Hostname: page-component-586b7cd67f-l7hp2 Total loading time: 0 Render date: 2024-11-27T17:10:58.365Z Has data issue: false hasContentIssue false
  • English
  • Français

Fit-risk in development projects: role of demonstration in technology adoption

Published online by Cambridge University Press:  15 August 2016

Moon Parks Open the ORCID record for Moon Parks [Opens in a new window] ,
Sangeeta Bansal  and
David Zilberman
Moon Parks
Affiliation:
Department of Agricultural and Resource Economics, University of California, 325 Giannini Hall, UC Berkeley, Berkeley, CA 94720, USA. E-mail: [email protected]
Sangeeta Bansal
Affiliation:
Centre for International Trade and Development, Jawaharlal Nehru University, India.
David Zilberman
Affiliation:
Department of Agricultural and Resource Economics, University of California, USA.
Get access Rights & Permissions [Opens in a new window]

Abstract

The introduction and adoption of new technologies is an important component of development projects. Many technologies that could spur considerable increase in welfare, however, are often adopted at low rates even when donors and NGOs have invested their effort in them heavily. This paper develops a framework to analyze inefficiencies caused by fit-risk (potential users are not certain whether the technology will fit their needs, lifestyles, social feedback or capabilities), and the role of marketing tools, such as demonstration, in reducing fit-risk and enhancing the efficiency of development projects. We find that, in the presence of fit-risk, there is always unrealized demand and resource waste. Donors who ignore fit-risk always overestimate the project value and over-subsidize the products they are promoting. We identify conditions under which introducing demonstration may help alleviate fit-risk and improve the overall project values. The impact of eliminating fit-risk on the project uptake depends on the probability of fit.

Type
Research Article
Information
Environment and Development Economics , Volume 21 , Issue 6 , December 2016 , pp. 742 - 766
Copyright
Copyright © Cambridge University Press 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Austin, J.E. (2000), ‘Strategic collaboration between nonprofits and business’, Nonprofit and Voluntary Sector Quarterly 29(suppl. 1): 69–97.Google Scholar
Bandiera, O. and Rasul, I. (2006), ‘Social networks and technology adoption in northern Mozambique’, Economic Journal 116(514): 869–902.Google Scholar
Dupas, P. (2014), ‘Short-run subsidies and long-run adoption of new health products: evidence from a field experiment’, Econometrica 82(1): 197–228.Google Scholar
Edmondson, A.C., Winslow, A.B., Bohmer, R.M., and Pisano, G.P. (2003), ‘Learning how and learning what: effects of tacit and codified knowledge on performance improvement following technology adoption’, Decision Sciences 34(2): 197–224.Google Scholar
Eikenberry, A.M. and Kluver, J.D. (2004), ‘The marketization of the nonprofit sector: civil society at risk?’, Public Administration Review 64(2): 132–140.CrossRefGoogle Scholar
Giné, X. and Yang, D. (2009), ‘Insurance, credit, and technology adoption: field experimental evidence from Malawi’, Journal of Development Economics 89(1): 1–11.Google Scholar
Hanna, R., Duflo, E., and Greenstone, M. (2012), ‘Up in smoke: the influence of household behavior on the long-run impact of improved cooking stoves’, NBER Working Paper No. 18033, Cambridge, MA.Google Scholar
Heffetz, O. and Shayo, M. (2009), ‘How large are non-budget-constraint effects of prices on demand?’, Applied Economics 1(4): 170–199.Google Scholar
Heiman, A., McWilliams, B., and Zilberman, D. (2001), ‘Demonstrations and money-back guarantees: market mechanisms to reduce uncertainty’, Journal of Business Research 54(1): 71–84.Google Scholar
Katz, M.L. and Shapiro, C. (1986), ‘Technology adoption in the presence of network externalities’, Journal of Political Economy 94: 822–841.Google Scholar
Klein, L.R. (1998), ‘Evaluating the potential of interactive media through a new lens: search versus experience goods’, Journal of Business Research 41(3): 195–203.Google Scholar
Koundouri, P., Nauges, C., and Tzouvelekas, V. (2006), ‘Technology adoption under production uncertainty: theory and application to irrigation technology’, American Journal of Agricultural Economics 88(3): 657–670.Google Scholar
Laxmi, V., Erenstein, O., and Gupta, R.K. (2007), ‘Impact of zero tillage in India's rice-wheat systems’, [Available at] http://ageconsearch.umn.edu/bitstream/56093/2/zeroTill_india.pdf.Google Scholar
Lindenberg, M. (2001), ‘Are we at the cutting edge or the blunt edge? Improving NGO organizational performance with private and public sector strategic management frameworks’, Nonprofit Management and Leadership 11(3): 247–270.Google Scholar
Roberts, J.H. and Urban, G.L. (1988), ‘Modeling multiattribute utility, risk, and belief dynamics for new consumer durable brand choice’, Management Science 34(2): 167–185.Google Scholar
Scott, C.A. (1976), ‘The effects of trial and incentives on repeat purchase behavior’, Journal of Marketing Research 13: 263–269.Google Scholar
Straub, E.T. (2009), ‘Understanding technology adoption: theory and future directions for informal learning’, Review of Educational Research 79(2): 625–649.Google Scholar
Zilberman, D., Lipper, L., and McCarthy, N. (2008), ‘When could payments for environmental services benefit the poor?’, Environment and Development Economics 13(3): 255–278.CrossRefGoogle Scholar
Zilberman, D., Zhao, J., and Heiman, A. (2012), ‘Adoption versus adaptation, with emphasis on climate change’, Annual Review of Resource Economics 4(1): 27–53.Google Scholar