The role of geographical clusters in the success of reward-based crowdfunding campaigns

Abstract

Crowdfunding is a relatively new phenomenon, which disrupted the classic way to fund a venture. It consists in retrieving the capital needed to start an entrepreneurial activity drawing funds from a large base of small investors – generally common people – rather than from the traditional financial sources. Although many studies have been conducted on this topic, little focus has been put on the geography of this phenomenon. This article addresses this issue analysing whether regions characterized by the presence of geographical clusters are able to raise the probability of a successful crowdfunding campaign for projects located there. Drawing on a data set of 792 crowdfunded projects, we conduct an empirical study aimed at studying the role played by geographical clusters in fostering the crowdfunding of new entrepreneurial ventures. The results offer insights into the phenomenon of crowdfunding and shed light on the role of geographical clusters in the success of reward-based crowdfunding campaigns of early-stage entrepreneurial projects.

Keywords

crowdfunding entrepreneurial finance geographical cluster new ventures

Introduction

Crowdfunding is a relatively new way for funding a variety of new ventures, allowing individual founders of entrepreneurial projects to request funding from many individuals (the crowd), often in return for future products or equity (Mollick, 2014). The fundraising starts with ‘an open call’ posted on a website devoted to the topic by the founder of a new entrepreneurial project for the provision of financial resources. The entrepreneur provides a pitch, usually a business plan, detailing the business activities and objectives, and how he or she will use the funds that are sought to be raised. The Internet platform informs funders what, if anything, he or she will receive in return for the capital contribution. The crowdfunding therefore refers to the efforts by entrepreneurial individuals to fund their new ventures by relying on relatively small contributions from a relatively large number of individuals using the Internet, without standard financial intermediaries.

The increasing success of the crowdfunding model in funding new ventures has recently spurred the interest of scientific research to investigate this phenomenon. Particularly, a few theoretical studies have focused on the comparison of different forms of crowdfunding (Belleflamme et al., 2014). Empirical studies have been also developed to examine the dynamics of crowdfunding and the determinants of success of crowdfunding campaigns (Ahlers et al., 2015; Colombo et al., 2015; Cordova et al., 2015; Frydrych et al., 2014; Mollick, 2014). Other works have explored the behaviour of the backers, that is, the contributors of the crowdfunding campaigns (Agrawal et al., 2015; Burtch et al., 2013; Cholakova and Clarysse, 2015; Kuppuswamy and Bayus, 2015; Mollick and Nanda, 2016; Ordanini et al., 2011). Although evidence exists that local characteristics affect entrepreneurs’ ability to attract external financing (Guiso et al., 2004a, 2004b), only few studies in the crowdfunding literature have explored the role of geography in the crowdfunding of early-stage entrepreneurial projects, showing that geographical proximity between proponents and backers helps attract contributions as it reduces information asymmetries between the two parties (Agrawal et al., 2015; Chan et al., 2018; Mollick, 2014; Mollick and Robb, 2016). However, despite the previous works have certainly enhanced our understanding of the crowdfunding phenomenon, less attention has been paid to the intriguing relationship between the success of the crowdfunding campaign and the founder’s location within a geographical cluster, where geographical clusters can be defined as geographical agglomerations of firms in particular, related, and/or complementary, activities, sharing a common vision, and exhibiting horizontal, vertical intra- and/or inter-sectoral linkages, embedded in a supportive socio-institutional setting, and cooperating and competing in national and international markets (Pitelis, 2012). Exploring if and how the location of a crowdfunding projects within a geographical cluster (GC) affect the success of a crowdfunding campaign is an interesting addition to this literature and relevant to the current debate on crowdfunding.

The present article helps to fill this gap by answering this main research questions: Does exist a relationship between the location of a project within a GC and its probability to succeed in a reward-based crowdfunding campaign? To address this question, I elaborate a conceptual framework based on a set of testable hypotheses, stemming from the literatures on entrepreneurship and economic geography, about the role of geographical clusters on hosting early-stage entrepreneurial projects that successfully raise funds via crowdfunding. The study contributes to the literature on new economic geography and to the emerging field of crowdfunding and specifically to the success drivers within campaigns, by investigating the attractiveness of GCs towards reward-based crowdfunding entrepreneurial projects and defining the key GC features that drive a successful crowdfunding campaign.

The article is organized as follows. The next two sections briefly review the different crowdfunding models and the literature background. The fourth section provides the theoretical framework and develops the research hypotheses. The fifth section illustrates the methodology and the data employed to empirically test the hypotheses. The sixth section reports and discusses the econometric findings, while the last section concludes with implications and plans for future research.

Crowdfunding models: An overview

Distinct models of crowdfunding are in use, differing each other mainly in terms of the return to investors, namely what the contributor receives from the entrepreneur in exchange for his/her capital (e.g. Tomczak and Brem, 2013; Zhang, 2013). Individuals composing the crowd generally receive rewards as material compensation, often in the form of monetary rewards (Vukovic et al., 2009), or as immaterial compensation, in the form of social acknowledgment (Kazai, 2011). In the case of material compensation, the reward can consist of monetary payments when the project initiators agree to refund the paid amount directly. This can also occur indirectly with rewards composed of products or services (Pelzer et al., 2012).

Overall, scholars divide crowdfunding into four models: donation-based crowdfunding, lending-based crowdfunding, equity-based crowdfunding and reward-based crowdfunding (e.g. Giudici et al., 2012; Leimeister, 2012).

Under the donation-based crowdfunding model, contributors receive nothing for their contribution, ‘not even the eventual return of the amounts they contributed’ (Bradford, 2012). Investors in exchange for their contribution receive just a social reward (e.g. acknowledgements). Although the majority of donation sites are for charities and non-profit institutions, a few websites seek donations for businesses (Griffin, 2013). These represent a small proportion of overall crowdfunding activity.

