Cognitive biases have a significant impact on the decision-making process in various spheres of life, and particularly — in economics. Different theories explain biased behavior and describe specific types of cognitive biases. This literature review focuses on exploring the most common variants of cognitive biases, such as risk aversion, anchoring, and overconfidence. Scholarly articles separated into themes related to certain theories and bias types were used as a primary source of information.
Prospect Theory, Risk Aversion
The Prospect Theory formulated by Daniel Kahneman and Amos Tversky states that agents evaluate their decisions under risk with a set of psychological criteria. Stemming from that knowledge, Gregoriou et al. (2019) created a seven-factor capital asset pricing model (CAPM), which explains variations in asset returns. According to Gregoriou et al. (2019), incorporating peak and end variables allowed to develop the most accurate asset pricing model. Therefore, the Prospect Theory proved its relevance for academics and practitioners in finances.
Since Prospect Theory assumes that decision-makers (DM) are not necessarily rational, it also proved to be helpful for the evaluation of decision-making units (DMU) performance. Liu et al. (2019) introduced a Prospect Cross-Efficiency (PCE) model for cross-efficiency performance evaluation of DMUs, which enabled to capture the risk attitude of the DM. Their approach could be potentially applied to different evaluation problems, such as financial management and investment selection.
However, the Prospect Theory has also been criticized from an ethical perspective. Dreisbach and Guevara (2019) argued that Prospect Theory could not support its claim that intuitive judgments are subject to the arbitrary effects of framing. The non-consequentialist ideals of human’s intrinsic value and non-violability of rights explained judgment in ethical dilemmas better (Dreisbach & Guevara, 2019). As a result, the use of Prospect Theory might be suboptimal for resolving ethical matters.
Economists often assume that the majority of people dislike taking risks. However, O’Donoghue and Somerville (2018) concluded that real-world risk aversion is not that straightforward as the standard expected utility model suggests. For instance, people systematically demonstrate risk-seeking behavior in certain situations, such as horse races and gambling, where almost all bets have negative expected payoffs (O’Donoghue & Somerville, 2018). Therefore, there is a strong need for alternative risk aversion models which could explain non-rational behavior better.
Anchoring: Positive and Negative
Anchoring (a disproportional influence of a particular value) is one of the most important biases for studying since it may impair decisions due to wrong estimates. According to Costa et al. (2017), the anchoring theme is widely popular in scholarly discourse since 435 out of 923 articles found on cognitive bias were focused on it. The effect directly affects decision-making by forcing the decision-maker to tend to one of the previously presented values.
As a cognitive bias, anchoring is often understood as a manifestation of irrational behavior. However, Lieder et al. (2018) argued that anchoring and adjustment could be understood as a signature of resource-rational information processing rather than an irrationality of the mind. Their rational process model explained ten anchoring phenomena and illustrated the potential for developing theories, which can reconcile the human mind with seemingly irrational cognitive biases.
Moreover, certain works revealed a positive influence of the anchoring effect. For example, Parveen and Siddiqui (2018) found that anchoring improved the probability of increasing investment profits and making better financial decisions, unlike the other common bias — overconfidence. On certain occasions, a high or positive anchor can lead to beneficial outcomes. Wu et al. (2018) studied a so-called newsvendor problem from the anchoring perspective and came to a conclusion that a high anchor under the homogeneous competition prevents understocking and leads to an increase in profit. Therefore, anchoring bias does not necessarily lead to ultimately bad decision-making.
However, the anchoring effect has a negative implication for target setting and achievement. Timing and planning are essential for any business project since its success relies on the ability to meet the schedule. Anchoring could affect the manager’s estimations of project duration, making them overly optimistic or pessimistic, depending on the anchor. A low anchor would lead to underestimation of required time, while a high anchor would make the manager overestimate it (Lorko et al., 2019). As a result, the project team would face a lack of time and fail to meet the schedule unless the team works fast, or the project would be considered too lengthy.
Lorko et al. (2019) found that the anchoring effect influences estimation of task duration and, most importantly, that bias persists over time. Moreover, the bias does not disappear even if the task is repeatedly estimated and executed (Lorko et al., 2019). Therefore, businesses should utilize specific tools such as databases instead of relying on human estimations in these matters.
Furthermore, anchoring impaired judgment and created biased in spheres that require maximal impartiality. For example, research by Bellé et al. (2017) proved that public sector managers and employees exposed to a high anchor received better performance ratings than their colleagues subjected to a low anchor. In addition, people who received positive economic or financial information were also exposed to an increased anchoring effect (Costa et al., 2018). Furthermore, the anchoring effect was prevalent, as 96% of members of the test group demonstrated it to a certain degree (Costa et al., 2018). Overall, the anchoring effect remains an issue for decision-making, and its benefits are rare and highly situational.
Overconfidence can be defined as an exaggeration of chances to perform a specific task successfully. Combrink and Lew (2019) found that experienced investors rated themselves higher in terms of performance in comparison to their peers. As a result, the resulting overconfidence may have consequences for their view of the causes of mistakes (Combrink & Lew, 2019). Therefore, there is an evident need for developing investor performance criteria, which would prevent the emergence of overconfidence bias.
