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How to prioritize perceived quality attributes from consumers' perspective? Analysis through social media data

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Abstract

Social media data is gaining attention as consumers become accustomed to sharing and finding product perceptions on social media. Perceived quality is consumers' subjective perceptions, and it is important for manufacturers to prioritize perceived quality attributes. However, existing studies mainly use survey data, which is prone to bias, and lack analysis of why perceived quality arises. We propose a three-stage framework to prioritize perceived quality attributes using social media data based on text mining techniques. First, a deep-learning approach is used to identify perceived quality; second, the group perceived quality, attribute importance, and quality category of the attribute are synthetically analyzed to quantify the perceived quality, and perception causes are mined; finally, importance-performance analysis is used to prioritize attributes and a bottom-up cause chart is built. In the case study, an automobile dataset is crawled to apply the proposed framework and the results are validated in a user experiment.

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References

  1. 12365auto. (2022). Complaints of cadillac CT4–2020 model 28T. Retrieved September 24, 2022, from http://www.12365auto.com/series/c-2799-7-1.shtml

  2. Abbas, Y., & Malik, M. S. I. (2021). Defective products identification framework using online reviews. Electronic Commerce Research. https://doi.org/10.1007/s10660-021-09495-8

    Article  Google Scholar 

  3. Abrahams, A. S., Fan, W., Wang, G. A., Zhang, Z. J., & Jiao, J. (2015). An integrated text analytic framework for product defect discovery. Production and Operations Management, 24(6), 975–990. https://doi.org/10.1111/poms.12303

    Article  Google Scholar 

  4. Abrahams, A. S., Jiao, J., Fan, W., Wang, G. A., & Zhang, Z. (2013). What’s buzzing in the blizzard of buzz? Automotive component isolation in social media postings. Decision Support Systems, 55(4), 871–882. https://doi.org/10.1016/j.dss.2012.12.023

    Article  Google Scholar 

  5. Abrahams, A. S., Jiao, J., Wang, G. A., & Fan, W. (2012). Vehicle defect discovery from social media. Decision Support Systems, 54(1), 87–97. https://doi.org/10.1016/j.dss.2012.04.005

    Article  Google Scholar 

  6. Akdeniz, M. B., & Calantone, R. J. (2017). A longitudinal examination of the impact of quality perception gap on brand performance in the US Automotive Industry. Marketing Letters, 28(1), 43–57. https://doi.org/10.1007/s11002-015-9392-8

    Article  Google Scholar 

  7. Azzopardi, E., & Nash, R. (2013). A critical evaluation of importance–performance analysis. Tourism Management, 35, 222–233. https://doi.org/10.1016/j.tourman.2012.07.007

    Article  Google Scholar 

  8. Baidu API. (2021). NLP-Python-SDK. Retrieved July 3, 2022 from https://ai.baidu.com/ai-doc/NLP/tk6z52b9z

  9. Bawden, D., & Robinson, L. (2009). The dark side of information: Overload, anxiety and other paradoxes and pathologies. Journal of Information Science, 35(2), 180–191. https://doi.org/10.1177/0165551508095781

    Article  Google Scholar 

  10. Bi, J.-W., Liu, Y., Fan, Z.-P., & Zhang, J. (2019). Wisdom of crowds: Conducting importance-performance analysis (IPA) through online reviews. Tourism Management, 70, 460–478. https://doi.org/10.1016/j.tourman.2018.09.010

    Article  Google Scholar 

  11. Chen, Y., Fay, S., & Wang, Q. (2011). The role of marketing in social media: How online consumer reviews evolve. Journal of Interactive Marketing, 25(2), 85–94. https://doi.org/10.1016/j.intmar.2011.01.003

    Article  Google Scholar 

  12. Cui, S., Wang, D., Wang, Y., Yu, P.-W., & Jin, Y. (2018). An improved support vector machine-based diabetic readmission prediction. Computer Methods and Programs in Biomedicine, 166, 123–135. https://doi.org/10.1016/j.cmpb.2018.10.012

    Article  Google Scholar 

  13. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In: Paper presented at the Proceedings of NAACL-HLT 2019, Minneapolis, MN.

