IGNITING THE FIRE: THE IMPACT OF ANTICIPATORY ENTREPRENEURIAL PASSION ON EFFORT AND AFFECT IN NASCENT ENTREPRENEURS

by

DANIEL A. COHEN

Submitted in partial fulfillment of the requirements for the degree of

Doctor of Philosophy

Weatherhead School of Management

Designing Sustainable Systems

CASE WESTERN RESERVE UNIVERSITY

August, 2016

CASE WESTERN RESERVE UNIVERSITY

SCHOOL OF GRADUATE STUDIES

We hereby approve the thesis/dissertation of

Daniel A. Cohen

Candidate for the degree of Doctor of Philosophy*.

Committee Chair

Jagdip Singh, Ph.D., Case Western Reserve University

Committee Member

Melissa Cardon, Ph.D., Pace University

Committee Member

Kalle Lyytinen, Ph.D., Case Western Reserve University

Committee Member

Casey Newmeyer, Ph.D., Case Western Reserve University

Date of Defense

May 13, 2016

*We also certify that written approval has been obtained

for any proprietary material contained therein.

© Daniel A. Cohen, 2016

All rights reserved.

Dedication

This dissertation is dedicated to my family and close friends. First and foremost, this dissertation is dedicated to my wife, Lisa. We have done some awesome things in our twenty years of marriage and manage to have fun while doing it. Thank you for loving me just exactly the way I am and thank you for being there during this big Ph.D. challenge. Life simply would not be as worthwhile without you by my side. This dissertation is also dedicated to my wonderful children Maddie and Jack. Your smiling faces always lift my spirits even during the most trying of times. I love you both with all my heart. I also dedicate this dissertation to my sisters Debby and Carla. You are the two best big sisters that a guy could ask for. Thanks for caring so much for your little bro and always having my back. Speaking of having my back, I dedicate this study to my boyhood friends. You know who you are. Thanks for being great friends since forever and thanks for all the laughs and crazy times over the years. During trying times, I always get comfort thinking of the fun stuff we’ve done and continue to do. I would also like to dedicate this to my high school coach, Dr. Jack Wofford and my college coach, George Kochman. Thank you both for teaching me that achieving great things happens with hard work over extended periods of time—there is no finish line! Thank you also for helping me discover and unleash my competitive spirit. There is no way I would have achieved what I have in life without your coaching influence—you inspired me to develop young people the way you both developed me. Finally, I dedicate this dissertation to the memory of my parents, Seymour, and Jeanne. Thank you for loving me unconditionally and for believing in me with all your heart.

Table of Contents

List of Tables ...... vii List of Figures ...... viii Abstract ...... x Chapter 1: Introduction ...... 1 The Research Problem ...... 5 Research Questions ...... 8 Theoretical Background and Conceptual Model ...... 9 Chapter 2: Literature Review ...... 10 Anticipated Emotion ...... 10 Entrepreneurial Passion ...... 11 Identity Theory ...... 15 Construct Definitions ...... 18 Anticipatory Entrepreneurial Passion (AEP) ...... 18 Effort ...... 19 Progress ...... 20 Affect ...... 20 Chapter 3: Specific Theory and Hypotheses ...... 22 AEP over Time ...... 22 Affect to Effort or Effort to Affect? ...... 25 Affect to Effort ...... 26 Effort to Affect ...... 27 AEP Related to Effort ...... 29 Goal Progress Moderates Relationship between Effort and Affect ...... 31 Chapter 4: Research Methods ...... 35 Sampling and Data ...... 35 Participants ...... 35 Attrition ...... 35 Procedure ...... 36 Data Collection ...... 37 Measurement ...... 40 Measures ...... 40 Exploratory and Confirmatory Factor Analysis ...... 44 v Exploratory Factor Analysis ...... 44 Confirmatory Factor Analysis ...... 46 Validity and Reliability ...... 48 Common Method Bias ...... 50 Method of Analysis ...... 50 Chapter 5: Hypotheses Testing, Results, and Discussion ...... 52 Summary of Results ...... 52 Results H1a ...... 53 Results H1b ...... 54 Discussion of H1a and H1b ...... 58 Results H2a and H2b ...... 61 Discussion of H2a and H2b ...... 63 Results for H3 ...... 64 Discussion of H3 ...... 68 Discussion of H4 ...... 73 Chapter 6: Contributions, Limitations, and Conclusion ...... 75 Contributions ...... 75 Academic Contributions ...... 75 Contributions to Practice...... 80 Limitations ...... 80 Generalizing to Other Populations ...... 82 Conclusion ...... 84 Appendix A: Program Details ...... 86 Appendix B: Information about Study Participants ...... 94 Appendix C: Construct Table and Measures for Pre, Post and Weekly Surveys ...... 95 References ...... 101

vi List of Tables

Table 1. CFA Results ...... 47 Table 2. CFA Parameters and t-values ...... 48 Table 3. Validity and Reliability Results ...... 49 Table 4. Hypotheses Results Table ...... 52 Table 5. ANOVA Results ...... 53 Table 6. ANOVA Results Overall Model ...... 54 Table 7. Coefficient Results ...... 54 Table 8. Post Hoc Analysis of H1b ...... 56 Table 9. Results of Testing Relationships of Affect to Effort and Effort to Affect ...... 61 Table 10. Results for Testing the Relationship between AEP and Effort ...... 64 Table 11. Reporting Controls from Hypothesis 3 ...... 65 Table 12. Model Overview for Hypothesis 3...... 65 Table 13. Relationships between AEP and Effort ...... 66 Table 14. Multi-Collinearity Analysis Hypothesis 3 ...... 67 Table 15. Full model with Interaction Tested via Multiple Regression ...... 70 Table 16. Testing Interaction Effects of Progress ...... 72 Table 17. Test of Multi-Collinearity Hypothesis 4 ...... 73

vii List of Figures

Figure 1. Conceptual Model ...... 9 Figure 2. Conceptual Model ...... 39 Figure 3. Empirical Model ...... 39 Figure 4. Model for Hypothesis H1a ...... 53 Figure 5. Model for Hypothesis H1b ...... 55 Figure 6. Post Hoc Analysis Model for Hypothesis H1b ...... 56 Figure 7. Post Hoc Analysis Model for Hypothesis H1b Substituting Week 3 Data ...... 57 Figure 8. Post Hoc Analysis Model for Hypothesis H1b Substituting Week 5 Data ...... 57 Figure 9. Model for Testing Hypothesis H2a and H2b ...... 61 Figure 10. Model for Testing Hypothesis 3 ...... 64 Figure 11. Model for Testing Hypothesis 4 ...... 71

viii Acknowledgements

There is a long list of people that have supported me on this Ph.D. journey, and I am grateful to many. Without question, the first person I need to acknowledge is Jagdip Singh, Ph.D., my dissertation chair. Jagdip, I have the utmost respect for you and hold you in high esteem. You went so far above and beyond the call of duty in helping me to achieve a lifelong bucket list goal of earning my Ph.D. As I said to you, I can never repay you. I plan, though, to pay it forward by devoting myself to my students as you have devoted yourself to me. Thank you for pushing me, challenging me, helping me, guiding me, and encouraging me to be both precise and bold. You are truly exceptional at what you do, and I have had the greatest experience possible being your doctoral student. I worked harder and accomplished more than I ever thought possible on both our studies. Thank you, Jagdip, for giving some much of yourself to me. I promise to pay it forward! I would like to acknowledge and thank Melissa Cardon, Ph.D. for serving on my dissertation committee. Melissa, you have been so helpful to me and so generous in sharing your vast knowledge of entrepreneurial passion. You also keep me on the cutting edge by sending me the latest articles related to my work. It is an honor to have you on my committee, and my dissertation has benefited greatly from your expertise, feedback, information sharing, and good will. Thank you so much for being on my committee and for adding such immense value. Another acknowledgment and thank you is due to Kalle Lyytinen, Ph.D. Kalle, you are our fearless leader! You are an amazing scholar and an even better person! How you manage to listen to these multidisciplinary dissertations that span so many fields and add real value to each and every one is beyond me. Under your leadership, our program has grown into an outstanding, formidable and reputable program. Congratulations on the success of the program and a sincere thank you for being on my committee. Like everyone else in the program, my work has benefited greatly from your presence. Thank you for being on my committee. I would like to acknowledge and thank Casey Newmeyer, Ph.D. for serving on my dissertation committee. Casey, you have been involved since the very early days of my study and helped me to learn about doing an experimental design. You also helped me develop the very earliest conceptual model—I remember drawing it up on your white board in your office. You also helped me learn how to use Qualtrics! You are a very talented and capable scholar yet very accessible and down to earth. Thank you for always being available and willing to help. Thank you for being on my committee. Finally, I absolutely need to thank Sue Nartker and Marilyn Chorman. I know I probably drove you both to the brink of insanity with my simple requests—say, for example, defending two days before graduation—yet you both always exhibited such grace and dignity. Thank you both so much for all you do—you make this program great!

ix Igniting the Fire: The Impact of Anticipatory Entrepreneurial Passion on Effort and Affect in Nascent Entrepreneurs

Abstract

by

DANIEL A. COHEN

The number of nascent entrepreneurs continues to decline, and for those that do try

entrepreneurship, it often does not stick—they try it and move on to other careers.

Heightening our understanding of what motivates and guides the actions of nascent

entrepreneurs is key to addressing these negative trends. The current literature has

examined passion; although, for people who have never launched an entrepreneurial

venture, passion seems less relevant. Thus, in this study, we conceptualize anticipatory

entrepreneurial passion (AEP). AEP is the anticipation of intense positive feelings

associated with engaging in entrepreneurial activities meaningful to a “future self”

identity. AEP was empirically tested to analyze how it changes over time. The

relationship between effort and affect and affect and effort is simultaneously tested to

settle disparate findings in the literature. The use of a longitudinal design measuring

variables over seven time periods allows a basis for causal claims. Key findings include that AEP is both a malleable and enduring emotion and that AEP in current time period

predicts AEP in subsequent time periods. In terms of the effort to affect debate, findings

suggest that these variables are causally interlinked. Deepening our understanding of the

key variables under study that impacts the development and motivation of nascent x entrepreneurs is of interest to academics involved in teaching and researching entrepreneurship as well as practitioners that are either embarking on an entrepreneurial career or for mentors that may be advising nascent entrepreneurs.

Keywords: passion; effort; affect; progress; identity; anticipated emotions; anticipated passion; anticipatory entrepreneurial passion

xi CHAPTER 1: INTRODUCTION

“Without passion, you have no energy. Without energy, you have nothing.” — Warren Buffet

Passion for entrepreneurship is an active topic at the moment in both practitioner

and academic literature. A sampling of practitioner articles indicates “the path to success

starts with the passion inside of you…it makes you want to work harder than you ever

have before, and never give up” (Lopez, 2013: 2). A search of entrepreneurs and passion on Amazon.com yielded 827 books available on the subject. Hyrum W. Smith, Vice

Chairman of Franklin Covey Company, describes passion as “that fire in your belly…and even when people tell you that you’ve lost your mind, you don’t back down” (Lyden,

2001).

In academic literature, non-romantic passion is defined as “a strong inclination toward an activity that people like and that they find important” (Vallerand et al., 2003).

Passion has long been connected to entrepreneurship and Smilor (1997) called it “one of the most observed phenomenon in the entrepreneurial process” (p. 342). Shane, Locke, and Collins (2003) called passion that entrepreneurs experience selfish love of their work.

Baum and Locke (2004) found that passion is related to venture success. Even though entrepreneurial passion was being mentioned as an important motivational aspect of the entrepreneurial experience and driver of the entrepreneurial process, it was not well defined in academic literature until Cardon, Wincent, Singh, and Drnovsek (2009) conceptualized entrepreneurial passion as an intense positive feeling for an activity that is deeply meaningful to one’s identity. The passionate connection between an entrepreneur and his or her venture is so strong that it has been described using a parenthood metaphor

(Cardon, Zietsma, Saparito, Matherne, & Davis, 2005). 1 While we know what entrepreneurial passion is (an intense positive feeling for an activity that is important to one’s identity (Cardon et al., 2009)), and what it does (it motivates entrepreneurs to pursue meaningful and coherent goals (Cardon et al., 2009), and how it relates to one’s identity (Murnieks, Mosakowski, & Cardon, 2011), a gap exists as entrepreneurial passion does not explain how it motivates nascent entrepreneurs that are new to the entrepreneurial process and have not yet engaged in entrepreneurial activities. How can a nascent entrepreneur harbor an intense positive feeling for an activity that is meaningful to his or her identity without having first engaged in the activity? In this dissertation, I address this gap by introducing a new construct, anticipatory entrepreneurial passion (hereafter called AEP), that I believe motivates nascent entrepreneurs to engage in entrepreneurial activities.

I want to be precise about the focus of this dissertation. I want to define, develop scales for, and test this new construct AEP on important outcome variables that will be described shortly. In terms of defining AEP, it is important to first understand its genesis: entrepreneurial passion (hereafter called EP). Cardon et al. (2009) define EP as

“consciously accessible intense positive feelings experienced by engagement in entrepreneurial activities associated with roles that are meaningful and salient to the self- identity of the entrepreneur” (p. 517). This definition depicts experienced entrepreneurs that are drawing upon their present or past intense positive feelings that are connected to specific role identities (Cardon et al. 2009) that have been established and validated by meaningful others through prior experience (Markowska, Hartel, Brundin, & Roan,

2015). Nascent entrepreneurs, however, do not have these experiences to draw upon nor

2 have their role identities been fully formed or validated by meaningful others

(Markowska et al., 2015).

While related to EP, AEP is a distinctive construct in a two important ways. First,

is the temporal orientation of AEP compared to EP. AEP has a future or potential

orientation while EP is based on present or past experiences. AEP is an anticipated

emotion whereas EP is a felt emotion. While an experienced entrepreneur may have

consciously accessible intense positive feelings based on present or past experiences or

“felt” emotions (Cardon et al., 2009), a nascent entrepreneur’s consciously accessible

intense positive feelings are based on anticipated emotions. Rather than drawing on past

or present experiences, a nascent entrepreneur imagines these intense feelings—how good it will feel when they raise initial capital or make that all-important first sale.

Second, Cardon et al.’s (2009) definition describes how these intense positive feelings are

“meaningful and salient to the self-identity of the entrepreneur” (p. 517). For an identity to be actualized, it must be validated by relevant external parties (Markowska et al.,

2015). For an entrepreneur, this could be customer validation and could occur via making enough sales to turn a profit. Or it could be validation from an investor that believes enough in the entrepreneur to make a financial investment in him/her. For most nascent entrepreneurs, this validation is a work in progress as most fail to build a viable company

(Reynolds & White, 1997)and thus fail at an initial attempt at validating an entrepreneurial identity (Markowska et al., 2015).

Cardon et al. (2009) describe three entrepreneurial sub-identities, that of inventor, founder, and developer. Cardon et al.’s (2009) describes these role identities as “(1) an inventor identity, where the entrepreneur’s passion is for activities involved in

3 identifying, inventing, and exploring new opportunities; (2) a founder identity, where the entrepreneur’s passion is for activities involved in establishing a venture for commercializing and exploiting opportunities; and (3) a developer identity, where the entrepreneur’s passion is for activities related to nurturing, growing, and expanding the venture once it has been created” (p. 516). Cardon et al. (2009) point out that these constructs are correlated and by no means mutually exclusive—an entrepreneur, for instance, might have salient role identities as an inventor and founder. Given the age and lack of entrepreneurial experience in this sample, the developer identity—with its focus on growing the venture once established—was eliminated due to lack of relevance to nascent entrepreneurs. The roles of inventor or founder are more conceivable to entrepreneurs at this stage. Thus, AEP is defined as the anticipation of intense positive feelings associated with engaging in entrepreneurial activities related to a “future self”

(Ibarra, 1999) identity of inventor or founder.

A nascent entrepreneur is defined as someone who initiates serious activities that are intended to culminate in a viable business startup (Reynolds, 1994). Nascent entrepreneurs follow an evolutionary process beginning with their intentions and continuing through attempts at activities aimed toward founding a company (Aldrich &

Martinez, 2001). Operationally, one becomes a nascent entrepreneur when they move beyond thinking or talking about becoming an entrepreneur to actually engaging in entrepreneurial activities (Aldrich & Martinez, 2001). Most nascent entrepreneurs do not succeed in actually creating a company (Reynolds & White, 1997) as, in many cases, their idea is not feasible, or they could not obtain necessary resources.

4 The problem of practice in this exploratory study relates to the development of this new construct, AEP, and how it motivates nascent entrepreneurs via impact upon effort and affect. In order to examine this problem in depth, data has been collected longitudinally from participants in two summer entrepreneurship accelerator programs that are designed to help nascent entrepreneurs make progress toward starting an entrepreneurial venture in order to understand the changes these nascent entrepreneurs undergo as AEP fuels engagement in entrepreneurial activities. Along the way, these nascent entrepreneurs receive and learn via feedback from mentors, customers, and suppliers, as they try on and seek to validate “possible” entrepreneurial role identities.

Further, recent research indicates that, despite significant growth in universities offering courses and programs related to entrepreneurship, the number of young people choosing to become entrepreneurs is declining (Simon & Barr, 2015). Exacerbating this problem is the fact that most nascent entrepreneurs do not become entrepreneurs because their ideas do not turn out to be feasible (Reynolds & White, 1997). A dwindling pipeline of young people trying entrepreneurship that gets further restricted during nascent attempts is a disconcerting set of facts given the importance of entrepreneurship to the US economy. Findings in this study may help stem this tide by heightening our understanding of AEP and its impact on important outcome variables.

The Research Problem

Recent research conflicts on important temporal dimensions related to effort and affect and my model provides an opportunity to reconcile and extend this research in a meaningful way. Foo, Uy, and Baron (2009) found that affect impacts effort with negative affect positively relating to effort on immediate tasks and positive affect relating

5 to effort on tasks beyond those that are immediate. Broadly speaking, Foo et al. (2009) find that emotion causes effort. Alternatively, Gielnik, Spitzmuller, Schmitt, Klemann, and Frese (2014) found that effort causes passion. When one puts in more effort, he or she feels more passionate about being an entrepreneur. When one puts in less effort, his or her passion wanes. Broadly speaking, Gielnik et al. (2014) find that effort causes emotion. Untangling these disparate findings is important as the relationship between effort and emotion is an important component of the entrepreneurial process.

