Leveraging Generative Artificial Intelligence to Enhance Cognitive Agility in Supply Chain Decision-Making: A Real-Time Empirical Analysis

This repository entry contains information from a research study that looks at how Generative Artificial Intelligence (AI), particularly Generative Pre-trained Transformer (GPT) models, helps senior supply chain executives think more flexibly during challenging situations. The study combines scenario-based experiments with 200 senior managers and qualitative interviews with 25 executives, providing comprehensive quantitative and qualitative insights.Research Objectives Empirically examine the impact of generative AI on cognitive agility in supply chains. Assess how cognitive agility influences decision accuracy, adaptability, and response time. Identify implementation challenges, barriers, and prerequisites for strategic integration of generative AI within organisational contexts. Methodological Overview Design: Mixed methods (Quantitative experimentation, qualitative semi-structured interviews) Sample: Quantitative: 200 senior supply chain managers Qualitative: 25 supply chain executives (purposively sampled for depth of insights) Tools Used: Scenario-based experiments using GPT-based generative AI tools NVivo 12 software for thematic qualitative analysis SmartPLS (PLS-SEM), Bayesian network modelling for quantitative analysis Data Collected Quantitative Data: Decision accuracy, response time, adaptability (Likert-scale assessments) Pre- and post-experiment psychometric scales measuring perceived cognitive agility, decision-making confidence, and perceived usefulness of generative AI Qualitative Data: Semi-structured interview transcripts Thematic categories: cognitive capabilities enhancement, implementation challenges, and strategic alignment factors Direct participant quotes providing granular, context-specific insights Key Findings Quantitative Outcomes: Generative AI significantly enhances cognitive agility (β = 0.64, p Qualitative Outcomes (Thematic): 1. Enhanced Cognitive Capabilities: Real-time analytics, improved responsiveness, creative problem-solving, and strategic foresight. 2. Implementation Challenges: Technological integration issues, legacy system constraints, data privacy and ethical compliance concerns, and human capital limitations such as skill gaps and resistance. 3. Strategic Alignment and Readiness: The importance of executive leadership commitment, an agile organisational culture, strategic alignment, and dedicated resources for effective AI adoption.