The Effectiveness of On-line Networking For Non-Profits

The Effectiveness of
On-line Networking For
Non-Profits
(What Can Be Gained?)
By Steve Feder
April 30th, 2009
Haverford College Senior Thesis
Abstract:
The primary focus of this study is exploring whether or not to recommend on-line
networking as an effective marketing tool for non-profits. Three key marketing goals are
identified: donations, volunteers, and new connections. A survey containing questions
involving these three goals was sent out to non-profits located on Charity Navigator and
Facebook. 254 non-profits responded to the survey. Analysis of the data indicates that
currently, the majority of charities using on-line networking are attracting many new
connections, but are not receiving substantial numbers of donations or volunteers.
Table of Contents
Introduction: 1-2
Literature Review: 3-28
Implications of Literature
Review: 28-30
Data Description: 30-34
Procedure: 34-37
Graphical Analysis: 37-46
Conclusion: 47-49
Bibliography : 50
Data Appendix: 51-55
Graphs and Figures: 55-66
1
Introduction:
This thesis is a study of whether or not on-line social networking is effective for
non-profits. Non-profits can market themselves on sites such as Facebook, MySpace,
Linked In, etc. They can attract donations and volunteers through these sites as well as
advertise for events. Working as an intern for Philanthropy Action, an on-line newspaper
discussing current issues in charitable giving, I sent out a survey to randomly selected
non-profits from two different sources. Using a list from Guidestar (a non-profit search
engine), I sent the survey to all United States non-profits with revenues ranging from 1
million to 5 million dollars. I also sent the survey to a group of randomly selected nonprofits that advertise on Facebook with no specific revenue range. The survey contained
questions concerning how long non-profits have participated in on-line networking, how
much money they have raised, how the results of on-line networking have changed over
time, and other related issues. I received 256 responses from the non-profits I surveyed,
72 from Facebook and 184 from Guidestar.
The goal of my thesis is to determine whether or not I should recommend on-line
social networking as a beneficial marketing method for non-profits. To accomplish this
goal, I have investigated whether non-profits can effectively use on-line social
networking tools to gain donations, volunteers, and new connections with the public. A
new connection is defined as a member of a charity’s on-line social networking group
who had no previous connection to the charity. I measure new connections as the
percentage of people who join a non-profit’s on-line networking group that had no
previous connection to the charity. I recognize that donations, volunteers, and new
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connections are not the only ways to use on-line networking. However, they are the most
tangible and appear to offer the most appropriate way to measure effectiveness.
My preliminary analysis focuses on how much donations, volunteers, and new
connections non-profits have gained from their on-line networking activities. The
majority of charities gained only very small numbers of donations and volunteers. There
were a wide range of responses concerning the ability to generate new connections. Some
charities have generated a large proportion of new connections, while others have not.
My secondary analysis focused on the different factors that influence the success
at attracting donations, volunteers, and new connections. I asked each non-profit to
answer such questions as how much time it spends constructing social networking
profiles, when it began on-line networking, and which personnel (executives, volunteers,
etc.) are doing the work. My study found that charities that began on-line networking in
2005 or earlier are considerably more successful attracting donations and new
connections than charities that began on-line networking campaigns between 2006 and
2008. Furthermore, charities that update their on-line networking profiles at least an hour
a week generate substantially more donations and volunteers than charities than spend
less than an hour. The limited sample size of my data set prevented me from reaching
stronger conclusions and I hope my study is just the beginning of the exploration of the
benefits and costs of on-line networking for non-profits.
3
Literature Review:
Because on-line social networking has only recently become a popular tool for
non-profits, the literature that addresses its effectiveness is very limited. Therefore, to
guide my study, I have reviewed articles concerning a range of different subjects
including the traditional marketing practices of non-profits, how non-profits have used
the internet as a whole, and politicians’ participation in on-line social networking.
To
provide some background, I begin my literature review analyzing articles on the
economic state of non-profits and common methods non-profits use to attract volunteers
and donors. It is important to discuss non-profits’ economic state because the expected
funds available to organizations strongly impacts their ability to pursue and support
marketing ventures on-line. Furthermore, analyzing traditional marketing methods is
useful because it highlights the areas where non-profits have room for growth and
improvement.
The second part of my literature review focuses on articles that discuss how nonprofits have structured their operations on the internet, which contain limited but
insightful data on non-profits’ on-line social networking efforts. The overall evaluation
of internet marketing is helpful because it demonstrates the potential of on-line
advertising and publicity campaigns and also shows the gaps in communication between
donors, volunteers, and charities that social networking may be able to fill.
I conclude the literature review by discussing articles on how politicians have
employed social networking sites to gain publicity, popular support, donations, and votes.
The political arena provides useful information because candidates have been
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participating in on-line social networking for much longer than non-profit organizations.
Many studies evaluate whether or not politicians have used Facebook and MySpace
effectively. I analyze the results of candidates’ on-line efforts to see how successful they
have been and see if their success can be applied to non-profits.
In August 2004, a report was published on employment and earnings trends for
non-profits from 1990 to 2004 (Dwiggens, Spitzberg, and Roesch, 2004). The report is
particularly applicable because it discusses how non-profits fared in the recession of
2001, which can be used to predict how non-profits will be affected by the current
recession. The data for the study came from the Bureau of Labor Statistics (BLS)
Current Employment Statistics payroll survey. The survey is sent out monthly and
comprises approximately 400,000 public and private for-profit and non-profit businesses
throughout the United States. The authors used the “Membership associations and
organizations section” of the survey which was designed to report earnings, employment,
and other data for various types of non-profits.
Traditionally, non-profits have maintained their performance despite economic
downturns. The authors of the study even go so far as to say that non-profits are often
asked to support the nation during recessions, and make up for job losses and decreasing
opportunities in the government and private sectors. However, non-profits suffered
noticeably as a result of the aftermath of September 11th. From 1990 to 2004, the average
employment growth rate for non-profits was 2.4 percent. This was noticeably higher than
the total employment growth rate of 1.4 percent. However, from 2002 to 2004, there was
a large drop in the employment growth rate in the non-profit sector from a high of 6
percent in 2002 to .5 percent in July 2004.
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The employment growth rate was still positive, but the earnings and hours worked
by non-profit employees declined. From 1990 to 2002, the hours worked by non-profit
employees remained fairly constant in the range of 30.9 to 31.7 in various years.
However, after 2002, the average weekly hours worked declined by 4 percent, dropping
below 30 for the first time in over a decade. The weekly earnings of non-profit workers
also decreased significantly. From 2003 to 2004, weekly earnings decreased by 5.2
percent after accounting for inflation. Reductions in earnings can be caused by decreases
in hours worked and by the lowering of hourly wages. As stated earlier, workers’ hours
were reduced from 2002 to 2004. However, workers also suffered decreases in their
hourly wages. From 2003 to 2004, real hourly wages fell by 3.9 percent. State by state
analysis showed similar patterns to the national data.
The authors provide two hypotheses for the reduction in wages and hours worked
by non-profit employees. The good will resulting from 9/11 caused greater demand for
non-profit jobs, allowing non-profits to decrease wages. Furthermore, the U.S. economy
as a whole has been moving towards more part-time work and the non-profit sector could
have been affected by this trend. However, neither of these hypotheses explains the drop
in the employment growth rate. Non-profits have had to deal with an increase in costs
due to rising health insurance premiums and the fact that their services are in greater
demand because of the suffering economy. The increased costs are most likely the reason
for the decreased employment growth rate. Not only do increased costs reduce the funds
non-profits have available to pay their existing employees, they also limit the available
income to hire new ones (Dwiggens et all., 2004).
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Yet, regardless of the reasons for non-profits’ struggles after 9/11, the conclusions
of the study are clear. Unlike in past recessions, 9/11 caused tangible negative
consequences for non-profits. Whether the current recession will have a similar impact is
unclear. However, one can gather from this study that it would be short-sighted to assume
that non-profits will not be harmed by present economic conditions.
Despite the potential of more limited funding for non-profits, there are still ways
they can improve results without increasing income. The Volunteer Management
Capacity Study attempted to identify how many volunteers charities need to function
efficiently and the best methods to recruit these volunteers (Urban Institute, 2004). The
study looked at a representative sample of U.S. charities and religious institutions. 2,993
charities were randomly selected from the 214,995 charities who filed a 990 form with
the IRS. 1,003 religious congregations from varying faiths and denominations were
randomly selected out of the 382,231 provided by the American Church List. Each of the
organizations selected were called multiple times and asked to answer a series of
questions. The response for the survey was 69 percent.
The study found that four out of every five charities use volunteers in some
fashion. Furthermore, nine out of ten charities reported that they have the capacity to take
on more volunteers. The median of the sample could afford to take on twenty new
volunteers. The charities were asked to describe the various problems they encounter in
their volunteer programs. The results of the survey led the authors to identify five
common problems charities face. Sixty percent of charities stated that recruiting
volunteers during the workday and lack of adequate funds to support volunteers were
problems. Sixty-seven percent stated recruiting a sufficient number of volunteers was an
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issue. Sixty-two percent stated that recruiting volunteers with the right skills was
problematic, and 55 percent stated that a lack of paid staff to train and supervise
volunteers was a concern. Lack of funding is an issue that goes beyond the efficiency of
volunteer recruitment programs. However, the study also notes that some of the problems
facing volunteer programs can be improved without increases in income.
The survey identified nine practices from previous literature that produce efficient
and successful volunteer programs. Though on-line social networking is not related to all
of the practices, it can be used as a tool to promote many of them. The survey asked the
charities to describe to what degree they have implemented the recommended practices.
