Critical analysis of the impact of big data analytics on supply chain operations

References

• Addo-Tenkorang, R., and P. T. Helo. 2016. “Big Data Applications in Operations/Supply-Chain Management: A Literature Review.” Computers & Industrial Engineering 101: 528–543. doi:10.1016/j.cie.2016.09.023.
   Web of Science ®Google Scholar
• Ahmadov, Y., and P. Helo. 2018. “A Cloud Based Job Sequencing with Sequence-Dependent Setup for Sheet Metal Manufacturing.” Annals of Operations Research 270 (1–2): 5–24. doi:10.1007/s10479-016-2304-3.
   Web of Science ®Google Scholar
• Akter, S., S. Fosso Wamba, and S. Dewan. 2017. “Why PLS-SEM is Suitable for Complex Modelling? An Empirical Illustration in Big Data Analytics Quality.” Production Planning & Control 28 (11–12): 1011–1021. doi:10.1080/09537287.2016.1267411.
   Web of Science ®Google Scholar
• Bag, S. 2017. “Big Data and Predictive Analysis is Key to Superior Supply Chain Performance: A South African Experience.” International Journal of Information Systems and Supply Chain Management 10 (2): 66–84. doi:10.4018/IJISSCM.2017040104.
   Web of Science ®Google Scholar
• Bag, S., S. Luthra, S. K. Mangla, and Y. Kazancoglu. 2021. “Leveraging Big Data Analytics Capabilities in Making Reverse Logistics Decisions and Improving Remanufacturing Performance.” The International Journal of Logistics Management 32 (3): 742–765. doi:10.1108/IJLM-06-2020-0237.
   Web of Science ®Google Scholar
• Bag, S., L. C. Wood, L. Xu, P. Dhamija, and Y. Kayikci. 2020. “Big Data Analytics as an Operational Excellence Approach to Enhance Sustainable Supply Chain Performance.” Resources, Conservation and Recycling 153 (104559): 104559. doi:10.1016/j.resconrec.2019.104559.
   Google Scholar
• Barnett-Page, E., and J. Thomas. 2009. “Methods for the Synthesis of Qualitative Research: A Critical Review.” BMC Medical Research Methodology 9 (1): 1–11. doi:10.1186/1471-2288-9-59.
   PubMed Web of Science ®Google Scholar
• Bhattacharjya, J., A. B. Ellison, V. Pang, and A. Gezdur. 2018. “Creation of Unstructured Big Data from Customer Service: The Case of Parcel Shipping Companies on Twitter.” The International Journal of Logistics Management 29 (2): 723–738. doi:10.1108/IJLM-06-2017-0157.
   Web of Science ®Google Scholar
• Bhattacharjya, J., A. Ellison, and S. Tripathi. 2016. “An Exploration of Logistics-Related Customer Service Provision on Twitter: The Case of e-Retailers.” International Journal of Physical Distribution and Logistics Management 45 (9/10): 979–1006.
   Google Scholar
• Ben-Daya, M., E. Hassini, and Z. Bahroun. 2019. “Internet of Things and Supply Chain Management: A Literature Review.” International Journal of Production Research 57 (15–16): 4719–4742. doi:10.1080/00207543.2017.1402140.
   Web of Science ®Google Scholar
• Bock, S., and F. Isik. 2015. “A New Two-Dimensional Performance Measure in Purchase Order Sizing.” International Journal of Production Research 53 (16): 4951–4962. doi:10.1080/00207543.2015.1005769.
   Web of Science ®Google Scholar
• Boland, A., Cherry, G., and Dickson, R., eds. 2017. Doing a Systematic Review: A Student's Guide. Sage.
   Google Scholar
• Brandau, A., and J. Tolujevs. 2013. “Modelling and Analysis of Logistical State Data.” Transport and Telecommunication Journal 14 (2): 102–115. doi:10.2478/ttj-2013-0009.
   Google Scholar
• Brinch, M., J. Stentoft, J. K. Jensen, and C. Rajkumar. 2018. “Practitioners Understanding of Big Data and Its Applications in Supply Chain Management.” The International Journal of Logistics Management 29 (2): 555–574. doi:10.1108/IJLM-05-2017-0115.
   Web of Science ®Google Scholar
• Brouer, B. D., C. V. Karsten, and D. Pisinger. 2018. “Optimization in Liner Shipping.” Annals of Operations Research 271 (1): 205–236. doi:10.1007/s10479-018-3023-8.
   Web of Science ®Google Scholar
• Bumblauskas, D., D. Gemmill, A. Igou, and J. Anzengruber. 2017. “Smart Maintenance Decision Support Systems (SMDSS) Based on Corporate Big Data Analytics.” Expert Systems with Applications 90: 303–317. doi:10.1016/j.eswa.2017.08.025.
   Web of Science ®Google Scholar
• Caldera, H. T. S., C. Desha, and L. Dawes. 2017. “Exploring the Role of Lean Thinking in Sustainable Business Practice: A Systematic Literature Review.” Journal of Cleaner Production 167: 1546–1565. doi:10.1016/j.jclepro.2017.05.126.
   Web of Science ®Google Scholar
• Chavez, R., W. Yu, M. A. Jacobs, and M. Feng. 2017. “Data-Driven Supply Chains, Manufacturing Capability and Customer Satisfaction.” Production Planning & Control 28 (11–12): 906–918. doi:10.1080/09537287.2017.1336788.
   Web of Science ®Google Scholar
• Chawla, V., S. Angra, S. Suri, and R. Kalra. 2020. “A Synergic Framework for Cyber-Physical Production Systems in the Context of Industry 4.0 and Beyond.” International Journal of Data and Network Science 4 (2): 237–244. doi:10.5267/j.ijdns.2019.12.002.
   Google Scholar
• Chehbi-Gamoura, S., R. Derrouiche, D. Damand, and M. Barth. 2020. “Insights from Big Data Analytics in Supply Chain Management: An All-Inclusive Literature Review Using the SCOR Model.” Production Planning & Control 31 (5): 355–382. doi:10.1080/09537287.2019.1639839.
   Web of Science ®Google Scholar
• Chen, M. 2022. “The Influence of Big Data Analysis of Intelligent Manufacturing under Machine Learning on Start-Ups Enterprise.” Enterprise Information Systems 16 (2): 347–316. doi:10.1080/17517575.2019.1694180.
