The Pennsylvania State University

The Graduate School

School of Public Affairs

Predictors of Alumni Donor Behavior

in Graduates of the Traditional MBA and iMBA Programs

at The Pennsylvania State University

A Dissertation in

Public Administration

by

Jason W. Ketter

© 2013 Jason W. Ketter

Submitted in Partial Fulfillment of the Requirements

for the Degree of

Doctorate of Philosophy

December 2013

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The dissertation of Jason Ketter was reviewed and approved* by the following:

Jeremy Plant Professor of Public Policy and Public Administration Dissertation Advisor Committee Chair Steven A. Peterson

Director, School of Public Affairs Professor of Politics and Public Affairs

Stephen P. Schappe Director, School of Business Administration

Associate Professor of Management

Triparna Vasavada

Assistant Professor of Public Administration *Signatures are on file at the Graduate School

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ABSTRACT

The affordability of a degree from a public university is the subject of much

heated debate in the halls of many state governments. The taxpayer, as well as the

individual paying tuition, is asking the question: What is the return on investment for the

millions of dollars used to support public higher education? The taxpayer views public

universities as bloated, inefficient, staffed with overpaid faculty, prioritizing athletics

over academics, and unable to control costs. The recent graduate is asking: Was

obtaining a degree worth the thousands of dollars of debt only to find no job upon

graduation? Furthermore, local governments under pressure to meet balanced budgets are

asking local non-profits, including universities, who pay no local taxes to help with

expenses for such items as fire protection.

Public universities are asked to be competitive and control costs, but are not given

the autonomy to increase tuition or to close a branch campus to cut costs because state

legislators hear from their constituent that rising tuition is making the degree cost

prohibitive or closing a branch campus would be a disaster to a local economy. This

study aims to help expand the knowledge base for higher education administrators who

are seeking to enhance revenue streams, policy makers who are implementing

performance based funding for public higher education, and researchers who are seeking

to better understand predictors of alumni giving and the impact of the online program

graduate may have on philanthropy.

Online education is growing at a rapid pace in the United States. According to the

2012 Survey of Online Learning conducted by the Babson Survey Research Group in

collaboration with the Sloan Consortium found students taking at least online course now

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exceeds 6.7 million and this is an increase of over 570,000 from the year prior (Sloan

Constortium, 2013). Further the survey results found thirty-two percent of higher

education students now take at least one course online and seventy-seven percent of

academic leaders found learning outcomes coming from online education as the same or

superior to those in face-to-face classes.

This study examined predictors of alumni donor behavior of graduates of the

traditional and online MBA programs from a public research university. Logistic

regression was used to analyze the variables of student experience and alumni

experience. The study is grounded in the organizational identity theoretical framework

and a questionnaire was mailed to collect the data for the study.

The results suggest that online graduates have higher levels of student and alumni

experiences, and that student and alumni experiences overall are predictors of alumni

donor behavior. This study can be useful for professionals in the fundraising field and for

policy makers who are seeking enhanced revenue streams to support public higher

education. Additionally, this study fills the gap in the literature with the introduction of

research looking at online and traditional graduate alumni and student experiences.

Philanthropy can play a significant role in helping to secure more funds to support

student scholarships, fund annual operating expenses, new initiatives, and to help build

endowments that can help secure the future of a particular university.

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TABLE OF CONTENTS

LIST OF FIGURES………………………………………………………………………………………….…...vii

LIST OF TABLES…………………………………………………………………………………………………viii

ACKNOWLEDGEMENTS………………………………………………………………………………….xi

CHAPTER 1: THE PROBLEM STATEMENT …………..………..………………………….1 Introduction.…………………………………………………..………………………………………….1

Problem Statement………………………………………………………….…………………………8 Significance of Study………………………………………………………………………………..12 Research Question…………………………………………………………………………………….14 Predictive Model…………………………………………………………………….…….18

CHAPTER 2: LITERATURE REVIEW………………………………………………….………….22 Higher Education Fundraising ………………………………………………………….…....22 Reasons People Give…………………………………………………………………………………25

Giving Back to One’s Alma Mater……………………………………………..31 Fundraising in the U.S.…………………………………………………………………….……….37 CHAPTER 3: METHODOLOGY……………………………………………………………..…………39 Quantitative Method………………………………………………………………………………….39 Theoretical Framework…………………………………………………………………………….40

Research Design and Data Collection…………………………..………………………...42 Population and Participants………………………………………………………...42

Definition of Terms...……………………………………………………………………47 Independent and Dependent Variables…………………..…………………..47

Questionnaire……………………………………………………………………………………..……..47 Issues of Validity and Reliability ……………………………………………..…………….49 Delimitations………………………………………………………………………..…..……….……...50

CHAPTER 4: DATA ANALYSIS………………………………………………………..…………...51 Overview………………………………………………………………………………………………..….51 Descriptive Statistics……………………………………………………………..……………….…51 Distribution of Data………………………………………………………………….……………….57 Individual T-test……………………………………………………..…………………………………58 Independent Sample T-test Results for Each Item……………………….…………61

Descriptive Statistics for the Constructs………………………………………….……...65 Hypothesis Testing…………………………………………………………………………………….68

H1: The MBA graduate will demonstrate higher levels of student experience—relationships…………………….…………………..…..68 H2: The MBA graduate will demonstrate higher levels of student experience—academic…………………………………….…………..…72 H3: There is a difference in student experience—impact on

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career between the iMBA and MBA graduates……………………......75 H4: There is a difference in alumni experience—alumni association between iMBA and MBA graduates………………..……..77 H5: There is a difference in alumni experience—engagement between iMBA and MBA graduates………………………………………....80 H6: Student experience distinguishes alumni donors from non-donors………………………………………...………………………………………………….82 H7: Alumni experience distinguishes alumni donors from non-donors……………………………………………………………………………………..…....87

CHAPTER 5: DISCUSSION AND ANALYSIS OF FINDINGS……….……………92 Discussion……………..……………………………………………………………………..…………...92 Overview of Findings………………..……………………………………………………………..93 Limitations of Study…………………………………..…………………………………..…………97 Future Research..………………………………………………………..…………………………....99

CHAPTER 6: CONCLUSION……………………………………………………………….……….....100 Introduction………..……………………………………………..........................................100

Summary………………..………………………………………………………………………………….103

REFERENCES……...…………………………………………………….………………………..……………..108

APPENDIX A Cover Letter and Questionnaire……………………………………..............119

APPENDIX B Reminder Post Card……………………………………………………………………..128

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LIST OF FIGURES

Figure 1.1 Predictive Model Questions for Alumni and Student Experience……..…18

Figure 1.2 Predictive Model Factors for Alumni Donor Behavior………….....………….19

Figure 2.1 Donor Giving Cycle………..………………………………………………………………..………29

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LIST OF TABLES

Table 2.1 Seven Faces of Philanthropy…………………………………………………………………….27

Table 2.2 Theoretical Frameworks……………………………………………………………………………30

Table 3.1 Sample Population………………………………………...............................................43

Table 3.2 Response Rate Comparison…………………………………………..………………………....46

Table 3.3 Chi-Square Test for Response Rate Comparison…………………………………….46

Table 4.1 Descriptive Statistics for Demographic Information……..………................52

Table 4.2 Descriptive Statistics for Gender……………………………………..………………….…..52

Table 4.3 Descriptive Statistics for Salary……………………………………….………………………53

Table 4.4 Descriptive Statistics for Age……………………………………………..……………………54

Table 4.5 Descriptive Statistics for Ethnicity……………………………..……………………………55

Table 4.6 Descriptive Statistics for Marital Status………………………….……………………….56

Table 4.7 Descriptive Statistics for Philanthropy…………………………………………………….57

Table 4.8 Descriptive Statistics of each item for MBA and iMBA…..…………………..59

Table 4.9 Independent Sample T-test Results for Select Items..……………….……………64

Table 4.10 Descriptive Statistics for Constructs……………………………............................66

Table 4.11 Cases by Constructs and Group…………………………………….………….………........67

Table 4.12 Item-Total Statistics for Cronbach’s Alpha Impact on Relationships….69

Table 4.13 Cronbach’s Alpha for Items Underlying Impact on Relationships……...70

Table 4.14 Descriptive Statistics on Student Experience, Impact on Relationship.71

Table 4.15 One-way ANOVA for Student Experience, Impact on Relationship....71

Table 4.16 Cronbach’s Alpha Item-Total Statistics of Academic Experience…….…73

Table 4.17 Cronbach’s Alpha for Items Underlying Academic Experience………....73

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Table 4.18 Descriptive Statistics on Student Experience, Academic………………….....74

Table 4.19 ANOVA for Student Experience, Academic………………………………..………..74

Table 4.20 Item-Total Statistics for Cronbach’s Alpha of Impact on Career.………..75

Table 4.21 Cronbach’s Alpha for Items Underlying Impact on Career…………………..76

Table 4.22 Descriptive Statistics for Student Experience, Impact on Career…………76

Table 4.23 ANOVA for Student Experience, Impact on Career……………………………...76

Table 4.24 Item-Total Statistics for Cronbach’s Alpha of Alumni Association…...78

Table 4.25 Cronbach’s Alpha for Items Underlying Alumni Association……….……..78

Table 4.26 Descriptive Statistics for Alumni Association………………………………………..79

Table 4.27 ANOVA for Alumni Association…………………………………………………………….79

Table 4.28 Item-Total Statistics for Cronbach’s Alpha Alumni Engagement……....80

Table 4.29 Cronbach’s Alpha for Items Underlying Alumni Engagement……........81

Table 4.30 Descriptive Statistics for Alumni Engagement……………………………………….81

Table 4.31 ANOVA for Alumni Engagement……………………………………………..…….........81

Table 4.32 Iteration History Student Experience of Donors and Non-Donor….....…83

Table 4.33 Block Classification Student Experience of Donors and Non-Donors..83

Table 4.34 Omnibus Tests for Model Coefficients Student Experience……….……....84

Table 4.35 Model Summary for Student Experience ………………………….……………….....84

Table 4.36 Hosmer and Lemeshow Goodness of Fit Test for Student Experience..85

Table 4.37 Classification of Donor and Non-Donor for Student Experience…..…….85

Table 4.38 Block 1 Method Variables in Equation for Student Experience…..…......86

Table 4.39 Iteration History Alumni Experience of Donors and Non-Donors……....87

Table 4.40 Block Classification Alumni Experience of Donors and Non-Donors...88

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Table 4.41 Omnibus Tests for Model Coefficients Alumni Experience………..……….88

Table 4.42 Model Summary for Student Experience………………………………………………..89

Table 4.43 Hosmer and Lemeshow Goodness of Fit Test for Alumni Experience..89

Table 4.44 Classification of Donor and Non-Donor for Alumni Experience…..…….90

Table 4.45 Block 1 Method Variables in Equation for Alumni Experience……..…...90

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ACKNOWLEDGEMENTS

I would like to thank everyone who supported me on my journey. A special thank you is for my wife Nayana, son Nishantha, and daughter Nishani who were extremely supportive and sacrificed family time in order for me to pursue my doctoral degree. I also want to thank the extended Ketter family and friends Lew Tucker, Tom Leamer, and Yongjae Kim for their encouragement and support. Finally, I want to thank the chair of my dissertation committee Dr. Jeremy Plant and remaining members of the committee Drs. Steven Peterson, Stephen Schappe, and Triparna Vasavada for their insight, assistance, and support of my research.

