ArticlePDF Available

Third-Party Signals in Crowdfunded Microfinance: The Role of Microfinance Institutions


Abstract and Figures

Crowdfunded microfinance research has routinely examined how campaign characteristics drive funding to crowdfunding campaigns but has neglected to examine the critical role of the microfinance institution (MFI). We leverage signaling theory to contend that entrepreneurs’ MFI affiliation is a salient third-party signal that shapes the performance of their crowdfunding campaign and examine how the financial and social performance of MFIs drive campaign funding. Our examination of 220,649 loans paired 173 MFIs supports our arguments. We provide insight into the importance of third-party signals in crowdfunding and into how investors seek to balance social motives with financial concerns in crowdfunded microfinance.
Content may be subject to copyright.
Third-Party Signals in
Crowdfunded Microfinance:
The Role of
Microfinance Institutions
Aaron H. Anglin1 , Jeremy C. Short2, David J. Ketchen Jr.3,
Thomas H. Allison4 , and Aaron F. McKenny5
Crowdfunded microfinance research has routinely examined how campaign characteristics
drive funding to crowdfunding campaigns but has neglected to examine the critical role of the
microfinance institution (MFI). We leverage signaling theory to contend that entrepreneurs’
MFI affiliation is a salient third-party signal that shapes the performance of their crowdfunding
campaign and examine how the financial and social performance of MFIs drive campaign funding.
Our examination of 220,649 loans paired 173 MFIs supports our arguments. We provide insight
into the importance of third-party signals in crowdfunding and into how investors seek to bal-
ance social motives with financial concerns in crowdfunded microfinance.
microfinance, crowdfunding, signaling theory
Micronance holds vast potential as a weapon against poverty by providing impoverished indi-
viduals with modest unsecured loans enabling them to fund small ventures, thereby increasing
their standard of living (Bruton, Khavul, & Chavez, 2011; Canales & Greenberg, 2016). However,
the micronance institutions (MFIs) that make and service these loans have historically strug-
gled to nd sucient capital to fund their operations. MFIs are organizations that provide bank-
ing services to microenterprises in emerging markets and commonly operate with a social
mission (Randøy, Strøm, & Mersland, 2015). The emergence of crowdfunded micronance—the
pairing of localized MFIs in impoverished communities with nonprot crowdfunding
1Department of Entrepreneurship and Innovation, Texas Christian University, Fort Worth, TX, USA
2Tom Love Division of Entrepreneurship and Economic Development, Price College of Business, University of
Oklahoma, Norman, OK, USA
3Department of Management, Harbert College of Business, Auburn University, Auburn, AL, USA
4Department of Management, Information Systems, & Entrepreneurship, Carson College of Business. Washington State
University, Pullman, WA, USA
5Department of Management, College of Business Administration, University of Central Florida, Orlando, FL, USA
Corresponding Author:
Aaron H. Anglin, Department of Entrepreneurship and Innovation, Neeley School of Business, Texas Christian
University, 2900 Lubbock Avenue, Fort Worth, TX, USA.
Email: a. anglin@ tcu. edu
Research Note
Entrepreneurship Theory and Practice
00(0) 1–22
© The Author(s) 2019
Article reuse guidelines:
sagepub. com/ journals- permissions
DOI: 10.1177/1042258719839709
journals. sagepub. com/ home/ etp
Entrepreneurship Theory and Practice 00(0)2
organizations—provides a potential solution to the capital constraints faced by MFIs (Moss,
Neubaum, & Meyskens, 2015). Crowdfunded micronance enables MFIs to share the risk of a
microloan among a set of individuals in wealthy countries who provide funding for the loans
made by MFIs in poorer countries. For example, USA Today described how a Peruvian taxi
driver was able to sustain rather than lose his business by borrowing $725 to repair his vehicle
from 29 people, each of whom provided an average of $25 on Kiva. com—the world’s largest
crowdfunded micronance organization (Marotta & Russell, 2016).
As crowdfunded micronance has blossomed into a billion dollar industry, research on crowd-
funded micronance has focused primarily on how campaign-related characteristics, such as the
use of singular messaging or the gender of the entrepreneur, inuence the funding of individual
crowdfunding campaigns (e.g., Allison, Davis, Short, & Webb, 2015; Galak, Small, & Stephen,
2011; Moss, Renko, Block, & Meyskens, 2018). While valuable in furthering knowledge con-
cerning this important mechanism for nancing poor entrepreneurs, research has largely
neglected the role of the MFI in the crowdfunded micronance ecosystem. MFIs are vital mem-
bers of this ecosystem—they make and manage the initial loans, vet the entrepreneurs, and ser-
vice crowdfunding websites with a supply of crowdfunding campaigns. Crowdfunding investor
contributions made through the crowdfunding website act as a form of “insurance” for loans
already made by the MFIs and allow MFIs to lend more freely to the poor. However, if MFIs
mismanage loans, the crowdfunding investors may not be repaid—reducing their willingness to
provide funds in the future and limiting a valuable supply of capital to poor entrepreneurs (Galak
et al., 2011). Scholars have recognized the importance of the MFI and have called for research
examining the role of the MFI (e.g., Moss et al., 2015), but these calls currently remain
To begin addressing this gap, we draw from signaling research, particularly research examin-
ing third-party signaling, to address the question: How do entrepreneurs’ MFI afliations affect
the performance of their crowdfunding campaigns? Signaling theory contends that signals—
deliberate communications of organizational attributes—are used to infer the quality of the sig-
naler and make decisions when the receiver lacks insider information about the signaler
(Connelly, Certo, Ireland, & Reutzel, 2011). Third-party signaling research examines how ali-
ations with other parties (e.g., strategic alliances, prominent investors, certication boards) act as
signals to communicate the quality of the signaler (e.g., Gulati & Higgins, 2003; Sadeh & Kacker,
2018). Aliations are key signals in that it is less costly for high-quality actors to establish third-
party relations than it is for low-quality actors (Plummer, Allison, & Connelly, 2016; Spence,
1974). For example, an association with a prominent venture capital rm, given its track record
of making protable investments, serves as a positive signal for an entrepreneur intending to take
his or her company public by suggesting that the company is a good investment (e.g., Elitzur &
Gavious, 2003). Because entrepreneurs choose to associate with MFIs in an eort to obtain
nancing, the MFI aliation may serve as a salient signal to crowdfunding investors about the
entrepreneur. Therefore, the characteristics of an MFI should provide insight into whether asso-
ciated campaigns are worth funding. As such, we investigate how nancial and social character-
istics of 173 MFIs inuenced fundraising for 220,649 crowdfunding campaigns.
We oer at least two potential contributions. First, we introduce MFI aliation as a salient
signal in crowdfunded micronance by illustrating how the nancial and social characteristics of
MFIs drive funds to crowdfunding campaigns. A critical line of signaling research is devoted to
teasing out and testing the inuence of specic signals and determining under what conditions
they are inuential (e.g., Ozmel, Reuer, & Gulati, 2013; Reuer, Tong, & Wu, 2012). Following
this lead, crowdfunded micronance research has focused on identifying and testing signals
originating within campaign content. We extend this line of inquiry by being the rst to show the
importance of third-party aliations in raising crowdfunded micronance. In doing so, we
Anglin et al. 3
answer calls for investigation into (a) the role of MFIs in crowdfunded micronance (e.g., Moss
et al., 2015) and (b) how crowdfunding platforms can be used to communicate positive signals
to prospective investors (e.g., Drover et al., 2017).
Second, we extend prior work suggesting that crowdfunded micronance investors seek to
balance their social motives with nancial concerns, but have found social motivations exert a
greater inuence on funding performance than economic motivations. Allison et al. (2015) found
that crowdfunding campaigns cast as an opportunity to help others or as a business opportunity
both receive positive responses from investors; however, those cast as an opportunity to help
others are received more positively. We extend this logic to MFI aliations and show that both
social and nancial MFI characteristics shape funding performance, but that social characteris-
tics have a greater impact on funding. Our work also broadly extends Allison et al. (2015) by
advancing a signaling model of the social versus nancial inuences on funding. Finally, our
work responds to concerns expressed in a recent Entrepreneurship Theory and Practice special
issue that “scholarly knowledge about crowdfunding remains quite limited” despite its large and
rapidly increasing “contribution to entrepreneurial fundraising” (Short, Ketchen, McKenny,
Allison, & Ireland, 2017).
Background and Hypotheses
A Review of Crowdfunded Microfinance Research
While the business models for crowdfunded micronance platforms vary from platform to plat-
form, many models consist of an ecosystem comprising MFIs, entrepreneurs, and crowdfunding
investors brought together via a crowdfunding website such as Kiva or Babyloan. The MFIs are
located in poor communities around the world and are responsible for making and servicing
loans to entrepreneurs. After a loan is made, the MFI uploads information about the loan (e.g.,
amount, repayment terms, location), the entrepreneur (e.g., personal information, why the loan is
needed, their home country), and the lending MFI (Moss et al., 2015). Crowdfunding platforms
organize and post this information as crowdfunding campaigns. Investors, often located in
wealthy countries, can then browse these campaigns and choose to support a portion of the loan.
If the loan is fully funded on the crowdfunding website, the funds are transferred to the MFI who
can then use the funds to lend to other entrepreneurs. As entrepreneurs repay the loans to the
MFI, the crowdfunding investors are reimbursed for their individual contributions. Figure 1 pro-
vides a diagram of this process.
The rapid growth of crowdfunded micronance as a mechanism to fund impoverished entre-
preneurs has led to a burgeoning research stream. The majority of this research has focused on
how entrepreneurs’ characteristics and the contents of their campaigns facilitate fundraising
through crowdfunding platforms. For instance, this research has revealed a preference for female
borrowers (Galak et al., 2011). Similarly, campaigns expressing an entrepreneurial orientation
(Moss et al., 2015), framing the business as an opportunity to help others (Allison et al., 2015),
using charismatic rhetoric (Anglin, Allison, McKenny, & Busenitz, 2014), and leveraging singu-
lar messaging (Moss et al., 2018) all improve funding outcomes. In addition, research examining
the economic impact of crowdfunded micronance indicates that this funding process may serve
as an attractive mechanism to decrease nancial exclusion of poor individuals (e.g., Marakkath
& Attuel-Mendès, 2015).
A notable omission in this research stream is the examination of the role of the MFI. Without
MFIs, billions in individual loans to poor entrepreneurs would never be made (Thorpe, 2018).
