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1 – 10 of over 5000Lutz Kaufmann, Craig R. Carter and Christian Buhrmann
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…
Abstract
Purpose
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.
Design/methodology/approach
A systematic classification approach was used to build a debiasing taxonomy, combined with a Q‐methodology.
Findings
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.
Originality/value
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.
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Xiaohang (Flora) Feng, Shunyuan Zhang and Kannan Srinivasan
The growth of social media and the sharing economy is generating abundant unstructured image and video data. Computer vision techniques can derive rich insights from unstructured…
Abstract
The growth of social media and the sharing economy is generating abundant unstructured image and video data. Computer vision techniques can derive rich insights from unstructured data and can inform recommendations for increasing profits and consumer utility – if only the model outputs are interpretable enough to earn the trust of consumers and buy-in from companies. To build a foundation for understanding the importance of model interpretation in image analytics, the first section of this article reviews the existing work along three dimensions: the data type (image data vs. video data), model structure (feature-level vs. pixel-level), and primary application (to increase company profits vs. to maximize consumer utility). The second section discusses how the “black box” of pixel-level models leads to legal and ethical problems, but interpretability can be improved with eXplainable Artificial Intelligence (XAI) methods. We classify and review XAI methods based on transparency, the scope of interpretability (global vs. local), and model specificity (model-specific vs. model-agnostic); in marketing research, transparent, local, and model-agnostic methods are most common. The third section proposes three promising future research directions related to model interpretability: the economic value of augmented reality in 3D product tracking and visualization, field experiments to compare human judgments with the outputs of machine vision systems, and XAI methods to test strategies for mitigating algorithmic bias.
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The purpose of this paper is to design several methods for enforcing developed countries’ responsibilities under the Green Climate Fund (GCF). The GCF has been one of the core…
Abstract
Purpose
The purpose of this paper is to design several methods for enforcing developed countries’ responsibilities under the Green Climate Fund (GCF). The GCF has been one of the core subjects of the world climate summits held under the United Nations Framework Convention on Climate Change. However, the development of the GCF has not progressed smoothly, and many concerns remain unresolved.
Design/methodology/approach
This paper illustrates three approaches for financing the GCF that vary in terms of the relative weights accorded to environmental responsibility and economic capacity. These three methods include the historical responsibility (HR) principle, the ability to pay (AP) principle and the preference score compromises (PSC) approach (which is a combination of the HR and the AP principles).
Findings
The empirical analysis demonstrates that the USA is the largest contributor to the GCF under the HR principle due to the volume of its historical emissions, whereas the European Union bears the greatest financial responsibility under the AP principle, based on its gross domestic product. Under the PSC approach, the European Union and the USA each undertakes a financial burden that approximates 40 per cent of the total financing for the GCF. These nations are followed by Japan, which has a share of almost 9 per cent.
Originality/value
This study is the first attempt to introduce the PSC concept into discussions regarding GCF financing. A scheme of burden sharing that combines environmental responsibility and economic capacity factors is developed and introduced. The respective weights assigned to the two factors are determined based on the Borda rule in voting theory, which avoids the arbitrary allocation of weights between the HR and the AP. These findings will be useful for mobilising the GCF in the Post-Kyoto era.
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This study aims to propose a framework of bias in construction project dispute resolution (CPDR hereafter).
Abstract
Purpose
This study aims to propose a framework of bias in construction project dispute resolution (CPDR hereafter).
Design/methodology/approach
With reference to the literatures on effects of bias, manifestations of bias in CPDR were developed. Based on data obtained from construction professionals about their frequency of having these bias manifestations, the underlying constructs of biased behaviors were explored by a principal component factor analysis. A confirmatory factor analysis was further conducted to validate the framework of bias in CPDR.
Findings
Four types of bias were identified as the constructs that underlie biased behaviors in CPDR. These four biases were included in the bias framework proposed: preconception, self-affirmation, optimism and interest-oriented. The potency of these types of bias was also evaluated.
Practical implications
First, the findings inform that the existence of bias in CPDR is real. Early detection allows management to intervene and steer CPDR team back to rational courses. Second, this study suggests optimizing CPDR procedures to diminish the chance of bias occurring.
Originality/value
Bias is almost an uncharted area in CPDR. The study fills this research gap by conceptualizing the underlying constructs of biased behaviors. The findings inform construction professionals of the likelihood of practicing biased behaviors in CPDR. Repeated dispute decisions in the commonly used multi-tiered dispute resolution process would enable the creeping in of biases.
