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Group interaction and evolution of customer reviews based on opinion dynamics towards product redesign

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Abstract

Perception of customer requirements and intention is crucial for product redesign where customer reviews play a significant role. Customers dynamically make decision and interact with others, which lead to the evolution of customer reviews. A customer reviews evolution model (CREM) is proposed to analyse the dynamic evolution process of group customer reviews by using a modified Deffuant-Weisbuch model based on opinion dynamics. In the proposed methodology, negativity bias and the helpfulness of reviews are incorporated according to the characteristics of customers and reality. Based on the related literature reviews and survey, negativity bias is introduced to present that positive customers are still sensitive to negative reviews out of confidence radius and will interact with them. In addition, the helpfulness of reviews is used to reflect the rate of information acquisition since the ability of expression varies from person to person. Moreover, as a case study, the customer reviews evolution of a smartphone is modelled to support the redesigned attributes evaluation. Finally, the feasibility and effectiveness of the proposed CREM is expounded through result analysis and discussion.

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References

  1. Jiao, Y., Yang, Y., & Zhang, H. (2017). An integration model for generating and selecting product configuration plans. Journal of Intelligent Manufacturing, 30(3), 1291–1302.

    Article  Google Scholar 

  2. Ahmad, N., Wynn, D. C., & Clarkson, P. J. (2013). Change impact on a product and its redesign process: A tool for knowledge capture and reuse. Research in Engineering Design, 24(3), 219–244.

    Article  Google Scholar 

  3. Janthong, N. (2011). A methodology for tracking the impact of changes in (re)designing of the industrial complex product. Paper presented at the IEEE international conference on industrial engineering and engineering management, Singapore, December 6–9.

  4. Gautam, N., & Singh, N. (2008). Lean product development: Maximizing the customer perceived value through design change (redesign). International Journal of Production Economics, 114(1), 313–332.

    Article  Google Scholar 

  5. Shieh, M.-D., Yan, W., & Chen, C.-H. (2008). Soliciting customer requirements for product redesign based on picture sorts and ART2 neural network. Expert Systems with Applications, 34(1), 194–204.

    Article  Google Scholar 

  6. Wang, Y., & Tseng, M. M. (2013). A Naïve Bayes approach to map customer requirements to product variants. Journal of Intelligent Manufacturing, 26(3), 501–509.

    Article  Google Scholar 

  7. Jing, N., Jiang, T., Du, J., & Sugumaran, V. (2018). Personalized recommendation based on customer preference mining and sentiment assessment from a Chinese e-commerce website. Electronic Commerce Research, 18(1), 159–179.

    Article  Google Scholar 

  8. Martínez, A., Schmuck, C., Pereverzyev, S., Pirker, C., & Haltmeier, M. (2020). A machine learning framework for customer purchase prediction in the non-contractual setting. European Journal of Operational Research, 281(3), 588–596.

    Article  Google Scholar 

  9. Gupta, M., Mittal, H., Singla, P., & Bagchi, A. (2017). Analysis and characterization of comparison shopping behavior in the mobile handset domain. Electronic Commerce Research, 17(3), 521–551.

    Article  Google Scholar 

  10. Choi, C.-R., & Jeong, H.-Y. (2014). Quality evaluation and best service choice for cloud computing based on user preference and weights of attributes using the analytic network process. Electronic Commerce Research, 14(3), 245–270.

    Article  Google Scholar 

  11. Hu, M. & Liu, B. (2004). Mining and summarizing customer reviews. Paper presented at the proceedings of the 2004 ACM SIGKDD international conference on knowledge discovery and data mining-KDD '04, Seattle, WA, USA, August 22–25.

  12. Jin, J., Ji, P., & Gu, R. (2016). Identifying comparative customer requirements from product online reviews for competitor analysis. Engineering Applications of Artificial Intelligence, 49, 61–73.

    Article  Google Scholar 

  13. Alamsyah, A., & Indraswari, A. A. (2017). Social network and sentiment analysis for social customer relationship management in indonesia banking sector. Advanced Science Letters, 23(4), 3808–3812.

