1. Introduction
The ability to solve dynamic problems is critical in engineering design, and problem-solving abilities vary among designers (Atman et al., Reference Atman, Chimka, Bursic and Nachtmann1999; Kim et al., Reference Kim, Choi, Sung and Park2018; Vieira et al., Reference Vieira, Gero, Delmoral, Gattol, Fernandes, Parente and Fernandes2020). In fact, the design process itself can affect a designer’s ability to develop a successful design and bring a product to market. The framing of a design project significantly impacts how designers solve problems (Cross, Reference Cross2007) and resources – both physical and time related – are key aspects of a project’s framing. Most, if not all, teams struggle with a limited amount of resources that they need to use to get a product to market. As a result, knowing how to properly allocate and manage limited resources may be the difference between a team that succeeds and one that fails.
In the current work, we investigate technology-based startup companies as a unique subset of new product development teams. Similar to design teams, nascent startups operate in a resource-scarce environment and employ design methods and practices (Moogk, Reference Moogk2012) to develop innovative products from problem identification to launch (Jonson, Reference Jonson2005; Bradner et al., Reference Bradner, Iorio and Davis2014). While we are all familiar with the phrase “necessity is the mother of invention,” the literature remains split on the effects of resource scarcity on invention and innovation processes. For example, Love and Roper (Reference Love and Roper2015) found that a scarcity of resources adversely affects the abilities of small- to mid-sized enterprises to develop and promote innovative ideas. Yet, Weiss et al. (Reference Weiss, Hoegl and Gibbert2011) found that a scarcity of financial resources actually contributed to the development of more creative and innovative ideas. A significant body of research has identified individual traits that affect the trajectory of a team such as self-efficacy (Bandura, Reference Bandura1977), bricolage (Baker and Nelson, Reference Baker and Nelson2005), risk propensity (Mill, Reference Mill1871) and perceptions of psychological safety (Edmondson, Reference Edmondson1999). However, limited work has investigated how these traits combine and affect a team’s ability to manage resources, especially in a resource-scarce environment.
The goal of this work is to address these research gaps through an investigation of the individual traits exhibited by new product developers and how these traits affect their ability to manage resources. Presented in the next section are the theoretical underpinnings of which this work builds upon. Then, we present our research objectives, followed by methods. The analysis and the results from our mixed-methods study with 241 American technology-based startups are presented in the subsequent two sections respectively. Implications of these results are then discussed, followed by limitations, future work and conclusions.
2. Background
The new product development process is inherently ambiguous, and key facets of the process, such as the project scope (Stacey and Eckert, Reference Stacey and Eckert2003), are often unclear. Designers and product development teams experience sudden changes in problem constraints (Duclos et al., Reference Duclos, Otto and Konitzer2010; McComb et al., Reference McComb, Cagan and Kotovsky2015) or shifts in project requirements (Clarkson et al., Reference Clarkson, Simons and Eckert2004) and are tasked with generating well-defined solutions to ill-defined problems (Dorst, Reference Dorst2006). Part of the uncertainty in new product development stems from a gap between customers’ expectations and the resources available to designers to meet these expectations. This gap induces a resource scarcity, or the inability to cope with resource deficits (Zhao and Tomm, Reference Zhao and Tomm2018) such as time (Zika-Viktorsson et al., Reference Zika-Viktorsson, Sundström and Engwall2006), money (Koshe and Jha, Reference Koshe and Jha2016), human capital (Brown et al., Reference Brown, Adams and Amjad2007) and physical resources such as building materials or tooling capabilities (Jaselskis and Ashley, Reference Jaselskis and Ashley1991). Dealing with a resource scarcity changes how people approach problems and causes them to deviate from rational and logical decision-making (Shah et al., Reference Shah, Mullainathan and Shafir2012). For instance, decisions made in the presence of scarcity are often based on a fixation that causes individuals to neglect important tasks to accommodate urgent, but less important tasks (Kazmer and Haythornthwaite, Reference Kazmer and Haythornthwaite2001) that ultimately worsen the state of scarcity (Zhao and Tomm, Reference Zhao and Tomm2018). Dealing with these scarcities can hinder designers’ cognitive function, depleting cognitive resources such as attention and working memory (Zhao and Tomm, Reference Zhao and Tomm2018), inducing cognitive overload (Shah et al., Reference Shah, Mullainathan and Shafir2012) and decreasing their ability to make effective design decisions. Marston et al. (Reference Marston, Allen and Mistree2000) claimed that design decisions are an irrevocable allocation of resources and thus, a designer’s ability to utilize resources determines their ability to create successful products (Hazelrigg, Reference Hazelrigg1996).
The availability of resources, such as time, money or materials, can change throughout the new product development process and can affect a team’s ability to solve problems (Schrader et al., Reference Schrader, Riggs and Smith1993; Zhao and Tomm, Reference Zhao and Tomm2018). For instance, resource management skills such as resource planning, allocation and scheduling can decrease project duration (Schrader et al., Reference Schrader, Riggs and Smith1993) and are critical for success, especially in competitive environments such as new product development (Chua et al., Reference Chua, Kog and Loh1999; Xin Chen et al., Reference Xin Chen, Moullec, Ball and Clarkson2016). Proper resource utilization requires both efficiency, or how well something is done, and effectiveness, or how useful something is. To use resources efficiently and effectively, a team must understand the problem they are trying to solve and identify the appropriate resources to solve it. To quantify this ability, Letting et al. (Reference Letting, Calpin, Soria Zurita and Menold2023) developed a resource accuracy metric that defines an individual’s resource accuracy as their ability to allocate relevant resources to critical obstacles they face. The allocation of relevant resources was studied because not all resources are inherently valuable or useful (Ketchen et al., Reference Ketchen, Hult and and Slater2007); rather, the value of a resource is derived from the ability of a team to use the resource to overcome a particular obstacle. In an example from the prior work (Letting et al., Reference Letting, Calpin, Soria Zurita and Menold2023), a participant was struggling to educate their customer about their product; they indicated that “whenever [I] bring up the technology, it just goes over people’s heads.” The participant then utilized educational resources on LinkedIn to help them better communicate the product to their customer. In this instance, LinkedIn is considered to be a relevant resource for educating customers about obstacles because it directly helps the participant to overcome the obstacle. In contrast, another participant identified funding and building a business model as their main obstacles, but when asked about resources they were using, they only identified social networking. While networking may be generally helpful to the participant or their team, it will not directly solve their lack of funding nor will it teach them how to build a business model.
We argue that an interesting case of new product development teams are technology-based startups. Similar to design teams, startup founders employ resources to generate ideas (Kanchana et al., Reference Kanchana, Divya and Beegom2013; Yilmaz et al., Reference Yilmaz, Daly, Seifert and Gonzalez2015; Deo et al., Reference Deo, Blej, Kirjavainen and Hölttä-Otto2021), explore the diffusibility of these ideas through prototypes (Gill et al., Reference Gill, Sanders and Shim2011; Camburn et al., Reference Camburn, Viswanathan, Linsey, Anderson, Jensen, Crawford, Otto and Wood2017; Nelson et al., Reference Nelson, Mahan, McComb and Menold2020), and bring ideas to market just like any product development team (Stone et al., Reference Stone, Tumer and Van Wie2005). Due to these parallels, we argue that startup founders represent a unique subset of new product development teams and are ideal to study the effects of resource constraints on authentic problem-solvers. Employing over 1.5 million people in the United States and accounting for approximately 2.8 percent of US businesses (Wu and Atkinson, Reference Wu and Atkinson2017), startups have significant economic impacts both nationally and globally and are critical for innovation and knowledge transfer (Colombo and Piva, Reference Colombo and Piva2008). Despite their impact, only 10 percent succeed, many of which come close to dissolution (Marmer et al., Reference Marmer, Herrmann, Dogrultan, Berman, Eesley and Blank2011). These teams tend to fail because of the scarcity induced by dealing with resource constraints (Wymer and Regan, Reference Wymer and Regan2005). Often times, when a founder is experiencing a scarcity of financial resources, for example, they tend to fixate on obtaining more funding and neglect other obstacles or problems that they need to solve. In these cases, the founder could find novel ways to allocate resources that may be more abundant to solve their problems, but they are unable to do so because of the scarcity-induced fixation. Marcon and Ribeiro (Reference Marcon and Ribeiro2021) identified six categories of resources that foster success and competitive advantage for startup firms: financial, human, social, organizational, physical and innovation. Financial resources are often used to acquire other types or resources (Ireland et al., Reference Ireland, Hitt and Sirmon2003) and are commonly obtained from personal investments, venture capitalists’ investments and accelerators (Pauwels et al., Reference Pauwels, Clarysse, Wright and Van Hove2016). Human resources, or human capital, can include hiring and training team members to improve their knowledge and necessary skills (Barney, Reference Barney1991), whereas social resources include intrafirm and interfirm relationships (Fukugawa, Reference Fukugawa2018) from which resources can be shared (Adner, Reference Adner2006). Business resources such as the structure of the team and how it conducts work comprise the organizational resource category (Löfsten, Reference Löfsten2016). Physical resources include materials or equipment related to the necessary technology or product that is being developed (Barney, Reference Barney1991) and innovation resources are resources that lead to product development and commercialization (Ireland et al., Reference Ireland, Hitt and Sirmon2003; Löfsten, Reference Löfsten2016).
