AFTER THE ANNOUNCEMENT: HOW CEO MOTIVATIONAL ATTRIBUTES SHAPE THEIR PROPENSITY TO BE INFLUENCED BY STAKEHOLDER REACTIONS TO ANNOUNCEMENTS OF STRATEGIC ACTIONS By Daniel L. Gamache A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of Business Administration Strategic Management Doctor of Philosophy 20 15 ABSTRACT AFTER THE ANNOUNCEMENT: HOW CEO MOTIVATIONAL ATTRIBUTES SHAPE THEIR PROPENSITY TO BE INFLUENCED BY STAKEHOLD ER REACTIONS TO ANN OUNCEMENTS OF STRATEGIC ACTIONS By Daniel L. Gamache Over the past three decades, building on upper echelons theory, research has continually demonstrated that CEO s play a central role in strategic decision making and that differences a mongst CEO s can help to explain firm strategic actions. Independently, other research has explored how CEO s attend to , and learn from , feedback provided by external stakeholders following announcements of strategic actions. In this dissertation I integrate these two research streams by exploring how CEO psychological characteristics shape the propensity for CEO s to be influenced by stakeholder reactions. I develop and test a theory arguing that some CEO attributes will shape the degree that CEO s are influen ced by positive or negative stakeholder reactions to the announcement of a strategic action, while other CEO characteristics will influence the degree that CEO s are influenced by stakeholder reactions in general. More specifically, I focus on two proximal motivational constructs that have been shown to have strong and meaningful impact on behavior: CEO regulatory focus and CEO temporal focus. I develop predictions about how these important attributes influence how CEO s attend to and learn from the reactions by the media and stock market following announcements of large acquisitions. iii ACKNOWLEDGEMENTS I wish to begin by thanking my wife, Darla, for her support and sacrifice. Her love and encouragement has been vital for me through the PhD program. She be lieves in me and my abilities in a way that spurs me on toward greater accomplishments. I thank my sons, Noah and Nate, for their love. The joy that they bring to my life gives me a greater sense of balance and purpose. I also wish to thank my advisor, Ger ry McNamara , for his direction, guidance and scholar has been an important part of my time at Michigan State. I cannot imagine a better advisor and mentor for my acad emic career. I am thankful that I can call you a friend. I thank Cynthia Devers for her encouragement and support. I am very thankful that our times at Michigan State overlapped. I am a better and more productive scholar because of working with you. I than k Russ Johnson for his support. Russ has been an important sounding board for me as I work through integrating micro - level theories at a macro - level. I also thank Albert Cannella, Jr. for providing an important outside perspective on my work and some impor tant methodological advice. The support of all these people has made me a better scholar and made this a better dissertation. Lastly, I want to thank my parents for their support and encouragement and many excellent scholars at Brandon University and Brie rcrest College for their role in my development. Thanks also to the rest of the Management Department at Michigan State University as the faculty and other doctoral students have made my time at MSU excellent in many ways. iv TABLE OF C ONTENTS LIST OF TABLES ................................ ................................ ................................ ......................... vi LIST OF FIGURES ................................ ................................ ................................ ...................... vii INTRODUCTION ................................ ................................ ................................ .......................... 1 CEO Motivational Constructs ................................ ................................ ................................ ..... 3 External Stakeholders ................................ ................................ ................................ ................. 5 Contributions ................................ ................................ ................................ ............................... 8 LITERATURE REVIEW ON CEO CHARACTERISTICS ................................ ........................ 12 Research on CEO Characteristics ................................ ................................ ............................. 12 Do C EOs Matter? ................................ ................................ ................................ ...................... 13 Observable Managerial Characteristics ................................ ................................ .................... 15 CEO Psychological Characteristics ................................ ................................ .......................... 16 CEO Personality Constructs ................................ ................................ ................................ ..... 17 CEO Self - Concept Constructs ................................ ................................ ................................ .. 19 CEO Motivational Constructs ................................ ................................ ................................ ... 20 Affect / Emotions/ Moods ................................ ................................ ................................ ..... 21 Temporal Focus ................................ ................................ ................................ ..................... 22 Regulatory Focus ................................ ................................ ................................ ................... 24 Concluding Thoughts ................................ ................................ ................................ ................ 26 LITERATURE REVIEW ON ACQUISITIONS ................................ ................................ .......... 28 Performa nce Implications of Acquisitions ................................ ................................ ................ 28 Antecedents to Acquisition Activity ................................ ................................ ......................... 30 Learning from Acquisition Experience ................................ ................................ ..................... 32 Learning and Subsequent Acquisition Performance ................................ ............................. 32 Learning and Subsequent Propensity to Acquire ................................ ................................ .. 35 Learning and Acquisition Completion ................................ ................................ .................. 36 Acquisition Learning and CEO characteristics ................................ ................................ ..... 37 Concluding Thought s ................................ ................................ ................................ ................ 38 THEORY AND HYPOTHESES ................................ ................................ ................................ .. 39 Upper Echelons Theory ................................ ................................ ................................ ............ 39 Field of Vision ................................ ................................ ................................ ....................... 40 Selective Perception ................................ ................................ ................................ .............. 41 Interpretation ................................ ................................ ................................ ......................... 41 CEO Learnin g ................................ ................................ ................................ ........................... 42 Main Effect Relationships ................................ ................................ ................................ ......... 43 The Moderating Effect of CEO Motivational Characteristics ................................ .................. 45 Regulatory Focus Theory ................................ ................................ ................................ .......... 46 CEO Promotion Focus ................................ ................................ ................................ ........... 47 CEO Prevention Focus ................................ ................................ ................................ .......... 49 v CEO Temporal Focus ................................ ................................ ................................ ............... 52 CEO Future Focus ................................ ................................ ................................ ................. 53 CEO Present Focus ................................ ................................ ................................ ................ 56 CEO Past Focus ................................ ................................ ................................ ..................... 59 METHODS ................................ ................................ ................................ ................................ ... 62 Sample ................................ ................................ ................................ ................................ ....... 62 Independent Variables ................................ ................................ ................................ .............. 63 Positive Market Reactions/ Negative Market Reactions ................................ ....................... 63 Positive Media Reactions/ Negat ive Media Reactions ................................ .......................... 65 Dependent Variables ................................ ................................ ................................ ................. 66 Acquisition Completion ................................ ................................ ................................ ........ 66 Acquisition Activity ................................ ................................ ................................ .............. 67 Moderator Variables ................................ ................................ ................................ ................. 67 CEO Promotion Focus and CEO Prevention Focus ................................ .............................. 69 CEO Past Focus, CEO Present Focus, and CEO Future Focus ................................ ............. 70 Control Variables ................................ ................................ ................................ ...................... 70 Firm - Leve l Controls ................................ ................................ ................................ .............. 71 CEO - Level Controls ................................ ................................ ................................ .............. 71 Board - Level Controls ................................ ................................ ................................ ............ 72 Indu stry - Level Controls ................................ ................................ ................................ ........ 72 Deal Characteristic Controls ................................ ................................ ................................ . 73 Additional Controls Tested ................................ ................................ ................................ ... 73 Analysis ................................ ................................ ................................ ................................ ..... 74 RESULTS ................................ ................................ ................................ ................................ ..... 77 Descriptive Statistics ................................ ................................ ................................ ................. 78 Acquisition Completion ................................ ................................ ................................ ............ 79 Subsequent Acquisition Activity ................................ ................................ .............................. 82 Supplemental Analysis ................................ ................................ ................................ .............. 89 Summary of Findings ................................ ................................ ................................ ................ 90 Additional Findings ................................ ................................ ................................ .................. 91 DISCUSSION ................................ ................................ ................................ ............................... 94 Future Directions ................................ ................................ ................................ ...................... 98 Impact on Management Practice ................................ ................................ ............................. 101 CONCLUSIONS ................................ ................................ ................................ ......................... 103 APPENDIX ................................ ................................ ................................ ................................ . 104 REFERENCES ................................ ................................ ................................ ........................... 123 vi LIST OF TABLES Table 1 - Descripti ve Statistics ................................ ................................ ................................ ... 105 Table 2 - Acquisition Completion ................................ ................................ ............................... 107 Table 3 - Heckman 2 - Stage Predicting Acquisition Completion ................................ ................ 109 Table 4 - Number of Acquisitions ................................ ................................ .............................. 1 11 Table 5 - Value of Acquisitions ................................ ................................ ................................ .. 113 Table 6 - Rate of Acquisition Activity ................................ ................................ ........................ 115 Table 7 - Comparing Event Windows ................................ ................................ ........................ 117 Table 8 - Comparing Event Windows ................................ ................................ ........................ 119 Table 9 - Comparing Event Windows ................................ ................................ ........................ 121 vii LIST OF FIGURES Figure 1 - High Level Theoretical Model ................................ ................................ ..................... 11 Figure 2 - Proposed Model (Positive Stakeholder Reactions) ................................ ...................... 44 Figure 3 - Proposed Model (Negative Stakeholder Reactions) ................................ .................... 45 Figure 4 - Interaction of CEO Prevention Focus and Negative Media Reactions ........................ 86 Figure 5 - Interaction of CEO Future Focus and Negative Media Reactions ............................... 87 Figure 6 - Interaction of CEO Past Focus and Negative Media Reactions ................................ ... 88 1 INTRODUCT ION CEO s play an important role in strategy formulation, directing firm resources, monitoring the environment, and communicating with external stakeholders ( Finkelstein, Hambrick, & Cannella, 2009 ) . Research has demonstrated that over recent decades CEO s have be come more important, playing an increasingly significant role in firm outcomes ( Quigley & Hambrick, 2015 ) . Their role is especially central in major strategic decisions such as guiding new product introductions ( Nadkarni & Chen, 2014 ; Yadav, Prabhu, & Chandy, 2007 ) , alliance formation ( Das & Kumar, 2011 ) , and the decision of whether or not to undertake an acquisition ( Devers, McNamara , Haleblian , & Yoder, 2013 ; Haleblian, Devers, McNamara , Carpenter, & Davison, 2009 ; Sanders & Hambrick, 2007 ) . Stemming from this understanding, two imp ortant lines of research have developed. First, significant research has emphasized that the psychological attributes of these executives may help explain differences in the types of strategic decisions they make ( Hambrick & Mason, 1984 ) . Upper echelons theory argues that psychological differences serve to shape the ision, directing a selective perception of stimuli, and shaping the interpretations of information that they do receive ( Hambrick, 2007 ; Hambrick & Mason, 1984 ) . Initial research into top executives focused on observable managerial c haracteristics as a measurable proxy for unobservable psychological characteristics ( Finkelstein et al., 2009 ) . More recently, researchers have focused on measuring CEO psychological characteristics more directly. As Finkelstein and colleagues (2009:50) note cts have the advantage of conceptual clarity, and they provide a pointed causal link to the executive behavior 2 A second line of research has focused on how CEO s, and the organizations they lead, learn from strategic events. In this context, learning occurs as a result of interactions between organizations and their environments ( Hedberg, 1981 ) . Learning within organizations can be defined as increasing the understanding of reality through observing the results of actions ( Hedberg, 1981 ) . As such, learning involves interpreting the consequences (feedback) that follow a behavior and using that feedback in future decisions ( Haleblian & Finkelstein, 1999 ; Haleblian, Kim, & Rajagopalan, 2006 ; Luo, 2005 ) . In particular, this type of learning is best categorized as l earning from experience and refers to learning that occurs following direct experiences of those involved ( Huber, 1991 ) . Learning from the announcements of strategic actions occurs when executive s use information provided by external stakeholders (e.g., media, stock market) in subsequent actions. Research has demonstrated that stakeholder reactions to organizational decisions influence how firms respond following succession announcements ( Graffin, Boivie, & Carpenter, 2013 ) , governance violations ( Dyck, Volchkova, & Zingales, 2008 ) , financial restatements ( Gomulya & Boeker, 2014 ; Palmrose, Richardson, & Scholz, 2004 ) , and acquisitions ( Haleblian et al., 2006 ; Luo, 2005 ) . For example, following an acquisition announcement executives may attend to and learn from external stakeholder reactions and be influenced by this learning in decisions about completing (or failing to complete) the focal acquisition and in subsequent acquisition activity ( Haleblian et al., 2006 ; Luo, 2005 ) . It is surprising however, that almost no research to date has integrated research on CEO psychological characteristics with research on how CEO s attend to, and learn from, external feedback. For CEO s to learn from the response of external stakeholders, the executive s must direct their attention to the information (f ield of vision), notice the stakeholder response (selective perception), and interpret the stakeholder response in a way that motivates them to 3 change their subsequent behavior. According to upper echelons theory, all three of these factors play an importa nt role in shaping managerial perceptions and strategic choices ( Hambrick & Mason, 1984 ) . Accordingly, it seems that research on CEO psychological characteristics would be a natural fit in helping to inform our understanding of how CEO s learn from stakeholder reactions to strategic announcements. To my knowledge, only one paper has indirectly touched on this by exploring how CEO narcissism shapes learning from recent performance ( Chatterjee & H ambrick, 2011 ) . This paper found that for narcissistic CEO s, recent media praise had a stronger positive relationship with subsequent firm risk taking. This initial finding helps set the stage for this dissertation by indicating that CEO psychological ch aracteristics may play an important role in shaping how CEO s learn from external stakeholders. To integrate these two research streams, this dissertation focuses on how CEO s psychological characteristics influence the ir propensity to attend to and learn f rom external stakeholders following the announcement of a strategic action. I develop and test a theory arguing that some CEO attributes will influence the degree that CEO s are influenced by positive or negative stakeholder reactions to the announcement of a strategic event, while other CEO characteristics will influence the degree that CEO s are influenced by external stakeholder reactions in general. CEO Motivational Constructs Research on CEO characteristics has provided important insights into firm stra tegic actions based on a wide variety of CEO characteristics. Studies on observable CEO characteristics explored characteristics such as CEO functional background ( Beal & Yasai - Ardekani, 2000 ; Crossland, Zyung, Hiller, & Hambrick, 2014 ) , education level ( Rajagopalan & 4 Datta, 1996 ) and organizational tenure ( Hambrick & Fukutomi, 1991 ; Henderson, Miller, & Hambrick, 2006 ) . More recently, this research has transitioned to a focus on measuring CEO psychological characteristics more directly . This research has primarily focused on CEO personality constructs (such as the five - factor model of personality (e.g., Herrmann & Nadkarni, 2014 ; Nadkarni & Herrmann, 2010 ; Peterson, Smith, Martorana, & Owens, 2003 ) and risk propensity (e.g., Gupta & Govindarajan, 1984 ; Wally & Baum, 1994 ) ) a nd on CEO self - concept constructs (such as hubris (e.g., Hayward & Hambrick, 1997 ; Hiller & Hambrick, 2005 ) and narcissism (e.g., Chatterjee & Hambrick, 2007 , 2011 ) ). It is noticeable; however, that there has been only minimal research on CEO motivational constructs. Unlike more stable personality characteristics, motivational constructs take a middle ground between stable traits and si tuation states. Distal - proximal motivational theories argue that motivational constructs such as regulatory focus and temporal focus have a more direct relationship with work behavior than more distal personality traits ( Barrick & Mount, 2005 ; Hoyle, 2010 ; Lanaj, Chang, & Johnson, 2012 ) . For strategy resear ch this suggests that CEO motivational constructs likely have a more proximal and substantive influence on executive behavior and strategic decision making. Consistent with this understanding, a number of strategy scholars have theorized that proximal mot ivational constructs, such as CEO regulatory focus, are likely to have important strategic implications for the firm (e.g., Das & Kumar, 2011 ; Wowak & Hambrick, 2010 ) . Early empirical work in this area has provided some evidence supporting these claims with studies of CEO emotions ( Delgado - Garcia & De La Fuente - Sabate, 2010 ) , regulatory focus ( Wallace, Little, Hill, & Ridge, 2010 ) , and temporal focus ( Nadkarni & Chen, 2014 ) . However, compared to research on more distal CEO psychological characteristics, like personality and self - concept 5 constructs, this research is very limited with only a few studies exploring the strategic implications of these more proximal motivational constructs. To build on our understanding of CEO motivational constructs, this dis sertation will integrate upper echelons theory with regulatory focus theory ( Higgins, 1997 ; Higgins, 1998 ) and the theory of temporal focus ( Shipp, Edwards, & Lambert, 2009 ) . Regulatory focus theory suggests that people pursue their goals through two distinct regulation systems: a promotion focus and a prevention focus ( Higgins, 1997 ; Higgins, 1998 ) . A promotion focus is concerned with accomplishment, aspirations, advancement, growth, and a sensitivity to the presence and absen ce of positive outcomes ( Crowe & Higgins, 1997 ) . A prevention focus is concerned with sec urity, safety, responsibility, duty, and a sensitivity to the presence and absence of negative outcomes ( Crowe & Higgins, 1997 ) ( Shipp et al., 2009: 1 ) . CEO regulatory focus and CEO temporal focus represent two motivational constructs likely to be especially important influences on CEO s field of vision, perception of stimuli, and interpretations of information. In this dissertation, I explore ways that CEO regulatory focus and CEO temporal focus influence learning from stake holder reactions to announcements of major strategic actions. I argue that CEO regulatory focus is likely to shape the propensity of CEO s to learn from either positive or negative stakeholder feedback while CEO temporal focus will influence the degree that CEO s pay attention to stakeholder feedback more generally. External Stakeholders In this dissertation I focus on how CEO motivational constructs shape the degree that CEO s learn from two important external stakeholders: the stock market and the media. Th e stock 6 market represents one of the most salient stakeholders to the organization. Stock price changes are driven by investors and represent the interpretation of investors of publically available information which they use to assess the managerial percep tions and motivations of executives making the strategic decision ( Schijven & Hit t, 2012 ) . Stock price represents a strategic performance variable that organizations consider when making decisions ( Beatty & Zajac, 1987 ) . Further, stock prices influence firm actions because CEO s and other top executives are frequently incentivized through equity based compensation such as stock options and restricted stock grants ( Devers, McNamara , Wiseman, & Arrfelt, 2008 ) . Some have argued that the influence of the stock marke - where firms are influenced by the stock market to emphasize short - term performance targets sometimes at the expense of longer - term performance ( Laverty, 1996 ; Marginson & McAulay , 2008 ) . The media is another important stakeholder that influences firm actions. The reciprocal effects model foc uses on the impact of mass media coverage on those who are the subjects of that media coverage ( Kepplinger, 2007 ) . Subjects of media coverage tend to overestimate the influence of these reports and as such try to take advantage of popularity provided by positive media coverage or attempt to minimize the effects of negative coverage ( Kepplin ger, 2008 ) . Findings from management research on media coverage of organizations are consistent with the reciprocal effects model demonstrating that: 1) the media responds to firm actions with coverage about the firm and its executives (e.g., Chen & Meindl, 1991 ; Zavyalova, Pfarrer, Reger, & Shapiro, 2012 ) ; 2) this coverage shapes public opinions about the firm (e.g., Pollock & Rindova, 2003 ; Pollock, Rindova, & Maggitti, 2008 ) ; 3) firms and their executives attempt to influence media coverage (e.g. , Westphal & Deephouse, 2011 ; Westphal, Park, McDonald , & Hayward, 7 2012 ) ; and 4) that these reports influence organizational decisions ( Bednar, 2012 ; Durand & Vergne, In Press ) . Considering how CEO s learn from both the stock market and the media is beneficial because they represent two very different types of stakeholder reactions. The stock market provides CEO s with hard quantitative evidence about the perceptions investors have about the acquisition. The media response represents more of a form of soft evidence in that media reports include both facts and th e interpretations and biases of individual reporters and news agencies responsible for the reports ( Ch en & Meindl, 1991 ) . Further, media reports are likely to frequently contain both positive and negative elements within the same reports. In line with upper echelons theory it is here that CEO motivational constructs will play a role in shaping which elem ents the CEO becomes aware of (field of vision), and how they selectively perceive and interpret those reports ( Hambrick & Maso n, 1984 ) . The limited strategy research that has explored both market and media coverage has consistently demonstrated a low correlation between the two ( Gomulya & Boeker, 2014 ; Pollock et al., 2008 ) suggesting that the stock market and media provide two independent reactions to an event. In order to explore the influence of CEO motivati onal characteristics on how CEO s learn from external stakeholder, I will focus on responses following acquisition announcements. Acquisitions are an ideal context for the study of CEO psychological characteristics because they represents an important strat egic decision for organizations that requires significant involvement from top executives and faces competing pressures from firm value - enhancing and personal self - interest motivations ( Devers et al., 2013 ; Haleblian et al., 2009 ) , and are likely to trigger stakeholder reactions that may influence subsequent behavior ( e.g., Haleblian et al., 2006 ) . Existing evidence shows that, on average, acquisitions provide shareholders of the acquiring firm 8 with no performance benefits and frequently result in negative marke t returns ( Datta, Pinches, & Narayanan, 1992 ; Haleblian et al., 2006 ; King, Dalton, Daily, & Covin, 2004 ) . As such, understanding how CEO psychological characteristics motivate acquisition activity is important for firm governance decisions. Prior research has used