Amsterdam, 01-07-2017
Recruitment agencies for startups
Thesis of Sahithyan Shanmugaratnan MSc. Entrepreneurship Supervisor: R. Van der Voort VU Student number: 2525162 UvA Student number: 11383372
STATEMENT OF ORIGINALITY
This document is written by Sahithyan Shanmugaratnan 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.
TABLE OF CONTENTS
1.0| Introduction 6
1.1| Research gap 8
2.0| Theory analysis 10
2.1 Labour market 10
2.2 Recent trends in the labour market 13
2.3 Defining ecosystem of Amsterdam 14
2.3.1. Amsterdam’s ecosystem 15
2.4 Recruitment and recruitment agencies in Amsterdam 16 2.5 Business model of recruitment agencies 17
2.6 Recruitment strategies 18
2.7 Hypotheses and theoretical explanation 19
3.0| Method 21 3.1. Sample Design 21 3.2. Research context 21 3.3. Data collection 22 3.4. Measures 23 3.5. Independent variables 23 3.6. Model 26 3.7. Data analysis 27 3.8. Testing 29 4.0| Results 30 4.1. Sample characteristics 30 4.2. Biodata 30 4.3. Independent variables 32 4.4. Wilcoxon tests 33
4.5. Testing the hypotheses 33
4.6. In-depth research 35
5.0| Discussion 36
5.1. Limitations and future research advice 38
6.0| Conclusion 40
7.0| References 41
8.2. Influences towards the labour market 47 8.3. Information about recruitment agencies in Amsterdam 49
8.4. Questionnaire 51 8.5. SPSS tables 54
ABSTRACT
There is a lack of knowledge within the research area among recruitment in startups. The purpose to research if there is a demand of hiring services for startups, started by noticing that recruitment agencies were focusing especially on big companies. It seems that recruitment agencies are focusing on big companies, because they have a stable cashflow which enables them to pay the fees to the recruitment agencies’ demand. However, startups seem to have less financial capital, which makes it difficult to pay the fees as a startup. In the interest of the problem stated in the previous sentence, this paper analysed startups and non-startups to research if there is an opportunity to create a business model for recruitment agencies towards startups. The core of the research will be a quantitative analysis which is supported by a qualitative analysis. Based upon aspects above, the following research question was formed: to what extent do startups have a demand for hiring services in Amsterdam? Therefore, several hypotheses will be tested. There is a lacking consensus if recruitment agencies are financially affordable for startups (H1). Moreover, the focus of hiring strategies according to startups and non-startups are tested (H2 and H3). On top of that, this paper investigated if the recruitment agencies should focus on trends, such as Internet of Things (IoT) (H4). As a result of this paper, the main research question is confirmed. In agreement with H1, recruitment agencies are too expensive for startups. Thus recruitment agencies should make it less expensive for startups. Furthermore, there is no significant difference between startups and non-startups, in consonance with their hiring focus. However, this paper recommends recruitment agencies to focus on human capital, creativity, and jack-of-all-trades when hiring for startups, because these are the variables which are chosen the most by founders of startups. When focusing on non-startups, recruitment agencies should focus on human capital.
1.0 INTRODUCTION
To the author’s knowledge, hiring services for startups have been scarcely investigated from the theoretical point of view. Therefore, this paper is analysing startups and non-startups with the aim to research if there is an opportunity to create a business model for recruitment agencies towards startups. Two particular definitions of startups and non-startups will be used in this paper. Definition of a startup is: a ‘temporary organization designed to search for a repeatable and scalable business model’ (Blank, 2010). Definition of a non-startup is: an ‘independently owned and operated, organized for profit, and not dominant in its field’ (SBA., 1999b). In other words, creating a new product to new customers with a new business model is a startup, while a non-startup is selling known products to known customers with a known business model.
Due to the fluctuation of the labour market and the development of technology, it seems important for startups to keep innovating. Usually, the United States (US) is the precursor of innovation due to a large range of innovators, and Silicon Valley which is known as an innovation hub. Because the US is the precursor, it seems that Europe is the next destination when something new is invented in the US. However, Isenberg (2011) claims that imitating Silicon Valley is not recommended. Nevertheless, the trend that the municipality of Amsterdam is willing to create a startup-hub, is because of the trend that self-employment is increasing (Stam, 2014). As reported by SBA (1999a), two out of the three new created jobs were created by startups. An increase in self-employment as in the US is an aspect of globalization. The aspect of globalization is created by its movement towards integration of economics, financials, and trade (Stam, 2014). The business dictionary defines globalization in the following way:
In the past, it was less difficult to hire people while using recruitment compared to the present-day. The success of a company’s recruitment was based on their inner circle, or on the timing at which applicants stepped into the application procedure. During that time, skills and abilities were not the priorities when choosing an employee. According to Taylor (1911), companies needed to select to those people who were suitable due to their skills, which was a psychometric approach.
Nowadays, the global environment is changing rapidly. To continue the success of a company, companies need individuals with numerous competences. When a company has a vacancy, there is still a chance to get the ‘wrong’ individual on board. There is a possibility that the hired individual is more of a liability than an asset, because the individual is not contributing to the success of the firm. For most of the jobs, companies choose individuals based on their curriculum vitae (cv). However, also differences in the psychology and sociability of potential employees are playing a significant role. The differences in psychology and sociability consist of emotions, personality, motivation, and abilities. In order to find the right employees, the company needs to search for the individual which fits best. As a result, finding the right employees could be performed by a recruiter. Recruitment agencies will support companies to hire the competent people by using effective recruitment and selection procedures. Companies need to make a good decision and invest time to find the right person (Newell, 2005). Otherwise, startups could lose financial capital. The financial loss could go up to 30% of the year-salary of the ‘wrong’ individual (Hacker, 1997). The financial loss exists of training costs, advertisement costs, recruitment costs, too little productivity, and loss of clients (Smith and Graves, 2002; Williamson et.al., 2002).
