Does looking good on social media sites affect startup fundraising? : a study of whether IT startups’ social media efforts influence fundraising

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Does looking good on social media sites affect

startup fundraising?

A study of whether IT startups’ social media efforts influence

fundraising

Author: Bettina Uri Student number: 10605347

MSc. in Business Studies - Entrepreneurship & Innovation Faculty of Economics and Business

University of Amsterdam

Supervisor: dr. Yang Song

Second reader: dhr. dr. G. Tsvi Vinig Date: 30th June, 2014

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Abstract

Social media has a pioneering role in the field of communication in today’s society. It eliminates the barriers of time and space, and broadens the way we communicate and build networks. Facebook and Twitter, to name just the most popular ones, capture more and more users, who spend extremely large amount of time on online networking sites. Apart from individual users, many entrepreneurs have realized the various opportunities of online social media that they can create for businesses; and have become engaged users.

Offline social networking is considered an important success factor for young companies that often lack financial capital. However, online social network of businesses has not received considerable research attention yet. In response, this Master thesis represents a research on the role of online social networking sites when the financial support of innovative companies, so-called startups, comes into question. In order to discuss the benefits of Facebook and Twitter for startups and to indicate whether social media usage has influence on fundraising, a quantitative research was conducted based on three conceptual models. The research involved Twitter and Facebook social media metrics as independent variables and funding data from CrunchBase as the dependent variable. Hypotheses about key factors of online networking were paralleled with theories of offline networking discussed in previous academic studies.

Results of the study revealed new findings in the field of online social networking. They showed that social media counts for startups. Putting effort into using online social media and using these platforms consciously contributed to their financial success. Thus, startups which are popular among online fans and followers, managed to raise larger amount of funding in the early-stages. Nevertheless, little is known about the full extent of social media, it is presumed that it will have greater effect on businesses in the future. However, the study had some limitations, therefore additional researches are needed to further the knowledge about online social media.

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1 Introduction

1.1 Background

The nature of business and society is undergoing fundamental changes in light of the information age. Use of online communication is increasing and has become widespread. This trend also contributes to significant changes in internal and external organizational environments (Aggarwal, 1999). Aggarwal (1999) identified the underlying relationship between technology and globalization, arguing that technology fosters globalization while globalization affects technological development, which leads to a new era of economy with a growing number of knowledge-intensive enterprises. Emerging online networking has also opened the door for innovative companies to connect with each other and to operate more efficiently (Ellison et al., 2007; Ross et al., 2009).

The duality of financial crisis and numerous opportunities offered by the Internet created an economic environment where the role of effective, knowledge-intensive businesses, so-called startups, have increased and become highly important. This trend is largely premised on the assumption that entrepreneurship has a positive effect on economic progress (GEM, 2013). The role of entrepreneurship in economic development was the focus of earlier studies as well. According to Wennekers and Thurik (1991) entrepreneurship is a multidimensional concept because it can be defined at the level of firms, industries and nations. They discussed whether entrepreneurship matters in modern economic development and ultimately concluded that it does. As a result of globalization, developed information and communication technology that require structural change, entrepreneurship has garnered a more important role in economic growth than ever before. Although it is obvious that not all entrepreneurial activities contribute equally to economic growth, knowledge-based enterprises might lead to the revitalization of the economy by influencing innovation, competition and industry dynamics. However, many early-stage businesses still face a lack of capital and background information about markets and competition that interfere with future opportunities. Investigating entrepreneurial success and survival

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of newly founded businesses, many researches came to the conclusion that social contacts - combined with social networks - are essential to rewarding businesses (Burt, 1992; Brüderl and Preisendörfer, 1998).

The popularity of online websites for networking is increasing and they provide a new platform for socialization and business. A recent study of online social media carried out by Comscore revealed that European Internet users spent an average of 27 hours online every month. Looking more in depth, it has been found that social media sites are frequented by users an average of 6.7 hours per month, thus making them the most-visited webpages (Comscore, 2013). Besides personal users who mainly sign up to follow their friends, social media has also captured many businesses (Kaplan and Haenlein, 2010). Previous studies mainly focused on social media as a marketing tool, but recently a growing number of entrepreneurs have realized its various potentials (Clark and Melancon, 2011). There has also been research to suggest that entrepreneurs with broader social networks are more likely to receive funding from investors and accomplish business development (Baron and Markman, 2000). Furthermore Hong (2013) discussed that an increasing number of venture capitalists rely on social media sites and monitor promising startups’ social-media efforts to assess the investment potential of each entity.

1.2 Research purpose

Online social media sites and the way companies incorporate social media outlets into their business platforms inspired the topic of this Master thesis. Online social sites disregard geographic boundaries, thereby enabling the free flow of information and a worldwide connection, which in turn accelerates opportunity recognition and implementation. Taking into consideration the relevance of network building and the increasing number of social media users, the question arises: how can innovative, fast-growing businesses benefit from free social media platforms to run their businesses more efficiently and effectively. As discussed before, financial difficulties are liable to occur during startups’ operation. Therefore, establishing professional relationships online could aid them in gaining better results and reaching potential business supporters.

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This thesis aims to analyse the importance of online social networks when the successful launch of a business, especially a startup’s, comes into question. More precisely, it strives to elucidate the benefits of online social networking for startups in terms of fundraising. Besides this, the study sets out to enhance academic knowledge of startups’ social media activity by conducting quantitative research, addressing gaps in the literature related to online social networking and startups’ financing. Furthermore, this research is expected to provide a relevant analysis about the relationship between startup companies’ social media activity on Facebook and Twitter and the amount of raised funding and what their most important activity features are on these platforms.

1.3 Research questions

The questions discussed by this thesis are: What influence do startups’ online social media performance have on fundraising? Do online activities of startups influence the chance for them to raise a greater amount of funding and, if so, what metrics matter? What trends can be noted in their behaviour?

1.4 Relevance of the study

The value of offline social networks has been discussed in many academic articles and lots of research has been carried out to examine how social relationships affect entrepreneurs’ performance and business opportunities (Elfring and Hulsink, 2003; Hite and Hesterly, 2001). However, online social networking of businesses has not yet received considerable research attention. Although this concept is relatively new, the opportunities of fast growing Internet based networking sites such as Facebook, Twitter, Instagram and LinkedIn cannot be ignored since they have changed the way we communicate. This research will add value by analysing whether social media provides a useful tool in the struggle for fledgling entrepreneurs to attract investors.

