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A Survey Study of Factors Influencing

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A Survey Study of Factors Influencing Decision Analysis Vol. 3, No. 3, September 2006, pp. 163–176 issn 1545-8490 �eissn 1545-8504 �06 �0303 �0163 informs ® doi 10.1287/deca.1060.0075 ©2006 INFORMS A Survey Study of Factors Influencing Risk-Taking Behavior in Real-World Decisions Under Uncertainty ...
A Survey Study of Factors Influencing
Decision Analysis Vol. 3, No. 3, September 2006, pp. 163–176 issn 1545-8490 �eissn 1545-8504 �06 �0303 �0163 informs ® doi 10.1287/deca.1060.0075 ©2006 INFORMS A Survey Study of Factors Influencing Risk-Taking Behavior in Real-World Decisions Under Uncertainty Manel Baucells IESE Business School, Avenida Pearson, 21, 08034 Barcelona, Spain, mbaucells@iese.edu Cristina Rata Universitat Pompeu Fabra, Ramon Trias Fargas 25-27, 08005 Barcelona, Spain, cristina.rata@upf.edu With the goal of investigating decision making under uncertainty in real-world decisions, we conduct asurvey requiring 261 subjects to describe a recent real-life decision and to answer questions about several dimensions of such decision, including reference-dependence, domain, the default alternative, and the type of consequences. We confirm a key prediction of prospect theory, namely, that perceiving the sure outcome as a loss increases risk-taking behavior. Such perception of losses also increases the attractiveness (perceived probabilities and estimated consequences) of the risky option. The results also confirm that the domain (professional versus private) of a decision is a factor influencing risk-taking behavior. Risk-taking behavior does not vary across the three groups considered (undergraduates, MBA students, and executives) and does not depend on the type of consequences (monetary or not). We confirm that reference-dependence, and not the default alternative, is the driver of risk-taking behavior. Key words : real-world decisions; risk-taking behavior; reference-dependence; domain; default alternative; type of consequence History : Received on October 13, 2005. Accepted by Don Kleinmuntz and George Wu on August 5, 2006, after 2 revisions. 1. Introduction Most of the descriptive research on decision making under uncertainty is done by means of laboratory experiments (Wu et al. 2004). Laboratory research has the advantage of performing controlled experiments, while allowing for the use of real monetary incen- tives. The extent to which the results of these exper- iments can be generalized to real-world decisions is still under debate (e.g., Kühberger et al. 2002). Our study aims to contribute to this debate by investigat- ing risk-taking behavior in decisions that are likely to be more representative of real-world decisions. The methodology we chose was to design a sur- vey in which subjects were asked to describe certain aspects of a recent real-life decision. We approached three groups of subjects—undergraduates, MBA stu- dents (hereafter “MBAs”), and executives. The sub- jects were required to fit the description in a simple decision analytic framework consisting of two alter- natives and one uncertainty. Subjects then answered questions regarding several dimensions of their decisions including reference-dependence, domain, default alternative, and type of consequences. Based on this survey, our goal is to examine the general- izability of various phenomena that have been doc- umented in the laboratory, as well as to investigate some new factors. We see our methodology as a com- plement to standard laboratory studies. Before designing the survey, we collected a list of potential factors that may influence risk-taking behav- ior. Traditional decision theory argues that the mag- nitude of outcomes and their probabilities, together with stable risk preferences over outcomes, are the major factors that influence risk-taking behavior (Keeney and Raiffa 1976, Clemen and Reilly 2001). Behavioral decision theorists expanded the list of rel- evant drivers of risk attitudes and risk perceptions. 163 Baucells and Rata: Survey Study of Factors Influencing Risk-Taking Behavior 164 Decision Analysis 3(3), pp. 163–176, © 2006 INFORMS Specifically, Kahneman and Tversky (1979) proposed reference-dependence as an important factor influenc- ing the risk-taking propensity. Subjects rely on com- parisons to form judgments, so the comparison with respect to a reference point induces outcomes to be perceived as a gain or a loss. In studies of deci- sion under uncertainty, reference-dependence is often referred to as framing, a term that admits multiple connotations (Levin et al. 1998). In this paper, the term framing will refer specifically to the perception of the sure outcome as a gain, neutral, or a loss. The ten- dency to choose the default alternative (Samuelson and Zeckhauser 1988, Schweitzer 1994, Johnson and Goldstein 2003) has also been proposed as a factor influencing the risk-taking behavior. The default alter- native is often taken as the reference point to com- pare outcomes, and we