Search results

The purpose of this study is to raise awareness about the ethical implications of artificial intelligence (AI) in the library and information industry, specifically focusing on bias and discrimination. It aims to highlight the need for proactive measures to mitigate these issues and ensure that AI technology is developed and implemented in an ethical and unbiased manner.

This viewpoint paper presents a critical analysis of the ethical implications of bias and discrimination in the library and information industry with respect to AI. It explores current practices and challenges in AI implementation and proposes strategies to address bias and discrimination in AI systems.

The findings of this study reveal that bias and discrimination are significant concerns in AI systems used in the library and information industry. These biases can perpetuate existing inequalities, hinder access to information and reinforce discriminatory practices. This study identifies key strategies such as data collection and representation, algorithmic transparency and inclusive design to address these issues.

This study contributes to the existing literature by examining the specific challenges of bias and discrimination in AI implementation within the library and information industry. It provides valuable insights into the ethical implications of AI technology and offers practical recommendations for professionals to confront and mitigate bias and discrimination in AI systems, ensuring equitable access to information for all users.

The purpose of this study is to address a critical gap in enterprise resource planning (ERP) implementation process for small and medium-sized enterprises (SMEs) by acknowledging and countering cognitive biases through a cognitive bias awareness matrix model. Cognitive biases such as temporal discounting and optimism bias often skew decision-making, leading SMEs to prioritize short-term benefits over long-term sustainability or underestimate the challenges involved in ERP implementation. These biases can result in costly missteps, underutilizing ERP systems and project failure. This study enhances decision-making processes in ERP adoption by introducing a matrix that allows SMEs to self-assess their level of awareness and proactivity when addressing cognitive biases in decision-making.

The design and methodology of this research involves a structured approach using the problem-intervention-comparison-outcome-context (PICOC) framework to systematically explore the influence of cognitive biases on ERP decision-making in SMEs. The study integrates a comprehensive literature review, empirical data analysis and case studies to develop the Cognitive Bias Awareness Matrix. This matrix enables SMEs to self-assess their susceptibility to biases like temporal discounting and optimism bias, promoting proactive strategies for more informed ERP decision-making. The approach is designed to enhance SMEs’ awareness and management of cognitive biases, aiming to improve ERP implementation success rates and operational efficiency.

The findings underscore the profound impact of cognitive biases and information asymmetry on ERP system selection and implementation in SMEs. Temporal discounting often leads decision-makers to favor immediate cost-saving solutions, potentially resulting in higher long-term expenses due to the lack of scalability. Optimism bias tends to cause underestimating risks and overestimating benefits, leading to insufficient planning and resource allocation. Furthermore, information asymmetry between ERP vendors and SME decision-makers exacerbates these biases, steering choices toward options that may not fully align with the SME’s long-term interests.

The study’s primary limitation is its concentrated focus on temporal discounting and optimism bias, potentially overlooking other cognitive biases that could impact ERP decision-making in SMEs. The PICOC framework, while structuring the research effectively, may restrict the exploration of broader organizational and technological factors influencing ERP success. Future research should expand the range of cognitive biases and explore additional variables within the ERP implementation process. Incorporating a broader array of behavioral economic principles and conducting longitudinal studies could provide a more comprehensive understanding of the challenges and dynamics in ERP adoption and utilization in SMEs.

The practical implications of this study are significant for SMEs implementing ERP systems. By adopting the Cognitive Bias Awareness Matrix, SMEs can identify and mitigate cognitive biases like temporal discounting and optimism bias, leading to more rational and effective decision-making. This tool enables SMEs to shift focus from short-term gains to long-term strategic benefits, improving ERP system selection, implementation and utilization. Regular use of the matrix can help prevent costly implementation errors and enhance operational efficiency. Additionally, training programs designed around the matrix can equip SME personnel with the skills to recognize and address biases, fostering a culture of informed decision-making.

The study underscores significant social implications by enhancing decision-making within SMEs through cognitive bias awareness. By mitigating biases like temporal discounting and optimism bias, SMEs can make more socially responsible decisions, aligning their business practices with long-term sustainability and ethical standards. This shift improves operational outcomes and promotes a culture of accountability and transparency. The widespread adoption of the Cognitive Bias Awareness Matrix can lead to a more ethical business environment, where decisions are made with a deeper understanding of their long-term impacts on employees, customers and the broader community, fostering trust and sustainability in the business ecosystem.