Currently, the most recruitment enterprises focus on big non-startups, where recruitment agencies gain the biggest profits. The main goal of recruitment agencies is to link employees with employers. A business model of a recruitment company consists of gaining a fee between
15% and 40% of the gross year-salary of the employee (Marchington et al., 2016). Employees of non-startups seem to be one out of many because the employees are having a superficial relationship with the owner and shareholders. However, in startups, employees seem to feel more important, because the employees are more connected to the owner of a startup and seem to have a larger influence on the owner and its core-business (Ewens and Marx, 2016). Thus, it seems important to invest time and financial capital to get the right employee as a startup. There is a big difference in recruiting for startups compared to non-startups. In financial terms, startups have a less stable cashflow in comparison with non-startups, which makes it difficult for startups to pay recruitment companies (Williamson et al. (2002); Smith and Graves (2002)). It seems that a recruitment company will play a significant role in a startup in the early stage in the life cycle of the firm. As a matter of fact, variables which play an important role is the legitimacy of norms, beliefs, and values which an employer and recruiter will have (Williamson, 2000). In line with Suchman (1995), legitimacy is the societal comparison between characteristics and norms. In the current literature, several aspects are understudied or lack consensus.
1.1. Research gap
This research aims to offer implications that can help scientists find consensus in whether startups need specific hiring services from recruitment agencies, which will be elaborated by answering the research question and research topics. The research question is: to what extent do startups have a demand for hiring services in Amsterdam? The research topics below will
provide an overview of the history of the labour market. Furthermore, it will provide the definition of recruitment and elaborate on the initial start of recruitment. Research topic 1 will give the reader background information about the labour market of the Netherlands from the past and present-day. Research topic 2: what is the current situation of Amsterdam’s startup-ecosystem? Second, the paper will focus on the startup ecosystem which is rapidly changing the last decades. Research topic 2 will show the reader in what kind of ecosystem the recruitment market is changing into. Research topic 3: how is the recruitment market in Amsterdam? Hereafter, the term recruitment will be clarified. On top of that, recruitment in Amsterdam specifically will be elaborated. Research topic 3 will explain how the changing ecosystem will influence the recruitment industry and what kind of innovations seem necessary to adapt to competitors in Amsterdam. Research topic 4: how is the business model of a recruitment agency? Furthermore, a general business model of a recruitment company will be demonstrated, which will give the reader insight into the particular decisions a recruitment firm makes towards startups and non-startups. Research topic 5: where do recruitment agencies focus on? Lastly, the gap in the market for recruitment agencies will be analysed regarding their focus on whether startups or non-startups. These answers will provide a fundament to answer the main research question. There is a growing consensus among recruitment agencies that startups need specific hiring services. In the current literature, several aspects are understudied or lack consensus.
At the end of the theory analysis, four hypotheses will be given to test. After testing these hypotheses, this paper will continue by discussing the uncertainties and by providing advice to future researchers who are willing to investigate on this topic. Finally, a brief summary of the findings and the answer on the main research question is shown in the conclusion.
2.0| THEORY ANALYSIS
2.1. Labour market
History of the Netherlands
To explain the phenomenon of the startup-ecosystem, one must look at the degree of its labour market. The Netherlands is a relatively small country. However, the Netherlands has been a leader in productive entrepreneurship in the past. The VOC in 1602 was an invention of the Netherlands where they were one of the best in the stock corporation. Furthermore, in the golden age of the Netherlands in the 17th century, entrepreneurship was crucial. On top of that, at the end of the 19th century and the beginning of the 20th century, entrepreneurship was important for companies due to its multinationals which arose, such as Philips, Shell, and Unilever (Stam, 2014). The labour market is changing rapidly in the millennium. Especially computer-related jobs are fastest growing jobs (Trend, 1997). Due to technology, the labour market is increasing in its employees’ (figure 1) (Stam, 2014).
Additionally, the Netherlands has a relatively low unemployment rate in comparison with surrounded countries. Furthermore, the last decades, the Dutch labour market became more dynamic due to entrepreneurship (figure 2). More people were willing to work independently and became self-employed (Stam, 2014).
Figure 2: Unemployment rates, 1990-2013 (Stam, 2014).
Hereupon, big non-startups, such as Philips and Unilever, were already established decades or even a century ago. However, these big non-startups are decreasing their employment the last decade, due to the increase of employment (figure 3). The non-startups pay those self-employers rather than hire them, which will increase the non-startup’s flexibility. Nowadays, startups are creating innovative vacancies. Big non-startups are collaborating with startups and self-employed people to adapt to recent trends (Stam, 2014). According to the rise of entrepreneurship in the Netherlands, in figure 4 can be seen that the Global Entrepreneurship Monitor (GEM) emphasizes the increasing entrepreneurship in the Netherlands. Additionally,
final point, figure 6 shows the gap which is created between solo self-employed and employers the last decade (Stam, 2014).
