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).     

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.     

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...     

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.     

An MEP.2 plan in 3 factorial experiment and its probability of correct identification

The present work consider the problem of constructing search designs for searching at most two active hidden two-factor interactions in 3ⁿ factorial setup under the assumption that the three-factor and higher-order interactions are negligible. The designs presented here are also capable of estimating all the main effects, general mean and the effects of identified active two-factor interactions. The performance of the designs has been studied in the noisy case by computing probability of correct identification.     

A note on estimating the false discovery rate under mixture model

In this note, we focus on estimating the false discovery rate (FDR) of a multiple testing method with a common, non-random rejection threshold under a mixture model. We develop a new class of estimates of the FDR and prove that it is less conservatively biased than what is traditionally used. Numerical evidence is presented to show that the mean squared error (MSE) is also often smaller for the present class of estimates, especially in small-scale multiple testings. A similar class of estimates of the positive false discovery rate (pFDR) less conservatively biased than what is usually used is then proposed. When modified using our estimate of the pFDR and applied to a gene-expression data, Storey's q-value method identifies a few more significant genes than his original q-value method at certain thresholds. The BH like method developed by thresholding our estimate of the FDR is shown to control the FDR in situations where the p  -values have the same dependence structure as required by the BH method and, for lack of information about the proportion _π0${\pi }_0$ of true null hypotheses, it is reasonable to assume that _π0${\pi }_0$ is uniformly distributed over (0,1).