Process evaluation of a community-based program for prevention and control of non-communicable disease in a developing country: The Isfahan Healthy Heart Program, Iran

<p>Abstract</p> <p>Background</p> <p>Cardiovascular diseases are the most common cause of mortality in Iran. A six-year, comprehensive, integrated community-based demonstration study entitled Isfahan Healthy Heart Program (IHHP) conducted in Iran, and it started in 2000. Evaluation and monitoring are integrated parts of this quasi-experimental trial, and consists of process, as well as short and long-term impact evaluations. This paper presents the design of the "process evaluation" for IHHP, and the results pertaining to some interventional strategies that were implemented in workplaces</p> <p>Methods</p> <p>The process evaluation addresses the internal validity of IHHP by ascertaining the degree to which the program was implemented as intended. The IHHP process evaluation is a triangulated study conducted for all interventions at their respective venues. All interventional activities are monitored to determine why and how some are successful and sustainable, to identify mechanisms as well as barriers and facilitators of implementation.</p> <p>Results</p> <p>The results suggest that factory workers and managers are satisfied with the interventions. In the current study, success was mainly shaped by the organizational readiness and timing of the implementation. Integrating most of activities of the project to the existing ongoing activities of public health officers in worksites is suggested to be the most effective means of implementation of the health promoting activities in workplaces.</p> <p>Conclusion</p> <p>The results of our experience may help other developing countries to plan for similar interventions.</p

Born Global firms from emerging economies: Investigating their success factors in international markets

Over the last two decades, the Born Global phenomenon has grown into a fascinating field of internationalization studies. Several perspectives have been studied to enrich this rather new area of business research, however little has been studied in context of the types of economies from where these firms emerge. More especially, insufficient amounts of literature cover the Born Global phenomenon from the emerging market context.   Therefore, the purpose of this thesis is to gain a deeper understanding of this phenomenon from the emerging market context by investigating the factors that influence these born global firms to succeed in their international markets.   We conducted a case study of two companies one from Mexico and another from Ghana. We adopted a qualitative approach for the literature review, data collection and analysis during the course of the study. We also utilized theoretical concepts to build a conceptualized framework to guide our study. Both primary and secondary data sources were used in this research.   Our study revealed five main factors that influence born global firms from emerging economies to succeed in the international scene. These factors are both internal and external and are as follows; strategic management of the firm, networks, product differentiation, technology and markets. We found that the significance of each of the factors’ influence on the companies was specific to type of industry and product/service offering

Solving large-scale two-stage stochastic optimization problems by specialized interior point method

Two-stage stochastic optimization models give rise to very large linear problems (LP). Several approaches have been devised for efficiently solving them, among which are interior-point methods (IPM). However, using IPM, the linking columns that are associated with first-stage decisions cause excessive fill-ins for the solutions of the normal equations, thus making the procedure computationally expensive. We have taken a step forward on the road to a better solution by reformulating the LP through a variable splitting technique which has significantly reduced the solution time. This work presents a specialized IPM that first applies variable splitting, then exploits the structure of the deterministic equivalent formulation of the stochastic problem. The specialized IPM works with an algorithm that combines Cholesky factorization and preconditioned conjugate gradients for solving the normal equations when computing the Newton direction for stochastic optimization problems in which the first-stages variables are large enough. Our specialized approach outperforms standard IPM. This work provides the computational results of two stochastic problems from the literature: (1) a supply chain system and (2) capacity expansion in an electric system. Both linear and quadratic formulations were used to obtain instances of up to 39 million variables and six million constraints. When used in these applications, the computational results show that our procedure is more efficient than alternative state-of-the-art IP implementations (e.g., CPLEX) and other specialized methods for stochastic optimization.Los modelos de optimización estocástica de dos etapas dan lugar a problemas lineales (PL) muy grandes. Se han ideado varios enfoques para resolverlos de manera eficiente, entre los que se encuentran los métodos de punto interior (MPI). Sin embargo, al usar MPI, las columnas de enlace que están asociados con las decisiones de la primera etapa provocan rellenos excesivos para las soluciones de las ecuaciones normales, lo que hace que el procedimiento sea computacionalmente costoso. Hemos dado un paso adelante en el camino hacia una mejor solución al reformular el PL mediante una técnica de división variable que ha reducido significativamente el tiempo de solución. Este trabajo presenta un MPI especializado que primero aplica la división de variables y luego explota la estructura de la formulación determinista equivalente del problema estocástico. El MPI especializado trabaja con un algoritmo que combina la factorización Cholesky y gradientes conjugados precondicionados para resolver las ecuaciones normales al calcular la dirección de Newton para problemas de optimización estocástica en los que las variables de las primeras etapas son lo suficientemente grandes. Nuestro enfoque especializado supera al MPI estándar. Este trabajo proporciona los resultados computacionales de dos problemas estocásticos de la literatura: (1) un sistema de cadena de suministro y (2) expansión de capacidad en un sistema eléctrico. Se utilizaron formulaciones tanto lineales como cuadráticas para obtener instancias de hasta 39 millones de variables y seis millones de restricciones. Cuando se utiliza en estas aplicaciones, los resultados computacionales muestran que nuestro procedimiento es más eficiente que las implementaciones de PI alternativas de última generación (por ejemplo, CPLEX) y otros métodos especializados para la optimización estocástica.Postprint (published version

