Timing is Everything
Success Factors for Startups
Marleen van Noord (10446974)
Dr. Tsvi Vinig
Master Thesis Business Administration
Entrepreneurship & Innovation
STATEMENT OF ORIGINALITY
This document is written by Student Marleen van Noord who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
ABSTRACT
This study offers key insights into success factors for startups. This study aims to contribute to entrepreneurial literature by providing a greater understanding about the impact of idea potential, team, potential, funding potential, business model potential and optimal timing on initial success of startups. This study will be first to measure the relative importance of timing and the way it interacts with the other factors. Optimal timing is the finding the right balance between demand uncertainty, complementary technologies and competition intensity. Timing showed to be the most important factor for startups to achieve initial success. Sub factor existence of a dominant design is considered most important. Followed by technology benefits of complementary technologies. Overall, this study contributes to prior literature by providing insight about the impact of timing on startups success by answering to the main question ‘how important is timing for startups to have success?’ On top of that, this study can function as interesting starting point in for further research about timing and entrepreneurial success.
INTRODUCTION
What makes startups succeed or fail? An increasing success rate for startups has the potential to dramatically increase economic growth globally. Entrepreneurship in the form of new business formation contributes to job creation, innovation and economic wealth (Fairlie, 2013). Entrepreneurs who bring cutting-edge innovations to the market contribute to economic growth and welfare. By recognizing the commercial opportunities offered by innovations and by transforming these opportunities into new products or services that improve the lives of all citizens, entrepreneurship contributes to increased productivity throughout the economy (Baumol & Strom, 2007). If successful entrepreneurship is a key source of economic growth, how can successful entrepreneurship be achieved?
Why do many startups with high potential fail when others are successful? What matters most for the success of a startup? It is unlikely that there is a magic formula that guarantees a successful startup. However, if we knew what factor or factors matter most to the success of a startup, we would be one step closer to finding this formula.
Many researchers and economists have attempted to find answers to these questions. In an inspiring TED talk, well-known entrepreneur Bill Gross (2015) emphasizes five elements that matter most for startup success: the idea, an ability to adapt, the business model, funding, and timing. According to his research, there appears to be one factor that rises above all others in importance: timing matters the most. However, support for this conclusion in academic literature can barely be found. Academic literature appears to be overlooking a very important success factor. To illustrate why, imagine that there is a great idea, a theoretically brilliant business model, a talented team, and enough funding to commence business operations. It seems likely that the startup would achieve success; however, if the idea arrives too early and consumers are not ready, they will not readily adopt it. Equally, if the idea arrives too late and a number of competitors are already operating in the market, it will be very challenging to establish the business. To illustrate the other end of the spectrum, there may be an idea that is reasonable though not great, a business model with a few drawbacks, a few dedicated though averagely skilled people, and barely enough funding to start the business. If the release is perfectly timed, under conditions where there exists a strong need for the idea and it enters the market before any competitor has, initial success would be strongly anticipated. Initial success will assist in improving the business model in order to provide the startup with sufficient
money and knowledge to solve the other shortcomings. According to Gross (2015), timing cannot be ignored and it cannot be substituted merely by paying more attention to one of the other elements. Certainly, having a good idea, business model, team and the available capital can all increase the chances of success, but without the critical factor of good timing, there is likely a great chance of the business failing, or at least struggling.
Although the scenarios describe above appear to be reasonable, support for them cannot be found in academic literature. It has always been necessary for entrepreneurs and investors to understand the key factors involved in creating a successful startup. Armed with this knowledge, opportunities can be utilized and pitfalls avoided. A substantial amount of scientific research has been conducted addressing the criteria for predicting successful startups. However, on average, nine out of ten startups still fail (Krishna, Agrawal & Choudhary, 2016). The current study suggests that previous research is overlooking the most important point. In contrast to the large number of studies that investigate the factors that relate to the idea, team, funding, and business model, there exists minimal research on the matter of timing as it pertains to startups’ success. There is consensus among scholars that timing matters; however, the combination of numerous contingencies, challenging methodological issues, and varying conceptual lenses undermines the development of a complete theoretical foundation. This study aims to address this research gap by examining the importance of timing for startups’ success. How important is timing for startups to be successful?
The structure of this study will be as followed. In the next Chapter, an overview of the literature on four of the five success factors – idea, team, business model, and funding – is presented. This is followed by an overview of the concept of timing drawn from the literature, real life examples. Elements that define timing are proposed. Chapter 3 provides an overview of the conceptual framework of this study. In Chapter 4 the methodology section, describes the sample selected, the measurement scales used, and the statistical analyses performed. The results section presents the findings of these analyses. The final Chapter provides a discussion of the findings, including a short summary of the most interesting findings and implications for future research, and a conclusion with an answer to the central question; How important is timing for startups to be successful?
LITERATURE OVERVIEW
In this study, a startup is defined as “a firm that is in its early stages of development and growth. Generally, such firms are in the process of bringing their initial product or service offerings to market, forming a customer base, and putting into place organizational processes and procedures” (Klotz, Hmieleski, Bradley & Busenitz, 2014). Despite a, substantial amount of research on success factors for startups in the field of entrepreneurship current academic literature does not yet offer complete insight into this issue. To address this gap in literature, an overview of high-quality studies in literature is provided here.
First, a short overview of the theoretical frameworks on startup performance and survival is provided. Gartner’s (1985) framework suggests that startup efforts differ in terms of the characteristics of the team, the organization they create, the environment surrounding the startup, and the process by which the startup is founded. According to Roure and Keeley (1990), for a startup to succeed, a series of challenges must be surmounted. In order to achieve this, startups should have the ability to identify an attractive market opportunity and to develop a plan to obtain a large share of it; to obtain sufficient funds to commence execution of the plan; to attract additional key employees; as well as the ability to modify the business strategy according to changing conditions (Roure & Keeley, 1990). This implies that the quality of the founding team, the environment, and the business strategy should largely explain the success or failure of startups. Stuart and Abetti (1990) explain the relationship between the characteristics of the team and exceptional performance. Mitchell, Mitchell and Smith (2004) develop this and investigated startup experience and startup expertise as it concerns establishing successful startups. Aldrich (1990) describes startups’ success from an ecological perspective, where the given environmental conditions generate variations in success of startups. Van Gelderen, Thurik and Bosma (2006) suggest that success or failure is primarily determined in the pre-startup phase by the entrepreneurs, the environment, and the process by means of which the startup is created. According to Zahra and Bogner (2000), a startup’s technology strategy is one of the most important factors explaining success, especially in dynamic contemporary environments. A startup’s technology strategy is the sum of the startup’s knowledge and skills: it determines the ability to offer new products or services, gain market acceptance, survive, and achieve financial success (Zahra & Bogner, 2000). Despite the different approaches and focal points, in general in the literature the
importance of the idea, the team, funding, and the business model as they concern the initial success of startups is widely acknowledged.
