Abstract
Our study explores links between the initial business model design and the post-founding development of new technology-based firms (NTBFs) through a selection of recently established NTBFs across Sweden, Finland, and France (481 firms). The trajectory of these firms’ performance has been observed and analyzed over the span of five years. The study examines the long-term effects of NTBFs’ initial business model design on their development and the path dependency of these designs on future firm performance. Our results indicate that high-growth and stable NTBFs have distinct initial designs, implying that new firms’ founders and their business design decisions have important consequences for subsequent NTBF development. Specifically, our study examines how business model characteristics impact the growth trajectory of NTBFs by empirically differentiating between the foundational characteristics of high-growth and stable NTBFs. We find that maintaining stable activity is linked to a low level of uniqueness, less demanding business objectives, and a lack of risk while high-growth is related to a larger early access to resources. We propose an explanation using path dependency literature, arguing how early business model choices predetermine long-term growth paths. Our research also provides valuable insights for stakeholders in designing targeted strategies for sustainable development.
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1 Introduction
The startup ecosystem traditionally prioritizes high-growth, disruptive companies with the potential to become the next “unicorn” startup (Atzmon et al., 2022; Kuratko & Audretsch, 2022). However, this narrow focus overlooks the significance of “stable” new firms in driving economic growth and job creation (Kuckertz et al., 2023). Entrepreneurship researchers have predominantly concentrated on high-impact “black swan” events in the entrepreneurial realm (Mahroum, 2016), despite initial public offerings and venture capital-backed high-growth firms comprising only a small percentage of new firms and job creation. This oversight disregards the crucial role that stable and sustainable small and medium-sized enterprises play in propelling economic growth (Aldrich & Ruef, 2018). Our study aims to redirect this focus by investigating factors supporting the stability and long-term success of new technology-based firms (NTBFs) as they navigate their post-founding phase.
From a conceptual point of view, path dependency theory, as proposed by Arthur (1994) and David (1985), posits that initial choices have lasting effects, influencing future outcomes (Bergek & Onufrey, 2014). Previous studies have explored how path dependency affects the founding process (e.g., Boxstael & Denoo, 2020; Snihur & Zott, 2020; Tornikoski & Renko, 2014) and post-founding growth (e.g., Bamford et al., 2000), but have not been very specific in specifying what impacts founding process and subsequent development. In this study, we introduce the notion of business model as one possible conduit through which path dependency works in NTBFs.
Indeed, while NTBFs’ post-founding development is marked by diverse trajectories, including high growth, stable path, or failure, this variation can be attributed to the business models adopted by these firms. Business models play a critical role in extracting value from innovation, enhancing firm performance, and building organizational capabilities (Aversa et al., 2015; Chesbrough & Rosenbloom, 2002; Jacobides, 2006). However, these business models often lack clarity and undergo multiple iterations before firm establishment (Loch et al., 2008; Kessler et al., 2012). Recent studies have explored scalable and non-scalable business models (Nair & Blomquist, 2019; Nielsen & Lund, 2018) and their link to resilience (Buliga et al., 2016) and financial risk (Patzelt et al., 2008). Analyzing NTBFs’ founding business models sheds light on their growth potential and long-term viability. The success of NTBFs hinges on their initial business models, which delineate how a startup intends to generate revenue from its products or services. A robust initial business model articulates the value proposition to customers, addresses market needs, and identifies avenues for scalability. Investors often scrutinize the initial business model to evaluate its viability and potential return on investment.
Applying path dependency theory in our context, we anticipate discovering differences in founding business models between stable and high-growth NTBFs. This investigation is crucial for technology transfer, as the stability and adaptability of NTBFs are central to the effective dissemination and application of new technologies. To this end, our study builds on previous research by investigating the influence of founding business model design on NTBFs’ sustained development. We consider NTBFs to be founded when they have initiated business activities and generate revenue, thus identifying their founding business models. Specifically, we aim to understand NTBFs’ post-founding trajectories by examining the developmental impact of founding business models. Guided by path dependency theory, our primary research question is: To what extent do founding business models and their different elements explain why some NTBFs witness stable post-founding development while others witness high growth?
To investigate our research question, we conduct a quantitative, exploratory study using a sample of 481 NTBFs established across Finland, France, and Sweden in 2013. Tracking their progress until 2018, corresponding to their first five years of operation, our findings suggest that certain elements of founding business models significantly influence subsequent NTBF development. Additionally, we observe distinct differences in founding business models between stable and high-growth NTBFs. Overall, our study provides some evidence supporting the utility of path dependency theory in comprehending the lasting effects of founders’ decisions on NTBFs’ post-founding development. By identifying the characteristics and strategies enabling NTBFs to maintain positive activity over time and contribute to economic growth, this study provides valuable insights for policymakers, entrepreneurs, and researchers in their efforts to support the development of stable, job-creating startups. As such, our findings have practical implications for entities interested in investing in NTBFs, such as venture capitalists, as they underscore the importance of understanding the conditions under which NTBFs are founded.
The paper is organized as follows. Section 2 outlines the theoretical framework. Section 3 details the dataset, data collection methods, and analyzed variables. Section 4 presents the findings, and Sect. 5 discusses their implications. Finally, Sect. 6 presents the conclusions.
2 Theoretical framework
2.1 Growth and survival
Policymakers and researchers recognize NTBFs’ significant impact on long-term economic growth (Spencer & Kirchhoff, 2006; Storey & Tether, 1998). High-growth NTBFs can spur job creation (Coad et al., 2014; Henrekson & Johansson, 2010), leading to policies favoring resource allocation for their support (Mason & Brown, 2013; Shane, 2009). However, critics highlight the potential unsustainability of high growth (Daunfeldt & Halvarsson, 2015) and difficulty in identifying high-growth firms early (Hölzl, 2009; Rannikko et al., 2019; Storey, 1994). Evolving firm growth theories include neoclassical, transaction cost, and Penrose’s theories. Modern economic theory, emphasizing competition and rapid technological change, aligns with contemporary growth and industrial understanding. This concurs with Schumpeter’s concept of capitalism as “creative destruction.” Alchian (1950) argues for the economic progress-driving mechanism of successful firm survival and growth, contrasting with the exit of less viable firms. However, new firms face challenges, with average survival rates around 50–55% (Dunne et al., 1989; Löfsten, 2016; van Praag, 2003). In 2005, the five-year survival rate for EU-founded enterprises was 46.4% (Eurostat, 2014), making survival and growth critical for economic development.
Policymakers acknowledge high-growth NTBFs’ role in driving economic growth through R&D investments and innovative products (Martínez-Ros & Labeaga, 2009; García-Manjón and Remero-Merino, 2012). Several studies focus on NTBFs and early processes and support (Davidsson & Klofsten, 2003) as well as on the human capital side of NTBFs (De Cleyn, et al., 2015). However, high-growth NTBFs remain uncommon (Autio & Rannikko, 2016). Typically, small firms exhibit tent-shaped growth rate distributions, implying limited high-growth instances (Coad & Hölzl, 2009). High-growth firms struggle to maintain growth over time (Coad, 2007; Coad & Hölzl, 2009), and are often labeled “temporarily unstable populations” (Delmar et al., 2003).
With this focus on high-growth firms, firms that maintain stability over time have received little attention, despite their economic importance. Although much is known regarding these firms’ survival rates, how and why they achieve stability is less understood. To address this knowledge gap, this study applies path dependency theory to investigate the characteristics of founding business models that may influence NTBF development over time. Specifically, we explore the relationship between NTBFs’ founding business models and subsequent development, focusing on both high-growth and stable firms.
2.2 Path dependency and initial conditions
We adopt path dependency theory to explore how new firms’ founding business models affect subsequent development, and whether these firms experience high growth or stability. The path dependency theory posits that past decisions and actions influence future ones, leading to a similar developmental pattern. This suggests that a system has a memory that shapes its future development. However, path dependency is not a clearly defined concept (Djelic & Quack, 2007; Morgan & Kubo, 2005; Pierson, 2000), and collecting acceptable empirical evidence for it can be challenging (Vergne & Durand, 2010).
Scholars such as David (1985), Arthur (1989), North (1990), Witt (1997), Rusko et al. (2019), Sydow et al. (2020), Cloutier and Messeghem (2021), Samuelsson et al. (2021), Ben-Hafaïedh and Hamelin (2022), and Hepp (2022) have been widely discussing this theory in the context of technological change. NTBFs are considered successful when they begin operations and generate sales revenue, at which point, past decisions regarding market access result in a founding business model. This model, validated by market success, continues to influence future decisions and determines a firm’s development path.
Stinchcombe (1965) presents supporting ideas when arguing that a new organization’s performance is significantly affected by the conditions and events surrounding its founding. Empirical evidence shows that founding conditions affect organizations over time (e.g., Boeker, 1988, 1989; Kimberly, 1975; Pennings, 1980; Tucker et al., 1990). Consequently, founding conditions are important sources of path dependency and imprinting effects for new firms at their inception (Romanelli, 1989). Moreover, imprinting can be understood as a founder-driven process for new firms (Johnson, 2007; Simsek et al., 2015). Founders’ decisions when establishing a firm have long-term effects on firm development (Bamford et al., 2004). Thus, previous studies have proposed or discovered firm founders’ imprint routines (Phillips, 2002), structures (Phillips, 2005), strategies (Boeker, 1989), capabilities (Klepper, 2002), values (Leung et al., 2013), and networks (Marquis, 2003; McEvily et al., 2012; Milanov & Fernhaber, 2009).