Solving large-scale two-stage stochastic optimization problems by specialized interior point method

Two-stage stochastic optimization models give rise to very large linear problems (LP). Several approaches have been devised for efficiently solving them, among which are interior-point methods (IPM). However, using IPM, the linking columns that are associated with first-stage decisions cause excessive fill-ins for the solutions of the normal equations, thus making the procedure computationally expensive. We have taken a step forward on the road to a better solution by reformulating the LP through a variable splitting technique which has significantly reduced the solution time. This work presents a specialized IPM that first applies variable splitting, then exploits the structure of the deterministic equivalent formulation of the stochastic problem. The specialized IPM works with an algorithm that combines Cholesky factorization and preconditioned conjugate gradients for solving the normal equations when computing the Newton direction for stochastic optimization problems in which the first-stages variables are large enough. Our specialized approach outperforms standard IPM. This work provides the computational results of two stochastic problems from the literature: (1) a supply chain system and (2) capacity expansion in an electric system. Both linear and quadratic formulations were used to obtain instances of up to 39 million variables and six million constraints. When used in these applications, the computational results show that our procedure is more efficient than alternative state-of-the-art IP implementations (e.g., CPLEX) and other specialized methods for stochastic optimization.Los modelos de optimización estocástica de dos etapas dan lugar a problemas lineales (PL) muy grandes. Se han ideado varios enfoques para resolverlos de manera eficiente, entre los que se encuentran los métodos de punto interior (MPI). Sin embargo, al usar MPI, las columnas de enlace que están asociados con las decisiones de la primera etapa provocan rellenos excesivos para las soluciones de las ecuaciones normales, lo que hace que el procedimiento sea computacionalmente costoso. Hemos dado un paso adelante en el camino hacia una mejor solución al reformular el PL mediante una técnica de división variable que ha reducido significativamente el tiempo de solución. Este trabajo presenta un MPI especializado que primero aplica la división de variables y luego explota la estructura de la formulación determinista equivalente del problema estocástico. El MPI especializado trabaja con un algoritmo que combina la factorización Cholesky y gradientes conjugados precondicionados para resolver las ecuaciones normales al calcular la dirección de Newton para problemas de optimización estocástica en los que las variables de las primeras etapas son lo suficientemente grandes. Nuestro enfoque especializado supera al MPI estándar. Este trabajo proporciona los resultados computacionales de dos problemas estocásticos de la literatura: (1) un sistema de cadena de suministro y (2) expansión de capacidad en un sistema eléctrico. Se utilizaron formulaciones tanto lineales como cuadráticas para obtener instancias de hasta 39 millones de variables y seis millones de restricciones. Cuando se utiliza en estas aplicaciones, los resultados computacionales muestran que nuestro procedimiento es más eficiente que las implementaciones de PI alternativas de última generación (por ejemplo, CPLEX) y otros métodos especializados para la optimización estocástica.Estadística i investigació operativ

Critical factors that impact purchase online of new telecom convergent services in the Mexican market

The following article analyzes the principal factors that have an impact in the adoption of new telecom convergent services, through electronic commerce, that have been explored and studied primarily in developed markets such as the United States and that have been deemed as critical factors in the development and growth of online electronic transactions. Specifically, factors and latent variables of this study derive from the models of Technology Acceptance (Davis, 1989) and Diffusion of Innovations (Rogers, 2003). A summary of past empirical studies is provided deriving from the aforementioned theoretical models followed by results of an exploratory field study comprising of 253 valid observations randomly selected from within the population of urban internet users in Mexico. The methodology used to determine the causal relationship between variables (Betas) was factor analysis (Principal Components) and structural equation modeling, specifically Smart-PLS. The study determined that perceived utility and trust variables are statistically relevant and significant in determining purchase online of new telecom convergent services and the development of electronic commerce in the Mexican Market