Analysis of Information Service Pricing Scheme Model Based on Customer Self-Selection
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Keywords:
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Utility Function, Perfect Substitute, Marginal Cost, Monitoring Cost, Pricing Schemes, Heterogeneous Customers
Abstract
This study attempts to analyze pricing schemes with monitoring cost and marginal cost for perfect substitute and quasi-linear utility functions for achieving Internet service Provider (ISP) in gaining benefit. Two types of customers analyzed, namely customers who are heterogeneous (both high-end and low-end) as well as heterogeneous customers (high-demand and low-demand) based on Flat-fee, usage-based, and two-part tariff are the three types of pricing methods employed. The results show that usage-based pricing schemes gain maximum profit optimal for heterogeneous customers (high-end and low-end), while for heterogeneous customers (high-demand and low-demand) type of pricing scheme two-part tariff obtains maximum profit optimal. The results of this study are more directed to the lemma of the perfect substitute utility function which compares the lemma of heterogeneous customers. This model was solved using LINGO 13.0 software and ISP to get maximum profit.
References
Barrios, P. S. C., & Cruz, D. E. (2017). A mixed integer programming optimization of bundling and pricing strategies for multiple product components with inventory allocation considerations. In 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM).
Caiati, V., Rasouli, S., & Timmermans, H. (2020). Bundling, pricing schemes and extra features preferences for mobility as a service: Sequential portfolio choice experiment. Transportation Research Part A: Policy and Practice, 131, 123–148. https://doi.org/10.1016/j.tra.2019.09.029
Cunningham, K., & Schrage, L. (2004). The LINGO Algebraic Modeling Language. In J. Kallrath (Ed.), Applied Optimization (pp. 159–171). Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-0215-5_9
Giraldo, M. D. O. (2017). Solving a Classical Optimization Problem Using GAMS Optimizer Package: Economic Dispatch ProblemImplementation. Ingeniería y Ciencia, 13(26), 39–63. https://doi.org/10.17230/ingciencia.13.26.2
Gizelis, C. A., & Vergados, D. D. (2011). A survey of pricing schemes in wireless networks. IEEE Communications Surveys & Tutorials , 13(1), 126–145.
Gu, C., Zhuang, S., & Sun, Y. (2011). Pricing incentive mechanism based on multistages traffic classification methodology for QoS-enabled networks. Journal of Networks, 6(1), 163–171.
Guan, Y., Yang, W., Owen, H., & Blough, D. M. (2008). A pricing approach for bandwidth allocation in differentiated service networks. Computers & Operations Research, 35, 3769–3786.
Hitt, L. M., & Chen, P. Y. (2005). Bundling with customer self-selection: A simple approach to bundling low-marginal-cost goods. Management Science, 51(10), 1481–1493. https://doi.org/10.1287/mnsc.1050.0403
Indrawati, Irmeilyana, Puspita, F. M., & Lestari, M. P. (2014). Cobb-Douglass utility function in optimizing the internet pricing scheme model. Telkomnika (Telecommunication Computing Electronics and Control), 12(1). https://doi.org/10.12928/TELKOMNIKA.v12i1.1800
Indrawati, Irmeilyana, Puspita, F. M., & Sanjaya, O. (2015). Internet pricing on bandwidth function diminished with increasing bandwidth utility function. Telkomnika (Telecommunication Computing Electronics and Control), 13(1). https://doi.org/10.12928/TELKOMNIKA.v13i1.117
Kopczewski, T., Sobolewski, M., & Miernik, I. (2018). Bundling or unbundling? Integrated simulation model of optimal pricing strategies. International Journal of Production Economics, 204(August), 328–345. https://doi.org/10.1016/j.ijpe.2018.08.017
Kuo, W.-H., & Liao, W. (2007). Utility-based Optimal Resource Allocation in Wireless Networks. IEEE Transactions on Wireless Communications , 6(10), 3600–3606.
L I Zu-Xin, Wan-Liang, W., & Xin-Min, C. (2008). Optimal bandwidth scheduling for resource-constrained networks. Acta Automatica Sinica, 20(10).