expect reference-dependence to be related to the default alternative. Most studies of these factors use experimental settings in which the reference point and the default alternative are manip- ulated or induced. Our goal is to separately measure and examine the effects of reference-dependence and the default alternative. The domain of a decision has been proposed as another factor that influences risk perceptions and behavior (Slovic 1972; Hershey and Schoemaker 1980, 1994; March and Shapira 1987; Schoemaker 1990; Weber et al. 2002; Blais and Weber 2006; see also the survey by Levin et al. 1998 on the interaction between domain and reference point effects for risky choices). However, most of the studies considered only a few, exogenously given, domains (Fagley and Miller 1990, Rettinger and Hastie 2001). The domain of a decision, of course, can be defined and measured in multiple ways. Therefore, the effects of domain are more dif- ficult to isolate than the influence of the reference point. This explains why more studies are devoted to framing effects than to domain effects. Our approach allows us to analyze a range of domains. Specifi- cally, we compare the broad categories of professional versus private domains, together with a finer clas- sification (investment, career, leisure, etc.) of up to 17 domains. Incentives can be real or hypothetical, as well as monetary or nonmonetary (e.g., use candies instead of money). Most behavioral research in laboratories is conducted using monetary outcomes (hypothetical or real), or hypothetical stimuli about nonmonetary decisions. An implicit assumption often made is that the conclusions of studies with monetary stimuli also apply to nonmonetary outcomes. Fagley and Miller (1997) compared the way people make choices in decisions having a monetary component as opposed to being nonmonetary (human life), and concluded that the effect of the reference point is independent of whether the outcomes are monetary or nonmonetary. Since in reality many decisions are either nonmone- tary or a combination of nonmonetary and monetary outcomes it is important to measure the influence, or lack of influence, of the types of consequences (mone- tary and nonmonetary such as comfort, convenience, social, time, etc.) on risk-taking behavior. The contribution of this paper is to develop an orig- inal decision-making survey to explore risk behav- ior under uncertainty. Our study complements the laboratory studies on decision making under uncer- tainty by sampling from actual decisions made by subjects, thus adding realism and descriptive rel- evance. This provides an alternative way to ver- ify several laboratory findings, for instance, whether reference-dependence influences risk-taking behavior in the direction predicted by prospect theory.1 Besides reference-dependence, we analyze other factors such as domain, default, and the type of consequences of a decision. Finally, our varied subject pool gives us the opportunity to observe similarities and differences in decision making among different subjects, not only undergraduates. Other methodologies exist to study real-world de- cisions. For instance, in the experience sampling method (ESM), subjects are alerted by mobile phone messages and requested to fill out a short question- naire reporting a recent decision at random times of the day. For instance, Hogarth (2006) used the ESM to look at the effect of feedback on confidence in everyday decision making. One of the methodologi- cal advantages of the ESM method compared to our method is that it avoids memory biases in reporting past decisions. 1 We are not the first to study prospect theory in the real world. However, previous research examined mainly reference depen- dence (e.g., Camerer 2000) and risk attitudes (e.g., Binswanger 1980) for given domains. Baucells and Rata: Survey Study of Factors Influencing Risk-Taking Behavior Decision Analysis 3(3), pp. 163–176, © 2006 INFORMS 165 Table 1 Characteristics of Subjects Undergrads MBAs Executives Total N 77 131 53 261 Median age 24�5 28 36 28 Female (%) 45 27 4 28 Country 86% United States 30 diff. countries 91% Spain The remainder of the paper is organized as fol- lows. Section 2 describes the survey design, and dis- cusses the measurement, coding of the variables, and methodological issues. Section 3 explains the statisti- cal results. Section 3.1 presents an overview of sim- ilarities and differences across the three groups with respect to the measured dimensions. Section 3.2 stud- ies the relative attractiveness of the risky outcome (in terms of probabilities and outcomes) and its relation- ship with the other factors. In §3.3, we apply a logistic regression model to examine the effect of reference- dependence and domain on risk-taking propensity. Section 3.4 offers an expanded logistic regression model to examine the effect of the default alterna- tive, type of consequences, and group. Finally, §3.5 discusses the relationship between default alternative and reference points. Section 4 concludes. We include an appendix which briefly reviews the predictions of prospect theory as applied to our decisions (sure out- come versus a binary gamble), as one varies the ref- erence point continuously from the worst to the best outcome. 