Exploring the 8 Mechanisms that Drive Charitable Giving

Copenhagen Business School

MSc.IT Business Administration and Information Systems

Master Thesis

The Antecedents of Interest in Social Crowdfunding: Exploring the 8 Mechanisms that Drive Charitable Giving

A u t h o r s :

M a r t i i n a S r k o c

R o z a r i n a A b u Z a r i m

S u p e r v i s o r :

P r o f e s s o r K a i H o c k e r t s

[ 1 9 D e c e m b e r 2 0 1 3 ]

C h a r : 1 8 4 , 7 8 2


1

Preface

This master thesis is the results of research that has been done as part of the MSc in Business

Administration and Information Management at Copenhagen Business School.

We would like extend our gratitude to our thesis supervisor: Professor Kai Hockerts for his

guidance, support and encouragement throughout the whole thesis process.

We would also like to thank all our colleagues and friends for their help in the initial stages of

our investigation and to thank our families for their tireless support.

Martiina Srkoc and Rozarina Abu Zarim

Copenhagen, December 2013


2

Contents

Preface ...............................................................................................................................................1

Abstract ..............................................................................................................................................4

1. Introduction ....................................................................................................................................4

1.1. The Changing Landscape of Philanthropy .................................................................................4

1.2. The Rise of Crowdfunding .........................................................................................................6

1.3. Social Crowdfunding: The Case of Kiva......................................................................................8

2. Research Objective .........................................................................................................................9

2.1. Research Area ..........................................................................................................................9

2.2. Research Question ...................................................................................................................9

2.3 Structure of the paper ............................................................................................................. 10

2.4. Delimitation ........................................................................................................................... 10

3. Literature Review .......................................................................................................................... 11

3.1. Philanthropy........................................................................................................................... 11

3.2. Microfinance .......................................................................................................................... 11

3.3. Crowdfunding and Social crowdfunding.................................................................................. 12

3.4. Kiva ........................................................................................................................................ 15

4. Methodology ................................................................................................................................ 29

4.1. Research Purpose ................................................................................................................... 29

4.2. Research Strategy................................................................................................................... 29

4.3. Research Design ..................................................................................................................... 30

4.4. Data Collection: Primary and Secondary data ......................................................................... 31

4.5. Research Process .................................................................................................................... 31

4.6. Research Reliability and Validity ............................................................................................. 32

4.6.1. Validity ............................................................................................................................ 32

4.6.2. Reliability......................................................................................................................... 33

4.7. Survey Process, Design, & the Development of Measures ....................................................... 33

4.7.1 Survey Process.................................................................................................................. 33

7.4.2. Survey Design .................................................................................................................. 35

4.7.3. Survey structure .............................................................................................................. 36

4.7.4. Pre-testing the Survey ..................................................................................................... 36

4.7.5. Developing the measures ................................................................................................ 38

4.8. Data Sampling ........................................................................................................................ 43


3

4.8.1. SurveyMonkey Audience – Demographics ....................................................................... 44

4.8.2. SurveyMonkey Audience - Recruitment Procedure .......................................................... 45

4.9. Data Analysis .......................................................................................................................... 47

4.9.1. Exploratory Factor Analysis (EFA) ..................................................................................... 47

4.9.2. Structural Equation Model (SEM) ..................................................................................... 49

5. Results .......................................................................................................................................... 49

5.1. Demographic Information ...................................................................................................... 50

5.2. Exploratory Factor Analysis (EFA) ........................................................................................... 53

5.3. Structural Equation Modelling (SEM) ...................................................................................... 62

6. Discussion ..................................................................................................................................... 71

7. Conclusion .................................................................................................................................... 79

8. Limitations and implications for future research ........................................................................... 80

BIBLIOGRAPHY .................................................................................................................................. 82

APPENDIX A ...................................................................................................................................... 93


4

Abstract

This study consists of an exploratory study of social crowdfunding using data collected from

302 participants in an online survey. The study investigates the relatively new phenomenon of

social crowdfunding, specifically Kiva with a view to understanding antecedents of investor

interest. Further, it looks at what role Bekkers & Wiepkings’s identified eight mechanisms play

in predicting interest in these social crowdfunding platforms. What can be surmised is that

some mechanisms, namely those of altruism, reputation, and efficacy do appear to be

manifested with regards to both donation behaviours and intention or interest in social

crowdfunding sites such as Kiva. Other mechanisms such as solicitation and psychological

benefits appear to impact donation behaviour but not interest in using Kiva. Links between an

awareness of need, costs and values in relation to Kiva could not be confirmed.

Keywords: Crowdfunding; Social Crowdfunding; Philanthropy; Intention; Donation

1. Introduction

1.1. The Changing Landscape of Philanthropy

Traditional forms of philanthropy and methods of giving are in the throes of change;

philanthropy is no longer relegated to charitable organizations, foundations and individuals and

has permeated every corner of governmental, corporate, civic and private life. It is manifested

daily through international aid, corporate social responsibility, Fair trade, and ethical businesses

through to telethons. Even the humble collection tin is undergoing a technological

transformation through the use of 2-D barcodes, allowing for donations to be made for those

caught without loose change at hand. But it is not just technological advances that are impacting

the philanthropic field there is also a shift in attitudes to traditional philanthropy. There is

mounting opposition to just handing over funds for quick fixes and indiscriminate causes;

instead there is an increasing demand for efficacious and sustainable solutions - not just giving

donations to needy victims but helping people help themselves. But with these new attitudes

and avenues, questions arise as to what impacts these may have on motivations to give

philanthropically. Are traditional motivations to give still applicable in this new scenario?


5

A definition for philanthropy that is often cited is that by W.K.Kellogg Foundation as

“…the giving of time, money, and know-how to advance the common good…” (W.K. Kellogg

Foundation, 2000: 6). Payton seems to agree with this, as he suggests that philanthropy involves

other facets such as the mission, shared values, and the organization involved in giving money

(Payton, 2008:30). Therefore the act of philanthropy involves some form of organization and

the means to effect the giving from donors who are motivated to give. By understanding what

motivates people to give and improving the process of donating, charities and other social

organizations would be better placed to employ the right strategies to take advantage of this.

One of the major developments in recent years that has influenced philanthropy is the

advent of the Internet, and general advancements in technology. This has opened a world of

opportunities by making the process of donating much simpler, faster and more transparent.

This, in turn, has led to a rise of popularity in online giving which has been increasing yearly.

Recent statistics by the eNonprofit Benchmarks 2013 study shows that online giving increased

by 21% in 2012 compared to the previous year (eNonProfit, 2013). Further evidence can be

seen from the quarterly results for 2012 from Network for Good’s Digital Giving Index, which

stated that over 59% of donations online were for charity websites, while social giving that is,

peer- to-peer fundraising sites has seen a rise of 13% (Network for Good, 2012). Moreover, the

statistics shows that not only has there been increased activity in giving online, the amount

given has been steadily rising, with Giving USA reporting that in the US, $298.3 billion was

donated which accounts for a 3.9% increase from previous years (Giving USA, 2012).

Fuelled by the rising popularity of online giving, a plethora of technologies and services

have been developed that is transforming traditional notions of philanthropy. Individuals can

now easily donate online through a charity’s website or through sites created specifically to

raise funds for a specified cause. The internet has given charities and non-profits the ability to

reach a far greater audience than traditionally, removing the constraints of geographical

boundaries and allowing information and donations to be aggregated and communicated almost

instantaneously throughout the globe. But this revolution has not come without some caveats

to be heeded.

The growth of social networks, supported by the changing demographics of a younger

generation utilising these networks more heavily, has seen a dramatic change in how social

media can be utilised to raise the profile of certain charities and causes. A case in point is the


6

Kony2012 video by the non-profit organization called Invisible Children, whose video went

viral through the social networks, creating one of the most watched videos of 2012 (Wasserman,

2012). The video managed to raise awareness concerning the plight of children being abducted

and force to fight for the Lord’s Resistance Army (LRA) headed by Joseph Kony. However,

the media interest that it created, also inadvertently put a spotlight on Invisible Children and

their activities – some of which were highly controversial. This illustrates the challenges faced

by charities when engaging with media technologies such as the internet: with its ubiquity,

philanthropic organization’s actions come under even greater public scrutiny, not least owing

to greater public demand for more openness, accountability and transparency. These issues have

brought about charity evaluator websites such as Charity Navigator to assuage some of the

public’s concerns. The site lists a guide of all the charities they have evaluated and the rating

that each charity has received – the purpose being to help donors make better-informed giving

decisions for the charities and causes that they wish to support.

As previously mentioned, another trend emerging is how philanthropic giving is

becoming increasingly more automated and social. Novel and innovative ways of donating and

raising funds are being developed to solve a myriad of social issues. Philanthropy is evolving

from its traditional roots and now encompasses more than advancing the common good It’s

about doing good socially that is, harnessing the power of technology and networks to do good

in an intersectoral world (W.K. Kellogg Foundation, 2000).

In the next section, one such phenomenon that is garnering great interest will be

investigated, that of crowdfunding.

1.2. The Rise of Crowdfunding

Crowdfunding has been referred to as a disruptive innovation in the world of finance

and as an alternative method to the traditional ways of funding (Gajda et al., 2012). Essentially,

crowdfunding refers to collectively raising small amounts of money, from a large number of

people using the internet or “the financing of a project or a venture by a group of

individuals instead of professional parties... an open call, essentially through the Internet,

for the provision of financial resources either in form of donation or in exchange for some

form of reward and/or voting rights in order to support initiatives for specific


7

purposes”(Schweinbacher & Larralde, 2010:4). This concept was originally derived from the

term crowdsourcing – and in fact they share similar characteristics namely the existence of a

“crowd” and the collaborative participation of these individuals in achieving a task or goal,

which in the case of crowdfunding, is raising funds (Lambert & Schwienbacher, 2010).

This technology-facilitated method of raising funds has mobilized activism, and created

new ways of social engagement and fundraising. This area has seen a tremendous growth over

the years, with a recent report stating that from 2009 to 2012 there was an increase of global

funding volume from $530 million to $2.7 billion US dollars with the greatest jump seen in

donation and reward based crowdfunding. The most active category was cited to be social

causes accounting for a 30% share of all crowdfunding activity (2009, 2012CF Crowdfunding

Industry Report, Massolution.com 2012).

Two examples of crowdfunding sites that have received a lot of media attention are the

two main players in the field of reward-based crowdfunding, namely Kickstarter and Indiegogo.

Kickstarters The Pebble E-paper watch project had an initial funding goal of $100, 000,

however by the end of the funding period, it had managed to amass pledges as much as $10,

266, 845 from 68,929 people (Kickstarter, 2012). However, the Ubuntu Edge Smartphone failed

to reach its funding target of $32 million funding on Indiegogo (Indiegogo, 2012) which raises

questions as to the efficacy of using crowdfunding for very large amounts of money.

Is raising funds the only purpose or benefit from Crowdfunding? Some commentators

have argued that these crowdfunding sites have evolved from a source of financing, to creating

“community benefits”, that funders are willing to support the project because of its perceived

benefit to the community in general, or as potential consumers (Belleflamme, Lambert, &

Schwienbacher, 2012a). Such sites can also be used by the project creator as a communication

channel in which to engage their customers, or generate new ideas, and form discussions around

a product. Feedback that the funders give can be used as a testing ground and be used to improve

future products (E. M. Gerber, Hui, & Kuo, 2009).


8

1.3. Social Crowdfunding: The Case of Kiva

The term social crowdfunding is applied here to denote the raising of money online for

social causes by using the “crowd.” Kiva.org provides an illustration of such a social

crowdfunding platform. First established in 2005, it was one of the first organizations to use the

crowd to raise funds. (Pope, 2011)

Kiva’s mission as stated on their website is “…to connect people through lending to

alleviate poverty…” and to empower these individuals to “…create opportunities for

themselves and others…and lift themselves out of poverty” (Kiva.org, 2013). The fundamental

basis of this non-profit organization lies in offering peer-to-peer based microloans to low

income entrepreneurs mainly from developing countries.

Kiva brings together potential lenders and borrowers on their platform. Lenders can

browse through the profiles of entrepreneurs seeking funds to start their business and once they

have selected an entrepreneur and an amount they wish to lend, the money is disbursed through

Kiva to one of its field partners who lends it to the borrower and is also responsible for handling

the repayments and updating the borrower profile. As the loan is repaid, the lenders on Kiva

can decide to withdraw their money or to relend it to another entrepreneur. What differentiates

Kiva from other peer-to-peer lending sites is that they do not charge any interest on the loans

and thus the focus is on social lending and support for entrepreneurs and the community.

Heavily inspired by the work of Professor Muhammad Yunus and his success with the

Grameen Bank, and the principles of microfinance, Kiva set out with the idea of creating social

change by offering an alternative method of finance to people often denied access to traditional

forms of finance. Concurrently, by conducting the lending process online, it opened up it has

dramatically raised the number of potential social lenders, by offering an easy to use platform

to connect to borrowers and lenders. So far Kiva has managed to lend nearly $500,000,000 to

a total of over 1.1 million recipients (Kiva.org, 2013). To further simplify the process of

lending, Kiva recently launched a new service called Kiva Zip which allows lenders to loan

money to the entrepreneurs directly through mobile payments, made via mobile devices

allowing for quicker disbursement of loans and for lenders to communicate directly with the

recipients (Kivazip, 2013). Technology as can be seen here is an enabling factor that has opened

up new possibilities in philanthropic giving. No longer are we tied to the traditional notions of


9

philanthropy, but instead crowdfunding has brought about a democratising effect in bringing

funding to the masses, with the added benefit of creating a sense of community, and providing

an opportunity to bring about social change

2. Research Objective

2.1. Research Area

Owing to crowdfunding’s relative novelty, very little academic research has been

conducted in this area to date. Most of the extant research has centred on entrepreneurs and

startups utilising crowdfunding as a method of raising financing (Belleflamme et al., 2012a),

classification of crowdfunding forms and business models (Hemer, 2011; Gajda et al., 2012).

Further, Lehrer offers that very little has been written regarding crowdfunding from a social

entrepreneurship perspective (Lehner, 2013). Moreover, there has been very little research into

what attracts people to give on crowdfunding sites. Conversely, research in philanthropy has

mainly focused on traditional notions of philanthropy and charitable giving without much

investigation into the impact and changes that technology provides with new, contemporary

ways of giving.

One academic paper by René Bekkers and Pamela Wiepking ( Bekkers & Wiepking,

2011) that has extensively explored the area of philanthropic giving, is Mechanisms That Drive

Charitable Giving A Literature Review of Empirical Studies of Philanthropy: Eight

Mechanisms That Drive Charitable Giving and serves as the theoretical foundation for our

paper. The focus of our research is to therefore to apply Bekkers and Wiepking’s eight

mechanisms of charitable giving and to test the theory against a real social crowdfunding case,

that of Kiva. Our particular interest lies in what attracts people to give on social crowdfunding

sites and to see if or which of the eight mechanisms (variables) impact giving, to what extent,

and whether they impact each other.

2.2. Research Question

The aim of this paper is therefore to explore the gap that exists in extant research

between social crowdfunding and philanthropy; in particular we are interested in what factors

attract interest to invest or give on social crowdfunding sites.

Specifically, the study tries to answer the following research questions:


10

1. What are the antecedents of investor interest in social crowdfunding sites?

2. In particular, what roles do the eight mechanisms identified by Bekkers & Wiepking

play in predicting interest in social crowdfunding sites such as Kiva?

2.3 Structure of the paper

The first section looks at the background to the area of philanthropy and changes

to the face of crowdfunding. We then describe the research objective, structure and limitations

of the paper. Following this, an exploration surrounding the themes of philanthropy,

crowdfunding and microfinance is conducted. The following section elaborates on the

methodology employed in conducting this research including the design of our online survey,

sample, measures and analytical tools utilized. This if followed by a presentation of the results

of our survey through an exploratory factor analysis in SPSS and the creation of a model fit

using AMOS. The final two sections see a discussion of the results of our research based on our

review of the literature, implications, and conclusions that can be drawn

2.4. Delimitation

Due to the breadth of crowdfunding as an area of research, we have decided to

focus solely on the crowdfunding sites with a social cause or goal. Furthermore, due to time

and resource constraints we have limited our investigation to the features of social

crowdfunding that pertain to our research topic.


11

3. Literature Review

3.1. Philanthropy

Philanthropy has been a subject of research for numerous years comprising of public,

private and corporate philanthropy and spanning a wide spectrum of interests from national and

historical philanthropy to the competitive advantages of philanthropy (Bremner, 1988; Porter

& Kramer, 2002). This has led to a growing discussion regarding philanthropic giving and pro-

social action. Interest stems from a wide variety of fields such as social psychology, (Twenge,

Baumeister, DeWall, Ciarocco, & Bartels, 2007; Yinon, Yoel & Sharon, 1985) communication,

(Abrahams & Bell, 1994; Simon, 1997) economics,(J Andreoni, 2007; Soetevent, 2005a)

politics (Cho, 2002), marketing (Diepen, Donkers, & Franses, 2009; Schlegelmilch,

Diamantopoulos, & Love, 1997) and the not-for-profit sector (Kottasz, 2004; Schervish &

Havens, 1997) and cover not only donors and donations, but beneficiaries as well as solicitation

by charitable, religious and non-profit organizations. It should be noted that a vast proportion

of work examining philanthropy stems predominantly from, economic journals, by scholars

such as Andreoni (GoogleScholar.com, 2013).

There are a myriad of themes evolving from the subject of philanthropy such as the concept of

altruism (Fehr & Fischbacher, 2003), patterns or behaviours in relation to philanthropic giving

(Andreoni, Brown, & Rischall, 2003; Ribar & Wilhelm, 2013; Rick, Cryder, & Loewenstein,

2008 Bekkers & Schuyt, 2008) and motivations for giving (R. Bekkers & Wiepking, 2010;

Goette, Stutzer, & Frey, 2010; Ribar & Wilhelm, 2013; Schervish & Havens, 1997).

3.2. Microfinance

Beyond these themes, there is also a growing body of work examining new and

traditional avenues for philanthropic giving. Of these, microfinance has received a great deal of

attention driven, most notably, by the attention afforded it by Muhammad Yunus, Nobel Peace

Prize winner in 2006 and founder of Grameen bank. Yunus and the Grameen bank have been

viewed as pioneers in the development of microfinance or microcredit (Yunus, Moingeon, &

Lehmann-Ortega, 2010). Of late, however, the view of microfinance as the panacea for poverty

is starting to come under scrutiny. Some critics have questioned the efficacy of microfinance

(Morduch, 1999; Morduch 2000) as a system for alleviating endemic poverty (Karnani, 2007).

Others have cited cases of high interest rates being charged, coercive methods of recovery, the


12

creation of indebtedness arising from second and consecutive loans being used to pay back

previous loans, inappropriate or undesirable use of the loans, and social issues stemming from

the sudden influx of money (Sharma, 2010).

3.3. Crowdfunding and Social crowdfunding

Supplements to microfinance have emerged of late as extensions or alternate tools for

philanthropic giving, one of which is crowdfunding. Crowdfunding, due to its relative newness,

is a somewhat under researched domain and hence our desire to expand on the body of work

that is presently available.

Our interest lies in crowdfunding for social entrepreneurships and what we have chosen

to term social crowdfunding (SCF), an expression often deemed interchangeable with

crowdfunding and a reflection of the lack of clarity as regards terminology within the field. The

research to date surrounding crowdfunding as a whole has focused predominantly on peer-to-

peer funding and addresses mainly financial themes, such as commercial models for

crowdfunding and equity investment, (whereby individuals receive small stakes in a privately

owned young business in return for investment and default risk) (Belleflamme et al., 2012a)

(Collins & Pierrakis, 2012)(Everett, 2010) to name but a few themes. Beyond the fundamentally

financial themes, other areas of research focus on individual crowdfunding practises and

successes as well as investigations in to the motivations underlying crowdfunding

(Belleflamme, Lambert, & Schwienbacher, 2012b; E. Gerber, Hui, & Kuo, 2012; E. M. Gerber

et al., 2009) as well as some generic research on the state of crowdfunding (Lambert &

Schwienbacher, 2010)(Hemer, 2011) and literature examining the advantages and

disadvantages of crowdsourcing applications applied to disaster relief coordination (Gao,

Barbier, & Goolsby, 2011).

Surprisingly little research addressing SCF has been conducted to date but this maybe,

in part, due to the novelty of the phenomenon of SCF and the timeliness of research. However

there is some indication of the potential for SCF as compared with non-SCF. Belleflamme,

Lambert & Schwienbacher point to “crowdfunding initiatives that are structured as non-profit

organizations tend to be significantly more successful than other organizational forms in

achieving their fundraising targets, even after controlling for various project characteristics”

(Belleflamme et al., 2012b : 1;Lambert & Schwienbacher, 2010 : 1).


