domain: qfin shs

text:
Benjamin Le Pendeven
email: blependeven@audencia.com

Armin Schwienbacher
email: armin.schwienbacher@skema.edu.

Karima Bouaiss

Gilles Garel

Alexis Collomb

Equity Crowdfunding: The Influence of Perceived Innovativeness on Campaign Success *

Keywords: equity crowdfunding, entrepreneurial finance, start-ups, innovativeness

This article examines the impact of perceived innovativeness on the success of equity crowdfunding campaigns. Building on the investor perspective, we hypothesize a positive impact of perceived innovativeness on the campaign outcome. Our database covers 191 campaigns launched in France on different platforms, drawing on over 2,000 individual assessments of the perceived innovativeness of the start-ups involved, carried out by 176 participants with diverse backgrounds. We find support for our hypothesis from the investor perspective in that highly innovative projects attract more crowd investors and, in turn, raise more capital. We contribute to the understanding of how the crowd makes investment choices.

INTRODUCTION

Crowdfunding is an emerging way to finance small, early-stage firms. It enables groups of individuals to pool money together in projects advertised on the Internet through dedicated platforms [START_REF] Belleflamme | Crowdfunding: Tapping the right crowd[END_REF][START_REF] Mollick | The dynamics of crowdfunding: An exploratory study[END_REF], including in the form of equity finance.

The analysis of investment drivers of equity investors is a traditional and important topic for scholars in entrepreneurial finance [START_REF] Coakley | New developments in equity crowdfunding: A review[END_REF][START_REF] Colombo | On growth drivers of high-tech start-ups: Exploring the role of founders' human capital and venture capital[END_REF][START_REF] Macmillan | Criteria distinguishing successful from unsuccessful ventures in the venture screening process[END_REF][START_REF] Rossi | Equity crowdfunding: New evidence from US and UK markets[END_REF][START_REF] Zacharakis | A lack of insight: Do venture capitalists really understand their own decision process[END_REF]. While researchers have studied different success factors of equity crowdfunding (ECF) campaigns [START_REF] Ahlers | Signaling in equity crowdfunding[END_REF][START_REF] Bapna | Complementarity of signals in early-stage equity investment decisions: Evidence from a randomized field experiment[END_REF][START_REF] Block | Which updates during an equity crowdfunding campaign increase crowd participation?[END_REF][START_REF] Lukkarinen | Success drivers of online equity crowdfunding campaigns[END_REF][START_REF] Vismara | Equity retention and social network theory in equity crowdfunding[END_REF][START_REF] Vismara | Information cascades among investors in equity crowdfunding[END_REF], the degree of innovation of the start-up as a factor and potential driver for ECF investments remains a largely underexplored factor [START_REF] Mahmood | What's in a logo? The impact of complex visual cues in equity crowdfunding[END_REF], despite its importance in the traditional entrepreneurial finance mechanisms. Our paper explores the extent to which the perceived innovativeness of an equity-crowdfunded start-up affects its funding outcome.

The traditional categories of early-stage investors, such as business angels (BAs) and venture capitalists (VCs), are attracted by innovative companies when they invest because they associate innovation with potential profitability [START_REF] Bottazzi | Venture capital in Europe and the financing of innovative companies[END_REF][START_REF] Croce | Investment motivations and UK business angels' appetite for risk taking: The moderating role of experience[END_REF][START_REF] Hellmann | The interaction between product market and financing strategy: the role of venture capital[END_REF] and the creativity of a project [START_REF] Anderson | Innovation and creativity in organizations: A stateof-the science review and prospective commentary[END_REF] with potentially good business performance [START_REF] Rosenbusch | Is innovation always beneficial? A metaanalysis of the relationship between innovation and performance in SMEs[END_REF]. Whether crowd investors make similar decisions as professional investors is unclear since, unlike crowd investors, business angels and venture capitalists have significant experience in selecting and assisting entrepreneurial start-ups, take advantage of physical meetings, and have conducted more thorough due diligence before investing. In contrast, due diligence by crowd investors is limited to examining the information made available by the entrepreneur on the platform, due to lack of time, experience, and skills [START_REF] Ahlers | Signaling in equity crowdfunding[END_REF], which is likely to lead crowd investors to have stronger information asymmetry than professional investors. Thus, the perception of the project based on the online presentation-including the perception of innovativeness-may matter far more because it is the only interaction between the crowd and the entrepreneur. The importance of investor perception in ECF is also demonstrated by [START_REF] Mahmood | What's in a logo? The impact of complex visual cues in equity crowdfunding[END_REF].

In this study on ECF, we take an investor perspective, which considers innovation in a different light than the consumer perspective taken in reward-based crowdfunding [START_REF] Chan | Crowdfunding innovative ideas: How incremental and radical innovativeness influence funding outcomes[END_REF]. In the former, innovation is related to the potential for growth and investment profitability [START_REF] Kortum | Entrepreneurial inputs and outcomes: New studies of entrepreneurship in the United States[END_REF][START_REF] Mason | What do investors look for in a business plan?: A comparison of the investment criteria of bankers, venture capitalists and business angels[END_REF]. Thus, our main hypothesis is that perceived innovativeness will attract more crowd investors, which leads to these most innovative startups raising more funds. To test this prediction, we conducted indepth research involving the four main ECF platforms in France, which, together, launched 230 campaigns from 2010 to 2017; these were analyzed by 176 participants in 2,973 different assessments in order to replicate as much as possible the perception of real crowd investors.

Our final sample for the regression analyses comprises 190 campaigns for which we have full information. We constructed two measures to assess the perceived innovativeness of the startups. The first measure is the well-established Diamond Framework [START_REF] Shenhar | Toward a typological theory of project management[END_REF][START_REF] Cumming | Government policy towards entrepreneurial finance: Innovation investment funds[END_REF]. The second is an empirically driven measure developed by the leading European rating agency for start-ups called Early Metrics.

Our results indicate that the ECF campaigns of the most innovative ventures are able to attract more investors and more capital than those of less innovative ones. These results are obtained with both measures of perceived innovativeness, which confirm our prediction that crowd investors consider innovativeness similarly to professional and/or qualified investors (business angels and venture capitalists), in line with a traditional investor perspective.

Our research contributes to the literature on entrepreneurial finance by offering a better understanding of the funding strategies and preferences of crowd investors. Our study complements the previous analysis (e.g., Ahlers et al., 2015, among others) by enlarging the scope of factors to include the perception of the innovation by potential investors. Similar to previous work on reward-based crowdfunding and innovation [START_REF] Chan | Crowdfunding innovative ideas: How incremental and radical innovativeness influence funding outcomes[END_REF][START_REF] Davis | Funders' positive affective reactions to entrepreneurs' crowdfunding pitches: The influence of perceived product creativity and entrepreneurial passion[END_REF][START_REF] Oo | User entrepreneurs' multiple identities and crowdfunding performance: Effects through product innovativeness, perceived passion, and need similarity[END_REF], our study provides an empirical contribution to the stream by demonstrating that the backers' perceptions of a venture's innovativeness affect their decisions [START_REF] Mitteness | Horse vs. jockey? How stage of funding process and industry experience affect the evaluations of angel investors[END_REF]. On that front, we highlight the need for distinct streams of theoretical and empirical research for different types of crowdfunding, since they involve different crowds and thus different motivations to participate.

Our paper starts with a literature review on ECF, innovativeness in the context of early-stage firms, and the derivation our main hypothesis based on the investor perspective of crowdfunding. Then, we complete the review by examining the suggested ways in which innovativeness can be measured. In the follow-up section, we explain our methodology and the unique dataset constructed for the analysis. Then, we present the results of our research. In the last section, we present the discussion and the limitations of our study.

EQUITY CROWDFUNDING AND INNOVATIVENESS

ECF as alternative financing mechanisms

The research on ECF has grown significantly in the recent years [START_REF] Mochkabadi | Equity crowdfunding: a systematic review of the literature[END_REF]. We build on a set of studies that investigate the drivers of campaign success as measured by the amount of funding achieved and whether the self-declared funding goal was attained [START_REF] Ahlers | Signaling in equity crowdfunding[END_REF][START_REF] Bapna | Complementarity of signals in early-stage equity investment decisions: Evidence from a randomized field experiment[END_REF][START_REF] Block | Which updates during an equity crowdfunding campaign increase crowd participation?[END_REF][START_REF] Lukkarinen | Success drivers of online equity crowdfunding campaigns[END_REF][START_REF] Vismara | Equity retention and social network theory in equity crowdfunding[END_REF][START_REF] Vismara | Information cascades among investors in equity crowdfunding[END_REF]. [START_REF] Vismara | Information cascades among investors in equity crowdfunding[END_REF] finds that participation in the first days triggers a cascade effect, meaning that convincing crowd investors to participate early on is crucial for the final outcome. [START_REF] Block | Which updates during an equity crowdfunding campaign increase crowd participation?[END_REF] and [START_REF] Hornuf | Market mechanisms and funding dynamics in equity crowdfunding[END_REF] extend this research towards understanding the dynamics during a campaign with regards to the impact of new information arrival and the choice of share allocation mechanisms.

