MANAGEMENT THEORY APPLICATIONS OF PROSPECT THEORY: ACCOMPLISHMENTS, CHALLENGES, AND OPPORTUNITIES R. MICHAEL HOLMES JR. (corresponding author) Department of Management College of Business Florida State University Tallahassee, FL 32306 Tel: +1 (770) 605-6977 Email:
[email protected] PHILIP BROMILEY The Paul Merage School of Business University of California, Irvine Irvine, CA 92697-3125 Tel: +1 (949) 824-6657 Email:
[email protected] CYNTHIA E. DEVERS A. B. Freeman School of Business Tulane University New Orleans, LA 70118 Tel: +1 (989) 400-7525 Email:
[email protected] TIM R. HOLCOMB College of Business Florida State University Tallahassee, FL 32306-1110 Tel: +1 (850) 644-7851 (Voice) Email:
[email protected] JEAN B. MCGUIRE E.J. Ourso College of Business Louisiana State University Baton Rouge, LA 70803 Tel: +1 (225) 578-5187 Email:
[email protected] Keywords: prospect theory, risk taking, value function, probability weighting function, reference points, loss aversion, diminishing sensitivity, framing, decision weights Acknowledgements: We would like to thank Rhett Brymer, Mario Krenn, Eric Liguori, and Jennifer Sexton for their feedback on earlier drafts of this manuscript. Accepted at Journal of Management 1
Electronic copy available at: http://ssrn.com/abstract=1714797
MANAGEMENT THEORY APPLICATIONS OF PROSPECT THEORY: ACCOMPLISHMENTS, CHALLENGES, AND OPPORTUNITIES ABSTRACT We review management research drawing on prospect theory, focusing primarily on studies in strategic management and organizational behavior/human resource management. These studies have made valuable contributions to several prominent research streams. However, they commonly underutilize or misinterpret central arguments from prospect theory. Further, they illustrate that applying prospect theory in organizational settings poses several theoretical and methodological challenges. Thus, we review these studies, critically analyze them, and make suggestions to enrich future work.
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Electronic copy available at: http://ssrn.com/abstract=1714797
Scholars have studied individuals’ risk-taking decisions for centuries (Edwards, 1954). Many research questions in the social sciences involve such decisions. For example, risk is relevant to decisions as diverse as whom to marry, whether to select red or black at the roulette table, when a country should go to war, and how much insurance to buy. Historically, scholars relied heavily on normative models (e.g., expected value and expected utility theory) to explain both (a) the decisions individuals should make and (b) the decisions individuals actually make (Einhorn & Hogarth, 1981). Departing from the normative approach, Kahneman and Tversky (1979) offered a highly influential descriptive model of decision making under risk. This model, termed prospect theory (PT), explained findings from laboratory studies that documented how individuals’ risk-taking decisions depart from the predictions of normative models. The elegance and apparent simplicity of PT led to its widespread adoption by scholars in many fields, including management. Indeed, over 500 articles in leading management journals1 have cited Kahneman and Tversky (1979). We review management research drawing on PT, focusing specifically on studies in strategic management and organizational behavior/human resource management (OB/HR). In addition to discussing the contributions of this work, we raise four areas of concern. First, most of these studies use only one or two of the constructs and arguments that comprise PT, and few studies use it as a coherent whole. Second, many studies claiming to draw on PT make predictions inconsistent with it (Bromiley, 2010). Third, scholars implement PT in disparate ways, limiting comparability across studies. Fourth, scholars often use PT, which is an individual-level theory, to explain higher-level phenomena, but the extent to which PT translates theoretically and empirically to higher levels of analysis is open to debate. Given these concerns, we conclude that despite decades of management research drawing on PT, (a) its past contributions to our field remain unclear and (b) a continuation of extant practices would limit its 3
potential to contribute in the future. As such, we encourage scholars to pursue new questions and revisit old ones by using PT more precisely and comprehensively. We begin by describing PT in detail. Specifically, we place PT in its historical context by outlining the basis of much decision-making research prior to its development. In addition, we explain the individual constructs and arguments embedded in its two central components: the value function and the probability weighting function. We also discuss how PT differs from two other important theories that explain decision making under risk, expected utility theory (EUT) and the behavioral theory of the firm (BTOF), thereby highlighting PT’s distinctiveness. We next review management research drawing on PT. First, we describe how we selected studies to review. The studies coalesced around key research streams, which we organize by level of analysis. At the individual level, we review research drawing on PT to examine executive compensation, negotiations, affect and motivation, and human resource management (HRM). At higher levels of aggregation, we review research using PT to explain relations between organizational risk and return, as well as the antecedents and consequences of firms’ risk-taking behaviors. From this base, we analyze extant use of PT in the management literature and offer suggestions to improve future research. We illustrate that scholars must utilize PT in its entirety to derive accurate predictions consistent with the theory (Wakker, 2003). We also discuss the many challenges that arise when scholars attempt to apply concepts from PT to the complex multi-level context of organizations. In doing so, we question whether PT definitively explains the findings of many management studies that have used it. In light of these concerns, we urge scholars to derive their hypotheses directly from PT’s two central components. By extension, they should identify clearly which aspects of PT they use. Finally, we encourage more valid and
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consistent measurement of PT’s constructs. In short, we hope that our work will lead to additional, yet richer, use of PT in the future. AN INTRODUCTION TO PROSPECT THEORY The Historical Context of PT The literature on decision making under risk emerged from efforts to understand individuals’ preferences in games of chance (Bernstein, 1996; Tversky & Kahneman, 1986). Before proceeding with our discussion of this literature, we clarify several definitions in Table 1. In general, researchers working in this area follow Knight (1921), who used the term risk to describe a situation in which an individual making a choice knows both the potential outcomes of each available option and the probabilities that those outcomes will occur. Scholars often use expected value, which is the probability-weighted average of the outcomes that could result from a choice, as the baseline against which to evaluate individuals’ risk-taking preferences. For example, as Table 1 illustrates, choosing a sure option (i.e., the outcome has a 100% probability of occurrence) over a probabilistic option with greater expected value is evidence of risk aversion, whereas selecting a probabilistic option with a lower expected value is evidence of risk seeking (Kühberger, 1998).2 Table 1 also contains several other definitions pertinent to the following discussion. INSERT TABLE 1 ABOUT HERE In a seminal work, Bernoulli (1738/1954) offered a theory to explain why individuals often do not choose the available option with the highest expected value (Schoemaker, 1982). His explanation distinguished between the objective value of an outcome and its utility, which reflects the pleasure it provides for the decision maker. Rather than expected value, he suggested that people value a gamble based on its expected utility, which is a probability-weighted average of the utilities for each of the outcomes (i) that could result from the choice (see Table 1): 5
(1) where u(i) connotes the utility of outcome i and p(i) connotes the probability that i will occur. In expected utility theory (EUT), individuals derive utility from their final wealth positions (i.e., the outcome of the gamble plus their current wealth). More specifically, the utility individuals derive from additional units of an outcome depends on how much of it they already possess. Bernoulli, for example, argued that a poor person would derive greater added utility from a given sum of money than a rich person would. According to this view, the relation between money and utility is concave, because additional money provides progressively less added utility. Thus, “Bernoulli’s great innovation was to abandon the standard way of evaluating gambles by their expected value” in favor of a focus on individuals’ idiosyncratic preferences (Kahneman, 2003: 703). Von Neumann and Morgenstern (1944; 1947) developed several decision-making axioms to argue that rational individuals, who know and can process all information relevant to a choice, should choose the available alternative with the highest expected utility. By extension, an individual’s choices should be invariant to the way s/he receives information (Edwards, 1954). Over time, EUT became the normative standard in research on decision making under risk. Further, the rational prescriptions EUT offered became the conceptual foundation for much of the descriptive literature on such decision making (Schoemaker, 1982; Slovic, Fischhoff, & Lichtenstein, 1977). Against this backdrop, management researchers have emphasized two alternative theories to explain risk-related behaviors: the BTOF and PT. In the BTOF, March and Simon (1958) and Cyert and March (1963) described how bounded rationality, which reflects human limitations in accessing, processing, and using information (e.g., Simon, 1957), influences decision-making processes in organizations. For example, the BTOF suggests that organizations compare their 6
performance to aspiration levels, which are akin to goals, and this comparison shapes their risktaking preferences. PT, our main focus here, came from a vast literature in behavioral decision theory (BDT) demonstrating that EUT models make “predictions that are not borne out by actual behavior” (Slovic et al., 1977: 11). Like the BTOF, a central theme of BDT is that people’s decisions often reflect bounded rationality. Tversky and Kahneman (1974) brought this work to a wider audience, emphasizing heuristics and biases that individuals use to make decisions. However, prior to the development of PT, BDT lacked a unifying theory of individual risk taking. Kahneman and Tversky (1979) drew on findings from earlier BDT research to develop PT, which challenged EUT’s predictions about individual-level decision making under risk. PT “departs from the tradition that assumes the rationality of [individuals]; it is proposed as a descriptive, not a normative, theory” (Tversky & Kahneman, 1992: 317). As we introduce PT below, we also draw attention to an extension of PT, termed cumulative prospect theory (CPT). Developed by Tversky and Kahneman (1992), CPT makes substantively similar predictions to PT in many respects, but the two theories differ slightly. In turn, we discuss these differences and their implications where appropriate. In our discussion PT below, we also emphasize how its assumptions and arguments depart from those of EUT. Subsequently, we will discuss how PT differs from the BTOF. We summarize the differences between these three theories in Table 2. Our intent is to describe PT’s predictions in detail, while highlighting their specificity and uniqueness. INSERT TABLE 2 ABOUT HERE Prospect Theory PT predicts individuals’ choices in decisions that involve risk. In developing the theory, Kahneman and Tversky (1979) relied on controlled experiments that offered people choices 7
