THE IDENTIFICATION THEORY

THE IDENTIFICATION THEORY: A QUANTITATIVE STUDY OF WOMEN’S

CHARITABLE GIVING MOTIVATION

By

Jared G. Beard

SUZANNE HOLMES, PhD, Faculty Mentor and Chair

INGLES MORGAN-GARDNER, PhD, Committee Member

LARRY SANDERSON, PhD, Committee Member

Elizabeth Koenig, JD, Dean, School of Public Service Leadership

A Dissertation Presented in Partial Fulfillment

Of the Requirements for the Degree

Doctor of Philosophy

Capella University

May, 2015

© Jared G. Beard 2015

Abstract

This quantitative research study examined differences in women’s giving motivation in a large

membership based nonprofit across 3 types of women. The theoretical framework was based on

the identification theory that postulates an individual‘s personal motivation to give was directly

correlated to self-identification with an organization. The findings of this study indicated that

overall 89% of the 855 respondents donated to another nonprofit with only 14% donating to the

selected membership based nonprofit. As boundaries between nonprofit revenue sources and

philanthropy are increasingly fluid, our theoretical understanding as well as our empirical

research on fund development must expand to encompass these new fundraising strategies. The

study summarizes the extant empirical literature on nonprofit financial development programs

and compares this research to emerging work on women’s motivation for giving. Drawing on

this literature, the study specifically calls for research on nonprofit fundraising that (a) gives

greater attention to the links between volunteerism, identification and women’s giving

motivation, (b) target marketing efforts of volunteerism and philanthropy to members with a

bachelor’s or higher educational level, and (c) the data suggests the nonprofit sector should focus

their efforts on approaches that deepen identification with the female donor base through

programs that allow; service on boards, ongoing volunteer activities that change lives and more

frequently asking for giving of financial resources.

iii

Dedication

This work is dedicated to my mother, Sandra Beard, to whom I promised I would

complete this study. She has been one of the driving forces in my life to never settle.

iv

Acknowledgments

There are many people to whom I owe so much appreciation and gratitude. Importantly, I

would like to thank God for giving me the opportunity to pursue this degree, and I would like to

thank my family for their love, support throughout this process. If it were not for the uncondi-

tional love and encouragement of my wife, Rachael, this dissertation would not have been possi-

ble. I have the deepest love and respect for her. I also wanted to thank my son Cole that sacri-

ficed our time together, that I promise to make up in the coming months. Next, I wish to thank

my mother and father, Sandy and Ron, as they instilled in me a drive and work ethic to complete

any task I begin and taught me the importance of faith, family, and dreams. They have been the

constant throughout my life and their belief in me and my abilities always lifted me to new lev-

els. I am sincerely blessed to have all these marvelous and inspirational people in my life.

I wish to extend a special thank you to my doctoral committee. First, a huge thank you to

my dissertation mentor, Dr. Suzanne Holmes, whose support, encouragement, and belief in me

never waned once during this process. She has challenged me and yet joined with me on this

journey, making certain that I created knowledge and scientific merit along the way. She is a

wonderful woman. Also, I would like to thank my committee members Dr. Ingles Morgan –

Gardner and Dr. Larry Sanderson. Without these individuals, their patience, and their practical

advice and suggestions, this study would have never materialized.

Finally, I would like to pay tribute to the many remarkable women who took part in this

study and opened the door to bring about change.

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Table of Contents

Acknowledgments iv

List of Tables viii

CHAPTER 1. INTRODUCTION OF THE STUDY 1

Statement of the Problem 1

Statement of the Purpose 3

Significance of the Study 4

Operational Definitions 8

Assumptions 9

Limitations 9

Delimitations 9

Summary 10

CHAPTER 2. LITERATURE REVIEW 11

Introduction 11

History of American Philanthropy 11

Fundraising Theories 16

Volunteerism 20

Donor Motivation 22

Women: the Emerging Donors 26

Theoretical Framework 32

Summary 33

CHAPTER 3. METHODOLOGY 35

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Introduction 35

Setting 37

Population 37

Research Design 36

Collection of the Data 36

Instrumentation 37

Research Questions 39

Data Analysis and Validity 39

Ethical Challenges 41

Sampling Plan 41

Research Philosophy 42

Summary 43

CHAPTER 4. RESULTS 45

Introduction 45

Reliability 45

Quantitative Findings 46

Demographics 46

Research Question 1 46

Research Question 1 Summary 51

Research Question 2 53

Research Question 2 Summary 65

Research Question 3 66

Research Question 3 Summary 75

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Chapter 4 Summary 76

CHAPTER 5. CONCLUSIONS, RECOMMENDATIONS, AND SUMMARY 79

Introduction 79

Review of the Research Problem and Purpose 80

Summary of Results by Research Question 83

Evaluation of the Results 91

Theoretical Implications 95

Implications for Practice 96

Recommendations for Future Research 99

Summary 100

Conclusion 101

REFERENCES 104

APPENDIX A. STATEMENT OF ORIGINAL WORK 110

viii

List of Tables

Table Page

1. Descriptive statistics of sample, age Categories 47

2. Descriptive statistics of sample, Age 47

3. Descriptive statistics of sample, education Categories 48

4. Descriptive statistics of sample, Education 49

5. Descriptive statistics of sample, hours volunteered at the Y 49

6. Descriptive statistics of sample, hours volunteered at the Y Categories 49

7. Descriptive statistics of sample, hours volunteered at another Non-profit 50

8. Descriptive statistics of sample, hours volunteered at another non-profit Categories 50

9. Descriptive statistics of sample, donation to the YMCA last Year 50

10. Descriptive statistics of sample, donation to the YMCA last year Categories 51

11. Descriptive statistics of sample, donation to another nonprofits last Year 51

12. Descriptive statistics of sample, donation to another nonprofit

last year Categories 51

13. Pearson chi square, group status of Sample 54

14. Pearson chi square, group status categorical for independent variable education 54

15. Pearson chi square for independent variable Education 55

16. Pearson chi square for independent variable amount of donation to the Y 55

17. Pearson chi square for independent variable donation to the Y 55

18. Pearson chi square for independent variable amount of

donation to the another NPO 56

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19. Pearson chi square for independent variable donation to the another NPO 56

20. Multivariate analysis of variance (MANOVA), descriptive analysis

for group differences for age, hours volunteered at the YMCA,

and hours volunteered at another NPO 58

21. Box's test of equality of covariance matrices for age, hours

volunteering at the Y and hours volunteering at another NPO 58

22. MANOVA results for age, hours volunteering at the Y and

hours volunteering at another NPO 59

23. Levene's test of equality for age, hours volunteering at the Y and

hours volunteering at another NPO 59

24. MANOVA, tests of between-subjects effects for group Status 60

25. Univariate descriptive statistics for group 1 differences for age,

hours volunteered at the Y and hours volunteered at another NPO 61

26. Univariate descriptive for group 1 differences for Age 61

27. Univariate frequency for group 1 differences for

hours volunteered at the Y 62

28. Univariate frequency for group 1 differences for

hours volunteered outside the Y 62

29. Univariate descriptive for group 2 differences for age,

hours volunteered within the Y, and hours volunteered outside the Y 63

30. Univariate descriptive for group 2 differences for Age 63

31. Univariate frequency for group 2 differences for hours volunteered at the Y 63

32. Univariate frequency for group 2 differences for

x

hours volunteered at another NPO 64

33. Univariate descriptive for group 3 differences for age,

hours volunteered at the Y, and hours volunteered at another NPO 64

34. Univariate descriptive for group 3 differences for Age 65

35. Univariate frequency for group 3 differences for hours volunteered at the Y 65

36. Univariate frequency for group 3 differences hours volunteered at another NPO 65

37. Multiple regression descriptive statistics for number of volunteer

hours at the Y, number of volunteer hours at another

NPO, years of Education 67

38. Pearson correlation for number of volunteer hours at the Y,

number of volunteer hours at another NPO and years of Education 68

39. ANOVA multiple regression for number of volunteer hours at the Y,

number of volunteer hours at another NPO and years of Education 68

40. Multiple regression, coefficients volunteer hours at the Y,

number of volunteer hours another NPO, years of Education 69

41. Multiple regression, model summary for number of volunteer

hours at the Y, number of volunteer hours at another

NPO and years of Education 69

42. Hierarchical multiple regression, descriptive Statistics 70

43. Pearson correlations, for independent subgroups

amount of volunteerism and Identification 71

44. Pearson correlations, model summary results for amount of

volunteerism and amount of Identification 73

xi

45. ANOVA for independent variables subgroups amount of volunteerism and

amount of Identification 74

46. Hierarchical multiple regression, coefficients of subgrouping

amount of volunteerism and Identification 75

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CHAPTER 1. INTRODUCTION TO THE STUDY

Chapter 1 provides background information that supports the need for this study. A

theoretical framework that supports the study was discussed. The purpose of the study, problem

statement, research questions and definition of key terms were also presented. Limitations and

delimitations of the study are introduced. Chapter 1 ends with the significance of the study and

anticipated benefits of this research.

Statement of the Problem

Recent research examined how women influence charitable giving in large, international,

voluntary service organizations (Kou et al.,2013). The results indicated that women are joining

service organizations in a greater percentage than men and through identification with the group

are donating at a higher percentage. Additional findings from the study indicated that

organizations could benefit from strategies that would encourage women’s participation and

cultivate a nurturing and welcoming environment. Kou, Hayat, Mesch, and Osili (2013) also

recommends future research should examine other nonprofit membership based organizations to

find the tipping point in which female representation begins to influence the culture within a

service organization.

According to Mesch (2010), female headed households at five different income levels

from $23,509 to $103,000 were more likely to give to charity than male-headed households.

From the same study, women also gave more than men when comparing amount given in every

income group except for group 2 (>23,509 and <43,500). In the past, women valued time as

much as giving money; however today many women are realizing the impact of money on the

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organization and the impact of time on the person. According, to Debra Mesch (2010), director

of the Women’s Philanthropy Institute at the Center on Philanthropy at Indiana University,

women still want to connect with the place where they give money. She also states that the

likelihood of giving a gift increases with the amount of time volunteered (Shaw-Hardy & Taylor,

2010).

The identification theory stems from the term “caritas” or care. Caritas was described as

the self – identification with the needs of others. The behavior of caring extends beyond the

individualistic nature of self to include; family, friends, neighbors, groups, communities, and

other associates. Havens and Schervich (2001) found that donors provided money and time to

individuals or organizations to which they were involved with in the past or felt a sense of

identity with.

The identification theory was supported by previous research from Shaw- Hardy and

Taylor (2010) that suggests women give as a result of passion or compassion to a cause. Women

are searching for community needs that can be solved through their gift. The theory also provides

a foundation for this proposed research to understand the giving motivation between women and

the nonprofit sector. The premise of caritas as a basis for motivation may deepen an

understanding of women’s giving and volunteering thereby served as the theoretical framework

for this research.

Based on the future fund development predictions of Damen and McCuistion (2010),

women represent the sector’s greatest opportunity for growth. Research indicates that women

over the next 20 years will control 80% of wealth in the United States. Recent research from

Women Give 2012 (Mesch, 2012), found that even though women earn less than men, have less

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money in retirement, and outlive their spouses, they are more likely to give and give more to

charity than men.

Based on the previous research on this topic and the foundational framework of

identification theory, membership organizations with large female membership percentages

should have a culture of giving based on identification. According to Klein (2006), 7 out of 10

people regularly give to charities or 70%. On the contrary, total member giving for the national

YMCA only represents 3.6% of men and women with women representing 51.8 % of that total

(YMCA of the USA [YUSA], 2013c). The targeted research site has a population of 10,577

female members and 708 are annual donors representing 6%.

In its recent history, the YMCA organization has struggled to connect members to their

philanthropic case for support. In 2010, the Y nationally engaged in a rebranding effort to

improve donor cultivation and member identification to build a platform to communicate their

charitable case (YUSA, 2010). The findings provided financial development program’s

strategies to engage women, the largest group of potential donors (Damen & McCuistion, 2010).

Statement of the Purpose

The purpose of this study is to provide empirical insights into the factors that contribute

to the giving motivation of women to help the nonprofit sector improve capacity building and

sustainability by learning how to use identification and volunteerism to increase charitable

giving. The overarching research questions examined to what degree the amount of volunteerism

and identification with the YMCA can predict a donation to the YMCA, and are there

correlations with descriptive variables that are statistically significant across the 3 groups of

women (a.) those who donate specifically to the YMCA, (b.) those who have donated to

organizations other than the YMCA, and (c.) those who haven’t donated to any organization?

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This quantitative research study examined differences in female giving motivation of

YMCA members across 3 types of women. The purpose of this empirical study was to better

understand the cause of identification with an organization. The nonprofit sector has struggled to

grow philanthropy at the same rate as other revenue sources such as; grants, sales of goods,

membership fees and programs (Dees, 1998). In the same article, the YMCA was mentioned to

have lost sight of its mission to promote the “spiritual, mental, and social condition of young

men” (p. 57). To that end, as a nonprofit the organization has an obligation to meet community

needs through programs that encourage members to donate to their cause. The significance of

this study could assist professional fundraisers to balance subsidies and contributions by

increasing giving motivation of women through identification with the nonprofit.

Significance of the Study

By identifying a gap in the research this study contributed to the fundraising industries’

identification theory. According to Mason nonprofits would greatly benefit from field

experiments in giving of time and money (2013). Mason also claimed that his review of 500

articles only produced three where the primary research was an experiment. This study added to

the collective body of research from the setting of a large volunteer membership based nonprofit.

The study added findings from the perspective of a nonprofit practitioner to provide greater

depth to the field of philanthropy and the topic of giving motivation. The majority of other

research has been conducted by economists and social psychological researchers that lack the

understanding of membership based nonprofits. Mason recommends leading theoretical models,

such as the identification theory, be tested in partnership with nonprofits to contribute useable

knowledge to the sector (2013).

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According to Women Give 2012 (Mesch, 2012), 76 million Baby Boomers will change

the face of philanthropy predicting that women are more likely to give to charity and give more

than their male counterparts when other factors affecting giving are taken into consideration. In

the past, women valued time as much as giving money; however today many women are

realizing the impact of money on the organization and the impact of time on the person (Shaw-

Hardy & Taylor, 2010). Women still want to connect to where they give money. Volunteering as

a mentor with students or girls more specifically are ways women choose to stay engaged.

According, to Debra Mesch, director of the Women’s Philanthropy Institute at the Center on

Philanthropy at Indiana University, stated that the likelihood of giving a gift raised with the

amount of time volunteered (Shaw-Hardy &Taylor, 2010). She continued on to encourage

matching volunteers to activities in an effort to increase the amount of funds raised.

This study addressed a gap in research identified by a lack of literature in the setting of a

large membership based nonprofit financial development program. The purpose was to advance

the knowledge in the area of philanthropy by building on the current literature through the

examination of the relationship between the independent variables (age, education, number of

hours volunteered last week at YMCA, number of hours volunteered last week at another

organization, money donated to the YMCA, money donated to other organizations) and the

dependent variables (participants amount of volunteerism and identification with the

organization). Based on the identification theory there should be a statistically significant finding

across the 3 groups of women of independent and dependent variables that predict a donation to

the YMCA.

According to documents available from the research location, the site was selected based

on its low current donor penetration rate of 6% out of a total membership base of 25,519 (18 and

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older) comprised of 14,235 female (56%) and 11,284 males (44%). This research provided

insights into the giving motivation of women that should have a high amount of identification as

members of the research location; however current member giving trends and volunteer activities

indicated that the female members are choosing not to support the organization with time or

money.

This research study was the evaluation of the financial development program of a

nonprofit organization in an effort to provide recommendations for increasing identification with

female members. According to Havens and Schervish (2001), the identification theory was based

on the premise that an individual‘s motivation to give was directly linked to self-identification.

This study contributed to the theory by examining the predictability of identification to the

outcome of a donation to the YMCA. The study examined the future impact of women on

philanthropy in the nonprofit sector. The targeted audience was professional fundraisers in the

nonprofit sector. The overarching impact on the sector was increasing charitable giving revenue

as a means to justify their tax exempt status and offset declining contributions.

According to Choi and DiNitto (2012), women volunteer in greater amounts and

demonstrate a greater amount of interest in meeting social needs. Additional findings indicated

that higher levels of income and education have a positive correlation to volunteering and

charitable giving. These additional findings may seem obvious however only approximately 14%

of the research site’s female members volunteer meaning that there was opportunity for

nonprofits to target individuals. The study also found that individuals are more likely to

volunteer at religious institutions than secular (Choi & DiNitto, 2012).The findings of Choi and

DiNitto’s 2012 study are significant when evaluating the setting of this proposed study of

YMCA members. The YMCA was a religious nonprofit that struggled to attract volunteers and

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charitable donations. The YMCA’s programs and services attract members with high income and

education so the question remains why the organization would struggle with volunteerism and

charitable giving. The proposed research site had a 25, 519 membership base of men and women

over the age of 18 with only 8% volunteering and 6% donating annually.

The findings contributed to the field of study by examining the factors that influence

volunteering and charitable giving. Specific to the YMCA was the factors that cause a YMCA

member to donate at another organization. The findings of this research also identified strategies

that nonprofits can implement to increase giving motivation of female members. The

overarching impact on the fundraising field could assist other nonprofit researchers and

development officers in understanding the motivations of the largest emerging donor group in

history (Damen & McCuistion, 2010). Recommendations could be made to the national YMCA

to be used to guide future fund development strategies and educate development officers. For

example, the Y engages 9 million youth and 12 million adults in 10,000 communities across the

U.S.; however the Y only attracts 500,000 volunteers and receives donations from only 3.6% of

members annually (YUSA, 2013c).

Past fundraising efforts by nonprofits have had a broad audience appeal based on the

traditional industry standard. The nonprofit sector was best described by the institutional theory

that postulates organizations are under pressure from peers in a similar industry to operate in a

traditional structure to remain legitimate (Harris, 1990).

The nonprofit sector has struggled to grow philanthropy at the same rate as other revenue

sources such as; grants, sales of goods, membership fees, and programs (Dees, 1998). The lack

of contributed revenue growth in comparison to others sources was causing taxing bodies and

other watchdog groups to question the exempt status of the nonprofit sector (Behn et al., 2010).

8

The loss of the exemption to nonprofits would cost millions in new taxes to the industry. To that

end, nonprofit organizations have an obligation to meet community needs through programs that

compel donations to their cause. The significance of this study was to assist development officers

to balance subsidies and contributions by increasing the giving motivation of women.

The findings of this research moved financial development programs away from

traditional fundraising techniques to a more targeted approach to gender groups. By more deeply

understanding the emerging women donor nonprofits can target fundraising marketing efforts,

program selection and volunteer opportunities to increase contributed revenue to meet

community needs.

Operational Definitions

The following terms were used operationally for the purpose of conducting this study:

Advancement: A term encompassing all fundraising effort to advance government,

churches, nonprofit agencies, and Universities.

Bequest: to will or endow property to a person or charity

Caritas: Describes the caring behavior and reflects the presence of self-identification

with other in need.

Charitable Organization: Organizations that meet social needs within the community or

help the needy. Also known as 501(c) 3.

501 (c) 3: Internal revenue service description of a nonprofit.

Development: the process of raising funds for programs, activities, and projects for

nonprofit organizations. Same as fundraising.

