ANTECEDENTS OF RADICALITY AND COMMERCIAL SUCCESS OUTCOMES IN SBIR PROJECTS
by
J. KRIST SCHELL
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
January, 2020
CASE WESTERN RESERVE UNIVERSITY
SCHOOL OF GRADUATE STUDIES
We hereby approve this thesis/dissertation of
J. Krist Schell
Candidate for the degree of Doctor of Philosophy *.
Committee Chair
Jagdip Singh, Ph.D., Case Western Reserve University
Committee Member
Kalle Lyytinen, Ph.D., Case Western Reserve University
Committee Member
Nicholas Berente, Ph.D., University of Georgia
Committee Member
Daniel Cohen, Ph.D., Wake Forest University
Committee Member
Lori Kendall, Ph.D., Case Western Reserve University
Date of Defense
May 14, 2019
*We also certify that written approval has been obtained
for any proprietary material contained therein.
Copyright © J. Krist Schell, 2019
All rights reserved.
Dedication
For Dana, Saralinda, and Daniel, for support unimaginable.
Table of Contents
List of Tables ...... vii List of Figures ...... viii Abstract ...... x CHAPTER 1: INTRODUCTION ...... 1 Study Context and Contribution ...... 3 Research Question ...... 6 Theoretical Background ...... 8 CHAPTER 2: LITERATURE REVIEW ...... 11 Construct Definitions ...... 15 Conceptual Model ...... 22 Hypotheses ...... 26 CHAPTER 3: METHODS ...... 37 SBIR Database ...... 37 Demographic Comparison ...... 38 Preliminary Data Analysis ...... 39 Data Analyses Strategies...... 41 Measures ...... 41 CHAPTER 4: RESULTS ...... 43 Hypotheses Results and Analyses ...... 45 Mediation Relationships ...... 50 Interaction Effects ...... 56 CHAPTER 5: DISCUSSION ...... 59 Contributions...... 61 Implications for Strategy Policy ...... 66 Implications for Innovation Development ...... 69 Implications for Public Policy Investment ...... 70 Study Limitations ...... 70 Conclusion ...... 73 Appendix A: Literature Review and Analyses ...... 75 Appendix B: Measurement Definition Table ...... 84 Appendix C: Literature Review Article Subject Aggregation ...... 91 Appendix D: Demographic Comparisons ...... 94
v Appendix E: IV Cohort EFA Analyses ...... 96 Appendix F: IV Cohort Validity Analyses ...... 97 Appendix G: IV CFA Analyses ...... 98 Appendix H: IV Cohort CFA Model Fit Analyses ...... 99 Appendix I: IV Cohort Common Method Bias Analyses ...... 100 Appendix J: DV Cohort EFA Analyses ...... 101 Appendix K: DV Validity Measurement ...... 102 Appendix L: DV Cohort CFA Analyses ...... 103 Appendix M: DV Cohort CFA Model Fit Analyses ...... 104 Appendix N: DV Cohort Common Method Bias Analysis ...... 105 Appendix O: Skewness and Kurtosis: All DV and IV Cohort Data ...... 106 Appendix P: Skewness and Kurtosis: All Matched Pair Cohort Data ...... 107 Appendix Q: Conceptual Model ...... 108 Appendix R: Bayesian Analyses Methodology ...... 109 Appendix S: Proposed Mediation Analyses ...... 114 References ...... 118
vi List of Tables
Table 1. Independence Test Results for Demographic Effects upon Matched/ Unmatched Respondent Cohort Inclusion ...... 39 Table 2. Hypotheses Results ...... 44 Table 3. Mediation Test Results ...... 45 Table 4. Comparison of Variable Directional Effects ...... 60 Table D1. Respondent Proximity to Major Client for Project vs. Respondent Presence in Matching/Nonmatching Response Cohort ...... 94 Table D2. No. of Personnel in Firm When Grant Awarded vs. Respondent Presence in Matching/Nonmatching Response Cohort* ...... 94 Table D3. SBIR Grant Issuing US Gov. Dept. vs. Respondent Presence in Matching/Nonmatching Response Cohort* ...... 94 Table D4. Respondent Region vs. Respondent Presence in Matching/Nonmatching ...... 95 Table D5. SBIR Award Size vs. Respondent Presence in Matching/Nonmatching ...... 95 Table R1. Bayesian EFA Analysis...... 110 Table R2. Hypotheses Testing and Goodness of Fit Measures ...... 111 Table R3. Moderation and Mediation Analyses ...... 112 Table R4. Mediating Effect Hypotheses Summary ...... 113
vii List of Figures
Figure 1. Conceptual Model and Path Results ...... 22 Figure 2. Interaction Effect of Planning on the Relationship between Experimentation and Radicality ...... 58 Figure 3. Interaction Effect of Planning on the Relationship between Experimentation and Commercial Success ...... 58
viii Acknowledgments
This dissertation journey has succeeded because of unrelenting, patient support from many special people. I am indebted to the entire Doctor of Management group—
Kalle Lyytinen, Sue Nartker, Alexis Antes, and especially Marilyn Chorman—for fielding the unique challenges this project presented. James Gaskin and Nick Berente continually offered insight and guidance that could never have been found elsewhere.
Jagdip Singh understood from the beginning the balance between thinking creatively and acting practically. At all times, he created a space where I was able to apply his lessons but also precipitated further understanding—a rare talent in any instructor. Lori Kendall
and Dan Cohen were continual sources of inspiration at the most critical of times. In
particular, Jing Tang provided Mplus technical expertise, which enabled the study to reach many of its objectives. No acknowledgment would be complete without my appreciation to my fellow students in the program, who from the beginning created the space for the exploration of the many paths the project encountered.
ix Antecedents of Radicality and Commercial Success Outcomes in SBIR Projects
Abstract
by
J. KRIST SCHELL
Small Business Innovation Research (SBIR) projects, despite significant commercial intent in the initial application process, typically do not result in clear financial success, with many successful technology outcomes falling into the “Valley of Death” without commercial application. This study investigates the antecedent effects upon Radicality and Commercial Success in SBIR projects to consider the duality of positive and negative outcomes that entrepreneurs’ variance-decision styles may simultaneously induce upon innovation processes. Using the constructs of Experimentation and Planning, this study also considers the causal inference from stakeholder communication and Risk Tolerance.
Entrepreneurial orientation has had considerable literature support of positive association innovation contexts. However, this study investigates the possible double-edged sword effect that variance-inducing development styles (EO) may be both a positive and negative when it comes to effects upon Commercial Success. The implications for investments in innovation and Radicality which may influence greater Commercial
Success are discussed.
Keywords: innovation; SBIR; radicality; planning; commercial success; entrepreneurial orientation; EO
x CHAPTER 1: INTRODUCTION
A firm’s capacity to generate innovation as sustainable advantage depends upon
preconditions which are complicated, mercurial, and difficult to replicate over time; more
broadly, the genesis of innovation across different industries is barely understood
(Coccia, 2017: 644). This study investigates antecedents of innovation through selected variables that together may create the mechanisms which result in innovative outcomes.
More specifically, this study focuses on the potential of Risk Tolerance and the frequency
and quality of stakeholder contact to affect the creative process that ultimately results in
the utility of Commercial Success. Its conceptual history develops from multiple
descriptions of the innovation process as advantage: an iterative sequence of process of
trial and error or learning by doing (Simon, 1981); developing and inculcating signature
firm capacities to form competitive advantages (Leonard‐Barton, 1992); and the tension
between creativity and purposeful utilization (March, 1991).
The theoretical mechanisms I use in this study is a conception of Entrepreneurial
Orientation-as-Experimentation, detailed extensively below, to provide hypothetical
structure to investigate the potential causal inferences within the data. This conception of
Experimentation is useful because it outlines the duality of variance in an innovation
system, that is, variance (expressed as Experimentation) that increases both positive and
negative outcomes. It is this double-edged sword effect which I hypothesize may be responsible for the lack of clear Commercial Success seen in the organizational ecology I
have chosen to study.
This specific ecology/unit of study is SBIR (Small Business Innovation Research)
grant awardee projects. These projects are undertaken by firms engaged in creating novel
1 solutions to questions posed by one of 11 U.S. government departments: Department of
Defense, NASA, and NIH, for example. SBIR grant projects are specifically tasked with using exceptional skills of the awardee firms to solve novel problems important to the grant issuer. These projects make plausible study targets due to the significant vetting and competitive application and selection process which conceptually control for a variety of technical ability, funding, skills, and experience, and to presumably increase the overall chance of successful outcomes which may be linked to observable antecedent variables.
Innovative outcomes that arise from specific contractual agreements between highly qualified and engaged counterparties could provide an opportunity to investigate the transmutation of inspiration into Commercial Success by testing for the effects of antecedent variables proximate to that outcome. A process of Experimentation might then be observable in an SBIR’s project development beginning from pre-established plan becomes evident if the project’s reported outcome diverted from its initial intent and yet showed a measurable degree of Commercial Success. Conversely, firms that show a predilection for following an established plan might also experience Commercial Success but provide observable lesser degrees of Experimentation. I, therefore, examine the potential mediating roles of Experimentation and Planning between Risk Tolerance and stakeholder communication behavior and the project outcomes of Radicality and
Commercial Success.
The specific problem of practice I address is the lack of overt Commercial
Success generated by SBIR projects, detailed below in the discussion of the Research
Question. This study contributes by investigating potential causal mechanisms in a high innovation environment that may naturally control for sources of negative variance
2 through the commonality of the SBIR grant process: lack of experience, funding, and
technical ability. Studying preconditions to innovation is important to make some
judgment as optimal configurations of strategy, a standard resource-based view of the firm consideration (Barney, 1991). More specifically, this study may contribute to the understanding of the innovation process by illuminating the roles of Experimentation and
Planning in inferring degrees of Commercial Success. Particularly regarding publicly financed technology investment, some measure of relative return on the public tax dollar would presumably be of value during times of shrinking budgets and competing priorities. This study may then contribute to creating policy incentives within the SBIR program to help realize critical U.S. governmental technical objectives and support domestic skills and expertise.
As such, this organizational ecology provides ample opportunities to associate antecedent variables with Commercial Success outcomes; as a problem of practice, the lack of Commercial Success in SBIR projects has been clear and pervasive. In the following section, I detail how this study may contribute by investigating ways in which an SBIR firm views and interacts with antecedents logically active in this ecology.
Study Context and Contribution
By investigating the mediating roles of Experimentation and Planning and their follow-on influence upon economic value creation and radical project outcomes, this study contributes by illuminating the links between innovation outcomes and specific behaviors reported by SBIR-grant recipient firms. In the following section, I briefly
describe the complex SBIR project development environment and historically low
probability significant Commercial Success. I then show how the antecedent constructs in
3 this study are potentially active and may potentially be exploited to increase the probabilities of commercial return.
In a previous study, I interviewed 20 SBIR project team members regarding lived experiences and project outcomes; the overall finding was that Commercial Success was
elusive at best, and the projects often represented ways to improve market knowledge and
gain important domain skills (Schell & Berente, 2014). Few interviewees reported
significant follow-on funding subsequent to the completion of the Phase II contract,
either from the issuer or from secondary market sources. The common response was that
such successes are rare and isolated events. As one organization’s leader put it, "The
whole commercial side is just more of a wish than reality…" (Firm 266) (Schell &
Berente, 2014). Additionally, this same study recorded SBIR personnel detailing
systemic disincentives for large defense contractors (“Primes”) to include SBIR
technology into larger platforms due to its disruption of long-term development projects and the typical inability of small firms to adequately insure against product liability.1
More generally, a 2007 conference report by the National Academies of Science
regarding the challenges of follow-on funding for SBIR Phase II projects detailed specific impediments to commercialization. “Many speakers supported the view that the Valley of
Death between development and acquisition was a real and substantial problem for small businesses” (Committee on Capitalizing on Science, Program, & Council, 2007: 19).
Among the among the causes cited by SBIR firms in the report were long time lags
before acquisition, the complexity of the acquisition environment and difficulties and cost
1 This paragraph adapted from Schell, J. K., & Berente, N. (2014). Avoiding the Valley of Death: A Cross-Case Analysis of SBIR Innovation Processes. Presented at the Academy of Management Conference, Philadelphia, PA. 4 management, a lack of Planning for eventual commercialization from before the very beginning of the project, a small firm’s inability to convince large firms to place its SBIR technology into large project platforms [i.e., a new jet fighter], and the vagaries of intermittent government financing choices and budget availability (Committee on
Capitalizing on Science et al., 2007).
Further challenges to SBIR commercialization and technology transfer were later detailed in 2014 by the same National Academies of Sciences (NAS) in its review of the
SBIR program and the Department of Defense. That report details some of the difficulties
SBIR firms face: resentment from issuing agencies legislatively required to finance
SBIR projects, complicated auditing requirements which result in development delays, changes in advocating program liaisons/Technical Point of Contact (TPOC) personnel, and a lack of incentives for prime contractors to cooperate/not to compete with SBIR firms (National Research Council, 2014). Our study addresses these challenges by measuring as antecedent variables the quality and frequency of TPOC communication and a firm’s Risk Tolerance.
Nevertheless, these NAS reports do quantify some degree of commercialization
successes from a variety of metrics: follow-on investment, licensing, timing effects due to
survey completion preceding commercialization, and in markets outside of DoD tracking.
Broadly, these reports imply some 30% to 45% of projects reach some stage of
Commercial Success at some point.
However, the distribution of these returns is important within the context of
overall successes. Overall, the net picture is that roughly half SBIR projects likely
provide some commercial return, though most are relatively small, derivative, and accrue
5 over a considerable time frame. “Most projects that reach the market generate minimal
revenues. A few awards generate substantial results, and a small number bring in large
revenues.” (Committee on Capitalizing on Science, Program, & Council, 2008: 116).
Data from this same 2008 Phase II SBIR assessment report large-scale Commercial
Successes rarely occur: 1.4% of the 790 Phase II survey cases accounted for over half the
total sales reported by all 790 projects. Further sales above $1MM were heavily skewed
by just several projects: only two respondents reported sales greater than $100MM, and
these two accounted for 17% of all revenue. This report concludes that in general
approximately one-third of the projects had no sales and at best a small number of these
would find market success and that these results are typical of other studies of SBIR
project market performance. Additionally, the long lag time between development and
market success made predictions problematic (Committee on Capitalizing on Science et
al., 2008: 116-118). The Valley of Death, therefore, and as noted above, continues.
Given this historically low likelihood of SBIR market successes, measuring Risk
Tolerance as antecedents in the innovation development process is important. If the
mediating variables of Experimentation and Planning serve to offset the degree of risk the project reportedly carries, then emphasizing either mediation event could have important implications creating greater innovative and or Commercial Successes.
Research Question
The preceding complex ecology into and by which SBIR technology is born frames the key research question I investigate in the study: What are the qualities and interactions of the antecedent conditions that influence radicality and commercial success in SBIR projects?
6 This is an important question in the SBIR context because the relatively high
degree of Planning and contractual direction in a project would imply a greater
Commercial Success rate than nominally achieved-see the “Valley of Death” discussion above. Note that given the uncertainty regarding SBIR project outcomes detailed before,
a rational actor may seek to create a multiplicity of options during the development of an
SBIR project. Long development time lags, shifting issuer priorities, variable government
funding, the unpredictable readiness of exogenous markets for a firm’s technology, and
diverse technology applications across multiple platforms would further argue for a
flexible, adaptive approach that could create radical outcomes, markedly different from
original intent, but appropriate for new contexts not visible at the beginning of a project.
However, such an emergent strategy could, in fact, be a liability. A quest for radicality
may, in fact, be a double-edged sword that could simultaneously both support and negate
Commercial Success and thus sets the stage for this study’s investigation of factors that affect variance-seeking and their outcomes.
To examine this contradictory duality, I follow a line of inquiry suggested from a derivative conception of entrepreneurial orientation (EO): EO-as-Experimentation
(Wiklund & Shepherd, 2011). Under this view, the authors posit that the degree of
Experimentation inherent in EO can induce both positive and negative variance, in contrast to previous research, which uniformly describes EO as a wholly beneficial capability. Wiklund and Shepherd further cite as support a literature review that indicated
“none of the 51 studies[…] took into account the possibility that EO may be associated with failure” (Wiklund & Shepherd, 2011: 926). In contrast to the studies cited in that review, our study delves precisely into this void by examining the degree to which the
7 antecedents to Radicality may be associated with decreased Commercial Success.
Wiklund and Shepherd specifically cite the need for studies which explore:
[…] the role of EO on the relationship between the distribution and the mean of performance; by avoiding an anti-failure bias to investigate the role of EO in how firms manage failure to skew the distribution of outcomes by investigating moderators in the relationship between EO and the distribution of outcomes; […] by choosing more homogenous samples that are suitable for answering EO research questions and choosing (creating) samples that include failures as well as successes (within or across firms);[…]. (p. 926)
This study, whose research design is specifically outlined below, specifically addresses these objectives. In the broader context of SBIR projects, elucidating qualities of potential causal links between variance seeking behaviors and outcomes may have utility in optimizing public investment decisions that may, prima facie, have the stated intent of fostering revolutionary outcomes, when in fact more evolutionary outcomes occur.
Conversely, projects with initially very clear commercial return objectives may become more realistically valued for radical results.
Theoretical Background
This Theoretical Background section places this study in the context of widespread entrepreneurial research regarding explanations of entrepreneurial action and results. The next section, Construct Definitions, defines and elucidates the constructs developed through the data in order to better understand their roles in the conceptual model. The following section, Hypotheses and Supporting Theory, specifically outlines
hypotheses and supporting theory, which may infer explanatory support via empirical testing for the mechanisms in the conceptual model.
Entrepreneurial orientation-as-experimentation. Entrepreneurial Orientation
(EO) has become the dominant paradigm in entrepreneurship research theory and 8 germane to my study because one of its derivative lines of inquiry, EO-as-
Experimentation, may offer explanatory power by describing Experimentation as having
a simultaneous positive and negative affect upon project outcomes (Wiklund & Shepherd,
2011). As a construct, five subcomponents characterize Entrepreneurial Orientation: acting autonomously, innovation bias, risk-taking, aggressive postures versus the competition, and proactive toward market opportunities (Lumpkin & Dess, 1996).
Though each component may be independent and capable of acting within a firm to different degrees, their net effect operating within a firm (or by an individual) results in a new entry event also known as entrepreneurship. This classic view of EO posits it as a firm advantage, linked conceptually to organizational configuration theories such as contingency and resource-based views of firm structure. With relevance to the SBIR project context which spans eleven different government entities and many different technological domains, this logically suggests a multiplicity of outcomes and effects would be the result of different degrees of EO components at work. Moreover, SBIR projects are typically narrowly defined by their issuers and open to bidding by a relatively
small number of potential awardees. These highly specific projects can create significant
competition between a small number of firms battling in a critical area of highly
specialized technical expertise. March (1991) notes that “[a]s the number of competitors
increases, the contribution of the variance to competitive advantage increases until at the
limit, as N goes to infinity, the mean becomes irrelevant” (March, 1991: 83); this would
imply that increasing Experimentation as a contributor to the variance of the project may
be a rational incentive for an SBIR project firm. Wiklund and Shepherd (2011) note this
as well: "Assume instead that the firms […] introduce a strategy that is associated with
9 Experimentation (e.g., they invest in and start experimenting with some new technology).
[…]This strategy increases performance variance but has no effect on mean
performance” (Wiklund & Shepherd, 2011: 928). Experimentation, its effects, and
outcomes appear to be justified targets for investigation from a theoretical perspective as
antecedents to radicality and commercial success, to address this study’s research
question.
Similarly to EO, I have adapted items for the IV constructs from research that investigates the role of alliance building, use of current skills, and one’s belief in the capacity to control future events (Sarasvathy, 2001). This formative heuristic, often referred to collectively as effectuation, is often cited as being associated with success in high uncertainty contexts: a meta-review of 91 studies with a combined total of 17,000 samples found statistically significant, positive correlations with firm performance and all effectuation tenants, with the exception of affordable loss (Read, Song, & Smit, 2009b); effectual and causal processes can be used in complementary manners depending on the stage of the innovation development process (Sitoh, Pan, & Yu, 2014); effectuation-based decision making is correlated with successful innovation in high innovation contexts; causation improves outcomes in low-innovation contexts (Küpper & Burkhart, 2009); and early development firms following effectuation processes are more likely to reach successful operations than those using causation-based strategies in high uncertainty contexts (Garonne, Davidsson, & Steffens, 2010). A study that provides evidence of non- positive outcomes for effectuation principles used in high innovation contexts such as
SBIR projects may help to explain the double-edged sword of positive and negative variance.
10 CHAPTER 2: LITERATURE REVIEW
To situate this dissertation within the scholarly discussion of innovation and its
antecedents, I searched for studies related to integrated effects of effectuation causation
published in peer-reviewed journals within the last 10 years using the search terms
“effectuation and causation,” “moderation,” and “interaction effect.” This search of the
article databases Business Source Premier and Business Source Complete returned
approximately 300 initial articles, 100 of which were further selected to determine
relevancy and/or similarity with the objectives of this study, particularly with regard to
the identification of possible mediating and interaction effects upon/between effectuation
(experimentation) and causation (planning). In total, 42 studies were ultimately identified
as representative investigations of the interplay and interaction, if any, of the antecedents
and components of planning and experimentation. Appendix A reports how each
study/paper addresses contexts of planning (causation) and experimentation
(effectuation): confirmation of effectuation/causation mechanisms, correlation with
innovation success, evidence of decision preferences, association with discrete outcomes,
presence of explicit moderators, interview or case study, and/or quantitative analysis.
Note that while in this study I have used experimentation and planning in lieu of effectuation and causation to improve reader accessibility, the specific instrument items owe their lineage to oft-cited measures of effectuation and causation detailed in Appendix
B. These 42 peer-reviewed articles were published within the last 10 years; 32 (76%) in
2015 or later. Of the 42, 19 are case studies and/or interview investigations, 16 are
quantitative studies relying on survey data, one relies on both interviews and instruments,
six are conceptual pieces reviewing theoretical issues regarding effectuation and
11 causation research, and one a mathematical simulation. Appendix C generally
summarizes the contexts and moderation contexts investigated in each article. By
necessity, these are generalizations and/or conceptual averages across the variety of
claims each article may make. The forty-two articles are categorized as follows, though several warranted inclusion in multiple categories. In addition to criticisms of, or rebuttals to, effectuation theory as a general construct (6 articles), the articles’ primary
investigations of effectuation contexts included: evaluation mechanisms that bear upon
(moderate/mediate) effectuation or causation principles such internationalization
strategies (6), perceptions of risk and uncertainty (5), individual traits such as
entrepreneurial self-efficacy (6), individual career experience (3) stakeholder
connectedness (2), cultural traits (1), moderation by effectuation theory components (4),
learning intent (1), science or non-science intent of venture (1), degree of market growth
(1), effective group versus individual decision processes (1), size of firm (1), and project
portfolio management strategy (2).