between alternatives, each of which contained possible outcomes and their respective probabilities of occurrence. Based on the results of such experiments, Kahneman and Tversky (1979) concluded that people’s choices could be described by a model that (a) converted the outcomes of gambles into subjective values (i.e., the pleasure the outcomes provide) and (b) weighted these subjective values by decision weights (i.e., the impact of probabilities on choice). In addition, they developed (a) the value function to describe how individuals determine the subjective values of outcomes and (b) the probability weighting function to define the relations between probabilities and decision weights. We refer to these two functions as the two central components of PT. Moreover, PT predicts that individuals choosing between two gambles will select the one with the highest value. As Table 2 suggests, whereas EUT uses Equation 1 to calculate the value of a gamble, PT does so using one of two equations (depending on the type of gamble). Before describing either of these two equations or PT’s two central components in detail, we emphasize that PT does not assume individuals consciously perform the operations depicted in the value function and the probability weighting function, nor does PT assume individuals consciously calculate the value of gambles. Rather, both of PT’s central components and both equations are analytical tools inferred from extensive experimental data (Kahneman, 2003). PT uses Equation 2a to calculate the value of mixed gambles, which include both positive and negative outcomes, and of gambles with probabilities that sum to less than one. According to PT, the value of such gambles is a weighted sum of the subjective values and decision weights for the i possible outcomes. For example, consider a gamble with outcomes x and y with probabilities p and q, respectively. PT represents the value of the gamble as follows: (2a)
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where v() connotes the subjective value of an outcome and π() connotes the decision weight for a probability. PT uses Equation 2b to calculate the value of gambles which contain only positive or negative outcomes (i.e., pure gambles) and have probabilities that sum to one. In particular, PT divides the gamble into a riskless portion and a risky portion. For example, consider the above gamble with outcomes x and y and probabilities p and q, and assume the subjective value of each outcome is positive. If v(y) < (v)x, then the decision maker is guaranteed to receive at least (v)y from the gamble. Thus, (v)y is the riskless portion of the gamble. The difference between v(y) and v(x) is the risky portion, which will occur with probability p. In turn, to calculate the value of the gamble, PT (a) multiplies the risky portion by the decision weight for p and (b) adds this product to the riskless portion. In other words, PT calculates the value of the gamble as follows: (2b) Importantly, as Equations 2a and 2b illustrate, PT is primarily concerned with individuals’ choices between two alternatives. Conversely, CPT “applies to any finite [gamble],” regardless of the number of outcomes (Tversky & Kahneman, 1992: 302), a point to which we will return later. We now discuss the two central components of PT in detail. The PT Value Function. Figure 1 illustrates a hypothetical value function, which depicts the relation between outcomes and their subjective values.3 As Kahneman and Tversky (1979) and Tversky and Kahneman (1992) explained, four properties of the value function merit emphasis. First, individuals judge outcomes relative to reference points. Thus, whereas the utilities of a decision’s outcomes in EUT depend on one’s final wealth position, PT assumes that people code the outcomes of a decision into gains or losses, which are “defined relative to some neutral reference point” (Kahneman & Tversky, 1979: 274). Outcomes above the reference point are 9
gains, and outcomes below it are losses. In this sense, PT assumes that “perception is reference dependent” (Kahneman, 2003: 703). INSERT FIGURE 1 ABOUT HERE Second, as Figure 1 illustrates, the value function is concave above the reference point and convex below it. This pattern produces risk-seeking preferences for gambles involving only losses (i.e., outcomes below the reference point) and risk-averse preferences for gambles involving only gains (i.e., outcomes above it).4 In contrast, most EUT applications assume that individuals are risk averse for both positive and negative outcomes (Schoemaker, 1982), though some applications also allow risk seeking for some decisions (e.g., Markowitz, 1952). Third, the value function incorporates diminishing sensitivity. Specifically, when additional increments of an outcome are further from the reference point, they provide progressively smaller increments of subjective value. For example, if the reference point is $0, the difference between the subjective values of $100 and $200 is greater than the difference between the subjective values of $10,100 and $10,200. Stated differently, the subjective values of outcomes above (below) the reference point increase (decrease) at decreasing rates. In turn, for gambles involving two outcomes far above or far below the reference point, the value function is nearly linear, and individuals are essentially risk neutral. On the surface, PT’s notion of diminishing sensitivity appears similar to EUT’s argument that additional increments of wealth provide progressively less utility as individuals have more wealth. However, PT suggests that the rate at which added subjective value declines depends on the distance of the outcomes from the reference point, whereas EUT suggests that the rate of decline depends on one’s final wealth position. Fourth, PT assumes that many individuals are loss averse, meaning that they find the displeasure of losses to be greater than the pleasure of equivalent magnitude gains. In other 10
words, reflecting earlier findings, PT assumes that people simply do not “like to lose” (Edwards, 1954: 396). For example, many individuals would prefer a gamble with equally likely outcomes of $0 and $200 to a gamble with equally likely outcomes -$100 and $300, even though the expected values (i.e., $100) are identical. Reflecting this loss aversion, Figure 1 is steeper for losses than it is for gains. In contrast, loss aversion plays no role in EUT. The PT Probability Weighting Function. The probability weighting function converts a gamble’s probabilities into decision weights. Because PT uses decision weights to calculate the value of gambles (see Equations 2a and 2b), the probability weighting function is an important aspect of PT. Figure 2 contains hypothetical probability weighting functions depicting relations between probabilities and decision weights.5 Unless stated probabilities are 0 or 1 (in which case decision weights are 0 and 1, respectively), “decision weights do not coincide with stated probabilities” (Kahneman & Tversky, 1979: 277). Specifically, PT suggests that people underweight most probabilities, particularly large ones, but overweight probabilities near zero. Underweighting (overweighting) implies that the influence of the probability on the value of the gamble is less than (greater than) the influence of the actual probability would be (see Equations 1, 2a, and 2b). INSERT FIGURE 2 ABOUT HERE Consistent with arguments proposed by Allais (1953), PT also suggests that small differences in probabilities near the endpoints of the probability scale (i.e., 0% and 100%) elicit large differences in decisions weights. In contrast, people are rather insensitive to differences in probabilities in the middle of the scale (Fennema & Wakker, 1997). Therefore, according to PT, relations between decisions weights and stated probabilities, though positive, are nonlinear. This pattern of underweighting and overweighting gives a reverse S-shape to the probability 11
weighting function. Thus, in PT, the influence of a small change in probability on the value of a gamble depends on where along the probability scale that changes occurs. Conversely, in EUT, changes in probability have linear effects on the value of a gamble (i.e., the effect is the same regardless of where along the probability scale that change occurs). Finally, we note that a key difference between CPT and PT lies in the probability weighting function. Unlike PT, CPT “allows different decision weights for gains and losses” (Tversky & Kahneman, 1992: 302). Specifically, as Figure 2 illustrates, the probability weighting function for gains exhibits greater curvature than does the probability weighting function for losses. In this sense, CPT suggests that people have “different attitudes toward probability for gains than for losses” (Fennema & Wakker, 1997: 55). In contrast, PT implicitly assumes that probabilities influence decisions similarly for gains and losses. Framing The reference point plays a critical role in PT. In particular, the reference point determines how individuals frame (i.e., interpret) the outcomes of a decision. For example, individuals using one reference point may frame a given outcome as a gain, while individuals using a different reference point may frame the same outcome as a loss (Bazerman, 1984). Thus, scholars can test PT by manipulating framing. Frequently, framing manipulations involve presenting identical information using different wording (Kühberger, 1998). In particular, manipulations that alter the reference points people use are often appropriate for testing PT (Levin, Schneider, & Gaeth, 1998). To illustrate, many experimenters have tested PT using the Asian disease problem. Study participants are told that a given disease is expected to kill 600 people. Some individuals are given a decision option that would save 200 people, encouraging them to use 600 deaths as the reference point. Thus, they frame this option as a gain. Conversely, other individuals are given a 12
decision option that would kill 400 people, encouraging them to use 0 deaths as the reference point. Thus, they frame this option as a loss. Although saving 200 people is equivalent to killing 400 in this example, study participants tend to be risk averse when they view choices involving the former decision option (i.e., the gain frame), but tend to be risk seeking when they view choices involving the latter (i.e., the loss frame) (Kühberger, Schulte-Mecklenbeck, & Perner, 1998; Tversky & Kahneman, 1981).6 Distinguishing PT from the BTOF We now turn to the other major theory used in management studies of risk, the BTOF. As Table 2 illustrates, the BTOF uses organizational performance relative to aspirations to predict risk taking. For example, Cyert and March (1992: 228) argued that when organizations are performing “close to a target [i.e., aspiration level], they appear to be risk-seeking below the target, [and] risk-averse above it.” Likewise, PT suggests that individuals are often risk seeking and risk averse for gambles involving outcomes below and above the reference point, respectively, especially when those outcomes are near the reference point. Thus, in some respects, the BTOF and PT are similar. However, they also differ in important ways. A basic difference is that PT is a theory of individual behavior, while the BTOF is a theory of organizational behavior. In turn, the two theories emphasize different subject matter. Specifically, the BTOF offers a detailed account of decision-making processes in organizations.7 In contrast, although PT predicts behavior, it is relatively silent on the cognitive processes underlying such behavior. As such, the BTOF specifies many constructs (e.g., organizational slack and routines) that do not have counterparts in PT. Similarly, although the reference point in PT and the aspiration level in the BTOF each divide outcomes into those that are desirable versus undesirable, the BTOF has a theory that describes the source of aspiration levels, whereas PT does not have a theory of reference points. 13
Specifically, as Table 2 illustrates, the BTOF suggests that an organization’s aspiration levels reflect its stakeholders’ preferences, its past aspiration levels, its past performance, and the performance of comparable organizations (Cyert & March, 1963). This level of specificity has enabled researchers to offer a number of proxies for aspiration levels, and, in some cases, to measure aspiration levels directly. Although PT research often uses the status quo as the reference point (Kahneman, 2003), like other individual-level theories of decision making under risk, PT generally lacks “a satisfactory theory of how reference [points] are established and, for that matter, good empirical ways of estimating them directly” (Luce, 1996: 192). Instead of measuring reference points, PT experimenters often impose reference points using framing manipulations similar to the Asian disease problem described above (Kahneman, 1992). In other respects, however, PT offers greater specificity than the BTOF does. For example, PT’s predictions rest on its two central components, which together define the values of gambles. By extension, PT explains decisions that involve specific outcomes and probabilities. Conversely, the BTOF describes how managers search for and select solutions that will produce organizational performance above aspirations. Thus, the BTOF does not require that managers know either the outcomes of the decisions or the probabilities of those outcomes. In this respect, the BTOF may apply to more ambiguous and ill-defined choices than those PT describes. Likewise, in part due to the decisions the BTOF describes, its predictions about risk taking are also more complex than are those researchers have derived from PT. To illustrate, the BTOF suggests that when firm performance exceeds aspirations by a large amount, firms’ risktaking preferences may switch from risk aversion to risk seeking (Cyert & March, 1963; March & Shapira, 1992). Conversely, firms with exceptionally poor performance may change aspiration levels and aspire simply to survive (March & Shapira, 1987). The BTOF’s predictions regarding whether low-performance firms become risk averse or risk seeking depend on whether managers 14