While information asymmetry with regard to entrepreneurs is a recurrent problem for all forms of entrepreneurial finance [START_REF] Mäkelä | Interorganizational commitment in syndicated crossborder venture capital investments[END_REF], it is especially true for ECF. Hence, scholars have studied the impact of different quality indicators on the funding outcomes of ECF campaigns (e.g., [START_REF] Ahlers | Signaling in equity crowdfunding[END_REF]. [START_REF] Vismara | Equity retention and social network theory in equity crowdfunding[END_REF] showed that equity retention is a strong signal of investment quality in ECF.

Innovation as an important indicator of quality in crowdfunding has not been investigated very much beyond [START_REF] Chan | Crowdfunding innovative ideas: How incremental and radical innovativeness influence funding outcomes[END_REF], [START_REF] Davis | Funders' positive affective reactions to entrepreneurs' crowdfunding pitches: The influence of perceived product creativity and entrepreneurial passion[END_REF][START_REF] Oo | User entrepreneurs' multiple identities and crowdfunding performance: Effects through product innovativeness, perceived passion, and need similarity[END_REF]. All these studies focus on reward-based crowdfunding campaigns (see also [START_REF] Coakley | New developments in equity crowdfunding: A review[END_REF]. Additionally, their findings did not converge, even though they all worked on the same US platform, i.e., Kickstarter. Chan and Parhankangas concluded that, on average, the most radically innovative projects on Kickstarter faced greater difficulty in achieving their funding goals than projects with more incremental innovations. One of the reasons is the fact that backers fear experiencing delays in receiving the products. [START_REF] Mollick | The dynamics of crowdfunding: An exploratory study[END_REF] shows that 75% of the technology and design projects on Kickstarter are delayed and often have cost over-runs in product development, manufacturing and logistics projects [START_REF] Agrawal | Some simple economics of crowdfunding[END_REF]. In contrast, Davis et al.'s research concluded that the degree of perceived creativity (a concept closely related but not identical to innovativeness) of the projects positively impacted crowdfunding outcomes. In the same way, [START_REF] Oo | User entrepreneurs' multiple identities and crowdfunding performance: Effects through product innovativeness, perceived passion, and need similarity[END_REF] find that the product innovativeness has a positive impact on the relationship between user entrepreneurship and reward-based crowdfunding performance. The nature of the relationship between innovativeness and online crowdfunding performance is, therefore, still unclear. Part of these contradictory findings may further be due to the different concepts and measurements used across different studies.

The few studies on the impact of the innovativeness perception of the crowd in an ECF context are by [START_REF] Mahmood | What's in a logo? The impact of complex visual cues in equity crowdfunding[END_REF]Di Pietro, Grilli, andMasciarelli (2020). The authors show that in ECF campaigns, startups with more complex company logos have a higher likelihood to be funded. The authors argue that more complex company logos are associated by backers with a perception of greater innovativeness, creating a positive association between innovativeness and ECF. Di Pietro, Grilli, and Masciarelli (2020) study the relationship between innovativeness and ECF campaign success. Although they only study it as moderating factor, it is however one that comes closest to ours in terms of studying innovation. Using signaling theory, they find that more innovative startups avoid getting punished from using costless signals ("cheap talk") during the campaign, enabling them to uncover a distinct channel (and different from ours) through which radical innovation can help entrepreneurs during their campaign.

Investor perspective of financing early-stage innovative start-ups

The literature on entrepreneurial finance in the past decades has demonstrated that innovative ventures face many specific difficulties in obtaining funds [START_REF] Hall | The Financing of Innovation[END_REF][START_REF] Hall | The Financing of R&D and Innovation[END_REF].

The first reason for this is related to the risk induced by innovation [START_REF] Hall | The Financing of Innovation[END_REF]. In early-stage situations, the entire start-up is an innovation, which could lead to a situation of liability of newness, making these startups particularly risky and vulnerable and thus potentially less attractive to investors [START_REF] Schoonhoven | Liability of newness[END_REF]. The second reason is information asymmetry [START_REF] Cumming | Government policy towards entrepreneurial finance: Innovation investment funds[END_REF][START_REF] Kerr | The consequences of entrepreneurial finance: Evidence from angel financings[END_REF]. Every innovation generates significant differences between the levels of knowledge and understanding of, on the one hand, entrepreneurs and, on the other hand, fund providers (equity investors and banks). A key source of information asymmetry in innovation is the strong component of intangible assets involved. This view contrasts with the more positive one that suggests that the most innovative startups will attract more funds and, in particular, more equity investors than less innovative startups [START_REF] Rossi | Equity crowdfunding: New evidence from US and UK markets[END_REF]. Indeed, equity investors like BAs are looking for financial performance [START_REF] Cholakova | Does the possibility to make equity investments in crowdfunding projects crowd out reward-based investments[END_REF][START_REF] Davis | Funders' positive affective reactions to entrepreneurs' crowdfunding pitches: The influence of perceived product creativity and entrepreneurial passion[END_REF] and will likely equate innovativeness with profit expectations. Research has provided evidence that professional investors consider innovation as a key factor in their selection process and an important element of their due diligence. For instance, [START_REF] Tyebjee | A model of venture capitalist investment activity[END_REF] document that patents, uniqueness, and technical edge are important selection criteria for venture capitalists. More recently, [START_REF] Gompers | How do venture capitalists make decisions[END_REF] find that technological novelty is one of the most important criteria for venture capitalists, after the quality of the management team. These studies suggest that for equity investors, innovation is likely to be viewed as an attractive dimension for investment, most likely because these investors gain from the upside potential through their equity position on the startups.

Crowd investors on ECF platforms are not acting from a consumer perspective, who dislike having to wait a long time before receiving the product they have invested in [START_REF] Chan | Crowdfunding innovative ideas: How incremental and radical innovativeness influence funding outcomes[END_REF]. They mainly are looking to maximize their profits through an investor perspective, such as BAs and VCs. From this investor perspective, crowd investors are considered to be quite similar to BAs because they share similar financial expectations and both make equity investments where financial upsides may be important [START_REF] Cholakova | Does the possibility to make equity investments in crowdfunding projects crowd out reward-based investments[END_REF][START_REF] Vismara | Information cascades among investors in equity crowdfunding[END_REF]. Consequently, we hypothesize that the more a venture is innovative, the more the ECF campaign will be successful. In other words, the innovativeness of a venture positively impacts the ECF campaign's funding outcomes. This prediction will be formally tested in the results section.

METHODOLOGY AND DATA

This section presents the methodology, the construction of key variables, and the list of control variables used in the multivariate analysis.

Construction of the data set

To obtain a representative sample, we base our empirical research on the four largest ECF platforms in France, i.e., Anaxago, Smart Angels, Sowefund, and WiSEED. Between their launch and December 2017, they had raised more than €130m for more than 250 projects (all types) involving over 40,000 different crowd investors.

We focus our research on multiple platforms for several reasons. The first reason is that a single platform could not launch a sufficient number of campaigns to generate a sufficient amount of representative quantitative data. Indeed, during the sample period, the largest platform in France funded a maximum of 58 campaigns. The second reason is to avoid the risk bias induced by a unique platform setting, i.e., each platform may have its own specific characteristics and track record [START_REF] Coakley | New developments in equity crowdfunding: A review[END_REF][START_REF] Rossi | Equity crowdfunding: New evidence from US and UK markets[END_REF]. For example, Sowefund has historically been close to business angels networks, while WiSEED has historically had a significant proportion of entrepreneurial projects in the social economy. For these different reasons, crowdfunding studies increasingly use data from multiple platforms (Cumming et al., 2019;[START_REF] Dushnitsky | Are we missing the platforms for the crowd? Comparing investment drivers across multiple crowdfunding platforms[END_REF].