Li, M., Feng, H., Chen, F., & Kou, J. (2013). Numerical investigation on mixed bundling and pricing of information products. International Journal of Production Economics, 144(2), 560–571. https://doi.org/10.1016/j.ijpe.2013.04.015
Merayo, N., Pavon-Marino, P., Aguado, J. C., Durán, R. J., Burrull, F., & Bueno-Delgado, V. (2016). Fair Bandwidth Allocation Algorithm for PONs Based on Network Utility Maximization. Journal of Optical Communications and Networking, 9(1), 75–86.
Moriya, T., Ohnishi, H., Ogawa, T., & Ito, T. (2005). Method of improving bandwidth and connectivity with multiple links in nomadic network environment. In 6th Asia-Pacific Symposium on Information and Telecommunication Technologies. IEEE.
Puspita, F.M., Wulandari, A., Yuliza, E., Sitepu, R., & Yunita. (2020). Modification of Wireless Reverse Charging Scheme with Bundling Optimization Issues. 2020 3rd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2020. https://doi.org/10.1109/ISRITI51436.2020.9315348
Puspita, Fitri Maya, Wulandari, A., Yuliza, E., Sitepu, R., & Yunita. (2021). End-to-end delay qos attribute-based bundling strategy of wireless improved reverse charging network pricing model. Science and Technology Indonesia, 6(1), 30–38. https://doi.org/10.26554/sti.2021.6.1.30-38
Puspita, Fitri Maya, Yuliza, E., Herlina, W., Yunita, Y., & Rohania, R. (2020). Improved Multi Service-Reverse Charging Models for the Multi Link Internet wireless Using QOS Bit Error Rate QoS Attribute. Science and Technology Indonesia, 5(1), 6. https://doi.org/10.26554/sti.2020.5.1.6-13
Rabbani, M., Salehi, R., & Farshbaf-Geranmayeh, A. (2017). Integrating assortment selection, pricing and mixed-bundling problems for multiple retail categories under cross-selling. Uncertain Supply Chain Management, 5(4), 315–326.
Schrage, L. (2009). Optimization Modeling with LINGO (6th ed.). LINDO Systems, Inc.
Sitepu, R., Puspita, F. M., Pratiwi, A. N., & Novyasti, I. P. (2017). Utility function-based pricing strategies in maximizing the information service provider’s revenue with marginal and monitoring costs. International Journal of Electrical and Computer Engineering, 7(2). https://doi.org/10.11591/ijece.v7i2.pp877-887
Sitepu, Robinson, Puspita, F. M., & Apriliyani, S. (2017). Utility Function-Based Mixed Integer Nonlinear Programming (MINLP) Problem Model of Information Service Pricing Schemes. In IEEE-4th International Conference on Data and Software Engineering, Palembang, Indonesia.
Sitepu, Robinson, Puspita, F. M., Tanuji, H., & Novyasti, I. P. (2016). Cobb-Douglas Utility Function Of Information Service Pricing Scheme Based On Monitoring And Marginal Costs. 2nd International Conference on Education, Technology and Science, (ICETS) .
Varadarajan, R. (2020). Customer information resources advantage, marketing strategy and business performance: A market resources based view. Industrial Marketing Management, 89(March), 89–97. https://doi.org/10.1016/j.indmarman.2020.03.003
Wu, S., & Banker, R. D. (2010). Best Pricing Strategy for Information Services. Journal of the Association for Information Systems, 11(6), 339–366.
Yassine, N., AlSagheer, A., & Azzam, N. (2018). A bundling strategy for items with different quality based on functions involving the minimum of two random variables. International Journal of Engineering Business Management, 10(Icom 2017), 1–9.
Ye, L., Xie, H., Wu, W., & Lui, J. C. S. (2017). Mining Customer Valuations to Optimize Product Bundling Strategy. 2017 IEEE International Conference on Data Mining (ICDM).