My general conceptual model, which will be described at the end of this chapter, tests the interrelationships between AEP, effort, goal progress and positive affect. Does effort cause emotion or does emotion cause effort? In addition to answering this important question, the additional constructs in my model allows for a more fine-grained analysis that provides an answer to this question along with heightening our understanding of whether effort causes emotion or emotion causes effort. Further, it is predicted that goal progress moderates the relationship between effort and affect.

Another research problem I seek to address is whether AEP sparks entrepreneurial effort. Gielnik et al. (2014) found that effort is positively related to passion. Gielnik et al.

(2014) also tested the opposite direction and found a non-significant relationship between passion and effort. Will Gielnik et al.’s (2014) finding be replicated with AEP in place of

EP or will the anticipatory nature of AEP more strongly motivate effort? The theory of anticipated emotion may help explain how AEP drives effort in nascent entrepreneurs.

A dominant paradigm in emotion research is that emotion causes behavior

(Russell, 2003). According to Russell (2003), “everyone knows that fear brings flight and anger brings fight” (p. 161). In certain situations, such as seeing a wild animal

6 approaching in a threatening manner, this direct causation is certainly applicable.

Baumeister, Vohs, DeWall, and Zhang (2007) challenged the paradigm that emotion directly causes behavior and created a new theory of how emotion shapes behavior.

Rather than direct causation, Baumeister et al. (2007) conceptualize a theory that emotion

shapes behavior through feedback, anticipation, and reflection.

Research indicates that anticipated emotion is a stronger motivator than felt

emotion because people believe their emotions will be bigger and longer-lasting than they

turn out to be in actuality (Wilson & Gilbert, 2003). For instance, a study by Gilbert,

Pinel, Wilson, Blumberg, and Wheatley (1998) found that untenured professors, when

asked about what their lives would be like if they failed to make tenure, predicted severe

and long-lasting emotional distress if they failed. In reality, for those that failed to make tenure, they got over the distress rapidly. For challenging and/or uncertain future events such as making tenure or trying to get a new startup successfully launched, the stronger

motivation of anticipated, rather than felt or actual, emotion makes sense. If making

tenure was no big event, assistant professors would not put in such concerted effort

(Baumeister et al., 2007). If failing as an entrepreneur was a trivial matter, entrepreneurs

would not work such long, intense hours. While these examples focus on negative

emotion (avoiding failure), other researchers have found that anticipated emotions

motivate positive outcomes as well. Bagozzi and Pieters’ (1998) study on dieting and

exercising behavior found that anticipating reaching their goal motivated people to exert

more effort leading to greater goal attainment. Bandura (1989) posited that when people

set more challenging goals, they anticipate positive outcomes and persist more when they

endure setbacks.

7 I argue that nascent entrepreneurs, being new to entrepreneurship, actually

anticipate entrepreneurial passion—that is they anticipate intense positive feelings related to engaging in an entrepreneurial role such as founding a company or inventing a new product and this anticipation fuels effortful tacking of entrepreneurial tasks designed to move them in the direction of making this anticipated and intense positive feeling a reality. I propose that it is not EP that motivates the efforts of these nascent entrepreneurs. Rather, it is AEP—how great they will feel in the future when they actually experience the passion they are pursuing. In this scenario, AEP motivates effort.

However, if that effort fails to yield progress, then it is predicted the passion will wane thus supporting Gielnik et al.’s (2014) findings. If the entrepreneurs make progress, it is predicted that they will feel positive affect about that progress and that affect will cause the entrepreneur to grow AEP in a subsequent time period.

This study examines a developmental angle revealed over four time periods so that we can understand what ignites passion in nascent entrepreneurs. So, one research problem this research attempts to resolve is understanding, through the creation and testing of this new construct AEP, how AEP motivates nascent entrepreneurs to partake in effortful engagement in entrepreneurial tasks. Is AEP in one time period related to

AEP in a subsequent time period? Does AEP endure like EP or is it more malleable or episodic similar to affect? Finally, as discussed, I attempt to reconcile conflicting findings in the literature regarding the temporal relationship between effort and affect.

Research Questions

Integrating the broad theories around EP, AEP, effort, and affect leads me to ask the following research questions:

8 1. Is AEP an enduring or malleable emotion?

2. As it relates to nascent entrepreneurs, does effort cause emotion or does

emotion cause effort?

Theoretical Background and Conceptual Model

Figure 1. Conceptual Model

This section provides an introduction to relevant theories that underpin the development of my conceptual model. In Chapter 2, the literature review, I will review these theories in depth.

9 CHAPTER 2: LITERATURE REVIEW

Anticipated Emotion

Anticipated emotion is the projection of how one will feel after a future event or

scenario has occurred. Anticipated emotion is based on the interplay between enduring

and episodic emotion and is dependent on the “distinction between automatic affect and

full- fledged conscious emotion” (Baumeister et al., 2007: 172). Conscious emotions are

long-lasting, powerful positive or negative emotions that often are accompanied by

physical arousal (Baumeister et al., 2007). Automatic affect is a momentary feeling that is

either positive or negative and typically guides behavior toward something (approach) or

away from something (avoidance) (Carver, 2004). Automatic affect (also known as state

affect) can provide quick directional feedback to approach something that feels good or

to avoid something that feels bad. According to Baumeister et al. (2007), “automatic

affective responses can preserve the lessons and information from previous emotional

experiences” (p. 172). So, conscious emotion is an intense, long-lasting positive or

negative emotion that typically takes some time to activate while an affective emotion is a nearly instantaneous short-lived emotion that guides behavior to approach or avoid

(Baumeister et al., 2007). Thus, “the combination of previous emotional outcomes and current affect also contributes to making people start anticipating emotional outcomes— and to choose their actions according to the emotions they expect will ensue”

(Baumeister et al., 2007: 172). As a result, people, motivated by pursuing the intense, long-lasting emotional feeling sought, act in ways designed to make this visionary feeling a reality. While in pursuit of this anticipated emotion, affective (or state) affect serves to

10 guide behavior in intended direction via quick feedback and to store key learnings that

serve to guide future behavior.

Anticipated emotion is positively related to goal pursuit via volitional processes

necessary to turn anticipated outcomes into real outcomes (Bagozzi & Pieters, 1998).

These volitional processes—directive (intentions), motivational (effort exerted), and regulatory (planning and progress)—act to transform anticipated emotion into active

pursuit of goals (Bagozzi, 1992). Bagozzi and Pieters (1998) study concluded that a “key

to the emotional goal system is the functioning of anticipatory emotions. Here, a

decision-maker must be capable of ‘imagining the possible’” (p. 20).

For goals to be actively pursued, though, it goes beyond imagining to the

previously mentioned volitional processes. As it relates to this study, AEP leads to

volitional processes of effort and progress. Bagozzi and Pieters (1998) found that the

anticipated emotion of wanting to lose weight—that is the anticipation of how good it

will feel to be thinner in the future—lead to the volitional processes of meal planning

which lead to goal attainment. As applied to this study, AEP will not lead to actually

pursuing the goal of becoming an entrepreneur without the accompanying volitional acts

of exerting effort and making progress. If effort is exerted and progress made, positive affect results as a feedback and learning system that affirms the direction and begins the

process of turning anticipated outcomes into real outcomes.

Entrepreneurial Passion

Cardon et al. (2009) describe, as mentioned, EP as a “consciously accessible, intense positive feelings experienced by engagement in entrepreneurial activities associated with roles that are meaningful and salient to the self-identity of the

11 entrepreneur” (p. 517). Despite much discussion about passion and entrepreneurship

(Smilor, 1997), it was not until Cardon et al.’s (2009) conceptualization of the construct

that is had been clearly defined in academic literature.

EP hailed from work done in passion for activities, identity research, and

grounding in the domain of entrepreneurship (Cardon et al., 2009). Passion for activities

(non-romantic passion) is about engaging in activities that people love and that are

important to them personally (Vallerand et al., 2003). Such passions are important to

people because they have ties to their identity—they begin to self-identify as a basketball player, musician, or entrepreneur, by way of example. Vallerand et al. (2003) espoused two types of passion—harmonious and obsessive. When these passion-driven identities integrate well with other salient identities, such as mother, sibling, doctor, or spouse, this is known as harmonious passion. By way of example, a golfer that enjoys the harmonious

passion for golf loves to play golf but does not play golf every day or ignore other salient

role identities just to pursue the passion of playing golf. A person that does not integrate a

passion-driven identity well with other identities is said to have obsessive passion. The

golfer in the previous example would want to play golf regardless of other identity

commitments and would feel a constant tension—if they spent time tending to another

identity, such as spouse or parent, they would want to be playing golf and if they played

golf, they would feel guilty about not meeting the needs put forth by a competing identity

(Vallerand et al., 2003). In the domain of entrepreneurship, Thorgren and Wincent (2015) applied this dualistic nature of passion (harmonious and obsessive) to compare nascent and habitual entrepreneurs and found that habitual entrepreneurs scored higher on obsessive passion.

12 While not widely tested empirically, some previous work has been done linking passion with important outcome variables. Baum and Locke (2004) found an indirect positive relationship between passion and venture growth. Mitteness, Sudek, and Cardon

(2012) found that displayed passion had an impact on investor’s evaluations of funding decisions. Murnieks et al. (2011) found a positive relationship between EP and engaging in entrepreneurial behaviors. In a qualitative study, Bird (1989) found that passion is related to persistence and tenacity. Further, Bird (1989) found that passion makes entrepreneurs feel the inevitable ups and downs as personal events. Cardon and Kirk

(2013) found the entrepreneurial passion for inventing fully mediated the relationship between self-efficacy and persistence while entrepreneurial passion for founding partially mediated the same relationship.

In Cardon et al.’s (2009) review of previous work on passion in the domain of entrepreneurship noted several definitions that shared commonalities noting “across these definitions, passion invariably involves feelings that are hot, overpowering, and suffused with desire. This fire of desire is referred to in virtually all writings on entrepreneurial passion with words such as enthusiasm, zeal, and intense longing” (p. 515).

Entrepreneurs feel such a strong emotional connection to their ideas, concepts, and companies that a parent/baby metaphor applies (Cardon et al., 2005).

While entrepreneurial passion is related to motivation, it is a separate and distinct construct (Murnieks et al., 2011). While motivation includes a wide variety of psychological factors that induce an individual to exert effort, “passion refers more specifically to intense, positive inclinations aimed at specific tasks” (Murnieks et al.,

2011). In terms of distinguishing passion, Vallerand et al. (2003) found empirical

13 differences between passion and other motivational constructs such as intrinsic motivation and extrinsic motivation stating that “although both passion and intrinsic and extrinsic motivation are both motivational concepts, they represent different constructs”

(p. 761). Passion is a domain- specific motivational construct (Chen, Yao, & Kotha,

2009) meaning that passion for entrepreneurship is similar to passion for other activities in some ways, yet it differs in other ways due to the dynamic, fast-paced, intense environment applicable only to entrepreneurship. Entrepreneurs are motivated to persist in these challenging environments because they associate passion with a connection to their identity (Cardon & Kirk, 2013; Cardon et al., 2009).

EP has been studied as a starting point—an independent variable that motivates effort (Cardon et al., 2009) or persistence (Bird, 1989) or other outcome variables. Two recent studies, however, have looked at EP as a dependent variable. Collewaert, Anseel,

Crommelinck, De Beuckelaere, and Vermeire (2016) found that passion for founding fades over time and that those entrepreneurs that either try a multitude of new ideas or seek feedback from mentors see less of a diminishment of passion for founding. Gielnik et al. (2014) also looked at factors that influence passion by examining the relationship of effort to passion finding that as effort wanes, so does EP suggesting that EP may be malleable or dependent on other factors. These are interesting findings, as EP has been characterized as an enduring emotion (Cardon et al., 2009) that does not change or fade over time (Cardon & Kirk, 2013). This dissertation has an opportunity to add to this discourse by examining AEP over time to determine if it is an enduring or malleable emotion or, perhaps, both.

14 Identity Theory

Identity theory hails from the field of social psychology and focuses on the

content and organization of a self-concept (Gecas, 1982; Stryker & Burke, 2000). Self-

concepts are composed of multiple identities, and these identities represent societal

expectations of behavior (Stryker, 1968). Individuals possess and manage these multiple identities such as sibling, spouse, doctor, entrepreneur, or mother and organize them

hierarchically according to prominence (importance of identity) (McCall & Simmons,

1966) and salience (probability of enacting an identity) (Stryker, 1968). Prominence, or

importance of an identity, is determined by many different factors including each

identity’s “degree of self and social support, one’s degree of commitment to and

investment in it, and the extrinsic and intrinsic gratifications associated with it” (McCall

& Simmons, 1966: 76). A salient entrepreneurial identity concerns the probability of

enacting an entrepreneurial identity. Some people become entrepreneurs simply to make

a living. They have enacted an entrepreneurial identity; thus, it is salient, but it may not

be central to a person’s self-concept, and therefore, it may not be prominent.

Identity centrality and identity salience relating to entrepreneurs are

complementary rather than competing concepts (Murnieks, 2007). For instance, if a

person perceives their entrepreneurial identity as central (important) they are more likely

to enact the identity (salience) (Murnieks, 2007). Because EP is about an intense longing

for an activity that is important to one’s identity (Cardon et al., 2009), identity centrality

is more relevant to this research. Murnieks et al. (2011) found that identity centrality is

positively related to and is a pre-cursor to EP.

15 Identity scholars, it should be noted, draw a distinction between roles and identity.

An identity is a cognitive schema resulting from the internalization of a role into a person’s self-concept (Stryker & Burke, 2000). A role is a set of expected behaviors attached to certain social statuses or positions (Cast, 2004) such as entrepreneur, sibling, or professional athlete. Cast provides a good differentiation between roles and identities,

“the status of mother has an attached set of role expectations, such as feed the child, clothe the child, rock the child, and these expectations are internalized in an identity so that each mother has an identity that reflects these expectations of herself in the status of mother” (p. 57). According to Stryker (1989), a self-concept contains a “structure of differentiated identities organized in a hierarchy of salience” (p. 54). According to

Murnieks (2007), “people’s conceptions of who they are rest on an aggregation of multiple different identities (parent, friend, entrepreneur, etc.) which they assume depending on time and place. Entrepreneurs’ self-concepts likely contain varying identities (spouse, father or mother, community leader, etc.) in addition to their entrepreneurial one” (p. 24).

As mentioned previously, Cardon et al. (2009) presented three entrepreneurial role identities—inventor, founder, and developer—that are specific to the domain of entrepreneurship. For use in this dissertation, the roles of inventor and founder are most salient to nascent entrepreneurs in my sample because the role of developer is related to growing a business over a long time horizon and that role may seem less accessible to nascent entrepreneurs.

There is a paucity of research on the topic of how entrepreneurs form an entrepreneurial identity. Recent research has proposed a conceptual framework that

16 suggests a relationship between positive emotion and the formation of an entrepreneurial

identity and my study has an opportunity to test some of these relationships empirically

(Markowska et al., 2015). According to Markowska et al. (2015), “the driver behind

entering an entrepreneurial role plays a key role in a person’s motivation orientation (an

approach or avoidance orientation) towards entrepreneurship” (p. 217). More fully,

Markowska et al.’s (2015) theoretical model proposes that the development of an entrepreneurial identity starts with the driver or motivation (whether the entrepreneur is being pushed into entrepreneurship versus being drawn or attracted to entrepreneurship) and then examines “the role of emotions in the perception and enactment of an entrepreneurial role and identity” (p. 217). For those “pushed” into entrepreneurship, they

“dis-identify” by seeing “the entrepreneurial role as (the) only choice for career;

receiving negative feedback from others when enacting the entrepreneurial role” (p. 217).

For those pulled or drawn to entrepreneurship, they see the “entrepreneurial role as

enabling a career vision; receiving positive feedback from others when enacting

entrepreneurial role” (p. 217). Thus, starting with their driver (positive emotion being

“pulled” or drawn to enacting an entrepreneurial role) and continuing with their

emotional response to enacting the entrepreneurial role (getting positive feedback fuels

their positive emotion) the nascent entrepreneur continues this virtuous cycle

continuously until the entrepreneurial identity fully forms.

There are interesting similarities between Markowska et al.’s (2015) theoretical

model and my empirical study that provides an opportunity to test part of their theoretical model and to meaningfully extend their work. While Markowska et al. (2015) theorize

that a “driver” is key to igniting this process, they do not discuss what driver may be at

17 work in drawing nascent entrepreneurs to entrepreneurship leaving a significant gap for

my contribution. I argue that this ignitor or “driver” is AEP and its sub-identities of future inventor or founder. This dissertation has an opportunity to make a contribution to the identity literature as there is a lack of research on how entrepreneurial identities form.

Construct Definitions

This study examines the relationships among the following constructs: anticipatory entrepreneurial passion, effort, goal progress, affect, and entrepreneurial identity enactment. Next, I will describe and define each construct.

Anticipatory Entrepreneurial Passion (AEP)

This construct and associated measures of AEP, while based on of Cardon et al.’s

(2009) definition of entrepreneurial passion, is conceptually quite distinct as AEP is the

anticipation of wanting to become an entrepreneur—the belief that being an entrepreneur is an experience worth pursuing. Nascent entrepreneurs may find that being an entrepreneur, in reality, is quite different than what they have envisioned. This could partially explain why most nascent entrepreneurs do not pursue entrepreneurial careers

(Reynolds & White, 1997). Also, as it relates to identity, EP is an intense positive feeling for an activity that is meaningful to the self-identity of the entrepreneur. Meanwhile, AEP relates to a “possible” or “future” identity that may be one of a few different identities that the nascent entrepreneur is contemplating (Ibarra, 1999) or attempting to validate

(Markowska et al., 2015). Thus, key distinctions between AEP and EP are that AEP is the anticipation of passion whereas EP is experienced passion and AEP is the pursuit of, or validation of, a “possible” or “future” identity whereas EP is an identity that is already meaningful in the life of an entrepreneur. I am defining AEP as the anticipation of intense

18 positive feelings associated with engaging in entrepreneurial activities related to a “future self” (Ibarra, 1999) identity of founder or inventor.

