dc.contributor.authorChoi, Jaewon
dc.contributor.authorLee, Hong Joo
dc.contributor.authorChoeh, Joon Yeon
dc.contributor.otherEkonomická fakultacs
dc.description.abstractThe automotive industry evaluates various success factors to achieve competitive advantage in selling products. Existing studies have predicted the success of newly launched automobiles based on an economic perspective. However, factors such as dynamic changes in consumer preferences and the emergence of numerous automobile brands pose difficulty in understanding product quality. This study proposes a method of understanding the automotive market using text mining techniques and online user opinions for newly launched cars. By analyzing customer experiences and expectations through their opinions, we can anticipate automobile demand in the market more easily. The proposed method is based on online reviews from an online portal for automobiles. Based on a literature review, this study presents a framework for analyzing input versus output word-of-mouth (WOM). It also integrates the success factors from existing automobile studies and derives functional categories and relevant keywords. The analysis identifies differences in consumer-interest factors that lead to short-term success or normal results in automobile sales. In addition, it confirms that the elements of WOM produces varying results depending on the timing these are employed in relation to the product launch (i.e., before or after a product’s launch). It revealed which dimensions of automobile characteristics are important factors in identifying sales volume and market share for specific types and brands of automobile models. The results of this study provide theoretical advantage in predicting market success in the automobile industry. In addition, the study derives practical insights into characteristics of classification information for market forecasts in the automotive industry. The paper provides empirical insights about how input WOM and output WOM which are analyzed differently can have predictive power in forecasting market share and sales volume for automobiles.en
dc.publisherTechnická Univerzita v Libercics
dc.publisherTechnical university of Liberec, Czech Republicen
dc.relation.isbasedonAhmad, T., & Doja, M. N. (2012). Rule Based System for Enhancing Recall for Feature Mining from Short Sentences in Customer Review Documents. International Journal of Computer Science and Engineering, 4(6), 1211–1219.
dc.relation.isbasedonAppel, G., Grewal, L., Hadi, R., & Stephen, A. T. (2020). The future of social media in marketing. Journal of the Academy of Marketing Science, 48(1), 79–95. https://doi.org/10.1007/s11747-019-00695-1
dc.relation.isbasedonBhattacherjee, A. (2001). Understanding Information Systems Continuance: An Expectation-Confirmation Model. MIS Quarterly, 25(3), 351–370. https://doi.org/10.2307/3250921
dc.relation.isbasedonBlei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.
dc.relation.isbasedonBone, P. F. (1995). Word-of-mouth effects on short-term and long-term product judgments. Journal of Business Research, 32(3), 213–223. https://doi.org/10.1016/0148-2963(94)00047-I
dc.relation.isbasedonBreteau, V., & Weber, S. (2013). Reconsidering the Choice between Gasoline- and Diesel-Powered Cars: Modeling Demand and Automakers’ Reactions. Transportation Research Record: Journal of the Transportation Research Board, 2375(1), 18–28. https://doi.org/10.3141/2375-03
dc.relation.isbasedonBrownstone, D., & Train, K. (1998). Forecasting new product penetration with flexible substitution patterns. Journal of Econometrics, 89(1–2), 109–129. https://doi.org/10.1016/S0304-4076(98)00057-8
dc.relation.isbasedonButtle, F. A. (1998). Word of mouth: understanding and managing referral marketing. Journal of Strategic Marketing, 6(3), 241–254. https://doi.org/10.1080/096525498346658
dc.relation.isbasedonChen, H., Chiang, R. H., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS Quarterly, 36(4), 1165–1188. https://doi.org/10.2307/41703503
dc.relation.isbasedonChen, K., Kou, G., Shang, J., & Chen, Y. (2015). Visualizing market structure through online product reviews: Integrate topic modeling, TOPSIS, and multi-dimensional scaling approaches. Electronic Commerce Research and Applications, 14(1), 58–74. https://doi.org/10.1016/j.elerap.2014.11.004
dc.relation.isbasedonChen, Y., Yang, S., & Wang, Z. (2016). Service cooperation and marketing strategies of infomediary and online retailer with eWOM effect. Information Technology and Management, 17(2), 109–118. https://doi.org/10.1007/s10799-015-0237-1
dc.relation.isbasedonCheung, M.-S., Anitsal, M. M., & Anitsal, I. (2007). Revisiting Word-of-Mouth Communications: A Cross-National Exploration. Journal of Marketing Theory and Practice, 15(3), 235–249. https://doi.org/10.2753/MTP1069-6679150304
dc.relation.isbasedonChoi, H. S., & Leon, S. (2020). An empirical investigation of online review helpfulness: A big data perspective. Decision Support Systems, 139, 113403. https://doi.org/10.1016/j.dss.2020.113403
dc.relation.isbasedonChoi, J., Lee, H. J., & Kim, H.-W. (2017). Examining the effects of personalized App recommender systems on purchase intention: A self and social-interaction perspective. Journal of Electronic Commerce Research, 18(1), 73–102.
