Skip to main content
SpringerLink
Log in

Research trends analysis by comparing data mining and customer relationship management through bibliometric methodology

  • Published:
Scientometrics Aims and scope Submit manuscript

Abstract

There are few comprehensive studies and categorization schemes to discuss the characteristics for both data mining and customer relationship management (CRM) although they have already become more important recently. Using a bibliometric approach, this paper analyzes data mining and CRM research trends from 1989 to 2009 by locating headings “data mining” and “customer relationship management” or “CRM” in topics in the SSCI database. The bibliometric analytical technique was used to examine these two topics in SSCI journals from 1989 to 2009, we found 1181 articles with data mining and 1145 articles with CRM. This paper implemented and classified data mining and CRM articles using the following eight categories—publication year, citation, country/territory, document type, institute name, language, source title and subject area—for different distribution status in order to explore the differences and how data mining and CRM technologies have developed in this period and to analyze data mining and CRM technology tendencies under the above result. Also, the paper performs the K–S test to check whether the analysis follows Lotka’s law. The research findings can be extended to investigate author productivity by analyzing variables such as chronological and academic age, number and frequency of previous publications, access to research grants, job status, etc. In such a way characteristics of high, medium and low publishing activity of authors can be identified. Besides, these findings will also help to judge scientific research trends and understand the scale of development of research in data mining and CRM through comparing the increases of the article author. Based on the above information, governments and enterprises may infer collective tendencies and demands for scientific researcher in data mining and CRM to formulate appropriate training strategies and policies in the future. This analysis provides a roadmap for future research, abstracts technology trends and facilitates knowledge accumulations so that data mining and CRM researchers can save some time since core knowledge will be concentrated in core categories. This implies that the phenomenon “success breeds success” is more common in higher quality publications.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  • Adam, D. (2002). The counting house. Nature, 415, 726–729.

    Google Scholar 

  • Ahmed, S. R. (2004). Effectiveness of neural network types for prediction of business failure. Information Technology: Coding and Computing, 2, 455–459.

    Google Scholar 

  • Berry, M. J. A., & Linoff, G. S. (2004). Data mining techniques second edition—for marketing, sales, and customer relationship management. New York: Wiley.

    Google Scholar 

  • Berson, A., Smith, S., & Thearling, K. (2000). Building data mining applications for CRM. New York: McGraw-Hill.

    Google Scholar 

  • Bortiz, J. E., & Kennedy, D. B. (1995). Effectiveness of neural network types for prediction of business failure. Expert Systems with Applications, 9, 503–512.

    Article  Google Scholar 

  • Brachman, R. J., Khabaza, T., Kloesgen, W., Piatetsky-Shapiro, G., & Simoudis, E. (1996). Mining business databases. Communication of the ACM, 39(11), 42–48.

    Article  Google Scholar 

  • Broadus, R. N. (1987). Toward a definition of bibliometrics. Scientometrics, 12(5–6), 373–379.

    Article  Google Scholar 

  • Chen, M. S., Han, J., & Yu, P. S. (1996). Data mining: an overview from a database perspective. IEEE Transactions on Knowledge and Data Engineering, 8(6), 866–883.

    Article  Google Scholar 

  • Coille, R. C. (1977). Lotka’s frequency distribution of scientific productivity. Journal of American Society for Information Science, 28, 366–370.

    Article  Google Scholar 

  • Eustace, R., & Merrington, G. (1995). A probabilistic neural network approach to jet engine fault diagnosis. In Proceedings of the 8th international conference on industrial and engineering applications of artificial intelligence and expert systems (pp. 67–76) Melbourne, Australia.

  • Fayyad, U. M., Piatetsky-Shapiro, G., & Smyth, P. (1996a). From data mining to knowledge discovery: an overview. In: Advances in Knowledge Discovery and Data Mining (pp. 1–34). California: American Association for Artificial Intelligence.

