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Journal ArticleDOI

Causation and effectuation processes: A validation study

TL;DR: In this paper, the authors developed and validated measures of causation and effectuation approaches to new venture creation and test their measures with two samples of entrepreneurs in young firms and found that effectuation is a formative, multidimensional construct with three associated sub-dimensions (experimentation, affordable loss, and flexibility) and one dimension shared with the pre-commitments.
About: This article is published in Journal of Business Venturing.The article was published on 2011-05-01. It has received 682 citations till now. The article focuses on the topics: Effectuation & Causation.
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Journal ArticleDOI
Greg Fisher1
TL;DR: In this paper, a critical examination of how different theoretical perspectives in entrepreneurship research translate into individual behavior, and whether such behavior is evident in the creation and development of new ventures is provided.
Abstract: This study provides a critical examination of how different theoretical perspectives in entrepreneurship research translate into individual behavior, and whether such behavior is evident in the creation and development of new ventures. Using an alternative templates research methodology, the behaviors underlying the theories of effectuation, causation, and bricolage are evaluated to see whether such behaviors are observable in case study data describing the early development of six new ventures. The analysis highlights behavioral similarities and differences between the various theoretical perspectives in entrepreneurship research, providing insight into how these perspectives contrast and complement one another, and how they could be integrated in future research.

722 citations


Cites background or methods or result from "Causation and effectuation processe..."

  • ...…strong behavioral alignment Table 4 Causation Approach to Entrepreneurship 37signals Bloglines del.icio.us Six Apart Flickr Trip Advisor Causation (Chandler et al., 2011; Sarasvathy, 2001) Identified and assessed long-run opportunities in developing the firm ✕ ✕ ✕ ✕ ✓✓ ✓ Calculated the returns of…...

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  • ...Table 1 Entrepreneurship Theories 1....

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  • ...The foundational articles relating to each of these theoretical perspectives were carefully reviewed, along with other articles that have examined behaviors pertaining to the various perspectives (e.g., Chandler et al., 2011, for causation and effectuation; Senyard et al., 2009, for bricolage)....

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  • ...To this end, the items used in their Figure 3 Bricolage Approach to Entrepreneurship (Baker & Nelson, 2005) Penurious environment Bricolage: making do by applying combinations of resources on hand Seek resources; attempt to acquire standard resources Avoid new challenges Bricolage domains Inputs Regulatory/ institutional Customers Parallel bricolage: community of practice & bricolage identity Routinization Broader, richer, more demanding market No growth Growth Selective bricolage 1028 ENTREPRENEURSHIP THEORY and PRACTICE measurement instruments were adapted as a foundation to describe the entrepreneurial behaviors underlying each of the entrepreneurship theories....

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  • ...Effectuation (adapted from Chandler et al., 2011; Sarasvathy, 2001) Items pertaining to the effectuation construct loaded onto four factors: Experimentation • Develops multiple variations of a product or service to arrive at a commercial offering: Creation of multiple different product prototypes…...

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Journal ArticleDOI
TL;DR: A review of the psychology of entrepreneurship can be found in this article, where meta-analytic findings show that personality dimensions, such as selfefficacy and need for achievement, and entrepreneurial orientation are highly associated with entrepreneurship (business creation and business success).
Abstract: In this review of the psychology of entrepreneurship, we first present meta-analytic findings showing that personality dimensions, such as (general) self-efficacy and need for achievement, and entrepreneurial orientation are highly associated with entrepreneurship (business creation and business success). We then discuss constructs that were developed within entrepreneurship research, such as entrepreneurial alertness, business planning, financial capital as resources, and entrepreneurial orientation, and how they can be better understood by taking a psychological perspective. Next, we elaborate how traditional psychological constructs have been utilized in entrepreneurship and how this may enhance our knowledge in industrial and organizational psychology (with respect to, for example, knowledge, practical intelligence, cognitive biases, goals and visions, personal initiative, passion, and positive and negative affect). Finally, we provide an overall framework useful for the psychology of entrepreneurship...

603 citations

Journal ArticleDOI

527 citations


Cites methods from "Causation and effectuation processe..."

  • ...Building on relevant exemplars from this and other journals (Chandler et al., 2011; Lewis, 2003; Shipp et al., 2009; Tang et al., 2012), we followed a three-stage procedure to assess our instrument in terms of multiple dimensions of validity (Cook and Campbell, 1979; Scandura andWilliams, 2000)....

