Incumbent capability enhancement in response to radical innovations

Schumpeterian market disequilibrium marked by entrepreneurial entry and incumbent exit has long held an important place in management literature. The extant literature has overwhelmingly championed the newcomer, despite incumbents' obvious advantages in resources, experience and market knowledge. The current research provides evidence for the effectiveness of the incumbent's strategy of capability enhancement (along an established technological trajectory) while responding to radical technological innovations. We develop a cognitive process model that integrates managerial cognition with capability development and deployment views, depicting the dynamics of the incumbent's capability enhancement process. We analyze the cognitive drivers of organizational actions in all stages (rigidity, triggering event, and capability renewal) and elucidate the role of top management cognition in the processes of detecting and correcting errors in a strategic course of action. We ground our model in the case of a cork-stopper industry veteran's decline as corks ceded ground to screw tops and other stoppers in the wine industry. How a major company fought back in response to the emergence of these, in the industry context, radical technological innovations, provides the basis for our narrative. The proposed theoretical model contributes to literature on technology management (with regard to incumbent strategies in response to radical innovation threats) as well as the role of cognition in strategy (providing an explanation of the cognitive underpinnings of capability development).     

The effect of natural disaster on fertility,birth spacing,and child sex ratio: evidence from a major earthquake in India

Natural disasters can lead to significant changes in health, economic, and demographic outcomes. However, the demographic effects of earthquakes have been studied only to a limited degree. This paper examines the effect of the 2001 Gujarat earthquake on reproductive outcomes. This earthquake killed more than 20,000 people; injured 167,000; and caused massive losses to property and civic assets. Using data from two large-scale District-Level Household Surveys (2002–2004 and 2007–2008), we employ difference-in-difference and fixed-effect regression models to compare the outcomes across earthquake-affected districts and their neighboring districts during 5 years before and after the earthquake. We find that the earthquake led to significant rises in childbirth rates. It also reduced birth spacing among uneducated, tribal, and Muslim women, and the incidence of male births among rural women. We find considerable variation in the demographic effects of the earthquake across location, household socioeconomic status, and parental age and education.     

A Method of Finding Predictor Genes for a Particular Disease Using a Clustering Algorithm

Clustering Algorithms are nowadays really important tools in microarray data analysis. The different clustering algorithm generally used in biological science does not take into consideration the underlying probability distribution of the data. In this sense, they are heuristic in nature. In this work we proposed a clustering algorithm based on EM Algorithm. It gives 28% less misclassification than the K-means algorithm (which is mostly use in Bio science). We have also shown on a real data set that this algorithm can be efficiently used for detecting the genes which are responsible for a particular disease.     

On some consistent tests of mutual independence among several random vectors of arbitrary dimensions

Statistics and Computing - Testing for mutual independence among several random vectors is a challenging problem, and in recent years, it has gained significant attention in statistics and machine...     

Beyond the “digital divide”: the “computer girls” of Seelampur

In the last decade, India’s position as a global information economy has aided its status as the poster child for Information and Communication Technologies for Development (ICTD) initiatives in the global South. Policy discourse emphasizes that crossing of the “digital divide” through access to technology will bring a leveling of economic and social hierarchies. Based on sustained ethnographic observations and interviews in Seelampur in New Delhi, my paper critically examines the promise of inclusion in the information society for low income “Muslim women.” In following the lives of the “computer girls” of Seelampur, I argue that access to computer training contradictorily reproduced and reified interconnected divisions of gender, class, caste, and religion. Ethnographic research reveals the complexities of everyday lived experiences among Seelampur women. The space of the ICT center was rigidly stratified and the center primarily attracted women from relatively higher class and caste positions within Seelampur. However, I argue that the women had contradictory experiences related to their status as India’s “middle-class” that restricted their economic and social mobility outside the ICT center. Their experiences testified to structural disempowerment that disrupted deterministic assumptions about ICT and mobility.     

Consumer bankruptcy and default: The role of individual social capital

In this paper, we empirically assess the role of individual social capital on personal bankruptcy and default outcomes in the consumer credit market. After controlling for a borrower’s risk score, debt, income, wealth, and legal and economic environments, we find that default/bankruptcy risk rises and then falls over the lifecycle, while a borrower who owns a home or is married has a lower risk of default/bankruptcy. Moreover, a borrower who migrates 190 miles from his “state of birth” is 17% more likely to default and 15% more likely to file for bankruptcy, while a borrower who continues to live in his state of birth is 14% and 10% less likely to default and file for bankruptcy, respectively. A borrower who moves to a rural area is 9% and 7% less likely to default and declare bankruptcy, respectively. We also find that measures of social networks, norms, and cooperation and trust (i.e., aggregate social capital) are inversely related to consumer bankruptcy.     

A note on nonlinear regression for the autoregressive moving average with non-hd errors

Estimation by nonlinear regression of the parameters for the stationary and invertible autoregressive moving average (ARMA) model with mixing or martingale difference errors is considered. Simple proofs of consistency and asymptotic normality for the nonlinear least squares estimator are given by exploiting results from nonlinear estimation theory and mixing and mixingale theory.     

Robust inference for generalized partially linear mixed models that account for censored responses and missing covariates – an application to Arctic data analysis

In this article, we propose a family of bounded influence robust estimates for the parametric and non-parametric components of a generalized partially linear mixed model that are subject to censored responses and missing covariates. The asymptotic properties of the proposed estimates have been looked into. The estimates are obtained by using Monte Carlo expectation–maximization algorithm. An approximate method which reduces the computational time to a great extent is also proposed. A simulation study shows that performances of the two approaches are similar in terms of bias and mean square error. The analysis is illustrated through a study on the effect of environmental factors on the phytoplankton cell count.