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Mix and Match? Exploring the Strategy Mix and Subsequent Performance of Transaction Platforms

Gary Dushnitsky, London Business School

Evila Piva, Politecnico di Milano School of Management

Cristina Rossi-Lamastra, Politecnico di Milano School of Management

ABSTRACT Some of the best-performing companies nowadays are transaction platforms (e.g., Alibaba, AriBnB, Uber). The platform literature offers insights on different pricing and non-pricing strategies, yet it remains silent on two fronts. First, we know about individual strategies but far less about strategy mix; namely, which strategic choices do platforms undertake concurrently? Second, we have detailed studies on the impact of specific strategies on matching outcomes, but there is no work on the performance implications of platforms’ overall strategy mix. The paper addresses these gaps using a hand-collected dataset of the population of 756 crowdfunding platforms – a prominent example of transaction platforms – across EU-15 countries. Our findings advance knowledge at the intersection of the platform and strategic management literature. Keywords: Transaction platforms, pricing strategies, non-pricing strategies, strategy mix, crowdfunding

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Mix and Match? Exploring the Strategy Mix and Subsequent Performance of Transaction Platforms

1. INTRODUCTION

Platforms businesses are rapidly expanding from traditional industries, such as

advertising-supported media, real-estate brokerages, and credit cards, to new-economy

businesses (Evans and Schmalensee, 2007). Some of the best-performing companies of the past

decade are platform businesses (Pasquale, 2017). One of the major types of platform businesses

are transaction platforms (Evans and Gawer, 2016; Cennamo, 2018).1 These platforms, often

referred to as multi-sided platforms, create value by enabling interactions between two or more

groups of participants (Evans and Schmalensee, 2007; Gawer, 2014; Rochet and Tirole, 2003).

The market value of the top transaction platforms surpasses $1.1 trillion (Evans and Gawer,

2016), with prominent platforms present across the globe; for example, Alibaba and eBay, Uber

and Didi Chuxing.

Strategy scholars have sought to explain the strategic choices platforms undertake

(Eisenmann, Parker and Van Alstyne, 2011; Schilling, 2002). The explanations draw on a

distinct feature of platforms businesses; the presence of indirect network effects (i.e.,

participation of one group raises the value of participating for the other), and direct network

effects (i.e., the value of participation increases in the number of same-group participants)

(Boudreau and Jeppesen, 2015; Hagiu, 2014; Rochet and Tirole, 2003).

A platform undertakes strategic choices with an eye to shaping the underlying network

effects and maximising overall transactions. To that end, transaction platforms (simply referred

to as ‘platforms’ hereafter) resort to two broadly defined categories of strategic choices: pricing

and non-pricing strategies. Works in economics focus on pricing strategies, exploring who is

1 There are a few platform typologies in the literature (e.g., Cennamo, 2018; Evans and Gawer, 2016; Gawer,

2014). For example, in addition to transaction platforms, the literature also points to platforms for complementary innovation, or marketplaces for information.

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subject to fees and how much are they charged. The technology and information systems

literature has focused on non-pricing strategies; namely, the design and architecture of a

platform (Eisenmann, Parker, and Van Alstyne, 2006; Boudreau and Hagiu, 2009; McIntyre

and Subramaniam, 2009; McIntyre and Srinivasan, 2017). Collectively, these studies identify

seven major strategic choices: decisions about subscription fees, transaction fees, the allocation

of fees across participant groups, as well as the level of platform accessibility, product variety,

bundling, and platform quality (Table 1).

There are hundreds of possible ways in which a platform can combine these choices.2

Which strategic choices do platforms commonly pursue? It is a critical question as network

effects can give rise to winner-takes-all dynamics (Arthur, 1989; Katz and Shapiro, 1992). We

know platforms unlock these dynamics through different pricing and non-pricing strategies;

however, we know much less about the overall mix of strategic choices that platforms actually

undertake. As a result, we may be ‘missing the forest for the trees’; investigating the

implication of specific strategic choices rather than exploring the cumulative impact of the

overall set of strategies. Thus, there is a need for a holistic view of platform strategies. The gap

was duly noted in a recent review of the platform literature (McIntyre and Srinivasan, 2017:

150); “Strategic management scholars have attempted to address many issues related to firm-

specific actions to leverage network effects, yet significant uncertainty remains about optimal

strategies in platform development and management.”

The purpose of this study is to fill the gap. Specifically, we bring a holistic approach to

the investigation of platforms’ strategic choices. The study covers the following questions:

Which strategic choices do platforms usually mix together? And what are the implications to

2 Assume each strategic choice is binary (i.e., product variety can be either generalist or specialist). It follows

platforms choose strategy mix among hundreds of possibilities; 1,711 to be exact [ = (14!) /((2)(7!)(14-7)!)].

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platforms’ performance? We believe that addressing these questions can advance strategy

research into platform businesses.

To that end, we exploit privileged access to a large-scale dataset to conduct what

Lyngsie and Foss (2017: 488) define as an inductive large-N study. We contribute to theory

development through a data-driven rather than hypotheses-testing approach (e.g., Birhanu,

Gambardella and Valentini, 2015; Claussen, Essling, and Kretschmer, 2015). The benefits of

this approach are twofold. First, it follows a broader call for strategic management scholars to

conduct research “which seeks to understand the real world [without always putting] theories

before facts” (Helfat, 2007: 185). Second, it complements existing efforts in the platform

literature which either investigate a large number of strategies over a handful of platforms (e.g.,

Boudreau and Hagiu, 2009; Li and Penard, 2014; Schilling, 2002), or utilise data from several

dozen platforms while concentrating on a couple of strategic choices (e.g., Cennamo and

Santalo, 2013; Zhu and Iansiti, 2012).

The setting of our study is the crowdfunding sector. Crowdfunding platforms are

transaction platforms serving as a conduit between individuals who seek capital to fund an

innovative idea, a social cause or life plans (crowdfundees hereafter), and prospective capital

providers (crowdfunders hereafter) (Belleflame, Omrani, and Peitz, 2015). There are hundreds

of crowdfunding platforms across the globe (Wardrop, Zhang, Rau and Gray, 2015;

Dushnitsky, Guerini, Piva and Rossi-Lamastra, 2016). We construct a hand-collected dataset

of the population of 756 crowdfunding platforms that operated in EU-15 countries up to 2017.

For each platform, we observe a host of pricing and non-pricing strategies as well as subsequent

performance indicators. The quality and scale of the dataset lends itself to an inductive large

N-approach (Helfat, 2007).

The analyses uncover several patterns. First, we observe the choice of one strategy is

sensitive to the pursuit of another. For example, there is evidence of trade-offs; e.g., platforms

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that cross-subsidise participants, are more likely to engage in a specialist product variety rather

than a generalist strategy. Next, we go beyond analysis of strategy pairs and proceed to explore

the overall combinations of strategic choices, following prior work in economics and strategy

(e.g., Ichniowski, Shaw, Prennushi, 1997; Gruber, Heinemann, Brettel, Hungeling, 2010). A

second pattern arises when we take into account the full set of pricing and non-pricing

strategies. Specifically, we observe that platforms cluster into three strategy mix profiles; non-

financial transaction platforms, financial transaction platforms and bundled transaction

platforms. Notably, not all platforms strictly adhere to the profile they are affiliated with. Many

platforms differentiate one or more of their choices. Third, the analyses show platforms’

performance is sensitive to the mix of strategic choices. Specifically, there is a curvilinear

association between a focal platform’s performance and the degree to which its strategic

choices differentiate from its ‘strategy mix’ profile.

The rest of the paper proceeds as follows. The next section begins with a brief literature

review and makes the case for a holistic study of platforms’ choices. The methodology section

describes our data and the findings section presents the inductive analyses. The interpretation

section derives five key insights which validate and expand the platform literature. We

conclude with the paper’s main contributions and sketch managerial implications and

directions for future research.

2. TRANSACTION PLATFORMS AND THEIR STRATEGIC CHOICES

Transaction platforms have existed for decades (e.g., credit card platforms). They have

proliferated in recent years (Evans and Gawer, 2016); in part due to the speed and scale

advantages afforded by the internet (Teece, 2018). Prominent transaction platforms include

Alibaba.com and eBay (which connect buyers and sellers); Airbnb (bringing together dwelling

owners and renters); Uber and Didi Chuxing (linking drivers and passengers); Fandango and

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Ticketmaster (connecting cinema or event venues with consumers); as well as crowdfunding

platforms such as Kickstarter, FundingCircle and CrowdCube (facilitating interactions between

those seeking capital and those who provide it).

Transaction platforms exhibit two common features. The first is the presence of

network effects.3 Indirect network effects imply the greater the number or variety of

participants in one group creates value to the other group; e.g., sellers derive more value from

eBay when there are more buyers and vice versa (Haigu, 2014). Direct network effects refer to

same-side value creation (e.g., renters on Airbnb benefit from the availability of reviews by

other renters). Second, platforms create value by facilitating interactions and transactions

between participants (Gawer, 2014; Rochet and Tirole, 2003).4

We study the mix of strategic choices that transaction platforms undertake concurrently.

The level of analysis is the platform. Below, we review key platform strategies and articulate

the rational for a holistic study thereof.

2.1 Transaction platforms’ strategic choices: a brief literature review

We compile a list of platform strategic choices. To that end, we leverage a recent

literature review (McIntyre and Srinivasan, 2017). For each study we listed (a) the strategic

choices it investigates, and (b) the number and type of platforms included in the empirical

analyses, where relevant. This exercise, summarised in Table 1, highlights seven broad

strategic choices, of which three are pricing strategies and four are non-pricing strategies.

Pricing Strategies. An important set of choices every platform undertakes has to do

with the fees it sets its participants. These are known as ‘pricing strategies’. Extant work

identifies three major pricing strategies: subscription fees, transaction fees and fee-allocation.

3 Direct and indirect network effects are also known as same-side and cross-side network effects, respectively. 4 The presence of both direct and indirect network effects and the need to coordinate distinct groups of participants

are the sine qua non conditions for a platform being a transaction platform. For instance, Skype offers instant messaging and voice-over IP phone services, yet it is not a transaction platform because it includes just one type of participant (those who want to communicate through Skype) and thus exhibits only direct network effects.

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The first two strategies determine how much participants pay, while the last strategy has to do

with who – participants on which side of the market – pays the fees.

