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Pricing and bargaining strategy of e-retail under hybrid operational patterns. (English) Zbl 1411.90180

Summary: Dual-channel, as a significant retail strategy, has got more and more attention for academia and industry. While most literature focus on the conflicts between traditional channel and online channel, there are few works consider the conflicts of online retail channels. This paper focuses on the pricing and bargaining strategy of manufacturer and e-retailer under hybrid operational patterns which are adopted by e-commerce platforms. The operational patterns are divided into two types: other-organization e-pattern, such as Amazon, and self-organization e-pattern, such as Alibaba. We consider the commission charge which is collected by self-organization e-platform; and the analysis reveals that a fixed commission only has an effect on the total profit of manufacturer, but a variable commission would influence the wholesale price of other-organization e-platform and e-retail prices of both e-platforms, respectively. The results also suggest that, the wholesale price and the e-retail price are both affected by the service quality and this effect is also influenced by the variable commission. In addition, we also discuss the possibility of the manufacturer and e-retailer adjust their pricing strategy based on big data implementation.

MSC:

90B50 Management decision making, including multiple objectives
91B24 Microeconomic theory (price theory and economic markets)
Full Text: DOI

References:

[1] Amrouche, N., & Yan, R. (2015). A manufacturer distribution issue: How to manage an online and a traditional retailer. Annals of Operations Research. doi:10.1007/s10479-015-1982-6. · Zbl 1406.91139
[2] Bernstein, F., Song, J. S., & Zheng, X. (2008). Bricks-and-mortar vs. clicks-and-mortar: An equilibrium analysis. European Journal of Operational Research, 187(3), 671-690. · Zbl 1137.90600
[3] Cai, G. G., Zhang, Z. G., & Zhang, M. (2009). Game theoretical perspectives on dual-channel supply chain competition with price discounts and pricing schemes. International Journal of Production Economics, 117(1), 80-96.
[4] Cattani, K., Gilland, W., Heese, H. S., & Swaminathan, J. (2006). Boiling frogs: Pricing strategies for a manufacturer adding a direct channel that competes with the traditional channel. Production and Operations Management, 15(1), 40.
[5] Chae, B. K. (2015). Insights from hashtag# supply chain and twitter analytics: Considering twitter and twitter data for supply chain practice and research. International Journal of Production Economics, 165, 247-259.
[6] Chen, C. P., & Zhang, C. Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on big data. Information Sciences, 275, 314-347.
[7] Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165-1188.
[8] Chen, Y. C., Fang, S. C., & Wen, U. P. (2013). Pricing policies for substitutable products in a supply chain with internet and traditional channels. European Journal of Operational Research, 224(3), 542-551. · Zbl 1292.90042
[9] Chiang, W. Y. K., Chhajed, D., & Hess, J. D. (2003). Direct marketing, indirect profits: A strategic analysis of dual-channel supply-chain design. Management Science, 49(1), 1-20. · Zbl 1232.90231
[10] Chu, J., Chintagunta, P. K., & Vilcassim, N. J. (2007). Assessing the economic value of distribution channels: An application to the personal computer industry. Journal of Marketing Research, 44(1), 29-41.
[11] Dan, B., Qu, Z., Liu, C., Zhang, X., & Zhang, H. (2014). Price and service competition in the supply chain with both pure play internet and strong bricks-and-mortar retailers. Journal of Applied Research and Technology, 12(2), 212-222.
[12] Dan, B., Xu, G., & Liu, C. (2012). Pricing policies in a dual-channel supply chain with retail services. International Journal of Production Economics, 139(1), 312-320.
[13] Desai, V. S. (1992). Marketing-production decisions under independent and integrated channel structure. Annals of Operations Research, 34(1), 275-306. · Zbl 0729.91037
[14] Dhar, V. (2013). Data science and prediction. Communications of the ACM, 56(12), 64-73.
[15] Dubey, R., Gunasekaran, A., Childe, S. J., Wamba, S. F., & Papadopoulos, T. (2016). The impact of big data on world-class sustainable manufacturing. The International Journal of Advanced Manufacturing Technology, 84(1-4), 631-645.
[16] Dumrongsiri, A., Fan, M., Jain, A., & Moinzadeh, K. (2008). A supply chain model with direct and retail channels. European Journal of Operational Research, 187(3), 691-718. · Zbl 1137.91388
[17] Dutta, D., & Bose, I. (2015). Managing a big data project: The case of ramco cements limited. International Journal of Production Economics, 165, 293-306.
[18] Hazen, B. T., Boone, C. A., Ezell, J. D., & Jones-Farmer, L. A. (2014). Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications. International Journal of Production Economics, 154, 72-80.
[19] Hsieh, C. C., Chang, Y. L., & Wu, C. H. (2014). Competitive pricing and ordering decisions in a multiple-channel supply chain. International Journal of Production Economics, 154, 156-165.
