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

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

William Robert Stromeyer

2016

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.

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.

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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

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

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

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

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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 is unknowable ex-ante (DeQuech, 2006, Knight, 1921) is the backdrop under which most entrepreneurial process unfolds (Alvarez and Barney, 2005; 2007; Dimov, 2010;

Hmieleski and Baron, 2008). Fundamental uncertainty is a powerful concept in entrepreneurship, yet with few exceptions, researchers have tended to steer away from the theoretical implications of this construct (DeQuech, 2000) and there is a paucity of research on learning under conditions of fundamental uncertainty. In order to maximize the theoretical value of entrepreneurial process research, scholars need to develop a more in-depth and robust understanding of learning under these conditions.

However, a dominant assumption of traditional learning literature is that the macro environment is given and the agent acts within and reacts to that environment (Osman, 2010). In this view, with few exceptions, the actions of the agent may occur in a complex-dynamic environment, but macro level environmental changes are attributed to exogenous forces (Funke,

2001). In contrast, a major contribution of entrepreneurial process research is that agents have the potential to enact meaningful change in the state of the macro environment (Wood & McKinley,

2010). Further it is assumed that the change induced by these agents is undertaken with conscious intent and some degree of foresight. Yet, it is also acknowledged that the change that emerges during the entrepreneurial process is not fully definable ex ante (Wiltbank, Dew, Read, &

Sarasvathy, 2006). It is this fundamental paradox that guides the current study, how do

2 entrepreneurs learn under fundamental uncertainty at the same time that they are enacting structural change given that they may not fully understand the change they are enacting? In particular this study sets out to examine the implications of this alternative viewpoint for understanding cognitive learning mechanisms that might underlie how entrepreneurs envision alternatives, generate potential causal structures underlying these alternatives, and iteratively test these understandings in the social marketplace during the enactment of opportunities

This paper investigates the cognitive mechanisms of how individuals learn under conditions of fundamental uncertainty empirically using an in-depth explanation building study

(Eisenhardt, 1989; Tripsas & Gavetti, 2000; Walsh & Bartunek, 2011; Yin, 2009) of the emergence of the pet health insurance industry in the U.S. during the time period 2002-2012. This development of an integration of theoretical implications derived from entrepreneurial process research with cognitive theory regarding the emergence of causal induction and structural form provides insights into how entrepreneurs learn about the opportunities that they themselves are forming. The sections that follow provide an overview of the literature and theory that influenced the extent of this case study.

Implications of Evolutionary Realism and Creation Theory for the Study of Entrepreneurial

Action, Process, and Learning

In the last several years there has been an increasing interest in examining entrepreneurship from a perspective that makes entrepreneurial opportunities an endogenous consequence of entrepreneurial action. This opportunity creation perspective (Alvarez & Barney,

2007) is built on an evolutionary realist epistemology (Alvarez & Barney, 2010). This epistemology brings together components of pragmatic realism (Peirce, 1905), with its focus on knowledge manifested in the ideal, social construction’s (Berger & Luckmann, 1967) emphasis on human institutions and shared knowledge, and the evolutionary perspective (Campbell, 1974). 3

These various epistemological underpinnings have implications for the creation perspective’s assertions regarding entrepreneurial acts, entrepreneurial processes, the nature of uncertainty, and the emergence of opportunities.

The concept of evolutionary realism emerged out of the work of Charles Pierce (1905),

William James (1907) and John Dewey (Dewey & Bentley, 1949). In particular, Pierce’s work aimed to strike a balance between the schools of ‘idealism’ (the notion or conviction that the source and foundation of knowledge is thought itself, i.e. the source of knowledge is the mind itself) and realism (there is something to be reckoned with that is independent of the mind and is not constituted by thought alone). While these forms of inquiry are as old as the study of philosophy, they received increased examination with the work of Berger & Luckmann’s (1967) investigation of “The Social Construction of Reality”. The central premise of this work was that persons and groups interacting in social systems generate shared conceptions of reality that become habituated and generate the institutionalization of knowledge. This work was subsequently taken by others to an extreme position in which all knowledge is a product of social agreement and that no states of reality exists outside of that which is conceptualized in a society.

Needless to say, this perspective received much push back.

In contrast Donald Campbell (1960, 1974) and others married the concept of social construction with earlier work in realism, to develop modern concepts of evolutionary realism.

This perspective asserts that there are aspects of reality that are independent of thought, i.e. forces such as gravity exist regardless of how or when we perceive them, but there are also many aspects of human social life that are based on fluid, renegotiated shared meaning. The classic example of this is the dollar bill, which clearly has a material aspect of paper and ink, but has value only so much as a shared agreement is maintained through constant re-enactment. This conceptualization has been made axiomatic, by some authors such as Dopfer & Potts (2004), via the assertions that

(1) all existences are bimodal matter-energy actualizations of ideas, (2) all existences associate, and (3) all existences are processes.

Evolutionary realism’s acknowledgement of social processes in the formation of knowledge has implications for how we think about markets and the entrepreneurial function.

Crucially rather than being able to postulate markets as a given reality, they are instead the outcome of a continual process of human action, negotiation, and perception. This perspective emphasizes the important role of social structures in guiding human interaction and behavior. In light of the role of shared social structure, one can propose that enacted opportunities may arise along a spectrum of contexts. With such a priori contexts lying between those that embody well defined, shared social meaning and those settings where there is a lack of such pre-exiting social structure. Likewise this also implies that in well-established markets there will be a body of communally shared knowledge and understanding, but that in those as of yet emerged markets there may be little known and much yet to create (or negotiate).

The creation perspective proposes that entrepreneurs will face a set of shared characteristics when attempting to enact opportunities. These include the presence of fundamental uncertainty, iterations and experimentation as primary mechanisms for generating information and understanding, and learning by doing within a path-dependent/path-creation framework

(Alvarez, Barney, & Anderson, 2013). If we assume that the presence of a market necessitates the co-existence of a social structure, then entrepreneurs engaged in opportunity creation processes in contexts that lack a priori shared social meaning must somehow also be enabling the creation of this structure. This facilitation of the creation of social structure must occur within the firm, but more importantly it must also occur for the various related stakeholders with which the firm interacts. This potential for change also begs the questions: do entrepreneurs know how to change the status quo, how do entrepreneurs learn if the future they are creating is unknowable ex-ante,

5 and what do we mean by learning under fundamental uncertainty? The first step in examining these issues is to clearly articulate what is meant by the term ‘fundamental uncertainty’.

Understanding Uncertainty

It is important to clarify what the term fundamental uncertainty means in this particular study. While it seems that such a term should be rather straight forward there is actually a fair amount of complexity and confusion below the surface. One of the challenges faced in studying decision-making and learning under fundamental uncertainty is that the term ‘uncertainty’ has historically been used to mean many different things across different fields and even within individual fields. This has led to a situation in which scholars who publish in the area of

‘uncertainty’ may be starting from radically different assumptions and axioms. The following section gives a brief overview of the relevant terminology and clarifies how various terms are defined in this study. Table 1.1 (see end of chapter) provides a further breakdown of terminology related to the concept of uncertainty and illustrates how the same terms may have different uses and connotations even within the same field. This confounding makes it a challenge to integrate work and clearly identify gaps in our understanding and knowledge of learning under

‘fundamental uncertainty’.

The three terms of import to this study are risk, ambiguity, and fundamental uncertainty.

In order to maintain consistency the distinctions made by Dequech (2000) amongst these three interrelated terms are adopted. Risk is present when future events occur with measurable probability. A clear example of this is a roll of the dice, or playing a lottery with known pay-out and odds. This concept is the base assumption for utility maximization, most classic economic theory, the majority of financial theory, and a large portion of the judgment and decision-making literature. Work in these areas often examines normative or prescriptive behavior, while a

6 counter-stream examines why individuals show regular deviations from ‘optimal’ behavior (i.e. biases and heuristics). Most of the decisions faced in life are not truly risk in a pure sense, but as a theoretical tool and an approximation of what we face the concept certainly has validity.

In contrast ambiguity “is uncertainty about the probability, created by missing information that is relevant and could be known” (DeQuech, 2000: pg 45). This is generally taken to mean that an agent knows what the potential outcomes can be, but is unable to access adequate information to make even subjective probabilistic estimates for their occurrences. Ambiguity can be further broken down into substantive uncertainty wherein the lack of all information inhibits the ability to make decisions with certain outcomes and procedural uncertainty wherein limitations of the computational and cognitive abilities of the agent prevents the agent from electing optimal choices given the available information. This complexity is akin to Simon’s notion of bounded rationality (substantive and procedural rationality). It should be noted that both neo-classical and main-stream economics tends to place “Knightian uncertainty” in the ambiguity silo (DeQuech, 2006).

Fundamental uncertainty is “characterized by the possibility of creativity and structural change and therefore by significant indeterminacy of the future” (DeQuech, 2000: pg. 48). “The list of possible events is not predetermined or knowable ex ante, as the future is yet to be created”

(DeQuech 2006: pg.112). This form of uncertainty implies that models of normative and prescriptive behavior may not be applicable. In the absence of fundamental uncertainty it makes sense to behave by rule-guided conventions as they have been enshrined in the utility maximization paradigm. However, under fundamental uncertainty unconventional acts may lead to innovation, competitive advantage, and structural change. The field of economics has for the most part not attempted to address this conceptualization of uncertainty. The set of axioms and assumptions needed to generate meaningful analytical models have not been identified at this

7 time and it is likely that even if they are the resulting models will not be tractable (DeQuech,

2006). Similarly there has only been minimal inquiry into this area in the field of psychology. The vast majority of studies completed in the field of judgment and decision-making are based on experimental approaches. Designing experiments that both permit an open state space and simultaneously provide control comparative conditions has proven to be exceptionally difficult.

Recently there has been some ground gained in this area (Payzan-LeNestour & Bossaerts, 2011), but there is much work still to be done.

A major critique of the concept of fundamental uncertainty, at least from a theoretical perspective, is the belief that fundamental uncertainty necessarily implies an ‘anything goes’ theory of behavior (Coddington, 1982; DeQuech, 2000). This reductionist, theoretical, nihilism is only appropriate if we assume that fundamental uncertainty is synonymous with ‘total ignorance’.

While this study acknowledges that fundamental uncertainty does imply unknowable future states, it also recognizes that the world we live in is full of constraints and enablers and thus it is not a world of ‘anything goes’. Further actors bring a body of prior knowledge and awareness with them when they face fundamental uncertainty, providing both tools to address this uncertainty, but likewise biases and assumptions that may restrict the set of considered choices. In essence this perspective asserts that there can be degrees of fundamental uncertainty, it is not a pure binary condition of either risk or total ignorance.

Another crucial aspect that facilitates movement away from the nihilistic perspective is the acknowledgement that opportunity creation, as embedded in creation theory, is a path- dependent, emergent phenomenon (Arthur, 1989; Garud & Karnoe, 2001; Mintzberg & Waters,

1985). This immediately adds an important temporal component to the entrepreneurial process, such that opportunities do not simply appear with a snap of the fingers. Rather entrepreneurs in their experimentations iterate new understandings and new market potentials, oscillating between

8 successes and failures without always knowing why. This experimentation and exploration in undefined, or ill-defined contexts is a crucial area for entrepreneurship literature to more deeply address.

The fundamental nature of such inquiry gets at the heart of entrepreneurship and further developments in these areas will clearly articulate the field’s unique contribution to the other streams of business, economics, and psychology literature. At the same time, the challenges that economics and psychology has faced in addressing issues of decision-making, judgment, and learning under fundamental uncertainty highlights the difficulty faced in undertaking such a task.

A priori it is not clear what route theoretically should be pursued in addressing these issues. This study has chosen to focus on the role of learning under fundamental uncertainty, and thus the next section lays out a set of inter-related theoretical frameworks that shed light on the phenomenon of learning under uncertainty. These frameworks were chosen in iteration with the data originating from the qualitative study. In essence the stories emerging from the study of the emergence of the pet health insurance market were used to drive questions of interest and insights into the phenomenon, which were subsequently ruminated on and distilled into aspects needing explanation. In tandem potential explanations and concepts from the current learning literature were explored in an effort to seek meaningful connections with aspects emerging from the qualitative data.

Avenues of Inquiry for Learning under Uncertainty

A significant portion of the learning literature makes the assumption that the macro environment is a state separate from the agent; not in that the agent lies outside the macro environment rather that the agent’s effects on the macro environment are minor enough that they are of little consequence (Shanks, 2010). In contrast, the major contribution of the creation

9 perspective of entrepreneurial opportunities, is that agents not only have the potential to enact meaningful change in the macro environment (i.e. the market), but that they actually do facilitate the emergence of this change. It is this fundamental paradox that guides the current study, how can entrepreneurs learn under fundamental uncertainty at the same time that they are enacting structural change (particularly given that they may not understand the change they are inducing)?

Entrepreneurship opens a new avenue of exploration in that it is specifically focused on an agent who is not only attempting to profit from structural change, but may also be a potential source of the creativity and innovation that predicates this change. This observation in regards to the learning literature led to the formation of a set of interrelated fundamental questions that guided both the qualitative analysis of the pet health insurance data and the theory domains that were explored.

The first and most significant question is “Under conditions of fundamental uncertainty, how do entrepreneurs learn from their experiments and how are these experiments conceived?”

Closely related, the answer to such a question would address issues of what agent and environmental aspects both constrain and enable this process of learning (lest we slip into the domain of “anything goes”). Secondly, “Overtime, how is noisy feedback both from the

‘experiments’ and from the environment interpreted?” In conditions of uncertainty what are the mechanism that allow entrepreneurs to extract ‘the signal from the noise.’ Finally, “How does social context influence the learning process and how does the process influence social context?”

Substantial research addresses the first part of this question, but we have little understanding of the later.

Frameworks for Understanding Learning under Uncertainty

The information-processing perspective in cognitive research focuses on those mechanisms of cognition that influence what information is perceived, how it is processed, what response is elicited, and how this response is enacted through behavior (Newell & Simon, 1956).

In regards to learning, two divergent streams tend to dominate this field of inquiry (Courville,

Daw, & Touretzky, 2006). One is based on knowledge-independent, statistical mechanisms of inference that rely on underlying processes of similarity and association. These cognitive interpretations envision learning as the testing and refinement of probabilities. Models of learning arising from this first stream tend to be based on the rational or bounded-rational actor (Langley

& Simon, 1981; Simon, 1982) or on the mechanisms that circumvent rationality, i.e. biases and heuristics (Tversky & Kahneman, 1974).

The other stream is task-specific, dependent on robust domain-specific knowledge, and relies on processes driven by representations and intuitions (Tenenbaum, Griffiths, & Kemp,

2006). This cognitive perspective depicts learning arising from intuitive theories, schemas, and knowledge structures (Markus. 1977). Models from this second stream are found primarily in research on task-specific learning, developmental cognition, and experimental choice models

(Courville, Daw, & Touretsky, 2006; Tenenbaum, Kemp, Griffiths, & Goodman, 2011).

Information-processing based theories tend to be the preferred avenue for experimental work and theoretical development in judgment and decision-making research, cognitive modeling, learning studies, and computational simulation (Shanks, 2010). Such theories provide formalized mechanisms for how information is perceived, encoded, retrieved, processed, and outputted. This formalization permits the falsification of proposed theory through experimentation, ease of testing proposed contingency mechanisms (i.e. such as affect, priming, etc.), and robust specification in computer driven simulation (which is also amenable to cross-

11 validation with data from human experimentation). Although this formalization provides many beneficial avenues, one of the stronger critiques of this line of inquiry has been its inability to integrate the complexity of context, particularly when the task domain might be ambiguous, task ordering is non-sequential, and information is contradictory (Clark, 2016; Griffiths &

Tenenbaum, 2009). However, recent advances in the area of developmental, cognitive learning theory based on hierarchical Bayesian representations, supported by emerging findings from biological neuroscience, machine learning, and artificial intelligence, are permitting an integrated investigation of the emergence of complex, abstract human knowledge domains (i.e. areas such as language formation, grammars, object taxonomies, the acquisition of new cause-effect relationships) (Jacobs & Kruschke, 2010). These approaches marry aspects of the two previously articulated, divergent streams of information processing to hypothesis the mind as a probabilistic, computational machine capable of utilizing abstract knowledge to infer domain and task specific attributes, while simultaneously leading to the emergence of generalization learning and the formation of causal hypotheses.

At its heart, the hierarchical Bayesian cognitive approach to learning is based on the fairly simple logic of Bayes rule, but this belays a rich complexity that permits approaches to learning that are able to side-step the classic either-or dichotomy of general abstraction vs. domain specific and nativism vs. empiricism, (Tenenbaum et al., 2011). The basic logic of Bayes rule is that the observation of new data permits the formation of an updated belief for any underlying hypothesis (posterior distribution), which in turn is a function of initial beliefs about that hypothesis (the prior) and of the character and implications of the observed data (the likelihood). Bayes rule provides the logic for inferential updating, which in of itself is useful but hardly new. The contribution of recent advances in the cognitive understanding of development and learning is the focus on the hypothesis space that provides the relationships that are to be

12 examined (i.e. the priors in the Bayes rule equation) (Perfors, Tenenbaum, Griffiths, & Xu, 2011).

The hypothesis space represents the range of potential hypotheses that an individual considers when confronted with a new learning task. It dictates both what hypotheses will be examined, as well as explains where these hypotheses originate from in relation to the individual’s existing knowledge.

In particular the theory of hierarchical Bayesian learning has moved beyond the classic use of Bayesian learning in which a single task or fixed set of tasks is analyzed at one level of inference. These earlier models of learning, denoted by the term discriminative models, focus only on the data relevant to the specific task or conditioning response at hand. Hierarchical models focus instead on a generative approach by marrying structured knowledge, domain insight, and statistical inference in order to explain how individuals generalize from sparse and sometimes contradictory data to form both domain-specific inferences and higher-level abstract understanding (Tenenbaum et al., 2006). This is accomplished by articulating the hypothesis spaces as a multi-tiered knowledge structure. At the lowest level the hypothesis space is related to the particular question, task, or domain-specific character of interest. Each upper level of the hypothesis space imposes a logic based on abstraction and structured knowledge which dictates the possible domain of the lower levels. Such a structure permits the learning of complex knowledge entities, such as new abstract causal frameworks, new representational schema, and categorical taxonomies. These complex entities in turn accelerate the learning of specific relations at the domain-specific level, a feature which has come to be known colloquially as the ‘blessing of abstraction” (Griffiths & Tenenbaum, 2009).

The mathematical nature of such models quickly becomes extremely complex, particularly when researchers attempt to examine how such inferential mechanisms may actually be accomplished in the brain (Gershman, Blei, & Niv, 2010). As the focus of this study is the

13 generation of a conceptualization of learning under fundamental uncertainty, this mathematical complexity will not be directly addressed, and rather the focus will be kept on the concepts of multiple levels of hypothesis generation, the role of prior knowledge, and the social structure surrounding the entrepreneur. Most of the hierarchical Bayesian cognitive literature is a mix of formal mathematical modeling and experimental data, often with the aim of examining how well a proposed models explains the experimental data (Jones & Love, 2011).

There are limitations to the hierarchical Bayesian approach as it has been implemented to date. Utilization of this theoretical approach in the current cognitive literature implies a learner who is constrained by an exogenously given task. For our purposes we need to acknowledge constraints for learning under fundamental uncertainty, lest we fall prey to ‘anything goes’, but these constraints may not be fully explicable a priori and they may be subject to change over time. The ad hoc, status quo challenging processes of entrepreneurial creation imply that significant aspects of the opportunity are learned through experimentation and iteration. However the information available in such a setting will be notoriously noisy and multiple sources will compete for attention. This makes it challenging for the entrepreneur to generate causal attributions or to develop a coherent logic as to the underling latent principles at play, and yet this happens anyhow.

Implementing this shift in the boundary conditions of the theory supporting the hierarchical Bayesian conceptualization of learning requires careful consideration to account for the notion of fundamental uncertainty and entrepreneurial process, whilst not jettisoning the primary logic underlying this approach. An examination of the emergence of the pet health insurance industry was used to guide this exploration and theory integration. This qualitative study was used as a means to explore how propositions can be put together in an iterative manner,

14 going back and forth between received theory and the data to generate a unifying concept of entrepreneurial learning under uncertainty.

Research Method

This study used a qualitative research approach (Gephart, 2004; Stake, 2005). As entrepreneurship is a younger field of study and many of the questions of interest are focused on temporal processes (i.e. learning), the rich empirical data provided by qualitative methods permits the illumination of aspects not previously recognized in current theory. Suddaby (2006) highlights this methodology as a viable means for extending extant theory and for addressing theory that may be incomplete in regards to complex phenomenon. Similarly when theoretical constructs are deployed into new domains (i.e. transitioning learning theory that was developed under the assumptions of ambiguity into the domain of fundamental uncertainty) qualitative methods can be a viable approach for articulating relevant theoretical boundaries and imperatives

(Siggelkow, 2007).

In particular this study utilized a pattern-matching analytical technique in order to derive a series of explanations for the phenomenon under observation (Wynn & Williams, 2012; Yin,

2009). Figure 1.1 presents a pictorial representation of the iterative process used in developing initial propositions, comparing these against case findings, refining propositions and reiterating the process of questioning the data for alternative explanations. “To ‘explain’ a phenomenon is to stipulate a presumed set of casual links about it, or ‘how’ or ‘why’ something happened” (Yin,

2009: p. 141). As this technique relies heavily on narrative development, it is well suited to embedding tests of theoretical statements or propositions within rich descriptive content

(Eisenhardt & Graebner, 2007). This permits the gradual development and refinement of a series of ideas, while also entertaining the possibility of rival explanations and significant contingencies.

2) Comparing 6) Revising 1) Making 4) Comparing 5) Comparing the findings the statements initial 3) Revising other the revisions of an initial or theoretical the statements contextual to the investigation propositions, statements or or details of the findings of a against such accounting initial propositions case against second, third, statements or for propositions the revisions or more cases propositions contingencies

Figure 1.1: Iterative Nature of Explanation Building via Case Based Pattern-Matching (Adapted from Yin, 2009: p. 143)

Data Setting & Sources

This study used data from the emergence of four firms (one primary and three comparative) in the pet health insurance industry in the United States during the period of 2000-

2012, supplemented by validating information from several other institutions, sources, and related parties, and the ongoing social change that surrounded this industry to examine the processes by which entrepreneurs learn under fundamental uncertainty during the enactment of new opportunities. Table 1.2 (see end of chapter) provides information about these four firms, which will be referred to by the fictitious pseudo-names: Toto (primary case), Asta, Gromit, and Snowy

(comparative cases). This table identifies both when the founders began working on the initial concept of pet health insurance as a market offering and when the founders legally created a firm to begin the process of formalizing financing and regulatory approvals. The founders of these four firms had varied levels of knowledge in regards to veterinary science and the practice of veterinary medicine. Likewise, across the firms the founders had varying prior exposure to the 16 operation of an insurance entity and the creation of insurance products. Finally the table provides the self-reported reason for why the founders first became involved with pet health insurance.

Although pet health insurance has been available in the United States since the 1980’s, the emergence of a clearly identifiable industry did not occur until the 2000’s (some would argue that is still not a ‘Category’). The thought that pet health insurance is not yet a recognized

‘category’ in the common vernacular or in the US insurance industry in general was a comment that was echoed by two different CEOs and by insurance regulators. Pet health insurance is an appropriate context in which to investigate the research questions as it is socially complex, is embedded in changing social perceptions of how we view pets and our relationships with them, demonstrates a spectrum of iterative approaches to implementing business models, written documents from the process are still readily available, and is recent enough as to provide direct access to involved actors.

In order to triangulate the data and provide greater validity, data was collected from both within the studied firms and from external sources. Internal interviews were conducted with founders, staff, and the immediate parties that they dealt with in forming their firm (i.e. funding sources, underwriters, etc.). External interviews were conducted with parties that had an active engagement with the industry, such as regulators, veterinarians, and clients. Interviews were further supplemented with regulatory records, internal firm documents, newspaper and trade journal archives, veterinary trade group commentaries, and other contemporaneous evidence.

Table 1.3 (see end of chapter) presents a summary of data sources used for this study.

Analytic Strategy In order to stay true to the explanation building modality this study proceeded in stages.

While it was known that the study would be focused on the question of how entrepreneurs learn under fundamental uncertainty, it was unclear at the start what theoretical learning frameworks 17 might guide the eventual data analysis and what level of unit of analysis would best illuminate entrepreneurial learning mechanism. As opposed to a pure grounded-theory approach that might look to generate de-novo theory this study was informed from the start by the assumptions of opportunities that guide entrepreneurial action (Alvarez, Young, Wooley, 2015). The propositions of opportunity process theory were used as guide posts in the initial exploration of the pet health insurance industry context and to illuminate what might be relevant to understanding learning in uncertain contexts (step 1).

This guided exploration of the pet health insurance industry revealed that there had been significant market and social uncertainty regarding the future viability of pet health insurance as a service in the early 2000s. As the data shows a variant of pet health insurance was already available in the USA, but many viewed it as a failed industry, a hurdle that a new round of entrepreneurs would have to overcome. This initial examination of the data revealed important aspects that would have to be addressed in the data analysis including the role of prior beliefs

(both the entrepreneur and stakeholders), the process of belief updating, the formation of alternative hypotheses, the generation of causal understanding, iterative testing to extract information from alternatives, the differential functions of both task and abstract learning, and the interplay between these two types of learning (step 2).

Utilizing these primary guideposts the body of cognitive learning literature was examined and the logic of hierarchal Bayesian learning was identified as a viable theory with explanatory power to articulate relationships amongst these components (step 3). Equipped with the dual theories of opportunity process and hierarchal Bayesian learning, data from one firm that emerged recently in the pet health insurance industry was examined in-depth for specific incidents in which the founders devised new alternatives to the status quo and through iterative enactment learned about their viability. Early in the analysis, data was organized in a chronological manner

18 for each firm in order to detect meaningful phenomenon that would articulate how entrepreneurial learning develops and how this relates to changes in uncertainty over time (Miles & Huberman,

1994). Later, this within-firm data was subdivided along the boundaries of three identified firm resources, while maintaining the original chronological ordering to preserve the interactions between these resources and the meaningful context.

While the learning process is ubiquitous throughout the opportunity creation process, at this point in the study it was discovered that the initial firm level case contained meaningful sub- cases that could help guide the analysis by facilitating pattern-matching. In order to generate a consistent and analyzable case-logic it was elected to treat particular business resources developed by the focal firm as units of learning. The initial genesis, development, adaptation, and eventual deployment of each of these resources provided a structure by which to identify the underlying sequences of entrepreneurial learning as they progressed from initial simple hypotheses to complex integration. These learning units were identified both via how the entrepreneurs intuitively presented aspects of the business as distinctive domains during the various interviews and from triangulation with firm documents and the other available evidence.

While there were certainly more learning units at play than are examined here, it was elected to examine a subset that played a significant role in facilitating the entrepreneur’s alternative vision for the social status quo, those that were crucial to the structure of the emerging opportunity, and those that played the most important role in bringing other members of the social interaction into alignment with the entrepreneur’s vision. This initial case analysis highlighted the prevalence of fundamental uncertainty, the important role of generative causal structures, the role of noisy feedback, and the role of constraints and enablers for examining the question of learning under fundamental uncertainty (step 4).

This within-firm theory development was then challenged by examining learning outcomes for three additional firms, in order to test the previously developed explanations against similarities and differences across-firms. The various founders of these firms brought different background experiences and capabilities to their ventures, and interacted with a different cast of internal and external actors (although there was some overlap in particular domains, such as regulators) leading to different contexts within which learning occurred. In order to support consistency in the analysis, these three comparative firms were examined in regards to how their founders created analogous firm resources during the opportunity formation process (step 5).

These cross-case comparisons permitted the illumination and refinement of the theoretical propositions in regards to similarities and differences in the relevant contexts and highlighted relevant contingencies (step 6). This re-examination of developing theoretical propositions under varying contexts serves to strength the pattern-matching approach by delineating when patterns hold and when and why they fall apart (Locke, 2001). This in turn leads to the caveat that the propositions presented in this manuscript are the culmination of the entire process and for parsimony the intermediate propositions are not articulated, excepting a section on contingencies emerging out of step five.

As a clarification for the reader the analyses in this study are focused on the learning processes of the firm founders. The number of primary founders ranged from one to three across the four studied firms. For simplification purposes this study treats founders from a single firm as a homogenous learning unit, rather than attempting to parse the specific learning of each individual founder. This parsimonious step was taken in order to facilitate a focus on the process of hypothesis formation and testing, the emergence of causal structures, the paradox of learning from noisy feedback, and the influence of fundamental uncertainty without simultaneously having to account for group/team level effects generated by individuals. As the founders of these firms

20 worked very closely together with constant engagement and the learning occurred over many years it is believed that such individual-team level effects can be safely parceled out from the phenomenon of interest. For ease of prose this paper will often use just the pseudo-name of the firm as a proxy for the founder(s). It is expected that the use of the firm pseudo-names will ease the burden of the reader who would otherwise have to keep track of the various founders’ names.

Study Landscape and Prior History

While this study is concerned with the processes of learning under fundamental uncertainty, as examined in the emergence of a focal firm and three comparative firms within the pet health insurance industry in the 2000s, it is important to understand some broader trends in social, demographic, technical, and regulatory change that influenced the processes investigated.

These changes had direct implications for the social milieu that guided, constrained, enabled, and inspired the involved entrepreneurs and those who they interacted with in resourcing their firms.

Two primary social-level changes included the changing role of the pet as a member of the family and huge advancements in both the way that veterinary medicine was practiced and its perceived value. While Americans have always had an abiding fascination with pets (Grier, 2006, p. 12), the rate of change in both of these areas picked up speed throughout the eighties and nineties.

Between 1979 and 2009 the US dog population increased from an estimated 49 million to

77.5 million and likewise a similar increase occurred in the cat population. This period also witnessed a marked increase in the amount that pet owners were willing to spend on their pets and the degree to which they identified pets as an integral member of the family. The 1980s and

1990s were periods of dramatic upheaval in veterinary medicine; with the emergence of veterinary specialists and the increased adoption of advanced medical techniques that in the past were found only in the domain of human medicine. Together these changes led to a major

21 increase in the demand for advanced veterinary care, which came with major increases in the cost of care. For those readers who are interested appendix A provides a more thorough discussion of these issues and also provides in-depth information about the early days of pet health insurance.

This data reveals the depth and magnitude of the uncertainty (primarily social) faced by the pet health insurance entrepreneurs examined in this study.