Our data collection covers projects published online during the period of 2010 until 2017. We collected data over several years to increase our sample and to be as representative as possible of the French market. Our final sample consists of 191 ECF campaigns published on these four platforms, excluding all the real-estate campaigns that were run on these same platforms (which are not entrepreneurial projects). Not all of these campaigns achieved their funding goals; nevertheless, most of these campaigns received the funds that were collected during the campaign since they were generally run under a keep-it-all funding model [START_REF] Hervé | Determinants of individual investment decisions in investment-based crowdfunding[END_REF].

This finding means that entrepreneurs obtained some financing even if the initially set target was not achieved. The ventures included in our sample are very diverse in terms of industry, ranging from new drug development (Acticor Biotech, a campaign of Anaxago) to a food doit-yourself kit (Cook Angels, a campaign on Smart Angels), and a digital service helping descendants to manage administrative formalities around funerals (Testamento, a campaign on Sowefund).

The selected platforms provided some of the data, as follows: amount targeted, amount collected, number of crowd investors, number of followers, and collection pace in days. We collected many more details manually from the campaign webpages to construct our variables of startups and campaigns. We also completed these data with external sources. We collected data on the establishment date of the firm from the official website societe.com. Regarding the number of patents at the time of the campaign and the number of patent citations at the time of the campaign, we used worldwide patent databases (on Espacenet, the Patent Search browser of the European Patent Office). Regarding the data about technological and innovation awards, we used the webpages of the campaigns. For the profiles of the CEOs and two other main managers, we mainly used LinkedIn to specify the degree level (PhD or Master's in a scientific subject) and to assess whether they potentially have a great deal of experience in the high-tech sector or a scientific institution.

Assessments and participants

Our preference was to generate a large quantity of assessments per campaign and to have more control over the respondents' profiles, to be as close as possible to the real crowd that invests in ECF. Our aim was to include entrepreneurial finance and innovation experts on the one side (because part of the crowdfunders on the equity platforms are qualified investors such as technological experts and business angels, for instance) and a non-expert crowd with no experience in online funding (because part of the crowdfunders on equity platforms are nonqualified investors such as people who want to reduce taxation by investing in small firms, for instance), on the other. Thus, for each campaign, we collected 13.1 independent assessments, on average. These assessments were carried out by examining the online campaign pages (including videos and online discussions) of ventures that had raised funds in previous years.

We mobilized different populations of participants, which we divided into the following six main categories:

-Finance people: 22 real business angels or real ECF crowd investors, and 5 bankers specialized in SMEs. They generated 16.0% of the assessments (N: 476); -Early Metrics: the leading rating agency for innovative start-ups in Europe mobilized three professional analysts. Each ECF campaign was evaluated by one of these analysts. They generated 7.9% of the assessments (N: 191); -Experts: 20 scholars in the fields of innovation management, technology management, entrepreneurial finance, and entrepreneurship also rated the campaigns. They generated 12.9% of the assessments (N: 272); -Practitioners in innovation management: 11 adults with executive responsibilities in innovation and in various organizations (large firms, SMEs, start-ups, NGOs and public sector). They generated 5.1% of the assessments (N: 267); -The crowd of adults: 12 people with no particular knowledge or specialty in innovation, technologies, entrepreneurial finance, or crowdfunding. They generated 8.1% of the assessments (N: 241); -Students in innovation and entrepreneurship: 56 students in a Master's Degree program specialized in innovation management but with different academic and professional backgrounds (some were engineers or students in law, management sciences, political sciences, etc.) and 47 students in a French Master's program majoring in entrepreneurship at a business school (some had previously graduated as engineers). They generated 49.9% of the assessments (N: 1484).

In total, we received assessments from 176 participants out of 275 people initially contacted.

21 participants had previous experience in ECF, 13 had previous experience as business-angels, and 10 had both types of experience. We mobilized these people on a voluntary basis, i.e., they were not paid for their contributions. To obtain their involvement, we first mobilized our professional, academic, personal, and student networks. Then, we also suggested to professional associations and groups of BAs that they might like to contribute in exchange for the results and a valuable analysis of ECF dynamics. Finally, the rating agency Early Metrics also contributed for free thanks to a strong relationship between the company and the researchers and their interest in a better understanding of the crowdfunding world.

All potential participants received a one-page document explaining the approach and the instructions to be followed. We made sure that each campaign was assessed by the different profiles of participants, and not just one or two types of individuals. Each of the 176 participants was asked to fill out between 11 and 24 assessment forms, depending on the time they accepted to allocate to this research project. In total, we collected 2,973 assessments. They were conducted between March 8, 2017 and December 6, 2017.

The assessments took place under the following two different sets of conditions: on-site with student participants during 90 minutes per group, with the possibility to finish later (N: 1,637 assessments), and remotely for all other categories (N: 1,336 assessments). The allocation of projects to participants was randomized, with a proportional allocation of each platform to each participant.

In order to assess a sufficiently large number of projects, a large majority of the assessments were conducted after the crowdfunding campaigns took place, thereby adopting a similar approach to other empirical studies on crowdfunding [START_REF] Chan | Crowdfunding innovative ideas: How incremental and radical innovativeness influence funding outcomes[END_REF][START_REF] Oo | User entrepreneurs' multiple identities and crowdfunding performance: Effects through product innovativeness, perceived passion, and need similarity[END_REF][START_REF] Vismara | Information cascades among investors in equity crowdfunding[END_REF]. We compared the assessments of the few projects evaluated both during and after their campaign (in a similar fashion as the other studies), and we did not identify any major differences between both sets of assessments.

As shown in Table 1, the ages of the participants are very diverse, ranging from 21 to 70 years old (mean: 31.2, median: 25), consistent with the profiles of funders on French crowdfunding platforms during this period (approximately 50% are < 35 years old; Finance Participative France, 2016). Regarding gender, we mobilized a crowd with the following characteristics: 63% male and 37% female. In comparison, WiSEED alone has fewer women (7% only, according to [START_REF] Hervé | Determinants of individual investment decisions in investment-based crowdfunding[END_REF] and Sowefund has 10% only on their list of the 50 most active investors (following their internal statistics they shared with us).

[TABLE 1 About Here]

Dependent variable: Funding outcome

A fundraising campaign on a crowdfunding platform may be considered successful in more than one way [START_REF] Ahlers | Signaling in equity crowdfunding[END_REF][START_REF] Chan | Crowdfunding innovative ideas: How incremental and radical innovativeness influence funding outcomes[END_REF][START_REF] Davis | Funders' positive affective reactions to entrepreneurs' crowdfunding pitches: The influence of perceived product creativity and entrepreneurial passion[END_REF]. Consequently, we selected two criteria that define the success of an ECF campaign, in line with the existing literature. Amount Collected is about how much funding was raised during the campaign. This method of measuring success is widely used in the crowdfunding literature [START_REF] Ahlers | Signaling in equity crowdfunding[END_REF][START_REF] Chan | Crowdfunding innovative ideas: How incremental and radical innovativeness influence funding outcomes[END_REF][START_REF] Davis | Funders' positive affective reactions to entrepreneurs' crowdfunding pitches: The influence of perceived product creativity and entrepreneurial passion[END_REF] and is considered to be the key metric. Number of Backers describes how many crowd investors were attracted to the project. Because the most successful campaigns attract more crowd investors, the capacity for a venture to raise funds from many crowd investors is a sign of good performance in terms of funding outcomes. In France, with its keep-it-all ECF market, the Amount Collected is not limited at the funding goal, and the Number of Backers is not simply the sign of the entrepreneur's decision to limit the amount to be collected, it is a sign of the attraction of the campaign and of how many people are interested in investing.3

Main explanatory variable: Perceived innovativeness

Numerous methodologies and frameworks have been developed to measure the innovation degree of projects and companies. Following Adams, Bessant, and Phelps (2006) and [START_REF] Edison | Towards innovation measurement in the software industry[END_REF], we can classify them into the following four main approaches:

1) Output measures of innovation: proxies based on intellectual property statistics about R&D and/or sales statistics related to new products, which include patents, patent citations, R&D expenses, quantity of new products introduced to the market per year, and the proportion of sales from new product or services.

2) Input measures of innovation: proxies based on financial data, e.g., R&D expenses, IP statistics, acquisition of technological assets (licenses, patents, start-ups), evolution of intangible assets in the balance sheet, marketing expenditures for new products, and training expenditures for innovative processes.

