A finite mixture logit model to segment and predict electronic payments system adoption

Information Systems Research

Volumn 22, Issue 1, 2011, Pages 118-133

  Bapna, Ravi ^a   Goes, Paulo ^b   Wei, Kwok Kee ^c   Zhang, Zhongju ^d  

^a UNIVERSITY OF MINNESOTA   (United States)

^b UNIVERSITY OF ARIZONA   (United States)

^c CITY UNIVERSITY OF HONG KONG   (Hong Kong)

^d UNIVERSITY OF CONNECTICUT   (United States)

Author keywords

Clustering analysis; Electronic payments systems; Finite mixture model; Hierarchical logit regression; Logistic regression; Market segmentation

Indexed keywords

EID: 79955821711     PISSN: 10477047     EISSN: 15265536     Source Type: Journal    
DOI: 10.1287/isre.1090.0277     Document Type: Article

References (68)

1. Abrahamson, E., L. Rosenkopf. 1997. Social networks on the extent of innovation diffusion: A computer simulation. Organ. Sci. 8(3) 289-309.
2. Agarwal, R., J. Prasad. 1997. The role of innovation characteristics and perceived voluntariness in the acceptance of information technologies. Decision Sci. 28(3) 557-582.
3. Agarwal, R., V. Sambamurthy, R. M. Stair. 2000. Research report: The evolving relationship between general and specific computer self-efficacy-An empirical assessment. Inform. Systems Res. 11(4) 418-430.
4. Allison, P. 1999. Logistic Regression Using the SAS System. Wiley-SAS Institute, Chichester, UK.
5. Andrews, R. L., I. S. Currim. 2003. A comparison of segment retention criteria for finite mixture logit models. J. Marketing Res. 40(2) 235-243.
6. Ang, S., S. Slaughter, K. Y. Ng. 2002. Human capital and institutional determinants of information technology compensation: Modeling multilevel and cross-level interactions. Management Sci. 48(11) 1427-1445.
7. Association for Financial Professionals (AFP). 2004. Electronic payments survey. http://www.afponline.org.
8. Au, Y. A., R. J. Kauffman. 2003. What do you know? Rational expectations in information technology adoption and investment. J. Management Inform. Systems 20(2) 49-76.
9. Au, Y. A., R. J. Kauffman, F. J. Riggins. 2005. A rational expectations theory of technology adoption: Evidence from the electronic billing industry. Workshop Inform. Systems Econom. University of California at Irvine, Irvine.
10. Bapna, R., P. Goes, A. Gupta, Y. Jin. 2004. User heterogeneity and its impact on electronic auction market design: An empirical exploration. MIS Quart. 28(1) 21-43.
11. Bozdogan, H., D. E. Ramirez. 1988. FACAIC: Model selection algorithm for the orthogonal factor model using AIC and CAIC. Psychometrika 53(3) 407-415.
12. Brancheau, J. C., J. C. Wetherbe. 1990. The adoption of spreadsheet software: Testing innovation diffusion theory in the context of end-user computing. Inform. Systems Res. 1(2) 115-143.
13. Bucklin, R. E., S. Gupta. 1992. Brand choice, purchase incidence, and segmentation: An integrated modeling approach. J. Marketing Res. 29(2) 201-215.
14. Burke, K. 2005. The impact of firm size on Internet use in small businesses. Electronic Markets 15(2) 5-19.
15. Chakravorti, S., E. Davis. 2004. An electronic supply chain: Will payments follow. Chicago Federal Letter. Number 206a (September). http://www.chicagofed.org/digital_assets/publications/ chicago_fed_letter/2004/cflseptember2004_206a.pdf.
16. Chin, W. W., B. L. Marcolin, P. R. Newsted. 2003. A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Inform. Systems Res. 14(2) 189-217.
17. Chwelos, P., I. Benbasat, A. S. Dexter. 2001. Empirical test of an EDI adoption model. Inform. Systems Res. 12(3) 304-321.
18. Claycamp, H., W. Massy. 1968. A theory of market segmentation. J. Marketing Res. 5 388-394.