13

A nascent body of work examining the realm of crowdfunding and social

entrepreneurships is emerging from the entrepreneurship perspective and examining, for

example, solicitation for funding (Lehner, 2013) Lehner draws upon extant literature to

highlight eight crowdfunding- related research themes and concludes that there is a need for a

schema of crowdfunding. Others examine the relationship between geographic distance and

funding and note that contrary to traditional finance theory of distance creating distrust, that

sensitivity to distance is mitigated on crowdfunding (Agrawal, Catalini, & Goldfarb, 2011;

Sorenson, Stuart, American, & May, 2011). This may be a significant finding in relation to

SCF, as many beneficiaries of loans are located continents apart from lenders. Other studies

have focused on crowdfunding as an alternate method of financing projects (Schweinbacher &

Larralde, 2010), various crowdfunding initiatives (Lambert & Schwienbacher, 2010) and

individual crowdfunding practises (Belleflamme et al., 2012b) to name but a few areas.

SCF is a valuable market to tap as most entrepreneurial ventures, social or otherwise,

find acquiring funds from traditional sources such as banks hard to achieve (Belleflamme et

al., 2012a; Berger, 2009; Cassar, 2002). Others have also cited traditional means of financing

entrepreneurial enterprises as ”inadequate in starting and sustaining growth of the many forms

of social entrepreneurship”(Lehner, 2013) and hence the importance of gaining a deeper

understanding of SCF and the possible funding opportunities they may provide.

The aggregate body of work is almost entirely concerned with the elicitation of funds

by entrepreneurs: there are some exceptions that focus on motivations for participating in

crowdfunding such as the works of Gerber et al(E. Gerber et al., 2012) but on the whole,

research and literature tends to favour the entrepreneur’s perspective. Our particular interest

lies not with the entrepreneur or soliciting organization but with the individuals providing the

funds, in particular the motivations that underlie funding on SFC. One notable exception is

research conducted by Liu et al that attempts to address the motivations underlying lending to

Kiva and examines lending activities from lender motivation and team affiliations (Liu, Chen,

Chen, Mei, & Salib, 2012).

Social crowdfunding (SCF) acts in a variety of ways. Unlike traditional avenues for

philanthropy, SCF attracts not only donors but investors and thereby creating an arena of mixed

motives, that is made up of people not solely with purely philanthropic motives but perhaps


14

with economic motives as well. Our primary focus with regards to SCF is to try and explain

what mechanisms stimulate interest in giving or lending on SFCs sites such as Kiva.

But in order to explain why people are attracted to sites such as Kiva, we need to

understand the motivations underlying charitable giving as a whole. In the UK alone the number

of households choosing to support charities has been declining or at best remained static for the

past twenty years while the number of registered charities continues to rise further exacerbating

the funding arena. (Sargeant, 1999; Schlegelmilch et al., 1997).

There have been a significant number of investigations into what enhances or impacts

philanthropic giving and prosocial behaviour. Cialdini et al among others investigate how

saddened mood impacts prosocial action by enhancing prosocial activity (Cialdini & Kenrick,

1976; Rosenhan, Karylowski, Salovey, & Hargis, 1981,) in (Cialdini & Baumann, 1981 ).

Attention is also given to what precedes acts of giving such as the anticipated pain of parting

with money. Rick, Cryder, & Lowenstein introduce and validate the hypothesis surrounding the

anticipatory pain of paying in their “spendthrift-tightwad scale”, a scale measuring the

individual differences related to the pain of paying. They suggest that “tightwads” spend less

than they ideally would like to for altruistic causes due to the anticipated pain of paying whereas

“spendthrifts”, due to too little anticipatory pain, typically end up paying more than they

initially wished to spend.(Rick et al., 2008) Other research examines how a focus on time versus

money can lead to two distinct mindsets, affecting people´s willingness to donate to

philanthropic causes. When asked to consider how much time people would be prepared to

donate or how much money they would be willing to contribute, Liu and Aaker noted that this

prompting increased the amount of time and/or money ultimately donated (Liu & Aaker, 2008).

Other works find that paradoxically, enduring pain and having to exert efforts could promote

contributions to a cause (Olivola & Shafir, 2013).

Perhaps owing to the diversities of fields and the need to draw on extensive literature

from these sometimes disparate sources, an overarching study into the motivations for giving

has led to few scholarly explorations with some notable exceptions (Sargeant, 1999)( Bekkers

& Wiepking, 2011; Lindahl & Conley, 2002; Sargeant & Woodliffe, 2007; Sargeant, 1999b)(

Liu et al., 2012).


15

3.4. Kiva

As stated earlier, one example of a SCF platform is Kiva. Due it its relative infancy,

empirical studies and scholarly literature are scarce; there are however a number articles that

discuss Kiva related issues and quite a number of references to Kiva regarding micro finance,

micro loans and crowdfunding/sourcing in scholarly literature (Agrawal et al., 2011; R. Bekkers

& Wiepking, 2011; Belleflamme & Lambert, 2010; Everett, 2010; Lehner, 2013; Pope, 2011;

Wiepking & Bekkers, 2010). Sources of literature here stem, not unsurprisingly from a variety

of field including economics, the social sciences, business administration, law and the non-

profit sectors.

Some empirical work that does address Kiva directly is Burtch et al who, using data drawn from

Kiva, find evidence to suggest that lenders show a preference for culturally similar and

geographically proximate borrowers (Burtch, Ghose, & Wattal, 2013), a view supported by

Galak et al (Galak, Small, & Stephen, 2011). An even more pertinent investigation conducted

by Liu et al examines the motivations underlying lending to Kiva using items developed for 10

classifications, namely General altruism, Group-specific altruism, Empathy, Reciprocity,

Equality and social safety net, Social responsibility and social norms, Effective development,

Personal satisfaction, Religious duty and External reasons ( Liu et al., 2012) Results here

indicate that lenders motivated by general or group-specific altruism made fewer and smaller

loans per month than others and external reasons also lowered loans. People who view Kiva as

an effective development tool lend more and more frequently, religious duty was also shown to

positively affect lending frequency and a lender belonging to any team (s) made more loans

than those not belonging to teams ( Liu et al., 2012).

Theoretical Framework

Bekkers and Wiepking: Mechanisms that drive charitable giving

Our research advances investigations in philanthropy by testing whether motivations to

give philanthropically are applicable to SCF by applying the eight mechanisms developed by

René Bekkers and Pamala Wiepking in their literature review of empirical studies of

philanthropy. Bekkers is Head of Research at the Center for Philanthropic Studies at VU

University Amsterdam in the Netherlands and also the research chair of the European Research

Network on Philanthropy (ERNOP)(René Bekkers, 2013). Pamala Wiepking, previously


16

affiliated with the Center for Philanthropic Studies, Faculty of Social Sciences, VU University

Amsterdam is currently working as assistant professor at the Department of Business-Society

Management, Rotterdam School of Management at the Erasmus University Rotterdam and is

also affiliated with the Erasmus Centre for Strategic Philanthropy.(Wiepking, 2013)

Although there are some worthy models for motivations regarding philanthropic giving

available, namely Sargeant & Woodliffe’s 2007 Gift giving: an interdisciplinary review, their

point of departure, however, like numerous other models, is on solicitations by charities, non-

profits and the like (Sargeant & Woodliffe, 2007a). Our interest lies more in the motivations

and interests of the donors or lenders themselves in order to establish if the motivations to give

charitably are also applicable to SCFs such as Kiva. One notable investigation conducted by

Liu et al investigates lender motivations to Kiva by employing theories stemming from social

identity and social preferences (Y. Liu et al., 2012). We have however opted to use Bekkers &

Wiepkings’s model in our investigation in that it is the most comprehensive, multidisciplinary

study conducted to date about motivations for philanthropic giving. Another factor that

impacted our selection of the Bekkers & Wiepking model was that much of what exists

regarding motivations and SFC is highly nascent owing to the novelty of SCF and could

theoretically have been investigated and/or included in their study. Bekkers & Wiepking base

their model on a literature review of over five hundred articles on charitable giving in order to

try and establish why people donate to charitable organizations. Their investigation point to

eight mechanisms (awareness of need, solicitation, costs & benefits, altruism, reputation,

psychological benefits, values and lastly efficacy) as being the most important forces driving

philanthropic giving. They further illustrate which mechanisms are impacted by other

mechanisms and how.

Disambiguation of Social crowdfunding and charitable organizations

Strictly speaking, Kiva and other SCF sites do not constitute a charitable organization

in the purest sense in that they are non-profits rather than registered charities. They do however

resemble charities closely in that their goals are philanthropic in nature and that the money

donated falls under Bekkers and Wiepking’s own definition that of “charitable giving as the

donation of money to an organization that benefits others beyond one’s own family” ( Bekkers

& Wiepking, 2011: 925). The eight mechanisms can be moderated or even mediated by other

factors such as personal characteristics or conditions. Further they categorize the eight


17

mechanisms in terms of what (tangibility and/or intangibility), where (within, outside or

between people) and who (actors , such as NGOs etc and targets i.e. donors and/or beneficiaries)

( Bekkers & Wiepking, 2011). They are presented in the same order as offered by Bekkers &

Wiepking as they correspond to the chronological order in which they affect giving in the

typical act of donation ( Bekkers & Wiepking, 2011 : 929). It should be noted that with regards

SCF, one might suggest Bekkers and Wiepking’s second mechanism Solicitation should

precede Awareness of Need as the first mechanism as this often may prove to be the mechanism

that initially acquaints an individual with the SCF sites and the specific needs addressed by

them.

Our intention is to glean whether the eight mechanisms provide information about

two aspects of philanthropy, namely donation behaviour and intention to donate or lend. In

some instances, a mechanism’s item should shed light on both our dependent and independent

variable, and sometimes they are naturally specific to a single variable. It is, in particular, the

items related to SCF and Kiva that have compelled us to develop new focus lenses through

which to view mechanisms such as awareness of need due to the novelty of SCF and Kiva.

The eight mechanisms

1. Awareness of need

Awareness of need is naturally a precondition in order for philanthropy to exist. It is the

awareness of a need or needs that precedes any act of giving. Needs can take on a variety of

forms from physical, emotional, social and mental needs to tangible artefacts – money,

medicine, disaster relief and so on and objectively or subjectively impact donations (Lee &

Farrell, 2003 in Bekkers & Wiepking : 930). According to Bekkers & Wiepking, moderating

factors that affect awareness of need are costs, reputation, psychological benefits, and efficacy

of donations. They also point to certain situational conditions and personal characteristics of

the donor such as dependence of the beneficiary on the donor, perceptions of deservingness and

political orientation to name but a few.

Needs are perceived by Bekkers & Wiepking as being tangible as well as intangible,

residing within, between, and outside people and originating from beneficiaries and

organizations and target donors ( Bekkers & Wiepking, 2011 : 929) We have modified Bekkers

and Wiepking’s approach somewhat to understand not only the awareness of people and


18

poverty but also to what extent people are aware of SCF as an opportunity to give or loan

philanthropically. Much of the literature and research surrounding awareness of need looks at

the relationship between personal experience and its impact on charitable giving (Bekkers &

Wiepking, 2011; Radley & Kennedy, 1995) which bears few parallels with our investigation.

Instead we assume that potential lenders are, overall, unaware of SCF sites such as Kiva due to

its novelty and thereby the need of the potential social entrepreneurs. We also assume that

people are aware of poverty and its existence in the world, however our interest lies more in

trying to understand to what extent people view poverty as a major social problem.

Some literature aims to link awareness of a need with the degree of willingness to help

or donate (Levitt & Kornhaber, 1977; Schwartz, 1974; Staub & Baer, 1975). Our focus is not

in the degree of willingness to help but more on the degree to which people are aware of SCF

and the extent to which poverty is perceived as a major social problem.

Bekkers & Wiepking report mass media as facilitating awareness of need (Simon, 1997

in Bekkers & Wiepking, : 930 ). They forward that support for a specific cause among the

general public increases over time as charities working for the cause continue to exist (Bekkers

& Wiepking, 2011), a claim, ironically, disputed by Callen (Callen, 1994) What appears evident

is that there is little knowledge of the concept of SCF and the way in which it address poverty;

with what has been investigated, researched and written, across academic fields. We are

therefore inclined to believe that greater media exposure, increased solicitation and further

research will bring about greater awareness of both SCF and Kiva and the social issues it

attempts to address.

As the descriptions for awareness of need given by both Bekkers & Wiepking and others

do not provide a good fit for our research purposes, we have elected to develop a new approach

to the interpretation of the awareness of need. Our approach does not address the awareness of

need in terms of poverty per se. Instead, our principal focus is on the awareness of SCF as a

platform for philanthropic lending and the awareness of the plight of the beneficiaries soliciting

through SCF and thereby inadvertently addressing the issue of poverty.

There is undoubtedly awareness among most people living in the developed world of

the existence of poor people in developing countries but perhaps little or very limited awareness

of poverty, not in the form of abject poverty but in people’s entrepreneurial endeavours and

limited access to financial institutions and financing opportunities. Our contention, therefore is


19

that at present, there is little awareness of the social entrepreneurs and their plight, however if

people are aware of SCF then they, in turn, must be aware of the social entrepreneurial ventures

being solicited for. Given this, it seems prudent not to investigate the antecedent of awareness

of the “social issue” and instead focus more directly on the awareness of the avenue to address

the social need in question, namely awareness of SCF itself.

H 1) People who are aware of SCF sites such as Kiva are more likely to be interested in

using it.

2. Solicitation

Bekkers & Wiepking define solicitation as “the mere act of being solicited to donate”

(Bekkers & Wiepking, 2011: 931). Further, they posit that the way in which donors are solicited

determines the effectiveness of solicitations. Under Bekkers & Wiepking’s classification

solicitations are viewed as both tangible and intangible, citing personal request as an example

of an intangible form of solicitation, are “interactions between people, originate from

beneficiaries or charitable organizations and target potential donors” (Bekkers & Wiepking,

2011 : 931). From the perspective of our investigation, the intangible solicitations as suggested

by Bekkers and Wiepking would be unlikely due to a number of factors not least that donors

and beneficiaries are unlikely to be aware of each other prior to tangible solicitation .

An area of research of importance to us is the channels used to solicit. With the novelty

of SCF, methods of solicitation are still being investigated and developed, therefore there is

very little empirical evidence to show how using channels such as electronic media for

solicitation impacts philanthropic giving (Sargeant & Woodliffe, 2007a). There is however

some research to suggest that there is indeed an impact on contributions that is dependent on

the modes of solicitation used (Sargeant, Jay, & Lee, 2006) (Jay, 2001) such as for example,

the use of pictures potentially having a negative impact on donations. Sargeant & Woodliffe’s

empirical study puts forwards arguments suggesting that distressing photographs create a sense

of donors feeling locked in to giving and as such, reducing their ability to choose what and

whom to give to. This ironically creates barriers to giving. Further, they suggest that images

thought of as depicting excessive need are perceived to be manipulative and reduces compliance

( Isen, & Noonberg, 1979; Sargeant & Woodliffe, 2007).


20

While SCF sites are not generally in the practise of using “distressing” images per se,

they do, largely, use photographs of the potential beneficiaries and other visual media to elicit

solicitations so the implications of employing photographs and the like need to be carefully

monitored and researched.

There are other caveats highlighted in the extant research and literature such as warnings

of “donor fatigue” brought on by over solicitation via direct mailing. It is suggested that this

may lower the average contribution (Diepen et al., 2009). Further unselective soliciting leads

to reduced contributions (Piersma & Jonker, 2004). These latter studies however refer to direct

mailing solicitation and may not necessarily be applicable in the case of SCF sites and Kiva

that are more likely to employ online solicitation since most activities are performed online.

We have also borne in mind Benson & Catt who claim that “donors receiving positively framed

messages, designed to make them feel good, are statistically more likely to respond than those

donors offered primarily negative messages, designed to make them feel bad” (Benson & Catt,

1978 in Sarangent & Woodliffe 2007,: 281). This point in particular is perhaps pertinent to the

field of SCF in that the potential loan recipients on Kiva and many other such sites are portrayed

as trying to help and empower themselves and not as needy victims requesting charity.

From empirical analyses of active solicitation, it has been concluded that the more

opportunities to give people encounter, the more likely they are to give (Bekkers, 2005; Bryant,

Slaughter, Kang, Hyojin, & Tax, 2003), and a concept we also assume to be true. Lindskold et

al point to direct, personal solicitations positively impacting donations (Lindskold, Forte,

Haake, & Schmidt, 1977) while Bekkers, using data from the Giving in the Netherlands Panel

Survey, finds ninety five percent of all donations are made in response to solicitations. Bryant

et al quoting a 1994 Independent Sector Survey of Giving and Volunteering conducted by the

Gallup Organization found that of the approximately 78% of respondents asked to donate

money or property in 1994, 85% donated money or property (Bryant et al., 2003).

Based on the research conducted by Bekkers and Bryant et al, we have developed

hypothesis H 2a and owing to the lack of pertinent literature regarding SCF we have developed

hypothesis H 2b.

H 2a) People who are regularly solicited are more likely to report giving regularly.

H 2b) People who are regularly solicited are more likely to be interested in using Kiva.


21

3. Cost and Benefits

Bekkers & Wiepking perceive “Tangible consequences that are associated with a

monetary value” as their definition of costs and benefits (R. Bekkers & Wiepking, 2011 : 932).

They have opted to focus predominantly on the material costs and benefits associated with

donating and, as such, view costs and benefits as tangible, residing outside of the donor,

originating from organizations and affecting donors. Bekkers & Wiepking’s material

perspective looks at studies investigating the price effects of giving donations such as

administrative costs, tax price on philanthropy and donation size, (Doob & McLaughlin,

1989,Weyant & Smith, 1987,Desmet, 1999 in Bekkers & Wiepking, 2011 : 933), as well as

techniques for eliciting donations such as foot-in-the-door, door-in-the face and low-ball

techniques (Abrahams & Bell, 1994; Brownstein & Katzev, 1985; Cialdini et al., 1975; Cialdini,

Cacioppo, Bassett, & Miller, 1978; Reingen, 1978 in Bekkers & Wiepking, 2011 : 948).

In terms of benefits, this paper has opted to view benefits as psychological benefits and

as such, Bekkers & Wiepking’s mechanism as given by them does not apply in the strict sense.

Their perspective is to view benefits as the tangible benefits to donors such as gift giving to

potential donors as incentives to donate. Since there are few, if any physical benefits to be

garnered by lenders to SCF sites, it would not be of much relevance to explore this avenue.

There is also, however, an examination of literature concerning costs of philanthropy

beyond the monetary such as the effect of weather conditions. This however lacks relevance to

this investigation; our paper addresses not the monetary aspects associated with donating but

rather the costs related to the action of philanthropy. These include aspects such as time, effort

and the ease of process etc.

A survey conducted by Smith and McSweeney and cited in Bekkers & Wiepking

predicting donating intentions and behaviour, found a correlation between people’s giving and

the amount of obstacles they perceived ( Bekkers & Wiepking, 2011). The survey however

refers to concerns such as whether a donation reaches the needy and not believing in or agreeing

with the cause (Smith & McSweeny, 2007); we have chosen to address these issues under the

mechanisms efficacy and values. An investigation by Wiepking and Breeze also attempts to

investigate a dimension of costs not directly linked to any actual monetary costs but to the

perceived psychological costs of giving. It finds that a donor’s own perception of their financial

state impacts donations regardless of the reality of a donor’s finances.(Wiepking & Breeze,


22

2012). An interesting study conducted by Olivola & Shafir find that, contrary to given

expectations, the prospect of pain and exerting effort increases willingness to contribute to pro-

social causes, for example, one experiment showed that people are willing to donate more to

charity when they anticipate having to suffer to raise money. (Olivola & Shafir, 2013) This may

be significant to SCF in that this arena is relatively new and as such, the terms and conditions

by which one lends can be seen as complicated, time consuming and as such, “painful.” On the

whole, however, we bow to literature regarding philanthropic giving that indicate a negative

correlation between obstacles and philanthropic giving.

Overall, however, there does not appear to be any significant body of work addressing

the issues of donor time, effort or ease of process and actual charitable giving with the exception

of nascent works examining organ and blood donations (Cialdini & Ascani, 1976; Goette et al.,

2010; Houston, 2004; Morgan & Miller, 2002).

