A Data-Driven Analysis of Instagram Tourism Behavior: Integrating Technology Acceptance, Planned Behavior, and Knowledge Management Perspectives

This study investigates Instagram-based tourism behavior through an integrated framework combining Stimulus-Organism-Response (SOR), Technology Acceptance Model (TAM), Theory of Planned Behavior (TPB), and Knowledge Management (KM) perspectives. Using data from 189 active Instagram users in Indonesia analyzed via PLS-SEM, we examine how Instagram content quality, influencer credibility, and electronic word-of-mouth shape cognitive and affective responses, ultimately influencing behavioral intentions and outcomes. Results reveal that content sharing behavior emerges as the strongest predictor of destination loyalty (β=0.789, p<0.01), validating a post-visit behavioral reinforcement loop from intention to actual visit to content sharing and loyalty. Expected social return significantly mediates the relationship between EWOM and behavioral intention (β=0.277, p<0.01), highlighting the role of anticipated social benefits in digital tourism decisions. The findings demonstrate that Instagram functions as a knowledge management platform where information quality, knowledge sharing intention, and social credibility jointly shape perceived usefulness. This research extends digital tourism theory by empirically identifying mechanisms through which social media platforms influence tourist behavior beyond traditional intention-based models, offering both theoretical insights and practical implications for destination marketing organizations.