Of particular relevance to this dissertation is the extensive criticism of effectuation
theory by Arend, Sarooghi, and Burkemper (2015) that details the construct’s strengths,
weaknesses, and suggests further elucidation. So much so, in fact, that three other teams’
rebuttals were subsequently published: a defense of the process view of effectuation
(Gupta, Chiles, & McMullen, 2016), a conception of human creativity as counterpoint to
[predictable] habit (Reuber, Fischer, & Coviello, 2016), and the conception of human
[effectual] action as inherently outside the bonds of empirical, positivist-based measurement (Read, Sarasvathy, Dew, & Wiltbank, 2016). Further examination of the
subcomponents and testing for predicted results of effectuation theory with outcomes.
12 One of the authors’ central criticisms of effectuation theory (and germane to the
solutions proposed in this dissertation study) is that researchers observe effectuation as a
de facto active mechanism rather than causally linked by identifying the presence of the
behavior but not necessarily tying it to outcomes or with antecedent causes. “This appears
to put effectuation into the category of a purely instrumentalist theory where connections
among units are described but not explained, […].” (Arend et al., 2015: 642). Their
criticism describes the components of effectuation which offset uncertainty as the only
logical action steps possible and are then, in fact, already bound up into the effectuation’s broad description. By concluding that effectuation exists, and then positing that it is an accurate, operative theory, researchers have actually only found confirmation through observations of its subcomponents-which were inherently extant in the presence of the effectual mechanism and not a consequence of its workings. It is analogous to stating if a thing is cake, it is therefore sweet, implying that a thing could cause itself with pieces of itself, an inherent tautology (Arend et al., 2015).
To address these deficiencies, the authors call for investigations of specific interrelationships between effectual components:
There is a need to move from an arguably tautological connection between conditions and behaviors to a decoupled system. Identifying the underlying causes of the proposed relationships among units would also help to define the boundaries and to construct testable hypotheses (involving variable relationships or process patterns). Extending effectuation in this manner may lead to answers to further research questions involving action-oriented approaches in general, in terms of their benefits, costs, and risks, and involving what coevolution is possible among co-creators in emerging industries. (Arend et al., 2015: 644) and,
…[W]e recommend that effectuation focus on providing an explicit explanation for value creation—how the artifacts that effectuators produce 13 are better performance-to-price offerings than substitutes for their customers and how those artifacts are produced at costs sufficiently below prices—which is an explanation that could possibly build on the insightful uncertainty resolution activities described in the model. (Arend et al., 2015: 645)
This dissertation falls squarely on the side of the positivist perspective of Arend et al.
(2015) by directly addressing the authors’ call to address these questions. This
dissertation therefore examines discrete (“decoupled,” per Arend et al., 2015) units of the
effectuation process—TPOC Communication Frequency and Prior Commitments—and
tests their influence upon Experimentation and Planning. It then links these specific
behaviors to an outcome variable (an “artifact,” per Arend et al., 2015) reported
separately per the matched-pair instrument design. The study design explicitly tests the
moderating effect of Planning upon Experimentation with the follow-on analysis of the
degree of value capture through measurement of Commercial Success data. This study
may be the first to directly investigate this moderation relationship effect. By pointing to
specific behaviors and not attitudes, the study design reduces the potential the
tautological fallacy noted above. It also specifically provides testable hypotheses by
linking measurable changes in separate antecedents.
Other studies have called for similar investigation which this dissertation study addresses: “endogenizing selection mechanisms” and “delineating means and resources”
(Read et al., 2016: 532). Both TPOC communication data and testing for the moderation of Experimentation by Planning and the resulting effect upon Commercial Success answer these calls. Other studies in the sample review investigated moderating mechanisms within the Experimentation (effectuation) context: degree of innovation as a positive moderator of the effectiveness of effectuation logics upon outcomes (Brettel,
14 Mauer, Engelen, & Kupper, 2012); industry class (science or non-science) moderates choice of decision logics between effectuation or causation or both (Villani, Linder, &
Grimaldi, 2018); and effectuation principles positively affect the Experimentation component of entrepreneurial orientation (Mthanti & Urban, 2014). While these are examples of studies that investigate variables within the overall context of innovation, none isolate and then directly measure a moderating effect of Planning upon
Experimentation.
Construct Definitions
Risk Tolerance. This study measures Risk Tolerance as the degree to which a principal investigator reported his/her firm’s comfort with ambiguity. A firm which reports being comfortable operating in and accepting higher degrees of uncertainty would presumably report higher levels of Risk Tolerance; risk assessment of a project would feasibly be a component of a firm’s decision to undertake it. Risk assumption is an important antecedent variable if it represents respondents’ view of the innate conformity of the particular project to the firm’s overall portfolio of risk and the capacity of the firm to manage it. Risk Tolerance bears upon the overall nomological framework of the model as risk seeking/avoiding is logically a component of variance-seeking/avoiding behavior and then be logically connected to decisions which may positively or negatively affect
Radicality and/or Commercial Success.
Communication with stakeholders. A numerical report or estimation of the frequency of TPOC (Technical Point of Contact) meetings with the project team is this study’s metric for communication with stakeholders. Qualitative interviewees frequently cited communication with the SBIR project issuer as crucial for a successful project
15 (Schell & Berente, 2014), and understood the benefits of networking within their technological domains to develop more radical products which would indicate the firm’s technological leadership (e.g., Eggers, Kraus, & Covin, 2014). One interviewee firm made clear and open communication with the issuer’s Technical Point of Contact (TPOC) a mandatory precondition before even considering a project application, echoing Lettl
(2007) and his finding that highly engaged counterparties “can increase the creative capacity of an organization as radically new ideas and solutions can be gained” (p. 69).
This directly echoes themes of connectedness to key market trends and advantages implicit in EO as an advantage. Theory; it, therefore, seems prudent to measure communication behavior during a project regarding sourcing outside information and how a project’s path meshed with its original, stated intent. If the value to a firm of an
SBIR project can be in future non-commercial returns, it appears logical to assume that creating a project and developing relationships in an important client space would be important, regardless of the eventual project outcome. A high degree of communication intent and action may imply close tracking to the issuer’s goals; conversely, a lack of communication may indicate the firm believes the project may have (or have intended) an outcome other than the initial stated goal. This may be the expression of a variance- seeking process and maps to themes of Experimentation, expressed in the Entrepreneurial
Orientation-as-Experimentation theory detailed above.
Prior commitments. Literature that describes stakeholder and innovator relationships clearly infer a complex, involved, and important landscape for an SBIR firm to navigate, regardless of the firm’s internal incentives. Those who engaged in innovative projects face managing significant amounts of information (Dunne & Dougherty, 2016),
16 which is likely true for SBIR project firms. Moreover, attempting to understand
“interface specifications too early in the process are detrimental to the progress of highly
innovative product development as this prevents developers from optimizing the
interaction of product components” (Schmickl & Kieser, 2008: 488), effectively asking
for too much commitment to detail from the firm-issuer relationship may impede the project’s outcome. More positively, De Propris (2002) found that “radical innovation is also positively related to cooperation over innovation with client firm’s suppliers, as well as R&D expenditure” and “client firms are 21% more likely to be innovators if they engage in joint innovation [with buyers]”; the same study found that buyers were more than twice as likely to benefit from innovation cooperation than suppliers and “firms that cooperate with innovation with both suppliers and client firms and invest in R&D almost
40% more likely than other firms to be innovators” (De Propris, 2002: 349–350). These benefits may include later development of innovations as well as recognition of technical domain competency [for the innovating firm] (Partanen, Chetty, & Rajala, 2014). In total, managing the stakeholder-innovator interface is clearly an important part of understanding the innovation ecology in the SBIR context, hence the necessity of including it as an indicator variable. Therefore, measuring Prior Commitments as the degree to which principal investigators reported undertaking agreements or commitments prior to the beginning of the project with entities external to the firm is included in the conceptual model.
Flexibility. This study measures Radicality to understand if and how variance, per the EO-as-Experimentation construct, is active in the model and how it might presumably influence Commercial Success. Considerable literature exists linking a firm’s propensity
17 to experiment with new products and resulting competitive vitality. Gaining proprietary
experimental skill through a novel [SBIR] project can confer an inimitable advantage
(Thomke, von Hippel, & Franke, 1998). Experimentation and its associated creativity
may come with a cost: Mainemelis (2010) argues that new ideas result in more radical
outcomes but with concurrent stress on available resources. This strain may be absorbed
by firms with more leadership experience in technology adoption, creating a resource
advantage per RBV concepts (Barney, 1991; Ettlie, 1990). In short, flexibility-biased behavior may induce greater outcome variance (per EO-as-Experimentation); conversely, firms with greater flexibility may also be able to take advantage of arbitrage opportunities to create Commercial Successes to a greater degree. In either case, flexibility may be a blessing or curse, hence its inclusion in the conceptual framework for the antecedents of
Radicality and Commercial Success.
Experimentation. Experimentation is the degree to which a project followed an undefined path in development as reported by principal investigators for the firm. Darwin suggests that individual variations from a species’ norm could grant competitive advantage; conversely, I must also allow that individual variations could negatively affect survivability. This Experimentation construct follows a similar logic as an extension of the conceptualization of EO-as-Experimentation: a firm's variance-inducing strategy may in some contexts provide positive economic return; conversely, greater variance may also incur negative economic return. As a construct, it is built upon themes of flexibility and
Experimentation, as previously discussed. It is appropriate to propose Experimentation as an explanatory construct to capture amorphous progress or an indefinite sense of
18 purposeful wandering undertaken by an SBIR firm and act as a counterpoint to Planning
development intent formed by the boundaries of an SBIR contract.
Planning. This study defines Planning as the degree to which company principals
reported taking dedicated time to strategize and identify pre-project strategies and
identification of intended markets prior to beginning the project. In order to provide
further support for our conceptual model, I propose to investigate the role of Planning
concepts, Radicality and Commercial Success. This conceptualization directly borrows
from the effectuation/causality literature originally expressed by (Sarasvathy, 2001) and
the aforementioned degree of connectedness implicit EO.
Particularly for the SBIR context, Planning or causation decision styles may be
active in contrast to a more undefined project development process. The specifically
delineated terms of an SBIR contract would presumably equate with an extensive and
well-thought-out set of prior-to-implementation steps per the contract approval proposal such as specific target markets, if not particular buyers, which would purchase a specific outcome of the project in the future. Causation decision styles stress internal information sources as the basis for development decisions; the SBIR firm would not reach out to exterior sources for development direction and would intend to have a target product with final specifications or capabilities at the end to exist as conceived of in the beginning. It is important to draw the distinction between a deterministic/causality view (what I do now will result in the discrete outcome I desire and can conceive of today) versus an effectual/experimentational view (I can overtly intend, as a strategy, to experiment as the future unfolds to create an outcome I don’t know about today).
19 Note here how the project outcome obligations and stated commercialization
goals of an SBIR project may set up natural contradictions with the high innovation contexts of new technology development in SBIR projects. For example, Brettel et al.
(2012) report causal antecedents to be more effective in low-innovation R&D environments. These acknowledged high-innovative contexts of SBIR projects may be fertile ground to test hypotheses that causality-biased decision styles may or may not lead to greater Commercial Success and provide a useful contrast to the EO-as-
Experimentation construct detailed above.
Radicality. I define Radicality as the level of general innovation and the degree of novelty and advancement past current technologies the project achieved as reported by a principle investigator for each project. As one of the two central outcome variables in this study, the degree of Radicality is a key component of the “double-edged sword” conception that variance-seeking may have both positive and negative outcomes.
It is important to highlight the logical requirement to evaluate Radicality in the
SBIR context, given the likelihood of falling into the “Valley of Death.” That firms would intentionally seek out Radicality make sense in this observation from a study of new product development portfolio and risk management:
[…,] if managers would rather reduce risk than increase expected performance, then a higher probability of technological or market disruption coupled with a complex performance landscape may call for an NPD [New Product Development] portfolio that includes a greater number of radical programs. This final insight is important as it challenges commonly accepted wisdom regarding NPD portfolio strategy and risk. (Chao & Kavadias, 2008: 918)
This may be, in fact, why taking on a project that has a low probability of commercial
return (seemingly high risk) would make sense in an SBIR context. More concretely, if
20 the return from a portfolio of SBIR projects is subject to relatively higher variability and
that the actual, intended outcomes are generally understood to provide uncertain-at-best
benefits, then it would logically seem to be the best interest of an SBIR firm to follow
strategies theorized by EO-as-Experimentation.
Commercial success. This study defines Commercial Success as to the degree to
which the project provided commercial return as reported by a Company Principle. It is
the penultimate outcome measure for the conceptual model as I also evaluate the effect of
Radicality upon Commercial Success in accordance with the research questions described earlier to investigate its antecedents.
Evaluating an SBIR project on the basis this outcome variable is a logical
objective for this study as commercialization is an explicit, strategic intention of the
program and is likely the most generally accepted conception of a “success.” The U.S.
Small Business Administration is chartered to assist qualifying firms through the SBIR program “to maintain and strengthen the competitive free enterprise system and the national economy” and defines commercialization as “the process of developing products, processes, technologies, or services in the production and delivery (whether by the originating party or others) of the products, processes, technologies, or services for sale to or used by the Federal government or commercial markets” and “[i]ncreasing private-sector commercialization of innovations derived from SBIR […] funds is one of the key pillars of the program.” (15 U.S.C. § 638(a), per the 2014 US government Inter-
Agency Policy Committee Report on Commercialization (SBIR & STTR Inter-Agency
Policy Committee, 2014). This 2014 policy directive further extensively details steps to establish commercialization databases, facilitate commercialization through the hiring of
21 third-party vendors, and other targeted activities designed to increase the odds that a
project’s output will make it over the “Valley of Death.”
Conceptual Model
Figure 1. Conceptual Model and Path Results
Specific path theoretical support. In this section, I define the anticipated results from the direct, mediation, and interactive effects of Experimentation and Planning upon
Radicality and Commercial Success, the study’s two dependent variables. On a path-by- path basis, I will describe my predicted (hypothetical) outcomes within the context of the
SEM relationships expressed in the conceptual model.
22 To look for statistically significant differences in the effects of these two decision
preferences as antecedents of Radicality and Commercial success, I measured
Experimentation and Planning biases expressed by the Principal Investigators (PIs) of
each project. Experimentation items measured the fluid path-seeking attitudes and actions
PIs took to allow the outcome of their SBIR project to emerge and align with the firm’s
success objectives. Planning items measured causation-intentioned actions PIs undertook
to align the project with a clearly defined goal and procedural steps which existed before
the initiation of the project. I intend to show that the inherent tension between the two
decision preferences of Experimentation and Planning will be evident in statistically
significant differences in their direct, indirect, and interaction effects upon Radicality and
Commercial Success.
Experimentation: Direct, indirect, and interaction effects. I predict that the
direct effect of Experimentation upon Radicality will be positive. This prediction follows
directly from the perspective that the future is pliable (through the effect of an
entrepreneur’s action) through choices taken in the present. Particularly in the SBIR
context where firms understand that the probability of Commercial Success is historically
low, firms may have the incentive to allow the project to evolve into outcomes that were
not necessarily in alignment with the original intent of the project if potentially greater
economic benefits become apparent. If there is risk and uncertainty in SBIR projects then risk mitigation through Experimentation is a logical tactic.
Direct effects of experimentation. I expect that Experimentation will have a direct positive effect upon Radicality because EO’s core tenants of Experimentation and
23 continuous adaptation suggest that more Experimentation would lead to projects which
were novel and unique.
Experimentation, for logic similar to the anticipated positive effect of
Experimentation upon Radicality, may produce a positive direct effect upon Commercial
Success. That is consistent with entrepreneurship literature that suggests in risky or
unknown uncertain environments EO principles are associated with greater Commercial
Success. However, within the context of the model, controlling for the effect of
Radicality will split off the positive success of Experimentation upon Commercial
Success. Despite the flexible and emergent components of Experimentation which have
implied intentional transitions toward the increased Commercial Success, in the context
of this model and the SBIR program dynamics, I hypothesize that Experimentation will
have a direct negative effect upon Commercial Success. Hypothesis 8 (above) outlines
this "double-edged sword" construct that tests the effect of Experimentation when I
control for Radicality on this relationship.
Indirect effects of experimentation. I predict the indirect effect of
Experimentation will decrease the negative effect of Planning upon Radicality, again
consistent with the emergent themes within the Experimentation construct. As detailed
above, the Planning construct is based upon causation principles that describe a decision
process that interprets the future as immutable, a single point in time in the future is
reachable through discrete prior action steps defined and determined now, without the
expectation of change or adaptation in the future. As such, I expect that Planning would
have a negative effect upon Radicality.
24 Within the context of my model, I expect that the indirect effect of
Experimentation upon the direct relationship between Planning and Commercial Success
will be to decrease the hypothesized positive effect of Planning upon Commercial
Success. In the SBIR context, I predict that a greater likelihood of Commercial Success
exists if a firm adopts causation-based strategies and does not experiment or allow the project to emerge or drift away from its original intent. The intended outcome of the project, as detailed through the application process includes a significant commercial analysis, carried out at the beginning all that of the project, and represents a vetting and
Planning process that should presumably result in greater Commercial Success than
Experimentation processes.
Interaction effects of experimentation and planning. I hypothesize an interaction effect of Experimentation will be to decrease the negative effect of Planning upon
Radicality. This interaction effect may capture an emergent decision process that will reduce the didactic and conforming intent of Planning at the inception of the project. As such, this is a juxtaposition of Experimentation and Planning principles as expressed above.
Conversely, I expect that another interaction effect of Experimentation will be to reduce the positive effect of Planning upon Commercial Success. One of the central theoretical constructs in our study is that EO can have both a positive and negative influence on Commercial Success. If Planning is more conducive to Commercial Success in the SBIR environment I anticipate that Experimentation would interact to reduce the positive effect of Planning upon Commercial Success thereby.
25 Direct effects of planning. Consistent with the Hypothesis 8, I predict Planning
will have a positive effect upon Commercial Success and Planning will have a negative
effect upon Radicality, per Hypothesis 9. In the context of the SBIR project, a causation-
oriented decision process (Planning) would logically produce an output that was close to
the original intent of the project, whether that project was a radical invention or not. The
operative point here is that deviation from its initial intent is less likely through Planning-
based decision processes. Conversely, I predict Planning will have a positive effect upon
Commercial Success, a positive direct effect upon Commercial Success in this context
that due to the highly defined nature of an initial SBIR contract.
Indirect effects of planning. I predicted the indirect effect of Planning will decrease the strength of the direct effect of Experimentation upon Radicality. Planning,
according to EO logic, would limit emergent choice behavior, and hence reduce the direct
effect of Experimentation upon Radicality. Conversely, I would expect that Planning
would have a positive indirect effect upon the direct relationship between
Experimentation and Commercial Success. That is, the mediating effect of Planning
would reduce the negative effect of Experimentation upon Commercial Success in the
SBIR context where Planning may infer a higher degree of economic return than
emergent processes.
Hypotheses
This section specifically outlines the testable hypotheses whose results may infer
explanatory support for the mechanisms in the conceptual model via empirical results.
Each of the following subsections delineates eleven hypothesized links suggested in the
conceptual model toward these objectives: what is the empirical test result I expect to
26 occur given the study’s theoretical context and how does that result help answer how
antecedent conditions influence Radicality and Commercial Success in SBIR projects?
Risk tolerance. SBIR project participants are likely rational actors and operate under an informed understanding of the probability that the project will result in a deliverable outcome. However, given the acknowledged likelihood that there will be little direct Commercial Success, an SBIR project firm would presumably allow a project's development and implementation to evolve toward a non-originally defined of result.
This would particularly be true if it meant recognizing the potential for greater economic return or other value creation such as greater domain experience, market presence, or other kinds of advantages that would accrue to the firm.
Risk Tolerance may bear upon Radicality and Commercial Success in the following ways. Firms comfortable with ambiguity may undertake a high-risk project may result in less Commercial Success simply by the difficult and/or complicated nature of the technological objective, regardless of the firm’s innate technological development skills. A more complicated technological objective undertaken by a more experienced firm may also eventually result in a more radical or innovative solution, tempered by its experience in that technical domain. I also acknowledge the possibility that firms may view a project as relatively low risk, but still actively follow an emergent path. In fact, the degree to which the firm views it can undertake an emergent strategy may, in fact, decreased its perceived value of risk, forming a biconditional relationship. Nevertheless, I suggest the more central operative effect within the causal chain will be that increased
Risk Tolerance would infer a greater likelihood that a firm will undertake emergent strategies for a project’s development:
27 Hypothesis 1. Risk Tolerance will have a positive effect on Experimentation
In SBIR projects there may exist a tension between strategizing and committing to
a discrete technological realization of the contract goals and experiencing low
Commercial Success (a.k.a. falling into the “Valley of Death”) and purposely undertaking
the project with the knowledge that the technological event may not be realizable but
hoping for eventually yet-undefined Commercial Success to accrue. In this context, EO-
as-Experimentation may predict less up-front commitment to strategizing for discrete future goals in SBIR projects. If a firm is more comfortable assuming ambiguity in this context the reported higher Risk Tolerance, then it would presumably spend less focus on
Planning such as defining discrete future markets, regardless of the commercialization
intent required in the SBIR contract. Similarly, to the effect of Risk Tolerance upon
Experimentation, I suggest that firms comfortable with ambiguity would perform less
overt strategizing prior to implementation, suggesting:
Hypothesis 2. Risk Tolerance will have a negative effect on Planning.
