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Kiva: pushing the boundaries of charity


In general, it is relatively easy to obtain a loan. However, the interest rate differs significantly between, and even within, countries. The interest rate fundamentally reflects how expensive a loan is, which is often too high for the borrower to accept, especially in developing countries (Fernando, 2006). This phenomenon is further thwarted by the fact that central organizations such as banks or other financial institutions, define the loan’s extent and interest. Many are consequently unable to realize their ideas or even sustain costs of life.


Kiva, an international nonprofit organization, perceived these issues and decided to take action. Founded in 2005, Kiva aims to connect people through lending in order to alleviate poverty (Kiva, 2018). They specifically focus on underdeveloped regions and are active in 84 countries. Kiva recently surpassed the $1 billion mark (Price, 2017), which bears testimony to their success. But why does it work? To answer that question, we will dive deep into Diva’s business model, the value system surrounding Kiva, and how Kiva organizes its operations in order to facilitate that value.

The process of borrowing

Kiva is best described as a platform for microfinancing, where borrowers can apply for loans with 0% interest rate. Borrowers have to pass strict application criteria in order to be posted on the platform. Thereafter, basically anyone can lend this applicant funds through a “crowdlending” system. After everything is in operation, the borrower starts to repay the exact amount that is borrowed. As Kiva is a nonprofit organization, they can simply cover their costs through voluntary donations by Kiva lenders (2/3) and other foundations and supporters (1/3). This business model allows them to re-envision charity and stimulate growth in previously deserted regions.

CCDC blog Kiva process


Value creation

Kiva is a crowdfunding platform, consisting of borrowers and lenders. As the term value is distinctly different for each, we will elaborate on both entities separately, and in a jointly manner thereafter.

Borrowers – This entity usually represent entrepreneurs who want to contribute to their local communities or simply sustain costs of life. As such, most of the borrowers are located in developing countries. Borrower’s value within this system is acquired through three components:

  • Access to capital – borrowers can obtain much-needed funds through Kiva, which were previously inaccessible for them.
  • No additional costs – loans are given to the borrower for a 100%.
  • Realize ideas – a somewhat softer value component for borrowers is the fact that they are able to realize their dreams. The sole notion of gaining capital would be insufficient to account for the total value created for this entity, as it neglects the emotional component.

Lenders – This entity is formed by anyone who is willing to lend money (for any amount) and is usually located in developed countries. Their value is depicted by the following components:

  • Feelings of altruism: lenders participate on Kiva, mainly because of altruistic reasons and is of crucial importance to Kiva’s existence. After all, lenders receive no monetary reward, despite evident risks.
  • No middleman: knowing that 100% of the loan goes to the borrower adds to the lender’s feeling of righteousness.

Kiva as operating mechanism

Kiva’s role is one of provider service logic (Saarijärvi, Kannan & Kuusela, 2013), as its limited to facilitating interactions between these two entities. Generally, It is inherently difficult for firms like Kiva to become part of the interaction process (Grönroos, 2011), but they have succeeded by a twofold of operations. First, the platform acts as a catalyst by connecting them. As the user base grows, additional value can be created due to network effects. That is, lenders have more projects to choose from, whereas borrowers’ chances of success increase. For Kiva, such positive network effects increase switching costs, allowing them to keep users (Farrell & Klemperer, 2007). Second, the screening and structured loaning procedures provide lenders with much needed security as they bear substantial risks. Most of Kiva’s resources are devoted to the latter, which we will scrutinize in further depth hereafter.

Mollick (2013) estimated that over 75% of crowdfunding projects do not fulfill their initial obligations. In Kiva’s case, such numbers are incredibly relevant since lenders’ value is created through acts of altruism. Even if loans cannot be repaid in full, it inherently means that the project was unsuccessful. Knowing that, as a supporter, your initial feeling and drive of supporting entrepreneurs is diminished.

Furthermore, projects are diverse in nature and vary substantially in terms of potential. As is the case with unsolicited ideas, these are of high quantity and often low potential. Alexy, Criscuolo & Salter (2012) recommend both a filtering beyond submission process and adjust a central-decentralized approach, which Kiva adhered to. Their local presence is fitting for small local ideas, whereas the filtering is fundamentally a two-step process. Potential is roughly assessed by Kiva itself, but the eventual filtering is conducted by the lenders in the form of reaching crowdfunding goals (Zvilichovsky, Inbar & Barzilay, 2015).


Thus, Kiva is a microfinance non-profit organization that brings entrepreneurs and funders together. Their business model allows them to connect previously incompatible partners through means of altruism and screening operations. On the other side, entrepreneurs rely on funds in order to realize ideas and contribute to their local communities. The platform is a unique case of how crowdfunding mechanisms can be deployed in order to further good in the world and is therefore a worthwhile consideration in addition to commercial crowdfunding platforms.


Alexy, O., Criscuolo, P., & Salter, A. (2011). No soliciting: strategies for managing unsolicited innovative ideas. California Management Review, 54(3), 116-139.

Farrell, J., & Klemperer, P. (2007). Coordination and lock-in: Competition with switching costs and network effects. Handbook of industrial organization3, 1967-2072.

Fernando, N. A. (2006). Understanding and dealing with high interest rates on microcredit: A note to policy makers in the Asia and Pacific region.

Grönroos, C. and Ravald, A. (2011). Service as a business logic: implications for value creation and marketing, Journal of Service Management, Vol. 22 No. 1, pp. 5-22.

Kiva. 2018. Official website. Retrieved from

Mollick, E., 2014. The dynamics of crowdfunding: An exploratory study. Journal of Business Venturing, 29(1), 1-16.

