Home Integrated Online Consumer Preference Mining for Product Improvement with Online Reviews
Article
Licensed
Unlicensed Requires Authentication

Integrated Online Consumer Preference Mining for Product Improvement with Online Reviews

  • Jie Li EMAIL logo , Qiaoling Lan , Lu Liu and Fang Yang
Published/Copyright: March 18, 2019
Become an author with De Gruyter Brill

Abstract

Exploring consumer preferences for a product is essential for the enterprise in product improvement. Many studies have been conducted in consumer preference. However, few studies have concentrated on evaluating the product and service characteristics of a specific product, to facilitate product and service improvements. This study proposes a systematic research framework for exploring major product and service features that reflect consumer preferences based on the online reviews. By creatively integrating quantitative studies of multiple linear regression and meta-analysis, this study expects to generate a feature-based preference importance ranking. Furthermore, by adopting an importance-satisfaction analysis, we can draw a matrix that is valuable in product improvement. Coupled with the preference rankings, implications for competitive strategies that facilitate product improvement can be drawn. The effectiveness of this methodology is verified by a case study of laptop on the basis of the online reviews from amazon.cn.


Supported by the National Social Science Foundation Project of China (16FGL014)


References

[1] Shiau C S, Tseng I H, Heutchy A W, et al. Design optimization of a laptop computer using aggregate and mixed logit demand models with consumer survey data. ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 2007: 175–185.10.1115/DETC2007-34883Search in Google Scholar

[2] Jun S P, Park D H, Yeom J. The possibility of using search traffic information to explore consumer product attitudes and forecast consumer preference. Technological Forecasting and Social Change, 2013, 86(340): 237–253.10.1016/j.techfore.2013.10.021Search in Google Scholar

[3] Lynch J G. Mission creep, mission impossible, or mission of honor? Consumer behavior research in an internet age. Journal of Marketing Behavior, 2015, 1(1): 37–52.10.1561/107.00000002Search in Google Scholar

[4] Xie Y, Batra R, Peng S. An extended model of preference formation between global and local brands: The roles of identity expressiveness, trust, and affect. Journal of International Marketing, 2015, 23(1): 50–71.10.1509/jim.14.0009Search in Google Scholar

[5] Kontot K, Hamali J, Abdullah F. Determining factors of customers’ preferences: A case of deposit products in islamic banking. Procedia-Social and Behavioral Sciences, 2016, 224: 167–175.10.1016/j.sbspro.2016.05.435Search in Google Scholar

[6] Cheung C M, Chan G W, Limayem M. A critical review of online consumer behavior: Empirical research. Journal of Electronic Commerce in Organizations, 2005, 3(4): 1–19.10.4018/jeco.2005100101Search in Google Scholar

[7] Cetinăa I, Munthiu M C, Răadulescu V. Psychological and social factors that influence online consumer behavior. Procedia-Social and Behavioral Sciences, 2012, 62: 184–188.10.1016/j.sbspro.2012.09.029Search in Google Scholar

[8] Liao S H, Chung Y C. The effects of psychological factors on online consumer behavior. IEEE International Conference on Industrial Engineering and Engineering Management, 2011: 1380–1383.10.1109/IEEM.2011.6118142Search in Google Scholar

[9] Chattaraman V, Rudd N A, Lennon S J. Identity salience and shifts in product preferences of Hispanic consumers: Cultural relevance of product attributes as a moderator. Journal of Business Research, 2009, 62(8): 826–833.10.1016/j.jbusres.2008.04.002Search in Google Scholar

[10] Fatimah F, Malgorzata L, Agata W. An empirical study of the factors influencing consumer behvaiour in the eclectic appliances market. Contemporary Economics, 2012, 6(3): 76–86.10.5709/ce.1897-9254.52Search in Google Scholar

