Algorithms in Marketing Systems: A Framework for Distributed Agency and Market Authority

Suraj Commuri & Ashita Aggarwal

Journal of Macromarketing2026https://doi.org/10.1177/02761467261440359article
AJG 2ABDC A
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0.50

Abstract

Markets increasingly operate through algorithmic systems exercising decision-making authority traditionally reserved for humans. This article develops the Algorithmic Sovereignty Framework to theorize how constitutive market authority, the capacity to determine what is visible, transactable, and choosable, redistributes across human-algorithmic assemblages in ways that challenge assumptions about market systems. We extend marketing systems theory to accommodate nonhuman agents that drive market evolution, exchange theory to theorize transactions completed through algorithmic mediation beyond human comprehension, and consumer sovereignty to introduce configuration sovereignty, which concerns who determines what is choosable rather than who chooses. The framework identifies five sovereignty dimensions (cognitive, temporal, informational, relational, and normative) where algorithms determine market outcomes, and it theorizes four evolutionary phases from human-dominant through hybrid configurations toward algorithmic autonomy. The analysis also reveals four novel market failures (algorithmic lock-in, preference manufacture, sovereignty asymmetries, and temporal exploitation) that compound rather than self-correct and that traditional governance cannot address.

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https://doi.org/https://doi.org/10.1177/02761467261440359

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@article{suraj2026,
  title        = {{Algorithms in Marketing Systems: A Framework for Distributed Agency and Market Authority}},
  author       = {Suraj Commuri & Ashita Aggarwal},
  journal      = {Journal of Macromarketing},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1177/02761467261440359},
}

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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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