Browsing by Author "Pauleen DJ"

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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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    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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    Mitigating cognitive biases in developing AI-assisted recruitment systems: A knowledge-sharing approach
    (IGI Global, 2022) Soleimani M; Intezari A; Pauleen DJ
    Artificial intelligence (AI) is increasingly embedded in business processes, including the human resource (HR) recruitment process. While AI can expedite the recruitment process, evidence from the industry, however, shows that AI-recruitment systems (AIRS) may fail to achieve unbiased decisions about applicants. There are risks of encoding biases in the datasets and algorithms of AI which lead AIRS to replicate and amplify human biases. To develop less biased AIRS, collaboration between HR managers and AI developers for training algorithms and exploring algorithmic biases is vital. Using an exploratory research design, 35 HR managers and AI developers globally were interviewed to understand the role of knowledge sharing during their collaboration in mitigating biases in AIRS. The findings show that knowledge sharing can help to mitigate biases in AIRS by informing data labeling, understanding job functions, and improving the machine learning model. Theoretical contributions and practical implications are suggested.