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|عنوان فارسی مقاله:||برخورد (مواجه) با مشکلات ناهمگن (نامتجانس) و برآورد (تخمین) اثرسببی (علی) در تحقیق کارآفرینی|
|عنوان انگلیسی مقاله:||DEALING WITH HETEROGENEITY PROBLEMS AND CAUSAL EFFECT ESTIMATION IN ENTREPRENEURSHIP RESEARCH|
|رشته های مرتبط:||مدیریت، کارآفرینی، مدیریت کسب و کار، مدیریت استراتژیک|
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This paper deals with causal effect estimation strategies in highly heterogeneous empirical settings such as entrepreneurship. We argue that the clearer used of modern tools developed to deal with the estimation of causal effects in combination with our analysis of different sources of heterogeneity in entrepreneurship can lead to entrepreneurship with higher internal validity. We specifically lend support from the counterfactual logic and modern research of estimation strategies for causal effect estimation.
۱٫ EXECUTIVE SUMMARY Entrepreneurship comes in many shapes and forms, driven by a broad variety of motivations and in a diversity of contexts. While this heterogeneity contributes to making entrepreneurship fascinating it also makes it very challenging for entrepreneurship researchers to arrive at strong and credible conclusions regarding causal relationships. Building on experiences from within and outside of entrepreneurship research this article provides an integrated discussion of strategies for dealing with the problem of heterogeneity with particular application to the entrepreneurship domain and the estimation of causal effects. Specifically, we deal with three problems: 1) unobserved heterogeneity, i.e., that unmeasured or unavailable variables may bias estimated relationships; 2) causal heterogeneity, i.e., that the structure, strength, direction or form of relationships may vary among sub-groups of the studied population, and 3) uneven validity, i.e., that the validity of chosen operationalizations may vary by sub-group or context. We discuss how these problems can be reduced at different stages of the research process, i.e., through theory and theorizing; in choosing a basic design for the study (including sampling); at the operationalization stage, and through approaches chosen for analysis, respectively. We conclude each section with summarized advice that should help entrepreneurship researchers design more robust studies and arrive at more validconclusions from extant data sets. Throughout, we illustrate with examples from entrepreneurship studies ۲٫ INTRODUCTION This paper deals with causal effect estimation strategies in highly heterogeneous empirical settings such as entrepreneurship. Business ventures are started by individuals and teams with very different backgrounds and motivations, pursuing different objectives based on business ideas of very variable inherent quality in environments that also show tremendous variability. Certain aspects of this great variability or heterogeneity is an important, fundamental and theoretically interesting characteristic of the entrepreneurship phenomenon (Alvarez & Busenitz, 2001; Davidsson, 2004). After all, it is in great part their ability to deviate from norms in new and unexpected ways that makes new and growing ventures fascinating and – sometimes – financially successful. However, the great variability also makes it difficult for researchers to arrive at valid causal inference, and studies that try to ‘reflect reality’ by including all the multi-dimensional variance at once risk arriving at weak or confusing results. Similarly, studies seemingly addressing the same questions using different samples, operationalizations or analysis approaches may arrive at conflicting results. Over the years, this has led to frustration that “entrepreneurs seem to defy aggregation” (Low & MacMillan, 1988) and that we are “getting more pieces of the puzzle, but no picture is emerging” (Koppl & Minniti, 2003 In this article we use heterogeneity as an umbrella term for the simultaneous variability along three different dimensions that makes it challenging to adequately measure theoretical constructs and to correctly model and to estimate causal relationships. Numerous factors contribute to problematic heterogeneity. They are (1) unobserved heterogeneity, (2) causal heterogeneity and (3) uneven or differential validity. While all scientific fields have to deal to some extent with heterogeneity problems there are several reasons why they are of particular significance for entrepreneurship research. First, the phenomenon itself may be more heterogeneous as it concerns emerging ventures,industries and populations. In more mature stages of development, market forces (Lawless & Tegarden, 1991), learning (Jovanovic, 1982) and institutional pressures (Henrekson, 2007) tend to limit the range of variation along at least some dimensions. Second, due to the cost and difficulty of obtaining primary data on such emerging phenomena researchers may turn to archival data that do not include all variables needed to avoid severe omitted variables problems (Shane, 2006). Third, within the multi-disciplinary field of entrepreneurship research, each theory or discipline emphasizes its specific set of variables and neglects others (Acs & Audretsch, 2003; Ireland & Webb, 2007). Partial absorption of what can be considered unified bodies of theory (and related empirical works) across a range of fields can lead to seriously misreading the theory and results. Many results may only be valid under certain theoretical assumptions and in specific empirical contexts. Hence, the construction of an integrated field of knowledge for entrepreneurship – important as it is – is associated with considerable risks1 .