The field of psychology has identified individual traits that affect the ability of individuals to solve problems and manage resources. While there are many individual traits that affect individuals’ behaviors, such as creativity (Glăveanu, Reference Glăveanu2018), perseverance (Scherer and Gustafsson, Reference Scherer and Gustafsson2015) and cognitive flexibility (Krems, Reference Krems2014), this work focuses on four specific traits that are particularly relevant within the context of resource-scarce environments: risk propensity, self-efficacy, bricolage and perceptions of psychological safety. These traits are particularly interesting within the context of new product development teams because not only have they been linked with increased problem-solving skills (Edmondson, Reference Edmondson1999; Baker and Nelson, Reference Baker and Nelson2005; McGee et al., Reference McGee, Peterson, Mueller and Sequeira2009; Zheng et al., Reference Zheng, McAlack, Wilmes, Kohler-Evans and Williamson2009) but they have also been shown to improve team performance (Edmondson et al., Reference Edmondson, Kramer and Cook2004; Hmieleski and Baron, Reference Hmieleski and Baron2008; Kariv and Coleman, Reference Kariv and Coleman2015; Raveendra et al., Reference Raveendra, Rizwana, Singh, Satish and Kumar2018). Here, it is important to distinguish between risk tolerance and risk propensity. Risk tolerance represents the level of uncertainty, or risk, that an individual is willing to accept in decision-making (Grable, Reference Grable2000). In contrast, risk propensity represents an individual’s inherent tendency, or willingness, to take risks (Sitkin and Weingart, Reference Sitkin and Weingart1995). In the context of problem-solving, dealing with resource scarcities has been shown to exaggerate risk-averse behaviors (Zhao and Tomm, Reference Zhao and Tomm2018), and prior work has shown that risk-averse designers tend to choose less creative concepts (Toh and Miller, Reference Toh and Miller2016) and abandon novel ideas (Starkey et al., Reference Starkey, Toh and Miller2016), ultimately decreasing team performance. In fact, risk propensity has long been considered a defining characteristic of founders (Mill, Reference Mill1871; Kilby, Reference Kilby1971; Al-Mamary et al., Reference Al-Mamary, Abdulrab, Alwaheeb and Alshammari2020), and recent work has found risk propensity to have a positive impact on entrepreneurial intention (Moraes et al., Reference Moraes, Iizuka and Pedro2018; Al-Mamary and Alshallaqi, Reference Al-Mamary and Alshallaqi2022). Further, founders with high levels of self-efficacy are more comfortable taking risks than those with low self-efficacy (Densberger, Reference Densberger2014).
Social cognitive theory posited by Bandura (Reference Bandura1989) suggests that self-efficacy, or the belief that an individual has in their ability to achieve a certain outcome (Bandura, Reference Bandura1977), determines their motivation and perseverance through challenges and obstacles. Self-efficacy, however, is considered to be domain-specific. Stemming from Bandura’s self-efficacy, entrepreneurial self-efficacy (ESE) represents an individual’s belief in their own ability to successfully execute entrepreneurial tasks. ESE has been linked with persistence and success in entrepreneurial endeavors (Cardon and Kirk, Reference Cardon and Kirk2015; Santoro et al., Reference Santoro, Ferraris, Del Giudice and Schiavone2020) and high levels of ESE have been shown to increase firm performance (Hmieleski and Baron, Reference Hmieleski and Baron2008) and resource management skills (McGee et al., Reference McGee, Peterson, Mueller and Sequeira2009). Chen et al. (Reference Chen, Greene and Crick1998) suggested the use of ESE to assess the potential of an individual’s ability to effectively manage resources. More specifically, they assert that individuals assess their ESE in reference to available resources and obstacles they face. While multiple studies have demonstrated the importance of ESE, very few studies have investigated its relationship with other critical traits such as bricolage.
The ability to make use of limited resources to solve emerging problems, or bricolage (Baker and Nelson, Reference Baker and Nelson2005), has also been individually linked with increased performance (Kariv and Coleman, Reference Kariv and Coleman2015). In a study on startup firms, Baker and Nelson (Reference Baker and Nelson2005) found that successful teams possessed the ability to consistently adapt, recycle or reimagine resources in novel ways. This ability is also correlated with new venture adaptability and firm growth (Yu et al., Reference Yu, Li, Su, Tao, Nguyen and Xia2020). Even for nascent, resource-constrained firms, Senyard et al. (Reference Senyard, Baker, Steffens and Davidsson2014) identified bricolage as a pathway to innovation. Extending this work, Stenholm and Renko (Reference Stenholm and Renko2016) found that successful bricoleurs exhibited greater entrepreneurial passion and were less likely to quit entrepreneurial endeavors. For teams that operate under resource constraints, such as those that persist in startups and new product development, this ability to be resourceful and leverage combinations of resources is critical for success.
Team success is largely dependent on team cohesion as well. Edmondson’s theory of psychological safety (Edmondson, Reference Edmondson1999), or the shared belief that a team is safe for interpersonal risk-taking, is a team-level construct that can be used to approximate team climate. Psychological safety is particularly critical for team performance in knowledge-intensive and complex tasks that require creativity and sense-making (Sanner and Bunderson, Reference Sanner and Bunderson2015). A team’s level of psychological safety affects their willingness to share knowledge and communicate openly (Edmondson et al., Reference Edmondson, Kramer and Cook2004), and when team members are willing to communicate openly, they contribute more efficiently to the team (Edmondson and Lei, Reference Edmondson and Lei2014) and leverage the wisdom of the collective (Salas et al., Reference Salas, Rosen, Burke and Goodwin2009), where teams that successfully interact yield greater success. Psychological safety has been shown to mediate the relationship between inclusive leadership and innovative work behavior in management (Javed et al., Reference Javed, Naqvi, Khan, Arjoon and Tayyeb2019) and is positively related to innovation performance and innovation capabilities when studied as an organizational-level construct (Andersson et al., Reference Andersson, Moen and Brett2020). In the context of design, psychological safety has been identified as a dynamic and reliable measure of team climate over time, meaning that a team’s psychological safety can be fostered and measured throughout the design process (Cole et al., Reference Cole, O’Connell, Gong, Jablokow, Mohammad, Ritter, Heininger, Marhefka and Miller2022a). Cole et al. (Reference Cole, Marhefka, Jablokow, Mohammed, Ritter and Miller2022b) found that teams with greater levels of psychological safety generated higher-quality ideas than those with lower levels of psychological safety. Cauwelier et al. (Reference Cauwelier, Ribiere and Bennet2019) studied French and American engineering teams and discovered that increased team psychological safety leads to increased learning and knowledge creation.
While psychological safety is a team construct, the effects of individual perceptions of psychological safety have also been studied in prior work. Individual perceptions of psychological safety represent a single team member’s own perceptions of their team’s climate. By studying individual perceptions of psychological safety, we can draw insights on how members of the same team experience varying levels of psychological safety. For instance, in a study on design teams, Cole et al. (Reference Cole, Jablokow, Mohammed and Miller2023) identified a discrepancy in perceptions of psychological safety between men and women. Specifically, women exhibited greater levels of perceived psychological safety when working with other women when compared to working with men. In contrast, men did not indicate any difference in perceived psychological safety with either gender. Startup teams, like design teams, operate in resource-scarce environments and work to solve complex problems. As such, team psychological safety has also been investigated in the field of new product development; it has been linked with greater levels of bricolage (Cunha, Reference Cunha2005; Faia-Correia and Cunha, Reference Faia-Correia and Cunha2007), or the ability to “create something from nothing” (Baker and Nelson, Reference Baker and Nelson2005). In preliminary work, the authors studied founders and identified individual perceptions of psychological safety to be a predictor of entrepreneurial bricolage (Letting and Menold, Reference Letting and Menold2023). This finding suggests that members of problem-solving teams who perceive their team as psychologically safe may be better equipped to combine resources in novel ways.
