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doi: 10.3934/jimo.2021148
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The evolution mechanism of the multi-value chain network ecosystem supported by the third-party platform

School of Economics and Business Administration, Chongqing University, Chongqing, 400044, China

*Corresponding author: zhangxumei@cqu.edu.cn

Received  March 2021 Revised  June 2021 Early access September 2021

This paper aims to study the evolution mechanism of the third-party platform ecosystem. A multi-value chain network ecosystem composed of multiple manufacturers, multiple suppliers, several logistics providers and a third-party platform for manufacturing is considered. The system dynamics method is used to build the model, and this paper collects relevant industry and platform data to simulate the evolution of user scale and participants' revenues. Furthermore, the influence of platform subsidy and matching service level on the evolution is studied. The results show that the platform's evolution can be divided into four stages: emergence, growth, maturity and upgrade. This paper also finds that, at the emergence stage and the growth stage, the augmentation of the subsidies to manufacturers makes the manufacturers' scale expand but let their revenues decline. Meanwhile, the platform's revenues reduce at the emergence stage while increase at the growth stage. When the subsidy amount is high and continues to augment, its positive effect on the user scale is weakened while its negative effect on manufacturers' revenues is enhanced. Besides, improving the matching service level is not conducive to the platform's revenues at the emergence stage, but after entering the growth stage, it can increase user scale and the platform's revenues simultaneously.

Citation: Xumei Zhang, Jiafeng Yuan, Bin Dan, Ronghua Sui, Wenbo Li. The evolution mechanism of the multi-value chain network ecosystem supported by the third-party platform. Journal of Industrial & Management Optimization, doi: 10.3934/jimo.2021148
References:
[1]

M. H. AzizS. QamarM. T. Khasawneh and C. Saha, Cloud manufacturing: A myth or future of global manufacturing?, Journal of Manufacturing Technology Management, 31 (2020), 1325-1350.  doi: 10.1108/JMTM-10-2019-0379.  Google Scholar

[2]

N. Bajgoric, Information systems for e-business continuance: A systems approach, Kybernetes, 35 (2006), 632-652.  doi: 10.1108/03684920610662377.  Google Scholar

[3]

Y. Cen and L. Li., Effects of network externalities on user loyalty to online B2B platforms: An empirical study, Journal of Enterprise Information Management, 33 (2019), 309-334.   Google Scholar

[4]

T. R. Casey and J. Töyli, Dynamics of two-sided platform success and failure: An analysis of public wireless local area access, Technovation, 32 (2012), 703-716.  doi: 10.1016/j.technovation.2012.08.003.  Google Scholar

[5]

J. Chu and P. Manchanda, Quantifying cross and direct network effects in online consumer-to-consumer platforms, Marketing Science, 35 (2016), 870-893.  doi: 10.1287/mksc.2016.0976.  Google Scholar

[6]

E. J. GhomiA. M. Rahmani and N. N. Qader, Cloud manufacturing: Challenges, recent advances, open research issues, and future trends, International Journal of Advanced Manufacturing Technology, 102 (2019), 3613-3639.  doi: 10.1007/s00170-019-03398-7.  Google Scholar

[7]

A. GoliH. K. ZareR. T. Moghaddam and A. Sadeghieh, A comprehensive model of demand prediction based on hybrid artificial intelligence and metaheuristic algorithms: A case study in dairy industry, MPRA Paper, 11 (2018), 190-203.   Google Scholar

[8]

A. GoliH. Khademi-ZareR. Tavakkoli-MoghaddamA. SadeghiehM. Sasanian and R. M. Kordestanizadeh, An integrated approach based on artificial intelligence and novel meta-heuristic algorithms to predict demand for dairy products: A case study, Network Computation in Neural Systems, 1 (2021), 1-35.   Google Scholar

[9]

A. Goli and B. Malmir, A covering tour approach for disaster relief locating and routing with fuzzy demand, International Journal of Intelligent Transportation Systems Research, 18 (2020), 140-152.  doi: 10.1007/s13177-019-00185-2.  Google Scholar

[10]

A. GoliH. K. ZareR. Tavakkoli-Moghaddam and A. Sadeghieh, Application of robust optimization for a product portfolio problem using an invasive weed optimization algorithm, Numer. Algebra Control Optim., 9 (2019), 187-209.  doi: 10.3934/naco.2019014.  Google Scholar

