Cognitive bias in defense acquisition decision-making: insights from the Armed Forces of the Philippines

Behavioral finance incorporates the field of psychology into finance and studies the behavior of individual which are guided by behavioral biases. The current study aims to examine the behavioral biases which can be seen in Nepalese stock investor and studies if the behavioral biases affect the financial decisions of investor or not. The study tested the following behavioral bias: Loss Aversion, Overconfidence, Optimism, Mental Accounting, Illusion of Control, Confirmation and Status Quo Bias. The data was collected from 136 respondents. The sample size was set as minimum of 120 on the basis of rule of thumb of Roscoe (1975). Likewise, four in-depth interviews were taken in order to collect response from institutional investor. The number of interviews for institutional investor was determined on the basis of Rao soft Sample Size Calculator. The study showed that Loss aversion, overconfidence and confirmation bias were correlated with financial decision making of the investor. The correlations were significant. But the regression analysis showed that there is influence of loss aversion, overconfidence and optimism bias in the financial decisions. Confirmation bias did not have significant relationship. Also, the behavioral bias as a whole affects the financial decisions. Likewise, the study also showed that status quo bias and mental accounting bias are prevailed in the institutional investor. These biases also influenced the individual investor financial decisions. As a whole the study shows that Nepalese investor are influenced by behavioral biases.

With the gradual increase of social competition, the enterprise's financial investment management has become an important way to obtain economic benefits and achieve sustainable development. Based on the author's work practice, this paper first analyzes the enterprise investment decision makers' cognitive bias and irrational behavior, and then, it puts forward the financial innovation strategies of the enterprise investment decision: The enterprise investment decision maker must be familiar with the characteristics of various cognitive biases and irrational behaviors in the investment decision, be good at learning, understanding and accepting new investment idea of behavior finance, keep calm, self-discipline mentality, implement collective decision-making and scientific decision-making, establish an investment project termination mechanism.

Cognitive biases significantly influence decision-making in the business world, often leading to irrational choices that impact organizational success. This paper explores the role of cognitive biases in business decision-making and their implications for achieving business excellence. It categorizes key biases, such as confirmation bias, anchoring bias, and loss aversion, and examines their effects on leadership, strategy, and organizational behavior. Additionally, it discusses methods to mitigate these biases through evidence based decision-making and psychological interventions. Understanding and managing cognitive biases can enhance business performance, foster innovation, and drive sustainable success.

Cognitive biases significantly influence decision-making in the business world, often leading to irrational choices that impact organizational success. This paper explores the role of cognitive biases in business decision-making and their implications for achieving business excellence. It categorizes key biases, such as confirmation bias, anchoring bias, and loss aversion, and examines their effects on leadership, strategy, and organizational behavior. Additionally, it discusses methods to mitigate these biases through evidence-based decision-making and psychological interventions. Understanding and managing cognitive biases can enhance business performance, foster innovation, and drive sustainable success.

Background: Behavioral bias factors influence individual decision-making. Technological innovations in the financial services industry have introduced automated financial advisors, or robo-advisors, to assist in mutual fund investment decisions and reduce behavioral biases. Purpose: This study aims to prove the influence of overconfidence and loss aversion behavior bias on mutual fund investment decisions by using robo-advisors as moderator variables.Design/methodology/approach: The research sample was 100 respondents with the criteria of young investors in the age range of 18 to 25 who invested in mutual funds for the last five years and were officially registered with the Financial Services Authority. The data processing method uses multiple linear analysis with moderation dummy variable, using a robo-advisor or not.Finding/Result: The results indicate that overconfidence and loss aversion biases significantly impact mutual fund investment decisions positively. Apart from that, the results also show that robo-advisors succeed in weakening the relationship between overconfidence bias and mutual fund investment decisions. Meanwhile, robo-advisors show results that cannot moderate the relationship between loss aversion and mutual fund investment decisions.Conclusion: Robo-advisors moderate the relationship between overconfidence bias and investment decisions but do not moderate the relationship between loss aversion and mutual fund investment decisions. The high overconfidence is caused by the ease of access to information related to investment assets that is widely spread through social media. Young investors are expected to be able to screen all information related to investment knowledge to reduce loss aversion from young investors. It can help investors make more rational decisions.Originality/value (State of the art):This research is unique because it examines the behavioral biases associated with robo-advisors on investment decisions, especially investments in mutual funds. This research is novel and includes artificial intelligence technology developing in finance using robo-advisor and mutual fund investment. These have managerial implications, such as the high overconfidence in the younger generation due to easy access to information related to investment assets, which is widely spread via social media. Knowledge related to finance is considered capable of reducing loss aversion from young investors to help them make more rational and better decisions. Robo-advisor technology has reduced the irrationality of mutual fund investors' investment decisions. The research results show that overconfidence and loss aversion bias positively and significantly influence investment decisions. Apart from that, the results also show that robo-advisors succeed in weakening the relationship between overconfidence bias and investment decisions. Meanwhile, robo-advisors show results that cannot moderate the relationship between loss aversion and investment decisions. Keywords: Robo-advisor, behavioral bias, overconfidence, loss aversion, mutual fund investment decision