acquisitions to study the influence of CEO self - concept constructs. This research has demonstrated that CEO narcissism and CEO hubris influence the proclivity to engage in acquisitions ( Brown & Sarma, 2007 ; Chatterjee & Hambrick, 2007 ) and the performance of those acquisitions ( Hayward & Hambrick, 1997 ; Malmendier & Tate, 2008 ) . Due to their more proximal relationship to individual behavior, CEO motivational constructs are also likely to play very important r oles in shaping acquisition activity. Contributions This dissertation will make several contributions to management literature. First, this dissertation will extend our knowledge about how CEO s attend to and learn from stakeholder reactions, and in partic ular, demonstrate how CEO motivational characteristics shape that learning. Only limited research has focused on how differences amongst CEO s influence how they learn. The research that has been conducted almost exclusively has focused on changes across CE O tenure with in organization s ( Henderson et al., 2006 ; Miller & Shamsie, 2001 ) . Because CEO characteri stics influence their field of vision, perception of phenomena, and interpretation of events ( Hambrick & Mason, 1984 ) , it is likely that these characteristics also shape how CEO s learn. This dissertation adds to upper echelons research in this way by integrating CEO attributes and research on CEO learning. Further, and specific to the acquisition context, this research adds to o ur understanding of how CEO s attend to and learn from 9 stakeholder reactions to acquisition announcements. Only limited research has explored how learning from stock market reactions shape the proclivity to engage in subsequent acquisitions ( e.g., Haleblian et al., 2006 ) or the willingness to complete the focal acquisition ( Luo, 2005 ) . Even less research has explored how CEO s or firms learn from media coverage. Second, this dissertation will add to our understanding of how CEO motivational characteristics influence firm strategic actions. As descr ibed earlier, most upper echelons research that explores CEO psychological characteristics has studied CEO personality constructs or CEO self - concept variables. I extend the limited research on more proximal motivational constructs by focusing on CEO regul atory focus ( Higgins, 1997 ; Higgins, 1998 ) and CEO temporal focus ( Nadkar ni & Chen, 2014 ; Shipp et al., 2009 ) . These motivational constructs are likely to have a stronger, more direct influence on organizational actions than more distal personality traits ( Barrick & Mount, 2005 ; Lanaj et al., 2012 ) and thus, are important for expanding our understanding of how characteristics of top executives inf luence firm strategic actions. Th ird, this dissertation will demonstrate how positive and negative stakeholder reactions to the firm can have differential influence in shaping subsequent actions. The limited strategy scholarship looking at how firms learn from stakeholder reactions, has n ot considered how differences in CEO s characteristics may make positive or negative reactions more salient. Instead, these papers have generally focused completely on either positive or negative reactions ( e.g., Bednar, Boivie, & Prince, 2013 ) or made the reactions a continuous scale, usually used to represent performance ( e.g., Haleblian et al., 2006 ) . I theorize that differences amongst CEO s in their regulatory focus will make either positive or negative stak eholder reactions more important 10 and thus provide stronger (or weaker) effects of learning from the stakeholder reactions to the This dissertation also adds to research on the role of the media in shaping firm act ions. Recent strategy scholarship has demonstrated that media coverage influences decision making in areas such as executive compensation, corporate governance, and strategic change ( Bednar, 20 12 ; Bednar et al., 2013 ) . None of this research, however, has looked at media reactions to specific events. Building off of the reciprocal effects model of media influence ( Kepplinger, 2007 , 2008 ) I argue that media reactions to the announcement of a strategic action will shape subsequent strategic actions, and differences amongst CEO s in motivational charac teristics will influence the strength of these relationships. As a final contribution, this dissertation expands research on how motivational constructs influence leadership activities. Most of the existing research in this area has focused on non - executi ve leaders and individual outcomes rather than broader firm level outcomes that CEO s influence ( Kark & Van Dijk, 2007 ; Neubert, Kacmar, Carlson, Chonko, & Roberts, 2008 ) . Researchers in both regulatory focus theory and temporal focus have stressed the importance of research on the influence of these constructs on organizational - level outcomes ( Kark & Van Dijk, 2007 ; Shipp et al., 2009 ) . By exploring theses constructs at the executive level of analysis with firm level outcomes, we can see the ways that regulatory focus and temporal focus influence leadership of large organizations. 11 Figure 1 - High Level Theoretical Model 12 LITERATURE REVIEW ON CEO CHARACTERISTICS To provide a star ting point for developing theory on how CEO psychological characteristics shape how CEO s attend to and learn from external stakeholders I start by providing a detailed literature review of research on CEO characteristics. I start with a broad overview of r esearch on CEO characteristics, discuss research on to whether CEO s matter, and then explore research on CEO psychological characteristics. I categorize CEO psychological characteristics into three categories: personality characteristics, self - concept cons tructs, and motivational constructs. As this dissertation focuses on CEO motivational constructs in particular, I provide a brief overview of research on CEO personality constructs and self - concept constructs before going into greater depth on the research of CEO motivational characteristics. Research on CEO Characteristics The study of CEO s is not new ( Finkelstein et al., 2009 ) . While certainly not the first to Cyert and March (1963 ) focus on the his work placed responsibility for organizational decisions in the hands of a small number of key decision makers and emphasized behavioral consequences of this decision making ( Mahoney, 2005 ) . If Cyert and March (1963 ) provided the kindling, it was H ambrick and Mason (1984 ) that provided the spark setting off a blaze of research on top executives. This theory paper argued that top executives matter and that studying these executives could improve our understanding of firm performance and strategic c hoices. Hambrick and Mason (1984 ) emphasized the study of observable managerial characteristics as a starting poi nt for research on executives suggesting that these characteristics can serve as a proxy for psychological characteristics. Research on this area has 13 brought significant understanding of the role of CEO s and their influence on firm outcomes. Until recently , however, with the focus on observable characteristics, very little was known about the psychological processes that lead to specific strategic choices ( Hambrick, 2007 ) with Don Hambrick noting this as one of his biggest disappointments with upper echelons research ( Hambrick, 2005 ) . More recently, researchers have begun to open the black box and study CEO psychological characteristics more directly. Because this dissertation is grounded in upper echelons research in general, and in expanding our understanding of the influence of CEO m otivational constructs in particular, I will begin this literature review, with a brief summary of research discussing the importance of the CEO in firm strategic decisions. Do CEO s Matter? For a study of CEO characteristics to have relevance to managers and strategy research it must be based on the assumption that CEO s make a difference to performance and actions of the firm. Any reading of media coverage of firms would make this question seem trivial as articles frequently attribute firm performance to the CEO ( Hayward, Rindova, & Pollock, 2004 ; Meindl, 1995 ) . This sort of consensus, however, has not alwa ys been found in management scholarship ( Finkelstein et al., 2009 ; Mackey, 2008 ) . Most prominently, Lieberson and O'connor (1972 ) used a variance decomposition technique and found that executive leadership explain ed only a minimal proportion of company performance and that environmental factors are much more important. This research and others concluded that CEO s decision making ability is constrained by both the internal structure and external demands on the organ ization ( Lieberson & O'connor, 1972 ; Salanick & Pfeffer, 1977 ) . 14 Over the last decades a number of studies have provided evidence suggesting a much greater influence of CEO s on firm performance and actions. One part of this is evident in the critiques of the Lieberson and O'connor (1972 ) study; detractors have challenged the choice of performance measures used, the exclusion of diverse firms from their sample, and their designation of a new leader based on changing board chairs or presi dents rather than focusing on CEO s ( Finkelstein et al., 2009 ) . More recent variance decomposition research that addressed these concerns found much higher CEO effects. Mackey (2008 ) sampled firms with at least two CEO changes during the sampl e period and showed that CEO s have an effect of corporate performance that was greater than either industry or firm effects. This research suggests that, on average, the CEO effect on corporate level performance is 29.2 percent of the variance ( Mackey, 2008 ) . Similarly, Bertrand and Schoar (2003 ) tracked CEO s across multiple firms over their career and found significant influences of these CEO s on both firm performance and investment decisions. In the most recent variance decomposition paper, Quigley and Hambrick (2015 ) explored the variance in firm performance attributed to CEO s over the past 60 years. Their analysis found that CEO s are becoming increasingly significant in influencing firm performance suggesting that the increase in media attention given to these leaders might be warranted ( Quigley & Hambrick, 2015 ) . A second source of evidence on the impact of the CEO is research on CEO succession ( Finkelstein et al., 2009 ) . If CEO s do not have a majo r influence on firm performance and actions, then changes in CEO should have little influence. Instead, research has demonstrated that the choice of a new CEO can impact stock market reactions (e.g., Lubatkin, Chung, Rogers, & Owers, 1989 ; Shen, 2003 ) , accounting measures of firm performance (e.g., Shen & Cannella, 2002 ; Zhang & Rajagopalan, 2004 ) , and the effectiveness of subsequent strategic actions (e.g., 15 Bigley & Wiersema, 2002 ; Zhang & Rajagopalan, 2010 ) . Similar evidence of the importance of the CEO can be seen in research demonstrating that CEO compensation influences fir m actions and performance ( Devers, Cannella, Reilly, & Yoder, 2007 ) . Most prominen tly, this research has demonstrated that the compensation mix (in particular stock option pay and stock ownership) has important influences on strategic risk taking, acquisitions, and divestitures (e.g., Devers et al., 2008 ; Sanders, 2001 ; Sanders & Hambrick, 2007 ) . It is not only the pay mix that matters ; relative pay of the CEO compared to other employees or CEO s of other firms can also influence firm performance and actions (e.g., Connelly, Haynes, Tihany i, Gamache, & Devers, In Press ; Fong, 2010 ; Fong, Misangyi, & Tosi, 2010 ) . Collectively this research shows that CEO compensation has important influe nces on firm actions and performance, something that would not be expected if the CEO did not matter. Finally, strong evidence for the importance of the CEO can be seen in the significant results found in many studies exploring how CEO characteristics infl uence firm actions and outcomes. These findings would not be so prevalent if CEO s did not have an important influence on firm outcomes. Observable Managerial Characteristics As noted earlier, most upper echelons research over the past three decades has focused on observable managerial characteristics. The initial arguments by Hambrick and Mason (1984 ) have proven accurate as this research has provided a deeper understanding of the influence of executives on firm actions and performance. Research in this area has explored the influence of the CEO ( Beal & Yasai - Ardekani, 2000 ; Crossland et al., 2014 ; Finkelstein et al. , 2009 ; Rajagopalan & Datta, 1996 ) . Likely the most prominently studied of these characteristics is CEO tenure. Early theoretical work in this area 16 argued that CEO s tend to go through distinct pa tterns of behavior during their tenure ( Hambrick & Fukutomi, 1991 ) . Subsequent empirical work found support for this idea in a study of Hollywood studi o heads ( Miller & Shamsie, 2001 ) . This research noted that experimentation declined over the tenure of these executives, however, expe rimentation was more valuable late in the executives tenure ( Miller & Shamsie, 2001 ) . The relationship between CEO tenure and firm per formance has proven to be a little more complex. Miller and Shamsie (2001 ) found an inverted - U shaped relationship between top executive tenure and firm performance. Henderson and colleagues (2006) followed up on this study by comparing the impact of CEO tenure in industries with differing levels of dynamism. This study found that CEO tenure was positively associated with per formance in the stable industry while CEO tenure was negatively associated with performance in the dynamic industry ( Henderson et al., 2006 ) . Observable managerial characteristics have been studied in a large part because of the difficulties associated with directly measuring CEO psychological traits ( Hambrick & Mason, 1984 ) ; however, over recent years there has been an increased focus on more direct study of CEO psychological characteristics. CEO Psychological Characteristics Hambrick (2007 ) noted that ex amining underlying psychological and social mechanisms is important for future research on Upper Echelons. The ability to capture psychological characteristics can provide less noise and greater conceptual clarity than only using observable executive chara cteristics ( Fink elstein et al., 2009 ; Hambrick & Mason, 1984 ) . The increased use of non - intrusive measures of CEO characteristics has facilitated studies that provide this kind of understanding. In what follows I w ill summarize research, both theoretical and empirical, on the 17 CEO psychological characteristics most prevalent in the strategic management literature. I divide research on CEO psychological constructs into three primary categories: personality characteris tics, self - concept constructs, and motivational constructs. Building on distal - proximal motivational theories ( Barrick & Mount, 2005 ; Hoyle, 2010 ; Lanaj et al., 2012 ) , I focus on CEO motivational constructs because they are likely to have a stronger influence on strategic behavior. In order to provide a bro ader context of the research on CEO psychological characteristics I start by providing a brief summary of research on CEO personality characteristics and self - constructs. The majority of research on CEO psychological attributes fit into these categories an d, accordingly, a brief review of these literatures will provide a contrast for the limited research on CEO motivational constructs. Because this dissertation focuses on CEO motivational constructs I will end this literature review with a more detailed exp loration into research in this area. CEO Personality Constructs The first category of psychological characteristics includes those characteristics classified as personality constructs. Included in this category is the five - factor model of personality (th e Big Five) along with need for achievement, risk propensity, and charisma. These personality traits represent enduring characteristics of individuals that demonstrate significant stability across time and situations throughout adulthood ( Costa & McCrae , 1996 ; McCrae & Costa, 1999 ) . Research into these constructs have demonstrated that CEO personality is related to top management team behavior and integration, choice of organizational structure, strategic decision making and even firm performance. Early strategy studies on CEO personality characteristics focused on need for achievement. These papers have primarily f ocused on how CEO s need for achievement 18 influences choice of strategy and organizational structure. Collectively, these findings suggest that CEO s with high need for achievement tend to structure the organization in ways to provide them with more c ontrol over organizational activities ( Miller & Dröge, 1986 ; Miller & Toulouse, 198 6 ) and that CEO need for achievement is positively associated with firm performance ( Wainer & Rubin, 1969 ) . More recently, studies have explored how the Big Five personality dimensions influence organizational outcomes such as strategic flexibility ( Nadkarni & Herrmann, 2010 ) , strategic change ( Herrmann & Nadkar ni, 2014 ) , and firm performance ( Nadkarni & Herrmann, 2010 ) . Other research on CEO personality has focused more on how CEO s relate to others within the firm , such as those with the top management team ( Peterson et al., 2003 ) . Another CEO personality attribute that has seen some research is risk propensity. These studies have focused on how risk propensity influences firm technological innovativeness ( Souitaris, 2001 ) , export involvement ( Halikias & Panayotopoulou, 2003 ) , business - uni t performance ( Gupta & Govindarajan, 1984 ) , and CEO decision making speed ( Wally & Baum, 1994 ) . A final CEO personality characteristic that has been studied is CEO charisma. CEO charisma has been shown to be positiv ely related to firm performance ( Agle & Sonnenfield, 1994 ) , although this relationship is stronger in situations characterized by high uncertainty ( Tosi, Misangyi, Fanelli, Waldman, & Yammarino, 2004 ; Waldman, Ramirez, House, & Puranam, 2001 ) . CEO charisma is also important because it can allow CEO s to influence external stakeholders such as securities analysts ( Fanelli & Misangyi, 2006 ; Fanelli, Misangyi, & Tosi, 2009 ) . 19 CEO Self - Concept Constructs A second subset of CEO characteristics can be grouped under the category of self - c oncept constructs including locus of control, core self - evaluation, hubris (or overconfidence), narcissism, and humility. The constructs discussed in this section reflect how the CEO s view themselves and their abilities. These constructs are still viewed a s relatively stable but are generally considered to be somewhat less stable than personality traits ( e.g., core self - evaluation; Judge, Bono, & Locke, 2000 ) and can be shaped by a combination of personality and context ( e.g., hubris; Finkelstein et al., 2009 ) . This categ ory represents the largest portion of research into CEO psychological characteristics, especially in recent years. Early studies in this area focused on CEO locus of control. These studies found significant relationships between CEO internal orientation an d firm innovativeness ( Miller, De Vries, & T oulouse, 1982 ) , strategic choices ( Boone, Braband er, & Witteloostuijn, 1996 ) , and risk taking ( Miller et al., 1982 ) . Several studies in this area found positive relationships between CEO internal control and firm performance ( Boone, De Brabander, & Hellemans, 2000 ; Miller & Toulouse, 1986 ; Roth, 1995 ) . Boone and colleagues (1996:687) noted that based on their study, CEO s achieve higher organizational perf Another self - concept construct that has received significant attention from researchers in recent years is CEO narcissism. Chatterjee and Hambrick (2007 ) studied CEO s from the computer industry showing that CEO dynamism, the number and size of acquisitions, and extreme performance (both positive and negative) ( Chatterjee & Hambrick, 2007 ) . Other research on CEO narcissism has found that CEO narcissism is related to firm entrepreneurial orientation ( Engelen, Neumann, & Schmidt, In Press ; Wales, Patel, & Lumpkin, 2013 ) , and that narcissistic CEO s are especially susceptible to 20 the influence of social praise ( Chatterjee & Hambrick, 2011 ; Gerstner, König, Enders, & Hambrick, 2013 ) . Similar research has also explored CEO core self - evaluation (CSE), or hubris, which reflects especially high levels of CSE ( Hiller & Hambrick, 2005 ) . Studies in hubris ( also referred to as overconfidence, Finkelstein et al., 2009 ) have found that hubris is associated with higher levels of risk taking ( Li & Tang, 2010 ) , firm innovativeness ( Galasso & Simcoe, 2011 ; Tang, Li, & Yang, In Press ) , and entrepreneurial orientation ( Engelen, Neumann, & Schwens, In Press ; Simsek, Heavey, & Veiga, 2010 ) . A few studies have also looked at CEO hubris in the context of acquisitions finding that hubristic CEO s engage in mor e acquisitions ( Malmendier & Tate, 2005 ) , pay greater premiums for those acquisitions ( Hayward & Hambrick, 1997 ) , and receive more negative market reactions for those acquisitions ( Brown & Sarma, 2007 ; Malmendier & Tate, 2005 ) . CEO Motivational Constructs A final category of CEO psychological characteristics is CEO motivational constructs. Motivational constructs are cognitive and affective i ndividual differences that lie in the middle ground between stable personality traits and situational states. Motivational constructs are important for understanding CEO behavior because they play a more direct role in how people set and pursue goals. Dist al - proximal theories argue that dispositions and traits have an indirect effect on behavior and that motivational constructs influence behavior more directly ( Bar rick & Mount, 2005 ; Hoyle, 2010 ; Lanaj et al., 2012 ) . In spite of the more direct connection between motivational constructs and behavior, researc h into CEO motivational constructs has been limited. In this review, I 21 will briefly summarize research on CEO affect, and then focus on the motivational constructs of this study: temporal focus and regulatory focus. Affect / Emotions/ Moods The study of a ffect and emotions can be a confusing endeavor even amongst trained psychologists ( Russell, 2003 ) . A wide range of constructs are used within this domain, sometimes with diffe ring meanings for the same term. Affect, then, can umbrella term encompassing a broad range of feelings that individuals experience, including feeling states, which are in - the - moment, short - term affective experiences, and feeling trait s, ( Barsade & Gibson, 2007: 37 ) . As such, affect can be thought of as being relatively stable but shaped by current moods and experiences. Research into CEO affect is extremely limited with all studies relyi ng on small sample surveys. One series of studies of CEO s in Spanish banks by Delgado - Garcia and colleagues has provided some initial findings in this area showing that CEO affect does influence firm strategies. They found that CEO positive affect was nega tively associated with strategic conformity but that CEO negative affect was positively associated with strategic conformity; further strategic conformity mediated the relationship between CEO negative affect and typical firm performance ( Delgado - Garcia & De La Fuente - Sabate, 2010 ) . In a subseq uent study, they also found that the CEO negative affect to typical performance relationship carried over as one measure of bank risk taking. Also considering several measures of bank portfolio risk taking , this paper concluded that CEO negative affect was associated with lower risk taking ( Delgado García, La Fuente Sabaté, Manuel, & Quevedo Puente, 2010 ) . Most recently, an additional study utilizing a sample of Spanish entrepreneurs found that positive affect was positively associ ated 22 with goal breadth, goal levels, and satisfaction with business performance ( Delgado García, Rodríguez Escudero, & Martín Cruz, 2012 ) . On the other hand, negative affect was negatively rmance ( Delgado García et al., 2012 ) . Temporal Focus Temporal focus refers to an individual difference in the predominant emphasis an individual has towards the past, present, and future ( Gjesme, 1979 ) . Temporal focus is important in shaping many aspects of life including goal setting, risk taking, and achievement motivations ( Bartel & Milliken, 2004 ; Zimbardo & Boyd, 1999 ) . 1 In spite of its relationships with these important managerial decision making constructs, very little research has been conducted into CEO temporal focus. Instead, strategy scholars have focused on temporal orie ntation at an organizational and institutional level. Much of this work has been built around the concept of short - termism which suggests that businesses in the U.S. frequently make decisions based around short - term ideals even at the expense of long - term performance ( Laverty, 1996 ; Marginson & McAulay , 2008 ) . Short - termism suggests that organizations hav e a near - future (or even present) time orientation that is less than ideal ( Souder & Bromiley, 2012 ) . R ecently, Souder and Bromiley (2012 ) applied a behavioral perspective to the issue demonstra ting that firm temporal orientation (as measured by life time expected durability of newly acquired assets) was influenced by performance relative to an aspiration point. 1 Temporal focus and temporal orientation are frequently used interchangeably. There is some debate in the liter ature as to whether these represent separate constructs (Shipp et al, 2009) or are different names for the same constructs (Mohammed & Nadkarni, 2011). Because strategy research has used both the terms temporal orientation and temporal focus to refer to th e same construct, I will use them interchangeably throughout this dissertation. To be consistent with the most recent strategy literature my hypotheses will use the term temporal focus (Nadkarni & Chen, In Press). 23 of institutional investors that have ownership in the firm. For example, research suggests that pension funds have a long - term orientation while professional investment funds have a short - term orientation ( Tihanyi, Johnson, Hoskisson, & Hitt, 2003 ) . Recently, Connelly and colleagues (In Press) demonstrated that dedicated institutio nal investors are associated with firms having lower pay disparity between top management and average employee pay while transient institutional investors are associated with firms having higher pay disparity. These findings may be indicative of differing temporal foci of these investors, and subsequently in the firms they own, because pay dispersion was positively associated with short - term performance and negatively associated with the long - term performance trend ( Connelly et al., In Press ) . There are a few limited examples of researchers exploring the temporal foc us of CEO s or other top executives. Das (1987 ) used questionnaires to explore temporal orientation amongst executives at a large U.S. bank , finding that longer - term future time orientations were associated with a preference for a longer time horizon in strategic planning. Other research has focused on CEO temporal focus and innovation outcomes demonstrating that a future focus was positively associated with the ability to detect new technolog ical opportunities, speed of product development, and deployment of resources in response to a technology change ( Yadav et al., 2007 ) . Most recently, Nadkarni and Chen (2014 ) explored CEO tem rate of new product introduction. The authors found that CEO past focus, present, and future focus were all related to the rate of new product introductions in dynamic environments, but that these relationships changed substantia lly in stable environments ( Nadkarni & Chen, 2014 ) . Both Yadav and col leagues (2007) and Nadkarni and Chen (2014 ) utilized CEO w ritings to measure 24 temporal focus. The utilization of non - obtrusive measures , such as these , provides a significant potential for future research in this area. Regulatory Focus One other motivational construct that has received some limited attention in t he study of top executives is regulatory focus ( Higgins, 1997 ; Higgins, 1998 ) . Regulatory focus theory a rgues that people have two distinct self - regulation systems which direct their strategic orientation towards goal pursuit ( Crowe & Higgins, 1997 ) . The first of these is a promotion focus. A promotion focus is associated with the tendency to view situations in a gain/ non - gain frame, sensitivity to the presence (or absence) of positive outcomes , and the desire to insure hits and insure against omission errors ( Higgins, 1997 ) . People with a strong promotion focus are concerned with accomplishment, growth, and advancement ( Higgins, 1998 ) . On the other hand, a prevention focus is associated with high security needs, the tendency to view situations in a loss/ non - loss frame, sensitivity to the prese nce (or absence) of negative outcomes, and the desire to insure correct rejections and insure against committing errors ( Higgins, 1997 ) . A prevention focus is associated with responsibility, p rotection, and safety ( Higgins, 1998 ) . Much of the strategy research on regulatory focus is conceptual in nature. Early work in this area tied regulatory focus theory to the entrepreneurial process arguing that promotion focus will be associated with the ability to generate ideas while a prevention focus is associated with the due diligence needed to screen ideas ( Brockner, Higgins, & Low, 2004 ) . Further, these authors suggest that a prevention focus may be beneficial in procuring resources while a promotion focus may ( Brockner et al., 2004 ) . Das and Kumar (2011 ) attitudes towards alliance partners. They argue that a promotion focus will be associated with 25 increased speed in selectin g an alliance partner, a lower sensitivity to partner opportunistic behaviors, and a greater willingness to engage in opportunistic acts. A prevention focus, meanwhile, will be associated with decreased speed in partner selection, a greater sensitivity to partner opportunistic behavior, and a lower willingness to engage in opportunistic acts ( Das & Kumar, 2011 ) . This paper goes on to outline differences in expected negotiation styles, co nflict management behaviors, and desired control systems, suggesting that prevention and promotion focus may have an important role in many aspects of alliance development. One final conceptual paper that utilized regulatory focus theory is Wowak and Hambrick (2010 ) interaction of CEO characteristics and compensation. They suggest that stock opt ion pay will have a limited influence on the risk taking behaviors of CEO s with a strong promotion focus or a strong prevention focus but will continue to influence risk taking of CEO s with a moderate prevention or promotion focus. To my knowledge only t hr ee published papers have empirically studied regulatory focus of CEO s . The first two of which looked at executives of small entrepreneurial firms both utilizing traditional survey measures for capturing CEO regulatory focus. Wallace and colleagues (2010) f ound that CEO promotion focus was positively related to firm performance, and CEO prevention focus was negatively related to firm performance, and that these relationships were moderated by perceptions of dynamism in the environment. Similarly, Hmieleski and Baron (2008 ) found a po sitive relationship between promotion focus and firm performance and a negative relationship between prevention focus and firm performance but only for firms in dynamic environments. The conceptual work on CEO regulatory focus suggests that further empiric al research in this area may be important in furthering upper echelons research. 