Based on what was gleaned from extant literature, as well as what we believe to be a logical

supposition, we have developed hypothesis H 3 with regards to philanthropic giving and the

impact of obstacles

H 3) People who find using Kiva to be easy are more likely to be interested in using

Kiva

4. Altruism

This area has received considerable attention from a wide range of fields but by far, the

fields that are best represented are altruism and economics as well as altruism and blood and

organ donation. A strict definition of altruism is hard to pin down however there are a number

of definitions describing the components that make up the term such as (a) an intention to help

another person; (b) that the act is initiated by the helper voluntarily; and (c) that it is performed

without expectation of reward from external sources (Bierhoff, 1987) in (Radley & Kennedy,

1995 : 686). From an economy perspective, cited by Bekkers & Wiepking to be the dominant

research leg for altruism to date, “Altruism is part of the behavior that you cannot capture with

a specifically defined ulterior motive”(James Andreoni, Harbaugh, & Vesterlund, 2007: 1).

Experimental investigation of altruism is thus focused around eliminating any possible ulterior

motives rooted in selfishness. “One of the central motives that potentially confound altruism is

the warm-glow of giving, that is, the utility one gets simply from the act of giving without any


23

concern for the interests of others.” (Andreoni 1989, 1990) in Andreoni, Harbaugh, &

Vesterlund, 2007: 1).

Bekkers & Wiepking elect also to view altruism through an economic lens. Their

primary focus is that of crowding out, an economic concept where increased public sector

spending replaces, or drives down, private sector spending. (Investopedia.com, 2013). Our

paper views altruism less from a monetary perspective and more from a perspective of how

concerned an individual is for the wellbeing of others, the lengths they are willing to go to help

alleviate social problems, their perceptions of how big a given social problem is, and how

important solving social problems is to the individual concerned. Consequently, we have

reviewed the extant literature on measures of altruism to help shed greater light on our area of

interest. Rushton et al introduce a self-report altruism scale, (Rushton, Chrisjohn, & Fekken,

1981); while empirically interesting, it proved too broad, covering too many aspects of altruism

for our purposes. Our hypothesis is based on the overall sentiment found in the literature on

philanthropy that greater altruism leads to increased likelihood of giving or pro-social behaviour

(Radley & Kennedy, 1995a; Sargeant & Woodliffe, 2007a) and this in turn, increases the

likelihood of giving on SCF sites such as Kiva.

H 4a) People with a higher level of altruism are more likely to report giving

philanthropically

H 4b) People with a higher level of altruism are more likely to be interested in using

Kiva

5. Reputation

We concur with Bekkers & Wiepking’s definition of reputation as “...the social

consequences of donations for the donor”( Bekkers & Wiepking, 2011 : 936). Literature

regarding reputation and charitable giving has examined the willingness to incur costs, the

negative costs of not giving, contributions brought on from the feeling of being seen, anonymity

and conspicuous compassion to name but a few themes (Alpizar, Carlsson, & Johansson-

Stenman, 2008; Bateson, Nettle, & Roberts, 2006; Clark & Wilson, 19961; Silverman,

Robertson, Middlebrook, & Drabman, 1984; Soetevent, 2005b in Bekkers & Wiepking :

937);(Grace & Griffin, 2006; Andreoni & Petrie, 2003).


24

Research conducted in to reputation, status and group pressure as instigators of

behaviour is based on works stemming from sociological, psychological, and anthropological

research and indicate that philanthropic and pro-social behaviour is motivated to a large extent

by "social" factors, such as the desire for prestige, esteem, popularity, or acceptance (Bernheim,

2013; Muehleman, Bruker, & Ingram, 1976). Satow finds that more money is donated under

public conditions than under private conditions and by individuals who are very much in need

of approval compared to those low in need of approval. (Satow, 1975)This research is, however,

generic and not specifically targeting reputation and altruistic behaviour. One work by Smith

& McSweeny, however, examining the social psychological factors underlying decisions to

donate money to charitable organisations does provide some valuable insights and was used to

help create items. They find that attitudes, perceived behavioural control, and injunctive norms

all significantly predicted intentions to donate. That is, individuals with who believed that

people of significance to them would approve of the behaviour were more likely to intend to

engage in charitable giving. Further, this study points to a link between intention to donate and

actual donation ( Smith & McSweeny, 2007).

What appears evident is that there is a growing body of work investigating the

relationship between charitable contributions, pro-social activity and reputation however there

is little documentation on the relationship between altruism and reputation. A notable

exceptions is Fehr & Fischbacher who point to experimental evidence which strongly suggests

that a considerable part of human altruism is driven by concerns about reputation (Fehr &

Fischbacher, 2003)

H 5a) People who feel that altruism is important for their reputation are more likely to

report giving philanthropically

H 5b) People who feel that altruism is important for their reputation are more likely to

be interested in using Kiva

6. Psychological benefits

Bekkers & Wiepking separate the psychological benefits that are attributed to donors

taking part in charitable giving in to the “joy of giving” and “self-image”. Self-image here is an

individuals’s norms and perception of themselves in relation to charitable giving and pro-social

help. The view here is that negative feelings are brought on by not giving and as such acts as


25

the impetus to give philanthropically. Our focus however, lies not in how individuals view

themselves but the association between feeling good while doing good; how elevated

psychological sensations might drive interest in donating and subsequently social

crowdfunding. Attention to self-image in the sense illustrated by Bekkers& Wiepking was

deemed not to be feasible due to the nature, time and scope of this particular investigation.

Bekkers and Wiepking report numerous works addressing positive psychological

benefits from philanthropic giving and pro-social behaviour. The origins of the “joy of giving”

or warm glow stem back to Andreoni, who posit that the reason people give is “First, people

simply demand more of the public good. This motive has become known in the literature as

"altruism." Second, people get some private goods benefit from their gift per se, like a warm

glow... if people "enjoy" making gifts or bequests, then the warm-glow effects will always

dominate altruism (Andreoni, 1989 : 1448-1449). Another field that has shown interest in

psychological benefits and charitable giving is, perhaps unremarkably, psychology in

particular, the field of neuropsychology (Harbaugh, Mayr, & Burghart, 2007; Moll et al., 2006)

with practitioners investigating neurological activity and giving. Links have also been drawn to

charitable giving and mood especially in the manipulation of mood through a 1“foot-in-the-

mouth” approach. For a more direct link to positive psychological benefits, Wunderink suggests

that giving to a charity has a positive effect on the giver’s self-esteem (Wunderink, 2000);

Bekkers & Wiepking point to donors self-reporting “feeling good” as a motive for donating to

charitable causes and offer three reasons for why humans may experience pleasurable

psychological experiences when giving, that is:-

1. People may alleviate feelings of guilt (avoid punishment)

2. People may feel good for acting in line with a social norm

3. People may feel good for acting in line with a specific (pro-social,

altruistic) self-image (Bekkers & Wiepking, 2011)

What we presume is that there is a link between feeling good about philanthropy and

the likelihood of giving philanthropically.

1 Findings indicate that there is greater compliance with a charitable donation request if the person making the request first

asks the potential donor how he or she is feeling, and then acknowledges the donor's response. The potential donor is then

expected to behave in accordance with his or her publicly stated feeling-state.(Aunel & Basil, 1994)


26

H 6a) People who assert having higher psychological benefits from donating are more

likely to report giving philanthropically

H 6b) People who assert having higher psychological benefits from donating are more

likely to be interested

7. Values

Research and literature regarding values are to be found in among other sources

sociology, psychology and philanthropic journals, but as Bekkers & Wiepking claim,

experimental studies on the effects of social values on philanthropy are non-existent ( Bekkers

& Wiepking, 2011). Bekkers & Schuyt find that pro-social values promote contributions to

other organizations more strongly than to church as pro-social values such as altruism motivate

contributing to the “wellbeing of fellow citizens in general, not just to members of one’s own

religious group.” ( Bekkers & Schuyt, 2008: 79) Further, they report that generosity of religious

people to non-religious causes, was less to do with integration in social networks promoting

norm conformity and mainly due to greater pro-social values (Bekkers & Schuyt, 2008). Van

Lange et al point to pro-social people being more likely to donate “…in an attempt to enhance

the well-being of the poor and the sick because they are concerned not only with helping others,

but also with seeking fairness by making a contribution to improving the outcomes for those

who are less well off” (Van Lange, Bekkers, Schuyt, & Vugt, 2007a). Coliazzi et al. note that

individuals are more likely to help groups or individuals perceived as being similar to

themselves, Heider posits similarity of values as an equally powerful determinant (Coliazzi et

al. 1988; Heider 1958) in (Sargeant & Woodliffe, 2007). Radley & Kennedy suggest similarity

of persons or groups create stronger empathy and thereby strengthen intentions to donate

(Radley & Kennedy, 1995a). Smith & McSweeny find individuals who felt a strong moral

obligation to donate reported stronger donating intentions ( Smith & McSweeny, 2007) while

others point to norms as a major determinant of donating behaviour (Armitage & Conner, 2001;

Bernheim, 2013; Radley & Kennedy, 1995a; Sargeant & Woodliffe, 2007a).

Bekkers & Wiepking claim that there are little to no studies testing moderators in

relation to philanthropy with the notable exception of Fong who tests whether or not prior

beliefs and values are stable characteristics of donors. (Fong, 2007) in (René Bekkers &

Wiepking, 2011). One addition is an investigation looking at the relationship between personal

values and inclinations by Bennet who found that these two factors powerfully influenced the


27

selection of charitable cause to donate to. Further a correlation was found between certain

personal values and inclinations and specific organisational values most admired by

respondents was established (Bennett, 2003). In keeping with Bennet (personal values and

inclinations) as well as Heider and Coliazzi, (perceived similarities), the following hypothesis

was developed.

H 7) People whose values are in congruence with Kivas’ are more likely to be interested

in using Kiva.

8. Efficacy

Investigations of efficacy relating to philanthropic giving often concern efficacious

methods or caveats of eliciting donations.(Callen, 1994; Jackson & Mathews, 1995; Ormstedt,

1994) Our point of departure lies in the donations themselves and how “effective” these

donations are at alleviating or solving social issues as perceived by philanthropic givers. Hence,

efficacy refers to the perception by donors of their contribution making a difference or not to

the cause they are supporting (Bekkers & Wiepking, 2011), a definition in line with this

investigation. Bekkers & Wiepking point to a relatively substantial body of literature examining

charitable giving and efficacy and stemming mainly from the fields of philanthropic, economic

and psychology studies. Diamond & Kashyap find that perceived need and efficacy predict

intention to contribute money (Diamond & Kashyap, 1997) and perceived efficacy and positive

impact of the donor’s actions was significantly influential (Iyer, Kashyap, & Diamond, 2012).

Confidence in a particular organization may also improve the way people perceive the efficacy

of their donation (Bekkers, 2006).

Perceived inefficacy may include charitable organizations using donations in a manner

that donors view as a misappropriation of funds for operating and start-up costs (Duncan, 2004)

and general concerns for how donated resources will actually be used (Shuptrine and Moore,

1980) in (Sargeant & Woodliffe, 2007). Negative perceptions of fundraising costs has also led

to underestimations regarding the proportion of funds raised that actually serve the purposes of

the organization (Bekkers, 2003).

Interest regarding what impact donations have is beginning to draw more attention even

if the onus is remains firmly grounded in the generation of donations for such endeavours as


28

the arts (Buraschi & Cornelli, 2013). However we assume that donation impact may act as a

strong incentive for potential donors (Buchheit & Parsons, 2006; Buraschi & Cornelli, 2013).

Hypothesis H8 follows this vein of thought.

H8) People who think that Kiva is a good way to address poverty are more likely to be interested

in using Kiva

Intention and Donation Behaviour

The hypotheses above draw to a final, encompassing, hypothesis that of the connection

between philanthropic giving behaviour and intention to donate. This is with regards to the

influence of attitudes, norms perceived behavioural control, and past behaviour on intentions to

donate (Smith & McSweeny, 2007 ), effect of prior behaviours, intentions and attitude

variability (P. Norman & Smith, 1995) and perceptions of givers to non-profits and the resulting

impact on donations (Sargeant, Ford, & West, 2006).

With what has been unearthed both in terms of extant literature and researched to date,

it would not be inconceivable to hypothesize a link between giving behaviour and intention to

donate. With this in mind, and taking in to account literary indications of the link between

Bekkers & Wipking’s eight mechanisms and donation behaviour /intentions, it may be

suggested that this link be extended to SCF, and Kiva.

H 9) People who report giving philanthropically are more likely to be interested in using

Kiva

In the following sections, the methodology of how we intend to investigate our

hypotheses and our ultimate purpose for doing so are addressed.


29

4. Methodology

4.1. Research Purpose

Our research examines the themes of philanthropic giving and crowdfunding. The paper

seeks to investigate and analyse what the antecedents of investor interest in Kiva and other such

social crowdfunding sites are. By applying Bekkers and Wiepking’s eight mechanisms that

drive philanthropic giving as our theoretical foundation, we hope to shed light on what attracts

people to invest on these sites. Furthermore, the results from our research may be used to build

upon Bekkers and Wiepking’s findings and to add to the extant body of work addressing

philanthropic giving. Additionally, our intention is drive more discussion into the areas of

philanthropy and social crowdfunding with the hope of encouraging further empirical research

by bringing together disparate fields of study and creating greater homogeneity of

interpretation. Our ultimate wish is to see proliferation in the creation of new theories and

models and an expansion of literature surrounding philanthropic giving and crowdfunding for

social good.

4.2. Research Strategy

According to Bryman & Bell, the main approaches in research fall traditionally within

two categories namely, deductive or inductive research (Bryman, A. and Bell, E., 2007). “The

deductive approach starts with a theory explaining a phenomenon, and from there

operationalizing and testing the hypothesis through empirical means” (Bryman, A. and Bell,

E., 2007:11). The advantage of this approach is that by using an established theory or theories,

the foundation on which the research being conducted on is built is likely to be empirically

strong especially if it falls within a well-researched area. However, the disadvantage of this

approach is that if the theory relied on is empirically unsound or unsubstantiated then this will

have an impact on future research, which may exacerbate the error. An inductive approach on

the other hand refers to using specific findings from conducted research and generalizing it

through a theory. The strength of this is that findings can be backed by observational evidence.

The weakness of inductive research is that if the data observed is too specific, it may be difficult

to generalize and raise the possibility of false conclusions being drawn.


30

For our purposes, we have selected Bekkers & Wiepking as our theoretical foundation

hence our approach is therefore deductive. The rationale for this decision lies in the lack of

general literature on which to base our research of social crowdfunding and in particular the

antecedents for giving on., Bekkers & Wiepking are well-established and recognised in the

field of philanthropic giving having conducted extensive research in this area. By employing

the eight mechanisms and developing our own hypotheses (as mentioned in the literature review

section), we are endeavouring to test Bekkers & Wiepking’s proposed mechanisms for

charitable giving against our dependent variable (DV) (see Figure 1)

Figure 1: Structure of the Hypothesized Model

4.3. Research Design

Concerning the form or design of our research, this study falls within the descriptions

of a cross-sectional design. The research involves observing a representative subset of the

general population at a specific point in time. Since our study is limited to a relatively short

Awareness of

Need

Investor Interest

in Social

Crowdfunding

(DV)

Solicitation

Cost and

Benefits

Altruism

Values

Reputation

Psychological

Benefits

Efficacy


31

time period it does not fall within the definition of a longitudinal study and does not involve the

manipulation or control of variables such as in an experiment. The intention here is to gain a

snapshot of the current relationship of our variables against the representative sample we have

chosen in order to infer what the present investor interest in social crowdfunding is.

4.4. Data Collection: Primary and Secondary data

There are two main types of data that can be collected: primary and secondary data

(Curtis K. 1994). Primary data refers to data that has been gathered for the specific purpose of

the study. Secondary data on the other hand refers to data that has been collected in another

context and produced by someone else. This would include data collected from published online

documents, academic articles, online newspapers and other such sources. In selecting the

method appropriate for our study, we not only considered the nature and objective, our research.

Hence, the method we have selected is mainly quantitative in nature using the online survey

provider Survey Monkey to conduct our survey. This forms our primary data. Further, the

quantitative approach is most appropriate for our investigation as we have used SPPS as a

statistical tool to extract and analyse the data.

Most of the secondary data was collected from academic journals found on Ebscohost

databases, Science Direct, Wiley Online Library, Taylor & Francis online, Academic Search

Elite and GoogleScholar, which helped in our research for the literature review section of the

paper. For statistical data and general information regarding crowdfunding, information was

sought from online crowdfunding organizations and philanthropy websites such as

charitynavigator.org and Network for Good. Finally, for recent updated news regarding

crowdfunding, online technology news sites such as TechCrunch and Venturebeat were utilised.

4.5. Research Process

The research phases as depicted in Figure 2 started with preparatory work involving

finding an area of research that was both relevant and contemporary but also topical, which in

the case of this paper was deemed to be crowdfunding. This area was subsequently then

narrowed to the specific sphere of social crowdfunding. Once consensus and agreement

regarding the subject of social crowdfunding was struck with our supervisor, the next steps

involved formulating the research question and preparing our strategy. Firstly, a general review

of literature pertaining to philanthropy was conducted, before specific themes were identified


32

and a more in-depth literature review particularly of nascent literature for the specific themes

was carried out. Concurrently, a review of relevant literature cited in Bekkers & Wiepking’s A

Literature Review of Empirical Studies of Philanthropy: Eight Mechanisms That Drive

Charitable Giving was performed.

As this paper employs a quantitative method for data collection, our next step was to

create and plan our survey design. The questions for our survey were continuously refined as

we tested it at various different stages over a period of several months with selected

respondents. Once the final survey was deployed, the results were then collected and statistical

tools employed to extract the data. A thorough analysis and consolidation of the paper was

undertaken in the last phase, followed by the editing process and completed by the final draft

of this paper. The various phases of the research process are illustrated in Figure 2.

Figure 2: The Research Process

4.6. Research Reliability and Validity

4.6.1. Validity

Bryman and Bell cite that, both reliability and validity are important concepts in the

research criteria (Bryman, A. and Bell, E., 2007). Validity refers to the extent to which a

selected method does indeed gauge what it claims to measure and whether the instruments used

are appropriate. The more comprehensible the language used to describe the measures, the more

likely it will be understood by respondents in the way desired. In an attempt to test our

hypothesis, the measures were compiled into items (questions) in a multiple choice survey

format. Items were then formulated with the objective of making Bekkers & Wiepking’s eight


33

mechanisms comprehensible to respondents. Items were designed such as to help infer

respondents’ general philanthropic attitudes and in the second part of the survey, such as to

infer respondent’s interest in investing in social crowdfunding sites such as Kiva. As there is

no prevailing or universally accepted measure for philanthropic giving in the field of social

crowdfunding, we have adopted Bekkers and Wiepking’s measures of philanthropy or drivers

of charitable giving (R. Bekkers & Wiepking, 2010) and applied it to our investigation.

Furthermore, the survey items have been refined after several iterations and pre-tests done with

smaller respondent numbers to ensure for uniform and consistent interpretation of the measures

and items before the final survey was deployed.

In order to achieve greater accuracy of our data, we elected to use an online survey service that

is, SurveyMonkey to try and expand on the original sample group size so as to provide a more

representative picture of the general population. This also eliminated the issue of using less

empirical reliable methods such as a snowballing technique

4.6.2. Reliability

With regards to reliability, this refers to consistency of the results, and whether the study

can be replicated by others to receive similar and consistent results. Since the sample

respondents used were procured from a cross-section of the general population of the United

States, it is possible that results will differ if tested in different national populations from other

countries. This could potentially be due to factors such as differing cultural norms and values,

access and exposure to technologies, variations in income levels and attitudes to social

entrepreneurs, social crowdfunding and philanthropy to name but a few potential mitigating

factors.

4.7. Survey Process, Design, & the Development of Measures

4.7.1 Survey Process

The first step in the survey process involved gathering all the requirements needed

to create the survey. This included thinking about what kind of survey were are planning to do.

Whether it should be online or offline. What kind of resources do we need? What should the

timelines be and who should be the respondents. All these activities formed the basis of the

survey requirements step. Once that was settled we moved on to designing and creating the


34

survey, followed by pre-testing and then collecting the data and finally extracting and analysing

the data. The specific steps will be explained in greater detail in the next section.

Figure 3: Survey Process

The whole process was cyclical although within each step, there were many iterations

as we had to make adjustments to deal with unexpected changes. On the whole the process took

over 4 months from start to finish. The longest period was the extracting and analysing of the

data and the data collection parts. The pre-testing was useful as it set the final requirements

before the survey was launched online. During this phase, we tested the measures identified in

our hypotheses to ensure that they would be understood correctly regarding the contents and

language used to form the questions.

