can be made for those committed to change out of their sense of obligation. In support of these arguments, Herscovitch and Meyer (2002) reported that both affective and normative forms of commitment to change were significant predictors of behavioral support for change among nurses who experienced various types of organizational change. Therefore, we posit that affective and normative commitment to change will be positively related to behavioral support for change. Second, behavioral resistance to change refers to the degree to which employees engage in behaviors aimed at preventing the success of the change or ensuring its failure. Change scholars have long considered resistance to be a distinct psychological and behavioral response to change, independent of degree of supportiveness (cf., Piderit, 2000). Although Herscovitch and Meyer (2002) treat resistance to change as the bipolar opposite of behavioral support for change, we disagree based on past research in the motivation literature. A number of researchers have suggested that approach (support) and avoidance (resistance) behaviors are stimulated by two distinct and relatively independent systems of self-regulation (cf., Aspinwall & Taylor, 1997; Carver & Scheier, 1998). As a result, both the triggers and the underlying processes that produce resistant behaviors are expected to differ from those that produce supportive behaviors. We therefore treat resistance to change as a unique behavioral dimension


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separate from behavioral support for change. Further, we argue that employees’ commitment to change, whether based on its inherent benefits or a sense of obligation, is likely to create cognitive and motivational dissonance with the enactment of various resistant behaviors intended to block or hinder the success of the organizational change (e.g., Elliott & Devine, 1994). As a result, both affective and normative commitments to change are likely to be negatively related to behavioral resistance to change. Finally, the display of creative behaviors for change is another important behavioral dimension particularly relevant to organizational change. Successful change requires innovative insights and ideas to identify new ways of behaving that are consistent with the spirit and goals of the change (cf., Heifetz & Laurie, 2001). In the midst of the constant uncertainty produced by organizational change, employees are unlikely to exhibit such behaviors in the absence of a strong commitment to the change (cf., Eisenberger, Fasolo, & Davis-LaMastro, 1990). We argue that affective and normative commitment to change will foster creative behavior for change by providing attentional and motivational resources required for generating creative ideas and new thoughts and also by directing them in ways to support the change. In summary, we hypothesize the following relationships between commitment to change and long-term behavioral responses to the change:

Hypothesis 3a: Employees’ affective commitment to change during the initial phase of organizational change (Time 1) will be related to their change-related behaviors in the later phase of change (Time 2); specifically, affective commitment will be positively related to behaviors in support for change (Hypothesis 3a1), negatively related to behaviors resistant to the change (Hypothesis 3a2), and positively related to creative behaviors for the change (Hypothesis 3a3). Hypothesis 3b: Employees’ normative commitment to change during the initial phase of organizational change (Time 1) will be related to their change-related behaviors in the later phase of change (Time 2); specifically, normative commitment will be positively related to behaviors in support for change (Hypothesis 3b1), negatively related to behaviors resistant to the change (Hypothesis 3b2), and positively related to creative behaviors for the change (Hypothesis 3b3).

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Affective Experience and Behavioral Responses to Change

Our discussion so far suggests an indirect relationship between employees’ affective experiences early in the change and their behavioral responses in the later phases of change mediated through their commitment to change. However, there is also reason to expect a direct relationship between positive and negative affective experiences and behavioral responses to change. A growing body of emotion research has found that affective experience serves an inherently motivational function by directly influencing behavior rather than being mediated by cognitive judgments or conscious awareness (e.g., Bargh & Chartand, 1999; Winkielman, Zajonc, & Schwarz, 1997; Zajonc, 1980). In particular, we draw on broaden-andbuild theory of positive emotions (e.g., Fredrickson, 2001; Fredrickson et al., 2008) and posttraumatic stress theory (e.g., Bacharach & Bamberger, 2007; Flannery, 1999) to hypothesize several direct paths through which positive and negative affect influence employees’ behavioral responses in the long term. A considerable body of research suggests that positive affect fosters prosocial behaviors such as supporting and cooperating with others (cf., Barry & Oliver, 1996; George & Bettenhausen, 1990; Rhoades, Arnold, & Jay, 2001). Therefore, employees experiencing positive affect are likely to engage in various supportive and cooperative behaviors during organizational change. Moreover, the broaden-and-build theory of positive emotions (e.g., Fredrickson, 2001; Fredrickson et al., 2008) suggests that ongoing experience of positive emotions builds a range of personal resources over time, including physical (e.g., physical health), social (e.g., friendship and social support networks), and psychological resources (e.g., resilience and optimism). By providing such enduring resources, positive affect may assist employees to continue to exhibit supportive behaviors during organizational change over an extended period of time. Fredrickson and colleagues’ broaden-and-build theory also suggests that positive emotions extend individuals’ attention, thinking, and behavioral repertories (e.g., Fredrickson, 2001; Fredrickson et al., 2008), which enhances their creativity (e.g., Amabile, Barsade, Mueller, & Staw, 2005; Isen, Daubman, & Nowicki, 1987). Furthermore, positive affect and broadened thinking that stimulates creativity are mutually enhancing, leading to an upward spiral towards experiencing greater positive emotions and creative coping (e.g., Fredrickson & Joiner, 2002). Therefore, employees who experience more positive affect during organizational change are likely to exhibit more supportive and creative behaviors in the long term. In contrast, negative affect tends to foster defensiveness (Seo, Barrett, & Bartunek, 2004) and various competitive behaviors (cf., Barry