Why a firm’s initial founding conditions and events shape its developmental trajectory is rooted in path dependency (Boeker, 1989). Once a firm establishes its initial strategies and configurations, the tendency to preserve these initial choices makes deviating from them difficult (Miller & Friesen, 1984; Quinn, 1980). This can be problematic for new firms if founders become overly optimistic and commit to decisions that ultimately hinder a firm’s ability to adapt and adjust during its development (Cooper et al., 1988). Once an NTBF initiates operations and generates sales revenue, choices made during its founding will shape its development path (Sydow et al., 2009).
Research on new firms commonly assumes that high-growth firms are successful and low-growth firms are failures. However, this ignores the importance of firms that maintain stability over time, which do not qualify as high-growth firms. Accordingly, policymakers and researchers have paid little attention to these stable firms’ economic importance and survival rates. Previous research has primarily focused on identifying high-growth firms’ characteristics or determinants (e.g., Siegel et al., 1993; Barringer et al., 2005; Moreno & Casillas, 2008; Audretsch, 2012; Bianchini et al., 2017; Moschella et al., 2019; Rydehell et al., 2019a), which can be related to growth opportunities, ability, and motivation (Löfsten & Lindelöf, 2001, 2002). However, growth orientation cannot sufficiently explain new firm growth, particularly in uncertain and unstable environments (Autio et al., 2000; Christensen & Raynor, 2013; Yli-Renko et al., 2002).
Some entrepreneurs refrain from pursuing certain growth strategies for non-economic reasons, such as employee well-being or the risk of losing control (Wiklund et al., 2003). Isaksson et al. (2021) analyze how initial business models in NTBFs influence their growth intentions and strategies. They underscore the significance of key partners, activities, resources, and value propositions in shaping the firm’s strategic orientation towards growth. Notably, while Isaksson et al. (2021) identify critical differences in the business model components between high-tech manufacturing and knowledge-intensive firms, they report minimal direct correlations between the specific design of the business model and actual early growth outcomes. This suggests that, although the choice of business model elements is strategic, it does not necessarily translate into a willingness or capability for growth. Thus, it aligns with the notion that not all founders aim to expand their new businesses rapidly or extensively (Napier et al., 2012; Storey, 1994), pointing to a nuanced understanding of growth strategies where the willingness to grow is as pivotal as the strategic choices made in the business model design. We push this study further by exploring longer term growth outcomes.
According to Kirchhoff (1994), a lack of motivation and resources can impede innovative small firms’ development. Research shows that founders’ attitudes toward growth play a significant role in entrepreneurial firms’ performance (Autio et al., 2000; Isaksson et al., 2013; Wiklund & Shepherd, 2003). Rydehell et al. (2019b) highlights the relationship between founders’ growth orientation and actual growth. However, whether this relationship also applies to perceived performance (i.e., compared to the founders’ goals) remains unclear. A firm’s perceived performance can be influenced by unrealistic expectations at its inception (Rydehell et al., 2019b); however, not all entrepreneurs aspire for high growth (Gartner & Liao, 2012; Wiklund et al., 2003). Research also shows that founders may differ in their attitudes toward growth (Gartner & Liao, 2012; Kirchhoff, 1994; Wiklund et al., 2003), which should be considered when examining high-growth firms’ characteristics and determinants (e.g., Siegel et al., 1993; Barringer et al., 2005; Moreno & Casillas, 2008; Audretsch, 2012; Bianchini et al., 2017; Moschella et al., 2019).
The discussion above highlights the crucial role that founders’ aspirations and motivations play in shaping a new firms’ developmental trajectory (Löfsten & Lindelöf, 2002; Yli-Renko et al., 2002). The decisions founders make at a new firm’s inception, such as the type of business model adopted, likely have lasting effects on firm development. This aligns with path dependency and imprinting mechanism theories, which suggest that the choices made during a firm’s early stages shape its future trajectory. However, while these theories provide insight into the potential impact of founders’ decisions, they offer little guidance on what specific elements of a founding business model may have the most significant effects on new firm development. To address this gap, our study explores what elements of founding business models, if any, have an imprinting effect on subsequent new firm development. Through an explorative examination of the types of decisions entrepreneurs can make regarding new firms’ founding business models, our study aims to provide a more nuanced understanding of the relationship between founding business models and new firm development.
Based on imprinting effects Snihur and Zott (2020) identify how founders achieve novelty imprinting, which they conceive as imprinting processes that result in and explain how cognitive imprints reinforce structural imprints in the context of business model innovation. Other researchers studying the connection between organizational models and high-tech firms found that the employment relations models firm founders adopt shape how human resource management evolves, with a strong path dependency in the development of employment systems. Baron et al. (2001) find that changes in organizational leaders’ accepted employment models increase turnover, thereby affecting subsequent organizational performance.
Our study demonstrates the utility of path dependency theory in understanding how founders’ decisions regarding founding business models have imprinting effects on NTFBs’ post-founding development. A business model delineates the manner in which a company generates, delivers, and enhances value (Amit & Zott, 2001; Osterwalder et al., 2005). In our study’s context, the founding business model pertains to the framework of a nascent firm concerning its surroundings (Chesbrough & Rosenbloom, 2002). We hypothesize that low-growth, stable activity is not on the same continuum as high firm growth, which is inherently prone to instability. Stable, low-growth firms could benefit from being embedded in an entrepreneurial ecosystem to face difficulties despite their weaknesses. This same ecosystem might hinder high-growth companies, giving them less control and flexibility over their activities; however, if they assume their risk, they might have more freedom to differentiate themselves from competitors.
3 Sample and method
To investigate the relationship between a new firm’s founding business model and its development, we mixed survey data (to collect information in the business model elements) with registry data (to gather annual financial records). We used logistic regression to analyze the connection between founding business models and subsequent development.
3.1 Definition of new technology-based firms
We used the NACE Rev. 2 classification to identify NTBFs, categorizing firms by R&D expenditure to value-added ratio. Our focus was on technology intensity, encompassing high-tech manufacturing, mid-tech manufacturing, and knowledge-intensive service industries. Notably, for knowledge-intensive services, we recognize that R&D expenditures are not the sole indicator of their intensity. Consistent with the Eurostat’s high-tech classification, knowledge-intensive services can also be characterized by the proportion of employees with higher education degrees, reflecting their reliance on intellectual capabilities and expertise (Eurostat, n.d.). This broader definition allows for a more inclusive identification of NTBFs across various sectors, aligning our approach with prior research (Butchart, 1987; Daunfeldt et al., 2015; Ejermo & Xiao, 2014).
We conducted telephone surveys with 589 NTBFs founded in 2013 across Finland, France, and Sweden to gather business model information. We obtained annual financial records from founding to 2018 via official registries. Employing logistic regression, we explored the connection between founding business models and long-term success factors. Further details are available in Appendix Table 6.
3.2 Data collection
3.2.1 Initial data collection about the founding business model
Initially, we identified firms registered in 2013 within our target industries through business registries. Our second step involved conducting a telephone survey in early 2016. This timing was strategically chosen to ensure the firms had been operational long enough for their foundational business models to be established, typically a span of two to three years, which we considered an adequate period for meaningful data collection. In addition, surveying the firms in 2016 allowed targeting them in a phase where their business models were neither too nascent to reflect mere aspirations nor too matured to have significantly deviated from their original concepts. Business model configuration is often established through trial-and-error learning (Andries et al., 2013; Berends et al., 2016). Surveying firms after months of existence ensures that the founders provide insights about a business model on which they engaged enough time and energy to become path dependent while being, at the same time, very close to its initial implemented configuration. The survey yielded 589 responses, with 481 meeting our criteria for inclusion in the analysis.
Overall, selecting Sweden, Finland, and France allowed us to gather a diverse and representative sample of NTBFs, providing a strong foundation for our research and making our findings more generalizable to the broader European industrial sector. We selected firms from Sweden, Finland, and France to ensure the relevance of our results to the European industrial sector. These countries were chosen for several reasons. First, they have a strong history of innovation and entrepreneurship, with Sweden and Finland known for their high-tech industries (Autio et al., 2014; Balawi & Ayoub, 2022), and France having a thriving startup ecosystem (Davies, 2022), making them ideal for studying NTBF development. Second, they offer a diverse range of cultural and economic environments, providing a comprehensive understanding of the factors that influence NTBFs. Third, they have developed infrastructures for innovation and entrepreneurship, including access to funding, training, and mentoring programs, supporting NTBF growth and development. Finally, the availability and quality of data in these countries is excellent, allowing us to track the development of NTBFs over time. Selecting these countries enhances the validity and reliability of our findings. To study French NTBFs, we focused on the Rhône-Alpes region, known for its high business activity and concentration of cities and industrial centers, such as Lyon and Grenoble. Its business register provided a comprehensive sample, improving the robustness of our findings.