19. Compeau, D. R., C. A. Higgins. 1995. Computer self-efficacy: Development of a measure and initial test. MIS Quart. 19(2) 189-211.
20. Cotteleer, M., C. Cotteleer, A. Prochnow. 2007. Cutting checks. Challenges and choices for the adoption of B2B electronic payments. Comm. ACM 50(6) 56-61.
21. Davis, F. 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quart. 13(3) 319-339.
22. Davis, F., R. Bagozzi, P. Warshaw. 1989. User acceptance of computer technology: A comparison of two theoretical models. Management Sci. 35(8) 982-1002.
23. Dayton, M. C., G. B. Macready. 1988. Concomitant variable latent class models. J. Amer. Statist. Soc. 83(401) 173-179.
24. DeSarbo, W., R. Grewal, C. Scott. 2008. A clusterwise bilinear multidimensional scaling methodology for simultaneous segmentation and positioning analyses. J. Marketing Res. 45(3) 280-292.
25. Fader, P. S., B. G. S. Hardie. 1996. Modeling consumer choices among SKUs. J. Marketing Res. 33(4) 442-452.
26. Fishbein, M., I. Ajzen. 1975. Beliefs, Attitude, Intention and Behavior: An Introduction to Theory and Research. Addison-Wesley, Reading, MA.
27. Forman, C. 2005. The corporate digital divide: Determinants of Internet adoption. Management Sci. 51(4) 641-655.
28. Gowrisankaran, G., J. Stavins. 2004. Network externalities and technology adoption: Lessons from electronic payments. RAND J. Econom. 35(2) 260-276.
29. Greene, W. H. 2000. Econometric Analysis, 4th ed. Prentice-Hall, Upper Saddle, NJ.
30. Gupta, S., P. K. Chintagunta. 1994. On using demographic variables to determine segment membership in logit mixture models. J. Marketing Res. 31(1) 128-136.
31. Hagenaars, J., A. McCutcheon. 2001. Applied Latent Class Analysis. Cambridge University Press, Cambridge, UK.
32. Hart, P., C. S. Saunders. 1997. Power and trust: Critical factors in the adoption and use of electronic data interchange. Organ. Sci. 8(1) 23-42.
33. Hart, P., C. S. Saunders. 1998. Emerging electronic partnerships: Antecedents and dimensions of EDI use from the supplier's perspective. J. Management Inform. Systems 14(4) 87-111.
34. Hayashi, F., E. Klee. 2003. Technology adoption and consumer payments: Evidence from survey data. Rev. Network Econom. 2(2) 175-190.
35. Hofmann, D. A. 1997. An overview of the logic and rationale of hierarchical linear models. J. Management 23(6) 723-744.
36. Iacovou, C., I. Benbasat, A. Dexter. 1995. Electronic data interchange and small organizations: Adoption and impact of technology. MIS Quart. 19(4) 465-485.
37. Jeyaraj, A., J.W. Rottman, M. C. Lacity. 2006. A review of the predictors, linkages, and biases in IT innovation adoption research. J. Inform. Tech. 21(1) 1-23.
38. Kamakura, W. A., G. Russell. 1989. A probabilistic choice model for market segmentation and elasticity structure. J. Marketing Res. 26(4) 379-390.
39. Kamakura, W. A., B. Kim, J. Lee. 1996. Modeling preference and structural heterogeneity in consumer choice. Marketing Sci. 15(2) 152-172.
40. Karahanna, E., D. W. Straub. 1999. The psychological origins of perceived usefulness and perceived ease of use. Inform. Management 35(4) 237-250.
41. Kauffman, R. J., J. McAndrews, Y. M. Wang. 2000. Opening the "black box" of network externalities in network adoption. Inform. Systems Res. 11(1) 61-82.
42. Kotler, P., K. L. Keller. 2006. Marketing Management, 12th ed. Prentice-Hall, Upper Saddle River, NJ.
43. Lazarsfeld, P. F., N. W. Henry. 1968. Latent Structure Analysis. Houghton Mifflin, Boston.
44. Magidson, J., J. K. Vermunt. 2002. Latent class models for clustering: A comparison with K-means. Canadian J. Marketing Res. 20 36-43.
45. McLachlan, J., D. Peel. 2000. Finite Mixture Models. John Wiley and Sons, New York.
