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Manexia
Published by UDEX Institute
ISSN : -     EISSN : 31246532     DOI : https://doi.org/10.66203
Core Subject : Economy,
Economics, Econometrics & Finance
Manexia: Journal of Business, Management, and Creative Economy is a peer-reviewed academic journal that publishes original research articles, conceptual papers, and case studies in the fields of business, management, and creative economy. The journal aims to advance scholarly discussion and practical insights in areas including strategic management, human resource management, marketing, entrepreneurship, digital business, innovation, and creative industry development. The journal particularly emphasizes interdisciplinary approaches and contemporary issues such as digital transformation, sustainable business practices, and the development of human capital in the creative economy. Manexia welcomes contributions from academics, researchers, practitioners, and policymakers that provide theoretical contributions as well as practical implications for business and organizational development in both local and global contexts.
Arjuna Subject : Bisnis, Menejemen, dan Akutansi (semua kategori) - Bisnis dan Menejemen Intetrnasional
Articles 28 Documents
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Compliance as Strategic Capability: Trust, Legitimacy, and Competitive Advantage in Digital Ecosystems Dianti Eka Aprilia
Manexia: Journal of Business, Management, and Creative Economy Vol. 1 No. 3 (2025): Governing Digital Markets: Structure and Power
Publisher : UDEX Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66203/manexia.01306

Abstract

Regulatory intensification in digital ecosystems has repositioned compliance from episodic obligation to persistent organizational condition. Yet compliance remains predominantly framed as a cost of participation rather than a potential source of strategic differentiation. This article advances a capability-based perspective arguing that compliance becomes strategically consequential when embedded as an organizational capability rather than executed as minimal rule adherence. Integrating resource-based theory, dynamic capabilities, trust theory, signaling logic, and legitimacy scholarship, the analysis develops a mediated framework in which compliance capability generates competitive differentiation through relational and institutional mechanisms. Embedded and credible compliance routines function as costly signals of integrity and competence, fostering stakeholder trust under conditions of uncertainty. Stabilized trust accumulates into institutional legitimacy, which enhances ecosystem positioning through preferential partner selection, reduced coordination friction, and reputational resilience. Competitive advantage thus emerges indirectly through credibility-based amplification rather than direct regulatory conformity. The framework contributes a credibility-centered theory of strategic compliance and extends capability research into the governance domain of digitally mediated markets.
Algorithmic Market Structuring: How AI Reconfigures Competitive Boundaries in Platform-Based Economies Agus Riyanto
Manexia: Journal of Business, Management, and Creative Economy Vol. 2 No. 1 (2026): Algorithmic Restructuring of Digital Markets
Publisher : UDEX Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66203/manexia.02102

Abstract

Digital platforms increasingly rely on algorithmic systems to allocate visibility, define evaluative metrics, and recalibrate participation conditions. While prior research has examined platform governance, market shaping, and algorithmic control, strategic management scholarship has yet to fully theorize how algorithmic infrastructures reconfigure the boundaries of competition itself. This article introduces the concept of algorithmic market structuring to explain how competitive boundaries in platform-based economies become endogenous to governance architectures. Integrating boundary theory, competitive dynamics, ecosystem strategy, attention-based theory, performance feedback models, and increasing returns logic, we develop a mechanism-based framework comprising four interrelated processes: visibility-based boundary making, metric re-specification, feedback-loop concentration, and rule volatility–induced adaptation. We argue that algorithmic centrality transforms rivalry from category-based competition to exposure-mediated competition, generating cumulative advantage dynamics and accelerating boundary reconfiguration. Importantly, these effects are not deterministic; their structural consequences depend on differentiation levels, switching costs, and governance automation intensity. By reframing competitive arenas as dynamically curated through algorithmic infrastructures rather than statically defined by industry structure, this study advances strategic management theory and clarifies how digital governance architectures shape the evolution of rivalry in contemporary platform markets.
Algorithmic Market Transformation Lina Marlina
Manexia: Journal of Business, Management, and Creative Economy Vol. 2 No. 1 (2026): Algorithmic Restructuring of Digital Markets
Publisher : UDEX Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66203/manexia.02101

