https://doi.org/10.1177/0266242617741534

International Small Business Journal: Researching Entrepreneurship

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Small Firmsisbj

Founding logics, technology validation, and the path to commercialization

Michael P CiuchtaUniversity of Massachusetts Lowell, USA

Anne S MinerUniversity of Wisconsin–Madison, USA

June-Young KimUniversity of Illinois at Urbana–Champaign, USA

Jay O’TooleGeorgia State University, USA

AbstractConsiderable research has demonstrated that small- and medium-sized enterprises (SMEs) who obtain institutionalized third-party endorsements experience higher performance. In this study, we develop an important boundary condition around this process. Drawing on institutional logics, we introduce the novel concept of founding logics. We then develop and test a theory in which founding logics play a role in both an SME’s decision to seek a third-party endorsement for the firm’s technology and then the likelihood that the SME will generate revenues based on the technology receiving the endorsement. Notably, our theory and results suggest that a founding logic that may compel an SME to seek technology validation can also impede the SME’s ultimate commercialization ability.

Keywordsentrepreneurship, imprinting, institutional logics, third-party endorsements

Corresponding author:Michael P Ciuchta, Department of Marketing, Entrepreneurship & Innovation, Manning School of Business, University of Massachusetts Lowell, Lowell, MA 01856, USA. Email: Michael_Ciuchta@uml.edu

741534 ISB0010.1177/0266242617741534International Small Business JournalCiuchta et al.research-article2017

Full Paper

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Small- and medium-sized enterprises (SMEs) bringing a new technology to market face consider-able obstacles (Eisenhardt and Schoonhoven, 1990; Gans and Stern, 2003). The lack of resources and the reputations of larger competitors put these SMEs at a technological and operational disad-vantage (Choi and Shephard, 2005). One way technology-based SMEs can overcome this disad-vantage is by obtaining credible third-party endorsements for their technology.1 Formal endorsements convey positive attributes that might otherwise be hidden to those seeking informa-tion about the focal firm (Djupdal and Westhead, 2013; Lounsbury and Glynn, 2001). Technology validation is a special type of endorsement that provides guidance regarding the validity of the technology being developed (Natsheh et al., 2015). In turn, this guidance can greatly facilitate the future development of SMEs (Djupdal and Westhead, 2013; Takalo and Tanayama, 2010). In this article, we explore boundary conditions that can alter the impact of these external endorsements on an SME’s development trajectory.

We draw on theories of institutional logics (Thornton et al., 2012) to develop a framework that suggests that a firm’s founding logic influences both the likelihood of pursuing a particular type of endorsement (in this case, technology validation) and responses to those endorsements. In develop-ing our framework, we introduce the concept of a founding logic, which we define as the values, beliefs, and motivations around which a focal firm was founded (Agle et al., 1999; Albert and Whetten, 1985; Biggart, 1991; Gruber and Fauchart, 2011). The founding logic focuses on the original purposes and values associated with the firm’s creation such logics draw from the more broad-based institutional logics, which are the overall pattern of beliefs, values, and practices that shape what is meaningful and legitimate to actors in a given context (Thornton and Ocasio, 1999). We focus on a context in which firms are founded under multiple institutional logics (Dufays and Huybrechts, 2016; Kraatz and Block, 2008), and in particular one in which those logics are at least partially in opposition (Almandoz, 2014; Pache and Santos, 2013).

Specifically, we introduce two types of founding logics: business-based founding logics, which are associated with commercial enterprises; and science-based founding logics, which are associ-ated with scientific endeavors. We show that these founding logics impact upon an SME’s likeli-hood of pursuing a technology validation endorsement. Furthermore, if the SME receives at least one technology validation endorsement, founding logics moderate the impact of additional endorsements on technology commercialization, defined as the generation of sales based on the technology (Schoonhoven et al., 1990).

This article makes several major contributions. First, our study adds to the debate regarding whether SMEs pursue third-party endorsements for external (Sun and Cheng, 2002) or internal (Heras-Saizarbitoria and Boiral, 2013) reasons. Our founding logics perspective suggests these need not be opposing rationales and can be encapsulated under a single founding logic. Next, we contribute to the understanding of the impact of third-party endorsements on SME performance. Generally, receiving endorsements benefits SME performance (Djupdal and Westhead, 2013), but we develop an important boundary condition for this process. Moreover, we find that these endorsements create an ‘adverse hazard’, which combines elements of adverse selection and moral hazard (Amit et al., 1998; Bruton et al., 2009). Said differently, logics influence both ex-ante selection into seeking endorsements, as well as an ex-post behavior change that results from receiving the endorsements. Next, we advance recent research that has suggested institutional logics can be imprinted at founding (Dufays and Huybrechts, 2016). We explicate a specific underexplored mechanism by which the founding elements can be replicated over time (Johnson, 2007). That is, founding logics exert their influence over time through their continued influence on how firms respond to receiving technology validation endorsements. Additionally, by establishing and directly assessing two potential founding logics, this study advances research on institutional logics in a unique way. Rather than using founder experience as a proxy for the

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logics brought to their ventures (Almandoz, 2014; Scarlata et al., 2015), we identify the actual underlying logics associated with founding a focal firm. Finally, the study has implications for management practice in a technological context. Prior research has indicated that engaging scien-tists in business activities can both help (Agrawal, 2006; Zucker et al., 2002) and harm (Durand et al., 2008; Gittelman and Kogut, 2003) commercial outcomes for technology-based SMEs. We suggest that actors from a similar institutional class, for example, scientists, may enact different underlying founding logics which will have an impact upon the firm’s commercial outcomes. This finding has important practical implications for both entrepreneurial firms and nationally spon-sored technology validation programmes.

Literature review

Third-party endorsements

Third-party endorsements play a pivotal role in the success and survivability of organizations (Lounsbury and Glynn, 2001). Given the uncertainty that can surround technology-based SMEs, these endorsements provide institutionalized cultural capital that lends legitimacy to these firms (De Clercq and Voronov, 2009) and may also provide specific guideposts around which organiza-tions can align actions (Singh et al., 2011). We focus on institutionalized, external third-party endorsements that serve as certifications regarding the recipient’s standing on quality or perfor-mance dimensions (Wade et al., 2006). Research has shown that certifications can be beneficial to the receiver’s performance. For example, Djupdal and Westhead (2013) studied firms participating in the Eco-Lighthouse Certification sustainable entrepreneurship programme in Norway. The authors found that small firms achieved higher performance if they earned certification. Similarly, in a study of the emergence of the independent power sector, Sine et al. (2007) found that firms that obtained certification from the United States Federal Regulatory Energy Commission reached operational status more quickly than those that did not become certified.

