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

Scientometrics

Volumn 87, Issue 3, 2011, Pages 425-450

  Tsai, Hsu Hao ^a  

^a NATIONAL CHENGCHI UNIVERSITY   (Taiwan)

Author keywords

Bibliometric methodology; Customer relationship management; Data mining; Research trend analysis

Indexed keywords

EID: 79955467658     PISSN: 01389130     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11192-011-0353-6     Document Type: Article

References (47)

1. Adam, D. (2002). The counting house. Nature, 415, 726-729.
2. Ahmed, S. R. (2004). Effectiveness of neural network types for prediction of business failure. Information Technology: Coding and Computing, 2, 455-459.
3. Berry, M. J. A., and Linoff, G. S. (2004). Data mining techniques second edition-for marketing, sales, and customer relationship management. New York: Wiley.
4. Berson, A., Smith, S., and Thearling, K. (2000). Building data mining applications for CRM. New York: McGraw-Hill.
5. Bortiz, J. E., and Kennedy, D. B. (1995). Effectiveness of neural network types for prediction of business failure. Expert Systems with Applications, 9, 503-512.
6. Brachman, R. J., Khabaza, T., Kloesgen, W., Piatetsky-Shapiro, G., and Simoudis, E. (1996). Mining business databases. Communication of the ACM, 39(11), 42-48.
7. Broadus, R. N. (1987). Toward a definition of bibliometrics. Scientometrics, 12(5-6), 373-379.
8. Chen, M. S., Han, J., and Yu, P. S. (1996). Data mining: an overview from a database perspective. IEEE Transactions on Knowledge and Data Engineering, 8(6), 866-883.
9. Coille, R. C. (1977). Lotka's frequency distribution of scientific productivity. Journal of American Society for Information Science, 28, 366-370.
10. Eustace, R., and 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.
11. Fayyad, U. M., Piatetsky-Shapiro, G., and 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.
12. Fayyad, U. M., Piatetsky-Shapiro, G., and Smyth, P. (1996b). The KDD process for extracting useful knowledge from volumes of data. Communications of the ACM, 39(11), 27-34.
13. Fletcher, D., and Goss, E. (1993). Forecasting with neural networks: An application using bankruptcy data. Information and Management, 24(3), 159-167.
14. Gupta, D. K. (1987). Lotka's law and productivity of entomological research in Nigeria for the period 1900-1973. Scientometrics, 12, 33-46.
15. He, Z., Xu, X., Huang, J. Z., and Deng, S. (2004). Mining class outliers: Concepts, algorithms and applications in CRM. Expert Systems with Applications, 27, 681-697.
16. Kincaid, J. W. (2003). Customer relationship management: Getting it right. New Jersey: Prentice Hall.
17. Langley, P., and Simon, H. A. (1995). Applications of machine learning and rule induction. Communication of the ACM, 38(11), 54-64.
18. Lau, H. C. W., Wong, C. W. Y., Hui, I. K., and Pun, K. F. (2003). Design and implementation of an integrated knowledge system. Knowledge-Based Systems, 16, 69-76.
19. 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.
20. Ling, R., and Yen, D. C. (2001). Customer relationship management: An analysis framework and implementation strategies. Journal of Computer Information Systems, 41, 82-97.
21. Lotka, A. J. (1926). The frequency distribution of scientific productivity. Journal of the Washington Academy of Sciences, 16(12), 317-324.
22. Moed, H. F., and 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.
23. Ngai, E. W. T. (2005). Customer relationship management research (1992-2002): An academic literature review and classification. Marketing Intelligence and Planning, 23, 582-605.
24. 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.
25. Pao, M. L. (1985). Lotka's law, a testing procedure. Information Processing and Management, 21, 305-320.
26. Pao, M. L. (1989). Concept of information retrieve. Colorado: Libraries Unlimited.
27. Parvatiyar, A., and Sheth, J. N. (2001). Customer relationship management: Emerging practice, process, and discipline. Journal of Economic and Social Research, 3, 1-34.
28. Potter, W. G. (1981). Lotka's law revisited. Library Trends, 30(1), 21-39.
29. Potter, W. G. (1988). 'Of making many books there is no end': Bibliometrics and libraries. The Journal of Academic Librarianship, 14, 238a-238c.
30. Pritchard, A. (1969). Statistical bibliography or bibliometrics. Journal of Documentation, 25(4), 348-349.
31. Rao, I. K. R. (1980). The distribution of scientific productivity and social change. Journal of American Society for Information Science, 31, 111-122.
32. Salchenberger, L. M., Cinar, E. M., and Lash, N. A. (1992). Neural networks: A new tool for predicting thrift failures. Decision Sciences, 23, 899-916.
33. Su, C. T., Hsu, H. H., and Tsai, C. H. (2002). Knowledge mining from trained neural networks. Journal of Computer Information Systems, 42, 61-70.
34. Swift, R. S. (2001). Accelerating customer relationships: Using CRM and relationship technologies. New Jersey: Prentice Hall.
35. Tam, K. Y., and Kiang, M. Y. (1992). Managerial applications of neural networks: The case of bank failure predictions. Management Science, 38, 926-947.
36. Teo, T. S. H., Devadoss, P., and 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.
37. Tsai, H. H., and 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.
38. Tsai, H. H., and 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.
39. Tsai, H. H., and 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).
40. Turban, E., Aronson, J. E., Liang, T. P., and Sharda, R. (2007). Decision support and business intelligence systems (8th ed.). Taiwan: Pearson Education.
41. van Raan, A. F. J. (1996). Advanced bibliometric methods as quantitative core of peer review based evaluation and foresight exercises. Scientometrics, 36, 397-420.
42. van Raan, A. F. J. (2000). The Pandora's box of citation analysis: Measuring scientific excellence, the last evil? In B. Cronin and 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.
43. van Raan, A. F. J., and 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.
44. Vlachy, J. (1978). Frequency distribution of scientific performance: A bibliography of Lotka's law and related phenomena. Scientometrics, 1, 109-130.
45. 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.
46. Weingart, P. (2004). Impact of bibliometrics upon the science system: inadvertent consequences? In H. F. Moed, W. Glanzel, and U. Schmoch (Eds.), Handbook on quantitative science and technology research. The Netherlands: Kluwer Academic Publishers.
47. Zhang, G., Hu, M. Y., Patuwo, B. E., and Indro, D. C. (1999). Artificial neural networks in bankruptcy prediction: General framework and cross validation analysis. European Journal of Operational Research, 116, 16-32.