Cognitive bias impairs an individual’s financial decision-making. Investors can make less biased and rational financial decisions by addressing and understanding cognitive bias. Many cognitive biases, i.e., overconfidence, loss aversion, confirmation bias, availability heuristics, anchoring bias, etc., may depend on demographic and socio-economic factors. On the other hand, individuals’ risk attitudes can also distort financial decision-making, such as loss aversion, even when gains and losses are of equal magnitude. This paper intends to examine the risk attitude of the respondents and behavioural bias through primary data analysis. We have examined the risk attitude and behavioural biases of the selected respondents. . A “proportional test”, a Pearsonian chi-squared test, and Yates correction were used for data analysis. Herd behaviour is prominent among the younger generation, who has comparatively less economic obligations than the older populace.

Objective: The study aims to investigate the key cognitive and emotional biases influencing the investment decisions of financially literate investors. It focuses on major cognitive biases like overconfidence, anchoring, availability heuristics, representativeness, and confirmation bias, alongside emotional biases such as the endowment effect, regret aversion, loss aversion, FOMO (fear of missing out), and herd behavior. Methods: A systematic literature review (SLR) and meta-analysis were conducted using peer-reviewed academic articles. Effect sizes were calculated to determine the magnitude of the biases, while heterogeneity across studies was analyzed using the I² statistic. Publication bias was assessed using funnel plots and Egger’s test. Results: The meta-analysis revealed moderate effect sizes for both cognitive (0.37) and emotional (0.39) biases. Overconfidence, herd behavior and biases stemming from the influence of technological factors were found to be dominant biases. The heterogeneity was moderate, with I² values of 62% for cognitive biases and 58% for emotional biases. No significant publication bias was detected, and sensitivity analysis confirmed the stability of the results. Conclusion: Financial literacy does not shield investors from cognitive and emotional biases, as even knowledgeable investors fall prey to these influences. The study highlights the need for strategies to reduce these biases to improve investment performance.  

The purpose of this study was to examine the impact of behavioral biases, such as overconfidence, disposition effect, herding, risk aversion, and financial literacy, on investment decision making. The sample was collected using a convenient method, and 338 respondents participated in the study. The study utilized descriptive statistics, ANOVA, independent sample t-tests, correlation, and linear regression analysis to analyze the data. The findings of the study suggest that overconfidence, disposition effect, and risk aversion have a significant positive impact on investment decision making, while herding does not have a significant effect. Furthermore, the results indicate that financial literacy moderate’s overconfidence, disposition effect, risk aversion, and herding negatively. This implies that higher financial literacy levels can help mitigate the impact of these biases on investment decisions. The study provides valuable insights for policymakers, stakeholders, and financial institutions to develop policies and strategies aimed at improving financial literacy. It can be useful for researchers and the general public as well, as it provides a deeper understanding of the behaviour bias and investment decision. Future research can broaden the scope of the study by including new independent variables, such as loss aversion and confirmation bias. Additionally, future research can explore the moderating effect of other factors, such as age and gender, on the relationship between behavioral biases and investment decision-making. Overall, the study highlights the importance of understanding and managing behavioral biases in investment decision making, and suggests that increasing financial literacy can help individuals make more informed investment decisions.

