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The effect of virtuous and entrepreneurial orientations on microfinance lending and repayment: a signaling theory perspective.

The availability of capital for microenterprises has grown rapidly due to microfinancing platforms such as Kiva. The investment decisions of microlenders are challenged due to the limited information about the microenterprises' characteristics and behavioral intentions. Extending signaling theory, we suggest that microenterprises' narratives on microfinancing platforms are an important means to signal valuable characteristics and behavioral intentions to prospective lenders. Results indicate that microenterprises, which signal autonomy, competitive aggressiveness, and risk-taking, are more likely to receive funding, and to receive it more quickly. Microenterprises that signal conscientiousness, courage, empathy, and warmth are less likely to get funded. Rhetorical signaling proactiveness, conscientiousness, courage, warmth, or zeal is negatively associated with loan repayment.

Introduction

Most research analyzing the financing of entrepreneurs focuses on venture capital, bank loans, and angel investments (Bygrave, 2009). Nevertheless, this type of capital represents only a portion of the financing used by entrepreneurs worldwide (Bygrave, Hay, Ng, & Reynolds, 2003; Kelley, Singer, & Herrington, 2011). Microfinance has become a viable means to supply critical capital to microenterprises, especially those in emerging countries that have limited access to traditional means of financing (Dorado, 2001; Khavul, 2010). In recent years, microfinance has emerged as a vital industry providing over $25 billion in small loans to over 150 million individuals (CGAP, 2013; Diekman, 2007). Microfinance has been heralded as a means to create both economic and social value as it provides financing to microenterprises, subsequently assisting in venture growth and socially benefiting families and communities. Despite the potential promise of microloans, there is a gap between what we know and what we need to know about the microfinancing phenomenon and how to improve its effectiveness and impact.

While it has become easier for entrepreneurs in emerging markets to gain access to capital, significant obstacles still remain (Yoshikawa, Rasheed, Datta, & Rosenstein, 2006). In particular, the lack of credible, reliable information about borrowers poses serious challenges to lenders (Yiu, Bruton, & Lu, 2005) as potential lenders may face particularly uncertain ex-ante investment decisions. This information asymmetry can seriously dampen microlenders' willingness to provide capital to microenterprises. Further, it may prove difficult for microlenders to choose between competing investment opportunities.

Recent research on initial public offerings (IPOs) has suggested that investors seek tangible and intangible information about the characteristics of firms in which they might invest, as this information can be used to alleviate concerns about risk and uncertainty (Bruton, Chahine, & Filatotchev, 2009; Payne, Benson, & Finegold, 2009). In settings featuring information asymmetry, the ability of the firm to signal, or convey value, reliability, or potential success to the investment community can be a critical factor in gaining funding. Thus, within the context of raising funds in the IPO market, signaling theory (e.g., understanding how information is communicated between two parties with access to different information) has been extensively researched (for a review, see Connelly, Certo, Ireland, & Reutzel, 2011) and the signals IPO firms send to the market via the language of their prospectuses can be an important input to the funding decision (e.g., Amit, Brander, & Zott, 1998; Daily, Certo, & Dalton, 2005).

Extending signaling theory to IPOs in foreign and emerging markets, Payne, Moore, Bell, and Zachary (2013) examined how the use of rhetoric reflecting a virtuous orientation (VO; i.e., rhetoric reflecting organizational values of integrity, empathy, warmth, courage, conscientious, and zeal) (Chun, 2005) in an IPO's prospectus subsequently influenced the IPO's pricing. These researchers argued that because of the limited amount of information available to investors in foreign and emerging markets, the rhetoric used in an IPO's prospectus can send signals to the market, which might increase investors' confidence or reduce their perception of the risk of their investment. Results showed that signaling a VO helped reduce IPO uncertainty as the use of rhetoric related to organizational virtue positively influenced investors' perceptions of IPO firms and IPO performance. While signaling "lies at the very heart of IPO research" (Payne et al., p. 233), our understanding of (1) the determinants of microlending and repayment and (2) the role that signaling may play in the context of microlending is far more uncertain.

In this study, we extend signaling theory to the microfinance literature and argue that the manner in which the rhetoric is used in the narratives describing the microenterprises on microfinancing platforms, such as Kiva, can help microenterprises signal critical organizational characteristics to the microfinance market. Given that publicly available market information about a specific microenterprise is likely to be nonexistent, or at least very limited, we believe that the investment decision of microlenders will be significantly influenced by these narratives. As such, the rhetoric reflected in these narratives will determine whether or not the microenterprise is funded, as well as the speed at which it receives funding.

Specifically, we believe that two organizational characteristics may be particularly important to signal the microfinance market. According to the signaling theory, information asymmetry is particularly relevant when one party is not fully aware of (1) the characteristics (e.g., quality or reliability) of the other party and (2) the behavioral intentions of the other party (Stiglitz, 1990). Therefore, considering the means to address information symmetries surrounding characteristics and behavioral intentions, we believe that the use of rhetoric reflecting a VO may signal a microenterprise's positive characteristics toward ethical and virtuous values and behaviors (Payne et al., 2013). These values, in turn, can improve microlenders' trust and faith in the microenterprise and reduce microlenders' uncertainty. Therefore, microenterprises whose narratives signal a VO are more likely to receive microfunding and will receive their funding more quickly than those microenterprises that do not. Second, to overcome information asymmetries related to behavioral intentions, we believe that the use of rhetoric related to entrepreneurial orientation (EO, e.g., rhetoric reflecting autonomy, competitive aggressiveness, innovativeness, proactiveness, and risk-taking behaviors) (Lumpkin & Dess, 1996) will also help microenterprises receive microloans and receive them more quickly. Rhetoric conveying an EO will send positive signals to potential microlenders about how the microenterprise intends to compete, as well as the strength of its business model and competitive position. A strong EO signal will enhance microlenders' confidence about the microenterprises' intentions and chances to succeed and grow revenues and profits, and in turn, repay their loans. In addition to the important signals that either VO or EO rhetoric may send to microlenders, we also hypothesize that they will be positively associated with the likelihood and speed of microloan repayment.

Our study makes several contributions to the literature. First, it extends signaling theory to the microfinance market and builds on the existing literature on the importance of signaling in the face of information asymmetry. Second, it adds to the microfinance literature by identifying organizational characteristics that influence microlenders' assessment of the microenterprise, as well the likelihood and speed with which microloans are repaid. Third, it extends the literature on VO and EO in that it suggests that these orientations can be influential to the perceptions and actions of key organizational stakeholders (i.e., microlenders), as well as the performance of firms. Fourth, it contributes to the social entrepreneurship literature by establishing a distinction between microfinance and other types of social financing for different socially valuable activities. Finally, it builds on existing work that uses computer-aided text analysis (Short, Broberg, Cogliser, & Brigham, 2010) to show that organizational documents germane to microfinance, such as loan descriptions, are fruitful sources for a variety of management constructs. To accomplish these objectives, we examine microloans made through one well-known microfinance Web site, Kiva. Drawing from signaling theory and the VO and EO literatures, we present hypotheses and explore how rhetoric may signal certain characteristics and behavioral intentions to potential microlenders and how these signals affect the funding and repayment of loans made through the Kiva Web site. Thus, we investigate multiple microlending performance measures in our study. Understanding the factors that lead to both microfinance funding and repayment are needed to improve the overall effectiveness of microfinance and crowdfunding platforms.

Theory Development and Hypotheses

Microfinance

Microfinance is being increasingly viewed as a powerful means to alleviate poverty as over $25 billion has been distributed in loans to entrepreneurs in emerging markets around the globe (Diekman, 2007; Khavul, 2010). Direct-to-borrower microfinancing platforms, such as Kiva, enable individual lenders to provide small, uncollateralized loans to entrepreneurs and entrepreneurial groups through a pass-through agent or partner microfinance institution (MFI; Allison, McKenny, & Short, 2013). These partner MFIs find the entrepreneurial ventures, determine their financing needs, and work directly with the ventures to service the loans. They also work with Kiva by providing them with the narratives about the ventures. Thus, microlenders invest in entrepreneurial ventures through the Kiva platform, which in turn provides funding to MFIs that are working directly with the ventures (Kiva, 2013). In the case of Kiva, prospective lenders can search the Kiva.org Web site, which presents hundreds of potential entrepreneurial ventures that are seeking small loans. For each venture, the Kiva Web site provides information featuring the name and location of the borrower, as well as a short narrative about the entrepreneurial opportunity being funded. The Web site also includes information about the size of the loan, the purpose of the loan, the loan repayment schedule, and the MFI that will service the loan on behalf of Kiva. The date of when the loan was first posted on the Kiva Web site and how much progress the borrower has made in achieving their goal of full funding are also provided. When an entrepreneur's loan request is fully funded, Kiva releases the loan to the MFI, which has already made the loan to the venture. As the venture makes payments on the loan, the lender receives repayment of their principal; the interest paid by the venture is captured by the MFI, which uses these proceeds to cover any expenses and to retain any remaining interest for profit. Thus, microlenders using Kiva do not earn interest from their loans; rather, the field partner MFI uses this interest as part of their operating revenue (Kiva).