  • Fayyad, U. M., Piatetsky-Shapiro, G., & Smyth, P. (1996b). The KDD process for extracting useful knowledge from volumes of data. Communications of the ACM, 39(11), 27–34.

    Article  Google Scholar 

  • Fletcher, D., & Goss, E. (1993). Forecasting with neural networks: An application using bankruptcy data. Information and Management, 24(3), 159–167.

    Article  Google Scholar 

  • Gupta, D. K. (1987). Lotka’s law and productivity of entomological research in Nigeria for the period 1900–1973. Scientometrics, 12, 33–46.

    Article  Google Scholar 

  • He, Z., Xu, X., Huang, J. Z., & Deng, S. (2004). Mining class outliers: Concepts, algorithms and applications in CRM. Expert Systems with Applications, 27, 681–697.

    Article  Google Scholar 

  • Kincaid, J. W. (2003). Customer relationship management: Getting it right. New Jersey: Prentice Hall.

    Google Scholar 

  • Langley, P., & Simon, H. A. (1995). Applications of machine learning and rule induction. Communication of the ACM, 38(11), 54–64.

    Article  Google Scholar 

  • Lau, H. C. W., Wong, C. W. Y., Hui, I. K., & Pun, K. F. (2003). Design and implementation of an integrated knowledge system. Knowledge-Based Systems, 16, 69–76.

    Article  Google Scholar 

  • Lejeune, M. A. P. M. (2001). Measuring the impact of data mining on churn management. Internet Research: Electronic Networking Applications and Policy, 11, 375–387.

    Article  Google Scholar 

  • Ling, R., & Yen, D. C. (2001). Customer relationship management: An analysis framework and implementation strategies. Journal of Computer Information Systems, 41, 82–97.

    Google Scholar 

  • Lotka, A. J. (1926). The frequency distribution of scientific productivity. Journal of the Washington Academy of Sciences, 16(12), 317–324.

    Google Scholar 

  • Moed, H. F., & Van Leeuwen, Th. N. (1995). Improving the accuracy of the institute for scientific information’s journal impact factors. Journal of the American Society for Information Science, 46, 461–467.

    Article  Google Scholar 

  • Ngai, E. W. T. (2005). Customer relationship management research (1992–2002): An academic literature review and classification. Marketing Intelligence & Planning, 23, 582–605.

    Article  Google Scholar 

  • Nicholls, P. T. (1989). Bibliometric modeling processes and empirical validity of Lotka’s law. Journal of American Society for Information Science, 40(6), 379–385.

    Article  Google Scholar 

  • Pao, M. L. (1985). Lotka’s law, a testing procedure. Information Processing and Management, 21, 305–320.

    Article  Google Scholar 

  • Pao, M. L. (1989). Concept of information retrieve. Colorado: Libraries Unlimited.

    Google Scholar 

  • Parvatiyar, A., & Sheth, J. N. (2001). Customer relationship management: Emerging practice, process, and discipline. Journal of Economic & Social Research, 3, 1–34.

    Google Scholar 

  • Potter, W. G. (1981). Lotka’s law revisited. Library Trends, 30(1), 21–39.

    Google Scholar 

  • Potter, W. G. (1988). ‘Of making many books there is no end’: Bibliometrics and libraries. The Journal of Academic Librarianship, 14, 238a–238c.

    Google Scholar 

  • Pritchard, A. (1969). Statistical bibliography or bibliometrics. Journal of Documentation, 25(4), 348–349.

    MathSciNet  Google Scholar 

  • Rao, I. K. R. (1980). The distribution of scientific productivity and social change. Journal of American Society for Information Science, 31, 111–122.

    Article  Google Scholar 

  • Salchenberger, L. M., Cinar, E. M., & Lash, N. A. (1992). Neural networks: A new tool for predicting thrift failures. Decision Sciences, 23, 899–916.

