Leadership and Innovation in Start-Ups:
An Unexpected Result
A Master’s Thesis
Student: Melanie M. R. Botman Student ID: 10351620
Supervisor: B. Szatmari Second assessor: Unknown Version: Final version
Faculty of Economics and Business MSc. Business Administration Entrepreneurship and Innovation Academic Year 2016-2017 Date of submission: 18-08-2017
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Statement of originality
This document is written by Student Melanie Botman 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.
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Abstract
The amount of new companies created each year is growing steadily. Many of these start-ups, however, do not survive. There is currently a growing interest in which factors can lead to entrepreneurial survival and success. This study deals with this topic by determining the effect of innovation on revenues growth. The connection between leadership and innovation outcomes is also tested, by using the Ohio State leadership styles. This research was conducted by surveying 103 Dutch start-ups between one and five years old.
Although innovation was expected to have a positive effect on revenues growth, no such relationship was found. The same applies to both leadership styles, while Consideration was expected to have a positive moderation effect and Initiating Structure a negative one, with neither of them showing a significant relationship.
There was another unexpected outcome in this study as the data showed a very strong correlation between the two leadership styles. This correlation means that these two styles cannot be seen as two separate variables, but are in fact one overarching leadership style containing behaviours from both original styles. When using this new variable to test the moderating effect, the relationship was still shown to be insignificant. It can therefore be concluded that innovation is not affected by leadership behaviour.
The results from this study show that theories introduced by business studies may not fit the profile of start-up companies. Entrepreneurs are advised to focus on other things than innovation practises when trying to build a successful company through growing revenues. Those who still place an importance on innovating can note that differences leadership do not seem to affect the outcomes of an innovation orientation.
Keywords: Innovation, Leadership, Entrepreneurship, Start-ups, Consideration, Initiating
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Table of Contents
1. Introduction
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5
2. Literature Review
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7
2.1 Start-up success ………7
2.1.1 Entrepreneur specific factors ………..8
2.1.2 Firm specific factors ……….9
2.2 Company Innovation ………10 2.2.1 Applying innovation ………11 2.3 Leadership Styles ………11 2.3.1 Consideration ………..12 2.3.2 Initiating Structure ………..13 2.4 Research Gap ………..13
3. Theoretical Framework
……….
14
3.1 Propensity to innovate affects start-up success ……….14
3.2 Leadership styles act as moderators ………16
3.2.1 Consideration ……….16 3.2.2 Initiating Structure ……….18 3.3 Conceptual Model ………..19
4. Research Design
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20
4.1 Survey Design ………..20 4.1.1 Measurement scales ………20 4.2 Targeting Respondents ……….205. Results
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21
5.1 Variables and Measurements ……….21
5.2 Hypotheses’ Analysis ………24
5.2.1 Hierarchical regression ……….24
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6. Discussion
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27
6.1 Limitations ……….29 6.2 Managerial Implications ………..30 6.3 Theoretical Implications ………..307. Conclusions
….………
31
8.
References
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35
9.
Appendices
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41
Appendix A: Measurement Scales … ……….41
Appendix B: Statistical Output ………43
Appendix B.1 Factor analysis ………..43
Appendix B.2 Process model 2 ……….47
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1. Introduction
Start-ups are becoming a more common marketplace occurrence, with globally about 100 million new companies being started each year (How many start-ups are there, 2014). Start-ups are classified as new firms recently started by entrepreneurs (Freeman & Engel, 2007). These new firms, however, suffer from low rates of survival and overall success (Song, Song & Parry, 2010). There are many different factors that have been attributed to start-up success, such as establishing alliances (Baum, Calabrese & Silverman, 2000), learning from and searching for new network relationships (Lee & Tsang, 2001; Prashantham & Dhanaraj, 2010), or gaining knowledge from the geographic location the start-up resides in (Gilbert, McDougall & Audretsch, 2008). With the number of new start-ups growing steadily over the years, this area can be quite interesting for business researchers (Burn-Callander, 2015; Anderson, 2015). Although researchers agree that start-ups are important for regional economic growth, there is no consensus on exactly how a start-up can successfully establish themselves (Acs & Szerb, 2007; Hormiga, Batista-Canino & Sánchez-Medina, 2011b). Researchers from differing backgrounds, such as business, management, psychology and even biology, have been working to find a comprehensive list of start-up success factors (Ciavarella, Buchholtz, Riordan, Gatewood & Stokes, 2004; White, Thornhill & Hampson, 2006; Baron & Tang, 2009). The more information on these success factors the research community can gather, the more we will be able to help the development of these companies in the initial stages of the business cycle (Hormiga, Batista-Canino & Sánchez-Medina, 2011b).
In this paper the focus is on the owner’s propensity to innovate, as the lack of innovation in a firm is linked to a lack of growth strategy (Gray, 2006). One can explain propensity to innovate as the degree to which a firm uses organisational structure to reach a state of innovativeness or how much the owner is inclined to create an innovative atmosphere within their company (Dobni, 2008a). Companies with a strong innovation orientation are capable of developing innovative, specialised capabilities and offerings (Rosenbuch, Brinckmann & Bausch, 2011). These capabilities and offerings can lead to future growth and sustainability for a company. A firm that has the propensity to innovate can adapt to changes in the environment and is therefore more likely to survive and thrive in harsh conditions (Hormiga, Batista-Canino & Sánchez-Medina, 2011b). Research shows that innovation has a stronger impact on younger companies than more established small or medium enterprises
6 (Rosenbusch, Brinckmann & Bausch, 2011), which means that start-ups should be able to benefit more from an innovation orientation than other companies.
In order to maintain a steady flow of innovations, the company’s employees need to be willing and able to participate in the innovation process (De Jong & Den Hartog, 2007). In small and medium sized firms, the leadership style that the top management implements can have a strong impact on the innovativeness of the company (Matzler, Schwarz, Deutinger & Harms, 2008). When management has a weak commitment to innovation, it signals to employees that the organisational culture does not appreciate or support innovation, which can create a significant barrier to employees participating in innovation (Carayannis & Provance, 2008; Madrid-Guijarro, Garcia & Van Auken, 2009). This means that successful innovation in the firm is dependent on how the owner or boss leads and manages the employees (García-Morales, Jiménez-Barrionuevo & Gutiérrez-Gutiérrez, 2012).
Although innovation is often stated as an important factor in entrepreneurship, there is only a limited body of research focusing on this relationship (Kraus, 2004; Dickson, Solomon & Weaver, 2008). The community also often agrees that leaders’ behaviour is important for triggering innovation participation, however, there is also a limited amount of research that deals with this topic (De Jong & Den Hartog, 2007; Love & Roper, 2015). The amount of studies declines even further when one filters for an entrepreneurial environment. As it is important to study the start-up success factors from all angles, this research fills in the gaps in the literature on innovation and leadership in entrepreneurial environments. The topic of innovation and leadership in start-ups leads to the following research question:
“What effect do the different leadership styles have on the relationship
between innovation propensity and the success rate of start-ups?”