Technical point of contact (TPOC) communication. The opportunity for clear
and open Technical Point of Contact (TPOC) Communication was a key value expressed
in the qualitative interviews; the capacity to coordinate with an issuer’s desires was
clearly important to multiple participants (Schell & Berente, 2014). This follows
literature support that describes stakeholder and innovator relationships clearly infer a
complex landscape for an SBIR firm to navigate, regardless of the firm’s internal project
goals. Closely coordinating with SBIR issuers “interface specifications too early in the process are detrimental to the progress of highly innovative product development as this prevents developers from optimizing the interaction of product components” (Schmickl
28 & Kieser, 2008: 488). More positively, De Propris (2002) found that “radical innovation is also positively related to cooperation over innovation with client firm’s suppliers, as well as R&D expenditure” and “client firms are 21% more likely to be innovators if they engage in joint innovation [with buyers]”; the same study found that buyers were more than twice as likely to benefit from innovation cooperation than suppliers and “firms that cooperate with innovation with both suppliers and client firms and invest in R&D almost
40% more likely than other firms to be innovators” (De Propris, 2002: 349–350). These benefits may include later development of innovations as well as recognition of technical domain competency [for the innovating firm] (Partanen et al., 2014). In total, managing the stakeholder-innovator interface was clearly an important part of understanding the innovation ecology in the SBIR context, hence the necessity of including this and the related construct, Prior Commitments, as indicator variables.
The preceding stakeholder communication context descriptions have a direct bearing upon this study’s theoretical underpinnings. EO themes of anticipation and proactive-ness through development of strategic alliances would be operative through
TPOC coordination if, as a project evolved, it became substantially different than the issuer’s original intent through that issuer’s inclusion in the project’s development.
EO/Experimentation theory would also predict that Experimentation increased through greater TPOC communication if the issuer’s project goals evolved over time. However, the highly specific terms of an SBIR contract would imply a commitment by the issuer to discrete, defined outcomes. However, a firm may have less incentive to communicate with the issuer if the firm saw potential benefit in arriving at a project outcome that was further from the initial project/issuer’s goal. These potentialities drive to the heart of the
29 double-edged sword conflict described earlier: TPOC/issuer coordination may, in fact,
both decrease and increase the chances of Commercial Success; the project firm may be
able to develop commercial opportunity through a non-project outcome as well as by
adhering to the original technological project goal. Conversely, closer coordination with
the issuer may result in less Commercial Success or greater Radicality. However, given
the specification process of an SBIR contract, I suggest that the most central effect of
TPOC Communication would be to suppress variance-increasing development decisions
(Experimentation) and increase the likelihood that SBIR firms would engage in Planning:
Hypothesis 3. TPOC Communication will have a negative effect on Experimentation; and
Hypothesis 4. TPOC Communication will have a positive effect on Planning.
I include Prior Commitments as an indicator variable because it also maps conceptually to co-creation as a logical component of proactiveness and anticipation in
EO via strategic alliances with external entities, similarly to TPOC Communication.
More directly than TPOC Communication, Prior Commitments apply direct attempts to manage future events that shape the value of a project outcome through sequential, possibly circuitous process of co-creating opportunities of mutual benefit, in this case between the SBIR issuer or other beneficiaries of the project’s outcome. This would appear as close coordination of an issuer’s requirements through timely sharing of information and regular meetings with the SBIR firm project team. In contrast, a causation decision process conceives of an eventual endpoint outcome as the same as the conception of the initial solution at the start of the project and may present as relatively little issuer coordination.
30 Again, similar to TPOC communication discussed in the section above, creation of Prior Commitments may, in fact, both decrease and increase the chances of
Commercial Success and potentially incur greater or lesser Radicality. I, therefore, also suggest that the most central effect of Prior Commitments, given the specifics of an SBIR contract process, would be to suppress Experimentation and increase the likelihood that
SBIR firms would engage in Planning:
Hypothesis 5. Prior Commitments will have a negative effect on Experimentation; and
Hypothesis 6. Prior Commitments will have a positive effect on Planning.
Experimentation. As one of two central intermediary predicted antecedent variables in the conceptual model, I suggest Experimentation carries the combined effects of experimentation and flexibility; I propose Experimentation is important because both theoretical and conceptual logic support its investigation as a possible antecedent to
Radicality and Commercial Success.
EO-as-Experimentation is derived from the core theory of Entrepreneurship
Orientation as a collection of behaviors that result in entry into a new market by a firm or individual. Experimentation in this context may be either or both a positive and negative with regard to economic value generation-the double-edged-sword conflict described above in the Construct Definitions section. I theorize that Experimentation is a process by which an SBIR project develops as a variance-seeking event; the central research question here is how and to what degree Experimentation may simultaneously increase
Radicality and Commercial Success or positively affect one and negatively affect one or the other or both.
31 A broader explanation of entrepreneurship orientation (EO) is useful in order to
place Wiklund and Shepherd’s (2011) interpretation of EO-as-Experimentation in
context, and, therefore, its analogous use in this study. As a construct, five action
subcomponents characterize the classic construct of Entrepreneurial Orientation: acting
anonymously, innovation bias, risk-taking, aggressive postures versus the competition,
and proactive toward market opportunities (Lumpkin & Dess, 1996). Though
independent and capable of acting in differing degrees, the net result of these activities
within a firm or by an individual result in a new entry event or entrepreneurship, per
Lumpkin et al. The classic view of EO posits EO as a firm advantage, directly linking to
organizational configuration theories such as contingency and resource-based views of firm structure.
More specifically, bound up in the five EO active attributes is the overarching theme of experimentation; germane to this study is the Wiklund and Shepherd’s (2011) proposition that Experimentation can lead to both positive and negative outcomes, ostensibly by increasing the variance of the potential result. I, therefore, offer a parallel view of Experimentation as a variance-increasing mechanism that may function similarly in SBIR project outcomes that favor Radicality but not necessarily Commercial Success, as detailed later in this section.
EO-as-Experimentation theory is primarily a way to describe how a firm or individual seeks to create economic value through entrepreneurship by acting autonomously, maintaining an innovation bias, seeking risk, maintaining an aggressive posture versus the competition, and engaging proactively with market opportunities
(Lumpkin & Dess, 1996). This study posits that the experimentation bias bound up in the
32 EO components would lead to greater variance in project outcomes. I suggest that acting
together, these components exist in an SBIR context and create an emergent process that
is an antecedent effect upon Radicality, and, given its theoretical and context within
entrepreneurship literature, infers:
Hypothesis 7. Experimentation will have a positive effect on Radicality.
Conversely, where emergent decision styles are less prevalent, I believe
Experimentation will have a depressive effect upon Commercial Success, principally
because Experimentation-biased styles induce more variance into the development
process and less actualization of Planning and commercialization goals. Conceptually, if
an emergent process results in a radical outcome, then this may augur for greater
Commercial Success. However, absent a radical, unique, or original outcome, an
emergent process may have a depressive effect upon Commercial Success. A central test
then of the double-edged-sword nature of Experimentation would then be:
Hypothesis 8. Experimentation will have a negative effect on Commercial Success when controlled for the effects of Radicality.
Planning. Planning is important as a predictive antecedent variable to account for the acknowledged significance of pre-contract commitment to commercialization in
SBIR contracts. Due to the clear commitment to commercialization intent required in the contractual arrangement between issuer and awardee, this may place SBIR projects in a high causality context. However, SBIR projects are at least nominally new and innovative efforts; since there is some evidentiary support that low innovation projects have better outcomes through causation-based decisions (Küpper & Burkhart, 2009), the data may provide an interesting contrast if causation has either a supportive or suppressive effect upon outcomes. 33 Particularly for this study, causality constructs may be active in contrast to a more
undefined project development process (Experimentation). Causality constructs stress
preplanning and discrete execution steps towards a clearly defined goal. The specific and
well-delineated nature of an SBIR contract would presumably equate with an extensive
and delineated set of steps per the initial proposal. I propose to test that projects which
were evaluated by participants as intending to follow a concrete development process
(high Causality) towards a discrete goal would show higher correlation for Commercial
Success per the successful SBIR grant application. These projects would presumably also
show a negative or low correlation with Radicality (i.e., outcomes which diverged from the initial stated goals of the project). Therefore, of particular interest is in contrasting high and low innovation project antecedents and outcomes in high-innovative contexts.
For example, causation antecedents may be more effective in low-innovation R&D environments (Brettel et al., 2012). I propose that the acknowledged high-innovative contexts of SBIR projects may be fertile ground to test hypotheses that causality-biased processes lead to greater Commercial Success. I suggest that this would be a useful relationship to explore in contrast with EO/Experimentation theory logics that imply high innovation projects benefit from Experimentation and flexibility. Those SBIR projects, which, according to instrument respondents, are structured via a Planning development process, may show greater Commercial Success as outcomes. Firms that prefer prior goal setting and adherence to a relatively structured project development paths may develop fewer radical projects. If Planning is a variance-avoiding behavior, then the degree of more radical outcomes may be suppressed. Accordingly, these two hypotheses contrast causation effects upon Radicality and Commercial Success:
34 Hypothesis 9. Planning strategizing will have a negative effect on Radicality; and
Hypothesis 10. Planning strategizing will have a positive effect on Commercial Success.
Radicality and commercial success. Radicality, despite being an expression of innovation and outcome novelty, may predict greater commercial results than
Experimentation. The logic for this assertion is the assumption that there would feasibly be some positive effect of Radicality upon Commercial Success to account for the continued intent by SBIR firms undertake these projects. In a prior qualitative study, firms cited the opportunity to retain important skills, make customer connections, become known as domain leader's, and develop important relationships with key issuers of projects that were important government priorities as reasons to undertake SBIR projects; the on-going, three-decade success of the program also implies the benefits are certainly not zero. Ostensibly there should be some derivative commercial success; even by chance, some number of projects diverging from their original goals through an emergent process could result in at least some positive commercial outcomes.
A boundary-expanding or radical project would, therefore, require the creation of
new (to the firm) skills that may increase its ability to create value in other contexts. To
this point, Bosma, Chia, and Fouweather (2016) describe novel events within an
organization as instigators of new semantic (linguistic) structures that, when complete,
constitute “radical learning” (Bosma et al., 2016: 24). A development process that looks to “potential future markets” and incorporates previously undiscovered customer preferences can provide more creative, radical outcomes (Forsman, 2009: 515), a benefit to an SBIR firm that desires to maintain domain knowledge leadership. “In particular,
[…] radical innovations are a key to organizational success […] to support breakthroughs 35 and achieve scientific goals in the presence of rapidly evolving scientific fields in high- velocity environments” (Coccia, 2016: 389).
Viewed more broadly, scientific knowledge and technology development are interrelated in a symbiotic manner, where one replenishes the other (Clark, 1987) as innovation begets knowledge and vice versa. Why intentionally seeking out Radicality makes sense for SBIR firms may have an answer in this observation from a study of new product development portfolio and risk management: “[…,] if managers would rather reduce risk than increase expected performance, then a higher probability of technological or market disruption coupled with a complex performance landscape may call for an NPD (new product development) portfolio that includes a greater number of radical programs. This final insight is important as it challenges commonly accepted wisdom regarding NPD portfolio strategy and risk” (Chao & Kavadias, 2008: 918).This may be in fact why taking on a project that has a low probability of commercial return
(seemingly high risk) would make sense in an SBIR context. More concretely, if the economic return from a portfolio of SBIR projects is subject to relatively higher variability, and the actual intended outcomes are generally understood to provide indirect or substantially delayed benefits, then it would logically to be the best interest of an SBIR firm to consider adaptive strategies other than those strategies determined before the project’s inception.
A hypothesized positive effect of Radicality upon Commercial Success is part of the effort to answer the double-edged sword quandary of the dual positive and negative nature of variance. If the Risk/Experimentation/Radicality causal chain reflects an overt strategy by SBIR firms, then it is at least feasible and certainly reasonable to suggest that
36 Radicality may have a small positive effect upon Commercial Success. More concretely,
Radicality may result in greater Commercial Success:
Hypothesis 11. Radicality will have a positive effect on Commercial Success.
CHAPTER 3: METHODS
SBIR Database
The study population for this study was drawn exclusively from the U.S. government's publicly available SBIR-awardee database
(www.sbir.gov/sbirsearch/award/all) and through two Qualtrics-administered matched-
pair, dyadic instruments, given one each to the identified Company Contact (DV
instrument) and Principal Investigator (IV instrument) for specific SBIR Phase I and
Phase II projects2. These respective participant targets were specifically identified
through this database, connected by a single, shared grant award. Separate survey
instruments were developed for each of these two contacts, using a combination of new
and existing measurement items, with the goal of analyzing matched pairs of business
and technical leadership around the single, discrete SBIR project shared by both
individuals. The database identified directors/principles as “Company Contact” and
included descriptors reported by firm personnel as President, CEO, and similar
identifiers. A Company Contact may evaluate a specific project with regards to the
outcomes the project may afford the firm; the “Principal Investigator” identified in the
database in an SBIR grant is specifically delineated as responsible for the direct control
and oversight of the project.
2 Case Western Reserve University IRB-2015-1204; approved: 06/08/2015 37 The unit of measurement was a specific Phase I or Phase II grant awarded from
2007 through 2012, inclusively. I chose this range to eliminate newer awards where outcomes are not yet clear and eliminate older awards where participants may have limited recall of processes and outcomes. Those chosen were awards with full e-mail and
phone contact data for two specified individuals. After accounting for multiple project
awards for a firm and removing duplicate recipient identities, the resulting database
included approximately 9,000 individual participant targets for approximately 4,500
unique projects records.
In mid-2015, approximately 9,000 DV and IV instruments were administered to
the SBIR database as described above. After multiple reminders, including direct phone solicitation, I received 516 total DV (230) and IV (286) total complete and partial responses. This resulted in 64 usable matched pair responses: two discrete IV and DV responses pertaining to a single, shared, unique SBIR project. Following Podsakoff,
MacKenzie, Lee, and Podsakoff (2003), I constructed this matched pair design to
substantially eliminate common method bias and need for statistical correction
(Podsakoff et al., 2003; see discussion and diagram pp. 897 & 898).
Demographic Comparison
To determine if presence in the matched or unmatched respondent cohorts was
related to specific demographic parameters, I measured the effects of client proximity,
number of personnel at time of award, grant issuer, respondent region, and SBIR grant
award size, through a chi-square test of independence for each demographic variable. An
expected proportionality for each group is derived and then compared to the actual
proportions. A derived statistic less than the chi-square statistic from a significance table
38 provides a test of consistent proportionality to indicate that the matched-pair cohort does
not differ from the full sample based on the specific demographic variable examined.
Table 1. Independence Test Results for Demographic Effects upon Matched/Unmatched Respondent Cohort Inclusion
Consistent Chi-square proportionality Derived statistic at .05 (test of chi- significance level Independence) Demographic Variable square df per indicated (Derived Chi2 test degrees of value < Indicated statistic freedom Chi2 value from table) Proximity to major client for 0.74 7.82 3 Yes project1 Personnel at time of award1 0.00 3.84 1 Yes U.S. government department issuer2 1.66 7.82 3 Yes U.S. region of respondent2 1.44 9.49 4 Yes Grant award size2 12.36 7.82 3 No 1 Company principle-reported data 2 Data from US Gov SBIR database
With the exception of SBIR award size, the results in Table 1 support the conclusion that
the matched pair cohort is not significantly different from the non-matched pair responses
with regard to distribution for the indicated demographics. Supporting tables for each
demographic calculation are reproduced in the Appendix D, Tables D1 through D5.
Preliminary Data Analysis
The preliminary analyses examined the data in two ways: two distinct IV and DV
cohorts of 271 and 189 complete case records, respectively, and a preliminary simple
regression analysis of the 64-matched pair IV and DV responses. I performed EFA, CFA,
and reliability tests for the for each of the two large cohorts and initial EFA and
regression tests of initial hypothetical relationships before developing the model central
to this study. The highlights of results are discussed briefly below in relation to the central themes of this study and reference supporting tables in the appendices. 39 The 271 IV cases are separated out into eight factors as shown in Appendix E after removing for weak loadings and those below .20. For this study, I have chosen not to theorize in the conceptual model new product capability, new competency, retaining key skills, and market acuity. Items loaded as anticipated with some exceptions: four flexibility and experimentation items loaded together. Based upon the shared conceptualization apparent within the items’ texts, I elected to treat these as a single construct, Experimentation. Flexibility did not load coherently into a single construct.
The quality of information exchanged construct split cleanly into a qualitative measure and the frequency measure of interaction with the project issuer as reported by the principal investigator. This is not surprising as the TPOC communication items were highly specific and numerically based. As reported in Appendix F, validity and reliability metrics for the IV contract constructs were generally within acceptable parameters, as were CFA and goodness-of-fit results, and reported in Appendices G and I. There were no excessive correlations. Testing for common method bias via a common latent factor I found regression weight differences less than .20 in all the variables but for those in
Quality of Information Exchanged and Causation as noted in Appendix I.
I performed equivalent analyses on the DV data of 187 responses. The DV EFA analysis resulted in seven constructs as reported in Appendix J. Validity and reliability results were acceptable overall with only measures cases marginal in near metric borders and all on separate constructs and there were not a large number of excessive correlations, all reported in Appendix K. The DV cohort CFA analyses and acceptable goodness-of-fit metrics are reported in Appendices L and M. Common method bias analyses as shown in Appendix N. In contrast to the common method bias analysis for the
40 IV data cohort, the DV cohort showed no CMB issues with no difference between
regression weights greater than .083. Skewness and kurtosis screening returned minor
and isolated issues for both DV and IV data for both separate cohort (Appendix O) and
matched pair groups (Appendix P) using the absolute value of the skewness and kurtosis
statistics being less than 2 as a metric.
Data Analyses Strategies
To test hypothesized relationships of the model’s antecedents upon Radicality and
Commercial Success, I selected Mplus analyses as it is more appropriate for use with smaller data sets; in this study, there were 50 pairs of complete usable responses. The
preliminary analyses of the individual IV and DV cohorts were conducted with SPSS.
Measures
The IV and DV instruments contained 39 and 35 items, respectively. These are reproduced along with the original scales in Appendix B which reports construct name, the effect it is intended to measure, the original scale item is retrieved during research, factor and Cronbach’s alpha measures if reported, the resulting item revised for SBIR project context, and comments regarding theoretical structure and loading results. The text of each original item was examined and edited to refer to a specific project context.
For example, the text of an original item read, "I allowed the business to evolve as opportunities emerged" and became "As I worked through the project, I allowed it to evolve as new directions emerged." This is in keeping with the matched pair design conception that the items would refer to a single project held in common between the recipients and that I judged generic questions to be less useful than specific references to
41 the project in question. The resulting scales were subjected to an extensive Q-sort analysis to confirm consistent interpretation and grouping into intended constructs.
All questions were five-point Likert scales except those for TPOC
Communication and company demographics. To act as a prompt during respondent viewing, the project name was included as a prompt above each item.
The DV scales were emailed to Company Principle email addresses, and the IV instrument is emailed to those individuals identified as Principal Investigator for the US government SBIR database. I selected these scales to capture constructs relevant to the research question: possible antecedent effects to Radicality and Commercial Success.
Where possible I found close matches to the context of the SBIR project environment, I used these scales, particularly those that developed by Chandler, DeTienne, McKelvie, and Mumford (2011) to capture the effects of effectuation and causation decision styles: causation, experimentation, flexibility, and pre-commitments.
42 CHAPTER 4: RESULTS
In the section below, I report how the statistical results in this study help answer to the central research purposes of this study, “What are the qualities and interactions of the antecedent conditions that influence Radicality and Commercial Success in SBIR projects?”, and, when evaluated through the Entrepreneurial Orientation-as-
Experimentation (EOE) construct contribute to our understanding of why SBIR projects are less successful commercially than might be supposed. The conceptual model, reported as Appendix Q, shows the predicted direction and empirical result for each hypothesis.
These data were obtained using the statistical package Mplus because of its capacity to analyze small sample size data sets. In this case, 50 matched-pair cases were analyzed where each pair member carried completely intact replies to each respective IV or DV instrument. Appendix R details this Mplus Bayesian methodology analyses: CFA
(R1), goodness-of-fit measures and hypotheses testing (R2), moderation and mediation
analyses (R3) and mediation hypotheses summary (R4). R3 and R4 are reproduced for readability from these data in the body of the text, below as Tables 2 and 3, respectively.
Of the 11 central study hypotheses, reported in Table 2, which tested the relationships between the study variables, four were supported at the p <= .100 level; five were the unanticipated direction and significant at the p <= .10 level, leaving two neither significant nor statistically significant.
Of the 21 mediation tests proposed with theoretical justification reported in
Appendix S, I selected nine for analyses based on relevancy to the central objective of investigating the role of Experimentation and Planning. These nine selected mediation
43 relationships are reported in Table 3: five were significant at the p <= .100 level, with three in the anticipated direction, two were significant but in the opposite/unanticipated direction, leaving four nonsignificant. Each mediation relationship was evaluated controlled for the effect of Planning within a range of -1.5 SD (standard deviations) below the average value of Planning, at the average value for Planning, and 1.5 SD above the average value of Planning.
