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All Journal International Journal of Management Science and Application
Elikplim Dzreke, Semefa
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Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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The Relational Algorithm: Axiomatizing the Divergent Social Calculus of Trust in Collectivist and Individualist Market Ontologies Dzreke, Simon Suwanzy; Elikplim Dzreke, Semefa
International Journal of Management Science and Application Vol. 5 No. 1 (2026): IJMSA
Publisher : Sultan Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/ijmsa.v5i1.500

Abstract

Global brands incur annual losses of around $23 billion due to culturally incompatible trust practices, as demonstrated by Uber's contractual misalignment in China's guanxi-centric markets. This ongoing insufficiency highlights a significant theoretical void: cross-cultural marketing lacks a foundational framework that elucidates ontological differences in the formation of trust. This study employs ethnographic fieldwork (n = 42 industry experts), agent-based computer modelling, and discrete-choice experiments (DCEs; n = 1,200 participants across 4 markets) to address the issue. Findings indicate that trust functions through incommensurable cultural relational algorithms individualistic contractarian principles vs collectivist contextualist principles. Violating these ontological principles diminishes purchase intent by 38–61% (hierarchical Bayesian estimation, 95% HDI), highlighting the behavioral repercussions of infringing ontological expectations. This paper proposes a new axiomatic framework for market ontology that facilitates the algorithmic adaptation of trust methods across cultural barriers. The framework provides a theoretically informed method for mitigating relational friction in international trade, with clear implications for market entry strategy, partnership formation, and platform management.
The Algorithmic Canvas: On the Autopoietic Redefinition of STP in the Age of Strategic Resilience Dzreke, Simon Suwanzy; Elikplim Dzreke, Semefa
International Journal of Management Science and Application Vol. 5 No. 1 (2026): IJMSA
Publisher : Sultan Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/ijmsa.v5i1.501

Abstract

Traditional Segmentation, Targeting, and Positioning (STP) frameworks demonstrate significant deficiencies in unstable markets, with actual data revealing a 67% decline after six months. This research redefines STP not as a structured process but as an autopoietic system—an entity that self-organizes and constantly redefines its limits. It presents the Algorithmic Canvas as the operational medium that facilitates this paradigm, in which segmentation, targeting, and positioning parameters dynamically evolve through human-AI collaboration. Using a sequential mixed-methods design that included a 6-month Fortune 500 lab ethnography (n=23), a computational analysis of 150 million customer interactions, and an empirically based agent-based simulation (ABS), the study shows that autopoietic STP implemented through the Canvas is 44% more resilient (p < 0.01) to market shocks and cuts strategic planning cycles by 90% compared to traditional models. Algorithmic co-creation methods enhanced the identification of substantial market fluctuations by a factor of 5.8. The study enhances the Autopoietic STP Framework and empirically substantiates Canvas Design Principles, effectively addressing algorithmic myopia and offering businesses a framework for improved adaptability and resource efficiency during turbulent conditions.
Co-Authors Dzreke, Simon Suwanzy