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& Oliver, 1996; Rhoades et al., 2001). In times of organizational change, the defensive stance activated by negative affect can be expressed in various forms of resistant behaviors, such as passively withdrawing from change initiatives and/or actively sabotaging them to make them fail. In particular, intense negative emotions can lead to posttraumatic distress that may have severe adverse effects on employees’ emotional well-being (e.g., Bacharach & Bamberger, 2007; Flannery, 1999). For example, such stress can trigger severe negative emotions over a longer period of time (e.g., Brewin & Holmes, 2003), which results in a continuous display of defensive and avoidance behaviors (e.g., Creamer, Burgess, & Pattison, 1992). In addition, prolonged experience of intensive negative emotions can lead to rapid and prolonged depletion of mental and physical resources (e.g., Hobfoll, Johnson, Ennis, & Jackson, 2003), which may directly undermine the ability to continue to engage in supportive behaviors over time. Therefore, employees experiencing more negative affect during organizational change are likely to engage in more resistant behaviors and less supportive behaviors in the long term:

Hypothesis 4a: Employees’ positive affective experience during the initial phase of organizational change (Time 1) will be positively and directly related to their levels of behavioral support for change (Hypothesis 4a1) and creative behavior for change (Hypothesis 4a2) in the later phase of change (Time 2). Hypothesis 4b: Employees’ negative affective experience during the initial phase of organizational change (Time 1) will be positively and directly related to their level of behavioral resistance to change (Hypothesis 4b1) and negatively and directly related to their level of behavioral support for change (Hypothesis 4b2) in the later phase of change (Time 2).

Based on both theory and empirical research, our previous hypotheses have posited the long-term, direct, and indirect relationships between employees’ affective experience and their commitment and behavioral responses to change. We turn now to developing hypotheses regarding the cross-level relationships between the leadership of employees’ direct managers and the employees’ affective experience during organizational change.

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Cross-Level Relationships Between Direct Managers’ Transformational Leadership and Employees’ Responses to Change

Transformational leadership has consistently been found to relate to employee psychological and behavioral outcomes (Judge & Piccolo, 2004) and is considered particularly important during times of organizational change (cf., Bass, 1985). This is because transformational leadership explicitly draws employees’ attention to a desired future state (vision) and instills confidence in their ability to meet high expectations (Bass, 1985; Bass, Avolio, Jung, & Berson, 2003; Bommer, Rich, & Rubin, 2005; Bono & Judge, 2003; Eden, 1992). Transformational leaders engage in behaviors that articulate a clear vision, demonstrate enthusiasm and passion, and inspire and motivate employees to work hard (Bass, 1985). By doing so, transformational leaders stimulate followers’ growth and self-actualizing needs and emphasize a higher order purpose in service of a larger community beyond their individual needs, desires, and welfare (Bono & Judge, 2003; Zaccaro, 2001). Herold et al. (2008) recently examined the effects of transformational leadership exhibited by managers from multiple organizations and at various levels of their firm’s hierarchy. The researchers found transformational leadership to be positively related to affective commitment to change as reported by organizational members in the manager’s work unit. We extend this line of inquiry by hypothesizing that the transformational leadership exhibited by employees’ direct managers in their workgroups during organizational change will influence employees’ affective reactions and commitment to change, which in turn will affect their long-term behavioral reactions to change. We follow previous scholars in conceptualizing transformational leadership at the leader’s workgroup level within the organizational hierarchy (e.g., Bono & Judge, 2003; Kark, Shamir, & Chen, 2003; Shamir, Zakay, Breinin, & Popper, 2000). These scholars argue that transformational leaders influence not only outcomes of individual followers but also collective outcomes at the workgroup level, for example, by emphasizing the collective identity and shared values of a work unit and/or by building strong culture, norms, and cohesiveness within a workgroup. Therefore, our hypotheses relating the transformational leadership of employees’ direct manager (workgroup level) to individual employee’s affective experience and commitment to change (employee level) are cross-level in nature. In addition, the major tasks of transformational leadership, such as developing a vision for change and inspiring employees towards the vision, are most likely to be needed in the initial phase of change where organizations set the direction for change and mobilize resources for change rather than in the later phases where organizations focus on sustaining and