Only 35 percent of the charities stated that they make a significant effort to recognize the
contributions of volunteers. Thirty percent stated that they have methods to measure the
impact and results of volunteering. Finally, only 44 percent of charities stated they had
detailed written policies and job descriptions for volunteer involvement. On-line
networking could potentially help solve these issues at low cost to charities. Facebook
allows charities to post documents and announcements as well as directly contact specific
groups of advocates. Non-profits could post job descriptions on social networking
websites, leading them to attract more qualified and knowledgeable volunteers. They
could also recognize volunteers’ accomplishments through group or individual messages
or by announcing their gratitude for volunteer efforts through the bulletin board available
on the charities’ networking homepage.
Another way on-line social networking could help solve some of charities’
marketing issues is by providing background on potential volunteers. Eighty-two percent
of the non-profits surveyed stated that they needed more information about the people in
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their community who want to volunteer. Thirty-nine percent described the need for more
information as great. The need for more information is a problem that the internet and
specifically on-line social networking could address. Facebook, MySpace, and other
social networking services offer inexpensive (even free) ways to gain information on
which people are interested in volunteering, what causes and social problems they want
to help solve, and some background information about them.
The authors of this study do not mention the internet as a potential way for nonprofits to increase the number of volunteers. They highlight the need for the
establishment of volunteer outreach centers and the need to give communities greater
access to information about the charities. They also mention the importance of training
staff to work efficiently with volunteers to produce the best possible results. Yet, many of
their recommendations require greater income, and given the state of our economy and
the previous research, we should not anticipate gains in funding. Due to the budget
constraints of U.S charities, the internet and social networking could be the easiest way to
act on the study’s findings by providing low cost opportunities to gain more information
and effectively communicate with volunteers. (Urban Institute, 2004)
On-line social networking appears to have great potential to improve the
relationship between volunteers and charities. However, charities’ fund-raising problems
do not seem to lend themselves to solutions through social-networking campaigns. In
August 2002, a study was published describing how U.S. non-profits conduct their fundraising activities (Hagar, Rooney, and Pollack, 2002). The authors created a survey that
they sent out to randomly selected non-profits obtained from the 2000 Core File, which is
a list of all the non-profits in the United States created by the National Center for
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Charitable Statistics. The authors rejected all non-profits that had less than $100,000 in
gross receipts. The authors sent out their survey to 3,082 non-profits and received 1,540
responses.
The authors’ survey generated some surprising results. Sixty-three percent of nonprofits responded that they had no full-time fund-raising staff. Only 14 percent reported
that they had more than one full-time fund-raising staffer. Non-profits that received over
one million dollars a year in grants and contributions were more likely to employ fundraising staff. However, even for that specific group, only 43 percent employed more than
one full-time staff person. The survey also asked whether non-profits employed any fundraising staff part- time or full-time. The percentage of charities employing at least one
fund-raising staff member increased slightly.
One possible explanation why non-profits seem to have limited fund-raising staff
is that they hire consultants to organize fund-raising activities. Yet, the data indicates that
92 percent of non-profits do not hire professional fundraisers. If fund-raising staffs are
uncommon, someone in the non-profit’s organization must be doing the fund-raising.
Executive directors seem to be bearing a significant part of the fund-raising load. Survey
respondents were asked to rank executive directors, volunteers, development staff, board
members, and others on a 1 to 5 scale, 5 being the most involved in fund-raising
activities. Survey respondents on average gave executive directors a 4. This was
significantly higher than the average rating of any other group.
However, fund-raising is not these executives’ primary duty. Only 3 percent of
respondents stated that executives spend more than half their time fund-raising. Though
volunteers, board members, and development staff do not spend large amounts of time on
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fund-raising, they each participate in the process. For example, three out of four
organizations surveyed used volunteers in some capacity to do fund-raising. The diverse
groups of people participating in fund-raising demonstrate that people with limited
experience and credentials in the area are playing substantial roles. The study did not
measure the success different groups have had in their fund-raising efforts. Yet, the fact
that people who are predominately assigned to other tasks are involved in the fundraising process could indicate a lack of efficiency (Hagar et al., 2002).
The authors formed a number of important conclusions from their data. Most
non-profits do not have extensive fund-raising staff. The authors separated non-profits
into two groups: those who do not rely on grants and donations to fund their operations
and those who need yearly contributions to continue their work. There is a significant
portion of non-profits who employ no personnel for fund-raising. These organizations
most likely rely on endowments and do not need fund-raising staff. The findings of the
study are more easily directed at the non-profits who need fund-raising to generate
income. Relying on executives, volunteers, and other personnel who do not necessarily
have experience in fund-raising is a difficult way to achieve success. Hiring fund-raising
staff or pursuing the services of professional firms may be costly, but it still might lead to
better results.
The most recent report on non-profits’ marketing activities was published in May
2008 (The American Marketing Association, 2008). The study assessed the methods nonprofits use to market themselves and the problems they encounter. The organization
surveyed 1,012 non-profits from a range of fields including health care, social services,
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universities, professional, and arts organizations. All the participants were marketing
executives and 64 percent were senior marketing directors.
When asked to rate their highest marketing priorities, small, medium-sized, and
large organizations were all most likely to list building awareness as their top priority.
Still, only 33 percent of the total non-profits surveyed gave this response, indicating a
large variety of answers. Generating revenue was the second most frequent response for
non-profits’ top marketing priority and was listed by about 20 percent of those surveyed.
Member acquisition and retention as well as customer engagement were the third and
fourth most likely responses. When distinguishing by organization type, the patterns
remained the same except in the professional association category. Forty-three percent of
professional association executives described member acquisition and retention as the
most important issue. Only 12 percent listed building awareness as having top marketing
priority.
The survey also asked participants to rate on a 1 to 5 scale how effective different
methods were at building awareness, revenue generation, member acquisition,
positioning and branding, and customer engagement. To build awareness and visibility,
public and community relations received the highest average rating. Not surprisingly,
development and fund-raising were considered the most effective at revenue generation.
Participants gave only average ratings to website and digital media. According to the
marketing executives, websites and digital media are most effective at positioning and
branding and least effective at revenue generation. This finding was very similar to my
results.
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The survey not only investigated marketing methods, but also gathered data on
non-profits’ marketing challenges and problems. Non-profits were asked to list their three
greatest marketing challenges today and five years from now. Forty-four percent listed
revenue generation as one of their three greatest challenges today and 41 percent stated
that it will be one of their three greatest challenges five years from now. Interestingly,
building awareness and visibility was the most common listed challenge today (49
percent), but only 20 percent of organizations predicted that it would be one of their three
greatest challenges five years from now. The reason for this disparity could be growth
through the internet. Non-profits have just begun to explore the benefits of on-line
marketing and progress through this medium could improve their awareness and visibility
in the future. Finally, 11 percent of non-profits described entering new markets and
channels as one of the three greatest challenges today, but the percentage increased to 28
percent when non-profits evaluated challenges five years from now. The change could be
explained by the fact that as the younger generation ages, non-profits will be forced to
use on-line tools like social networking to recruit volunteers and donors.
Problems associated with marketing are very much related to budgeting.
Regardless of the methods, marketing efforts will not be successful unless non-profits
have enough money to spend. On average, a non-profit's marketing budget was 2 percent
to 3 percent of the non-profit's total budget. Forty-four percent of non-profits spend less
than $50,000 on marketing and 56 percent spend less than $100,000. Similar to the
findings of previous research (Hagar et al., 2002), not only do most non-profits spend
very little on marketing, they also employ limited marketing staffs. Forty-two percent of
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non-profits have less than two full-time marketing staff members and less than 20 percent
have staffs with four or more people.
The American Marketing Association’s report can help non-profits to better
structure their marketing efforts. Non-profits do not have a lot of money to spend on
marketing endeavors. However, many rely on their marketing departments to perform
important functions. Forty-five percent of non-profits stated that their marketing
department was solely responsible for building awareness. Due to the importance of
marketing to building awareness and the limited funds available, the internet and social
networking could be critical tools for marketing personnel of non-profit organizations.
Social networking has had tangible success building name recognition and awareness for
charities. Over 20 million people have joined Facebook groups sponsored by charities.
On-line social networking is not currently an effective fund-raising tool, but raising
money is not the primary role of most marketing staffs. Forty-one percent of non-profits
stated that fund-raising was not a part of marketing department duties and only 15 percent
stated that the marketing department was solely responsible for raising donations. The
American Marketing Association’s findings indicate that despite budget constraints, there
is substantial room for improvement in marketing operations and on-line social
networking could be one way to achieve growth. (American Marketing Association,
2008)
The internet provides great potential to improve donor and volunteer recruitment
for non-profits. The Online Marketing Nonprofit Benchmark Index Study analyzed the
prevalence and effectiveness of on-line marketing for various groups of non-profits
(Bhagat et al., 2007). The survey included thirty non-profit organizations whose activities
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were monitored from July 1, 2005 to June 30, 2006. The participants were divided into
three different sizes based on the non-profits’ amount of revenue. Charities were labeled
small if they had less than $5 million dollars of revenue, medium if they had between $5
and $20 million dollars of revenue, and large if they had greater than $20 million dollars
of revenue. The charities were also divided into six categories based on subject matter:
health, animal welfare, environmental, Christian, public affairs, and public broadcasting.
The study asked participants to disclose a range of information including the
organizations’ operational budgets, monthly website traffic, registration rates, and on-line
donations.
The authors generated notable findings from their data. The total amount of
money raised on-line by non-profits grew substantially from 2005 to 2006. The median
donation growth rate for non-profits was 27 percent and the median total number of
donations was $362,485. The amount of money raised was directly related to the number
of e-mail addresses non-profits had access to. Organizations with more than 100,000 email addresses raised two or three times the donations of those with smaller files.