3) Methodologies developed with an ambition of generalization, from a theoretical perspective, mainly in the strategy and innovation management fields, such as [START_REF] Cooper | Benchmarking the firm's critical success factors in new product development[END_REF], [START_REF] Chiesa | Development of a technical innovation audit[END_REF], Shenhar andDvir (1996, 2007), [START_REF] Cormican | Auditing best practice for effective product innovation management[END_REF], [START_REF] Burgelman | Strategic Management of Technology and Innovation[END_REF], and [START_REF] Mahmood | What's in a logo? The impact of complex visual cues in equity crowdfunding[END_REF]. 4) Methodologies developed ad hoc by authors for specific purposes. Regarding the specific topic of the relation between the degree of innovation and the success in crowdfunding, the article of [START_REF] Chan | Crowdfunding innovative ideas: How incremental and radical innovativeness influence funding outcomes[END_REF] is an example of this method.

Earlier studies on ECF have taken into account output-based measures of innovation, such as patents, patent citations, and patent applications (e.g., [START_REF] Ahlers | Signaling in equity crowdfunding[END_REF][START_REF] Ralcheva | Forecasting success in equity crowdfunding[END_REF][START_REF] Shafi | Investors' evaluation criteria in equity crowdfunding[END_REF][START_REF] Vismara | Information cascades among investors in equity crowdfunding[END_REF][START_REF] Walthoff-Borm | Equity crowdfunding: First resort or last resort?[END_REF]. The challenge with these units of measure is that, in ECF, most of them are small and at a very early stage of development, so they rarely hold patents. [START_REF] Walthoff-Borm | Equity crowdfunding: First resort or last resort?[END_REF] found that start-ups on Crowdcube at best had ongoing patent applications (if any) at the time of running their campaigns. Thus, the lack of patents is not necessarily an indication of lack of innovation but is simply due to the very early stage of development of the venture.

Innovativeness is also a personal and subjective notion. Consequently, we consider it necessary to record the perceptions of various people, which is a key point because our study aims to understand the extent to which project innovativeness, as perceived by potential crowd investors, impacts a project's funding success.

In our study, we score projects on scales of perceived innovativeness on several dimensions. This is based on two complementary frameworks, the first one coming from the literature on innovation and project management [START_REF] Shenhar | Toward a typological theory of project management[END_REF][START_REF] Cumming | Government policy towards entrepreneurial finance: Innovation investment funds[END_REF]) and the second one being an empirical construct based on the methodology used by the start-up rating agency Early Metrics, which is the market leader in Europe in the sector of start-up rating.

Both frameworks are summarized in Table 2. The first one is the "Diamond Framework" of Shenhar andDvir (1996, 2007), which proved useful for characterizing the innovation dimension of projects and ventures. The aim of the framework is to better classify projects according to their degree of innovation, novelty, and uncertainty, using a multi-dimensional perspective. The framework is also easy to implement.

[TABLE 2 About Here] The Diamond Framework contains the following four items: Novelty (level of newness and novelty), Technology (level of technological uncertainty), Complexity (level of complexity of the product, the process and the project), and Pace (level of urgency for deploying the innovation). For every element of the framework, we used the qualitative scale suggested by the authors. Then, we transcribed this scale into a quantitative five-item scale, from 0 (which is the minimum value) to 4 (maximum value).

The second framework used for measuring innovativeness is based on the methods used by Early Metrics. Their approach is based on the following four parameters: The Degree of innovation of the product and/or service on offer, to understand how innovative the product or service is on the market; the Degree of innovation of the production process, to understand the level of innovation of the production process; the Degree of innovation of the business model, to understand the degree of innovativeness of the business model and the revenue model; and the Global degree of innovation of the project / start-up, for an assessment of the venture's overall innovativeness. According to their rating protocol, each item can have a value between 0 and 4, following the same logic as the Diamond Framework. This framework has been used several hundred times already by the rating agency since 2014, especially for young ventures at an early-stage stage of development.

Next to evaluating the perceived innovation of the startups, we asked respondents to assess each startup's quality and campaign quality along several dimensions: business potential, quality of entrepreneurial team, probability of failure, internationalization (i.e., international orientation of clients, offices, etc.), quality of the online description on the campaign website, and quality of the video shown on that campaign website. Each of these dimensions were assessed by respondent on a scale ranging from 0 (lowest) to 4 (highest).

Control variables

We control for numerous characteristics of the ventures and online campaigns, as suggested by numerous studies about crowdfunding and success outcomes [START_REF] Agrawal | Some simple economics of crowdfunding[END_REF][START_REF] Chan | Crowdfunding innovative ideas: How incremental and radical innovativeness influence funding outcomes[END_REF]. In particular, we control for three main types of variables, i.e., campaigns, ventures, and entrepreneurs.

Regarding the first type of variables, we collect variables that capture quality indicators of the startups, as follows: the number of press references reported on the ECF campaign webpage (quotations in a newspaper, TV, etc.), which we label Number of Press References; and the number of awards (the variable Awards) presented on the campaign page. Next, we check whether the campaign is the first financing round. Indeed, some ventures in our sample have raised follow-up financing on ECF platforms. The variable Financing Round gives the sequence of the round, i.e., whether it is the first or second round of ECF. Then, we collect information on the Funding Goal, which has been shown to affect the success of a fundraising campaign [START_REF] Ahlers | Signaling in equity crowdfunding[END_REF][START_REF] Allison | Persuasion in crowdfunding: An elaboration likelihood model of crowdfunding performance[END_REF][START_REF] Mollick | The dynamics of crowdfunding: An exploratory study[END_REF]. Finally, we to control for the development of the ECF market over the years, which may have impacted the performance of campaigns differently in a given year. Regarding the second category of control variables, we control for the characteristics of the ventures themselves. To begin with, we collect the usual variables used in the academic literature about innovation, such as Patents at the campaign launch, as well as Patents' Citations. We also control for the Start-Up Year of Incorporation and the Platform, to ensure that any remaining idiosyncrasies do not affect the results of the study.

Finally, we collect data about the management. We control for the characteristics of the CEOs and the two other main managers presented on the campaign pages. For each campaign, we check manually (using LinkedIn and complementary sources available online) whether the CEO and managers had a PhD and/or an MSc in Science or Engineering and whether they had accumulated Long Experience (experience > 4 years) in a high-tech sector or a scientific organization. We code 1 if yes and 0 if not, which results in a set of dummy variables.

RESULTS

In this section, we present our results. First, we describe our sample and present the basic summary statistics of our variables. Then, we investigate the relationship between perceived innovation and the output-based innovation measures. Finally, we provide the results on the testing of our hypotheses.

Summary statistics

Our final sample is composed of 191 observations, each of which is a unique ECF campaign on one of the four largest platforms in France from 2010 to 2017. Table 3 provides the summary statistics of the variables used in the analysis. Most firms are quite young, with the average year of incorporation being 2012. However, there is one outlier, a firm that originated in 1987. The average funding goal is €482,278, which is slightly below the average amount collected in a campaign (€381,432). This figure is quite close to the average value of €411,341 reported in the French national industry statistics for 2017 (Financement Participatif France, 2017).

However, there are large variations among the campaigns, as evidenced by the difference between the minimum and maximum values.

[TABLE 3 About Here]

In terms of managerial characteristics, we distinguish between the CEO and, more broadly, the managerial team ("management"), which may include up to the three main entrepreneurs in the firm. On average, 13.1% of CEOs hold a PhD degree, 37.2% a Master's degree in a scientific subject (MSc), and 39.3% already have significant (more than 4 years) previous experience in a high-tech sector or in scientific research. Finally, 90.2% of CEOs are men and 94.5% are Caucasian. When considering the management team, 75.9% of teams include at least one member with a PhD, 89.5% at least one with a Master's degree in a scientific subject, and 88.5% at least one with previous experience in a high-tech sector or scientific organization. These findings suggest that management teams possess significant overall levels of expertise in the scientific and technological sectors.

The last panels in Table 3 show the statistics concerning our measurements of perceived innovation and the different output-based innovation measures. As mentioned before, the first unit of measure (Measure of Innovativeness 1) is a composite index that sums up the value of the four proxies of Technology, Novelty, Complexity, and Pace, following the methodology of Shenhar andDvir (1996, 2007). Similarly, the second one (Measure of Innovativeness 2) is the sum of the other four proxies, derived from the methodology of Early Metrics. Although this is not reported in the table, both measurements are highly correlated since their pairwise correlation is 0.8805 and statistically significant at the 1% level.

The output-based innovation proxies are based on tangible measurements and are, thus, complementary to the perceived ones. Prior to starting the ECF campaign, the average firm has 0.592 patents, 9.314 patent citations, 0.623 technological awards, and 1.314 technological partnerships. However, the median start-up has none of these, suggesting that-based on these traditional units of measures-they are not innovative at all. However, this is not the case.