Other issues that we considered included whether there should be different language

versions of the survey for the different respondent groups – in our pre-test many of the

respondents were Danish. However, in the end it was agreed that we would keep the survey in

English.

1. Survey requirements

2. Survey Design

3. Pre-testing4. Data

Collection

5. Extract and Analyse data


35

7.4.2. Survey Design

As previously stated, the survey was designed using the online survey tool

SurveyMonkey – a provider of online survey software and services (www.surveymonkey.com).

They have currently over 14 million users of which 360,000 are paid customers (Merced, 2013).

The diagram below shows how their customers are segmented.

Figure 4: SurveyMonkey Customers (*data taken from surveymonkey site)

Our decision to select SurveyMonkey was based on a number of factors. It is a well-

established company in this field of survey creation and analysis and has a number of appealing

features such as creation and design of surveys, large sample of potential respondents,

possibilities for specific demographic group selection, the option to custom-specify targeting

criteria such as different interest groups.

Our first task involved formulating the survey items and developing measures to be used.

Initially, the survey consisted of 83 questions based on extrapolating information on Bekkers

& Wiepking´s eight mechanisms, exposure to philanthropy and social crowdfunding and Kiva.

The respondents were asked to evaluate items presented in statement form and to select a choice

based on the 5-point Likert scale as seen below:

For Profit -42%

Non-profit -27%

Education -27%

Government -9%

Breakdown of SurveyMonkey Customers

For Profit Non-profit Education Government Personal

Personal - 1%


36

Strongly Disagree Disagree Unsure Agree Strongly Agree

Figure 5: 5-Point Likert Scale

The questions were compulsory (ergo an answer was required in order for respondents

to continue). The Likert scale is a widely employed tool used in scaling responses (G. Norman,

2010) and is ideal for helping to provide information on preferences.

4.7.3. Survey structure

The survey was divided in to two parts. Part one involved questions concerning

the eight mechanisms and for each mechanism, 4 – 5 questions were created. In Part two of the

survey, the questions were directed towards social crowdfunding and in particular, Kiva. Before

answering part two, specific instructions were given to the respondents directing them to Kiva’s

website at www.kiva.org Here respondents were asked to familiarise themselves with the site,

especially regarding how the donation process worked. After viewing the website, the

respondents were asked to return to the survey in order to complete the survey, namely by

completing part two. Finally, at the end of the survey, respondents were encouraged to leave

comments. The estimated time predicted for the completion of the entire survey was

approximately 10-15 minutes. To distribute the survey, a link was created using a web collector

for the address https://www.surveymonkey.com/s/socialcrowdfunding which was then

automatically sent to the respondents once the final survey went live. The survey was open for

a week to allow the respondents’ time to participate in and complete the survey. Throughout

the weeklong process, progress on the survey was carefully monitored so as to check for errors,

apparent discrepancies resulting from technical glitches and to monitor the number of daily

responses on the front page panel which displayed the number of responses received and the

most recent reply.

4.7.4. Pre-testing the Survey

Pre-tests were conducted on the survey before the final version was released. To test the

general understanding of the questions and receptivity to the measures, we generated an initial

sample of 100 respondents who were selected through a snowball sampling technique. Using a

Linkedin account, several posts including the link to the survey were posted on different

LinkedIn groups related to philanthropy and non-profits, and also on the Copenhagen Business

http://www.kiva.org/

https://www.surveymonkey.com/s/socialcrowdfunding


37

School Alumni group. Furthermore, a link to the survey was also sent to our personal contacts

on our LinkedIn Network via their messaging system. This survey was also conducted online

using SurveyMonkey, however, this period was extended to a two-week period owing to

insufficient initial responses and slow take up rate in doing the survey. Since the majority of

the respondents in this pre-test were based in Europe, it was decided that a European social

crowdfunding platform, 2MyC4 (www.myc4.com) would be used as our source of research.

The predominant motivation for the pre-test was to improve the survey questions, by checking

for language inaccuracies and comprehensibility of terms used as well as address any survey

structure issues that may have been brought to light. Moreover, invaluable feedback was

provided regarding the survey process. Based on the feedback that we received from the pre-

test survey, the general consensus was that there were too many questions; the language and

terms used were for the most part, comprehensible to all respondents. Bearing this feedback in

mind, minor changes were undertaken regarding some restructuring and in some instances,

rewording of questions to help facilitate comprehension and flow as well as issues pertaining

to the survey length.

One major issue however concerned the potential drop-off points in the number of

respondents when they reached part two of the survey. This had been an issue that had been

drawn to our attention from the feedback received. Owing to the length of the survey,

respondents may be reluctant to return to complete the rest of survey once they had left

SurveyMonkey to examine Kiva’s website. Taking on board the feedback we received and the

results of the pre-tests, we reduced the number of questions to 50 survey questions and provided

the information regarding Kiva within the survey itself, and so eliminating the need for people

to leave the survey in order to complete it. Furthermore, it was decided at this point that the

snowball sampling technique had considerable drawbacks; our respondents were not entirely

random as they stemmed from our personal networks and that of our colleagues and

acquaintances and thereby creating a higher probability of community bias. This therefore led

us to select a larger and greater randomised sample that would give a more representative and

2 It was reasoned that there would be a greater probability of respondents being familiar with MyC4 is a Danish

social crowdfunding platform and as such has a reasonable probability of being familiar with Danish

respondents who made up a substantial proportion of the initial sample group in Denmark


38

reliable sample group. This is in turn helped to eradicate any bias that may have been incurred

from utilizing the snowball technique.

4.7.5. Developing the measures

For the purposes of our research, empirical measures were first and foremost,

extrapolated based on our theoretical review of Bekkers and Wiepking’s eight mechanisms. (R.

Bekkers & Wiepking, 2010).The 8 measures identified are: a) Awareness of Need, b)

Solicitation, c) Cost d) Altruism, e) Reputation, f) Psychological Benefits, g) Values h)

Efficacy. Where there were divergences of definition such as with awareness of need, where

Bekkers & Wiepking discuss the needs of beneficiaries and our focus lies in awareness of an

intermediary instrument, namely Kiva and other social crowdfunding sites we drew on

alternative sources for inspiration.

Item generation

Items were generated after a thorough literature review. An initial pool of 80 items was

developed for Bekkers & Wiepkings’s eight mechanisms with a view to gaining an

understanding of philanthropic behaviours and intention (the dependent and independent

variable for this paper) and the goal of this research. Some mechanisms and therefore items,

were jointly grouped (see values and altruism) and in some instances, based on our parameters,

mechanisms were addressed under an alternate mechanism (see costs and benefits). The survey

items were divided in to two parts, the first addressing mainly generic motivations and

behaviours with regards to philanthropic giving and the second part dealing specifically with

motivations and intentions in relation to Kiva. Each item was presented on a 5-item Likert scale

ranging from 1 (strongly disagree to) to 5 (strongly agree). The 80 items also included a number

of items developed to glean generic information about the respondents. The initial pool included

a number of control items to help ensure reliability. The items were developed through

numerous iterations and with the assistance of colleagues who helped with issues of item

validity, comprehensibility and aptness in eliciting the desired information. Aptness was tested

by taking a number of random items for each mechanism and asking test respondents to indicate

which mechanism they believed the item addressed (See example below)

I have never been approached by any social crowdfunding sites to invest or donate

money


39

Awareness of need Solicitation Costs & Benefits Altruism Reputation

Psychological Benefits Values Efficacy

A definition for each mechanism accompanied the items so as to help ensure

consistency of meaning among the testing respondents (See example below)

Solicitation is whether a person has been approached either in person or otherwise to

give or charitably invest money in causes and/or social crowdfunding

Any inconsistencies were then addressed and revisions or deletions made when

required. The final item pool was reduced to 20 items with all generic items such as the age and

sex of respondents removed as this information was subsequently provided by SurveyMonkey.

As this investigation addresses not only general motivations, behaviours and intentions

with regards to philanthropy but also the fairly novel SCF example of Kiva, many items,

especially those relating specifically to Kiva or SCF could not be directly taken from other

existing measures and as such needed to be specifically created based on theoretical literary

conceptualizations. Others consisted of items adapted from measures to suit the specific context

of this investigation.

Due to the number of mechanisms which we explored, the following section addresses

the various items in the eight mechanisms under which the fall.

Awareness of Need

Items here initially addressed both poverty, awareness of SCF and social entrepreneurs

soliciting for funds through charitable lending. With regards to SCF, no measures could be

found. There were also no appropriate item examples from which to draw inspiration from.

Items or item measures addressing awareness in general proved elusive. One exception was a

study investigating awareness of high school students’ awareness of counselling services

(Gallant & Zhao, 2011) however the items were deemed too generic to use as inspiration. Final

iterations of the survey resulted in items developed to elicit information regarding intention to

give charitably through SCF sites such as Kiva. As such, these items were developed for part 2

of the survey. Owing to the very specific nature of survey items in part 2, to our knowledge

similarity of item measures or items have not been developed, and as such, we opted to develop


40

specific items for awareness and Kiva. One such item sample is I am aware of social

crowdfunding sites such as Kiva.

Solicitation

Items were developed here are based on Bekkers & Wiepking’s empirical analyses of

active solicitation that concludes that the greater the number of opportunities to give people

encounter, the more likely they are to give (Bekkers, 2005; Bryant, Slaughter, Kang, Hyojin, &

Tax, 2003). Hence the items developed addressed how often (or not) respondents were solicited

to give philanthropically. An example of an item used to draw inspiration from was from Smith

& McSwenny’s Charitable Giving: The Effectiveness of a Revised Theory of Planned Behaviour

Model in Predicting Donating Intentions and Behaviour with items such as ‘I usually donate

money to charities and community service organisations’ A sample of an item developed for

part 1 is It has been quite a while since I was asked to donate money. Another source of

inspiration was Lee & Farrel’s Buddy, Can You Spare A Dime?: Homelessness, Panhandling,

and the Public (Lee & Farrell, 2003). As there were no items or measures that could be directly

adopted we have developed new items reflecting the literature highlighted above. Items

developed here were created with a view to eliciting generic information regarding solicitation

and not in relation to SCF and Kiva.

Costs and Benefits

Items here pertained solely to intentions to give philanthropically and as such were

introduced in the part 2 of the survey. As indicated earlier, we opted to view “benefits” as

psychological benefits. Information that could have been gleaned regarding the benefits of Kiva

would have been purely speculative as respondents were, by and large, unfamiliar with SCF

and Kiva.

As previously mentioned in the literature review, there does not appear to be any

significant body of work addressing the issues of donor time, effort or ease of process and actual

philanthropic giving with the exception of nascent works examining organ and blood donations

(Cialdini & Ascani, 1976; Goette et al., 2010; Houston, 2004; Morgan & Miller, 2002). As

such, it was decided to develop items that would elicit information regarding the process of

giving philanthropically in particular with reference to time and ease of lending through Kiva.


41

Again, owing to the very specific nature of survey items in here, no similarity of item

measures or items developed to date, could be discerned. As such we opted to develop new

items for costs and Kiva. This is a sample of an item developed for costs and Kiva The process

of lending on Kiva appears to be complicated.

Altruism

Early iterations of survey items revealed that items under altruism were often seen as

pertaining to values. As such, it was decided to group altruism and value items together, while

still attempting to develop items addressing both mechanisms. Our primary focus, with regards

to items investigating altruism fell on how important and the lengths people would be willing

to go to, to address social issues. Two sources of great inspiration came from Webb et al’s

Development and Validation of Measures to Measure Attitudes Influencing Monetary

Donations to Charitable Organizations (Webb, Brashear, & Green, 2000) and Rushton’s The

altruistic personality and the self-report altruism scale. (Rushton et al., 1981) Another source

of information was Muehleman et al’s The generosity shift (Muehleman et al., 1976) A sample

of an item here is I would go to great lengths to help alleviate suffering. As discussed earlier,

the interchangeability of items referring to altruism and values as perceived by the test

respondents led us to combine these 2 sets of items. This as well as no similarity of item

measures or items having been developed to our knowledge led us to develop new items that

would cover both issues of values and altruism. For the purposes of continuity, we have chosen

to let Bekkers & Wiepking’s mechanism names remain unchanged here.

Reputation

Items to glean insights in to generic motivations, behaviours and intentions and

reputation were inspired by Mathur’s Older Adults’ Motivations for Gift Giving to Charitable

Organizations: An Exchange Theory Perspective (Mathur, 1996) as well as Smith &

McSweeny’s Charitable Giving : The Effectiveness of a Revised Theory of Planned Behaviour

Model in Predicting Donating Intentions and Behaviour.(Smith & McSweeny, 2007) Further

inspiration was taken from Bernheim and Muehleman’s descriptions of motivations underlying

philanthropic and prosocial behaviour, that of “social factors” such as desire for prestige,

esteem, popularity, or acceptance (Bernheim, 2013; Muehleman et al., 1976). These terms in

particular stood as the framework or reference for our understanding of reputation and the

guiding force underlying our items such as It is important to me that people recognise I am a


42

charitable person. Since no items or measures were directly adaptable, we opted to develop

new items based on theoretical literary conceptualizations.

Psychological benefits

Item development was partially inspired items developed by Bekkers’in Keeping the

Faith Origins of Confidence in Charitable Organizations and its Consequences for

Philanthropy (René Bekkers, 2006) and what Andreoni refers to as the “warm glow” and

Bekkers and Wiepking describe as “feeling good” as motivators to give philanthropically.

(James Andreoni, 1990; René Bekkers, 2006) Our items too, reflect psychological benefits as

feeling good by doing good and are in keeping with Bekkers’ item example ‘Giving to charities

makes me happy’. As with many of the items previously mentioned, items and item measures

for psychological benefits and philanthropic giving were not found to be adoptable and as such,

new items were developed based on theoretical literary conceptualizations as with this sample

item cited Giving money to charitable causes gives me a sense of pleasure.

Values

Items here address values associated with lending and Kiva. As noted earlier, we

registered in preliminary steps that values and altruism were often seen as interchangeable and

as such, many of the sources used for inspiration for items in altruism were also used for items

pertaining to values. No items or item measures were found directly addressing philanthropy

and values and as such, the horizon was extended to include norms. We therefore developed

items inspired by what was cited for altruism and Smith & McSweeny’s items for moral norms

such as ‘It goes against my beliefs to donate money to charities or community service

organisations’ (Smith & McSweeny, 2007) As was the situation stated earlier regarding the

perceived interchangeability of items addressing altruism and values, we have again opted to

develop new items. This was also due to the very case-specific nature of items required for this

part. A sample of such an item is as follows It is acceptable that Kiva requires poor borrowers

to pay back the money they have borrowed.

Efficacy

Here inspiration was drawn from Webb et al’s Development and Validation of Measures

to Measure Attitudes Influencing Monetary Donations to Charitable Organizations who have


43

developed items addressing both perceived efficacy (Charitable organizations have been quite

successful in helping the needy) and inefficacy of charitable donations (Much of the money

donated to charity is wasted) Other sources of inspiration were garnered from Smith &

McSweeny.

Our focus with regards to efficacy involved the efficacy or perceived efficacy of Kiva

both in terms of Kiva as an organization able to fulfil its goals and efficacy of impacting poverty

through Kiva. As such, new items were developed as no items or item measures were found

addressing this highly specific area. A sample of an item addressing Kiva and efficacy is given

here I feel that Kiva can achieve its goals.

The following section addresses the progression from item generation and compilation

of the survey to selection of the survey service utilized.

4.8. Data Sampling

In the final survey conducted, we utilised a more random sampling method by using the

SurveyMonkey Target Audience feature (SurveyMonkey Audience). What this offered was the

possibility of allowing our survey to reach a broader and targeted population and the possibility

to set the criteria that we want. Our decision to select Kiva as opposed to remain with the Danish

SCF platform MYC4 was based on a number of factors. Primarily, the SurveyMonkey

respondent pool is predominantly based in the USA with some representation in France and the

UK. The likelihood of respondents outside of Denmark, France or the USA being familiar with

MYC4 were marginal. However, by opting to select an American SCF platform such as Kiva

that is well-established and with a relative degree of notoriety, the likelihood of any prior

knowledge to Kiva and therefore SCF would be increased. The targeting criteria that we set for

the respondents were:-

300 respondents (n=300)

18 years of age and above

Male and Female with a 50-50 even split

Random selection from the US general population


44

To calculate the number of respondents that we needed, we based our decision on the

need to calculate the Cronbach Alpha which is the measure for internal consistency, that is, the

instrument we have opted to show reliability for survey items. It has been suggested that a

number between 50-500 sample respondents are required for a sound factor analysis (Aleamoni,

1973; Comrey, 1978). Hence a sample of 300 is generally acceptable (Nunnally, J. C., &

Bernstein, 1994). Furthermore, in order to eliminate gender bias, the sample was evenly

distributed between the two genders. Additionally to remove selection bias, we insisted on

random selection from a representative sample of the general population be used. According to

SurveyMonkey, the issue of representative samples has been addressed as they have the

potential to recruit from an over 30 million people strong group made up of the visitors to the

SuveyMonkey site (SurveyMonkey.com). Comparison tests carried out against similar research

businesses such as Gallup show that the responses from their target audience match the

benchmarks in Gallup (Surveymonkey Data Quality White Paper, 2012). Since, our survey is

heavily reliant on SurveyMonkey services, the next part of the paper addresses how

SurveyMonkey recruits for their targeted audience service, that of the audience demographic

composition and how the process works.

4.8.1. SurveyMonkey Audience – Demographics

Since 2011, SurveyMonkey has offered their product SurveyMonkey Target Audience

to their customers as an add-on to their basic survey service. This add-on allows customers to

specify their own targeting requirements based on age, gender, income and location and the like

(SurveyMonkey Target Audience Criteria, 2013). According to SurveyMonkey, although their

target audience is taken randomly from “…a diverse group of people and is reflective of the

U.S. population…”(SurveyMonkey, Demographics, 2013). It should also be noted that the pool

in which the audience is selected is slanted more towards internet users, and “…compared to

the U.S. adult population, Internet users tend to skew more highly educated, higher income,

and younger, among other demographic skews...” (SurveyMonkey, Demographics, 2013).

However, one research has shown that online sampling is just as representative and the results

are generally consistent with results from traditional methods (Gosling, Vazire, Srivastava, &

John, 2000). As the sample from which our survey is drawn is relatively large and diverse, we

believe it falls under acceptable criteria our study.


45

4.8.2. SurveyMonkey Audience - Recruitment Procedure

The SurveyMonkey “audience” consists of a pool of survey respondents who have been

specially recruited to participate in SurveyMonkey’s target audience program. In return for

rewards, the participants agree to complete surveys created by SurveyMonkey customers. The

respondents of this program are recruited through three primary methods:

SurveyMonkey

Global Partner Network

SurveyMonkey Ads

SurveyMonkey Contribute refers to the website that is setup to recruit people to do

surveys. In return for completing the survey, the members receive rewards such as a donation

to their selected charity of $0.50 for every survey they complete. The list of charities that

SurveyMonkey donates to can be found on their website. Furthermore, they are entered into a

weekly lottery for an opportunity to win $100 (SurveyMonkey Contribute, 2013). The Global

Partner network refers to SurveyMonkey’s relationship with various international data

providers that offer access to respondents located worldwide. Apart from advertisements placed

on general websites and social networks, SurveyMonkey Ads are also designed to attract a

target audience that is already familiar with surveys, by placing these advertisements on a

landing page seen when respondents complete a survey. With over 30 million visitors a month,

this allows for a rich pool from which to recruit new members for SurveyMonkey Contribute –

which currently stands at over a 3 million survey respondent pool (Surveymonkey, 2013).

The recruitment process works as such; when signing up to be a respondent on

SurveyMonkey Contribute, the respondents are asked to fill out their personal profile and to

answer a few prepared questions. Throughout this process, a screening filter is employed to

categorize the participants based on the information provided. From this information,

SurveyMonkey is able to collect demographic information such as their age, gender, location

and profession, but also from question answered, behaviours and attitudes can also be gleaned.

This allows for a better targeting of the surveys, based on customers set criteria. SurveyMonkey

matches the criteria with the potential respondents based on their profiles and interests and

notifies them of the survey. Should respondents choose to do the survey, they can then decide

which charity should receive a donation from SurveyMonkey’s short-list.


46

To ensure reliability with their service, SurveyMonkey have undertaken certain steps to

ensure data and sample quality (Surveymonkey, Data Quality White Paper. 2012):

Scale and Diversity – By building a large database of respondents with diverse

demographic profiles and covering international respondents and ensuring that the

recruitment is conducted from various traffic sources and social networks.

