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Drawing on conservation of resources (CoR) and speech act theories, the authors tested the relationship between managers’ motivating language (ML) and employee service quality and psychological relatedness and competence as mediating variables between their associations.

Using a convenient sampling technique, the authors collected 366 hotel employees’ opinions in Malaysia and analysed them in partial least squares-structural equation modelling.

Three forms of ML, psychological competence and relatedness correlate with employees’ service quality. Although direction-giving language is correlated with competence, empathetic and meaning-making language are not; thus, competence only mediates the relationship between direction-giving language and service quality. Three types (direction-giving, empathetic and meaning-making) of managers’ communication are correlated with relatedness; thus, relatedness mediates the association between the three types of language and service quality.

Hospitality managers are encouraged to enhance psychological relatedness and competence by practising an appropriate ML. Psychological relatedness and competence are significant mechanisms that enlighten the effects of supervisory communicant on service quality, indicating employees’ need satisfaction should be improved.

Our study contributes to speech act and CoR theories by explaining the relationship between ML, psychological relatedness, competence and service quality.

The study investigates the consumer’s attitude to using artificial intelligence (AI) devices in hospitality service settings considering social influence, hedonic motivation, anthropomorphism, effort expectancy, performance expectancy and emotions.

This study employed a quantitative methodology to collect data from Bangladeshi consumers who utilized AI-enabled technologies in the hospitality sector. A total of 343 data were collected using a purposive sampling method. The SmartPLS 4.0 software was used to determine the constructs' internal consistency, reliability and validity. This study also applied the partial least squares structural equation modeling (PLS-SEM) to test the research model and hypotheses.

The finding shows that consumer attitude toward AI is influenced by social influence, hedonic motivation, anthropomorphism, performance and effort expectancy and emotions. Specifically, hedonic motivation, social influence and anthropomorphism affect performance and effort expectations, affecting consumer emotion. Moreover, emotions ultimately influenced the perceptions of hotel customers' willingness to use AI devices.

This study provides a practical understanding of issues when adopting more stringent AI-enabled devices in the hospitality sector. Managers, practitioners and decision-makers will get helpful information discussed in this article.

This study investigates the perceptions of guests' attitudes toward the use of AI devices in hospitality services. This study emphasizes the cultural context of the hospitality industry in Bangladesh, but its findings may be reflected in other areas and regions.