Significant prior literature has shown these factors – self-efficacy, bricolage, risk propensity and psychological safety – to be critical to the success of problem-solving teams, and more specifically, new product development teams. No work has studied these traits together, especially within the context of resource-constrained, problem-solving teams. However, prior work in the fields of design and entrepreneurship have employed various techniques to identify distinct groups, or archetypes, of individuals based on a combination of relevant traits. Archetypes allow researchers to study patterns of behaviors or traits across groups of individuals. For instance in design, Begnum et al. (Reference Begnum, Pettersen and Sørum2019) employed qualitative methods to identify different archetypes of interaction designers and described the strengths of each archetype. In entrepreneurship, Ambos and Birkinshaw (Reference Ambos and Birkinshaw2010) identified three distinct archetypes of ventures to study new venture evolution. However, little work has explored how salient individual traits might combine to uncover archetypes of founders, particularly with respect to new product development.
To address this gap, we are interested in investigating how self-efficacy, bricolage, risk propensity and psychological safety might combine to affect resource use. These traits represent how an individual perceives their team environment, their own abilities, their resources and their behaviors. Psychological safety fosters an environment where individuals feel safe taking interpersonal risks, which may enhance their willingness to engage in innovative behaviors. This environment may positively influence ESE, where individuals with higher self-efficacy are more confident in themselves. Entrepreneurial bricolage compliments this relationship by enabling individuals to be resourceful and innovative in resource-scarce environments. Finally, individuals with a greater risk propensity by definition are more likely to take action in uncertain situations. Thus, these constructs may combine to uncover distinct patterns of behavior.
3. Research objectives
In this study, we followed an exploratory QUANT
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explanatory qual approach where qualitative data were collected and analyzed to further explain the quantitative results (Giddings and Grant, Reference Giddings and Grant2006). This work aimed to explore how individual traits of founders affect their resource management abilities. We argue that technology-based startup founders represent a unique subset of new product developers that can provide significant insights into design behaviors. To explore these topics, two research questions drive this work:
RQ1: Are there distinct archetypes that emerge based on salient entrepreneurial traits and if so, what are the characteristics of these archetypes?
Prior work has shown that individual traits of founders such as self-efficacy, bricolage, risk propensity and psychological safety are important factors of team and startup success. In pursuit of our first research question, we investigate if archetypes of participants emerged based on these individual traits. In this work, we refer to archetypes as distinct groups of founders that are characterized by shared traits, behaviors and decision-making patterns. These archetypes represent generalized profiles that capture similar traits within each group while highlighting differences from other archetypes, which could help to identify common behaviors and challenges faced by different types of founders. We first hypothesize that unique archetypes will emerge, with participants in each archetype exhibiting similar characteristics to one another. Next, we further investigate if the archetypes that emerge based on individual traits will exhibit differences in firm-level characteristics such as the stage of development and firm performance. Here, we hypothesize that based on archetype, there will be differences in how developed the firms are as well as their financial performance. An important distinction in this work is that the results from RQ1 were used to develop RQ2 to further investigate the nuances between the archetypes that were identified in RQ1.
RQ2: Does participants’ resource accuracy differ based on archetype?
Resource accuracy represents an individual’s ability to identify relevant resources to mitigate current obstacles. It is important to note here that resource accuracy differs from resource acquisition capability which represents an individual’s ability to obtain the resources necessary for firm survival (Pulka et al., Reference Pulka, Ramli and Bakar2018). In this work, we are interested specifically in individuals’ ability to identify necessary, or relevant, resources. We added the qualifier “relevant” to resources to make an important distinction: not all resources are valuable or useful, but they depend on the obstacles a participant faces. For example, if a participant is struggling with obtaining funding to hire another team member, a physical resource such as new laboratory equipment is not relevant to this specific obstacle. However, new laboratory equipment is a relevant resource to a participant struggling with broken equipment. As such, we are specifically interested in these relevant resources, or the resources that directly mitigate obstacles the team currently faces. We hypothesize that participants with higher levels of these traits will exhibit greater resource accuracy.
4. Materials and methods
To identify distinct archetypes, 241 technology-based startup company founders were surveyed and thirty-two were interviewed in a mixed-methods research study.
4.1. Recruitment and participants
In this work, 241 participants were recruited from ten universities across the Mid-Atlantic Hub of the National Science Foundation’s (NSF) Innovation Corps (I-Corps) program. The NSF I-Corps program is a seven-week training program that was founded in 2011 with three main aims to (1) train the entrepreneurial workforce, (2) bring cutting-edge technologies to market and (3) foster a national innovation ecosystem (NSF, 2022). Over 5000 students, university researchers and professionals have been trained through I-Corps (Nnakwe et al., Reference Nnakwe, Cooch and Huang-Saad2018). It is also important to note that because students, university researchers and professionals may participate in this program, the firms represented in this study include, but are not limited to, academic-based startups. Studying these founders provides a unique opportunity to investigate authentic problem-solving teams that operate in resource-constrained environments. All participants were informed that their participation was voluntary and would not affect their involvement with the NSF I-Corps program in any way. As with any voluntary study, we do acknowledge that the nature of this recruitment may introduce self-selection bias, which should be considered when deriving insights from this study. Informed consent was obtained in accordance with the Pennsylvania State University’s Institutional Review Board policies.
In this sample, participants’ firms were at varying stages of development. As shown in Figure 1, 8 (3.3%) firms had paused development, 38 (15.8%) firms were in the idea stage, 139 (57.7%) firms were developing their idea, 47 (19.5%) firms had tested their product with customers, 6 (2.5%) firms were ready for sale and 3 (1.2%) firms were existing in the market. The distribution of participants’ age is shown in Figure 2. Participants’ demographics with respect to gender and race are shown in Figure 3. Of the 241 participants, 113 were men, 64 were women and 64 participants did not provide demographics pertaining to gender and are excluded from Figure 3. Of those represented here, 71 (43%) were Caucasian or White, 44 (26.6%) were Asian, 37 (22.4%) were African American or Black and 13 (8%) were Hispanic or Latinx. Compared to the general population, this study lacks an appropriate representation of women. However, in the context of startup teams, our representation of women (31.3%) aligns well with some of the most startup-oriented ecosystems such as Chicago (30%) and Boston (29%) (Herrmann et al., Reference Herrmann, Gauthier, Holtschke, Berman and Marmer2015; Berger and Kuckertz, Reference Berger and Kuckertz2016). Our sample, however, is not exactly representative of startups with respect to race and ethnicity. Harvard reports that 79.6% of entrepreneurs are Caucasian or White, 15.8% are Asian, 3.8% are Hispanic or Latinx and 0.4% are African American or Black (Gompers and Wang, Reference Gompers and Wang2017).
Distribution of the stage of development of participants’ firms.
Distribution of participants’ age.
Distribution of participants’ race and gender.
4.2. Data collection
This section presents the data collection methods used in this work. Surveys and interviews were employed to gather both quantitative and qualitative data.
4.2.1. Survey
A survey was created and distributed to participants from the NSF I-Corps program to capture individual traits and the state of their firm. The full survey instrument is presented in the Appendix. At the start of the survey, all participants were informed that their participation was voluntary, in accordance with the institutional review board at the Pennsylvania State University. Due to the relatively long nature of this survey, it is possible that survey fatigue may affect these data. However, responses were checked for common signs of survey fatigue such as straight-lining and tapering off, which were not observed in these data.
Participants first responded to the ESE questionnaire developed by McGee et al. (Reference McGee, Peterson, Mueller and Sequeira2009). In general, self-efficacy is an individual’s belief in their own ability to accomplish a given task (Bandura, Reference Bandura1977). More specifically, however, ESE captures an individual’s confidence in their own ability to succeed in entrepreneurial tasks or ventures (McGee et al., Reference McGee, Peterson, Mueller and Sequeira2009). The ESE questionnaire consists of nineteen Likert-type items that are divided into five dimensions of ESE: (1) searching, (2) planning, (3) marshaling, (4) implementing – people and (5) implementing – financial. The searching phase refers to the participant’s ability to develop a unique idea, and the planning phase refers to converting the idea into a business plan. Next, the marshaling phase involves allocating resources to bring the idea into existence. The final phase, or the implementing phase, refers to managing both people and financial resources. In their work, McGee et al. (Reference McGee, Peterson, Mueller and Sequeira2009) conducted a factor analysis that indicated both convergent validity and discriminant validity. This finding revealed that the items for each dimension load on each other, but do not load on the other dimensions (Bagozzi et al., Reference Bagozzi, Yi and Phillips1991), meaning that five unique dimensions of ESE exist. For this reason, this questionnaire was chosen over composite measures that fail to provide insight into these specific dimensions. Participants rated their agreement with each of the nineteen items on a five-point scale ranging from one (very little confidence) to five (a great amount of confidence). Example items from each dimension of this scale include their confidence in their ability to (1) “brainstorm (come up with) a new idea for a product or service,” (2) “design an effective marketing or advertising campaign for the new product or service,” (3) “clearly and concisely explain verbally or in writing their business idea in everyday terms,” (4) “deal effectively with day-to-day problems and crises” and (5) “read and interpret financial statements.” Participants received a score for ESE in each of these five dimensions. Each dimension score was calculated as an average of the participant’s responses from the statements in that dimension. Cronbach’s alpha for our sample was excellent (0.92) (Cronbach, Reference Cronbach1951), indicating excellent internal reliability.