We first consider subscription fees. Simply put, a subscription fee is the price

participants pay to access the platform (Caillaud and Jullien, 2003; Evans, 2003). The decision

to set subscription fees has implications for platform performance. For example, setting

subscription fees at zero is likely to boost adoption and can lead to fast growth in the number

of platform participants (i.e., the platform’s installed based). However, that may come at a cost.

It is possible that many participants are of low quality, which in turn can impede total

transactions on the platform. Some platforms, such as credit card operators, apply subscription

fees to their participants (Stango, 2002), while others allow free access (e.g., dating platforms,

Bryant and Sheldon, 2017).

Another pricing strategy consists of charging transaction fees. A transaction fee is

incurred by the participants whenever they transact through the platform (Rochet and Tirole,

2003; Parker and Van Alstyne, 2005; Rochet and Tirole, 2006). The fee can be set as a fraction

of the value of the transaction or as a nominal amount. A fee may take the form of a usage fee;

i.e., participants are charged for the right to interact with others (e.g., LinkedIn charges a fee

for those who wish to send a message to other participants). Alternatively, a platform may set

a success fee; charging only participants who have successfully completed a transaction (e.g.,

Kickstarter levies a fee on successfully crowd-funded projects, but if funding is not successful,

no fees accrue).

The third pricing strategy, fee-allocation, is aimed at solving the ‘chicken-egg-problem’

that is common for platforms (Parker and Van Alstyne, 2005; Rochet and Tirole, 2006). The

optimal pricing strategy for a platform may be to allocate different fees to different participant

groups (Caillaud and Jullien, 2003). Namely, the group that is more price-sensitive should be

allocated lower fees in order to attract participants. In turn, this will induce participants on the

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‘other side’ to join and the platform can recover its loss by also allocating fees to these ‘other

side’ participants. At the extreme, we observe platforms that allocate subscription fees solely

to the price-sensitive group. Uber, for example, allocates subscription fees only to drivers.

Similarly, eBay charges only sellers (Rochet and Tirole, 2003; Li, Liu and Bandyopadhyay,

2010; Wan et al, 2017). The same can be said of transaction fees: many videogame platforms

charge a fee only to game-developers, whereas wireless carriers charge fees to both consumers

and content providers (Hagiu, 2009).

Non-pricing Strategies. Platforms also engage in a host of strategies that go beyond

fee setting. They are known as ‘non-pricing strategies’ and involve various strategic choices

regarding platform design. Extant work speaks of four non-pricing strategies: accessibility,

product variety, bundling and platform quality.

Accessibility refers to a number of non-fee strategies aimed at managing access to the

platform. Broadly speaking, these strategic choices fall into two categories: (a) controlling

participants’ access and behavior within the focal platform, or (b) specifying the scope of

feasible interactions across platforms (these are also known as ‘exclusivity strategies’).

The former set of accessibility strategies are geared towards shaping the number and

profile of prospective participants. The goal is to attract the ‘right’ kind of participants

(Boudreau and Hagiu, 2009). For example, dating platforms often restrict access to certain

social groups, with the goal of maximising positive interactions while minimising negative

ones (Evans and Schmalensee, 2008). Other platforms chose to ‘open up’ in an effort to attract

a diverse participant pool (e.g., videogame developers; Boudreau, 2010). International reach is

another important accessibility decision; namely, a platform decision to service participants

beyond its domestic market (Fuentelsaz, Garrido, Maicas, 2015). Similarly, a platform may opt

to support multiple languages to attract more participants (e.g., a France crowdfunding platform

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may opt to run its interface in German as well as French as a way to appeal and attract a broader

participant base).

The latter set of strategies concerns the level of exclusivity adopted by a focal platform.

A platform can restrict participants from sharing offerings or within-platform data on other

platforms (Cennamo and Santalo, 2013). The goal is to impede the growth of rival platforms.

Some platforms pursue a strict exclusivity strategy. OnTheMarket, a real-estate transaction

platform set up by UK real-estate agencies, prohibited participants from listing their offerings

on other platforms. Others adopt more lenient approaches; for instance, payment cards use

rewards programmes to encourage exclusive usage (Rysman, 2009).

Product Variety concerns the scope of offerings a platform supports (Evans, 2003). It

is a strategic choice that shapes the pool of participants, the value they derive and, ultimately,

platform’s outcomes. Past evidence suggests platforms view product variety as an important

strategy along with pricing strategies (Clements and Ohashi, 2005). Moreover, platforms differ

in terms of the product variety they pursue (Eisenmann et al, 2006). For example, FarFetch

specialises in luxury apparel and fashion, with over 700 boutiques and leading brands, whereas

eBay pursues an expansive set of sales categories, including apparel as well as automobiles,

memorabilia and so on. There is notable variation even within a given sector; Match.com is a

generalist dating platform serving a broad set of participants, whereas Tastebuds.com

specialises in those with a love of music.

Bundling is a strategic choice that defines a platform’s market identity (Cennamo and

Santalo, 2013). A platform may choose to bundle functionalities from its current segment with

those common in a neighbouring market segments because it stands to enjoy economies of

scale and scope (Gawer and Cusumano, 2008; Eisemann et al, 2011). At the other extreme,

platforms may opt to unbundle their offerings in an effort to sharpen their unique identity. For

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example, online classified ads platforms decided to unbundle and support only some of the

functionalities that online newspaper platforms offer (Seamans and Zhu, 2013).

Platform Quality. The final set of strategies has to do with the quality of the platform

itself (Sheremata, 2004; Suarez, 2004; Schilling, 2003; Tiwana et al, 2010). It covers a host of

choices regarding technical performance, user interface and ease of use (Gawer, 2014; Zhu and

Iansiti, 2012). For example, eBay’s visually appealing user experience stands in contrast to

another major classified platform, Craiglist, with its choice of a text-only interface. Similarly,

Uber is celebrated – in part – due to the ease of use of its mobile platform, and LinkedIn is

applauded for its productivity benefits (Hagiu, 2014).

To conclude, past work discusses seven major platform strategies. Table 1 and the

above discussion showcase the significant body of knowledge accumulated to date, yet past

work examines two or three platform strategies at a time. To the best of our knowledge, no

work has undertaken a holistic study of all platform’s strategies.

2.2. From individual strategies to strategy mix

The strategic management field has long recognised the value of studying the overall

mix of firm strategies. Porter and Siggelkow (2008) emphasise that superior performance is the

result of consistent combinations of firm strategies because the outcome of any strategy

decision crucially depends on other strategic choices. Within the platform literature there are

voices calling for a holistic analysis of “how price and non-price instruments coexist [and]

interact” (Boudreau and Hagiu, 2009: 25), and more broadly noting that “significant

uncertainty remains about optimal strategies in platform development and management”.

(McIntyre and Srinivasan, 2017: 150). We advocate the exploration of platforms’ overall

strategy mix. Our argument is threefold.

First, there is a prolific body of work on platforms (Baldwin and von Hippel, 2011;

Gawer, 2014; Schilling, 1998, 2002; Zhu and Iansiti, 2012), yet the work is fragmented across

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several streams of literature that have evolved independently (McIntyre and Srinivasan, 2017).

As a result, there is a host of distinctive theoretically motivated factors, but no single all-

encompassing theory of platform strategies. Works in the economics field, for example, view

arm’s-length pricing as the central strategic choice of a transaction platform. Other studies view

platforms as architects delineating strategies and structures to attract certain participants and

shape the interactions between them (Baldwin and von Hippel, 2011; Boudreau, 2010;

Schilling, 2002; 1998; West, 2003; Zhu and Iansiti, 2012).

Second, there is growing evidence that platforms’ success is determined not by any

individual strategic choice, but rather by the combination of multiple strategic choices. Some

evidence is qualitative in nature (e.g., Boudreau and Hagiu, 2009; Li and Penard, 2014). Large-

sample analyses further uncover subtle trade-off between selected strategy pairs; for example,

accessibility and exclusivity (Cennamo and Santalo, 2013), or accessibility and platform

quality (Zhu and Iansiti, 2012). Common to these studies is the insight that a platforms’ success

is a cumulative function of its strategic choices.

Third, transaction platforms have both the incentives and capabilities to manage their

overall strategy mix (Iansiti and Levien 2004; Gawer and Cusumano, 2002). The profitability

and longevity of a given platform are directly tied to its ability to foster voluminous

transactions. As a result, a platform has substantial incentives to carefully assess the viability

of each strategic choice as well as their cumulative effect. Platforms also possess access to the

necessary information to do so. By definition, a transaction platform occupies a unique position

at the nexus of bilateral relations among participants. The position affords access to quality

information necessary to develop and evaluate possible strategies (Venkatraman and Lee,

2004; McIntyre and Subramaniam, 2009).

In sum, the platform literature is ready to relax two implicit assumptions; (a) the impact

of a strategic choice is independent of other concurrent choices; and (b) platforms are unaware

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of, or unwilling to, manage strategic interdependencies. Relaxing these assumptions paves a

way for an investigation of platforms’ strategy mix.

3. METHODOLOGY: DATA AND APPROACH

The current stage of theoretical knowledge offers keen insights into the role-specific

strategic choices. However, it is not yet sufficiently developed to deductively formulate

hypotheses about the complex interplay between multiple pricing and no-pricing strategies.

Absent an all-encompassing theoretical framework, we avoid unwise hypotheses development

that is formulated solely for the purpose of legitimising statistically significant results (Bettis,

2012; Helfat, 2007). Rather, we follow Bettis and colleagues (Bettis, Gambardella, Helfat and

Mitchell, 2014) and advance an inductive study of platforms’ strategy mix.

Taking advantage of a large hand-collected dataset (details below), we conduct what

Lyngsie and Foss (2017: 488) define an inductive large-N study. It is a quantitative-based

approach that seeks to distil key facts and interesting patterns from a comprehensive large

dataset. Inductive large-N studies are increasingly more common in the field of strategic

management (apart from Lyngsie and Foss, 2017, see Birhanu et al, 2016; Claussen et al,

2015). Hence, our study is positioned at a mid-point between the testing of specific hypotheses

and an open-ended exploratory contribution.5 Below we describe the data and variables. The

findings section reports exploratory analyses. The interpretation section maps our findings onto

extant work and proceeds to inform our research questions.