To come to a conclusive result it is necessary to split this research question into the following sub-questions:
- How could the propensity to innovate impact start-up success? - How might different leadership styles impact this effect?
7 To compare leadership behaviours in this paper, the Ohio State leadership styles approach will be used (Judge, Piccolo & Ilies, 2004; Northouse, 2013). This leadership model has recently been enjoying a resurgence in the literature (Piccolo, Bono, Heinitz, Rowold, Duehr & Judge, 2012). The leadership styles approach focuses on the task behaviour and the relationship behaviour of the leaders. The focus is on the dimensions of Consideration and Initiating Structure. These dimensions are more descriptive than other leadership dimensions that have been developed later, and therefore allow for a better analysis of the situation (Northouse, 2013).
To answer the research question it is necessary to determine a set of hypotheses based on previously published articles in the fields of leadership, innovation management and entrepreneurship. These hypotheses will then be tested through a survey of start-ups in the Netherlands and analysed accordingly. The results will then be discussed in the discussion section. This paper will end with the theoretical and managerial implications of these findings, and some ideas for future research.
2. Literature Review
2.1 Start-up success
Over the past few decades research into the field of entrepreneurship has grown significantly (Gartner 2001). The amount of entrepreneurs starting new ventures has grown as more people aim to start their own companies (Burn-Callander, 2015). These new ventures, however, suffer from low success rates, with some studies even reporting more than half of the ventures they followed failing to survive for more than two years (Song, Song & Parry, 2010). Although economists, such as Joseph Schumpeter, have argued that recessions create opportunities for entrepreneurs to start new ventures (Gans, 2009), research has shown that economic downturns actually lead to higher failure rates for start-ups (Song, Song & Parry, 2010). Poor market conditions are also detrimental to a new venture’s success (Franco & Haase, 2010). This is an example of how external factors can affect a new venture’s success.
The majority of start-ups tend to be located in specific geographic regions, often referred to as clusters (Pe’er & Keil, 2013). These clusters make it easier for start-ups to attract a higher number of skilled employees, as there are more of them available in these areas. The same applies to gaining more customers and specialised suppliers. The start-up can also gain
8 knowledge from residing in these clusters (Gilbert, McDougall & Audretsch, 2008). Placing the company within these strategic locations can result in good profits and a strong level of growth (Hormiga, Batista-Canino & Sánchez‐Medina, 2011b). Especially start-ups with lower amounts of resources benefit from residing in clusters. Clusters can, however, have a negative impact on start-up survival, as having a large amount of competitors can lead to rivalry (Franco & Haase, 2010; Pe’er & Keil, 2013). The different clusters and their differing structures call for certain organisational strategies, which means that start-ups need to be wary of their environment when planning their activities (McDougall, Robinson & DeNisi, 1992). Depending on the barriers to entry, new ventures need to employ different strategies to gain financial success. These strategies are mediated by the knowledge creation process (Tsai & Li, 2007). By mobilising knowledge and triggering new spirals of knowledge creation the start-up can develop and implement successful strategies.
Although there are external factors that lead to start-up success or failure, there are many internal factors that have been researched by the scientific community. As these factors have more adaptability, they are very interesting for entrepreneurs. These factors can be segmented into two groups, namely firm specific and entrepreneur specific factors.
2.1.1 Entrepreneur specific factors
Goals, self-efficacy, passion, tenacity and communicated vision all shown to have an effect on venture growth, which demonstrates how important the behaviour of the entrepreneur is (Korunka, Frank, Lueger & Mugler, 2003; Markman & Baron, 2003; Baum & Locke, 2004). A conscientious and focused entrepreneur with a high need for achievement will have a more successful start-up (Lee & Tsang, 2001; Ciavarella, Buchholtz, Riordan, Gatewood & Stokes, 2004) The entrepreneur’s social skills also play an important role, as social perception, expressiveness, accuracy in reading others and self-promotion lead to a rise in financial measures of start-up performance (Baron & Markman, 2003; Baron & Tang, 2009). An entrepreneur needs to become a successful leader, as this is necessary for a start-up to have a chance at long-term survival (Ciavarella, Buchholtz, Riordan, Gatewood & Stokes, 2004; Makhbul & Hasun, 2010).
Belief in one’s self as an entrepreneurial type is also related to venture survival and the level of financial success achieved (Miskin & Rose, 2015). A contradiction to this is that the level of optimism an entrepreneur has can have a negative effect on the performance of the
9 start-up (Hmieleski & Baron, 2009). This is because highly optimistic entrepreneurs tend to undervalue new information and are less likely to learn from their past experiences. Previous management experience in a large business is a strong indicator of venture survival, while previous ownership and small business experience determine how profitable the company will be (Lee & Tsang, 2001; Miskin & Rose, 2015).
The level of education obtained by the entrepreneur can also have an effect on start-up performance, as for each year of education added there are productivity increases (Dickson, Solomon & Weaver, 2008). An entrepreneur’s familiarity with the proposed target market and the geographical area also affect the success of the start-up, as it implies previous market experience, and local knowledge and relationships (Gilbert, McDougall & Audretsch, 2008; Miskin & Rose, 2015).
2.1.2 Firm specific factors
There are several firm specific features, such as size, resources and strategies, that can have an effect on new ventures success and survival. Smaller companies are shown to be easier to get started, leading to higher levels of success and survival (Van Gelderen, Thurik & Bosma, 2006). The amount of intended and available start-up capital can also affect this success, as research shows that it is easier to start with smaller amounts of capital (Van Gelderen, Thurik & Bosma, 2006).
According to research done by Song, Song and Parry (2010) another firm specific factor that affects start-up success is how well the company’s first product performs (Song, Song & Parry, 2010). They state that the most successful products come from ideas that are focused on an analysis of customer needs paired with technology development. If that idea came from the venture’s founder, the performance of the product and the company can be significantly higher. Formal processes for collecting and using market information are, therefore, invaluable for entrepreneurial success (Song, Wang & Parry, 2010).
The organisation can also impact its short term success by creating a good reputation for itself (Hormiga, Batista-Canino & Sánchez-Medina, 2011a; Hormiga, Batista-Canino & Sánchez-Medina, 2011b). Building a good reputation can win customer loyalty and can have a decisive effect on other stakeholders, such as financial entities and suppliers. Also, the number of trade fairs and business events the entrepreneur attends has a positive effect on the company’s success, as the entrepreneur can build meaningful relationships and a strong
10 reputation (Lee & Tsang, 2001; Prashantham & Dhanaraj, 2010; Hormiga, Batista-Canino & Sánchez-Medina, 2011a). By establishing alliances, entrepreneurs can increase their chances of success (Baum, Calabrese & Silverman, 2000).
