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Content provision strategy selection for a media platform in the presence of traffic revenue

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Abstract

Inspired by the diversity of content provision and the importance of traffic revenue in media market, this paper investigates the pricing and advertising decisions for a media platform considering traffic revenue under three content provision strategies: single paid-content-without-ads strategy, single free-content-with-ads strategy, and hybrid content strategy. The optimal solutions among different content provision strategies are compared and analyzed, thereby guiding the media platform and consumers in choosing the best content provision mode, while also providing some valuable insights for the media platform in its decision-making. In presence of traffic revenue, findings show that the hybrid content provision strategy can achieve a win–win situation for the media platform and consumers, in terms of platform profit, consumer surplus, and decision-making speed, whereas the single paid or free content provision strategy cannot achieve such a situation. We also surprisingly find that the media content with higher quality fails in creating more welfare for consumers, when the traffic revenue sensitivity coefficient and the free content’s quality discount factor are high. Numerical experiments verify the theoretical results. We also study two model extensions of endogenous content quality and platforms competition, and find that the major findings of the base model are robust.

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References

  1. Zhang, W., Chen, Z., & Xi, Y. (2020). Traffic media: How algorithmic imaginations and practices change content production. Chinese Journal of Communication, 14(1), 58–74.

    Article  Google Scholar 

  2. Zhou, Y.-W., Chen, C., Zhong, Y., et al. (2020). The allocation optimization of promotion budget and traffic volume for an online flash-sales platform. Annals of Operations Research, 291(1), 1183–1207.

    Article  Google Scholar 

  3. De Cornière, A., & Sarvary, M. (2023). Social media and news: Content bundling and news quality. Management Science, 69(1), 162–178.

    Article  Google Scholar 

  4. Bakker, P., & Hille, S. (2013). I like news. Searching for the ‘Holy Grail’ of social media: The use of Facebook by Dutch news media and their audiences. European Journal of Communication, 28(6), 663–680.

    Article  Google Scholar 

  5. Lin, X., Hou, R., & Zhou, Y. (2020). Platform competition for advertisers and viewers in media markets with endogenous content and advertising. Journal of Systems Science and Systems Engineering, 29(1), 36–54.

    Article  Google Scholar 

  6. Chatterjee, P., & Zhou, B. (2021). Sponsored content advertising in a two-sided market. Management Science, 67(12), 7560–7574.

    Article  Google Scholar 

  7. Subramanian, H., Mitra, S., & Ransbotham, S. (2021). Capturing value in platform business models that rely on user-generated content. Organization Science, 32(3), 804–823.

    Article  Google Scholar 

  8. Zia, M., & Rao, R. C. (2019). Search advertising: Budget allocation across search engines. Marketing Science, 38(6), 1023–1037.

    Google Scholar 

  9. Alibakhshi, R., & Srivastava, S. C. (2022). Post-story: Influence of introducing story feature on social media posts. Journal of Management Information Systems, 39(2), 573–601.

    Article  Google Scholar 

  10. Amaldoss, W., & Du, J. (2023). How can publishers collaborate and compete with news aggregators? Journal of Marketing Research. https://doi.org/10.1177/00222437231153607

    Article  Google Scholar 

  11. Dietl, H., Lang, M., & Lin, P. (2013). Advertising pricing models in media markets: Lump-sum versus per-consumer charges. Information Economics and Policy, 25(4), 257–271.

    Article  Google Scholar 

  12. Peitz, M., & Valletti, T. M. (2008). Content and advertising in the media: Pay-tv versus free-to-air. International Journal of Industrial Organization, 26(4), 949–965.

    Article  Google Scholar 

  13. Amaldoss, W., Du, J., & Shin, W. (2021). Media platforms’ content provision strategies and sources of profits. Marketing Science, 40(3), 527–547.

    Article  Google Scholar 

  14. Wang, W., & Guo, Q. (2023). Subscription strategy choices of network video platforms in the presence of social influence. Electronic Commerce Research, 23(1), 577–604.

    Article  CAS  Google Scholar 

  15. Wu, C.-H., & Chiu, Y.-Y. (2023). Pricing and content development for online media platforms regarding consumer homing choices. European Journal of Operational Research, 305(1), 312–328.

    Article  MathSciNet  Google Scholar 

  16. Liu, Y., Yildirim, P., & Zhang, Z. J. (2022). Implications of revenue models and technology for content moderation strategies. Marketing Science, 41(4), 831–847.

    Article  Google Scholar 

  17. Su, W., Li, Y., Zhang, H., et al. (2021). How the attributes of content distributors influence the intentions of users to pay for content shared on social media. Electronic Commerce Research, 23(1), 407–441.

    Article  Google Scholar 

  18. Anand, A., Irshad, M. S., & Dwivedi, Y. K. (2022). Modeling view count dynamics for YouTube videos: A multimodal perspective. Kybernetes, 51(10), 2964–2986.

    Article  Google Scholar 

  19. Kanuri, V. K., Mantrala, M. K., & Thorson, E. (2017). Optimizing a menu of multiformat subscription plans for ad-supported media platforms. Journal of Marketing, 81(2), 45–63.

    Article  Google Scholar 

  20. Ma, B., Di, C., & Hsiao, L. (2020). Return window decision in a distribution channel. Production and Operations Management, 29(9), 2121–2137.