We chose our sample selection criteria carefully to ensure the data accurately represents the population of interest. We only included firms organized as limited companies, as this legal structure clearly separates the firm from the individual and eliminates hobby firms from our sample. Additionally, detailed annual report data is typically only available for limited companies, making this structure more suitable for our study. By limiting our sample in this way, ensured a more robust and representative dataset, ultimately strengthening the validity of our findings. To ensure the homogeneity of our sample, we took several measures to control for heterogeneity among new firms. As Wennberg (2005) and Davidsson (2007) note, the characteristics and origins of new firms can vary greatly. Thus, we focused on independent firms that started as “de novo” entities, rather than spin-offs from existing businesses or other types of startups. Furthermore, to ensure that the sample firms were operationally active, we only included those that were registered in a specific year (2013), active (i.e., not deregistered or liquidated), and liable for value added tax and tax prepayment. These measures allowed us to create a sample of firms that were truly independent, operationally active, and representative of the studied population.
This sample represented 18.8% of all NTBFs established in 2013 in the geographical areas of study. This response rate is consistent with previous mail surveys of small and medium-sized firms (Chandler & Hanks, 1994; McDougall et al., 1994; Yli-Renko et al., 2001). The non-respondents included firms that were inactive, could not be located, or chose not to participate without providing a specific reason. We found that the performance of non-respondents did not differ significantly from that of the firms in our sample, indicating that our results are generalizable to the population of NTBFs in the studied geographical areas. In the Appendix, Tables 7 and 8 respectively provide the response rates for each country and an analysis of the non-responses. Table 8 shows that firms decided not to report on their performance for reasons other than the characteristics of their founding business model.
3.2.2 Performance data and screening criteria
In 2020, we conducted a follow-up study to assess the growth and performance of our sample firms. Data were collected from reliable sources including the Retriever Business database (Sweden), Voitto + database (Finland), and Rhône-Alpes business register (France). These trusted sources maintained by organizations such as Asiakastieto Oy and French commercial courts ensured accurate, up-to-date, and comprehensive performance data.
We employed rigorous screening to ensure our sample represented genuine “de novo” firms. Firms reporting their business as “a purchase or takeover of an existing business” were excluded (44 firms). Additionally, 64 firms without established founding business models or initial sales were excluded. Data availability issues led to 75 firms being excluded, resulting in a final sample of 406 observations with performance and business model information. This meticulous screening process ensured that true independent startups were represented in our sample.
3.3 Questionnaire design and administration
To ensure reliable and valid data collection, we collaborated with TNS-Sifo, a leading research agency, to conduct telephone interviews in March 2016 across all three study countries. This approach enhanced data quality and control. Experienced professional callers, chosen randomly, were employed to further bolster data reliability. Telephone interviews yielded high response rates and minimized question misinterpretation risks. Interviewers were trained to guide respondents through the survey, reducing confusion.
Our survey development followed a two-step process. We initiated discussions with entrepreneurs to identify key resource dimensions for quantitative measurement. Thorough pre-testing and questionnaire modifications occurred through telephone discussions with firms to eliminate uncertainties. TNS-Sifo reviewed the questionnaire for language and clarity, enhancing result validity.
Interviews were monitored and recorded to ensure response consistency and quality. The master questionnaire was initially developed in English and later translated by proficient researchers into Swedish, Finnish, and French. Six NTBFs participated in a telephone pre-test to identify uncertainties and refine the survey. A pilot study with 26 NTBFs assessed questionnaire effectiveness. A pre-test in each country enhanced question clarity. Our research design prioritized reliability and validity in the data collection process.
3.4 Variables
3.4.1 Dependent variables
In the conceptual section of this paper, we suggest that firms witnessing stable and high-growth development are, by nature, different from inception. For this reason, we analyze the correlations between the business model characteristics and the firm trajectories in a different set of analyses, with two different dependent variables: a dichotomous measure for stable firms and one for high-growth firms.
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(1)
Stable firms: We identified stable firms as those consistently reporting positive sales over at least three consecutive years: 2014–2016, 2015–2017, or 2016–2018 (294 firms in the sample). Stability was determined by consistent reporting, excluding firms with irregular activity reporting, failed firms, or those missing activity reports.
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(2)
High-growth firms: Eurostat and OECD (2007) suggest defining high-growth firms as those with a minimum of ten employees in the founding year and an annualized employment growth surpassing 20% over three years (Hölzl, 2014). However, Coad et al. (2014) and Halabinsky et al. (2006) propose defining high-growth as those achieving at least 50% sales growth across three financial years. In our study, high-growth status was assigned to firms reporting sales for three consecutive years (meeting stability criteria) with an average annual growth rate exceeding 50% (103 firms in the sample). We calculated year-to-year growth (2014–2015, 2015–2016, 2016–2017, and 2017–2018), and the high-growth dummy was assigned if the four-period average exceeded 50%. Figure 1a presents the average annual sales growth distribution, showing sparse values above 50%. Our sample comprised three groups: (1) Non-stable firms, (2) Stable firms, and (3) High-growth firms. Figure 1b displays total sales distribution, accounting for new firms’ inherent small initial values. To avoid disproportionate effects of extreme values, we dichotomized high growth (above/below 50%). Median annual growth for NTBFs in the sample was 18%.
3.4.2 Independent variables
We focused on founders’ decisions regarding the design of their business, specifically the founding business model. This area of interest is particularly relevant, as new firms often encounter a high uncertainty in terms of markets and technologies (Andries et al., 2013), which poses challenges when deciding on a business model (Trimi & Berbegal-Mirabent, 2012). Furthermore, the technological uncertainties inherent in new firms’ products, services, and processes exacerbate these difficulties (Bollinger et al., 1983; Storey & Tether, 1998).
A business model is a description of how a firm creates, delivers, and adds value (Amit & Zott, 2001; Osterwalder et al., 2005). In the context of our study, the founding business model refers to the architecture of a new firm in relation to its environment (Chesbrough & Rosenbloom, 2002). Founding business models also reflect the market knowledge and resources that a firm can acquire and utilize (Andries & Debackere, 2007, 2013).
A new firm’s architecture is commonly described in relation to its environment by dividing its business model into nine interconnected elements: value proposition, target customer segments, customer relationships, distribution channels, revenue streams, vital resources, key activities, strategic partners, and cost structure (Osterwalder et al., 2005). When designed effectively, these elements determine how a firm interacts with current and potential customers and organizes its internal operations and supply chain to meet customer needs (Sohl et al., 2020). The nine business model elements are represented by independent variables grouped into nine categories (Osterwalder & Pigneur, 2010):
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(1)
Value proposition was measured using three five-point scales: differentiation from competitors, pricing relative to industry peers, and product quality.
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(2)
Customer segment was assessed through three dummy variables: customer size, payers, and exporting.
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(3)
Customer relationship was measured using a five-point scale reflecting the proximity of a firm’s relationship with primary customers.
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(4)
Channel was represented as a dummy variable indicating direct customer interaction.
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(5)
Revenue streams were denoted by a dummy variable for multiple revenue sources.
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(6)
Key activities were a measure comprising three five-point scales evaluating the extent of internalization for expertise, technology, production, and sales-related functions.
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(7)
Cost structure was a measure comprising two five-point scales reflecting the dominance of fixed costs and economies of scale in production.
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(8)
Key partners were quantified by averaging the number of different partner types, such as suppliers, subcontractors, distributors, local authorities, and universities.
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(9)
Key resources were assessed through two five-point scales gauging a firm’s advanced expertise and recognition, along with counting external funding sources, such as banks, venture capital, crowdfunding, or public sources.
In operationalizing the components of founding business models, our methodology employed a nuanced approach by distinguishing between reflective, formative, and single-item measures to capture the multifaceted nature of new firm development. Reflective measures were strategically applied to constructs such as the value proposition and customer relationships, where the underlying concept is believed to cause the observed indicators, ensuring coherence and inter-correlation among these indicators, reflective of the firm’s strategic emphasis (Jarvis et al., 2003). This approach underscores the reflective nature of these variables, where changes in the construct are expected to be mirrored by all indicators. Formative measures were utilized for constructs where indicators collectively contribute to the formation of the latent variable, as seen in key activities and cost structure. These components independently affect the business model, highlighting the formative nature of these variables where each aspect contributes uniquely to the construct, necessitating a composite measure to account for the diverse elements (Diamantopoulos & Winklhofer, 2001).
Lastly, single-item measures were applied to straightforward constructs, where a singular, well-defined indicator sufficiently captures the construct’s nature, such as channel and revenue streams. This decision, guided by the need for parsimony and the aim to reduce respondent burden, did not compromise the depth of insights into the founding business models’ influence on firm development (Edwards & Bagozzi, 2000).
Together, these methodological choices, reflective measures for strategic emphasis constructs, formative measures for composite business model elements, and single-item measures for straightforward constructs, ensure a robust and nuanced understanding of how foundational business model elements drive new firm growth and evolution.
3.4.3 Control variables
We included four control variables to isolate the effects of different settings (i.e., all chosen variables may be associated with NTBF sales growth, in addition to the chosen independent variables): (1) Type of goods for sales measured on a five-point scale whether firms were selling “only products” (1), “Mainly products” (2), “Products and services equally” (3), “Mainly services” (4) or “Only services” (5); (2) Growth satisfaction as a composite measure of how satisfied the founder was with the time required to commercialize the product or service, with an increase in sales, business profitability, and employment; (3) Startup experience; and (4) Industry experience of the founders (dummies). We used dummies to assess whether founders had experience in startups (“true” if they had founded a prior business) or in the industry (“true” if the number of years of experience was greater than (5) To address the highly skewed distribution in our sample. We did not choose one year as the threshold, because 94% of the respondents had at least some prior industry experience.