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1.5 Structure of the thesis

This chapter first introduced the main topic and objectives of the thesis and subsequently provided the main research questions for the study. The remaining part of the thesis is structured as follows. First, a comprehensive literature review of existing findings is presented and gaps in the discussed literature are extrapolated for further discussion. The study continues with a brief analysis of social media trends and SEC regulations. After, based on the academic findings and recent social media statistics, a conceptual model and formulated hypotheses are presented. The hypotheses are then tested empirically. Before that, the methodology of the research will be described, which summarizes the data collection and the data sampling methods and discusses the applied analysis techniques. In the Results chapter the analysis and findings of the tests are covered. Then, based on these outcomes, the discussion and the conclusion of the research are presented and compared with the theoretical literature. The thesis ends with the limitations of the study and with implications for future research.

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2 Literature review

The term “social media” has recently been high on the business executives’ agenda who tried to identify how companies could benefit from social media tools (Kaplan, Haenlein, 2010). Generally, users only utilize tools such as Facebook, Instagram or Twitter for personal use and do not take into consideration the opportunities such online resources can create for businesses. However, such sites can provide many possibilities for firms. Brown (2011) discussed how online social media sites paint a richer picture of companies. He emphasized their importance in gaining real time information about companies and about changes in the industry trends.

Before identifying if startups’ online social media presence affects their performance in terms of fundraising, it is important to consider the theoretical background of the research. This theoretical framework includes various theories about entrepreneurship and its challenges and about network building both offline and online. Furthermore it reviews the literature on startups, investors, online social media and on online networking.

2.1 Entrepreneurship and startups

Early academic studies barely mentioned or defined entrepreneurship. The lack of commonly agreed-upon definition of entrepreneurship can be explained by the high incidence of equilibrium theory in previous academic studies (Baumol, 2005; Knudsen, Swedberg and 2009). According to the equilibrium theory, participants are satisfied with the current combination of prices and quantities on the market, and therefore they are not likely to change their present actions. It is seen as a Pareto-optimal state, where no one can benefit from trade (Eckhardt and Shane, 2003). This statement is contradictory with entrepreneurial processes since in an entrepreneurial environment changes are likely to happen by putting together new combinations (Knudsen and Swedberg, 2009). There were a few early scholars who showed interest in the topic of entrepreneurship and conducted several analyses to gain insight into the concept of entrepreneurship.

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Entrepreneurship in terms of behaviour was first defined by Richard Cantillon in his book “Essai sur la Nature Commerce en Général” in 1755 (Saucier and Thornton, 2010). His approach to entrepreneurship was mainly based on the economic role of entrepreneurs rather than an individual performance. He studied entrepreneurs’ buying and selling behaviour and considered their risk-taking activity as the most important feature of them. One of the major economists, who have worked on the theory of entrepreneurship, is Schumpeter. He took a specific view that disrupts the equilibrium theory. He defined entrepreneurs as innovative and profit oriented individuals who seek new opportunities (Schumpeter, 1911). He stated that “Everyone is an entrepreneur when he actually carries out new combinations, and loses that character as soon as he has built up his business, when he settles down to running it as other people run their businesses” (Schumpeter, 1911, p. 78). Schumpeter paralleled the process of finding new combinations with that of entrepreneurial discovery and exploiting profitable opportunities. He asserted that an innovative attitude is an essential element of entrepreneurial success. His concept of innovative entrepreneurship (i.e., individuals willing to break the circular flow, has been accepted by most economists. Due to his work the term entrepreneur means more than just a traditional businessman.

Since then, the definition of what it means to be an entrepreneur has been redefined and his views were extended in many academic studies. Stevenson and Gumpert (1985) argued that entrepreneurship cannot be defined only by terms such as “innovative, flexible, dynamic, risk taking, creative”, neither the act of starting and operating new ventures describes it accurately. Rather, they enhanced the way how entrepreneurs take action. More precisely, they tried to show how individuals can execute innovatively and operate creatively while simultaneously pursuing outside opportunities. Consequently they described entrepreneurs in terms of a range of behaviour. Kirzner (1985), Shane and Venkataraman (2000) considered entrepreneurs quick-witted innovators, who recognize profitable opportunities and take action to indulge their needs. Acs (2006) related entrepreneurs’ activities to innovation and technological development. Entrepreneurs build new enterprises (which in turn create jobs), strengthen

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competition and improve productivity. As a consequence, entrepreneurial actions might result in economic growth.

“The first success of a firm is its birth” (Van Gelderen et al., 2005, p. 365). However, in order to start a business, entrepreneurs need entrepreneurial opportunities. Stevenson and Jarillo (1990) suggested three key components that affect the development of entrepreneurial processes: opportunity detection, pursuit of the opportunity and the possibility of succeeding. Ardichvili (2003) also specified entrepreneurial opportunities; he distinguished opportunity identification in three essential steps: recognition, development and evaluation. He suggested that opportunity recognition is necessary but less important than opportunity development. The process of opportunity development is cyclical, which is influenced by five major factors: “entrepreneurial alertness, information asymmetry and prior knowledge, social network, personality traits, type of opportunity itself” (Ardichvili et al., 2003). Besides valuable opportunities, entrepreneurs’ characteristics and their environment are also relevant to entrepreneurial success.

The main conclusion to be drawn from these definitions is that entrepreneurship is more than just setting up new ventures. Although there is not a widely-accepted description of a “typical” entrepreneur (Bull and Willard, 1993), based on the academic literature entrepreneurs can be seen as individuals who strive for success by creating innovative and profitable businesses. They are willing to take risks to generate growth and are likely to make profit not only from their own knowledge and ideas but also from their social networks.

Since entrepreneurship can be defined as the creation of new combinations of economic means, entrepreneurs are often associated with small business ownership and management (Gibb, 1966; Carland et al., 1984). However, they are not equal. Kirby (2004) indicated that the concept of entrepreneurial firms extends beyond small businesses. The main difference between the two concepts stems from entrepreneurial traits such as “need for achievement, internal locus of control, risk taking propensity, need for independence, need for responsibility, and need for power” (Carland et al., 1984). However,

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innovation is the most important differential factor in the relation between entrepreneurship and small business.

This thesis relies on certain kind enterprises, which are considered strongly entrepreneurial and innovative, namely startups. This study attempts to introduce startups’ activity in respect of their social media presence, therefore it is highly important to understand the attributes of startups and differentiate them from other small enterprises.