    Article  Google Scholar 

  21. Xu, J., Huang, Y., Avgerinos, E., et al. (2021). Dual-channel competition: The role of quality improvement and price-matching. International Journal of Production Research, 60(12), 3705–3727.

    Article  Google Scholar 

  22. Alaei, S., Makhdoumi, A., Malekian, A., et al. (2022). Revenue-sharing allocation strategies for two-sided media platforms: Pro-rata versus user-centric. Management Science, 68(12), 8699–8721.

    Article  Google Scholar 

  23. Alaei, S., Makhdoumi, A., & Malekian, A. (2023). Optimal subscription planning for digital goods. Operations Research. https://doi.org/10.1287/opre.2023.2468

    Article  MathSciNet  Google Scholar 

  24. Anderson, S. P., Foros, Ø., & Kind, H. J. (2018). Competition for advertisers and for viewers in media markets. The Economic Journal, 128(608), 34–54.

    Article  Google Scholar 

  25. Kübler, R., Seifert, R., & Kandziora, M. (2020). Content valuation strategies for digital subscription platforms. Journal of Cultural Economics, 45(2), 295–326.

    Article  Google Scholar 

  26. Kim, S.-M. (2016). How can we make a socially optimal large-scale media platform? Analysis of a monopolistic Internet media platform using two-sided market theory. Telecommunications Policy, 40(9), 899–918.

    Article  Google Scholar 

  27. Liu, Q., Nedelescu, D., & Gu, J. (2021). The impact of strategic agents in two-sided markets. Journal of Economics, 134(3), 195–218.

    Article  Google Scholar 

  28. Duan, Y., Liu, P., & Feng, Y. (2022). Pricing strategies of two-sided platforms considering privacy concerns. Journal of Retailing and Consumer Services, 64, 102781.

    Article  Google Scholar 

  29. Jha, P. D. S., Khalf, M. F., & Karthick, M. F. (2023). Convolutional neural networks for breast cancer detection using regions of interest from infrared images. Tamjeed Journal of Healthcare Engineering and Science Technology, 1(2), 44–53.

    Article  Google Scholar 

  30. Kumar, D. B. S., & Sani Mohammed, D. B. (2023). Detection of human protein structures by select deep learning models and dynamic systems. Tamjeed Journal of Healthcare Engineering and Science Technology, 1(1), 35–42.

    Article  Google Scholar 

  31. Rs, A., Kumar, R., & Punitha, P. (2023). The effect of grain size and silicon content on non-oriented grain steel anomalous loss through frequency excitation in the medical healthcare by using big data analysis. Tamjeed Journal of Healthcare Engineering and Science Technology, 1(1), 43–53.

    Article  Google Scholar 

  32. Ahmed, F. M., & Mohammed, D. B. S. (2023). Feasibility of breast cancer detection through a convolutional neural network in mammographs. Tamjeed Journal of Healthcare Engineering and Science Technology, 1(2), 36–43.

    Article  Google Scholar 

  33. Kadhim, N. M., Mohammed, H. A., Radhawi, S. N., et al. (2023). Investigation of the next generation science standards including in the science book according to E-learn: Analytical study. Tamjeed Journal of Healthcare Engineering and Science Technology, 1(2), 30–35.

    Article  Google Scholar 

  34. Du, A. Y., Das, S., Gopal, R. D., et al. (2014). Optimal management of digital content on tiered infrastructure platforms. Information Systems Research, 25(4), 730–746.

    Article  Google Scholar 

  35. De Haan, E., Wiesel, T., & Pauwels, K. (2016). The effectiveness of different forms of online advertising for purchase conversion in a multiple-channel attribution framework. International Journal of Research in Marketing, 33(3), 491–507.

    Article  Google Scholar 

  36. Lee, J.-Y., Fang, E., Kim, J. J., et al. (2018). The effect of online shopping platform strategies on search, display, and membership revenues. Journal of Retailing, 94(3), 247–264.

    Article  Google Scholar 

  37. Rhuggenaath, J., Akcay, A., Zhang, Y., et al. (2019). Optimal display-ad allocation with guaranteed contracts and supply side platforms. Computers & Industrial Engineering, 137, 106071.

    Article  Google Scholar 

  38. Hao, P., Hu, L., Zhao, K., et al. (2019). Dynamic pricing with traffic engineering for adaptive video streaming over software-defined content delivery networking. Multimedia Tools and Applications, 78(3), 3471–3492.

    Article  Google Scholar 

  39. Zhao, Y., Song, P., & Feng, F. (2019). What are the revenue implications of mobile channel visits? Evidence from the online travel agency industry. Electronic Commerce Research and Applications, 36, 100865.

    Article  Google Scholar 

  40. Sun, H., Fan, M., & Tan, Y. (2020). An empirical analysis of seller advertising strategies in an online marketplace. Information Systems Research, 31(1), 37–56.

    Article  Google Scholar 

  41. Lee, S., Manjunath, D., & Joo, C. (2022). On the economics effects of CDN-mediated delivery on content providers. IEEE Transactions on Network and Service Management, 19(4), 4449–4460.