4 Results
4.1 Descriptive statistics
Table 1 presents the descriptive statistics for our initial sample of NTBFs (complete observations, minus respondents screened out; N = 481), for the complete dataset after collecting performance measures in the follow-up study in 2020 (n = 406), and for the group of stable firms (n = 294). Overall, the values are very similar across groups, suggesting that a firm’s founding business model and its different elements, as measured in 2016, had little to no effect on subsequent firm development (follow-up and stable firms).
Table 2 shows the correlations between variables in our model. No major multicollinearity issues were found overall, although some business model elements were somewhat correlated. The highest correlation was 0.36, between the number of external sources of funding and number of key partners. To avoid spurious effects due to some collinearity between variables, we ran our two models with each business model element separately and all elements together.
4.2 Regression analysis
The next step was the regression analysis. Table 3 presents the results of the logistic regression to assess the correlations between the business model measurements and three-year positive sales reporting (at least) to identify stable NTBFs. Blocks 1–9 present the different business model elements individually. Model 10 aggregates all business models elements. The results were consistent across models. Product quality was strongly significantly correlated was a lower likelihood of reporting stable sales (β = -0.925; p-value < 0.001), as was customer size (many small clients, as opposed to a few big clients; β = -0.661; p-value < 0.05). Key activities (higher value means that more firm’s activities are done externally; β = 0.481; p-value < 0.05) and key resources (higher value means a firm’s expertise is more advance and their reputation is better; β = 0.498; p-value < 0.001) were associated with a higher likelihood of reporting stable sales. Thus, the NTBFs in our sample were more likely to achieve stable sales (as opposed to non-stable sales) when characterized by lower product quality, having few big clients, higher externalization of key activities, and better resources (more advanced expertise and a better reputation).
Table 4 focuses on the stable firms (reported positive sales for at least three consecutive years) and predicts which business model elements are correlated with a high-growth development path. The results were consistent across models. Two business model elements were correlated with high-growth. External funding (higher value means more funding sources) was associated was a higher likelihood of reporting high growth (β = 0.576; p-value < 0.01), as were revenue streams (higher value means higher diversity in revenue streams; β = 0.865; p-value < 0.01). Overall, the NTBFs in our sample were more likely to achieve an average annual growth above 50% (as opposed to stable sales) when they were characterized by more diverse revenue streams and funding sources.
4.3 Robustness checks
Most recently founded NTBFs are in the computer programming industry, which was also the case in our sample (60% of firms). To assess the extent to which our results are robust to industry differences, we computed multiple models with industry dummies and subsamples by industry (computer programming or others). The results are available in Table 9 in the Appendix. For the models that predicted sales stability, our results were robust to industry controls. Maintaining the same significance level is difficult when splitting a sample, likely because of a lack of power related to a much smaller sample size. For the models predicting high growth, although the direction remained the same and the model with industry controls was identical, the significance level varied by industry. Further studies could consider the industry-specific effects of business model characteristics with a larger sample of firms.
5 Discussion, limitations and future research directions
This study applied path dependency theory to investigate if variations in founding business models impact NTBFs’ development paths, distinguishing between stable growth and high growth. We analyzed a 2013 NTBF cohort in Sweden, Finland, and France, tracking their development from 2014 to 2018. Our findings reveal two key insights. First, NTBFs achieve sales stability by displaying traits such as lower product quality, fewer major clients, increased externalization of key activities, and superior resources (enhanced expertise and reputation). Second, NTBFs attain high growth through diverse revenue streams and funding sources. However, few founding business model elements overall influence NTBF development. Elements driving stable growth and high growth diverge, with no single element aligning with both trajectories. We explore the theoretical and practical implications of these findings further below.
5.1 Theoretical implications
While classical approaches to path dependency theory focus on external conditions surrounding new organizations, our study focuses on imprinting factors under founders’ volitional control: newly founded firms’ business models. Founders face several possibilities when considering whether to start a new firm; however, once they begin to make decisions and take action, these initial choices trigger further developments that determine a firm’s organizational path (Sydow et al., 2009). Our findings appear to support this, as they show differences in founding business models that apparently result in different development paths between stable and high-growth NTBFs. These observations provide support for path dependency theory in that choices made at a new firm’s inception have significant effects well beyond the founding phase (Aspelund et al., 2005; Geroski et al., 2010). Thus, our findings complement existing studies demonstrating the benefits of adopting path dependency theory to explain performance and success when founding new firms (e.g., Boxstael & Denoo, 2020; Snihur & Zott, 2020). Boxstael and Denoo (2020) analyzed business model design during new venture creation and provided a dynamic view on founder identity imprinting on business models, finding that novelty-centered business models are derived from cognitive work developed by founder identity construction and verification processes.
The founding business model elements explored in our study did not all exert significant path dependency effects on subsequent NTBF development. Specifically, the nine business model elements were not shown to be equally important for imprinting NTBFs’ development paths. Notably, some business model elements are related to stable development while others are associated with high-growth; however, the rationale behind these differences in path dependency effects deserves further exploration.
First, we observed that high-growth NTBFs’ founding business models are characterized by highly diverse funding sources and revenue streams. External funding has been consistently associated with high growth, as a requirement or enabler (Assenova & Sorenson, 2017; Delmar and Shane, 2003; Cavallo et al., 2019; Huergo et al., 2020). Thus, external financing can be used to acquire other resources (Katila et al., 2008) and enhance the development of the new offer to accelerate market development (Festel et al., 2013; Homburg et al., 2014; Katila et al., 2008). Firms with access to more resources can diversify their revenue sources early. Resource slack enables parallel development (as opposed to sequential) of different revenue logics. Although these observations are not particularly surprising, the underlying path dependency remains in question. We refer to this business model path dependency as “increasing resources” and posit that, when external funding brings in new powerful external stakeholders, this creates a moral and legal obligations for NTBFs to deliver on promises they made to secure financing (Table 5). Thus, the entry of external stakeholders in the form of new funding imprints NTBF development toward the sole goal of high growth. Acquiring other required resources through external funding can also cement an NTBF’s path toward high growth.
Second, stable NTBFs have a distinct path dependency. We observed that stable NTBFs’ founding business models are characterized by lower product quality, few big customers, externalizing key activities, and better resources (higher expertise and reputation). Adopting these elements in their founding business model will help NTBFs experience stable development. These elements provide an intriguing picture about stable NTBFs. To design firms that will experience stable development, founders should focus on lower-quality products, offer their products to few big clients, and maintain a lean organization (i.e., externalize key activities), while honing their expertise and reputation. We refer to this business model path dependency as “preserving resources,” (Table 5) and posit that, when founders aim to maintain sales, overextending their resources may not be sensible.
The assumption that founders focus on lower-quality products might be explained by their lack of funding to develop high-quality products. Stable growth might be result from an inability to incorporate venture capital or other investments, not a deliberate design choice, and externalizing key activities may not lead to lower quality, but rather a basic lack of funding to internalize activities. This could be a critical design issue related to a trade-off between product-related choices and financial arrangements. The selection of economies of scope versus scale and product quality may be evident design choices enabled by financial resources and risk.
Thus, NTBFs may face drawbacks in developing perfect products (which takes time), while missing potential revenue from offering less-finished products on the market (DeKinder & Kohli, 2008). Externalizing the maximum number of key activities might also make it harder for NTBFs to pursue high-quality products. Instead, keeping an organization lean, and focusing on honing its own internal expertise and reputation, might be the right decision if internal resources are used to target a few large clients and keep them satisfied with lower-quality products. Externalizing key activities might also allow NTBFs to remain flexible when facing potential market disturbances (Kim & Pennings, 2009) and adjust needed operations to maintain sales. When adopting path dependency theory to explain stable NTBF development, several initial decisions of founders should be considered: the organization of key activities (maximum number externalized), quality of firm resources, and less-than-perfect products initially offered to a few big clients. Compared with the other five, these four business model elements appear to result in more imprinting effects on NTBFs’ subsequent stable development.
Our study indicates that new firms’ founders and their business design decisions have important consequences for subsequent NTBF development. However, designing business models is complex, can require multiple iterations, and may be characterized by regression, which conflicts with path dependency theory. After a business model is implemented and exploited, path dependency might hinder changes, particularly in exploration phases where new high-tech startups have more freedom and entrepreneurs appear completely free to choose their path. Several trade-offs between technology, financial, product/service and financial dimensions in a business model are intently related and interdependent, and the most important issues can be related to customers, resources, and expertise or an entrepreneurial ecosystem. A founder’s decision itself might not imprint a certain business model, but rather the degree to which the founder succeeds in attracting external funding. Certain partners being included in an entrepreneurial ecosystem leads to access to capital. Including a venture capitalist’s relevant knowledge and partners including intellectual capital is often why startups grow quickly. However, receiving external funding may not be an intentional choice founders make, but dependent on selection mechanisms that venture capital funding agencies commonly use.