26 Most recently, Gamache and colleagues (In Press) used letters to the shareholders to measure CEO regulatory focus. This study found that CEO promotion focus was positively ass ociated with the quantity and value of acquisition activity, while CEO prevention focus was negatively associated with the quantity and value of acquisition activity. Further, the authors demonstrated that incentive compensation in the form of stock option pay attenuated the negative impact of CEO prevention focus on acquisition activity but did not influence the relationship between CEO promotion focus and acquisition activity. Concluding Thoughts Research on CEO motivational constructs is just starting to gain a place in strategy research. Much of the research that has explored these constructs has been conceptual in nature and there has been very limited empirical research in this area. Proximal motivational theories suggest that these motivational cons tructs are important because they can have a more direct and more powerful influence on behavior ( Barrick & Mount, 2005 ; Hoyle, 2010 ; Lanaj et al., 2012 ) . At the heart of this dissertation is the argument that CEO motivational constructs will influence the degree that CEO s will attend to and learn from the reactions of external stakeholders following the announcement of a strategic action. In particular, my theory suggests that CEO regulatory focus will shape the propensity of CEO s to be influenced by positive or negative stakeholder reactions while CEO temp oral focus will shape the propensity of CEO s to be influenced by external stakeholder reactions in general. Doing so will increase our understanding of how CEO s learn and help to expand our limited understanding of how CEO motivational constructs influence strategic actions. 27 I choose not to include CEO affect in my hypotheses for two reasons. First, as I note earlier, research on affect and emotions includes a wide range of constructs, sometimes with overlapping or conflicting meanings ( Russell, 2003 ) . Second, regulatory focus is associated with emotional experiences. A promotion focus is associated with stronger feelings of positive emotion, while a prevention focus is associa ted with stronger feelings of negative emotions ( Brockner & Higgins, 2001 ; Lanaj et al., 2012 ) . Due to these similarities I decided to not include both sets of constructs. Because regulatory focus also has broader implications, such as in strategic preferences, I choose to include regulatory focus theory in my model and do not include affect. 28 LITERATURE REVIEW ON ACQUISITION S The context for my study of how CEO , and learn from , external stakeholder reactions centers on firm acquisition activity. In particular, I argue that CEO motiva tional constructs will influence how CEO s learn from stakeholder reactions to acquisition announcements. As such, this section of the literature review focuses on acquisitions. Over the past three decades, there has been a significant amount of research on firm acquisitions. Researchers have provided a strong understanding about the general performance implications of acquisitions and are starting to get a better understanding of antecedents of acquisition activity. Still, there remains much to learn about acquisition activity and acquisition learning, particularly surrounding the role of CEO psychological characteristics in shaping acquisition activity. In summarizing this literature, I will start with a broad summary of research on acquisitions including b oth the performance consequences of acquisitions and the antecedents of that acquisition activity. Following that, I will concentrate the review on research of learning from acquisition experiences. Performance Implications of Acquisitions The most cons istent finding in acquisition research is that acquiring firms generally do not benefit from making acquisitions ( Barkema & Schijven, 2008 ; Haleblian et al., 2009 ) . Meta - analytic research has been consistent in demonstrating this finding, showing that, on average, the of the acquiring firms receive no benefits and frequently are left with negative returns ( Datta et al., 1992 ; King et al., 2004 ) . 29 In their meta - analysis, King and colleagues (2004) did find evidence suggesting that moderators were likely present however none of the moderators with sufficient sample size to be tested demonstrated significant interactions. One possible moderate that has seen some empirical support is that of firm size; Moeller, Schlingemann, and Stulz (2004 ) found that acquisitions made by small firms were much more successful than acquisitions by larger firms. The ability to ret ain the executives from the target firm also influences acquisition performance for the acquirer. The departure of top executives from the acquired firm is harmful to acquirer performance ( Ber gh, 2001 ; Cannella & Hambrick, 1993 ) . This negative effect on acquisition performance is particularly strong when the departure involves long - tenured executives or executives from the highest positi ons in the target company ( Bergh, 2001 ; Cannella & Hambrick, 1993 ) . Other factors associated with acquisition performance include deal characteristics such as payment type (stock vs. cash), managerial factors such as ownership and board governance, environmental factors such as position in a merger wave, and firm factors such as recent performance, and as I will discuss more below, learn ing from firm experience ( Haleblian et al., 2009 ) . How a CEO is paid can also influence the types of acquisitions the CEO engages in, thereby, influencing acquisition performance. Sanders and Hambrick (2007 ) found that CEO stoc k option pay was associated with more extreme financial performance for investments (including acquisitions), and that the performance of these investments was more likely to be negative than positive. There is also evidence that individual differences am ongst CEO s influence the performance of acquisitions. For example, Hayward and Hambrick (1997 ) found that CEO hubris was nega tively associated with acquisition performance in large acquisitions. Similarly, 30 research from finance demonstrates that CEO overconfidence is associated with more negative market reactions ( Malmendier & Tate, 2008 ) . Individual differences in experiences and multicultural acceptance can also influence post - merger integration thus influencing acquisition performance ( Chatterjee, Lubatkin, Schweiger, & Weber, 1992 ; Pablo, 1994 ) . Antece dents to Acquisition Activity ( Haleblian et al., 2009: 472 ) . The generally accepted un derstanding that, on average, most acquisitions fail to provide positive returns to shareholders, makes understanding why firms continue to engage in acquisitions very important. Research has found support for a wide range of antecedents that suggest both value enhancing and private interest motives ( D evers et al., 2013 ) . Value enhancing motives suggest that acquisitions are done with the best interests of the shareholders in mind as firms attempt to create synergy through acquisitions by increasing market power, enhancing firm efficiency , or redeplo ying complementary assets ( Haleblian et al., 2009 ) . Private interest motives include attempts by executives to maximize their compensation, increase their personal discretion, or to diversify their risk position ( Devers et al., 2013 ) . The recent study by Devers and colleagues (2013) found support for the private interest motives by demonstrating tha t following an acquisition CEO s are likely to sell firm stock and exercise firm options; these actions are consistent with the idea that they are not confident about the success of their own acquisitions. Compensation is frequently viewed as an important p rivate interest 31 acquisition activity ( Sanders, 2001 ) . Other research has demonstrated that CEO compensation increases following acquisitions regardless of the performan ce of the acquisitions ( Harford & Li, 2007 ) . Naturally, if CEO s receive financial benefits from acquiring other firms we can expect them to continue eng aging in these behaviors. There are, however, risks for CEO s who acquire firms. Lehn and Zhao (2006 ) found that poor stock market performance of an acquisition was associated with increased likelihood th at the CEO would not remain in the position five years later. These same CEO s, however, had a better chance of staying in the ir role with the firm through the next five year s if they cancelled the acquisition before completion following the negative market reaction ( Lehn & Zhao, 2006 ) . This suggests that CEO s who learn from stakeholder reactions do not receive the same career penalties as those who do not learn. CEO characteristics also play a role in shaping the le vel of firm acquisition activity. Research in this area has demonstrated that value enhancing motives may result in value destroying decisions if the motives are misplaced. For example, CEO overconfidence (hubris) is positively associated with acquisition activity ( Brown & Sarma, 2007 ; Malmendier & Tate, 2005 , 2008 ) . Similarly, in the paper by Hayward and Hambrick (1997 ) noted earlier, the authors found that CEO hubris w as positively associated with the size of premiums paid for acquisitions. Another CEO self - concept construct, CEO narcissism, has also been shown to influence acquisition activity. Chatterjee and Hambrick (2007 ) found that narcissistic CEO s engaged in more acquisitions and larger acquisitions. These initial findings suggest that CEO characteristics acquisition activity. 32 Learning from Acquisition Experience Learning and Subsequent Acquisition Performance T he wide range of evidence consistently demonstrates that firms learn from their prior acquisition experience. Findings show that prior acquisition experience can influence both the proclivity to engage in acquisitions and the performance of those acquisitions ( Haleblian et al., 2009 ) . Most of this research took a learning curve approach to study how acquisition experience influences performance of subsequent acquisitions; while the underlying assumpt ion in this line of theorizing is that learning from experiences would be highly beneficial to performance , the empirical results have been equivocal ( Barkema & Schijven, 2008 ) . Departing from the learning curve arguments, Haleblian and Finkelstein (1999 ) took a behavioral learning perspective approach to suggest that learning may not always be beneficial. This theory suggested that performance consequences following an acquisition serve as either a reward or punishmen t for the action; however, organizations can draw either correct or incorrect generalizations about these consequences. Their findings confirmed this, demonstrating a U - shaped relationship between acquirer experience and acquisition performance and showing that similar acquisition experience was more valuable than dissimilar acquisition experience ( Haleblian & Finkelstein, 1999 ) . Building on Haleblian and Finkelstein (1999 ) arguments that learning is not always beneficial, Zollo (2009 ) argued that some of the negative performance implications from acquisition experience may be a result o f superstitious learning. Superstitious there is a negative relationship between perceptions of prior acquisition performance and actual performance of the focal acquisition ( Zollo, 2009 ) . 33 Similarly, some research has explored the types of experience that matter for effective learning. For example, executives may be drawing on experi ence from prior dissimilar events resulting in declining performance on subsequent acquisitions ( Finkelstein & Haleblian, 2002 ) . Hayward (2002 ) found that small acquisition losses were beneficial to the performance of subsequent acquisitions, and that there is an inverted U - shaped relationship between the similarity of prior acquisitions and focal acquisition performance ( Hayward, 2002 ) . Another argument for why acquisition experience does not improve performance of subsequent acquisitions is that firms may forget the lessons they have learned ( Meschi & Metais, 2013 ) . In a study of acquisitions by French companies, Meschi and Metais (2013 ) found support for a decay in the value of acquisition experiences. Acquisition experi ences that were too old (five or more years before) did not decrease the likelihood of acquisition failure. Further, they found that only medium - term acquisition experiences (three or four years prior) improved focal acquisition survival rates suggesting t hat some time was needed for the experience to be integrated ( Meschi & Metais, 2013 ) . Recently, Kim, Haleblian, and Finkelstein (2011 ) used behavioral learning theory and the theory of desperation to extend our understanding of why firms overpay for acquisitions. They found that when organic firm growth is low relative to social and historical aspirations, the firm is more likely to pay higher acquisition premiums. Further, they found support for the hypothesis that firms that become dependent on acquisitions for growth tend to overpay for acquisitions. or an reduced the effect of desperation on acquisition premium paid ( Kim et al., 2011 ) . These findings 34 are interesting because they suggest that firms who do not have their own adequate level of acquisition experience may benefit from hiring advisors who have that experience, and that advisor experienc es may, at times, be more important than focal firm experience. Other research also demonstrates that firms can learn from the acquisition experiences of others. McDonald , Westphal, and Graebner (2008 ) experience that their outside directors bring from their own firms or from other firms they serve as a director. Other research suggests that learning from others can be detrimental if it lead s to bandwagon effects. During a merger wave, acquisition performance is higher for firms who capture a first mover adva wave ( McNamara , Haleblian, & Dykes, 2008 ) . Of note, most of this research measured acquisition performance based on cumulative abnormal returns (CARS) (e.g., Finkelstein & Haleblian, 2002 ; Haleblian & Finkelstein, 1999 ; Wright, Kroll, Lado, & Van Ness, 2002 ) . It might be more accurate to consider CARS as representative of market or investor reactions rather than fully reflecting the performance of the acquisition. Schijven and Hitt (2012: 1248 ) announcements rarely, if ever, represent the objective, rational - deductive calculations th at Instead, they demonstrated, that acquisition factors including premiums paid, use of stock in the purchase, industry similarity, involvement of ( Schijven & Hitt, 2012 ) . Interestingly experience did not significantly moderate the relationship between premium paid and investor reaction ( Schijven & Hitt, 2012 ) . 35 Learning and Subsequent Propensity to Acquire Considering the uncertainty about why firms continue to engage in acquisitions , it is surprising tha t only a small subset of the acquisition learning research has focused on the influence of acquisition experiences on the proclivity to engage in further acquisitions. Early work in this area suggested that acquisition activity developed into organizationa l routines that provided momentum for the direction of future firm actions ( Amburgey & Miner, 1992 ) . This ( Amburgey & Miner, 1992: 345 ) . Similarly research has demonstrated that experience shapes decisions about locations of acquisitions ( Baum, Li, & Usher, 2000 ) and whether the acquiring firm continues to make related or unrelated acquisitions ( Yang & Hyland, 2006 ) . Haleblian and colleagues (2006) directly tested the effect of prior acquisition experience on the likelihood of making a subsequent acquisition . Building off the arguments of Amburgey and Miner (1992 ) described earlier, these a uthors suggested that firms learn from the feedback provided by acquisition performance . They found that acquisition experience was positively related to the likelihood of a subsequent acquisition. Further they found that acquisition performance was positi vely related to the propensity to engage in a subsequent acquisition and that strong acquisition performance also positively moderated the relationship between acquisition experience and likelihood of making subsequent acquisitions ( Haleblian et al., 2006 ) . organizational events. In one study, Barkema and Vermeulen (1998 ) found that firms learn from the diversity of international markets that they do business in , and this shapes the cho ice between an acquisition or new venture for international expansions with firms with high multinational 36 diversity preferring to expand by way of new ventures. Alliance experience can also shape propensity to acquire. For example, Kogut (1991: 29 ) argued that joint ventures r epresented an option to acquire, found ing equent work finding that experience as alliance partners increases the likelihood that one firm will acquire the other ( Vanhaverbeke, Duysters, & Noorderhaven, 2002 ) . One series of studies has provided evidence that learn ing from the acquisition experiences of other firms in their network can influence the propensity to acquire . Haunschild (1993 ) found that the number of prior acquisitions by a fi positively associated with the number of acquisitions completed by the focal firm. A subsequent study tested and found support for several moderators of this relationship ; the influence of director interlocks on focal firm acquisitions was weaker in larger firms and when the CEO was also a member of other business councils but the influence of director interlocks on large firms was stronger when recent press coverage about acquisitions was high ( Haunschild & Beckman, 1998 ) . This research stream also explored performance implications of vicarious learning. The authors found that firms pay lowe r premiums on the acquisitions when they have network partners with heterogeneous premium experience and when those network partners have completed deals with a diversity of target sizes ( Beckman & Haunschild, 2002 ) . Learning and Acquisition Completion Another outcome of acquisition learning that has been explored in recent research is the likelihood of acquisition completion. Research in this context has demonstrated that firms apply learning both from past acquisition performance and the market reaction to the focal acquisition in making the decision to complete an acquisition. In addition, Luo (2005 ) found that the market 37 reaction to an acquisition is positively related to the likelihood of subsequent acquisition completion. Looking at the influence of prior acquisition experience on focal acquisition completion, Muehlfeld, Rao Sahib, and Van Witteloostuijn (2012 ) found that cumulative successful acquisition experiences had an inverted U - shaped relationship with the likelihood of focal acquisition completion . Further, they noted that cumulative acquisition failure experiences had a U - shaped relationship with focal acquisition completion. Interestingly , they found that failure experiences with acquisitions only influenced an acquisition in similar co ntexts ; however, success experiences with acquisitions had spillover effects on non - similar acquisitions ( Muehlfeld et al., 2012 ) . These findings may suggest that firms attribute past successes to their acquisition capability but attribute the cause of ac quisition failures to more context specific issues. Acquisition Learning and CEO characteristics While CEO self - concept characteristics such as narcissism and hubris have been studied in regards to both the antecedents of acquisition activity and in shapin g the performance implications of acquisitions, there has been a dearth of research connecting CEO characteristics to the process of learning from acquisition experience. One paper, although somewhat indirectly, does explore how differences amongst CEO s in fluence the impact of experience on subsequent acquisitions. Chatterjee and Hambrick (2011 ) studied the influence of CEO narcissism on how cues about recent performance shape subsequent risk taking (which included acquisitions). Their findings suggest that narcissistic CEO s were unresponsive to recent perfor mance but were highly responsive to social praise from the media ( C hatterjee & Hambrick, 2011 ) . Although very preliminary, these findings suggest that differences amongst CEO s shape how the CEO learns 38 from feedback about firm performance. Clearly there is significant room to add to our understanding about how difference s amongst CEO s shape how they learn from feedback and this dissertation proposes to do just that. Concluding Thoughts Acquisitions provide an important context suitable for exploring how CEO motivational characteristics influence CEO learning from extern al stakeholder reactions to an announcement of a strategic action. Acquisitions are an important strategic action requiring significant involvement from the CEO (e.g., Devers et al., 2013 ; Sanders, 2001 ) . Further, because acquisitions frequently fail to provide financial returns to the shareholders, understanding antecedents of acquisition ac tivity (including learning) is important for both scholarship and practice ( Haleblian et al., 2009 ) . In addition, existing research has provided some initial evidence that individual differences of CEO s influence acquisition propensity ( Hayward & Hambrick, 1997 ; Malmendier & Tate, 2008 ) . This suggests that research on other CEO constructs may be fruitful in advan cing our understanding of why CEO s acquire. Finally, the existing research on learning from acquisitions provides a strong foundation for this dissertation. There is significant research on how CEO s learn from acquisitions ( Barkema & Schijven, 2008 ) including some research on subsequent propensity to acquire ( Haleblian et al., 2006 ; Haunschild, 1993 ) and likelihood of completing the focal acquisition ( Luo, 2005 ) . These findings provide evidence of an underlying main effect relationship between acquisition learning and subsequent actions. I contribute to this research by exploring how CEO motivational characteristics shape the propensity of CEO s to le arn in these ways. 39 THEORY AND HYPOTHESE S The theoretical foundation for this dissertation is upper echelons theory. Upper echelons theory argues that executive characteristics influence how they interpret their environment and how this influences their s trategic decisions. I focus on theory surrounding two types of motivational attributes: regulatory focus and temporal focus. Before developing specific hypotheses related to these two types of constructs, I will provide a theoretical overview of upper eche lons theory. Following that, I will briefly discuss theory on CEO learning and demonstrat e its consistency with upper echelons theory for explaining how top executives learn from stakeholder reactions to strategic announcements. Building on this framework I explore CEO motivational characteristics from regulatory focus theory and temporal focus. I argue that these CEO attributes will influence CEO learning. In particular, my theory suggests that CEO regulatory focus will shape the degree that CEO s are influ enced by positive or negative stakeholder reactions to the announcement of a strategic event, while CEO temporal focus will shape the degree that CEO s are influenced by external stakeholder reactions in general. Upper Echelons Theory Upper echelons theor y, as put forth by Hambrick and Mason (1984 ) , argued that integrating the study of top executives into strategic management research had the potential to provide greater capability for predicting organizational outcomes. At a basic level, upper personalized interpretatio ns of the strategic situations they face, and (2) these personalized ( Hambrick, 40 2007: 334 ) . As a result, organizations become a reflection of these top executives ( Hambrick, 2005 ) . The central mechanisms in the upper echelons theory reflect an explanation of how top executives filter information. As Hambrick (2005: 112 ) information proc essing theory, offering a way to systematically explain how executives act under ion, and interpretation ( Hambrick & Mason, 1984 ) psychological orientation inclu ding their values, cognitive models, and personality characteristics ( Finkelstein et al., 2009 ) . When dealing with some environmental or processing that then shapes the strategic ch oices, executive behaviors and ultimately organizational performance ( Hambrick, 2005 ) . Field of Vision The three major steps in information processing all influence how CEO s respond to feedback provided by external stakeholders following th e announcement of a strategic decision. The first of these is CEO internal and external environment to which CEO s direct their attention ( Hambrick & Mason, 1984 ) . The field of vision is limited because it is not possible for executives to pay attention to all the events going on around them ( Hambrick, 2005 ) . As such, the degree that CEO s learn from external stakeholder reactions depends, first of all, on how much attention they pay to the stakeholder reaction; in the case of this dissertation, how much attention they pay to the stock market and media following an announcement of a strategic action. While existing research has 41 demonstrated that, in general, CEO s do appear to pay attention to both the stock market and the media (e.g., Bednar et al., 2013 ; Haleblian et al., 2006 ) , according to upper echelons theory, the degree that particular CEO s are atte ntive to these stakeholders will be shaped by their psychological characteristics. For example, CEO s with a high future focus may be focused more on future outcomes associated with the strategic action such that they do not pay close attention to current s takeholder reactions to the announcement of the action. Selective Perception The second information processing step is selective perception. Even if the information about an event is within the field of vision of executives it may not be perceived if it f alls outside of their selective perception ( Hambrick & Mason, 1984 ) notices only a sub ( Hambrick, 2005: 112 ) . Instead, only some of the information received by executives will be especially salient while other information will seem much less important and fade into the ir subconscious , and some other information will be missed entirely ( Finkelstein et al., 2009 ) . CEO psychological characteristics also play a role in shaping what information executives will perceive. For example, a promotion focus is associated with a high sensitivity to the pres ence or absence of positive outcomes ( Higgins et al., 2001 ) . As such, CEO s with a high promotion focus are likely to be more keenly aware of positive content in the stakeholder reactions than of negative content . Interpretation According to upper echelon theory the third element of the filtering process for executives is their interpretation of the information that they perceive. Interpretation occurs when executives attaches meaning to the information that they have noticed ( Hambrick, 2005 ) . During the interpretation step executives considers the implications, weigh the risks and benefits, and 42 draw conclusions about the new information ( Finkelstein et al., 2009 ; Hambrick, 2005 ) . CEO s wi th a high present temporal focus, for example, are likely to view the responses from external stakeholders as having important implications for current strategic decisions. CEO Learning Most strategy research on learning has focused on learning as an org anizational level phenomenon (e.g., Fiol & Lyles, 1985 ; March, 1991 ; Miller, 1996 ) . This research suggests that organizations learn when they store knowledge through the use of procedures, rules, and organizational norms ( March, 1991 ) . This organizational learning, h owever, is based on the learning of individuals within the organization ( Cohen & Levinthal, 1990 ; Hedberg, 1981 ) . Hedberg (1981: 3 ) notes CEO s play an important role i n directing strategy formulation and decision making ( Finkelstein et al., 2009 ) . As such, it is surprising that little research has applied upper echelons theory to learning of CEO s. Learning in organizations involves a wide range of processes that rely heavily on the individuals involved, including environmental scanning, performance monitoring, and interpreting information ( Huber, 1991 ) . When the firm announces a major strategic action, the CEO is likely to be particula rly involved in scanning the environment, monitoring external reactions, and interpreting this information. CEO motivational characteristics are likely to influence the way in CEO s do all of these tasks. 