2.2 Definition of enterprise and startup

The term “enterprise” can be explained dependent on the stage of a business’ life cycle. Enterprise is defined as a human activity, which aims to satisfy customer needs while generating profit (Chikan, 2008). Kallay and Imreh (2004) extended Chikan’s definition of enterprise further and, argued that there is a two-sided approach to this description: an enterprise can either be defined as a profit oriented organization or as a profit-seeking activity that is not necessarily related to an organization. They stated that business actions start during the design phase of a company, so a profit generating idea is part of the business’ life cycle from the very first moment of idea conception.

Just as with entrepreneurship there is no generally accepted definition for the term startup. Startups are considered as fledgling, knowledge-intensive business enterprises that have bright ideas and the ability to grow rapidly. According to Autio’s (1997) linear model, startups are technology-based, new companies that are beyond the phase of idea conception and have already established a firm. Ries (2011) defined startups as “human institutions designed to deliver a new product or service under conditions of extreme uncertainty”. He argued that startup success is not about the coincidence of being at the right place at the right time, but rather that the key to startup success can be learned and taught. Startups are linked with the act of amassing financial and human capital to evolve their product and turn the idea into a fast growing business. However, not every newly founded business can be regarded as a startup.

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With these definitional contours in mind, startups can be seen as embodiment of discoveries that combine innovative ideas with great technology, which is the best source of rapid change.

2.3 Startup investors

There are certain factors that are crucial for innovative startups: a unique idea, a great and courageous team and financial support. However, having a good idea will not lead to success easily. The most challenging part is getting the money during early stages to transform the idea into a rewarding business model.

Startup founders usually find it difficult to raise funding. Most of them are not eligible for bank loans or debt due to the fact that their businesses’ operating history is limited. Some entrepreneurs are able to get funding from “family, friends and fools” (from the so-called three Fs) but in many cases it is not efficient (Mason, 2006). Consequently, financial support regularly comes from the investor market. Among numerous financing possibilities, funding mainly stems from three sources: angel investors, corporate venture capitals (CVCs) and venture capital firms (VCs) who invest money in the hope of high returns generated by high-growth, enticing companies (Van Osnabrugge, 2000; Bottazzi et al., 2002). Although entrepreneurs usually have technological competence they typically lack managerial experience and industry knowledge. Institutional investors therefore provide more than capital, they possess entrepreneurial and business experience, unique knowledge and access to networks.

Venture capital is defined as equity or equity-based investment in recently established companies by financial intermediaries, which invest in early-stage businesses. In return for the high risk of investment, besides ownership, venture capitalists get control over the company. They often play the role of director, advisor or manager of the firms due to their rich knowledge of markets and professional experience (Kortum and Lerner, 2000).

Venture capital industry has gone through considerable development over the last three decades in the United States and boosted the commercialization of technological innovation. Modern venture capital

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financing started in 1945 and shortly after it has become the main financial supporter of startup companies, operating in high-tech industries such as information technology, e-commerce and biotechnology. Today’s most successful and outstanding companies have also received funding from VCs at their initial stages: Apple, Amazon, Intel, to name just a few (Bottazzi, L. et al., 2002). According to the MoneyTree Report (2014) conducted by PricewaterhouseCoopers LLP and the National Venture Capital Association (NVCA) the above mentioned industry categories remained the number one investment sectors in 2013 also, for both aggregate dollar investments and total number of deals.

Previous studies investigated the role of venture capitalists from different aspects. They argued that their role extends that of traditional financial intermediaries, and shed light on their non-financial values for companies. VC backed startups proved to be more innovative and faster in business implementation. (Kortum and Lerner 2000; Hellman and Puri, 2002; Kanniainen and Keuschnigg, 2003). Hellmann and Puri (2002) measured the performance of 170 high-technology startups in Silicon Valley by the criterion whether venture capitalists have a role in the development of startup firms beyond the role of traditional financial intermediaries. They revealed that venture capitalists perform additional roles and provide value-added inputs by formulating new human resource policies, adapting stock option plans, or hiring a VP of sales and marketing. As a result they suggested that startups funded by VCs are dynamic companies that likely to act more innovative and operate more efficiently. Venture capital also has influence on patented inventions. Kortum and Lerner (2000) found positive relationship between venture-backed companies and the amount of patents they produced compared to non-ventured-backed companies in the US. Furthermore, venture capital funding events are considered as a positive sign of the quality of startups (Davila et al., 2003).

Besides venture capitalists, angel investors also have a crucial role in funding companies during their early stage of growth. Based on current estimates of the Center for Venture Research the number of US angel investors is growing, the total investments in Q1 and Q2 2013 increased by 5,2 % compared to Q1 and Q2 2012 (MoneyTree Report, 2014; Sohl, 2013). Unlike venture capitalists, who manage the pooled money of other individuals in a fund, angel investors are individuals, who invest their own money

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in new, small companies. Angel investors tend to make decisions based on intrinsic motivation and can be involved in the company’s processes even more than venture capitalists (Cardon et al., 2012). In spite of high-risk, they prefer investing in early-stage deals and being active members of the venture. The bulk of angel investors have entrepreneurial experience that can be accounted for their willingness to take risks. Considering other preferences of angel investors, it has been noticed that businesses located geographically close to BAs appeal to them more (Freear, J. et al., 1994).

Startup success is embedded in a broad set of skills and expertise of the entrepreneur. As it was discussed earlier, resources, such as human and financial capital and access to networks, are necessary for running successful startup business. However, startups usually cannot build on the entrepreneur's human and financial capital alone. Therefore, networks are indispensable for getting the necessary complementary resources and capabilities (Wu, 2007).

Hubbard (1998) related the imbalance between the growth of venture capital industry and the amount of supported businesses to inefficient information flow. He discovered information asymmetry, so-called “knowledge gap”, between businesses and investors that is accounted for improper market functions. Baron and Markman (2003) found positive relation between social relations and financial success. Their study revealed that the broader a social network is, the easier it is to gain access to venture capitalists and to raise funding. These findings also strengthen the importance of networking. Effective network building is considerably more important for early-stage companies (Hoang, Antoncic, 2003; Burt, 1992) since networks provide them access to information, goods and services, expressions of affects, support and advice (Tichy et al, 1979).