Because many of the entrepreneurs do not have the resources or expertise to create campaigns
themselves, without MFIs, the supply of crowdfunding campaigns to the crowdfunding
Entrepreneurship Theory and Practice 00(0)4
platforms would be drastically reduced. Further, it is incumbent on the MFI to ensure that loans
are repaid, ensuring that the crowdfunding investors are subsequently repaid. As such, poor MFI
performance could decrease the likelihood that individual investors will be repaid and reduce the
Figure 1. Crowdfunded microfinance loan process (adapted from Allison, McKenny, & Short, 2013).
Anglin et al. 5
willingness of investors to provide additional funds (Galak et al., 2011). A loss of crowdfunding
contributions, then, decreases the supply of nancial capital to MFIs that can be used to lend to
other entrepreneurs. Likewise, crowdfunded micronance is an inherently social cause (e.g.,
Anglin et al., 2014; Moss et al., 2018). MFIs viewed as not helping or taking advantage of the
poor work against the overall mission of crowdfunded micronance. Maintaining this social
mission is critical to attracting funds because investors want to feel like they are participating in
a good cause (e.g., Allison et al., 2013).
In sum, because MFIs play a critical in role in the crowdfunded micronance ecosystem,
researchers need to examine this important facilitator of the micronance process to develop a
better understanding of this ecosystem. Crowdfunding investors have access to information con-
cerning the MFIs facilitating the loans they fund. It is likely, then, that characteristics of MFIs
inuence funding to crowdfunding campaigns. We turn to research on third-party signaling for
insight into how the MFI may shape funding for individual crowdfunding campaigns.
Third-Party Signals in Crowdfunding Microfinance
Third-party signals are signals that arise from a signaler’s aliation with other parties (Elitzur &
Gavious, 2003; Dineen & Allen, 2016). Third-party signaling research examines how these al-
iations act as a signal to communicate quality concerning the signaler (e.g., Gulati & Higgins,
2003; Sadeh & Kacker, 2018). An aliation with a third party is an important signal because the
third party possesses characteristics that can be used to make inferences about the signaler (e.g.,
Dineen & Allen, 2016; Gulati & Higgins, 2003; Sadeh & Kacker, 2018). For example, when an
investment bank has a track record of backing successful IPOs, an entrepreneur whose IPO is
underwritten by this bank signals that his or her rm is a good investment. Here the investment
bank’s track record positively shapes investors’ perceptions about the likelihood that the IPO will
be successful. Likewise, an aliation with venture development organizations (e.g., Techstars,
JumpStart, Y-Combinator) is a valuable signal for early-stage startups because these organiza-
tions are believed to be highly selective in their application process; consequently, only high
quality rms are admitted to the programs (Plummer et al., 2016). For science-based organiza-
tions, an aliation with a prestigious university, as measured through the number of bibliographic
citations, is a positive signal during an IPO (Colombo, Meoli, & Vismara, 2019). In addition,
third parties are often believed to possess insider information that the receiver does not have
(Bergh, Connelly, Ketchen, & Shannon, 2014). If the third party is willing to associate with an
entrepreneur, it is likely that the “inside information” is positive. Therefore, the aliation is a
positive indicator of the rm’s quality.
Entrepreneurs who use crowdfunded micronance often aliate with MFIs in an attempt to
improve their chances of obtaining nancing. Therefore, an MFI aliation may serve as a salient
signal to crowdfunding investors concerning individual entrepreneurs. While the track records of
individual entrepreneurs are unknown, investors can observe the history of the aliated MFI.
For instance, MFI default rates, protability, and social performance metrics are monitored and
reported on crowdfunding websites. Crowdfunded micronance investors can use this informa-
tion to draw inferences about entrepreneurs and their crowdfunding campaigns. For example, a
low default rate would indicate that an MFI tends to make viable loans; therefore, entrepreneurs
associated with this MFI are likely to repay their obligations. Further, because MFIs vet entre-
preneurs in the initial loan process, an MFI should possess insider information concerning an
entrepreneur. If a well-performing MFI is willing to lend to an entrepreneur, this likely suggests
that the “insider information” is positive. In sum, the characteristics of an MFI should provide
insight on whether a campaign is worth funding, thereby making MFI aliation a potentially
Entrepreneurship Theory and Practice 00(0)6
valuable signal for entrepreneurs. To probe this line of inquiry further, we investigate how the
nancial and social characteristics of MFIs facilitate fundraising in crowdfunding campaigns.
MFI Financial Performance and the Likelihood of Funding
Default rates are a key indicator of nancial performance, because they provide a direct indica-
tion of the likelihood that loans will be repaid (Ahlin, Lin, & Maio, 2011; Field, Pande, Papp, &
Rigol, 2013). Likewise, MFIs with high default rates are unsustainable and limit the social out-
reach of the MFI (Bhatt & Tang, 2001). The default rates of MFIs signal the creditworthiness of
the aliated entrepreneurs. An aliation with an MFI that has a lower default rate is a costly
signal, because only those entrepreneurs who are most likely to repay a loan are able to make this
aliation, while entrepreneurs less likely to repay may have to aliate with MFIs exhibiting
higher default rates. As crowdfunding investors decide whether to provide funds to a crowdfund-
ing campaign, higher MFI default rates signal that the individual entrepreneurs associated with
the MFI represent greater risks. Investors can use the track record of the MFI to infer the likeli-
hood that the entrepreneur will repay the loan. Though crowdfunding investors desire to make a
social impact, research suggests that they are sensitive to the risks of lending to the poor (Allison
et al., 2015). Accordingly, as the risk of lending increases, their willingness to fund the campaign
should decrease with increasing default rates.
While the relationship between default rates and the likelihood of funding should be negative,
this relationship is likely nonlinear. Although risk preferences likely vary among crowdfunding
investors, as default rates rise, investors should become less willing to lend. Indeed, despite
making relatively small investments, crowdfunding investors tend to be risk averse (e.g., Chan
& Parhankangas, 2017; Paravisini, Rappoport, & Ravina, 2017). Likewise, research examining
risk preferences among individuals indicates that the distribution of risk preferences is not nor-
mal, with a greater concentration of individuals exhibiting “lower” risk preferences and a
decreasing number of individuals willing to assume risk as the chance of loss increases (e.g.,
Chavas & Holt, 1996). Those with the lowest risk preferences will stop lending rst, which
should make up a greater volume of potential lenders, leaving a smaller population of potential
lenders as default rates rise. Because fewer individuals are available to lend, the overall impact
of lenders continuing to drop out will diminish. Eventually, lending will simply stop at higher
levels. Thus, we expect the overall negative relationship between default rates and the likelihood
of funding to exhibit a nonlinear relationship where the impact of default rates levels o as
default rates rise. Stated formally,
Hypothesis 1: Higher MFI default rates will be negatively associated with the likelihood of a crowd-
funding campaign being funded. This relationship will be nonlinear, accelerating as default rates
rise, but leveling off at higher default rates.
Another salient indicator of nancial performance is protability. MFIs that are more protable
are viewed as better managed, more selective of their clientele, and capable of making good
lending decisions (e.g., Daher & Le Saout, 2013; Kyereboah‐Coleman & Osei, 2008). An entre-
preneur’s association with more protable MFIs may signal that the entrepreneur is a less-risky
investment. Indeed, management and nance research long has shown that superior protability
increases a third party’s credibility (e.g., Chemmanur & Fulghieri, 1994) and illustrated links
between protability and reputation (e.g., Roberts & Dowling, 2002). Accordingly, associations
with these third parties may act as a key signal within the venture nancing process (Drover
et al., 2017). An association with a more protable MFI is a costly signal because it can only be
realized by entrepreneurs who meet the selective criteria of the MFI. However, because
Anglin et al. 7
crowdfunded micronance investors are socially inclined (Galak et al., 2011), high levels of
protability could indicate an overemphasis on nancial concerns at the expense of social con-
cerns. Seeking greater protability often reduces the nancial inclusiveness of MFIs and results
in a shift away from helping the poor (Aduda & Kalunda, 2012). Thus, although protability
indicates less risk, at higher levels, the perceived lack of emphasis on helping the poor should
negatively impact the willingness to fund crowdfunding campaigns. Accordingly, we expect
MFI protability to exhibit an inverted-U relationship with the likelihood of a crowdfunding
campaign being funded. Stated formally,
Hypothesis 2: An MFI’s protability has an inverted-U relationship with the likelihood of a crowd-
funding campaign being funded.
MFI Social Performance and the Likelihood of Funding
A key indication of social performance in crowdfunded micronance is an MFI’s average loan
size as a percentage of per capita income of the country in which the loan is made (Mersland &
Urgeghe, 2013). When loan size is held constant, this metric increases as larger loans are made
in poorer countries. As such, it is widely seen as indicative of an MFI’s willingness to help
poorer entrepreneurs because the MFIs are willing to incur the risk of helping individuals who
are the least able to repay the loan (e.g., D’Espallier, Hudon, & Szafarz, 2013; Randøy et al.,
Crowdfunded micronance investors lend, in part, because they want to aid poor individuals
and derive good feelings from doing so (Allison et al., 2013, 2015). Because an MFI’s average
loan size as a percentage of per capita income is an indication of an MFI’s willingness to aid the
poor (Randøy et al., 2015), loans made by MFIs higher in this metric are likely viewed as made
to truly poor individuals. Entrepreneurs associated with MFIs higher in this metric signal that
they are the most in need of funds. In addition, the willingness of impoverished entrepreneurs to
incur the risk of the loan, despite their circumstances, is a costly endeavor and may signal a
strong commitment to their business (e.g., Becchetti & Conzo, 2011). Funding these individuals
provides investors with an opportunity to fulll the desire to enable poor entrepreneurs to launch
or grow a business. Accordingly, we expect that as the loan size as a percentage of per capita
income increases that there is an increase in the likelihood of funding for individual loans asso-
ciated with these MFIs.