Summary of Prior Industry History and its Implications for this Study

In 1982, pet health insurance was introduced by Veterinary Pet Insurance (VPI) (should this get a pseudo name as well?) into the United States, but it grew anemically up through the start of the 2000s. While pet insurance stayed under the radar for most Americans, this time period had important implications for the nature of the context within which a new wave of firms subsequently co-created opportunities during the 2000s. In order to articulate these issues the following section provides some needed background information that explains the environment that the firms under study confronted. The initial goal for the formation of VPI was to reduce the incident of ‘economic euthanasia’, which is when a pet owner elects to have an animal put down rather than pay for veterinary care. For a broad range of reasons, which are articulated in more depth in Appendix A, VPI created an insurance product that according to the founder was

“fundamentally flawed.” While VPI survived as a firm in order to enjoy the subsequent rebirth of the industry in the 2000s, it was kept alive through repeated cash-infusions having never had a year of positive cash flow. The major downside of the ‘flawed’ insurance products of VPI was that it created negative sentiment for the concept of pet health insurance in the US amongst veterinarians, regulators, insurance underwriters, and pet owners (i.e. the complete body of significant external stakeholders).

The prior history of pet health insurance, for the period 1982-2001, provides an explication of the complex context within which a new wave of entrepreneurs reimagined and reinvigorated the industry. The industry that subsequently emerged during the 2000s looked nothing like the one that VPI earlier brought to the market. However in order to understand the cognitive learning processes that these new entrepreneurs engaged, we must account for the socio-cognitive framework that was already on the ground. The unfolding of a shared consensus (Canon-Bowers

& Salas, 2001; Lee, 2001) around the failure of pet health insurance had led to mutually held beliefs along several dimensions that had the potential to derail any new attempts at the concept of pet health insurance. Such embedded negative associations were acting as a repository of shared knowledge, one that was functioning as a short circuit for efforts to reinvigorate the industry. This strongly shared negative sentiment generated substantial uncertainty for those entrepreneurs in the 2000s who envisioned alternatives for the exiting status quo. All of the various firm founders interviewed related a similar sentiment as captured by one in particular:

“there was really a lot of uncertainty about what might work, or how we might make it happen.”

Table 1.4 (see end of chapter) provides an overview of the conflicted socio-cultural environment that founders engaged in the entrepreneurial process faced at the beginning of the

2000s. In particular it highlights the overall impressions that various parties had for the future of pet health insurance, and the negative associations that had been formed. These knowledge structures, shared understandings, had become fairly solidified over twenty years and as can be seen appear to be rather unfavorable to the re-emergence of the pet health insurance industry.

In order for entrepreneurs to break out of the mold of how pet health insurance was perceived at the start of the 2000s they needed to integrate several aspects of the context. Firstly they needed to not only comprehend these negative sentiments and the reason for their existence, but then they needed to envision alternatives. With such concepts in mind they then needed to

23 propose and tests solutions (based on perceived causal structures) in the marketplace (Alvarez &

Barney, 2007). Relying on noisy feedback, they needed to nonetheless infer results and the reasons underlying these results; which would then guide them in further iterations. If successful they would eventually enact a change in these prior sentiments, leading to new ones that would consider pet health insurance as a desirable state of affairs.

The section that follows recounts the history of how one firm, the primary case (Toto), went about this task. This single case overview is used to illuminate three firm resources that emerged during the opportunity formation process. These three identified resources were fundamental to the nature of the specific firm that eventually emerged and possessed the significant benefit that variants of them were developed by each of the other three firms used in the comparative stage of the analysis (Snowy, Asta, and Gromit). This overview of Toto is followed by a short section introducing the three comparative firms and articulating the rationale of each founder for initially pursuing the pet health insurance concept. For brevity’s sake a complete history of each of the three comparative firms is not provided, but rather highlights and insights from this additional data analysis is included in the later theoretical and proposition development.

Toto Pet Insurance: Brief Firm History

Toto Pet Insurance had its 2002, pre-firm days start amongst a group of MBA students at

Wharton College, University of Pennsylvania. Bob and Sara (names changed), a couple from the

UK, during their studies had a cat that became very ill and required emergency veterinary care.

The cost of treatment for this animal was close to $5,000 and they had to pay for it out of pocket.

Being from the UK, where there was already a well-established pet health insurance market, they wondered why pet health insurance in the US was relatively unknown amongst the public and

24 generally disliked by the veterinary community. With the help of Simon, a fellow MBA student, they developed a basic business plan, which they subsequently entered in the MBA jungle business plan challenge at Wharton. While the early plan was ambitious it was out of sync with the regulatory structures for how insurance products can be developed and sold in the US. Further many of the assumptions used in the plan were not in line with the reality on the ground. It had not sunk in yet with the team how badly the pet health insurance marketplace had been damaged by prior attempts by other firms.

While this first business plan made it through the initial competition, it was realized that there was inadequate knowledge amongst the team concerning the insurance industry in general.

This is when the team was connected with Karen, a fellow student, who had previously worked as an actuarial specialists for Canada Life and had twelve years of insurance industry experience.

With her help the business plan was redrafted with a focus on building a quality pet health insurance product that was priced “using actuarial principles from the outset.” The four MBA students and their new concept joined Wharton’s venture initiation program. As part of this undertaking they were able to enlist the advisory board assistance of a well-known veterinarian with ties to the professional journal Veterinary Economics, and a successful property & casualty insurance entrepreneur. At this time they also began the lengthy and challenging process of beginning to develop actuarial models to support the types of policies they envisioned for the marketplace. They went on to win the Wharton business plan competition in 2003, beating out various technology and biotechnology offerings.

Shortly after this success, disagreement amongst the team members on how to proceed and increasing team acrimony led to the dissolution of the group. Bob and Sara left to start their own pet health insurance firm based on licensed material from the UK. Karen and Simon struck out on their own, spending the next year writing the business plan for their enterprise concept,

25 now called Toto Pet Health Insurance. This work involved substantial efforts to finalize dog and cat actuarial models that supported the policy concept, pricing the new product, searching for insurance partners that might be willing to be involved as underwrites, and traveling the funding circles. In order to support himself and gain more insurance industry experience Simon got a job at Progressive insurance, while Karen moved to Ohio with her family. In September, 2004 an

Ohioan non-profit development fund based in Cleveland selected Toto as one of three firms amongst 150 for start-up support funding.

With the support of this funding Karen and Simon were able to refine the concept and in

October, 2005 they signed a letter of intent with Lloyd’s of London syndicate to provide underwriting for Toto’s pet health insurance policies. However, it was still another year until finally in October 10, 2006 Toto sold its first policy which covered Karen’s newly adopted cat. In

March, 2007 Toto launched its ecommerce website which provided an informative portal for potential and current customers, as well as for veterinarians and other related parties. Based on successful launch and growth, a venture capital firm stepped in as a partner in 2008 providing needed capital and support. In June 2011, Toto reached break even with the continuing prospect for strong growth.

The next section examines the development of three specific firm resources (units of learning) and the events that surrounded them that were integral to the emergence of Toto and the formation of the opportunity that they enacted. Narrowing the focus to these three resources permits the development of propositions and theory regarding how entrepreneurs learn under uncertainty. Variants of the three identified resources are also found in the stories of the three comparative firms.

Resource 1: The Actuarial Model

When discussing insurance policies, it is important to know that an actuarial model lies at the heart of insurance products. Insurance is premised on the notion that the policy holder in return for providing a premium is entitled to compensation after the occurrence of a covered event. An actuarial model is used to create and price insurance policies by matching the risk of coverable events, the outgoing payment in response to event occurrence, and the inflow from received premiums. There are many ways an actuarial model can be structured. Amongst property and causal insurance a primary difference is found in how the underlying actuarial model determines the payout amount and the list of covered events. With pet health insurance the primary inputs are the morbidity rates of accidents and illnesses amongst cats and dogs, and the cost of associated veterinary treatments and interventions. At its core the theory and structuring of an actuarial model is not very complicated, but this simplification belays significant difficulties and complexities in real world implementation.

Early on the founders of Toto realized that they had the opportunity to provide a new alternative to the then currently available pet health insurance products (i.e. policies from VPI).

When VPI was initially brought to market in the 1980s the firm elected to use a schedule of benefits actuarial model. This type of model pays out a set amount in response to a covered event, regardless of the actual treatment provided by the veterinarian or the billing practices of the veterinarian. As further articulated in appendix A, VPI’s choose this model because it was implementable with the technology that they had at the time, meet their goals of keeping the cost of the policies low, and allowed the product to be brought to the market quickly. The major downside was that over time increasing mismatches between what VPI paid out for veterinary care and what veterinarians actually billed led to angry customers, substantial policy / customer turn-over, and the formation of negative sentiment towards pet health insurance.

Toto’s initial business plans underestimated how challenging shifting the actuarial model underlying pet health insurance ended up being. This observation was put forth unprimed by the founders during one of the early interviews. As Table 1.4 illustrates there was significant negative sentiment for the concept. Several key players who needed to be involved in the process of recreating the pet health insurance industry had seen prior missteps and were wary of or actively avoided future involvement. The initial business concept attempted to address these issues by envisioning a firm that would engage these various parties in a dialogue about the potential value of insurance for the veterinary industry, the insurance industry, and the pet owner.

While the very earliest business plans of Toto do not discuss the particulars of how policies would be developed and priced, with the addition of Karen this aspect became a significant part of the opportunity creation process. It was realized that if the conversation about pet health insurance was going to change the product needed to be redesigned from the ground up. With her significant prior experience in actuarial practice, Karen proposed the concept of a percentage of bill policy. VPI’s policies were still based on the schedule of benefits model with its inherent limitations. Over the years VPI’s polices had become more and more complicated as problems occurred and were resolved through add-ons that eventually led to policies where it was essentially impossible for the customer to know how much of a claim was going to be refunded.

Toto believed that a percentage of bill policy approach would solve this problem. Such a concept is also seen in human health insurance, often under the name of co-insurance. In this type of setup a policyholder buys a policy that will pay a fixed percentage of a claim, such amounts often range from 70-90%. When a veterinary bill is paid by the policyholder they submit a claim to Toto, which then reimburses the agreed on percentage (thus the policyholder pays a co- insurance in the amount of 10-30%). The advantages to such an approach are that it keeps claim pay-outs current with veterinary service market-pricing and payment is automatically adapted to

28 local market conditions. This is of great benefit to the policyholder who no longer needs to worry about how their vet prices relative to the schedule of benefits, and who no longer needs to understand all of the veterinary terminology that underlies such a schedule.

The disadvantages are that such an approach is more data intensive and involves significantly more complex actuarial methods as compared to the schedule of benefits approach as implemented by VPI. Without a deep understanding of this complexity and proper risk management policies can quickly get out of hand. Toto faced several challenges in enacting this resource and admitted that they had almost given up several times. The first major hurdle they faced was two-fold, no one on the team had an intimate understanding of the veterinary industry or veterinary terminology, and they had no data on which to build the cat and dog morbidity tables that would be needed for policy pricing. What they did have was persistence, the ability to envision an alternative solution, and fortuitous network connections. An economics professor at

Wharton, who had been conducting a multi-year study of the veterinary industry, agreed to share his data with the team. “If we hadn’t been at Wharton, we would never have met him and he probably would have never shared with us.” Armed with this data the team members immersed themselves in reading veterinary materials and recruited vets to their advisory board who helped them start the process of understanding veterinary procedures.

With these initial steps the Toto team was able to begin the formalization of their first version of the actuarial models. However the realities of the marketplace began to hit home: “a hard market hit home as we were turned down or ignored for the most part.” It seems that the other parties needed for the creation of an opportunity simply didn’t want to be part of the process. Regulators had through their experiences with VPI become fairly suspicious of the validity of the concept of pet health insurance. VPI had faced regulatory investigations in the

1990s with eventual resolution of outstanding concerns by the 2000s. The regulatory apparatus

(which in the USA is state by state, but fairly homogenous in standards across the country) was satisfied with the concept of polices based on a schedule of benefits actuarial model. There was outstanding data to support that policies from VPI, while not necessarily well-loved by customers, were at least meeting regulatory requirements and achieving acceptable claims payout levels. A percentage of bill model meant something new and untried, with regulators being well-aware that such policies entail more risk and put more demands on the firms that administer them. As is usual in such situations they put up many hurdles for Toto to clear.

Along with regulatory approval, Toto was also in the position of having to convince an established insurance entity to provide underwriting services. Underwriting requires a several million dollar capital reserve, something that simply was not feasible for Toto to undertake itself.

Of course underwriters had many of the same reservations as the regulators. Further these firms knew that if they underwrote a product and it failed they would be on the hook, or if it was disliked their association might generate negative sentiment in the marketplace towards their other insurance offerings. At the same time if a new insurance product was well structured and had good market penetration it could provide a robust revenue stream for the underwriter, albeit a small stream in comparison to existing insurance offerings. Through much iteration and fine- tuning Toto was eventually able to convince Lloyd’s of London to sign on as their initial underwriter and received initial regulatory approval from the state of Ohio (subsequently registering in all fifty states). It took roughly two years for Toto to court an underwriter and three years to finally clear regulatory hurdles in order to sell its first policy.

The eventual acceptance of the percentage of bill policy structure by underwriters and regulators was an important step in the opportunity creation process. Equally important were the socio-cultural shifts that permitted this event and the spillover that occurred into other relationships that the firm was cultivating. A percentage of bill policy meant that a policy could

30 be structured from the ground up to provide the features that Toto thought would be most appealing to the market (it is important to keep in mind that the knowledge of what was appealing to the market wasn’t a given, but rather took significant time and effort to cultivate). Observation of the failures of VPI revealed many aspects of the prior product that had alienated customers and vets alike. Toto’s primary competency in actuarial sciences and the data they now had, allowed them to design policies that permitted full customizability, guaranteed renewal for the life of the pet, level premiums for life, and variable maximum annual payout limits. This flexibility permitted the creation of a product that was in harmony with the rest of the mission of Toto and integrated eloquently with the other crucial firm resources.

Resource 2: Ecommerce Website

Initially it was believed that the best way to market policies was by a “two-pronged” strategy.

The most important community to breakthrough to was the veterinary community, as they served the role of primary gate keeper to the pet owner, acting as a portal for information and recommendations to potential customers. Toto initially planned to entice veterinarians to become involved by offering a monetary incentive tied to policy sales. While monetary incentives seem like a ready solution to the problem of enlisting the help of vets there were two major problems with this approach. First such monetary compensation is not permitted within the US regulatory system, as only licensed agents & brokers are allowed to provide advice or recommendations on the purchase of insurance. Second, vets neither had the training for this, nor did they want to spend the time to become licensed, and likewise they had no appetite for the related liability. Toto surmised that active institutional efforts to change the regulatory system to permit the utilization of economic incentives would have inevitably been futile. The second prong was to target pedigree dog breeders with short-term complementary plans to be given when a pet was sold to a

31 new owner. While such an approach was legal, Toto eventually concluded that alone this step would be insufficient to drive adequate policy sales as there were simply too few pedigree pets sold relative to the overall dog & cat population which is mostly mixed-breed.

With the development of the actuarial model and more time spent probing stakeholder responsiveness (testing hypotheses), a multi-tiered marketing strategy was developed. This included efforts to further public relations, promotion by word of mouth, corporate giving to cat- dog charities, marketing alliances and affiliations, and most importantly internet marketing. Time in the trenches revealed to the founders that prior efforts at direct mass-marketing by classic channels (newspaper, TV, etc.) had been fatal to several of the prior short-lived firms that were involved in the early years of the pet health insurance industry. The cost to conduct such forms of advertising simply did not generate adequate sales, particularly with the uphill battle against the already existing negative sentiment towards the entire concept of pet health insurance. Owing to the scalability of websites and their ability to be easily updated as needed, the internet was elected as the primary distribution channel for Toto both in regards to marketing, but also as a sales engine.

Partly for employment in the period when Toto was not up and running yet, and partly to gain valuable experience Simon had sought employment at Progressive insurance. This firm was known, and still is known, for innovativeness in the insurance industry, the introduction of customizable auto insurance policies (Name Your Price Tool), and a strong internet presence.

With Simon’s experience on the front-end and Karen’s expertise in claims handling and pricing, they developed and launched a website that allowed customers to explore the product. The website also provided educational material that explained how veterinary care worked and how pet health insurance policies could be integrated in a manner such that they actually paid-out on claims in a manner that the customer would understand. Importantly the website also provided

32 free policy pricing where a customer could enter their pets information, the location they lived in, and pick a customizable level of coverage. This function provided live feedback on the fly so that customers could explore different options that might best suit their budget. Additionally the website provided a direct route to actually purchase the policy and complete the enrollment process for placing a specific pet onto a policy. Later development advanced the website to integrate with the claims processing system, providing customers with direct online account access, expedited claims processing, and trackable, commented policy histories.

Resource 3: Claims Processing System

The earliest plans of Toto did not address the issue of claims processing, a fundamental step in the insurance process, however it was eventually recognized that this would need to be addressed. At the outset it was believed that the claims handling process could be readily outsourced under the assumption that this was a fairly routine task (many existing insurance firms already utilized outsourced claims processing) and could be implemented through a claims manual stipulating Toto’s policies. The founders felt that they didn’t have the internal training and implementation capabilities and that they were not likely to get adequate funding to support the hiring and training of a claims staff.

As they explored the landscape further and started to understand how radically their concepts for pet health insurance were departing from the old ways, it became more and more apparent that they needed to develop internal claims capabilities. “The Toto Pet Insurance program is unique: efficient, customer-friendly claims processing is central to our operating tenets. With pet insurance there is little in the way of past practices to rely upon in the US and there are no third-party administration that specializes in pet insurance coding or claims management.” Exiting third party services simply didn’t have the requisite knowledge to

33 understand veterinary procedures and the claims process that would be needed to support the percentage of bill actuarial model.

Importantly, Toto eventually came to realize that it is at the point of claims payment that the customer truly judges a pet health insurance policy. If they received a fair payment that matched their expectations they would be content, if they received an unexpected payment that was based on complex and convoluted claims information they would be upset. As heard from one prior VPI executive: “the only happy VPI policyholders were the ones that never filed a claim.”

In order to implement the claims handling process Toto developed a claims administration system with a backbone based on automation. With the frequent, small transactions expected from pet health insurance policies it was realized that automation would be crucial to controlling costs. Further such a system could directly integrate with the actuarial models, feeding in claims information, and pulling out policy quotations. The ability to data mine claims information was important for driving the businesses analytical engine that could be used to develop projections of earnings by source, sales analysis, a range of other crucial metrics, and provide information necessary for the development and refinement of future insurance policy offerings.

Comparative Firms: A Brief Overview of Snowy, Asta, & Gromit

The prior section provides a brief history of the formation of the firm Toto (the primary case) and initial insights into the experience of the founders during the opportunity formation process. Three resources generated by the entrepreneurial process were highlighted, as they played a central role in the development and structuring of Toto’s insurance offerings, facilitated pivotal integration with stakeholders, and helped to illuminate the process of entrepreneurial

34 learning. As these identified resources are fundamental components of any insurance product variants of them were also developed by the three comparative firms examined in this study. In total this permitted pattern-matching the processes of learning under fundamental uncertainty across 12 units of learning. The emergence of these three comparative firms substantially overlapped the time period during which Toto was maturing from an idea for a business plan contest to a fully operational business.

The first of these, Snowy, was launched in response to the founder’s recent success with an unrelated consumer-level startup. This individual was looking for a challenge, enjoyed and actively sought out business experiences where he knew nothing or very little a priori, and had witnessed the thriving pet health insurance market in Europe during travels for his prior business.

Although not at first, the development of the three resources examined in this study was strongly influenced by the necessity that they would support the eventual formation of a ‘monoline’ insurance company. Usually a specialty insurance policy, like pet health insurance, is written by the issuing firm, but underwritten by an existing large capital provider. All of the existing pet health insurance companies, excepting Snowy, are structured in this manner. In contrast, a monoline insurer places both policy issuance and underwriting under one roof. For Snowy, the founder gradually came to believe that this alternative structure would permit a hyper-focus on cost-minimization efficiency, maximization of policy issuance flexibility, positioning of the firm for future lock-in product offerings, and rapid progress towards IPO or M&A. This strong focus of the founder did not emerge for several years into his efforts to launch his firm, as he too faced the same fundamental uncertainty that Toto’s and the other firms’ founders were confronting.

Asta, the second comparative case, was established by a founder who had previously played a central role in the formation of VPI in the 1980s. This founder emphasized the missteps of VPI and what might have been done differently. He saw this new firm as a chance to do it right

35 without all the cemented cultural and institutional norms of an existing entity. For Asta the founder had a desire for growth tempered with the necessity to provide the best product to the specific customer base the might want a mid-range policy (mid-range price and mid-range coverage). For this firm the implementation of the three resources focused on the development of this mid-range offering for the well-informed customer.

The final comparative case, Gromit, was initiated as a side-project for a large existing firm in the pet supplies industry. This firm initially envisioned pet health insurance as an extension offering to their current broad portfolio of pet products, animal feeds, and branded veterinary care. The formation of a stand-alone pet health insurance firm was placed under the purview of a president tasked with its development and integration with the parent firm. This manager approached three individuals to acts as firm founders, two of whom had prior experience with

VPI. From the start Gromit’s attempted implementation of a new offering in the pet health insurance marketplace was the least well-developed. This struggle was reflected in the formation of the three resources of interest and the slowed pace of learning amongst the founders. While

Gromit eventually launched an insurance policy offering, conflicting demands on the firm led to its demise during the time period in which this study was undertaken.

Armed with this basic history of the primary case and the three comparative cases, specific attention is now turned to processes by which entrepreneurs learn under fundamental uncertainty.

Entrepreneurial Learning under Fundamental Uncertainty

The prior section provided a brief history of the formation of the firm Toto and initial insights into the experience of the founders during the opportunity formation process in the pet health insurance industry. Three resources with economic relevance were highlighted, as they played a

36 central role in the development and structuring of Toto’s eventual insurance offerings, and facilitated pivotal integration with stakeholders. Two of these identified resources, the actuarial model and the claims processing system, are fundamental components of any insurance product.

For this reason variants of both of these resources were developed by the comparative firms.

Although the functionality and business domain of the third resource, the e-commerce website, could have been met with alternative resources (door-to-door sales, advertising and sales through exciting insurance brokers) isomorphic market forces led the three comparative firms to develop their own dedicated e-commerce websites.

In summary, the actuarial model dictated the fundamental structure of the policy governing what claims would be covered, how much would be paid out for covered claims, and policy pricing that supported the generation of a profit while assuring a continuous and renewable capital pool. The claims processing system provided the backbone by which policies were administered and policyholder obligations were fulfilled. The ecommerce website served as a unification platform, providing the primary method of communication with active policy holders and a means for acquisition of new customers, while simultaneously integrating bi-directional information from both the actuarial model and the claims processing system. This level of complexity and integration however did not simply spring into fully integrated existence, but emerged gradually during the co-creation of the opportunity. On average it took the various founders five to six years to develop, structure, and deploy these resources.

By examining the chronological history of the development of each resource a picture of how entrepreneurs learn under fundamental uncertainty during the formation of opportunities coalesced. Iterating between the data regarding Toto’s development of the actuarial model and the assumptions, predictions, and propositions of entrepreneurial process theory a viable temporal delineation emerged. Subsequently, Toto’s development of its claims processing system and its e-

37 commerce website were also mapped into this organizing approach. Once the data was organized in this manner particular features of each temporal category became more prominent and an overarching structure became apparent. Mapping the other firms in a similar manner revealed the same structure emerging, albeit with some contingencies around prior experience and the firm’s position in the social conversation that surrounds the emergence of a new opportunity.

Earliest days

In the earliest days of the opportunity formation process there is little in the way of social structures to guide the entrepreneur (Berger & Luckmann, 1967). The entrepreneur has many dreams, and visions of what might come to be, but there is a dearth of information to validate these ideations (Alvarez & Barney, 2010). If as argued, the emergence of an opportunity is the creation of a new social arrangement, then the entrepreneur faces the uphill battle of enlisting others into a shared reality (Kaplan, 2008). However, exiting social structures are not monolithic, and the entrepreneur will come into contact with many different parties who hold different viewpoints and respond to the entrepreneur in differing ways (Searle, 1995). This means that it will be challenging for the entrepreneur to determine when the new social contract they are putting forth is receiving mixed response because it is not the right solution or that they are talking to the wrong people. Further, external parties themselves are not temporally homogenous in their response to proposed alternatives, they can be swayed this way and that (Woolley, 2013), and they may not even understand their own preferences (Lichtenstein & Slovic, 2006).

The pet health insurance industry posed a further challenge in that a social structure that had already been formed was significantly antithetical to the re-launch of the industry. The four firms examined in this study came to the opportunity formation process with different stores of prior knowledge about what had previously transpired in the industry. The founders of Toto had

38 extensive experience in casualty and property insurance, but knew little about veterinary medicine or the pet health insurance industry’s history. The founder of Snowy, who originally started a pet health insurance firm in Canada before eventually making the move to the USA, began his exploration of the opportunity with no knowledge about either insurance or veterinary medicine. Although his success in Canada provided him with the experience of bringing a pet health insurance firm to that market, the knowledge he gained there was not as readily transferable to the USA market as he initially expected. Snowy: “After what I had accomplished in Canada, I thought the move down south would be fairly straight forward, it wasn’t and it took me several years to figure out why”

The founders of the two firms, Asta and Gromit, both had prior experience at VPI and thus they were aware that pet health insurance had never successfully caught on in the USA, although many efforts had been made. Both of these groups of founders had extensive veterinary experience, but their insurance experience had been with a firm that was never able to innovate itself out of its troubles and one that carried dysfunctional cultural norms. Unlike the founders of

Toto who had seen highly successful insurance firms up close in person or the founder of Snowy who started with a blank slate, the founders of Asta and Gromit needed to unlearn what they had been exposed to at VPI and develop a deeper understanding of what caused the problems at VPI beyond their currently held beliefs. Both Asta and Gromit envisioned their new firms as a chance to “get it right the second time around” at the same time they were aware that they faced significant challenges in the inertia that had already been formed in the industry.

Turning back to the resources that each of these firms developed, we can see the consequences of both the unusual social structure that had emerged around the value of pet health insurance (or perhaps the destructive value) and the prior knowledge that each founding team brought to the table. In the earliest days of the formation of these resources the various firms

39 envisioned the resources purely as utilitarian and necessary for the conduct of business. All four firms initially focused on the creation of the actuarial model as this is the defining lynch pin of an insurance offering and is necessary for going through the process of underwriting and regulatory approval. Eventually all four firms turned away from VPI’s approach based on a schedule of benefits, and instead adopted the percentage of bill model.

The founder of Snowy, who knew nothing about insurance, studied what other insurance firms were doing, realized he needed to know more about actuarial models and began educating himself. His reason for choosing a percentage of bill model was “I just looked at what all the other property and causal insurance firms were doing and decided I better do the same thing.”

The founders of Toto initially elected the percentage of bill model because “there has been a trend over the last decades for all insurance products of this type to move towards percentage of bill as actuarial models have matured and the technology to manage them has improved … as more competitors started doing this customers began to expect it and if you didn’t do the same you were no longer in the game.” The founder of Asta elected the percentage of bill model early in his development of the actuarial model because “I had seen what it was like in the early days of VPI with a warehouse full of files needing to be accessed, it was a nightmare, we choose the schedule of benefits because it was the only thing we knew how to do at the time … this time around with

Asta I had a feeling that this is the way to do it right in regards to a robust policy.” After the founders of Gromit were approached by the large parent company, they realized “we have a chance to try to do it again and get it right this time … the schedule of benefits model at VPI has a historic cryptic nature with lots of exclusions and etcetera which lead to limited reimbursements

… a negative experience for the client meant a negative experience for the vet”.

Although all of the entrepreneurs envisioned the actuarial model as a means to offer something new to the marketplace, early on the focus was on the nuts and bolts of how to

40 construct such a model, where the data might come from, what insurance risks to take on and what to exclude from the policies, etc. There was little if any outwards facing engagement with others beyond those needed for the actual implementation of the models. Snowy: “I just put my noise to the grindstone and learned everything I could about actuarial models, without ever looking up to think about what it meant beyond the policy”. When there was outward engagement it rarely provided useful information. Gromit: “We realized in the early days that talking to others could be detrimental to what we were trying to do, you get excited about achieving a breakthrough and the response is either mildly positive from someone who already thinks pet health insurance is needed or blanket fear, perhaps caused by severe ignorance, from those vets who still hate you.”

Returning to entrepreneurial process theory, it is logical that entrepreneurs in the early days would be focused inward and would tend to hyper-focused on the details of the task at hand. The context they face is extremely noisy and contradictions abound. At this stage the entrepreneur is a hypothesis maker, in whom prior experience and imagination dominates the social reality at hand.

The entrepreneur is testing their own hypotheses about what might come to be, but the potential hypothesis space is so large that the individual can only focus on small pieces at a time. The existing social structure still dramatically constrains the feedback they can receive from their hypotheses, but the complexities of this social structure is poorly understood by the entrepreneur.

The entrepreneur is able to make small local changes, receive feedback, and update to the best of their abilities. This is the beginning of learning. This insight leads to the first proposition:

Proposition 1

Under conditions of fundamental uncertainty task and domain specific learning will be used by the entrepreneurs in the earliest of stages of hypotheses formation. The uncertainty is deeply

41 pervasive and these early hypotheses are superficial and the resulting feedback is noisy and difficult to interpret.

Emergence of abstract learning

As the development of the actuarial models progressed, the founders of the various firms moved into a new stage of the entrepreneurial process. In this stage they became more intimately involved with outside parties and those who would eventually become critical stakeholders in the industry. Some of these individuals were discovered accidently, some were actively recruited, and others were brought into the discussion owing to institutional and regulatory forces. These various parties brought their own beliefs about the value and validity of pet health insurance.

They shared their viewpoints of the current social order. At this point the various founders started engaging in a manner that extended beyond simply accomplishing the task at hand, and instead started crafting a narrative of what they hoped to accomplish in the industry (Searle, 1995). This narrative however was still in its infancy and lacked strong consensus amongst the involved parties.

One of the first major hurdles that the firms faced was receiving regulatory approval to offer their insurance products to the market. In the United States regulatory approval for insurance policies is granted state by state. Amongst the various states the firms faced different responses. All of the states required the various firms to go through multiple iterations of the screening process before finally granting approval. For some of these states pet health insurance

“wasn’t high on the radar” and the involved regulators would address the relevant paperwork in less than a timely manner. Other states were outright hostile to the idea, although they hid this opposition behind a wall of bureaucracy.

State regulator: “I still remember the first time we dealt with that firm, they were the first to apply for approval based on that form of actuarial model (percentage of bill). I didn’t know the exact reason why, but my supervisor told me to make it hard for them to get approved. Now, I couldn’t simply say no, because we have to justify what we flag in approvals, but you know you can do that in a way that makes it a challenge … I found out later that my supervisor’s boss was the one who wanted to deny approval. Not sure, but he had experience many years ago with pet insurance and I think medical friends of his opposed the idea.”