Recent research has started to examine boundary conditions regarding the relationship between certification and performance. In his study of home health agencies, Desai (2016) found that the performance benefits of receiving external certification was conditional on the number of quality problems at the recipient organization, as well as the number of other entities receiving certifica-tion. In our research, we advance a contingency approach by focusing on the impacts of a type of third-party endorsement known as a ‘selective support scheme’ (Colombo et al., 2012). In selective support schemes, endorsements are provided on a competitive basis and only after submitted pro-posals are evaluated carefully (Colombo et al., 2012). Given these awards are based on selective screening criteria, they can provide validation. In our case, the criteria involve the proposal’s tech-nical merits; therefore, the endorsement conveys technology validation. This technology validation can be especially critical for the success of technology-based SMEs (Natsheh et al., 2015; Takalo and Tanayama, 2010). In developing our theoretical framework, which explicates causal processes by which SMEs first apply for, and then respond to, technology validations, we draw from insights on institutional theory.

Institutional logics

Institutional logics are patterns of beliefs, values, and practices that shape what is meaningful and legitimate to actors in a given context (Thornton and Ocasio, 1999). As such, logics are overarching principles that provide actors with guidelines to interpret and respond to reality (Greenwood et al., 2011; Thornton, 2004). The logics perspective assumes that actors are embedded in social, cultural,

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and political structures and are guided by cognitively bounded intentions and goals (Thornton et al., 2012: 80). More generally, the framework suggests that logics provide specific mechanisms by which organizational members focus their attention and establish goals (Thornton et al., 2012).

Logics can play an especially important role in new firms by providing cognitive models and other cultural material that guides formation and early development (Gruber and Fauchart, 2011). These logics can then become imprinted in the organization, reflecting the early intentions of the founders and other key stakeholders (Dufays and Huybrechts, 2016; Johnson, 2007; Powell and Colyvas, 2008). Ultimately, logics become rooted in organizational stories, practices, and artifacts (Lounsbury and Glynn, 2001).

In this article, we introduce founding logics as a special type of imprinted institutional logic. We define founding logics as the central and core values, norms, and practices around which the firm was founded (Albert and Whetten, 1985; Gruber and Fauchart, 2011). Founding logics refer to the mission, values, and hoped-for outcomes related to the firm itself. They may incorporate specific employment relationships or business models (Beckman et al., 2007; Burton and Beckman, 2007). In other words, while institutional logics provide founders, employees, and stakeholders with the broad scripts for organizational life and interactions with the environment, founding logics, more specifically, provide the overarching rationale regarding why the organization was created. They refine the broad potential scripts that prevailing institutional logics provide into more focused ori-entations toward the goals and purpose of the focal organization.

To clarify the distinction between institutional and founding logics, consider the example of a bank start-up. In creating the bank, the particular founders will rely on broad-based institutional logics, including the cultural symbols and material practices associated with a bank in general. The founding logics will be those elements from the broad institutional logics associated with banking that they draw upon when founding their particular bank, such as we are a bank founded to serve our community, or we are a bank founded to generate the highest possible returns to our investors.

The degree to which any single firm draws on prevailing institutional spheres will influence its specific founding logics (Brickson, 2005; Lee and Battilana, 2014). We acknowledge that this perspective assumes significant agency for the actor (Battilana, 2006). Because founding logics are specifically rooted in the founding of the organization, they are distinct from the institutional logics to which an organization adheres to over time that may change, such as the case of mission drift among socially oriented ventures (Ebrahim et al., 2014).

Similar to the broader institutional environment from which they are derived, founding logics can be based on various and potentially competing underlying logics. At the institutional level, this is known as institutional pluralism (Dufays and Huybrechts, 2016), which is defined as situations in which firms operate in multiple institutional spheres (Kraatz and Block, 2008: 243). Often, these multiple institutional spheres are considered to be in conflict (Almandoz, 2012; Pache and Santos, 2013; Wry and York, 2017). Examples include: trustee versus performance logics among mutual fund companies (Lounsbury, 2007), commercial versus social logics among venture capital firms (Scarlata et al., 2015), financial versus community logics among commercial banks (Almandoz, 2012; Almandoz, 2014), profit versus non-profit logics among microfinance organizations (Shahriar et al., 2016), and social versus economic logics among social enterprises (Moss et al., 2011; Stevens et al., 2015). We now turn to the broad institutional logics we use to classify indi-vidual organizations to their specific founding logics.

Business logics and science logics

Observers have argued that business and science are governed by different ethos (Merton, 1957). Business-oriented logics include a strong focus on achieving profitability or other financial-related

Ciuchta et al. 5

outcomes such as valuation (Battilana and Dorado, 2010; De Clercq and Voronov, 2011), as well as attention on the marketing needed to attract and maintain customers (Thornton and Ocasio, 1999). Science-oriented logics, on the other hand, focus on scientific norms and practices, includ-ing experimentation and developing and sharing new knowledge (Dasgupta and David, 1994; Fini and Toschi, 2016). Put succinctly, ‘science is concerned with producing knowledge while business is concerned with producing profit’ (Zabusky and Barley, 1997: 362). Indeed, science-oriented firms are ‘in business to do science’ (Powell and Sandholtz, 2012: 104), whereas business-oriented firms are ‘in science to do business’ (Powell and Sandholtz, 2012: 107).

Substantial empirical evidence shows that these two types of firm logics persist over time and between organizations (Dasgupta and David, 1994; Dubinskas, 1985; Shinn and Lamy, 2006). Toole and Czarnitzki (2009) posited that academic scientists who pursue ‘academic goals’ develop particular skills that may not translate into a commercial realm, and industrial scientists who pur-sue ‘industrial goals’ may be ill-suited for advancing scientific knowledge in an academic research environment. In a study comparing academic and non-academic entrepreneurs, Fini and Toschi (2016) found that academic entrepreneurs leveraged their awareness of technical competencies more than their non-academic counterparts and their entrepreneurial self-efficacy and managerial skills less. Overall, this brief review highlights how the broad presence of these alternative logics offers a menu from which a start-up can choose elements of its founding logics. For our hypothe-ses, it is important to note that we examine the impact of each type of logic independently, rather than construing some kind of hybrid or dominant logic within the organization (Brickson, 2005).

