Skip to main content
Springer Nature Link
Log in

Sales switching decision to the online platform of liquefied petroleum gas enterprise with asymmetric information

  • Special Issue
  • Published:
Evolutionary Intelligence Aims and scope Submit manuscript

Abstract

With the development of the internet technology, many energy companies sell products via online platform, e.g., Irving Oil gift cards are sold at Amazon. Liquefied petroleum gas (LPG), as an important kind of energy, is widely used in many area where the natural gas can not reach, especially in the developing countries. Although the sales of the LPG need the order information from the customers, and the order can be sent through the online platform, the online platform’s cost information is unknown to the LPG. In this paper, we consider the switching problem of LPG sale to the online platform, and establish the LPG enterprize’s optimal switching model with asymmetric information. Then we derive the optimal solution through the analysis of the incentive constraint and the participation constraint to the online platform. Moreover, the comparison with the optimal solution with symmetric information is also addressed. Finally, a numerical example is given to illustrate the effectiveness of the proposed model and the switching strategy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Calantone RJ, Gassenheimer JB (1991) Overcoming basic problems between manufacturers and distributors. Ind Mark Manag 20(3):215–221

    Article  Google Scholar 

  2. Lee HL, Padmanabhan V, Whang S (1997) Information distortion in a supply chain: the bullwhip effect. Manag Sci 43(4):546–558

    Article  MATH  Google Scholar 

  3. Mentzer JT, DeWitt W, Keebler JS, Min S, Nix NW, Smith CD, Zacharia ZG (2001) Defining supply chain management. J Bus Logist 22(2):1–25

    Article  Google Scholar 

  4. Merritt NJ, Newell SJ (2001) The extent and formality of sales agency evaluations of principals. Ind Mark Manag 30(1):37–49

    Article  Google Scholar 

  5. Swaminathan JM, Smith SF, Sadeh NM (1998) Modeling supply chain dynamics: a multiagent approach. Decis Sci 29(3):607–632

    Article  Google Scholar 

  6. Lan Y, Liu Z, Niu B (2017) Pricing and design of after-sales service contract: the value of mining asymmetric sales cost information. Asia Pac J Oper Res 34(01):1740002

    Article  MathSciNet  MATH  Google Scholar 

  7. Clement J (2019) Statistics and facts about global e-commerce. https://www.statista.com/topics/871/online-shopping

  8. Johnson WC, Kang J-K, Masulis RW, Yi S (2018) Seasoned equity offerings and customer–supplier relationships. J Financ Intermediation 33:98–114

    Article  Google Scholar 

  9. Blessley M, Mir S, Zacharia Z, Aloysius J (2018) Breaching relational obligations in a buyer-supplier relationship: feelings of violation, fairness perceptions and supplier switching. Ind Mark Manage 74:215–226

    Article  Google Scholar 

  10. Brynjolfsson E, Smith MD (2000) Frictionless commerce? A comparison of internet and conventional retailers. Manag Sci 46(4):563–585

    Article  Google Scholar 

  11. Zhu K (2002) Information transparency in electronic marketplaces: why data transparency may hinder the adoption of b2b exchanges. Electron Mark 12(2):92–99

    Article  Google Scholar 

  12. Kamrad B, Siddique A (2004) Supply contracts, profit sharing, switching, and reaction options. Manag Sci 50(1):64–82

    Article  Google Scholar 

  13. Wagner SM, Friedl G (2007) Supplier switching decisions. Eur J Oper Res 183(2):700–717

    Article  MATH  Google Scholar 

  14. Pfeiffer T (2010) A dynamic model of supplier switching. Eur J Oper Res 207(2):697–710

    Article  MathSciNet  MATH  Google Scholar 

  15. Friedl G, Wagner SM (2012) Supplier development or supplier switching? Int J Prod Res 50(11):3066–3079

    Article  Google Scholar 

  16. Zhang J, Tang W, Mingmao H (2015) Optimal supplier switching with volume-dependent switching costs. Int J Prod Econ 161:96–104

    Article  Google Scholar 

  17. Shugan SM (1980) The cost of thinking. J Consum Res 7(2):99–111

    Article  MathSciNet  Google Scholar 

  18. Berman O, Wang J, Sapna KP (2005) Optimal management of cross-trained workers in services with negligible switching costs. Eur J Oper Res 167(2):349–369

    Article  MathSciNet  MATH  Google Scholar 

  19. Yu X, Lan Y, Zhao R (2019) Strategic green technology innovation in a two-stage alliance: vertical collaboration or co-development? Omega 102–116

  20. Burnham TA, Frels JK, Mahajan V (2003) Consumer switching costs: a typology, antecedents, and consequences. J Acad Mark Sci 31(2):109–126

    Article  Google Scholar 

  21. Heide JB, Weiss AM (1995) Vendor consideration and switching behavior for buyers in high-technology markets. J Mark 59(3):30–43

    Article  Google Scholar 

  22. Klemperer P (1995) Competition when consumers have switching costs: an overview with applications to industrial organization, macroeconomics, and international trade. Rev Econ Stud 62(4):515–539

    Article  MATH  Google Scholar 

  23. Myerson RB (1979) Incentive compatibility and the bargaining problem. Econometrica 47(1):61–73

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

This work was supported by Chinese Postdoctoral Science Foundation No. 2015M581301, the National Social Science Foundation of China No. 17BGL238, Natural Science Foundation of Chinese Hubei Provincial No. 2016CFB402, the Science and Technology Research Project of Chinese Hubei Provincial Department of Education No. Q20171804, and Key Laboratory of Automotive Power Train and ElectronicsHubei University of Automotive Technology, China No. ZDK1201802.

Author information

Authors and Affiliations

  1. College of Management and Economics, Tianjin University, Tianjin, 300072, China

    Mingmao Hu & Guang Ji

  2. College of Mechanical Engineering, Hubei University of Automotive Technology, Shiyan, 442002, Hubei, China

    Mingmao Hu

  3. Department of Industrial and Systems Engineering, Wayne State University, Detroit, MI, 48202, USA

    Evrim Dalkiran

  4. School of Vocational Education, Tianjin University of Technology and Education, Tianjin, 300222, China

    Xiaoli Liu

Authors
  1. Mingmao Hu
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Guang Ji
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Evrim Dalkiran
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Xiaoli Liu
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Guang Ji.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hu, M., Ji, G., Dalkiran, E. et al. Sales switching decision to the online platform of liquefied petroleum gas enterprise with asymmetric information. Evol. Intel. 16, 1767–1777 (2023). https://doi.org/10.1007/s12065-019-00320-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12065-019-00320-8

Keywords