   Google Scholar
• Chen, P. C., and M. Kezunovic. 2016. “Fuzzy Logic Approach to Predictive Risk Analysis in Distribution Outage Management.” IEEE Transactions on Smart Grid 7 (6): 2827–2836. doi:10.1109/TSG.2016.2576282.
   Web of Science ®Google Scholar
• Chen, Z.-L. 2010. “Integrated Production and Outbound Distribution Scheduling: Review and Extensions.” Operations Research 58 (1): 130–148. doi:10.1287/opre.1080.0688.
   Web of Science ®Google Scholar
• Chien, C. F., C. W. Liu, and S. C. Chuang. 2017. “Analysing Semiconductor Manufacturing Big Data for Root Cause Detection of Excursion for Yield Enhancement.” International Journal of Production Research 55 (17): 5095–5107. doi:10.1080/00207543.2015.1109153.
   Web of Science ®Google Scholar
• Choi, Y., H. Lee, and Z. Irani. 2018. “Big Data-Driven Fuzzy Cognitive Map for Prioritising IT Service Procurement in the Public Sector.” Annals of Operations Research 270 (1–2): 75–104. doi:10.1007/s10479-016-2281-6.
   Web of Science ®Google Scholar
• Chopra, S., and P. Meindl. 2010. Supply Chain Management: Strategy, Planning, and Operation. 4th ed. Upper Saddle River, NJ: Pearson Education Inc.
   Google Scholar
• Davenport, T. H. 2014. “How Strategists Use “Big Data” to Support Internal Business Decisions, Discovery and Production.” Strategy & Leadership 42 (4): 45–50.
   Google Scholar
• Delbufalo, E. 2012. “Outcomes of Inter-Organizational Trust in Supply Chain Relationships: A Systematic Literature Review and a Meta-Analysis of the Empirical Evidence.” Supply Chain Management: An International Journal 17 (4): 377–402. doi:10.1108/13598541211246549.
   Web of Science ®Google Scholar
• Demey, Y. T., and M. Wolff. 2016. “SIMISS: A Model Based Searching Strategy for Inventory Management Systems.” IEEE Internet of Things Journal 4 (1): 1–1. doi:10.1109/JIOT.2016.2638023.
   Web of Science ®Google Scholar
• Denyer, D., and D. Tranfield. 2009. “Producing a Systematic Review.” In The Sage Handbook of Organisational Research Methods, edited by Karen Lyons, Merl C. Hokenstad, Manohar S. Pawar, and Nigel Hal, 671–689. Thousand Oaks, CA: SAGE Publications.
   Google Scholar
• DiBiano, R., and S. Mukhopadhyay. 2017. “Automated Diagnostics for Manufacturing Machinery Based on Well-Regularized Deep Neural Networks.” Integration 58: 303–310. doi:10.1016/j.vlsi.2017.03.012.
   Web of Science ®Google Scholar
• Dimaggio, P. J., and W. W. Powell. 2000. “The Iron Cage Revisited Institutional Isomorphism and Collective Rationality in Organizational Fields.” In Economics Meets Sociology in Strategic Management (Advances in Strategic Management) Vol. 17, edited by Joel A. C. Baum and Frank Dobbin, 143–166. Bingley: Emerald Group Publishing Limited.
   Google Scholar
• Dubey, R., A. Gunasekaran, and S. J. Childe. 2019. “Big Data Analytics Capability in Supply Chain Agility.” Management Decision 57 (8): 2092–2112. doi:10.1108/MD-01-2018-0119.
   Web of Science ®Google Scholar
• Dubey, R., A. Gunasekaran, S. J. Childe, C. Blome, and T. Papadopoulos. 2019. “Big Data and Predictive Analytics and Manufacturing Performance: integrating Institutional Theory, Resource‐Based View and Big Data Culture.” British Journal of Management 30 (2): 341–361. doi:10.1111/1467-8551.12355.
   Web of Science ®Google Scholar
• Dubey, R., A. Gunasekaran, T. Papadopoulos, S. J. Childe, K. T. Shibin, and S. F. Wamba. 2017. “Sustainable Supply Chain Management: framework and Further Research Directions.” Journal of Cleaner Production 142: 1119–1130. doi:10.1016/j.jclepro.2016.03.117.
   Web of Science ®Google Scholar
• Dubey, R., A. Gunasekaran, S. J. Childe, D. J. Bryde, M. Giannakis, C. Foropon, D. Roubaud, and B. T. Hazen. 2020. “Big Data Analytics and Artificial Intelligence Pathway to Operational Performance under the Effects of Entrepreneurial Orientation and Environmental Dynamism: A Study of Manufacturing Organisations.” International Journal of Production Economics 226 (107599): 107599. doi:10.1016/j.ijpe.2019.107599.
   Google Scholar
• Ekşioğlu, S. D., H. Edwin Romeijn, and P. M. Pardalos. 2006. “Cross-Facility Management of Production and Transportation Planning Problem.” Computers & Operations Research 33 (11): 3231–3251. doi:10.1016/j.cor.2005.02.038.
   Web of Science ®Google Scholar
• Furneaux, B. 2012. “Task-Technology Fit Theory: A Survey and Synopsis of the Literature.” In Information Systems Theory, Vol. 28, edited by Y. K. Dwivedi, M. R. Wade, and S. L. Schneberger, 87–106. New York: Springer.
   Google Scholar
• Giannakis, M., and M. Louis. 2016. “A Multi-Agent Based System with Big Data Processing for Enhanced Supply Chain Agility.” Journal of Enterprise Information Management 29 (5): 706–727. doi:10.1108/JEIM-06-2015-0050.
   Web of Science ®Google Scholar
• Gandomi, A., and M. Haider. 2015. “Beyond the Hype: Big Data Concepts, Methods, and Analytics.” International Journal of Information Management 35 (2): 137–144. doi:10.1016/j.ijinfomgt.2014.10.007.
   Web of Science ®Google Scholar
• Goodhue, D. L., and R. L. Thompson. 1995. “Task-Technology Fit and Individual Performance.” MIS Quarterly 19 (2): 213–236. doi:10.2307/249689.
   Web of Science ®Google Scholar
• Gough, D., Oliver, S., and Thomas, J., eds. 2017. An Introduction to Systematic Reviews. Sage.
   Google Scholar
• Gunasekaran, A., T. Papadopoulos, R. Dubey, S. F. Wamba, S. J. Childe, B. Hazen, and S. Akter. 2017. “Big Data and Predictive Analytics for Supply Chain and Organizational Performance.” Journal of Business Research 70: 308–317. doi:10.1016/j.jbusres.2016.08.004.
