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

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.

  • Adam, D 2002 The counting house. Nature 415:726729.

  • Ahmed, SR 2004 Effectiveness of neural network types for prediction of business failure. Information Technology: Coding and Computing 2:455459.

    • Search Google Scholar
    • Export Citation
  • Berry, MJA, Linoff, GS 2004 Data mining techniques second edition—for marketing, sales, and customer relationship management Wiley New York.

    • Search Google Scholar
    • Export Citation
  • Berson, A, Smith, S, Thearling, K 2000 Building data mining applications for CRM McGraw-Hill New York.

  • Bortiz, JE, Kennedy, DB 1995 Effectiveness of neural network types for prediction of business failure. Expert Systems with Applications 9:503512 .

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brachman, RJ, Khabaza, T, Kloesgen, W, Piatetsky-Shapiro, G, Simoudis, E 1996 Mining business databases. Communication of the ACM 39 11 4248 .

  • Broadus, RN 1987 Toward a definition of bibliometrics. Scientometrics 12 5–6 373379 .

  • Chen, MS, Han, J, Yu, PS 1996 Data mining: an overview from a database perspective. IEEE Transactions on Knowledge and Data Engineering 8 6 866883 .

  • Coille, RC 1977 Lotka's frequency distribution of scientific productivity. Journal of American Society for Information Science 28:366370 .

  • 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. 6776) Melbourne, Australia.

    • Search Google Scholar
    • Export Citation
  • 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. 134). California: American Association for Artificial Intelligence.

    • Search Google Scholar
    • Export Citation
  • Fayyad, UM, Piatetsky-Shapiro, G, Smyth, P 1996 The KDD process for extracting useful knowledge from volumes of data. Communications of the ACM 39 11 2734 .

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fletcher, D, Goss, E 1993 Forecasting with neural networks: An application using bankruptcy data. Information and Management 24 3 159167 .

  • Gupta, DK 1987 Lotka's law and productivity of entomological research in Nigeria for the period 1900–1973. Scientometrics 12:3346 .

  • He, Z, Xu, X, Huang, JZ, Deng, S 2004 Mining class outliers: Concepts, algorithms and applications in CRM. Expert Systems with Applications 27:681697 .

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kincaid, JW 2003 Customer relationship management: Getting it right Prentice Hall New Jersey.

  • Langley, P, Simon, HA 1995 Applications of machine learning and rule induction. Communication of the ACM 38 11 5464 .

  • Lau, HCW, Wong, CWY, Hui, IK, Pun, KF 2003 Design and implementation of an integrated knowledge system. Knowledge-Based Systems 16:6976 .

  • Lejeune, MAPM 2001 Measuring the impact of data mining on churn management. Internet Research: Electronic Networking Applications and Policy 11:375387 .

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ling, R, Yen, DC 2001 Customer relationship management: An analysis framework and implementation strategies. Journal of Computer Information Systems 41:8297.

    • Search Google Scholar
    • Export Citation
  • Lotka, AJ 1926 The frequency distribution of scientific productivity. Journal of the Washington Academy of Sciences 16 12 317324.

  • Moed, HF Th N Van Leeuwen 1995 Improving the accuracy of the institute for scientific information's journal impact factors. Journal of the American Society for Information Science 46:461467 .

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ngai, EWT 2005 Customer relationship management research (1992–2002): An academic literature review and classification. Marketing Intelligence & Planning 23:582605 .

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nicholls, PT 1989 Bibliometric modeling processes and empirical validity of Lotka's law. Journal of American Society for Information Science 40 6 379385 .

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pao, ML 1985 Lotka's law, a testing procedure. Information Processing and Management 21:305320 .

  • Pao, ML 1989 Concept of information retrieve Libraries Unlimited Colorado.

  • Parvatiyar, A, Sheth, JN 2001 Customer relationship management: Emerging practice, process, and discipline. Journal of Economic & Social Research 3:134.

    • Search Google Scholar
    • Export Citation
  • Potter, WG 1981 Lotka's law revisited. Library Trends 30 1 2139.

  • Potter, WG 1988 ‘Of making many books there is no end’: Bibliometrics and libraries. The Journal of Academic Librarianship 14:238a238c.