Validation Tools - The use of validation tools such as 3TrueSample to check for

duplicate sign-ups, and verifying a person’s email address and physical location.

Filtering tools to remove outliers from their data sets and respondents that provide

inaccurate data.

Incentive programs - Non-monetary rewards to encourage survey participation when

surveys are completed and incentivizing respondents to philanthropic acts. This is done

through their donation of time in completing surveys and the donations through

SurveyMonkey to charity of choice.

Representativeness of sample – Performing periodical audience quality benchmarking

testing to assuage quality concerns regarding the data provided by respondents. The

results are frequently compared with industry standards such as Gallup.

3 TrueSample® utilizes these technologies: RealCheck Postal™ which collects and evaluates names and postal

addresses against third-party consumer databases to determine if they're legitimate and correspond with one

another. RealCheck Social™ - is used for records that fail RealCheck Postal, where respondents’ names and

email addresses are checked against third-party databases of social and other online sources. UniqueCheck

collects and examines digital fingerprints from respondents’ computers to determine if they’ve already

completed the survey. Also examines known information about the respondents’ identities to flag those that

appear to be duplicates (taken from www.truesample.com)


47

4.9. Data Analysis

In order to analyse the data collected from SurveyMonkey, we utilised the statistical

software SPSS in order to conduct our exploratory factor analysis (EFA) and AMOS to create

a structure equation model (SEM). Since we have limited experience with both software and

statistics, we were assisted partially with obtaining the results by our supervisor, Professor Kai

Hockerts.

4.9.1. Exploratory Factor Analysis (EFA)

Hair describes exploratory factor analysis (EFA) as a useful technique for analysing a

large set of variables that involve “complex, multidimensional relationships” for the purpose

of discovering the “underlying patterns or relationships” and to “condense or summarized

into smaller set of factors or components” (Hair et. al, 2009: 90) . Field argues that “reducing

a data set from a group into interrelated variables into a smaller set of factors…achieves

parsimony by explaining the maximum amount of common variance in a correlation matrix

using the smallest number of explanatory concepts…” (Field, 2005,:654). Since our survey

involves a large number of variables, we have elected to use EFA to find the correlations and

relationships between them in order to get a better understanding of and aiding our analysis of

the survey results.

SPSS allows different configurations to be set when conducting an EFA. This paper

utilises the following method in construction of the final results which is presented in the next

section 5.2 diagram 8.

Steps taken in SPSS for Exploratory Factor Analysis

Selection of relevant variables – survey questions (items) were selected for analysis

1. Selection of extraction method – Principal Component Analysis was selected

2. Selection of rotation method to utilise – Varimax Rotation was selected

3. Recoding of any negative variables - questions that were worded in negative form were

recoded


48

4. Construction of scale for the reliability analysis – KMO Bartlett test & Cronbach Alpha

was selected.

For a reliable factor analysis, the larger the sample size the better the result (Field, 2005).

According to Comrey, generally a sample size of 50 and under constitutes very poor sample

size, 100 = poor ; 200 = fair; 300 = good; 500 = very good (Comrey, A. L., & Lee, 1992). In

total we received 333 responses, however only 302 were completed survey responses. This

however still falls well in to Comfery’s criteria for a “good” sample size.

In deciding on the extraction method for factors, we used the one most commonly used

extraction methods that of Principal Component Analysis (PCA) and the Eigenvalue set at the

default 1. (Field, 2005) Eigenvalues represent the variance accounted for each factor and is used

to decide how many factors, what factors to extract in the EFA. A scree plot was often used to

show this variance in a graph format. Eigenvalues above 1 are normally selected as the cut value

however if a factor component is below 1 it does not mean it should be immediately discarded

(Field, 2005) because if it is very close to 1, it can still be relevant to the analysis especially if

there are not enough factors to examine.

The rotation method helps to simplify the structure of the analysis thus aiding in its

interpretation by reducing its uncertainties (Hair et. al, 2009) . It does this by maximising the

high loaded factors and minimising the low ones. For the purposes of this paper, the Varimax

Rotation solution (orthogonal type) was selected as it is one of the most commonly used

methods. Finally, for the reliability analysis, the scores that were calculated are for the KMO

Bartlett Sphericity Test and Cronbach Alpha. The KMO Bartlett test “…examines whether the

population correlation matrix resembles an identity matrix…(all correlation coefficients are

zero)…” (Field, 2005, p.642) If the coefficients are zero then it means the variables correlate

badly with each other. Therefore the minimum coefficient should be 0.5 (Field, 2005, p.642).

The lower the coefficient is to this number, the more significance the correlation between the

variables.

Cronbach Alpha is commonly used as a way to measure reliability where scales have

been used in a survey. Since our survey utilizes a Likert scale, it may be prudent to include this

in our analysis. Furthermore, Field suggests that questions which are negatively encoded such

as “I do not get asked for donations often” should be re-coded in SPSS when conducting

reliability testing. Although there is some debate regarding the minimum value acceptable for


49

Cronbach Alpha, widely agreed acceptable Cronbach Alpha value is 0.7/0.8 (Field, 2005) which

is the cut off value that is employed in this investigation.

4.9.2. Structural Equation Model (SEM)

Once the EFA was completed, the next step in the process was to create a Structural

Equation Model (SEM) based on our EFA. SEM was used to test our hypotheses and to prove

the relationships between the variables.

5. Results

The final survey was run for one week and the total number of responses received

was 333. From this number, 302 were complete responses and 31 were partial answers –

representing a 90.7% response rate. As mentioned earlier, our targeting criteria for our sample

population was a sample group of 300; we however received 333 responses in total that is 9.9%

more than originally requested for from SurveyMonkey.

SurveyMonkey’s front panel revealed information regarding the respondent’s

activity while completing the survey. This indicated that, on average, most people completed

the survey within 10 minutes. Moreover, 90% of the responses completed the survey within the

first 2-3 days of its launch which meant the 300 target number was achieved early in to the

launch. In the optional comments section at the end of the survey, 78 people left personal

responses – which were generally positive. Generally comments pertained to not previously

knowing about Kiva, not being aware SCF, empathy with the plight of the social entrepreneurs,

as well as praise for acquainting respondents with Kiva and SCF. The SurveyMonkey text

analysis tool revealed that the top 5 words recorded in the comments were: Money, Donate,

Kiva, Interesting and Survey – words that factor with not only our dependent and independent

variable (Kiva) but with our investigation as a whole.

Ironically the survey may go some way to confirming our first hypothesis people

who are aware of SCF sites such as Kiva are more likely to be interested in using it if the survey

respondents do indeed lend through Kiva. This would require further investigation and is

beyond the time and scope of this paper, but could prove to be of empirical interest for future

studies.


50

5.1. Demographic Information

SurveyMonkey provided us with the data regarding the demographic composition

of the respondents who took our survey. The data given showed results from 301 respondents,

indicating a slight discrepancy from the 302 responses received in total and hence the lack of

demographic data for one respondent.

From the data collected we were able to glean the age, gender and profession of

the respondents. Although these demographic results do not form part of our final analysis,

given that there are bias already in force, created by factors such as our selection criteria and

SurveyMonkey’s respondent pool (all respondents must be 18 or over, have prior knowledge

of SurveyMonkey, have access to the internet etc.) our demographic results do demonstrate

heterogeneity in our sample pool of respondents. Figure 6 below shows that there is, as

requested prior to the survey launch, a near even split between the respondents gender with

males dominating slightly in the numbers at 51% and totalling 154 respondents and females at

49% with 147 respondents.

Figure 6: Respondents Gender

Female; 147; 49%

Male; 154; 51%

Respondents gender

Female Male


51

Figure 7: Respondents Age

With regards to the age of respondents, respondent age ranged from 18 to 65 years

old, the average age of respondents was 40.7. Figure 7 also reveals that the age of most survey

respondents was 55 years old with 17 respondents in total, followed by 23 year olds with 14.

Furthermore, the largest number of respondents were in the 40-65 range, which suggests an

older demographic in this sample provided by SurveyMonkey. A further breakdown of the age

and number of participants can be found in Appendix A

7

4

5

2

6

14

6

2

8

6

5

9

8

11

9

6

9

2 2

6

5

6

4

6

7

8

6

8

6

7 7 7

11

9

13

5

8

17

4

2

1

6

3 3 3 3

5

4

0

2

4

6

8

10

12

14

16

18

18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64

Tota

l

Age

Respondents age


52

Figure 8: Respondents Profession

Figure 8 shows the total number of respondents based on their profession in

descending order. It should be noted that the majority of the respondents selected “other” as

their profession that is, their profession did not fall within the given categories and a further 67

people did not provide SurveyMonkey with any information regarding their profession. Of the

given professions, education accounted for the highest number of respondents at 16 in total,

followed by Healthcare at 13 and manufacturing at 11 people. Since survey respondents

complete surveys in return for donations to charities via SurveyMonkey, this may imply some

degree of altruism and as such, there may be a correlation between these professional areas and

altruism.

108

67

29

16 13 11 9 9 6 4 3 3 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 10

20

40

60

80

100

120O

the

r

Did

no

t an

swer

No

t A

vaila

ble

Edu

cati

on

Hea

lth

care

Man

ufa

ctu

ring

Co

nstr

uct

ion

Gov

ern

men

t/P

ublic

Sec

tor

Co

mpu

ter

Res

elle

r (h

ard

war

e/so

ftw

are)

Ret

ail

Ban

king

/Fin

anci

al

Med

ia/E

nte

rtai

nm

ent

Arc

hite

ctur

e

Ener

gy/U

tilit

ies/

Oil

& G

as

Rea

l Est

ate/

Pro

pert

y

Adv

erti

sing

No

n-p

rofi

t

Tele

com

mun

icat

ions

Tran

spo

rtat

ion

Agr

icu

ltur

e/Fi

shin

g

Info

rmat

ion

Tec

hn

olo

gy/I

T

Mar

keti

ng

& S

ales

Acc

ou

nti

ng

Ship

pin

g/D

istr

ibu

tion

Co

nsu

ltin

g

Ho

spit

alit

y/To

uri

sm

Inte

rne

t

Phar

mac

euti

cals

Tota

l Nu

mb

ers

Profession

Respondents Profession

* Data provided by SurveyMonkey


53

5.2. Exploratory Factor Analysis (EFA)

The results from the final survey were analysed using EFA to uncover the

underlying relationship between variables and to identify the factors that exist in our results.

This was performed in order to explore how the variables correlate with each other and the

level of significance they maintained with each other. This would then assist us in ascertaining

which variables load significantly together by showing a high correlation, which would

demonstrate a good result in EFA.

The first result achieved from the EFA was the Kaiser-Meyer-Olkin measure of

sampling adequacy (KMO) and Bartlett’s Test of Sphericity. The KMO test is useful in testing

whether a sample will yield adequate and reliable factors. Generally speaking, values that are

closest to a value of 1 are considered optimal The minimum acceptable value should be 0.5

(Kaiser, 1974). Furthermore values between 0.7 and 0.8 are considered sound (Field, 2005).

Therefore, the result achieved on the KMO test that of 0.763 is considered to be good. Bartlett’s

test measures the significance of the correlations in a correlation matrix. Since our results is

0.000 which is less than 0.05, this shows significance and that sufficient correlation exists with

the variables for us to proceed with the EFA.

Figure 9: KMO and Bartlett Test

.


54

Figure 10 below shows the results for Communalities

The items on the left represent the survey questions (items) that were left

remaining after the PCA was run several times (to remove items with low loading scores), and

the extraction score for each item. A communality represents the extent to which each item

correlates with all the other items in the EFA. Communalities range from 0 to 1. The higher the

score (closer to 1), the more optimal it is considered to be (Hair, 2009). The results show that

all the items have relatively high scores and therefore correlate with each other and were

therefore retained. In the next figure, figure 11 the Eigenvalue for each factor (component) is

shown together with their percentage of variance from each other.


55

Figure 11: Eigenvalues & Variances

The Eigenvalue is the total variance described by each factor. It is used to decide how many of

the factors should be extracted for the overall factor analysis. Normally, only Eigenvalues above

1 are considered significant (Kaiser, 1960), however in some cases it is acceptable to retain

values that are below but close to 1 where the factors that are kept will not be able to explain

all the variance and therefore the value below 1 should not be rejected as they may provide

useful information in the analysis (Field, 2005). Therefore for the purposes of this investigation

and in order to retain 8 factors it was decided that factors 1- 8 will be kept as they do not vary

significantly from the value of 1. However, after point 8 there is a dramatic drop to 0.631 on

factor 9 which therefore is unacceptable for the analysis. Another way to view this would be


56

using a Scree Plot as depicted in figure 12 below which shows the eigenvalues of the factors

and where they are located on the plot. The dotted line represents our cut-off point when

deciding which factors to retain.

Figure 12: Scree Plot with Eigenvalues

An alternate method by which to view this would be to look at the percentage of

variance for each factor as shown in figure 13. Where a factor has a high Eigenvalue such as

factor 1 at 4,809, it contributes the highest variance accounting for 24% of the total variance.

As the Eigenvalue drops for the next factors so does the % of variance. Therefore it is important

to retain factors that contribute the most to explain the variances in the variables for a sound

analysis. By selecting the cut off at factor 8, based on the cumulative percentage, these factors

represent 76% of the total variance and therefore this confirms that we have selected the most

important factors for our investigation and as such factors 9 to 20 are deemed redundant.

According to Hair, in social sciences, a solution that explains 60% of the total variance is

acceptable (Hair, 2009).


57

Figure 13: Total Variances

Rotated Component Matrix

Once the factors were extracted, the Varimax rotation method was employed to facilitate

reading the results. Varimax seeks to find the optimal loading of the factors for easier

interpretation. The larger the sample, the smaller the loadings can be to be considered

significant. Both Fields and Hair mention that the minimum for the factor loadings can be as

low as 0.30 but this depends on the sample size (Fields, 2005). For a sample size of 300 as in

our case, the factor loading should be a minimum of 0.35 as is the percentage given for a 250

sample to have practical significance. The guidelines are depicted in the table below (Hair,

2009).

Guidelines for Identifying Significant Factor Loadings based on Sample Size*

Factor Loading Sample Size needed for significance

.30 350

.35 250

.40 200

.45 150

.50 120

.55 100

.60 85

.65 70


58

Component

1 2 3 4 5 6 7 8

I am aware of social crowdfunding sites such as Kiva. 0,112 0,106 0,815 0,012 0,028 0,033 0,284 -0,022

I have heard of social crowdfunding before today. 0,081 0,048 0,882 -0,037 0,063 0,050 0,082 -0,063

People have approached me about social crowdfunding before. 0,024 0,044 0,732 -0,010 0,316 -0,116 0,088 0,073

It has been quite a while since I was asked to donate money. -0,015 0,021 0,033 0,869 -0,179 0,140 0,037 -0,024

I do not get asked for donations very often. -0,013 0,005 -0,001 0,905 -0,058 0,070 0,055 -0,127

The process of lending through Kiva looks time consuming. -0,065 0,023 0,072 0,073 -0,071 0,892 -0,003 -0,138

The process of lending on Kiva appears to be complicated. -0,007 -0,002 -0,088 0,137 -0,060 0,879 0,041 -0,163

More can be done to address the issue of poverty. 0,001 0,726 -0,070 -0,165 -0,129 0,074 0,163 0,159

I would go to great lengths to help alleviate suffering. 0,208 0,764 0,084 -0,018 0,207 -0,044 0,094 -0,050

I have a genuine concern for the well-being of others less fortunate than myself. 0,290 0,716 0,065 0,156 0,011 0,000 0,015 0,098

Helping solve social problems is really important to me. 0,265 0,724 0,162 0,066 0,146 -0,008 0,114 -0,021

I feel uncomfortable that Kiva requires poor borrowers to pay me back the money I have lent. 0,031 0,047 0,086 -0,093 0,059 -0,169 -0,066 0,848

I would feel awkward taking out money that poor recipients have paid back. 0,084 0,090 -0,107 -0,057 0,079 -0,129 0,029 0,851

I care what people think about my charitable giving. 0,149 0,057 0,192 -0,102 0,829 -0,103 0,138 0,069

It is important to me that people recognise I am a charitable person. 0,174 0,113 0,135 -0,163 0,837 -0,037 0,093 0,085

Giving money to charitable causes gives me a sense of pleasure. 0,830 0,282 0,067 0,032 0,095 -0,045 0,037 0,019

Charitable giving makes me experience positive sensations. 0,839 0,226 0,101 -0,005 0,113 -0,009 0,074 0,016

Giving to charitable causes makes me feel really good about myself. 0,870 0,137 0,048 -0,066 0,132 -0,034 0,064 0,100

I feel that my lending on Kiva would have a real impact on poverty. 0,115 0,135 0,203 -0,022 0,201 0,027 0,830 0,023

I feel that Kiva can achieve its goals. 0,044 0,175 0,198 -0,001 0,041 0,013 0,861 -0,060 Extraction method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. Rotation converged in 6 iterations Figure 14 : Rotated Component Matrix


59

As can be seen in figure 14, the results show that we have an 8-factor solution.

Factor loads that are below 0.3 are greyed out whereas those that are considered significant are

bolded. The diagram shows how the items (survey questions) load on the various factors. As

previously mentioned, what was being sought was to see the relationship between the items and

the measures. We have labelled each factor based on the measures we identified earlier on in

our methods section (Bekkers & Wipkings’s 8 mechanisms).

Furthermore, we investigated how closely the items load to each other and

whether they load on more than 1 factor. We analysed the results and found that in general the

factors loaded significantly on the measures that we were expecting except for one item

(“people have approached me about social crowdfunding before”) loading on two different

factors. However, since the item loads highly on the first factor, (factor 3) we have chosen to

ignore the low result on the 2nd factor (factor 5). Below is the summary of the results from the

table and the measures identified for the following items.

SURVEY ITEMS MEASURES

I am aware of social crowdfunding sites such

as Kiva.

Factor 3 ( Awareness of need)

I have heard of social crowdfunding before

today.

Factor 3 ( Awareness of need)

People have approached me about social

crowdfunding before.

Factor 3 (Awareness of need) & 5

(Reputation)

It has been quite a while since I was asked

to donate money.

Factor 4 (Solicitation)

I do not get asked for donations very often.

The process of lending through Kiva looks

time consuming.

Factor 6 (Costs and Benefits)

The process of lending on Kiva appears to

be complicated.

More can be done to address the issue of

poverty.

Factor 2 (Altruism)

I would go to great lengths to help alleviate

suffering.


60

I have a genuine concern for the well-being

of others less fortunate than myself.

Helping solve social problems is really

important to me.

I feel uncomfortable that Kiva requires poor

borrowers to pay me back the money I have

lent.

Factor 8 (Values)

I would feel awkward taking out money

that poor recipients have paid back.

I care what people think about my charitable

giving.

Factor 5 (Reputation)

It is important to me that people recognize I

am a charitable person.

Giving money to charitable causes gives me

a sense of pleasure.

Factor 1 (Psychological benefits)

Charitable giving makes me experience

positive sensations.

Giving to charitable causes makes me feel

really good about myself.

I feel that my lending on Kiva would have a

real impact on poverty.

Factor 7 (Efficacy)

I feel that Kiva can achieve its goals.

Figure 16: Items and Measures

Cronbach Alpha and Testing Reliability

Cronbach alpha is often used to measure internal consistency i.e. whether items in a group are

closely related or not. It can also be used to measure the reliability of the scale used. The general

rule is that the closer Cronbach alpha in the result is to 1.0 the more reliable it is. George and

Mallery have posited that the following guide can be used (George, D., & Mallery, 2003):

> 0.9 = Excellent

> 0.8 = Good

> 0.7 = Acceptable

> 0.6 = Questionable

> 0.5 = Poor

< 0.5 = Unacceptable


61

In order to calculate the Cronbach Alpha for the survey items. We firstly grouped them based

on the measure identified in the Rotated Component Matrix and the items for each measure.

Then the Cronbach Alpha was measured each group in turn. The results can be seen in figure

17.

Measures Cronbach Alpha

Awareness of Need 0.798

Solicitation 0.795

Cost and Benefit 0.793

Altruism 0.771

Values 0.705

Reputation 0.787

Psychological Benefit 0.869

Efficacy 0.766

Figure 17: Cronbach Alpha Results

Based on the results, it can be inferred that all group items had an internal

consistency that was > 0.7 and therefore acceptable. It should be noted that items measuring

psychological benefit achieved the highest Cronbach Alpha value.

These results were examined further using the Structured Equation Model and

tested to see whether a good model fit could be achieved. This was performed to test our

hypotheses and confirm the relationships that we identified in the EFA in order to gain

additional insight into our data and results. This is further illustrated in the following section.


