Advertisement

Cluster Computing

, Volume 22, Supplement 4, pp 7925–7932 | Cite as

Incentive mechanism on customer knowledge collaborative acquisition with relational contract under double-sided moral hazard in big data context

  • Nali ShenEmail author
Article
  • 367 Downloads

Abstract

In the big data context, the customer data are distributed out of the enterprise boundary. It’s the key to maintain competitive advantage that applying the customer knowledge acquisition to the enterprise innovation. From the perspective of the supply chain, there is complementarity of customer knowledge for the manufacturer and the retailer. Sharing the collaborative acquisition of customer knowledge can improve the efficiency of both sides, and even of the whole system. However, since the imbalance of the information between both sides and the imbalance aggravated because of the pending verification of customer big data value, result in speculation behaviors that called bilateral moral hazard. Reasonable design of synergy incentive contract is an important way to solve bilateral moral hazard in customer knowledge collaborative acquisition. As the big data customer knowledge needs to be examined, the relational contract model of customer acquisition knowledge is established and contract analysis of the incentive effects between the formal contract and relational contract is made. Consequently, the incentive of formal contract will result in system profit loss, while relational contract incentive has positive inspiration for collaboration between the manufacturer and the retailer, which can improve the efficiency of the acquisition. In the process, when the discount rate is large enough to reach a certain threshold, the optimal system revenue can be achieved through the relational contract.

Keywords

Customer knowledge Big data Incentive Moral hazard Relational contract 

Notes

Acknowledgements

The author gratefully acknowledge the financial support from The Ministry of education of Humanities and Social Science Youth Project of China (No. 15YJC630108) and project by Southwest University of Political Science and Law (2014XZQ-16). The research also Supported by National Natural Science Foundation of China (No. 71402152 )and National Social Science Foundation of China (15CGL020) and The Ministry of Education of Humanities and Social Science foundation of China (16YJC630181).

References

  1. 1.
    Mayer-Schonberger, V., Cukier, K.: Big Data: A Revolution that will Transform How We Live, Work, and Think. John Murray Publishers Ltd., London (2013)Google Scholar
  2. 2.
    Vries, E.J., Brijder, H.G.: Knowledge management in hybrid supply channels: a case study. Int. J. Technol. Manage. 20(5–8), 569–587 (2000)CrossRefGoogle Scholar
  3. 3.
    Garcia, M.M., Annabi, H.: Customer knowledge management. J. Oper. Res. Soc. 53(8), 875–884 (2002)CrossRefGoogle Scholar
  4. 4.
    Simon, P.: Too Big to Ignore: The Business Case for Big Data. Wiley, Hoboken (2013)Google Scholar
  5. 5.
    Feng Z., Guo X., Zeng D., Chen Y., Chen G,: Several frontier research topics on business management under the big data context. J. Manag. Sci. (2013)Google Scholar
  6. 6.
    Lijie.: Sticking Customers–Cooperation Between Vanka and Data. [EB/OL]. http://panjin.leju.com/news/2014-09-13/08132906688.shtml. Accessed 13 Sep 2014
  7. 7.
    Holmstrom, B.: Moral hazard in teams. Bell J. Econ. 13(2), 324–340 (1982)CrossRefGoogle Scholar
  8. 8.
    Wang, Y., Wallace, S.W., et al.: Service supply chain management: a review of operational models. Eur. J. Oper. Res. 247(3), 685–698 (2015)CrossRefGoogle Scholar
  9. 9.
    Maestrinia, V., Luzzini, D.: Supply chain performance measurement systems: a systematic review and research agenda. Int. J. Prod. Econ. 183(09), 299–315 (2017)CrossRefGoogle Scholar
  10. 10.
    Corbett, C.J., Decroix, G.A., Ha, A.Y.: Optimal shared-savings contracts in supply chains: linear contracts and double moral hazard. Eur. J. Oper. Res. 163(3), 653–667 (2005)CrossRefGoogle Scholar
  11. 11.
    Baker, G., Gibbons, R., Murphy, K.J.: Relational contracts and the theory of the firm. Q. J. Econ. 117(1), 39–84 (2002)CrossRefGoogle Scholar
  12. 12.
    Wei, F., Li, Y., et al.: The new progress in the study of psychological contract at home and abroad. J. Manag. Sci. 8(5), 82–89 (2005)Google Scholar
  13. 13.
    Shuangyan, L., Difang, W.: BPO governance mechanism research based on formal contract and relational contract. Econ. Manag. 30(18), 4–8 (2008)Google Scholar
  14. 14.
    Daido, K.: Formal and relational incentives in a multitask model. Int. Rev. Law Econ. 26(3), 380–394 (2006)CrossRefGoogle Scholar
  15. 15.
    Wang, A., Si, C.: The relational contract in R&D outsourcing. Res. Manag. 27(6), 103–108 (2006)Google Scholar
  16. 16.
    Sloof., R., Sonnemans, J.: The interaction between explicit and relational incentives: an experiment. Games Econ. Behav.  https://doi.org/10.1016/j.geb.2011.03.006 (2011)
  17. 17.
    Zhuang, X., Wang, J.: Dynamic incentive contracts research based on overconfidence and supervision mechanism. J. Syst. Eng. 25(5), 642–650 (2010)Google Scholar
  18. 18.
    Chen, Y., Jiang, N.: The information aggregation value of supply chain and creative ability formation mechanism in the era of big data. J. Infor Stud: Theory Appl. 52(7), 80–85 (2015)Google Scholar
  19. 19.
    Grime, S.: Unstructured Data and the 80 Percent Rule [EB/OL], 2011, http://clarabridge.com/default.aspx?tabid=137&ModuleID=635&ArticleID=551. Accessed 11 Dec 2012
  20. 20.
    Zhang, W.: Game Theory and Information Economics. Shanghai People’s Publishing House, Shanghai (2004)Google Scholar
  21. 21.
    Bolton, P., Dewatripont, M.: Contract Theory. The MIT Press, Cambridge (2004)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Department of Marketing, Business SchoolSouthwest University of Political Science and LawChongqingPeople’s Republic of China

Personalised recommendations