Signaling Theory

Decision makers need information, and individuals have access to two types of information (Connelly et al., 2011; Stiglitz, 2002). First, individuals have access to public information, which is widely and freely available to all parties. Second, individuals may have access to private information, which is information available to only some parties. Since "different people know different things" (Stiglitz, p. 469), some individuals may possess private information that others may find valuable when making decisions. Under these circumstances, individuals with private information may wish to provide signals to others in an effort to reduce the information asymmetries that exist between parties. Conversely, individuals lacking private information may be particularly attuned to signals that may reflect unobservable characteristics, behavioral intentions, or private information. Signaling theory (Spence, 1973) has been used to explain how information asymmetries influence decision making in a wide variety of contexts. These include how organizations may signal social responsibility by having a diverse board of directors (Miller & Triana, 2009) or how an entrepreneurial firm can signal value through top management team characteristics (Lester, Certo, Dalton, Dalton, & Cannella, 2006) or founder involvement (Busenitz, Fiet, & Moesel, 2005).

According to Stiglitz (1990), two types of private information are particularly important: (1) information about characteristics (e.g., quality or reliability) where one party is not fully aware of the characteristics of another party, and (2) information about intent, where one party is not fully aware of the behavioral intentions of another party. Signals, however, can be used to help parties resolve information asymmetry related to these unobservables. In the context of microfinance, lenders are largely unaware of the characteristics and behavioral intentions of the borrower. However, the short narrative about the entrepreneur and the entrepreneurial opportunity being funded, which is written by the MFI, is clearly a means to signal the characteristics and behavioral intentions of the borrower. We, therefore, believe these signals will be particularly relevant to the microfinance decision-making process.

Signaling theory is concerned with the manner in which insiders intentionally convey imperceptible, positive information about themselves to outsiders (Connelly et al., 2011). While insiders may be constantly communicating, not all of these conveyances are signals. Typically, to be considered productive, signals must be (1) observable and (2) costly. Observability refers to the extent to which outsiders are able to notice the signal. Costly refers to the sender's expense associated with signaling desirable characteristics, such as quality, reliability, or genuineness. Educational degrees, industry certifications, or professional credentials are examples of observable and costly signals.

While most signaling theory addresses costly signals, some researchers (see Farrell & Rabin, 1996; Almazan, Banerji, & DeMotta, 2008; Payne et al., 2013) have observed that cheap talk, or less costly signals, can also be used by senders to reduce information asymmetries between parties. While on the surface it might seem that the entrepreneurial narratives on the Kiva.org Web site are costless signals, we argue that these signals can carry significant costs. First, the MFI could face considerable costs if they are caught providing disingenuous signals about the entrepreneurial opportunity. Kiva's Terms of Use agreement (http://www.kiva.org/legaFterms) gives Kiva the right to terminate its relationship with deceptive MFIs. Thus, while there might be a short-term incentive for MFIs to provide misinformation in their signals, the long-term costs can clearly outweigh those benefits. Second, if certain misleading signals were determined to be valuable, then such signals would proliferate, and in the long run, these dishonest signals would lose their value (Connelly et al., 2011). Finally, since senders can choose from a variety of signals to send, the transmission of signals must be strategically managed (Austen-Smith & Banks, 2000). Signaling one type of characteristic or behavioral intention comes with the opportunity cost of not conveying another. For these reasons, we conclude that microenterprises' narratives are costly signals.

While interest in microloan funding has increased dramatically over the past few years, empirical research of the microfinance decision process, however, is still in its infancy. Two recent studies have examined the conditions that affect the propensity of microloans being made. First, in their study of lender and borrower characteristics, Galak, Small, and Stephenson (2011) find that lenders prefer to fund individual entrepreneurs over groups. Further, Galak et al. find that lenders tend to more swiftly fund borrowers who were similar to them along three lines of social proximity, namely sex, occupation, and sharing the same first initial. This finding is consistent with the trust literature, which suggests that individual similarity and commonalities can foster attraction and trust (Mayer, Davis, & Schoorman, 1995). Galak et al. (p. 135) conclude that "despite the financially-orientated nature of microfinance, people are nonetheless affected by more psychological factors."

In the second study, Allison et al. (2013) rely upon the theory of political rhetoric and content analyze 6,051 entrepreneurial narratives from Kiva for groups and individuals from the Democratic Republic of the Congo, Liberia, Mozambique, Rwanda, and Cambodia. These authors argue the language used in the brief narratives describing the borrower and the entrepreneurial opportunity, that is the rhetorical content of the narrative, influences the speed and likelihood with which microloans are funded by lenders. They specifically hypothesize that those narratives that possess language with the "warm-glow effect" (i.e., the positive affect individuals receive when they provide assistance to those in need) (Andreoni, 1990) will gain the attention of lenders and increase the likelihood of funding. Allison et al. find that narratives that contain higher levels of language indicating blame and concern are more likely and more swiftly funded than those narratives that contain language related to accomplishment, tenacity, and variety. The results from these two studies suggest that the rhetoric used in microenterprises' narratives is influential in the microfinancing decision-making process.

Our investigation builds upon the work of Galak et al. (2011) and Allison et al. (2013) by examining the microlending decision through a signaling theory perspective. Given the information asymmetry and uncertainty surrounding the microlending decision, we reason that microlenders will be particularly attuned to information and signals that would reduce their uncertainty. In addition, we ask what factors contribute to the likelihood that one venture will repay its loan over another venture? In other words, is there a difference in the likelihood that a venture will repay its loan based on whether or not the microenterprise has a VO or EO? Since VO (Chun, 2005) and EO (Lumpkin & Dess, 1996) are considered key strategic tools an organization can use to differentiate itself from others to gain a competitive advantage, we reason that these orientations can play critical roles in the way microenterprises position themselves relative to other borrowers, as well as the manner in which they behave and compete against their rivals. As such, these orientations will influence not only their likelihood of getting funded, but will also be associated with their ability to repay loans. We address these questions in the paragraphs below.

VO and Microfinance Lending Decisions and Repayment

Defined as "ethical character traits that are learnt from an accumulative perception of a firm's behaviors in everyday business life, that drives internal and external stakeholder satisfaction, and that is aligned with its ethical values used for strategic positioning," (Chun, 2005, p. 272), organizational virtue is the integrated set of beliefs that inform the organization's ethical traits and virtuous behavior. Character traits associated with a strong VO include regularly and routinely displaying principles such as honesty, fairness, reliability, trustworthiness, and commitment to others (Paine, 1991). An organization's VO is important because it can have two significant consequences (Payne, Brigham, Broberg, Moss, & Short, 2011). First, the nature of an organization's VO can affect the organization's processes, methods, and decisions (Cameron, Bright, & Caza, 2004). Second, the content of the organization's VO can influence the manner in which it presents itself to its stakeholders. Communications, marketing, and narratives, which are presented to external stakeholders will reflect the stance the organization takes on the importance of ethical and virtuous behaviors and actions. In the context of this study, we argue that the microventures' VO will not only be reflected in the manner in which their narratives present them, but it will also affect how they conduct their operations.

While numerous researchers have considered the dimensions that should be included in a comprehensive list of business virtues (e.g., Murphy, 1999; Shanahan & Hyman, 2003; Solomon, 1992, 1999), Chun (2005) conducted a content analysis of companies' ethical statements and provided an empirically verified list of six dimensions of organization virtue, namely conscientiousness, courage, empathy, integrity, warmth, and zeal. Conscientiousness refers to the extent to which an organization can be considered dependable and hardworking (Barrick & Mount, 1991). Courage is defined as the attainment of success in achieving a desired goal or objective and the level of effort taken to accomplish the outcome (Harris, 2001). Empathy is portrayed by the ability to perceive or consider the feeling of others (Sawyer, 1975) and showing a sense of concern or sympathy for the circumstances of others (Davis, 1983). Integrity is defined as the trustworthiness and honesty of an individual or organization (Butler & Cantrell, 1984). Warmth is characterized by open and pleasant expressions of courtesy, friendliness, and generosity to others (Chun). Finally, zeal is an organization virtue characterized as including elements such as humor, excitement, passion, or fun.

