Browsing by Author "Bui TX"

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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.
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.
How can live streamers enhance viewer engagement in eCommerce streaming?
(HICCS, 2021-01-05) Liu GHW; Sun M; Lee NCA; Bui TX
eCommerce live streaming has enabled new forms of customer engagement, where live streamers, viewers and platform owners engage each other in real time to hawk and trade goods and services. Central to live streaming sales are live streamers. It is therefore critical to discover techniques to maximize live streamers' engagement with viewers. Based on the intimacy theory, we propose the perceived intimacy live streamers created improves online engagement with viewers. Our survey results suggest streamers' authenticity, attitudinal similarity and customer response capability enhance intimacy perceived by online viewers, leading to viewers' online engagement. Contributions of our study are discussed.
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.