EXPLORING AND RANKING AI-RELATED RISKS IN DOCTORAL STUDIES: AN EMPIRICAL AHP STUDY OF BUSINESS ADMINISTRATION RESEARCHERS IN VIETNAM
DOI:
https://doi.org/10.62985/j.huit_ojs.vol26.no3E.491Keywords:
Analytic Hierarchy Process (AHP), artificial intelligence, Business Administration, doctoral students, risks in research.Abstract
Amidst the swift transition to digital platforms, integrating artificial intelligence (AI) into academic inquiries offers substantial advantages but simultaneously introduces critical challenges for PhD candidates in the field of Business Administration. This research focuses on identifying and classifying the core risks linked to the adoption of AI, covering dimensions such as data integrity, ethical standards, technical vulnerabilities, and researcher proficiency. Utilizing the Analytic Hierarchy Process (AHP) framework, the investigation gathered insights from a sample of 175 PhD candidates across prominent Vietnamese Universities to evaluate the relative importance of these risk factors. The findings identify four major risk groups: (1) Data risks (weight: 0.56), encompassing incomplete data, biased data and data security concerns; (2) Ethical risks (0.26), involving challenges such as transparency, privacy violations, and AI decision-making bias; (3) Technical risks (0.12), including software errors and inaccurate AI models; and (4) Competence risks (0.06), which pertain to a lack of data analysis skills and difficulties in selecting appropriate AI tools. The research emphasizes the necessity of recognizing and controlling these potential threats, alongside offering strategic recommendations to reduce their impact and bolster AI-related expertise for PhD scholars.
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