Abstract
The pricing of tickets is a crucial decision when managing entertainment events like concerts, operas, ballets, and theater performances. In this study, we examine the pricing decisions made by two major decision-makers before an entertainment event: the venue and the online retailer. In a game-theoretic approach with uncertain information, the interactions between decision-makers are studied in a single and dual-channel structure. A confidence-level-based uncertainty theory is utilized in order to formulate the amount of uncertainty, and the optimal solutions are analytically computed to maximize the total profit for both players. There are three distinct cases studied, including two single-channel market cases (one in which only the venue sells the tickets, and another in which only the online platform sells the tickets) as well as a dual-channel market case (in which both players sell tickets). Stackelberg and Nash games are used to illustrate the interactions between players in each game. The results indicate the optimal pricing, service level improvement, and total profit for both the venue and the online retailer. Additionally, it is shown that the amount of risk (confidence level) the decision-makers are willing to accept strongly affects the optimal solution. In some cases, the roles of the market participants do not have a significant impact on the optimal solution. The findings of this paper can help practitioners in the entertainment industry make better ticket pricing decisions in an uncertain and competitive environment.
Similar content being viewed by others
References
Alamdar, S. F., Rabbani, M., & Heydari, J. (2018). Pricing, collection, and effort decisions with coordination contracts in a fuzzy, three-level closed-loop supply chain. Expert Systems with Applications, 104, 261–276. https://doi.org/10.1016/j.eswa.2018.03.029
Ando, T. (2018). Merchant selection and pricing strategy for a platform firm in the online group buying market. Annals of Operations Research, 263(1), 209–230. https://doi.org/10.1007/s10479-015-2036-9
Arslan, H. A., Easley, R. F., Wang, R., & Yılmaz, Ö. (2021). Data-driven sports ticket pricing for multiple sales channels with heterogeneous customers. Manufacturing & Service Operations Management, 24(2), 1241–1260. https://doi.org/10.1287/msom.2021.1005
Aswad, J. (2019). Live nation confirms placing tickets directly on secondary market at artists’ request. Variety. Retrieved from https://variety.com/2019/biz/news/live-nation-placed-thousands-of-tickets-on-secondary-market-metallica-1203273337/
Batarfi, R., Jaber, M. Y., & Zanoni, S. (2016). Dual-channel supply chain: A strategy to maximize profit. Applied Mathematical Modelling, 40(21–22), 9454–9473.
Chen, T.-H. (2015). Effects of the pricing and cooperative advertising policies in a two-echelon dual-channel supply chain. Computers & Industrial Engineering, 87, 250–259.
Chen, X., & Ralescu, D. A. (2013). Liu process and uncertain calculus. Journal of Uncertainty Analysis and Applications, 1(1), 3.
Courty, P. (2000). An economic guide to ticket pricing in the entertainment industry. Recherches Économiques De Louvain/louvain Economic Review, 66(2), 167–192.
Dye, C.-Y., & Yang, C.-T. (2016). Optimal dynamic pricing and preservation technology investment for deteriorating products with reference price effects. Omega, 62, 52–67.
Ghalehkhondabi, I., Ahmadi, E., & Maihami, R. (2020a). An overview of big data analytics application in supply chain management published in 2010–2019. Production, 30.
Ghalehkhondabi, I., Maihami, R., & Ahmadi, E. (2020b). Optimal pricing and environmental improvement for a hazardous waste disposal supply chain with emission penalties. Utilities Policy, 62, 101001.
He, Y., Huang, H., & Li, D. (2018). Inventory and pricing decisions for a dual-channel supply chain with deteriorating products. Operational Research, 20, 1–43.
Hua, G., Wang, S., & Cheng, T. E. (2010). Price and lead time decisions in dual-channel supply chains. European Journal of Operational Research, 205(1), 113–126.
Huang, Z., Huang, W., Chen, W.-C., & Lanham, M. A. (2021, 2021//). Dynamic Pricing for Sports Tickets. Paper presented at the Advances in Data Science and Information Engineering, Cham.