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2.4 Network building and social capital

Networking is strongly related to entrepreneurship, which is “the process by which individuals - either on their own or inside organizations – pursue opportunities without regard to the resources they currently control” (Stevenson and Jarillo, 1989). Entrepreneurs might meet with an obstacle when they are about to use their resources. As a consequence, they have to take advantage of their existing social networks or try to establish new relationships. Dubini and Aldrich defined social network (1991) as “patterned relationship between individuals, groups, and organizations”, which plays an important role in entrepreneurial processes. Brüderl and Preisendörfer (1996) found that broad and diverse social network is linked to entrepreneurial success. A detailed research of the role of networks in the entrepreneurial context was conducted by Hoang and Antoncic (2003), wherein the entrepreneurs’ network revealed three essential components: the content of network relationships, governance, and structure.

Network content describes the resources that flow and exchange between individuals and organisations (Steier and Greenwood, 1999). On one hand, relationships provide advice, information and emotional support to entrepreneurs, while on the other hand, networks can also lead to exchange in resources (Hoang and Antoncic, 2003). Schonsheck (2000) suggested that the practice of networking is about making business friends, who can advance our business results: “it’s not who, you know, it’s who knows you”. Networking aims to improve relations by “establishing, maintaining and expending the circle of business friends”.

Relying on theoretical and empirical research, Hoang’s and Antoncic’s (2003) study showed some positive outcomes of strong network structure and its influence on business development. Network structure is defined as “the pattern of direct and indirect ties between actors” that vary in size, strength and diversity (Dubini and Aldrich, 1991). The types of entrepreneurial resources during the evolution of businesses have become a controversial topic. Individuals tend to rely on resources persistently during all venture stages. Although the type of information may change parallel with the venture phase, the

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development of ties is more important when considering the emergence of firms (Hoang and Antoncic, 2003; Elfring and Hulsink, 2003).

Entrepreneurial ties are categorized according to the relation or agreement between two individuals. The difference between pairs of entrepreneurs is embedded in the strength of ties (Dubini and Aldrich, 1991). Dubin and Aldrich (1991) suggested two concepts of networks based on the intensity and diversity of relationships: personal (direct) and extended (indirect) networks. The pattern of direct and indirect ties is defined as a network structure (Hoang and Antoncic, 2003). Personal ties are considered as “strong ties”, which include direct and frequent relationships such as friends, family and previous professional relationships from whom entrepreneurs obtain support (either emotional or financial), advice and services. In contrast with these relationships, extended networks are “weak ties” that depict formal and loose networks from separate clusters leading to new business partners and information resources (Burt, 1992; Dubini and Aldrich, 1991). The development and change of weak and strong ties over a firm’s evolution have recently received high attention. Elfring and Hulsink (2003) examined how entrepreneurial activities in different phases of a company’s development affect tie formation processes and how the proportion of strong and weak ties changes during these development stages. They used the perspective of entrepreneurial processes, such as “seeking opportunities, acquiring resources and gaining legitimacy”, and argued that entrepreneurs focus on these activities differently based on initial connections. The main idea of their research was to see whether the mix of weak and strong ties develops over time according to the evolving environment.

Considering the competitiveness of businesses, social networks and access to information are dominant factors of successful enterprises and appreciated by entrepreneurs. Burt (1992) highlighted that there are three kinds of capital, which lead to competitive advantage: financial, human and social capital. Social context and environment have been mentioned as a necessary entrepreneurial tool in other economic studies as well (Brüderl and Preisendörfer, 1966; Hoang and Antoncic, 2003). Social capital exists in the relations among persons (Coleman, 1988). Unlike financial and human capital, social capital is owned jointly by parties. Furthermore investing time and effort in building social capital brings

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opportunities to transform human and financial capital to profit in return (Burt, 1992). The importance of the concept of social capital creating opportunities was emphasized by Aldrich and Zimmer (1986). They found positive relations between the extension of social networks and the scope of opportunities. Apart from opportunity formulation, networking also contributes to an innovative atmosphere that helps firms gain a better position on the market (Dubini and Aldrich, 1991). For example, MacMillan (1983) referred to network and contact building as the major factor of a firms’ success. Numerous studies examined the importance and significance of connections and stock of information between entrepreneurs. Shane and Venkataraman (2000) explained that only certain people recognize entrepreneurial opportunities, which they refer to as entrepreneurs’ prior possession of information. Since no one shares and receives entirely the same information at the same time, the probability of opportunity discovery and development are distinct and correlate with network building. It has been shown that startups that have larger informal communication networks - the weak ties - increased their chance to overcome external shocks. The intensity of startups’ communication correlates with the probability of continuance of businesses. It leads to the assumption that informal communication networks make it easier to obtain information that normally would require years (Raz, Gloor, 2007). Baum, Calabrese and Silverman (2000) also came to a similar conclusion during their research analysing how startup’s alliance network configuration affects their early performance.

Steier’s and Greenwood’s (1999) research also supported the influence of social networks on development and evolution of financial networks within young companies. They described a four-stage model of an angel investors’ network development:

1. “Locating sources and building network from contacts.” 2. “Strengthening strategic relationships.”

3. “Optimizing resources and developing relationships into multi-dimensional network.” 4. “Managing complex networks and making new ties to expand business.”

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Their study emphasized the value of ties, particularly the significance of weak ties that grow and change during the stages of network revolution and contribute to a higher probability of investment.

To sum up, all the above mentioned studies focused on networking and establishing relationships offline. However, in the era of Internet, the concept of networking has changed significantly. Online platforms have become a prevalent and key source of information sharing. This means that there is a new channel that supports establishing weak ties with individuals and organizations while also maintains existing relationships (Ellison, N. et al., 2007).

2.5 Online social networks

The concept of social networking has always been present as a result of interpersonal connections such as friendships, common interests or ideas. However, network building also started online due to the introduction of broadband Internet. The World Wide Web turned 25 years old on 12th_{March 2014. The} invention of Sir Tim Berners-Lee made users life easier over a network of computers called the Internet. Soon after, the first social media websites appeared offering new and meaningful ways to communicate, to engage in events, companies or brands all around the world. Although the emergence of social media started two decades ago, it is still growing rapidly and giving increasingly innovative experience to users. Online social networking is undoubtedly a global phenomenon.