Zhang, Z., Luo, X., Kwong, C. K., Tang, J., & Yu, Y. (2018). Impacts of service uncertainty in bundling strategies on heterogeneous consumers. Electronic Commerce Research and Applications, 28, 230–243.
Zhou, Y., Zhang, T., Mo, Y., & Huang, G. (2020). Willingness to pay for economy class seat selection: From a Chinese air consumer perspective. Research in Transportation Business and Management, 37(April), 100486. https://doi.org/10.1016/j.rtbm.2020.100486
Caiati, V., Rasouli, S., & Timmermans, H. (2020). Bundling, pricing schemes and extra features preferences for mobility as a service: Sequential portfolio choice experiment. Transportation Research Part A: Policy and Practice, 131, 123–148. https://doi.org/10.1016/j.tra.2019.09.029
Cunningham, K., & Schrage, L. (2004). The LINGO Algebraic Modeling Language. In J. Kallrath (Ed.), Applied Optimization (pp. 159–171). Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-0215-5_9
Giraldo, M. D. O. (2017). Solving a Classical Optimization Problem Using GAMS Optimizer Package: Economic Dispatch ProblemImplementation. Ingeniería y Ciencia, 13(26), 39–63. https://doi.org/10.17230/ingciencia.13.26.2
Gizelis, C. A., & Vergados, D. D. (2011). A survey of pricing schemes in wireless networks. IEEE Communications Surveys & Tutorials , 13(1), 126–145.
Gu, C., Zhuang, S., & Sun, Y. (2011). Pricing incentive mechanism based on multistages traffic classification methodology for QoS-enabled networks. Journal of Networks, 6(1), 163–171.
Guan, Y., Yang, W., Owen, H., & Blough, D. M. (2008). A pricing approach for bandwidth allocation in differentiated service networks. Computers & Operations Research, 35, 3769–3786.
Hitt, L. M., & Chen, P. Y. (2005). Bundling with customer self-selection: A simple approach to bundling low-marginal-cost goods. Management Science, 51(10), 1481–1493. https://doi.org/10.1287/mnsc.1050.0403
Indrawati, Irmeilyana, Puspita, F. M., & Lestari, M. P. (2014). Cobb-Douglass utility function in optimizing the internet pricing scheme model. Telkomnika (Telecommunication Computing Electronics and Control), 12(1). https://doi.org/10.12928/TELKOMNIKA.v12i1.1800
Indrawati, Irmeilyana, Puspita, F. M., & Sanjaya, O. (2015). Internet pricing on bandwidth function diminished with increasing bandwidth utility function. Telkomnika (Telecommunication Computing Electronics and Control), 13(1). https://doi.org/10.12928/TELKOMNIKA.v13i1.117
Kopczewski, T., Sobolewski, M., & Miernik, I. (2018). Bundling or unbundling? Integrated simulation model of optimal pricing strategies. International Journal of Production Economics, 204(August), 328–345. https://doi.org/10.1016/j.ijpe.2018.08.017
Kuo, W.-H., & Liao, W. (2007). Utility-based Optimal Resource Allocation in Wireless Networks. IEEE Transactions on Wireless Communications , 6(10), 3600–3606.
L I Zu-Xin, Wan-Liang, W., & Xin-Min, C. (2008). Optimal bandwidth scheduling for resource-constrained networks. Acta Automatica Sinica, 20(10).
Li, M., Feng, H., Chen, F., & Kou, J. (2013). Numerical investigation on mixed bundling and pricing of information products. International Journal of Production Economics, 144(2), 560–571. https://doi.org/10.1016/j.ijpe.2013.04.015
Merayo, N., Pavon-Marino, P., Aguado, J. C., Durán, R. J., Burrull, F., & Bueno-Delgado, V. (2016). Fair Bandwidth Allocation Algorithm for PONs Based on Network Utility Maximization. Journal of Optical Communications and Networking, 9(1), 75–86.
Moriya, T., Ohnishi, H., Ogawa, T., & Ito, T. (2005). Method of improving bandwidth and connectivity with multiple links in nomadic network environment. In 6th Asia-Pacific Symposium on Information and Telecommunication Technologies. IEEE.