43 Further supporting this argument, research on learn ing has noted that individual characteristics play an important role in how people learn. Jarvis (1987: 73 ) possible for individuals to perceive what are apparently the same facts from a situation and experience them differently, even to experience them in such a manner as to confer diametrically characteristics can shape learning by influencing this process ; t hese include personality factors such as the Big Five personality traits ( Zhang & Sternberg, 2005 ) , self - concept constructs such as self - esteem ( Hall, 2005 ) , and motivational constructs such as emotions and sense of time ( Jarvis, 2005 ) . Consistent with upper echelons theory, research on learning suggests tha t differences amongst individuals in their psychological attributes shapes what the learner is aware of, what they perceive, and how they interpret information. In short, these dif ferences shape how they learn. Main Effect Relationships This dissertation focuses on providing an understanding for how CEO motivational constructs influence how CEO s learn from stakeholder reactions to acquisition announcements. I build off of existing research which demonstrates that stakeholder reactions to acquisition annou ncements influence the likelihood of completing the focal acquisition ( Luo, 2005 ; Muehlfeld et al., 2012 ) and their propensity to engage in subsequent acquisitions ( Haleblian et al., 2006 ) . In so doing, I do not hypothesize main effect relationships. However, consistent with prior research, I expect that positive reactions by stakeholders to acquisition announcements will be positively associated with both the likelihood of completing the focal acquisition and with subsequent acquisition activity. On the other hand, I expect that negative reactions by 44 stakeholders to acquisition announcements will be negatively associated with the likeli hood of completing the focal acquisition and subsequent acquisition activity. The moderator hypotheses I focus on utilize upper echelons theory and consider how motivational characteristics shape how differences amongst CEO s shape this learning. Figure 2 - Proposed Model (Positive Stakeholder Reactions) 45 Figure 3 - Proposed Model (Negative Stakeholder Reactions) The Moderating Effect of CEO Motivational Characteristics As noted above, a wide varie ty of individual characteristics shape the learning process ( Hall, 2005 ; Jarvis, 1987 ; Zhang & Sternberg, 2005 ) . Similarly, as argued in the literature review, researchers in strategic leadership have explored the influences of a wide variety of psychological characteristics including personality characteris tics, self - concept attributes, and motivational constructs. The distal - proximal theory of motivation argues that compared to more distal personality traits, motivational constructs provide a more proximal influence on work behavior ( Barrick & Mount, 2005 ) . As such, the influence of motivational constructs on behaviors is likely to be stronger and more meaningful than more distal personality traits ( Lanaj 46 et al., 2012 ) . These proximal motivations play an important role in shaping individual information processing ( Hoyle, 2010 ) which, integrated with the upper echelons theory, suggests that these motivational characteristics may have an important role in influence strategic decisions. As such, in this dissertation I fo cus on two types of motivational constructs that are likely to influence how CEO s learn from stakeholder reactions to the announcement of strategic actions. Regulatory Focus Theory The first proximal motivational constructs that I explore in this disserta tion stem from regulatory focus theory ( Higgins, 1997 ; Higgins, 1998 ) . According to regulatory focus the ory individuals pursue their goals through two distinct regulatory mechanisms: a promotion focus and a prevention focus ( Higgins, 1997 ; Higgins, 1998 ) . A promotion focus motivates individuals towards goal pursuit through a concern with accomplishment and a desire for growth and advancement ( Crowe & Higgins, 1997 ) . A prevention focus motivates individuals towards goal pursuit through a concern with responsibility and a desire for security and saf ety ( Crowe & Higgins, 1997 ) . Both of these foci can lead people towards successful goal a chievement but do so through very different types of behaviors. For example, in studying for an exam, a promotion focus will lead an individual to focus on tasks designed to ensure a good grade (such as reading the textbook and studying class notes) ( Lanaj et al., 2012 ) . A prevention focus, meanwhile, directs individuals to focus on tasks designed to avoid getting a bad grade (such as avoidi ng television or parties) ( Lanaj et al., 2012 ) . Promotion focus and prevention foci, thus, represent independent constructs and not opposi te ends of a continuum, making it possible for people to be 47 high on one or the other, high on both foci, or low on both foci ( Forster, Higgins, & Bianco, 2003 ; Lanaj et al., 2012 ) . 2 Regulatory focus shapes the types of strategic actions that CEO s are likely to pursue. Strategy scholars have suggested that CEO regulatory focus is likely to influence how firms interact with alliance partners ( Das & Kumar, 2011 ) , the generation and implementation of entrepreneurial ideas ( Brockner et al., 2004 ) , and firm risk taking ( Wowak & Hambrick, 2010 ) . Further, CEO regulatory focus is likely to influence t he types of information that individual s are likely to pay attention to and how they interpret that information. I argue that CEO regulatory focus will influence the degree that CEO s are motivated by either positive or negative reactions by stakeholders following the announcement of an acquisition. CEO Promot ion Focus A promotion focus motivates behavior through a drive to approach desired end states ( Higgins, 1997 ) . People high in promotion focus are sensitive to the presence and absence of posit ive outcomes and are eager to advance and achieve gains ( Crowe & Higgins, 1997 ) . A promot ion focus also sensitizes people to positive environmental signals leading to greater job satisfaction ( Lanaj et al., 2012 ) . This sensitiv ity is likely to limit the field of vision of CEO s and shape the type of information that they pay attention to (selective perception). CEO s with a strong promotion focus are likely to pay careful attention to positive stakeholder reactions. Supporting thi s, prior research has demonstrated that a promotion focus is associated with an increased sensitivity to positive environmental signals ( Lan aj et al., 2012 ) . As such, CEO s are 2 Lanaj and colleagues (2012) demonstrated through meta - analytic analysis that the correlation between these two constructs is relatively 48 likely to spend time reading positive media coverage or dwelling on positive stock market responses following the announcement of an acquisition. 3 CEO s with a high promotion focus are also likely to be influenced more strongly by the positive emotions expressed in the stakeholder responses. After experiencing a favorable outcome, an individual with a strong promotion focus will feel more intense positive emotions ( Brockner & Higgins, 2001 ) . This suggests that the positive media and market responses are likely to be especially impactful to CEO s with a high promotio n focus because it creates a powerful emotional reaction. CEO promotion focus is also likely to shape how CEO s interpret the positive information that they receive. Positive reactions are likely to be especially important to how CEO s perceive the initial success, or lack of success, of the acquisition. A promotion focus is associated with a desire for accomplishment and growth ( Crowe & Higgins, 1997 ) . Although many benefits that a firm may see from an acquisition take place in the long run ( Haleblian et al., 2009 ) , the immediate reactions from the media and the market provide immediate performance feedback. Positive reactions provide CEO s who have a strong promotion focus with the quick sense of accomplishment that they are seeking. As such, CEO s with a high promotion focus are likely to interpret positive reactions to the acquisition announcements as positive affirmation for the decision to ac quire. 3 I argue that CEO s are likely to view positive market and media reactions to an acquisition as a gain situation. Prior research has established that the acquirer generally fails to benefit from an acquisition (Haleblian et al., 2009). As such, CEO s are likely to expect a neutral reaction from the media and the stock market following the acquisition. Any positive reaction, therefore, will be a gain to CEO s who are likely to benefit from positive reactions in terms of increased positive repu tation, higher compensation and increased board support for future strategic actions. Recent research by Devers and colleagues (2013) provides some support for this claim. These authors demonstrate that CEO s exercise more options and sell more firm stock f ollowing acquisitions that experience positive market reactions. This suggests that the positive reactions were an unexpected gain that CEO s moved to take advantage of. 49 Further, a promotion focus influences the strategic choices that CEO s are likely to take in response to the external stakeholder responses. A promotion focus is associated with strategies designed to maximize gains and minimize non - gains ( Higgins, 1997 ) . People with a strong promotion focus, therefore, will take steps to CEO s with a strong promotion focus will be more likely take the positive reactions from external stakeholders as evidence that the acquisition they are undertaking is likely to be a hit, increasing their willingness to persist in this direction. Similarly, th e positive reactions will lead these CEO s to see acquisitions, in general, as being an effective strategy in making gains. The drive of CEO s with a strong promotion focus to see advancement and gains will then drive them to make continued acquisitions. As such, I hypothesize: H 1 : The relationship between positive stakeholder reactions to acquisition announcements and a) completion of the focal acquisition and b) subsequent acquisition activity will be moderated by CEO promotion focus such that the relations hip will be stronger for CEO s with high promotion focus. CEO Prevention Focus A prevention focus motivates behavior through a drive to avoid mismatches to desired end - states ( Higgins, 1997 ) a nd a sensitivity to the presence and absence of negative outcomes ( Crowe & Higgins, 1997 ) . People high in prevention focus have high security needs and are guided by a sense of duty and responsibility ( Crowe & Higgins, 1997 ) . A prevention focus also sensitizes people to negative environmental signals ( Lanaj et al., 2 012 ) . As such, CEO 50 prevention focus is likely to influence how CEO s process information about external stakeholder reactions. The limited field of vision of CEO s with a strong prevention focus is likely to emphasize attention to negative stakeholder reac tions because of their sensitivity to negative outcomes. In addition, people high in prevention focus also tend to experience more negative emotions ( Lanaj et al., 2012 ) . Following an unfavorable outcome a strong prevention focused individual will feel more intense negative emotions than will a weak prevention focused person ( Brockner & Higgins, 2001 ) . As a result, CEO s high in prevention focus are likely to be especially perceptive of negative stakeholder reactions and these negative reactions are likely to be especially impactful because of the negative emotional reaction they create in these CEO s. 4 CEO prevention focus is also likely to influence the ways that CEO s interpret negative information that they receive. These interpretati ons are likely to be centered on a loss framing. As I argued earlier, the initial reactions are likely to shape how CEO s perceive the success of an acquisition. A prevention focus is associated with a desire to avoid losses ( Higgins, 1997 ) . As such CEO s high in prevention focus are likely to interpret negative stakeholder reactions as evidence that the acquisition is a loss situation. Further, these CEO s are likely to be very sensitive to the l oss of support of the general public (through media coverage) and investors (stock market). Finally, CEO prevention focus is likely to shape the strategic choices that the executives make in reaction to the stakeholder responses. In taking actions toward s their goals, someone 4 I argue that CEO s are likely to view negative market and media reactions as a loss s ituation. CEO s are likely to know that acquirers do not tend to receive positive feedback following an acquisition announcement. However, CEO s are also likely to believe that their acquisition has positive merit. Accordingly, I believe CEO s are likely to h ave neutral expectations for the reactions to the acquisition. Any negative reaction, therefore, will be a loss to CEO s who are likely to be hurt by negative reactions in terms of negative reputational effects, compensation loss and loss of board support f or future strategic actions. 51 ( Crowe & Higgins, 1997: 126 ) . In other words, someone with a strong prevention focus would rather avoid taking action if they felt it might lead to a mistake. A prevention foc us is associated with vigilance and someone high in prevention focus is more likely to work slowly with a focus on accuracy ( Forster et al., 2003 ; Lanaj et al., 2012 ) . Further, a prevention focus is associated with high security needs; people wi th a high prevention focus will take steps to avoid threats to their security ( Higgins, 1997 ) . Therefore, following negative stakeholder reactions CEO s with a strong prevention focus are like ly to take actions to avoid making further mistakes and ensure their personal job security. Research has demonstrated that negative stock market reactions following an acquisition announcement has negative implications on the job security for the CEO s resp onsible for those acquisitions ( Lehn & Zhao, 2006 ) . However, by taking actions to cancel the acquisitions before completion these CEO s are able to successfully lower the negative effect of these acquisitions ( Lehn & Zhao, 2006 ) . This suggests that CEO s high in prevention focus will take steps to withdraw from the announced acquisition both to avoid potential losses and to increase their personal security. CEO s with a high prev ention focus are also likely to avoid potential future losses by reducing subsequent acquisition activity. Because they are highly attuned to the negative reactions, these will become especially salient to these CEO s when they are considering future acquis itions. They more clearly associate acquisitions with potential losses and as such will be less likely to pursue them in the future. Taking these arguments together, I formally hypothesize that: 52 H 2 : The relationship between negative stakeholder reactions to acquisition announcements and a) completion of the focal acquisition and b) subsequent acquisition activity will be moderated by CEO prevention focus such that the relationship will be stronger for CEO s with high prevention focus. CEO Temporal Focus Th e second motivational characteristic I explore in this dissertation is CEO temporal focus. Across the literature in psychology and organizational behavior a wide range of titles are given to this temporal construct including temporal orientation, time pers pective, and time orientation ( Mohammed & Nadkarni, 2011 ; Shipp et al., 2009 ; Zimbardo & Boyd, 1999 ) . T emporal focus can shape the motives and behaviors of individuals. For with a present - time perspective focus on immediate pleasure, take more risks, and make plans with shorter ti me frames, whereas individuals with a future - time perspective are highly goal - oriented, make longer - ( Mohammed & Na dkarni, 2011: 490 ) . Temporal focus is particularly important for research into attention to and how they perceive and evaluate that information ( Shipp et al., 2009 ) . Temporal focus is partly stable, developed as a result of upbringing and cultural, but is also influenced by current knowledge and moods and can change over time as a result of personal, social and institutional pressures ( Karniol & Ross, 1996 ; Zimbardo & Boyd, 1999 ) . Temporal focus is made up of three distinct constructs: future focus, present focus, and past focus ( Shipp et al., 2009 ) . These constructs are independent of each other such that a person can be high on only one focus 53 ( Shipp et al., 20 09: 3 ) . CEO temporal focus is likely to shape the types of strategic actions that CEO s chose to pursue. As explained in more detail earlier, scholars have found that CEO temporal focus is related to the length of strategic plans ( Das, 1987 ) , rate of new product introduction ( Nadkarni & Chen, 2014 ) , and how firms respond to strategic change ( Yadav et al., 2007 ) . CEO temporal focus is likely to be espec ially impactful in shaping the degree that CEO s prefer strategic actions with short - term performance implications compared to those actions that take longer to see benefits from. CEO s with a high future focus are likely to be more willing to take on projec ts that require a long - time to complete such as investments in long - term research and development projects ( Yadav et al., 2007 ) . CEO s with a high present focus are more likely to focus on projects that can make an impact now such as short - term investments in advertisements. Finally, CEO s with a high past focus are likely to make repeated use of the types of strategic actions that have w - term and long - term performance implications ( Haleblian et al., 2009 ) , so CEO s may pursue an acquisition strategy regardless of temporal focus. However, CEO temporal focus is likely to play an important role in how CEO s learn from external stakeholder reactions to an acquisition. Unlike CEO regulatory focus, which I argue shapes the degree that CEO s are influenced by either positive or negative stakeholder reactions, my theory suggests that CEO temporal focus wil l influence the degree that CEO s pay attention to stakeholder reactions in general. CEO Future Focus A future focus is associated with thinking that is primarily concerned with future events, mak es long - term plans, and frequently considers what the future holds ( Mohammed & Harrison, 54 2013 ; Nadkarni & Chen, 2014 ) . A future focus can be beneficial in terms - setting, motivation and achievement strivings, but it can hinder well - being when the pursuit of these goals creates time - ( Shipp et al., 2009: 2 ) . An individual high in future focus is likely to procrastinate less and be willing to take acti on towards a future that they are generally optimistic about ( Shipp et al., 2009 ) . Further, a strong future focus allows individuals to take a high level view and clearly distinguish between primary concerns and more minor secondary issues ( Mohammed & Harrison, 2013 ) . CEO future focus is likely to influence the im portance that CEO s place on both positive and negative stakeholder reactions to announcements of acquisitions. CEO s with a strong future focus are more likely to be concerned with the long - term implications of the acquisition and therefore not be sensitive to short - term reactions from stakeholders. CEO s with a strong future focus are likely to make assessments of their current situation based on their anticipated future rather short - term results ( Shipp & Jansen, 20 11 ) . This long - term perspective will shape the degree that these CEO s become aware of the stakeholder reactions. CEO s with a high future focus are likely to limit their field of vision and selective perception to future oriented issues. A future focus i s associated with striving for future goals and rewards and less concern with current results ( Gibson, Waller, Carpenter, & Conte, 2007 ) . As such, the reactions of external stakeholders are likely to be less important and CEO s with a high future focus are likely to pay less atte ntion to them. Instead, CEO s with a high future focus will direct their limited attention to events and opportunities that address future strategic issues ( Yadav et al., 2007 ) . To the extent that CEO s do become aware of the stakeholder reactions, those with a high future focus will interpret them in light of the future and discount their current importance. A 55 future focus is associated with a concern for long - term plans and future consequences ( Mohammed & Harrison, 2013 ) . Further, people with a strong future focus tend to be optimistic, believing the best about future outcomes ( Shipp et al., 2009 ) . Accordingly, CEO s with a strong future focus are likely to be particularly concerned with long - ter m performance implications while being less interested in short - term reactions of stakeholders. These CEO s have an ultimate outcome in mind when engaging in an acquisition, and are likely to continue to believe in the probability of successfully achieving those outcomes. As such, these CEO s are less likely to modify their actions based on current feedback. So the positive and negative external stakeholder reactions that CEO s with a high future focus do become aware of are less likely to influence their perc eptions of the focal acquisitions success and as such will have less of an influence on the likelihood of focal acquisition completion. Similarly, people with a strong future focus are less likely to consider prior experiences as important indicators of fu ture success. High future focus people are less effective at engaging in feedback based learning ( Nadkarni & Chen, 2014 ) . In this way, CEO s with a strong future focus will make subsequent acquisition decisions based on the assessments they make of the individual merits of each potential acquisition independently. They will r ely less on past experiences and, as such, reactions of external stakeholders, both positive and negative, to a focal acquisition are less likely to influence the subsequent level of acquisition activity that they engage in. Taken together, I hypothesize t hat CEO future focus will reduce the degree that both positive and negative stakeholder reactions will influence completion of the focal acquisition or their propensity to undertake subsequent acquisitions. More formally, I hypothesize: 56 H3: The relations hip between negative stakeholder reactions to acquisition announcements and a) completion of the focal acquisition and b) subsequent acquisition activity will be moderated by CEO future focus such that the relationship will be weaker for CEO s with high fut ure focus. H4: The relationship between positive stakeholder reactions to acquisition announcements and a) completion of the focal acquisition and b) subsequent acquisition activity will be moderated by CEO future focus such that the relationship will be weaker for CEO s with high future focus. CEO Present Focus Individuals high in present focus tend to be concerned with immediate pleasures and short - term plans ( Mohammed & Harrison, 2013 ) . These people are oriented to issues associated r current circumstances in making decisions ( Nadkarni & Chen, 2014 ) . A present focus can lead people to impulsive behaviors and the ability to quickly take advantage opportunities ( Shipp et al., 2009 ) . On the other hand, individuals with a strong present focus may fail to adequately consider long - term consequences and may engage in reckless risk - taking ( Mohammed & Harrison, 2013 ; Shipp et al., 2009 ) . CEO present focus will shape the way that CEO s process info rmation about both positive and negative stakeholder reactions and the propensity to act on this information. First, CEO present focus is likely to increase the attention that executives place on the current environment ( Nadkarni & Chen, 2014 ) , thereby widening their field of vision. Further, CEO s with a high 57 present focus a re likely to place a high value on the reactions of external stakeholders because they care about immediate performance of strategic actions they engage in. Mohammed and Harrison (2013: 246 ) note that worth to short - term information over long - term information . People with a high present focus make plans with shorter - time horizons ( Mohammed & Harrison, 2013 ) suggesting that CEO s with a high present focus will have short - term expectations for their acquisitions and will attenti ve to the feedback provided by these external stakeholders. CEO s with a high present focus are also likely to act quickly based on both positive and negative external stakeholder reactions. People with a high present focus are likely to be more impulsive and seek ways to gain immediate satisfaction ( Gibson et al., 2007 ; Mohammed & Harrison, 2013 ) . As such, a CEO with a high present focus will want to act quickly following these stakeholder reactions. A present focus is associated with the ability to be flexible and make adjustments to current plans ( Nadkarni & Chen, 2014 ) . Therefore, if the stakeholder reactions are negative, CEO s with a high present focus will want to find wa ys of shifting stakeholder sentiments. Withdrawing from the announced acquisition is a quick way of doing this. If the stakeholder reactions are positive, CEO s with a high present focus will want to move quickly to complete the acquisition. These CEO s are unlikely to drag out the acquisition process as they focus on what is happening right away and want to get it done. Similarly, CEO s with a strong present focus are likely to take information as a guide in influencing subsequent acquisition decisions. If th e stakeholders react positively to the acquisition announcement, high present focus CEO s will likely sense an opportunity to capitalize on the positive sentiment with additional acquisitions. A present focus is associated with a willingness to act quickly to take advantage of opportunities and a risk taking attitude ( Gibson et 58 al., 2007 ; Shipp et al., 2009 ) bot h of which will drive these CEO s to quickly undertake addition acquisitions. On the other hand, if the stakeholders react negatively to the acquisition announcement, high present focus CEO s will likely sense that any plans for subsequent acquisitions shoul d be abandoned. They are unlikely to consider long - term benefits from potential subsequent acquisitions but instead be very sensitive to the current reactions of stakeholders. In this case, the negative reactions of the external stakeholders to the current acquisition is likely to color the CEO s perceptions of what stakeholders want more than any past experiences or expectations about the future. Based on these arguments, I believe that CEO s with a high present focus will be more likely to be aware of the e xternal stakeholder reactions to the acquisition announcements, will more likely interpret this information to be an important indicator of the acquisition success and, therefore, will be more likely to quickly act on that information. As a result, I hypot hesize: H5: The relationship between negative stakeholder reactions to acquisition announcements and a) completion of the focal acquisition and b) subsequent acquisition activity will be moderated by CEO present focus such that the relationship will be str onger for CEO s with high present focus. H6: The relationship between positive stakeholder reactions to acquisition announcements and a) completion of the focal acquisition and b) subsequent acquisition activity will be moderated by CEO present focus such that the relationship will be stronger fo r CEO s with high present focus. 