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Decision-making competence (DMC) of entrepreneurs and managers is a longstanding topic in this increasingly globalized world. These figures operate in conditions not within their own control, and good levels of DMC are often considered to be desirable for the flourishing of business and society. This paper reports an empirical investigation on the DMC of entrepreneurs and managers, in an attempt to inform about their tendencies to incur in risky and costly choices.

Three cognitive biases associated with operational strategies and individual characteristics of entrepreneurs and managers, namely under/overconfidence (UOC, i.e. self-confidence in taking decisions), resistance to sunk costs (RSC, i.e. propensity to take cost investments) and consistency in risk perception (CRP, i.e. how well individuals understand probability rules) were considered . Cognitive biases measures were used in a cross-sectional study on a sample of n = 639 entrepreneurs and n = 512 managers. Data collected via online survey were analyzed using descriptive statistics and inferential statistics to determine differences among entrepreneurs and managers DMC.

Analyses reveal that entrepreneurs exhibit higher levels of UOC compared to managers with a marked presence of UOC among entrepreneurs at younger ages. Conversely, performance regarding RSC improves with higher education levels while age and RSC are positively correlated only for managers, regardless of education. Lastly, entrepreneurs and managers resulted as not being affected by CRP. This study discusses these results to provide initial insights for further avenues of research and practice.

The study offers an innovative, evidence-based viewpoint on how entrepreneurs and managers deal with risky and costly decisions. It offers an initial understanding of the role of UOC, RSC and CRP, that is specific cognitive biases associated with operational strategies and individual characteristics, in the DMC of these working figures. The study forwards avenues of scrutiny of quick-witted entrepreneurs and systematic managers.

Expert evaluation is the backbone of the multi-criteria decision-making (MCDM) techniques. The experts make pairwise comparisons between criteria or alternatives in this evaluation. The mainstream research focus on the ambiguity in this process and use fuzzy logic. On the other hand, cognitive biases are the other but scarcely studied challenges to make accurate decisions. The purpose of this paper is to propose pilot filters – as a debiasing strategy – embedded in the MCDM techniques to reduce the effects of framing effect, loss aversion and status quo-type cognitive biases. The applicability of the proposed methodology is shown with analytic hierarchy process-based Technique for Order-Preference by Similarity to Ideal Solution method through a sustainable supplier selection problem.

The first filter's aim is to reduce framing bias with restructuring the questions. To manipulate the weights of criteria according to the degree of expected status quo and loss aversion biases is the second filter's aim. The second filter is implemented to a sustainable supplier selection problem.

The comparison of the results of biased and debiased ranking indicates that the best and worst suppliers did not change, but the ranking of suppliers changed. As a result, it is shown that, to obtain more accurate results, employing debiasing strategies is beneficial.

To the best of the author's knowledge, this approach is a novel way to cope with the cognitive biases. Applying this methodology easily to other MCDM techniques will help the decision makers to take more accurate decisions.

The purpose of this paper is to investigate if and how firms approach debiasing and what determines its success. In particular, this study examines if debiasing is effective in reducing cognitive decision biases. This paper also investigates organizational characteristics that determine the effectiveness of debiasing.

This study uses survey data from German firms to answer the research questions. Target respondents are individuals in a senior management accounting function.

In line with the hypotheses, this paper finds that debiasing can reduce cognitive biases. Moreover, this study finds that psychological safety not only directly influences the occurrence of cognitive biases but is also an important factor that determines the effectiveness of debiasing.

This paper provides evidence that debiasing can serve as a powerful management accounting tool and discusses debiasing in the context of recent management accounting literature. This study also adds to the stream of research that investigates the role of psychological safety in organizations by highlighting its importance for successful debiasing.

This paper informs firms that use or intend to use debiasing about crucial determinants to consider when debating its implementation, i.e. psychological safety. This study also identifies risk management as a potential interface for the implementation of systematic debiasing.

While previous research primarily addresses specific cognitive biases and debiasing mechanisms using lab experiments, this is – to the best of the knowledge – the first study investigating cognitive biases and debiasing on a broad conceptual level using survey data.

