Critical Mass, Diffusion Channels, and Digital Divide

References

• Agarwal, R., V. Sambamurthy, R. M. Stair. “Research report: The evolving relationship between general and specific computer self-efficacy — An empirical assessment,” Information Systems Research (11:4), 2000, pp. 418–430.
   Web of Science ®Google Scholar
• Allen, D. “New telecommunications services,” Telecommunications Policy (12), 1988, pp. 257–264.
   Web of Science ®Google Scholar
• Becker, G. S. Human Capital, National Bureau of Economic Research Columbia, NY: 1964.
   Google Scholar
• Bresnahan, T., E. Brynjolfsson, L. Hitt. “Information technology, workplace organization and the demand for skilled labor: Firm-level evidence,” The Quarterly Journal of Economics (117:1), 2002, pp. 339–376.
   Web of Science ®Google Scholar
• Brown, S., V. Venkatesh. “Bringing non-adopters along: The challenge facing the PC industry,” Communications of the ACM (46:4), 2003, pp. 76–80.
   Web of Science ®Google Scholar
• Brown, S., V. Venkatesh. “Model of adoption of technology in households: A baseline model test and extension incorporating household life cycle,” MIS Quarterly (29:3), 2005, pp. 399–426.
   Web of Science ®Google Scholar
• Davis, F. D., R. P. Bagozzi, P. R. Warshaw. “User acceptance of computer technology: A comparison of two theoretical models,” Management Science (35:8), 1989, pp. 982–1003.
   Web of Science ®Google Scholar
• Dewan, S., F. J. Riggins. “The digital divide: Current and future research directions,” Journal of the Association for Information Systems (6:12), 2005, pp. 298–337.
   Google Scholar
• DiMaggio, P. J., W. W. Powell. “The iron cage revisited: institutional isomorphism and collective rationality in organizational fields,” American Sociological Review (48:2), 1983, pp. 147–160.
   Web of Science ®Google Scholar
• DiMaggio, P. J., E. Hargittai, C. Celeste, S. Shafer. “From unequal access to differentiated use: A literature review and agenda for research on digital inequality”, in Social Inequality, K. Neckerman. (ed.), Russell Sage Foundation, New York, NY, 2004.
   Google Scholar
• Goodhue, D. L., R. L. Thompson. “Task-technology fit and individual performance,” MIS Quarterly (19:2), 1995, pp. 213–236.
   Web of Science ®Google Scholar
• Goolsbee, A., P. Klenow. “Evidence of learning and network externalities in the diffusion of home computer,” Journal of Law and Economics (45:2), 2002, pp. 317–343.
   Web of Science ®Google Scholar
• Granovetter, M. “Economics action and social structure: The problem of embeddedness,” American Journal of Sociology (91), 1985, pp. 481–510.
   Web of Science ®Google Scholar
• Greene, W. H. Econometric Analysis (5th ed.) Prentice-Hall, NJ, 2003.
   Google Scholar
• Hargittai, E. “Second level digital divide: Differences in people's online skills,” First Monday (7:4), 2002, pp. 125.
   Google Scholar
• Henderson, J. C., N. Venkatraman. “Strategic alignment: Leveraging information technology for transforming organizations,” IBM Systems Journal (38:2&3), 1993, pp. 472–484.
   Google Scholar
• Hong, S., K. Y. Tam. “Understanding the adoption of multipurpose information appliances: The case of mobile data services,” Information Systems Research (17:2), 2006, pp.162–179.
   Web of Science ®Google Scholar
• Hsieh, J. J., A. Rai, M. Keil. “Understanding digital inequality: Comparing continued use behavioral models of the socio-economically advantaged and disadvantaged,” MIS Quarterly (32:1), 2008, pp.97–126.
   Web of Science ®Google Scholar
• International Telecommunication Union. 2007. Africa, ICT indicator, 2007. Available at http://www.itu.int/ITU-D/ict/statistics/at_glance/af_ictindicators_2007.html.
   Google Scholar
• Jasperson, J., P. E. Carter, R. Zmud. “A comprehensive conceptualization of post-adoptive behaviors associated with information technology enabled work systems,” MIS Quarterly, (29:3), 2005, pp. 525–557.
   Web of Science ®Google Scholar
• Jovanovic, B., P. Rousseau. “General Purpose Technologies,” NBER Working Paper No. 11093, 2005.
   Google Scholar
• Katz, L. F., K. M. Murphy. “Changes in relative wages, 1963-1987: Supply and demand factors,” The Quarterly Journal of Economics (107:1), 1992, pp. 35–78.