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This dissertation is dedicated to

John F. Ketter

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CHAPTER 1

THE PROBLEM STATEMENT

Introduction

The nonprofit sector is an integral part of our civil society. The line between the

nonprofit sector and the public sector began to blur in earnest in the 1960s, when

traditional relationships between state, federal, and local government and nonprofit

organizations changed. Through government contracts during the War on Poverty the

number of nonprofit organizations increased to carry out social and health programs,

mental health centers, and child welfare (Smith, 2008; Smith, 2010). The growth of the

nonprofit sector has continued since the 1960s and today warrants increased evaluation

and review within the public administration literature. This heightened need for review

stems from the need to examine issues such as accountability, performance management,

and future funding sources given the use of taxpayers’ funds to support the plethora of

nonprofits operating in the United States (Brooks A. C., 2002; Smith, 2008; Smith,

2010). Public higher education has been the recipient of such government support for

many years. Although not considered public agencies per se they are integral to a civil

society, and their welfare is a barometer on the future health of our democracy.

The coordination of a public higher education system is a complex process. Prior

to the 1950’s governance of the public higher education systems resembled that of private

colleges, with lay boards that made the fiduciary and policy decisions with a fair amount

of autonomy (McLendon, Deaton, & Hearn, 2007). This structure created competition

between state universities. With the postwar boom of enrollments states began to

examine efficiencies and long-term planning for the state universities to tackle the influx

of student enrollments never before experienced. The solution was seen to be

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consolidation. From the 1950’s to the late 1980’s states embraced consolidating

governing boards “States achieved a highly centralized form of campus governance.

Under such arrangements, states granted a central board line authority over constituent

campuses, empowering the board to make many day-to-day decisions over institutions

with a given system, sector, or state” (McLendon, Deaton, & Hearn, 2007, p. 647).

Since the 1980’s, along with the decrease in state funds available for state public

higher education there has been a considerable amount of change in the governance of

public higher education. The state-wide board structure is seen to help regulate increases

in tuition and other policy decisions to control and coordinate a state system of higher

education to promote equity, standardization, and centralized decision making processes.

The deregulating of a central board is seen as favoring efficiency, choice, decentralized

decision making, and performance (McLendon, Deaton, & Hearn, 2007).

Governance structures are not the same across public higher education.

“Different states use different types of governance structures…most states are organized

with either regulatory or consolidate coordinating boards” (Delaney & Doyle, 2011, p.

354). In 2004 43 percent of states used a regulatory governance structure and 47 percent

used a centralized structure (Delaney & Doyle, 2011). For example, The Pennsylvania

State University was chartered in 1855 by the legislature of the Commonwealth of

Pennsylvania and today has a shared governance structure:

The Pennsylvania State University’s 32-member Board of Trustees is composed

of the following: Five trustees serve in an ex officio capacity by virtue of their position

within the University or the Commonwealth of Pennsylvania. They are the President of

the University; the Governor of the Commonwealth; and the state secretaries of the

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departments of Agriculture; Education, and Conversation and Natural Resources. Six

trustees are appointed by the Governor; nine trustees are elected by the alumni, six are

elected by organized agricultural societies, within the Commonwealth; and six are elected

by the Board of Trustees representing business and industry endeavors. (Pennsylvania

State University, 2013)

Conversely the Pennsylvania State System of Higher Education’s governance

structure has more political appointees with the exception of student representatives:

The 20-member Board of Governors is responsible for planning and

coordinating the development and operation of the Pennsylvania State System of

Higher Education [PASSHE]. The Board establishes broad educational, fiscal

and human resources (including labor relations) policies, and oversees the

efficient management of PASSHE. Among other tasks, the Board appoints the

Chancellor and university presidents, approves new academic programs, sets

tuition, approves human resources policies and collective bargaining agreements

and approves PASSHE’s annual operating budget.

Eleven Board members are appointed to four-year terms by the Governor

of Pennsylvania. Their appointments are confirmed by the Pennsylvania State

Senate. Three students, selected from among the universities’ student government

association presidents, serve on the Board until graduation. Four legislators are

selected by the majority and minority leaders of the Pennsylvania State Senate

and House of Representatives. The Governor of Pennsylvania or a designee also

is a Board member, as is the state secretary of education or a designee.

(Pennsylvania State System of Higher Education, 2013)

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Despite the view that public universities may not be seen as public agencies they

play an integral part in public policy given the level of resources allocated. The Morrill

Act of 1862 provided the framework for federal assistance for higher education and the

National Defense Education Act of 1958 affected higher education institutions by

providing support to students to study science, mathematics, and foreign languages (Dye,

2008). Government plays a role in supporting higher education through student

assistance, e.g., Pell Grants, low interest loans, and federal research dollars through

government agencies such as the National Science Foundation. States are responsible for

establishing higher education institutions. “The first state university to be chartered by a

state legislature was the University of Georgia in 1794” (Dye, 2008, p. 141). Although

the federal government provides support, the majority of the financial burden to support

public higher education rests with state governments (Dye, 2008; Fethke & Policano,

2012). And, unfortunately, these funds have been steadily decreasing.

For generations public universities have been supported by state funding;

however, there now appears to be a permanent decline of state support. The decrease of

this government subsidy means higher tuition for students and parents. Consider these

statistical indices “Following each recession starting in the early 1980s, the percentage of

educational expense paid by net tuition increased: in 1985 net tuition amounted to 23.3

percent; in 2000 it increased to 29.3 percent; and by 2010 it accounted for 40.3 percent.”

(Fethke & Policano, 2012, p. 12). A small change in a state subsidy requires a significant

increase in tuition, but because the state subsidy remains a significant part of the overall

state budget legislators clamor for tuition to be held constant or to rise less than the rate

of inflation. Higher education has had to respond to the tremendous pressure from rising

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costs in health care, employee benefits, higher construction and maintenance costs. The

end result is that public higher education must broaden and/or enhance current funding

sources. Public universities must remain vigilant in operational efficiency and

effectiveness to maintain state funding levels that continue to trickle in.

Public higher education institutions have traditionally relied upon philanthropy to

help advance their mission, to distinguish themselves from their competitors, or to

transform themselves, e.g., moving from college to university status. A college may elect

to add programming, i.e., graduate degrees, and reorganize existing college majors into

undergraduate schools/colleges to seek university status from state and regional

accreditation agencies. From an international recruitment perspective this may aid a

university, as in certain parts of the world colleges are known as high schools or colleges

are actually the residence halls within a university. Private donations can assist with the

resource investment needed to carry forward with the accreditation process. Reliance on

philanthropy is no longer just for added extras, but for some universities for its survival,

as noted by Drezner (2011).

Although private institutions have solicited alumni, friends, foundations, and

corporations for support for centuries, public colleges and universities are turning

more to private giving to meet budgetary demands. As government support of

higher education decreases and the cost to educate a student rises, the need for

philanthropic support to maintain higher education’s excellence and increased

access is great. (Drezner, 2011, p. ix)

Public higher education, therefore, is turning more and more to philanthropy to

make up for financial shortfalls. A targeted source to fill this financial gap is the body of

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alumni. Of the $28 billion donated to support higher education in 2010, $7.10 billion,

over 25 percent came from alumni (Council for Aid to Education, 2011). The only group

which gave more was private foundations, which donated $8.40 billion or 30 percent of

total support in 2010 (Council for Aid to Education, 2011). Private foundations,

however, use the alumni participation rate as a factor in awarding a grant to an

institution—the higher the rate the better. Philanthropy in public higher education has

become big business. Many four-year public institutions are currently conducting very

aggressive fundraising campaigns. For example, The University of Texas at Austin has

raised $1,198,752,800 of a $3,000,000,000 campaign; The University of Illinois has

raised $1,964,000,000 of a $2,250,000,000 campaign; and The Pennsylvania State

University has raised $1,031,284,673 of a $2,000,000,000 campaign (Chronicle of Higher

Education, 2012). In light of the financial need, the changing landscape of public higher

education, and the magnitude of fundraising campaigns, more research is needed on

voluntary support (Drezner, 2011; Frumkin, 2000; Lindahl & Conley, 2002; Mael &

Ashforth, 1992). Despite this call for research most literature on fundraising is centered

on generic best practices and is atheoretical (Drezner, 2011). Furthermore, research on

an entirely new type of constituent, the online alumnus, is virtually nonexistent (Distance

Education Report, 2009).

A significant portion of the future alumni body will have had a non-traditional

college delivery experience. Total enrollment of at least one online course, i.e., private

and public universities, in the fall of 2007 was reported to be 3.9 million (Lei & Gupta,

2010). The Sloan Online Survey of 2,500 colleges and universities in 2009, reported 5.6

million students enrolled in at least one online course. The growth rate for online

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students was twenty-one percent compared to the less than two percent growth of the

overall higher education student population (Allen & Seaman, 2010).

David Schejbal, Dean, Continuing Education, Outreach and E-Learning Division

at the University of Wisconsin questions whether “to the extent that universities are

developing online programs, they might be eroding their alumni giving base and, i.e.,

shooting themselves in the foot” (Distance Education Report, 2002, p. 8). The online

college experience does not afford a student, a future alumnus, the same traditional

college delivery experiences such as face-to-face interaction with professors and fellow

students during class or during class breaks, interaction in study areas or dining areas

within the university community, or opportunities to participate in extracurricular

activities. Therefore, it is important for universities to learn about the impact an

increased number of online graduates will likely have on future philanthropy.

This study is aimed to aid the nonprofit sector and public higher education by

studying alumni experience (alumni association and alumni engagement) and student

experience (academic, impact on career, and impact on relationships), factors to help

predict alumni donation behavior, with an examination of traditional MBA graduates and

online MBA graduates. The field of Public Administration will benefit from this study

because of its relevancy to the funding and management of non-profit organizations,

governance issues and the relationship of these issues to political institutions.

Furthermore, this study will help contribute to the dialogue for public legislators as they

grapple with the myriad of public policies surrounding the support of public higher

education.

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Problem Statement

Motivations to attend college are varied, but most individuals graduate with an

expectation that, armed with a university degree, there will be a better job with higher

pay waiting. Results from the U.S. Census Bureau (2011) show the 2008 mean annual

earnings for levels of educational attainment: high school degree $31,283; associate’s

degree $39,506; bachelor’s degree $58,613; master’s degree $70,856; professional degree

$125,019; and doctorate degree $99,697. Financial reward remains a valid rationale for

pursuing a degree beyond the contributions of enlightenment, socialization, pursuit of a

passion, or playing sports. However, the cost of achieving a university degree continues

to rise.

The average published tuition and fee charges for undergraduates to attend a

public four-year in-state institution for the 2011-2012 academic year was $8,244, an 8.3

percent increase from the previous year. At a public four-year out-of-state institution the

cost was $20,770, a 5.7 percent increase from the previous year; at a private nonprofit

four-year institution it was $28,500, a 4.5 percent increase from the previous year; and at

a for-profit institution it was $14,487, a 3.2 percent increase from the previous year

(College Board, 2012). The increased costs can be attributed to many issues, but for

public institutions across the U.S the decrease in appropriations per full-time equivalent

student (FTE) is a serious one. The trend in state funding is long-term and downward.

The annual percentage changes in state appropriations per FTE, excluding the federal

stimulus, decreased 11.29 percent in 2008-2009, decreased 8.71 percent in 2009-2010,

and decreased 1.76 percent in 2010-2011 (College Board, 2012).

