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The Relationship between Overconfidence and the Introduction of Risky Products: Evidencefrom a Field StudyAuthor(s): Mark Simon and Susan M. HoughtonSource: The Academy of Management Journal, Vol. 46, No. 2 (Apr., 2003), pp. 139-149Published by: Academy of ManagementStable URL: http://www.jstor.org/stable/30040610 .

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c Academy of Management Journal 2003, Vol. 46, No. 2, 139-149.

THE RELATIONSHIP BETWEEN OVERCONFIDENCE AND THE INTRODUCTION OF RISKY PRODUCTS:

EVIDENCE FROM A FIELD STUDY

MARK SIMON Oakland University

SUSAN M. HOUGHTON

Georgia State University

To date, no field research has examined the effects of overconfidence on ill-structured decisions made by managers, such as product introductions. We explored this gap in the literature and found, in a study of high-technology firms, that overconfidence was positively related to the degree to which product introductions were pioneering (risky). Further, managers introducing pioneering products were more apt to express extreme certainty about achieving success, but these products were less likely to achieve success.

Scholars know little about how managers select risky action, although such decisions are the es- sence of strategic choice (Palmer & Wiseman, 1999). Some research evidence indicates that individuals' perceptions of risky situations may explain why they engage in risky behavior (e.g., Sit- kin & Weingart, 1995). Busenitz and Barney (1997) demonstrated that the perceptual bias of overconfidence may be associated with entrepreneurial behavior. Camerer and Lovallo (1999) greatly refined understanding of the role of overconfidence in risky decision making through a rigorous laboratory study. In this simulated environment, they found that overconfidence was associated with the risky action of entering a new market. While their study, along with Busenitz and Barney's work, highlighted the importance of overconfidence in risky decision making, Camerer and Lovallo noted that "experimental data are hardly conclusive evidence that overconfidence plays a role in actual entry decisions by firms" (1999: 307). They stated that a critical "scientific payoff" would come when these relationships could be observed in the field. To date, however, no studies have examined the role of overconfidence in actual entry decisions, such as product introductions.

Overconfidence occurs when an individual's certainty that his or her predictions are correct ex- ceeds the accuracy of those predictions (Klayman, Soll, Gonzalez-Vallerjo, & Barlas, 1999). Although dozens of laboratory studies have documented the prevalence of overconfidence, few scholars have explored overconfidence in the field. The limited field research on overconfidence suggests that overconfidence is more likely to occur when individuals make predictions regarding less repetitive decisions. For instance, meteorologists do not over- estimate the accuracy of their knowledge when forecasting rain (Murphy & Winkler, 1977), but they are overconfident when predicting tornadoes (Murphy & Winkler, 1982). Field research also suggests that receiving little or ambiguous feedback about prior decisions also increases overconfidence. For example, doctors are not overconfident when predicting patient mortality in intensive care units (Winkler & Poses, 1993), yet they exhibit overconfidence when diagnosing disease (Chris- tensen-Szalanski & Bushyhead, 1981). In the former case, doctors usually know the outcomes of their prior predictions, but in the latter case they often lack feedback (Winkler & Poses, 1993). Similarly, those who receive delayed feedback have a much more difficult time learning to calibrate their judgments than those who receive rapid feedback, such as meteorologists forecasting rain or bridge players making bids (Keren, 1991).

Although the above field studies have implied that overconfidence is greater when a decision environment is nonrepetitive or ambiguous, all of these studies examined relatively structured decision environments. Strategic business decisions, however, occur in extremely ill-structured decision

We thank Irene Duhaime, Harry Barkema, and three anonymous reviewers for their helpful comments on drafts of this work.

This research was funded in part by an Oakland Uni- versity School of Business Administration Research Grant, the Center for Entrepreneurial Leadership, Inc., and the Ewing Marion Kauffman Foundation. The con- tents of this publication are solely the responsibility of the authors.