On average it took each of the firms roughly two to three years to go through the regulatory process with the first state. Subsequent approvals were faster, as in general, states weigh the evidence of what another state has done in the regulatory process in a positive manner.

During the process of pursing regulatory approval from the various states in which each firm planned to first launch, the founders were also beginning the process of developing the claims processing systems and the e-commerce websites for their firms. While these undertakings still exhibited a significant amount of task-dependent learning, the process of abstract learning was starting to be recognizable. These two resources were not simply about meeting their planned utilitarian need, but they also embedded the founders growing understanding of the social complexity that surrounds people’s connections with their pets, the value that society places on veterinary care, and the missteps of prior attempts at pet health insurance.

Snowy: “It was around the time that we were going through regulatory approval in

Canada that I realized just how fouled up things had been in both Canada and the US. The regulators are pushing back on you, and you know that part of that is just their job, but also I got the sense that there was real unease with the whole thing. Too many bad apples in the past, lots of older pet owners who I would never be able to get back to the table, and vets who wouldn’t even talk about it. The same thing happened when we moved into the USA. You would think after

43 having success in the Canada it would have been an easy move, it wasn’t. I talked to over a hundred venture capital and private equity shops before one was willing to actually deal with us.

We had to show them not only that our various business functions worked, but that the story we were telling was actual market potential”

Gromit: “I wanted to know what the roadblock to pet health insurance was. Why wouldn’t vets accept this paradigm? I think there were two big factors inhibiting adoption. The first is the assumption that pet insurance is equivalent to human insurance. That means a fear of an HMO regulatory structure. I was at the Iowa conference and a vet in the audience said that a physician friend had told him to run away, there would be massive constraints. The second factor is the fault of pet health insurance itself. The prior negative experience had poisoned the well.

The inertia at VPI was just too strong and we kept doing the wrong thing. My partner says that the problem with VPI is that they have pissed in the chili (a southern expression meaning that everyone contributes to the night’s meal, but one party ruins it for everyone else).”

As part of resource development, all of the firms began to actively engage the veterinary community as they came to understand that the veterinarian plays a significant role in the customer’s decision to purchase pet health insurance. If a vet strongly opposes the concept then the various firms would be unable to place advertisement materials in their offices and if asked the vet would steer clients away from the product. Many older vets who had experienced VPI were still strongly opposed to pet health insurance, with one vet stating that “it is one of the worst things to ever happen to veterinarian medicine.” Younger vets who had not been exposed to this earlier history, might be amenable to the concept but they did not understand the product and were hesitant to make any recommendations. The various firms all started efforts to engage the veterinary community through providing educational lectures at veterinary schools, symposium at veterinary conferences, and office visits. This higher level understanding of the social structure

44 around veterinary care was facilitating the formation of causal understandings on the part of the various founders.

Through this engagement with the veterinary community the various founders developed a deeper understanding of the relationship between vet and client, and the relationship that clients had with their pets. For example, Toto came to understand pet owners as “pet parents”. “The strengthening of the bond between human and animal, had moved beyond the relationship between master and subject, to loved family member. As we came to understand this we came to realize that pet health insurance isn’t just about paying for medical care, it is about your commitment to the ones you love.” This deeper understanding was reflected in the development of the claims processing system and the e-commerce website. Although Toto initially planned to outsource claims processing, they discovered that existing entities that provided this service simply did not have the capabilities to handle veterinary claims data. This pushed them to develop internal capabilities to accomplish this task, but as reflected in their writings and business plan at the time the desire to turn inwards was more than simply needing to fulfill a business function.

They envisioned the claims processing function as a vital touch point with the customer, one in which they could demonstrate that they were an important member in the “pet parent” relationship. Likewise, the e-commerce website became more than simply a tool to drive marketing and sales, but also a platform for relationship management and a means to keep abreast of the community of “pet parents” they were working to foster. The three other firms demonstrated similar advances in the complexity of the thought and rationale put forth for the decisions they were making in regards to the firm resources they were developing.

During this later stage of the early development of the opportunity, entrepreneurs’ prior hypothesis testing has generated feedback that aided them in refining their initial conceptualizations of the tasks at hand. At the same time this hypothesis testing and task specific

45 learning has begun to illuminate the social constraints that currently surround the potential opportunity. The entrepreneurs’ increasing awareness of the role of social context is beginning to illuminate what aspects of the social structure have plasticity and might be modified. The cross- talk amongst the firm, outside parties, and stakeholders is beginning to share some degree of consensus, but strong and differing opinions still hold sway. Entrepreneurs still face the challenging task of determining what feedback means above and beyond the task at hand and they still struggle to engage others in dialogue supported by shared understanding. This insight leads to the second proposition:

Proposition 2

Under conditions of fundamental uncertainty general abstract learning will start to be used by the entrepreneurs in the later part of the early stages of hypotheses formation. The uncertainty is still deeply pervasive and the resulting feedback continues to be noisy and difficult to interpret.

Integration & Formalization

As the four firms studied here began to receive regulatory approval and the resources they needed to support business functions came to fruition, the new market for pet health insurance went online. For all of the firms the initial days were slow. Toto sold one policy initially to one of its founders for her cat. Gromit struggled in its first month with anemic sales and Asta mostly spent its time trying to entice new customers rather than selling policies.

Snowy’s reputation from Canada gave it a boost in the US market, although initial sales were below projected targets. However for the three firms, Toto, Snowy, and Asta continued efforts in the marketplace began to quickly show surprising results with commensurately staggering growth rates. Gromit became mired in the demands of its large corporate owner and although the

46 founders were equally engaged in the learning process as their brethren in the other firms, the actions they could take were too constrained. Gromit was unable to generate the growth that the other firms achieved and eventually withdrew from the marketplace.

Interestingly it was at the point that these various firms brought their policies to the marketplace that the pace of learning substantially accelerated. By engaging various stakeholders in the process of formalizing the format and structure of the insurance offering the firms had built a community of interested parties that shared a common language. Major veterinary organizations had come back to the table with a willingness to view what these firms were doing, separated from the problems of the past. These organizations were pushing information and opinion out to their members that showed that the ‘new’ pet health insurance was fundamentally different and might offer a means for veterinary offices to aid their clients in light of increasing costs for veterinarian medicine. Likewise the various state regulators were now on board with the new industry. Asta: “it was clear to the regulators that we were becoming more important, sometimes they would require negotiation, but for the most part they were letting us do it our way.”

Learning was still ongoing, the process hadn’t stopped. It was around this time that several of the firms got together informally and began discussing how to support and grow the industry. Out of this an industry trade group was formed. This group provided a platform to engage in industry promotion and lobbying, but more importantly it served as a mechanism to share learning amongst the firms. “It seemed like we had everything all figured out, ready to go, like you could just throw the switch and start printing money, but we quickly found out that there was much we didn’t know.” “We knew how to do everything but the most important, we didn’t know how to sell this thing.” The trade group was a platform to share what worked and what didn’t, to debate how to move forward, and a place to both compete and support one another.

Through the trade group firms were sharing task specific know-how, bolstered by higher level concepts that supported the validity of what had been learned.

At this stage the various firms began demonstrating the ability to seamlessly engage both task-specific learning and abstract learning simultaneously. Integration of their varied entrepreneurial visions with reformation of the social constraints that surrounded the industry had created the opportunity to engage in new market activity. Increasing awareness of their ability to influence and shift social constraints in tandem with others, empowered the founders.

Gromit: “We were changing the attitude of the profession (veterinarians) by giving them a better experience. We realized that one way to do this was to support the push for more education about pet health insurance … The fascinating part was that at the same time I was focused on getting vet school talks done, we were taking everything we heard there and feeding it back into the firm.”

Snowy: “I remember it as one of those moments were a light goes off. All of a sudden things start to make sense, you understand that the reason something isn’t working is because something is broken elsewhere. Initially there is too much going on, you get distracted and go this way and that. Once it all comes together you are no longer just playing with pieces.”

For Toto, as for the other firms, this was also the time when differentiation became more salient and each firm began to hone its own specific strength. Toto’s advantage lay in information. The resources they had been creating in the early days were individually robust, but as they came to understand the industry more deeply they created deep linkages between these resources. The actuarial model supported the policy formation, but it was also linked to the e- commerce website permitting the production of on the spot policy quotations with a huge range of options for potential customer. The claims processing system was linked into both of these as well, providing a wealth of information as to what was happening in veterinarian care and the

48 performance of Toto’s insurance offerings. This allowed the firm to tailor its offerings to the customers that it wanted, while shifting those individuals who weren’t willing to pay for their risk profile to other firms. Without the integration of task-specific and abstract learning this competitive differentiation would not have been attainable by Toto. Toto: “It wasn’t just about having a good product, it was about having the best. Behind the scenes you have to get everything right, but to the pet parent you have to come across as transparent, honest, genuine, and committed to the relationship. Our strength in information is what let us accomplish this. It was integration across all facets of the firm.”

In the later stages of the opportunity formation process broadly shared social consensus emerges as a new way of doing business begins to take hold in the marketplace (Lounsbury &

Crumley, 2007). The various parties involved in the market process are approaching common ground and shared beliefs are formed (Porac & Baden-Fuller, 1989). Dissent may still be present, but the involved parties can now communicate with a common language and the aspects on which they disagree are more readily understood (Searle, 1995). The dual features of both isomorphic tendencies and competitive differentiation begin to be recognizable driven by the emergence of shared institutions (regulations), the formation of common stakeholder vernacular, and path- dependent resource differentiation (Aldrich & Fiol, 1994; Dacin, 1997; Garud & Karnoe, 2001).

Learning is becoming robust and there is a smooth flow between task-dependent efforts and abstract exploration. These insights lead to proposition three:

Proposition 3

After initial hypotheses testing has generated new knowledge and data allowing the entrepreneurs to distinguish results from noise the entrepreneurs begin to understand the social complexity of

49 the context and how their previous actions have begun to shape the context. Entrepreneurs now use task and domain specific learning and general abstraction learning in an integrated manner.

Integrating Study Findings with Extant Learning Theory

This examination of the data has revealed important aspects that need to be addressed in furthering our understanding of learning under fundamental uncertainty including the role of prior beliefs the process of belief updating, the formation of alternative hypotheses from the status quo, the generation of causal understanding, iterative testing to extract information from alternatives, the differential functions of both task and abstract learning, and the interplay between these two types of learning.

This proposed temporal structure envisions three broad epochs in the evolution of how entrepreneurs learn during the opportunity formation process. Given the initial assumption of fundamental uncertainty, as an opportunity is formed aspects of the uncertainty begin to resolve and the information available to the entrepreneur undergoes fundamental shifts. This shift in the character of information leads to changes in how the entrepreneur relates with the environment and to changes in the efficiency and type of learning employed. The primary forms of learning identified included task/domain specific (i.e. the answer to a specific question or the solution to a particular problem) and higher-level abstract learning (i.e. organizing principles, social structures). The need to integrate these two forms of learning and the need to articulate a mechanism by which an individual could transition between them highlights a linkage between entrepreneurial process theory and the logic underlying the hierarchical Bayesian theory (HBT) of learning.

HBT assumes that the process of learning integrates both task-specific learning and generalized abstract learning (Kemp & Tenenbaum, 2008). An individual engaged in a specific

50 activity proposes hypotheses for the results they see based on higher order organizing principles.

These higher organizing principles both enable the formation of task-specific hypotheses, but they also constrain the type and variety of alternative hypotheses (Griffiths & Tenenbaum, 2009).

As information from a task is collected beliefs about the potential hypotheses that could underlie that task are updated. At the same time the refinement of these local, task-specific hypotheses leads to refinement of the higher order principles (Tenenbaum, Griffiths, & Kemp, 2006). This hierarchy of knowledge accelerates the process of local learning and facilities the ability to carry the lesson learned in one domain into other domains of similar character (Kemp & Tenenbaum,

2008). At its core the HBT approach to learning is about the formation of generative models. An individual is capable of more than simply describing what is happening, but they are also able to inductively reason potential causes for what is happening (Steyvers, Tenenbaum, Wagenmakers,

& Blum, 2003). The ability to form generative models allows the individual to extract relationship properties well in advance of the data that would be necessary to achieve this by pure probabilistic means (Kemp, Perfors, & Tenenbaum, 2007). The early imposition of an inductive causal understanding on the part of the individual, also provides the individual with robust mechanisms to differentiate signal from noise (Payzan-LeNestour & Bossaerts, 2011). As with all

Bayesian approaches to learning the process of belief updating is rationale, but it is important to note that the individual is rationale to the information they receive, not in any global manner. This means that HBT learning is compatible with other entrepreneurial logics such as effectuation

(Sarasvathy, 2011) and other conceptualizations of learning (Holcomb, Ireland, Holmes, & Hitt,

2009). Further there is no reason that various forms of information screening mechanisms

(Hutchins, 1995), such as biases and heuristics, cannot play a significant role in the information gathering and processing components of the HBT approach (Gigerenzer & Todd, 1999;

Gigerenzer, 2000; Payne, Bettman, & Johnson, 1993).

In examining the linkages between the case-study derived propositions and the HBT approach we can identify some modification and contingencies to include when applying this theory. The entrepreneurs from the case study also faced a hierarchy of knowledge. For them the most significant higher order principles were the social structure that surrounded the old perceptions of pet health insurance and the newly emerging social consensus about what the new pet health insurance could mean. Much like Bayesians they brought their prior knowledge to the problem at hand. However we must be cautious in our assumptions of the importance of prior knowledge for the entrepreneurial process. Unlike a simple learning task, the process of opportunity creation unfolds over a significantly lengthy time period. Yesterday’s new discoveries become today’s prior knowledge. Although the various founders began with different prior knowledge backgrounds within a few years of starting they all shared a similar set of perceptions of what had gone wrong with the industry and how their firms might play a role in the new marketplace. In situations where multiple groups of entrepreneurs are working towards the same opportunity set, it is possible that initial prior knowledge plays only a very small role in what transpires over the coming years of the opportunity formation process.

The HBT literature to date has been applied to clearly defined tasks, wherein the learning individual may not know the potential dynamic nature of the system, but where the dynamic component is externally pre-determined (Navarro, Newell, & Schulze, 2015). Such tasks include activities such as sorting objects into taxonomies, the formation of causal relationships about the hidden relationships between variables, learning names for things, and other forms of inductive reasoning (Griffiths & Tenenbaum, 2009). Providing robust explanations for how the human mind is able to generate such understandings is no small undertaking, but in relation to the questions we examine in entrepreneurship such tasks are certainly more circumscribed. In extending the concepts behind HBT to further our understanding of learning under fundamental

52 uncertainty aspects of the formalized nature of HBT models will need to be pushed to their extreme boundaries.

Current HBT models envision a hypothesis space that is informed by higher and higher levels of organizing principles. These various levels are directly linked to one another and as such any task based learning necessarily effects all levels of the knowledge hierarchy. This study argues that initially entrepreneurs learning under conditions of uncertainty rely almost exclusively on task-based learning as the complexities of the social structure they face are too great and they do not yet understand their potential role in effecting higher level changes. If one extends the

HBT concept of higher-order organizing principles to include not only direct linkages with lower levels (where task based-learning resides) but also to include complex linkages amongst the higher levels, then it is possible that a greater volume of task-based learning is required before the refinement of higher levels occurs as compared to classic HBT models. Likewise as abstract learning accelerates in the later stage of entrepreneurial hypothesis testing, then the resolution of understanding of the complex linkages at the higher levels will accelerate the performance of learning at the task level. The insight in this study point towards the potential to revisit HBT concepts in a formalized manner to both strengthen the logic herein, but also as an opportunity for entrepreneurship literature to contribute back to the developmental cognitive literature that brought forth HBT.

Conclusion

This study has examined the emergence of new opportunities created in the pet health insurance industry by motivated entrepreneurs who had visions to radically transform the perceptions of what the product provided and how it could be implemented. In particular the phenomenon of how entrepreneurs learn under fundamental uncertainty was examined. In the

53 process of creating firm resources the entrepreneurs also enacted significant change in the context, bringing several parties that were previously opposed to the concept back to the table.

Repeatedly throughout interviews with firm founders in this sector it was mentioned by these individuals that they didn’t think of themselves as anything particularly special, and yet the change in the industry was remarkable. Likewise an often heard comment was “we didn’t really know what we were doing at the start” and yet these individuals under conditions that would have deterred many, learned new ways of doing things and in the process re-introduced a concept to the US market that many believed had already failed. In this sense the entrepreneurs were also changed by the process itself, with most stating that they couldn’t possibly have imagined were they ended up.

In the years since the ‘re-launch’ of pet health insurance by Toto and a group of other firms who were implementing similar, although not identical, policy structures and business strategies the industry has undergone a significant rebirth. As of 2005 the penetration rate of pet health insurance was estimated at less than 1% of the cat and dog population. By 2007, with the entrance of a small group of firms doing things in a new way, sales of pet health insurance grew to an estimated $230 million from an estimated $120 million in 2004, a 92% increase. VPI, in response to the actions of these new entrants, began the process of redesigning its policies, claims handling processes, and communication with customers. As of 2000 VPI sales were roughly 200,000 policies annually, this number grew to an estimated 415,000 by 2007, finally breaking the company out of its long stall. Fetch, Inc. (a contemporary of Toto) saw its revenue of $812,000 in

2007, grow to an estimated $18.7 million by 2011, an outstanding 2203% increase. As of 2012

USA Today reported that there were eleven companies offering pet health insurance in the US market, with sector revenue of $303 million in 2009.

An information processing cognitive approach was utilized in this paper as a means to understand how entrepreneurs integrate information from the context with their own inductions and intuitions (Clark, 1997). Positing the entrepreneur as a motivated actor seeking to enact opportunities in a socially constructed marketplace provides both challenges and options for examining the mechanisms that underlie the entrepreneurial process. While there has certainly been progress in understanding how individuals make decisions under uncertainty, this work tends to focus on uncertainty defined either as risky choice or parametric uncertainty (Knight,

1921). The theoretical assertion that entrepreneurs may endogenously influence the context as a means of generating opportunities, means that we must address the issue of decision-making and learning under fundamental uncertainty. This fundamental uncertainty means that at certain time points of the entrepreneurial process it will not be possible to know what will be most relevant to the future, i.e. what information is most pertinent may not be identifiable (as some of this information may not yet even exist). At the same time fundamental uncertainty doesn’t mean anything goes. Markets are not socially created by entrepreneurs alone, rather they are created by the interaction between entrepreneurs and the many others involved in the process (Garud &

Karnoe, 2001). This means that there are parts of the environment that are both exogenous and endogenous to the entrepreneurial process, and that even those parts that are endogenous are not under the strict control of the entrepreneur. Likewise learning in this setting isn’t just about searching the environment for the optimal solution, but rather entails repeated iteration in an effort to understand how to integrate with the socio-cultural milieu. This moves the investigation of entrepreneurship out of the realm in which exploration and exploitation only occurs in a given external context, a land of ‘search’ (March, 1991)

In order to illuminate concrete cognitive processes this study focused on the manifestation of resources that facilitated new opportunities. The resources that were examined

55 embodied both tangible and intangible components. For example the actuarial model is manifested in a set of tangible data artifacts and physical output. But the true value of the actuarial model lies in its ability to support the firm’s alternative vision of a transparent, easy to understand, and reliable insurance product. This is what the consumer ended up developing a belief about, not how the policy actually was operationalized in the background. Tangible aspects of resources can embody technical and creative breakthroughs which are vital to the future success of the firm within the opportunity it is creating. However alone these aspects are insufficient to generate value as they do not provide adequate social understanding to consumers in order to warrant their engagement in transactions for the firms potential outputs. The intangible aspects of the resources provide a means by which entrepreneurial creativity is manifested in social meaning. By generating social meaning they permit the formation of consumer demand, thus inherently they are also serving as part of the mechanism that generates consumer need

(clearly within a context that was already favorable to increased spending on pet care).

Clearly this paper’s conceptual development is a simplification of an extremely complex process. In assuming that both individual entrepreneurs and teams of entrepreneurs act as single entities it removed interesting aspects of how groups process information in interdependent ways and the effect of different motivational tendencies (DeDreu & Nijstad, 2008). Likewise the knowledge domain of a group is not a simple additive function of the group members. Rather diversity of knowledge and depth of knowledge should play different roles in how groups perceive both exiting alternatives in the given environment along with how they imagine not as of yet exiting alternatives (Taylor & Greve, 2006).

Further work is needed to understand what the cognitive mechanisms are for how entrepreneurs envision novel alternatives to the status quo, an area that creativity research has long struggled to articulate. The philosophy of art speaks of the moment of ‘incept’ (Beardsley,

1965), Freud talked of “phantasying” (1908), and more recently scholars such as T. Amabile have examined how creativity is influenced (1994). In more recent work Amabile & Muller (2008: 33-

34) argue:

Creativity research has enjoyed only a slightly better reputation among the broader

group of psychology scholars, management scholars, and business leaders. Many who

are unfamiliar with recent advances in the field assume that is has little broad relevance

because its focuses only on the arts (and perhaps the sciences), has little validity because

creativity is too ill defined, ephemeral, and “soft” to study rigorously, and provides little

practical applicability because creativity cannot be influenced. But they are wrong.

Likewise, there is clearly a role for prior experience, put perhaps it is does not have as significant a role to play as it does in the discovery perspective (Shane, 2000; Shane &

Venkataraman, 2000). Prior experience may certainly influence the environmental cues that are attended to by entrepreneurs (Tversky & Kahnmen, 1974), but little is known about how prior experience will influence the imagined alternatives and the link between prior experience and acts that modify the social setting. For example, one of the new pet health insurance firms, was started by a previously successful entrepreneur who had no experience in either the veterinary field or the insurance industry. At the same time this study illustrates that the process of market formation may lead entrepreneurs from different backgrounds towards mutually shared prior knowledge.

Another avenue that might provide further fruitful inquiry is an examination of the interplay between entrepreneurial motivation, information-processing, and persistence. Clearly the entrepreneurs in this study faced much more significant odds than they initially perceived, the deck was heavily stacked against them. Yet even in light of many negative signals there are those who persisted and in the process were amongst the group that resurrected the industry. The motivation to succeed was huge and the belief in the new way of structuring the product was

57 robust. One limitation of this study is that while some failed firms were observed, their failure occurred in the mid-1990s, in-depth data wasn’t available to understand why their motivation and persistence wasn’t adequate. This limitation may not be severe as from what can be gleaned from the data these firms were simply imitating VPI, and thus were unlikely attempting the effort to shift the social conversation. On the other hand, the specter of unobserved entrepreneurial entities that never made it to the firm formation stage in the 2000s, presents a data challenge that cannot be directly handled. Did these pre-firms not survive because the entrepreneurs had the wrong mix of motivation and persistence, were they trying to engage in a different social conversation, did they fail to learn from their hypothesis testing, could they not differentiate the signal from the noise, was it simply errors of execution, or was it some combination of all of these?

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Conceptualization Used Term Implications Alternative Names in This Paper Probability of all Normative and predictive Uncertainty outcomes known, utility maximization Risk effect/type of each holds Irreducible uncertainty outcome known Lotteries Probability of some or all Events are pre- Uncertainty outcomes are unknown, determined and effect/type of each knowable, but some Parametric uncertainty outcome known aspect of agent (computational & Estimation uncertainty cognitive limits) or environment (complexity) Weak uncertainty prevents full acquisition of information (i.e. Knightian Risk Ambiguity bounded rationality) Knightian uncertainty Normative and predictive learning and decision Savage’s uncertainty models based on the notion of risk don’t hold Substantive uncertainty owing to ambiguity aversion (Ellsberg Procedural uncertainty paradox, 1961) Probability of outcomes is Both risk and ambiguity Uncertainty unknown, effect/type of models don’t hold owing outcomes unknown to changeability of the Unexpected uncertainty state space (i.e. structural change). Knightian uncertainty

Fundamental Strong uncertainty Uncertainty Structural uncertainty

Radical uncertainty

Genuine uncertainty

Table 1.1. Risk, Ambiguity, & Uncertainty

Primary Case Comparative Comparative Comparative Case One Case Two Case Three Pseudo-name Toto Snowy Asta Gromit Year Founder(s) 2002 2000 (Canada) 2003 2005 Started Working 2004 (USA) on Idea Year of US Firm 2006 2007 2006 2008 Formation # of Founders 2 1 1 3 Prior Veterinary None None Extensive Extensive Experience Prior Insurance Extensive None Medium Medium Industry Experience Impetus for Extremely high Prior Getting it right Extension of Initial Concept bill for entrepreneurial the second time parent firm’s veterinary exit, looking to around product portfolio services do something new

Table 1.2. List of four firms focused on in this study

Note: Asta was the Charles’ wire fox terrier in the book and several movies starting with the “Thin Man” in the 1930s. Gromit is Wallace’s companion in several animated claymation movies created by Nick Park. From the Belgian comic books by Hergé Remi, Snowy is a fox terrier that faithfully follows Tintin in his adventures around the world. Toto, a terrier, is Dorothy’s intrepid companion in L. Frank Baum’s series of Oz children books.

Note: Although Snowy had its start in Canada, this study focuses more narrowly on its preparation and subsequent expansion into the United States. This period also corresponds with the firm’s main growth phase and acquisition of significant funding. Further, while certain aspects of the Candian operations were adaptable to the US market, many aspects required novel learning.

Table 1.3: Data Sources for Study

Group Knowledge & Sentiment Representative Data

Pet-Owners Few owners have heard of pet health insurance, “Consumers didn’t know the asymmetry of the risk and those who have often don’t understand the information, there were shifting underwriter and claims product. policies.”

Policyholder sentiment is mixed with some “I get mixed reviews on it. Reimbursements seems to be positive feedback, but also many negative at the whim of someone at the other end of the telephone experiences and hostility. … many people hesitate to get it.” Veterinarians Recognition of rising cost of care and rising cost “The American Veterinary Medical Association supports of student debt from veterinary training. pet insurance, calling such coverage ‘important to the future of the veterinary profession’s ability to provide Desire to balance providing the best care with high quality and up-to-date veterinary services” care that the owner can afford, ethical dilemma. “…it is the worst thing that ever happened to veterinary Anger at guilt by association effects from medicine” – prominent older vet available pet health insurance products. “a vet in the audience said that physician friends had told Fear of going down the same road as human him to run away, there would be massive constraints” health insurance, desire to avoid HMO system that tells Vets what they can and can’t do “…lack of understanding difference between HMO and indemnity.” Regulators & Wary of alternative solutions as current solutions “…company (VPI) has had some problems with the state Underwriters have been problematic insurance department, which has required it to increase its capital reserves.” Small, niche product that has to be regulated owing to legislation but gets passed to the low “We didn’t really want to deal with it again … the new man on the totem guy got stuck with it.” – state regulator

Awareness of prior history of claims problems “California Insurance Commissioner John Garamendi … and firm investigations in the industry recently filed charge against Veterinary Pet Insurance Co (VPI)” Insurance Perception of category as insignificant and “There was a person who walking in looking for a Industry problematic Lloyd’s representative saying he had invented a policy to insure the world against nuclear war … along with some esoteric forms of coverage like pet health insurance.” “Should aliens kidnap an earthling … more than 100,000 US citizens have taken out insurance against just this Perception of category as a joke possibility” in the same article as a discussion of pet health insurance

“Pet Insurance, for example, grabbed the headline in May Perception of category that succeeded elsewhere, when Patsy Bloom sold the company she founded 20 but for structural reasons would not in the US years ago – PetPlan – for pounds 16m to Cornhill (UK)” Economists Perception of coverage as a junk product that is “If you are really worried that someday you will have a not needed. big veterinary bill, put $50 a year away in a bank account and collect interest on it.” “This is in the junk coverage category” Assumed causal link between the creation of an insurance product an subsequent inflation in the “If everybody buys insurance we will get CAT scans for pricing of veterinary care cats and dog scans for dogs and all kinds of crazy machines for pets that nobody would ever have thought of using … and pet owners will pay for it.” Misallocation of societal resources “But Orin Kramer, an economist and consultant in Princeton, NJ, who specialized in insurance issues, says that widespread insurance for pets may have results that mirror human health care strikingly.”

Table 1.4: State of Pet Health Insurance Industry as of Early 2000s

Chapter 2: The Influence of Mentoring on Entrepreneurial Self-Efficacy and the Desire to

Become an Entrepreneur

Chapter Abstract

This study proposes and tests a model of the relationships between entrepreneurial career mentoring, traditional career mentoring, and the desire and intent to become an entrepreneur.

Career commitment, career satisfaction, and entrepreneurial self-efficacy were examined as mediators. The sample included 4,027 university alumni who provided survey data. A multi- group analysis strategy including calibration and validation samples was used to test the model.

The results support the model fit and study hypotheses. Both types of mentoring were positively related to entrepreneurial self-efficacy. However, entrepreneurial career mentoring had a positive relationship with desire and intent to become an entrepreneur while traditional career mentoring had a negative relationship. The implications of the results for mentoring and entrepreneurship research and practice are discussed.

Introduction

The choice to become an entrepreneur is a daunting notion for most individuals because they may feel uncertain if they have the personal, financial, and social resources needed to be successful. However, despite their uncertainty, individuals do intentionally choose to shift from an organizational to entrepreneurial career (Bird, 1988; Katz & Gartner, 1988; Krueger &

Brazeal, 1994). Researchers have shown that attitudes towards entrepreneurial behavior are an important predictor of intentions to become an entrepreneur (Douglas & Shepherd, 2002;

Krueger, Reilly, & Carsrud, 2000). The theory of social cognition stresses the role of self-efficacy as a primary determinant of motivation, willingness to engage, and perseverance in undertaking tasks (Bandura, 1977). Many studies of entrepreneurship have utilized self-efficacy as a predictor of intentions (Hmieleski & Baron, 2008). More specifically, studies have shown a relationship between entrepreneurial self-efficacy (ESE) and entrepreneurial career preferences (Chen et al.,

1998; DeNoble et al., 1999; Krueger, Reilly, & Carsrud, 2000; Segal, Borgia, & Schoenfeld,

2002).

Individuals contemplating career decisions often rely on their mentors for advice, encouragement, and to serve as a sounding board for their ideas (Kram, 1983). However, we know little about the impact of mentoring on individual’s intentions to engage in an entrepreneurial career, i.e. entrepreneurial career intentionality. There are three reasons for our lack of understanding about the role of mentoring in entrepreneurial career decisions. First, mentoring research has traditionally explored career advancement within existing companies. We refer to this form of mentoring as traditional career mentoring. Traditional career mentoring occurs both formally and informally within existing companies and should, at least logically, be inversely related to a decision to become an entrepreneur. Entrepreneurial career mentoring, on the other hand, is substantially different from traditional career mentoring. It consists of mentoring that encourages departure from the corporate setting and ‘transitioning’ into entrepreneurship.