Development Officer: Person who is responsible for engage with donors on behalf of a

nonprofit organization.

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Donor: Person that contributes to a charity or nonprofit

Endowment: The act of donating an asset to a charity that is held with the principle

investment and draws the interest revenue for nonprofit support.

Fundraising: The act of asking individuals to support a cause or nonprofit organization.

Philanthropy: The act or effort to better society through monetary donation.

Assumptions

For the purpose of this study, the following three assumptions were accepted.

Theoretically, there was a priority structure to giving based on meeting basic needs such as

family security and association with other feelings. Once basic needs are met and the individual

feels comfortable they will give to nonprofit organizations. The second assumption was that

identification will motivate giving to an organization (Havens & Schervish, 2001). Topically,

there are differences in the motivational rationale of females when deciding to make a giving

decision. Methodologically, it was assumed that participants were truthful when filling out the

survey.

Limitations

The current study accepted the limitation that the setting of this examination only focused

on women that are members of the research site and their motivation to donate to the

organization. Therefore, the study may have limited ability to make generalizations to the entire

nonprofit sector (Field, 2013). The study also accepts the limitations for data collection expected

to take place during the summer of 2014. The findings are reliant on the timeframe and not

reflective of a different time of year. The study accepts the limitation of the ability of the

participants to self-report accurate and truthful information.

Delimitations

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The primary delimitation of the study was that several other dependent variables with

regard to donor motivation could have been addressed. Also, the researcher has experience and

working knowledge of philanthropy through professional fundraising efforts. The final

delimitation was the lack to scientific research on women’s motivation for giving in the nonprofit

sector.

Summary

Chapter 1 provided an overview that demonstrated the need for this research study. A

theoretical framework with linkages to the purpose of the study was discussed. The purpose of

the study, problem statement and operational definitions were also presented. Assumptions,

limitations and delimitations of the study were introduced. Chapter 1 ended with the significance

of the study and anticipated benefits of this research.

Chapter 2 introduces the brief history of American philanthropy, female philanthropy and

YMCA philanthropy. Secondly, an overview of well-known fundraising theories and the linkage

to volunteerism and donor motivation are examined. Third, the topic of women as emerging

donors was presented with the current literature for support of study. Finally, the chapter

concludes with a discussion of the primary theoretical framework that was used to examine the

motivation of females making financial contributions to a membership based nonprofit

organization.

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CHAPTER 2. LITERATURE REVIEW

Introduction

This chapter focused on the history of American philanthropy and women as an emerging

donor. While the focus of this chapter was on the giving motivation of women the overall

motivation factors were examined for greater context. Today, men have been viewed as the

primary donor requiring development officers to focus on their giving motivation and interest.

As women continue to have a longer life expectancy than men they will begin to possess the

majority of wealth in America. The review of literature was from existing scientific journals,

books and trade journals on the topic of philanthropy, volunteerism and donor motivation. The

following topics; history of American philanthropy, fundraising theories, volunteers, donor

motivation, women as emerging donors and the theoretical framework provide the context for

this examination.

History of American Philanthropy

By the beginning of the 19th

century, an established tradition of giving had taken hold and

became a constant characteristic of society to support nonprofit organizations. The tradition was

documented by Alexis de Tocqueville that traveled America in 1835 writing and documenting

his experiences (Probst, 1962). Tocqueville detailed his observations specifically when a

community recognized a widespread unmet need for a church, school or hospital they would

form a committee, select a leader and solicited support for the cause (Probst, 1962).

One of the most famous early philanthropic efforts was for Harvard College by three

ministers. The campaign took place in early 1600 when the minsters traveled back to England

12

from America to raise funds. One came back with E150 (English currency); the second stayed in

England and the third died in the gallows (Sargeant, 2010). Harvard was built in 1636 with the

largest donations coming from two Englishmen John Harvard and Elihu Yale (Rudolph, 1990).

From the founding of institutions, such as Harvard, men had a place to be educated developing a

donor pool that would lead the way for the next 300 years.

Female Philanthropy

Females were educated to be teachers in 1790 as an effort to promote the ideals of the

Christian roots. The purpose of educating women came out of the idea of a Christian wife,

mother, and teacher (Solomon, 1985). The evolution of the educated women ignited an increase

in philanthropy from 1790 through the 1850 (Parsons, 2004). In general, all religious groups

supported the education of women in an effort to Christianize the western frontiers (Solomon,

1985). Although, many feared the education of women would result in a decrease in their

motherly role, others felt men and women should receive equal education.

Today, females have climbed to the top of organizations, although still unrepresented,

such as Sheryl Sandberg the Chief Operating Officer of Facebook. Sandberg attributes her

success to her grandmother “Girlie” that modeled a progressive style that she later adopted

(2013). She also stated that her mother dropping out of a Ph. D program in 1965 to give birth and

stay at home to raise the family had a lasting impact on her. At that time it was thought to be sign

of weakness for a wife to work and not stay at home with the family. These events were common

at the time and have provided the guiding force to many females who strive to change the future.

The educational expansion of women has played a large role in the increase of philanthropy. In

1982, Harvard professor Carol Gilligan claimed that women’s intentions had been

miscommunicated and should be allowed to speak with their own voice and with their own sense

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of integrity (1982). Many feminists took offence to the claim as they argued that there was no

difference between men and women. Gilligan observed children at play and determined that

women have differing moral and psychological tendencies than men (1982). She also claimed

that women think more in terms of relationships and men are more rules driven. As a result of

Gilligan’s work, philanthropist began relationship driven methods such as giving circles and

women’s philanthropic initiatives. Over the last 300 years many females have contributed to

society and the growth of this nation. One such female was Anna Richardson Harkness who

distributed $40 million dollars over the course of her life in the 19th

century (Shaw-Hardy &

Taylor, 1995). She gave $6 million to Yale University, $4 million to Columbia University and

founded the Commonwealth Fund with an endowment of $20 million. Her wealth came out of an

inheritance of $50 million from her husband in 1888, which she managed for the next thirty –

eight years increasing the estate to $85 million. At the time of her death in 1926, she designated

most of her wealth to nonprofits and $22 million to the Commonwealth Fund (Shaw-Hardy &

Taylor, 1995).

Since the late 1700’s through the 1800’s women have pooled their resources through

collective giving. This unique practice seems to attract many women based on the relationship

factor and the impact of larger gifts. One of the first collective groups formed in 1797 was the

Society for the Relief of Poor Widows with Small Children in New York City. The society was

founded by Isabella Martha Graham using her husband’s inheritance combined with gifts from

fifteen other women (Shaw-Hardy & Taylor, 1995). In the first year the Women’s Society helped

98 women and more than 200 small children. By 1816, the group supported more than two

hundred women and over five hundred children. At the beginning of the 20th

century, nearly

every town of any size had a similar organization that was managed by their affluent women

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(Shaw-Hardy & Taylor, 1995).Today, higher education has adopted this new trend of giving

circles as a way to increase identification with alumni and key stakeholders (Beeson, 2006).

Other nonprofits have been slow to develop giving circles at the same rate as colleges and

universities as a result of limited time and development staff. In the college setting development

officers have focused on male donors as a result of their higher pay on average (Matthews,

1991).

In 1931, Jane Addams was awarded the Nobel Peace Prize for her work as the founder of

the Hull House that provided educational, vocational and domestic training for women and

immigrants (Mc Henry, 1980). Supporters or workers of the Hull House were typically recent

graduates of newly founded women’s colleges. She felt the women benefited by the experience

and found bond to the less fortunate (Mc Henry, 1980).

Today, women control 60% of the wealth in the US and evidence indicates they will

inherit and manage even more wealth in the future (Damen & McCuistion, 2010). The simple

fact that women out live men by five years have been the basis of the prediction that women will

control 80% of their families’ financial affairs sometime in their life. Experts Havens and

Schervish estimate that $41 trillion dollars was expected to pass through the hands of Americans

by 2052 (2003). They also predict that $6 trillion will be given to charitable organization. The

estimates were found to be reasonable by the review and confirmation of the Council of

Economic Advisors and the Bureau of Labor Statistics. Furthermore, the Congressional Budget

Office staff of economists used the estimation to analyze the future wealth transfer (Havens &

Schervish, 2003).

Based on these findings from experts Damen and McCuistion, (2010) and Havens and

Schervish, (2003) women will play a critical role in defining the philanthropic landscape in the

15

next several decades. Recent findings from Women Give 2012 (Mesch, 2012) challenge the

perceptions about who was philanthropic, revealing that Boomer and older women are as or

more philanthropic than their male counterparts. Findings from Women’s Business Research

indicated that 54% of businesswomen make all of their decisions independently of advice or

counsel from anyone. With increases to overall wealth, greater amounts of giving than their

counterparts, coupled with independent decisions, women are quickly beginning to realize the

power they possess to change the face and future of our society.

YMCA Philanthropy

The YMCA was founded in 1844 in London, England, and then expanded to the US in

1851 with the primary objective to empower communities to address the social needs facing

them during the modern industrial society (YUSA, 2013a). Retired sea captain, Thomas V.

Sullivan convened the first meeting of the YMCA in the US to discuss drafting a constitution

(Hopkins, 1951). From 1840 to 1960 the civil rights movement and the industrial revolution

changed the YMCA organizational model to focus on recruiting volunteers to assist with the civil

unrest, as a result other social issues emerged such as; disease, homelessness and racism. For

example, the New Orleans YMCA raised $23,000 in 1858 to provide care for victims of the

yellow fever and other diseases. In the late 1860, YMCA leaders began buildings hotels in

response to the need for Christian based affordable housing for immigrant railroad workers

(YUSA, 2013a).

In the 20th

century, corporate philanthropy was very uncommon as businesses continued

to only focus on profits. However, the focus on profits changed during the time of the railroad

boom attracting immigrant workers from all over the world. The influx of these workers caused

social problems to communities such as alcoholism, prostitution and homelessness. The railroad

16

companies realized in order to complete their work they would need to address the social

environment that was preventing the productivity of the workforce (YUSA, 2013a). Knowing

that they were unable to address the issues themselves they turn to the leader of social

programming at the time, the YMCA. The railroad YMCA partnership launched the nonprofit

into the largest provider of transient hotels in the country. The first railroad YMCA, as they were

called, was founded in 1872 in Cleveland, OH (YUSA, 2013b). The blend of Christian

environment and safe housing met the need for the railroad to have a productive work force and

the YMCAs need to spread the message of Christianity (YUSA, 2013b).

Unknown at the time the decision to partner with the railroad had many unattended

consequences for the future of the YMCA. The YMCA organizational model dedicated to

ministry and addressing social issues had changed to address the new need of continuous cash

flow to offset building expenses. Hotel revenues were generated by charging workers to stay

overnight and providing financial assistance to those who could not afford accommodations.

However, as the railroad neared completion the need changed from overnight accommodations

to a place for social programs. By using the physical assets built up across American through

hotels now sets the stage for the YMCA to transition to the largest nonprofit provider of its time.

Fundraising Theories

Fundraising theories have provided the foundation for many of today’s donor

engagement strategies. This section examines the leading theories from the industry of fund

development. In the past, researchers, such as Harris (1990), have examined the interaction of

environment and institution using three theories; resource dependency theory, enactment theory,

and institutional theory. Resource dependency theory states that the key to the survival of an

organization was the ability to attract and maintain resources. The resource dependency theory

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postulates that nonprofits become dependent on revenue sources such as; programs, special

events, campaigns, memberships or products. Nonprofits are constantly planning and

strategically creating programs to generate new revenue in an effort to support their

philanthropic work in the community.

The enactment theory encourages an organization to rethink its constraints, threats and

opportunities to create a new environment (Harris, 1990). The concept would be to allow a

development officer to create a more donor centric environment for the organization. In a

practical application this theory would resemble a rebrand launch of a nonprofit. For instance, a

nonprofit might chose to redefine their mission or vision in an effort to improve their position in

the community.

The institutional theory postulates that organizations are under pressure from other

organizations in a similar industry to operate in a traditional structure to remain legitimate

(Harris, 1990). Based on the theory an organization may implement a fundraising structure that

has been successful at another organization.

In 1991, the topic of giving motivation reached a divide in the field of fundraising based

around two conflicting opinions. Simmons argued against the “warm glow theory” or altruism

that self-interest was at the heart of every act of giving, he went on to state that even the most

selfless act can be traced back to self-interest (1991). In contrast, Andreoni (1989) argued that

selfless or altruistic giving was the result of an intangible psychological effect of feeling better

termed “warm glow” theory. The theory explains the psychological effect of feeling better about

oneself after the act of charitable giving. The warm glow theory could be described as the

motivation for giving to others was out of a feeling of satisfaction after making the gift.

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Another theory that could have an influence on giving was the commitment – trust

theory. This marketing theory postulates that trust affects the commitment level of the donor and

the motivation to give a gift. The linkage from commitment and trust was based on the

assumption that the two elements already exist and drives decisions based on the premise.

Researchers’ Morgan, R. and Hunt S. were the first to postulate this theory and have since built

substantial research to support the theory’s findings (1994).

The identification or connection to a particular program or cause was beginning to

change among nonprofits as broad organizational appeals are declining. According to Ajzen’s

expectancy – values model theory attitude can be calculated as the product of evaluations of

behavioral outcomes value and how strongly people think a behavior may lead to that outcome

expectancy (1991). Furthermore, in a decision making process the donor will evaluate the cause

of the organization or values and the likelihood that their donation will make a difference. An

example of this type of motivation was the Royal National Lifeboat Institution that allows donors

to purchase needed equipment for life boat crews as they struggle to save lives at sea (Mort &

Rose, 2004). Donors have the simplistic recognition that a particular funding level will purchase

a particular item for the rescue worker, such as a lifejacket, boots, or a safety line. The donor’s

connection to a designated gift with a singular purpose has shown to increase the amount and

likelihood of a gift (Mort & Rose, 2004).

Another theory that could impact the female donor was from the same researchers’ Mort

and Rose (2004) titled means-end chain theory. The theory postulates the reason people develop

a particular giving interest is a result of the end goal that they want to achieve. To clarify, an

example of this theory would be giving or donating to the International Rotary Clubs efforts to

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eradicate polio from the Earth. A particular donor may be motivated by ending polio; however

decline to donate to support local needs of the community through the civic group.

In 1995, Kelly proposed four theories in an effort to explain the different fundraising

techniques. The four theories were; press agentry, public information, two-way asymmetrical,

and two way symmetrical. The press agentry theory was based on influence and control of the

donor by relationship building (Kelly, 1995). One of fund developers’ most common

fundraising techniques was by promoting their organization and building strong personal

connects with donors. The studies using this theory would examine the perception of the donor

and fundraiser relationship. The independent variable would be the giving motivation of the

donor and the dependent variables of age, gender, marital status, education, etc. The public

information theory was based on the theory of enlightenment and truth. Meaning a fundraiser

would use organizational information through a communication medium to influence giving. The

independent variables would include donor motivation and the dependent variables of trust and

commitment to the organization. The two-way asymmetrical model’s purpose was to use a two

way communication model to scientifically persuade giving (Kelly, 1995). The two-way

asymmetrical theory would examine the communication of the donor and the fundraiser. The

independent variable would include donor giving motivation and the dependent variables of

communication, perception of the organization and perceived relationship. The final model from

Kelly was the two-way symmetrical that also has a two way communication allowing the donor

and the organization to reach a common understanding. The model was founded on the principle

that donors benefit from making a gift because the organization benefits allowing the overall

society to benefit (1995).

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As the economy slowly trended upward and unemployment was trending down many

nonprofits are investigating in ways to generate greater amounts of contributions to their cause

(Nonprofit Research Collaborative, 2013). The nonprofit sector was facing an increased demand

for service and lower revenue from government grants, program fees and other resources

(Nonprofit Finance Fund, 2013). This section focused on effective fundraising theories that could

generate sustainable philanthropic growth as part of the solution to the growing pressures many

nonprofits are facing in today’s environment. The next section transitions from theory to practice

by examining volunteerism as a predictor of giving motivation.

Volunteerism

According to Mesch, the likelihood of giving a gift raises with the amount of time

volunteered (Shaw-Hardy &Taylor, 2010). Mesch’s 2009 research supports the findings that

women who volunteer at a particular organization give in greater amounts. She continues on to

encourage matching volunteers to activities in an effort to increase funds raised. Her final

recommendation was listing volunteer opportunities on the organizations website to inform the

public with a plan in place to train and schedule. However, the primary reason they donate was

unknown. Nationally, the YMCA is examining ways to improve fund development strategies and

educate development officers for greater amounts of contributions to the organization. For

example, the Y engages 9 million youth and 12 million adults in 10,000 communities across the

U.S. in membership; however the Y only attracts 500,000 volunteers (4%) and receives

donations from only 3.6% of members annually (YUSA, 2013c). The identification theory would

suggest that the primary motivation would be the identification with the organization and a cause

they care about. The act of caring would be demonstrated though the time they choose to

volunteer to an organization. According to Choi and DiNitto (2012), women volunteer in greater

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amounts and demonstrate a greater amount of interest in meeting social needs. Additional

findings indicated that higher levels of income and education have a positive correlation to

volunteering and charitable giving.

Psychological theories reference motivation in personality traits and self-concepts

(Wilson, 2012). Sociological theories cite more volunteer motivation with tendencies toward

race, gender, and social class (Wilson, 2012). However, overall the identity theory of

volunteering could be the best description of motivation on the topic. For example, in a Spanish

study young female volunteers who identified with the role were more likely to express an

interest to volunteer in the future (Marta & Pozzi, 2008). The implication was that females are

more connected by a relationship causing a greater interest in volunteering. Another Spanish

study found that identifying as a volunteer also helped to predict the duration of the service to an

organization (Chacon et al., 2007). In contrast, a more recent 2012 article questioned if there

was really an effect from volunteer identity given the variety of volunteer programs and activities

from across the organization (Wilson). In a small group of volunteer interviews qualitative

findings revealed five different role identities: the influencer, the helper, faith based, community

and success. Each group identified with a different purpose to the reason why they volunteered.

The attachment theory was another indicator of volunteerism. The theory suggests that

only people who feel reasonably secure themselves will invest time and energy in dealing with

others’ needs and suffering (Boelby, 1969). The theory uses an attachment avoidance dimension

with the key indication of distrust of another’s goodwill and the amount of independence viewed

by the volunteer. With regard to females this theory could hold some insight into how they view

volunteering and relationship building. Organizations that are recruiting for policy volunteers

would want to select women who demonstrate behaviors that show attachment to serving others.

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In summary, this section supported the predictor of the amount of volunteerism and time

on giving. Women are searching for causes that connect their passion and purpose into positive

results unlike; men that have been socialized to expect to receive payment for the work they

perform. The next section examines in more detail the gender difference with regard to giving.

Gender

In the United States, women do more volunteer work than men; however men still have a

slight advantage in resources and social capital (Einolf, 2010). Women are primarily motivated

by the pro-social nature of volunteering. Research indicated that men and women volunteer at

different rates, for different hours, different activities, and in different organizations (Wilson,

2012). Women do seem in general to be hardwired to be engaged in their communities; however

men have been shown to work very hard on boards and in governance of organizations (Shaw-

Hardy &Taylor, 2010). Women tend to consider volunteering as part of their DNA or something

they are expected to do (Shaw-Hardy &Taylor, 2010). On the contrary, men have been socialized

to expect to receive payment for the work they perform. For example, in youth sports men will

accept a coaching position while it would be common for women to accept a “team mom” role.