[11]

A. Goli, E. B. Tirkolaee and N. S. Aydin, Fuzzy integrated cell formation and production scheduling considering automated guided vehicles and human factors, IEEE Transactions on Fuzzy Systems, (2021), 1–10. Google Scholar

[12]

L. JiangY. Wang and D. Liu, Logistics cost sharing in supply chains involving a third-party logistics provider, CEJOR Cent. Eur. J. Oper. Res., 24 (2016), 207-230.  doi: 10.1007/s10100-014-0348-5.  Google Scholar

[13]

S. Khalilpourazari and S. Pasandideh, Bi-objective optimization of multi-product EPQ model with backorders, rework process and random defective rate, 2016 12th International Conference on Industrial Engineering (ICIE), IEEE, (2016). doi: 10.1109/INDUSENG.2016.7519346.  Google Scholar

[14]

S. KhalilpourazariS. TeimooriA. MirzazadehS. H. R. Pasandideh and N. G. Tehrani, Robust Fuzzy chance constraint programming for multi-item EOQ model with random disruption and partial backordering under uncertainty, Journal of Industrial and Production Engineering, 36 (2019), 276-285.  doi: 10.1080/21681015.2019.1646328.  Google Scholar

[15]

S. Khalilpourazari and H. H. Doulabi, Designing a hybrid reinforcement learning based algorithm with application in prediction of the COVID-19 pandemic in Quebec, Annals of Operations Research, (2021), 1462. doi: 10.1007/s10479-020-03871-7.  Google Scholar

[16]

S. Khalilpourazari, H. H. Doulabi, A. Ö. Çiftçioğlu and G. Weberde, Gradient-based grey wolf optimizer with Gaussian walk: Application in modelling and prediction of the COVID-19 pandemic, Expert Systems with Applications, 177 (2021), 114920. doi: 10.1016/j.eswa.2021.114920.  Google Scholar

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S. KhalilpourazariS. SoltanzadehG. W. Weber and S. K. Roy, Designing an efficient blood supply chain network in crisis: Neural learning, optimization and case study, Ann. Oper. Res., 289 (2020), 123-152.  doi: 10.1007/s10479-019-03437-2.  Google Scholar

[18]

J. Kim, The platform business model and business ecosystem: Quality management and revenue structures, European Planning Studies, 24 (2016), 2113-2132.  doi: 10.1080/09654313.2016.1251882.  Google Scholar

[19]

J. LiJ. J. QiuY. ZhouS. WenK. Q. Dou and Q. Li, Study on the reference architecture and assessment framework of industrial internet platform, IEEE Access, 8 (2020), 164950-164971.  doi: 10.1109/ACCESS.2020.3021719.  Google Scholar

[20]

J. Liu and P. Jiang, A manufacturing network modelling and evolution characterizing approach for self-organization among distributed MSMEs under social manufacturing context, IEEE Access, 8 (2020), 119236-119251.   Google Scholar

[21]

Y. LiuD. Q. Chen and W. Gao, How does customer orientation (In) congruence affect platform firms' performance?, Industrial Marketing Management, 87 (2020), 18-30.   Google Scholar

[22]

Y. Liu, L. Zhang, F. Tao and L. Wang, Development and implementation of cloud manufacturing: An evolutionary perspective, In Proceedings of the ASME 2013 International Manufacturing Science and Engineering Conference, Madison, Wisconsin, 2 (2013). doi: 10.1115/MSEC2013-1172.  Google Scholar

[23]

R. LotfiN. Mardani and G. W. Weber, Robust bi-level programming for renewable energy location, International Journal of Energy Research, 45 (2021), 7521-7534.   Google Scholar

[24]

L. Li, X. Zhao and Z. Jian, Operation strategy of platform enterprises in network environment, Journal of Management Sciences in China, (in Chinese), 141 (2015), 19–37. Google Scholar

[25]

B. Lei, Population growth of e-retailing ecosystems: A system dynamics approach, Management Review, (in Chinese), 29 (2017), 152–164. Google Scholar

[26]

