Frame of the research. The accommodation sector is a central component of tourism systems and, in many European contexts, is largely composed of SMEs facing digital transformation, sustainability pressures, changing customer expectations, and increasing uncertainty. Purpose of the paper. This paper examines how the management and economics literature explains the role of artificial intelligence (AI) in shaping performance, customer experience, organizational structures, and strategic decision-making in the accommodation sector, with particular attention to SMEs. Methodology. The study adopts a systematic literature review informed by PRISMA and SPAR-4-SLR principles. Peer-reviewed journal articles published between 2010 and early 2026 and indexed in Scopus and Web of Science were selected through a multi-stage process, resulting in a final sample of 95 articles. Results. The findings identify four main dimensions of AI adoption: operational performance, customer experience, organizational transformation, and strategic decision-making. AI supports forecasting, revenue management, automation, personalization, review analytics, and market sensing, but its effects depend on data quality, managerial interpretation, organizational readiness, employee capabilities, customer trust, and ecosystem conditions. Research limitations. The review is limited to English-language peer-reviewed articles indexed in two databases and excludes grey literature and purely technical studies. Managerial implications. Accommodation SMEs should approach AI adoption as a staged, selective, and problemdriven process while preserving human hospitality and responsible governance. Originality of the paper. The paper offers a sector-specific and SME-sensitive framework for understanding AIenabled managerial transformation in accommodation firms. Keywords: artificial intelligence; accommodation sector; tourism; SMEs; hospitality management; systematic literature

Artificial Intelligence and Small and Medium-Sized Enterprises in the Accommodation Sector. A Systematic Literature Review from an Economic and Managerial Perspective

CLAUDIA FRABONI
;
TONINO PENCARELLI
2026

Abstract

Frame of the research. The accommodation sector is a central component of tourism systems and, in many European contexts, is largely composed of SMEs facing digital transformation, sustainability pressures, changing customer expectations, and increasing uncertainty. Purpose of the paper. This paper examines how the management and economics literature explains the role of artificial intelligence (AI) in shaping performance, customer experience, organizational structures, and strategic decision-making in the accommodation sector, with particular attention to SMEs. Methodology. The study adopts a systematic literature review informed by PRISMA and SPAR-4-SLR principles. Peer-reviewed journal articles published between 2010 and early 2026 and indexed in Scopus and Web of Science were selected through a multi-stage process, resulting in a final sample of 95 articles. Results. The findings identify four main dimensions of AI adoption: operational performance, customer experience, organizational transformation, and strategic decision-making. AI supports forecasting, revenue management, automation, personalization, review analytics, and market sensing, but its effects depend on data quality, managerial interpretation, organizational readiness, employee capabilities, customer trust, and ecosystem conditions. Research limitations. The review is limited to English-language peer-reviewed articles indexed in two databases and excludes grey literature and purely technical studies. Managerial implications. Accommodation SMEs should approach AI adoption as a staged, selective, and problemdriven process while preserving human hospitality and responsible governance. Originality of the paper. The paper offers a sector-specific and SME-sensitive framework for understanding AIenabled managerial transformation in accommodation firms. Keywords: artificial intelligence; accommodation sector; tourism; SMEs; hospitality management; systematic literature
2026
979-12-243-4261-8
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11576/2780351
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