59 CEO Past Focus ( Nadkarni & Chen, 2014: 6 ) . A past focus can include generally positive reflections (sentimental) and/or generally negative reflections (aversive) ( Gibson et al., 2007 ; Mohammed & Harrison, 2013 ) . As people r eflect on events of the past they can think about the how and why and use that to shape subsequent actions and by doing so improve their learning ( Karniol & Ros s, 1996 ; Shipp et al., 2009 ) . For someone high in past focus, their perception of past events shapes their expectations for future outcomes ( Shipp & Jansen, 2011 ) . I argue that CEO past focus will influence how a CEO responds to both positive and negative stakeholder reactions but will do so in different ways for the two dependent variables of this study. First, I will discuss the implications of CEO past f ocus in moderating the relationship between CEO stakeholder reactions and completion of the focal acquisition. CEO s with a strong past focus are likely to be less concerned with the stakeholder responses to the current acquisition (limited field of vision and reduced selective perception), be less concerned about the reactions (interpretation), and be less willing to change as a result of current information. When focusing on the present acquisition, CEO s with a strong past focus are likely to rely on prio r experiences. For these people the success or failure of past actions is likely to have a larger influence than feedback on current actions ( Nadkarni & Chen, 2014 ; Shipp et al., 2009 ) . A strong past focus is likely to suggest a reliance on past ways of doing things and ( Nadkarni & Chen, 2014 ) . CEO s with a high past focus will have taken the historical information into consideration when they initially decided to p roceed with the acquisition and the new information is unlikely to 60 change their perceptions of those events. In short, when they decided to acquire they made up their mind and remain committed to their decision. As such, I hypothesize: H7a: The relationshi p between negative stakeholder reactions to acquisition announcements and completion of the focal acquisition will be moderated by CEO past focus such that the relationship will be weaker for CEO s with high past focus. b: The relationship between positive stakeholder reactions to acquisition announcements and completion of the focal acquisition will be moderated by CEO past focus such that the relationship will be weaker for CEO s with high past focus. On the other hand, CEO s with a strong past focus are l ikely to consider how external stakeholders respond to the focal acquisition when making decisions about subsequent acquisitions. A past focus involves the use of past memories in making decisions ( Nadkarni & Chen, 2014 ) . When making an acquisition decision, CEO s with a high past focus are likely to consider the performance of prior acquisitions they have engaged in. As such, the focal acquisition quickly becomes part of the collective acquisition experiences that the CEO will draw on. Further, when considering past acquisitions, the focal acquisition will be especially salie nt because it will be most clearly in the memory of the CEO and as a result have a large influence. The time that goes by between the focal acquisition and subsequent acquisition decisions the CEO has time to reflect on the focal acquisition. People with a past focus learn over time from past events and consider how to improve future events ( Karniol & Ross, 1996 ; Shipp et al., 2009 ) . As a result, CEO s with a high past focus will reflect on the focal acquisition as time passes and this learning will shape subsequent acquisitions. Therefore, I hypothesize: 61 H8a: The relationship between negative stakeholder reac tions to acquisition announcements and subsequent acquisition activity will be moderated by CEO past focus such that the relationship will be stronger for CEO s with high past focus. b: The relationship between positive stakeholder reactions to acquisition announcements and subsequent acquisition activity will be moderated by CEO past focus such that the relationship will be stronger for CEO s with high past focus. 62 METHODS Sample The sample for this dissertation is the S&P 500 as of January 1, 2006. I capt ured media and stock market reactions for all large acquisitions (greater than $100M , Hayward & Hambrick, 1997 ; Singh & Montgomery, 1987 ) announced by these firms from 2006 until the end of 2011. The $100 million cutoff ensured that I focused on acquisitions for which the CEO is likely to be highly involved ( Hayward & Hambrick, 1997 ) and that the acquisition is likely to receive significant attention from externa l stakeholders. Data was gathered from several sources. First, firm and industry level controls were collected from Compustat and the Compustat Segments database. 5 Executive compensation and tenure data were collected from Execucomp and board data was col lected from Risk Metrics (formerly the Investor Responsibility Research Center) . Firm acquisition data was collected from the SDC M ergers and A cquisitions database . CEO motivational characteristics were measured utilizing a nnual reports which were primaril y collected through two sources: Mergent and each were made utilizing the Buckmaster database, ABI/ Inform , and Google searches. Stock market reactions were captured from the Eventus database provided by the Center for Research in Securities Pricing (CRSP) and media variables were captured through specific searches in Factiva . 5 I measured firm diversification as well as industry dynamism using data from the Compustat Segments database. ame industry in the main Compustat database. As such, the Segments database provides a more precise measure of these primary industry. Howeve r, some firms move sales from one segment to another, post hoc resulting in a number of negative sales in a given segment year. As such, I replace d all negative values as missing prior to calculating diversification and segment level complexity. 63 Through this data collection I identified 1180 acquisition announcements during my sample p eriod. Of these, 34 were removed because multiple acquisitions were announced by the same firm on the same day making it impossible to clearly identify which acquisition the stock market was reacting to. One further acquisition was removed because the acqu isition announcement occurred on the last day of the CEO s tenure. Next, I removed 204 acquisitions for which no media coverage was found in Factiva and 10 acquisitions for which no market reactions were found in Eventus. A further 66 acquisitions were remo ved because no letter to the shareholders were found and 107 acquisitions were removed because the firm had no reported values in the Compustat Segments database for any year making it impossible to calculate firm diversification levels. Finally an additio nal 32 acquisitions had at least one other variable missing data and as such were removed from the analyses. As such, the sample size for the final analyses was 726 acquisitions. In predicting acquisition completion, however, two additional acquisitions we re removed due to missing acquisition - level variables resulting in a sample size of 724. In all of the cases described above the acquisitions were still included in the calculations of the dependent variables number of subsequent acquisitions and value of subsequent acquisitions. Independent Variables Positive Market Reactions/ Negative Market Reactions . I captured market reactions to the acquisition announcement through the use of cumulative abnormal returns (CARS). The calculation for CARS predicts an e xpected (or normal) return for a particular security and compares that to the actual price change surrounding the focal event. The difference between the actual return and the predicted return represents the cumulative abnormal return for that announcement . For this study, my estimation period followed a 250 day trading window ranging 64 from 295 trading days before the acquisition announcement to 45 trading days before the acquisition announcement which represents approximately one year of trading ( Hayward, 2002 ; McNamara et al., 2008 ) . Following prior research ( e.g., Schijven & Hitt, 2012 ) , I used three different event windows in my analysis of CARS. For m y first event window, I utilized a 21 day window which ranges from 5 trading days prior to the acquisition announcement to 15 trading days following the announcement. This window is appropriate when the market may require time to make sense of the details surrounding the announcement ( Haleblian et al., 2006 ; McNamara et al., 2008 ) . Further, this window avo ids some of the misinterpretation problems associated with shorter event windows ( Oler, Harrison, & Allen, 2008 ) . On the other hand, some research has argued that shorter event windows avoid the potential for confounding events to influence the abnormal returns ( Schijven & Hitt, 2012 ) . Accordingly, I utilized a 3 day window, 1 day before the acquisition a nnouncement to 1 day after the announcement, to limit this possibility but still allow for differences in the exact timing of the announcement ( e.g., Sears & Hoetker, 2014 ) . Finally, my third event w indow captured a mid - range effect between the other two. This window will be a 7 day window ranging from 3 days before the acquisition announcement until 3 days after the announcement (e.g., Schijven & Hitt, 2012 ; Wright et al., 2002 ) . M y primary results are reported using the 7 day window with supplemental tables demonstrating the similarities and differences found when using the alternate event windows. Because my theory suggests that for some CEO s there may be differences in the relati ve importance of positive and negative stakeholder reactions, I used CARS to create two variables. Positive Market Reaction included the value returned by CARS if the value is positive and included a 0 otherwise. Negative Market Reaction included the value of CARS if the value is 65 negative and included a 0 otherwise. I then reversed the signs associated with the negative reaction so that a higher value for negative market reaction indicated a stronger negative reaction. Positive Media Reactions/ Negative Me dia Reactions . I captured media reactions based on mentions of the firm over a 21 day period surrounding the announcement of an acquisition starting 3 days before and going 17 days after the acquisition ( - 3,17). This time frame includes more than a bi - week ly news cycle (including appropriate lead times) following the acquisition which ensures that the weekly and bi - weekly periodicals sampled (e.g. BusinessWeek, Forbes) will have had an opportunity to publish stories about the acquisition. I collected media mentions from four prominent national business daily and weekly news outlets: Forbes, The Wall Street Journal, Bloomberg BusinessWeek, and s. Further, I collected posts from three influential news services because these represent posts that frequent ly receive significant coverage in local and national newspapers: Associated Press Newswires , Dow Jones Newswires, and Gannett News Service . 6 classified articles into specific categories based on the content of the article ( Bednar, 2012 ) . A company search was 7 In gene ral, the data pulled from the Factiva searches returned a broad collection of articles including articles not directly about the focal company and focal acquisition. As such, for each acquisition in the sample, I manually reviewed all the articles captured by the Factiva search and removed any articles not directly about the focal company 6 For all outlet. This allows for both web and print based material to be captured. As such, I captured all sources associated with each of these media outlet s. 7 category name only and not the artic les contained with those categories. 66 and the focal acquisition. I also removed any duplicate articles and any articles that were an exact reprint of a company press release. 8 Prior research has demonstrated t hat positive and negative valence are distinct constructs ( Baumeister, Bratslavsky, Finkenauer, & Vohs, 2001 ; Bednar, 2012 ) . Therefore, it is possible that an article about an acquisition may contain both positive and negative content. As such, I measured each construct by capturing both positive and negati ve content from the media coverage independently. Positive Media Reaction was measured by the percentage of positive words captured in the media coverage while Negative Media Reaction was measured by the percentage of negative words captured in the media c overage. The media content was then analyzed using the Linguistic Inquiry and Word Count software (LIWC) ( Pennebaker, Booth, & Francis, 2007 ) . LIWC contains pre - designed and pre - validated dictionaries of words measuring the positive and negative emotion (valence) within the text ( Pennebaker et al., 2007 ; Pennebaker & Francis, 1996 ) and are frequently used in evaluating the content of media coverage ( Bednar, 2012 ; Zavyalova et al., 2012 ) . Dependent Variables Acquisition Completion Following the work of Muehlfeld and colleagues (2012), I created a dummy variable with the value of 1 if an announced acquisition w as completed, and 0 following the end of our acquisition sampl e period ( Dikova, Sahib, & Van Witteloostuijn, 2010 ; 8 Other examples of articles that were removed from this process include articles about other acquisitions that only casually mention the focal acquisition, general market news that briefly mentions the acquisition but provides no 67 Muehlfeld et al., 2012 ) . 9 Muehfeld and colleagues (2012) argued that this was appropriate because the median time to completion is just over two months following the announcement, and Thomson contin ually and retrospectively updates information on past deals suggesting that the acquisition remains uncompleted if it is not identified as completed in the SDC platinum database. 10 Acquisition Activity Three measures of subsequent acquisition activity wer e used that capture different elements of acquisition activity: number of acquisitions ( Sanders, 2001 ) and value of acquisition ( Sanders & Hambrick, 2007 ) , and rate of acquisitions. First, n umber o f a cquisitions captured the count of how many large acquisitions (greater than $100 million) were announced during the 365 days following the acquisition. 11 The size of acquisitions was captured in the second measure of acquisition, value of acquisitions . V alue of Acquisitions was measured based on the total value of all large acquisitions announced during the 365 days following the acquisition. I log transformed both number of acquisitions and value of acquisitions due to skewness. My third measure of acqui sition activity was acquisition rate . This measure utilized event history analysis to capture the rate that firms engage in a subsequent acquisition following the focal acquisition. In supplemental analyses, I tested for robustness by considering all subse quent acquisition activity (including those with a value of less than $100 million). Moderator Variables Content analysis of letters to the shareholder has been used to improve our understanding of CEO s by studying issues including CEO values ( Daly, Pouder, & Kabanoff, 2004 ) , CEO 9 10 As discussed below, I conducted Rare Events Logistic re gression to analyze my predictions for Acquisition Completion. This required that I reverse code the Acquisition Complete to become Acquisition Incomplete. 11 As such, acquisition data was collected through the end of 2012 so I had at least 365 days of data for all acquisitions occurring in my sample (through the end of 2011). 68 cognition and atte ntion ( Eggers & Kaplan, 2009 ; Kaplan, 2008 ) , and psychological characteristics including commitment to the status quo ( McClelland , Liang, & Barker, 2010 ) , and charismatic vision ( Fanelli et al., 2009 ) . While some have argued that letters to the shareholders may have been written by someone other than the CEO (such as a public relations staff), there has been significant evidence suggesting that CEO s a re heavily involved with writing the letters ( Duriau, Reger, & Pfaffer, 2007 ) . CEO s carry a fiduciary duty to sign the letter attesting to its honesty and accuracy ( Kaplan, 2008 ) . One piece of evidence that CEO s do follow through with this duty is the within - CEO consistency of the se letters. Some studies have undertaken rigorous analysis finding that the style, word choice, and content of letters exhibits within - CEO consistency and between - CEO differences ( e.g., Eggers & Kaplan, 2009 ) . Further evidence comes from prior research which has demonstrated strong consistency in language used by CEO s across a number of formats includi ng letters to the shareholders, interviews, and speeches ( Nadkarni & Che n, 2014 ) . The fact that letters to the shareholders match the language used by CEO s in interviews and speeches is strong support for the claim that they write the letters to shareholders. A final, and powerful, point of evidence that CEO s write the lette rs is that analysis of CEO letters to the shareholders have strong predictive power, predicting outcomes as diverse as competitive attacks and retaliations ( Marcel, Barr, & Duhaime, 2010 ) , speed and direction of strategic actions ( Nadkarni & Barr, 2008 ; Nadkarni & Narayanani, 2007 ) , new product introductions ( Nadkarni & Chen, 2014 ) , post - merger performance ( Dal y et al., 2004 ) , and rate of entry into new technology markets ( Eggers & Kaplan, 2009 ; Kaplan, 2008 ; Yadav et al., 2007 ) . It is hard to imagine such predictive power of letters to the shareholders if they are indeed written by anonymous public relations staffers. 69 Letters to the shareholders provide a par ticular benefit to longitudinal research because they provide a non - intrusive measure based on a consistent format of communication comparable across time periods that is not found in CEO speeches or media interviews ( Eggers & Kaplan, 2009 ) . The letters to the shareholders were analyzed using the Linguistic Inquiry and Word Count software (LIWC) ( Pennebaker et al., 2007 ) . LIWC allows for the use of pre - validated dictionaries and the abi lity to develop your own dictionaries. LIWC is being increasingly used in management studies due to its reliability and strong predictive validity (e.g., Nadka rni & Chen, 2014 ; Pfarrer, Pollock, & Rindova, 2010 ; Zavyalova et al., 2012 ) . CEO Promotion Focus and CEO Prevention Focus w ere measured usin g the dictionaries developed and validated by Gamache, McNamara , Mannor, and Johnson (In Press ) . The dictionaries were created based on words that would be most closely connected to t he motivations, attitudes and behaviors associated with prevention and promotion foci including words used in regulatory focus survey and word fragment completion measures ( Johnson, Lanaj, Tan, & Chang, 2012 ; Johnson & Steinman, 2009 ; Lockwood, Jordan, & Kunda, 2002 ) . The list was then reduced to provi de the greatest alignment with regulatory focus theory. These dictionaries were then validated through two steps. First, to establish content validity, the list of words from the two dictionaries were combined, alphabetized, and sent to 25 organizational s cholars who identified whether each word was associated with a promotion focus, prevention focus, or if its association was uncertain. Strong support was found for the content validity of the dictionaries ( Gamache et al., I n Press ) . In step two, 174 students participated in a pilot study where they completed conventional measures of regulatory focus and other individual characteristics (e.g., Big Five personality traits and core self - evaluations) as well as a writing sampl e which was measured using the dictionaries developed for LIWC. Correlation and 70 regression results from this data strongly supported convergent and discriminant validity of the LIWC regulatory focus dictionaries ( Gamache et al., In Press ) . In my final measure, I used the number of prevention and promotion words in the letter to the shareholder the year prior to the acquisition divided by the total number of words in the letter to the shareholder. CEO Past Focus, CEO Prese nt Focus, and CEO Future Focus were measured using LIWC preset dictionaries ( Pennebaker et al., 2007 ) capture the CEO their future focus ( Pennebaker et al., 2007 ) . Nadkarni and Chen (2014 ) conducted a validation study of these measures with 144 mid - level executives who completed the Shipp et al. (2009 ) temporal focus scales . This validation study demonstrated strong convergent and divergent validity for the LIWC measure of past, present and future focus ( Nadkarni & Chen, 2014 ) . Consistent with my measures for CEO promotion and CEO prevention focus, my measures for CEO past focus, CEO present focus, and CEO future focus used the number of words the respective dictionary captured from the letter to the shareholders divided by the total number of words in the letter. Control Variables I controlled for several factors which could suggest alternative explanations for a CEO propensity to engage i n acquisition activity or their willingness to complete the announced acquisitions. I include several different types of controls including firm - level controls, CEO - level controls, board - level controls, industry - level controls, and for models predicting ac quisition completion, deal characteristic controls. Beyond the controls listed below, I will also control for the year of the acquisition in order to capture any macro - economic trends that may influence 71 acquisition activity or the completion of the focal a cquisition. Further, in models analyzing the influence of market reactions I controlled for positive and negative media coverage and in models analyzing the influence of media reactions I controlled for positive and negative market reactions . I also contro lled for media count to capture the total number of articles published about the focal acquisition. All control variables (except for characteristics of the focal acquisition were lagged to one year before the year of the acquisition announcement. Firm - Lev el Controls Prior research has found that firm size may influence acquisition performance ( Haleblian et al., 2009 ) acquisitions. I controlled for firm size by taking the natural log of sales. Firm performance may also influence a CEO e in acquisitions and the types of acquisitions undertaken ( Iyer & Miller, 2008 ; Kim et al., 2011 ) . To control fo r this I used return on assets. To leverage as measured by ge in acquisition activity I controlled for firm diversification using an entropy measure ( Palepu, 1985 ; S anders & Hambrick, 2007 ; Westphal & Fredrickson, 2001 ) . 12 CEO - Level Controls I controlled for several CEO - level factors that may influence the CEO he CEO acquisition experience as measured by the CEO CEO of the focal firm. Because recent research has noted that the value of acquisition experience decays over time ( Meschi & Metais, 2013 ) , and consistent with prior research ( e.g., Reuer, Tong, & Wu, 2012 ) , I calculated the CEO the focal acquisition date. CEO compensation can also influence a CEO al risk taking propensity and acquisition 12 Due to missing data from the Compustat Segments database, and the relative temporal consistency of firm diversification levels, I utilized within - firm mean - replace for firm diversifi cation. 72 decisions ( Haleblian et al., 2009 ; Sanders, 2001 ; Sanders & Hambrick, 2007 ) . As such, I controlled for the CEO salary, bonuses, and restricted stock held. Board - Level Controls The board of directors can also influence firm acquisition activity ( Haunschild & Beckman, 1998 ) . I controlled for two variables that help to capture the degree of influence a CEO is likely to have over the board; the greater the influence of the CEO over the board, the fewer the constraints on their ability to act based on their own motives. I controlled for CEO power over the board by using a composite measure of three factors ( Westphal & Fredrickson, 2001 ) . First, I calculated the CEO - to - director relative tenure. Second, to capture the degree o f loyalty that directors may have to the CEO who appointed them, I calculated the proportion of directors whose appointment occurred during the tenure of the current CEO ( Boeker, 1992 ) . The final indicator was a CEO duality measure which was a dichotomous variable recording a 1 if the CEO was also the board chair and 0 otherwise. I used principal component analysis (PCA) ( Jackson, 1991 ) on these factors to create one composite measure. Further, because board vigilance may influence acquisition activity ( Hoskisson & Turk, 1990 ) , I also controlled for board independence as measured by the proportion of independent directors on t he board. 13 Industry - Level Controls In order to control for industry conditions that may influence ( Haleblian et al., 2009 ) , I controlled for industry dynamism by regressing the five - year industry sales on a year - count variable and dividing the standard err or by the average industry sales over the five year period ( Dess & Beard, 1984 ; Pathak, Hoskisson, & Johnson, 2014 ) . 13 Due to some missing data in the Risk Metrics database, and the relative temporal consistency of board characteristics, I utilized within - firm mean - replacement for board - level control variables. 73 Deal Characteristic Controls For models predicting the dependent variable istics that might influence the likelihood of acquisition completion. I controlled for relative acquisition size, measured as the ratio of acquisition value relative to the total of the acquiring firm assets. I also controlled for multiple bidders with a d ichotomous variable equal to a 1 if there were multiple bidders for the target firm or 0 otherwise. Additional Controls Tested - My overall control variable strategy was based on the recommendations of Becker (2005 ) and Carlson and Wu (2012 ) who recommended against the use of unnecessary controls. As such, I developed a larger model with a number of additional controls and reduced this model to the controls listed above. In the first step I dropped any that were not significantly correlated with my dependent variables ( Becker, 2005: 285 ) as these unnece ssarily reduce power. As a result of this step I dropped free cash flow (measured by operating income less dividends, taxes, and interest expense ( McNamara et al., 2008 ) ), industry munificence (measured by taking the regression coefficient from the regression of industry sales on a year - count variable and dividing the coefficie nt by the average industry sales over the previous five year period ( Dess & Robinson, 1984 ) ), industry complexity , (measured using a Herfindahl index for concentration which measures the degree that an industry is dominated by few competitors ( B ertrand & Mol, 2013 ) ), stock options held, stock options granted ( Sanders, 2001 ; Sanders & Hambrick, 2007 ) , CEO tenure , and CEO age. In models predicting acquisition completion the larger model also controlled for three dichotomous variables reflecting acquis ition characteristics that were not significantly correlated with the dependent variable: whether the acquisition was a related acquisition , whether there was a termination fee in place for the acquisition, and whether or not the target was a foreign targe t .In 74 the second step, following Carlson and Wu (2012 ) I looked to drop any further control variables that had no correl ation with any other variable of the study at p<.10, however, no additional control variables were selected to be dropped based on this step. 14 Analysis Multiple forms of analysis were used in this dissertation. First, I utilized OLS regression techniques . I standardized all variables to be interacted before creating the interaction terms to avoid potential multicollinearity. For testing the hypotheses predicting acquisition completion, I utilized rare events logistic regression (relogit in Stata) . Logisti c regression is appropriate because I used a binary dependent variable ( Muehlfeld et al., 2012 ; Wooldridge, 2009 ) how ever, ( King & Zeng, 2001: 137 ) . 15 Rare events logistic regression provides a correction that pro vides a more accurate estimation when predicting rare events ( King & Zeng, 2001 ) . 16 For models predicting number of acquisitions and acquisition value, I utilized tobit regression which is useful fo r continuous variable that takes on only non - negative numbers ( Wooldridge, 2009 ) . In both cases, due to skewness, I logged the measure (x + 1) creating a value with a lower limit of zero. For all of these analyses I clustered standard errors based on the firm because many firms in my sample conducted multiple acquisitions during the sample period. 14 Results for the full model were generally consist ent with those presented although the strengths of the relationships were slightly weaker than those described in a couple of situations. This is consistent with diminished power caused by adding impotent control variables. 15 Announced acquisitions only re mained incomplete 9.16% of my sample. 16 Results for traditional logistic regression are generally consistent with those presented from rare events logistic regression. 75 Two additional forms of analyses were conducted to deal with the potential for endogeneity and censoring in my data. For analyses predicting the likelihood of acquisition completion I utilized the Heckman procedure. The two - stage Heckman procedure first estimates a probit model predicting the likelihood of a firm undertaking an acquisition in a given year. This calculation is used to create an inverse Mills ratio which is then used as a control variable in the primary regression analysis ( Bushway, Johnson, & Slocum, 2007 ; Krause & Semadeni, 2014 ; Laamanen, Brauer, & Junna, 2014 ) . The Heckman procedure requires the choice of an appropriate instrument. In this case, a valid instrument requires that the instrument is likely to be a significant predict or of the announc ement of an acquisition (and therefore the decision to start the acquisition process) but uncorrelated with the likelihood of acquisition completion. I used two instruments in my Heckman analysis: firm size and firm performance . Both of these are important variables in predicting the announcement of an acquisition but are not correlated with acquisition completion. The instruments are included in the first step of the Heckman procedure to calculate the inverse mills ratio and then are not included in the se cond stage model. Finally, I conducted an event history analysis using a Cox proportional hazard model to further test my predictions about subsequent acquisition activity. A Cox proportional hazard model is an event history survival analysis that examines the time it takes for an event to occur ( Cox, 1972 ; Machin, Cheung, & Parmar, 2006 ) ; in this analysis the d ependent variable is the acquisition rate measured as the time between the focal acquisition and the next acquisition undertaken by the firm. Event history analysis serves to help address the problems associated with censored data ( Allison, 1984 ) . This type of censoring occurs in my data because firms may or may not have completed a subsequent acquisition at the end of my time period and my other 76 dependent variables (number and value of acquisitions) places an artificial end point of one year following the acquisition announcement. For my analysis, I conducted a multiple failure survival analysis (also called recurrent event survival analysis) because it was possible for a CEO to engage in multiple acquisitions. In order to do this I set up a conditional risk set model where time is measured continuous ly, starts at the study entry (first acquisition), but where the clock is reset to zero after each failure ( Cleaves, 1999 ) . My analysis was required to be a little more complicated than traditional analysis of this type, however, because my pri mary interest is in predicting the rate of time between one acquisition (A) and the next acquisition (A+1) based on the characteristics of the first acquisition (A) (where as in most medical studies using this method the predictor variables are constant fo r each individual). In order to do that, I move d all of the predictor variables and control variables associated with acquisition A forward to be associated with acquisition A+1. That way the media and market reactions from acquisition A (as well as all co ntrol variables) were being used to predict the rate of time between acquisition A and acquisition A+1. To be consistent with my primary analysis I considered all acquisitions from 2006 until the end of 2011 and considered the first acquisition by the CEO in 2012 or the end of 2012 when no acquisitions were made in that year as the end point of my analysis. 77 RESULTS In what follows, I present the results of this dissertation in four sections. First, I will briefly discuss the descriptive statistics and correlations. These can be found in Table 1. Next, I will discuss the findings for when the dependent variable is acquisition completion. Perhaps, in part due to the rareness of these events, I found only limited support for these hypotheses. The results o f these analyses can be found in Table 2 (rare events logistic regression) and Table 3 (Heckman 2 - stage procedure). Following that, I focus on my hypotheses predicting the CEO subsequent acquisition activity. This section contains some interesting findin gs which serve to make important advancements in strategic management research. These results are found in Table 4 (number of subsequent acquisitions), Table 5 (value of subsequent acquisitions), and Table 6 (cox analysis predicting rate of acquisition act ivity). Beyond these analyses , I also include three tables (Tables 7 9) where I explore differences that occur when using different event windows. In each case , I include the full model with all interactions for each of the three event windows for each t ype of analysis I used. 17 Table 7 includes the comparison of event windows for the dependent variable acquisition completion and for the Heckman procedure. Table 8 includes the comparison of event windows for both number and value of subsequent acquisition activity, and Table 9 includes the comparison of event windows for the Cox Analysis predicting rate of acquisition activity. For the most part , there is strong consistency across the different event windows. Throughout my explanations of the findings , I wi ll draw attention to rare situations where there are important differences between the different event windows. 17 To save space I do not include control variables in these final comparisons. 78 Descriptive Statistics Table 1 presents the summary statistics and correlation matrix for the variables included in my study. As noted earlier , in my sample over 91% of the announced acquisitions were completed making the likelihood of an announced acquisition being left incomplete as a rare event. It is worth noting the correlations between the primary independent variables in my study. Positiv e media reactions and positive market reactions are correlated at r = - 0.036 while negative media reactions and negative market reactions are correlated at r = 0.122. These low correlations between media reactions and stock market reactions are consistent with prior research ( Gomulya & Boeker, 2014 ; Pollock et al., 2008 ) . Also worth noti ng, the correlation between CEO promotion focus and CEO prevention focus in my study is r = - 0.201 which is a stronger negative correlation than other recent work exploring CEO regulatory focus (Gamache and colleagues (in press) noted a correlation of r = - 0.10). CEO prevention focus has very low correlations with CEO temporal focus variables while CEO promotion focus has a modest and significant negative correlation with CEO present focus r = - 0.127 and CEO past focus r = - 0.150. Further, while CEO past fo cus is not significantly correlated with either CEO present focus or CEO future focus, there is a significant correlation between CEO present focus and CEO future focus of r = 0.284. While this is a stronger correlation than ( Nadkarni & Chen, 2014 ) who found a correlation of r = 0.02, it is in line with prior research on tem poral focus such as studies by Shipp and colleagues (2009) who found present focus and future focus correlated at r = 0.29 a nd r = 0.48 across two studies. 79 Acquisition Completion The first results I will examine are the results for my predictions regardi ng the completion of the focus acquisition. Hypotheses 1a, 2a, 3a, 4a, 5a, 6a, and both 7a and 7b focus on the moderating influence of CEO regulatory focus and CEO temporal focus on acquisition completion. Table 2 presents the findings utilizing rare event s logistic regression and Table 3 presents the findings utilizing the Heckman procedure to correct potential endogeneity in my data. The results between these two forms of analyses are very similar. In each of these tables Model 1 includes only the control variables, moderator variables and non - hypothesized main effect relationships. Models 2 through 4 include results for the interaction effects on the relationship between stock market reaction to the acquisition announcement and acquisition completion. Mod el 2 includes the interactions between market reactions and temporal focus variables while Model 3 includes the interactions between market reactions and regulatory focus variables and Model 4 includes all interactions between stock market reactions and bo th temporal focus and regulatory focus variables. Models 5 through 7 include results for the interaction effects on the relationship between media reactions to the acquisition announcement and acquisition completion. Model 5 includes the interactions betwe en temporal focus variables and media reactions while Model 6 includes the interactions between regulatory focus variables and media reactions and Model 7 includes all interactions between media reactions and both temporal focus and regulatory focus variab les. Finally, Model 8 includes all the interactions of both market reactions and media reactions with both temporal focus and regulatory focus variables. All conclusions on my findings are based off of Model 8 in each table except where otherwise noted. 80 A n initial observation is that there are no significant main effects of either positive and negative media or positive and negative market reactions on acquisition completion. While I did not hypothesize any main effects, I expected based on prior research that positive stakeholder reactions would likely be positively related to acquisition completion and negative stakeholder reactions would be negatively related to acquisition completion. The first set of moderators I hypothesized would influence the rela tionship between stakeholders and acquisition completion was for CEO regulatory focus. Hypothesis 1a predicted that CEO promotion focus would strengthen the relationship between positive stakeholder reactions and acquisition completion and Hypothesis 2a pr edicted that CEO prevention focus would strengthen the relationship between negative stakeholder reactions and acquisition completion. There was no support found for either of these predictions. In Hypotheses 3a and 4a , I argued for a moderating influence of CEO future focus. Specifically, I argued that CEO future focus would weaken the relationship between negative (H3a) and positive (H4a) stakeholder reactions to the acquisition announcement. In the final models (Model 8) for each of these analysis there was no support found for the hypothesized relationships. Of note, however, Models 5 and 7 , of both Tables 2 and 3 , showed marginal support for the interaction effect between future focus and negative market reaction (H3a) ; however , these limited effects g o away in the full model. Perhaps a study with a larger sample size would find some support for H3a ; however , the effect appears to be, at best, very small. Hypothesis 5a argued that CEO present focus would strengthen the relationship between negative sta keholder reactions to the announcement of the acquisition and acquisition completion while Hypothesis 6a argued that CEO present focus would strengthen the relationship between positive stakeholder reactions and acquisition completion. First, no support wa s found for the 81 interactions between media reactions and CEO present focus. Model 8 of Tables 2 and 3, however, show a marginally significant coefficient for the interaction between CEO present focus and negative market reaction suggesting some very limite d support for Hypothesis 5a ; however , in Models 2 and 4 of these same tables the coefficients are not significant. Further, it is worth noting, that with a narrower event window for measuring market reaction ( - 1, 1; see Table 7), the coefficients for the i nteraction between CEO future focus and negative market reaction are significant ( p < .05). In further analysis with the narrower event window, I found that this interaction is not significant when including only the temporal focus X market reactions inter actions but become significant when including the regulatory focus X market reactions interactions. Taken together these findings suggest that some degree of multicollinearity may be a factor in these findings, although variance inflation factors (VIF) run on the full model show no VIF scores greater than 3.0. Due to these potential concerns , I do not conclude any support for this hypothesis. Hypothesis 7 argued that CEO past focus would weaken the relationship between both negative (7a) and positive (7b) stakeholder reactions. In both cases no support was found for the interactions of CEO past focus and media reactions. For Hypothesis 7b, I found marginal support (Tables 2 and 3) suggesting that past focus may indeed have some weakening effect on the relat ionship between positive stakeholder reactions and acquisition completion. For Hypothesis 7a there was a marginally significant coefficient for the interaction between negative stakeholder reactions and acquisition completion ; however , this is in the oppos ite of the hypothesized direction. The results here have a similar pattern to those described above for H5a. The results are stronger with the narrower event window for market reaction ( - 1, 1; Table 7) but are only 82 significant when both temporal focus and regulatory focus interactions are considered in the same model. Again, I do not draw any strong conclusion from these findings. In summary, there is no strong support for any of the hypothesized moderating relationships predicting acquisition completion. There are several possible explanations for the lack of findings in this area. First, as mentioned earlier, failure to complete an acquisition is a rare event. As such , it is possible that there are not enough non - completed acquisitions in my sample to get a true understanding of the influence market and media reactions and the moderating role of CEO characteristics. It is also possible that there are some unique characteristics of the acquisitions that are not completed that play a much larger role in the decision to abandon an announced acquisition. One such possibility is the presence of multiple bidders. In all models presented on Tables 2 and 3 the coefficient for the control variable multiple bidders is negative and strongly significant ( p < .01 ). As s uch, it is possible that the decision to not complete an acquisition is a function of simply getting out bid by a competing offer and not a decision that results from media or market reactions. Other unmeasured factors that could be driving the decision to not complete an acquisition are regulatory factors and whether the acquisition was friendly or hostile. Subsequent Acquisition Activity Next , I will examine the results for my predictions predicting subsequent acquisition activity of the firm. Hypothese s 1b, 2b, 3b, 4b, 5b, 6b, and both 8a and 8b explore the moderating influence of CEO regulatory focus and CEO temporal focus on acquisition completion. Three different measures of subsequent acquisition activity are explored with generally strong agreement in findings. First, Table 4 presents findings predicting the number of 83 acquisitions conducted by the CEO in the 365 days following the announcement of the focal acquisition. Similarly, Table 5 presents findings predicting the value of acquisitions conduct ed by the CEO in the 365 days following the focal acquisition announcement. Finally Table 6 presents the results of a Cox Survival Analysis predicting the rate of acquisition activity based on the length of time that occurs between acquisitions. Similar to the tables used to present acquisition completion each of these tables includes 8 models. Model 1 includes only the control variables, moderator variables and non - hypothesized main effect relationships. Model 2 includes the interactions between market rea ctions and temporal focus variables. Model 3 includes the interactions between market reactions and regulatory focus variables while Model 4 includes all interactions between stock market reactions and both temporal focus and regulatory focus variables. Mo del 5, meanwhile, includes the interactions between temporal focus variables and media reactions , and Model 6 includes the interactions between regulatory focus variables and media reactions. Model 7 includes all interactions between media reactions and bo th temporal focus and regulatory focus variables. Lastly, all the interactions of both market reactions and media reactions with both temporal focus and regulatory focus variables are included in Model 8. Again, while I did not hypothesize any main effect relationships for the impact of positive and negative stakeholder reactions, prior research lead me to expect that positive stakeholder reactions would be positively associated with subsequent acquisition activity and negative stakeholder reactions would be negatively associated with subsequent acquisition activity. Consistent with this expectation, negative market reactions were consistently negatively associated with subsequent acquisition activity. For both number of subsequent acquisitions and value of subsequent acquisitions (Tables 4 and 5) the coefficient for negative market reactions was negative and significant ( p < .001). In the Cox analysis (Table 6) predicting rate of 84 acquisition activity the relationship was consistent but not as strong ( p < .1 0). On the other hand, the main effect of positive market reactions and subsequent acquisition activity was not significant in any of the forms of analysis. These results we re consistent with research in psychology which has consistently demonstrated that negative emotions and content is stronger than positive emotions and content ( Baumeister et al., 200 1 ) . There was much less consistency in the main effect relationships for media reactions. The coefficient for negative media reaction was not significant when predicting the number of subsequent acquisitions (Table 4), was negative and marginally signif icant in predicting the value of subsequent acquisitions ( p < .10; Table 5), but was positive and significant in predicting the rate of subsequent acquisition activity in the Cox analysis ( p < .05; Table 6). Meanwhile, the coefficient for positive media re action was not significant when predicting either the number or value of acquisitions (Tables 4 and 5) but was strongly significant when predicting the rate of subsequent acquisition activity ( p < .001; Table 6). Clearly, there was not the strong consisten t influence of media reactions on subsequent acquisition activity as there was for negative stock market reactions. Although the main effect of negative market reactions was consistently strong, the moderating influences of CEO characteristics are consist ent in their lack of significant influence. For each of the hypotheses predicting subsequent acquisition activity , the interactions of regulatory focus and temporal focus variables with both negative and positive market reactions were not significant. It s financial well - being , it has a strong negative effect and that this effect is not influenced by CEO characteristics. 85 As such, I focus ed the rest of my exploration on the results foun d with the interactions of CEO characteristics and media reaction. While the main effects of media reactions provide d inconsistent findings, there were some important and consistent findings when exploring how the CEO temporal focus and regulatory focus va riables interact with media reactions. Hypothesis 1(b) argued that CEO promotion focus will strengthen the relationship between positive stakeholder reactions and subsequent acquisition activity. I found some support for this hypothesis. While the coeffici ent for the interaction of CEO promotion focus and positive media coverage were not significant in predictions of the number and value of subsequent acquisition activity it was positive and significant in the Cox analysis predicting rate of acquisition act ivity ( p < .05; Table 6). Although limited to only one form of analysis , there was some support for H1(b) for media reactions. Hypothesis 2(b) argued that CEO prevention focus will strengthen the relationship between negative stakeholder reactions and sub sequent acquisition activity. I found consistent support for this hypothesis across all of the models. The coefficient for the interaction of CEO prevention focus and negative media reactions was significant and negative in predicting both number of acquis itions and value of acquisitions ( p < .05; Tables 4 and 5) and was marginally significant in predicting rate of acquisition activity ( p < .10; Table 6). As such, it appears that CEO prevention focus does strengthen the relationship between negative media c overage and subsequent acquisition activity supporting H2(b) for media reactions. Figure 4 provides a visual depiction of this interaction effect. 86 Figure 4 - Interaction of CEO Prevention Focus and Negative Media Reactions Hypothesis 3(b) and Hypothesis 4(b) explored the moderating influence of CEO future focus on subsequent acquisition activity. Hypothesis 3(b) argued that CEO future focus will weaken the relationship between negative stakeholder reactions and subsequent ac quisition activity while Hypothesis 4(b) argued that CEO future focus will weaken the relationship between positive stakeholder reactions and subsequent acquisition activity. I found no support for Hypothesis 4(b) ; however , I did find consistent support fo r Hypothesis 3(b) for media reactions. The coefficient for the interaction of CEO future focus and negative media reactions was positive and significant in predicting both the number of subsequent acquisitions and rate of acquisition activity ( p < .05; Tab les 4 and 6) and was marginally significant in predicting value of subsequent acquisitions ( p < .10; Table 5). This interaction effect is graphically displayed in figure 5. 87 Figure 5 - Interaction of CEO Future Focus and Negative M edia Reactions In Hypotheses 5(b) and 6(b) I argued that CEO present focus will strengthen the relationships between both negative and positive stakeholder reactions and subsequent acquisition activity. The results for all three dependent variables fail ed to find any support for these hypotheses suggesting that CEO present focus does not influence the relationship between stakeholder reactions and subsequent acquisition activity. Hypothesis 8 argued that CEO past focus will strengthen the relationship b etween both negative (H8a) and positive (H8b) stakeholder reactions. When examining the coefficients for the interactions between negative media reactions and CEO past focus , I found consistent support across all three dependent variables used for measurin g subsequent acquisition activity ( p < .01; Tables 4, 5 and 6). These findings support Hypothesis 8a for media reactions. When 88 examining the coefficients for the interactions between positive media reactions and CEO past focus , I found some limited support . When predicting subsequent number of acquisitions and subsequent value of acquisitions , I found no support for this hypothesis ; however , when predicting rate of acquisition activity the coefficient for the interaction between CEO past focus and positive media reaction was positive and significant ( p < .05; Table 6) supporting hypothesis 8b. So while I found strong support for Hypothesis 8a, I found only limited support for Hypothesis 8b. The interaction effect for Hypothesis 8a can be seen graphically in Figure 6. Figure 6 - Interaction of CEO Past Focus and Negative Media Reactions 89 Supplemental Analysis I conducted three additional forms of supplemental analysis to further test the moderating effect of CEO regulatory focus an d CEO temporal focus on the relationship between stakeholder reactions to an acquisition announcement and subsequent acquisition activity. First, I created a dummy variable in the next year to indicate whether or not the firm undertook an acquisition in th e next 365 days. The variable included a 1 if the CEO did engage in an acquisition in the next year and a 0 otherwise. Secondly, I measured both number of acquisitions and value of acquisitions by including all subsequent acquisitions instead of only large acquisitions. It is possible that CEO s will not be as quick to make changes in their plans for small acquisitions as they may believe that the market and media will not react strongly to small acquisitions anyways. The results of these supplemental analy ses are consistent with those described above. All three models showed strong main effects for negative market reactions on subsequent acquisition activity ( p < .001). The coefficient for negative media reactions was only significant in predicting value o f all acquisitions in the next 365 days ( p < .05). No support was found for a main effect impact of positive media or market reactions. Consistent with my primary analyses, these supplemental analyses also provided strong support for three of my hypothese s when considering the moderating influence of CEO characteristics on the relationship between media reaction and subsequent acquisition activity. For Hypothesis 2(b) reflecting the moderating influence of CEO prevention focus on the relationship between n egative media and subsequent acquisition activities , I found negative and significant coefficients for the interaction term with both the in the next year dependent variable ( p < .05) and the value of all acquisition s in the next 365 days ( p < .05). For Hy pothesis 3(b) that 90 proposed the moderating influence of CEO future focus moderating the relationship between negative media coverage and subsequent acquisition activity, I found positive and significant coefficients for the interaction term with both in th e next year dependent variable ( p < .05) and the number of all acquisitions in the next 365 days ( p < .05). Finally, for Hypothesis 8(a) predicting that CEO past focus will strengthen the relationship between negative stakeholder reactions and subsequent a cquisition activity , I found negative and significant coefficients for all three of the additional dependent variables: in the next year ( p < .01), number of all acquisitions ( p < .05), and value of all acquisitions ( p < .01). Summary of Findings In sum mary, my results suggest some interesting conclusions. First, consistent with research in psychology ( Baumeister et al., 2001 ) , my results indicate that negative reactions are generally stronger than positive reactions both in terms of main effects and in terms of significant interaction effects. I had expected that positive reactions would also have s ignificant impact and that CEO characteristics would bring out some of these characteristics ; however , there is only limited support in my findings in this area. Secondly, and contrary to how I frame d my hypotheses, I found important differences between th e two types of stakeholder reactions. Negative stock market reactions appear to have a strong main effect relationship on subsequent acquisition activity with minimal influence of CEO motivational characteristics on this relationship. In short, it appears that all CEO s are influenced by negative market reactions regardless of their regulatory focus and temporal focus attributes. On the other hand, I found much weaker and less consistency in the main effect relationship of negative media reactions on subseq uent acquisition activity. Instead, I found some 91 important moderating relationships. In this area, I found consistent support for three of my hypotheses regarding the influence of CEO characteristics on the relationship between media reactions and subseque nt acquisition activity. In support of Hypothesis 2(b), I found that CEO prevention focus strengthens the relationship between negative media reactions and subsequent acquisition activity. I also found consistent support for Hypothesis 3(b) showing that CE O future focus weakens the impact of negative media reactions on subsequent acquisition activity. Finally, I found consistent support for Hypothesis 8(a): CEO past focus strengthens the relationship between negative media reactions and subsequent acquisiti on activity. I did find some limited support for two hypotheses predicting how CEO characteristics might moderate the influence of positive media reactions ; however, both of these findings are only significant when I predict ed rate of acquisition activity using the Cox survival analysis. Here , I find support for H1(b) predicting that CEO promotion focus will strengthen the relationship between positive media reaction and subsequent acquisition activity. In the Cox analysis , I also found support for H8(b) w hich predicted that high CEO past focus will strengthen the relationship between positive media reactions and subsequent acquisition activity. Because these findings are only supported in the Cox survival analysis, I am hesitant to draw any strong conclusi ons ; however, it might suggest that the artificial censoring that occurs when I set my timeline for subsequent acquisitions within 365 days. Additional Findings There are a few additional findings worth noting from my analyses. First, I did not hypothesi ze an interaction effect of CEO promotion focus with negative media reactions , yet my results consistently demonstrated a significant interaction effect here with CEO promotion focus 92 consistently strengthening the impact of negative media reactions on subs equent acquisition activity. The coefficient of the interaction term between CEO promotion focus and negative media reactions is negative and significant across all three of my primary forms of analysis: number of subsequent acquisitions ( p < .05), value o f subsequent acquisitions ( p < .01) and rate of acquisition activity ( p < .10). Further, the interaction term between CEO promotion focus and negative stock market reactions was negative and marginally significant for both number of subsequent acquisitions and value of subsequent acquisitions ( p < .10). In both of these cases the relationship was stronger with both narrower event windows ( - 1,1; p < .01) and wider event windows ( - 5, 15; p < .05). These findings are interesting and suggest that the influence of CEO promotion focus is more nuanced than my theorizing suggested. CEO s with a high promotion focus are driven to accomplish, advance, and achieve ( Higgins, 19 97 ; Lanaj et al., 2012 ) . As such, negative stakeholder reactions might provide evidence to high promotion focus CEO s that acquisitions are not an effective way of achieving their goals for the organiz ation. Similarly, the strong desire to achieve goals may make CEO s with a high promotion focus more attentive to external feedback following large strategic actions. Because CEO s believe in the importance of media coverage and general market support to rea ch their goals for their organizations high promotion focused CEO s may pay close attention to the reactions from these stakeholders. Another observation from my data that deserves some attention is that in some models CEO prevention focus was positively a ssociated with subsequent acquisition activity ( p < .05 in predicting number of subsequent acquisition activity and p < .10 in predicting value of subsequent acquisition activity). This is opposite the findings of Gamache and colleagues (in press) who find that CEO prevention focus is negatively associated with acquisition activity. There are of course some important differences between this study and the prior work that may 93 suggest reasons for the different findings. First, this study looks at the number o f large acquisitions while Gamache and colleagues (in press) look at all acquisitions. Secondly, this study included media and stock market reactions as variables in the regression models. Third, this study looked at each acquisition individually rather th an considering acquisitions on an annual basis. The Gamache et al. (in press) study was conducted on an annual basis and used firm fixed effects. Finally, this study covers a much different time frame than the Gamache and colleagues (in press) study and in cluded the recent recession. I conducted some supplemental analyses to help explain this finding. I created base models without media coverage and tested these models for the number of large acquisitions and the number of all acquisitions. The coefficient for CEO Prevention Focus remained positive and significant ( p < .05) for the model predicting large acquisitions. For the model predicting all sizes of acquisitions the coefficient for CEO Prevention Focus was still positive but no longer significant (p = .679). As such, it appears that the size difference may explain some of these findings , but clearly other factors might also exist. Future research would benefit by exploring other moderators to the CEO Prevention Focus and acquisition relationship. 