139



140 Academy of Management Journal April

environments, lacking discrete input variables or measurement calibration (Roll, 1986). In such ill- structured decision environments as those sur- rounding new product introductions, overconfidence may be especially prevalent. Extending Camerer and Lovallo's (1999) line of inquiry, we examined overconfidence in the decision environment of new product introductions in the field. Importantly, not all new product introduction decisions are the same. Product introductions that are more pioneering-that is, that create a distinct market category (Robinson & Fornell, 1985)-are riskier than incremental product introductions, because pioneering introductions are unique and decision makers lack prior similar actions to help calibrate judgment (Golder & Tellis, 1993). These character- istics suggest that managers may be even more vul- nerable to overconfidence when deliberating about pioneering product introductions than they are when considering incremental introductions. Con- sequently, we examined whether overconfidence varied with the extent to which a product introduction was pioneering.

OVERCONFIDENCE AND THE EXTENT OF PIONEERING

Overconfidence is excessive certainty about one's prediction. One important prediction tool that managers use when attempting to forecast the success of their strategic actions is called a diagnostic cue. A diagnostic cue is an indicator that is present most of the time given one outcome and absent most of the time given the alternative outcome (Soll, 1996). Managers must rely on previous examples of the diagnostic cue to make their current prediction. For example, when predicting a product introduction's success, a manager may use "positive customer feedback prior to an introduction" as a diagnostic cue that has been frequently associated with the outcome of "achieving sufficient demand." A diagnostic cue may be a stronger predictor of success in common decision contexts because the current decision is similar to previous situations. In uncommon decision contexts, however, the diagnostic cue may be a weaker predictor of success. We argue that the decision context for considering a pioneering product introduction dif- fers substantially from the decision context of more incremental product introductions. Pioneering product introductions represent a decision context in which the cue is likely to have poor predictive validity because pioneering introductions, by definition, lack historical precedent. For example, early customer feedback is more weakly associated with the success of pioneering products than it is

with the success of incremental products because customers are not familiar with the product category in the case of the former (Atuahene-Gima, 1995).

Overconfidence occurs when managers overesti- mate the predictive validity of a cue. Managers may accurately assess the predictive validity of a diagnostic cue in common decision contexts, such as incremental product introduction decisions. Man- agers can recall a broad set of similar situations when the diagnostic cue was and was not associated with the outcome of interest. Therefore, the diagnostic cue in an incremental context does not lull the managers into overconfidence in their predictions. The usefulness of a diagnostic cue breaks down, however, in atypical decision contexts, such as pioneering product introductions, because the managers may not appreciate the poor predictive ability of the cue. Instead, the managers become overconfident because they disproportionately ob- serve instances of the cue's association with positive outcomes. Managers in a pioneering decision context may notice examples in which the diagnostic cue predicted success because successful pioneering efforts often yield dramatic results, such as reinventing whole industries. In these instances, the cue may be especially salient and available to the manager (Tversky & Kahneman, 1974). In contrast, pioneering products that fail are shorter lived and more easily overlooked than less pioneering products (Golder & Tellis, 1993). In addition, people's natural reluctance to discuss and publicize stories of instances when they did not achieve their goals (Levinthal & March, 1993) may disproportionately affect the availability of accounts of times when the diagnostic cue was not associated with success in pioneering products. Therefore, managers in a pioneering decision context may overesti- mate the predictive validity of a cue because they have salient information about the instances when the cue was associated with a positive outcome and limited information about instances when the cue was associated with a negative outcome, even if the negative outcome was more likely.

Consequently, we maintain that overconfidence is likely to occur in pioneering decision contexts more often than in incremental decision contexts. We further suggest that the presence of overconfidence, in turn, encourages managers to pursue actions that are riskier than those they might have pursued without a biased perception of risk. Many studies have demonstrated the importance of risk perception in explaining risky action (e.g., Hough- ton, Simon, Aquino, & Goldberg, 2000; Weber & Hsee, 1998). Scholars studying first movers (Lieber- man & Montgomery, 1988), plant expansion (Nutt,



2003 Simon and Houghton 141

1993), and innovation (Staw, 1991) have proposed that managers proceed with risky action because they do not perceive the action's riskiness (Kahne- man & Lovallo, 1993). Similarly, we argue that overconfidence is a specific cause of underestimat- ing the riskiness of a proposed action. Therefore, managers may be more likely to take the risky action of introducing a pioneering product introduction when they are overconfident. This leads us to hypothesize:

Hypothesis 1. Greater overconfidence is associated with introducing products that are more pioneering than incremental.

