Generative AI and Circular Economy Practices in Manufacturing Supply Chains: Survey Dataset of 240 Firms

 This dataset contains the responses of 240 manufacturing firms to a structured survey investigating the role of Generative Artificial Intelligence (GAI) in enabling Circular Economy (CE) practices and improving sustainability outcomes.The survey instrument was developed based on validated scales from prior literature, including Generative AI capability measures (Dwivedi et al., 2023) and Circular Economy practice measures (Kirchherr et al., 2018).It captures firm demographics, AI adoption levels, circular practice implementation, sustainability performance metrics, organizational readiness, adoption barriers, and open-ended insights.The dataset supports research on AI-enabled sustainable supply chains, mediation modeling (GAI \u2192 CE \u2192 Performance), and cross-sectoral benchmarking.Data DescriptionFile FormatExcel (.xlsx)generative_ai_ce_survey_responses.xlsx \u2014 full dataset with 240 anonymized firm responsessurvey_summary_statistics.xlsx \u2014 descriptive statistics for all variablessurvey_correlation_matrix.xlsx \u2014 correlation matrixsurvey_regression_results.xlsx \u2014 OLS regression coefficients predicting waste reduction from GAI and CEmediation_results.xlsx \u2014 mediation analysis output (GAI \u2192 CE \u2192 Waste Reduction)Survey StructureSection A \u2013 DemographicsIndustry sector, company size, region, annual revenueSection B \u2013 Generative AI Capabilities (7 items, 5-point Likert)Eco-design, process simulation, production optimization, sustainable packaging, predictive maintenance, supply chain reconfiguration, sustainability-integrated decision-makingSection C \u2013 Circular Economy Practices (9 items, 5-point Likert)Closing loops, slowing loops, narrowing loops, reverse logistics, material recovery, shared resourcesSection D \u2013 Sustainability Performance MetricsWaste reduction (%), energy intensity (MJ\/unit), material recovery rate (%), employee reskilling rate (%), SME supplier inclusion rate (%)Section E \u2013 Readiness and BarriersAI readiness score (1\u20135), adoption barriers (multiple choice)Section F \u2013 Open-Ended QuestionsExamples of GAI improving sustainabilityPolicy or incentives to scale AI\u2013CE practicesData Collection MethodologyPopulation: Manufacturing firms in automotive, electronics, consumer goods, and logisticsSampling frame: Industry directories and LinkedIn company listingsSample size: 240 valid responses (20% response rate)Collection period: [Insert period here]Instrument delivery: Online survey form (Qualtrics)Ethics: All responses anonymized, no personally identifying information collectedPotential UsesQuantitative modeling of GAI adoption impact on CE practices and performanceMediation\/moderation analysis in sustainability researchBenchmarking CE adoption across manufacturing sectorsTeaching dataset for statistics, machine learning, or supply chain management courses