    Article  Google Scholar 

  42. Qu, D., Gao, C., & Xu, B. (2023). Pricing and consumption in the P2P product sharing era: How does the dual-channel manufacturer cooperate with third-party sharing platforms? Electronic Commerce Research. https://doi.org/10.1007/s10660-023-09710-8

    Article  Google Scholar 

  43. Amaldoss, W., Du, J., & Shin, W. (2023). Pricing strategy of competing media platforms. Marketing Science. https://doi.org/10.1287/mksc.2021.0092

    Article  Google Scholar 

  44. Liu, J., Zhong, W., Zhang, J., et al. (2023). The effectiveness of cross-platform targeted advertising strategy. Electronic Commerce Research. https://doi.org/10.1007/s10660-022-09659-0

    Article  PubMed Central  Google Scholar 

  45. Jiang, B., & Yang, B. (2019). Quality and pricing decisions in a market with consumer information sharing. Management Science, 65(1), 272–285.

    Article  MathSciNet  Google Scholar 

  46. Zhou, Q., Meng, C., & Yuen, K. F. (2021). Remanufacturing authorization strategy for competition among OEM, authorized remanufacturer, and unauthorized remanufacturer. International Journal of Production Economics, 242, 108295.

    Article  Google Scholar 

  47. Zhao, S., You, Z., & Zhu, Q. (2021). Quality choice for product recovery considering a trade-in program and third-party remanufacturing competition. International Journal of Production Economics, 240, 108239.

    Article  Google Scholar 

  48. Zhu, X., Lang, M., & Dietl, H. M. (2023). Content quality assurance on media platforms with user-generated content. Journal of Theoretical and Applied Electronic Commerce Research, 18(3), 1660–1686.

    Article  Google Scholar 

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Acknowledgements

The research is supported by the National Natural Science Foundation of China under Grant No. 72071072.

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Correspondence to Lan Jiang.

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Appendix

Appendix

Proof of Proposition 1

\(p_{H}^{*} - p_{P}^{*} = \frac{{m\left( {2\delta m - \left( {1 + \delta } \right)\beta } \right)}}{{2\left( {2\delta m - \beta } \right)}} - \frac{{m\left( {m - \beta } \right)}}{2m - \beta } = - \frac{{m\left( {1 - \delta } \right)\beta^{2} }}{{2\left( {2\delta m - \beta } \right)\left( {2m - \beta } \right)}} < 0\); \(r_{H}^{*} - r_{F}^{*} = 0\). Thus, \(p_{H}^{*} < p_{P}^{*}\) and \(r_{H}^{*} = r_{F}^{*}\). □

Proof of Proposition 2

\(q_{H}^{*} - q_{F}^{*} = 0\), \(q_{H}^{*} - q_{P}^{*} = \frac{{m\left( {1 - \delta } \right)\beta }}{{\left( {2\delta m - \beta } \right)\left( {2m - \beta } \right)}} > 0\). Thus, \(q_{H}^{*} = q_{F}^{*} > q_{P}^{*}\). Similarly, \(R_{Ht}^{*} = R_{Ft}^{*} > R_{Pt}^{*}\) can be derived. □

Proof of Proposition 3

\(\pi_{H}^{*} - \pi_{F}^{*} = \frac{{m\left( {1 - \delta } \right)}}{4} > 0\), \(\pi_{H}^{*} - \pi_{P}^{*} = \frac{{m\left( {1 - \delta } \right)\beta^{2} }}{{4\left( {2\delta m - \beta } \right)\left( {2m - \beta } \right)}} > 0\), \(\pi_{P}^{*} - \pi_{F}^{*} = \frac{{m^{2} \left( {1 - \delta } \right)\left( {2\delta m - \left( {1 + \delta } \right)\beta } \right)}}{{2\left( {2\delta m - \beta } \right)\left( {2m - \beta } \right)}} > \frac{{m^{3} \left( {1 - \delta } \right)^{2} \delta }}{{2\left( {2\delta m - \beta } \right)\left( {2m - \beta } \right)}} > 0\) since \(\beta < \delta m\). Thus, \(\pi_{H}^{*} > \pi_{P}^{*} > \pi_{F}^{*}\). □

Proof of Proposition 4

\(CS_{H}^{*} - CS_{F}^{*} = \frac{{m\left( {1 - \delta } \right)}}{8} > 0\).

\(CS_{H}^{*} - CS_{P}^{*} = \frac{{3m\beta^{2} \left( {1 - \delta } \right)\left( {12\delta m^{2} - 4m\left( {1 + \delta } \right)\beta + \beta^{2} } \right)}}{{24\left( {2\delta m - \beta } \right)^{2} \left( {2m - \beta } \right)^{2} }} > \frac{{3\delta m^{3} \beta^{2} \left( {1 - \delta } \right)\left( {8 - 3\delta } \right)}}{{24\left( {2\delta m - \beta } \right)^{2} \left( {2m - \beta } \right)^{2} }} > 0\) since \(\beta < \delta m\).