The selection of males to leadership positions and females to “back up roles” demonstrates the

assumption about interests and capabilities of women towards nurturing (Messner & Bozada,

2009).

Donor Motivation

In the past, fundraising has operated on the assumption that trust will have a direct impact

on commitment and giving motivation (Sargeant & Lee, 2004).Sargeant and Lee’s 2004 study

used empirical data to determine the strategies needed to increase trust and commitment within

an organization. A key finding from the same study also found that the building of trust as a lone

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characteristic would be largely ineffective to increase giving motivation. The conclusion of the

study indicated that a combination of trust and commitment would influence giving to an

organization and effect donor giving behavior. For example, supporters of the cancer society

might reasonably be expected to exhibit higher levels of commitment if they have had a personal

experience with the cause resulting in an increased giving behavior (Sargeant, 2004).The author

hypothesizes two results. The first prediction was that a high degree of commitment would

correlate to giving motivation as a positive causal link. The second prediction was that a high

degree of trust would influence giving motivation; however mediated by commitment, would

have a positive causal link (Sargeant & Lee, 2004).The results enabled fundraisers to craft their

message to the correct audience increasing giving behavior and motivation.

Many nonprofits are struggling in the new environment of a poor economy and greater

competition for the contributed dollar. With a greater understanding of why donors chose to

contribute to a particular cause will enable fundraisers to continue the charitable work of their

nonprofit. The concern in countries such as Australia, the UK and Canada is that many of the

most wealthy citizens of $1 million plus earners are not claiming any tax deduction at all

meaning a decline in giving overall (Madden, 2006). The impact of tax as a motivating factor to

giving has recently been increasing as government officials examine ways to increase

government revenue. According to Hall (2010), donors would rather give to nonprofits to avoid a

high tax at year end. For example, at the University of Alabama a high profile donor contacted

the college and made an additional $100,000 donation to protect his assets while also fulfilling

his charitable commitments. The study indicates that many wealthy people that are worried about

impending tax increases could decide to give more at year end. In the US evidence indicates that

80% of high income individuals are interested in giving more back to their community (Prince,

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2000). The motivation and capacity to give was present in these countries; however nonprofits

must have a better understanding of what compels them to give. The assumption was that donors

will report factors that influence their culture have a link to their decision to donate. The findings

of Madden’s study revealed lower levels of motivation linked to identity with a social group and

higher levels with passion for social change (2006). Another assumption was that the trust a

donor has in the organization will need to be high to result in a contribution.

Recent research provides evidence that motivation and identity may be the result of the

donors’ employer. A 2013 study found that the workplace of a donor had a significant impact on

their philanthropic decision making (Smith). In this case study the findings revealed that

workplace attitudes and interactions strengthened employees’ philanthropic values and

influenced behaviors that increased both time and money donated. Researchers of the same study

also found that workplace identity drove decisions regarding their choice of charity. The concept

of identity was different than past research that would suggestion trust as a predictor of giving

motivation. Such as, Sargeant and Lee’s 2004 study that suggests trust and commitment levels

are the primary predictors of giving motivation. The results indicated that nonprofits cannot

focus on efforts to build trust alone. Even large increases in trust will have only a minor impact

on giving motivation where commitment levels are low (Sargeant & Lee, 2004). Based on this

study the UK government, for example, has reevaluated its policies aimed at increasing trust to

increase giving behavior. The implications of this study could be used in any fundraising setting

to demonstrate the important components that need to be present to increase the giving

motivation of donors. However, Smith (2013) would argue that work identity has supplanted

trust and commitment to override a person’s idea of self and giving motivation.

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Many nonprofits are being force to attempt new ways to offset the poor economy by

trying innovative approaches to giving motivation. A current example of this innovation in donor

motivation was a Greenpeace campaign to bring awareness of the Japanese government

legalizing whale hunting (Ohanian, 2009). The Greenpeace organization decided to release a

marketing campaign to name a whale that was filmed on video to bring awareness and support to

their cause (Ohanian, 2009).The video asked stakeholders to vote for a name of an anonymous

whale. The winning name was Mr. Splashy Pants that went viral among the social media

community bringing wide spread awareness of the issue.

Mister Splashy Pants was a phenomenal example of the power of social media and

audience centric marketing. The response rate of the target market was extraordinary voting the

whale to 78% over the next name at 3% (Ohanian, 2009). As financial development programs

design the marketing plans for their cause it will be critical for them to generate a grassroots

connection to donors and compel them to donate, such as the Mr. Splashy Pants campaign. A

similar grassroots campaign from the Australian Childhood Foundation featured a child covered

by white wall paper on the front of a business in downtown Melbourne, Australia (Sargeant,

Shang, & Associates, 2010).Of course, the child was not real; however the print over the ad read

“Neglected children are made to feel invisible”, (Australian Childhood Foundation, 2012). A

videographer filmed the reaction of people walking by that were struck by the ad. The video was

then upload to the foundations website and sent out using social media.

The way nonprofits are motivating their donors and communicating to the public is

changing and becoming increasingly competitive among the nonprofit sector. The nonprofits that

understand the motivation of their current donors and target new donors based on innovative

communication will be better positioned for the future competitive fundraising environment.

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Women: The Emerging Donor

For many women, the expansion of their passion and purpose was how they choose to

give time and money (Damen & McCuistion, 2010). Their motivation was tied directly to their

childhood experience and the model set by their parents. Self-expressed giving through wealth

and time represent our basic need of a sense of belonging and purpose. The father of humanistic

psychology, Abraham Maslow, explains this motivation through “self-actualization” written in

1943 (Damen & McCuistion, 2010). Maslow writes, “Might be phrased as the desire to become

more and more what one is, to become everything that one is capable of being” ( p. 35). New

research suggests that younger women are giving an estimated two times more money than men

of the same age (Mesch, 2014). The same study also found that young women who are

unaffiliated with a religion give roughly twice the amount to charitable organizations than

women who are affiliated and infrequently attend religious services.

Philanthropy in the next 10 to 20 years will change drastically and become more complex

than ever before. Successful nonprofits will need to identify emerging groups of new donors and

begin to understand their motivations for giving. According to Marx, there are three interrelated

economic and demographic streams that will dramatically increase giving in the near future

(2000). The first factor was the buildup of wealth based on the policies created by the Reagan

and Bush administrations in the 1980s. During that time the United States witnessed

unprecedented gains in wealth in the upper-income groups. The second factor will be the groups

of older Americans that have over preformed financially in the last 40 years that are expected to

bequest close to $41 trillion from 1998-2052 (Havens & Schervish, 2003). The transfer of wealth

during this specified time will represent the largest transfer of wealth the world as ever seen. The

final factor was the generation of Baby Boomers that are now reaching their peak age for giving.

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In contrast to the past dominate male donors; experts are predicting women will give

large portions of this wealth to programs that benefit women (Mesch, 2010). The future

implications for fund developers will be the paradigm shift of giving motivation and the

programs that will be supported by women. The giving motivations of women are very different

than that of men and will need to be understood for nonprofits to be successful in future

fundraising efforts. Today, more US wealth is produced jointly by working men and women

however; individually working women make up an estimated 60% of US wealth. As the largest

group of emerging donors nonprofits should target programs and volunteer opportunities that

attract women to begin the cultivation process. For example, the giving habits of Sheryl

Sandberg, Facebook COO, are best described in an interview with the Huffington Post. She

stated in this century the brutality inflicted on women around the globe (sex trafficking, acid

attacks, bride burnings and mass rape) has been the paramount moral challenge (Sandberg,

2009). The statement of moral challenge from Sandberg was a glimpse into her giving

motivation to empower women. She goes on to argue the linkage between global poverty and the

challenges facing women around the world. Sandberg’s argument is similar to Bill Gates’s

giving pledge statement to solve global poverty; however Gates does not link poverty to the

plight of women. Gates’s motivation is inspired by his belief that preventable diseases are the

primary cause of global poverty (Giving Pledge, 2013). The similarities are both admirable and

certainly valid, although the extent of giving from Gates will only last as far as his wife takes it

after his death. The more critical question will be if Melinda Gates is motivated by a linkage to

women or will she follow her husband’s lead and continue his method to solve global poverty.

Based on the life expectancy prediction of women it is likely that she will outlive her husband

and be left to decide the future of the $37 billion Bill and Melinda Gates Foundation (2013).

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Sheryl Sandberg has found her passion for giving and currently serves on the Women for

Women International Board of Directors (Sandberg, 2009). The humanitarian organization

strives to help women survivors of war move from crisis to self-sufficiency. Donors are paired

with women in need to provide financial assistance, job skill training, leadership training, rights

awareness training and microcredit loans (Sandberg, 2009). Financial commitment is $27 dollars

a month to offset the cost of rebuilding a life after war. Another aspect to the plan is a letter from

donors to the participant that is translated on their behalf. The organization has targeted the

emotional connection to donors and provides tangible outcomes.

Culture, environment and social trends all give evidence of women as the next large

group of emerging donors. According to Damen and McCuistion, single women compared to

single men currently give on average $630 more per year (2010). Women are earning and

receiving wealth primarily through inheritance and the workplace. The following section

examined the history and motivational components of the two distinctly different wealth

generators.

Workplace

Since 1980, women have progressively achieved 50% of the college graduates in the

United States (Goldin et al., 2006). Fifty percent of college graduates was an educational

achievement that took over 100 years to balance caused by environmental and cultural barriers.

Today, workplace barriers still exist for women in leadership roles in America. In top leadership

roles women hold 14% of executive officer positions, 17% of board seats, and constitute 18% of

our elected congressional officials (Catalyst, 2012). Unfortunately, to get percentages up to a

balanced 50% could take another 100 years if plans are not in place to encourage female

advancement. However, in recent years women have shown the ability to overcome workplace

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barriers earning more than ever before. Since 2004, women are the fastest growing sector of

wealthy individuals in the last decade by tripling the number of women who earn more than

$100,000 per year (Shaw-Hardy & Taylor, 2010).

According to Sheryl Sandberg, Facebook COO, the statistics are caused by external and

internal barriers (2013). She describes the external barriers as institutional conditions such as;

sexism, discrimination, lack of child care, parental leave and the criteria for advancement. These

external barriers are heightened by a 2011 Mckinsey report that found women are promoted on

past accomplishments and men are promoted by potential (Barsh & Lee, 2011). In contrast,

internal barriers are the conditions that are developed in early childhood and adulthood such as;

lacking self-confidence, not raising their hands, low achievement expectations, life balance,

housework, and child responsibilities. Both internal and external barriers are real obstacles to

women and their ability to achieve leadership positions.

Witter and Chen writer of The She Spot argue that women’s income has risen 60% over

the past thirty years in contrast to men’s median income increase of 6% (2008). The cause of this

gain was most certainly the educational increase of women over the last 20 years. Females are

increasingly outperforming males in the classroom, earning about 57% of the undergraduate and

60% of the master’s degrees in the United States (Sandberg, 2013). By out pacing men women

have found success in penetrating previously male dominated fields such as; astronauts, partners

in law firms, surgeons, rabbis, police and fire, Supreme Court Justices and Chief Executive

Officers.

Inheritance

The largest generation in America today is the Baby Boomer population at 76 million

strong (born1946-1964;Mesch, 2012). Holding more than 90% of the country’s net worth and

30

78% of all financial assets baby boomers represent the future of giving (Havens & Schervich,

2011). According to Damen and McCuistion (2010) it was predicted that by 2030, 54% of

American boomers will be women.

Men and women differ on their investment of inheritance. Men often use funds in

business opportunities and women are more likely to set up a charitable trust (Nichols, 1990).

This fact should outline the planning efforts of Development Officers to understand the

motivation of this potential group of donors. For most women passion and purpose are the

leading indicators for how they give their time and money (Damen & McCuistion, 2010).

Passion and purpose are grounded in childhood experiences that shape the phonological needs of

potential female donors (Damen & McCuistion, 2010). With the previously stated potential of

boomer women’s life expectancy to exceed men by five years places them in the philanthropic

future of the nonprofit sector. An example of female motivation and gift giving comes from

Jennifer Ladd and a $1million inheritance from Standard Oil (Shaw-Hardy & Taylor, 2010). In

1972, Ladd was twenty – one and felt overwhelmed by the gift and sought out other young

people with wealth and similar values. Ladd learned to give strategically and through

collaboration with others. Today, women are less afraid of their inheritance and are preparing for

it. The childhood experience of the Generation X and the Baby Boomers will play a key role in

how and who gifts are given.

Generation X, (born between 1961 and 1980) was a unique group in that it grew up in a

society characterized by soaring divorce rates, recession, chronic disease and technological

advancements never experienced by previous generations. Technology exploded with innovation

in electronics and communication, such as the cell phone and the internet. As young adults,

college graduates experienced an unstable economy and a deteriorating job market. The job

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market improved in the late 1990s, with the technology boom and the internet startups only to

see it decline once again a few years later. The X Generation became labeled over time as lazy

resulting from their technology addiction.

Today, Millennials and Generation Xers are beginning to overcome the lazy, lost and

tech addict stereotypes (Kleber & Associates, 2005). Many of those in Generation X started

careers later than the Boomer Generation (typically considered those born between 1946 and

1964). However, statistics show that, in their thirties, more members of Generation X are

homeowners and wealthier than their parents (Kleber & Associates, 2005). As a result,

Generation X donors are in need of information, question where money was going, and prefer to

communicate by email. Millennials and Generation X single women that are unaffiliated with a

particular religion have recently been found to give two and half times more money than their

older counterparts (Lindsay, 2014).

In contrast to the Generation X, Baby Boomers see a major difference between their

inheritance and their earned income (Riley, 2004). Boomers are characterized by the name the

Great Generation experienced World War II, Vietnam War, The Great Depression, The Civil

Rights Movement, and The Feminist Movement (LaBranche, 1992). The largest generation in

modern history is described as; well-educated, fiscally conservative, idealist, and perfectionists

(LaBranche, 1992). Over the next ten years this group will experience an unprecedented $41

trillion transfer of wealth, with $6 trillion possibly to nonprofits (Riley, 2004).

To reach this generation fundraisers will need to understand the motivation of this group

to give. Boomers will be inspired to give in honor of their parents and will look for causes that

their parents would have approved of in life (Riley, 2004). Unlike the Generation X, Boomers

feel responsible that the gift is used for the direct recipients and not administrative costs. A

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feeling of guilt and responsibility of unearned wealth will motivate helping the less fortunate.

Conservative by nature the group will align with religious organizations and build lasting

relationships. Driven by results they will also ask for tangible evidence that their gift is

strengthening the community.

Theoretical Framework

Based on the findings of Havens and Schervish the identification theory postulates that an

individual’s personal motivation to give was directly correlated to self-identification with an

organization (2001). The theory suggests:

It is self –identification with others and with the needs of others (rather than selflessness),

that motivates the transfer to individuals and to philanthropic organizations and that leads

givers to derive satisfaction from fulfilling those needs. The notion of identification is

grounded in the religious and philosophical tradition of the practice of human love. (p. 1)

Within the paradigms of the theory were the four elements based on the foundation of

care and the comparison (Havens & Schervish, 2001). Meaning, there is a priority structure to

giving based on four objective associations: meeting basic needs, religious traditions, experience

of blessing and the need to help others. Once the basic needs were met and the individual feels

comfortable they can give to trusted nonprofit organizations. The second association with

religious traditions that was found can be derived from identification with organizations. The

third association was an understanding of blessings that could cause an internal warm

psychological feeling and would be associated with social expression. Finally, the fourth

association was concluded with a feeling of judgment asking four questions:

1. Is there something you want to do with your wealth?

2. That fulfills the needs of others?

3. That you can do more efficiently and more effectively than government or private

enterprise?

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4. And that fulfills your happiness by expressing your gratitude; bring you satisfaction,

and actualizing your identification with the fate of other? (Havens & Schervish, 2001,

p. 2).

The identification theory stems from the term “Caritas” similar to the act of caring or

loving (Havens & Schervish, 2001). Caritas was described as the self – identification with the

needs of others. According to Freyhan (1948), Caritas was complicated by its relationship to the

love of God and the love of one’s neighbor. Most theologians at the time would argue that both

relationships together would define the term; however the New Testament clearly states the

primary object of Caritas was God (Freyhan, 1948). The behavior of caring extends beyond the

individualistic nature of self to include; family, friends, neighbors, groups, communities, and

other associates. The researchers found that donors provided money and time to individuals or

organizations to which they were involved with in the past or felt a sense of identity with.

The identification theory was supported by previous research from Shaw- Hardy and

Taylor (2010) that suggests that women give as a result of passion or compassion to a cause.

Women are searching for community needs that can be solved through their gift. The

identification theory provides a foundation for this study to understand the giving motivation

between women and the nonprofit sector. The premise of caritas as a basis for motivation may

deepen our understanding of women’s giving and volunteering thereby served as the theoretical

framework for this research.

Summary

Chapter 2 provides the supporting literature for an examination of women’s giving

motivation to the nonprofit organizations. The clear gap in research from the previously

mentioned researchers (Kou et al., 2013, Damen & McCuistion, 2010, Mesch, 2010, Shaw-

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Hardy & Talyor, 2003, Parson, 2004,Choi & DiNitto, 2012), lacks the presents of a large

membership based nonprofit, such as the YMCA. Current research examines institutes of higher

education and small NPOs. The identification theory in the setting of a large membership based

nonprofit should contribute to the field of study in new ways. Kou, Hayat, Mesch, and Osili

(2013) also recommends future research should examine exploration of other nonprofit

membership based organizations to find the tipping point in which female representation begins

to influence the culture within a service organization. This study contributed to theory by

examining if and to what degree the identification with the YMCA was strong enough to compel

a member to donate to the organization as predicted by the theory. Current research from the

fundraising industry was broad and focused on men or generational giving tendencies. In order to

generate new contributions from women a targeted approach will need to be planned and

executed based on motivational giving research. By more deeply understanding the emerging

women donor nonprofits can target fundraising marketing efforts, program selection and

volunteer opportunities to increase contributed revenue short falls. The chapter of literature

included the history of philanthropy, fundraising theories, volunteers, donor motivation, women

as emerging donors and the theoretical framework.

Chapter 3 presents the research design and methodology that were used to conduct this

study. The collection of data was reviewed, as are the three guiding research questions. The

setting, population, instrumentation, data analyses, research philosophy and researcher

positionality are also discussed.

35

CHAPTER 3. METHODOLOGY

Introduction

This chapter explains the chosen methodology and the connection to the topic of

women’s giving motivation. The purpose of the study was to develop a deeper understanding of

the variables that influence giving motivation and how a financial development program can

strategically design efforts to elicit greater funding. As females continue to accrue greater

amounts of wealth in the next 10 years professional development officers will need to implement

strategies to deepen engagement through greater understanding of their charitable motivations.

Setting

The research site was located in a Midwestern membership based YMCA. Over 36,000

members from a five county region are served by the nonprofit organization. The geographic

focus of the organization was a loose collection of rural, mid-sized and urban cities. The research

site was a leading nonprofit organization in the area focusing on youth development, healthy

living, and social responsibility. In addition to the membership base, the YMCA has built a

strong body of 3,000 volunteers; however the primary target for the study was the YMCA female

member base of 7,100. The YMCA’s mission and purpose brings an element of inclusion and

attracts members from all walks of life providing an excellent sample of diverse individuals.