Motivating on-line visitors of organizations’ homepages to register e-mail
addresses and other contact information appears to be highly related to fostering lasting
relationships and attracting donors and volunteers. Not surprisingly, the greater the
amount of web traffic (the number of people who visit your homepage), the more e-mail
addresses an organization receives. Finally, the authors concluded that organizations with
mid-size budgets are capable of raising significant donations. Although those with large
budgets still raise the most money, the growth rate of on-line donations for mid-size nonprofits is greater than the growth rate of donations for large non-profits. Mid-size non-
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profits experienced a growth rate of 45 percent from 2005 to 2006, compared to a 20
percent growth rate for large non-profits, and an 11 percent growth rate for small nonprofits (Bhagat et al., 2007).
The Online Benchmark study evaluated whether various groups of non-profits
have been able to use the internet to pursue donations, on-line traffic, and direct contact
with the public. A more recent study investigated non-profits’ correspondence with a
specific group of individuals (Convio Inc., 2008). The study analyzes the interactions of
“wired donors” with non-profits. 23 different non-profit organizations from a variety of
sectors participated in the study. These non-profits were asked to provide lists of all
donors who contributed at least $1,000 on-line over the past eighteen months and who
had given the non-profits access to their e-mail addresses. The non-profits provided
approximately 39,000 donors’ names and all of the donors were asked through e-mail to
complete an on-line survey. 3,443 people responded to the survey and the data was
weighted so as not to over-represent any particular charity. The poor response rate raises
concern regarding the reliability of their results.
Most of the study’s participants had high incomes, gave large and consistent
donations to charity, and were comfortable using the internet. More than 50 percent of
respondents made over $100,000 per year. The average yearly contribution for the
respondents was $10,896 and the median yearly contribution was $4,500. Finally, the
wired wealthy spend on average 18 hours on-line per week.
The most obvious conclusion of the findings is the “wired wealthy” are not afraid
to give money on-line. Eighty percent of the wired wealthy stated that they have donated
money on-line. Fifty-one percent stated that on-line giving was their preferred method to
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contribute to charity and only 30 percent of the participants preferred to contribute
through the mail. What might be even more important than the slight majority of people
who currently prefer giving on-line is that 46 percent of the participants stated they are
likely to make more donations on-line in the future compared to the 2 percent who stated
that they will decrease their on-line donations.
The reasons people preferred to give on-line were multi-faceted. Seventy-two
percent of respondents agreed with the statement “On-line giving is more efficient and
helps charities reduce their administrative expenses.” Seventy percent agreed that “Online giving lets you make a gift immediately when you are thinking about it, which
otherwise you might forget,” and 68 percent agreed that “On-line giving lets charities
respond more quickly in the event of a crisis or emergency.” What is notable about these
responses is that the donors seem very concerned about how on-line activities affect the
charities’ operations and the success of their missions. (Convio Inc., 2008)
Participants agree that on-line contributions have clear benefits for charities.
However, many express skepticism over the security of the on-line giving process. The
greatest concern of participants seemed to be that charities would use their e-mail
addresses in an undesired manner. Fifty-five percent were worried the charity would
send them too much e-mail. 54 percent were concerned that “e-mail solicitations might be
fraudulent,” (Convio Inc., 2008) and 51 percent were worried that the charity would share
their e-mail and contact information with others without their permission. What is notable
about these concerns is that for the most part, they are easy to overcome. Charities can
regulate how much e-mail their donors receive and they can gain donors’ trust by
thoroughly describing their operations and methods and keeping donors informed of the
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results and success of their contributions. Interestingly, only 45 percent stated they were
satisfied with off-line donation procedures. This significant percentage reveals that many
people are looking for a better source of interaction with their charities and on-line
services could fulfill that need.
The study only briefly mentions the wired wealthy’s use of on-line social
networking tools. The limited data indicates that on-line networking is not a strong source
for marketing campaigns aimed at this sub-group of the population. YouTube is the only
social networking tool that is used by a majority (51 percent) of the wired wealthy.
MySpace, Facebook, Flicker, and LinkedIn are much less common. Furthermore, out of
those who do use MySpace and Facebook, a significantly smaller percentage check their
profiles frequently. Of the 14 percent who are on MySpace and the 9 percent who are on
Facebook, 15 percent of MySpace users and 33 percent of Facebook users check
frequently.
The study also asked participants to describe how willing they would be to
interact with their charity through social networking tools. Thirty-four percent of
respondents said they would view an on-line video posted by a charity. Furthermore, 31
percent said they would forward a message from a charity to a friend or colleague.
However, only 9 percent said they would join a Facebook or MySpace group related to a
charity or cause. Moreover, non-profits seem to be having difficulty attracting the 9
percent who are willing to join Facebook and MySpace groups. Only 3 percent of survey
participants stated that they joined a MySpace or Facebook non-profit group. These
small percentages indicate that it will be difficult to connect to the wired wealthy through
social networking tools. YouTube and on-line videos appear to be the most popular
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source to gain the attention of the wired wealthy. However, YouTube is not an effective
tool to recruit and organize supporters of charities because it is restricted to video
viewings. Facebook and MySpace offer far greater opportunities to connect with and
mobilize users, but the wired wealthy seem reluctant to use these sources to interact with
non-profits.
Clearly, the majority of the wired wealthy are not using social networking to
connect with non-profits. Yet, homepage websites of charities do not appear to be a
commonly used tool either. Only 7 percent of the wired wealthy donors visited their
charities’ websites at least once a month and 61 percent visited homepages once or twice
a year. Most of the wired wealthy donors were unhappy with the charities’ homepage
web-sites. Less than 10 percent of participants strongly agreed with the statement that
most charities’ websites are well designed and only 28 percent strongly agreed that
charities’ websites make it easy to donate. The study does not explicitly ask how the
wired wealthy gain information about non-profits. However, an interesting note is that it
did discover that slightly less than 50 percent of participants used Charity Navigator (an
independent non-profit research site) to investigate charities’ financial information.
The authors reach a number of interesting conclusions based on the study’s
findings. The majority of the wired wealthy expressed concern about the way charities
use their e-mail addresses and the way they structure their websites. Making websites
more inspiring and easy to use for donors is very important. Regarding e-mail addresses,
providing donors with control over the content and frequency of the messages they
receive from charities is essential. Different donors desire different levels of contact.
Catering to the needs of individuals is ideal to achieving donor satisfaction. However,
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since most charities do not have the resources to cater to individuals, dividing donors into
groups by desired level of interaction could also be effective. The conclusion directly
related to my study is that social networking sites do not appear to be an effective way to
interact with the wired wealthy. It is a common perception that social networking sites
target a younger audience and this study reinforces the opinion that those over the age of
30 are not frequent users of this method of communication. (Convio Inc., 2008)
Politics provides a more detailed look at how effective on-line social networking
can be at achieving tangible results. Non-profits do not appear to have mastered the
internet, let alone social networking sites. On the other hand, politicians seem to have
more experience and expertise with internet campaigning and have successfully used the
internet to improve their chances of winning elections. A recent article published by the
Pew Research Center assessed the internet’s impact on the 2008 election (Rainie and
Smith, 2008). The data in the report is based on telephone interviews performed between
April 8 and May 11, 2008 by Princeton Survey Research Associates International.
Approximately 23,000 phone numbers were dialed, but only 2,251 complete responses
were given. At least ten attempts were made to call each household. Interviewers asked
to speak to the youngest male over eighteen years of age in the household.
The study found that the internet had a noticeable effect on the election and
political activity on the internet increased substantially from the 2004 to the 2008
presidential election.
In the spring of 2008, 40 percent of Americans used the internet to
receive news about the presidential campaign, compared to 31 percent in the spring of
2004. The percentage of all adults who donated on-line to any candidate increased from 2
percent in the fall of 2004 to 8 percent in the spring of 2008. Finally, out of all internet
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users, 23 percent in 2008 looked for campaign information on a typical day compared to
13 percent in 2004.
It appears that the primary way information was obtained about the campaign on
the internet was through e-mail. Out of all the users of e-mail, 35 percent received emails from presidential campaigns at least once a week and 14 percent say they received
such e-mails on a daily basis. However, though e-mail may have been the primary source
of communication, social networking sites also played a prominent role. Thirty-three
percent of all internet users have a social networking profile. Furthermore, 40 percent of
these users have become “engaged in political activity of some kind” through the site.
The percentages are much higher for younger age groups. Sixty-six percent of people
under the age of thirty have a social networking profile and half of these younger users
“get or share information about the candidates and the campaign.” The authors make very
clear that the political activities on these social networking sites are not simply
befriending candidates. Twenty-nine percent of social networking users found out about
friends’ political viewpoints and interests. Twenty-two percent obtained information
about candidates or campaigns. Ten percent recruited friends to support candidates, and 9
percent started or joined a political group. (Rainie and Smith, 2008)
Smith’s study does not address in detail the effect of social networking on the
outcome of political elections. In 2007, The American Political Science Association
published a study on the effect of Facebook political activity on the 2006 mid-term
elections (Williams and Gulati, 2007). The study accomplishes three important goals. It
describes the differences between political social networking campaigns and candidates’
websites. It illustrates how candidates have used on-line social networking as an election
21
tool, and it also analyzes the results of candidates’ on-line networking efforts.
Candidates have direct control of the messages and ads they show on their homepage
websites. Unlike homepage websites, candidates cannot always influence the content on
social networking sites. Social networking sites maintain a certain degree of power over
subject matter. Sometimes, the sites will accommodate candidates’ needs. For example,
You-Tube took off line a controversial video of McCain during a campaign stop. Yet, for
the most part, social networking sites’ main goal is to satisfy its users. Users can
comment and react to almost all of a political candidate’s activities, making it difficult for
a candidate to convey a unified message or stance. Moreover, social networking sites are
more willing to appease users’ wishes than candidates’ desires. When liberal activists
complained about the removal of the McCain video, YouTube placed it back on-line.
