How to cite this paper
Liu, Y., Wei, J & G, Y. (2025). Optimization decision of supply chain data governance involving data governance service providers.International Journal of Industrial Engineering Computations , 16(2), 295-306.
Refrences
Belhadi, A., Kamble, S., Jabbour, C. J. C., Gunasekaran, A., Ndubisi, N. O., & Venkatesh, M. (2021). Manufacturing and service supply chain resilience to the COVID-19 outbreak: Lessons learned from the automobile and airline industries. Technological Forecasting and Social Change, 163, 120447. doi: 10.1016/j.techfore.2020.120447
Bradlow, E. T., Gangwar, M., Kopalle, P., & Voleti, S. (2017). The Role of Big Data and Predictive Analytics in Retailing. Journal of Retailing, 93(1), 79-95. doi: 10.1016/j.jretai.2016.12.004
Chatterjee, S., Chaudhuri, R., Shah, M., & Maheshwari, P. (2022). Big data driven innovation for sustaining SME supply chain operation in post COVID-19 scenario: Moderating role of SME technology leadership. Comput Ind Eng, 168, 108058. doi: 10.1016/j.cie.2022.108058
Chen, F. R., Lai, G. M., & Xiao, W. Q. (2016). Provision of Incentives for Information Acquisition: Forecast-Based Contracts vs. Menus of Linear Contracts. Management Science, 62(7), 1899-1914. doi: 10.1287/mnsc.2015.2193
Chu, L. Y., Shamir, N., & Shin, H. (2017). Strategic Communication for Capacity Alignment with Pricing in a Supply Chain. Management Science, 63(12), 4366-4388. doi: 10.1287/mnsc.2016.2527
Dan, B., Zhang, S. G., & Zhou, M. S. (2018). Strategies for warranty service in a dual-channel supply chain with value-added service competition. International Journal Of Production Research, 56(17), 5677-5699. doi: 10.1080/00207543.2017.1377355
Dekimpe, M. G., & Geyskens, I. (2019). Retailing Research in Rapidly Changing Times: On the Danger of Being Leapfrogged by Practice. Journal of Retailing, 91(1), 6-9. doi: 10.1016/j.jretai.2019.02.001
Fosso Wamba, S., Gunasekaran, A., Dubey, R., & Ngai, E. W. T. (2018). Big data analytics in operations and supply chain management. Annals Of Operations Research, 270, 1-4. doi: 10.1007/s10479-018-3024-7
Giri, B. C., & Glock, C. H. (2017). A closed-loop supply chain with stochastic product returns and worker experience under learning and forgetting. International Journal Of Production Research, 55(22), 6760-6778. doi: 10.1080/00207543.2017.1347301
Giri, B. C., & Masanta, M. (2020). Developing a closed-loop supply chain model with price and quality dependent demand and learning in production in a stochastic environment. International Journal Of Systems Science-Operations & Logistics, 7(2), 147-163. doi: 10.1080/23302674.2018.1542042
Gupta, S., & Ramachandran, D. (2021). Emerging Market Retail: Transitioning from a Product-Centric to a Customer-Centric Approach. Journal of Retailing, 97(4), 597-620. doi: 10.1016/j.jretai.2021.01.008
Hossain, M. A., Akter, S., & Yanamandram, V. (2020). Revisiting customer analytics capability for data-driven retailing. Journal of Retailing and Consumer Services, 56, 102187. doi: 10.1016/j.jretconser.2020.102187
Huang, M.-H., & Rust, R. T. (2022). A Framework for Collaborative Artificial Intelligence in Marketing. Journal of Retailing, 98(2), 209-223. doi: 10.1016/j.jretai.2021.03.001
Jaber, M. Y., & El Saadany, A. M. A. (2011). An economic production and remanufacturing model with learning effects. International Journal Of Production Economics, 131(1), 115-127. doi: 10.1016/j.ijpe.2009.04.019
Liu, P. (2019). Pricing policies and coordination of low-carbon supply chain considering targeted advertisement and carbon emission reduction costs in the big data environment. Journal of Cleaner Production, 210, 343-357. doi: 10.1016/j.jclepro.2018.10.328
Liu, P., & Yi, S. P. (2018). Investment decision-making and coordination of a three-stage supply chain considering Data Company in the Big Data era. Annals Of Operations Research, 270(1-2), 255-271. doi: 10.1007/s10479-018-2783-5
Louhghalam, A., Akbarian, M., & Ulm, F.-J. (2017). Carbon management of infrastructure performance: Integrated big data analytics and pavement-vehicle-interactions. Journal of Cleaner Production, 142, 956-964. doi: 10.1016/j.jclepro.2016.06.198
Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2019). 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
Nilashi, M., Ahmadi, H., Arji, G., Alsalem, K. O., Samad, S., Ghabban, F., . . . Alarood, A. A. (2021). Big social data and customer decision making in vegetarian restaurants: A combined machine learning method. Journal of Retailing and Consumer Services, 62, 102630. doi: 10.1016/j.jretconser.2021.102630
Pantano, E., & Dennis, C. (2019). Store buildings as tourist attractions: Mining retail meaning of store building pictures through a machine learning approach. Journal of Retailing and Consumer Services, 51, 304-310. doi: 10.1016/j.jretconser.2019.06.018
Pi, Z. Y., Fang, W. G., & Zhang, B. F. (2019). Service and pricing strategies with competition and cooperation in a dual-channel supply chain with demand disruption. Computers & Industrial Engineering, 138. doi: 10.1016/j.cie.2019.106130
Piccarozzi, M., & Aquilani, B. (2022). The role of Big Data in the business challenge of Covid-19: a systematic literature review in managerial studies. Procedia Computer Science, 200, 1746-1755. doi: 10.1016/j.procs.2022.01.375
Rita, P., & Ramos, R. F. (2022). Global Research Trends in Consumer Behavior and Sustainability in E-Commerce: A Bibliometric Analysis of the Knowledge Structure. Sustainability, 14 (15), 9455. https://doi.org/10.3390/su14159455.