Next, participants responded to a set of statements that captured their perceptions of entrepreneurial bricolage skills, or their ability to use limited resources to solve problems and “make something from nothing” (Baker and Nelson, Reference Baker and Nelson2005). Participants were asked to respond to a set of nine statements regarding how often they “go about doing things for their startup” on a five-point Likert scale ranging from one (never) to five (always). Example items include “we use any existing resource that seems useful to respond to a new problem or opportunity” and “to deal with new challenges we acquire resources at low or no cost and combine them with what we already have.” The overall score for a participant’s entrepreneurial bricolage was calculated by taking an average of the participant’s scores from the nine statements because confirmatory factor analysis indicated that these statements are due to one single factor. Cronbach’s alpha for our sample was high (0.89) (Cronbach, Reference Cronbach1951), indicating high internal reliability.
To capture participants’ risk propensity, or willingness to take risks, they responded to the General Risk Question (GRQ) developed by Dohmen et al. (Reference Dohmen, Falk, Huffman, Sunde, Schupp and Wagner2011), which asks “how do you see yourself: are you generally a person who is fully prepared to take risks or do you try to avoid taking risks?” Participants responded to this question on an eleven-point Likert scale ranging from zero (not at all willing to take risks) to ten (very willing to take risks). The GRQ was chosen because it is predictive of real-world risk-taking (Dohmen et al., Reference Dohmen, Falk, Huffman, Sunde, Schupp and Wagner2011), and it captures risk conception as well as risk preference better than other measures of risk (Dohmen et al., Reference Dohmen, Quercia and Willrodt2018), indicating that multi-item risk assessments are not necessary. Additionally, the GRQ has been validated across domains such as migration (Jaeger et al., Reference Jaeger, Dohmen, Falk, Huffman, Sunde and Bonin2010), economics (Bonin et al., Reference Bonin, Dohmen, Falk, Huffman and Sunde2007; Grund and Sliwka, Reference Grund and Sliwka2010) and entrepreneurship (Caliendo et al., Reference Caliendo, Fossen and Kritikos2009).
The survey also included the psychological safety scale developed by Edmondson (Reference Edmondson1999), which consists of seven Likert-type items that captured participants’ perceptions of the psychological safety of their startup team. Although psychological safety is a team-level construct, when individual scores are used, Edmondson’s scale can be used as a measure of perceptions of psychological safety. Participants rated their agreement with each item on a seven-point scale ranging from one (very inaccurate) to seven (very accurate). A score of one represents a low level of perceived psychological safety, while a score of seven represents a high level of perceived psychological safety. Example items include “members on this team are able to bring up problems and tough issues” and “working with this team, my unique skills and talents are valued and utilized.” Three items on the psychological safety scale were worded to portray negative psychological safety. An example of a negatively worded item is “if you make a mistake on this team, it is often held against you.” These three items were reverse-coded to ensure that a low score in response to a negatively worded prompt represents a high level of psychological safety. Edmondson’s measure of psychological safety has been consistently shown to have strong construct validity (Edmondson, Reference Edmondson1999); thus, the overall score for a participant’s perceived psychological safety was calculated as the mean of the seven scale items. The scale also yielded an acceptable internal reliability for our sample, with a Cronbach’s alpha of 0.78 (Cronbach, Reference Cronbach1951).
In this work, we assess the state of each startup by identifying each firm’s stage of development and financial performance. Stage of development can be used to understand how nascent or established a firm is and ultimately evaluate its progress (Santisteban and Mauricio, Reference Santisteban and Mauricio2017). For instance, assessing this metric can provide insights into the team’s ability to solve problems, manage resources and capitalize on opportunities. To identify stage of development, participants were asked the following question: “at what stage of development is your product, service or system?” Participants chose from the following six options: still in idea stage; a model or idea is being developed; a prototype, procedure or minimum viable product has been tested with customers; completed and ready for sale or delivery; existing in market; or product development is paused. These response options represent the phases that constitute the idea-to-launch process that is central to new product development (Schuh et al., Reference Schuh, Studerus and Hämmerle2022). The financial performance of a team also serves as a key indicator of success or failure. In the field of entrepreneurship, financial performance is commonly used to measure firm performance (Zahra, Reference Zahra1993; Davila and Foster, Reference Davila and Foster2007; Adusei and Adeleye, Reference Adusei and Adeleye2021) and to understand the growth potential of the venture (Davila et al., Reference Davila, Foster and Gupta2003). Here, we quantify financial performance as the sum of any financial capital raised or acquired and the annual reported revenue. In this survey, participants were asked to share any financial streams available to them: “if you have received any business venture funding, please indicate what type and how much.” Participants were provided the following categories to report funding: founders capital, grants, friends and family, angel, venture capital and startup competitions. Annual reported revenue was determined through secondary research.
4.2.2. Interviews
At the conclusion of the survey, participants were asked if they would be willing to engage in a follow-up interview. Of the 241 survey respondents, thirty-two (22 men and 10 women) participants indicated they were willing to participate. We determined thirty-two participants to be a sufficient sample size for this study based on prior work that reviewed the recommended sample sizes for qualitative research to understand the experiences of individuals (Morse, Reference Morse1994; Guest et al., Reference Guest, Bunce and Johnson2006; Clarke and Braun, Reference Clarke and Braun2013). The first author conducted semistructured interviews with the participants. Participants were first asked to describe their venture, what problem their technology or innovation solves, their primary customers and the long-term goals of the company. Following these descriptions, the interviewer prompted founders to reflect upon current obstacles their companies face and identify resources they may use to overcome these obstacles. Example questions used during these interviews include but are not limited to:
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1. Please describe any obstacles or hurdles you have faced as a founder.
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• How did you manage to overcome these obstacles?
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• What resources helped you overcome these obstacles?
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2. What are the most pressing obstacles that your startup currently faces?
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• What plans do you have to mitigate these obstacles?
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These questions were developed based on the coding schema that includes Marcon and Ribeiro’s six categories of obstacles and resources which include financial, human, social, organizational, physical and innovation (see Table 1; Marcon and Ribeiro, Reference Marcon and Ribeiro2021). These questions were used to guide the general flow and structure of the interviews, but based on participants’ responses, follow-up questions and probes were used (Rubin and Rubin, Reference Rubin and Rubin2011) to gain richer insights into the participants’ unique experiences. Interviews lasted approximately thirty minutes and were conducted one on one via Zoom. All interviews were recorded and the audio was transcribed leveraging a professional transcription service. The first author verified all transcriptions for accuracy.
Codebook for obstacles (Letting et al., Reference Letting, Calpin, Soria Zurita and Menold2023)
4.2.3. Secondary data collection
In addition to the business venture funding data collected from the survey, we conducted a secondary data collection using LexisNexis, a news, business and legal database. The names of each startup firm were searched in the company and financial category of the database across all available dates. The researchers verified each firm by comparing the firm’s location and founding date from the company profile to the information participants provided in the survey. Of the 241 total firms in our study, we identified 194 firms and collected financial data. Specifically, the annual reported revenue was collected from each company profile. Using these data, financial performance was calculated as a sum of the business venture funding of each startup and the reported annual revenue.
5. Analysis
RQ1: Are there distinct archetypes that emerge based on salient entrepreneurial traits and if so, what are the characteristics of these archetypes?
To first understand if distinct archetypes exist, a k-means cluster analysis was conducted using R CRAN v. 4.2.0. A k-means cluster analysis is an unsupervised machine learning algorithm that partitions a dataset into distinct groups, or clusters, of participants with similar characteristics (Hair et al., Reference Hair, Anderson, Tatham and Black1984). The algorithm computes the number of clusters and the cluster size by iteratively minimizing the within-cluster sum of squares, which is a significant strength of the method. Emergent clusters should exhibit high within-group homogeneity and high between-group heterogeneity (Morissette and Chartier, Reference Morissette and Chartier2013). More generally, this method measures similarity between participants as the Euclidean distance, or the distance between vectors representing each participant’s traits. Ultimately, k-means clustering creates a smaller number of clusters with meaningful groupings of participants and this method was chosen because it is a simple, yet robust method for exploring patterns and analyzing high-dimensional data sets. In this work, we were interested in investigating if distinct archetypes of participants exist based on salient entrepreneurial traits. In our analysis, the k-means clustering method groups participants based on if they exhibit the following traits: ESE, entrepreneurial bricolage, risk propensity and perceptions of psychological safety. This method allows us to investigate how these individual factors interact, or hang together, and if these interactions result in the emergence of distinct archetypes of participants. Finally, the specific characteristics of each archetype can be analyzed by comparing these traits across the resulting clusters.