The study also gives the background of the progress of political on-line social
networking. Before 2006, there were few effective ways for candidates to employ social
networking. Candidates were not allowed to operate their own profiles. The only way
they could reach users was by motivating constituents to establish profiles and groups in
support of their candidacy. Furthermore, even when sites began to allow candidates to
create their own profile, they were limited to interacting with 5,000 friends. In 2006,
Facebook began to accommodate politicians’ needs. Facebook’s Election Pulse campaign
allowed candidates to create their own profiles, post photographs, messages, and videos
on these profiles, and provide access to all Facebook users. Facebook’s Election Pulse
campaign attracted the attention of many politicians. Thirty-three percent of U.S. Senate
candidates and 10 percent of U.S. House candidates created Facebook profiles compared
22
to the 21 percent of U.S. Senate candidates and 2.7 percent of U.S. House candidates who
created MySpace profiles.
Politicians clearly embraced Facebook’s new political measures. However, the
authors wanted to explore whether or not Facebook had a measurable effect on election
results. The authors constructed two different sets of regressions. The first regression
examined the effect both the candidate and the opponent’s Facebook activity had on the
candidates’ voting share. The authors created their own variable, “Facebook activity” by
giving a 1 to candidates who updated their Facebook profile and a 0 to those who did not.
The second regression analyzed the effect the natural log of the number of Facebook
supporters registered for a candidate and the natural log of the number of Facebook
supporters for the opponent had on their final voting shares. In both sets, different
regressions were composed for incumbent races and open elections. The control variables
included for incumbent races were party, incumbent’s voting record, the quality of the
challenger, whether or not the incumbent had been involved in a scandal, and a variety of
other factors. The control variables included for open seat elections were district
ideology, financial advantage, and relative experience, among others. The authors
focused on U.S. House races rather than U.S. Senate races because there were only 29
incumbent Senators running for re-election and 8 open seat races. They chose not to
combine the Senate data with the House data because past research indicates that
different models must be used for each.
The authors’ initial multivariable regression analysis showed that incumbents that
updated Facebook profiles did not perform any better than incumbents who did not.
However, in secondary analysis, when substituting the natural log of Facebook
23
incumbent supporters and challenger supporters, the results indicate that a 1 percent
increase in the number of Facebook supporters for incumbents increases their final vote
percentage by .011 percent and a 1 percent increase in the number of Facebook
supporters for challengers reduces incumbents winning percentage by .015 percent.
These changes do not appear to be very large, but stated in a different context, the
numbers prove to be important. An incumbent who increases his Facebook supporters by
100 percent would be predicted to gain a 1.1 percent greater share of votes than if he did
not increase his Facebook supporters. Furthermore, a challenger who increased his
Facebook supporters by 100 percent would be estimated to reduce the incumbent’s share
of victory by 1.5 percent. These statistics indicate that in a close election, increasing the
number of Facebook supporters could conceivably change the outcome. One important
factor to note is that there are diminishing returns to increasing supporters. For example,
if an incumbent increases his Facebook supporters from 10,000 to 20,000, he will be
predicted to gain an additional 1.1 percent of the vote share. However, it will take an
increase of 20,000 more supporters to gain an additional 1.1 percent.
Williams and Gulati’s study appears to demonstrate that on-line networking
efforts can lead to tangible results. However, the authors note that it is unclear whether or
not Facebook activities actually caused changes in voting patterns or whether increases in
the number of Facebook friends simply demonstrated that candidates were gaining more
popularity outside of Facebook. One definite conclusion that can be reached from the
study is that on-line social networking sites are not all the same. Facebook was clearly
more useful to politicians than MySpace because Facebook catered to the needs of
politicians through its Election Pulse campaign. This fact suggests that for my study, it
24
will be important to differentiate between on-line networking sites and evaluate whether
each separate site is an effective marketing tool for non-profits. (Gulati and Williams,
2007)
In August 2008, The Social Science Computer Review published a study on the
effects of MySpace and YouTube on political elections (Gueorguieva, 2008). The author
makes a number of convincing points concerning the importance of on-line social
networking. MySpace, YouTube, and other social networks attract a very large audience.
MySpace, created in 2003, has greater than 150 million profiles. YouTube contains more
than 100 million videos. Furthermore, both MySpace and YouTube have had enormous
growth. MySpace grew by 367 percent from 2005 to 2006 and each day approximately
65,000 new YouTube videos were uploaded. The large number of users and expansion of
social networking sites make them ideal for influencing the public to vote for a candidate
and attracting volunteers and donors for political campaigns.
One concern many people have had involving marketing and publicity on social
networking sites is that the users are teenagers and college students who have low
incomes and little influence. Gueorguieva believes this is a misconception. Reimer
concluded that between 48 percent and 65 percent of YouTube users are between 35 and
64 years of age (as cited in Gueorguieva, 2008). Furthermore, according to a 2006
MediaMetrix study, 52 percent of MySpace users are 35 or older. Moreover, the
demographics on social networking sites are changing substantially. Twelve to seventeen
year olds accounted for 24.7 percent of MySpace users in August 2005, but made up only
11.9 percent in August 2006 (as cited in Gueorguieva, 2008). As the United States
25
becomes increasingly technology savvy, the average age of users should continue to
climb.
Despite the fact that social networking attracts a vast number of people and that
the older generation is becoming more comfortable using such sites, there is still
skepticism whether marketing campaigns on social networking sites can generate tangible
results. President Obama’s success using social networking sites is well known. Obama
clearly was able to use on-line social networking far more effectively than his
competitors. Gueorguieva mentions that on MySpace, as of April 30, 2007, Obama had
161,720 friends. This was close to three times as many as any other candidate. Also, as of
April 30, 2007, Obama had 2,791,315 views on YouTube, almost four times as many as
any other candidate.
The publicity and positive media attention Obama received through social
networking were clearly valuable to his campaign. However, his results are not very
applicable to the non-profit world because Obama was in a very unique position. Not
only was he one of a select few of presidential candidates, he also was the first strong
minority candidate for President and a very charismatic leader who the younger
generation could relate to. By contrast, there are thousands of non-profit organizations
competing for volunteers and donors throughout the United States. Many of these nonprofits have similar missions and it is difficult for these organizations to distinguish
themselves from one another.
What is particularly interesting about Gueorguieva’s study is that it states that
local politicians as well as presidential candidates have been able to attract donors and
volunteers through social networking. This is quite different than my findings, which
26
indicate that the majority of non-profits have not been able to attract substantial amounts
of donation and volunteers through on-line networking. Gueorguieva stated that on-line
networking campaigns aimed at attracting volunteers have been particularly successful.
MySpace users are three times as likely to interact with political candidates. In 2006,
state comptroller Peter Franchot recruited 80 percent of his volunteers through MySpace
and Facebook. These volunteers combined to make 15,000 phone calls and mail out
50,000 brochures, spurring Franchot to victory in a Maryland primary
Social networking efforts to attract donors have had less tangible results. More
than half of the contributors to the Democratic Party and a quarter of the Republican
contributors have made at least one donation on-line. Yet, this statistic is not specific to
social networking. Few social networking campaigns have been able to generate a
significant amount of donations. One example that contradicts this lack of fund-raising
success was the campaign of Chuck Poochigian, a Republican running for California
Attorney General, who joined MySpace and saw his on-line donations increase by 50
percent after only two months.
Gueorguieva concludes her study by discussing both the challenges and future
outlook for social networking. She mentions that social networking sites are subject to
selection bias. People who use social networking sites for political purposes could
already be active in politics, and social networking might be simply an alternate way to
participate rather than a tool to encourage growth. Another issue is that there is a limit to
what can be done on social networking sites. It is difficult to form a unified campaign
platform on MySpace or YouTube because of the sheer number of varying viewpoints.
27
However, Gueorguieva believes that despite these potential problems, the future
outlook is good for political social networking. The expense of maintaining on-line
networking campaigns is very small. Moreover, the effectiveness of social networking
sites is increasing. MySpace and YouTube are both developing programs designed for
helping users to organize their political activities. The popularity of social networking
sites combined with the on-going efforts these sites are making to adapt to users’ political
needs lead Gueorguieva to conclude that campaigning on social networking sites is
becoming a necessity for the majority of political candidates.
Gueorguieva’s study, along with the other political studies, combines to
demonstrate that on-line social networking efforts can lead to tangible results. Politicians
on the national and local level have used MySpace, YouTube, and Facebook to increase
publicity for their campaigns. In some cases, on-line networking activities appear to have
led to increases in votes. However, looking at these studies as they relate to non-profits,
the impact on voting shares is not the critical issue. What is important is that politicians
used these sites to attract volunteers and donors. By creating profiles, posting
announcements on these profiles, and contacting on-line “friends,” candidates have
achieved the goals many non-profits desire. (Gueorguieva, 2008)
Because only limited research has been done on non-profits’ use of on-line social
networking sites, it is difficult to conclude whether or not non-profits will have similar
success as politicians have had. However, studies of on-line marketing efforts in general
show that people are willing to interact with and donate money to non-profits on-line.
Donors and volunteers have certain expectations of their non-profits. They want to know
the impact of their time and money; they want their contributions to be recognized; and
28
they want non-profits’ to be well-organized so that their donations and time can be
utilized as efficiently as possible. If non-profits can use on-line social networking to meet
these expectations, then sites like Facebook and MySpace could prove to be inexpensive
ways for non-profits to gain measureable results. Furthermore, though such sites may not
be for all age groups, people of all ages are becoming more and more comfortable with
on-line networking tools. Even if MySpace and Facebook are not generating significant
results now, their effectiveness will only increase in the future.
Implications of Literature Review:
The United States’ economy is undoubtedly facing difficult times. Dwiggins et
al’s., (2004) report clearly indicates that non-profits could suffer from the troubling
times. With decreased revenue, non-profits will be looking for inexpensive marketing
methods. On-line networking is extremely cheap and, if effective, could be a substitute
for more costly ventures. This makes my examination of the benefits of on-line
networking timely and appropriate.