Social networking sites (SNSs) were defined, analysed and valued in former academic reviews (Ellison et al., 2007). Boyd and Ellison (2008) described social network sites as “web-based services that allow individuals to (1) construct a public or semi-public profile within a bounded system, (2) articulate a list of other users with whom they share a connection, and (3) view and traverse their list of connections and those made by others within the system.” Kaplan (2010) defined SNSs similarly, he also highlighted the opportunity of creating personal profile to connect and communicate with friends or colleagues by emails or quick messages. Users’ profiles can include any type of information, for instance photos,

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videos, audio files, and blogs. Online social networking sites enable the maintenance of existing relationships and also the formation of new connections.

Researches have been conducted whether the Internet is weakening, transforming or enhancing our community. Furthermore scholars argued that the act of building social capital differs offline and online. Although the vast majority of them have positive views on the Internet’s impact on social life, some scholars found negative outcome on relationships that result in weak social capital. Nie (2001) argued that Internet usage is an isolating activity and users are becoming more unsociable. Their social interaction is decreasing, which might affect their social capital. According to the findings of Kraut et al. (1998) using Internet leads to a decline in social involvement and psychological well-being. Both scholars concluded that frequent Internet users are likely to be lonely and isolated that contribute to depression. However, many other relevant studies criticised the idea of “lonely crowd” and draw opposite conclusions (Wellman et al., 2001; Hampton and Wellman 2003; Donath and Boyd, 2004; Ellison et al., 2007). Even the follow up report of Kraut’s (2002) controversial study refuted previous findings on online networking. Hampton and Wellman (2003) examined the online networks of a local community. Their results showed positive effect on the group’s social interaction and social capital, thus online interactions filled the communication gaps of offline conversations (Wellman et al., 2001). Recent studies have also emphasized the importance of online networking for the formation of stronger weak ties (Donath and Boyd, 2004; Wellman et al., 2001).

Online opportunities differ by the nature of existing relations. Haythornthwaite (2002; 2005) was one of the first researchers, who presented a study on how the strength of social ties differ offline and online. She found that the new medium of communication has positive impact on both weak and strong-tie networks. On one hand, online connection may strengthen and develop weak-tie relationships while on the other hand, it broadens the opportunities of strongly tied pairs for communication. Beyond social ties she introduced another level of tie: latent ties. Latent social network ties are formed by computer or non-computer organizations which are “technically possible but not activated socially”. Only social activity can transform these ties to weak ties. Donath and Boyd (2004) speculated that online social

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networking increases the number of weak ties a person can have due to cheap technology and heterogeneous networks. Other studies also added findings about how new dimension of communication can contribute to growing weakly-tied networks and support community building (Hampton, 2003; Pinkett, 2003).

In contrast with changes in personal relationships, only a few studies have been carried out to analyse how business relationships modify due to online networking. However, it is important to understand how entrepreneurs of the 21st_{century use online social networking sites to support their businesses. In} 2009, McKinsey conducted a survey on companies’ use of Web 2.0 social media platforms. It revealed measureable results, which stemmed from better interaction with followers and fans, and from increased awareness of companies’ products. Significant benefits led to higher revenues and more innovative business outcome. Fischer and Reuber (2011) analysed 12 entrepreneurs to give new insights into social media usage in business context. Their study identified a positive relationship between the intensity of entrepreneurs’ social interactions through Twitter and advancement through effectuation process. Another study focused on the success factors of startups in Germany by looking at the social network structure of their founders. It brought out that online networking matters and certain structural properties of these networks contribute to firms’ success (Nann et al., 2009).

2.6 A new group of investors

Last year Accenture (2012) conducted a survey on investors’ changing attitudes and invented a new group called Generation D (Gen D). Due to the financial crash and the boom of online social channels a new group of tech savvy-investors is emerging. They are engaged users of social media - most of them are regular Facebook users - who have an important role in the group of US investors and tend to seek for information on online platforms to mitigate risk and get better insight into investment opportunities. Gen D investors are split into three groups based on their age: 21-30 years old - Millennial candidate (50%), 31-45 years old - Gen X candidate (25%), 46-70 years old - Boomer candidate (25%). Younger investors are interested in finding alternative and complimentary sources of information for investment

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decisions. Accenture’s study also raises the question how social media could influence investors’ decision and whether it will bring rapid changes in the future.

Despite the above mentioned studies, little is known about how the use of online social media platforms may affect startups’ performance, especially in the early stages. It is clear that online social networking facilitates a new way of connection and provides various opportunities. This master thesis assumes that online social networking sites lead to supportive opportunities in business development and accelerate young firms’ recognition and expansion on the market. However, there is still a lack of research on online social media and their benefits for entrepreneurs. To bridge this gap, this thesis examines startup activities on Twitter and Facebook in order to expand their professional network and raise funding. It intends to shed light on the connection between startups’ online activity and the amount of financial support received from investors. This work assumes that online networking could lead to a higher chance for fundraising and a better opportunity for business development for startups.

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3 Online social media: Facebook and Twitter

Recently, only a “share” button sunders us from entire publicity – either if we look at Facebook, Instagram or Twitter – however, the information age we live in and the mass amount of data, which we are able to obtain is something that an average person had never dreamt of a few years ago. A striking post can reach such a vast amount of people that a national newspapers would never do. Online audiences have become huge and their presence provide amazing opportunities for messaging and communication. Spending time online to follow and communicate with friends, family members, colleagues or total strangers is considered as a common daily activity.

According to the Pew Research Center (2014) 87% of American adults use the Web. Nearly 73% of them use at least one social media site. Although various social networking sites exist, Facebook is still the most dominant networking platform in terms of user numbers, 71% of online adults have an account on Facebook. By the end of 2013, it reached 1.23 billion monthly active users, from whom 757 million users logged on Facebook daily (The Guardian). Although the popularity of Facebook is undiminished, there are other platforms as well that created their own unique user groups. For instance Twitter, which is the fastest-growing social platform and mainly popular among younger adults. Or LinkedIn that appeals to college graduates and professionals from higher income households. Users are open to new networking platforms, 42% of them are already using multiple social networking platforms (Duggan and Smith, 2013). Surprisingly, the vast majority of social media users do not belong to the youngsters, according to the Pew Research Center report (2014) online networking platforms are frequently visited by older people as well.