perceive the firm’s survival is threatened. In contrast, PT’s value function clearly predicts risk neutrality when outcomes are far from the reference point (Kahneman & Lovallo, 1993). As this discussion and Table 2 suggest, PT’s predictions are distinct from those of both EUT and the BTOF. However, as we demonstrate later, PT’s predictions are more complex than many researchers have assumed (see Bromiley, 2010). In the following sections, we identify and review management research drawing on PT. This review will show that, although PT contains two central components, management research generally makes predictions exclusively based on the value function, while ignoring the probability weighting function. IDENTIFYING PT RESEARCH IN MANAGEMENT According to Web of Science, scholars have cited the Kahneman and Tversky (1979) article that introduced PT over 6,100 times. In addition, scholars have cited Tversky and Kahneman’s (1992) article that introduced CPT over 1,300 times. More than 550 articles citing either of these two studies (or both) have appeared in periodicals consistently recognized as top academic journals for management research (see Footnote 1 for a list of the journals we used). Given the considerable size of this literature and space limitations, we employed four steps to narrow our scope and attempt to capture the most appropriate research. We first searched for articles using the search terms prospect theory and loss aversion. We included the latter term because loss aversion is the most frequently used aspect of PT in the management literature. This search returned 189 articles. Second, we examined these articles for appropriateness and fit. To focus more precisely on PT-based research, we eliminated 15 articles that did not cite Kahneman and Tversky (1979) or Tversky and Kahneman (1992). Our review revealed that several influential studies in management that drew on PT did not appear in the sample (e.g., Amit & Schoemaker, 1993; Neale & Bazerman, 1985). Therefore, in our third step, we added the top 5% of articles (based on citation counts per year) published in leading 15
management journals that cited either of the two seminal PT articles. This step helped ensure that we captured the most impactful studies in management that drew on PT. This step added 17 articles to our sample,8 bringing the total to 191 articles. Of this 191, we focused on 81 articles that used PT to describe decisions frequently confronted by managers. We term these 81 articles management theory applications of PT. The other 110 articles were decision analysis applications of PT. Due to the advanced state of PT research in the decision analysis literature, we draw insights from these articles to enrich our analysis and future research suggestions. MANAGEMENT THEORY APPLICATIONS OF PT AT THE INDIVIDUAL LEVEL This section reviews management theory applications of PT at the individual level. Reflecting the focus of prior work, we consider studies using PT to examine executive compensation, negotiations, affect and motivation, and HRM. Executive Compensation We identified seven executive compensation studies at the individual level that drew on PT. Traditionally, much of the literature on executive compensation has emphasized agency theory (Devers, Cannella, Reilly, & Yoder, 2007). Agency theory generally assumes risk-averse executives, risk-neutral shareholders, and a positive association between organizational risk and return. With these assumptions, agency theorists suggest that shareholders should tie a portion of executives’ compensation to organizational returns, thereby discouraging executive risk aversion (Jensen & Meckling, 1976). In contrast to agency theory, PT states that individuals sometimes evidence risk-seeking, rather than risk-averse, behavior. Therefore, Eisenhardt (1989) suggested that PT’s insights about risk taking could inform agency theory research on executive compensation. Thus far, executive compensation research drawing on PT has emphasized two concepts from the value function: the 16
reference point and loss aversion. These studies illustrate why such compensation often fails to produce executive behavior consistent with shareholder interests (e.g., Walsh & Seward, 1990). For example, Wiseman and Gomez-Mejia (1998) integrated concepts from agency theory and PT to develop the behavioral agency model (BAM), which assumes that executives are loss averse and that their compensation plans create reference points for them. For example, executives may perceive that the exercise price of their stock options is an important reference point. When the price of the stock underlying their stock options exceeds the exercise price, executives adopt gain frames and, consistent with PT, become risk averse. Alternately, high incentive pay targets can lead executives to adopt loss frames and, therefore, become risk seeking. Thus, reflecting arguments from PT, the BAM suggests that executive compensation plans can either discourage or encourage risk aversion, depending on how executives view the relation between firm outcomes (e.g., the stock price) and the reference points (e.g., the exercise price) such plans make salient. Extending this insight, Devers, Wiseman, and Holmes (2007) used a policy capturing approach to examine the subjective values managers place on stock options. In particular, they used the prices managers assign to stock options as a proxy for subjective values. They argued that rising stock prices elicit gain frames and declining prices elicit loss frames. Further, they assumed that stock price volatility reflects the risk of the underlying stock (e.g., Sharpe, 1964). Consistent with their expectations, they found evidence that managers assigned a premium for stock price volatility when stock prices were declining but discounted for volatility when stock prices were increasing.9 They interpreted this result as evidence that, consistent with the value function, managers are risk seeking in loss frames and are risk averse in gain frames. This results contrasts with an assumption of the Black-Scholes (1973) model, which assumes that the value of stock options relates positively to volatility, regardless of stock price trends. Therefore, if 17
compensation committees rely exclusively on the Black-Scholes logic to design stock option packages, changes in the volatility and trend of the underlying stock price could result in managers perceiving that they are overcompensated or undercompensated. Scholars have also used PT to examine when and how executives decouple their wealth from firm performance, thereby circumventing some of the intended effects of incentive compensation (Bebchuck & Fried, 2006). For example, Matta and McGuire (2008) argued that CEOs are loss averse, and strong firm performance decreases their concerns about the potential losses stemming from equity-based pay. Consistent with this argument, these scholars found that CEOs were more likely to reduce equity holdings and reduced them by a larger magnitude when firm performance was poor. Likewise, also arguing that executives are loss averse, Dunford, Oler, and Boudreau (2007) found that executives increasingly leave their firms voluntarily when the price of the firm’s stock is far below the exercise price. Thus, although many firms use stock options as a retention mechanism (Fulmer, 2009), the value function provides insight into conditions when this form of compensation might increase executive turnover. In short, despite drawing only on PT concepts related to the value function, these studies have modified extant management theory by showing that executive compensation can motivate either risk-averse or risk-seeking behaviors, depending on whether recipients frame the potential consequences of those behaviors as gains or losses. The studies also identify conditions that encourage executives to alter their exposure to the downside consequences of equity-based pay. However, this area of study also illustrates two shortcomings of many management theory applications of PT. First, these findings rest heavily on whether scholars have correctly identified the reference point. Because PT lacks a theory of reference points, scholars have had to make somewhat arbitrary assumptions in this regard. For example, the observation that some 18
executives use the value of their stock options as loan collateral (Devers, Wiseman, & Holmes, 2007) suggests that they may use reference points other than the exercise price. Indeed, consistent with the notion that managers often focus on the best or worst possible scenario (March & Shapira, 1987), some executives may use a stock’s highest or lowest recent prices as a reference point. Second, none of these studies considers other important PT concepts, such as diminishing sensitivity and decision weights. We return to both of these issues in a broader discussion below. Negotiations We found six studies that have used PT to study negotiations. These studies often experimentally manipulate the frames negotiators use to make decisions, and they observe the influence of such frames on bargaining behavior and outcomes. For example, researchers can create gain frames by telling study participants that they are negotiating over profits they will receive. Conversely, researchers can create loss frames by telling the participants that they are bargaining over expenses they must pay (De Dreu, Carnevale, Emans, & Van de Vliert, 1994). In general, these studies rely on insights from the value function, especially loss aversion and reference points. However, some of the studies also examine the effects of diminishing sensitivity in the value function. Researchers have identified three consequences of loss aversion for negotiations. First, negotiators in loss frames may become risk seeking and behave aggressively, while negotiators in gain frames may become risk averse and behave cooperatively. For example, Neale and Bazerman (1985) told study participants either to minimize their losses in the negotiation or to maximize their gains, presumably placing them in loss frames and gain frames, respectively. These scholars found that the loss-frame negotiators made fewer concessions and acted more competitively than gain-frame negotiators did. 19
The second consequence of loss aversion can explain why agreements are sometimes difficult to reach in negotiations. Negotiators tend to view their own concessions as losses and those of their counterparts as gains. In turn, reflecting loss aversion, negotiators value their concessions (seen as losses) roughly twice as much as they value equivalent magnitude concessions by their counterparts (seen as gains), which would make them unwilling to make concessions (Kahneman & Tversky, 1995; Kahneman, 1992).10 If each negotiator approaches the negotiation this way, “the resulting four-to-one gap may be difficult to bridge” and the negotiators may have difficulty reaching an agreement (Tversky & Kahneman, 1986: S262; see also Bazerman, 1984). For example, negotiations involving two individuals attempting to minimize their losses (i.e., they are in loss frames) often end in impasses wherein no final settlement is reached (Bottom & Studt, 1993). Together, these two consequences of loss aversion provide a foundation for several negotiations studies. For example, Kristensen and Garling (1997) examined negotiations over the price of a good. They created gain (loss) frames for buyers by setting the seller’s initial offer price for the good below (above) the buyers’ reservation prices. They found that buyers in gain frames made fewer counteroffers, were more likely to reach an agreement, and purchased the good at a higher price than buyers in loss frames did. Thus, this study is consistent with earlier work suggesting that gain frames promote cooperation. It also suggests that gain frames may reduce the likelihood of impasse in negotiations. However, Bottom (1998) examined a situation in which loss frames can have similar effects. In particular, he created a scenario wherein individuals reaching a settlement in a negotiation would have to play a gamble to determine how much money they would receive or pay. As such, reaching a settlement created risk for negotiators. If gain-frame individuals are risk averse, they should be reluctant to reach a settlement in this scenario. Conversely, if loss-frame 20
