Insights from Dynamic Pricing Scenarios for Multiple-generation Product Lines with an Agent-based Model using Text Mining and Sentiment Analysis

Year 2024, , 24 - 45, 26.04.2024

Atefeh Anisi Gül Okudan Kremer Sigurdur Olafsson

https://doi.org/10.62743/uad.7352

Abstract

Corporations must constantly upgrade and improve their offerings due to changes in customer preferences. It is a common strategy for firms in technology-intensive markets to use online reviews as a source of product information to inform such changes. This user-generated information is valuable since it provides companies with valuable and low-cost input. In this paper, we propose an agent-based model for simulating potential cannibalization situations with respect to customer satisfaction throughout consecutive generations of a product line. The level of customer satisfaction is regarded as a parameter in the model, which is conceptualized to affect the product price. The proposed model provides insights into different pricing strategies regarding customer satisfaction levels affect the total lifecycle profitability of multiple-generation product lines, and how they can be used to assist organizations in developing appropriate dynamic pricing strategies.

Keywords

text mining, sentiment analysis, multiple-generation product lines, cannibalization, agent-based modeling, dynamic pricing scenarios.

References

• [1] Krankel, R. M., Duenyas, I., & Kapuscinski, R. (2006). Timing successive product introductions with demand diffusion and stochastic technology improvement. Manufacturing & Service Operations Management, 8(2), 119-135.
• [2] Morgan, L. O., Morgan, R. M., & Moore, W. L. (2001). Quality and time-to-market trade-offs when there are multiple product generations. Manufacturing & Service Operations Management, 3(2), 89-104.
• [3] Lin, C.-Y., & Kremer, G. E. O. (2014). Strategic decision making for multiple-generation product lines using dynamic state variable models: The cannibalization case. Computers in Industry, 65(1), 79-90.
• [4] Jiao, J., & Tseng, M. M. (1999). A methodology of developing product family architecture for mass customization. Journal of Intelligent Manufacturing, 10(1), 3-20.
• [5] Simpson, T. W. (2004). Product platform design and customization: Status and promise. Ai Edam, 18(1), 3-20.
• [6] Lin, C.-Y., & Okudan, G. E. (2013). Planning for multiple-generation product lines using dynamic variable state models with data input from similar products. Expert systems with applications, 40(6).
• [7] Lin, C.-y., Kilicay-Ergin, N. H., & Okudan, G. E. (2011). Agent-based modeling of dynamic pricing scenarios to optimize multiple-generation product lines with cannibalization. Procedia Computer Science, 6, 311-316.
• [8] Kilicay-Ergin, N., Lin, C.-y., & Okudan, G. E. (2015). Analysis of dynamic pricing scenarios for multiple-generation product lines. Journal of Systems Science and Systems Engineering, 24(1), 107-129.
• [9] Ofek, E., & Sarvary, M. (2003). R&D, marketing, and the success of next-generation products. Marketing Science, 22(3), 355-370.
• [10] Edelheit, L. S. (2004). The IRI Medalist's Address: Perspective On GE Research & Development. Research-Technology Management, 47(1), 49-55.
• [11] Norton, J. A., & Bass, F. M. (1987). A diffusion theory model of adoption and substitution for successive generations of high-technology products. Management science, 33(9), 1069-1086.
• [12] Mahajan, V., & Muller, E. (1996). Timing, diffusion, and substitution of successive generations of technological innovations: The IBM mainframe case. Technological Forecasting and Social Change, 51(2), 109-132.
• [13] Bardhan, A., & Chanda, U. (2008). A model for first and substitution adoption of successive generations of a product. International Journal of Modelling and simulation, 28(4), 487-494.
• [14] Huang, C.-Y., & Tzeng, G.-H. (2008). Multiple generation product life cycle predictions using a novel two-stage fuzzy piecewise regression analysis method. Technological forecasting and social change, 75(1), 12-31.
• [15] Dobson, G., & Kalish, S. (1988). Positioning and pricing a product line. Marketing Science, 7(2), 107-125.
• [16] Arslan, H., Kachani, S., & Shmatov, K. (2009). Optimal product introduction and life cycle pricing policies for multiple product generations under competition. Journal of Revenue and Pricing Management, 8(5), 438-451.
• [17] Li, H., & Graves, S. C. (2012). Pricing decisions during inter‐generational product transition. Production and Operations Management, 21(1), 14-28.
• [18] Gallego, G., & Wang, R. (2014). Multiproduct price optimization and competition under the nested logit model with product-differentiated price sensitivities. Operations Research, 62(2), 450-461.
