Kiva: pushing the boundaries of charity

March 10, 2018 nikogang Leave a comment

Introduction

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

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.

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).

Conclusion

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.

Bibliography

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 organization, 3, 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 http://www.kiva.org

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 https://www.forbes.com/sites/susanprice/2017/07/06/lending-pioneer-kiva-hits-the-one-billion-mark-and-launches-a-fund-for-refugees/#76dc79025dfe

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)

Articles

One-Way Mirrors in Online Dating

March 10, 2018 ninahoekstra28 Leave a comment

Increasingly, human interactions are communicated using electronic, internet-based medias. It allows for easy access to a lot of content in an organized format within a short amount of time. This creates for an ideal setting for facilitating online dating networks, where its users search for other users with the same intimate-based goals by using the community. Online dating communities are tailored specifically to users who are looking for a romantic partner, in contrast to social networking websites (Quesnel, 2010). The main difference between social media platforms and dating communities is that the first connects people who already know each other, and the second connects people that would like to know each other (Piskorski, 2014).

The growing popularity of online dating websites is altering one of the most fundamental human activities; finding a date or even a marriage partner. Research from the US Census Bureau has shown that 46% of the single population in the US uses online dating to initiate and engage in the process of finding a partner (Paumgarten, 2011). A recent trend is that online dating platforms offer new capabilities to users, such as extensive search, big data-based mate recommendations and varying levels of anonymity, whose parallels do not exist in the physical world (Gelles, 2011). However, little is known about the causal effects, which the authors of this paper seek to examine. Moreover, the authors of this article ran a randomized field experiment on a major North American online dating website, where 50,000 randomly selected users were gifted the ability to anonymously view profiles of other users. The control group was not able to anonymously view other profiles.

The effect of anonymity on users’ behavior

Anonymity may impact a user’s behavior through two distinct causal mechanisms (Bakos, 1997). First of all, lowering searching costs may lead to improved matching because users can express true preferences. An anonymous user has uninhibited access to information as compared to non-anonymous user, who may not visit a profile or regret visiting a profile, because the other user can see this. Because of anonymity, users do not need to worry about repeatedly visiting one’s profile, which is normally seen as stalking or inappropriate behavior. Furthermore, anonymity may impact the matching process because of the lack of signaling-related mechanisms, which are necessary to establish successful communication with a potential mate. It leads to an information asymmetry in which anonymous and non-anonymous users differ in the ability to gather information about the users they are interested in. Therefore, the research objective is to examine the net effect of disinhibition and signaling in online dating (Bapna, 2015).

Social norms may also inhibit the expression of what are considered taboo preferences, such as same-sex and interracial mate seeking (Panchankis and Goldfried, 2006). An anonymity feature may potentially lower this stigma, thereby lowering searching costs and resulting in improved search and improved matching.

Findings

The results of the article suggest that weak signaling is a key mechanism in increasing number of matches. Anonymous users ended up having fewer matches compared with their non-anonymous counterparts, as they were not able to leave a weak signal to the profile they viewed. This effect was particularly strong for women, as they tend not to make the first move and instead rely on the counterparty to initiate the communication. The reduction in quantity of matches by anonymous users is not compensated by a corresponding increase in quality of matches.

The results of this article also show that straight individuals of both genders significantly increase their likelihood of viewing profiles of users of the same gender when they are anonymous. Yet, total number of matches decreases for the anonymous users. Furthermore, this research shows that incoming views and messages decrease because of anonymity, while the number of outgoing messages remains unchanged. The findings of this article from the basis for further research on how the internet, social media and social communities are changing some of the fundamental activities we carry out as humans. Last, the results can also be used to further examine the impact of various levels of privacy protection on individuals’ behavior (Goldfarb and Tucker, 2011).

Sources

Bapna, R., Ramaprasad, J., Shmueli, G., and Umyarov, A. (2016) One-Way Mirrors in Online Dating: A Randomized Field Experiment. Management Science, 62(11), 3100-3122

Bapna R, Umyarov A (2015) Do your online friends make you pay?
A randomized field experiment on peer influence in online
social networks. Management Science, 61(8):1902–1920.

Bakos, J. (1997) Reducing buyer search costs: Implications for electronic marketplaces. Management Science, 43(12):1676–1692

Gelles, D. (2011) Inside Match.com. Financial Times, accessed 10 March 2018.

Goldfarb, A. and Tucker, C. (2011) Privacy regulation and online advertising. Management Science, 57(1):57–71.