2.2 Company Innovation
Another possible start-up success factor could be the strength of innovation orientation inside the company (Gray, 2006). Many innovations and new processes that small firms undertake can be essential for future growth and sustainability (Kickul & Gundry, 2002; Rosenbusch, Brinckmann & Bausch, 2011). Although most researchers focus on product development when they talk about innovation (Freeman & Engel, 2007; Rao, Chandy & Prabhu, 2008), this is actually one factor among many (Kickul & Gundry, 2002; Samuelsson & Davidsson, 2009). According to Samuelsson and Davidsson (2009) there are two different groups of innovations, namely creative change and optimising change. Creative change refers to the exploitation of innovative venture ideas, leading to a firm entering a new product-market that others have not recognised or sought to exploit. This is close to the definition many researchers use when discussing innovation practices. The other type is optimising change, which is when resources are not used optimally within an existing market or within the company, leaving room for a firm to optimise this process (Kickul & Gundry, 2002; Samuelsson & Davidsson, 2009). In Schumpeter’s words, "What we, unscientifically, call economic progress means essentially putting productive resources to uses hitherto untried in practice, and withdrawing them from uses they have served so far. This is what we call 'innovation'" (1928, p. 378). According to Yeh-Yun Lin and Yi-Ching Chen (2007), out of all the different types of innovation the administrative innovations have the highest predictive power on company sales. This shows that it is important to include all forms of innovation, and not solely product innovation, when researching the effects it has on companies. Research conducted by Johannessen, Olsen and Lumpkin (2001), however, shows that all these different types of innovation can be defined and measured together as one single construct. This construct can only be distinguished by the degree of innovation radicalness. By using this more holistic approach, one can target the research at a range of different companies, instead of focusing on the usual technology firms.
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2.2.1 Applying innovation
There is an assumption that small companies need to be innovative to compete against bigger and more established opponents (Rosenbusch, Brinckmann & Bausch, 2011; Kickul & Gundry, 2002). Research shows that there is a validity to this statement, although it is dependent on certain contextual factors (Rosenbusch, Brinckmann & Bausch, 2011). Firstly, fostering an innovative orientation has a greater positive effect on company performance than patents, innovative products or services (Rosenbusch, Brinckmann & Bausch, 2011). If companies focus solely on innovative offerings, they miss other important dimensions that can help their company create value.
Secondly, internal innovation projects lead to better firm performance than working with external partners on innovation projects (Rosenbusch, Brinckmann & Bausch, 2011). Internally working on innovation projects allows the company to keep control of the project, which can reduce administrative necessities and speed up the innovation process. It also allows the company to benefit fully from the returns from the project without sharing them with an external party. Innovation performance is, however, higher in companies that are proactive in creating and strengthening relationships with innovative suppliers, users and customers (Lasagni, 2012). The company, therefore, needs to create relationships with innovative others, while keeping the actual innovating processes inside the firm (Rosenbusch, Brinckmann & Bausch, 2011; Lasagni, 2012).
2.3 Leadership Styles
In this study leadership is studied, as it may have a relationship with the innovation outcomes in new ventures. Leadership has often been connected to business success (De Jong & Den Hartog, 2007). It can be explained as the process of influencing others towards achieving a desired outcome. In the case of maintaining a steady flow of innovations, the organisation’s employees need to be able and willing to participate in the innovation process (De Jong & Den Hartog, 2007). There are many different ways that leaders can affect these employees’ innovative behaviour (De Jong & Den Hartog, 2007; Dobni, 2008a). An organisation’s innovation propensity can be enhanced through the cultural acceptance of innovation within the company (Carayannis & Provance, 2008), which can be created and enhanced through the leader’s behaviour towards the employees (Dobni, 2008a).
12 To compare leadership behaviours in this paper, the Ohio State leadership styles approach will be used (Stogdill & Bass, 1981; Jago, 1982; Judge, Piccolo & Ilies, 2004; Northouse, 2013). This approach focuses on the task behaviour and the relationship behaviour of the leaders. The focus is on the dimensions consideration and initiating structure (Stogdill & Bass, 1981; Judge, Piccolo & Ilies, 2004; Northouse, 2013). This style approach is not a refined theory that provides a neatly organised set of prescriptions for effective leadership behaviour (Northouse, 2013). Rather, it provides a framework for assessing leadership in a broad way, as behaviours with a task and relationship dimension. It works by not telling leaders how to behave, but by describing major components of their behaviour (Northouse, 2013). It is therefore perfect for using as a research tool, as it can help determine how leaders have been behaving, allowing the conclusions to be inferred from the results. According to Judge, Piccolo and Ilies (2004) the dimensions affect leadership effectiveness in differing ways. Although they both have different effects, both leadership styles are associated with work-relevant behaviours and follower attitudes (Piccolo, Bono, Heinitz, Rowold, Duehr & Judge, 2012).
2.3.1 Consideration
The consideration dimension focuses on the degree to which a leader respects and shows concern for employees, looks out for their welfare and expresses support and appreciation (Judge, Piccolo & Ilies, 2004). Leaders who show the consideration style towards their subordinates are more follower focused, as they stress the importance of job satisfaction, see that subordinates are rewarded for a job well done and make sure their employees feel comfortable talking to them (Stogdill & Bass, 1981). These leaders also emphasise participative decision making, as they get the approval of subordinates on important matters before going ahead and put suggestions made by the employees into practice. The social distance between these leaders and their subordinates is also minimised, as people with this leadership are easy to approach and style treat subordinates as equals (Stogdill & Bass, 1981; Jago, 1982). This smaller social distance can maintain and strengthen the self-esteem of the employees. When the group is cohesive, consideration can lead to increased job satisfaction, clarity and performance (Stogdill & Bass, 1981).
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2.3.2 Initiating Structure
This second leadership style focuses on organising the roles of the employees towards goal attainment and creating well-defined channels and patterns of communication (Judge, Piccolo & Ilies, 2004). Initiating structure includes behaviours that focus on the task concern, such as insisting on maintaining standards, emphasising meeting deadlines and seeing that employees work to full capacity (Bass & Stogdill, 1981; Judge, Piccolo & Ilies, 2004). This leadership style also focuses on directiveness, as these leaders decide in detail what should be done and how the subordinates should go about doing this, while making their attitudes clear along the way (Stogdill & Bass, 1981). They define and structure their own roles and those of their subordinates towards goal attainment. A leader scoring high on this dimension provides communication channels to help employees complete predefined goals, while maintaining an emphasis on the importance of meeting the deadlines (Judge, Piccolo & Ilies, 2004). When cohesiveness is low in the group setting, this leadership style leads to satisfaction with supervision, higher subordinates’ self-rated performance and role clarity (Stogdill & Bass, 1981). This leadership style can also lead to grievances and a high turnover of employees, as it is very task-focused. Untrained personnel need more help and structure, but the trained employees feel too constricted and watched with this leadership style.
