Longitudinal Analysis of Big Data and Technological Interventions in Industrial Performance

This research aims to fill a gap in the body of knowledge by examining the impacts of technological advancements on operational productivity across various industries. Employing a longitudinal case study design encompassing three distinct industries (an Agri-Food firm, a construction firm, and a logistics firm), the research unfolds in two main phases aligned with Lewin's change management model: an unfreezing phase that was witnessed in January 2023 and refreezing phases that existed up to December 2023. Quantitative and qualitative data collection techniques used include face-to-face interviews, document reviews, and regression analysis to establish positive effects of technological solutions on organizational performance, sales, efficiency, and customer satisfaction. The research also recommends that increase of software infrastructure and engineering, market decision making and data analysis management are key factors to the performance of the industries in each sector. Using regression models, it also establishes that all the independent variables (SEI, DAMS, MDM) are majorly related to dependent variables, with coefficients (β) from 0. R2 value of 0.72 is relatively high, which indicates that the model has efficient explanatory power. These insights present recommendations to managers and policymakers who intend to utilize technology for organizational development and competitiveness.