It is important to note that overconfidence may occur at any given level of expressed certainty. For example, a manager who does not express much confidence in his or her predictions may still be overconfident if the predictions are consistently wrong, while a manager who is extremely certain of his or her predictions is not overconfident if the predictions are always correct. In this research, we identify the special case of extreme certainty. Ex- treme certainty occurs when an individual per- ceives virtually no possibility of failure. Being wrong when one expresses extreme certainty may be especially devastating for organizations. Thus, consistent with Fischhoff, Slovic, and Lichten- stein's research (1977), our study focuses on the most dramatic, and potentially damaging, case of overconfidence, extreme certainty.

Although managers in a pioneering decision context may be overconfident more often than managers in less pioneering decision contexts, they are not automatically more likely to express extreme certainty about their predictions. The question arises: Do managers introducing pioneering products express extreme certainty more or less frequently than managers introducing less pioneering products? On the one hand, managers in pioneering decision contexts might be less likely to express extreme certainty than managers in other decision contexts, because diagnostic cues are weaker predictors of success in pioneering contexts-an argu- ment that on its face has a certain intuitive appeal. On the other hand, managers in pioneering decision contexts may be relying on a few salient examples of past situations in which a given diagnostic cue was associated with success in order to determine their level of certainty about their own prediction. They may lack a larger, more represen- tative, set of examples that includes instances in which diagnostic cues were not associated with success. In contrast, managers in an incremental product introduction decision context may have a broader set of examples of diagnostic cues and out-

comes. In this broader set, it seems reasonable to assume that for at least some examples, a diagnostic cue would not be associated with success (Soll, 1996). These countervailing examples may temper expectations and may be less likely to generate extreme certainty.

Empirical studies examining other types of risky action provide some evidence, albeit tentative, of a positive relationship between extreme certainty and pioneering. Busenitz and Barney (1997) found that even though company founders are in very ambiguous environments, they more frequently express extreme certainty regarding their knowledge than other managers do. Cooper, Woo, and Dunkel- berg (1988) found that 35 percent of entrepreneurs were 100 percent confident that their new ventures would succeed, even though new ventures frequently fail. Similarly, Corman, Perles, and Van- cini (1988) determined that despite the inherent uncertainty that high-technology companies face, two-thirds of their managers believed their own firms' actions contained zero risk. Therefore, we hypothesize the following:

Hypothesis 2. Extreme certainty is associated with introducing products that are more pioneering than incremental.

In addition to extreme certainty, another variable in our study is achieving success. This led us to address the relationship between achieving success and pioneering. Product introductions are an evolving process, and the root of a failure may be in any part of the process. For example, managers often encounter technological problems well before an introduction is complete. In fact, researchers investigating overconfidence have often measured judgment errors that occur at the start of a process by observing results after the fact (e.g., Winkler & Poses, 1993). Thus, one could even view failure to succeed as capturing an initial prediction error associated with a decision's context. As we argued earlier, the actual predictive validity of a diagnostic cue may be weaker for managers in pioneering decision contexts than it is for those in incremental decision contexts. Yet these managers may still proceed with an introduction, not recognizing they are acting on unreliable cues. Because the cues they are acting on are in actuality less associated with success, we would expect them to encounter failure more frequently. Therefore, like other researchers (e.g., Golder & Tellis, 1993), we expected to find a negative relationship between achieving success and pioneering:




Hypothesis 3. Achieving success is negatively associated with introducing products that are more pioneering than incremental.