   Web of Science ®Google Scholar
• Kim, S. S., J. Son. 2009. “Out of dedication or constraint? A dual model of post-adoption phenomena and its empirical test in the context of online services,” MIS Quarterly (33:1), 2009, pp. 49–70.
   Web of Science ®Google Scholar
• Kraatz, M. S. “Learning by association? Interorganizational netoworks and adaptation to environmental change,” Academy of Management Journal (41:6), 1998, pp. 621–643.
   Web of Science ®Google Scholar
• Levina, N., M. Xin. “Comparing IT workers' compensation across country contexts: Demographic, human capital, and institutional factors,” Information Systems Research (18:2), 2007, pp. 193–210.
   Web of Science ®Google Scholar
• Lou, H., W. Luo, D. Strong. “Perceived critical mass effect on groupware acceptance,” European Journal of Information Systems (9:2), 2000, pp. 91–103.
   Web of Science ®Google Scholar
• Mahajan, V., E. Muller, F. M. Bass. “New product diffusion models in marketing: A review and directions for research,” Journal of Marketing (54:1), 1990, pp. 1–26.
   Web of Science ®Google Scholar
• Mahler, A., E. M. Rogers. “The diffusion of interactive communication innovations and the critical mass: The adoption of telecommunications services by German banks,” Telecommunications Policy (23), 1999, pp. 719–740.
   Web of Science ®Google Scholar
• Marakas, G., M. Yi, R. Johnson. “The multilevel and multifaceted character of computer of computer self-efficacy: Toward clarification of the construct and integrative framework for research,” Information Systems Research (9:2), 1998, pp.126–163.
   Web of Science ®Google Scholar
• Montazemi, A., J. Siam, A. Esfahanipour. “Effect of network relations on the adoption of electronic trading systems,” Journal of Management Information Systems (25:1), 2008, pp. 233–266.
   Web of Science ®Google Scholar
• Oliver, P., G. Marwell, R. Teixeira. “A theory of the critical mass: Interdependence, group heterogeneity, and the production of collective action,” American Journal of Sociology (91:3), 1985, pp. 522–556.
   Web of Science ®Google Scholar
• Owen-Smith, J., W. W. Powell. “Knowledge networks as channels and conduits: The effects of spillovers in the Boston biotechnology community,” Organization Science (15:1), 2004, pp. 5–21.
   Web of Science ®Google Scholar
• Parks Associates. 2008. National Technology Scan. Available at http://www.parksassociates.com/research/multiclients/summaries/parks-NationalScan.pdf.
   Google Scholar
• Rice, R. E., A. E. Grant, J. Schmitz, J. Torobin. “Individual and network influences on the adoption and perceived outcomes of electronic messaging,” Social Networks (12:1), 1990, pp. 27–55.
   Web of Science ®Google Scholar
• Robinson, J. P., P. DiMaggio, E. Hargittai. “New social survey perspectives on the digital divide,” IT & Society (1:5), 2003, pp.1–22.
   Google Scholar
• Rogers, E. M. Diffusion of Innovations (4th ed.), Free Press, New York, 1995.
   Google Scholar
• Shapiro, C., H. R. Varian. Information Rules, Harvard Business School Press, Boston, MA, 1999.
   Google Scholar
• Stafford, T. F. “Differentiating between innovators and laggards in the uses and gratifications for internet services,” IEEE Transactions on Engineering Management (50:4), 2003, pp. 427–435.
   Web of Science ®Google Scholar
• U.S. Census Bureau. 2001. Computer and Internet use in the United States: September 2001. Available at: http://www.census.gov/population/www/socdemo/computer/ppl-175.html
   Google Scholar
• Van Dijk, J. K. Hacker. “The digital divide as a complex and dynamic phenomenon,” The Information Society (19:4), 2003, pp. 315–326.
   Web of Science ®Google Scholar
• Venkatesh, V., S. Brown. “A longitudinal investigation of personal computers in homes: Adoption determinants and emerging challenges,” MIS Quarterly (25:1), 2001, pp. 71–102.
   Web of Science ®Google Scholar
• Venkatesh, V., S. A. Brown, L. M. Maruping, H. Bala. “Predicting different conceptualizations of system use: The competing roles of behavioral intention, facilitating conditions, and behavioral expectation,” MIS Quarterly (32:3), 2008, pp. 483–502.
   Web of Science ®Google Scholar