In their examination of how the strength of a firm's VO influenced the perceptions of IPO investors in foreign and emerging markets, Payne et al. (2013) found that IPOs that signaled a strong VO were viewed as less uncertain investments by investors. By signaling characteristics associated with ethicality and reliability, these firms were able to increase investor confidence and reduce perceived risk. Because of the limited information about ventures in foreign and emerging markets available to potential investors, investment decisions were particularly sensitive to the nature of the language used in the IPOs' prospectuses.

Extending this logic to the microfinance settings, we argue that rhetoric signaling the dimensions of a VO in the narratives of microenterprises will significantly enhance lenders' perceptions of the microenterprise and subsequently affect the speed with which the microloans are made. Microenterprises that signal high levels of conscientiousness are essentially telling prospective lenders that they are dependable, reliable, hardworking, and secure investments, which can reduce microlenders' uncertainty. Indeed, conscientious employees are more likely to have a calling orientation in their work and are thus more productive; the accomplishment of doing their work well is a sufficient end in itself (Wright & Cropanzano, 2004). We, therefore, believe that microenterprises that strongly signal conscientiousness will be more likely to be funded than those that send weaker conscientiousness signals.

Courageous microenterprises are ambitious, achievement oriented, leading, and competent. They signal a determination to succeed despite the factors that hinder business development and entrepreneurship, like corruption and weak enforcement of the rule of law (Bowen & De Clercq, 2008)--factors commonly found in emerging markets where microfinance takes place. As with conscientiousness, microenterprises signaling higher levels of courage should be funded more quickly than their less courageous rivals because their orientation signals their determination for success to potential lenders.

Microenterprises that display empathy are concerned, reassuring, supportive, and sympathetic, and empathic organizations are thought to have higher stakeholder satisfaction (Chun, 2005). Lloyd (1990) suggests that "nice" companies, characterized by empathy toward society, industry neighborliness, and good corporate manners, are actually more profitable than more "brutal" or "nasty" organizations because over time, society has come to value these characteristics. Additionally, organizations with higher levels of perceived compassion and forgiveness have been shown to have higher levels of perceived innovation and quality (Cameron et al., 2004). Extending this logic to microenterprises, we suggest that rhetoric associated with greater empathy reduce microlenders' uncertainty about reliability and behavioral intentions, which will increase the likelihood and speed with which the microenterprise will receive full funding.

Microenterprises that emphasize greater integrity are honest, sincere, socially responsible, and trustworthy. Organizations that operate with integrity have a relatively fixed character that is sustained over time (Moore, 2005). For example, Collins and Porras (1998) provide examples of companies such as 3M ("absolute integrity") and GE ("honesty and integrity"), which have enduring ideologies to guide the organizations. Integrity will help the enterprise to withstand the corrupting influences in the broader environment (Moore). Additionally, integrity is significantly related to business profitability, a key dimension of organizational performance (Cameron et al., 2004). In an effort to reduce their investment uncertainty, microlenders are more likely to invest in enterprises that signal honesty and trust, and will want to avoid investing in microenterprises perceived as being associated with fraudulence.

Organizations that display warmth are friendly, open, caring, pleasant, and straightforward. Exceedingly few empirical studies have been conducted on warmth as a dimension of organizational virtuousness since Chun's (2005) initial work. Payne et al. (2011) found that family firms displayed higher levels of warmth in the shareholder letters of company annual reports. Extensive work by Miller and Le Breton-Miller (2005) indicates that family firms outperform nonfamily firms over the long term. Findings relevant to warmth are that family firms that established a greater feeling of community within the organization and connection with entities outside of the organization were more successful. With regard to microenterprises, it is reasonable to expect that enterprises that signal greater warmth will be more attractive investments to microlenders because these microenterprises are more likely to create a stronger sense of community and connection with lenders.

Zealous microenterprises are fun, exciting, attractive, imaginative, and spirited, and such characteristics are frequently found in popular corporations today (Chun, 2005). Zeal is not limited to "exciting" popular corporations such as Apple or Virgin Atlantic; IPOs from developing countries have been shown to express greater zeal than IPOs from the developed world, and zeal as a part of VO contributes significantly to IPO performance in this context (Payne et al., 2013). Regarding our sample of microenterprises in developing countries, we expect zeal to play a positive role in enterprise performance. Microenterprises signaling a zest for business, and which take a more imaginative approach, should be more appealing to customers and lenders alike, and should thus provide a better lending opportunity than others with less zeal.

In sum, the microfinance context is one with limited information, which leads to high levels of investment uncertainty in the mind of potential microlenders. In particular, microlenders have difficulty assessing key characteristics (e.g., quality and reliability) of potential borrowers. Any conveyance that signals a VO to microlenders will likely increase lender confidence, which in turn will increase the likelihood of funding. If microenterprises' narratives can effectively signal characteristics associated with positive moral and ethical values, such as conscientiousness, courage, empathy, integrity, warmth, and zeal, then we believe that these characteristics are likely to reduce lender uncertainty. Fears over moral hazard and adverse selection, which are prevalent in the microlending setting (Bruton, Khavul, & Chavez, 2011), may also be reduced. Because of the inability to effectively monitor the borrower, microlenders may be particularly wary of microenterprises that do not convey an adequate level of trustworthiness or fail to demonstrate sound ethical principles. Thus, microlenders will prefer narratives which signal the various VO dimensions. This in turn will increase the speed with which borrowers' requests for loans are fully funded:

Hypothesis 1: The extent to which microenterprises signal (1) conscientiousness, (2) courage, (3) empathy, (4) integrity, (5) warmth, and (6) zeal to microlenders is positively associated with the likelihood that investors will fund microloan ventures.

Virtuous organizations are seen as fair, honest, reliable, trustworthy, and committed to the support of others (Payne et al., 2013). In the case of microloan borrowers, those entrepreneurs who possess dimensions of a strong VO are likely to feel a strong sense of moral obligation to their lenders. They will be highly motivated and ethically driven to maintain their positive perception in the mind of their stakeholders. The strength of their VO dimensions will not only foster their efforts to satisfy stakeholders, but also influence their efforts to improve their organizational performance and competitive position (Chun, 2005). Their efforts in the six VO dimensions will, in turn, increase the efficacy, as well as the likelihood, that borrowers will endeavor to follow through on repaying their microloan. We, therefore, propose the following hypothesis for empirical testing:

Hypothesis 2: The extent to which microenterprises signal (1) conscientiousness, (2) courage, (3) empathy, (4) integrity, (5) warmth, and (6) zeal to microlenders is positively associated with the likelihood that microloan ventures will repay their loans.

EO and Microlending Decisions and Repayment

It has long been argued that an organization's EO is positively associated with a number of organizational outcomes related to growth and success (e.g., Lumpkin & Dess, 1996; Wiklund & Shepherd, 2005). Defined as the processes, practices, and decision-making style of entrepreneurial firms (Lumpkin & Dess, 1996), EO reflects the mindset and methods organizations use to search and pursue opportunities for growth. As conceptualized by Lumpkin and Dess, EO consists of five dimensions, namely autonomy, competitive aggressiveness, innovativeness, proactiveness, and risk-taking.

In this paper, we use the Lumpkin and Dess (1996) conceptualization of EO with five dimensions for two reasons, as suggested by Covin and Lumpkin (2011). Lirst, Lumpkin and Dess present EO as a superordinate construct, a specific manifestation of the five independent constructs (dimensions) mentioned above; hence, our emphasis on these five dimensions is individually rather than collectively. This stands in contrast to other conceptualizations, such as the Covin and Slevin (1989) conceptualization, which suggests that EO is a latent construct existing only to the extent that innovativeness, proactiveness, and risk taking are concurrently manifested. Second, EO is considered a real phenomenon and is thus amenable to reflective measurement models rather than formative models. As we explain later in the Methods section, modeling EO via five independent dimensions more closely aligns the theory with our methods. We thus use reflective modeling of our constructs as independent EO dimensions to overcome inherent challenges with formative modeling, as recommended by Covin and Wales (2012).

