Bridging Data-Driven Decision Making and Predictive Analytics: A Scientometric and Co-occurrence Analysis Using VOSviewer

This study aims to examine the intellectual structure and thematic evolution of research at the intersection of data-driven decision making and predictive analytics, addressing a notable gap in the literature regarding their conceptual integration. Using a dataset of ۳۱۸ peer-reviewed articles from the Scopus database, a co-occurrence analysis of author keywords was conducted via VOSviewer. The analysis identified four main thematic clusters: (۱) predictive analytics in supply chain management and sustainability, (۲) machine learning and predictive modelling. (۳) data-driven decision frameworks integrated with artificial intelligence, and (۴) foundational technologies such as big data and business intelligence. Overlay visualization revealed the increasing prominence of emerging technologies such as blockchain and IOT in ۲۰۲۳-۲۰۲۴, signalling a shift toward real-time, decentralized decision models. Density visualization confirmed the conceptual centrality of predictive analytics, machine learning, and data-driven decision making. The study provides a structured overview for advancing interdisciplinary research and practice.

کلیدواژه ها:

Data-Driven Decision Making ، Predictive analytics ، VOSviewer ، Scientometrics ، Co-occurrence analysis