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


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.