   Web of Science ®Google Scholar
• Gunasekaran, A., Y. Y. Yusuf, E. O. Adeleye, and T. Papadopoulos. 2018. “Agile Manufacturing Practices: The Role of Big Data and Business Analytics with Multiple Case Studies.” International Journal of Production Research 56 (1–2): 385–397. doi:10.1080/00207543.2017.1395488.
   Web of Science ®Google Scholar
• Gupta, M., and J. F. George. 2016. “Toward the Development of a Big Data Analytics Capability.” Information & Management 53 (8): 1049–1064. doi:10.1016/j.im.2016.07.004.
   Web of Science ®Google Scholar
• Hazen, B. T., J. B. Skipper, C. A. Boone, and R. R. Hill. 2018. “Back in Business: Operations Research in Support of Big Data Analytics for Operations and Supply Chain Management.” Annals of Operations Research 270 (1–2): 201–211. doi:10.1007/s10479-016-2226-0.
   Web of Science ®Google Scholar
• Hosoda, T., and S. M. Disney. 2012. “On the Replenishment Policy When the Market Demand Information is Lagged.” International Journal of Production Economics 135 (1): 458–467. doi:10.1016/j.ijpe.2011.08.022.
   Web of Science ®Google Scholar
• Hopkins, J., and P. Hawking. 2018. “Big Data Analytics and IoT in Logistics: A Case Study.” The International Journal of Logistics Management 29 (2): 575–591. doi:10.1108/IJLM-05-2017-0109.
   Web of Science ®Google Scholar
• Hu, J., F. L. Lewis, O. P. Gan, G. H. Phua, and L. L. Aw. 2014. “Discrete-Event Shop-Floor Monitoring System in RFID-Enabled Manufacturing.” IEEE Transactions on Industrial Electronics 61 (12): 7083–7091. doi:10.1109/TIE.2014.2314068.
   Web of Science ®Google Scholar
• Huang, Y.-Y., and R. B. Handfield. 2015. “Measuring the Benefits of ERP on Supply Management Maturity Model: A “Big Data” Method”.” International Journal of Operations & Production Management 35 (1): 2–25. doi:10.1108/IJOPM-07-2013-0341.
   Web of Science ®Google Scholar
• Huang, T., and J. A. Van Mieghem. 2014. “Data and Inventory Management: Model and Empirical Analysis.” Production and Operations Management 23 (3): 333–347. doi:10.1111/poms.12046.
   Web of Science ®Google Scholar
• Irani, Z., M. M. Kamal, A. Sharif, and P. E. D. Love. 2017. “Enabling Sustainable Energy Futures: Factors Influencing Green Supply Chain Collaboration.” Production Planning & Control 28 (6–8): 684–705. doi:10.1080/09537287.2017.1309710.
   Web of Science ®Google Scholar
• Ivanov, D., and A. Dolgui. 2021. “A Digital Supply Chain Twin for Managing the Disruption Risks and Resilience in the Era of Industry 4.0.” Production Planning and Control 32 (9): 775–788.
   Web of Science ®Google Scholar
• Jackson, K., & Bazeley, P. (2019). Qualitative Data Analysis with NVivo. Sage.
   Google Scholar
• Jang, S. W., and G. Y. Kim. 2017. “A Monitoring Method of Semiconductor Manufacturing Processes Using Internet of Things–Based Big Data Analysis.” International Journal of Distributed Sensor Networks 13 (7): 155014771772181. doi:10.1177/1550147717721810.
   Web of Science ®Google Scholar
• Jain, A., N. Rudi, and T. Wang. 2015. “Demand Estimation and Ordering under Censoring: Stock-out Timing is (Almost) All You Need.” Operations Research 63 (1): 134–150. doi:10.1287/opre.2014.1326.
   Web of Science ®Google Scholar
• Ji, G., L. Hu, and K. H. Tan. 2017. “A Study on Decision-Making of Food Supply Chain Based on Big Data.” Journal of Systems Science and Systems Engineering 26 (2): 183–198. doi:10.1007/s11518-016-5320-6.
   Web of Science ®Google Scholar
• Ji, W., and L. Wang. 2017. “Big Data Analytics Based Fault Prediction for Shop Floor Scheduling.” Journal of Manufacturing Systems 43: 187–194. doi:10.1016/j.jmsy.2017.03.008.
   Web of Science ®Google Scholar
• Jin, Y., and S. Ji. 2013. “Partner Choice of Supply Chain Based on 3d Printing and Big Data.” Information Technology Journal 12 (22): 6822–6826. doi:10.3923/itj.2013.6822.6826.
   Google Scholar
• Kache, F., and S. Seuring. 2017. “Challenges and Opportunities of Digital Information at the Intersection of Big Data Analytics and Supply Chain Management.” International Journal of Operations & Production Management 37 (1): 10–36. doi:10.1108/IJOPM-02-2015-0078.
   Web of Science ®Google Scholar
• Kamal, M. M. 2020. “The Triple-Edged Sword of COVID-19: Understanding the Use of Digital Technologies and the Impact of Productive, Disruptive and Destructive Nature of the Pandemic.” Information Systems Management 37 (4): 310–317. doi:10.1080/10580530.2020.1820634.
   Web of Science ®Google Scholar
• Kamal, M. M., and Z. Irani. 2014. “Analysing Supply Chain Integration through Systematic Literature Review: A Normative Perspective.” Supply Chain Management. An International Journal 19 (5/6): 523–557.
   Web of Science ®Google Scholar
• Kato, T., and R. Kamoshida. 2020. “Multi-Agent Simulation Environment for Logistics Warehouse Design Based on Self-Contained Agents.” Applied Sciences 10 (21): 7552. doi:10.3390/app10217552.
   Google Scholar
• Khakifirooz, M., C. F. Chien, and Y. J. Chen. 2018. “Bayesian Inference for Mining Semiconductor Manufacturing Big Data for Yield Enhancement and Smart Production to Empower Industry 4.0.” Applied Soft Computing 68: 990–999. doi:10.1016/j.asoc.2017.11.034.
   Web of Science ®Google Scholar
• Kozjek, Dominik, Rok Vrabič, Borut Rihtaršič, and Peter Butala. 2018. “Big Data Analytics for Operations Management in Engineer-to-Order Manufacturing.” Procedia CIRP 72: 209–214. doi:10.1016/j.procir.2018.03.098.