[20] Hu, W., & Li, Y. (2012). Retail service for mixed retail and e-tail channels. Annals of Operations Research, 192(1), 151-171. · Zbl 1233.90090
[21] Huang, Z., & Benyoucef, M. (2013). From e-commerce to social commerce: A close look at design features. Electronic Commerce Research and Applications, 12(4), 246-259.
[22] Ji-fan Ren, S., Fosso Wamba, S., Akter, S., Dubey, R., & Childe, S. J. (2016). Modelling quality dynamics, business value and firm performance in a big data analytics environment. International Journal of Production Research. doi:10.1080/00207543.2016.1154209.
[23] Ji, G., & Yu, M. (2014). Pricing decisions in dual-channel supply chains with service cooperation under asymmetric information. In 2014 11th International conference on service systems and service management (ICSSSM), (pp. 1-6). IEEE.
[24] Kurata, H., Yao, D. Q., & Liu, J. J. (2007). Pricing policies under direct vs. indirect channel competition and national vs. store brand competition. European Journal of Operational Research, 180(1), 262-281. · Zbl 1114.90306
[25] Li, H., & Cui, N. F. (2013). Study on pricing scheme decision in dual channel supply chain under bargaining power. Application Research of Computers, 8, 021.
[26] Lu, Q., & Liu, N. (2013). Pricing games of mixed conventional and e-commerce distribution channels. Computers & Industrial Engineering, 64(1), 122-132.
[27] Mussa, M., & Rosen, S. (1978). Monopoly and product quality. Journal of Economic Theory, 18(2), 301-317. · Zbl 0403.90007
[28] O’leary, D. E. (2011). The use of social media in the supply chain: Survey and extensions. Intelligent Systems in Accounting, Finance and Management, 18(2-3), 121-144.
[29] Perrey, J., Spillecke, D., & Umblijs, A. (2013). Smart analytics: How marketing drives short-term and long-term growth. McKinsey Quarterly. http://refhub.elsevier.com/0925-5273(14)00425-3/sbref75.
[30] Roberts, F. S. (2008). Computer science and decision theory. Annals of Operations Research, 163(1), 209-253. · Zbl 1170.91303
[31] Rodrłguez, B., & Aydın, G. (2015). Pricing and assortment decisions for a manufacturer selling through dual channels. European Journal of Operational Research, 242(3), 901-909. · Zbl 1341.90077
[32] Su, Y., & Geunes, J. (2013). Multi-period price promotions in a single-supplier, multi-retailer supply chain under asymmetric demand information. Annals of Operations Research, 211(1), 447-472. · Zbl 1291.90026
[33] Tan, K. H., Zhan, Y., Ji, G., Ye, F., & Chang, C. (2015). Harvesting big data to enhance supply chain innovation capabilities: An analytic infrastructure based on deduction graph. International Journal of Production Economics, 165, 223-233.
[34] Teece, D. J. (2007). Explicating dynamic capabilities: The nature and microfoundations of (sustainable) enterprise performance. Strategic Management Journal, 28(13), 1319-1350.
[35] Tsay, A. A., & Agrawal, N. (2000). Channel dynamics under price and service competition. Manufacturing & Service Operations Management, 2(4), 372-391.
[36] Tsay, A. A., & Agrawal, N. (2004). Channel conflict and coordination in the e-commerce age. Production and Operations Management, 13(1), 93-110.
[37] Upasani, A., & Uzsoy, R. (2008). Incorporating manufacturing lead times in joint production-marketing models: A review and some future directions. Annals of Operations Research, 161(1), 171-188. · Zbl 1151.90389
[38] Viswanathan, S. (2005). Competing across technology-differentiated channels: The impact of network externalities and switching costs. Management Science, 51(3), 483-496.
[39] Waller, M. A., & Fawcett, S. E. (2013). Click here for a data scientist: Big data, predictive analytics, and theory development in the era of a maker movement supply chain. Journal of Business Logistics, 34(4), 249-252.
[40] Wamba, S. F., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How ‘big data’can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, 234-246.
[41] Xiao, T., Yu, G., Sheng, Z., & Xia, Y. (2005). Coordination of a supply chain with one-manufacturer and two-retailers under demand promotion and disruption management decisions. Annals of Operations Research, 135(1), 87-109. · Zbl 1112.90305
[42] Xiao, T., Choi, T. M., & Cheng, T. (2014). Product variety and channel structure strategy for a retailer-stackelberg supply chain. European Journal of Operational Research, 233(1), 114-124. · Zbl 1339.90177
[43] Yan, R. (2008). Pricing strategy for companies with mixed online and traditional retailing distribution markets. Journal of Product & Brand Management, 17(1), 48-56.
[44] Yoo, B., & Donthu, N. (2000). Developing a scale to measure the perceived quality of an internet shopping site (pqiss). Developments in Marketing Science, 23, 471-471.
[45] Zha, Y., Zhang, J., Yue, X., & Hua, Z. (2015). Service supply chain coordination with platform effort-induced demand. Annals of Operations Research, 235(1), 785-806. · Zbl 1332.90086
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