K-means clustering was performed iteratively with 25 random initializations until convergence was achieved with a maximum of 10 iterations. The algorithm varied the number of clusters to determine the best number of clusters to represent the true data groupings. Each iteration used a different number of clusters and computed the mean point-to-centroid distance, and any iterations resulting in a single-member cluster were to be omitted, although this was not observed in these data. We determined that our sample size, n = 241, was adequate for this analysis based on prior work that suggests a minimum sample size of 80 based on the number of clustering variables in this study (Dolnicar and Grün, Reference Dolnicar and Grün2008). Using the silhouette method, which plots the average silhouette width for different numbers of clusters, k = 2 clusters minimized the point-to-point centroid distance, yielding two clusters, or archetypes, of similar size with a discernible rationale behind the clustering based on visual inspection and statistical comparisons of the responses. The elbow method, which plots the within-cluster sum of squares against the number of clusters, was also implemented to validate that having k = 2 clusters was optimal. Thus, two archetypes were identified for further analysis and comparison: archetype 1 (n = 134) and archetype 2 (n = 107).
RQ2: Does participants’ resource accuracy differ based on archetype?
To investigate participants’ resource accuracy, we qualitatively analyzed the thirty-two semistructured interview transcripts. Deductive coding was employed, using a previously developed coding schema by Marcon and Ribeiro (Reference Marcon and Ribeiro2021) that categorizes firm-critical obstacles and resources into the following categories: financial, human, social, organizational, physical and innovation. The codebook we used to identify obstacles, as shown in Table 1, contains each category of obstacles, along with example statements for each code from our data set. The codebook we used to identify resources, as shown in Table 2, contains each category of resources, along with example statements for each code from our data set. Throughout the coding process, the appropriateness of the coding schema was discussed and the process itself was reviewed. The interview data were coded to agreement by two coders.
Codebook for resources (Letting et al., Reference Letting, Calpin, Soria Zurita and Menold2023)
Figures 4 and 5 show examples of how obstacles and resources that were identified for participants A and B, respectively. Red highlight indicates an identified obstacle and green highlight indicates an identified resource. For the purpose of this example, excerpts from the interview were sliced together and extraneous information was omitted.
Example of participant A’s coded interview (Letting et al., Reference Letting, Calpin, Soria Zurita and Menold2023).
Example of participant B’s coded interview (Letting et al., Reference Letting, Calpin, Soria Zurita and Menold2023).
Participants’ resource accuracy was quantified by summing the weighted frequency of orphaned obstacles, obstacle and resource matches, and extra resources, based on prior work (Letting et al., Reference Letting, Calpin, Soria Zurita and Menold2023). Here, orphaned obstacles were given a weight of −1, obstacle and resource matches were given a weight of +1, and extra resources were given a weight of +0.5. A weight of −1 was chosen for orphaned obstacles because having an obstacle with no resources to help mitigate it negatively impacts the team. However, when a resource is identified to mitigate an obstacle, this negative impact is turned positive. Thus, a score of +1 was chosen for an obstacle and resource match. Extra resources were weighted +0.5 because while they are helpful, they do not completely mitigate the burden of an unsolved obstacle. The total score for each participant was calculated using the following equation:
$$ ResourceAccuracy=-O+M+0.5R, $$
where O is the number of orphaned obstacles, M is the number of obstacle and resource matches, and R is the number of extra resources. The excerpts shown in Figures 4 and 5 were analyzed as shown in Tables 3 and 4 and received scores of +2.5 and −2.5, respectively.
Example of participant A’s obstacle and resource identification (Letting et al., Reference Letting, Calpin, Soria Zurita and Menold2023)
Example of participant B’s obstacle and resource identification (Letting et al., Reference Letting, Calpin, Soria Zurita and Menold2023)
6. Results
RQ1: Are there distinct archetypes that emerge based on salient entrepreneurial traits and if so, what are the characteristics of these archetypes?
To better understand the differences between founders in archetypes 1 and 2, pairwise comparisons were conducted across participants’ five dimensions of ESE, entrepreneurial bricolage, risk propensity and perceptions of psychological safety. Participants’ data for these traits were not normally distributed, as assessed by the Shapiro-Wilk test for normality that requires p
$ > $
0.05. Thus, Mann–Whitney U tests were conducted to determine if there were differences in participants’ traits between archetypes 1 and 2.
First, a series of Mann–Whitney U tests were conducted to determine if archetypes 1 and 2 participants exhibited differences in ESE during each phase. ESE ranged from one to five, with one representing low self-efficacy and five representing high self-efficacy. To account for the increased risk of Type I error due to multiple comparisons across ESE, the Bonferroni correction was applied by dividing the significance threshold (p < 0.05) by the number of tests (in this case, 5), and then comparing the p-values to the new significance threshold of p < 0.01. In the searching phase, the median ESE was significantly greater in archetype 1 (Q1 = 4.00, Mdn = 4.33, Q3 = 5.00) than in archetype 2 (Q1 = 3.33, Mdn = 4.00, Q3 = 4.33), U = 11,266, z = 7.62, p < 0.001. In the planning phase, the median ESE was significantly greater in archetype 1 (Q1 = 3.00, Mdn = 3.25, Q3 = 4.00) than in archetype 2 (Q1 = 1.75, Mdn = 2.25, Q3 = 2.75), U = 12,724, z = 10.33, p < 0.001. In the marshaling phase, the median ESE was significantly greater in archetype 1 (Q1 = 4.00, Mdn = 4.33, Q3 = 5.00) than in archetype 2 (Q1 = 3.00, Mdn = 3.33, Q3 = 3.84), U = 12,655, z = 10.20, p < 0.001. In the implementing people phase, the median ESE was significantly greater in archetype 1 (Q1 = 3.83, Mdn = 4.17, Q3 = 4.79) than in archetype 2 (Q1 = 2.67, Mdn = 3.33, Q3 = 3.67), U = 12,394, z = 9.72, p < 0.001. In the implementing financial phase, the median ESE was significantly greater in archetype 1 (Q1 = 3.00, Mdn = 3.67, Q3 = 4.33) than in archetype 2 (Q1 = 2.00, Mdn = 2.67, Q3 = 3.17), U = 11,023, z = 7.17, p < 0.001. Results from these tests indicate that archetype 1 participants exhibit greater levels of ESE in each of the five phases, shown in Figure 6.
Distribution of participants’ entrepreneurial self-efficacy (ESE) in each phase for archetypes 1 and 2.
A Mann–Whitney U test was conducted to determine if archetypes 1 and 2 participants exhibited differences in entrepreneurial bricolage. Entrepreneurial bricolage ranged from one to five, with one representing low bricolage and five representing high bricolage. The median entrepreneurial bricolage was significantly greater in archetype 1 (Q1 = 4.00, Mdn = 4.18, Q3 = 4.78) than in archetype 2 (Q1 = 3.22, Mdn = 3.56, Q3 = 3.89), U = 11,951, z = 8.89, p < 0.001. Figure 7 shows the distribution of participants’ average entrepreneurial bricolage in each archetype.
Distribution of participants’ entrepreneurial bricolage for archetypes 1 and 2.
A Mann–Whitney U test was conducted to determine if archetypes 1 and 2 participants exhibited differences in risk propensity. Risk propensity ranged from zero to ten, with zero representing high-risk aversion and ten representing high-risk propensity. The median risk propensity was significantly greater in archetype 1 (Q1 = 7.00, Mdn = 8.00, Q3 = 9.00) than in archetype 2 (Q1 = 5.00, Mdn = 6.00, Q3 = 8.00), U = 11,307, z = 7.70, p < 0.001. Figure 8 shows the distribution of participants’ risk propensity in each archetype.
Distribution of participants’ risk propensity for archetypes 1 and 2.
A Mann–Whitney U test was conducted to determine if archetypes 1 and 2 participants exhibited differences in perceptions of psychological safety, which was measured by average perceived psychological safety. Psychological safety ranged from one to seven, with one representing low psychological safety and seven representing high psychological safety. The median perceived psychological safety was significantly greater in archetype 1 (Q1 = 5.86, Mdn = 6.43, Q3 = 7.00) than in archetype 2 (Q1 = 4.86, Mdn = 5.57, Q3 = 6.14), U = 10,676, z = 6.52, p < 0.001. Figure 9 shows the distribution of participants’ psychological safety in each archetype.
Distribution of participants’ perceived psychological safety for archetypes 1 and 2.