   Google Scholar
• Krumeich, J., D. Werth, and P. Loos. 2016. “Prescriptive Control of Business Processes.” Business & Information Systems Engineering 58 (4): 261–280. doi:10.1007/s12599-015-0412-2.
   Web of Science ®Google Scholar
• Ku, J. H. 2018. “A Study on Prediction Model of Equipment Failure through Analysis of Big Data Based on RHadoop.” Wireless Personal Communications 98 (4): 3163–3176. doi:10.1007/s11277-017-4151-1.
   Web of Science ®Google Scholar
• Kumar, A., R. Shankar, A. Choudhary, and L. S. Thakur. 2016. “A Big Data MapReduce Framework for Fault Diagnosis in Cloud-Based Manufacturing.” International Journal of Production Research 54 (23): 7060–7073. doi:10.1080/00207543.2016.1153166.
   Web of Science ®Google Scholar
• Lai, Y., H. Sun, and J. Ren. 2018. “Understanding the Determinants of Big Data Analytics (BDA) Adoption in Logistics and Supply Chain Management: An Empirical Investigation.” The International Journal of Logistics Management 29 (2): 676–703. doi:10.1108/IJLM-06-2017-0153.
   Web of Science ®Google Scholar
• Lamba, K., and S. P. Singh. 2017. “Big Data in Operations and Supply Chain Management: current Trends and Future Perspectives.” Production Planning & Control 28 (11–12): 877–890. doi:10.1080/09537287.2017.1336787.
   Web of Science ®Google Scholar
• Lau, R. Y. K., W. Zhang, and W. Xu. 2017. “Parallel Aspect-Oriented Sentiment Analysis for Sales Forecasting with Big Data.” Production and Operations Management 27 (10): 1–20.
   Web of Science ®Google Scholar
• Lavalle, S., E. Lesser, R. Shockley, M. S. Hopkins, and N. Kruschwitz. 2011. “Big Data, Analytics and the Path from Insights to Value.” MIT Sloan Management Review 52 (2): 21–32.
   Web of Science ®Google Scholar
• Lele, Q., and K. Lihua. 2016. “Technical Framework Design of Safety Production Information Management Platform for Chemical Industrial Parks Based on Cloud Computing and the Internet of Things.” International Journal of Grid and Distributed Computing 9 (6): 299–314. doi:10.14257/ijgdc.2016.9.6.28.
   Google Scholar
• Lee, C., and R. Nair. 2021. “Robust Transit Line Planning Based on Demand Estimates Obtained from Mobile Phones.” EURO Journal on Transportation and Logistics 10: 100034. doi:10.1016/j.ejtl.2021.100034.
   Web of Science ®Google Scholar
• Lee, J. Y., J. S. Yoon, and B. H. Kim. 2017. “A Big Data Analytics Platform for Smart Factories in Small and Medium-Sized Manufacturing Enterprises: An Empirical Case Study of a Die Casting Factory.” International Journal of Precision Engineering and Manufacturing 18 (10): 1353–1361. doi:10.1007/s12541-017-0161-x.
   Web of Science ®Google Scholar
• Lee, C. K. H. 2017. “A GA-Based Optimisation Model for Big Data Analytics Supporting Anticipatory Shipping in Retail 4.0.” International Journal of Production Research 55 (2): 593–605. doi:10.1080/00207543.2016.1221162.
   Web of Science ®Google Scholar
• Li, D., H. Tang, S. Wang, and C. Liu. 2017. “A Big Data Enabled Load-Balancing Control for Smart Manufacturing of Industry 4.0.” Cluster Computing 20 (2): 1855–1864. doi:10.1007/s10586-017-0852-1.
   Web of Science ®Google Scholar
• Li, H., Y. Ji, G. Luo, and S. Mi. 2016. “A Modular Structure Data Modeling Method for Generalized Products.” The International Journal of Advanced Manufacturing Technology 84 (1–4): 197–212. doi:10.1007/s00170-015-7815-6.
   Web of Science ®Google Scholar
• Li, J., F. Tao, Y. Cheng, and L. Zhao. 2015. “Big Data in Product Lifecycle Management.” The International Journal of Advanced Manufacturing Technology 81 (1–4): 667–684. doi:10.1007/s00170-015-7151-x.
   Web of Science ®Google Scholar
• Li, L., T. Chi, T. Hao, and T. Yu. 2018. “Customer Demand Analysis of the Electronic Commerce Supply Chain Using Big Data.” Annals of Operations Research 268 (1–2): 113–128. doi:10.1007/s10479-016-2342-x.
   Web of Science ®Google Scholar
• Li, Y., G. Jia, Y. Cheng, and Y. Hu. 2017. “Additive Manufacturing Technology in Spare Parts Supply Chain: A Comparative Study.” International Journal of Production Research 55 (5): 1498–1515. doi:10.1080/00207543.2016.1231433.
   Web of Science ®Google Scholar
• Li, Z., H. Yang, S. Zhang, and G. Liu. 2016. “Unrelated Parallel Machine Scheduling Problem with Energy and Tardiness Cost.” The International Journal of Advanced Manufacturing Technology 84 (1–4): 213–226. doi:10.1007/s00170-015-7657-2.
   Web of Science ®Google Scholar
• Liu, Z., Y. Wang, L. Cai, Q. Cheng, and H. Zhang. 2016. “Design and Manufacturing Model of Customized Hydrostatic Bearing System Based on Cloud and Big Data Technology.” The International Journal of Advanced Manufacturing Technology 84 (1–4): 261–273. doi:10.1007/s00170-015-8066-2.
   Web of Science ®Google Scholar
• Liu, P., and S. P. Yi. 2018. “A Study on Supply Chain Investment Decision-Making and Coordination in the Big Data Environment.” Annals of Operations Research 270 (1–2): 235–253. doi:10.1007/s10479-017-2424-4.
   Web of Science ®Google Scholar
• Ma, L., F. Nie, and Q. Lu. 2015. “An Analysis of Supply Chain Restructuring Based on Big Data and Mobile Internet - A Case Study of Warehouse-Type Supermarkets.” Proceedings of IEEE International Conference on Grey Systems and Intelligent Services, GSIS, 446–451.
   Google Scholar
• Manyika, J., M. Chui, B. Brown, J. Bughin, R. Dobbs, C. Roxburgh, and A. H. Byers. 2011. “Big Data: The Next Frontier for Innovation, Competition, and Productivity.” Accessed on January 2, 2019. Available at http://www.mckinsey.com/insights/business_technology/big_data_the_next_frontier_for_innovation.
