THE ROLE OF DATA ANALYTICS AND AI IN IMPROVING MANAGERIAL DECISION-MAKING

Abstract

This paper aims to analyze the effects of Data Analytics Capability (DAC) and Artificial Intelligence Adoption (AIA) on the efficiency of Managerial Decision-Making (DME). In the current Industry 4.0 environment and under data-driven management, organizations use analytics and AI solutions to make strategic decisions. Prior studies show that data analytics and AI are capable of improving the process of decision making by increasing its speed, accuracy, and efficiency; however, empirical evidence about their joint influence is still insufficient. This research conducts a survey of 35 business experts to test two hypotheses: (H1) DAC positively impacts DME, and (H2) AIA positively impacts DME. A set of questions with four items each to measure DAC, AIA, and DME constructs were used on a five-point Likert scale (refer to Annex). The survey was held online using a Google form, and collected data was analyzed with the help of Microsoft Excel. The scores for each construct were calculated as average scores for corresponding items (e.g., DAC_avg = (DAC1 + DAC2 + DAC3 + DAC4)/4). According to the descriptive statistics, all three variables had moderate to high mean scores (DAC ≈ 3.91, AIA ≈ 3.85, DME ≈ 4.18). Pearson correlation showed very strong positive correlations between all pairings (r ≈ 0.838 for DAC-DME and r ≈ 0.826 for AIA-DME). Both simple linear regression models (DME ~ DAC: β ≈ 0.75, p < 0.001, R² = 0.703 and DME ~ AIA: β ≈ 0.80, p < 0.001, R² = 0.683) proved the significance of these correlations. Thus, both hypotheses have been confirmed. The findings have been represented through the use of tables and charts. In summary, there is clear indication of considerable impacts that arise from having the ability to analyze data and making use of artificial intelligence. The recommendations call for investment in appropriate technology and training. These are some of the issues discussed in Chapter 5.