In the lending-based crowdfunding, entrepreneurs post loan-requests to finance their business. The model refers to those projects borrowing money from the crowd with a predetermined payback amount and period. Two examples of lending crowdfunding sites are Kiva (http://www.kiva.org/) and Prosper. The former does not offer interests: entrepreneurs post loan-requests on the Kiva site for lenders support, lenders crowdfund the loan in increments of US$25 or more, when the fundraising is complete the borrower repays the loan and lenders use repayments to fund new loans, donate or withdraw the money. Differently from Kiva website, the US-based example, Prosper, offers interests. Lenders purchase notes issued by the sites, which use those funds to lend through WebBank or PayPal to borrowers. Transaction fees and interest on loans depend on the borrowers’ credit risk.

In the equity-based crowdfunding, entrepreneurs ask individuals to finance the project in exchange for a share of equity securities (Belleflamme et al., 2014). Here, the crowd buys shares of the fundraised company, thus taking the role of equity stakeholders with the goal of profit-sharing in the future (Kraus et al., 2016). Although equity-based crowdfunding is projected to potentially change the landscape of venture financing most significantly, the corresponding investment regulations and legislations for equity-based crowdfunding are still not well defined in the majority of countries and they have only been recently approved in United States.

Differently from the other models, the reward-based crowdfunding offers both material and immaterial compensation. Rewards, in fact, could range from notes of thank you to small tokens of appreciation. Other rewards comprise the product that will be commercialized by the entrepreneur if the project is successful. In this case, funders can benefit from pre-selling or pre-ordering, since sites offer a pre-purchase option that enables contributors to receive the financed project or product before publication or market entrance, often at a reduced price (Griffin, 2013). Leading reward and pre-purchase sites include Kickstarter (http://www.kickstarter.com/) and IndieGoGo. Reward-based projects are often non-profit organizations, for example a registered association (this is an ‘E.V.’ in Germany) (Kraus et al., 2016). Based on existing research, reward-based projects tend to be more successful than other crowdfunding models (Belleflamme et al., 2013). For its innovativeness respect to the lending-based model, which can be compared to traditional bank lending, and its higher rate of success respect to other crowdfunding models, the reward-based model is the focus of most researchers (e.g. Chan et al., 2018; Colombo et al., 2015; Mollick, 2014) including this study.

Literature background

Success factors of crowdfunding campaigns

Due to the remarkable success that the crowdfunding model has achieved as a viable method of funding new ventures, an increasing number of academic studies are recently investigating this phenomenon and the opportunities related to it (Conway, 2013). Particularly, a few theoretical studies have focused on the comparison of different forms of crowdfunding (Belleflamme et al., 2014). Empirical studies have been developed to examine the advantages offered by crowdfunding over more traditional sources of early-stage capital for entrepreneurs seeking funding (Culkin et al., 2016) and its role in sustaining innovation (Gupta et al., 2017). Others have explained the dynamics of crowdfunding and the determinants of success of crowdfunding campaigns (Ahlers et al., 2015; Belleflamme et al., 2013; Colombo et al., 2015; Mollick, 2014). Cordova et al. (2015) analyse what factors influence the probability of getting more than 100% of the required funds and whether those factors can influence the quantity of overfunds in successful projects. The findings show that three variables heavily influence the ability to gain the required amount or more: success is negatively related to the logarithm of the amount requested, while it has a positive relation with the duration of the project – being the number of days available for making donations – and with the logarithmic transformation of the mean amount collected per day. Mollick (2014) analyses the relationship between the quality of the project and the odds of success. Signals such as the number of updates given by the founder and the presence of a presentation video are related to a higher success rate, while spelling errors lead to a lower probability to raise the desired amount of funds. Frydrych et al. (2014), by using a data set collected from Kickstarter, investigate the characteristics of successful projects, considering legitimating signals and content. Chan et al. (2018) analyse the effects of project, product category, entrepreneur and location on crowdfunding outcomes. They find that the project and the characteristics of the entrepreneur have greater effect on reward-based crowdfunding success, whereas the product category and the location have lower but still significant effects.

Other works have explored the behaviour of the backers, that is, the contributors of the crowdfunding campaigns (Agrawal et al., 2015; Burtch et al., 2013; Cholakova and Clarysse, 2015; Kuppuswamy and Bayus, 2015; Mollick and Nanda, 2016; Ordanini et al., 2011), as well as the role of the entrepreneurs on the crowdfunding success. Löher et al. (2018) investigate the role of the entrepreneurs’ financial commitment. Bernardino and Santos (2016) study the role played by social entrepreneurs’ personality traits on the choice between the traditional donation model and social crowdfunding to finance social projects.

The role of geographical location

Only few studies have been developed to explore the role of geography in the crowdfunding of early-stage entrepreneurial projects, evidenced that despite the omnipresent reach of the Internet, geography matters in crowdfunding. The study of Agrawal et al. (2015), focused on the crowdfunding in the recording industry, analysing the campaigns on the Sellaband online platform1 shows that the geography of crowdfunding varies from that of other forms of funds raising, mitigating the distance effects and virtually reducing the distance between founder and funders. This makes the distribution of crowdfunded projects more uniform than that of venture capital investments, providing an additional – and sometimes pivotal – source of funds to those ventures located in remote regions (Agrawal et al., 2015). Mollick (2014), analysing the role played by geography in affecting the probability of success of a crowdfunding campaign, finds an uneven distribution of the successful projects, as well as that ‘the project mix of founders echoes the cultural products of the cities in which they are based’ (Mollick, 2014: 9). However, as Mollick and Robb (2016) point out, this does not prove that there is not a common path in the location of successful crowdfunded projects: the phenomenon indeed presents a tendency to clustering, although not as marked as for venture capital investments. It appears indeed that successful projects are located in a limited area, especially when it comes to some categories of project – like the high-tech ones. Lin and Viswanathan (2013), on the other hand, investigate a similar issue, demonstrating the existence of a home bias due to the fact that funders prefer to invest on projects located in their own state, indicating that the geographic dispersion of crowdfunding isn’t accidental. Altogether, these studies suggest that although crowdfunding mitigates geography-related frictions (Agrawal et al., 2015), location influences crowdfunding outcomes: projects that were closer to banks attracted less funding from local investors (Kim and Hann, 2015), whereas those located where there are more creative population enjoy a higher rate of success (Mollick, 2014). Location effects also take shape through local altruism or by promoting projects that share similar values with local communities (Josefy et al., 2017). Recently, Giudici et al. (2018) show that some salient characteristics of the geographical area in which entrepreneurs reside affect the success of the crowdfunding projects they propose. Specifically, using a data set of 618 proponents that launched 457 crowdfunding projects on 13 Italian reward-based platforms, they find that the existence of social relations among people residing in a specific geographical area increases the likelihood of success of reward-based entrepreneurial projects.