62

5.3. Structural Equation Modelling (SEM)

SEM is a highly useful statistical technique for testing the relationship between

variables (observed and latent) and the causal inferences that can be deduced from these

relations (Hair, 2009; Kline, 2011). In addition, SEM is able to explore the multivariate

relationships simultaneously in an integrated method.

By using the results from the EFA, several items were found to be associated with

certain factors hence the next step taken was to test and confirm these relationships and

concurrently test our hypotheses with the data collected. This was performed, in particular, to

determine the model fit that is, the degree to which the SEM fit the data being sampled. Using

the Maximum Likelihood (ML) method estimate, and utilizing the default model in SPSS

AMOS, the results were then evaluated using several of the absolute fit indices measures

mentioned below as they were deemed most relevant for our analysis.

CMIN

MODEL NPAR CMIN DF P CMIN/DF

Default Model 89 389,329 211 ,000 1,845

Saturated Model 300 , 000 0

Independence model 24 3149,560 276 , 000 11,411

* Model fit summary results produced by SPSS AMOS

CMIN refers to the minimum value discrepancy. The smaller the number the less

discrepancy there is. CMIN/DF refers to CMIN divided by its degree of freedom and although

1 is considered a good fit, 1-2 is acceptable (AMOS Fit indices). Therefore the result achieved

here falls within the acceptable parameters.

RMR, GFI

MODEL RMR GFI AGFI PGFI

Default Model ,057 ,907 ,867 ,638

Saturated Model ,000 1, 000

Independence model ,260 ,402 ,350 , 370



63

RMR, (root mean square residual), is the amount that shows the variations

between what is estimated and observed in the model. Although 0 is ideal, the result 0.057 is

good (Hair, 2009) and when this is compared with GFI or the goodness of fit index, the

recommended number is below 0.9 and therefore considered a good fit by this measure (Hair,

2009), The PGFI (Parsimony Goodness of Fit) should be a value more than 0. The closer it is

to a value of 1 the better, hence a result of 0.6 is an acceptable result.

Baseline Comparisons

MODEL

NFI

Delta1

RFI

rho1

IFI

Delta2

TLI

rho2 CFI

Default Model ,876 ,838 ,939 ,919 ,938

Saturated Model 1,000 1,000

1,000

Independence model ,000 ,000 ,000 , 000 ,000


The Comparative Fit Index (CFI) utilises an incremental fit index by comparing

the model with alternative baseline models. Values range from 0 to 1, however values above

0.90 indicates good fit. The result for CFI of 0.938 shows that this requirement is satisfied in

our results.

RMSEA

MODEL RMSEA LO 90 HI 90 PCLOSE

Default Model ,053 ,045 ,061 ,268

Independence model ,186 ,180 ,192 ,000


RMSEA stands for Root Mean Square Error of Approximation and it measures the lack of

fit when comparing with the saturated model. According to Kline, an RMSEA value of less

than 0.05 is a “close approximate fit”, between 0.05 and 0.08 is a “reasonable approximate fit”,

whereas values of 0.1 or more is an indication of a poor fit (Kline, 2011, p.206). Therefore the

value obtained here for RMSEA of 0.053 is just slightly above 0.05 close fit but still otherwise

good. To assess the confidence interval, the values from LO90 and HI90 are computed and for

LO90 it should be 0.5 and below and HI90 should be not more than 0.8 which in our results is


64

shown to be the case. The PCLOSE or p of Close Fit is normally used to check for sampling

errors in RMSEA, a high results shows that is close to the model fit which is proven here.

In summary, the results show that a good fit is achieved on some of the fit indices such as

CFI, GFI, RMR, RMSEA whereas on others such PGFI, CMIN/DF have acceptable results.

However, it’s important to keep in mind that a good fit does not mean that the model is correct,

only that it is conceivable that SEM fits the data being sampled and therefore the results need

to be considered as a whole and not in isolation.

Main Results

In this part of the paper, the main results from the SEM is presented, along with

the path model that shows the relationships and causal inferences that can be made between the

variables. In figure 18, the hypothesized causal link is shown in the arrows pointing from the

independent variables (on the right side) to the dependent variable (donation behavior and

intention to donate).

Estimate S.E. C.R. P Label

DonationBehaviour <--- Solicitation 0,408 0,072 5,679 ***

DonationBehaviour <--- Altruism 0,269 0,124 2,165 0,030

DonationBehaviour <--- Reputation 0,281 0,091 3,077 0,002

DonationBehaviour <--- PsychBenefits 0,294 0,122 2,398 0,016

Intention <--- Awareness 0,106 0,071 1,487 0,137

Intention <--- Solicitation -0,081 0,048 -1,711 0,087

Intention <--- CandB 0,069 0,066 1,047 0,295

Intention <--- Altruism 0,196 0,087 2,242 0,025

Intention <--- Values 0,054 0,071 0,757 0,449

Intention <--- Reputation 0,286 0,073 3,944 ***

Intention <--- Efficacy 0,449 0,101 4,453 ***

Intention <--- DonationBehaviour 0,167 0,057 2,902 0,004

Intention <--- PsychBenefits -0,072 0,078 -0,924 0,350

Estimates, Scalar Estimates, Maximum Likelihood Estimates, and Regression Weights (Group 1 – Default Model) results produced in SPSS AMOS

Figure 18: Main SEM Results


65

The two results were are most interested in is the effect size which is can be seen

in the values for Estimate and significance of the results which is shown in P-value. The other

values here are Standard Error (S.E.) and Critical ratio (CR). The measures that will be used for

statistical significance in the P-value is given in AMOS as seen below, namely the higher the

star rating, the more statistically significant the result (Rice, 1989); (Anderson et al, 2000):

*** Statistically significant if < 0.001

** Statistically significant if < 0.05

* Statistically significant if < 0.1

Reference from http://www.statsdirect.co.uk/help/basics/pval.htm

The p-value (calculated probability) is applied when trying to reject the null hypothesis. The

null hypothesis where “…there is no relationship between two quantities…” (Business

Dictionary.com). What we are trying to prove is that there is a relationship between the

measured items and hence the lower the number, the more significant it is.

Based on our results above:

Estimate S.E. C.R. P Label

DonationBehaviour <--- Solicitation 0,408 0,072 5,679 ***

Intention <--- Reputation 0,286 0,073 3,944 ***

Intention <--- Efficacy 0,449 0,101 4,453 ***

These three results have the highest significance and therefore will impact our findings the

most.

Intention <--- CandB 0,069 0,066 1,047 0,295

Intention <--- Values 0,054 0,071 0,757 0,449

Intention <--- PsychBenefits -0,072 0,078 -0,924 0,350

On the other hand, the following results show the lowest significance, due to their high p-values

and therefore, are less consequential to our findings. However, they will still be discussed when

compared to our hypotheses and also in the discussion part of the paper.

http://www.statsdirect.co.uk/help/basics/pval.htm


66

A path diagram showing the relationship and causal inferences between the variables can be

seen below.

Figure 19: SEM Model

Donation Behaviour

Intention to donate

Solicitation

Altruism

Reputation

Psychological

benefits

Values

Cost & Benefit

Awareness

Efficacy


67

The diagram below shows the variables in the oval shape and the arrows denotes the

hypothesized causal direction. The variables on the left side are independent variables where

else intention to donate and donation behavior are the dependent variables. Items (survey

questions) have been removed from this diagram for easier viewing. Furthermore, the

significance of the relationships are identified with the arrows.

Figure 20: SEM Path Diagram & Significance

Donation Behaviour

Intention to donate

Solicitation

Altruism

Reputation

Psychological

benefits

Values

Cost & Benefit

Awareness

Efficacy

***

** 0.030

** 0.002

0.16

0.137

***

***

0.449

** 0.025

0.295

* 0.087

0.356

**0.004


68

Comparisons of hypotheses and results

1. H1 – Awareness

H1 - People who are aware of SCF sites such as Kiva, are more likely to be interested in using

it.

The results for this shows that although a positive relationship between awareness

and the intention to use Kiva was indicated, the relationship was statistically insignificant

(0.137) even with the effect size of (0.106) which is not considered very low. No relationship

was found between awareness and donation behavior. Therefore H1 could not be confirmed.

2. H2 – Solicitation

H2a – People who are regularly solicited are more likely to report giving regularly.

H2b – People who are regularly solicited are more likely to be interested in using Kiva.

A statistically significant result of *** was achieved on the p-value for H2a which

is the highest rating achievable regarding the relationship between solicitation and donation

behaviour. Furthermore the effect size is the largest in the total results for donation behaviour

at 0.408. It can therefore be argued that, this proves that donation behaviour is impacted

positively and quite significantly with an increase in solicitation. Therefore H2a can be

confirmed.

With regards to H2b, the results still show a significant relationship at 0.87 (*) in

relation to intention/interest in using Kiva, however, the effect size is weak and negative at -

.081. This does not mean that there is a connection between the two, but it is more likely that

there is no direct connection. What this suggests is that people who are solicited regularly are

less likely to be interested in using Kiva. We observed that the results were contrary to our

hypothesis, however since the effect is so small we do not believe that this is an important

effect. Therefore H2b, cannot be categorically confirmed.


69

3. H3 – Cost and Benefit

H3 – People who find using Kiva to be easy are more likely to be interested in using Kiva.

As stated in previous sections, our interest lies solely in costs incurred in terms of

time and ease. The results suggest that there is no statistically significant relationship between

cost and the intention/interest to use Kiva (0.295). Furthermore, the effect size is small at 0,069.

Therefore we are unable to confirm H3.

4. H4 – Altruism

H4a – People with a higher level of altruism are more likely to report giving philanthropically.

H4b – People with a higher level of altruism are more likely to be interested in using Kiva.

With a p-value of ** (0.030) there exists a statistically significant relationship

between altruism and donation behaviour. The effect size is also relatively large at 0.269. When

we compare with the results for altruism and intention/interest to use Kiva, a similar high

significant relationship can be deduced (0.025), accompanied by a reasonable size effect.

Therefore both H4a and b can be confirmed.

5. H5 – Reputation

H5a – People who feel that altruism is important for their reputation are more likely to report

giving philanthropically.

H5b – People who feel that altruism is important for their reputation are more likely to be

interested in using Kiva.

For both intention/interest in using Kiva and donation behavior showed results

that are highly significant (** and ***) and achieving large size effects (.0286, 0.281). This

indicates that reputation figures strongly with people’s philanthropic behaviour if they feel

altruism is important and this impacts their likelihood of giving and interest in using Kiva. Both

H5a and H5b can be confirmed.

6. H6 – Psychological Benefits


70

H6a – People who assert having higher psychological benefits from donating are more likely

to report giving philanthropically.

H6b - People who assert having higher psychological benefits from donating are more likely

to be interested in using Kiva.

A positive and statistically significant result was achieved (0.016), together with a large size

effect in relation to donation behaviour. However, the results from psychological benefits with

regards interest in giving using Kiva were somewhat unexpected. The P-value was statistically

insignificant at 0.356 and coupled with a weak negative size effect. This would suggest that

people who claim to have higher psychological benefits from donating are less likely to be

interested in using Kiva. As such, H6a can be confirmed but H6b cannot be confirmed.

7. H7 – Values

H7 – People whose values are in congruence with Kivas’ are more likely to be interested in

using Kiva.

The relationship between values and intention/interest in using Kiva is statistically insignificant

(0.449) and has a small size effect (0.054) which suggests no direct connection between the two

and therefore H7 cannot be confirmed.

8. H8 – Efficacy

H8 – People who think that Kiva is a good way to address poverty are more likely to be


Efficacy shows up with the strongest results for intention/interest in using Kiva by having a ***

p-value and the highest size effect in the results as whole at 0.449. Furthermore the standard

error and critical ratios are also very high. H8 can therefore be confirmed.

9. H9 – Intention/Interest in using Kiva

H9 – People who report giving philanthropically are more likely to be interested in using Kiva.


71

The final result involved looking at the relationship between donation behavior and

intention/interest in using Kiva. A statistically significant result can be seen (0.004) with a large

size effect – therefore H9 can be confirmed.

To summarize this section, SEM was utilised to test the relationship and causal inferences

between the variables. A good model fit was achieved using some of the absolute fit indices

showing how close the data sampled fits the model and finally, the 9 hypotheses were compared

with the results. For donation behaviour, solicitation showed the most significance and had the

largest effect. Furthermore altruism and reputation also figured strongly in the results. For

intention/interest in using Kiva, efficacy showed the strongest significance, while altruism and

reputation and donation behaviour also figured persuasively. Some of the hypotheses however

could not be confirmed. The following section will elaborate on these results.

6. Discussion

In this study, we have investigated the 8 mechanisms identified by Bekkers &

Wiepking that drive charitable giving and applied them in the context of social crowdfunding,

specifically to the case of Kiva. Based on these 8 mechanisms, we have constructed 9

hypotheses that were tested against the data sample collected from our online survey. The

results were extracted using exploratory factor analysis and then further analyzed using the

structural equation method.

This section of the paper will discuss the theories surrounding each of the 8

mechanisms, with a special focus on what Bekkers and Wiepking has postulated and comparing

this with the results that were attained in our study. Since social crowdfunding is a nascent

concept phenomenon, with very little research and literature extant to date, the hope is that our

research can provide new insights into this emerging area that could prove valuable for social

crowdfunding sites similar to Kiva, but also other organizations involved in philanthropy and

social issues.

It is also hoped that this research will add to the scant body of work addressing

social crowdfunding and philanthropy. Furthermore, we have attempted to actively test the

theoretical framework created by Bekkers and Wiepking by applying it to tangible case, that of

Kiva. (Bekkers & Wiepking, 2011).


72

Bekkers and Wiepking: Mechanisms that drive charitable giving

1. Awareness of need

One of the preconditions for philanthropy to exist is the awareness of the need

that precedes any act of giving (R. Bekkers & Wiepking, 2010). The four dimensions identified

by Bekkers & Wiepking, relating to needs are that they are both tangible and intangible, reside

within, between, and outside people, and may originate from beneficiaries, organizations and

target donors (Bekkers & Wiepking, 2011 : 929). Our focus however, lies predominantly with

the organization, and only indirectly with the beneficiaries and donors when addressing needs.

As such, we have modified Bekkers and Wiepking’s approach to awareness of need by focusing

on the awareness of the social crowdfunding sites which in our case was Kiva and the social

causes attributed to them. By increasing awareness of these social crowdfunding sites and what

the raison d'etre is behind them, that is, addressing the needs of social entrepreneurs it is

believed that people will become aware of the beneficiaries soliciting for help. We presume that

the greater the awareness of social crowdfunding sites such as Kiva and what they do, the

greater the likelihood of altruistic people starting to use it.

Our results however reveals that there is an insignificant relationship between

awareness of need and the intention to donate on social crowdfunding sites such as Kiva. Hence

H1 could not be confirmed. This does not mean that no connection exists, but it is more likely

that the effect is too small to make a significant connection between the two. A possible

explanation for this could be the argument that social crowdfunding is a new phenomenon and

that many of the survey respondents indicated that they were not familiar with social

crowdfunding sites such as Kiva. The importance of utilizing mass media to assist in raising

awareness of a need was highlighted by Bekkers & Wiepking (Bekkers & Wiepking, 2010:930)

As such, a further investigation needs to be conducted regarding how Kiva and other social

crowdfunding sites have used mass media to spread awareness of their organization and the

social causes they address on their respective sites. Solicitation is another possible factor that

could explain the low significance results and this will be discussed further below.

2. Solicitation

Bekkers & Wiepking define solicitation as “the mere act of being solicited to

donate” (Bekkers & Wiepking, 2011: 931). Furthermore, they argue that the way in which


73

donors are solicited affects the effectiveness of solicitations. Of particular interest to our

research is the channels or mediums used to solicit donations. Social crowdfunding sites have

benefitted greatly from technology. This can be seen in the platform, structure, organization,

payment systems and communication that all rely on technology as their backbone. Donations

are often solicited when profiles of the recipients are uploaded on the platform together with

their pictures and a background story regarding the recipient. Furthermore, their profiles and

stories can be shared on the donors’ social network to extend the solicitation activities even

further afield. The implication that can be derived from extant research and literature is that the

more people are solicited to donate money, the more likely they are to give (Bekkers, 2005;

Bryant, Slaughter, Kang, Hyojin, & Tax, 2003). However, one should be aware that donor

fatigue has been cited as one of the reasons for the low numbers in contributions (Diepen et al.,

2009). Further unselective soliciting leads to reduced contributions (Piersma & Jonker, 2004).

Therefore, this suggests that taking a more targeted and personal approach to solicitation could

make a difference in the level of contributions received.

Based on our results for solicitation and donation behaviour, a significant

relationship was proved and the effect size was similarly large which confirms H2a that is,

people who are regularly solicited to donate to philanthropic causes are more likely to give,

reinforcing what has been cited in extant research and literature. In fact this gave the strongest

results for donation behaviour compared to the other 8 mechanisms. However, a significant

relationship could not be confirmed for H2b with regards to solicitation and intention. This does

not suggest that there is no relationship between intention and solicitation, but the effect seems

weak and also it shows to be negative that is, the more people are regularly solicited, the less

interest they have in using Kiva. If we compare this with our results for awareness earlier, this

suggests that possibly, the lack of awareness of Kiva and other social crowdfunding sites could

be the reason for a low results finding for solicitation with regards to H2b and that Kiva should

look at their communications with the potential donors and focus on creating a more personal

and targeted approach as suggested by Lindskold et al to soliciting for donations (Lindskold,

Forte, Haake, & Schmidt, 1977)


74

3. Costs and Benefits

With regards to costs and benefits, Bekkers & Wiepking have decided to

concentrate their attentions on materials costs and benefits normally associated with donating

such as administrative cost, tax benefits etc. (R. Bekkers & Wiepking, 2010). For the purposes

of this paper, we have decided not to apply Bekkers & Wiepking’s definition for benefits which

mainly viewed from a financial costs perspective to the donor, but instead to apply this term as

in the form of psychological benefits received by the donor. Psychological benefits will be

addressed at a later point in this discussion. For the investigation into costs, this paper focuses

on the real or perceived costs of giving rather than the financial costs. Our assumption was that

if ease and time were issues that attracted or repelled from giving, then this would naturally be

reflected in people’s willingness to be interested in using Kiva.

Our research, however suggests that there is an insignificant relationship between

costs and intention to donate on Kiva, indicating that people are indifferent to issues of time

and ease of use. Therefore, we cannot confirm H3 that states people who find using Kiva easy,

are more likely to be interested in using Kiva. Perhaps there are other mechanisms at play

impacting costs such as altruism (where we have found a positive correlation to exist between

altruism and interest in Kiva). Bekkers & Wiepking have suggested the impacts of other

mechanisms, however they do not note report any impact of altruism on costs.

4. Altruism

The term altruism is hard to define strictly as it involves different components that make up the

term such as (a) an intention to help another person; (b) that the act is initiated by the helper

voluntarily; and (c) that it is performed without expectation of reward from external sources

(Bierhoff, 1987) in (Radley & Kennedy, 1995 : 686). Another definition provided by Andreoni

states “One of the central motives that potentially confound altruism is the warm-glow of giving,

that is, the utility one gets simply from the act of giving without any concern for the interests of

others.” (Andreoni 1989, 1990) in Andreoni, Harbaugh, & Vesterlund, 2007: 1). As Bekkers

& Wiepking view altruism from a predominantly economic perspective, we have elected to

focus more on the non-pecuniary angle by following what has been stated in general

philanthropic literature regarding altruism, that of the concern for the wellbeing of others and

that greater altruism leads to increased likelihood of giving or prosocial behaviour (Radley &

Kennedy, 1995b; Sargeant & Woodliffe, 2007b). These sentiments were borne in mind when


75

formulating H4 and we suggested that people with a higher level of altruism are more likely to

give philanthropically and as such, be drawn to lending through Kiva.

The results showed a positive and significant relationship with both donation behaviour (H4a)

and intention to donate (H4b), with marked effect in size in relation to both aspects, therefore

confirming our hypothesis for altruism. It is somewhat unsurprising that altruism is an important

mechanism that drives interest to give charitably. The fact that the results are significant for

both donation behaviour and intention suggests that Kiva would have a higher chance of success

in increasing donations by attracting more altruistic people to donate on their platform. What is

perhaps difficult to ascertain is how these altruistic persons would be located and identified as

well as drawn specifically to SCF sites as Kiva and not to other soliciting social organizations.

