Using Decision Lattice Analysis to Model IOT-based Companies’ profit
الموضوعات :Nazanin Talebolfakhr 1 , Seyed Babak Ebrahimi 2 , Donya Rahmani 3
1 - K. N. Toosi University of Technology
2 - K. N. Toosi University of Technology
3 - K. N. Toosi University of Technology
الکلمات المفتاحية: IOT, , Pricing Strategies, , Demand Uncertainty, , Binomial Decision Lattice, , Real Options,
ملخص المقالة :
Demand uncertainty and high initial investments for IOT-based projects lead to analyzing various types of options, especially real options in project execution to decrease these uncertainties. In this study, we investigate the firms’ expected profits that resulted from appropriate chosen static and dynamic pricing strategies namely low-pricing, high-pricing, and contingent pricing combined with binomial decision lattices. Besides, the reciprocal influence between pricing strategies and IOT investment could provide useful insights for the firms that confront demand uncertainties in selling the firms’ products. We propose a model which is the integration of binomial decision lattices, which have been calculated by Real Option Super Lattice Solver 2017 software, and pricing policies under uncertainty. The results provide insights into what pricing strategies to choose based on the project’s real option value and the level of the firm uncertainty about the purchasing of the high-value consumer. Among the mentioned static and dynamic pricing strategies, high-pricing and contingent pricing strategies under different situations can be selected and expected profits of each of the strategies will be calculated and compared with each other. On the contrary, as the low-pricing strategy resulted in the lowest option value, it will not be scrutinized in this study. Experimental results show that if the IOT investment level and high-value consumer purchasing likelihood are high, the firm will implement the high-pricing strategy, otherwise choosing the contingent pricing due to the demand uncertainty would be appropriate.
[1] X. Zhang and W. T. Yue, “Transformative value of the Internet of Things and pricing decisions,” Electronic Commerce Research and Applications, vol. 34, No. November 2018, 2019, p. 100825.
[2] L. Atzori, A. Iera, and G. Morabito, “The Internet of Things: A survey,” Computer Networks, vol. 54, No. 15, 2010, pp. 2787–2805.
[3] S. Kim and S. Kim, “A multi-criteria approach toward discovering killer IoT application in Korea,” Technological Forecasting and Social Change, vol. 102, 2016, pp. 143–155.
[4] S. Cuomo, P. De Michele, F. Piccialli, A. Galletti, and J. E. Jung, “IoT-based collaborative reputation system for associating visitors and artworks in a cultural scenario,” Expert Systems with Applications, vol. 79, 2017, pp. 101–111.
[5] Y. H. Tehrani, Z. Fazel, and S. M. Atarodi, “A survey of two dominant low power and long range communication technologies,” Journal of Information Systems and Telecommunication, vol. 6, No. 2, 2018, pp. 60–66.
[6] M. Fasanghari and A. Keramati, “Customer Churn Prediction Using Local Linear Model Tree for Iranian Telecommunication Companies,” Journal of Industrial Engineering, University of Tehran, 2011, pp. 25–37.
[7] M. Fasanghari, “E-Commerce Assessment in Fuzzy Situation,”in E-commerce, Shangha, China: InTech, 2010, ch. 2, pp. 21–31.
[8] J. C. Cox, S. A. Ross, and M. Rubinstein, “Option pricing: A simplified approach,” Journal of Financial Economics, vol. 7, No. 3, 1979, pp. 229–263.
[9] P. Tufano and T. Copeland, “A Real-World Way to Manage Real Options,” Harvard Business Review, Vol. 82, 2004, pp. 90-99. [10] T. Copeland and V. Antikarov, Real options, New York: Texere LLC, 2001.
[11] L. E. Brandão, J. S. Dyer, and W. J. Hahn, “Using Binomial Decision Trees to Solve Real-Option Valuation Problems,” Decision Analysis, vol. 2, No. 2, 2005, pp. 69–88.