The following sections provide an overview of key finding per success factor in more detail.
1. The Idea
All startups begin with an idea. However, not every idea leads to a successful startup. How can ideas with high potential be distinguished from poor business ideas? Understanding why some ideas are more successful than others is crucial to building a high-potential startup. Knowing how to evaluate ideas is particularly important because ideas for startups involve much uncertainty, insufficient information, and rapid decision-making (Human, Clark, Baucus & Eustis, 2004). In this study, “the idea” describes “the offering of the startup,” which includes the products or services they provide and the manner in which they provide this offering. How may business ideas be evaluated? Which screening criteria do formal investors, such as venture capitalists, bankers, and “angel” investors, use to evaluate ideas? The criteria used by formal investors to make their investment decisions are of interest to gain deeper understanding of what factors could lead to a higher success rate of startups (Hall & Hofer, 1993). According to formal investors, superiority, innovativeness, and global expanding potential are the principal characteristics of high-potential ideas (Zider, 1998). It is unsurprising that delivering superior products or services with unique benefits and real value to customers separates winning ideas from losing ones (Cooper, 2013). The extent of uniqueness and the level of advanced technologies protect ideas from competitors who may rapidly copy the idea, and therefore forms a significant source of sustainable competitive advantage (Human et al., 2004). Cooper (2013) suggests that superior products and services have in common that they provide: (1) high value for money for the customer, as well as reducing the customer’s total costs, and boasting excellent price and performance characteristics; (2) excellent product quality compared to competitors, including customers’ perceptions of quality; (3) superiority in terms of meeting customer needs, offering unique advantages that cannot be found in competing offerings, or solving problems that cannot be solved by competing offerings; (4) highly visible benefits, as well as being perceived as useful by customers. One other sub factor that should be taken into account is the level of global potential. If a startup wants to grow substantially it has to have global potential. Startups that have the potential to globalize early and rapidly will succeed ahead of those that do not. Startups that globalize early have a greater possibility of becoming successful because
they reduce revenue-source risks and increase the size of their addressable market (Bailetti, 2012).
Hypothesis (1a): The higher the level of idea superiority, the higher the level of initial
success of a startup
Hypothesis (1b): The higher the level of global potential, the higher the level of initial
success of
In the literature there is consensus about idea superiority as a predictor of the success of startups. Whether an idea is new or radical is a more complex matter. Introducing a radical new offering can spell the difference between success and failure. A unique innovation is embodied in an original or unique product, process, system, or service; it is specialized or customized, beyond the state-of-the-art, based on proprietary and patentable technology or on complex systems engineering, and the research and development requirements are high (Stuart & Abetti, 1990). Innovative ideas provide a unique advantage over the offerings of the competition, significantly reduce consumers’ perceived risks, and are associated with better financial performance (Henard & Szymanski, 2001). Startups with innovative ideas are likely to generate significant market share earlier, enhance cash flows, boost their external visibility and legitimacy, and increase the likelihood of survival of startups (De Jong & Vermeulen, 2006; Henard & Szymanski, 2001). Offering a completely new product or service can provide the startup with the possibility of targeting lucrative market segments, building a favorable market image and a strong brand recognition, and developing and controlling access to distribution channels, all of which are outcomes leading to a high success potential for startup (Zahra & Bogner, 2002). Unique innovation ideas may seem ridiculous at first but produce excellent results. Radically new offerings can be risky as customers may not accept them right away. Such offerings require public education; however, if they are successful they are likely to result in a huge competitive advantage for the startup (Human et al., 2004). An idea for a new startup can be placed along an innovation continuum. At the one end are radical innovations that deliver creative destruction of a whole segment of an industry. According to Schumpeter (1934) creative destruction is the cycle of relatively fluid industries, where new entrants innovate with technologically superior offerings and displace incumbent ventures. Examples include eBay, Airbnb, Uber, and Netflix. At the other end are incremental innovations that modify and refine existing practices. Incremental innovations create value by
imitation, extension, limitation, or improvement of existing offerings (Amason, Shrader & Tompson, 2006). Low levels of innovation rarely achieve major success (Human et al., 2004).
Hypothesis (1b): The higher the level of innovation, the higher the initial success of a startup 2. The Team
A longstanding belief in academic literature is that the product of service is the most important determinant of a startup’s success. However, developing impressive new technologies, even breakthrough ones, is rarely sufficient to enable survival and to achieve market success (Zahra & Bogner, 2000). Ideas are worth little without a team to execute the plan successfully. This study’s focus is on the competence of entrepreneurial teams. As the field of entrepreneurship research has matured, scholars have agreed that business formation is commonly accomplished by teams rather than individual entrepreneurs (Klotz et al., 2014). Startups managed by a team are likely to have greater potential for success than those managed by a single entrepreneur, due to the increasing technological complexity and the high intensity of competition (Eisenhardt & Schoonhoven, 1990). According to Eisenhardt and Schoonhoven (1990), this can be explained simply: more founders mean more necessary skills and knowledge to perform the difficult job of building a successful startup. A team allows greater agility in entering markets quickly and in maintaining responsiveness to changing market conditions (Miloud, Aspelund & Cabrol, 2012). Much research has been conducted on the compositional profile of an entrepreneurial team. While there have been some difference in focus, there appears to be consensus among researchers on the fact that the quality and competence of entrepreneurs play a critical role in startups’ success (Carland & Carland 2012; Miskin & Rose, 2015). In this study, the definition of Schjoedt and Kraus (2009) is used: “an entrepreneurial team consists of two or more individuals who have an interest in financial commitment to a startup’s future and success; whose work is interdependent in the pursuit of common goals and the startup’s success; who are considered to be at the executive level with executive responsibility in the early phases of the firm, including founding and pre-startup; and who are seen as social entity by themselves as by others.” In an ideal situation, the entrepreneurial team incorporates a high level of knowledge of the technology it utilizes, the market it aims at, and has general management and startup experience (Stuart & Abetti, 1990). MacMillan, Siegel and Narashimha (1985) suggest that the most influential factors are managerial or leadership experience and the level of familiarity with relevant target markets and geographical areas. Bhide (1994), Grant (1992),
and Zider (1998) suggest a range of skills that indicate startup success, namely, business skills, including functional, technical, and persuasion and motivation skills; interpersonal skills including communication, listening, networking, and conflict management; and creative problem-solving and change management.