METHODS

Sample Selection

To test the hypothesized relationships, we examined the product introduction decisions of managers of small companies in the computer industry. We studied a single industry to help establish the boundary conditions needed for theory development (Wiseman & Catanach, 1997). We chose the computer industry because its product introductions vary greatly in risk, given the industry's short product life cycles and rapidly changing customer needs. Smaller firms provide an ideal setting in which to explore ill-structured decision making because they have fewer formal procedures, greater centralization, and lower organizational inertia; these conditions maximize the effects of managerial cognition on organizational outcomes. Using a comprehensive list of Georgia-based computer companies, the Georgia Technology Sourcebook, we identified 135 small computer companies (each with fewer than 100 employees) that anticipated launching a new product within 30 days or had just launched a product within the past three months. Executives in 65 (48%) of these firms agreed to participate, and 55 (41%) of these firms completed all phases of the project. The participating firms had an average age of 13 years and an average size of 48 employees and did not differ from nonpartic- ipants in age (p = .27) or size (p = .42). In each

sampled company, the responding executive (58 percent were at the CEO or president level, and 42 percent reported directly to their CEOs) was the person judged by company managers to be the most influential in the focal product introduction decision.

Research Design

McNamara and Bromiley (1999) argued that researchers may make drastic errors in the design and interpretation of risk-taking models in field studies if they fail to examine the issues managers actually attend to. Therefore, we did not ask managers about their confidence in the overall success of their

product introductions, because we believed that question was too broad for most managers to an- swer critically. Instead, we disaggregated the product introduction decision into more discrete com-

ponents that were salient and important to the managers. We asked managers to describe what factors they believed were important in order for

their product introductions to succeed. By examining managers' beliefs about specific success factors, rather than their beliefs about the overall success of a product introduction, we attempted to focus on the finer-grained aspects of the managers' decision- making perceptions. This approach is consistent with other scholars' assertions that beliefs about success factors are often a prerequisite to determining whether an action is feasible, and that they may thus play a crucial role in the relationship between risk perception and action (Mason & Mitroff, 1981).

We first collected data from each participating firm around the time the company launched its product to assess the extent to which the product was pioneering, the success factors the manager was focusing on, and the manager's level of certainty about achieving each success factor. Eigh- teen months later, we collected data to determine whether the new product introduction had achieved the specified success factors.

Instrument development and data collection. We pretested the interview protocols with eight individuals who were either product introduction or computer industry experts, or experts in both areas. We refined our interviewing protocol in view of their feedback so that the questions were clear, relevant, not leading, and not value-laden. For example, the pretest experts uniformly told us that managers of small companies do not conceptualize success in terms of achieving a specific number (for instance, a 30 percent market share). Accordingly, we did not require respondents to provide quanti- tative responses to express their level of certainty about achieving each success factor. Once the interview protocols were established, we spent four hours training three MBA students, who conducted most of the audiotaped interviews. The training focused on how to start the interview, clarify interview questions, distinguish between complete and incomplete answers, and probe for more detail without leading the interviewee. To further stan- dardize the process, we accompanied each inter- viewer on his or her first five interviews. The interviews were conducted in the responding managers' own offices; each interview lasted about 30 minutes. At the time of the interview, we also collected other documentation about the new product to assist in determining the extent to which the introduction was pioneering. Specifically, we talked with other managers in the firm, interviewed competitors, examined the physical products and promotional materials, and gathered externally available market information.

Eighteen months after we gathered the first phase of data, we mailed the original respondents a questionnaire about whether or not the product had




achieved certain success factors. Fifty-five (85%) of the 65 original firms completed this final segment of data collection. The high overall response rate, as well as the fact that nonrespondents had had potentially positive (having been acquired) and negative (having failed) outcomes minimized con- cerns about response bias (McDougall & Oviatt, 1996). Additionally, the 10 nonresponding firms did not differ from the rest of the sample in number of employees (p = .81), firm age (p = .83), extent to which the product was pioneering (p = .95), or initial level of certainty (p = .82).

Content analysis. The audiotaped interviews from the first phase of data collection were tran- scribed and content-analyzed. Content analysis is a valid way to uncover and measure underlying decision processes and cognition because the language an individual uses reflects his or her cognition (Winograd, 1983). Researchers have used content analysis to identify the heuristics used by the managers of innovative firms (Manimala, 1992) and to determine how cognitive biases affect decision processes (Haley & Stumpf, 1989). We followed the four-step process recommended by (Winograd, 1983) to ensure reliable and valid coding of the success factors that the managers considered. We repeated this coding process to determine each manager's level of certainty about achieving each of the success factors and the extent to which the product was pioneering. To code pioneering, however, we considered all other product documentation, in addition to the focal manager's interview statements.