R. LotfiM. NayeriS. M. Sajadifar and N. Mardani, Determination of start times and ordering plans for two-period projects with interdependent demand in project-oriented organizations: A case study on molding industry, Journal of Project Management, 2 (2017), 119-142.  doi: 10.5267/j.jpm.2017.9.001.  Google Scholar

[27]

R. Lotfi, Z. Yadegari, S. H. Hosseini, A. H. Khameneh, E. B. Tirkolaee and G. W. Weber, A robust time-cost-quality-energy-environment trade-off with resource-constrained in project management: A case study for a bridge construction project, Journal of Industrial & Management Optimization, (2020). doi: 10.3934/jimo.2020158.  Google Scholar

[28]

R. LotfiY. Z. MehrjerdiM. S. PishvaeeA. Sadeghieh and G. W. Weber, A robust optimization model for sustainable and resilient closed-loop supply chain network design considering conditional value at risk, Numer. Algebra Control Optim., 11 (2021), 221-253.  doi: 10.3934/naco.2020023.  Google Scholar

[29]

J. C. LuY. C. Tsao and C. Charoensiriwath, Competition under manufacturer service and retail price, Economic Modelling, 28 (2011), 1256-1264.  doi: 10.1016/j.econmod.2011.01.008.  Google Scholar

[30]

W. LiuX. YanW. Wei and D. Xie, Pricing decisions for service platform with provider's threshold participating quantity, value-added service and matching ability, Transportation Research Part E: Logistics and Transportation Review, 122 (2019), 410-432.  doi: 10.1016/j.tre.2018.12.020.  Google Scholar

[31]

M. MohtaramzadehT. Ramayah and C. Jun-Hwa, B2B E-commerce adoption in Iranian manufacturing companies: Analyzing the moderating role of organizational culture, International Journal of Human-Computer Interaction, 34 (2018), 621-639.  doi: 10.1080/10447318.2017.1385212.  Google Scholar

[32]

S. M. Pahlevan, S. Hosseini and A. Goli, Sustainable supply chain network design using products' life cycle in the aluminum industry, Environmental Science and Pollution Research, (2021), 263. doi: 10.1007/s11356-020-12150-8.  Google Scholar

[33]

Y. QueW. ZhongH. ChenX. Chen and X. Ji, Improved adaptive immune genetic algorithm for optimal QoS-aware service composition selection in cloud manufacturing, International Journal of Advanced Manufacturing Technology, 96 (2018), 4455-4465.  doi: 10.1007/s00170-018-1925-x.  Google Scholar

[34]

L. RenJ. CuiY. WeiY. LaiLi and L. Zhang, Research on the impact of service provider cooperative relationship on cloud manufacturing platform, International Journal of Advanced Manufacturing Technology, 86 (2016), 2279-2290.  doi: 10.1007/s00170-016-8345-6.  Google Scholar

[35]

S. RuutuT. Casey and V. Kotovirta, Development and competition of digital service platforms: A system dynamics approach, Technological Forecasting & Social Change, 117 (2017), 119-130.  doi: 10.1016/j.techfore.2016.12.011.  Google Scholar

[36]

Sealand Securites, Depth report of guolian (60613): A fast-growing b2b e-commerce and industrial internet leader, https://www.fxbaogao.com/pdf?id=2382617&query=%7B%22keywords%22%3A%22B2B%22%7D&index=0&pid=, 2021-01-28. Google Scholar

[37]

A. W. Siddiqui and S. A. Raza, Electronic supply chains: Status & perspective, Computers & Industrial Engineering, 88 (2015), 536-556.  doi: 10.1016/j.cie.2015.08.012.  Google Scholar

[38]

O. SohaibM. NaderpourW. Hussain and L. Martinez, Cloud computing model selection for e-commerce enterprises using a new 2-tuple fuzzy linguistic decision-making method, Computers & Industrial Engineering, 132 (2019), 47-58.  doi: 10.1016/j.cie.2019.04.020.  Google Scholar

[39]

B. ShengC. ZhangX. YinQ. LuY. ChengT. Xiao and H. Liu, Common intelligent semantic matching engines of cloud manufacturing service based on OWL-S, International Journal of Advanced Manufacturing Technology, 84 (2016), 103-118.  doi: 10.1007/s00170-015-7996-z.  Google Scholar