Pachankis, J. and Goldfried, M. (2006) Social anxiety in young gay
men. J. Anxiety Disorders 20(8):996–1015

Paumgarten N (2011) Looking for someone: Sex, love, and loneliness on the Internet. New Yorker, http://www.newyorker.com/reporting/2011/07/04/110704fa_fact_paumgarten, accessed 10 March 2018.

Piskorski MJ (2014) A Social Strategy: How We Profit from Social Media, Princeton University Press, Princeton, NJ.

Quesnel, A. (2010) Online Dating Study: User Experiences of an Online Dating Community. Inquiries Journal, 2(11): 1-3.

Articles

Why do people engage in collaborative consumption?

March 10, 2018 485772ed Leave a comment

Collaborative consumption is a large scale trend which involves millions of users and constitutes a profitable business model for many companies to invest in (Botsman and Rogers, 2010). It is often associated with the sharing economy and takes place in organized systems or networks, in which participants conduct sharing activities in the form of renting, lending, trading, bartering, and swapping of goods, services, transportation solutions, space, or money (based on Owyang et al., 2014; Belk, 2014; Bardhi and Eckhardt, 2012; Botsman and Rogers, 2010; Chen, 2009).

Despite the rising importance of collaborative consumption, there is not much knowledge on why users engage in collaborative activities nor why many people are still reluctant to participate in this emerging trend. To address this gap, Möhlmann, in his paper “Collaborative Consumption: Determinants of Satisfaction and the Likelihood of Using a Sharing Economy Option Again” (2015), adopts a holistic approach to study the determinants of the usage of collaborative consumption services, providing empirical evidence from both business-to-consumer (B2C) and consumer-to-consumer (C2C) settings. As a matter of fact, collaborative consumption might refer to both B2C services, such as commercial car sharing, or C2C sharing in the form of redistribution markets or collaborative lifestyles (Bardhi and Eckhardt, 2012; Botsman and Rogers, 2010; Mont, 2004), such as accommodation sharing marketplaces. While nowadays users of sharing services can mainly be found among young age groups, the future generation will be growing up with this trend (Möhlmann, 2015).

Möhlmann (2015) analyzes ten factors that are expected to have an effect on the variable satisfaction with a sharing option, which itself has an effect on the likelihood of choosing a sharing option again. These ten determinants are: community belonging, cost savings, environmental impact, familiarity, internet capability, service quality, smartphone capability, trend affinity, trust, and utility (see Figure 1). The hypotheses of the paper suppose that each determinant has a positive effect on the two dependent variables, with satisfaction with a sharing option also having a positive impact on the likelihood to use a sharing option again. The empirical analysis was conducted on two different collaborative consumption services, specifically the B2C car sharing service car2go (study 1) and the C2C accommodation sharing service Airbnb (study 2). Two independent quantitative online studies were rolled out in July 2014, distributing questionnaires via a mailing list to students of the University of Hamburg (Germany) by a research laboratory.

The findings (see Table 1) show that respondents seem to predominantly be driven by rational reasons, serving their self-benefit, when using collaborative consumption services. Users pay attention to the fact that collaborative consumption helps them to save money and that respective service is characterized by a high utility, in a way that it well substitutes a non-sharing option. In addition, familiarity with a service was found to be an important determinant, probably because it lowers transaction costs of getting to know the specifics of the sharing process (Henning-Thurau et al., 2007). Furthermore, both studies reveal the important role of trust as an essential determinant of the satisfaction with a sharing option. This is an interesting result because trust has not been analyzed in relation to other determinants in the context of collaborative consumption in quantitative studies so far (Möhlmann, 2015). Some differences are also present in the two studies, specifically, in study 1 (B2C car sharing context car2go), two additional determinants with significant effects were identified: community belonging and service quality. While in study 2 (C2C accommodation sharing context Airbnb), a relationship between the satisfaction with a sharing option and the variable likelihood of choosing a sharing option again was estimated. This relationship was not revealed in study 1.

The main strength of this paper is that it is both academically and managerially relevant. Academically speaking, the results of this study contribute to close a research gap and hold valuable implications for researchers. Findings indicate that indeed there are many similarities among the determinants of the use of different collaborative consumption services. However, a detailed analysis might also reveal context or industry specifics, as shown in this paper. While for managers of B2C and C2C collaborative consumption services, the results of this paper offer important and relevant insights for the acquisition but also retention of customers. Managers of B2C and C2C services should adapt their market activities to respond to the fact that rational and self-centred determinants were found to be essential, including utility, cost savings, and familiarity. Furthermore, managers need to make sure that trust building measures are implemented and communicated to respective stakeholders.