Table 2. Hypotheses Results
Estimate Significance Significance Standardized divided by at p= .10 Hypotheses Estimate at p = .10 Result Error Standardized [Two-tailed [two-tailed] Error P-value/2] H1. Risk Tolerance will have a positive effect -0.136 0.081 -1.675 0.094 0.047 Not supported on Experimentation H2. Risk Tolerance will have a negative effect -0.152 0.073 -2.081 0.037 0.019 Supported upon Planning H3. Technical Point of Contact (TPOC) Communication will have a negative effect on -0.177 0.081 -2.181 0.029 0.015 Supported Experimentation H4. TPOC Communication will have a positive -0.189 0.057 -3.328 0.001 0.001 Not supported effect upon Planning H5. Prior Commitments will have a negative 0.060 0.144 0.419 0.675 0.338 Not supported effect upon Experimentation H6. Prior Commitments will have a positive 0.223 0.113 1.963 0.050 0.025 Supported effect upon Planning H7. Experimentation will have a positive -0.198 0.155 -1.281 0.200 0.100 Not supported effect upon Radicality Exper x Plan -0.028 0.491 -0.057 0.954 0.477 Prior Commitments as a control upon 0.300 0.130 2.315 0.021 0.011 Radicality H8. Experimentation will have a negative effect upon Commercial Success when -0.767 0.199 -3.856 0.000 0.000 Supported controlled for the effects of Radicality Exper x Plan 0.003 0.446 0.008 0.994 0.497 Prior Commitments as control upon 0.358 0.145 2.479 0.013 0.007 Commercial Success H9. Planning will have a negative effect on 1.268 0.207 6.124 0.000 0.000 Not supported Radicality H10. Planning will have a positive effect on 1.393 0.245 5.696 0.000 0.000 Supported Commercial Success H11. Radicality will have a positive effect on -0.380 0.144 -2.640 0.008 0.004 Not supported Commercial Success Results significant Statistically Supported hypothesis, significant at at p <=.100, but Unsupported hypothesis, nonsignificant at p > .100 significant at p <=.100 hypothesis not p <= .100 supported 44 Table 3. Mediation Test Results
Moderation test beta Mediation test within Planning significance within Mediation Hypotheses interaction effect Planning interaction range effect range Independent Dependent Hypotheses Mediator -1.5 SD M 1.5 SD -1.5 SD M 1.5 SD Variable Variable M1 Risk Tolerance Experimentation Radicality 0.023 0.027 0.031 0.350 0.155 0.313,
Commercial M2 Risk Tolerance Experimentation 0.096 0.094 0.092 0.141 0.079 0.162 Success Frequency of M5 Experimentation Radicality 0.032 0.035 0.038 0.283 0.132 0.264 TPOC Contact
Frequency of Commercial M6 Experimentation 0.124 0.122 0.121 0.092 0.044 0.088 TPOC Contact Success
Prior M9 Experimentation Radicality -0.009 -0.012 -0.015 0.433 0.357 0.415 Commitments
Prior Commercial M10 Experimentation -0.043 -0.042 -0.040 0.374 0.352 0.368 Commitments Success Quality of Removed for out of bounds multicollinearity M13 Information Experimentation Radicality values Exchanged Quality of Commercial Removed for out of bounds multicollinearity M14 Information Experimentation Success values Exchanged Commercial M18 Experimentation Radicality 0.059 0.075 0.091 .414 .111 .377 Success
Results significant at p <= .100, Unsupported hypothesis, Supported hypothesis, significant at p <= .100 but hypothesis not supported nonsignificant at p > .100
Hypotheses Results and Analyses
How do these results explain the qualities and interactions of the study variables, particularly Experimentation and Planning? To what degree does the EOE construct help explain the patterns and resulting narrative of the data? And lastly, what does this imply regards to relevancy to innovation development and the economic value creation goals of the SBIR program?
45 Hypothesis 1. Risk Tolerance will have a positive effect on Experimentation.
Risk Tolerance had a statistically significant and unpredicted negative effect upon
Experimentation: p = .047, β = -.14. This result represents a paradox since risk tolerant
firms would logically be expected to prefer experimentation strategies, consistent with
the success of Experimentation (effectuation) inferred as a project development strategy
in high innovation contexts (Blauth, Mauer, & Brettel, 2015; Brettel et al., 2012; Wiklund
& Shepherd, 2011). It is not necessarily clear that firms are risk-averse, however. In the
SBIR context, these projects, as discussed extensively above, seldom result in direct
economic gain. Therefore, to undertake an SBIR project is an inherently risky project.
This unexpected finding may, in fact, reflect firms’ behaviors to decrease risk in an already uncertain environment. This may infer that a firm may believe in this context that it does not need to experiment, and that increased variance decreases the chances of success. This finding would not support of EOE as both a positive and negative effect if firms recognize that experimentation would be or could induce an undesired outcome, as
firms would presumably understand the dual nature of experimentation as increasing the
probability of both success and failure.
Hypotheses 2. Risk Tolerance will have a negative effect on Planning.
H2 was supported at p=.019, β = -.15 and in the anticipated direction.
Conceptually, this is a logical finding: project teams that accept greater risk would
presumably be less inclined to follow a predetermined strategy. In this SBIR context,
which is nominally one of investigation and innovation, project teams may prefer overt
experimentation strategies which could be inherently riskier. A frequent proposition in
effectuation literature (e.g., Brettel et al., 2012) is that variance seeking behaviors
46 (Experimentation/effectuation) occur more frequently in high innovation contexts (Blauth
et al., 2015)
Hypothesis 3. Technical Point of Contact Communication will have a negative effect on Experimentation.
H3 returned as predicted, with a significant and negative effect upon
Experimentation by Technical Point of Contact (TPOC) Communication Frequency: p =
.015, β = -.18. SBIR project teams that tended to confer more often with a respective
project TPOC could be more firmly committed to a preconceived strategic development
process and/or be amenable to iterative direction as the project developed. These teams
would presumably not overtly experiment or deviate from the initial development plan, or
at least be constrained during the development process through close coordination with
the issuer and the issuer’s objectives.
Hypothesis 4. TPOC Communication will have a positive effect on Planning.
The effect of TPOC Communication Frequency had a significant though
unanticipated negative effect upon Planning: p = .001, β = -.19. The implication that
communication frequency suppresses strategic planning is counterintuitive; how could an
issuer’s increased insertion into the development process inhibit conceptualization? A
possible interpretation is that increased communication took the place of [prior] strategic
planning and became a de facto iterative decision-making process as the project evolved.
Note here that the same antecedent tested in both H3 and H4—TPOC Communication—
had a statistically significant negative correlation with both Experimentation and
Planning. However, both Experimentation and Planning are associated with statistically
significant and opposing effects upon Commercial Success. A preference for greater
TPOC communication would, therefore, be consistent with the EO behavior component 47 to be “…proactive, innovative, and risk-taking…” (Covin & Slevin, 1989: 77). Meeting
with the SBIR grant issuer to review the project’s trajectory, build industry relationships,
or acquire domain prowess would qualify as exploitation of knowledge bases, and
arguably proactive behavior. As was found during the qualitative interviews where
interviewees described SBIR projects as important ways to build relationships, TPOC
Communication could justifiably be used to spur Experimentation, that is, innovation,
and yet suppress Planning which would in other contexts and later in the model infer
greater Commercial Success. As such, it is an example of the EO-E duality construct.
Hypothesis 5. Prior Commitments will have a negative effect on Experimentation.
Hypothesis 6. Prior Commitments will have a positive effect on Planning.
H5 was not supported: p = .338, β = -.06 while H6 was supported and in the
anticipated direction: p = .025, β = .22. This result may be indicative of the contractually based nature of an SBIR project that generally lays out specific, initially-agreed-upon deliverables. A logical assumption would be that a contractual agreement would be viewed as a specific and controlling prior commitment and as such be tightly bound up into the perception of a planning process as expressed by Principal Investigator respondents. In contrast, an initial intent to commit to a variance—inducing process—
Experimentation-may be less clearly delineated and hence not perceived by respondents as an intent emanating from an initial agreement.
Hypothesis 7. Experimentation will have a positive effect on Radicality.
Experimentation had a significant and unanticipated negative effect upon
Radicality: p = .100, β = -.20. A possible interpretation may be that if the firm's objective is to create an outlier project (radical outcome), then inducing further variance through
48 Experimentation may be less productive. Stated differently, in the SBIR context a project
may, in fact, be from its initial conception and intent a radical project, and the SBIR
project development process might follow very concrete (Planning) steps and result in
radical outcome when compared to other products or services.
Hypothesis 8. Experimentation will have a negative effect on Commercial Success when controlled for the effects of Radicality.
H8 was supported by the significant and negative effect of Experimentation upon
Commercial Success, given the mediating effect of Radicality: p = .000, β = -.77.
Experimentation appears here to suppress Commercial Success and thus does not infer an anticipated positive return for Experimentation-induced variance as implied by the EOE construct. However, if Experimentation induces variance in outcomes, then the negative influence upon economic gain partially supports the conception of EOE as a negative mechanism. Within the SBIR context, H8 results further imply that when
Experimentation and exploration have predominant emphasis within the project development process, the efforts of the team are less likely to result in economic gain.
Again, this runs counter to effectuation-based arguments that hold within an innovation
construct greater variance results in greater financial success. Indeed, for a project team,
it may, in fact, be that the act of Experimentation is, in and of itself, the principle reasons
undertake the project, a point developed further below.
Hypothesis 9. Planning will have a negative effect on Radicality.
The effect of Planning upon Radicality was significant but in the unanticipated
and positive direction: p = .000, β = 1.27. Planning here expresses a duality of effects,
both positive, on Radicality and Commercial Success (per H10), implying causational
49 intent to create an outcome, rather than through a revolutionary, experimental process, in this study context.
Hypothesis 10. Planning will have a positive effect on Commercial Success.
The effect of Planning on Commercial Success was significant and in the predicted positive direction: p = .000, β = 1.39. Planning would seem to be a desirable project development strategy. As such, it does not support Experimentation as a pathway to economic value inference as discussed above and held broadly throughout the effectuation literature that supports economic value creation in an innovation-dependent environment the result of fluid, dynamic processes.
Hypothesis 11. Radicality will have a positive effect on Commercial Success.
H11 was significant but in the unanticipated direction: p = .004, β = -.38.
Radicality’s negative effect upon Commercial Success would support variance-as-a- negative component of EOE. Within SBIR projects, a divergent (radical) project may be an overt, intentional goal, as is the stated aim of the program. Within the SBIR project context, however, Radicality may not be linked to specific economic value creation but reflect a desire to create an exceptional technical or reputational outcome.
Mediation Relationships
The following section’s details result from eight tests of Experimentation as a mediator upon the dependent variables, Commercial Success and Radicality and one test using Radicality as the mediating variable. I chose to test Radicality’s role as a mediator between Experimentation and Commercial Success to contrast its direct effect upon
Commercial Success. These are selected for relevance to the study objectives from the 21 hypothesized mediation relationships shown in Appendix S. In all cases, Planning is a
50 moderating variable to understand the interplay between antecedents, a central objective
of the study.
Mediation 1: Experimentation will partially mediate the relationship between TPOC Communication and Radicality.
I predicted that as a mediator, Experimentation will decrease the negative effect
of TPOC Communication upon Radicality; stronger TPOC Communication will have a
dampening effect on the radical nature of a project’s outcome under the presumption that
a firm would be more inclined to follow the TPOC/issuer objectives, reinforced through
higher/consistent/continual contact. Experimentation would thereby reduce the
effectiveness of ongoing TPOC contact.
The data showed that at no values between -1.5 to 1.5 standard deviations effect
range of Planning was the mediation effect of Experimentation upon the relationship between Risk Tolerance and Radicality significant. These findings reflected other central trends in the data when experimentation had relatively less significant mediating influence within the model.
Mediation 2: Experimentation will positively mediate the relationship between Risk Tolerance and Commercial Success when controlled for the effects of Planning.
At the average value of Planning, Experimentation has a significant and positive mediating effect on the effect of Risk Tolerance on Commercial Success: p = .079, β =
09. This result provides a counterpoint to the negative effect of Experimentation upon
economic value creation. Per EO-as-Experimentation, Experimentation can be associated with both higher levels of failure and higher levels of success in an innovation environment. As such, Risk Tolerance would be expected to have a positive effect upon
Commercial Success which would be increased (facilitated) by the positive action of 51 Experimentation as a mediator. The mediating effect upon the Risk Tolerance–
Commercial Success relationship is consistent across the effect range of Planning from -
1.5 to 1.5 standard deviations, though the significance of Experimentation falls off
slightly to just above the .10 threshold. This is likely due to low power within the sample
size, but directionally, it supports the role of Experimentation active role as a mediating
variable.
I interpret this weak support for the positive EOE mechanism within the model:
those firms which perceive themselves as comfortable assuming project risk find
inferential support for Commercial Success within this part of the model. Note that the
mediation effect of Experimentation was significant across almost the entire Planning
control value range, though with the relatively small beta effect of .09, this broad result
suggests support for the variance-as-advantage conception of EOE through the positive mediating role of Experimentation on the relationship between Risk Tolerance and
Commercial Success. Note here the evidence for the EOE duality effect: Experimentation
directly reduces the effect of a variable associated with positive commercial gain;
however, Experimentation positively mediates the relationship between Risk Tolerance
and Commercial Success.
Mediation 5: Experimentation will positively mediate the relationship between frequency of TPOC Contact and Radicality when controlled for the effects of Planning.
Under no values of Planning was the mediation effect of Experimentation upon
the relationship between Frequency of TPOC Contact on Radicality significant, though at
the mean value of Planning, its effect was marginally significant at p = .13. All effects
were small, ranging from .03 to .04 per Table CCC. These findings reflected central
52 trends in the data where Experimentation had relatively small degrees of influence in the
model.
Mediation 6: Experimentation will positively mediate the relationship between Frequency of TPOC contact and Commercial Success when controlled for Planning.
I predicted Experimentation would increase the positive effect of TPOC
communication on Commercial Success; per EOE, Experimentation can result in positive
and negative outcomes. In this case, I assume greater TPOC communication in the
presence of Experimentation would increase Commercial Success because experimental
project development may be either more tightly focused per issuer objectives, which may
include Commercial Success. Additionally, greater TPOC communication may also
actually encourage greater Experimentation, greater variance, with the commensurate
possibility of creating greater outcomes that result in economic value.
The data infer that at the average value of Planning, Experimentation had a
positive and significant mediating effect on the relationship of frequency of TPOC
communication with Commercial Success. This effect was significant across the effect
range of Planning: β = .12 and p = .092 (-1.5 SD), .04 (mean) and .09 (1.5 SD). This
result offers at least partial support for the central EOE conception of the study, though
the interrelationships implied by these path components suggest the need for a subtle
analysis for greater clarity. The data imply TPOC communication has a significant and
negative effect upon Experimentation; Experimentation likewise has a significant and
negative effect upon Commercial Success. The net effect in this implied causal chain
would be increased Frequency of TPOC contact would infer less Experimentation, and then increased Commercial Success. In this instance, the [coordination with market
53 leaders] component of EOE would appear to be active and Experimentation performing
as an economic value spoiler, per the EOE-as-negative theoretical construct underpinning this study I will develop this theme later in the discussion section with a more holistic analysis of intertwined antecedent effects
Mediation 9: Experimentation will positively mediate the relationship between Prior Commitments and Radicality when controlled for Planning.
I predicted that Experimentation’s action as a mediator would be to account for greater proportion of the total effect upon Radicality and positively mediate the
relationship. Per EOE theory, firms that react to market opportunities as a result of
communication with stakeholders, perhaps reflected as intentions the result of Prior
Commitments, would proactively experiment in creating a more divergent or radical project outcome.
However, under no values of Planning was the mediation effect of
Experimentation upon the relationship between Prior Commitments on Radicality significant. These findings reflected central trends in the data where Experimentation had relatively small degrees of influence in the model.
Mediation 10: Experimentation will positively mediate the relationship between Prior Commitments and Commercial Success when controlled for Planning.
I predicted that Experimentation would have a significant partial negative effect upon the relationship between Prior Commitments and Commercial Success due to
Experimentation’s role in increasing variance. Consistent with the central hypotheses of this study, pre-conceptualization or Planning activities would be associated with greater
Commercial Success, so aligning project goals through pre-project inception
54 arrangements would create greater economic value. Experimentation, therefore, to
increase variance, would be associated with creating less direct commercial value.
However, at no value of Planning as a moderator did Experimentation infer a statistically significant mediation effect on the relationship between Prior Commitments
and Commercial Success. This was consistent with the overall data trends, which showed
advance strategy antecedents to have greater explanatory power within the model than
Experimentation effects.
Mediation 13: Experimentation will positively mediate the relationship between quality of information exchanged in Radicality when controlled for Planning.
When controlling for the effects of Experimentation, I predicted that quality of
information’s effect upon Radicality would decrease, supporting the Experimentation
component of EOE as a mechanism by which firms can increase variance. SBIR firms
would presumably receive more information about an issuer’s intent and therefore
explore more varied development paths if the overall objective of the project was to
create a more radical outcome.
However, quality of information exchanged as a variable showed an excessive
multicollinearity value greater than .8 with Planning and was removed from the model.
Conceptually, valuable exogenous information from the issuer of an SBIR grant, and
more generally, any important stakeholder, would foreseeably have an impact upon
planning; hence, the multicollinearity effect. Put differently, it is hard to imagine that data
deemed important and operative by a project team would not be inculcated into a decision
process.
Mediation 14: Experimentation will positively mediate the relationship between quality of information exchanged and Commercial Success when controlled for Planning. 55 I predicted that the mediating effect of Experimentation will be to reduce the
effect of quality of information upon Commercial Success, supporting the conception of
EOE as able to both create economic value and variance. However, due to the multicollinearity issue detailed in Mediation 14 (above), quality of information was removed from the model.
Mediation 18: Radicality will negatively mediate the relationship between Experimentation and Commercial Success when controlled for Planning.
When moderated by Planning, I predicted that Radicality should positively
mediate the relationship between Experimentation and Commercial Success, presumably because, given the history of SBIR projects, Commercial Success is often elusive, and a more divergent project may be more economically viable.
However, at the average value of Planning, Radicality was only marginally significant at p = 0.111 and with a relatively small beta of .08. This mediating effect became less significant at both higher and lower SD values of Planning: -1.5 SD/p = .414 and +1.5 SD/p = .377, respectively. This result again echoes the relatively small effect size of Experimentation within the model.
Interaction Effects
As Reported in Table 2, the interaction effects of Experimentation and Planning were both nonsignificant. However, upon closer inspection, the moderating effect of
Planning implies an important interplay between antecedents. Within specific standard deviations of its effect range, Planning shows the capacity to influence the relationship between both Experimentation and Experimentation’s effect upon both Radicality and
Commercial Success. Shown in Figure 2, between a range of approximately -.75 and
+1.35 standard deviations (SD) and at a 90% confidence level, Planning reduces 56 Experimentation’s effect upon Radicality. Similarly, Figure 3 shows that within a 90% confidence interval and within an SD range of approximately -1.42 to +1.25, Planning reduces the positive effect of Experimentation upon Commercial Success. As discussed above in the Theory and Literature Analyses, the negative moderation effect of Planning upon Experimentation is a central contribution of this study. This finding contrasts with the conclusions in the literature which support the association between variance-inducing decision preferences, high-innovation contexts, and degrees of success (if or when measured).
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57 Figure 2. Interaction Effect of Planning on the Relationship between Experimentation and Radicality
Figure 3. Interaction Effect of Planning on the Relationship between Experimentation and Commercial Success
58 CHAPTER 5: DISCUSSION
In the section that follows, I highlight specific results that illustrate the potential interplay and connection between study variables and the degree to which these variables interact to support the central theoretical construct of EOE and interpret these results within the context of the SBIR structural ecology. The discussion below describes the evidentiary support for each hypothesis and mediation test and then contextualizes each within the Research Question and broader pragmatic goals of this study.
These results imply a complicated intertwining of factors that bear upon SBIR project outcomes, summarized in Table 4 below. For example, risk-tolerant firms may be expected to prefer risker projects, but the data indicate a counterintuitive finding via the significant and negative effect of Risk Tolerance upon Experimentation: β = -.14, p =
.047. Moreover, the significant and negative effect of Risk Tolerance upon Planning (and
Planning’s comparatively strong positive effect upon Radicality) implies that Risk
Tolerance may simultaneously promote and inhibit Radicality. Similarly, frequent TPOC
Communication has a significant and negative effect upon both Planning and
Experimentation. Experimentation is associated with statistically significant decreases in
both Radicality and Commercial Success. Therefore, increased Risk Tolerance and
increased TPOC Communication would imply a decrease in Experimentation with a
commensurate increase in Radicality and Commercial Success. While Prior
Commitments’ effect upon the intermediary variables was nonsignificant, its effect on
Radicality and Commercial Success was both significant and positive, adding to the
countervailing forces within the model.
59 Table 4. Comparison of Variable Directional Effects
Commercial Variable Planning Experimentation Radicality Success Risk Tolerance N N P1 Not Tested Frequency of TPOC N N P1 Not Tested Communication Prior Commitments N N P1 P1 Planning Not Tested Not Tested P P Experimentation Not Tested Not Tested N N Radicality Not Tested Not Tested Not Tested N Negative/Positive: significant at p <= .10; Negative/Positive: non-significant at p > .10; 1Not Hypothesized but tested
The offsetting effects within the antecedents and intermediary variables’
relationships may provide incentives for complex behavior that may help explain fewer
SBIR commercial successes, the explanations for which is one of this study’s central goals. For example, frequency of TPOC Communication simultaneously depresses the effects of Planning and Experimentation. A firm and/or project team may, therefore, be in the position of suppressing the development of economic value if it chooses or the issuer chooses increased interaction. Given the approximately equivalent beta effects, greater
TPOC Communication may decrease Planning (depressing Planning’s effect upon
Commercial Success) while implying greater positive effect on Commercial Success by decreasing Experimentation. Risk Tolerance has a similar bifurcated effect: greater Risk
Tolerance decreases Planning and Planning’s effect upon Commercial Success; however, greater Risk Tolerance implies less Experimentation and the reduction of
Experimentation’s negative effect upon Commercial Success. While not hypothesized,
Prior Commitment is associated with a positive effect upon both Radicality and Success.
Intermediary variables appear to act in opposition to each other; note that
Radicality had a negative effect upon Commercial Success which could then infer that
60 Experimentation’s negative and significant effect upon Radicality might be a possible strategy to increase economic value. However, the interaction effects of Experimentation and Planning upon the dependent variables, while small, indicates support for EO-as-
Experimentation’s negative and positive duality. Overall, the countervailing actions of the antecedents within the model imply a complicated terrain for these projects.
Contributions
In this section, I suggest how these findings may contribute to our understanding of the innovation process within the SBIR framework, and more generally for innovation realization in other contexts. I also develop suggestions for how the study could advance our understanding of incentives created by public investment in technology innovation.
This study contributes to academic inquiry and practitioner application to innovation research by investigating the potential for more effective innovation pathways and antecedents to Commercial Success, within a defined group of innovation projects.
As originally conceived, using the SBIR universe of awardees’ project success provided a
degree of control for the measurement of entrepreneurial success by assuming a level of
expertise and evaluation extant at each project’s inception. Becoming a Phase I or Phase
II grant awardee assumes the successful completion of not only an extensive grant
application, but also the vetting of that successful firm’s intent by an informed and
competitive process that is somewhat standardized across eleven SBIR-issuing government agencies. More broadly, in concept, the SBIR project database aggregates projects that should be the result of a thorough Planning process by experienced
researchers and/or service providers by an issuer with a demonstrated commitment to at
the least a successful technological outcome. This process is distinct from a university or
61 public innovation incubator environment where the prerequisites (skills, experience,
funding, institutional aims) for inclusion may vary even more widely.
