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The emerging business partner role of management accountants (MAs) results in an increased requirement of MAs to make business decisions. Frame dependence cognitive biases regularly influence decisions made in conditions of uncertainty, as is the case in business decision-making. Consequently, this study aims to examine susceptibility of MAs to frame dependence bias.

A survey was conducted among an international sample of practising MAs. The proportion of MAs influenced by framing bias was analysed and compared to findings in other populations. Logistic regression was then used to determine whether MAs who exhibit a higher preference for evidence-based (as opposed to intuitive) decision-making are more susceptible to framing bias.

Despite a comparatively high preference for evidence-based decision-making, the prevalence of framing bias among MAs is comparable to that of other populations. A higher preference for evidence-based decision-making was found to only be associated with higher susceptibility to endowment effect bias.

To the best of the authors’ knowledge, this is the first study to comprehensively examine framing bias for MAs as a group of decision-makers. Additionally, this study’s sample consists of practising MAs, and not only students.

The main objective of this paper is to analyze the dynamic volatility spillovers between the investor's behavioral biases, the macroeconomic instability factors and the value at risk of the US Fintech stock market before and during the COVID-19 pandemic.

The authors used the methodologies proposed by Diebold and Yilmaz (2012) and the wavelet approach.

The wavelet coherence results show that during the COVID-19 period, there was a strong co-movement among value at risk and each selected variables in the medium-run and the long-run scales. Diebold and Yilmaz's (2012) method proved that the total connectedness index raised significantly during the COVID-19 period. Moreover, the overconfidence bias and the financial stress index are the net transmitters, while the value at risk and herding behavior variables are the net receivers.

This study offers some important implications for investors and policymakers to explain the impact of the COVID-19 pandemic on the risk of Fintech industry.

The study findings might be useful for investors to better understand the time–frequency connectedness and the volatility spillover effects in the context of COVID-19 pandemic. Future research may deal with investors' ability of constructing portfolios with another alternative index like cryptocurrencies which seems to be a safer investment.

To the best of the authors' knowledge, this is the first study that relies on the continuous wavelet decomposition technique and spillover volatility to examine the connectedness between investor behavioral biases, uncertainty factors, and Value at Risk of US Fintech stock markets, while taking into account the recent COVID-19 pandemic.

Research in the domain of behavioral finance has proven that investors demonstrate irrational behavior while making investment decisions. In a similar domain, the primary objective of this research is to prioritize the behavioral biases that influence cryptocurrency investors' investment decisions in the Indian context.

A fuzzy analytic hierarchy process (F-AHP) was used to prioritize the behavioral factors impacting cryptocurrency investors' investment decisions. Overconfidence and optimism, anchoring, representativeness, information availability, herding, regret aversion, and loss aversion are among the primary biases evaluated in the present study.

The findings suggested that the two most important influential criteria were herding and regret aversion, with loss aversion and information availability being the least influential criteria. Opinions of family, friends, and colleagues about investment in cryptocurrency, the sale of cryptocurrencies that have increased in value, the avoidance of selling currencies that have decreased in value, the agony of holding losing cryptocurrencies for too long rather than selling winning cryptocurrencies too soon, and the purchase of cryptocurrencies that have fallen significantly from their all-time high are the most important sub-criteria.

This survey only covered active cryptocurrency participants. Additionally, the study was limited to individual crypto investors in one country, India, with a sample size of 467 participants. Although the sample size is appropriate, a larger sample size might reflect the more realistic scenario of the Indian crypto market.

The study is relevant to individual and institutional cryptocurrency investors, crypto portfolio managers, policymakers, researchers, market regulators, and society at large.

To the best of the authors' knowledge, no prior research has attempted to explain how the overall importance of various criteria and sub-criteria related to behavioral factors that influence the decision-making process of crypto retail investors can be assessed and how the priority of focus can be established, particularly in the Indian context.

This article aims to systematically review the literature published in recognized journals focused on cognitive heuristic-driven biases and their effect on investment management activities and market efficiency. It also includes some of the research work on the origins and foundations of behavioral finance, and how this has grown substantially to become an established and particular subject of study in its own right. The study also aims to provide future direction to the researchers working in this field.

For doing research synthesis, a systematic literature review (SLR) approach was applied considering research studies published within the time period, i.e. 1970–2021. This study attempted to accomplish a critical review of 176 studies out of 256 studies identified, which were published in reputable journals to synthesize the existing literature in the behavioral finance domain-related explicitly to cognitive heuristic-driven biases and their effect on investment management activities and market efficiency as well as on the origins and foundations of behavioral finance.

This review reveals that investors often use cognitive heuristics to reduce the risk of losses in uncertain situations, but that leads to errors in judgment; as a result, investors make irrational decisions, which may cause the market to overreact or underreact – in both situations, the market becomes inefficient. Overall, the literature demonstrates that there is currently no consensus on the usefulness of cognitive heuristics in the context of investment management activities and market efficiency. Therefore, a lack of consensus about this topic suggests that further studies may bring relevant contributions to the literature. Based on the gaps analysis, three major categories of gaps, namely theoretical and methodological gaps, and contextual gaps, are found, where research is needed.