All these papers offer valuable contributions by identifying the location-level determinants influencing crowdfunding success; however, they do not consider a specific characteristic of regions, namely the existence of GC. GCs are geographically defined production systems, characterized by a large number of highly specialized firms and associated institutions, integrated through a complex network of inter-organizational relationships and linked by commonalities and complementarities (Becattini, 1990; Maskell, 2001; Porter, 1998), where positive agglomeration economies arise. By neglecting this issue, the existing literature does not investigate if and to what extent the belonging of a start-up project to a GC may be related to its ability to attract funding through crowdfunding.

Geographical clusters have attracted much attention in the academic literature. The large number of studies developed within different streams of research – social sciences, regional economics, economic geography, political economy and industrial organization – have explained the reasons of GC competitiveness and recognized that the existence of GCs within a region is a distinctive characteristic of that region that positively affects its overall performance (Rothgang et al., 2017). Specifically, it has been proved that GCs allow for promoting national, regional and local competitiveness, innovation and growth (Porter, 1998; 2000) and sustain the competitive advantages of regions by fostering innovation (Malecki, 1981; Saxenian, 1994; Sweeney, 1987). Prior studies have highlighted significant effects of location advantages because regional networks prompt information flows (Stuart and Sorenson, 2003) enabling spillover effects of start-up success and thus affecting the creation of new firms at regional level (Rocha and Sternberg, 2005). In addition, other studies shown the investor’s tendency to invest in locally headquartered firms (Coval and Moskowitz, 1999), thus demonstrating that GCs assure the success of traditionally funded entrepreneurial ventures (Chen et al., 2010; Owen-Smith and Powell, 2004; Shane and Cable, 2002; Stuart and Sorenson, 2007).

Drawing on this, one can argue that studying the geography of crowdfunding without considering the effects of geographical proximity of founders seeking crowdfunding and, in particular, the localization of a start-up project in a GC may not give rise to some interesting insights and bias the results. Moreover, despite the considerable body of existing theoretical and empirical GC studies, to the best of our knowledge, none of these has investigated the effects of GC on successful crowdfunding.

Both the streams of study induce to think that there may actually be a relationship between the location of a project within a GC and its probability to succeed in a crowdfunding campaign, although, to our knowledge, there are no studies to date that have investigated this issue.

In this article, we attempt to fill this gap, by answering at the following research questions: Do GCs foster the crowdfunding of new entrepreneurial ventures? Is being embedded in a GC positively related with a successful fundraising on crowdfunding platforms? Does the level of specialization and the size of GCs influence the success gained on crowdfunding platforms? Does the GCs’ level of innovativeness affect on the success of crowdfunding campaigns?

In doing this, this article identifies a novel driver of success on top of those highlighted in the crowdfunding literature: the localization of the crowdfunding projects within a GC and its characteristics. In so doing, it defines what type of ecosystem is required to help the success of crowdfunding campaigns.

This evidence adds both to the stream of the crowdfunding research, which has documented that the geographic concentration of new venture activity is still apparent in crowdfunding, as crowdfunding projects are not evenly distributed across the country, and to the economic geography literature, which has proved the positive effects of GCs on the development of entrepreneurial firms as well as on the fundraising process for new ventures.

Theoretical framework

Numerous studies have examined the effect of GCs on entrepreneurship (Beugelsdijk, 2007; Cooke, 2016; McRae-Williams et al., 2007; Pascal and Stewart, 2008; Westlund and Bolton, 2003). New entrepreneurial firms are attracted to clusters by the pool of skilled and specially trained personnel, access to risk capital, favourable demand conditions, reduced transaction costs and motivational factors, such as prestige and priorities (Krugman, 1991; Marshall, 1920; Storper, 1997). Locating in a GC may enhance a company’s visibility, legitimacy and survival chances (Pouder and St. John, 1996). Furthermore, the presence of complementary economic activities within GCs creates externalities that enhance incentives and reduce barriers for new business creation (Delgado et al., 2010) as well as the co-location of companies, customers, suppliers and other institutions increases innovation opportunities while amplifying the pressure to innovate (Inkinen, 2015; Porter, 2000). Thus, the founder’s location within a GC may convey information on the potentiality of its business and thus can constitute a credible basis upon which funders evaluate the new entrepreneurial project that they intend to finance. Whit this regard, studies in the entrepreneurial finance literature evidence the positive effect of space on early-stage venture capital investments. According to Sunny and Shu (2019), regional clustering, especially spatial proximity to an industry cluster, reinforces the relationship between capital and firm formation due to spillover in knowledge, human capital and service networks. Additionally, research in various countries shows that venture capital investments are geographically clustered (e.g. Chen et al., 2010; Florida and Kenney, 1988; Florida and Smith, 1990; Martin et al., 2002, 2005; Mason, 1987; Mason and Harrison, 1991, 2002; Zook, 2002). This is explained in terms of a combination of both supply and demand-side factors. On the supply side, venture capital funds are clustered in a small number of cities. On the demand side, the uneven geography of venture capital investments clearly reflects the uneven geography of entrepreneurial activity – and of growth potential firms in particular – and the clustering of technology-based firms (Mason and Pierrakis, 2013). Building on these arguments, we formulate our first hypothesis:

H1: Crowdfunding campaigns are more likely to be successful if new entrepreneurial projects are developed within geographical clusters.