Although crowdfunded micronance investors are concerned with the social impact of their
investments, they are also conscious of the risk of lending to the poor and expect to be repaid
(Moss et al., 2015). Indeed, crowdfunded micronance research indicates that investors seek to
balance their desire to help others with the risks associated with lending to the poor (Allison
et al., 2015). As larger loans are made to poorer individuals, the risk that these loans may not be
paid back increases (Mersland & Øystein Strøm, 2009). If the loans are not paid back, investors
are not reimbursed for their contributions to the loan. It is unlikely, then, that increasing the aver-
age loan size as a percent of per capita income is viewed as favorable at all levels. In fact,
because higher loan/income ratios are associated with higher repayment risk, at higher levels
further increases may dissuade investors’ willingness to lend. Thus, at higher levels of MFI aver-
age loan size as a percent of per capita income, the aliation with an MFI is no longer viewed as
a positive signal and the likelihood that a campaign will be funded will decrease. In sum, we
expect that this metric has an inverted-U relationship with the likelihood of a loan being funded.
Stated formally,
Entrepreneurship Theory and Practice 00(0)8
Hypothesis 3: An MFI’s average loan size as a percentage of per capita income will have an invert-
ed-U relationship with the likelihood of a crowdfunding campaign being funded.
Another indicator of social performance is the cost to borrowers (e.g., Roberts, 2013; Sun & Im,
2015). MFIs with a stronger emphasis on helping the poor seek to reduce borrowing costs. For
instance, research has found that MFIs with lower interest rates may have a stronger shared
vision of poverty alleviation among loan ocers and employees of an MFI (Sun & Im, 2015).
Further, MFIs charging higher interest rates are often viewed as exploiting the poor, because
these individuals have limited alternative sources of funding (Rosenberg, Gonzalez, & Narain,
2009). An entrepreneur aliated with an MFI with lower borrowing costs signals that investing
in the entrepreneur provides an opportunity to help a poor individual in a fair, socially impactful
manner. In addition, while less-costly to the entrepreneur in monetary terms, obtaining loans at a
lower borrowing cost can be considered costly from a signaling perspective because such loans
suggest that entrepreneurs are more creditworthy. Indeed, micronance research has found that
demonstrated creditworthiness operates as positive signals to other parties (e.g., Becchetti &
Conzo, 2011). As the cost to borrowers rises, however, crowdfunded micronance investors
should be less likely to fund campaigns because this reduces the social impact of their
This relationship should be nonlinear. Although lower borrowing costs are preferred, it is
likely that crowdfunding investors expect there to be costs associated with originating and ser-
vicing the loan. Kiva, for example, states “…most Kiva borrowers do pay interest to our Field
Partners in some form” (Kiva, 2018a). It is reasonable to expect that some investors continue to
lend to entrepreneurs with higher borrowing cost loans. However, the number of investors will-
ing to contribute should shrink as borrowing costs rise in light of the reduced social impact.
Because fewer individuals are available to contribute, the overall impact of investors continuing
to drop out on the willingness to fund a campaign will diminish. Eventually, contribution rates
will stay the same beyond a higher borrowing cost threshold. Thus, we expect the overall nega-
tive relationship between borrowing costs and the likelihood of funding to exhibit a nonlinear
relationship where the impact of borrowing costs levels o as borrowing costs rise. Stated
Hypothesis 4: Higher average loan cost to the entrepreneur will be negatively associated with the
likelihood of a crowdfunding campaign being funded. This relationship will be nonlinear, accelerat-
ing as borrowing costs rise, but leveling off at higher levels of borrowing costs.
Our sample is collected from Kiva, the world’s largest crowdfunded micronance platform,
which has facilitated over 1.1 billion USD in loans (Kiva, 2018a) and partners with MFIs around
the globe that make and administer loans to the poor (Kiva, 2018b). Throughout 2016, Kiva
underwent a redesign of their platform to improve the user experience leading to its current
design. This design features information concerning the MFI administering each loan on the
right-hand side of the individual crowdfunding campaign page and includes nancial and social
performance metrics. Accordingly, crowdfunding investors can easily assess the performance of
the MFI. Because of the redesign of the platform in 2016, we collected data from Kivatools. com
on individual loans posted from January 1, 2017 through February 28, 2018 (when the dataset
ended at the time of collection) and paired these loans with the corresponding data on the MFI
administering the loan. We began with 224,671 loans. The loan data includes direct loans (i.e.,
Anglin et al. 9
loans made without an MFI) because Kiva began allowing direct loans in recent years as a
growth strategy. However, these loans are largely made to U.S.-based small businesses and most
loans posted Kiva are not direct loans. Accordingly, 2,244 direct loans are excluded from our
analysis. This left us with 222,427 loans. We further excluded loans made by Kiva partners oper-
ating under “experimental” status. This status is used for partners to test their Kiva lending pro-
gram before scaling their program. These partnerships often do not have complete information
about MFI performance reported. We exclude an additional 1,778 loans made by these partners.
In total, we were able to match 220,649 loans with 173 MFIs. This represents approximately
98% of all loans posted to Kiva over the time frame of the study.
Dependent Variable
The dependent variable, success, reects whether or not the campaign met its funding target set
at the beginning of the campaign (e.g., Allison et al., 2013; Anglin et al., 2018). This is one of the
most commonly used dependent variables in crowdfunding research and in broader venture
nancing research (e.g., Ahlers, Cumming, Günther, & Schweizer, 2015; Anglin et al., 2018;
Josefy, Dean, Albert, & Fitza, 2017). If the campaign met its target, the success variable was
coded as “1.” If the campaign missed its target, the success variable was coded as “0.”
Independent Variables
Our study addresses four independent variables: two nancial performance variables and two
social performance variables. All of these variables can be viewed on the right-hand side of the
individual crowdfunding campaign web page. For nancial performance, we assess MFI default
rate and protability. Default rate is calculated as the percentage of funded loans that were not
repaid. To capture protability, we use the MFIs’ return on assets (e.g., Kyereboah‐Coleman &
Osei, 2008), which is a commonly used measure of rm-level nancial performance (Jeong &
Harrison, 2017) and the main protability measure for MFIs reported by Kiva. To capture social
performance, we use an MFI’s average loan size as percentage of per capita income and average
cost to borrower. The average loan size as percentage of per capita income is calculated by
dividing the MFI’s average loan size by its country’s gross national income (per capita) and mul-
tiplying by 100. For example, if an MFI’s average loan size is $100 and per capita income is
$1000, this metric would equal 10%. If a country’s per capita income is $10,000 this metric
would equal 1%. We use the MFI’s portfolio yield, calculated as the MFI’s nancial earnings
divided by its average loan portfolio outstanding during a given year, to approximate an MFI’s
average cost to borrower. The default rate and average loan size as percentage of per capita
variables are right-skewed and include zero values. As such, we use the inverse hyperbolic sine
(ihs) transformation to normalize these variables. This is akin to a natural log transformation, yet
it can account for zero values, and is interpreted in the same way as a natural log (Sauerwald, Lin,
& Peng, 2016).
We introduce several controls to account for established antecedents of funding success. We
include the natural log of loan size, natural log of loan term, the natural log of the word length of
the loan descriptions, dummy variables to account for the repayment interval (i.e., monthly, bul-
let, or irregular), and a dummy variable coded as “1” if the loan was made to a group and “0” if
made to an individual to control for loan characteristics shown to inuence funding (e.g., Allison
et al., 2013; Moss et al., 2018). We use a dummy variable coded as “1” for female if the
Entrepreneurship Theory and Practice 00(0)10
crowdfunding campaign includes a female borrower because of the demonstrated preferences for
funding females through crowdfunding (Moss et al., 2015). We control for the risk rating assigned
to the MFI by Kiva resulting from Kiva’s due diligence processes (Galak et al., 2011). Finally,
we use dummy variables to account for the economic sector classication of each loan (e.g.,
Allison et al., 2015).
Statistical Procedures
We use multilevel logistic regression to estimate our results using the melogit command in Stata.
Crowdfunding data are routinely nested, making multilevel designs benecial for estimating
models using crowdfunding data (Anglin et al., 2018). Nested observations violate the indepen-
dence assumptions associated with traditional regression techniques, consequently multilevel
approaches allow us to address this lack of independence (Aguinis & Culpepper, 2015). Further,
the logistic portion of our regression enables us to account for the fact that our dependent vari-
able can only take values of “0” and “1.’” In our dataset, each individual loan is paired with one
of 173 MFIs. As such, each loan is nested within an MFI. While our controls and independent
variables account for a portion of the variance attributed to funding success by the MFIs, there
may be other aspects of MFIs that we cannot account for that inuence success. By accounting
for the nesting of individual loans within MFIs, we are able to account for this unobserved vari-
ance. Each loan (and MFI) is further nested within the country in which they are located, which
may have unique characteristics (cultural, economic, etc.) that inuence funding success. We
must address this variance as well. Therefore, when we estimate our models, the individual loans
make up level 1 (i.e., the xed eects), the MFIs make up level 2, and home countries make up
level 3.
Table 1 provides the descriptive statistics for our sample and Table 2 provides the results for our
hypothesis tests. We provide the log odds coecients and marginal eects (ME), which lend
themselves to a more “linear” interpretation (i.e., a change in x is associated with change in y;
Hoetker, 2007). Hypothesis 1 proposed that an MFI’s default rate will have a negative relation-
ship with the likelihood of a crowdfunding campaign being funded as default rates rise, but level
o at higher default rates. Consistent with this relationship, the linear term is negative (b = −1.77,
p < .01; ME = −0.05, p < .01) and the quadratic term is positive (b = 0.58, p < .01; ME = 0.02, p
< .01) for default rates. Figure 2 indicates a convex relationship between MFI default rates and
funding success consistent with Hypothesis 1. Thus, Hypothesis 1 is supported. Our results sug-
gest that MFI default rates cease to inuence the likelihood of funding (i.e., the relationship is
at) at approximately 4.6%.
Hypothesis 2 proposed an inverted-U relationship between MFI protability and funding suc-
cess. The linear term is negative (b = −0.02, p > .05; ME = 0.00, p > .05) and the quadratic term
is positive (b = 0.00, p > .05; ME = 0.00, p > .05), but neither is signicant. Thus, Hypothesis 2
is not supported.
Hypothesis 3 proposed an inverted-U relationship between an average loan size as a percent-
age of per capita income and funding success. The linear term is positive (b = 0.67, p < .05; ME
= 0.02, p < .01) and the quadratic term is negative (b = −0.16, p < .01; ME = −0.004, p < .01).
Figure 3 is consistent with an inverted-U relationship. Thus, Hypothesis 3 is supported. The
inection point occurs at approximately 12.2% for the average loan size as a percentage of per
capita income.
Anglin et al. 11
Table 1. Descriptive Statistics (N = 220,649).
