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Electronic Commerce Research

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17-11-2020

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

Authors: Fan Zou, Yupeng Li, Jiahuan Huang

Published in: Electronic Commerce Research | Issue 4/2022

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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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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.CrossRef

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.CrossRef

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.

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

15.

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

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.CrossRef

17.

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

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.CrossRef

19.

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

20.

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

21.

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

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

26.

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

27.

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

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

42.

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

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.CrossRef

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.CrossRef

45.

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

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

53.

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

54.

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

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.CrossRef

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.CrossRef

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.CrossRef

58.

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

59.

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

60.

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

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.CrossRef

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.CrossRef

Title: Group interaction and evolution of customer reviews based on opinion dynamics towards product redesign
Authors: Fan Zou
Yupeng Li
Jiahuan Huang
Publication date: 17-11-2020
Publisher: Springer US
Published in: Electronic Commerce Research / Issue 4/2022
Print ISSN: 1389-5753
Electronic ISSN: 1572-9362
DOI: https://doi.org/10.1007/s10660-020-09447-8

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