Abstract

This editorial introduces a collection of conceptual articles examining how algorithmic infrastructures are reshaping the foundations of digital markets. Rather than functioning merely as operational tools, artificial intelligence increasingly acts as a structural coordination mechanism influencing market architecture, organizational boundaries, demand formation, pricing legitimacy, and productivity dynamics. Together, the contributions in this issue highlight how algorithmic governance redistributes control, restructures competitive interaction, and generates new forms of performance divergence within platform-mediated digital ecosystems.
Data Extractivism and Strategic Value Appropriation: Rethinking Firm Advantage in AI-Centric SME Ecosystems Taufik Wibisono
Manexia: Journal of Business, Management, and Creative Economy Vol. 2 No. 1 (2026): Algorithmic Restructuring of Digital Markets
Publisher : UDEX Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66203/manexia.02103

Abstract

Artificial intelligence–enabled platforms are transforming the foundations of competitive advantage in digital market ecosystems. Small and medium-sized enterprises (SMEs) generate substantial transactional and behavioral data through platform participation, yet control over data aggregation and model-training architectures typically resides with platform sponsors. This structural decoupling challenges the core assumption of the resource-based view that ownership and control of valuable resources ensure rent appropriation. Integrating resource-based theory, value appropriation logic, data-enabled learning research, and platform governance scholarship, this article develops a conceptual framework explaining how data extractivism operates as an architecture-mediated mechanism of value capture. The model argues that competitive advantage in AI-centric ecosystems increasingly derives from control over aggregation infrastructures rather than localized data generation. Cross-SME data pooling produces compounding learning rents that disproportionately accrue to actors controlling centralized architectures, especially under conditions of high switching costs, limited data portability, and governance opacity. By reframing advantage as architecture-dependent, the study extends strategic management theory and clarifies how SME performance becomes ecosystem-conditioned in AI-driven markets.
Algorithmic Integration Boundaries: Rethinking the Theory of the Firm in Platform Ecosystems Cici Aulia Permata Bunda; Faqihuddin
Manexia: Journal of Business, Management, and Creative Economy Vol. 2 No. 1 (2026): Algorithmic Restructuring of Digital Markets
Publisher : UDEX Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66203/manexia.02104

Abstract

The theory of the firm has long assumed alignment between ownership, coordination authority, and capability execution, conceptualizing firm boundaries as governance solutions that internalize control when markets become inefficient. The diffusion of artificial intelligence (AI) infrastructures within platform ecosystems destabilizes this alignment by relocating decision-relevant intelligence beyond formal ownership domains. Firms increasingly embed externally governed algorithmic systems into pricing, forecasting, visibility management, and workflow coordination, allowing coordination and monitoring to occur without asset transfer or hierarchical integration. This article reconceptualizes firm boundaries as algorithmic integration boundaries defined by infrastructural control over coordination and learning processes. A mechanism-based framework identifies four cumulative processes driving boundary reconfiguration: data dependency intensification, workflow embedding, algorithmic visibility and control redistribution, and capability redistribution. Together, these mechanisms produce algorithmic boundary permeability, a condition in which legal ownership persists while effective coordination authority and adaptive capacity extend into externally governed infrastructures. This reconceptualization refines boundary theory, extends resource-based and dynamic capability perspectives through the notion of infrastructure-dependent capabilities, and identifies algorithmic mediation as a structural source of interorganizational power asymmetry.
Demand Atomization and the Erosion of Competitive Coherence: Strategic Implications of Algorithmic Personalization Riza Saepul Millah
Manexia: Journal of Business, Management, and Creative Economy Vol. 2 No. 1 (2026): Algorithmic Restructuring of Digital Markets
Publisher : UDEX Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66203/manexia.02105