Figure 3: Dutch employment of the five largest firms Figure 4: Global Entrepreneurship Monitor
Figure 5: Self-employment in labour population Figure 6: Solo self-employed and employer firms
In agreement with Global Entrepreneurship Monitor (GEM), entrepreneurship is still growing, partly by accelerators and incubators as is described in the appendix (8.2). The growth of entrepreneurship will create the importance of startups. Big non-startups, such as Google and Apple, buy those startups and use them to stay innovative and scale up their services to avoid creative destruction (Schumpeter, 1942). Creative destruction is that innovative companies lead
| 2.2 RECENT TRENDS IN THE LABOUR MARKET
In recent years, the labour market has changed rapidly through innovations in technology. One of these important topics is Internet of Things (IoT), which has influenced the labour market the last decade. IoT is going to influence the labour market and its economy in the future as well (Fleish, 2010). IoT itself is defined as ‘the internet that is connected to every virtual object where interconnectivity plays a significant role (Fleish, 2010).’ Another definition is ‘a network of physical objects which are embedded in network connectivity (Zanella et al., 2014).’ IoT is mentioned as one of the biggest influencers of future technology and is used in a wide range of industries. IoT is a very recent development, which will create some insecurity for companies to implement it to recruitment agencies (Lee and Lee, 2015). As stated in Lee and Lee (2015), IoT consists of five technologies, which are IoT application software, computing the cloud, middleware, radio frequency identification (RFID) and wireless sensor networks (WSN). Thereupon, Yu et al. (2010) claim that in IoT infrastructure of the communication, sensor devices, objects, and units which will be processed, are essential. Thus, physical activities and objects are connected to the online world (Huang and Li, 2010a).
Furthermore, interconnectivity plays a significant role in IoT, which is explained by communication by sensing and actuation which bring devices together (Bresnahan et al., 2002). Interconnectivity will be used in future applications to automate particular interactions, which will create a smart system (Gubbi et al., 2010). Interconnectivity seems to have a positive influence towards recruitment agencies for online recruitment (Bartram, 2000). Despite the reported influence of IoT, academic research regarding IoT in recruitment agencies is relatively sparse.
2.3.DEFINING THE ECOSYSTEM OF AMSTERDAM
Ecosystem could be defined in several ways. In line with Willis (1997), an ecosystem is a unit which comprises a community (or communities) in an interactive open system by several organisms, which creates fluxes of matter, and energy. Another description of a startup ecosystem is the whole set of players who are relevant to the development of startups in a certain area (Masurel, 2016). The latter definition will be used in this paper.
A startup ecosystem and an entrepreneurial ecosystem have the same functions. However, the startup ecosystem will focus more on young risky startups, by trying to perceive financial capital in order to upscale the startups. As reported by Stam (2014), there are seven important pillars to create an entrepreneurial ecosystem: accessible markets, human capital, funding, mentors, government support, education, universities as catalysts and cultural support. On top of that, these pillars create value, such as productivity, income, employment, and well-being.
Moreover, innovation plays an important role for startup-ecosystems (Stam and Nooteboom, 2011). Figure 7 shows that exploration and exploitation strengthen each other to succeed as an innovation, by using earlier inventions, research, development, and knowledge, which is called the cycle of innovation. Stam and Nooteboom, emphasize that IoT may play a role in the future for recruitment agencies, because of the cycle of innovation.
Figure 7: Cycle of innovation (Stam and Nooteboom, 2011).
About 50% of the startups will not be able to exist anymore after one year (Startup statistics, 2016). Moreover, most of the startup owners start their business at their home, and often fund it by their personal funding and funding of family, friends, and fools (FFF). The chance of success is relatively low for a startup. One of the causes is a lack of managerial skills and sales skills, or in other words human capital (Bosma et.al., 2004). In this paper human capital is defined as: ‘the collective value of the organization's intellectual capital (competencies, knowledge, and skills)’ (Human capital, n.d.).
How to get human capital in a company is different for every industry, and it depends on the companies’ capacity. Startups are obviously smaller and likely to have less money to spend in comparison with non-startups. Because startups have less financial capital to spend on recruitment, recruitment agencies should change their business model towards startups (Williamson et al. (2002); Smith and Graves (2002)).
2.3.1. Amsterdam’s ecosystem
By zooming into the labour market and the ecosystem of Amsterdam, it is shown that Amsterdam is, at the moment of writing, number nineteen of the top startup-ecosystems. An estimation shows that there are between 1900-2600 startups in Amsterdam (Startup statistics, 2016). In accordance with the rates about how long startups live, there is a 7% probability that startups are still existing after numerous years (Blodget, 2013).
An example which shows that the municipality of Amsterdam is using innovation, such as IoT, is the Amsterdam School of Data Science, which is an initiative of Amsterdam Data Science (ADS). ADS shows the ecosystem of Amsterdam and is a network organization founded by 600 scientific researchers of: Amsterdam University of Applied Sciences, Centrum Wiskunde and Informatica, University of Amsterdam, and VU Amsterdam.
At ADS all kinds of scientific researchers are working, which result in a diverse and multidisciplinary team. ADS works with several big companies, such as Facebook, Google, IBM and so forth. With these kinds of collaborations, ADS wants to strengthen its data science. Furthermore, Amsterdam has Amsterdam Smart City (ASC), which is a unique partnership between companies, governments, knowledge institutions and the people of Amsterdam. A smart city is ‘a city where social and technological infrastructures and solutions facilitate and accelerate sustainable economic growth. This improves the quality of life in the city for everyone’ (Amsterdam Smartcity, 2015).