VariableaMean SD 12 3 4 5 6 7 8 9 10 11 12 13
1 Success 0.95 0.21
2 Loan term (ln) 2.47 0.41 −0.21
3 Loan amount (ln) 6.14 0.91 −0.20 0.22
4 Risk rating 3.12 0.96 0.00 0.09 0.06
5 Length (ln) 4.76 0.44 0.00 0.00 0.00 0.00
6 Female 0.83 0.38 0.15 −0.21 −0.09 0.02 0.00
7 Group 0.17 0.38 −0.02 −0.29 0.29 −0.19 0.00 0.16
8 Bullet 0.08 0.27 −0.04 0.03 0.05 0.00 0.00 −0.20 0.14
9 Irregular 0.39 0.49 0.12 −0.42 −0.16 0.10 0.00 0.22 −0.13 −0.24
10 Monthly 0.53 0.50 −0.09 0.39 0.13 −0.10 0.00 −0.11 0.05 −0.31 −0.85
11 Default rate (ihs) 0.48 0.64 −0.11 0.17 0.02 −0.16 0.00 −0.21 −0.03 0.27 −0.32 0.17
12 Profitability 3.30 8.15 0.05 0.02 −0.15 0.39 0.00 0.00 −0.21 0.07 0.27 −0.30 −0.25
13 Avg. loan size per capita income (ihs) 2.78 1.78 −0.09 0.20 0.18 0.40 0.00 −0.13 −0.25 −0.18 −0.09 0.19 0.18 −0.03
14 Borrower cost 33.60 16.47 0.03 −0.19 −0.03 0.20 0.00 0.11 −0.25 −0.29 0.44 −0.28 −0.05 0.28 0.26
Note. aBecause of the large sample size, all correlations with an absolute value greater than 0.01 are significant at p < .001.
Entrepreneurship Theory and Practice 00(0)12
Table 2. Financial and Social Performance on the Likelihood of Funding.
(log odds)
Main effects
(log odds)
Main effects
Loan term (ln) −2.33***
Loan amount (ln) −1.87***
Risk rating −0.40
Length (ln) −0.00
Female 1.56***
Group 0.12
Irregular 0.05
Monthly 0.12
Fourteen sector dummiesbIncluded Included Included Included
Default rate (ihs) −1.77**
Default rate2 (ihs) 0.58**
Profitability −0.02
Profitability2 0.00
Avg. loan size per capita income (ihs) 0.67*
Avg. loan size per capita income2 (ihs) −0.16***
Cost to borrower −0.14***
Cost to borrower2 0.002***
Constant 23.51***
Variance components
MFI 0.00
Country 5.87***
Log likelihood −25,610.84 −25,527.08
χ29,731.93 9,761.62
Number of loans 220,649 220,649
Number of MFIs 173 173
aStandard errors in parentheses. bEleven sectors significant at p < .001 and one sector significant at p < .05.
Note. *p < .05. **p < .01. ***p < .001.
Anglin et al. 13
Hypothesis 4 proposed that a higher average cost to borrowers will be negatively associated
with the likelihood of a crowdfunding campaign being funded, but will level o at higher levels
of borrowing costs. The linear term is negative (b = −0.14, p < .001; ME = −0.004, p < .001) and
the quadratic term is positive (b = 0.002, p < .001; ME = 0.0001, p < .001). Figure 4 is consistent
with the notion of a negative relationship that levels o at higher borrowing costs. Thus,
Hypothesis 4 is supported. The relationship attens at an approximate cost to borrowers of 35%.
Post Hoc: Dominance Analysis
While our results suggest that an entrepreneur’s MFI aliation serves as a salient signal in
crowdfunded micronance, they provide less insight into which concerns (nancial vs. social)
are most important to investors. Given that investors balance both nancial and social concerns
when evaluating whether to fund entrepreneurs (e.g., Allison et al., 2015; Moss et al., 2015), a
natural extension of such ndings is to examine how investors weight MFI nancial versus social
performance. Accordingly, we conduct a dominance analysis on our independent variables.
Dominance analysis is a means of determining the relative importance of independent variables
through a pairwise comparison of all predictors (Budescu, 1993). The relative importance of a
variable based on the variable’s “reduction of error” in predicting the dependent variable, which
is used to produce dominance statistics for each variable that enables researchers to rank the
importance of variables (Johnson & Lebreton, 2004). Because we examine quadratic eects,
Figure 2. Default rates on success.
Entrepreneurship Theory and Practice 00(0)14
each variable is ranked on the combined standardized dominance score of the linear and qua-
dratic term. The combined standardized dominance scores were as follows: default rates = 0.30,
protability = 0.02, average loan size as a percentage of per capita income = 0.42, and cost to
entrepreneur = 0.27. From most important to least important, the ranking is as follows: (a) aver-
age loan size as a percentage of per capita income (social), (b) default rate (nancial), (c) cost to
borrower (social), and (d) protability (nancial).
We leverage a signaling lens to examine how the nancial and social performance of MFIs shape
the funding of crowdfunded micronance campaigns. While prior studies have demonstrated the
importance of entrepreneurs’ characteristics in attracting funds for crowdfunding campaigns
(e.g., Allison et al., 2013; Moss et al., 2018), the role of the MFI in attracting funding has been
largely neglected. This dearth of research has remained despite calls for more investigation into
the MFI’s role in crowdfunded micronance (e.g., Moss et al., 2015) and calls for further inves-
tigation into how crowdfunding platforms can be used to communicate positive signals to other
prospective investors and stakeholders (Drover et al., 2017). Our work takes a step forward in
answering these calls by providing evidence that an entrepreneur’s MFI aliation is a salient
third-party signal in crowdfunded micronance. Specically, our results suggest that investors
Figure 3. Average loan size as a percent of per capita income on success.
Anglin et al. 15
may consider the nancial and social characteristics of MFIs in drawing inferences about the
quality of individual crowdfunding campaigns. We provide insight into how investors balance
the social and nancial aspects of lending in crowdfunded micronance. We show that both
nancial and social concerns are important with regard to MFIs, with an emphasis on helping
being the largest factor driving funding. Our work has both theoretical implications for the
understanding of third-party signals and practical implications for crowdfunded micronance
Theoretical Implications
Third-party signaling research routinely examines how aliation with another party shapes
outcomes for the signaler. For example, a rm’s aliation with prestigious underwriters during
an IPO sends a positive signal to investors because such underwriters are believed to have an
ability to recognize quality (Bergh et al., 2014). This is often because the underwriters have a
history of making protable investments (Higgins & Gulati, 2003). While it is common to view
these third-party aliations in binary (i.e., they exist or they do not; (Plummer et al., 2016) or
linear terms (e.g., more prestige is better; Chen, Hambrick, & Pollock, 2008), research has
shown that third-party signals often accrue nonlinear benets to the signaler (e.g., Pollock,
Chen, Jackson, & Hambrick, 2010). Our work lends further support to this nonlinear view. We
demonstrate that the nancial and social characteristics of MFIs exhibit nonlinear relationships
with the likelihood of funding. For instance, our results suggest that while investors value the
social impact of their investment and seek to fund entrepreneurs associated with MFIs lending
to the poorest individuals, this relationship diminishes as the average loan size becomes a larger
percentage of per capita income, eventually becoming negative (at approximately 12.2%).
Accordingly, an aliation with a socially-minded MFI cannot be viewed as a universally
Figure 4. Cost to borrower on success.
Entrepreneurship Theory and Practice 00(0)16
positive signal, but rather is dependent on the extent of the MFI’s social performance. Likewise,
while increasing default rates have a negative impact on funding, investors appear to be willing
to continue lending up to a point (until defaults exceed 4.6%). Thus, our results suggest a
threshold up to which investors are willing to risk loss in order to fund impoverished
Our work also furthers understanding of how the socially oriented characteristics of the third
party may benet the signaler. Third-party signaling research has largely focused on the econom-
ically oriented characteristics of third parties, such as IPO track record or market position (e.g.,
Chang, 2004; Hoehn-Weiss & Karim, 2014), paying much less attention to third parties’ socially
oriented characteristics. However, organizations that engage in socially oriented causes can
enhance the reputation of the rm (Aguinis & Glavas, 2012). For example, recent research in
strategic management suggests that engaging in socially oriented causes may lead to positive
investment evaluations (e.g., Ioannou & Serafeim, 2015). It follows, then, that collaborating with
organizations engaged in social activities may confer benets as well, sending a positive signal
to receivers. Our results support this idea: entrepreneurs can increase their likelihood of funding
when associated with MFIs high in social performance (i.e., lower borrowing costs and higher
loan size per capita).
Our study provides insight into investor motivations in crowdfunded micronance. Using
cognitive evaluation theory, past research indicates that while both cues are positively related to
funding (Allison et al., 2015), intrinsic cues (i.e., framing the crowdfunding campaign as an
opportunity to help others) dominate extrinsic cues (i.e., framing crowdfunding campaign as a
business opportunity). When examining MFIs, our results appear consistent with this work. Our
dominance analysis suggests that average loan size as a percentage of per capita income is the
most dominant eect in our models (social), default rates (nancial) and cost to borrowers
(social) have similar eects, and protability (nancial) has little inuence on funding.
Accordingly, while investors balance social and nancial concerns when examining their cam-
paign contributions, social concerns appear to weigh more heavily in investment decisions. This
adds credence to the idea put forward by Allison et al. (2015) that the emphasis on social con-
cerns speaks to the “relative importance of intrinsic cues among a group of lenders who are
intrinsically motivated and self-select into participating in crowdfunded micronance” (p. 66).
Contrary to our second hypothesis, MFI protability had no inuence on the likelihood that a
campaign was funded. Researchers have long noted that superior nancial performance increases
a third party’s credibility (e.g., for VCs or investment banks; Chemmanur & Fulghieri, 1994),
thereby making associations with such parties a key signal (Drover et al., 2017). However, our
nding might be explained by the unique aims of micronance investors. Prior work has charac-
terized these investors as seeking to balance social and economic concerns (Galak et al., 2011;
Allison et al., 2015). Our results may hint at nuances regarding the latter. MFI default rates signal
the creditworthiness of aliated entrepreneurs and provide a direct indication of the likelihood
that an individual investor will be repaid. Repayment personally aects investors’ ability to
recoup their contribution and to direct future contributions toward other loans. MFI protability,
on the other hand, has no direct implications for investors in that there is no immediate personal
eect. Although entrepreneurs may endure a more rigorous screening process to partner with
more protable MFIs, because investors are not personally aected by MFI protability, it is
possible that investors assign little or no value to aliations with an MFI higher in protability.