5. Reputation

Literature regarding reputation has mainly examined this area from a sociological,

psychological, and anthropological perspective and shows that philanthropic and social

behavior is motivated by social factors such as the desire for prestige, esteem, popularity, or

acceptance (Bernheim, 2013; Muehleman et al., 1976). Bekkers & Wiepking define reputation

as “...the social consequences of donations for the donor” (Bekkers & Wiepking, 2011 : 936).

Furthermore Smith & McSweeny, have identified a link between intention and donation in that

people are more likely to engage in charitable giving when they believe that people who are

significant to them would approve of this behavior (Smith & McSweeny, 2007).

The use of social media to raise funds is becoming an increasingly important strategy for

charities and non-profit organizations (McCurry, 2010). With this in mind, the ability to

potentially enhance a person’s reputation with regards to donations made online has been

increased with tools and technologies that allow people to share, recommend, and link their

actions on their social networks. In contrast to dropping donations in to a collection box, online

philanthropy affords an opportunity to report ones philanthropic acts to vast potential audiences.

Given this, it is understandable why a significant relationship and large effects were reflected

in the results for both donation behaviour and intention to donate which confirms H5a and b.

People who feel that altruism was important for their reputation were more likely to report

giving philanthropically and are more likely to be interested in using Kiva. The chance of

increasing their reputation among their peers by being seen to be altruistic, is shown to be.


76

These findings with regards to reputation could be significant for Kiva and other SCF sites. By

enhancing opportunities for donors to be seen behaving altruistically possibly through greater

ties to social media, this may in turn attract others who wish to be seen behaving altruistically.

6. Psychological Benefits

Bekkers & Wiepking define psychological benefits ascribed by donors who give charitably in

terms of the “joy of giving” and “self-image”. Joy of giving or warm glow was posited by

Andreoni as a reason why people give is “First, people simply demand more of the public good.

This motive has become known in the literature as "altruism." Second, people get some private

goods benefit from their gift per se, like a warm glow... if people "enjoy" making gifts or

bequests, then the warm-glow effects will always dominate altruism (Andreoni, 1989 : 1448-

1449). Self-image on the other hand is attributed to one’s self image as “…an altruistic,

empathic, socially responsible, agreeable, or influential person…” (Bekkers & Wiepking, 2011

: 938). For the purposes of our research, we have opted to associate psychological benefits with

feeling good, while doing good. Therefore H6a states that people who assert having higher

psychological benefits from donating are more likely to give philanthropically, and they are

also more likely to be interested in using Kiva (H6b). Our results however cannot confirm either

hypothesis because of the insignificant relationship between both donation behavior and

intention to donate. The size effects for both parts also showed some weakness, with the

relationship between psychological benefits and intention found to be in the negative. This

suggests that the people who assert having higher psychological benefits, are less likely to be


It may be suggested that the nature of SCF may lie at the source of the issue here. If people do

indeed experience positive sensations from philanthropic behavior, it may be that the prospect

of lending rather than giving would not be anticipated to produce the same level of “warm

glow” as would be achieved from giving. As such, organizations such as Kiva should give

further thought to how they promote potential beneficiaries and the social causes they support.


77

7. Values

Some commentators such as Van Lange suggests that prosocial people are more

likely to donate owing not only to their values, but their social norms and sense of altruism

(Van Lange, Bekkers, Schuyt, & Vugt, 2007b). Others have theorized that people are more

likely to help others perceived as being similar to themselves. Heider posits that its similarity

of values that is paramount Coliazzi et al. 1988; Heider 1958) in (Sargeant & Woodliffe, 2007).

A reason for this is provided by Radley & Kennedy who suggest that people who are similar

have stronger empathy with each other and this thereby strengthens intentions to donate (Radley

& Kennedy, 1995b). However, the lack of research in this area with regards to philanthropy

was duly noted in Bekkers & Wiepking which suggests that values might pose difficulties when

attempting to test empirically. In formulating our hypothesis, we focused on the concept of

similarity in values as suggested by Heider and developed H7 – People whose values are in

congruence with Kiva’s are more likely to be interested in using Kiva. When this hypothesis

was tested, the result showed an insignificant relationship between values and intention, with a

small size effect perceived. As such, this hypothesis could not be confirmed. A possible

explanation for this could be that values are difficult to define and measure as we have attested

to in the issues experienced during the development of survey items for this mechanism.A

person’s values are subjective and hence it would be harder to compare if they are in congruence

with an organization’s values. Furthermore, since Kiva’s focus specifically addresses social

entrepreneurs, it may be highly likely that this might not be in line with people’s values or

interests – Due to the novelty of social crowdfunding it is not inconceivable to assume that

people may not have acquired a moral compass or values with regards to social lending or

social entrepreneurships.

It should also be noted that values have been shown to be linked to altruism, and

since altruism has shown a significant result in our results, it can be argued that values as a

factor does not figure strongly in people’s decision to be interested in Kiva; instead it may be

more to do with being altruistic which as we have shown is often seen as interchangeable with

values.

8. Efficacy

Being efficacious could be a vital consideration that donors consider when deciding on

philanthropic giving (Callen, 1994; Jackson & Mathews, 1995; Ormstedt, 1994) The standpoint


78

we have selected lies in our interpretation of how “effective” the donations are perceived to be

at alleviating or solving social issues by philanthropic givers. An important difference here is

we are referring to the perception by donors of their contribution making a difference (or not)

to the cause they are supporting, which is in line with what Bekkers & Wiepking suggest with

efficacy (Bekkers & Wiepking, 2011).

Part of this process of being efficacious is eliciting confidence in the organization, from

the perception of the donors, that their donations will make a difference (Bekkers, 2006).

Equally therefore when organizations are seen to have misused the funds, confidence in the

organization is lost and they are likely to be perceived to be inefficacious (Duncan, 2004). For

H8 we argue that people who think Kiva is a good way to address poverty, are more likely to

lend through Kiva. The results point to a highly significant relationship between efficacy and

intention achieving a three star rating on the p-scale and an estimate of 0.449. These findings

show that efficacy is an important factor in determining interest in using Kiva. This is also

attested to on Kiva´s website where success stories of recipients are regularly updated and

statistics regarding the total amount of donations, donors, causes and recipients are clearly

visible (Kiva.org, 2013). Efficacy of the organization is also supported by the low rates for loan

defaults. Seeing positive outcomes from loans serves to further boost confidence regarding

donation efficacy.

9. Donation Behavior and Intention to Donate

In the final hypothesis (H9), a link between philanthropic giving behaviour and intention

to donate was drawn. This was done firstly by drawing upon our previous hypotheses and

secondly by bearing in mind extant research and literature such as the possible influence of

attitudes, norms perceived behavioural control, and past behaviour on intentions to donate

(Smith & McSweeny, 2007) the effect of prior behaviours, intentions and attitude variability

(P. Norman & Smith, 1995) and perceptions of givers to non-profits and the resulting impact

on donations (Sargeant, Ford, et al., 2006). Our goal was to test whether donation behaviour

predicted the likelihood that someone would lend through Kiva.

From the results, it can be discerned that a significant relationship is established

between donation behaviour and intention. People who are likely to report that they give

philanthropically, are most likely interested in sites such as Kiva.


79

The next step is to ask how these mechanisms relate to each other and to what degree.

Bekkers & Wiepking argue that it is possible for these mechanisms to exist simultaneously,

however the mix and the effect would be different as it is dependent on factors such as “…time,

place, organization and donors…” involved (Bekkers & Wiepking, 2011 : 944).

What we have found from our investigation testing these mechanisms are that these

mechanisms should not be considered alone but as whole when interpreting the results. Some

of the mechanisms proved easier to measure than others. Values and altruism were difficult to

separate and were often viewed as interchangeable and as such were deemed difficult to.

Psychological benefits proved difficult to assess accurately in terms of Kiva as any benefits

were assumptions or best guesses from respondents largely unfamiliar with social

crowdfunding and its social causes.

What has also been brought to light is the need for careful definition. What Bekkers and

Wiepking refer to as “Values” draws from research and literature examining social norms,

altruism and other similar associations. Mechanisms are often viewed from a particular

perspective for example that of the organization so greater research is required to fine tune

definitions and perspectives.

7. Conclusion

Crowdfunding is a new phenomenon that has grown tremendously in the past few years.

Technology has aided in this development through the development of tools and software that

allow organizations such as Kiva to create a platform from which to help raise funds online.

Besides providing the structure and controlling the processes that occur in running such an

organization online, the platform serves as an important convergence point where people can

meet others with similar interests and where actions can be coordinated to achieve their social

goals.

As defined earlier, the term social crowdfunding is applied here to denote the raising of

money online for social causes by using the “crowd”. For the purposes of our research we have

selected to use Kiva as an example of social crowdfunding. Our results have shown that in the

case of Kiva, efficacy, reputation, altruism and donation behaviour are highly relevant

mechanisms that impact the intention to donate on Kiva. On the other hand, solicitation and

psychological benefits show a negative relationship with a weak size effect with intention, and


80

awareness and costs and benefit point to insignificant results. Therefore it seems, based on our

results, that these 4 mechanisms efficacy, reputation, altruism and donation behaviour are

potentially the antecedents of investor interest in social crowdfunding sites such as Kiva. With

this knowledge organizations like Kiva, could galvanise their efforts in ensuring that their

processes, communications and actions reflect the 4 mechanisms as they are shown to be most

effective at stirring interest in social crowdfunding platforms.

With regards to the methods used, an online survey using SurveyMonkey was conducted with

302 data sample. Exploratory factor analysis and Structural equation model was then utilised.

The exploratory factor analysis is a useful technique to extract and analyse the large data sets

(Hair, 2009) that we received from our survey. Being able to reduce the data and group variables

that are related facilitated finding correlations between the variables but also in identifying the

8 factors that we did. The purpose of the using structural equation model was to try to achieve

a model fit with the data sample that we had. Furthermore, our main findings helped us to test

our hypotheses by checking them against various standard statistical tests such as p-value.

Overall, the eight mechanisms performed better in relation to explaining the antecedents of

interest in social crowdfunding but less so, in indicating what role mechanisms play in the

prediction of interest.

What is clear however, is that social crowdfunding is a highly under researched field and in

need of empirical investigation. Bekkers & Wiepking’s eight mechanisms also requires further

applications and testing to help draw out its potential as a tool to for investigating philanthropy.

Issues of definition as well as issues pertaining to perspectives of investigation have highlighted

this.

8. Limitations and implications for future research

Although our study involves looking at philanthropic giving in the context of

social crowdfunding, due to limitation of resources and time, we were only able select one

extant case of a social crowdfunding site as part of our empirical research. However, mentioned

below are several ways in which further research could generalize and improve upon our

research:


81

Collect data from another or more social crowdfunding site and perform a

comparison

Prepare follow-up questions for respondents

Conduct qualitative interviews with selected respondents from the survey

and staff from social crowdfunding sites

Increase the population sample for the survey

Repeat the survey with a different population sample e.g. Europe

Conduct a longitudinal study

As mentioned earlier, the lack of literature in the area of social crowdfunding

owing to its relative newness, means that there is a lot of potential areas in which to investigate.

We have attempted to study one part of it by looking at philanthropic giving and what attracts

people to donate to such sites. For our theoretical framework, we elected to use Bekkers &

Wiepking’s “8 mechanisms that drive charitable giving” as the foundation for our theory due

Bekkers & Wiepking’s well-established authority in the field of philanthropic literature and the

relative up to datedness of the work.

Based on this, our results identified 4 mechanisms that proved to be significant in

garnering interest in social crowdfunding sites. Our hope is that more research can be conducted

on these 4 mechanisms identified and tested empirically with some of the suggestions

mentioned above.


82

BIBLIOGRAPHY

Abrahams, M. F., & Bell, R. a. (1994). Encouraging Charitable Contributions: An

Examination of Three Models of Door-in-the-Face Compliance. Communication Research,

21(2), 131–153. doi:10.1177/009365094021002001

Agrawal, A., Catalini, C., & Goldfarb, A. (2011). The Geography of Crowdfunding. SSRN

Electronic Journal. Retrieved from

http://strategy.sauder.ubc.ca/schiff/seminars/The_Geography_of_Crowdfunding_2011_01_06

_acg.pdf

Aleamoni, L. M. (1973). Notes Effects of size of sample on Eigenvalues. Observed 58(2),

269–272.

Alpizar, F., Carlsson, F., & Johansson-Stenman, O. (2008). Anonymity, reciprocity, and

conformity: Evidence from voluntary contributions to a national park in Costa Rica. Journal

of Public Economics, 92(5-6), 1047–1060. doi:10.1016/j.jpubeco.2007.11.004

AMOS Fit indices. (n.d.). Retrieved from

http://www8.stat.umu.se/kursweb/vt012/staf30mom1/?download=List of AMOS fit

indices.doc

Anderson, W., Burnham, D.R.,Thompson, K.P. (2000). Null hypothesis testing: Problems,

prevalence and an alternative. Journal of Wildlife Management, 64 (4), 912-923

Andreoni, J. (2007). Charitable Giving. American journal of economics and sociology, 70,

152–86. doi:10.1111/j.1536-7150.2010.00767.x

Andreoni, James. (1989). Giving with Impure Altruism: Applications to Charity and

Ricardian Equivalence. Journal of Political Economy. doi:10.1086/261662

Andreoni, James. (1990). Impure Altruism and Donations to Public Goods: A Theory of

Warm-Glow Giving. The Economic Journal, 1000(401), 464–477.

Andreoni, James, Brown, E., & Rischall, I. (2003). Charitable Giving by Married Couples:

Who Decides and Why Does it Matter? J. Human Resources, XXXVIII(1), 111–133.

doi:10.3368/jhr.XXXVIII.1.111

Andreoni, James, Harbaugh, W. T., & Vesterlund, L. (2007). Altruism in Experiments. In

New Palgrave Dictionary of Economics, 2nd Edition, 2007 (pp. 1–8).

Armitage, C. J., & Conner, M. (2001). Efficacy of the Theory of Planned Behaviour: A meta-

analytic review. British Journal of Social Psychology, 40(4), 471–499.

doi:10.1348/014466601164939

Bateson, M., Nettle, D., & Roberts, G. (2006). Cues of being watched enhance cooperation in

a real-world setting. Biology letters, 2(3), 412–4. doi:10.1098/rsbl.2006.0509


83

Bekkers, R., & Wiepking, P. (2010a). A Literature Review of Empirical Studies of

Philanthropy: Eight Mechanisms That Drive Charitable Giving. Nonprofit and Voluntary

Sector Quarterly (Vol. 40, pp. 924–973). doi:10.1177/0899764010380927

Bekkers, R., & Wiepking, P. (2010b). A Literature Review of Empirical Studies of


Sector Quarterly (Vol. 40, pp. 924–973). Retrieved from

http://nvs.sagepub.com/cgi/doi/10.1177/0899764010380927

Bekkers, R., & Wiepking, P. (2011). A Literature Review of Empirical Studies of


Sector Quarterly (Vol. 40, pp. 924–973). doi:10.1177/0899764010380927

Bekkers, René. (2003). Trust, Accreditation, and Philanthropy in the Netherlands. Nonprofit

and Voluntary Sector Quarterly, 32(4), 596–615. doi:10.1177/0899764003258102

Bekkers, René. (2006). Keeping the Faith Origins of Confidence in Charitable Organizations

and its Consequences for Philanthropy. In NCVO/VSSN Researching the Voluntary Sector

Conference 2006 (pp. 1–23).

Bekkers, René. (2013). About Rene Bekkers. wordpress.com. Retrieved November 17, 2013,

from http://renebekkers.wordpress.com/about/

Bekkers, René, & Schuyt, T. (2008a). And who is your neighbor? Exlaining denominational

differences in charitable giving and volunteering in the Netherlands. review of religious

research, 50(1), 74–96. doi:Article

Bekkers, René, & Schuyt, T. (2008b). And Who is your neighbor? Explaining denominational

difference in charitable giving and volunteering in the Netherlands. Review of Religious

Research, 50(1), 74–96.

Bekkers, René, & Wiepking, P. (2011). A Literature Review of Empirical Studies of

Philanthropy. Nonprofit and Voluntary Sector Quarterly, 40, 924 –973.

doi:10.1177/0899764010380927

Belleflamme, P., & Lambert, T. (2010). Crowdfunding : An Industrial Organization.

Belleflamme, P., Lambert, T., & Schwienbacher, A. (2012a). Crowdfunding : Tapping the

Right Crowd ∗.

Belleflamme, P., Lambert, T., & Schwienbacher, A. (2012b). Crowdfunding : Tapping the

Right Crowd ∗. Retrieved from http://ssrn.com/abstract=1578175

Belleflamme, P., Lambert, T., & Schwienbacher, A. (2012c). Individual Crowdfunding

Practices ∗. Retrieved from http://ssrn.com/abstract=2151179


84

Bennett, R. (2003). Factors underlying the inclination to donate to particular types of charity.

International Journal of Nonprofit & Voluntary Sector Marketing, 8(1), 12.

doi:10.1002/nvsm.198

Benson, P. L., & Catt, V. L. (1978). Soliciting Charity Contributions: The Parlance of Asking

for Money1. Journal of Applied Social Psychology, 8(1), 84–95. doi:10.1111/j.1559-

1816.1978.tb00767.x

Berger, S. C. (2009). Emergence of Financial Intermediaries in Electronic Markets : The Case

of Online P2P Lending, 2(1).

Bernheim, B. D. (2013). A Theory of Conformity. Journal of Political Economy, 102(5),

841–877.

Bremner, R. H. (1988). American Philanthropy. (D. J. Boorstin, Ed.) (2nd ed.). University of

Chicago Press. Retrieved from http://books.google.com/books?hl=en&lr=&id=xRIodAUj1-

wC&pgis=1

Bryman, A. and Bell, E. (2007). Business Research Methods. Oxford University Press.

Buchheit, S., & Parsons, L. M. (2006). An experimental investigation of accounting

information’s influence on the individual giving process. Journal of Accounting and Public

Policy, 25(6), 666–686. doi:10.1016/j.jaccpubpol.2006.09.002

Buraschi, A., & Cornelli, F. (2013). The Economics of Donations and Enlighted Self-interest.

Burtch, G., Ghose, A., & Wattal, S. (2013). Cultural Differences and Geography as

dererminants of online pro-social lending. SSRN Electronic Journal, 1, 1–44.

doi:10.2139/ssrn.1692661

Callen, J. (1994). Money Donations, Volunteering and Organizational Efficiency. The Journal

of Productivity Analysis, 5,. Retrieved from

http://download.springer.com/static/pdf/360/art%3A10.1007%2FBF01073908.pdf?auth66=13

84964066_70f004a47e8c39f8a8291f7fe731a8d0&ext=.pdf

Cassar, G. (2002). The Financing of Business Start-Ups. Journal of Bu, 19, no. 2. Retrieved

from http://papers.ssrn.com/abstract=1404862

Cho, W. K. T. A. M. (2002). Tapping Motives and Dynamics: Insights From the Asian

American Case. American Politics Research, 30(4), 347–383.

Cialdini, R. B., & Ascani, K. (1976). Test of a concession procedure for inducing verbal,

behavioral, and further compliance with a request to give blood. Journal of Applied

Psychology, 61(3), 295–300.

Clark, P. B., & Wilson, J. Q. (19961). Incentive Systems: A Theory of Organizations.

Administrative Science Quarterly, 6(2), 129–166.


85

Collins, L., & Pierrakis, Y. (2012). The venture crowd: Crowdfunding Equity investment into

business. Nesta (pp. 1–36).

Comrey, A. L. (1978). Common methodological problems in factor analytic studies. Journal

of Consulting and Clinical Psychology, 46(4), 648–659. doi:10.1037//0022-006X.46.4.648

Comrey, A. L., & Lee, H. B. (1992). A first course in factor analysis (2nd ed.). Hillsdale, NJ:

Lawrence Erlbaum Associates, Publishers.

Curtis, K. R., & Ph, D. (1994). Conducting Market Research Using Primary Data, 1–10.

Diamond, W. D., & Kashyap, R. K. (1997). Extending Models of Prosocial Behavior to

Explain University Alumni Contributions1. Journal of Applied Social Psychology, 27(10),

915–928. doi:10.1111/j.1559-1816.1997.tb00278.x

Diepen, M. Van, Donkers, B., & Franses, P. H. (2009). Does Irritation Induced by Charitable

Direct Mailings Reduce Donations ? International Journal of Research in Marketing Volume,

26(3), 180–188.

Duncan, B. (2004). A theory of impact philanthropy. Journal of Public Economics, 88(9-10),

2159–2180. doi:10.1016/S0047-2727(03)00037-9

eNonProfit. (2013). eNonprofit Benchmarks 2013.