\(CS_{P}^{*} - CS_{F}^{*} = \frac{{2m^{3} \left( {1 - \delta } \right)\left( {4\delta^{2} m^{2} - 4\delta m\left( {1 + \delta } \right)\beta + \left( {1 + \delta^{2} + \delta } \right)\beta^{2} } \right)}}{{4\left( {2\delta m - \beta } \right)^{2} \left( {2m - \beta } \right)^{2} }}\),

\(f_{1} \left( \beta \right) = \left( {1 + \delta^{2} + \delta } \right)\beta^{2} - 4\delta m\left( {1 + \delta } \right)\beta + 4\delta^{2} m^{2}\), \(\frac{{2\delta m\left( {1 + \delta } \right)}}{{1 + \delta^{2} + \delta }} - \delta m = \frac{{\delta m\left( {1 - \delta^{2} + \delta } \right)}}{{1 + \delta^{2} + \delta }} > 0\),

\(f_{1} \left( \beta \right)|_{\beta = \delta m} = \delta^{2} m^{2} \left( {1 - 3\delta + \delta^{2} } \right)\): if \(0 < \delta < \frac{3 - \sqrt 5 }{2}\), \(f_{1} \left( \beta \right)|_{\beta = \delta m} > 0\); if \(\frac{3 - \sqrt 5 }{2} < \delta < 1\), \(f_{1} \left( \beta \right)|_{\beta = \delta m} < 0\). \(\frac{{2\delta m\left( {1 + \delta - \sqrt \delta } \right)}}{{1 + \delta^{2} + \delta }}\) and \(\frac{{2\delta m\left( {1 + \delta + \sqrt \delta } \right)}}{{1 + \delta^{2} + \delta }}\) are the roots of \(f_{1} \left( \beta \right) = 0\).

Thus, when \(0 < \delta < \frac{3 - \sqrt 5 }{2}\), \(CS_{H}^{*} > CS_{P}^{*} > CS_{F}^{*}\). When \(\frac{3 - \sqrt 5 }{2} < \delta < 1\), if \(0 < \beta < \frac{{2\delta m\left( {1 + \delta - \sqrt \delta } \right)}}{{1 + \delta^{2} + \delta }}\), then \(CS_{H}^{*} > CS_{P}^{*} > CS_{F}^{*}\); if \(\frac{{2\delta m\left( {1 + \delta - \sqrt \delta } \right)}}{{1 + \delta^{2} + \delta }} < \beta < \delta m\), then \(CS_{H}^{*} > CS_{F}^{*} > CS_{P}^{*}\). □

Proof of Corollary 1

  1. 1.

    \(\frac{{\partial p_{P}^{*} }}{\partial \beta } = - \frac{{m^{2} }}{{\left( {2m - \beta } \right)^{2} }} < 0\), \(\frac{{\partial p_{P}^{*} }}{\partial \beta } - \frac{{\partial p_{H}^{*} }}{\partial \beta } = \frac{{\beta m^{2} \left( {1 - \delta } \right)\left( {4\delta m - \left( {1 + \delta } \right)\beta } \right)}}{{\left( {2\delta m - \beta } \right)^{2} \left( {2m - \beta } \right)^{2} }} > \frac{{\delta \beta m^{3} \left( {1 - \delta } \right)\left( {3 - \delta } \right)}}{{\left( {2\delta m - \beta } \right)^{2} \left( {2m - \beta } \right)^{2} }} > 0\), Thus, \(\frac{{\partial p_{H}^{*} }}{\partial \beta } < \frac{{\partial p_{P}^{*} }}{\partial \beta } < 0\).

  2. 2.

    \(\frac{{\partial r_{F}^{*} }}{\partial \beta } = \frac{{\partial r_{H}^{*} }}{\partial \beta } = - \frac{{\delta^{2} m^{2} }}{{\left( {2\delta m - \beta } \right)^{2} }} < 0\). Thus, \(\frac{{\partial r_{F}^{*} }}{\partial \beta } = \frac{{\partial r_{H}^{*} }}{\partial \beta } < 0\).

  3. 3.

    \(\frac{{\partial q_{F}^{*} }}{\partial \beta } - \frac{{\partial q_{P}^{*} }}{\partial \beta } = \frac{{m\left( {1 - \delta } \right)\left( {4\delta m^{2} - \beta^{2} } \right)}}{{\left( {2\delta m - \beta } \right)^{2} \left( {2m - \beta } \right)^{2} }} > \frac{{\delta m^{3} \left( {1 - \delta } \right)\left( {4 - \delta } \right)}}{{\left( {2\delta m - \beta } \right)^{2} \left( {2m - \beta } \right)^{2} }} > 0\), \(\frac{{\partial q_{P}^{*} }}{\partial \beta } = \frac{m}{{\left( {2m - \beta } \right)^{2} }} > 0\), Thus, \(\frac{{\partial q_{H}^{*} }}{\partial \beta } = \frac{{\partial q_{F}^{*} }}{\partial \beta } > \frac{{\partial q_{P}^{*} }}{\partial \beta } > 0\).

  4. 4.

    \(\frac{{\partial \pi_{H}^{*} }}{\partial \beta } - \frac{{\partial \pi_{P}^{*} }}{\partial \beta } = \frac{{\beta m^{2} \left( {1 - \delta } \right)\left( {4\delta m - \left( {1 + \delta } \right)\beta } \right)}}{{2\left( {2\delta m - \beta } \right)^{2} \left( {2m - \beta } \right)^{2} }} > \frac{{\delta \beta m^{3} \left( {1 - \delta } \right)\left( {3 - \delta } \right)}}{{2\left( {2\delta m - \beta } \right)^{2} \left( {2m - \beta } \right)^{2} }} > 0\), \(\frac{{\partial \pi_{F}^{*} }}{\partial \beta } = \frac{{\partial \pi_{H}^{*} }}{\partial \beta } = \frac{{\delta^{2} m^{2} }}{{2\left( {2\delta m - \beta } \right)^{2} }} > 0\), \(\frac{{\partial \pi_{P}^{*} }}{\partial \beta } = \frac{{m^{2} }}{{2\left( {2m - \beta } \right)^{2} }} > 0\). Thus, \(\frac{{\partial \pi_{F}^{*} }}{\partial \beta } = \frac{{\partial \pi_{H}^{*} }}{\partial \beta } > \frac{{\partial \pi_{P}^{*} }}{\partial \beta } > 0\).