    Article  Google Scholar 

  14. Marrese-Taylor, E., Velásquez, J. D., Bravo-Marquez, F., & Matsuo, Y. (2013). Identifying customer preferences about tourism products using an aspect-based opinion mining approach. Procedia Computer Science, 22, 182–191.

    Article  Google Scholar 

  15. Castellano, C., Fortunato, S., & Loreto, V. (2009). Statistical physics of social dynamics. Reviews of Modern Physics, 81(2), 591–646.

    Article  Google Scholar 

  16. Kusmartsev, F. V., & Kürten, K. E. (2008). Physics of the mind: opinion dynamics and decision making processes based on a binary network model. International Journal of Modern Physics B, 22(25n26), 4482–4494.

    Article  Google Scholar 

  17. Dong, Y., Ding, Z., Martínez, L., & Herrera, F. (2017). Managing consensus based on leadership in opinion dynamics. Information Sciences, 397–398, 187–205.

    Article  Google Scholar 

  18. Luo, G.-X., Liu, Y., Zeng, Q.-A., Diao, S.-M., & Xiong, F. (2014). A dynamic evolution model of human opinion as affected by advertising. Physica A: Statistical Mechanics and its Applications, 414, 254–262.

    Article  Google Scholar 

  19. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–291.

    Article  Google Scholar 

  20. Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5(4), 297–323.

    Article  Google Scholar 

  21. Zhang, K. (2019). Encountering dissimilar views in deliberation: Political knowledge, attitude strength, and opinion change. Political Psychology, 2019(40), 315–333.

    Article  Google Scholar 

  22. Nicholls, R. (2020). What goes on between customers? A cross-industry study of customer-to-customer interaction (cci). Journal of Service Theory and Practice, 30(2), 123–147.

    Article  Google Scholar 

  23. Hu, K. C., Lu, M., Huang, F. Y., & Jen, W. (2017). Click “like” on facebook: The effect of customer-to-customer interaction on customer voluntary performance for social networking sites. International Journal of Human-Computer Interaction, 33(2), 135–142.

    Article  Google Scholar 

  24. Gimenez, M. C., Paz García, A. P., Burgos Paci, M. A., & Reinaudi, L. (2016). Range of interaction in an opinion evolution model of ideological self-positioning: Contagion, hesitance and polarization. Physica A: Statistical Mechanics and its Applications, 447, 320–330.

    Article  Google Scholar 

  25. Fang, S., Zhao, N., Chen, N., Xiong, F., & Yi, Y. (2019). Analyzing and predicting network public opinion evolution based on group persuasion force of populism. Physica A-Statistical Mechanics and Its Applications, 525, 809–824.

    Article  Google Scholar 

  26. Moe, W. W., & Schweidel, D. A. (2012). Online product opinions: incidence, evaluation, and evolution. Marketing Science, 31(3), 372–386.

    Article  Google Scholar 

  27. Dou, R., Zhang, Y., & Nan, G. (2017). Iterative product design through group opinion evolution. International Journal of Production Research, 55(13), 3886–3905.

    Article  Google Scholar 

  28. Zhao, Y., Kou, G., Peng, Y., & Chen, Y. (2018). Understanding influence power of opinion leaders in e-commerce networks: An opinion dynamics theory perspective. Information Sciences, 426, 131–147.

    Article  Google Scholar 

  29. Varma, V. S., Morarescu, I.-C., Lasaulce, S., & Martin, S. (2017). Opinion dynamics aware marketing strategies in duopolies. In: 2017 IEEE 56th annual conference on decision and control (CDC), Melbourne, Australia, pp. 3859–3864.

  30. Wang, Y., Wang, J., & Yao, T. (2019). What makes a helpful online review? A meta-analysis of review characteristics. Electronic Commerce Research, 19(2), 257–284.

    Article  Google Scholar 

  31. Zheng, X., Zhu, S., & Lin, Z. (2013). Capturing the essence of word-of-mouth for social commerce: Assessing the quality of online e-commerce reviews by a semi-supervised approach. Decision Support Systems, 56, 211–222.