To determine whether on-line networking is an effective marketing tool, it is
important to identify what non-profits hope to gain from it. The Volunteer Management
Capacity Study demonstrates that recruiting volunteers is essential to non-profits’
operations (Urban Institute, 2004). 67 percent of the charities in the survey stated
recruiting adequate number of volunteers was a problem, and 90 percent stated they have
the capacity to take on more volunteers. Clearly, if on-line networking could be used to
gain a significant number of volunteers, it would be very attractive to non-profits.
29
The American Marketing Association (2008) identified that building awareness is
on average ranked as non-profits’ top marketing priority. Forming new connections with
the public is a form of building awareness, and if on-line networking can attract
previously unconnected people, then it will be fulfilling a recognized need. Not
surprisingly, the second highest marketing priority was generating revenue. Though most
organizations rely on contributions to support their activities, many lack organized
donation campaigns. Hagar et al. (2002), found that 63 percent of non-profits had no fulltime fund-raising staff and that fund-raising duties were shared across departments. Online networking profiles can be created by any type of personnel, do not require extensive
expertise, and are very inexpensive. If on-line networking can prove a profitable method
to gain donations, it could be very useful to non-profits.
Evaluations of non-profits’ marketing campaigns show that forming new
connections and gaining donations and volunteers are important priorities. However, it is
important to evaluate whether these tasks can be achieved through the internet. The Online Benchmark Study’s report (Bhagat et al., 2007) on the wired wealthy conclude that
people are very willing to interact with and donate money to charities on-line. In fact, the
younger and middle-aged generations would prefer to use the internet over other more
established communication methods. In The On-line Benchmark study, the wired wealthy
expressed a desire for the improvement and better organization of non-profits’ on-line
activities. On-line networking could potentially play a role in this improvement.
The internet appears to be an effective way for non-profits to interact with the
public. However, whether on-line networking can be used to achieve non-profits’
marketing goals is less clear. Since on-line networking is such a new resource, very
30
limited literature exists to evaluate its effectiveness for non-profits. The studies by Gulati
and Williams (2007), and Gueorguieva (2008) conclude that on-line networking has
played an integral role in political campaigns since 2000. Both local and national
politicians have used on-line networking to successfully gain donations, volunteers, and
build awareness. In fact, Gueorguieva even argues that on-line networking has changed
the outcomes of elections. Whether politicians’ success can be transferred to non-profits
is unclear. Still, the fact that on-line networking can be employed to achieve important
marketing objectives leaves room for optimism.
Data Description:
The majority of the analysis in my study involves seven different variables. The
variable year is important to my question because it allows me to discover whether the
year when organizations started on-line networking has affected their ability to attract
donations, volunteers, and new connections. I hope to find out whether successful on-line
networking campaigns take time to develop or whether organizations can expect to obtain
immediate results. One hundred and twenty-six organizations responded to this question
out of one hundred and fifty-four possible respondents. 32.5 percent of the respondents
stated they started in 2008, 42.1 percent started in 2007, 18.3 percent started in 2006, and
7.1 percent started in 2005 or earlier. (Figure A) There were twenty-six missing values.
The large percentage of respondents that answered that they started on-line networking
ventures in 2008 or 2007 indicates that on-line networking is a very new marketing tool
for non-profits.
31
The variable time measures how many hours per week an organization spends
changing, maintaining, or updating its on-line networking profile. I will use this variable
to discover whether or not more hours spent working on on-line social networking sites
leads to a greater number of donations, volunteers, and new connections. One hundred
and twenty-five organizations responded to this question out of a possible one hundred
and fifty-two responses. 38.4 percent of respondents stated they spent less than an hour
per week, 51.2 percent spent between 1 and 5 hours, 7.2 percent spent between 5 and 10
hours, .8 percent spent between 10 and 15 hours, 1.6 percent spent between 15 and 20
hours, and .8 percent spent 20 hours or more. (Figure B) There were 27 missing values.
To some extent, on-line networking profiles sustain themselves, so the small number of
hours non-profit spend updating their profiles is not surprising. However, if greater time
leads to greater results, charities clearly have room for improvement.
The variable staff indicates who maintains an organization’s on-line networking
profile. I will use the staff variable to compare which personnel are the most successful at
generating donations, volunteers, and new connections. It is important to note that
respondents could select more than one answer. To make the analysis simpler, I divided
the variable into eight dummy variables: executives only, volunteers only, program staff
only, marketing/fundraising staff only, executives plus, volunteers plus,
marketing/fundraising staff plus, and program staff plus. I describe these dummy
variables in my data description because it is the dummy variables, not the actual staff
variable that is used in my analysis.
The plus dummy variables include all responses that contained more than one
staff group. For example, executives plus contains all responses in which non-profits
32
selected executives and at least one other branch. I will compare the success of
organizations that used only one type of personnel to the success of organizations that
divided the work between multiple departments. In the original survey, 121 out of a
possible 152 organizations gave thirteen responses. Dividing the responses into the eight
dummy variables, 2.5 percent of the responses selected executives only, 6.6 percent
selected volunteers only, 34.7 percent selected marketing staff only, 26.4 percent selected
program staff only, 9 percent selected executives plus, 24.8 percent selected marketing
staff plus, 19 percent selected volunteers plus, and 19 percent selected program staff plus.
There were 31 missing values. Marketing and program staff are by far the most common
personnel that participate in on-line networking maintenance. Comparing the success of
marketing and program staff with the success of volunteers could help charities identify
whether or not on-line networking is worth the time of paid personnel.
The variable change measures how the effectiveness of on-line networking has
changed over time. I will compare change to the time spent updating an organization’s
on-line networking site to see whether greater time spent leads to improvement of on-line
networking results. Out of the 123 responses, 59.4 percent of organizations stated that
results increased over time, 37.4 percent said they stayed the same, and 3.3 percent said
they decreased. (Figure D) There were 29 missing values. Clearly, it appears that the
benefits of on-line networking increase the longer organizations remain on the sites.
The variable donations measures the monetary contributions organizations have
received from on-line networking sites. I will analyze the affects time, year, and staff
have on donations in order to recommend the most effective ways for non-profits to
attract donors. Respondents could choose from seven different ranges of donations (for
33
example between $100 and $500). They could also select an unknown category which is
considered a missing value. To simplify the analysis, I label each category as a mean of
the range. For example, I call the $100 to $500 category 300. Out of 79 responses, 58.2
percent of the respondents raised between $0 and $100, 20.3 percent raised between $100
and $500, 8.9 percent raised between $500 and $1000, 5.1 percent raised between $1000
and $5000, 1.3 percent raised between $5000 and $10,000, 3.8 percent raised between
$10,000 and $50,000, and 2.5 percent raised $50,000 or more. (Figure E) There are 73
missing values. The responses suggest that the majority of non-profits have not been able
to raise significant amounts of money through on-line networking. Yet, the fact that over
6 percent have raised at least $5000 is important to note.
The variable volunteers measures how many volunteers organizations have
attracted through on-line networking. Similar to the donations variable, I will analyze the
effects time, year, and staff have on attracting volunteers. The volunteers variable is
divided into five different ranges and an unknown category which is again treated as a
missing value. Organizations were able to choose between 1000 and 5000 volunteers or
5000 or more volunteers, but none of the organizations chose these categories. Once
again, I labeled each category as a mean of the range of volunteers. Out of 76 responses,
75 percent attracted between 1 and 25 volunteers, 7.9 percent attracted between 25 and 50
volunteers, 4 percent attracted between 50 and 100 volunteers, 9.2 percent attracted
between 100 and 500 volunteers, and 4 percent attracted between 500 and 1000
volunteers (Figure F). There were 76 missing values. The vast majority of the sample
attracted a very small number of volunteers, but the fact that greater than 13 percent
gained at least 100 volunteers signifies that at least some charities have had success.
34
The last variable in my study is called connections. This variable measures the
percentage of people who join an organization’s on-line social network that have some
previous connection to the non-profit. As with donations and volunteers, I will analyze
the effects time, year, and staff have on making new connections. The variable is divided
into five ranges and an unknown category. Out of 95 responses, 16.8 percent stated that
between 95 percent and 100 percent of the people who joined their on-line networking
group were new connections, 22.1 percent selected between 75 percent and 90 percent,
8.4 percent selected between 50 percent and 75 percent, 28.4 percent selected between 25
percent and 50 percent, and 24.2 percent selected between 0 percent and 25 percent
(Figure G). There were 57 missing values. Clearly, from the wide range in responses,
some organizations have successfully generated a large proportion of new connections,
while others have not. The fact that about 17 percent of the responses stated that their
groups were made up of 95 to 100 percent of new connections shows that generating a
substantial percentage of new connections is definitely a possibility. However, the 24
percent that generated groups consisting of only 0 to 25 percent new connections shows
that many charities have also struggled in this area.
Procedure:
In the summer of 2008, I worked as an intern for an on-line newspaper called
Philanthropy Action1. Started in 2007, the newspaper covers contemporary social issues
related to charitable giving. My thesis began as a prospective piece for the newspaper, but
became a more detailed quantitative study. I began the project by creating a survey
containing 21 questions. Respondents were divided into three groups. Those who had
1
www.philanthropyaction.com
35
never participated in on-line networking were asked one set of questions. Those who
previously participated in on-line networking but stopped were asked a second set of
questions, and those who were currently participating in on-line networking were asked a
third set of questions. As an incentive to participate in the survey, all those who were
willing to provide contact information were entered in a contest to win an IPod Shuffle.
I used an on-line tool called Survey Monkey2 to construct the survey, send it out, and
collect results.
