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Annals of Operations Research

, Volume 257, Issue 1–2, pp 379–394 | Cite as

E-business system investment for fresh agricultural food industry in China

  • Ziping Wang
  • Dong-Qing Yao
  • Xiaohang Yue
Article

Abstract

A fresh agricultural food online exchange system is gradually becoming an emerging e-business model in China. However, how and when to make an investment in this new business model is risky due to the unique characteristics of fresh agricultural food. In this paper, from a third party decision maker’s perspective, we propose an evolutionary discounted cash flow model to investigate the optimum time point for investment. Based on the assumptions of base demand, the derived e-demand occurring on the pendent e-business system can be characterized by non-stationary stochastic processes. Then our new model can further be innovatively analyzed by considering the dynamic scheme of the cash flow and its evolution. The analytical results suggest the optimal investment time point depends upon the consumers’ switch rate from the physical store to e-store and on the urbanization rate. A Monte Carlo simulation is further presented to compare the effects of multiple uncertainties embedded into the system. Our uncertainty analysis reveals that when government financial support fluctuates greatly, the optimal investment time could be either in the very beginning or in the end. This optimal time strategy also holds for the random demand factor after the coefficient variation of the demand reaches a certain threshold.

Keywords

E-business system Evolutionary discounted cash flow  Transparent short supply chain Investment Adoption 

Notes

Acknowledgments

The third author was supported in part by the Fund for International Cooperation and Exchange of the National Natural Science Foundation of China (Grant No.71110107024).

References

  1. Aristides, M., Maro, V., & Vicky, M. (2009). Understanding the factors affecting e-business adoption and impact on logistics processes. Journal of Manufacturing Technology Management, 20(6), 853–865.CrossRefGoogle Scholar
  2. Aung, M., & Chang, Y. (2014). Traceability in a food supply chain: Safety and quality perspectives. Food Control, 39, 172–184.CrossRefGoogle Scholar
  3. Benaroch, M., Lichtenstein, Y., & Robinson, K. (2006). Real options in information technology risk management: An empirical validation of risk-validation of risk-option relationships. MIS Quarterly, 30(4), 827–864.Google Scholar
  4. Chari, M., Devaraj, S., & David, P. (2008). The impact of information technology investments and diversification strategies on firm performance. Management Science, 54(1), 224–235.CrossRefGoogle Scholar
  5. Choi, T. M., (2013). Multi-period risk minimization purchasing models for fashion products with interest rate, budget, and profit target considerations. Annals of Operations Research, (in press). doi: 10.1007/s10479-013-1453-x.
  6. Choi, T. M., & Chiu, C. H. (2012). Risk analysis in stochastic supply chains: A mean-risk approach. Springer, International Series in Operations Research and Management Science.Google Scholar
  7. Choi, T. M., Chow, P., & Xiao, T. (2012). Electronic price-testing scheme for fashion retailing with information updating. International Journal of Production Economics, 140, 396–406.CrossRefGoogle Scholar
  8. Chow, J., & Regan, A. C. (2011). Real option pricing of network design investments. Transportation Science, 45(1), 50–63.CrossRefGoogle Scholar
  9. Dabbene, F., Gay, P., & Tortia, C. (2014). Traceability issues in food supply chain management: A review. Biosystems Engineering, 120, 65–80.CrossRefGoogle Scholar
  10. Eksoz, C., Mansouri, S. F., & Bourlakis, M. (2014). Collaborative forecasting in the food supply chain: A conceptual framework. International Journal of Production Economics, 158, 120–135.CrossRefGoogle Scholar
  11. Hult, G. T., Craighead, C., & Ketchen, D. (2010). Risk uncertainty and supply chain decisions: A real option perspective. Decision Science, 41(3), 435–457.CrossRefGoogle Scholar
  12. Knowledge@Wharton (2014). From vaccines to cheese caves: Energizing the ‘cold chain’. Knowlwdge @Wharton, Sep. 14.Google Scholar
  13. Lankton, N., & Luft, J. (2008). Uncertainty and industry structure effects on managerial intuition about information technology real options. Journal of Management Information Systems, 25(2), 203–240.CrossRefGoogle Scholar
  14. Li, X., & Johnson, J. (2002). Evaluate IT investment opportunities using real options theory. Information Resources Management Journal, 15(3), 32–47.CrossRefGoogle Scholar
  15. Longstaff, F., & Schwartz, E. (2001). Valuing American options by simulation: A simple least-squares approach. The Review of Financial Studies, 14(1), 113–147.CrossRefGoogle Scholar
  16. MGI (2013). China’s E-tail revolution: Online shopping as a catalyst for growth. Mckinsey Global Institute.Google Scholar
  17. Migliore, G., Schifani, G., & Cembalo, L. (2015). Opening the black box of food quality in the short supply chain: Effects of conventions of quality on consumer choice. Food Quality & Preference, 39, 141–146.CrossRefGoogle Scholar
  18. Nguyen, H. (2013). Critical factors in e-business adoption: Evidence from Australian transport and logistics companies. International Journal of Production Economics, 146(1), 300–312.CrossRefGoogle Scholar
  19. Oberholtzer, L., Dimitri, C., & Jaenicke, E. C. (2014). Examining US food retailers’ decisions to procure local and organic produce from farmer direct-to-retail supply chains. Journal of Food Products Marketing, 20(4), 345–361.CrossRefGoogle Scholar
  20. Petruzzi, N. C., & Dada, M. (1999). Pricing and newsvendor problem: A review with extensions. Operations Research, 47(2), 183–194.CrossRefGoogle Scholar
  21. Samis, M., Laughton, D. & Poulin, R., (2013). Risk discounting: The fundamental difference between the real option and discounted cash flow project valuation methods. In Working paper.Google Scholar
  22. Schwartz, E., & Zozaya-Gorostiza, C. (2003). Investment under uncertainty in information technology: Acquisition and development projects. Management Science, 49(1), 57–70.CrossRefGoogle Scholar
  23. Trigeorgis, L. (1996). Real options: Managerial flexibility and strategy in resource allocation. Cambridge: MIT Press.Google Scholar
  24. Validi, S., Bhattacharya, A., & Byrne, P. J. (2014). A case analysis of a sustainable food supply chain distribution system: A multi-objective approach. International Journal of Production Economics, 152, 71–87.CrossRefGoogle Scholar
  25. Wagner, S., Padhi, S., & Zanger, I. (2014). A real option-based supply chain project evaluation and scheduling method. International Journal of Production Research, 52(12), 3725–3743.CrossRefGoogle Scholar
  26. Wu, I., & Chen, J. (2006). A hybrid performance measure system for e-business investments in high-tech manufacturing: An empirical study. Information & Management, 43(3), 364–377.CrossRefGoogle Scholar
  27. Zarei, M., Fakhrzad, M. B., & Paghaleh, M. J. (2011). Food supply chain leanness using a developed QFD model. Journal of Food Engineering, 102, 25–33.CrossRefGoogle Scholar
  28. Zhao, T., Sundararajan, S., & Tseng, C. (2004). Highway development decision-making under uncertainty: A real options approach. Journal of Infrastructure System, 10(1), 23–32.CrossRefGoogle Scholar
  29. Zhou, H. M., Ding, Q., & Otto, J. (2013). The reality and prospect of fresh agricultural product supply chains in China. International Journal of Applied Management Science, 5(3), 251–264.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.School of Business and ManagementMorgan State UniversityBaltimoreUSA
  2. 2.College of Business and EconomicsTowson UniversityTowsonUSA
  3. 3.Lubar School of BusinessUniversity of Wisconsin-MilwaukeeMilwaukeeUSA

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