Meta-Analysis Dataset for AI in Consumer Behavior

1. General InformationTechnical Note: Meta-Analysis Dataset for AI in Consumer BehaviorVersion: 1.0Date of Completion: October 27, 2025Primary Researcher: Luane DannoMaster's Advisor: Professor Dr. Diego Nogueira Rafael - São Carlos State UniversityMethodological Supervision: Professor Dr. Valter Afonso Vieira - Maringa State UniversityFunding: This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.Affiliation: UFSCar - PPGA.Date of Creation: 27-Oct-2025 (Conforme sua decisão estratégica).This repository adheres to the FAIR Principles (Findable, Accessible, Interoperable, and Reusable). The meta-analytical integration was performed using Random-Effects Models, and the effect size conversion followed the formulas provided by Borenstein et al. (2009). All coding was cross-checked to ensure inter-rater reliability. 2. OverviewThis dataset contains the systematic identification and screening flow for a meta-analysis regarding Artificial Intelligence (AI) and Consumer Behavior. The search strategy was conducted across 46 scientific databases (via Web of Science, Scopus, ProQuest, and EBSCO) and complemented by grey literature via Google Scholar. 3. Methodology, Search Strategy & IdentificationKeywords used: "artificial intelligence" AND "consumer behav*".Languages: English, Portuguese, Spanish.Period: 1995 – 2025.Total Records Identified: n = 2008Databases & Registers: n = 1988Google Scholar: n = 20Deduplication: A total of 408 duplicate records were removed (401 from database exports and 7 from manual search results), resulting in a unique pool of 1600 records for screening. 4. Screening & Eligibility CriteriaThe screening process followed the PRISMA 2020 Protocol. A total of 1494 records were excluded during the initial phase.Primary Exclusion Reason: Theoretical and Statistical Saturation reached (n = 1,409). The screening was concluded when the sample provided sufficient statistical power to represent the population effect sizes, as validated by a Senior Methodological Review.Technical Exclusions: * Qualitative Studies (n = 65)Bibliometric/Quantitative-only without effect sizes (n = 14)Retracted Articles (n = 1)Lack of statistical data/No author response (n = 5) 5. Final Sample CompositionReports Assessed for Full-Text Eligibility: =n = 104 (excluding 2 reports not retrieved).Included Studies: 104 articles met all inclusion criteria for the meta-analytical model.Quality Audit: The final sample of 104 articles was subjected to a technical audit to ensure reliability and alignment with ABS 4* journal standards. 6. Data Curation & Version ControlInitial Extraction (Jan-Mar 2025): Primary data gathering from Web of Science (n=205).Expansion Phase (Oct-Nov 2025): Expansion to 45+ additional databases to ensure sample saturation and minimize publication bias.Technical Refinement (Dec 2025-Feb 2026): Recoding of variables to align with meta-analytical standards and inter-rater reliability checks (referenciando as reuniões com os professores como "Technical Committee Review").Final Consolidation: Technical validation by Senior Researchers (Prof. Valter) confirming statistical power and reliability. 7. Data Usage & DOIThis record serves as a formal timestamp for the research methodology and data collection. All procedures follow the CC BY 4.0 license. 8. Codebook (TBD)Effect Size Columns: xxxxxxOther Collumns: xxxxxModerators: xxxxx