dc.relation.isbasedonChong, A. Y. L., Li, B., Ngai, E. W. T., Ch’ng, E., & Lee, F. (2016). Predicting online product sales via online reviews, sentiments, and promotion strategies. International Journal of Operations & Production Management, 36(4), 358–383. https://doi.org/10.1108/IJOPM-03-2015-0151
dc.relation.isbasedonChowdhury, M., Salam, K., & Tay, R. (2016). Consumer preferences and policy implications for the green car market. Marketing Intelligence & Planning, 34(6), 810–827. https://doi.org/10.1108/MIP-08-2015-0167
dc.relation.isbasedonChristopher, M., Payne, A., & Ballantyne, D. (1991). Relationship Marketing: Bringing quality customer service and marketing together. Oxford: Butterworth-Heinemann.
dc.relation.isbasedonCruz, R. A. B., & Lee, H. J. (2014). The Brand Personality Effect: Communicating Brand Personality on Twitter and its Influence on Online Community Engagement. Journal of Intelligence and Information Systems, 20(1), 67–101. https://doi.org/10.13088/jiis.2014.20.1.067
dc.relation.isbasedonDe Janosi, P. E. (1959). Factors Influencing the Demand for New Automobiles. Journal of Marketing, 23(4), 412–418. https://doi.org/10.1177/002224295902300408
dc.relation.isbasedonDewey, J. (2007). How We Think & Education And Experience. London: Frederick Ellis.
dc.relation.isbasedonEast, R., Hammond, K., & Wright, M. (2007). The relative incidence of positive and negative word of mouth: A multi-category study. International Journal of Research in Marketing, 24(2), 175–184. https://doi.org/10.1016/j.ijresmar.2006.12.004
dc.relation.isbasedonEmpson, L., Muzio, D., Broschak, J. P., & Hinings, C. R. (2015). The Oxford Handbook of Professional Service Firms. Oxford University Press.
dc.relation.isbasedonFishbein, M., & Ajzen, I. (1977). Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Boston, MA: Addison-Wesley.
dc.relation.isbasedonFornell, C., & Wernerfelt, B. (1988). A Model for Customer Complaint Management. Marketing Science, 7(3), 287–298. https://doi.org/10.1287/mksc.7.3.287
dc.relation.isbasedonFrasquet, M., Miquel, M. J., & Mollá, A. (2017). Complaining at the Store or Through Social Media: The Influence of the Purchase Channel, Satisfaction, and Commitment. In F. Martínez-López, J. Gázquez-Abad, K. Ailawadi, & M. Yagüe-Guillén (Eds.), Advances in National Brand and Private Label Marketing (pp. 87–94). Cham: Springer.