In the first step, we decided on the size of the text units (for instance, word, phrase, sentence, para- graph, and so forth) to analyze. Generally, the smaller the text unit, the more reliable the coding but the greater the potential to miss the phenomenon of interest. We determined that individual sentences, or occasionally strings of sentences, contained discrete ideas. Second, on the basis of the new product introduction literature, we developed a preliminary list of success factors (for instance, being able to develop a quality product). Using a "hold-out" sample of responses, we matched interview text units to the preliminary list of success factors to clarify classification decisions. This process resulted in a list of success factor categories, along with extensive definition write-ups. The category definitions became the basis for the coding rules. In the third step, we used these coding rules to practice coding with a different hold-out sample of interviews. We each coded the hold-out sample independently and obtained nearly identical results. We then discussed each coded text unit to eliminate possible ambiguity in the coding rules.

The coding taxonomy proved to be both comprehensive and reliable.

In the fourth step, the final coding, we independently coded each interview and then recoded the same transcripts in a different order, to establish test-retest reliability by ensuring that there were no coding biases due to text unit order or time frame. For the success factors categories, we attained test- retest reliability of 100 percent and interrater agreement for 95 percent of the cases. We then followed the four-step procedure to code both a manager's level of certainty with respect to each success factor (test-retest reliability of 100 percent; interrater agreement of 96 percent) and the extent of pioneering (test-retest reliability of 100 percent; interrater reliability of 96 percent). All coding discrepancies were resolved through discussion to mutual consensus.

Variable Measurement

Extreme certainty. When individuals are making relatively few predictions, or predicting unique events, such as whether they will achieve a success factor, overconfidence can only be determined to exist when they express extreme certainty and turn out to be wrong (Fischhoff et al., 1977). Conse- quently, we used a conservative measure of certainty in this study by differentiating between extreme certainty and all other levels of certainty. The response for each success factor was coded either "extreme certainty" (1) or "not" (0). Extreme certainty included statements like "I have no doubt," "definitely," "completely sure," and "absolutely positive." Responses that indicated some window of doubt, such as "maybe" were coded 0. The final extreme certainty score was the percentage of times a manager was coded 1 for each success factor he or she had identified.

Achieved success factors. To determine whether a new product introduction achieved the success factors that the manager associated with it had identified, we matched the initial success factors to the data in the second-phase survey about the product introduction's success. We adapted the second- phase survey from past product introduction research (Cooper & Kleinschmidt, 1996) to capture whether the product achieved the success factors according to six multi-item scales. The average rating on each of five two-item scales measured whether a firm successfully coped with external technological change (a = .80), maintained quality standards (a = .77), achieved sufficient demand (a = .74), controlled product-related expenses (a =

.81), and overcame competitive challenges (a =

.86). The average of a three-item scale measured




whether the firm's product introduction proceeded at an acceptable speed (a = .70). Managers re- sponded to each item on a seven-point Likert-type scale where 1 represented "strongly agree" and 7 represented "strongly disagree." For example, an item on the quality standards scale read "The product was bug-free" (reverse-coded). Any score lower than 4, the midpoint on the scale, indicated that to some degree the respondent perceived that the firm had failed to achieve success in that factor. The final measure was the percentage of the success factors originally mentioned by the respondent that were subsequently achieved. Table 1 provides descriptive information about the success factor categories.

Overconfidence. We measured overconfidence by first comparing the extreme certainty measure to the achieved success factor measure for each success factor the respondent identified. An individual was overconfident on any given success factor if he or she received an extreme certainty score of 1 but the subsequent achieved success factor score was below 4 (indicating lack of success). Next, the composite overconfidence measure was calculated as the percentage of success factors for which each respondent was overconfident.