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Table 1: Social networking site use by age group, 2005-2013 Source: Pew Research Center, Social Media Update 2013

Apart from individual users, social media has also become popular among companies. Over the last few years, increasing number of companies have become involved in online social media and started using these channels to communicate (Joyce, 2013). Online social networks let firms engage in discussions, share information and connect with their stakeholders such as customers, employees, communities, analysts and investors. The quotation from Jill Schlesinger illustrates the importance of participating in social media: “If you’re not on the platform, you have no credibility”. Investigators agree on the fact that social media will further increase in importance over the following years. It is has been proven that companies’ sales and marketing results have improved due to their social media presence (Fischer and Reuber, 2011; Clark and Melancon, 2013; Mangold and Faulds, 2009). Results of Go-Gulf’s (2013) survey on “How social media influences businesses” shows that social media has doubled companies’ marketing leads, helped find new customers and increased conversion rates.

Apart from reaching target groups and broad audience, social media platforms are being considered as suitable methods for communicating with investors. In April 2013 Twitter and Facebook, along with other social media, have been officially deemed by the Securities and Exchange Commission (SEC) to be acceptable venues for companies to announce key information to investors, as long as investors have

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been alerted in advance, which media will be used1_{. Due to SEC’s press report (April, 2013) companies} are officially allowed to take advantage of social media sites to attract investors’ attention and share with them their stories. On one hand, the richness and speed of these websites enable firms to be involved in conversations, while on the other hand, followers can obtain and discuss real-time information. At first, SEC’s revolution of social media use mainly concerned individual investors, who are free to access Facebook pages and follow companies on Twitter. However, as The New York Times reported Bloomberg made a change and it integrated Twitter into its terminals, creating a channel between social media, companies and Wall Street (Alden, 2013). Whether public companies take the advantages of social media properties or rather stick to traditional channels, it is also interesting to investigate how smaller companies use Facebook and Twitter. Young companies might need to use social networking sites even greater to impress investors and enhance their business.

However, it is controversial which platform is more valuable. They are vary in terms of scope and functionality (Kietzmann et al., 2011). Previous studies on social media usage categorized social media sites according to different aspects. Kaplan and Haenlein (2010) classified them by social presence/media richness and self-presentation/self-disclosure, whereas Kietzmann (2010) developed a “honeycomb” model of seven functional building blocks (“identity, conversations, sharing, presence, relationships, reputation and groups”) to compare online networking platforms. Regarding Twitter and Facebook, both of them have similar goals such as connecting people, bringing together supporters and establishing a community, however, they reach their purposes in different ways. Twitter is meant to be more active due to its real time data stream platform, IAB (2009) described it as an online micro-blog. In contrast, Facebook seems rather passive since it is more about ensuring engagement with fans by enabling them to comment more and to share in-depth thoughts about a company. Clang (2012) discussed their differences as follows: “Twitter is great for establishing relationships whereas Facebook strengthens existing ones”. Baker (2010) also strengthened the previous idea. He claimed that companies are able to get more Facebook fans than Twitter followers because many of their fans had already known

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the company before they “liked” its page, while on Twitter users mainly follow companies they want to know and were not familiar with them earlier.

To summarize, neither one is better than the other, nor is it the aim of this report to explain which one is more useful. What matters is how companies use these sites to increase and attract more attention. Therefore their value is mainly based on companies’ social media strategy.

4 Conceptual framework

After reviewing the academic literature and social media statistics to build a theoretical background for the study, different hypotheses are formulated. Though the Literature Review part of this thesis is quite broad and discusses various theories, only a few emerging questions can be answered in this study due to its limitations. Therefore, the hypotheses introduced in this separated section explain the highlights of previous studies.

In the Literature Review mainly the traditional (offline) social networks of the entrepreneurs were described in relation to opportunity recognition and entrepreneurial success. The major focus was on the size, diversity and degree of these social connections. Several studies found positive relationship between the aforementioned variables and business performance (Brüderl and Preisendörfer, 1998; Duchesneau and Gartner, 1990; Walker et al., 1997), however, in case of online social networks these parameters should be analysed differently due to the differences between online and offline communication structures. Although a wide range of sources were used for finding relevant academic studies for the research, there were only a small number of them related to online network building. To compare offline and online networking factors, content of the hypotheses about the effect of online networking are paralleled with theories of offline networking studies. Consequently, different elements of social networking were applied for hypotheses generation. In the followings these are discussed separately for each social media site.

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4.1 Social media metrics

Users share a vast amount of content on social media platforms every day. “Social media data are largely user-generated content on social media sites. Social media data are vast, noisy, distributed, unstructured, and dynamic”, however they might contain valuable and beneficial data for businesses, users and consumers (Gundecha and Liu, 2012). Social media data can be defined by several metrics and evaluated through various techniques. Culnan, McHugh and Zubillaga’s (2010) study also encouraged using diverse metrics to measure the value of social media usage for businesses. The Interactive Advertising Bureau (IAB) specified standard definitions for Social Media Metrics.

4.1.1 Network size

There was a positive relation between the size of social networks and the success of early-stage businesses (Brüderl and Preisendörfer, 1996; Dubini and Aldrich, 1991; Hoang and Antoncic, 2003). According to Witt’s model (2004) the more contacts an entrepreneur has, the more support he receives, which positively influences the success of startups. Due to diverse, extended social networks, including many weak ties, companies can obtain valuable information easier and recognize opportunities faster than businesses, relying mainly on the three Fs (Aldrich and Zimmer, 1986; Burt, 1992). Besides better performance, the chance for financial capital raising also increases with the amount of connections (Evans and Jovanovic, 1989). These contacts, which lead to better performance, are considered as their social capital and play major role in networking (Aldrich and Zimmer, 1986; Burt, 1992).

Regarding this research, the success of startups can be explained by the total amount of funding that they managed to raise, while the number of fans and followers describe their network size. The size of online networks exceeds dramatically that of offline connections. Here the size of online network refers to the number of Facebook fans and Twitter followers of the startup and not to the size of personal connections of the entrepreneurs. Based on the above mentioned arguments the first hypothesis assumes that investors are more likely to invest larger amount of money in startups with larger online network.

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Positive relation is expected between the online network size of a startup and the amount of funding it raised in total. The following hypotheses are formulated.

Hypothesis 1:

H1.(F): The size of a startup company’s network on Facebook (number of Facebook fans) is positively

related to the total amount of its funding.

H1.(T): The size of a startup’s network on Twitter (number of Twitter followers) is positively related to

the total amount of its funding.