Hypotheses

Founding logics and the pursuit of technology validation

Given our assertions that founding teams can be influenced by prevailing science and business logics and a founding logic can have lasting firm effects, we turn to hypothesizing how such found-ing logics influence the firm’s decision to seek technology validation for one of its technologies. Firms founded on business logics institutionalize norms and practices associated with rationalized firm processes such as financial planning and budgets (Hwang and Powell, 2009; Sanders and Tuschke, 2007). Following a logic of business, pursuing technology validation is seen as an instru-mental action that signals key stakeholders and resource providers (Connelly et al., 2011). This signal can be particularly critical for firms pursuing a high-growth strategy (Colombo and Grilli, 2005).

Although we argue that firms founded on business logics will pursue technology validation, we expect firms founded on science logics to have an even higher likelihood of pursuing technology validation for several reasons. First, the logic of appropriateness (March, 1994) asserts that organi-zations pursue actions based on that action’s appropriateness in the given situation and the firm’s identity. Thus, there is an underlying science logic that points to pursuing technology validation as something that is ‘correct’ or consistent with the firm’s underlying identity as embodied in the founding logic. Second, along with this intrinsically motivated rationale, firms with science-based founding logics will draw upon an instrumental rationale, given the prestige that receiving technol-ogy validations can bestow upon recipients (Burris, 2004). Technology validation can give the firm reputational status in the world of science, regardless of financial or investor impact. Finally, firms with a founding logic of science are more likely to develop individuals who personally identify with the underlying technology (George and Bock, 2008). Even as they engage in commercial activities, academic scientists often preserve their underlying scientific role identities (Jain et al., 2009). One way they can accomplish this goal is by identifying closely with the technology.

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Validation of the technology corroborates the images scientists have of themselves and their firm. Thus, we hypothesize,

Hypothesis 1. SMEs with science-based founding logics will be more likely than firms with business-based founding logics to seek a technology validation endorsement.

Founding logics, technology validation endorsements, and commercialization

We now turn to the impact of third-party endorsements on commercialization of the underlying technology. We define commercialization as the process of developing and then selling a technology in a market (Kirchberger and Pohl, 2016; Mitchell and Singh, 1996). Although commercialization is a process, by technology commercialization, we refer specifically to generating sales in the market based on the underlying technology (Schoonhoven et al., 1990). In this way, technology commercialization represents a specific point in the process of moving from invention or idea inception to a viable commercial product or service that has been known as crossing the ‘Valley of Death’ (Markham, 2002).

In this article, we emphasize the role of founding logics in this process through their influence on the firm’s noticing, interpreting, and responding to the technology validation endorsements. Thus, rather than depicting endorsements as objective, unambiguous signals of quality for exter-nal constituents, the theory we develop emphasizes the subjective meaning of endorsements to their recipients. The subjective meaning of endorsements, we argue, depends on the specific found-ing logic of the firm (Baron et al., 1999; Besharov and Smith, 2014; Shahriar et al., 2016), which in turn influences the commercialization path of the firm.

First, underlying founding logics will influence what is most salient about the endorsement (Hoffman and Ocasio, 2001; Taylor and Fiske, 1975). For firms founded on business logics, salient aspects of the endorsements include their potential to be instrumental in reaching firm objectives, such as attracting customers or generating profits (De Clercq and Voronov, 2011). For firms founded on science logics, salient aspects of the endorsements are more likely to emphasize the technology’s technical aspects and contributions to creating knowledge (Golish et al., 2008).

In addition to influencing the salience of the endorsements themselves, founding logics will also influence how those endorsements are interpreted by the receiving firm and, given that interpreta-tion, how resources are deployed. For example, interpretations according to business logics should indicate that the firm has strong business capabilities, which could be applied to at least one com-mercially viable technology. Indeed, the business logic will facilitate the development of what are known as substantive growth capabilities (Koryak et al., 2015). Firms founded around business logics will develop capabilities and knowledge related to market details, product design, customer interests, and production for market sales (Danneels, 2002; Dougherty, 1990). Receiving more validation provides additional opportunities to deploy knowledge to technologies with the most perceived potential presenting an enhanced ability to leverage experience gained in a given tech-nology to other projects with revenue-producing potential.

Science-based founding logics, in contrast, are likely to lead to interpretations rooted in the context of science-based values and norms. A science-based logic nurtures and is hospitable to scientists who are intrinsically motivated by the puzzle-solving nature of science (Mallon et al., 2005; Stephan and Levin, 1992). Therefore, technology validation will be interpreted as an indication that the firm has created an appropriate means to engage in exploratory scientific activities. In turn, these firms are likely to deploy resources favoring future science activities over commercial endeavors (Toole and Czarnitzki, 2009). For example, rather than building stronger capabilities in

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market research and analysis (Danneels, 2002; Maurer and Ebers, 2006), such firms will be more inclined to focus on developing scientific depth, such as communicating within science networks (Baker et al., 2003), developing or refining research instruments, and more deeply probing scientific puzzles as they pursue additional research opportunities. Technology validation may encourage these firms to disseminate their knowledge as scientific research in academic publications and presentations, which further diverts their attention from commercial endeavors. Therefore, we hypothesize,

Hypothesis 2. The effect of technology validation on the likelihood of technology commerciali-zation is less positive for SMEs with science-based founding logics than it is for SMEs with business-based founding logics.

Methods

Research context: Small Business Innovation Research programme

Enacted in 1982 in the United States, the Small Business Innovation Research (SBIR) programme is designed to support small firm commercialization and innovation (Lerner, 1999). Eligibility is restricted to firms with fewer than 500 employees. Under this public programme, each federal agency with an extramural budget for research and development that exceeds US$100 million must allocate a certain percentage to fund SBIR. Currently, 11 federal agencies participate in the programme.

SBIR is a multi-phase program. In this study, we focus on Phase I, a feasibility stage with awards of up to US$100,000. As noted in the SBIR programme policy directive (SBIR, 2002), the focus in this phase is on prototyping or developing proof of concept, but firms must also address the potential for technology commercialization and how the technology would ultimately lead to generating revenue. Depending on the agency to which a firm applies, proposals are either reviewed in-house or sent for peer review. As Wallsten (2000) noted, the regulatory guidelines do not encour-age SBIR programme managers to fund marginal projects (i.e. those with the most need). Rather, the guidelines suggest managers should use selection criteria commensurate with criteria private investors use.