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Journal ArticleDOI
TL;DR: In this paper, the authors review the effectuation literature and make suggestions for how to design and conduct empirically rigorous effectuation studies consistent with the developmental state of the research stream.
Abstract: Effectuation represents a paradigmatic shift in the way that we understand entrepreneurship. Since its introduction, however, few researchers have attempted to empirically test effectuation. Our purpose is to encourage effectuation research. To do so, we review the effectuation literature and make suggestions for how to design and conduct empirically rigorous effectuation studies consistent with the developmental state of the research stream.

424 citations


Cites background or methods from "Causation and effectuation processe..."

  • ..., 2009), exploratory factor analysis (Chandler et al., in press), and ordinary least squares regression (Wiltbank et al., 2009). Such methods and techniques appear to be appropriate for this state of development. As effectuation research moves into an intermediate state, it is also increasingly important to implement rigorous methods to separate real from spurious results (Chandler & Lyon, 2001). This includes the use of appropriate control variables to strengthen the claim that a study’s independent variables are the cause of the observed effect in the dependent variable. A control variable is a variable that is included in an analysis to allow researchers to tap into the relationship between independent and dependent variables without interference. If relevant influences are not controlled, true effects may go unobserved and spurious effects may occur. If the introduction of appropriate control variables does not change the original relationship between the cause and effect variables, then claims of non-spuriousness are strengthened (Trochim, 2001). For example, the existing nonexperimental empirical effectuation literature has not measured or controlled for environmental uncertainty. Instead, Wiltbank et al. (2009) used control variables that seem to capture individuals’ general risk propensities. Although researchers have claimed that entrepreneurs generally possess higher risk propensities than non-entrepreneurs (Stewart & Roth, 2001) and therefore, an individual’s risk propensity may be related to the degree to which an individual uses effectuation versus causation, we do not believe that an individual’s risk propensity is an adequate proxy for situational uncertainty. Because the use of effectual and causal logics is a choice that an individual may make dependent on the amount of uncertainty that he or she perceives, we suggest that researchers who examine the effects of effectuation and causation should attempt to measure uncertainty and control for it. This leads to another related data analysis issue—the issue of endogeneity. In research models, a variable is endogenous if it is a function of other variables in the model. For example, a change in environmental circumstances that changes the level of uncertainty is an exogenous change if the level of uncertainty is not correlated with the error term. Perceptions of uncertainty, however, may be endogenous and may lead to endogenous changes in causation and effectuation and outcomes. The application of effectuation behaviors may also inject uncertainty into the process, another endogenous change. If perceived uncertainty is endogenous and entrepreneurs self-select into effectual or causal modes of operation, then one should also provide instrumental measures of effectuation. For a reference on how to control for endogeneity in empirical models, see Hamilton and Nickerson (2003). An additional issue related to endogeneity is the choice of independent and dependent variables studied in effectuation studies....

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  • ...Note that Chandler et al. (in press) used methodologies consistent with the formative nature of the effectuation construct. Using these methodologies, they found that effectuation can be better understood as a formative construct rather than as a reflective construct, and they provided a validated effectuation measure consisting of four sub-constructs—experimentation, affordable loss, flexibility, and precommitments. They concluded, however, that additional measures should be developed. These new measures could incorporate other elements of effectuation that are shown to be central to effectuation (e.g., beginning with a given set of means). Future researchers could use different sample types and data collection methods, and they could include effectuation outcome variables as a means of validating effectuation as a formative construct (cf. MacKenzie et al.). Therefore, we suggest that researchers should consider the formative nature of the construct, and use appropriate methods to validate their measures. In addition, we suggest two specific formative measurement models that might be used for effectuation. MacKenzie et al. (2005) suggested that when a composite construct is the focus of research, investigators may want to use a mixed indicator measurement model such as the one diagrammed in Figure 1....

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  • ..., 2009), exploratory factor analysis (Chandler et al., in press), and ordinary least squares regression (Wiltbank et al., 2009). Such methods and techniques appear to be appropriate for this state of development. As effectuation research moves into an intermediate state, it is also increasingly important to implement rigorous methods to separate real from spurious results (Chandler & Lyon, 2001). This includes the use of appropriate control variables to strengthen the claim that a study’s independent variables are the cause of the observed effect in the dependent variable. A control variable is a variable that is included in an analysis to allow researchers to tap into the relationship between independent and dependent variables without interference. If relevant influences are not controlled, true effects may go unobserved and spurious effects may occur. If the introduction of appropriate control variables does not change the original relationship between the cause and effect variables, then claims of non-spuriousness are strengthened (Trochim, 2001). For example, the existing nonexperimental empirical effectuation literature has not measured or controlled for environmental uncertainty. Instead, Wiltbank et al. (2009) used control variables that seem to capture individuals’ general risk propensities....