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Higher education fundraising appeals attempt to communicate the philosophy that

a philanthropic investment is making a difference in the lives of today’s students and

helps to promote a better future for the individual student and for society as a whole. The

theme is that a better educated society is a better society. In the solicitation of alumni it

is often communicated that their education was subsidized in part by contributions made

by alumni before them, friends, faculty, staff, parents, foundations, and corporations. In

other words, a person was not charged the true cost of his or her education; therefore, a

donor gives back because it is deemed “owed” for the received subsidy. Additionally, a

solicitation will communicate how a donation makes possible the ability for a current

student to attend via a scholarship and the reality that without said scholarship, regardless

of the already subsided total costs, the student could not pursue his or her educational

dreams.

Alumni support their alma mater philanthropically for a plethora of reasons, such

as alignment with a personal passion, affinity, feelings of gratitude, organization identity,

pride, tax incentives, feeling of giving back, or altruism (Baade & Sundberg, 1996;

Bruce, 2007; Clotfelter, 2003; Lindahl & Conley, 2002; Mann, 2007; Sun, Hoffman, &

Grady, 2007; Tsao & Coll, 2005). Additionally, alumni who strongly identify with their

alma mater are shown to exhibit higher levels of volunteerism and support (Drezner,

2009; Mael & Ashforth, 1992). The emotional element in giving back to one’s alma

mater stems traditionally from the social exchange that took place during the educational

experience. The more positive this educational experience the greater the likelihood of

making a donation when solicited (Baade & Sundberg, 1996; Clotfelter, 2001; Sun,

Hoffman, & Grady, 2007). This experience is thought of in terms of the traditional

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educational experience: living on campus, attending class in person, participating in

extracurricular activities, and having personal face-to-face interaction with fellow

students and faculty. In addition to these assumptions one can also assume a certain

degree of economic reward achieved from obtaining a degree that may influence the

likelihood of giving back as an expression of appreciation.

With the rising costs of higher education and the financial pressures public

institutions are facing, the need for philanthropic support will increasingly become more

important for the future of higher education in the U.S. Institutions run the risk of pricing

themselves out of business if the return on investment from tuition paying students and

parents fails to yield good paying jobs that pays for the initial investment. If state

governments are forced to continue to cut higher education appropriations, institutions

will be required to make up the difference through higher tuition, expansion of

educational programming with lower delivery costs, e.g., online education, auxiliary

services revenue, and private charitable contributions.

The online educational model, however, breaks with the traditional social

exchange that takes place in the traditional residential education delivery model. There is

no in-person class interaction with faculty and fellow students, no in-person office hours

interaction with faculty, no involvement in in-person extracurricular activities, or no

leadership opportunities beyond strictly online groups or discussions. A strict online

educational experience is vastly different than the traditional model. Therefore, what

impact may this have on future alumni giving? If, as aforementioned, institutions of

higher education must rely more and more on alternative sources of funding to include

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philanthropy, will online education produce an apathetic alumni body that views the

educational experience in a more purely economic and transactional sense?

The online education model sprung from the for-profit private sector. It continues

to produce thousands of private sector higher education alumni who were charged actual

costs (Buchen, 2005; Distance Education Report, 2009). Under this model the tuition

dollars contribute to the profits of shareholders and provide less emotional ties and

incentives to give back philanthropically after graduation. This reinforces the notion of a

college education as a commodity (Slaughter & Rhoades, 2004) and future alumni will

socially construct the college experience as transactional and not transformational

(Wastyn, 2009). Do online students at non-profit institutions view their educational

experience as merely an economic transaction as well because the online model lacks the

experiences of the traditional educational model?

In other words, higher education leadership faces a dilemma. If non-profit higher

education institutions continue to promote and expand the online educational model to

deal with immediate and projected funding issues, what impact will this have on alumni

giving in the future? Larger graduating classes under the traditional model already

present challenges to development offices to maintain respectable alumni giving

participation rates. It is more challenging to secure a 15 percent giving participation rate

for a graduating class of 1,000 than one of 500, and the giving participation rate is a

factor foundations consider when deciding to award an institution’s grant application.

Also, are institutions treating all alumni the same in their respective databases, i.e., not

differentiating between traditional and online? If not, this will present significant

messaging problems that attempt to invoke emotional connections for the purpose of

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philanthropic investment. For example, a solicitation appeal that attempts to take an

alumnus down memory lane with references to buildings or on campus activities would

be inappropriate for an online graduate.

Significance of Study

Public institutions are under enormous financial pressure to cut costs, freeze

tuition, consolidate programming, and seek alternative funding sources. One avenue of

alternative funding is through fundraising; therefore, universities benefit from a graduate

who has demonstrated a strong organizational identity to his or her alma mater as this

identity can translate into charitable contributions (Drezner, 2009; Mael & Ashforth,

1992; Mann, 2007; McDearmon & Shirley, 2009; Sun, Hoffman, & Grady, 2007;

Tsiotsou, 2007). In addition to current financial pressures, institutions recognized years

ago that, with the ebb and flow in student demographics, there was a need to broaden the

range beyond the traditional students by instituting adult education and continuing

education programs (Pfeffer & Salancik, 1978). Today the question is not just about

expanding the age demographic of potential students, but also the location of potential

students. Traditionally, education was offered within a geographic region so a student

could live on campus or make a reasonable commute. This is no longer the case with

online education, or distance education. Today, students can literally attend from all

corners of the globe through a variety of distance based education models.

Distance education is the phenomenon where the student and instructor are

removed from each other and there is mediated interaction during the learning transaction

(Kanuka & Conrad, 2003). Originally established as correspondence courses, distance

education has evolved today into “web-based form of instruction [that] allows instructors

13  

to reach a much larger audience and encourages more flexibility with a student’s

schedule” (Lei & Gupta, 2010, p. 616). As distance education has evolved, in many ways

it has revolutionized higher education.

This revolution sprang, ironically, from the for-profit private sector of higher

education. Walden University, founded in 1970, was one of the first, followed by

Capella University (originally called the Graduate School of America) and, most notable,

the University of Phoenix, which enrolls over 200,000 students (Buchen, 2005). The

public sector institutions, awakening to the revenue potential and demands of the market,

are inundating their curricula with distance education courses and programs. Total

enrollment, i.e., private and public universities, was reported to be 3.9 million students

enrolled in at least one online course by the fall of 2007 (Lei & Gupta, 2010). The

success that private sector educational institutions have had with distance education in

building their customer base has awakened the sleeping giants, i.e., the traditional public

institutions.

Distance education revenue is fast becoming a new foundation of higher

education delivery. This is shifting the tuition dependency from the sole traditional

education delivery model for institutions. Additionally, pertinent to higher education

fundraising, it will produce an ever-increasing online graduate alumni body.

Understanding this critical shift in terms of what it means for tuition revenue and future

philanthropic dollars is paramount for the financial sustainability of a public higher

education institution. Historically, the funds given by state government to public

institutions were in exchange to control access and tuition. State governments continue

to want this control despite providing less funding. Traditional restrictions are hindering

14  

universities’ ability to react to the market place. According to one study, “the most

critical of these factors are rising competition from local and international competitors,

shifting technologies for delivering instruction, greater understanding and appreciation of

what tuition and fees actually purchase, and an altered willingness to pay students,

parents, and taxpayer” (Fethke & Policano, 2012, p. 216). Public institutions need to

prepare for financial diversification, e.g., away from dependence on state funding for

survival. Private funding can play a significant role in achieving this objective.

Research Question

The study tested whether the student experience and alumni experience,

traditional versus online, can help predict alumni donor behavior. The study also tested if

there is a difference in the alumni and student experience between online and traditional

MBA graduates. A student who was satisfied with his or her academic experience, and

felt that it contributed to his or her career success, would be more likely to give back to

his or her alma mater. However, academic experience regarding career is just a part of

the student experience. There is also the student experience of relationship building

during a student’s educational experience and student’s experiences that had an impact on

their career. Sun, Hoffman, and Grady (2007) found among seven significant predictor

variables for alumni giving, student experience –impact on career was not found to be

significant, but student experience – relationships and student experience –

extracurricular activities was found to be significant.

Their study did not, however, distinguish between online students and traditional

attending students. Additionally, this study categorized alumni experience as a one-way

communication focusing solely on what the alumni receive from the university, e.g.,

15  

newsletter, website, and not the interaction between the alumni association and the

alumni. Identifying those predictors that an institution can influence such as the alumni

association and levels of alumni engagement would be valuable information for

fundraising operations. Sun, Hoffman, and Grady (2007) also did not look at the student

academic experience as a potential factor in predicting alumni donation behavior. Unlike

offering one educational delivery system, i.e., the traditional method, universities now

offer the online educational experience which is distinctly different. To accommodate

this significant change, this study explores two groups that attended the same university,

but did not have the same educational delivery experience. This study does not address

the economic motivators as a predicator of future giving even though the MBA is a

graduate degree and an assumption could be made that graduate alumni are more likely to

be economically motivated than undergraduates.

The MBA and iMBA are the two groups examined in the study. The independent

variables employed were types of alumni experiences (alumni association and

engagement) and student experiences (academic, impact on career, impact on

relationships). The dependent variable was donation behavior. A greater understanding

of any links between the independent and dependent variables will provide guidance to

university development operations in targeting potential alumni donors. Gender, age,

income, marital status and ethnicity were controlled variables. These variables were

controlled to better understand the relationship between the independent and dependent

variables. Additionally, controlling for these variables will help avoid confounding

variables that could jeopardize internal validity.

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The iMBA student is a student that is studying for his or her master of business

administration from The Pennsylvania State University by taking courses exclusively

online. The Pennsylvania State University iMBA program employs the cohort model and

provides some personal interaction for students during a two week in-person workshop.

The iMBA student must work independently, and be self-reliant in dealing with the

technology and securing answers to questions that arise during his or her educational

experience. Like the iMBA student, the residential MBA can also interact with fellow

students and professor electronically or participate in the open-source structure.

However, the residential MBA student has more opportunity to interact with fellow

students and professors because of the informal opportunities that are presented, such as

breaks during lectures or chance hallway or dining hall meetings. In addition, by

attending the physical location of the university the residential MBA student is exposed

to more extra-curricular opportunities, including flyers on bulletin boards or student club

recruitment desks.

A sample taken from alumni of The Pennsylvania State University was used to

test the following hypotheses:

Hypothesis 1: The MBA graduate will demonstrate higher levels of student

experience—relationships.

Hypothesis 2: The MBA graduate will demonstrate higher levels of student

experience—academic.

Unless asked, and told, during an interview, a potential employer would be unable

to differentiate between an iMBA and a MBA graduate from The Pennsylvania State

17  

University. There is nothing on the graduate’s record or diploma other than he or she

received his or her MBA. The following hypothesis was tested:

Hypothesis 3: There is a difference in student experience—impact on career

between the iMBA and MBA graduates.

Hypotheses testing the alumni association and alumni engagement experiences

was done:

Hypothesis 4: There is a difference in alumni experience—alumni association

between iMBA and MBA graduates.

Hypothesis 5: There is a difference in alumni experience—engagement between

iMBA and MBA graduates.

Finally, hypotheses testing alumni giving was conducted:

Hypothesis 6: Student experience distinguishes alumni donors from non-donors.