Contribution to academic research. Wiklund and Shepherd’s (2011) conception of EO-as-Experimentation inquiry (and this study) suggest that Experimentation increases variance and thus the likelihood of failure. This study builds on this construct through its parallel conception of Experimentation as an antecedent to both Radicality
and Commercial Success through a finer-grain investigation of the antecedents and
interactions of Experimentation.
Experimentation and Planning. The qualitative phase of this project is an inquiry and the overall ecology of the SBIR project, determined that risk, stakeholder communication, and Experimentation were important components of a firm’s SBIR project experience. These are also components of effectuation theory, a collection of constructs which describe how firms respond to an uncertain future (Read et al., 2009b) and the principle driving theory behind the antecedent variables of this study. As such, this study contributes by the call by Arend et al. (2015) to map the interplay of effectuation theory components with precise, targeted methodology, particularly through investigating the interaction effect between Experimentation and Planning. As detailed in
Chapter 2 above, this study may be the first to explicitly investigate the capacity of
Planning to moderate Experimentation’s effects. The interaction effect I found though small, was statistically significant and indicated that Planning, as its effect increased, would reduce the ability of Experimentation to positively influence both Radicality and
Commercial Success. A possible explanation of this effect may the decision logic
62 preferences preferred by “science-based [SB]” or “non-science-based [nSB]” projects as suggested by Villani et al. (2018):
Indeed, we show that to successfully get started, SB founders need an approach more inclined towards causation, where business Planning is important in at least one configuration out of two. On the other hand, SB entrepreneurs always need to be flexible and, therefore, adhere to the effectual approach. (Villani et al., 2018: 180)
The authors delineated science based as those ventures “originated in their work in
academic, scientific, or applied research…” (p. 175); while this dissertation did not
control for technology type, all the government issuers of the SBIR projects in the 50
matched pair samples (DOD, DHHS, DOA, DOC, DOE, DHS, NASA, EPA, DOT)
presumably have a strong technical focus. If a preference for Planning is indeed evident
in more technical ventures (per Villani et al., 2018), then greater time and effort applied
in Planning behaviors could suppress the effects of other decision logics, in this case,
Experimentation.
Is there is an intrinsic component of Planning that suppresses the act of
Experimentation within the human cognitive processes? When one plans, does that force
out flexible thinking? Said differently, would the same project group progress faster if it
were told to find an innovation solution without a specific, preordained deliverable
product? For the moment, these data infer as an interaction effect Planning suppresses
Radicality and Commercial Success by suppressing the positive of Experimentation upon
each outcome, respectively, what is in the precise nature of the active Planning that
occasions this suppression.
Part of an explanation may be in the intrinsic nature of the SBIR contractual
process. Note that through H9 and H10, Planning had the two strongest effects upon
63 Radicality and Commercial Success within the model. This may be the result of a high
Planning (causation) component of SBIR projects due to the application review process which lays out discrete project deliverables. In an environment in which very discrete expectations about Commercial Success are determined at the very inception of the project by sophisticated researchers and a well-understood review process, causation
(Planning) constructs infer greater Commercial Success. This is an interesting counterpoint to this inquiry given that, like EO, much entrepreneurship literature equates
Experimentation processes with success in high innovation contexts such as SBIR projects.
Contribution to practitioner application. This study has the potential to improve investment outcomes in innovation projects in several ways. First, do contractual commitments impede development of innovation? A common theme in the qualitative interviews was that stakeholder (a SBIR grant issuer) was critical for successful SBIR outcomes. However, preliminary results show some direct positive correlation between issuer communication and Commercial Success outcomes, but negative correlations with the antecedents Planning and Experimentation. While close coordination with the issuer’s technical point of contact (TPOC) might serve to ensure an issuer-desired outcome, close coordination may, in fact, be detrimental to the SBIR firm’s economic best interests, given the negative influence of TPOC communication and Risk Tolerance upon Planning and its positive influence on Commercial Success. Generalizing to venture capital projects or those with high follow-on investor participation, this participation may actually result in a negative or less Commercial Success outcomes as a result of active investor participation.
64 Second, and more generally, under what circumstances does investment incite
evolutionary versus revolutionary outcomes? Does contractually forcing an expectation
of commercial commitment actually serve to increase the variance associated with
commercial return? While this study did not segregate responses by SBIR issuing entity
(a limitation) several points are clear: an SBIR project can result in access to important
industry contacts and develop leading domain knowledge, despite the general mandate of
Commercial Success. These are important benefits for a firm in relatively small
competitive domains with highly specific technologies with a limited number of
competitors. It is possible that a firm would choose to actively deviate from a pre-
prescribed path (causation) if more non-commercial successes might eventually accrue to it, regardless of the eventual economic gain. Assuming that SBIR firms are rational actors, while the results infer that Experimentation has a negative correlation with both
Radicality and Commercial Success, that Experimentation is an active strategy at all
(note its positive association with quality of information exchanged) imply that non- economic goals of Experimentation practices are nevertheless important. As a result, both
SBIR grant issuers and recipients may actually benefit by openly and clearly delineating together an outcome not immediately commercially successful.
Lastly, my study may contribute to greater return on investment of SBIR dollars through better valuation of different cognitive Planning preferences. Given the significant professional skills and public funding and the opportunity cost of not pursuing more innovative, domain-expanding projects, this study may show that specific Planning strategies (causation) to develop a highly radical outcome may, in fact, garner more
65 economic success rather than a project that is, from the beginning, targeted towards more
discrete or improbable commercial outcomes.
Implications for Strategy Policy
The narrative that develops from the data provides support for the action of the
EOE mechanism, the oddly weak effects of the action of Prior Commitments in the SBIR context which is presumably based upon the needs of project-external stakeholders, and the inferred contradiction between the effects of both Experimentation and Radicality upon Commercial Success. There is a consistent trend in the data supporting overt
Planning as a pathway to Commercial Success in contrast to the effectuation theory that implies a develop-as-you-go path. There are undoubtedly other narratives which can develop from these data; what I present here are initial findings meant to highlight application to the research objective.
When acting directly, Experimentation appears to suppress Commercial Success; however, as a mediator Experimentation positively mediates the relationships between both Risk Tolerance (M2) and Frequency of TPOC Contact (M6) and Commercial
Success, respectively. However, Experimentation also negatively mediates the relationship between quality of information exchanged and both Radicality (M13) and
Commercial Success (M14). M2 would logically imply that risk-tolerant firms would use
Experimentation to create economic value; however, per H1, Risk Tolerance has a
statistically supported negative effect upon Experimentation. I interpret this to mean that
risk-tolerant firms would use an experimental development process (by necessity)
however SBIR firms may not overtly seek an SBIR project as a chance to experiment,
with Experimentation the intrinsic goal. As such a SBIR project would be viewed as
66 carrying significant intrinsic risk and Experimentation would not be productive as it would effectively add to the potentiality for overall variance in outcomes. This is borne out by H8 where Experimentation has a negative effect upon Commercial Success when controlled for the effects of Radicality, Prior Commitments and quality of information.
Resultant economic value creation in a highly innovative environment, therefore, may be more of the product of overt intentionality rather than a more random, inspirational model described by effectuation literature.
One of the challenges of this study has been to elucidate the multiple trends and implications from the underlying assumption that both the issuer and recipients of SBIR grants are rational actors, given the extensive application and vetting process required of the grant program, but also suggest cogent reasons for the lack of Commercial Success.
However, the data do suggest multiple successful pathways based upon initial preferences, capacities, and ultimate intentions of the SBIR issuer and the SBIR project team.
For example, the data imply an SBIR project team that intended to create tangible economic value would accept risk a relatively higher degree of risk, experiment [M2] and communicate frequently with the issuer [M6]. This is clearly at the crux of the EOE contradiction; the data also imply more stakeholder contact reduces Planning, which is then may reduce economic value outcomes (H10) but reduce the negative effect of
Radicality upon Commercial Success (H9).
If a radical outcome project were the SBIR project team’s goal, H11 implies the team would also accept a lower probability of economic gain. H9 suggests that Planning has a strong and positive role upon a radical project outcome and economic value
67 creation per H9 and H 10; the negative effect of Experimentation upon Radicality per H7 further supports Planning as an appropriate strategy for a radical outcome project.
Moreover, somewhat paradoxically, a project team may further a radical project outcome by either restricting frequent issuer communication as an overt strategy or choosing a project where the issuer (or the project team) would tend not to communicate frequently, per H4.
A radical project outcome, therefore, seems the result of an overt, intentional strategy process, with less Experimentation, and with the expectation of lower financial gain. Stated differently, a project team that wanted to create a radical project would prefer an issuer to communicate infrequently and be itself relatively risk intolerant (H2).
A potentially important trend within the data for SBIR project teams is a
surprising lack of clear benefit for connection with outside information sources. TPOC
communication (H3 and H4) was associated with less Experimentation and less Planning; the effect of Prior Commitments (H5 and H6) a central tenant of effectuation theory and a component of entrepreneurship orientation was both small and non-significant.
Moreover, Experimentation both positively and negatively mediated the relationship of
TPOC contact frequency (M5) on Commercial Success, implying inbound information could be affected by investigation policy. Mediation tests of prior commitment’s effect upon Radicality and Commercial Success via Experimentation were small and non- significant (M9 and M10). Does this imply that SPR teams are better off ignoring or negating inbound information? TPOC communication is associated with less Planning
(H4), despite Planning’s robust positive effect upon Commercial Success (H10). This may point to the overriding need to define economic return from the beginning and
68 throughout all phases of the project despite what would appear to be a distraction from outside sources. The implication for SBIR issuers would be to more clearly align the issuer’s goals with either a radical project outcome or economic return. For example,
M13 would imply that less Experimentation in the presence of high-quality information would lead to a more radical outcome. Careful coordination of the development process with the development firm (the SBIR grant awardee in this study), that is, less
Experimentation as a shared goal, would seem a rational goal given these analyses.
Implications for Innovation Development
The model, as supported by the study data, describes an ecology in which seemingly logical, overt innovation development tactics appear to work in opposition to each other. An SBIR firm, therefore, could be acting in good faith in its intention to create economic value yet be undertaking actions that paradoxically decrease the chances of doing so. For example, a project team that chose to connect less frequently with its
TPOC as an overt strategy may benefit engender greater Commercial Success through possible suppression of the negative effects of Experimentation, though it may also suffer from the reduced positive benefit of Planning. One of this study's objectives was to understand why there are fewer Commercial Successes than might be anticipated-that is, why projects don't succeed in crossing the "Valley of Death." These antecedent variables acting in countervailing ways may underscore the difficulty in creating economic overt economic value; the antecedent implement and intermediary variables seem to act in offsetting ways that both decrease positive and negative effects that both increase/decrease negative and positive effects simultaneously.
69 Implications for Public Policy Investment
This study contributes by suggesting that issuers and SBIR grant awardee firms may benefit by early delineating outcome goals as discrete and generally mutually exclusive. The study provides evidence that Experimentation per the EOE construct may not be beneficial for either the development of a radical outcome or economic value. It also shows that attitudes towards risk and connection with outside entities are, at best, complicated, and in conjunction with overt Planning strategies and Experimentation practices may introduce countervailing effects that offset the positive effects of innovation-seeking behaviors. The overall strength of Planning to infer, positive effect upon both Radicality and Commercial Success would imply a firm may want to establish
discrete goals from the outset, and perhaps decrease reliance upon variance seeking
behaviors such as Experimentation. An important contribution is the discovery of the unexpectedly negative effects of the antecedents; their consistent negative association with both Planning and Experimentation but yet positive direct effects upon Radicality and Commercial Success, would imply a potentially real cost to these Planning and
Experimentation. As a consequence, SBIR projects have so many cooks in the kitchen that a clear path to economic value may be inherently more complicated than other ventures. As such, the model suggests that a positive outcome may be more due to the reduction of multiple negative effects rather than promotion of a single process.
Study Limitations
A total of 50 matched IV and DV pairs may limit the statistical power of this study to infer statistically significant relationships, hence the choice of MPlus as the statistical analysis tool. MPlus relies upon Bayesian analysis assumptions which, while
70 intellectually attractive, may be subject to unknown biases not experienced in more
traditional or more broadly used statistical packages.
I was unable to sample equally or on a prorated basis matched pairs from representative geographical U.S. areas. SBIR funding and awards show large West Coast
and East-central preferences; disparate effects may occur from more regional
development or investment activity versus more isolated regions of the United States. My
sample did not control for the number of awards or the industry experience of each firm
which may have a bearing upon the capacity of the firm to bring a project from
conception to eventual success, be it innovative or commercial or both. The sampling
also did not control for industry type where the commercial opportunity may be more
prevalent in one than the other-software security versus tank treads, for example; this
may be especially important when small firms are targeting issuer solutions that have
such specific parameters that their commercial application may be initially quite narrow.
There may also be a difference in issuer priorities with regard to project importance and
relevancy, which would result in a high or low level of communication. Similarly, I did
not control for issuer preferences for radical or highly defined project outcomes. Some
SBIR firms may find themselves in facing greater or lesser constraints based upon the
issuer's outcome preference. For example, EPA SBIR contracts may be more exploratory,
while DOD projects may be highly targeted towards specific technological solutions.
Lastly, the matched pair sample did not differentiate between Phase I and Phase II
awards; Phase II awards may have a skewed probability of financial and commercial
return because Phase II awards are given to firms after passing from an initial exploratory
phase into greater funding and knowledge.
71 A significant limitation of this study is the low number of matched pairs and
accompanying potential for response bias. These data were difficult and complicated to
collect and aggregate, requiring multiple requests and broadcasts of reminders and.
Approximately 500 overall responses were returned; however, the intersection of these
was 50 complete matched pairs of instruments. Those responding to the instrument may
have a somewhat stilted view regarding the likelihood of commercial success, and
whether or not external or internal forces had a bearing upon the projects’ economic
success. Moreover, there was undoubtedly a degree social acceptance factor active in
answers as the SBIR grant process has a component of accountability to the US
government, which may have influenced respondents to exaggerate positive outcomes.
Greater clarity could have been elucidated through items that directly compared
the actual perception of the risk profile of the project (as the unit of analysis) to the Firm
Principals’ and Principal Investigators’ conception of the firm’s overall risk profile. The
items within the instrument reflected firm values as a whole; because the unit of
measurement was the unique SBIR project (its intent, outcome, and development
process), a better focus would have been to specifically delineate the firm’ risk evaluation of each unique project and not the firms’ overall risk assumption preferences. In doing so, the development process of a unique particular project (directly referenced by the
instrument) would have been a tighter connection to the innovation process and
correlation with risk. Second, Radicality as an initial conception describes an outcome that was, when compared to prior solutions, a clear outlier. The model may have been to elucidate the project outcome differences between a low-risk firm that took comparatively higher risk projects, or a high-risk firm that took lower risk projects. A
72 radical project may, in fact, be groundbreaking, though not necessarily developed through
in an experimental pathway, nor be considered high-risk, and not undertaken with the
intent of economic gain. The extension of a firm’s core competency into a new domain
may, in fact, be a radical insertion of technology, but, in fact, be a comparatively low-risk commitment of firm assets.
Additionally, this model does not control for the industry, age of the firm, number of personnel, phase (Phase I or Phase II in the SBIR award hierarchy), though independence test results reported in Table 1, above, for client proximity, number of personnel, issuer, and region indicate no significant difference between matched and un- matched pair respondents.
Conclusion
This study investigates antecedents of innovation outcomes in order to better understand why some SBIR projects result in more commercial success than others.
Using variable constructs adapted from effectuation theory and through the conceptual lens of Entrepreneurial Orientation-as-Experimentation, it identifies statistically significant support for Experimentation and Planning effects upon Radicality and
Commercial Success. It identifies the possibly novel claim that Planning has the capacity to suppress the positive influence of Experimentation upon both Radicality and
Commercial Success, a claim not found in recent literature. It answers its intended research question regarding the lack of commercial success in SBIR projects by describing the SBIR project ecology as a complex network of often countervailing antecedents. It derives a central contribution that positive commercial or experimental
73 outcomes could be mutually exclusive, and the probability for each outcome may be enhanced by clearly defining initial goals.
74 APPENDIX A: Literature Review and Analyses
Compare and contrast reported instances of Explicit description of moderators Interview and or case Quantitative or effectuation/causation and upon effectuation or causation Comment Citation data survey data associated with discrete processes? outcomes What are the more explicit causal factors in entrepreneurship? Important article calling for the greater Effectuation research often specification of the “behavioral authenticates the existence of the fundamentals”, roles of counterparties (Arend et al., 2015) 1 process by noting its components through explicit modeling and how it but does not clearly link the transmits to value creation and actual components of the processes to goals, express interesting propositions outcomes. (Response to ASB 2015 in AMR) general claim is that process theory as a way to view effectuation is valid; therefore, noting the existence of effectuation (Gupta et al., 2016) 2 components, temporally ordered (as authors claim) is enough to infer causality and generalize to other contexts as a proscriptive group of behaviors [Response to ASB 2015 in AMR] As original proponents of effectuation theory, these authors refute ASB criticisms by defining effectuation as the result of innate human actions, (Read et al., 2016) 3 inherently outside and separate from the explanations provided by positivist perspectives which are the result of empirical observation resultant data analysis.