2.3 Research Gap
Many different start-up success factors are based on intuition instead of research (Dickson, Solomon & Weaver, 2008; Hmieleski & Baron, 2009; Hormiga, Batista-Canino & Sánchez-Medina, 2011b). Research into the field of entrepreneurship has been growing recently, however there is still plenty left unknown (Gartner, 2001). Although the importance of innovation in entrepreneurship is often stated, there is only a limited body of research focusing on innovation (Kraus, 2004; Dickson, Solomon & Weaver, 2008). This study contributes to the literature gap, as it focuses on the effects of an innovation orientation on start-ups. Researchers often only state that there are possible effects, without giving much support for the assumption.
The same is true for the field of leadership, despite the community agreeing on the importance of leaders in triggering innovation, there is only a limited amount of research that deals with the integration of leadership and innovation (De Jong & Den Hartog, 2007; Love &
14 Roper, 2015). When combining this with an entrepreneurial environment, the amount of research papers declines even further. As start-ups are usually more open to change, human variation can have a stronger effect on these companies (Markman & Baron, 2003), making them an interesting case to study.
Currently there are no studies that have been conducted that use a set of more descriptive leadership styles, such as the Ohio State Leadership Styles approach, to study the relationship between innovation and start-up success. This gap in the literature leads to the following research question:
“What effect do the different leadership styles have on the relationship
between innovation propensity and the success rate of start-ups?”
3. Theoretical Framework
3.1 Propensity to innovate affects start-up success
First it is important to define exactly what is meant by the propensity to innovate. The innovation propensity helps explain the degree of innovativeness that a company achieves (Ryan & Tipu, 2013). An organisation’s propensity to innovate can best be explained as the degree to which it is inclined to use organisational architecture to achieve a state of innovativeness (Dobni, 2008a), or in other words, how innovation-oriented the company is.
Secondly it is important to define start-up success as it will be measured in this research. There are many different measures of start-up success, such as a growth in sales, growth in profits, venture survival or some measure of innovation (Dickson, Solomon & Weaver, 2008). This shows there are many different ways to study entrepreneurial performance. In this case two measures are chosen. Firstly all the companies selected for this research are currently active and have survived at least one year. The measure used for the hypotheses is revenues growth, as this is a traditional corporate valuation parameter that shows the start-up has a healthy performance (Weinzimmer, 1997; Eccles & Serafeim, 2013)
Many innovations that companies undertake are known to be essential to the sustainability and future growth of a small firm (Kickul & Gundry, 2002; Gray, 2006). Innovation can help a company achieve superior performance in a competitive environment
15 (Lyon & Ferrier, 2002). This is because a firm that has the propensity to innovate can adapt to changes in the environment faster and is ,therefore, more likely to survive and thrive in harsh or turbulent conditions (Hormiga, Batista-Canino & Sánchez-Medina, 2011b). As start-ups often reside in competitive and turbulent conditions (Song, Song & Parry, 2010; Franco & Haase, 2010), a higher propensity to innovate will have a positive effect on the growth and survival of these firms (Hormiga, Batista-Canino & Sánchez-Medina, 2011b). Smaller companies, such as start-ups, are more flexible and can therefore apply organisational changes faster than larger companies, leading to better performance and growth (Rosenbuch, Brinckmann & Bausch, 2011).
Companies with a strong innovation orientation are also capable of developing specialised, innovative capabilities and offerings (Rosenbuch, Brinckmann & Bausch, 2011). By introducing innovative products, processes, services or business models specifically tailored to attractive niches, start-ups can stand out from the competition (Porter, 1980). Serving these niches with innovative offerings is particularly advantageous for small companies compared to their large counterparts due to their nimbleness and size, which allows them to bring the new offerings to market faster. By offering highly innovative products or services, start-ups can also avoid a price competition in the market they enter into (Rosenbusch, Brinckmann & Bausch, 2011). These new products or services might create a new level demand and therefore facilitate growth (Porter, 1980). These innovative offerings may also help the start-ups strengthen their position in the market by setting high barriers to market entry, which can in turn lead to higher returns (Rosenbusch, Brinckmann & Bausch, 2011).
Another reason why innovation might create a positive effect on start-up success is that it can create a positive perception among market participants, which can lead to a higher brand equity, attracting skilled employees and obtaining better collaboration partners (Rosenbusch, Brinckmann & Bausch, 2011). An innovation orientation can create these positive perceptions by forming new distribution or promotional channels (Kickul & Gundry, 2002; Rosenbusch, Brinckmann & Bausch, 2011). These channels can raise brand awareness and levels of efficiency, which leads to higher levels of value for the company and evidently a possible growth in revenues (Kickul & Gundry, 2002.)
Although an innovation orientation is known to require high investments and comes with many risks and uncertainties, it still seems to create overall value for new and established small companies (Rosenbusch, Brinckmann & Bausch, 2011). The benefits of innovation seem
16 to outweigh the costs and create overall value. Research has also shown that the age of the firm affects the strength of the positive relationship between innovation and the firm’s performance, with younger companies, such as start-ups, benefitting more from an innovation orientation than older firms (Rosenbusch, Brinckmann & Bausch, 2011). A start-up will gain more from having an innovation orientation than more established firms and is often more able to implement the new innovations faster, which leads to the following hypothesis:
H1: Innovation Propensity has a positive effect on Start-up Success.
3.2 Leadership styles act as moderators
In small and medium sized companies, the top management’s leadership style can have a strong impact on the innovativeness and the overall performance of a firm (Matzler, Schwarz, Deutinger & Harms, 2008). In order to realise a steady flow of innovations, employees need to be both able and willing to participate in innovating (De Jong & Den Hartog, 2007). One of the more significant barriers to innovation that companies suffer from is a weak management commitment to innovation, as this signals to the employees that the organizational culture does not support innovation (Carayannis & Provance, 2008; Madrid-Guijarro, Garcia & Van Auken, 2009). This shows that the leader’s particular behaviour can affect the innovation orientation of a company. The following sections will show how the two leadership styles are expected to affect the company’s innovation outcomes.
3.2.1 Consideration
The Consideration leadership style emphasises a participative decision making focus, with leaders discussion important matters with their subordinates and putting their suggestions into practise (Stogdill & Bass, 1981). This style should lead to a higher level of employee motivation and involvement towards innovation practises, as it increases employee empowerment and shared leadership (Dobni, 2008a; De Jong & Den Hertog, 2007). Empowering employees has a positive effect on innovation within a company, as it drives them to go above and beyond what is normally expected of them (Dobni, 2008a). Employees need to feel confident that they can perform their work and sense that this work can make a
17 difference. Empowerment is best achieved if subordinates feel that they have a choice in how things are implemented in the company (Dobni, 2008a). By consulting the employees before initiating chances that may affect them, and by incorporating their suggestions and ideas into decisions, leaders can enhance the subordinates’ involvement in innovation practices (De Jong & Den Hartog, 2007). A lack of consultation can undermine the employees’ motivation and deprive the project of performance improving fresh ideas that might have come from these employees.