Jafari, H., Hejazi, S. R., & Rasti-Barzoki, M. (2016). Pricing decisions in dual-channel supply chain including monopolistic manufacturer and duopolistic retailers: A game-theoretic approach. Journal of Industry, Competition and Trade, 16(3), 323–343.
Jørgensen, S., & Zaccour, G. (2019). Optimal pricing and advertising policies for a one-time entertainment event. Journal of Economic Dynamics and Control, 100, 395–416.
Kemper, C., & Breuer, C. (2016). Dynamic ticket pricing and the impact of time–an analysis of price paths of the English soccer club Derby County. European Sport Management Quarterly, 16(2), 233–253.
Li, X., Gao, L., & Li, W. (2012). Application of game theory based hybrid algorithm for multi-objective integrated process planning and scheduling. Expert Systems with Applications, 39(1), 288–297. https://doi.org/10.1016/j.eswa.2011.07.019
Liu, B. (2007). Uncertainty theory (pp. 205–234). Springer.
Liu, B. (2013). Toward uncertain finance theory. Journal of Uncertainty Analysis and Applications, 1(1), 1.
Liu, M., Cao, E., & Salifou, C. K. (2016). Pricing strategies of a dual-channel supply chain with risk aversion. Transportation Research Part E: Logistics and Transportation Review, 90, 108–120.
Liu, Z., Chen, J., Diallo, C., & Venkatadri, U. (2021). Pricing and production decisions in a dual-channel closed-loop supply chain with (re)manufacturing. International Journal of Production Economics, 232, 107935. https://doi.org/10.1016/j.ijpe.2020.107935
Ma, N., Gao, R., Wang, X., & Li, P. (2019). Green supply chain analysis under cost sharing contract with uncertain information based on confidence level. Soft Computing, 24, 1–19.
Maihami, R., & Ghalehkhondabi, I. (2022). Pricing problem in a medical waste supply chain under environmental investment: A game theory approach. Journal of Industrial and Production Engineering, 39(8), 1–17.
Maihami, R., Ghalehkhondabi, I., & Ahmadi, E. (2021). Pricing and inventory planning for non-instantaneous deteriorating products with greening investment: A case study in beef industry. Journal of Cleaner Production, 295, 126368.
Maihami, R., Karimi, B., & Ghomi, S. M. T. F. (2017). Pricing and inventory control in a supply chain of deteriorating items: A non-cooperative Strategy with Probabilistic Parameters. International Journal of Applied and Computational Mathematics, 3(3), 2477–2499.
Mao, Z., Liu, T., & Li, X. (2021). Pricing mechanism of variable opaque products for dual-channel online travel agencies. Annals of Operations Research. https://doi.org/10.1007/s10479-021-04163-4
Meng, Q., Li, M., Liu, W., Li, Z., & Zhang, J. (2021). Pricing policies of dual-channel green supply chain: Considering government subsidies and consumers’ dual preferences. Sustainable Production and Consumption, 26, 1021–1030. https://doi.org/10.1016/j.spc.2021.01.012
Modak, N. M., & Kelle, P. (2019). Managing a dual-channel supply chain under price and delivery-time dependent stochastic demand. European Journal of Operational Research, 272(1), 147–161.
Mu, R., Lan, Y., & Tang, W. (2013). An uncertain contract model for rural migrant worker’s employment problems. Fuzzy Optimization and Decision Making, 12(1), 29–39.
Sadeghi, A., & Zandieh, M. (2011). A game theory-based model for product portfolio management in a competitive market. Expert Systems with Applications, 38(7), 7919–7923. https://doi.org/10.1016/j.eswa.2010.11.054
Şahin, M., & Erol, R. (2017). A dynamic ticket pricing approach for soccer games. Axioms, 6(4), 31.
Shapiro, S. L., Drayer, J., & Dwyer, B. (2016). Examining consumer perceptions of demand-based ticket pricing in sport. Sport Marketing Quarterly, 25(1), 34–46.
Sikhar, B., Gaurav, A., Zhang, W. J., Biswajit, M., & Tiwari, M. K. (2012). A decision framework for the analysis of green supply chain contracts: An evolutionary game approach. Expert Systems with Applications, 39(3), 2965–2976. https://doi.org/10.1016/j.eswa.2011.08.158
Talluri, K. T., & Van Ryzin, G. J. (2006). The theory and practice of revenue management (Vol. 68): Springer Science & Business Media.
