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How to Quantify Perceived Quality from Consumer Big Data: An Information Usefulness Perspective

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Knowledge and Systems Sciences (KSS 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1927))

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Abstract

Perceived quality reflects consumers’ subjective perceptions of a product and is important for manufacturers to improve quality. In recent years, social media becomes a new channel for consumers to share perceived quality, but existing studies overlooked the information usefulness of each piece of data, which creates barriers for manufacturers to process impactful information. This paper proposes a two-stage approach to quantifying perceived quality based on information usefulness. First, the usefulness categories of perceived quality are identified through a combination of deep learning and the knowledge adoption model; then, multiple usefulness categories are considered to quantify the perceived quality information. In the empirical study, the method was validated using an automobile dataset from Autohome. Results show that the method obtains more effective perceived quality information. The proposed method contributes to the research on both perceived quality quantification and information usefulness.

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References

  1. Paul, J.: Toward a ‘masstige’ theory and strategy for marketing. Eur. J. Int. Manag. 12(5–6), 722–745 (2018)

    Article  Google Scholar 

  2. Golder, P.N., Mitra, D., Moorman, C.: What is quality? An integrative framework of processes and states. J. Mark. 76(4), 1–23 (2012)

    Article  Google Scholar 

  3. Harju, C.: The perceived quality of wooden building materials—a systematic literature review and future research agenda. Int. J. Consum. Stud. 46(1), 29–55 (2022)

    Article  Google Scholar 

  4. Akdeniz, M.B., Calantone, R.J.: A longitudinal examination of the impact of quality perception gap on brand performance in the US Automotive Industry. Mark. Lett. 28(1), 43–57 (2017)

    Article  Google Scholar 

  5. Chowdhury, H.K., Ahmed, J.U.: An examination of the effects of partitioned country of origin on consumer product quality perceptions. Int. J. Consum. Stud. 33(4), 496–502 (2009)

    Article  Google Scholar 

  6. Yieh, K., Chiao, Y., Chiu, Y.: Understanding the antecedents to customer loyalty by applying structural equation modeling. Total Qual. Manag. Bus. Excell. 18(3), 267–284 (2007)

    Article  Google Scholar 

  7. Yu, C.J., Wu, L., Chiao, Y., Tai, H.: Perceived quality, customer satisfaction, and customer loyalty: the case of lexus in Taiwan. Total Qual. Manag. Bus. Excell. 16(6), 707–719 (2007)

    Article  Google Scholar 

  8. Duraiswamy, V., Campean, F., Harris, S., Munive-Hernandez, J.E.: Development of a methodology for robust evaluation of perceived quality of vehicle body panel gaps. In: The Proceedings of the DESIGN 2018 15th International Design Conference, (2018)

    Google Scholar 

  9. Hazen, B.T., Boone, C.A., Wang, Y., Khor, K.S.: Perceived quality of remanufactured products: construct and measure development. J. Clean. Prod. 142, 716–726 (2017)

    Article  Google Scholar 

  10. Sun, B., Mao, H., Yin, C.: How to identify product defects and segment consumer groups on an online auto forum. Int. J. Consum. Stud. 46(6), 2270–2287 (2022)

    Article  Google Scholar 

  11. Danner, H., Thøgersen, J.: Does online chatter matter for consumer behaviour? A priming experiment on organic food. Int. J. Consum. Stud. 46(3), 850–869 (2021)

    Article  Google Scholar 

  12. He, L., Zhang, N., Yin, L.: The evaluation for perceived quality of products based on text mining and fuzzy comprehensive evaluation. Electron. Commer. Res. 18(2), 277–289 (2018)

    Article  Google Scholar 

  13. Bawden, D., Robinson, L.: The dark side of information: overload, anxiety and other paradoxes and pathologies. J. Inf. Sci. 35(2), 180–191 (2009)

    Article  Google Scholar 

  14. Stylidis, K., Wickman, C., Söderberg, R.: Perceived quality of products: a framework and attributes ranking method. J. Eng. Des. 31(1), 37–67 (2020)

    Article  Google Scholar 

  15. Sussman, S.W., Siegal, W.S.: Informational influence in organizations: an integrated approach to knowledge adoption. Inf. Syst. Res. 14(1), 47–65 (2003)

    Article  Google Scholar 

  16. Wu, R., Wu, H.H., Wang, C.L.: Why is a picture ‘worth a thousand words’? Pictures as information in perceived helpfulness of online reviews. Int. J. Consum. Stud. 45(3), 364–378 (2020)

    Article  Google Scholar 

  17. Filieri, R.: What makes online reviews helpful? A diagnosticity-adoption framework to explain informational and normative influences in e-WOM. J. Bus. Res. 68(6), 1261–1270 (2015)

    Article  Google Scholar 

  18. Liu, X., Wang, G.A., Fan, W., Zhang, Z.: Finding useful solutions in online knowledge communities: a theory-driven design and multilevel analysis. Inf. Syst. Res. 31(3), 731–752 (2020)