Hypothesis (2a): If the entrepreneurial team has a high level of skills, the startup is more
likely to be successful.
Scholars have most often examined three kinds of experience, namely, previous entrepreneurial or leadership experience, industry experience, and educational experience (MacMillan et al., 1985; Hall & Hofer, 1993). Startup experience provides the team with learning opportunities that can be exploited; educational experience provides skills that might serve in the accomplishment of the many tasks that setting up a business entails; and industry experience can be helpful in the discernment and valuation of new business ideas (Van Gelderen, Thurik & Bosma, 2006). According to Larson and Star (1993), prior entrepreneurial experience is assumed to provide a set of entrepreneurial skills, a valuable network of contacts and a business reputation that makes it easier to gain legitimacy and to surmount the obstacles faced in building a startup. In addition, industry experience will build creditability early on and is commonly associated with startup success (Human et al., 2004). Ideally, the entrepreneurial team will possess all three types of experience since this will increase the probability of success (Human et al., 2004).
Hypothesis (2b): If the entrepreneurial team possesses all three kind of experience, the
startup is more likely to be successful.
Startups are continually faced with diverse challenges originating in uncertainties as regards business processes, markets and technologies (Chowdhury, 2005). Coping with unforeseen, unpredictable challenges is critical for startups (Loch, Solt & Bailey, 2008). New business creation is a novel and mostly unstructured task. Uncertainties require flexibility from the entrepreneurial team. They require continuous development of new skills, knowledge and ways to perform from members of the team. According to Schjoedt and Kraus (2009), the entrepreneurial team requires heterogeneity in its human capital, for example, experiences, knowledge, skills, and abilities; as well as homogeneity in order for the members to be able to function together and to be effective in managing the startup in response to the external
environment. The contingency “fit” theory suggests that the potential for success is higher if startups adopt strategies, innovations, and organizational procedures in consonance with the markets served (Stuart & Abetti, 1990). Critical to this purpose is the ability of entrepreneurs to adapt and to change their behavior to transform their circumstances into viable opportunities (Gartner, Starr & Bhat, 1998). Startups often do not correctly foresee the best means of addressing opportunities, and may be forced to adapt and modify their approach over time (Loch, Solt & Bailey, 2008). Adaptability can be defined as “a change in team performance in response to environmental contingencies, making the necessary modifications to meet new challenges and uncertainties, and shifting focus, plans, actions, and priorities in response to unpredictable circumstances” (Pulakos, Smitt, Dorsey, Arad, Hedge & Borman, 2002; Burke, Stagl, Salas, Pierce & Kandall, 2006). Adaptability is the level at which a team achieves correspondence between its behavior and a set of novel demands (LePine, 2005). Due to the high level of uncertainties a startup faces, it is important that teams are able to adapt to new technologies and sudden environmental changes. The ability to anticipate future market needs and to adapt to meet these needs provides the fundamental foundation for success (Harvey & Evans, 1995).
Hypothesis (2c): The greater the ability of the team to adapt, the higher the initial success of
a startup.
2.3 The Business Model
The next step is to transform the idea and assets into a business model. According to Osterwalder, Prgneur, and Tucci (2005) is the business model the “blueprint” to run a business. Amit and Zott (2001) describe a business model as “the structure, content, and governance of transactions between the focal firm and its exchange partners.” A business model is a structural template that contains information about all the relationships, activities, costs, and revenue generation paths the business has. It is a description of how an idea might be actualized in the real world (Human et al., 2004). Zott and Amit (2010) consider the business model as key activity system and entrepreneurial task. Composing a decent business model is probably the most widely used teaching tool in entrepreneurship education and training (Lange et al., 2007). However, a stream in business model research suggests a more novel business model design. Cavalcante, Kesting and Ulhoi (2011) suggest business models should be challenged to provide stability on one hand, and flexibility to allow for change on the other. It is always important to consider in advance how to generate revenue, though it is
possible to start a business without a business model and create one over time. If the business model is to static, entrepreneurs might fail to recognize, explore, seize and exploit valuable new market or technological opportunities since they are not in line with the present business model. For instance, when Hurly, Chen, and Karim started Youtube, they did not have a complete business model (Gross, 2005). The same applies to today’s entrepreneurial champions, such as Bill Gates, and Michael Dell: they started their world-changing businesses without completely worked-out business models (Lange, Mollov, Pearlmutter, Singh and Bygrave, 2007).
Why should (would-be) entrepreneurs spend time composing a detailed business model? Is it worth the effort? Johnson, Christiansen, and Kagermann (2008) address the importance of a good business model with the following example of Apple. “When Apple introduced the iPod, they introduced not only good technology in a fancy design but more primarily a great business model. By combining hardware, software and service, the business model provided game changing convenience for consumers, resulting in extremely high profits for Apple. Great business models have the potential to reshape industries” (Johnson, Christiansen & Kagermann, 2008). In addition, in most cases, the proposed business model is used as an important screening tool by formal investors, such as venture capitalists, business angels and bankers. Even if entrepreneurs do not aim to raise capital from formal sources, it is reasonable to think that careful preparation and building a business model provides the entrepreneurs with the advantages of an overview of all the facets of the startup, an examination of potential strategies, and a determination of the necessary human and financial resources for starting a business. Johnson, Christiansen and Kagerman (2008) believe that a business model with high potential consists of four interlocking elements that, taken together, create and deliver value. The first element is the customer value proposition: it creates a means of dealing with fundamental problems that require a solution. The second element is the profit formula, the blueprint that defines how the company creates value for itself while providing value for the customer. It consists of a revenue model, cost structure, margin model, and resource velocity. The third element is the definition of key resources and assets, elements that create value for the customer and the firm, and the manner in which those elements interact. The last element is the key processes, which describe the operational and managerial processes that allow the delivery of value and have the potential to increase in scale (Jonshon, Christiansen & Kagerman, 2008). These elements form the building blocks of high-potential business models. To establish a successful startup, a stable system in which these elements bond to one another
in consistent and complementary ways is required (Johnson, Christiansen & Kagerman, 2008).
Hypothesis (3a): A higher level of completeness (of all four interlocking elements) has a
positive impact on the initial success of startups.
Hypothesis (3b): The higher the perceived potential of the four interlocking elements, the
higher the level of initial success.
Hypotheses (3c): A higher level of business model potential has a positive impact on the
initial success of startups.
