Business Analytics Strategies in Port Economics from a Systems-Theory Perspective: A Bibliometric Analysis and Future Research Directions

Open Access
Article
Conference Proceedings
Authors: Alen JugovićMiljen Sirotic

Abstract: Business analytics in the context of port economics encompasses data-driven insights to optimize port operations, streamline logistics, inform infrastructure planning, and enhance stakeholder coordination. Contemporary research in business analytics strategies in port economics from a systems – theory perspective is fragmented, as varied approaches and themes make it challenging for scholars and industry practitioners to form a clear vision of current integrated business analytics strategies. To address this gap, this study conducts a bibliometric analysis of 142 articles regarding business analytics strategies in port economics. The articles were published in 98 academic journals and authored by 498 scholars. The application of the bibliographic coupling methodology in the VOSviewer software enabled the identification of four clusters: (1) Contemporary Maritime Transport Systems; (2) Port Systems Analysis; (3) Performance Optimization; (4) Data – Driven Decision Support. Content analysis of the identified clusters indicates future research directions regarding business analytics strategies that contemporary ports should incorporate: (1) Improved efficiency and resource allocation via utilization of predictive analytics, real – time monitoring and performance measurement; (2) Cost optimization via reduced waiting times, improved equipment utilization, and predictive maintenance; and (3) Enhanced decision – making via data – driven insights, risk management, and sustainability goals. The findings offer a scientifically robust foundation for scholars and industry practitioners aiming to improve their understanding of how systems – theory informed business analytics strategies can be utilized to optimize the port as a system.

Keywords: Port Economics, Business Analytics Strategies, Systems Theory, Operational Efficiency, Predictive Analytics, Bibliometric Analysis

DOI: 10.54941/ahfe1006763

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