Extent of pioneering. To assess the riskiness of a firm's action, we measured the extent to which the product introduction was pioneering. Most risk measures in field studies use corporate-level stock market and accounting data. By aggregating the risk entailed in multiple actions, however, those studies mask the relationship between a manager's decision process and the taking of actions that vary in riskiness (McNamara & Bromiley, 1997). We used a finer-grained measure of risk that isolates the relationship between components of a manager's decision process (that is, the different success factors) and taking a strategic action that varies in riskiness (that is, introducing a new product). We measured the extent to which each new product was market- pioneering using the coding procedure described above. Our pioneering coding categories, which were based on other studies of pioneering (e.g., Golder & Tellis, 1993), included the following: how the product differed from competitive offerings,

what distribution channels the manager planned to use, which customers the product targeted, how those customers currently were meeting their needs, and when other companies introduced products within the same market category. We occasionally encountered apparently conflicting evidence, but we were able to resolve the contradic- tions simply by asking a few follow-up questions. The final coding ranged from 1, "not at all pioneering," to 4, "very pioneering."

For example, one company in our sample, which we call MotorView, introduced a pioneering new product that was coded 4. The product was a digital video imaging system for monitoring heavily trav- eled street intersections in cities with high accident rates that can electronically transmit vehicle iden- tification information for ticketing motor vehicle violations. At the time of the introduction, the firm had no U.S. competitors, and only two European companies provided a service that was remotely similar; the European systems, however, relied on camera film technology requiring remote film pro- cessing and handling.

In contrast, another company, which we call Bricklayer, had a nonpioneering new product introduction that was coded 1. It introduced a com- bination software and hardware product that acts as a firewall between an untrusted network of computers and a trusted network of computers. This product monitors the data traffic going into and coming out of the customer's trusted network, keeping unauthorized users out of the network, not allowing any computers in the network to interact with untrusted computers. There are 28 other companies that have firewall products and 11 that have products in the class of the Bricklayer product. Thus, the product is not pioneering because the firewall concept is well known in that marketplace.

Control variable. We controlled for risk propensity, which refers to one's general tendency to take or avoid risk (Sitkin & Weingart, 1995), because it

may be associated with taking a specific risky action. The respondents completed a risk propensity questionnaire approximately one week before the initial interview to reduce any potential influence of the risk questions on the responses to other seg-

TABLE 1 Descriptive Data Regarding Success Factors

Achieved Maintained Coped with Overcame Controlled Proceeded at

Satisfactory Quality Technological Competitive Product Acceptable Success Factors Demand Standards Change Challenges Expenses Speed

Percent mentioned 53% 18% 12% 7% 6% 4%

Percent achieved 60% 38% 59% 60% 80% 63%




ments of the research. The instrument was a six- item (a = .74), five-point Likert scale adapted from Busenitz and Barney (1997).

Analysis

This study tested the hypotheses using hierarchical multiple regression. To examine whether overconfidence was associated with products that are more pioneering than incremental (Hypothesis 1), in step 1 we regressed the control variable, risk propensity, against the criterion variable, extent of pioneering. Step 2 then added overconfidence to the equation. To test Hypotheses 2 and 3, we en- tered risk propensity and, in step 2, added extreme certainty and the achieved success factors.

RESULTS

Table 2 displays the descriptive statistics of the variables. As expected, overconfidence is highly correlated with both extreme confidence and the achieved success factors, suggesting that we were correct to include overconfidence in a separate equation. Table 3 presents the result of the hierarchical regression equations. The results of model 2 indicate that greater overconfidence is associated with products that are more pioneering than incremental. The results of model 3 confirm that extreme confidence (Hypothesis 2) and the achieved success factors (Hypothesis 3) are both associated with pioneering introductions in the directions predicted. To ensure that the significance of Hy- pothesis 3 was not an artifact of our use of a dichot- omous measure of success (that is, any rating lower than 44 indicated failure on the seven-point scales measuring the achievement of success), we also tested the relationship using the average of the participants' responses to these six scales. Results obtained using the averaged metric (3 = -.32, p <