75
Reply to ASB 2015: This conceptual piece suggests a kind of soft determinism: pragmatism-based view that is expressed by human a habit but still allows humans (Reuber et al., 2016) 4 creative agency. Humans develop habituation out of convenience; creativity and habit are non-positivist views of the effectuation process theory. Changing from causal to effectual logics occurs when More generally a description of the study’s firms sought to (Kalinic, Sarasvathy, & Forza, 5 case studies existence of the presence of the effectual 5 enter complex and 2014) behavior unknown international contexts General claim is effectual choices Effectual choices become negative Observed effectual behavior negatively moderated discrete Planning and positive moderators upon positively associated with for resources, but positively moderated (Gabrielsson & Gabrielsson, opportunity creation, learning, 4 case studies 6 increased international the effects of building networks, 2013) resource capabilities, network commercialization entrepreneurial orientation, upon firm creation growth and survival. Reported effectuation/causation decision preference based upon liability. No explicit statement of interaction effects, only that interviewees reported effectuation Process study where existence of Generalized assumption for behaviors when experiencing 8 case studies reported effectual/causation behavior (Dash & Ranjan, 2019) 7 improved performance increased liabilities; implies relevancy of generalized theory “disadvantaged” firms exhibited effectual component affordable loss, suggesting economic resources moderate decision choice Degree of intensity of traits such as Self-reported survey; Positive psychological decision-making self-efficacy, optimism and (Zhang, Cui, Zhang, No 116 US cases/132 preferences as antecedents; moderation 8 perspective-taking influence choice Sarasvathy, & Anusha, 2019) Chinese cases effect unclear of effectual or causal logics
76
65 German companies Dependent variable was Appears to be mediation (though who were finalists in An interesting finding was that the split between firms describe his moderation) study national greater the “Psychic Distance”, a choosing more flexible where CEO experience and entrepreneurship measure of familiarity with target export 9 exporting versus greater perception of environment contest; self-reported country, the more likely the CEO was to (Harms & Schiele, 2012) commitment of financial mediated by E/C decision logic instrument-items use causation logics; overall, results resources e.g. direct choices, influenced method of measuring showed causation preferences in place of financial investment market entry effectuation/causation classical effectuation predictions decision preferences Impact of the evolution of a firm’s lifecycle on decision choices of No full-text copy available; notation from N/A 1 case study (Servantie & Rispal, 2018) 10 effectuation, causation, and abstract bricolage Enhanced growth of firms seems to be tied to customer/stakeholder Observed effectuation dominant logic in (Matalamäki, Vuorinen, No 10 case studies 11 mutuality, though list of growth strategies Varamäki, & Sorama, 2017) interview questions was not explicit in defining results Decision model: “aspiration, novelty, efficiency, or strategic” influenced choice Appears to be wholly descriptive, using of decision logic; most Business model/phase of described instances of firm objectives 1 company case, 23 salient point is that development determines choice of that correlate with E/C logics; difficult to (Sitoh et al., 2014) 12 interviews effectuation causation decision logics defend causal direction as by clearly can exist at once and conditionality likely be active in the same firm at the same time. The greater the transitory career path preference the more likely the entrepreneur was to prefer Interesting individual-as unit-of- Not explicit in terms of 291 cases/new effectuation-based strategies; in measurement data on career path project outcome but in independent Swedish the presence of startup experience, evolution preference. Implies that more (Gabrielsson & Politis, 2011) 13 generalized career firms’ CEO or higher startup experience experience will create more effectuation trajectory managing director increased the positive affect of preferred decision logics. career path preference upon effectuation logic choice. 5 interviews with Implication that pragmatism, high Descriptive study of the process with entrepreneurs across goalsetting and preference for little correlation or tie to innate attitudes (Pfeffer & Khan, 2018) 14 five different opportunity capture infer causation or process preferences industries
77
Uncertainty positively moderates the positive effect of effectuation Survey; 142 cases of constructs on problem-solving Uncertainty used as a proxy for Increased competitive firm employees who speed; uncertainty negatively innovation. A singular project experience (Blauth et al., 2015) 15 advantage had participated in a moderates the effect of causation used as the unit of measurement. new product project constructs on problem-solving speed No explicit description of moderators; authors make the general point that entrepreneurs in the case studies used relied more Observations of instances where firms on creating relationships when/as exhibited effectual behavior when (Galkina & Lundgren- Internationalization efforts needed rather than explicit 7 case studies seeking collaboration with interested 16 Henriksson, 2017) advance Planning. parties seeking international Internationalization intent, opportunities therefore, may be a moderator that increases ad hoc/effectual relationship building. 123 experienced German research and Degree of innovation as a develop managers moderator of the effectiveness of A frequently cited study that suggests developed through decision logics: Effectuation and emphasizing effectuation behaviors in authors’ personal causation logics show different corporate contexts “when innovativeness connections; 400 Research and development degrees of influence depending on is high” improves research and responses from (Brettel et al., 2012) 17 output the innovation context. In low development. A specific product was the publicly available innovation contexts, causal unit of measurement; no explicit database of German decision logics imply greater measure of the moderation effect companies greater research and development success; provided. than 50 employees. and high innovation contexts Single respondent per case. Overall context is uncertainty as the general moderator to affect Found causation active when resource 4 case studies, 10 (Reymen, Berends, causation or effectuation choice; shortage emerges; effectuation used to 18 interviews Oudehand, & Stultiëns, 2017) and resource scarcity implies define customer benefits causation choices
78
To enter new markets is to be inherently 129 complete surveys flexible and form alliances with other Authors claim effectuation as a of Greek individually entities; to suggest that these positive moderator of improved owned companies “diversifiers” could have been successful (Deligianni, Voudouris, & Yes performance versus causation 19 seven years old or solely by other means but chose Lioukas, 2017) logics in new market entry less; founders were effectuation strategies instead (when ventures the target there are logically few other choices) makes little intuitive sense. Cultural components of high-power Effectuation seems to be used in Kuwaiti distance, low individualism, low Interviews with 13 culture as well, with low individualism (Magalhaes & Abouzeid, masculinity, high uncertainty 20 Kuwaiti entrepreneurs and high uncertainty driving certain 2018) avoidance as a moderator upon the effectuation components. use of effectuation Learning and outside support A successful formation of a business influenced effectuation behaviors One case with four Yes based upon effectual concepts driven by (Jones & Li, 2017) 21 which resulted in the ultimate interview targets learning and external support success of the business Experience moderates the reliance Transcriptions of self- Experience infers greater use of upon effectual thinking processes; reported cognition: 27 effectuation; however, disparity in (Read, Dew, Sarasvathy,
22 causality more prevalent with less expert entrepreneurs groups may create confounding variables Song, & Wiltbank, 2009a) experience and 37 MBA students that are active in addition to experience Moderating effect of industry class: 86 total: 39 are science or non-science based new Dependent variable was new venture science based and 47 ventures. Implication is both view performance; interestingly, successful Yes are not science base; (Villani et al., 2018) 23 risk or partnerships and about the science-based startups use a combination longitudinal. Publicly same way. Rely on partnerships to of effectuation and causation. available survey diversify risk in low risk situations. Causal logic triggered/moderated by entrance a venture capitalist; implication is while both effectuation and causation Use of effectuation logics logics are used at once, entrepreneurial correlated with increased Single case; six (Ciszewska-Mlinaric, Obloj, & Yes talent made be maintained within the 24 internationalization and lack of interviews Wasowska, 2016) firm if effectuation-possible roles are still international experience available, in juxtaposition to the causation/goal setting financial return objectives of venture capital Applicable study as authors found that Demonstrates statistically effectuation positively affects the EO-as- significant evidence that Survey results from is Experimentation component of EO. effectuation positively moderates what EO is 94 high- (Mthanti & Urban, 2014) 25 However, in my view it is difficult to the relationship between EO and technology companies separate out Experimentation and firm performance. effectuation components
79
Foreign market selection and foreign market entry imply different decision logics-using both Study notes the presence of effectual effectuation and causation Case studies of 10 (Chetty, Ojala, & Leppäaho, No behaviors but infers that causation 26 processes; the relationship firms 2015) speeds learning. utilization component of effectuation appears to be active in selection and entry choices The readiness of low-cost alternatives in assets and technical Home-based businesses use parts of support moderates entry, given effectuation though making affordable loss construct and low 21 interview cases for commitments/strategic alliances not in (Daniel, Domenico, & Sharma, efficacy, though this is clearly 27 home-based trading action. High self-efficacy not necessary; 2015) correlation, and likely means that authors extend affordable loss to low self-efficacy positively social/psychological constructs and moderates choice of small business/home-based business Uncertainty in international Confirms that firms seeking to enter 28 contexts increases the use of 7 firm case studies international markets use effectuation (Galkina & Chetty, 2015) effectuation network building precepts References the theory of Giddens and structuration to look at the co- Conceptual piece that makes the point creation and “agentic” nature of 3 case studies of that effectuation is likely about (Bhowmick, 2011) 29 how an entrepreneur shapes entrepreneurs expanding means and less than about structure and how structure shapes controlling the future the entrepreneur 128 self-reported Industry growth moderates the cases; participants Study does not explicitly link the choice of effectuation or causation; were moderation mechanism to changes in (Futterer, Schmidt, & Yes high growth positively influences entrepreneurship and effectuation/causation and relies on the 30 Heidenreich, 2018) effectuation, while low growth innovation executive reported presence of effectuation to environments support causation contacts from publicly show causality by association traded German firms
80
Not explicit moderation, but predictive based on the degree/strength of the Study uses associations between combination of traits and decision fsQCA analysis of personality traits: harmonious passion, choices. Effectual or causation responses from 50 obsessive passion, entrepreneurial self- choices are linked to combinations entrepreneurs, efficacy, and risk perception and (Stroe, Parida, & Wincent, of the traits with no single trait average of 34 years 31 causation/effectuation choices. Results 2018) able to produce either effectuation old with experience implied measurable emotional traits or causation outcomes. Generally, founding a working steer decision logic choice on an intrinsic appeal of the venture entity individual level. (harmonious passion) associates with effectuation, esp. in the presence of risk. The authors interesting claim is effectuation strategies “…offer[] a Unique mathematical simulation posits more consistent path to higher effectuation decision styles in risky and performance regardless of the uncertain contexts positively influences entrepreneur’s ability to predict firm performance until predictive the future” (p.101). Though not In the simulation capacity by the entrepreneur can reach (Welter & Kim, 2018) 32 explicitly stated as moderation by over 75%. The greater returns that map the authors, high predictive ability to greater predictive abilities in the as modeled/described in the simulation support high returns to “high- simulation interacts with causation growth startups” (p.101), decision styles to produce the “maximum returns” (p.101) Summation of current research and suggestions for further exploration. Does not mention studying moderators but does call for “relationships between (Perry, Chandler, & Markova, effectuation and established constructs 33 2012) (p.848)”; “stakeholder interactions or relationships (p. 851)” and “that measure these processes [effectuation and causation] separately (p. 855). 6 one to two-person ventures interviewed Regressions show discrete identity to determine types traits can infer preferences for 350 instrument cases and measures of (Alsos, Clausen, Hytti, & effectuation or causation; no from Norwegian Multimethod study 34 entrepreneurial Solvoll, 2016) explicit test of moderation or business registry identity: Missionary, interaction Darwinian, Communitarian.
81
A somewhat preliminary study of 104 complete cases two hypotheses but providing derived from 1400 Tying/developing moderation measures evidence for the entrepreneur’s randomly selected to discrete events has been an oft- (Parida, George, Lahti, & Yes interesting perception of gain and 35 single-founder suggested area of research to and expand Wincent, 2016) control moderates the positive Swedish venture CEOs understanding of effectuation/causation. relationships on effectuation or begun in 2012 causation on initial venture sales. Decision path tracking implies teams “a collective” produce more innovative Related to Planning-Experimentation and varied outcomes. inquiry by implying that a “discovery Participants subsequently Study implies that individual versus Self-reports of orientation” produces less innovation related their decisions were group processes imply less or more decision paths from 13 than a “collective decision-making (Agogué, Lundqvist, & more causal, when path effectual behaviors, respectively, 36 teams of nascent oriented” process. Interaction test would Middleton, 2015) tracking data also suggested with resulting more positive and entrepreneurs therefore be logical next step. Authors that at the time both varied outcomes in innovation agree that tracking decision processes effectuation and causality post hoc is difficult. logics were in play, with a great proportion of effectuation logics in action Five specific target Overall conclusion is that small firms firms; events detailing differ from larger firms by using more ad No explicit moderation, however, product development hoc and classically organized new- (Berends, Jelinek, Reymen, & an attempt to document and were categorized and product development paths, and 37 Stultiëns, 2014) model empirically reported events then linked through effectuation is a likely and appropriate implied causality and descriptor of the process smaller firms sequence. use. Study investigates entrepreneurial capability: “sensing, selecting, shaping and synchronizing 174 completed opportunities”” as a mediator of While a directionally correct attempt to Opportunity exploitation instruments/usable the relationships between understand the interplay between E/C, was measured by the “sum cases from a sample of effectuation and causation upon self-reported exploitation values are of the number of entrepreneurs and top opportunity exploitation, vague. The study does not answer and in opportunities exploited”; executives from (Guo, 2018) 38 respectively. No significant fact builds on the problem of association the paper is silent as to the Beijing and Changchun interaction effect between and assumes of the validity of the relative outcome versus new ventures (less causation and effectuation found. effectuation construct by finding pieces Commercial Success than 10 years old) in Author reports mediation of of the construct. high-tech industries. effectuation/causation effects upon opportunity exploitation by entrepreneurial capability
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A moderation [Entrepreneurial Self-Efficacy] and mediation The study separates Uncertainty into [Flexibility] study; Flexibility as a Dynamism and Hostility and Resource mediator between Environmental Combination into Cohesion and Coupling; Uncertainty and Environmental explanation of results is cursory, but the Resource Combination and tracking of personality traits of self- Entrepreneurial Self-Efficacy as a 287 senior-level efficacy and flexibility are interesting to moderator of the relationship (Xuebing, Yueling, & Yi, 2015) 39 respondents see how entrepreneurs might react. Only between Environmental study seen to use moderated mediation: Uncertainty and Flexibility. As a the great the self-efficacy, the less moderator, study reports that ESE entrepreneurs relied on flexibility to strengthens the relationship b/t manage resources in the competitive dynamism and flexibility but environments. weakens the relationship b/t hostility and flexibility. Matching pair study similar to this one; overseeing coordinator and project What is the impact of 108 coordinator and managers per each firm’s portfolio were portfolio management and Straight regressions but 442 project manager targets. However, the causal logic project innovation on the directionality interesting as study responses from the confirms support for effectuation and (Nguyen, Killen, Kock, & prevalence of decision-logic looks at the way management 40 108 coordinators; unit then goes on to claim that should be Gemünden, 2018) components: means-driven, choice drive project development of measurement was proscribed objective; particularly by affordable loss, and behavior. the project. showing that overt control decreases adaptability? “adaptability and affordable loss logics”, which are presumably desirable. Decision logic preference Numerically counted instances of moderated by developmental stage effectuation or causation and observed of venture; earlier to later stage of earlier-stage development used 9 case study firms (Reymen et al., 2015) 41 development moderated effectuation preferences more and later preferences for effectuation to used causation logics as venture causation developed Relies on self-reporting and no apparent Study investigates use of outcome measure. Interesting study in effectuation logics with the firm as Multiple scale that it creates Control Orientation as the a unit of measurement; Control validations: n = 163 antecedent construct to the newly (Werhahn, Mauer, Flatten, & No 42 Orientation is developed and n = 258 developed scales’ other factors (though Brettel, 2015) somewhat ex nihilo as antecedent respectively these are clearly related to effectuation to effectuation factors constructs noted in the literature) as reflective, not formative constructs.
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APPENDIX B: Measurement Definition Table
Factor Adapted Instrument Item loading as Text as Used reported by Construct Definition Original Items Source Green = Items for Comment original Preliminary Factory scale Structure authors 1. Risk12I: Compared to our Company principles typical projects, this one instrument also contained was a real gamble for us. these items; #6 reflects the 1. Taking gambles is part of (Jambulingam, 2. Risk13I: We take above- Affordable Loss component our strategy for success. Kathuria, & Doucette, .839 average risks in our of Causation theory. DV 2. We take above-average 2005), 1,2,4,5; business. and IV items. risks in our business. (Stewart & Roth, .879 3. Risk14I: The most 3. The most important intent 2001; see p. 150 for important intent of this of this project was to grow detail on risk Degree to which project was to grow future future revenue. categorization of Principal revenue. 4. Taking chances is an entrepreneurs based Investigator .887 4. Risk15I: Our firm is Risk Tolerance element of our business on income/growth views firm’s usually very risk averse. strategy. objectives in support comfort with .867 RC 5. Our strategy can be of this item), 3; Schell, ambiguity; 5. Risk16I: Our strategy can characterized by a strong 6 be characterized by a tendency to take risks. strong tendency to take 6. We were able to take this Cronbach’s alpha risks. project on because the firm reported at .920 per 6. Risk17I: We were able to could afford the loss if the Jab lingam items take this project on project failed. because the firm could afford the loss if the project failed.
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1. QLIN24: Our Technical Items 1-3 loaded as Quality Point of Contact (TPOC) of Information Exchanged; provided relevant items 4 and 5 loaded as 1. Our foreign partner firm information whenever We TPOC Communication as a has provided relevant asked. numerical report/estimation information whenever We 2. QLIN25: We were unable to by Principal Investigator of asked them for it. learn about important the frequency of TPOC 2. We are promptly notified by changes in the project's meetings with project our foreign partner context from the issuer. RC team.; whenever any major 3. QLIN26: We received clear, change occurs at their firm. timely information about the 3. We get clear information issuer's plans. about the plans of our Qualitative 4. QLIN27: After We received foreign partner concerning measure of the grant, the project team the collaboration well in Krishnan Quality of value and met with the Technical advance. information frequency of Point of Contact (TPOC): 1: 4. How often do senior Cronbach’s alpha exchanged communication Not really at all, 2: A few managers from your firm reported at .800 with project times a year, 3: About once communicate with their issuer. a quarter, 4: Several times counterparts in the foreign a month, 5: About every partner firm? (1 - daily; 5 - week once a month or less) 5. QLIN28: How often did the 5. How often do senior and project team meet face-to- middle managers in your face with the issuer or the company make business technical point of contact trips to your foreign partner (TPOC) after the award was firm? (1 - twice a month or received? 1: Not really at more; 5 - once a year or all, 2: A few times a year, 3: less). About once a quarter, 4: Several times a month, 5: About every week 1. We used a substantial IV items testing a 1. PriCom22: We negotiated Degree to which number of agreements with component of effectuation future arrangements with Principal customers, suppliers and theory outside organizations for Investigator other organizations and .069 the outcome of this project. Prior relates use of people to reduce the (Chandler et al., 2011) 2. PriCom23: We used prior Commitments previous amount of uncertainty. agreements/commitments relationships in 2. We used pre-commitments from customers, suppliers, project from customers and .830 or others to help this development suppliers as often as project. possible.
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1. We allowed the business to 1. *Flex18: As We worked *= Use in Experimentation evolve as opportunities through the project, we construct; IV items emerged. allowed it to evolve as new 2. We adapted what We were directions emerged. Degree to which doing to the resources We 1. Flex19: We adapted what project direction .750 had. We were doing to the could differ from 3. We were flexible and took resources We had pre-prescribed .700 Flexibility advantage of opportunities (Chandler et al., 2011) 2. *Flex20: We took path. (*Indicates as they arose. advantage of unexpected items used in .710 4. We avoided courses of events as they arose for Experimentation action that restricted our this particular project. construct) .620 flexibility and adaptability. 3. Flex21: We avoided decisions that restricted the flexibility or adaptability of the project’s outcome. 1. We experimented with 2. Exp8: As the project IV items different products and/or developed, we business models. experimented with different 2. The product/service that products and/or prototypes.
Degree to which We now provide is 3. Exp9: The outcome of the .630 project options essentially the same as project was what We had
tested. originally conceptualized. originally planned for. RC .850 Experimentation (*indicated 3. The product/service that (Chandler et al., 2011) 4. Exp10: The project's
items used in We now provide is outcome is substantially .820 Experimentation substantially different than different from what We first
construct) We first imagined. imagined it would be. .750 4. We tried a number of 5. *Exp11: We tried a number different approaches until of different approaches until We found a business We found a solution that model that worked. worked. 1. We allowed the business 1. Flex18: As We worked Experimentation construct to evolve as opportunities through the project, we comprised of Flexibility and emerged. allowed it to evolve as new Experimentation items 2. We tried a number of directions emerged. loading together; central different approaches until .750 2. Exp11: We tried a number antecedent construct from Degree to which We found a business of different approaches until IV items project followed Based on Chandler model that worked. .750 We found a solution that Experimentation an undefined Experimentation and 3. We tried a number of worked. path in Flexibility items different approaches until .630 3. Exp8: As the project development. We found a business developed, we model that worked. .710 experimented with different 4. We were flexible and took products and/or prototypes. advantage of opportunities 4. Flex20: We took advantage as they arose. of unexpected events as
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they arose for this particular project.
1. We analyzed long run IV items Reported by opportunities and selected 1. Cau2: We spent significant Principal Investigator. what We thought would time planning strategies for provide the best returns .620 this project before it started. Cau2 and Cau3 make up 2. We designed and planned 2. Cau3: From the beginning Planning construct business strategies .740 of this project, our firm 3. We organized and developed and followed Cau5 included in implemented control internal control processes. Experimentation Construct processes to make sure 3. Cau4: Before starting We met objectives .620 development, we identified Degree of prior 4. We researched and and analyzed target determinant selected target markets markets. Planning decision-making (Chandler et al., 2011) and did meaningful 4. Cau5: As we went along, guided project competitive analysis the design of the project development. 5. We designed and planned .720 continuously evolved. production and marketing 5. Cau6: We chose to do this efforts project because of the 6. We developed a strategy to .740 resources We had available best take advantage of at the time. resources and capabilities 6. Cau7: When We started, 7. We had a clear and .550 we had a clear and consistent vision for where consistent vision for where We wanted to end up We wanted to end up. .510 1. Rad29I: The outcome of Outcome IV items reported 1. Innovation is a minor this project was a minor by Principle Investigator improvement over the improvement over previous Level of general previous technology (Gatignon, Tushman, technology. RC innovation of 2. Innovation was based on a Smith, & Anderson, 2. Rad30I: This project project as revolutionary change in 2002) Radicality outcome came from (was viewed by technology based on) a revolutionary Company 3. Innovation was a Cronbach’s Alpha advance in technology. Principle breakthrough technology reported at .780 3. Rad31I: This project 4. Innovation lead to products outcome was a that were difficult to breakthrough technology.
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replace with substitute 4. Rad32I: Older technology using older technology. could not replace or 5. Innovation represents a substitute for this: project major advance in a outcome. subsystem 5. Rad33I: This project's outcome was/is/or will be an important part of a larger system. 1. CmSc18: The outcome Outcome DV items technology was reported by Company 1. This Project was successfully implemented Principle Degree to which successfully implemented by the intended end- the project was by unit user(s). viewed by (Gatignon et al., 2002) 2. This Project has been 2. CmSc19: The project Commercial Company commercially successful outcome has been Success Principle as Cronbach’s Alpha for unit commercially successful for providing reported at .910 3. This Project has met unit's the firm. commercial expectations regarding the 3. CmSc20: This project has return. impact on sales met the firm's expectations regarding its impact on sales. 1. NwPro25: Doing this project gave us new ways 1. Application service of making products. provider (ASP) allows my 2. NwPro26: The project's company to use new outcome allowed us to applications. .80 create new product ideas. 2. ASP will stimulate new 3. NwPro27: This project Construct ideas concerning the opened up opportunities designed to applications in my firm. .84 (Verwaal, that were not available capture the 3. ASP allows applications Commandeur, & before for my firm. Outcome DV items New Product capacity of the that thus far were not Verbeke, 2009) 4. NwPro28: This project did reported by Company Capability project to available for my firm. .68 not result in any great leaps Principle; not used in enable firm to 4. ASP allows us to make big Cronbach’s Alpha forward in creating new theorization for this model create new leaps forward in using the reported at .900 products. RC products. new application 5. NwPro29: This project technologies. .65 didn't help us understand 5. ASP allows my firm to keep product development track of new applications. .73 trends more. RC 6. ASP allows my firm to 6. NwPro30: This project introduce new applications. .76 allowed our firm to introduce new products that it couldn't before.
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1. NewCm 12: This project involved fundamentally new 1. Innovation involved concepts or principles for fundamentally new this firm. concepts or principles for 2. NewCm 13: Developing the business unit project technology required 2. Innovation required new us to use new skills We did skills which business unit not possess previously. Construct did not possess 3. NewCm 14: We gained designed to 3. Innovation required important capabilities as a capture the business unit to develop firm by developing the Outcome DV items capacity of the (Gatignon et al., 2002) many new skills outcome technology. reported by Company New Competency project to Cronbach’s Alpha 4. Innovation required 4. NewCm 15: This project Principle; not used in enable firm reported at .870 business unit learned from required the firm to learn theorization for this model garner new completely new or different from new knowledge skills and/or knowledge bases bases. knowledge 5. Innovation required this 5. NewCm 16: We completed unit to adapt different this project with skills and methods and procedures knowledge We already 6. Innovation required had. RC business unit to carry out a 6. NewCm 17: We needed to great deal of retraining conduct a lot of retraining to achieve the project's outcome. Capacity of the 1. RetSkill31: A key reason to 1. A key reason to do this project to help do this project was to project was to firm retain an keep/maintain important keep/maintain important Retaining Key important Investigator created skills within this firm. Not used in theorization for skills within this firm Skills technical items 2. RetSkill32: Motivating key this model 2. Motivating key personnel to capacity personnel to stay was an stay was an important goal important in important goal of this of this project SBIR contexts project. 1. MktA21: Because We did 1. Have a good this project, our firm gained
understanding of a strategic competitive .720 competitors’ strengths and advantage.
strategies 2. MktA22: Because of this .820 2. Foresee customers’ (Rosenzweig & Roth, project We can now better Not used in theorization for Market Acuity product or service needs. 2007) predict customers’ needs. this model
3. Understand target markets 3. MktA23: A main goal of .690 better than competitors doing this project was to
4. Able to sense shifting obtain market information. .740 boundaries of industry 4. MktA24: This project allowed us to sense shifting
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industry or technology boundaries.
1. FmSzDV33_1: Approximately many employees were working in your firm in [study year]? 1- 10, 11-20, 21-30, 31-40, >50 2. FmDsCDV34_1: 1. Size of the firm Approximately what is/was 2. Proximity to key stakeholder contact the distance in miles from 3. Commercialization potential your firm to the most 4. Commercial success defined as non-issuer significant customer/client Demographic and funding Investigator created for this project? 0-10, 11- Outcome 5. Age of the firm items 30, 31-50, 51-100, 101- Measures 6. Did this Phase We project result in a 300, >301 Phase II award? [Conditional item if project 3. PatntDV25_1: Did this was Phase, We] project directly result in a patent? 4. Phs3FDV35_1: Did Phase III financing of this project occur? 5. FrmYrDV37_1: What year was your firm started? 6. Did this Phase We project result in a Phase II award?