The Consideration approach also emphasises supporting, respecting and showing concern for employees’ welfare, while putting their suggestions into practice (Stogdill & Bass, 1981), which can have a positive effect on employees’ innovation participation by raising employee constituency (Dobni, 2008a). This constituency is based on how the subordinates feel about their connection to their co-workers and the organisation in general. This includes having trust and respect in management, feeling like they can contribute and feeling like they are heard, valued and seen as equals. Leaders following the Consideration approach make sure their employees feel comfortable talking to them by being easy to approach and treating them as equals (Stogdill & Bass, 1981; Jago, 1982), which has a positive effect on the level of employee constituency (Dobni, 2008a). By listening, being patient and helpful, and looking out for someone’s interests if problems arise, employees can be motivated towards working idea generation and application. If subordinates’ suggestions are never implemented, however, they can become de-motivated, which can lead to a drop in these innovation practices (De Jong & Den Hartog, 2007). Considerate leaders will be more inclined to implement suggesting made by employees, which can keep them motivated to keep innovating.
Recognition is another factor that positively affects employees’ innovation participation (De Jong & Den Hartog, 2007). Recognition includes giving compliments, awards and ceremonies for subordinates innovative solutions and applications. Studies have shown that a leader can trigger both idea generation and application behaviour in employees by being keen to recognise and reward innovative contributions (De Jong & Den Hartog, 2007). Leaders who follow the Consideration approach see that subordinates are rewarded for a job well done (Stogdill & Bass, 1981) , which in the case of innovation management can lead to an increase in employees’ idea generation and application, as this shows that the company values such behaviour (De Jong & Den Hartog, 2007).
18 By increasing employee empowerment, applying participative decision making, supporting and respecting employees, and recognising and applying innovative ideas the Consideration leadership style can have a positive effect on the outcomes of having an innovation propensity, which leads to the following hypothesis:
H2: Consideration has a positive effect on the relationship between Innovation Propensity and Start-up Success.
3.2.2 Initiating Structure
Initiating structure is the degree to which a leader organises the roles of the employees towards goal attainment and creates well-defined channels and patterns of communication (Judge, Piccolo & Ilies, 2004). A leader with this style monitors their employees’ efficiency, decides on exactly what should be done and how the tasks must be performed and insists on maintaining a set of standards (Bass & Stogdill, 1981; Judge, Piccolo & Ilies, 2004). According to research conducted by De Jong and Den Hartog (2007), applying monitoring behaviour by ensuring employee effectiveness, checking-up on people and stressing tried and tested routines has a negative effect on idea generation and application. This means that these particular behaviours associated with the Initiating Structure leadership style can be expected to have a negative effect on the firm’s innovation practises.
The Initiating Structure leadership style prefers to create a set of standards and maintain the status quo (Bass & Stogdill, 1981; Judge, Piccolo & Ilies, 2004). The leader decides exactly what each employee should be doing and the subordinates do not have the freedom to decide on their own approaches to their jobs. This could have a negative effect on the relationship between innovation and start-up success, as having a high degree of structure is not supportive of innovation (Ryan & Tipu, 2013). This is because innovation is aimed at challenging the status quo. By granting subordinates freedom and autonomy innovative behaviour is strengthened (Krause, 2004; De Jong & Den Hartog, 2007). The organisation needs to embrace new ideas and discard traditional approaches. Allowing the employees the freedom to decide how they do their jobs has a positive effect on both idea generation and implementation. One can conclude that the Initiating Structure’s behaviours could have a
19 negative effect on the effectiveness of innovation propensity, as they do not support innovative behaviour from the employees.
By monitoring the employees closely, maintaining standard practices and not allowing for freedom or autonomy, the Initiating Structure leadership style is expected to have a negative effect on the outcomes of Innovation Propensity, which leads to the following hypothesis:
H3: Initiating Structure has a negative effect on the relationship between Innovation Propensity and Start-up Success.
3.3 Conceptual Model
In this study the focus is on how a start-up’s propensity to innovate affects their success rate. This success rate is defined by the growth in revenues the firm has had in the preceding year. The two leadership styles are expected to moderate the relationship between these two variables. These relationships and their expected directions can be seen in Figure 1.
Figure 1: Conceptual Model
Start-up Success Consideration Innovation Propensity H1: + H3: - H2: + Leadership Styles Initiating Structure
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4. Research Design
4.1 Survey Design
To come to an understanding of the aforementioned hypotheses, it was necessary to conduct a survey among the leaders/owners of start-ups. This survey helps receive answers to a certain set of comparable questions, allowing statistical and holistic conclusions to be drawn from the results. A web-based survey was used, as it allows the participants to feel anonymous and makes the survey more convenient for them to fill in (Blumberg, Cooper & Schindler, 2008). The respondents were offered the option to sign up to receive a copy or summary of the results. As it could allow them insights into the success of their own leadership styles, this was thought to be a good incentive to get them to participate.
4.1.1 Measurement scales
Identifying measurement scales for each of the variables in the model was necessary for the creation of the survey. Stogdill’s leadership scales from the LBDQXII survey were used to measure the Consideration and Initiating Structure (Schriesheim, Kinicki & Schriesheim, 1979). There are 10 items for each of the two scales, with an alpha of 0.86 for Initiating Structure and 0.90 for Consideration (Ford, 1981). For the Innovation Propensity variable, scales created by Dobni (2008b) were used, with an alpha of 0.71. The three scales can be found in Appendix A. A five-point Likert scale ranging from strongly disagree (1) to strongly agree (5) was attached to each statement derived from these scales. A few of the scales were reversed to determine if the participants have actually read all the questions properly and responded accordingly. The two different leadership styles scales were shuffled to keep respondents from realising the differences between the two. The control measures included: number of employees, age of the firm, and whether it is a technology firm.
4.2 Targeting Respondents
The start-ups eligible for surveying were started between 1 and 5 years ago, as less than one year will not allow the measurement of success. More than five years will make it hard to measure the original leadership style, as the organisational structure might have changed due to the organisation growing and the owner might have delegated leadership to new managers (Weinzimmer, 1997). The start-ups had to have at least 4 employees, as this
21 allowed for leadership behaviours to take place. The focus was on start-ups in the Netherlands, meaning that the selection of participants to send surveys to could be done by using the search machine made available by the Kamer van Koophandel (Adressenbestand, 2017). The survey was sent to 497 respondents, who had been selected from the population using convenience sampling. There were originally 709 possible respondents in the population, however only the respondents for which an email address could be found were included.
After sending out the surveys, the business owners who had not yet responded were contacted by phone to be invited to participate in this research. They were sent personalised emails if they agreed to participate. In total there were 103 people who filled in the survey, which means there was a 20% response rate and in total 14,5% of the entire population has been surveyed.