  5. 5.

    \(\frac{{\partial CS_{F}^{*} }}{\partial \beta } = \frac{{\partial CS_{H}^{*} }}{\partial \beta } = \frac{{\delta^{3} m^{3} }}{{\left( {2\delta m - \beta } \right)^{3} }} > 0\), \(\frac{{\partial CS_{P}^{*} }}{\partial \beta } = \frac{{m^{3} }}{{\left( {2m - \beta } \right)^{3} }} > 0\).

\(\frac{{\partial CS_{H}^{*} }}{\partial \beta } - \frac{{\partial CS_{P}^{*} }}{\partial \beta } = \frac{{m^{3} \left( {\left( {1 - \delta^{3} } \right)\beta^{3} - 6\delta m\left( {1 - \delta^{2} } \right)\beta^{2} + 12\delta^{2} m^{2} \left( {1 - \delta } \right)\beta } \right)}}{{\left( {2\delta m - \beta } \right)^{3} \left( {2m - \beta } \right)^{3} }} = \frac{{m^{3} f_{2} \left( \beta \right)}}{{\left( {2\delta m - \beta } \right)^{3} \left( {2m - \beta } \right)^{3} }}\),

\(\frac{{\partial f_{2} \left( \beta \right)}}{\partial \beta } = 3\left( {1 - \delta } \right)f_{1} \left( \beta \right)\), where \(f_{1} \left( \beta \right) = \left( {1 + \delta^{2} + \delta } \right)\beta^{2} - 4\delta m\left( {1 + \delta } \right)\beta + 4\delta^{2} m^{2}\) and \(\beta \in \left( {0,\delta m} \right)\). When \(0 < \delta < \frac{3 - \sqrt 5 }{2}\), we obtain that \(\frac{{\partial f_{2} \left( \beta \right)}}{\partial \beta } > 0\), which indicates that \(f_{2} \left( \beta \right)\) increases with \(\beta \in \left( {0,\delta m} \right)\). When \(\frac{3 - \sqrt 5 }{2} < \delta < 1\), if \(0 < \beta < \frac{{2\delta m\left( {1 + \delta - \sqrt \delta } \right)}}{{1 + \delta^{2} + \delta }}\), then \(\frac{{\partial f_{2} \left( \beta \right)}}{\partial \beta } > 0\), which indicates that \(f_{2} \left( \beta \right)\) increases with \(\beta\); if \(\frac{{2\delta m\left( {1 + \delta - \sqrt \delta } \right)}}{{1 + \delta^{2} + \delta }} < \beta < \delta m\), then \(\frac{{\partial f_{2} \left( \beta \right)}}{\partial \beta } < 0\), which indicates that \(f_{2} \left( \beta \right)\) decreases with \(\beta\). Consequently, when \(0 < \delta < \frac{3 - \sqrt 5 }{2}\) and \(0 < \beta < \delta m\), since \(f_{2} \left( \beta \right) > f_{2} \left( \beta \right)|_{\beta = 0} = 0\), we obtain that \(\frac{{\partial CS_{H}^{*} }}{\partial \beta } > \frac{{\partial CS_{P}^{*} }}{\partial \beta }\). When \(\frac{3 - \sqrt 5 }{2} < \delta < 1\) and \(0 < \beta < \frac{{2\delta m\left( {1 + \delta - \sqrt \delta } \right)}}{{1 + \delta^{2} + \delta }}\), since \(f_{2} \left( \beta \right) > f_{2} \left( \beta \right)|_{\beta = 0} = 0\), we obtain that \(\frac{{\partial CS_{H}^{*} }}{\partial \beta } > \frac{{\partial CS_{P}^{*} }}{\partial \beta }\). When \(\frac{3 - \sqrt 5 }{2} < \delta < 1\) and \(\frac{{2\delta m\left( {1 + \delta - \sqrt \delta } \right)}}{{1 + \delta^{2} + \delta }} < \beta < \delta m\), since \(f_{2} \left( \beta \right) > f_{2} \left( \beta \right)|_{\beta = \delta m} = \delta^{3} m^{3} \left( {1 - \delta } \right)\left( {\delta^{2} - 5\delta + 7} \right) > 0\), we obtain that \(\frac{{\partial CS_{H}^{*} }}{\partial \beta } > \frac{{\partial CS_{P}^{*} }}{\partial \beta }\). Then, \(\frac{{\partial CS_{H}^{*} }}{\partial \beta } > \frac{{\partial CS_{P}^{*} }}{\partial \beta }\) for \(\beta \in \left( {0,\delta m} \right)\).

Thus, we can conclude that \(\frac{{\partial CS_{F}^{*} }}{\partial \beta } = \frac{{\partial CS_{H}^{*} }}{\partial \beta } > \frac{{\partial CS_{P}^{*} }}{\partial \beta } > 0\). □

Proof of Corollary 2

  1. 1.

