Share:


DYNAMIC PRICING STRATEGIES AND CONSUMER BEHAVIOR: EVIDENCE FROM E-COMMERCE PLATFORMS

    Vidushi Tewari

Abstract

This study explores dynamic pricing strategies inside e-commerce structures, focusing at the interaction between purchaser conduct and pricing decisions made through both producers and e-trade companies in a global context. We establish a choice-making model where the e-trade firm acts as a pacesetter and the manufacturer as a follower. Two eventualities are analyzed: one in which the producer operates its own on-line save and any other wherein it makes use of the e-commerce firm’s platform. Through mathematical modeling and numerical evaluation, we become aware of key equilibrium strategies and pricing behaviors for both members. Findings display that after producers preserve their own web sites, e-commerce corporations can provide lower costs to new clients to decorate competitiveness. Conversely, when manufacturers leverage the e-trade platform, their pricing strategy is prompted through the referral fee charge, with decrease costs for brand spanking new customers being not unusual. The results indicate that changes in referral expenses and franchise costs significantly impact the producers' desire to sell thru the e-commerce platform rather than establishing unbiased web sites. This studies affords precious insights into how dynamic pricing and consumer segmentation impact strategic decisions in the rapidly evolving panorama of on line income.

Keyword : Dynamic Pricing, Consumer Behavior, E-Commerce Platforms, Pricing Strategies, Manufacturer Decisions, Referral Fees, Franchise Fees, Online Sales, International Market, Competitive Strategy.

Published in Issue
October 13, 2024
Abstract Views
02
PDF Downloads
03
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References


1. Choi, Sunmee, and Anna S. Mattila. 2009. Perceived equity of fee differences throughout channels: The moderating function of price frame and norm perceptions. Journal of Marketing Theory and Practice 17: 37–48 2. Cox, Jennifer Lyn. 2001. Can differential expenses be honest? Journal of Product and Brand Management 10: 264–seventy five. 3. Dai, Bo. 2010. The Impact of Perceived Price Fairness of Dynamic Pricing on Customer Satisfaction and Behavioral Intentions: The Moderating Role of Customer Loyalty. Auburn: Auburn University. 4. Deksnyte, Indre, and Zigmas Lydeka. 2012. Dynamic Pricing and Its Forming Factors. International Journal of Business and Social Science 3: 213–20. 5. Devaraj, Sarv, Ming Fan, and Rajiv Kohli. 2002. Antecedents of B2C channel delight and preference: validating ecommerce metrics. Information Systems Research 13: 316–33. 6. Devon, Holli A., Michelle E. Block, Patricia Moyle-Wright, Diane M. Ernst, Susan J. Hayden, Deborah J. Lazzara, Suzanne M. Savoy, and Elizabeth Kostas-Polston. 2007. A psychometric Toolbox for checking out Validity and Reliability. Journal of Nursing Scholarship 39:155–64. 7. Elmaghraby, Wedad, and Pinar Keskinocak. 2003. Dynamic pricing in the presence of inventory concerns: Research evaluation, modern-day practices, and future instructions. Management Science 49: 1287–309. 8. Garbarino, Ellen, and Olivia F. Lee. 2003. Dynamic pricing in Internet retail: Effects on purchaser receive as proper with. Psychology Marketing 20: 495–513 9. Gefen, David. 2002. Customer loyalty in E-trade. Journal of the Association for Information Systems 3: 27–51. 10. Gerbin, David W., and Janet G. Hamilton. 1996. Viability of Exploratory Factor Analysis as a Precursor to Confirmatory Factor Analysis. Structural Equation Modeling 3: 2–72. 11. Hair, Joseph F., Rolph E. Anderson, Ronald L. Tatham, and William C. Black. 1995. Multivariate records analysis. In Englewood Cliffs. Upper Saddle River: Prentice-Hall Inc. 12. Hair, Joseph F., William C. Black, Barry J. Babin, and Rolph E. Anderson. 2010. Multivariate Data Analysis: A Global Perspective, 7th ed. New York: Pearson. 13. Haws, Kelly L., and William O. Bearden. 2006. Dynamic pricing and patron fairness perceptions. Journal of Consumer Research 33: 304–305. 14. Henson, Robin K., and J. Kyle Roberts. 2006. Use of Exploratory Factor Analysis in Published Research: Common Errors and Some Comment on Improved Practice. Educational and Psychological Measurement sixty six. 15. Hogarty, Kristin Y., and Constance V. Hines. 2005. The Quality of Factor Solutions in Exploratory Factor Analysis: The Influence of Sample Size, Communality, and Overdetermination. Educational and Psychological Measurement. Sixty 5: 202–26 .Sims, C.A. Macroeconomics and reality. Econ. J. Econ. Soc. 1980, 1–48. 16. Takada, H.; Bass, F.M. Multiple time series evaluation of competitive marketing behavior. J. Bus. Res. 1998, forty three, ninety seven–107. 17. Franses, P.H. Forecasting in advertising. Handb. Econ. Forecast. 2006, 1, 983–1012. 18. Dekimpe, M.G.; Hanssens, D.M. Time-collection fashions in advertising: Past, present and future. Int. J. Res. Market. 2000, 17, 183–193. 19. Hsu, N.J.; Hung, H.L.; Chang, Y.M. Subset choice for vector autoregressive techniques the usage of Lasso. Comput. Stat. Data Anal. 2008, 52, 3645–3657.