This paper is also subject to a number of limitations. Firstly, even though it is true that collaborative consumption services are mainly used by a young age group, the fact that approximately 88% of the respondents were under the age of 30 does not provide true generalizability of the results. Especially considering that collaborative consumption is a growing trend that will soon involve people of any age group, a more heterogeneous sample should have been utilized. Secondly, it is likely that interrelations among determinants exist, which is something that has not been studied here. For example, it seems straightforward that determinants such as cost saving and utility, or familiarity and trend affinity might be correlated. Future research should construct a more comprehensive research model also considering such interdependencies. Thirdly, one of the most significant determinants in the analysis was utility, however, such variable showed low values of Cronbach alpha, respectively 0.57 in study 1 and 0.60 in study 2. Considering that the generally accepted cut-off is that alpha should be 0.70 or higher for a set of items to be considered a scale (Garson, 2012), the internal consistency of such variable is very poor. This undermines the reliability of the significant relationship between utility and the two dependent variables. Future studies should therefore create surveys which construct the utility variable in a different way. Lastly, in this paper, only the likelihood of using a sharing option again was investigated, but not actual behaviour. A more comprehensive and reliable analysis should consider the real behaviour of users. Longitudinal studies or experimental designs can be used in future research in order to address this issue.

To conclude, it can be said that there are without doubt several determinants which can affect satisfaction with collaborative consumption services and the likelihood of choosing such services again. Future studies might consider various additional determinants such as, for example, burden of ownership (ownership is usually associated with responsibility and effort), process risk (sharing can involve procedural risks), or product variety (sharing offers a wide range of different products and services). The list goes on as the relevant causal factors can be numerous. So what other determinants do you believe to be crucial in explaining user engagement in collaborative consumption?

References

Bardhi, F., & Eckhardt, G. M. (2012). Access-based consumption: The case of car sharing. Journal of consumer research, 39(4), 881-898.

Belk, R. (2014). You are what you can access: Sharing and collaborative consumption online. Journal of Business Research, 67(8), 1595-1600.

Botsman, R., & Rogers, R. (2011). What’s mine is yours: how collaborative consumption is changing the way we live.

Chen, Y. (2008). Possession and access: Consumer desires and value perceptions regarding contemporary art collection and exhibit visits. Journal of Consumer Research, 35(6), 925-940.

Garson, G. D. (2012). Testing statistical assumptions. Asheboro, NC: Statistical Associates Publishing.

Hennig-Thurau, T., Henning, V., & Sattler, H. (2007). Consumer file sharing of motion pictures. Journal of Marketing, 71(4), 1-18.

Möhlmann, M. (2015). Collaborative consumption: determinants of satisfaction and the likelihood of using a sharing economy option again. Journal of Consumer Behaviour, 14(3), 193-207.

Mont, O. (2004). Institutionalisation of sustainable consumption patterns based on shared use. Ecological economics, 50(1-2), 135-153.

Owyang, J., Samuel, A., & Grenville, A. (2014). Sharing is the new buying: How to win in the collaborative economy. Vision Critical/Crowd Companies.

Business Case

What3Words: Changing the world, 3 words at a time.

March 10, 2018 twanvanh Leave a comment

Have you ever struggled to explain where you are, get a package delivered to a wrong address, or have a taxi taken you to the wrong address? This is an occurrence in everyone’s daily lives. How do you describe where you are, or how do you explain it when the addresses are unclear? What3Words aligns with your struggles and believes that the addressing system can be, and should be better.

How does it work?

What3Words found that the current addressing system isn’t suitable for everyday needs. Street addresses can be incorrect, ambiguous, or even non-existent. Businesses and homes are located nowhere near a zip code. And a large chunk of the world doesn’t even have an address. What3Words came up with a solution to this problem by dividing the world in a grid of thee square meters, each with its own unique three word address. If you need a package delivered in the slums, you’re looking for your friends at a festival, or you need medical aid in the middle of the Sahara, What3Words can guide you there. With worldwide partners as UNICEF (medical aid), Mercedes (navigation), and Domino’s (off the grid pizza delivery) is What3Words becoming ready to conquer the world. The below video shows what it’s all about.

