Big data adoption and performance: mediating mechanisms of innovation, supply chain integration and resilience
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| Publicat a: | Supply Chain Management vol. 30, no. 1 (2025), p. 67-85 |
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Emerald Group Publishing Limited
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| 024 | 7 | |a 10.1108/SCM-03-2024-0186 |2 doi | |
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| 045 | 2 | |b d20250101 |b d20251231 | |
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| 100 | 1 | |a Kumar, Rajeev Ranjan |u Department of Operations Management, Indian Institute of Management Ranchi, Ranchi, India | |
| 245 | 1 | |a Big data adoption and performance: mediating mechanisms of innovation, supply chain integration and resilience | |
| 260 | |b Emerald Group Publishing Limited |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a PurposeThis study aims to examine how big data adoption (BA) helps to improve innovation capability, supply chain integration, resilience and organizational performance through direct and mediating mechanisms.Design/methodology/approachThis study uses a combination of meta-analytic approaches (meta-structural equation modeling and meta-regression) using 205 effect sizes from 76 prior empirical studies. It leverages the organization information processing theory as a theoretical lens to analyze the proposed relationships. This study estimates heterogeneity in the relationship between BA and innovation capability based on the meta-regression by considering different types of moderators: digital competitiveness score (DCS), national culture, type of economies and gross domestic product (GDP) per capita.FindingsThe findings indicate that BA improves the innovation capability of the organization, supply chain integration and resilience, which consequently drives organizational performance. The results show that the innovation capability mediating effect is higher between BA and supply chain integration than between BA and supply chain resilience link. However, supply chain resilience and integration are equally effective in translating innovation capability influence to organizational performance. The authors find that developing countries reap more benefits from BA in driving innovation, and country culture plays a vital role in driving innovations.Research limitations/implicationsThis study offers multiple theoretical implications. First, deriving from organization information processing theory, the authors recognized that BA and innovation capability complement each other, which improves the information processing capacity of the organizations, enabling supply chain integration, resilience and organizational performance (Bahrami et al., 2022; Gupta et al., 2020; Chatterjee et al., 2022). This study is one of few that analyzed how BA and innovation capability work together to drive supply chain integration, resilience and organizational performance, which was not collectively studied in existing studies, meta-analyses or reviews to ascertain the direct and mediating mechanisms (Aryal et al., 2020; Oesterreich et al., 2022; Ansari and Ghasemaghaei, 2023; Bag and Rahman, 2023; Alvarenga et al., 2023). Second, our study offers integrated and more definitive results regarding identified relationships. More precisely, the study provides statistically significant direct effects with the help of meta-analysis and meta-structural equation modeling to remove the ambiguity in the literature. Third, apart from the above definitive relationships, mediation analysis contributes to academia in identifying significant mediating mechanisms related to innovation capability, supply chain integration and resilience. Innovation capability partially and significantly mediates between BA and supply chain integration/resilience. Fourth, meta-regression provides valuable insights related to DCS, national culture and type of economies in the supply chain context. In fact, this study is the first one to examine the effects of DCS and all dimensions of national culture on the BA−INV relationship and overcome certain limitations that exist in the literature (Oesterreich et al., 2022; Ansari and Ghasemaghaei, 2023; Nakandala et al., 2023).Practical implicationsBig data is captured through evolving digital technologies such as intelligent sensors, radio frequency identification tags, global positioning system (GPS) locations and social media, which generate large data sets. Thus, managers must extract value from such a large data set and transition from big data to BA. This transition encompasses retrieving unknown patterns and insights from big data, its interpretations and extracting meaningful actions (Gupta et al., 2020; Hallikas et al., 2021). This study confirms that organizational capabilities in terms of BA and innovation enable supply chain integration and resilience. Managers must concentrate on BA and innovation capability simultaneously rather than making a trade-off between capabilities (Morita and Machuca, 2018) to drive supply chain integration, resilience and performance. For example, Morita and Machuca (2018) study revealed that many companies are doing trade-offs between capabilities and innovation. Hence, the findings clarified confusion among practitioners and confirmed that BA improves innovation capability, consequently enabling higher supply chain integration and resilience. Thus, managers investing in innovation capability will be more confident about integration, resilience and performance outcomes.Originality/valueThis is one of the early studies that examine the underlying mechanisms of innovation capability, supply chain integration and resilience between BA and organizational performance. Moderation analysis with a DCS, national culture, type of economies and GDP per capita explains the heterogeneity between the BA and innovation capability relationship. | |
| 653 | |a Innovations | ||
| 653 | |a Big Data | ||
| 653 | |a Regression | ||
| 653 | |a Datasets | ||
| 653 | |a Culture | ||
| 653 | |a Data processing | ||
| 653 | |a Collaboration | ||
| 653 | |a Managers | ||
| 653 | |a Electronic data interchange | ||
| 653 | |a Resilience | ||
| 653 | |a Chain drives | ||
| 653 | |a Modelling | ||
| 653 | |a Decision making | ||
| 653 | |a Tradeoffs | ||
| 653 | |a Radio frequency identification | ||
| 653 | |a Supply chains | ||
| 653 | |a Data analysis | ||
| 653 | |a Information processing | ||
| 653 | |a Heterogeneity | ||
| 653 | |a Developing countries--LDCs | ||
| 700 | 1 | |a Raj, Alok |u Department of Production, Operations and Decision Sciences, XLRI Xavier School of Management, Jamshedpur, India | |
| 773 | 0 | |t Supply Chain Management |g vol. 30, no. 1 (2025), p. 67-85 | |
| 786 | 0 | |d ProQuest |t ABI/INFORM Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3161440048/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3161440048/fulltext/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3161440048/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |