AI for Data Analytics in Management Research

Artificial Intelligence as an analytical assistant that accelerates the movement from data to information, insight and decision, while leaving problem definition, validation and final judgment with the researcher. He contrasted traditional analysis with AI-enhanced analysis, which adds machine learning, natural-language interaction and generative explanations, and showed how generative AI can support code generation in R and Python, data cleaning, and exploratory data analysis through AI-generated visualisations. The covered descriptive and diagnostic analytics (cautioning that correlation is not causation), predictive analytics using regression, decision trees, random forests, neural networks and time-series methods, and multivariate techniques including Factor Analysis, Cluster Analysis, MANOVA and Structural Equation Modelling, along with interpreting model-fit indices. The session also addressed segmentation and clustering, managerial optimisation, multi-criteria decision-making methods such as AHP, TOPSIS and PROMETHEE, and the use of institutional data for forecasting enrolment and dropout risk. He introduced a structured prompt-engineering framework for data analysis and closed with a discussion of AI hallucination, bias and reliability, stressing that human accountability for analytical and managerial decisions cannot be delegated to an algorithm