The Divorce of Word and Deed—A Data-Mining Approach to Identify and Evaluate Customer Requirements

References

• Al-Natour S, Turetken O (2020) A comparative assessment of sentiment analysis and star ratings for consumer reviews. Internat. J. Inform. Management 54:102132.Crossref, Google Scholar
• Archak N, Ghose A, Ipeirotis PG (2011) Deriving the pricing power of product features by mining consumer reviews. Management Sci. 57(8):1485–1509.Link, Google Scholar
• Bettman JR, Luce MF, Payne JW (1998) Constructive consumer choice processes. J. Consumer Res. 25(3):187–217.Crossref, Google Scholar
• Bi JW, Liu Y, Fan ZP, Cambria E (2019) Modelling customer satisfaction from online reviews using ensemble neural network and effect-based Kano model. Internat. J. Production Res. 57(22):7068–7088.Crossref, Google Scholar
• Brown TB, Mann B, Ryder N, Subbiah M, Kaplan J, Dhariwal P, Neelakantan A, et al. (2020) Language models are few-shot learners. Preprint, submitted May 28, https://arxiv.org/abs/2005.14165v4.Google Scholar
• Büschken J, Allenby GM (2016) Sentence-based text analysis for customer reviews. Marketing Sci. 35(6):953–975.Link, Google Scholar
• Chandon P, Morwitz VG, Reinartz WJ (2005) Do intentions really predict behavior? Self-generated validity effects in survey research. J. Marketing 69(2):1–14.Crossref, Google Scholar
• Chen Y, Xie J (2008) Online consumer review: Word-of-mouth as a new element of marketing communication mix. Management Sci. 54(3):477–491.Link, Google Scholar
• Chevalier JA, Mayzlin D (2006) The effect of word of mouth on sales: Online book reviews. J. Marketing Res. 43(3):345–354.Crossref, Google Scholar
• Chib S, Greenberg E, Chen Y (1998) MCMC Methods for Fitting and Comparing Multinomial Response Models (University Library of Munich, Munich, Germany).Google Scholar
• Churchill GA, Surprenant C (1982) An investigation into the determinants of customer satisfaction. J. Marketing Res. 19(4):491–504.Crossref, Google Scholar
• Courage C, Baxter K (2005) Understanding Your Users: A Practical Guide to User Requirements Methods, Tools, and Techniques (Morgan Kaufmann Publishers, San Francisco).Google Scholar
• Culotta A, Cutler J (2016) Mining brand perceptions from twitter social networks. Marketing Sci. 35(3):343–362.Link, Google Scholar
• Devlin J, Chang MW, Lee K, Toutanova K (2018) BERT: Pre-training of deep bidirectional transformers for language understanding. Preprint, submitted October 11, https://arxiv.org/abs/1810.04805v2.Google Scholar
• Do HH, Prasad P, Maag A, Alsadoon A (2019) Deep learning for aspect-based sentiment analysis: A comparative review. Expert Systems Appl. 118:272–299.Crossref, Google Scholar
• Engelbrektsson P, Söderman M (2004) The use and perception of methods and product representations in product development: A survey of Swedish industry. J. Engrg. Design 15(2):141–154.Crossref, Google Scholar
• Floyd K, Freling R, Alhoqail S, Cho HY, Freling T (2014) How online product reviews affect retail sales: A meta-analysis. J. Retailing 90(2):217–232.Crossref, Google Scholar
• Friedman EMS, Savary J, Dhar R (2018) Apples, oranges, and erasers: The effect of considering similar versus dissimilar alternatives on purchase decisions. J. Consumer Res. 45(4):725–742.Crossref, Google Scholar
• Gardial SF, Clemons DS, Woodruff RB, Schumann DW, Burns MJ (1994) Comparing consumers’ recall of prepurchase and postpurchase product evaluation experiences. J. Consumer Res. 20(4):548–560.Crossref, Google Scholar
