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On Entrepreneurial Learning, Mentoring, and the Logic of Bayes Dissertation Presented in Partial Fulfillment of the Requirements On Entrepreneurial Learning, Mentoring, and the Logic of Bayes Dissertation Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University By William Robert Stromeyer, B.F.A., M.B.A Graduate Program in Business Administration The Ohio State University 2016 Dissertation Committee: Sharon A. Alvarez, Co-Advisor Raymond A. Noe, Co-Advisor Benjamin A. Campbell Robert B. Lount 1 Copyright by William Robert Stromeyer 2016 2 Abstract This dissertation is comprised of three essays that examine entrepreneurial learning, entrepreneurial mentoring, and the logic of Bayes and Bayesian analysis. The first essay delves into the cognitive mechanisms involved in learning under fundamental uncertainty by entrepreneurs engaged in the process of forming new opportunities. An examination of the emergence of the pet health insurance marketplace in the United States during the period 2002- 2012 drives a qualitative analysis that integrates propositions concerning the entrepreneurial process with theoretical assertions from the hierarchical Bayesian theory of learning. The second essay examines how entrepreneurial career mentoring, mentoring in support of a transition to entrepreneurial employment, leads to increased entrepreneurial intentions mediated by entrepreneurial self-efficacy. The final essay provides a commentary and suggestions for best usage of new techniques developed in Bayesian structural equation modeling, through a Bayesian based analysis of entrepreneurial self-efficacy. ii Acknowledgements This dissertation would not have been possible without the loving support of my family. My deepest gratitude to my advisor and dearest friend, Sharon Alvarez. Thank you for guiding me on this journey, letting me make my own mistake, but always putting me back on the right path. I am also grateful for the support and encouragement of the rest of my committee. Thank you for your insights, perseverance, and guidance as I pursued this body of research. Finally, I wish to thank my fellow PhD students, all the wonderful members of the management department, and the people of Fisher College for supporting a nurturing, but academically rigorous environment. iii Vita 2006 ..................................................................... B.F.A., Rochester Institute of Technology 2007 ..................................................................... M.B.A., Rochester Institute of Technology 2010 to present ..................................................... Graduate Teaching and Research Assistant, Department of Management & HR, The Ohio State University Publications Stromeyer, W. R., Miller, J. W., Sriramachandramurthy, R., & DeMartino, R. (2015). The Prowess and Pitfalls of Bayesian Structural Equation Modeling Important Considerations for Management Research. Journal of Management, 41(2), 491-520. Miller, J. W., Stromeyer, W. R., & Schwieterman, M. A. (2013). Extensions of the Johnson- Neyman technique to linear models with curvilinear effects: Derivations and analytical tools. Multivariate Behavioral Research, 48(2), 267-300. Stromeyer, W.R., & Barney, J. (2012). Cost-Benefit Analysis. In D. Teece and M. Augier (Eds.) The Palgrave Encyclopedia of Strategic Management. Fields of Study Major Field: Business Administration Focus: Entrepreneurship Minor Field: Quantitative Psychology – Judgement & Decision Making iv Table of Contents Abstract ............................................................................................................................... ii Acknowledgments.............................................................................................................. iii Vita..................................................................................................................................... iv List of Tables ..................................................................................................................... vi List of Figures ................................................................................................................... vii Chapter 1: Entrepreneurial Learning................................................................................... 1 Chapter 2: Entrepreneurial Mentoring .............................................................................. 68 Chapter 3: Bayesian SEM ................................................................................................. 99 References ....................................................................................................................... 148 Appendix A: Prior History in the Pet Health Insurance Market ..................................... 161 Appendix B: In-Depth Timeline of Pet Health Insurance (1977-2012) .......................... 174 Appendix C: SRMR & pseudo-SRMR (pSRMR) .......................................................... 181 Appendix D: 횯훿 Matrix Estimation ................................................................................ 183 v List of Tables Table 1.1: Risk, ambiguity, & uncertainty ........................................................................ 64 Table 1.2: List of four firms focused on in this study ....................................................... 65 Table 1.3: Data Sources for Study .................................................................................... 66 Table 1.4: State of pet health insurance industry as of early 2000s .................................. 67 Table 2.1: Means, standard deviations, and correlations among study variables ............. 95 Table 2.2: Invariance between calibration and validation models .................................... 96 Table 2.3: Regression parameters for model 4 ................................................................. 97 Table 2.4: Indirect effect for model 4 ............................................................................... 98 Table 3.1: Benefits and cautions for specifying informative priors ................................ 141 Table 3.2: PCS-CFA measurement model fitted using ML estimator ............................ 142 Table 3.3: Bayesian model with informative priors specified for cross-loadings. .......... 143 Table 3.4: Modified Bayesian model .............................................................................. 144 Table 3.5: Cross-Validation of Modified Bayesian model ............................................. 145 Table 3.6: Bayesian model where the 횯훿 matrix was freely estimated .......................... 146 Table 3.7: Demonstration of priors in context of structural model ................................. 147 vi List of Figures Figure 1.1: Iterative nature of explanation building via case based pattern ...................... 16 Figure 2.1: Model of desire and intent for entrepreneurship ............................................ 72 Figure 2.2: Path diagram with coefficients ....................................................................... 86 Figure 3.1: Factor loadings for a perfect cluster solution and a Bayesian model ........... 112 Figure 3.2: Density Plot of 횯훿 matrix for the estimated PCS-CFA model. .................... 132 Figure 3.3: Density Plot of 횯훿 matrix for complexity one model .................................. 133 Figure A.1: Adoption rate of pet health insurance .......................................................... 168 Figure A.2: Pet health insurance timeline ....................................................................... 169 vii Chapter1: Entrepreneurial Learning under Fundamental Uncertainty: An Examination of the Pet Health Insurance Industry Chapter Abstract Utilizing an explanation-building case study this paper examines the implications of the opportunity creation perspective for theorizing in the domain of entrepreneurial cognition. Based on findings from an in-depth exploration of the pet health insurance industry in the 2000s propositions are developed regarding the means by which entrepreneurs learn under fundamental uncertainty. As motivated actors, entrepreneurs develop hypotheses, some of which coincide with expectations for change in the context, and then test these in the socially-constructed marketplace. This cycle of experimentation and feedback leads to the refinement of hypotheses and the updating of beliefs amongst the entrepreneur, as well as within the context permitting a shift in the social-cultural conversation. As the entrepreneurs understanding of the socially complex context matures they transition from task-specific learning to a complex integration of multiple forms of learning. Introduction There is a growing appreciation for the role of process in the field of entrepreneurship (McMullen & Dimov, 2013). Recent work on the formation and exploitation of entrepreneurial opportunities has focused on the iterative, enactment processes associated with opportunities (Alvarez & Barney, 2007; Dimov, 2007). Work on resource recombination and the unique 1 application of resources in constrained environments is explicitly process oriented (Baker and Nelson, 2006). Moreover, work on new venture creation suggests that process is an essential consideration of nascent venture formation (Gruber, 2007; Dimov, 2011). This recent process orientation acknowledges entrepreneurial action and learning under conditions of uncertainty as principal mechanisms by which change is facilitated (Alvarez, Barney, Anderson, 2013). Fundamental uncertainty, information contexts in which future states and the probability of these states
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