The first group I sent the survey to was made up of non-profits that advertise on
Facebook. Facebook has a special application called “Causes” that is designed to make it
easy for non-profits to advertise. Facebook members can not only join non-profit groups,
they can actually donate money to organizations directly through the site. Facebook’s
Causes separates the non-profits into different categories. I selected eight out of ten
categories to sample. The categories included Animals, Arts and Culture, Education,
Environment, Health, Human Services, Public Advocacy, and International. I did not
sample religion or political campaigns because it is debatable whether or not these causes
can be classified as non-profits.
Facebook’s Causes application is divided into pages. The pages on Facebook are
separated by category. Each page contains twenty five causes for that category. Because
there were too many causes and non-profits to sample all of them, I randomly selected
using a random number generator3 two pages for every twenty pages of each category.
Out of the causes examined, only certified American non-profits were selected.
Furthermore, causes that did not seem to be operated by the non-profit the cause was
2
3
www.surveymonkey.com
http://www.psychicscience.org/random.aspx
36
supporting were ignored. In other words, if I believed that no member of a certified nonprofit was involved in starting the cause, I ignored the entry. Finally, branches of national
non-profits were also not examined.
Before I sent out the survey, I researched all the non-profits that were randomly
selected, finding the most appropriate e-mail addresses to send the inquiry. The ideal
recipient was a marketing director, and the next best was a development or
communications director. If none of these personnel had available contact information, I
settled for an executive director or other position. In the worst case scenario, I sent the
survey to the company’s general e-mail address: for example, [email protected]. All 339
non-profits randomly selected on Facebook were sent e-mails, and 72 of these non-profits
responded. The survey sorts answers into two categories: those completed and those
unfinished. All given responses will be included in the analysis.
For my second group of analysis, an e-mail was sent to Charity Navigator requesting
a list of all mid-sized non-profits with revenues between 1 and 5 million. The list
contained 2,254 non-profits. The non-profits were separated into the categories Animals,
Arts, Culture and Humanities, Education, Environment, Health, Human Services,
International, Public Benefit, and Religion. The religion category was eliminated. I also
eliminated all the branches of bigger non-profits: for example, the SPCA of Chester
County. After these removals, the list was reduced to 1,657 non-profits. Out of this list, I
randomly selected 339 non-profits to e-mail. The random selection process involved
separating the non-profits by category, finding out what percent of the total non-profits
each category contained, and then multiplying that percent by 339 to find the total
number of non-profits in each category that would be randomly selected. For each non-
37
profit selected, I followed the same e-mail procedures that I did for the Facebook group.
In order to separate the two different groups (Facebook and Charity Navigator), I created
a second link on Survey Monkey for the Charity Navigator group. Charity Navigator nonprofits filled out the same survey as Facebook non-profits, but their answers were
collected by Survey Monkey in a different group.
The third analysis group included the remainder of the non-profits in the Charity
Navigator list that were not randomly selected. This group was different from the second
group because I did not research these non-profits to find the appropriate person to
contact. The original list sent by Charity Navigator included an e-mail address for each
non-profit. Most of the addresses were in the format info@non-profit, but some were
personal addresses for specific non-profit representatives. Using a Survey Monkey tool, I
sent out a mass e-mail to the all the non-profits in this third category. 1,298 non-profits
received e-mails. Because some of the e-mail addresses were invalid and some of
Charity Navigator’s non-profits did not contain contact information, 20 out of the 1,657
non-profits were not contacted from the Charity Navigator list.
All of the charities’ responses were collected by Survey Monkey which automatically
performed a basic analysis. Survey Monkey calculated the percentage of companies who
gave a particular answer to each question. Survey Monkey also allowed me to download
all of the responses into an EXCEL document. After cleaning up the EXCEL sheet, the
data was transported into STATA for analysis.
38
Graphical Analysis:
The fundamental goal of my study is to identify whether or not to recommend online networking as a marketing tool for non-profits. To address this main goal, I chose to
test whether or not organizations can obtain tangible benefits through on-line networking
and the best methods to do so. As indicated by the American Marketing Association
(2008), obtaining donations, hiring volunteers, and making new connections are three of
the most important goals of charities’ marketing campaigns. At first glance, it does not
appear that on-line networking is an effective method for attracting donations or
volunteers. Figure E demonstrates that slightly less than 60 percent of the sample
generates between $0 and $100 and less than 10 percent generates greater than $1,000.
Figure F demonstrates that about 75 percent of the sample generates between 1 and 25
volunteers.. The evidence on attracting new connections is less one-sided. Figure G
demonstrates that about 50 percent of the sample’s on-line networking groups had a
majority of members who had no previous connection to the non-profit. Furthermore, 23
percent of the sample generated between 75 and 90 percent of new connections and 15
percent of the sample generated between 90 and 100 percent of new connections. Clearly,
a notable portion of the charities that responded to my survey have been successful using
on-line networking tools to generate new connections. The findings are consistent with
the American Marketing Association’s conclusion that websites and digital media are
most effective at positioning and branding and least effective at revenue generation.
Despite the fact that the preliminary analysis seems to demonstrate that the
majority of charities have not been able to use on-line networking sites to attract
donations and volunteers, one must consider whether the problem lies with the sites
39
themselves or non-profits use of the tools. Gulati and Gueorguieva demonstrated that
politicians can use on-line networking to attract large numbers of donations and
volunteers. It is therefore possible that non-profits are not spending enough time
updating and maintaining on-line networking sites to reap the benefits. For this reason, I
wanted to find out whether increases in time spent updating and maintaining on-line
networking sites led to increases in the number of volunteers, donations, and new
connections.
Figures H through K evaluate the effect that the independent variable time has on
the dependent variables donations, volunteers, and new connections. The majority of
charities that use on-line networking tools spend between 0 and 10 hours per week
updating and maintaining the site (Figure B). Since only about 2 percent of the sample
spent greater than 10 hours maintaining and updating the site, I combined all charities
that spent greater than 10 hours updating and maintaining their on-line networking sites
into a 10 or more category.
Gueorguieva (2008) mentions in her study that politicians have been especially
successful at recruiting volunteers through on-line networking sites. Figure H shows that
some charities have also been successful at recruiting volunteers. The graph appears to
demonstrate that there is a positive relationship between time and average volunteers
recruited. As time increases, the average number of volunteers increases substantially.
However, this graph is somewhat misleading. To test whether the differences in average
number of volunteers were statistically significant, I ran a t-test measuring the chance that
the means of volunteers for two consecutive time intervals were actually different from
one another. The p-values provided throughout the figures section indicate the level of
40
statistical significance. Values highlighted in bold are significant at greater than a 90
percent confidence level, while values highlighted in red are significant at greater than a
95 percent confidence level. Values that are that significant at the 90% confidence level
are important to note, but should be taken with some degree of skepticism. Values that
are significant at the 95% confidence level provide strong evidence of a relationship
between the two variables.
The only difference in Figure H that is statistically significant at greater than a 90
percent confidence level is between 0 and 2.5 hours ( p-value .089). Charities that spend
0 hours updating and maintaining their on-line networking site attract about 50 less
volunteers than charities that spend 2.5 hours. This could signify that an increase from 0
to 2.5 hours will help charities attract more volunteers, but causality concerns discussed
in the conclusion section of my thesis prevent me from making a strong recommendation.
The relationship between time and donations (Figure I) is less clear. Not
surprisingly, charities that spend 0 hours updating their on-line networking site make
close to $4000 less than charities that spend 2.5 hours. This relationship is significant at
greater than a 98 percent confidence interval (p-value 0.0170 ). What I found very
difficult to explain is why the average number of donations decreases substantially from
2.5 to 7.5 hours (p-value .0763). Outliers in the 2.5 hour category seem to be the cause of
this discrepancy.
Figure J looks at the median number of donations for a given time interval. Most
of my analysis does not examine medians because there is little difference between the
mean and median figures. Furthermore, because the majority of the sample selected the
minimum choices for volunteers and donations, the medians for groups of charities with
41
varying times, years, and personnel are often the same. Figure J is one of the few cases in
which the median is important to consider. Unlike in Figure I, the number of donations is
much greater for charities that spend 7.5 hours than for charities that spend 2.5 hours.
However, the difference is not statistically significant.
To calculate the statistical significance of the difference in medians, I performed
a chi-squared test (with a continuity correction) for equality of the two independent
samples. The only difference in medians that is statistically significant is between 0 and
2.5 hours (p-value .001). The mean and median graphs of the effect of time on donations
demonstrate two important points. There is a clear difference between the donations
attracted by charities that spend 0 hours updating their on-line networking profiles
compared to charities that spend 2.5 hours. Furthermore, though the difference in mean
donations between 2.5 hours and 7.5 hours is significant at greater than a 90% confidence
level, the median graph shows that this is most likely the result of outliers.
The relationship between time and new connections appears to reinforce the
preliminary conclusions that on-line networking can effectively attract people with no
previous connection to the non-profit. Figure K appears to illustrate that the greater the
amount of time spent updating the on-line networking profile, the more likely a charity
will be able to make new connections with people who had no previous relationship with
the charity. However, none of the means are significantly different from one another.
This is somewhat surprising because one would think the greater amount of time
organizations spent updating their on-line networking profiles, the greater number of new
connections they would make.
42
On-line networking is not simply about attracting volunteers, donors, and new
connections. Publicity, posting messages, and promoting a specific event are all also
other important goals of on-line networking. Because of the varying goals of on-line
networking, I wanted to test whether time spent maintaining and updating a charity’s online networking increases the general effectiveness of on-line networking over time.
Figure L is a bar graph with time as the independent variable and effectiveness as the
dependent variable. As stated in the data description, charities could choose whether
effectiveness increased, stayed the same or decreased over time. In order to make
analysis easier, I assigned numerical values to these three answer choices. Increased was
assigned a 3, stayed the same was assigned a 2, and decreased was assigned a 1.