To better understand the link between these characteristics and the state of the firm, we next evaluated the firms’ stage of development and financial performance. The stage of development included six categories: paused development, idea stage, the idea is being developed, the product tested with customers, ready for sale and existing in the market. To compare the distribution of the stage of development between archetypes 1 and 2, a Chi-square goodness-of-fit test was conducted. The Chi-square test showed that the distribution of firms’ stage of development significantly differed between the archetypes (
$ {\chi}^2 $
= 12.33, df = 5, p = 0.03). Additionally, the Chi-square test indicated a Cramer’s V of 0.23, indicating a large effect size (Rea and Parker, Reference Rea and Parker1992; Akoglu, Reference Akoglu2018). Figure 10 shows the distribution of participants’ stage of development in each archetype. Using visual inspection of Figure 10, archetype 1 exhibits firms that are ready for sale and existing in the market, while archetype 2 does not exhibit firms in these more developed categories.
Distribution of firms’ stage of development for archetypes 1 and 2.
Next, we compared participants’ financial performance; data were available for 194 of the original 241 participants, with 112 in archetype 1 and 82 in archetype 2. The financial performance data were not normally distributed. Thus, a Mann–Whitney U test was conducted to determine if there were differences in financial performance between archetypes 1 and 2, which includes any business venture funding participants have received and the annual revenue of their firm. The median financial performance was not significantly different in archetype 1 (Q1 = 0, Mdn = 28,132, Q3 = 101,250) than in archetype 2 (Q1 = 0, Mdn = 375, Q3 = 74,899), U = 5043, z = 1.17, p = 0.22. Figure 11 shows the overall distribution of firms’ financial performance. To further explore these data, Figure 12 provides a zoomed-in view of the distribution of firms’ financial performance, fitted to the box plot.
RQ2: Does participants’ resource accuracy differ based on archetype?
Distribution of firms’ financial performance for archetypes 1 and 2.
Fitted distribution of firms’ financial performance for archetypes 1 and 2.
Prior to understanding if resource accuracy – or the ability to identify relevant resources to mitigate obstacles – of participants differs across the two archetypes, we first provide descriptive information of the types of obstacles and resources that participants identified. Interviews were conducted with thirty-two participants, one of which was missing clustering data. Thus, data from thirty-one participants were analyzed from archetype 1 (n = 20) and archetype 2 (n = 11). Using the codebooks in Tables 1 and 2, obstacles and resources were organized into six categories: financial, human, social, organizational, physical and innovation. The frequencies of obstacles and resources identified by the participants for each of the six categories are shown in Figure 13 for archetypes 1 and 2. The frequencies of the obstacles and resources were normalized because the number of participants in each archetype is different. The normalized frequency was calculated by dividing the true frequency for each category in each archetype by the number of participants in that respective archetype; all frequencies for obstacles and resources in archetype 1 were divided by n = 20 and n = 11 for archetype 2. This process yields a frequency that represents frequency per person. Archetype 1 obstacles (solid) and resources (striped) are denoted in dark blue, and archetype 2 obstacles (solid) and resources (striped) are denoted in light blue. From these interviews, a total of 172 (archetype 1 = 102, archetype 2 = 70) obstacles were identified and a total of 128 (archetype 1 = 86, archetype 2 = 42) resources were identified.
Normalized distribution of the obstacles and resources that founders identified in each category for archetypes 1 and 2.
In Figure 13, it can be observed that participants in archetype 1 identify more obstacles than resources in the social, organizational and physical categories but identify more resources than obstacles in the financial, human and innovation categories. In contrast, participants in archetype 2 identify more obstacles than resources in every category except for human. Thus, participants in archetype 2 may struggle in more categories and tend to identify more obstacles per participant than those in archetype 1.
To determine if there were differences in participants’ resource accuracy between archetypes 1 and 2, an independent samples t-test was conducted. Resource accuracy was normally distributed and homogeneity of variances was met, as assessed by Levene’s Test for Equality of Variances. The differences in participants’ resource accuracy were significantly different, t(29) = 2.23, p = 0.03, d = 0.87, indicating a large effect size. Resource accuracy was significantly greater in archetype 1 participants (M = 0.35, SD = 2.76) than archetype 2 participants (M = −1.86, SD = 2.40). Figure 14 shows the distribution of participants’ resource accuracy in each archetype.
Distribution of participants’ resource accuracy for archetypes 1 and 2.
7. Discussion
Our work was motivated by two research questions: 1) are there distinct archetypes that emerge based on participants’ salient entrepreneurial traits and if so, what are the characteristics of these archetypes? and 2) does participants’ resource accuracy differ based on archetype? Because limited work has studied self-efficacy, bricolage, risk propensity and perceptions of psychological safety together, we sought to investigate how the combination of these traits affects participants’ abilities to successfully match resources to perceived obstacles. We argue that these findings hold significant value for the field of design because startup teams are a unique case of new product development teams that can serve as a proxy for resource-constrained design teams. Both startup teams and product development teams are tasked with bringing an innovative product to market, working with limited time, money and human capital. We highlight four main findings from the study as follows:
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1. Two distinct archetypes of participants were identified. Archetype 1 comprised individuals with greater levels of ESE, entrepreneurial bricolage, risk propensity and perceived psychological safety than those in archetype 2.
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2. The distribution of firms’ stage of development significantly differed across archetypes.
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3. Financial performance was not significantly different between archetypes.
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4. Archetype 1 participants exhibited significantly greater resource accuracy than archetype 2 participants.
RQ1: Are there distinct archetypes that emerge based on salient entrepreneurial traits and if so, what are the characteristics of these archetypes?
The first result from this research question supported our hypothesis that unique archetypes of participants will emerge, with participants in each archetype exhibiting similar characteristics to one another. Specifically, two distinct archetypes of participants emerged based on salient entrepreneurial traits. Participants in archetype 1 (n = 134) exhibited greater levels of ESE across five dimensions, entrepreneurial bricolage, risk propensity and perceptions of psychological safety than participants in archetype 2 (n = 107). Thus, participants in archetype 1 exhibit greater confidence in their abilities to complete entrepreneurial tasks and to combine existing resources to solve new problems. They are also more willing to engage in risks, and perceive their team climate to be more positive when compared to participants in archetype 2. These findings support prior literature that has observed greater levels of entrepreneurial bricolage in startup participants with greater levels of ESE and perceptions of psychological safety (Letting and Menold, Reference Letting and Menold2023), and that participants with greater levels of self-efficacy are more comfortable taking risks (Densberger, Reference Densberger2014). Specifically, our work suggests that two distinct archetypes of new product developers may exist – one that exhibits stronger entrepreneurial traits compared to the other. Because prior literature has linked these defining traits with the state of a firm, it is important to investigate if participants’ startups differ across archetypes.
Here, the state of a firm refers to its stage of development and financial performance. The second result from this research question supported our hypothesis that participants with greater levels of these traits will positively affect the stage of development. A Chi-square goodness-of-fit test revealed that the distribution of firms’ stage of development was different between the archetypes. Further, we note that firms in archetype 1 tend to be further along the product development pipeline than firms in archetype 2. Specifically, in archetype 1 we observed six firms that were ready for sale and three firms that were existing in the market, but in archetype 2, no firms were observed in either of these more developed categories. This finding suggests either that (1) startups from archetype 2 are much more nascent than startups from archetype 1 or that (2) participants from archetype 1 are more likely to have a startup that continues further in the product development pipeline. While it is possible that archetype 2 startups are more nascent than archetype 1 startups, this scenario is unlikely given the large number of startups present in this study. Thus, it is more likely that archetype 1 participants may be better at navigating product development processes. This finding also supports prior literature that suggests participants’ entrepreneurial capabilities determine the speed of launching or developing a new product (Ardyan, Reference Ardyan2016).
The third result from this research question, however, refuted our hypothesis that participants with greater levels of these traits will positively affect their firm’s financial performance. No significant differences were observed between the financial performance of firms in archetypes 1 and 2. This result was surprising and contradicts prior work that has shown that increased entrepreneurial skills are related to increased financial performance (Baer and Frese, Reference Baer and Frese2003; Senyard et al., Reference Senyard, Baker and Davidsson2009; Choi et al., Reference Choi, Ullah and Kang2021). In the context of this study, we hypothesize that differences in financial performance might not yet have emerged due to the nascent nature of the firms. For instance, only three of the 241 firms were existing in market and six were ready for sale. In total, these firms represent less than four percent of those represented in this study, meaning that the overwhelming majority of these firms have not yet begun to sell their product. Further, the financial performance data that we collected are sparse. In fact, nearly forty-five percent of these data are zeros, indicating that we may not yet be able to capture the true financial performance of these firms. For these firms, it is likely that they are either still in the idea stage or developing their idea and may not have yet begun raising funds or earning any revenue. These findings indicate that while significant prior work has suggested that increased entrepreneurial skills are related to firm performance, this may not be true for such nascent firms who are early on in the product development pipeline.
