|
on Information and Communication Technologies |
| By: | Frank J. van Rijnsoever; Carolina Castaldi |
| Abstract: | Innovation is a process that involves searching for new information. This paper builds upon theoretical insights on individual and organizational learning and proposes a knowledge based model of how actors search for information when confronted with innovation. The model takes into account different search channels, both local and non local, and relates their use to the knowledge base of actors. The paper also provides an empirical validation of our model based on a study on the search channels used by a sample of Dutch consumers when buying new consumer electronic products. |
| Keywords: | knowledge base, learning, information search, innovation, consumer behaviour |
| Date: | 2008–02 |
| URL: | http://d.repec.org/n?u=RePEc:uis:wpaper:0802&r=ict |
| By: | Ramana Nanda (Harvard Business School, Entrepreneurial Management Unit); Tarun Khanna (Harvard Business School, Strategy Unit) |
| Abstract: | This study explores the importance of cross-border social networks for entrepreneurship in developing countries by examining ties between the Indian expatriate community and local entrepreneurs in India's software industry. We find that entrepreneurs located outside software hubs - in cities where monitoring and information flow on prospective clients is harder - rely significantly more on diaspora networks for business leads and financing. Relying on these networks is also related to better firm performance, particularly for entrepreneurs located in weaker institutional environments. Our results provide micro-evidence consistent with a view that cross-border social networks serve an important role in helping entrepreneurs to circumvent the barriers arising from imperfect local institutions in developing countries. |
| Keywords: | Diasporas, Informal Networks, Institutions, Entrepreneurship. |
| JEL: | F22 L14 L26 L86 O17 O19 |
| Date: | 2007–06 |
| URL: | http://d.repec.org/n?u=RePEc:hbs:wpaper:08-003&r=ict |
| By: | Jana Eklund; Sune Karlsson |
| Abstract: | This paper is concerned with the efficient implementation of Bayesian model averaging (BMA) and Bayesian variable selection, when the number of candidate variables and models is large, and estimation of posterior model probabilities must be based on a subset of the models. Efficient implementation is concerned with two issues, the efficiency of the MCMC algorithm itself and efficient computation of the quantities needed to obtain a draw from the MCMC algorithm. For the first aspect, it is desirable that the chain moves well and quickly through the model space and takes draws from regions with high probabilities. In this context there is a natural trade-off between local moves, which make use of the current parameter values to propose plausible values for model parameters, and more global transitions, which potentially allow exploration of the distribution of interest in fewer steps, but where each step is more computationally intensive. We assess the convergence properties of simple samplers based on local moves and some recently proposed algorithms intended to improve on the basic samplers. For the second aspect, efficient computation within the sampler, we focus on the important case of linear models where the computations essentially reduce to least squares calculations. When the chain makes local moves, adding or dropping a variable, substantial gains in efficiency can be made by updating the previous least squares solution. |
| Date: | 2007–05 |
| URL: | http://d.repec.org/n?u=RePEc:ice:wpaper:wp35&r=ict |