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stabilizing the change (Kotter, 1996; Seo & Hill, 2005). Therefore, we focus our hypotheses on the impacts of transformational leadership within the initial phase of change (Time 1). Consistent with the psychological process model of individual follower reactions developed earlier, we argue that transformational leadership is directly related to employees’ experience of positive and negative affect. In support of this argument, a growing body of research has found that transformational leadership works through influencing followers’ affective experiences (e.g., Bono & Ilies, 2006; Rubin, Munz, & Bommer, 2005). First, by expressing enthusiasm and optimism for change and demonstrating confidence in themselves and others, transformational managers’ are likely to directly affect their employees’ experience of positive feelings. Second, managers’ display of individualized consideration for each employee, another important dimension of transformational leadership (Bass, 1990; Bono & Judge, 2003), is likely to alleviate their employees’ negative emotions such as fear and anger that frequently arise during organizational change (Kiefer, 2005). Therefore, we hypothesize a positive cross-level main effect of manager’s transformational leadership behavior on employees’ positive affect and a negative cross-level main effect on their negative affect during organizational change. We also posit that managers’ transformational leadership during organizational change affects employee commitment to change. Transformational leaders are likely to engender affective commitment to change by providing a clear and engaging vision and also expressing high expectations and confidence in their employees’ capabilities (e.g., Herold et al., 2008). In addition, they are likely to build normative commitment to change. When transformational leaders, whom employees tend to view as agents of the organization (Eisenberger, Stinglhamber, Vandenberghe, Sucharski, & Rhoades, 2002), listen to their concerns and help them address problems, employees are likely to feel an obligation to reciprocate this positive treatment by supporting organizational goals. Further, beyond this sense of indebted obligation, transformational leaders should also generate feelings of loyalty and a moral obligation to support the organization’s initiatives (Meyer & Parfyonova, 2009) because such leaders appeal to employees’ higher order values, describe the group’s work in ideological terms, and emphasize the collective responsibility to contribute to the organization’s future (Bono & Judge, 2003; Shamir, House, & Arthur, 1993). Therefore, we hypothesize two positive main effects of supervisors’ transformational leadership on employees’ affective and normative commitment to change Hypothesis 5a: During the initial phase of organizational change (Time 1), managers’ transformational leadership will

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be positively related to their employees’ positive affect (Hypothesis 5a1) and negatively related to their employees’ negative affect (Hypothesis 5a2). Hypothesis 5b: During the initial phase of organizational change (Time 1), managers’ transformational leadership will be positively related to their employees’ affective commitment (Hypothesis 5b1) and normative commitment to change (Hypothesis 5b2).

Method

We tested the hypothesized conceptual model in a field setting, the headquarters of a large, government agency in the transportation area, which was in the process of integrating multiple work units into a larger stand-alone organization that would run more like a private-sector business. The organization aimed to build cross-functional capabilities to satisfy the needs of internal and external customers by emphasizing employee input and innovation. Therefore, this change involved altering fundamental change to the organizational structure and major processes, as well as its identity. We invited employees from all agency departments that were impacted by the restructuring to participate in this study. The study included two points of time, approximately at the beginning and the middle stages of the overall change effort. The first stage focused on the major restructuring of the organization in order to redirect organizational resources including human capital, to activities most critical to the organization’s mission. This phase involved the allocation of resources and the development of basic financial processes, which directly affected employees’ tasks and reporting relationships in the new organization. Our first survey (Time 1) was administered approximately when the reorganization plan was mostly implemented and completed. The second phase immediately followed the first phase, focusing on cost control and productivity improvement. Major change efforts in this phase included developing operating plans, establishing cost management, and providing appropriate training for employees and managers. The second phase of the change took another full fiscal year, and our second survey (Time 2) was conducted at about the time the second phase of the change was mostly over. Research Design

The study utilized a nonexperimental (naturally occurring), longitudinal research design at two levels of analysis. At Time 1, employees assessed their direct managers’ transformational leadership and reported


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their own affective, attitudinal, and behavioral responses to change. We collected their responses through a Web-based survey in which participants were assigned a unique ID number that safeguarded the confidentiality of their responses. This procedure allowed our research team to connect different measures over time and to link individuals from the same work group together. Employee affect, commitment, and behavioral data formed the first level of analysis in the study. Work group members’ assessments of their immediate manager’s leadership, aggregated across group members, constituted the second level of analysis. Approximately a year after the first survey (Time 2), employees reported their own attitudinal and behavioral responses to change using the same procedure followed in the initial survey. Sample