2.4 Funding
Starting and growing a new business requires a considerable amount of financial resources (Manigart & Struyf, 1997). Decisions about the use of debt, equity and financial structure in establishing a startup have been shown to have a significant influence on risk of failure, and on firm performance and growth potential (Cassar, 2004). Funding acts as initial fuel for any startup: it supports startups through their initial hurdles and at the same time acts as an accelerator (Krishna et al., 2016). Over and above the general financial requirements, additional capital is necessary for research and development in order to be able to develop superior offerings (Manigart & Struyf, 1997). Financial capital is one of the most visible resources; it creates a buffer against random shocks and allows the pursuit of more capital-intensive strategies, which are better protected from limitation (Cooper, Gimeno-Gascon & Woo, 1994). How do startups obtain access to financial capital? Entrepreneurial finance literature describes a large range of possibilities for startups to raise capital, from more traditional sources such as bank loans or financial bootstrapping, to venture capital and angel finance, and the emergence of “new” sources of finance, such as crowdfunding (Bellavitis, Filatotchev, Kamuriwo & Vanacker, 2017). The proliferation of new sources of entrepreneurial finance results in it being much easier for startups to raise capital and to grow. A few relatively “new” sources of finance are accelerator and incubator programs, proof-of-concepts centers, university-based seed funds, crowdfunding platforms, and intellectual property-backed financial instruments (Bellavitis, 2017). The globalization of financial markets has allowed startups to obtain funding from sources around the world (Devigne et al., 2013). In practice, startups often raise funding from a multitude of sources. In most cases the
funding cycle starts with the three Fs: friends, family and fools. In general, when entrepreneurs aim to start a business, they turn to their networks of close ties (Bellavitis et al., 2017). When entrepreneurs succeed in building a prototype and approaching new clients they advance to the next stage, in which they attempt to broaden the funding cycle by approaching formal investors, such as angel investors and venture capitalists (Bellavitis et al., 2017). Venture capital funding events provide important signals about the quality of a startup (Davila, Foster & Gupta, 2003). The majority of startups fail during the first years. In general, venture-backed startups are more successful and grow faster than non-venture-backed startups (Davilla et al., 2003). Due to careful selection by venture capitalist, the survival ratio of startups funded by venture capitalist is much higher (Stuart & Abetti, 1990). Funding sources such as venture-capital firms, banks, angel investors have developed sophisticated investment-analysis tools and models to evaluate the potential of startups (Human et al., 2004). If funding is only being received from the three Fs of the entrepreneurs, or from the entrepreneurs themselves, the problem arises that these sources do not possess the same depth of analytic tools and the same amounts of available funding to invest as formal investors (Human et al., 2004). Obtaining external funding is considered a valuable sign of legitimacy and enables access to an important network of contacts (Bellavitis et al., 2017). It is seen as a hallmark of quality and success. The majority of highly successful companies was able to raise capital from formal investors (Bellavitis et al., 2017). The amount of financial capital to which a startup has access has an influence on its survival prospects and its potential success (Cooper et al, 1994). If a startup receives large amounts of funding, this empowers it to execute more ambitious strategies, and reduces the risk of it being unable to meet its financial demands and as a result go out of business early. Larger amounts of funding provide the startup with greater flexibility to exploit opportunities.
Hypothesis (4a): The greater the level of funding, the greater the possibility of initial success
of startups
Hypothesis (4b): The greater the ability to rise external funding, the greater the likelihood of
the startup achieving initial success.
While it may appear as though all the important assets required for building a successful startup have been reviewed, according to this study, one factor has been omitted.
2.5 Timing
Despite the acknowledged importance of timing for the performance and survival of startups, scholars have struggled to generate an integrative theoretical foundation that includes timing. There exist different streams of research that aim to identify optimal timing. Scholars have converged on the notion that success can be achieved through the improved use of time (Wagner & Digman, 1997). However, consensus is still lacking as regards optimal timing, and a generalizable framework has not yet been established (Suarez, Grodal & Gotsopoulos, 2015). Zachary, Gianiodis, Payne and Markman (2015) fault the large number of contingencies that cause conceptual and methodological complexities for the lack of a rigorous theory of timing. Given the scarcity of empirical work on the importance of timing for startups, this part of the study is mainly exploratory. In such cases of scarcity of relevant research, practical examples are used to explain why hypotheses are proposed.
In this study, timing refers not to a specific moment in time (e.g. a month or a year), but to the moment a startup enters into a new or existing space (a market, industry, or geographic region), relative to its competitors, the development of technology, and industry life cycle. It concerns the broader condition of the economy, industry and culture. It involves developments that usually are out of the control of entrepreneurs and can barely be defined.
The leading mistaken assumption that an entrepreneur can make is that superior ideas will sell themselves. This will only happen if the matter of timing is handled in a proficient manner. The decision to enter the market should be timed to balance the risks of premature entry against the opportunities missed by arriving late. There are only limited periods of time when the “fit” between the needs of a market and the offering of a firm is optimal (Abell, 1987). If the market is not ready for an offering, it is analogous to offering a Ferrari in a world without highways or gasoline: an admirable feat of engineering, but not one that creates value for customers. Startups must time their entry into the market in accordance with the evolution of complementary technologies and customer requirements, rather than simply emphasizing rapid market entry (Christensen, 1998; Choi and Thum 1998; Regibau and Rockett, 1996; Schilling, 2002). How to decide whether the market is ready? This begins with careful investigation of, and sensitivity to, market needs, competition and the ability to identify the suboptimal deployment of resources. This study proposes that optimal timing entails discovering the right balance between market readiness and competition intensity. How can this balance be defined?