Background: Management Information Systems (MIS) play a critical role in enhancing decision-making and operational efficiency in the banking sector. However, the effectiveness of these systems is often undermined by cognitive biases that distort how individuals process and respond to system-generated information. In the context of Nepalese commercial banks, where MIS adoption is increasing, the role of cognitive bias remains underexplored and insufficiently addressed in both research and practice. Objectives: This study aims to investigate the extent to which cognitive biases anchoring bias, overconfidence bias, loss aversion, and confirmation bias impact the use and effectiveness of MIS in Nepalese commercial banks. The goal is to identify how these psychological factors influence user interaction with MIS and their subsequent decisions. Methods: A quantitative descriptive-correlational research design was used, employing a structured 23 item questionnaire based on validated constructs. A purposive sample of 571 participants, 371 banking customers and 200 employees from Nepalese commercial banks was selected to gather diverse perspectives on MIS usage and cognitive biases. Data analysis involved SPSS 26.0 for descriptive statistics and AMOS 26.0 for confirmatory factor analysis (CFA) and structural equation modeling (SEM) to evaluate model fit and test the hypothesized relationships between cognitive biases and MIS utilization. Results: The study found that cognitive biases limit the effective use of MIS. Overconfidence, anchoring, loss aversion, and confirmation bias affect user decisions. These biases lead to ignored alerts, trust on early data, fear of change, and resistance to updates. Conclusion: Cognitive biases are critical impairments to MIS effectiveness in Nepalese commercial banks. Addressing these through targeted training, cognitive debiasing, and user-centric system design is essential to promote rational decision-making and optimize MIS utility. JEL Classification: G21, M15, D83, C38, O33

AI-Based Detection and Mitigation of Cognitive Bias in Managerial Decision-Making within Management Information Systems

Cognitive biases play a crucial role in shaping corporate financial decisions, particularly in the high-stakes arenas of mergers, acquisitions, and investments. This paper investigates the various cognitive biases that influence the decision-making processes of executives and boards, often leading to suboptimal financial outcomes. By conducting a comprehensive review of existing literature, performing detailed case studies, and employing robust data analysis techniques, this research aims to illuminate the intricate mechanisms through which cognitive biases operate within the realm of corporate finance. The study identifies key biases such as overconfidence, anchoring, confirmation bias, and loss aversion, examining how these psychological phenomena can distort judgment and lead to flawed decision-making. For instance, overconfidence may drive executives to pursue aggressive growth strategies without adequately assessing risks, while confirmation bias can result in the neglect of critical information that contradicts existing beliefs. Furthermore, the paper explores the broader implications of these biases on organizational performance and financial health, highlighting the potential for significant economic repercussions when biases go unchecked. By synthesizing insights from behavioral finance and organizational psychology, this research not only elucidates the impact of cognitive biases but also offers practical recommendations for mitigating their effects. Strategies such as fostering diverse decision-making teams, implementing structured decision-making frameworks, and enhancing awareness through training programs are proposed to help organizations navigate the complexities of corporate finance more effectively. Ultimately, this paper contributes to the understanding of how cognitive biases can shape financial decision-making in corporate settings and underscores the importance of adopting a more analytical and evidence-based approach to enhance decision quality and organizational outcomes.

PURPOSE – Investment decisions are pivotal in shaping individuals' financial well-being and long- term wealth accumulation. However, these decisions are not always made rationally and objectively. Instead, they are often influenced by various psychological factors, including cognitive biases and sociocultural factors, such as gender. Understanding the interplay between gender dynamics and cognitive biases in investment decision-making is crucial for devising effective strategies to enhance financial literacy, promote gender equality, and optimize investment outcomes. Hence, the purpose of this research is to confirm the variables influencing cognitive behavioral biases such as overconfidence bias, confirmation bias, representativeness bias and anchoring bias of male and female investors in their investment decisions.RESEARCH DESIGN – In the current research study, a sample of 400 responses was gathered with the help of questionnaires and retail investors in India were the respondents for the study. Cronbach’s Alpha as used to check the reliability of the data gathered and Confirmatory Factor Analysis (CFA) was used to confirm the variables for the study.FINDINGS – This research paper delivers a second-order CFA model that displays adequate fit for both genders, with some variances in factor loadings, variances, and residuals. This proposes that while the overall structure is dependable, there are gender-specific gradations or shades.ORIGINALITY/VALUE – The study of behavioral bias in decision-making is a dynamic field, with each research study yielding varying results. There are numerous scales available for measuring behavioral biases. The items and dimensions of behavioral biases are well-defined, leading the study to employ Confirmatory Factor Analysis (CFA) to validate the variables influencing behavioral biases in the context of investment decisions among both men and women. The findings of this research are intended to lay the groundwork for further large-scale research.