5.2 Practical implications
Our study has practical relevance for not only NTBFs but also individuals interested in investing in such firms, such as venture capitalists. To make informed investment decisions, potential investors should understand the conditions under which an NTBF is founded. The job-creation ability of high-growth firms implies that policymakers should allocate more resources to support these firms, rather than investing in startups that generally have no growth ambition. However, this suggestion has been criticized because high-growth does not appear to have long-term stability, and potential high-growth firms are difficult to identify early. However, stable firms can also benefit from being embedded in an entrepreneurial ecosystem, which can provide them with the support and resources necessary to face difficulties despite their weaknesses. Therefore, policymakers should consider allocating resources to support both high-growth and stable NTBFs to promote the overall development of technology-based industry.
While it’s true that stable firms can benefit from being embedded in an entrepreneurial ecosystem by gaining access to new ideas, talent, and potential collaboration opportunities, the practical implementation of this recommendation may encounter several challenges. Firstly, stable firms may have different organizational cultures and risk appetites compared to start-ups and other entrepreneurial ventures within the ecosystem, which could lead to difficulties in integrating and collaborating effectively. Secondly, the priorities and objectives of stable firms may not always align with those of the entrepreneurial ecosystem. These firms may prioritize efficiency, predictability, and risk mitigation over innovation and experimentation, making it challenging to navigate within the dynamic and uncertain environment of an entrepreneurial ecosystem.
The approach to allocate more resources to support high-growth NTBFs over start-ups with no growth ambition is a topic that warrants careful consideration and discussion. High-growth NTBFs have the potential to significantly impact economic growth, job creation, and innovation. By allocating resources to support these firms, policymakers can catalyze economic development and drive technological advancements that benefit society as a whole. NTBFs often pioneer disruptive technologies and business models that enhance competitiveness and drive industry innovation. By prioritizing support for high-growth NTBFs, policymakers can foster a culture of innovation and entrepreneurship that propels industries forward. High-growth NTBFs have the potential to scale rapidly and expand into international markets, bringing revenue and investment back to the home country. Allocating resources to support these firms can position the country as a leader in emerging industries and create opportunities for global market penetration. However, supporting start-ups with no growth ambition can contribute to job stability, community resilience, and the diversification of the business landscape. Policymakers must strike a balance between supporting high-growth NTBFs and start-ups with no growth ambition. Both types of firms play valuable roles in the economy, and a holistic approach that considers the diverse needs of entrepreneurs and businesses is essential. A balanced approach that considers the unique contributions and challenges of different types of firms is essential for fostering a dynamic and resilient entrepreneurial ecosystem.
In examining the influence of initial business models on the growth trajectories of NTBFs, it is pertinent to consider the broader context of governmental support mechanisms, such as the Small Business Innovation Research (SBIR) program in the United States. The SBIR program exemplifies a strategic government initiative aimed at fostering innovation and growth within small businesses, serving as a critical comparison point for our study. As highlighted by Link and Scott (2013), the SBIR program underscores the economic role of government in facilitating business innovation and development. This comparison elucidates the nuanced differences in governmental roles across different national contexts and their impact on NTBF growth. Furthermore, our analysis aligns with the concept of institutional path dependence, as discussed by Goel and Saunoris (2016), which emphasizes the enduring influence of initial conditions and institutional frameworks on firms’ international research intensity and growth patterns.
5.3 Limitations and future research directions
This study has limitations that can provide a foundation for future research. First, our study has limitations in terms of measuring business models. Although we used a comprehensive framework of nine business model elements, alternative, more effective measures may exist for some elements, particularly those we found to have no significant impact on NTBF development, such as customer relationships, key partnerships, and customer-centric value proposition. Also, conceptually, the role of critical design in business model innovation has received some attention (Clauss, 2017; Sánchez & Ricart, 2010; Schneckenberg & Velamuri, 2019) because the design aspect and how these nine business model elements of Osterwalder and Pigneur (2010) does not make the interdependencies (trade-offs) between design choices explicit. Trade-offs with respect to market access include customer segmentation and channels used to access customers and may be part of a business model’s design, but not the result of design choices. Additionally, we did not collect data on respondents’ growth mindset, which might provide insights into how it aligns with various imprinting elements. Thus, future research should explore these issues in greater depth and develop new methods for measuring business model elements in the context of NTBF development.
Second, we also acknowledge that relying on founders’ declarations about their business may lead to some biases in the answer, including desirability biases. This is a common risk of surveys. We tried to mitigate this risk by measuring the business model elements in a neutral way, giving no hint to the respondent on possible preference in answer. We also collected the performance measure independently of the survey, from registry. However, future studies may try to assess business characteristics using, for instance, archival data and observations rather than declaration, for increased objectivity of the measures.
Third, regarding the sampling frame, we chose NTBFs created (registered) in 2013 and surveyed them in 2016. This allowed us to analyze the founding business models of NTBFs that had generated sales revenue, which is an important indicator of a company’s progress and growth potential. Additionally, this sampling method allowed us to track NTBF development over time, which is crucial for understanding factors that influence firm development. However, this might have excluded some NTBFs created in 2013 that had not reported sales revenue at the time of our survey but might have succeeded in doing so after 2016. Future research can expand and improve our sampling method by incorporating a wider range of NTBFs.
Reflecting further on our methodological choices, particularly the exclusion of firms without established founding business models or those that did not report performance data, we acknowledge the potential for selection bias. This exclusion was predicated on the focus of our study on the tangible impacts of specific business model configurations on firm performance. Including firms at too nascent or undefined a stage could have compromised the clarity and relevance of our findings. However, this approach may limit the generalizability of our results across a broader spectrum of NTBFs. Recognizing this, future research could endeavor to include a wider range of NTBFs, perhaps those in earlier stages of development or with incomplete data sets, employing innovative methodologies to infer missing data points. Such studies could provide valuable insights into the early dynamics of business model formation and performance outcomes, enriching our understanding of NTBF development trajectories. This expansion of the research scope would not only address potential biases but also enhance the robustness and applicability of findings across different contexts and stages of firm development.
Fourth, we used a survey to collect information on founding business models, resulting in cross-sectional data. Thus, while we can provide a snapshot of the situation of NTBFs in 2016, we have limited means to identify founders’ intentions and development paths in firms’ early stages. However, our design was able to capture the state of a founding business model in 2016, and the subsequent development data collected through business registries provides valuable insights into these firms’ post-founding development. Although our design has its limitations, we have provided a detailed description of our methodological approach to allow future researchers to build upon and improve our measures and design. Due to the structure of our data and the type of analyses we conduct, correlational in nature, we make no causal claims, but we highlight significant differences in the initial business model characteristics of some NTBFs with stable development trajectories and with high-growth trajectories. We hope that further studies may overcome empirical limitations of ours, including an integrated measure and modeling of the new firm growth trajectories.
Fifth, the study’s claim that specific business model elements dominate path dependency effects on subsequent high-growth NTBFs overlooks the dynamic nature of entrepreneurial processes. While certain business model elements may indeed influence the trajectory of NTBFs, attributing dominance to specific factors oversimplifies the complex interplay of variables shaping entrepreneurial outcomes. Entrepreneurial ventures are inherently dynamic, influenced by various internal and external factors that evolve over time. Path dependency effects, while significant, are just one aspect of a multifaceted process that includes adaptation, learning, and response to changing market conditions, technological advancements, and competitive landscapes. Advocating for a more nuanced approach to understanding NTBF development involves recognizing the interactive nature of entrepreneurial processes. Rather than attributing outcomes solely to specific business model elements, it is crucial to consider how these elements interact with each other and with external factors to shape the growth trajectory of NTBFs.
Our investigation was guided by path dependency theory. We acknowledge that alternative perspectives exist to investigate the same phenomenon. In fact, we encourage future scholarly work to build on our insights and continue exploring the phenomenon of stable development among NTBFs by adopting alternative theoretical perspectives to complement and build on our results. However, rather than categorizing founding business model elements into distinct path dependency effects, another research approach is to study the complex interplay of factors that shape new firm development. This approach acknowledges that the process of founding and developing a new firm involves a dynamic interaction of various internal and external factors. These factors can include market conditions, technological advancements, regulatory environments, entrepreneurial skills and capabilities, industry dynamics, and socio-cultural influences, among others. By focusing on the interplay of these factors, researchers and practitioners can gain a deeper understanding of how new firms emerge and evolve over time. Hopefully, our findings will inspire further scholarly work and serve as a catalyst for further conceptual development in this field. Future research can also focus on different types of business models, industries, or countries to establish our findings’ generalizability. In addition to investigating the imprinting effects of founding business models on subsequent NTBF development, it would be valuable for future research to explore the origins of founding business models and extent to which they are shaped by path dependency and imprinting mechanisms. A further challenge for future scholars is also to develop improved methods for measuring business model elements (for example customer relationships and key partnerships) in the context of NTBFs to better guide decision making of investors and policy makers.