  14. Dou, W. Y., Lim, K. H., Su, C. T., Zhou, N., & Cui, N. (2010). Brand positioning strategy using search engine marketing. MIS Quarterly, 34(2), 261–279. https://doi.org/10.2307/20721427

    Article  Google Scholar 

  15. Eskildsen, J. K., & Kristensen, K. (2006). Enhancing importance-performance analysis. International Journal of Productivity and Performance Management, 55(1), 40–60. https://doi.org/10.1108/17410400610635499

    Article  Google Scholar 

  16. Feng, J., & Papatla, P. (2011). Advertising: Stimulant or suppressant of online word of mouth? Journal of Interactive Marketing, 25(2), 75–84. https://doi.org/10.1016/j.intmar.2010.11.002

    Article  Google Scholar 

  17. Feng, J., & Papatla, P. (2012). Is online word of mouth higher for new models or redesigns? An investigation of the automobile industry. Journal of Interactive Marketing, 26(2), 92–101. https://doi.org/10.1016/j.intmar.2012.01.001

    Article  Google Scholar 

  18. Gao, G., Greenwood, B. N., Agarwal, R., & McCullough, J. S. (2015). Vocal minority and silent majority how do online ratings reflect population perceptions of quality. MIS Quarterly, 39(3), 565–589. https://doi.org/10.2139/ssrn.2629837

    Article  Google Scholar 

  19. Golara, S., Dooley, K. J., & Mousavi, N. (2021). Are dealers still relevant? How dealer service quality impacts manufacturer success. Production and Operations Management, 30(10), 3560–3578. https://doi.org/10.1111/poms.13450

    Article  Google Scholar 

  20. Golder, P. N., Debanjan, M., & Christine, M. (2012). What is quality?An integrative framework of processes and states. Journal of Marketing, 76, 1–23. https://doi.org/10.1016/j.jbusres.2011.02.012

    Article  Google Scholar 

  21. Gottlieb, U. R., Brown, M. R., & Drennan, J. (2011). The influence of service quality and trade show effectiveness on post-show purchase intention. European Journal of Marketing, 45(11/12), 1642–1659. https://doi.org/10.1108/03090561111167324

    Article  Google Scholar 

  22. Gruss, R., Abrahams, A. S., Fan, W., & Wang, G. A. (2018). By the numbers: The magic of numerical intelligence in text analytic systems. Decision Support Systems, 113, 86–98. https://doi.org/10.1016/j.dss.2018.07.004

    Article  Google Scholar 

  23. Guo, Y., Barnes, S. J., & Jia, Q. (2017). Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent dirichlet allocation. Tourism Management, 59, 467–483. https://doi.org/10.1016/j.tourman.2016.09.009

    Article  Google Scholar 

  24. Hazen, B. T., Boone, C. A., Wang, Y., & Khor, K. S. (2017). Perceived quality of remanufactured products: Construct and measure development. Journal of Cleaner Production, 142, 716–726. https://doi.org/10.1016/j.jclepro.2016.05.099

    Article  Google Scholar 

  25. He, L., Zhang, N., & Yin, L. (2018). The evaluation for perceived quality of products based on text mining and fuzzy comprehensive evaluation. Electronic Commerce Research, 18(2), 277–289. https://doi.org/10.1007/s10660-018-9292-0

    Article  Google Scholar 

  26. He, Z., Zheng, L., & He, S. (2022). A novel approach for product competitive analysis based on online reviews. Electronic Commerce Research. https://doi.org/10.1007/s10660-022-09534-y

    Article  Google Scholar 

  27. Huang, L., Tan, C.-H., Ke, W., & Wei, K.-K. (2014). Do we order product review information display? How? Information and Management, 51(7), 883–894. https://doi.org/10.1016/j.im.2014.05.002

    Article  Google Scholar 

  28. Husbands, P., Simon, H. D., & Ding, C. (2000, OCT 22). On the use of the singular value decomposition for text retrieval. In: Paper Presented at the 1st Annual Meeting of the Computational Information Retrieval Workshop (CIR 00), RALEIGH, NC.

  29. Islam, A., Laato, S., Talukder, S., & Sutinen, E. (2020). Misinformation sharing and social media fatigue during COVID-19: An affordance and cognitive load perspective. Technological Forecasting and Social Change, 159, 120201. https://doi.org/10.1016/j.techfore.2020.120201

    Article  Google Scholar 

  30. Kano, N., Seraku, N., Takahashi, F., & Tsuji, S. (1984). Attractive quality and must-be quality. The Journal of Japanese Society for Quality Control, 14(2), 147–156.

    Google Scholar 

  31. Kim, K., Park, O.-J., Yun, S., & Yun, H. (2017). What makes tourists feel negatively about tourism destinations? Application of hybrid text mining methodology to smart destination management. Technological Forecasting and Social Change, 123, 362–369. https://doi.org/10.1016/j.techfore.2017.01.001

    Article  Google Scholar 

  32. Kim, M., Kim, S., & Lee, J. (2018). Spatial heterogeneity of country-of-origin effects within a country: Analysis of online review ratings in the US car market. Marketing Letters, 29(2), 189–205. https://doi.org/10.1007/s11002-018-9451-z

    Article  Google Scholar 

  33. Koksal, O., & Akgul, O. (2022). A comparative text classification study with deep learning-based algorithms. In: Paper Presented at the 2022 9th International Conference on Electrical and Electronics Engineering (ICEEE).