5. Results
To analyse and verify the data the statistical software package SPSS was used. This software is widely used and accepted as a statistical package to analyse both business and social data (Doane & Seward, 2013). The first section deals with verifying the data, determining the reliability of the scales and checking for correlations among the variables. In the second section the hypotheses are tested through regression analysis.
5.1 Variables and measurements
Firstly, frequencies were examined to determine if there were any errors in the data. There were no errors to be found in the data. Missing variables were dealt with by excluding the cases list-wise, meaning that only completed surveys were used for the analysis. There were 103 people who participated in this research, but one of these responses was incomplete. After this the counter-indicative items in the leadership variables were recoded. Then a univariate outliers check was performed, which found two cases that needed to be excluded. These two cases had a Zscore of -3.31682 on the Innovation Propensity variable, which was outside the acceptable level of -3. The final total of useable responses amounted to 100.
22 Although pre-validated scales were used for this research, the consistency of the measurements was re-examined through a reliability check. The Innovation Propensity scale has a high reliability, with Cronbach’s Alpha = 0.832. The corrected item-total correlations show that all the items have a good correlation with the total score of the scale, as all are above 0.30. Also, the reliability would not be substantially affected if any of the items were to be removed. The Initiating Structure scale also has a high reliability, with Cronbach’s Alpha = 0.904, and a good correlation with the total of the scale. In this case the Cronbach’s Alpha would not be significantly affected by removing any of the scale items. The Consideration scale also has a high reliability, as the Cronbach’s Alpha = 0.931. There is a good correlation with the total score of the scale and removing any of the items would not significantly affect the Cronbach’s Alpha. The results from this check can be found in Table 1.
Table 1: Reliability Results
Construct Number of items
Chronbach’s Alpha
Scale
improvement Skewness Kurtosis Innovation
Propensity 9 0.832 No - 0.645 0,472
Initiating
Structure 10 0.904 No - 1.009 0.979
Consideration 10 0.931 No - 1.004 0.684
The next step was to compute the scale means, with which the scales could be checked for skewness and kurtosis. All the scales were within the predefined empirical limits for both measures, meaning that normality can be assumed for all three scales (table 1). The means and standard deviations can be found in table 2. This table also shows the correlation analysis results. It can be seen that all relationships between the independent variables are positive. The same applies to the relationships between the independent variables and the dependent variable. The control variables have been added to this table for extra clarification. The number of employees has a significant correlation with the age of the company. There are no other significant relationships between the control variables and any other variables. Both leadership variables have a small significant effect on Revenues Growth and a large significant
23 effect on innovation. This means that when those styles are used innovation and revenues go up. The reverse could also be true, as it is only a correlation and not a causation, however, this seems unlikely. Innovation does not seem to have a significant effect on revenues. The two leadership styles strongly correlate to each other, which means that they seem to have a linear effect together.
Table 2: Mean, Standard deviation and Correlations of Study Variables
M SD 1 2 3 4 5 6 7 1. Revenues 50.78 82.70 - 2. Firm age 3.15 2.94 0.03 - 3. N of Employees 22.61 41.73 -0.08 0.30** - 4. Industry Type 1.64 0.48 0.02 -0.14 -0.06 - 5. Innovation 3.55 0.67 0.15 0.02 0.02 0.08 (0.83) 6. Initiating Structure 3.65 0.86 0.20* 0.03 -0.03 0.09 0.50** (0.90) 7. Consideration 3.73 0.92 0.21* -0.00 -0.05 0.12 0.47** 0.92** (0.93)
Industry type: two conditions (1 = technology firm 2 = non-technology firm) **. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
As the two variables are so highly correlated a factor analysis was run to determine if there were any conflicting items (see Appendix B.1). The Keyser-Meyer Olkin criteria and the Bartlett’s test of Sphericity show that this data is appropriate for conducting a factor analysis. The factor analysis showed that, with the exception of one low loading item, both variables actually belong to one factor. Therefore, I propose a fourth hypothesis:
H4: The combination of both leadership styles will positively/negatively moderate the relationship between Innovation Propensity and Start-up success.
This new hypothesis will also be tested through a regression analysis. The original variables will be maintained to determine if there is a difference in effect between the two leadership styles. This will help determine if the combination effect in hypothesis four will have a negative or positive relationship. The same factor analysis also showed that Innovation Propensity loads on two factors. The first factor has innovation specific items, while the second factor can be explained as more strategic management factors. The two factors do
24 belong together, as the strategic management is part of the Propensity to Innovate. Three items have been removed from this variable, as they scored lower than 0.512, which is the cut-off point for a sample of 100 data sets (Stevens, 2002).
5.2 Hypotheses’ Analysis
In this section the moderation hypotheses will be tested through regression analysis. First H1 will be tested by running a linear regression. After this the moderation hypotheses will be tested with the help of a macro written by Andrew F. Hayes (Hayes, 2012). First H2 and H3 will be tested and then H4 will be tested separately.
5.2.1 Hierarchical regression
To test the first hypothesis a hierarchical regression analysis was used to examine the relationship between the independent variable (Innovation Propensity) and the dependent variable (Revenues Growth), while controlling for Firm Type, Firm Age and Number of Employees. The first step was to enter the three control variables, so that a shared variability of these variables with the Innovation Propensity could be controlled. By doing this one can be certain that the observed effect of Innovation Propensity on Revenues Growth is independent of the effect of these three control variables.
Table 3: Hierarchical Regression Model of Revenues Growth
R R2 R2 Change B SE β T Sig. Step 1 0.110 0.012 0.012 Firm Age 2.038 3.024 0.072 0.674 0.502 Number of Employees -0.208 0.211 -0.105 -0.985 0.327 Firm Type 4.217 17.566 0.025 0.240 0.811 Step 2 0.178 0.032 0.020 Firm Age 2.033 3.010 0.072 0.675 0.501 Number of Employees -0.223 0.210 -0.113 -1.061 0.291 Firm Type 3.117 17.501 0.018 0.178 0.859 Innovation Propensity 17.363 12.540 0.140 1.385 0.169 Statistical significance: *p <.05; **p <.01; ***p <.001
25 In step 1 the model was not statistically significant, F(3, 96) = 0.390; p > 0.05, and only explains 1.2% of the variance in Revenues Growth. It can be concluded that the variance in the variable Revenues Growth is explained by other factors. In step 2 Innovation Propensity was entered as a predictor, raising the total variance explained by the model as a whole to 3.2%. This second model was also not significant, F(4, 95) = 0.774; p > 0.05. The independent variable (Innovation Propensity) does not have a significant effect on Revenues Growth, therefore it is not possible to reject the null hypothesis regarding H1.
H1: not supported.
5.2.2 Testing the moderators
Although the effect of Innovation Propensity on Revenues Growth is not supported, we aim to test the moderating effects of the leadership styles on the relationship, as suggested by hypothesis two, three and the new hypothesis four In order to test these effects, the Process macro for SPSS was used (Hayes, 2012).