From a signaling perspective, for a signal to be a separating mechanism between lower and
higher quality rms, receivers must assign value to the signal (Bergh et al., 2014). This concept
is sometimes referred to as “signaling t,” wherein a particular signal must align with the expec-
tations or concerns of receivers in order to be valuable (Connelly et al., 2011). Even though an
aliation with a more protable MFI may be observable and costly in a signaling sense, perhaps
Anglin et al. 17
it is not an important signal because it does not “t” with the personal concerns of the
Practical Implications
Our results hold practical implications for Kiva and other crowdfunded micronance platforms.
Specically, because crowdfunding platforms need individuals to contribute to loans, our nd-
ings can be leveraged by such platforms in making and managing MFI partnerships. Crowdfunding
organizations typically conduct due diligence to assess the nancial stability and social impact of
MFIs when forming a new partnership. This due diligence is performed in an eort to provide
investors with a sense of security when making crowdfunding contributions and to provide evi-
dence that their contributions are going to a good cause (e.g., Kiva, 2018c). Our results provide
direct insight into how investors may react to the partnerships they make. For instance, our
results suggest that the inuence of default rates on the likelihood of funding levels out at values
above 4.6% (22 are above this rate and 8 have default rates above 10%). Because Kiva promotes
a 97% repayment (3% default) rate (Kiva, 2018a), expectations regarding payments are high and
investors appear to only tolerate a small amount of defaults above this rate. As such, Kiva should
be cautious about maintaining partnerships with MFIs with default rates in excess of 4.6%.
Although a relatively small portion (13%) of MFIs have default rates beyond the inection
point, this is not the case for the two social performance variables: 51 MFIs (30%) have a cost to
borrower greater than 35% and 58 MFIs (34%) have an average loan size as a percentage of per
capita income greater than 12.2%. Our results suggest that this could be troublesome for Kiva in
attracting funds to individual campaigns aliated with these MFIs. However, it is worth noting
the default rates average about 1% for these MFIs. As such, it is possible to that low default rates
could oset the negative impact of these metrics when they are at levels beyond our calculated
inection points. While we hope our results provide crowdfunding platforms with insight into
how investors may react to the nancial and social performance of MFIs, we hasten to add that
our analysis only examines each performance metric in isolation and weights each metric indi-
vidually. We do not address the interplay among these performance metrics. Thus, future research
might dive deeper into our ndings and explore how performance congurations impact fund-
raising. Such research would provide insight into the tradeos that accompany nancial and
social performance of MFIs.
Limitations and Future Research
Our research examines how MFI aliation can serve as a salient signal for entrepreneurs in
crowdfunded micronance. MFIs, however, may send signals in their own right that shape either
crowdfunding performance or their partnership with Kiva, which we do not address. These sig-
nals may consist of traditional costly signals, such as partnerships, awards, or certications.
However, they may also include “costless” signals, which are dened as nonbinding and unver-
iable messages (Bhattacharya & Dittmar, 2003). Costless signals are intriguing in crowdfund-
ing contexts because, while typically believed to be of little value, they have shown to be
important in driving crowdfunding outcomes (e.g., Anglin et al., 2018). Kiva provides MFI
descriptions and mission statements made by MFIs on its website. For instance, one MFI in our
sample states
Credo stands out in the Georgian micronance sector because of its innovative, high quality products
and its unique delivery mechanism focused on clients’ wellbeing…It strives to oer exible, conve-
nient, transparent products and services to best meet the needs of its clients.
Entrepreneurship Theory and Practice 00(0)18
Such statements are “costless” in the sense that they are dicult to verify and are a matter of
perspective. Nevertheless, an MFI describing itself as “innovative,” “exible,” and “transparent”
may shape both the willingness of investors to provide funds to crowdfunding campaigns and
Kiva’s willingness to partner with MFIs. Future researchers could leverage a series of qualitative
interviews with Kiva to determine if/which costless signals resonate during the partnership pro-
cess. These signals could then be evaluated for their inuence on driving funds to individual
crowdfunding campaigns.
Our independent variable measures reect important aspects of nancial and social perfor-
mance. However, a complete understanding of the implications of organizational performance
requires accounting for the heterogeneity of environments, strategies, and management practices
as well as the desires of multiple stakeholders (Richard, Devinney, Yip, & Johnson, 2009). As
such, it is unlikely that any one study could fully account for the complexity of how MFI perfor-
mance shapes lending decisions. Thus, while we provide a foundation upon which to investigate
how MFI performance shapes lending decisions, additional research is needed to fully under-
stand the performance indicators that drive investment. For example, community initiatives (e.g.,
AIDS education or literacy programs) that go beyond making loans to the poor may serve as an
indicator of social performance (e.g., van Rooyen, Stewart, & de Wet, 2012). Because Kiva pro-
vides‘certications’ on the MFI’s webpage for engaging in such activities, future research might
explore how engaging these community initiatives impacts investment in crowdfunding
Our work provides evidence that MFI aliation operates as a key signal for entrepreneurs in
crowdfunded micronance. We nd that an MFI’s nancial and social performance are predic-
tive of the likelihood of an individual crowdfunding campaign being funded. For researchers, we
take a needed step forward in understanding the role of the MFI as an important, but often over-
looked, contextual factor aecting crowdfunded micronance. For practitioners, we provide
insight into how investors should be attentive to the characteristics of partnering MFIs, which we
hope will inform crowdfunding platform–MFI partnership decisions.
Declaration of Conflicting Interests
The author(s) declared no potential conicts of interest with respect to the research, authorship, and/or
publication of this article.
The author(s) received no nancial support for the research, authorship, and/or publication of this article.
Aaron H. Anglin https:// orcid. org/ 0000- 0003- 0857- 2081
Thomas H. Allison https:// orcid. org/ 0000- 0001- 8873- 9798
Aduda, J., & Kalunda, E. (2012). Financial inclusion and nancial sector stability with reference to Kenya:
A review of literature. Journal of Applied Finance and Banking, 2(6), 95–120.
Aguinis, H., & Glavas, A. (2012). What we know and don’t know about corporate social responsibility: A
review and research agenda. Journal of Management, 38(4), 932–968.
Anglin et al. 19
Aguinis, H., & Culpepper, S. A. (2015). An expanded decision-making procedure for examining cross-level
interaction eects with multilevel modeling. Organizational Research Methods, 18(2), 155–176.
Ahlers, G. K. C., Cumming, D., Günther, C., & Schweizer, D. (2015). Signaling in equity crowdfunding.
Entrepreneurship Theory and Practice, 39(4), 955–980.
Ahlin, C., Lin, J., & Maio, M. (2011). Where does micronance ourish? micronance institution perfor-
mance in macroeconomic context. Journal of Development Economics, 95(2), 105–120.
Allison, T. H., McKenny, A. F., & Short, J. C. (2013). The eect of entrepreneurial rhetoric on microlending
investment: An examination of the warm-glow eect. Journal of Business Venturing, 28(6), 690–707.
Allison, T. H., Davis, B. C., Short, J. C., & Webb, J. W. (2015). Crowdfunding in a prosocial microlend-
ing environment: Examining the role of intrinsic versus extrinsic cues. Entrepreneurship Theory and
Practice, 39(1), 53–73.
Anglin, A. H., Allison, T. H., McKenny, A. F., & Busenitz, L. W. (2014). The role of charismatic rhetoric in
crowdfunding: An examination with computer-aided text analysis. In J. C. Short (Ed.), Social entrepre-
neurship and research methods (Vol. 919–48). Bingley, UK: Emerald Group Publishing.
Anglin, A. H., Short, J. C., Drover, W., Stevenson, R. M., McKenny, A. F., & Allison, T. H. (2018). The
power of positivity? The inuence of positive psychological capital language on crowdfunding perfor-
mance. Journal of Business Venturing, 33(4), 470–492.
Becchetti, L., & Conzo, P. (2011). Enhancing capabilities through credit access: Creditworthiness as a sig-
nal of trustworthiness under asymmetric information. Journal of Public Economics, 95(3-4), 265–278.
Bergh, D. D., Connelly, B. L., Ketchen, D. J., & Shannon, L. M. (2014). Signalling theory and equilibrium
in strategic management research: An assessment and a research agenda. Journal of Management Stud-
ies, 51(8), 1334–1360.
Bhatt, N., & Tang, S. -Y. (2001). Delivering micronance in developing countries: Controversies and policy
perspectives. Policy Studies Journal, 29(2), 319–333.
Bhattacharya, U., & Dittmar, A. (2003). Costless versus costly signaling: Theory and evidence from share
Bruton, G. D., Khavul, S., & Chavez, H. (2011). Microlending in emerging economies: Building a new line
of inquiry from the ground up. Journal of International Business Studies, 42(5), 718–739.
Budescu, D. V. (1993). Dominance analysis: A new approach to the problem of relative importance of pre-
dictors in multiple regression. Psychological Bulletin, 114 (3), 542–551.
Canales, R., & Greenberg, J. (2016). A matter of (relational) style: Loan ocer consistency and exchange
continuity in micronance. Management Science, 62(4), 1202–1224.
Chan, C. S. R., & Parhankangas, A. (2017). Crowdfunding innovative ideas: How incremental and radical
innovativeness inuence funding outcomes. Entrepreneurship Theory and Practice, 41(2), 237–263.
Chang, S. J. (2004). Venture capital nancing, strategic alliances, and the initial public oerings of Internet
startups. Journal of Business Venturing, 19(5), 721–741.
Chavas, J. -P., & Holt, M. T. (1996). Economic behavior under uncertainty: A joint analysis of risk prefer-
ences and technology. The Review of Economics and Statistics, 78(2), 329–335.
Chemmanur, T. J., & Fulghieri, P. (1994). Investment bank reputation, information production, and nan-
cial intermediation. The Journal of Finance, 49(1), 57–79.
Chen, G., Hambrick, D. C., & Pollock, T. G. (2008). Puttin' on the Ritz: Pre-IPO enlistment of prestigious
aliates as deadline-induced remediation. Academy of Management Journal, 51(5), 954–975.