The SBIR programme offers an unusually powerful setting to use as a proxy for technology validation. According to the SBIR’s website,

The objective of Phase I is to establish the technical merit, feasibility, and commercial potential of the proposed R/R&D efforts and to determine the quality of performance of the small business awardee firm prior to providing further Federal support in Phase II. (http://www.sbir.gov/about/about-sbir; downloaded 5 July 2013)

Thus, establishing and validating technical merit is a key objective of the programme.

Sample

To test our first hypothesis, we needed to identify firms before they applied for SBIR. We could not rely on the SBIR database to choose our sample because this database only contains firms that have won SBIR grants (the U.S. Small Business Administration (SBA) does not publish the list of all applicant firms). Therefore, to construct our sample, we used several sources to compile a list of as many SMEs in the area that could potentially apply for an SBIR grant. We focused on firms located around a large research-based university in the Midwest of the United States. Therefore, we relied

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on the university’s technology transfer office and the university’s office of corporate relations for lists of university spin-outs. We also acquired a list of the tenants of the university’s research park. In addition, we also used a local utility directory with a list of start-ups in the area. Finally, we sup-plemented this search with information from Dun and Bradstreet. From these sources, we were able to generate a list of 421 firms for which we had valid contact information. From those, we obtained a sample of 142 firms with complete information to test the first hypothesis. To test the second hypothesis, we used a subsample from the first sample, consisting of those 63 firms that won at least one SBIR award.

We piloted our survey instrument with experts and then distributed an Internet survey to can-didate firms. Firms that did not respond were contacted in a second wave of survey collection efforts. In total, 421 firms were contacted, which yielded 144 usable surveys, representing a response rate of 34.9%. This rate is in line with others in firm survey research (Baruch and Holton, 2008). We tested for the presence of non-response bias by comparing survey responses from those in the first wave to those in the second wave, assuming that later responders more closely resembled non-responders (Armstrong and Overton, 1977). This analysis resulted in no statistically different means (p > .05). We also compared respondents to non-respondents on vari-ables contained in archival data (Rogelberg and Stanton, 2007). This analysis suggested that respondents won more SBIR awards, were younger, and more likely to have licensed their tech-nology. Because these mean differences were statistically significant, we analysed the sensitivity of our reported findings using weighting class adjustment (Lohr, 1999). Results reported were robust to this sensitivity test.

We relied on the survey responses of a single respondent from each firm. Two key factors miti-gate potential bias from this strategy. First, prior to beginning the survey, respondents were informed that questions would pertain to founding processes of their firm and could by-pass any questions they were unprepared to answer. Second, these firms were very small when they were established, averaging 2.4 founders and 2.1 full-time equivalent employees at the date of establishment.

To check for the potential presence of common methods bias, we conducted a Harman single-factor test (Podsakoff et al., 2003) and found that the largest factor explained 57% of the variance. To investigate the possible presence of common methods bias further, we estimated a measure-ment/structural model of the key variables and then added an uncorrelated method factor (Williams et al., 1989). The fit statistic comparison indicated that including the uncorrelated method factor failed to provide a better fitting model, suggesting common methods bias is unlikely. The concrete nature of our dependent variables also mitigates the presence of common methods bias (Doty and Glick, 1998: 380).

Although it is common to use responses from key informants to obtain organization-level meas-ures (Stevens et al., 2015), we conducted sensitivity analysis to investigate the potential impact of the respondent’s role within the firm. We determined whether respondents were founders in 142 of the 144 usable surveys. Of those 142, 118 (83%) were part of the original founding team. To inves-tigate potential bias, we conducted two sets of analyses. First, we conducted t-tests on the responses of founders and non-founders on our key logics variables and found no statistically significant dif-ference. Second, we repeated our analyses and included a dummy variable for founder respondents. This variable was not statistically significant, and the results were robust to those presented here.

Measures

Dependent variable: apply to SBIR. We created a dichotomous variable that takes the value of 1 if the firm ever applied for an SBIR grant, 0 otherwise, to test our first hypothesis. We captured this

Ciuchta et al. 9

information through a series of survey questions that asked respondents about their firm’s history with SBIR.

Dependent variable: technology-generated sale. Because our dependent variable for the second hypothesis indicates a specific point in time in which the firm generates revenues based on the technology, we recorded the dependent variable as a series of binary outcomes denoting whether the firm generated revenues from the technology during the observation period. We recorded a 1 if the firm generated sales in that period, 0 otherwise. Although our dependent variable is measured by a sequence of 0s and 1s, the information this sequence conveyed is equivalent to that conveyed by the actual duration of time (Muñoz-Bullón and Cueto, 2011: 86)

Technology validation (SBIR awards). We measured technology validation by creating a rolling sum of the number of Phase 1 SBIR awards a firm won, updated annually. The primary data source for this variable was the SBA’s TECH-Net online searchable database (http://web.sba.gov/tech-net/docrootpages/index2.cfm), which has been used in previous academic research (e.g. Toole and Czarnitzki, 2009). For the analysis, we lagged this variable by one year.

Founding logics. The literature has indicated that certain values can be imprinted (Baron et al., 1999; Johnson, 2007), but we specifically wanted to assess the founding logic in terms of the organizational purpose as seen during the founding process. We also sought a measure that is not inferred simply from the prior experiences of the founders. Therefore, we constructed our founding logics measures using a new single, eight item survey question that asked respondents to evaluate factors that influenced the founding of their firm (see Appendix 1). The items correspond to reasons founders often give for starting their firms (Carter et al., 2003; Jain et al., 2009). We constructed two theoretically driven additive indexes for each founding logic from these items (Law et al., 1998). The founding logics represent formative scales in which the presence of theoretically defined items indicate membership in a class, but do not necessarily correlate with each other (Law et al., 1998).

To double-check our classification of the indicators into the two indexes we created, we conducted an exploratory factor analysis that did not constrain the indicators. In this analysis, two factors emerged with eigenvalues greater than one. We present the factor loadings from a varimax rotated solution in Table 1. To ease interpretation, we also present the solution with just the loadings indicated with absolute values greater than 0.3. As shown in the table, the indicators load on the factors, as we suggest, and there are no meaningful cross-loadings. These results support our approach.

Business founding logic. We used the average of three of the eight survey items to assess business founding logic: firm wealth creation, personal income, and business prestige. For all items, individuals responded on a seven-point Likert scale anchored by 1 = not at all important to 7 = extremely important. The mean for this variable is 4.0 in the full sample and 3.8 in the subsample.