Tiwari, S., Cárdenas-Barrón, L. E., Goh, M., & Shaikh, A. A. (2018). Joint pricing and inventory model for deteriorating items with expiration dates and partial backlogging under two-level partial trade credits in supply chain. International Journal of Production Economics, 200, 16–36.
Watanabe, N. M., & Soebbing, B. P. (2015). Ticket price behavior and attendance demand in Chinese professional soccer The sports business in the Pacific Rim (pp. 139–157): Springer.
Widodo, E., & Januardi. (2021). Noncooperative game theory in response surface methodology decision of pricing strategy in dual-channel supply chain. Journal of Industrial and Production Engineering, 38(2), 89–97. https://doi.org/10.1080/21681015.2020.1848932
Wu, W., Zhang, L., & Qiu, F. (2017). Determinants of tourism ticket pricing for ancient villages and towns: Case studies from Jiangsu, Zhejiang, Shanghai and Anhui provinces. Tourism Management, 58, 270–275.
Wu, X., Zhao, R., & Tang, W. (2014). Uncertain agency models with multi-dimensional incomplete information based on confidence level. Fuzzy Optimization and Decision Making, 13(2), 231–258.
Xiao, T., & Shi, J. J. (2016). Pricing and supply priority in a dual-channel supply chain. European Journal of Operational Research, 254(3), 813–823.
Xu, H., Liu, Z. Z., & Zhang, S. H. (2012). A strategic analysis of dual-channel supply chain design with price and delivery lead time considerations. International Journal of Production Economics, 139(2), 654–663.
Yao, K. (2013). Extreme values and integral of solution of uncertain differential equation. Journal of Uncertainty Analysis and Applications, 1(1), 2.
Zhang, C., Liu, Y., & Han, G. (2021). Two-stage pricing strategies of a dual-channel supply chain considering public green preference. Computers & Industrial Engineering, 151, 106988. https://doi.org/10.1016/j.cie.2020.106988
Zohoori, S., Jafari Kang, M., Hamidi, M., & Maihami, R. (2022). An AIS-Based approach for measuring waterway resiliency: A case study of Houston ship channel. Maritime Policy & Management, 1–21. https://doi.org/10.1080/03088839.2022.2047813
Acknowledgement
This research was supported by National Natural Science Foundation of China Project (72072021, 71772032).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix
Appendix
Proof of Lemma 1
The optimal selling price and service level are as follows:
In case 1, the venue, as the only ticket seller, wants to maximize its total profit, Eq. (5). The venue’s total profit depends upon the margin sale price \({m}_{V}\) and the level of service time \(L\). Thus, solving the following simultaneous equations systems will determine the optimal solution:
Proof of Proposition 1
We will prove the second-order conditions with respect to \({m}_{V}\) and \(L\). Thus, we need to compute the Hessian matrix as follows:
A function will be concave if the first principal minor of H is negative; this means that \({H}^{1}< 0\). If the second principal minor of H is positive, \({H}^{2}>0\). Hence, we have:
To prove that \({H}^{2}>0\), \({\Phi }_{2V}^{-1}\left(\alpha \right)\) should be greater than \(\frac{{{(\Phi }_{3}^{-1}(1-\alpha ))}^{2}}{4k}\). This completes the proof.
Proof of Lemma 2
Equation (15) only depends on \({P}_{\mathrm{OR}}\). To obtain the optimal value, we take the first derivative of \({\Pi }_{J}\) with respect to \({P}_{\mathrm{OR}}\) and solve the resultant equation:
Proof of Proposition 2
We examine the second-order condition for \({\Pi }_{J}\) with respect to \({P}_{\mathrm{OR}}\): \(\frac{{d}^{2}{\Pi }_{J}}{d{{P}_{\mathrm{OR}}}^{2}}=-2{\Phi }_{2OR}^{-1}(\alpha )<0\)
This completes the proof.