[40]

B. TanS. L. PanX. Lu and L. Huang, The role of is capabilities in the development of multi-sided platforms: The digital ecosystem strategy of alibaba.com, Journal of the Association for Information Systems, 16 (2015), 248-280.  doi: 10.17705/1jais.00393.  Google Scholar

[41]

E. B. TirkolaeeS. HadianG. Weber and I. Mahdavi, A robust green traffic-based routing problem for perishable products distribution, Computational Intelligence, 36 (2020), 80-101.  doi: 10.1111/coin.12240.  Google Scholar

[42]

O. F. Valilai and M. Houshmand, A collaborative and integrated platform to support distributed manufacturing system using a service-oriented approach based on cloud computing paradigm, Robotics and Computer-Integrated Manufacturing, 29 (2013), 110-127.  doi: 10.1016/j.rcim.2012.07.009.  Google Scholar

[43]

T. WuM. ZhangX. TianS. Wang and G. Hua, Spatial differentiation and network externality in pricing mechanism of online car hailing platform, International Journal of Production Economics, 219 (2020), 275-283.  doi: 10.1016/j.ijpe.2019.05.007.  Google Scholar

[44]

S. Yablonsky, A multidimensional platform ecosystem framework, Kybernetes, 49 (2020), 2003-2035.   Google Scholar

[45]

B. YooV. Choudhary and T. Mukhopadhyay, A model of neutral B2B intermediaries, Journal of Management Information Systems, 19 (2002), 43-68.   Google Scholar

[46]

F. Zhu and M. Iansiti, Entry into platform-based markets, Strategic Management Journal, 33 (2012), 88-106.   Google Scholar

[47]

H. ZhangG. F. JiangK. Yoshihira and H. Chen, Proactive workload management in hybrid cloud computing, IEEE Transactions on Network and Service Management, 11 (2014), 90-100.  doi: 10.1109/TNSM.2013.122313.130448.  Google Scholar

show all references

References:
[1]

M. H. AzizS. QamarM. T. Khasawneh and C. Saha, Cloud manufacturing: A myth or future of global manufacturing?, Journal of Manufacturing Technology Management, 31 (2020), 1325-1350.  doi: 10.1108/JMTM-10-2019-0379.  Google Scholar

[2]

N. Bajgoric, Information systems for e-business continuance: A systems approach, Kybernetes, 35 (2006), 632-652.  doi: 10.1108/03684920610662377.  Google Scholar

[3]

Y. Cen and L. Li., Effects of network externalities on user loyalty to online B2B platforms: An empirical study, Journal of Enterprise Information Management, 33 (2019), 309-334.   Google Scholar

[4]

T. R. Casey and J. Töyli, Dynamics of two-sided platform success and failure: An analysis of public wireless local area access, Technovation, 32 (2012), 703-716.  doi: 10.1016/j.technovation.2012.08.003.  Google Scholar

[5]

J. Chu and P. Manchanda, Quantifying cross and direct network effects in online consumer-to-consumer platforms, Marketing Science, 35 (2016), 870-893.  doi: 10.1287/mksc.2016.0976.  Google Scholar

[6]

E. J. GhomiA. M. Rahmani and N. N. Qader, Cloud manufacturing: Challenges, recent advances, open research issues, and future trends, International Journal of Advanced Manufacturing Technology, 102 (2019), 3613-3639.  doi: 10.1007/s00170-019-03398-7.  Google Scholar

[7]

A. GoliH. K. ZareR. T. Moghaddam and A. Sadeghieh, A comprehensive model of demand prediction based on hybrid artificial intelligence and metaheuristic algorithms: A case study in dairy industry, MPRA Paper, 11 (2018), 190-203.   Google Scholar

[8]

A. GoliH. Khademi-ZareR. Tavakkoli-MoghaddamA. SadeghiehM. Sasanian and R. M. Kordestanizadeh, An integrated approach based on artificial intelligence and novel meta-heuristic algorithms to predict demand for dairy products: A case study, Network Computation in Neural Systems, 1 (2021), 1-35.   Google Scholar

[9]