At Time 1, we e-mailed invitations to all 3,264 employees working in the headquarters (100%) and received a return rate of 30.7% (1,001 responses). Only employees from groups where two or more individuals responded were included in the Time 1 sample. This requirement reduced the number of participating employees to 906 employees who reported to 217 managers and supervisors. The number of respondents per workgroup ranged from 2 to 10, with a mean size of 4.2 employees. We also collected data on gender (64.6% male), age (34.46% 35 and under, 63.61% between 36 and 55, 1.93% over 55), race (75% White, 10.6% Black, 4% Hispanic or Latin, 3.5% Asian, 6.9% other), highest education level (49.2% with a bachelor’s degree or higher), organizational tenure (13.8% less than 5 years, 34.9% between 6 and 15 years, 51.3% greater than 15 years), and tenure in current position (34.9% less than 2 years, 65.1% greater than 2 years). At Time 2 (approximately a year later), we e-mailed invitations to the 2,993 employees who received the first survey, and 31.8% of them (951 employees) responded. Among the respondents were 430 employees (45.2%) who responded to both the first and the second surveys. This group formed the longitudinal, final sample. Independent t-tests revealed no significant differences in demographics (gender, age, and tenure) between the Time 1 and Time 2 samples, between the final and the rest of the Time 1 samples, and between the final and the rest of the Time 2 samples, except for two minor differences: The participants in the final sample included slightly (7%) more men than those in the rest of the Time 1 sample (p < .05) and were slightly older (1.2 years) than those in the rest of the Time 2 sample (p < .05). Overall, our final sample reasonably represents both the Time 1 and Time 2 samples, which in turn, represent the entire employees in the organization.

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Measures2

Affective experience. We measured employees’ affective experience by using 14 items of affective adjectives selected from the PANAS and its subscales developed by Watson and Clark (1992). The selection of these 14 items was guided by the organizational change literature. For example, certain affect items in the PANAS scale, such as alert, ashamed, or guilty, were not selected because they are not considered as positive or negative feelings typically experienced during organizational change. We asked employees to report their current affective experiences during organizational change using the 14 items on a 5-point scale (1 = not at all; 5 = extremely so). We measured positive affect (PA) by averaging the scores on seven positive mood items: happy, excited, active, energetic, interested, enthusiastic, and proud (alpha = .89); and negative affect (NA) by averaging the scores on seven negative mood items: afraid, scornful, resentful, nervous, threatened, irritable, and scared (alpha = .89). Affective and normative commitment to change. We measured employees’ affective commitment to change using three items selected from the six-item scale developed by Herscovitch and Meyer (2002). Similarly, normative commitment to change was measured by three items selected from the six-item scale developed by Herscovitch and Meyer (2002). These items were selected because they showed the highest factor loadings with the affective and normative commitment to change, respectively, based on the factor analysis results reported in Herscovitch and Meyer (2002: 477). For each item, participants rated the degree to which they were affectively and normatively committed to the organizational change on a 5-point scale ranging from 1 = strongly disagree to 5 = strongly agree. The scores on these items were averaged to index participants’ affective commitment to change (sample item: “I believe in the value of this change”; alpha = .93) and normative commitment to change (sample item: “I feel a sense of duty to work toward this change”; alpha = .68). Behavioral responses to change. The three dimensions of behaviors during organizational change were measured using scales adapted from several existing ones. First, behavioral support for change was measured using four items selected from Herscovitch and Meyer’s (2002) scale of behavioral support for change that includes 17 items describing a range of cooperative behaviors from reluctant cooperation (compliance: 3 items), to passive forms of cooperation (8 items), to active forms of cooperation 2 We note that we used the shortened scales to measure several key variables in this study due to the organizational limitations of the survey length. For each shorted scale, we explain the empirical or conceptual basis for item selection.