Population

The population was comprised of 5,914 females who were members of the research site

and over the age of 18. Other demographics of age, race, education, and partner status varied

based on the inclusive nature of the organization. The sample size was in accordance with the

36

requirements of quantitative research and large enough to allow for generalizations for the group

to be made for valid findings. The desired power was a minimum of 120 completed surveys. The

power analysis was calculated using the larger of two estimates (regression vs. MANOVA). The

MANOVA analysis compared three groups, and minimum sample to detect significant

differences using medium effect sizes was 40 per group (Tabachnick & Fidell, 2007).In the event

that the survey’s results returned a biased representation of the three groups of women the survey

would have been resent until a fair representation was reached in each grouping. Each group

needed a minimum of 40 respondents to be equally distributed among the three groups and meet

the minimum power of 120. If the response rate was higher than needed a random sampling

technique would have been used to achieve a workable data set.

Research Design

The research design was a non-experimental cross sectional quantitative study to examine

the aspect of the program with regard to women’s motivation to give to a charitable organization.

Three groups of female YMCA members were examined (a.) those who donated specifically to

the YMCA, (b.) those who have donated to organizations other than the YMCA, and (c.) those

who haven’t donated to any organization. The design utilized independent variables of giving to

find if there was a significant difference in predicting the outcome (donation to the YMCA).

Several analyses were used such as: descriptive, Chi-square, MANOVA and multiple regression

to determine to what degree the independent variables could predict the giving motivation of

female YMCA members (Field, 2013).

Collection of Data

A modified questionnaire developed by Pam Parsons(2004) was used for the collection of

data. The questionnaire collected demographic information such as; age, education level, number

37

of hours volunteered at the YMCA, number of hours volunteered at other nonprofits, amount of

money donate to the YMCA and amount of money donated to other nonprofits. The remainder of

the questionnaire continued to collect data on volunteerism and identification with the YMCA.

The approval to collect this data was subject to permission given by Capella University

Institutional Review Board for working with human subjects. The questionnaire was sent in the

winter of 2014 by electronic mail with introduction letter from YMCA, introduction of the study,

statement for informed consent and survey instrument. The informed consent was attached to the

emails that potential participants received as well as the first page of the survey that they had to

agree to before moving forward. After one week a follow up reminder email was sent to

encourage a good response rate. Data was examined as a collective group to avoid singling out

an individual. There was no pilot and field testing requirement as a result of the validated

instrument chosen from Parson’s study in 2004. Permission to use the instrument was given by

email from the author Dr. Parsons. To encourage participation the YMCA offered a 3 month

credit to a random participant. The YMCA received the names and contact information to

conduct the drawing completely separate from the study and the researcher. Participants sent

their names in a separate email to a YMCA representative and in no way did the researcher have

access to the participant information. All survey data was collected by the researcher with no

participant identification and the YMCA was provided a report at the conclusion of the study.

Instrumentation

The Women’s Philanthropy: Motivations for Giving Survey was developed by Parsons to

collect data on the motivation of women to give to philanthropic cause in a higher education

setting (2004). A modified questionnaire developed by Pam Parsons was used for the collection

of data (2004).

38

Variables

Descriptive independent variables (IV) were collected first by asking information on; IV

1: Age (Descriptive), IV 2: Education (Interval level variable) IV 3: Number of hours

volunteered last week at the YMCA (Interval level variable), IV 4: Number of hours volunteered

last week at other nonprofits (Interval level variable). Predictor Independent variables (IV) 7:

(b1: amount of volunteerism (survey questions 1-3), IV 8: (b2: the amount of identification with

the YMCA (survey questions 4-9) are groupings to align with the identification theory by

measuring the degree to which they can predict the outcome (money donated to the Y). The

dependent variables (DV) were outcomes resulting from respondents’ self-reported donation

history DV: 5 (money donated to the Y last year). DV 6: Money donated/gifted to other

nonprofit last year (categorical) (descriptive).

Validity & Reliability

The instrument uses a four point Likert- type scale with responses ranging from “strongly

agree” to “strongly disagree” including (1) from “strongly disagree,” (2) “disagree,” (3)

“agree,” and (4) “strongly agree.” Using a Likert-type scale provided an excellent valid response

based on the design of the questionnaire (McMillan & Schumacher, 1989). The initial reliability

was validated with a cronbach alpha score of .850 (Parson, 2004). The responses provided valid

evaluation of opinions of the participants’ motivations. The questionnaire was modified slightly

replacing two terms “higher education” and “the University of Alabama” to the “YMCA.” The

researcher received approval to use the instrument by email from the University of Alabama and

by the researcher. Revisions to the survey were minimal with the changes adopting a different

setting from the University of Alabama to the nonprofit sector or more specifically the YMCA.

In the first sentence alumni was replaced with YMCA member. The question of “are you an

39

alumni of the University?” was removed. Revised questions; 20, 21, 22, 23, 28 and 29 the

wording of “the University of Alabama” was replaced with “YMCA.” Questions 1, 2, 5- 8, 10 –

19 and 24, 25, 27 were omitted from the original instrument. The levels of donations were added

for categorical separation of the three groups of women.

Research Questions

RQ 1: What are the demographic characteristics of the respondents (age, education level,

number of hours volunteered last week at YMCA, number of hours volunteered last week at

other organization)?

RQ 2: To what degree do demographic characteristics significantly differ across the three

groups of women: (a.) those who donate specifically to the YMCA, (b.) those who have donated

to organizations other than the YMCA, and (c.) those who haven’t donated to any organization?

RQ 3: To what degree does the number of volunteer hours at the Y, number of volunteer

hours elsewhere, years of education, amount of volunteerism and amount of identification with

the YMCA predict a donation to the YMCA?

Data Analysis and Validity

The study contributed to the theory by examining if and to what extent the identification

with the YMCA is strong enough to compel a member to donate to the organization as predicted

by the theory. This study contributed to the theory by examining a voluntary membership based

nonprofit’s ability to attract donors though identification. The research questions 1-3 are listed

below with a description for data analysis.

RQ 1: What are the demographic characteristics (age, education level, hours volunteered

last week at YMCA, hours volunteered last week at other organization, amount donated)?

40

A descriptive analysis was used to examine the demographic data of the sample. For

categorical data (education level, amount donated to the YMCA, and amount donated outside the

YMCA) number and percentages have been reported. For continuous data (age, number of hours

volunteered within the YMCA, and number of hours volunteered at another NPO) the means and

standard deviations were reported.

RQ 2: To what degree do demographic characteristics significantly differ across the three

groups of women: (a.) those who donate specifically to the YMCA, (b.) those who have donated

to organizations other than the YMCA, and (c.) those who haven’t donated to any organization?

Chi-square analyses were used to examine group differences regarding education level,

amount donated to the YMCA, and amount donated outside the YMCA. A MANOVA, followed

by univariate analyses, was utilized to examine group differences for age, number of hours

volunteered within the YMCA, and number of hours volunteered at another NPO.

RQ 3: To what degree does the number of volunteer hours at the Y, number of volunteer

hours elsewhere, years of education, amount of volunteerism and amount of identification with

the YMCA predict a donation to the YMCA?

Multiple regression was used to examine how well the independent variables (number of

volunteer hours at the Y, number of volunteer hours elsewhere, years of education) predicted the

amount of money donated to the YMCA. Hierarchical multiple regression was used to analyze

two groupings of independent variables: (b1: amount of volunteerism (survey questions 1-3) and

(b2: the amount of identification with the YMCA (survey questions 4-9) (Field, 2013). Both

predictors were analyzed for their ability to predict the outcome (a donation to the Y). The

resulting b values indicated if there was a positive or negative relationship based on the

coefficient to the outcome (a donation to the Y).

41

Findings indicated if there was a significant difference using a probability (p) value of

less than .05 in the independent and dependent variables and if there was a correlation to a

donation to the Y (Field, 2013).

Ethical Challenges

Ethical challenges associated with this study were evaluated against the findings of the

Belmont report and received IRB approval from Capella University. No information was

collected that could identify participants to protect anonymity. Lack of coercion was addressed

by participants’ ability to not respond to the survey; however a three month YMCA membership

credit was offered to a randomly selected individual to encourage participation (National

Commission for the Protection of Human Subjects of Biomedical and Behavioral Research,

1979). Informed consent form was sent to introduce the study to the participant. In the letter the

participant had the researcher’s contact information for questions and a clear understanding of

the intent of the study. To encourage participation a drawing for a 3 month credit on their

membership (value: $189) was offered. Information was stored in a digital database for a

minimum of seven years with security parameters install for protection of participants. After the

seven year period data will be permanently and irreversibly destroyed or "sanitized" in

accordance with National Institutes of Standards and Technology best practices. Any

publications or findings did not identify participants to maintain confidentiality.

Sampling Plan

The population was comprised of 5,914 female participants that were current members of

the research site. Other demographics of age, race, education, and partner status varied based on

the inclusive nature of the organization. The sample reached 855 completed surveys with a 14%

response rate. The desired power is a minimum of 120 completed surveys. The power analysis

42

was calculated by taking the 8 variables and multiplying by 8 with a starting point of 50 totaling

114 completed surveys needed (Tabachnick and Fidell, 2007).

In the event that the survey’s results returned a lower number causing a biased

representation of the three groups of women the survey would have been resent until a fair

representation was reached in each grouping. Each group needed a minimum of 40 per group to

allow comparison of groups (40 per group x 3 groups = 120). If the response rate was higher

than needed a random sampling technique would have been used to achieve a workable data set.

Research Philosophy

The following section describes and justifies the foundation of philosophy of the

research. Each research view point was described to give the major postpositivism assumptions

such as; axiological assumptions, epistemological assumptions, ontological assumptions, and

methodological assumptions.

The axiological viewpoint of postpositivism measured the researcher’s values against

subjectivity. Trochim, 2006 stated that critical realism as a descriptor of postpositivism was the

reality independent of our perceptions that science could study. Critical realism supported the

theory that research by observation can be imperfect influenced by the view of the researcher.

In the same article, Popper recommended that social science as a movement must begin

to argue theories and examine a theory for disapproval rather than validation (Trochim, 2006).

He goes on to state that new research should be scrutinized to be proven invalid. Using a more

objective view should improve findings and the credibility of the research in the field.

Postpositivism was described as the viewpoint that research could be entirely unbiased

from the researcher using objectivity. Observation was the hallmark of the positivism theory

comprised the use of sense to conduct and perform social research. Today, postmodernists have

43

moved beyond epistemological tendencies to a more postmodern view of social science. The new

approach to methodology tests the relevance of the more traditional research in social theory

(Mirchandani, 2005).

The ontological view of postpositivism argued that researchers are influenced by their

own senses and intellectual abilities making objectivity difficult (Letourneau & Allen, 1999).

The same theorists viewed social research with an understanding that researchers had been

influenced by their past experience and beliefs.

In conclusion, the benefits of postpositivism improved the credibility of findings by

requiring researchers to respect both qualitative and quantitative methods. The critical nature of

postpositivism removed more opportunities for biases than positivism. Additionally, the overall

approach to research was in an effort to produce valid findings and to bring awareness to

researchers of their own influences. The continued challenge for the postpositivism view was the

longer timeframe needed to get credible research to the field. Researchers using a method of

scrutiny to only disprove a theory would improve objectivity; however the increase time of

implementation could be significant.

Summary

Chapter 3 presented the research design and methodology that was used for this study.

The collection of data was reviewed, as were the three guiding research questions. The setting,

population, instrumentation, data analyses and research philosophy were also described.

Chapter 4 offers a presentation of the results and analysis of the data. Analyses of the

data were presented with regard to each research question and findings are discussed with

depictions of data through tables and figures. Analyses included interpretation of findings

44

supporting descriptive statistics of the female donor and correlation between the three groups

with linkages to independent variables.

45

CHAPTER 4. RESULTS

Introduction

The following chapter documented the analysis of data with regard to the motivations of

women based on the amount of identification and volunteerism with a membership based

nonprofit. The descriptive analysis brought to light the type of women that were most

philanthropic for future targeted approaches through a financial development program. The

overarching research questions examined to what degree does the amount of identification and

the amount of volunteerism predicts a donation to the YMCA. The YMCA being a large

membership based nonprofit builds on the 2013 work of Kou, Hayat, Mesch, and Osili that

recommended finding the tipping point in which female representation begins to influence the

culture within a service organization.

Reliability

The quantitative analysis included the use of SPSS® Version 22 (2013) for data

manipulation. The survey instrument was tested by Parsons for Cronbach’s alpha and item-to-

item correlations (2004). The original survey consisted of 22 items with item to total correlations

ranging from .331 to .759 and a Cronbach’s alpha score of .860. Item to total correlations greater

than .30 met the standard for validity (Nunnally & Bernstein, 1994). An alpha score of .80 or

more met the standard for reliability (Carmines & Zeller, 1979).

Chi-square analyses were used to examine group differences regarding education level,

donation to the YMCA, and donation to another nonprofit. A MANOVA, followed by univariate

analyses, was utilized to examine group differences for age, hours volunteered within the

46

YMCA, and hours volunteered outside the YMCA. Multiple regression examined how well the

independent variables (volunteer hours at the Y, volunteer hours elsewhere, years of education)

predict a donation to the YMCA. Hierarchical multiple regression examined two groupings of

independent variables: (b1: amount of volunteerism (survey questions 1-3) and (b2: the amount

of identification with the YMCA (survey questions 4-9) (Field, 2013). Both predictors were

analyzed for their ability to predict the outcome (donation to the Y). The resulting b values

indicated if there was a positive or negative relationship based on the coefficient to the outcome

(donation to the YMCA). Findings indicated a significant difference using a probability (p) value

of less than .05 in the independent and dependent variables and if there was a correlation to the a

donation to the YMCA (Field, 2013).

Quantitative Findings

Demographics

The survey was emailed to a sample that consisted of 5,914 women. Nine hundred and

seventy responses were received for a response rate of 16.4%. Out of the 970 respondents, 115

did not complete the entire survey and were eliminated leaving the completed survey count at

855 and a response rate of 14.4%. The desired power was a minimum of 120 completed surveys.

The power analysis was calculated by taking the 8 variables and multiplying by 8 with a starting

point of 50 totaling 114 completed surveys needed (Tabachnick & Fidell, 2007). Data was

analyzed using the Statistical Package for the Social Sciences 22.0 (SPSS) to determine

descriptive data for demographics.

Research Question 1

47

RQ 1: What are the demographic characteristics (age, education level, number of hours

volunteered last week at YMCA, number of hours volunteered last week at other organization,

amount donated)?

Descriptive analyses were used to examine the demographic data of the sample. For

categorical data (education level, amount donated to the YMCA, and amount donated to another

NPO and continuous data (age, number of hours volunteered within the YMCA, and number of

hours volunteered at another NPO) numbers and percentages were reported.

Of the 855 respondents the age ranged from 20 – 84. Respondents were divided into five

categories according to reported age. Age category one 20- 29 had 46 respondents representing

5.4%. Age category two 30-39 had 224 respondents representing 26.2%. Age category three 40-

49 had 231 respondents representing 27%. Age category four 50-59 had 147 respondents

representing 17.2%. Age category five 60 and older had 207 respondents representing 24.2%.

The age variable had a mean score of 48. These results are illustrated in Tables1and 2.

Table 1

Age Categories

Age Frequency Percent Cumulative Percent

20-29 46 5.4 5.4

30-39 224 26.2 31.6

40-49 231 27 58.6

50-59 147 17.2 75.8

60 and older 207 24.2 100

Total 855 100

Table 2

Age Descriptive Statistics

N Minimum Maximum M

Age 855 20 84 48.007

48

Of the 855 respondents, the education ranged from less than high school to post

doctorate. Respondents were divided into eight categories according to reported education level.

Education category one less than high school had 1 respondents representing .1%. Education

category two high school or GED had 45 respondents representing 5.3%. Education category

three vocational or technical school had 37 respondents representing 4.3%. Education category

four some college had 193 respondents representing 22.6%. Education category five Bachelor’s

degree had 289 respondents representing 33.8%. Education category six Master’s degree had 249

respondents representing 29.1%. Education category seven Doctorate had 36 respondents

representing 4.2%. Education category eight Post-Doctorate had 4 respondents representing .5%.

The education variable had a mean score of 4.9 or a bachelor’s degree. These results are

illustrated in Tables 3 and 4.

Table 3

Education Categories

Frequency Percent Cumulative Percent

N/A 1 0.1 0.1

less than High

School 1 0.1 0.2

HS/GED 45 5.3 5.5

Vocational/Tech 37 4.3 9.8

Some College 193 22.6 32.4

Bachelor’s Degree 289 33.8 66.2

Master’s Degree 249 29.1 95.3

Doctorate 36 4.2 99.5

Post Doctorate 4 0.5 100

Total 855 100

Note. Education categories were coded 1 for less than high school; 2 HS/GED; 3 Vocational/Tech;

4 Some college; 5 Bachelor's Degree; 6 Master's Degree; 7 Doctorate; 8 Post Doctorate

49

Table 4

Education Descriptive Statistics

N M SD Education (categories 1-8) 855 4.908 1.189

Note. Education categories were coded 1 for less than high school; 2 HS/GED;

3 Vocational/Tech; 4 Some college; 5 Bachelor's Degree; 6 Master's Degree; 7 Doctorate;

8 Post Doctorate

Of the 855 respondents, 98% (838) did not volunteer at the YMCA with the other

2% (17) volunteering 70.5% of the time (from 1-5 hours). These results are illustrated in

Tables 5 and 6.

Table 6

Average number of hours volunteered at the YMCA last week

Frequency Percent Cumulative Percent

Answered No 838 98 98

1 - 5 Hours 12 1.4 99.4

5 -10 Hours 1 0.1 99.5

10 Hours or more 1 0.1 99.6

Unknown 3 0.4 100

Total 855 100

Of the 855 respondents, 44% (375) reported they volunteered for another nonprofit or

church, with 272 or 73% volunteering from 1-5 hours. The other 56% (480) reported they had

not volunteered the week prior to completing the survey. These results are illustrated in Tables 7

and 8.

Table 5

Average number of hours volunteered at the YMCA last week

Frequency Percent Cumulative Percent

Yes 17 2 2

No 838 98 100

Total 855 100

50

Table 7

Average number of hours volunteered at another non-profit (including church) last week

Frequency Percent Cumulative Percent

Yes 375 43.9 43.9 No 480 56.1 100 Total 855 100

Of the 855 respondents, 14.3% had donated to the YMCA in the past year with 71%

donating $0-499. Eighty six percent reported they had not made a donation to the YMCA in the

previous year. These results are illustrated in Tables 9 and 10.

Table 9

Donation to the YMCA Last Year

Frequency Percent Cumulative Percent

Yes 122 14.3 14.3 No 733 85.7 100 Total 855 100

Table 8

Average number of hours volunteered for any other non-profit (including church) last week

Frequency Percent Cumulative Percent

Answered No 480 56.1 56.1 1-5 Hours 272 31.8 88 5-10 Hours 45 5.3 93.2 10 Hours or more 50 5.8 99.1 Unknown 8 0.9 100 Total 855 99.9

51

Of the 855 respondents, 89% (759) donated to another nonprofit or church with 27.5%

donating $0-499, 12.3% from $500 -999, 18.8% from $1000-4999, 8.9% from $5000 or more.