dc.relation.isbasedonGhose, A., Ipeirotis, P. G., & Li, B. (2012). Designing Ranking Systems for Hotels on Travel Search Engines by Mining User-Generated and Crowdsourced Content. Marketing Science, 31(3), 493–520. https://doi.org/10.1287/mksc.1110.0700
dc.relation.isbasedonGuo, X. (2014). A novel Bass-type model for product life cycle quantification using aggregate market data. International Journal of Production Economics, 158, 208–216. https://doi.org/10.1016/j.ijpe.2014.07.018
dc.relation.isbasedonHassani, H., Beneki, C., Unger, S., Mazinani, M. T., & Yeganegi, M. R. (2020). Text Mining in Big Data Analytics. Big Data and Cognitive Computing, 4(1), 1. https://doi.org/10.3390/bdcc4010001
dc.relation.isbasedonHeath, C. (1996). Do People Prefer to Pass Along Good or Bad News? Valence and Relevance of News as Predictors of Transmission Propensity. Organizational Behavior and Human Decision Processes, 68(2), 79–94. https://doi.org/10.1006/obhd.1996.0091
dc.relation.isbasedonHerr, P. M., Kardes, F. R., & Kim, J. (1991). Effects of Word-of-Mouth and Product-Attribute Information on Persuasion: An Accessibility-Diagnosticity Perspective. Journal of Consumer Research, 17(4), 454–462. https://doi.org/10.1086/208570
dc.relation.isbasedonHsiao, Y.-H., Chen, M.-C., & Liao, W.-C. (2017). Logistics service design for cross-border E-commerce using Kansei engineering with text-mining-based online content analysis. Telematics and Informatics, 34(4), 284–302. https://doi.org/10.1016/j.tele.2016.08.002
dc.relation.isbasedonHuang, S., Liu, X., Peng, X., & Niu, Z. (2012). Fine-grained product features extraction and categorization in reviews opinion mining. In Proceedings of the 2012 IEEE 12th International Conference on Data Mining Workshops. Washington, DC: IEEE Computer Society.
dc.relation.isbasedonJalilvand, M. R., & Samiei, N. (2012). The effect of electronic word of mouth on brand image and purchase intention. Marketing Intelligence & Planning, 30(4), 460–476. https://doi.org/10.1108/02634501211231946
dc.relation.isbasedonJones, M. A., Reynolds, K. E., Mothersbaugh, D. L., & Beatty, S. E. (2007). The Positive and Negative Effects of Switching Costs on Relational Outcomes. Journal of Service Research, 9(4), 335–355. https://doi.org/10.1177/1094670507299382
dc.relation.isbasedonKahn, K. B., & Chase, C. W. (2018). The State of New-Product Forecasting. Foresight: The International Journal of Applied Forecasting, 51, 24–31.
dc.relation.isbasedonKarimi, S., Holland, C. P., & Papamichail, K. N. (2018). The impact of consumer archetypes on online purchase decision-making processes and outcomes: A behavioural process perspective. Journal of Business Research, 91, 71–82. https://doi.org/10.1016/j.jbusres.2018.05.038
dc.relation.isbasedonKim, T., & Hong, J. (2015). Bass model with integration constant and its applications on initial demand and left-truncated data. Technological Forecasting and Social Change, 95, 120–134. https://doi.org/10.1016/j.techfore.2015.02.009
dc.relation.isbasedonKotler, P. (2009). Marketing Management: A South Asian Perspective. Pearson Education India.