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(championing: 6 items). We excluded items reflecting reluctant or passive forms of cooperation and included four items indicating most active forms of cooperative behaviors in support for change. Participants rated the items on a 5-point scale ranging from 1 = strongly disagree to 5 = strongly agree indicating the degree to which they engaged in task behaviors supporting the change (sample item: “I’ve put in a good deal of effort in trying to do what I can to make the transition succeed”; alpha = .84). Second, we measured behavioral resistance to change using two items adapted from the descriptive measures of resistance to change (both active and passive forms) developed by Herscovitch and Meyer (2002). Participants rated the items on the same 5-point scale, assessing the degree to which they displayed behaviors resisting or sabotaging the change initiatives (sample item: “When we have been asked to do new things as part of the (organization’s name) transition, I have just kept to what I had been doing before the change”; alpha = .68). Third, we assessed creative behavior for change using six items selected from the 13-item scale of a creativity measure developed by Zhou and George (2001), which we modified slightly to fit the organizational change context. Out of the 13 items of creativity scale, we retained only six items pertaining to suggesting new and creative ideas to improve change processes and/or performance, while excluding other items regarding an individual’s general creative tendency or general searching behaviors. Participants rated the degree to which they suggested creative ideas and developed new solutions to facilitate the change (sample item: “I have come up with innovative solutions to problems that the changes have brought to my work group”; alpha = .91) on the same 5-point scale. We conducted confirmatory factor analysis (CFA) to ensure that these three measures of behavioral response to change were distinct from each other. The CFA results suggested that the three-factor measurement model generally fit the data (χ 2 = 236.72, df = 51; CFI = .93; IFI = .93; RMSEA = .09) with correlations among the three factors ranging from −.40 (between behavioral resistance and creativity) and .68 (between supportive and creative behavior). Additional CFA results showed that this measurement model fits significantly better than several other alternative measurement models such as a model in which all three measures were combined into one factor (χ 2 (3) = 308.96, p < .001) or a two-factor model in which supportive and creative behavior measures were combined (χ 2 (2) = 251.75, p < .001). Thus, the data supported the discriminant validity of these measures. In addition, because these behavioral outcome measures were based on participants’ self-reports, further validation is needed regarding whether these measures meaningfully predict other conceptually relevant and observable variables. Such observable variables were not available at the

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time of our survey for this study. Therefore, we collected additional data from an organizational change simulation where 229 senior students enrolled in a university participated and played the role of employees in a company undergoing an organizational change. In this simulation, participants first watched a short (3 minutes) video in which the CEO of the company introduced the organizational change to the employees and then performed two 10-minute tasks within a randomly assigned, 3 to 5 person workgroup.3 After completing both tasks, participants assessed their own behavioral characteristics during the two group tasks using the same measures used in our field study to measure the three dimensions of behavioral responses to change. Using the same measures, they also assessed each member in their assigned work group regarding the extent to which he or she demonstrated the three behavioral characteristics during the two group tasks. We found that the self-report measures of supportive, resistant, and creative responses to change significantly predicted the peer-rated measures (r = .30, p < .01; r = .38, p < .01; r = .46, p < .01, respectively), meeting the requirements of convergent validity. Transformational leadership. We assessed managers’ transformational leadership behavior using the data collected from their direct reports. Using the 12 items from Podsakoff, MacKenzie, and Fetter (1990), we asked employees to describe the leadership that their direct manager displayed during the organizational change. These items tapped four subdimensions of transformational leadership. Sample items for each of these dimensions, respectively, are: “My leader provides us with a compelling vision to work toward” (charisma/vision); “My leader inspires others when s/he discusses our direction for the future” (inspiration); “My leader challenges us to think about old problems in new ways” (intellectual stimulation); and “My leader considers people’s feelings before acting” (individual consideration). Scores from all 12 items were averaged to attain the transformational leadership indices (alpha = .95). We followed established procedures to verify whether managers’ transformational leadership behavior as assessed by their direct reports could be aggregated to the workgroup level (e.g., Bommer et al., 2005; Rubin et al., 2005; Shamir et al., 2000). These included computing r wg , the level of within group agreement (James, Demaree, & Wolf, 1993), and two intraclass coefficients, ICC(1) and ICC(2), which, respectively, refer to the proportion of variance in the variable of interest that is attributable to differences between groups and the reliability of the group level means (Bliese, 2000). The median r wg , ICC(1), and ICC(2) values were .90, .18, 3 The first task was to determine as a group the extent to which this work unit would adopt the proposed change (from 0% to 100%). The second was a creative task in which participants listed as many slogans as possible to promote the change for the company.