Abstract

Algorithmic personalization has been widely conceptualized as a performance-enhancing capability that improves targeting precision and customer alignment. However, its structural consequences for market organization and strategic stability remain under-theorized. This conceptual article advances a market-structure perspective by introducing the constructs of demand atomization and competitive coherence. It argues that increasing algorithmic personalization intensity reduces shared exposure across consumers, dispersing preferences into dynamically reconfigured micro-clusters. Simultaneously, reinforcement mechanisms embedded in digital platforms may concentrate transactional outcomes among highly visible actors. This dual dynamic—fragmentation in preference formation alongside concentration in transaction distribution—creates structural pressures that erode competitive coherence, defined as the firm’s ability to maintain integrative strategic alignment across heterogeneous market contexts. The analysis proposes non-linear effects of personalization intensity and identifies privacy salience and firm size as critical boundary conditions. Small and medium-sized enterprises are theorized to face amplified vulnerability due to limited orchestration capacity. The framework reframes personalization from a tactical optimization tool to a market-structuring force with long-term strategic implications.
Algorithmic Pricing Intensity and the Curvilinear Reconfiguration of Consumer Fairness Norms Uus Muhamad Husni Tamyiz
Manexia: Journal of Business, Management, and Creative Economy Vol. 2 No. 1 (2026): Algorithmic Restructuring of Digital Markets
Publisher : UDEX Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66203/manexia.02106

Abstract

Algorithmic pricing is widely framed as a technological instrument for efficiency and revenue optimization. Yet as pricing decisions become increasingly embedded within autonomous computational systems, their implications extend beyond performance outcomes to the normative foundations of market exchange. This article develops a conceptual framework explaining how algorithmic pricing intensity reshapes consumer fairness norms through curvilinear dynamics. Drawing on justice theory, reference price stability, attribution processes, and institutional legitimacy, the analysis proposes that algorithmic pricing intensity exhibits an inverted-U relationship with normative legitimacy. At low to moderate levels, algorithmic systems enhance procedural objectivity and enable adaptive updating of reference expectations, thereby strengthening fairness norms. Beyond a critical threshold, however, heightened volatility, granular personalization, and causal opacity destabilize reference anchors and intensify exploitative attributions, resulting in legitimacy erosion. By reframing fairness as a dynamic normative constraint rather than a static perception, the article contributes to research on digital market governance and strategic legitimacy, highlighting the bounded nature of algorithmic optimization in competitive digital environments
The AI Productivity Paradox Revisited: A Multi-Level Theory of Performance Divergence in SME-Dominated Digital Ecosystems Ahmad Mundzir
Manexia: Journal of Business, Management, and Creative Economy Vol. 2 No. 1 (2026): Algorithmic Restructuring of Digital Markets
Publisher : UDEX Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66203/manexia.02107

Abstract

Artificial intelligence (AI) has intensified debates surrounding the contemporary productivity paradox, where rapid technological progress coexists with uneven improvements in measured productivity. Although growing evidence shows that AI can significantly enhance task-level performance—reducing completion time, improving output quality, and standardizing decision processes—these gains do not always translate into consistent firm-level productivity outcomes, particularly among small and medium-sized enterprises (SMEs) operating in platform-mediated digital markets. This article develops a conceptual framework that revisits the AI productivity paradox through a multi-level theoretical perspective. Integrating insights from productivity paradox research, general-purpose technology theory, task-based technological change, and platform ecosystem scholarship, the study proposes that AI-induced productivity gains propagate unevenly across four analytical layers: tasks, SMEs, platforms, and digital ecosystems. Three generative mechanisms—complement lag, measurement wedge, and compounding learning effects—explain how productivity gains are translated, partially observed, or redistributed across these levels. While SMEs may experience delayed or weakly measured productivity improvements due to complement constraints and measurement limitations, platform infrastructures can accumulate accelerated gains through data-enabled learning and cross-merchant aggregation. The framework introduces productivity divergence as a concept explaining how ecosystem-level efficiency can increase even when individual firms experience uneven productivity outcomes.

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