[11] Bray J P. Consumer behaviour theory: Approaches and models. Bournemouth University. 2008. http://libvolume3.xyz/economics/ba/semester1/principlesofmicroeconomics/theoriesofconsumersbehaviour/theoriesofconsumersbehaviourtutorial2.pdf, 1–33.Search in Google Scholar

[12] Chung J, Rao V R. A general consumer preference model for experience products: Application to internet recommendation services. Journal of Marketing Research, 2012, 49(3): 289–305.10.1509/jmr.09.0467Search in Google Scholar

[13] Rangaswamy E A. A study on the critical success factors of ipad focusing on the buyer behaviour and involvement. Amity Global Business Review, 2015, 10: 95–109.Search in Google Scholar

[14] Ray S. Building a model for purchase decision of laptops and price-performance analysis of major players. IUP Journal of Management Research, 2009: 8(1): 7.Search in Google Scholar

[15] Gicheol J, Jongsu L. Estimating consumer preferences for online music services. Applied Economics, 2010, 42(30): 3885–3893.10.1080/00036840802360153Search in Google Scholar

[16] Baltas G, Saridakis C. An empirical investigation of the impact of behavioural and psychographic consumer characteristics on car preferences: An integrated model of car type choice. Transportation Research Part A: Policy and Practice, 2013, 54(2): 92–110.10.1016/j.tra.2013.07.007Search in Google Scholar

[17] Izogo E E, Ogba I E. Service quality, customer satisfaction and loyalty in automobile repair services sector. International Journal of Quality and Reliability Management, 2015, 32(3): 250–269.10.1108/IJQRM-05-2013-0075Search in Google Scholar

[18] Lepmets M, Cater-Steel A, Gacenga F. Extending the it service quality measurement framework through a systematic literature review. Journal of Service Science Research, 2012, 4(1): 7–47.10.1007/s12927-012-0001-6Search in Google Scholar

[19] Xiao S, Wei C P, Dong M. Crowd intelligence: Analyzing online product reviews for preference measurement. Information and Management, 2015, 53(2): 169–182.10.1016/j.im.2015.09.010Search in Google Scholar

[20] Teng S, Khong K W, Chong Y L, et al. Examining the impacts of electronic word-of-mouth message on consumers’ attitude. Journal of Computer Information Systems, 2016: 1–14.10.1080/08874417.2016.1184012Search in Google Scholar

[21] Jie L, Wenyi X, Fang Y, et al. An Integrated Research Framework for Effect of EWOM. Journal of Systems Science and Information, 2017, 5: 343–355.10.21078/JSSI-2017-343-13Search in Google Scholar

[22] Decker R, Trusov M. Estimating aggregate consumer preferences from online product reviews. International Journal of Research in Marketing, 2010, 27(4): 293–307.10.1016/j.ijresmar.2010.09.001Search in Google Scholar

[23] Wu W Y, Liao Y K, Chatwuthikrai A. Applying conjoint analysis to evaluate consumer preferences toward subcompact cars. Expert Systems with Applications, 2014, 41(6): 2782–2792.10.1016/j.eswa.2013.10.011Search in Google Scholar

[24] Rosario A B, Sotgiu F, Valck K D, et al. The effect of electronic word of mouth on sales: A meta-analytic review of platform, product, and metric factors. Journal of Marketing Research, 2016, 53(3): 297–318.10.1509/jmr.14.0380Search in Google Scholar

[25] Yoo S H. Consumers’ preferences for the attributes of post-pc: Results of a contingent ranking study. Applied Economics, 2006, 38(19): 2327–2334.10.1080/00036840500427486Search in Google Scholar

[26] Liao H F, Robert W P, Gavriel S. Content preparation for e-commerce involving chinese and U.S. online consumers. Journal of Human-Computer Interaction, 2009, 25(8): 729–761.10.1080/10447310903025503Search in Google Scholar

[27] Zhang K Z K, Zhao S J, Cheung C M K, et al. Examining the influence of online reviews on consumers’ decision-making. Decision Support Systems, 2014, 67(C): 78–89.10.1016/j.dss.2014.08.005Search in Google Scholar