Autonomy refers to the extent to which individuals or teams are able to act independently to foster new ideas and bring those to fruition (Lumpkin & Dess, 1996). Autonomous actors are free to actively pursue new opportunities without being constrained by existing organizational processes or structures, or the interest of other stakeholders. The freedom to act is associated with innovation, the creation of new businesses, and organizational effectiveness. We suggest that autonomy plays a significant role in microenterprise performance, just as it does in other contexts (Lumpkin, Cogliser, & Schneider, 2009). A principal purpose of microfinance is to increase the autonomy of microenterprises (Bardy, Drew, & Kennedy, 2012). Autonomous microenterprises have the freedom to exploit whatever opportunities arise in their locales and shift production accordingly, such as making the move to raise goats instead of chickens. The opposite also seems to hold true; microenterprises that lack autonomy would be unable to make such a shift despite changes in demand, and thus be less successful.

Competitive aggressiveness considers the competitive posture the venture assumes in the marketplace. It is characterized as offensive tactics aimed at gaining a strong competitive position, or aggressive reactions intended to maintain a position in the face of a strong threat from industry rivals (D'Aveni, 1994). Ventures that assume a strong, aggressive stance in the face of competition are thought to perform well by swiftly exploiting opportunities through decisive actions (Hamel, 2007). Likewise, competitive aggressiveness should improve the fortunes of microenterprises because in developing economies, resources are harder to come by than in the developed world (Prahalad & Hart, 2002), implying that competition for those scarce resources is required for firm survival. Competition is also thought to improve performance by motivating individuals to invest in greater intragroup cooperation (Tauer & Harackiewicz, 2004) to improve the likelihood of resource acquisition. Aggressiveness may also be displayed as a drive to achieve success for the family or community, rather than to put a rival out of business (West, Bamford, & Marsden, 2008). Therefore, microenterprises a displaying greater competitive aggressiveness should outperform others that are less aggressive.

Innovativeness refers to the propensity of ventures to introduce new products, processes, and routines as the result of experimentation and creativity (Lumpkin & Dess, 1996). Firms with an orientation toward innovation behave more entrepreneurially as they are committed to disrupting current market conditions by stimulating new demand or creating novel resources combinations. Continuous reinvention of organizational systems, products, and services through innovative efforts is seen as the key to organization survival and prosperity. The innovativeness dimension has the highest correlation with increased performance than any other EO dimension, and EO has a greater impact on microbusinesses when compared with small businesses (Rauch, Wiklund, Lumpkin, & Frese, 2009). Additionally, case studies in a microenterprise context indicate that innovativeness in production methods leads to entrepreneurial success, while a lack of innovativeness stymies effective resource use (West et al., 2008). These findings suggest that in a microenterprise context, innovative enterprises will signal to lenders that they will outperform other less innovative enterprises.

Proactiveness is a function of foresight and the extent to which organizations anticipate changes forthcoming in the environment and position themselves in advance of those evolving conditions (Lumpkin & Dess, 1996). Proactive firms are viewed as change or market leaders as opposed to market laggards or followers as they develop and implement innovations in advance of other actors. Crowdfunding has allowed microfinance to become a globalized phenomenon, expanding capital availability and thus placing opportunities (whether discovered or created) within reach of microenterprises proactive enough to seize them (Zahra, Newey, & Li, 2014). We, therefore, suggest that proactive microenterprises will signal a more successful venture to lenders and outperform those ventures that are not as proactive.

Finally, a firm's risk-taking propensity reflects the extent to which it is capable and comfortable in launching new, bold, and often costly action in the face of considerable uncertainty. Calculated risk taking is the quintessential entrepreneurially characteristic (Brockhaus, 1980) and "practically all theorists agree that entrepreneurship involves, by definition, taking risks of some kind" (McClelland, 1960, p. 210). West et al. (2008) show how microenterprises in one community used resources to successfully take economic risks despite negative incentives, while those in another community were not as successful in their entrepreneurial activities despite resource abundance in the area. It thus stands to reason that microenterprises that display a risk-taking propensity will signal a more successful venture than those that appear more risk averse.

In the context of our study, we believe that signaling dimensions of a strong EO will be particularly beneficial to microenterprises. Specifically, we argue that lenders will prefer to fund ventures that strongly signal the five EO dimensions. These microenterprises will be perceived as more desirable investments as expressions and rhetoric associated with EO dimensions will reduce information asymmetry related to behavioral intentions. Also, microenterprises with narratives that strongly signal aggressive entrepreneurial intentions consistent with EO will likely be viewed as having more of the characteristics necessary for success than those that do not. Those narratives that possess direct, repeated references to EO dimensions and paint those ventures as those that aggressively pursue new ideas, products, and processes in an innovative manner are more likely to gain the attention of investors as uncertainty over behavior intentions are reduced. Concerns associated with moral hazard and adverse selection may also be reduced. We, therefore, propose that narratives that reflect the five dimensions of an EO are more likely to be funded:

Hypothesis 3: The extent to which microenterprises signal (1) autonomy, (2) competitive aggressiveness, (3) innovativeness, (4) proactiveness, and (5) risk taking to microlenders is positively associated with the likelihood that investors will fund microloan ventures.

Consistent with research on EO, we expect that those borrowers who signal and strongly possess the five EO dimensions in their loan narratives will likely perform well in the marketplace (Habbershon & Pistrui, 2002). We believe that the strength of borrowers' beliefs of the importance of an entrepreneurial mindset will be reflected in the narratives requesting their loans. Those ventures that are innovative, aggressive, and risk taking will be well positioned to assume strong competitive positions over their rivals. As such, they can more effectively build and execute strong business models, which will lead to revenue growth and profitability. It naturally follows that these ventures will be in a better position to repay their loans, and they will do so at a quicker pace than other borrowers who are less focused on building and maintain strong EO dimensions. We, therefore, present the following hypotheses for empirical testing:

Hypothesis 4: The extent to which microenterprises signal (1) autonomy, (2) competitive aggressiveness, (3) innovativeness, (4) proactiveness, and (5) risk taking to microlenders is positively associated with the likelihood that microloan ventures will repay their loans.

Methods

Sample

The sample includes over 400,000 loans made to entrepreneurs who use the microfinance crowdfunding platform Kiva to access capital for their ventures, from 2006 to 2012. Data on each of these entrepreneurs were obtained through the Web site http:// build.kiva.org/docs/data/loans in the format of XML files. These files were converted into alternate file types so that the loan data could be analysed through computer-assisted textual analysis and statistical software (CATA; Short et al., 2010).

Crowdfunding has become a popular means for individuals to pool their resources and support clients served by MFIs (Davis & Webb, 2012; Schenk & Horska, 2012). Through Kiva, individuals can invest $25 or more in a chosen entrepreneur profiled on their Web site. Kiva has developed relationships with over 160 MFI field partners who know the local communities and find entrepreneurs in need of financing. The MFI field partner is responsible for the distribution, administration, and collection of the loan, while Kiva serves as the intermediary between investors and the MFI. Throughout the life of a loan, an individual investor can receive updates regarding the entrepreneur's venture and repayment. Once the loan is repaid, the individual investor receives Kiva credit, which they can use to invest in another entrepreneur, donate to Kiva, or be repaid. As of January 14, 2013, Kiva has made a total of US$392,925,875 in loans to 956,512 entrepreneurs through 515,843 loans (http://www.kiva.org/about/stats).

Dependent Variables

Following previous research (Allison et al., 2013; Galak et al., 2011), we created two variables necessary for survival analysis: whether or not the loan was funded, and how long it took to fund the loan. The first variable, to gauge whether or not the loan was funded, was a dichotomous variable with the value of 1 if the loan was funded, and 0 if the loan was not funded. A second, continuous variable measured the length of time, in days and minutes, from which the loan was displayed on the Kiva Web site to the time it was fully funded (or the date study closed). Thus, loans funded within 24 hours of their release had a value less than 1, and those taking longer than 24 hours to fund had a value greater than 1.

To provide an additional measure of venture performance, we also created two additional variables related to loan repayment: whether or not the loan was repaid, and how long it took to repay the loan. As with the funding performance measures, we created a dichotomous variable and a continuous variable for repayment.