    Article  Google Scholar 

  32. Ahmad, S. N., & Laroche, M. (2015). How do expressed emotions affect the helpfulness of a product review? Evidence from reviews using latent semantic analysis. International Journal of Electronic Commerce, 20(1), 76–111.

    Article  Google Scholar 

  33. Ullah, R., Zeb, A., & Kim, W. (2015). The impact of emotions on the helpfulness of movie reviews. Journal of Applied Research and Technology, 13(3), 359–363.

    Article  Google Scholar 

  34. Salehan, M., & Kim, D. J. (2016). Predicting the performance of online consumer reviews: A sentiment mining approach to big data analytics. Decision Support Systems, 81, 30–40.

    Article  Google Scholar 

  35. Wang, C. C., Chou, F. S., Chen, C. C., & Yang, Y. J. (2015). Negativity bias effect in helpfulness perception of word-of-mouths: The influence of concreteness and emotion. In K. Okuhara, K. Wang, L. Wang, S. Uesugi, & I-Hsien. Ting (Eds.), Multidisciplinary social networks research. Berlin: Springer.

    Chapter  Google Scholar 

  36. Malik, M. S. I., & Hussain, A. (2017). Helpfulness of product reviews as a function of discrete positive and negative emotions. Computers in Human Behavior, 73, 290–302.

    Article  Google Scholar 

  37. Felbermayr, A., & Nanopoulos, A. (2016). The role of emotions for the perceived usefulness in online customer reviews. Journal of Interactive Marketing, 36, 60–76.

    Article  Google Scholar 

  38. East, R., Uncles, M. D., Romaniuk, J., & Lomax, W. (2016). Measuring the impact of positive and negative word of mouth: A reappraisal. Australasian Marketing Journal (AMJ), 24(1), 54–58.

    Article  Google Scholar 

  39. Wu, P. F. (2013). In search of negativity bias: An empirical study of perceived helpfulness of online reviews. Psychology & Marketing, 30(11), 971–984.

    Article  Google Scholar 

  40. Yin, D., Mitra, S., & Zhang, H. (2016). Research note—when do consumers value positive vs negative reviews? An empirical investigation of confirmation bias in online word of mouth. Information Systems Research, 27(1), 131–144.

    Article  Google Scholar 

  41. Ito, T. A., Larsen, J. T., Smith, N. K., & Cacioppo, J. T. (1998). Negative information weighs more heavily on the brain: The negativity bias in evaluative categorizations. Journal of Personality and Social Psychology, 75(4), 887–900.

    Article  Google Scholar 

  42. Rozin, P., & Royzman, E. B. (2001). Negativity Bias, Negativity Dominance, and Contagion. Personality and Social Psychology Review, 5(4), 296–320.

    Article  Google Scholar 

  43. Yuan, J., Zhang, Q., Chen, A., Li, H., Wang, Q., Zhuang, Z., et al. (2007). Are we sensitive to valence differences in emotionally negative stimuli? Electrophysiological evidence from an ERP study. Neuropsychologia, 45(12), 2764–2771.

    Article  Google Scholar 

  44. Ofir, C., & Simonson, I. (2001). In search of negative customer feedback: The effect of expecting to evaluate on satisfaction evaluations. Journal of Marketing Research, 38(2), 170–182.

    Article  Google Scholar 

  45. Garcia, D., Garas, A., & Schweitzer, F. (2012). Positive words carry less information than negative words. EPJ Data Science, 1(1), 3.

    Article  Google Scholar 

  46. Bebbington, K., MacLeod, C., Ellison, T. M., & Fay, N. (2017). The sky is falling: Evidence of a negativity bias in the social transmission of information. Evolution and Human Behavior, 38(1), 92–101.

    Article  Google Scholar 

  47. Weisstein, F. L., Song, L., Andersen, P., & Zhu, Y. (2017). Examining impacts of negative reviews and purchase goals on consumer purchase decision. Journal of Retailing and Consumer Services, 39, 201–207.

