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Antecedents and Outcomes of Big Data Adoption in Supply Chain: A Meta-Analytic Investigation

Author

Listed:
  • Raj, Alok

    (XLRI - Xavier School of Management Jamshedpur, Jharkhand, India)

  • Kumar, Rajeev Ranjan

    (Indian Institute of Management Ranchi, Jharkhand, India)

  • Jeyaraj, Anand

    (Wright State University, Ohio, U.S.A)

Abstract

This paper aims to provide a comprehensive understanding of the big data–performance relationship based on the existing empirical evidence. Using a meta-analysis approach, big data adoption (BDA) related 446 effect sizes reported in 133 prior empirical studies were gathered from 118848 informants in more than 30 countries. Results confirm ten significant antecedents and eight outcomes of BDA based on identified literature. We further estimate the heterogeneity based on subgroup analysis by considering two types of moderators as (a) economic regions (developed vs developing), and (b) type of industry. We find that organizations in developed countries adopt big data largely due to environmental and organizational factors. Further, developed countries can harness the potential of big data for better performance (e.g., supply chain integration, collaboration, customer relationship management, and innovation). This study provides multifaceted insights for practitioners and academia alike regarding the use of big data.

Suggested Citation

  • Raj, Alok & Kumar, Rajeev Ranjan & Jeyaraj, Anand, 2024. "Antecedents and Outcomes of Big Data Adoption in Supply Chain: A Meta-Analytic Investigation," American Business Review, Pompea College of Business, University of New Haven, vol. 27(2), pages 775-797, November.
    • Handle: RePEc:ris:ambsrv:0122
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    More about this item

    Keywords

    Big Data; Meta-Analysis; Economic Regions; Type of Industry;
    All these keywords.

    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • J10 - Labor and Demographic Economics - - Demographic Economics - - - General

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