Hypothesis 7: Alumni experience distinguishes alumni donors from non-donors.

The study has both practical and theoretical significance. A better understanding

of the differences between the experiences of the MBA and iMBA graduate will assist

universities as they allocate resources in fundraising initiatives and in the area of alumni

relations. Philanthropy and associated development functions operate much like a private

business within a university. It is easier to measure outcomes and understand the return

on investment than in other aspects of university administration. For example, investing

funds in direct mail or phonathon activities, or an Annual Fund program, can be easily

compared to the funds these activities raised. If an Annual Fund expends more than it is

receiving in donations and alumni participation continues to decline this provides

18  

valuable information to decision makers and suggest that changes are needed in the

operation of the Annual Fund.

Predictive Model

Thirty-eight questions were used in the questionnaire to collect data on student

and alumni experiences. Figure 1.1 delineates the questions that measure each of the five

variables. For details of each question see the questionnaire, Appendix A. The Likert-

scale to measure respondents’ answers was utilized. The scale ranged from 1 (disagree)

to 5 (agree) and if a respondent answered N/A (non-applicable) this case was dropped

and assigned a 0. Figure 1.1 delineates the questions that measure each variable and

Figure 1.2. delineates the model’s measurements to be used to predict alumni donor

behavior.

Figure 1.1: Predictive Model Questions for Alumni and Student Experiences Q1, 2, 3, 4, 5,

6, 7, 8, 9 , 10  

Alumni Experience Alumni AssociationMBA Q11, 12, 13,

14, 15, 16, 17   Alumni Experience Engagement

Q18, 19, 20,

21, 22, 23, 24, 25

  Student Experience Academic

iMBA Q26, 27, 28,

29, 30, 31   Student Experience Impact on Career

Q32, 33, 34,

35, 36, 37, 38  

Student Experience Impact on Relations

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Figure 1.2: Predictive Model Factors for Alumni Donor Behavior

Alumni Experience Alumni Association  

Alumni Donor Behavior

Alumni Experience Engagement  

Student Experience Academic  

Student Experience Impact on Career  

Student Experience Impact on Relations  

This research contributes to the literature gap that exists in the fundraising

profession. The bulk of the literature written for the fundraising profession is largely

atheoretical, and are usually so-called best practices pieces. “Further research on

philanthropy and fundraising will allow practitioners to enhance their advancement

programs, expanding them to new prospects pools by better understanding how donors

choose to participate in prosocial manner” (Drezner, 2011, p. 60). Furthermore, the

literature is scant on the studies studying if there is a difference between the online

graduate and the traditional graduate who graduated from the same university. With the

ability of students to graduate from one of two distinctly different educational delivery

systems within the same university institutions need to be prepared to address these

groups appropriately as it engages in fundraising.

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This research has implications for professionals in the field of university

development. A better understanding of the prospect pool will assist with better

fundraising campaigns and improve efficiency of the fundraising operation.

Understanding a donor’s motivation from a theoretical viewpoint can be used to help

implement more successful fundraising activities for the practitioner. Also, this research

has implications for public policy.

Despite the decline in state appropriations, state governments are still allocating

millions of dollars for public higher education. The Commonwealth of Pennsylvania

allocated 4.6 percent of the $27.3 billion budget for higher education in 2011-2012

(Commonwealth of Pennsylvania, 2012). Part of the allocation process involves

performance base funding. Although the Pennsylvania State System of Higher Education

(PASSHE) is separate from The Pennsylvania State University the following point

illustrates the movement towards performance based funding for public higher education.

All of the indicators will be based on the three basic themes of student success,

access and stewardship, which, in turn, support PASSHE’s recently developed

strategic initiatives. The strategic initiatives are predicated on the need to

transform how, when and where student learning occurs; how the resources

necessary to ensure learning need to be recruited, retained and sustained; how the

PASSHE universities relate to their various communities; and how the State

System partners with the Commonwealth in creating and delivering a shared

vision of the future The performance funding program will measure how well the

universities succeed in transforming teaching and learning, securing resources,

21  

engaging their communities and regions and providing leadership for the future.

(Pennsylvania State System of Higher Education, 2011)

The Commonwealth is monitoring fundraising performance, delineated in the words

“securing resources,” and funds from the taxpayer are in essence matched based, up to a

preset maximum, on the fundraising success of the PASSHE university. The

Commonwealth recently passed the Higher Education Modernization Act to allow

employees of the PASSHE system to fundraise (Pennslyvania State System of Higher

Education, 2012).

The two remaining pieces of the legislative package were crafted to enable the

universities to generate additional revenue through private fundraising and to

reduce their purchasing costs. Under the former, university presidents, faculty and

other employees will be able to be more involved in fund-raising. With the

continuing decline in state funding support, private fund raising has become more

important as a revenue source to the universities. (Pennslyvania State System of

Higher Education, 2012)

With the heightened expectations for public universities regarding fundraising success,

this study contributes to the research literature and also aids in the public discourse to

help policy makers acquire a better understanding of the practice of fundraising that

public universities must embrace. This study contributes to understanding non-profit

management which is concerned with fundraising and accountability. Furthermore, this

study contributes to the relationship that non-profit organizations have with political

institutions that are also concerned with revenues streams and accountability of non-

profit organizations.

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CHAPTER 2

LITERATURE REVIEW

Higher Education Fundraising

In 1638 John Harvard’s bequest to the Colonies’ first college can be viewed as the

start of higher education philanthropy in the United States (Cutlip, 1965; Drezner, 2011).

The notion of securing private donations for a college motivated individuals to take

action. Cotton Mather, a Harvard alumnus who was passed up for the presidency at

Harvard, solicited Elihu Yale, a wealthy individual who made his fortune working with

the East India Company to support another college, not Harvard. As a result of Reverend

Mather’s request, “Mr. Yale despite his great wealth, responded with a quantity of

assorted day goods which in the end realized over £550 in the American market place and

placed a new name on the American collegiate landscape” (Rudolph, 1990, p. 9). It was

not long after that securing private donations helped to launch other colleges; in addition

to Harvard and Yale, William and Mary, Dartmouth, Brown, Columbia, Rutgers,

Princeton, Pennsylvania, and Delaware (Rudolph, 1990). These private donations were

in addition to some of the public funds that some of these colleges were receiving, e.g.,

Dartmouth from New Hampshire and Vermont.

Fundraising originally in the U.S. was ad hoc, “the first systematic effort to raise

money on this continent was for a college” (Cutlip, 1965, p. 3). The key word in this

statement is systematic. Higher education fundraising was responsible for developing

systems for funding, systems that would eventually include paid fundraisers, to secure the

needed resources to run their endeavors. Higher education institutions today employ

predictive dialing phonathon operations, variable data infused direct mail campaigns,

high level donor research to identify top prospects, sophisticated planned (estate) giving

23  

vehicles, and large-scale fundraising (marketing) campaigns. These campaigns are used

to energize the donor base and to reach lofty fundraising goals, some that exceed $1

billion.

As a result of the absence of direct state government support, private institutions

are more versed in the art of fundraising than public institutions. Private institutions have

been forced to learn and expand their fundraising craft. This expertise is demonstrated by

the recent or ongoing fundraising campaigns conducted by private institutions. For

example, Stanford University just completed a campaign that raised $6.2 billion, Cornell

University has raised more than $3 billion towards a $4.75 billion goal, and Columbia

University just raised its goal from $4 billion to $5 billion because of early success

(Chronicle of Higher Education, 2012). In comparison, The Pennsylvania State

University is working towards a goal of $2 billion to be raised by June 2014. Although

some select public institutions have broken the $1 billion fundraising goal, public

institutions are presently no match in keeping pace with the private institutions in

fundraising initiatives. Public institutions also do not have the comparable endowments

that the leading private institution counterparts enjoy.

Some public institutions are tailoring their fundraising campaign message to

highlight that the need of the campaign is a direct result of the decline in state funding.

Public institutions are increasingly becoming more dependent on charitable giving. Some

institutions utilize the earned income from endowments, made possible by private

support, to cover annual operating expenses. Often, these resources are lacking. A recent

example can be seen in the state of Wisconsin and the financial hit the University of

Wisconsin (UW) took from declining State appropriations. “In June 2011 the UW Board

24  

of Regents raised tuition 5.5 percent to generate $37.5 million, which offset only about

one-third of the budget cut from the state. If, instead, private funds had been raised, the

additional endowment necessary would be about $770 million’’ (Fethke & Policano,

2012, p. 51). The Commonwealth of Pennsylvania also experienced steep cuts in higher

education in funding is recent years.

For the second year in a row, Pennsylvania Gov. Tom Corbett announced in his

budget for 2012-2013 that it would include cuts in higher education funding. The state-

related universities The Pennsylvania State University, Temple and Pitt would experience

a 30 percent reduction in funds (on top of the 20 percent cut they took last year) and the

14 state colleges and universities would see cuts in state funding by 25 percent (WHYY,

2012).

Significantly for fundraisers, donors do not want to make up the difference from

the budget cuts that public higher institutions are experiencing. Although the institution

may have experienced a cut in its budget, potential donors as taxpayers did not

experience a comparable savings in their taxes. Understanding the motivations of

individuals in the prospect and donor pool is critical for public higher education

fundraising because fundraising has certainly changed since the days of Reverend Mather

and Mr. Yale.

There are many challenges facing public higher education. The research

conducted for public higher education covers, for example, curriculum, access, diversity,

and efficiency. Furthermore, higher education fundraising research covers such topics as

annual funds, planned giving, and mega campaigns (Drezner, 2011). The literature

review for this study is focused on understanding the donor and the knowledge to help

25  

predict donor behavior. The next section reviews the literature concerned with donor

behavior as it applies to philanthropy generally, i.e., not limited to public higher

education, and the various theoretical frameworks used in the study of donor behavior.

The chapter concludes by looking at donor behavior as it applies to higher education.

Reasons People Give

As philanthropy has becomes more sophisticated so too have donors. They seek a

return on their investment and want to make gifts that are more personal. The decision to

give is much more thoughtful and seeks to align passion with interest. Birkholz (2008)

speaks to this challenge:

There is a fundamental change emerging in the 21st-century fundraising.

This change is not driven by increasingly sophisticated nonprofit

organizations; nor is it propelled primarily by the integration of MBAs or

other smart people into the sector. Not even the compelling need for

support of worthy causes produced this evolutionary leap. In fact, the

most important component of the philanthropic partnership that is moving

us forward: We are changing because of our donors. (p. 1)

Public universities are starting to accept the reality that fundraising is part of the

fabric of the university operation. Knowledge acquired in understanding the prospect and

the donor will contribute significantly to the fundraising bottom line and ultimately the

university’s revenue base. Researchers continue to expand upon the more traditional

notion of altruism. It is no longer assumed to be the sole reason as to why people donate

to charitable organizations (Lindahl & Conley, 2002; Mann, 2007). In addition to

altruistic motives individuals can view charitable giving from the lens of economics,

26  

organizational identity, social identity, or service lenses. An individual may donate

because he or she views the nonprofit sector as more efficient than the government in

delivering a particular service. Therefore, perceived efficiency of the nonprofit is

important in donor decision making (Bennett & Savani, 2003; Brooks A. C., 2006;

Hyndman, 1990; Iwaarden, Wiele, William, & Moxham, 2009). Under an economic

theory of charitable giving the donor seeks to understand the need and value of the gift in

economic terms. The economic theory may also be combined with the traditional

altruism viewpoint suggesting that the donor is not always exhibiting the self-interest

model of rational choice theory because the rational choice fails to explain those

occasional acts by these same individuals that are selfless acts of kindness (Frank, 1996).