A. Goli and B. Malmir, A covering tour approach for disaster relief locating and routing with fuzzy demand, International Journal of Intelligent Transportation Systems Research, 18 (2020), 140-152.  doi: 10.1007/s13177-019-00185-2.  Google Scholar

[10]

A. GoliH. K. ZareR. Tavakkoli-Moghaddam and A. Sadeghieh, Application of robust optimization for a product portfolio problem using an invasive weed optimization algorithm, Numer. Algebra Control Optim., 9 (2019), 187-209.  doi: 10.3934/naco.2019014.  Google Scholar

[11]

A. Goli, E. B. Tirkolaee and N. S. Aydin, Fuzzy integrated cell formation and production scheduling considering automated guided vehicles and human factors, IEEE Transactions on Fuzzy Systems, (2021), 1–10. Google Scholar

[12]

L. JiangY. Wang and D. Liu, Logistics cost sharing in supply chains involving a third-party logistics provider, CEJOR Cent. Eur. J. Oper. Res., 24 (2016), 207-230.  doi: 10.1007/s10100-014-0348-5.  Google Scholar

[13]

S. Khalilpourazari and S. Pasandideh, Bi-objective optimization of multi-product EPQ model with backorders, rework process and random defective rate, 2016 12th International Conference on Industrial Engineering (ICIE), IEEE, (2016). doi: 10.1109/INDUSENG.2016.7519346.  Google Scholar

[14]

S. KhalilpourazariS. TeimooriA. MirzazadehS. H. R. Pasandideh and N. G. Tehrani, Robust Fuzzy chance constraint programming for multi-item EOQ model with random disruption and partial backordering under uncertainty, Journal of Industrial and Production Engineering, 36 (2019), 276-285.  doi: 10.1080/21681015.2019.1646328.  Google Scholar

[15]

S. Khalilpourazari and H. H. Doulabi, Designing a hybrid reinforcement learning based algorithm with application in prediction of the COVID-19 pandemic in Quebec, Annals of Operations Research, (2021), 1462. doi: 10.1007/s10479-020-03871-7.  Google Scholar

[16]

S. Khalilpourazari, H. H. Doulabi, A. Ö. Çiftçioğlu and G. Weberde, Gradient-based grey wolf optimizer with Gaussian walk: Application in modelling and prediction of the COVID-19 pandemic, Expert Systems with Applications, 177 (2021), 114920. doi: 10.1016/j.eswa.2021.114920.  Google Scholar

[17]

S. KhalilpourazariS. SoltanzadehG. W. Weber and S. K. Roy, Designing an efficient blood supply chain network in crisis: Neural learning, optimization and case study, Ann. Oper. Res., 289 (2020), 123-152.  doi: 10.1007/s10479-019-03437-2.  Google Scholar

[18]

J. Kim, The platform business model and business ecosystem: Quality management and revenue structures, European Planning Studies, 24 (2016), 2113-2132.  doi: 10.1080/09654313.2016.1251882.  Google Scholar

[19]

J. LiJ. J. QiuY. ZhouS. WenK. Q. Dou and Q. Li, Study on the reference architecture and assessment framework of industrial internet platform, IEEE Access, 8 (2020), 164950-164971.  doi: 10.1109/ACCESS.2020.3021719.  Google Scholar

[20]

J. Liu and P. Jiang, A manufacturing network modelling and evolution characterizing approach for self-organization among distributed MSMEs under social manufacturing context, IEEE Access, 8 (2020), 119236-119251.   Google Scholar

[21]

Y. LiuD. Q. Chen and W. Gao, How does customer orientation (In) congruence affect platform firms' performance?, Industrial Marketing Management, 87 (2020), 18-30.   Google Scholar

[22]

Y. Liu, L. Zhang, F. Tao and L. Wang, Development and implementation of cloud manufacturing: An evolutionary perspective, In Proceedings of the ASME 2013 International Manufacturing Science and Engineering Conference, Madison, Wisconsin, 2 (2013). doi: 10.1115/MSEC2013-1172.  Google Scholar

[23]

R. LotfiN. Mardani and G. W. Weber, Robust bi-level programming for renewable energy location, International Journal of Energy Research, 45 (2021), 7521-7534.   Google Scholar

[24]