  34. Kumar, S. (2021). Deep learning based affective computing. Journal of Enterprise Information Management, 34(5), 1551–1575. https://doi.org/10.1108/jeim-12-2020-0536

    Article  Google Scholar 

  35. Li, Q., Zeng, D. D., Xu, D. J., Liu, R., & Yao, R. (2020). Understanding and predicting users’ rating behavior: A cognitive perspective. Informs Journal on Computing. https://doi.org/10.1287/ijoc.2019.0919

    Article  Google Scholar 

  36. Liem, A., Abidin, S. A., & Warell, A. (2009). Designers’ perceptions of typical characteristics of form treatment in automobile styling. In: Paper Presented at the 5th International Workshop on Design and Semantics of Form and Movement, DesForm, Taipei.

  37. Liu, X., Zhang, B., Susarla, A., & Padman, R. (2020). Go to YouTube and call me in the morning: Use of social media for chronic conditions. MIS Quarterly, 44(1), 257–283. https://doi.org/10.25300/misq/2020/15107

    Article  Google Scholar 

  38. Liu, Y., & Shankar, V. (2015). The dynamic impact of product-harm crises on brand preference and advertising effectiveness: An empirical analysis of the automobile industry. Management Science, 61(10), 2514–2535. https://doi.org/10.1287/mnsc.2014.2095

    Article  Google Scholar 

  39. Ma, M.-Y., Chen, C.-W., & Chang, Y.-M. (2019). Using Kano model to differentiate between future vehicle-driving services. International Journal of Industrial Ergonomics, 69, 142–152. https://doi.org/10.1016/j.ergon.2018.11.003

    Article  Google Scholar 

  40. Malla, S., & Alphonse, P. J. A. (2021). COVID-19 outbreak: An ensemble pre-trained deep learning model for detecting informative tweets. Applied Soft Computing, 107, 107495. https://doi.org/10.1016/j.asoc.2021.107495

    Article  Google Scholar 

  41. Martilla, J. A., & James, J. C. (1977). Importance-performance Analysis. Journal of Marketing, 41(1), 77–79. https://doi.org/10.2307/1250495

    Article  Google Scholar 

  42. Matzler, K., Bailom, F., Hinterhuber, H. H., Renzl, B., & Pichler, J. (2004). The asymmetric relationship between attribute-level performance and overall customer satisfaction: A reconsideration of the importance–performance analysis. Industrial Marketing Management, 33(4), 271–277. https://doi.org/10.1016/s0019-8501(03)00055-5

    Article  Google Scholar 

  43. Miao, Y., Du, R., Li, J., & Westland, J. C. (2019). A two-sided matching model in the context of B2B export cross-border e-commerce. Electronic Commerce Research, 19(4), 841–861. https://doi.org/10.1007/s10660-019-09361-8

    Article  Google Scholar 

  44. Mitra, D., & Golder, P. N. (2006). How does objective quality affect perceived quality? Short-term effects, long-term effects, and asymmetries. Marketing Science, 25(3), 230–247. https://doi.org/10.1287/mksc.1050.0175

    Article  Google Scholar 

  45. Nadeau, J., & Casselman, R. M. (2008). Competitive advantage with new product development: implications for life cycle theory. Journal of Strategic Marketing, 16(5), 401–411. https://doi.org/10.1080/09652540802480894

    Article  Google Scholar 

  46. Nguyen, H. T., & Chaudhuri, M. (2019). Making new products go viral and succeed. International Journal of Research in Marketing, 36(1), 39–62. https://doi.org/10.1016/j.ijresmar.2018.09.007

    Article  Google Scholar 

  47. Oh, H. (2001). Revisiting importance–performance analysis. Tourism Management, 22, 617–627. https://doi.org/10.1016/S0261-5177(01)00036-X

    Article  Google Scholar 

  48. Qian, Y., Jiang, Y., Du, Y., Sun, J., & Liu, Y. (2020). Segmenting market structure from multi-channel clickstream data: A novel generative model. Electronic Commerce Research, 20(3), 509–533. https://doi.org/10.1007/s10660-019-09393-0

    Article  Google Scholar 

  49. Qiao, J., Wang, C., Guan, S., & Shuran, L. (2022). Construction-accident narrative classification using shallow and deep learning. Journal of Construction Engineering and Management. https://doi.org/10.1061/(asce)co.1943-7862.0002354