ASC is aware of the challenges of the city, but also of the improvements which could be applied to the city. Another benefit of Amsterdam’s ecosystem is its close network. Relevant contacts are nearby and Amsterdam has a wide international network. Regularly there are meetups with different kinds of companies with entrepreneurs in Amsterdam (Meetup, n.d.). At the moment of writing, there are more than 3000 international companies settled in the Amsterdam Metropolitan Area (Business ecosystem, 2017). Besides the international companies, Amsterdam is also a place for startups to test an innovative product in a particular market. Furthermore, Amsterdam is a multi-cultured city. Amsterdam has over 180 different nationalities and has 90% of the population speaking English in Amsterdam (Business ecosystem, 2017). Drawing upon Amsterdam, it seems that this city is able to easily do business with international companies, because of their multicultural city and their well English speaking population.
qualified candidate (from within or outside of an organization) for a job opening, in a timely and cost-effective manner’ (Recruitment, n.d.).
To use recruitment agencies for startups, it is more difficult due to lower revenues in comparison with non-startups. Startups need to create a stable cashflow, because startups are at the beginning of their life-cycle. Non-startups have stable a cashflow, which they could use to invest in their team and thus in recruitment companies (Williamson et al. (2002); Smith and Graves (2002)). Lastly, innovation is important for hiring procedures of a company due to recent trends which is elaborated earlier on in 2.2.
In line with the founder of ACE Venture Lab (incubator) Erik Boer, the recruitment market is fluctuating. By making use of Google Analytics, all people with a Google account are able to rate and review companies. A numerous of the most recent successful recruitment agencies based in Amsterdam, in consonance with Google Analytics, is shown further in this sentence and are briefly summarised as is mentioned in the appendix (8.3): SIRE Life Sciences, Young Capital, Monsterboard, Harvey Nash, and YER.
Besides these recruitment agencies, there are more recruitment agencies in Amsterdam. Most of those recruitment agencies did start their company within the last five to ten years. The relatively recent start of recruitment agencies implies that the recruitment industry has a big demand which needs sufficient supply. Drawing upon the examined recruitment agencies (8.3), different kinds of business models will fit in the recruitment industry.
2.5. Business model of recruitment agencies
Usually, recruitment agencies are trying to fill in the gaps between employers and employees. Employers use recruitment agencies in order to find the right person for a vacancy. After an employer explained what kind of competences and skills they are looking for, a recruitment
agency will start to use its network to find the right person. After finding numerous employees for the employer, the employer will get start an application procedure with some of the potential employees. In continuation, the employer could accept one of the applicants. By accepting an applicant, the employer must pay a one-time fee between 15% and 40% of the gross year-salary of their new employee to the recruitment agency (Marchington et al., 2016). Based on the current business model presented in this paragraph, the purpose of this paper is to create a new effective business model for particular startups.
2.6. Recruitment strategies
In the recruitment industry, there are different manners to hire an employee. For instance, the recruitment agencies use characteristics which fit best in the organization rather than having the requirements of the job itself (Bowen et al., 1991). Generally, companies could focus on their current employees or could focus on strategic recruitment. As reported by Maliranta and Asplund (2007), investing in current employees is costly. However, investing in current employees will positively influence the productivity of the company. Two manners of recruitment are hiring according to knowledge, skills, and abilities (KSA), or hiring according to personality and culture (PC) of the employees (Bowen et al., 1991). In agreement with KSA, the employee needs to fit in the organization and its task demands. In line with PC, the employer focusses more towards the fit between culture and its personality of the employee. A ‘good’ personality consists of an easy way of adaptation with the colleagues, which will increase the atmosphere within the team.
2.7. Hypotheses and theoretical explanation
In this section, the four hypotheses are shown and elaborated.
Hypothesis 1: recruitment agencies are too expensive for startups. Theoretical explanation: as reported by the literature, startups do not have a large financial capital to invest in their company. For this reason, fees which need to be paid to recruitment agencies are too expensive for startups (Williamson et al. (2002); Smith and Graves (2002)). Based upon described above, hypothesis 1 was formed.
Hypothesis 2: non-startups are focusing more on potentially experienced employees than startups. Theoretical explanation: non-startups are focusing more on known products in known markets. Thus, when non-startups need to hire new employees, it seems that they will focus on experienced employees in their preferred markets (Isenberg, 2011). Based upon described above, hypothesis 2 was formulated.
Hypothesis 3: startups are focusing more on creative employees, jack of all trades employees, and employees with a lot of knowledge and skills (human capital) than
non-startups. Theoretical explanation: building on the literature described above, startups are more focusing on creative employees, because they focus on new products in new markets. Because of the changing labour market and the ecosystem, which are changing and creating a startup-hub, it seems that startups need employees who are thinking creative (Isenberg, 2011) Moreover, it seems that startups need people who have several capabilities, and not only expertise in one sector. This paper calls these people with several capabilities ‘jack-of-all-trades’ (Wagner, 2003). Because these people have several capabilities, they are
multi-disciplined, and they can be used in several ways. Thus, it seems that the employees should be more creative and should have more various competences (jack-of-all-trades) (Wagner, 2003). Based upon described above, hypothesis 3 was formed.