TABLE 2 Means, Standard Deviations, and Correlationsa

Mean s.d. 1 2 3 4

1. Pioneering 2.04 1.15 2. Overconfidence 0.22 0.29 .24* 3. Extreme certainty 0.49 0.39 .30** .48*** 4. Achieved success factors 0.56 0.35 -.17' -.53*** .13 5. Risk propensity 3.70 0.59 .21t -.07 .07 .13

a n = 55; one-tailed test results are reported. p < .10

* p < .05 ** p < .01

*** p < .001

TABLE 3 Results of Hierarchical Regression Analysisa

Pioneering

Predictor Variable Model 1 Model 2 Model 3

Step 1: Control Risk propensity 0.21' 0.23* 0.22*

Step 2: Predictors Overconfidence 0.26* Extreme certainty 0.31 **

Achieved success factors -0.24* AR2, change from model 1 .07 .14 AF, change from model 1 3.84* 4.16** R2 .04 .11 .18 Adjusted R2 .03 .08 .13 F 2.34' 3.15* 3.65**

a n = 55; standardized coefficients and one-tailed test results are reported.

tp < .10 *

p < .05 ** p < .01

.01) indicated the relationship was consistent across different aggregation measures.

DISCUSSION

Hundreds of laboratory studies on cognitive biases, including overconfidence, have greatly added to knowledge about the pervasiveness of these decision processes (Hogarth, 1980). These studies have demonstrated that the extent of biases can be extremely sensitive to decision context, leading scholars to investigate the predominance of system- atic judgment errors outside of the lab, in natural decision settings. Accordingly, researchers have examined the predictions of TV game show guests (Berk, Hughson, & Vandezande, 1996), real estate agents (Northcraft & Neale, 1987), commercial bankers (McNamara & Bromiley, 1999), auditors (Ashton & Hubbard-Ashton, 1990), and professional bridge players (Keren, 1991), and found mixed results across these applied situations. In each of these field studies, however, the researchers have studied relatively structured decisions. Although these investigations of structured situations have broadened understanding of the choice behavior, cognitive bias scholars have argued that ill-structured decisions represent the most interest- ing and important judgments of choice (e.g., Soll, 1996). Consistent with this, McNamara and Bromi- ley (1997) suggested that ill-structured decision situations may be the most common arena for the bias of overconfidence, yet no research published to date has examined this relationship in the field.




The current study addressed this gap with a field study of the prevalence of overconfidence in one type of ill-structured decision situation, introducing a new product. In addition, we examined overconfidence across a range of product introduction decisions, varying from lower-risk, incremental, product introductions to higher-risk, pioneering, introductions. We found that overconfidence was associated with introducing products that were more pioneering than incremental. We also found that managers who were extremely certain that they would achieve certain success factors were more likely to introduce pioneering rather than incremental products. Finally, as predicted, achieving the measured aspects of success was less associated with pioneering product introductions than with incremental introductions.

This study makes a significant contribution to an ongoing methodological debate in the cognitive bias literature. Some scholars have argued that experimental designs may overstate the presence of bias because the studies lack meaningful monetary incentives, which decreases subjects' motivation to act rationally (Schwarz, 1994). Furthermore, laboratory settings may provide subjects with especially salient signals that would not occur in more natural environments (Schwarz, 1994). Others, however, have countered that the uncertainty, com- plexity, and stress associated with making actual strategic decisions might increase the presence of cognitive biases (e.g., Duhaime & Schwenk, 1985). These scholars have asserted that many real-life situations are like the laboratory settings, forcing people to make decisions without the benefit of experience, and therefore, the bias (for example, the primacy effect) that is found in the lab may also occur in actual organizations. Our study provides some insight into this debate by confirming that overconfidence occurs in actual strategic decision situations that are ill-structured. Further, our study highlights a pattern of an increase in overconfidence as the riskiness of the decision context increases. In more general terms, the research illus- trates the benefits of iterative alternation between the laboratory and field by using well-established laboratory findings to examine phenomena in natural environments. We also believe that returning to a laboratory design may be the best next step for teasing out the causal directions of the relationships that we found. Similarly, an experimental design may be the best approach for isolating the underlying dynamics of actual diagnostic cues and their relationship to levels of confidence (Soll, 1996).