    Article  Google Scholar 

  19. Cao, Q., Duan, W., Gan, Q.: Exploring determinants of voting for the ‘helpfulness’ of online user reviews: a text mining approach. Decis. Support Syst. 50(2), 511–521 (2011)

    Article  Google Scholar 

  20. Liu, Z., Park, S.: What makes a useful online review? Implication for travel product websites. Tour. Manage. 47, 140–151 (2015)

    Article  Google Scholar 

  21. Schindler, R.M., Bickart, B.: Perceived helpfulness of online consumer reviews: the role of message content and style. J. Consum. Behav. 11(3), 234–243 (2012)

    Article  Google Scholar 

  22. Chen, Y., Zhang, Z.: Research on text sentiment analysis based on CNNs and SVM. In: 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA), pp. 2731–273 (2018)

    Google Scholar 

  23. Zeithaml, V.A.: Consumer perceptions of price, quality, and value: a means-end model and synthesis of evidence. J. Mark. 52(3), 2–22 (1988)

    Article  Google Scholar 

  24. Mitra, D., Golder, P.N.: How does objective quality affect perceived quality? Short-term effects, long-term effects, and asymmetries. Mark. Sci. 25(3), 230–247 (2006)

    Article  Google Scholar 

  25. Yoon, B., Jeong, Y., Lee, K., Lee, S.: A systematic approach to prioritizing R&D projects based on customer-perceived value using opinion mining. Technovation 98, 102164 (2020)

    Article  Google Scholar 

  26. Gottlieb, U.R., Brown, M.R., Drennan, J.: The influence of service quality and trade show effectiveness on post-show purchase intention. Eur. J. Mark. 45(11/12), 1642–1659 (2011)

    Article  Google Scholar 

  27. Singh, A., Jenamani, M., Thakkar, J.J., Rana, N.P.: Propagation of online consumer perceived negativity: quantifying the effect of supply chain underperformance on passenger car sales. J. Bus. Res. 132, 102–114 (2021)

    Article  Google Scholar 

  28. Wang, Y.-Y., Guo, C., Susarla, A., Sambamurthy, V.: Online to offline: the impact of social media on offline sales in the automobile industry. Inf. Syst. Res. 32(2), 582–604 (2021)

    Article  Google Scholar 

  29. Slotegraaf, R.J., Inman, J.J.: Longitudinal shifts in the drivers of satisfaction with product quality: the role of attribute resolvability. J. Mark. Res. 41(3), 269–280 (2004)

    Article  Google Scholar 

  30. Stylidis, K., Wickman, C., Söderberg, R.: Defining perceived quality in the automotive industry: an engineering approach. Procedia CIRP 36, 165–170 (2015)

    Article  Google Scholar 

  31. Stylidis, K., Dagman, A., Almius, H., Gong, L., Söderberg, R.: Perceived quality evaluation with the use of extended reality. In: Proceedings of the Design Society: International Conference on Engineering Design, vol. 1, no. 1, pp. 1993–2002 (2019)

    Google Scholar 

  32. Ma, M.-Y., Chen, C.-W., Chang, Y.-M.: Using Kano model to differentiate between future vehicle-driving services. Int. Ind. Ergon. 69, 142–152 (2019)

    Article  Google Scholar 

  33. Sweller, J.: Cognitive load during problem solving effects on learning. Cogn. Sci. 12(2), 257–285 (1988)

    Article  Google Scholar 

  34. Moradi, M., Zihagh, F.: A meta-analysis of the elaboration likelihood model in the electronic word of mouth literature. Int. J. Consum. Stud. 46(5), 1900–1918 (2022)

    Article  Google Scholar 

  35. Oh, Y.K., Yi, J.: Asymmetric effect of feature level sentiment on product rating: an application of bigram natural language processing (NLP) analysis. Internet Res. 32(3), 1023–1040 (2022)

    Article  Google Scholar 

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Authors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Tong Yang, Yanzhong Dang & Jiangning Wu

Authors
  1. Tong Yang
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  2. Yanzhong Dang
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  3. Jiangning Wu
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Corresponding author

Correspondence to Tong Yang .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Jian Chen

  2. Japan Advanced Institute of Science and Technology, Ishikawa, Japan

    Van-Nam Huynh

  3. CAS Academy of Mathematics and Systems Science, Beijing, China

    Xijin Tang

  4. Dalian University of Technology, Dalian, China

    Jiangning Wu

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Yang, T., Dang, Y., Wu, J. (2023). How to Quantify Perceived Quality from Consumer Big Data: An Information Usefulness Perspective. In: Chen, J., Huynh, VN., Tang, X., Wu, J. (eds) Knowledge and Systems Sciences. KSS 2023. Communications in Computer and Information Science, vol 1927. Springer, Singapore. https://doi.org/10.1007/978-981-99-8318-6_5

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  • DOI: https://doi.org/10.1007/978-981-99-8318-6_5

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8317-9

  • Online ISBN: 978-981-99-8318-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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