Hypothesis 4: recruitment agencies should focus more on IoT by adapting to recent trends. Theoretical explanation: as was discussed earlier in this paper, in consonance with SBA (1999a), scientific papers show that two out of three vacancies come from startups, which indicates that hiring the right employees for those vacancies at startups is important for the economy. Furthermore, a recent trend is for example IoT, which seems important for the upcoming years. Suggesting that IoT is important in the future, recruitment agencies should use IoT in their business model (Fleish, 2010). Additionally, the researcher of this paper had an interview with the co-founder of SIRE Life Sciences, which started four and a half years ago and agreed to follow the trends and use IoT in the future. Furthermore, the co-founder emphasized that the lack of good employees creates the most of his inconvenience. In line with this theoretical and experimental background, it seems that there is a demand for hiring strategies for startups. Based upon described above, hypothesis 4 was formulated.
Contribution
The above described lacking knowledge is an interesting area of research and creates a significant research gap, as the literature has found no consensus about recruitment for particular startups. When solving the described significant gap in the current literature, this research will be a significant attribution to the current field of research. This paper offers an interesting theoretical and practical contribution when filling in the research gap and minimize this research gap.
3.0| METHOD
The methodology will consist of the following contents: sample design, research context, data collection, measures, and data analysis.
3.1. Sample Design
This research used a mainly consisted of a quantitative design, supported by an in-depth research. The independent variables will be analysed by doing surveys and interviews with the managers and founders of startups about their hiring procedures, and why they use recruitment companies or not. The sample design in this paper which will be used, is a combination of a quantitative and qualitative design. The data is retrieved from 70 respondents: 52 of them are startups and 18 are non-startups. Startups and non-startups are the control variables (dependent variables). The founders and managers of the companies are questioned, because they both share the same vision on the company. The founders and managers are the supervisors of the company itself (Boeker and Wiltbank, 2005). Furthermore, a supportive in-depth research is executed by conducting interviews. Moreover, the surveys are conducted in Amsterdam, because many startups and non-startups are settled in Amsterdam.
3.2. Research context
The research context of this paper is to create a business model for recruitment agencies towards startups. Startups have less financial capital for new employees, because they need financial capital to create a stable cashflow. Having less financial capital is a reason for startups not to invest in recruitment companies (Colombo and Piva, 2008). However, human capital and other independent variables are important for startups, which indicates that recruitment could be important for startups as well.
The questionnaire is separated in two sections. The first part consists of the biodata, which is general information, such as age of the founder or manager, experience of the founder or manager, education of the founder or manager, age of the company, and sector of the company. In continuation, the independent variables are implemented in the questionnaire. Every independent variable has been changed into a question. Besides the biodata, the independent variables are focused on two sections. The two sections are hiring and recruitment agencies. First, there are five different question about hiring, which consists of experience, expertise, creativity, jack-of-all-trades, and human capital. Second, the questionnaire is focused on recruitment agencies, which consists of topics about technology, financial affordance and their focus on startups. Why independent variables are used, can be seen in 3.5.
3.3. Data collection
The founders or managers of companies were asked to fill in the survey or to conduct an interview. Avoiding socially desirable answers, the founders or managers were not informed about the research topic (Pruyn and Wilke, 2001). The anonymity is at all times guaranteed for the founders and managers of the company. The questionnaire has been written in English and conducted in English, unless the founder or manager preferred Dutch. The founders or managers are found trough social networks, meetups and warm connections. Preferable, the questionnaires are conducted face to face in order to acquire more inside information from the founder or manager. When face to face is not possible, the questions are conducted by calling the founder or manager. The final option is to send the questionnaire by e-mail.
3.4. Measures
This paper assumed that all questions were not too difficult to understand for the founders and managers. These surveys will be realised by using the Likert-scale (5). Hereupon, the literature review will support by testing the hypotheses and answering the research question. After the surveys, this paper will compare the answers of these two types of companies, namely startups and non-startups to get an answer to the research question. The surveys are especially conducted under startups and non-startups, to create a model for startups which will be recommended towards recruitment agencies. In this paper, startups have been seen as companies younger than five years. At the end of paragraph 3.0 (method), there is shown an outline of the conceptual model.
3.5. Independent variables
The hiring processes can be influenced by several resources. These resources can be split up in tangible resources and intangible resources (Jones et.al., 2013) (figure 8):
Figure 8: Tangible and intangible resource (Jones et.al., 2013).
machinery, and equipment. On the other hand, intangible resources are resources which are represented in a non-physical form, such as knowledge, skills, and relationships. The tangible and intangible resources are important for the entrepreneur and logically for his startup.
As illustrated in the entrepreneurial ecosystem model (figure 9) by Isenberg (2011), policy, finance, culture, supports, human capital, and markets are important pillars for entrepreneurs, which can be used for startups. Isenberg’s model is a multi-disciplined perspective which is used by several scientists (Mack and Mayer, 2016). Isenberg’s model has, even more, disciplines which create a precise model.
variable, because labour and education seem important for an employee. From Isenberg’s domain finance, financial affordance is chosen as an independent variable, because it seems that recruitment agencies are not financially affordable for startups. From Isenberg’s domain culture, creativity, jack-of-all-trades, IoT are chosen as independent variables, because creativity is part of societal norms (see Isenberg’s model). Moreover, jack-of-all-trades seem part of the societal norms of a startup-hub (Wagner, 2003). Furthermore, innovation is a societal norm of a culture. From Isenberg’s domain supports, IoT is chosen as an independent variable, because IoT plays a role for technical experts, who are supporting the ecosystem. IoT is partly chosen as an independent variable as well, because of the domain culture. From Isenberg’s domain markets, expertise and experience are chosen as independent variables. Expertise is a variable which has influence on its market and early customers. Moreover, networks are gained by experience. Network is a sub-domain of the domain markets. From Isenberg’s domain policy, financial affordance is chosen as an independent variable, because there are support professionals who financially support recruitment agencies, such as investment bankers. Financial affordance is chosen as an independent variable as well in the domain finance.