3.1 Data collection

We constructed a proprietary, hand-collected database including information on the

population of crowdfunding platforms in the EU-15 countries. Crowdfunding platforms are a

5 The latter is associated with qualitative small-N research design that is usually employed in inductive studies

(Eisenhardt, 1989). For an excellent example in the platform literature, see Boudreau and Hagiu (2009).

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class of transaction platforms. A crowdfunding platform enables individuals and/or

organisations (crowdfundees) to aggregate funding from a host of investors (crowdfunders) in

exchange for an equity stake, interest payments, non-pecuniary rewards, or simply as an

outright donation (Mollick, 2014). We exclude platforms geared towards one specific project

or organisation. We also drop platforms that exclusively serve professional investors.6

The data collection proceeded as follows. First, we identified the population of

crowdfunding platforms launched in the EU-15 countries between the year of creation of the

first crowdfunding platform in Europe (i.e., 1999) and the end of 2015. We scanned all

European and national crowdfunding associations and listed their members. We also reviewed

crowdfunding studies that focused either on Europe as a whole or on individual EU-15

countries. We then visited every platform’s website to check whether it met our definition.

Second, once included in the database, we collected detailed data on platforms’ strategies and

performance between platform creation and the end of 2017. The information was gleaned from

platforms’ websites (both current and past pages accessed using the Wayback Machine internet

archive), the associated Facebook and LinkedIn pages (if available) and the crowdfunding

studies mentioned above.

These efforts resulted in a comprehensive dataset covering the population of

crowdfunding platforms launched in the EU-15 countries. We observe 756 crowdfunding

platforms launched between 1999, the year of creation of the first crowdfunding platform in

Europe, and their performance through the end of 2017. During that period, the crowdfunding

6 Funding platforms dedicated to wealthy investors are excluded because they fail to meet the crowd-funding

criteria. A common feature across crowdfunding platforms is aggregation of capital (Belleflamme, Lambert and Schwienbacher, 2014); “external financing from a large audience [the ‘crowd’], in which each individual provides a very small amount”. The presence of multiple, small contributions is associated with distinct network effects; both of direct (e.g., validating the investment and creating momentum among other investors; Agrawal, Catalini and Goldfarb, 2015; Kuppuswamy and Bayus, 2018), and indirect nature (e.g., aggregating diverse customers’ and investors’ opinions reduces demand uncertainty and uncovers features of interest; Strausz, 2017). Finally, we note that while certain funding platforms cater to wealthy investors, it does not mean that wealthy individuals are excluded from participation in the 756 platforms we study.

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sector experienced three phases. The period leading up to the financial crisis (i.e., up to 2008),

the following years until crowdfunding achieved wide market and regulatory acceptance (i.e.,

between 2009 and 2013) and finally the period post-2013.7

3.2 Variables

We constructed empirical proxies of (pricing and non-pricing) strategic choices as well

as the performance of crowdfunding platforms. The strategic choices we consider mirror those

identified through the literature review.

Platform pricing strategies are captured by the following variables. The variable

D_Subscription equals 1 if the focal platform charges participants a subscription fee, and 0

otherwise. We employ a binary variable for two reasons. First, as we describe later, the vast

majority of crowdfunding platforms do not adopt a subscription fee. Second, among those that

charge a subscription fee, some charge an absolute amount, while others set it as a percentage

of crowdfundees’ funding targets. Hence, it is impossible to create a meaningful continuous

measure that is systematically available for all the platforms in our dataset.

The variable D_Transaction_Fee equals 1 if the platform charges a fee to participants

when a transaction materialises. Crowdfunding platforms are highly consistent in that they set

the fee as a percentage of the transaction amount. We carefully collected the information and

created a continuous measure: Transaction_Fee is the percentage of the total capital raised that

a platform charges when transactions materialise. We also observe that some platforms employ

a fee schedule; for example, the French platform Ulule charges fees that range from 1.67% to

4.17%, depending on the campaign’s funding target. For such platforms, Transaction_Fee is

7 This pattern mirrors broader global events: (a) the rise of alternative funding mechanisms following the global

financial crisis of 2008, which resulted in a sudden reduction in the availability of capital from traditional players, (b) the successful crowdfunding campaign of the US presidential candidate Barack Obama increased popularity and market awareness to the space, and (c) the JOBS Act of 2012 and related regulatory frameworks brought further interest and stability to the space.

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calculated as the average percentage value.

The variable D_Fee_Allocation equals 1 when the platform charges both crowdfunders

and crowdfundees, while it equals 0 if platform fee(s) is (are) charged only to one group of

participants.8

We also construct empirical proxies for three non-pricing strategies: accessibility,

product variety and bundling.9 The first variable captures platforms’ accessibility. As internet-

based platforms, crowdfunding platforms are quite easy to access. That said, our data collection

reveals a clear accessibility impediment across European platforms: language. A platform that

runs solely in German may not be readily accessible to Spanish speakers and vice versa.

Accordingly, we focus on a platform decision to support multiple languages as a proxy of

platform accessibility choice. The variable D_Accessibility is a binary variable equal to 1 if the

focal platform supports more than one language, and zero if it runs in a single language. In line

with the theoretical logic that accessibility choices shape the number of a platform’s

participants, we further construct a continuous variable that captures the number of prospective

participants affected by this strategic choice; Accessibility is computed as the number in

millions of people for whom the language(s) supported by the focal platform is their native

language (source: most recent Ethnologue data).

The variable D_Project_Variety is a binary variable equal to 1 if the platform hosts a

diverse set of projects, and 0 otherwise. It is an indicator of the level of project variety on a

focal platform. For example, the variable is equal to 1 for generalist platforms such as Seedrs,

a UK platform for investing in startups and later-stage businesses which does not apply any

8 Data on pricing strategies was unavailable for 174 platforms. For these platforms, we imputed the missing data by substituting in the population median. The results are robust to dropping these observations.

9 We do not proxy for exclusivity strategy. The reason is the crowdfunding setting features a high level of exclusivity de jure and de facto. Many platforms explicitly prohibit running a campaign on other platforms concurrently. Moreover, crowdfundees avoid running several campaigns simultaneously because it bifurcates their efforts; it dilutes the momentum of growing number of investors (and investment amount) on any one platform and reduces the chances of meeting the funding target (where applicable).

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industry or sector restrictions; and BuonaCausa, an Italian platform where people collect

money for personal projects, charities and causes, which does not impose any domain

limitations. Conversely, the variable is equal to 0 if the platform hosts projects that are

dedicated to specific products or services (e.g., Musicstarter, a German platform launched to

fund music projects by musicians and bands), projects focused on a specific geographical area

(e.g., Ginger, an Italian platform launched in the Emilia Romagna region which targets local

projects), and/or projects proposed by a specific group of fundraisers (e.g., Eduklab, a French

platform dedicated to funding projects proposed by students).

The variable D_Bundling captures whether a focal platform bundles functionalities of

multiple crowdfunding types. The crowdfunding literature identifies four distinct

crowdfunding types (often referred to as crowdfunding models) based on the types of

incentives offered to crowdfunders in exchange for their money. We briefly describe them and

assign a binary variable to each of them. On equity-based crowdfunding platforms the

crowdfunders receive an equity stake in the funded venture (D_Equity = 1, else zero). On

lending-based platforms crowdfunders earn interest payments in return for a loan (D_Lending

= 1, else zero). On reward-based platforms crowdfundees offer tangible or intangible rewards

in exchange for funding (D_Reward = 1, else zero). Finally, donation crowdfunding, as the

name suggests, does not entail extrinsic reward to crowdfunders (D_Donation = 1, else zero).

Many platforms operate a single crowdfunding type. For these platforms the variable

D_Bundling equals 0. However, there are cases where a focal platform opts to bundle several

crowdfunding types (Dushnitsky et al, 2016). For these platforms D_Bundling equals 1.

We employ two proxies of platform performance. The first one captures the total value

of transactions a focal platform facilitates during 2016 and 2017. Remember, the core

proposition of a transaction platform is to facilitate matching and voluminous transactions

between the participants. Accordingly, the measure is a direct proxy of a platform’s ability to

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realise this objective. The variable Ln_Transactions is the logarithm of the total amount (in

thousands of Euro) secured on the platform that year (e.g., Ln_Transactions_2016 when

focusing on transactions facilitated in 2016). To build this variable, we hand-collected

information from the following sources: (a) several platforms report the annual transaction

amount in a dedicated section including detailed platform stats (e.g., stats.goteo.org), (b) others

report only the cumulated amount raised, usually on the platform’s home page (e.g.,

www.lendahand.com). We retrieved annual transaction amount by using the Wayback

Machine internet archive to capture screenshots of the homepages in the last days of every year,

and (c) where applicable, we calculated the annual amount by summing the individual capital

raised by all crowdfunding campaigns in that year. It was a manual and lengthy effort.

Unfortunately, the data is unavailable for 50% of platforms active during the period 2015-2017.

The other performance measure is D_Exited. It is a binary variable equal to 1 if the

platform exited at any point prior to the end of 2017, and zero else. The benefit of this measure

is that we observe it for each and every platform in our dataset. Moreover, it is a well-

established proxy of performance; termination of operations has been long utilised as an

indicator of performance (e.g., Schilling, 2002; Seamans and Zhu, 2017). A platform is

classified as “exited” if it experienced one of the following scenarios: the platform website

does not operate as of December 31 2017 (e.g., the Belgian platform Belgodisc), or it had not

been updated for several years (e.g., the German platform Crowdenergy); second, the website

explicitly states the organisation operating the platform had either failed or exited the

crowdfunding sector (e.g., the UK platform Solar Schools); third, the website revealed that the

organisation was still active but had forgone all crowdfunding activity. Lastly, a platform is

deemed exited if it merged with another one (e.g., the Italian platform Crowdfunding-Italia,

which merged with Kapipal) or redirected all traffic to another platform (e.g., the UK platform

Wedidthis merged with Crowdfunder).

18

Finally, we include a number of control variables in the performance analysis.

Ln_Platform_Age, as the label suggests, is the logarithm of the age of a platform in years. We

also include a proxy for a platform’s installed base. Past work emphasises network effects are

crucial to platforms’ success. Therefore, we include Project_Number, a proxy for the number

of participants attracted to the focal platform. This variable is computed as the logarithm of the

cumulated number of projects posted on the platform through the end of 2015.10 We control

for environmental factors that proxy for socio-economic characteristics of the country where

the platforms are based. GDP_Per_Capita is the gross domestic product (in thousand Euros)

per capita in 2015 (source: World Bank), and Population_Density is country population in 2015

divided by country land area (source: Eurostat).