Individuals give to nonprofit organizations for a plethora of reasons: alignment

with the mission and objectives of the organization, tax incentives, he or she is altruistic,

or he or she directly benefited or had a close loved one who directly benefited from the

organization (Prince & File, 1994). Prince and File (1994) conducted research to assist

fundraisers in developing a donor-centered fundraising approach. Using a preliminary

segmentation study of 476 affluent individuals donors (donor maintains $1 million or

more in discretionary investment and has made a gift to a nonprofit of $50,000 or more in

the past two years) followed by an intensive testing of the motivational items derived

from the initial donor segments the seven faces of philanthropy was developed. The

objective in developing these identity tags is to assist fundraisers in properly targeting the

nonprofit’s message that would best resonate with a particular donor, see Table 2.1.

27  

Table 2.1: Seven Faces of Philanthropy. ______________________________________________________________________________ 

Donor personality Utilization of charity network ______________________________________________________________________________ 

Altruists Small charity network made up of close friends and family.

Communitarians Charity network made up of other business

owners and network overlaps with other affiliations.

Devout Religious based network. Dynasts Break away from parent’s charity network

and develop own from his or her own generation of friends.

Investors Qualify organizations through business

contacts. Repayers The exception: selection of nonprofit based

on own beneficiary experience. Socialites Charity network very important, group

decision-making regarding support of nonprofits. 

______________________________________________________________________________ 

Adapted from The Seven Faces of Philanthropy, by Russ Prince and Karen File, 1994.

However, one cannot assume that the seven faces of philanthropy are mutually

exclusive. These identity tags may overlap. For example, a donor may indeed feel the

need to repay the institution, but this same donor may also be a business owner in the

local community where the institution is located, an altruistic person, and very sociable.

The more the institution can align the multiple faces of philanthropy of the donor, the

better position the institution is in when soliciting the donor.

28  

Williams (2007), in a study to learn donor preferences among young donors (18 –

39 years old), baby boomers (40 – 58 years old), and mature donors (59 years and older)

found baby boomers valued information more than mature donors; and young donors

valued the efficiency of nonprofits less. “When making a decision to give, 72 percent of

young donors used the internet and 74 percent used a charity’s website to look for

information about a charity. Mature donors were the least likely group to use the internet

or a website” (Williams, 2007, p. 185). Understanding not only what motivates the

donor, but also how and what the donor needs regarding information is paramount. For

older adults the personal relationship, more face-to-face interaction, between organization

and donor is a critical component.

For older donors the social exchange, i.e., the personal relationship, significantly

impacted gift giving (Mathur, 1996). Using data collected from 240 surveys from

residents, 50 and older, living in a single metropolitan statistical area in a Midwestern

state Mathur (1996) found that the expectation of social interaction resulting from giving

a gift is positively related to gift giving behavior. Donors may provide a smaller gift to

test the stewardship program of the nonprofit. Stewardship is the action (thank you letter,

sending a birthday card, providing special invitations to events, and the like) taken by the

nonprofit to demonstrate appreciation for a gift given or volunteer work provided by an

individual. If after giving a smaller gift the older individual feels appreciated and

receives the expected social interaction from the nonprofit additional, and possibly larger

gifts, often follow.

The social exchange theory helps professionals in the field understand the donor

giving cycle (see Figure 2.1). Social exchange involves the communication necessary to

29  

build a relationship with an individual (Blau, 1964). Without this communication an

individual cannot engage in the donor giving cycle and move from prospect to donor.

Additionally, it is not enough having the nonprofit’s outward actions (attempts to contact,

letters, etc.) towards a potential donor. The prospect needs to reciprocate and accept

incoming messages by acting on them, either negatively or positively.

Figure 2.1: Donor Giving Cycle.

Communication for all stages of the cycle (Repeat cycle to seek another gift in the future)

 

 

Suspect Prospect Cultivation Pre-Solicitation Solicitation Stewardship

Identify potential

donor

Begin Relationship

Strengthen Relationship

Confirmation of aligning passion and interest with university needs

Present proposal

and ask for gift

Report on impact of gift and thank

 

Table 2.2 summarizes theoretical frameworks used to understand or predict donor

behavior. The challenge in attempting to place an individual into one framework, e.g.,

economic is that it fails to take into account the complexity of the individual. Much like

the seven faces of philanthropy aforementioned the line for the theoretical framework can

be blurred as motivations for giving can be multifaceted. This study used the

organizational identity framework, which is covered in the next chapter.

30  

Table 2.2: Theoretical Frameworks.

Theory Attributes Importance to Fundraising Charitable giving People are motivated to

give because they are altruistic, reciprocity, and direct benefits.

Donors feel an obligation to give back to make society a better place.

Economics Donors look at the

efficiency of the organization and impact from a resource allocation point of view.

Donors that see a good return on their philanthropic investment are more inclined to support the organization.

Service Donor behavior is shaped

by service value, service quality, and his or her level of satisfaction.

Donors feel they received professional service and value from the nonprofit they are likely to respond positively to fundraising appeals.

Social exchange/relationship marketing

Donors require an exchange with the organization that is personal and their social identity is partially defined via association with the organization. Relationships with donors can range from transactional to highly relational.

Some donors need a personal exchange with a segment of the organization so the donors does not feel like a just another number, i.e., donor. Other donors are satisfied with more of a transactional relationship.

Adapted from College fund raising using theoretical perspectives, by Timothy Mann, 2010.

In higher education one expectation, or hope, is that alumni give back because

they are repaying the institution that did not charge the full cost of their educational

experience. Another element could be that an alumnus feels a debt of gratitude to a

mentor, or that the degree acquired is linked to career advancement. Public institutions

31  

benefit in many ways from strong relations with alumni. The stakes are high in

fundraising campaigns, some with billion dollar plus goals, and understanding the alumni

body warrants extensive research. The next section reviews alumni giving and alumni

donor behavior studies.

Giving Back to One’s Alma Mater

Researchers have sought to assist fundraising professionals in the field of higher

education to help understand more specific predictors of alumni donor behavior. In

addition to the demographic variables and personal motivation variables, alumni

predictors can be categorized into the student experience and the alumni experience

(Baade & Sundberg, 1996; Clotfelter, 2003; Sun, Hoffman, & Grady, 2007; Tsao & Coll,

2005). The latter, alumni experience, the fundraising and alumni teams at the university

can more readily influence. Therefore, it behooves higher education leadership,

fundraising professionals, and academic researchers to expand the knowledge base in the

area of student experience with an examination of the online student experience.

The ability to identify reasons or factors for giving back to one’s alma mater is

critical for university fundraisers in the highly competitive fundraising world. There are

demographic variables such as income, age, and gender, and other factors that have been

researched in search of the holy-grail of predictors of alumni giving. For example,

Bristol (1990) found that the number of years since graduation and initial giving to one’s

alma mater related to the level of alumni giving. More importantly, however, are those

factors the university can influence as it relates to alumni giving. A university cannot

influence demographic variables nor directly influence deep-seated internal motivation

32  

for giving in general to nonprofits, but it can influence two specific areas that factor into

alumni giving: student experiences and alumni experiences.

One factor to consider is the efficiency of fundraising activities as delineated by

Hashemi et al., “Identification of potential donor for any charitable and philanthropic

organizations is essential because it saves considerable amounts of money, including the

time of professional staff” (Hashemi, Le Blanc, Bahrami, Bahar, & Traywick, 2009, p.

28). For example, if the university in a direct mail campaign is able to weed out non-

potential donors from the list considerable resources can be saved in postage and printing

costs. Hashemi et al. (2009) found the identification of data items, participation in

university events as a student, select class years, graduation from a select college within

the university, and a wealth index score that appear frequently together may be a key in

the identification of the potential donors.

In a study using a subset of the College and Beyond survey, a collection of

information from three cohorts of 34 colleges and universities, Clotfelter (2003) found

higher levels of contributions are associated with alumni income, if the alumnus

graduated from the university he or she first attended, and if the individual level of

satisfaction with his or her undergraduate experience was positive. This survey applied

only to the traditional education experience. Satisfaction is partially explained by overall

satisfaction with extracurricular activities and athletics. Another critical aspect of

satisfaction collected in the survey was a mentor variable, defined as someone taking an

interest in the individual during his or her college time. “Especially noteworthy is the

significance of the MENTOR variable, which speaks to the importance that alumni attach

to personal contacts with faculty and other college staff” (Clotfelter, 2003, p. 117). This

33  

raises the question as to whether online education can provide the mentoring experience

found to be significant in the securing of donations from alumni.

In a study of 125 public and private doctoral-grant research universities, Baade

and Sundberg (1996) examined variables to represent student characteristics because

understanding the student body is helpful is determining alumni giving as students of

today are alumni of the future. The results indicate better students and the higher quality

of the experience, measured by instructional spending per student, and the experience a

student has influences attitudes toward the institution as an alumnus/alumna all correlate

with higher levels of giving. As in other research this study did not isolate online

students from traditional students.

Students who received scholarships were more likely to give back to his or her

alma mater while students who graduated with student loans were less engaged in alumni

giving (Monk, 2003). Monk (2003) examined patterns of giving of young graduates from

28 highly selective private institutions and found satisfaction with undergraduate

experience was the single most significant determinant of alumni giving. “Respondents

who reported that they are ‘very satisfied’ with the undergraduate experience gave over

2.6 times as much to their alma mater as graduates who were ‘ambivalent,’ ‘generally

dissatisfied,’ or ‘very dissatisfied.’” (Monk, 2003, p. 126). Previous studies have found

that an individual’s salary is also a determinant. Career earnings information is difficult

to obtain, but the level of debt acquired as a student or receipt of a scholarship is

information that universities can use in soliciting alumni.

Using the database of public doctoral-granting research universities in the U.S.,

Newman (2011) found that being a current member of the alumni association was a

34  

strong predictor of alumni giving. The results of the study demonstrated that when an

alumnus was a member of the alumni association he or she was 4.8 times more likely to

be a current donor than non-members of the association. Additionally, life-time members

of the alumni association were the best donors, defined as making participation gifts

(Newman, 2011). Related to the notion of belonging and organizational identity is this

notion of legacy.

Meer and Rosen (2010) found that alumni who currently had a relative attending

their alma mater had strong effect on alumni giving behavior. This was particularly

strong when the student was a child or a niece/nephew of the alumnus or alumna. The

effect if the student was of an older generation, e.g., parent or grandparent, was not as

great. Additionally, there was no difference in the effect if the student was male or

female.

There are many motivations to giving to one’s alma mater as noted by Tsunoda,

“Motivations of alumni giving associated with institutional prestige and academic

quality, alumni college experience, alumni-institutional relationships and background

characteristics of the donors to be significant determinants of alumni giving” (Tsunoda,

2010, p. 6). Alumni satisfied with their MBA experience, university prestige, and salary

were all found to be significant predictors of donating behavior (Baruch & Sang, 2012).

Baruch and Sang also found in their constructed model using a database of 3,677 MBA

graduates that the level of engagement with the university, e.g., alumni activities, was a

significant mediating factor related to donating behavior.

Weerts and Ronca (2007) developed a model looking at inclination variables:

social exchange, expectation theory, and investment model against capacity variables.