Everett, C. R. (2010). Group membership , relationship banking and loan default risk : the

case of online social lending. West Lafayette. Retrieved from

http://ssrn.com/abstract=1114428

Fehr, E., & Fischbacher, U. (2003). The nature of human altruism. Nature, 425, 785–791.

doi:10.1038/nature02043

Field, A. P. (2005). Discovering Statistics Using SPSS (2nd ed.). London, England: SAGE.

Fong, C. M. (2007). Evidence from an expriment on charity to welfare recipients: reciprocity,

altruism and the empathic responsiveness hypothesis. The Economic Journal, 117, 1008–

1024. doi:10.1111/j.1468-0297.2007.02076.x

Gajda, O., Klaes, M., Hartzell, J., Collins, L., Pereira, P. S., & Dean-johns, S. (2012). A

Framework for European Crowdfunding (pp. 1–40).

Galak, J., Small, D., & Stephen, A. T. (2011). Micro-finance Decision making: A field study

of prosocial lending. Journal of Market Research, 48,(SPL), 1–55.

doi:10.1509/jmkr.48.SPL.S130

Gallant, D. J., & Zhao, J. (2011). High School Students’ Perceptions of School Counseling

Services: Awareness, Use, and Satisfaction. Counseling Outcome Research and Evaluation,

2(1), 87–100. doi:10.1177/2150137811402671


86

Gao, H., Barbier, G., & Goolsby, R. (2011). Harnessing the Crowdsourcing Power of Social

Media for Disaster Relief. IEEE Intelligent Systems, 26, 10–14. doi:10.1109/MIS.2011.52

George, D., & Mallery, P. (2003). SPSS for Windows step by step: A simple guide and

reference. 11.0 (4th ed.). Boston: Allyn & Bacon.

Gerber, E., Hui, J., & Kuo, P. (2012). Crowdfunding: Why People are Motivated to

Participate. mech.northwestern.edu.

Gerber, E. M., Hui, J. S., & Kuo, P. (2009). Crowdfunding : Why People Are Motivated to

Post and Fund Projects on Crowdfunding Platforms. Retrieved from

http://www.juliehui.org/wp-content/uploads/2013/04/CSCW_Crowdfunding_Final.pdf

Giving USA. (n.d.). Giving USA 2012. 2012.

Goette, L., Stutzer, A., & Frey, B. M. (2010). Prosocial Motivation and Blood Donations: A

Survey of the Empirical Literature. Transfusion medicine and hemotherapy : offizielles Organ

der Deutschen Gesellschaft fur Transfusionsmedizin und Immunhamatologie, 37(3), 149–154.

doi:10.1159/000314737

GoogleScholar.com. (2013). James Andreoni. Google.scholar.com. Retrieved from

http://scholar.google.com/citations?user=FvzjvLoAAAAJ&hl=en&oi=ao

Gosling, S. D., Vazire, S., Srivastava, S., & John, O. P. (2000). Should we trust web-based

studies? A comparative analysis of six preconceptions about internet questionnaires. The

American psychologist, 59(2), 93–104. doi:10.1037/0003-066X.59.2.93

Grace, D., & Griffin, D. (2006). Exploring conspicuousness in the context of donation

behaviour. International Journal of Nonprofit and Voluntary Sector Marketing, 11(2), 147–

154. doi:10.1002/nvsm.24

hair - Multivariate Data Analysis 7th Edition.pdf. (n.d.).

Harbaugh, W. T., Mayr, U., & Burghart, D. R. (2007). Neural responses to taxation and

voluntary giving reveal motives for charitable donations. Science (New York, N.Y.), 316,

1622–1625. doi:10.1126/science.1140738

Harms, M. (2007). What Drives Motivation to Participate Financially in a Crowdfunding

Community? Vrije Universiteit.

Hemer, J. (2011). A Snapshot on Crowdfunding. Business. Retrieved from

http://www.econstor.eu/bitstream/10419/52302/1/671522264.pdf

Houston, D. J. (2004). “Walking the Walk” of Public Service Motivation: Public Employees

and Charitable Gifts of Time, Blood, and Money. Journal of Public Administration Research

and Theory, 16(1), 67–86. doi:10.1093/jopart/mui028

Indiegogo. (n.d.). Ubuntu Edge Smartphone.


87

Investopedia.com. (2013). Crowding Out Effect Definition. Retrieved from

http://www.investopedia.com/terms/c/crowdingouteffect.asp

Iyer, E. S., Kashyap, R. K., & Diamond, W. D. (2012). Charitable Giving: Even the Willing

Need to be Persuaded. Journal of Current Issues & Research in Advertising, 33(1), 115–127.

doi:10.1080/10641734.2012.676489

Jackson, N., & Mathews, R. M. (1995). Using Public Feedback to increase contributions to a

multipurpose senior center. Journal of Applied Behaviour Analysis, 4(Winter), 449–455.

Jay, E. (2001). The rise and fall? of face-to-face fundraising in the United Kingdom. New

Directions for Philanthropic Fundraising, (33), 83–94. Retrieved from

http://onlinelibrary.wiley.com/doi/10.1002/pf.3306/abstract

Kaiser, H. . (1960). The application of electronic computers to factor analysis. Educational

and Psychological Measurement, 20, 141–151.

Kaiser, H. . (1974). An index of factorial simplicity. Pyschometrika, 39, 31–36.

Karnani, A. (2007). Microfinance Misses Its Mark. Stanford Social Innovation Review.

Retrieved November 09, 2013, from

http://www.ssireview.org/articles/entry/microfinance_misses_its_mark

Kickstarter. (n.d.). Kickstarter - Pebble E-Paper Watch.

Kiva.org. (2013). Kiva.org.

Kivazip. (2013). Kivazip.

Kottasz, R. (2004). How should charitable organisations motivate young professionals to give

philanthropically? International Journal of Nonprofit and Voluntary Sector Marketing, 9(1),

9–27. doi:10.1002/nvsm.230

Lambert, T., & Schwienbacher, A. (2010). An Empirical Analysis of Crowdfunding. Analysis,

1578175, 1–23. Retrieved from

http://www.crosnerlegal.com/images/47770544_An_Empirical_Analysis_of_Crowdfunding.p

df

Lee, B. a., & Farrell, C. R. (2003). Buddy, Can You Spare A Dime?: Homelessness,

Panhandling, and the Public. Urban Affairs Review, 38(3), 299–324.

doi:10.1177/1078087402238804

Lehner, O. M. (2013). Crowdfunding Social Ventures : A Model and Research Agenda.

Routledge Venture Capital Journal, 15(3), 1–28.

Levitt, L., & Kornhaber, R. C. (1977). Stigma and Compliance: A Re-Examination. The

Journal of Social Psychology, 103(1), 13–18. doi:10.1080/00224545.1977.9713290


88

Lindahl, W. E., & Conley, A. T. (2002). Literature Review: Philanthropic Fundraising.

Nonprofit Management and Leadership, 13(1), 91–112. doi:10.1002/nml.13108

Liu, W., & Aaker, J. (2008). The Happiness of Giving: The Time‐Ask Effect. Journal of

Consumer Research, 35(3), 543–557. doi:10.1086/588699

Liu, Y., Chen, R., Chen, Y., Mei, Q., & Salib, S. (2012). “ I Loan Because ...”: Understanding

Motivations for Pro-Social Lending. In Proceedings of the fifth ACM international conference

on Web search and data mining (2012) (pp. 503–512).

Massolution.com. (2012). 2009, 2013CF - Crowdfunding Industry Report. Massolution.com.

Mathur, A. (1996). Older Adults ’ Motivations for Gift Giving to Charitable Organizations :

An Exchange Theory Perspective. Psychology & Marketing, 13(1), 107–123.

Merced, M. J. D. La. (n.d.). SurveyMonkey Raises Nearly $800 Million While Staying

Private. New York Times.

Moll, J., Krueger, F., Zahn, R., Pardini, M., de Oliveira-Souza, R., & Grafman, J. (2006).

Human fronto-mesolimbic networks guide decisions about charitable donation. Proceedings

of the National Academy of Sciences of the United States of America, 103, 15623–15628.

doi:10.1073/pnas.0604475103

Morduch, J. (1999). The Microfinance Promise. Journal of Economic Literature. Retrieved

from

http://www.nyu.edu/projects/morduch/documents/microfinance/Microfinance_Promise.pdf

Morduch, J. (2000). The Microfinance Schism. World Development, 28(4), 617–629.

doi:10.1016/S0305-750X(99)00151-5

Morgan, S. E., & Miller, J. K. (2002). Communicating about Gifts of Life: The Effect of

Knowledge, Attitudes and Altrusim on Behavior and Behavioral Intentions Regarding Organ

Donation. Journal of Applied Communication Research, 30(2), 163–178.

Muehleman, J. T., Bruker, C., & Ingram, C. M. (1976). The generosity shift. Journal of

Personality and Social Psychology, 34, 344–351.

Network for Good. (2012). Network for Good’s Digital giving Index.

Norman, G. (2010). Likert scales, levels of measurement and the “laws” of statistics.

Advances in health sciences education : theory and practice, 15, 625–632.

doi:10.1007/s10459-010-9222-y

Norman, P., & Smith, L. (1995). The theory of planned behaviour and exercise: an

investigation into the roles of prior behaviour, behavioural intentions and attitude variability.

European Journal of Social Psychology, 25, 403–415.


89

Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd Editio.). New York,

USA: McGraw- Hill.

Olivola, C. Y., & Shafir, E. (2013). The Martyrdom Effect: When Pain and Effort Increase

Prosocial Contributions. Journal of behavioral decision making, 26(1), 91–105.

doi:10.1002/bdm.767

Ormstedt, D. E. (1994). Government regulation of fundraising: A struggle for efficacy. New

Directions for Philanthropic Fundraising, 1994(3), 129–141. doi:10.1002/pf.41219940309

Payton, Robert L. Moody, M. P. (2008). Understanding Philanthropy : Its Meaning and

Mission (p. 224).

Piersma, N., & Jonker, J.-J. (2004). Determining the optimal direct mailing frequency.

European Journal of Operational Research. doi:10.1016/S0377-2217(03)00349-7

Pope, N. (2011). Crowdfunding Microstartups: It’s Time for the Securities and Exchange

Commission to Approve a Small Offering Exemption. Santa Clara Law Santa Clara Law

Digital Commons. Retrieved from

http://digitalcommons.law.scu.edu/cgi/viewcontent.cgi?article=1187&context=facpubs

Porter, M. E., & Kramer, M. R. (2002). The Competitive Advantage of Corporate

Philanthropy. Harvard Business Review, (December), RO212D.

Psychology, A. S., Defects, B., Isen, A. M., & Noonberg, A. (1979). The effect of

photographs of the handicapped on donation to charity: When a thousand words may be too

much. Journal of Applied Social Psychology, 9, 426–431. doi:10.1111/j.1559-

1816.1979.tb02716.x

Radley, A., & Kennedy, M. (1995). Charitable Giving by Individuals: A Study of Attitudes

and Practice. Human Relations, 48(6), 685–709. doi:10.1177/001872679504800605

Ribar, D. C., & Wilhelm, M. O. (2013). Altruistic and Joy of Giving Motivations in

Charitable Behavior Altruistic and Joy-of-Giving Motivations in Charitable Behavior. Journal

of Political Economy, 110(2), 425–457 The University of Chicago Press.

Rick, S. I., Cryder, C. E., & Loewenstein, G. (2008). Tightwads and Spendthrifts. Journal of

Consumer Research, 34(April), 767–779.

Rushton, J. P., Chrisjohn, R., & Fekken, C. (1981). The altruistic personality and the self-

report altruism scale. Personality and Individual Differences, 2(July 1980), 293–302.

Sargeant, A. (1999a). Charitable Giving: Towards a Model of Donor Behaviour. Journal of

Marketing Management, 15(4), 215–238. doi:10.1362/026725799784870351

Sargeant, A. (1999b). Charitable Giving: Towards a Model of Donor Behaviour. Journal of

Marketing Management, 15(4), 215–238. doi:10.1362/026725799784870351


90

Sargeant, A., Ford, J. B., & West, D. C. (2006). Perceptual determinants of nonprofit giving

behavior. Journal of Business Research, 59(2), 155–165. doi:10.1016/j.jbusres.2005.04.006

Sargeant, A., Jay, E., & Lee, S. (2006). Benchmarking Charity Performance : Returns from

Direct Marketing in Fundraising. Journal of Nonprofit and Public Sector Marketing 16(1/2):,

16(1/2), 77–94. doi:10.1300/J054v16n01

Sargeant, A., & Woodliffe, L. (2007). Gift giving : an interdisciplinary review. International

Journal of Nonprofit and Voluntary Sector Marketing, 307(August), 275–307.

doi:10.1002/nvsm.308

Satow, K. L. (1975). Social approval and helping. Journal of Experimental Social

Psychology, 11(6), 501–509. Retrieved from

http://www.sciencedirect.com/science/article/pii/0022103175900013

Schervish, P. G., & Havens, J. J. (1997). Social participation and charitable giving: A

multivariate analysis. Voluntas, 8(3), 235–260. doi:10.1007/BF02354199

Schlegelmilch, B. B., Diamantopoulos, A., & Love, A. (1997). Characteristics affecting

charitable donations: empirical evidence from Britain. Journal of Marketing Practice: Applied

Marketing Science, 3(1), 14–28. doi:10.1108/EUM0000000004318

Schwartz, S. H. (1974). Awareness of interpersonal consequences, responsibility denial, and

volunteering. Journal of Personality and Social Psychology, Vol 30(1), 57–63.

doi:10.1037/h0036644

Schweinbacher, A., & Larralde, B. (2010). Crowdfunding of Small Entrepreneurial Ventures.

In Handbook of Entrepreneurial Finance (Vol. 2010, pp. 1–23). Oxford University Press.

Retrieved from http://ssrn.com/abstract=1699183

Sharma, S. (2010). MFIs lay small-debt trap in Andhra. The Telegraph. London. Retrieved

from http://infochangeindia.org/microfinance/news/mfis-lay-small-debt-trap-in-andhra.html

Silverman, W. K., Robertson, S. J., Middlebrook, J. L., & Drabman, R. S. (1984). An

investigation of pledging behavior to a national charitable telethon. Behavior Therapy.

Retrieved from http://www.sciencedirect.com/science/article/pii/S0005789484800337

Simon, A. (1997). Television news and international earthquake relief. Journal of

Communication, 47(3), 82–93. doi:10.1111/j.1460-2466.1997.tb02718.x

Smith, Joanne .R., & McSweeny, A. (2007). Charitable Giving : The Effectiveness of a

Revised Theory of Planned Behaviour Model in Predicting Donating Intentions and

Behaviour. Journal of Community & Applied Social Psychology, 17(September 2006), 363–

386. doi:10.1002/casp.906

Smith, Joanne R, & McSweeny, A. (2007). Charitable Giving : The Effectiveness of a

Revised Theory of Planned Behaviour Model in Predicting Donating Intentions and


91

Behaviour. Journal of Community & Applied Social Psychology, 386(17), 363–386.

doi:10.1002/casp

Soetevent, A. R. (2005a). Anonymity in giving in a natural context—A field experiment in 30

churches. Journal of Public Economics, 89, 2301–2323.

Soetevent, A. R. (2005b). Anonymity in giving in a natural context—a field experiment in 30

churches. Journal of Public Economics, 89(11), 2301–2323. Retrieved from

http://www.sciencedirect.com/science/article/pii/S0047272704001756

Sorenson, O., Stuart, T. E., American, S., & May, N. (2011). Syndication Networks and the

Spatial Distribution of Venture Capital Investments 1. American Journal of Sociology, 106(6),

1546–1588.

Staub, E., & Baer, R. S. (1975). Stimulus characteristics of a sufferer and difficulty of escape

as determinants of helping., Journal of(2), 279–284. doi:10.1037/h0036645

SurveyMonkey. (n.d.-a). SurveyMonkey Target Audience.

Surveymonkey. (n.d.). Data Quality: Measuring the Quality of Online Data Sources,

SurveyMonkey Audience White Paper, 1–15.

SurveyMonkey. (n.d.-b). SurveyMonkey Target Audience Criteria.

SurveyMonkey. (n.d.-c). SurveyMonkey demographics.

SurveyMonkey Contribute, 2013. (n.d.).

SurveyMonkey.com. (n.d.). SurveyMonkey.

Twenge, J. M., Baumeister, R. F., DeWall, C. N., Ciarocco, N. J., & Bartels, J. M. (2007).

Social exclusion decreases prosocial behavior. Journal of personality and social psychology,

92(1), 56–66. doi:10.1037/0022-3514.92.1.56

Van Lange, P. a. M., Bekkers, R., Schuyt, T. N. M., & Vugt, M. Van. (2007). From Games to

Giving: Social Value Orientation Predicts Donations to Noble Causes. Basic and Applied

Social Psychology, 29(4), 375–384. doi:10.1080/01973530701665223

W.K. Kellogg Foundation. (2000). e-Philanthropy, Volunteerism, and Social Changemaking:

A New Landscape of Resources, Issues, and Opportunities.

Wasserman, T. (n.d.). KONY 2012’ Tops 100 Million Views, Becomes the Most Viral Video

in History [STUDY]. Mashable.

Webb, D. J., Brashear, T. G., & Green, C. L. (2000). Development and Validation of Scales to

Measure Attitudes Influencing Monetary Donations to Charitable Organizations. Journal of

the Academy of Marketing Science. doi:10.1177/0092070300282010


92

Wiepking, P. (2013). Welcome to my website Pamala Wiepking. The Pamala Wiepking

website. Retrieved November 17, 2013, from http://www.wiepking.com/

Wiepking, P., & Bekkers, R. (2010). Does Who Decides Really Matter? Causes and

Consequences of Personal Financial Management in the Case of Larger and Structural

Charitable Donations. VOLUNTAS: International Journal of Voluntary and Nonprofit

Organizations. doi:10.1007/s11266-010-9124-x

Wiepking, P., & Breeze, B. (2012). Feeling poor, acting stingy: the effect of money

perceptions on charitable giving. International Journal of Nonprofit and Voluntary Sector

Marketing, 17(1), 13–24. doi:10.1002/nvsm.415

Wunderink, S. R. (2000). The economics of consumers’ gifts and legacies to charitable

organisations. International Journal of Nonprofit and Voluntary Sector Marketing, 5(3), 268–

287. doi:10.1002/nvsm.119

Yinon, Yoel & Sharon, I. (1985). Similarity in Religiousness of the Solicitor, the Potential

Helper, and the Recipient as Determinants of Donating Behavior’. Journal of Applied Social

Psychology, 15, 726–734.

Yunus, M., Moingeon, B., & Lehmann-Ortega, L. (2010). Building Social Business Models:

Lessons from the Grameen Experience. Long Range Planning, 43(2-3), 308–325.

doi:10.1016/j.lrp.2009.12.005


93

APPENDIX A

Gender Total

Female 147

Male 154

Grand Total 301

D.O.B Age Total

1995 18 7

1994 19 4

1993 20 5

1992 21 2

1991 22 6

1990 23 14

1989 24 6

1988 25 2

1987 26 8

1986 27 6

1985 28 5

1984 29 9

1983 30 8

1982 31 11

1981 32 9

1980 33 6

1979 34 9

1978 35 2

1977 36 2

1976 37 6

1975 38 5

1974 39 6

1973 40 4

1972 41 6

1971 42 7

1970 43 8

1969 44 6

1968 45 8

1967 46 6

1966 47 7

1965 48 7

1964 49 7

1963 50 11

1962 51 9

1961 52 13

1960 53 5

1959 54 8

1958 55 17

1957 56 4

1956 57 2

1955 58 1


94

1954 59 6

1953 60 3

1952 61 3

1951 62 3

1950 63 3

1949 64 5

1948 65 4

Profession Respondents

Other 108

Did not answer 67

Not Applicable 29

Education 16

Healthcare 13

Manufacturing 11

Construction 9

Government/Public Sector 9 Computer Reseller

(hardware/software) 6

Retail 4

Banking/Financial 3

Media/Entertainment 3

Architecture 2

Energy/Utilities/Oil & Gas 2

Real Estate/Property 2

Advertising 2

Non-profit 2

Telecommunications 2

Transportation 2

Agriculture/Fishing 1

Information Technology/IT 1

Marketing & Sales 1

Accounting 1

Shipping/Distribution 1

Consulting 1

Hospitality/Tourism 1

Internet 1

Pharmaceuticals 1

Exploring the 8 Mechanisms that Drive Charitable Giving

Documents

Transcript of Exploring the 8 Mechanisms that Drive Charitable Giving