To test for H2 and H3 the conceptual and statistical Process model 2 was used, for two moderators in one model (Appendix B.2). This model does not suffer from multicollinearity as the VIF scores are 6.968 for Initiating Structure, 6.592 for Consideration and 1.313 for Innovation Propensity, which are all well below the maximum score of 10. One can see from the moderation analysis that the model of H2 and H3 is not significant, as p > 0.05. The model also only measures 4.95% of the variance. The interaction outline shows that neither of the interaction effects are significant (see table 4). Therefore there is no evidence of a moderation effect from these moderators and the null hypotheses cannot be rejected.
H2: not supported. H3: not supported.
Surprisingly the sign of the coefficients for hypothesis two and three are actually the opposites of what was expected in the hypotheses. The negative coefficient is much smaller than the positive, so we shall assume that the combination leadership variable will have an overall positive effect, leading to:
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H4: The combination of both leadership styles will positively moderate the relationship between Innovation Propensity and Start-up success.
Table 4: Moderator Analysis for Hypothesis 2 & 3
Coeff SE T P LLCI ULCI
Constant -5.5784 118.4086 -0.0471 0.9625 -240.6820 229.5252 Initiating Structure -21.9043 131.9463 -0.1660 0.8685 -283.8874 240.0787 Innovation Propensity -2.4688 35.8282 -0.0689 0.9452 -73.6067 68.6692 Interaction Effect 1 5.7554 37.6070 0.1530 0.8787 -68.9143 80.4251 Consideration 28.8439 137.3681 0.2100 0.8341 -243.9043 301.5921 Interaction Effect 2 -2.8455 8.2368 -0.0744 0.9408 -78.7657 73.0746
Table 5: Moderator Analysis for hypothesis 4
Coeff SE T P LLCI ULCI
Constant 1.5119 110.1576 0.0137 0.9891 -217.1496 220.1733 Leadership Combined 6.8167 37.1476 0.1835 0.8548 -66.9209 80.5544 Innovation Propensity -6.8009 32.5872 -0.2087 0.8351 -71.4962 23.3785 Interaction Effect 3.5838 9.9722 0.3594 0.7201 -16.2110 23.3785
To test for H4 the conceptual and statistical Process model 1 for simple moderation was used. The model design can be found in Appendix B.3. This model does not suffer from multicollinearity as the VIF scores are 1.265 for Innovation Propensity and 1.282 for Combined Leadership Styles. The model summary of this moderation analysis shows that the model of
27 H4 is significant with p < 0.05. The model also only explains 5.3% of the variance in Revenues Growth. The interaction outline shows that the interaction effect is not significant, as p > 0.05 (see table 5). Therefore one can conclude that there is no evidence of moderation and the null hypothesis cannot be rejected.
H4: not supported.
6. Discussion
The aim of this study is to create a better understanding of some of the mechanisms that influence start-up performance. In the previous sections the data is analysed based on the hypotheses proposed. In this section the findings will be discussed and compared to the existing literature in the field.
In hypothesis one the expectation is that having an innovation propensity will have a positive effect on start-up success, by causing a growth in revenues. This hypothesis is in line with the work of several researchers, who all agree that innovation has a positive effect on companies (Porter, 1980; Kickul & Gundry, 2002; Lyon & Ferrier, 2002; Hormiga, Batista-Canino & Sánchez-Medina, 2011b; Rosenbusch, Brinckmann & Bausch, 2011). It is also the assumption of many practitioners that this relationship exists and must be nurtured (Kraus, 2004; Dickson, Solomon & Weaver, 2008). Surprisingly, in contrast with the expectations given by the literature, having an innovation propensity does not have a significant relationship with revenues growth in a start-up. This means that the first hypothesis is rejected. One explanation could be that the turbulent environments that the start-ups find themselves in, as this environment can affect the innovation outcomes (Droge, Calantone & Harmancioglu, 2008). This turbulence can specifically affect new product or service success, which can lead to a smaller growth in revenues. Another reason would be that although there is a high focus on innovation by top management, employees are still not participating in innovative practises. Perhaps the employees’ cultural background does not encourage innovative behaviour (Shane, 1993).
For the second and third hypotheses a moderating effect was expected on the relationship between innovation propensity and revenues growth. In the literature there is no
28 consensus on what this relationship might entail, however based on the arguments given in this study, one can expect a positive effect for Consideration and a negative effect for Initiating Structure (Stogdill & Bass, 1981; De Jong & Den Hartog, 2007l Dobni, 2008a). In contrast with these expectations, neither variables have a significant moderating effect. Therefore the second and third hypotheses are rejected. It must be said that these two leadership variables are shown to be highly correlated with each other, therefore these results must be viewed with caution. To deal with this correlation a fourth hypothesis was proposed, ‘The combination of the leadership styles has a positive effect on the relationship between innovation propensity and start-up success’. This new hypothesis, however, must also be rejected, as the data analysis does not show a significant moderating effect for this new combined variable. One can therefore conclude that the leadership styles, and perhaps leadership itself, does not have an effect on the outcomes of an innovation orientation in start-ups. Perhaps employees that are willing to work for start-ups expect a certain level of innovation orientation within the firm and do not need leadership to point them in that direction. Another explanation could be that start-ups tend to have smaller amounts of employees, which might create a close knit community. This community will know what needs to happen and do it together, instead of needing leadership to influence them towards achieving a specific goal.
The most unexpected result in this study is the high correlation (0.92) between Consideration and Initiating Structure. In the literature these variables are treated as completely independent and are expected to behave as such (Judge, Piccolo & Ilies, 2004). However, this research shows that when an entrepreneur scores high on one style, they will also score high on the other style. A few other researchers have come to the conclusion that the two leadership styles are not always empirically independent, even if they are expected to be (Weissenberg & Kavanagh, 1972; Bass, 1990). Some have found a positive correlation (Judge, Piccolo & Ilies, 2004), while for others this correlation was negative (Lowin, Hrapchak & Kavanagh, 1969). Weissenberg and Kavanagh (1969) conducted research into these correlations and found that a significant amount of the research papers written at the time had a positive relationship (51%) or negative relationship (10%). According to Kerr, Schriesheim, Murphy and Stogdill (1974) these correlations might be due to environmental factors. Perhaps in this case start-ups need to apply both leadership styles to be able to survive, therefore the current sample of start-ups that are more than one year old might
29 consistently apply both styles. This would explain why this high correlation exists in this dataset. It could also be caused by respondents being unable to consider the two dimensions separately (Kerr, Schriesheim, Murphy & Stogdill, 1974). Another reason for this correlation would be that these leadership styles have evolved in the past few decades, and have merged into one factor. This could be due to contemporary management practices being taught by business schools, which stress the importance of both leadership styles for business success (Northouse, 2013).