Rather, it highlights the fact that the formal measures of innovativeness used in the literature are not the most appropriate measure for early-stage start-ups. It should also be noted that 2.1% of start-ups are a spin-off from a scientific organization or technological firm.

Links between perceived and output-based innovation

Before testing our hypotheses, we examine whether perceived innovation is related to the usual output-based innovation measures, i.e., to proxies generally used to measure the degree of real innovation of a start-up in the entrepreneurial finance literature and referred to in the literature as output-based measures. While this question is not directly related to our hypotheses, we believe it is interesting to determine whether our measurements of perceived innovation capture at least some of the more quantitative innovation proxies. Our analysis allows us to infer which of these real innovation measurements are captured by the perceived innovation reported by individuals. As mentioned in the previous section, we construct the following measurements of 5 presents pairwise correlations between the different measures. While the two perceived innovation measures show a very strong correlation of 0.905, the other variables are much less correlated. Still, strong correlations exist between our perceived innovation measures and the set of output-based measures. Most notably, we find strong correlations with the number of partnerships, the number of tech awards, the number of patents, and patent citations. A more complete correlation matrix between the main variables is presented in Appendix Table 1.

The results of regression analyses are reported in Panel B of Table 5. Models 1-2 show the results obtained through the first innovation measurement technique, and Models 3-4 show the results obtained through the second one. We use Number of Patents and Number of Patent Citations interchangeably since they both measure the patent intensity but in two distinct ways.

We obtain different findings for these two variables. Perceived innovation is associated with a greater presence of patents, spin-offs, and with a higher number of strategic partnerships and (in some specifications) tech awards, but we find no relationship with patent citations. Press coverage is not associated with higher perceived innovation. Overall, these findings suggest that perceived and formal innovation only partially concur, which demonstrates that both approaches to measuring innovativeness are relevant and potentially complementary. As mentioned before, this lack of a strong correlation is not surprising in our context since we consider early-stage start-ups that-in most cases-do not own any patents yet.

Impact of perceived innovation on the funding outcome

We now test our prediction concerning the impact of perceived innovation on the fundraising outcome of equity-crowdfunded start-ups. We predict a positive effect on campaign outcomes, applying an investor perspective on ECF. We use both measurements of perceived innovation and-as explained before-we consider two different methods to measure campaign success, as follows: the total amount of funds raised at the end of the campaign (variable Ln(Amount Collected/1000 + 1)) and the number of crowd investors who have pledged money by the end of the campaign as a measurement of investor participation (variable Ln(Number of Backers + 1)).

Table 6 presents the estimated results for the first variable based on the amount collected. All OLS regressions include platform, industry and year fixed effects. 4 Model 1 shows the results without any innovation measure to observe the impact of the control variables absent of these measures. Model 2 uses the first type of measurement of perceived innovation, while Model 3 includes the second type of measurement. We find statistically significant results in support of our prediction, i.e., both measurements are positive and significant at the 1% level. Thus, in ECF where investors benefit from the upside potential of the firms they assist, we find that the start-ups that are perceived to be more innovative are able to raise larger amounts. 5 In economic terms, a one-standard deviation increase in the first innovativeness measure translates into a 42.8% increase in the amount raised. Taken at the mean of €381,432, this corresponds to an additional €163,253, which is meaningful. The second measure of innovativeness yields a similar economic impact. In terms of the control variables, although most are not significant, we do find that firms with higher funding goals raise more, which is consistent with previous findings [START_REF] Hervé | Determinants of individual investment decisions in investment-based crowdfunding[END_REF][START_REF] Mollick | The dynamics of crowdfunding: An exploratory study[END_REF]. Models 4-6 in Table 6 show the results for the same specification but applying the second to measure campaign success, i.e., the Number of Backers variable. The number of observations is lower (169 instead of 191) since we do not have the information on the exact number of backers for all campaigns. We again find support for our prediction since both our perceived innovation variables are associated with more crowd investors, which supports the notion that the most innovative ventures do attract more crowd investors. The economic significance is again large: a one-standard deviation increase in the first innovativeness measure translates into a 37.0% increase in the number of backers. Taken at the mean of 126 backers, this corresponds to an additional 46 backers, which is again meaningful.

One possible critique of our work could be that our measures of perceived innovation largely captures the perceived quality of the startups and the description of the project on the campaign website, in particular given the lack of specific controls for quality. To overcome this limitation, we added further measures of startup and project description quality as assessed by the respondents. We use average values across all respondents for each campaign. As shown in Table 7, adding these variables does not alter our results on perceived innovation. While not explicitly reported, regressions in Table 7 include all other control variables used in Table 6, for each specification. Given these results, we conclude that our perceived innovation measures are distinct from quality measures and that our results on the positive impact of perceived innovation on campaign outcome is robust to their inclusion. 6Another risk of bias may stem from the possibly endogenous nature of the funding goal in ECF [START_REF] Cumming | Investors' choices between cash and voting rights: Evidence from dual-class equity crowdfunding[END_REF]. Entrepreneurs may increase their funding goal if other recent campaigns also had higher goals. We therefore instrument the variable Funding Goal in Table 6, using the average of funding goals of campaigns done in the studied platforms the previous year. Table 7 shows the results of 2SLS regressions. The F-statistics reported at the bottom of the table indicate the instrument is strongly significant. Overall, while the significance of the variable Funding Goal is affected, the one of our primary variables of interest (Measure of Innovativeness 1 and Measure of Innovativeness 2) are not.

Next, we propose several robustness checks that pertain to the composition of our "crowd" of assessors. First, we recalculate both innovativeness measures using only assessments made by participants with prior (equity or reward-based) crowdfunding experience. This sample may be closer to the true characteristics of equity investors. Second, we recalculate both innovativeness measures but excluding instead all assessments made by students, who represent about 50% of the sample but may not represent as much equity investors. And third, we recognize the fact that, given the way assessors were hired, some of the assessors may have had the capacity to obtain additional online information on the projects, which could also have affected the measures of perceived innovation and consequently our main results. In order to exclude this possibility, we perform an additional test by excluding assessments of all the assessors we are not fully sure that they did no access extra online information. Results on these three robustness checks are provided in Appendix Tables 2 to 4 and lead to similar conclusions as in Table 6.

In summary, we may conclude that project innovativeness positively affects campaign success in ECF, consistent with our prediction. 7 Our result is consistent with the fact that, in ECF, contributors are interested in financial returns (investor perspective), where innovation is associated with potentially greater benefits for owners. Our result also complements the recent work of [START_REF] Mahmood | What's in a logo? The impact of complex visual cues in equity crowdfunding[END_REF], which concludes, with a very different methodology, that crowd investors positively associate logo complexity (which they use as proxy for perceived innovation) with funding.

DISCUSSION AND CONCLUDING REMARKS

During the last decade, different forms of crowdfunding have emerged, and each needs to be studied separately since they do not attract the same types of contributors or projects [START_REF] Vismara | Information cascades among investors in equity crowdfunding[END_REF]. In ECF, start-ups seek equity capital for investment, R&D, sales development, and to purchase equipment; contributors provide funds in exchange for expected financial returns upon exit. Following the findings regarding professional investors, the degree of perceived innovativeness is likely to positively affect the outcome of an ECF campaign. We argue that a different perspective is needed to understand how innovation affects ECF more than some other forms of crowdfunding, and our results are consistent with an investor perspective, namely, highly innovative start-ups can raise more capital and attract more participants during the campaign than less innovative ones. Our results hold both by applying a measurement of perceived innovativeness based on the "Diamond Framework" of Shenhar andDvir (1996, 2007) and an alternative measurement technique developed by the rating agency Early Metrics.

Contributions to the literature

This study provides new evidence of the importance of perceptions in ECF campaign success, in particular, perceptions concerning innovation. It offers novel insights into the investment preferences of crowd investors. As noted by [START_REF] Chan | Crowdfunding innovative ideas: How incremental and radical innovativeness influence funding outcomes[END_REF] in their concluding discussion, different results should be obtained for alternative types of crowdfunding. Our study offers a clear theoretical contribution by developing an investor perspective on the impact of innovation perception on equity crowdfunding participation and has found empirical support for this perspective in the ECF market. Empirical evidence is based on two separate measures, one of which is grounded in a solid theoretical framework. Thus, our conclusions support the claim made by [START_REF] Chan | Crowdfunding innovative ideas: How incremental and radical innovativeness influence funding outcomes[END_REF] that different forms of crowdfunding need to be studied separately, and different theoretical frameworks may be needed to explain what occurs on these different platforms.