Eleven percent reported they had not made a donation in the previous year. These results are

illustrated in Tables 11 and 12.

Table 11

Donation to Another Nonprofits Last Year

Frequency Percent Cumulative Percent

Yes 759 88.8 88.8 No 96 11.2 100 Total 855 100

Table 12

Donation to Another Nonprofits Last Year Categories

Frequency Percent Cumulative Percent

Answered No 96 11.2 11.2 $ 0 - 499 235 27.5 38.7 $500 - 999 105 12.3 51 $1000 - 4999 161 18.8 69.8 $5000 or more 76 8.9 78.7 Unknown 182 21.3 100 Total 855 100

Summary of Demographic Data

Table 10

Donation to the YMCA Last Year

Frequency Percent Cumulative Percent

Answered No 733 85.7 85.7 $ 0 - 499 86 10.1 95.8 $500 - 999 6 0.7 96.5 $1000 - 4999 4 0.5 97 $5000 or more 3 0.4 97.3 Unknown 23 2.7 100 Total 855 100

52

Overall, the descriptive analyses results of the demographic data highlighted the

categorical data (education level, amount donated to the YMCA, and amount donated outside the

YMCA) and continuous data (age, number of hours volunteered within the YMCA and number

of hours volunteered outside the YMCA). The 855 respondents had a mean age of 48 and an

educational level of a bachelor’s degree. Ninety – eight percent of the sample (838 respondents)

did not volunteer at the YMCA leaving only 17 respondents who volunteered 71% of the time at

the lowest category of 1-5 hours per week. However, 44% of the respondents volunteered for

another nonprofit or church with 73% (272) volunteering from 1-5 hours. The respondents also

donated to the YMCA at 14.3% (122 of the 855 respondents), reporting a donation of $0-499

with 71%. The same respondents reported a much higher donation to other nonprofits at 89%;

however, the most common donation remained the same with 30% donating between $0-499.

After reporting either a donation to the Y or another nonprofit, 11% were identified who made

no donation to either representing 87 respondents.

From the above information, respondents were categorized into three groups for further

examination. Group status was determined by survey descriptive questions 5 and 6 that

determined a donation to the Y, a donation to another nonprofit or non-donation for each

respondent. Group one (n = 113) was comprised of respondents who donated to the YMCA and

to another NPO. Nine respondents only donated to the Y were removed to remain consistent

within the group. Group two (n = 655) was comprised of respondents who only donated to

another nonprofit and not the Y removing 104 respondents. Group three (n = 87) respondents did

not donate to either group one or group two.

Research Question 1 Review

53

RQ 1: What are the demographic characteristics (age, education level, number of hours

volunteered last week at YMCA, number of hours volunteered last week at other organization,

amount donated)?

As previously stated, using SPSS®, research question 1 used descriptive analyses to

examine the demographic data of the sample. Descriptive analyses were used to examine the

demographic data of the sample to establish group status for research questions 2 and 3. For

categorical data (education level, amount donated to the YMCA, and amount donated outside the

YMCA) and continuous data (age, number of hours volunteered within the YMCA, and number

of hours volunteered outside the YMCA) categories, numbers and percentages were reported.

The following section addressed research questions 2 and 3.

Research Question 2

RQ 2: To what degree do demographic characteristics significantly differ across the three

groups of women: (a.) those who donate specifically to the YMCA, (b.) those who have donated

to organizations other than the YMCA, and (c.) those who haven’t donated to any organization?

The sample of 855 respondents, (N = 855), were categorized into group status determined

by a donation to the Y, donation to another nonprofit or non-donation for each participant. Group

one was comprised of respondents that donated to the YMCA and to another NPO (n = 113).

Group two was comprised of respondents that only donated to another nonprofit (n = 655).

Group three respondents did not donate to either group one or group two (n = 87). These results

are illustrated in Table 13.

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Table 13

Group Status

Frequency Percent Cumulative Percent

Donated to YMCA & NPO 113 13.2 13.2 Donated to another NPO 655 76.6 89.8 No donation to either 87 10.2 100 Total 855 100

The highest response rate for group one was Master’s degree at 37% (42). The highest

response for group two was Bachelor’s degree at 36% (234). The highest amount for group three

was some college at 38% (33). These results are illustrated in Table 14.

Table 14

Group Status Education

Group Status 1 (n = 113) 2 (n = 655) 3 (n = 87)

N/A 0 1 0 1

Less than HS 0 1 0 1

HS/GED 2 31 12 45

Vocational/Tech 2 29 6 37

Some College 25 135 33 193

Bachelors 33 234 22 289

Masters 42 197 10 249

Doctorate 8 25 3 36

Post Doc 1 2 1 4

Total 113 655 87 855

The independent variable education was found to be statistically significant with a Chi

square value 48.390 (p = .000), which was less than the significance level of .05(X2 (16) =

48.390a, p < .05). Fourteen cells did not reach a count more than 5 which could represent an

underrepresentation of a category; however, the categories represented education levels which

were anticipated to have a lower count in the lowest and highest education levels. These results

are illustrated in table 15.

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Table 15

Group Status Education Pearson Chi-Square Results

Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 48.390a 16 0 Likelihood Ratio 46.933 16 0 Linear-by-Linear Association 28.962 1 0 N of Valid Cases 855

a. 14 cells (51.9%) have expected count less than 5. The minimum expected count is .10.

The independent variable donation to the Y was found to be statistically significant with a

Chi Square value 785.084a (p = .000), which was less than the significance level of .05 (X2 (10)

= 785.084a, p < .05). Eleven cells did not reach a count more than 5 which could represent an

underrepresentation of a category; however, the categories represented giving levels which were

anticipated to have lower count, in higher giving amounts. These results are illustrated in Tables

16 and 17.

Table 16

Amount of Money Donated to the YMCA Last Year

No $ 0 - 499 $500-999 $1000-4999 $5,000 + Unknown Total

Group 1 0 80 5 4 2 22 113

Group 2 646 6 1 0 1 1 655

Group 3 87 0 0 0 0 0 87

Total 733 86 6 4 3 23 855

The independent variable amount of money donated to another nonprofits last year was

found to be statistically significant with a Chi Square value 769.021a (p = .000), which was less

Table 17

Pearson Chi-Square, Amount of Money Donated to the YMCA Last Year

Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 785.084a 10 0

Likelihood Ratio 609.004 10 0

Linear-by-Linear Association 276.006 1 0

N of Valid Cases 855

56

than the significance level of .05 (X2 (10) = 769.021a, p < .05). These results are illustrated in

Tables 18 and 19.

Table 18

Amount of Money Donated to Any Other Nonprofits Last Year

No $ 0 - 499 $500-999 $1000-4999 $5,000 + Unknown Total

Group 1 0 28 17 24 13 31 113

Group 2 9 207 88 137 63 151 655

Group 3 87 0 0 0 0 0 87

Total 96 235 105 161 76 182 855

Table 19

Amount of Money Donated to Any Other Nonprofits Last Year Pearson Chi-square Results

Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 769.021a 10 0

Likelihood Ratio 508.398 10 0

Linear-by-Linear Association 130.301 1 0

N of Valid Cases 855

a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 7.73.

Pearson Chi-Square Summary

After performing the Pearson Chi-Square, to find the degree to which demographic

characteristics differ across the three groups of women, all three independent variables

(education, donation to the Y last year and donation to another NPO last year) were found to be

statistically significant (p < .05). The independent variable education was found to be statistically

significant with a Chi square value 48.390 (p = .000). The independent variable donation to the

Y last year was found to be statistically significant with a Chi Square value 785.084a (p = .000).

The independent variable amount of money donated to another nonprofits last year was found to

be statistically significant with a Chi Square value 769.021a (p = .000). The next section

analyzed the continuous data of age, hours volunteered within the YMCA, and hours volunteered

at another NPO.

57

Multivariate Analysis of Variance

A multivariate analysis of variance (MANOVA) followed by univariate analyses,

examined group differences for age, hours volunteered within the YMCA, and hours volunteered

at another NPO.

Age

Of the entire sample (N = 855) the average age was 40-49 (M = 3.2865, SD = 1.239).

When divided into subgroups, group 1 (n =113) the average age was 40-49 (M = 3.7168, SD =

1.15317). Group 2 (n = 655) the average age was 40-49 (M = 3.3053, SD = 1.22345). Group 3 (n

= 87) the average age was 30-39 (M = 2.5862, SD = 1.18667). These results are illustrated in

Table 20.

Hours volunteered at the Y last week

Of the entire sample (N = 855) on average the respondents reported volunteering 0 hours

at the Y last week (M =.0339, SD = .29039). When divided into subgroups, group 1 (n = 113) on

average the respondents reported that they did not volunteer at the Y last week (M =.0000, SD =

.0000). Group 2 (n = 655) on average the respondents reported that they did not volunteer at the

Y (M = .0015, SD = .03907). Group 3 (n = 87) on average the respondents reported that they did

not volunteer at the Y (M =.3218, SD = .85582). These results are illustrated in Table 20.

Hours volunteered at another NPO last week

Of the entire sample (N = 855) on average the respondents reported volunteering 1-5

hours at another NPO last week (M =.6363, SD = .89487). When divided into subgroups, group 1

(n =113) on average the respondents reported volunteering at another NPO 1-5 hours last week

(M = 1.0265, SD = 1.08940). Group 2 (n = 655) on average the respondents reported

volunteering at another NPO 0 hours last week (M = .5740, SD = .81852). Group 3 (n = 87) on

58

average the respondents reported volunteering at another NPO 0 hours last week (M = .5977, SD

= 1.03945). These results are illustrated in Table 20.

Table 20

MANOVA Descriptive for Group Differences for Age,

Hours Volunteered at the YMCA, and Hours Volunteered at Another NPO

Group Status M SD N

Age 1 3.7168 1.15317 113

2 3.3053 1.22345 655

3 2.5862 1.18667 87

Total 3.2865 1.23985 855

Hours volunteered at the Y 1 0 0 113

2 0.0015 0.03907 655

3 0.3218 0.85582 87

Total 0.0339 0.29039 855

Hours volunteered at another NPO 1 1.0265 1.0894 113

2 0.574 0.81852 655

3 0.5977 1.03945 87

Total 0.6363 0.89487 855

When comparing the three groups for differences among the variables of age, hours

volunteering at the Y and hours volunteering at another NPO The Box’s M value 2487.200 was

associated with a p value of .000, which was interpreted as non-significant based on Field’s

recommendation of p < .05 (2013). Thus, the homogeneity of covariance matrices between the

groups was assumed to be unequal or non-significant among the groups for analysis of the

MANOVA. These results are illustrated in Table 21.

Table 21

Box's Test of Equality of Covariance Matrices

for Age, Hours Volunteering at the Y and Hours Volunteering at Another NPO

Box's M 2487.2 F 409.212 df1 6 df2 127731.96 Sig. 0

59

The MANOVA was found to be statistically significant across all tests (Pillai's Trace,

Wilks' Lambda, Hotelling's Trace and Roy's Largest Root) with a p = .000. The significance

level was established at p < .05 (Field, 2013). These results are illustrated in Tables 22 and 23.

Table 22

MANOVA Results for Age, Hours Volunteering at the Y and

Hours Volunteering at Another NPO

Effect

Value F Error df Sig.

Intercept Pillai's Trace 0.789 1057.63 850 0

Wilks' Lambda 0.211 1057.63 850 0

Hotelling's Trace 3.733 1057.63 850 0

Roy's Largest Root 3.733 1057.63 850 0

Group Status Pillai's Trace 0.162 25.012 1702 0

Wilks' Lambda 0.842 25.429 1700 0

Hotelling's Trace 0.183 25.846 1698 0

Roy's Largest Root 0.15 42.548 851 0

Table 23

Levene's Test of Equality for Age, Hours Volunteering at the Y and

Hours Volunteering at Another NPO

F df1 df2 Sig.

Age 0.776 2 852 0.461

Hours volunteered at the Y 227.546 2 852 0

Hours volunteered at another NPO 13.031 2 852 0

Based the MANOVA tests, there were known statistically significant differences among

the three independent variables. To find the difference of the independent variables a follow up f

test between subjects found that each variable among the groups were statistically significant

across all groups (Age, f = 21.769, p = .000), (Hours volunteered at the Y, f = 53.457, p = .000),

and (Hours volunteered at another NPO, f = 12.753, p = .000). The significance level was

established at p < .05. These results are illustrated in Table 24.

60

Table 24

Tests of Between-Subjects Effects for Group Status

Source Dependent Variable Type III df M Sq F Sig.

Sum of

Squares

Corrected Model Age 63.823a 2 31.911 21.769 0

Hours at the Y 8.029b 2 4.015 53.457 0

Hours at another

NPO 19.877c 2 9.939 12.753 0

Intercept Age 4221.236 1 4221.236 2879.561 0

Hours at the Y 4.781 1 4.781 63.662 0

Hours at another

NPO 220.96 1 220.96 283.521 0

Group Status Age 63.823 2 31.911 21.769 0

Hours at the Y 8.029 2 4.015 53.457 0

Hours at another

NPO 19.877 2 9.939 12.753 0

Error Age 1248.973 852 1.466

Hours at the Y 63.987 852 0.075

Hours at another

NPO 663.999 852 0.779

Total Age 10548 855

Hours at the Y 73 855

Hours at another

NPO 1030 855

Corrected Total Age 1312.795 854

Hours at the Y 72.016 854

Hours at another

NPO 683.876 854

a. R Squared = .049 (Adjusted R Squared = .046)

b. R Squared = .111 (Adjusted R Squared = .109)

c. R Squared = .029 (Adjusted R Squared = .027)

MANOVA Summary

After performing the MANOVA, there were no type 1 errors and all three variables were

found to be statistically significant. The following section used univariate analyses for each

group to carry on the more focused comparisons of the dependent variables of age, hours

volunteered at the Y, and hours volunteered at another NPO.

61

Univariate Analyses

Group 1 (n = 113) were identified by their responses that they had made a donation to

the Y last year and to another NPO. Respondents were grouped into the already established five

categories according to reported age. To determine independent variables volunteering at the Y

and volunteering at other nonprofits, respondents’ answers were coded 1 for yes they volunteered

or 2 for no they didn’t volunteer. These results are illustrated in Table 25.

Table 25

Univariate Descriptive Statistics for Group 1 Differences for Age, Hours

Volunteered at the YMCA, and Hours Volunteered at Another NPO

Age Volunteered at YMCA Volunteered at Another NPO Group 1

n 113 113 113 113

Mean 3.7168 1.9381 1.4513 1

Median 4 2 1 1

Std. Deviation 1.15317 0.24213 0.49984 0

The mean age of the group was 3.7 or age category 40-49. Group 1 had the highest

frequency of respondents in age category 5 (60 and older) with 40 out of the 113 or 35.4%.

These results are illustrated in Table 26.

Table 26

Univariate Descriptive for Group 1 Differences for Age

Frequency Percent Cumulative Percent

20-29 1 0.9 0.9

30-39 20 17.7 18.6

40-49 29 25.7 44.2

50-59 23 20.4 64.6

60 and older 40 35.4 100

Total 113 100

Of the 113 respondents of Group 1, only 7 or 6.2% reported that they volunteered at the

Y and 106 or 93.8% did not volunteer at the Y. These results are illustrated in Table 27.

62

Table 27

Univariate Frequency for Group 1 Differences for Hours Volunteered at the Y

Frequency Percent Cumulative Percent

Yes 7 6.2 6.2

No 106 93.8 100

Total 113 100

Of the 113 respondents of Group 1, 62 or 54.9% reported that they volunteered at another

NPO last week and 51 or 45.1% did not volunteer at another NPO last week. These results are

illustrated in Table 28.

Table 28

Univariate frequency for group 1 differences for hours volunteered at another NPO

Frequency Percent Cumulative Percent

Yes 62 54.9 54.9

No 51 45.1 100

Total 113 100

Group 2 (n = 655) were identified by their responses that they had made a donation to

another nonprofit. Respondents were grouped into the already established five categories

according to reported age. To determine independent variables volunteering at the Y and

volunteering at other nonprofits, respondents’ answers were coded 1 for yes they volunteered or

2 for no they didn’t volunteer. The mean age of the group was 3.3 or age category 40-49. The

mean of group 2 for volunteering at the Y was 1.98 with a standard deviation of .11650 and 646

out of 655 or 98.6% indicated that they did not volunteer at the Y; however, group 2 did

volunteer at other nonprofits with a mean score of 1.5344 with a standard deviation of .11650

and 305 out of 655 or 46.6%. These results are illustrated in tables 29, 30, 31 and 32.

63

Table 29

Univariate Descriptive for Group 2 Differences for Age,

Hours Volunteered within the YMCA, and Hours Volunteered at Another NPO

Age Volunteered Volunteered Group 2

at the YMCA at another NPO

n 655 655 655 655

Mean 3.3053 1.9863 1.5344 2

Median 3 2 2 2

Std. Deviation 1.22345 0.1165 0.4992 0

Group 2 had the highest amount of respondents in age category 3 (40-49) with 179 out of

the 655 or 27.3%. These results are illustrated in Table 30.

Table 30

Univariate Descriptive for Group 2 Differences for Age

Frequency Percent Cumulative Percent

20-29 29 4.4 4.4

30-39 174 26.6 31

40-49 179 27.3 58.3

50-59 114 17.4 75.7

60 and older 159 24.3 100

Total 655 100

Of the 655 respondents of Group 2, only 1.4% (f = 9) reported that they volunteered at

the Y. These results are illustrated in Table 31.

Table 31

Univariate Frequency for Group 2 Differences for Hours Volunteered at the

YMCA

Frequency Percent Cumulative Percent Yes 9 1.4 1.4 No 646 98.6 100 Total 655 100

Of the 655 respondents of Group 2, (f = 305) or 46.6% reported that they volunteered at

another NPO. These results are illustrated in Tables 32.

64

Table 32

Univariate Frequency for Group 2 Differences for Hours Volunteered at Another NPO

Frequency Percent Cumulative Percent Yes 305 46.6 46.6 No 350 53.4 100 Total 655 100

Group 3 (n = 87) were identified by their responses that they did not make a donation to

either the Y or another nonprofit. Respondents were grouped into the already established five

categories according to reported age. To determine independent variables volunteering at the Y

and volunteering at other nonprofits respondents’ answers were coded 1 for yes they volunteered

or 2 for no they didn’t volunteer. The mean age of the group was 2.6 or age category 30-39. The

mean of group 3 for volunteering at the Y was 1.98 with a standard deviation of .10721 and 86

out of 87 answered no; however, group 3 didn’t volunteer at other nonprofits either with a mean

score of 1.90 with a standard deviation of .29064 and 79 out of 87 or 90.8%. These results are

illustrated in tables 33, 34 and 35.

Table 33

Univariate Descriptive for Group 3 Differences for Age,

Hours Volunteered at the YMCA, and Hours Volunteered at Another NPO

Age Volunteered Volunteered Group 3

at the YMCA at Another NPO

n 87 87 87 87

Mean 2.5862 1.9885 1.908 3

Median 2 2 2 3

Std. Deviation 1.18667 0.10721 0.29064 0

Group 3 had the highest amount of respondents in age category 2 (age 30-39) with 30 out

of the 87 or 34.5%. These results are illustrated in Table 34.