   Google Scholar
• Mentzer, T. J., and K. B. Kahn. 1995. “A Framework of Logistics Research.” Journal of Business Logistics 16 (1): 231–250.
   Google Scholar
• Mignerat, M., and S. Rivard. 2009. “Positioning the Institutional Perspective in Information Systems Research.” Journal of Information Technology 24 (4): 369–391. doi:10.1057/jit.2009.13.
   Web of Science ®Google Scholar
• Mikalef, P., M. Boura, G. Lekakos, and J. Krogstie. 2019a. “Big Data Analytics Capabilities and Innovation: The Mediating Role of Dynamic Capabilities and Moderating Effect of the Environment.” British Journal of Management 30 (2): 272–298. doi:10.1111/1467-8551.12343.
   Web of Science ®Google Scholar
• Mikalef, P., M. Boura, G. Lekakos, and J. Krogstie. 2019b. “Big Data Analytics and Firm Performance: Findings from a Mixed-Method Approach.” Journal of Business Research 98: 261–276. doi:10.1016/j.jbusres.2019.01.044.
   Web of Science ®Google Scholar
• Mikalef, P., J. Krogstie, I. O. Pappas, and P. Pavlou. 2020. “Exploring the Relationship between Big Data Analytics Capability and Competitive Performance: The Mediating Roles of Dynamic and Operational Capabilities.” Information & Management 57 (2): 103169. doi:10.1016/j.im.2019.05.004.
   Web of Science ®Google Scholar
• Min, Q., Y. Lu, Z. Liu, C. Su, and B. Wang. 2019. “Machine Learning Based Digital Twin Framework for Production Optimization in Petrochemical Industry.” International Journal of Information Management 49: 502–519. doi:10.1016/j.ijinfomgt.2019.05.020.
   Web of Science ®Google Scholar
• Mishra, N., and A. Singh. 2018. “Use of Twitter Data for Waste Minimisation in Beef Supply Chain.” Annals of Operations Research 270 (1–2): 337–359. doi:10.1007/s10479-016-2303-4.
   Web of Science ®Google Scholar
• Mishra, S., and S. P. Singh. 2020. “Distribution Network Model Using Big Data in an International Environment.” Science of the Total Environment 707 (135549): 135549. doi:10.1016/j.scitotenv.2019.135549.
   PubMedGoogle Scholar
• Mishra, N., A. Singh, N. P. Rana, and Y. K. Dwivedi. 2017. “Interpretive Structural Modelling and Fuzzy MICMAC Approaches for Customer Centric Beef Supply Chain: application of a Big Data Technique.” Production Planning & Control 28 (11–12): 945–963. doi:10.1080/09537287.2017.1336789.
   Web of Science ®Google Scholar
• Moretto, A., S. Ronchi, and A. S. Patrucco. 2017. “Increasing the Effectiveness of Procurement Decisions: The Value of Big Data in the Procurement Process.” International Journal of RF Technologies 8 (3): 79–103. doi:10.3233/RFT-171670.
   Google Scholar
• Mourtzis, D. 2016. “Challenges and Future Perspectives for the Life Cycle of Manufacturing Networks in the Mass Customisation Era.” Logistics Research 9 (1): 1–20. doi:10.1007/s12159-015-0129-0.
   Google Scholar
• Nazari-Shirkouhi, S., H. Shakouri, B. Javadi, and A. Keramati. 2013. “Supplier Selection and Order Allocation Problem Using a Two-Phase Fuzzy Multi-Objective Linear Programming.” Applied Mathematical Modelling 37 (22): 9308–9323. doi:10.1016/j.apm.2013.04.045.
   Web of Science ®Google Scholar
• Nguyen, Truong, Li Zhou, Virginia Spiegler, Petros Ieromonachou, and Yong Lin. 2018. “Big Data Analytics in Supply Chain Management: A State-of-the-Art Literature Review.” Computers & Operations Research 98: 254–264. doi:10.1016/j.cor.2017.07.004.
   Web of Science ®Google Scholar
• Nnamdi, O. 2018. “Strategies for Managing Excess and Dead Inventories: A Case Study of Spare Parts Inventories in the Elevator Equipment Industry.” Operations and Supply Chain Management: An International Journal 11 (3): 128–138. doi:10.31387/oscm0320209.
   Google Scholar
• Oliveira, T., and M. R. O. Martins. 2011. “Literature Review of Information Technology Adoption Models at Firm Level.” The Electronic Journal Information Systems Evaluation 14 (1): 110–121.
   Google Scholar
• Pal, B., S. S. Sana, and K. Chaudhuri. 2014. “A Multi-Echelon Production–Inventory System with Supply Disruption.” Journal of Manufacturing Systems 33 (2): 262–276. doi:10.1016/j.jmsy.2013.12.010.
   Web of Science ®Google Scholar
• Pang, J., N. Zhang, Q. Xiao, F. Qi, and X. Xue. 2021. “A New Intelligent and Data-Driven Product Quality Control System of Industrial Valve Manufacturing Process in CPS.” Computer Communications 175: 25–34. doi:10.1016/j.comcom.2021.04.022.
   Web of Science ®Google Scholar
• Pang, Y., X. Zhao, H. Yan, and Y. Liu. 2021. “Data-Driven Trajectory Prediction with Weather Uncertainties: A Bayesian Deep Learning Approach.” Transportation Research Part C: Emerging Technologies 130: 103326. doi:10.1016/j.trc.2021.103326.
   Web of Science ®Google Scholar
• Pramanik, D., S. C. Mondal, and A. Haldar. 2020. “Resilient Supplier Selection to Mitigate Uncertainty: Soft-Computing Approach.” Journal of Modelling in Management 15 (4): 1339–1361. doi:10.1108/JM2-01-2019-0027.
   Web of Science ®Google Scholar
• Pishdad, A., A. Koronios, and G. Geursen. 2014. “Identifying Gaps in Institutional Theory.” Australasian Conference on Information Systems. 1–11.
   Google Scholar
• Pishdad, A., and A. Haider. 2013. “Responding to External and Internal Changes: Organizational Responsiveness Pressures in Institutionalising ERP Systems.” Proceedings of the Portland International Center for Management of Engineering and Technology (PICMET), 2305–2312.