Colombo, M. G., Meoli, M., & Vismara, S. (2019). Signaling in science-based IPOs: The combined eect
of aliation with prestigious universities, underwriters, and venture capitalists. Journal of Business
Venturing, 34(1), 141–177.
Connelly, B. L., Certo, S. T., Ireland, R. D., & Reutzel, C. R. (2011). Signaling theory: A review and assess-
ment. Journal of Management, 37(1), 39–67.
Daher, L., & Le Saout, E. (2013). Micronance and nancial performance. Strategic Change, 22(1-2),
Entrepreneurship Theory and Practice 00(0)20
Dineen, B. R., & Allen, D. G. (2016). Third party employment branding: Human capital inows and
outows following “Best Places to Work” certications. Academy of Management Journal, 59(1),
Drover, W., Busenitz, L., Matusik, S., Townsend, D., Anglin, A., & Dushnitsky, G. (2017). A review and
road map of entrepreneurial equity nancing research: Venture capital, corporate venture capital, angel
investment, crowdfunding, and accelerators. Journal of Management, 43(6), 1820–1853.
D’Espallier, B., Hudon, M., & Szafarz, A. (2013). Unsubsidized micronance institutions. Economics Let-
ters, 120(2), 174–176.
Elitzur, R., & Gavious, A. (2003). Contracting, signaling, and moral hazard: a model of entrepreneurs,
‘angels,’ and venture capitalists. Journal of Business Venturing, 18(6), 709–725.
Field, E., Pande, R., Papp, J., & Rigol, N. (2013). Does the classic micronance model discourage entre-
preneurship among the poor? Experimental evidence from India. American Economic Review, 103(6),
Galak, J., Small, D., & Stephen, A. T. (2011). Micronance decision making: A eld study of prosocial
lending. Journal of Marketing Research, 48(SPL), S130–S137.
Gulati, R., & Higgins, M. C. (2003). Which ties matter when? The contingent eects of interorganizational
partnerships on IPO success. Strategic Management Journal, 24(2), 127–144.
Higgins, M. C., & Gulati, R. (2003). Getting o to a good start: The eects of upper echelon aliations on
underwriter prestige. Organization Science, 14(3), 244–263.
Hoehn-Weiss, M. N., & Karim, S. (2014). Unpacking functional alliance portfolios: How signals of viabil-
ity aect young rms' outcomes. Strategic Management Journal, 35(9), 1364–1385.
Hoetker, G. (2007). The use of logit and probit models in strategic management research: Critical issues.
Strategic Management Journal, 28(4), 331–343.
Ioannou, I., & Serafeim, G. (2015). The impact of corporate social responsibility on investment recommen-
dations: Analysts' perceptions and shifting institutional logics. Strategic Management Journal, 36(7),
Jeong, S. -H., & Harrison, D. A. (2017). Glass breaking, strategy making, and value creating: Meta-an-
alytic outcomes of women as CEOs and TMT members. Academy of Management Journal, 60(4),
Johnson, J. W., & Lebreton, J. M. (2004). History and use of relative importance indices in organizational
research. Organizational Research Methods, 7(3), 238–257.
Josefy, M., Dean, T. J., Albert, L. S., & Fitza, M. A. (2017). The role of community in crowdfunding suc-
cess: Evidence on cultural attributes in funding campaigns to “save the local theater”. Entrepreneurship
Theory and Practice, 41(2), 161–182.
Kiva. (2018a). About us. Retrieved from https://www. kiva. org/ about
Kiva. (2018b). Where Kiva works. Retrieved from https://www. kiva. org/ about/ where- kiva- works/ partner
Kiva. (2018c). Due diligence and monitoring. Retrieved from https://www. kiva. org/ about/ due- diligence
Kyereboah‐Coleman, A., & Osei, K. A. (2008). Outreach and protability of micronance institutions: The
role of governance. Journal of Economic Studies, 35(3), 236–248.
Marakkath, N., & Attuel-Mendès, L. (2015). Can micronance crowdfunding reduce nancial exclusion?
regulatory issues. International Journal of Bank Marketing, 33(5), 624–636.
Marotta, D. J., & Russell, M. (2016). Micro loans: Give a little, help a lot. USA Today, January, 16.
Retrieved from https://www. usatoday. com/ story/ money/ personalnance/ 2016/ 01/ 16/ advice- iq- micro-
loans/ 78877246/
Mersland, R., & Øystein Strøm, R. (2009). Performance and governance in micronance institutions. Jour-
nal of Banking & Finance, 33(4), 662–669.
Mersland, R., & Urgeghe, L. (2013). International debt nancing and performance of micronance institu-
tions. Strategic Change, 22(1-2), 17–29.
Anglin et al. 21
Moss, T. W., Neubaum, D. O., & Meyskens, M. (2015). The eect of virtuous and entrepreneurial orienta-
tions on micronance lending and repayment: A signaling theory perspective. Entrepreneurship Theory
and Practice, 39(1), 27–52.
Moss, T. W., Renko, M., Block, E., & Meyskens, M. (2018). Funding the story of hybrid ventures: Crowd-
funder lending preferences and linguistic hybridity. Journal of Business Venturing, 33(5), 643–659.
Ozmel, U., Reuer, J. J., & Gulati, R. (2013). Signals across multiple networks: How venture capital and
alliance networks aect interorganizational collaboration. Academy of Management Journal, 56(3),
Paravisini, D., Rappoport, V., & Ravina, E. (2017). Risk aversion and wealth: Evidence from person-to-per-
son lending portfolios. Management Science, 63(2), 279–297.
Plummer, L. A., Allison, T. H., & Connelly, B. L. (2016). Better together? signaling interactions in new
venture pursuit of initial external capital. Academy of Management Journal, 59(5), 1585–1604.
Pollock, T. G., Chen, G., Jackson, E. M., & Hambrick, D. C. (2010). How much prestige is enough? Assess-
ing the value of multiple types of high-status aliates for young rms. Journal of Business Venturing,
25(1), 6–23.
Randøy, T., Strøm, R. Ø.., & Mersland, R. (2015). The impact of entrepreneur-CEOs in micronance insti-
tutions: A global survey. Entrepreneurship Theory and Practice, 39(4), 927–953.
Reuer, J. J., Tong, T. W., & Wu, C. W. (2012). A signaling theory of acquisition premiums: Evidence from
IPO targets. Academy of Management Journal, 55(3), 667–683.
Richard, P. J., Devinney, T. M., Yip, G. S., & Johnson, G. (2009). Measuring organizational performance:
Towards methodological best practice. Journal of Management, 35(3), 718–804.
Roberts, P. W. (2013). The prot orientation of micronance institutions and eective interest rates. World
Development, 41, 120–131.
Roberts, P. W., & Dowling, G. R. (2002). Corporate reputation and sustained superior nancial perfor-
mance. Strategic Management Journal, 23(12), 1077–1093.
Rosenberg, R., Gonzalez, A., & Narain, S. (2009). The new moneylenders: are the poor being exploited by
high microcredit interest rates? In Moving beyond storytelling: Emerging research in micronance (pp.
145–181): Emerald Group Publishing Limited.
Sadeh, F., & Kacker, M. (2018). Quality signaling through ex-ante voluntary information disclosure in
entrepreneurial networks: Evidence from franchising. Small Business Economics, 50(4), 729–748.
Sauerwald, S., Lin, Z. J., & Peng, M. W. (2016). Board social capital and excess CEO returns. Strategic
Management Journal, 37(3), 498–520.
Short, J. C., Ketchen, D. J., McKenny, A. F., Allison, T. H., & Ireland, R. D. (2017). Research on crowd-
funding: Reviewing the (very recent) past and celebrating the present. Entrepreneurship Theory and
Practice, 41(2), 149–160.
Spence, A. M. (1974). Market signaling: Informational transfer in hiring and related screening processes.
Cambridge, MA: Harvard University Press.
Sun, S. L., & Im, J. (2015). Cutting micronance interest rates: An opportunity co-creation perspective.
Entrepreneurship Theory and Practice, 39(1), 101–128.
Thorpe, D (2018). Kiva is really a crowdfunded bank for refugees and other 'unbankables'. Forbes, Septem-
ber, 24. Retrieved from https://www. forbes. com/ sites/ devinthorpe/ 2018/ 09/ 24/ kiva- is- really- a- crowd-
funded- bank- for- refugees- and- other- unbankables/# 533b491a220a
van Rooyen, C., Stewart, R., & de Wet, T. (2012). The impact of micronance in sub-Saharan Africa: A
systematic review of the evidence. World Development, 40(11), 2249–2262.
Author Biographies
Aaron H. Anglin (PhD, University of Oklahoma) is an Assistant Professor in the Department of
Entrepreneurship and Innovation at Texas Christian University. His research is primarily focused
on novel forms of new venture nancing in both developed and emerging markets, family
Entrepreneurship Theory and Practice 00(0)22
business, and the social and psychological aspects of entrepreneurship. His research has appeared
in journals such as Journal of Management, Journal of Business Venturing, Entrepreneurship
Theory and Practice, The Leadership Quarterly, Family Business Review, and Journal of
Business Venturing Insights and has been featured in outlets such as Forbes.
Jeremy C. Short (PhD, Louisiana State University) serves as the Michael F. Price Chair in
Entrepreneurship in the Tom Love Division of Entrepreneurship and Economic Development,
Price College of Business, University of Oklahoma. His research explores the determinants of
performance in entrepreneurship, franchising, family business, and strategic management. His
work has appeared in journals such as Academy of Management Journal, Journal of Management,
Journal of Business Venturing, Strategic Entrepreneurship Journal, Entrepreneurship Theory
and Practice, and Strategic Management Journal and has been featured in outlets such as Forbes,
CNBC, Scientic American Mind, and the Wall Street Journal.
David J. Ketchen Jr. (PhD, PennsylvaniaState University) serves as a Harbert Eminent Scholar
and Professor of Management in the Harbert College of Business at Auburn University. His
research interests include entrepreneurship and franchising, methodological issues in organiza-
tional research, strategic supply chain management, and the determinants of superior organiza-
tional performance. His work has appeared in journals such as Academy of Management Journal,
Administrative Science Quarterly, Entrepreneurship Theory and Practice, and Strategic
Management Journal.
Thomas H. Allison (PhD,University of Oklahoma) is an Associate Professor in the Department
of Entrepreneurship and Innovation of the Neeley College of Business at Texas Christian
University. His research centers on entrepreneurial nance and the eects of narrative and rhet-
oric on investment decisions. His work has appeared in journals such as Academy of Management
Journal, Journal of Business Venturing, Strategic Entrepreneurship Journal, and Entrepreneurship
Theory and Practice.