65

Table 34

Univariate Descriptive for Group 3 Differences for Age

Frequency Percent Cumulative Percent

20-29 16 18.4 18.4 30-39 30 34.5 52.9 40-49 23 26.4 79.3 50-59 10 11.5 90.8 60 and older 8 9.2 100 Total 87 100

Of the 87 respondents of Group 3, only 1 or 1.1% reported that they volunteered at the Y.

These results are illustrated in Table 35.

Table 35

Univariate Frequency for Group 3 Differences for Hours Volunteered at the YMCA

Frequency Percent Cumulative Percent

Yes 1 1.1 1.1 No 86 98.9 100 Total 87 100

Of the 87 respondents of Group 3, 8 out of 87 or 9.2% reported that they volunteered at

another NPO. These results are illustrated in table 36.

Table 36

Univariate Frequency for Group 3 Differences Hours Volunteered at Another NPO

Frequency Percent Cumulative Percent

Yes 8 9.2 9.2 No 79 90.8 100 Total 87 100

Research Question 2 Summary

Overall, the univariate analyses for the three groups identified some key findings that are

reported in Chapter 5 in more detail. The Group 1 independent variable age had a higher mean

score of 3.7 than the other two groups with 35.4% aged 60 and older. Group 1 also had the

66

highest amount of volunteering hours at the Y with 6.2% of respondents; however this group was

self-identified as annual donors to the Y last year. Group 1 also reported that 54% volunteered at

another organization.

Group 2 was the largest reported group at 655 respondents who made a donation to

another nonprofit last year. Group 2 was a younger group than Group 1, with 27.3% aged 40-49

and a mean of 3.3053. Volunteering at the Y within group 2 was 1.2% or (f = 9). Consistent with

Group 1, Group 2 volunteered at other nonprofits 46.6 percent of the time or 305 out of 655.

Group 3 the smallest group with 87 respondents who did not make a donation to either

the Y or another nonprofit. Group 3 represented the youngest reported age category at 34.5% age

30-39 or 30 out of 87 respondents. Group 3 also had the lowest percentage of reported

volunteering at the Y at 1.1% and the lowest percentage of volunteering at other nonprofits at

9.2%. Overall, group 3 did volunteer 8.1% more often at another nonprofit than at the YMCA.

Research question 3 examined to what degree does the number of volunteer hours at the Y,

number of volunteer hours at another NPO, years of education, amount of volunteerism and

amount of identification with the YMCA predict a donation to the YMCA using multiple

regression.

Research Question 3

Multiple Regression

RQ 3: To what degree does the number of volunteer hours at the Y, number of volunteer

hours elsewhere, years of education, amount of volunteerism and amount of identification with

the YMCA predict a donation to the YMCA?

Multiple regression was used to examine how well the independent variables (number of

volunteer hours at the Y, number of volunteer hours elsewhere, years of education) predict a

67

donation to the YMCA. The independent variables (IV) were continuous and then grouped into

categories. The IV “number of volunteer hours at the Y” was grouped as followed: 1= 1-5 hours,

2 =5-10 hours, 3 = 10 hours or more and 4 = unknown. The IV “number of volunteer hours

elsewhere” was grouped as followed: 1= 1-5 hours, 2 =5-10 hours, 3 = 10 hours or more and 4 =

unknown. The IV “years of education” was grouped as followed: 1 = less than High School, 2 =

HS/GED, 3 = Vocational/Tech, 4 = Some College, 5 = Bachelor’s Degree, 6 = Master’s Degree,

7 = Doctorate, 8 = Post Doctorate.

The dependent variable “amount of donation to the YMCA” was continuous and then

grouped into four categories (1= $0-499, 2 = $500-999, 3 = $1000 – 4999, 4 = $5000 or more

and 5 = unknown). The mean score and standard deviation of the dependent variable and three

independent variables are listed in Table 37.

Table 37

Multiple Regression Descriptive Statistics for Number of Volunteer Hours at the Y,

Number of Volunteer Hours at Another NPO and Years of Education

M SD N

Amount of money to Y 0.2772 0.90749 855

Hours of volunteering at the Y 0.0339 0.29039 855

Hours of volunteering at another NPO 0.6363 0.89487 855

Years of education 4.9088 1.18977 855

Of the 855 respondents, there was a statistically non-significant negative correlation of -

.036 of hours volunteering at the YMCA (p = .148). The second independent variable was hours

volunteering at another nonprofit that had a positive correlation of .059 and was found to be

statistically significant (p = .041). The third independent variable was education that had a

positive correlation of .100 that was found to be statistically significant (p = .002). Both hours of

volunteering at another nonprofit and years of education demonstrated a positive correlation to a

prediction of a donation to the YMCA. These results are illustrated in Table 38.

68

Table 38

Pearson Correlation for Number of Volunteer Hours at the YMCA,

Number of Volunteer Hours at Another NPO and Years of Education

Donation Hours at the Hours at another Edu

to YMCA YMCA NPO

Pearson Donation to YMCA 1 -0.036 0.059 0.1

Correlation Hours at the YMCA -0.036 1 -0.083 -0.045

Hours at another NPO 0.059 -0.083 1 0.025

Education 0.1 -0.045 0.025 1

Sig. Donation to YMCA . 0.148 0.041 0.002

(1-tailed) Hours at the YMCA 0.148 . 0.008 0.093

Hours at another NPO 0.041 0.008 . 0.234

Education 0.002 0.093 0.234 .

N Donation to YMCA 855 855 855 855

Hours at the YMCA 855 855 855 855

Hours at another NPO 855 855 855 855

Education 855 855 855 855

Of the 855 respondents, there was an overall statistically significant probability (p =.007);

(F = 4.039) when comparing independent variables: volunteer hours at the Y, number of

volunteer hours elsewhere, years of education to their ability to predict a donation to the YMCA.

These results are illustrated in Table 39

Table 39

ANOVA Multiple Regression for Number of Volunteer Hours at the Y,

Number of Volunteer Hours at Another NPO and Years of Education

Model 1

Sum of Squares df Mean Square F Sig.

Regression 9.874 3 3.291 4.039 .007b

Residual 693.431 851 0.815

Total 703.305 854

a. Dependent Variable: Donation to the YMCA

b. Predictors: (Constant), Education, Hours of volunteering at another NPO, Hours of volunteering at the Y

Hours of volunteering at the Y had a negative coefficient (beta = -.027); (p = .434) .Hours

of volunteering at another NPO had a positive coefficient (beta = .055); (p =.109). However, the

probability levels of both variables were found to be statistically non-significant (p > .05).

69

Education was the only statistically significant variable that predicted a donation to the Y, (beta

= .098); (p = .004). These results are illustrated in Tables 40 and 41.

Table 40

Coefficients Volunteer Hours at the YMCA, Number of

Volunteer Hours at Another NPO and Years of Education

Model 1

Std. Error Beta t Sig.

(Constant) 0.133

-0.916 0.36

Hours of volunteering at the YMCA 0.107 -0.027 -0.782 0.434

Hours of volunteering at another NPO 0.035 0.055 1.603 0.109

Education 0.026 0.098 2.872 0.004

a. Dependent Variable: Donation to YMCA

Table 41

Model Summary for Number of Volunteer Hours at the Y,

Number of Volunteer Hours at Another NPO and Years of Education

Model 1 R R Adjusted R Square F df1 df2 Sig. F

Square R Square Change Change

Change

.118a 0.014 0.011 0.014 4.039 3 851 0.007

a. Predictors: (Constant), Education, Hours of volunteering at another NPO, Hours of volunteering at the Y

Hierarchical Multiple Regression

Hierarchical multiple regression were used to analyze two groupings of independent

variables: b1: amount of volunteerism (survey questions 1-3) and b2: the amount of

identification with the YMCA (survey questions 4-9) (Field, 2013). Both predictors were

analyzed for their ability to predict the outcome (a donation to Y). The resulting b values

indicated if there was a positive or negative relationship based on the coefficient to the outcome

(a donation to Y). The independent variables were collected using a 4 point Likert scale (strongly

agree =1, agree = 2, disagree = 3, strongly disagree = 4). The means scores reflect the average

participants ranking of each variable as it pertains to agreeability. The most agreeable variable

response with the lowest mean (M = 1.7591); (SD = .62197) was “I believe YMCA youth

70

programs change lives.” The most disagreeable variable response with the highest mean (M =

3.2351); (SD = .76955) was “I plan to leave my estate or part of my estate to benefit the

YMCA.” These results are illustrated in Table 42.

Table 42

Hierarchical Multiple Regression Descriptive Statistics

M SD N

A donation to Y 0.277 0.90749 855

My financial support goes to support organizations where I volunteer. 2.323 0.8903 855

I want to make a difference in the community by giving of my time. 1.945 0.68645 855 I want to make a difference in my community by giving of my financial re-

sources. 2.114 0.6878 855

I believe the YMCA changes lives. 1.798 0.59663 855

I believe YMCA youth programs change lives. 1.759 0.62197 855

I believe the YMCA promotes social justice. 1.888 0.74282 855

I plan to leave my estate or part of my estate to benefit the YMCA. 3.235 0.76955 855

I attend YMCA events and participate in programs. 2.256 0.79142 855

I serve or have served in the past on the YMCA boards/committees. 3.204 0.75948 855

According to the Pearson Correlations, all variables had a negative correlation to the

predictor a donation to the Y. Three variables were found to be statistically significant with p

values greater than .05. Independent variable “I want to make a difference in my community by

giving of my financial resources” had a negative correlation of -.109 (p = .001). Independent

variable “I believe the YMCA changes lives” had a negative correlation of -.061 (p =.038).

Independent variable “I serve or have served in the past on the YMCA boards/committees” had a

negative correlation of -.090 (p = .004). These results are illustrated in Table 43.

71

Table 43

Pearson Correlations, for Independent Subgroups

Amount of Volunteerism and Identification

Donation Financial My Financial Y Change

to Y Support Time Resources Lives

Pearson A donation to Y 1 -0.004 -0.045 -0.109 -0.061

Correlation Financial support -0.004 1 0.416 0.328 0.123

My time -0.045 0.416 1 0.44 0.139

Financial resources -0.109 0.328 0.44 1 0.087

Y Change Lives -0.061 0.123 0.139 0.087 1

Youth programs -0.014 0.09 0.114 0.078 0.676

Social justice -0.032 0.076 0.089 0.128 0.422

Leave my estate -0.008 0.132 0.16 0.208 0.142

Participate in programs -0.022 0.07 0.097 0.142 0.172

Y Board -0.09 0.152 0.138 0.196 0.104

Sig. A donation to Y . 0.458 0.094 0.001 0.038

(1-tailed) Financial support 0.458 . 0 0 0

My time 0.094 0 . 0 0

Financial resources 0.001 0 0 . 0.005

Y Change Lives 0.038 0 0 0.005 .

Youth programs 0.338 0.004 0 0.012 0

Social justice 0.175 0.013 0.005 0 0

Leave my estate 0.409 0 0 0 0

Participate in programs 0.257 0.02 0.002 0 0

Y Board 0.004 0 0 0 0.001

N A donation to Y 855 855 855 855 855

Financial support 855 855 855 855 855

My time 855 855 855 855 855

Financial resources 855 855 855 855 855

Y Change Lives 855 855 855 855 855

Youth programs 855 855 855 855 855

Social justice 855 855 855 855 855

Leave my estate 855 855 855 855 855

Participate in programs 855 855 855 855 855

Y Board 855 855 855 855 855

72

Table 43 (continued)

Pearson Correlations, for Independent Subgroups

Amount of Volunteerism and Identification

Youth Social Estate Participate Y Board

Programs Justice

Programs

Pearson A donation to Y -0.014 -0.032 -0.008 -0.022 -0.09

Correlation Financial support 0.09 0.076 0.132 0.07 0.152

My time 0.114 0.089 0.16 0.097 0.138

Financial resources 0.078 0.128 0.208 0.142 0.196

Y Change Lives 0.676 0.422 0.142 0.172 0.104

Youth programs 1 0.479 0.118 0.145 0.094

Social justice 0.479 1 0.218 0.135 0.099

Leave my estate 0.118 0.218 1 0.205 0.389

Participate in programs 0.145 0.135 0.205 1 0.2

Y Board 0.094 0.099 0.389 0.2 1

Sig. A donation to Y 0.338 0.175 0.409 0.257 0.004

(1-tailed) Financial support 0.004 0.013 0 0.02 0

My time 0 0.005 0 0.002 0

Financial resources 0.012 0 0 0 0

Y Change Lives 0 0 0 0 0.001

Youth programs . 0 0 0 0.003

Social justice 0 . 0 0 0.002

Leave my estate 0 0 . 0 0

Participate in programs 0 0 0 . 0

Y Board 0.003 0.002 0 0 .

N A donation to Y 855 855 855 855 855

Financial support 855 855 855 855 855

My time 855 855 855 855 855

Financial resources 855 855 855 855 855

Y Change Lives 855 855 855 855 855

Youth programs 855 855 855 855 855

Social justice 855 855 855 855 855

Leave my estate 855 855 855 855 855

Participate in programs 855 855 855 855 855

Y Board 855 855 855 855 855

The results of the correlation analysis revealed statistically significant correlations

between model b1 (subscale amount of volunteerism) (r = .013) and model b2 (subscale amount

of identification) (r = .025); (p = .011). However, the predictability of model b1 (subscale

73

amount of volunteerism); (F = 1.721) over model b2 (subscale amount of identification) was not

statistically significant (p = .113), which was greater than the established significance level (p <

.05). These results are illustrated in Table 44.

Table 44

Pearson Correlations Model Summary Results for

Amount of Volunteerism and Amount of Identification

Model R

R

Square Adjusted

R

Square F df1 df2 Sig.

R Square Change Change F Change

b1 .114a 0.013 0.01 0.013 3.74 3 851 0.011

b2 .158b 0.025 0.015 0.012 1.721 6 845 0.113

a. Predictors: (Constant), DiffwFinR, MyFinancialSupportGoesWhereIVol, DiffTime

b. Predictors: (Constant), DiffwFinR, MyFinancialSupportGoesWhereIVol, DiffTime,

YProgramsChangeLives, IAttendYeventsPrograms, YBoard, EstateY, YPromotesSocialJustice, YChangeLives

The hierarchical regression model run for subscale b1 (amount of volunteerism) was

found to be a statistically significant predictor of a donation to the YMCA (F = 2.401); (p =

.011). In the second model results indicated that subscale b2 (amount of identification) was also

found to be a statistically significant predictor of a donation to the YMCA (F =3.740); (p =

.011). Results indicated that subscale b1 (amount of volunteerism) and subscale b2 (amount of

identification) were both statistically significant predictors of a donation to the YMCA (p = .011)

using a significance level of p < .05. These results are illustrated in Table 45.

74

Table 45

ANOVA of Subgroups Amount of Volunteerism and Amount of Identification

Model

Sum of

Squares df

Mean

Square F Sig.

b1 Regression 9.151 3 3.05 3.74 .011b

Residual 694.154 851 0.816

Total 703.305 854

b2 Regression 17.534 9 1.948 2.401 .011c

Residual 685.772 845 0.812

Total 703.305 854

a. Dependent Variable: Donation to the Y

b. Predictors: (Constant), DiffwFinR, MyFinancialSupportGoesWhereIVol, DiffwTime

c. Predictors: (Constant), DiffwFinR, MyFinancialSupportGoesWhereIVol, DiffwTime,

YProgramsChangeLives, IAttendYeventsPrograms, YBoard, EstateY, YPromotesSocialJustice, YChangeLives

Beta weights indicated that none of the independent variables had positive correlations

less than the established probability level (p < .05). However, the beta weights did indicate a

negative correlation in three of the independent variables less than the established probability

level (p < .05). The first independent variable with a negative correlation from subscale b1, “I

want to make a difference in my community by giving of my financial resources” (beta = -.117);

(p = .002) was found to be statistically significant. The second independent variable with a

negative correlation from subscale b2 “I believe the YMCA changes lives” (beta = -.089); (p =

.059) was found to be statistically significant. The third independent variable with a negative

correlation from subscale b2 “I serve or have served in the past on the YMCA

boards/committees” (beta = -.092); (p = .014). These results are illustrated in table 46.

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Research Question 3 Summary

Data suggested that there was a positive correlation of .059 hours volunteering at another

nonprofit that was found to be a statistically significant predictor of a donation to the Y (p =

.041). Education also demonstrated a statistically significant positive correlation of .100 to the

prediction of a donation to the Y (p = .004). Although, both hours of volunteering at another

nonprofit and years of education were found to be statistically significant predictors of a

donation to the YMCA the strength of the correlations would be considered statistically weak.

Hierarchical multiple regression were used to analyze two subgroupings of independent

variables: model b1: amount of volunteerism (survey questions 1-3) and model b2: the amount of

identification with the YMCA (survey questions 4-9) (Field, 2013). According to the Pearson

correlations all variables had a negative correlation to the predictor of a donation to the Y. Three

variables met the significance level (p > .05) and were found to be statistically significant

Table 46

Coefficients of Subgrouping Amount of Volunteerism and Identification

Model

Unstandardized Standardized t Sig.

B Beta

b1 (Constant) 0.537

4.533 0

Financial support 0.04 0.039 1.021 0.308

My time -0.013 -0.01 -0.243 0.808

Financial resources -0.154 -0.117 -3.038 0.002

b2 (Constant) 0.745

3.868 0

Financial support 0.05 0.049 1.273 0.203

My time -0.008 -0.006 -0.151 0.88

Financial resources -0.146 -0.111 -2.825 0.005

Y Change Lives -0.136 -0.089 -1.893 0.059

Youth programs 0.087 0.06 1.235 0.217

Social justice -0.019 -0.015 -0.383 0.702

Leave my estate 0.063 0.053 1.389 0.165

Participate in programs 0.008 0.007 0.193 0.847

Y Board -0.11 -0.092 -2.453 0.014

a. Dependent Variable: Amount of money to Y

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predictors of a donation to the Y. Independent variable “I want to make a difference in my

community by giving of my financial resources” had a negative correlation of -.109 ( p = .001).

Independent variable “I believe the YMCA changes lives” had a negative correlation of -.061 (p

= .038). Independent variable “I serve or have served in the past on the YMCA

boards/committees” had a negative correlation of -.090 (p = .004). The results of the correlation

analysis revealed statistically significant correlations between model b1 (subscale amount of

volunteerism) (r = .013) and model b2 (subscale amount of identification) (r = .025); (p = .001).

However, the predictability of model b1 (subscale amount of volunteerism); (F = 1.721) over

model b2 (subscale amount of identification) was not statistically significant (p = .113), which

was greater than the established significance level (p < .05); (see Table 44). Findings indicated

that model b1 (subscale amount of volunteerism) and model b2 (subscale amount of

identification) are both statistically significant predictors of a donation to the YMCA (p = .011).

Chapter 4 Summary

Research Question 1 Summary

RQ 1: What are the demographic characteristics (age, education level, number of hours

volunteered last week at YMCA, number of hours volunteered last week at other organization,

amount donated)?