Besides the independent variables, which are made from Isenberg’s model, this paper changed the main research question into an additional independent variable, which is called recruitment for startups (see method). Thus, the dependent (control) variables are: startups and non-startups, and the independent variables are: experience, expertise, creativity, jack-all-trades, human capital, IoT, financial affordance and recruitment for startups. The conceptual model is shown in figure 10.
3.6. Model
Figure 10: Conceptual model research method for startups and non-startups.
The independent variables:
To what extent there is a demand for hiring services for startups in Amsterdam?
Startup Non-startup Independent variables Expertise Creativity Jack-of-all-trades Recruitment for startups IoT Financial affordance Experience Human capital
3.7. Data analysis
Table 1 illustrates that the following variables are used.
Question (Q) 1 till question 8 are biodata, question 9 till 16 are independent variables (Q9 till Q13 are focused on the hiring strategies; Q14 till Q16 are focused on recruitment agencies):
Table 1: all variables
In table 2, this paper will distinguish two different groups (control variables): the startup group and the non-startup group in order to find differences between the two groups. In table 3, this paper analyses the independent variables which are used the most by startups or by non-startups. Based upon these criteria’s, the research question will be answered and the hypotheses will be tested. Variable Number Gender Q1 Involvement of recruitment Q2 Founder/manager Q3 Age Q4 Education Q5 Company existence Q6 Number of employees Q7 Sector/Industry base Q8 Experience Q9 Expertise Q10 Creativity Q11 Jack-of-all-trades Q12 Human capital Q13 Financial capital Q14 Focus on startups Q15 IoT Q16
Each question in the questionnaire represented no more than one independent variables, which leads to the usage of Cronbach’s alpha was unnecessary. Moreover, independent variables need to be analysed between startups and non-startups, to verify if there is a significant difference, by executing a Man Whitney u test. Furthermore, within the independent variables, there needs to be an analysis of which independent variables are chosen the most by founders and managers. The Wilcoxon test will be analysed separately for startups and separately for non-startups to examine the scores within the independent variables. The latter test will be executed in order to provide a recommendation towards recruitment agencies about the independent variables which are the most important for startups and non-startups.
Table 2: Man Whitney u test
Topic Question Question in
questionnaire Test
Recruitment
business model for startups
Is there a demand for hiring services for startups? Q15 Man Whitney u test
Financial affordance
Is recruitment nowadays too expensive for startups or non-startups?
Q14 Man
Whitney u test Hiring strategy
non-startup and non-startups
Is there a significant difference between the hiring strategies of startups and non-startups?
Q9 till Q16 Man Whitney u test Biodata startups and Is there a significant difference between the Q1 till Q8 Man
Table 3: Wilcoxon test
Topic Question Question in
questionnaire Test
Important independent variables for a startup
Is there a significant difference between all independent variables for startups?
Q9-Q16 Wilcoxon test
Important independent variables for a non-startup
Is there a significant difference between all independent variables for non-startups?
Q9-Q16 Wilcoxon test
3.8. Testing
Man Whitney u tests and Wilcoxon tests will be executed to get relevant information. A Man Whitney u test, with the grouping variable ‘startups or non-startups’ has been executed. As a test variable list, the independent variables will be calculated to analyse if the independent variables differ from non-startups in comparison with startups. Moreover, Wilcoxon tests were executed for the data analysis of the surveys. With the option select cases, first, the start-ups are analysed particularly within their independent variables. Second, the non-startups are analysed particularly within their independent variables (experience vs. expertise vs. creativity vs. jack-of-all-trades vs. human capital vs. Iot vs. recruitment for startups vs. financial affordance). Due to these tests, results will show which variables are the most important for particular startups and particular non-startups.
4.0| RESULTS
4.1. Sample characteristics
The final sample consisted of 70 participants with an average age of 30 years old (SD = 1.30). The participants were derived from 52 startups and 18 non-startups. For both groups, the conditions were the same. The biodata consists of the following independent variables: gender, the involvement of recruitment, age, education, the existence of the company, number of employees and their industry base. The independent variables, according to hiring and recruitment agencies, consist of the following independent variables: experience, expertise, creativity, jack-of-all-trades, human capital, IoT, recruitment for startups and financial affordance. Furthermore, a supportive in-depth research is executed (qualitative). These interviews are analysed to have an additional in-depth view. The interviews are done with 5 different companies.
4.2. Biodata
To give an overview of the biodata, the results are shown in table 4.
Table 4: Descriptive statistics biodata.
Situation N Percent
Gender 57 Male (82%)
Age 24 19-25 (34%)
23 26-35 (33%)
10 36-45 (14%)
6 46-55 (9%)
7 56-65 (10%)
Education founder 2 HGSE (HAVO) (3%)
4 PUE (VWO) (6%) 6 IVE (MBO) (8%) 17 HVE (HBO) (24%) 18 WO-Bachelor (26%) 23 WO-Master (33%) Existence company 5 0-1 (7%) 16 1-2 (23%) 12 2-3 (17%) 12 3-4 (17%) 7 4-5 (10%) 18 > 5 (26%) Capacity start-up 34 0-5 (49%) 11 6-10 (16%) 7 11-15 (10%) 10 16-20 (14%) 0 21-25 (0%) 8 >25 (11%)
Sector company 10 Software (14%)
16 Tech (23%)
4 E-commerce (6%)
18 Health (26%)
2 Management (3%)
16 Other (23%)
The variables age (p = .000), existence of the company (p = .000) and the capacity of the company (p = .000) significantly differ between startups and non-startups.