Whereas Cardon et al. (2009) conceptualized three sub-identities that are salient to entrepreneurs (inventor, founder, and developer identities), I only conceptualize two

(inventor and founder identities) as the developer identity fits better with those that have experience in building companies over time.

Effort

According to Uy, Foo, and Ilies (2015), “effort is critical to new venture implementation” (p. 378). Despite this importance, there has not been much empirical work done that examines effort in the entrepreneurial domain. This could be because the construct of effort is hard to define and measure because it is “an invisible, internal, hypothetical construct that is not observable” (Yeo & Neal, 2004: 231). Yeo and Neal define effort as “a limited-capacity resource that can be allocated to a range of different activities, including on-task, off-task, and self-regulation activities. These allocations can vary in terms of intensity and persistence” (p. 231). Effort intensity describes how hard a person tries to execute a given behavior (Kanfer, 1990; Vroom, 1964). Given that effort, as described, is a “limited capacity resource,” we focus on time expended as a measure of effort. Given that effort intensity describes how hard a person tries, we also measure an individual’s self-report of how their effort impacted the startup’s progress toward achieving goals. Thus, I am defining effort as the time (measured self-report by time worked) and intensity of effort (measured by individual impact on startup goals) allocated to the startup.

19 Progress

Experiencing progress, even small bits of progress, can positively impact work

motivation. Amabile and Kramer (2011) argued that incremental, ordinary bits of progress can have a significant impact on day-to-day motivation observing that participants noted a “good day” when they made progress towards achieving goals.

Weick’s (1984) theory of small wins discusses the importance of small “wins” or advances in progress that build confidence and empower people to persist and move forward. Huang, Zhang, and Broniarczyk (2012) found that people’s perceptions of goal progress sustained their efforts and kept them focused on goal striving. There is a connection in all these studies between progress and achieving goals. Progress is an indicator that an individual or group is either achieving their goals or are moving in a positive direction toward achieving their goals. For this study, I am defining progress as the extent of perceived progress made this week in relation to overall startup goals. This is measured via self-report and validated via mentor report.

Affect

First, it is important to note that we are interested in the state rather than trait affect in this study. State affect reflects a participant’s current feeling or mood rather their longer, term average feelings or moods. According to Baron (2008), entrepreneurs experience strong affective responses because of the chaotic, unpredictable nature of entrepreneurship and because of the level of investment in time, money, and effort that entrepreneurs make in their startup. Affect reflects a signal of how one feels about how things are going (Carver, 2004). If a person’s state affect is negative, this is a signal that things are not going well and that a change needs to occur. On the other hand, if a person

20 experiences positive affect, it is a sign that things are going well, and change may not be needed (Carver, 2004). These moods are episodic and fleeting compared to the more enduring emotion evident from core affect (Russell, 2003). I am defining state affect as the episodic, fleeting positive and negative emotions one encounters in everyday life.

Examples of state positive affect include feeling inspired or enthusiastic. Examples of negative state affect include feeling irritable or nervous.

21 CHAPTER 3: SPECIFIC THEORY AND HYPOTHESES

AEP over Time

This hypothesis is exploratory in nature combining a new construct, AEP, with a question about whether it endures or changes over time. As mentioned, Cardon et al.

(2009) conceptualized EP as an enduring emotion. Empirically, Cardon and Kirk (2013) also found EP to be an enduring emotion with very little change over an eighteen month period. Gielnik et al. (2014) found that effort impacted EP and with low effort, EP waned. Collewaert et al. (2016) found that EP wanes over time and that certain activities such as frequency of changing ideas and approaches or seeking feedback from mentors slowed but did not stop the waning of EP.

In analyzing the samples of Cardon and Kirk (2013), Gielnik et al. (2014) and

Collewaert et al. (2016), there is a significant age and experience differential between

Cardon and Kirk’s (2013) sample, with an average age of 48 and 85% having founded at least one company and both the Gielnik et al. (2014) sample (average age 36 and significantly less entrepreneurial experience) and Collewaert et al.’s (2016) sample

(average age 33 and described as nascent entrepreneurs). Perhaps the enduring nature of passion is related to more experiences as an entrepreneur and, thus, more external validation of an entrepreneurial identity (Markowska et al., 2015). Alternatively, as most nascent entrepreneurs do not become entrepreneurs (Reynolds & White, 1997) perhaps

Cardon and Kirk’s (2013) more experienced sample enjoyed the benefit of having less passionate or less validated, from a role identity standpoint, nascent entrepreneurs culled from the sample. In Gielnik et al. (2014) and Collewaert et al.’s (2016) samples of

22 younger, less experienced entrepreneurs, EP was malleable—it changed based on exposure to other variables.

In my eight years’ experience in guiding nascent entrepreneurs in an entrepreneurship accelerator program at a prestigious university located in the northeastern U.S., I noticed that they were quite susceptible to external cues or comments made by others—particularly potential customers or investors. Whereas more experienced entrepreneurs would take such feedback in stride and think of it as a lesson learned while testing the merits of their idea, a nascent entrepreneur would typically feel significant negative affect after such interactions. I would discuss this dynamic with the other members of my teaching team and with key external industry-specific mentors, and we collectively would work to help the nascent entrepreneurs make sense of this feedback in the context of their overall entrepreneurial goals. Nascent entrepreneurs would sometimes take bits of customer feedback and globalize to all customers. Instead of “this specific customer did not see the value in our product”, it sometimes became

“customers will not buy our products.” A similar dynamic occurred when faced with investor rejection. Experienced entrepreneurs, especially those that have tasted success, have been known to talk openly about how many investors rejected their startup— somewhat of a badge of honor. For example, Howard Schultz, founder of Starbucks, was legendarily turned down by the first 242 investors that he pitched

(https://www.flickr.com/photos/deeplifequotes/7381350066/).

For many nascent entrepreneurs, though, rejection by early stage investors or customers can be hard to handle emotionally because it may be perceived as an invalidation of their emerging entrepreneurial identity (Markowska et al., 2015).

23 Murnieks et al. (2011) found that early empirical studies on passion did not find a

significant impact on individual attitudes or behaviors because the identity piece was

omitted from EP until Cardon et al. (2009) included identity as a meaningful component

in the definition of EP. Further, according to Murnieks et al. (2011), “entrepreneurial

identities are key correlates capable of influencing the rise and fall of passion” (p. 6). In

this sample, nascent entrepreneurs are in the process of testing or validating their entrepreneurial identities. According to Stryker (1984), emerging identities seek validation and that the more important the identity, the more it is in need of being validated. Rejection from prospective customers or investors may slow or stop this validation. As a result, it was quite typical for these nascent entrepreneurs to lose some passion for their startup over the course of the accelerator program. Through learning, effective mentoring, and perhaps a rekindling of residual passion via later occurring identity validation, it would typically resume to prior levels over time. Thus,

Hypothesis 1a. Mean values of AEP show significant variation over time such that the value becomes lower in the middle of the program relative to the beginning or end.

My experience in mentoring hundreds of nascent entrepreneurs led me to conclude that their passion would typically stumble but not fall. If AEP was a flame, negative feedback from customers or potential investors would be wind causing it to flicker. However, the flame would rarely blow out permanently. This is the enduring aspect of AEP. In the short term, certain factors might cause it to change; however, over time, it typically comes back to previous levels.

To explore this in more detail, it is necessary to examine the core components of

EP as a basis to explain behaviors observed in AEP. According to Cardon et al. (2009),

24 “as a feeling, EP differs from episodic changes in core affect. While the latter is

subconsciously or unconsciously activated by external objects or activities that may be

inert or irrelevant to an individual’s identity meaning, passion involves “intense longing”

that one feels for objects or activities that are deeply meaningful to one’s identity,

whether those objects are real, remembered, desired, imagined, or anticipated” (p. 515).

Thus, EP is different from the episodic nature of core affect due to its enduring nature.

I argue that AEP, while different from EP due to the future temporal orientation

of the construct and the “future self” orientation of the role identities of inventor and

founder, will also have an enduring characteristic. This theory of future or possible selves

(Ibarra, 1999; Markus & Nurius, 1986) posits that people “try out” future identities that

they would like to validate because they anticipate these identities will be meaningful

given their career aspirations. Therefore, it is the anticipation of an intense positive

feeling felt through engaging in activities that align with a relevant role identity (future

self) that causes AEP to be an enduring emotion. As such, current AEP will be predicted

by previous AEP. Thus,

Hypothesis 1b. The effects of AEP at the current time period will be positively related to the effects of AEP in the previous time period.

Affect to Effort or Effort to Affect?

The next two hypotheses proposed are competing hypotheses in the literature— does effort cause affect or does affect cause effort? Gielnik et al. (2014) found that effort impacted passion—as effort increased, passion increased and as effort decreased, passion waned. In this study, however, Gielnik et al. (2014) used only two items to measure EP— one item representing a founder identity and one item representing an inventor identity— rather than using all of the EP measures—thus washing out the effects of each role 25 identity in his measurement of EP. According to Murnieks et al. (2011), without the

identity component, passion rises and falls more readily and is less enduring. So, while

called passion in the study, as it is operationalized, it is really an episodic emotion like

state affect. On the other hand, Foo et al. (2009) found that affect was related to effort.

The longitudinal design of this study allows me to test both of these competing

hypothesis to determine whether effort causes affect or affect causes effort.

Affect to Effort

Foo et al. (2009) found support that affect leads to effort exerted. According to

Carver and Scheier (1990), negative affect is a signal that progress toward goal

attainment has been inadequate and that increased effort is necessary to reduce this

discrepancy. Positive affect is a sign that things are going well (Carver, 2003) and that

the entrepreneur can look beyond immediate tasks and begin to exert effort on longer

term, more strategic tasks (Foo et al., 2009). Drawing on Carver’s (2003) affect-as-

information theory, Foo et al. (2009) found that negative affect leads to greater effort on venture tasks that needed immediate attention, and that positive affect leads to effort on

longer term, more strategic tasks.

Foo et al.’s (2009) findings suggest that affect impacts effort exerted—both in the

immediacy and longer term. Foo et al. (2009) suggest that “affect serves as a source of

information for entrepreneurs” (p. 1092). Consistent with affect-as-information theory,

Foo et al.’s (2009) findings indicate that negative affect suggests something is wrong, and

the focus needs to be on fixing it immediately. Foo et al.’s (2009) interpretation of

positive affect, meanwhile, differs from Carver and Scheier’s (1990) view that it indicates

an opportunity to cruise. Rather, Foo et al. (2009) suggest that positive affect allows the

26 entrepreneur to acknowledge that all is going well, and his/her vision can be refocused

from immediate, day-to-day tasks toward “bigger picture” tasks.

While there is no reason to believe that Carver’s (2003) affect-as-information theory does not apply to nascent entrepreneurs, it is worth noting that Foo et al.’s (2009) conclusion that affect serves as a source of information for entrepreneurs has its limits given this sample’s lack of entrepreneurial experience. In short, they have had less opportunity to learn from negative and positive affect and how affective experiences

motivate their entrepreneurial efforts. Given the exploratory nature of this study, it will

be interesting to see if these findings apply to nascent entrepreneurs. Thus,

Hypothesis 2a. Greater the positive affect at time t, the higher the effort invested in entrepreneurial activities in the subsequent time period.

Effort to Affect

I will draw on two theories—Vroom’s expectancy theory and the theory of

emotion as a feedback system—to support my argument that effort impacts affect. A key

theory that underpins motivation in nascent entrepreneurship is Vroom’s (1964)

expectancy theory. In Vroom’s theory, three sub-components are: startup-specific

instrumentality (if I put in effort I will be rewarded—i.e. the link between effort and

affect), expectancy (if I work hard I can start my own business), and valence (I feel

strongly about succeeding in my entrepreneurial effort because of the value to me as an

individual). While I use different nomenclature, this study examines nascent

entrepreneurs’ development via expectancy theory by examining effort and affect

simultaneously.

A second theory that is relevant to the argument that effort causes affect is the

theory of emotion as a feedback system. A basis of this theory is that we behave (exert 27 effort) in anticipation of what that behavior will yield (positive affect). This theory is

related to anticipatory emotion, and it is counter to long-held beliefs that emotion causes effort (Baumeister et al., 2007). As mentioned, the notion that fear leads to flight or anger leads to fighting are common examples of how emotion causes behavior. And, in some cases, that is true. However, in many cases, we exert effort because we know that hard work is necessary in the present to achieve something meaningful in the future. We exert effort today because we anticipate positive affect in the future.

Anticipatory emotion is a strong motivator, in fact, it is more potent than felt or actual emotion (Baumeister et al., 2007). The strength of anticipatory emotion’s motivation lies in the weight people put on future outcomes—and these scenarios are often exaggerated (Baumeister et al., 2007). To a nascent entrepreneur, this exaggerated scenario may be fear of their startup failing, thus motivating more effort to make that fear less likely to be realized. Alternatively, it could be the anticipation of intense positive feelings when their startup gets valued at $1 billion. There is also “an affective residue of prior emotional outcomes” (Baumeister et al., 2007: 74) that drives behavior. This could be positive—we made our first sale last week as a result of hard work and if I want to preserve this good feeling (affect) I need to call on more customers (exert effort) this week. Or negative—I have not put in the effort, and my results show it. This learning is quite important to nascent entrepreneurs who are not yet used to the dynamic, up and down nature of entrepreneurship. In a sense, “emotion provides salient feedback about one’s actions, but the function of this feedback is mainly to help the person learn a lesson and leave a strong affective cue that may guide future behavior” (Baumeister et al., 2007:

174).

28 It takes concerted effort to introduce a new product, service or organization to the

world. The entrepreneur (and affiliated team) must put forth significant effort in order to

achieve the positive affect that accompanies achievement and to learn from these

experiences to guide future behavior (Baumeister et al., 2007). Positive affect results

from doing something well such as exerting effort, while negative affect results from not

doing something well such as not giving a concerted effort (Carver & Scheier, 1990).

We approach activities that we do well, and we avoid activities that we do not do

well (Carver & Scheier, 1990), and a feedback system is created that helps us move

closer to meaningful goals that we want to pursue and further away from outcomes that

we want to avoid (Carver & Scheier, 1990). We create “negative (discrepancy reducing)

feedback loops whereby people examine their behavior relative to the goal being pursued

(affect) and adjust their behavior (effort exerted) if they are not closing this discrepancy

gap” (Carver & Scheier, 1990: 26). Thus,

Hypothesis 2b. The greater the effort exerted at time t, the greater the affect felt in subsequent time periods.

AEP Related to Effort

As mentioned, AEP shares its basis in the emergent theory of anticipated emotion

along with EP. Baumeister et al. (2007) conceptualized a new theory—emotion as

feedback whereby emotion pursues behavior while using feedback as a guiding mechanism. For example, “whereas fear has often been a favorite example of theorists who wish to argue that emotion directly initiates behavior, guilt may be a useful example of the feedback theory. A person performs a behavior that causes distress to a friend. The person, therefore, feels guilty afterward. The guilt prompts the person to consider what he or she did wrong and how to avoid similar outcomes in the future. The next time a 29 comparable situation arises, there may be a brief twinge of guilty affect that helps the person choose a course of action that will not bring distress to friends (and more guilt to self”)” (Baumeister et al., 2007: 173).

Russell (2003) had a similar notion related to guilt that it signals “bad idea” and the person simply learns from this situation to avoid that behavior in the future. Thus,

“much behavior is emotion regulating, insofar as it attempts to bring about a desired emotional state later on” (Baumeister et al., 2007: 173). Therefore, people create a vision of what they want to achieve or pursue “and anticipate the likely consequences of prospective actions, they set goals for themselves, and they otherwise plan courses of action that are likely to produce desired outcomes” (Baumeister et al., 2007: 173).

Through exercise of forethought, people motivate themselves and guide their actions in an anticipatory or proactive way (Bandura, 1991: 248). Similarly, we argue that this anticipatory forethought is really anticipatory passion for engaging in an entrepreneurial role. In the case of nascent entrepreneurs, it is AEP—a vision of an intense positive emotion that serves to motivate efforts necessary to help them validate their entrepreneurial identity.

Entrepreneurial effort is key to survival in early stage firms. Effort can be thought of as “a limited-capacity resource that can be allocated to a range of activities” (Yeo &

Neal, 2004: 231). This effort, as it relates to nascent entrepreneurs, is typically aimed at creative tasks (such as generating ideas and matching them to marketplace needs) and administrative tasks (applying for licenses or ordering supplies) (Uy et al., 2015).

Entrepreneurs need to exert substantial effort to improve success chances for their new venture (Uy et al., 2015). In fact, organizational survival depends on the degree of fit

30 between entrepreneurial effort and environmental forces (Aldrich & Martinez, 2001).

Persistent effort is especially important for entrepreneurs (Shane et al., 2003) due, in part, to the numerous challenges and obstacles that entrepreneurs face in the entrepreneurial process (Markman, Baron, & Balkin, 2005). It is not only effort, but rather repeated effort in the face of adversity, challenge, or difficulty that is necessary for survival (Markman et al., 2005). Entrepreneurs engage in a “continuation of effortful action despite failures, impediments, or threats, either real or imagined” (Gimeno, Folta, Cooper, & Woo, 1997:

758). This effort must be sustained during the initial time required to get a business started successfully (Wu, Gerlach, & Young, 2007). Nascent entrepreneurs, fueled by

AEP, will be motivated to exert significant effort. Thus,

Hypothesis 3. Greater the AEP at time t, the higher the effort invested in entrepreneurial activities in the subsequent time period.

Goal Progress Moderates Relationship between Effort and Affect

Progress does not typically just happen. Instead, effort exerted in a directed capacity leads to progress (Uy et al., 2015). In terms of making progress, greater motivation is a key factor that distinguishes those nascent entrepreneurs that make progress towards creating a successful venture from those that do not make progress

(Renko, Kroeck, & Bullough, 2012).