The overall the sample (N = 855) had a mean age of 48 and an educational level of a

bachelor’s degree. Ninety – eight percent of the sample did not volunteer at the YMCA leaving

only 17 respondents who volunteered, with 71% of those at the lowest category of 1-5 hours per

week. However, 44% of the sample (N = 855) volunteered for another nonprofit or church with

73% (f = 272) volunteering from 1-5 hours. The sample (N = 855) also donated to the YMCA

14.3% (f = 122) with 71% of those reporting a donation of $0-499. The overall sample reported a

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much higher donation to other nonprofits at 89%; however, the most common donation remained

the same with 32% donating between $0-499.

From the above information respondents were categorized into three groups for further

examination. Group status was determined by survey descriptive questions 5 and 6 that

determined donation to the Y, donation to another NPO or non-donation for each participant.

Group 1 (n =113) was comprised of participants who donated to the YMCA and another NPO.

Group 2 (n = 655) was comprised of participants who only donated to another nonprofit. Group 3

(n = 87) did not donate to either the YMCA or another nonprofit.

Research Question 2 Summary

RQ 2: To what degree do demographic characteristics significantly differ across the three

groups of women: (a.) those who donate specifically to the YMCA, (b.) those who have donated

to organizations other than the YMCA, and (c.) those who haven’t donated to any organization?

The group 1 independent variable age was found to be higher than the other two groups

with 35.4% aged 60 and older (M = 3.7). Group 1 also had a lower amount of volunteer hours at

6.2% of respondents volunteering at the Y; however this group was self-identified as annual

donors to the Y last year. More than half (54%) of group 1respondents also reported that they

volunteer at another NPO.

Group 2 (n = 655) reported they had made a donation to another nonprofit last year.

Group 2 was a younger than group 1 with 27.3% aged 40-49 (M = 3.3053). Volunteering at the Y

within group 2 was 1.4% (f = 9) which was 4.8% lower than group 1. Similar to Group 1 almost

half (46.6%) of Group 2 respondents reported that they had volunteered at another NPO (f =

305).

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Group 3 (n = 87) reported that they did not make a donation to either the Y or another

nonprofit in the past year. Group 3 represented the youngest reported age category at 34.5% age

30-39 (f = 30). Group 3 also had the lowest percentage of reported volunteering at the Y (1.1%)

and the lowest percentage of volunteering at other nonprofits (9.2%). These findings indicated

that group 3 comprised of non-donors volunteered 8.1% more frequently at another NPO than at

the Y.

Research Question 3 Summary

RQ 3: To what degree does the number of volunteer hours at the Y, number of volunteer

hours elsewhere, years of education, amount of volunteerism and amount of identification with

the YMCA predict a donation to the YMCA?

The results indicated that 2% of the sample (N = 855) volunteered at the Y last week and

43% volunteer at another nonprofit last week. Results also indicated that 14% of the sample

made a charitable donation to the Y in the previous 12 months, while 89% of the sample gave to

another nonprofit.

When comparing the amount of identification with the Y and amount of volunteerism,

both were statistically significant predictors of a monetary donation to the Y. The two highest

negative predictors within the subgroups statistically were the independent variables “I want to

make a difference in the community with my financial resources” and “I have served on a

YMCA board or committee.” Both subscale survey questions received a high amount of

disagreement to the statement using the dependent variable predictor of a donation to the Y in the

last year. Chapter 5 provides the conclusions, recommendations and summary.

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CHAPTER 5. DISCUSSION, IMPLICATIONS, RECOMMENDATIONS

This empirical study has examined the differences in giving motivation of a large

membership based nonprofit across 3 groups of women. The study involved a quantitative

methodology using a validated survey instrument developed by Parsons (2004). The findings

supported the literature from the field providing evidence that the overall group of women

contribute to where they have the highest amount of identification and volunteerism (Shaw-

Hardy & Taylor, 2010); (Mesch, 2012). However, there were many differences and learnings

across the three groups that led to the conclusion and recommendation to the field. The findings

indicated that overall, 89.8% of the sample (N = 855) donated to both the Y and another

nonprofit. Also found, was that while 44% of the overall respondents volunteered at another

nonprofit, 2% reported that they volunteered for the YMCA.

The results could be considered contrary to the identification theory that states an

individual‘s personal motivation to give was directly correlated to self-identification with an

organization (Havens & Schervish, 2001). However, respondents may have found greater

identification with another NPO or, according to Choi and DiNitto (2012), women volunteer in

greater amounts and demonstrate a greater amount of interest in meeting social needs (2012).

The findings could indicate that the YMCA was not viewed as an organization that meets their

expectation of an organization that is meeting a social need. Additional findings indicated that

higher levels of income and education have a positive correlation to volunteering and charitable

giving. The remainder of Chapter 5 examines in more detail the data to find the answers to each

of our three research questions.

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Review of the Research Problem and Purpose

Women possess the greatest charitable giving opportunity for non-profit organizations in

the very near future. Recent research from Women Give 2012 (Mesch, 2012), found that even

though women earn less than men, have less money in retirement, and outlive their spouses, this

study suggested that Boomer and older women are more likely to give and give more to charity

than men. According to Shaw-Hardy & Taylor, (2010) women will become the dominant

audience in the future of fundraising based on educational achievements and accrued wealth.

Historically, philanthropy has been focused on male giving motivations causing a critical

disconnect with females as potential donors. As our societal and economic environment becomes

more volatile, understanding the giving motivation of women will become a focus for a future

untapped donor pool.

Research Problem

Recent research examined how women influence charitable giving in large, international,

voluntary service organizations (Kou et al., 2013). The results indicated that women are joining

service organizations in a greater percentage than men and through identification with the group,

are donating at a higher percentage. Additional findings from the study indicated that

organizations could benefit from strategies that will encourage women’s participation and

cultivate a nurturing and welcoming environment for women. Kou, Hayat, Mesch, and Osili

(2013) also recommends future research should examine other nonprofit membership based

organizations to find the tipping point in which female representation begins to influence the

culture within a service organization.

According to Mesch (2010), female headed households at five different income levels,

from $23,509 to $103,000, are more likely to give to charity than male-headed households. From

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the same study, women also gave more than men when comparing amount given in every income

group except for Q2 (>23,509 and <43,500). In the past, women valued time as much as giving

money; however, today many women are realizing the impact of money on the organization and

the impact of time on the person. According to Debra Mesch, director of the Women’s

Philanthropy Institute at the Center on Philanthropy at Indiana University, women want to

connect with the place where they give money. She also states that the likelihood of giving a gift

increases with the amount of time volunteered (Shaw-Hardy & Taylor, 2010).

Identification Theory stems from the term “caritas” or care. Caritas was described as the

self – identification with the needs of others. The behavior of caring extends beyond the

individualistic nature of self to include family, friends, neighbors, groups, communities, and

other associates. Havens and Schervich (2001) found that donors provided money and time to

individuals or organizations to which they were involved with in the past or felt a sense of

identity with.

Identification Theory was supported by previous research from Shaw - Hardy and Taylor

(2010) that suggests women give as a result of passion or compassion to a cause. Women are

searching for community needs that can be solved through their gift. Identification Theory

provides a foundation for this proposed research to understand the giving motivation between

women and the nonprofit sector. The premise of caritas as a basis for motivation could be the

link to a greater understanding of women’s giving and volunteering, thereby, serving as the

theoretical framework for this research.

Based the foundational framework of the Identification Theory and supporting research,

membership organizations with a high percentage of female members are likely to have higher

percentages of total giving. According to Klein (2006), 7 out of 10 people regularly give to

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charities or 70%. On the contrary, total member giving for the national YMCA only represents

3.6% of men and women, with women representing 51.8% of that total (YUSA, 2013c). The

targeted research population has 10,577 female members and 708 are annual donors representing

6 %.

The overarching research questions examined the degree to which the amount of

volunteerism and the amount of identification with the YMCA predict, a donated to the YMCA,

and are there other descriptive correlations that are statistically significant across the 3 groups of

women (a) those who donate specifically to the YMCA, (b) those who have donated to

organizations other than the YMCA, and (c) those who haven’t donated to any organization?

This quantitative research study examined differences in female giving motivation of

YMCA members across 3 types of women. In its recent history, the YMCA organization has

struggled to connect members to their philanthropic purpose or cause. In 2010, the Y nationally

engaged in a rebranding effort to improve donor cultivation and member identification to build a

platform to communicate their charitable case (YUSA, 2010). The findings provided financial

development program’s strategies to engage women, the largest group of potential donors

(Damen & McCuistion, 2010).

Purpose

The primary purpose of this research was to contribute to the fundraising industries and

the YMCA’s deeper understanding of the giving motivation of women. In the past, the nonprofit

sector has struggled to grow philanthropy at the same rate as other revenue sources, such as

grants, sales of goods, membership fees and programs (Dees, 1998). To that end, as a nonprofit,

the organization has an obligation to meet community needs through programs that encourage

members to donate to their cause. The significance of this study will assist professional

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fundraisers to balance subsidies and contributions by increasing giving motivation of women

through identification with the nonprofit.

Summary of Results by Research Question

Three research questions guided the study. The findings are briefly discussed, research

questions are restated and data analysis was based on Chapter 4.

Research Question 1.What are the demographic characteristics (age, education level,

number of hours volunteered last week at YMCA, number of hours volunteered last week at

other organization, amount donated)?

Research question 1 was asked to glean an understanding of the demographics of all

respondents as a whole. Research questions 2 and 3 divided the group for comparisons of

volunteerism and identification based on survey descriptive questions 5 and 6 indicating where

respondents contributed annually. The results of research question 1 revealed that the average

age of the respondents was 48 years old with a bachelor’s degree. These respondents were more

likely to have volunteered at another nonprofit at 44%, where only 2% volunteered at the

YMCA. Of the 855 respondents who volunteered at either another nonprofit (including church)

or the YMCA, the amount was the same at 1-5 hours of volunteering per week. The number of

respondents who donated to the YMCA represented 122 of the 855 respondents or 14.3%;

however the number who donated to other nonprofits (including a church) swelled to 759 of the

855 or 89%. The highest level of donation amount reported from group 1 and 2 was the same

with 71% donating to the Y at $0-499 and 30% donating to another nonprofit at $0-499. The

descriptive data supports the literature review and previous research from Shaw-Hardy & Taylor

(2010), which suggests the likelihood of giving a gift increased with the amount of time

volunteered and higher levels of education.

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The purpose of research question 1 was the clear picture to the overall female donor

demographics to use for comparison and understanding. The results for research question 1

indicated that this sample (N = 855) of female Y members are philanthropic in nature; however

the low response of contributions to the Y at only 14.3% (122) in comparison to the 89% (759)

who donated to another nonprofit or church. Another key was the amount of donations and

volunteer hours were at the lowest level for both the Y and other NPOs. This finding could be a

sign of the times with a poor economy and lack of time, as societal trends continue to embrace

technology over community causing social isolation. According to Baek, our next generation is

more comfortable in the cyber-world rather than actively participating real society (2014). Based

on the study results and the supporting literature, the female demographic is the sectors best

chance to develop passionate and cause driven donors for the next generation. Research

questions 2 and 3 used the overall demographics to define the three groups and allows for further

comparisons. Research question 2 examined the group demographics in more depth as to what

degree the characteristics significantly differ across the three groups.

Research Question 2. To what degree do demographic characteristics significantly differ

across the three groups of women: (1.) those who donate specifically to the YMCA, (2.) those

who have donated to organizations other than the YMCA, and (3.) those who haven’t donated to

any organization?

Research question two examined the group differences among the reported demographic

data from the sample (N = 855). The groups were categorized into group status determined by a

donation to the Y, donation to another nonprofit or non-donation for each participant. Group one

was comprised of participants that donated to both the YMCA and another NPO (n = 113).

Group two was comprised of participants that only donated to another nonprofit (n = 655). If a

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respondent donated to both the Y and another nonprofit they were assigned to group one. Group

two was comprised solely of respondents that donated to another nonprofit including a church.

Group three participants did not donate to either group one or group two (n = 87). Chi-square

analyses examined group differences regarding education level, amount donated to the YMCA,

and amount donated outside the YMCA. A MANOVA, followed by univariate analyses, was

utilized to examine group differences for age, number of hours volunteered within the YMCA,

and number of hours volunteered outside the YMCA.

Education Level Differences

Education declined among each group with group 1 reporting the highest with a Master’s

degree, group 2 with a Bachelor’s degree and group 3 with some college. The independent

variable education were found to be significantly significant with a Chi square of 48.390(p =

.000), which were less than the significance level (X2 (16) = 48.390a, p < .05). The results

indicated that there was a significant difference among the groups between education and a

donation to the Y or another nonprofit. The findings indicated that respondents that donate to

both the YMCA and another NPO are at a higher level of education than groups 2 and 3.

Furthermore, Group 2 had a higher level of education with a Bachelor’s degree than group 3 with

some college. The link from education to where the participants chose to give is important to the

nonprofit sector for planning of outreach efforts, marketing and engagement. Outreach to women

of membership based nonprofit are well educated and will likely be motivated by programs and

initiatives that help with education of other people. For the YMCA specifically, as they begin

to implement programs to increase educational opportunities, such as achievement gap

programs, the need to communicate to female members will be critical. Financial

development programs should focus on engaging the women of the nonprofit sector by linking

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them to philanthropic programs to increase educational opportunities for increased identification

with the organization.

Amount donated to the YMCA Differences

The independent variable donation to the Y was found to be statistically significant with a

Chi Square value 785.084a (p = .000), which was less than the significance level of p < .05 (X2

(10) = 785.084a, p < .05). The results indicated that among the three groups, there is a

significant difference in the amount donated to the Y. The results indicated that the highest

volume of donations among the three groups was to another NPO (n = 655); however both

groups 1 and 2 donated most frequently in the lowest category ($0-499). Thus, both groups that

donated to either the Y or another NPO did so at a $0- $499 amount. Eighty respondents out of

113 that donated to the Y did so at the lowest level $0-$499. Financial development programs

should focus on cultivation of this group to higher amounts through targeted efforts to

increase identification. Linking volunteerism to educational programs, such as tutoring,

could be a way to deepen identification of the donor to higher levels. Another finding is the

lack of major donors in the category above $1000. The finding indicates a lack of identification

with the nonprofit causing lower levels of contributions. To achieve a higher level of major

donors, the Y will need to engage women with targeted approaches such as; board of directors’

participation, volunteer activities around education and improved communication of

organizational priorities that link to their motivation.

Amount Donated to Another NPO Differences

The independent variable amount of money donated to any other nonprofits last year was

found to be statistically significant with a Chi Square value 769.021 (p = .000), which was less

than the significance level (X2 (10) = 769.021a, p < .05). The results indicated that group 1

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donated to both the Y and another nonprofit. Of the 855 respondents, 9 donated only to the

Y. Overall, 89.8% (768 respondents) of the 855 sample indicated that they gave to either the Y or

another nonprofit. This study’s finding is 20% higher than, Klein’s (2006) claim that, 7 out

of 10 or 70% of people regularly give to charities. Further research would be needed to

validate reasons behind the 20% increase; however it is likely the entire sample comprised

of women would be more philanthropic in comparison to the national average of men and

women. For the nonprofit sector, this particular finding demonstrates the giving nature of

women and the emerging power that they possess to change the landscape of philanthropy.

Group Differences

The MANOVA was found to be statistically significant across all tests; Pillai's Trace,

Wilks' Lambda, Hotelling's Trace and Roy's Largest Root (p = .000). The significance level was

established at p < .05. The results indicated a statistically significant difference among the three

independent variables (age, hours volunteered at the Y and hours volunteered at another NPO).

Further analysis using univariate analysis for each group found that group 1 had a much higher

age of 60 and older (f = 40); however the mean remained similar to group 2 with both falling into

category 3 (age 40-49). Group 3 was slightly younger with highest amount of respondents in age

category 2 (30-39) with 30 out of the 87 or 34.5%. The results indicated that the age group

40-49 has the highest probability to predict a donation to the Y. For the nonprofit sector,

the findings indicated that females aged 40-49 should be a target for marketing and

communication of financial development programs. Other findings specific to the YMCA

would indicate planned giving education should be a focus in the future based on the high

frequency of respondents 60 and older.

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With regard to volunteering at the Y, group 1 reported that 6.2% volunteered at the Y,

group 2 reported only 1.2% and group 3 reported 1.1%. The results indicate that group 1 was 6

times more likely to volunteer with the Y than groups 2 and 3. Although, volunteerism reporting

was low overall for the YMCA the results still support the research from Shaw-Hardy and Taylor

(2010) which stated the likelihood of giving a gift rises with the amount of time volunteered and

higher levels of education. These findings indicated that respondents that volunteer as little

as 1-5 hours per week within a particular organization are more likely to give to that same

organization. Furthermore, discussed in more detail later in the chapter are the findings that the

amount of volunteerism and identification are statistically significant predictors of giving a gift

to the YMCA.

With regard to volunteering at another NPO, group 1 reported that 54.9% volunteered at

another NPO, group 2 reported only 46.6% and group 3 reported 9.2%. The results indicate that

almost half of group 1 and 2 volunteer for another nonprofit with group 3 four times less likely to

volunteer. The findings continued to support the linkage of volunteerism to the prediction of a

gift based the comparison of groups.

In summary, the results support that there was a statistically significant probability that

demographic characteristics of age, hours volunteered at the Y and hours volunteered at another

NPO differ across the three groups of women. The findings support the literature that higher

amounts of volunteerism are strong predictors of giving motivation. According to mean scores of

age across the three groups, category 3 (age 40-49) has the highest probability to donate to the Y

or another NPO.

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Research Question 3. To what degree does the number of volunteer hours at the Y,

number of volunteer hours elsewhere, years of education, amount of volunteerism and amount of

identification with the YMCA predict a donation to the YMCA?

The test of Persons correlations found a negative correlation of -.036 of hours

volunteering at the YMCA; however, the significance level was greater than .05 making the

finding statically non-significant (p = .148). The second independent variable was hours

volunteering at another nonprofit that had a positive correlation of .059 and was found to

be statistically significant (p = .041). The third independent variable was education that had

a positive correlation of .100 that was found to be statistically significant (p = .002). Both

hours of volunteering at another nonprofit and years of education were predictors of a donation

to the YMCA. The findings indicated that by targeting members of a nonprofit with a

bachelor’s degree or higher who volunteer at any nonprofit, there is a statistically

significant correlation to predicting a donation to the YMCA. Using education and

volunteering could be generalized to all nonprofits as an identifier for potential donors for

marketing and donor cultivation.

Part two of research question 3 used hierarchical multiple regression to find the

predictability of a donation to the YMCA using two subscale groups of nine independent

variables: Model b1: amount of volunteerism (survey questions 1-3) and model b2: the amount

of identification with the YMCA (survey questions 4-9). The descriptive statistics found that out

of the nine independent variables, respondents agreed with seven of the statements and disagreed

with 2 according to mean scores (table 46). The most agreeable response with the lowest mean

score 1.7591 was “I believe YMCA youth programs change lives.” The results indicate overall

the respondents are agreeable to subscale model b1 amount of volunteerism. Subscale model b2

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amount of identification had 4 out of 6 agreeable responses with 2 disagrees on survey questions,

“I plan to leave my estate or part of my estate to benefit the YMCA” (M =3.23) and “I serve or

have served in the past on the YMCA boards/committees” (M =3.20). Utilizing the Pearson

correlation test, the next section examined statistically significant results related to the subscales

and the predictor.