dc.relation.isbasedonLee, J., Park, D.-H., & Han, I. (2008). The effect of negative online consumer reviews on product attitude: An information processing view. Electronic Commerce Research and Applications, 7(3), 341–352. https://doi.org/10.1016/j.elerap.2007.05.004
dc.relation.isbasedonLee, T. Y., & Bradlow, E. T. (2011). Automated Marketing Research Using Online Customer Reviews. Journal of Marketing Research, 48(5), 881–894. https://doi.org/10.1509/jmkr.48.5.881
dc.relation.isbasedonLi, Z., Zhang, M., Ma, S., Zhou, B., & Sun, Y. (2009). Automatic Extraction for Product Feature Words from Comments on the Web. In Asia Information Retrieval Symposium, Lecture Notes in Computer Science (Vol. 5839, pp. 112–123). Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-642-04769-5_10
dc.relation.isbasedonLiu, Y., Bi, J.-W., & Fan, Z.-P. (2017). Ranking products through online reviews: A method based on sentiment analysis technique and intuitionistic fuzzy set theory. Information Fusion, 36, 149–161. https://doi.org/10.1016/j.inffus.2016.11.012
dc.relation.isbasedonLv, Z., Jin, Y., & Huang, J. (2018). How do sellers use live chat to influence consumer purchase decision in China? Electronic Commerce Research and Applications, 28, 102–113. https://doi.org/10.1016/j.elerap.2018.01.003
dc.relation.isbasedonMaru File, K., Cermak, D. S., & Alan Prince, R. (1994). Word-of-Mouth Effects in Professional Services Buyer Behaviour. Service Industries Journal, 14(3), 301–314. https://doi.org/10.1080/02642069400000035
dc.relation.isbasedonMazurova, E. (2017). Exploratory Analysis of the Factors Affecting Consumer Choice in E-Commerce: Conjoint Analysis. Journal of Information Systems Engineering and Management, 2(2), 12. https://doi.org/10.20897/jisem.201712
dc.relation.isbasedonMeyer, D., Dimitriadou, E., Hornik, K., Weingessel, A., & Leisch, F. (2021). Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071). Wien: TU. Retrieved from https://CRAN.R-project.org/package=e107
dc.relation.isbasedonMitra, S., & Jenamani, M. (2020). OBIM: A computational model to estimate brand image from online consumer review. Journal of Business Research, 114, 213–226. https://doi.org/10.1016/j.jbusres.2020.04.003
dc.relation.isbasedonMuller, E., & Yogev, G. (2006). When does the majority become a majority? Empirical analysis of the time at which main market adopters purchase the bulk of our sales. Technological Forecasting and Social Change, 73(9), 1107–1120. https://doi.org/10.1016/j.techfore.2005.12.009
dc.relation.isbasedonNetzer, O., Feldman, R., Goldenberg, J., & Fresko, M. (2012). Mine Your Own Business: Market-Structure Surveillance Through Text Mining. Marketing Science, 31(3), 521–543. https://doi.org/10.1287/mksc.1120.0713
dc.relation.isbasedonO’Brien, J. (2018). Age, autos, and the value of a statistical life. Journal of Risk and Uncertainty, 57(1), 51–79. https://doi.org/10.1007/s11166-018-9285-3
dc.relation.isbasedonPark, D.-H., Lee, J., & Han, I. (2007). The Effect of On-Line Consumer Reviews on Consumer Purchasing Intention: The Moderating Role of Involvement. International Journal of Electronic Commerce, 11(4), 125–148. https://doi.org/10.2753/JEC1086-4415110405
dc.relation.isbasedonQiu, G., Liu, B., Bu, J., & Chen, C. (2011). Opinion Word Expansion and Target Extraction through Double Propagation. Computational Linguistics, 37(1), 9–27. https://doi.org/10.1162/coli_a_00034
dc.relation.isbasedonRawal, P. (2013). AIDA Marketing Communication Model: Stimulating a Purchase Decision in the Minds of the Consumers through a Linear Progression of Steps. IRC’s International Journal of Multidisciplinary Research in Social Management, 1, 37–44.
dc.relation.isbasedonReimer, T., & Benkenstein, M. (2016). When good WOM hurts and bad WOM gains: The effect of untrustworthy online reviews. Journal of Business Research, 69(12), 5993–6001. https://doi.org/10.1016/j.jbusres.2016.05.014
dc.relation.isbasedonRichins, M. L. (1983). Negative Word-of-Mouth by Dissatisfied Consumers: A Pilot Study. Journal of Marketing, 47(1), 68–78. https://doi.org/10.1177/002224298304700107
dc.relation.isbasedonSallee, J. M., West, S. E., & Fan, W. (2016). Do consumers recognize the value of fuel economy? Evidence from used car prices and gasoline price fluctuations. Journal of Public Economics, 135, 61–73. https://doi.org/10.1016/j.jpubeco.2016.01.003
dc.relation.isbasedonSchlosser, F. K., & McNaughton, R. B. (2009). Using the I‐MARKOR scale to identify market‐oriented individuals in the financial services sector. Journal of Services Marketing, 23(4), 236–248. https://doi.org/10.1108/08876040910965575
dc.relation.isbasedonSchneider, M. J., & Gupta, S. (2016). Forecasting sales of new and existing products using consumer reviews: A random projections approach. International Journal of Forecasting, 32(2), 243–256. https://doi.org/10.1016/j.ijforecast.2015.08.005
dc.relation.isbasedonSimon, H. A. (2019). The Sciences of the Artificial. Cambridge, MA: MIT Press.