L. Li, X. Zhao and Z. Jian, Operation strategy of platform enterprises in network environment, Journal of Management Sciences in China, (in Chinese), 141 (2015), 19–37. Google Scholar

[25]

B. Lei, Population growth of e-retailing ecosystems: A system dynamics approach, Management Review, (in Chinese), 29 (2017), 152–164. Google Scholar

[26]

R. LotfiM. NayeriS. M. Sajadifar and N. Mardani, Determination of start times and ordering plans for two-period projects with interdependent demand in project-oriented organizations: A case study on molding industry, Journal of Project Management, 2 (2017), 119-142.  doi: 10.5267/j.jpm.2017.9.001.  Google Scholar

[27]

R. Lotfi, Z. Yadegari, S. H. Hosseini, A. H. Khameneh, E. B. Tirkolaee and G. W. Weber, A robust time-cost-quality-energy-environment trade-off with resource-constrained in project management: A case study for a bridge construction project, Journal of Industrial & Management Optimization, (2020). doi: 10.3934/jimo.2020158.  Google Scholar

[28]

R. LotfiY. Z. MehrjerdiM. S. PishvaeeA. Sadeghieh and G. W. Weber, A robust optimization model for sustainable and resilient closed-loop supply chain network design considering conditional value at risk, Numer. Algebra Control Optim., 11 (2021), 221-253.  doi: 10.3934/naco.2020023.  Google Scholar

[29]

J. C. LuY. C. Tsao and C. Charoensiriwath, Competition under manufacturer service and retail price, Economic Modelling, 28 (2011), 1256-1264.  doi: 10.1016/j.econmod.2011.01.008.  Google Scholar

[30]

W. LiuX. YanW. Wei and D. Xie, Pricing decisions for service platform with provider's threshold participating quantity, value-added service and matching ability, Transportation Research Part E: Logistics and Transportation Review, 122 (2019), 410-432.  doi: 10.1016/j.tre.2018.12.020.  Google Scholar

[31]

M. MohtaramzadehT. Ramayah and C. Jun-Hwa, B2B E-commerce adoption in Iranian manufacturing companies: Analyzing the moderating role of organizational culture, International Journal of Human-Computer Interaction, 34 (2018), 621-639.  doi: 10.1080/10447318.2017.1385212.  Google Scholar

[32]

S. M. Pahlevan, S. Hosseini and A. Goli, Sustainable supply chain network design using products' life cycle in the aluminum industry, Environmental Science and Pollution Research, (2021), 263. doi: 10.1007/s11356-020-12150-8.  Google Scholar

[33]

Y. QueW. ZhongH. ChenX. Chen and X. Ji, Improved adaptive immune genetic algorithm for optimal QoS-aware service composition selection in cloud manufacturing, International Journal of Advanced Manufacturing Technology, 96 (2018), 4455-4465.  doi: 10.1007/s00170-018-1925-x.  Google Scholar

[34]

L. RenJ. CuiY. WeiY. LaiLi and L. Zhang, Research on the impact of service provider cooperative relationship on cloud manufacturing platform, International Journal of Advanced Manufacturing Technology, 86 (2016), 2279-2290.  doi: 10.1007/s00170-016-8345-6.  Google Scholar

[35]

S. RuutuT. Casey and V. Kotovirta, Development and competition of digital service platforms: A system dynamics approach, Technological Forecasting & Social Change, 117 (2017), 119-130.  doi: 10.1016/j.techfore.2016.12.011.  Google Scholar

[36]

Sealand Securites, Depth report of guolian (60613): A fast-growing b2b e-commerce and industrial internet leader, https://www.fxbaogao.com/pdf?id=2382617&query=%7B%22keywords%22%3A%22B2B%22%7D&index=0&pid=, 2021-01-28. Google Scholar

[37]

A. W. Siddiqui and S. A. Raza, Electronic supply chains: Status & perspective, Computers & Industrial Engineering, 88 (2015), 536-556.  doi: 10.1016/j.cie.2015.08.012.  Google Scholar

[38]

O. SohaibM. NaderpourW. Hussain and L. Martinez, Cloud computing model selection for e-commerce enterprises using a new 2-tuple fuzzy linguistic decision-making method, Computers & Industrial Engineering, 132 (2019), 47-58.  doi: 10.1016/j.cie.2019.04.020.  Google Scholar