This research introduces the original concept of the Cognitive Bias Awareness Matrix, a novel tool designed specifically for SMEs to evaluate and mitigate cognitive biases in ERP decision-making. This matrix fills a critical gap in the existing literature by providing a structured, actionable framework that effectively empowers SMEs to recognize and address biases such as temporal discounting and optimism bias. Its practical application promises to enhance decision-making processes and increase the success rates of ERP implementations. This contribution is valuable to behavioral economics and information systems, offering a unique approach to integrating cognitive insights into business technology strategies.

This paper aims to examine how fear of missing out (FOMO) and investment intention mediate the relationship between behavioral biases and investment decisions of retail investors in the Indian stock market.

The present research comprises two cross-sectional quantitative studies, where Study A involves data from 405 self-employed and Study B involves 393 salaried investors. Data was attained through questionnaires – the partial least squares structural equation modeling was used for data analysis.

The outcomes show that herding, overconfidence and loss aversion bias significantly impact investment intention and FOMO on both studies. Furthermore, the outcomes also indicate that herding and loss aversion bias significantly influence investment decisions in studies (A and B); however, overconfidence bias insignificantly affects the investment decisions in Study A. Besides, the results also reveal a substantial relationship between FOMO, investment intention and investment decision.

The findings of this paper assist practitioners (financial analysts and retail investors) in considering the various ways of analyzing investment decision outcomes by considering the joint effect of several biases.

This paper is an initial attempt to propose a new theoretical framework and empirically examine the impact of behavioral biases on investment decisions by considering the FOMO and investment intention among self-employed and salaried investors. This study also contributes to the behavioral finance literature; other researchers may find it valuable to attain their goals.

The cognitive biases exhibited by investors could hinder their capacity for logical reasoning and impact their perception and reaction to information when making financial choices. So, this study was done to identify the behavioral biases that hinder investors' sound decision-making at the Pakistan Stock Exchange (PSX).

A cross-sectional study was undertaken employing a causal research design approach. Questionnaires were administered to individual investors of the PSX as the data collection methodology. The data were subsequently analyzed through the utilization of the Smart PLS Structural Equation Modeling (SEM) technique.

The results suggest that information factors and cognitive biases, namely home bias, geographical bias, investor sentiment, salience, and over/under reaction have a positive association with the investors' choices at PSX.

The study’s emphasis is on the impact of behavioral biases on individual investors only, even though such biases also influence the investment decisions of institutional investors.

The study holds implications for scholars engaged in the field of behavioral finance as well as professionals involved in the stock market, particularly those interacting with individual investors and personal finance. Additionally, the current study will take into account investors, financial advisors, practitioners, policymakers, investment experts, stakeholders or target groups, etc. to support various groups in their professional activity and to help them overcome such biases that influence their sound decision-making power.

The innovative aspect of this research is its ability to advance the understanding of the conceptual underpinnings and social structure of behavioral biases by critically analyzing the body of prior research and adding value to the existing body of literature on behavioral finance in Pakistan by investigating the combined impact of never-studied variables, i.e. geographical bias and information variables, understudied behavioral variables, i.e. home bias and salience and studied variables, i.e. investor sentiment and over/under reaction on individual investor investment decisions at PSX.

The purpose of this paper is to analyse five biases in the valuation of financial investments using a mental time travel framework involving thought investments – with no objective time passing.

An investment’s initial value, together with any periodic funding cash-flows, are mentally projected forward (at an expected rate of return) to give the value at the investment horizon; and this projected value is mentally discounted back to the present. If there is a difference between the initial and present values, then this can imply a bias in valuation.

The study identifies (and gives examples of) five real-world valuation biases: biased funding cash-flow estimates (e.g., mega infrastructure projects); biased rate of return projections (e.g., market crises, tech stock carve-outs); biased discount rate estimates (e.g., dual-listed shares, dual-class shares, short-termism, time-risk misperception, and long-termism); time-duration misestimation or perception bias when projecting (e.g., time-contracted projections which lead to short-termism); and time-duration misestimation or perception bias when discounting (e.g., time-extended discounting which also leads to short-termism). More than one bias can be operating at the same time and we give an example of low levels of retirement savings being the result of the biased discounting of biased projections. Finally, we consider the effects of the different biases of different agents operating simultaneously.

The paper examines key systematic misestimation and psychological biases underlying financial investment valuation pricing anomalies.

Enterprise social media (ESM) are expressive spaces where users exchange emotional workplace communication. While some studies have explored how positive emotions may be contagious, little research explored the notion that negative communication may accumulate on enterprise social media. This study explores perceived negativity bias and its correlates in the context of ESM.