Business Model

What3Words believes in a better addressing system worldwide, and therefore provides its algorithm through an API to everyone that is willing to use it. However, how does this provide joint profitability? What3Words mainly acts as a service provider, a platform where businesses and non-profit organizations can utilize their API to provide a better service to individuals. Lives can be saved by locating people faster, but also businesses experience an increase in value through this new addressing system. One example is a pilot study at global logistics and transportation company Aramex who tested the system with 100 deliveries in Dubai, comparing both the old system and the 3 word addresses. They found that the deliveries with 3 word addresses were 42% faster and reduced the total distance travelled by 22%, proving the profitability of the system in this segment (What3Words n.d.). However, how is What3Words profiting as their API is up for grasps? Firstly, What3Words basically crowdsources the application of its system, lowering its own innovation costs (Bockstedt et al. 2016). Governments, organizations, individuals, everyone can contribute to the application of 3 word addresses. Secondly, What3Words generates revenue by offering its API for free but charging for high volume usage (Henderson 2017).

The switching costs of the 3 word addressing system are currently influenced by only two alternatives. Individuals either return to their old addressing system, or secondly, switch to regular GPS coordinates. However, does What3Words suffer from these alternatives when its addressing system is already adopted? This system is developed as such to be comprehensive, easy to use, and to fill the gaps that other systems leave. The old addressing systems are incomplete, ambiguous, or even non-existent, and GPS coordinates are difficult to communicate. Once adopted it is expected that the switching costs of this system are high as alternatives cannot live up to the same standard.

When evaluating the institutional arrangements it can be seen that What3Words adopted cleaver restrictions. By providing an open API it can limit the customization possibilities of its technology to such extend that users act within boundaries that not harm the purpose, or the company. Furthermore, What3Words provides flexibilities to organizations by providing personalized pricing mechanisms that never transfer any ownership of the technology. As discussed earlier there is a free tier for low volume use of the API, and a payed tier for high volume usage, but on top of that offers the organization also special arrangements for qualifying non-profit organizations Henderson (2017). The institutional environment, on the other hand, does not has an answer to this system, as there are no restrictions being placed nation – or worldwide yet.

Great stuff! Why not use it?

Saving lives, never missing out on a delivery, and always getting where you want to be. Is this reality, or is this too good to be true? Although many practical implications already have proven its value (e.g. Aramex delivery pilot) and many businesses started leveraging 3 word addresses, there still are some concerns to the applicability of the system. One of the main concerns is that the 3 word addresses only provide you with an horizontal location, but does not specify the height at for example a multi-storey building. Another downside is the randomness of the word combination. Knowing where you are now doesn’t help you in getting somewhere else.

To conclude it seems that What3Words has a system in hand that could truly benefit the world. Those locations that lack reliable addressing systems, possess remote locations, or encounter a natural hazard seem to be well suited, however, the system may not be as applicable to all.

References:

Bockstedt, J., Druehl, C. and Mishra, A. (2016) ‘Heterogeneous Submission Behavior and its Implications for Success in Innovation Contests with Public Submissions’. Production and Operations Management, 25(7): 1157-1176.

Carson S. J., D. T. (1999). Understanding Institutional Designs Within Marketing Value Systems. Journal of Marketing Vol 63, 115-130.

Henderson (2017) ‘ How does what3words create revenue?’ Accessed on 10 March 2018 on https://support.what3words.com/hc/en-us/articles/207065989-How-does-what3words-create-revenue-

What3Words (n.d.) ‘ Simpler, faster, better: 3 word addresses take on Dubai’s street addresses in Aramex delivery challenge’. Accessed on 10 March 2018 on https://what3words.com/partner/aramex-delivery-challenge/

Video:

What3Words (n.d.) ‘About’. Accessed on 10 March 2018 on https://what3words.com/about/.

Articles, Opinion

The dark and the bright side of co-creation

March 10, 2018 382727kg Leave a comment

Nowadays, companies are really engaged with consumers as they participate in a company’s development process. Active contribution leads to ideas, solutions and positive word-of-mouth(WOM). Collaborative innovation creates a sense of community among the participants. However, it may not always live up to the expectations of its members or is seen as a success. So, are there only positive sides of co-creation? The goal of this paper was to explore triggers for positive and negative reactions from engagement in online innovation communities (Gebauer et al., 2013).

How did they study it?

A qualitative study was conducted to find triggers in online innovation communities and a quantitative study tested how triggers influence the behavior of members of online innovation communities. Continue reading The dark and the bright side of co-creation →