[12] D. Singh, G. Tripathi, and A. J. Jara, “A survey of Internet-of-Things: Future vision, architecture, challenges and services,” in 2014 IEEE World Forum on Internet of Things (WF-IoT), Mar. 2014, pp. 287–292.
[13] A. Čolaković and M. Hadžialić, “Internet of Things (IoT): A review of enabling technologies, challenges, and open research issues,” Computer Networks, vol. 144, 2018, pp. 17–39.
[14] M. H. Miraz, M. Ali, P. S. Excell, and R. Picking, “A review on Internet of Things (IoT), Internet of Everything (IoE) and Internet of Nano Things (IoNT),” in 2015 Internet Technologies and Applications, ITA 2015 - Proceedings of the 6th International Conference, 2015, pp. 219–224.
[15] H. Lamaazi, N. Benamar, A. J. Jara, L. Ladid, and D. El Ouadghiri, “Challenges of the Internet of Things: IPv6 and Network Management,” in 2014 Eighth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, Jul. 2014, pp. 328–333.
[16] H. Hamidi, “Safe use of the internet of things for privacy enhancing,” Journal of Information Systems and Telecommunication, vol. 4, No. 3, 2016, pp. 145–151.
[17] S. Fay and J. Xie, “Probabilistic goods: A creative way of selling products and services,” Marketing Science, vol. 27, No. 4, 2008, pp. 674–690.
[18] G. Gallego, S. G. Kou, and R. Phillips, “Revenue management of callable products,” Management Science, vol. 54, No. 3, 2008, pp. 550–564.
[19] V. Mak, A. Rapoport, and E. J. Gisches, “Competitive dynamic pricing with alternating offers: Theory and experiment,” Games and Economic Behavior, vol. 75, No. 1, 2012, pp. 250–264.
[20] I. P. L. Png and H. Wang, “Buyer uncertainty and two-part pricing: Theory and applications,” Management Science, vol. 56, No. 2, 2010, pp. 334–342.
[21] M. Kremer, B. Mantin, and A. Ovchinnikov, “ Dynamic Pricing in the Presence of Myopic and Strategic Consumers: Theory and Experiment,” Production and Operations Management, vol. 26, No. 1. 2017, pp. 116-133.
[22] L. Cabral, “Dynamic pricing in customer markets with switching costs,” Review of Economic Dynamics, vol. 20, 2016, pp. 43–62.
[23] J. J. Anton, G. Biglaiser, and N. Vettas, “Dynamic price competition with capacity constraints and a strategic buyer,” International Economic Review, vol. 55, No. 3, 2014, pp. 943–958.
[24] E. Biyalogorsky and E. Gerstner, “Contingent Pricing to Reduce Price Risks,” Marketing Science, vol. 23, No. 1, 2004, pp. 146-155.
[25] H. A. Najafabadi, A. Shekarchizadeh, A. Nabiollahi, N. Khani, and H. Rastegari, “The innovation roadmap and value creation for information goods pricing as an economic commodity,” Journal of Information Systems and Telecommunication, vol. 7, No. 2, 2019, pp. 154–164.
[26] H. K. Bhargava and S. Sundaresan, “Contingency Pricing for Information Goods and Services under Industrywide Performance Standard,” Journal of Management Information Systems, vol. 20, No. 2, 2003, pp. 113–136.
[27] F. Black and M. Scholes, “The pricing of options and corporate liabilities,” Journal of Political Economy, vol. 81, No. 3, 1973, pp. 637–657.
[28] P. Kodukula and Ch. Papudesu , Project Valuation Using Real Options Analysis, A practitioner's Guide, J. Ross Publishing, 2006.
[29] X. Li and J. D. Johnson, “Opportunities Using Real Options Theory,” Information Resources Management Journal, vol. 15, No. 3, 2002, pp. 32–47.
[30] J. Zhang, S. Bandyopadhyay, and S. Piramuthu, “Real option valuation on grid computing,” Decision Support Systems, vol. 46, No. 1, 2008, pp. 333-343.