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and .42, respectively. The r wg value exceeded .70, which is commonly used to justify aggregation of individual-level measures to the group level (Klein & Kozlowski, 2000). In addition, the ICC(1) value was statistically significant, and both ICC(1) and ICC(2) were within the acceptable range of values summarized in the literature (Bliese, 2000; James, 1982) and also comparable to previously reported values (e.g., Schneider, White, & Paul, 1998). Considered in combination, these statistics provided justification for aggregation (Bliese 2000). Controls. We controlled for several individual-level (level-1) variables that might systematically affect the results of this study. First, prior studies have found that more experienced employees are likely to respond to organizational change less favorably (e.g., van Dam, Oreg, & Schyns, 2007). Therefore, we measured and controlled for employees’ years of organizational tenure. Second, given the pervasive link between affect and cognition (e.g., Forgas, 1995; LeDoux, 1996), it is important to hold cognition relatively constant when examining the impact of affect in the context of organizational change. Particularly important is an individual’s belief regarding whether the given organizational change will result in positive or negative outcomes in various aspects of his/her organizational life, such as job, career, and working relationships (e.g., Herold et al., 2008; Newman & Krzystofiak, 1993; Schweiger & Walsh, 1990), and this belief can considerably vary across individuals depending on their functions and positions in the organization (Frahm & Brown, 2007). Therefore, we measured and controlled for employees’ perceived impact by asking the participants to report on a 5-point scale (1 = very negative, 2 = somewhat negative, 3 = no change, 4 = somewhat positive, 5 = very positive) the degree to which the organizational change has either positively or negatively impacted eight areas of organizational life: (a) the nature of responsibilities, (b) the status of position, (c) compensation, (d) future career advancement, (e) workload, (f) relationships with supervisors, (g) job security, and (h) worker relationships. Scores on these items were averaged to index participants’ perceived impact (alpha = .86). On average, the respondents perceived the impact of the organizational change negatively at Time 1 (mean = 2.60, SD = .62) and maintained a similar level of negativity at Time 2 (mean = 2.76, SD = .72). Third, Herold et al. (2008) recommended controlling for employees’ commitment to their overall organization when examining their commitment to a specific organizational change because the former can systematically influence the latter. Therefore, we measured and controlled for employees’ general organizational commitment, using four items (alpha = .90) adapted from the affective commitment scale developed by Allen and Meyer (1990). A sample item is “I feel like I’m really a

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member of the (organization name).” Finally, we analytically controlled for all the group-level (work unit) variances as we explain below. Analysis

The model to be tested was hierarchical in nature with two levels of analysis, consisting of the employee responses at the individual level (Level 1) and the leadership of their immediate manager at the group level (Level 2). We used hierarchical linear modeling (HLM; Bryk & Raudenbush, 1992) because it allows analysis of within-group (lower-level) variance while taking into account the nonindependence in the level-1 data at level 2. HLM also allows analysis of the relationships between variables across levels of analysis. The employee-level (level-1) variables in the model were affective experience, commitment to change, and behavioral responses to change; the work-group level (level-2) variable was the manager’s transformational leadership. We tested the hypothesized model in two stages. First, we tested the hypothesized level-1 (within-group) relationships among the level-1 variables controlling for all individual-level control variables (tenure, perceived impact, organizational commitment, and, where appropriate, the dependent variable at Time 1) as well as all the group-level variances (fixed effects). In specifying the HLM models, we simultaneously entered both the level-1 predictors and the level-1 controls by centering their scores relative to each work group’s mean in order to obtain estimates that were based exclusively on within-group variance. (This eliminated all betweengroup variance in the predictor scores.) Second, we tested the hypothesized effect of the manager’s leadership on employee’s affective experience and commitment to change by developing and examining an intercept-asoutcomes model in which we regressed the intercept estimates obtained from level-1 analyses on the level-2 leadership variable—transformational leadership—as a predictor. Here, we centered the predictor scores relative to the grand mean of all the work group mean scores in order to obtain estimates based on between-group variance (Enders & Tofighi, 2007). Thus, our analytic approach reflects the separation model, one of the four approaches suggested by Hofmann and Gavin (1998), which is based on the conceptualization that all the individual-level (level-1) variables in our model are related to each other within groups, while the group-level (level-2) variables (transformational leadership in this study) predict the between group variances of the individual-level variables. Results

Table 1 shows the means, standard deviations, and correlations for the constructs in the theoretical model shown in Figure 1. Based on

Tenure Perceived impact Organizational commitment Positive affect (T1) Negative affect (T1) Affective commitment to change (T1) Normative commitment to change (T1) Affective commitment to change (T2) Normative commitment to change (T2) Behavioral support for change (T2) Behavioral resistance to change (T2) Creative support for change (T2) Transformational leadership (T1)

4.88 2.62 2.70 2.77 2.24 3.00 3.47 2.99 3.20 3.28 2.01 3.43 3.11

1.33 .63 1.01 .91 1.00 1.17 .93 1.19 .94 .89 .89 .89 .74

2

3

4

– .01 – −.02 .45∗∗ – −.01 .49∗∗ .57∗∗ – .03 −.43∗∗ −.29∗∗ −.33∗∗ .01 .37∗∗ .68∗∗ .46∗∗ .02 .19∗∗ .50∗∗ .41∗∗ −.12∗ .31∗∗ .55∗∗ .40∗∗ −.03 .14∗∗ .38∗∗ .33∗∗ −.01 .15∗∗ .52∗∗ .43∗∗ .02 −.02 −.21∗∗ −.17∗∗ .02 .04 .32∗∗ .34∗∗ ∗∗ .03 .33 .18∗∗ .22∗∗