    \(\frac{{\partial p_{P}^{*} }}{\partial m} = \frac{{\beta^{2} - 2\beta m + 2m^{2} }}{{\left( {2m - \beta } \right)^{2} }} > \frac{{m^{2} \left( {1 + \left( {1 - \delta } \right)^{2} } \right)}}{{\left( {2m - \beta } \right)^{2} }} > 0\),

    \(\frac{{\partial p_{H}^{*} }}{\partial m} - \frac{{\partial p_{P}^{*} }}{\partial m} = \frac{{\beta^{2} \left( {1 - \delta } \right)\left( {4\delta m^{2} - \beta^{2} } \right)}}{{2\left( {2\delta m - \beta } \right)^{2} \left( {2m - \beta } \right)^{2} }} > \frac{{\delta m^{2} \beta^{2} \left( {1 - \delta } \right)\left( {4 - \delta } \right)}}{{2\left( {2\delta m - \beta } \right)^{2} \left( {2m - \beta } \right)^{2} }} > 0\), Thus, \(\frac{{\partial p_{H}^{*} }}{\partial m} > \frac{{\partial p_{P}^{*} }}{\partial m} > 0\).

  2. 2.

    \(\frac{{\partial r_{F}^{*} }}{\partial m} = \frac{{\partial r_{H}^{*} }}{\partial m} = \frac{{\delta \left( {2\delta^{2} m^{2} - 2\beta \delta m + \beta^{2} } \right)}}{{\left( {2\delta m - \beta } \right)^{2} }} > \frac{{\delta^{3} m^{2} }}{{\left( {2\delta m - \beta } \right)^{2} }} > 0\). Thus, \(\frac{{\partial r_{F}^{*} }}{\partial m} = \frac{{\partial r_{H}^{*} }}{\partial m} > 0\).

  3. 3.

    \(\frac{{\partial q_{H}^{*} }}{\partial m} = \frac{{\partial q_{F}^{*} }}{\partial m} = - \frac{\beta \delta }{{\left( {2\delta m - \beta } \right)^{2} }} < 0\), \(\frac{{\partial q_{P}^{*} }}{\partial m} = - \frac{\beta }{{\left( {2m - \beta } \right)^{2} }} < 0\), \(\frac{{\partial q_{F}^{*} }}{\partial m} - \frac{{\partial q_{P}^{*} }}{\partial m} = - \frac{{\beta \left( {1 - \delta } \right)\left( {4\delta m^{2} - \beta^{2} } \right)}}{{\left( {2\delta m - \beta } \right)^{2} \left( {2m - \beta } \right)^{2} }} < 0\), Thus, \(\frac{{\partial q_{H}^{*} }}{\partial m} = \frac{{\partial q_{F}^{*} }}{\partial m} < \frac{{\partial q_{P}^{*} }}{\partial m} < 0\).

  4. 4.

    \(\frac{{\partial \pi_{P}^{*} }}{\partial m} - \frac{{\partial \pi_{H}^{*} }}{\partial m} = \frac{{\beta^{2} \left( {1 - \delta } \right)\left( {4\delta m^{2} - \beta^{2} } \right)}}{{4\left( {2\delta m - \beta } \right)^{2} \left( {2m - \beta } \right)^{2} }} > \frac{{\delta m^{2} \beta^{2} \left( {1 - \delta } \right)\left( {4 - \delta } \right)}}{{4\left( {2\delta m - \beta } \right)^{2} \left( {2m - \beta } \right)^{2} }} > 0\), \(\frac{{\partial \pi_{H}^{*} }}{\partial m} - \frac{{\partial \pi_{F}^{*} }}{\partial m} = \frac{1 - \delta }{4} > 0\), \(\frac{{\partial \pi_{F}^{*} }}{\partial m} = \frac{{\delta^{2} m\left( {\delta m - \beta } \right)}}{{\left( {2\delta m - \beta } \right)^{2} }} > 0\). Thus, \(\frac{{\partial \pi_{P}^{*} }}{\partial m} > \frac{{\partial \pi_{H}^{*} }}{\partial m} > \frac{{\partial \pi_{F}^{*} }}{\partial m} > 0\).

  5. 5.

    \(\frac{{\partial CS_{F}^{*} }}{\partial m} = \frac{{\delta^{3} m^{2} \left( {2\delta m - 3\beta } \right)}}{{2\left( {2\delta m - \beta } \right)^{3} }}\), Thus, if \(\beta < \frac{2\delta m}{3}\), then \(\frac{{\partial CS_{F}^{*} }}{\partial m} > 0\); if \(\frac{2\delta m}{3} < \beta < \delta m\), then \(\frac{{\partial CS_{F}^{*} }}{\partial m} < 0\).

\(\frac{{\partial CS_{P}^{*} }}{\partial m} = \frac{{m^{2} \left( {2m - 3\beta } \right)}}{{2\left( {2m - \beta } \right)^{3} }}\). Thus, when \(0 < \delta < \frac{2}{3}\), \(\frac{{\partial CS_{P}^{*} }}{\partial m} > 0\). When \(\frac{2}{3} < \delta < 1\), if \(0 < \beta < \frac{2m}{3}\), then \(\frac{{\partial CS_{P}^{*} }}{\partial m} > 0\); if \(\frac{2m}{3} < \beta < \delta m\), then \(\frac{{\partial CS_{P}^{*} }}{\partial m} < 0\).