The skillful understanding and knowledge of the cognitive heuristic-driven biases will help the investors, financial institutions and policymakers to overcome the adverse effect of these behavioral biases in the stock market. This article provides a detailed explanation of cognitive heuristic-driven biases and their influence on investment management activities and market efficiency, which could be very useful for finance practitioners, such as an investor who plays at the stock exchange, a portfolio manager, a financial strategist/advisor in an investment firm, a financial planner, an investment banker, a trader/broker at the stock exchange or a financial analyst. But most importantly, the term also includes all those persons who manage corporate entities and are responsible for making their financial management strategies.

Currently, no recent study exists, which reviews and evaluates the empirical research on cognitive heuristic-driven biases displayed by investors. The current study is original in discussing the role of cognitive heuristic-driven biases in investment management activities and market efficiency as well as the history and foundations of behavioral finance by means of research synthesis. This paper is useful to researchers, academicians, policymakers and those working in the area of behavioral finance in understanding the role that cognitive heuristic plays in investment management activities and market efficiency.

This paper examines the relationship between behavioral biases, and demand decisions for life insurance products in Uganda.

Data were collected from 351 life insurance policyholders in Uganda. The authors used a cross-sectional survey by applying a structured questionnaire. Descriptive analysis was conducted and hypothesized relationships between the constructs were evaluated through the use of structural equation modeling.

Results indicate that, behavioral biases are significant predictors of life insurance demand among Ugandan policyholders. Also, the two behavioral bias variables (heuristic bias and prospect bias) are significant predictors of demand decisions for life insurance products.

These results are helpful for both insurers and regulators. For insurers, it is now evident that demand decisions for life insurance products are not fully rational. It is imperative for insurers to simplify life insurance product information (heuristics), integrate product education and widen dissemination of product information (prospect bias) to allow policyholders to come up with optimal demand decisions. While for insurance policymakers, the study provides an understanding of behavioral biases. With such insights, policymakers can identify exploitative and deceptive information that target policyholders to better guide life insurance documentation and product designs.

This study is the first to offer insights into behavioral biases' influence on demand decisions for life insurance products in a developing country like Uganda. By integrating prospects and expected utility theory, this study examines rationality and irrationality in demand decisions for life insurance products.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-03-2023-0201

The adoption of mobile payment remains low in certain regions, highlighting the need to identify the factors that enable and inhibit its adoption. This study aims to address this gap by investigating the role of information security, loss aversion and the moderating influence of the herd effect on Inertia and behavioral intentions in the adoption of mobile payment systems.

A structural equation model was developed and tested with 332 valid questionnaires to examine the proposed hypotheses.

The empirical results reveal that information security plays a significant role as an enabler, while loss aversion acts as an inhibitor of mobile payment adoption. Furthermore, the study uncovers the moderating influence of the herd effect on the relationship between Inertia and behavioral intentions.

This study was conducted in a specific region and may not be generalizable to other regions. Future studies could expand the sample size and scope to enhance the external validity of the findings.

This study offers practical implications for mobile payment service providers. Understanding the key enabling and inhibiting factors identified in this study can guide providers in designing and improving their services. Strengthening information security measures can help build trust among potential adopters, while offering incentives can mitigate the impact of loss aversion and encourage early adoption.

The findings of this study have social implications as they contribute to promoting the adoption of mobile payment systems. Increased adoption can enhance financial inclusion and stimulate economic development.

This study provides novel insights into the enabling and inhibiting factors of mobile payment adoption and highlights the moderating role of the herd effect. By shedding light on the influence of social norms on individual behavior in the context of mobile payment adoption, this study contributes to the existing literature and advances our understanding of this phenomenon.

This study aims to use a qualitative approach to explore and clarify the mechanism by which heuristic-driven biases influence the decisions and performance of individual investors actively trading on the Pakistan Stock Exchange (PSX). It also aims to identify how to overcome the negative effect of heuristic-driven biases, so that finance practitioners can avoid the expensive errors which they cause.

This study adopts an interpretative approach. Qualitative data was collected in semistructured interviews, in which the target population was asked open-ended questions. The sample consists of five brokers and/or investment strategists/advisors who maintain investors’ accounts or provide investment advice to investors on the PSX, who were selected on a convenient basis. The researchers analyzed the interview data thematically.

The results confirm that investors often use heuristics, causing several heuristic-driven biases when trading on the stock market, specifically, reliance on recognition-based heuristics, namely, alphabetical ordering of firm names, name memorability and name fluency, as well as cognitive heuristics, such as herding behavior, disposition effect, anchoring and adjustment, repetitiveness, overconfidence and availability biases. These lead investors to make suboptimal decisions relating to their investment management activities. Due to these heuristic-driven biases, investors trade excessively in the stock market, and their investment performance is adversely affected.