[39]

B. ShengC. ZhangX. YinQ. LuY. ChengT. Xiao and H. Liu, Common intelligent semantic matching engines of cloud manufacturing service based on OWL-S, International Journal of Advanced Manufacturing Technology, 84 (2016), 103-118.  doi: 10.1007/s00170-015-7996-z.  Google Scholar

[40]

B. TanS. L. PanX. Lu and L. Huang, The role of is capabilities in the development of multi-sided platforms: The digital ecosystem strategy of alibaba.com, Journal of the Association for Information Systems, 16 (2015), 248-280.  doi: 10.17705/1jais.00393.  Google Scholar

[41]

E. B. TirkolaeeS. HadianG. Weber and I. Mahdavi, A robust green traffic-based routing problem for perishable products distribution, Computational Intelligence, 36 (2020), 80-101.  doi: 10.1111/coin.12240.  Google Scholar

[42]

O. F. Valilai and M. Houshmand, A collaborative and integrated platform to support distributed manufacturing system using a service-oriented approach based on cloud computing paradigm, Robotics and Computer-Integrated Manufacturing, 29 (2013), 110-127.  doi: 10.1016/j.rcim.2012.07.009.  Google Scholar

[43]

T. WuM. ZhangX. TianS. Wang and G. Hua, Spatial differentiation and network externality in pricing mechanism of online car hailing platform, International Journal of Production Economics, 219 (2020), 275-283.  doi: 10.1016/j.ijpe.2019.05.007.  Google Scholar

[44]

S. Yablonsky, A multidimensional platform ecosystem framework, Kybernetes, 49 (2020), 2003-2035.   Google Scholar

[45]

B. YooV. Choudhary and T. Mukhopadhyay, A model of neutral B2B intermediaries, Journal of Management Information Systems, 19 (2002), 43-68.   Google Scholar

[46]

F. Zhu and M. Iansiti, Entry into platform-based markets, Strategic Management Journal, 33 (2012), 88-106.   Google Scholar

[47]

H. ZhangG. F. JiangK. Yoshihira and H. Chen, Proactive workload management in hybrid cloud computing, IEEE Transactions on Network and Service Management, 11 (2014), 90-100.  doi: 10.1109/TNSM.2013.122313.130448.  Google Scholar