46. Mithas, S., N. Ramasubbu, M. S. Krishnan, C. Fornell. 2006-2007. Designing Web sites for customer loyalty across business domains: A multilevel analysis. J. Management Inform. Systems 23(3) 97-127.
47. Oh, S., S. Kurnia, R. Johnston, H. Lee, B. Lim. 2006. A stakeholder perspective on successful electronic payment systems diffusion. Proc. 39th Hawaii Internat. Conf. Systems Sci., Kauai, HI.
48. Pfeiffer, H. K. C. 1992. The Diffusion of Electronic Data Interchange. Springer-Verlag, New York.
49. Plouffe, C. R., J. S. Hulland, M. Vanderbosch. 2001. Research report: Richness versus parsimony in modeling technology adoption decisions-understanding merchant adoption of a smart card-based payment system. Inform. Systems Res. 13(2) 208-222.
50. Premkumar, G., K. Ramamurthy, M. Crum. 1997. Determinants of EDI adoption in the transportation industry. Eur. J. Inform. Systems 6(2) 107-121.
51. Raudenbush, S. W., A. S. Bryk. 2002. Hierarchical Linear Models: Applications and Data Analysis Methods, 2nd ed. Sage Publications, Thousand Oaks, CA.
52. Reich, B. H., I. Benbasat. 1990. An empirical investigation of factors influencing the success of customer-oriented strategic systems. Inform. Systems Res. 1(3) 325-347.
53. Rogers, E. M. 1995. Diffusion of Innovations, 4th ed. Free Press, New York.
54. Saunders, C. S., S. Clark. 1992. EDI adoption and implementation: A focus on interorganizational linkages. Inform. Resources Management J. 5(1) 9-19.
55. Shmueli, G., O. Koppius. 2007. Predictive vs. explanatory modeling in IS research. Proc. Conf. Inform. Systems Tech., Seattle. http://www.citi.uconn.edu/cist07/5c.pdf.
56. Shmueli, G., N. R. Patel, P. C. Bruce. 2006. Data Mining for Business Intelligence. John Wiley and Sons, Hoboken, NJ.
57. Stavins, J. 2003. Perspective on payments. Regional Rev. 1 6-9.
58. Straub, D. W., M. Keil, W. Brenner. 1997. Testing the technology acceptance model across cultures: A three country study. Inform. Management 33(1) 1-11.
59. Taylor, S., P. A. Todd. 1995. Understanding information technology usage: A test of competing models. Inform. Systems Res. 6(2) 144-176.
60. Teo, H. H., B. C. Y. Tan, K. K. Wei. 1995. Innovation diffusion theory as a predictor of adoption intention for financial EDI. Proc. 1995 Internat. Conf. Inform. Systems, Amsterdam, 155-165.
61. Teo, H. H., K. K. Wei, I. Benbasat. 2003. Predicting intention to adopt interorganizational linkages: An institutional perspective. MIS Quart. 27(1) 19-49.
62. Tornatzky, L. G., K. J. Klein. 1982. Innovation characteristics and innovation adoption-implementation: A meta-analysis of findings. IEEE Trans. Engrg. Management 29(1) 28-45.
63. Venkatesh, V. 2000. Determinants of perceived ease of use: Integrating perceived behavioral control, computer anxiety and enjoyment into the technology acceptance model. Inform. Systems Res. 11(4) 342-365.
64. Venkatesh, V., F. Davis. 1996. A model of the antecedents of perceived ease of use: Development and test. Decision Sci. 27(3) 451-481.
65. Vermunt, J. K., J. Magidson. 2005. Technical Guide for Latent GOLD 4.0: Basic and Advanced. Statistical Innovations Inc., Belmont, MA.
66. Wedel, M., W. DeSarbo. 1993. A latent class binomial logit methodology for the analysis of paired comparison choice data: An application reinvestigating the determinants of perceived risk. Decision Sci. 24(6) 1157-1169.
67. Wedel, M., W. A. Kamakura. 2000. Market Segmentation: Conceptual and Methodological Foundations, 2nd ed. Kluwer Academic Publishers, Boston.
68. Yap, C. S., C. P. P. Soh, K. S. Raman. 1992. Information systems success factors in small business. OMEGA Internat. J. Management Sci. 20(5/6) 597-609.