Adoption of Artificial Intelligence in Chatbot Recommendation Systems for Complex Customer Preferences: A Case Study of Shopee E-Commerce - Apriyanti Sijabat; Dana Indra Sensuse (Scientific Journal of Informatics, 2026)
Purpose: This study identifies and analyzes factors influencing AI chatbot recommendation system adoption and proposes optimization strategies based on user perceptions and organizational decisions. Methods: A qualitative case study was conducted with Shopee as the unit of analysis. Nine participants three internal Shopee personnel and six active users were interviewed and selected via purposive sampling. Thematic analysis followed three stages (open, axial, and selective coding) guided by the TOE–TAM framework. Trustworthiness was ensured through member checking, peer debriefing, and four criteria: credibility, transferability, dependability, and confirmability. Result: Shopee's AI chatbot delivers personalized recommendations but users frequently experience information overload from irrelevant results. Three TOE dimensions technology readiness, organizational readiness, and external pressure, were found to drive adoption, while four TAM factors perceived usefulness, ease of use, trust, and satisfaction, shape user acceptance. Five strategic recommendations are proposed: algorithm enhancement, data quality improvement, adaptive personalization, deeper customer profiling, and information overload reduction. Novelty: Prior studies examine organizational adoption (TOE) or user acceptance (TAM) of AI chatbots in isolation, leaving a gap in understanding how macro-level institutional readiness interacts with micro-level user cognitive barriers. This study addresses that gap by integrating TOE and TAM as a dual-perspective lens, explaining how institutional readiness spanning technology, organization, and environment directly reduces cognitive barriers during automated recommendations. The study further foregrounds the "Complex Customer Preferences vs. Information Overload" paradox as a central challenge: AI chatbots deployed to manage complex preferences often generate overload that undermines user trust and satisfaction, a tension prior TOE–TAM integrations have not addressed.