Investigating the determinants of performance of artificial intelligence adoption in hospitality industry during COVID-19

Yuangao Chen · Yuqing Hu · Shasha Zhou · Shuiqing Yang

116 citations0/0 files storedInternational Journal of Contemporary Hospitality Management

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Technology Adoption and User Behaviour AI in Service Interactions Impact of AI and Big Data on Business and Society

Abstract

Purpose Drawing on the technology-organization-environment (TOE) framework, this study aims to investigate determinants of performance of artificial intelligence (AI) adoption in hospitality industry during COVID-19 and identifies the relative importance of each determinant. Design/methodology/approach A two-stage approach that integrates partial least squares structural equation modeling (PLS-SEM) with artificial neural network (ANN) is used to analyze survey data from 290 managers in the hospitality industry. Findings The empirical results reveal that perceived AI risk, management support, innovativeness, competitive pressure and regulatory support significantly influence the performance of AI adoption. Additionally, the ANN results show that competitive pressure and management support are two of the strongest determinants. Practical implications This research offers guidelines for hospitality managers to enhance the performance of AI adoption and presents policy-making insights to promote and support organizations to benefit from the adoption of AI technology. Originality/value This study conceptualizes the performance of AI adoption from both process and firm levels and examines its determinants based on the TOE framework. By adopting an innovative approach combining PLS-SEM and ANN, the authors not only identify the essential performance determinants of AI adoption but also determine their relative importance.

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References

2015Common Method Bias in PLS-SEM 2005Post-Adoption Variations in Usage and Value of E-Business by Organizations: Cross-Country Evidence from the Retail Industry 2021Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance 2012Organizational innovation as an enabler of technological innovation capabilities and firm performance 2021Artificial Intelligence and Business Value: a Literature Review 2021Understanding AI adoption in manufacturing and production firms using an integrated TAM-TOE model 2011Absorptive capacity, innovation, and financial performance 2021Understanding managers’ attitudes and behavioral intentions towards using artificial intelligence for organizational decision-making

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