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  • ...For example, in terms of effectuation- and causation-related behaviors, researchers could develop items similar to the following Likert scale items used by Chandler et al. (in press) to measure experimentation—“We experimented with different products and/or business models” and “We tried a number of different approaches until we found a business model that worked.” In terms of cognitive processes, researchers could develop measures similar to the following Likert scale items used by Wiltbank et al. (2009)—“As you assemble information Figure 1 Mixed Indicator Measurement Model for Effectuation...

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Posted Content
TL;DR: In this article, a review of existing literature on financing social ventures as well as crowdfunding is presented, and a research agenda of eight themes for crowdfunding of social ventures is set up.
Abstract: Crowdfunding (CF) in a social entrepreneurship context is praised in narrations for its multifaceted potential - to access much needed financial resources, to gain legitimacy through crowd participation, and to further tap the crowd as a resource for numerous activities of the venture. From an academic point of view however, little has been written about CF as a whole, and inquiries from the social entrepreneurship sphere are so far mostly concerned with CF donations. In order to overcome the scarcity of the resource ‘crowd’ being asked for gifts, new approaches, including tailored reward systems, more structured bond-like investments and equity based CF are experimented with. Finance literature scarcely addresses these new forms, and no article so far shows concern for the idiosyncrasies of social ventures and the differing rationale of the social entrepreneurs and investors in CF activities.This paper thus sets out to first review existing literature on financing social ventures as well as on crowdfunding. Based upon the findings, the author subsequently draws up an early scheme of CF in order to structure future inquiries and to provide a common ground for discussion. Based upon the two streams, and in reflection to perspectives from traditional finance, a research agenda of eight themes for CF of social ventures is set up. The themes proposed are: investor types and utility-functions; corporate governance and structure in CF ventures; investor relations, risk and disclosure; applications and comparative approaches; network tie formation; legitimacy, institutions and democracy; challenging finance metrics; and legal and regulative hurdles for equity and debt CF.

371 citations


Cites background from "Causation and effectuation processe..."

  • ...…and Scott 2010; Lam 2010) – for example the effectuation principles used by entrepreneurs are barely compatible with the traditional rationales of banks, basing their financing decisions in project finance upon the long-term planning of stable cash flows (Chandler et al. 2011; Perry et al. 2011)....

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References
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TL;DR: The book aims to provide the skills necessary to begin to use SEM in research and to interpret and critique the use of method by others.
Abstract: Designed for students and researchers without an extensive quantitative background, this book offers an informative guide to the application, interpretation and pitfalls of structural equation modelling (SEM) in the social sciences. The book covers introductory techniques including path analysis and confirmatory factor analysis, and provides an overview of more advanced methods such as the evaluation of non-linear effects, the analysis of means in convariance structure models, and latent growth models for longitudinal data. Providing examples from various disciplines to illustrate all aspects of SEM, the book offers clear instructions on the preparation and screening of data, common mistakes to avoid and widely used software programs (Amos, EQS and LISREL). The book aims to provide the skills necessary to begin to use SEM in research and to interpret and critique the use of method by others.

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TL;DR: In this paper, a six-step framework for organizing and discussing multivariate data analysis techniques with flowcharts for each is presented, focusing on the use of each technique, rather than its mathematical derivation.
Abstract: Offers an applications-oriented approach to multivariate data analysis, focusing on the use of each technique, rather than its mathematical derivation. The text introduces a six-step framework for organizing and discussing techniques with flowcharts for each. Well-suited for the non-statistician, this applications-oriented introduction to multivariate analysis focuses on the fundamental concepts that affect the use of specific techniques rather than the mathematical derivation of the technique. Provides an overview of several techniques and approaches that are available to analysts today - e.g., data warehousing and data mining, neural networks and resampling/bootstrapping. Chapters are organized to provide a practical, logical progression of the phases of analysis and to group similar types of techniques applicable to most situations. Table of Contents 1. Introduction. I. PREPARING FOR A MULTIVARIATE ANALYSIS. 2. Examining Your Data. 3. Factor Analysis. II. DEPENDENCE TECHNIQUES. 4. Multiple Regression. 5. Multiple Discriminant Analysis and Logistic Regression. 6. Multivariate Analysis of Variance. 7. Conjoint Analysis. 8. Canonical Correlation Analysis. III. INTERDEPENDENCE TECHNIQUES. 9. Cluster Analysis. 10. Multidimensional Scaling. IV. ADVANCED AND EMERGING TECHNIQUES. 11. Structural Equation Modeling. 12. Emerging Techniques in Multivariate Analysis. Appendix A: Applications of Multivariate Data Analysis. Index.

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Abstract: This article is concerned with measures of fit of a model. Two types of error involved in fitting a model are considered. The first is error of approximation which involves the fit of the model, wi...