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APPENDIX C: Literature Review Article Subject Aggregation
Citation A B C D E F G H I J K L M N O (Arend, Sarooghi, & Burkemper, 2015) 1 1 (Gupta, Chiles, & McMullen, 2016) 2 1 (Read, Sarasvathy, Dew, & Wiltbank, 2016) 3 1 Reuber, Fischer, & Coviello, 2016) 4 1 (Kalinic, Sarasvathy, & Forza, 2014) 5 1 (Gabrielsson & Gabrielsson, 2013) 6 1 (Dash & Ranjan, 2019) 7 1 (Zhang, Cui, Zhang, Sarasvathy, & Anusha, 2019) 8 1 (Harms & Schiele, 2012) 9 1 (Servantie & Rispal, 2018) 10 1 (Matalamäki, Vuorinen, Varamäki, & Sorama, 2017) 11 1 (Sitoh, Pan, & Yu, 2014) 12 1 (Gabrielsson & Politis, 2011) 13 1 (Pfeffer & Khan, 2018) 14 1 (Blauth, Mauer, & Brettel, 2015) 15 1 (Galkina & Lundgren-Henriksson, 2017) 16 1
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(Brettel, Mauer, Engelen, & Küpper, 2012) 17 1 (Reymen, Berends, Oudehand, & Stultiëns, 2017) 18 1 (Deligianni, Voudouris, & Lioukas, 2017) 19 1 (Magalhaes & Abouzeid, 2018) 20 1 (Jones & Li, 2017) 21 1 1 (Dew, Read, Sarasvathy, & Wiltbank, 2009) 22 1 (Villani, Linder, & Grimaldi, 2018) 23 1 (Ciszewska-Mlinaric, Obloj, & Wasowska, 2016) 24 1 (Mthanti & Urban, 2014) 25 1 (Chetty, Ojala, & Leppäaho, 2015) first text 26 1 (Daniel, Domenico, & Sharma, 2015) 27 1 (Galkina & Chetty, 2015) 28 1 1 (Bhowmick, 2011) 29 1 (Futterer, Schmidt, & Heidenreich, 2018) 30 1 (Stroe, Parida, & Wincent, 2018) 31 1 (Welter & Kim, 2018) 32 1 (Perry, Chandler, & Markova, 2012) 33 1 (Alsos, Clausen, Hytti, & Solvoll, 2016) 34 1
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(Parida, George, Lahti, & Wincent, 2016) 35 1 (Agogué, Lundqvist, & Middleton, 2015) 36 1 (Berends, Jelinek, Reymen, & Stultiëns, 2014) 37 1 (Guo, 2018) 38 1 (Xuebing, Yueling, & Yi, 2015) 39 1 (Nguyen, Killen, Kock, & Gemünden, 2018) 40 1 (Reymen et al., 2015) 41 1 (Werhahn, Mauer, Flatten, & Brettel, 2015) 42 1 6 6 5 6 3 4 2 1 4 1 1 1 1 1 2
A: Literature review/Theory criticism or defense B: Internationalization choices C: Perception of risk/liability/uncertainty D: Entrepreneurial self-efficacy/individual traits E: Individual career experience F: Evolution of firm lifecycle/Type of business model G: Stakeholder interconnection H: Cultural traits and influences I: Effectuation components as moderators J: Learning K: Science or non-science venture L: Market growth M: Group vs. individual decision process N: Firm size O: Project portfolio management strategy
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APPENDIX D: Demographic Comparisons
Table D1. Respondent Proximity to Major Client for Project vs. Respondent Presence in Matching/Nonmatching Response Cohort
Proximity in Miles to Most Significant Client for This Project* Cohort 0 to 10 11 to 100 101 to 300 >301 Totals Matched-pair data 5 8 10 27 50 Non-matched pair data 14 24 23 90 151 Totals 19 32 33 117 201 *Company Principle Reported Data
Table D2. No. of Personnel in Firm When Grant Awarded vs. Respondent Presence in Matching/Nonmatching Response Cohort*
Personnel at Time of Award
Cohort <=2 >=3 Total Matched-pair data 38 12 50 Non-matched pair data 118 37 155 Total 156 49 205 *Company Principle Reported Data
Table D3. SBIR Grant Issuing US Gov. Dept. vs. Respondent Presence in Matching/Nonmatching Response Cohort*
US Government Issuing Dept. DOA, DHS, NASA, Cohort DOD DHHS Totals DOC, DOE EPA, DOT Matched-pair data 22 16 7 5 50 Non-matched pair data 201 96 44 38 379 Totals 223 112 51 43 429 *U.S. Gov. reported data
94 Table D4. Respondent Region vs. Respondent Presence in Matching/Nonmatching
SBIR Respondent by US Region* South- Cohort West Central Southeast Northeast Totals Central Matched-pair data 13 11 6 6 14 50 Non-matched pair data 97 65 36 55 126 379 Totals 110 76 42 61 140 429 *U.S. Gov. reported data
Table D5. SBIR Award Size vs. Respondent Presence in Matching/Nonmatching
Non-matched Pair Percentile Grouping Breaks Lowest 20th 60th 80th to through all through all through all highest of 10th of 50th of 70th $98,930 $154,835 >= Cohort <= $98,881 thru thru Totals $712,046 $152,012 $710,806 Non-matched pair data 75 152 76 76 379 Matched-pair data 14 8 11 17 50 Totals 89 160 87 93 429 *U.S. Gov. reported data
95 APPENDIX E: IV Cohort EFA Analyses
Quality of Current IV EFA Pattern Matrix 12-31-16 Risk TPOC Prior Radicality Emergence Information Planning Experimentation Tolerance Communication Commitments Exchanged Rad31I This project outcome was a breakthrough technology. 0.807 Rad30I This project outcome came from (was based on) a revolutionary advance in technology. 0.707 0.164 Rad29I The outcome of this project was a minor improvement over previous technology. RC 0.650 Rad32I Older technology could not replace or substitute for this project outcome. 0.485 0.109 Rad33I This project's outcome was/is/or will be an important part of a larger system. 0.445 0.122 0.107 -0.185 Flex18 As we worked through the project, we allowed it to evolve as new directions emerged. 0.103 0.718 -0.167 Exp11 We tried a number of different approaches until we found a solution that worked. 0.616 0.140 -0.122 Exp8 As the project developed, we experimented with different products and/or prototypes. 0.540 -0.108 0.200 Flex20 We took advantage of unexpected events as they arose for this particular project. 0.458 0.169 -0.121 QLIN24 Our Technical Point of Contact (TPOC) provided relevant information whenever we asked. 0.805 -0.126 QLIN25 We were unable to learn about important changes in the project's context from the issuer. RC 0.708 0.105 QLIN26 We received clear, timely information about the issuer's plans. 0.103 0.686 0.102 0.105 Risk13I We take above-average risks in our business. 0.782 Risk16I Our strategy can be characterized by a strong tendency to take risks. 0.744 0.102 Risk15I Our firm is usually very risk averse. RC 0.687 QLIN27 After we received the grant, the project team met with the Technical Point Of Contact (TPOC)… 0.845 QLIN28 How often did the project team meet face-to-face with the issuer or the technical point of contact (TPOC) 0.679 after the award was received? PriCom23 We used prior agreements/commitments from customers, suppliers, or others to help this project. 0.683 PriCom22 We negotiated future arrangements with outside organizations for the outcome of this project. 0.140 0.665 Cau2 We spent significant time planning strategies for this project before it started. 0.767 Cau3 From the beginning of this project, our firm developed and followed internal control processes. 0.113 0.456 -0.117 Cau4 Before starting development, we identified and analyzed target markets. 0.185 0.422 Exp10 The project's outcome is substantially different from what we first imagined it would be. 0.796 Exp9 The outcome of the project was what we had originally planned for. RC -0.134 -0.131 0.715 Extraction Method: Principal Axis Factoring. Rotation Method: Oblimin with Kaiser Normalization.
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APPENDIX F: IV Cohort Validity Analyses
97
APPENDIX G: IV CFA Analyses
98
APPENDIX H: IV Cohort CFA Model Fit Analyses
IV Cohort CFA Model Fit Analyses IV CFA Recommended Reference (Hair, Black, Babin, Chi-square/df 1.484 Between 1 and 3 & Anderson, 2010) Chi-square 332.5 Deg. of Freedom 224 P-value 0.000 CFI 0.934 > 0.950 (Hu & Bentler, 1999) GFI 0.911 AGFI 0.881 SRMR 0.055 RMSEA 0.043 < 0.060 (Hu & Bentler, 1999) PCLOSE 0.898 > 0.050 (Gaskin, 2012)
99 APPENDIX I: IV Cohort Common Method Bias Analyses
Common Method Bias Analysis: IV 100% Redacted 1/1/17 IV CFA Model Without CLF IV CFA Model With CLF Regression Weight Standardized Regression Weights Standardized Regression Weights Delta RadIV31_1 <--- Radicality 0.829 RadIV31_1 <--- Radicality 0.772 0.057 RadIV30_1 <--- Radicality 0.711 RadIV30_1 <--- Radicality 0.705 0.006 RadIV29_1RC <--- Radicality 0.649 RadIV29_1RC <--- Radicality 0.619 0.030 RadIV32_1 <--- Radicality 0.503 RadIV32_1 <--- Radicality 0.459 0.044 FlexIV18_1 <--- Emergence 0.561 FlexIV18_1 <--- Emergence 0.477 0.084 ExpIV11_1 <--- Emergence 0.741 ExpIV11_1 <--- Emergence 0.527 0.214 ExpIV8_1 <--- Emergence 0.725 ExpIV8_1 <--- Emergence 0.576 0.149 QLInfIV24_1 <--- Qual_Info_Exch 0.718 QLInfIV24_1 <--- Qual_Info_Exch 0.301 0.417 QLInfIV25_1RC <--- Qual_Info_Exch 0.735 QLInfIV25_1RC <--- Qual_Info_Exch 0.254 0.481 QLInfIV26_1 <--- Qual_Info_Exch 0.797 QLInfIV26_1 <--- Qual_Info_Exch 0.275 0.522 RiskIV13_1 <--- Risk_Pref 0.818 RiskIV13_1 <--- Risk_Pref 0.813 0.005 RiskIV15_1RC <--- Risk_Pref 0.653 RiskIV15_1RC <--- Risk_Pref 0.646 0.007 RiskIV16_1 <--- Risk_Pref 0.736 RiskIV16_1 <--- Risk_Pref 0.737 -0.001 QLInfIV27_1 <--- Freq_TPOC_Cont 0.856 QLInfIV27_1 <--- Freq_TPOC_Cont 0.810 0.046 QLInfIV28_1 <--- Freq_TPOC_Cont 0.714 QLInfIV28_1 <--- Freq_TPOC_Cont 0.708 0.006 PrComtIV23_1 <--- Prior_Com 0.531 PrComtIV23_1 <--- Prior_Com 0.395 0.136 PrComtIV22_1 <--- Prior_Com 0.893 PrComtIV22_1 <--- Prior_Com 0.931 -0.038 CausIV2_1 <--- Causation 0.603 CausIV2_1 <--- Causation 0.273 0.330 CausIV3_1 <--- Causation 0.639 CausIV3_1 <--- Causation 0.228 0.411 ExpIV10_1 <--- Diff_Out_Expect 0.711 ExpIV9_1RC <--- Diff_Out_Expect 0.886 FlexIV20_1 <--- Emergence 0.341 FlexIV20_1 <--- Emergence 0.206 0.135 CausIV4_1 <--- Causation 0.476 CausIV4_1 <--- Causation 0.177 0.299 RadIV33_1 <--- Radicality 0.540 RadIV33_1 <--- Radicality 0.453 0.087 100
APPENDIX J: DV Cohort EFA Analyses
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APPENDIX K: DV Validity Measurement
DV Validity Measurement New New Retaining Commercial Risk Market CR AVE MSV Mar(H) Skill Product Radicality Skills in Success Tolerance Acuity Capacity Capability Firm Commercial Success 0.822 0.608 0.461 0.851 0.780 New Skill Capacity 0.759 0.392 0.140 0.904 0.174 0.626 New Product Capability 0.831 0.516 0.461 0.942 0.679 0.341 0.718 Risk Tolerance 0.836 0.630 0.081 0.955 0.029 0.225 0.223 0.793 Radicality 0.815 0.477 0.394 0.965 0.419 0.374 0.628 0.284 0.690 Retaining Skills in Firm 0.667 0.502 0.060 0.967 -0.002 0.244 0.073 0.125 0.019 0.708 Market Acuity 0.661 0.503 0.335 0.970 0.560 0.263 0.579 0.061 0.449 0.203 0.709 VALIDITY CONCERNS Convergent Validity: the AVE for New Skill Capacity is less than 0.50. Convergent Validity: the AVE for Radicality is less than 0.50. Reliability: the CR for Retaining Skills in Firm is less than 0.70. Reliability: the CR for Market Acuity is less than 0.70.
102
APPENDIX L: DV Cohort CFA Analyses
103
APPENDIX M: DV Cohort CFA Model Fit Analyses
DV Cohort CFA Model Fit Analyses DV CFA Recommended Reference Between 1 and Chi-square/df 1.445 (Hair et al., 2010) 3 Chi-square 401.8 Deg. of Freedom 278 P-value 0.000 CFI 0.930 > 0.950 (Hu & Bentler, 1999) GFI 0.865 AGFI 0.829 SRMR 0.063 RMSEA 0.049 < 0.060 (Hu & Bentler, 1999) PCLOSE 0.548 > 0.050 (Gaskin, 2012)
104 APPENDIX N: DV Cohort Common Method Bias Analysis
DV CFA Model Without CLF DV CFA Model With CLF Regression Standardized Regression Weights Standardized Regression Weights Weight Delta
NewCmDV13_1 <--- New_Skill_Capacity 0.797 NewCmDV13_1 <--- New_Skill_Capac 0.778 0.019 NewCmDV16_1RC <--- New_Skill_Capacity 0.613 NewCmDV16_1RC <--- New_Skill_Capac 0.593 0.020 NewCmDV12_1 <--- New_Skill_Capacity 0.585 NewCmDV12_1 <--- New_Skill_Capac 0.534 0.051 NewCmDV17_1 <--- New_Skill_Capacity 0.541 NewCmDV17_1 <--- New_Skill_Capac 0.481 0.060 NewCmDV15_1 <--- New_Skill_Capacity 0.558 NewCmDV15_1 <--- New_Skill_Capac 0.461 0.097 RiskDV5_2 <--- Risk_Tolerance 0.818 RiskDV5_2 <--- Risk_Tolerance 0.769 0.049 RiskDV2_1 <--- Risk_Tolerance 0.808 RiskDV2_1 <--- Risk_Tolerance 0.751 0.057 RiskDV4_1RC <--- Risk_Tolerance 0.754 RiskDV4_1RC <--- Risk_Tolerance 0.701 0.053 RadDV9_1 <--- Radicality 0.862 RadDV9_1 <--- Radicality 0.841 0.021 RadDV8_1 <--- Radicality 0.796 RadDV8_1 <--- Radicality 0.766 0.030 RadDV11_1 <--- Radicality 0.619 RadDV11_1 <--- Radicality 0.562 0.057 RadDV7_1RC <--- Radicality 0.604 RadDV7_1RC <--- Radicality 0.554 0.050 RadDV10_1 <--- Radicality 0.507 RadDV10_1 <--- Radicality 0.425 0.082 RtCrtDV31_1 <--- Retaining_Skills_in_Firm 0.689 RtCrtDV31_1 <--- Retaining_Skills_ 0.664 0.025 RtCrtDV32_1 <--- Retaining_Skills_in_Firm 0.719 RtCrtDV32_1 <--- Retaining_Skills_ 0.676 0.043 ComScDV19_1 <--- Commercial_Success 0.881 ComScDV19_1 <--- Commercial_Succ 0.853 0.028 ComScDV20_1 <--- Commercial_Success 0.725 ComScDV20_1 <--- Commercial_Succ 0.681 0.044 ComScDV18_1 <--- Commercial_Success 0.724 ComScDV18_1 <--- Commercial_Succ 0.684 0.040 MktAcDV24_1 <--- Market_Acuity 0.586 MktAcDV24_1 <--- Market_Acuity 0.547 0.039 NProCpDV29_1RC <--- Market_Acuity 0.614 NProCpDV29_1RC <--- Market_Acuity 0.538 0.076 MktAcDV22_1 <--- Market_Acuity 0.71 MktAcDV22_1 <--- Market_Acuity 0.707 0.003 RiskDV3_1 <--- New_Product_Capability 0.28 RiskDV3_1 <--- New_Product_Ca 0.199 0.081 NewCmDV14_1 <--- New_Product_Capability 0.769 NewCmDV14_1 <--- New_Product_Ca 0.686 0.083 NProCpDV30_1 <--- New_Product_Capability 0.809 NProCpDV30_1 <--- New_Product_Ca 0.781 0.028 NProCpDV27_1 <--- New_Product_Capability 0.788 NProCpDV27_1 <--- New_Product_Ca 0.75 0.038 NProCpDV26_1 <--- New_Product_Capability 0.797 NProCpDV26_1 <--- New_Product_Ca 0.742 0.055 105
APPENDIX O: Skewness and Kurtosis: All DV and IV Cohort Data
Total DV and IV Response Data Descriptive Statistics If Stat > If Stat > N Range Minimum Maximum Mean Skewness 3x Stan Kurtosis 3x Stan Statistic Statistic Statistic Statistic Statistic Statistic ABS >2 Std. Error Err Statistic ABS >2 Std. Error Err StartDate 228 182.518 42185.96 42368.481 42209.29 2.921 2.921 0.161 Skew 7.962 7.962 0.321 Kurt EndDate 228 182.5176 42185.97 42368.487 42209.88 2.919 2.919 0.161 Skew 7.975 7.975 0.321 Kurt No. Phase I 228 23 0 23 2.19 3.427 3.427 0.161 Skew 16.089 16.089 0.321 Kurt Total $ PI 228 3076936 0 3076936 307379.2 3.365 3.365 0.161 Skew 14.324 14.324 0.321 Kurt No. Phase II 228 15 0 15 1.04 4.151 4.151 0.161 Skew 26.349 26.349 0.321 Kurt Total $ PII 228 11258060 0 11258060 943480.3 3.195 3.195 0.161 Skew 15.09 15.09 0.321 Kurt Award Year 228 5 2007 2012 2009.97 -0.385 0.385 0.161 -1.207 1.207 0.321 Kurt Amount of Award 228 4161482 22702 4184184 414015.4 3.436 3.436 0.161 Skew 15.672 15.672 0.321 Kurt MktAcDV21_1 224 4 1 5 3.51 -0.589 0.589 0.163 Skew -0.592 0.592 0.324 MktAcDV22_1 227 4 1 5 2.98 -0.178 0.178 0.162 -1.118 1.118 0.322 Kurt MktAcDV23_1 226 4 1 5 1.62 1.622 1.622 0.162 Skew 1.757 1.757 0.322 Kurt MktAcDV24_1 226 4 1 5 3.26 -0.387 0.387 0.162 -0.828 0.828 0.322 MarkAcu_FormINDX 228 15 4 19 11.25 -0.163 0.163 0.161 -0.536 0.536 0.321 NewCmDV12_1 218 4 1 5 3.53 -0.405 0.405 0.165 -0.846 0.846 0.328 NewCmDV13_1 220 4 1 5 3.55 -0.613 0.613 0.164 Skew -0.376 0.376 0.327 NewCmDV14_1 303 4 1 5 4.02 -1.255 1.255 0.14 Skew 3.298 3.298 0.279 Kurt NewCmDV15_1RC 215 4 1 5 2.34 0.769 0.769 0.166 Skew 0.211 0.211 0.33 NewCmDV16_1 218 4 1 5 3.25 -0.198 0.198 0.165 -1.105 1.105 0.328 Kurt NewCmDV17_1 218 4 1 5 2.24 0.617 0.617 0.165 Skew 0.109 0.109 0.328 NProCpDV25_1 303 4 1 5 3.37 -0.542 0.542 0.14 Skew 0.227 0.227 0.279 NProCpDV26_1 303 4 1 5 3.92 -1.327 1.327 0.14 Skew 2.175 2.175 0.279 Kurt NProCpDV27_1 303 4 1 5 3.78 -1.076 1.076 0.14 Skew 1.549 1.549 0.279 Kurt NProCpDV28_1RC 214 4 1 5 3.46 -0.324 0.324 0.166 -0.949 0.949 0.331 NProCpDV29_1RC 213 4 1 5 3.27 -0.296 0.296 0.167 -0.799 0.799 0.332 NProCpDV30_1 303 4 1 5 3.71 -0.841 0.841 0.14 Skew 0.602 0.602 0.279 RadDV7_1RC 303 4 1 5 3.44 -0.378 0.378 0.14 -0.203 0.203 0.279 RadDV8_1 303 4 1 5 3.25 -0.228 0.228 0.14 -0.147 0.147 0.279 RadDV9_1 303 4 1 5 3.36 -0.439 0.439 0.14 Skew 0.295 0.295 0.279 RadDV10_1 212 4 1 5 3.95 -1.045 1.045 0.167 Skew 1.381 1.381 0.333 Kurt RadDV11_1 213 4 1 5 3.71 -0.727 0.727 0.167 Skew -0.214 0.214 0.332 ComScDV18_1 303 4 1 5 2.93 -0.087 0.087 0.14 -0.16 0.16 0.279 ComScDV19_1 303 4 1 5 2.85 -0.054 0.054 0.14 0.203 0.203 0.279 ComScDV20_1 303 4 1 5 2.85 -0.109 0.109 0.14 0.129 0.129 0.279 RtCrtDV31_1 303 4 1 5 2.78 -0.009 0.009 0.14 -0.198 0.198 0.279 RtCrtDV32_1 303 4 1 5 2.35 0.632 0.632 0.14 Skew 0.222 0.222 0.279 Risk DV1_1 209 4 1 5 2.59 0.333 0.333 0.168 -0.754 0.754 0.335 Risk DV2_1 303 4 1 5 3.66 -0.811 0.811 0.14 Skew 1.332 1.332 0.279 Kurt Risk DV3_1 209 4 1 5 3.78 -0.608 0.608 0.168 Skew -0.455 0.455 0.335 Risk DV4_1RC 303 4 1 5 3.83 -0.942 0.942 0.14 Skew 1.747 1.747 0.279 Kurt Risk DV5_2 303 4 1 5 3.49 -0.782 0.782 0.14 Skew 1.33 1.33 0.279 Kurt Risk DV6_2 209 4 1 5 3.02 -0.253 0.253 0.168 -0.903 0.903 0.335 FmSzDV33_1 206 6 1 7 2.12 1.678 1.678 0.169 Skew 1.848 1.848 0.337 Kurt FmDsCDV34_1 202 5 1 6 4.86 -1.31 1.31 0.171 Skew 0.168 0.168 0.341 PatntDV25_1 207 1 1 2 1.21 1.415 1.415 0.169 Skew 0.003 0.003 0.337 Phs3FDV35_1 206 1 1 2 1.22 1.373 1.373 0.169 Skew -0.117 0.117 0.337 FrmYrDV37_1 205 70 1942 2012 1998.93 -2.196 2.196 0.17 Skew 7.005 7.005 0.338 Kurt P1_P2DV38_1 138 1 1 2 1.49 0.059 0.059 0.206 -2.026 2.026 0.41 Kurt StartDate 303 175.4455 42185.88 42361.328 42206.96 3.511 3.511 0.14 Skew 12.272 12.272 0.279 Kurt EndDate 303 175.4351 42185.9 42361.332 42207.51 3.509 3.509 0.14 Skew 12.291 12.291 0.279 Kurt No. Phase I 303 30 0 30 2.55 3.812 3.812 0.14 Skew 16.284 16.284 0.279 Kurt Total $ PI 303 3410413 0 3410413 327957 3.419 3.419 0.14 Skew 12.947 12.947 0.279 Kurt No. Phase II 303 16 0 16 1.19 3.935 3.935 0.14 Skew 19.042 19.042 0.279 Kurt Total $ PII 303 11805600 0 11805600 1027447 3.118 3.118 0.14 Skew 11.816 11.816 0.279 Kurt Award Year 303 5 2007 2012 2009.97 -0.449 0.449 0.14 Skew -1.189 1.189 0.279 Kurt Amount of Award 303 2943369 56631 3000000 378308.4 2.603 2.603 0.14 Skew 8.668 8.668 0.279 Kurt CausIV1_1 298 4 1 5 4.09 -1.066 1.066 0.141 Skew 0.52 0.52 0.281 CausIV2_1 298 4 1 5 3.86 -0.787 0.787 0.141 Skew -0.022 0.022 0.281 CausIV3_1 295 4 1 5 3.67 -0.734 0.734 0.142 Skew 0.1 0.1 0.283 CausIV4_1 297 4 1 5 3.8 -0.84 0.84 0.141 Skew 0.413 0.413 0.282 CausIV5_1 296 4 1 5 3.96 -0.957 0.957 0.142 Skew 0.811 0.811 0.282 CausIV6_1 294 4 1 5 3.7 -0.62 0.62 0.142 Skew -0.375 0.375 0.283 CausIV7_1 298 4 1 5 4.14 -1.237 1.237 0.141 Skew 2.023 2.023 0.281 Kurt CausalityIV_FormINDX 303 35 0 35 26.65 -1.65 1.65 0.14 Skew 5.855 5.855 0.279 Kurt ExpIV8_1 303 4 1 5 3.75 -0.814 0.814 0.14 Skew 0.089 0.089 0.279 ExpIV9_1RC 303 4 1 5 2.52 0.472 0.472 0.14 Skew -0.538 0.538 0.279 ExpIV10_1 303 4 1 5 2.59 0.475 0.475 0.14 Skew -0.485 0.485 0.279 ExpIV11_1 303 4 1 5 3.52 -0.608 0.608 0.14 Skew -0.224 0.224 0.279 RiskIV12_1 290 4 1 5 2.32 0.599 0.599 0.143 Skew 0.189 0.189 0.285 RiskIV13_1 303 4 1 5 3.51 -0.415 0.415 0.14 -0.136 0.136 0.279 RiskIV14_1 291 4 1 5 3.46 -0.35 0.35 0.143 -0.772 0.772 0.285 RiskIV15_1RC 303 4 1 5 3.51 -0.548 0.548 0.14 Skew -0.088 0.088 0.279 RiskIV16_1 303 4 1 5 3.31 -0.254 0.254 0.14 -0.345 0.345 0.279 RiskIV17_1 291 4 1 5 2.95 -0.069 0.069 0.143 -0.869 0.869 0.285 Kurt FlexIV18_1 287 4 1 5 3.86 -1.073 1.073 0.144 Skew 1.511 1.511 0.287 Kurt FlexIV19_1 287 4 1 5 3.93 -1.039 1.039 0.144 Skew 1.089 1.089 0.287 Kurt FlexIV20_1 287 4 1 5 3.69 -0.465 0.465 0.144 Skew 0.228 0.228 0.287 FlexIV21_1 286 4 1 5 3.41 -0.398 0.398 0.144 -0.737 0.737 0.287 FlexibleIV_FormINDX 287 12 8 20 14.88 -0.23 0.23 0.144 0.104 0.104 0.287 RadIV29_1RC 303 4 1 5 3.57 -0.406 0.406 0.14 -0.87 0.87 0.279 Kurt RadIV30_1 303 4 1 5 3.44 -0.436 0.436 0.14 Skew -0.623 0.623 0.279 RadIV31_1 303 4 1 5 3.49 -0.433 0.433 0.14 Skew -0.569 0.569 0.279 RadIV32_1 303 4 1 5 4.07 -0.806 0.806 0.14 Skew -0.177 0.177 0.279 RadIV33_1 286 4 1 5 3.94 -0.959 0.959 0.144 Skew 0.419 0.419 0.287 PrComtIV22_1 303 4 1 5 2.92 -0.054 0.054 0.14 -1.005 1.005 0.279 Kurt PrComtIV23_1 303 4 1 5 3.17 -0.4 0.4 0.14 -0.911 0.911 0.279 Kurt QLInfIV24_1 303 4 1 5 3.64 -0.591 0.591 0.14 Skew -0.3 0.3 0.279 QLInfIV25_1RC 303 4 1 5 3.35 -0.104 0.104 0.14 -0.419 0.419 0.279 QLInfIV26_1 303 4 1 5 3.17 -0.189 0.189 0.14 -0.495 0.495 0.279 QLInfIV27_1 303 4 1 5 2.28 0.704 0.704 0.14 Skew -0.342 0.342 0.279 QLInfIV28_1 303 4 1 5 2.04 1.082 1.082 0.14 Skew 0.744 0.744 0.279 Valid N (listwise) 122