• Ghose A, Ipeirotis PG, Li B (2012) Designing ranking systems for hotels on travel search engines by mining user-generated and crowdsourced content. Marketing Sci. 31(3):493–520.Link, Google Scholar
• Gray D, Brown S, Macanufo J (2010) Gamestorming: A Playbook for Innovators, Rulebreakers, and Changemakers, 1st ed. (O’Reilly, Sebastopol, CA).Google Scholar
• Griffin A, Hauser JR (1993) The voice of the customer. Marketing Sci. 12(1):1–27.Link, Google Scholar
• Guadagni PM, Little JDC (1983) A logit model of brand choice calibrated on scanner data. Marketing Sci. 2(3):203–238.Link, Google Scholar
• Hensher DA, Rose JM, Greene WH (2015) Applied Choice Analysis, 2nd ed. (Cambridge University Press, Cambridge, UK).Crossref, Google Scholar
• Hou T, Yannou B, Leroy Y, Poirson E (2019) Mining changes in user expectation over time from online reviews. J. Mech. Design 141(9):091102.Crossref, Google Scholar
• Hu N, Pavlou PA, Zhang J (2017) On self-selection biases in online product reviews. MIS Quart. 41(2):449–475.Crossref, Google Scholar
• Hurst A, Lerer A, Goucher AP, Perelman A, Ramesh A, Clark A, OpenAI, et al. (2024) GPT-4o system card. Preprint, submitted October 25, https://arxiv.org/abs/2410.21276.Google Scholar
• Information Resources Management Association (2018) Digital Marketing and Consumer Engagement: Concepts, Methodologies, Tools, and Applications (IGI Global, Hershey, PA).Crossref, Google Scholar
• Jain HK, Tanniru MR, Fazlollahi B (1991) MCDM approach for generating and evaluating alternatives in requirement analysis. Inform. Systems Res. 2(3):223–239.Link, Google Scholar
• Kahneman D (2003) Maps of bounded rationality: Psychology for behavioral economics. Amer. Econom. Rev. 93(5):1449–1475.Crossref, Google Scholar
• Kano N (1984) Attractive quality and must-be quality. J. Japanese Soc. Quality Control 14(2):147–156.Google Scholar
• Kettinger WJ, Teng JTC, Guha S (1997) Business process change: A study of methodologies, techniques, and tools. MIS Quart. 21(1):55–80.Crossref, Google Scholar
• Kim Y (2014) Convolutional neural networks for sentence classification. Preprint, submitted August 25, https://arxiv.org/abs/1408.5882.Google Scholar
• Kim J, Allenby GM, Rossi PE (2002) Modeling consumer demand for variety. Marketing Sci. 21(3):229–250.Link, Google Scholar
• Krishnan V, Ulrich KT (2001) Product development decisions: A review of the literature. Management Sci. 47(1):1–21.Link, Google Scholar
• Kumar V, Reinartz W (2018) Customer Relationship Management: Concept, Strategy, and Tools 3rd ed. (Springer, Berlin, Heidelberg).Crossref, Google Scholar
• Lancaster KJ (1966) A new approach to consumer theory. J. Political Econom. 74(2):132–157.Crossref, Google Scholar
• Lee S, Park S, Kwak M (2022) Revealing the dual importance and Kano type of attributes through customer review analytics. Adv. Engrg. Informatics 51:101533.Crossref, Google Scholar
• Li X, Hitt LM (2008) Self-selection and information role of online product reviews. Inform. Systems Res. 19(4):456–474.Link, Google Scholar
• Liu Y, Jiang C, Zhao H (2019) Assessing product competitive advantages from the perspective of customers by mining user-generated content on social media. Decision Support Systems 123:113079.Crossref, Google Scholar
• Lu Y, Wang R, Yan X, Yu Y (2024) Product information display optimization with quality uncertainty and return. Preprint, submitted April 15, https://doi.org/10.2139/ssrn.4771026.Google Scholar
• Lu Y, Liu Q, Dai D, Xiao X, Lin H, Han X, Sun L, Wu H (2022) Unified structure generation for universal information extraction. Preprint, submitted March 23, https://arxiv.org/abs/2203.12277.Google Scholar