Due to the significant parallels between startup and new product development teams, we argue that startups represent a unique subset of designers and that these findings hold significant implications to the field of design. Past work in this field has studied the effects of individual traits such as self-efficacy (Nolte et al., Reference Nolte, Berdanier, Menold and McComb2021; Singh et al., Reference Singh, Cascini and McComb2022) and risk propensity (Vermillion et al., Reference Vermillion, Malak, Smallman and Linsey2015) on designers and design performance. However, these studies often investigate traits in isolation. In practice, these traits do not exist in a vacuum; designers are multifaceted and should not be defined by one metric. Limited work has attempted to study the interaction of individual traits to form a more holistic evaluation of designers. As such, our findings highlight the need to conduct design research with multifaceted designers in mind. We do note that there may be other traits that are relevant to designers in this work. For example, we did not factor demographic identity into our analysis. Rather than attempting to capture every relevant trait of designers, which is likely impossible, we identified and studied specific traits based on prior literature.
RQ2: Does participants’ resource accuracy differ based on archetype?
In response to our second research question, the fourth result in this study supported our hypothesis that participants with greater levels of ESE, entrepreneurial bricolage, risk propensity and perceived psychological safety will exhibit greater resource accuracy. Participants’ resource accuracy, or their ability to allocate relevant resources to critical obstacles they face, was determined through qualitative analysis of thirty-two interviews. Results indicated that participants in archetype 1 with greater levels of these traits exhibited significantly greater resource accuracy than participants in archetype 2. Thus, archetype 1 participants more accurately identified and allocated resources to mitigate specific obstacles. This finding supports prior work that suggests a positive relationship between the ability to manage resources and the individual traits that were used to partition archetypes 1 and 2. For instance, Frey (Reference Frey2010) studied 577 small businesses and found that participants’ self-efficacy influences their ability to allocate resources and even improves the ability to predict one’s resource allocation strategy. Similarly, in a study of 29 startup firms, Baker and Nelson (Reference Baker and Nelson2005) found that Lévi-Strauss’ bricolage (Lévi-Strauss, Reference Lévi-Strauss1966) enables firms to apply combinations of resources to solve new problems. In design, Qiu et al. (Reference Qiu, Ge and Yim2008) have used risk perception to model resource allocation, and Letting et al. (Reference Letting, Calpin, Soria Zurita and Menold2023) found a positive relationship between individual perceptions of psychological safety and resource accuracy.
The ability to properly manage resources is paramount to the success of new product development teams (Chua et al., Reference Chua, Kog and Loh1999; Joglekar and Ford, Reference Joglekar and Ford2005). As such, many design researchers have developed methods to aid in resource planning and allocation (Georgiopoulos et al., Reference Georgiopoulos, Jonsson and Papalambros2005; Farhang Mehr and Tumer, Reference Farhang Mehr and Tumer2006; Qiu et al., Reference Qiu, Ge and Yim2008; Xin Chen et al., Reference Xin Chen, Moullec, Ball and Clarkson2016). Yet, our findings reveal that some designers may be better equipped than others to manage resources, particularly in resource-constrained environments. This disparity is further exacerbated by the inherent resource-scarce nature of design that can hinder cognitive function (Shah et al., Reference Shah, Mullainathan and Shafir2012; Zhao and Tomm, Reference Zhao and Tomm2018). A significant body of work in design has focused on improving design methods for ideation (Hay et al., Reference Hay, Duffy, Gilbert, Lyall, Campbell, Coyle and Grealy2019; Sosa, Reference Sosa2019), concept selection (Zheng et al., Reference Zheng, Ritter and Miller2018; Lee et al., Reference Lee, Daly, Vadakumcherry and Rodriguez2023) and concept development (Goetz et al., Reference Goetz, Kirchner, Schleich and Wartzack2021; Boudier et al., Reference Boudier, Sukhov, Netz, Le Masson and Weil2023) strategies. However, these studies are largely generalized to all designers and often times do not consider multiple traits of designers. We argue that design research may benefit from studying the effects of these methods on different archetypes of designers. Existing methods may benefit some designers over others, and designers who struggle to effectively solve problems may need unique methods that do not yet exist. As such, we highlight the need for investigating design methods and practices across designers of varying abilities to better equip all designers to develop successful solutions.
7.1. Practical implications
Insights from this work, specifically in the context of trait interactions, can be drawn to inform individual skills, organizational design and policy interventions. Our findings suggest that there exist two distinct archetypes of founders, one of which exhibits greater levels of ESE, entrepreneurial bricolage, risk propensity and perceptions of psychological safety than the other. Further, founders that exhibit greater levels of these traits exhibit greater resource accuracy. Given these distinct differences, founders may benefit from examining their own individual traits to potentially improve their resource accuracy. For instance, research suggests that risk propensity is a dynamic characteristic that can change with entrepreneurial experience (Arpiainen and Kurczewska, Reference Arpiainen and Kurczewska2017). Given that risk-taking is widely considered a defining characteristic of entrepreneurs (Mill, Reference Mill1871; Kilby, Reference Kilby1971; Al-Mamary et al., Reference Al-Mamary, Abdulrab, Alwaheeb and Alshammari2020), founders may significantly benefit from self-analysis and ultimately experiential intervention to increase risk propensity and the ability to successfully navigate taking risks. Prior research has shown that ESE can be increased via interventions that focus on fostering a growth mindset, yielding greater task persistence (Burnette et al., Reference Burnette, Pollack, Forsyth, Hoyt, Babij, Thomas and Coy2020). With this knowledge, it is possible that founders could intentionally seek interventions or trainings that aim to improve their own ESE. As such, we hypothesize that it may be possible for a founder in archetype 2 that exhibits low levels of risk propensity, ESE or entrepreneurial bricolage may be able to remediate and improve these traits, ultimately improving their ability to manage resources and better navigate the obstacles their startup faces. Similarly, founders with lower levels of perceived psychological safety within their team may improve their firms’ ability to navigate resource constraints by first addressing issues within their team. In fact, many studies have shown that interventions effectively increase psychological safety (O’Donovan and McAuliffe, Reference O’Donovan and McAuliffe2020; Scarpinella et al., Reference Scarpinella, Cole, Ritter, Mohammad, Jablokow and Miller2023) and consequently increase team performance, group learning and communication (Marder et al., Reference Marder, Ferguson, Marchant, Brennan, Hedler, Rossi, Black and Doig2021).
Findings from this work can also be used to inform organizational design. In the context of team composition and dynamics, significant prior work has highlighted the importance of forming diverse teams (Lau et al., Reference Lau, Beckman and Agogino2012; Alzayed et al., Reference Alzayed, Miller and McComb2022; Ruiz and Wever, Reference Ruiz and Wever2024). Understanding the interactions between salient traits such as ESE, entrepreneurial bricolage, risk propensity and perceptions of psychological safety can help to inform teams to balance these traits across members in a group. Further, these insights could help allocate roles within a team to position individuals in roles that maximize their strengths. Prior work has also shown that managers should be diversity leaders and that it is important to consider individualized management techniques that fit the needs of each team member (Homan et al., Reference Homan, Gündemir, Buengeler and van Kleef2020). Managers could draw insights from this work to adapt their management and leadership styles to better support the diverse needs of their team members.
This work is also particularly relevant given the significant resources the federal government has invested in promoting founders and increasing the success of startups, particularly high-tech startups. Policies can be designed to offer tailored support to founders based on their individual traits or archetypes. For example, startup accelerator programs such as the NSF’s Innovation Corps can draw insights from these findings, possibly realigning program offerings and founder trainings to meet the needs of founders in archetype 1 or 2. One of the main goals of programs such as I-Corps is to provide guidance and training to founders to ultimately move toward commercialization. As such, I-Corps mentors may be able to use these findings to better understand how to tailor their guidance to each participant based on their individual traits. The findings from this work highlight potential foci for improvement in accelerator curricula and resources. For instance, accelerators may consider implementing interventions and resources to improve founders’ risk tolerance, self-efficacy and bricolage skills. The addition of these research-based implementations could improve the marketability of the accelerator to founders as well.
8. Limitations and future work
Although we found significant results in this study, as with any work, there are limitations that should be considered when interpreting our findings. One limitation of the current study is that our participants were recruited exclusively from the NSF I-Corps program. Because of this recruitment strategy, there is the possibility that self-selection bias may be affecting the results. For example, participants from the NSF I-Corps program may represent a different subset of founders than those who have not participated because they may have more time to participate in these training programs or they may now have more resources available to them. The participants in this study were also geographically clustered in the Mid-Atlantic region of the United States, and prior work has found that geographic location can affect cultural norms and resource availability (Peng et al., Reference Peng, Menold and Miller2022). As a result of these limitations, we acknowledge that the findings from this study may not be generalizable to broader geographic locations or founders that have not participated in the NSF I-Corps program. Future work will focus on recruiting participants across the United States both within and outside of the NSF I-Corps program to compare these findings.