As mentioned, nascent entrepreneurs fueled by AEP are likely to exert strong initial effort while working on their startup. AEP (Baumeister et al., 2007) is a strong motivator as the nascent entrepreneur envisions working hard and imagines how good he or she will feel as a result of exerting effort on something that is meaningful to his/her identity (Cardon et al., 2005). Raw effort alone, however, is not sustainable. The entrepreneur (and affiliated team) must make progress toward goals in order to feel good 31 about the effort put forth (Uy et al., 2015). Positive affect results from doing something well such as making progress, while negative affect results from doing something poorly

such as not making progress (Carver & Scheier, 1990).

We approach activities that we do well and we avoid activities that we do not do well (Carver & Scheier, 1990), and a feedback system is created that helps us move closer to meaningful goals that we want to pursue and further away from outcomes that we want to avoid (Carver & Scheier, 1990). We create “negative (discrepancy reducing)

feedback loops whereby people examine their behavior relative to the goal being pursued

and adjust their behavior if they are not closing this discrepancy gap” (Carver & Scheier,

1990: 26). Bandura (1991) argues, though, that it cannot simply be a negative feedback loop because that disconnect from progress would derail an individual. Positive outcomes are indicative of progress, and that positively impacts motivation (Bandura, 1991).

Therefore, both negative and positive outcomes serve to motivate effort and progress

(Bandura, 1991). Experiencing a sense of progress helps stimulate motivation for future

effort. Amabile and Kramer (2011) argued that making progress, even day-to-day

incremental progress that is hard to observe can significantly impact a person’s day-to-

day motivation. These small bits of progress, known as “small wins” (Weick, 1984) can

inspire confidence and empower people to persist in pursuit of meaningful tasks and

move forward. These “small wins” significantly impact day-to-day motivation and these small steps accumulate into critical steps that can sustain effort over time (Amabile &

Kramer, 2011). Moreover, Amabile and Kramer (2011) found that participants were more likely to report “good days” when they experienced progress (even small bits of progress) towards achieving their goals.

32 People put in more effort when they expect to succeed (Olson, Roese, & Zanna,

1996). Seeing evidence of one’s progress leads to deep engrossment in, and the pursuit of, important life pursuits (Csikszentmihalyi & Beattie, 1979). Experiencing progress over time would cause the nascent entrepreneur to attribute the progress to his/her own levels of effort (Weiner, 1985). This internal attribution would increase one’s good feeling about making progress on something meaningful to their sense of self, and this would lead to future increased efforts via attempts made to preserve the positive affect derived from putting in the effort and making progress.

In terms of positive affect, “persons experiencing positive affect tend to perceive objects, other persons, ideas, and almost anything else more favorably than individuals experiencing neutral or negative affect” (Baron, 2008: 331). We are interested in state affect in this study because we are examining how nascent entrepreneurs feel on an episodic basis immediately after meeting weekly with mentors and assessing the progress

(or lack of progress) made during the previous week. These weekly meetings are part of the accelerator program’s process of helping the entrepreneurs and their concepts develop. When meeting with experienced entrepreneurial mentors affiliated with the accelerator programs, the nascent entrepreneurs share how they spent time the previous week and where they exerted effort and made progress in developing their concept. The mentors then provide guidance and feedback so that the entrepreneur can make more progress in subsequent weeks. The mentors, in a sense, are evaluating and providing feedback on how the nascent entrepreneurs are performing in their entrepreneurial role.

Thus, the “feedback about their entrepreneurial role performance gives rise to emotional reactions, for example, joy, happiness, anger, and frustration” (Markowska et al., 2015:

33 218). Positive affect indicates that things are going well while negative affect indicates that things are not going well (Carver, 2004). Thus,

Hypothesis 4. Greater goal progress will strengthen the positive relationship between effort and affect.

34 CHAPTER 4: RESEARCH METHODS

Sampling and Data

Participants

The study participants were 27 adults that attended two entrepreneurship accelerator programs affiliated with two prestigious universities located on the east coast of the United States (For more information about the programs, please see Appendix A).

The sample population consists of nascent entrepreneurs. The average age of participants is 24.61. 87.5% of participants have less than two year’s entrepreneurial experience. In terms of stage of a startup, 82% described their startup as being at the idea stage. The idea stage is the stage where the idea exists as the vision of the founder and founding team and has not yet been validated or accepted by customers. When that validation

occurs, it is known as the concept stage. These participants fit the description of a nascent

entrepreneur (Aldrich & Martinez, 2001) and their participation in an entrepreneurship accelerator program provided an opportunity to observe and measure their development and growth. For more detailed information about this study’s participants, please see

Appendix B.

Attrition

This research study began with 27 participants and ultimately had 25 cases that,

had I been able to use either week 6 or week 7 data interchangeably, would have been

complete cases. A complete case consists of all surveys filled out completely by the

participant and by the mentor across all nine time points. 19 such cases fit that

requirement. Thus, the final N is 19. Attrition is common in longitudinal cases as the

demand of filling out each and every survey becomes too burdensome for certain

35 participants—especially toward program’s end as participants gear up for Demo Day—a

high profile event attended by potential investors. In comparing the six cases that did not

completely fill out all surveys with the nineteen cases that did so, A T-Test was conducted to compare values across groups and found no discernable differences

(program p-value = .85, education p-value = .55, and age p-value = .59) across groups.

So, after attrition, the response rate was 70.3%. By way of comparison, another

longitudinal study on passion collecting data over three waves had a response rate of

22.3% (Collewaert et al., 2016). Granted, they had a larger initial sample; however, it

shows that the participants were quite engaged given that data was collected in nine

waves. While an N of 19 is small, as we can all agree, I was able to find valid and

interesting statistical results that will be discussed in the next section.

At T1, 96% of participants filled out surveys (26 out of 27). At T2, 92 %

participated (25 of 27). At T3 92% (25 out of 27) participated, and at T4 70% (19 out of

27) participated yielding a final participation rate study-wide of 70%. Thereby yielding

an N of 19 and 76 data points. In terms of mentors, I had feedback from five mentors

assessing effort and progress at all requested intervals for all 19 cases.

Procedure

A repeated measure longitudinal design to analyze within-person differences over

multiple time periods was utilized in this analysis. The 19 entrepreneurs in this study

were contacted weekly over a nine week period. A weekly study design covers a suitable

timeframe to give nascent entrepreneurs sufficient time to develop their business concept,

consult with program mentors, meet with customers—basically immerse themselves in

the entrepreneurial process in a manner that would allow me to investigate dynamic

36 changes in the relationship between AEP, effort, progress and affect. Previous research indicates that people are accurate in their evaluations of emotional states over the course of a week (Parkinson, Briner, Reynolds, & Totterdell, 1995). After receiving IRB approval from Case Western Reserve University and the other universities participating in this study. Weekly emails were sent to participants of both programs at the conclusion of their weekly mentoring session in order to most accurately gauge their emotional states of affect and AEP.

Data Collection

Data was collected at nine time points over a nine week period—pre-program, week’s one through seven, and post program. This data collection was designed to coincide with the structure of both programs that called for weekly mentor meetings. In these meetings, the entrepreneurs would discuss their progress from the previous week and set goals for the upcoming week. The mentors would also give the entrepreneurs feedback on key areas related to their startup and helped the nascent entrepreneurs make sense of interactions with customers, potential investors, prospective employees, and suppliers. Sometimes this feedback can be supportive and encouraging while other times it can be a bit harsh and discouraging. Given the variables of interest in this study, data was collected each week immediately after the mentors provided feedback to participants.

The four time periods selected are week one of the program (T1) in order to measure the model in the first week of the program when they get their first round of mentor feedback. Week one is extremely important as the participants typically bring great enthusiasm and go after their entrepreneurial tasks with concerted effort; (T2) is week three of the program and by now the nascent entrepreneurs may be finding that it is

37 harder than just having a good idea—they have to execute on the idea and validate the ideas with customers. Sometimes customers are extremely interested in the new offering; other times they are not so interested, and this can be challenging for nascent entrepreneurs to internalize. This mid-point is when nascent entrepreneurs start to get a sense of what the entrepreneurial process is like in reality. (T3) is week four of the program. By analyzing weeks three and four, I get a strong assessment at the mid-point in the program. (T4) is the last week of the program (week 7), so I can assess the end of the programmatic experience. This last week is a crucial time in an accelerator program as they are preparing for Demo Day—the day when they showcase their emergent concept in front of prospective investors. It is a stressful time and really mirrors the entrepreneurial experience well. Having a wave of measurement at that time is key.

In terms of the time lag between measurement waves, there is a two week lag between T1 and T2, a one-week lag between T2 and T3, and a three week lag between T3 and T4. These time lags were selected in order to allow enough time for meaningful change to occur while also measuring closely enough to account for predicted changes. In conclusion, I have one wave of measurement at beginning and end of the program and two waves of measurements in the middle of the program. This will enable me to trace participants’ growth and change as they go through the entire program.

Even though data was collected at nine time points, the four time points selected were specifically chosen because of their timing relative to important activities or milestones given the nature and goals of the respective programs. I also was able to capture these four critical time points without burdening the estimation function given my small sample size.

38 Figure 2 is the conceptual model developed to explain this study’s logic while

Figure 3 is the empirical model used to test this study’s hypotheses. There are relationships within one time period, and there are relationships across time periods.

These relationships are analyzed at four different points as these nascent entrepreneurs go through these intensive entrepreneurship accelerator programs to better understand how they develop and change.

Figure 2. Conceptual Model

Figure 3. Empirical Model

T1 T2 T3 T4

AEP AEP AEP AEP

Effort Effort Effort Effort

GP GP GP GP

Affect Affect Affect Affect

39 Measurement

In total, AEP items were asked nine times—pre and post program along with all

seven weeks covering the duration of the program. Questions about effort, progress, and affect—self-report and mentor report—were asked seven times over the duration of the

program as I sought to capture those variables while they occurred. I asked AEP, effort,

progress, and affect questions across all seven weeks in order to have a fully cross-

lagged design. The questions on AEP were asked based on how they anticipated feeling

in the future. The questions about affect were asked about how they were feeling at that

particular moment in time while the questions on effort and progress were asked about

participants’ effort and progress during the previous week. Mentors were asked only

about participants’ effort and progress over the previous week.

Measures

Anticipatory entrepreneurial passion (AEP): In this study, AEP is introduced as a new construct. This construct traces its origin to the construct EP created by Cardon et al.

(2009). EP measures connect intense positive feelings (items starting with “it is exciting” or “I enjoy” or items ending with “excites me”) with entrepreneurial identity components of founder, inventor, and developer (for example, “establishing a new company excites me”). Given that study participants are new to the entrepreneurial process, the construct of entrepreneurial passion does not fit quite as well and needed to be modified to better capture the futuristic aspect of what motivates nascent entrepreneurs to pursue entrepreneurship. AEP was assessed using two sets of self-report items that measured how confident (measured using a sliding scale from zero confidence to 110% confident) these nascent entrepreneurs were when surveyed about enacting entrepreneurial roles of

40 inventor or founder in the future. While confidence itself is not part of the passion

construct, I asked these nascent entrepreneurs to assess the likelihood of a future state—

how confident they were that they would become an inventor or founder in the future. For

example, in the first set of items the stem of “in 3–5 years, how confident are you that

you will be seen as a (n)…inventor that disrupted the market with a new technology or

idea” (for inventor identity) is used. For a founder identity, by way of example, “in 3–5

years, how confident are you that you will be seen as a (n) founder that created a bold

new company” (founder identity). There were three items that reflect a future inventor

identity and three items that reflect a future founder identity. Given that a developer

identity involves building a company over a long time horizon, this particular sub-

dimension of entrepreneurial passion was not relevant to nascent entrepreneurs and was

not included in this study. The second set of items also measured AEP for inventing and

founding. This set of items sought to measure the intense positive feelings associated

with anticipated passion. So, the stems of inventor and founder follow: “just thinking of

myself as an inventor in 3–5 years that disrupted the market with a new technology or

idea…” (Inventor identity) or “Just thinking of myself as a founder in 3–5 years that

created a bold new company” (founder identity). These stems preceded the choices of “I

feel a rise of intense joy”, “I feel a high of extreme excitement”, and “I am filled with

enthusiasm.” This construct married the main characteristics of EP (an intense positive feeling for an activity that is important to one’s identity) with an anticipated view of engaging in entrepreneurship and enacting an entrepreneurial role of inventor or founder

in the future. AEP was measured at nine points in time—T1-T7 during the program in addition to measuring pre and post program.

41 Effort: Participants rated their individual effort on three items that each measure a

distinct component of effort—time spent, time spent on new and unfamiliar tasks, and the

impact their individual performance contributed to achieving overall startup goals.

Previous researchers have used time on task to measure effort (e.g. Blau, 1993; Brown &

Peterson, 1994; Fisher & Ford, 1998). For time, I used the following item: “During the past week, how much time did you personally devote to working on your startup?” Given my interest in nascent entrepreneurs that were “trying on” new role identities as founders or inventors, I also wanted to measure time spent on new and unfamiliar tasks. I used the following item: “Of these hours, what percent of your time did you devote to new and unfamiliar tasks related to the startup” to examine time spent on new role tasks.

Ultimately, a composite variable of effort called combined effort was created by multiplying time spent in total by time spent proportionally on new and unfamiliar tasks.

Effort was self-reported and was substantiated by asking the same questions of mentors that worked closely with the nascent entrepreneurs. Effort was measured at seven points during the program (T1-T7).

Progress: Progress was measured using the following self-report item: “during the past week, to what extent did your individual performance contribute to progress toward achieving your startup goals”? Progress from the perspective of mentors was measured

by utilizing the same item. Entrepreneurs are biased toward their startup and may see

“progress” that is not really there (Cassar & Craig, 2009). Mentors are more objective

about the startup and lend credibility to a progress claim. Thus, I plan to use the mentor

report data to represent progress made by participants. Progress was measured both self-

report and mentor report seven times during the program (T1-T7).

42 Positive and Negative affect (listed as Affect in the model): Participants rated

their affect using the PANAS survey (Watson & Tellegen, 1999). State affect, as

previously described, examines how one feels at a particular moment in time as opposed

to trait affect which measures average feelings over time or how one usually feels. The

stem of this set of questions starts with “tell us how you feel at this particular moment in

time. Right now I am feeling…” I used a sliding scale from 0–10 to measure each aspect

affiliated with positive affect (inspired, alert, determined, attentive, and active) and

negative affect (upset, hostile, ashamed, nervous, and afraid). I measured PANAS each

week immediately after entrepreneurs met with their mentors to discuss the previous

week’s accomplishments and challenges along with future strategies and plans. The goal

was to capture the state affect as soon as possible relative to the time they received

feedback from mentors. I plan to combine the answers into individual “positivity” ratio

for each participant. Russell, Weiss, and Mendelsohn (1989) created such a composite

known as the “affect grid.” They found that by combining pleasure and displeasure along with arousal and sleepiness, they were able to explain a significant amount of variance

across the entire PANAS construct. Combining positive and negative affect into one

“positivity score” is a valid approach “when subjects are called upon to make affective

judgments in rapid succession or to make a large number of judgments, especially when

those judgments are to be aggregated” (Russell et al., 1989: 499). PANAS was measured

seven times during the program (T1-T7). Please note that all measures are reported in

Appendix C.

43 Exploratory and Confirmatory Factor Analysis

To assess convergent and discriminate validity of the latent constructs in the

measurement model, I employed factor analyses. Even though one of the constructs used

in the empirical modeling in this dissertation was well established (PANAS and called

Affect in the model), others had never been used before (AEP) or used in other contexts

(effort and progress). Therefore, both exploratory and confirmatory factor analysis were

used to assess whether the individual items were loading onto the appropriate constructs.

Exploratory Factor Analysis

First, I conducted an exploratory factor analysis using Principal Axis Factoring

with Promax rotation method to see if the observed variables of AEP, positive affect,

negative affect, effort and progress loaded together as expected, were highly correlated,

and met criteria for reliability and validity. For this phase, SPSS was utilized to extract

relevant factors and to assess the data structure inherent in my responses and to assess

discriminate validity between theoretically related factors. Given the small sample and

wanting to ensure the reliability and validity of these items over time an initial

exploratory factor analysis was conducted using a split week analysis to make sure that the results were consistent across time. The initial exploratory factor analysis yielded the following results: The KMO and Bartlett’s test for sampling adequacy were significant

(KMO .884, significance .ooo) and the communalities were sufficiently high (all above

0.50 and mean communality score of .72); thus, indicating the chosen variables were adequately correlated for a factor analysis. A clean pattern matrix emerged identifying four distinct factors with high loadings, and the four factors explained 67.4% of the variance. During this initial exploratory factor analysis, four items were eliminated with

44 two being eliminated from the construct AEP and two being eliminated from affect. The two items eliminated from the construct AEP were: “entrepreneur who founded a company that changed the world” and “just thinking of myself as a founder in 3–5 years that created a bold new company I am filled with enthusiasm.” The two items removed from affect were: “upset” and “hostile.”

Next, an exploratory factor analysis using week 1 through 3 data and week 4 through 7 data was conducted and no material differences between groups was evident. I did find two AEP items that cross-loaded in a problematic manner in the week 1 through

3 data. Hence, I removed the following items from AEP: “idea person that started it all” and “just thinking of myself as founder in 3–5 years (stem) I feel a high of extreme excitement.” Removing these two items left the AEP construct with eight items—four that represent an inventor sub-identity and four that represent a founder sub-identity.

Given that there were no other differences between the weeks 1 through 3 and weeks 4 through 7 data, I returned to the full week 1 through 7 data and conducted a final exploratory factor analysis with the cross-loaded items removed.

