Explaining the ambiguous impact of overconfidence on corporate decision-making: a critique of the research methodology
Explaining the ambiguous impact of overconfidence on corporate decision-making: a critique of the research methodology

Summary/Abstract: Prior entrepreneurship research shows that individuals often possess biased expectations regarding their chances of success in the market compared to objective reality, as well as to their success and profitability compared to their peers. This distorted, biased view on one’s chances of success is referred to as overconfidence. The present study addresses the effect of overconfidence on corporate decision-making with regard to the methodology used in economic and psychological studies. Current research provides contradictory and inconclusive results about the effect of overconfidence on various Chief Executive Officers’ decisions and profitability. In this study, I try to explain this inconclusiveness by outlining some of the most important methodological issues in the overconfidence research. In psychological literature, there is a wide consensus among researchers about the robustness of overconfidence in human reasoning. This cognitive bias has been demonstrated in many populations and work domains; like clinical psychologists, drivers, financial analysts, investors, stock market specialists, statisticians, basketball players, or managers. In the literature, overconfidence appears mainly in three different constructs – calibration of probabilities, overestimation, and overplacement. The calibration of probabilities is measured by comparing individuals’ subjective probability judgments with the real objective probability. Overestimation is based on comparing individuals’ performance in a particular task with their belief about how they will perform or how they performed. Finally, overplacement is measured by comparing individuals’ belief about their own performance with the belief about the performance of other individuals. According to these three constructs, overconfidence can be defined as a systematic tendency to overestimate one’s own ability to make accurate forecasts, or as an overestimation of one’s own performance, or knowledge, compared to his/her actual performance, or others’ knowledge. In recent decades, authors from economic disciplines started to omit the direct measurement of overconfidence and instead they have often searched for various indirect variables that could serve as proxies for overconfidence; like holding options beyond rational thresholds, purchasing stocks of one’s own company despite the high exposure to risk, or chief executive officers’ press portrayals. Additionally, the effect of overconfidence has started to be linked and sometimes confused with other similar concepts like optimism or illusion of control. Authors often use findings from multiple different constructs as a basis for their hypotheses about the effect of overconfidence in corporate decision-making. Moreover, they often use different measurement tools or other proxies for examining overconfidence compared to the previous studies they reported. This confusion of different forms of overconfidence together with different operationalizations causes difficulty in integrating knowledge about particular overconfidence constructs. In this paper, I describe, firstly, the origins and differences in operationalization between economic and psychology studies. Several widely-used measures and proxies of overconfidence in economic research are described and the diversity of using these measures in previous studies is showed. Subsequently, I discuss how different forms of overconfidence impact the decision-making and performance of entrepreneurs. In this part, the study focuses on the three most frequent areas that are reflected in the current literature; namely the effect of overconfidence on financial decision-making, firm profitability, and entrepreneurs’ innovativeness. It is showed that studies in these areas often bring contradictory findings; mainly in the context of risk-taking, debt usage, or dividend payment, and this contradiction seems to result mostly from using different operationalizations of overconfidence. The final part of the study outlines several possible ways how problems with methodology and inconclusiveness in the overconfidence research could be solved. Firstly, is the importance of finding and using a valid direct overconfidence measure in entrepreneurship research. The ability to make an accurate reasoning about one’s own performance or abilities depends on how the question/task format is designed, what information about testing an individual possesses, how their reasons will be evaluated by researcher, or what reference group will be used in the evaluation. Considering this, many overconfidence measures do not meet these conditions. Moreover, indirect measures of overconfidence not only miss these conditions, but they also miss the main principle of direct overconfidence measures, which is the comparison of one’s reasoning about one’s performance or abilities with one’s real performance or abilities. Therefore, it is questionable whether indirect measures and proxies used in economic literature really measure overconfidence, i.e. they investigate the biased reasoning about one’s performance, ability, or knowledge. Considering this validity issue, the direct measures for examining overconfidence should be preferred in future research. They could be focused on a direct examination of individuals’ beliefs about their performance, knowledge, and abilities necessary for any entrepreneurial activity; like managerial or functional skills (finance, distribution, sales, marketing, leadership, etc.). The second way to address methodology issues in the overconfidence research is to test whether and to what extent some of the widely-used indirect overconfidence measures correlate with direct measures. Economic studies often use two or more overconfidence measures, but most of these measures are indirect. Combining indirect and direct measures could help to find out whether indirect measures are really associated with biased reasoning about one’s performance or abilities. Finally, the third way to solve the knowledge integration problem is conducting metaanalyses regarding the effect of overconfidence on specific CEOs’ corporate decision-making. In these analyses the type of overconfidence measurement should be examined as a moderator of the effect of overconfidence on corporate decision-making. This could identify how different overconfidence measures affect specific corporate decisions and hopefully explain some contradictory findings in current literature. Besides the three main proposals, there are also other more general crucial factors that need to be taken into account when designing measurement tools or improving validity and reproducibility of the overconfidence research methodology. This concerns mostly various questionable research practices; like selective reporting of variables or results, p-hacking, or harking, in order to support the widespread notion of robustness of overconfidence in human reasoning and decision-making. Considering the proposed ways of improving the methodology in overconfidence research, a joint and vital step is to properly distinguish overconfidence constructs and also other related constructs like optimism, or illusion of control.

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