    Article  Google Scholar 

  50. Raithel, S., Sarstedt, M., Scharf, S., & Schwaiger, M. (2012). On the value relevance of customer satisfaction: Multiple drivers and multiple markets. Journal of the Academy of Marketing Science, 40(4), 509–525. https://doi.org/10.1007/s11747-011-0247-4

    Article  Google Scholar 

  51. Ray, B., Garain, A., & Sarkar, R. (2021). An ensemble-based hotel recommender system using sentiment analysis and aspect categorization of hotel reviews. Applied Soft Computing, 98, 106935. https://doi.org/10.1016/j.asoc.2020.106935

    Article  Google Scholar 

  52. Sabbah, T., Selamat, A., Selamat, M. H., Al-Anzi, F. S., Viedma, E. H., Krejcar, O., & Fujita, H. (2017). Modified frequency-based term weighting schemes for text classification. Applied Soft Computing, 58, 193–206. https://doi.org/10.1016/j.asoc.2017.04.069

    Article  Google Scholar 

  53. ShomyLiu. (2020). pytorch_bert_elmo_example. Retrieved September 27, 2022, from https://github.com/ShomyLiu/pytorch_bert_elmo_example

  54. Singh, A., Jenamani, M., Thakkar, J. J., & Rana, N. P. (2021). Propagation of online consumer perceived negativity: Quantifying the effect of supply chain underperformance on passenger car sales. Journal of Business Research, 132, 102–114. https://doi.org/10.1016/j.jbusres.2021.04.027

    Article  Google Scholar 

  55. Strydom, G., Ewing, M. T., & Heggen, C. (2020). Time lags, non-linearity and asymmetric effects in an extended service-profit chain. European Journal of Marketing, 54(10), 2343–2363. https://doi.org/10.1108/ejm-12-2019-0906

    Article  Google Scholar 

  56. Stylidis, K., Dagman, A., Almius, H., Gong, L., & Söderberg, R. (2019). Perceived quality evaluation with the use of extended reality. Proceedings of the Design Society: International Conference on Engineering Design, 1(1), 1993–2002. https://doi.org/10.1017/dsi.2019.205

    Article  Google Scholar 

  57. Stylidis, K., Wickman, C., & Söderberg, R. (2020). Perceived quality of products: A framework and attributes ranking method. Journal of Engineering Design, 31(1), 37–67. https://doi.org/10.1080/09544828.2019.1669769

    Article  Google Scholar 

  58. Sweller, J. (1988). Cognitive load during problem solving effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1016/0364-0213(88)90023-7

    Article  Google Scholar 

  59. Vernon, R. (1992). International investment and international trade in the product cycle. In: International economic policies and their theoretical foundations (pp. 415–435). San Diego, California: Academic Press.

  60. Wang, R., Li, Z., Cao, J., & Chen, T. (2019). Chinese text feature extraction and classification based on deep learning. In: Paper presented at the proceedings of the 3rd international conference on computer science and application engineering.

  61. Wang, W. Q., & Benbasat, I. (2009). Interactive decision aids for consumer decision making in e-commerce: The influence of perceived strategy restrictiveness. MIS Quarterly, 33(2), 293–320. https://doi.org/10.2307/20650293

    Article  Google Scholar 

  62. Wang, Y.-Y., Guo, C., Susarla, A., & Sambamurthy, V. (2021). Online to offline: The impact of social media on offline sales in the automobile industry. Information Systems Research, 32(2), 582–604. https://doi.org/10.1287/isre.2020.0984

    Article  Google Scholar 

  63. Wu, E.H.-K., Chen, S.-E., Liu, J.-J., Ou, Y.-Y., & Sun, M.-T. (2020). A self-relevant CNN-SVM model for problem classification in K-12 question-driven learning. IEEE Access, 8, 225822–225830. https://doi.org/10.1109/access.2020.3039531

    Article  Google Scholar 

  64. Wu, J., & Zhao, N. (2022). What consumer complaints should hoteliers prioritize? Analysis of online reviews under different market segments. Journal of Hospitality Marketing and Management. https://doi.org/10.1080/19368623.2022.2119187

    Article  Google Scholar 

  65. Xu, Y., Armony, M., & Ghose, A. (2021). The interplay between online reviews and physician demand: An empirical investigation. Management Science, 67(12), 7344–7361. https://doi.org/10.1287/mnsc.2020.3879

    Article  Google Scholar 

  66. Xu, Z., & Dang, Y. (2020). Automated digital cause-and-effect diagrams to assist causal analysis in problem-solving: A data-driven approach. International Journal of Production Research, 58(17), 5359–5379. https://doi.org/10.1080/00207543.2020.1727043