   Google Scholar
• Preethi, V. V., S. R. Sankar, and R. J. Mathew. 2016. “Predictive Analysis Using Big Data Analytics for Sensors Used in Fleet Truck Monitoring System.” International Journal of Engineering and Technology 8 (2): 714–719.
   Google Scholar
• Ranjan, S., J. Vijay, and P. Pralay. 2016. “Antecedents and Imperatives of Integrating SCM and ERP in Manufacturing Organizations: A Conceptual Perspective with Big Data Analytics.” International Journal of Control Theory and Applications 9 (10): 4423–4431.
   Google Scholar
• Rao, P., M. R. Muller, and G. Gunn. 2017. “Conducting a Metering Assessment to Identify Submetering Needs at a Manufacturing Facility.” CIRP Journal of Manufacturing Science and Technology 18: 107–114. doi:10.1016/j.cirpj.2016.10.005.
   Web of Science ®Google Scholar
• Rahman, M., M. M. Kamal, E. Aydin, and A. Ul-Haque. 2022. “Impact of Industry 4.0 Drivers on the Performance of Service Sector: Comparative Study of Cargo Logistic Firms in Developed and Developing Regions.” Production Planning and Control 33 (2-3): 228–243.
   Web of Science ®Google Scholar
• Rehman, M. Hur., V. Chang, A. Batool, and T. Y. Wah. 2016. “Big Data Reduction Framework for Value Creation in Sustainable Enterprises.” International Journal of Information Management 36 (6): 917–928. doi:10.1016/j.ijinfomgt.2016.05.013.
   Web of Science ®Google Scholar
• Rüßmann, M., M. Lorenz, P. Gerbert, M. Waldner, J. Justus, P. Engel, and M. Harnisch. 2015. Industry 4.0: The Future of Productivity and Growth in Manufacturing Industries. Boston Consulting Group.
   Google Scholar
• Ren, S., H. L. Chan, and T. Siqin. 2020. “Demand Forecasting in Retail Operations for Fashionable Products: methods, Practices, and Real Case Study.” Annals of Operations Research 291 (1–2): 761–777. doi:10.1007/s10479-019-03148-8.
   Web of Science ®Google Scholar
• Ren, S., X. Zhao, B. Huang, Z. Wang, and X. Song. 2019. “A Framework for Shopfloor Material Delivery Based on Real-Time Manufacturing Big Data.” Journal of Ambient Intelligence and Humanized Computing 10 (3): 1093–1108. doi:10.1007/s12652-018-1017-7.
   Web of Science ®Google Scholar
• Sang, G. M., L. Xu, and P. de Vrieze. 2021. “A Predictive Maintenance Model for Flexible Manufacturing in the Context of Industry 4.0.” Frontiers in Big Data 4: 663466. doi:10.3389/fdata.2021.663466.
   PubMedGoogle Scholar
• Scott, W. 2005. “Institutional Theory: Contributing to a Theoretical Research Program.” In Great Minds in Management: The Process of Theory Development, edited by Ken G. Smith and Michael A. Hitt, 460–485. Oxford UK: Oxford University Press.
   Google Scholar
• See-To, E. W., and E. W. Ngai. 2018. “Customer Reviews for Demand Distribution and Sales Nowcasting: A Big Data Approach.” Annals of Operations Research 270 (1–2): 415–431. doi:10.1007/s10479-016-2296-z.
   Web of Science ®Google Scholar
• Shan, S., X. Wen, Y. Wei, Z. Wang, and Y. Chen. 2020. “Intelligent Manufacturing in Industry 4.0: A Case Study of Sany Heavy Industry.” Systems Research and Behavioral Science 37 (4): 679–690. doi:10.1002/sres.2709.
   Web of Science ®Google Scholar
• Sharma, S., Y. Cui, Q. He, R. Mohammadi, and Z. Li. 2018. “Data-Driven Optimization of Railway Maintenance for Track Geometry.” Transportation Research Part C: Emerging Technologies 90: 34–58. doi:10.1016/j.trc.2018.02.019.
   Web of Science ®Google Scholar
• Shukla, M., and M. K. Tiwari. 2017. “Big-Data Analytics Framework for Incorporating Smallholders in Sustainable Palm Oil Production.” Production Planning & Control 28 (16): 1365–1377. doi:10.1080/09537287.2017.1375145.
   Web of Science ®Google Scholar
• Singh, A. K., N. Subramanian, K. S. Pawar, and R. Bai. 2018. “Cold Chain Configuration Design: Location-Allocation Decision-Making Using Coordination, Value Deterioration, and Big Data Approximation.” Annals of Operations Research 270 (1–2): 433–457. doi:10.1007/s10479-016-2332-z.
   Web of Science ®Google Scholar
• Sivarajah, S., M. M. Kamal, Z. Irani, and V. Weerakkody. 2017. “Critical Analysis of Big Data Challenges and Analytical Methods.” Journal of Business Research 70: 263–286. doi:10.1016/j.jbusres.2016.08.001.
   Web of Science ®Google Scholar
• Son, J.-Y., and I. Benbasat. 2007. “Organizational Buyers’ Adoption and Use of B2B Electronic Marketplaces: Efficiency- and Legitimacy-Oriented Perspectives.” Journal of Management Information Systems 24 (1): 55–99. doi:10.2753/MIS0742-1222240102.
   Web of Science ®Google Scholar
• Srinivasan, R., and M. Swink. 2018. “An Investigation of Visibility and Flexibility as Complements to Supply Chain Analytics: An Organizational Information Processing Theory Perspective.” Production and Operations Management 27 (10): 1849–1867. doi:10.1111/poms.12746.
   Web of Science ®Google Scholar
• Straub, E. T. 2009. “Understanding Technology Adoption: Theory and Future Directions for Informal Learning.” Review of Educational Research 79 (2): 625–649. doi:10.3102/0034654308325896.
   Web of Science ®Google Scholar
• Sugrue, D., and P. Adriaens. 2021. “A Data Fusion Approach to Predict Shipping Efficiency for Bulk Carriers.” Transportation Research Part E: Logistics and Transportation Review 149: 102326. doi:10.1016/j.tre.2021.102326.
   Web of Science ®Google Scholar
• Sushil, 2017. “Multi-Criteria Valuation of Flexibility Initiatives Using Integrated TISM–IRP with a Big Data Framework.” Production Planning and Control 28 (11–12): 999–1010. doi:10.1080/09537287.2017.1336794.