    Article  Google Scholar 

  • Su, C. T., Hsu, H. H., & Tsai, C. H. (2002). Knowledge mining from trained neural networks. Journal of Computer Information Systems, 42, 61–70.

    Google Scholar 

  • Swift, R. S. (2001). Accelerating customer relationships: Using CRM and relationship technologies. New Jersey: Prentice Hall.

    Google Scholar 

  • Tam, K. Y., & Kiang, M. Y. (1992). Managerial applications of neural networks: The case of bank failure predictions. Management Science, 38, 926–947.

    Article  MATH  Google Scholar 

  • Teo, T. S. H., Devadoss, P., & Pan, S. L. (2006). Towards a holistic perspective of customer relationship management implementation: A case study of the housing and development board, Singapore. Decision Support Systems, 42, 1613–1627.

    Google Scholar 

  • Tsai, H. H., & Chi, Y. P. (2011). Trend analysis of supply chain management by bibliometric methodology. International Journal of Digital Content Technology and its Applications, 5(1), 285–295.

    Google Scholar 

  • Tsai, H. H., & Chiang, J. K. (2011). E-commerce research trend forecasting: A study of bibliometric methodology. International Journal of Digital Content Technology and its Applications, 5(1), 101–111.

    Google Scholar 

  • Tsai, H. H., & Yang, J. M. (2010). Analysis of knowledge management trend by bibliometric approach. In Proceeding(s) of the WASET on knowledge management (Vol. 62, pp. 174–178).

  • Turban, E., Aronson, J. E., Liang, T. P., & Sharda, R. (2007). Decision support and business intelligence systems (8th ed.). Taiwan: Pearson Education.

    Google Scholar 

  • Van Raan, A. F. J. (1996). Advanced bibliometric methods as quantitative core of peer review based evaluation and foresight exercises. Scientometrics, 36, 397–420.

    Article  Google Scholar 

  • Van Raan, A. F. J. (2000). The Pandora’s box of citation analysis: Measuring scientific excellence, the last evil? In B. Cronin & H. B. Atkins (Eds.), The web of knowledge. A festschrift in honor of Eugene Garfield. Chap. 15 (pp. 301–319). New Jersey: ASIS Monograph Series.

    Google Scholar 

  • Van Raan, A. F. J., & Van Leeuwen, Th. N. (2002). Assessment of the scientific basis of interdisciplinary, applied research. Application of bibliometric methods in nutrition and food research. Research Policy, 31, 611–632.

    Article  Google Scholar 

  • Vlachy, J. (1978). Frequency distribution of scientific performance: A bibliography of Lotka’s law and related phenomena. Scientometrics, 1, 109–130.

    Google Scholar 

  • Weingart, P. (2003), Evaluation of research performance: the danger of numbers. In: Bibliometric analysis in science and research. Applications, benefits and limitations. Second conference of the Central Library (pp. 7–19) Forschungszentrum Jülich.

  • Weingart, P. (2004). Impact of bibliometrics upon the science system: inadvertent consequences? In H. F. Moed, W. Glanzel, & U. Schmoch (Eds.), Handbook on quantitative science and technology research. The Netherlands: Kluwer Academic Publishers.

    Google Scholar 

  • Zhang, G., Hu, M. Y., Patuwo, B. E., & Indro, D. C. (1999). Artificial neural networks in bankruptcy prediction: General framework and cross validation analysis. European Journal of Operational Research, 116, 16–32.

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Management Information System, National Chengchi University, No. 64, Sec. 2, Zhinan Rd., Wenshan District, Taipei City, 11605, Taiwan, R.O.C.

    Hsu-Hao Tsai

Authors
  1. Hsu-Hao Tsai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hsu-Hao Tsai.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Tsai, HH. Research trends analysis by comparing data mining and customer relationship management through bibliometric methodology. Scientometrics 87, 425–450 (2011). https://doi.org/10.1007/s11192-011-0353-6

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-011-0353-6

Keywords

Search

Navigation