Decoding the digital influence: how Trivago’s information ecosystem drives hotel booking intentions?

Safdar Khan · Mohd Azhar · Sana Azeem

4 citations0/0 files storedJournal of Hospitality and Tourism Insights

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Technology Adoption and User Behaviour Digital Marketing and Social Media Consumer Behavior and Marketing Influence

Abstract

Purpose This study uncovers the determinants affecting hotel booking intentions in the Trivago’s information ecosystem. For this purpose, the information adoption model and the theory of planned behavior were integrated with additional constructs viz., trust (TR) and satisfaction (ST). Design/methodology/approach A cross-sectional online survey was administered through Trivago’s official Facebook page. The data were analyzed using Statistical Package for the Social Sciences (SPSS) 20 and Analysis of Moment Structures (AMOS) 22.0, with hypotheses tested through structural equation modeling. Findings This study presents a thorough model that explains the drivers of hotel booking intentions in the Trivago’s information ecosystem. The results confirm most of the proposed hypotheses, with information quality significantly influencing attitude, while attitude, subjective norms, perceived behavioral control and satisfaction show significant positive effects on behavioral intention (BI). However, the paths from source credibility to attitude and trust to behavioral intention are not significant, leading to the rejection of H2 and H6. Research limitations/implications This study offers several theoretical and practical implications for scholars and practitioners. It advances understanding of how Trivago’s information ecosystems shape booking intentions beyond technology-focused models and guides travel platforms in designing effective strategies to influence customer decisions and improve bookings. Originality/value This study uniquely examines the influence of Trivago’s information ecosystem on consumers’ hotel booking intentions, a less explored area. While prior studies emphasize habits, cuisine and mobility, this work offers new insights into the travel sector by underscoring the role of information-driven factors in booking behavior.

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References

1981Evaluating Structural Equation Models with Unobservable Variables and Measurement Error 2003Common method biases in behavioral research: A critical review of the literature and recommended remedies.2003User Acceptance of Information Technology: Toward A Unified View1 1988Structural equation modeling in practice: A review and recommended two-step approach.2000A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies 1985From Intentions to Actions: A Theory of Planned Behavior 1995Understanding Information Technology Usage: A Test of Competing Models 2007The Effect of On-Line Consumer Reviews on Consumer Purchasing Intention: The Moderating Role of Involvement

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