These findings have implications both for theory and practice. On the theoretical front, our work contributes to a better understanding of how perceived innovativeness affects corporate fundraising outcomes in entrepreneurial firms. By examining ECF, we investigate the contributors' contexts and incentives from a different perspective. In doing so, we contribute to the emerging literature related to the innovativeness and the success of crowdfunding campaigns [START_REF] Chan | Crowdfunding innovative ideas: How incremental and radical innovativeness influence funding outcomes[END_REF][START_REF] Davis | Funders' positive affective reactions to entrepreneurs' crowdfunding pitches: The influence of perceived product creativity and entrepreneurial passion[END_REF][START_REF] Oo | User entrepreneurs' multiple identities and crowdfunding performance: Effects through product innovativeness, perceived passion, and need similarity[END_REF][START_REF] Mahmood | What's in a logo? The impact of complex visual cues in equity crowdfunding[END_REF]. Crowd investors purchase shares in start-ups and thus become shareholders, either directly in entrepreneurial firms or indirectly through a financial vehicle that is set up by the platform to manage the crowd's investment, which leads them to hold a longer-term view, where the main driver for pledging money is the expectation of a future financial return. The existing studies have not examined the impact of perceived innovativeness on ECF, which is the aim of this study. One exception is the study by [START_REF] Mahmood | What's in a logo? The impact of complex visual cues in equity crowdfunding[END_REF], which shows that perceptions (in their case, the perceptions of a company's logo complexity) do affect crowd investors' behavior.

More broadly, it enriches the scientific knowledge in entrepreneurial finance about the investment drivers of nonprofessional investors, such as crowd investors. We do understand that they value innovation but, more generally, seem to be attracted by innovativeness, as are BAs and VCs. In doing so, we enlarge the knowledge on the stream of investment drivers in equity entrepreneurial finance. An open question is whether they view innovation the same way; our study has focused on perceived innovation.

Our findings also contribute to the literature on investment decision criteria in equity. Following the traditional [START_REF] Fried | Toward a model of venture capital investment decision making[END_REF][START_REF] Tyebjee | A model of venture capitalist investment activity[END_REF] and more recent articles in the field (Gompers et al., 2018;[START_REF] Petty | In pursuit of the real deal": A longitudinal study of VC decision making[END_REF][START_REF] Zacharakis | A lack of insight: Do venture capitalists really understand their own decision process[END_REF], we document similarities in the criteria used by crowd investors and professional investors. While multiple criteria enter their equation, we document that innovativeness is important for both types of investors.

Practical implications

This study could also prove valuable for practitioners. Entrepreneurs will gain insights into the expectations of crowd investors, especially that they are attracted by innovative projects. This knowledge will help entrepreneurs of high-tech startups with fears of being under-financed due to high information asymmetries and level of complexity to be more confident about their capacity to raise funds through online platforms.

Moreover, ECF platforms will note the success criteria leading to increased investor participation and larger volumes of funds raised online. Platforms may benefit from our findings inasmuch as they select the start-ups that are allowed to launch a campaign. Since highly innovative start-ups will be more successful, these platforms will have an incentive to select the more innovative ones, leaving the others aside.

Limitations and future research

Future research may help cope with the limitations of our own work. One limitation is that the different studies examining project innovativeness in crowdfunding use different measures, making comparison across these studies difficult. This restriction also applies here, in particular when comparing our study with the one by [START_REF] Chan | Crowdfunding innovative ideas: How incremental and radical innovativeness influence funding outcomes[END_REF] and [START_REF] Oo | User entrepreneurs' multiple identities and crowdfunding performance: Effects through product innovativeness, perceived passion, and need similarity[END_REF] in reward-based crowdfunding and [START_REF] Mahmood | What's in a logo? The impact of complex visual cues in equity crowdfunding[END_REF] in ECF. Another limitation is about the geographical scope of the study, which is based on a single country. Cultural differences possibly affect the impact of perceived innovativeness and willingness to fund innovative projects, and replication studies would be needed to improve our understanding of this phenomenon. While we have included as many controls for differences in platform structure, some are missing and would require including more platforms. For instance, other platforms offers different types of securities to the crowd and these securities may be allocated differently, as illustrated by Hornuf andSchwienbacher (2017, 2018). These aspects affect crowd participation as well as the dynamics of participation during the campaign.

One avenue for future research would be to directly compare platforms of different crowdfunding types. This avenue of research would make it possible to explore whether startups deliberately self-select into different types of crowdfunding according to their degree of innovativeness. The contrasting findings between reward-based and equity crowdfundingwith regard to innovativeness-suggest a trade-off for entrepreneurs that is based on the extent to which their projects are innovative. Given that less innovative start-ups tend to be more successful with reward-based crowdfunding than their more innovative counterparts, it is possible that the innovation level directly affects the choice of crowdfunding type. This unexplored research question could shed light on how entrepreneurs decide on the type of crowdfunding.

An additional research avenue could be to investigate further interrelations between achieved innovation and ECF, beyond the selection criteria. Similar to [START_REF] Eldridge | What impact does equity crowdfunding have on SME innovation and growth? An empirical study[END_REF], it would be interesting to complement our study with one on the impact of the post-campaign outcomes such as the achieved innovation and ultimate economic performance of the funded firms. This would grant insight into whether the crowd is able to assess not only the perceived innovation but also the likelihood of firms becoming innovative in the future. Moreover, studying the firm's ultimate economic performance allows one to assess whether the risks taken by crowd investors pays off. [START_REF] Shenhar | Toward a typological theory of project management[END_REF][START_REF] Cumming | Government policy towards entrepreneurial finance: Innovation investment funds[END_REF] Novelty

  Regarding more specific control variables with a stronger focus on technological innovation, which we called "output measures of innovation", we collect data on Technological & Innovation Awards, Spin-Off from Scientific Organization, and Number of Technological and Innovation Partnerships. Technological & Innovation Awards are all the awards related to innovation, technology, or science received by the start-ups and presented on the campaign pages. These do not include other types of awards, such as "Entrepreneur of the Year Award" or "Responsible firm of the region 2015". The Spin-Off from Scientific Organization variable indicates (stated on the campaign webpage) whether the venture is a spin-off from a public research organization (e.g., INSERM, CNRS, CEA, etc.) or from a large technological firm (Intel, BASF, etc.). Finally, Number of Technological and Innovation Partnerships refers to the number of technological, scientific, and innovation relations and agreements mentioned on the campaign webpage.

"

  formal innovation": Number of Patents; Number of Patent Citations, Number of Tech Awards, whether the start-up is a Spin-Off, and Number of Partnerships. We further include platform and year fixed effects to control for platform and time invariant effects. For instance, Cumming et al. (2019) offer evidence that platforms play a role in the due diligence of campaigns, which may affect their outcome. Year fixed effects help controlling for market development over time. Panel A in Table

  Level of newness and novelty 0 -No New / 1 -Derivative / 2 -Platform / 3 -New to market / 4 -New to world Technology Level of technological uncertainty 0 -No-tech /1 -Low-tech / 2 -Medium-tech / 3 -High-tech / 4 -Super high-tech Complexity Level of complexity of product, process, and project 0 -No complexity / 1 -Low / 2 -Medium / 3 -High / 4 -Array Pace Level of urgency for deploying the innovation 0 -Slow / 1 -Regular / 2 -Fast / Competitive / 3 -Time-critical / 4 of innovation of product and/or service on offer Level of innovation specifically concerning the product or service rather than the capacity to deliver or the production model Measurement scale from 0 to 4 Production process: Degree of innovation of the production process Level of innovation specifically concerning the production model Measurement scale from 0 to 4 Business model: Degree of innovation of the business model Level of innovation concerning the business and revenue model Measurement scale from 0 to 4 Global: Global degree of innovation of project / startup Overall feeling about the innovativeness of the venture beyond the previous characteristics Measurement scale from 0 to 4

TABLE 1 : Participants' profiles and distribution

 1

	Variables	No. Obs.	Mean	Median	Std. Dev.	Min. Max.
	Age of Respondent	176	31.153	25	11.335	21	70
	Gender of Respondent (1=Male)	176	0.631	1	0.484	0	1
	Type: Practitioners	176	0.017	0	0.130	0	1
	Type: Crowd	176	0.045	0	0.209	0	1
	Type: Experts	176	0.068	0	0.253	0	1
	Type: Finance persons	176	0.142	0	0.350	0	1
	Type: Rating Agency	176	0.159	0	0.367	0	1
	Type: Students	176	0.568	1	0.497	0	1

