POSTER SUMMARY
A Knowledge-Based Approach to Opportunity Recognition
Patrick J. Murphy, DePaul University
Rodney C. Shrader, University of Illinois at Chicago
Principal Topic
Explaining opportunity recognition (OpR) is a key challenge for the entrepreneurship field. Much OpR research, drawing from multiple conceptual underpinnings (e.g., economics, marketing, sociology, anthropology, psychology), cites the important role of knowledge. However, this work has not drawn effectively from epistemology: A discipline specializing in the nature and derivation of knowledge, the scope of knowledge, and the reliability of claims to knowledge. Our undertaking traces epistemology’s conceptual and methodological usefulness for OpR research, taking steps toward delineating a knowledge-based paradigm.
Method
A random sample of 1,261 entrepreneurs from the Panel Study for Entrepreneurial Dynamics provided initial data. The 149 of these that went on to incorporate a new business venture (NBV) and achieve some level of viability provided primary data. Based on past theory, we held direct reports of knowledge (e.g., industry experience, critical relationships, expert guidance) as important empirical elements. Categorical OpR-related items operationalized these reports. As theoretic foundations allow knowledge to transcend person and situation objectively (i.e., not a Lewinan-based framework), all such reports (e.g., one individual giving two identical reports of two different circumstances; two individuals giving two different reports of one circumstance) were treated as valid. Thus, we did not control for error via scaling or factor analytic techniques. This tactic explained variance of interest while allaying two previously cited concerns for entrepreneurship research: It supported nonparametric analyses (i.e., eliminating violations of parametric assumptions), and mitigated levels of analysis issues (i.e., maintaining similar relevance across individual, firm, and system levels).
Results and Implications
Using Hierarchical Loglinear Analysis (HLA), we delineated three OpR models; one for each type of OpR (i.e., idea-first, business-first, simultaneous), derived from nine knowledge-based indicators, all noted in previous research. Results reflect and integrate various past findings via convergences of indicators. The initial HLA delineated models of OpR and allowed calculation of odds ratios (i.e., forecasting deductively versus predicting inductively). Logit analysis then cast the models as forecasters of NBV incorporation. Finally, MANOVA (after testing assumptions) cast them as forecasters of viability one year later. Results were middling for demographics and individual difference covariates and control variables. Industry sector effects were interpreted via the North American Industry Classification System. One model (Idea-first) forecasted NBV incorporation and none forecasted viability. This research offers an early theoretic justification and methodological demonstration of a (1) knowledge-based approach to OpR research and (2) utilization of entrepreneurial opportunities (i.e., versus entrepreneurs) as empirical units of analysis.
CONTACT: Patrick J. Murphy, Department of Management, Suite 7000, DePaul University, 1 East Jackson Boulevard, Chicago, IL 60604; (T) 312-362-8487; (F) 312-362-6973; pmurph12@depaul.edu