    Article  Google Scholar 

  67. Yilmaz, S., & Toklu, S. (2020). A deep learning analysis on question classification task using Word2vec representations. Neural Computing and Applications, 32(7), 2909–2928. https://doi.org/10.1007/s00521-020-04725-w

    Article  Google Scholar 

  68. Yoon, B., Jeong, Y., Lee, K., & Lee, S. (2020). A systematic approach to prioritizing R&D projects based on customer-perceived value using opinion mining. Technovation. https://doi.org/10.1016/j.technovation.2020.102164

    Article  Google Scholar 

  69. Zeithaml, V. A. (1988). Consumer perceptions of price, quality, and value a means-end model and synthesis of evidence. Journal of Marketing, 52(3), 2–22. https://doi.org/10.2307/1251446

    Article  Google Scholar 

  70. Zhang, B., Pavlou, P. A., & Krishnan, R. (2018). On direct vs. indirect peer influence in large social networks. Information Systems Research, 29(2), 292–314. https://doi.org/10.1287/isre.2017.0753

    Article  Google Scholar 

  71. Zhao, A., & Yu, Y. (2021). Knowledge-enabled BERT for aspect-based sentiment analysis. Knowledge-Based Systems, 227, 107220.

    Article  Google Scholar 

  72. Zhao, K., Hu, Y., Hong, Y., & Westland, J. C. (2021). Understanding characteristics of popular streamers on live streaming platforms: Evidence from Twitch.tv. Journal of the Association for Information Systems, 22(4), 1076–1098. https://doi.org/10.17705/1jais.00689

    Article  Google Scholar 

  73. Zheng, L., He, Z., & He, S. (2020). A novel probabilistic graphic model to detect product defects from social media data. Decision Support Systems. https://doi.org/10.1016/j.dss.2020.113369

    Article  Google Scholar 

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Acknowledgements

The authors acknowledge the Center for Big Data & Intelligent Decision-Making of Dalian University of Technology for providing computing resources.

Funding

This work was supported by the National Natural Science Foundation of China [Project No. 71871041], and the China Scholarship Council [202106060118].

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Authors and Affiliations

  1. Institute of Systems Engineering, Dalian University of Technology, No. 2 Linggong Rd., Dalian, 116024, People’s Republic of China

    Tong Yang, Yanzhong Dang & Jiangning Wu

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Correspondence to Tong Yang.

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Appendices

Appendix A: Quality categories of each attribute

Class

Quality category

Attribute

D

Attractive

CP, Power, Appearance

One-dimensional

Comfort, Space

Must-be

Interiors, Handling, EC

C

Attractive

CP, Appearance, Power

One-dimensional

EC

Must-be

Comfort, Interiors, Handling, Space

B

Attractive

CP, Appearance

One-dimensional

Power, EC

Must-be

Comfort, Interiors, Handling, Space

A

Attractive

CP, Appearance, Power

One-dimensional

Interiors, EC

Must-be

Comfort, Handling, Space

A0

Attractive

CP, Power

One-dimensional

Space, Appearance, EC

Must-be

Comfort, Interiors, Handling

A00

Attractive

CP

One-dimensional

Comfort, Handling, Power, Appearance, EC

Must-be

Interiors, Space

Appendix B: Perception cause details of CT4

Attribute

Component

Parameter

Opinion

Comfort

Seat

Leather

Comfy, soft, not suitable, hard

Sound insulation

Multilayer soundproof glass

Not bad, good

Interiors

Screen

Touch LED screen

Small

Hand feel

Steering wheel: leather

Good

EC

Fuel consumption

100 km fuel consumption:9.1

Low

Energy consumption

100 km fuel consumption:9.1

High

Handling

Underpan

Five link independent suspension

Sturdy

Brake

Brake: ventilated disc type

Sensitive

Appearance

Design

6 color optional

Not bad

Facade

4-door, 5-seat, 3-box

Aggressive, good, fond

Power

Dynamic quality

Maximum power:174

Ordinary, sufficient, comfortable-going

Gear

8 block H-Matic

Comfortable-going

Space

Trunk

Trunk:364

Big

Interspace

4760*1815*1428

Big, ordinary, fair to middling, average

CP

Price

259,700

Appropriate, inexpensive

Configuration

Tire pressure display

Not bad, multifunctional

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Yang, T., Dang, Y. & Wu, J. How to prioritize perceived quality attributes from consumers' perspective? Analysis through social media data. Electron Commer Res 25, 39–67 (2025). https://doi.org/10.1007/s10660-022-09652-7

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