This study provides a practical framework to explore and clarify the mechanism by which heuristic-driven biases influence investment management activities. To the best of authors’ knowledge, the current study is the first to focus on links between heuristic-driven biases, investment decisions and performance using a qualitative approach. Furthermore, with the help of a qualitative approach, the investigators also highlight some factors causing an increased use of heuristic variables by investors and discuss practical approaches to overcoming the negative effects of heuristics factors, so that finance practitioners can avoid repeating the expensive errors which they cause, which also differentiates this study from others.

This paper aims to identify the impacts of sociodemographic covariates on behavioural biases (BB) scores; the psychometric evidence of the BB measurement instruments; and the main BB that influences the decision-making of individual investors.

Papers were retrieved through search using keywords in ten databases. This systematic review is based on 69 peer-reviewed papers, most of which were published between 2017 and 2021. The relevance of the included papers was assessed through the analysis of statistical/psychometric methods used, and content analysis of the BB literature and its sociodemographic correlations.

Overconfidence is higher in men and not related to age. There was no consensus regarding the relationship between BB and other sociodemographic variables. Most measuring instruments are ad hoc, showing ≤ 4 types of psychometric evidence and assessing ≤ 9 BB. Therefore, the findings demonstrate that there is no gold standard instrument for measuring investors’ BB. Furthermore, 37 BB were cited as influencers of individual investors’ decision-making and overconfidence, herding, anchoring, representativeness and loss aversion were the most prevalent.

Considering that very few systematic reviews have been published in the behavioural finance area, this paper highlights the current state-of-the-art and identifies significant gaps in the literature that can be explored by further research.

To the best of the authors’ knowledge, this is the first systematic review that analyses the psychometric properties of instruments used for individual investors BB assessment.

This paper aims to review, systematize and integrate existing research on disposition effect and investments. This study conducts bibliometric analysis, including performance analysis and science mapping and thematic analysis of studies on disposition effect.

This study adopted a thematic and bibliometric analysis of the papers related to the disposition effect. A total of 231 papers published from 1971 to 2021 were retrieved from the Scopus database for the study, and bibliometric analysis and thematic analysis were performed.

This study’s findings demonstrate that research on the disposition effect is interdisciplinary and influences the research in the domain of both corporate and behavioral finance. This review indicates limited research on cross-country data. This study indicates a strong presence of work on investor psychology and behavioral finance when it comes to the disposition effect. The findings of thematic analysis further highlight that most of the research has focused on prospect theory, trading strategies and a few cognitive and emotional biases.

The findings of this study can be used by investors to minimize their biases and losses. The study also highlights new techniques in machine learning and neurosciences, which can help investment firms better understand their clients’ behavior. Policymakers can use the study’s findings to nudge investors’ behavior, focusing on minimizing the effects of the disposition effect.

This study has performed the quantitative bibliometric and thematic analysis of existing studies on the disposition effect and identified areas of future research on the phenomenon of disposition effect in investments.

Given the complexity of building information modeling (BIM) adoption decisions in small- and medium-sized enterprises (SMEs) in the Architecture, Engineering and Construction (AEC) industry, understanding BIM adoption decision-making through the net effect of a single factor on BIM adoption decisions alone is limited. Therefore, this paper analyzed the co-movement effect of managers' psychological factors on the BIM adoption decisions from the perspective of managers' perceptions. The purpose is to let managers have a deep understanding of their BIM adoption decisions, and put forward targeted suggestions for the AEC industry to promote the adoption of BIM by SMEs.

Data from 192 managers in SMEs collected by the questionnaire were used in a fuzzy set qualitative comparative analysis (fsQCA). Due to the limitations of fsQCA in making the best use of the data used, as a complement to fsQCA, necessary conditions analysis (NCA) was used to analyze the extent to which necessary conditions influenced the outcome.

(1) NCA analysis shows that high perceived resource availability (PRA) and high performance expectancy (PE) are necessary conditions for high BIM adoption intention (AI). (2) fsQCA analysis shows that high PE is the single core condition for high AI. fsQCA analysis identifies three configurations of managers' psychological factors, reflecting three types of managers' decision preferences, namely benefit preference, loss aversion and risk avoidance, respectively. Different decision preferences may lead to different BIM adoption strategies, such as full in-house use, partial in-house/outsourcing and full outsourcing of BIM processes. (3) High perceived risk (PR) and low perceived business value of BIM (PBV) are the core conditions for low AI.

This paper expands on the application of fsQCA to context of BIM adoption decisions. Based on the results of fsQCA analysis, this paper also establishes the relationship between managers' decision-making psychology and BIM adoption strategy choice and analyzes the impact of different decision biases on BIM adoption strategy choice. It concludes with suggestions for encouraging managers to adopt BIM and for avoiding decision-making bias.