Despite a large number of studies shown that GCs enhance the probability of entry, survival and growth of new firms (Beaudry and Swann, 2001; Dumais et al., 2002; Pe’er and Vertinsky, 2006; Rosenthal and Strange, 2005; Stough et al., 1998), other studies indicate that the performance of new firms is negatively affected by locating in a GC or even more that location in a GC decreases the survival chances of new firms (Folta et al., 2006; Sorenson and Audia, 2000). Different arguments can be used to explain such a potentially negative effect. One can resort to the literature on agglomeration economies that have investigated the impact of different types of agglomeration economies on economic growth. This field of studies has seen two opposing theories. One suggests that the concentration of a particular industry within a specific geographic region induces positive externalities – MAR externalities² – that lead to higher rates of growth. The other appraises the virtues of diversified economies or Jacobs’s externalities (e.g. Glaeser et al., 1992) and suggests that regions with a diversified set of industries will be characterized by high economic growth, because local diversity sparks creativity, triggers new ideas, induces knowledge spillovers and provides valuable resources that are required for innovation to take place. This debate, however, has been resolved by several empirical studies (Boschma and Iammarino, 2009; Greunz, 2004; Henderson et al., 1995; Martin and Ottaviano, 1999), showing that both MAR and Jacobs externalities positively affect regional economic growth and innovation. Put it differently, Porter (2003) argues that specialization in clusters of related industries, not in industries per se, should lead to better regional performance and that a range of overlapping clusters (caused by related industries that belong to more than one cluster) may be more beneficial for regional growth than having a diversity of clusters that are unrelated. Introducing the notion of regional related variety that tries to capture a delicate balance between technological proximity and distance across sectors in a region, other authors suggest that technological and industry relatedness is a major asset for economic growth in regions (Boschma and Frenken, 2011; Frenken et al., 2007; Neffke et al., 2011).

This is in line with an expanding economic geography literature that has investigated the impact of the different dimensions of proximity on learning, innovation and more in general economic growth in regions. Studies on this issue argue that proximity facilitates interactive learning, thus stimulating innovation, but may also have negative impacts on innovation due to the problem of lock-in. Accordingly, not only too little proximity between local firms active in different industries but also too much proximity due to regional specialization may be detrimental to interactive learning and innovation (Boschma, 2005).

Building on these arguments, we formulate our second hypothesis:

H2: A moderate level of GC industry variety increases the likelihood of success of crowdfunding projects: the GC level of specialization has an inverse-U-shaped effect on the success gained on crowdfunding platforms.

A wide and consolidated literature has sought the determinants of variation in new firm formation on a regional basis (Audretsch and Fritsch, 1994; Guesnier, 1994; Keeble and Walker, 1994; Reynolds, 1991; Reynolds et al., 1993; Sutaria, 2001). Among these determinants, the industrial intensity has been recognized positively linked to firm birth rates (Armington and Acs, 2002; Reynolds, 1991; Reynolds et al., 1994). Regions more densely populated have more start-up activity because of the concentration of several firms in a single location ensures a pooled market for workers with industry-specific skills, offers a more developed infrastructure of services, supports the production of non-tradable specialized inputs and generates informational spillovers that give clustered firms a better production function than isolated producers have (Armington and Acs, 2002; Krugman, 1991). On the other side, there is an economic explanation of the potentially negative effect of GCs on the performance of new entrepreneurial firms. Namely, while moderate levels of clustering are beneficial for new firms, very strong clusters densely populated might produce adverse effects due to congestion and hypercompetition among firms for resources and personnel (Beaudry and Swann, 2001; Folta et al., 2006; Prevezer, 1997).

From these arguments, the third hypothesis being tested is as follows:

H3: The industry density (ID) and industry growth (IG) in a GC is expected to have a curvilinear effect (inverse U-shape relationship) on the success of a crowdfunding campaign.

The relationship between regional innovation, economic growth and new firm formation has been the topic of a number of theoretical and empirical research efforts in the last years (Chen et al., 2010; Florida and Smith, 1993; Martin et al., 2005; Mason and Harrison, 1991, 2002; Mason and Pierrakis, 2013; Zook, 2002). An influential line of research recognizes that creativity and innovation are the underlying forces of entrepreneurship and, building on this, suggests that entrepreneurship is positively associated with regional environments that promote creativity and innovation (Jacobs, 1961; Lee et al, 2010; Lucas, 1988).

Lee et al. (2010) empirically show that new firm formation is strongly correlated with creativity. Kirchhoff et al. (2002) hypothesizing that new firms will tend to form in regions characterized by high level of innovative activity empirically demonstrate that university research and development expenditures are positively related to new firm formations. Almeida and Kogut (1997) find that the success of start-up firms is strongly linked to the richness of the technological opportunities offered by the local knowledge networks they are tied into. Furthermore, several papers in entrepreneurial finance literature document an uneven geography of venture capital investments that clearly reflects the uneven geography of entrepreneurial activity and, in particular, the geography of technology-based clusters (Avnimelech et al., 2007; Chen et al., 2010; Mason and Pierrakis, 2013).

Based on the extensive literature that highlights the broader relationship between entrepreneurship and the regional innovation system (e.g. Audretsch, 1995; Feldman, 2001) we formulate the fourth hypothesis:

H4: A higher level of GC innovativeness increases the likelihood that a new entrepreneurial venture successfully raises funds in a crowdfunding campaign.