1

6

7

8

9

10

11

12

– −.27∗∗ – −.19∗∗ .49∗∗ – −.32∗∗ .68∗∗ .36∗∗ – −.24∗∗ .36∗∗ .50∗∗ .55∗∗ – −.26∗∗ .52∗∗ .47∗∗ .65∗∗ .61∗∗ – .14∗∗ −.26∗∗ −.37∗∗ −.22∗∗ −.42∗∗ −.37∗∗ – −.14∗∗ .31∗∗ .34∗∗ .33∗∗ .41∗∗ .62∗∗ −.40∗∗ – −.16∗ .12 .17∗ −.03 .00 −.01 .05 −.06

5

a For level-1 variables, means, standard deviations, and correlations were calculated between individuals (Time 1 variables, n = 906 employees; Time 2 variables, n = 430). Only for supervisor transformational leadership variable, its mean, standard deviation, and correlations were calculated between groups (using aggregated scores for within-group variables: level 2, n = 217 supervisors). ∗ p ≤ .05, ∗∗ p ≤ .01.

1 2 3 4 5 6 7 8 9 10 11 12 13

Mean SD

TABLE 1 Means, Standard Deviations, and Correlations Among Variablesa

MYEONG-GU SEO ET AL. 143

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Table 1, two control variables—perceived impact and organizational commitment—were highly correlated with many of the hypothesized variables. In particular, the correlations between organizational commitment and several other variables were unusually high, for example, those with affective commitment to change (r = .68), positive affect (r = .57), normative commitment to change (r = .50), and behavioral support for change (r = .52). On the one hand, these results may point to the importance of controlling for these variables in testing our hypotheses in this study to minimize the possible spurious effects that these control variables might have on the results. On the other hand, however, controlling for these variables, organizational commitment in particular, can also distort the results by removing too much common variance from the variables under investigation and the relationships being modeled. Therefore, we report and discuss all the results below both with and without controlling for organizational commitment, whereas perceived impact was entered into all HLM analyses. Prior to the HLM analyses, we examined the validity of our measurement model by conducting a series of CFAs. The CFA results suggested that the 8-factor measurement model—positive affect (7 items), negative affect (7 items), affective commitment to change (3 items), normative commitment to change (3 items), behavioral support (4 items), behavioral resistance (2 items), creative behavior (6 items), and transformational leadership (12 items)—generally fits the data (χ 2 = 1,499.03, df = 566; CFI = .91; IFI = .91; RMSEA = .06). Additional CFA results showed that this model fits significantly better than all other alternative measurement models in which two or more of the variables in the measurement model were assumed to be one factor, for example, seven-factor models that either combined affective and normative commitment to change into one factor (χ 2 (7) = 230.26, p < .001) or combined PA and NA into one (χ 2 (7) = 1200.25, p < .001), six-factor model that combined the three behavioral outcomes (χ 2 (13) = 460.37, p < .001), three-factor model that combined all the affect and commitment variables (χ 2 (25) = 2569.54, p < .001), or one-factor model that combined all variables (χ 2 (28) = 5300.62, p < .001). Consequently, the data supported the discriminant validity of our measurement model. The results of the HLM analyses are discussed below. We summarize all the significant paths from the results of our analyses in Figure 2. Links Between Affect and Commitment to Change

The results of the HLM level-1 analyses testing Hypothesis 1a and Hypothesis 1b are shown in Table 2. First, Hypothesis 1a predicted a positive relationship between employees’ experience of positive affect

+.12

Negative Affect T1

-.12

+.10

+.14

Positive Affect T1

Time 2

+.41

+.13

-.27

-.10

+.27

+.17

T2

Normative Commitment to Change

+.28

T2

Affective Commitment to Change

+.20

-.10

-.11

+.56

Figure 2: The Summary Results.

T1

Normative Commitment to Change

T1

Affective Commitment to Change

+.37

+.33

Time1

Creative Behavior for Change T2

Behavioral Resistance to Change T2

Behavioral Support for Change T2

Note. The results are based on Table 2 (Models 1, 3, 4, and 6), Table 3 (Models 2, 4, and 6), and Table 4. The level-1 coefficients were obtained within groups (group-mean centering), while the cross-level coefficients were obtained between groups (grand-mean centering). Scores are standardized HLM ˆ x / S y )). Organizational commitment was not included in the analyses. ∗ p < .05, ∗∗ p < .01, ∗∗∗ p < .001. coefficient (B(S

Employee-level (Level-1)

Transformational Leadership T1

Workgroup-level (Level-2)

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.42∗∗∗ (.11∗ ) −.05 (−.04)

.05 (.05) .04 (.04)

–

.03 (.02) .07 (.07) – (.04) –

.00 (.01) .39∗∗∗ (.14∗ ) – (.62∗∗∗ ) –

–

SE

Bˆ

.33∗∗∗ (.08) −.18∗∗ (−.15∗ )

–

−.05 (−.07) .32∗ (.14) – (.49∗∗∗ ) –

Bˆ

.07 (.09) .07 (.05)