When assessing timing literature, it is noticeable that the majority of studies perceive timing as order of entry. Scholars have largely studied the first-mover advantage perspective (Zachary et al., 2015) According to this perspective, the most profitable strategy for startups, especially in technology markets, is to be the first to bring innovative offerings to the market, thereby gaining advantages over (potential) competitors (Boyd & Mason, 1999). Startups enter the market immediately in order to exploit the opportunities associated with being pioneers and to enjoy a period of monopoly. If startups successfully enter the market in the early stage, this may grant the benefits of being the first-mover. The startup may be able to establish (1) the industry standard, (2) a reputation in the market (3), higher customer awareness, (4) high switching costs, (5) control of scare resources, (6) control of distribution channels, and (7) barriers to subsequent entry (Miloud, Aspelund & Cabrol, 2012). However, another stream of research holds that aiming for a first-mover strategy is risky and that advantages are context specific (Zachary et al., 2015). Christensen, Suarez, and Utterback (1998) suggest that first-mover advantages might not hold true in fast-changing industries. Early entry is accompanied by great uncertainty because new industries are seldom well-defined, customer needs are unknown or hard to predict, and a there may be a lack of understanding of industry dynamics (Suarez, Grodal & Gotopoulus, 2015). Founding a startup is risky under any conditions, but especially when the entrepreneurs must fashion a new market, raise capital from skeptical sources, and recruit untrained employees (Aldrich & Fiol, 1994). Entrepreneurs may take time and gather information to reduce uncertainties and build the necessary resources and capabilities before making the decision to enter the market (Choi & Shepherd, 2004). What is the right moment to step in the industry? Venture capitalists generally focus on the middle part of the classic industry S-curve (Zider, 1998). At this stage, growth is the most rapid. In this manner venture capitalists avoid the risks of the early stage in which technologies are uncertain and market needs unknown. They simultaneously avoid the risks of the later stage, which is characterized by competitive shakeouts, consolidations and slow growth rates (Miloud, Aspelund & Cabrol, 2012). Suarez and colleagues (2015) refer to a window of opportunity for entry between the emergence of a dominant category, referring to offerings that address similar needs and compete for the same market space, and the emergence of a dominant design. If startups enter the market before the emergence of a dominant category, they may lack guidance on how the market space will be understood, defined, and delimited (Suarez et al., 2015). Early entrants are at risk of committing to categories that do not prevail. Startups that enter after a dominant category has been
established can rely on boundaries for positioning and technological priorities (Suarez et al., 2015). According to Suarez and colleagues (2015) high performance is associated with entry during the window of strategic opportunity that occurs prior to the emergence of a dominant design. Scholars have agreed that the emergence of a dominant design defines resource conditions that are associated with competitive advantages and triggers the onset of industry maturity (Suarez et al., 2015). Once the industry shifts to maturity, it becomes increasingly hostile to new entrants because the locus of competition changes to production processes, economies of scale, and to a higher degree of commoditization.
Hypothesis (5a): Entering the industry before the emergence of a dominant design is
positively related to initial success of startups.
There is consensus among scholars on the attractiveness of the growth stage. This study argues that defining timing as the moment a startup enters the industry life-cycle is not completely sufficient. The concept of timing is not limited to the stage at which the startup enters the market, but it is a construction involving larger and, unfortunately, more abstract concepts. What makes this stage a good time to become involved? Lilien and Yoon (1990) emphasize that, as mentioned above, in a dynamic competitive environment, the decision to enter the market should be timed to balance the risks of premature entry against the opportunities missed by entering late. The risks and opportunities associated with offering something new to market vary due to several factors, for instance, changes in the general economy, changes in customer preferences, and the evolution of technologies within the industry (Lilien & Yoon, 1990). Entrants have to deal with diverse contingencies that differ in risk exposure, resource capability commitment, and the degree of control over entry processes and outcomes (Zachary, Gianiodis, Payne & Markman, 2015). What makes the growth stage an attractive stage to entry? This study aims to discover which concepts define timing for startups and to demonstrate their importance. Timing involves fulfilling a demand in the market or creating an entire market out of a latent demand. Venture capitalists find startups in large markets with high-growth potential the most interesting when qualifying new venture opportunities. Investors prefer to see maximum potential when an industry is in the growth stage (Zider, 1998). This presupposes, inter alia, that the level of demand potential determines optimal timing. Demand potential exists when there is an underserved need. Occasionally (technology) startups offer ideas so advanced that they do not yet fit customer expectations or habits, and with which customers do not yet feel comfortable (Schilling, 2002). Adapting to
new technologies and rejecting old habits will only occur if there is a pressing need to do so (Human et al., 2004). That is, it will only happen if the offering arrives at the right time. This can be illustrated with the example of Airbnb. At first, the idea of renting out one’s home, including one’s personal belongings, to strangers appears not to have been one that everyone would be content with. However, when Airbnb launched in 2008, the recession was at its height. People needed extra income, which made them more willing to rent space in their homes. At the same time, going on holiday and paying for (expensive) hotels or other accommodation did not belong to the realm of possibilities either, as travelers were seeking ways to save money on accommodation too. Demand potential will depend on attribute values as well as the presence or absence of close substitutes, which may already exist. One can only speak of market readiness on condition that there exists an underserved need that results in a definable demand potential. That is, a decision regarding the “right” timing to exploit opportunities depends on potential customer demand (Chrisman & McMullan, 2000). In order for a startup to succeed, the offering must be based on real customer needs, not on the assumption that if one introduces a product or service, there will be demand for it (Choi & Shepherd, 2004).
Hypothesis (5b): The lower the level of demand uncertainty, the higher the level of initial
success of startups.
Technological evolution in the social system provides opportunities for entrepreneurial activity. Technological change is attractive for startups due to conditions for high variability (Roure & Keely, 1990). According to Schumpeter (1934), entrepreneurial opportunities are the result of the introduction of new knowledge. Technological change can be a source of new knowledge (Acs & Varga, 2005). According to Choi and Shepherd (2004), the best time to exploit entrepreneurial opportunities is when there is high perceived knowledge of customer demand for the new offering and the necessary technologies are fully developed. The evolution of supporting technologies suggests another indication of market readiness. Supporting technologies regards the fact that ideas have higher value when they are combined with complementary assets and ideas and are therefore characterized by a certain degree of interdependence in specific contexts (Natalicchio, Petruzzelli & Garavelli, 2014). The value of a new offering depends on the existence of technologies that enable the meeting of quality and efficiency expectations (Choi & Shephard, 2004). If these technologies are not fully developed, the startup is at risk of not achieving the desired technical success (Choi &
Shephard, 2004). As result of the absence of academic literature that describes the relationship between complementary technologies and startup success, a real-life example is presented to illustrate the likelihood of the existence of this relationship. Letsbuyit.com can be comprehended as the pre dated Groupon; however, although the idea is quite similar, Letsbuyit.com did not succeed. Letsbuyit.com started in 1999 before there was the opportunity of utilizing social networking platforms. While Groupon, which is able to use social media, is a great success, letsbuyit.com resulted in failure. If supporting goods and services are available, startups are more likely to possess sets of attributes that meet customer demand (Schilling, 2002). The evolution of complementary technologies is not under control of entrepreneurs, but knowing when to seize upon opportunities or when to postpone doing so is important to achieving success. Supporting technologies or infrastructures should be reliable and aligned; however, waiting for the market to be served with various available supporting technologies will not guarantee success. Waiting increases the risk that competitors will already have captured an unique advantage and become the dominant design (Schilling, 2002). In accordance with Anderson and Tushman (1990), who state that the first introduction is rarely adopted as the dominant design. Golder and Telli (1993) suggest that the majority of leaders are neither market pioneers nor late entrants (Schilling, 2002). With too early entry, the advantages of complementary technologies may not be fully utilized, while on the other hand, with late entry there is a risk of greater competition.
