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All Journal Inkubis: Jurnal Ekonomi dan Bisnis PENG: Jurnal Ekonomi dan Manajemen
Rokhman, Ibnu
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Economics, Econometrics & Finance

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Strategic Technology Adoption and Organizational Agility in the Digital Transformation Era Rokhman, Ibnu
PENG: Jurnal Ekonomi dan Manajemen Vol. 3 No. 3 (2026): Juli-September, Social Innovation and Community Participation for Inclusive and
Publisher : Teewan Journal Solutions

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62710/xagqbg06

Abstract

Digital transformation has changed organizational management patterns and encouraged companies to improve their adaptability through the adoption of strategic technology. However, the success of digital transformation still faces challenges in integrating technology with organizational capabilities. This study aims to analyze the influence of strategic technology adoption on organizational agility in companies implementing digital technology in Surabaya, Indonesia. The study uses a quantitative approach with a survey method of 212 respondents consisting of managers and employees at digital technology-based companies in Surabaya. Data collection was carried out through a structured questionnaire, then analyzed using Structural Equation Modeling based on Partial Least Square (SEM-PLS). The results of the study show that strategic technology adoption has a positive and significant effect on organizational agility through increasing business process flexibility, decision-making speed, and innovation capabilities. This research makes a theoretical contribution to the development of digital transformation management studies as well as practical implications for organizations in building adaptive and sustainable technology strategies.
Artificial Intelligence and Digital Innovation in Smart Tourism Destination Management: An Integrative Review and the AI-SDM Framework Rokhman, Ibnu
Inkubis : Jurnal Ekonomi dan Bisnis Vol. 8 No. 4 (2026): INKUBIS Jurnal Ekonomi Dan Bisnis
Publisher : Politeknik Siber Cerdika Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59261/inkubis.v8i4.520

Abstract

Background: Artificial intelligence (AI) is transforming tourism destinations through personalized services, conversational and generative interfaces, predictive analytics, and data-driven decision-making. Yet technology adoption alone does not explain how destinations convert AI-generated information into coordinated managerial action. Objective: This study addresses that organizational problem through a conceptual integrative review of research on AI-enabled tourism services, smart destination management, destination governance, visitor experience, organizational capabilities, workforce readiness, and responsible data practices. Methods: The synthesis identifies four interconnected application domains: AI-driven personalization, conversational and generative visitor interaction, predictive visitor-flow analytics, and data-driven destination governance. These domains are abstracted into the AI-Enabled Smart Destination Management (AI-SDM) framework, which connects AI-Enabled Service Capability, Smart Destination Governance Capability, and Visitor Experience Outcomes. Results: Visitor and operational intelligence functions as the principal conversion input, while visitor-generated behavioral and experiential feedback supports subsequent organizational learning and AI adaptation. Workforce AI Readiness and Responsible Data Governance are positioned as enabling conditions. The framework contributes to destination-management theory by shifting attention from AI adoption toward the organizational capability required to sense relevant visitor information, interpret and coordinate responses, and responsibly convert intelligence into destination action and visitor value. It complements broad smart-tourism and emerging cognitive-destination architectures by specifying the organizational intelligence-to-action mechanism that links AI-enabled interactions with destination governance and learning. Conclusion: The paper concludes with propositions and an empirical research agenda for measurement development and validation.
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