    • Search Google Scholar
    • Export Citation
  • Pritchard, A 1969 Statistical bibliography or bibliometrics. Journal of Documentation 25 4 348349.

  • Rao, IKR 1980 The distribution of scientific productivity and social change. Journal of American Society for Information Science 31:111122 .

  • Salchenberger, LM, Cinar, EM, Lash, NA 1992 Neural networks: A new tool for predicting thrift failures. Decision Sciences 23:899916 .

  • Su, CT, Hsu, HH, Tsai, CH 2002 Knowledge mining from trained neural networks. Journal of Computer Information Systems 42:6170.

  • Swift, RS 2001 Accelerating customer relationships: Using CRM and relationship technologies Prentice Hall New Jersey.

  • Tam, KY, Kiang, MY 1992 Managerial applications of neural networks: The case of bank failure predictions. Management Science 38:926947 .

  • 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, 16131627.

    • Search Google Scholar
    • Export Citation
  • 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), 285295.

    • Search Google Scholar
    • Export Citation
  • 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.

    • Search Google Scholar
    • Export Citation
  • 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. 174178).

    • Search Google Scholar
    • Export Citation
  • Turban, E, Aronson, JE, Liang, TP, Sharda, R 2007 Decision support and business intelligence systems 8 Pearson Education Taiwan.

  • AFJ Van Raan 1996 Advanced bibliometric methods as quantitative core of peer review based evaluation and foresight exercises. Scientometrics 36:397420 .

    • Crossref
    • Search Google Scholar
    • Export Citation
  • AFJ Van Raan 2000 The Pandora's box of citation analysis: Measuring scientific excellence, the last evil? B Cronin HB Atkins eds. The web of knowledge. A festschrift in honor of Eugene Garfield. Chap. 15 ASIS Monograph Series New Jersey 301319.

    • Search Google Scholar
    • Export Citation
  • AFJ Van Raan Th N Van Leeuwen 2002 Assessment of the scientific basis of interdisciplinary, applied research. Application of bibliometric methods in nutrition and food research. Research Policy 31:611632 .

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vlachy, J 1978 Frequency distribution of scientific performance: A bibliography of Lotka's law and related phenomena. Scientometrics 1:109130.

    • Search Google Scholar
    • Export Citation
  • 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. 719) Forschungszentrum Jülich.

    • Search Google Scholar
    • Export Citation
  • Weingart, P 2004 Impact of bibliometrics upon the science system: inadvertent consequences? HF Moed W Glanzel U Schmoch eds. Handbook on quantitative science and technology research Kluwer Academic Publishers The Netherlands.

    • Search Google Scholar
    • Export Citation
  • Zhang, G, Hu, MY, Patuwo, BE, Indro, DC 1999 Artificial neural networks in bankruptcy prediction: General framework and cross validation analysis. European Journal of Operational Research 116:1632 .

    • Crossref
    • Search Google Scholar
    • Export Citation

Manuscript submission: http://www.editorialmanager.com/scim/

  • Impact Factor (2019): 2.867
  • Scimago Journal Rank (2019): 1.210
  • SJR Hirsch-Index (2019): 106
  • SJR Quartile Score (2019): Q1 Computer Science Apllications
  • SJR Quartile Score (2019): Q1 Library and Information Sciences
  • SJR Quartile Score (2019): Q1 Social Sciences (miscellaneous)
  • Impact Factor (2018): 2.770
  • Scimago Journal Rank (2018): 1.113
  • SJR Hirsch-Index (2018): 95
  • SJR Quartile Score (2018): Q1 Library and Information Sciences
  • SJR Quartile Score (2018): Q1 Social Sciences (miscellaneous)

For subscription options, please visit the website of Springer

Scientometrics
Language English
Size B5
Year of
Foundation
1978
Volumes
per Year
4
Issues
per Year
12
Founder Akadémiai Kiadó
Founder's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Publisher Akadémiai Kiadó
Springer Nature Switzerland AG
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
CH-6330 Cham, Switzerland Gewerbestrasse 11.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 0138-9130 (Print)
ISSN 1588-2861 (Online)