However, as in the case of anchoring, overconfidence bias can sometimes yield positive results. Johnson and Fowler (2011) argued that overconfident managers could be audacious and make bold decisions which could provide a competitive edge (as cited in Moosa and Ramiah, 2017). Nevertheless, overconfidence is linked to other objectively harmful conditions, such as self-serving bias (Moosa and Ramiah, 2017). As a result, unmanaged overconfidence among the personnel may lead to dangerous consequences for the business.
Furthermore, overconfidence bias is often tied to other potentially harmful biases. Levy and Tasoff (2017) found a link between an exponential-growth bias (EGB) — a tendency to underestimate growth processes, and overconfidence. According to Levy and Tasoff (2017), a market solution for EGB is not developed since biased individuals are overconfident and show low demand for tools that would improve their financial decisions. Overall, the vicious connection between the exponential-growth and overconfidence biases undermines global financial literacy.
Another insight into overconfidence bias can be found in studying the entrepreneurs. Ilieva et al. (2018) conducted research on Austrian entrepreneurs and revealed that they are prone to overconfidence. Being a single founder of the enterprise led to an increased probability of overconfidence bias (Ilieva et al., 2018). In addition, overconfidence bias was associated with the failure of young businesses (Ilieva et al., 2018). Therefore, debiasing techniques for combating overconfidence, such as feedback provision, seem vital for entrepreneurs.
Overconfidence bias can also make a negative impact on corporate finances. Koo and Yang (2018) found that overconfident managers tend to overestimate the future payoffs of their investments and believe that they can control them. Moreover, overconfident managers reinforce optimism about their risky projects (Koo & Yang, 2018). In the end, the overconfidence of those managers leads to potential financial risks for their companies, especially if their risks pay off initially.
Financial analysts and stock portfolio managers represent another group of professionals affected by overconfidence bias. The impairment of judgment caused by the bias could either lead to prediction overconfidence or certainty overconfidence (Baker et al., 2017). In the former case, the manager sets a narrow confidence interval around their forecasts, leaving no room for market fluctuations. In the latter case, the manager believes that their predictions are bound to happen and has too much confidence in their accuracy (Baker et al., 2017). In the end, overconfidence can lead to the creation of concentrated stock portfolios, which can cause significant losses if the industry of choice faces an unexpected crisis. Therefore, overconfidence bias hampers the process of target setting and achievement in finances and stock trading.
The previous examples showed that even experienced professionals are vulnerable to overconfidence bias. Czaja and Röder (2020) claimed that nonprofessionals are also susceptible to overconfidence. Moreover, an overconfident trader attracts higher investment flows from the investors when showing their bias (Czaja & Röder, 2020). However, an overconfident behavior proved to be harmful on the long distance (Czaja & Röder, 2020). Overall, overconfidence bias poses a more significant threat for decision-making and appears to have even fewer advantages than the anchoring effect.
Correlation Between Experience and Cognitive Biases
Another important issue for research regarding the subject of cognitive biases lies in their possible correlation with professional experience. While several scholarly articles touched on the connection between the worker’s level of experience and their susceptibility to anchoring and overconfidence, they have not revealed a universal regularity. For instance, Lorko et al. (2019) argued that estimating and executing a similar task does not eliminate anchoring in project management. Therefore, even the experienced managers who had followed through the routine tasks repeatedly are prone to influence of anchoring effect.
However, a situation with biases and experience in business and entrepreneurship differs significantly. Ilieva et al. (2018) found that previous business experience boosts confidence among entrepreneurs, which can be considered a positive influence. In relation to business, overconfidence stemmed from being a sole owner rather than from previous experience (Ilieva et al., 2018). In addition, Szerb and Vörös (2021) revealed that slightly or moderately experienced business owners were more prone to overconfidence than their inexperienced or more experienced colleagues. Therefore, the role of professional experience in the emergence of cognitive biases among entrepreneurs remains unclear.
Finally, certain studies deny the correlation between the level of experience and susceptibility to biases. Huffman et al. (2019) concluded that the likelihood of overconfidence among the managers does not diminish with experience. In other words, both beginners and veterans were equally prone to overconfidence bias. Due to that fact, the apparent lack of a clear correlation between experience and cognitive biases leads to the hypothesis that a tendency towards biases is an inherent trait of human psychology. As a result, the anchoring effect and overconfidence could likely be present among the partner managers regardless of their professional experience.
Existing Research Gap
To summarize, the research efforts on cognitive biases in reviewed scholarly articles were primarily focused on investigating implications for stock trading, investments, and management in corporate finances. So far, the subject of anchoring and overconfidence bias in the decision-making for partnership managers has not been thoroughly studied. Therefore, research exploring the cognitive biases among the partner managers would be helpful for closing a research gap in several directions.