    Article  Google Scholar 

  48. Shihab, M. R., & Putri, A. P. (2019). Negative online reviews of popular products: Understanding the effects of review proportion and quality on consumers’ attitude and intention to buy. Electronic Commerce Research, 19(1), 159–187.

    Article  Google Scholar 

  49. Li, Z., & Shimizu, A. (2018). Impact of online customer reviews on sales outcomes: An empirical study based on prospect theory. The Review of Socionetwork Strategies, 12(2), 135–151.

    Article  Google Scholar 

  50. Li, Z., Li, F., Xiao, J., & Yang, Z. (2018). Effects of negative customer reviews on sales: Evidence based on text data mining. In: 2018 IEEE International conference on data mining workshops (ICDMW), Singapore, pp. 838–847.

  51. Li, Z., Li, F., Xiao, J., & Yang, Z. (2020). Topic features in negative customer reviews: Evidence based on text data mining. The Review of Socionetwork Strategies, 14, 19–40.

    Article  Google Scholar 

  52. Cajueiro, D. O. (2011). Enforcing social behavior in an Ising model with complex neighborhoods. Physica A: Statistical Mechanics and its Applications, 390(9), 1695–1703.

    Article  Google Scholar 

  53. Sznajd-Weron, K., Tabiszewski, M., & Timpanaro, A. M. (2011). Phase transition in the Sznajd model with independence. EPL (Europhysics Letters), 96(4), 48002.

    Article  Google Scholar 

  54. Galam, S. (2008). Sociophysics: A review of galam models. International Journal of Modern Physics C, 19(03), 409–440.

    Article  Google Scholar 

  55. Nathan DeWall, C., Twenge, J. M., Bushman, B., Im, C., & Williams, K. (2010). A little acceptance goes a long way: Applying social impact theory to the rejection-aggression link. Social Psychological and Personality Science, 1(2), 168–174.

    Article  Google Scholar 

  56. Wang, C., Li, Q., & E, W., & Chazelle, B. . (2017). Noisy Hegselmann-Krause systems: Phase transition and the 2R-conjecture. Journal of Statistical Physics, 166(5), 1209–1225.

    Article  Google Scholar 

  57. Zhang, J., & Hong, Y. (2013). Opinion evolution analysis for short-range and long-range Deffuant-Weisbuch models. Physica A: Statistical Mechanics and its Applications, 392(21), 5289–5297.

    Article  Google Scholar 

  58. Weisbuch, G. (2004). Bounded confidence and social networks. The European Physical Journal B-Condensed Matter, 38(2), 339–343.

    Google Scholar 

  59. Deffuant, G., Neau, D., Amblard, F., & Weisbuch, G. (2000). Mixing beliefs among interacting agents. Advances in Complex Systems, 03(01n04), 87–98.

    Article  Google Scholar 

  60. Weisbuch, G., Deffuant, G., & Amblard, F. (2005). Persuasion dynamics. Physica A: Statistical Mechanics and its Applications, 353, 555–575.

    Article  Google Scholar 

  61. Meng, F., Dipietro, R. B., Gerdes, J. H., Kline, S., & Avant, T. (2018). How hotel responses to negative online reviews affect customers’ perception of hotel image and behavioral intent: An exploratory investigation. Tourism Review International, 22(1), 23–39.

    Article  Google Scholar 

  62. Sparks, B. A., So, K. K. F., & Bradley, G. L. (2016). Responding to negative online reviews: The effects of hotel responses on customer inferences of trust and concern. Tourism Management, 53, 74–85.

    Article  Google Scholar 

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Acknowledgements

This project was supported by the National Natural Science Foundation, China (No. 51505480, 51875345), Postgraduate Research & Practice Innovation Program of Jiangsu Province (No. KYCX19_2175), and Postgraduate Research & Practice Innovation Program of China University of Mining and Technology (No. KYCX19_2175).

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Correspondence to Fan Zou.

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Zou, F., Li, Y. & Huang, J. Group interaction and evolution of customer reviews based on opinion dynamics towards product redesign. Electron Commer Res 22, 1131–1151 (2022). https://doi.org/10.1007/s10660-020-09447-8

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