Hypothesis (5c): The higher the level of technology benefits by complementary technologies
the higher the level of initial success
Startups may prefer waiting until more information about complementary technologies is available and uncertainty is reduced. However, when waiting, startups risk the emergence of a competitor. One might be too late if there already exist competitors with a substantial audience. If the market is already served, the time to offer a similar product or service might have already passed.
The risk of competition has been mentioned as important several times. A competitive environment is an essential component for success (Cooper, 2013), and can “make or break” a business. By way of illustration, the former market leader in video renting in the US, Blockbuster, had the perfect business model and was not concerned about the rise of new competitors, such as Netflix. When technology advanced, Blockbuster failed to see the
possibilities provided by the internet, and how it would change the industry, and consequently did not immediately upgrade its business model. By that time, Netflix had completely established their online-streaming services platform. Once Blockbuster realized the potential and introduced a streaming service it was simply too late as Netflix had already created a powerful network. The success of startups is heavily dependent upon competitive environmental developments, many of which may be very difficult to predict (Cooper,1993). Although technical capabilities can improve startups’ performance, this depends on the competitive environment the startup operates in, and the level at which it does so. A startup should match the conditions of its environment to be successful (Zahra & Bogner, 2000). To achieve the best position in the industry, a startup has to be aware of the opportunities and threats posed by competitors (Zahra & Bogner, 2000). Organizational ecology literature notes that a firm’s performance is influenced by the conditions in which a firm is born (Geroski, Mata & Portugal, 2010). The findings of Geroski, Mata and Portugal (2010) suggest that firms’ strategies, market conditions, and macroeconomic conditions are all important determinants of survival.
Hypothesis (5d): The higher the level of benefits of complementary technologies, the higher
the level of initial success.
Hypothesis (5e): Optimal timing has a positive impact on initial success of startups.
Hypothesis (6): Variance in timing explains a greater degree of variance in initial success
TIMING IS EVERYTING: SUCCESS FACTORS FOR STARTUPS H1 (a+b+c+d) H2 (a+b+c+d) H3 (a+b+c) H4 (a+b+c) H5 (a+b+c+d+e) H6 (++) 3. CONCEPTUAL FRAMEWORK
This section contains a visualization of the hypothesized relationships in a conceptual framework. A short summary of all hypotheses is given.
Figure 1. Conceptual Framework
Figure 1 provides a visualization of the concepts and the hypothesized relationships. This study aims to explore the role of timing in determining startups success and the role of timing related to other key success factors for startups as, idea, team, business model and funding. The main purpose is to determine the importance of timing. As described above the key factors are divided in sub factors to determine relative importance of the different sub factors. Business model potential (+)
- Completeness (+) - Interlocking potential (+) Funding potential (+) - Funding targets (+) - Source (+) Team potential (+) - Team skills (+) - Team adaptability (+) - Team experience (+) Idea potential (+) - Idea superiority (+) - Idea innovativeness (+) - Global potential (+) Optimal Timing (++) - Customer uncertainty (-) - Technology benefits (+) - Competition intensity (-) - Dominant Design (-) Initial Success - Revenue Growth - Employee Growth - Probability of Survival
Hypothesis (1a): The higher the level of idea superiority the higher the level of initial success
of a startup
Hypothesis (1b): The higher the innovation level the higher the level of initial success
Hypothesis (1c): The higher the level of global potential the higher the level of initial success
of a startup
Hypothesis (1d): The higher the idea potential the higher the level of initial success of a
startup
Hypothesis (2a): If the entrepreneurial team has high level of skills the startup is more likely
to be successful
Hypothesis (2b): If the entrepreneurial team possesses all three kind of experience the
startup is more likely to be successful
Hypothesis (2c): The higher the ability to adapt of the team the higher the initial success of a
startup
Hypothesis (2d): The higher the team potential the higher the level of initial success of a
startup
Hypothesis (3a): The level of completeness (all four interlocking elements) increases the
level of initial success.
Hypothesis (3b): The higher the perceived potential of the four interlocking elements the
higher the level of initial success.
Hypothesis (3c): The higher the business model potential the higher the level of initial
success of a startup
Hypothesis (4a): The higher the level of funding the higher the initial success of startups Hypothesis (4b): The higher the ability to raise external funding the startup is more likely to
achieve initial success
Hypothesis (4c): The higher the funding potential the higher the level of initial success of a
startup
Hypothesis (5a): The higher the optimal timing the higher the initial success of a startup Hypothesis (5b): Entering the industry before the emergence of a dominant design is
Hypothesis (5c): The lower the level of demand uncertainty the higher the level of initial
success of startups
Hypothesis (5d): The higher the technology benefits of complementary technologies the
higher the level of initial success of a startup
Hypothesis (5e): The lower the level of competition intensity the higher the level of initial
success of a startup
Hypothesis (6): Variance in optimal timing explains a greater degree of variance in initial
4. METHODOLOY
In this section the research method, design and analysis will be explained. The purpose of this study was to get a better understanding of what factors determine a startup’s success, and to what extent. Although a great amount of research exists on factors, idea, team, business model and funding. One factor seems to be left out, namely timing. The current study aimed to uncover whether timing is of importance for the success of startups.
Design
In this study two data collection methods were employed: 1) desk research, and 2) an online questionnaire. First, to determine the field, key issues, and development in the literature, a great number of peer-reviewed studies in the fields of management, entrepreneurship, and strategy were analyzed. The literature review aimed to include the most influential and highest cited articles on this topic available and therefore contains a substantial amount of literatures published in A-list journals. The included studies vary in their publication date, starting from 1934 to 2017. Web of Science and Google Scholar were the search engines used to access the Academic Literature. Examples of the search included keywords as “founding conditions”, “market timing”, “success factors”, and “startup characteristics”. Second, the questionnaire was based on desk research and two exploratory interviews. The questionnaire was composed of existing questionnaires from previous scientific research in combination with new input. The questionnaire was tested to determine whether questions were clear and whether the concepts represented relevant factors. All questions were asked in English. The answers were presented on a Likert-scale (1-5) with a series of descriptive or numerical responses indicative of the relative magnitude of the particular component. The response possibilities were designed to establish a common basis of understanding, and wherever possible, numeric rangers were incorporated.