106 APPENDIX P: Skewness and Kurtosis: All Matched Pair Cohort Data
107 APPENDIX Q: Conceptual Model
108 APPENDIX R: Bayesian Analyses Methodology
This study to conduct a confirmatory factor analysis utilized the Mplus statistical
program relying upon Bayesian precepts. Bayesian SEM analysis replaces parameter
specifications of exact zeros and equalities with approximate zeros and equalities based
on informative, small-variance priors. This approach can avoid unnecessarily strict
models and represent hypotheses derived from substantive theory by permitting cross-
loadings during the CFA analyses. The specified informative priors for the cross-loadings
in CFA are hypothesized not be influenced by certain factors. “A prior that probably
more accurately reflects substantive theory uses a mean of zero and a normal distribution
with small variance” (Muthén & Asparouhov, 2012: 13). This study applied a set of
cross-loadings λ ~ N(0, 0.05). The 95% of the loading variance is between -0.44 and
+0.44, which can be considered as small loadings, implying that the cross-loadings are close to zero, but not exactly zero. To evaluate the model, a low Posterior Predictive P-
Value (PPP) indicates poor fit, but a PPP around 0.5 indicates an excellent-fitting model;
this model returned a PPP value of 0.16, implying acceptable model fit. Table R1 (below)
indicates the resultant factor groupings for this analysis.
109 Table R1. Bayesian EFA Analysis
Loadings RADI EMERG QUAL RISK FREQ PRIOR CAUSE COMSC ZRAD29 0.711 -0.011 0.081 0.03 0.021 0.059 0.017 0.017 ZRAD30 0.671 0.005 -0.083 -0.053 0.046 0.02 -0.023 0.002 ZRAD31 0.782 0.084 0.056 0.009 0.004 0.035 0.094 0.064 ZRAD32 0.374 0.003 -0.03 0.045 -0.048 -0.053 -0.028 -0.032 ZFLEX18 0.058 0.681 0.006 -0.002 -0.015 0.079 -0.038 0.025 ZCAUS5 -0.006 0.692 -0.007 -0.007 -0.04 -0.059 0.029 0.005 ZQLINF24 0.014 -0.004 0.620* -0.03 0.153 0.099 0.012 -0.002 ZQLINF25 -0.015 0.003 0.861* 0.046 -0.056 0.006 0.022 0.029 ZQLINF26 0.04 0.034 0.826* -0.02 -0.008 -0.011 0.12 -0.027 ZRISK13 0.116 0.051 -0.017 0.823* 0.002 -0.052 -0.012 -0.049 ZRISK15 0.01 0.051 0.102 0.847* -0.011 0.045 0.057 -0.043 ZRISK16 -0.098 -0.088 -0.075 0.768* 0.025 0.035 0.028 0.068 ZQLINF27 0.029 0.013 0.05 -0.039 0.885 0.045 0.006 -0.021 ZQLINF28 0.004 -0.03 -0.01 0.042 0.821 0.009 0.006 0.007 ZPRCOMT22 0.099 0.049 0.024 -0.142 0.073 0.536 0.076 0.04 ZPRCOMT23 -0.013 -0.046 0.068 0.094 0.021 0.493 0.029 0.062 ZCAUS2 0.074 -0.025 0.081 -0.012 -0.037 0.055 0.542 0.079 ZCAUS3 0.011 0.005 0.189 0.089 -0.009 0.054 0.591 -0.019 ZCOMSC18 0.037 0.007 -0.012 0.033 0.067 0.004 0.02 0.707* ZCOMSC19 0.027 -0.018 0.01 -0.024 -0.049 0.075 0.046 0.765* ZCOMSC20 0.004 0.063 0.007 -0.059 -0.041 -0.004 -0.013 0.716* *Notes: RADI: Radicality; EMERG: Experimentation; CAUSE: Planning; RISK: Risk Tolerance; FREQ: TPOC Communication Frequency; PRIOR: Prior Commitments; QUAL: Quality of Information Exchanged
110 Table R2. Hypotheses Testing and Goodness of Fit Measures
Dependent Variable Commercial Radicality Experimentation Planning Success
Intercept -0.05 (0.06) -0.07 (0.05) 0.00 (0.06) 0.03 (0.04) Independent variables Radicality -0.38 (0.14)*** Experimentation -0.77 (0.2)*** -0.20 (0.16) Planning 1.39 (0.25)*** 1.27 (0.21)*** Experimentation * 0.00 (0.45) -0.03 (0.49) Planning Risk Tolerance 0.51 (0.08)*** -0.14 (0.08)* -0.15 (0.07)** TPOC Communication 0.37 (0.10)*** -0.18 (0.08)** -0.19 (0.06)*** Frequency Prior Commitments 0.36 (0.15)** 0.30 (0.13)** 0.06 (0.14) 0.22 (0.11)*
Control variables Common method bias 1.24 (0.22)*** 1.17 (0.15)*** 0.20 (0.12)* -0.42 (0.07)***
R-square 0.58 (0.08)*** 0.75 (0.05)*** 0.19 (0.11)* 0.42 (0.1)***
Goodness of Fit Measures χ2 (df) 0.91 (2) CFI/TLI 1.00/ 1.12 SRMR 0.01 RMSEA (90 percent C.I.) 0.00 (0.00, 0.22)
P-close 0.67
111 Table R3. Moderation and Mediation Analyses
Unstandardized P-Value: P-Value: Independent Variable S.E. Est./S.E. Estimate Two-tail Single-tail Mediating effect of Experimentation on Commercial Success RISK CAUSE = -1.5sd 0.096 0.089 1.079 0.281 0.141 CAUSE = 0 0.094 0.066 1.417 0.157 0.079 CAUSE = 1.5sd 0.092 0.093 0.988 0.323 0.162 FREQ CAUSE = -1.5sd 0.124 0.093 1.329 0.184 0.092 CAUSE = 0 0.122 0.072 1.707 0.088 0.044 CAUSE = 1.5sd 0.121 0.089 1.353 0.176 0.088 PRIOR CAUSE = -1.5sd -0.043 0.134 -0.322 0.747 0.374 CAUSE = 0 -0.042 0.11 -0.381 0.703 0.352 CAUSE = 1.5sd -0.04 0.119 -0.339 0.735 0.368 Mediating effect of Experimentation on Radicality RISK CAUSE = -1.5sd 0.023 0.059 0.386 0.7 0.350 CAUSE = 0 0.027 0.026 1.017 0.309 0.155 CAUSE = 1.5sd 0.031 0.063 0.489 0.625 0.313 FREQ CAUSE = -1.5sd 0.032 0.055 0.575 0.565 0.283 CAUSE = 0 0.035 0.031 1.119 0.263 0.132 CAUSE = 1.5sd 0.038 0.061 0.633 0.527 0.264 PRIOR CAUSE = -1.5sd -0.009 0.054 -0.17 0.865 0.433 CAUSE = 0 -0.012 0.033 -0.366 0.714 0.357 CAUSE = 1.5sd -0.015 0.069 -0.215 0.83 0.415 Mediating Effect of Radicality on the relationship between Experimentation and Commercial Success EMERG CAUSE = -1.5sd 0.059 0.273 0.217 0.828 0.414 CAUSE = 0 0.075 0.062 1.223 0.221 0.111 CAUSE = 1.5sd 0.091 0.29 0.315 0.753 0.377
112 Table R4. Mediating Effect Hypotheses Summary
Mediation Indirect effect b (SE) LLCI M1 Risk Tolerance --> Experimentation --> Radicality 0.03 (0.03) -0.01 Risk Tolerance --> Planning --> Radicality -0.19 (0.10)** -0.35 Risk Tolerance --> Radicality --> Commercial Success -0.19 (0.07)*** -0.31 M2 Risk Tolerance --> Experimentation --> Commercial Success 0.10 (0.07) -0.004 Risk Tolerance --> Planning --> Commercial Success -0.21 (0.11)** -0.39 Risk Tolerance --> Experimentation --> Radicality --> Commercial Success -0.01 (0.01) -0.03 Risk Tolerance --> Planning --> Radicality --> Commercial Success 0.07 (0.04)* 0.01 Total Risk Tolerance --> Commercial Success -0.24 (0.10)** -0.4 M5 TPOC Communication Frequency --> Experimentation --> Radicality 0.04 (0.03) -0.01 TPOC Communication Frequency --> Planning --> Radicality -0.24 (0.07)*** -0.36 TPOC Communication Frequency --> Radicality --> Commercial Success -0.14 (0.06)** -0.25 TPOC Communication Frequency --> Experimentation --> Commercial M6 0.14 (0.07)* 0.03 Success TPOC Communication Frequency --> Planning --> Commercial Success -0.26 (0.08)*** -0.4 TPOC Communication Frequency --> Experimentation --> Radicality --> -0.01 (0.01) -0.03 Commercial Success TPOC Communication Frequency --> Planning --> Radicality --> 0.09 (0.04)** 0.03 Commercial Success Total TPOC Communication Frequency --> Commercial Success -0.19 (0.10)* -0.35 M9 Prior Commitments --> Experimentation --> Radicality -0.01 (0.03) -0.05 Prior Commitments --> Planning --> Radicality 0.28 (0.13)** 0.05 Prior Commitments --> Radicality --> Commercial Success -0.11 (0.07)* -0.23 M10 Prior Commitments --> Experimentation --> Commercial Success -0.05 (0.12) -0.28 Prior Commitments --> Planning --> Commercial Success 0.31 (0.16)* 0.05 Prior Commitments --> Experimentation --> Radicality --> Commercial 0.01 (0.01) -0.02 Success Prior Commitments --> Planning --> Radicality --> Commercial Success -0.11 (0.06)* -0.21 Total TPOC Communication Frequency --> Commercial Success 0.05 (0.15) -0.22 M18 Experimentation --> Radicality --> Commercial Success 0.07 (0.06) -0.01 Planning --> Radicality --> Commercial Success -0.48 (0.18)*** -0.78
113 APPENDIX S: Proposed Mediation Analyses
Dependent Antecedent Mediator Predicted result Theoretical justification variable When Experimentation is controlled as a mediator, effect of Risk Tolerance on Radicality will decrease owing to the likelihood that firms which except higher levels of risk will be predisposed Partial positive toward Experimentation. Firms that tend to operate with greater M1 Risk Tolerance Experimentation Radicality mediation Risk Tolerance may undertake either more radical projects, or be comfortable pushing the project into a direction beyond its original intent; Experimentation bias would presumably be the mechanism by which a firm would reach a more radical outcome When Experimentation is controlled as a mediator, the effect of Risk Tolerance on Commercial Success will decrease. Per EO-as- Experimentation, Experimentation can be associated with both Commercial Partial positive higher levels of failure and higher levels of success in an M2 Risk Tolerance Experimentation Success mediation innovation environment. As such, Risk Tolerance would be expected to have a negative effect upon Commercial Success which would be decreased by the positive action of Experimentation as a mediator. When controlled for as a mediator, Planning will decrease the Partial positive positive effect of Risk Tolerance upon Radicality; firms with M3 Risk Tolerance Planning Radicality mediation higher risk thresholds would presumably attempt more radical projects and be less inclined to follow a prescriptive path. When Planning is controlled for as a mediator, the negative path Commercial Partial negative effect of Risk Tolerance upon Commercial Success will increase. M4 Risk Tolerance Planning Success mediation Planning would presumably partially mitigate the negative effect of Risk Tolerance on Commercial Success. As a mediator, Experimentation will decrease the negative effect of TPOC communication upon Radicality; stronger TPOC communication will have a dampening effect on the radical Partial positive nature of a project’s outcome under the presumption that a firm M5 TPOC Communication Experimentation Radicality mediation would be more inclined to follow the TPOC/issuer objectives will reinforced through higher/consistent/continual contact. Experimentation would thereby reduce the effectiveness of ongoing TPOC contact.
114
Experimentation will increase the positive effect of TPOC Communication on Commercial Success; per EOE, Experimentation can result in positive and negative outcomes-in Commercial Partial positive this case I assume greater TPOC communication in the presence M6 TPOC Communication Experimentation Success mediation of Experimentation would increase Commercial Success because experimental project development may be either more tightly focused per issuer objectives-which may include Commercial Success. When controlling for Planning as a mediator, the negative effect Partial negative of TPOC communication on Radicality will increase; firms that M7 TPOC Communication Planning Radicality mediation tended to follow more proscriptive paths would be less likely to create radical outcomes. When controlling for Planning as a mediator, the positive effect Commercial Partial positive of TPOC communication upon Commercial Success will decrease M8 TPOC Communication Planning Success mediation due to the greater connection with issuer objectives which would presumably also include Commercial Success. When controlling for Experimentation as a mediator, the positive effect of Prior Commitments upon Radicality will decrease; Per Partial positive M9 Prior Commitments Experimentation Radicality EOE theory, firms that react to market opportunities, perhaps mediation reflected as Prior Commitments, would proactively experiment in create a more divergent project outcome. When controlling for Experimentation, the effect of Prior Commitments upon Commercial Success will decrease, indicating Experimentation’s positive role in a firm’s inclination to use Commercial Partial positive M 10 Prior Commitments Experimentation relationships to drive Commercial Success. Under this view, firms Success mediation would harness EOE by selecting market opportunities they could presumably act upon through greater trial and error to find greater Commercial Success. When controlling for Planning, the positive effect of Prior Partial negative Commitments upon Radicality will increase, according to the view M 11 Prior Commitments Planning Radicality mediation that EOE would enable greater diversion outcomes while Planning result in a more proscribed outcome. When controlling for Planning, the positive effect of Prior Commitments upon Commercial Success will decrease; similarly, Commercial Partial positive M 12 Prior Commitments Planning to M11, EOE application would suggest that a firm would also Success mediation actively engage in Planning activities based upon Prior Commitments that would create Commercial Success
115
When controlling for the effects of Experimentation, Quality of Partial positive M 13 Quality of Information Experimentation Radicality Information’s effect upon Radicality will decrease, supporting mediation EOE as a mechanism by which firms can increase variance. When controlling for the Experimentation, the effect of Quality of Commercial Partial positive Information upon Commercial Success will decrease, again M 14 Quality of Information Experimentation Success mediation supporting our conception of EOE as able to both create economic value and variance. Experimentation positively will moderate the mediation capacity of Radicality to decrease the influence of Quality of Information upon Commercial Success; Experimentation is a pervasive Commercial Positive moderated attitude and the ability of a firm to inculcate information to M 15 Quality of Information Radicality/Experimentation Success mediation create an exceptional [radical] outcome that is commercially successful should be influenced by it at many different points. EOE would therefore be supported by an overarching diffuse effect of Experimentation. [See figure below]
When controlling for Planning, the effect of Quality of Partial negative M-16 Quality of Information Planning Radicality Information on Radicality will increase, owing to the suppressive mediation effect of Planning upon an expected outcome. When controlled for Planning, the effect of Quality of Information Commercial Partial positive on Commercial Success will increase, presumably because, given M 17 Quality of Information Planning Success mediation history of SBIR projects, Commercial Success is often not achieved by adhering to issuer When controlled for Radicality, the negative effect of Commercial Partial negative M 18 Experimentation Radicality Experimentation upon Commercial Success will increase per the Success mediation conception of EOE that EOE increases variance
116
When controlled for Radicality, the positive effect of Planning on Commercial Partial negative M 19 Planning Radicality Commercial Success will increase, per the conception EOE as a Success mediation mechanism that can increase variance Experimentation, when controlled as a mediator, will decrease the effect of Radicality upon Commercial Success; per EOE, Experimentation can also act as a double-edged sword-this would Commercial Partial positive M 20 Radicality Experimentation be an indicator of unintentional radical outcome, fostered Success mediation through Experimentation, that would decrease the chance Commercial Success. Compare with Experimentation as a moderator, above. As a mediator, Planning will increase the effect of Radicality upon Commercial Partial negative Commercial Success; active Planning would improve the capacity M 21 Radicality Planning Success mediation of the firm to proactively seek a radical outcome when the intent was Commercial Success.
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