Managerial decisions significantly influence the trajectory of corporate performance and governance. However, these decisions are not always purely rational; they are often affected by psychological tendencies known as behavioral biases. This paper examines the various types of behavioral biases—such as overconfidence, anchoring, loss aversion, confirmation bias, and herd behavior— that managers exhibit during decision-making. The study explores how these biases impact corporate strategies, resource allocation, risk assessment, and stakeholder engagement. Further, it outlines the implications of such biases on governance structures and long-term business outcomes. The paper concludes by suggesting strategies to mitigate the negative effects of behavioral biases, including training, diversified boards, and implementation of checks and balances.

In the realm of investment decisions, the influence of behavioral biases has emerged as a captivating area of exploration. This article embarks on a comprehensive journey through the landscape of behavioral biases in investment choices, delving into their profound impact on financial markets. Contrary to traditional finance theories assuming rationality, a multitude of empirical evidence attests to the pervasive effects of cognitive and emotional biases. Through an extensive literature review, this article elucidates the intricacies of key biases such as overconfidence, loss aversion, anchoring, confirmation bias, herding behavior, disposition effect, framing effects, and regret aversion. By examining the distinct ways these biases distort investors' judgment and decision-making processes, we unveil the often unexpected deviations from rationality. Each bias, rooted in human psychology, can lead to suboptimal investment behaviors, portfolio misalignments, and heightened market volatility. However, recognizing the impact of these biases provides opportunities for transformative insights. As investment professionals, policymakers, and individuals alike comprehend the subtle nuances of behavioral biases, tailored interventions, educational initiatives, and adaptive strategies can be devised to mitigate their adverse effects. This article not only synthesizes the prevailing research but also charts a course for future investigations. The implications of understanding and addressing behavioral biases extend beyond financial realms, offering a bridge between finance and psychology. As interdisciplinary collaboration gains momentum, pathways for future research become evident, beckoning scholars to delve deeper into the uncharted territories of human behavior and its intricate relationship with investment decisions. Through the exploration of these biases and their potential remedies, this article illuminates the evolving landscape of investment decision-making in a world where cognitive fallacies intersect with financial choicest.

Most investors view the stock market as a place to trade shares where everything is well organized based on data and economic factors. Investors behave rationally after analyzing the market based on related data and news. However, behavioural finance shifts the traditional view, emphasising human emotions and cognitive bias. This review explores the intersection of how human psychology, such as cognitive bias, emotion, and cultural background, drives retail investment decisions across global stock markets in the past decades. Important psychological aspects of loss aversion, overconfidence, herding, and anchoring are examined to determine how they affect market efficiency and the pricing of assets. In the review, we outline the evolution, strengths, and weaknesses of financial theory and models from classical paradigms like efficient markets hypothesis, CAPM, through behavioral finance to emerging neurofinance. In addition, we consider both qualitative and quantitative research concerning cognitive and emotional biases, such as overconfidence, herding, and loss aversion, which influence the decision-making processes of individual investors. By combining empirical data and theoretical models, this review offers a better view of the reasons behind unregulated markets and the role played by cognitive biases in destabilizing the market. The findings point to applying approaches in investment and financial literacy to help curb the detrimental effects of irrational thinking. Keywords: Behavioral Finance, Cognitive Biases, Investor Psychology, Stock Market Investments, Market Volatility, Loss Aversion, Overconfidence, Herd Behavior, etc.