The individual Pearson correlations found that all variables had a negative correlation to

the predictor (donation to the Y). The negative correlation was consistent with previous results of

group 2 (n = 655) giving to other nonprofits. However, only three variables were found to be

statistically significant. The first independent variable found to be significant was “I want to

make a difference in my community by giving of my financial resources” which had a negative

correlation of -.109 (p = .001). The respondents indicated they agree to give financially to make

a difference (M = 2.11) (2 = agree); although the predictability was negatively related to the

outcome of a donation to the YMCA. The results provide evidence that the amount of

volunteerism with the YMCA was not a strong enough motivator for female members to

donate to the organization or other motivations they may identify with more strongly.

The second independent variable that was found to be significant was “I believe the

YMCA changes lives” which had a negative correlation of -.061 (M = 1.7; p = .038). The

respondents are indicating that although they agree the YMCA changes lives, it was not a

high enough amount of identification to motivate a donation to the YMCA.

The third independent variable that was found to be significant was “I serve or have

served in the past on the YMCA boards/committees” which had a negative correlation of -.090

(M = 3.2035; p = .004). The respondents are indicating that they have not been on a Y board

or committee causing a negative correlation to the ability to predict a donation to the

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YMCA. The results indicated that there was a correlation to not being a board member

and the ability to predict a donation to the YMCA to the overall sample.

In closing, when comparing the two subscales model b1 (the amount of volunteerism)

and subscale model b2 (amount of identification), both were found to be statistically significant

predictors of a donation to the YMCA (p = .011) (see Table 49). The results of the correlation

analysis revealed statistically significant correlations between model b1 (subscale amount of

volunteerism) (r = .013) and model b2 (subscale amount of identification) (r = .025); (p = .001).

However, the predictability of model b1 (subscale amount of volunteerism); F (1.721) over

model b2 (subscale amount of identification) was not statistically significant (p = .113), which

was greater than the established significance level (p < .05); (see Table 44). Findings indicated

that model b1 (subscale amount of volunteerism) and model b2 (subscale amount of

identification) are both statistically significant predictors of a donation to the YMCA (p = .011).

Further, overall recommendation and conclusions are listed later in the chapter specific to the

YMCA, as well as additional generalizations to the larger nonprofit sector.

Evaluation of the Results

The results overall demonstrate the linkage between the identification theory and the

practical application of a financial development program. Both the theoretical and the practical

implication are discussed to validate the expansion of the theory to the membership based

nonprofit sector and to the YMCA organization. The study produced statistically significant

valid findings that can be generalized across the nonprofit sector with the known listed

limitations.

Past research tells us that women will control 80% of the wealth of the nation and

evidence indicates they will inherit and manage even more wealth in the future (Damen &

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McCuistion, 2010). The simple fact that women out live men by five years have been the basis of

the prediction that women will control 80% of their families’ financial affairs sometime in their

life. Experts Havens and Schervish (2003) estimate that $41 trillion dollars is expected to pass

through the hands of Americans by 2052. Based on this research, it was clear that women

represent the largest untapped and underutilized resource in a financial development program.

The capacity of women to contribute will define philanthropy over the next ten to twenty years

based on positive identification with a nonprofit. This study as identified three results that

required further evaluation; women in governance positions, amount of volunteerism and

identification and level of education.

Women in Governance Positions

This research suggests that women would have higher amounts of identification with a

NPO if they served in board positions. By embracing women in the governance structure of

nonprofits would likely improve identification and philanthropic motivation. Women comprise

less than 15% of corporate board members in the US, UK, Canada, Australia and many European

countries (Singh & Terjesen, 2008). Demographical information of the community and the

membership base should be used to identify gaps in diversity among the nonprofit’s governance

profile. Specifically, women should have equal representation on governance as men in

nonprofits who have a similar demographical membership base. In this study, the respondents

are indicating they have not been on a Y board or committee causing a negative correlation to the

ability to predict a donation to the YMCA.

When respondents were asked to answer “I serve or have served in the past on the

YMCA boards/committees” the mean response was 3.20 in disagree (M =3.20). Furthermore,

within the study population and setting, the YMCA of Southwest Illinois, 55% of the nonprofit’s

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membership base was female in comparison to the board of directors that was comprised of only

24% female (YMCA of Southwest Illinois, 2014). Another strategy to improve diversity would

be adhering to term limits based on the bylaws of the organization. The result should allow for a

transition of long time male represented seats to be transitioned to females resulting in improved

diversity among the board profile. The long term outcome would increase the amount of

volunteerism and identification of women within nonprofit organizations. The end result of

implementing this identification strategy predicts the increased frequency of annual contributions

and improves the effectiveness of an organization’s financial development program.

Amount of Volunteerism and Identification Needed

When comparing mean scores for the two subscales, amount of volunteerism and amount

of identification, 7 out of the possible 9 questions were answered with an agree or strongly agree.

This suggests that the respondents have a positive opinion that volunteerism was important and

they have a positive identification with the Y; however the respondents only donated at a rate of

14% to the Y.

The answer could be found in the first independent variable of the identification subscale

that was found to be statistically significant “I believe the YMCA changes lives.” The

independent variable had a negative correlation of -.061 (M = 1.7, p = .038). The 855

respondents are indicating that although they strongly agree the YMCA changes lives only 14%

were motivated to donate as a result.

The results also indicated the need to increase volunteer opportunities specifically for

women in YMCA programs. Of the 855 respondents, 768 or 89.8% donate to either the Y or

another nonprofit; however only 14% donate to the Y. Group 1 comprised of Y donors was

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found to volunteer 6 times more than groups 2 and 3. This finding was a strong indication that

increasing the amount of Y volunteer opportunities will increase the likelihood of a donation.

Level of Education

Education was the only coefficient predictor of a donation using multiple regression with

a positive correlation that was found to be statistically significant (beta =.075, p =.004). The

study also found across the three groups of 855 respondents, 89.9% of the sample donated to

either the Y or another NPO with a mean educational level of a Bachelor’s degree. Also,

supporting the results from the literature, women’s income has risen 60% over the past thirty

years in contrast to men’s median income increase of 6% (Witter & Chen, 2008). The cause of

this gain was most certainly the educational increase of women over the last 20 years. Females

are increasingly outperforming males in the classroom, earning about 57% of the undergraduate

and 60% of the master’s degrees in the United States (Sandberg, 2013). By out pacing men,

women are beginning to penetrate previously male dominated fields such as astronauts, partners

in law firms, surgeons, rabbis, police and fire, Supreme Court Justices and Chief Executive

Officers.

In closing, the results indicated the need to target education as criteria for donor

stewardship and cultivation of women. Development Officers should target programs and

volunteer opportunities that will attract women of higher education to deepen identification with

the organization. Programs such as women’s giving circles, women’s educational empowerment

or women’s volunteer opportunities will enable nonprofits to increase the amount of

identification they have with the next generation of emerging donors.

Theoretical Implications

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Study findings validated the identification theory that postulates an individual’s personal

motivation to give was directly correlated to self-identification with an organization (Havens &

Schervish, 2001). The results from the Pearson correlation found that the second independent

variable (hours volunteering at another nonprofit) had a positive correlation of .059 and was

found to be statistically significant (p = .041). Also, linked to the theory was the third

independent variable (education) that had a positive correlation of .100 and was found to be

statistically significant (p = .002). Both hours of volunteering at another nonprofit and years of

education were predictors of a donation to the Y. The theory states that it is self –identification

with others and with the needs of others (rather than selflessness), that motivates the transfer to

individuals and to philanthropic organizations. The findings support the postulation of the theory

demonstrated by the correlation of volunteering or self-identification with others to the

prediction of a gift to the organization.

Further support of the theory was found by comparing the two subscales model b1 (the

amount of volunteerism) and subscale model b2 (amount of identification). Both were found to

be statistically significant predictors of a donation to the Y (p = .011) (see Table 49). The theory

suggests that there is a priority structure to giving based on four objective associations: meeting

basic needs, religious traditions, experience of blessing and the need to help others. Based on the

results of the subscales, a validation can be confirmed of the four associations that were linked

through the designed survey questions.

The theory also states that behavior of caring extends beyond the individualistic nature of

self to include family, friends, neighbors, groups, communities, and other associates. The

researchers found that donors provided money and time to individuals or organizations to which

they were involved with in the past or felt a sense of identity (Havens & Schervish, 2001). The

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current study found that respondents did donate to organizations to which they identified;

however the identification was not found in a great percentage in the study setting of the YMCA.

Respondents donated 89.8% overall, with 76.6% of the donations going to an organization other

than the YMCA (see Table 13). The implication to the theory validates the four objective

associations and the overall self – identification of donors. Unfortunately for the YMCA, the

study suggests the idea that their members are giving in higher frequency to other nonprofits

with greater amounts of identification.

Implications for Practice

The findings have identified an emerging donor group, women’s motivations for giving

and the impact of identification with an NPO. The implications for the nonprofit sector have

been supported by the literature and validated through research findings. The remainder of this

section discussed implications on the practice of a financial development program in the

nonprofit sector.

Women’s giving has become of more interest recently, as financial development

programs find ways to target donor groups and understand that they are motivated differently

than their male counterparts. According to Choi and DiNitto (2012), women volunteer in greater

amounts and demonstrate a greater amount of interest in meeting social needs. Additional

findings indicate that higher levels of income and education have a positive correlation to

volunteering and charitable giving. The findings from this study confirm Choi and DiNitto’s

2012 research demonstrated by the 89.9% of the sample that donated to a NPO or the YMCA.

An additional linkage to Choi and DiNitto’s (2012) findings was the high level of volunteerism

among group 1 (volunteering 54.9%) and group 2 (volunteering 46.6%) both at nonprofits other

than the YMCA. The implication for the YMCA is the lower level of volunteering of the same

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donor groups with group 1 volunteering 6.2% and group 2 volunteering 1.4% at the YMCA. A

potential opportunity for the YMCA could be to increase volunteer activities that attract group 2,

thereby increasing identification and possibly contributions. The opportunity for the nonprofit

sector would be to target volunteer activities to increase identification with their organization to

increase contributions.

Educational levels of groups 1(donors to the Y) and group 2 (donors to other NPO)

confirm Choi & DiNitto’s findings, as well. Group 1 reported the highest percentage at the

educational level of a Master’s degree (37%), group 2 reported a Bachelor’s degree (36%) and

overall combining the two donor groups revealed that 79% were at a Bachelor’s degree or higher

(2012). The implication for the Y would be to target marketing efforts of volunteerism and

philanthropy to members with a bachelor’s or higher educational level. The same could be

generalized to the nonprofit sector to yield greater results in volunteerism and contributions to a

financial development program.

Women’s motivations for giving from this study support Kou, Hayat, Mesch, and

Osili 2013’s findings that organizations could benefit from strategies that will encourage

volunteer participation with a nurturing and welcoming environment for women. Three

variables were found to be statistically significant with p values greater than .05. Independent

variable “I want to make a difference in my community by giving of my financial resources” had

a negative correlation of -.109 and a p value of .001. Independent variable “I believe the YMCA

changes lives” had a negative correlation of -.061and a p value of .038. Independent variable “I

serve or have served in the past on the YMCA boards/committees” had a negative correlation of

-.090 and a p value of .004.

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All three variables have implications on the YMCA that can be generalized to the

nonprofit sector. The negative correlation between identification and a donation to the Y

demonstrated that female Y members agree to the statements; however are not motivated enough

to make a charitable donation. The data suggests that the Y and the nonprofit sector should

focus their efforts on approaches that deepen identification with female donors through

programs that allow; service on boards, ongoing volunteer activities that change lives and

more frequently asking for giving of financial resources. The study findings have identified

and validated that these changes will increase identification and the literature has demonstrated

that adhering will result in increased contributions to a financial development program.

The impact of identification is concurrent with where someone spends time and money.

According, to Debra Mesch, director of the Women’s Philanthropy Institute at the Center on

Philanthropy at Indiana University, women still want to connect with the place where they give

money. She also states that the likelihood of giving a gift increases with the amount of time

volunteered (Shaw-Hardy & Taylor, 2010). The study findings again confirm the literature with

low amounts of time volunteering that mirrored the low amounts of funds donated. The overall

855 respondents volunteered 31.8% (272 respondents) from 0-5 hours per week and donated

27.5% (235respondents) from $0-499 at NPO last year.

The findings imply that by increasing volunteer time in turn will increase contributions

through greater amounts of identification. Financial development programs with a greater

emphasis on developing volunteers along a continuum; from causal, to connected, to committed

would have the effect of increased volunteer time, identification and contributions. In the future,

as women take a more prominent role as philanthropist, organizations that possess the ability to

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form deep relationships through long-term volunteerism will yield the greatest financial impact

through increases in the amount of identification.

Limitations of the Study

The current study accepted the limitation to generalize results to a larger population by

accessing a sample drawn from one test site. Therefore, the study may have limited ability to

make generalizations to the entire nonprofit sector (Field, 2013). The study also accepts the

limitations for data collection expected to take place during the summer of 2014. The findings

are reliant on the timeframe and not reflective of a different time of year. The study accepts the

limitation of the ability of the participants to self-report accurate and truthful information. The

bias that may have resulted from the survey only being distributed electronically; respondents

may have been limited only to women who had access to email or access to a computer, felt

comfortable using a computer and have heard about and taken interest in the study topic (Remler

& Van Ryzin, 2011). The over or under representation of test results caused by common

“method-bias,” which is frequently associated with data collected using the utilization of self-

report survey tools, including over or under overstated responses and errors resulting from

participants’ misunderstanding of questions (Remler & Van Ryzin, 2011). The final limitation

was the sensitive nature of the subject caused some respondents to submit incomplete surveys or

withhold donation amounts.

Recommendations for Future Research

The evaluation of a financial development program revealed a significant relationship

between the amount of identification and the prediction of a contribution within a large

membership based nonprofit; however many questions will require further examination. Since

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negative correlations exist between identification and the population the following questions

remain as recommendations for future research:

1. Could this study be replicated at other membership based nonprofits to

broaden the generalizations of the sector?

2. Could this study be focused solely on multiple YMCAs nationally to

determine regional differences?

3. Could this study be sent to male Y members to determine their motivations for

giving and whether men give differently than women?

4. Could the YUSA research and development department could replicate this at

YMCA across the country to gather a broader understanding of the

motivations behind women’s giving to a YMCA?

5. Items that were part of the survey that could still be analyzed or addressed for

future research are as follows:

a. Is there a correlation to gift size and the amount of identification with

the nonprofit?

b. Is there a correlation to gift size and the amount of volunteerism with

the nonprofit?

c. What influences women that have a high amount of identification with

the YMCA to make a charitable donation to the organization?

d. What influences women that have a high amount of volunteerism with

the YMCA to make a charitable donation to the organization?

Summary

To better understand why women are not volunteering and contributing to the YMCA in

similar amounts as other NPO, further study will need to be conducted aimed at building upon

101

results stemming from this research. A closer study of why they are donating to other nonprofits

will lead to more findings that could offer greater insights into the emerging donor group. Or,

additional examination should be given to how organizational members assess and define

identification. Exploration into these factors may uncover why a positive correlation was not

identified between the amount of volunteerism, amount of identification and the predictor of the

contribution.

In closing, the findings have indicated that identification and volunteerism are strong

predictors of attracting future contributions from women. Nonprofit financial development

departments should begin to focus on stewardship that will increase the motivation of women to

give to their cause. Selecting women to serve in governance positions should be a priority for

nonprofit’s Chief Development Officers, Chief Executive Officers and financial development

programs. By targeting programs that increase the educational opportunities of women will

increase identification with the nonprofit resulting in higher contributions. In closing, over the

next 10 years nonprofits that embrace a culture to intentionally develop relationships with

women will be best positioned to meet the needs of their communities.

Conclusion

A growing body of demographic data, literature and research indicates that women’s

philanthropy is an important aspect of the nonprofit sector locally, nationally and globally. In the

future women donors have the ability to reshape the philanthropic landscape and influence the

larger growth of social programs. Experts, Havens and Schervish, estimated that $41 trillion

dollars was expected to pass through the hands of Americans by 2052 (2003). They also

predicted that 6 trillion will be given to charitable organization. The estimates have been

reviewed and confirmed by the Council of Economic Advisors and the Bureau of Labor Statistics

102

that found the prediction to be reasonable. Furthermore, the Congressional Budget Office staffs

of economists have used the estimation to analyze the future wealth transfer (Havens &

Schervish, 2003).

This study provides support to the important role that women will play in the future fund

development of nonprofits, such as the YMCA. The findings again indicated that females are

motivated by caring for others through volunteerism causing increased amounts of identification

with the organization. This study allowed for collection of demographic information to be

collected and key insights into motivation for women to volunteer and identify with a large

membership based organization. The findings indicated that there is a correlation to volunteer

hours and predicting a donation to a nonprofit. The findings imply that by increasing volunteer

time in turn will increase contributions through greater amounts of identification. Also found,

that respondents donated to organizations that they had identification with; however the

identification was not found in the study setting of the YMCA as a member. Respondents

donated 89.8% overall with 76.6% of the donations going to an organization other than the

YMCA (see Table 13).

This study supports the growing literature on the motivations of women, showing that

greater amounts of identification influence charitable giving. Based on these findings from

experts Damen and McCuistion, 2010, and Havens and Schervish (2003) women will play a

critical role in defining the philanthropic landscape in the next several decades. Recent findings

from Women Give 2012 (Mesch, 2012) challenge the perceptions about who was philanthropic,

revealing that Boomer and older women are as or more philanthropic than their male

counterparts. Findings from Women’s Business Research indicate that 54% of businesswomen

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make all of their decisions independently of advice or counsel from anyone. Women are

beginning to realize the power they possess to change the face and future of our society.

The findings also indicated that there was a negative correlation to respondents that had

never served on a board and the prediction of a donation. Previous research from Erkut, Kramer

and Konrad (2008), also indicates having three or more women serving on a corporate board

could have a critical influence on the discussion and process of board direction. Likewise,

Marquis and Lee’s (2011) study found that corporations with a greater percentage of women in

senior management roles made significantly higher charitable contributions. Based this current

research, the selection process of nonprofit boards will greatly influence the organizations

success in fund development in the future.

The study findings have direct implication to the nonprofit sector with regard to

engagement and recruitment of female members. Confirming Kou, Hayat, Mesch, and Osili

(2013) recommendations organizations would benefit from strategies that encourage women to

participate in leadership roles and have a welcoming environment to their involvement. Future

success in financial development will depend on an organization’s ability to change the culture

within their organizations and pay more attention to the distinctive giving motivation of women.

104

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APPENDIX A. STATEMENT OF ORIGINAL WORK

Statement of Original Work and Signature

I have read, understood, and abided by Capella University’s Academic Honesty Policy (3.01.01)

and Research Misconduct Policy (3.03.06), including the Policy Statements, Rationale, and Def-

initions.

I attest that this dissertation or capstone project is my own work. Where I have used the ideas or

words of others, I have paraphrased, summarized, or used direct quotes following the guidelines

set forth in the APA Publication Manual.

Learner name

and date , Jared G. Beard

Mentor name

Dr. Suzanne Holmes