A second reason for our lack of understanding about the role of mentoring in entrepreneurial career decisions is that the very notion of entrepreneurial career mentoring is conceptually confounded with more general notions of entrepreneurial mentoring. Entrepreneurial

69 mentoring includes the mentor-provided networks, relationships, expertise and assistance provided to entrepreneurs already operating within the entrepreneurial process (e.g. Deakins,

Graham, Sullivan, & Whittam, 1998; St-Jean & Audet, 2012; Sullivan, 2000). This form of mentoring is not career-decision focused, but rather focused on aiding the success of the entrepreneur and their venture. Entrepreneurial mentoring thus occurs after the decision to become an entrepreneur, while entrepreneurial career mentoring occurs before this decision.

Although these two forms of entrepreneurial mentoring may not be mutually exclusive, they differ in the types of activities and outcomes received by protéges.

Third, research shows that protégés self-esteem and general and contextual self-efficacy increase as a result of participating in mentoring relationships (Waters, McCabe, Kiellerup, &

Kiellerup, 2002). These improvements in self-efficacy are positively related to intentions to engage in related actions or behaviors (Byrne & Keefe, 2002). However, the generalizability of results of current mentoring research to entrepreneurial mentoring is limited because most studies have focused on mentoring relationships in which both the mentor and the protégé are from the same organization, i.e., traditional career mentoring. Also, studies have focused on identifying the career and psychosocial outcomes that can benefit the protégés while they remain with the current organization such as increased affective commitment and satisfaction with their current role (see

Noe, Greenberger, and Wang, 2002). In contrast, mentoring in which an individual or group provides guidance and support to a mentee’s decision to become an entrepreneur, i.e. entrepreneurial career mentoring, is likely to occur outside traditional organizational boundaries and influences the protégés intentions to leave their current organization to start a new business venture. Finally, although research suggests that mentoring enhances protégés self-efficacy, it is unknown if it further influences the desire and intent to choose an entrepreneurial career.

The purpose of this study is to examine the relationships among traditional and entrepreneurial career mentoring, entrepreneurial self-efficacy, career commitment, career satisfaction, and an individual’s desire and intention to become an entrepreneur. This contributes to our understanding of mentoring and entrepreneurship in several ways. The study offers insights into the role of mentoring by investigating entrepreneurial career mentoring; a type of mentoring that has received little research attention. Further, the outcome variable used in the study, intention to become an entrepreneur, answers calls for considering a broader range of personal outcomes in mentoring research (Kram and Ragins, 2007) and contributes to our understanding of the role of mentoring in entrepreneurial career decision-making. Finally, the study provides insight into the mechanism through which mentoring may influence intent to choose an entrepreneurial career by investigating entrepreneurial self-efficacy as a potential mediator.

Figure 2.1 presents the conceptual model for the study. Below we discuss the theoretical background for the model and the study hypotheses.

ESE-Searching + ESE-Planning Entrepreneurial + Career Mentoring + ESE-Marshaling + Desire & Intent ESE-People for + Entrepreneurship ESE-Financing Traditional Career - Mentoring +

+ Career Commitment -

Career Satisfaction

Figure 2.1: Model of Desire and Intent for Entrepreneurship

Entrepreneurial Career Mentoring and Traditional Career Mentoring

Entrepreneurial career mentoring refers to a relationship in which a senior more experienced mentor provides encouragement, guidance, and feedback to a less experienced individual in regards to the transition to an entrepreneurial position. This transition requires cognitive, skill-based, and affective learning which can be facilitated by a mentor (St-Jean &

Audet, 2012; Sullivan, 2000). The mentor provides feedback that enables the prospective entrepreneur to reflect on their actions, choices, attitude, and intended behavior (Sullivan, 2000).

The mentor may also serve as a role model to the protégé, increasing the saliency and desirability of the ‘entrepreneurial life style’ (Scherer et al., 1989; Scherer et al., 1991). The mentoring

72 relationship likely fosters entrepreneurship as an option to current employment for the protégé by increasing the salience of its potential benefits and challenges.

Hypothesis 1: Entrepreneurial career mentoring will be positively related to the desire and

intent to become an entrepreneur.

Traditional career mentoring refers to “an intense interpersonal exchange between a senior experienced colleague (mentor) and a less experienced junior colleague (protégé) in which the mentor provides support, direction, and feedback regarding career plans and personal direction” (Russell & Adams, 1997, p. 2). Congruent with the majority of mentoring research this study constrains career mentoring to mentoring that occurs in an organizational setting and is primarily focused on benefitting the organization and the individual’s career within the organization. Kram (1983) asserted that career mentors provide their protégés with career and psychological support. Career support is provided through coaching, sponsorship, protection, exposure, the assignment of challenging work, and advocacy. Psychosocial support is provided through role modeling, counseling, confirmation, and friendship. Traditional career mentoring has been shown to be inversely related to a protégé’s intentions to turnover and subsequent turnover

(Lankau & Scandura, 2002; Viator & Scandura, 1991)).

Traditional career mentoring has been advocated as a means to accelerate the process of organizational socialization, increase the retention of high performing individuals, and generate organizational commitment (Payne & Huffman, 2005). Through organizational socialization individuals develop an understanding of the organization’s goals and values leading to greater affective commitment (Griffeth et al., 2000). Repeated exposure to an organizational culture may lead to a shift in an individual’s goals and values such that they become more congruent with the espoused organizational values (O’Reilly & Chatman, 1996). Insuring congruence with organizational values and developing commitment are the mechanisms through which mentoring

73 inhibits turnover intentions. As a result, because an individual’s decision to become an entrepreneur or move to self-employment necessitates leaving the current organization it is proposed that exposure to traditional career mentoring will suppress the desire and intention to engage in this behavior.

Hypothesis 2: Traditional career mentoring will be negatively related to the desire and intent

to become an entrepreneur.

Self-Efficacy & Entrepreneurial Self-Efficacy

The concept of self-efficacy, an individual’s belief in their ability to accomplish tasks within a particular domain, has played a central role in theories of social learning and social cognition (Wood & Bandura, 1989). The expectations and motivation that arises from self- efficacy influence coping behaviors, the degree that effort will be expended, tolerance to adversity, goal setting, and the choice of actions to undertake (Bandura, 1977; Gist, 1987).

According to social cognition theory, self-efficacy is posited as a central mechanism for the enactment of human agency (Bandura, 1982, 1989, 2001).

Social cognition theory recognizes that self-efficacy is not a static trait, but rather malleable based on external and internal influences. Self-efficacy is generally conceptualized to be task or event specific, i.e., an individual can have a high level of self-efficacy in one domain, but low self-efficacy in another. This does not mean that the formation of self-efficacy in response to a particular task is confined to only that specific task. The generative capability of self-efficacy (Bandura, 1982) asserts that the formation of self-efficacy for one task can influence the formation of self-efficacy for related tasks. This influence is attenuated as the similarity in tasks declines and task independence increases.

Entrepreneurial self-efficacy (ESE) refers to an individual’s belief in their personal capabilities related to the formation of a new venture (Boyd & Vozikis, 1994). This specification of self-efficacy is based on the assumption that the entrepreneurial process involves a range of inter-related tasks that are unique to such a degree that they cannot be readily captured in a general measure of self-efficacy (Chen, Greene, & Crick, 1998).

Entrepreneurship is a multi-phase process (McGee et al., 2009; Mueller and Goic, 2003).

The phases of the process model include searching and evaluating the opportunity, developing the business concept, acquiring needed resources, and managing the venture (Stevenson, Roberts, and

Grousbeck, 1985). During the searching phase the entrepreneur develops a novel idea or identifies a market opportunity. As part of this process the entrepreneur relies on their creativity and innovativeness to explore many alternatives. The planning phase (developing the business concept & assessing required resources) involves formalizing the entrepreneurial concept into an implementable plan that fits within the entrepreneur’s abilities and goals. During the marshaling phase (acquiring needed resources) the entrepreneur acts to gain control over the resources needed to implement the business. The implementing stage (managing and harvesting the venture) is focused on managing the venture and assuring its successful growth past incubation. The implementing stage has been conceptualized as including managing people (implementing- people) and managing the finances of the business (implementing-finance). Entrepreneurs vary in the extent to which they believe they will be successful in each phase of the entrepreneurial process. As a result, it is necessary to separately consider individual’s self-efficacy for each phase of the entrepreneurship process.

Mentors likely exert their influence on entrepreneurship through influencing protégés self-efficacy (Bandura, 1982; Waters et al., 2002). Mentors may have protégés engage in activities that expose them to entrepreneurial activities and provide them with a sense of

75 accomplishment or mastery experiences. In these situations the mentor provides an outlet for the protégé to experiment in a career transition without incurring its full risk, thus increasing the likelihood of success and mitigating the negative consequence of failure. Interactions with the mentor also provide the protégé with vicarious experiences, e.g., stories, related to successful entrepreneurship. This heightens the protégé’s sense that they too will be successful if they choose to engage in entrepreneurial behaviors. Further, the encouragement and engagement of a mentor likely serves as a source of verbal and social persuasion to assure the protégé that they possess the necessary skills and attributes for success. Finally, a mentor who is able to communicate their experiences, present opportunities to the protégé, and provide feedback and assurance can generate a positive change in the protégé’s attitude toward and willingness to engage in entrepreneurship.

Entrepreneurial career mentoring will likely influence all aspects of ESE. The exposure to tasks related to the entrepreneurial process will heighten the protégé’s perception that they have the ability to engage and persevere in these domain related tasks. Also, the presence of a mentor, a supportive other who is respected and admired, enhances the protégé’s self-perceptions that they are capable of success in entrepreneurial tasks.

Hypothesis 3: Entrepreneurial career mentoring will be positively related to searching,

planning, marshalling, implementing-people, and implementing-finance dimensions of ESE.

Individuals with a high level of self-efficacy within a domain are more likely to engage and persist in tasks related to that domain (Gist & Mitchell, 1992; Chen, Gully, Eden, 2004).

Studies have demonstrated that higher levels of entrepreneurial self-efficacy are associated with increases in individual’s intentions to engage in entrepreneurial activities and behaviors (Baum &

Locke, 2004; Chen et al., 1998; Zhao, Seibert, & Hills, 2005). Although we cannot assume that the formation of intentions will necessarily lead to the career decision to become an entrepreneur,

76 intent has been shown to have a strong influence on subsequent actions (Armitage & Connor,

2001). As a result, increases in individual’s ESE will likely lead to a greater desire and intent to become an entrepreneur.

Hypothesis 4: Searching, planning, marshalling, implementing-people, and implementing-

finance dimensions of ESE will be positively related to the desire and intent to become an

entrepreneur.

In combination, Hypotheses 1, 3, and 4 suggest that the positive relationship between entrepreneurial career mentoring and desire and intent is mediated through ESE.

Hypothesis 5: The relationship between entrepreneurial career mentoring and the desire and

intent to become an entrepreneur is mediated through the searching, planning, marshalling,

implementing-people, and implementing-finance dimensions of ESE.

Career Commitment and Career Satisfaction

Career commitment refers to one’s attitude towards a profession or vocation. As such, career commitment is related to a broader range of referents than is organizational commitment (Blau,

1985). Allen et al. (2004) in a meta-analysis of mentoring notes that the most consistent benefit of mentoring is probably “the impact on affective reactions to the workplace and positive psychological feelings regarding one’s career” (p.132). These positive changes in affective reactions toward the workplace and the generation of feelings of commitment have been shown to lead to higher levels of satisfaction (Aryee & Chay, 1994). Exposure to traditional career mentoring is also likely to heighten one’s sense of capability for managing and implementing challenging undertakings. The capabilities required for engaging in more complex or managerial tasks within an organizational setting including acquiring resources and managing people and finances are similar to those required in managing a new venture. As a result, it traditional career

77 mentoring influences protégés self-efficacy related to acquiring resources and managing people and finances as well as their commitment to and satisfaction with their current career.

Hypothesis 6: Traditional career mentoring will be positively related to marshaling,

implementing-people, and implementing-finance dimensions of ESE, career commitment, and

career satisfaction.

An increase in ESE resulting from career mentoring will likely be associated with a positive increase in desire and intent to become an entrepreneur. Improvements in an individual’s believe in their capabilities to persevere in the face of challenging and uncertain tasks likely leads to beliefs that one will succeed as an entrepreneur. However, an increase in career commitment and career satisfaction should reinforce the benefits of the current position and reduce the desire to leave the current organization for an entrepreneurial career.

Hypothesis 7: Career commitment and career satisfaction will be negatively related to the

desire and intent to become an entrepreneur. Marshaling, implementing-finance, and

implementing-people dimensions of ESE will be positively related to the desire and intent to

become an entrepreneur.

In combination Hypotheses 2, 6, and 7 suggest that the negative relationship between traditional career mentoring and desire and intent to become an entrepreneur is mediated through three dimensions of ESE, career commitment, and career satisfaction.

Hypothesis 8: The relationship between traditional career mentoring and the desire and

intent to become an entrepreneur is mediated through marshaling, implementing-finance, and

implementing-people dimensions of ESE, career commitment, and career satisfaction.

Method: Sample and Procedure

A survey was administered to a large and diverse population of college-educated individuals who were in different career stages. Survey data was collected from the alumni of a large Northeastern university in the United States. Approximately 70,000 potential respondents were contacted via email to participate in the study. 5,300 participants completed the survey representing a response rate of 7.6%. An examination of educational and demographic variables revealed no potential response bias.

Because the study purpose was to examine factors that influence desire and intent to become an entrepreneur, respondents who classified themselves as an entrepreneur or self- employed were excluded. The remaining sample included 4,027 participants ranging in age from

16 to 72 (mean age =35.52). Males accounted for 64.4% of the sample, which is typical of the university’s alumni.

Measures

Entrepreneurial Self-Efficacy (ESE). ESE was assessed using a measure that focuses on the five specific tasks that entrepreneurs engage in when launching a business venture (McGee et al., 2009). The measure used a seven point Likert-type response scale (1=Disagree to 7=Agree).

Sample items included “Think of new ideas for a product or service” and “Design an effective marketing campaign for a new product or service.” The scales representing each task included searching ( = .84), planning ( = .83), marshaling ( = .83), implementing-people ( = .92), and implementing-finance ( = .93).

Career Commitment. Blau’s (1985) career commitment scale was adapted for use. Items were reworded to be more industry agnostic. The measure used a seven point Likert response

79 scale (1=Disagree to 7=Agree). Sample items included “I want a career in my current industry” and “If I could start again I would not choose this field”( = .80).

Desire and Intent for Self-Employment or Entrepreneurship (D&I). This four-item measure assessed an individual’s desire and intent for job and career, conditions that are associated with becoming an entrepreneur ( = .78). The four items included a desire to be an entrepreneur or to be self-employed, a desire for company ownership, a desire to be free from close supervision (1=Not at all important to 7=Very Important), and intention to become an entrepreneur or to transition to self-employment in the next five years (1=Not Likely to 7=Very

Likely).

Career Satisfaction. Two items were used to assess current employment satisfaction ( =

.80). The items included “How satisfied are you with your current job” and “Overall, how satisfied are you with your current career” (1=Not Satisfied to 7=Very Satisfied).

Entrepreneurial Career Mentoring. One item asked participants to estimate the amount of mentoring that they have received for starting a new business or for being an entrepreneur. A seven point Likert-type response scale was provided (1=Very little to 7=A lot).

Traditional Career Mentoring. One item asked participants to estimate the amount of mentoring that they have received in their career or field of professional employment. A seven point Likert-type response scale was provided (1=Very little to 7=Alot).

Demographics. Age and gender were collected through either the survey or via matching to university records. Age and gender were included as control variables in this study because they have both been shown to have an influence on self-efficacy, desire, and intent (Betz &

Hackett, 1981; Maurer, 2001; Wilson et al., 2007).

Analytical Strategy

A calibration and validation based data analysis strategy was used because of the large sample size and the desire to reduce problems associated with excessive model fitting (Cudeck &

Browne, 1983). Calibration and validation with a holdout sample relies on testing and fitting the model to the calibration sample. The model specified using the calibration sample is then assessed against the validation holdout sample to confirm that it fits this new data equally well.

This approach adds rigor to the data analysis while improving the generalizability of the tested model.

The data was split into a calibration sample (n=2,500) and a validation sample (n=1,527) by random assignment. All descriptive statistics and modeling leading up to the validation stages are from the calibration sample only.

Results

Table 2.1 (see end of chapter) presents the means, standard deviations, and correlations for the study variables. There was variability in the amount of traditional career mentoring and entrepreneurial career mentoring received by the study respondents. Twenty percent of respondents reported that they had received some entrepreneurial mentoring, 8.2% had received a great amount of entrepreneurial mentoring, and 71.8% had received very little or none. Also,

72.6% of respondents reported that they had received some traditional mentoring, 12.8% had received a great amount of traditional mentoring, and 14.6% had received very little or none. The values shown in Table 2.1 represent the composite average scale scores of the respective items. In the structural equation modeling analysis, the scale scores are treated as latent variables

Testing the Model

To test the study hypotheses, D&I was the dependent variable, entrepreneurial career mentoring and traditional career mentoring were independent variables, the five dimensions of

ESE, career commitment, and career satisfaction were mediators. Age and gender were included as covariates.

AMOS 19 was used to test the measurement model (Arbuckle, 2010). This model included all of the multi-item measures previously tested as well as dummy-latent variables for the single-item variables. To test model fit all latent variables were allowed to covary. The model fit was good as indicated by the relevant fit indices (χ2=3828.884, df=432, RMSEA=.056,

CFI=.926, NFI=.918).

The measurement model was respecified with regression pathways to test the study hypotheses. A nested model comparison approach was used because the model proposes that specific pathways are important for the link between mentoring and D&I. The initial model tested (Model A) included all possible regression pathways between the antecedents (age, gender, entrepreneurial career mentoring, traditional career mentoring), mediators (ESE dimensions), and outcome (DSI). This test of the full structural model yielded a model with good fit (χ2=3828.884, df=432, RMSEA=.056, CFI=.926, NFI=.918).

To examine the model specified in Figure 2.1 the paths between entrepreneurial career mentoring and career commitment and career satisfaction, and the paths between traditional career mentoring and the searching and planning dimensions of ESE were constrained to zero

(Model B). All of these paths were non-significant in the full structural model (Model A). As anticipated, a nested model comparison between Model A and Model B revealed that it provided

82 equivalent fit based on the delta CFI criteria (Δχ2=7.0549, df=4, p=.133, ΔCFI=.000).1 Model B also provided a good fit to the data (χ2=3835.943, df=436, RMSEA=.056, CFI=.926, NFI=.917).

We chose to use Model B for testing with the validation sample and for testing the hypotheses because of the invariance equivalence and it is more parsimonious than Model A.

Model Validation Using Multi-Group Analysis

We assessed the fit of the model specified in the first stage against the holdout sample. A multi-group analysis strategy was used in which the same model was tested across both samples and increasing constraints were imposed. For this analysis, Model B was specified for both the calibration and validation sample. Estimation of model fit was determined concurrently from both sets of data. A series of increasingly constraining invariance tests were imposed between the calibration and validation samples.

The two most important comparisons were tests for configural invariance and metric invariance. The unconstrained, configural fit of Model B estimated from both calibration and validation was good (Model 1) (χ2=6501.2, df=877, RMSEA=.040, CFI=.922, NFI=.911). Metric invariance (Model 2) demonstrated that the measurement loadings (i.e. the loadings of factors on items) are equivalent between the two groups. A nested comparison between these two models reveals that the models were equivalent by the delta CFI criteria (Δχ2=30.924, df=21, p=.086,

ΔCFI=.000). Structural invariance was confirmed by further constraining both the structural weights (Model 3) and the structural covariances (Model 4) equivalent between the groups.

Constraining the structural weights demonstrated equivalence (Δχ2=29.629, df=31, p=.537,

ΔCFI=.000), as did constraining the structural covariances (Δχ2=21.902, df=10, p=.016,

1 The delta CFI method is useful in situations where the sample size is very large and delta χ2 becomes overly sensitive. Cheung and Rensvold (2002) recommend that if the change in CFI between two nested models is less than or equal to 0.01 then the models can be treated as if they are invariant. 83

ΔCFI=.000). These results showed that Model B was configural, metric, and structural invariant between the calibration and validation sample. Table 2.2 (see end of chapter) provides the details of the calibration and validation.

Demonstrating invariance of the model between the calibration and validation samples showed that the model fits equally well in the sample in which model exploration occurred as well as a sample that was not involved in model specification. To examine the proposed hypotheses it was necessary to examine the regression parameters, the total effects, and the indirect effects. Because the potential of non-normality exists, particularly in regards to the indirect effects and the gender dichotomy, we used bootstrapping to derive empirical estimates of parameters. Tables 2.3 and 2.4 (see end of chapter) report the bootstrapping estimates from

Model 4 (Model 4 was used because it provided the most invariance constraints). The estimates and confidence intervals were derived from 1,000 bootstrap samples and represent unstandardized coefficients (Preacher & Hayes, 2008).

Results of Hypothesis Testing Age and gender, the control variables, had significant relationships with D&I (b=-.011,

95% CI -.018, -.007 and b=.434, 95% CI .313, .542, respectively). Hypotheses 1 and 2 described the relationship between entrepreneurial career mentoring and traditional career mentoring and

D&I. Both Hypotheses 1 and 2 were supported. We found a positive and significant relationship between entrepreneurial career mentoring and D&I (b=.515, 95% CI .457, .576). Also, as hypothesized the effect of traditional career mentoring on D&I was negative and significant (b=-

.131, 95% CI -.161, -.098).

Hypotheses 3 predicted a positive relationship between entrepreneurial career mentoring and the searching, planning, marshaling, implementing-people, and implementing-finance

84 dimensions of ESE. Hypothesis 3 was supported. As shown in Table 2.3, we found significant and positive relationships between entrepreneurial career mentoring and searching (b=0.306, 95%

CI. .271, .341), planning (b=0.377, 95% CI. .334, .415), marshaling (b=0.282, 95% CI. .251,

.313), implementing-people (b=0.115, 95% CI .088, .139), and implementing-finance (b=0.231,

95% CI. 0.184, .270) dimensions of ESE.

Hypothesis 4 predicted a positive relationship between D&I and each of the ESE dimensions. Hypothesis 4 was partially supported. Searching and planning dimensions of ESE were significantly related to D&I search, b=.339, 95% CI .227, .411; planning, b=.330, 95% CI

.224, .429), but the other three ESE dimensions were not (see Table 2.3). These results extend to the mediation hypothesis (Hypothesis 5). Hypothesis 5 was only partially supported. Only searching (b=.104, 95% CI .081, .130) and planning (b=.124, 95% CI .083, .164) dimensions of

ESE were significant. The complete mediation results are presented in Table 3.4.

Hypotheses 6 suggested a positive relationship between traditional career mentoring and marshaling, finance, and people dimensions of ESE and career commitment and career satisfaction. Hypothesis 6 was partially supported. Although the relationships between career mentoring and marshalling (b=.026, 95% CI .013, .042) and people (b=.031, 95% CI .018, .047) dimensions of ESE were significant, the path between career mentoring and the finance dimension of ESE was not. The estimates were positive and significant for career commitment

(b=.141, 95% CI .119, .167) and career satisfaction (b=.224, 95% CI .195, .253). Similarly,

Hypothesis 7 was only partially supported. Marshaling, people, and finance dimensions of ESE were not significantly related to D&I, but both career commitment (b=-.142, 95% CI -.212, -.060) and career satisfaction (b=-.118, 95% CI -.182, -.063) were significantly and negatively related to

D&I. Estimates of the indirect effects between traditional career mentoring and D&I provide partial support for Hypothesis 8. The indirect effects through the three ESE dimensions were not

85 significant, but we found a significant and negative indirect effect for both career commitment

(b=-.020, 95% CI -.032, -.009) and career mentoring (b=-.027, 95% CI -.042, -.014). Figure 2.2 shows the individual path coefficients and the full path diagram with the respective path coefficients.

.277

.306 ESE-Searching .339 Entrepreneurial .377 ESE-Planning Career .330 Mentoring .282

.115 ns .231 ESE-Marshaling .028 Desire & Intent ns ESE-People -.038 for .026 .031 .031ns Entrepreneurship Professional .015ns ESE-Financing Career Mentoring -.142 .141

Career Commitment -.118 .224

-.084 Career Satisfaction

Figure 2.2: Path Diagram with Coefficients Note: All coefficients are significant at p<.05, except those marked with ns.

Discussion Overall, this study contributes to our understanding of how mentoring can influence career change involving entrepreneurship. Specifically, we found that traditional career mentoring increases protégés satisfaction and commitment to their current career while entrepreneurial career mentoring increases the desire and intent to become an entrepreneur. This suggests that it is necessary to abandon a “one size fits all’ approach when considering how mentoring influences career change, at least in the case for individual’s considering a career change to entrepreneurship.

Our results for ESE as a mediating variable adds to our understanding of the mechanisms through which mentoring can influence desire and intent to become an entrepreneur. Traditional career mentoring influenced the two dimensions of ESE that deal with marshaling resources and managing people. However, neither of these dimensions a significant influence on desire and intent to engage in entrepreneurship. Entrepreneurial career mentoring had a significant influence on entrepreneurial desire and intent through searching and planning efficacy, dimensions related to entrepreneurial ideation. This suggest that the career and psychosocial functions that mentors need to provide protégés to entice them to change to entrepreneurial careers do not completely overlap with those typically investigated in mentoring research, i.e., serve as a sounding board, provide guidance about ideas, help develop their business concept, and identify necessary resources.

One practical implication of the results is that a specific type of mentoring, entrepreneurial career mentoring, can enhance individual’s desire and intent to become an entrepreneur. Initiatives designed to work with aspiring entrepreneurs to get their new ideas and products to the market through starting new businesses may benefit from providing them with access to an experienced entrepreneur who can serve as a mentor. The mentor should provide

87 guidance on how to successfully make the transition to an entrepreneurial career, increasing protégés motivation to search and plan for an entrepreneurship career.

Study Limitations & Future Research

The results and conclusions of this research should be interpreted with caution for several reasons. First, cross-sectional data was used to test the study hypotheses. The causal directions proposed in the model could be reversed. For example, it could be that individuals who possess high levels of desire and intent for self-employment may actively seek out mentoring. The directionality issue is partially addressed by examining the mediating effect of self-efficacy. It is logical to assume that mentoring improves self-efficacy, which in turn, drives desire and intent. It is far less likely the case that desire and intent directly lead to changes in self-efficacy. Also, increases in entrepreneurial self-efficacy are undoubtedly influenced by other factors and experiences, which in turn, increase desire and intent to become an entrepreneur.

Another potential limitation of the cross-sectional data is the presence of common method variance. The study attempted to address this in the design of the survey by separating related constructs across the survey and by changing the response format across question clusters.

As a robustness check the models presented in this study were examined with the inclusion of a

CFA marker test (Richardson et al., 2009). This test was conducted with a four-item personal/lifestyle orientation scale that prior research has shown is not related to entrepreneurial status. Results revealed no statistical detection of either congeneric common method variance

(ΔCFI=0.000) or non-congeneric common method variance (ΔCFI=-0.002).

Second, we used single-item measures to assess entrepreneurial and traditional career mentoring. Single-item measures are often presumed to have unacceptably low reliability that cannot be estimated. However work by Wanous & Reichers (1997) and Gardner, Cummings,

Dunham, and Pierce (1998) has shown that single-item measures may actually exhibit acceptable

88 reliabilities. In this study we sought to focus on participants’ overall exposure to two types of mentoring, a unidimensional report, rather than on varied characteristics of the mentoring process or individual perceptions of the mentoring experience (Wanous & Hudy, 2001). An analysis using reliability correction for these two single items leads to the same results of significant and non-significant outcomes. This analysis was conducted with both mentoring indicators attenuated as if their reliabilities were .70 (Coffman & MacCallum, 2005).

Eby et al. (2008) provided a meta-analysis of the effects of mentoring across a broad range of outcomes. The form of mentoring examined by Eby et al. (2008) is similar to what we consider as traditional career mentoring in this study. Eby et al. (2008) provides a basis for comparison of how the effects found in our study compare to prior studies. If we assume that intent to become an entrepreneur is similar to intent to withdraw from the current organization, a comparison of the effect size of traditional career mentoring on desire and intent (b=-.131) falls within one standard deviation of the corresponding effect size for withdraw intentions documented in Eby et al. (2008) (rc=-.10, s.d.=.03) and within the 95% C.I. for this meta effect (-

.15 to -.05). A similar relationship is shown for the relationship between traditional career mentoring, career commitment and career satisfaction. This similarity of the effect sizes supports the integrity of the single-item measures used in this study.

A third limitation is that this study only examined intentions rather than the actual decision to become an entrepreneur. Also, we did not examine the relationship between mentoring and success as an entrepreneur. Prior studies have shown links between entrepreneurial self-efficacy and willingness of individuals to persevere in entrepreneur settings (Baum & Locke,

2004). Future research needs to explore how changes in self-efficacy influence changes in subjects’ motivations, and how events that occur during the entrepreneurial process influence self-efficacy (Forbes, 2005). While venture performance depends on many factors (Hmieleski &

Baron, 2008), prior research has shown a tentative link between supportive mentoring and venture success (Deakins, Graham, Sullivan, & Whittman, 1998). Future research should explore what types of mentoring are needed during particular stages of venture formation, what forms of mentoring are best suited to individuals’ styles, and how mentor-protégé relationships influence the mentoring process (Ragins, Cotton, & Miller, 2000).

Considering the different influence of traditional career mentoring and entrepreneurial career mentoring on desire and intent to become an entrepreneur suggests that future research needs to examine the impact of antecedents that increase ESE but reduce the desire and intent to pursue an entrepreneurial career. This is especially important for understanding how organizations can motivate individuals to engage in entrepreneurial activities that benefit the firm, but do not encourage the employees to leave to start their own businesses. Such antecedents might involve participation in specific type of mentoring programs as well as communities of practice, work-related projects, development and training activities, and rewards and recognition for creative and innovative ideas. Also, further conceptual development is necessary to better define and understand the nature of entrepreneurial career mentoring and its relationship to the larger career mentoring literature. Significant questions remain regarding the nature of entrepreneurial career mentoring and the role of family, friends, associations, trade groups, and government programs.

