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

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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.
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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
Abstract
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.
Keywords
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
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DOI: 10.1177/1042258719839709
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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
unanswered.
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.,
2015).
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
investment.
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
formally,
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.
Method
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).
Controls
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.
Results
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.
Variablesa
Controls
(log odds)
Controls
(ME)
Main effects
(log odds)
Main effects
(ME)
Loan term (ln) −2.33***
(0.06)
−0.06***
(0.01)
−2.34***
(0.06)
−0.06***
(0.01)
Loan amount (ln) −1.87***
(0.03)
−0.05***
(0.01)
−1.86***
(0.03)
−0.05***
(0.01)
Risk rating −0.40
(0.17)
−0.01
(0.01)
0.11
(0.18)
0.00
(0.00)
Length (ln) −0.00
(0.03)
0.00
(0.00)
0.00
(0.03)
0.00
(0.00)
Female 1.56***
(0.00)
0.04***
(0.00)
1.56***
(0.03)
0.04***
(0.00)
Group 0.12
(0.08)
0.00
(0.00)
0.11
(0.08)
0.00
(0.00)
Irregular 0.05
(0.09)
0.00
(0.00)
0.07
(0.09)
0.00
(0.00)
Monthly 0.12
(0.09)
0.01
(0.00)
0.12
(0.09)
0.01
(0.00)
Fourteen sector dummiesbIncluded Included Included Included
Default rate (ihs) −1.77**
(0.56)
−0.05**
(0.02)
Default rate2 (ihs) 0.58**
(0.19)
0.02**
(0.00)
Profitability −0.02
(0.01)
0.00
(0.00)
Profitability2 0.00
(0.00)
0.00
(0.00)
Avg. loan size per capita income (ihs) 0.67*
(0.31)
0.02**
(0.00)
Avg. loan size per capita income2 (ihs) −0.16***
(0.04)
−0.004**
(0.00)
Cost to borrower −0.14***
(0.03)
−0.004***
(0.00)
Cost to borrower2 0.002***
(0.00)
0.0001***
(0.00)
Constant 23.51***
(0.52)
24.69***
(0.65)
Variance components
MFI 0.00
(0.00)
0.00
(0.02)
Country 5.87***
(0.82)
3.77***
(0.52)
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).
Discussion
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
platforms.
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
entrepreneurs.
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
investors.
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
campaigns.
Conclusion
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.
Funding
The author(s) received no nancial support for the research, authorship, and/or publication of this article.
ORCID ID
Aaron H. Anglin https:// orcid. org/ 0000- 0003- 0857- 2081
Thomas H. Allison https:// orcid. org/ 0000- 0001- 8873- 9798
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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.
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... Consistent with prior work, we drew our sample from Kiva, the world's largest crowdfunded microlending platform (Allison et al., 2015(Allison et al., , 2020Moss et al., 2015). The fundamental mission of Kiva, like many crowdfunded microlending platforms, is providing loans to poor individuals in an effort to promote entrepreneurship (e.g. ...
... The fundamental mission of Kiva, like many crowdfunded microlending platforms, is providing loans to poor individuals in an effort to promote entrepreneurship (e.g. Allison et al., 2013Allison et al., , 2015Anglin et al., 2020;Bruton et al., 2015, Moss et al., 2015, 2018. Our target population was the most recent two quarters of loans as of the commencement of the study: July-December, 2018. ...
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... This approach allows us to account for spatial autocorrelation between participants from the same country (Figueroa-Armijos 2019) and avoid overparameterization. This method also follows previous crowdfunding research dealing with similar sample data (e.g., Anglin et al. 2020;Yu et al. 2017). ...
... We use these control variables to account for other information available to the crowdfunder from the borrowing entrepreneur's profile on the prosocial crowdfunding platform. This is appropriate as the information on these publicly available profiles form the bases for the crowdfunders' funding decisions (Anglin et al. 2020;Mollick 2014). ...
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... Signaling theory has increased our understanding of when prospective investors choose to fund new ventures in an environment fraught with uncertainty and informational asymmetry (Baid & Allison, 2019;Bergh et al., 2014;Colombo, 2021;Connelly et al., 2011). Firms can ease resource attraction through signaling with their boards (Certo, 2003), team member characteristics (e.g., Plummer et al., 2016), early accomplishments (Hallen, 2008), and endorsement relationships (Anglin et al., 2020;Plummer et al., 2016;Stuart et al., 1999). Firms can also benefit from conveying low-cost signals, including visual cues, optimistic speech, press releases, and forecasts (Ahlers et al., 2015;Anglin et al., 2018;Mahmood et al., 2019, Steigenberger & Wilhelm, 2018Vanacker et al., 2020). ...
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... It is within this standard context that the crowdfunding literature emerges. On the one hand, the dominant view of matchmaking supply and demand enables a large body of studies on success-factors of online calls such as signaling credibility through text, rewards, video and interactions in all the different types of crowdfunding models 1 (Younkin and Kashkooli, 2016;Vismara, 2018;Anglin et al, 2020;Colombo, 2021;Montfort et al, 2021). On the other hand, case-based studies show how gift-giving motivations emerge in combination with community benefits and new forms of fan-engagement Brzozowska, 2016, 2017;Gerber and Hui, 2016;Josefy et al., 2017;Navar-Gill, 2018;Rykkja et al., 2020). ...
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This chapter analyzes MicroFinTech applications after the microfinance background, examined in Chapter 2, the microfinance issues, illustrated in Chapter 3, and the impact of technology (discussed in Chapter 4) and FinTechs (illustrated in Chapter 5). Technology-driven sustainability boosts scalability and outreach. Crowdfunded resources can back microfinance projects, as shown by well-known templates like Kiva.
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