4 FINDINGS

The first set of descriptive statistics documents the frequency of strategic choices

undertaken by European crowdfunding platforms (Figure 1). They yield several insights. First,

there is substantial variation in platforms’ choice of deploying certain strategies; while 84% of

the platforms charge transaction fees to crowdfundees who successfully secured funding, only

4% levy fees on both participant groups (i.e., crowdfundees and crowdfunders). Second, within

the set of pricing strategies, the vast majority of platforms charge transaction fees, while a small

minority opts for subscription fees. Third, there is also notable variation in the adoption of non-

pricing strategies. The platforms are almost evenly split in terms of product variety strategy;

with 48% (52%) of the platforms operating as generalists (specialists). About 22% of the

platforms opt to broaden accessibility by supporting multiple languages and 16% bundle

different crowdfunding types within the same platform.

[Figure 1 about here]

10 When the exact number of projects posted is missing, the variable is computed as the logarithm of the cumulated

number of funded projects divided by the average success ratio of European platforms (56%).

19

Building on these observations, we shift our focus to study pairs of strategic choices.

Table 2 presents cross-tabulation of selected pairs. Panel A illuminates the co-occurrence of

fee-allocation versus product variety (A1) and accessibility (A2). Among the platforms that

choose to charge both crowdfundees and crowdfunders, 72% also decide to pursue a generalist

product variety strategy. The fraction is much lower among platforms that charge only

crowdfundees; only 47% employ a generalist strategy. The Wilcoxon Test shows the difference

is highly statistically significant (p<0.00). We observe a reverse pattern for the accessibility

strategy (Panel A2). The fraction of platforms adopting an inclusive accessibility strategy is

lower among platforms that charge both participant groups compared to those that do not; 12%

versus 22%, respectively. The Wilcoxon Test shows the difference is close to significance at

traditional levels (p<0.11).

Panel B presents similar cross-tabulation for another pricing strategy, subscription fees.

Panel B1 shows the fraction of platforms adopting a generalist strategy is higher among those

charging subscription fees (61%) compared to those that do not (46%). The difference is highly

significant (p<0.00). Panel B2 documents a reverse pattern for accessibility strategy. The

fraction of platforms with an inclusive accessibility strategy is lower among platforms that

charge a subscription fee compared to those that do not; 16% versus 23%, respectively. The

difference is insignificant at traditional levels (Wilcoxon Test; P<0.156).

[Tables 2 and 3 about here]

To further capture co-occurrence among any pair of strategic choices, we present

Tetrachoric pairwise correlations of the six strategic choices.11 Table 3 uncovers several

interesting patterns. Among pricing strategies, platforms that choose to charge subscription

fees are less likely to adopt transaction fees (-0.19, p<0.02). And within non-pricing strategies,

11 Tetrachoric correlation uses data in 2x2 contingency matrices to estimate the Pearson correlations that one

would obtain had the variables been continuous, bivariate normal and linearly related (Drasgow, 1988).

20

we observe consistent positive correlations of low to moderate magnitude. Table 3 also

indicates correlation between pricing and non-pricing strategies. For example, the decision to

levy a subscription fee is positively correlated with the adoption of a generalist product variety

strategy (0.20, p<0.01), which echoes the cross-tabulation analysis in Table 2, yet it is not

significantly correlated with an inclusive accessibility through the adoption of multiple

languages (-0.12, p<0.18). In sum, we observe positive as well as negative correlations of

meaningful magnitudes for many strategy pairs. In other words, our inductive analyses indicate

there are systematic patterns to the strategic choices that platforms undertake.

Platforms’ Strategy Mix. To the extent platforms undertake a holistic approach to

their strategic choices, we are in need of a methodology that can capture the fact that decisions

are taken in combination. Following prior work, we run a cluster analysis to unveil similarities

in platforms’ strategy mixes (e.g., Gruber et al, 2010; Ichniowski et al, 1997). We used a two-

step clustering procedure. First, we determined the number of clusters using the hierarchical

cluster analysis developed by Ward (1963), then we assigned platforms to clusters using the k-

means clustering method. In line with Milligan and Hirtle (2003), we scaled all the variables

to prevent our findings being influenced by variables measured on larger scales.

The inputs to the cluster analysis are the measures for the six strategic choices discussed

so far. While the binary variables are helpful in succinctly presenting frequency of strategy

adoption, the cluster analysis methodology can take advantage of more fine-grained data.12

Therefore, we utilise detailed measures of platforms’ strategic choices where available. They

include: platforms’ transaction fees (i.e., Transaction_Fee is the percentage fee a focal platform

charges when transactions materialise); accessibility strategy (i.e., Accessibility is the number

of people who use the languages supported by a given platform as their native language); and

12 We thank an anonymous reviewer for this suggestion.

21

the crowdfunding types facilitated by bundled and unbundled platforms (i.e., D_Donation,

D_Reward, D_Lending and D_Equity).

The cluster analysis highlights three groups of platforms deploying similar strategy

mix.13 Table 4 (Panel A) reports cluster mean for each variable considered in the analysis.14

The table also reports the mean for the overall sample and the p-value of the one-way analysis

of variance (ANOVA) tests. The Scheffé post hoc tests are used to gauge variable values that

statistically differ across clusters. Where there are no significant differences between clusters,

the variable is assigned the same superscript label. Table 4 (Panel B) translates these

superscripts into verbal bracket names for ease of interpretation. Specifically, we denote

whether each strategic choice within a focal cluster is associated with low or high values,

compared to the other clusters (Gruber et al, 2010). Finally, we label the three clusters:

Financial Transaction Platforms; Non-Financial Transaction Platforms; and Bundled

Transaction Platforms. While the labels may simplify the actual nature of each strategy mix,

they facilitate discussion of our findings.

[Table 4 about here]

The first cluster is characteristic of Financial Transaction Platforms. A common

feature of these platforms is that both participant groups engage in a financial transaction. The

crowdfundees seek funding and the crowdfunders participate in anticipation of a monetary

return. In the crowdfunding literature these platforms are known as lending and equity

platforms. As for pricing strategies, Financial Transaction Platforms stand out in that they

commonly allocate fees to both participant groups. This is in contrast to platforms in the other

strategy mix groups. As for the fees components, the analysis suggests Financial Transaction

Platforms are (a) more likely to impose subscription fees, and (b) transaction fees are set at a

13 As robustness check, we run the cluster analysis on the 594 platforms that were active at the end of 2015 and

obtained the same three clusters. 14 Table 4 reports non-standardised values for clarity and ease of interpretation.

22

lower percentage than other platforms. As for non-pricing strategies, the results point at

concurrent efforts to broaden the participant pool. A typical Financial Transaction Platform

hosts a highly diverse set of projects (i.e. a generalist product variety strategy). These platforms

are also broad in their reach; on average, a Financial Transaction Platform supports languages

spoken by over 200 million individuals.

One may argue that these platforms cleverly juxtapose pricing and non-pricing

strategies as a way to maximise successful transactions by broadening the number of

prospective participants while deterring low-quality participants. Indeed, the choice of high

accessibility and product variety are geared towards increasing the number of prospective

participants. At the same time, the pricing choices operate in tandem to levy (a) a fee of

significant magnitude, (b) upfront, and (c) on both sides of the market. These pricing strategies

operate as an effective screening mechanism, deterring crowdfundees with low-quality projects

as well as ‘tourist’ crowdfunders who do not intend to actively invest. To facilitate the

performance analyses reported below, we define an indicator binary variable, Financial_Mix,

which equals 1 for every platform that is affiliated in this cluster, else zero.

The second cluster is characteristic of Non-Financial Transaction Platforms. It consists

of donation-based and reward-based crowdfunding platforms. Contrary to participants of

platforms affiliated with the previous strategy mix, the participants of Non-Financial platforms

are not motivated solely by a financial rational. Specifically, crowdfunders do not commit their

money in anticipation of financial returns. In terms of pricing strategies, the average Non-

Financial Transaction Platform sets high transaction fees to crowdfunders. It does not pursue

subscription fees, nor does it allocate fees to the other participant group (i.e., crowdfunders).

As for the non-pricing strategies, the results point to a subtle approach. They are highly

inclusive on one dimension: a typical Non-Financial Transaction Platform supports multiple

languages and reaches hundreds of millions of prospective participants, yet they are more

23

focused on another dimension. A typical Non-Financial Transaction Platform is likely to

pursue a narrow product variety, acting as a specialist.

In an attempt to maximise successful matching and overall transactions, the approach

of Non-Financial Transaction Platforms differs from that of their Financial Transaction peers.

High accessibility and narrow product variety imply that Non-Financial Transaction Platforms

draw like-minded participants in terms of domains of interest, while reaching beyond their

home country. At the same time, pricing choices encourage prospective participants to join the

platform and browse or post projects of interest. To that end, these platforms charge (a) zero

or trivial fees to crowdfunders, and (b) high fee to crowdfundees, but only once they have

successfully transacted. Taken together, one may argue that Non-Financial Transaction

Platforms ‘run deep’, whereas Financial Transaction Platforms choose a ‘run wide’ strategy

mix. To facilitate performance analyses, we define Non_Financial_Mix to equal 1 for every

platform that is affiliated in this second cluster, else zero.

The third cluster consists of Bundled Transaction Platforms. As the label suggests,

these platforms bundle functionalities of several crowdfunding types. It is the smallest cluster

in terms of the number of platforms, yet it is not a negligible group, with well over 100 affiliated

platforms. A limited fraction (16% of the platforms in the cluster) bundle functionalities across

three or even four crowdfunding segments. The most common approach is to bundle reward

and donation in a single platform (62% of the platforms in the cluster). The choice reflects an

assumption of communalities in participants’ preferences and therefore an opportunity for

economies of scale and scope. It further indicates the competences needed to facilitate reward-

based crowdfunding may partially overlap with the competences required in donation

crowdfunding (e.g., mobilising communities of participants who share a complex set of social

and financial motivations. (See Ryu, Kim, Kim and Kim, 2017).

24

This strategy mix stands apart from the other two clusters. While Bundled Transaction

Platforms share one pricing and one non-pricing strategy with Non-Financial Platforms (i.e.,

fee allocation and accessibility, respectively), they also differ on both strategy categories (i.e.,

subscription fee and product variety, respectively). A key feature of Bundled Transaction

Platforms is the highly inclusive approach manifest in their non-pricing choices. A typical

platform pursues a generalist, high-accessibility and bundled approach. The set of pricing

choices exhibits a less consistent pattern; upfront subscription fee is common but is usually

levied only on one group of participants. Finally, we define Bundled_Mix to equal 1 for every

platform that is affiliated in this cluster, else zero.

In conclusion, European crowdfunding platforms cluster into three common strategy-

mix profiles. Each profile features a set of choices geared towards supporting successful

transactions. Every platform is affiliated with a strategy mix (i.e., it exhibits a similar profile

of strategic choices). However, platforms do not strictly adhere to the strategy-mix profile. In

fact, a study of platforms’ choices would be incomplete without recognising that platforms can

differentiate from the exact strategy-mix profile. The proxy Differentiation captures the extent

to which a given platform’s strategic choices differ from the strategy-mix profile it is affiliated

with. Formally, it is calculated as the Euclidian distance between the focal platform and the

centroid of the cluster it is affiliated to, along the vector of the strategic choices.15

[Figure 2 about here]

Figure 2 presents a histogram of Differentiation. Several insights follow. First, we note

some level of differentiation in platforms’ strategic choices; many platforms differ from the

exact profile of the strategy mix they are affiliated with. It underscores the fact that platforms

proactively make choices which shape the pool of participants and their interactions. This

15 Strategic differentiation refers to the extent a platform differs from its strategy-mix profile on multiple strategic

choices. It is not to be confused with differentiation on a single strategic choice (e.g., pursue a specialist focus where other platforms operate as generalists).

25

observation advances an agentic view of platforms (i.e., platforms are agents that are in control

of their strategy). Second, at the same time, the level of differentiation is moderate. About 40%

of the platforms closely adhere to the set of choices in their respective strategy mix. And among

the platforms departing from their strategy mix, the majority differentiate along only one or

two strategic choices. It is a striking observation, given there are thousands of possible

permutations of the six platform strategies. One may view this ‘stickiness’ to a smaller set of

choices as an indication of the interdependencies among platform strategies. It corroborates the

need for a holistic approach to the study of platform strategies. Taken together, these

observations beg the question whether differentiation breeds results. We address this below.

Platforms’ Strategy Mix and Performance. We turn to investigate the association

between platforms’ current strategic choices and their future performance.16 The annual

amount of transactions a platform facilitates is the main performance proxy. The average

annual transaction amount is 13.5 million and 14.1 million Euros for 2016 and 2017,

respectively. The amount varies across platforms. Platforms in the top 75% quartile facilitate

transaction volume that is 28-fold that of platforms in the bottom 25%. Accordingly, our

performance measure is the logarithm of the total amount (in thousands of Euro). We regress

platform performance on indicators of platforms’ strategic choices, as well as a host of controls.

The models are estimated using Ordinary Least Squares regressions with robust standard errors

and clustering platforms by affiliations to the three strategy mixes.

We can run these estimates only for platforms that did not exist before 2015. Therefore,

a survivorship bias may influence the relationships between strategic choices and transaction

amount. We thus include in the performance model the Inverse Mills ratio obtained from the

16 Our approach complements a stream of platform literature which studies a reverse relationship, investigating

how current platforms’ performance (e.g., installed base) drives future strategic choices. In line with the strategic management literature, our focus is on how strategy drives performance rather than the other way around. Methodologically, we control for the effects of previous platform performance.

26

estimates of a selection equation (Heckman, 1979). The selection equation estimates the

likelihood that a platform has exited prior to 2015. It is a Logit model with D_Exited on the

left-hand side and the following variables on the right-hand side: the dummy variables denoting

strategy mix affiliation, the measure of differentiation level as well as the environmental

controls GDP per capita and population density as of the year of platform creation, and

(logarithm of the) years elapsed between platform creation and 2015. Because the Heckman

procedure is susceptible to identification problems (Sartori, 2003), we include a variable that

explains selection (i.e. platform exit) but is unrelated to platform performance in terms of

transaction amount. The exclusion restriction variable is Country_Failure_Rate. It is the ratio

between the number of companies exited and the number of companies active in the country

where platform is (was) located, as of the year of platform creation (source: Eurostat). The

estimates of the selection equation are reported in the Appendix Table A1.

[Table 5 and Figure 3 about here]

Table 5 reports multivariate regression analyses of platforms’ performance. Panels A

and B present similar specifications where the dependent variable is the amount of transactions

in 2016 and 2017, respectively. The results are very similar. Below, we discuss the findings in

Panel B. Model 1 presents the base model including only controls. The coefficients for both

platform’s past transaction amount and the cumulative number of projects posted are positive

and statistically significant (β = 0.90, p-value = 0.00; β = 0.11, p-value = 0.05, respectively).

As for the other control variables, the GDP per capita is positive and of statistical significance

(β = 0.48, p-value = 0.09) while population density is negative and significant (β = -0.00, p-

value = 0.01).

In Model 2, we add the strategy mix indicators. Specifically, we include Non-

Financial_Mix, and Bundled_Mix, while keeping Financial_Mix as the omitted category. We

also include the level of platforms’ differentiation. The control variables retain their signs,

27

although with a lesser level of statistical significance. The coefficients of both Non-

Financial_Mix, and Bundled_Mix are negative and statistically significant (β = -0.96, p-value

= 0.08, and β = -1.24, p-value = 0.00, respectively). The results suggest that the annual

transaction amounts facilitated by Financial Transaction Platforms are larger than either Non-

Financial or Bundled Transaction Platforms. This is consistent with broader patterns in the

crowdfunding sector (Wardrop et al, 2015) and underscores the important role financially

orientated crowdfunding plays in the economy nowadays. However, the relative transaction

volume is less informative for the purpose of our investigation of platforms’ strategy mix.17

Interestingly, Differentiation is clearly not statistically significant (β = -0.00, p-value = 0.98).

It implies that performance is not a monotone function of platform’s differentiation level. Of

course, it is possible that there is a more complex relationship between differentiation and

performance.

In Model 3 we further explore the effect of the level of a platform’s differentiation by

introducing the square term of the differentiation measure. The coefficient of the main term is

positive while the square term is negative and both are statistically significant (β = 2.24, p-

value = 0.0, and β = -0.34, p-value = 0.02, respectively). Figure 3 plots the predicted values

within the range of parameters. It records an inverse U-shaped relationship between the level

of a platform’s differentiation and its subsequent performance. Because our specification

includes the strategy-mix indicators, the interpretation is as follows. Irrespective of the specific

strategy mix, the higher the level of a platform’s differentiation, the stronger its performance,

up to moderate levels of differentiation, beyond which an increase in differentiation is

associated with performance decreases. Put differently, the strategic choices a focal platform

17 We thank the guest editors and an anonymous reviewer for this suggestion.

28

undertakes, and specifically the extent to which it undertakes choices that deviate from the

strategy mix it is affiliated with, carry systematic implications for its performance.

To test the robustness of our findings, we performed a number of additional analyses

that are reported in Appendix Table A2. Model 1 presents the ‘skeleton’ specification, where

we drop all the control variables (with the sole exception of the Inverse Mills ratio). Model 2

presents an alternative specification, which retains the platform level controls but drops the

country-level variables. As the distribution by cluster of the 202 platforms for which

performance data are available may differ from that of the population, Model 3 is estimated

using sampling weights. The weights denote the inverse of the probability that each platform

is included in the sample. Finally, in Model 4, we report the estimates of a baseline OLS

regression rather than a two-stage Heckman analysis. Across the various specifications,

differentiation and its squared term keep their respective economic and statistical significance.

In other words, the curvilinear association between a platforms’ differentiation and subsequent

performance is robust to alternative specifications.

Unfortunately, detailed transaction amount is available for only some of the platforms.

Therefore, we proceed to report a supplementary analysis which takes advantage of the

population of platforms. To that end, we utilise an alternative performance variable: platform

exit. Table 6 reports the results of a Logit regression of platform exit. In interpreting the results,

one should keep in mind that the analyses in Table 5 focus on platforms’ success, whereas the

current analyses predict platforms’ exit.

[Table 6 and Figure 4 about here]

Model 1 presents a base model with control variables. Neither GDP per capita nor

population density are statistically significant at traditional levels. The Time Trend coefficient

is positive and statistically significant at traditional levels (β = 1.51, p-value = 0.00). Model 2

adds the strategy-mix indicators. Specifically, we include Non_Financial_Mix and

29

Bundled_Mix, while keeping Financial_Mix as the omitted category. Both coefficients are

positive, although only the Non_financial_Mix is statistically significant at traditional levels;

(β = 0.37, p-value = 0.05). It implies that exit is 20% more likely for Non-Financial Transaction

Platforms in comparison to the omitted category, Financial Transaction Platforms. The results

are in line with the previous analysis: platforms affiliated with a Financial strategy mix are

more robust in that they are (a) less likely to exit and also (b) facilitate greater transaction

amounts.

Model 3 introduces the differentiation measure and its square term. The coefficient of

the main term is negative, while the square term is positive, and both are statistically

significant; (β = -1.88, p-value = 0.00) and (β = 0.25, p-value = 0.00), respectively. Recall that,

in non-linear models, the marginal effects cannot be immediately deduced from the coefficients

(Hoetker, 2007); therefore, we plot the predicted values within the range of parameters while

holding the other variables at their means. Figure 4 records a U-shaped relationship between

the platform’s level of differentiation and the likelihood of exit. Because exit is an inverse

indicator of performance success, the interpretation of a U-shaped relationship is consistent

with the inverted U-shaped patterns observed in Figure 3. At initial levels of differentiation,

the decision is associated with a decrease in the likelihood a platform experiences an exit.

However, beyond a moderate level of differentiation, the opposite holds: an increase in

differentiation is associated with greater likelihood of exit. In line with the transaction-amount

analyses, the decision to deviate from the strategy-mix profile carries systematic implications

for platforms’ performance.

Again, in the Appendix we show robustness to alternative specifications (see Table A3).

