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RETRACTED ARTICLE: A comparative study of the market demand for Chinese proficiency test preparation books: evidence from e-commerce data

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This article was retracted on 21 March 2024

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Abstract

Language test preparation books are essential for second language learners to pass a specific test. Although numerous books of this type have been published, yet little attention has been paid to different market demands between different areas. To address this issue, the present study used E-commerce data, in particular positive comments, retrieved from four online shopping platforms to compare the differences and similarities in market demand for Chinese language proficiency test preparation books between China and four English-speaking countries. The data were analyzed using a long short-term memory model using Python from the perspectives of major market demand, customer profile, functional expectations and non-functional expectations. Theoretical and practical implications were proposed based on the findings.

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Notes

  1. HSK test includes six bands, with band 1 and 2 as the elementary level, band 3 and 4 as the intermediate level, and band 5 and 6 as the advanced level [1].

  2. http://www.chinesetest.cn/gosign.do?id=1&lid=0#.

  3. The Snow NLP module can calculate the sentiment coefficient of a text as a value between 0 and 1. A value closer to 0 indicates that the text contains mostly negative information, while a value closer to 1 indicates that the text contains mostly positive information; for e-commerce review mining, 0.3 is generally used as the threshold between positive and negative sentiment [20].

  4. http://report.iresearch.cn/report/202102/3736.shtml, successfully accessed on 2021–03-15.

  5. https://www.iresearch.com.cn/Detail/report?id=3391&isfree=0.

  6. The data sources for the Chinese market were from: JingDong.com: https://www.jd.com, successfully accessed on 2020–08-02; DangDang: http://search.dangdang.com, successfully accessed on 2020–08-02; TianMao: https://www.tmall.com/, successfully accessed on 2020–08-02.

  7. Amazon is an international e-commerce platform. The data sources for the English-speaking were from: USA: https://www.amazon.com/s?k=HSK+test&language=en_US&ref=nb_sb_noss_2, successfully accessed on 2020–08-01; UK: https://www.amazon.co.uk/s?k=HSK+test&ref=nb_sb_noss, successfully accessed on 2020–08-01; Canada: https://www.amazon.ca/s?k=HSK+test&ref=nb_sb_noss, successfully accessed on 2020–08-01; Australia: https://www.amazon.com.au/s?k=HSK+test&ref=nb_sb_noss_2, successfully accessed on 2020–08-01.

  8. LSTM model assigns specific classification weights to different words, and the larger the weights, the more important these words are for text classification [32]. For e-commerce comments mining, the 20 most frequently used words are generally analyzed as classification keywords [20, 23].

Abbreviations

L2:

Second language

HSK:

Hanyu Shuiping Kaoshi

TOEFL:

Test of English as a Foreign Language

IELTS:

International English Language Testing System

CS:

Chinese as a second language

FL:

Foreign language

TSA:

Target simulation analysis

LSTM:

Long-short term memory model

CSL:

Chinese as a second language for learners

CFL:

Chinese as a foreign language for learners

References

  1. Zhang, J., & Fu, H. (2016). The influence of the number of new HSK candidates on the difficulty estimation of test paper. Test Study, 20(01), 68–73.

    Google Scholar 

  2. Green, A. (2006). Washback to the learner: Learner and teacher perspectives on IELTS preparation course expectations and outcomes. Assessing Writing, 11(2), 113–134.

    Article  ADS  Google Scholar 

  3. Rashidi, N., & Javanmardi, F. (2011). The IELTS preparation washback on learning and teaching outcomes. Cross-Cultural Communication, 7(3), 132–144.

    Google Scholar 

  4. Mayzlin, D., Dover, Y., & Chevalier, J. A. (2014) Promotional reviews: An empirical investigation of online review manipulation. American Economic Review, 26(03).

  5. Xu, H., Liu, D., Wang, H., & Stavrou, A. (2015). E-commerce reputation manipulation: The emergence of reputation-escalation-as-a-service. ACM.

  6. Rathor, A. S., Agarwal, A., & Dimric, P. (2018). Comparative study of machine learning approaches for Amazon reviews. Procedia Computer Science, 132, 1552–1561.

    Article  Google Scholar 

  7. Allwright, R. (1982). Perceiving and pursuing learner's needs. In Geddes, M., & Sturtridge, G. (Eds.), Individualisation. Oxford.

  8. West, R. (1994). Needs analysis in language teaching. Language Teaching, 27(01), 1–17.

    Article  Google Scholar 

  9. Hutchinson, W. (1987). A English for specific purposes—A learning centered Approach. Cambridge University Press.

    Book  Google Scholar 

  10. Munby, J. (1978). Communicative syllabus design. Cambridge University Press.

    Google Scholar 

  11. Zhang, L. (2006). Business Chinese needs analysis. Language Teaching and Linguistic Studies, 03, 55–60.

    Google Scholar 

  12. Li, J. (2014). Needs analysis: An effective way in business English curriculum design. Theory and Practice in Language Studies, 4(9).

