Second Order Confirmatory Components of a Measurement Model of Challenges in Adopting Artificial Intelligence in Human Resource Management in the MICE Industry
Keywords:
Artificial Intelligence, Human Resource Management, MICE Industry, Second-Order Confirmatory Factor AnalysisAbstract
The objectives of this study were: (1) to analyze the second-order confirmatory factor structure of a measurement model of challenges in adopting artificial intelligence (AI) in human resource management within the MICE industry, and (2) to examine the goodness-of-fit of the proposed measurement model. The sample comprised 400 employees, ranging from operational staff to senior executives, working in human resource management departments of MICE businesses located in Bangkok and its metropolitan area. The sample was selected using purposive sampling. The research instrument was a structured questionnaire. Data were analyzed using descriptive statistics, the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy, and second-order confirmatory factor analysis (CFA). The results revealed that: (1) the measurement model of challenges in adopting AI in human resource management within the MICE industry consisted of four key components, namely technological challenges (TEC), ethical challenges (ETH), human resource challenges (HRM), and legal and regulatory challenges (LAW), with all subcomponent factor loadings meeting established criteria; and (2) the model demonstrated a good fit to the empirical data, as indicated by the fit indices: χ² = 293.941, df = 126, CMIN/df = 2.333, RMSEA = 0.058, RMR = 0.024, GFI = 0.935, CFI = 0.979, and NFI = 0.965. These findings suggest that the model for measuring the challenges of applying artificial intelligence in human resource management in the MICE industry is consistent with the model and empirical data.
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