4.1.2 Social media activity of users: Fan activity score and Follower activity score

Besides monitoring a company’s online community size, the fraction of its active fans or followers, who frequently share, like or comment online content, also identifies whether social media presence is beneficial. Value of offline networks was measured by the improvement and the strength of ties (Hoang and Antoncic, 2003). The more diverse and better the relationship is between an entrepreneur and its stakeholders, the better its business performs (Baum et al., 2000; Hoang and Antoncic, 2003; Elfring and Hulsink, 2003).

To evaluate online networks, social media platforms provide supplemental tools for page admins to calculate a useful metric, the so-called “Engagement Rate”. It interprets what proportion of a page’s audience engaged with its content. On Facebook it is available under the “Page Insights” function. Facebook Engagement Rate takes into account the total number of likes, comments and shares. Then the total can be multiplied by the number of fans to provide a percentage measurement. (Simply Measured, Socialbakers, 2012). According to Simply Measured, Twitter Engagement Rate calculation is done similarly. Number of replies, retweets and mentions of the tweets are added up. To get a percentage rate the total is multiplied by the number of followers (Socialbakers, 2012). However, some of these metrics are post metrics, while some are not available to the public. Therefore an own measurement of engagement was calculated for this research. In this report the value of relationships is

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measured by “Facebook fan activity score” and “Twitter follower activity score”, which are estimated on the basis of the retrieved metrics. Chapter 5, Research design and methodology, introduces the formula and factors in detail.

Based on the discussed literature, it is hypothesised that companies which build an active online community show better business performance and can increase their awareness easier. In this report performance is indicated by the amount of total funding, engagement rate is indicated by the activity score. Based on the aforementioned theories the following hypothesis is created.

Hypothesis 2:

H2.(F): The more active fans a company has on Facebook, the more funding it raises in total.

H2.(T): The more active followers a company has on Twitter, the more funding it raises in total.

4.1.3 Frequency of social media usage

Besides the size and value of networks, Burt (1992) found that the amount of time and effort invested in building social capital contribute to business success. In this report the time spent on treating existing and building new relationships on online platforms is measured by the frequency of startups’ social media activity. It is represented by the number of their posts and tweets.

Hypothesis 3

H3.(F): The number of Facebook posts is positively related to a startup’s total amount of funding.

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4.2 Venture capital

Venture capitalists are considered as investors, who provide startups with more than just financial help. Venture-backed companies are proved to be more innovative and more successful due to venture capitalists’ additional support (Hellmann and Puri, 2002; Kortum and Lerner, 2000). Baron and Markman’s research (2003) suggested that companies with broader social networks receive venture funding easier. Thus, the following case is hypothesized.

Hypothesis 4: The broader and more active a company’s network on Twitter (number and activity score

of followers) and Facebook (number and activity score of fans), the more likely it raises venture funding.

4.3 Stages of startup funding

Apart from the lack of capital, startups have to face the challenge of gaining awareness and trust of people, especially investors’, to get access to necessary sources. Wu (2007) argued that network building increases the chance to gain complementary capital. It can be assumed that in order to increase awareness and gain credibility through online platforms the quality of the content and the activity of the companies is crucial. In this case, these can be measured by the number of users who found online content remarkable and by the number of posts. As it was discussed before, the type of information might change in line with the venture phase (Hoang and Antoncic, 2003; Elfring and Hulsink, 2003). In addition, Greve and Salaff (2003) explored that each phase of establishing a business requires different type of network activities. Entrepreneurs tend to limit the number of their activities in Phase 1, when they do not feel confident about their business plan yet. However, after the idea is public and their business is set up, they are inclined to share more about their ideas and needs with their network to get support and attract potential business partners. This research assumes that like offline behaviour, online networking activity also changes during a business development.

In this study the different rounds of funding will be used to identify the phases in the establishment of businesses. Based on the above discussed studies, the following hypothesis is formulated.

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Hypothesis 5: After a business is established, startups become more active on social media sites to

attract their network’s attention. Social media activity increases between the first few rounds of funding.

4.4 Conceptual models

Conceptual model 1.

Models were tested for Facebook and Twitter separately:

 Hypothesis (Twitter): Startup use of Twitter affects positively the total amount of their raised funding.

 Hypothesis (Facebook): Startup use of Facebook affects positively the total amount of their raised funding.

Conceptual Model 2

Conceptual Model 3

Independent variable

 Social network size

 Activity score

 Frequency of social media usage

Dependent variable

 Total amount of funding

Independent variable

 Social network size

 Activity score

Dependent variable

 Venture funding (yes/no)

Independent variable  Time 1  Time 2  Time 3 Dependent variable  Activity on SNS

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5 Research design and methodology

This research project set out to enhance the academic knowledge of startups’ social media activity by conducting a research, which addresses gaps in the literature related to online social networking and startups’ financing, and to analyse the relation between startups’ practices of using social media sites and the amount of investment they received. Online social media analysis is a relatively new research field, which enables tremendous opportunities for researches in various topics. Public, large-scale data can be easily retrieved from social media platforms for analysis, which then can be analysed from different perspectives (DiGrazia et al, 2013).

In this study two social media platforms were used as data sources and various social media metrics were applied to carry out an observational study. Taking the research questions of this study into account, which addresses the relation between the different variables of startups, a quantitative research method was chosen opposed to the qualitative research methods. Aliaga and Gunderson (1999) defined quantitative research technique as a method, which explains phenomena and provides a meaningful concept “by collecting numerical data that are analysed using mathematically based methods”. Qualitative studies lead to a deeper understanding of social facts and help gaining a more detailed insight into a question (Silverman, 2000; Maxwell 2005). Furthermore, the analysis is based on different hypotheses, which need to be tested and verified. To confirm the generated hypotheses and explore the influence of startups’ social media activity on their fundraising, a substantial amount of objective data is required. Thus, quantitative research is the most appropriate method to apply (Gephart, R. P., 2004).

This research relies on a wide range of quantitative data, which was obtained from three different online sources. The dataset was retrieved by using website’s so called Application Programming Interfaces (APIs), which enables the collection of novel metrics from free sources that provide a well-structured dataset (Priem and Hemminger, 2010). Hypotheses were tested on a longitudinal dataset comprising general and investment data, and social media metrics of startups. However, first, a sample from the database was selected to ensure the availability and reliability of the data. After the sampling, all the

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selected companies’ Twitter and Facebook activities were observed and metrics were analysed. SPSS statistics software was used for correlation and regression analysis.

5.1 Research sample and quantitative data set

In the following sections different sources of the quantitative data and features of the sample are described.