In the final exploratory factor analysis conducted after the initial and split analysis, and after the removal of problematic cross-loaded items, the KMO and

Bartlett’s test for sampling adequacy was significant, and the communalities were sufficiently high (all above 0.50 and mean communality score of .74); thus, indicating the chosen variables were adequately correlated for a factor analysis. A clean pattern matrix emerged identifying four distinct factors with high loadings (average loading for AEP

=.849) on each factor and no cross-loaded items. Interestingly, eight items loaded cleanly onto AEP. Given that the AEP items originated from the EP construct adjusted

45 temporally with a future identity focus, I conceptualized that AEP would have similar

sub-categories of inventor and founder identity. Conceptually, I linked identity enactment with the notion that AEP would have two dimensions—that of founder and that of

inventor—thus connecting to the nascent entrepreneurs’ development of a specific

entrepreneurial identity. Instead, one factor loaded cleanly for AEP. I believe the identity

piece is quite relevant; however, it appears it has not yet crystalized in these nascent

entrepreneurs. I believe, at this early stage of their careers, they more broadly think of

themselves as entrepreneurs and that the identity of inventor or founder cements itself

later—perhaps after the company gets acceptance in the market or after a successful exit

or some other form of external validation occurs (Markowska et al., 2015).

Similar convergent and discriminate results were noted for affect, effort, and

progress. In terms of reliability, Cronbach’s alpha for AEP items were .970, for affect

.818, for effort and progress .727. In terms of discriminant validity, a factor correlation

matrix was created and none of the factors were correlated above .70 level, and there are

no problematic cross-loadings. The four-factor model had a total variance explained of

67%, with all extracted factors having eigenvalues above 1.0.

Confirmatory Factor Analysis

Confirmatory factor analysis was conducted using SEM in an attempt to

determine the amount of variance explained by each factor in relation to each item

indicator. This was done by assessing the factor loadings and t-values for each factor. In addition, modeling all of the factors as latent constructs in SEM allowed for an indication of the relative degrees of inter-correlation between constructs, as well as an overall judgment how well the measurement model fit the data. The measurement model was

46 built in AMOS and included the following latent factors: AEP, positive affect, negative affect, and progress. All of the items loaded significantly onto their designated factors and the overall model fit was acceptable. Please see the results of the overall model fit in

Table 1.

Table 1 indicates that the goodness of fit for my measurement model.

Table 1. CFA Results

Metric Observed Value Criterion Criterion Source Chi-Square 373.437 CMIN and Degrees of Degrees of Freedom Cmin/DF <3 Byrne (2001) Freedom 144 CMIN/DF = 2.59 CFI .921 >.90 Hair et al. (2010) NFI .878 >.90 Hair et al. (1998) TLI .906 >.90 Hair et al. (1998) .092 RMSEA Upper Limit =.010 <.10 Hair et al. (2010) Lower Limit = .083 P Close .000 >.05 Byrne (2001)

In Table 2, please find the items retained after Confirmatory Factor Analysis along with their factor loadings and t-values. The average loading across all items is .78, and none of the items had loadings lower than .60. More specifically, the average loading of the AEP construct is .83; positive affect is .77. Negative affect is .78, and effort/progress is .75. These results indicate that the items load strongly on the appropriate construct and that the constructs are distinct from one another.

The t-values “measure the significance of the partial correlation of the variable reflected in the regression coefficient. As such, it indicates whether the researcher can confidently say, with a stated level of error, that the coefficient is not equal to zero” (Hair et al., 2010: 212). After comparing the t-values and degrees of freedom, all t-values

47 below are greater than 2.576 indicating that at the .001 level these coefficients are not equal to zero.

Table 2. CFA Parameters and t-values

CFA Parameters and t-values Factor Abbreviated Items Construct t-values P Loadings Inventor that entered market <--- AEP 0.776 Tinkerer that improved product <--- AEP 0.639 9.395 *** I feel a rise of intense joy <--- AEP 0.945 15.438 *** I feel extreme excitement <--- AEP 0.981 16.272 *** I am filled with enthusiasm <--- AEP 0.966 15.928 *** Founder that created company <--- AEP 0.632 9.273 *** Nurturer that birthed company <--- AEP 0.749 11.363 *** I feel a rise of intense joy <--- AEP 0.898 14.363 *** Alert <--- PA 0.652 Inspired <--- PA 0.754 8.951 *** Determined <--- PA 0.873 10.005 *** Attentive <--- PA 0.726 8.682 *** Active <--- PA 0.873 10 *** Nervous <--- NA 0.783 Afraid <--- NA 0.901 10.018 *** Ashamed <--- NA 0.659 8.947 *** Final three items from mentors: <--- Effort/Progress 0.634 Time devoted to startup Percent of time devoted to new <--- Effort/Progress 0.77 6.887 *** tasks Individual’s impact on overall <--- Effort/Progress 0.733 6.947 *** progress *Note: full items are available in Appendix C

Validity and Reliability

To test for convergent validity, the Average Variance Extracted—a measure to assess convergent validity by indicating how much variance a given factor explains—was

48 calculated and analyzed. For all factors, the AVE was above 0.50. Specifically, the AVE

for negative affect is .62, AEP is .68, positive affect is .60, and progress is .51. To test for

discriminant validity, the square root of the AVE to all factor correlations was compared.

Also, the maximum shared variance (MSV) and average shared variance (ASV) were

examined to determine the extent to which one variable can be explained via another

variable. MSV and ASV to AVE were compared, and both were less than AVE indicating

that variance explained by the variables in this study are greater than could be possibly

explained in other variables. Thus, all factors demonstrated adequate discriminate

validity.

Composite reliability for each factor was also computed. In all cases, the composite reliability was above the minimum threshold of .70. Taken together, this indicates reliability in this study’s factors.

Table 3. Validity and Reliability Results

CR AVE MSV ASV NA AEP PA Progress NA 0.828 0.620 0.024 0.015 0.788 AEP 0.945 0.689 0.360 0.125 -0.124 0.830 PA 0.885 0.609 0.360 0.123 0.071 0.600 0.780 Progress 0.756 0.510 0.024 0.010 0.156 -0.004 0.073 0.714

After conducting this confirmatory factor analysis and specifically examining

model fit, each item parameter, t-values, and convergent and discriminate reliability

metrics, the final remaining items and constructs all show high degrees of validity and

reliability and demonstrate convergent reliability in that each construct distinctly

measures what it sets out to measure along with discriminate reliability in that the

49 constructs are also distinct from one another. Thus, the items and constructs are ready for the data analysis phase of this dissertation.

Common Method Bias

Given the longitudinal design employed in this study, the contemporaneous effects of repeated measures minimize the risk of common method bias. When the relationship between effort and affect was tested, for example, the preceding relationship from effort to effort was used as a control when examining the relationship between effort and affect. By controlling for these previous effects, the effect under examination is a real effect and not an artifact from the instrument or any other source of common method bias. Other issues of bias exist due to antecedent conditions that can create confounding and will be controlled by directly modeling these effects.

Method of Analysis

All hypotheses were tested while controlling for age, program, and education level. The two programs were selected because they have a similar length, format and structure and both emphasize helping nascent entrepreneurs launch a company after completing the program.

Given the small sample size, I used a p-value of .10 for significance. In order to ensure power for statistical testing, I followed a particular three-part approach. First, a model was created without controls to get coefficients and overall fit for estimation.

Second, controls were added to hypothesized paths as well as constraining paths that were invariant over time. By identifying paths that were invariant over time and constraining them to be equal, the number of variables were minimized resulting in fewer coefficient paths thus enabling an increase in statistical power to analyze differences—a

50 necessary step given the small sample size. To illustrate, the effects of age on

hypothesized paths were found to be invariant. So, all paths from age were constrained to

be the same, and this reduced the number of coefficients and increased statistical power

without altering any other effects. All controls were tested in a similar manner, and

separate nested models were constructed to parse them out and, using model comparison,

test each model in comparison to the default model to examine differences. An outcome

of this approach was finding paths that were invariant and constraining them to be equal

as described previously. Third, using the testing to determine which non-hypothesized paths were invariant, all such paths were constrained to be equal in the baseline model.

Then, to test hypotheses, constraints were imposed on hypothesized paths in the early to middle parts of the programs (t1-t2 and t2-t3) to be equal, and then constrained in the middle to end of the programs (t2-t3 and t3-t4) to be equal and these different models were compared to the baseline model and the resulting Chi Square differences were evaluated.

For testing hypotheses, which will be discussed specifically and in detail in the

next section, I conducted a one-way Analysis of Variance (ANOVA) to test H1a,

Structural Equations Model in Amos to test H1b, H2a, and H2b, and multiple regression

in SPSS to test H3 and H4.

51 CHAPTER 5: HYPOTHESES TESTING, RESULTS, AND DISCUSSION

In this chapter, I test, analyze and discuss the relationships between AEP and effort, the relationships between effort and affect and vice versa, and whether goal progress moderates the relationship between effort and affect.

Summary of Results

Table 4. Hypotheses Results Table

Hypotheses Results H1a: Mean values of AEP show significant variation over time such that the Supported value becomes lower in the middle in the middle of the program relative to the beginning or end H1b: The effects of AEP at the current time period will be positively related Supported to the effects of AEP in the previous time period. H2a: Greater the positive affect at time t, the higher the effort invested in Not Supported entrepreneurial activities in the subsequent time period. H2b: Greater the effort exerted at time t, the greater the affect felt in the Supported subsequent time period. H3: Greater the AEP at time t, the higher the effort invested in Not Supported entrepreneurial activities in the subsequent time period. H4: Greater goal progress will strengthen the positive relationship between Not Supported effort and affect.

Hypothesis 1a. Mean values of AEP show significant variation over time such that the value becomes lower in the middle of the program relative to the beginning or end.

Hypothesis 1b. The effects of AEP at the current time period will be positively related to the effects of AEP in the previous time period.

52 Results H1a

Table 5. ANOVA Results

Dependent Variable: AEP 90% Confidence Interval Std. Upper Time Period Mean Lower Bound Error Bound 1 .114a .227 -.264 .492 2 .073a .224 -.300 .446 3 -.232a .234 -.622 .158 4 .045a .226 -.331 .421

Figure 4. Model for Hypothesis H1a

Estimated Marginal Means of AEP

Covariates appearing in the model are evaluated at the following values: Effort = .0000000, Progress = .0000000, Affect = .0000000

In this model, I controlled for age, education level, and program differences. In testing this model, paths from each control to each variable were estimated. For the sake of visual clarity, I have omitted the controls and paths to variables from this figure.

53 Table 6. ANOVA Results Overall Model

Source F Sig. Corrected Model 1.886 .096 Intercept .000 1.000 Effort .063 .803 Progress 2.465 .121 Affect 7.997 .006 Time Period .434 .730

A one-way ANOVA test was run with AEP as the dependent variable and time period as the independent variable and effort, affect and progress utilized as controls. As a precondition of the validity of an ANOVA test, I ran Levene’s test for homogeneity of variances (F test .778, degrees of freedom 1, and .510 significance). This tests the null hypothesis that the error variance is equal across groups and a non-significant result, as obtained here, indicates that the groups under study here are invariant. The data fits the overall model (F 1.886 and significant .096*, R-square .141 and adjusted R Square .066).

The means of AEP went from .114 at t1, to .073 at t2, then dropped significantly to -.232 at t3 before rebounding at t4 to .045 (see Table 5 and Figure 4 for visual depiction). The results indicate a sharp drop at t3 before recovering at t4, thus supporting H1a.

Results H1b

Table 7. Coefficient Results

AEP To AEP Estimate S.E. P AEP Time 1 → AEP Time 2 .863 .146 *** AEP Time 2 → AEP Time 3 .513 .148 *** AEP Time 3 → AEP Time 4 -.078 .380 .837

54 Figure 5. Model for Hypothesis H1b

In this model, I controlled for age, education level, and program differences. In testing this model, paths from each control to each variable were estimated. For the sake of visual clarity, I have omitted the controls and paths to variables from this figure.

I tested these effects at four time points (t1-t2, t2-t3, and t3-t4) and the results are presented in the table above. This model demonstrated adequate fit (CMIN 10.458, DF 6,

CFI .946, NFI.899, TLI .811, RMR .062, RMSEA,.203 P Close .124). In this model, constraints were imposed on controls to examine equality of longitudinal coefficients. I found age and education to be invariant across all paths, so I constrained them all to be equal and built those constraints into the baseline model. Further, I constrained the control program to two rather than three paths. The effect of this procedure was to reduce the coefficient paths that were equivalent and incorporate those equal paths in the baseline model so that more statistical power could be utilized to examine hypothesized paths. In terms of specifying the best fitting model, I constrained the AEP model from early to middle part of the program (chi-square = 2.706, DF=1, p < .10) and from the middle to the end of the program (Chi-square 1.948, DF=1, p<.10) and compared those models to the constrained baseline model and noted the differences in parentheses above.

I also included paths from t1-t3 and t2-t4 as those longer horizons helped achieve better overall model fit.

As is evident from the beta weights, fluctuation is evident across time points. The relationship from AEP t1 to t2 is positive and significant (b=.86, SE .14, P<***). The relationship from AEP t2 to t3 is positive and significant (b=.51, SE=.14, P<***). The 55 relationship from AEP t3 to t4 is negative and non-significant (b=-.07, SE.38. P<.83).

Based on these results, the initial conclusion would be partial support of H1b because the t3 result does not predict t4 outcome. Curious about these results being slightly different than hypothesized, I conducted a post hoc analysis (see Table 8) and looked at slightly longer time horizons from t1-t3 (b=.50, SE .15, P<***) and t2 to t4 (b=.96, SE .40,

P<.016**) and found both those relationships positive and significant.

Table 8. Post Hoc Analysis of H1b

AEP Time 1 → AEP Time 3 .501 .157 *** AEP Time 2 → AEP Time 4 .964 .401 .016**

Figure 6. Post Hoc Analysis Model for Hypothesis H1b

In this model, I controlled for age, education level, and program differences. In testing this model, paths from each control to each variable were estimated. For the sake of visual clarity, I have omitted the controls and paths to variables from this figure.

Taken together, even though AEP dips short term from t3 to t4, the effects of AEP

casts a long shadow and impacts results over a longer time horizon from t2-t4. Even

though a dip occurs from t3 to t4 and the previous time period is not predicted by the

current time period, this effect is overridden over the slightly longer time horizon from t2

to t4. So, while AEP may dip in the short term, it endures over a longer term, thereby

fully supporting H1b.

56 To ensure that the results from this hypothesis test were not reflective of

programmatic events or other confounding artifacts, a post hoc analysis was conducted

(see Figures 7 and 8) substituting different time periods not included in the hypothesis test.

Figure 7. Post Hoc Analysis Model for Hypothesis H1b Substituting Week 3 Data

In this model, I controlled for age, education level, and program differences. In testing this model, paths from each control to each variable were estimated. For the sake of visual clarity, I have omitted the controls and paths to variables from this figure.

Figure 8. Post Hoc Analysis Model for Hypothesis H1b Substituting Week 5 Data

In this model, I controlled for age, education level, and program differences. In testing this model, paths from each control to each variable were estimated. For the sake of visual clarity, I have omitted the controls and paths to variables from this figure.

Given that seven weeks of data existed, the three excluded weeks of data were

tested to ensure that the results obtained were a result of the hypothesized relationships

and not a result of programmatic activities or other confounding artifacts. The three other

weeks of data were tested and found to be invariant—that is, taken together, the results

57 do not change in a statistically significant way when substituting data from weeks that were not included in this analysis.

Discussion of H1a and H1b

In order to discuss these results in an integrative manner, it is important to look at this new construct AEP and the themes that underpin it. As discussed in the theory section, AEP hails from EP—an intense desire or love for an activity that is meaningful to one’s identity (Cardon et al., 2009). AEP, though, is not EP—it is the anticipation of becoming an entrepreneur and the belief that an entrepreneurial identity may be worth pursuing. Nascent entrepreneurs in these intense summer accelerator programs are trying on a version of their future or possible selves (Ibarra, 1999). According to Markowska et al. (2015), for an identity to be meaningful, it must first be validated by important external parties such as customers, investors, or seasoned entrepreneurs. Experienced entrepreneurs are known to these external others as entrepreneurs as their identity has been previously validated. The nascent entrepreneurs in this study, as I found out in examining the AEP construct in exploratory factor analysis, do not yet think of themselves in specific terms as inventors or founders in part because this identity has not yet been fully established or validated by others.

Whereas Cardon and Kirk (2013) found that entrepreneurial passion endures over time, in this study findings indicate that AEP is both malleable and enduring. As shown in the results for H1a and H1b, AEP is malleable—it can dip in the short term—and enduring as it comes back over slightly longer time horizons. In the Cardon and Kirk study, the average age is 48.6 and 85% of the sample had previously engaged in an entrepreneurial venture. In this study, the average age is 26 and 85% have never engaged

58 in an entrepreneurial venture. In the Cardon and Kirk (2013) study, their sample exhibited EP that endured because they had fallen in love with entrepreneurship and their identities as entrepreneurs had been established. In this study, the results indicate that these nascent entrepreneurs’ AEP is still forming—their anticipatory passion and tie to an entrepreneurial identity, a work in progress.

How can AEP be malleable and enduring at the same time? As mentioned previously, nascent entrepreneurs seem to be more liable to be influenced by feedback from the environment—particularly feedback from customers, investors and mentors— than seasoned entrepreneurs. Over the course of my accelerator program, we (me and the other mentors on the formal teaching team) would meet with the entrepreneurs weekly and discuss their previous week’s activities. We would discuss their goal progress, what specific challenges they were facing, and we would almost always discuss their interactions with prospective customers. Over the course of leading this program for eight years, I noticed a trend. In most cases, and typically over a short period of time, entrepreneurs would lose their zeal for their startup. As mentors, we would talk to them to find out the cause. It was often a result of a prospective customer or a run of customers giving them negative feedback about their product or prototype. This feedback would often cause an extrapolation to occur in the minds of the nascent entrepreneurs—they perceived that this negative reaction would be shared by many prospective customers— causing a temporary dip in passion to occur. A similar dynamic happened as the nascent entrepreneur tried to raise seed capital. Rejection by one angel investor or venture capitalist would not phase a more experienced entrepreneur. To an unproven nascent entrepreneur, however, such feedback can cause feelings of rejection and can cause their

59 AEP to wane. As a practitioner at the time, I did not appreciate that this negative

feedback from prospective customers and investors may also have been perceived as

failed attempts at validating an entrepreneurial identity.