This study relies on survey data collected from 599 employees of a global organization. The response rate was 18.7%. Structural equation modeling was used to test the hypotheses.

The results contribute to research on ESM by demonstrating that perceived negativity bias is positively related to feelings of accountability and negatively associated with social support. Furthermore, the results indicate that unmet communication expectations on ESM can have implications for perceived social support beyond online contexts and accountability through perceived negativity bias.

The findings demonstrate how employees' unmet expectations about ESM use increase feelings that a digital environment is disproportionately negative, which may create an “unsafe” space for employees and a fear of being held accountable for their contributions. This study highlights how the Expectation-Disconfirmation Theory provides a fruitful framework for studying enterprise social technologies.

This study suggests that work is not merely a rational endeavor, and that emotions and personal feelings (including negative ones) may shape workplace communication on ESM. We contribute to research on ESM use by using the Expectation-Disconfirmation Theory as a lens to study antecedents and implications of perceived negativity bias.

This paper aims to conduct a comparative analysis of the impact of market uncertainty on the degree of accuracy and bias of analysts' earnings forecasts versus four model-based earnings forecasts.

The study employs panel regression analysis on a sample of Egyptian listed companies from 2005 to 2022 to examine the impact of market uncertainty on the accuracy and bias of each type of earnings forecast.

The empirical analysis reveals that market uncertainty significantly affects analysts’ earnings forecast accuracy and bias, while model-based earnings forecasts are less affected. Furthermore, the Earnings Persistence and Residual Income model-based earnings were found to be superior in terms of exhibiting the least susceptibility to the impact of market uncertainty on their forecast accuracy and biasness levels, respectively.

The findings have important implications for stakeholders within the financial realm, including investors, financial analysts, corporate executives and portfolio managers. They emphasize the importance of considering market uncertainty when formulating earnings forecasts, while concurrently highlighting the potential benefits of using alternative forecasting methods.

To our knowledge, the influence of market uncertainty on analysts' earnings forecast accuracy and bias in the MENA region, particularly in the Egyptian market, remains unexplored in existing research. Additionally, this paper contributes to the existing literature by pinpointing the forecasting method, specifically distinguishing between analysts-based and model-based approaches, whose predictive quality is less adversely impacted by market uncertainty in an emerging market.

The purpose of this study is to investigate the impact of behavioral biases on investment decisions through a serial mediation of overconfidence and disposition effects.

The authors assess the behavioral biases affecting the investment decisions of life insurance policyholders through the serial mediation of overconfidence and disposition effects using a structured questionnaire. The study included 501 life insurance policyholders who were selected using a snowball sampling technique.

The results of this study revealed that behavioral biases influence the investment decisions of life insurance policyholders. The results also support the serial mediation model, where behavioral biases influence the investment decisions of life insurance policyholders via overconfidence and disposition effects.

This study makes a theoretical contribution to the field of behavioral finance by exploring the influences of behavioral biases on investment decisions. It also introduces overconfidence and disposition effects as serial mediators between behavioral biases and investment decisions. The study will be helpful for researchers, academicians and policymakers in the development of a more comprehensive model in the area of behavioral finance and in raising awareness regarding those biases among policyholders in order to improve their investment strategy.

This study has extended the ongoing simple mediation model by integrating overconfidence and disposition effects in a serial mediation model between behavioral biases and investment decisions. The study will contribute to the area of behavioral finance, as it is the first time this particular study has been conducted according to the authors’ knowledge.

Examines the relationship of individual demographic characteristics, work‐setting factors and work outcomes to perceived bias, both personally experienced and observed in organizational decision making. Data were collected from 829 women and 766 men employed in a single professional services firm using anonymously completed questionnaires. Although both women and men perceived bias, women reported significantly higher levels of both personally experienced and observed bias. Perceived bias was correlated with work settings and work outcomes similarly for women and men. Women and men experiencing and observing more bias, described the work setting as less favourable and were less satisfied, more likely to quit and saw the organization as less committed to fairness and due process. Draws implications for management and organizations.

Using a large‐scale database of a major business services company, self‐reported usage volume data were compared with actual usage volume. Several business‐related factors were then examined in relation to the survey response bias. Survey response bias was found to be lower for clients that use the services more extensively, had been in business for a longer period of time, and have smaller number of employees. Survey response bias was also found to be lower when the level of involvement with the service (lower level of management in this study) was greater. Such response bias information would be useful for managers when making sales forecasts or market share estimations using survey responses.