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

PurposeDrawing 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/approachA 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. FindingsThe 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 implicationsThis 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/valueThis 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.

Making Sense of Risk Information amidst Uncertainty: Individuals' Perceived Risks Associated with the COVID-19 Pandemic

During a global pandemic such as COVID-19, laypeople bear a large burden of responsibility for assessing risks associated with COVID-19 and taking action to manage risks in their everyday lives, yet epidemic-related information is characterized by uncertainty and ambiguity. People perceive risks based on partial, changing information. We draw on crisis informatics research to examine the multiple types of risk people perceive in relation to the COVID-19 pandemic, the information sources that inform perceptions of COVID-19 risks, and the challenges that people have in getting the information they need to understand risks, using qualitative interviews with individuals across the United States. Participants describe multiple pandemic-related threats, including illness, secondary health conditions, economic, socio-behavioral, and institutional risks. We further uncover how people draw on multiple information sources from technological infrastructures, people, and spaces to inform the types of their risk perceptions, uncovering deep challenges to acquiring needed risk information.