• Ma X, Khansa L, Deng Y, Kim SS (2013) Impact of prior reviews on the subsequent review process in reputation systems. J. Management Inform. Systems 30(3):279–310.Crossref, Google Scholar
• Martilla JA, James JC (1977) Importance-performance analysis. J. Marketing 41(1):77–79.Crossref, Google Scholar
• McFadden D (2001) Economic choices. Amer. Econom. Rev. 91(3):351–378.Crossref, Google Scholar
• Medhat W, Hassan A, Korashy H (2014) Sentiment analysis algorithms and applications: A survey. Ain Shams Engrg. J. 5(4):1093–1113.Crossref, Google Scholar
• Millson MR, Raj SP, Wilemon D (1992) A survey of major approaches for accelerating new product development. J. Product Innovation Management 9(1):53–69.Crossref, Google Scholar
• Mithas S, Ramasubbu N, Sambamurthy V (2011) How information management capability influences firm performance. MIS Quart. 35(1):237–256.Crossref, Google Scholar
• Moe WW, Schweidel DA (2012) Online product opinions: Incidence, evaluation, and evolution. Marketing Sci. 31(3):372–386.Link, Google Scholar
• Oliver RL (1980) A cognitive model of the antecedents and consequences of satisfaction decisions. J. Marketing Res. 17(4):460–469.Crossref, Google Scholar
• Peng H, Cambria E, Hussain A (2017) A review of sentiment analysis research in Chinese language. Cognitive Comput. 9(4):423–435.Crossref, Google Scholar
• Pieters R, Zeelenberg M (2005) On bad decisions and deciding badly: When intention–behavior inconsistency is regrettable. Organ. Behav. Human Decision Processes 97(1):18–30.Crossref, Google Scholar
• Pinheiro JC, Bates DM (1996) Unconstrained parametrizations for variance-covariance matrices. Statist. Comput. 6(3):289–296.Crossref, Google Scholar
• Qi J, Zhang Z, Jeon S, Zhou Y (2016) Mining customer requirements from online reviews: A product improvement perspective. Inform. Management 53(8):951–963.Crossref, Google Scholar
• Ribeiro FN, Araújo M, Gonçalves P, André Gonçalves M, Benevenuto F (2016) SentiBench—A benchmark comparison of state-of-the-practice sentiment analysis methods. EPJ Data Sci. 5(1):23.Crossref, Google Scholar
• Rosario AB, Sotgiu F, Valck KD, Bijmolt THA (2016) The effect of electronic word of mouth on sales: A meta-analytic review of platform, product, and metric factors. J. Marketing Res. 53(3):297–318.Crossref, Google Scholar
• Saldanha TJV, Mithas S, Krishnan MS (2017) Leveraging customer involvement for fueling innovation: The role of relational and analytical information processing capabilities. MIS Quart. 41(1):367–396.Crossref, Google Scholar
• Shamshiri A, Ryu KR, Park JY (2024) In-context learning for long-context sentiment analysis on infrastructure project opinions. Preprint, submitted October 15, https://arxiv.org/abs/2410.11265.Google Scholar
• Sireli Y, Kauffmann P, Ozan E (2007) Integration of Kano’s model into QFD for multiple product design. IEEE Trans. Engrg. Management 54(2):380–390.Crossref, Google Scholar
• Smith RE, Swinyard WR (1983) Attitude-behavior consistency: The impact of product trial versus advertising. J. Marketing Res. 20(3):257–267.Crossref, Google Scholar
• Stanko MA, Molina‐Castillo F, Munuera‐Aleman J (2012) Speed to market for innovative products: Blessing or curse? J. Product Innovation Management 29(5):751–765.Crossref, Google Scholar
• Taboada M, Brooke J, Tofiloski M, Voll K, Stede M (2011) Lexicon-based methods for sentiment analysis. Comput. Linguistics 37(2):267–307.Crossref, Google Scholar