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Mean SD ECM TCM ES EP EM EIP EIF CC CS D&I Entrepreneurial Career 1.49 1.04 n/a Mentoring (ECM) Traditional Career 3.19 1.74 .234** n/a Mentoring (TCM) ESE Search (ES) 5.13 1.15 .276** .060** 0.84 ESE Plan (EP) 4.19 1.42 .294** -.002 .567** 0.83 ESE Marshall (EM) 4.94 1.24 .298** .086** .615** .679** 0.83 95 ESE Imp People (EIP) 5.55 1.03 .182** .059** .431** .459** .609** 0.92 ESE Imp Finance (EIF) 4.75 1.50 .172** .000 .242** .511** .388** .439** 0.93 Career Commitment (CC 4.83 1.26 .027 .170** .069** -.033 .112** .062** -.019 0.80 Career Satisfaction (CS) 5.35 1.44 .049** .216** .057** .001 .115** .103** .035 .527** 0.78 Desire & Intent (D&I) 3.92 1.39 .292** -.074** .403** .404** .342** .218** .213** -.126** -.128** 0.78

Table 2.1 Means, standard deviations, and correlations among study variables

Note: N=2500; *p<.05, **p<.01. Scale reliabilities are presented on the diagonal. ESE= Entrepreneurial self-efficacy.

Model df Δdf Χ2 Δ Χ2 p CFI ΔCFI NFI RMSEA SRMR 1-Configural 877 6501.2 .922 .911 .040 .0474 Invariance 2-Measurement 898 21 6531.5 30.294 .086 .922 .00 .911 .039 .0478 Weights 3-Structural 929 31 6561.1 29.629 .537 .922 .00 .910 .039 .0483 Weights 4-Structural 939 10 6583.0 21.902 .016 .922 .00 .910 .039 .0484 Covariance

Table 2.2: Multi-Group Analysis of Invariance between Calibration and Validation Models

Note: Each model is nested in the prior model directly above it

Antecedent Outcome beta 95% LLCI 95% ULCI Entrepreneurial ESE Search .306* .271 .341 Career Mentoring ESE Plan .377* .334 .415 ESE Marshall .282* .251 .313 ESE Imp People .115* .088 .139 ESE Imp Finance .231* .184 .270 Desire & Intent .277* .220 .335 Traditional ESE Marshall .026* .013 .042 Career Mentoring ESE Imp People .031* .018 .047 ESE Imp Finance .015 -.009 .036 Career Commitment .141* .119 .167 Career Satisfaction .224* .195 .253 Desire & Intent -.084* -.115 -.052 Age ESE Search .007* .003 .011 ESE Plan .019* .015 .022 ESE Marshall .006* .003 .010 ESE Imp People .015* .013 .018 ESE Imp Finance .021* .017 .026 Career Commitment .005* .002 .010 Career Satisfaction .014* .009 .020 Desire & Intent -.018* -.023 -.013 Gender ESE Search .135* .053 .217 ESE Plan .041 -.047 .133 ESE Marshall .007 -.064 .077 ESE Imp People -.086* -.144 -.035 ESE Imp Finance .099 .011 .186 Career Commitment -.001 -.086 .089 Career Satisfaction .213* .101 .316 Desire & Intent .393* .294 .499 ESE Search Desire & Intent .339* .272 .411 ESE Plan Desire & Intent .330* .224 .429 ESE Marshall Desire & Intent .028 -.199 .166 ESE Imp People Desire & Intent -.038 -.135 .066 ESE Imp Finance Desire & Intent .031 -.024 .086 Career Commitment Desire & Intent -.142* -.212 -.060 Career Satisfaction Desire & Intent -.118* -.182 -.063

Table 2.3 Regression Parameters for Model 4

Note: *p<.05. LLCI = lower limit of confidence interval, ULCI = upper limit of confidence interval, ESE= Entrepreneurial self-efficacy.

Antecedent Mediating Pathway beta 95% LLCI 95% ULCI Entrepreneurial ESE Search .104* .081 .130 Career Mentoring ESE Plan .124* .083 .164 ESE Marshall .008 -.034 .048 ESE Imp People -.004 -.016 .007 ESE Imp Finance .007 -.005 .021 Traditional ESE Marshall .001 -.003 .005 Career Mentoring ESE Imp People -.001 -.005 .002 ESE Imp Finance .000 .000 .002 Career Commitment -.020* -.032 -.009 Career Satisfaction -.027* -.042 -.014

Table 2.4: Indirect Effect for Model 4

Note: *p<.05. Desire and intent to become an entrepreneur is the outcome for the indirect effects. ESE= Entrepreneurial self-efficacy.

Chapter 3: Tensions between Theory and Data: Integrating Subjective Interpretations in a

Bayesian Structural Equation Modeling Examination of Entrepreneurial Self-Efficacy

Chapter Abstract

Recent advances using small-variance priors in conjunction with MCMC estimation, termed Bayesian structural equation modeling (BSEM), have the potential to create a new paradigm in scale development, measurement modeling, and structural testing in covariance based structure modeling (CSM). However, theoretical and statistical considerations have been raised about BSEM which need to be addressed before BSEM is adopted by scholars. This article aims to discuss these concerns, provide an overview of the method, and develop guidelines on how to best utilize BSEM in order to realize its full potential. Using a large dataset, this article employs a BSEM approach to validate a multidimensional measure of entrepreneurial self- efficacy. This example illustrates how this technique can be used to address complex measurement structures. Drawing on factor analytic theory, important issues and the appropriate application of the technique along with guidelines are presented and discussed. At a more fundamental level this article illuminates the tension between pragmatic subjective interpretations of data that give precedence to theory and view models as imperfect simplifications of complex phenomenon, as opposed to positivist views that more heavily weight the voice of the data and view the potential models that generate this data as knowable, finite entities.

Introduction The development and dissemination of new statistical techniques often creates challenges. New statistical methods sow promising theoretical seeds through their potential to (1) allow for the testing of hypotheses in ways more consistent with the underlying theory, (2) relax unrealistic model assumptions, and (3) allow for previously un-testable relationships to be empirically examined. On the other hand, the introduction of new approaches may reap a crop of dubious findings if they are inappropriately utilized (MacCallum, Edwards, & Cai 2012; Muthén

& Asparouhov 2012b). Furthermore, the capabilities of new techniques may clash with existing methodological paradigms, creating frustration for writers, reviewers, and editors alike.

The use of Bayesian approaches to conduct covariance structure modeling2 (CSM) for multidimensional reflective constructs3 (MRCs) provides an exemplar of these tensions. A key benefit of Bayesian CSM is that the measurement model of MRCs can be more realistically specified by allowing for the estimation of cross-loadings (Muthén & Asparouhov 2012a) as opposed to specifying that each observed variable has only one loading as in most CFA models, which Browne (2001) terms a perfect cluster solution (PCS). Failure to incorporate these cross- loadings can result in poor model fit and inflated correlations between the underlying latent variables (Marsh et al. 2009, 2010). On the other hand, Bayesian CSM also allows researchers to model all correlated unique variances (CUVs) between observed measures, which can raise

2 We utilize the term covariance structure modeling (CSM) throughout this manuscript rather than the acronym SEM to avoid confusion given that many scholars use terms such as CFA to refer to the measurement model and SEM to refer to (potentially) the exact same model with regression pathways in place of correlations between the latent variables (Anderson & Gerbing 1988). The term CSM encompasses both measurement and structural models.

3 Law, Wong, & Mobley (1998:741) define a multidimensional construct as a construct which contains “a number of interrelated attributes or dimensions and exists in multidimensional domains.” MRCs occupy an important place in several organizational and managerial theories, examples of which include the “Big 5” (Marsh et al. 2010), relational norms (Heide & John 1992), and organizational citizenship behavior (Organ 1988).

100 important concerns about the theoretical meaning of such solutions (Rindskoff 2012) and the value of such models given they may fit any data structure (MacCallum 2003). Furthermore, the practice of specifying cross-loadings is contradictory to the current reflective measurement paradigm developed in the 1980s by marketing scholars (Anderson, Gerbing, & Hunter 1987;

Gerbing & Anderson 1988) and propagated today by well-accepted textbooks such as Hair et al.

(2010). According to this perspective “A necessary condition for assigning meaning to estimated, latent variables is that the measures posited as alternative indicators of each construct be acceptably unidimensional,” (Anderson et al. 1987: 435). Thus a paradox exists between the emerging Bayesian CSM paradigm4 and the current paradigm, which we term the “CFA paradigm.” This paradox (Poole & Van de Ven 1989) requires illumination and resolution.

This manuscript addresses the dialectic created by the emerging paradigm of Bayesian

CSM. Manuscripts (Kaplan & Depaoli 2012; Muthén & Asparouhov 2012a; Scheines, Hoijtink,

& Boomsma 1999), books (Lee 2007), and technical reports (Asparouhov & Muthén 2010;

Dunson, Palomo, & Bollen 2005) provide an extensive overview of the mathematical foundations and implementation of Bayesian approaches to conduct Bayesian CSM. However important theoretical questions remain unaddressed, such as (1) when and why are some cross-loadings permissible, (2) for cross-sectional research, is their utility in correlating the unique variances of the manifest variables, and (3) what latitude should be granted to the analyst in specifying priors on structural relationships? Addressing these questions gets to the core principles of the philosophy of modeling (MacCallum 2003), measurement (Bagozzi 2011), and theory testing

(Roberts and Pashler 2000).

4 It should be noted that the “Bayesian CMS paradigm” is based off of the work of Thurstone (1947) and classic psychology. While the utilization of Bayesian techniques for model estimation in a CSM context are novel, the underlying philosophy of measurement represented in this approach shares a kinship with the earliest solutions to latent variable measurement models. As in many settings, we can clearly see the ‘swing of the pendulum’ between paradigms representing contrasting viewpoints (Kuhn, 1977)

101

The remainder of this manuscript is structured in several sections. First, a brief overview of the Bayesian and frequentist approaches to statistical inference is provided for the unfamiliar reader. This discussion is utilized as a springboard to reconcile the clear inconsistencies between authors such as Browne (2001), Marsh et al. (2009) and Muthén & Asparouhov (2012a) who argue that manifest variables can exhibit multiple large factor loadings and work by Anderson et al. (1987), Gerbing & Anderson (1988), and Hair et al. (2010) that strongly advocates for the requirement of unidimensionality. Drawing on works concerning measurement theory (Bagozzi

2007, 2011; Edwards & Bagozzi 2000), the theoretical meanings of cross-loadings are examined in order to provide a more nuanced, theoretically driven rationale for when and why cross- loadings are permissible. A review of the benefits and cautions for incorporating informative priors when conducting Bayesian CSM is next presented, and these issues are empirically demonstrated using survey data from a study of entrepreneurship. The discussion section provides additional reflection on these issues, in particular noting that (1) when studying multidimensional constructs, obtaining measures with high reliability should take precedence over unidimensionality and (2) for cross-sectional data, researchers are urged to avoid specifying a prior distribution to correlate manifest variable unique variances as it appears that freeing these parameters is almost assured to guarantee perfect model fit and obscure potentially important relationships. Lastly, the manuscript concludes by noting the subjective nature of models and the importance of recognizing all models are incomplete representations of a complex reality.

Bayesian Modeling of Covariance Structures

The two greatest differences between Bayesian and frequentitst approaches are their treatment of the nature of population parameters5 and the incorporation of prior information. In

5 Frequentists view parameter values as fixed in the population, and through random sampling, estimates of these parameters are obtained. Armed with parameter estimates and associated standard errors, inferences 102 regards to the first difference, Frequentists view parameter values as fixed in the population and through random sampling estimates of these parameters are obtained. In Bayesian inference population parameters are not fixed but rather are treated as random variables with their own distributions (Bolstad 2007). This distinction results in interpretational differences between frequentist confidence intervals and Bayesian credibility intervals (Yuan & MacKinnon 2009).

The second difference between frequentist and Bayesian statistics is that the former is agnostic to prior information concerning population parameters whereas the latter allows for the incorporation of prior information. In Bayesian statistics prior information about the distribution of parameters is updated with new data to produce a posterior distribution of the parameters. This can be written following Scheines et al. (1999, 38) as:

푝(푦|휽)푝(휽) 푝(휽|푦) = ∝ 푝(푦|휽)푝(휽) (1) ∫ 푝(푦|휽)푝(휽)푑휽

In Equation 1 푝(휽|푦) is termed the posterior distribution of 휽, which represents the distribution of parameters incorporating the data and 푝(푦|휽) is the distribution of the data given parameters, which is equivalent to the likelihood of the parameter estimates given the data, denoted 퐿(휽|푦). Equation 1 shows that this likelihood is weighted by the prior distribution of the

are made whether parameters differ from a specified value, most often zero, in the population. As an example, imagine that a sample regression coefficient’s estimate is 0.35 with a standard error of 0.10, resulting in a 95% confidence interval of [0.15, 0.55]. Counter-intuitively, this confidence interval says nothing about the probability that the true population parameter falls in this range. Rather, the interpretation of a confidence interval is based on the infinite repeated sampling framework: “If we were to draw repeated samples from the population and calculate the confidence interval many times, we would assume that 95% of these intervals would contain the parameter. Given that this interval does not contain zero, we will infer that the parameter differs from zero in the population.”

In Bayesian inference population parameters are not fixed but rather are treated as random variables with their own distributions (Bolstad 2007). As such, statistical inference is more straightforward in that a Bayesian credibility interval is probabilistic in that a 95% credibility interval of [0.15, 0.55] can be interpreted by saying “there is a 95 percent probability that the population parameter falls within this interval.” Such inference is more in line with how most individuals think about statistical results.

103 parameters, 푝(휽), and lastly ∫ 푝(푦|휽)푝(휽)푑휽 is termed the marginal distribution 6. Equation 1 is often rewritten as:

푝(휽|푦) ∝ 퐿(휽|푦)푝(휽) (2)

The symbol ∝ can be interpreted as “proportional to” indicating that the posterior distribution of the parameters given the data is proportional to the likelihood of the parameters weighted by the prior information about the distribution of the parameters. This ability to incorporate prior information about parameters is a defining characteristic and advantage of

Bayesian statistics. As noted by Bolstad (2007, xxi) “The ‘objectivity’ of frequentists statistics has been obtained by disregarding any prior knowledge about the process being measured…

Throwing away this prior information is wasteful of information… Bayesian statistics uses both sources of information.” Apart from incorporating researchers’ intuition and earlier findings, prior information improves the precision of posterior estimates (Yuan & MacKinnon 2009). In situations where little information is available, diffuse (also called non-informative) prior distributions can be specified, in which case point estimates (i.e. the mean) of posterior distributions approach maximum likelihood estimates in large samples (Dunson et al. 2005).

Despite these advantages, as noted by Kaplan & Depaoli (2012), a primary limitation in the application of Bayesian methods to complex models has been the challenge of developing the posterior distribution of parameters given the mathematical intractability of high dimension integrals. With increased computing capabilities and the development/refinement of Markov

Chain Monte Carlo (MCMC) techniques utilizing the Gibbs sampler (Geman & Geman 1984)

6 The challenge of analytically solving for 푝(휃|푦) is that this can require high dimensional integration for the marginal distribution. However, the use of Markov Chain Monte Carlo simulation allows for us to make “draws” from the posterior distributions of interest. Such draws are often conducted using the Gibbs sampler because while the distribution of the posterior may not be known, the conditional distribution of the posterior given the data and other model parameters is known (Asparouhov & Muthén 2010; Dunson et al. 2005; Edwards 2010).

104 combined with their incorporation into user-friendly software (WinBugs, Mplus, R), estimating complex models using Bayesian approaches has become accessible to applied researchers who are not trained statisticians/mathematicians. Readers interested in a technical discussion of

MCMC and the Gibbs sampler implemented in CSM are referred to Asparouhov & Muthén

(2010), Dunson et al. (2005), Edwards (2010), and Lee (2007).

For the applied researcher studying MRCs, the increased availability of user-friendly software to conduct Bayesian inference affords increased modeling capabilities including estimating models that would not be identified using frequentist estimators (Scheines et al. 1999).

Furthermore the incorporation of prior information, particularly knowledge of the magnitude of cross-loadings, allows for more realistic modeling of covariance structures (Muthén &

Asparouhov 2012a). However to apply this approach one must reject the current paradigm of unidimensional measurement as espoused by Gerbing & Anderson (1988) and Hair et al. (2010).

Hair et al. (2010: 674) state “the existence of significant cross-loadings is evidence of a lack of construct validity” and recommend that “You…should not run CFA models that include…cross- loadings…evidence that a significant cross-loading exists also shows a lack of discriminant validity” (675). However, Marsh et al. (2009, 447) have a diametrically opposite view:

“Although there are advantages to having “pure” items that load on a single factor, this is clearly not a requirement of a well-defined, useful factor structure, nor even a requirement of traditional definitions of “simple structure” in which nontarget loadings are ideally small relative to target loadings but not required to be zero.” This presents a paradox (Poole & Van de Ven 1989) that must be rectified if the flexibility of Bayesian CSM in management research is to be accepted and subsequently leveraged.

105

Rectifying the ‘old’ and ‘emerging’ measurement paradigms

Is unidimensionality a required property of manifest variables as argued by Anderson et al. (1987) and Hair et al. (2010), or has this paradigm emerged owing to a fundamental misunderstanding of the relationship between RLVs and their manifest indicators? To answer this question, two issues must be examined: (1) the nature of the relationship between a RLV and observed measures and (2) the source of the theoretical meaning of a RLV. In regards to the first point, auxiliary measurement theory provides the logical rationale for why a given RLV should be related to an observed measure (Edwards & Bagozzi 2000). 7 Auxiliary measurement theory arises from the RLV’s theoretical definition, which provides a parsimonious, precise meaning for the concept represented by this RLV (Bollen 1989). In other words, one can think, “given this

RLV’s definition, does it make sense that this RLV would be related to this observed measure?”

In regards to the second point, the theoretical meaning of the RLV arises from the concept the RLV represents (Bollen 2011). Importantly as Bagozzi (2007, 2011) notes, RLVs contain “surplus” meaning beyond the empirical meaning created through their operationalization. This recognition that RLVs have surplus meaning is contradictory to the

7 There is some disagreement about the relationship between a RLV and an observed indicator. Traditionally scholars have stated that the RLV “causes” variation in the observed indicator (Bollen an Lennox 1991; Howell, Breivik, & Wilcox 2007). Bagozzi (2007, 2011), on the other hand, avoids the use of causal language and instead states that correspondence rules indirectly connect a latent variable with observed measures:

“an auxiliary hypothesis concerning theoretical mechanisms, empirical criteria, and a rule connecting the mechanisms and criteria. A correspondence rule is a complex conceptualization consisting of a logical expression, some theoretical meaning, and some empirical meaning. It bridges the abstract meaning that should be specified formally in the latent variable and the observational meaning residing in the empirical operations defining the manifest variable…A connection thus exists between a latent variable and manifest variable, but the connection is an indirect one. Latent variables are not identified or defined by manifest variables; manifest variables provide only part of the meaning of the latent variable.”

While this distinction does not impact the core arguments we make in this manuscript, due to this contention in measurement theory, we avoid the use of causal language when describing the relationship between a RLV and an observed measure.

106 aforementioned Anderson et al. (1987: 435) excerpt because their statement implies that a RLV’s meaning is a direct function of its indicators8. One way to understand that a RLV has surplus meaning is that the exclusion of one or more measures does not alter the theoretical definition of said latent variable (Bollen and Lennox 1991), which is contradictory to the argument that an

RLV’s theoretical meaning is a function of its indicators9.

Apart from unidimensionality not being a requirement to establish the meaning of a RLV, the requirement that measures be unidimensional is not consistent with the original literature on factor analysis, particularly Thurstone’s (1947) work on simple structure. As noted by Browne

(2001), Thurstone’s argument for simple structure was that the matrix of factor loadings needed to be easily interpretable, with the core requirement being that a manifest variable not be allowed to load on all latent factors. In other words, if there are m latent factors, then each manifest variable can have at most m – 1 “large” loadings. If all items have only one large loading then the matrix of factor loadings is said to have a “perfect cluster solution” (Browne 2001), which is the most restrictive form of simple structure, but not the only form of permissible simple structure. An example of a more complex pattern of loadings is Holzinger & Swineford’s (1937) bi-factor model whereby each manifest variable is posited to load onto a general factor and one

8 Further evidence that these scholars adopt the conceptualization that a RLV’s definition is a direct function of its indicators can be seen in Gerbing & Anderson’s (1988: 189) statement that “Factors in an exploratory analysis do not correspond directly to the constructs represented by each set of indicators because each factor from an exploratory factor analysis is defined as a weighted sum of all observed variables in the analysis.”

9 To provide more detail, imagine that there is a series of 10 indicators that operate as indicators for a given RLV, but for parsimony, the researcher only includes a set of four on the measurement instrument. According to the Anderson et al. (1987), the theoretical meaning of this RLV would differ from study to study depending on the series of indicators selected, which is actually consistent with the determination of meaning for a formative construct (Howell et al. 2007). However, as noted by Bagozzi (2011) and Edwards (2011) one advantage of using RLVs over formative constructs is that they are generalizable beyond the empirical operationalization in a given study precisely because indicators are interchangeable. Furthermore, Anderson et al.’s (1987) conceptualization of an RLV’s definition being a function of its indicators is inconsistent with the ontological necessity that an RLV exist independent of its measures (Borsboom, Mellenbergh, & van Heerden 2003)

107 specific factor. Bi-factor models, which are not permitted by Gerbing & Anderson’s (1988) and

Hair et al.’s (2010) logic, have recently seen increased application in the education and medical literature as reviewed by Reise (2012). A second example of a more complex structure is

Thurstone’s “box” data, where the majority of measures exhibit a complexity greater than one10

(Browne 2001).

A natural question following from the above arguments is when are more complex factor structures permissible? An answer can be found by drawing on the aforementioned concept of auxiliary measurement theory: when two RLVs have related theoretical domains, such as with

MRCs, cross-loadings would be permissible given there is theoretical justification for such loadings (Marsh et al. 2009). On the other hand, if two RLVs have distinct theoretical domains, then large-magnitude cross-loadings would suggest that the measures are not operating as the researcher expects, which requires refinement of the current measurement theory. However, there is no definitive statistical test whether cross-loadings are permissible; rather, such cross- loadings must be justified by the researcher and their inclusion or exclusion is ultimately a subjective judgment. Thus, like most statistical urban legends (Spector 2006), there are “kernels” of truth underlying the use of unidimensional measure. Complexity one measures, and consequently perfect cluster solutions, encourage researchers to formalize measurement instruments and develop scales with an easily-interpretable simple structure and avoid theoretically-inconsistent cross-loadings and increase the parsimony of measurement models

(Asparouhov & Muthén 2009).

An important lesson can be taken from examining the emergence of the CFA measurement paradigm: readily-available and easily-implemented software, combined with

10 An observed variable’s complexity refers to the number of large loadings that it is posited to have on the set of m latent variables. As noted by Browne (2001) and Myers, Ahn, & Jin (2013) complexity one measures have dominated applied applications in the social sciences.

108 unchallenged recommendations, can lead to a paradigm inconsistent with the theory underlying the statistical procedure. In his review of the history of factor rotation Browne (2001) notes that when factor rotation was completed by hand researchers were able to include his/her subjective knowledge at each step of the process. He then notes that with the advent of computers:

“First of all the time consuming aspect of factor rotation was eliminated. Rotating factor matrices became quick and easy. Secondly the opportunity for use of background knowledge concerning the variables during the rotation process was eliminated. Some regarded this as a desirable change of direction to greater objectivity, since the rotation process was no longer influenced by the investigator and depended only on the choice of rotation algorithm.” (113) Browne (2001), citing Yates (1987), notes that the ability of rotation algorithms to recover perfect cluster solutions rather than more complex structures such as Thurstone’s (1947)

“box” data was an important cause of applied researchers seeking measures that exhibited a complexity of one. Asparouhov & Muthén (2009) further note that with the development of CFA in the late 1960s and popularization of LISREL in the 1970s, combined with the limitations of

EFA for conducting structural analysis, researchers shifted to utilizing CFA models. Perfect cluster solutions were preferable given the necessity in CFA of a priori specifying the factor structure, which when augmented with the recommendations of Anderson et al. (1987), Anderson

& Gerbing (1988), Gerbing & Anderson (1988), and the popular text by Hair et al. (2010), resulted in a “perfect storm” that has led us to the CFA paradigm. As House (1996: 333, 346) articulated: “Clearly, social scientists need to escape the boundaries of prevailing paradigms and to question prevailing wisdom,” lest we “…get trapped in our measurement system and apply it blindly to new questions for which it is inappropriate.” However, by the same token, clearly articulating key benefits and pitfalls of the Bayesian CSM paradigm is essential to help researchers best leverage these techniques.

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Incorporation of prior information into Bayesian CSM

Given the unfamiliarity of many researchers with the specification of prior distributions in Bayesian analysis, this section provides an overview of the benefits and cautions of specifying prior distributions on three sets of parameters: (1) factor loadings, (2) correlated unique variances, hereafter noted as CUVs, and (3) structural coefficients. Attention is limited to these parameters because decisions about specifying priors for these parameters will be necessary when conducting the structural analysis of any model, whereas specifying priors on parameters such as direct effects between independent variables and the intercepts of endogenous manifest variables, as in

Muthén and Asparouhov (2012a), are less-frequently seen in management research.

As a broad overview, a few words about priors in the context of CSM need mentioning.

First, as noted by Yuan and MacKinnon (2009: 306) “A strong prior that dominates the likelihood usually is not recommended. The inference should be mostly driven by currently observed data.”

Thus, as expanded on in the discussion, it is the responsibility of the researcher to disclose what priors, both diffuse and informative, were specified and the robustness of the results to the specification of different priors. Second, different types of parameters are specified to have different prior distributions. Typically intercepts, factor loadings, and structural regression coefficients are specified as using normal distributions, an individual variance parameter (such as a unique variance or error variance) is specified using an inverse gamma distribution, and covariance/correlation matrixes are specified using an inverse Wishart distribution (Asparouhov

& Muthén 2010). Third, as summarized in Table 3.1 (see end of chapter), there are distinct theoretical implications for specifying informative priors for these different parameter types.

Factor Loadings

The use of informative priors on factor loadings represents one of the most valuable features of Bayesian CSM relative to frequentist CFA approaches. When conducting traditional 110

CFA with complexity one indicators, researchers specify one freely-estimated factor loading and fix the remaining loadings at zero for a given manifest variable. However, the requirement that loadings be fixed to zero in the population is often unrealistic as Asparouhov & Muthén (2009:

398) note “although technically appealing, CFA requires strong measurement science that is often not available in practice. A measurement instrument often has many small cross-loadings that are well motivated by either substantive theory or by the formulation of the measurements.” In

Bayesian CSM, researchers can more realistically model the hypothesized factor structure by specifying informative priors for expected small loadings and use diffuse priors for expected large loadings (Muthén & Asparouhov 2012a). For example, imagine a researcher has data for a multidimensional construct measured by 12 items each assumed to have a complexity of one that serve as indicators for three RLVs. Assuming that the variances of the latent variables are fixed to one for identification, Figure 3.1 Panel A presents the traditional CFA specification with large loadings denoted with a ‘?’ and remaining loadings fixed to zero whereas Figure 1 Panel B shows a Bayesian CSM specification with large loadings also denoted as a ‘?’ (i.e., diffuse priors are utilized) and remaining loadings specified using an informative normal prior11 with mean equal to zero and a standard deviation of 0.1012.

11 The reader will notice that by frequentist standards Panel B is not identified because with the factor correlations freely estimated, at least two zero loadings would need fixed in each column (Bollen 1989). However as noted by Dunson et al. (2005) identification in a Bayesian framework is distinct in that identification means that the posterior distribution can be updated from the data. Using informative priors can allow for this updating to occur, which provides an explanation for why Bayesian methods can allow for the estimation of CSMs that would have previously been unidentified (Muthén & Asparouhov 2012a; Scheines et al. 1999).

12 Given that the latent variables are assumed to have a variance of 1.0, the factor loadings are standardized in this setting. Specifying that the standard deviation of the factor loading is 0.10 would indicate that the loading is expected to have a value between -0.20 and 0.20, which we believe can be considered inconsequential from a substantive perspective.

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Panel A: Traditional CFA Model Panel B: Bayesian Model

? 0 0 ? 푁(0,0.1) 푁(0,0.1) ? 푁(0,0.1) 푁(0,0.1) ? 0 0

? 0 0 ? 푁(0,0.1) 푁(0,0.1)

푁(0,0.1) ? 푁(0,0.1) 0 ? 0

0 ? 0 푁(0,0.1) ? 푁(0,0.1)

0 0 ? 푁(0,0.1) 푁(0,0.1) ?

0 0 ? 푁(0,0.1) 푁(0,0.1) ? 0 0 ? 푁(0,0.1) 푁(0,0.1) ? ( ) 0 0 ? (푁(0,0.1) 푁(0,0.1) ? )

Figure 3.1: Specification of the factor loadings for a perfect cluster solution (PCS) CFA model where small loadings are fixed to zero (Panel A) and a Bayesian model where small loadings are given a small-variance normal prior with mean equal to zero and a standard deviation equal to 0.1.

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As seen in Figure 3.1, the Bayesian specification using informative priors provides a more realistic representation of the measurement model in that all small loadings need not be fixed to a value of zero. Apart from providing a more realistic representation of the measurement model, two statistical benefits arising from the use of small-variance cross-loadings are (1) improved model fit and (2) reduced correlation between the latent variables13. Furthermore, imagine that Panel B is estimated on pilot data and the researcher finds that three loadings which had been a priori hypothesized to be small are large enough to be of substantive concern. Upon collecting a subsequent dataset these three loadings could be freely-estimated using diffuse priors14. In addition, information concerning the magnitudes of the cross-loadings from the pilot study could be incorporated in subsequent research to improve the precision of parameter estimates.

However the use of small-variance priors for factor loadings presents a drawback by imposing increased methodological and theoretical challenges as summarized in Table 3.1 Panel

A. First, if the researcher specifies informative priors with too large a variance (i.e. standard deviation of 0.30 rather than 0.10 in our example) the model may not converge (Muthén &

13 Marsh et al. (2009, 2010) note that many measurement instruments such as the Big Five personality scale display unacceptable fit when modeled using CFA models where all indicators have a complexity of one. Furthermore, in order to recreate the underlying covariance matrix when there are multiple small cross- loadings, the correlations between the latent variables will be inflated, which can threaten the discriminant validity of the constructs.

14 Concurring with MacCallum et al. (2012), we caution researchers that simply freeing these three parameters in the original sample is akin to utilizing modification indices to conduct a specification search (MacCallum et al. 1992). It should be noted there are differences between this Bayesian approach and using modification indices to conduct specification searchers using ML estimation. As noted by Steiger (1990), modification indices, also termed Lagrangian multipliers (Bollen 1989), are calculated assuming that the remainder of the model is properly specified. As a result, the resulting change in the χ2 from freeing a given parameter based in its modification index may not equal the reported value of the modification index. Thus, as noted by Muthén & Asparouhov (2012a) the Bayesian approach using small- variance priors provides a more complete picture than using ML modification indices. However, it is important to remember that the researcher is still modifying the originally hypothesized model as noted by MacCallum et al. (2012).