Proof of Proposition 3
The second-order condition completes the proof as follows:
Proof of Proposition 4
Like proposition 3, the second-order condition shows the concavity as follows:
Proof of Lemma 3
\({\Pi }_{J}\) depends on \({P}_{V},L,\) and \({P}_{\mathrm{OR}}\). Thus, taking the first derivative with respect to these three variables and solving the resultant simultaneous equations systems will complete the proof:
Proof of Proposition 5
. In this case, the joint total profit depends upon three variables: \({P}_{V},L\), and \({P}_{\mathrm{OR}}\). Thus, the corresponding Hessian matrix is as follows:
The first principal minor of \(H\) is \({H}^{1}=-2{\Phi }_{2V}^{-1}\left(\alpha \right)<0\). The second principal minor of \(H\) is \({H}^{2}=det\left(\begin{array}{cc}-2{\Phi }_{2V}^{-1}\left(\alpha \right)& {\Phi }_{3}^{-1}(1-\alpha )\\ {\Phi }_{3}^{-1}(1-\alpha )& -2k\end{array}\right)\), and the third principal minor of \(H\) is \({H}^{3}=\mathrm{det}(H)=2({\Phi }_{2OR}^{-1}(\alpha )(-4k{\Phi }_{2V}^{-1}\left(\alpha \right)+{({\Phi }_{3}^{-1}(1-\alpha ))}^{2})+{\Phi }_{2V}^{-1}\left(\alpha \right){({\Phi }_{3}^{-1}(\alpha ))}^{2}+({\Phi }_{3OR}^{-1}\left(1-\alpha \right)+{\Phi }_{3V}^{-1}(1-\alpha ))(-{\Phi }_{3}^{-1}(1-\alpha ){\Phi }_{3}^{-1}(\alpha )+k({\Phi }_{3OR}^{-1}\left(1-\alpha \right)+{\Phi }_{3V}^{-1}(1-\alpha ))))\). If \({\Phi }_{2V}^{-1}\left(\alpha \right)\)>\(\frac{{{(\Phi }_{3}^{-1}(1-\alpha ))}^{2}}{4k}\) holds true, then \({H}^{2}>0\) and \({H}^{3}<0\). Thus, alternating algebraic signs has been proven for the principal minors of \(H\). This shows that \(H\) is negative definite and the joint total profit is concave.
Proof of Lemma 4
Proof of Proposition 6
The second-order condition shown below completes the proof:
Proof of Proposition 7
We compute the Hessian matrix as follows:
Similar to proposition 5, we have \({H}^{1}=-2{\Phi }_{2V}^{-1}\left(\alpha \right)<0\), \({H}^{2}=det\left(\begin{array}{cc}-2{\Phi }_{2V}^{-1}\left(\alpha \right)& {\Phi }_{3}^{-1}(1-\alpha )\\ {\Phi }_{3}^{-1}(1-\alpha )& -2k\end{array}\right)\), and \({H}^{3}=\mathrm{det}\left(H\right)=2{\Phi }_{2OR}^{-1}(\alpha )(-4k{\Phi }_{2V}^{-1}\left(\alpha \right)+{({\Phi }_{3}^{-1}(1-\alpha ))}^{2})+{\Phi }_{3}^{-1}(\alpha )(2{\Phi }_{2V}^{-1}\left(\alpha \right){\Phi }_{3}^{-1}(\alpha )-{\Phi }_{3}^{-1}(1-\alpha )({\Phi }_{3OR}^{-1}\left(1-\alpha \right)+{\Phi }_{3V}^{-1}(1-\alpha )))\). If \({\Phi }_{2V}^{-1}\left(\alpha \right)\)>\(\frac{{{(\Phi }_{3}^{-1}(1-\alpha ))}^{2}}{4k}\) holds true, then \({H}^{2}>0\) and \({H}^{3}<0\). This completes the proof.
Proof of Lemma 5
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Maihami, R., Kannan, D., Fattahi, M. et al. Ticket pricing for entertainment events under a dual-channel environment: a game-theoretical approach using uncertainty theory. Ann Oper Res 331, 503–542 (2023). https://doi.org/10.1007/s10479-023-05192-x
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10479-023-05192-x