   Web of Science ®Google Scholar
• Tayal, A., and S. P. Singh. 2018. “Integrating Big Data Analytic and Hybrid Firefly-Chaotic Simulated Annealing Approach for Facility Layout Problem.” Annals of Operations Research 270 (1–2): 489–514. doi:10.1007/s10479-016-2237-x.
   Web of Science ®Google Scholar
• Tao, F., L. Zhang, Y. Liu, Y. Cheng, L. Wang, and X. Xu. 2015. “Manufacturing Service Management in Cloud Manufacturing: Overview and Future Research Directions.” Journal of Manufacturing Science and Engineering 137 (4): 1–11. doi:10.1115/1.4030510.
   Web of Science ®Google Scholar
• Tao, F., J. Cheng, Q. Qi, M. Zhang, H. Zhang, and F. Sui. 2018. “Digital Twin-Driven Product Design, Manufacturing and Service with Big Data.” The International Journal of Advanced Manufacturing Technology 94 (9–12): 3563–3576. doi:10.1007/s00170-017-0233-1.
   Web of Science ®Google Scholar
• Tranfield, D., D. Denyer, and P. Smart. 2003. “Towards a Methodology for developing evidence-Informed Management Knowledge by Means of Systematic Review.” British Journal of Management 14 (3): 207–222. doi:10.1111/1467-8551.00375.
   Web of Science ®Google Scholar
• Trivedi, A., A. Chauhan, S. P. Singh, and H. Kaur. 2017. “A Multi-Objective Integer Linear Program to Integrate Supplier Selection and Order Allocation with Market Demand in a Supply Chain.” International Journal of Procurement Management 10 (3): 335. doi:10.1504/IJPM.2017.083466.
   Google Scholar
• van der Spoel, S., C. Amrit, and J. van Hillegersberg. 2017. “Predictive Analytics for Truck Arrival Time Estimation: A Field Study at a European Distribution Centre.” International Journal of Production Research 55 (17): 5062–5078. doi:10.1080/00207543.2015.1064183.
   Web of Science ®Google Scholar
• Waller, M. A., and S. E. Fawcett. 2013. “Click Here for a Data Scientist: Big Data, Predictive Analytics, and Theory Development in the Era of a Maker Movement Supply Chain.” Journal of Business Logistics 34 (4): 249–252. doi:10.1111/jbl.12024.
   Web of Science ®Google Scholar
• Wamba, S. F., and S. Akter. 2019. “Understanding Supply Chain Analytics Capabilities and Agility for Data-Rich Environments.” International Journal of Operations & Production Management 39 (6/7/8): 887–912. doi:10.1108/IJOPM-01-2019-0025.
   Web of Science ®Google Scholar
• Wamba, S. F., S. Akter, A. Edwards, G. Chopin, and D. Gnanzou. 2015. “How ‘Big Data’ Can Make Big Impact: findings from a Systematic Review and a Longitudinal Case Study.” International Journal of Production Economics 165: 234–246. doi:10.1016/j.ijpe.2014.12.031.
   Web of Science ®Google Scholar
• Wamba, S. F., R. Dubey, A. Gunasekaran, and S. Akter. 2020. “The Performance Effects of Big Data Analytics and Supply Chain Ambidexterity: The Moderating Effect of Environmental Dynamism.” International Journal of Production Economics 222: 107498. doi:10.1016/j.ijpe.2019.09.019.
   Web of Science ®Google Scholar
• Wamba, S. F., A. Gunasekaran, R. Dubey, and E. W. Ngai. 2018. “Big Data Analytics in Operations and Supply Chain Management.” Annals of Operations Research 270 (1–2): 1–4. doi:10.1007/s10479-018-3024-7.
   Web of Science ®Google Scholar
• Wan, J., S. Tang, D. Li, S. Wang, C. Liu, H. Abbas, and A. V. Vasilakos. 2017. “A Manufacturing Big Data Solution for Active Preventive Maintenance.” IEEE Transactions on Industrial Informatics 13 (4): 2039–2047. doi:10.1109/TII.2017.2670505.
   Web of Science ®Google Scholar
• Wang, L., and C. A. Alexander. 2015. “Big Data Driven Supply Chain Management and Business Administration.” American Journal of Economics and Business Administration 7 (2): 60–67. doi:10.3844/ajebasp.2015.60.67.
   Google Scholar
• Wang, G., A. Gunasekaran, and E. W. Ngai. 2018. “Distribution Network Design with Big Data: Model and Analysis.” Annals of Operations Research 270 (1–2): 539–551. doi:10.1007/s10479-016-2263-8.
   Web of Science ®Google Scholar
• Wang, Gang, Angappa Gunasekaran, Eric W. T. Ngai, and Thanos Papadopoulos. 2016. “Big Data Analytics in Logistics and Supply Chain Management: Certain Investigations for Research and Applications.” International Journal of Production Economics 176: 98–110. doi:10.1016/j.ijpe.2016.03.014.
   Web of Science ®Google Scholar
• Wang, J., J. Yang, J. Zhang, X. Wang, and W. Zhang. 2018. “Big Data Driven Cycle Time Parallel Prediction for Production Planning in Wafer Manufacturing.” Enterprise Information Systems 12 (6): 714–732. doi:10.1080/17517575.2018.1450998.
   Web of Science ®Google Scholar
• Wang, J., P. Zheng, W. Qin, T. Li, and J. Zhang. 2019. “A Novel Resilient Scheduling Paradigm Integrating Operation and Design for Manufacturing Systems with Uncertainties.” Enterprise Information Systems 13 (4): 430–447. doi:10.1080/17517575.2018.1526322.
   Web of Science ®Google Scholar
• Wang, S., J. Wan, D. Li, and C. Zhang. 2016. “Implementing Smart Factory of Industrie 4.0: An Outlook.” International Journal of Distributed Sensor Networks 12 (1): 3159805. doi:10.1155/2016/3159805.
   Web of Science ®Google Scholar
• Wang, Y., L. Kung, and T. A. Byrd. 2018. “Big Data Analytics: Understanding Its Capabilities and Potential Benefits for Healthcare Organizations.” Technological Forecasting and Social Change 126: 3–13. doi:10.1016/j.techfore.2015.12.019.
   Web of Science ®Google Scholar
• Wong, E., and Y. Wei. 2018. “Customer Online Shopping Experience Data Analytics: Integrated Customer Segmentation and Customised Services Prediction Model.” International Journal of Retail & Distribution Management 46 (4): 406–420. doi:10.1108/IJRDM-06-2017-0130.