4.3. Independent variables
To give an overview of the independent variables, the results are shown in table 5. All independent variables between startups and non-startups are not significant (p > 0.05) with exception of the independent variables financial affordance (p = .034) and recruitment for startups (p = .020).
Table 5: Descriptive statistics independent variables.
Independent variables SU: Mean (SD) Non-SU: Mean (SD) P-value
Experience 3.48 (0.92) 3.61 (1.24) 0.40
Expertise 3.67 (0.11) 3.50 (0.23) 0.57
Creativity 4.12 (0.12) 3.89 (0.20) 0.33
Recruitment for startups 3.85 (0.12) 3.28 (0.23) 0.020
IoT 3.50(0.18) 3.28 (0.27) 0.48
4.4. Wilcoxon tests
As analysed in the Wilcoxon tests for startups, the independent variable human capital shows a significant difference within the independent variables (p = .000 (vs. experience); p = 0.000 (vs. expertise); p = 0.002 (vs jack of all trades)). Hereupon, jack of all trades (p = .025 (vs. creativity)) differs significantly. Lastly, creativity (p = .002) is significantly important for recruitment agencies. The other independent variables do not differ significantly for startups (p > 0.05).
As stated in the Wilcoxon tests for non-startups, the independent variable human capital (p = .018 (vs. expertise) is the most important variable for non-startups. The other independent variables do not differ significantly for non-startups (p > 0.05).
4.5. Testing the hypotheses
In this chapter, the hypotheses that were sketched earlier in this paper are tested. Paragraph 4.6. will provide the answers to the main research question and the hypotheses, whether they are rejected or accepted.
The main research question was implemented in the questionnaire as a statement: recruitment agencies should also focus on startups. On this statement, startups answered significantly different than non-startups (p = 0.020). The significance claims that startups want a specific hiring service from recruitment agencies. Thus, the main research question can be
answered by the findings of this paper. For hypothesis 1, startups find recruitment agencies too expensive. Startups have more need than non-startups on reducing the prices what recruitment agencies currently ask for (p = .034). Thereupon, hypothesis 1, recruitment agencies are too expensive for startups in comparison with non-startups, is accepted.
For hypothesis 2, the independent variables between startups and non-startups were not significantly different (p > 0.05). Therefore, hypothesis 2, non-startups are focusing more on potentially experienced employees than startups, is clearly rejected. For hypothesis 3, as stated in the Wilcoxon tests for startups, the independent variable human capital (p = .000 (vs. experience); p = 0.000 (vs. expertise); p = 0.002 (vs jack of all trades), jack of all trades (p = .025 (vs. creativity)), and creativity (p = .002) is significantly important for recruitment agencies when focusing on startups, whereas the other independent variables are not important for recruitment agencies (p > 0.05). These important independent variables (human capital, creativity, and jack of all trades) could have an influence on the KSA and PC (Bowen et al., 1991), which should be used in the hiring strategies of startups. On the other hand, the independent variables are not significantly different between startups and non-startups. Therefore, hypothesis 3, startups are focusing more on creative employees, jack of all trades employees, and employees with a lot of knowledge and skills (human capital) than non-startups, is clearly rejected. Finally, hypothesis 4, recruitment agencies should focus more on IoT by adapting to recent trends, is clearly rejected (p > 0.05). Based upon the results, there is no support that IoT needs to be implemented in recruitment agencies.
4.6. In-depth research
Besides the quantitative analysis, this paper has used an in-depth research by executing interviews with founders of startups. The results are shown below:
Founder number one (26) has a company which focuses on innovative print software (startup). The founder himself did not finish his university and started a company. This conversation showed that the founder found motivation and creativity very important. The founder used his own network to find people, due to lack of financial capital to find the right people through recruitment agencies. Founder number two (54) has a real estate firm, who does not have a good relationship with her employees. This was probably created by the online recruitment which was done by the founder herself. She told that online recruitment resulted in extrinsically motivated employees. However, she has still a successful company. Founder number three (28) has an all-round IT firm. He started his company during his study and used his own network. He said that creativity and human capital are the most important variables to succeed as an employee. He said that recruitment is expensive nowadays, and do not distinguish from the rest. However, he would pay the agencies if they will distinguish from their competitors, and will positively contribute to their IT firm. Founder number four (38) has a successful company in data analytics. The founder showed that his company is created through his own network and network of its employees. The referrals resulted in a good relationship within his team, and resulted in intrinsically motivated people who worked to achieve the goals of founder number four. He emphasized that it is too expensive for startups to pay amounts like 15%-40% of gross year salaries as a fee for recruitment agencies. Finally, founder number five (61) was a founder of a big employment agency, which has been sold to another company. The founder told that he used his own network to recruit his employees. He said that human capital and motivation are the most important variables for long-term success.
5.0. Discussion
It is the main purpose of this study to draw attention to the business model of recruitment agencies, according to startups. The main research question was implemented in the questionnaire as a statement: recruitment agencies should also focus on startups. As a result of the statement, startups answered significantly higher than non-startups, which indicates that startups would like to have more help from recruitment agencies. Thus, the main research question can be answered by using the findings of this paper. In line with Williamson (2000), small businesses need particular recruitment strategies. The biodata shows clear differences between startups and startups: in startups the founders/managers are older, the non-startup exist for a longer time, and the non-non-startup has a larger capacity than the start-up.