35  

The results demonstrated that alumni donors who are likely to give and who volunteered

expected to be involved. This level of involvement translates into supporting institutions

and built lifelong relationships. The motivations behind why graduates voluntarily

contribute vary, including for example, altruism, reciprocity, or direct benefits (Mann,

2007; Liu, 2006; Tom & Elmer, 1994). Furthermore, a graduate’s decision to support his

or her alma mater is influenced by the level of organizational identity the graduate

exhibits (Drezner, 2009; Mael & Ashforth, 1992; Sun, Hoffman, & Grady, 2007).

In an interesting qualitative study that examined non-donors and why they do not

give, Wastyn (2009) found both donors and non-donors did have positive feelings toward

their alma mater, had good experiences, and remained engaged as alumni, but they

differed in how they socially constructed those experiences into personal narratives. Four

major themes came to light in why these alumni do not give: “they considered college a

commodity not a charity, they do not believe the college needs their money, they have

misperceptions and uncertainties about giving and they do not make their giving

decisions logically” (Wastyn, 2009, p. 100). The theme of considering college a

commodity and not a charity is interesting, given that the subjects in this study all had the

traditional education experience. The online educational experience, launched via the

private sector, has its roots as transactional and can be viewed more as a commodity than

a traditional college experience.

Considering college education a commodity and not a charity is perhaps a view

that will increasingly complicate the fundraising efforts of universities. The online

education experience was launched by the for-profit companies/universities. This

development inherently undermines the traditional notion that students were not actually

36  

paying the full cost of his or her educational experience. Paying for an online education

was viewed as comparable to purchasing any other commodity online. However,

consumers do not, or are expected, to make donations to for-profit companies from which

they have purchased an item or service. The traditional educational experience provides

more than just the classroom experience and helps to rationalize why the actual costs of

education exceeds what a student is charged.

The online educational experience is more transactional: the purchase of an item

or service is in exchange for a certain purchase price. Is the production of an online

degree, a degree without the traditional educational experience, going to produce a higher

percentage of future alumni that will view the experience as a purely transactional

economic exchange because the student was charged the actual costs for the delivery of

the education received? If so, this raises the question: is there any motivation for the

online alumnus to give back to the degree granting institution?

Alumni often identify strongly with their alma mater. In recognition of this,

organizational identity theory has been used to examine levels of philanthropic support

exhibited by graduates of educational institutions (Mael & Ashforth, 1992). When

alumni identify with their alma mater, sharing with people who are alumni of institutions

from which they graduated or taking very personally the actions of their alma mater, even

if they live on the other side of the world, they are exhibiting traits of an organizational

identity with their university. Organizational identity level can influence levels of

external philanthropic support institutions receive and, as delineated by resource

dependency theory (Pfeffer & Salancik, 1978), institutions are dependent on various

37  

sources of external support, including philanthropic support. A positive alumni and

student experience may be viewed as creating positive organization identity.

There are many reasons why an individual donates to a nonprofit organization.

Again, some of the reasons people give are tax incentives, prestige, name recognition,

personal relationships with internal members of the organization, fulfilling a spiritual

need, or seeking to build a relationship with an organization for future business gain.

However, “one of the best reasons to give to a charity is that it has communicated a clear

and compelling mission to which donors can identify” (Frumkin & Kim, 2001, p. 274).

Although there is a plethora of public institutions, having seen one does not imply one

has seen them all. The last section of this chapter briefly addresses the rising competition

of fundraising in the U.S.

Fundraising in the U.S.

The growth of institutional fundraising is an example of the interdependence

between donor and institution. The donor has to satisfy a charitable need, perhaps his or

her altruistic gap, and the institution needs the financial resources to sustain itself or to

secure a competitive advantage in differentiating itself. Resource dependency theory

implies the creation of alliances that are interdependent. This interdependency can create

uncertainty for institutions as they become more reliant on philanthropic dollars.

Therefore, institutions seek to steward donors to increase behavioral interdependence

between donor and institution. “Interdependence is a consequence of the open-systems

nature of organizations—the fact that organizations must transact with elements in the

environment in order to obtain the resources necessary for survival” (Pfeffer & Salancik,

1978, p. 43). This interdependence is certainly not limited to public universities. The

38  

increased sophistication of nonprofits is demonstrated through targeted campaigns, co-

branding, a robust consulting industry benefiting nonprofits, and the billions of dollars

raised annually.

In 2011 contributions reached $298.42 billion in the United States. The top

recipients of the contributions were religious organizations with 32 percent, educational

institutions with 13 percent and human service organizations with 12 percent (Urban

Institute, 2013). There are over 1.5 million nonprofit organizations incorporated in the

U.S, with a combined total revenue of $1.4 trillion, expenses of $1.4 trillion, and assets of

$2.7 trillion (Urban Institute, 2013). Explosion of nonprofits has heightened competition

for philanthropic dollars, increasing the pressure on institutions to sustain existing

relationships with current donors, lest they write checks elsewhere. Institutions are

dependent on a variety of sources for revenue as delineated by resource dependency

theory. Donors are one source of this revenue.

The degree to which an institution relies on philanthropic support to sustain its

reputation or even keep its doors open varies. With rapid growth in online education, the

future alumnus/alumna segment is building rapidly. However, the literature is scant on

the impact this will have on alumni donations and alumni giving participation rates. This

study addresses this gap in understanding the constituent’s level of organizational identity

and donor behavior that is critical to successful fundraising.

39  

CHAPTER 3

METHODOLOGY

Quantitative Method

The study tested if the student educational and alumni experience between the

traditional MBA graduates and the online MBA graduates can help predict alumni donor

behavior. A quantitative approach to measure predictors of alumni donors was used.

This research strategy involves the “collection of numerical data, as exhibiting a view of

the relationship between theory and research as deductive and a predilection for a natural

science approach (and of positivism in particular), and as having an objectivist

conception of social reality” (Bryman, 2008, p. 140). Using a quantitative approach

allows a researcher to help understand the best predictors of outcomes (Creswell, 2009).

The following variables were used in the study: alumni experiences in alumni

association and engagement; and student experiences in academic, impact on

relationships, and impact on career. Sun, Hoffman, & Grady (2007) found among seven

significant predictor variables for alumni giving, student experience –impact on career

was not found to be significant, but student experience – relationships and student

experience – extracurricular activities were found to be significant. Their study did not,

however, distinguish between online students and traditional attending students.

Additionally, Sun et al. (2007) categorized alumni experience as a one-way

communication focusing solely on what the alumni receive from the university, e.g.,

newsletter, website, and not the interaction between the alumni association and the

alumni. Unlike offering only the traditional educational delivery system many

universities now offer the online educational experience which is distinctly different.

Therefore, this study aims to fill the gap in the literature by exploring two groups that

40  

attended the same university pursuing a similar graduate degree, but who did not have the

same educational delivery experience.

Furthermore, the importance of the study is not only to shed light on this new

development but also to assist universities with resource allocation for fundraising and

alumni relations purposes. With the introduction of the online option for students,

university fundraisers are faced with the additional challenge of reaching a segment of

the alumni population that has had distinctly different student and educational

experiences than the traditional student. The pursuit of a higher education degree is

largely looked at in today’s market by potential students, and their parents, in terms of the

return on investment. This has created pressure on universities to keep tuition costs

stable while at the same time expenses, such as health care for staff and retirement costs,

continue to rise. Philanthropy is a viable option to help subsidize the costs for student

scholarships and other ways of reducing the cost of attending college. This chapter

includes an outline of the theoretical framework, the research design and collection of the

data, and a discussion of the population and sample.

Theoretical Framework

Alumni are assumed to identify with their alma mater. Organizational identity

theory has been used to examine levels of philanthropic supported exhibited by graduates

of educational institutions (Mael & Ashforth, 1992). Mael and Ashforth found that

alumni identifying with their alma mater correlates with participation in fundraising

campaigns. In their words, “Organizational identification is defined as a perceived

oneness with an organization and the experience of the organization’s successes and

failures as one’s own” (Mael & Ashforth, 1992, p. 103). Individuals need to define the

41  

relationship with an organization by answering “Who am I?” and “Who are we?” (Albert,

Ashforth, & Dutton, 2000). Therefore, an individual can identify himself or herself as an

alumnus/alumna of his or her university. There are many factors that can influence

alumni donation behavior: the fondness an individual holds towards his or her alma

mater is influenced by his or her time spent at the university, interaction with

faculty/students/mentor, time spent on extracurricular activities, outcome of career

placement upon graduation, and even how the individual feels towards his or her alma

mater’s actions taken today, e.g., budget allocation towards athletics instead of the liberal

arts (Gaier, 2005; Mael & Ashforth, 1992; Miller & Casebeer, 1990).

Organizational identity can influence the levels of external philanthropic support

institutions receive and, as delineated by resource dependency theory (Pfeffer &

Salancik, 1978), institutions are dependent on various sources of external support,

including philanthropic support. A positive alumni and student experience can be viewed

as influencing positive organization identity. By defining oneself in part by the

associations he or she keeps the person is setting a course for his or her future action. If

the organization is successful the individual will join in the celebration of said success.

On the other hand if the organization is failing the individual may elect to disassociate

him or herself from the organization. If an individual has positive and distinctive identity

with an organization, this can translate into support and loyalty (Ashforth & Mael, 1989;

Mann, 2007).

Of course the ability to donate, i.e., resources to give, is different than the

willingness to give. If an individual has the resources but does not identify positively

with his or her alma mater, then securing a donation is a challenge. On the other hand

42  

there are alumni who positively identify with their alma mater; however, they do not have

the financial ability to donate. “The consideration of organization identification theory in

college fundraising is significant because it provides a rational explanation of why people

make donations” (Mann, 2007, p. 40). Fundraising is not unlike business in that time is

best spent with prospects who can provide the most business, i.e., donations. For

fundraisers, spending time with those alumni with the ability to provide the leadership or

the largest gift and who have a positive institutional identification can contribute to a

more efficient and effective fundraising operation.

Research Design and Data Collection

This section commences with a review of the data collected used to complete the

study. The sampling frame, the participants, and the definition of terms is discussed.

The quantitative approach allowed for the testing of the hypotheses by collecting data.

Confirmation or refutation of the hypotheses through observation and measuring results

numerically was done.

Population and Participants

The sampling frame used consists of MBA and iMBA graduates from The

Pennsylvania State University. There are several programs across the university; the

sample frame contained MBA and iMBA graduates from all the programs. The

university has a traditional MBA program that requires a student to attend classes in

person and have in person time with professors and his or her fellow students throughout

the life of the program. The iMBA is comprised of 19 online courses and two one-week

residency experiences (Pennsylvania State University, 2010).

43  

The university tracks whether a student enrolled and graduated from the MBA

program or the iMBA program. Therefore, a stratified random sample was utilized. The

advantage of this method is that it “ensures that the resulting sample will be distributed in

the same way as the population in terms of the stratifying criterion” (Bryman, 2008, p.

173). There are several subcategories codes of MBA graduates: ACCTG, B ADM,

BUS, BUSAD, FIN, MGMT, MKTG, etc. There were no similar subcategories, i.e.,

ACCTG, B ADM, BUS, BUSAD, FIN, MGMT, MKTG, etc. for the iMBA graduates.