   Web of Science ®Google Scholar
• Wu, M., and K. Yang. 2012. “Research on the Algorithms and Key Factors in Optimizing Supply Chain Inventory.” IEEE 5th International Conference on Advanced Computational Intelligence, 433–436.
   Google Scholar
• Xu, Y., G. Chen, and J. Zheng. 2016. “An Integrated Solution – KAGFM for Mass Customization in Customer-Oriented Product Design under Cloud Manufacturing Environment.” The International Journal of Advanced Manufacturing Technology 84 (1–4): 85–101. doi:10.1007/s00170-015-8074-2.
   Web of Science ®Google Scholar
• Yan-Qiu, L., and W. Hao. 2016. “Optimization for Logistics Network Based on the Demand Analysis of Customer.” 2016 Chinese Control and Decision Conference (CCDC), 4547–4552. doi:10.1109/CCDC.2016.7531804.
   Google Scholar
• Yang, H., J. Wen, X. Gu, Y. Liu, S. Wang, W. Cai, and Y. Li. 2017. “A Mathematical Model for Flow Maldistribution Study in a Parallel Plate-Fin Heat Exchanger.” Applied Thermal Engineering 121: 462–472. doi:10.1016/j.applthermaleng.2017.03.130.
   Web of Science ®Google Scholar
• Yang, C., S. Lan, W. Shen, G. Q. Huang, X. Wang, and T. Lin. 2017. “Towards Product Customization and Personalization in IoT-Enabled Cloud Manufacturing.” Cluster Computing 20 (2): 1717–1730. doi:10.1007/s10586-017-0767-x.
   Web of Science ®Google Scholar
• Yu, W., M. A. Jacobs, R. Chavez, and M. Feng. 2017. “The Impacts of IT Capability and Marketing Capability on Supply Chain Integration: A Resource-Based Perspective.” International Journal of Production Research 55 (14): 4196–4211. doi:10.1080/00207543.2016.1275874.
   Web of Science ®Google Scholar
• Yu, W., R. Chavez, M. A. Jacobs, and M. Feng. 2018. “Data-Driven Supply Chain Capabilities and Performance: A Resource-Based View.” Transportation Research Part E: Logistics and Transportation Review 114: 371–385. doi:10.1016/j.tre.2017.04.002.
   Web of Science ®Google Scholar
• Yu, C.-S., and H.-L. Li. 2000. “A Robust Optimization Model for Stochastic Logistic Problems.” International Journal of Production Economics 64 (1–3): 385–397. doi:10.1016/S0925-5273(99)00074-2.
   Web of Science ®Google Scholar
• Yuan, H., W. Xu, Q. Li, and R. Lau. 2018. “Topic Sentiment Mining for Sales Performance Prediction in e-Commerce.” Annals of Operations Research 270 (1–2): 553–576. doi:10.1007/s10479-017-2421-7.
   Web of Science ®Google Scholar
• Yunusa-Kaltungo, A., and J. K. Sinha. 2017. “Effective Vibration-Based Condition Monitoring (eVCM) of Rotating Machines.” Journal of Quality in Maintenance Engineering 23 (3): 279–296. doi:10.1108/JQME-08-2016-0036.
   Web of Science ®Google Scholar
• Yue, X., H. Cai, H. Yan, C. Zou, and K. Zhou. 2015. “Cloud-Assisted Industrial Cyber-Physical Systems: An Insight.” Microprocessors and Microsystems 39 (8): 1262–1270. doi:10.1016/j.micpro.2015.08.013.
   Web of Science ®Google Scholar
• Zhao, P., X. Liu, W. Shi, T. Jia, W. Li, and M. Chen. 2020. “An Empirical Study on the Intra-Urban Goods Movement Patterns Using Logistics Big Data.” International Journal of Geographical Information Science 34 (6): 1089–1116. doi:10.1080/13658816.2018.1520236.
   Web of Science ®Google Scholar
• Zhao, Y., H. Zhang, L. An, and Q. Liu. 2018. “Improving the Approaches of Traffic Demand Forecasting in the Big Data Era.” Cities 82: 19–26. doi:10.1016/j.cities.2018.04.015.
   Web of Science ®Google Scholar
• Zheng, P., H. Wang, Z. Sang, R. Y. Zhong, Y. Liu, C. Liu, K. Mubarok, S. Yu, and X. Xu. 2018. “Smart Manufacturing Systems for Industry 4.0: Conceptual Framework, Scenarios, and Future Perspectives.” Frontiers of Mechanical Engineering 13 (2): 137–150. doi:10.1007/s11465-018-0499-5.
   Web of Science ®Google Scholar
• Zheng, M., and K. Wu. 2017. “Smart Spare Parts Management Systems in Semiconductor Manufacturing.” Industrial Management and Data Systems 116 (2): 322–348.
   Google Scholar
• Zhong, R. Y., S. Lan, C. Xu, Q. Dai, and G. Q. Huang. 2016. “Visualization of RFID-Enabled Shopfloor Logistics Big Data in Cloud Manufacturing.” The International Journal of Advanced Manufacturing Technology 84 (1–4): 5–16. doi:10.1007/s00170-015-7702-1.
   Web of Science ®Google Scholar
• Zhong, R. Y., G. Q. Huang, S. Lan, Q. Y. Dai, X. Chen, and T. Zhang. 2015. “A Big Data Approach for Logistics Trajectory Discovery from RFID-Enabled Production Data.” International Journal of Production Economics 165: 260–272. doi:10.1016/j.ijpe.2015.02.014.
   Web of Science ®Google Scholar
• Zhong, R. Y., C. Xu, C. Chen, and G. Q. Huang. 2017. “Big Data Analytics for Physical Internet-Based Intelligent Manufacturing Shop Floors.” International Journal of Production Research 55 (9): 2610–2621. doi:10.1080/00207543.2015.1086037.
   Web of Science ®Google Scholar
• Zhou, H., Y. Shou, X. Zhai, L. Li, C. Wood, and X. Wu. 2014. “Supply Chain Practice and Information Quality: A Supply Chain Strategy Study.” International Journal of Production Economics 147 (PART C): 624–633. doi:10.1016/j.ijpe.2013.08.025.
   Web of Science ®Google Scholar
• Zigurs, I., and B. K. Buckland. 1998. “A Theory of Task/Technology Fit and Group Support Systems.” MIS Quarterly 22 (3): 313–334. doi:10.2307/249668.
   Web of Science ®Google Scholar