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TL;DR: This chapter discusses Structural Equation Modeling: An Introduction, and SEM: Confirmatory Factor Analysis, and Testing A Structural Model, which shows how the model can be modified for different data types.
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Book
21 Jul 2011
TL;DR: Structural Equation Models: The Basics using the EQS Program and testing for Construct Validity: The Multitrait-Multimethod Model and Change Over Time: The Latent Growth Curve Model.
Abstract: Psychology is a science that advances by leaps and bounds The impulse of new mathematical models along with the incorporation of computers to research has drawn a new reality with many methodological progresses that only a few people could imagine not too long ago Such progress has no doubt revolutionized the panorama of research in the behavioral sciences Structural Equation Models are a clear example of this Under this label are usually included a series of state-of-the-art multivariate statistical procedures that allow the researcher to test theoryguided hypotheses with clearly confi rmatory ends as well as to establish causal relations among variables Confi rmatory factor analysis, the study of measurement invariance, or the multitraitmultimethod models are some of the procedures that stem from this methodology In this sense, it would be diffi cult to fi nd a scientifi c journal that publishes empirical works in psychology that does not address some of these issues, so their current transcendence is undeniable The manual written by the Full Professor of the University of Ottawa, Barbara M Byrne, is a link in a series of books that address this topic Throughout her long academic trajectory, Professor Byrne developed interesting and popular work focused on bringing the researcher and the professional layman—and not so layman—closer to the diverse statistical programs available on the market for data analysis from the perspective of structural equation models (ie, LISREL, AMOS, EQS) (Byrne, 1998, 2001, 2006) Bearing this in mind, the main goal of this work is to introduce the reader to the basic concepts of this methodology, in a simple and entertaining way, avoiding mathematical technicisms and statistical jargon For this purpose, we used the statistical program Mplus 60 (Muthen & Muthen, 2007-2010), an extremely suggestive software that incorporates interesting applications The authoress provides a practical guide that leads the reader through illustrative examples of how to proceed step by step with the Mplus, from the initial specifi cations of the model to the interpretation of the output fi les On the one hand, we underline that the data used proceed from prior investigations and can be consulted in the Internet, offering the reader the possibility of practicing with them (http://wwwpsypresscom/sem-with-mplus/ datasets/); on the other hand, updating the information with novel and apt bibliographic references allows the reader to study in more depth the diverse topics that are presented in the manual, if he or she so desires The book consists of four sections, with a total of 12 chapters The fi rst section, Chapters 1 and 2, addresses introductory terms related to structural equation models and working with the Mplus program at a user-level The second unit focuses on data analysis with a single group In Chapter 3, the factor validity of the self-concept is tested by means of confi rmatory factor analysis In Chapter 4, the authoress performs a fi rst-order confi rmatory factor analysis, in which she examines the validity of the scores of the Maslach Burnout Inventory (MBI) in a sample of teachers In Chapter 5, the internal structure of the scores on the Beck Depression Inventory-II is analyzed by means of second-order confi rmatory factor analysis in a sample of Chinese adolescents In the next chapter, the complete model of structural equations is tested, and the authoress examines the causal relation established between diverse variables (ie, work climate, self-esteem, social support) and Burnout The third section of the manual is, in my opinion, the most interesting, not only because of the expansion of the study of measurement invariance in recent years but also because of the expansion it may possibly have in the future In this section, Professor Byrne goes into multigroup comparisons Specifi cally, in Chapter 7, she examines the factor equivalence of the MBI in two samples of teachers by means of the analysis of covariance structures In this chapter, she introduces relevant concepts, such as types of invariance (confi gural, metric, and strict) or the invariance of partial measurement In Chapter 8, she also analyzes measurement invariance, using for this purpose the analysis of mean and covariance structures This analysis, in comparison to the analysis of covariance structures, allows contrasting the latent means of two or more groups With this goal, she verifi es whether there is measurement invariance between the scores on the Self-description Questionnaire-I in Nigerian and Australian adolescents In Chapter 9, she proposes a complete model of structural equations in which she tests the causal structure through the procedure of cross validation Lastly, in the fourth section, she reveals three very interesting topics, that are also up-to-date and that, to some degree, go beyond the initial goal of the book, such as the multitrait-multimethod models, latent growth curves, and multilevel models Summing up, the work “Structural Equation Modeling with Mplus: Basic concepts, applications, and programming” is of enormous interest and utility for all professionals of psychology and related sciences who, without having exhaustive knowledge of the details of structural equation models, wish to test their hypothetical models by means of the Mplus program No doubt, this is a reference manual, a must-read that is accessible and that has a high degree of methodological rigor We hope that the readers

16,616 citations