To test these hypotheses, an econometric analysis on 792 crowdfunded projects is applied. This is presented in the rest of the article.

Data and methodology

Data

To test the above hypotheses, an empirical research has been conducted on a data set of reward-based crowdfunded projects. Data about crowdfunding campaigns – such as the total amount collected and the location where they were started – have been retrieved on Kickstarter and Indiegogo in a period of 2 months (January and February 2018), the two largest and dominant reward-based crowdfunding platforms. The need for a coherent data set, characterized by uniformed data, urged to consider only those projects started in the United States, while excluding those developed in Europe, China, Australia, Japan or anywhere else in the world. However, this is not expected to alter the results of the analysis for two main reasons. First, the percentage of US-based projects over the total number of crowdfunding requests is very high,3 so it is presumable that an eventual path shown in the US crowdfunding would be applicable to other regions in the world. Second, the phenomenon started in the United States, and it is therefore reasonable to assume that it is far more developed in North America than in other regions and that the evolution pattern in the world will follow – at least for the first period – the pattern of the American crowdfunding model.

The main source of data regarding the geographical clusters has been the US Cluster Mapping Tool (CMT), powered by the Institute for strategy and competitiveness of the Harvard Business School and the US Economic Development Administration. The CMT proposes a classification of clusters according to the North American Industry Classification System, so that each cluster corresponds to an area of specialization referring to a single sector or to a small group of connected industries. The geographic unit of analysis is the Bureau of Economic Analysis’s economic areas (EAs), which define the relevant regional markets surrounding metropolitan or micropolitan statistical areas. EAs consist of one or more economic nodes – metropolitan or micropolitan statistical areas that serve as regional centres of economic activity – and the surrounding counties that are economically related to the nodes through commuting patterns. The EAs represent the relevant regional markets for labour, products and information. They are mainly determined by labour commuting patterns that delineate local labour markets and that also serve as proxies for local markets where businesses in the areas sell their products (Johnson and Kort, 2004).

Opting for wider unit – such as states – would have ended up with losing the differences in the economic environment that may occur between two regions, as much as close they can be. On the other hand, choosing a political entity – such as counties – would have left out of the analysis many of the economic aspects that can be shared between two

EAs seemed therefore the best suiting option, since they are enough wide to consider the possibility that economy may not follow the borders tracked by politics and not excessively narrowed to avoid cutting out those synergies that undeniably occur between different districts sharing common economic infrastructures, eventually leading to ignore some of the features that could affect the crowdfunding process. Furthermore, because EAs capture the boundaries of labour pools as described by commuting patterns between work and home, they provide the relevant geographic contours of factor pools associated with skilled labour, specialized suppliers and knowledge (Alcácer and Chung, 2014), thus well fitting the definition of GCs.

Data set construction

To have a homogeneous data set, in terms of the amount requested and the amount actually reached, only projects near their deadline have been examined. In particular, I select all the crowdfunding campaigns with at most 48 h remaining, thus collecting 882 crowdfunding projects. Such a choice allows taking into account the different behaviours of donors in terms of the timing of the donation (Agrawal et al., 2015; Kuppuswamy and Bayus, 2015).

The projects selected on Kickstarter and Indiegogo span within a list of 17 categories, ranging from arts (e.g. theatre, music, etc.) to technology. Based on the information about the location where projects were started gathered on Kickstarter and Indiegogo, each project has been assigned to an EA. We collected reward-based projects spread all over the country, covering the 45% of the total EAs (i.e. 81 of 179).

The 882 projects have than classified in two main categories depending on the nature of the project: ‘technology’ and ‘fashion and creative’. The former includes projects related to technological innovation, the latter is made of creative products and projects related to fashion, apparel, art and so on. These two categories allow catching the opposite nature of the proposed projects based on the underlying features of the product, namely a technology-based and a creative-based product. In doing this, I have excluded 36 projects and by eliminating the outliers the final sample consists of 792 projects (37.4% belonging to the category ‘technology’ and 62.6% to the category ‘fashion and creative’). The size of our final sample is in line with previous studies on crowdfunding (e.g. Ahlers et al., 2015; Giudici et al., 2018; Kraus et al., 2016; Zheng et al., 2014).

Tables 1 and 2 show the distribution of the 792 reward-based crowdfunding projects in the sample by categories and geographic location, respectively.

Table 1.

Characteristics of RB crowdfunding projects by category.

	All projects		Successful projects		Success rate*
	N	%	N	%	%
Project’s category
Video production and distribution	193	24.4	71	15.2	36.8
Information technologies and analytical instruments	157	19.8	142	30.5	90.4
Recreational and small electric goods	126	15.9	110	23.6	87.3
Apparel	58	7.3	29	6.2	50.0
Printing services	58	7.3	34	7.3	58.6
Performing arts	60	7.6	24	5.2	40.0
Music	57	7.2	12	2.6	21.1
Leather and related products	16	2.0	15	3.2	93.8
Food processing and manufacturing	21	2.7	4	0.9	19.0
Education and knowledge creation	17	2.1	5	1.1	29.4
Medical devices	9	1.1	6	1.3	66.7
Downstream metal products	6	0.8	3	0.6	50.0
Textile manufacturing	7	0.9	5	1.1	71.4
Footwear	2	0.3	2	0.4	100.0
Production technology and heavy machinery	2	0.3	1	0.2	50.0
Trailers, motor homes and appliances	2	0.3	2	0.4	100.0
Furniture	1	0.1	1	0.2	100.0
Total	792	100.0	466	100	58.8
Project’s macro-category
Technology	296	37.4	261	56.0	88.2
Fashion and creative	496	62.6	205	44.0	41.3
Total	792	100.0	466	100.0	58.8

RB: reward-based.