This paper aims to link findings from laboratory-based decision-making research and decision-making competence (DMC) aspects that may be central for career-related decision-making processes. Past research has identified individual differences in rational responses in decision situations, which the authors refer to as DMC. Although there is a robust literature on departures from rational responses focused on heuristics and biases (H&B) in decision-making, such evidence is largely confined to group-level differences observed in psychology laboratories and has not been extended to the realm of career development and workforce behavior.

By first introducing the concept of DMC and contextualizing it within organizations and the work environment, the paper outlines a review on recent development concerning debiasing interventions in organizations and provides insights on how these may be effective with regard to organizational performance and individual career development.

The contribution presents a perspective to improve knowledge about career decision-making competence (C-DMC) by presenting an approach linking decision-making research to interventions aiming at managing H&B and systematic misperceptions in career processes.

This contribution is one of the few linking decision-making research to the applied context of organizations and of career competences. Moreover, while some research has treated decision-making skills as traits, this contribution provides support to consider them developable as competencies.

This paper aims to empirically compare the degree to which two technological interventions, based on the computer-supported collaborative learning (CSCL) and the technology acceptance model (TAM), were associated with a different incidence of financial biases.

The study adopted a quasi-experimental research design. The authors randomly assigned the participants (N = 507) to one of two training conditions or a control group, and in turn, we assessed the incidence of financial biases after the training interventions.

Participants who took part in the TAM-based group reported lower financial biases than those in the CSCL-based training group and the control group.

Literature suggests that two educational approaches, i.e. the CSCL and the TAM, can implement individuals’ financial decision-making. These educational approaches involve technology to support individuals in reducing the incidence of cognitive biases. This study contributes by advancing empirical evidence on technological supports for interventions to improve financial decision-making.

Suboptimal decision-making may lead to adverse consequences both at the individual and social levels. This paper contributes to the literature on debiasing interventions by offering initial evidence on technological-based interventions in the domain of financial decision-making. The authors discuss the application of this evidence in lifelong training.

This study provides evidence on how different technological interventions are associate with a lower incidence of financial biases.

Despite evidence showing that cognitive biases – the systematic errors made by humans during cognitive processing, are prevalent among decision-makers, there is a lack of theoretical models providing insight into how these limitations of human mind might affect decisions made during performance management. This study aims to fill this gap and contribute to performance management scholarship by proposing a conceptual framework broadening our understanding of the role of cognitive biases in performance improvements practices and by highlighting remedies for cognitive biases.

Using benchmarking as an example, the authors integrate the knowledge from performance management and cognitive psychology literature. Examples of cognitive biases possible during benchmarking are used to illustrate how the limitations of human mind might have a critical role in performance management.

The cognitive biases might diminish the positive effect of performance improvement practice on organizational performance. As there is a prevalence of cognitive biases coupled with the inability of individuals to recognize and face them, the remedy for cognitive biases should be sought not at an individual but rather on an organizational level, in creating organizational cognitive biases policy (CBP).

The presented model provides new insights into the role of cognitive biases in performance management and allows seeing CBP as a safeguard against the effects of cognitive biases on performance. By referring to cognitive biases and CBP, our model also helps to understand why the same performance improvement practices might incite different opinions among decision-makers.

Gender bias in artificial intelligence (AI) should be solved as a priority before AI algorithms become ubiquitous, perpetuating and accentuating the bias. While the problem has been identified as an established research and policy agenda, a cohesive review of existing research specifically addressing gender bias from a socio-technical viewpoint is lacking. Thus, the purpose of this study is to determine the social causes and consequences of, and proposed solutions to, gender bias in AI algorithms.

A comprehensive systematic review followed established protocols to ensure accurate and verifiable identification of suitable articles. The process revealed 177 articles in the socio-technical framework, with 64 articles selected for in-depth analysis.