Science founding logic. We measured this variable by averaging five items: fund own lab/students, shape the direction of science, scientific prestige, want technology/product in use by many peo-ple, and social welfare, for example, cure AIDS. For all items, individuals responded on a seven point Likert scale anchored by 1 = not at all important to 7 = extremely important. The mean for this variable is 3.7 in the full sample and 4.2 in the subsample.

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Control variables

To control for industry norms and technology constraints on the likelihood of commercialization (e.g. Libaers and Meyer, 2011), we created dummy variables for the following industry sectors: life sciences (NAICS 3254) and research and development (NAICS 5417). We obtained these data from the National Establishment Time-Series (NETS) (Walls, 2010) and LexisNexis databases, as well as a local utility directory. For the 13 companies for which we were unable to obtain NAICS codes, we had three doctoral students assign NAICS codes based on an open-ended industry description that the respondents provided in the survey.

SBIR awards provide funding (Shane, 2004) that may affect technology commercialization. Therefore, we used the dummy variable angel funding to control for potential financial motiva-tions, as well as the potential strength of the opportunity as seen by outside investors. Because intellectual property rights can play an important role in competitive positioning and strategy (Gans et al., 2008), we included a dummy variable for firms that formally licensed technology from the university’s Technology Transfer Office as part of the founding process.

Formal education provides would-be entrepreneurs with both specific, relevant knowledge and a social network that can be either beneficial or detrimental in starting a business and generating sales (Helfat and Peteraf, 2003). Furthermore, these qualities may be separate from the logics cre-ated in their new firms. To capture resource issues associated with team size and the impact of formal education on the likelihood of commercialization, we constructed two variables: (1) found-ers with business degrees, which represents the count of founding team members that have an undergraduate or graduate degree in business or economics and (2) founders with science degrees that counts the number of founders with advanced science degrees. We created start-up founders by counting the number of founders with prior start-up experience.

Table 1. Factor loadings.

Indicator Factor 1 Factor 2 Uniqueness

Complete loadings

Shape direction of science 0.78 −0.12 0.38Social welfare, for example, cure AIDS 0.58 −0.01 0.66Personal income −0.21 0.62 0.57Fund own lab/students 0.31 0.10 0.89Scientific prestige 0.70 0.10 0.50Firm wealth creation −0.02 0.62 0.61Want technology/product in use by many people 0.50 −0.02 0.75Business prestige 0.19 0.57 0.63

Loadings |abs| > .3

Shape direction of science 0.78 0.38Social welfare, for example, cure AIDS 0.58 0.66Personal income 0.62 0.57Fund own lab/students 0.31 0.89Scientific prestige 0.70 0.50Firm wealth creation 0.62 0.61Want technology/product in use by many people 0.50 0.75Business prestige 0.57 0.63

Ciuchta et al. 11

In the models with technology-generated sale as the dependent variable, we included several SBIR-related controls. We included SBIR proposal success, which measured the approximate per-centage of the firm’s SBIR proposals that resulted in an award. The survey provided 10 percentage point increments (e.g. less than 10% and 10%–19%) and asked the respondents to indicate the appropriate category that captured their firm’s overall success rate on submitted SBIR proposals. DOD is a dummy variable if any of the firm’s SBIR awards were granted by the U.S. Department of Defense, which has a contracting mechanism unlike the other agencies. We also controlled for how old the firm was when it won its first SBIR award (age at first SBIR).

Analysis

The dependent variable for our first hypothesis is a binary outcome: apply to SBIR. Therefore, we used logistic regression for our analysis. The dependent variable for our second hypothesis is also a binary outcome (if the firm generates sales from an SBIR technology), but a time element is also associated with the outcome. We estimated the probability that a firm would generate sales in the next period, given the firm had not yet generated sales up to and including the current period. In other words, each firm was at risk of generating sales from an SBIR technology beginning when the firm won its first award. To analyse the data, we used a discrete-time hazard model (Allison, 1995), which is a common approach for examining entrepreneurial transitions (Muñoz-Bullón and Cueto, 2011; Nanda and Sorensen, 2010; Wilson et al., 2013). An event history framework has advantages over other approaches (such as ordinary least squares regression) because it uses infor-mation provided by subjects that do not experience an event and those that do (Singer and Willett, 2003). In event history specifications, the hazard is an unobservable variable that controls both the timing and occurrence of events and is the fundamental dependent variable in these models (Allison, 2014).

In the discrete-time model, let Pit be the conditional probability that firm i has generated sales in year t, given that the firm has not already generated sales. The model indicates that Pit is related to the covariates by a logistic regression equation

logP

Px xit

itt it k itk1 1 1−

= ++ +α ββ

where t = 1, 2, 3, …We control for a time main effect by including the variable period, which relates the logit hazard

linearly to time (Singer and Willett, 2003).

Results

Descriptive statistics and bivariate correlations

Table 2 presents summary statistics and bivariate correlations of all the variables included in the statistical models. Panel A contains summary statistics and correlations of the variables used to test our first hypothesis (144 firms). Panel B contains summary statistics and variables used to test our second hypothesis (63 firms and 379 firm-periods). Of the 144 firms in the full sample, 85 (59%) applied for an SBIR award. Of those that applied, 63 (74%) won an award, and of those, 29 (46%) generated sales from the SBIR technology. The median waiting time of firms that generated sales

12 International Small Business Journal: Researching Entrepreneurship 00(0)T

able

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Sum

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Age

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Ciuchta et al. 13

was three years, with a mean of 3.2 years. These results are consistent with other empirical findings on time to commercialization (Schoonhoven et al., 1990).

The correlation matrix indicates only moderate correlations between any of the variables, reduc-ing concerns about collinearity. We also calculated variance inflation factors (VIFs) for each of the variables (Greene, 2003). Each variable had a VIF under 10, the level that typically indicates seri-ous multicollinearity (Cohen et al., 2003).

Model results

Table 3 shows the results of our logistic regression analysis to test our first hypothesis. Model 1 includes control variables and Model 2 adds the theory variables Business founding logic and Science founding logic. Hypothesis 1 predicted that firms founded around science logics would be more likely to apply for SBIR than would firms founded around business logics. As shown in the table, the coefficient for science logic is positive (β = .61, p < .01). The coefficient on business logic is not statistically significant. To formally test our hypothesis, we calculated a z-statistic of the coefficient difference (Clogg et al., 1995; Paternoster et al., 1998). The results of this test indicate we can reject the null hypothesis that the coefficients are equal (z = −3.02, p < .01). To provide additional insights into interpreting these results, we calculated predicted probabilities of applying for SBIR at high and low levels of Science founding logic. At a value of one standard deviation below the mean value of Science founding logic, the predicted probability of applying for an SBIR award (holding all other variables at their median) is 25%. This increases to 65% with a Science founding logic of one standard deviation above the mean.

