Knowledge source preferences as determinants of strategic entrepreneurial orientation

In the knowledge intensive context, firms’ capacity to integrate external and internal sources of knowledge becomes an important competitive advantage and may distinguish entrepreneurial from conservative firms. This paper explores the proposition that differences in strategic entrepreneurial orientation (EO) across firms may be significantly determined by differences in firms’ preferences regarding knowledge sources. Our research is based on 208 firms operating in knowledge intensive industries in six Central and East European countries (CEEC). We identified three types of firms in terms of patterns of sources of knowledge: external R&D knowledge based firms, in-house knowledge based firms and value chain dependent firms. By using different proxies or different dimensions of EO, we have found that the EO is strongest in firms based on external knowledge. Firms with inhouse based knowledge have an intermediate strength of the EO, and firms dependent on value chains are the least entrepreneurially oriented. We have also found moderate support for grouping different proxies of EO into three dimensions identified in literature – innovativeness, pro-activeness and risk-taking. Value chain firms are not pro-active, have the lowest innovativeness, and are the most risk averse. External knowledge based firms are the most active in all three dimensions of EO, while inhouse knowledge based firms are in an intermediate position. Our results point to strong systemic features of entrepreneurial activities; i.e., EO is inherently different in different sub-populations of firms depending on their patterns of sources of knowledge. It seems that these patterns operate as a moderating factor between performance and the EO, which explains mixed results from the literature

Exploiting graph structure in Active SLAM

Aplicando análisis provenientes de la teoría de grafos, la teoría espectral de grafos, la exploración de grafos en línea, generamos un sistema de SLAM activo que incluye la planificación de rutas bajo incertidumbre, extracción de grafos topológicos de entornos y SLAM activo \'optimo.En la planificación de trayectorias bajo incertidumbre, incluimos el análisis de la probabilidad de asociación correcta de datos. Reconociendo la naturaleza estocástica de la incertidumbre, demostramos que planificar para minimizar su valor esperado es más fiable que los actuales algoritmos de planificación de trayectorias con incertidumbre.Considerando el entorno como un conjunto de regiones convexas conectadas podemos tratar la exploración robótica como una exploración de grafos en línea. Se garantiza una cobertura total si el robot visita cada región. La mayoría de los métodos para segmentar el entorno están basados en píxeles y no garantizan que las regiones resultantes sean convexas, además pocos son algoritmos incrementales. En base a esto, modificamos un algoritmo basado en contornos en el que el entorno se representa como un conjunto de polígonos que debe segmentarse en un conjunto de polígonos pseudo convexos. El resultado es un algoritmo de segmentación que produjo regiones pseudo-convexas, robustas al ruido, estables y que obtienen un gran rendimiento en los conjuntos de datos de pruebas.La calidad de un algoritmo se puede medir en términos de cuan cercano al óptimo está su rendimiento. Con esta motivación definimos la esencia de la tarea de exploración en SLAM activo donde las únicas variables son la distancia recorrida y la calidad de la reconstrucción. Restringiendo el dominio al grafo que representa el entorno y probando la relación entre la matriz asociada a la exploración y la asociada al grafo subyacente, podemos calcular la ruta de exploración óptima.A diferencia de la mayoría de la literatura en SLAM activo, proponemos que la heurística para la exploración de grafos consiste en atravesar cada arco una vez. Demostramos que el tipo de grafos resultantes tiene un gran rendimiento con respecto a la trayectoria \'optima, con resultados superiores al 97 \% del \'optimo en algunas medidas de calidad.El algoritmo de SLAM activo TIGRE integra el algoritmo de extracción de grafos propuesto con nuestra versión del algoritmo de exploración incremental que atraviesa cada arco una vez. Nuestro algoritmo se basa en una modificación del algoritmo clásico de Tarry para la búsqueda en laberintos que logra el l\'imite inferior en la aproximación para un algoritmo incremental. Probamos nuestro sistema incremental en un escenario de exploración típico y demostramos que logra un rendimiento similar a los métodos fuera de línea y también demostramos que incluso el método \'optimo que visita todos los nodos calculado fuera de línea tiene un peor rendimiento que el nuestro.<br /

Technology and the economy

Overview of economics of innovatio

The importance of ideas: an a priori critical juncture framework

This paper sets out an improved framework for examining critical junctures. This framework, while rigorous and broadly applicable and an advance on the frameworks currently employed, primarily seeks to incorporate an a priori element. Until now the frameworks utilized in examining critical junctures were entirely postdictive. Adding a predictive element to the concept will constitute a significant advance. The new framework, and its predictive element, termed the “differentiating factor,” is tested here in examining macro-economic crises and subsequent changes in macro-economic policy, in America and Sweden

Aid and Growth: Have We Come Full Circle?