TABLE 2 : Perceived innovation -variable definitions

 2

	Innovation Index	Variable	Description
	Diamond
	Framework /
	NTCP Model

TABLE 3 : Summary statistics

 3

		No.
	Variables	Obs.	Mean	Median	Std. Dev.	Min.	Max.
	Start-Up and Campaign Characteristics:
	Campaign Year	191	2015.0	2015.0	1.376	2010	2017
	Start-Up Year of Incorporation	191	2011.5	2012.0	3.447	1987	2017
	Funding Goal (€)	191	482278.1 350000.0	644627.2	100000	8000000
	Amount Collected (€)	191	381432.5 252800.0	656020.8	0	8480000
	Ln(Amount Collected + µ)	191	13.421	13.347	0.404	12.830	15.996
	No. of Backers	169	125.550	59.000	173.756	0	1137
	No. of Press References	191	2.853	0.000	5.761	0	30
	No. of Awards (all types)	191	0.597	0.000	1.664	0	11
	Financing Round	191	1.068	1.000	0.273	1	3
	Minimum Ticket Size (€)	191	1206.1	1000.0	1397.2	0	10000
	No. of Words (website description)	186	1581.0	885.5	1547.2	87	8921
	Video Length (seconds)	167	185.2	180.0	87.2	29	562
	No. of Pictures (website description)	182	2.973	1.000	4.096	0	24
	Managerial Characteristics:
	CEO has a PhD (1=yes)	191	0.131	0.000	0.338	0	1
	CEO has an MSc (1=yes)	191	0.372	0.000	0.485	0	1
	CEO has Previous Experience (1=yes)	191	0.393	0.000	0.490	0	1
	CEO is a man (1=yes)	183	0.902	1.000	0.299	0	1
	CEO is Caucasian (1=yes)	183	0.945	1.000	0.228	0	1
	Management has a PhD (1=yes)	191	0.759	1.000	0.429	0	1
	Management has an MSc (1=yes)	191	0.895	1.000	0.307	0	1
	Management has Previous Experience (1=yes)	191	0.885	1.000	0.320	0	1
	Perceived Innovation:
	Measure of Innovativeness 1	191	7.691	7.750	2.624	0.615	13
	Technology	191	1.949	1.929	0.943	0	3.846
	Novelty	191	2.070	2.067	0.733	0	3.500
	Complexity	191	1.916	1.900	0.740	0.077	3.385
	Pace	191	1.762	1.800	0.468	0.538	2.667
	Measure of Innovativeness 2	191	8.098	8.214	2.278	0.846	13.462
	Supply	191	2.275	2.308	0.737	0.077	3.647
	Production process	191	1.895	1.867	0.612	0.077	3.615
	Business model	191	1.711	1.714	0.448	0.462	2.692
	Global	191	2.217	2.231	0.668	0.231	3.667
	Output-based Innovation Measure:
	No. of Patents	191	0.592	0.000	1.381	0	10
	No. of Patent Citations	191	9.314	0.000	33.520	0	376
	No. of Tech Awards	191	0.623	0.000	1.144	0	7
	Spin-Off (1=yes)	191	0.021	0.000	0.144	0	1
	No. of Partnerships	191	1.314	0.000	2.056	0	11
	Project Quality during Campaign:
	Business Potential	191	2.651	0.473	2.667	0.692	3.733
	Quality of Entrepreneurial Team	191	2.576	0.449	2.600	1.333	3.833

TABLE 4 : Sample of participants' responses

 4

	Variables	No. Obs. Mean Median Std. Dev. Min. Max.
	Measure of Innovativeness 1	2954	7.696	8	3.495	0	16
	Technology	2972	1.959	2	1.211	0	4
	Novelty	2971	2.073	2	1.201	0	4
	Complexity	2972	1.926	2	1.078	0	4
	Pace	2957	1.748	2	1.003	0	4
	Measure of Innovativeness 2	2926	8.153	8	3.764	0	16
	Supply	2973	2.301	2	1.173	0	4
	Production process	2944	1.931	2	1.202	0	4
	Business model	2947	1.690	2	1.090	0	4
	Global	2972	2.238	2	1.041	0	4

TABLE 5 : Determinants of perceived innovation

 5

	Panel A -Correlation matrix of the main innovation variables. Significance level: * < 1%.
		[1]	[2]	[3]	[4]		[5]	[6]	[7]
	1. Measure of Innov. 1	1.000
	2. Measure of Innov. 2	0.905* 1.000
	3. No. of Patents	0.378* 0.357* 1.000
	4. No. of Patent Cit.	0.263* 0.257* 0.832* 1.000
	5. No. of Tech Awards	0.293* 0.327* 0.222* 0.235* 1.000
	6. Spin-Off (1=yes)	0.177 0.151	-0.036 -0.033	0.176	1.000
	7. No. of Partnerships	0.472* 0.428* 0.340* 0.362* 0.270*	0.227*
	8. No. of Press Ref.	-0.081 -0.045 -0.039 -0.066 -0.103	0.017	-0.167
	Panel B -OLS regressions. The dependent variable is Measure of Innovativeness 1 in Reg. (1)
	and (2), and Measure of Innovativeness 2 in Reg. (3) and (4). Standard errors are reported in
	brackets. Significance level: * < 10%; ** < 5%; *** < 1%.
				Dep. Var. = Measure of		Dep. Var. = Measure of
				Innovativeness 1		Innovativeness 2
				[1]	[2]		[3]	[4]
	No. of Patents			0.391**			0.317**
				(0.165)			(0.140)
	No. of Patent Citations				0.005			0.004
					(0.007)			(0.006)
	No. of Tech Awards			0.158	0.222**		0.171	0.222**
				(0.128)	(0.124)		(0.106)	(0.109)
	Spin-Off (1=yes)			1.988***	1.495***	1.620***	1.228**
				(0.513)	(0.503)		(0.613)	(0.599)
	No. of Partnerships			0.327***	0.386***	0.221***	0.268***
				(0.076)	(0.083)		(0.064)	(0.072)
	No. of Press References			0.007	0.008		0.022	0.023
				(0.034)	(0.035)		(0.039)	(0.039)
	Platform FE incl.			Yes	Yes		Yes	Yes
	Industry FE incl.			Yes	Yes		Yes	Yes
	Year FE incl.			Yes	Yes		Yes	Yes
	No. of obs.			191	191		191	191
	R-squared			0.522	0.492		0.443	0.417

TABLE 6 : Determinants of the amount collected and investor participation during the ECF campaign

 6 All OLS regressions include platform, industry and year fixed effects. The dependent variable in Models (1)-(3) is Ln(Amount Collected/1000 + 1).

	The

TABLE 7 : Determinants of the amount collected and investor participation, controlling for project quality

 7 All OLS regressions include platform, industry and year fixed effects, as well as all control variables used in Table6for the same specifications.

	The

TABLE 8 : Determinants of the amount collected and investor participation, controlling for endogeneity of funding goal

 8 All OLS regressions include platform, industry and year fixed effects, as well as all control variables used in Table6for the same specifications. The dependent variable in Models (1)-(3) is Ln(Amount Collected/1000 + 1). The dependent variable in Models (4)-(6) is Ln(No. of Backers +1). The variable Funding Goal is instrumented in all the specifications, using the average funding goal the previous year as instrument. The results of the first-stage F-stat test is reported below. P-values are reported in brackets.

