This paper aims to study the pricing strategies of an online trading platform with indirect network externalities by considering heterogeneous trading behavior in the downstream market and the long tail.
The game theory, optimization and comparative static are used in this research. The equilibria are derived from the game theory, and with them, the authors optimize the platform’s profit function. Comparative static is used to study pricing strategies.
It is found that with heterogeneous trading behavior, the transaction-based model is more profitable than the subscription-based model by reason of the feasibility of “price discrimination”. However, with certain advantages of subscription fees such as avoiding offline transactions, the subscription-based model is better with a concentrated distribution of sellers’ revenues (the Gini coefficient is small). With a lucrative long tail, the platform should set a low price to attract small sellers in the long tail. Besides, if the Gini coefficient is large, the effects of the market entry barrier of sellers on the optimal price in each model may be opposite.
It implies that the choice of revenue models and pricing strategies are influenced by the Gini coefficient or the long tail. The exogenous setting in which buyers can use the platform for free needs further extension.
The authors provide insights on how to choose revenue models and how to price the sellers with the long tail phenomenon.
This paper emphasizes the role of the long tail on pricing strategies and the effect of heterogeneous trading behavior on model selection.
The authors gratefully acknowledge financial support from the Natural Science Foundation of China (71671036, 71171046) and the major project of philosophy and social science research in colleges and universities in Jiangsu province (2018SJZDA005).
Geng, Y. and Zhang, Y. (2019), "Pricing on monopoly online trading platform with heterogeneous trading behavior and the long tail", Kybernetes, Vol. 49 No. 3, pp. 852-875. https://doi.org/10.1108/K-10-2018-0553Download as .RIS
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