\(\frac{{\partial CS_{H}^{*} }}{\partial m} = \frac{{\left( {\delta - 1} \right)\beta^{3} - 6\delta m\left( {\delta - 1} \right)\beta^{2} - 12\delta^{2} m^{2} \beta + 8\delta^{3} m^{3} }}{{8\left( {2\delta m - \beta } \right)^{3} }}\),

\(f_{3} \left( \beta \right) = \left( {\delta - 1} \right)\beta^{3} - 6\delta m\left( {\delta - 1} \right)\beta^{2} - 12\delta^{2} m^{2} \beta + 8\delta^{3} m^{3}\).

Since \(\frac{{\partial f_{3} \left( \beta \right)}}{\partial \beta } = 3\left( {\delta - 1} \right)\beta^{2} - 12\delta m\left( {\delta - 1} \right)\beta - 12\delta^{2} m^{2} < 0\) if \(\beta < \delta m\), \(f_{3} \left( \beta \right)\) decreases with \(\beta \in \left( {0,\delta m} \right)\). \(f_{3} \left( \beta \right)|_{\beta = \delta m} = \delta^{3} m^{3} \left( {1 - 5\delta } \right)\): if \(0 < \delta < \frac{1}{5}\), \(f_{3} \left( \beta \right)|_{\beta = \delta m} > 0\); if \(\frac{1}{5} < \delta < 1\), \(f_{3} \left( \beta \right)|_{\beta = \delta m} < 0\). \(\frac{{2\delta m\left( {\left( {1 - \delta } \right)\left( {\delta_{1} + \delta } \right) - \delta_{1}^{2} } \right)}}{{\delta_{1} \left( {1 - \delta } \right)}} \in \left( {0,\delta m} \right)\) is the root of \(f_{3} \left( \beta \right) = 0\), where \(\delta_{1} = \left( {\delta \left( {1 - \delta } \right)^{2} \left( {1 + \delta_{11} } \right)} \right)^{\frac{1}{3}}\) and \(\delta_{11} = \sqrt {\frac{1}{1 - \delta }}\).

Thus, when \(0 < \delta < \frac{1}{5}\), \(\frac{{\partial CS_{H}^{*} }}{\partial m} > 0\). When \(\frac{1}{5} < \delta < 1\), if \(0 < \beta < \frac{{2\delta m\left( {\left( {1 - \delta } \right)\left( {\delta_{1} + \delta } \right) - \delta_{1}^{2} } \right)}}{{\delta_{1} \left( {1 - \delta } \right)}}\), then \(\frac{{\partial CS_{H}^{*} }}{\partial m} > 0\); if \(\frac{{2\delta m\left( {\left( {1 - \delta } \right)\left( {\delta_{1} + \delta } \right) - \delta_{1}^{2} } \right)}}{{\delta_{1} \left( {1 - \delta } \right)}} < \beta < \delta m\), then \(\frac{{\partial CS_{H}^{*} }}{\partial m} < 0\), where \(\delta_{1} = \left( {\delta \left( {1 - \delta } \right)^{2} \left( {1 + \delta_{11} } \right)} \right)^{\frac{1}{3}}\) and \(\delta_{11} = \sqrt {\frac{1}{1 - \delta }}\). □

Proof of Corollary 3

  1. 1.

    \(\frac{{\partial p_{H}^{*} }}{\partial \delta } = \frac{{m\beta^{2} }}{{2\left( {2\delta m - \beta } \right)^{2} }} > \frac{{\partial p_{P}^{*} }}{\partial \delta } = 0\). Thus, \(\frac{{\partial p_{H}^{*} }}{\partial \delta } > \frac{{\partial p_{P}^{*} }}{\partial \delta } = 0\).

  2. 2.

    \(\frac{{\partial r_{F}^{*} }}{\partial \delta } = \frac{{\partial r_{H}^{*} }}{\partial \delta } = \frac{{m\left( {2\delta^{2} m^{2} - 2\beta \delta m + \beta^{2} } \right)}}{{\left( {2\delta m - \beta } \right)^{2} }} > \frac{{\delta^{2} m^{3} }}{{\left( {2\delta m - \beta } \right)^{2} }} > 0\). Thus, \(\frac{{\partial r_{F}^{*} }}{\partial \delta } = \frac{{\partial r_{H}^{*} }}{\partial \delta } > 0\).

  3. 3.

    \(\frac{{\partial q_{H}^{*} }}{\partial \delta } = \frac{{\partial q_{F}^{*} }}{\partial \delta } = - \frac{\beta m}{{\left( {2\delta m - \beta } \right)^{2} }} < 0\), \(\frac{{\partial q_{P}^{*} }}{\partial \delta } = 0\). Thus, \(\frac{{\partial q_{H}^{*} }}{\partial \delta } = \frac{{\partial q_{F}^{*} }}{\partial \delta } < \frac{{\partial q_{P}^{*} }}{\partial \delta } = 0\).

  4. 4.