Sample
The sample consisted of 87 startups (entrepreneurs filled in the survey in name of their startup) whereof 18 of them were software/platform-oriented, 16 were operating in the retail industry, 12 were marketing-oriented, and 8 were active in the creative industry. The 33 other startups were not clearly assignable to any of these categories. All startups were less than 10 years old. All respondents received the same online questionnaire. The questionnaire is composed with Qualtrics and distributed by email, social media, startup-working spaces, peers, friends and family. All respondents were kindly asked to send the survey to other
entrepreneurs. All responses were collected anonymously.
Validity and Reliability
This study aimed to achieve high viability and reliability. Cronbach and Meehl (1995) defined construct validity as “the degree to which an instrument measures the construct it is intended to measure”. Saunders and Lewis (2009) defined reliability as “the extent to which data collection techniques or analysis procedures yield consistent findings”. Therefore, in order to minimize any bias resulting from confidentiality issues, all of the respondents’ answers were kept anonymous. Also, respondents could fill in the survey in their natural habitat, to make sure no pressure was felt from the researcher, of time-related pressure. Although respondents were asked to be as honest and critical as possible on their startup’s performance, it was important to keep in mind that answers are based on their own judgments. To ensure high reliability on all levels, only existing scales from literature with acceptable reliability scores and theoretical relevance were used. To determine whether the scales with multiple items would form a reliable scale, Cronbach’s Alpha tests were performed. The correlation between the items was tested, including ‘scale if item deleted’ to reach the highest reliability score. Table 2 shows an overview of the Cronbach’s alpha scores.
Measures
All startups were asked to evaluate indirectly their idea potential, team potential, funding potential, business model potential and optimal timing at the moment of founding and in its early years. The startups were asked to rate themselves on a 5 point Likert-scale or ratio scale. Table 2 consist a summary of measures, sources, level and reliability.
To measure variable Idea Superiority the scale of Langerak, Hultink and Robben (2004) with a Cronbach’s Alpha 0.81 was used. Level of Innovation was measured using the 5-point Likert scale of Gartner, Starr and Bhat (1998). Global Potential was measured with a 5-point Likert scale, varying form 1=very low potential to 5=very high potential. To measure the level of entrepreneurial skills of the team the “venture screening questionnaire” of Gartner, Starr and Bhat (1998) with Chronbach’s alpha 0.74 was used. The rating scale for Team Experience, of Gartner, Starr & Bhat (1998), scores a Chronbach’s alpha of 0.78. Team Adaptably was measured on an 8-item descriptive 5-point Likert-scale, which was developed by Pulakos et al., (2000) and had a Chronbach’s alpha of 0.85. For the measurement of Ability to Meet Funding Target the 5-point Likert scale of Gartner, Starr and Bhatt (1998) was used. The
amount of internal and external were asked to be filled in as well. A scale to measure Business Model Potential was based on the requirements of Jonhson, Christiansen & Kagerman (2008) for high potential business models, with Cronbach’s alpa 0.63 on completeness and Cronbach’s alpha 0.65 on interaction potential. Optimal Timing was measured as the balance between Market Readiness, Competition Intensity and Technology Benefits. Competition Intensity was measured with a scale of Schilling (2002), with Cronbach’s alpa 0.82. The measurement for Demand Uncertainty was based on a scale of Choi & Shephard (2004). with Cronbach’s alpha 0.72. Technology Benefits were also measured with a scale from Schilling (2002) with Chronbach’s alpha 0.80. In relevance of the used Startups, which all started less than 10 years ago, it was suitable to measure the variable Initial Success, over for example variables as ’overall’ or ’long-term’ success. Initial success, in general, is a prerequisite for attracting new customers, maintaining a steady flow of capital, and a conformation to the entrepreneurs and employees that their initial sacrifices will be rewarded (Stuart & Abetti, 1987). Initial Succes was constructed of the items ’revenue growth’ and ’employee growth’ and scores Cronbach’s Alpa 0.86 which was a highly reliable scale. Startup Age and Sector They Operate In were included as control variables.
For the variable Idea Potential, a scale was constructed with a score of Cronbach’s Alpha 0.86, which means high reliability. Also the variable Team Potential was highly reliable with a Cronbach’s Alpha of 0.82. Business Model Potential had an acceptable reliability, with a Cronbach’s Alpha of 0.65. The constructed scale of Funding Potential with a Cronbach’s Alpha of 0.77 could aswell be considered reliable. And Optimal Timing scored a Cronbach’s Alpha of 0.89, which there was a highly reliable scale.
Almost all Cronbach’s alpha scores were higher than Chronbach’s Alpha 0.70, which means good reliability (Nunally, 1978). The measurement scales of Business Model Potential and Business Model Completeness and Interlocking Potential score Chronbacks’s Alpha >0.60, which means they were reliable. In addition, the corrected item-total correlations indicate that all the items had a good correlation with the total score of the scale, because all score above 0.30. Also, none of the items would substantially affect reliability if they were deleted.
Recoded Variables
All measurement scales are designed in a way that low scores are negatively keyed items and high scores are positively keyed items. Demand Uncertainty and Competition Intensity
however, were asked the other way around. Therefore, to compute the variable optimal timing, demand uncertainty and competition intensity were recoded to make sure they align ed with the other scales. In this way, all scores could be interpreted the same way. Furthermore, two dummy variables were computed. Dummy variables indicate either the absence or presence of a characteristic or trait. Existence of Dominant Design was recoded into a dummy variable, where no clear existence of a dominant design had a value of 0, and clear existence of dominant design had a value of 1. The same holds for Source of Funding, where mostly internal funded had a value of 0, and mostly external funded had a value of 1).
Analysis
In this section, the statistical analyses performed in this study will be clarified. All the quantitative analyses in this study were performed with IMB SPSS Statistics 23. After cleaning and sorting the database, the first step of the analysis process was conducting a reliability analyses. Second, to test correlation between the variables, a Pearson correlation analysis was performed. The correlation analysis quantifies the intensity and the direction of the relation between two or more variables. Third, the effects of idea potential, team potential, funding potential, business model potential, optimal timing and their sub (factors) on initial success were analyzed with the use of a multivariate linear regression analysis, with Initial Success as the dependent variable. A multivariate linear regression analysis is a test to examine the linear relation between two ore more independent variables and one dependent variable. The test can be used when the independent variables are continuous; to predict a dependent variable starting from a set of correlated independent variables. Generalizability was tested by the formula of Tabachnick and Fidell (2007, p.123) for calculating sample size requirements, taking the number of independent variables into account: N > 50 +m (where m = number of independent variables). A normality check was done by a histogram to indicate a normal distribution. Furthermore, to check linearity, a scatterplot was composed to indicate a straight line. The same scatterplot was used to detect outliers.