Figure 1.  Multi-value chain network of the third-party platform
Figure 2.  SD diagram of the third-party platform ecosystem's evolution
Figure 3.  The evolution of the user scale
Figure 4.  The evolution of participants' revenues
Figure 5.  The evolution of the user scale under different subsidy amount
Figure 6.  SD diagram of the third-party platform ecosystem's evolution
Figure 7.  The evolution of the user scale under different matching service levels
Figure 8.  The evolution of the participants' revenues under different matching service levels
Table 1.  Survey on related works
Reference Research object Research problem Certain stage/
Different stages
Siddiqui and Raza [37]; Sohaib et al. [38]; Valilai and Houshmand [42]; Yoo et al. [45]; Cen and Li [3]; Sheng et al. [39]; Que et al. [33]; Liu et al. [21] Third-party platform for manufacturing Issues in the early development stage or operation process Certain stage
Liu et al. [22]; Ren et al. [34] Third-party platform for manufacturing Dynamic evolution of the platform Certain stage
Tan et al. [40]; Kim [18] Third-party platform for consumption/service Strategies used in the evolution of the platform Different stages
Zhu and Iansiti [46]; Casey and TÖyli [4]; Manchanda [5]; Ruutu et al. [35] Third-party platform for consumption/service Dynamic evolution of the platform Certain stage
Goli et al. [7]; Goli et al. [8]; Khalilpourazari et al. [15]; Khalilpourazari et al. [16]; Lotfi et al. [23]; Tirkolaee et al. [41]; Goli et al. [9]; Khalilpourazari et al. [17]; Goli et al. [11]; Lotfi et al. [26]; Khalilpourazari et al. [13]; Khalilpourazari et al. [14]; Goli et al. [10]; Pahlevan et al. [32]; Lotfi et al. [27]; Lotfi et al. [28] Value chain without considering the participation of the third-party platform Optimization and operation of the value chain Certain stage
This research The multi-value chain network ecosystem supported by the third-party platform for manufacturing Dynamic evolution of the platform Different stages
Reference Research object Research problem Certain stage/
Different stages
Siddiqui and Raza [37]; Sohaib et al. [38]; Valilai and Houshmand [42]; Yoo et al. [45]; Cen and Li [3]; Sheng et al. [39]; Que et al. [33]; Liu et al. [21] Third-party platform for manufacturing Issues in the early development stage or operation process Certain stage
Liu et al. [22]; Ren et al. [34] Third-party platform for manufacturing Dynamic evolution of the platform Certain stage
Tan et al. [40]; Kim [18] Third-party platform for consumption/service Strategies used in the evolution of the platform Different stages
Zhu and Iansiti [46]; Casey and TÖyli [4]; Manchanda [5]; Ruutu et al. [35] Third-party platform for consumption/service Dynamic evolution of the platform Certain stage
Goli et al. [7]; Goli et al. [8]; Khalilpourazari et al. [15]; Khalilpourazari et al. [16]; Lotfi et al. [23]; Tirkolaee et al. [41]; Goli et al. [9]; Khalilpourazari et al. [17]; Goli et al. [11]; Lotfi et al. [26]; Khalilpourazari et al. [13]; Khalilpourazari et al. [14]; Goli et al. [10]; Pahlevan et al. [32]; Lotfi et al. [27]; Lotfi et al. [28] Value chain without considering the participation of the third-party platform Optimization and operation of the value chain Certain stage
This research The multi-value chain network ecosystem supported by the third-party platform for manufacturing Dynamic evolution of the platform Different stages
Table 2.  Initial value of constants in the model
Notation Variables Value Unit
$ {{N}_{M0}} $ Initial scale of manufacturers 10 User
$ {{N}_{S0}} $ Initial scale of suppliers 50 User
$ a $ Potential market demand 4500 Auto/(user*month)
$ s $ Supply per supplier 20000 Part/(user*month)
$ {{p}_{M}} $ Product price 100000 Yuan/auto
$ {{p}_{S}} $ Part price 800 Yuan/part
$ {{C}_{S}} $ Part cost 700 Yuan/part
$ \Delta t $ Duration of subsidy 6 Month
$ r $ Unit subsidy 500 Yuan/(user*month)
$ \rho $ Commission rate 0.005 -
$ f $ Unit logistics fee 18 Yuan/part
$ F $ Membership fee 200 Yuan/(user*month)
$ \Delta T $ Time before expansion 12 Month
$ \lambda $ Matching service level 0.8 -
$ \zeta $ Logistics service level 0.8 -
$ {{M}_{M0}} $ Manufacturer capacity before expansion 300 User
$ {{M}_{S0}} $ Supplier capacity before expansion 10000 User
$ {{M}_{M}} $ Manufacturer capacity after expansion 500 User
$ {{M}_{S}} $ Supplier capacity after expansion 15000 User
Notation Variables Value Unit
$ {{N}_{M0}} $ Initial scale of manufacturers 10 User
$ {{N}_{S0}} $ Initial scale of suppliers 50 User
$ a $ Potential market demand 4500 Auto/(user*month)
$ s $ Supply per supplier 20000 Part/(user*month)
$ {{p}_{M}} $ Product price 100000 Yuan/auto
$ {{p}_{S}} $ Part price 800 Yuan/part
$ {{C}_{S}} $ Part cost 700 Yuan/part
$ \Delta t $ Duration of subsidy 6 Month
$ r $ Unit subsidy 500 Yuan/(user*month)
$ \rho $ Commission rate 0.005 -
$ f $ Unit logistics fee 18 Yuan/part
$ F $ Membership fee 200 Yuan/(user*month)
$ \Delta T $ Time before expansion 12 Month
$ \lambda $ Matching service level 0.8 -
$ \zeta $ Logistics service level 0.8 -
$ {{M}_{M0}} $ Manufacturer capacity before expansion 300 User
$ {{M}_{S0}} $ Supplier capacity before expansion 10000 User
$ {{M}_{M}} $ Manufacturer capacity after expansion 500 User
$ {{M}_{S}} $ Supplier capacity after expansion 15000 User
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