Journal of Logistics, Informatics and Service Science | 2026
Authors: Salhab H.; Zoubi M.; Istatia H.; Khrais L.
DOI: 10.33168/JLISS.2026.0305
Journal: Journal of Logistics, Informatics and Service Science
Year: 2026
Publisher: Success Culture Press
Document Type: Article
Open Access: All Open Access; Gold Open Access
Cited by: 0
This manuscript examines how young consumers respond to AI chatbots in social commerce by conceptualizing chatbots as informatics-enabled front-line service systems. Building on a unified model that assigns Stimulus-Organism-Response (SOR) as the system structure, the Persuasion Knowledge Model (PKM) as the ethical-cognition mechanism, and Trust Theory as the service-outcome logic, we test how two service-design choices-identity disclosure (transparency) and conversational tone (personalized vs. generic)-shape trust and perceived manipulation, and ultimately purchase intention. Using a 2x2 between-subjects experiment with UAE youth (18-25), standardized chatbot dialogues were generated and pretested using a large-language-model workflow to ensure consistent stimuli; this design enables controlled comparison but does not fully capture the adaptivity of live chatbots. PLS-SEM results show that transparent AI disclosure and empathetic personalization increase trust and reduce perceived manipulation; trust is the dominant mediator linking design cues to purchase intention, while perceived manipulation imposes a significant negative effect. Digital literacy attenuates the negative influence of manipulation on intention, highlighting a boundary condition relevant for service governance. The results can also be used to inform guidelines for the development and delivery of service systems, which involve the provision of transparency by default, personalization that is explainable, the adaptation of tone to the needs of the user, and the development of an escalation process. © 2026, Success Culture Press. All rights reserved.
Chatbots; Commerce; Literacy; Transparency; Trust; Youth