Model 1 presents a ‘skeleton’ specification with only the key independent variables. Model 2

includes only platform-related measures. And Model 3 replicates the full specification of

Model 3 in Table 6 and adds another country-level control: the business failure rate in the

30

country. The coefficients of the differentiation variables retain their sign and significance

across the models.

5 INTERPRETATION

Analysis of six canonical strategic choices across 756 transaction platforms offers some

instructive insights. In particular, it tackles the research questions laid in the introduction:

Which strategic choices do platforms usually mix together? And what are the implications for

platforms’ performance? We believe our findings inform work at the intersection of the

platform and strategic management literatures (e.g., Schilling, 1998; Cennamo, 2018).

Insight (1). With respect to the first question, the findings indicate a clear trade-off

between platform strategies. The cross-tabulation analyses suggest the focus of investigation

should shift from specific strategic choices towards the mix of multiple strategies. Indeed, our

findings highlight that transaction platforms manage a trade-off between expanding and

curating their participants’ pool. On the one hand, a platform seeks to increase the number of

participants to realise network effects. On the other hand, it has to curtail participation to assure

participants’ quality to facilitate successful transactions. Importantly, platforms pursue this fine

balance through a subtle mix of pricing and non-pricing strategies.

The advantage of observing six different strategic choices lies in better insights into this

trade-off. We illustrate that by focusing on the three strategy mixes that emerge from the cluster

analysis. First, we observe that platforms trade off pricing versus non-pricing strategies. It is

particularly clear by looking at the cluster of Bundled Transaction Platforms. These platforms

pursue an inclusive agenda in their choice of non-pricing strategies, engaging in a generalist

product variety, enhancing accessibility by supporting millions of people who speak different

languages and bundling several crowdfunding types within a single platform. At the same time,

the pricing strategies they deploy are more constricting in nature; Bundled Transaction

31

Platforms adopt high subscription fees and often charge high transaction fees. This observation

echoes a budding stream of work which explicitly studies the interplay between pricing and

non-pricing strategies (e.g., Boudreau and Hagiu, 2009; Cennamo and Santalo, 2013; Li and

Penard, 2014; Zhu and Iansiti, 2012).

Insight (2). The analyses also reveal trade-offs within each strategy category. Consider

Non-Financial Transaction Platforms, which typically encourage participation by setting

subscription fees low (or at zero) while charging high transaction fees. They adopt an equally

balanced approach to the category of non-pricing strategies; supporting millions of people who

speak different languages, while usually adopting a narrow product variety. The example

illustrates that trade-offs are also managed within each strategy category, with a few choices

geared towards expansion and the others towards curation. To the best of our knowledge,

within-category balancing received little attention in the literature, especially when it comes to

the non-pricing-strategies category. It is fertile ground for future work.

Insight (3). There are insights into the theoretical mechanisms which underlie the

strategic trade-offs. Extant theory discusses two distinct and potentially competing

mechanisms shaping network effects (Cennamo, 2018; Rysman, 2009): (a) participants’ utility

due to having a large and diverse pool of participants on the other side of the platform, while

at the same time (b) their dis-utility due to over-crowding and competition on their own side

of the platform. Which of these mechanisms is at play is a theoretical and empirical puzzle. A

careful investigation of the three strategic clusters may resolve the puzzle for transaction

platforms. Recall our earlier observations that financial platforms choose a ‘run wide’ strategy

mix, whereas non-financial platforms ‘run deep’. While seemingly contradictory, both

exemplify a case where mechanism (a) dominates mechanism (b). Consider Financial

Transaction Platforms, where participants’ utility functions are more homogenous; the goal is

to maximise financial returns. The platforms employ a combination of generalist and high-

32

accessibility strategic choices in an effort to broaden the pool of investors and investees, such

that each side meets more attractive financial terms (per mechanism (a)). Of course, it comes

at the cost of over-crowding (per mechanism (b)). Next, consider Non-Financial Transaction

Platforms where participants’ utility functions are heterogeneous.18 Accordingly, platforms

adopt a specialist strategy in an effort to enhance alignment and increase the likelihood of

successful matching (per mechanism (a)). Again, it comes at the cost of over-crowding (per

mechanisms (b)). In sum, the transaction platforms we study seem to prioritise larger

participant pool while managing the risk of intense same-side competition.

With respect to the second question, we observe platforms’ performance is sensitive to

the mix of strategic choices they adopt. Many platforms differentiate, adopting some but not

all of the strategic choices associated with their strategy mix – and differentiation carries

performance implications.19 We find that a moderate level of differentiation is associated with

peak platform performance. A couple of insights follow.

Insight (4). Our findings are consistent with a theoretical prediction that, in the

presence of network effects, even small differences between platforms can have a big impact

on their performance (Arthur, 1989; Katz and Shapiro, 1992). Past empirical work supports

this prediction. For example, new platforms that pursue a different (i.e., more open)

accessibility strategy experience greater attractiveness and performance (Baldwin and von

Hippel, 2011; Boudreau, 2010; Eisenmann et al, 2006). Our analysis expands on this work. It

shows that limited differentiation matters, irrespective of the specific strategies on which the

platform chooses to differentiate. In other words, we contribute generalisable (i.e., strategy-

agnostic) evidence in support of the theoretical prediction.

18 The utility functions of participants in donation and reward crowdfunding likely exhibit a diverse set of

objectives along multiple dimensions (e.g., consumption vs investment, social vs. individual, etc.). 19 Strategic differentiation refers to the extent a platform differs from its strategy mix on multiple strategic choices.

It is not to be confused with differentiation on a single strategic choice. (Also see footnote 14.)

33

Insight (5). The findings further inform the scope of the above dynamics. To see this,

consider the underlying mechanisms. For example, we know that differentiation can realise

network effects through the discovery of latent demand (Boudreau, 2010; Eisenmann et al,

2006). It may also strengthen platforms’ value proposition to its existing participant pool (Lee

et al, 2006; Shankar and Bayus 2003; Suarez 2005). Finally, differentiation crystalises and

augments a focal platform’s value-add vis-à-vis other platforms (Cennamo and Santalo, 2013;

Seamans and Zhu, 2017. These advantageous mechanisms, however, are not boundless. The

curvilinear association between differentiation and performance marks a clear boundary. It

indicates that the decision to differentiate on multiple fronts results in a loss of alignment and

blurring of strategic focus (Cennamo and Santalo, 2013). Extensive differentiation alters the

number as well as the profile of participants and also the way in which they interact. The

resulting loss of alignment erodes platforms’ ability to realise beneficial network effects and

carries adverse performance implications.

In summary, our findings corroborate extant platform research and further highlight

areas for further work. In line with past work, we observe substantial level of interplay between

pricing and non-pricing strategies (e.g., Boudreau and Hangiu, 2009; Cennamo and Santalo,

2013; Zhu and Iansiti, 2012). For example, we discern systematic patterns for a few pairs of

strategic choices (Baldwin and von Hippel, 2011; Boudreau, 2010; Eisenmann et al, 2006;

West, 2003). At the same time, the analyses identify opportunities for theory development.

Chief among them is the evidence that platforms cluster into three common strategy-mix

profiles. This directs future work to focus on the cumulative impact of platforms’ overall

strategy mix, rather than the narrower investigation of specific strategic choices. Moreover, per

insight (3), one could explore the extent to which seemingly distinct strategy mix can equally

stimulate network effects. Finally, the curvilinear relationship between the level of strategic

differentiation and subsequent performance raises interesting questions at the intersection of

34

the platform and strategic management literature (e.g., Cennamo, 2018; Schilling, 1998, 2002).

Per insight (5), we ponder whether a platform should focus solely on its participants when

crafting its strategic choices, or should it deliberate the nature and number of other platforms?

The dual focus on both internal participants and external platforms can prove fertile ground for

future work.

6 CONCLUSIONS

We conducted a large-N inductive study of the population of European crowdfunding

platforms. Across these 756 transaction platforms, we document a few key patterns. First, there

is notable difference in the deployment of well-established platform strategies (Figure 1).

Second, the analyses suggest strategic choices are not taken in isolation. Rather, platforms

cluster into three groups deploying similar strategy mixes: Non-Financial Transaction

Platforms, Financial Transaction Platforms and Bundled Transaction Platforms. Third, we

show platforms’ performance is sensitive to their strategic choices. Specifically, there is a

curvilinear association between the degree to which the choices of a focal platform differentiate

from its strategy mix and its subsequent performance. Finally, we interpret these patterns and

derive five key insights.

Our contributions lie at the intersection of the platform and strategic management

literatures. Empirically, the sheer scale of the empirical investigation – documenting six

strategic choices across hundreds of platforms – makes a contribution. It offers validation and

generalisation of extant work. Specifically, we supplement the platform literature where

empirical studies either investigate a large number of strategies over a handful of platforms

(e.g., Boudreau and Hagiu, 2009; Li and Penard, 2014; Schilling, 2002), or utilise data from

several dozen platforms while concentrating on a couple of strategic choices (e.g., Cennamo

and Santalo, 2013; Zhu and Iansiti, 2012).

35

Importantly, the unique data facilitates theoretical development. Drawing on strategy

work (e.g., Porter and Siggelkow, 2008; Gruber et al, 2010), we introduce a holistic view of

strategic choices to the platform literature. We do so by documenting patterns that uncover

platforms’ strategy mix. The advantage of a holistic approach is evident, for example, by

looking at the closely related crowdfunding literature. Extant work employs a crowdfunding

typology where crowdfunders’ investment logic is the sole classification rationale (Fleming

and Sorenson, 2016). Our results show that crowdfunding type is not a single, standalone

choice but rather is associated with a broad mix of non-pricing and pricing strategic choices.

Limitations and directions for future research. Like any study, our paper has

limitations that open up avenues for future research. One set of limitations comes from our

empirical context. First, crowdfunding platforms are highly popular transaction platforms.

Nonetheless, it would be of interest to replicate our study on other types of transaction

platforms to understand whether these results generalise to other contexts. For instance, a

possible replication context is that of dating platforms, which are gaining momentum among

laypersons and scholars alike (e.g., Bryant and Sheldon, 2017). Second, we welcome studies

that expand the geographical coverage beyond the EU-15 countries. Although crowdfunding

platforms are supposedly accessible from all over the world, prior work finds a geographical

proximity effect (Agrawal, Catalini, and Goldfarb, 2015). The proximity effect may reflect

similarity in socio-economic, culture and values that result in excessive fraction of within-

country matches. Replicating our analysis across different countries could establish boundary

conditions to our findings. Third, we encourage scholars to investigate the strategy mix-

performance association using other performance indicators. For instance, common

performance indicators in the platform literature are market dominance or, more generally,

relative market shares (see e.g., Cennamo and Santalo, 2013).