6 Conclusions
This study provides valuable insights into the role of path dependency of founding business models on NTBFs’ post-founding development. Given the crucial role NTBFs play in the economy, gaining a deeper understanding of their development paths is of great importance. This study’s findings provide support for path dependency theory, indicating that the choices made at the inception of a new organization have profound effects on its future development. The study reveals that differences in founding business models result in distinct development paths for NTBFs with stable compared with high growth. NTBFs with stable growth are typically characterized by lower-quality products, fewer big clients, higher externalization of key activities, and better resources, while high-growth NTBFs have more diverse revenue streams and funding sources. This study emphasizes the significance of considering theory-based logic to explain the post-founding development of NTBFs and has implications for both academia and practitioners. Furthermore, for policymakers the idea that founding conditions play a significant role in NTBF development should also be of importance. Understanding concerning this relationship would help to develop conditions under which desired types of NTBFs could be created. By enriching the ongoing discussion regarding the importance of founding business models and their imprinting role in NTBFs’ post-founding development, we aim to contribute to the field and provide useful insights for future research.
Differences in founding business models indeed dictate distinct development paths for NTBFs. However, oversimplification of these paths may hinder practical relevance by overlooking the nuanced entrepreneurial trajectories that exist. Each NTBF, depending on its unique business model, faces a set of challenges and opportunities that shape its development journey. For instance, a knowledge-intensive service startup may have a different growth trajectory compared to a manufacturing startup due to differences in market dynamics, customer acquisition strategies, and revenue models. By recognizing and understanding these differences, policymakers, investors, and entrepreneurs can tailor their support, funding, and strategies to better suit the specific needs of NTBFs. Failure to acknowledge the complexity of entrepreneurial trajectories may result in generic solutions that do not effectively address the diverse challenges faced by NTBFs.
Furthermore, whether an NTBF targets many small customers or a few big customers does not appear to matter; once they accept external funding, they need to pursue high growth no matter what type of customers they target. Similarly, regardless of whether key activities are mostly external or internal, or their own expertise is high quality, an NTBF that accepts external funding needs to pursue high growth. Almost all other business model elements are irrelevant (except the revenue model) from a path dependency perspective if an NTBF accepts external funding. Thus, when adopting path dependency to explain the development of NTBFs toward a high-growth path, only at two of the founders’ initial decisions need to be examined: whether they accepted external funding and if they decided to develop multiple revenue streams simultaneously with the firm’s founding. These two business model elements appear to override any path dependency effects the other seven might have on the subsequent high growth of NTBFs. We aimed to provide conceptual arguments, highlighting why certain business model elements appear to have a greater impact on NTBF development. Our study has limited ability to examine the factors that influence founding business model formation, particularly during the nascent entrepreneurial stage when founders are trying to establish their business and generate their first sales. This could include exploring the constraints and forces that shape the development of an entrepreneurial project, and how they may affect the creation of the desired founding business model. Understanding these underlying mechanisms could provide insight into how founding business models emerge and evolve over time.
References
Alchian, A. A. (1950). Uncertainty, evolution and economic theory. Journal of Political Economy, 58, 211–222.
Aldrich, H. E., & Ruef, M. (2018). Unicorns, gazelles, and other distractions on the way to understanding real entrepreneurship in the United States. Academy of Management Perspectives, 32(4), 458–472.
Amit, R., & Zott, C. (2001). Value creation in e-business. Strategic Management Journal, 22(6–7), 493–520.
Andries, P., & Debackere, K. (2007). Adaptation and performance in new businesses: Understanding the moderating effects of independence and industry. Small Business Economics., 29(1–2), 81–99.
Andries, P., Debackere, K., & van Looy, B. (2013). Simultaneous experimentation as a learning strategy: Business model development under uncertainty. Strategic Entrepreneurship Journal, 7(4), 288–310. https://doi.org/10.1002/sej.1170
Arthur, W. B. (1989). Competing technologies, increasing returns, and lock-in by historical events. Economic Journal, 99(394), 116–131.
Arthur, W. B. (1994). Increasing returns and path dependence in the economy. University of Michigan Press.
Aspelund, A., Berg-Utby, T., & Skjevdal, R. (2005). Initial resource´s influence on new venture survival: A longitudinal study of new technology-based firms. Technovation, 25(11), 1337–1347.
Assenova, V. A., & Sorenson, O. (2017). Legitimacy and the benefits of firm formalization. Organization Science, 28(5), 804–818.
Atzmon, M. B., Vanderstraeten, J., & Albers, S. (2022). Small-firm growth-enabling capabilities: A framework for young technology-based firms. Technovation, 117, 102542.
Audretsch, D. (2012), Determinants of high-growth entrepreneurship. Report prepared for the OECD/DBA International Workshop on “High-Growth Firms: local policies and local determinants. Copenhagen, 28th of March, 2012.
Autio, E., Rannikko, H., Handelberg, J., and Kiuru, P. (2014). Analyses on the Finnish high-growth entrepreneurship ecosystem.
Autio, E., & Rannikko, H. (2016). Retaining winners: can policy boost high-growth entrepreneurship? Research Policy, 45(1), 42–55.
Autio, E., Sapienza, H., & Almeida, J. (2000). Effects of time to internationalization, knowledge-intensity, and imitability on growth. Academy of Management Journal, 43(5), 909–924.
Aversa, P., Furnari, S., & Haefliger, S. (2015). Business model configurations and performance: A qualitative comparative analysis in Formula One racing, 2005–2013. Industrial and Corporate Change, 24(3), 655–676.
Balawi, A., & Ayoub, A. (2022). Assessing the entrepreneurial ecosystem of Sweden: A comparative study with Finland and Norway using Global Entrepreneurship Index. Journal of Business and Socio-Economic Development, 2(2), 165–180.
Bamford, C. E., Dean, T. J., & Douglas, T. J. (2004). The temporal nature of growth determinants in new bank foundings: Implications for new venture research design. Journal of Business Venturing, 19(6), 899–919.
Bamford, C. E., Dean, T. J., & McDougall, P. P. (2000). An examination of the impact of initial founding conditions and decisions upon the performance of new bank start-ups. Journal of Business Venturing, 15(3), 253–277.
Baron, J. N., Hannan, M. T., & Burton, M. D. (2001). Labor pains: Change in organizational models and employee turnover in young, high-technology firms. The American Journal of Sociology, 106(4), 960–1012.
Barringer, B. R., Jones, F. F., & Neubaum, D. O. (2005). A quantitative content analysis of the characteristics of rapid-growth firms and their founders. Journal of Business Venturing, 20(5), 663–687.
Ben-Hafaïedh, C., & Hamelin, A. (2022). Questioning the growth dogma: A replication study. Entrepreneurship Theory and Practice, 47, 10422587211059992.
Berends, H., Smits, A., Reymen, I., & Podoynitsyna, K. (2016). Learning while (re)configuring: Business model innovation processes in established firms. Strategic Organization, 14(3), 181–219. https://doi.org/10.1177/1476127016632758
Bergek, A., & Onufrey, K. (2014). Is one path enough? Multiple paths and path interaction as an extension of path dependency theory. Industrial and Corporate Change, 23(5), 1261–1297.
Bianchini, S., Bottazzi, G., & Tamagni, F. (2017). What does (not) characterize persistent corporate high-growth? Small Business Economics, 48(3), 633–656.
Boeker, W. (1988). Organizational Origins: entrepreneurial and environmental imprinting of the time of founding. Ecological Models of Organizations, 1988, 33–51.
Boeker, W. (1989). Strategic change: The effects of founding and history. Academy of Management Journal, 32(3), 489–515.
Bollinger, L., Hope, K., & Utterback, J. M. (1983). A review of literature and hypotheses on new technology-based firms. Research Policy, 12(1), 1–14.
Boxstael, A. V., & Denoo, L. (2020). Escaping the founder identity trap: A process view on business model design during new venture creation. In K. J. Sund, R. J. Galavan, & M. Bogers (Eds.), Business models and cognition (pp. 57–94). Emerald Publishing Limited.
Buliga, O., Scheiner, C. W., & Voigt, K. I. (2016). Business model innovation and organizational resilience: Towards an integrated conceptual framework. Journal of Business Economics, 86(6), 647–670.
Butchart, R. L. (1987). A new UK definition of the high technology industries. Economic Trends, 400, 82–88.
Cavallo, A., Ghezzi, A., Dell’Era, C., & Pellizzoni, E. (2019). Fostering digital entrepreneurship from startup to scaleup: The role of venture capital funds and angel groups. Technological Forecasting and Social Change, 145, 24–35. https://doi.org/10.1016/j.techfore.2019.04.022
Chandler, G. N., & Hanks, S. H. (1994). Market attractiveness, resource-based capabilities, venture strategies, and venture performance. Journal of Business Venturing, 9(4), 331–349.
Chesbrough, H., & Rosenbloom, R. S. (2002). The role of the business model in capturing value from innovation: Evidence from Xerox Corporation’s technology spin-off companies. Industrial and Corporate Change., 11(3), 529–555.
Christensen, C. M., & Raynor, M. E. (2013). The Innovator´s Solution: Creating and Sustaining Successful Growth. Harvard Business Scholl Press.
Clauss, T. (2017). Measuring business model innovation: Conceptualization, scale development and proof of performance. R&D Management, 47(3), 385–403.
Cloutier, L., & Messeghem, K. (2021). Whirlwind model of entrepreneurial ecosystem path dependence. Small Business Economics. https://doi.org/10.1007/s11187-021-00553-x
Coad, A. (2007). A closer look at serial growth rate correlation. Review of Industrial Organization, 31(1), 69–82.
Coad, A., Daunfeldt, S.-O., Hölzl, W., Johansson, D., & Nightingale, D. (2014). High-growth firms: Introduction to the special section. Industrial and Corporate Change, 23(1), 91–112.