Hypothesis 2 predicted that a focal firm’s founding logic would moderate the impact of technol-ogy validation on the likelihood of technology commercialization. Table 4 includes the results of our discrete-time event history analyses, which were used to test this hypothesis. Model 3 includes the control variables. In Model 4, we add the variables SBIR awards, Business founding logic, and Science founding logic, which are the three variables needed to test this predicted interaction effect.

Table 3. Apply to SBIR logistic regression.

Model 1 Model 2

Constant −0.61* (0.28) −2.48** (0.87)Angel funding 0.14 (0.47) 0.10 (0.53)Life science 0.60 (0.69) 0.16 (0.77)Research and development 1.09* (0.54) 0.85 (0.59)Licensed tech 1.97** (0.55) 1.79** (0.61)Team start-ups 0.28 (0.21) 0.44† (0.26)Founders with business degrees −0.50* (0.24)Founders with science degrees 0.34 (0.22)Business founding logic −0.08 (0.14)Science founding logic 0.61** (0.18)Observations 144 144Log-likelihood −81.0 −68.8Chi-squared 33.0** 57.2**Pseudo R2 .17 .29

SBIR: Small Business Innovation Research.Standard errors in parentheses.†p < .10; *p < .05; **p < .01 (two-tailed tests).

14 International Small Business Journal: Researching Entrepreneurship 00(0)

Although we did not hypothesize unconditional effects for these variables, it warrants noting that the coefficient on SBIR awards is positive and significant (β = .13, p < .05). Thus, securing additional awards increases the likelihood that a firm will generate revenues based on SBIR technology. Somewhat surprisingly, the coefficient for the unconditional impact of Business founding logic is negative and significant (β = −.33, p < .05). Thus, having a stronger business logic decreases the chances of generating revenue, if one does not account for interaction effects. This may seem counterintuitive, but one explanation could be that firms with business-based founding logics are pursuing riskier, higher payoff opportunities that either take longer to generate revenues or never actually generate sales.

We include the interaction terms in Models 5 through 7. In Model 5, we include just the Business logic × SBIR awards interaction; in Model 6, just the Science logic × SBIR awards; and in Model 7, we include both interaction terms. The coefficient on the science logic interaction term is negative and significant (β = −.13, p < .01), whereas the coefficient on the business logic interaction term is not significant (β = .02, NS). Again, to formally test our hypothesis, we conducted a z-test on the coefficient difference. Results of this test (z = 2.34, p < .05) support our hypothesis that the impact of technology endorsements on commercialization is less positive for science logics than it is for

Table 4. Technology-generated sale discrete time hazard models.

Model 3 Model 4 Model 5 Model 6 Model 7

Constant −2.64** (0.84) −0.56 (1.06) −0.50 (1.12) −2.17 (1.40) −2.07 (1.45)Period −0.07 (0.04) −0.11† (0.06) −0.11† (0.06) −0.13* (0.06) −0.13* (0.07)Angel funding 0.77† (0.45) 1.08* (0.55) 1.08* (0.55) 0.92 (0.58) 0.93 (0.59)Life science −0.45 (0.56) −0.60 (0.64) −0.61 (0.64) −0.65 (0.71) −0.70 (0.71)Research and development

−0.06 (0.51) −0.13 (0.52) −0.14 (0.53) −0.14 (0.58) −0.13 (0.59)

Licensed tech −0.38 (0.64) −0.53 (0.62) −0.53 (0.62) −0.63 (0.79) −0.61 (0.80)SBIR proposal success 0.12 (0.08) 0.27* (0.12) 0.27* (0.12) 0.23* (0.11) 0.23* (0.11)DOD −0.18 (0.51) −0.49 (0.65) −0.47 (0.65) −0.63 (0.67) −0.58 (0.67)Age at first SBIR 0.02 (0.04) 0.00 (0.04) 0.00 (0.04) 0.03 (0.04) 0.03 (0.04)Team start-ups 0.30 (0.25) 0.65* (0.30) 0.64* (0.31) 0.78* (0.32) 0.76* (0.33)Founders with business degrees

0.28 (0.22) 0.47† (0.25) 0.48† (0.25) 0.53† (0.28) 0.53† (0.28)

Founders with science degrees

−0.60* (0.25) −1.11** (0.36) −1.10** (0.36) −1.08** (0.35) −1.05** (0.35)

SBIR awards 0.13* (0.06) 0.11 (0.14) 0.74** (0.23) 0.69** (0.26)Business founding logic −0.33* (0.14) −0.35* (0.15) −0.30† (0.16) −0.33* (0.17)Science founding logic −0.43 (0.26) −0.43† (0.26) −0.06 (0.30) −0.05 (0.30)Business logic × SBIR awards

0.01 (0.04) 0.02 (0.04)

Science logic × SBIR awards

−0.13** (0.05) −0.13** (0.05)

Log-pseudo likelihood −93.5 −87.2 −87.2 −84.3 −84.2Chi-squared 19.0† 31.5** 33.7** 34.9** 38.1**Firm periods 379 379 379 379 379Pseudo R2 0.09 0.15 0.15 0.18 0.18

SBIR: Small Business Innovation Research.Standard errors in parentheses.†p < .10; *p < .05; **p < .01 (two-tailed tests).

Ciuchta et al. 15

business logics. To further interpret the results, we include in Figure 1 a graph of the relationship between SBIR awards and the probability of commercializing technology at high and low levels of science logics (holding all other variables at their median). As shown in Figure 1, at high levels of science logic, there is no impact upon winning additional awards. At low levels of science logic, however, there is a pronounced positive contribution of additional awards on the chances of commercialization.

In addition to our hypothesized relationships, several other interesting patterns emerged in our empirical results. First, the coefficient on period is negative and significant (β = −.13, p < .05). This result indicates that the longer firms go without commercializing (after they have won their first award), the less likely they are to ever generate revenues from an SBIR technology. As might be expected, the coefficient on founder start-ups is positive and significant (β = .76, p < .05). In practical terms, adding an additional founder with start-up experience more than doubles the odds of generating sales (exp(.76) = 2.14). Finally, we point out that the coefficient on founders with science degrees is negative and significant (β = −1.05, p < .01). This suggests that founding teams with more scientists are less likely to commercialize their technology. It could be that these teams are also pursuing riskier opportunities, but it could also be these teams lack the appropriate skills and connections needed to generate sales. This finding also lends more substance to our hypothesized results. We find that the interaction between Science founding logics and SBIR awards has an impact on commercialization success above and beyond what can be explained by the number of scientists on the founding team.