Over time, though, with effective mentoring, learning on the part of the

entrepreneur, and subsequent progress made with other customers or investors, AEP

returns to previous levels. As can be seen in the H1a and H1b results above, while AEP

may be quite malleable over the short term, it ultimately endures and resumes previous

levels. Thus, through the results of both H1a and b above, and confirmed by my years as

a practitioner spent developing nascent entrepreneurs, AEP in a nascent entrepreneur is both malleable and enduring. To use pottery as an analogy, the clay has to dry to “leather hard” before being put in the kiln. At this stage, the clay is hardening and taking shape, yet it is still malleable. Once it goes through the kiln, though, that clay loses its malleability. AEP in nascent entrepreneurs is the “leather hard” stage of this process— partially formed—whereas EP and seasoned entrepreneurs have been baked in the kiln and their EP and identity are fully formed.

Hypothesis 2a. Greater the positive affect at time t, the higher the effort invested in entrepreneurial activities in the subsequent time period.

Hypothesis 2b. Greater the effort exerted at time t, the greater the affect felt in the subsequent time period.

60 Results H2a and H2b

Table 9. Results of Testing Relationships of Affect to Effort and Effort to Affect

Affect To Effort Estimate S.E. P Affect T1 → Effort T2 -.254 .105 .016** Affect T2 → Effort T3 -.254 .105 .016** Affect T3 → Effort T4 0.04 .237 0.866

Effort To Affect Estimate S.E. P Effort T1 → Affect T2 -.002 .131 .990 Effort T2 → Affect T3 -.002 .131 .990 Effort T3 → Affect T4 .258 .084 ***

Figure 9. Model for Testing Hypothesis H2a and H2b

In this model, I controlled for age, education level, and program differences. In testing this model, paths from each control to each variable were estimated. For the sake of visual clarity, I have omitted the controls and paths to variables from this figure.

I also used nested models to examined effort and affect variables along with the same controls—age, program, and education. This model demonstrated adequate fit

(CMIN 18.100, DF 17, CFI .978, NFI.826, TLI .927, RMR .075, RMSEA .060, P Close

.433) and constraints were imposed on controls to examine equality of longitudinal 61 coefficients. In this nested model, I constrained effort t1 to effort t2 to be the same,

education, program, and age to be the same, and affect from t1 to t2 were found to be

invariant and thus constrained as equivalent allowing me to isolate the hypothesized paths

from effort to affect and vice versa. I built these constraints into the baseline model and

then constrained the hypothesized paths on the affect/effort model from early in the

program to middle of program (chi-square difference = 1.034, DF=1, p < .50) and then

from middle of program to end of program (chi-square difference = 3.63, DF=1, p < .30).

These two hypotheses are designed and tested to shed light on competing

hypotheses in previous studies with one study finding that effort causes affect and

another study finding that affect causes effort. The longitudinal design of this study

allows us to test both of these hypotheses simultaneously to figure out which is correct—

does effort cause affect or does affect cause effort? As can be seen in the table above, for

H2a, affect is significant and negatively related to effort in the next time period for t1-t2

and t2-t3; however, it changes to non-significant and positive from t3 to t4. Regarding

H2b, the results indicate that the relationship between effort and affect is not significant

initially but that it changes to a positive and significant relationship over the duration of

the program. Thus, it appears that both studies are correct—it seems that affect and effort

are causally interlinked in a nuanced manner—affect has a negative causal relationship

with effort that ultimately wanes over time while effort has a positive causal relationship

with affect yet it takes time for this relationship to emerge. Thus, H2a is not supported,

and H2b is supported.

62 Discussion of H2a and H2b

As mentioned previously, affect is an episodic emotion that indicates how a person feels at a given moment. In analyzing these two competing hypotheses—does affect cause effort or vice versa—I had the benefit of measuring and evaluating these variables as they interacted over time given my longitudinal model. Positive affect is a sign that all is going well (Carver & Scheier, 1990). In the dynamic, fast paced environment of a startup, those moments when things are going well can be a signal to catch your breath and, perhaps, give yourself permission to momentarily take your foot off the pedal, so to speak, before needing to reach down deep in preparation for another tough stretch of busy activity. Affect—as a signal that all is going well—can lead to complacency hence the result of a negative relationship between affect and effort. In the fast-paced world of startups, however, this complacency can be a real danger if long lasting. So, it might be healthy to enjoy the fruits of past labor and feel good about what has been accomplished for a short period. Past a certain point, however, positive affect will give way if it erodes effort indefinitely. At some point in an entrepreneur’s existence, he/she needs to manage the relationship between affect and effort or run the risk of losing positive affect due to complacency and lack of effort.

Looking at the relationship from the other vantage point, effort to affect, the results indicate a positive causal relationship; however, it takes time for it to emerge.

From a practitioner’s standpoint, entrepreneurs are known to work very hard. There is a saying amongst entrepreneurs—you have total freedom as an entrepreneur—you can work whenever you want. Any 18 hours a day you choose! Failure is tolerated—even accepted—however, the surefire way to harm a reputation as an entrepreneur is to not

63 work hard and avoid putting in the requisite effort. New ideas do not market and sell themselves—it takes intense effort to introduce something new to the world. When one works extremely hard over time on something meaningful—such as starting a company—a sense of pride and positive affect results. Thus, over time, effort leads to affect for entrepreneurs.

Hypothesis 3. Greater AEP at time t will be positively related to greater effort invested in entrepreneurial activities in the subsequent time period.

Results for H3

Table 10. Results for Testing the Relationship between AEP and Effort

IVs DVs Effort T2 Effort T3 Effort T4 AEP T1 b = -.41**, SE = .23 AEP T2 b= .17 ns, SE = .25 AEP T3 b = -.27 ns, SE = .24

Figure 10. Model for Testing Hypothesis 3

In this model, I controlled for age, education level, and program differences. In testing this model, paths from each control to each variable were estimated. For the sake of visual clarity, I have omitted the controls and paths to variables from this figure.

64 Hypothesis 3 was tested by conducting multiple regression analysis with effort at

t2, t3 and t4 as dependent variables and AEP t1, t2, and t3 as independent variables.

Effort t1, t2, and t3 and Affect t1, t2, and t3 were regressed as independent variables.

This analysis was executed in two steps. In step one, the appropriate controls (see Table

11) were regressed against effort at t1, t2, and t3.

Table 11. Reporting Controls from Hypothesis 3

Education b = -.15, SE = .53 Program b = -.79***, SE = .43 Age b = .28, SE = .056

In step two, AEP at t1, t2, and t3 was regressed against Effort at t1, t2, and t3 while including controls from step 1 and results are reported above in Table 11. Effort t1, t2, and t3 was regressed along with affect t1, t2, and t3. Overall model results are reported in Table 12.

Table 12. Model Overview for Hypothesis 3

Change Statistics R Adjusted Model Square R Square R Square F Sig. F Change Change Change 1 0.545 0.454 0.545 5.98 0.007 2 0.814 0.722 0.27 5.813 0.011

By analyzing the overall model analysis, I can determine the overall effects the

controls and the independent variables had on the dependent variable. The change in R-

Square is significant (.27, 0.011***) when introducing the independent variables of AEP,

effort, and affect from preceding period to the initial model that consisted of controls of

age, program, and education. Taken as a whole, the independent variables have a

65 significant effect on the dependent variables as shown via the significant F change in the table above after accounting for controls.

To determine the specific impact, I then examined the regression coefficients to determine the relative impact each independent variable has on the dependent variable. A regression coefficient is “the estimated change in the dependent variable for a unit change of the independent variable. If the regression coefficient is found to be statistically significant (i.e. the coefficient is significantly different from zero), the value of the regression coefficient indicates the extent to which the independent variable is associated with the dependent variable” (Hair et al., 2010: 163). In examining the impact of the regression coefficients in this model, AEP is the strongest predictor of effort compared to the other independent variables of affect and antecedent effort (see Table 13). AEP has a stronger effect on subsequent effort than even effort from the preceding period. It is worth noting that a control was significant in this analyses. The program was positively related to effort (b=-.79***, SE =.43).

Table 13. Relationships between AEP and Effort

Independent Standard Coefficient Significance Variables Error Effort T1 0.47 0.175 0.012 Affect T1 -0.141 0.131 0.328 AEP T1 -0.554 0.208 0.007 Effort T2 0.426 0.256 0.264 Affect T2 -0.637 0.242 0.088 AEP T2 0.655 0.3 0.074 Effort T3 0.077 0.268 0.769 Affect T3 0.288 0.331 0.413 AEP T3 -0.474 0.331 0.232 Dependent Variable: Effort in subsequent t

66 Given the small sample size and the fact that these latent variables are closely related—for instance, effort is the same variable being measured at different time points—evaluating issues of collinearity and multicollinearity are important. A simple initial test of collinearity was conducted to make sure that none of the independent variables was too highly correlated with another. According to Hair et al. (2010), correlations above .90 are a cause for concern. None of the independent variables in this multiple regression models were correlated higher than .49, so issues of collinearity are not present in this analysis. Using SPSS, tests for issues of multicollinearity were conducted to collectively evaluate the correlations of all independent variables simultaneously. According to Hair et al. (2010), VIF of <5.0 is acceptable and VIF<3.0 is good indicator that issues of multicollinearity are not present. As can be seen in Table 14, all VIF results in this model are <3.0 and correlations are not high enough to cause concern for multi-collinearity. Taken together, the results of this model are not hampered by issues of collinearity or multi-collinearity.

Table 14. Multi-Collinearity Analysis Hypothesis 3

Multi-collinearity Check Independent Variables Correlation VIF Effort T1 .573 1.524 Affect T1 -.203 1.412 AEP T1 .063 1.149 Effort T2 .110 2.050 Affect T2 -.244 2.407 AEP T2 .098 2.141 Effort T3 .368 2.242 Affect T3 .490 1.025 AEP T3 .236 1.821 Dependent Variable: Effort in subsequent time period

67 Discussion of H3

There is a negative and significant relationship between AEP t1 to Effort t2. This is a perplexing result as it is unexpected that AEP would be negatively and significantly related to effort. This relationship then changes to positive and non-significant and then reverts back to negative and non-significant at t4. So, there are some rather abrupt changes in the relationship between AEP and effort over the duration of the program.

Perhaps the excitement of launching a startup in a competitive accelerator program caused these nascent entrepreneurs to take relax their day to day efforts. I tested the relationship between AEP and Affect and found a strong, positive and significant relationship from the beginning (t1-t2 b= .74 SE= .26 P value<.004) to the middle of the program (t2-t3 b=.63 SE=.21 P value<.013). Perhaps, in the short run, AEP has a similar impact as affect has on effort—that of causing a temporary reduction in effort. Perhaps the excitement associated with high levels of AEP combined with the official launch of the startup in the accelerator program has the nascent entrepreneurs looking ahead to the success of the venture and overlooking the everyday effort necessary to make that success a reality. Foo et al. (2009), referencing control theory (Carver & Scheier, 1981), describe it as when things are going well, they are likely to relax efforts, and when things are not going well, efforts are redoubled.

Another possibility that explains the negative relationship between AEP and effort is that certain effortful tasks were negatively anticipated and thought to be overly challenging, difficult, or not aligned with the strengths of the entrepreneur(s). A recent study labeled anticipation of challenging or unpleasant tasks as “action aversion” (Van

Gelderen, Kautonen, & Fink, 2015). Thus, effort might quickly wane due to action

68 aversion. For example, “one explanation is that action aversion may be anticipated but

also experienced when the action is taken. For example, a person makes an effort to

undertake customer validation, and receives negative feedback, making the customer

validation task an aversive experience” (Van Gelderen et al., 2015: 668).

While AEP is a different construct than EP, they do share some similarities. In

Gielnik et al.’s (2014) study, they hypothesized and found a positive relationship between

effort and passion. They also tested the reverse relationship and found that EP in current time period does not predict entrepreneurial effort in the subsequent time period (b=0.01,

SE = 0.08, ns). So, my finding are theoretically similar to the Gielnik study. I predicted a positive relationship because of the anticipatory nature of AEP—surmising that it would serve to motivate the effort necessary to make AEP a realized state.

The findings indicate that these nascent entrepreneurs do not yet connect AEP

with effort. A corollary would be teenage smoking. Teens smoke for a variety of

reasons—addiction among them—however, a main reason is because it is perceived to be

an adult activity (Jessor, 1992) and a pleasurable activity (Amos, Gray, Currie, & Elton,

1997). Youths are not ignorant of the fact that it may harm their health much later in life.

So why exert the effort today to quit something they enjoy that will only pay dividends in

a long time? At some point, they know they’ll have to exert the effort but in the

meantime, they get some value (enjoyment or feeling of achieving adult autonomy) from

it in the moment. Perhaps the entrepreneurs know they will have to put in a substantial

effort to make their startup a proven entity over time. In the meantime, they are enjoying

the anticipation of that success and the good feeling it imparts during the earliest stages

of forming the venture. As a result, Hypothesis 3 is not supported.

69 Hypothesis 4. Greater goal progress will strengthen the positive relationship between effort and affect.

Table 15. Full model with Interaction Tested via Multiple Regression

IVs DVs

Affect T2 Affect T3 Affect T4

AEP T1 b = .77, SE.33, P<.017**

AEP T2 b = .60, SE .25, P<.042**

AEP T3 b = .34, SE = .09, P<.101 borderline *

Effort T1 b = -.07 ns, SE .28,

Effort T2 b = -.06 ns, SE = .26,

Effort T3 b = .45 SE = .09, P<.019**

Progress T1 b = .12 ns, SE .22

Progress T2 b = .11 ns, SE .30

Progress T3 b = -.02 ns, SE .10

Interaction T1 b = .06 ns, SE .30

Interaction T2 b = .08 ns, SE .34

Interaction T3 b = .15ns, SE.09

70 Figure 11. Model for Testing Hypothesis 4

In this model I controlled for age, education level, and program differences. In testing this model, paths from each control to each variable were estimated. For the sake of visual clarity, I have omitted the controls and paths to variables from this figure. For path coefficients, please see interaction in table 15.

Results: I conducted multiple regression to test Hypothesis 4. I followed three

steps to conduct this analysis. In step 1, I regressed controls of age, education, and

program against the dependent variable affect. None of the controls were significantly

related to affect. In step 2, I regressed the main effects of the independent variables AEP,

effort, and progress on the dependent variable of affect. In the third step, I isolated the

interaction effect (effort x progress) to see if it moderated the relationship between effort

and affect. The results of all three steps are presented earlier in Table 14. In terms of

examining the results, progress did not moderate the positive relationship between effort

and affect at any of the three time points measured—from t1-t2 (b=.06 ns, SE .30), t2-t3

(b=.08 ns, SE .34), and t3-t4 (b=.15 ns, SE .09).

In terms of assessing the overall model analysis, in Table 16, I report the impact the interaction variable (progress and effort) has on affect. In looking at the three time periods in Table 16, the moderation effect is presented as model 3 at each time point. The 71 interaction effect does not create a statistically significant change in the model at any of

the three periods (t1-t2 F change is .794 ns, t2-t3 F change is .821 ns, and t3-t4 F change is .417 ns). The interaction effect is model number three listed at each time point in Table

16.

Table 16. Testing Interaction Effects of Progress

Adjusted Change Statistics R Model R Square Square R Square Change F Change Sig. F Change t1-t2 1 0.195 0.034 0.195 1.213 0.339 2 0.571 0.356 0.376 3.501 0.05 3 0.574 0.302 0.003 0.072 0.794 t2-t3 1 0.081 -0.102 0.081 0.442 0.726 2 0.432 0.148 0.351 2.471 0.112 3 0.435 0.075 0.003 0.053 0.821 t3-t4 1 0.382 0.259 0.382 3.094 0.059 2 0.679 0.519 0.297 3.709 0.043 3 0.699 0.507 0.019 0.711 0.417 1. Predictors: (Constant), Age, Program, Education (controls) 2. Predictors: (Constant), Age, Program, Education (controls), Progress t 1, AEP t 1, Effort t 1 (same for t2, and t3) 3. Predictors: (Constant), Age, Program, Education (controls), Progress t 1, AEP t 1, Effort t 1, Interaction T1 (same for t2 and t3)

As described in the results section of Hypothesis 3, multi-collinearity must be examined given the small sample size and close relationship between latent variables. In

Table 17, results of multi-collinearity check indicate no issues. The correlations are not high enough to be a cause for concern, and the VIF test indicates that all variables are

<3.0. Taken together, multicollinearity is not an issue with this model. 72 Table 17. Test of Multi-Collinearity Hypothesis 4

Multi-collinearity

Check Independent Correlation VIF Variables AEP T1 .335 1.524 Effort T1 .107 1.412 Progress T1 .136 1.156 Mod T1 -.062 1.336 Effort T2 .515 1.175 Affect T2 -.216 2.196 AEP T2 .157 1.423 Mod T1 -.059 1.900 Effort T3 .576 1.340 Affect T3 .360 1.024 AEP T3 .272 1.886 Mod T1 .134 1.155 Dependent Variable Affect in Subsequent t

Discussion of H4

I did not find support for Hypothesis 4. The interaction effect (effort x progress) was not significant at any time point. This finding does not mesh with my experience of mentoring nascent entrepreneurs on a consistent basis over the past ten years.

Theoretically, Gielnik et al. (2014) found support for their hypothesis that progress mediates the relationship between effort and passion. Given that previous research and

practical experience indicate that progress is a significant factor in the relationship between effort and affect, it is difficult to draw conclusions as to why these results did not follow the expected pattern.

In reflecting on my practical experience running an entrepreneurship accelerator program at a prestigious university located in the northeastern U.S., the program I

73 previously directed for eight years, I realized that the program there is significantly

longer than either of the programs under study in this sample. The program I previously

directed is 26 weeks compared to 7 weeks for both programs in my sample. And in the

first seven weeks of the longer term entrepreneurship accelerator program I previously

directed—we spent a lot of internal time getting the concepts ready to meet with customers by helping them create a prototype or initial product offering or teaching them how to approach customers for the first time. Then, we assigned independent work over winter break. Upon returning to campus for the spring semester, they had to demonstrate the attainment of product/ market fit in order to advance to the second part of the program that helped them develop their concept into a company. Product/Market fit is an important progress milestone as entrepreneurs have to show that they have identified a problem that customers are willing to pay to solve, and the customer has validated their solution as a viable one. This product/market fit is a big progress point for entrepreneurs and is often thought of as a turning point where they either fail or move forward to building a company. In my experience with the program, only about half of the teams got to product market fit in 12 weeks.