* Success rate calculated as the ratio between the number of successful projects and the total number of projects in the corresponding category.

Table 2.

Characteristics of RB crowdfunding projects by localization.

	All projects		Successful projects		Success rate*
	N	%	N	%	%
Project’s localization (EAs)
Los Angeles	130	16.4	79	17.0	60.8
New York	129	16.3	67	14.4	51.9
San Jose – San Francisco	113	14.3	92	19.7	81.4
Seattle	41	5.2	33	7.1	80.5
Atlanta	30	3.8	19	4.1	63.3
Boston	28	3.5	11	2.4	39.3
Chicago	27	3.4	13	2.8	48.1
San Diego	21	2.7	16	3.4	76.2
Washington	18	2.3	4	0.9	22.2
Portland	17	2.1	12	2.6	70.6
Salt Lake City	17	2.1	14	3.0	82.4
Others (70 EAs)	221	27.9	106	22.7	48.0
Total	792	100.0	466	100.0	58.8
Project’s localization (region)
West	380	48.0	260	55.8	68.4
Midwest	74	9.3	37	7.9	50.0
Northeast	201	25.4	101	21.7	50.2
South	137	17.3	68	14.6	49.6
Total	792	100.0	466	100.0	58.8

RB: reward-based; EA: economic area.

* Success rate calculated as the ratio between the number of successful projects and the total number of projects in the corresponding category.

When examining the projects’ localization, we observe an uneven geographical distribution (more than 70% of the projects are localized in 11 EAs). Similarly, substantial polarization exists in terms of projects’ category, with the 60% of the projects developed in only three categories, that is, ‘video production and distribution’, ‘information technologies and analytical instruments’ and ‘recreational and small electric goods’. Furthermore, tables show that the success rate is higher for the project’s macro-category ‘technology’ (88.2%), whereas substantial heterogeneity exists in success rates by the projects’ location, with those located in the West Region having the highest success rate (68.4%).

Variables

Dependent variable

To test our hypotheses, as the phenomenon under study is the ability of a project to collect funds on a crowdfunding platform, we use as dependent variable the funding amount at 48 h remaining by the end of each campaign. In line with previous studies (e.g. Colombo et al. 2016; Vismara, 2016), the Funding_Amount variable is measured as the ratio between the amount pledged and the amount requested, namely the percentage of target capital collected. This variable is a fine-tuned measure of crowdfunding campaign success that indicates how much capital has been raised. We fixed a threshold value equal to 1 to establish the success of the campaign:

when Funding_Amount ≥ 1, the crowdfunding campaign is successfully,

when Funding_Amount is comprised between 0 and 1, the campaign is unsuccessfully as it does not reach the target.

Independent variables

The explanatory variables have been introduced to test our hypotheses.

To test the first hypothesis, we introduce a dummy variable (LQ _i ) taking value one if the location quotient is greater than 1, zero otherwise. Location quotients have been used by geographers and economists for a number of years to gauge a region’s specialization in a given industry (Blair, 1995; Stimson et al., 2006). The location quotient compares an area’s employment structure with a larger geographic area, such as the state or nation. It is calculated as follows:

{LQ}_{i} = \frac{E_{i, j} / E_{i}}{E_{US, j} / E_{US}},

where

E_i,j is the number of employees in industry j for area i

E_i is the is total number of employees in area i

E _{US, j} is the number of employees in industry j for the United States

E _US is the total number of employees in the United States

As such, location quotient greater than 1 denotes the existence of a GC specialized in a given industry (Carrol et al., 2008; Miller et al., 2001; Wennberg and Lindqvist, 2010).

To test our second hypothesis and measure the level of GC specialization, we use the employment share in industry j, given by

{Specialization}_{i} = \frac{E_{i, j}}{E_{i}} .

This index offers a measure of the specialization of the cluster. Its maximum values (close to 1) happen when all employees in the area working in the same industry, hence a high specialization exists. Its minimum values happen when there is an equal distribution of employees over all industries, hence high variety and no specialization exist.

To test our hypothesis 3, we introduce two variables: the ID and the IG. The ID index (ID _i ) is measured as the number of firms in the GC specialization industry divided by the total area. The number of firms located in the area has been approximated by the number of establishments – where an establishment is a physical location where the business is conducted.

As an indicator of the industry growth (IG _i ), we use the annual rate of new firm formation in the GC specialization industry, approximated by the establishment growth rate, measured as follows:

{IG}_{i} = \frac{N_{i j, t} / N_{i j, 0}}{N_{i j, 0}},

where

N_j,0 is the number of establishment in industry j for area i at year 0

N_j,t is the number of establishment in industry j in area i at year t.

These two variables suggest the existence in the region of some underlying economic benefits that can create entrepreneurial opportunities, but they also offer an insight of the competition existing within the area.

The variable adopted as a proxy for the level of innovativeness of GC relates to the patents registered in the area (Patent_ratio _i ). Patents are widely used to assess the ability of an industry, a firm and a geographical area to come up with valuable and marketable innovations. Considering that different industries provide different opportunities to register patents, we have defined the variable as follows:

{Patent_Ratio}_{i} = \frac{P_{i, j}}{{max}_{k = 1 \dots m} [P_{k, j}]},

where

P_{i, j} is the number of patent registered in industry j for area i

max _{k = 1…m} [P_{k, j}] is the highest number of patents registered in a k area in industry j.

The closer this indicator gets to 1, the more innovative is that region compared to others.

Control variables

In addition to the variables of interest, I used a number of control variables that prior studies have indicated as important to study the potential of a region in encouraging the start-up of new enterprises. In particular, I used (1) the full population density of the GC (ID_full), measured as the total number of firms in the area divided by the total area; (2) the full IG (IG_full) calculates by the annual rate of new firm formation in the GC for all industries; (3) the total number of patents registered in the GC (Patent). The first two variables control for the industry size of GCs and their entrepreneurial growth and capture urbanization economies. The third variable explains the effect of clusters on the innovative performance of firms (Porter, 2003).

