Data-Driven Decision Making and Competitive Advantage of Brewery Firms in the South-East, Nigeria

Abstract

The ability to make informed decisions based on data has become a critical factor in achieving a competitive advantage. Manufacturing firms in South East Nigeria face numerous challenges, including fluctuating consumer preferences, supply chain disruptions, and intensifying market competition from both local and international brands. Despite the increasing availability of advanced data analytics tools and technologies, many of these firms still rely heavily on traditional decision-making approaches that lack precision and adaptability. The study examined the effect of Data-Driven Decision-Making on brewery firms' competitive advantage in South - East Nigeria using firms in the brewery industry as the study area. The study adopted descriptive survey research design. From a population of 2170 senior employees of the brewery firms in the South - East Nigeria, a sample of 418 employees was determined using the Borg and Gall formula. The study made use of a structured questionnaire which was duly validated and tested for reliability using a test-re-test method. Major statistical tools of analysis were summary statistics of percentages, Pearson correlation and multiple regression analysis through the application of SPSS. Findings of the study indicate that there is a high adoption rate of data-driven decision making among the brewery firms in SE Nigeria. Specifically, adoption of data analytics tools, decision-making speed and data driven culture have a significant positive effect on competitive advantage of brewery firms in SE. Nigeria.  The study concluded that the adoption of data-driven decision-making process promotes competitiveness among the brewery firms in South-East Nigeria by enhancing responsiveness, innovation and market share through customer satisfaction, loyalty and retention. The study recommends among others that brewery firms should prioritise data analytics training initiatives to enhance the quality of their data-driven decision-making in their respective organisations.