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Food waste in the hospitality sector has emerged as a global concern. Various technology-driven online food services such as the food delivery apps (FDA) contribute to hospitality food waste. FDA users might behave irresponsibly by ordering more foods than required which may lead to food waste generation. To date, limited studies have been attempted to understand how consumers’ over-ordering behavior through FDA result in hospitality food waste.

The authors used partial least squares structural equation modeling (PLS-SEM) to analyze survey data from 248 FDA users.

The results indicated that perceived convenience and trust positively influence consumers' attitude toward FDA, which in turn promotes over-ordering behavior. Interestingly, the anticipated positive relationship between price advantage and attitude toward FDA was not supported by the data. Furthermore, the authors confirmed that over-ordering behavior contributes to food waste, an outcome that has crucial implications for both the hospitality sector and sustainability efforts.

The current study employs the stimulus-organism-behavior-consequence (SOBC) theory to investigate the catalysts and consequences of over-ordering behavior via FDA. This study thus highlights the importance of the SOBC model in understanding consumer behavior.

The pressure to survive in a highly competitive market by using artificial intelligence (AI) has further demonstrated the need for automation in business operations during a crisis, such as COVID-19. Prior research finds managers' mixed perceptions about the use of technology in business, which underscores the need to better understand their perceptions of adopting AI for automation in business operations during COVID-19. Based on social exchange theory, the authors investigated managers' perceptions of using AI in business for effective operations during the COVID-19 pandemic.

This study collected data through a survey conducted in China (N = 429) and ran structural equation modeling to examine the proposed research model and structural relationships using Smart PLS software.

The results show that using AI in supply chain management, inventory management, business models, and budgeting are positively associated with managers' satisfaction. Further, the relationship between managers' satisfaction and effective business operations was found to be positively significant. In addition, the findings suggest that top management support and the working environment have moderating effects on the relationship between managers' satisfaction and effective business operations.

The results of this study can guide firms to adopt an AI usage policy and execution strategy, according to managers' perceptions and psychological responses to AI.

The study can be used to manage the behavior of managers within organizations. This will ultimately improve society's perception of the employment of AI in business operations.

The study's outcomes provide valuable insights into business management and information systems with AI application as a business response to any crisis in the future.