In addition, I used another local-level control, the local projects (LPs), namely the natural logarithm of the total number of reward-based crowdfunding projects launched within each area and already ended (i.e. projects whose funding deadline had expired). The inclusion of this variable allows controlling a potential bias due to the fact that the number of successful reward-based projects in a GC could be higher merely because the total number of posted projects is higher (Giudici et al., 2018).

The econometric model

An ordinary least squares model is applied to estimate what role, if any, GCs play in the crowdfunding success, measured by the percentage of funds raised over the amount requested, and which GC features may foster the crowdfunding of innovative start-ups.

Empirical results

Descriptive statistics and correlation coefficients are reported in Table 3. The correlation matrix shows that the variables are not significantly correlated, furthermore the variance inflation factor test has been performed to check for multicollinearity between independent variables. All variables have values lower than 5 in this test. Therefore, it is expected that multicollinearity does not bias the regression results.

Table 3.

Descriptive statistics and correlation matrix.

Variables	Funding_Amount	LQ	Specialization	ID	IG	Patent_ratio	ID_full	IG_full	Patent	LPs
Funding_Amount	1
LQ	0.12	1
Specialization	0.02	0.49	1
ID	−0.03	0.43	0.67	1
IG	−0.06	−0.00	0.17	0.26	1
Patent_Ratio	0.14	0.40	0.33	0.43	−0.17	1
ID_full	−0.11	0.38	0.27	0.00	0.07	−0.02	1
IG_full	−0.04	0.08	0.02	−0.13	0.46	0.46	−0.00	1
Patent	0.28	0.29	0.12	0.09	−0.24	0.45	−0.04	−0.01	1
LPs	0.13	0.27	0.02	0.04	0.02	0.06	−0.03	−0.02	0.62	1
Observations	792	792	792	792	792	792	792	792	792	792
Minimum	0	0	0.02	0.01	−0.08	0	0.17	−0.52	0	4
Maximum	44.85	1	11.30	0.31	0.11	1	14.94	0.65	9559	10
Mean	7.79	0.52	3.86	0.09	−0.00	0.38	8.50	0.01	923.37	7
Standard deviation	11.32	0.50	1.86	0.05	0.02	0.37	4.12	0.03	2544.09	1.39
VIF	2.81	1.61	2.07	2.81	1.36	1.85	1.95	1.02	1.51	2.01

ID: industry density; IG: industry growth; LQ: location quotient; LPs: local projects; VIF: variance inflation factor.

The empirical findings obtained from the estimation are reported in Table 4. First model considers only the effect of being part of a GC. In models 2 and 3, the variable Specialization (model 2) and its squared term (model 3) are introduced to test the hypothesized inverted U-shaped relationship between the industry specialization across GCs and the success of crowdfunding campaigns. Fourth model adds ID and IG to take into account the effects of the size and the entrepreneurial dynamics of GCs. Model 5 takes into account the GC’s innovativeness by adding Patent_ratio. This model is the full model that includes all explanatory variables and control variables. In the last two columns (models 6 and 7) of Table 4, I stratified the data between ‘technology’ and ‘fashion and creative’ projects.

Table 4.

OLS estimation results.

Variables	Model 1(all)	Model 2 (all)	Model 3 (all)	Model 4 (all)	Model 5 (all)	Model 6 (technology)	Model 7 (fashion and creativity)
LQ	6.728*** (0.000)	6.587*** (0.000)	4.842*** (0.000)	3.474*** (0.000)	4.733*** (0.000)	6.077*** (0.003)	5.673*** (0.000)
Specialization		0.593*** (0.000)	0.881*** (0.000)	0.822*** (0.006)	0.966* (0.094)	0.064* (0.062)	0.106* (0.051)
Specialization2			−0.780*** (0.001)	−0.652** (0.077)	−0.966* (0.094)	−0.042* (0.090)	−0.086* (0.066)
ID				−0.017** (0.024)	−0.019** (0.002)	−0.002 (0.288)	−0.001*** (0.006)
IG				−100.106*** (0.000)	−67.472*** (0.000)	−91.231* (0.068)	−82.688*** (0.000)
Patent_Ratio					8.403*** (0.000)	3.754 (0.162)	9.955*** (0.000)
ID_full	−4.590e⁻³ (0.849)	−6.341e⁻³ (0.745)	−3.111e⁻³ (0.205)	−4.052e⁻³ (0.822)	−4.122e⁻³ (0.555)	−5.610e⁻³ (0.333)	−6.310e⁻³** (0.022)
IG_full	21.382** (0.043)	22.435** (0.047)	29.454*** (0.005)	24.156** (0.017)	27.651** (0.006)	145.134 (0.208)	25.639*** (0.001)
Patent	0.002*** (0.000)	0.002*** (0.000)	0.001*** (0.000)	0.002*** (0.000)	0.002*** (0.000)	0.001*** (0.006)	0.001 (0.500)
LPs	1.572*** (0.000)	1.614*** (0.000)	1.721*** (0.000)	1.926*** (0.000)	2.083*** (0.000)	2.103*** (0.003)	1.023*** (0.000)
Observations	792	792	792	792	792	296	496
R2	0.124	0.146	0.207	0.285	0.526	0.211	0.320
Adj-R²	0.090	0.132	0.177	0.192	0.434	0.197	0.288

OLS: ordinary least squares; ID: industry density; IG: industry growth; LQ: location quotient; LPs: local projects; VIF: variance inflation factor. p > |t| in parentheses.

***p < 0.001; **p < 0.05; *p < 0.1.

As an overall result, the regressions show that being part of a GC has a significant effect on the success of a crowdfunding campaign. Hence, following our first hypothesis, being part of a GC increases the likelihood to obtain funds via crowdfunding.