For the iMBA graduates there was only iMBA classification and no subcategories;

therefore, the subcategory B ADM of the traditional MBA program was utilized as it

represents the most generic rubric, Business Administration, for the overall general study

programs for the MBA program. The population for the B ADM subcategory was 860

and the iMBA population was 527, for a total population size of N = 1387 (see Table

3.1). A sample size of 500 was used. This size was based on providing a reasonable

response target number. Using the stratified method the sample was randomly selected

for the B ADM MBA and iMBA graduates.

Table 3.1: Sample Population. ______________________________________________________________________________ 

Graduates Population % of Total Stratified Sample ______________________________________________________________________________ 

B ADM MBA 860 62% 310 iMBA 527 38% 190 Total 1387 100% 500 ______________________________________________________________________________ 

44  

A total of 181 usable questionnaires were returned for a 36 percent response rate.

In an analysis of 1607 academic studies published from 2000 to 2005 Baruch and Holtom

(2008) found the mean mail response rate was 44.7 percent with a standard deviation of

21.8 percent. This study was less than the mean found by Baruch and Holtom (2008).

Despite the numerous studies they were not able to include because the response rate was

not reported, Rogelberg and Stanton (2007) state putting response rates into acceptable

categories fails to help demonstrate that the report research is free from nonresponse bias.

Rogelberg and Stanton (2007) further explain,

If the standard response rate in one’s research area is 45% and a new article in the

same area achieves 55%, we suggest that using this achievement to claim superior

results is folly because the difference between the new study’s response rate and the

standard contain little if any information about the presence, magnitude, or direction

of nonresponse bias present in the data…in contrast, if a study does obtain a

response rate well below some industry or area standard, this also does not

automatically signify that the data obtained from the research were biased. (p. 198)

In an evaluation of Information Systems research, Sivo et al., (2006) found in the

publications of the Journal of AIS, Information Systems Research, Management

Information Systems Quarterly, European Journal of Information Systems, Management

Science, and Journal of MIS the average response rate in research in which data were

gathered by questionnaire averaged 22 percent to 59.4 percent. In an evaluation of mail

surveys for election forecasting Vissner et al., (1996) found mail surveys were more

accurate than telephone surveys, even with a response rate below 30 percent. “Mail

surveys have limitations, and low response rates are clearly one of them, but it is also

45  

clear that these limitations are not necessarily as debilitating as some assume” (Visser,

Krosnick, Marquette, & Curtin, 1996, p. 216). Visser et al., further state, “So to view a

high response rate as a necessary condition of accuracy is not necessarily sensible, nor is

the notion that a low response rate necessarily means low accuracy” (p. 216).

For this study nine respondents were dropped from the final analysis because their

questionnaires arrived after the analysis had begun or were not filled out. Ninety-five

questionnaires were returned from the MBA graduates for a 31 percent response rate and

eighty-six questionnaires were returned from the iMBA graduates for a 45 percent

response rate. Chi-square test, see Tables 3.2 and 3.3, was used to explore the

relationship between the iMBA and the MBA by returned and no returned surveys. The

Continuity Correction value is 10.275 and has an associated significance value of .001.

The result is significant; the proportion of iMBA that returned the survey is significantly

different from the proportion of the MBA surveys that were returned.

46  

Table 3.2: Response Rate Comparison.

No returned Returned VAR00002

iMBA Count 104 86 190 Expected Count 121.2 68.8 190.0 % within VAR00002

54.7% 45.3% 100.0%

% within VAR00003

32.6% 47.5% 38.0%

% of Total 20.8% 17.2% 38.0% MBA Count 215 95 310

Expected Count 197.8 112.2 310.0 % within VAR00002

69.4% 30.6% 100.0%

% within VAR00003

67.4% 52.5% 62.0%

% of Total 43.0% 19.0% 62.0% Total Count 319 181 500

Expected Count 319.0 181.0 500.0 % within VAR00002

63.8% 36.2% 100.0%

% within VAR00003

100.0% 100.0% 100.0%

% of Total 63.8% 36.2% 100.0%

Table 3.3: Chi-Square Test for Response Rate Comparison.

Value df

Asymp. Sig. (2-sided)

Exact Sig. (2-sided)

Exact Sig. (1-sided)

Pearson Chi-Square 10.899a 1 .001

Continuity Correctionb 10.275 1 .001

Likelihood Ratio 10.808 1 .001

Fisher's Exact Test .001 .001Linear-by-Linear Association

10.877 1 .001

N of Valid Cases 500

a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 68.78. b. Computed only for a 2x2 table

47  

Definition of Terms

iMBA graduate is defined as an alumnus or alumna of The Pennsylvania State

University who received his or her educational learning experience online.

MBA graduate is defined an alumnus or alumna of The Pennsylvania State

University who received his or her education learning experience in person.

Online program is defined as the online educational learning experience.

Traditional program is defined as the in person education learning experience.

Independent and Dependent Variables

The independent variables are types of alumni experiences (alumni association and

engagement) and student experiences (academic, impact on career, impact on

relationships). These factors were used as predicators of alumni donation behavior. For

example, alumni who were treated well as students and had a positive student experience,

both in relationships and impact on career, are more receptive to making gifts. The

alumni experience is also a factor in predicting alumni donation behavior as the engaged

alumnus or alumna is likely to be better informed and aware of the philanthropic needs of

the institution. The dependent variable is donation behavior. Gender, age, income,

marital status and ethnicity were all controlled in order to determine if there is a

relationship between the independent and dependent variables.

Questionnaire

The questionnaire was mailed in September 2012 to collect the data. The five page

questionnaire (see Appendix A) was printed on both the back and front pages to minimize

weight and save on postage expense. The questionnaire included an introduction letter

(see Appendix A) that explained the purpose of the research, that participation was

48  

strictly voluntary, that all responses would be kept confidential and no personal

identifying information will be recorded, the respondent could elect to stop at any time or

not answer any questions they did not want to, and finally, they could contact me or The

Pennsylvania State University Institutional Review Board with any questions.

To promote a high response rate each envelope was hand-addressed and each

envelope had a postage stamp affixed rather than metered postage. Twenty-eight days

following the questionnaire a reminder postcard (see Appendix B) was mailed to the

entire population and because the survey is anonymous the reminder postcard was written

in such fashion so as to be appropriate for both those individuals who had not yet

returned the questionnaire and for those that had. The return envelope was addressed to

the researcher both in the addressee block and the return address block to discourage

individuals from placing his or her return address on the envelope. Additionally, each

return envelope had a postage stamp affixed rather than metered postage.

The survey was a self-administered questionnaire and used a 5-point value

congruence Likert-scale to measure determinants of alumni donation behavior. The

questionnaire used to collect the data had forty-six questions, including demographic

questions. The instrument’s questions represent the subscales alumni experience—alumni

association, alumni experience—engagement, student experience—impact on career,

student experience—relationships, and student experience—academic for The

Pennsylvania State University MBA.

The instrument provided preselected options for the respondent to answer using

the 5-point value congruence Likert scale: 5 points for agree, 4 points for somewhat

agree, 3 points for neutral, 2 points for somewhat disagree, 1 point for disagree, and no

49  

points assigned for N/A (non-applicable). There were ten questions to measure the

alumni experience—alumni association, seven questions to measure the alumni

experience—engagement, eight questions to measure the student experience—academic,

six questions to measure the student experience—impact on career, and seven questions

to measure student experience—impact on relationships. Questions to measure and

control for the variables age, gender, income, ethnicity, and marital status were asked.

The variables were controlled to better understand the relationship between the dependent

and independent variables.

Issues of Validity and Reliability

Research must consider the validity and reliability of the data along with the

methods used to collect the data in analyzing the data. Overall this provides confidence

in the research results. Validity is whether an instrument measures what the instrument

sets out to measure. Reliability is whether an instrument is consistent when repeated

(Bryman, 2008; Field, 2009). Validity and reliability provide the necessary confidence

for the evaluation. To assist in minimizing issues related to validity and reliability is to

use questionnaires and constructs that have demonstrated validity and reliability.

The questionnaire was developed based on some of the constructs of the student

experience and alumni experience from Sun, Hoffman, and Grady (Sun, Hoffman, &

Grady, 2007). The validity and reliability of the data was checked in the analysis stage

delineated in the next chapter. The next chapter presents the analysis of the data that was

collected.

50  

Delimitations

The research was limited to MBA graduates and not bachelor graduates because

the ability to secure an online graduate degree from The Pennsylvania State University

has more history than the undergraduate degree. The sample was limited to B ADM

MBA traditional graduates rather the entire population of traditional MBA graduates. It

was cost prohibitive to expand the study to the entire traditional MBA graduates and to

have a representative iMBA graduate sample in portion to a group of traditional

graduates so the B ADM population was used. The iMBA graduate does not graduate

with a specialization; therefore, the B ADM, the general rubric MBA was used in

comparing the online iMBA graduates and the traditional MBA graduates.

The study was limited with respect to graduate degree holders at The

Pennsylvania State University. This study was limited in that only MBA graduates were

studied, not other graduate degrees or undergraduates. Further, the study was of

graduates of a public university versus a private university because of the university’s

link to state government funding and public policy. Finally, the collection of the data

was limited by mail only and no online or over the phone option was available. The data

provided had physical addresses for each record, but an email was not available for each

record. Also, surveys were mailed out first class and if a participant had moved the

survey would be forwarded. If an email is no longer current the email becomes

undelivable. The next chapter covers in detail the analysis of the data.

51  

CHAPTER 4

DATA ANALYSIS

Overview

This chapter reviews the data analysis that was used for the data collected from

the questionnaire. The data was analyzed using the Statistical Package for Social

Sciences (SPSS). Statistical methods included correlation coefficients analyses, t-tests,

analyses of variances, One-way ANOVA, and logistic regression. The data was used for

the model to determine predictors of alumni donor behavior and to understand the

differences between the MBA graduate and the iMBA graduate alumni and student

experiences. The results of the descriptive statistics are first explained, followed by how

the data was distributed.

This design allowed testing of the hypotheses by using analysis of One-way

ANOVA and logistic regression to analyze results and report the findings. The procedure

in conducting the data analysis was as follows: descriptive statistics, individual T-tests,

and to test H1- H5, One-way ANOVA was used and to test H6 and H7, logistic

regression was used.

Descriptive Statistics

Descriptive statistics on the data were run to acquire an understanding of the

respondent pool and to check how the data was distributed. Table 4.1 provides the

summary of the demographic information. Only marital status had a higher percentage of

missing cases compared with all other demographic information. The higher number of

missing cases of marital status could be attributed to the fact the survey did not provide

the option of “partner” as an option.

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Table 4.1: Descriptive Statistics for Demographic Information. 

Cases

Valid Missing Total N Percent N Percent N Percent

Gender 179 98.9% 2 1.1% 181 100.0%Salary 173 95.6% 8 4.4% 181 100.0%Age 179 98.9% 2 1.1% 181 100.0%Ethnicity 177 97.8% 4 2.2% 181 100.0%Marital status 155 85.6% 26 14.4% 181 100.0%

As indicated by Table 4.2 a majority of the respondents were male (69%) to

female (31%) and these percentages held both for the overall group and within the MBA

and iMBA groups. A Chi-square test was performed to determine if the males and

females were distributed differently across the MBA and iMBA groups. The test failed

to indicate a significant difference, X2 (1, N=179) = .037, p >.05.

Table 4.2: Descriptive Statistics for Gender.

