Mapping the dynamics of herd behavior in tourism and hospitality: A bibliometric and text mining approach (1985–2025)

This study explores herd behavior in tourism and hospitality through a comprehensive approach that integrates bibliometric analysis, thematic mapping, and text mining of 146 publications indexed in Scopus and Web of Science from 1985 to 2025. The research identifies five dominant thematic clusters: decision antecedents and satisfaction; experience-loyalty-performance linkages; perception and risk in destination image formation; information-driven herding and market impact; and a China-focused contextual framework. Findings reveal that herd behavior operates through two primary theoretical channels, informational cascades to reduce cognitive load under uncertainty and normative conformity for social acceptance, with digital platforms acting as critical mediators. Furthermore, the analysis highlights a methodological shift from macro-level aggregate proxies to micro-level, data-driven, and experimental approaches, underscoring the need for robust causal identification and transparent reporting. The study also highlights a critical gap in cross-cultural comparative frameworks, underscoring the need to apply cultural dimensions (e.g., individualism vs collectivism) to understand divergent herding tendencies. Practical implications include strategies for curating digital information architectures, leveraging social learning cues to promote sustainable tourism, and aligning expectation formation with service delivery. By synthesizing intellectual, social, and conceptual structures, this study advances the theoretical integration of social influence in tourism. It underscores the dual nature of herd behavior as both an efficiency driver and a potential source of systemic risk, calling for adaptive management to balance economic gains with environmental resilience.