Another set of limitations is associated with the operationalisation of platforms’

36

strategic choices. First, other strategies (e.g., advertising, innovating the internal organisation

or opening new branches abroad) may influence transaction platforms’ success. Studying these

strategies is beyond the scope of this paper. Moreover, we are not aware of any systematic

approach for collecting and coding data on these strategic choices across hundreds of different

platforms. Future work can explore questions such as: Do some strategy mixes require more

investments in advertising as they are less attractive for participants? Do some strategy mixes

require an innovative internal organisation as they are challenging to deploy?

Second, our work focuses on the direct effects of the strategy mixes on performance,

while it is silent on the boundary conditions of the strategy-mix—performance association.

One can envisage several moderators of this association, ranging from the aforementioned

country-level factors to individual-level characteristics of platforms’ managers. For example,

skilled managers will likely engage in wise differentiations of the strategy mix of their

platforms, thus obtaining superior performance from these differentiations.

Managerial implications. Despite these limitations, our study offers interesting

insights for practitioners and policy-makers involved and/or interested in the development of

transaction platforms. In particular, we witness that platforms’ (pricing and non-pricing)

strategic choices are associated with total value of transactions on the platforms. This finding

reassures managers that the fate of transaction platforms is not sealed by the first-mover

advantage that inevitably leads to a winner-takes-all outcome (Eisenmann et al, 2006, Hagiu,

2009). Transaction platforms can succeed by wisely devising the mix of choices to balance the

tensions and trade-offs which leverage the diverse aims and preferences of their distinct groups

of participants. Platform managers should also consider the option to differentiate their

platform’s strategy mix from platforms combining similar strategies. Our results, indeed,

suggest that if such a differentiation is not too radical, it can pay off. In sum, our results

encourage transaction platforms’ managers to avoid focusing on a single strategy and to adopt

37

instead a holistic approach whereby the various elements fit together to better coordinate

participants from the distinct groups.

38

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FIGURES AND TABLES

Figure 1 – Frequency of strategic choices among the population of European crowdfunding platforms

Figure 2 – Histogram of the level of strategic differentiation

0.22

0.48

0.160.12

0.84

0.04

0.2

.4.6

.8

mean of Accessibility mean of Prd_Varietymean of Bundling mean of Fee_Subscriptionmean of Fee_Transaction mean of Fee_Allocation

0.2

.4.6

.81

Den

sity

1 2 3 4 5 6Euclidean distance from the cluster centroid

42

Figure 3 – Predicted values of platform performance (Transaction Amount) for different levels of platform differentiation

Figure 4 – Predicted values of the platform performance (Platform Exit) for different levels of platform differentiation

02

46

8Li

near

Pre

dict

ion

0 .5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6Differentiation

Predictive Margins with 90% CIs.2

.4.6

.81

Pr(

D_E

xite

d_20

17)

0 .5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6Differentiation

Predictive Margins with 90% CIs

43

Table 1 – Review of Seminal Platform Studies, based on McIntyre and Srinivasan (2017: 145-147). Authors Year Journal Strategies examined Type Nu. and type of platforms Caillaud and Julian 2003 RAND Journal of

Economics • Subscription fees (pricing strategy) • Transaction fees (pricing strategy) • Fee-allocation (pricing strategy) • Exclusivity (non-pricing strategy)

Theoretical

Evans 2003 Review of Network Economics

• Subscription fees (pricing strategy) • Product variety (non-pricing strategy)

Theoretical

Rochet and Tirole 2003 Journal of the European Economic Association

• Subscription fees (pricing strategy) • Transaction fees (pricing strategy) • Fee-allocation (pricing strategy)

Theoretical

West 2003 Research Policy • Accessibility (non-pricing strategy) Empirical qualitative

3 Computer platforms

Sheremata 2004 Academy of Management Review • Accessibility (non-pricing strategy) • Platform quality (non-pricing strategy)

Theoretical

Venkatraman and Lee

2004 The Academy of Management Journal [Focus on platforms’ attributes rather than strategies] Empirical quantitative

8 Video game consoles

Clements and Ohashi

2005 Journal of Industrial Economics

• Subscription fees (pricing strategy) • Transaction fees (pricing strategy) • Fee-allocation (pricing strategy) • Product variety (non-pricing strategy)

Empirical quantitative

8 Video game consoles

Parker and Van Alstyne

2005 Management Science • Subscription fees (pricing strategy) • Transaction fees (pricing strategy) • Fee-allocation (pricing strategy) • Bundling (non-pricing strategy)

Theoretical

Suarez 2005 Academy of Management Journal [Focus on platforms’ attributes rather than strategies] Empirical quantitative

177 Cellular operators

Lee et al 2006 Management Science [Focus on platforms’ attributes rather than strategies] Theoretical Rochet and Tirole 2006 RAND Journal of Economics • Subscription fees (pricing strategy)

• Transaction fees (pricing strategy) • Fee-allocation (pricing strategy)

Theoretical

Evans and Schmalensee

2008 Issues in competition, Law and Policy • Subscription fees (pricing strategy) • Transaction fees (pricing strategy) • Fee-allocation (pricing strategy) • Accessibility (non-pricing strategy)

Theoretical

Gawer and Cusumano

2008 Sloan Management Review • Fee-allocation (pricing strategy) Theoretical

44

• Bundling (non-pricing strategy) Baldwin and Woodard

2009 Platforms, Markets and Innovation

[Focus on platforms’ attributes rather than strategies] Theoretical

Rysman 2009 Journal of Economics Perspectives

• Subscription fees (pricing strategy) • Transaction fees (pricing strategy) • Fee-allocation (pricing strategy) • Accessibility (non-pricing strategy) • Platform quality (non-pricing strategy)

Theoretical

Tee and Gawer 2009 European Management Review

[Focus on innovation platforms] Empirical qualitative

1 Mobile internet service platform

Boudreau 2010 Management Science • Accessibility (non-pricing strategy) Empirical quantitative

21 Handheld computing systems

Tiwana et al 2010 Information Systems Research

[Focus on innovation platforms] Theoretical

Eisenmann et al 2011 Strategic Management Journal • Subscription fees (pricing strategy) • Transaction fees (pricing strategy) • Bundling (non-pricing strategy)

Theoretical

Zhu and Iansiti 2012 Strategic Management Journal • Platform quality (non-pricing strategy) Empirical quantitative

2 video game consoles

Afuah 2013 Strategic Management Journal [Focus on platforms’ attributes rather than strategies] Theoretical Cennamo and Santalo

2013 Strategic Management Journal • Product variety (non-pricing strategy) • Exclusivity (non-pricing strategy)

Empirical quantitative

14 home video consoles

Kapoor and Lee 2013 Strategic Management Journal [Focus on innovation platforms] Empirical quantitative

5,367 hospitals

Kay 2013 Research Policy [Focus on platforms’ attributes rather than strategies] Empirical qualitative

1 case: Qwerty vs. Dvorak

Fuentelsaz et al 2015a Strategic Management Journal [Focus on platforms’ attributes rather than strategies] Empirical quantitative

134 mobile service providers

Fuentelsaz et al 2015b Journal of Management • Accessibility (non-pricing strategy) • Entry timing (non-pricing strategy)

Empirical quantitative

65 mobile communication companies

Boudreau, Jeppesen

2015 Strategic Management Journal [Focus on platforms’ attributes rather than strategies] Empirical quantitative

85 online multi‐player game platforms

The table does not report a few excellent review papers: Gawer (2009, 2014), Eisenmann et al (2009) and McIntyre and Subramaniam (2009).

45

Table 2 – Cross-tabulation of selected strategic choices pairs

Panel A1: Co-occurrence of Fee Allocation and Product Variety Product Variety

Specialised Generalist Total Fee

Allocation One group 53% 47% 100%

Both groups 28% 72% 100% Total 52% 48% 100%

Panel A2: Co-occurrence of Fee Allocation and Accessibility Accessibility

Low High Total Fee

Allocation One group 78% 22% 100%

Both groups 87% 12% 100% Total 78% 22% 100%

Panel B1: Co-occurrence of Subscription Fees and Product Variety Product Variety

Specialised Generalist Total Subscription

Fee No 54% 46% 100%

Yes 39% 61% 100% Total 52% 48% 100%

Panel B2: Co-occurrence of Subscription Fees and Accessibility Accessibility

Low High Total Subscription

Fee No 77% 23% 100%

Yes 84% 16% 100% Total 78% 22% 100%

Table 3 - Tetrachoric pairwise correlations of Platform Strategic Choices

Accessibility Product Variety

Bundling Fee Subscription

Fee Transaction

Fee Allocation

Accessibility 1.00 Product Variety

0.15 1.00

Bundling 0.09 0.13 1.00 Fee Subscription

-0.12 0.20 0.09 1.00

Transaction Fee

0.08 0.04 0.17 -0.19 1.00

Fee Allocation

-0.17 0.29 -0.14 0.28 0.07 1.00

46

Table 4 – Clusters of platforms with similar strategy mix

Panel A: Cluster analysis results

Variables Sample mean

1 2 3

ANOVA p-value

(n = 281) (n = 357) (n = 118) Financial_Mix

Transaction platforms Non-Financial_Mix

Transaction platforms Bundled_Mix

Transaction platforms

D_Subscription 0.123 0.214a 0.042b 0.153a 0.000

Transaction_Fee 4.927 4.496b 5.182a 5.182a,b 0.029

D_Fee_Allocation 0.042 0.100a 0.003b 0.025b 0.002

Accessibility 280.994 256.673a 291.315a 307.686a 0.095

D_Project_Variety 0.479 0.665a 0.305b 0.559a 0.000

D_Bundling 0.156 0.000b 0.000b 1.000a 0.000

D_Equity 0.246 0.505a 0.000c 0.373b 0.000

D_Lending 0.233 0.495a 0.000c 0.314b 0.000

D_Reward 0.385 0.000c 0.538b 0.839a 0.000

D_Donation 0.323 0.000c 0.462b 0.669a 0.000

Legend: In the columns capturing the three clusters, cluster means are reported. In each row, cluster means with the same superscript are not significantly different (p<0.1) on the basis of Sheffe post hoc test. The highest bracket is labeled with superscript ‘a,’ the next highest bracket with superscript ‘b,’ etc.