Perhaps the shorter version of the programs under study did not allow enough time for them to make the kind of progress—especially progress validated by customers—necessary for it to make an impact. There is an opportunity for future research to test this interaction over a longer time period.

74 CHAPTER 6: CONTRIBUTIONS, LIMITATIONS, AND CONCLUSION

This chapter is dedicated to discussing the contributions this dissertation makes to

theory and practice as well as its limitations and a conclusion.

Contributions

This study advances our collective understanding of passion through the creation and empirical testing of AEP as well as the finding that AEP is enduring and malleable in

nature. A second major contribution of this dissertation is the causally interlinked

relationship between behavior (effort) and emotion (affect). Finally, we make a

contribution to the anticipated emotion literature by testing AEP in the domain of

entrepreneurship. Specific contributions to theory and practice are discussed in the relevant categories below.

Academic Contributions

First and foremost, I make a contribution to the literature on EP by extending this research through introducing this new construct, AEP, designed for nascent entrepreneurs that are either new to the field of entrepreneurship or are anticipating an entrepreneurial career. While EP is now well understood and has been tested empirically in a number of studies, AEP is a different construct altogether as it relates to nascent entrepreneurs that, while they may have intense feelings of being or becoming an entrepreneur, have yet to form an entrepreneurial identity. By testing and empirically validating AEP, future scholars that are interested in passion related to nascent or prospective entrepreneurs now have a scale to utilize in conducting future research.

Second, findings indicate that AEP is both enduring and malleable in nature.

Cardon et al. (2009) conceptualized EP as an enduring, rather than episodic, emotion.

75 Cardon and Kirk (2013) found empirical support that EP is an enduring emotion. Later

studies by Gielnik et al. (2014) and Collewaert et al. (2016) found it to be an episodic

emotion. Interestingly, my findings suggest that while it may change in the short term,

passion casts a long shadow and ultimately resumes prior levels.

My conclusion as to why this occurs is that nascent entrepreneurs may have

strong anticipatory passion—they badly want to become entrepreneurs—however, the

identity component is not yet fully validated and causes ebbs and flows in passion as

nascent entrepreneurs seek this validation (Markowska et al., 2015) via interactions with

prospective customers, employees or investors.

I base this conclusion on four items. First, Murnieks et al.’s (2011) point that

without identity included in the equation, passion rises and falls. Second, Markowska et

al.’s (2015) conceptualization that an identity must be validated by meaningful external parties for it to feel real to the beholder. Third, my empirical testing of the AEP construct included exploratory and confirmatory factor analysis that showed AEP loading on only

one factor rather than two even though AEP was conceptualized as including two role

identities—that of inventor and that of founder. This tells me that nascent entrepreneurs have not yet validated their role identity with such a fine point. Rather, they more broadly think of themselves as nascent entrepreneurs. Fourth, and finally, is the significant age and experiential differences evident in the samples when comparing Cardon and Kirk’s

(2013) finding that EP is enduring and Gielnik et al. (2014) and Collewaert et al.’s (2016) findings that EP was subject to change over time. I suggest that this age and experience gap means the difference between fully and partially validated entrepreneurial identities.

76 Interestingly, and a key contribution, is the difference between this study’s

finding that AEP is malleable (it sharply dropped in t3) and enduring (before resuming in

t4) and Collewaert et al.’s (2016) finding that passion for founding fades and does not

resume. While both samples predominantly were made up of nascent entrepreneurs, the

participants in my study enjoyed the benefit of weekly mentoring sessions with

experienced entrepreneurs. Collewaert et al.’s (2016) suggestion to rekindle fading

passion was to “tweak their ideas and seek feedback…by developing their learning

orientation” (p. 35). Further, Collewaert et al.’s (2016) suggests nascent entrepreneurs join networks to more formally seek feedback from experienced mentors.

Engaging in startup activity is inherently uncertain (Venkataraman, 1997; Zahra

& Dess, 2001). The risk and fear of failure is real. This study makes an important contribution by empirically validating Collewaert et al.’s (2016) conclusion that mentoring matters and can help rekindle passion when it fades. Experienced mentors can help the nascent entrepreneurs make sense of customer and investor feedback and see the

bigger picture. Further, mentors, by sharing their own trials and tribulations with nascent

entrepreneurs, can help nascent entrepreneurs learn that the process of becoming an

entrepreneur—and validating an entrepreneurial identity—takes time, effort, and patience

and that others before them have been able to successfully navigate this uncertain path.

Accelerator and incubator programs are in the business of helping nascent

entrepreneurs reconcile the difference between their present self and their imagined

entrepreneurial self. They might envision becoming the next Mark Zuckerberg, Steve

Jobs, Elon Musk, Arianna Huffington or some other entrepreneurial role model and our

program (and others like it including the programs examined in this dissertation) help

77 them along on the journey of creating their future entrepreneurial self. Of course, at the program’s end, the outcome of their future identity will remain unknown and will not reveal itself for some time. We help them move in the direction of their dreams.

Third, my longitudinal model allowed us to settle and advance the disparate findings of Foo et al. (2009) finding that emotion causes effort and Gielnik et al.’s.

(2014) finding that effort causes emotion. Unlike my colleagues, I have the data and analysis to make causal claims. By controlling for all previous occurrences in my design,

I am able to distill down to pure effects and make causal claims.

In terms of effort’s relationship to affect, Gielnik et al. (2014) found that effort influences passion and that as effort wanes so does passion. By analyzing this over time, and by controlling for preceding effects, the relationship between effort and affect materializes; however, it takes time for the effects to settle in. As is reported in the results section, the relationship between effort and affect is non-significant from t1 to t2 and from t2 to t3. It does not become significant until t3-t4. This rise to significance takes time as the nascent entrepreneurs become embedded in learning from mentors, customers, and fellow program participants. Ultimately, it takes a bit of time for them to figure out that their efforts are significantly related to positive affect.

In terms of affect’s relationship to effort, I find a significant but negative relationship between affect and effort. Here, Foo et al. (2009) are both right and terribly wrong. They are correct in noting the significant relationship; this relationship, though, is negative and significant. Participants are excited to begin the accelerator programs—they are competitive to get into and the nascent entrepreneurs are full of AEP, affect and energy. Often, instead of engaging in challenging and meaningful tasks such as calling on

78 prospective customers or meeting with prospective investors, they focus on something

familiar—such as coding—or choose to spend time on extraneous tasks. The signal that

affect gives off is that all is well and that it is fine to cruise by either engaging in

extraneous or familiar tasks or relax efforts (Carver & Scheier, 1990). By signaling that

all is well, entrepreneurs can become complacent and this complacency can ultimately reduce effort. I have witnessed this occurrence after observing entrepreneurs after they have raised funds. They are full of positive affect after raising money and, to some extent, they have to exert effort based on pressure from investors. What happens, though, is that the team feels insulated from the market by the cushion of funding. Often the founders do less selling or customer validation because, while basking in the afterglow of raising funds, they feel less of a need to make sales to cover expenses. If this affect induced lack of effort persists too long, it can hurt the overall performance of the venture.

Of course, my thoughts here are bounded by my sample and relate to nascent entrepreneurs and may not generalize to a broader relationship between affect and effort.

The findings here, though, do generalize to nascent entrepreneurs and those that mentor

and coach them.

A final contribution comes via extending anticipated emotion into the domain of

entrepreneurship. Anticipated emotion has been conceptualized as a stronger motivator

than felt emotion (Baumeister et al., 2007). The notion of anticipated rather than

experienced or felt emotion has not been empirically tested in the domain of

entrepreneurship. Further, and in answering Cardon et al.’s (2009) and Gielnik et al.’s

(2014) query to examine anticipatory passion, we examine nascent entrepreneur’s AEP

over time. By doing so, we extend the literature on anticipated emotion.

79 Contributions to Practice

This study has important implications for practice. First, by creating and empirically testing AEP, an instrument now exists for screening and testing nascent entrepreneurs interested in participating in advanced entrepreneurship classes or university or community accelerators or incubators in order to admit those with the highest AEP. As mentioned, during my tenure directing a collegiate accelerator program,

I noticed that nascent entrepreneurs’ passion would typically drop and rebound over the course of the program. Knowing that AEP is enduring and malleable is important in terms of teaching and mentoring nascent entrepreneurs. Now it has been tested empirically and those involved in coaching and mentoring nascent entrepreneurs have a mechanism to restore or rejuvenate waning AEP. Nascent entrepreneurs may experience dips in AEP, however, with proper coaching and feedback mechanisms, AEP will ultimately endure.

Learning about the interplay between effort and affect will help professors and mentors help nascent entrepreneurs set goals, learn from their feelings (affective residue) as well as their behavior (I didn’t work hard enough or I worked inefficiently or on wrong tasks) and ultimately be more productive. Taken together, these practical contributions may help solve two major problems of practice by increasing the number of nascent entrepreneurs that ultimately become career entrepreneurs as well as increasing the number of student entrepreneurs that take the leap to become nascent entrepreneurs.

Limitations

While this paper makes significant contributions as discussed in the previous sections, like all research, it is not without limitations.

80 One limitation is time lag in retrospective reporting. We asked entrepreneurs to assess the effort and progress made during the past week. Memory biases can occur during when time lags are present. We also asked them to report their situational mood after receiving feedback from mentors. Although time lag biases are possible, previous research indicates that reasonable agreement between assessing current emotional states and a weekly summative report (Parkinson et al., 1995).

My sample of 19 is quite small; however, we followed these entrepreneurs over a seven-week period and collected data from them each week leading to 76 data points for our analysis. There is precedence for this style of study in the entrepreneurship literature

(see Gielnik et al., 2014; Foo et al., 2009) for examples of studies using this format that were published in reputable journals).

Another limitation is that my measurement instrument relies somewhat heavily on self-reported data. I did measure effort and progress from self and mentors. All other measures are self-reported. It would be optimal to measure a motivational construct such as AEP without having to rely on survey data. Using physiological data such as heart rate, blood pressure, or brain waves would be an interesting and effective way to measure a motivational construct such as AEP.

Finally, I am only following them for the duration of their time in the eight-week summer accelerator program. Future work examining a longer time horizon is necessary to see this transformation from nascent entrepreneur to seasoned entrepreneur occur.

After my dissertation, for the purposes of publication, I plan to follow up with this group annually.

81 These limitations notwithstanding, I believe I have attempted to tackle important research questions related to passion, anticipated emotion, and the relationship between effort and affect. It has been a challenging and, ultimately, an enlightening and rewarding experience, and I appreciate the opportunity to indulge my practical experience working with nascent entrepreneurs and test it empirically and rigorously.

Generalizing to Other Populations

While the findings of this study are interesting, it is important to note some boundaries that may exist when considering the impacts of these findings on other populations. There are three key boundaries that must be examined when thinking about how these findings may apply to other sample populations: age and other work experiences, entrepreneurial experience, and structured versus unstructured environments. Each boundary condition will be explained in the coming paragraphs.

While the average age in this sample is 26, the average age of most first time entrepreneurs is 40 (https://hbr.org/2013/06/entrepreneurs-get-better-with/). As it relates to the effort to affect and affect to effort results, the same effects found in this study are presumed to hold when applied to an older, more experienced sample. However, the effects may be “smoothed” out as a result of the experiential gap. One finding in this study is that the nascent entrepreneurs are sensitive to the cues they receive from the external environment including prospective customers, investors, supply partners, or team members. While new to entrepreneurship, nascent entrepreneurs that are forty are probably not new to the workplace. While a typical workplace environment may not be as chaotic or dynamic as an entrepreneurial environment, especially if you are not an owner or founder, this type of environment still offers its share of ups and downs and

82 opportunities to manage affect and interpret external cues. Thus, findings from this study

may generalize to older, more experienced nascent entrepreneurs. However, the effects may not be as pronounced.

In terms of generalizing to more experienced entrepreneurs, or those that have founded or owned businesses previously and are engaging in another startup, the findings in this study are not expected to generalize to this group. The reason that nascent entrepreneurs are so susceptible to these external cues is that they have not experienced the dynamic, up and down nature of entrepreneurship previously. Lacking this experience, the chaotic nature of entrepreneurship can be hard to manage emotionally— especially for the first time. So, when a few early stage customers give early feedback that is not favorable, a more experienced entrepreneur will take that feedback in stride and continue to speak to numerous other customers before drawing any conclusions. The nascent entrepreneurs are more likely to make a rushed judgment that their concept will not be embraced by the market based on the skewed data that comes from a small sample.

More experienced entrepreneurs are less sensitive to early stage feedback and realize that they are just beginning the entrepreneurial process and need to be patient before drawing conclusions on the needs of the market. Also, experienced entrepreneurs are used to the chaotic nature of entrepreneurship, having experienced big swings in affect in previous entrepreneurial roles, and are able to control their responses to these external cues. Thus, findings from this study will not generalize as well to experienced entrepreneurs.

Finally, let us examine structured versus unstructured environments in relation to the generalizability of these findings. The nascent entrepreneurs in this study were all participating in structured accelerator programs that featured weekly meetings with 83 experienced entrepreneurs as well as workspace to share with fellow participants that were experiencing the same dynamics at the same time. Whereas participants this study could seek the counsel and gain perspective from mentors, nascent entrepreneurs in less structured environments have no such structure or support system to lean upon. This could be the reason in Collewaert et al. (2016) that passion declined and did not resume prior levels. The experience and perspective sharing that occurs during such a structured program allows accelerated learning and helps the nascent entrepreneur learn to manage emotions and handle the chaotic nature inherent in the entrepreneurial process. Without such structure, nascent entrepreneurs would have a longer learning curve as it relates to managing emotions and dealing with the ups and downs that are part of this process.

Thus, the findings of this study generalize well to nascent entrepreneurs in less structured environments and, in fact, the findings may be even stronger. Unlike more experienced nascent entrepreneurs, however, the findings in this study, particularly the relationship between effort and affect, are more likely to be more pronounced without the structure and coaching present.

Conclusion

This dissertation covers a set of topics that are important to developing, motivating and mentoring nascent entrepreneurs. Through the creation and empirical testing of AEP, to the discovery that AEP is both malleable and enduring, to the causally interlinked relationship of effort and affect, this dissertation has added to our collective understanding in meaningful ways. By deepening our knowledge in this domain, academic theory and practice will be advanced to improve teaching, research and

84 mentoring nascent entrepreneurs in a manner that may help spark their AEP into a burning fire of desire for future entrepreneurial activity.

85 Appendix A: Program Details

Below is a table of key program details. Many important details, such as how many cohorts will be formed, which industries the cohorts will cover, which member of the teaching team will be responsible for each cohort, etc., are not yet known, and this table will be updated as this information becomes known.

Accelerator Name Program 1 Program 2 Program Length Eight Weeks Eight Weeks Scheduled Dates June and July 2015 June and July 2015 Number of estimated 20 21 Participants Teaching Philosophy Lean Startup Lean Startup Dedicated Yes Yes Mentors/Teaching Team Guest Mentors Yes Yes Program Structure Regular meetings with a couple Regular meetings with a couple of extended sessions held during of extended sessions held during the course of the program. the course of the program. Participants and teaching team Participants and teaching team expected to attend each session. expected to attend each session. Guest mentors will also show up Guest mentors will also show up on a rotational basis. When the on a rotational basis. When the program begins, respondents are program begins, respondents are typically broken up into four (or typically broken up into four (or so) cohorts based on common so) cohorts based on common interests or industries. For interests or industries. For example, a typical year might example, a typical year might have a software cohort, a have a software cohort, a hardware cohort, a clean tech hardware cohort, a clean tech cohort and a service oriented cohort and a service oriented cohort. Each cohort is led by a cohort. Each cohort is led by a member of the teaching team. member of the teaching team. That teacher mentors and gives That teacher mentors and gives feedback to each individual in feedback to each individual in the the program. We will not know program. We will not know the the makeup of the cohorts or the makeup of the cohorts or the teaching member assigned until teaching member assigned until just before the start of the just before the start of the programs. programs. Program Activities Boot camp: The boot camp is Boot camp: The boot camp is designed to familiarize designed to familiarize participants with the program, participants with the program, the 86 the timing of events, meetings, timing of events, meetings, and and mentor sessions, and overall mentor sessions, and overall expectations for successfully expectations for successfully engaging in the program. engaging in the program. Additionally, boot camp lays out Additionally, boot camp lays out the key activities that will occur the key activities that will occur over the duration of the program over the duration of the program culminating in demo day—a culminating in demo day—a high high profile event attended by profile event attended by prospective investors. In terms prospective investors. In terms of of content, the boot camp begins content, the boot camp begins by by each team giving their first each team giving their first concept pitch to the teaching concept pitch to the teaching team team and program mentors. and program mentors. Then the Then the team shares their work team shares their work plans/goals with the mentors. plans/goals with the mentors. After the pitch, each team gets After the pitch, each team gets initial feedback from mentors on initial feedback from mentors on the clarity of their concept and the clarity of their concept and the the quality of their goals. The quality of their goals. The teaching team then presents an teaching team then presents an overview of lean principles. overview of lean principles. The boot camp also provides an The boot camp also provides an opportunity to assess where the opportunity to assess where the startup teams are in the overall startup teams are in the overall entrepreneurial process. The entrepreneurial process. The assessment focuses on the assessment focuses on the startup’s clarity of their value startup’s clarity of their value proposition to customers, the proposition to customers, the distinctiveness of their product distinctiveness of their product as as a solution to a customer’s a solution to a customer’s problem, the logic of their problem, the logic of their revenue model and revenue revenue model and revenue generation plan, their acuity generation plan, their acuity with with their operational their operational model/cost model/cost structure, and their structure, and their financial plan financial plan and funding and funding strategy. strategy. In general, these summer In general, these summer accelerator programs are quite accelerator programs are quite similar—they are designed to similar—they are designed to help these nascent entrepreneurs help these nascent entrepreneurs “accelerate” their overall progress “accelerate” their overall as they take an untested idea and progress as they take an untested try to turn it into a concept that idea and try to turn it into a has been validated by customers concept that has been validated and/or inventors and then, by customers and/or inventors eventually, into a company.