individuals are risk seeking, they should be eager to reach a settlement. Consistent with his expectations, Bottom (1998) found that loss-frame negotiators were more cooperative and were more likely to reach a settlement than gain-frame negotiators were. This study underscores why scholars should understand what constitutes risk in a given decision. Examining another interesting phenomenon, De Dreu et al. (1994) considered how negotiators behave when they know their counterparts are in gain or loss frames. These scholars found that negotiators perceived that their counterparts were more cooperative when they knew the counterparts were in loss frames. Perhaps as a result, they conceded less to and demanded more from opponents in loss frames. Thus, this study suggests that people's negotiating behavior may depend on what they know about their counterpart’s frame. A third way scholars have used loss aversion in negotiations research is to explain the types of concessions a negotiator will value most. Specifically, a counterpart’s concessions may mean more to a negotiator when they reduce the negotiator’s losses, relative to when they increase his or her gains (Northcraft, Brodt, & Neale, 1995). Scholars have examined this argument by drawing on another aspect of the value function, diminishing sensitivity. For example, Northcraft et al. (1995) proposed that, due to the curvature of the value function, a negotiator will value a counterpart’s concessions more when they involve outcomes near the negotiator’s reference point. This diminishing sensitivity can produce several interesting paradoxes in negotiations. To illustrate, as Northcraft et al. (1995) explained, assume an individual is bargaining for concessions in two different categories of outcomes, and one of these categories is more important to him or her than the other one is. If that person receives a concession near the reference point in the less important category, s/he may value that concession more than a concession further from the reference point in the more important
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category. This study demonstrates the importance of understanding the reference points people use to determine the subjective values of outcomes. In another interesting demonstration of diminishing sensitivity, Northcraft, Preston, Neale, Kim, and Thomas-Hunt (1998) created schedules specifying the outcomes participants were allowed to concede in a negotiation. These schedules resembled value functions of different shapes. Specifically, some of the schedules reflected diminishing sensitivity in that, as individuals made more concessions, subsequent ones were less important. The schedules for other individuals specified either that all concessions were equal (i.e., linear sensitivity) or that subsequent successions were more important than were earlier concessions (i.e., increasing sensitivity). Consistent with their expectations, these scholars found that diminishing sensitivity in the value function enables the negotiators to reach an agreement beneficial to both of them. In sum, studies on negotiations provide several insights into the effects of framing on decisions. Further, the studies underscore the need to understand both how people view risk in a given decision and which reference point they use to evaluate the relevant outcomes. For example, owing to diminishing sensitivity, individuals may be ambivalent about changes in gains and losses far from the reference point. Likewise, a person may frame an outcome as a loss, even if that outcome only reduces his or her gains. Affect and Motivation We identified eight studies drawing on PT to study affect (i.e., positive or negative states, feelings, moods, or emotions that change over time; e.g., George, 1991) and motivation (i.e., internal forces that encourage individuals to engage and maintain certain behaviors; e.g., Steers & Porter, 1991) in diverse settings. These studies drew their predictions primarily from the value function. In particular, they generally drew on the concept of loss aversion and, to lesser extents, reference points and diminishing sensitivity. 22
Three studies drew on loss aversion to explain employee motivation. Steel and Konig (2006), for example, argued that individuals separate attributes of a task into gains (e.g., its rewards) and losses (e.g., its costs). Consistent with loss aversion, they hypothesized that individuals view tasks more favorably and are more motivated to engage in them when they offer immediate gains and delayed losses. Similarly, also drawing on loss aversion, Grant (2007) theorized that employees are attuned to the impact their work has on other people, and they perceive that their work has greater impact when that work minimizes organizational losses (e.g., prevents crises), relative to when the work creates gains for the organization (e.g., improves its image). Furthermore, Heath, Knez, and Camerer (1993) argued that employees become accustomed to the financial and other benefits the organization provides. In turn, reflecting loss aversion, employees may react negatively and their motivation may decline when their organizations eliminate or reduce such benefits. In addition, van Buiten and Keren (2009) examined the language people use to motivate others. Specifically, they examined the tactics people use to convince others to select high-risk or low-risk options. They found that people who promote a low-risk option try to encourage other people to view that option through a gain frame rather than a loss frame. Because gain frames tend to evoke risk-averse preferences, using gain frames to promote low-risk policies may be an effective way to motivate employees. In contrast, however, people promoting high-risk options did not believe that loss frames were more convincing than gain frames were. In turn, they may have difficulty convincing people to select high-risk policies. As we describe below, this study illustrates the importance of understanding how individuals interpret decisions. Regarding affect, Coughlan and Connolly (2001) examined how reference points and loss aversion influenced individuals’ satisfaction with their performance on a task. Specifically, individuals’ experienced dissatisfaction or satisfaction when their performance was below or 23
above, respectively, the performance level they expected to achieve. Thus, a person’s performance expectations may constitute an important reference point for him or her. Moreover, consistent with loss aversion, individuals may experience greater dissatisfaction when performance is below their expectations than the satisfaction they experience when performance exceeds their expectations. Accordingly, this study suggests that individuals can evaluate the same outcome differently depending on the reference point they use. Other scholars have drawn on diminishing sensitivity in the value function to examine individuals’ affective responses to their level of voice in decisions (i.e., their perceptions that they have been able to convey their preferences, concerns, etc.). For example, Hunton, Hall, and Price (1998) found that voice positively influences individuals’ views about procedural fairness and decision control, as well as the individuals’ levels of satisfaction with the decision. Consistent with diminishing sensitivity, however, the positive influence of additional voice declined as it reached higher levels. Extending this work, Price, Hall, van den Bos, Hunton, Lovett, and Tippett (2001) studied relations between voice and perceptions of fairness in participants from four countries (Great Britain, Mexico, The Netherlands, and the U.S.). Price et al. (2001) argued that individuals may compare their level of voice to a reference point reflecting national norms for voice in their home countries. For values of voice above and near the reference point, the relation between voice and fairness had a concave shape. This result is consistent with the shape of the value function. For values of voice far above the reference point, however, the relation between voice and fairness had a convex shape, which is inconsistent with the value function. Price et al. (2001) also showed that these results generally held for participants from all four countries. Despite the insights these studies have provided, they generally use concepts from PT in a manner somewhat detached from the original arguments of the theory. For example, as a theory 24
of decision making under risk, PT does not speak directly to affect and motivation. Thus, although relations between the variables in some of the studies coincided with the shape of the PT value function, the results of such studies do not necessarily support PT. Indeed, the PT value function only describes relations between outcomes and their subjective values to decision makers. In addition, several of these studies did not specify a reference point. Absent a clear reference point, the gains and losses of interest may be somewhat ill defined. HRM Six studies drew on PT to examine HR-related questions. This research generally examined selection and performance evaluation, but one study examined employees’ willingness to accept HR policies. Again relying primarily on the value function, the studies in this area considered loss aversion, reference points, and diminishing sensitivity. For example, Kristof (1996) argued that selection resembles a gamble with positive and negative outcomes. To illustrate, Highhouse and Johnson (1996) argued that selectors used job incumbents’ performance as a reference point and evaluated the anticipated performance of prospective candidates as gains or losses relative to this point. Consistent with loss aversion, they found that people prefer a candidate who represents no performance change from the incumbent over candidates whose possible performance represents a loss. In addition, Wong and Kwong (2005a; 2005b) found diminishing sensitivity in selection and performance evaluation. Specifically, decision makers perceived that differences between job candidates were larger when low magnitude numbers quantified such differences (e.g., the percentage of errors is 5% vs. 6%) than when high magnitude numbers did (e.g., the percentage of non-errors is 95% vs. 94%). In other words, consistent with a concave value function, additional units of high (low) magnitude outcomes were less (more) important to decision
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makers. Thus, individuals may be able to manipulate similar decisions in organizations by varying the presentation of information. Finally, Bamberger and Fiegenbaum (1996) drew on concepts in PT to examine how managers evaluate HR strategies. They argued that managers adopt loss or gain frames depending on how HR-related outcomes compare to reference points, and this framing increases the managers’ openness toward risk-seeking and risk-averse HR policies, respectively. They further argued that, because individuals in the same organization may use different reference points, some managers will be in gain frames, while others will be in loss frames. The resulting differences in risk-taking preferences may create conflict that disrupts HR strategy implementation. In short, some of these studies demonstrate that the value function may shape personnel decisions in organizations. Again, however, this research largely ignores the probability weighting function. More broadly, the body of management research applying PT at the individual-level of analysis identifies several reasons that people in the same organization may interpret identical decisions differently. With this thought in mind, we now turn to reviewing research that has used PT at higher levels of aggregation. MANAGEMENT THEORY APPLICATIONS OF PT AT HIGHER LEVELS OF AGGREGATION Although it is an individual-level theory, scholars often apply PT to explain higher-level phenomena. Indeed, in contrast to the 28 individual-level studies we identified, we found 53 at higher-levels of aggregation. Research utilizing PT at higher levels primarily falls into one of two broad categories. The first uses firm performance distributions to explain relations between firm risk and return. The second examines the antecedents and consequences of firms’ specific risk-taking actions (e.g., acquisitions). 26