Panel B. Verbal description of cluster average strategy mix

Variables

1 2 3 Financial_Mix

Transaction platforms Non-Financial_Mix

Transaction platforms Bundled_Mix

Transaction platforms

D_Subscription HIGH LOW HIGH

Transaction_Fee LOW HIGH LOW, HIGH

D_Fee_Allocatiom HIGH LOW LOW

Accessibility HIGH HIGH HIGH

D_Project_Variety HIGH LOW HIGH

D_Bundling LOW LOW HIGH

D_Equity HIGH LOW MEDIUM

D_Lending HIGH LOW MEDIUM

D_Reward LOW MEDIUM HIGH

D_Donation LOW MEDIUM HIGH

47

Table 5 – Platform performance analysis: Annual Transaction Amount

Panel A – 2016 Transaction Amount

Model 1 Model 2 Model 3

Coeff. (std) [p] Coeff. (std) [p] Coeff. (std) [p]

Constant -2.087 (0.971) [0.165] -0.103 (1.323) [0.945] -1.783 (1.471) [0.349]

Non-Financial_Mix - -1.056 (0.187) [0.030] -1.020 (0.168) [0.026]

Bundled_Mix - -0.584 (0.067) [0.013] -0.620 (0.086) [0.019]

Differentiation - 0.001 (0.315) [0.998] 1.215 (0.124) [0.010]

Differentiation_Squared - - -0.179 (0.056) [0.086]

Ln_Transactions 0.850 (0.031) [0.001] 0.780 (0.044) [0.003] 0.771 (0.045) [0.003]

Ln_Platform_Age 0.375 (0.643) [0.619] -0.107 (1.289) [0.942] -0.129 (1.241) [0.927]

Project_Number 0.068 (0.026) [0.117] 0.105 (0.038) [0.112] 0.104 (0.037) [0.108]

GDP_Per_Capita 0.375 (0.129) [0.101] 0.406 (0.218) [0.204] 0.398 (0.210) [0.199]

Population_Density -0.002 (0.001) [0.185] -0.001 (0.001) [0.216] -0.001 (0.001) [0.215]

Inverse_Mills_Ratio 0.708 (0.204) [0.074] 0.280 (0.607) [0.690] 0.262 (0.583) [0.697]

Number of observations 202 202 202

Pseudo R2 0.746 0.760 0.763 F Test Differentiation = Differentiation_Squared = 0

- - 48.07 [0.020]

The omitted strategy mix variable is Financial_Mix. The first-stage regression results are reported in the Appendix (Table A1).

Legend. Robust standard errors in parentheses, p-values in square brackets.

Panel B – 2017 Transaction Amount

Model 1 Model 2 Model 3

Coeff. (std) [p] Coeff. (std) [p] Coeff. (std) [p]

Constant -1.661 (0.635) [0.120] -0.609 (1.269) [0.679] -3.851 (1.455) [0.118]

Non-Financial_Mix - -0.964 (0.299) [0.084] -0.895 (0.273) [0.082]

Bundled_Mix - -1.245 (0.085) [0.005] -1.314 (0.122) [0.009]

Differentiation - -0.101 (0.203) [0.667] 2.242 (0.449) [0.038]

Differentiation_Squared - - -0.345 (0.042) [0.015]

Ln_Transactions 0.903 (0.063) [0.005] 0.826 (0.082) [0.010] 0.809 (0.077) [0.009]

Ln_Platform_Age -0.209 (0.659) [0.782] -0.074 (1.097) [0.953] -0.117 (1.044) [0.921]

Project_Number 0.109 (0.024) [0.046] 0.127 (0.066) [0.193] 0.125 (0.064) [0.192]

GDP_Per_Capita 0.482 (0.154) [0.089] 0.506 (0.224) [0.152] 0.489 (0.184) [0.117]

Population_Density -0.002 (0.000) [0.009] -0.001 (0.000) [0.045] -0.002 (0.001) [0.108]

Inverse_Mills_Ratio 0.401 (0.230) [0.223] 0.326 (0.444) [0.539] 0.292 (0.420) [0.559]

Number of observations 202 202 202

Pseudo R2 0.679 0.696 0.703 F Test Differentiation = Differentiation_Squared = 0

- - 33.75 [0.029]

The omitted strategy mix variable is Financial_Mix. The first-stage regression results are reported in the Appendix (Table A1).

Legend. Robust standard errors in parentheses, p-values in square brackets.

48

49

Table 6 – Platform performance analysis: Likelihood of exit

Model 1 Model 2 Model 3

Coeff. (std) [p] Coeff. (std) [p] Coeff. (std) [p]

Constant -3.174 (0.562) [0.000] -2.757 (0.635) [0.000] -0.502 (0.972) [0.605]

Non-Financial_Mix - 0.366 (0.190) [0.054] 0.291 (0.194) [0.134]

Bundled_Mix - 0.342 (0.247) [0.166] 0.348 (0.250) [0.165]

Differentiation - -0.292 (0.123) [0.017] -1.876 (0.547) [0.001]

Differentiation_Squared - - 0.245 (0.081) [0.003]

Time Trend 1.510 (0.246) [0.000] 1.540 (0.255) [0.000] 1.590 (0.258) [0.000]

GDP_Per_Capita_Creation 0.002 (0.011) [0.822] 0.004 (0.011) [0.732] 0.003 (0.011) [0.766]

Population_Density_Creation -0.001 (0.001) [0.360] -0.001 (0.001) [0.340] -0.001 (0.001) [0.336]

Number of observations 756 756 756

Pseudo R2 0.044 0.061 0.070 F Test Differentiation = Differentiation_Squared = 0

- - 14.92 [0.000]

The omitted strategy mix variable is Financial_Mix. Results for Logit regression.

Legend. Robust standard errors in parentheses, p-values in square brackets.

50

APPENDIX

Table A1 – Estimates of the selection equation on exit before 2015

D_Exited

(Logit model)

Constant -3.201 (0.893) [0.000]

Non_Financial_Mix 0.343 (0.235) [0.145]

Bundled_Mix 0.141 (0.306) [0.645]

Differentiation -0.426 (0.178) [0.017]

Ln_Years_Elapsed_2015 1.721 (0.203) [0.000]

GDP_Per_Capita_Creation -3.295 (15.420) [0.831]

Population_Density_Creation 0.000 (0.001) [0.655]

Country_Failure_Rate 0.108 (0.053) [0.041]

Number of observations 748

Pseudo R2 0.138 Legend. Robust standard errors in parentheses, p-values in square brackets.

51

Table A2 – Platform performance analysis: checks of robustness

Model 1 – Heckman without

platform/country controls Model 2 – Heckman without

country controls Model 3 – Heckman

with mix weights Model 4 – No Heckman:

simple OLS

Constant -2.595 (1.323) [0.189] -3.884 (1.172) 0.080 -4.078 (1.262) [0.084] -3.107 (1.121) [0.109]

Non-Financial_Mix -0.824 (0.215) [0.062] 0.834 (0.079) 0.009 -0.863 (0.258) [0.079] -0.972 (0.240) [0.056]

Bundled_Mix -1.218 (0.112) [0.008] 2.104 (0.526) 0.057 -1.282 (0.125) [0.009] -1.337 (0.090) [0.005]

Differentiation 2.416 (0.406) [0.027] -0.353 (0.044) 0.015 2.202 (0.422) [0.035] 2.364 (0.382) [0.025]

Differentiation_Squared -0.373 (0.047) [0.016] -0.791 (0.280) 0.106 -0.350 (0.045) [0.016] -0.346 (0.042) [0.014]

Ln_Transactions_2015 0.868 (0.059) [0.005] -1.234 (0.089) 0.005 0.824 (0.080) [0.009] 0.808 (0.076) [0.009]

Ln_Platform_Age_2015 - 0.647 (0.673) 0.438 0.125 (0.863) [0.898] -0.575 (0.392) [0.280]

Project_Number_2015 - 0.114 (0.073) 0.260 0.120 (0.065) [0.208] 0.125 (0.063) [0.185]

GDP_Per_Capita_2015 - - 0.045 (0.018) [0.134] 0.051 (0.015) [0.080]

Population_Density_2015 - - -0.002 (0.000) [0.059] -0.002 (0.001) [0.101]

Inverse_Mills_Ratio 0.289 (0.239) [0.349] 0.755 (0.214) 0.072 0.384 (0.307) [0.338] -

Number of observations 204 202 202 202

Pseudo R2 0.684 0.697 0.706 0.703 F Test Differentiation = Differentiation_Squared = 0

100.20 [0.009] 226.59 [0.004] 30.34 [0.032] 39.29 [0.025]

Legend. Robust standard errors in parentheses, p-values in square brackets.

52

Table A3 – Platform exit analysis: checks of robustness

D_Exited Model 1 Model 2 Model 3

Constant 1.491 (0.827) [0.071] -0.553 (0.897) [0.537] -1.507 (1.097) [0.169]

Non-Financial_Mix 0.414 (0.185) ]0.025] 0.283 (0.193) [0.142] 0.272 (0.201) [0.176]

Bundled_Mix 0.370 (0.238) [0.119] 0.333 (0.249) [0.182] 0.378 (0.255) [0.139]

Differentiation -1.522 (0.530) [0.004] -1.874 (0.548) [0.001] -1.987 (0.553) [0.000]

Differentiation_Squared 0.201 (0.079) [0.011] 0.244 (0.081) [0.003] 0.260 (0.082) [0.002]

Time Trend - 1.604 (0.256) [0.000] 1.815 (0.273) [0.000]

GDP_Per_Capita_Creation - - 0.005 (0.012) [0.654]

Population_Density_Creation - - -0.001 (0.001) [0.241]

Number of observations 756 756 756

Pseudo R2 0.044 0.069 0.088 F Test Differentiation = Differentiation_Squared = 0

10.30 [0.006] 14.99 [0.000] 16.02 [0.000]

Legend. Robust standard errors in parentheses, p-values in square brackets.