Table 4.
Most cited articles in the application of I4Es-in-SCM research.
| Authors | Title of document | Journal | Total Citations | FWCI | Norm. Citations |
|---|---|---|---|---|---|
| Waller and Fawcett (2013) | Data science, predictive analytics, and big data: A revolution that will transform supply chain design and management | Journal of Business Logistics | 561 | 18.27* | 14.45 |
| Wang et al. (2016) | Big data analytics in logistics and supply chain management: Certain investigations for research and applications | International Journal of Production Economics | 423 | 26.97* | 9.92 |
| Hazen et al. (2014) | Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications | International Journal of Production Economics | 373 | 24.06* | 10.80 |
| Gunasekaran et al. (2017) | Big data and predictive analytics for supply chain and organizational performance | Journal of Business Research | 259 | 20.75* | 11.19 |
| Zhong et al. (2015) | A big data approach for logistics trajectory discovery from RFID-enabled production data | International Journal of Production Economics | 235 | 26.52* | 5.82 |
| Zhong et al. (2016) | Big Data for supply chain management in the service and manufacturing sectors: Challenges, opportunities, and future perspectives | Computers and Industrial Engineering | 216 | 29.16* | 5.06 |
| Carbonneau et al. (2008) | Application of machine learning techniques for supply chain demand forecasting | European Journal of Operational Research | 195 | 5.70* | 2.18 |
| Schoenherr and Speier-Pero (2015) | Data science, predictive analytics, and big data in supply chain management: Current state and future potential | Journal of Business Logistics | 190 | 16.18* | 4.71 |
| Tan et al. (2015) | Harvesting big data to enhance supply chain innovation capabilities: An analytic infrastructure based on deduction graph | International Journal of Production Economics | 182 | 21.06* | 4.51 |
| Papadopoulos et al. (2017) | The role of Big Data in explaining disaster resilience in supply chains for sustainability | Journal of Cleaner Production | 179 | 15.11* | 7.74 |
Note: *FWCI - Field-Weighted Citation Impact shows how well cited this document is when compared to similar documents. A value greater than 1.00 means the document is more cited than expected according to the average.