Organizational Risk and Return An early application of PT in the management literature examined relations between organizational risk and return. We identified 13 management studies in this area. Much of this work draws on Bowman (1980), who measured returns as mean return on equity (ROE) over time and risk as the variance in such returns. He found negative associations between organizational risk and return and termed this relation a paradox, because it runs counter to the positive risk-return relation assumed in many finance and economics models (Armour & Teece, 1978; Sharpe, 1964). Drawing on PT to explain negative risk-return relations, Bowman (1982) argued that firms use median returns in the industry as a reference point. Noting that PT implies risk seeking when outcomes are below the reference point, he argued that firms performing below the industry median take more risks. Subsequent studies extended Bowman’s work by considering additional industries and countries and by examining more intricate relations between organizational risk and return. Studying U.S. firms in nearly 50 industries, for example, Fiegenbaum and Thomas (1988) found a negative risk-return association for firms performing below the industry median but found a positive association for firms performing above the industry median. Jegers (1991) also found this general pattern of results in a sample of Belgian firms. The authors of both of these studies concluded that their results supported PT. More recent work offers three important criticisms challenging this conclusion. First, many scholars have raised concerns about the methodology in these studies.11 For example, managers may associate risk more with losses than with the variability in outcomes, raising questions about the use of variance measures to capture risk (March & Shapira, 1987). Building on this observation, Miller and Leiblein (1996) introduced a measure of downside risk, which they based on the lower partial moments of firms’ return distributions (see also Miller & Reuer, 27
1996). In contrast to studies supporting Bowman’s paradox, Miller and Leiblein (1996: 91) found that “downside risk results in improved subsequent performance. Performance shows a negative relation with subsequent downside risk.” Thus, the findings of organizational risk-return studies may hinge on the risk measure used. As we discuss below, this possibility underscores the need to select risk-taking measures carefully when testing PT. A second criticism concerns the reference point. The BTOF research on aspiration levels we discussed earlier suggests that managers may compare firm performance to criteria other than industry performance metrics. Specifically, challenging the widespread use of median performance in the industry as a reference point, Bromiley (1991a) argued that high-performing firms clearly do not aspire to lower performance. Instead, such firms may use their own performance history as a reference point. Likewise, as noted, firms in danger of bankruptcy may focus on survival (March & Shapira, 1987). Of course, if firms are not using the reference point researchers assume, results that appear to support PT may not do so (Nickel & Rodriguez, 2002). The third criticism concerns whether PT would actually predict a negative relation between organizational risk and return. As we noted above, the PT value function implies that risk aversion and risk seeking are strongest near the reference point, but they decline toward risk neutrality when outcomes are far above and below the reference point, respectively. Thus, as Bromiley (2010) argued, the PT value function would imply either a positive association between organizational risk and return or no association at all. In sum, Bowman’s (1980) early insights led to a number of studies investigating organizational risk-return relations. However, this research stream is subject to criticisms related to the measurement of risk and reference points. Further, in our view, the PT value function is inconsistent with a negative risk-return association, despite the claims of many studies. To these criticisms, we add that this literature ignores the probability weighting function. 28
Additionally, we note that the risk evident in return distributions depends partly on the risk-taking decisions of managers (Palmer & Wiseman, 1999). Reflecting this argument, more recently, scholars have moved away from measures of firm risk based on the return distribution in favor of specific measures of firm risk-taking behaviors. Firm Risk-Taking Behaviors Using similar arguments to those found in the risk-return studies above, several studies have drawn on PT to examine firm risk-taking behaviors. Nearly half of all the articles (40 out of 81) we reviewed fell within this area of research. This work generally assumes that firm-level actions are manifestations of the risk-taking preferences PT predicts for individuals. Although the dependent variables are typically at the firm level, some of the studies consider predictors at the individual level, while others consider predictors at higher levels. As with the other work we have reviewed, these studies draw their predictions only from the value function, and few of them consider diminishing sensitivity in the value function. Individual-level Antecedents of Firm Risk-taking Behaviors. Amit and Schoemaker (1993) argued that the frames managers adopt influence their decisions about how to use organizational resources. Some studies examine this possibility by arguing that managers perceive external threats increase the possibility of potential losses (Jackson & Dutton, 1988). In turn, they use loss aversion to explain how managers respond to a variety of perceived external threats to the firm. For example, Chattopadhyay, Glick, and Huber (2001) found that CEOs who perceived threats to their organizations’ resources responded with externally-directed actions (e.g., adjustments to targeted markets), which the authors presumed were riskier than were internally directed actions (e.g., adjustments to administrative procedures). Similarly, George, Chattopadhyay, Sitkin, & Barden (2006) theorized that managers become risk seeking when they perceive that threats to the legitimacy of the organization will reduce its access to resources. In 29
turn, they might respond through risky organizational responses that are inconsistent with industry norms. Conversely, Sharma (2000) found that managers may initiate strategies that conform to institutional pressures when threats involve issues related to the organization’s impact on the natural environment. As earlier discussed, scholars have also argued that incentive compensation creates gain and loss frames for executives.12 Like the research we reviewed above, this work frequently assumes that CEOs use the exercise price of stock options as a reference point. For example, Zhang, Bartol, Smith, Pfarrer, and Khanin (2008) argued that CEOs frame underwater stock options as possible losses, particularly when firm performance is poor. In turn, they argued that this loss frame facilitates risky, unethical behavior (Lehman & Ramanujam, 2009). In support, they found evidence that firms were more likely to exhibit earnings manipulations when CEOs’ stock options were underwater, and this effect was stronger when the firms were performing poorly. Other studies have drawn on PT to explain specific organizational actions involving large resource commitments. For example, also assuming that CEOs use the exercise price of stock options as a reference point, Larraza-Kintana, Wiseman, Gomez-Mejia, and Welbourne (2007) found that as the values of the CEOs’ in-the-money stock options increased, firms took less risk, which they measured using several organizational actions (e.g., new market entries and capital investments). This result supports the notion that CEOs with positively valued stock options adopt gain frames, which promote risk-averse behaviors at the firm level. Likewise, Matta and Beamish (2008) argued that because CEOs are loss averse, they adopt gain frames and become risk averse when they have lucrative equity holdings and in-the-money stock options, especially when they near the end of their tenures. Using international acquisitions to measure risk taking,
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they found that as the value of aging CEOs’ equity holdings and in-the-money stock options increased, firms took less risk. Departing from most prior work drawing on PT at higher levels of aggregation, Devers, McNamara, Wiseman, and Arrfelt (2008) provided evidence of a curved value function for inthe-money stock options. Using a variant of Miller and Bromiley’s (1990) strategic risk measure (i.e., R&D intensity, capital investment intensity, and debt-to-equity ratio), Devers and her colleagues (2008) provided evidence that the value of restricted stock held by CEOs was negatively associated with firm risk taking. However, Devers et al. (2008) also argued that, consistent with PT, the subjective values CEOs attach to stock options rise at a decreasing rate. In other words, when the stock price is extremely high, further increases appear to be less important to CEOs. In support, Devers et al. (2008) found that increases in the value of stock options exhibited progressively weaker marginal impacts on the level of firm risk CEOs initiate. Finally, a creative study by Pennings and Smidts (2003) estimated individuals’ value functions and examined their implications in a field setting. Specifically, they constructed farmers’ value functions using the individuals’ preferences in a series of hypothetical choices, and they found that the shape of those value functions differed across farmers. Farmers with value functions exhibiting the S-shape PT assumes (see Figure 1) tended to use production systems where pricing fluctuations frequently produce significant gains and losses (e.g., purchasing animals). However, farmers whose value functions were not S-shaped were more likely to use production systems where gains and losses due to pricing fluctuations were less of a concern (e.g., raising animals). They offered two alternative explanations for this result. First, people who frequently make decisions involving gains and losses may develop S-shaped value functions. Second, people with S-shaped value functions may prefer production systems in which decisions involving gains and losses are commonplace. 31
Higher-level Antecedents of Firm Risk-taking Behaviors. Scholars have also drawn on the value function, primarily loss aversion, to study higher-level antecedents of firm risk-taking behaviors. Like the organizational risk and return studies, these studies argue that conditions at the firm- or industry-level produce gain and loss frames for organizations as a whole, thereby facilitating firm-level risk aversion and risk seeking, respectively. For example, declining industry demand may create loss frames that facilitate risk seeking. Consistent with this argument, Wood (2009) argued that declining industry demand encourages acquisitions. Scholars have made similar arguments in several studies examining firms’ investments in innovation, which are presumably risky. For example, Abrahamson and Rosenkopf (1993: 492) argued that many organizations copy other firms’ innovations because, consistent with loss aversion, the “perceived threat of a competitive disadvantage far outweighs the perceived value of an equally large competitive advantage.” In other words, the possibility of incurring losses by not copying innovations often weighs heavily in organizations. Similarly, Mone, McKinley, and Barker (1998) theorized that firms experiencing performance declines encounter possible losses, which encourage risk-seeking investments in innovation. Strong performance, however, can place organizations in gain frames, facilitating investments in low-risk, existing products and services (Smith & Tushman, 2005). In addition, two studies drew on loss aversion to explain interactions between organizations and external stakeholders. D’Aveni (1989) argued that creditors are loss averse, which makes them hesitant to realize losses by forcing borrowers into bankruptcy. He argued that this hesitancy is evidence of risk-seeking behavior because it reduces the funds available to the creditor (e.g., through liquidation) if the borrower ultimately fails. Jawahar and McLaughlin (2001) suggested that the risk-averse approach to managing stakeholders is to address all of the needs of all stakeholders. Arguing that near-failing firms adopt loss frames and become risk 32
seeking, these scholars theorized that such firms neglect the interests of some stakeholders and attend only to the interests of stakeholders most critical to immediate survival. Like the organizational risk-return studies above, some scholars have measured firm performance relative to a reference point. These studies argue that performance below the reference point creates loss frames and risk-seeking behavior, whereas performance above the reference point creates gain frames and risk-averse behavior (Palmer & Wiseman, 1999). Citing this argument, Markovitch, Steckel, and Yeung (2005) assumed that firms with stock returns above (loss) the industry average are in gain (loss) frames. In turn, they found that firms with above-average (below-average) stock returns subsequently invested less (more) in high-risk actions than in low-risk actions. In addition, arguing that divesting a previously acquired business reflects risk aversion, Shimizu (2007) found that firms are likely to divest such businesses when their performance is negative, but this effect declines as their performance decreases further. Finally, some research has drawn on PT to examine how firm risk-taking influences firm performance. For example, using average performance in the industry as the reference point, Wiseman and Catanach (1997) found evidence that below-average firm performance creates loss frames and facilitates risk-seeking behavior. Further, they found that the relations between such behavior and subsequent performance were generally negative. Conversely, Morrow, Sirmon, Hitt, and Holcomb (2007) found that investors responded positively when firms with declining performance engaged in certain risk-taking behaviors (i.e., valuable and inimitable new product introductions and mergers and acquisitions). They interpreted their results as evidence that investors value some forms of risk seeking when firms are experiencing losses. In short, drawing primarily on the value function, research on the antecedents of firm risk-taking behaviors generally suggests that gain frames give rise to risk-averse behavior and 33