5.1.1 Data sample

Information about startup companies, and data about their raised funding were collected from a website, called CrunchBase. CrunchBase is TechCrunch’s public database, which is described as a free database that provides comprehensive information about startup activity and can be edited by anyone (CrunchBase). It includes information about founders, individual investors, financial institutions, trends, milestones, rounds of funding and acquisitions (automatically generated report retrieved from CrunchBase). The dataset of CrunchBase has been used in prior studies as well, however, all of these researches examined other fields of investor and startup behaviour and activity (Yuxian, and Sor-Tsyr, 2012; Xiang et al., 2012). For this research data was extracted by using the CrunchBase Application Programming Interface (API). Using CrunchBase API provides access to its free directory of technology startup companies.

In April 2014 the database included 37,875 companies with recorded fundraising. Companies are categorized by industry; country; status; date of founding; date, round and amount of raised funding. This study used only a sample of the collected dataset. Research was carried out only within startup companies, operating in the U.S, which has been leader in churning out startups. The sample needed to stem from a sector, in which companies are active on the Internet, and where the level of investment is high. IT industry had the foremost position in the investment sector on the basis of total amount of investment and number of deals (MoneyTree Report, 2014). For this reason, firms were chosen from two high technology sectors: “web” and “software” (company category names are used by CrunchBase).

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The selection criteria was also supported by CrunchBase’s report, which was carried out by its data export tool as shown below. Web and software companies were among the top company categories in terms of total amount of received funding between 2000 and 2013.

Table 2: Top company categories by amount of funding 2000-2013 Source: www.crunchbase.com

Furthermore, according to the Facts and Figures of the Inc. 5000 report2_{, companies that are operating} in the Software and IT services industry performed 136% and 111% aggregated growth in terms of revenues respectively.

Apart from focusing only on “web” and “software” companies from the U.S., the following criteria were used for narrowing down the data sample:

 Status: operating. Only operating companies are included in the report.

 Identified Facebook and Twitter account. Since the study uses social media data, only those companies were selected, which have active user account on the examined platforms.

 Date of founding: 2009. Startup companies’ activity was analysed on two different social media platforms: Twitter and Facebook. To sample similar companies, which had equal opportunities

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to take advantage of social media platforms, an exact founding year had to be chosen. In order to gain sufficient amount of social media data, 2009 was selected due to the fact that social media traffic increased steadily after that year. For instance, from January to November 2010 Facebook was the most-visited website in the US (Rosoff on BusinessInsider.com). Furthermore the number of registered Twitter users increased dramatically between February 2008 and February 2009. Nielsen ranked Twitter as “the fastest-growing website in the Member Communities category” in February 2009 (McGiboney, 2009).

Finally, 123 companies were selected for the final dataset.

5.1.2 Dependent variables

The amount of raised funding was taken as the dependent variable of the study. The amount of funding was calculated in total and by the type of funding.

5.1.3 Independent variables: Social media metrics

Social media activity of the selected startups was monitored between 2009 and 2013. “Social media monitoring is the active monitoring of social media channels for information about a company or organisation” (Financial Times Lexicon)3_{. To measure and reveal the impact of companies’ social media} activity different set of metrics can be used. For this study, social media metrics of each selected startup company were extracted from Facebook and Twitter, for instance the number of fans or followers, or the amount of content (posts, tweets, images, shares or other content). However, it is controversial, which of these metrics are considered to be the best indicator of companies’ success in social media. The metric of presence, interactivity and reach are necessary to determine the traffic of a social media page. Sterne (2010) argued that the size of a company’s online social network does not have an important role in its success in social media. What matters most is how many people find its online, shared content remarkable and then spread the word.

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For the purpose of this study, some of the commonly used Facebook and Twitter metrics were chosen for monitoring. These metrics of each social networking platform are described below.

Facebook

Facebook metrics were collected through the use of Graph API. The following variables were studied:

 Fans: This variable refers to the number of Facebook users who “like” the company’s Facebook page.

 Posts: It refers to the number of how many times a day a company posted content to its page.

 Likes: This variable reveals how many “likes” a company’s post got each day.

 Comments: It shows how many times a day users commented on a firm’s Facebook posts

 Shares: It reveals how many times a company’s Facebook content was shared by users each day.

 Fan activity score: It measures what fraction of a Facebook page’s (in this case a startup’s) contents (posts) have caught its fans’ attention. For this report the metric of “fan activity score” is calculated based on the retrieved metrics (introduced above). First daily fan activity score was calculated for each startup for 5 years as follows: 𝑙𝑖𝑘𝑒𝑠+𝑐𝑜𝑚𝑚𝑒𝑛𝑠+𝑠ℎ𝑎𝑟𝑒𝑠

𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑝𝑜𝑠𝑡𝑠

Then the average of the daily activity scores was estimated for each company.

Twitter

Likewise data mining on Facebook, Twitters’ API - so called REST API v1.1 - was applied for data collection. However, in contrast with Facebook’s database, access to Twitter data is limited, only a certain number of calls can be made in a certain time period. This study analysed the following variables:

 Followers: The number of users who have chosen to follow the company.

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 Retweets: This metric reflects to the number of times that a company’s tweet was retweeted by its followers.

 Favorites: This is similar to Likes on Facebook, it represents the volume of engagement of Twitter users.

 Follower activity score: Twitter activity score on Twitter also indicates how a company or brand engages its followers on Twitter. Calculation was done similarly, as done for Facebook fans. First the daily Twitter activity score for all startups was calculated with the following formula between 2009 and 2013: 𝑟𝑒𝑡𝑤𝑒𝑒𝑡𝑠+𝑓𝑎𝑣𝑜𝑟𝑖𝑡𝑒𝑠

𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑡𝑤𝑒𝑒𝑡𝑠

Then the average amount of the daily activity score was estimated for each company.

5.2 Data reduction and analysis

The data file was created in Excel. Consequently, a codebook was prepared to convert the obtained information into a format that SPSS can understand. Variables were defined in SPSS. Since the dataset did not contain any counter-indicative items, recoding was not necessary. Scales were not used, therefore check of reliability and validity of each scale were not part of this analysis. After defining the codebook, the data was reviewed for errors. No error occurred, therefore the descriptive phase of the analysis could be started. Descriptive statistics were followed by regression analysis (Model 1 & 2) and ANOVA analysis (Model 3) to test the hypotheses.

Does looking good on social media sites affect startup fundraising? : a study of whether IT startups’ social media efforts influence fundraising