87 and then, eventually, into a Boot camp Objectives: company. 1. Get acquainted/create a Boot camp Objectives: community out of teams 2. Establish a common 1. Get acquainted/create a vocabulary and explain community out of teams the “lean” philosophy 2. Establish a common 3. Get each team to create vocabulary and explain and “own” a series of the “lean” philosophy weekly, measurable 3. Get each team to create targets based on their and “own” a series of overall team goals weekly, measurable 4. Explain how to prepare targets based on their for the weekly sessions overall team goals 4. Explain how to prepare Week 1: Acknowledging that for the weekly sessions teams are at different points of progression, teams set goals to All teams and mentors are achieve by demo day. For invited to a happy hour example, if a concept is vague celebrating the start of the and broad, their goals might be to Startup Hoya program. This finish with much greater clarity happy hour is designed to kick around the value proposition, start the program and help the target customer, and the creation mentors and participants get to of a minimally viable product know each other in a relaxed (MVP). If a concept is more and laid back atmosphere. advanced and generating revenue, Week 1: Acknowledging that they will achieve greater clarity teams are at different points of around the customer acquisition: progression, teams set goals to the most efficient way to convert achieve by demo day. For a customer, how to expedite the example, if a concept is vague sales cycle, how to reach target and broad, their goals might be market most cost effectively. to finish with much greater Each concept develops and shares clarity around the value their specific goals with their proposition, target customer, mentors and get feedback—thus and the creation of a minimally starting a process that will repeat viable product (MVP). If a each week over the duration of concept is more advanced and the program. Each team also sets generating revenue, they will short term goals to discuss with achieve greater clarity around mentors in the following week. the customer acquisition: the most efficient way to convert a Each week, all participants and customer, how to expedite the mentors affiliated with the LCL sales cycle, how to reach target program are invited to a weekly market most cost effectively. community dinner to celebrate the accomplishments of the prior Each concept develops and week and to facilitate shares their specific goals with their mentors and get

88 feedback—thus starting a relationships between nascent process that will repeat each entrepreneurs and mentors. week over the duration of the program. Each team also sets Week 2: A basic goal for week 2 short term goals to discuss with is for all teams to engage in mentors in the following week. customer discovery. For early stage teams, this may involve Week 2: A basic goal for week 2 multiple customer interviews to is for all teams to engage in understand their customers’ customer discovery. For early problem in great depth. For more stage teams, this may involve advanced teams, this week of the multiple customer interviews to program would involve showing understand their customers’ prospective customers a problem in great depth. For minimally viable product solution more advanced teams, this week (MVP) of their previously of the program would involve identified problem. showing prospective customers a minimally viable product Each team meets with their solution (MVP) of their mentor(s) and discusses their previously identified problem. goals set the previous week and their performance in terms of Each team meets with their meeting or not meeting previous mentor(s) and discusses their goals. At the end of the session, goals set the previous week and goals are set for the upcoming their performance in terms of week. meeting or not meeting previous goals. At the end of the session, Week 3: Teams are continuing to goals are set for the upcoming engage in customer discovery. week. The earlier stage teams are continuing to interview customers Week 3: Teams are continuing to further understanding of their to engage in customer needs while later stages teams are discovery. The earlier stage showing customers MVP and are teams are continuing to using customer feedback to interview customers to further improve their product offering. understanding of their needs while later stages teams are Each team meets with their showing customers MVP and mentor(s) and discusses their are using customer feedback to goals set the previous week and improve their product offering. their performance in terms of meeting or not meeting previous Each team meets with their goals. At the end of the session, mentor(s) and discusses their goals are set for the upcoming goals set the previous week and week. their performance in terms of meeting or not meeting previous Week 4: All teams are continuing goals. At the end of the session, to engage in customer discovery. goals are set for the upcoming Early stage teams at this point week. should be taking what they learned from customer interviews and using that knowledge to

89 Week 4: All teams are create an MVP that will solve the continuing to engage in most basic needs of their target customer discovery. Early stage market customers. More teams at this point should be advanced teams will be morphing taking what they learned from into selling by taking pre-orders customer interviews and using from target market customers that knowledge to create an anxious for a solution to their MVP that will solve the most problem. For those later stage basic needs of their target companies that do not get pre- market customers. More orders, they begin to assess advanced teams will be whether they are really solving a morphing into selling by taking meaningful problem and pre-orders from target market contemplate a potential pivot to a customers anxious for a solution new product offering or a new to their problem. For those later group of customers. stage companies that do not get pre-orders, they begin to assess Each team meets with their whether they are really solving a mentor(s) and discusses their meaningful problem and goals set the previous week and contemplate a potential pivot to their performance in terms of a new product offering or a new meeting or not meeting previous group of customers. goals. At the end of the session, goals are set for the upcoming Each team meets with their week. mentor(s) and discusses their goals set the previous week and Week 5: All teams are continuing their performance in terms of to engage in customer discovery. meeting or not meeting previous Early stage teams should now be goals. At the end of the session, showing their MVP’s to target goals are set for the upcoming market customers in an attempt to week. determine customer interest and to secure pre-orders. Later stage Week 5: All teams are concepts are attempting to show continuing to engage in traction and growth—they have customer discovery. Early stage either found product/market fit or teams should now be showing are contemplating a pivot. their MVP’s to target market customers in an attempt to All teams are also shifting their determine customer interest and focus towards Demo Day—a high to secure pre-orders. Later stage profile day where they get to concepts are attempting to show pitch their concept in front of traction and growth—they have angel investors and venture either found product/market fit capitalists. Teams develop pitches or are contemplating a pivot. and share these pitches with the teaching team and external All teams are also shifting their mentors to get feedback and make focus towards Demo Day—a their pitch as strong as possible. high profile day where they get to pitch their concept in front of Each team meets with their angel investors and venture mentor(s) and discusses their capitalists. Teams develop goals set the previous week and 90 pitches and share these pitches their performance in terms of with the teaching team and meeting or not meeting previous external mentors to get feedback goals. At the end of the session, and make their pitch as strong as goals are set for the upcoming possible. week. Each team meets with their Week 6: All teams are engaging mentor(s) and discusses their in customer discovery. Early goals set the previous week and stage teams are now showing an their performance in terms of MVP prototype to customers to meeting or not meeting previous gauge their interest and to secure goals. At the end of the session, pre-orders. At this point, the early goals are set for the upcoming stage teams are determining if week. customers are interested in their offering. More advanced teams Week 6: All teams are engaging are trying to get traction and show in customer discovery. Early evidence of growth potential. stage teams are now showing an They are also examining the size MVP prototype to customers to of their prospective market to gauge their interest and to determine how they might be able secure pre-orders. At this point, to scale their concept. All teams the early stage teams are are practicing their Demo Day determining if customers are pitches—this becomes a interested in their offering. More dominant activity late in the advanced teams are trying to get program as Demo Day affords traction and show evidence of significant opportunities to raise growth potential. They are also funds, secure mentoring examining the size of their relationships and to market their prospective market to determine nascent companies. how they might be able to scale their concept. All teams are Each team meets with their practicing their Demo Day mentor(s) and discusses their pitches—this becomes a goals set the previous week and dominant activity late in the their performance in terms of program as Demo Day affords meeting or not meeting previous significant opportunities to raise goals. At the end of the session, funds, secure mentoring goals are set for the upcoming relationships and to market their week. nascent companies. Week 7: This is the final week of Each team meets with their the program and all teams are mentor(s) and discusses their focused on Demo Day. Teams are goals set the previous week and continuing to engage in customer their performance in terms of discovery with the overall goal meeting or not meeting previous being the achievement of goals. At the end of the session, product/market fit—have they goals are set for the upcoming identified a real problem that week. customers are willing to pay for and have customers accepted their Week 7: This is the final week solution as a viable one for their of the program, and all teams given problem? If so, they have 91 are focused on Demo Day. achieved product market fit and Teams are continuing to engage are ready to proceed into in customer discovery with the company building mode. If not, overall goal being the they may have more customer achievement of product/market discovery work to do or they may fit—have they identified a real need to pivot. Another reality is problem that customers are failure—they have not identified willing to pay for and have a real problem worth solving and customers accepted their need to start over with another solution as a viable one for their idea. Teams are working around given problem? If so, they have the clock to perfect their pitch— achieved product market fit and typically a six to eight-minute are ready to proceed into pitch in front of prospective company building mode. If not, investors. they may have more customer discovery work to do or they Each team meets with their may need to pivot. Another mentor(s) and discusses their reality is failure—they have not goals set the previous week and identified a real problem worth their performance in terms of solving and need to start over meeting or not meeting previous with another idea. Teams are goals. At the end of the session, working around the clock to goals are set for the upcoming perfect their pitch—typically a week. six- to eight-minute pitch in Demo Day: This is the front of prospective investors. culmination of the summer Each team meets with their program and a very high profile mentor(s) and discusses their event. The participants work very goals set the previous week and hard as this could be the make or their performance in terms of break event for their fragile meeting or not meeting previous company. This can be a scenario goals. At the end of the session, where they secure investment or goals are set for the upcoming procure a large pre-order or week. otherwise validate their startup. The teams work hard to make the Demo Day: This is the most of this important event. culmination of the summer program and a very high profile event. The participants work very hard as this could be the make or break event for their fragile company. This can be a scenario where they secure investment or procure a large pre-order or otherwise validate their startup. The teams work hard to make the most of this important event.

92 Relationship Between After analyzing all program After analyzing all program Program Activities and activities, there is no theoretical activities, there is no theoretical AEP or experiential basis to conclude or experiential basis to conclude that a relationship exists that a relationship exists between between the program activities the program activities and AEP. and AEP. The participants bring The participants bring a certain a certain level of AEP to the level of AEP to the program and program and that may impact that may impact their motivation their motivation to engage in to engage in differing degrees differing degrees with the with the activities described activities described above. The above. The opposite effect, opposite effect, though, does not though, does not make sense. In make sense. In other words, other words, more mentoring or more mentoring or more more customer discovery does not customer discovery does not lead to more AEP. lead to more AEP. Program Objectives To develop nascent To develop nascent entrepreneurs entrepreneurs and to help them and to help them validate and validate and refine an refine an entrepreneurial idea entrepreneurial idea (exists in (exists in the minds of the team) the minds of the team) into an into an entrepreneurial concept entrepreneurial concept (validated by customers) so that (validated by customers) so that the startup can begin the process the startup can begin the process of forming a company. Some of forming a company. Some companies advance into the companies advance into the exploitation phase as they are exploitation phase as they are either farther along or make faster either farther along or make progress during the program. faster progress during the program. Typical Program 1. Launch company 1. Launch company Outcomes 2. Raise capital 2. Raise capital 3. Apply to a professional 3. Apply to a professional accelerator program accelerator program (typically includes funding) (typically includes funding) 4. Pivot to a new idea 4. Pivot to a new idea 5. Realize that this idea is not 5. Realize that this idea is not viable viable 6. Realize that 6. Realize that entrepreneurship entrepreneurship is the right is the right career path career path 7. Realize that entrepreneurship 7. Realize that is not the right career path entrepreneurship is not the right career path

93 Appendix B: Information about Study Participants

Category Program 1 Program 2 Average age of participants 26 23 Age range of participants 20–35 19–32 Previous entrepreneurial 80% have less than two 95% have less than two experience years of entrepreneurial years of entrepreneurial experience experience Percentage that has 47% yes 47% yes completed a feasibility study for their startup 53% no 53% no Incorporation status of 26% incorporated 37% incorporated startups 74% not incorporated 63% not incorporated Current stage of startup Ideation: 32% Ideation: 21% activity Concept: 37% Concept: 74% Beginning to produce Beginning to produce revenue: 16% revenue: 0% Regularly producing Regularly producing revenue: 16% revenue: 0%

94 Appendix C: Construct Table and Measures for Pre, Post and Weekly Surveys

Cronbach’s Alpha of Authors Construct Name Measures Scale (full references at (average end of work) across items) Anticipatory The scale for this part will be a Cronbach’s Significantly adapted Passion (measured confidence intensity scale where 1 = Alpha .970 from Cardon et al. pre and post 0% confident, 2= (2009) paper that program and conceptualized three weekly during 25% confident, 3= 50% confident, 4 = entrepreneurial sub- program) 75% confident and 5 = 100% identities confident, 6 =110%

confident

In 3–5 years, how confident are you that you will be seen as a (n)…

1. inventor that entered the market with a new technology or idea

2. idea person that started it all

3. “tinkerer” that took a decent idea and made it a great product/service

4. founder that created a bold new company

5. nurturer that “birthed” an innovative company

6. Entrepreneur who founded a company that changed the world.

Just thinking of what I aspire to be as an inventor in 3–5 years that disrupted the market

with a new technology or idea …

1. I feel a rise of intense joy.

2. I feel a high of extreme excitement

3. I am filled with enthusiasm

Just thinking of what I aspire to be as a founder in 3–5 years that created a bold new

company … 95 Cronbach’s Alpha of Authors Construct Name Measures Scale (full references at (average end of work) across items)

1. I feel a rise of intense joy.

2. I feel a high of extreme excitement

3. I am filled with enthusiasm Current 1. It is exciting to figure out new Cronbach’s Cardon et al. (2009) Entrepreneurial ways to meet unmet market needs Alpha .86 Passion (measured 2. Searching for new ideas for pre and post products/services is enjoyable to program) me 3. I am motivated to figure out how to make existing products/services better 4. Scanning the environment for new opportunities really excites me 5. Inventing new solutions to problems is an important part of who I am 6. Establishing a new company excites me 7. The idea of owning my own company in the future energizes me 8. Nurturing a new business through its emerging success is enjoyable 9. Being the founder of a business is an important part of who I strive to become 10. I really like finding the right customers to market my product/service to 11. Assembling the right people to work for my business is exciting

Concerted Effort Individual effort Cronbach Adapted from Gielnik (collected weekly 1. During the past week, how much Alpha .83 et al. (2014) during program) time did you personally devote to working on your startup? Slide a slide scale from 0 to 60 hours 2. Of these hours, what percent did you devote to new and unfamiliar tasks related to the startup ….Include a sliding scale from 0 to 100%

96 Cronbach’s Alpha of Authors Construct Name Measures Scale (full references at (average end of work) across items) Team effort

1. During the past week, how much time do you think did your team collectively devote to working on your startup? (your best judgment)

Slide a slide scale from 0 to 300 hours instead

2. Of these hours, what percent do you think did the team collectively devote to new and unfamiliar tasks related to the startup: (your best judgment)

Include a sliding scale from 0 to 100%

3. In the way your team interacted over the last one week, would you say that the team members worked… Well together Cohesively 0-10 sliding scale with appropriate end points

Developmental 1. During the past week, how would Cronbach’s Adapted from Gielnik Progress you evaluate the progress of your Alpha .93 et al. (2014) team toward achieving your Self-report and startup goals? collected from mentors slide scale from 0% to 100% (Measured pre- achievement of startup goals program as a control and will be 2. During the past week, to what measured weekly extent did your individual during program). performance contribute to progress toward achieving your startup goals?

slide scale from 0% to 100% achievement of startup goals

Situational Affect Tell us how you feel at this particular Cronbach’s Watson and Tellegen (PANAS) moment in time. Alpha .88 (1999) measured weekly Right now I am feeling…

97 Cronbach’s Alpha of Authors Construct Name Measures Scale (full references at (average end of work) across items) during the program Sliding 0-10 scale from “not feel this at immediately after all” to “very strongly feel this.” receiving weekly feedback on Upset progress from Hostile mentors Alert Ashamed Inspired Nervous Determined Attentive Afraid Active Interval measure: never 1 2 3 4 5 always

Entrepreneurial This next set of questions seeks to Cronbach’s Adapted from Cardon Identity Enactment understand how you consider different Alpha .86 et al. (2009) entrepreneurial roles (measured pre and Inventor Identity post program) How likely is it that you will have invented a new product or idea in the next...? (use a 0–100% probability sliding scale) a. One year b. 2–3 years c. 4–5 years

How ready are you to develop an idea that has appeal in the marketplace in the next…? (use 0–100% readiness sliding scale) a. One year b. 2–3 years c. 4–5 years

How likely is it that you will improve an existing product or service so that it is market-ready in the next…? (use 0– 100% probability sliding scale)

a. One year b. 2–3 years c. 4–5 years

Founder Identity How likely is it that you will have founded a viable company in the

98 Cronbach’s Alpha of Authors Construct Name Measures Scale (full references at (average end of work) across items) next...? (use 0–100% probability sliding scale) a. One year b. 2–3 years c. 4–5 years

How ready are you to take a company through its emerging stages in the next…? (use 0–100% readiness sliding scale) a. One year b. 2–3 years c. 4–5 years

How likely is it that you will identify the right customer segment or market in the next…? (use 0–100% probability sliding scale)

a. One year b. 2–3 years c. 4–5 years

Entrepreneurial 1. Are you currently self-employed? Cronbach’s Lüthje and Franke Intention a. Yes Alpha .86 (2003) b. No (measured pre and post program) 2. Do you plan to be self-employed and working in your startup in the foreseeable future? a. Yes b. No

3. If yes to question 2 above, do you plan to be self-employed a. Immediately upon completing the startup accelerator program b. Within 3 months of completing the program c. Within 6 months of completing the program d. Within one year of completing the program e. Beyond one year of completing the program

99 Cronbach’s Alpha of Authors Construct Name Measures Scale (full references at (average end of work) across items) 4. How much of your personal wealth would you be willing to invest in your startup? Use a sliding scale of 0 to 100%

Entrepreneurial 1–5 scale from 1 (not at all like me) to Cronbach’s Duckworth and Grit 5 (very much like me) alpha .91 Quinn (2009)

(measured pre and At this point in your life, how do you post program) evaluate yourself in terms of…

Consistency of Interest 1. I often set a goal but later choose to pursue a different one 2. I have been obsessed with a certain idea or project for a short time but later lost interest 3. I have difficulty maintaining my focus on projects that take more than a few months to complete 4. New ideas and projects sometimes distract me from previous ones

Perseverance of Effort 1. I finish whatever I begin 2. Setbacks don’t discourage me. 3. I am diligent.

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