loss frames give rise to risk-seeking behavior at the firm level. Some studies have also examined the firm performance consequences of the risk-taking behavior, but these results may depend on both the type of risk examined and on the performance measure used. These studies also illustrate several inconsistencies in research that draws on PT to explain higher-level phenomena. In particular, scholars have used different proxies for the reference point. The BTOF and other work (Fiegenbaum, Hart, & Schendel, 1996; Short & Palmer, 2003) argue that firms use many reference points and they may change over time. However, our lack of understanding of which reference points firms are using limits our ability to compare results across studies and to conclude that the research on firms’ risk-taking behaviors definitively supports PT. For example, Chen (2008) argued that firms can use either past performance or industry median performance as reference points. Importantly, he also found that performance above past performance levels increased R&D intensity, but performance above the industry median decreased R&D intensity. Thus, we could conclude that this study either supports PT or contradicts PT, depending on the reference point we assume. Further, several of the studies we reviewed did not specify a reference point at all. This practice resembles modeling risk taking as a function of wealth, which may not fit PT’s assumptions. In addition, the definition of risk can be especially problematic when scholars use PT at higher levels. For example, whereas Shimizu (2007) used divestitures to measure risk-reducing actions, Markovitch et al. (2005) used divestitures to measure high-risk actions. Further, although Latham and Braun (2009) used R&D as an indicator of risk seeking, the results of factor analyses suggest that R&D intensity loads negatively when other measures of risk seeking, such as capital investments (Miller & Bromiley, 1990) or diversification (Palmer & Wiseman, 1999), load positively. Likewise, whereas Mone et al. (1998) argued that performance declines would facilitate risk-seeking investment in innovation, Markovitch et al. (2005) argued that 34
several forms of such investment (e.g., technology alliances) are low-risk actions. In turn, these two studies reach conclusions that are somewhat inconsistent with one another. Like inconsistencies in the reference point, the plethora of risk taking metrics makes it difficult to discern whether the firm risk-taking studies, as a whole, definitively support PT. In the section that follows below, we offer suggestions to help scholars operationalize reference points and risk taking measures more consistently. AN ANALYSIS OF MANAGEMENT THEORY APPLICATIONS OF PT AND SUGGESTIONS FOR FUTURE RESEARCH The previous two sections illustrated that research drawing on PT has made contributions in diverse areas of management scholarship. We now turn to analyzing the use of PT in the management literature and making suggestions for future research. Before proceeding, we would like to emphasize that although few management theory applications draw on CPT, it may be more appropriate for studying many topics than PT is. Strictly speaking, as noted above, PT only applies to two outcome gambles, whereas CPT can accommodate more than two outcomes. Thus, to the extent that many management decisions involve more than two possible outcomes, we encourage greater use of CPT. With this thought in mind, we organize the remainder of this section by discussing concerns related to (a) using parts of PT while ignoring others, (b) interpreting the value function, (c) interpreting the probability weighting function, and (d) using PT in organizational settings. Table 3 summarizes many of the issues we raise in this section. INSERT TABLE 3 ABOUT HERE Partial use of PT As our review demonstrated, management research drawing on PT typically considers only the value function. More specifically, nearly all of the 81 studies drew on loss aversion and, 35
to a lesser extent, reference points. Few studies drew on diminishing sensitivity. However, studies incorporating the probability weighting function were virtually absent. Table 3 includes some of the consequences of using PT partially. We stress that the implications of ignoring aspects of PT are non-trivial. For example, Levy and Levy (2002) claimed that their study participants made choices inconsistent with PT. However, when calculating the value of the gambles the participants were evaluating, Levy and Levy (2002) inserted probabilities in lieu of decision weights. Wakker (2003) recalculated the value of the gambles using decision weights, as specified in PT (see Equation 2a above), and he concluded that Levy and Levy’s (2002) data agreed fully with PT. Moreover, Bromiley (2010) demonstrated that PT makes different predictions depending on whether scholars use the value function alone, the probability weighting function alone, and or both functions together. Therefore, if scholars do not use all of PT, they cannot readily argue that their results either support or refute it. PT itself is a collection of concepts that were somewhat disjointed before Kahneman and Tversky (1979) integrated them into a coherent theory (Wu, Zhang, & Gonzalez, 2004). Thus, when scholars use some aspects of PT while not accounting for others, they are not using PT. Rather, they are drawing on individual constructs and arguments (e.g., the influence of loss aversion on choice), which may be useful but do not reflect PT itself. In this respect, we do not suggest that PT’s individual concepts cannot inform management research. However, scholars must be clear whether they are deriving hypotheses from PT itself or from a subset of its individual constructs. Absent the unifying framework PT provides, scholars must also carefully explain how those constructs will shape the phenomena of interest. Accordingly, we now discuss implications of the concepts embedded in PT’s two central components: the value function and the probability weighting function. Interpretations of the PT Value Function 36
In Table 3, we list four implications of the value function our review suggested were either underutilized or misrepresented in management research. First, the curvature of the value function is essential to PT (Tversky & Kahneman, 1981). Because the function is nearly linear far from the reference point, risk seeking and risk aversion do not increase as outcomes move further from the reference point. Rather, as we have emphasized, the value function posits risk neutrality at large distances from the reference point. Second, because PT assumes individuals evaluate outcomes by comparing them to a reference point, past outcomes should not influence choice, beyond their possible effect on the reference point. Some scholars have challenged PT’s assumption that choices do not depend directly on past outcomes (Sitkin & Pablo, 1992), but it is fundamental to PT. Specifically, it reflects PT’s isolation assumption: individuals consider choices in isolation rather than considering their impact on total wealth. In turn, concepts such as loss aversion and diminishing sensitivity pertain to the subjective values of possible (i.e., future) gains and losses. Although studies often operationalize the value function using historical (i.e., experienced) gains and losses, we emphasize that potential outcomes should be the focus of research drawing on PT. Third, many decisions in organizations constitute mixed gambles because both gains and losses are possible. For example, most risky business choices (e.g., acquisitions or new product introductions), can have positive or negative outcomes. Indeed, many managers would not consider a choice risky unless it could have negative outcomes (March & Shapira, 1987). Although management theory applications of PT have largely ignored the implications of mixed gambles, they have important consequences for risk taking. Importantly, the PT value function implies strong risk aversion for mixed gambles (Bromiley, 2010). Tversky and Kahneman (1992: 316) argued that this risk aversion is due to “[t]he pronounced asymmetry in the value function.” Specifically, in Figure 1, the kink at the 37
reference point produces a sharp difference between the subjective value of a gain and the subjective value of an equivalent loss. This large kink exists because loss aversion applies to negative outcomes, whereas it does not apply to positive ones (Bromiley, 2009). In other words, the potential losses mean more to the individuals than the potential gains do. If managers’ decisions often constitute mixed gambles yet scholars do not consider the resulting risk aversion, results appearing to support PT actually may not do so. At the same time, incorporating this PT prediction into our scholarship can enrich our understanding of managers’ decisions and their implications. Fourth, research should consider the possibility that the value function varies across individuals and firms. Although experimental studies have elicited functional forms and parameters that define the shape of the value functions for individuals (e.g., Abdellaoui, 2000; Abdellaoui, Bleichrodt, & Paraschiv, 2007), these studies nearly always use monetary gambles with university students. However, as Pennings and Smidts (2003) demonstrated, we cannot assume that the parameters or shapes of the value function are the same for all individuals. Thus, research needs to document value functions in organizations, determine how to aggregate individuals’ value functions up to the level of the firm, and identify the conditions under which such aggregation is warranted (e.g., Chen, Mathieu, & Bliese, 2004; Klein, Dansereau, & Hall, 1994). The parameters or shape of the value function should also vary systematically across firms. For example, losses that appear immense for small businesses may appear trivial for many large firms. Likewise, it seems quite problematic to assume all firms view potential risks the same way. This possibility addresses boundary conditions for PT, as Kahneman and Tversky (1979) cautioned that the PT value function may not accurately predict choices involving either ruinous or insignificant losses. 38
Interpretations of the PT Probability Weighting Function In light of our earlier conclusion that management research frequently ignores decision weights, Table 3 emphasizes four aspects of the probability weighting function future management research should consider. First, although management scholars often attribute PT’s predictions about risk taking to loss aversion, Kahneman and Tversky (1979: 280) argued that “decision weights could produce risk aversion and risk seeking” even without loss aversion. To illustrate, consider a choice between either $5 with certainty or a gamble with a 50% probability of $0 and a 50% probability of $10. The underweighting of the probabilities (see Figure 2) reduces the value of the gamble (see Equation 2) below its expected value, resulting in a preference for the certain outcome (i.e., $5 for sure). Thus, the probability weighting function produces risk aversion in this example. Alternatively, if the same gamble involved losses, the underweighting of probabilities would make the value of the gamble greater than its (negative) expected value. According to PT, individuals would then prefer the gamble, evidencing risk seeking. Thus, in these examples, loss aversion (from the value function) is not necessary to generate the “risk averse above; risk seeking below” prediction scholars often attribute to PT. Importantly, the probability weighting function’s decision weights can also generate the opposite prediction. The probability weighting function suggests that individuals overweight low probabilities (see Figure 2). When the decision weight is larger than the probability, the value of gambles involving gains can exceed the expected value, and the value of gambles involving losses can be below the (negative) expected value (see Equation 2a and 2b, for example). By extension, due to the overweighting of low probabilities, individuals may be willing to accept low-probability gambles involving gains but avoid such gambles involving losses.