As for the level of specialization, the positive (and significant) coefficients of the explanatory variable across the different models seem to negate hypothesis two. However, by performing a quadratic regression between the Funding_Amount and the level of GC specialization (model 3), we find that the sign of the regression coefficient of the quadratic term – Specialization² – is negative thus indicating an inverted U-shape relationship, implying that the Funding_Amount is low for low level of GC specialization, then increases achieving its peak for moderate level of GC specialization, and decreases as the value of Specialization keeps decreasing. This result thus confirms the second hypothesis. An excess of specialization reduces the GC knowledge variety and creates empty of experience and know-how related to other industries. This might become critical when a project requires, to be developed, a set of entrepreneurial skills, resources and know-how that aren’t limited to the sole sector where the project belongs but range along different industries. A moderate level of intra-industry specialization generate positive externalities that, in turn, increase the probability for a new venture to collect money through a crowdfunding campaign.

Hypothesis 3 is not supported by the results. Coefficients of the variables explaining the effects of the GC ID and IG are both negative and significant. Even the results of a quadratic regression do not show a curvilinear relationship between the variables as hypothesized. Thus the higher the number of firms within a GC, the lower is the likelihood that projects started in that GC will succeed in a crowdfunding campaign. This finding may have a double explanation. Firstly, it may be due to the fact that the intensity of local rivalry, together with the presence of a large number of incumbents, may make it harder for a new venture to emerge and gain a relevant position within the GC. Entrepreneurial ventures started in regions with strong established GCs paradoxically could count on a more limited set of resources, with the most advantageous being captured by the incumbents. A different explanation, according to Giudici et al. (2018), is that in GCs where it easier to start a business, the most promising ideas likely obtain financing through traditional channels, leaving less promising projects to seek crowdfunding.

Finally, results support our hypothesis 4 (the variable Patent_ratio shows an estimated coefficient that is positive and significant at p < 0.001), thus hinting at the existence of a correlation between the success of a crowdfunding campaign and the innovativeness of the area in which the new entrepreneurial project is based.

Hence, the most innovative is the GC the higher is the likelihood that a project raises at least its goal or more than that.

This general pattern holds across the projects’ macro-categories, in fact results from models 6 and 7 resemble the results obtained for all projects shown in model 5.

Conclusion

This article examines if and how the geographical area in which early-stage entrepreneurial projects are located affects the success of a reward-based crowdfunding campaign. In particular, it theoretically discusses the positive relationship between the location of a project within a GC and its probability to succeed in a crowdfunding campaign. Furthermore, employing a data set of 792 reward-based crowdfunded projects, the article empirically shows that being embedded in a GC matters, and that the level of GC innovativeness increases the chance for new ventures to succeed in a reward-based crowdfunding campaign, by raising the amount of requested funds or more.

This study contributes to the literatures on entrepreneurial finance and economic growth and agglomeration in economic geography. To the best of our knowledge, it is the first that analyses the effects of the geography in the crowdfunding of early-stage entrepreneurial projects by explicitly focusing on the role played by GCs on the success of reward-based crowdfunding campaigns.

In particular, the article enriches the stream of studies on the success factors for crowdfunding campaigns, by identifying a novel driver of success, namely the belonging of a start-up project to a GC. In so doing, the article offers also a valuable contribution to the literature on the role of geography in crowdfunding, by providing further support for the idea that space and the characteristics of the geographical area matter in crowdfunding. This work adds to the previous studies by identifying an additional fundamental element of the geographical area that affects the success of a reward-based crowdfunding project, namely the existence of a GC. The advantages of GCs, including agglomeration and external economies from co-location, the concentration of skilled human resources, social embeddedness and capital that reduce transaction costs due to trust, the flexibility and entrepreneurship of small firms involved in clusters, economies of diversity, as well as the existence of untraded interdependencies, are recognized by the potential backers who are therefore more willing to invests.

Results of this study are consistent with prior studies that have found that social capital, personal social networks (Colombo et al., 2015; Mollick, 2014; Zheng et al., 2014) and local social relationships (Giudici et al., 2018) are important drivers of crowdfunding success, because of social capital and networks are distinctive characteristics of GCs.

As for the economic geography literature, the extant literature on GCs emphasized their advantages in attract investment and stimulating the new firms formation. The article adds to these contributions by showing not only that GCs can increase the likelihood for a new entrepreneurial project to succeed in a crowdfunding campaign but also which GC’s characteristics are more beneficial for the success of crowdfunding campaigns.

While these results are intriguing, they have a number of limitations. The analysis did not consider a number of factors that may influence a successful crowdfunding. Specifically, other independent and control variables that characterize the proponents, the geographical clusters and the backers could be added to improve the model. Information on proponents’ social capital and personal social networks would allow to capture additional important aspects that come from being located in a GC, namely trust, shared norms and values. Information on the presence of local bank branches will allow controlling for favourable local conditions. Information on the localization of the backers and their relationships with the proponents will allow considering the effects of both geographical and social proximity among proponents and backers.

Further analysis will be devoted to address this issue by controlling for the above relevant aspects that could influence investors’ decisions in crowdfunding new entrepreneurial projects.

Finally, this study focuses on reward-based crowdfunding, crowdfunding projects launched on US platforms and developed within US geographical clusters. It would be worth enlarge the analysis by considering different crowdfunding models. Of particular interest could be to distinguish between equity crowdfunding and reward-based crowdfunding, as one could expect that because GCs attract professional investors and high growth companies they could more favour the success of equity crowdfunding rather than reward -based crowdfunding (prevalent in more deprived communities). Further analysis will be devoted to address this issue. Finally, it would be interesting to repeat the analysis for other countries with diverse culture and where it is strong the presence of GCs.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Nunzia Carbonara

Notes

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