Browsing by Author "Taskin N"

Now showing 1 - 9 of 9
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    Cognitive biases in developing biased artificial intelligence recruitment system
    (University of Hawai‘i at Mānoa, 2021-01-01) Soleimani M; Intezari A; Taskin N; Pauleen D; Bui TX
    Artificial Intelligence (AI) in a business context is designed to provide organizations with valuable insight into decision-making and planning. Although AI can help managers make decisions, it may pose unprecedented issues, such as datasets and implicit biases built into algorithms. To assist managers with making unbiased effective decisions, AI needs to be unbiased too. Therefore, it is important to identify biases that may arise in the design and use of AI. One of the areas where AI is increasingly used is the Human Resources recruitment process. This article reports on the preliminary findings of an empirical study answering the question: how do cognitive biases arise in AI? We propose a model to determine people's role in developing AI recruitment systems. Identifying the sources of cognitive biases can provide insight into how to develop unbiased AI. The academic and practical implications of the study are discussed.
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    Different perspectives on engagement, where to from here? A systematic literature review
    (John Wiley and Sons Ltd and the British Academy of Management, 2023-12-29) Wittenberg H; Eweje G; Taskin N; Forsyth D
    Engagement has emerged as a significant focus in contemporary management research, widely acknowledged for its positive impact on wellbeing and performance. However, over 30 years since its introduction, the concept of engagement remains fractured with multiple definitions, ongoing theoretical debates, and inconsistent empirical evidence of practical value. This review addresses the evolving nature of work-related engagement, recognizing the need for fresh perspectives to better understand this complex phenomenon. To facilitate progressing the research agenda beyond current debates, we used a meta-narrative review as a systematic approach for synthesizing our findings and problematizing techniques to generate innovative ideas. Our review identified six distinct groups, each arguing for different conceptualizations of engagement. We illuminated opportunities for further research directions by mapping and challenging dominating narratives. Specifically, our review highlights the need to conduct research outside the predominant positivist/postpositivist perspective. It also identifies a need for additional research to understand how task-level engagement happens through the interplay of individuals and the environment. Our study makes significant conceptual contributions by offering clear boundaries of existing knowledge, an alternative conceptualization of engagement, and a platform for new directions. Contribution to literature review methodology using integrative and generative approaches is also discussed.
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    Harvesting Wisdom on Social Media for Business Decision Making
    (HICSS, 2022-01-01) Yu J; Taskin N; Pauleen DJ; Jafarzadeh H; Bui TX
    The proliferation of social media provides significant opportunities for organizations to obtain wisdom of the crowds (WOC)-type data for decision making. However, critical challenges associated with collecting such data exist. For example, the openness of social media tends to increase the possibility of social influence, which may diminish group diversity, one of the conditions of WOC. In this research-in-progress paper, a new social media data analytics framework is proposed. It is equipped with well-designed mechanisms (e.g., using different discussion processes to overcome social influence issues and boost social learning) to generate data and employs state-of-the-art big data technologies, e.g., Amazon EMR, for data processing and storage. Design science research methodology is used to develop the framework. This paper contributes to the WOC and social media adoption literature by providing a practical approach for organizations to effectively generate WOC-type data from social media to support their decision making.
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    Introduction to the Judgement, Big Data-Analytics and Decision-making Minitrack
    (University of Hawai‘i at Mānoa, 2021-01-05) Pauleen D; Weerasinghe K; Taskin N; Intezari A; Bui TX
    2021 is the first year that the Judgement, Big Data-Analytics and Decision-making mini-track has been offered. The track's objective is to monitor and advance our knowledge of the convergent technologies of Big Data and analytics and their role in augmenting knowledge for better management decision-making. The track attracted seven submissions of which five were accepted. The papers form a diverse group, offering case studies of big data analytics projects and critical analysis of various factors that impact the successful or unsuccessful use of data/analytics in organizational settings.