Price, S. (2017). Lending Pioneer Kiva Hits The One Billion Mark And Launches A Fund For Refugees. Retrieved from

Saarijärvi, H., Kannan, P. K., & Kuusela, H. (2013). Value co-creation: theoretical approaches and practical implications. European Business Review, 25(1), 6-19.

Zvilichovsky, David and Inbar, Yael and Barzilay, Ohad, Playing Both Sides of the Market: Success and Reciprocity on Crowdfunding Platforms (2015). (available at SSRN)

Great ideas from ordinary people

Article discussed: Schemmann, B., Herrmann, A. M., Chappin, M. M., & Heimeriks, G. J. (2016). Crowdsourcing ideas: Involving ordinary users in the ideation phase of new product development. Research Policy, 45(6), 1145-1154.


Crowdsourcing ideas from customers is a contemporary phenomenon that has enjoyed widespread attention from both scholars and business practitioners. These events, albeit temporary or long-term, generate countless of ideas for the focal company, but only a handful are actually implemented. This is particularly true when ideas originate from ordinary users as opposed to experts, or lead users. It is thus pivotal to scrutinize this ordinary user group in order to improve efficiency of the yet expensive and time-consuming filtering process.

With this in mind, Schemmann et al. (2015) conducted a cross-sectional research in an effort to derive ideator and idea-related characteristics that could explain whether a crowdsourced idea from ordinary users is implemented. Based on prior studies and their analysis, they conclude that the following factors all positively relate to actual implementation:

  • Attention paid to crowdsourced ideas of others (H2)
  • Idea popularity (H3)
  • Idea potential innovativeness (H4)

Synthesis of theory: value from juxtaposing user bases

The most interesting theoretical contribution of this paper stems from its focus on the ordinary user, contrasting most existing research, which emphasized the role of lead users. That is, whether ideator and idea-related characteristics can determine actual adoption by the organization. Motivation is found to result in more successful ideas due to individuals being stimulated to develop a qualitative strong idea (Bayus, 2013). However, no such finding is found to be significant within the current research. In addition to the ideator-based characteristic, idea-related characteristics also influence the eventual adoption of the idea. Whereas lead users are valued for their knowledge and expertise, ordinary users leverage an even more powerful mechanism called Wisdom of the Crowds (Surowiecki, 2004). The sheer number of ordinary users results in an extremely accurate prediction of whether an idea is likely to be successful or not. Furthermore, given that innovative ideas are positively related to implementation (Witell et al., 2011), we can infer that the increased diversity within ordinary users is likely to produce more novel ideas than lead users.

Such a juxtaposition of user base enables firms to tailor crowdsourcing initiatives to their respective objectives and move away from crowdsourcing failure rates that have been observed to be as high as 40% (Castellion & Markham, 2013). That is, firms can tweak two mechanisms that naturally emerge from this study’s insights. First, crowdsourcing participants can be limited to a certain user base. Secondly, crowdsourcing platforms can be designed to includes functions such as peer evaluations and idea visibility. The latter brings us to the study by Bockstedt et al. (2016) who distinguishes between blind and unblind innovation contests. Specifically, unblind contests are likely to enhance the positive relation of hypothesis 2 as the platform facilitates learning from others.

Methods used: weaknesses and improvements

The present study was conducted on a sample of 1456 ideas for which the company’s review process was completed. The data stems from an open idea call of a large beverage producer, which was publicly available. Although such a method bears generalizability issues, it can still be justified from a practical perspective. However, in addition to the limitations stated by the authors, another weakness relates to the codification of variables. That is, all variables are examined as dichotomous variables, with exception of ideator motivation. All hypotheses measured with dichotomous values are found to be significant, whereas the continuous variable used to test hypothesis 1 is rejected. Not only does such an outcome arouse suspicion, there is much room for improvement. As illustration, the independent variable attention paid to other ideas could also be coded as a continuous or count variable, measured by relatively time spent on other’s idea page or the number of other’s idea pages visited, respectively. Although arguments for both methods are evident, the authors could at least include such an empirical analysis as a robustness check. As a result, findings become more credible, increasing the likelihood of actual utilization when managing crowdsourcing platforms.


Thus, the present study presents insights into ideator and idea-related characteristics that determine idea implementation in long-term crowdsourcing initiatives. By analysis of ordinary users, the authors complement existing views with another relevant dimension that could help business practitioners manage difficult socio-technical dynamics inherent to crowdsourcing. The higher success rate of crowdsourcing is in turn, likely to spur innovation across a multitude of industries. Although such aspirations seem distant, achieving great feats is realized by taking one step at a time.


Bayus, B.L., 2013. Crowdsourcing new product ideas over time: an analysis of the Dell IdeaStorm Community. Manage. Sci. 59 (1), 226–244

Surowiecki, J., 2004. The Wisdom of Crowds: Why the Many are Smarter than the Few and How Collective Wisdom Shapes Business, Economies, Societies, and Nations. Doubleday, New York, NY.

Witell, L., Löfgren, M., Gustafsson, A., 2011. Identifying ideas of attractive quality in the innovation process. TQM J. 23 (1), 87–99.

Castellion, G., Markham, S.K., 2013. Perspective: new product failure rates:influence of argumentum ad populum and self-interest. J. Prod. Innov. Manage.30 (5), 976–979.

Bockstedt, J., Druehl, C., & Mishra, A. (2016). Heterogeneous submission behavior and its implications for success in innovation contests with public submissions. Production and Operations Management25(7), 1157-1176.