AI-Enabled Customer Engagement, Online Brand Trust, and Sustainable Brand Equity in Platform-Based E-Commerce Distribution: A Conceptual Framework
Keywords:
Artificial Intelligence, Customer Engagement, Online Brand Trust, Sustainable Brand Equity, E-Commerce, Thailand, Perceived RiskAbstract
Artificial Intelligence (AI) has significantly influenced interactions between e-commerce businesses and their customers on a day-to-day basis today. Recommendation engines, chatbots, automated service replies, and customer analytics can improve the frequency and efficacy of a brand's interactions with customers. However, increasing contact does not necessarily add value to a brand. This is a conceptual framework paper that presents a theoretical model that describes the impact of AI-Enabled Customer Engagement (AICE) on Sustainable Brand Equity (SBE) via online brand trust. This paper does not seek to make an environmental or social sustainability claim, but rather a claim that SBE is an attribute of customer-based brand value in the long term. The model is grounded in customer engagement theory, online trust research, the customer-based brand equity tradition, and the Stimulus-Organism-Response (SOR) framework. It proposes four core relationships: AICE is proposed to influence SBE, AICE is proposed to influence online brand trust, online brand trust is proposed to influence SBE, and online brand trust is proposed to partially mediate the AICE-SBE relationship. Three new boundary conditions are formulated to extend this framework: non-linear (inverted-U) engagement-trust linkage, the role of perceived risk as a moderator, and the importance of algorithmic transparency as a condition that shifts the saturation point. The Thai e-commerce environment is chosen because official market statistics show that e-marketplaces and social commerce play significant roles as sales channels where AI-mediated engagement can be observed in customer journeys. As a conceptual contribution, this work presents a theory-building framework as opposed to empirical results in a platform-based and social-commerce distribution channels setting. Its main contribution is the introduction of the AI Engagement Saturation Model (AIESM), which postulates an inverted-U saturation effect of AICE on online brand trust until a certain saturation point, after which additional AI engagement deteriorates online brand trust.
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