Puspita, F.M., Wulandari, A., Yuliza, E., Sitepu, R., & Yunita. (2020). Modification of Wireless Reverse Charging Scheme with Bundling Optimization Issues. 2020 3rd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2020. https://doi.org/10.1109/ISRITI51436.2020.9315348
Puspita, Fitri Maya, Wulandari, A., Yuliza, E., Sitepu, R., & Yunita. (2021). End-to-end delay qos attribute-based bundling strategy of wireless improved reverse charging network pricing model. Science and Technology Indonesia, 6(1), 30–38. https://doi.org/10.26554/sti.2021.6.1.30-38
Puspita, Fitri Maya, Yuliza, E., Herlina, W., Yunita, Y., & Rohania, R. (2020). Improved Multi Service-Reverse Charging Models for the Multi Link Internet wireless Using QOS Bit Error Rate QoS Attribute. Science and Technology Indonesia, 5(1), 6. https://doi.org/10.26554/sti.2020.5.1.6-13
Rabbani, M., Salehi, R., & Farshbaf-Geranmayeh, A. (2017). Integrating assortment selection, pricing and mixed-bundling problems for multiple retail categories under cross-selling. Uncertain Supply Chain Management, 5(4), 315–326.
Schrage, L. (2009). Optimization Modeling with LINGO (6th ed.). LINDO Systems, Inc.
Sitepu, R., Puspita, F. M., Pratiwi, A. N., & Novyasti, I. P. (2017). Utility function-based pricing strategies in maximizing the information service provider’s revenue with marginal and monitoring costs. International Journal of Electrical and Computer Engineering, 7(2). https://doi.org/10.11591/ijece.v7i2.pp877-887
Sitepu, Robinson, Puspita, F. M., & Apriliyani, S. (2017). Utility Function-Based Mixed Integer Nonlinear Programming (MINLP) Problem Model of Information Service Pricing Schemes. In IEEE-4th International Conference on Data and Software Engineering, Palembang, Indonesia.
Sitepu, Robinson, Puspita, F. M., Tanuji, H., & Novyasti, I. P. (2016). Cobb-Douglas Utility Function Of Information Service Pricing Scheme Based On Monitoring And Marginal Costs. 2nd International Conference on Education, Technology and Science, (ICETS) .
Varadarajan, R. (2020). Customer information resources advantage, marketing strategy and business performance: A market resources based view. Industrial Marketing Management, 89(March), 89–97. https://doi.org/10.1016/j.indmarman.2020.03.003
Wu, S., & Banker, R. D. (2010). Best Pricing Strategy for Information Services. Journal of the Association for Information Systems, 11(6), 339–366.
Yassine, N., AlSagheer, A., & Azzam, N. (2018). A bundling strategy for items with different quality based on functions involving the minimum of two random variables. International Journal of Engineering Business Management, 10(Icom 2017), 1–9.
Ye, L., Xie, H., Wu, W., & Lui, J. C. S. (2017). Mining Customer Valuations to Optimize Product Bundling Strategy. 2017 IEEE International Conference on Data Mining (ICDM).
Zhang, Z., Luo, X., Kwong, C. K., Tang, J., & Yu, Y. (2018). Impacts of service uncertainty in bundling strategies on heterogeneous consumers. Electronic Commerce Research and Applications, 28, 230–243.
Zhou, Y., Zhang, T., Mo, Y., & Huang, G. (2020). Willingness to pay for economy class seat selection: From a Chinese air consumer perspective. Research in Transportation Business and Management, 37(April), 100486. https://doi.org/10.1016/j.rtbm.2020.100486
Authors
Indrawati, Puspita, F. M., Resmadona, Yuliza, E., Dwipurwani, O. ., & Octarina, S. . (2021). Analysis of Information Service Pricing Scheme Model Based on Customer Self-Selection. Science and Technology Indonesia, 6(4), 337–343. https://doi.org/10.26554/sti.2021.6.4.337-343
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