  13. Barnes, M. (2016). The washback of the TOEFL iBT in Vietnam. Australian Journal of Teacher Education, 41(7), 158–174.

    Article  Google Scholar 

  14. Jiang, L. (2015). Practice of compilation from the perspective of “combination of examination and teaching”—Taking vocabulary compilation of HSK standard course as an example. Selected papers from the 12th International Symposium on Chinese Language Teaching. World Chinese teaching association, Confucius Institute Headquarters: World Chinese Teaching Association (pp. 8–11).

  15. Liang, Y. (2017). An empirical study on the evaluation standards of international Chinese teaching materials with teachers as evaluators. International Chinese Language Education (Chinese and English), 2(3), 48–58.

    Google Scholar 

  16. Tan, P. (2017). Assessment of Chinese teaching materials: Present situation, problems and solutions. Journal of Yunnan Normal University (Teaching and Research of Chinese as a Foreign Language), 15(04), 32–38.

    Google Scholar 

  17. Liang, Y. (2016). Pilot study on Chinese language teaching materials evaluation standards from the perspective of learner-as-evaluator. Overseas Chinese Education, 6, 796–804.

    Google Scholar 

  18. Jiang, L. (2015). The underlying idea and practice of HSK standard course. Research on international Chinese Language Teaching, 02, 53–59.

    Google Scholar 

  19. Potthoff, P., & Eller, M. (2000). Survey with questionnaire: Advantages and disadvantages of various data collection techniques. Journal of Public Health, 14(4).

  20. Wu, Y. (2015). Taobao store big data marketing: Data analysis, mining, efficient transformation. Posts and Telecom Press.

    Google Scholar 

  21. Zhang, X., & Jing, F. (2012). Literature review on e-commerce website attributes based on consumer buying behaviors. Library and Information Service, 56(04), 136–142.

    Google Scholar 

  22. Muniz, A., & Guinn, T. (2001). Brand community. Journal of Marketing, 27(3), 412–423.

    Google Scholar 

  23. Shu, C. (2016). Jing Dong platform data operation. Electronic Industry Press.

    Google Scholar 

  24. Liao, C., Cai, C., & Li, Y. (2013). An empirical study on influence factors of online reviews’ helpfulness in e-commerce. Soft Science, 27(05), 46–50.

    Google Scholar 

  25. Fitzsimmons, J. (2014). Service management: Operations, strategy, and information technology. International Journal of Service Industry Management, 10(2), 263.

    Google Scholar 

  26. Tadelis, S. (2016). Reputation and feedback systems in online platform markets. Annual Review of Economics, 8(1), 321–340.

    Article  Google Scholar 

  27. Baye, M. R., Morgan, J., & Scholten, P. (2010). Temporal price dispersion: Evidence from an online consumer electronics market. Journal of Interactive Marketing, 18(4), 1–31.

    Google Scholar 

  28. Gong, S., Liu, X., Liu, Y., & Zhao, P. (2012). Does online word-of-mouth determine product’s fate: An empirical analysis of online book reviews. NaiKai Business Review, 15(04), 118–128.

    Google Scholar 

  29. Lavoi, N. M., Becker, E., & Maxwell, H. D. (2007). Coaching Girls": A content analysis of best-selling popular press coaching books. Women in Sport & Physical Activity Journal, 16(2), 7–20.

    Article  Google Scholar 

  30. Xiong, F., Deng, Y., & Tang, X. (2015). The architecture of word2vec and its applications. Journal of Nanjing Normal University (Engineering and Technology Edition), 15(01), 43–48.

    Google Scholar 

  31. Mikolov, T. S., Kopecky, J., & Burget, L. (2009). Neural network based language models for highly inflective languages. IEEE International Conference on Acoustics, Taibei

  32. Sundermeyer, M., Schluter, R., & Ney, H. (2012). LSTM neural networks for language modeling. Inter-Speech.

    Book  Google Scholar 

  33. Huang, C., & Li, G. (2010). Washback effect of HSK on teaching Chinese as a second. Language China Test, 02, 26–32.

    Google Scholar 

  34. Nel, E. N., Krog, S., Lebeloane, L. D. M., & Zhou, Q. (2019). A comparative study on teaching and learning Chinese characters by primary non-native Chinese learners in South Africa and China. Per Linguam, 35(2).

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Funding

Zhejiang Normal University Research Special Fund for Young Doctors in 2021 (Fund No: ZZ323205020520013080).

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Authors and Affiliations

  1. College of International Education, Zhejiang Normal University, Jinhua, 321004, Zhejiang, China

    Chengang Zeng

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  1. Chengang Zeng
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Correspondence to Chengang Zeng.

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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s10660-024-09838-1

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Zeng, C. RETRACTED ARTICLE: A comparative study of the market demand for Chinese proficiency test preparation books: evidence from e-commerce data. Electron Commer Res 23, 207–230 (2023). https://doi.org/10.1007/s10660-022-09592-2

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