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Asparouhov 2012a). In other words, this approach is unlike exploratory factor analysis (EFA) where the analyst lets the data speak for itself, given that a degree of prior knowledge about loadings and the number of factors is required. Second, as of this point the use of normal priors for factor loadings has been assumed, which have a range of minus infinity to positive infinity.

However, a researcher could knowingly or unknowingly specify different distributions (i.e. uniform) on small loadings that “force” hypothesized small loadings to be small, which underscores why reporting priors is critical. Lastly, as argued in the previous section, practically- significant cross-loadings need to have a theoretical justification, which places an increased burden on the researcher to defend such cross-loadings.

Correlated Unique Variances (CUVs)

15 As shown in Table 3.1 Panel B, specifying informative priors for the 횯훿 matrix to correlate the unique variances (UVs) of the observed variables has different theoretical implications from using informative priors for factor loadings. It should be noted this is not referring to instances where researchers allow for individual CUVs due to a priori expectations such as longitudinal designs (Bollen 1989) or item-wording effects such as reverse scoring

(Marsh et al. 2010), which are permissible. Rather, this refers to the specification of an informative inverse-Wishart distribution for 횯훿 that allows for all parameters in this matrix to be estimated as in Muthén & Asparouhov (2012a).

To understand the distinct theoretical implication from modeling 횯훿, it is important to define the statistical meaning of a CUV. Imagine item X1 has a positive loading onto Factor1

15 횯훿is the LISREL notation for the symmetric matrix for the unique variances of the observed variables on the diagonal and the correlations between the unique variances on the off-diagonal. In the factor analysis model 횯훿is assumed to be a diagonal matrix, which means that the partial correlation between the observed variables, holding the latent factors constant, is assumed to be zero (MacCallum & Tucker 1991).

114 and X4 has a positive loading on Factor2. If Factor1 and Factor2 are positively correlated, a positive CUV indicates that the model under-estimates the correlation between X1 and X4 whereas a negative CUV indicates that the model over-estimates this correlation (Gerbing &

Anderson 1984). If Factor1 and Factor2 are negatively correlated, a positive CUV indicates that the model over-estimates the correlation whereas a negative CUV indicates that the model under- estimates this correlation. Given this statistical meaning the first cause for apprehension is that, unlike cross-loadings, there is theoretical ambiguity as to the cause of CUVs. For example, without a priori expectations, is the CUV between X1 and X4 the result of a method factor such as social desirability bias, is there another underlying latent variable that is not modeled such as a second order-factor (Gerbing & Anderson 1984), or is the CUV the result of sampling error

(Muthén & Asparouhov 2012a)? Compounding matters, when 횯훿 is estimated using an informative prior the researcher has to contend with p(p-1)/2 CUVs which can create severe interpretational challenges when many of these CUVs are large16. Rindskoff’s (2012: 338) statement is apropos to summarize this problem in the context of Muthén & Asparouhov (2012a) analysis of a male and female sample of the Big Five where there were 17 and 37 significant

CUVs:

“If I were a personality researcher, I do not think I would be happy with a “Big 5 plus moderate to small 27, give or take 10” theory. If there are supposed to be five factors, then the number of failures to fit the model (by adding extra correlations) should be small, and either their numerical value should be small or there should be a theoretical explanation for why these residuals are correlated. Of course, this theoretical explanation would be post hoc (if it were known ahead of time, the model would have included the expected parameters.) In this case, the theoretical explanation would be tentative and would need to be corroborated on a different data set.”

16 Given that statistical significance is a function of sample size, we believe that a focus on the magnitude of the CUVs is more important than whether they are statistically significant. For example, in a large sample a CUV of 0.10 may be significant whereas in a small sample a CUV of 0.20 may not be significant, even though the CUV of 0.20 is more important from a practical standpoint.

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A second reason for apprehension with the estimation of 횯훿 is that, as will subsequently be demonstrated, it appears that estimation of this matrix may result in outstanding model fit for any specified model. For example in Muthén & Asparouhov (2012a) when the authors estimate

17 횯훿 the hypothesized structure of the Big Five fits perfectly based on the PPC fit criteria.

Furthermore, all factor loadings specified with diffuse priors exhibited large loadings whereas small loadings specified with zero mean and small variance informative priors were near zero.

The concern with this procedure is that if the estimation of 횯훿 will result in perfect model fit even with model misspecification, then the model has little value. MacCallum (2003: 131) captures this well in regards to the flexibility of models by stating “In practice, if a highly flexible model fits observed data well, support is still weak, because the model would fit a wide range of data well. On the other hand, if an inflexible model were found to fit well in an empirical study, support for that model would be stronger. If two models were found to fit equally well, we should prefer the one that is less complex or flexible” and further notes “the evaluation of a given model should take into account the capacity of the model to fit a wide array of data. And models should be devalued to the extent that they are able to achieve good fit to nearly any data” (133).

17 PPC stands for posterior predictive checking, which is a common way to establish fit for Bayesian CSM models. Essentially, the PPC is calculated at each k iteration of the MCMC whereby a discrepancy function is calculated for the parameter estimates from the kth iteration and the observed data and at the same iteration a simulated dataset of the same size as the sample data is generated from the specified model and the parameters estimated in the kth iteration. A discrepancy function is also calculated for this simulated dataset given the model parameters. A PPC confidence interval not containing zero indicates that the discrepancy function for the observed data given the parameters fits better than the simulated data given the parameters (Asparouhov & Muthén 2010).

The posterior distribution of the predicted data can be mathematically from Kaplan & Depaoli (2012) as:

푝(푦푟푒푝|푦) = ∫ 푝(푦푟푒푝|휃) ∗ 푝(휃|푦)푑휃

As they note, given that 푝(휃|푦) is proportional to 푝(푦|휃) ∗ 푝(휃), PPC incorporates both uncertainty about the model parameters and the data. As before, the core idea underlying PPC is that the replicated data should closely match the observed data, and if not, this indicates a problem with the model. However, it is important to note that PPC does not take model parsimony into account, unlike the Bayesian and Deviance Information Criteria (BIC and DIC).

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Structural Regression Parameters

Regression parameters between observed or latent variables represent a “middle ground” of sorts with equally-weighted benefits and cautions for using informative priors as summarized in Table 1 Panel C. The benefit of specifying informative priors is that the incorporation of information from previous studies (Bolstad’s 2007) allows for more precise estimates of regression parameters (Yuan & MacKinnon 2009) and allows the researcher to incorporate his/her knowledge of the substantive area (Rindskopf 2012). This use of prior information is especially valuable in studies with small sample sizes given this prior information can drastically improve the precision of the estimated posterior (Yuan & MacKinnon 2009)

At the same time, it is important for researchers to recognize that the results from study to study are rarely interchangeable due to different samples, operationalizations of measures, and included covariates. As a result prior information should be discounted by using a larger variance when specifying informative priors (Yuan & MacKinnon 2009). A second challenge exists when theory predicts that variable X should have a stronger impact on Y than variable M does on Y.

While such tests are critical to allow for theory pruning (Leavittt et al., 2010) and improve the precision of our theorizing (Edwards & Berry 2010), researchers face the challenge that they can

(1) let the data speak for itself by using diffuse priors for these regression parameters or (2) specify informative priors to incorporate the theory’s predictions whereby the X to Y parameter has a larger mean than the M to Y parameter. The clear tradeoff is that by using the informative priors the researcher may “force” the model to say what the theory predicts, but at the same time, incorporation of prior knowledge is a core benefit of Bayesian CSM (Kaplan & Depaoli 2012).

Methodology - Research Setting

As part of a larger scale study on the antecedents and consequences of entrepreneurship a questionnaire-based survey was administered to the alumni of a large US university in the 117 summer of 2011. Among the measures collected was a multi-dimensional entrepreneurial self- efficacy scale developed by McGee et al. (2009). The concept of self-efficacy has played a central role in theories of social learning and social cognition (Wood & Bandura 1989). Self- efficacy can be adequately summarized as one’s belief in one’s ability to accomplish tasks within a domain. The expectations and motivation that arises from an individual’s self-efficacy have an influence on that individuals’ coping behaviors, expended effort, adversity tolerance, goal setting, and choice of actions (Bandura 1977; Gist 1987). When self-efficacy is used to appraise individuals’ belief in their personal capabilities related to the formation of a new venture, it is further delineated as entrepreneurial self-efficacy (abbreviated as ESE) (Boyd & Vozikis 1994).

This specification of self-efficacy is based on the assumption that the entrepreneurial process involves a range of inter-related tasks that are unique to such a degree that they cannot be readily captured in a general measure of self-efficacy (Chen, Greene, & Crick 1998).

McGee et al.’s development of an ESE scale specified a five-factor PCS-CFA solution comprising the dimensions of search, plan, marshal, implement-finance, and implement-people.

This scale conceptualizes the process of entrepreneurship as a multi-staged life cycle. Stevenson,

Roberts, and Grousbeck (1985) proposed a process model that separates new venture creation into multiple phases: evaluating the opportunity, developing the business concept, acquiring needed resources, and managing the venture. During the searching phase (evaluating the opportunity,

Stevenson’s et al.’s term) the entrepreneur develops a novel idea or identifies a market opportunity. As part of this process the entrepreneur relies on their creativity and innovativeness to explore many alternatives. The planning phase (developing the business concept & assessing required resources) is focused on formalizing the entrepreneurial concept into an implementable plan that fits within the entrepreneur’s abilities and goals. During the marshaling phase (acquiring needed resources) the entrepreneur acts to gain control over the resources needed to implement the business. The implementing stage (managing and harvesting the venture) is focused on 118 managing the venture and assuring its successful growth past incubation. The implementing stage has been conceptualized involving both an aspect of managing people (implementing-people) and managing the finances of the business (implementing-finance).

A CFA analysis presented in their original article showed acceptable fit (CFI= .96, TLI=

.95, RMSEA= .06) with good factor loadings (range 0.70-0.92). However, a pressing concern was that the inter-factor correlations showed several extreme values (range 0.55-0.94, median= 0.70).

These high factor correlations raise the concern that items might be cross loading and that the discriminant validity of the proposed factors was not robust. The new data collected as part of this large-scale study provided the perfect opportunity to contrast the utility of the ICS-CFA approach with a Bayesian approach to measurement modeling, in the context of a complex multi-factored measure.

Data Collection

Approximately 70,000 potential respondents were queried for participation via email, with 7,891 participants completing the survey instruments (a response rate of ~11.3%). As this study is primarily exploratory it was elected to drop any respondent who did not totally complete the ESE scale rather than to rely on an imputation strategy. This election was made as imputation in a Bayesian context is a unique area of inquiry that this article will not attempt to address (see

Rubin, 1996). This list-wise deletion strategy reduced the sample to 6,306 respondents. In order to focus the measurement model on a consistent population, it was elected to remove those individuals who reported that they were either retired, unable to work, or were already an entrepreneur or self-employed. This resulted in a final sample size of 4,041 respondents. For analysis purposes two random samples of 500 participations was drawn from this final sample, in order to create sample sizes more in line with average study populations. Note the results presented here are relatively consistent across sample sizes ranging from 200-500.

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CFA Analysis

The first step in the analysis was to fit a CFA model to the data to determine if the originally hypothesized perfect cluster solution displayed acceptable fit and discriminant validity.

The raw data were used as input into Mplus Version 7. (Muthén & Muthén 2012) To provide comparability to the majority of published studies, the default ML algorithm was used for estimation18. The standardized factor loadings and inter-factor correlation for this model are displayed in Table 3.2 (see end of chapter).

Table 3.2 indicates that while all items have high-standardized loadings on their respective constructs, overall model fit is highly suspect. Specifically, the CFI (0.900) is well below the 0.95 recommendation (Hu & Bentler 1999), the RMSEA (0.100) point estimate indicates unacceptable fit based on Browne & Cudeck’s (1992) guidelines, and the SRMR is large at 0.075. An examination of the inter-factor correlations reveals concerns about discriminant validity of the RLVs given the large correlations (r > 0.60) between search & plan, plan & marshal, and marshal & implement people19. Finding that this specification of ESE displays unacceptable fit illustrates Marsh et al.’s (2010) contentions that many MRCs are difficult to model using the standard PCS-CFA model. This provided the opportunity to explore the use of different approaches to Bayesian CSM to successfully model ESE.

Bayesian Analysis – Measurement Model

18 Mplus has several “robust” estimators including the MLR estimator that applies the Sattora-Bentler correction to the χ2 test static to address nonnormality and utilizes a sandwich estimator to calculate the standard errors of the estimated parameters (Muthén & Muthén 2012).

19 It should be noted that the correlations between the LVs reported in Table 3.2 are substantially less than those reported in McGee et al.’s (2009) original article.

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Given the overlap of the theoretical definitions of the facets of ESE, a Bayesian model was developed similar to Figure 3.1 Panel B where all posited zero loadings were modeled using informative priors with a mean of zero and a standard deviation of 0.14120. Since the setting of the prior is sensitive to the scale of the model, all observed variables were standardized prior to the analysis, which is permissible given that the model is scale free21. The hypothesized large loadings were specified using Mplus’ default diffuse normal prior. Estimation was completed using the default Gibbs sampler. Convergence was evaluated through examination of the PSR, evaluating trace plots of parameters, and evaluating the autocorrelation plots of parameters. The median values for the estimated parameters are reported below in Table 3.3 (see end of chapter).

One of the disadvantages of Bayesian CSM is that model fit indices are not as developed, i.e. fit statistics such as CFI and RMSEA are not available (Levy 2011). Based on the PPC criteria, model misfit is suggested as the PPC confidence interval does not contain zero [231.2,

339.1], with the model’s DIC = 20,344 and BIC = 21,003. However, as argued by Gelman

(2003) and Levy (2011), PPC can be viewed more as a diagnostic tool than as a measure of model fit per se. Under this philosophy, the PPC for the model where cross-loadings were estimated fits better than the model (results not reported) where degenerate priors22 with a value of zero were utilized, resulting in a PPC confidence interval of [654.5, 750.8].

20 In Mplus normal priors are specified using the mean and variance; for simplicity we set the variance at 0.02, which corresponds to a standard deviation of 0.141.

21 Scale free means that the results from estimating a given model will not change if the observed data is transformed in a linear fashion (Cudeck 1989). The ML estimator is scale free whereas other estimators such as ULS (unweighted least squares) are not scale free (Bollen 1989). In the context of all of our present models we are not imposing parameter restrictions such as equality constraints which allow us to linearly transform the raw data from its original metric to conduct the analysis. Cudeck (1989) provides a more in-depth examination of the topic of this issue.

22 Degenerate priors do not have a distribution and are a fixed point (MacCallum et al. 2012). As such, estimating the original measurement model using degenerate priors with a value of zero is equivalent to estimating the model reported in Table 3.1 using the Gibbs sampler rather than maximum likelihood. In other words, this is a PCS-CFA model estimated in a Bayesian framework.

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Owing to the complexity of this measurement model, i.e. five factors with nineteen observed measures, it is not surprising that the proposed solution fails to totally replicate the data even when allowing for cross-loadings given that all models are to some degree incorrect

(Cudeck & Henly 1991; MacCallum 2003). While fit statistics such as CFI and RMSEA are unavailable in a Bayesian framework, a proxy for SRMR can be calculated for this measurement model. However, in Bayesian CSM SRMR is actually a distribution (Levy 2011), but Mplus does not yet have the capability of calculating SRMR during MCMC iterations to develop this distribution. Given this limitation in this study SRMR is calculated using the median values for the parameters, which we term pseudo-SRMR (pSRMR). Details about the calculation of pSRMR are provided in Appendix C. Using the pSRMR to evaluate model fit, the model with degenerate priors demonstrates poor fit with a pSRMR = 0.075 (equivalent to the ML-CFA model) whereas for the model with cross-loadings the pSRMR = 0.028, indicating that the model with cross-loadings better replicates the observed data correlation matrix.

Returning to Table 3.3, calls attention to two findings. First, the correlations between the

LVs are smaller, which is to be expected (Marsh et al. 2009; Muthén & Asparouhov 2012a) and reduces concerns about discriminant validity. The average correlation is 0.518 and the largest correlation is 0.714. Second, there is evidence that there are three cross-loadings (S3 on Plan, P1 on Search, and P4 on Marshall) that have practical significance (i.e. > 0.30) and warrant further examination. Examining the wording of these items in the McGee at al. scale it becomes apparent that this might be a model in which items should be expected to show theoretically meaningful cross-loadings. The wording of item S3: “Design a product or service that will satisfy customer needs and wants” (McGee et al., pg. 978) would appear to refer to both the constructs of

Search (i.e. identifying a new opportunity or market) and Plan (formalizing the entrepreneurial concept). Likewise, Item P1: “Estimate customer demand for a new product or service” is clearly related to Plan for a new business, but may also be considered by some to be a central aspect of 122 identifying an opportunity (Search). Lastly, P4: “Design an effective marketing/advertising campaign for a new product or service” requires the ability to Plan, but the act of engaging in efficient action can also be tied to Marshal (acquiring needed resources).

Based on reasoning these three important cross-loadings it was elected to conduct the analysis a second time with these three cross-loadings freely estimated. This was accomplished by replacing the respective zero mean, small variance informative priors with diffuse priors.

Results in Table 3.4 reveal that freeing up these loadings allows the model to place larger weights on them; in each case the freely estimated loading is greater than in the previous model. In terms of model fit, the models are quite similar with the PPC, BIC, and pSRMR being nearly identical.

However, importantly, there is an improvement in the discriminant validity of the LV correlations. The average correlation is 0.483 and the largest correlation is 0.632 as compared to the prior Bayesian model with values of 0.518 and 0.714. This substantial improvement leads to the specification of this model as the final measurement structure.

In order to avoid the possibility that the previous results are an outcome of sample specific characteristics, a calibration/validation strategy was implemented per the recommendations of MacCallum et al. (2012). The validation was completed by drawing a second independent sample of 500 observations. At this time multi-group analysis is not yet available in Bayesian CSM (it can be partially implemented through mixture models, but that is beyond the scope of this article). Nonetheless the results in Tables 3.5a & 3.5b reveal that the measurement model with three complexity two indicators fits both samples in a roughly equivalent manner.An alternative Bayesian approach to address the misfit of the PCS-CFA model is to apply a technique not possible using ML approaches. This approach allows for the estimation of the off-diagonal entries of the 횯훿 matrix, through specification of an informative prior. Estimation of 횯훿 is not possible using ML due to the fact that the model would have negative degrees of freedom, but is possible in Bayesian approaches due to the difference 123 between frequentist and Bayesian identification (see Endnote x). In order to estimate 횯훿 one can follow Muthén & Asparouhov’s (2012a) example of specifying an inverse-Wishart prior distribution for 횯훿 where the parameters of this distribution are ~IW(I, p+6), given p is the number of observed measures.

In order to understand the implications of estimating 횯훿, a measurement model was estimated where all observed measures were assumed to have a complexity of one, small cross- loadings were permitted, and 횯훿 was freely estimated. This model is equivalent to the one previously presented in Table 3.3, with the addition of the estimation of all CUVs. The results from this model are reported in Table 3.6.

This model displays outstanding fit judged by the PPC criteria; the 95% PPC confidence interval contains zero, indicating the replicated data closely matches the sample data, which is not surprising given the enormous increase in the number of parameters. The pSRMR value further indicates excellent fit (pSRMR=0.018). However, examination of the factor loadings reveals an important concern: with the CUVs estimated evidence that S3 and P4 have a complexity greater than one is not apparent, which would lead one to conclude on a different measurement structure.

Furthermore, the outstanding model fit raises the concern highlighted earlier: can estimation of

횯훿 essentially allow the researcher to obtain acceptable fit for the model he/she desires? To check this concern, the PCS-CFA model where all cross-loadings were specified to have degenerate priors was run, with the addition of allowing for the estimation of 횯훿. These results

(not reported) lend credence to this concern: the PPC confidence interval contains zero [-60.0,

54.1] and the pSRMR for this model is outstanding (pSRMR = 0.013).

Bayesian Analysis – Structural Model

Having explored issues in regards to the use of priors for measurement models specified in a Bayesian CSM context, attention is now turned to their use in relation to structural pathways. 124

In order to demonstrate the previous observation that informative priors can improve parameter estimation, or when misused can produce corrupted results, three example are presented. In each case the complexity two measurement model specified in Table 3.4 is utilized for the structure of

ESE. Since prior research has shown that self-efficacy (Bandura, 1991) is predictive of an individual's intent to engage in an activity, a simple structural model is tested in which the five dimensions of ESE are used to predict an individual's desire and intent to become an entrepreneur

(measured by four items). It should be noted that the result presented here should not be interpreted as research results (several important covariates have been excluded in order to simplify the example), but rather serve to illustrate the principles of specifying structural priors.

Table 3.7 presents results from specifying this model with diffuse priors, informative priors, and inappropriate priors. The first two of these are used to present a hypothetical case in which data collected from a pilot or pre-study is used to develop informative priors for subsequent estimation of a second sample. Using the first group of 500 observations, the structural model was estimated with diffuse priors as such priors are most appropriate when there is little prior guidance on potential effects sizes or the researcher wishes to let the data speak the loudest. The parameters estimates derived from this estimation and the standard deviation of these estimates were then used to generate informative priors for estimating the same model in the second group of 500 observations. In-line with Yuan & McKinnon's (2009) recommendation to inflate the standard deviation specified for the prior a value of 150% of the observed standard deviation was used.

Examining the results of estimating the structural model in group 2, it can be seen that the inclusion of the informative priors leads to improvement in the precision of the parameter estimates for the structural regressions. While the median values are different, which would be expected since they are from a new sample, the 95% C.I.s on are roughly 73% the width of the same intervals estimated in group 1with diffuse priors. Note that this is not just an effect of being 125 a different sample, if these same priors were used for estimating the original set of observations a similar, if not superior, improvement in the width of the 95% C.I.s would be noted.

The last column in Table 3.7 presents an example in which inappropriate priors have been used in the context of a structural model. This example shows that if a researcher believed that only the last two dimensions of ESE (implement-people and implement-finance) should be predictive of desire and intent, that specification of strong priors can be used to force the desired results. In this case near-degenerate priors were specified for the first three regression pathways and informative priors with large means were specified for the desired significant pathways.

These strong priors dominate the data, and lead to the radically skewed results. An examination of the model comparison statistics reveals that this model provides a dramatically inferior fit in comparison to the prior two examples. However if this comparison was not provided, or the fact that strong informative priors were used was not revealed, a reader may not be aware of the sleight-of-hand going on here. While this example is clearly contrived it should highlight the caution necessary in using informative priors.

Discussion

The increased availability and usability of Bayesian techniques for modeling covariance structures, especially MRCs, could represent a watershed methodological moment for management researchers similar to the development and diffusion of ML-based CSM during the

1970s and 1980s. Bayesian approaches afford researchers a degree of flexibility previously unseen, but as with all new statistical approaches, such flexibility can come at a cost. The

Bayesian CSM paradigm allows for, and indeed advocates for, the specification of measurement models for MRCs that are not permitted in the current CFA measurement paradigm. This manuscript has sought to address these tensions by rectifying the differences between the

Bayesian and CFA measurement paradigms along with highlighting the benefits and cautions 126 associated with Bayesian CSM pertaining to the specification of informative priors on factor loadings, estimation of CUVs, and structural pathways. From the results of the empirical analysis, in this section two key findings are described in more detail: (1) having reliable measures trumps unidimensional measures when modeling MRCs and (2) estimating all CUVs using informative priors should be limited to a diagnostic role and not included as standard practice until this approach is subjected to further simulation analysis and inquiry.

Reliable Measures over Unidimensional Measures (Within Reason)

The reliability of observed measures can be thought of as the proportion of variance of the observed measure explained by the set of m latent variables23 (Bollen 1989). Numerous benefits exist for using highly-reliable measures. First, as analytically demonstrated by

MacCallum & Tucker (1991) and shown empirically via simulation in MacCallum et al. (2001), the primary driver of model misfit arises from items having low reliability24. Second, holding sample size constant, there is a greater probability of factor extraction when items have a high

23 In CSM there are three sources of variance for an observed measure: (1) common variance (termed communality) explained by the latent variable, (2) specific variance, which is variance associated with an individual item, and (3) error variance, which arises from imperfect measurement. We will define common 2 2 2 2 variance as 휎퐶 , specific variance as 휎푆 , and error variance as 휎퐸 . The total variance of an item (휎푇 ) is the sum of these three sources of variance.

The reliability of an observed measure is the ratio of the common and specific variance over the measure’s total variance. However, in most CSM applications specific variance is unknown, and thus specific 2 variance and error variance are summed together in what is termed unique variance (휎푈). This unique variance is the diagonal entry on the 횯훿 matrix. As such, the statement that an item’s reliability can be 2 2 found as 휎퐶 /휎푇 is an underestimate of the true reliability as the specific variance is not included in the numerator (Bollen 1989).

24 Two important assumptions in the common factor model are (1) unique variances are uncorrelated and (2) there is no correlation between the latent factors and unique variances (MacCallum & Tucker 1991). However, these assumptions apply to the population model; when fitting the common factor model to a sample covariance or correlation matrix, these assumptions are unlikely to hold due to sampling variability. Given that a low reliability of an item implies that the elements of the vector of unique variances will be large, violating these assumptions when there is low reliability is more serious when there is high reliability.

127 reliability (MacCallum et al. 1999). Third, high reliability items reduce the negative consequences of multicollinearity in CSM, with Grewal, Cote, and Baumgartner (2004: 527) stating “Probably the most important safeguard against the damaging effects of multicollinearity is to make sure that all constructs are measured as reliably as possible.” Consistent with our views, Asparouhov & Muthén (2009: 430) state “One can argue that it is more important to find accurate measurements than to find a pure set of measurements.”

Despite the importance of utilizing highly reliable measures, several caveats need to be made to the argument that highly reliable measures are more important than having unidimensional measures. First, this argument is most applicable when studying MRCs given that cross-loadings are more likely to be theoretically permissible due to measures having overlap with interrelated RLVs’ theoretical definitions. Such an example occurred with the S3 and P1 measures loading onto the search and plan RLVs. As the McGee et al. scale is derived from a life- stage model of the entrepreneurial process, one would expect there to be some theoretical bleed over between constructs representing various stages. Further, according to R2 (0.58 for S3 and

0.64 for P1), the complexity two measurement structure explains a large portion of the observed variance. Given the theoretical justification for these cross-loadings, we would not want to discard these measures25 given the large R2. Second, following Thurstone (1947), no measure should load onto all m RLVs given the minimum requirement for simple structure, according to

Browne (2001), is that a measure can have at most m-1 large loadings. Third, the factor loading matrix should be easily interpretable, which is purpose of identifying a simple structure

(Thurstone 1947). Fourth, while cross-loadings are permissible, a measure should display a large

25 Hair et al. (2010: 675) contend that “evidence that a significant cross-loading exists…shows a lack of discriminant validity.” This statement is not valid as discriminant validity is the property of the RLV, not manifest measures. Their statement is consistent with the aforementioned Anderson et al. (1987) quote that the empirical operationalization of an RLV determines its definition, which has already been shown to be inconsistent with factor analytic theory (Browne 2001; Thurstone 1947).

128 loading on the RLV it is intended to empirically operationalize. Absence of a hypothesized large loading suggests interpretation confounding (Burt 1976), which implies that a disconnection between the theoretical concept the RLV represents and its empirical operationalization26. Thus, one should not interpret the argument that reliable measures are more valuable than unidimensional measures as an argument to blindly utilize observed measures with the greatest explained variance, per these above reasons.

Correlated Unique Variances: Concerns & Recommendations

As previously noted, the ability to freely estimate all CUVs using an informative inverse-

Wishart prior is unique to Bayesian CSM as such an analysis would be impossible using frequentist approaches. However, this is an example of new modeling flexibility that raises important theoretical concerns. First, an inherent assumption necessary to estimate 횯훿 and maintain that the hypothesized factor loadings and specified RLV relationships have theoretical meaning is that model misfit arises solely from sampling error and unimportant minor factors.

This assumption allows the researcher to treat 횯훿 as a “vacuum” that captures all noise arising

26 One can think of interpretational confounding as occurring when an observed measure that is posited to be influenced by a RLV does not exhibit a large loading. As noted by Bollen (1989), the first process for developing items representative of a RLV is to articulate a theoretical definition of the RLV to establishing the meaning of the concept the RLV represents. Given this theoretical definition, the researcher then develops a set of observed measures that are expected to be highly correlated given the theoretical definition of the RLV (Bollen & Lennox 1991). Interpretational confounding would occur is a measure that is expected to be highly correlated with the other items is not, and thus would display a small factor loading. The theoretical issue that arises is that the observed measure and theoretical definition of the RLV are thus disconnected because, given the definition of the RLV, the researcher would have expected the observed measure to be highly correlated with the other measures. This then raises the question of why the measure was not correlated with the other measures, which implies that the original auxiliary measurement theory is in need of modification.

A second example of interpretational confounding would occur if a researcher articulates a theoretical definition for an RLV but then operationalizes the RLV using a set of measures that are not consistent with the theoretical definition of the RLV. In this instance there is interpretational confounding even if the measures are highly correlated because there is little correspondence between the observed measures and the theoretical definition of the RLV.

129 from model error27 and sampling error (Cudeck & Henley 1991; MacCallum and Tucker 1991).

Thus estimation of 횯훿 is problematic if model misfit is the result of the researcher failing to model theoretically-meaningful effects, such as including additional structural paths between

RLVs or modeling an additional RLV. The previous examples demonstrated this in that estimating 횯훿 would lead the researcher to affirm that a PCS-CFA model fits the data perfectly.

A second problem as shown in Muthén & Asparouhov’s (2012a) Big Five example and with the models reported earlier where 횯훿 was estimated is that the PPC criterion and pSRMR are rendered “worthless” measures of fit as estimation of the 횯훿 allows the researcher’s specified model to replicate the data (covariance matrix). Appendix D further examines this issue by asking the question can estimation of the 횯훿 matrix allow a grossly ill-specified model to nonetheless recreate the underlying data. The ability of a CSM model with 횯훿 estimated to fit any data raises a concern parallel to Roberts and Pashler’s (2000: 359) caution about model fit affirming theory “Theorists who use good fit as evidence seem to reason as follows: If our theory is correct, it will be able to fit the data; our theory fits the data therefore it is more likely that our theory is correct. However, if a theory does not constrain possible outcomes, the fit is meaningless.” Their logic drove the aforementioned quote from MacCallum (2003) that a model that can fit any data has little value.

