Internationalization,Innovation and Institutions: The 3 I's Underpinning the Competitiveness of Emerging Market Firms

The past decade has seen an increase in the extent of research focused on and around emerging market firms (EMFs) and their rising levels of competitiveness in both their home markets and more importantly in the global market place. At the same time, the practitioner-oriented literature has been documenting a growing number of corporate success stories that originate in emerging market economies. We posit that the growing prominence of EMFs is a result of three interrelated phenomena: the fast-paced internationalization of EMFs into both developing and developed market economies; the rapidly increasing extent to which business enterprises in emerging economies are focusing on knowledge-intensive processes and innovation; and the continuous evolution of institutions in these markets, particularly in terms of economic liberalization.     

Ensemble Habitat Mapping of Invasive Plant Species

Ensemble species distribution models combine the strengths of several species environmental matching models, while minimizing the weakness of any one model. Ensemble models may be particularly useful in risk analysis of recently arrived, harmful invasive species because species may not yet have spread to all suitable habitats, leaving species‐environment relationships difficult to determine. We tested five individual models (logistic regression, boosted regression trees, random forest, multivariate adaptive regression splines (MARS), and maximum entropy model or Maxent) and ensemble modeling for selected nonnative plant species in Yellowstone and Grand Teton National Parks, Wyoming; Sequoia and Kings Canyon National Parks, California, and areas of interior Alaska. The models are based on field data provided by the park staffs, combined with topographic, climatic, and vegetation predictors derived from satellite data. For the four invasive plant species tested, ensemble models were the only models that ranked in the top three models for both field validation and test data. Ensemble models may be more robust than individual species‐environment matching models for risk analysis.     

Application of Multivariate Analysis to Increase the Yield of Dry Cell Batteries

In an organization, the manufacturing process of a dry cell battery was suffering from the problem of frequent stoppages in the assembly line. The complete battery manufacturing operation is highly automated and mechanized. It was suspected that excessive variation in overall height of bobbin was the major reason for such stoppages. The bobbin, the inside part of a dry cell battery acting as cathode, is formed by the battery extrusion process. A planned experiment was carried out on the extrusion process that identified the setting of extrusion machines and the amount of water content in the cathode mixture as the parameters causing variation in the bobbin characteristics. The problem of frequent stoppages was eliminated when appropriate action was taken on these two parameters. Finally, multivariate and univariate control schemes were developed for online control of the bobbin characteristics.     

Union–intersection principle and constrained statistical inference

Most statistical models arising in real life applications as well as in interdisciplinary research are complex in their designs, sampling plans, and associated probability laws, which in turn are often constrained by inequality, order, functional, shape or other restraints. Optimality of conventional likelihood ratio based statistical inference may not be tenable here, although the use of restricted or quasi-likelihood has spurred in such environments. S.N. Roy's ingenious union–intersection principle provides an alternative avenue, often having some computational advantages, increased scope of adaptability, and flexibility beyond conventional likelihood paradigms. This scenario is appraised here with some illustrative examples, and with some interesting problems of inference on stochastic ordering (dominance) in parametric as well as beyond parametric setups.     

Regression models for analyzing clustered binary and continuous outcomes under an assumption of exchangeability

Scientific experiments commonly result in clustered discrete and continuous data. Existing methods for analyzing such data include the use of quasi-likelihood procedures and generalized estimating equations to estimate marginal mean response parameters. In applications to areas such as developmental toxicity studies, where discrete and continuous measurements are recorded on each fetus, or clinical ophthalmologic trials, where different types of observations are made on each eye, the assumption that data within cluster are exchangeable is often very reasonable. We use this assumption to formulate fully parametric regression models for clusters of bivariate data with binary and continuous components. The regression models proposed have marginal interpretations and reproducible model structures. Tractable expressions for likelihood equations are derived and iterative schemes are given for computing efficient estimates (MLEs) of the marginal mean, correlations, variances and higher moments. We demonstrate the use the ‘exchangeable’ procedure with an application to a developmental toxicity study involving fetal weight and malformation data.     

Some remarks on classical and bayesian reliability estimation of binomial and poisson distributions

The problems of estimating the reliability function and Pr{X₁+...+X_k ≤ Y} are considered. The random variables X’s and Y are assumed to follow binomial and Poisson distributions. Classical estimators available in the literature are discussed and Bayes estimators are derived. In order to obtain the estimators of these parametric functions, the basic role is played by the estimators of factorial moments of the two distributions.     

Formulating Contextual Building Regulations for Hill Towns of India: The Case of Shimla

Hill towns of India are best known for their salubrious climate, scenic beauty, picturesque setting and healthy living environment. Hill towns have been experiencing high pressure for development for the last three decades and as a result, are facing numerous problems related to new development that is contextually inappropriate and leads to deteriorated living conditions and poor environmental quality. Most of the issues of existing development are due to contextually inappropriate building regulations, which are formulated without consideration of the geoenvironmental and developmental context of environmentally sensitive hill towns. Moreover, no scientific and systematic approach is followed for the formulation of regulations. A new approach based on various intrinsic characteristics and factors is required for formulation of building regulations in the particular context to hill towns of India. The relevant issues related to building regulations are highlighted in this paper along with the identification of a more suitable building regulatory system for the specific context of hill towns. The article suggests a new approach for the formulation of building regulations for hill towns, based on various intrinsic characteristics and different factors, which are crucial for contextual development in hill towns.     

Knowledge management for sustainability in operations

Abstract

The purpose of this paper is to examine the effect of Knowledge Management (KM) as a platform to enable sustainability in firms’ operations and to provide recommendations for managers to integrate sustainable operations into their business strategies. The urgency to create and implement sustainable operations is also argued. The conceptual framework is based on the hypothesis that KM contributes to the achievement of more suitable operations. To test this hypothesis, a model of structural equations was developed with data collected from 345 SMEs. Empirical evidence supported the hypothesis, indicating that KM represents an important alternative to implementing sustainability. The results suggest that managers need to implent KM, as they will enable a better understanding and awareness regarding the global dangerous impacts from unsustainable operations mainly focused on sales and cost reduction. For this reason, this research provides evidence that KM offers an alternative impulse on the quest for more sustainable operations.     

Evaluating the human resource related soft dimensions in green supply chain management implementation

Because of increased carbon emissions, environmental protection initiatives have gained significant attention at global level. One of the major initiatives taken by the industrial sector to minimize the negative environmental effect of the value chain activities is Green Supply Chain Management (GSCM). In industry, soft (human resource-related) dimensions influence the implementation of GSCM process greatly. In the literature, relatively less discussion is provided on assessing the significance of soft dimensions in efficient GSCM acceptance in industry. The present work is an attempt to construct a structural framework for assessing the significance of the soft dimensions in adopting GSCM concepts by taking a case of automotive company in India. A hybrid approach of Best Worst Method (BWM) and Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach is employed in this work. BWM is used to prioritize the GSCM oriented soft dimensions, and DEMATEL is employed to extract interrelationships among them. The result shows that ‘Top management commitment’, ‘Employee involvement’, ‘Organizational culture’ and ‘Teamwork’ are the highly prioritized causal soft dimensions in efficient GSCM adoption. This research work would help industry managers and practitioners to decide where to concentrate for GSCM concepts in context of soft dimensions for sustainable business development.