Most previous research has focused on technical rather than social causes, consequences and solutions to AI bias. From a social perspective, gender bias in AI algorithms can be attributed equally to algorithmic design and training datasets. Social consequences are wide-ranging, with amplification of existing bias the most common at 28%. Social solutions were concentrated on algorithmic design, specifically improving diversity in AI development teams (30%), increasing awareness (23%), human-in-the-loop (23%) and integrating ethics into the design process (21%).

This systematic review is the first of its kind to focus on gender bias in AI algorithms from a social perspective within a socio-technical framework. Identification of key causes and consequences of bias and the breakdown of potential solutions provides direction for future research and policy within the growing field of AI ethics.

The peer review history for this article is available at https://publons.com/publon/10.1108/OIR-08-2021-0452

Reaching decisions in a deliberative manner is of utmost importance for boards, as their decision-making impacts entire organisations. The current study aims to investigate (1) the quality of group decisions made by board members, (2) their confidence in, satisfaction with, and reflection on the decision-making, and (3) the effect of two discussion procedures on objective decision quality and subjective evaluations of the decision-making.

Board members of various Dutch non-profit organisations (N = 141) participated in a group decision-making task and a brief questionnaire. According to the hidden-profile paradigm, information was asymmetrically distributed among group members and should have been pooled to reach the objectively best decision. Half of the groups received one of two discussion procedures (i.e. advocacy decision or decisional balance sheet), while the other half received none.

Only a fifth of the groups successfully chose the best decision alternative. The initial majority preference strongly influenced the decision, which indicates that discussion was irrelevant to the outcome. Nevertheless, board members were satisfied with their decision-making. Using a discussion procedure enhanced participants' perception that they adequately weighed the pros and cons, but did not improve objective decision quality or other aspects of the subjective evaluation. These findings suggest that board members are unaware of their biased decision-making, which might hinder improvement.

Rather than using student samples, this study was the first to have board members participating in a hidden-profile task.

Scoring is a widely used, long-established, and universally applicable method of measuring risks, especially those that are difficult to quantify. Unfortunately, the scoring method is often misused in real estate practice and underestimated in academia. The purpose of this paper is to supplement the literature with general rules under which scoring systems should be designed and validated, so that they can become reliable risk instruments.

The paper combines the rules, or axioms, for coherent risk measures known from the literature with those for scoring instruments. The result is a system of rules that a risk scoring system should fulfil. The approach is theoretical, based on a literature survey and reasoning.

At first, the paper clarifies that a risk score should express the variation of a property’s yield and not of its quality, as it is often done in practice. Then the axioms for a coherent risk scoring are derived, e.g. the independence of the risk factors. Finally, the paper proposes procedures for valid and reliable risk scoring systems, e.g. the out-of-time validation.

Although it is a theoretical work, the paper also focuses on practical applicability. The findings are illustrated with examples of scoring systems.

Rules for risk measures and for scoring systems have been established long ago, but the combination is a first. In this way, the paper contributes to real estate risk research and risk management practice.

The authors perform a large‐scale review of debiasing literature with the purpose of deriving a mutually exclusive and exhaustive debiasing taxonomy. This taxonomy is used to conceptualize debiasing activities in the supplier selection process. For each supplier selection‐debiasing construct, scale items are proposed.

A systematic classification approach was used to build a debiasing taxonomy, combined with a Q‐methodology.

Based on the developed and externally validated debiasing taxonomy, five debiasing activities for the supplier selection context are derived. The conceptual investigation of these supplier selection‐oriented debiasing measures helps both researchers and supply managers to gain a better understanding of debiasing mechanisms and to effectively further improve the supplier selection process by integrating behavioral aspects.

This research extends the taxonomy of decision biases developed by Carter, Kaufmann, and Michel, by systematically analyzing strategies to debias the decision‐making process. The highly fragmented research landscape on debiasing was inventoried and structured. As a result, a debiasing taxonomy was created that extracted five main debiasing categories. These were then conceptualized within the context of the supplier selection process. In doing so, debiasing literature from different research streams such as economics, psychology, and behavioral and strategic decision making was systematically integrated into the field of supply management. Proposed scale items allow for empirical investigation as a next step in the development of the nascent field of behavioral supply management.