Coad, A., & Hölzl, W. (2009). On the autocorrelation of growth rates. Journal of Industry, Competition and Trade, 9(2), 139–166.
Cooper, A. C., Woo, C. Y., & Dunkelberg, W. C. (1988). Entrepreneurs’ perceived chances for success. Journal of Business Venturing, 3(2), 97–108.
Daunfeldt, S. O., Elert, N., & Johansson, D. (2015). Are high-growth firms overrepresented in high-tech industries? Industrial and Corporate Change, 25(1), 1–21.
Daunfeldt, S.-O., & Halvarsson, D. (2015). Are high-growth firms one-hit wonders? Evidence from Sweden. Small Business Economics, 44(2), 361–383.
David, P. A. (1985). Clio and the economics QWERTY. The American Economic Review, 75(2), 332–337.
Davidsson, P. (2007). Interpreting Performance in Research on Independent Entrepreneurship. In L. M. Gillin (Ed.), 4th AGSE International Entrepreneurship Research Exchange 2007. Brisbane.
Davidsson, P., & Klofsten, M. (2003). The business platform: Developing an instrument to gauge and to assist the development of young firms. Journal of Small Business Management, 41(1), 1–26.
Davies , P., (2022), The future of French tech: why start-ups are optimistic despite economic gloom and cyber attacks. https://www.euronews.com/next/2022/09/20/the-future-of-french-tech-why-start-ups-are-optimistic-despite-economic-gloom-and-cyber-at
De Cleyn, S. H., Braet, J., & Klofsten, M. (2015). How human capital interacts with the early development of academic spin-offs. International Entrepreneurship and Management Journal, 11(3), 599–621.
DeKinder, J. S., & Kohli, A. K. (2008). Flow signals: How patterns over time affect the acceptance of start-up firms. Journal of Marketing, 72, 84–97.
Delmar, F., Davidsson, P., & Gartner, W. (2003). Arriving at the high-growth firm. Journal of Business Venturing, 18(2), 189–216.
Delmar, F., & Shane, S. (2003). Does Business planning facilitate the development of new ventures? Strategic Management Journal, 24(12), 1165–1185.
Diamantopoulos, A., & Winklhofer, H. M. (2001). Index construction with formative indicators: An alternative to scale development. Journal of Marketing Research, 38(2), 269–277.
Djelic, M.-L., & Quack, S. (2007). Overcoming path dependency: Path generation in open systems. Theory and Society, 36(2), 161–186.
Dunne, T., Roberts, M., & Samuelson, L. (1989). The growth and failure of US manufacturing plants. The Quarterly Journal of Economics., 104(4), 671–698.
Edwards, J. R., & Bagozzi, R. P. (2000). On the nature and direction of relationships between constructs and measures. Psychological Methods, 5(2), 155–174.
Ejermo, O., & Xiao, J. (2014). Entrepreneurship and survival over the business cycle: How do new technology-based firms differ? Small Business Economics, 43(2), 411–426.
Eurostat., (2014), Eurostat indicators on high-tech industry and knowledge–intensive services (Annex 3 – High-tech aggregation by NACE Rev. 2).
Eurostat-OECD, (2007), Eurostat-OECD Manual on Business Demography Statistics. Luxembourg Office for Official Publications of the European Communities.
Festel, G., Wuermseher, M. & Cattaneo, G. (2013). Valuation of early stage high-tech start-up companies. Journal of Business Venturing, 8(3), 216–231.
García-Manjón, J. V., & Romero-Merino, M. E. (2012). Research, development, and firm growth. Empirical evidence from European top R&D spending firms. Research Policy, 41(6), 1084–1092.
Gartner, W., & Liao, J. (2012). The effects of perceptions of risk, environmental uncertainty, and growth aspirations on new venture creation success. Small Business Economics, 39(3), 703–712.
Geroski, P. A., Mata, J., & Portugal, P. (2010). Founding conditions and the survival of new firms. Strategic Management Journal, 31(5), 510–529.
Goel, R. K., & Saunoris, J. W. (2016). Institutional path dependence and international research intensity. Economic Modelling, 52, 851–858.
Henrekson, M., & Johansson, D. (2010). Gazelles as job creators: A survey and interpretation of the evidence. Small Business Economics, 35(2), 227–244.
Hepp, J. (2022). Being small at the right moment: Path dependence after a shift in the technological regime. Industrial and Corporate Change, 31(2), 464–499.
Hölzl, W. (2009). Is the R&D behaviour of fast-growing SMEs different? Evidence from CIS III data for 16 countries. Small Business Economics, 33(1), 59–75.
Hölzl, W. (2014). Persistence, survival and growth: A closer look at 20 years of fast-growing firms in Austria. Industrial and Corporate Change, 23(1), 199–231.
Homburg, C., Hahn, A., Bornemann, T., & Sandner, P. (2014). The role of chief marketing officers for venture capital funding: endowing new ventures with marketing legitimacy. Journal of Marketing Research, 51(5), 625–644.
http://ec.europa.eu/eurostat/cache/metadata/Annexes/htec_esms_an3.pdf [Accessed 2016–05–20]
Huergo, E., & López, A. (2020). Growth effects of economic conditions at birth: the role of public funding for technology-based start-ups. Economics of Innovation and New Technology, 30(2), 511–538.
Isaksson, A., Löfsten, H., & Rannikko, H. (2021). The influence of initial business models on early business performance—a study of 589 new high-tech firms. International Journal of Innovation Management, 25(5), 2150055. https://doi.org/10.1142/S1363919621500559
Isaksson, A., Vanyushyn, V., & Hultén, P. (2013). The impact of managers’ attitudes on SMEs’ growth in northern Sweden. International Journal of Entrepreneurship and Small Business, 18(3), 298–312.
Jacobides, M. G. (2006). The architecture and design of organizational capabilities. Industrial and Corporate Change, 15(1), 151–171.
Jarvis, C. B., MacKenzie, S. B., & Podsakoff, P. M. (2003). A critical review of construct indicators and measurement model misspecification in marketing and consumer research. Journal of Consumer Research, 30(2), 199–218.
Johnson, V. (2007). What Is Organizational Imprinting? Cultural Entrepreneurship in the Founding of the Paris Opera. American Journal of Sociology, 113(1). https://doi.org/10.1086/517899.
Katila, R., Rosenberger, J. D., & Eisenhardt, K. M. (2008). Swimming with sharks: technology ventures, defense mechanisms and corporate relationships. Administrative Science Quarterly, 53(2), 295–332.
Kessler, A., Korunka, C., Frank, H. & Lueger, M. (2012). Predicting founding success and new venture survival: A longitudinal nascent entrepreneurship approach. Journal of Enterprising Culture, 20(1), 25–55. https://doi.org/10.1142/S021849581250002125.
Kim, H. E., & Pennings, J. M. (2009). Innovation and strategic renewal in mature markets: A study of the tennis racket industry. Organization Science, 20(2), 368–383.
Kimberly, J. R. (1975). Environmental constraints and organizational structure: A comparative analysis of rehabilitation organizations. Administrative Science Quarterly, 20(1), 1–9.
Kirchhoff, B. (1994). Entrepreneurship and Dynamic Capitalism. Westport.
Klepper, S. (2002). The capabilities of new firms and the evolution of the US automobile industry. Industrial and Corporate Change, 11(4), 645–666.
Kuckertz, A., Scheu, M., & Davidsson, P. (2023). Chasing mythical creatures–A (not-so-sympathetic) critique of entrepreneurship’s obsession with unicorn startups. Journal of Business Venturing Insights, 19, e00365. https://doi.org/10.1016/j.jbvi.2022.e00365
Kuratko, D. F., & Audretsch, H. E. (2022). The future of entrepreneurship: The few or the many? Small Business Economics, 59(1), 269–278.
Leung, A., Foo, M. D., & Chaturvedi, S. (2013). Imprinting effects of founding core teams on HR values in new ventures. Entrepreneurship Theory and Practice, 37(1), 87–106.
Loch, C. H., Solt, M. E., & Bailey, E. M. (2008). Diagnosing unforeseeable uncertainty in a new venture. Journal of Product Innovation Management, 25(1), 28–46.
Löfsten, H. (2016). Business and innovation resources: Determinants for the survival of new technology-based firms. Management Decision, 54(1), 88–106.
Löfsten, H., & Lindelöf, P. (2001). Science parks in Sweden—Industrial renewal and development? R&D Management, 31(3), 309–322.
Löfsten, H., & Lindelöf, P. (2002). Science parks and the growth of new technology based firms—Academic-industry links, innovation and markets. Research Policy, 31(6), 859–876.
Mahroum, S. (2016). Black swan start-ups: Understanding the rise of successful technology business in unlikely places. Palgrave Macmillan, London.
Marquis, C. (2003). The pressure of the past: Network imprinting in intercorporate communities. Administrative Science Quarterly, 48(4), 655–689.
Martínez-Ros, E., & Labeaga, J. M. (2009). Product and process innovation: Persistence and complementarities. Europan Management Review, 6(1), 65–75.
Mason, C., & Brown, R. (2013). Creating good public policy to support high-growth firms. Small Business Economics, 40(2), 211–225.
