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International Journal of Management Science and Application
Published by Sultan Publisher
ISSN : -     EISSN : 29632056     DOI : https://doi.org/10.58291/ijmsa
Core Subject : Economy, Science,
Decision Sciences, Operations Research & Management Economics, Econometrics & Finance
The aim is to publish empirical research that advances management theory by testing, improving, or augmenting it. All empirical techniques, such as mixed methods, meta-analytical techniques, field, laboratory, and qualitative and quantitative techniques, are welcome. Research should provide significant theoretical and empirical advances, and articles should emphasize how to put these findings into practice. The International Journal of Management Science and Application (IJMSA) publishes manuscripts in a variety of business-related fields, including entrepreneurship, human resource management, leadership, operations management, marketing, and finance.
Arjuna Subject : Bisnis, Menejemen, dan Akutansi (semua kategori) - Bisnis, Menejemen, dan Akutansi (Lain-Lain)
Articles 65 Documents
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The AI Co-pilot: Navigating Market Turbulence and Charting a Course for Sustainable Advantage Simon Suwanzy Dzreke
International Journal of Management Science and Application Vol. 4 No. 2 (2025): IJMSA
Publisher : Sultan Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/ijmsa.v4i2.442

Abstract

This study addresses the gap in frameworks for effective human-AI collaboration in strategic decision-making during turbulent market conditions. Using a mixed-methods approach (longitudinal case studies in manufacturing, finance, and logistics; large-scale executive surveys; computational simulations), we empirically evaluate the "AI co-pilot" model, where AI augments human strategic cognition. Results show AI co-pilots improve market disruption prediction accuracy by 30-50% and reduce strategic response latency. However, these benefits critically depend on governance frameworks ensuring algorithmic accountability, dynamic trust calibration, and human agency preservation. Case studies (e.g., AI-enabled semiconductor shortage detection enabling proactive diversification) demonstrate value, while instances of algorithmic opacity highlight the necessity of human oversight. Maintaining competitive advantage requires interfaces ("algorithmic diplomacy"), balancing AI's computational power with human judgment, wisdom, and ethics. Organizations achieving this symbiosis gain superior resilience, transforming volatility into adaptive innovation opportunities.
The Enterprise Lingua Franca: A Foundational Framework for Semantic Interoperability and Cross-Functional Cognition Simon Suwanzy Dzreke; Semefa Elikplim Dzreke
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.v5i1.467

Abstract

Digital transformation often fails at the conceptual level: Seventy-three percent of cross-functional initiatives fail because the functional mental models don't match up, making it impossible to solve difficult problems like making sustainability profitable. This research identifies a significant deficiency in enterprise interoperability, wherein disparate departmental epistemologies manifested in conflicting interpretations of fundamental constructs such as "customer" or "value" among Marketing, Finance, and Operations—result in strategic incoherence despite technological integration. Technological solutions are inadequate in addressing these profound philosophical gaps. This paper introduces the Enterprise Lingua Franca, a new cognitive framework created through design science research that combines case studies, ontology engineering, and cognitive task analysis to make organizational intelligence more cohesive. It creates the first theory of Cross-Functional Cognition and gives tangible steps for semantic alignment that turn conceptual fragmentation into strategic coherence, which opens up new ways to solve problems.
The Cognitive Chrysalis: Engineering Metamorphic Resilience in Tourism Through Post-Outbreak Intelligence and Adaptive Design Simon Suwanzy Dzreke; Semefa Elikplim Dzreke
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.v5i1.468

Abstract

This work fills a major theoretical gap in tourist resilience: the systemic imbalance between cognitive processes and physical infrastructure, which increases susceptibility during hydrometeorological crises. Existing frameworks fail to explain why locations with similar hazard exposure display substantial outcome disparities, as seen by Venice's lengthy flood disruption against Singapore's predictive mitigation success. The study makes two major theoretical contributions: the Resilience Engineering Framework (REF), which combines cognitive load theory, behavioral intelligence, and AI-mediated feedback loops to model systemic brittleness; and the Adaptive Design Protocol (ADP), which applies REF principles to spatial, governance, and infrastructural interventions. The study takes a sequential mixed-methods approach, with (1) big data analytics across 20 destinations quantifying cognitive stressors (e.g., decision fatigue amplifying evacuation errors by 22%), (2) stakeholder surveys identifying governance misalignments, and (3) agent-based modeling validating REF dynamics. Empirical results show that ADP implementation reduces rebound time by 41% and infrastructure damage costs by 37% through metamorphic adaptation, as demonstrated by Bali's AI-driven crowd-flow systems, which speed up recovery by 58% through cognitive load optimization. The findings demonstrate that shifting fragility into anticipatory capacity necessitates cognitively grounded design, providing a reproducible approach for regenerative tourist ecosystems.
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.

Page 7 of 7 | Total Record : 65

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