Since the fit of a model with 횯훿estimated has little practical meaning, does this technique in fact have any value? Our position is that it is too early to answer this question definitively, as further research is needed to identify what additional knowledge may be gleamed from estimating all CUVs. For the time being we strongly recommend against the use of this technique as a

27 Traditionally it is assumed that the common factor model fits perfectly in the population and thus all misfit is a function of random sampling. However, as MacCallum & Tucker (1991) and Cudeck & Henley (1991) argue, the assumption that the posited model fits perfectly in the population is untenable due to many factors such as failure to include minor factors, nonlinear relationships, and violating the assumption that all observations are homogeneous (MacCallum et al. 2001). 130 method for validating measurement structures. However with adequate caution to avoid sample- dependent alterations, one potential use for this technique might be to help identify why a measurement scale is not operating according to a priori expectations. Figure 3.2 and 3.3 present matrix-density plots of the estimated 횯훿 matrix for the model specified as a PCS-CFA structure

(Figure 3.2) and the model where all observed measures had a complexity of one and cross- loadings were specified with informative priors (Figure 3.3). These plots represent the estimated

CUVs via gray tones, with darker colors indicating larger values. As can be seen in the plot from the PCS-CFA Model, there are many large CUVs, with several concentrated clusters. All of these non-white colors indicate areas where the proposed model was unable to accurately explain the relationships between the observed measures. The plot of the model that includes cross-loadings shows that more of the underlying relationships are being explained by the model and that most of the problematic clusters have been resolved.

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1 5 10 15 19

1 1

5 5

10 10

15 15

19 19 1 5 10 15 19

Figure 3.2: Density Plot of 횯훿Matrix for the estimated PCS-CFA model. Darker off-diagonal elements indicate entries with a larger absolute value. The numbers 1-19 are in numerical order for the observed measures, thus 1 = S1, 2 = S2,...,19 = IF3.

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1 5 10 15 19

1 1

5 5

10 10

15 15

19 19

1 5 10 15 19

Figure 3.3: Density Plot of 횯훿Matrix for the estimated model where all observed measures had a complexity of one and cross-loadings were specified using informative priors. Darker off- diagonal elements indicate entries with a larger absolute value. The numbers 1-19 are in numerical order for the observed measures, thus 1 = S1, 2 = S2,...,19 = IF3.

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Limitations In this manuscript attention has been purposely focused on the theoretical meanings and implications inherent in new options provided by a Bayesian approach to CSM. In order to avoid a long side-track several additional issues have been left uncovered or passed over quickly. The actual implementation and execution of MCMC and the Gibbs sampler was left unexplored, several excellent sources are available for readers interested in the technicalities underlying these, see Asparouhov & Muthén (2010), Dunson et al. (2005), Edwards (2010), and Lee (2007).

Further examination of measurement models was restricted to cross-sectional, reflective, multidimensional scales. Alternatives to such specifications exist including the bi-factor model,

MIMCs, and various mixture models. In regards to longitudinal models the issues addressed here are still pertinent, although additional complexities are introduced. One of these issues, which is also relevant in multi-group research designs, is the role of invariance (both measurement and temporal). Muthén & Asparouhov (2013), along with other scholars, are examining issues related to the role of near-invariance. In much the same way that informative priors on cross-loadings removes the restrictive assumption of unidimnesionality, such approaches relax strict invariance assumptions.

Conclusions

Modeling covariance structures using Bayesian approaches, particularly the

BSEM technique outlined by Muthén & Asaporouhov (2012a), combined with the increased synthesis of categorical and continuous latent variable models (Muthén 2002), appears to herald the dawn of a second generation of CSM (Kaplan & Depaoli 2012).

However, to best leverage the flexibility provided by Bayesian CSM, as a community we must rectify the contradictory arguments of the Bayesian CSM paradigm and the current

CFA paradigm and establish the boundaries of modeling flexibility to ensure that models 134 remain theoretically meaningful. This manuscript has sought to address these issues by arguing that Bayesian CSM is more consistent with factor analytic theory than the current

CFA-dominated approach and articulating the theoretical implications for utilizing informative priors to estimate cross-loadings and correlated unique variances. To conclude, we would like to leave the reader with the following quotation from Cudeck &

Henly (1991: 512) which provides an elegant summary of our core points:

“In the study of mathematical models, the process of developing and justifying a

model is the most fundamental of issues, because every other feature associated

with the use of quantitative models is influenced by the final form of the

structure. Yet no model is completely faithful to the behavior under study.

Models usually are formalizations of processes that are extremely complex. It is

a mistake to ignore either their limitations or their artificiality. The best one can

hope for is that some aspect of a model may be useful for description, prediction,

or synthesis. The extent to which this is ultimately successful, more often than

one might wish, is a matter of judgment.”

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Parameter Type Panel A: Factor Loadings Panel B: Correlated Unique Variances Panel C: Regression Parameters •Ability to more realistically specify the •No clearly known theoretical benefits •Ability to incorporate prior information from measurement model given the imprecision of previous and/or pilot studies to improve the most scales by specifying small-variance •Potential benefits for diagnostics and scale precision of estimates and incorporate priors for loadings that are expected to be development, but utility unknown and not substantive knowledge Benefits small. validated.

•Ability to incorporate prior information from previous studies and/or pilot studies concerning the magnitude of loadings. •Use of priors with too large a variance for •Important to consider the equivalence of expected small loadings can result in the •Conceptual ambiguity about the theoretical previous studies to specify priors; in most

141 model not being identified. meaning of a correlated unique variance—the cases given heterogeneous samples and correlated unique variance could be the result variables, prior information should be •If utilizing a set of different small-variance of sampling error, a common method factor, or discounted priors, researchers should report which priors another underlying latent variable resulted in the best model fit and whether •Researchers can, knowingly or unknowingly, substantive conclusions about the •Can result in models with limited theoretical specify small-variance priors that "force" measurement model change depending on the meaning structural pathways to be statistically (non)- Cautions choice of prior. significant rather than letting the new data be •Potential evidence that freeing the Θδ matrix the primary driver of inference •Specification of priors with a limited range may result in perfect model fit for the (i.e. uniform) could be utilized to "force" researcher's specified model problematic loadings to fit the researcher's hypothesized model. •To our knowledge no simulation study has examined the theoretical implications for •Cross-loadings must have theoretical freeing the Θδ matrix, with current simulation justification only examining the robustness of different sampling algorithms Table 3.1: Benefits and cautions from specifying informative priors on factor loadings (Panel A), correlated unique variances (Panel B), and structural pathways (Panel C)

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PCS-CFA Model (ML) Implement Implement Search Plan Marshal People Finance S1 0.887 S2 0.942 S3 0.675 P1 0.716 P2 0.783 P3 0.763 P4 0.707 M1 0.851 M2 0.738 M3 0.781 IP1 0.880 IP2 0.848 IP3 0.851 IP4 0.777 IP5 0.789 IP6 0.736 IF1 0.913 IF2 0.963 IF3 0.817 Fit Statistics: χ2=845.0; DF=142; RMSEA=0.100; CFI=0.900; SRMR=0.075

Latent Variable Correlation Matrix Implement Implement Search Plan Marshal People Finance Search 1

Plan 0.633 1

Marshal 0.569 0.789 1

Implement 0.420 0.524 0.639 1 People Implement 0.269 0.617 0.443 0.443 1 Finance Table 3.2: PCS-CFA measurement model fitted using the ML estimator.

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Bayesian Model w/ Small Variance Cross-Loadings (Original Structure) Implement Implement Search Plan Marshal People Finance S1 0.922 0.017 -0.056 -0.028 -0.009 S2 1.007 -0.104 -0.008 0.010 0.032 S3 0.415 0.326 0.096 0.057 -0.031 P1 0.402 0.430 0.077 0.026 -0.046 P2 -0.037 0.988 -0.187 0.033 0.000 P3 -0.087 0.788 -0.025 -0.035 0.157 P4 -0.059 0.520 0.366 -0.064 -0.030 M1 0.015 0.070 0.825 -0.023 -0.009 M2 -0.057 0.092 0.655 0.077 -0.011 M3 0.023 -0.103 0.771 0.075 0.064 IP1 -0.051 -0.022 -0.067 0.969 -0.007 IP2 -0.113 0.021 0.041 0.858 0.015 IP3 -0.077 0.045 -0.113 0.955 -0.029 IP4 0.124 -0.015 0.014 0.699 0.054 IP5 0.049 -0.013 0.073 0.759 -0.061 IP6 0.045 -0.122 0.045 0.745 0.013 IF1 0.028 -0.086 0.043 0.025 0.927 IF2 0.028 -0.025 -0.003 -0.056 1.012 IF3 -0.054 0.126 -0.055 0.028 0.771 Free Para.=143; PPC= 231.2-339.1; DIC=20,344; BIC=21,003; pSRMR=0.028

Latent Variable Correlation Matrix Implement Implement Search Plan Marshal People Finance Search 1

Plan 0.586 1

Marshal 0.565 0.714 1

Implement 0.448 0.520 0.624 1 People Implement 0.259 0.589 0.404 0.466 1 Finance Table 3.3: Bayesian model with informative priors specified for cross-loadings. Factor loadings in bold were freely estimated using diffuse priors.

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Modified Bayesian Model w/ Small Variance Cross-Loadings Implement Implement Search Plan Marshal People Finance S1 0.911 0.060 -0.059 -0.039 -0.001 S2 0.984 -0.039 -0.029 -0.002 0.042 S3 0.437 0.389 0.081 0.056 -0.063 P1 0.470 0.415 0.087 0.020 -0.053 P2 0.051 0.888 -0.088 0.034 -0.014 P3 -0.017 0.689 0.068 -0.035 0.149 P4 -0.022 0.406 0.511 -0.096 -0.028 M1 0.032 0.062 0.842 -0.032 -0.018 M2 -0.040 0.082 0.671 0.069 -0.021 M3 0.025 -0.091 0.763 0.066 0.059 IP1 -0.041 -0.013 -0.057 0.951 -0.002 IP2 -0.099 0.017 0.059 0.841 0.020 IP3 -0.062 0.056 -0.106 0.942 -0.028 IP4 0.132 0.011 0.006 0.689 0.055 IP5 0.059 -0.004 0.080 0.742 -0.057 IP6 0.045 -0.102 0.039 0.731 0.020 IF1 0.016 -0.069 0.033 0.029 0.925 IF2 0.017 -0.013 -0.010 -0.049 1.008 IF3 -0.048 0.115 -0.040 0.033 0.768 Free Para.=143; PPC= 227.4-334.9; DIC=20,339; BIC=21,001; pSRMR=0.031

Latent Variable Correlation Matrix Implement Implement Search Plan Marshal People Finance Search 1

Plan 0.446 1

Marshal 0.549 0.632 1

Implement 0.429 0.452 0.618 1 People Implement 0.239 0.597 0.415 0.452 1 Finance Table 3.4: Modified Bayesian model with informative priors specified for cross-loadings. Factor loadings in bold were freely estimated using diffuse priors.

144

Group 1 Group 2

Range of Range of Latent Primary Range of Cross- Primary Range of Cross- Factor Loadings Loadings Loadings Loadings min max min max min max min max Search 0.437 0.984 -0.099 0.132 0.440 0.931 -0.110 0.155 Plan 0.389 0.888 -0.102 0.115 0.318 0.978 -0.065 0.092 Marshal 0.511 0.842 -0.106 0.087 0.503 1.050 -0.090 0.152 Imp-People 0.689 0.942 -0.096 0.069 0.650 0.976 -0.094 0.177 Imp-Finance 0.768 1.008 -0.063 0.149 0.784 1.003 -0.086 0.169

Group 1 Group 2

Free Parameters 143 143 PPC 231.2 - 339.1 227.4 - 334.9 DIC 20344 20339 BIC 21002 21001 pSRMR 0.034 0.035

Table 3.5: Comparison of Modified Bayesian model between Group 1 (calibration) and Group 2 (validation). Top portion shows the range of observed primary and cross loadings. Bottom portion shows the model fit results.

145

Bayesian Model w/ Small Variance Crossloadings & CU Implement Implement Search Plan Marshal People Finance S1 0.937 -0.005 -0.049 -0.035 -0.011 S2 0.912 -0.044 0.004 0.016 0.002 S3 0.463 0.219 0.115 0.060 0.035 P1 0.323 0.464 0.082 0.046 -0.023 P2 -0.033 0.887 -0.065 0.005 0.028 P3 -0.057 0.812 -0.033 -0.028 0.131 P4 -0.035 0.767 0.112 -0.014 -0.064 M1 0.054 0.098 0.755 0.003 -0.014 M2 -0.049 0.002 0.846 0.005 -0.008 M3 -0.013 -0.072 0.864 0.036 0.024 IP1 -0.013 -0.029 -0.037 0.907 0.005 IP2 -0.066 0.023 0.038 0.831 0.011 IP3 -0.052 -0.019 -0.037 0.903 -0.006 IP4 0.067 -0.011 0.034 0.755 0.037 IP5 0.033 0.009 0.026 0.809 -0.047 IP6 -0.007 -0.036 -0.044 0.845 -0.026 IF1 0.006 -0.040 0.008 0.037 0.906 IF2 0.006 0.020 -0.009 -0.025 0.920 IF3 -0.022 0.033 -0.012 -0.003 0.852 Free Para.=314; PPC= -58.6-58.1; DIC=20,134; BIC=21,699; pSRMR=0.018

Correlation Matrix: BSEM Model w/ Small Variance Crossloadings Implement Implement Search Plan Marshal People Finance Search 1

Plan 0.579 1

Marshal 0.545 0.669 1

Implement 0.451 0.497 0.605 1 People Implement 0.288 0.564 0.425 0.459 1 Finance Table 3.6: Bayesian model with informative priors specified for factor loadings and correlated unique variances. Factor loadings in bold were freely estimated using diffuse priors.

146

Diffuse Prior (Grp 1) Informative Prior (Grp 2) Inappropriate Prior (Grp 1) Free Para. 160 160 160 Est. Para. 126 124 254 PPC 279-397 274-394 350-1559 BIC 26095 25990 26427 DIC 25360 25252 25941 Median 95% C.I. Median 95% C.I. Median 95% C.I. Search 0.342 0.196 0.468 0.368 0.265 0.459 0.038 -0.025 0.100 Plan 0.451 0.337 0.549 0.394 0.314 0.467 0.013 -0.074 0.098 Marshall 0.351 0.217 0.473 0.368 0.283 0.447 0.042 -0.021 0.109

147 Imp-Ppl 0.248 0.118 0.369 0.221 0.125 0.316 0.502 0.402 0.588

Imp-Fn 0.350 0.216 0.464 0.250 0.147 0.344 0.680 0.504 0.762

Table 3.7: Demonstration of priors in the context of a structural model. Median parameter estimates are the regression pathways between each latent factor of ESE and the individual's desire and intent to become an entrepreneur.

147

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Appendix A: Prior History in the Pet Health Insurance Market

Study Landscape and Prior History

While this study is concerned with the processes of resource co-creation, as examined in the emergence of a particular firm within the pet health insurance industry in the 2000s, it is important to understand some broader trends in social, demographic, technical, and regulatory change that influenced the processes investigated. These changes have direct implications for the social milieu that guides, constrains, and inspires the involved entrepreneurs and those who they interacted with in resourcing their firms. These shifts can be grouped into families of related changes, with two primary social-level changes including the changing role of the pet as a member of the family and huge advancements in both the way that veterinary medicine is practiced and its perceived value. While Americans have always had an abiding fascination with pets (Grier, 2006), the rate of change in both of these areas picked up speed throughout the eighties and nineties. In 1982, pet health insurance was introduced into the United States, but grew anemically through this same period. While pet insurance stayed under the radar for most

Americans, this time period had important implications for the nature of the context within which a new wave of firms subsequently co-created resources during the 2000s. In order to articulate these issues the following sections provide some needed background information that explains the environment that the firm under study confronted.

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The Changing Role of Pet as Property and Pet as Family Member

America has long been a nation of pet-owners, even from the earliest days of the

European settlers (Grier, 2006: p.2). Spanish conquistadors and other European groups brought dogs with them as war beasts, guards, workers, and companions; along with a host of other small animals, including domestic cats and birds. While there is a long, interwoven history of

Americans and their love for dogs and more recently cats, changes in the nature of these relationships have accelerated in the last several decades. Huge breakthroughs in crop sciences and infrastructure in the last century allowed America to conquer the persistent threat of widespread hunger and food availability volatility. Along with the invention of two of the more taken for granted conveniences of modern pet care, clay-based cat litter and viable multi-life stage flea control (Grier, 2006: p. 87). These changes dramatically increased the rate at which households welcomed domesticated animals into their homes.

While exact statistics for the number of dogs, cats, and families with pets are not available, it should be observed that the population of both has been on a steady increase over the last three decades. The US dog population between 1979 and 2009 increased from an estimated

49 million to 77.5 million28, with 58.3% of families reporting at least one pet as of 2001 and 62% by 2003.29 On average the cat population has mirrored the growth in the dog population, although it probably contains roughly ten million more animals.

This same time period also saw a dramatic increase in the actual amount that pet owners reported spending on their pets, their willingness to spend on their pets, and the offerings available from the pet industry. The pet supply industry has grown steadily over the last decades, with total U.S. pet industry expenditures growing from $17 billion in 1994 to $53.3 billion in

28 Give cite for this information 29 AVMA Survey 2011 162

201230. Petsmart, the nation’s largest pet supply chain, was started in 1987 with two stores and subsequently IPO’ed in 1994 with 107 stores. By 2002 the chain had 600 stores and net sales totaling $2.7 billion (Grier, 2006, p. 270), with sales increasing to $6.8 billion in 2012.

The social manner by which pets are identified within the family has also changed, and continues changing. Pets have always been considered property in the United States, as reflected in the law and the very nature of the term ‘pet-owner’. However it is becoming more common for pet owners to report that they consider their pet to be a member of the family or a best friend31

Recently some pet owners have begun rejecting the term ‘pet owner’ in favor of the more familial

‘pet parent’. For a period of time the Humane Society of America attempted to only use the term

‘companion animal’ in the believe that ‘pet’ denoted a dominant form of ownership and implied a hierarchical relationship (Grier, 2006, p. 7).

While there is no one clear reason for this change in how Americans allocate their income and time, several factors have been identified as potential explanations. Amongst these are changes in the demographic composition of the population with more families consisting of a smaller number of children, a larger elderly population, and the tendency for families to fragment into separate units. It is speculated that animal companionship serves an important social role for the estimated 25% of US individuals who live on their own. Likewise changes in discretionary income, the availability of ready-made pet foods and pet care products, the quality of veterinary care and human medical care (extending live spans in both humans and animals) have contributed to the ease with which individuals may now possess pets. There is also a general change in how society views its role as a steward of the planet and by extension the animals that we bring under our domain. While change is never uniform across a society, the overall trend of pet owner’s willingness to spend (and dote) on their pets has not been restricted only to the affluent.

30 http://www.americanpetproducts.org/press_industrytrends.asp 31 APPMA Survey, 2004 163

Changes in the Veterinary Profession

Veterinary medicine has changed dramatically in the last several decades as well. The field has become more professionalized, more advanced, and more in-line with the current state of human medicine. These rapid changes in veterinary medicine have been partly driven by an increased adoption of techniques from human medicine, with their associated complexities and high costs, and partly by the previously cited willingness of pet owners to incur the costs for these more advanced practices. A 1980 report from the American Kennel Club cited that veterinary bills had on average doubled in the prior five years32, this pace has remained relatively consistent over the last three decades. The cost of veterinary medicine has increased annually at roughly

5.6% since 200033.

The 1980s and 1990s saw two large shifts in veterinary practice, one of these being the emergence of specialists, and the other the appearance of veterinary care facilities that possessed high-end technology akin to human hospitals. As heard from one interviewee this time period was the end of the “James Herriot era.”34 As demand for quality veterinary medicine increased and more techniques were transferred into veterinary medicine from human medicine (although in many cases such techniques might have originally been developed with animals) it became possible for veterinarians to specialize in narrower fields of medicine. These shifts in demand for advanced veterinary care made it possible for specialists to cover the additional cost of the associated training. Likewise these specialists needed access to equipment that was too expensive for any one small practice to afford (i.e. Ct scan, etc.). While initially located around veterinary

32 Newsweek, September 15, 1980. “Man Insures Dog: How Pets Get Vets.” 33 Bureau of Labor Statistics, US Department of Labor, Consumer Price Index 2010 edition 34 James Herriot (pen name) was an English veterinarian who wrote a well-loved series of books about his experience as a small-town country vet. His stories centered on his adventures as a vet in which any day he might be called on to tend the upset stomach of a small, spoiled lap dog or spend his evening assisting a farmer with a cow’s breach birth. 164 teaching and research schools, high-end animal medical centers sprung up around the country.35

These centers provided a location for specialists to receive referral traffic from local veterinary practices, provide emergency case management, and facilitate procedures that were unavailable at the average veterinarian’s office.36

While these specialists had, on average, received the same level of training (and likewise incurred similar debt) as their human-medicine counterparts the procedures they completed were billed at a fraction of what equivalent care at a doctor’s office would have cost.37 This trend of improving quality of care, cost of service, and minimal remuneration continues to be a primary concern amongst the veterinary profession. As one veterinarian interviewed for this study commented: “We often ethically feel torn between providing the best care that our training has prepared us for, and providing the care that the pet owner can actually afford.” This echoes a sentiment that has been iterated throughout the last several decades: “… the possibilities afforded by high-tech pet care and its costs create difficult ethical questions.”38

This ethics of this line of inquiry extended to the broader societal concern of where resources should be allocated. As these seismic shifts were ongoing in the practice of veterinary medicine, more than once the concern that it is inappropriate to spend thousands treating a sick dog when there are people in desperate need of medicine echoed through the field39. Yet growth in demand for such services remained unabated and the veterinary field responded by graduating more specialists and increasing the overall level of training for general veterinarians.40 By 1998, amongst the 60,000 vets in the USA there were 5,600 specialists in such diverse areas as

35 St Petersburg Times, September 11, 1988. “Extraordinary Pet Care on Rise” 36 The Toronto Star, October 23, 1990. “Say Woof” 37 The Globe and Mail, January 17, 1985. “Clawed by Pet Owners” 38 Newsweek, May 20, 1991. “In No Time, Back on All Four Feet” 39 The Washington Post, July 2, 1991. “High-tech Medicine for Pets: How Much Are Owners Willing to Spend?” 40 Kiplinger’s Personal Finance Magazine, July 1997. “Money (Ouch!) Can Cure Fido” 165 endocrinology, cardiology, toxicology, and psychology.41 As the veterinary field and their clients

(by which is meant the pet owners) discovered that interventions taken from human medicine could dramatically affect outcomes, the rate of technology transfer accelerated. This adoption included pharmaceuticals, which Novartis estimated in 1999 as a $3 billion market in the US alone, growing at 20 percent annually.42 Interestingly amongst all these sweeping changes in the field of veterinary medicine, one aspect that has remained essentially the same is that it is the only major medical field that is paid for, essentially solely, by client’s discretionary cash flow.

Some Details about Pet Health Insurance

As this study is not particularly about the intricacies of insurance, but rather the relationships between entrepreneurs and resources, some simplifications will be utilized in regards to discussing insurance. The pricing, issuance, and regulation of insurance is a complex field, a complexity that would most likely get in the way. In order to address this complexity some aspects of the story have been simplified when it was felt that such simplification would not compromise the data or the theorizing.

Pet health insurance is an insurance product designed to defray the potential medical costs that can occur when a pet sustains an injury or an illness that requires veterinary care. Like most insurance products it is premised on the notion that the pooling of risks allows policy holders to pay in a steady stream of premiums and receive compensation when a covered event occurs. Unlike more commonly available livestock insurance, which is designed to provide the insured party with economic value coverage, pet health insurance is designed as a tool for absorbing unanticipated veterinary expenses.

41 The Times, April 6, 1998. “Bright Eyes and Bushy Tails Cost US Pounds 6 Billion” 42 Newsweek, October 11, 1999. “When Pets Pop Pills”. 166

Like many smaller, niche insurance products, policies are sold by the issuer (the pet health insurance firm in this case), but are underwritten by larger, established multiline insurance firms. These smaller firms are called MGAs (managing general agents) and are responsible for marketing and sales, issuing policies, handling claims, risk assessment and pricing. An MGA manages the day to day of providing the insurance product and pays a share to the underwriter for providing capital coverage as mandated by regulation and other services. In order for an MGA to remain in business they must offer a product that appeals to customers and also one that works economically. Built into the premium for a policy is the actuarial estimate of how much will be paid out to settle claims, a fee to be paid to the underwriter, any regulatory or taxation fees, the estimated expenses that the MGA will incur in servicing the policy, and any remaining profit for the MGA. The largest portion of the premium can be linked to the expected claims payouts, which is guided by the underlying actuarial model. For an MGA to remain viable their actuarial model must be accurate relative to the product type they are offering, otherwise the MGA may find itself in a situation where it has to pay out more than it budgeted. Such a situation would lead to the underwriter having to cover the difference, an occurrence the underwriter clearly wants to avoid. Further the amount that a pool of insurance policies is expected to pay out is governed by the regulatory authority of the state. Successful niche, insurance products thread the needle between providing a well-priced product that attracts consumers and also providing adequate margins to account for expenses and profit, all the while being squeezed by regulators and underwriters.

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The Early Emergence of Pet Health Insurance (1980s-1990s)

While pet health insurance had been previously tried in certain Scandinavian countries, this story starts in 1977 with the formation of Pet Plan in the UK.43 Patsy Bloom, a former charity worker, and David Simpson launched the business with 500 pounds in capital and the believe that the “I had to sell the concept before I could sell the product.”44 Mrs. Bloom was renowned for her ability to enlist the involvement of others (in particular the veterinary community) as she traveled the country with her dog Annie on a perpetual sales pitch. As Figure A.1 illustrates, the growth of pet insurance in the UK was phenomenal, growing steadily through the late nineties and then exploding thereafter. By 1993 Pet Plan was collecting premiums amounts in the tens of millions and in 1996 was acquired by a large multi-line insurance firm, Cornhill Insurance.45 Several other firms entered the market, including Tesco the multinational grocery and general merchandise box store. By 2013 market penetration is approaching 25% in both the cat and dog markets.

Pet Health Insurance Penetration Rate: 1979-2011 30.0% 25.0% 20.0% USA 15.0% 10.0% Canada 5.0% Britian 0.0%

Figure A.1: Adoption rate of pet health insurance in USA, Canada, and Britain between 1979 and 2011. Rate is determined by the number of outstanding dog policies divided by the owned dog population in each region. Some values have been interpolated from known data based on estimated growth rates.

43 The Guardian, May 27, 1989. “Cover for creatures great and small – Vets’ bills are proving a growing burden for pet owners” 44 The Times, April 17, 1993. “Dogged determination wins through” 45 The Independent, May 2, 1996. “Cornhill buys Pet Plan for pounds 32.5m” 168

The start of pet health insurance in the USA was not the same rosy picture. Pet health insurance got its start in 1982 with the founding of Veterinary Pet Insurance (VPI) by Jack

Stephens, DVM with funds raised from several hundred veterinary practices in California.46 The main driving force behind this joint effort was the desire to reduce the occurrence of economic euthanasia (when a pet’s owner elects to have a pet put down rather than incur the cost of veterinary treatment).47 Growth in policy sales for VPI was anemic, with Dr. Stephens admitting that his company lost money every year between 1982 and 2000.48 During this same time period several firms attempted to enter the marketplace with similar offerings, and either failed after a short time or never made it past the regulatory approval phase. A similar story played out in

Canada with several firms attempting entry, a few limping along and most simply failing outright.

Pet Health Insurance Timeline AHIA launched in USA VPI finally makes a profit PetPlan & Pet Sure UK market saturated launced in Canada Pet Plan luanched in Embrace & ASPCA PetPlan & Petsure Britian launched VPI market share falls consolidated from >90% to ~50%

1975 1980 1985 1990 1995 2000 2005 2010 2015 Nationwide acquires VPI VPI launched in USA Trupanion & PetPlan Lloyd's of London Pet Plan, UK sold to launched in USA underwirintg Pet Plan Cornhill AHIA fails

Figure A.2: Pet Health Insurance Timeline for USA, Canada, and Britain

Contemporaneous data from these time periods shows that many characteristics of the cultures, regulatory structures, and institutional regimes were nearly identical in the US, UK, and

Canada (including aspects such as pet owners willingness to pay for veterinary care, perceptions of pet as a family member, other forms of insurance available and regularly purchased, regulation

46 The Washington Post, February 18, 1982. “Pets: Mr. Rover, Your Policy?” 47 The New York Times, July 15, 1982. “Insurance for the family pet” 48 The New York Times, June 30, 2002. “Break a leg, Fluffy, if you have insurance” 169 and pricing of veterinary care, etc.).49 Given very similar contexts it is a paradox that the rate of adoption of pet health insurance was so very divergent amongst these countries.

Whilst VPI eventually became a successful business, it became cash flow positive in the

2000s and was later acquired by Nationwide Insurance in 2008, the history it laid down in the process had important implications for the research question. For the vets who initially funded and formed VPI an overriding goal was to reduce the occurrence of economic euthanasia and to provide pet owners an alternative means to pay for care. They felt the best way to accomplish this goal was to introduce a product that was as cheap as possible and thus would get into the most hands. While they were not oblivious to the economics of running a business, many of them being veterinary practice owners, neither were they experts in the field of insurance.

The primary pricing mechanism of an insurance product is the underlying actuarial model.

An actuarial model provides a set of probabilistic estimates for how likely a covered event will occur during the life of the policy. Based on a pool of policyholders one can calculate the expected amount that will be paid out across the pool. Individual policies are then priced such that there are adequate reserves to cover the expected payout plus additional monies to provide for fees, expenses, and profit to the insurance entities. There are many forms of models to choose from; in this case a schedule of benefits model was elected. A schedule of benefits policy is designed to provide a given payment of y dollars for a given procedure x (for example a policy might pay $200 for the removal of a foreign object from a dog’s stomach). The advantage to such a model is that it is less data intensive than many of the other options. In this case VPI needed to estimate the average occurrence rate of each illness or accident (called morbidity) that the policy covered and make an overall average estimate of the amount that should be paid out to cover the related treatment. The disadvantage of such a model is that it assumes that care is priced the same

49 These findings are from various studies and reports put out by both veterinary (AVMA, NCVEI, BVA, CVMA) and animal welfare organizations (ASPCA, RSPCA, SPCA) in all three countries. 170 for all policyholders (i.e. vets charge the same for the same procedure) and if payouts for procedures are not adjusted often enough they can quickly fall out of sync with the market. It can be seen that in some cases policyholders will file for a claim and receive reimbursement that is most or all of what they paid out for treatment. Such cases will occur when a veterinarian’s fees are in line with the schedule of benefits model’s assumptions. Other policyholders will receive only a fraction of their claim when for various reasons a veterinarian’s fees are greater than those assumed by the schedule of benefits. Further it can be seen that if on average veterinarians’ fees increase (things never seem to get cheaper) and the model is not adjusted, on average policyholders will receive a smaller reimbursement as a percentage of their bill.