Discussion

This article provides several important contributions regarding the interplay between logics and third-party endorsements and their roles in technology commercialization among SMEs. First, our

Figure 1. Science logic × SBIR awards interaction graph.

16 International Small Business Journal: Researching Entrepreneurship 00(0)

first hypothesis examined why SMEs pursue a technology validation endorsement. Among SME researchers, there is some debate as to whether firms pursue external accreditation for internal or external reasons. Some suggest that SMEs pursue these endorsements primarily in response to external pressures such as customer demand (Sun and Cheng, 2002). Others suggest that the pur-suit of these endorsements is driven by the SME’s own internal idiosyncratic needs and contingen-cies (Heras-Saizarbitoria and Boiral, 2013). By incorporating insights from institutional logics, we suggest that internal and external reasons need not be opposing motivations for seeking endorse-ments. For example, we found that firms with science-based founding logics were more likely to pursue SBIR grants. Importantly, the science logic incorporated both internally focused logics, such as funding own lab/students, as well as more outwardly focused logics such as enhancing scientific prestige.

In our second hypothesis, we examined the role of founding logics in affecting an organization’s commercialization trajectory through the influence of logics upon how firms respond to receiving third-party endorsements. Prior research has suggested that SMEs generally benefit from receiving third-party endorsements as they promote greater legitimacy in the eyes of key stakeholders (Djupdal and Westhead, 2013; Lounsbury and Glynn, 2001). These endorsements are perceived as conveying some underlying attribute that is deemed valuable in a particular field (De Clercq and Voronov, 2009) that can lead to higher firm performance (Sine et al., 2007). However, research has started to examine boundary conditions regarding the impact of third-party endorsements on firm performance (Desai, 2016). Our study joins that conversation and offers a distinct contribution. Desai’s emphasis is still very much on the external signaling impact of certifications and, therefore, the impact upon key stakeholders. Our emphasis here is much more on the internal processes within the recipient organization itself and how receiving validation leads to different types of behavior depending on the underlying founding logic of the recipient firm.

Findings regarding our second hypothesis also contribute to an understanding of the imprinting of logics (Dufays and Huybrechts, 2016). For imprinting to occur, initial elements need to be rep-licated over time (Johnson, 2007). In our study, we identify the receipt of third-party endorsements as one such mechanism that can influence the path-dependent patterns of values and behaviors. We offer theory and evidence for the non-obvious influence of a founding logic, a pathway not previ-ously presented in the literature to our knowledge. The founding logic influences later action pat-terns through its impact on first seeking, and then interpreting, and acting upon, technology validations.

Taken together, the pattern of findings across the two hypotheses provides several new insights regarding the entrepreneurial process. First, these findings have particular implications for adverse selection and moral hazard problems in funding entrepreneurial ventures. Generally speaking, adverse selection arises when information asymmetries exist between an agent (the entrepreneur) and a principal (the funder). In the typical setup, the unobservable information possessed by the entrepreneur relates to his or her ability or willingness to exert effort to create value or to the ven-ture’s underlying quality (Darrough and Stoughton, 1986; Takalo and Tanayama, 2010). Considerable research examines how entrepreneurs overcome those information asymmetries by signaling their unobservable quality to the investor (Ahlers et al., 2015; Busenitz et al., 2005; Eddleston et al., 2016). Moral hazard arises when the agent’s actions are unobservable and have different value to the agent and the principal. For example, this problem arises if after receiving funding the entrepreneur exerts less effort than the investor would desire (Amit et al., 1998; Elitzur and Gavious, 2003).

Although adverse selection and moral hazard are often studied separately, Bruton et al. (2009) suggest, in their study of initial public offering (IPO) pricings, that the presence of both can occur in entrepreneurial contexts. As such, the authors proposed that a founder’s retained equity

Ciuchta et al. 17

percentage (following IPO) served as a viable signal to investors to overcome concerns about adverse selection. However, at very high levels of retained equity percentage, investor fears about managerial entrenchment, a moral hazard problem, predominated.

This article illustrates another situation in which elements of both adverse selection and moral hazard are present. However, unlike in Bruton et al. (2009), our results do not suggest an effective signal to overcome one or both of the problems and so we call the situation one of ‘adverse hazard’. More specifically, our results suggest a two-stage process in which the first stage looks like adverse selection. Firms with higher science-based founding logics were more likely to seek validation, but then ultimately, they were less likely to commercialize the technology. As the principals, SBIR programme managers could not rely on a signal to overcome this information asymmetry.

Furthermore, an element of moral hazard was also present because our results suggest that these same firms were no less likely to commercialize the technology than firms lower in science-based founding logics unless they received subsequent validation. Thus, the event of receiving the valida-tion itself led to an ex-post change in the firm’s behavior contrary to that which the programme managers desired when they granted the awards. Applying these results to an entrepreneur–venture capitalist setting, our results would imply that low effort entrepreneurs may be more likely to pur-sue venture capital than high effort entrepreneurs. However, they may not actually start to sustain a low effort until they receive follow-on rounds of funding.

Next, this study also moves beyond existing approaches to capture the imprinting of institu-tional logics at founding. Significant work infers logics from proxies, such as the founding team member’s prior experience levels and industry backgrounds (Almandoz, 2014) or the legal form the organization adopted at founding (Shahriar et al., 2016). Rather, we established theoretically motivated conceptualizations and measures of actual logics that were rooted in and instrumental to the firm’s founding. Importantly, under our conceptualization, having a science-based founding logic in and of itself did not have an unconditional effect on commercialization success. It had an impact, however, through its effect as an important moderator on commercialization outcomes. At the same time, using the proxy measure of counting the number of scientist founders did result in an unconditional (negative) effect on the likelihood of commercialization. These findings emphasize the complex motivations and goals that occur within an institutional class such as scientists (Hayter, 2011; Hayter, 2015; Sauermann and Stephan, 2013). Thus, our results suggest continued caution in using simple demographic measures as proxies for underlying goals, interests, or identities.