Taleizadeh, A. A., & Sadeghi, R. (2019). Pricing strategies in the competitive reverse supply chains with traditional and e-channels: A game theoretic approach. International Journal Of Production Economics, 215, 48-60. doi: 10.1016/j.ijpe.2018.06.011
Tarakci, H. (2016). Two types of learning effects on maintenance activities. International Journal Of Production Research, 54(6), 1721-1734. doi: 10.1080/00207543.2015.1055847
Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J.-f., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356-365. doi: 10.1016/j.jbusres.2016.08.009
Xu, G. Y., Dan, B., Zhang, X. M., & Liu, C. (2014). Coordinating a dual-channel supply chain with risk-averse under a two-way revenue sharing contract. International Journal Of Production Economics, 147, 171-179. doi: 10.1016/j.ijpe.2013.09.012
Liu, M.; Jia, W.; Yan, W.; He, J. (2023). Factors Influencing Consumers? Repurchase Behavior on Fresh Food e-Commerce Platforms: An Empirical Study. Advanced Engineering Informatics. 56, 101936. https://doi.org/10.1016/j.aei.2023.101936.
Zhou, Y. W., Guo, J. S., & Zhou, W. H. (2018). Pricing/service strategies for a dual-channel supply chain with free riding and service-cost sharing. International Journal Of Production Economics, 196, 198-210. doi: 10.1016/j.ijpe.2017.11.014
Bradlow, E. T., Gangwar, M., Kopalle, P., & Voleti, S. (2017). The Role of Big Data and Predictive Analytics in Retailing. Journal of Retailing, 93(1), 79-95. doi: 10.1016/j.jretai.2016.12.004
Chatterjee, S., Chaudhuri, R., Shah, M., & Maheshwari, P. (2022). Big data driven innovation for sustaining SME supply chain operation in post COVID-19 scenario: Moderating role of SME technology leadership. Comput Ind Eng, 168, 108058. doi: 10.1016/j.cie.2022.108058
Chen, F. R., Lai, G. M., & Xiao, W. Q. (2016). Provision of Incentives for Information Acquisition: Forecast-Based Contracts vs. Menus of Linear Contracts. Management Science, 62(7), 1899-1914. doi: 10.1287/mnsc.2015.2193
Chu, L. Y., Shamir, N., & Shin, H. (2017). Strategic Communication for Capacity Alignment with Pricing in a Supply Chain. Management Science, 63(12), 4366-4388. doi: 10.1287/mnsc.2016.2527
Dan, B., Zhang, S. G., & Zhou, M. S. (2018). Strategies for warranty service in a dual-channel supply chain with value-added service competition. International Journal Of Production Research, 56(17), 5677-5699. doi: 10.1080/00207543.2017.1377355
Dekimpe, M. G., & Geyskens, I. (2019). Retailing Research in Rapidly Changing Times: On the Danger of Being Leapfrogged by Practice. Journal of Retailing, 91(1), 6-9. doi: 10.1016/j.jretai.2019.02.001
Fosso Wamba, S., Gunasekaran, A., Dubey, R., & Ngai, E. W. T. (2018). Big data analytics in operations and supply chain management. Annals Of Operations Research, 270, 1-4. doi: 10.1007/s10479-018-3024-7
Giri, B. C., & Glock, C. H. (2017). A closed-loop supply chain with stochastic product returns and worker experience under learning and forgetting. International Journal Of Production Research, 55(22), 6760-6778. doi: 10.1080/00207543.2017.1347301
Giri, B. C., & Masanta, M. (2020). Developing a closed-loop supply chain model with price and quality dependent demand and learning in production in a stochastic environment. International Journal Of Systems Science-Operations & Logistics, 7(2), 147-163. doi: 10.1080/23302674.2018.1542042
Gupta, S., & Ramachandran, D. (2021). Emerging Market Retail: Transitioning from a Product-Centric to a Customer-Centric Approach. Journal of Retailing, 97(4), 597-620. doi: 10.1016/j.jretai.2021.01.008
Hossain, M. A., Akter, S., & Yanamandram, V. (2020). Revisiting customer analytics capability for data-driven retailing. Journal of Retailing and Consumer Services, 56, 102187. doi: 10.1016/j.jretconser.2020.102187