Hypothesis 1 assumed that recruitment agencies are too expensive for startups. The results illustrate that, in agreement with hypothesis 1, startups find recruitment agencies too expensive. To be more accessible for startups, recruitment agencies should reduce the price. Based on these results, hypothesis 1 is clearly accepted. The in-depth research confirms hypothesis 1 as well, because founder number three of a startup is willing to pay a fee to recruitment agencies when they distinguish themselves by reducing the price. Also founder number one has a lack of financial capital to pay these fees. The study demonstrates that startups have less financial capital to invest compared to non-startups (Williamson et al., 2002; Smith and Graves, 2002; Colombo and Piva, 2008).
Hypothesis 2 assumed that non-startups are focusing more on potentially experienced employees than startups. Contrary to hypothesis 2, the result of the independent variable
because human capital is chosen the most by founders and managers of non-startups. As reported by Isenberg (2011) and Stam (2014), human capital is one of the most important pillars of the entrepreneurial ecosystem. This research provides insights that human capital is extra important for non-startups, in stead of experience which was assumed. Besides, human capital consists partly of experience, which indicates that experience also could be important for non-startups (Davidsson and Honig, 2003).
Hypothesis 3 assumed that startups are focusing more on creative employees, jack of all trades employees, and employees with a lot of knowledge and skills (human capital) than non-startups. There are no significant differences between the scores of the independent variables of startups and the scores of the independent variables of non-startups. Based on these results, hypothesis 3 is clearly rejected. Although, this research does not show a significant difference between the groups, a small dissimilarity between the groups can be seen. The results show that startups focus the most on human capital, creativity, and jack-of all trades, but not significant more than non-startups. These independent variables (human capital, creativity, and jack of all trades) could have an influence on the KSA and PC (Bowen et al., 1991), which could be used in the hiring strategies of startups. As stated by Wagner (2003), jack of all trades have a positive influence on entrepreneurial situations. The in-depth research is a confirmation, as founder number four emphasizes his good relationship with his employees, because of using his own network to find creative people and jack-of-all-trades. Additionally, founder number five states that human capital and motivation are the most important variables to succeed as an employee. In short, hypothesis 3 is clearly rejected. Nonetheless, the levels of the independent variables differed, it still offers interesting implications.
Hypothesis 4 assumed that recruitment agencies should focus more on IoT by adapting to recent trends. Based on the results, hypothesis 4 is clearly rejected. Therefore, recruitment agencies do not have to focus on recent trends, such as IoT. In line with Lee and Lee (2015),
IoT is a very recent development, which will create some insecurity for companies to implement it to their structure. This could be explained by companies which prefer the personal approach of recruitment agencies rather than technologies similar to IoT, because of its insecurity. Likewise, founder number four of the in-depth research emphasized this insecurity as well. Founder number four also stated that employees who joined the company via referrals, created a better atmosphere in the company and intrinsic motivation as well. However, in accordance with Fleish (2010), IoT itself is defined as the internet that is connected to every virtual object where interconnectivity plays a significant role. Interconnectivity seems to have a positive influence towards recruitment agencies for online recruitment (Bartram, 2000).
5.1. Limitations and future research advice
When interpreting this research, some comments should be taken into account. Firstly, in the questionnaire, there are questions conducted with the term prefer, which could be understood as a wide term. Prefer seems not precise enough, so advice for future researchers will be to precisely create the questions. Besides prefer, terms as experience and creativity were not elaborated in the questionnaire, because this paper assumed that the contestants had the same understanding. Therefore, future advisors should elaborate terms in the questionnaire to decrease the bias. Furthermore, this paper made a distinction in startups and non-startups, which can be expanded in startups, SME’s and corporates. Additionally, the paper focused on Amsterdam only, which seems to be an upcoming startup-hub. An advice for future researcher will be to focus on several startup-hubs all over the world to create a heterogeneous sample. Moreover, the researcher of this paper used a sample of nonprobability convenience sampling,
of a small sample size (Bryman and Bell, 2011). The final point of limitations is that this research focused on the founders and managers of startups and non-startups. To be more precise on this research, only founders should be questioned due to bias.
To sum up, there is enough to improve this research in the future. First, particular terms seem not elaborated specific enough, thus advice for future researchers will be to precisely create the questions. Second, making a good distinction between startups, SME’s and corporates will give more insights. Third, focus on several startup-hubs all over the world will create a heterogeneous sample. Fourth, aim for random samples to create probability convenience sampling. Fifth, analyse only founders to prevent bias in future research. Last, aim for a bigger sample size to get a more reliable result. All in all, when implementing these future advices, more insight will be acquired about hiring services for startups.
6.0| CONCLUSION
The main research question: ‘to what extent there is a demand for hiring services for startups in Amsterdam?', can be answered, based on the findings of this paper. H1 shows that according to start-ups, recruitment agencies are too expensive. The recruitment agencies should change their business model, by decreasing their fee for startups. Moreover, the recruitment agencies should have a different focus for startups in comparison with startups (H2). For non-startups, the recruitment agencies should focus on human capital. Furthermore, the results indicate that recruitment agencies could focus on human capital, jack of all trades, and creativity for startups, because these pillars seem to be important for start-ups when hiring an employee (H3). In conclusion, this paper gives recommendations towards recruitment agencies to create a separate business model for startups and a separate business model for non-startups. Therefore, this paper gives sufficient insights and a scientific contribution towards recruitment agencies to minimize the gap.
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