[28] Xiao S, Wei C P, Dong M. Crowd intelligence: Analyzing online product reviews for preference measurement. Information and Management, 2015, 53(2): 169–182.10.1016/j.im.2015.09.010Search in Google Scholar

[29] Amarouche K, Benbrahim H, Kassou I. Product features extraction from opinions according to time. World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering, 2016, 10(6): 1176–1183.Search in Google Scholar

[30] Qi J, Zhang Z, Jeon S, et al. Mining customer requirements from online reviews: A product improvement perspective. Information and Management, 2016, 53(8): 951–963.10.1016/j.im.2016.06.002Search in Google Scholar

[31] Jones C. A general consumer preference model for experience products: Application to internet recommendation services. Social Science Electron Publishing, 2011, 49(3): 289–305.Search in Google Scholar

[32] Kwong C K, Wong T C, Chan K Y. A methodology of generating customer satisfaction models for new product development using a neuro-fuzzy approach. Expert Systems with Applications, 2009, 36(36): 11262–11270.10.1016/j.eswa.2009.02.094Search in Google Scholar

[33] Cai Z, Aguilar F X, Cai Z, et al. Consumer stated purchasing preferences and corporate social responsibility in the wood products industry: A conjoint analysis in the US. and china. Ecological economics, 2013, 95(4): 118–127.10.1016/j.ecolecon.2013.08.017Search in Google Scholar

[34] Varela P, Beltrán J, Fiszman S. An alternative way to uncover drivers of coffee liking: Preference mapping based on consumers’ preference ranking and open comments. Food Quality and Preference, 2014, 32: 152–159.10.1016/j.foodqual.2013.03.004Search in Google Scholar

[35] Kurt M, Elmar S, Kenneth H. Importance-performance analysis revisited: The role of the factor structure of customer satisfaction. The Service Industries Journal, 2003, 23(2): 112–129.10.1080/02642060412331300912Search in Google Scholar

[36] Martilla J A, James J C. Importance-performance analysis. The Journal of Marketing, 1977, 41(1): 77–79.10.1177/002224297704100112Search in Google Scholar

[37] Tonge J, Moore S A. Importance-satisfaction analysis for marine-park hinterlands: A western australian case study. Tourism Management, 2007, 28(3): 768–776.10.1016/j.tourman.2006.05.007Search in Google Scholar

[38] Lipsey M W, Wilson D B. Practical meta-analysis. Thousand Oaks, CA: Sage publications, 2001.Search in Google Scholar

[39] Latour S A, Peat N C. Conceptual and methodological issues in consumer satisfaction research. Advances in Consumer Research, 1979, 6: 431–437.Search in Google Scholar

[40] Oliver R L, Linda G. Effect of satisfaction and its antecedents on consumer preference and intention. Advance in Consumer Research, 1981, 8: 88–93.Search in Google Scholar

[41] Matzler K, Bailom F, Hinterhuber H H, et al. The asymmetric relationship between attribute-level performance and overall customer satisfaction: A reconsideration of the importance-performance analysis. Industrial Marketing Management, 2004, 33(4): 271–277.10.1016/S0019-8501(03)00055-5Search in Google Scholar

[42] Cohen J. Statistical power analysis for the behavioral sciences. Hillsdale: Lawrence Erlbaum Associates Inc., 1988.Search in Google Scholar

[43] Zhan J, Han T L, Liu Y. Gather customer concerns from online product reviews: A text summarization approach. Expert Systems with Applications, 2009, 36(2): 2107–2115.10.1016/j.eswa.2007.12.039Search in Google Scholar

Received: 2017-09-08
Accepted: 2018-05-03
Published Online: 2019-03-18

© 2019 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 20.11.2025 from https://www.degruyterbrill.com/document/doi/10.21078/JSSI-2019-017-20/html
Scroll to top button