Independent and Control Variables

We use generalizable measures of EO and VO that utilizes a rare analysis technique in microfinance research, namely computerized textual analysis of organizational narratives. Content analysis, a qualitative method that classifies or categorizes communications, allows for contextual inferences that other methods do not (Krippendorff, 2004; Weber, 1990). It is often used to explore issues of significance to management scholars that are difficult to examine (Carley, 1997; Woodrum, 1984). Content analysis is often applied to organizational narratives such as annual reports, letters to shareholders, and organizational mission statements (e.g., Duriau, Reger, & Pfarrer, 2007; Moss, Short, Payne, & Lumpkin, 2011; Palmer & Short, 2008).

While there are many content analysis techniques, in this paper, we use LIWC (Linguistics Inquiry and Word Count), a computer-assisted text analysis program, to examine loan descriptions (e.g., Pennebaker, Francis, & Booth, 2001). Loan descriptions are a vital narrative for ventures seeking funding through Kiva and provide a generalizable data source. LIWC has been used in content analysis studies in many contexts such as customer service interactions (King, Shapiro, Hebl, Singletary, & Turner, 2006), negotiations (Olekalns & Smith, 2009), newspaper and magazine articles (Humphreys, 2010; Pfarrer, Pollock, & Rindova, 2010), and shareholder letters (Yadav, Prabhu, & Chandy, 2007). LIWC allows users to create custom dictionaries, which makes it ideal for use in the present study. LIWC also provides standardized output to control for narrative length, since longer narratives could naturally contain more instances of a specific type of language.

EO and VO. To improve the construct validity of analysis, we used dictionaries employed in previous studies. Doing so also serves to improve the generalizability of those dictionaries as they are applied to additional contexts. First, we used validated measures to explore the dimensions of VO (conscientiousness, courage, empathy, integrity, warmth, and 0 zeal) (Payne et al., 2011; see article for complete word list). Examples of words used in these dictionaries include confident and prepared (conscientiousness), competent and strong (courage), compassionate and patient (empathy), honest and sincere (integrity), gracious and pleasant (warmth), and eager and passionate (zeal). (1)

Next, we also used the dictionaries created by Short, Payne, Brigham, Lumpkin, and Broberg (2009) in which they gauged the extent to which chief executive officers used entrepreneurial language in their letters to shareholders (see article for complete word list). Their dictionary has also been used to gauge entrepreneurial language used in mission statements of social and commercial ventures (Moss et al., 2011). We measured all five EO dimensions (autonomy, competitive aggressiveness, innovativeness, proactiveness, and risk taking) used in the loan narratives. We examined each EO dimension individually (Lumpkin & Dess, 1996) as the dimensions were conceptualized as independent constructs. Examples of the types of words used in each dimension include independence and do-it-yourself (autonomy), ambitious and intensive (competitive aggressiveness), creativity and initiative (innovativeness), explore and prospect (proactiveness), and bold and enterprising (risk taking). We follow Covin and Wales's (2012) recommendation to model the five EO dimensions as independent constructs when using computer-assisted text analysis in conjunction with Lumpkin and Dess's conceptualization, thus matching the theory with our methods.

Control Variables. We also included a number of control variables that could be expected to influence the results, as has been done in other studies using Kiva data (e.g., Allison et ah, 2013; Galak et ah, 2011). We included a dummy variable for year to account for differences in loan-funding rates over time, as well as continuous variables for gross domestic product per capita and infant mortality rate to account for individual country-level effects. We controlled for loan size, since larger loans could be expected to take longer to fund/repay, or even to not be funded/repaid at all. Since Kiva administers its loans through over 160 MFI partners, who have different creditworthiness levels, we used Kiva's 5-star risk rating (with 0.5 star increments) to control for the effect of MFI partner on loan funding. Kiva also provides the sex of the venture's leader, or in the case of a group loan, the sex of the person leading the group seeking a loan. We thus created a dummy variable for sex: female (1) and male (0). Finally, since Kiva users may have different preferences in loaning money to individuals or groups, we created a dummy variable for a group loan (1) or individual loan (0).

Analysis

We use a Cox proportional hazards model (Cox, 1972) to model our hypotheses. This analysis method is used to estimate the probability of an event occurring given the values of the independent variables (Spruance, Reid, Grace, & Samore, 2004). In most cases, when studying the length of time for an event to occur, the final state of some cases remain unknown at the end of the study. Using ordinary least squares regression with this type of data may result in significant bias (Spruance et al.). The usefulness of the Cox proportional hazards model is thus its ability to account for biases that could arise due to data censoring at the study's end (right censoring). The Cox proportional hazards model accounts for right censoring in our study by estimating the impact of the independent variables on the probability of the dependent variable occurring (e.g., funded or not). None of the cases in our sample began to seek funding prior to the beginning of our study; as such, our data are not left censored. The nature of Kiva's funding process also eliminates the possibility of random censoring due to dropouts in that the loan has already been made once the funding request is released.

The output of the Cox models are exponentiated beta values (e[beta]), which specify the effect that each independent variable has on the rate and probability of the event happening. In our study, e[beta] greater than 1.0 suggest that the variable increases the probability of funding, while e[beta] less than 1.0 suggest that it decreases the probability of funding. Exponentiated betas also signify that the event is likely to occur sooner for e[beta] values greater than one and later for e[beta] values less than one (Spruance et al., 2004). Cox hazard models have been used in marketing research on microlending where the similarity of the borrower to the lender was used to predict time to funding (Galak et al., 2011), and in microlending studies, examining political discourse used to influence venture funding (Allison et al., 2013).

Results

Table 1 presents the descriptive statistics and correlations for our sample of loans. All correlations between the variables representing language used in the loan were 0.10 or less, suggesting that each variable measures distinct language elements. Combs (2010) recommends that studies employing large data sets report effect size, and given our sample size, statistical power of 0.80, and significance of 0.001, the effect size is 0.0007. We highlight the practical significance of our results in the Discussion section.

We conducted the Cox hazard models in steps as reported in Tables 2 and 3, to gauge whether or not the loans reached funded status and whether or not the loans were repaid. Control variables were entered in Model 1, followed by the independent variables in Models 2 and 3. Given small effect sizes, Combs (2010) suggests reporting standardized coefficients so that the relative magnitude of the coefficients can be assessed by the reader. The e[beta] coefficients in the Cox hazard proportion model are standardized for the independent variables.

We found contrasting findings for Hypothesis 1, which proposed that ventures signaling the six dimensions of VO would be more likely to be funded. Results for conscientiousness (e[beta] = 0.99, /?<0.05), courage (e[beta] = 0.99, p<0.01), empathy (e[beta] = 0.99, p < 0.05), and warmth (e[beta] = 0.99, p < 0.05) were significant and in the opposite direction as hypothesized, while integrity and zeal were not significant. Ventures in our sample were less likely to be funded if they signaled conscientiousness, courage, empathy, or warmth.

We also found contrasting results for Hypothesis 2, which was significant in the opposite direction for four out of the six VO dimensions: conscientiousness (e[beta] = 0.98, p< 0.001), courage (e[beta] =0.99, p< 0.001), warmth (e[beta] = 0.99, < 0.01), and zeal (e[beta] = 0.97, p < 0.001). Results for empathy and integrity were not significant. Contrary to our hypothesis, this finding supports the idea that ventures that signaled conscientiousness, courage, warmth, or zeal are less likely to repay their loans, and when they do repay them, the loans tend to be repaid less quickly.

Hypothesis 3 suggested that the extent to which microenterprises signaled the five dimensions of an EO would be positively associated with likelihood of being funded. This hypothesis was supported for three out of the five EO dimensions: autonomy (e[beta] = 1.03, p < 0.01), competitive aggressiveness (e[beta] = 1.02, p < 0.01), and risk taking (e[beta] = 1.02, p < 0.05). Innovativeness and proactiveness were not supported.

Finally, with regard to EO dimensions and repayment, Hypothesis 4 was not supported. Four of the five EO dimensions were not significant, with proactiveness being the only dimension that was significant, but in the opposite direction hypothesized (e[beta]) = 0.97, p < 0.001). Ventures signaling high levels of proactiveness in their loan descriptions are less likely to repay their loans, with nonsignificant results for the dimensions of autonomy, competitive aggressiveness, innovativeness, and risk taking.

Discussion and Conclusion

Financing new ventures is an extremely important yet challenging component of the entrepreneurial process (Aldrich & Ruef, 2006; Cassar, 2004). Microfinance represents an important tool to assist microenterprises in their growth (Bruton et al., 2011; Khavul, 2010). In recent years, crowdfunding sources such as Kiva have served as an increasingly popular medium to assist in the funding and growth of these ventures worldwide. Management scholars are only at the beginning phases of analyzing different components of these phenomena (e.g., Allison et al., 2013; Galak et al., 2011). This study provides further insight into how different organizational characteristics and the rhetoric in entrepreneurial narratives influence both the financing of microenterprises as well as the repayment of this funding.