Aaron F. McKenny (PhD, University of Oklahoma) is an Assistant Professor of entrepreneur-
ship at the Kelley School of Business, Indiana University. His research is primarily focused on
the intersection of entrepreneurship and strategic management with an emphasis on the role of
social and other non-economic phenomena in organizational settings. He is on the review boards
for the Journal of Management, Journal of Business Venturing and Family Business Review. His
research has been published in several journals, including Journal of Management, Journalof
Business Venturing, Strategic Entrepreneurship Journal, Entrepreneurship Theory and Practice,
Organizational Research Methods, Journal of the Academy of Marketing Science, Annual Review
of Organizational Psychology and Organizational Behavior, and Family Business Review.
... We predict that MFIs can take on a similar role in crowdfunding microfinance settings. But to do so, they must maintain strong reputations (Kleinert et al., 2020), such as by affiliating with entrepreneurs about which they have positive inside information (Anglin et al., 2020). Because the crowdfunding platforms have long-term incentives to support highquality projects (Roma et al., 2021), which enables them to retain lenders interested in future campaigns, MFIs also must address information asymmetry and adverse selection issues to identify good borrowers (Stiglitz & Weiss, 1981), despite the inherent uncertainties of long-term crowdfunding outcomes (Mollick, 2014). ...
... With these findings, our research makes two main contributions. First, with regard to research on thirdparty signalling, we extend prior findings about the importance of third-party affiliations for raising crowdfunding finance (Anglin et al., 2020) by specifying which characteristics of and precisely how third-party signals can address the refugee finance gap. Notably, social performance is more difficult to measure than financial performance, and no consensus exists regarding what motivates the crowd of lenders, such as whether they prioritize financial or social motives when making lending decisions (Galak A. P. M. Gama et al. et al., 2011). ...
... Our findings help clarify fragmented entrepreneurial signalling findings and reveal which signals are most useful for entrepreneurs (Colombo, 2021), and we do so in the critical crowdfunded microfinance context, at the frontier of literature pertaining to refugees and entrepreneurship (Desai et al., 2021). In addition to answering calls to examine how MFIs can facilitate resource acquisition by underserved entrepreneurs (Anglin et al., 2020;Moss et al., 2015), we offer cross-disciplinary insights into which characteristics of intermediaries can facilitate links between the supply (crowd of lenders) and demand (refugee entrepreneurs) sides in prosocial crowdfunding. ...
Full-text available
Despite the relevance of crowdfunding as a financing tool for underrepresented entrepreneurs, prior research pays scant attention to the funding gap for refugee entrepreneurs. Using a composite framework that integrates both entrepreneurship research and signalling theory, the current study investigates how microfinance institutions (MFIs) and refugee entrepreneurs can deploy signals to pursue entrepreneurial opportunities on digital platforms. The results, based on refugee data pertaining to 5615 loans on Kiva during 2015–2018, reveal that when refugee loan campaigns are affiliated with an MFI that itself features lower default rates, achieves high profitability, adopts an entrepreneurial support orientation, operates transnationally and is digitally focused, the campaign achieves better crowdfunding performance outcomes than refugees campaigns affiliated with an MFI that lacks these features. These findings provide clear evidence that when MFIs offer reputational signals, visible to the crowd of lenders, it can increase entrepreneurial financing and democratize resource acquisition among financially excluded refugee entrepreneurs. Plain English Summary Can microfinance institutions boost crowdfunding among refugee entrepreneurs and their small businesses? Yes, they can. Third-party signals may support growth in alternative finance for #refugees. While research on entrepreneurship has largely targeted immigrant entrepreneurs, the refugee context has been neglected, namely how refugee entrepreneurs fund their economic activities. With signalling literature on new venture financing of entrepreneurship being greatly fragmented, we contribute to the understanding of how crowdfunding microfinance boost venture financing of refugees. We study the gain of legitimacy by refugee entrepreneurs displayed through reputational signals intertwined with the reputation of microfinance institutions (MFI). Our results reveal higher success in funding outcomes when the loan campaign is linked with microfinance institutions with lower loan default rates, higher profitability, driven by entrepreneurial support, operating internationally and with a digital presence, compared with MFI that lacks these features. Our work has relevant implications for underrepresented refugee entrepreneurs, crowdfunding actors, policymakers and scholars. Our findings indicate that the affiliation between refugees-microfinance institutions creates certain reputational signals which enhance entrepreneurial finance and shape conditions for societal integration in the host country. For crowdfunding platforms, we show that to develop an effective, self-perpetuating entrepreneurial ecosystem, they should work to build their reputation among lenders, by capitalizing on and making third-party signals more readily available. At the same time, they must conduct due diligence to assess and monitor MFIs’ behaviour. Policy makers are recommended to build up on this digital microfinance experience to enhance new venturing finance refugee programs. We, thus, extend prior findings about the importance of third-party affiliations by establishing a composite framework of third-party signals in the context of new venture financing for financially excluded communities, and refugees in particular. Accordingly, for scholars, we offer cross-disciplinary insights into which characteristics of intermediaries can facilitate links between the supply (crowd of lenders) and demand (refugee entrepreneurs) sides in prosocial crowdfunding.
... First, the countless number of signaling constructs available tend to be ambiguous and overlapping, and researchers often use different labels to refer to signal constructs with similar meanings. Consider the terms used to refer to signals from third parties: third-party signals (Anglin et al., 2020), investor-generated signals (Wang, Mahmood et al., 2019), external signals (Colombo et al., 2019), or endorsement signals (Courtney et al., 2017). Second, entrepreneurship signaling research keeps introducing new constructs. ...
... If receivers embrace an economic return logic, they prefer signals that indicate return potential, such as financial forecasts or patents, but if other receivers adopt a community logic, they might ignore such signals (Goethner, Luettig et al., 2021;Scheaf et al., 2018). A social impact logic tends to mark impact accelerators and microfinance investors, so these receivers interpret signals about entrepreneurs' social credentials as more critical than signals about their economic quality (Anglin et al., 2020;Yang et al., 2020). Second, receivers with different institutional logics might draw different conclusions from the same signal (Luo et al., 2020). ...
... Second, exchanges on platforms have followed from developments in digitalization, so new ventures often interact on online platforms with heterogeneous, geographically dispersed, and anonymous groups of signal receivers, such as donors, lenders, or investors (e.g., Ahlers et al., 2015;Anglin et al., 2020). Unlike targeting, say, a single selected venture capitalist, it is not possible for entrepreneurs on digital platforms to establish dedicated signals that will resonate with all of the many, diverse receivers on online platforms. ...
Full-text available
A rapidly expanding body of entrepreneurship literature draws on signaling theory. Yet as the field grows, common understanding of the theory’s underlying constructs has become increasingly fuzzy and riddled with ambiguities. To establish a common ground for entrepreneurship scholars, we take stock of 172 articles in a systematic literature review and develop a taxonomy of signal constructs. In an effort to increase the clarity of signal constructs further, we apply this taxonomy to assess the signal constructs’ boundary conditions, relationships, and interplays with complementary theories in entrepreneurship contexts. Finally, we leverage the novel insights to identify promising opportunities for further theory-based developments in the field.
... According to the model, there are four perspectives/areas of business activity: research and development, internal (operational) and customer and financial [36]. The main advantage of this model is the identification of value drivers affecting the financial and non-financial (intangible) value and the study of their mutual relationships [37,38]. The value drivers identified by Norton and Kaplan correspond to, among others, those proposed by Walters [39,40] and Rappaport [7]. ...
Full-text available
This paper presents the results of a survey of small and medium-sized enterprises (SMEs) in Poland that have benefited from crowdfunding (CF). Based on these results, a new business model was developed. On this basis, the CF equity, reward and donation models were analyzed, and the impact of CF on the way of creating value in the company in the context of sustainable development was determined. The survey results show that the use of CF promotes the sustainable development of SMEs in Poland and significantly impacts their business model. In practical terms, this research has contributed to a better understanding of value creation by these companies. The results of our analysis are useful for consulting companies and CF platforms that help SMEs organize campaigns. In theoretical terms, the study conducted and the methodology used allow the presentation of a new definition of CF and a sustainable business model for a company using CF as well as contribute to the value management theory in SMEs.
... Meanwhile, according to [14], the usage of crowdfunding has been widely used in Islamic banking and finance (IBF) industry especially as microfinance and as well as one of the tools in Islamic finance technology (FinTech) [21]. However, crowdfunding has less disclosed as an alternative in research funding especially in the field of IBF [15]. ...
Full-text available
This study aims to investigate the factors that influence BIMB customers' intention to utilise Sadaqa House, a digitalized Islamic crowdfunding platform. This study utilised the Theory of Planned Behavior (TPB) as a conceptual framework with four (4) independent variables, namely attitude, subjective norms, perceived behavioural control, and religiosity, and one dependent variable, namely intention to use digitalized Islamic crowdfunding. BIMB clients in Selangor were sent an online questionnaire, Google Form, via several social media platforms. There was a total of 250 respondents collected. The findings revealed that subjective norm was the most influential factor in predicting consumer intention to use Sadaqa House BIMB. This was followed by an attitude indicating that supportive ideas from others, such as families, relatives, peers, and acquaintances, can influence the respondent's choice to utilise Sadaqa House. However, perceived behavioural control and religiosity have little impact on the intention to use Sadaqa House BIMB. The results suggested that BIMB should permit all non-profit organizations, private and public sectors, and communities to contribute to this digitalized Islamic crowdfunding initiative organised by Sadaqa House BIMB. Additionally, the study suggests that more campaigns be conducted to create and promote awareness. With increased awareness, not only the individual but also the individual's circle will be influenced to utilise this digitalized Islamic crowdfunding. This study differs from previous research in that it provides a new perspective by employing TPB and introducing a new variable, religiosity.
... First, our results may not generalize to other crowdfunding types. For example, in reward crowdfunding or microloan markets, resource providers often operate with a social or community logic (Anglin et al., 2020;, rather than an economic return logic (Cholakova & Clarysse, 2015). Thus, they may be less suspicious and more inclined to believe entrepreneurs' claims. ...
