AI-driven knowledge and intelligence: a strategic framework for competitive advantage in tourism and hospitality industry
Bassam Samir Al-Romeedy
Abstract
Purpose This study aims to examine the impact of artificial intelligence (AI) adoption (AI) on sustaining competitive advantage (SCA) in the tourism and hospitality industry, focusing on the mediating roles of knowledge management (KM) and business intelligence (BI). Design/methodology/approach Grounded in the knowledge-based view, the research adopts a quantitative approach, collecting data from 662 managers and supervisors working in five-star hotels and travel agencies in Saudi Arabia. Findings Using structural equation modeling, the results revealed that AI adoption significantly influences sustainable competitive advantage both directly and indirectly. AI adoption was found to enhance KM and BI, and both constructs independently contributed to strengthening competitive advantage. Mediation analysis confirmed that KM and BI partially mediate the effect of AI adoption, demonstrating that the full value of AI adoption is realized when paired with effective internal knowledge and intelligence systems. Originality/value This study integrates AI adoption, KM and BI within a KBV-based explanatory framework to clarify how AI-enabled knowledge contributes to sustainable competitive advantage in tourism and hospitality. Rather than treating AI adoption as a standalone information technology-enabled capability, the study highlights the mediating roles of KM and BI as organizational mechanisms that absorb, structure and operationalize AI-driven knowledge into sustained performance outcomes. Using empirical evidence from Saudi Arabia, the study offers refined theoretical insights and context-specific managerial implications for digitally transforming tourism and hospitality organizations.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.