dc.relation.isbasedonSohrabpour, V., Oghazi, P., Toorajipour, R., & Nazarpour, A. (2021). Export sales forecasting using artificial intelligence. Technological Forecasting and Social Change, 163, 120480. https://doi.org/10.1016/j.techfore.2020.120480
dc.relation.isbasedonStokes, D., & Lomax, W. (2002). Taking control of word of mouth marketing: the case of an entrepreneurial hotelier. Journal of Small Business and Enterprise Development, 9(4), 349–357. https://doi.org/10.1108/14626000210450531
dc.relation.isbasedonTARP. (1985). Consumer Complaint Handling in America: An Update Study. Technical Assistance Research Programs Institute.
dc.relation.isbasedonTitov, I., & McDonald, R. (2008). Modeling online reviews with multi-grain topic models. In Proceedings of the 17th International Conference on World Wide Web (pp. 111–120). Beijing: ACM. https://doi.org/10.1145/1367497.1367513
dc.relation.isbasedonTseng, C. H., & Wei, L. F. (2020). The efficiency of mobile media richness across different stages of online consumer behavior. International Journal of Information Management, 50, 353–364. https://doi.org/10.1016/j.ijinfomgt.2019.08.010
dc.relation.isbasedonVardakas, J. S., Zorba, N., & Verikoukis, C. V. (2014). A Survey on Demand Response Programs in Smart Grids: Pricing Methods and Optimization Algorithms. IEEE Communications Surveys & Tutorials, 17(1), 152–178. https://doi.org/COMST.2014.2341586
dc.relation.isbasedonVieira, V., Santini, F. O., & Araujo, C. F. (2018). A meta-analytic review of hedonic and utilitarian shopping values. Journal of Consumer Marketing, 35(4), 426–437. https://doi.org/10.1108/JCM-08-2016-1914
dc.relation.isbasedonWang, X., Yang, Z., & Liu, N. R. (2009). The Impacts of Brand Personality and Congruity on Purchase Intention: Evidence From the Chinese Mainland’s Automobile Market. Journal of Global Marketing, 22(3), 199–215. https://doi.org/10.1080/08911760902845023
dc.relation.isbasedonWei, W., Liu, H., He, J., Yang, H., & Du, X. (2008). Extracting feature and opinion words effectively from chinese product reviews. In Proceedings of the 5th International Conference on Fuzzy Systems and Knowledge Discovery (Vol. 4, pp. 170–174). Shandong: IEEE Computer Society.
dc.relation.isbasedonWest, R., & Turner, L. (2007). Introducing Communication Theory. New York, NY: McGraw Hill.
dc.relation.isbasedonWong, T.-L., & Lam, W. (2008). Learning to extract and summarize hot item features from multiple auction web sites. Knowledge and Information Systems, 14(2), 143–160. https://doi.org/10.1007/s10115-007-0078-2
dc.relation.isbasedonYan, Z., Xing, M., Zhang, D., & Ma, B. (2015). EXPRS: An extended pagerank method for product feature extraction from online consumer reviews. Information & Management, 52(7), 850–858. https://doi.org/10.1016/j.im.2015.02.002
dc.relation.isbasedonZeithaml, V. A., Parasuraman, A., & Berry, L. L. (1992). Strategic positioning on the dimensions of service quality. Advances in Services Marketing and Management, 2, 207–228.
dc.relation.ispartofEkonomie a Managementcs
dc.relation.ispartofEconomics and Managementen
dc.rightsCC BY-NC
dc.subjectmarket successen
dc.subjecttext miningen
dc.subjectpurchase decisionen
local.facultyFaculty of Economics
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