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This paper aims to explore how artificial intelligence (AI) technologies have redefined the hospitality industry. It develops a theoretical framework to evaluate its impact on employee engagement, retention and productivity levels, stemming from its potential implications for service quality and customer satisfaction.

Based on the exploration of relevant literature, role theory and service-profit chain were used to develop – role-service-profit chain.

Role-service-profit chain is an analytical tool which has strong implications for investment and deployment analysis of the new technologies in hospitality and tourism businesses. It proposes how managers can evaluate how the role expectation of technological innovations relate to service quality and customer satisfaction through its impact on employee-related outcomes (such as employee engagement, retention and productivity), and assess the corresponding impact on profitability and growth, in the context of their own unique internal environment and position in the market.

Although an empirical assessment of the hypothesised relationships in the model is required to evaluate and validate it in the hospitality industry, role-service-profit chain presents promising implications for tourism and hospitality practice and future research.

Role-service-profit chain is an analytical tool from which managers can make improvements on talent and talent management practices and adjust expectations and behaviours in ways that facilitate improvements in service quality and customer satisfaction.

This paper makes an important contribution to hospitality and tourism literature, as it explores how AI technologies implemented to improve on talent and talent management practices impact on service quality and customer satisfaction, and develops analytical tools by which this may be evaluated.

This study aims to explain why online reviews in Airbnb are skewed toward positive ratings. The authors examine customer perceptions of the service quality of an Airbnb stay as a relevant antecedent of whether customers leave an online review of that Airbnb stay. To this end, the authors test the hypothesis that the relationship between service quality and leaving an online review is linear and positive.

To test the hypothesis, the authors rely on primary survey data from 177 Airbnb customers combined with secondary data coming from their personal online Airbnb accounts. The authors conducted a binary logistic regression analysis to test the hypothesis.

The results show that customers’ service quality perceptions are positively and linearly related to leaving an online review of an Airbnb stay. In other words, satisfied customers are more likely to leave a review after an Airbnb stay than those who are dissatisfied.

The study is original in two respects. First, it reconsiders the role of customer experiences in explaining online customer reviews. In doing so, it empirically shows that the conventional wisdom of a U-shaped relationship between customer experiences and online reviewing does not hold in the context of the sharing economy. Second, by relying on primary survey data, the authors reveal the risk of dissatisfied customers creating an underreporting bias in online reviews, which ultimately make online reviews of Airbnb skewed toward positive ratings.

The hospitality and tourism industry faces a number of workforce challenges, especially the high turnover rates and associated replacement costs associated with continually identifying and hiring new employees. The purpose of this paper is to discuss how hospitality and tourism organizations can use electronic human resource management (eHRM) and artificial intelligence (AI) to help recruit and select qualified employees, increase individual retention rates and decrease the time needed to replace employees. Specifically, it discusses how e-recruiting and e-selection and AI tools can help hospitality and tourism organizations improve recruiting and selection outcomes.

Research on eHRM, AI, employee recruitment and employee selection are applied to the hospitality and tourism industry and insights for how eHRM and AI can be applied to the industry are discussed.

eHRM and AI have the potential to transform how the hospitality and tourism industry recruit and select employees. However, care must be taken to ensure that the insights gained and the decisions made are well received by employees and lead to better employee and organizational outcomes.

This paper represents the first research that integrates research from eHRM and AI and applies it to the hospitality and tourism industry.

This paper represents the first research that integrates research from eHRM and AI and applies it to the hospitality and tourism industry.

The research and practice agree that social media are reshaping strategy and organization rules across industries. Nevertheless, how social media can become a source of competitive advantage remains under-investigated and there is no evidence about which capabilities and competencies can effectively and strategically exploit social media. By merging the literature on social media management and hospitality, the authors develop and test a theoretical framework that identifies the most relevant capabilities and competencies for using social media in the food service sector. The paper aims to map them and understand which ones are relevant according to different strategic choices of social media use.

The authors adopted a qualitative methodology using semi-structured interviews to managers or owners of 14 restaurants in a big city in Northern Italy.

The theoretical framework suggests that social media could be strategically used for different aims by relying on specific capabilities and competencies. The authors tested it and found that, though nowadays restaurant managers mainly focus on a narrow set of social media competencies linked to relational and marketing capabilities, some also rely on social media to promote organizational change and innovation.

The authors propose a theoretical framework and preliminary evidence on capabilities and competencies declined for the food service sector. The model considers different uses of social media and related capabilities and competencies by mapping them accordingly to their strategic use. The authors preliminarily validate our framework and highlight the competencies possessed by the restaurant managers of our sample and their alignment with the strategic use of social media.