In addition to contributing these methodological findings, this study also sheds some light on risk-

taking behavior in the field. Some researchers have concluded that individuals decide to take certain high-risk actions because they perceive them to be less risky than others do (e.g., Sitkin & Weingart, 1995). With a few exceptions (e.g., McNamara & Bromiley, 1997), however, field research has not measured the accuracy of perceptions of risk. Our findings suggest that managers taking riskier actions are too certain they will achieve success and thereby underestimate risk. We also extend previous studies by comparing the same phenomenon across varying levels of risk. To date, studies have examined biases at a single level of risk (e.g., Si- mon, Houghton, & Aquino, 2000). The current study overcomes this limitation, indicating that overconfidence and extreme certainty play a greater role in riskier actions than in less risky ones.

Future research should build on the findings of this study to address its limitations. First, as is typical of many organizational studies of risk taking (Palmer & Wiseman, 1999), our research did not address the studied managers' perceptions of return. Because the link between risk taking and return is complex (Jia, Dyer, & Butler, 1999; Weber & Milliman, 1997) and often depends on context, examining this relationship was beyond the scope of this study, but it should be incorporated into future research. Second, we examined smaller firms in one industry, a sampling pattern that limits the study's generalizability. Third, the project's sample was relatively small, primarily because of the heavy demands of studying overconfidence in the field, which required respondents to participate in interviews, provide archival information, and complete instruments 18 months later. Despite the small sample, the study detected the hypothesized associations, suggesting that our findings may be robust.

We also guarded against retrospective bias (Hu- ber & Power, 1985) by interviewing the managers close to the time of the product introduction, interviewing the most knowledgeable person involved in each decision, guaranteeing confidentiality, and making the study's results useful to the respondents. In our study, any retrospective bias would have reduced the presence of overconfidence in two ways. First, managers who had already encountered problems with the emerging product introduction of concern might be reluctant to state they had been certain, at the time of the decision, that the product could achieve the success factors. Second, managers might not admit they failed to achieve a success factor, if 18 months earlier they had stated that they were certain they would achieve the success factor. We minimized the




chance of a spurious relationship between certainty and achieving the success factors by measuring them differently, collecting data on each variable at times 18 months apart, and not reminding the managers of their responses to earlier segments of the research.

Limitations notwithstanding, the study has some important managerial implications. While overconfidence may facilitate a society's economic progress by spurring experimentation (Levinthal & March, 1993), it can lead many individual firms down "pathways to disaster" and to ultimate failure (Barnes, 1984). Given the importance of the various aspects of success examined here to product introductions, managers who are overconfident of achieving them may be especially likely to hurt overall performance. Although overconfidence is difficult to control, researchers have suggested some decision-making tools to help managers guard against overconfidence. Schweiger, Sand- berg, and Ragan (1986) highlighted several group decision-making techniques, such as devil's advocacy and dialectical inquiry, that may help mini- mize individual-level biases during decision making. Also, Winkler and Poses (1993) suggested that individuals may limit their own overconfidence by writing down all the reasons supporting their predictions and all the reasons disconfirming them.

It is important to note that we are not necessarily recommending that managers decrease risk taking, but instead are suggesting that managers should not rely on inappropriately extreme certainty to facilitate the decision process, because such reliance can lead to overcommitting up-front resources, hinder- ing willingness to monitor assumptions, and failing to make needed adjustments (Sykes & Dunham, 1995). Instead, organizations should build appro- priate cultures and rewards systems that facilitate informed risk taking and learning.

Taken as a whole, the current research indicates that considering certain types of actions may place managers in a decision environment that increases their levels of certainty, which, in turn, encourages action. Ironically, it may be more difficult to achieve success in these environments. It is our

hope that these findings will spur additional field and laboratory studies, thereby generating a greater accumulation of knowledge about the relationship between overconfidence and risky action in different decision contexts.

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Mark Simon ([email protected]) is an associate professor of strategic management at Oakland University. He received his Ph.D. from Georgia State University. His research interests include risk perception and cognitive biases as they relate to proactive firm behaviors.

Susan M. Houghton ([email protected]) is an as-

sociate professor of strategic management at the J. Mack Robinson College of Business, Georgia State University. She received her Ph.D. from the University of North Carolina at Chapel Hill. Her research focuses on the cognitive aspects of strategic decision making and executive team cognitive processes.



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