–

.05 (.06) .12 (.11) – (.07) –

SE

.06 (−.00) −.12∗ (−.11∗ )

−.05 (−.05) .11∗ (.07) – (.21∗ ) .57∗∗∗ (.49∗∗∗ ) –

Bˆ

.07 (.07) .07 (.06)

.04 (.05) .11 (.10) – (.09) .05 (.07) –

SE

.38∗∗∗ (.20∗∗∗ ) −.05 (−.04)

–

.01 (.02) −.02 (−.16∗∗ ) – (.36∗∗∗ ) –

Bˆ

.04 (.05) .04 (.04)

–

.02 (.02) .06 (.06) – (.05) –

SE

Model 4: DV at Time 1

.27∗∗∗ (.14∗ ) −.14∗ (−.13∗ )

–

.00 (−.01) .05 (−.05) – (.26∗∗∗ ) –

Bˆ

.07 (.06) .06 (.05)

–

.05 (.04) .11 (.10) – (.07) –

SE

Model 5: DV at Time 2 (without mediator)

Model 3: DV at Time 2 (with mediator)

Model 2: DV at Time 2 (without mediator)

.09 (.05) −.10∗ (−.10∗ )

.41∗∗∗ (.37∗∗∗ )

−.02 (−.02) .09 (.04) – (.11) –

Bˆ

.07 (.06) .05 (.05)

.05 (.07)

.04 (.04) .10 (.09) – (.07) –

SE

Model 6: DV at Time 2 (with mediator)

Note. Bˆ = unstandardized HLM coefficient, SE = robust standard error. Scores in parentheses were estimates with organizational commitment as a control variable. All predictor scores were centered at groups’ means. Results are based on 906 employees at Time 1 and 430 employees at Time 2. ∗ p < .05, ∗∗ p < .01, ∗∗∗ p < .001.

Negative affect (T1)

Predictors: Positive affect (T1)

(Organizational commitment) Affective commitment (T1) Normative commitment (T1)

Perceived impact

Variables Controls: Tenure

Model 1: DV at Time 1

DV: Normative commitment

DV: Affective commitment

TABLE 2 Within-Group Relationships Between Affect and Commitment to Change

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and commitment to change within the initial time frame (Time 1) and was fully supported: Employees’ positive affect experienced at Time 1 was significantly and positively related to both affective commitment at Time 1 (Hypothesis 1a1: Model 1 in Table 2) and normative commitment to change at Time 1 (Hypothesis 1a2: Model 4 in Table 2). Second, in contrast, our prediction (Hypothesis 1b) that negative affect would be negatively related to commitment to change within the initial time frame (Time 1) was not supported. Employees’ experience of negative affect at Time 1 was not significantly related to either affective or normative commitment to change reported at Time 1 (Model 1 and Model 4 in Table 2). Although these relationships were significant and negative as predicted when negative affect was entered in the HLM equations without positive affect (β = −.11, p < .05 and β = −.09, p < .05, respectively), the significance disappeared when negative affect was entered together with positive affect. In Hypothesis 2a, we predicted that positive affect at Time 1 would be positively and indirectly related to affective and normative commitment to change at Time 2 (Hypothesis 2a1 and Hypothesis 2a2), fully mediated by affective and normative commitment to change at Time 1 respectively (Hypothesis 2a3 and Hypothesis 2a4). To test these hypotheses, we followed the steps suggested by Baron and Kenny (1986). First, as shown in Model 2 and Model 5 in Table 2, positive affect was significantly and positively related to both affective commitment to change and normative commitment to change at Time 2 when no mediator was entered, which supported Hypothesis 2a1 and Hypothesis 2a2. We note that the highly significant relationship between positive affect at Time 1 and affective commitment to change at Time 2 (Bˆ = .33, p < .001) became nonsignificant when organizational commitment was controlled as shown in Model 2 in Table 2. However, we believe that this result mainly reflected the high covariance with the control variable (r = .55 and .57, respectively) instead of a nonsignificant relationship. Second, the independent variable, positive affect, was significantly and positively related to each of the mediators, affective or normative commitment to change at Time 1 (as discussed above), which in turn, was significantly and positively related to affective commitment (β = .60, p < .001) and normative commitment to change (β = .45, p < .001) at Time 2, respectively. Thus, the second condition for mediation was met. Finally, when the mediator was entered together with the independent variable, positive affect was no longer significantly related to either affective or normative commitment to change at Time 2, while each of the mediators continued to significantly predict the dependent variable (Model 3 and Model 6 in Table 2). In addition, the Sobel (1982) test results suggest that both of the indirect effects carried by affective and normative commitment to change at Time 1 were significant

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(t = 6.76, p < .001 and t = 6.21, p