2. Research Methods 2.1. Subjects We distributed a questionnaire to three groups of par- ticipants.2 The first group consisted of 77 undergradu- ate students from Duke University. The second group was made up of 131 MBA students at IESE Business School in Barcelona, Spain. The third group consisted of 53 executives who were enrolled in the executive education program at IESE Business School. Table 1 summarizes the different demographical character- istics of the undergraduates (subsequently “Under- grads”), MBA students, and Executives. 2 The questionnaire can be downloaded from the research section at http://webprofesores.iese.edu/mbaucells/. 2.2. Survey Design Figure 1, not shown in the questionnaire, underlies the design of the questionnaire. r is the reference point, xs is the monetary equivalent of the safe alternative (S), and xs − r is the perceived monetary equivalent gain or loss associated with such outcome. Likewise, xb− r and xw − r , with xb > xw, are the perceived gains or losses associated with the better and worse outcomes, respectively, of the risky alternative (R). Finally, p is the probability of the better outcome. The questionnaire begins with introducing the pur- pose of the study, and asking the subjects to describe briefly one recent decision. To conform to the simple decision analytic scheme of Figure 1, the decision is constrained to two alternatives, a sure alternative S and a risky alternative R� Furthermore, the potential outcomes of the risky alternative had to be summa- rized in two scenarios, a better outcome scenario and a worse outcome scenario. Subjects were asked to briefly describe the two alternatives and the three out- comes. Then subjects were required to answer a number of questions which are meant to measure several dimen- sions of a decision. Some of those dimensions (e.g., p, xb − xw) correspond to the elements of a decision depicted in Figure 1, while others are meant to help us to further characterize a decision (the type of con- sequence, default alternative, reference-dependence, and final choice). In what follows, we briefly describe Figure 1 Decision Framework Underlying the Questionnaire Risky alternative (R) p 1–p xs–r xb–r xw–r Safe alternative (S ) Better outcome Worse outcome Sure outcome Baucells and Rata: Survey Study of Factors Influencing Risk-Taking Behavior 166 Decision Analysis 3(3), pp. 163–176, © 2006 INFORMS these questions, in the same order in which they were presented in the questionnaire. Type of Consequence. Subjects were asked to clas- sify the outcomes of their decisions according to one or more of the following seven categories: monetary, comfort (or discomfort), convenience, time (arriving on time or late, delays, waiting), social consequences (fame, embarrassment), career, and other. Probability p. Subjects were presented with a di- rect linear scale between 0 and 1 with increments of 10% and were requested to use a cross to indicate the estimated probability of the better outcome.3 Default Alternative. We explained to the sub- jects that “a default or nonproactive alternative is actually chosen if nothing is done.” Subjects were then requested to decide whether in their decisions (a) the safe alternative was the default alternative, or (b) the risky alternative was the default alternative, or (c) neither alternative was the default due to the fact that both alternatives required some action to be taken. The outcome of the default alternative will be highly correlated with the continuation of the current state of the world, also called status quo. The Attractiveness of the Safe Alternative q. In the same way that p measures probabilities, q mea- sures consequences. The fraction q = xs − xw xb − xw indicates the position of the sure outcome relative to the better and worse outcomes in a 0–1 scale. We call q the attractiveness of the safe alternative. For non- trivial decisions, q takes values strictly between 0 to 1. The difference between p and q measures the relative attractiveness of the risky option. A risk-neutral deci- sion maker strictly prefers the risky outcome if and only if p > q. This observation can be easily seen by setting r = xw, and observing that the expected values of R and S are p xb − xw� and q xb − xw�, respectively. 