94 DIS CUSSION In this dissertation , I have integrated research on upper echelons theory ( Finkelstein et al., 2009 ; Hambrick & Mason, 1984 ) with research on how stakeholder reactions to organizational decisions influence subsequent actions of the organization (e.g.,` Graffin et al., 2013 ; Haleblian et al., 2006 ; Palmrose et al., 2004 ) . In doing so, I developed and tested a theory arguing that some CEO characteristics influence the degree to which CEO s are influenced by positive or negative stakeholder reactions and th at other attributes influence the degree to which CEO characteristics are influenced by stakeholder reactions more generally. My findings demonstrate that while some types of stakeholder reactions appear to influence most or all CEO s, the motivational attr ibutes of the CEO influence the propensity of CEO s to be shaped by other types of stakeholder reactions. In particular, negative stock market reactions appear to exert a strong main effect influence on CEO s while the influence of negative media reactions i s subject to the motivational attributes of CEO s. More specifically, the influence of negative media reactions to the announcement of an acquisition on subsequent acquisition activity is shaped by both CEO regulatory focus and CEO temporal focus. First, I f ou nd that both CEO promotion focus and CEO prevention focus strengthen the influence of negative media reactions on future acquisition activity. In part, this aligns with my theory that argued that CEO prevention focus would strengthen the influence of n egative media reaction. However, I did not expect that CEO promotion focus would influence the effect of negative stakeholder reactions. As noted earlier, these results might suggest that high promotion focus CEO s look closely to the media to evaluate whet her or not they are successfully progressing towards their goals. Secondly, in support of my hypotheses , I f ou nd that CEO past focus strengthens the relationship between negative media reactions and subsequent 95 acquisition activity and that CEO future focus weakens the impact of negative media reactions on subsequent acquisition activity. Through this work, my dissertation makes several contributions to management research. First, I build on upper echelons theory which states that CEO characteristics influ ence their field of vision, perception of phenomena, and how they interpret events ( Hambrick & Mason, 1984 ) . In doing so, I d emonstrated that CEO regulatory focus and CEO temporal focus influence the degree to which CEO s attend to , and learn from , how the media reacts to the announcement of a strategic event. Prior research on how CEO s learn from stakeholder reactions did not co nsider how CEO characteristics might shape this learning. Secondly, this dissertation adds to our understanding of how CEO motivational characteristics influence firm actions. In recent years, strategy scholars have begun to study how CEO motivational cha racteristics such as CEO temporal focus ( Nadkarni & Chen, 2014 ) and CE O regulatory focus ( Gamache et al., In Press ) influence firm strategic actions. I extend this research by demonstrating that motivational characteristics shape the degree to which negative media reactions influence subseq uent acquisition activity. This adds to existing research that not only do CEO motivational attributes have direct effects on strategic actions ( Gamache et al ., In Press ; Nadkarni & Chen, 2014 ) but that they also shape the way executives attend to and interpret external feedback. Third, this dissertation extends recent scholarship on the role of the me dia in influencing executive decision making ( Bednar, 2012 ; Bednar et al., 2013 ) . I do this by examining media reactions to a specific ev ent, the announcement of an acquisition. I show ed that the importance of media reactions in influencing future firm behavior is contingent on CEO motivational attributes. This adds to the growing conversation in strategy scholarship over the attempts of th e 96 firm to influence the media ( Westphal & Deephouse, 2011 ; Westphal et al., 2012 ) and the reciprocal effects caused as media coverage influences the firm. A related contribution that I make emphasizes the differential influence of stock market and media reactions. I f ou nd that negative market reactions to the acquisition announcement have a strong main effect on subsequent acquisition activity while CEO regulatory focus and temporal focus shape the degree to which the negative media reactions have an influence. There may be several reasons for this finding. First, the stock market reaction provides CEO s with hard quantitative evidence about the perceptions that investors have about the acquisition. Research in psychology has demonstrated that precise numbers serve as salient anchors that people grasp on to in making decisions ( Janiszewski & Uy, 2008 ) . On the other hand, media reactions are a form of soft evidence as media reports include both facts and interpretations of th ose facts shaped by the biases of the reporters and new agencies responsible for those reports ( Chen & Meindl, 1991 ) . Further, negative stock market reactions are likely to be especially salient to CEO s as they directly influence the pocketbook of executives as they are likely to own significant stock and options in their organization ( Devers et al., 2008 ) . In addition, negative stock market reactions ors and as such require a response from the CEO . On the other hand, negative media reactions are not tied directly to the opinions of investors and CEO s may have some leeway in choosing to respond or not respond. Because CEO s have a choice in whether to re spond to negative media reactions , there is more opportunity for CEO attributes to shape the likelihood that they will respond or not. Another difference between market and media reactions is that market reactions are singularly positive or negative while m edia reactions frequently contain both positive and negative elements within the same 97 article. As such, there is no ambiguity surrounding negative market reactions while negative media reactions may be somewhat offset by positive media reactions in the sa me article. This research also contributes to our understanding of why executives continue to engage in acquisition activity in spite of significant evidence suggesting that acquisitions provide little in the way of financial performance benefit ( Haleblian et al., 2009 ) . Recent research has shown that CE O self - interest and CEO characteristics influence acquisition activity ( Devers et al., 2013 ; Gamache et al., In Press ; Seo, Gamache, Devers, & Carpenter, In Press ) . Haleblian and colleagues (2006) demonstrated that stock market performance of recent acquisitions influenced the propensity to engage in subsequent acquisitions. By splitting apart positive and negative market reactions this dissertation provides some evidence that their findings may have been driven primarily by the negative influence of negative market reactions. Further, I demonstrate d that negative media reactions influence subsequent acquisition activity and that this effect is stronger for CEO s with high past f ocus . This extends our understanding of how acquisition activity is influenced both by stakeholder reactions and by individual characteristics of executives. Finally, this research makes important contributions to our understanding of temporal focus and regulatory focus. First, this research builds on calls to explore the influence of these psychological constructs on organizati onal - level outcomes ( Kark & Van Dijk, 2007 ; Shipp et al., 2009 ) . I show that temporal focus and regulatory f ocus can influence firm acquisition activity by shaping the propensity of CEO s to attend to negative media reactions. I also extend research on regulatory focus theory by demonstrating that both CEO promotion focus and CEO prevention focus strengthen the r elationship between negative media reactions and subsequent acquisition activity. While some have argued that both promotion focus and prevention focus are positively 98 related to job performance, most research on regulatory focus looks at differential influ ence of promotion and prevention focus on different outcomes ( Lanaj et al., 2012 ) . In addition, I contribute to research on temporal focus by demonstrating that past focus and future focus, but not present focus, shape the propensity of executives to attend to, and respond to, negative media reactions. I had expected that CEO present focus would strengthen the relationship between stakeholde r reactions and subsequent acquisition activity because CEO s with a high present focus would likely be attentive to the current environment and willing to respond quickly based on the stakeholder responses. Instead, CEO present focus did not seem to change the propensity of CEO s to attend to, and respond to, the stakeholder reactions. Rather, CEO past focus and CEO future focus had strong and opposing influence on the relationship between negative media reactions and subsequent acquisition activity. This se rves to increase our understanding of the intricate way in which temporal focus influences how people attend to information in the environment. Future Directions This dissertation also opens up several avenues for future research. The finding that market and media reactions have very different influences on subsequent firm actions suggests that future research would benefit by exploring why these differences exist. Earlier, I suggested several possible reasons for these differences and these could be test ed empirically. For example, I argued that negative stock market reactions may be influential because they directly influence the financial position of CEO s. If this is the case, the effect of negative media reactions might be stronger for CEO s with a high level of stock ownership. I also argued that the effect of media reactions might be more influenced by CEO motivational characteristics because the media 99 reactions contain both positive and negative elements within the same article. Future research could explore this further by looking at a subset of articles that are most strongly negative (with very little positive content), or by creating a measure that subtracts positive coverage from the negative coverage in an article. The difference in the influen ce of market and media reactions also suggests that research would benefit by exploring the influence of other stakeholders. A natural first place to start might be the role of securities analysts. Investment analysts play an important role in shaping the opinion of investors and have been shown to influence organizational decisions such as the decision to fire CEO s ( Wiersema & Zhang, 2011 ) . Further, similar to their attempts to influence the media, CEO s have been shown to take steps to exert influence on securities analyst ( Washburn & Bromiley, 2014 ) . Investment analyst ratings are closely connected to stock market performance and provide quantitative feedback to executives ( Washburn & Bromiley, 2014 ) , but also reflect biases and opinions of the analyst themselves. Since they have characteristics similar to both stock market and the media, it is possible that analyst rating may have both main effect influences and be shaped by CEO motivational characteristics. As such, studying investment a nalysts may shed further light on why executives respond differently to stock market and media reactions. Future research could also consider the role of other CEO characteristics as moderators of the relationship between stakeholder reactions and subsequ ent firm actions. As argued earlier, CEO motivational characteristics are more directly connected to firm performance than self - concept constructs. This does not mean, however, that some self - concept constructs might not also moderate the relationship betw een stakeholder reaction and subsequent firm actions. For example, it is possible that CEO locus of control ( Boone et al., 1996 ; Miller et al., 1982 ) could 100 influence CEO by external stakeholders. It is possible that a CEO with high internal control may be more dismissive of stakeholder reactions instead believing that they have a better understanding of the situation within the firm than outsiders do. Existing research has also demonstrated that CEO core self - evaluation influences acquisition activity ( Hayward & Hambrick, 1997 ; Malmendier & Tate, 2005 ) . Hiller and Hambrick (2005: 298 ) note is reason to expect that many executives have relatively high CSE, and a significant - - CSE as hubris. Hayward and Hambrick (1997 ) found that CEO hubris was positively associated with the size of premiums paid for acquisitions and subsequent shareholders returns. This hubris may also lead executiv es to ignore reactions of the market and media and persistently move forward with their own plans. Strategy research has also explored the influence of CEO narcissism on firm strategic actions including acquisitions ( Chatterjee & Hambrick, 2007 ) . Recent findings have demonstrated that CEO s high in narcissism are more influenced by media praise ( Chatterjee & Hambrick, 2011 ) . It would be interesting to see if these strong effects carry over and strengthen the effect of stock market reactions, or if consistent with my findings for temporal focus and regulatory focus, narcissism influences media reactions but not market reactions. Finally, in this dissertation I found that both CEO promotion focus and CEO prevention focus stren gthened the relationship between negative media reactions and subsequent acquisition activity. This surprising finding suggests important avenues for future research. Research on regulatory focus theory frequently explores ways in which promotion focus and prevention focus have differential impact on behavior. However, both high promotion focus and high prevention focus lead individuals to be highly motivated to work toward reaching their goals but they use different means of doing so ( Leonardelli, Lakin, & Arkin, 2007 ; Wallace & C hen, 2006 ) . It is 101 possible that there may be a number of situations where CEO promotion focus and CEO prevention focus have similar influence on strategic actions. Future studies in this area would benefit both research in strategic management and resear ch in psychology by exploring when promotion and prevention focus have similar influences on behavior. Impact on Management Practice This dissertation suggests several implications for management practice. First, it suggests another behavioral factor tha t can contribute to acquisition activity. Boards of directors monitoring executive behavior would benefit by understanding how recent negative media and If the firm is intent on expanding through acquisitions the board of directors may want to be more encouraging when recent acquisitions have received negative reactions. Similarly, it is likely that negative reactions to other risky organization actions may furt her limit CEO risk taking propensity. As such, boards may choose to encourage the CEO to continue aggressive actions in spite of the negative reactions. Additionally, these findings can inform executives about how they might be influenced by media coverag e. Research has demonstrated that executives take significant efforts to influence media coverage ( Westphal & Deephouse, 2011 ; Westphal et al., 2012 ) . It is possible, making. Building on the reciprocal effects model of media coverage this dissertation demonstra tes that at least some CEO s are highly influenced by negative media reactions. Providing executives with a deeper understanding of how their motivational attributes shape their behavior is also important. As executives gain a greater understanding about their 102 natural tendencies in the face of negative reactions they are better able to set aside those reactions and make better subsequent decisions. Understanding their own regulatory and temporal foci can better equip CEO s to understand their natural tenden cies and be able to recognize their inherent strengths and weaknesses. 103 CONCLUSIONS Clearly, CEO motivational characteristics play an important role in shaping the decisions that CEO s make on behalf of the firm. While some other research has begun to de monstrate some important main effect relationships of CEO regulatory focus and CEO temporal focus , this dissertation emphasizes how these attributes influence the way CEO s are influenced by stakeholder reactions following the announcement of a strategic ac tion. In doing so, I demonstrated that negative stock market reactions have a significant main effect on subsequent actions , but that the effect of negative media coverage are contingent on the characteristics of the executive. In particular, I found that negative media reactions were stronger for CEO s with a high prevention focus or high promotion focus, and for CEO s with a high past focus. Further, I found that negative media reactions were weaker for CEO s with a high future focus. I believe these finding s make important contributions for strategic management research and management practice. 104 APPENDI X 105 Table 1 - Descriptive Statistics Variables Mean s.d. 1 2 3 4 5 6 7 8 9 10 11 12 1. (ln) Number of Acquisitio ns 0.664 0.789 1.000 2. (ln) Value of Acquisitions 1.516 1.696 0.897 1.000 3. Acquisition Completion 0.916 0.278 - 0.013 0.005 1.000 4. Postive Market Reaction ( - 3,3) 0.015 0.028 - 0.018 - 0.015 - 0.065 1.000 5. Ne gative Market Reaction ( - 3,3) 0.078 0.033 - 0.146 - 0.143 - 0.087 - 0.284 1.000 6. Negative Media Reaction 0.447 0.436 - 0.122 - 0.124 - 0.070 0.001 0.122 1.000 7. Positive Media Reaction 2.515 1.169 0.000 - 0.015 - 0.095 - 0.036 0.040 0.110 1.000 8. Promotion Focus 1.898 0.663 0.077 0.046 0.071 0.037 - 0.001 - 0.093 - 0.044 1.000 9. Prevention Focus 0.296 0.293 0.016 0.007 - 0.012 - 0.014 - 0.024 0.129 0.050 - 0.201 1.000 10. Future Focus 0.573 0.327 0.115 0.076 - 0.041 0.051 - 0.025 0.013 0.065 - 0.036 0.082 1.000 11. Present Focus 3.779 1.192 0.246 0.172 - 0.037 0.001 - 0.044 0.015 0.097 - 0.127 - 0.020 0.284 1.000 12. Past Focus 1.296 0.515 0.067 0.046 - 0.068 - 0.009 - 0.015 0.018 - 0.016 - 0.150 - 0.023 - 0.012 - 0.022 1.000 13. Firm Size 9 .753 1.296 0.412 0.383 0.005 - 0.052 - 0.084 0.035 0.036 - 0.034 0.066 0.082 0.275 0.044 14. Firm Performance 0.069 0.064 - 0.125 - 0.047 0.030 - 0.078 0.006 0.004 - 0.027 0.010 - 0.140 - 0.154 - 0.057 - 0.102 15. Leverage 1.002 1.808 0.250 0.196 - 0.100 - 0.005 0 .113 - 0.011 0.003 0.062 0.017 0.170 0.088 0.177 16. Diversification 0.800 0.586 0.282 0.254 - 0.038 - 0.045 - 0.077 - 0.006 0.005 0.083 0.120 0.020 0.174 0.189 17. CEO Power 0.003 1.170 0.113 0.105 0.007 - 0.018 0.006 - 0.019 0.083 - 0.023 0.073 0.013 - 0.007 - 0.031 18. Board Independence 0.788 0.129 - 0.126 - 0.069 0.043 - 0.002 - 0.040 0.140 - 0.056 0.074 0.124 - 0.086 - 0.175 - 0.006 19. Industry Dynamism 0.033 0.040 - 0.074 - 0.075 - 0.032 0.239 0.023 - 0.014 - 0.019 - 0.036 0.034 0.045 - 0.080 0.239 20. (ln) Acquisi tion History (#) 1.162 1.030 0.639 0.557 - 0.020 - 0.073 - 0.043 - 0.082 0.049 0.059 - 0.005 0.106 0.232 0.057 21. (ln) Acquisition History ($) 5.691 3.727 0.437 0.407 - 0.004 - 0.087 - 0.011 - 0.013 0.040 - 0.002 0.035 - 0.011 0.107 0.001 22. Salary 1229.310 630 .196 0.342 0.310 - 0.022 - 0.051 - 0.108 0.006 0.061 0.087 - 0.006 0.206 0.319 0.041 23. Bonus 2112.077 5164.036 0.288 0.239 - 0.042 0.052 0.044 - 0.131 0.133 0.123 - 0.051 - 0.011 0.032 - 0.058 24. Restricted Stock Held 12469.850 33838.560 0.216 0.177 0.021 - 0.049 - 0.042 - 0.093 - 0.089 0.144 - 0.072 - 0.005 0.001 - 0.004 25. Media Count 4.829 7.279 - 0.060 - 0.065 - 0.230 0.142 0.193 0.309 0.065 - 0.061 - 0.029 0.020 0.021 0.028 26. Multiple Bidders 0.039 0.193 - 0.017 - 0.035 - 0.326 0.070 0.098 0.123 0.125 0.045 - 0. 030 0.078 0.029 0.007 27. Relative Size 0.069 0.148 - 0.223 - 0.239 - 0.169 0.031 0.371 0.162 0.070 0.084 - 0.056 0.022 - 0.096 - 0.075 N = 726 except for variables 26 and 27 where N = 723 p < 0.05 for correlations in bold; two - tailed test 106 Variables 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 1. (ln) Number of Acquisitions 2. (ln) Value of Acquisitions 3. Acquisition Completion 4. Postive Market Reaction ( - 3,3) 5. Negative Market Reaction ( - 3,3) 6. Negative Media Reaction 7. Positive Media Reaction 8. Promotion Focus 9. Prevention Focus 10. Future Focus 11 . Present Focus 12. Past Focus 13. Firm Size 1.000 14. Firm Performance - 0.131 1.000 15. Leverage 0.199 - 0.314 1.000 16. Diversification 0.346 - 0.163 0.134 1.000 17. CEO Power 0.127 - 0.063 0.129 0.023 1.000 18. Board Independence 0.204 - 0.003 - 0.070 0.034 0.059 1.000 19. Industry Dynamism 0.040 - 0.133 0.156 0.007 - 0.067 0.060 1.000 20. (ln) Acquisition History (#) 0.486 - 0.135 0.252 0.341 0.20 7 - 0.103 - 0.035 1.000 21. (ln) Acquisition History ($) 0.425 - 0.069 0.126 0.259 0.206 - 0.017 - 0.050 0.849 1.000 22. Salary 0.487 - 0.042 0.053 0.365 0.055 0.009 - 0.146 0.409 0.290 1.000 23. Bonus 0.144 - 0.098 0.233 - 0.020 0.222 - 0.213 0 .036 0.277 0.205 - 0.059 1.000 24. Restricted Stock Held 0.223 - 0.134 0.213 0.046 0.089 0.043 - 0.026 0.269 0.201 0.061 0.161 1.000 25. Media Count 0.114 0.074 - 0.012 0.034 - 0.014 0.055 0.125 - 0.052 - 0.023 - 0.018 - 0.015 - 0.039 1.000 26. Multipl e Bidders - 0.005 - 0.016 0.122 0.000 0.044 - 0.013 0.058 - 0.044 - 0.022 - 0.012 - 0.009 0.006 0.275 1.000 27. Relative Size - 0.325 0.105 - 0.091 - 0.150 - 0.063 0.017 0.012 - 0.214 - 0.159 - 0.137 - 0.103 - 0.075 0.371 0.255 1.000 N = 726 except for variables 26 an d 27 where N = 723 p < 0.05 for correlations in bold; two - tailed test 107 Table 2 - Acquisition Completion Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Pos. Market Reactions - 0.161 - 0.13 2 - 0.140 - 0.065 - 0.141 - 0.162 - 0.151 - 0.022 (0.142) (0.162) (0.130) (0.182) (0.128) (0.143) (0.128) (0.186) Neg. Market Reactions - 0.038 - 0.055 0.227 0.246 - 0.027 - 0.060 - 0.042 0.262 (0.154) (0.162) (0.174) (0.181) (0.154) (0.155) (0.154) (0.193) Pos. Media Reactions - 0.169 - 0.158 - 0.184 - 0.166 - 0.169 - 0.170 - 0.156 - 0.152 (0.136) (0.136) (0.147) (0.144) (0.146) (0.150) (0.172) (0.169) Neg. Media Reactions 0.028 0.031 0.015 0.018 0.079 0.000 0.033 0.007 (0.157) (0.161) (0.155) (0.156) (0.17 0) (0.166) (0.176) (0.173) Future X Neg. Market - 0.059 - 0.126 - 0.086 (0.158) (0.209) (0.214) Present X Neg. Market 0.021 - 0.196 - 0.290+ (0.132) (0.180) (0.181) Past X Neg. Market - 0.024 - 0.209 - 0.240+ (0.125) (0.12 8) (0.141) Future X Pos. Market - 0.188 - 0.165 - 0.156 (0.173) (0.180) (0.170) Present X Pos. Market 0.033 0.052 - 0.016 (0.229) (0.238) (0.244) Past X Pos. Market - 0.215 - 0.290 - 0.363+ (0.197) (0.241) (0.239) Promotion X Neg. Market - 0.280** - 0.315** - 0.329** (0.089) (0.120) (0.125) Prevention X Neg. Market 0.204 0.311 0.212 (0.172) (0.203) (0.205) Promotion X Pos. Market - 0.145 - 0.180 - 0.176 (0.163) (0.175) (0.169) Prevention X Pos. Market 0.075 0.148 0.081 (0.160) (0.157) (0.165) Future X Neg. Media 0.323+ 0.316+ 0.267 (0.209) (0.220) (0.233) Present X Neg. Media - 0.195 - 0.188 - 0.209 (0.200) (0.199) (0.199) Past X Neg. Media - 0.155 - 0.076 0.039 (0.147) (0.175) (0.214) Future X Pos. Media 0.140 0.164 0.168 (0.156) (0.148) (0.145) Present X Pos. Media - 0.126 - 0.160 0.043 (0.121) (0.124) (0.135) Past X Pos. Media 0.041 0.02 1 0.021 (0.129) (0.150) (0.150) Promotion X Neg. Media 0.109* 0.099 0.045 (0.195) (0.215) (0.215) Prevention X Neg. Media 0.304 0.275 0.324 (0.173) (0.190) (0.212) Promotion X Pos. Media - 0.078 - 0.087 - 0.151 (0.114) (0.138) (0.139) Prevention X Pos. Media - 0.154 - 0.180 - 0.138 (0.145) (0.147) (0.151) Promotion Focus 0.406** 0.396** 0.485** 0.464** 0.399** 0.392* 0.369* 0.394* (0.156) (0.152) (0.171) (0.171) (0.154) (0.167) (0.164) (0 .169) 108 Prevention Focus - 0.060 - 0.027 - 0.053 - 0.010 - 0.094 - 0.033 - 0.053 - 0.003 (0.132) (0.137) (0.151) (0.158) (0.132) (0.154) (0.155) (0.181) Future Focus 0.100 0.140 0.102 0.139 0.060 0.093 0.042 0.052 (0.157) (0.16 2) (0.163) (0.163) (0.155) (0.156) (0.152) (0.156) Present Focus 0.022 - 0.000 0.019 0.012 0.050 0.027 0.063 0.034 (0.147) (0.147) (0.154) (0.160) (0.148) (0.149) (0.149) (0.162) Past Focus - 0.193 - 0.171 - 0.199 - 0.184 - 0.248 - 0.210 - 0.264 - 0.271 ( 0.160) (0.163) (0.154) (0.161) (0.161) (0.155) (0.161) (0.166) - 2.319*** - 2.316*** - 2.131*** - 2.152** - 2.356*** - 2.340*** - 2.338*** - 2.080** Multiple Bidders (0.539) (0.587) (0.602) (0.645) (0.538) (0.556) (0.561) (0.660) - 0.254 - 0.231 - 0.265 - 0.28 2 - 0.307 - 0.208 - 0.266 - 0.282 Relative Size (0.210) (0.240) (0.196) (0.224) (0.213) (0.203) (0.204) (0.211) Firm Size 0.366+ 0.375+ 0.298 0.346 0.367+ 0.391+ 0.382+ 0.368 (0.196) (0.190) (0.206) (0.218) (0.197) (0.201) (0.203) (0.225) Firm Performa nce 0.083 0.115 0.096 0.114 0.096 0.063 0.075 0.090 (0.119) (0.144) (0.132) (0.163) (0.116) (0.115) (0.115) (0.160) Leverage - 0.145 - 0.124 - 0.168 - 0.207 - 0.113 - 0.167 - 0.131 - 0.189 (0.088) (0.087) (0.101) (0.124) (0.089) (0.096) (0.093) (0.122) D iversification - 0.131 - 0.121 - 0.121 - 0.098 - 0.137 - 0.148 - 0.146 - 0.113 (0.149) (0.150) (0.154) (0.153) (0.152) (0.148) (0.153) (0.155) CEO Power 0.153 0.125 0.175 0.178 0.134 0.147 0.131 0.148 (0.143) (0.158) (0.145) (0.158) (0.146) (0.148) (0.152 ) (0.167) Board Independence - 0.033 0.011 - 0.004 0.059 - 0.041 - 0.038 - 0.056 0.022 (0.152) (0.155) (0.156) (0.155) (0.153) (0.160) (0.160) (0.161) Dynamism 0.129 0.087 0.094 0.074 0.182 0.158 0.204 0.185 (0.191) (0.217) (0.202) (0.205) (0.199) (0. 