6.2 Limitations
The aim of this study is to clarify the relationships between innovation propensity and start-up success and the mechanisms that play a role in this relationship. Despite some unexpected findings and the contributions to the existing literature base, this study is subject to some limitations. The limitations and their consequences will be discussed in this section.
One of the limitations of this research design is the use of cross-sectional data, as it only gives a snapshot of the situation. As all the companies in the sample have different ages, they might be in different stages of their start-up phase, and therefore might give different results. Another limitation is the survival bias of the start-ups, as companies who have not managed to survive until the date of surveying are not included in the population. Therefore the results given do not include the less successful entrepreneurs, and therefore cannot show what might have had a negative effect on start-up success. As all the entrepreneurs surveyed had companies in the Netherlands, the results can only be inferred for countries with the same cultural backgrounds. Countries in for example Asia, have largely different cultural background and may differ when it comes to the leadership styles or how they approach innovation.
A more statistically based limitation is that the data was collected through a non-probability sampling method, based on convenience sampling, resulting in a possible low external validity. However, as the chosen sample composed of 70% of the total population, the external validity may not be too low. A more obvious limitation is the high level of correlation between the two leadership variables. Therefore one cannot take the results for hypothesis two and three too seriously. To deal with this problem hypothesis four was introduced, which included both leadership styles in one variable.
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6.3 Managerial implications
Many start-up success factor are based on intuition, without the empirical research to back these claims up (Dickson, Solomon & Weaver, 2008; Hmieleski & Baron, 2009; Hormiga, Batista-Canino & Sánchez-Medina, 2011b). The importance of innovation is often agreed on, however this research shows that in the case of start-ups having an innovation orientation has no effect on revenues growth and start-up success. This does not mean that new firms need to refrain from innovating, but it does show that the importance business leaders place on an innovation orientation is unnecessary. Start-ups should, therefore, let innovations grow organically, without placing too much importance on the activities that lead to these innovations.
Another surprising result was that different leadership styles and the combination of these styles had no effect on the innovation outcomes. Managers often feel that they need to lead employees towards achieving a desired outcome (De Jong & Den Hartog, 2007). Especially in start-ups the entrepreneur’s personality and leadership are seen as important (Markman & Baron, 2003; Hormiga, Batista-Canino & Sánchez-Medina, 2011a). However, this research shows that employees do not need managers to act or lead a certain way for them to act innovatively. This means that managers can focus on helping employees achieve other outcomes, without having to think about the effect it might have on the company’s innovation outcomes.
6.4 Theoretical implications
This study contributes to the literature by focusing on innovation and leadership in start-ups. Although some practitioners have found reasons to believe that innovation has a significant effect on start-up success, the results from this research show that this might not be the case. The same applies to the effect of leadership on innovation outcomes. This shows that although these theories might apply to normal companies, start-ups could have a different set of rules. This conclusion leads to the importance of focusing on more entrepreneurial situations when studying business and leadership topics, as there is much that is unknown in these situations. An interesting future research topic would be to determine exactly what the different outcomes of innovation would be in different contexts. Perhaps future research could also focus on different leadership theories and their applications within
31 entrepreneurial situations. It would also be interesting to test these different theories on the specific innovation outcomes, to determine their exact effects.
This research also unexpectedly contributed to the leadership literature, by showing that there is a strong correlation between Consideration and Initiating structure. Although this is not what the study originally aimed at discovering, it is still an interesting outcome for the leadership community. The Ohio State leadership styles have recently become more popular, however, this research shows that these styles might have evolved in the past several decades. One could even say that in the case of start-ups these two styles have merged together to become one leadership style. It is not known if this is only the case for start-ups or that this is more common across the board. It would be interesting to conduct research into this correlation and determine what exactly causes it. Judge, Piccolo and Ilies had attempted to explain this correlation in 2004, however they received mixed results. Perhaps research now can determine the causes of this correlation.
7. Conclusions
Research into entrepreneurial success factors has recently been increasing (Gartner, 2001; Hormiga, Batista-Canino & Sánchez‐Medina, 2011b). This study aims to contribute to this entrepreneurship literature by determining the effect of innovation on start-up success. This research then studies this relationship further by focussing on the effect of an entrepreneur’s leadership style on this innovation process. The study was conducted by surveying Dutch start-up companies that were between 1 and 5 years old at the moment of data collection. There were 103 respondents out of a total population of 709. The respondents were asked to answer questions about their innovation propensity, leadership style and the growth in revenues. They were also asked for information about the age of the company, the number of employees and if the firm was a technology start-up. The received results were examined for missing results, outliers and correlations. The data was then analysed through several regression analyses, to determine whether the four hypotheses could be accepted or needed to be rejected.
Having a high level of innovation propensity is expected to lead to a positive effect on start-up success. This is because innovation allows companies to adapt to environments faster
32 and be more flexible, which can be useful in turbulent environments (Hormiga, Batista-Canino & Sánchez-Medina, 2011b; Rosenbuch, Brinckmann & Bausch, 2011). It can help a firm stand out from the competition by creating innovative products or services (Porter, 1980; Rosenbuch, Brinckmann & Bausch, 2011). It can also strengthen the firm’s position and create a new level of demand (Porter, 1980). Having an innovation orientation can also create a positive perception of the company, which can lead to higher levels of brand equity, better collaboration partners and more skilled employees (Kickul & Gundry, 2002; Rosenbusch, Brinckmann & Bausch, 2011). All of these innovation benefits can lead to firm survival and growth, which means they can raise start-up success.
Having a high level of Consideration is expected to have a positive moderation effect on the relationship between innovation propensity and start-up success. This is because it can lead to empowered employees, a higher level of employee constituency, and a higher level of employee motivation towards innovating (Stogdill & Bass, 1981; Dobni, 2008a). By caring for employees, listening to and supporting them, giving recognition for their work and sharing the decision making process with them, the Consideration behaviour can lead to higher levels of employee participation in innovation practises, which in turn can lead to higher innovation outcomes (Stogdill & Bass, 1981; De Jong & Den Hartog, 2007; Dobni, 2008a).
Initiating Structure is expected to have a negative moderation effect on the relationship between innovation propensity and start-up success, because monitoring and having a high degree of structure have a negative effect on innovation outcomes (De Jong & Den Hartog, 2007; Ryan & Tipu, 2013). This leadership behaviour does not support innovative behaviour from the employee , as the organisation needs to embrace new ideas and discard their traditional approaches to create the necessary innovative culture (De Jong & Den Hartog, 2007). Leaders who display an Initiating Structure behaviour can be quite rigid in their thinking, as they like to stick to a predefined set of rules (Judge, Piccolo & Ilies, 2004).
The results of this study suggest that:
1. Having an innovation orientation does not affect start-up success.
2. Different leadership behaviours do not have a moderating effect on the relationship between having a propensity to innovate and start-up success.
3. The two Ohio State leadership styles, Consideration and Initiating Structure, are highly correlated.