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Thus, PT predicts that individuals can exhibit a fourfold pattern of risk taking: “risk aversion for gains and risk seeking for losses of high probability; risk seeking for gains and risk aversion for losses of low probability” (Tversky & Kahneman, 1992: 297). Perhaps owing to the general tendency to ignore the probability weighting function, however, we found no management theory applications of PT incorporating this prediction. This omission limits our ability to explain commonly observed phenomena using PT. For example, risk-seeking behavior for low-probability gains explains the popularity of lotteries, and risk-averse behavior for lowprobability losses explains the prevalence of insurance against catastrophic yet unlikely phenomena (Kahneman & Tversky, 1979). Likewise, managers face some choices that involve extremely high probabilities (e.g., production cost savings from new equipment) and extremely low probabilities (e.g., developing a new drug). Thus, considering PT’s fourfold pattern of risk taking provides a new lens through which to examine many managerial decisions. Second, the fourfold pattern notwithstanding, the probability weighting function also contributes to a general pattern of risk aversion for many choices. Figure 2 reveals that most probabilities are underweighted. By extension, even though the probabilities of all possible outcomes in a gamble obviously sum to one, the decision weights for all of the possible outcomes of a risky choice will often sum to less than one (Kahneman & Tversky, 1979). That the decision weights typically sum to less than one contributes to a general tendency toward risk aversion. Management research drawing on PT has ignored this general pattern of risk aversion the probability weighting function frequently implies.13 Third, the importance of the probability weighting function poses challenges to management researchers. Although experimental studies can apply it by providing outcomes and probabilities to participants, researchers conducting field studies must identify the outcomes managers are considering and must develop reasonable estimates of the probabilities managers 40
assign to such outcomes. Moreover, if researchers wish to utilize the probability weighting function CPT implies, they must be able to identify which outcomes are gains and which ones are losses, because the relations between probabilities and decision weights are different for gains and losses (see Figure 2). Thus, the difficulties of properly representing outcomes and probabilities may be insurmountable in many areas of management research. However, using the probability weighting function might be plausible in some situations. For example, researchers examining well-defined CEO incentives might be able to make plausible assumptions about the probabilities of potential outcomes. Further, if there is rich data specifying the outcomes of past decisions and it is plausible managers consider such data, scholars may be able to infer the influence of probabilities on managerial choices. Likewise, the argument that individuals infer probabilities is evident in Sine, Haveman, and Tolbert’s (2005) study of the effects of external institutions on firm founding rates and in Steel and Konig’s (2006) illustration that CPT can enrich theories of motivation. Fourth, the shape of the probability weighting function implies that managers’ sensitivities to differences in probabilities vary. For probabilities near 50%, for example, the nearly horizontal slope of the probability weighting function (see Figure 2) implies that “people are relatively insensitive to probability difference[s]” (Tversky & Kahneman, 1992: 312). However, note the sharp increase in the slopes near 0% and 100%. In this region, slight differences in probabilities lead to dramatic differences in decision weights. Thus, there is a need to understand how individuals infer probabilities (see Smith & von Winterfeldt, 2004 for a review of the vast literature on how individuals infer probabilities). Using PT in Organizational Settings We now turn to issues specifically associated with applying PT outside experimental settings. Our purpose is not to discourage researchers from using PT outside of the lab. Rather, it 41
is to note the challenges in such applications in the hopes of encouraging richer use of PT. Herein, we discuss issues related to (1) levels of analysis, (2) accounting for alternative explanations in empirical work, (3) identifying reference points, and (4) measuring risk taking. Again, we summarize this discussion in Table 3. Level of Analysis. As our review demonstrated, applying PT to complex, multi-level organizational settings can present several theoretical and methodological challenges. In our view, many studies using PT at higher levels implicitly use it as a homologous theory. In other words, they assume its constructs have the same meanings and relations across levels (Chen, Bliese, & Mathieu, 2005). As Table 3 suggests, PT may not be as homologous as many scholars assume. For example, in organizations, it may be unclear who makes the decisions researchers examine. Many studies attempt to minimize this concern by focusing on the CEO, but other top (Hambrick, 1994) and middle-level managers (Kuratko, Ireland, Covin, & Hornsby, 2005) influence decisions as well. As our review of studies at the individual level showed, there are several reasons to expect that these different individuals interpret decisions differently. Depending on their location and job, for example, managers attend to different immediate concerns (Lawrence & Lorsch, 1967) and have different access to information (Thompson, 1967). Therefore, it is unlikely that all organizational members perceive potential outcomes and probabilities similarly. The possibility that some individuals do not have value functions and probability weighting functions resembling those in Figures 1 and 2 produces additional complexity (Abdellaoui, 2000; Bleichrodt & Pinto, 2000; Pennings & Smidts, 2003). Given these sources of variation, generalizing PT’s predictions about individual-level behavior to draw conclusions about the behavior of firms increases the potential for cross-level fallacies (e.g., Rousseau, 1985). Consistent with arguments offered by others (Hitt, Beamish, 42
Jackson, & Mathieu, 2007; Klein et al., 1994), to test PT definitively in organizational settings, scholars must identify the appropriate decision makers and account for the possibility that they interpret decisions differently (Bromiley, Miller, & Rau, 2001; Svenson, 1996). Likewise, scholars need to consider alternative explanations for their findings. We now turn to this issue. Alternative Explanations. Scholars who use PT in organizational settings should recognize, and preferably account for, alternative explanations for evidence that appears to support PT. Table 3 includes five such alternative explanations. First, scholars should consider the possibility that managers are simply acting sensibly to achieve their personal or organizational goals. For example, if managers believe a risky behavior will increase performance, an empirical study finding that low-performing firms enacted the behavior more than did high-performing firms would not necessarily support PT. Rather, it is plausible that managers in the low-performing firms felt more pressure to improve performance, and so took an action expected to do so. Thus, to claim a purely PT-based explanation for risk taking, scholars need evidence that the behaviors observed are incompatible with either managers’ goals or those of the organization. For example, in a product pricing experiment, Ho and Zhang (2008) found that the risk-seeking behaviors of loss-frame individuals reduced their performance in a hypothetical task. The finding that the behaviors were inconsistent with performance-maximizing behavior bolstered the authors’ conclusion that the behaviors reflected risk-seeking preferences. Often, experiments using PT account for profit-seeking behavior by equating the expected values of the available gambles. For example, Miller and Shapira (2004) examined the prices individuals set when buying and selling call and put options, which provide rights to buy and sell assets (respectively) at a given exercise price. Arguing that call (put) options create gain (loss) frames, they found that individuals’ pricing of call options evidenced risk aversion, and 43
individuals’ pricing of put options evidenced risk seeking. These pricing decisions were inconsistent with the expected values of the options, thereby bolstering Miller and Shapira’s (2004) conclusion that the results reflected the individuals’ risk-taking preferences. Second, researchers need to differentiate between PT and the BTOF. Table 2 and our earlier discussion suggest that the two theories make distinct predictions. However, to differentiate the two theories, scholars will need to derive their predictions more directly and rigorously from the theories themselves. Third, in organizational settings, managers likely expect that the outcomes of many decisions involving risk will not occur immediately after the choice. Thus, in addition to PT explanations, researchers must account for the possibility that individuals’ preferences for future outcomes and immediate outcomes likely vary (Thaler, 1981; Shelley, 1993). Fourth, whereas risk-taking decisions in experiments generally involve no added cost, risk taking in organizations requires resources. For example, Voss, Sirdeshmukh, and Voss (2008) found evidence that risk-seeking managers invest more in exploration than exploitation, but only when their organizations had sufficient financial slack to support exploration. Likewise, Chattopadhyay et al. (2001) found that risk-seeking behavior is more likely when organizations have the financial resources to fund it. Thus, researchers applying PT in organizational settings must account for the cost to implement risk-averse or risk-seeking decisions. Fifth, scholars must differentiate PT predictions from the effects of known organizational and cognitive biases. Whereas the experiments on which Kahneman and Tversky (1979) based PT provided clear explanations of outcomes and their probabilities, in organizations, the specification of outcomes and their probabilities depend on organizational processes. Thus, individuals’ perceptions of the outcomes and probabilities may incorporate a variety of biases and other issues associated with both individual and organizational information processing. For 44
example, March and Simon (1958) note that re-transmissions of information may reduce uncertainty about that information, a phenomenon termed uncertainty absorption. Likewise, Bromiley (1987) documents numerous systematic biases in different kinds of organizational forecasts. While marketers proposing a new product may tend to optimistic forecasts, for example, sales people whose incentives tie to forecasts may tend to conservative forecasts. Regarding cognitive biases, loss aversion can help explain the tendency to continue tasks once investments are made (i.e., escalating commitment; Brockner, 1992; Arkes & Blumer, 1985), preferences for the current state of affairs (i.e., the status quo bias; Kahneman, Knetsch, & Thaler, 1991; Samuelson & Zeckhauser, 1988), and the reluctance to relinquish possessed goods (i.e., endowment; Kahneman, Knetsch, & Thaler, 1990; Tversky & Kahneman, 1991). Thus, scholars need to consider whether such cognitive biases are manifestations of loss aversion or are independent constructs that are related to loss aversion. More broadly, scholars should be aware that these cognitive biases can confound empirical tests of PT. As this discussion implies, scholars must contend with several confounds when testing PT. When applying PT in organizational settings, additional concerns related to the measurement of important constructs also emerge, as we now illustrate. Ambiguous Reference Points. Our reviewed showed that management scholars frequently draw on PT to argue that individuals or firms behave differently when outcomes are above versus below a reference point. Moreover, gains and losses with identical probabilities will have different decision weights in CPT (see Figure 2). Thus, as Table 3 suggests, identifying the reference point is critical. Given PT’s experimental roots, wherein researchers manipulated reference points, little guidance exists about how to identify and measure them in non-experimental settings. Scholars have noted the plethora of reference points used in both individual-level (Kahneman, 1992) and 45
organizational-level (Fiegenbaum et al., 1996) research. Thus, there is a need for more precise knowledge about the origins of reference points, especially for research conducted outside the lab (e.g., Fischhoff, 1996; Short & Palmer, 2003; Svenson, 1996). For example, without knowing when individuals or firms are likely to use one reference point versus another, PT can be difficult to falsify in organizational settings. Illustrating this concern, Fiegenbaum et al. (1996: 223) noted that in field research, “any variable(s) that highlights a particular target or objective seems capable of establishing a reference point.” Thus, results counter to PT could mean that the researcher assumed a different reference point than the decision maker used. Alternately, they could mean that the researcher used the correct reference point but PT was not predictive. Researchers may need to collect a variety of objective and subjective data to understand which reference points individuals and organizations use (Fiegenbaum et al., 1996). Indeed, exclusive reliance on archival data implicitly assumes that managers interpret a decision the way researchers interpret it (e.g., Short & Palmer, 2003). Ironically, however, the notion of framing highlights how and why individuals interpret decisions differently. Thus, “[t]o get a sense of how subjects are reasoning, it is useful to let them speak for themselves” (Lopes, 1996: 184). For example, Devers, Wiseman, and Holmes (2007) used a questionnaire to verify how study participants interpreted and used the information they were given. Further, scholars could use content analyses of company reports to identify the reference points firms are utilizing (Short & Palmer, 2003). Despite the dangers of desirability and selfserving biases (Short & Palmer, 2003), such qualitative data can help specification and reduce the possibility that researchers attribute outcomes to incorrect causes. Moreover, longitudinal, repeated measures research designs may provide insights into how decision makers alter their selection of reference points over time, thereby helping us move beyond the static choices that 46
has often characterized PT-based research (Hollenbeck, Ilgen, Phillips, & Hedlund, 1994; Slattery & Ganster, 2002). We also encourage the use of other under-used approaches to studying decision making, including case study techniques, quasi-experiments, field experiments, and surveys. Further, we encourage additional work along the lines of the aforementioned Pennings and Smidts (2003) study, which estimated participants’ value functions and used them to study attributes of the individuals’ organizations. More broadly, we urge scholars to identify reference points ex ante, especially when conducting research at higher levels of aggregation. If possible, scholars should also test their models with alternate assumptions about the reference point. Risk Taking Measures. In many experiments testing PT, one of the available gambles is certain, often making the risky option (i.e., the probabilistic one) readily apparent. Although risk has a well-defined meaning in such a simple experiment, it has numerous, different meanings in organizations. For example, the risks confronting managers (e.g., the possibility of termination) sometimes depart from those confronting organizations (e.g., Palmer & Wiseman, 1999). Furthermore, different firm-level measures of risk often capture different constructs (e.g., Miller & Bromiley, 1990). Firms may also take several risks simultaneously. Sometimes, one source of risk may exacerbate another. Wiseman and Catanach (1997), for example, found that financial firms paying more to access funding (i.e., interest rate risk) could also be at a higher risk of defaulting on other liabilities (i.e., liquidity risk). At other times, however, risk-seeking behavior in some areas may counterbalance risk-averse behavior in others (McNamara et al., 2002). To illustrate, Gomez-Mejia, Haynes, Nunez-Nickel, Jacobson, and Moyano-Fuentes (2007) demonstrated that founding families accept a higher risk of firm failure in an effort to preserve family control, yet they also minimize performance variability to avoid exacerbating this risk. However, if firms 47
were behaving in accordance with the isolation assumption of PT, they should address risky decisions sequentially (i.e., one at a time) rather than trading off one form of risk for other forms of risk. In this respect, we also emphasize that people have different beliefs about what is risky (Bromiley & Curley, 1992; Kühberger, 1998; Sitkin & Weingart, 1995; Weber & Milliman, 1997). Perhaps because of such variation, as our review showed, some researchers use a given behavior (e.g., a divestiture) to measure risk aversion, whereas other researchers use that behavior to measure risk seeking. Therefore, scholars should take greater care in defining, ex ante, what constitutes risk-averse and risk-seeking choices. Further, when possible, scholars should examine the construct validity of their risk measures. We also suggest that management scholars shift some attention away from exclusive reliance on secondary data and toward primary field data capable of capturing executives’ perceptions of risk in actual organizational environments and situations. For example, we are encouraged by Larraza-Kintana et al.’s (2007) approach to measuring risk taking. These scholars had CEOs assess (a) how frequently their firms engaged in nine strategic actions and (b) the risk associated with each of the nine actions. In turn, these scholars captured risk taking at the organizational level as a weighted average of the frequency with which the CEO’s firm used those actions and their perceived riskiness to him or her. This approach helps ensure that the measures scholars use to capture risk accurately reflect managers’ perceptions. Scholars could use a similar approach to distinguish different forms of risk taking. Such measures have construct validity and, thus, could advance the state of PT research in management (e.g., Boyd, Gove, & Hitt, 2005). CONCLUSION
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Management studies drawing on PT have made valuable contributions in diverse research streams, including executive compensation, negotiations, affect and motivation, HRM, relations between organizational risk and return, and firm risk-taking behaviors. Thus, we urge scholars to continue drawing on PT in the future. However, management scholars have yet to realize the full potential of PT. In particular, management studies typically draw predictions from only one of PT’s two central components. In addition, some research uses PT in ways inconsistent with the theory. Therefore, it remains unclear whether the body of management research definitively supports PT. Given this concern, we stress that scholars using PT should draw their predictions directly from both components of the theory. In this respect, we have offered suggestions to aid scholars seeking to use PT more precisely and comprehensively. In addition, we provided guidance to enrich the use of PT in organizational settings. Specifically, researchers should exercise caution when applying PT at higher levels of aggregation. They should also consider several alternative explanations for findings that appear to support PT. In addition, while emphasizing the importance of understanding the reference point used by decision makers, we also suggested how researchers can study reference points in the future. Finally, we noted challenges in measuring risk taking in organizational settings and highlighted approaches we believe can enhance the quality of future work in this regard. In short, we are excited about the contributions management theory applications of PT have made, and we hope our work will be a catalyst for additional, yet more precise and consistent, use of PT in the future.
