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TRUVACE RECORD VERSION record: TRV-2026-2647 version: 1 kind: certified reason: Certified into the record timestamp: 2026-11-12T16:56:24.033125Z status: published lens: g_space sector: lifestyle headline: When chatbots speak local: cultural understanding as a driver of value co-creation dek: Purpose This study investigates how chatbot cultural understanding shapes consumer responses in online retail, focusing on perceived brand localness, consumer–brand identification and value co-creation behaviors. Guided by social identity theory, the study also examines the moderating role of consumer ethnocentrism. Design/methodology/approach This study employs a sequential mixed-methods design. Study 1 involves 15 semi-structured interviews that explore consumer perceptions of culturally intelligent chatbots.… gain_title: In a field study of 303 online consumers of a cross-border e-retailer, chatbot cultural understanding raised perceived brand localness, which led to stronger brand identification and more value co-creation behaviours. problem_title: (none) trace_subject: (none) gain_reading: In a field study of 303 online consumers of a cross-border e-retailer, chatbot cultural understanding raised perceived brand localness, which led to stronger brand identification and more value co-creation behaviours. gain_evidence: Chatbot cultural understanding enhances perceived brand localness, which leads to consumer–brand identification and, in turn, value co-creation behaviors. problem_reading: (none) problem_evidence: (none) quick_read: A peer-reviewed study in International Marketing Review found that online shoppers who felt a retailer's chatbot understood their culture saw the brand as more local. That perception was linked to a stronger sense of identification with the brand and to more value co-creation behaviours. The work combined 15 semi-structured interviews with a field study of 303 consumers recruited online, analysed with structural equation modeling. It applies social identity theory to conversational AI, treating culturally attuned chatbots as cultural insiders. The evidence comes from one cross-border e-retailer in a single collectivist emerging market, so the results apply to encounters where international retailers actively localise chatbot communication. limitation: The evidence is drawn from a single cross-border e-retailer operating in one collectivist emerging market, and the findings are bounded to encounters in which international retailers actively localize chatbot communication. tag: Evidence-backed gain key_points: The study used a sequential mixed-methods design: 15 semi-structured interviews followed by a field study. | The field study covered 303 consumers recruited online and used structural equation modeling. | Chatbot cultural understanding raised perceived brand localness. | Perceived brand localness led to consumer–brand identification, which in turn led to value co-creation behaviours. | Consumer ethnocentrism did not moderate the link between chatbot cultural understanding and perceived brand localness. | Data came from a single cross-border e-retailer in one collectivist emerging market. rundown: The researchers first interviewed 15 consumers about their perceptions of culturally intelligent chatbots, then tested a proposed model in a field study of 303 online-recruited consumers. They analysed responses with structural equation modeling. The model ran in a chain: a chatbot's cultural understanding made the brand seem more local, that fostered consumer–brand identification, and identification was followed by value co-creation behaviours. Guided by social identity theory, the authors cast such chatbots as cultural insiders. The authors also tested whether consumer ethnocentrism changed these links. It did not moderate the association between chatbot cultural understanding and perceived brand localness, nor the indirect association with value co-creation behaviours. sources: - peer_reviewed | International Marketing Review | https://doi.org/10.1108/imr-10-2025-0500 | 2026-09-28 prev: 0000000000000000000000000000000000000000000000000000000000000000
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