Specifically, this study makes five contributions to the microfinance and microenterprise literatures. First, it extends signaling theory to microfinance and strengthens the signaling literature regarding the importance of signaling under conditions of information asymmetry. Second, it adds to the microfinance literature by highlighting venture characteristics that influence lenders' assessment of the venture, and the likelihood and speed with which microloans are funded and repaid. Third, it advances work on VO and EO by suggesting that certain dimensions of these orientations can influence the perceptions and actions of microlenders, as well as the performance of microborrowers. Fourth, it contributes to the social entrepreneurship literature by establishing a distinction between microfinance and other types of social financing. Finally, the study builds on other studies using computer-assisted textual analysis to study a variety of organizational constructs in a microenterprise context. We expand on each of these contributions and our results below, as well as discuss the study's limitations and highlight opportunities for future research.

Signaling theory provides a logic in which to situate our findings, suggesting that Kiva lenders are more likely to fund ventures that signal autonomy, competitive aggressiveness, and risk taking. These same lenders are less likely to fund ventures that signal conscientiousness, courage, empathy, or warmth. While prior research suggests that a strong VO creates a positive impression for stakeholders and reduces investor uncertainty (Fiol, Hatch, & Golden-Biddle, 1998; Payne et al., 2011, 2013), our study suggests these characteristics do not create the same impression in the minds of microlenders. This finding contributes to double bottom line research (Clark, Rosenzweig, Long, & Olsen, 2004; Emerson, 2003) that for investors focusing on the microfinance sector, signaling certain competitive or an economic characteristic is more important than emphasizing characteristics related to trust and ethics. In the case of microfinance, uncertainty associated with competitive behavioral intention (i.e., EO) might be more meaningful than uncertainty associated with characteristics (i.e, VO). This might be due to the fact that microlenders do not expect a return on their investment, which might make rhetoric signaling trust less important to their decision-making process. Given the lack of information about the microenterprise available to investors--essentially limited to loan descriptions on the Kiva Web site--our results show the impact that signaling can have on loan funding and repayment.

At the same time, for prosocial lenders perhaps all microenterprises demonstrate dimensions of virtuousness as these ventures are operated by "necessity entrepreneurs" who engage in entrepreneurship as a means to survive, provide for their family, and alleviate poverty (Khavul, 2010). Thus, microlenders may believe all microenterprises are in essence ethical and virtuous in that they provide an income to the poor. Or at least, from a signaling perspective, there might be more uncertainty surrounding microenterprises' intentions than characteristics. Social investors, therefore, may prefer those microenterprises that present themselves as more business-oriented, as arguably they could have a greater social and economic impact.

This finding also suggests that microenterprises need to signal the business dimensions of their venture in their narratives to social investors. Some of the narratives might have been written to appeal to the social sentiments of social investors (signaling the dimensions of VO) when they could have focused on the EO dimensions of the venture. As suggested by Allison et al. (2013), narratives appealing to investors should be carefully managed in order to improve chances of funding. Signals need to be strategically managed (Austen-Smith & Banks, 2000) as the benefits, and opportunity costs, of different signals must be considered. Future research could better assess who writes these narratives, with what prompts, and how this varies from MFI to MFI.

Our findings also have implications for the VO and EO literatures. Rather than taking a higher order approach to these constructs, our study examines their dimensions individually. It is important to reiterate that the results are not significant for all dimensions of these constructs. The preference for funding microenterprises that signal a higher EO and lower VO is significant for all dimensions of EO except proactiveness and innovativeness and for all dimensions of VO except integrity and zeal. This could be because these microenterprises are led by necessity entrepreneurs who are not necessarily known for their innovativeness or creativity or for their willingness to explore (proactiveness) or for their zealousness (eagerness, passion). Rather, they seek to meet their basic needs and those of their families (Kelley et al., 2011). Our study thus uncovers contextual conditions under which certain VO and EO dimensions may or may not be important to the performance of microenterprises.

Relatedly, our results highlight the importance of the independent variance of the five EO dimensions. The difference in how the EO dimensions affect funding versus repayment in Kiva loans emphasizes this variance. Ventures on Kiva tend to be funded--and to be funded more quickly--when they present themselves as having higher autonomy, competitive aggressiveness, and risk taking. They also tend to not repay their loans--or to do so more slowly--when they use language higher in proactiveness. These findings highlight the need for further research into how EO dimensions vary in different contexts since most studies present EO as a unitary construct (Rauch et al., 2009).

This study also contributes to the social entrepreneurship literature by providing a rigorous quantitative study establishing a distinction between microfinance and other types of social financing (Short, Moss, & Lumpkin, 2009). This study focuses on analyzing the factors that influence investors to provide loans to microenterprises that are underserved. Other studies in the social entrepreneurship space focus on analyzing different aspects of social finance or financing for people contributing to the social good or engaged in socially valuable activities (e.g., Scarlata & Alemany, 2010). The microenterprises that are the focus of this study concentrate on developing their own enterprise, and in the process, they create varying degrees of economic and social value. Investors supporting social entrepreneurs through social finance might give importance to different characteristics as the primary purpose of these social entrepreneurs is to create social good. Thus, this study contributes to the social entrepreneurship literature by focusing on analyzing the factors that influence lending to and repayment by microenterprises that operate in the developing world.

Finally, the study builds on other studies using computer-assisted textual analysis to study a variety of organizational constructs in a microenterprise context. By analyzing how a VO and EO of a microenterprise influences lenders' decision making as well as a microenterprise's repayment, we use these constructs in a novel way. Assessing VO and EO by content analyzing the narratives of microenterprises allows us to assess the lending and repayment tendencies of a great number of microlenders and microenterprises.

Like all studies, ours is not without its limitations. Our study can only draw conclusions with the way the ventures present themselves; we cannot draw causal attributions to actual venture behavior (Moss et al., 2011). Additionally, our analysis relies on word counts from specific dictionaries, which creates the possibility of taking words out of context or of missing the rich understanding available through using human coders (Duriau et al., 2007). Yet despite these limitations, computer-assisted text analysis was required to enable us to examine the hundreds of thousands of documents made available by Kiva. Additionally, our use of the Kiva data set limits the generalizability of our findings to crowdfunding microfinance solutions and may not be generalizable to other types of venture financing, such as traditional bank loans, venture capital, and angel investors.

Finally, it is statistical fact that larger sample sizes are able to detect significant relationships with smaller effect sizes, bringing the practical significance of the results into question. Discussing the real-life impact of the results is therefore even more important in such cases (Combs, 2010). At first glance, the e[beta] of the Cox regression results seems close to 1.0--the point at which no effect is present. Yet practically speaking, even a small change in the e[beta] in a sample size of over 400,000 has significant real-life implications in the lives of microentrepreneurs. For example, the coefficient for Autonomy in Table 2, Model 2 (e[beta] = 1.03, p < 0.01) suggests that for every incremental use of the word "autonomy" (for every 100 words in the narrative), there is a 3% increase in the likelihood of receiving funding. The same concept holds true for e[beta] values less than 1.0, meaning that for each incremental occurrence of a word in the narrative, there is less chance of being funded, or the loan being repaid. For example, in the case of zeal, (see Zeal in Table 3, Model 3: e[beta] = 0.97, p < 0.001), each incremental use of a word related to zeal will decrease the chance of repayment by 3%.

Building on the results of this study, we now discuss four specific areas for future research.

(1) Microlender Characteristics. Future research should more thoroughly assess the role of the lender in a crowdfunding platform. Previous studies suggest that lenders give on Kiva to feel a warm glow or to feel good about themselves (Allison et al., 2013) and that they make their funding decisions based on those that are more socially proximate to themselves (Galak et al., 2011; Loewenstein & Small, 2007). Nevertheless, there are many other aspects of the lender-borrower relationship that could be assessed. Future studies can further analyze the relationship between lender characteristics and those of borrowers. Are women more likely to lend to women or men? What stimulates lenders to invest in more ventures? Do investing patterns change over time? Do those who invest over longer time periods prefer certain types of ventures? Will these lenders eventually seek a return on their investment in the form of interest or a share in the venture or are they content as social investors?

