Big Data Driven Predictive Analytics and Strategic Decision-Making in Retail Shopping Malls in Southeast, Nigeria

This study explores the effect of big data-driven predictive analytics on operational efficiency and strategic decision-making in retail shopping malls in Southeast Nigeria. The rapid growth of retail markets has necessitated the adoption of innovative data analytics tools to enhance decision quality and competitiveness. The research investigates how various independent factors—namely data volume, data sources, data processing speed, use of analytics tools, insights generated, decision support systems, and staff skills in analytics -affect the operational efficiency of retail malls. A structured survey was administered to 131 respondents across selected malls in the region. To analyze the relationships among these variables, factor analysis was employed, helping to identify the underlying factors influencing operational efficiency. The factor analysis results revealed significant insights: data volume and data sources loaded strongly on a factor labelled "Data Infrastructure," with loadings of 0.78 and 0.81 respectively. Data processing speed and use of analytics tools loaded on a second factor, "Analytic Capabilities," with loadings of 0.75 and 0.77. The insights generated and decision support systems formed a third factor, "Decision Support Effectiveness," with loadings of 0.83 and 0.79. Staff skills in analytics contributed to a fourth factor, "Human Capital Readiness," with a loading of 0.76. Regression analysis indicated that these factors collectively explained 68% of the variance in operational efficiency (R² = 0.68, p < 0.01). The findings suggest that robust data infrastructure and advanced analytic capabilities significantly enhance operational efficiency. Moreover, well-trained staff and effective decision support systems further contribute to improved performance. Based on these results, it is recommended that retail malls in Southeast Nigeria invest in expanding their data collection and processing capacities, adopt sophisticated analytics tools, and prioritize staff training in big data analytics to foster strategic decision-making and operational excellence. Implementing these measures can position retail malls to better respond to market dynamics and sustain competitive advantage in the evolving retail landscape.

Publication Details

Journal
Umsida Repository (Universitas Muhammadiyah Sidoarjo)
Published
2025-05-01
Primary Topic
Big Data and Business Intelligence
Type
article
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article

Big Data Driven Predictive Analytics and Strategic Decision-Making in Retail Shopping Malls in Southeast, Nigeria

Umsida Repository (Universitas Muhammadiyah Sidoarjo)
Big Data and Business Intelligence
article

Big Data Driven Predictive Analytics and Strategic Decision-Making in Retail Shopping Malls in Southeast, Nigeria

article en

Abstract

This study explores the effect of big data-driven predictive analytics on operational efficiency and strategic decision-making in retail shopping malls in Southeast Nigeria. The rapid growth of retail markets has necessitated the adoption of innovative data analytics tools to enhance decision quality and competitiveness. The research investigates how various independent factors—namely data volume, data sources, data processing speed, use of analytics tools, insights generated, decision support systems, and staff skills in analytics -affect the operational efficiency of retail malls. A structured survey was administered to 131 respondents across selected malls in the region. To analyze the relationships among these variables, factor analysis was employed, helping to identify the underlying factors influencing operational efficiency. The factor analysis results revealed significant insights: data volume and data sources loaded strongly on a factor labelled "Data Infrastructure," with loadings of 0.78 and 0.81 respectively. Data processing speed and use of analytics tools loaded on a second factor, "Analytic Capabilities," with loadings of 0.75 and 0.77. The insights generated and decision support systems formed a third factor, "Decision Support Effectiveness," with loadings of 0.83 and 0.79. Staff skills in analytics contributed to a fourth factor, "Human Capital Readiness," with a loading of 0.76. Regression analysis indicated that these factors collectively explained 68% of the variance in operational efficiency (R² = 0.68, p < 0.01). The findings suggest that robust data infrastructure and advanced analytic capabilities significantly enhance operational efficiency. Moreover, well-trained staff and effective decision support systems further contribute to improved performance. Based on these results, it is recommended that retail malls in Southeast Nigeria invest in expanding their data collection and processing capacities, adopt sophisticated analytics tools, and prioritize staff training in big data analytics to foster strategic decision-making and operational excellence. Implementing these measures can position retail malls to better respond to market dynamics and sustain competitive advantage in the evolving retail landscape.

Umsida Repository (Universitas Muhammadiyah Sidoarjo)
Industry, innovation and infrastructure
Openalex Percentile: Top 34%
Big Data and Business Intelligence
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