So why did VPI elect a schedule of benefits model? First, as previously mentioned, such a model is less data intensive and requires less back-office actuarial modeling to upkeep. More complex models, such as the percentage of bill, require both morbidity data as well as pricing data by geographic regions (with finer grain data leading to more differentiable pricing). Not only is it more difficult to get this greater depth of information, it is harder to process this data into interpretable models. In the early 1980s, when VPI was started the founders and their staff simply didn’t have the horsepower to support these more complex models, both in terms of computational and actuarial capabilities. Secondly, VPI was started in California, a state well known for its heavy regulatory framework. At the time of VPI’s formation, regulators pushed VPI towards the schedule of benefits model, under many of the same concerns previously expressed.

Unfortunately the downsides of the schedule of benefits model outweighed the advantages in implementing the product, particularly when it came to customers and their relationship with veterinarians. Few, if any, customers (i.e. pet owners who bought a VPI policy) understood the schedule of benefits model or if they understood the concept, they could not navigate the minutiae of veterinary terminology. In essence, this policy format imposed a significant information asymmetry on the client. When an animal was treated and care was paid 171 for the customer had no idea how much they were going to receive back when they eventually filed a claim with VPI. This led to situations in which a pet owner would take their animal to the vet, receive care and pay for treatment, then receive a reimbursement some time later that was a fraction of the paid bill (estimated around 50-55% by firm founder during this time period).

Inevitably this upset the pet owner who would then accuse the vet of over-charging, engaging in over-care, or otherwise being dishonest. Further it was not uncommon for clients to assume, truthfully or not, that a vet had given their approval and recommendation for the insurance policy.

Such assumptions led to increased acrimony between client and vet. Vets for their part didn’t have the resources to aid clients in navigating the insurance process and resented being the accused party.

Further issues exacerbated the formation of negative sentiment towards the concept of pet health insurance. VPI felt that its policies might still be priced too highly, even with the schedule of benefits model. The easiest way to deal with this issue in insurance is to add exemptions to the policy, things that the policy will not cover. A primary exemption that was made was the exclusion of pre-exiting conditions, such an exemption is needed in order avoid the situation in which only pet owners with known issues would buy policies. Insurance, of this sort, is designed to pay for unexpected expenses, in the case of pre-exiting conditions clients already know that they will incur costs and thus have an incentive to a buy a policy that will shift some of the cost onto other policyholders. This exemption was reasonable and well understood by clients; most pet-health insurance policies to this day exclude pre-exiting conditions. Other exclusions found in

VPI policies were much more problematic. In particular policies excluded breed specific, predisposed diseases. For example the spitz family of dogs has a genetic predisposition to hip dysplasia (abnormal development of the hip joint that leads to poor fit between the ball and socket, often leading to painful dislocation injuries). Such exclusions remove a known pool of risk for the policy originator, thus allowing for an overall reduction of policy pricing. 172

Unfortunately they impose yet more information asymmetries on the policyholders, most of who will have no knowledge of the prevalence or perhaps even existence of predispositional diseases.

These policy exemptions, other steps taken to reduce the cost of policies, and other various factors led to policy products that were confusing for both customers and vets.

Additionally the few other firms that attempted entry during this period essentially copied VPI’s business model. None of these firms survived, but they did act to reinforce the assumptions of vets, clients, regulators, economists, and other interested parties that pet health insurance simply wasn’t a viable product. The overall sentiment concerning pet insurance as of 2000 was very negative, with many vets viewing the concept as anathema to the practice of veterinary care.

There was much information that provided a clear negative signal for the viability of pet health insurance as a service product (at least as it was understood at the time). However, in a broader context there was clear information that pet owners had a greater willingness and desire to provide quality veterinary care for their companions and that vets were willing to and wanted to provide this care. However, the general notion of insurance, in particular the role of human health insurance, was also playing a confounding role into how the market for pet health insurance might be viewed. In summary, entrepreneurs faced many contradictory signals, as did regulators, underwriters, vets, legislators, pet owners, and other related parties.

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Appendix B: In-Depth Timeline of Pet Health Insurance (1977-2012)

USA, Canada, Great Britain

 Since 1945 o According to the American Kennel Club at least 30 companies have testes the market for pet insurance and no has succeeded (as of Sept. 1980) o According to Guy Hodge, director of information services for the Human Society of the United States, more than 35 companies have come and gone in this sector since 1945 (as of Feb. 1982). Plans failed from such factors as undercapitalization, inadequate actuarial information, and lack of support from veterinarians.  1977 o Patsy Bloom and David Simpson launch Pet Plan in the UK with 500 pounds in capital. Patsy promotes this ‘new’ product along with her dog, Annie, by traveling around dog shows and veterinary practices. “I had to sell the concept before I could sell the product”. She sponsors local meetings on the British Small Animal Veterinary Association on condition that she can give a talk about her product. Originally underwritten by Dog Breeders Insurance Company, with 1,300 policies sold in the first year. By 1980 this had grown tenfold and Pet Plan adopted Llyod’s as underwriter.  1980 o Janruary - Pet Health Support of Anaheim begins offering pet health insurance with annual premiums ranging from $23 for cats and $47 for dogs. Effort goes nowhere. o September – Judi Goose of Santa Ana and Medial Pet Services (MPS) of San Diego will begin offering pet health insurance. Policies range from $31 for cats to $70 for dogs. Since June MPS has paid out claims of $10,000. Both fold.  1981 o November - California Veterinary Services (founded in 1980 with funds from 700 to 800 veterinarians in the state) will begin offering pet insurance in California in 1982 (the future parent of VPI). Rhulen Agency Inc. will do the same in New York.  1982 o February – Veterinary Pet Insurance (VPI), a division of California Veterinary Services will make policies available in March. Advertisements are posted in 500 of the state’s 1700 veterinary hospitals, more than 35,000 people have sent for information. Pre-existing conditions, intentional injuries, and congenital or hereditary defects will not be covered (this has implications for later in the life of VPI). Neither plan (catastrophe only or catastrophe with sickness and major medical) cover routine care. The lack of routine coverage is intentional to keep premiums low. VPI is still awaiting approval from the California Department of Insurance. o April – Frontier Insurance Company will offer pet insurance in New York as of April 27th (Frontier is the new name for the efforts of Rhulen Agency Inc.). Mr. Rhulen states that if pet insurance is successful in New York then Frontier will go national. Policy restrictions are similar to VPI’s offerings, with the additional condition that all pets of the same species within a household must be covered.

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o July – Concern is starting to be expresses that the availability of pet health insurance will lead to substantial increases in the cost of veterinary care. The National Insurance Consumer Organization argues that pet medical bills are not the type of expense for which people should buy insurance. “It’s an absurd expenditure for insurance” and “This is in the junk coverage category” according to J. Robert Hunter, president of NICO: “If you are really worried that someday you will have a big veterinary bill, put $50 a year way in a bank account and collect interest on it.” Hunter asserts that these plans will result in additional and unneeded costs to consumers: “If everybody buys the insurance we will get CAT scans for cats and dog scans for dogs and all kinds of crazy machines for pets that nobody would ever have through of using. And pet owners will pay for it.”  1983 o January – Frontier Insurance reports that it has 350 policyholders, roughly half in NYC (premiums range from $42-$79). Nearly $2,000 in claims has been paid out. VPI has sold 7,000 policies and paid close to $70,000 in claims (premiums range from $19-$120).  1985 o January – John Robbins (independent insurance agent) begins writing health insurance policies for pets in Westerville, Ohio. Not clear that his policies are in fact legal, according to Ohio statue. o November – Alpo plans to begin testing a pet insurance program for consumers in cooperation with an insurance company on the West Coast (probably VPI). Nothing more of this plan is heard.  1988 o March – Animal Health Insurance Co. began selling policies in Connecticut in 1987, hopes to start selling in 49 states (Tennessee does not allow pet-care policies). VPI has about 150,000 policyholders at this point and offers plans in 27 states, with plans to expand to 12 more states by mid-year. According to Rebecca Moore, marketing representative for VPI, veterinary costs rose 183% between 1981 and 1986 (this seems like an exaggeration). Michael Garvey, chairman of the department of medicine at New York’s Animal Medical Centre, says growth of the pet health insurance industry has been slow because of a lack of advertising and promotion. o May- AHIC receives the support of the Massachusetts Society for the Prevention of Cruelty to Animals and starts offering plans in that state.  1989 o May – In the UK, several insurance agencies drop out of the market including: Prudential stops accepting new customers for it Prupet contract, Vetex withdraws its credit-card type scheme, and Holman General Facilities ceases operating its Holdfast dog and cat plan. Pet Plan has over 175,000 policyholders (at least 51% of the UK market), policies are underwritten by Lloyd’s. o June- AHIC is now Animal Health Insurance Agency (AHIA), and is being underwritten by Llyod’s (which has long history of writing obscure policies). o October – Two companies enter the Canadian market. Pet Plan will be sold by Reed Stenhouse Ltd (Canada’s largest insurance brokerage), imitates the UK product. PetSure will be offered by PetSure Canada Inc., a subsidiary of Aegon Insurance Co. (a large European firm). o November – The Massachusetts Co. begins offering the Pet Lover’s Visa Card, which offers a 10% discount on pet health insurance through Lloyd’s of London.  1990 o October – Nichol Insurance Brokers Ltd. becomes the latest Canadian company to offer pet health insurance, called Medipet Anti-Maux.  1991 o January- Although VPI and AHIA claim to have sold over 250,000 policies between them, veterinarians say that they have no or only a few clients with coverage. A segment of clients are dropping the policies after discovering that the insurance does not cover what they expected, or reimbursements are very slow in coming (or not at all). Veterinaries are expressing the opinion that not covering routine care is a substantial reason for why they don’t recommend the insurance to their clients. Prior efforts in the area of insurance have failed because there were too many limits on coverage.

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o March- California Insurance Commissioner John Garamendi files charges against VPI for delays in paying claims. o May – Current estimate of 100,000 animals covered nationwide by VPI and AHIA. This is a tiny fraction of the potential market. Pet insurance is available in 49 states, except Tennessee which still does not allow it. o October – A South Florida chain of animal clinics is launching a health maintenance organization which would provide routine care and a discount on surgery for an annual premium.  1992 o February – Estimate of 100 million pets in the USA (meaning that VPI and AHIA cover roughly 0.091% of the market), 52 million dogs and 55 million cats. The Fireman’s Fund Insurance Company will start promoting the Medipet plan, in partnership with AHIA. They will be airing a cable television infomercial aimed at reaching 20 million viewers. This follows a direct mail campaign begun the prior fall through Sears Roebuck that targeted 600,000 households. Consumer advocates and some economists state that this new effort by a mainstream company is ridiculous. Mr. Hunter re-asserts the position that insurance in this sector will lead to unnecessary costs and cause clients to approve treatments that they wouldn’t okay if they were covering the cost directly. He expressed interest in life insurance for cats: “Imagine a type of life insurance where you don’t have to pay the first eight times the policyholder dies.” o July – Jardine, a Birmingham, UK-based insurance brokerage, launches “Moggies and Mongrels” pet insurance policy. Also offers ‘Paws’ plan. o Fall – Reed Stenhouse sells Pet Plan (Canada) to H.E.D. Leipsic. PetSure Canada Inc. was almost closed down, but Pet Plan Group Ltd. of London England took it over. Pet Sure and Pet Plan are combined into one entity.  1993 o April – Pet Plan in the UK, now is collecting premiums in the tens of millions of pounds. o August – Pet Plan (Canada) is receiving blowback from canceling policies for higher risk clients. The Ontario Insurance Commission is investigating claims of false advertising. Llyoyd’s the underwriter flagged policies it wanted dropped after the transition of Pet Plan and Petsure. Pet Plan’s 14,000 polices cover far less than 1% of Canadian pets, the company believes it needs to get 5% to stay in business.  1994 o April 1st – AHIA fails after a national effort to promote is unsuccessful. o May – Pet Plan Canada now has 12,000 policies. Medi-Pet has folded and Pet Sure was merged with Pet Plan. Pet insurance is now a $150 million dollar industry in the UK.  1995 o August - Current estimates of US pet population: 63 million cats and 54 million dogs, with Americans spending $17 billion a year on them. Increase in the number of newspaper articles discussing the changing nature of pets and their inclusion as a family member. o September – A report from the Animal Hospital Association finds that 70% of the population considers pets to part of the family. However 69% of pet owners don’t like insurance plans, saying they are too expensive. o For the year – In the UK pet owners paid $89 million in premiums to insure 700,000 dogs and cats (estimated at 5% of the population)  1996 o May – Pet Plan, UK is sold to Cornhill for 16 million pounds. o November – The Consumer Federation of America provides a list of insurance products that consumers should avoid, included in the list is pet health insurance. Mr. Hunter, now the director of insurance for this entity, reiterates the idea that it is better to bank the premiums and highlights the fact that the policies do not covering pre-existing conditions. o December – VPI is trying to design a policy that covers condition endemic to certain breeds, such as hip dysplasia.  1997 176

o June – The pet industry is now a $25 billion dollar market in the USA. o July – VPI now has an estimated 75,000 policyholders and is available in 43 states. Vets, clients, and the newspaper reports are increasingly frustrated with the caps on low-cost policies. They assert that these policies are more akin to a discount on veterinary services, rather than proper insurance. The American Veterinary Medical Association (AVMA) supports pet insurance calling such coverage “important to the future of the veterinary profession’s ability to provide high quality and up-to-date veterinary services.” Mr. Hunter retorts “It’s no accident that most pet insurance was invented by vets, who are jealous of health insurance for people and of the high-price procedures that allows.” o August – Pet insurance in the UK now covers roughly 13% of dogs and 5% of cats, as compared to Canada and the US were both of these percentages are below 1%. o September- Pet Assure is established as a pseudo HMO, offering a 25% discount on veterinary care and services through partner members (launched the prior year). Members choose from a list of participating vets. Vets in return receive referrals and marketing assistance. o October – Veterinary Centers of America Inc. invests $6 million in VPI.  1998 o April - The AVMA estimates that Americans spend $6.68 billion a year on pet health care. VPI has roughly 75,000 policies (estimate of $9 million in premiums). Rewards Plus of America adds pet health insurance to its offerings. “We were all laughing” says Frank Longwell, vice president of marketing. o August – Firms offering pet health insurance as a job benefit, in the increasing aggressive effort to recruit employees. A study by the American Pet Product Manufactures Association shows that 80% of pet owners celebrate their pet’s birthday. o November – Pet Assure moves into group marketing to target employee benefit packages. CEO Jay Bloom expects hundreds, if not thousands of companies to begin offering pet care as a benefit in the next few years.  1999 o January – Roughly 16,000 Canadians now have pet insurance. o March – Pets Health, in Canton, Ohio has sold 4,000 policies in the past 18 months in 40 states. o June – A bill is introduced to the New York assembly that would permit the establishment of court- administrated trusts for the care and feedings of dogs and other animals. o July – American spend ~$27 billion annually on pets, with $12 billion going to healthcare. The pet pharmaceutical market is estimated at $3 billion and 20% annual growth. o October – VPI has struggled, but sales rose 90% last year to $26 million (with premiums averaging $200, implying ~130,000 policies). o During the Year – Pet Care Insurance Brokers Ltd. is started in Oakville, Ontario.  2000 o February – Los Angeles-based Answer Financial Inc. AFI, an online provider of voluntary benefits of voluntary benefits ads five pet insurance plans to its portfolio. Joining an increasing group of providers. o May – Vancouver City Savings Credit Union begins offering the SafeRate pet insurance policy. “It all has an oh, what next ring to it” according to Marianne Chatten, sales rep with VanCity Insurance Services Ltd. PetPlan is still the dominant player in the Candian market, with an estimated 15,000 clients. Other competitors include Pet Care & Petcetera, a retail pet store that offers insurance. The Ontario, Alberta, and Canadian Veterinary Medical Associations endorse the PetCare program exclusively for a two-year period (probably a pay for endorsement deal). Valerie Goddard, marketing executive for Pet Plan, states “People thought we were this big monopoly making tons of money when we are not. We have waited for competition for a long time. It brings credibility.” o July – VPI annual premium is roughly $265 a year. o November – Reader’s Digest Association will start marketing Pethealth Inc’s accident and health insurance policies in the United States and Canada. Pethealth in Oakville, Ontario 177

o December – According to Dr. Stephens, VPI has about 200,000 policies in force. VPI is now majority owned by Nationwide Insurance (60%) via Scottsdale. Premier, in Wisconsin, is also insuring several thousand dogs, started 1998 by Tom Kurtz.  2001 o January – Vetinsurance, underwritten by Allianz Insurance Company of Canada (a subsidiary of Allianz AG) is introduced in Canada. Lincoln General Insurance Co. agrees to underwrite Pethealth Inc. entry into the United States. o July – The American Pet Product Manufactures Association estimates the pet market at $28.5 billion. A study by the AAHA reports that 75% of survey participants would be willing to go into debt in order to pay for veterinary care. AIG introduces Health Pet. VPI estimates that revenue will reach $55 million (roughly 250,000 policies). Dr. Stevens estimates that his four competitors have at best 25,000 to 30,000 policies combined.  2002 o April – Royal & Sun Alliance (RSA) will start fielding a 26 person team of pet bereavement counselors in an effort to increase the attractiveness of its UK policy offering. The pet insurance industry in the UK is estimated at 160 million punds, with 12% of dog owners and 7% of cat owners. There are now roughly 60 entities offering policies, Pet Plan is still the dominant player with 40%, Tesco Personal Finance has captured 20%. o June – Rescue shelters and adoption groups have begun offering two-months of pet health insurance coverage as part of the adoption package. VPI reports an eightfold increase in revenue over five years, nearly $72 million. The American Kennel Club enters a partnership with a British firm to start offering insurance. Pethealth Inc. (Candaian) forms an alliance with Petco to sell policies in the US. Current renewal rates at VPI average 82%. Dr Stephens (VPI) reveals that his company lost money every year between 1982 and 2000. Estimates profit of $2,000,000 on 250,000 policies in 2001. o November – Owing to economic troubles firms are cutting back on employee benefits, but pet health has remained relatively sticky (the number of companies offering this perk doubled in the last decade). This is mostly due to the voluntary nature of the offering, and the fact that few employers cover part of the cost. Complications that these benefits might fall under the purview of ERISA slow down adoption. o December – A study from AAHA finds that 47% of owners would spend any amount to save a pet’s life. A report that rescue dogs from the World Trade Center are receiving better care than human workers leads to investigations and complaints. VPI has donated lifetime medical policies to every rescue dog.  2003 o January – Major players in the US market include VPI, Petcare (Candian), and Petshealth Care Plan (which recently absorbed Premium Pet Insurance after it went bust and lost its underwriter). o June- The USA pet market is projected to grow 10% this year to $31 billion. Pharmaceutical growth is enormous, for example pain control is up 275% in six years to more than $150 million. o August – Petshealth sells 11,736 new core policies during the second quarter of 2003 and 42,761 ShelterCare policies (a cross promotion with petfinder.com). Sales are up 130% over the prior year. However payouts are up by 23%, and the business is not profitable owing to the high rate of claims. Packaged Facts, a division of MarketResearch.com, estimates that spending on Pet Insurance in the US climbed 342% from 1998 to 2002. o September – Estimate that VPI now has 340,000 policies and pays out 35,000 claims a month. Pethealth has now sold 21,723 new core policies for the first six months of 2003, a 76% increase over the 12,323 sold in the same period in 2002. Laura Bennet, plans to launch Embrace Pet Insurance in the US the following year.

 2004

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o March – Pethealth Inc’s fourth quarter loss is $349,701 for the three months ending Dec. 31, 2003, compared with a loss of $1.14 million during the same period a year earlier. Quarterly revenue grew to $2.04 million from $1.21 million. Chris Ashton starts Fetch Pet Insurance, which will later launch Petplan insurance in the USA (August, 28, 2006), underwritten by American National Property and Casualty Company. Fetch holds an exclusive license with Petplan Limited, a wholly owned subsidiary of Allianz Cornhill Insurance PLC. o June – The AVMA estimates that 58% of US households have at least one pet, with an estimated 68.9 million cats and 66.6 million dogs. HSBC joins an alliance with VPI to sell policies to its bank clients. o Novemeber – Petshealth reports a third quarter loss of about $50,000 with revenue of $2.99 million.  2005 o February – The pet industry has doubled in size in the last ten years to $34 billion. VPI has sold 360,000 policies during 2004, as compared to 157,000 in 2000. About 1,100 US companies offer VPI as an employee benefit. o May – Don Cherry, a famous Canadian hockey commentator, joins a partnership with Petheath Inc. to create an insurance program for dogs and Cats (named CherryBlue) o November – A study from the AKC reports that on average dog owners will incur $2,127 in one- time expenses and $2,489 in annual expenses. o December – In the UK open heart surgery is performed successfully on a cat for the first time.  2006 o May – Estimate that VPI now has 369,000 active policies (roughly 80% of the market). Less than 1% of the estimated 90.5 million cats and 73.9 million dogs. Roughly $110 million in premiums/ o August – PetPlan (Canada) reports that the company is growing 35-40% a year (yet less than 1% of owners in Canada have coverage) o July – Fetch Inc. begins selling policies under the PetPlan license from Cornhill Allianz. Chris Ashton pursues the PetPlan partnership in order to get the brand name cache and access to the actuarial data held by the parent company. o September – Petsecure (from Australia and Canada) plans to launch operations in the US market. Hollard insurance in the planned underwriter, with Petsecure providing all back office administration and risk profiling. o October – The ASPCA beings offering a plan in connection with Hartville Group. o November – PetPlan (Canada) insures 42,000 pets. SecuriCan General Insurance which underwrites and administrates PetPlan, also underwrites programs for PC Financial, the Canadian Automobile Association, and Overwaites Foods.  2007 o January – Sales of pet insurance in the US topped $160 million in 2005, up nearly 25% from 2004. Packaged Facts and Consumer Reports estimate that Americans spent $230 million on pet health insurance in 2006. o February – The pet pharmaceutical industry is $5 billion, growing 14% annually. There are now 90,000 pet policies on the books in Canada. VPI now covers 415,000 policies. An estimated 1,600 US firms now offer pet insurance as an employment benefit. o July – Nestle Purina PetCare Co. launches PurinaCare Pet Health Insurance in Canada, underwritten by SecuriCan General Insurance Co. PurinaCare, unlike most insurance, covers routine examinations. o August – Canadian pet insurance now covers roughly 110,000 pets. o October – Vsurance begins offering life insurance policies for dogs in the US. Coverage is available for up to $10,800. Eli Lily comes out with a canine version of Prozac.  2008 o February – Fetch Inc. has grown to 11 employees, with expectations to expand to 100 in the next three years. VPI has 400 employees and $150 million in annual premium sales. Datamonitor forecasts the UK pet health insurance market to grow to $1.17 billion in 2011 from nearly $740 million in 2006. 179

o March – AVMA reports that the national average for a veterinarian visit in 2006 was $135 for dogs and $112 for cats. o April – AVMA reports that $24.5 billion on health care for all pets in 2006. Pet population estimated at 81.7 million cats and 72 million dogs. There are 83,730 veterinarians in the nation. o August – VPI estimated at 450,000 policies, double six years earlier. o October – VPI now at 465,000 policies. Sales are remaining resilient in light of economic conditions.  2009 o August – The pet industry has grown to $46 billion, from $17 billion in 1994. The American Pet Products Association estimates that there are 93.6 million cats and 77.5 million dogs. Hurricane Katrina and other events are pushing legislatures and institutions (such as the Red Cross) to reexamine the classification of pets as property. o November – Central States Indemnity, a subsidiary of Berkshire Hathaway, wins a contract to underwrite PurinaCare in the US. Veterinary spending is expected to increase to $12.2 billion.  2010 o April – Fetch Inc. now employs 40 people. Their product PetPlan is rated tops by the Humane Society of the United States o June – Canada now at 140,000 pets covered of an estimated 14.4 million cats and dogs. SecuriCan General is now Western Financial Insurance Co. For 2009 revenue from policies was $32 million, up from $2.4 million in 2004. Net profit of $3.8 million, with roughly half of premium revenue paid out in claims. o August – VPI awards a bronze trophy to Ellie, a Labrador, for most unusual pet health insurance claim, after she ate a beehive containing pesticides and thousands of dead bees.  2011 o April – VPI now at 485,000 policies, up from 195,000 in 2001. The American Pet Products Association expects Americans to spend slightly less than $400 million on pet health insurance in 2011. o May – In the UK, RSA joins Tesco in a joint effort to sell Pet Health Insurance. o August- - Fetch, Inc. (i.e. PetPlan) reports a 2,207 % growth rate over three years. Revenue grew to $18.7 million in 2010 from $812,000 in 2007. Firm has roughly 100,000 policyholders. o October – Capital Blue Cross, in Harrisburg, starts insuring pets under a program managed by Petplan.  2012 o March – USA Today reports that there are now 11 companies offering pet insurance in the US market. Revenue in the sector was $303 million in 2009. Americans spent an estimated $14.1 billion in veterinary care in 2011 according to the American Pet Products Association.

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Appendix C: SRMR & pseudo-SRMR (pSRMR)

SRMR is a global fit measure of how closely the model-estimated correlation matrix differs from the correlation matrix of the observed data and is one of the most commonly used measures of fit (Hu & Bentler 1999). In order to calculate SRMR it is necessary to generate a matrix of the residuals (denoted 푹푅) between the original correlation matrix of the manifest variables (denoted RD) and the correlation matrix implied by the model parameters denoted

(denoted RM). Some software can output this implied correlation matrix, otherwise it can be calculated from the factor analysis model.

푹푅 = 푹퐷 − 푹푀 (A1)

′ 푹푅 = 푹퐷 − (횲횽횲 + 횯훿) (A2)

Assuming p observed variables and m latent factors, 횲 is a (p x m) matrix of factor loadings, 횽 is a (m x m) correlation matrix between the latent factors, and 횯훿 is a (p x p) matrix with unique variances on the diagonal and correlations between observed variable unique variances (UVs) on the off-diagonal. 푹푅 will thus contain p(p+1)/2 unique elements. Note that we include the diagonal in the calculation of SRMR, although in ML based factor analysis, accounting for rounding errors, the residuals on the diagonal will always equal zero. Following the Mplus Technical Appendix 5 SRMR is calculated as:

푝(푝+1) 푆푅푀푅 = √(∑ ∑ 푟2 )/ ( ) (A3) 푗 푘≤푗 푗푘 2

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In Equation A3 푟푗푘 is defined as:

푠푗푘 휎̂푗푘 푟푗푘 = − (A4) √푠푗푗√푠푘푘 √휎̂푗푗√휎̂푘푘

In Equation A4 푠푗푘 represents a covariance between two observed measures, 푠푗푗 is the variance of observed measure j, 푠푘푘 is the variance of observed measure k, 휎̂푗푘is the model- estimated covariance between measures j and k, 휎̂푗푗 is the model-estimated variance for measure j, and 휎̂푘푘 is the model-estimated variance for measure k. It should be noted that in Equations A1 and A2 the off-diagonal elements of 푹푅 correspond to 푟푗푘.

The general guideline for the use of SRMR in ML estimation is that SRMR should be <

0.08 and ideally less than 0.05 (Hu & Bentler 1999). However, in a Bayesian setting SRMR has its own distribution rather than being a point estimate (Levy 2011). At this time Mplus and other programs do not include a Bayesian implementation for SRMR, which would require calculating

SRMR at each iteration of the MCMC chain. Until such features are available we propose as an alternative, the psuedo-SRMR (pSRMR), which can be used for comparison purposes. This measure is calculated like SRMR but is derived by comparing the original correlation matrix of manifest indicators to a recreated correlation matrix using parameter values from the Bayesian analysis. We elected to calculate pSRMR using the median values from the posterior of each parameter in 횲, 횽, and 횯훿. Within a reasonable range of tolerance the pSRMR value derived from the median point estimates of the model parameters should closely approximate the true median value of the actual SRMR distribution.

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Appendix D: 횯δ Matrix Estimation

When conducting the initial groundwork for this article, two of the co-authors had rather divergent views on the potential utility of estimating the entire 횯훿 matrix. One felt that there was the potential for resolving issues related to model fit that were arising from modeling noise, unique sample characteristics, method factors, etc. The other was much more dubious that the technique offered any theoretical value and rather served as a means to sweep all of these issues under the carpet. In order to come to the consensus offered in this article, the more optimistic author decided to run a simple experiment utilizing 횯훿 estimation. While the results here are not definitive and do not carry the robustness of a simulation study, they certainly gave this individual cause to pause.

The experiment started with the simple premise: can this technique solve model misfit issues for models that are not theoretically justified? The hope was to find that the technique would fail in these situations, in which case it could be inferred that estimation of 횯훿 provided a means to handle modeling noise (rather than being a Band-Aid for model misspecification).

Along with the ESE scale demonstrated in the methodology section, the survey included many other scales including career commitment (Blau, 1985), career insight & career identity

(Noe et al., 1990), general self-efficacy, (Schwarzer et al., 1997) and a multi-dimensional career motivations scale (DeMartino et al., 2006). In total these scales are represented by 55 observed items. In order to generate a nonsensical model 18 items were randomly selected and used to create a measurement model with three factors connected to six manifest items each. As expected the estimation of this PCS model using ML indicated unacceptable fit (RMSEA= 0.139; CFI= 183

0.535; SRMR=0.116). Further indicative of poor modeling are a wide range of factor loadings: -

0.267 to 0.745. Likewise Bayesian estimation of the same PCS model with diffuse priors on the complexity one loadings, and degenerate priors on all cross-loadings produces a model with unacceptable fit (PPC= 1259-1365; BIC= 24417; DIC= 21818).

At this point a three-factor model with no theoretical meaning and poor fit had been developed. The question remained: what would estimating 횯훿 accomplish with this same model and would such a model even converge? After specifying an inverse-Wishart prior for 횯훿, estimation was attempted in MPlus. Although the model took many iterations to reach convergence, estimation was successful. According to the PPC= -52.1 – 73.2 criteria the model is able to faithfully replicate the underlying data correlation matrix. Upon further thought this is not surprising; estimating the off-diagonal entries in 횯훿 creates a model that has the potential to explain nearly all linear relationships in the underlying data. In a comparative sense it is clear from the BIC= 24607 and DIC= 22327 that this more complex model would not be accepted relative to the less complex PCS model, but without this comparison we would have no clear grounds for rejecting the complex model, at least on the grounds of its ability to reproduce the sample data.

While this model is contrived (a researcher would be likely to reject the model based on poor factor loadings and other attributes), it does highlight a concern with indiscriminant estimation of 횯훿. If estimation of 횯훿 allows a grossly misspecified model to recreate the underlying data, it is almost certain that this approach will obscure important theoretical model misspecifications. Certainly this is not the last word on the veracity of this technique, but it does highlight the cautions we have provided in this manuscript. Incidentally, the co-author who was relatively gung-ho about this technique at first is now inclined to take a cautious, pessimistic view going forward.

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