(2) Crowdfunding Impact. Crowdfunding is a relatively new mechanism for microenterprises to receive funding. Crowdfunding has been referred to as pass-through microlending as an MFI acts as an agent between the prosocial lender/social investor and the individual entrepreneur (Allison et al., 2013). Nevertheless, hundreds of millions of dollars have been raised for microenterprises via crowdfunding Web sites like Kiva. It would be interesting to better understand how this funding source and its process impact both microentrepreneurs and the MFI serving as the back-office for the loan. Do these investments really improve the economic and social value of the MFI and the microenterprise? Has this funding mechanism impacted the development of microenterprises and the overall alleviation of poverty? How do MFIs and microenterprises interact with lenders?

(3) Global Differences. This study assesses VO and EO on a global scale. However, perhaps some of these results vary by region or by institutional or cultural characteristics of different countries. Future studies could look at these same hypotheses in different regions. Lenders may also prefer to invest in different countries. What factors influence the choice of which country a social investor chooses? How do institutional pressures in different countries influence likelihood of funding and repayment?

(4) Microenterprises. This study focuses on the likelihood that microenterprises are funded and they repay their loans based on VO and EO. However, there are many elements of the microenterprises that could be further analyzed. We suggest that these microenterprises are primarily composed of necessity-based entrepreneurs, but is this really the case? Are some of these entrepreneurs more opportunity focused? In addition, are these microenterprises equivalent to family firms? How could research on family firms be applied to microenterprises? How many employees do these ventures have and what are their growth plans?

We look forward to future studies that develop these questions. Although microenterprises are a special category of business and Kiva/crowdfunding represents a different type of investing, together they represent a large segment of the entrepreneurial and financing populations. We present only a few areas in which research could be conducted. We invite scholars to offer their own contributions to further our knowledge of microfinancing.

DOI: 10.1111/etap.12110

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(1.) Both the EO and VO dictionaries in the literature contain phrases, such as jockey for position (EO: competitive aggressiveness). Since LIWC is not capable of detecting phrases, we hyphenated the phrases (i.e., jockey-for-position) to retain as much construct validity as possible given our analysis tools.

Todd W. Moss is an Assistant Professor of Entrepreneurship and Sustainability, Whitman School of Management, Syracuse University, Syracuse, NY 13244, USA.

Donald O. Neubaum is an Associate Professor of Strategy and Entrepreneurship, College of Business, Oregon State University, Corvallis, OR 97331, USA.

Moriah Meyskens is Instructor in Management in the Management Department, School of Business, University of San Diego, San Diego, CA 92110, USA.

Please send correspondence to: Donald O. Neubaum, tel.: 541-737-6036; email: don.neubaum@bus .oregonstate.edu, to Todd W. Moss at tmoss@syr.edu, and to Moriah Meyskens at mmeyskens@sandiego.edu.
Table 1
Descriptive Statistics and Correlations (N = 407,716)

     Variable                       Mean        SD          1

 1   Funded Y/N                       0.98       0.14
 2   2006                             0.00       0.03    0.00 *
 3   2007                             0.03       0.17    0.02 ***
 4   2008                             0.09       0.28    0.04 ***
 5   2009                             0.16       0.36    0.05 ***
 6   2010                             0.20       0.40    0.06 ***
 7   2011                             0.27       0.44    0.08 ***
 8   2012                             0.26       0.44    0.21 ***
 9   Loan amount                    792.44     788.15   -0.10 ***
10   Infant mortality                33.71      19.23    0.03 ***
11   GDP per capita               5,070.24   4,084.73   -0.02 ***
12   NGO risk                         3.26       0.84   -0.03 ***
13   Sex                              0.27       0.44   -0.09 ***
14   Group                            0.12       0.32    0.02 ***
15   Autonomy                         0.02       0.13   -0.00
16   Competitive aggressiveness       0.08       0.25   -0.00
17   Innovativeness                   0.09       0.29   -0.01 ***
18   Proactiveness                    0.05       0.20   -0.01 ***
19   Risk taking                      0.03       0.15   -0.00
20   Conscientious                    0.16       0.38   -0.01 ***
21   Courage                          0.38       0.60   -0.01 ***
22   Empathy                          0.11       0.32   -0.01 ***
23   Integrity                        0.08       0.27    0.01 ***
24   Warmth                           0.19       0.43   -0.01 ***
25   Zeal                             0.07       0.24    0.00

     Variable                         2           3           4

 1   Funded Y/N
 2   2006
 3   2007                         -0.01 ***
 4   2008                         -0.01 ***   -0.05 ***
 5   2009                         -0.01 ***   -0.07 ***   -0.13 ***
 6   2010                         -0.01 ***   -0.09 ***   -0.15 ***
 7   2011                         -0.02 ***   -0.10 ***   -0.19 ***
 8   2012                         -0.02 ***   -0.10 ***   -0.18 ***
 9   Loan amount                   0.00 *     -0.03 ***   -0.01 ***
10   Infant mortality              0.01 ***    0.01 ***    0.03 ***
11   GDP per capita                0.01 ***    0.03 ***    0.02 ***
12   NGO risk                     -0.01 ***   -0.01 ***   -0.02 ***
13   Sex                           0.01 ***   -0.02 ***   -0.03 ***
14   Group                        -0.01 ***   -0.05 ***    0.03 ***
15   Autonomy                     -0.00       -0.01 ***   -0.01 ***
16   Competitive aggressiveness   -0.01 ***   -0.04 ***   -0.08 ***
17   Innovativeness               -0.00 *     -0.04 ***   -0.06 ***
18   Proactiveness                -0.00 ***   -0.03 ***   -0.03 ***
19   Risk taking                  -0.00       -0.02 ***   --0.04 ***
20   Conscientious                 0.00       -0.02 ***   -0.02 ***
21   Courage                      -0.00       -0.04 ***   -0.07 ***
22   Empathy                      -0.01 ***   -0.03 ***   -0.05 ***
23   Integrity                     0.00       -0.02 ***   -0.04 ***
24   Warmth                       -0.00       -0.02 ***   -0.06 ***
25   Zeal                          0.00 *     -0.01 ***   -0.02 ***

     Variable                         5           6           7

 1   Funded Y/N
 2   2006
 3   2007
 4   2008
 5   2009
 6   2010                         -0.22 ***
 7   2011                         -0.26 ***   -0.30 ***
 8   2012                         -0.26 ***   -0.30 ***   -0.36 ***
 9   Loan amount                  -0.05 ***   -0.02 ***    0.01 ***
10   Infant mortality             -0.01 ***   -0.00 *     -0.01 ***
11   GDP per capita                0.01 ***   -0.01 ***    0.01 ***
12   NGO risk                     -0.03 ***   -0.03 ***    0.02 ***
13   Sex                          -0.04 ***   -0.00 *      0.04 ***
14   Group                        -0.01 ***    0.01 ***   -0.01 ***
15   Autonomy                     -0.00 ***    0.01 ***    0.00
16   Competitive aggressiveness   -0.03 ***    0.05 ***    0.04 ***
17   Innovativeness               -0.02 ***   -0.02 ***    0.04 ***
18   Proactiveness                -0.00 ***    0.01 ***    0.00
19   Risk taking                  -0.01 ***    0.02 ***    0.01 ***
20   Conscientious                -0.02 ***   -0.00 *      0.01 ***
21   Courage                       0.03 ***    0.00       -0.00
22   Empathy                      -0.00 *      0.02 ***   -0.01 ***
23   Integrity                     0.02 ***    0.03 ***   -0.00
24   Warmth                       -0.04 ***   -0.02 ***    0.03 ***
25   Zeal                          0.01 ***    0.03 ***   -0.00

     Variable                         8            9          10

 1   Funded Y/N
 2   2006
 3   2007
 4   2008
 5   2009
 6   2010
 7   2011
 8   2012
 9   Loan amount                   0.07 ***
10   Infant mortality             -0.00         0.04 ***
11   GDP per capita               -0.04 ***     0.21 ***   -0.59 ***
12   NGO risk                      0.05 ***     0.10 ***   -0.20 ***
13   Sex                           0.03 ***     0.07 ***    0.01 ***
14   Group                         0.01 ***     0.46 ***    0.24 ***
15   Autonomy                      0.01 ***     0.00        0.00
16   Competitive aggressiveness    0.01 ***    -0.00        0.00
17   Innovativeness                0.05 ***     0.00 ***   -0.00 *
18   Proactiveness                 0.02 ***     0.00        0.00
19   Risk taking                   0.01 ***     0.00       -0.00 *
20   Conscientious                 0.03 ***     0.00 ***   -0.00
21   Courage                       0.03 ***     0.00 ***   -0.00 *
22   Empathy                       0.03 ****    0.00        0.00
23   Integrity                    -0.01 ***     0.00       -0.01 ***
24   Warmth                        0.06 ***     0.01 ***   -0.00
25   Zeal                         -0.01 ***     0.00       -0.00