Table. 2. Summary table of measures, source, level and reliability
Variable Source Level Reliability
Idea Superiority Langerak, Hultink & Robben, 2004
Interval/Ratio .814
Innovativeness Global potential Potential
Gartner, Starr & Bhat, 1998 Constructed scale Interval/Ratio Interval/Ratio Interval/Ratio 1-item 1-item .847
Team Skills Gartner, Starr & Bhat,
1998
Interval/Ratio .743
Experience Gartner, Starr & Bhat, 1998 Interval/Ratio .780 Ability to adapt Team Potential Pulakos et al., 2000 Constructed scale Interval/Ratio Interval/Ratio .856 . 823
Funding Source Internal/External Dummy 1-item
Ability to meet funding targets
Funding potential
Gartner, Starr & Bhat, 1998 Constructed scale Interval/Ratio Interval/Ratio 1-item .769 Business Model
Completeness Jonshon, Christiansen & Kagerman, 2008 Interval/Ratio .634 Interlocking Potential BM potential Jonshon, Christiansen & Kagerman, 2008 Constructed scale Interval/Ratio Interval/Ratio .652 .653 Timing Demand Uncertainty Choi & Shepherd,
2004
Interval/Ratio .801
Complementary technologies Schilling, 2002 Interval/Ratio .849
Dominant Design No/Yes Dummy
1-item Competition intensity Optimal timing Schilling, 2002 Constructed scale Interval/Ratio Interval/Ratio .82 .887 Initial success
5. RESULTS
In total 87 startups filled in the online survey completely. The average age of startups was (?) (M =4.02, SD = 1,77), with a distribution of % national focused and % international focused startups. Startups are asked to fill in their sector of focus. Of the results 5 categories could be made; 18 of them are online/software/platform-oriented (20.7%), 16 operating in the retail industry (18.4%), 12 are marketing-oriented (13.8%), and 8 are active in the creative industry (9.2%), 33 others are not clearly assignable to a category (37.9%). Table 3 shows an overview of descriptive statics of the sample.
Table 3. Descriptive statics Sample
(N=87) N Age M(SD) 87 4.02(1.77) Focus - National (%) - International (%) 35 52 40.2 59.8 Sector - Software (%) - Retail (%) - Marketing (%) - Creative (%) - Other (%) 18 16 12 8 33 20,7 18,4 13,8 9,2 37,9
When looking at the descriptive statics of the variables it is notable that the averages scores do not differ that much from each other. The idea potential of the startups is rated as a little above moderate (M=3.62, SD=.51). The same withholds for team potential (M=3.55, SD=.39). Funding potential is on average rated as high potential (M=3.94, SD=.81). Business model potential is on average rated as moderate potential (M=3.23, SD=.43). On average optimal timing is rated as high (M=3.94, SD=1.01). An overview can be found in table 4.
Table 4. Overview Descriptive Statistics (N=87) M (SD) M Min Max Idea Potential 3.62 (.51) 3.56 2.33 4.44 Team Potential 3.55 (.39) 3.58 2.86 4.60 Funding Potential 3.94 (.81) 4.00 2.00 5.00 BM Potential 3.23 (.43) 3.21 2.29 4.21 Optimal Timing 3.69 (.92) 4.04 1.87 4.80 Initial Success 3.94 (1.01) 4.50 1.50 5.00
Table 5. Correlation Matrix
Table 5: Correlations, Means, Standard Neviations , Sample size Mean SD N 1 2 3 4 5 6 1. IDEA Potential 1,0502 87 -2. Team Potential 1,1655 87 0,440 0 - 3. Business Model Potential 0,9688 87 0,062 0 0,142 0 -4. Funding Potential 1,5682 87 -0,011 0 0,173 0 -0.301* * - 5. Optimal Timing 1,3422 87 -0.213 * -0.220 * -0,0310 -0,1920 - 6. Initial Success of Startups 0,7023 87 -0.303 ** -0,103 0 -0.225* 0,1740 0.799 **
-**. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).
Table 6. Hierarchical Multiple Linear Regression R R2 R2 change B SE β t Step 1 0,299 0,090 0,090 Startup Age -0,1790 0,0620 -0.2990** -2,8930 Step 2 0,903 0,815 0,725 Startup Age -0,2690 0,0390 -0.4500** -6,8870 Idea Potential 0,2990 0,1350 0.1460* 2,2200 Team Potential 0,1760 0,1450 0,0650 1,2090
Business Model Potential -0,3770 0,1330 -0.155** -2,8330
Funding Potential 0,0990 0,0770 0,0760 1,2870
Optimal Timing 0,9890 0,0640 0.8670** 15,5370
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
Hierarchical multiple regression was performed to investigate the ability of idea potential, team potential, business model potential, funding potential, and optimal timing to predict initial success, after controlling for age of startup. In the first step of performing hierarchical regression the control variable age of startup is entered. This model is statistically significant F(1,85)=8.56;p<.05 and explains 9% of variance in initial success of startups. The model being statistically significant means it can be used to evaluate predictors of initial success of startups. After idea potential, team potential, business model potential, funding potential, and optimal timing at step two the total variance explained by the model as a whole is 81,5% F(6,80)=58.79; p<.001. The introduction of the five success factors explained additional 72,5% variance in initial success of startups, after controlling for age of the startup (R2 Change=.72; F(5,80)= 62.79; p<.05). In the final model four out of six predictor variables are statistically significant, with optimal timing recording a higher beta value (b* = .86, p < .05) than age of startup (b* = .45, p < .05), idea potential (b* = .15, p < .05), business model potential (b* = -.16, p < .05). In other words, if optimal timing increases for one, startups initial success will increase for (.86). If the age of startup increases with one, initial success will increase with (.45). If idea potential increases for one, startups initial success will increase with (.15). Notable is if business model potential increases with one, initial success of a startup will decrease with (.16). Predictor variables team potential (b* = .07, p =.23) and funding potential (b* = .08, p = .20) are not statistically significant. Optimal timing shows a very strong positive relationship with initial success. Age of a startup shows a reasonable positive connection with initial success. Idea potential shows a weak positive linkage.