Huang, M.-H., & Rust, R. T. (2022). A Framework for Collaborative Artificial Intelligence in Marketing. Journal of Retailing, 98(2), 209-223. doi: 10.1016/j.jretai.2021.03.001
Jaber, M. Y., & El Saadany, A. M. A. (2011). An economic production and remanufacturing model with learning effects. International Journal Of Production Economics, 131(1), 115-127. doi: 10.1016/j.ijpe.2009.04.019
Liu, P. (2019). Pricing policies and coordination of low-carbon supply chain considering targeted advertisement and carbon emission reduction costs in the big data environment. Journal of Cleaner Production, 210, 343-357. doi: 10.1016/j.jclepro.2018.10.328
Liu, P., & Yi, S. P. (2018). Investment decision-making and coordination of a three-stage supply chain considering Data Company in the Big Data era. Annals Of Operations Research, 270(1-2), 255-271. doi: 10.1007/s10479-018-2783-5
Louhghalam, A., Akbarian, M., & Ulm, F.-J. (2017). Carbon management of infrastructure performance: Integrated big data analytics and pavement-vehicle-interactions. Journal of Cleaner Production, 142, 956-964. doi: 10.1016/j.jclepro.2016.06.198
Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2019). 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
Nilashi, M., Ahmadi, H., Arji, G., Alsalem, K. O., Samad, S., Ghabban, F., . . . Alarood, A. A. (2021). Big social data and customer decision making in vegetarian restaurants: A combined machine learning method. Journal of Retailing and Consumer Services, 62, 102630. doi: 10.1016/j.jretconser.2021.102630
Pantano, E., & Dennis, C. (2019). Store buildings as tourist attractions: Mining retail meaning of store building pictures through a machine learning approach. Journal of Retailing and Consumer Services, 51, 304-310. doi: 10.1016/j.jretconser.2019.06.018
Pi, Z. Y., Fang, W. G., & Zhang, B. F. (2019). Service and pricing strategies with competition and cooperation in a dual-channel supply chain with demand disruption. Computers & Industrial Engineering, 138. doi: 10.1016/j.cie.2019.106130
Piccarozzi, M., & Aquilani, B. (2022). The role of Big Data in the business challenge of Covid-19: a systematic literature review in managerial studies. Procedia Computer Science, 200, 1746-1755. doi: 10.1016/j.procs.2022.01.375
Rita, P., & Ramos, R. F. (2022). Global Research Trends in Consumer Behavior and Sustainability in E-Commerce: A Bibliometric Analysis of the Knowledge Structure. Sustainability, 14 (15), 9455. https://doi.org/10.3390/su14159455.
Taleizadeh, A. A., & Sadeghi, R. (2019). Pricing strategies in the competitive reverse supply chains with traditional and e-channels: A game theoretic approach. International Journal Of Production Economics, 215, 48-60. doi: 10.1016/j.ijpe.2018.06.011
Tarakci, H. (2016). Two types of learning effects on maintenance activities. International Journal Of Production Research, 54(6), 1721-1734. doi: 10.1080/00207543.2015.1055847
Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J.-f., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356-365. doi: 10.1016/j.jbusres.2016.08.009
Xu, G. Y., Dan, B., Zhang, X. M., & Liu, C. (2014). Coordinating a dual-channel supply chain with risk-averse under a two-way revenue sharing contract. International Journal Of Production Economics, 147, 171-179. doi: 10.1016/j.ijpe.2013.09.012
Liu, M.; Jia, W.; Yan, W.; He, J. (2023). Factors Influencing Consumers? Repurchase Behavior on Fresh Food e-Commerce Platforms: An Empirical Study. Advanced Engineering Informatics. 56, 101936. https://doi.org/10.1016/j.aei.2023.101936.
Zhou, Y. W., Guo, J. S., & Zhou, W. H. (2018). Pricing/service strategies for a dual-channel supply chain with free riding and service-cost sharing. International Journal Of Production Economics, 196, 198-210. doi: 10.1016/j.ijpe.2017.11.014