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).     

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...     

Nonparametric estimation of waiting time distributions in a Markov model based on current status data

We construct nonparametric estimators of state waiting time distribution functions in a Markov multistate model using current status data. This is a particularly difficult problem since neither the entry nor the exit times of a given state are directly observed. These estimators are obtained, using the Markov property, from estimators of counting processes of state entry and exit times, as well as, the size of “at risk” sets of state entry and transitions out of that state. Consistency of our estimators is established. Finite-sample behavior of our estimators is studied by simulation, in which we show that our estimators based on current status data compare well with those based on complete data. We also illustrate our method using a pubertal development data set obtained from the NHANES III [1997. NHANES III Reference Manuals and Reports (CD-ROM). Analytic and Reporting Guidelines: The Third National Health and Nutrition Examination Survey (1988–94). National Center for Health Statistics, Centers for Disease Control and Prevention, Hyattsville, MD] study.     

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.     

Temporal prediction of future state occupation in a multistate model from high-dimensional baseline covariates via pseudo-value regression

In many complex diseases such as cancer, a patient undergoes various disease stages before reaching a terminal state (say disease free or death). This fits a multistate model framework where a prognosis may be equivalent to predicting the state occupation at a future time t. With the advent of high-throughput genomic and proteomic assays, a clinician may intent to use such high-dimensional covariates in making better prediction of state occupation. In this article, we offer a practical solution to this problem by combining a useful technique, called pseudo-value (PV) regression, with a latent factor or a penalized regression method such as the partial least squares (PLS) or the least absolute shrinkage and selection operator (LASSO), or their variants. We explore the predictive performances of these combinations in various high-dimensional settings via extensive simulation studies. Overall, this strategy works fairly well provided the models are tuned properly. Overall, the PLS turns out to be slightly better than LASSO in most settings investigated by us, for the purpose of temporal prediction of future state occupation. We illustrate the utility of these PV-based high-dimensional regression methods using a lung cancer data set where we use the patients’ baseline gene expression values.     

The effects of internal resources and partnership quality on firm performance: An examination of Indian BPO providers

Firms from developed nations are increasingly resorting to business process outsourcing (BPO) as part of their global sourcing strategy. Although BPO is a growing practice, there has been limited empirical attention in understanding the phenomenon, particularly from the perspective of provider firms that execute important business processes for their overseas clients. In this paper we focus on the resources and capabilities that are utilized by the providers in fulfilling their clients' sourcing needs. Using resource-based view and social exchange as theoretical foundations, we argue that providers' human capital, organizational capital, management capability, and partnership quality are crucial assets that are deemed valuable by the clients and are utilized by the providers in attaining higher performance. Using a sample of Indian BPO providers, we empirically test three models (direct, mediating and moderating) to understand how these assets impact firm-level performance. Results show that resources and capabilities relate to performance in varying measures and partnership quality has partial-mediating and moderating effects on these relationships. We conclude by discussing the theoretical and practical implications of the study and highlighting avenues of future inquiry.     

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.