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    Investigating the Determinants of Big Data Analytics Adoption in Decision Making: An Empirical Study in New Zealand, China, and Vietnam
    (Association for Information Systems, 2022-06-28) Yu J; Taskin N; Nguyen CP; Li J; Pauleen DJ
    Background: As a breakthrough technology, big data provides an opportunity for organizations to acquire business value and enhance competitiveness. Many companies have listed big data analytics (BDA) as one of their top priorities. However, research shows that managers are still reluctant to change their work patterns to utilize this new technology. In addition, the empirical evidence on what determines their adoption of BDA in management decision making is still rare. Method: To more broadly understand the determinants affecting managers’ actual use of BDA in decision making, a survey was conducted on a sample of 363 respondents from New Zealand, China, and Vietnam who work in different managerial roles. The dual process theory, the technology–organization–environment framework, and the key associated demographic characteristics are integrated to form the theoretical foundation to study the internal and external factors influencing the adoption. Results: The findings illustrate that the common essential factors across countries linking BDA in decision making are technology readiness, data quality, managers’ and organizational knowledge related to BDA, and organizational expectations. The factors that are more situation-dependent and evident in one or two countries’ results are managers’ predilection toward valuing intuition and experience over analytics and organizational size. Conclusion: The findings enrich the current literature and provide implications for practitioners on how they can improve the adoption process of this new technology.
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    Investigating the theoretical constructs of a green lean six sigma approach towards environmental sustainability: A systematic literature review and future directions
    (MDPI (Basel, Switzerland), 7/10/2020) Farrukh A; Mathrani S; Taskin N
    Green lean six sigma (GLSS) is an emerging approach towards environmental sustainability in conjunction with operational achievements. The success of this approach is premised on an understanding of the different components of a GLSS program; being the determinants for its outcomes. The aim of this paper is to investigate the various constructs of GLSS that play an essential role in achieving environmental sustainability. For this purpose, a systematic review of available literature has been conducted to evaluate the drivers, enablers (tools), and outcomes of a GLSS strategy as well as its critical success factors and barriers. Findings reveal that these constructs of GLSS as a holistic approach can facilitate an organization to better accomplish environmental objectives such as waste minimization, emission reduction, and resource conservation as compared to constructs of only one or any two of these strategies. Based on the analysis, an integrated GLSS framework is developed for environmental sustainability in addition to identifying vital research gaps and future directions.
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    Making sense of COVID-19 over time in New Zealand: Assessing the public conversation using Twitter
    (PLOS, 2021-12-15) Jafarzadeh H; Pauleen DJ; Abedin E; Weerasinghe K; Taskin N; Coskun M; Mehmood R
    COVID-19 has ruptured routines and caused breakdowns in what had been conventional practice and custom: everything from going to work and school and shopping in the supermarket to socializing with friends and taking holidays. Nonetheless, COVID-19 does provide an opportunity to study how people make sense of radically changing circumstances over time. In this paper we demonstrate how Twitter affords this opportunity by providing data in real time, and over time. In the present research, we collect a large pool of COVID-19 related tweets posted by New Zealanders-citizens of a country successful in containing the coronavirus-from the moment COVID-19 became evident to the world in the last days of 2019 until 19 August 2020. We undertake topic modeling on the tweets to foster understanding and sensemaking of the COVID-19 tweet landscape in New Zealand and its temporal development and evolution over time. This information can be valuable for those interested in how people react to emergent events, including researchers, governments, and policy makers.
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    Redesigning the Management Capabilities Development Index
    (The Massey People, Organisation, Work and Employment Research (MPOWER) Group, 2018-03-15) Junaid F; Parker J; Taskin N
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    Reducing AI bias in recruitment and selection: an integrative grounded approach
    (Taylor and Francis Group, 2025-03-20) Soleimani M; Intezari A; Arrowsmith J; Pauleen DJ; Taskin N
    Artificial Intelligence (AI) is transforming business domains such as operations, marketing, risk, and financial management. However, its integration into Human Resource Management (HRM) poses challenges, particularly in recruitment, where AI influences work dynamics and decision-making. This study, using a grounded theory approach, interviewed 39 HR professionals and AI developers to explore potential biases in AI-Recruitment Systems (AIRS) and identify mitigation techniques. Findings highlight a critical gap: the HR profession’s need to embrace both technical skills and nuanced people-focused competencies to collaborate effectively with AI developers and drive informed discussions on the scope of AI’s role in recruitment and selection. This research integrates Gibson’s direct perception theory and Gregory’s indirect perception theory, combining psychological, information systems, and HRM perspectives to offer insights into decision-making biases in AI. A framework is proposed to clarify decision-making biases and guide the development of robust protocols for AI in HR, with a focus on ethical oversight and regulatory needs. This research contributes to AI-based HR decision-making literature by exploring the intersection of cognitive bias and AI-augmented decisions in recruitment and selection. It offers practical insights for HR professionals and AI developers on how collaboration and perception can improve the fairness and effectiveness of AIRS-aided decisions.