• [19] Kim, N., Srivastava, R. K., & Han, J. K. (2001). Consumer decision-making in a multi-generational choice set context. Journal of Business Research, 53(3), 123-136.
• [20] Schön, C. (2010). On the optimal product line selection problem with price discrimination. Management Science, 56(5), 896-902.
• [21] Chen, J.-M., & Chang, C.-I. (2013). Dynamic pricing for new and remanufactured products in a closed-loop supply chain. International Journal of Production Economics, 146(1), 153-160.
• [22] Fruchter, G., Fligler, A., & Winer, R. (2006). Optimal product line design: Genetic algorithm approach to mitigate cannibalization. Journal of optimization theory and applications, 131(2), 227-244.
• [23] Hotho, A., Nürnberger, A., & Paaß, G. (2005). A brief survey of text mining. Ldv Forum,
• [24] Zhan, J., Loh, H. T., & Liu, Y. (2009). Gather customer concerns from online product reviews–A text summarization approach. Expert Systems with Applications, 36(2), 2107-2115.
• [25] Thorleuchter, D., Van den Poel, D., & Prinzie, A. (2010). Mining ideas from textual information. Expert Systems with Applications, 37(10), 7182-7188.
• [26] Nasukawa, T., & Yi, J. (2003). Sentiment analysis: Capturing favorability using natural language processing. Proceedings of the 2nd international conference on Knowledge capture,
• [27] Wilson, T., Wiebe, J., & Hoffmann, P. (2005). Recognizing contextual polarity in phrase-level sentiment analysis. Proceedings of human language technology conference and conference on empirical methods in natural language processing,
• [28] Beineke, P., Hastie, T., & Vaithyanathan, S. (2004). The sentimental factor: Improving review classification via human-provided information. Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04),
• [29] Pang, B., & Lee, L. (2004). A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. arXiv preprint cs/0409058.
• [30] Pang, B., Lee, L., & Vaithyanathan, S. (2002). Thumbs up? Sentiment classification using machine learning techniques. arXiv preprint cs/0205070.
• [31] Turney, P. D. (2002). Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. arXiv preprint cs/0212032.
• [32] Hu, M., & Liu, B. (2004a). Mining and summarizing customer reviews. Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining,
• [33] Hu, M., & Liu, B. (2004b). Mining opinion features in customer reviews. AAAI,
• [34] Kamps, J., & Marx, M. (2001). Words with attitude.
• [35] Zhang, W., Xu, H., & Wan, W. (2012). Weakness Finder: Find product weakness from Chinese reviews by using aspects based sentiment analysis. Expert Systems with Applications, 39(11), 10283-10291.
• [36] Kang, D., & Park, Y. (2012). Measuring customer satisfaction of service based on an analysis of the user generated contents: Sentiment analysis and aggregating function based MCDM approach. 2012 IEEE International Conference on Management of Innovation & Technology (ICMIT).
• [37] Täckström, O., & McDonald, R. (2011). Semi-supervised latent variable models for sentence-level sentiment analysis. The 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies,
• [38] Mostafa, M. M. (2013). More than words: Social networks' text mining for consumer brand sentiments. Expert systems with applications, 40(10), 4241-4251.
• [39] Kontopoulos, E., Berberidis, C., Dergiades, T., & Bassiliades, N. (2013). Ontology-based sentiment analysis of twitter posts. Expert systems with applications, 40(10), 4065-4074.
• [40] Deng, Z.-H., Luo, K.-H., & Yu, H.-L. (2014). A study of supervised term weighting scheme for sentiment analysis. Expert Systems with Applications, 41(7), 3506-3513.
• [41] Dave, K., Lawrence, S., & Pennock, D. M. (2003). Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. Proceedings of the 12th international conference on World Wide Web,
• [42] Kang, D., & Park, Y. (2014). based measurement of customer satisfaction in mobile service: Sentiment analysis and VIKOR approach. Expert Systems with Applications, 41(4), 1041-1050.
• [43] Do, H. H., Prasad, P. W., Maag, A., & Alsadoon, A. (2019). Deep learning for aspect-based sentiment analysis: a comparative review. Expert systems with applications, 118, 272-299.
• [44] Nazir, A., Rao, Y., Wu, L., & Sun, L. (2020). Issues and challenges of aspect-based sentiment analysis: A comprehensive survey. IEEE Transactions on Affective Computing, 13(2), 845-863.
• [45] Zhuang, L., Jing, F., & Zhu, X.-Y. (2006). Movie review mining and summarization. Proceedings of the 15th ACM international conference on information and knowledge management.
• [46] Netzer, O., Feldman, R., Goldenberg, J., & Fresko, M. (2012). Mine your own business: Market-structure surveillance through text mining. Marketing Science, 31(3), 521-543.