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Consistent with the dynamic capabilities view tenets, this paper aims to conceptualize a theoretical framework of service innovation in the hotel industry.

This study uses a qualitative method with a content analysis approach. The data were collected using a snowball sampling method and semi-structured interviews with 14 experts in Tehran's hotel industry.

The findings demonstrate that the most significant factors are using the new technology, keeping up with it, training human labor, being up-to-date and adopting new infrastructures. Results also reveal that improper management and lack of knowledge are the most critical factors behind service innovation failure in the hotel industry. Regarding the infrastructures needed to develop service innovation in the hotel industry, the results show that adopting the newest technology in diverse aspects, human infrastructure, the capital and appropriate space and place are the key factors.

This paper contributes to the literature by linking the service innovation perspective to the dynamic capabilities view. It explains how hotels can enhance service innovation to gain a competitive advantage. Therefore, both academicians and hoteliers can develop action plans by selecting and managing the service innovation process.

Several review articles have been published within the Artificial Intelligence (AI) literature that have explored a range of applications within the tourism and hospitality sectors. However, how efficiently the applied AI methods and algorithms have performed with respect to the type of applications and the multimodal sets of data domains have not yet been reviewed. Therefore, this paper aims to review and analyse the established AI methods in hospitality/tourism, ranging from data modelling for demand forecasting, tourism destination and behaviour pattern to enhanced customer service and experience.

The approach was to systematically review the relationship between AI methods and hospitality/tourism through a comprehensive literature review of papers published between 2010 and 2021. In total, 146 articles were identified and then critically analysed through content analysis into themes, including “AI methods” and “AI applications”.

The review discovered new knowledge in identifying AI methods concerning the settings and available multimodal data sets in hospitality and tourism. Moreover, AI applications fostering the tourism/hospitality industries were identified. It also proposes novel personalised AI modelling development for smart tourism platforms to precisely predict tourism choice behaviour patterns.

This review paper offers researchers and practitioners a broad understanding of the proper selection of AI methods that can potentially improve decision-making and decision-support in the tourism/hospitality industries.

This paper contributes to the tourism/hospitality literature with an interdisciplinary approach that reflects on theoretical/practical developments for data collection, data analysis and data modelling using AI-driven technology.