• Tanaka M (1972) Evaluation of function and value improvement by rating approach. Paper presented at the SJVE Annual Conference, University of Wisconsin-Madison Libraries, Madison, WI.Google Scholar
• Taylor K, Burns M (1999) Changes in pre-and post-purchase evaluative criteria: Exploring the impact on consumer (dis) satisfaction. J. Consumer Satisfaction Dissatisfaction Complaining Behav. 12:90–99.Google Scholar
• Timoshenko A, Hauser JR (2019) Identifying customer needs from user-generated content. Marketing Sci. 38(1):1–20.Link, Google Scholar
• Traylor MB (1984) Ego involvement and brand commitment: Not necessarily the same. J. Consumer Marketing 1(2):75–79.Crossref, Google Scholar
• Valdivia A, Hrabova E, Chaturvedi I, Luzón MV, Troiano L, Cambria E, Herrera F (2019) Inconsistencies on TripAdvisor reviews: A unified index between users and sentiment analysis methods. Neurocomputing 353:3–16.Crossref, Google Scholar
• Van Atteveldt W, Van Der Velden MACG, Boukes M (2021) The validity of sentiment analysis: Comparing manual annotation, crowd-coding, dictionary approaches, and machine learning algorithms. Comm. Methods Measures 15(2):121–140.Crossref, Google Scholar
• Vanegas LV, Labib AW (2001) A fuzzy quality function deployment (FQFD) model for deriving optimum targets. Internat. J. Production Res. 39(1):99–120.Crossref, Google Scholar
• Violante MG, Vezzetti E (2017) Kano qualitative vs quantitative approaches: An assessment framework for products attributes analysis. Comput. Indust. 86:15–25.Crossref, Google Scholar
• von Hippel E (1988) The Sources of Innovation (Oxford University Press, New York).Google Scholar
• Wang R, Ke C, Cui S (2022) Product price, quality, and service decisions under consumer choice models. Manufacturing Service Oper. Management 24(1):430–447.Link, Google Scholar
• Wang Y, Lu X, Tan Y (2018) Impact of product attributes on customer satisfaction: An analysis of online reviews for washing machines. Electronic Commerce Res. Appl. 29:1–11.Crossref, Google Scholar
• Wilson T, Wiebe J, Hoffmann P (2009) Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis. Comput. Linguistics 35(3):399–433.Crossref, Google Scholar
• Xiao S, Wei CP, Dong M (2016) Crowd intelligence: Analyzing online product reviews for preference measurement. Inform. Management 53(2):169–182.Crossref, Google Scholar
• Xiao Y, Zhao Y, Shu K (2025) Understanding and tackling label errors in individual-level nature language understanding. Preprint, submitted February 18, https://arxiv.org/abs/2502.13297.Google Scholar
• Zhang L, Wang S, Liu B (2018) Deep learning for sentiment analysis: A survey. WIREs Data Mining Knowledge Discovery 8(4):e1253.Crossref, Google Scholar
• Zhang KZK, Zhao SJ, Cheung CMK, Lee MKO (2014) Examining the influence of online reviews on consumers’ decision-making: A heuristic–systematic model. Decision Support Systems 67:78–89.Crossref, Google Scholar
• Zhang W, Deng Y, Liu B, Pan SJ, Bing L (2023) Sentiment analysis in the era of large language models: A reality check. Preprint, submitted May 24, https://arxiv.org/abs/2305.15005v1.Google Scholar
• Zhao M, Zhang CX, Hu YQ, Xu ZS, Liu H (2021) Modelling consumer satisfaction based on online reviews using the improved Kano model from the perspective of risk attitude and aspiration. Tech. Econom. Development Econom. 27(3):550–582.Crossref, Google Scholar
• Zou C, Cai J, Chen L, Nah FFH (2024) An exploratory study of conventional machine learning and large language models for sentiment analysis. Degen H, Ntoa S, eds. HCI Internat. 2024—Late Breaking Papers (Springer Nature, Cham, Switzerland), 291–300.Crossref, Google Scholar