3 As a double check, we asked them to write the numerical estimate, which for most of the subjects was consistent. In case of disagree- ment, we took the numerical estimate. Figure 2 Scale to Locate the Sure Outcome with Respect to the Better and Worse Outcomes Worse outcome Better outcome Sure outcome q = 0.4 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 We elicited q using a direct scale.4 Specifically, sub- jects were presented with a scaled line as in Figure 2, and asked to estimate the location of the sure out- come with respect to the worse and better outcomes in terms of their preferences. Reference-Dependence. The locus of the reference point r relative to the outcomes yields a gain, neu- tral, or loss perception for each outcome. To measure reference-dependence, we directly asked subjects their perception. For each outcome, we provided the sub- jects with three checkboxes so that they could indicate their perception of the outcome: as a gain, a loss, or neutral (neither a gain nor a loss). Final Choice. Subjects were asked to write down whether they chose the safe or the risky alternative. This is the variable to be explained in our study. We also asked the subjects—in the case when the uncertainty was solved—to briefly describe “What happened?” Many subjects left this question blank and we do not consider this question in the analysis. 4 We also requested a quantitative measure of the monetary equiv- alents of the outcomes. Specifically, subjects were asked to imagine that they had chosen the risky alternative and the worse outcome had happened. In this case, they have to provide us with their willingness-to-pay to replace (a) the worse outcome with the better outcome and (b) the worse outcome with the sure outcome. This information provides us with the differences xb−xw� and xs−xw�. As a double check, we also requested xb − xs� in the same way. The sum of xb − xs� and xs − xw� should agree with xb − xw�. Although exact agreement between these two measures occurs in fewer than half of the cases, both estimates have the same order of magnitude, and the correlation between Ln xb−xs�+ xs−xw�� and Ln xb − xw� is 0.95. The following three ratios— xs − xw�/ xb − xw�, xs − xw�/ xb − xs�+ xs − xw��, and 1− xb − xs�/ xb − xw�—provide estimates of q. Let qm be the median of these three numbers. The correlation between qm and q is 0.56. While the correlation is some- what low, we confirm that our logistic regression coefficients do not change significantly if we use qm instead of q. We decided to use the direct scale estimate q because it was more straightforward and generated more valid answers. Hence, the elicited monetary equivalents are not used in our analysis. Baucells and Rata: Survey Study of Factors Influencing Risk-Taking Behavior Decision Analysis 3(3), pp. 163–176, © 2006 INFORMS 167 2.3. Coding of the Variables As a prerequisite to analyzing the results of the sur- vey, we coded the responses as follows. • Reference-Dependence. We classified the decisions according to the perception of the sure outcome, i.e., as gain in the cases that the sure outcome was reported as a gain, as loss if the sure outcome was perceived as a loss, and as neutral if the sure outcome was perceived as neutral. We call this the framing of the sure outcome. Strictly speaking, only 11 deci- sions were perceived as either all-gains or all-losses, in the sense that the worse outcome is perceived as neutral or gain, or the better outcome as neutral or loss. Hence, most of the decisions were mixed, having both gains and losses as consequences. We eliminated 17 cases that showed some inconsistency from the analysis of reference-dependence. For example, if the safe alternative was perceived as neutral, then the bet- ter outcome cannot be perceived as a loss, nor can the Table 2 Domain of the Decisions for the Different Groups Undergrads (%) MBAs (%) Executives (%) Total (%) Risky (%) Professional Human resources (assign tasks, choose collaborators, — — 19 4 100 organize subordinates) Start MBA (keep current job or start MBA) — 29 — 15 94 Business (decisions made in the current job) — 2 28 7 73 Job (change job or not) 4 8 30 11 88 Protocol (how to deal with superiors1) 5 2 4 3 83 Studying (continue education or not) 12 2 — 5 73 Subtotal Professional 21 44 81 44 Private Safety (undertake laser eye surgery, drive after drinking, 12 2 — 4 90 wear helmet) Location (move to another city/country or not) 6 3 2 4 50 Investment (invest personal wealth) — 7 2 4 70 Relationship (continue/start or not a relationship) 6 3 2 4 37 Buy/sell (whether to buy/sell something and choice 6 10 2 7 79 of supplier) Flat rental (rent a flat or wait for other opportunities) — 11 2 6 75 Ethics (tell the truth, break the law) 8 — — 2 48 Organization (plan activities, schedule, do now/do later) 16 13 6 12 77 Leisure (entertainment activities and sports2) 6 6 — 5 57 Traveling (traveling/vacation decisions) 3 2 4 3 73 Campout (camp out or do something else) 16 — — 5 80 Subtotal Private 79 56 19 56 1Examples of protocol are “to attend dinner after the interview/not attend,” or “abide with supervisor/confront him.” 2Since our questionnaire asks subjects to recall a risky decision, a few of them reported leisure decisions involving risky sports such as sky diving or paddling in the open ocean. worse outcome be perceived as a gain. We then intro- duce two binary variables, D_GAIN and D_LOSS, to account for the three levels of reference-dependence. D_GAIN = 1 for those reporting that the sure out- come was perceived as gain and 0 otherwise, and D_LOSS= 1 for those reporting that the sure outcome was perceived as a loss and 0 otherwise. • Domain. Subjects were not asked t
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