Figure L seems to demonstrate that the greater the amount of time spent updating
on-line networking profiles, the greater the value of effectiveness. The difference in
means between 2.5 and 7.5 hours is not statistically significant and the difference
between the means of 7.5 and 10 hours is only significant at the 90 percent confidence (pvalue .0845). However, the fact that the difference in means between 0 and 2.5 is
statistically significant at greater than a 99 percent confidence level (p-value 0.0011)
gives support for the conclusion that there is some positive relationship between the two
variables.
The effect that the year a charity started on-line networking had on donations,
volunteers, and making new connections is another important factor to consider.
Politicians began using on-line networking in 2000 and have had more time than nonprofits to gain experience using such sites. Examining the impact of year will provide
charities with information on whether they can expect instant results from on-line
43
networking campaigns, or whether tangible benefits increase over time. It is important to
note that about two-thirds of the charities surveyed started their on-line networking
campaigns in 2007 or 2008 (Figure A). This indicates that on-line social networking is a
recent development.
A bar graph (Figure M) with year started as the independent variable and the
number of volunteers as the dependent variable appears to display a negative relationship
between the two variables. Charities that started on-line networking in 2005 or earlier
have generated more than double the number of volunteers as compared to charities that
started on-line networking in 2006 and 2007. Yet, possibly because of the fact that only
a few charities started on-line networking in 2005 or earlier, the difference in average
volunteers between 2005 and 2006 is not statistically significant. The only difference in
means that is statistically significant at greater than a 90 percent confidence interval is
between the average number of volunteers for 2007 and 2008 (p-value .0603). Charities
that started on-line networking in 2007 on average attracted about 60 more volunteers
than charities that started in 2008, indicating that charities that have some experience
with on-line networking seem to have more success in this area
Figure N illustrates the effect of year on average donations. The bar graph
indicates that organizations that started on-line networking in 2005 or earlier have been
able to gain a substantial number of donations while organizations that have started online networking more recently have gained practically no donations. All the differences in
means are statistically significant at greater than a 95 percent confidence level (p-values
.0360, .0452, and .0425) . It is important to note that only five charities that started online networking in 2005 provided data on the number of donations attracted. Still, it is
44
interesting that both charities that attracted $50,000 of donations or more started on-line
networking in 2005 or earlier and that all of the five charities raised at least between
$5000 and $10,000. It would seem that these charities have benefited substantially from
on-line networking experience.
Surprisingly, the average number of donations increases from 2006 to 2007, but
decreases from 2007 to 2008. The increase from 2006 to 2007 is difficult to account for
because one would think the longer an organization participated in on-line networking the
more money they would attract. However, it is important to note that the average
numbers of donations for charities that began on-line networking in 2006, 2007, and 2008
are all less than $2,000, showing that in recent years on-line networking has not been an
effective fund-raising tool.
Examining the effect that year has on new connections (Figure O) shows that
once again organizations that began on-line networking in 2005 or earlier are the most
successful. On average, 80 percent of connections generated by these charities are new,
compared to 40 percent for 2006 charities. This difference is significant at greater than
the 99 percent confidence interval (p-value 0.0005). The percent of new connections
increases from 40 percent in 2006 to slightly less than 60 percent in 2007 and the
difference is statistically significant at greater than a 95 percent confidence interval (pvalue 0.0488). The difference between the means of new connections for charities that
began on-line networking in 2007 and 2008 are not statistically significant.
The relationship between year and new connections is similar to the relationships
between year and donations and volunteers. Charities that began on-line networking in
2005 or earlier seem to benefit from the increased experience. Furthermore, at least for
45
donations and new connections, charities that began on-line networking in 2007
outperform charities that began on-line networking in 2006. I have no clear explanation
for why this occurs and hope it can be explored in future research. A final important point
to note is that charities that began on-line networking in 2008 are able to generate a
substantial percentage of new connections. I gathered the data for this study in the
summer of 2008, so charities that stated they started on-line networking in 2008 had only
recently created profiles. While volunteers and donations do not appear to come quickly,
new connections are a benefit charities can expect in less than a year.
Another important factor non-profits must consider when starting on-line
networking campaigns is who should be doing the work. Hagar et al., (2002) concluded
that executives were the most effective at attracting donations. My results lead to a
different conclusion. When executives maintained on-line networking sites on their own
and when they combined to work with others, they generated very small average
donations (Figure P). Marketing staff seem to have been considerably more successful.
Compared to other personnel working alone, marketing staff raised the largest average
number of donations. Still, before I recommend that non-profits allocate on-line
networking maintenance to marketing staff, it is important to note that the difference
between the means and marketing staff only and program staff only was not statistically
significant. One statement I can make with some confidence is that the most effective
way to maximize donations is to have marketing or volunteers work together with other
departments. The difference in means between the Volunteers Plus category and the
Program Staff category was statistically significant at close to the 95% confidence level
(p-value .0562).
46
Examining median donations for a given personnel (Figure Q) reinforces the
conclusion that dividing the tasks between two departments seems to be the most
effective way to raise donations. However, I am somewhat cautious of the apparent
differences in medians. The volunteers only category seems to have a greater median
than the other three groups, but the difference is not statistically significant. Furthermore,
the difference between the volunteers plus category and the program staff only category
appears to be quite large, but it only significant at the 90% confidence level (p-value
.081) and the difference between volunteers only and volunteers plus is not statistically
significant.
The relationship between staff and average number of volunteers (Figure R) is
noticeably stronger. Once again when departments work together, they have greater
success, but the differences in means are more statistically significant. The difference
between the executives plus category and the marketing staff plus category is significant
at a 95% confidence level (p-value 0.0502), indicating that out of any personnel,
executives working with other departments are the most successful at attracting
volunteers. If non-profits do not have the resources to allocate on-line networking
maintenance to more than one department, it appears program staff are the most
successful when working alone. Program staff attract on average about 50 more
volunteers than marketing staff, but the difference is only significant at the 90%
confidence level (p-value .0991).
The relationship between staff and new connections (Figure S) is not very
notable. Staff working alone appeared to be more successful that staff working together.
For example, program staff generated slightly under 60% of new connections working
47
alone, but generated only 40% of new connections when combined with other
departments (p-value .0287). However, most of the differences in means were not
statistically significant.
Conclusion:
I began this project with the goal of discovering whether or not to recommend online social networking as a marketing tool for non-profits. Previous research indicated
that non-profits’ top marketing priorities included increasing donations and volunteers
and establishing new connections. The research also shows that politicians have
successfully used on-line networking tools to achieve these goals. After analyzing the
results of my survey sent out to non-profits of varying sizes and types, I find that though
there is reason for optimism, there is a long way to go before on-line networking becomes
a primary marketing tool.
Initially, it appeared that non-profits had been generally unsuccessful gaining
donations and volunteers using on-line networking tools and that some were able to gain
a substantial number of new connections while others struggled. However, the
relationship between time and donations and volunteers showed that non-profits may not
be spending enough time to gain significant results.
When organizations increased their time spent updating on-line networking
profiles from less than an hour per week to between one and five hours per week, the
number of volunteers and donations attracted increased substantially and the findings
were statistically significant. I do recommend that non-profits hoping to increase their
donations and volunteers consider spending at least an hour updating their profiles each
48
week. However, it is important to note causality concerns regarding the relationship
between time and donations, volunteers, and new connections. Charities that began online networking campaigns and initially experienced success attracting donations and
volunteers would likely increase the amount of time spent updating their on-line
networking profiles. Therefore it could be that the number of donations and volunteers
effects the amount of time spent and increases in time spent are not responsible for
increases in donations and volunteers.
Spending more time updating on-line networking profiles is not the only factor
non-profits should consider. My comparisons of the effect year has on donations,
volunteers, and new connections lead to important conclusions. Charities that started online networking between 2006 and 2008 are not gaining large numbers of donations.
However, the charities that started on-line networking in 2005 or earlier, though few in
number, have been very successful gaining both donations and new connections. One
reason this may be is because charities that started on-line networking campaigns in 2005
or earlier that were not successful simply stopped using on-line networking as a
marketing tool. Still, these charities clearly have found ways to use on-line networking
tools and future research could explore what methods these charities have used to obtain
their success.
My data does not lead to clear conclusions concerning what departments should
conduct on-line networking maintenance. A limited sample size seems to have prevented
me from obtaining many statistically significant results. It is important to note that
charities that allocate maintenance between multiple departments generate more
donations and volunteers. However, the causality of this relationship is an issue. It is very
49
possible that organizations that experience success attracting donations and volunteers are
more willing to allow personnel from multiple departments to participate in on-line
networking.
If a non-profit came to me tomorrow and asked me whether or not to start an online networking campaign, I would say undoubtedly yes. It is important to note that online networking is very inexpensive. The fact that non-profits can generate any tangible
benefits at close to no cost is very impressive. Furthermore, the majority of organizations
who spend more than one hour per week maintaining their sites describe the effectiveness
of on-line networking as improving, leaving room for growth and optimism. Non-profits
can certainly not rely on on-line networking as a primary source of donations or
volunteers. However, on-line networking is a very new marketing tool and my data
indicates that it has the potential to become an effective method for non-profits.
50
Bibliography
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2.
3.
4.
5.
Bhagat, V., Donovan, Q., and Hauf, B., (2007). The Online Marketing Nonprofit
Benchmark Index Study. Convio Inc.
Convio Inc. 2008. The Wired Wealthy: Using the Internet to Connect with Your
Middle and Major Donors.
Dwiggins-Beeler, R., Spitzberg,B., and Roesch,S, (2004). Vectors of
Volunteerism: Communication Correlates of Volunteer Retention, Recruitment,
and Job Satisfaction in a Nonprofit Organization. International Communication
Association.
Gueorguieva, V., (2008). Voters, MySpace, and YouTube. Social Science
Computer Review, 26(3), 288-300.
Gulati, G, and Williams, C. (2007). Social Networks and Political Campaigns:
Facebook and the 2006 Midterm Elections. American Political Science
Association.
