Opportunity Recognition: A Contingency Framework of Individual Attributes, Time Pressure, and Uncertainty
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This dissertation clarifies individual differences in opportunity recognition as a precursor to starting a new business. Using a contingency perspective, time pressure and uncertainty are hypothesized to moderate the effects of individual attributes – entrepreneurial self-efficacy, tolerance of uncertainty, and prior experience – on opportunity recognition. This framework answers the question whether certain individuals can leverage tensions to their benefit more than others. In this study, opportunity recognition has been operationalized by two technology-centric tasks and measured as opportunity quantity and quality. A total of 227 usable responses were collected from students enrolled at the Bloch’s REP program through an on-line experiment. I implemented negative binomial and linear regression analyses to measure quantity and quality respectively, and to test the individual differences of the focal variables. The analytical results demonstrate different main effects of entrepreneurial self-efficacy on quantity and quality. More importantly, the results deny the commonly-held assumption that time pressure and uncertainty adversely affect all individuals. My findings reinforce the importance of cultivating tolerance of uncertainty in students and practicing entrepreneurs for effective decision-making under high pressure business situations. This study’s theoretical framework has implications for research on opportunity recognition involving technology, and for policies designed to increase entrepreneurial behaviors.
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Introduction -- Theoretical development -- Research methodology -- Analysis and results for soft robot scenario -- analysis and results for virtual reality scenario -- Discussion and conclusion -- Appendix A. IRB approval -- Appendix B. Experiment -- Appendix C. Quality Scoring Scale for Raters: Soft Robot -- Appendix D. Quality Scoring Scale for Raters: Virtual Reality -- Appendix E. Measurement Components Results (Using GSEM: Family Ordinal, Link Logit)
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Ph.D.(Doctor of Philosophy)