     Variable                        11          12          13

 1   Funded Y/N
 2   2006
 3   2007
 4   2008
 5   2009
 6   2010
 7   2011
 8   2012
 9   Loan amount
10   Infant mortality
11   GDP per capita
12   NGO risk                      0.16 ***
13   Sex                           0.05 ***    0.04 ***
14   Group                        -0.08 ***   -0.10 ***   -0.14 ***
15   Autonomy                     -0.00       -0.00        0.00
16   Competitive aggressiveness   -0.01 ***    0.00        0.01 ***
17   Innovativeness               -0.00        0.01 ***    0.01 ***
18   Proactiveness                -0.01 ***   -0.00        0.00
19   Risk taking                   0.00        0.00        0.00
20   Conscientious                 0.00        0.00        0.00
21   Courage                       0.00        0.00        0.00 *
22   Empathy                      -0.01 ***    0.00        0.00
23   Integrity                     0.01 ***   -0.00       -0.00
24   Warmth                       -0.00 *      0.00        0.00 *
25   Zeal                         0.00        -0.00       -0.00

     Variable                        14          15          16

 1   Funded Y/N
 2   2006
 3   2007
 4   2008
 5   2009
 6   2010
 7   2011
 8   2012
 9   Loan amount
10   Infant mortality
11   GDP per capita
12   NGO risk
13   Sex
14   Group
15   Autonomy                      0.00
16   Competitive aggressiveness   -0.00       -0.00
17   Innovativeness                0.00        0.04 ***    0.00 *
18   Proactiveness                 0.00        0.00        0.03 ***
19   Risk taking                   0.00        0.00        0.06 ***
20   Conscientious                 0.00        0.02 ***   -0.02 ***
21   Courage                       0.00        0.03 ***    0.01 ***
22   Empathy                      -0.00       -0.01 ***    0.10 ***
23   Integrity                     0.00        0.04 ***    0.01 ***
24   Warmth                        0.01 ***    0.01 ***   -0.01 ***
25   Zeal                          0.00 *      0.01 ***   -0.00

     Variable                        17          18         19

 1   Funded Y/N
 2   2006
 3   2007
 4   2008
 5   2009
 6   2010
 7   2011
 8   2012
 9   Loan amount
10   Infant mortality
11   GDP per capita
12   NGO risk
13   Sex
14   Group
15   Autonomy
16   Competitive aggressiveness
17   Innovativeness
18   Proactiveness                 0.02 ***
19   Risk taking                   0.00 ***   0.03 ***
20   Conscientious                 0.02 ***   0.00        0.00 *
21   Courage                       0.07 ***   0.02 ***   -0.00
22   Empathy                      -0.04 ***   0.02 ***    0.02 ***
23   Integrity                     0.02 ***   0.00 *      0.02 ***
24   Warmth                        0.03 ***   0.03 ***   -0.00
25   Zeal                          0.02 ***   0.01 ***    0.01 ***

     Variable                        20         21          22

 1   Funded Y/N
 2   2006
 3   2007
 4   2008
 5   2009
 6   2010
 7   2011
 8   2012
 9   Loan amount
10   Infant mortality
11   GDP per capita
12   NGO risk
13   Sex
14   Group
15   Autonomy
16   Competitive aggressiveness
17   Innovativeness
18   Proactiveness
19   Risk taking
20   Conscientious
21   Courage                      0.05 ***
22   Empathy                      0.03 ***   -0.03 ***
23   Integrity                    0.06 ***    0.04 ***   -0.00
24   Warmth                       0.08 ***    0.10 ***    0.02 ***
25   Zeal                         0.04 ***    0.01 ***    0.02 ***

     Variable                        23         24

 1   Funded Y/N
 2   2006
 3   2007
 4   2008
 5   2009
 6   2010
 7   2011
 8   2012
 9   Loan amount
10   Infant mortality
11   GDP per capita
12   NGO risk
13   Sex
14   Group
15   Autonomy
16   Competitive aggressiveness
17   Innovativeness
18   Proactiveness
19   Risk taking
20   Conscientious
21   Courage
22   Empathy
23   Integrity
24   Warmth                       0.04 ***
25   Zeal                         0.00 *     0.03 ***

* p<0.05; ** p < 0.01; *** p< 0.001

GDP, gross domestic product; NGO, nongovernmental organization; SD,
standard deviation.

Table 2
Cox Proportional Hazard Model for Loan Funding (N = 403,445)

Variable                    Model 1         Model 2         Model 3

Control variables
    ([dagger])
  2006                        1.74 ***        1.75 ***        1.74 ***
  2007                        3.56 ***        3.57 ***        3.56 ***
  2008                        2.10 ***        2.10 ***        2.09 ***
  2009                        1.96 ***        1.96 ***        1.95 ***
  2010                        1.85 ***        1.85 ***        1.84 ***
  2011                        1.39 ***        1.39 ***        1.39 ***
  Loan amount                 1.00 ***        1.00 ***        1.00 ***

  Infant mortality            1.01 ***        1.01 ***        1.01 ***
  GDP per capita              1.00 ***        1.00 ***        1.00 ***
  Partner MFI risk            0.98 ***        0.98 ***        0.98 ***
  Sex                         1.76 ***        1.76 ***        1.76 ***
  Group                       0.91 ***        0.91 ***        0.91 ***
Independent variables
  Autonomy                                    1.03 **
  Competitive                                 1.02 **
    aggressiveness
  Innovativeness                              1.00
  Proactiveness                               1.00
  Risk taking                                 1.02 *
  Conscientiousness                                           0.99 *
  Courage                                                     0.99 **
  Empathy                                                     0.99 *
  Integrity                                                   1.00
  Warmth                                                      0.99 *
  Zeal                                                        1.01
Model [chi square]       94,627.61 ***   94,642.02 ***   94,647.65 ***
[DELTA][chi square]                          17.80 **        20.63 **
df                           12              17              18

* p< 0.05; ** p< 0.01; *** p < 0.001

([dagger]) Year 2012 excluded from analysis due to collinearity.

df, degrees of freedom; GDP, gross domestic product; MFI,
microfinance institution.

Table 3
Cox Proportional Hazard Model for Loan Repayment (N = 403,419)

Variable                 Model 1        Model 2         Model 3

Control variables
    ([dagger])
  2006                   2.91 ***         2.91 ***        2.91 ***
  2007                   4.16 ***         4.16 ***        4.15 ***
  2008                   4.51 ***         4.50 ***        4.49 ***
  2009                   2.40 ***         2.40 ***        2.40 ***
  2010                   1.43 ***         1.43 ***        1.43 ***
  2011                   1.41 ***         1.41 ***        1.41 ***
  Loan amount            1.00 ***         1.00 ***        1.00 ***
  Infant mortality       1.00 **          1.00 **         1.00 **
  GDP per capita         1.00 ***         1.00 ***        1.00 ***
  Partner MFI risk       1.03 ***         1.03 ***        1.03 ***
  Sex                    1.36 ***         1.36 ***        1.36 ***
  Group                  0.87 ***         0.87 ***        0.87 ***
Independent variables
  Autonomy                                0.99
  Competitive                             1.00
    aggressiveness
  Innovativeness                          1.00
  Proactiveness                           0.97 ***
  Risk taking                             1.01
  Conscientiousness                                       0.98 ***
  Courage                                                 0.99 ***
  Empathy                                                 1.00
  Integrity                                               1.00
  Warmth                                                  0.99 **
  Zeal                                                    0.97 ***
Model [chi square]      92.947 ***   92,960.70 ***   93,009.03 ***
[DELTA][chi square]                      14.23 *         63.15 ***
df                      12               17              18

* p < 0.05; ** p < 0.01; *** p < 0.001
([dagger]) Year 2012 excluded from analysis due to collinearity.

df, degree of freedom; GDP, gross domestic product; MFI, microfinance
institution.
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Author:Moss, Todd W.; Neubaum, Donald O.; Meyskens, Moriah
Publication:Entrepreneurship: Theory and Practice
Geographic Code:1U9CA
Date:Jan 1, 2015
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