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AI transforming B2C relationships and relational exchange theory

Aswo Safari

Marketing Theory2026https://doi.org/10.1177/14705931261437343article
AJG 3ABDC A
Weight
0.50

Abstract

The increasing integration of artificial intelligence (AI) in B2C marketing challenges the core principles of relational exchange theory (RET). Traditionally, RET has focused on human intentionality, moral agency, and emotional commitment as essential for maintaining long-term relationships. This paper aims to explore how AI-driven interactions affect the structure, dynamics, and management of consumer–firm relationships. Drawing on recent research in marketing, human–computer interaction, and critical algorithm studies, the paper identifies five primary tensions caused by AI: the simulation of trust, the replacement of human agency, the reversal of control, the virtualization of intimacy, and the imbalance of transparency. The paper suggests that RET must evolve into a dual-pathway framework, one that maintains its focus on human relationships and another that addresses machine-mediated, functional interactions. Based on this, the paper proposes a research agenda to rethink relationality, reevaluate trust and commitment, investigate algorithmic governance, and define the limits of RET in hybrid relationships. This work seeks to contribute to the reimagining of marketing theory in an era dominated by algorithms, in which the concept of “relationship” becomes more fluid, technologically mediated, and ethically complex.

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https://doi.org/https://doi.org/10.1177/14705931261437343

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@article{aswo2026,
  title        = {{AI transforming B2C relationships and relational exchange theory}},
  author       = {Aswo Safari},
  journal      = {Marketing Theory},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1177/14705931261437343},
}

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AI transforming B2C relationships and relational exchange theory

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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