    \(\frac{{\partial \pi_{F}^{*} }}{\partial \delta } = \frac{{\delta m^{2} \left( {\delta m - \beta } \right)}}{{\left( {2\delta m - \beta } \right)^{2} }} > 0\), \(\frac{{\partial \pi_{P}^{*} }}{\partial \delta } = 0\), \(\frac{{\partial \pi_{H}^{*} }}{\partial \delta } = - \frac{{m\beta^{2} }}{{4\left( {2\delta m - \beta } \right)^{2} }} < 0\). Thus, \(\frac{{\partial \pi_{F}^{*} }}{\partial \delta } > \frac{{\partial \pi_{P}^{*} }}{\partial \delta } = 0\), \(\frac{{\partial \pi_{H}^{*} }}{\partial \delta } < 0\).

  5. 5.

    \(\frac{{\partial CS_{P}^{*} }}{\partial \delta } = 0\). \(\frac{{\partial CS_{F}^{*} }}{\partial \delta } = \frac{{\delta^{2} m^{3} \left( {2\delta m - 3\beta } \right)}}{{2\left( {2\delta m - \beta } \right)^{3} }}\), Thus, if \(\beta < \frac{2\delta m}{3}\), then \(\frac{{\partial CS_{F}^{*} }}{\partial \delta } > 0\); if \(\frac{2\delta m}{3} < \beta < \delta m\), then \(\frac{{\partial CS_{F}^{*} }}{\partial \delta } < 0\). \(\frac{{\partial CS_{H}^{*} }}{\partial \delta } = - \frac{{m\beta^{2} \left( {6\delta m - \beta } \right)}}{{8\left( {2\delta m - \beta } \right)^{3} }} < 0\) since \(\beta < \delta m\). Thus, \(\frac{{\partial CS_{H}^{*} }}{\partial \delta } < 0\). □

1.1 Proof of endogenous content quality

All decisions are decided by the media platform, and thus whether the game sequence is simultaneous or sequential makes no mathematical difference. For convenience, we use the sequential game. Thus, based on the equilibrium profit with exogenous content quality, we can further derive the optimal content quality via the first-order derivative condition. The optimal content quality among different strategies are as follows:\({m}_{P}^{E*}=\beta\),\({m}_{F}^{E*}=\frac{\beta }{\delta }\),\({m}_{H}^{E*}=\frac{\beta \left(1+\sqrt{\delta }\right)}{2\delta }\). substituting the optimal content quality into the equilibrium solutions with exogenous content quality, the equilibrium profit and consumer surplus with endogenous content quality are as follows. Strategy P: \(\pi_{P}^{E*} = \frac{\beta }{2}\), \(CS_{P}^{E*} = \frac{\beta }{2}\). Startegy F: \(\pi_{F}^{E*} = \frac{\beta }{2}\), \(CS_{F}^{E*} = \frac{\beta }{2}\). Strategy H: \(\pi_{H}^{E*} = \frac{{\beta \left( {1 + \sqrt \delta } \right)^{2} }}{8\delta }\), \(CS_{H}^{E*} = \frac{{\beta \left( {1 + \sqrt \delta } \right)^{2} }}{8\delta }\).

\(m_{F}^{E*} - m_{H}^{E*} = \frac{{\beta \left( {1 - \sqrt \delta } \right)}}{2\delta } > 0\), \(m_{H}^{E*} - m_{P}^{E*} = \frac{{\beta \left( {1 + \sqrt \delta - 2\delta } \right)}}{2\delta } > 0\) since \(0 < \delta < 1\). Thus, \(m_{F}^{E*} > m_{H}^{E*} > m_{P}^{E*}\), which proves Proposition 5.

\(\pi_{H}^{E*} - \pi_{P}^{E*} = \frac{{\beta \left( {1 + 2\sqrt \delta - 3\delta } \right)}}{8\delta } > 0\) since \(0 < \delta < 1\). Thus, we can conclude that \(\pi_{H}^{E*} > \pi_{P}^{E*} = \pi_{F}^{E*}\), \(CS_{H}^{E*} > CS_{P}^{E*} > CS_{F}^{E*}\), which proves Proposition 6. □

1.2 Proof of platforms competition

With the goal of maximizing profit, Media platform A and media platform B compete with a Nash game and simultaneously make their decisions. Hence, by solving equations \(\frac{\partial {\pi }^{A}}{\partial {r}^{A}}=\frac{\partial {\pi }^{A}}{\partial {p}^{A}}=\frac{\partial {\pi }^{B}}{\partial {p}^{B}}=0\), we can obtain the equilibrium decisions for two competing media platforms: \({r}^{A*}=\frac{m\left(m-\beta \right)}{2m-\beta }\), \({p}^{A*}=\frac{m\left(m-\beta \right)}{2m-\beta }\), \({p}^{B*}=\delta m\). Then, the equilibrium profits and consumer surplus are as follows: \({\pi }^{A*}=\frac{{m}^{2}}{2\left(2m-\beta \right)}\), \({\pi }^{B*}=0\), \(C{S}^{*}=\frac{{m}^{3}}{2{\left(2m-\beta \right)}^{2}}\). As a result, when the platform B using strategy P competes with the platform A using strategy H, the platform A uses the joint decisions of price and ads to attract all customers, leaving the platform B with zero order (\({q}_{P}^{B*}=0\)) and zero profit (\({\pi }^{B*}=0\)). Proposition 7 (a) can be easily obtained. The proof of Proposition 7 (b) is similar to that of Corollary 3 (5). □

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Jiang, L. Content provision strategy selection for a media platform in the presence of traffic revenue. Electron Commer Res (2024). https://doi.org/10.1007/s10660-024-09823-8

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