scholarly journals Desire and intention: causal variables of entrepreneurial action

Author(s):  
Indu Peiris
2003 ◽  
Vol 19 (3) ◽  
pp. 164-174 ◽  
Author(s):  
Stephen N. Haynes ◽  
Andrew E. Williams

Summary: We review the rationale for behavioral clinical case formulations and emphasize the role of the functional analysis in the design of individualized treatments. Standardized treatments may not be optimally effective for clients who have multiple behavior problems. These problems can affect each other in complex ways and each behavior problem can be influenced by multiple, interacting causal variables. The mechanisms of action of standardized treatments may not always address the most important causal variables for a client's behavior problems. The functional analysis integrates judgments about the client's behavior problems, important causal variables, and functional relations among variables. The functional analysis aids treatment decisions by helping the clinician estimate the relative magnitude of effect of each causal variable on the client's behavior problems, so that the most effective treatments can be selected. The parameters of, and issues associated with, a functional analysis and Functional Analytic Clinical Case Models (FACCM) are illustrated with a clinical case. The task of selecting the best treatment for a client is complicated because treatments differ in their level of specificity and have unequally weighted mechanisms of action. Further, a treatment's mechanism of action is often unknown.


1990 ◽  
Vol 45 (8) ◽  
pp. 984-985 ◽  
Author(s):  
G. R. Patterson
Keyword(s):  

2020 ◽  
Vol 46 (2) ◽  
pp. 102-118
Author(s):  
Damien D. Nouvel

While Dubai's urban scene is dominated by planned and pre-designed developments, grassroots initiatives have always been present and have helped shape the trajectory of the city's evolution. In one case, an industrial area, Al Quoz, has seen the clustering of art businesses over a relatively short period turning it into a cultural destination. Accounting for most of such clustering, Alserkal Avenue became Dubai's art hot-spot that changed the cultural map of the city. This article describes the rise of Alserkal Avenue, not only as the result of the entrepreneurial action of the proprietors but also as a product of a complex melange of economic, cultural, and urban evolutionary processes that intertwine with the rise of the city itself.


2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Christian Lechner ◽  
Abeer Pervaiz

Abstract In the entrepreneurship literature, the phenomenon of industry emergence has been largely investigated from an institutional perspective. Appropriate institutions would allow then a group of individual entrepreneurs (“the heroes”) to create an industry through innovative ventures. New ventures create new industries and firm entry, survival, and exit drive industry evolution. Our research, however, explores what creates the favorable set of circumstances for new ventures to emerge and focuses on the pre-emergence phase and we propose that the patterns of emergence resemble those of social movements. Through an actor perspective, this research highlights the existence of diverse actors, not necessarily entrepreneurs, who are necessary to trigger a collective action during the pre-emergence phase of industries. This research is also distinct from entrepreneurial ecosystems as its development already requires some successful entrepreneurial action. The 3D printing industry was chosen as a single longitudinal case study, where the actors are the embedded units of analysis. The findings of the study lead to the identification of three aggregate dimensions—“Social Movement Composition,” Temporal Engagement,” and “Coalitions Development”—that were prevalent during the pre-emergence phase of the 3D printing industry. Our propositions emphasize the importance of large collective action and the role of multiple actors in order to create the conditions for, first, firm emergence and, the second, to the process of industry emergence.


2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Bechir Fridhi

AbstractThis article aims to understand the extent to which social entrepreneurship (SE) contributes to the construction of a collective dimension linked to social innovation (SI). We aim to propose new ideas that can deliver insights into the SE phenomenon. This research is also distinct from entrepreneurial ecosystems as its development already requires some successful entrepreneurial action and to do it, the structuring and consolidation of an entrepreneurial ecosystem constitutes a real challenge for the development of SI.This work has been based on a participant observation of eight major events dedicated to social entrepreneurship or the shared economy. In-depth interviews with Tunisian social entrepreneurs were also conducted in order to enrich our corpus. The results show the necessary cooperation of social entrepreneurs for a sustainable and responsible social innovation. Indeed, the analysis emphasizes that the viability and sustainability of a social innovation rests essentially on a collective construction, beyond common social values.


Author(s):  
Claus Wiemann Frølund

Abstract Entrepreneurial action takes place in a context of Knightian uncertainty. In order to overcome this uncertainty, entrepreneurs engage in a process of judgment resulting in a decision about the course of action. Institutions arise mainly to reduce economic friction by providing structure to human interaction and thus reducing uncertainty. However, institutions may also introduce further uncertainty and thus disrupt the judgment process preceding entrepreneurial action. The present paper builds upon recent efforts to integrate the concepts of uncertainty and institutions within the entrepreneurial context. Drawing on Frank H. Knight's seminal insight, the judgment-based view of entrepreneurship, and relevant concepts of entrepreneurial outcomes, the main contribution of the paper lies in the development of a model offering a coherent description of the way institutions affect uncertainty and the entrepreneurial process.


2019 ◽  
Vol 35 (19) ◽  
pp. 3663-3671 ◽  
Author(s):  
Stephan Seifert ◽  
Sven Gundlach ◽  
Silke Szymczak

Abstract Motivation It has been shown that the machine learning approach random forest can be successfully applied to omics data, such as gene expression data, for classification or regression and to select variables that are important for prediction. However, the complex relationships between predictor variables, in particular between causal predictor variables, make the interpretation of currently applied variable selection techniques difficult. Results Here we propose a new variable selection approach called surrogate minimal depth (SMD) that incorporates surrogate variables into the concept of minimal depth (MD) variable importance. Applying SMD, we show that simulated correlation patterns can be reconstructed and that the increased consideration of variable relationships improves variable selection. When compared with existing state-of-the-art methods and MD, SMD has higher empirical power to identify causal variables while the resulting variable lists are equally stable. In conclusion, SMD is a promising approach to get more insight into the complex interplay of predictor variables and outcome in a high-dimensional data setting. Availability and implementation https://github.com/StephanSeifert/SurrogateMinimalDepth. Supplementary information Supplementary data are available at Bioinformatics online.


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