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The Spillover Effect of Digitization on Entrepreneurship: Evidence from India, 2005-2011
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Author: Che-Wei Liu, U. of Maryland, College Park
Author: Sunil Mithas, U. of Maryland
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This study examines IT externalities on entrepreneurship by using two waves of high quality nationally representative data on approximately 12,000 households in India. Our results show a direct impact of computer use on entrepreneurship. We examine spillover effects by using the caste system in India as a measure of social distance. The results show that computer use by those belonging to the same caste in a district has a significant and positive spillover effect on entrepreneurship, but this result does not hold for those using computers belonging to a different caste. Additional analyses suggest that level of harmony in a district enhances the positive spillover effect, and individuals who live in a harmonious district enjoy higher social externalities from computer use. The results also indicate the spillover effects differ depending on technology, and we observe differences in spillover effects of computer use and mobile phone adoption. Although mobile phone adoption shows a negative association with entrepreneurship primarily due to negative spillover effects from other castes, the negative effect is positively moderated by harmony as in the case of computer use. Together, these findings provide important insights on the role of IT and social/geographic groups on entrepreneurship and suggest that spillover effects become stronger when there is a strong social harmony in a region. Our results also suggest that a more complete evaluation of technology policies should concern social returns, which can sometimes exceed private returns.
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Search Terms: Spillover effect | Entrepreneurship | Returns to computer skills
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Paper is No Longer Available Online: Please contact the author(s).
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Diffusion Through Generation Cohorts
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Author: Anil R. Doshi, UCL School of Management
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William H. Newman Award Nominee
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This paper studies technology adoption where sets of new members —generation cohorts—enter the population over time. Using diffusion of Twitter through eight generation cohorts of television shows debuting between 2005 and 2013, I show that the time to reach 25% diffusion decreased from 48 months to 1 month and rates of diffusion quadrupled. I also provide evidence that diffusion experienced by prior generation cohorts affects the diffusion path of those that follow. These results suggest that incumbent firms need to account for the renewed diffusion process with each generation cohort and entrant firms can successfully enter and compete in a market with a widely diffused incumbent technology by differentiating a new product offering to attract a new generation cohort. Results from this paper resolve some diffusion puzzles where the diffusion path does not appear logistic.
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Search Terms: diffusion | twitter | social media
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Paper is No Longer Available Online: Please contact the author(s).
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Hyperconnectivity: A Cross-Country Comparison
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Author: Erkan Bayraktar, American U. of the Middle East
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The aim of this study is to measure cross-country differences in the relative efficiency of economic and social impacts of hyperconnectivity with regard to its drivers using data envelopment analysis (DEA) and canonical correlation analysis (CCA). CCA confirms the existence of a strong and significant relationship between Environment, Readiness and Usage drivers of hyperconnectivity and social and economic outcomes on a country level. After eliminating the administrative differences among countries, DEA findings indicate that developing economies are the most efficient ones followed by the emerging and advanced economies. The main structural differences found among the different economies are: Political & Regulatory Environment, Infrastructure & Digital Content, Affordability, and Individual Usage. Relatively favorable legislative conditions in advanced economies do not contribute much to produce economic and social impacts. Emerging economies were the most effective ones to use infrastructure & digital content followed by developing and advanced countries respectively. For emerging economies, relatively better affordability of technologies did not produce much economic and social impact. Even though Individual usage was high in advanced economies, it did not generate sufficient impact. Policy-makers should aim to improve efficiency of transformation of hyperconnectivity into economic and social impacts in their respective countries.
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Search Terms: economic development and growth | knowledge and productivity spillovers | hyperconnectivity
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Paper is No Longer Available Online: Please contact the author(s).
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