Foresight in Smart Management: Emerging Approaches and Implementation Challenges
Keywords:
Foresight, Smart Management, Data-Driven Analysis, Algorithmic Governance, Implementation Challenges, Improvement StrategiesAbstract
Objective: The study aimed to systematically examine emerging foresight approaches in smart management, identify implementation challenges, and propose improvement strategies to enhance future-oriented decision-making in organizations.
Methods and Materials: This qualitative review employed a thematic analysis approach using NVivo software (version 14). Data were collected through a systematic review of academic literature from Scopus, Web of Science, ScienceDirect, and SID databases. Twelve articles meeting inclusion and quality criteria were selected for analysis. Open, axial, and selective coding were conducted, and the process continued until theoretical saturation was achieved.
Findings: Thematic analysis revealed three major categories: (1) innovative foresight approaches in smart management—including data-driven analysis, algorithmic governance, and participatory scenario design; (2) implementation challenges—including technological limitations, shortage of interdisciplinary experts, cultural resistance, and ethical concerns; and (3) improvement strategies—including developing hybrid foresight models, empowering smart human capital, institutionalizing data-driven policymaking, and strengthening international cooperation. The findings indicated that effective smart foresight requires the integration of both technological and human dimensions.
Conclusion: Foresight in smart management is not merely an analytical tool but a transformative paradigm for shaping desired organizational futures. The integration of data analytics, systems thinking, and ethical responsibility can foster intelligent, responsive, and sustainable decision-making systems.
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