Full-text available
When entrepreneurs express their ambitions to achieve extraordinary financial growth, it may signal growth potential to early-stage investors. However, as this study proposes, promising overly high growth ambitions might backfire for entrepreneurs—dismissed as cheap talk and even penalized due to investors’ credibility concerns. Therefore, entrepreneurs may need to complement their high ambitions with costly signals, such as citing their rich experiences or patents. Such costly signals can serve as credibility buffers and transform high ambitions from cheap talk into credible signals. Two empirical studies, based on campaign data from an equity crowdfunding setting and a conjoint experiment, support these arguments.
... To examine the effects of pandemic on crowdfunding performance, we estimated a logistic regression model and, in accordance with the crowdfunding literature, deploy funding success as the conventional dependent variable (Anglin et al. 2020). ...
Full-text available
The COVID-19 impact on global poverty dragged another 97 million people into poverty in 2020. Nonetheless, there is scant evidence reporting on the impacts on alternative means of financing designed to enable the poor during this global health crisis. This paper addresses this gap of funding impoverished entrepreneurs by studying the changes in their successfully funded campaigns on the largest crowdfunding microfinance platform prior and during COVID-19. After collecting data from January 2018 to November 2021 for a total of 767,112 campaigns, we report that the COVID-19 pandemic positively impacts on the funding success of the crowdfunding campaigns. However, rises in the daily number of COVID-19 cases negatively associate with campaigns getting fully funded. The odds of campaigns being fully funded decrease by 4.4% for a one thousand increase in new cases.
Much of the extant scholarship in supply chain management (SCM) has had a developed world focus, although most of the global population resides outside this area. SCM scholars are now recognizing this limitation in the coverage of our communities' research. They have recognized that the logistical challenges of getting products to these underserved markets at the bottom of the economic pyramid (BOP) may be fundamentally different from the “big box” mindset that prevails in the west. There is growing recognition that supply chain entrepreneurship is critical to the logistics and physical distribution systems that can get products to such markets in a cost‐effective manner. Yet, such entrepreneurs, who are often small, and weakly integrated into the global economy, face several challenges in their daily business. Many of them rely on microfinance to fund their business. Yet, the microfinance model itself is changing into a web‐supported crowdfunded model. The current study investigates how an entrepreneur's circumstances with regard to their borrowing status as a first‐time borrower, and their intent with regard to business expansion influence their success in fundraising on a crowdfunding platform. Results reveal that BOP entrepreneurs who are “repeat borrowers” have difficulty in obtaining funding for their business plans.
Reward‐based crowdfunding (CF) has emerged as a method to solicit funds for innovative projects. Yet, little is still known on the ability of reward‐based CF to act as a signal in the eyes of future consumers, and thus boost the future market performance of new products that innovators intend to commercialize using the campaign funds. In addition, scant research has clarified the boundary conditions that can magnify or weaken the efficacy of this CF signal. Given the relevance of reward‐based CF for supporting innovation, understanding when the CF campaign performance works as an effective signal is of great interest, especially in business settings characterized by high product quality uncertainty. By using the movie industry as a setting, we contribute to fill this gap. Specifically, we argue that the positive effect of the reward‐based CF performance is moderated by two important factors influencing consumers’ purchase decisions: the degree of product innovativeness and the expert judgment about the product. Elaborating on the effects of product innovativeness, we posit that this product feature should moderate the positive relationship between CF and subsequent market performances in an inverted U‐shaped fashion. Favorable expert recommendations, on the other hand, should weaken the efficacy of the CF performance as a signal. Results from a sample of 1,059 new movies (of which 152 released in theaters) confirm these predictions and offer several remarkable implications for innovators. This article is protected by copyright. All rights reserved.
Full-text available
Crowdfunded microloans are a suitable tool for financing basic economic activities in developing as well as developed countries, favouring female empowerment. Despite the loans being relatively small, the widespread use of this instrument merits analyzing the factors affecting the microloan. One of these factors is gender because microloans are an important tool to finance projects promoted by women in many developing countries where microfinance is widely diffused. This research aims to determine if the gender of crowdfunded micro-borrowers is related to the main features which define the conditions of a microloan: amount, term, number of lenders, length of time to contact with borrowers and repayment system. The methodology used is the multinomial logit regression. The sample used in this study has been obtained by applying sampling techniques to a extensive public database from Kiva. This provided information on microloans from 56 countries around the world. The results based on amount, term, repayment method and recruitment period indicate that women are the best borrowers. All these variables, except the term, are significant at a 5% level. These findings may be useful to improve financial inclusion and outreach, consistently with the Sustainable Development Goals. Future research is needed to assess how “green and pink” microfinance (with environmental strategies particularly favored by women) can attract more ESG-compliant crowdfunding resources.
Full-text available
We extend the entrepreneurship literature to include positive psychological capital-an individual or organization's level of psychological resources consisting of hope, optimism, resilience , and confidence-as a salient signal in crowdfunding. We draw from the costless signaling literature to argue that positive psychological capital language usage enhances crowdfunding performance. We examine 1726 crowdfunding campaigns from Kickstarter, finding that entrepreneurs conveying positive psychological capital experience superior fundraising performance. Human capital moderates this relationship while social capital does not, suggesting that costly signals may, at times, enhance the influence of costless signals. Post hoc analyses suggest findings generalize across crowdfunding types, but not to IPOs.
Full-text available
This paper examines antecedents of ex-ante voluntary information disclosures for standardized contracts in entrepreneurial networks. Entrepreneurs (e.g., franchisors) may make such disclosures to prospective business partners in order to signal profitability of partnering, attract financial and managerial resources, and develop their entrepreneurial networks. In practice, only a fraction of franchisors make financial performance representations (FPRs), an ex-ante voluntary information disclosure to prospective franchisees. We address gaps in the signaling, voluntary information disclosure, franchising, entrepreneurship, and small- and medium-enterprise (SME) literatures. We draw on signaling theory to develop a theoretical framework and investigate factors that influence a franchisor’s disclosure decision. We evaluate hypotheses from our theoretical framework through econometric analyses of multi-sector panel data for the US franchising industry. We estimate a logit model and use lagged independent variables to address our dichotomous independent variable and potential endogeneity, respectively. Our results support the view that firms signal their quality through FPRs to attract potential business partners and expand their entrepreneurial networks. Beyond the extant literature, we find that rigorous partner qualification is another driver of voluntary information disclosure in franchising. Our findings also provide empirical support for the complementary role played by multiple quality signaling mechanisms used by franchisors and yield public policy implications for franchising.
Full-text available
We investigate the effect of innovativeness on crowdfunding outcomes. Because crowdfunding campaigns characterized by greater incremental innovativeness are more comprehensible and generate more user value for typical crowdfunders, incremental innovativeness may result in more favorable funding outcomes. By comparison, campaigns that feature greater radical innovativeness are riskier to develop, harder for crowdfunders to understand and result in less favorable funding outcomes. This negative effect of radical innovativeness may be mitigated by incremental innovativeness, which may help crowdfunders to understand and appreciate radical innovativeness more. A sample of 334 Kickstarter campaigns provides support for our hypotheses.
Full-text available
Crowdfunding is a rapidly growing phenomenon wherein entrepreneurs seek funding for their entrepreneurial activities from a potentially large audience of interested individuals. Crowdfunding has exploded in popularity over the last decade and now accounts for tens of billions of dollars annually. But despite the importance and growth of crowdfunding, little scholarly knowledge exists about the topic. To address this gap, this special issue includes five articles that each advance knowledge about crowdfunding in important ways. We briefly review past work on crowdfunding in leading entrepreneurship and management journals. We then highlight the diverse contributions offered in the special issue articles.
Full-text available
Equity financing in entrepreneurship primarily includes venture capital, corporate venture capital, angel investment, crowdfunding, and accelerators. We take stock of venture financing research to date with two main objectives: (a) to integrate, organize, and assess the large and disparate literature on venture financing; and (b) to identify key considerations relevant for the domain of venture financing moving forward. The net effect is that organizing and assessing existing research in venture financing will assist in launching meaningful, theory-driven research as existing funding models evolve and emerging funding models forge new frontiers.
Full-text available
Despite increased interest in examining the factors that influence crowdfunding success, the effects of community context have been relatively unexamined. We address this void by examining the role of cultural context in crowdfunding success. Our unique data set of crowdfunding projects to “save the local theater” are homogenous in their goal, allowing us to test whether crowdfunding campaigns in certain communities lead to better funding outcomes than others. Theoretically, our results suggest the need for further integration of community and cultural constructs into models of venture funding, as such variables may have more relevance than previously believed.
This paper studies the combined effect of affiliation with prestigious universities, underwriters, and venture capitalists on the valuation of biotech ventures at IPO and their post-IPO performance. We argue that affiliation to a prestigious university provides the affiliated firm with a quality signal in the scientific domain. The pure quality signaling effect of the affiliation is isolated from the substantive benefits it provides by performing a difference-in-difference approach based on the scientific reputation of scientists in firms' upper echelons. The signal is stronger the weaker is the scientific reputation of scientists of the focal IPO-firm and is additive to those provided by prestigious venture capitalists and underwriters. Results for a sample of 254 European biotech ventures that went through an IPO between 1990 and 2009 confirm our predictions.
Prosocial crowdfunding platforms are venues for individual lenders to allocate resources to ventures that specifically pursue economic and social value. In a setting where hybridity is expected, do crowdfunders respond positively to category-spanning ventures, or do they prefer to fund ventures that are more clearly situated within a single category? Drawing on theory rooted in category membership and spanning, our hypotheses test whether prosocial crowdfunding lenders will more quickly allocate resources to hybrid microenterprises that communicate their hybridity, or to those that communicate a single one of their dual aims. Our study demonstrates that even in such a setting, crowdfunders lend more quickly to microenterprises that position themselves within a single linguistic category in which the social is emphasized over the economic. This suggests that how hybrid organizations position themselves in their linguistic narratives has a significant impact on resource allocation by external prosocial audiences.
We estimate risk aversion from investors' financial decisions in a person-toperson lending platform. We develop a method that obtains a risk-aversion parameter from each portfolio choice. Since the same individuals invest repeatedly, we construct a panel data set that we use to disentangle heterogeneity in attitudes toward risk across investors, from the elasticity of risk aversion to changes in wealth. We find that wealthier investors are more risk averse in the cross section and that investors become more risk averse after a negative housingwealth shock. Thus, investors exhibit preferences consistent with decreasing relative risk aversion and habit formation.