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Casey J. McNellis, John T. Sweeney and Kenneth C. Dalton
In crafting Auditing Standard No.3 (AS3), a primary objective of the PCAOB was to reduce auditors' exposure to litigation by raising the standard of care for audit documentation…
Abstract
In crafting Auditing Standard No.3 (AS3), a primary objective of the PCAOB was to reduce auditors' exposure to litigation by raising the standard of care for audit documentation. We examine whether the increased documentation requirements of AS3 affect legal professionals' perceptions of audit quality and auditor responsibility in the event of an audit failure. Our experiment consists of a 3 × 2 between-participants design with law students serving as proxies for legal professionals. The results of our experiment indicate that when an audit procedure, namely the investigation of inconsistent evidence, is not required to be documented, legal professionals perceive the performance of the work itself but not its documentation to significantly increase audit quality and reduce the auditor's responsibility for an audit failure. When documentation of the procedure is required, as per AS3, legal professionals perceive enhanced audit quality and reduced auditor responsibility only if the performance of the work is documented.
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James Prater, Konstantinos Kirytopoulos and Tony Ma
One of the major challenges for any project is to prepare and develop an achievable baseline schedule and thus set the project up for success, rather than failure. The purpose of…
Abstract
Purpose
One of the major challenges for any project is to prepare and develop an achievable baseline schedule and thus set the project up for success, rather than failure. The purpose of this paper is to explore and investigate research outputs in one of the major causes, optimism bias, to identify problems with developing baseline schedules and analyse mitigation techniques and their effectiveness recommended by research to minimise the impact of this bias.
Design/methodology/approach
A systematic quantitative literature review was followed, examining Project Management Journals, documenting the mitigation approaches recommended and then reviewing whether these approaches were validated by research.
Findings
Optimism bias proved to be widely accepted as a major cause of unrealistic scheduling for projects, and there is a common understanding as to what it is and the effects that it has on original baseline schedules. Based upon this review, the most recommended mitigation method is Flyvbjerg’s “Reference class,” which has been developed based upon Kahneman’s “Outside View”. Both of these mitigation techniques are based upon using an independent third party to review the estimate. However, within the papers reviewed, apart from the engineering projects, there has been no experimental and statistically validated research into the effectiveness of this method. The majority of authors who have published on this topic are based in Europe.
Research limitations/implications
The short-listed papers for this review referred mainly to non-engineering projects which included information technology focussed ones. Thus, on one hand, empirical research is needed for engineering projects, while on the other hand, the lack of tangible evidence for the effectiveness of methods related to the alleviation of optimism bias issues calls for greater research into the effectiveness of mitigation techniques for not only engineering projects, but for all projects.
Originality/value
This paper documents the growth within the project management research literature over time on the topic of optimism bias. Specifically, it documents the various methods recommended to mitigate the phenomenon and highlights quantitatively the research undertaken on the subject. Moreover, it introduces paths for further research.
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Lasse Mertins, Debra Salbador and James H. Long
This paper synthesizes the extant research on the outcome effect in the accounting domain, focusing primarily on the context of performance evaluation. It reviews the current…
Abstract
This paper synthesizes the extant research on the outcome effect in the accounting domain, focusing primarily on the context of performance evaluation. It reviews the current state of our knowledge about this phenomenon, including its underlying cognitive and motivational causes, the contexts in which the outcome effect is observed, the factors that influence its various manifestations, and ways in which undesirable outcome effects can be mitigated. It also considers various perspectives about the extent to which outcome effects represent undesirable judgmental bias, and whether this distinction is necessary to motivate research on this topic. The paper is intended to motivate and facilitate future research into the effects of outcome knowledge on judgment in the accounting context. Therefore, we also identify important unanswered questions and discuss opportunities for future research throughout the paper. These include additional consideration of instances in which the outcome effect is reflective of bias, how this bias can be effectively mitigated, ways in which outcome information influences judgment (regardless of whether this influence is considered normative), and how the underlying causes of the outcome effect operate singly and jointly to bring about the outcome effect. We also consider ways that future research can contribute to practice by determining how to encourage evaluators to retain and incorporate the relevant information conveyed by outcomes, while avoiding the inappropriate use of outcome information, and by enhancing external validity to increase the generalizability of experimental results to scenarios frequently encountered in practice.