Indeed, our results may help account for conflicting research regarding the contributions of scientists on founding teams, with some research pointing toward benefits (Agrawal, 2006; Zucker et al., 2002) and other research pointing toward detriments (Durand et al., 2008; Gittelman and Kogut, 2003). Our evidence suggests a potential basis of this mixed finding. Scientists provide key knowledge that can lead to strategic advantage but can also impart institutional logics that shape how salient events are interpreted (Sauermann and Stephan, 2013). Thus, what matters is not that a mere scientist is involved, but what logics that scientist brings to the venture. Our research indi-cates that the particular scientists involved, and the logics they bring, can play a pivotal role on a technology-based SME’s ability to cross the Valley of Death from invention to commercialization (Markham, 2002).

Practical implications and limitations

This article offers several important practical implications, limitations, and directions for future research. First, our results have practical implications for national technology commercializa-tion programmes such as SBIR. Although commercialization is not SBIR’s only objective, it is clearly an important intention. Increasingly, concern has arisen that SBIR produces ‘mills’ or

18 International Small Business Journal: Researching Entrepreneurship 00(0)

companies that are adept at winning grants but not at achieving commercial success (Lerner, 1999; Reich, 2013). In particular, academic spinoffs may be especially likely to follow these routes (Hayter, 2015), although other research has demonstrated that firms with closer ties to universities benefit more from winning SBIR grants when outcomes other than revenues are also considered (Siegel and Wessner, 2012). Like other research (Howell, 2017), this article suggests that winning awards by itself does not harm (and likely helps) commercialization suc-cess, but we find there is a strong contingency based on founding logics. Granted, capturing information about a firm’s founding logics through its SBIR application may not be desirable or feasible. In fact, we caution against using simple proxies, but establishing a more accurate screening of underlying motives of award recipients could be one way to limit mill-like out-comes or to more explicitly seek a mix of short-term and long-term economic impact goals. Going back to our discussion of adverse selection and moral hazard, a more effective process to enhance short-term commercialization might occur if firms could adequately signal that they do not have science-based founding logics. In that case, SBIR would rely on that signal, not in making the initial grant award, but in deciding to hold back on future grants until the firm dem-onstrates commercial success or to make explicit that the value of the work lies in longer term technology development for commercial potential.

Finally, like all research, the study has several limitations that present opportunities for future research. Given our operationalization of technology validation, we also considered whether the results could have arisen from the SBIR awards simply by providing more funds that allowed firms with science-based founding logics to pursue science objectives. Carefully inspecting the pattern of results does not support this interpretation. If science firms have an innate tendency toward a lower likelihood of commercialization, one would expect to see this in our results. However, as noted, the models show no unconditional effect of having a science logic on the likelihood of com-mercialization. Nonetheless, future work could be directed toward teasing out the subsidy effect from the validation effect (Takalo and Tanayama, 2010).

The study also involved specific founding logics and a specific type of third-party endorsement. Additional research could examine whether these patterns hold in other contexts. For example, considerable research has examined the environmental orientations of entrepreneurial firms (Hall et al., 2010; Hörisch et al., 2017; Meek et al., 2010). Other research has examined the performance effects of receiving environmental certifications (Djupdal and Westhead, 2013). We suggest that founding logics could provide a unifying perspective between these two streams of research, such that logics may influence both the likelihood that an organization pursues environmental certifica-tion and subsequently how it responds to receiving that certification. Finally, we did not attempt to explain the emergence of the particular logics within the sample SMEs; for example, were the log-ics the result of certain power struggles within teams (Greve and Zhang, 2017) or dependent on the identities of particular founders (Wry and York, 2017)? The emergence of founding logics is a ripe avenue for future research.

Conclusion

In this research, we introduced a new concept called founding logics. We then demonstrated that founding logics impact an SME’s decision to pursue a technology validation programme and then the likelihood of commercializing the technology developed for the programme. Interestingly, the results suggest that firms with science-based founding logics are both more likely to apply for the program but then less likely to commercialize the technology as they accrue additional endorse-ments. By incorporating logics into our theoretical framework, we suggest that the decision to pursue validation is driven by internal and external factors rather than the either/or that has been

Ciuchta et al. 19

portrayed in the literature. We also develop an important boundary condition around the impact of third-party endorsements. Whereas much of the prior research demonstrates the positive impact of endorsements on firm performance, we demonstrate an important condition in which SMEs that receive more endorsements can be less likely to reach commercial success, even as they continue to develop the technology.

Acknowledgements

The authors thank the editor, Professor Claire Leitch, and the anonymous referees for their valuable com-ments and suggestions. The authors also are grateful to Anthony Sadler and Sanjay Jain for their help in developing the scales used in this study.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publica-tion of this article: Portions of this research received financial support from Ewing Marion Kauffman Foundation.

Note

1. The type of endorsements we study are also known as accreditations or certifications Wade et al. (2006) The burden of celebrity: The impact of chief executive officer (CEO) certification contests on CEO pay and performance. Academy of Management Journal 49: 643–660.

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Author biographies

Michael P Ciuchta is an Assistant Professor in the Department of Marketing, Entrepreneurship & Innovation at the University of Massachusetts Lowell. He received his PhD in Organization Theory/Strategic Management from the University of Wisconsin – Madison. His research takes a behavioral approach to studying institu-tions and entrepreneurship.

Anne S Miner is an Emeritus Professor at the Wisconsin School of Business. She is a Fellow of the Academy of Management, whose Technology and Innovation Management Division named her Scholar of the Year in 2004. She received her bachelor’s degree from Harvard University (Radcliffe), and her MS and PhD degrees from Stanford University. Her research focuses on organizational learning, organizational improvisation, entrepreneurship, job creation, and industry level learning.

June-Young Kim is a Lecturer of strategy and entrepreneurship at the University of Illinois at Urbana-Champaign. He received his PhD from the University of Wisconsin-Madison. His research interests include learning from success and failure, innovative learning, and analogy.

Jay O’Toole is an Assistant Professor of Strategic Management at Georgia State University. His research focuses on social-psychological and behavioral theories to better understand team and firm innovativeness.

Appendix 1

Survey questions for logics measures

The list below includes factors other people have given as their key influences when they estab-lished their firms. On a scale from 1 to 7, with 1 = Not at all important and 7 = Extremely important, how important was each of the following in the founding process of your firm?

Fund own lab/students

Business prestige

Firm wealth creation

Want technology/product in use by many people

Shape direction of science

Personal income

Scientific prestige

Social welfare, for example, cure AIDS