Another limitation of the current work is a reliance on self-report data through surveys. While a commonly employed data collection technique across human-subjects research (Blösch-Paidosh and Shea, Reference Blösch-Paidosh and Shea2022; Singh et al., Reference Singh, Cascini and McComb2022; Nolte et al., Reference Nolte, Zurita, Starkey and McComb2023; Budinoff et al., Reference Budinoff, McMains and Shonkwiler2024), self-report surveys may fall victim to certain biases such as social desirability bias. As such, in future work, research should attempt to triangulate self-report data with secondary ratings or other avenues of empirical evidence which may provide a more complete picture of phenomenon of interest. Our survey also included questionnaires on multiple constructs that were chosen based on prior literature. While these were carefully chosen, their length may have induced survey fatigue in our participants and may have affected how participants responded to the questions that were asked. It is also difficult to determine if participants responded truthfully to the survey questions. We do note, however, that the survey responses were reviewed before analysis and we concluded that to the best of our knowledge, the responses seemed to be completed truthfully and thoughtfully. It should also be noted that individual traits and characteristics can change significantly over time, particularly during the early stages of a startup. Thus, it is possible that an individual’s archetype may change and that the results in this study represent an instantaneous measurement of each participant’s self-reported traits. Future work will focus on collecting more longitudinal data to determine how these traits and archetypes might evolve with time.
Regarding the analytical methods used in this work, k-means clustering was employed to explore patterns of behaviors and archetypes within the data without imposing assumptions about predictive relationships. As an unsupervised method of machine learning, k-means clustering allowed us to investigate naturally occurring patterns in these traits without relying on explanatory or predictive models. However, we recognize that this approach has limitations in terms of establishing causal or predictive relationships. Thus, a valuable area for future work could involve supervised methods to test predictive or explanatory hypotheses. Such analyses could assess specific relationships between identified archetypes and performance-based metrics such as resource accuracy or financial performance.
This work studied startup founders that are in the early stages of the new product development process, and as a result, the financial performance data may be affected. A significant area of future work will focus on studying these firms longitudinally as they become more developed. By doing so, we will be able to track the firm over time and gain important insights into their financial performance. Studying the success of these teams longitudinally will provide meaningful insights to the field of design by uncovering the complex relationship between individual traits of founders and startup success.
9. Conclusion
We sought to understand the interactions between salient traits for resource-constrained design teams, and how these interactions affect resource accuracy. To achieve this goal, a mixed-methods study was conducted with 241 technology-based startup company founders that participated in the Mid-Atlantic Hub of the NSF I-Corps program. These participants represent a unique subset of new product development teams and provide an opportunity to study authentic problem-solvers that operate in a resource-constrained environment. Participants were surveyed on their individual traits such as self-efficacy, bricolage, risk propensity and perceptions of psychological safety. Thirty-two participants were also interviewed to determine their ability to manage resources, measured by resource accuracy. Results suggest that two distinct archetypes of participants emerge when studying individual traits. Participants in archetype 1 exhibit greater levels of ESE, entrepreneurial bricolage, risk propensity and individual perceptions of psychological safety when compared to archetype 2. We also found that the distribution of firms’ stage of development differed significantly across archetypes, but their financial performance did not. Finally, archetype 1 participants were significantly better at allocating relevant resources to mitigate perceived obstacles than archetype 2 participants. In order to understand how problem-solvers operate, we aimed to identify emergent patterns in traits that are critical for teams operating in a resource-scarce environment. Overall, this study suggests that distinct groups of designers may exist, and that some may be better equipped to develop successful designs. Through this work, we highlight the need for further research into archetypes of designers and possible reevaluation of design methods to better equip all designers with the necessary skill set to develop successful solutions and drive innovation.
Acknowledgments
This work is supported by NSF Grant No. 2044502.
Appendix
1. With which Mid-Atlantic Hub NSF I-Corps program are you affiliated?
2. In which Mid-Atlantic Hub NSF I-Corps program did you participate?
3. Are you currently an undergraduate or graduate student?
(1) Yes
(2) No
4. What is your current academic standing?
(1) First-year (freshman)
(2) Sophomore
(3) Junior
(4) Senior
(5) Master’s student
(6) PhD student
5. Please list your major and any minors (intended or declared).
6. What is your current (or most recent) job title and organization/institution?
7. What is the name of your startup or company?
8. What is your role in the startup or company?
9. How long has your company or startup been in existence? Please provide an approximate date your company or startup was founded.
10. How many W-2 employees (including founders) does your venture have?
11. At what stage of development is your product, service or system?
(1) Existing in market
(2) Completed and ready for sale or delivery
(3) A prototype, procedure or minimum viable product that has been tested with customers
(4) A model or idea is being developed
(5) Still in idea stage
(6) Product development is paused at the moment
(7) N/A
12. If you have received any business venture funding, what type and how much? Please enter numerical values in USD.
13. How much confidence do you have in your ability to…
(1) Brainstorm (come up with) a new idea for a product or service
(2) Identify the need for a new product or service
(3) Design a product or service that will satisfy customer needs and wants
(4) Estimate customer demand for a new product or service
(5) Determine a competitive price for a new product or service
(6) Estimate the amount of startup funds and capital necessary to start my business
(7) Design an effective marketing/advertising campaign for a new product or service
(8) Get others to identify with and believe in my plans and vision for a new business
(9) Network, i.e., make contact with and exchange information with others
(10) Clearly and concisely explain my business idea
(11) Supervise employees
(12) Recruit and hire employees
(13) Delegate tasks and responsibilities to employees
(14) Deal effectively with day to day problems and crises
(15) Inspire, encourage and motivate my employees
(16) Train employees
(17) Organize and maintain financial records of my business
(18) Manage the financial assets of my business
(19) Read and interpret financial statements
14. Does the following represent how you never, rarely, sometimes, often or always go about doing things for your startup?
(1) We are confident of our ability to find workable solutions to new challenges by using our existing resources
(2) We gladly take on a broader range of challenges than others with our resources would be able to
(3) We use any existing resource that seems useful to responding to a new problem or opportunity
(4) We deal with new challenges by applying a combination of our existing resources and other resources inexpensively available to us
(5) When dealing with new problems or opportunities we take action by assuming that we will find a workable solution
(6) By combining our existing resources, we take on a surprising variety of new challenges
(7) When we face new challenges we put together workable solutions from our existing resources
(8) We combine resources to accomplish new challenges that the resources weren’t originally intended to accomplish
(9) To deal with new challenges we acquire resources at low or no cost and combine them with what we already have
15. Please rate your agreement with the statements below in regards to your startup team
(1) If you make a mistake on this team, it is often held against you
(2) Members on this team are able to bring up problems and tough issues
(3) People on this team sometimes reject others for being different
(4) It is safe to take a risk on this team
(5) It is difficult to ask other members of this team for help
(6) No one on this team would deliberately act in a way that undermines my efforts
(7) Working with this team my unique skills and talents are valued and utilized
16. Are you generally a person who is fully prepared to take risks or do you try to avoid taking risks?
17. With which gender do you identify?
(1) Man
(2) Woman
(3) Nonbinary
(4) Another gender
(5) Prefer not to answer
18. With which racial/ethnic groups do you identify?
(1) Hispanic or Latinx
(2) African American or Black
(3) Asian
(4) Native Hawaiian or other Pacific Islander
(5) Native American or Alaskan Native
(6) Caucasian or White
(7) Another
(8) Prefer not to answer
19. What is the highest level of school you have completed or the highest degree you have received?
(1) No high school diploma
(2) High school diploma
(3) Associates degree
(4) Some college
(5) Bachelor’s degree
(6) Master’s degree
(7) PhD
(8) Professional degree (MD, JD, DDS, DVM, etc.)
(9) I don’t know
(10) Not applicable
20. What is the highest degree achieved by your mother?
(1) No high school diploma
(2) High school diploma
(3) Associates degree
(4) Some college
(5) Bachelor’s degree
(6) Master’s degree
(7) PhD
(8) Professional degree (MD, JD, DDS, DVM, etc.)
(9) I don’t know
(10) Not applicable
21. What is the highest degree achieved by your father?
(1) No high school diploma
(2) High school diploma
(3) Associates degree
(4) Some college
(5) Bachelor’s degree
(6) Master’s degree
(7) PhD
(8) Professional degree (MD, JD, DDS, DVM, etc.)
(9) I don’t know
(10) Not applicable
22. How old are you?
(1) Under 18
(2) 18–24 years old
(3) 25–34 years old
(4) 35–44 years old
(5) 45–54 years old
(6) 55–64 years old
(7) 65+ years old
23. The researchers of this study are interested in collecting founder narratives of lived experiences during startup processes. Would you be willing to participate in a brief interview with the researchers to describe your experiences in greater detail?
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