6. Hager, M., Pollack,T., and Rooney, P., (2002). How fundraising is carried out in
US nonprofit organizations. The Urban Institute.
7. Lipman Hearne and the American Marketing Association, (2008) The State of
Non-Profit Marketing.
8. Mckinsey & Company. 2007. How Businesses are using Web 2.0. The Mckinsey
Quartely.
9. Merten, M.J., and Williams, A.L. (2008). A review of online social networking
profiles by adolescents. Adolescence.
10. Rainie, L. and Smith, A. (2008). The Internet and the 2008 Election. Pew Internet
and American Life Project
11. Urban Institute. 2004. Volunteer Management Capacity in America’s Charities
and Congregations.
51
Data Appendix:
Variable Label: Volunteers
Missing Observations: 76 / 152
Variable Description: This variable describes the average number of volunteers
charities stated they gained through on-line networking. The original answers were in
ranges for example between 1 and 25 volunteers. To make statistical analysis easier, this
variable takes the average of each range.
Variable values and coding:
Non-missing Observations
Volunteers |
Freq. Percent
Cum.
----------------------------------------------12.5 |
57
75.00
75.00
37.5 |
6
7.89
82.89
75 |
3
3.95
86.84
300 |
7
9.21
96.05
750 |
3
3.95
100.00
----------------------------------------------Total |
76
100.00
Variable Label: Donations
Missing Observations: 79/ 152
52
Variable Description: This variable describes the average number of donations
charities stated they gained through on-line networking. The original answers were in
ranges for example between $0-100. To make statistical analysis easier, this variable
takes the average of each range.
Variable Values and coding:
Non-Missing Observations
Donations ($)| Freq.
Percent
Cum.
----------------------------------------------50 |
46
58.23
58.23
250 |
16
20.25
78.48
750 |
7
8.86
87.34
2500 |
4
5.06
92.41
7500 |
1
1.27
93.67
25000 |
3
3.80
97.47
50000 |
2
2.53
100.00
----------------------------------------------Total |
79
100.00
Variable Label : Connections
Missing Observations: 95 / 152
Variable Description: This variable measures the percent of people who joined an
organization's on-line social network(s) that respondents stated had no prior connection to
the non-profit. The original answers were in ranges for example between 0 percent and
53
10 percent. To make statistical analysis easier, this variable takes the average of each
range.
Non-missing Observations
Variable Values and coding:
% of New Connections | Freq. Percent
----------------------------------------------12.5 | 23
24.21
37.5 | 27
28.42
62.5 | 8
8.42
83.5 | 21
22.11
95 | 16
16.84
----------------------------------------------Total | 95
100.00
Cum.
24.21
52.63
61.05
83.16
100.00
Variable Label: Staff
Missing Observations: 121 / 156
Variable Description: This variable describes who charities stated maintained their online networking sites. E stands for executives, V stands for volunteers, P stands for
program staff, and M stands for marketing / fundraising staff. Charities were able to
select more than one answer.
Variable values and coding:
Non-Missing Observations
Staff | Freq.
Percent
Cum.
----------------------------------------------E|
EM |
EMP |
EMPV |
EPV |
3
3
3
3
1
2.48
2.48
2.48
2.48
0.83
2.48
4.9
7.44
9.92
10.74
54
EV |
1
0.83
11.57
M|
42
34.71
46.28
MP |
7
5.79
52.07
MPV |
5
4.13
56.20
MV |
9
7.44
63.64
P|
32
26.45
90.08
PV |
4
3.31
93.39
V|
8
6.61
100.00
---------------------------------------------Total |
121
100.0
Variable Label: Year
Missing Observations: 126/ 156
Variable Description: This variable describes the year charities stated they began on-line
networking. 8 corresponds to 2008, 7 corresponds to 2007, 6 corresponds to 2006, and 5
corresponds to 2005 or earlier.
Variable values and coding:
(Year |
Freq.
Percent
Cum.
----------------------------------------------5|
9
7.14
7.14
6|
23
18.25
25.40
7|
53
42.06
67.46
8|
41
32.54
100.00
----------------------------------------------Total |
126
100.00
Variable Label: Time
55
Missing Observations: 125/ 152
Variable Description: This variable measures the time non-profits stated they spend per
week changing, maintaining, and updating their on-line networking profiles. The original
answers were in ranges for example between 1-5 hours. To make statistical analysis
easier, this variable takes the average of each range. 0 corresponds to the response less
than one hour.
Variable values and coding:
Time (Hours per week) |
Freq.
Percent
Cum.
----------------------------------------------0|
48
38.40
38.40
2.5 |
64
51.20
89.60
7.5 |
9
7.20
96.80
12.5 |
1
0.80
97.60
17.5 |
2
1.60
99.20
20 |
1
0.80
100.00
----------------------------------------------Total |
125
100.00
Variable Label: Change
Missing Observations: 123/ 152
Variable Description: This variable measures whether non-profits stated the effectiveness
of on-line networking increased, decreased, or stayed the same time over time.
56
Variable values and coding:
Change |
Freq.
Percent
Cum.
----------------------------------------------D|
4
3.25
3.25
I|
73
59.35
62.60
S|
46
37.40
100.00
----------------------------------------------Total |
123
100.0
57
Figures:
Figure A
.3
.2
0
.1
% of responses
.4
Year
2005 or Earlier
2007
2006
2008
Figure B
.3
.2
.1
0
% of responses
.4
.5
Time
Less than an hour
5-10 hours
15-20 hours
1-5 hours
10-15 hours
20 or more hours
58
Figure C
.3
.2
0
.1
% of responses
.4
Staff
Executives Only
Program Staff Only
Executives Plus
Volunteers Plus
Marketing Staff Only
Volunteers Only
Marketing Staff Plus
Program Staff Plus
Figure D
.4
.2
0
% of Responses
.6
Change In Effectiveness
Increased
Decreased
Stayed the Same
59
Figure E
0
.2
.4
% of Responses
.6
Donations
$0-100
$500-1000
$5000-10,000
$50,000 or more
$100-500
$1000-5000
$10,000- 50,000
Figure F
.6
.4
.2
0
% of Responses
.8
Volunteers
1-25
50-100
500-1000
25-50
100-500
60
Figure G
0
.1
.2
% of Responses
.3
New Connections
0-25%
50-75%
90-100%
25-50%
75-90%
200
100
0
mean of volunteer
300
Figure H
0
Relationship
(0,2.5)
(2.5, 7.5)
(7.5. 10)
2.5
7.5
Time (Hours Per Week)
10
P Values
0.089
0.3892
0.2191
61
6,000
4,000
0
2,000
mean of donations ($)
8,000
Figure I
0
Relationship
(0,2.5)
(2.5, 7.5)
(7.5. 10)
2.5
7.5
Time (Hours Per Week)
10
P Values
0.0170
0.0763
0.2407
1,000
0
500
Median of Donations ($)
1,500
Figure J
0
Relationship
(0,2.5)
(2.5, 7.5)
(7.5. 10)
2.5
7.5
Time (Hours Per Week)
10
P Values
0.001
0.880
0.741
62
0
20
40
60
mean of new connections (%)
80
Figure K
0
2.5
7.5
Time (Hours Per Week)
Relationship
P Values
(0,2.5)
(2.5, 7.5)
(7.5, 10)
0.1445
0.2934
0.3937
10
0
1
2
Mean of Effectiveness
3
Figure L
0
2.5
7.5
Time (Hours Per Week)
Relationship
P Values
(0,2.5)
(2.5, 7.5)
(7.5, 10)
0.0011
0.2620
0.0845
10
63
0
50
100
mean of volunteer
150
200
Figure M
5
6
7
8
Year Started
Relationship
P Values
(5,6)
(6,7)
(7,8)
0.1257
0.4613
0.0603
0
5,000
10,000
15,000
mean of donations ($)
20,000
25,000
Figure N
5
6
7
8
Year Started
Relationship
(5, 6)
(6,7)
(7,8)
P Values
0.0360
0.0452
0.0425
64
0
20
40
60
mean of new connections (%)
80
Figure O
5
6
7
8
Year Started
Relationship
P Values
(0, 2.5)
(2.5, 7.5)
(7.5. 10)
0.0005
0.0448
0.1277
6,000
4,000
0
2,000
Mean of Donations ($)
8,000
Figure P
Executives Only
Volunteers Only
Executives Plus
Volunteers Plus
Relationship
(Vplus,P)
(Vplus, Mplus)
(P,V)
(M,P)
(E,M)
Marketing Staff Only
Program Staff Only
Marketing Staff Plus
Program Staff Plus
P-value
0.0562
0.2260
0.2907
0.4083
0.3604
65
200
150
100
0
50
Median of Donations ($)
250
Figure Q
Executives Only
Volunteers Only
Executives Plus
Volunteers Plus
Relationship
P-value
(V,P)
(V, Vplus)
(Vplus, P)
0.854
0.539
0.081
Marketing Staff Only
Program Staff Only
Marketing Staff Plus
Program Staff Plus
200
150
100
50
0
Average Number of Volunteers
250
Figure R
Executives Only
Volunteers Only
Executives Plus
Volunteers Plus
Relationship
(M, Eplus)
(M, P)
(Eplus and Mplus)
(Mplus and P plus)
(Vplus and P)
Marketing Staff Only
Program Staff Only
Marketing Staff Plus
Program Staff Plus
P-values
0.0031
0.0991
0.0502
0.0536
0.2280
66
60
40
20
0
% of New Connections
80
Figure S
Executives Only
Volunteers Only
Executives Plus
Volunteers Plus
Relationship
(P, Pplus)
(P, Eplus)
(Mplus, Pplus)
(M, Mplus)
(V, Vplus)
Marketing Staff Only
Program Staff Only
Marketing Staff Plus
Program Staff Plus
P-values
0.0287
0.3985
0.1690
0.0471
0.0443
67