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FOOTNOTES 1.
We selected the following journals: Academy of Management Journal, Academy of Management Review, Administrative Science Quarterly, Human Resource Management, Journal of Applied Psychology, Journal of Management, Journal of Management Studies, Journal of Organizational Behavior, Management Science, Organization Science, Organizational Behavior and Human Decision Processes, Personnel Psychology, and Strategic Management Journal. We also used this list of journals to identify articles to review.
2.
For simplicity, we use the term outcome(s) in lieu of the term potential outcome(s) for the remainder of the paper, unless we are drawing specific attention the difference between potential and actual outcomes.
3.
Figure 1 is based on functional forms and parameters from Tversky and Kahneman (1992).
4.
To understand why the curvature of the value function reflects risk-taking preferences, consider a gamble with two equally probable outcomes of $0 and -$20. The expected value of the gamble is -$10. The total value of this gamble will be a weighted sum of the subjective value of 0 and the subjective value of -20, and so will fall on a line joining these two subjective values (depending on the probabilities). If the value function is convex, then the subjective value of -$10 will be below the line joining the subjective values of 0 and -20. Because the subjective value of the gamble is greater than the subjective value of -$10, the individual will prefer the gamble, thereby illustrating risk seeking. A symmetric argument provides risk aversion above the reference point.
5.
Figure 2 is based on functional forms and parameters from Tversky and Kahneman (1992).
6.
In this respect, many experimental tests of PT implicitly demonstrate that people’s risk-taking preferences are inconsistent with conventional notions of rationality, according to which such preferences should not vary when individuals receive identical information in slightly different ways (Tversky, Slovic, & Kahneman, 1990).
7.
We thank an anonymous reviewer for encouraging us to highlight this distinction.
8.
Of the 28 articles that we identified in this step, six of them were already in our sample. Five of the articles were entrepreneurship articles, which we eliminated. Therefore, we added 17 articles to our sample. We thank an anonymous reviewer for encouraging us to identify some of the most influential articles drawing on PT.
9.
Devers, Wiseman, and Holmes (2007) attempted to minimize the possibility that differences in the objective values of the stock options accounted for their results. For example, in the gain frame, the Black-Scholes value was higher for the high-volatility stock than for the low-volatility stock. However, in that same frame, the subjective values assigned by the study participants were higher for the low-volatility stock than for the highvolatility stock. We thank an anonymous reviewer for encouraging us to clarify this issue.
10. Tversky and Kahneman (1991) estimated the displeasure of losses to be more than twice the pleasure of equivalent gains. Subsequent studies have supported this assertion (Bleichrodt, Pinto, & Wakker, 2001; Tversky & Kahneman, 1992). 11. There are several methodological debates surrounding the statistical techniques and measures used to study relations between organizational risk and return. Some of these debates, while important, do not pertain directly to PT itself. Therefore, we refer interested readers to Andersen, Denrell, and Bettis, (2007), Baucus, Golec, and Cooper (1993), Bromiley (1991b), Bromiley and Rau (2010), Henkel (2009), Ruefli (1990), and Wiseman and Bromiley (1991). 12. Whereas the studies we include in the section reviewing research on executive compensation consider dependent variables at the individual level, here we consider dependent variables at the firm level. 13. The amount of risk aversion varies depending on the parameters in the probability weighting function, as well as those of the value function (Blavatskyy, 2005; Bromiley, 2010). 61
TABLE 1 Definitions of Key Conceptsa Concept
Definition
Decision weight
Depicts the influence of a probability on the value of a gamble.
Diminishing sensitivity
The difference between the subjective values of two outcomes is larger, the closer those outcomes are to the reference point.
Expected value
The probability weighted average of a gamble’s outcomes (Edwards, 1954).
Expected utility
The probability weighted average of the utilities of a gamble’s outcomes, where utility refers to the pleasure the final wealth positions (i.e., current wealth plus the outcome of the gamble) will provide (von Neumann & Morgenstern, 1944).
Framing
An individual’s interpretation of a decision (Tversky & Kahneman, 1981).
Gain frame
Anticipating an outcome in excess of one’s reference point.
Loss aversion
A tendency to prefer minimizing losses to maximizing equivalent magnitude gains.
Loss frame
Anticipating an outcome below one’s reference point.
Mixed gambles
Gambles that offer both positive and negative outcomes.
Probability weighting function
Translates probabilities into decision weights.
Pure gambles
Gambles that offer strictly positive or strictly negative outcomes.
Reference point
The neutral position used to determine the extent to which outcomes constitute gains (which are above this position) or losses (which are below this position).
Risk
Situations in which both outcomes and their probabilities of occurrence are known to the decision maker (Knight, 1921).
Risk aversion
Preferring sure outcomes to probabilistic outcomes with greater expected value.
Risk seeking
Preferring probabilistic outcomes to sure outcomes with greater expected value.
Subjective value
Depicts the value an individual perceives an outcome to be worth, reflecting the pleasure the outcome will provide.
Value function
Translates outcomes into subjective values.
a
Unless otherwise noted, these definitions are from Kahneman and Tversky (1979).
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TABLE 2. A Comparison of Expected Utility Theory, Prospect Theory, and the Behavioral Theory of the Firm Expected Utility Theory
Prospect Theory
Behavioral Theory of the Firm
Representative Work
Bernoulli (1738/1954) von Neumann & Morgenstern (1944)
Kahneman & Tversky (1979) Tversky & Kahneman (1992)
Cyert & March (1963) March & Simon (1958)
Level of Analysis
Individual
Individual
Organizational
Form of Rationality
Rationality
Bounded rationality
Bounded rationality
Reference Point
None
One neutral reference pointa
Many aspiration levelsb
Predictor of Risk-taking Preferences
∑ utility * probability; summation over all outcomes
v (x) * π (p) + v (y) * π (q)c v (y) + π (p) * [v (x) – v (y)]d
Performance relative to aspirations
a. Generally, PT assumes that the reference point is the status quo. Aspirations, expectations, norms, and social comparisons can shape the reference points people use (Tversky & Kahneman, 1991). b. Current aspiration levels may reflect stakeholder preferences, past aspiration levels, past performance, and the performance of comparable organizations (Cyert & March, 1963). c. This equation applies to mixed gambles or gambles for which the probabilities add to less than one. d. This equation applies to pure gambles with probabilities that add to one.
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TABLE 3 Analysis and Future Research Directions Partial Use of PT · · ·
Studies almost uniformly ignore the probability weighting function. Research that does not consider the full model is not testing PT. Studies not using the full model must explain the effects of PT’s constructs.
Interpretation of the Value Function · · · ·
Declining curvature predicts risk neutrality for large gains and losses. PT’s isolation assumption questions the relevance of past performance. Many decisions are mixed gambles and may promote risk aversion. The value function may vary across firms and individuals.
Interpretation of the Probability Weighting Function · · · ·
The effects of decision weights may counter the effects of the value function. Decision weights may promote general risk aversion. The probability weighting function requires specified outcomes and probabilities. Sensitivity to differences in probabilities varies with the level of probabilities.
Organizational Applications ·
Level of Analysis o PT is an individual-level theory; whether it is homologous is debatable. o It may be unclear who makes which decision in organizations. o Perceptions likely vary across individuals and organizations.
·
Alternative Explanations o Risk taking may reflect profit-maximizing behavior. o Researchers must differentiate PT from other theories (see Table 2). o Preferences for immediate versus future outcomes shape risk taking. o Costs of risk taking may influence behavior in ways not anticipated in PT. o Organizational and cognitive biases influence choices.
·
Ambiguous Reference Points o PT predictions depend heavily on the reference point. o We need more evidence about which reference points are used and when. o Research should specify the reference point, preferably ex ante.
·
Risk Taking Measures o Different measures of firm risk taking may capture different constructs. o Firms may take several risks simultaneously. o Risk taking measures in management research may not align with PT. o People perceive risk differently. 64
FIGURE 1 A Hypothetical Value Function (Tversky & Kahneman, 1992)
65
FIGURE 2 Hypothetical Probability Weighting Functions (Tversky & Kahneman, 1992) Panel A. Probability weighting function for gains
Panel B. Probability weighting function for losses
66