189) (0.198) (0.226) Acquisition History 0.018 0.002 0.022 - 0.034 0.011 0.021 0.023 - 0.049 (0.167) (0.173) (0.174) (0.178) (0.167) (0.166) (0.198) (0.179) Salary - 0.333* - 0.347* - 0.309+ - 0.389* - 0.316+ - 0.328+ - 0.314+ - 0.347+ (0.162) (0.162) (0.1 76) (0.182) (0.168) (0.168) (0.173) (0.188) Bonus - 0.222* - 0.233* - 0.115 - 0.132 - 0.214* - 0.218* - 0.224* - 0.262 (0.095) (0.097) (0.110) (0.117) (0.103) (0.097) (0.104) (0.129) Restricted Stock Held - 0.151 - 0.150 - 0.198 - 0.197 - 0.178 - 0.165 - 0.181 - 0. 207+ (0.140) (0.130) (0.117) (0.108) (0.144) (0.127) (0.137) (0.111) Media Count - 0.202 - 0.205 - 0.271 - 0.288 - 0.180 - 0.161 - 0.140 - 0.249 (0.125) (0.137) (0.134) (0.135) (0.115) (0.131) (0.125) (0.142) Constant 3.014*** 2.901*** 3.063*** 3.000*** 3.043*** 3.001*** 3.037*** 2.957*** (0.417) (0.419) (0.421) (0.435) (0.411) (0.424) (0.423) (0.434) n = 723 + p< .10; * p< .05; ** p< .01; *** p<.001 One tailed tests for hypothesized variables, two - tailed tests for control variables. Standard errors are in parentheses. Year dummy variables included. 109 Table 3 - Heckman 2 - Stage Predicting Acquisition Completion Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Pos. Market Reac tions - 0.155 - 0.128 - 0.137 - 0.058 - 0.139 - 0.153 - 0.147 - 0.018 (0.141) (0.161) (0.130) (0.181) (0.127) (0.141) (0.127) (0.185) Neg. Market Reactions - 0.029 - 0.050 0.234 0.250 - 0.021 - 0.048 - 0.034 0.266 (0.155) (0.159) (0.175) (0.181) (0.155) (0.156 ) (0.155) (0.195) Pos. Media Reactions - 0.165 - 0.156 - 0.18 - 0.165 - 0.168 - 0.161 - 0.150 - 0.148 (0.136) (0.136) (0.147) (0.145) (0.147) (0.150) (0.173) (0.170) Neg. Media Reactions 0.034 0.035 0.018 0.021 0.088 0.010 0.044 0.011 (0.157) (0.162) (0. 156) (0.157) (0.171) (0.166) (0.177) (0.173) Future X Neg. Market - 0.047 - 0.122 - 0.078 (0.161) (0.212) (0.216) Present X Neg. Market 0.019 - 0.195 - 0.282+ (0.132) (0.179) (0.181) Past X Neg. Market - 0.027 - 0.210 - 0.240+ (0.124) (0.129) (0.141) Future X Pos. Market - 0.183 - 0.162 - 0.151 (0.171) (0.177) (0.168) Present X Pos. Market 0.017 0.039 - 0.028 (0.222) (0.229) (0.239) Past X Pos. Market - 0.227 - 0.301 - 0.375+ (0.192) (0 .230) (0.233) Promotion X Neg. Market - 0.286** - 0.319** - 0.327** (0.089) (0.120) (0.126) Prevention X Neg. Market 0.206 0.318 0.223 (0.172) (0.203) (0.206) Promotion X Pos. Market - 0.147 - 0.182 - 0.172 (0.165) ( 0.176) (0.170) Prevention X Pos. Market 0.073 0.150 0.087 (0.162) (0.157) (0.166) Future X Neg. Media 0.328+ 0.323+ 0.273 (0.210) (0.221) (0.232) Present X Neg. Media - 0.202 - 0.195 - 0.210 (0.198) (0.198) (0. 200) Past X Neg. Media - 0.153 - 0.076 0.037 (0.145) (0.173) (0.212) Future X Pos. Media 0.137 0.160 0.166 (0.156) (0.148) (0.144) Present X Pos. Media - 0.12 - 0.154 - 0.166 (0.120) (0.124) (0.129) Past X Pos. Medi a 0.043 0.02 0.041 (0.130) (0.152) (0.135) Promotion X Neg. Media 0.105 0.097 0.039 (0.195) (0.216) (0.215) Prevention X Neg. Media 0.291 0.261 0.307 (0.171) (0.188) (0.211) Promotion X Pos. Media - 0.09 2 - 0.098 - 0.158 (0.112) (0.139) (0.137) Prevention X Pos. Media - 0.159 - 0.183 - 0.140 (0.146) (0.148) (0.150) Promotion Focus 0.381* 0.378* 0.476** 0.456** 0.379* 0.366* 0.347* 0.384* (0.156) (0.154) (0.173) (0.174) (0.154) (0.16 9) (0.165) (0.171) 110 Prevention Focus - 0.058 - 0.024 - 0.054 - 0.008 - 0.095 - 0.030 - 0.053 0.003 (0.130) (0.133) (0.149) (0.154) (0.130) (0.150) (0.150) (0.174) Future Focus 0.100 0.144 0.100 0.141 0.062 0.092 0.046 0.059 ( 0.159) (0.162) (0.165) (0.163) (0.156) (0.158) (0.153) (0.157) Present Focus 0.026 0.001 0.019 0.012 0.051 0.030 0.063 0.033 (0.150) (0.150) (0.156) (0.162) (0.150) (0.153) (0.152) (0.164) Past Focus - 0.207 - 0.181 - 0.208 - 0.191 - 0.260 - 0.223 - 0.276 - 0.279 (0.160) (0.164) (0.164) (0.160) (0.162) (0.156) (0.162) (0.165) - 1.451* - 1.663* - 1.315+ - 1.605* - 1.524* - 1.455* - 1.479* - 1.617* Inverse Mills Ratio (0.676) (0.762) (0.716) (0.802) (0.679) (0.666) (0.673) (0.816) - 2.302*** - 2.309*** - 2.125 *** - 2.149** - 2.346*** - 2.314*** - 2.324*** - 2.068** Multiple Bidders (0.535) (0.582) (0.599) (0.638) (0.538) (0.551) (0.560) (0.654) - 0.280 - 0.248 - 0.276 - 0.294 - 0.328 - 0.243 - 0.295 - 0.299 Relative Size (0.207) (0.238) (0.191) (0.217) (0.208) (0.201 ) (0.200) (0.206) Leverage - 0.110 - 0.097 - 0.142 - 0.181 - 0.083 - 0.113 - 0.087 - 0.148 (0.078) (0.075) (0.090) (0.113) (0.080) (0.083) (0.081) (0.113) Diversification - 0.209 - 0.221 - 0.198 - 0.198 - 0.220 - 0.220 - 0.221 - 0.209 (0.144) (0.152) (0.149) (0. 156) (0.149) (0.144) (0.150) (0.161) CEO Power 0.127 0.095 0.151 0.149 0.108 0.119 0.103 0.116 (0.144) (0.160) (0.146) (0.160) (0.148) (0.149) (0.154) (0.169) Board Independence - 0.050 - 0.021 - 0.031 0.023 - 0.064 - 0.051 - 0.071 - 0.009 (0.152) (0.15 3) (0.156) (0.153) (0.152) (0.159) (0.159) (0.160) Dynamism 0.251 0.210 0.189 0.187 0.300 0.283 0.321 0.301 (0.195) (0.221) (0.207) (0.207) (0.202) (0.193) (0.201) (0.230) Acquisition History 0.024 0.008 0.027 - 0.029 0.016 0.03 0.031 - 0.04 (0.165 ) (0.170) (0.171) (0.174) (0.165) (0.163) (0.170) (0.173) Salary - 0.218 - 0.231 - 0.218 - 0.283+ - 0.203 - 0.203 - 0.194 - 0.234 (0.149) (0.150) (0.162) (0.165) (0.154) (0.155) (0.161) (0.172) Bonus - 0.329** - 0.361** - 0.216+ - 0.256+ - 0.328** - 0.323** - 0.33 2** - 0.285+ (0.109) (0.115) (0.125) (0.139) (0.115) (0.107) (0.113) (0.146) Restricted Stock Held - 0.146 - 0.158 - 0.213+ - 0.216* - 0.179 - 0.163 - 0.184 - 0.226* (0.145) (0.133) (0.117) (0.107) (0.151) (0.134) (0.142) (0.111) Media Count - 0.196 - 0.201 - 0.271 - 0.286 - 0.177 - 0.155 - 0.138 - 0.248 (0.122) (0.135) (0.132) (0.134) (0.113) (0.127) (0.122) (0.141) Constant 4.756*** 4.888*** 4.641*** 4.918*** 4.865*** 4.733*** 4.796*** 4.884*** (0.957) (1.065) (1.022) (1.137) (0.948) (0.934) (0.931) (1.1 41) n = 723 + p< .10; * p< .05; ** p< .01; *** p<.001 One tailed tests for hypothesized variables, two - tailed tests for control variables. Standard errors are in parentheses. Year dummy variables included. 111 Table 4 - Number of Acquisitions Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Pos. Market Reactions - 0.005 - 0.006 - 0.004 - 0.001 - 0.001 - 0.007 - 0.003 0.001 (0.039) (0.040) (0.040) (0.039) (0.039) (0.040) (0.039) (0.04 0) Neg. Market Reactions - 0.211*** - 0.214*** - 0.209*** - 0.213*** - 0.212*** - 0.208*** - 0.209*** - 0.212*** (0.056) (0.058) (0.054) (0.056) (0.055) (0.056) (0.054) (0.054) Pos. Media Reactions - 0.038 - 0.038 - 0.035 - 0.035 - 0.043 - 0.039 - 0.049 - 0.045 (0.030) (0.030) (0.030) (0.030) (0.032) (0.031) (0.034) (0.034) Neg. Media Reactions - 0.056 - 0.056 - 0.054 - 0.053 - 0.054 - 0.052 - 0.042 - 0.040 (0.039) (0.038) (0.039) (0.039) (0.041) (0.040) (0.043) (0.042) Future X Neg. Market - 0.003 - 0.010 - 0.0 18 (0.082) (0.079) (0.074) Present X Neg. Market - 0.019 - 0.039 - 0.040 (0.067) (0.061) (0.061) Past X Neg. Market - 0.028 - 0.056 - 0.035 (0.049) (0.050) (0.050) Future X Pos. Market 0.024 0.025 0.023 (0.046) ( 0.044) (0.045) Present X Pos. Market - 0.065 - 0.078 - 0.082 (0.049) (0.047) (0.050) Past X Pos. Market 0.012 - 0.003 0.000 (0.043) (0.044) (0.045) Promotion X Neg. Market - 0.068+ - 0.090* - 0.072+ (0.040) (0.038) (0.037) Prevention X Neg. Market - 0.004 - 0.007 - 0.006 (0.062) (0.061) (0.060) Promotion X Pos. Market - 0.052 - 0.066 - 0.058 (0.045) (0.045) (0.045) Prevention X Pos. Market - 0.006 - 0.013 - 0.016 (0.044) (0.043) ( 0.045) Future X Neg. Media 0.083* 0.089* 0.086* (0.049) (0.049) (0.049) Present X Neg. Media 0.023 0.016 0.014 (0.046) (0.049) (0.043) Past X Neg. Media - 0.086* - 0.106** - 0.101** (0.038) (0.036) (0.038) Future X Pos. Media 0.047 0.042 0.039 (0.037) (0.038) (0.037) Present X Pos. Media - 0.018 - 0.018 - 0.023 (0.028) (0.027) (0.027) Past X Pos. Media 0.005 0.017 0.014 (0.033) (0.037) (0.036) Promotion X Neg. Media - 0.090* - 0.106* - 0.105* (0.040) (0.043) (0.044) Prevention X Neg. Media - 0.038 - 0.060* - 0.060* (0.034) (0.033) (0.034) Promotion X Pos. Media 0.005 0.024 0.019 (0.032) (0.038) (0.038) Prevention X Pos. Media 0.011 0.012 0.012 (0.031) (0.032) (0.033) Promotion Focus 0.052 0.053 0.049 0.047 0.056 0.041 0.046 0.042 (0.050) (0.051) (0.050) (0.050) (0.050) (0.051) (0.050) (0.050) 112 Table 4 Prevention Focus 0.094* 0.096* 0.090* 0.09 3* 0.092* 0.097* 0.100* 0.099* (0.039) (0.039) (0.040) (0.039) (0.038) (0.040) (0.040) (0.040) Future Focus - 0.031 - 0.027 - 0.032 - 0.03 - 0.024 - 0.031 - 0.023 - 0.023 (0.046) (0.047) (0.046) (0.047) (0.045) (0.046) (0.044) (0.046) Present Focus 0.029 0.023 0.029 0.021 0.031 0.027 0.029 0.021 (0.046) (0.048) (0.046) (0.047) (0.045) (0.046) (0.045) (0.047) Past Focus 0.048 0.046 0.047 0.04 0.043 0.052 0.049 0.043 (0.044) (0.044) (0.044) (0.044) (0.044) (0.044) (0.045) (0.043) Firm Size 0.191** * 0.192*** 0.187*** 0.188*** 0.197*** 0.190*** 0.195*** 0.191*** (0.047) (0.047) (0.046) (0.046) (0.047) (0.048) (0.048) (0.047) Firm Performance 0.045 0.052 0.046 0.057 0.055 0.041 0.052 0.062 (0.045) (0.046) (0.045) (0.046) (0.046) (0.045) (0.045 ) (0.044) Leverage 0.069 0.078 0.069 0.084 0.084 0.073 0.091 0.102 (0.054) (0.057) (0.054) (0.057) (0.055) (0.054) (0.055) (0.057) Diversification 0.024 0.025 0.022 0.022 0.023 0.026 0.024 0.022 (0.044) (0.044) (0.044) (0.044) (0.044) (0.044) (0. 044) (0.043) CEO Power - 0.057 - 0.061 - 0.053 - 0.056 - 0.064+ - 0.061 - 0.068+ - 0.066+ (0.039) (0.039) (0.039) (0.040) (0.038) (0.039) (0.038) (0.040) Board Independence - 0.053 - 0.053 - 0.054 - 0.054 - 0.052 - 0.054 - 0.051 - 0.052 (0.044) (0.044) (0.044) ( 0.044) (0.045) (0.045) (0.046) (0.046) Dynamism - 0.198** - 0.204** - 0.211** - 0.213** - 0.206** - 0.205** - 0.216** - 0.226** (0.076) (0.076) (0.079) (0.078) (0.076) (0.076) (0.077) (0.080) Acquisition History 0.480*** 0.473*** 0.478*** 0.468*** 0.481*** 0.476*** 0.475*** 0.465*** (0.043) (0.043) (0.043) (0.043) (0.044) (0.044) (0.044) (0.044) Salary 0.000 - 0.001 0.004 0.002 0.000 0.002 0.002 0.005 (0.053) (0.053) (0.051) (0.051) (0.053) (0.052) (0.052) (0.050) Bonus 0.116** 0.115** 0.127** 0.126 ** 0.121** 0.114** 0.118** 0.125** (0.041) (0.042) (0.042) (0.042) (0.041) (0.044) (0.044) (0.045) Restricted Stock Held - 0.010 - 0.010 - 0.014 - 0.014 - 0.018 - 0.012 - 0.020 - 0.022 (0.022) (0.022) (0.022) (0.022) (0.022) (0.022) (0.022) (0.022) Media Count 0.013 0.015 0.003 0.003 0.015 0.001 - 0.003 - 0.008 (0.055) (0.055) (0.053) (0.050) (0.054) (0.056) (0.056) (0.051) 0.095 0.090 0.100 0.094 0.097 0.093 0.094 0.086 Constant (0.086) (0.088) (0.086) (0.087) (0.088) (0.086) (0.088) (0.049) n = 726 + p< .10; * p< .05; ** p< .01; *** p<.001 One tailed tests for hypothesized variables, two - tailed tests for control variables. Standard errors are in parentheses. Year dummy variables included. 113 Table 5 - Value of Acquisitions Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Pos. Market Reactions - 0.015 - 0.020 - 0.012 - 0.009 - 0.005 - 0.018 - 0.009 - 0.001 (0.104) (0.108) (0.106) (0.108) (0.106) (0.106) (0.107) (0.110) Neg. M arket Reactions - 0.526*** - 0.543*** - 0.516*** - 0.529*** - 0.529*** - 0.513*** - 0.515*** - 0.520*** (0.141) (0.141) (0.142) (0.141) (0.139) (0.140) (0.137) (0.137) Pos. Media Reactions - 0.095 - 0.096 - 0.091 - 0.091 - 0.112 - 0.095 - 0.126 - 0.114 (0.071) (0 .072) (0.071) (0.072) (0.077) (0.076) (0.083) (0.083) Neg. Media Reactions - 0.221* - 0.219* - 0.216* - 0.209* - 0.216* - 0.220* - 0.192+ - 0.187+ (0.099) (0.099) (0.098) (0.099) (0.105) (0.101) (0.109) (0.108) Future X Neg. Market - 0.096 - 0.117 - 0.141 (0.189) (0.185) (0.177) Present X Neg. Market - 0.017 - 0.065 - 0.073 (0.154) (0.144) (0.143) Past X Neg. Market - 0.101 - 0.157 - 0.106 (0.119) (0.117) (0.116) Future X Pos. Market 0.056 0.058 0.047 (0.111) (0. 106) (0.109) Present X Pos. Market - 0.166 - 0.188 - 0.198 (0.127) (0.124) (0.130) Past X Pos. Market - 0.006 - 0.030 - 0.022 (0.105) (0.110) (0.115) Promotion X Neg. Market - 0.144 - 0.199* - 0.150+ (0.098) (0.093) ( 0.090) Prevention X Neg. Market 0.015 0.037 0.045 (0.148) (0.153) (0.147) Promotion X Pos. Market - 0.087 - 0.117 - 0.097 (0.113) (0.156) (0.113) Prevention X Pos. Market - 0.003 - 0.012 - 0.01 (0.110) (0.114) (0.114 ) Future X Neg. Media 0.172+ 0.187+ 0.190+ (0.131) (0.127) (0.128) Present X Neg. Media 0.065 0.043 0.037 (0.117) (0.110) (0.110) Past X Neg. Media - 0.244** - 0.292** - 0.275** (0.101) (0.098) (0.102) Future X P os. Media 0.117 0.105 0.099 (0.097) (0.096) (0.097) Present X Pos. Media - 0.035 - 0.035 - 0.051 (0.077) (0.074) (0.074) Past X Pos. Media 0.033 0.061 0.046 (0.083) (0.091) (0.090) Promotion X Neg. Media - 0. 270* - 0.315** - 0.313** (0.107) (0.110) (0.111) Prevention X Neg. Media - 0.090 - 0.144* - 0.150* (0.092) (0.085) (0.088) Promotion X Pos. Media - 0.008 0.047 0.033 (0.087) (0.099) (0.099) Prevention X Pos. Media 0 .049 0.055 0.06 (0.079) (0.079) (0.083) Promotion Focus 0.080 0.080 0.074 0.066 0.094 0.045 0.062 0.048 (0.148) (0.147) (0.148) (0.147) (0.149) (0.148) (0.147) (0.147) 114 Table 5 Prevention Focus 0.163 0.175+ 0.156 0.168+ 0 .159 0.160 0.169+ 0.176+ (0.099) (0.098) (0.100) (0.097) (0.099) (0.098) (0.098) (0.097) Future Focus - 0.018 - 0.027 - 0.022 - 0.037 - 0.005 - 0.017 - 0.003 - 0.024 (0.112) (0.112) (0.112) (0.113) (0.114) (0.112) (0.112) (0.114) Present Focus - 0.003 - 0. 017 - 0.004 - 0.025 0.004 - 0.008 - 0.001 - 0.025 (0.112) (0.124) (0.122) (0.122) (0.123) (0.123) (0.123) (0.122) Past Focus 0.110 0.097 0.108 0.085 0.097 0.123 0.114 0.092 (0.120) (0.120) (0.121) (0.121) (0.122) (0.120) (0.122) (0.121) Firm Size 0.56 5*** 0.563*** 0.555*** 0.552*** 0.579*** 0.562*** 0.579*** 0.560*** (0.146) (0.145) (0.145) (0.143) (0.147) (0.147) (0.148) (0.146) Firm Performance 0.263+ 0.285* 0.266+ 0.292* 0.291* 0.254+ 0.281* 0.305* (0.139) (0.140) (0.138) (0.138) (0.137) (0. 136) (0.133) (0.131) Leverage 0.242 0.280 0.248 0.298 0.283 0.252 0.299+ 0.341+ (0.170) (0.174) (0.167) (0.173) (0.174) (0.168) (0.173) (0.174) Diversification 0.171 0.172 0.164 0.162 0.167 0.174 0.168 0.16 (0.132) (0.131) (0.133) (0.131) (0.133) (0.131) (0.132) (0.130) CEO Power - 0.087 - 0.099 - 0.082 - 0.090 - 0.102 - 0.100 - 0.113 - 0.115 (0.125) (0.124) (0.127) (0.127) (0.123) (0.125) (0.123) (0.126) Board Independence - 0.168 - 0.168 - 0.170 - 0.169 - 0.164 - 0.171 - 0.16 - 0.162 (0.114) (0.115) ( 0.115) (0.117) (0.115) (0.119) (0.120) (0.122) Dynamism - 0.423* - 0.433* - 0.446* - 0.453* - 0.441* - 0.440* - 0.469* - 0.485* (0.211) (0.210) (0.220) (0.217) (0.213) (0.213) (0.215) (0.223) Acquisition History 0.725*** 0.711*** 0.726*** 0.704*** 0.718*** 0.714*** 0.701*** 0.683*** (0.125) (0.125) (0.124) (0.125) (0.123) (0.126) (0.124) (0.124) Salary 0.125 0.124 0.133 0.133 0.127 0.133 0.132 0.142 (0.165) (0.163) (0.161) (0.157) (0.164) (0.163) (0.161) (0.155) Bonus 0.320*** 0.307*** 0.339*** 0.3 29*** 0.337*** 0.318*** 0.330*** 0.335*** (0.078) (0.080) (0.083) (0.084) (0.081) (0.081) (0.083) (0.089) Restricted Stock Held 0.041 0.042 0.031 0.029 0.019 0.030 0.008 0.003 (0.073) (0.073) (0.074) (0.074) (0.075) (0.073) (0.073) (0.074) Media C ount - 0.043 - 0.036 - 0.063 - 0.059 - 0.038 - 0.078 - 0.087 - 0.090 (0.142) (0.142) (0.139) (0.134) (0.138) (0.145) (0.144) (0.138) Constant 0.046 0.022 0.055 0.034 0.054 0.041 0.046 0.032 (0.231) (0.235) (0.230) (0.233) (0.233) (0.231) (0.233) (0.235) n = 726 + p< .10; * p< .05; ** p< .01; *** p<.001 One tailed tests for hypothesized variables, two - tailed tests for control variables. Standard errors are in parentheses. Year dummy variables included. 115 Table 6 - Rate of Acquisition Activity Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Pos. Market Reactions - 0.030 - 0.000 - 0.033 0.001 - 0.029 - 0.025 - 0.025 0.007 (0.069) (0.068) (0.074) (0.067) (0.068) (0.065) (0.062) (0.06 4) Neg. Market Reactions - 0.078* - 0.082* - 0.069+ - 0.067 - 0.073* - 0.093* - 0.097* - 0.073+ (0.033) (0.040) (0.042) (0.042) (0.033) (0.038) (0.039) (0.044) Pos. Media Reactions 0.243*** 0.246*** 0.241** 0.245*** 0.261*** 0.240** 0.256*** 0.264*** (0. 070) (0.066) (0.071) (0.067) (0.070) (0.070) (0.054) (0.051) Neg. Media Reactions 0.086+ 0.082+ 0.081+ 0.079+ 0.135* 0.078+ 0.140* 0.128* (0.044) (0.045) (0.044) (0.045) (0.052) (0.046) (0.056) (0.056) Future X Neg. Market 0.001 - 0.000 - 0.009 (0.073) (0.072) (0.080) Present X Neg. Market - 0.005 - 0.022 - 0.011 (0.045) (0.053) (0.056) Past X Neg. Market - 0.017 - 0.021 - 0.019 (0.042) (0.043) (0.046) Future X Pos. Market - 0.067 - 0.072 - 0.080 (0.077) (0 .072) (0.071) Present X Pos. Market - 0.087 - 0.094+ - 0.076 (0.058) (0.055) (0.059) Past X Pos. Market - 0.068 - 0.067 - 0.052 (0.067) (0.067) (0.065) Promotion X Neg. Market - 0.009 - 0.023 - 0.025 (0.027) (0.030) ( 0.033) Prevention X Neg. Market - 0.001 0.009 0.008 (0.054) (0.056) (0.060) Promotion X Pos. Market - 0.070 - 0.112 - 0.105 (0.078) (0.083) (0.078) Prevention X Pos. Market - 0.123 - 0.093 - 0.069 (0.094) (0.077) (0.0 73) Future X Neg. Media 0.102+ 0.128* 0.115* (0.065) (0.068) (0.066) Present X Neg. Media - 0.045 - 0.046 - 0.031 (0.058) (0.055) (0.056) Past X Neg. Media - 0.125** - 0.155*** - 0.152** (0.042) (0.045) (0.045) Futu re X Pos. Media - 0.013 - 0.047 - 0.043 (0.066) (0.069) (0.063) Present X Pos. Media - 0.080+ - 0.061 - 0.075 (0.044) (0.046) (0.047) Past X Pos. Media 0.090* 0.128** 0.117* (0.052) (0.051) (0.051) Promotion X Neg. Me dia - 0.031 - 0.099* - 0.101+ (0.045) (0.050) (0.052) Prevention X Neg. Media - 0.031 - 0.079+ - 0.070+ (0.039) (0.049) (0.048) Promotion X Pos. Media 0.100* 0.147** 0.145* (0.055) (0.059) (0.063) Prevention X Pos. Media 0.063 0.074 0.071 (0.059) (0.067) (0.063) Promotion Focus 0.034 0.036 0.035 0.040 0.040 0.031 0.053 0.063 (0.064) (0.065) (0.065) (0.065) (0.066) (0.066) (0.069) (0.070) 116 Table 6 Prevention Focus 0.039 0.049 0.031 0.042 0.029 0.056 0.057 0.056 (0.052) (0.051) (0.047) (0.046) (0.051) (0.061) (0.062) (0.056) Future Focus - 0.081 - 0.081 - 0.080 - 0.080 - 0.075 - 0.086 - 0.077 - 0.076 (0.051) (0.053) (0.051) (0.052) (0.053) (0.051) (0.053) (0.054) Present Focus 0.010 0.008 0.021 0.019 - 0.001 0.020 0.010 0.017 (0.055) (0.055) (0.055) (0.055) (0.055) (0.054) (0.054) (0.053) Past Focus - 0.016 - 0.029 - 0.014 - 0.026 0.002 - 0.002 0.021 0.014 (0.046) (0.047) (0.047) (0.047) (0.053) (0.050) (0.056) (0.056) Firm Size 0.182** 0.188** 0.181** 0.185** 0.183** 0.183** 0.171* 0.172* (0.070) (0.069) (0.068) (0.068) (0.071) (0.072) (0.074) (0.071) Firm Performance 0.025 0.033 0.024 0.036 0.016 0.017 0.011 0.020 (0.055) (0.056) (0.055) (0.057) (0.056) (0.055) (0. 057) (0.059) Leverage 0.214** 0.240* 0.214** 0.246* 0.216** 0.220** 0.235** 0.262* (0.076) (0.099) (0.075) (0.105) (0.076) (0.076) (0.078) (0.110) Diversification 0.088 0.091 0.087 0.094 0.085 0.091 0.090 0.094 (0.057) (0.057) (0.057) (0.056) (0. 057) (0.055) (0.056) (0.056) CEO Power 0.025 0.028 0.028 0.033 0.027 0.028 0.028 0.032 (0.068) (0.068) (0.068) (0.068) (0.069) (0.067) (0.068) (0.067) Board Independence 0.098* 0.100* 0.101* 0.105* 0.095* 0.097* 0.094* 0.101* (0.046) (0.047) (0.0 47) (0.047) (0.045) (0.046) (0.045) (0.046) Dynamism 0.044 0.066 0.040 0.051 0.034 0.026 0.012 0.015 (0.100) (0.108) (0.102) (0.103) (0.105) (0.103) (0.106) (0.110) Acquisition History 0.424*** 0.419*** 0.429*** 0.422*** 0.426*** 0.427*** 0.430*** 0 .429*** (0.050) (0.052) (0.051) (0.052) (0.052) (0.050) (0.055) (0.056) Salary - 0.004 0.001 - 0.005 - 0.005 0.024 - 0.017 0.014 0.017 (0.071) (0.072) (0.071) (0.071) (0.078) (0.069) (0.078) (0.076) Bonus - 0.029 - 0.037 - 0.032 - 0.037 - 0.018 - 0.036 - 0. 025 - 0.031 (0.055) (0.055) (0.056) (0.057) (0.054) (0.055) (0.054) (0.053) Restricted Stock Held - 0.123** - 0.120** - 0.120** - 0.120** - 0.134*** - 0.127** - 0.145** - 0.142*** (0.037) (0.037) (0.037) (0.037) (0.038) (0.038) (0.039) (0.039) Media Count - 0.059 - 0.031 - 0.053 - 0.031 - 0.071 - 0.053 - 0.064 - 0.047 (0.100) (0.106) (0.108) (0.108) (0.099) (0.100) (0.097) (0.104) n = 724 + p< .10; * p< .05; ** p< .01; *** p<.001 One tailed tests for hypothesized variables, two - tai led tests for control variables. Standard errors are in parentheses. Year dummy variables included. 117 Table 7 - Comparing Event Windows Acquisition Completion Heckman Procedure Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 ( - 1,1) ( - 3,3) ( - 5,15) ( - 1,1) ( - 3,3) ( - 5,15) Pos. Market Reactions 0.062 - 0.022 0.176 - 0.054 - 0.018 0.176 (0.205) (0.186) (0.254) (0.209) (0.185) (0.254) Neg. Market Reactions 0.575* 0.262 0.059 0.602* 0.266 0.056 (0 .279) (0.193) (0.200) (0.288) (0.195) (0.203) Pos. Media Reactions 0.108 - 0.152 - 0.103 - 0.099 - 0.148 - 0.098 (0.183) (0.169) (0.192) (0.185) (0.170) (0.193) Neg. Media Reactions 0.096 0.007 - 0.045 0.099 0.011 - 0.042 (0.197) (0.173) (0.176) (0.197) (0.173) (0.176) Future X Neg. Market - 0.085 - 0.086 - 0.308 - 0.075 - 0.078 - 0.311 (0.212) (0.214) (0.242) (0.214) (0.216) (0.247) Present X Neg. Market - 0.541* - 0.290+ 0.038 - 0.551* - 0.282+ 0.044 (0.243) (0.181) (0.232) (0.245) (0. 181) (0.235) Past X Neg. Market - 0.433* - 0.240+ - 0.203+ - 0.438* - 0.240+ - 0.200+ (0.203) (0.141) (0.140) (0.204) (0.141) (0.141) Future X Pos. Market - 0.091 - 0.156 - 0.379* - 0.086 - 0.151 - 0.346* (0.179) (0.170) (0.205) (0.176) (0.168) (0.2 09) Present X Pos. Market - 0.135 - 0.016 0.141 - 0.152 - 0.028 0.141 (0.295) (0.244) (0.216) (0.283) (0.239) (0.223) Past X Pos. Market - 0.243 - 0.363+ - 0.013 - 0.241 - 0.375+ - 0.017 (0.268) (0.239) (0.233) (0.272) (0.233) (0.241) Promotion X Neg. Market - 0.452** - 0.329** - 0.426** - 0.465** - 0.327** - 0.432** (0.152) (0.125) (0.148) (0.153) (0.126) (0.150) Prevention X Neg. Market 0.437 0.212 - 0.159+ 0.441 0.223 - 0.167+ (0.287) (0.205) (0.116) (0.293) (0.206) (0.121) Promotion X Pos. Market - 0.004 - 0.176 - 0.204 - 0.005 - 0.172 - 0.209 (0.242) (0.169) (0.169) (0.247) (0.170) (0.173) Prevention X Pos. Market - 0.010 0.081 - 0.062 0.004 0.087 - 0.066 (0.127) (0.165) (0.166) (0.129) (0.166) (0.165) Future X Neg. Media 0 .281 0.267 0.277 0.282 0.273 0.279 (0.232) (0.233) (0.240) (0.232) (0.232) (0.240) Present X Neg. Media - 0.163 - 0.209 - 0.235 - 0.157 - 0.210 - 0.237 (0.206) (0.199) (0.185) (0.207) (0.200) (0.186) Past X Neg. Media 0.007 0.039 0.037 0.011 0 .037 0.041 (0.166) (0.214) (0.179) (0.164) (0.212) (0.180) Future X Pos. Media 0.215 0.168 0.105 0.214 0.166 0.099 (0.160) (0.145) (0.158) (0.160) (0.144) (0.159) Present X Pos. Media - 0.204 0.043 - 0.188 - 0.204 - 0.166 - 0.182 (0.141) ( 0.135) (0.134) (0.141) (0.129) (0.135) Past X Pos. Media 0.102 0.021 0.013 0.101 0.041 0.011 (0.137) (0.150) (0.143) (0.138) (0.135) (0.145) Promotion X Neg. Media 0.166 0.045 0.056 0.170 0.039 0.054 (0.222) (0.215) (0.210) (0.221) (0.21 5) (0.211) Prevention X Neg. Media 0.366 0.324 0.382* 0.347 0.307 0.363* (0.234) (0.212) (0.179) (0.240) (0.211) (0.178) Promotion X Pos. Media - 0.141 - 0.151 - 0.207 - 0.151 - 0.158 - 0.218 (0.153) (0.139) (0.155) (0.150) (0.137) (0.155) 118 Table 7 Prevention X Pos. Media - 0.156 - 0.138 - 0.261+ - 0.156 - 0.140 - 0.258* (0.172) (0.151) (0.144) (0.174) (0.150) (0.146) n = 723 + p< .10; * p< .05; ** p< .01; *** p<.001 One tailed tests for hypothesized variables, two - tailed tests for control variables. Standard errors are in parentheses. Year dummy variables included. 119 Table 8 - Comparing Event Windows Number of Acquisitions Value of Acquisitions Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 ( - 1,1) ( - 3,3) ( - 5,15) ( - 1,1) ( - 3,3) ( - 5,15) Pos. Market Reactions 0.005 0.001 0.028 - 0.040 - 0.001 0.088 (0.052) (0.040) (0.051) (0.130) (0.110) (0.126) Neg. Market Reactions - 0.170** - 0.212*** - 0.103* - 0.528** - 0.520*** - 0.219* (0.059) (0.054) (0.054) (0.153) (0.137) (0.107) Pos. Media Reactions - 0.043 - 0.045 - 0.047 - 0.108 - 0.114 - 0.116 (0.033) (0.034) (0.042) (0.082) (0.083) (0.083) Neg. Media Reactions - 0.048 - 0.040 - 0.049 - 0.210* - 0.187+ - 0.202+ (0.042) (0.042) (0.042) (0.106) (0.108) (0.109) Future X Neg. Market - 0.142+ - 0.018 - 0.153* - 0.389* - 0.141 - 0.273+ (0.074) (0.074) (0.065) (0.185) (0.177) (0.145) Present X Neg. Market - 0.027 - 0.040 0.057 - 0.052 - 0.073 0.120 (0.054) (0.061) (0.054) (0.124) (0.143) (0.116) Past X Neg. Market - 0.019 - 0.035 - 0.003 - 0.081 - 0.106 - 0.107 (0.048) (0.050) (0.037) (0.125) (0.116) (0.093) Future X Pos. Market - 0.047 0.023 - 0.014 - 0.058 0.047 0.054 (0.052) (0.045) (0.054) (0.113) (0.109) (0.122) Present X Pos. Market - 0.012 - 0.082 - 0.053 - 0.073 - 0.198 - 0.185 (0.065) (0.050) (0.061) (0.150) (0.130) (0.157) Past X Pos. Market - 0.001 0.000 - 0.054 - 0.501 - 0.022 - 0.161 (0.051) (0.045) (0.048) (0.113) (0.115) (0.121) Promotion X Neg. Market - 0.107** - 0.072+ - 0.069* - 0.261** - 0.150+ - 0.191* (0.036) (0.037) (0.034) (0.092) (0.090) (0.084) Prevention X Neg. Market - 0.011 - 0.006 0.000 - 0.028 0.045 - 0.035 (0.054) (0.060) (0 .027) (0.134) (0.147) (0.063) Promotion X Pos. Market - 0.071+ - 0.058 - 0.038 - 0.178+ - 0.097 - 0.069 (0.041) (0.045) (0.047) (0.106) (0.113) (0.120) Prevention X Pos. Market - 0.037 - 0.016 - 0.047 - 0.077 - 0.010 - 0.098 (0.040) (0.045) (0.058) (0.098) (0.114) (0.152) Future X Neg. Media 0.085* 0.086* 0.087* 0.179+ 0.190+ 0.183+ (0.050) (0.049) (0.048) (0.126) (0.128) (0.130) Present X Neg. Media 0.009 0.014 0.007 0.028 0.037 0.022 (0.041) (0.043) (0.041) (0.107) (0.110) (0.108) Past X Neg. Media - 0.100** - 0.101** - 0.101** - 0.274** - 0.275** - 0.278** (0.036) (0.038) (0.036) (0.097) (0.102) (0.103) Future X Pos. Media 0.049 0.039 0.047 0.118 0.099 0.109 (0.039) (0.037) (0.040) (0.098) (0.097) (0.101) Present X P os. Media - 0.024 - 0.023 - 0.024 - 0.053 - 0.051 - 0.047 (0.027) (0.027) (0.028) (0.072) (0.074) (0.074) Past X Pos. Media 0.009 0.014 0.005 0.033 0.046 0.036 (0.036) (0.036) (0.037) (0.091) (0.090) (0.090) Promotion X Neg. Media - 0.112* - 0.1 05* - 0.114* - 0.330** - 0.313** - 0.329** (0.045) (0.044) (0.046) (0.113) (0.111) (0.116) Prevention X Neg. Media - 0.055+ - 0.060* - 0.059* - 0.130+ - 0.150* - 0.144* (0.036) (0.034) (0.031) (0.099) (0.088) (0.085) Promotion X Pos. Media 0.024 0. 019 0.011 0.047 0.033 0.015 (0.039) (0.038) (0.037) (0.102) (0.099) (0.099) 120 Table 8 Prevention X Pos. Media 0.017 0.012 0.008 0.066 0.060 0.044 (0.034) (0.033) (0.034) (0.084) (0.083) (0.085) n = 726 + p< .10; * p< .05; ** p< .01; *** p<.001 One tailed tests for hypothesized variables, two - tailed tests for control variables. Standard errors are in parentheses. Year dummy variables included. 121 Table 9 - Compa ring Event Windows Rate of Acquisition Activity Model 4 Model 5 Model 6 ( - 1,1) ( - 3,3) ( - 5,15) Pos. Market Reactions - 0.091 0.007 - 0.012 (0.067) (0.064) (0.064) Neg. Market Reactions - 0.076 - 0.073 - 0.062 (0.053) ( 0.044) (0.047) Pos. Media Reactions 0.276*** 0.264*** 0.248*** (0.053) (0.051) (0.053) Neg. Media Reactions 0.128* 0.128* 0.136* (0.055) (0.056) (0.057) Future X Neg. Market - 0.013 - 0.009 - 0.053 (0.079) (0.080) (0.067) Present X Neg. Market - 0.005 - 0.011 0.042 (0.053) (0.056) (0.044) Past X Neg. Market 0.030 - 0.019 0.047 (0.050) (0.046) (0.033) Future X Pos. Market - 0.038 - 0.080 - 0.048 (0.066) (0.071) (0.065) Present X Pos. Market - 0.051 - 0.076 - 0.008 (0.067) (0.059) (0.076) Past X Pos. Market 0.066 - 0.052 - 0.032 (0.092) (0.065) (0.072) Promotion X Neg. Market - 0.023 - 0.025 - 0.050 (0.034) (0.033) (0.052) Prevention X Neg. Market - 0.008 0.008 - 0.009 ( 0.060) (0.060) (0.043) Promotion X Pos. Market - 0.098 - 0.105 - 0.089 (0.083) (0.078) (0.089) Prevention X Pos. Market - 0.181 - 0.069 - 0.127 (0.111) (0.073) (0.080) Future X Neg. Media 0.113* 0.115* 0.121* (0.064) (0.066) (0.0 67) Present X Neg. Media - 0.027 - 0.031 - 0.055 (0.053) (0.056) (0.054) Past X Neg. Media - 0.151** - 0.152** - 0.161*** (0.045) (0.045) (0.045) Future X Pos. Media - 0.035 - 0.043 - 0.048 (0.063) (0.063) (0.062) Present X Pos. Media - 0.080 - 0.075 - 0.058 (0.046) (0.047) (0.047) Past X Pos. Media 0.113* 0.117* 0.120* (0.051) (0.051) (0.053) Promotion X Neg. Media - 0.100+ - 0.101+ - 0.095+ (0.053) (0.052) (0.052) Prevention X Neg. Media - 0.093* - 0.07 0+ - 0.076 (0.049) (0.048) (0.062) Promotion X Pos. Media 0.144* 0.145* 0.135** (0.064) (0.063) (0.058) 122 Table 9 Prevention X Pos. Media 0.071 0.071 0.076 (0.071) (0.063) (0.076) n = 724 + p< .10; * p< .05; ** p< .01; *** p<.001 One tailed tests for hypothesized variables, two - tailed tests for control variables. Standard errors are in parentheses. Year dummy variables included. 123 REFERENCES 124 REFERENCES Agle, B. R., & Sonnenfield, J. A. 1994. Charismatic chief executive officers: Are they more effective? An empirical test of charismatic leadership theory. Academy of Management Proceedings , 1994: 2 - 6. 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