The micro-macro paradox has been revived. Despite broadly positive evaluations at the micro and meso-levels, recent literature has turned decidedly pessimistic with respect to the ability of foreign aid to foster economic growth. Policy implications, such as the complete cessation of aid to Africa, are being drawn on the basis of fragile evidence. This paper first assesses the aid-growth literature with a focus on recent contributions. The aid-growth literature is then framed, for the first time, in terms of the Rubin Causal Model, applied at the macroeconomic level. Our results show that aid has a positive and statistically significant causal effect on growth over the long run with point estimates at levels suggested by growth theory. We conclude that aid remains an important tool for enhancing the development prospects of poor nations.foreign aid, growth, aid effectiveness, causal effects

MCA4climate: A Practical Framework for Planning Pro-Development Climate Policy

MCA4climate is a major new UNEP initiative providing 1 Introduction practical assistance to governments in preparing their climate change mitigation and adaptation plans and strategies. It aims to help governments, particularly in developing countries, identify policies and measures that are low cost, environmentally effective and consistent with national development goals. It does this by providing a structured approach to assessing and prioritizing climate-policy options, while taking into consideration associated social, economic, environmental and institutional costs and benefits. In doing so, it seeks to counter the widely held perception that tackling climate change is costly, highlight the potential developmental benefits of addressing climate change and encourage action to that end

Essays on building and evaluating two-stage DEA models of efficiency and effectiveness

Researchers are not consistent in their choice of input and output variables when using two-stage data envelopment analysis (DEA) models to measure efficiency and effectiveness. This inconsistency has resulted in the development of many different two-stage DEA models of efficiency and effectiveness for the financial industry. In this dissertation, I improved the statistical method from the MASc dissertation (Attarwala, 2016) by adding more features. These features are documented in Chapter 2 on page 4 and page 5. This statistical method evaluates efficiency and effectiveness models in the banking industry. It relies on the semi-strong version of the efficient market hypothesis (EMH). The EMH is motivated by the wisdom of the crowds, discussed in Section 2.2.2. Previously (Attarwala, 2016), I found that the two-stage DEA model of Kumar and Gulati (2010) is not consistent with the semi-strong EMH for Indian and American banks. In this dissertation, using my improved statistical method, I show that the two-stage DEA model of Kumar and Gulati (2010) is not consistent with the semi-strong EMH for banks in Brazil, Canada, China, India, Japan, Mexico, South Korea and the USA from 2000- 2017. I address the question of whether a universal two-stage DEA model of efficiency and effectiveness exists by building a variable selection framework. This variable selection framework automatically generates two-stage DEA models of efficiency and effectiveness. To do this, it uses the improved statistical method and a genetic search (GS) algorithm. The variable selection framework finds the best, universal, two-stage DEA model of efficiency and effectiveness consistent with the semi-strong definition of EMH for banks in Brazil, Canada, China, India, Japan, Mexico, South Korea and the USA and from 2000-2017. I investigated the causal relationship between (a) the quantitative measures of efficiency and effectiveness from the best two-stage DEA model generated by the variable selection framework and (b) Tobin’s Q ratio, a financial market-based measure of bank performance. Not only do I provide bank managers with a reasonable proxy for measuring efficiency and effectiveness, but I also address the question of whether acting on these input and output variables improves the performance of banks in the financial market. Finally, I set up an optimization problem and find an optimal path from the two-stage DEA model of Kumar and Gulati (2010) to the best two-stage DEA model found by the variable selection framework. This optimal path provides a set of actionable items for converting a two-stage DEA model that is not consistent with the semi-strong EMH to one that is