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Danielle M. Gardner, Caitlin Q. Briggs and Ann Marie Ryan
As COVID-19 cases rose in the US, so too did instances of discrimination against Asians. The current research seeks to understand and document discrimination toward Asians in the…
Abstract
Purpose
As COVID-19 cases rose in the US, so too did instances of discrimination against Asians. The current research seeks to understand and document discrimination toward Asians in the US specifically linked to the global pandemic (study 1). The authors test hypotheses based in social categorization and intergroup contact theories, demonstrating perceived pandemic blame is a mechanism for discrimination (study 2).
Design/methodology/approach
In study 1, the authors survey Asians living in the US regarding experiences and perceptions of COVID-19-related discrimination. In study 2, a two-time point survey examined whether participant perceptions of pandemic blame toward China predict discriminatory behavior toward Asians.
Findings
Study 1 demonstrated that 22.5% of US-residing Asians report personally encountering pandemic-related discrimination. Study 2 indicated that COVID-19 blame attributions toward China predicted anticipated hiring bias and increased physical distancing of Asians at work, associated with higher levels of US identification.
Research limitations/implications
The findings have theoretical implications for research on blame and stigmatization, as well as practical implications regarding bias mitigation.
Originality/value
The present studies advance understanding of event-based blame as a driver of prejudice and discrimination at work and suggest organizations attend to bias mitigation in conjunction with uncertainty reduction communications in challenging times.
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Claartje J. Vinkenburg, Carolin Ossenkop and Helene Schiffbaenker
In this contribution to EDI's professional insights, the authors develop practical and evidence-based recommendations that are developed for bias mitigation, discretion…
Abstract
Purpose
In this contribution to EDI's professional insights, the authors develop practical and evidence-based recommendations that are developed for bias mitigation, discretion elimination and process optimization in panel evaluations and decisions in research funding. An analysis is made of how the expectation of “selling science” adds layers of complexity to the evaluation and decision process. The insights are relevant for optimization of similar processes, including publication, recruitment and selection, tenure and promotion.
Design/methodology/approach
The recommendations are informed by experiences and evidence from commissioned projects with European research funding organizations. The authors distinguish between three aspects of the evaluation process: written applications, enacted performance and group dynamics. Vignettes are provided to set the stage for the analysis of how bias and (lack of) fit to an ideal image makes it easier for some than for others to be funded.
Findings
In research funding decisions, (over)selling science is expected but creates shifting standards for evaluation, resulting in a narrow band of acceptable behavior for applicants. In the authors' recommendations, research funding organizations, evaluators and panel chairs will find practical ideas and levers for process optimization, standardization and customization, in terms of awareness, accountability, biased language, criteria, structure and time.
Originality/value
Showing how “selling science” in research funding adds to the cumulative disadvantage of bias, the authors offer design specifications for interventions to mitigate the negative effects of bias on evaluations and decisions, improve selection habits, eliminate discretion and create a more inclusive process.
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The online economy has not resolved the issue of racial bias in its applications. While algorithms are procedures that facilitate automated decision-making, or a sequence of…
Abstract
Purpose
The online economy has not resolved the issue of racial bias in its applications. While algorithms are procedures that facilitate automated decision-making, or a sequence of unambiguous instructions, bias is a byproduct of these computations, bringing harm to historically disadvantaged populations. This paper argues that algorithmic biases explicitly and implicitly harm racial groups and lead to forms of discrimination. Relying upon sociological and technical research, the paper offers commentary on the need for more workplace diversity within high-tech industries and public policies that can detect or reduce the likelihood of racial bias in algorithmic design and execution.
Design/methodology/approach
The paper shares examples in the US where algorithmic biases have been reported and the strategies for explaining and addressing them.
Findings
The findings of the paper suggest that explicit racial bias in algorithms can be mitigated by existing laws, including those governing housing, employment, and the extension of credit. Implicit, or unconscious, biases are harder to redress without more diverse workplaces and public policies that have an approach to bias detection and mitigation.
Research limitations/implications
The major implication of this research is that further research needs to be done. Increasing the scholarly research in this area will be a major contribution in understanding how emerging technologies are creating disparate and unfair treatment for certain populations.
Practical implications
The practical implications of the work point to areas within industries and the government that can tackle the question of algorithmic bias, fairness and accountability, especially African-Americans.
Social implications
The social implications are that emerging technologies are not devoid of societal influences that constantly define positions of power, values, and norms.
Originality/value
The paper joins a scarcity of existing research, especially in the area that intersects race and algorithmic development.
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