		Significance level: * < 10%; ** < 5%; *** < 1%.
	[1]	[2]	[3]	[4]	[5]	[6]

TABLE 1 : Pairwise correlations between the main variables

 1

	Significance level: * < 1%.
		[1]	[2]	[3]	[4]	[5]	[6]	[7]	[8]	[9]	[10]	[11]	[12]	[13]	[14]
	[1] Measure of Innovativeness 1	1.000
	[2] Measure of Innovativeness 2	0.905* 1.000
	[3] Funding Goal (€1000), in natural log	0.240* 0.210* 1.000
	[4] Ticket Size (€1000)	-0.021 0.101	0.134	1.000
	[5] No. of Press References	-0.081 0.045	0.137	0.054	1.000
	[6] No. of Awards (all types)	0.090	0.106	0.117	0.015	0.396*	1.000
	[7] Financing Round	0.079	0.045	0.086	0.056	0.061	0.090	1.000
	[8] Start-Up Age	0.109	0.099	0.040	0.153	0.087	0.008	0.045	1.000
	[9] Management has a PhD	0.058	0.082	0.041	0.017	0.014	0.003	0.051	0.163	1.000
	[10] Management has an MSc	0.160 0.195* 0.017	0.132	0.006	0.061	0.040	0.090 0.607* 1.000
	[11] Management has Previous Experience 0.161	0.159	0.132	0.031	0.103	0.011	0.030	0.081 0.602* 0.627* 1.000
	[12] No. of Patents	0.378* 0.357* 0.218*	0.024	0.039	0.059	0.172	0.185	0.162	0.122 0.119	1.000
	[13] No. of Tech Awards	0.293* 0.327* 0.088	0.092	0.103	0.105	0.167	0.040	0.025	0.022 0.111 0.222* 1.000
	[14] Spin-Off (1=yes)	0.177	0.151	0.150	0.034	0.017	0.036	0.098	0.057	0.003	0.050 0.053	0.036	0.176	1.000
	[15] No. of Partnerships	0.472* 0.428* 0.117	0.161	0.167	0.041	0.084	0.136	0.074	0.169 0.111 0.340* 0.270* 0.227*

TABLE 2 : Determinants of the amount collected and investor participation, based on measures of innovation by assessors with prior crowdfunding experience

 2 All OLS regressions include platform, industry and year fixed effects. The innovativeness variables are calculated with responses of assessors with prior (equity or reward-based) crowdfunding experience only. This leads to the loss of one observation compared to Table6. The dependent variable in Models (1)-(3) is Ln(Amount Collected/1000 + 1). The dependent variable in Models (4)-(6) is Ln(No. of Backers +1). Standard errors are reported in brackets. Significance level: * < 10%; ** < 5%; *** < 1%.

		[1]	[2]	[3]	[4]	[5]	[6]
	Measure of Innovativeness 1		0.115**			0.092**
	Measure of Innovativeness 2			0.122**			0.119***
	Funding Goal (€1000), in
	natural log	0.725***	0.700***	0.695***	0.368**	0.341**	0.317**
	Ticket Size (€1000)	-0.081	-0.090	-0.081	-0.269***	-0.277***	-0.271***
	No. of Press References	-0.011	-0.013	-0.012	-0.012	-0.014	-0.014
	No. of Awards (all types)	0.030	0.011	0.004	0.081*	0.067	0.060
	Financing Round	0.324	0.298	0.272	0.289*	0.260	0.193
	Start-Up Age	0.022	0.011	0.025	0.009	-0.002	0.006
	Management has a PhD	0.148	0.138	0.186	0.194	0.161	0.201
	Management has an MSc	-0.994*	-0.969*	-1.029*	-0.869*	-0.825*	-0.887*
	Management has Previous
	Experience	0.515	0.383	0.337	0.436	0.347	0.279
	No. of Patents	0.014	-0.009	0.015	-0.002	-0.013	0.018
	No. of Tech Awards	-0.004	-0.015	-0.032	0.063	0.054	0.036
	Spin-Off (1=yes)	-0.379	-0.461	-0.390	-0.485	-0.531	-0.451
	No. of Partnerships	0.061	0.026	0.034	0.100**	0.067	0.064
	No. of obs.	190	190	190	168	168	168
	R-squared	0.356	0.381	0.393	0.623	0.640	0.657

TABLE 3 : Determinants of the amount collected and investor participation, excluding students as assessors for the calculation of perceived innovativeness measures

 3 All OLS regressions include platform, industry and year fixed effects. The innovativeness variables are calculated with responses of all assessors but excluding students. The dependent variable in Models (1)-(3) is Ln(Amount Collected/1000 + 1). The dependent variable in Models (4)-(6) is Ln(No. of Backers +1). Standard errors are reported in brackets. Significance level: * < 10%; ** < 5%; *** < 1%.

		[1]	[2]	[3]	[4]	[5]	[6]
	Measure of Innovativeness 1		0.148**			0.127***
	Measure of Innovativeness 2			0.138**			0.123***
	Funding Goal (€1000), in
	natural log	0.727***	0.676***	0.679***	0.359**	0.315**	0.302**
	Ticket Size (€1000)	-0.0809	-0.0970	-0.0769	-0.270***	-0.283***	-0.266***
	No. of Press References	-0.0112	-0.0107	-0.0135	-0.0119	-0.0142	-0.0171
	No. of Awards (all types)	0.0299	0.0133	0.0206	0.0808*	0.0701	0.0790*
	Financing Round	0.325	0.210	0.260	0.283*	0.192	0.231
	Start-Up Age	0.0218	0.00723	0.0140	0.0105	-0.00314	0.00306
	Management has a PhD	0.136	0.187	0.159	0.240	0.257	0.229
	Management has an MSc	-0.987*	-0.964*	-1.069*	-0.896*	-0.841*	-0.928*
	Management has Previous
	Experience	0.522	0.360	0.478	0.412	0.283	0.414
	No. of Patents	0.0138	-0.0323	-0.0238	-0.00162	-0.0299	-0.0306
	No. of Tech Awards	-0.00454	-0.0161	-0.0173	0.0653	0.0510	0.0542
	Spin-Off (1=yes)	-0.377	-0.540	-0.624	-0.522	-0.575*	-0.765*
	No. of Partnerships	0.0609	0.00610	0.0318	0.101**	0.0471	0.0747*
	No. of obs.	191	191	191	169	169	169
	R-squared	0.356	0.392	0.389	0.623	0.650	0.649

TABLE 4 : Determinants of the amount collected and investor participation (replicating Table 6 in the manuscript), based on measures of innovation that exclude assessors with potential online access

 4 All OLS regressions include platform, industry and year fixed effects. The innovativeness variables are calculated with responses of assessors that could not have access to any additional inline information. The dependent variable in Models (1)-(3) is Ln(Amount Collected/1000 + 1). The dependent variable in Models (4)-(6) is Ln(No. of Backers +1). Standard errors are reported in brackets. Significance level: * < 10%; ** < 5%; *** < 1%.

		[1]	[2]	[3]	[4]	[5]	[6]
	Measure of Innovativeness 1		0.152***			0.118***
	Measure of Innovativeness 2			0.172***			0.139***
	Funding Goal (€1000), in
	natural log	0.727***	0.624***	0.658***	0.359**	0.270*	0.278*
	Ticket Size (€1000)	-0.0809	-0.0926	-0.0870	-0.270***	-0.278***	-0.275***
	No. of Press References	-0.0112	-0.0135	-0.0158	-0.0119	-0.0162	-0.0183
	No. of Awards (all types)	0.0299	0.00866	0.00942	0.0808*	0.0657	0.0669
	Financing Round	0.325	0.256	0.329	0.283*	0.210	0.251
	Start-Up Age	0.0218	0.00970	0.0190	0.0105	-0.000773	0.00565
	Management has a PhD	0.136	0.182	0.164	0.240	0.249	0.237
	Management has an MSc	-0.987*	-1.018*	-1.090*	-0.896*	-0.880*	-0.945**
	Management has Previous
	Experience	0.522	0.372	0.436	0.412	0.302	0.362
	No. of Patents	0.0138	-0.0306	-0.0306	-0.00162	-0.0272	-0.0266
	No. of Tech Awards	-0.00454	-0.0225	-0.0395	0.0653	0.0507	0.0365
	Spin-Off (1=yes)	-0.377	-0.707	-0.605	-0.522	-0.767**	-0.688*
	No. of Partnerships	0.0609	0.0143	0.0257	0.101**	0.0592	0.0647
	No. of obs.	191	191	191	169	169	169
	R-squared	0.356	0.396	0.400	0.623	0.648	0.653

In unreported analyses, we also used a binary variable that equals one if the funding goal was achieved, and zero otherwise. This alternative measure of funding success let to the same conclusions.

We further considered the classification of BtoB and BtoC, which yields similar outcomes. However they are highly correlated with our industry dummies. Therefore, we decided to consider industry dummies instead, which represent a finer classification.

As mentioned in Section 3.4, each measure of perceived innovativeness is itself composed of four subdimensions. When using each of these separately, we obtain positive and significant (at 5% level) coefficients for all the eight sub-dimensions, with the exception of Business Model in the second measure.

In unreported analyses, we use more objective measures of campaign quality, namely the number of words in the campaign description, the number of pictures, and the length of videos in seconds. Replacing the measures above with these objective measures does not alter our conclusions. However, we do not report them here because it leads to a reduced sample size.

We explored various moderating effects of this relationship using interaction terms, which however generated high multicolinearity in the analysis. As a result, the interaction terms were (almost) never statistically significant. Thus, we do not report them here.

impact bonds, discrimination in funding entrepreneurs).