Firstly, a quantitative analysis based on questionnaires would allow revealing how wide the cognitive biases are spread among the partner managers. The literature review showed that entrepreneurs, traders, investors, and public sector workers are prone to anchoring and overconfidence; a survey would prove whether the partner managers are different in that regard. Moreover, the knowledge about the prevalent types of biases would be valuable for suggesting debiasing countermeasures. Secondly, the research results would provide empirical evidence on how the revealed biases affect the partner managers’ decision-making. This information would make a valuable insight for companies that strive to forge strategic alliances since cognitive biases could potentially result in sub-optimal decisions from the partner managers. Finally, the research would clarify whether the cognitive biases, in case of their presence, have any unexpected positive implications for strategic partnership management.
Research on cognitive biases among the partner managers would be useful both for academics and business practitioners. On the one hand, the scholars could extrapolate the utilized approach to different, previously uncovered branches of management. On the other hand, the companies could use the questionnaire-based research technique to evaluate cognitive bias spread among their partner managers. Strategic partnership management is a particularly delicate discipline, which requires calculated decision-making. Therefore, it is important to close all gaps in knowledge to ensure that the company establishes beneficial partner relationships.
Baker, H. K., Filbeck, G., and Ricciardi, V. (2017). ‘How behavioural biases affect finance professionals’. The European Financial Review, pp. 25–29.
Bellé, N., Cantarelli, P., and Belardinelli, P. (2017). ‘Cognitive biases in performance appraisal: experimental evidence on anchoring and halo effects with public sector managers and employees’. Review of Public Personnel Administration, 37(3), pp. 275–294.
Combrink, S., and Lew, C. (2020). ‘Potential underdog bias, overconfidence and risk propensity in investor decision-making behavior’. Journal of Behavioral Finance, pp. 21(4), pp. 337–351.
Costa, D. F., de Melo Carvalho, F., de Melo Moreira, B. C., and do Prado, J. W. (2017). ‘Bibliometric analysis on the association between behavioral finance and decision making with cognitive biases such as overconfidence, anchoring effect and confirmation bias’. Scientometrics, 111(3), pp. 1775–1799.
Costa, D. F., de Melo Moreira, B. C., de Melo Carvalho, F., and Silva, W. S. (2018). ‘Anchoring effect in managerial decision-making in accountants and managers: an experimental study’. REBRAE, 11(3), pp. 425–445.
Czaja, D., and Röder, F. (2020). ‘Self-attribution bias and overconfidence among nonprofessional traders’. The Quarterly Review of Economics and Finance, 78, pp. 186–198.
Dreisbach, S., and Guevara, D. (2019). ‘The Asian Disease problem and the ethical implications of Prospect Theory’. Noûs, 53(3), pp. 613–638.
Gregoriou, A., Healy, J. V., and Le, H. (2019). ‘Prospect theory and stock returns: a seven factor pricing model’. Journal of Business Research, 101, pp. 315–322.
Huffman, D., Raymond, C., and Shvets, J. (2019). ‘Persistent overconfidence and biased memory: Evidence from managers’. Pittsburgh: University of Pittsburgh, pp. 1–45.
Ilieva, V., Brudermann, T., and Drakulevski, L. (2018). ‘“Yes, we know!” (Over) confidence in general knowledge among Austrian entrepreneurs’. Plos One, 13(5), pp. 1–15.
Koo, J. H., and Yang, D. (2018). ‘Managerial overconfidence, self-attribution bias, and downwardly sticky investment: evidence from Korea’. Emerging Markets Finance and Trade, 54(1), pp. 144–161.
Levy, M. R., and Tasoff, J. (2017). ‘Exponential-growth bias and overconfidence’. Journal of Economic Psychology, 58, pp. 1–14.
Lieder, F., Griffiths, T. L., Huys, Q. J., and Goodman, N. D. (2018). ‘The anchoring bias reflects rational use of cognitive resources’. Psychonomic Bulletin & Review, 25(1), pp. 322–349.
Liu, H. H., Song, Y. Y., and Yang, G. L. (2019). ‘Cross-efficiency evaluation in data envelopment analysis based on prospect theory’. European Journal of Operational Research, 273(1), pp. 364–375.
Lorko, M., Servátka, M., and Zhang, L. (2019). ‘Anchoring in project duration estimation’. Journal of Economic Behavior & Organization, 162, pp. 49–65.
Moosa, I. A., and Ramiah, V. (2017). ‘Overconfidence and self-serving bias’. In The Financial Consequences of Behavioural Biases. Palgrave Macmillan, Cham, pp. 83–95.
O’Donoghue, T., and Somerville, J. (2018). ‘Modeling risk aversion in economics’. Journal of Economic Perspectives, 32(2), pp. 91–114.
Parveen, S., and Siddiqui, M. A. (2018). ‘Anchoring heuristic, disposition effect and overconfidence bias in investors: a case of Pakistan stock exchange’. Abasyn Journal of Social Sciences, 11(2), pp. 280–294.
Szerb, L., and Vörös, Z. (2021). ‘The changing form of overconfidence and its effect on growth expectations at the early stages of startups’. Small Business Economics, 57(1), pp. 151–165.
Wu, M., Bai, T., and Zhu, S. X. (2018). ‘A loss-averse competitive newsvendor problem with anchoring’. Omega, 81, pp. 99–111.