McDougall, P. P., Scott, S. & Oviatt, B. (1994). Explaining the formation of international new ventures: The limits of theories from international business research. Journal of Business Venturing 9(6), 469–487.
McEvily, B., Jaffee, J., & Tortoriello, M. (2012). Not all bridging ties are equal: Network imprinting and firm growth in the nashville legal industry, 1933–1978. Organization Science, 23(2), 547–563.
Milanov, H., & Fernhaber, S. A. (2009). The impact of early imprinting on the evolution of new venture networks. Journal of Business Venturing, 24(1), 46–61.
Miller, D., & Friesen, P. H. (1984). Organizations: A Quantum View. Englewood Cliffs.
Moreno, A. M., & Casillas, J. C. (2008). Entrepreneurial orientation and growth of SMEs: A causal model. Entrepreneurship Theory and Practice, 32(3), 507–528.
Morgan, G., & Kubo, I. (2005). Beyond path dependency? Constructing new models for institutional change: The case of capital markets in Japan. Socio-Economic Review., 3(1), 55–82.
Moschella, D., Tamagni, F., & Xu, X. (2019). Persistent high-growth firms in China’s manufacturing. Small Busienss Economics, 52(1), 573–594.
Nair, S., & Blomquist, T. (2019). Failure prevention and management in business incubation: Practices towards a scalable business model. Technology Analysis & Strategic Management, 31(3), 266–278.
Napier, G., Rouvinen, P., Johansson, D., Finnbjörnsson, T., Solberg, E. and Pedersen, K. (2012), Nordic Growth Entrepreneurship Review 2012—Final report, Nordic Innovation Publication 2012:25, Nordic Innovation, Oslo
Nielsen, C., Lund, M., Montemari, M., Paolone, F., Massaro, M., & Dumay, J. (2018). Business Models: A Research Overview. Routledge. https://doi.org/10.4324/9781351232272
North, D. C. (1990). Institutions, institutional change and economic performance. Cambridge University Press.
Osterwalder, A., & Pigneur, Y. (2010). Business Model Generation. John Wiley and Sons.
Osterwalder, A., Pigneur, Y., & Tucci, C. L. (2005). Clarifying business models: Origins, present, and future of the concept. Communications of the Association for Information Systems, 16, 1. https://doi.org/10.17705/1CAIS.01601
Patzelt, H., Zu Knyphausen-Aufseß, D., & Nikol, P. (2008). Top management teams, business models, and performance of biotechnology ventures: An upper echelon perspective. British Journal of Management, 19(3), 205–221.
Pennings, J. M. (1980). Interlocking Directories: Origins and Consequences of Connections Among organization´s Board of Directors. Jossey-Bass.
Phillips, D. J. (2002). A genealogical approach to organizational life chances: The parent-progeny transfer among Silicon Valley law firms, 1946–1996. Administrative Science Quarterly, 47(3), 474–506.
Phillips, D. J. (2005). Organizational genealogies and the persistence of gender inequality: The case of silicon valley law firms. Administrative Science Quarterly, 50(3), 440–472.
Pierson, P. (2000). Increasing returns, path dependence, and the study of politics. American Political Science Review, 94(2), 251–267.
Quinn, J. B. (1980). Strategies for change: Logical incrementalism. Richard D. Irwin.
Rannikko, H., Tornikoski, E., Isaksson, A., & Löfsten, H. (2019). Survival and growth patterns among new technology-based firms: Empirical study of cohort 2006 in Sweden. Journal of Small Business Management, 57(2), 640–657.
Romanelli, E. (1989). Environments and strategies of organization start-up: Effects on early survival. Administrative Science Quarterly, 34(3), 369–387.
Rusko, R., Hietanen, L., Kohtakangas, K., & Järvi, T. (2019). Roles of career anchors and path dependency in the entrepreneurial process: Case Finland. International Journal of Entrepreneurship and Small Business, 37(3), 342–363.
Rydehell, H., Isaksson, A., & Löfsten, H. (2019a). Business networks and localization effects for new Swedish technology-based firms’ innovation performance. The Journal of Technology Transfer, 44(5), 1547–1576.
Rydehell, H., Isaksson, A., & Löfsten, H. (2019b). Effects of internal and external resource dimensions on the business performance of new technology-based firms. International Journal of Innovation Management, 23(1), 1950001.
Samuelsson, M., Söderblom, A., & McKelvie, A. (2021). Path dependence in new ventures’ capital structures. Entrepreneurship Theory and Practice, 45(2), 319–349.
Sánchez, P., & Ricart, J. E. (2010). Business model innovation and sources of value creation in low-income markets. European Management Review, 7(3), 138–154.
Schneckenberg, D., & Velamuri, V. (2019). The design logic of new business models: Unveiling cognitive foundations of managerial reasoning. European Management Review, 16(2), 427–447.
Siegel, R., Siegel, E. & Macmillan, I. C. (1993). Characteristics distinguishing high-growth ventures. Journal of Business Venturing, 8(2), 169–180.
Shane, S. (2009). Why encouraging more people to become entrepreneurs is bad public policy. Small Business Economics, 33(2), 141–149.
Simsek, Z., Fox, B. C., & Heavey, C. (2015). “What’s Past Is Prologue” A framework, review, and future directions for organizational research on imprinting. Journal of Management, 41(1), 288–317.
Snihur, Y., & Zott, C. (2020). The genesis and metamorphosis of novelty imprints: How business model innovation emerges in young ventures. Academy of Management Journal, 63(2), 554–583.
Sohl, T., Vroom, G., & Fitza, M. A. (2020). How much does business model matter for firm performance? A variance decomposition analysis. Academy of Management Discoveries, 6(1), 61–80. https://doi.org/10.5465/amd.2017.0136
Spencer, A., & Kirchhoff, B. (2006). Schumpeter and new technology based firms: Towards a framework for how NTBFs cause creative destruction. International Entrepreneurship and Management Journal, 2(2), 145–156.
Stinchcombe, A. L. (1965). Social Structure and Organizations. In J. P. March (Ed.), Handbook of Organizations (pp. 142–193). Rand McNally.
Storey, D. J. (1994). Understanding the Small Business Sector. Routledge.
Storey, D. J., & Tether, B. S. (1998). New technology-based firms in the European Union: An introduction. Research Policy, 26(9), 933–946.
Sydow, J., Schreyögg, G., & Koch, J. (2009). Organizational path dependency: Opening the black box. Academy of Management Review, 34(4), 689–709.
Sydow, J., Schreyögg, G., & Koch, J. (2020). On the theory of organizational path dependence: Clarifications, replies to objections, and extensions. Academy of Management Review, 45(4), 717–734.
Tornikoski, E., & Renko, M. (2014). Timely creation of new organizations: The imprinting effects of entrepreneurs’ initial founding decisions. Management, 17(3), 193–213.
Trimi, S., & Berbegal-Mirabent, J. (2012). Business model innovation in entrepeneurship. International Entrepreneurship and Management Journal, 8(4), 449–465.
Tucker, D. J., Singh, J. V., & Meinhard, A. G. (1990). Founding characteristics: imprinting, and organizational change. In J. V. Singh (Ed.), Organizational Evolution: New Directions (pp. 180–200). Newbury Park.
Van Praag, C. M. (2003). Business survival and success of young small business owners. Small Business Economics, 21(1), 1–17.
Vergne, J.-P., & Durand, R. (2010). The missing link between the theory and empirics of path dependence: Conceptual clarification, testability issue, and methodological implications. Journal of Management Studies, 47(4), 736–759.
Wennberg, K. (2005). Entrepreneurship research through longitudinal databases: Measurement and design issues. New England Journal of Entrepreneurship, 8(2), 9–19.
Wiklund, J., Davidsson, P., & Delmar, F. (2003). What do they think and feel about growth? An expectancy–value approach to small business managers’ attitudes toward growth. Entrepreneurship Theory and Practice, 27(3), 247–270.
Wiklund, J., & Shepherd, D. (2003). Aspiring for, and achieving growth: The moderating role of resources and opportunities. Journal of Management Studies, 40(8), 1921–1941.
Witt, U. (1997). ‘Lock-In’ vs ‘critical masses’—industrial change under network externalities. International Journal of Industrial Organization, 15(6), 753–773.
Yli-Renko, H., Autio, E., & Sapienza, H. J. (2001). Social capital, knowledge acquisition, and knowledge exploitation in young technology-based firms. Strategic Management Journal, 22(6–7), 587–613.
Yli-Renko, H., Autio, E., & Tontti, V. (2002). Social capital, knowledge, and the international growth of technology-based new firms. International Business Review, 11(3), 279–304.
Acknowledgements
The authors hereby gratefully acknowledge financial support for this study from the Peter Wallenberg Foundation for Economics and Technology. We are grateful for the comments of the participants at the Virtual BCERC (Babson College Entrepreneurship Research Conference) in Munich, Germany, June 8-11, 2021.
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Löfsten, H., Isaksson, A., Rannikko, H. et al. Impact of initial business model on the growth trajectory of new technology-based firms: a path dependency perspective. J Technol Transf (2024). https://doi.org/10.1007/s10961-024-10086-6
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DOI: https://doi.org/10.1007/s10961-024-10086-6
Keywords
- High-growth firms
- Stable firms
- Founding business model
- Path dependency
- Imprinting mechanism
- Post-founding success