Total Female Male Group MBA Count 30 64 94

% within Group 31.9% 68.1% 100.0% % within Gender 53.6% 52.0% 52.5% % of Total 16.8% 35.8% 52.5%

iMBA Count 26 59 85

% within Group 30.6% 69.4% 100.0% % within Gender 46.4% 48.0% 47.5% % of Total 14.5% 33.0% 47.5%

Total Count 56 123 179

% within Group 31.3% 68.7% 100.0% % within Gender 100.0% 100.0% 100.0%

53  

The MBA group fared slightly better at the highest end of the salary scale, thirty-

five percent, compared to twenty-one percent for the iMBA group as indicated by Table

4.3. This could be attributed to an older population in the MBA group (see Table 4.4)

and having spent more time in the workforce that provided more opportunity for

increases in salary. Sixty percent of all respondents indicated an annual salary greater

than $100,000. From a fundraising perspective both groups, based on salary alone, are

viable prospects for a major gift ($5,000 or more a year for five years). Ninety-five

percent of the group based on salary alone appeared to be excellent prospects for a

leadership annual gift of at least $1,000 or more a year.

A Chi-square test was performed to determine if salary was distributed differently

across the MBA and iMBA groups. The test failed to indicate a significant difference, X2

(4, N=173) = 8.646, p >.05.

Table 4.3: Descriptive Statistics for Salary

Less than

$50K

$50K-

$74.9K

$75K-

$99.9K

$100K-

$149.9K

More than

$150K Total

Group MBA Count 7 10 20 21 31 89

% within Group 7.9% 11.2% 22.5% 23.6% 34.8% 100.0%

% within Salary 87.5% 41.7% 52.6% 41.2% 59.6% 51.4%

% of Total 4.0% 5.8% 11.6% 12.1% 17.9% 51.4%

iMBA Count 1 14 18 30 21 84

% within Group 1.2% 16.7% 21.4% 35.7% 25.0% 100.0%

% within Salary 12.5% 58.3% 47.4% 58.8% 40.4% 48.6%

% of Total .6% 8.1% 10.4% 17.3% 12.1% 48.6%

Total Count 8 24 38 51 52 173

% within Group 4.6% 13.9% 22.0% 29.5% 30.1% 100.0%

% within Salary 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

% of Total 4.6% 13.9% 22.0% 29.5% 30.1% 100.0%

54  

It was not surprising to learn that the majority of the older respondents would be

in the MBA group, see Table 4.4. The traditional MBA program has been in operation

longer. Furthermore, younger generations have embraced new technology at a higher

rate than the older generations and may be less intimidated in pursuing the online

education delivery option. A Chi-square test was performed to determine if age was

distributed differently across the MBA and iMBA groups. The test indicated a significant

difference, X2 (4, N=179) = 51.708, p <.05.

Table 4.4: Descriptive Statistics for Age. 25 less 26-35 36-45 46-55 56+ Total

Group MBA Count 1 13 26 29 26 95

% within Group 1.1% 13.7% 27.4% 30.5% 27.4% 100.0%

% within Age 100.0% 23.6% 44.1% 78.4% 96.3% 53.1%

% of Total .6% 7.3% 14.5% 16.2% 14.5% 53.1%

iMBA Count 0 42 33 8 1 84

% within Group .0% 50.0% 39.3% 9.5% 1.2% 100.0%

% within Age .0% 76.4% 55.9% 21.6% 3.7% 46.9%

% of Total .0% 23.5% 18.4% 4.5% .6% 46.9%

Total Count 1 55 59 37 27 179

% within Group .6% 30.7% 33.0% 20.7% 15.1% 100.0%

% within Age 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

% of Total .6% 30.7% 33.0% 20.7% 15.1% 100.0%

Table 4.5 delineates the ethnicity between the two groups and it showed it did not

differ much. The majority (83.6%) of all respondents were white. A Chi-square test was

performed to determine if ethnicity was distributed differently across the MBA and

iMBA groups. The test failed to indicate a significant difference, X2 (5, N=177) = 3.413,

p >.05.

55  

Table 4.5: Descriptive Statistics for Ethnicity.

Asian/Pacific Black/African Hispanic Native White Other Total

Group MBA Count 7 3 2 1 77 3 93

% within

Group

7.5% 3.2% 2.2% 1.1% 82.8% 3.2% 100.0%

% within

Ethnicity

58.3% 75.0% 28.6% 50.0% 52.0% 75.0% 52.5%

% of

Total

4.0% 1.7% 1.1% .6% 43.5% 1.7% 52.5%

Asian/Pacific Black/African Hispanic Native White Other Total

iMBA Count 5 1 5 1 71 1 84

% within

Group

6.0% 1.2% 6.0% 1.2% 84.5% 1.2% 100.0%

% within

Ethnicity

41.7% 25.0% 71.4% 50.0% 48.0% 25.0% 47.5%

% of

Total

2.8% .6% 2.8% .6% 40.1% .6% 47.5%

Total Count 12 4 7 2 148 4 177

% within

Group

6.8% 2.3% 4.0% 1.1% 83.6% 2.3% 100.0%

% within

Ethnicity

100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

% of

Total

6.8% 2.3% 4.0% 1.1% 83.6% 2.3% 100.0%

Marital status between the two groups also did not differ much, see Table 4.6. A

majority of the MBA (75%) and iMBA (78%) graduates were married. A Chi-square test

was performed to determine if marital status was distributed differently across the MBA

and iMBA groups. The test failed to indicate a significant difference, X2 (1, N=155) =

.132, p >.05.

56  

Table 4.6: Descriptive Statistics for Marital Status.

Single Married Total Group MBA Count 20 62 82

% within Group 24.4% 75.6% 100.0% % within Marital status 55.6% 52.1% 52.9% % of Total 12.9% 40.0% 52.9%

iMBA Count 16 57 73

% within Group 21.9% 78.1% 100.0% % within Marital status 44.4% 47.9% 47.1% % of Total 10.3% 36.8% 47.1%

Total Count 36 119 155

% within Group 23.2% 76.8% 100.0% % within Marital status 100.0% 100.0% 100.0% % of Total 23.2% 76.8% 100.0%

Donor and non-donor between the two groups did not differ much, see Table 4.7.

A Chi-square test was performed to determine if donor status was distributed differently

across the two groups, MBA and iMBA. The test failed to indicate a significant

difference, X2 (1, N=179) = 2.785, p > .05.

57  

Table 4.7: Descriptive Statistics for Philanthropy.

Donor Non Donor Total Group MBA Count 45 48 93

% within Group 48.4% 51.6% 100.0%% within Philanthropy 59.2% 46.6% 52.0% % of Total 25.1% 26.8% 52.0%

iMBA Count 31 55 86

% within Group 36.0% 64.0% 100.0% % within Philanthropy 40.8% 53.4% 48.0% % of Total 17.3% 30.7% 48.0%

Total Count 76 103 179

% within Group 42.5% 57.5% 100.0% % within Philanthropy 100.0% 100.0% 100.0% % of Total 42.5% 57.5% 100.0%

Distribution of Data

Assumptions of normality tests for each variable were found to be acceptable

despite a slight distribution to the right. The slight distribution is attributed to all survey

question answers having the same scale running in the same direction, i.e., there were no

reverse questions where a higher score actually meant a negative indication. The scores

for Kurtosis, a measure of the "peakedness" or "flatness" of a distribution, were found to

be less than ± 2 and for skewness, the extent to which a distribution of values deviates

from symmetry around the mean scores, were found to be less than ± 2. Although these

scores are not ideal they are acceptable (Field, 2009). Negatives values of skewness

indicate scores are more on the right of the distribution and positive values of kurtosis

indicate a pointy and heavy-tailed distribution (Field, 2009). Kolmogorov-Smirnov and

58  

the Shapiro-Wilk tests were conducted to check for normality of the data and the test

results suggested violation of the assumption of normality.

Transforming data comes with additional challenges in analyzing data. “By

transforming the data you change the hypothesis being tested (when using a log

transformation and comparing means you change from comparing arithmetic means to

comparing geometric means). Transformation also means that you’re now addressing a

different construct to the one originally measured” (Field, 2009, p. 156). The log

transformation method for the data to test the assumption of normality was conducted.

After performing the log transformation method the test results indicated that the data

still violated the assumption of normality.

To examine the data visually Q-Q plots was run. This test and Boxplots affirmed

the scores fell to the right in the distribution. This case is not special. Often scales and

measures used in the social sciences have scores that are skewed. This does not

necessarily indicate a problem with the scale, but rather reflects the underlying nature of

the construct being measured (Tabachnick & Fidell, 2001). For example, student

teaching evaluation scores are often skewed, with most students being reasonably

satisfied with instructors’ performance.  Even though the data had distribution to the right

that does indicate higher scores, i.e., positive responses to alumni and student

experiences, indicating positive feelings by respondents to his or her Pennsylvania State

University educational experience.

Individual T-test

An individual T-test for each item was run to find the mean and standard

deviation for each of the items measuring for alumni experience—alumni association,

59  

alumni experience—engagement, student experience—impact on career, student

experience—relationships, and student experience—academic for the MBA and the

iMBA graduate. Overall the two groups did not show great disparity, but many of the

questions provided results indicating a higher level of satisfaction of one group over the

other. Higher mean scores were demonstrated by the iMBA group in all but six of the

questions (3, 4, 6, 10, 26, and 28) out of 38 questions. The descriptive statistics of each

item are delineated in Table 4.8.

Table 4.8: Descriptive Statistics of each item for MBA and iMBA Group N Mean Std. Deviation Std. Error MeanAlumni Assoc. Exp Q1 MBA 76 3.2368 .99154 .11374

iMBA 78 3.2821 1.03067 .11670Alumni Assoc. Exp Q2 MBA 65 3.2462 .90192 .11187

iMBA 70 3.3143 .86045 .10284Alumni Assoc. Exp Q3 MBA 63 3.2698 .90173 .11361

iMBA 68 3.2353 1.12127 .13597Alumni Assoc. Exp Q4 MBA 74 2.3784 1.17861 .13701

iMBA 69 2.3333 1.13328 .13643Alumni Assoc. Exp Q5 MBA 81 3.7901 1.08069 .12008

iMBA 74 4.1216 .96447 .11212Alumni Assoc. Exp Q6 MBA 76 3.4868 1.12538 .12909

iMBA 74 3.3243 1.00830 .11721Alumni Assoc. Exp Q7 MBA 69 3.4783 1.18332 .14246

iMBA 77 3.5455 .96716 .11022Alumni Assoc. Exp Q8 MBA 78 3.5000 1.31673 .14909

iMBA 74 4.1216 .96447 .11212Alumni Assoc. Exp Q9 MBA 83 1.9277 1.35055 .14824

iMBA 82 2.0244 1.36965 .15125Alumni Assoc. Exp Q10 MBA 83 2.3614 1.26470 .13882

iMBA 75 2.2533 1.25303 .14469Alumni Engage Exp Q11 MBA 87 3.1724 1.14330 .12257

iMBA 83 3.3012 1.26621 .13898Alumni Engage Exp Q12 MBA 87 3.4483 1.05388 .11299

iMBA 84 3.5595 1.23553 .13481

60  

Group N Mean Std. Deviation Std. Error MeanAlumni Engage Exp Q13 MBA 88 3.3750 1.10706 .11801

iMBA 84 3.7619 1.18849 .12967Alumni Engage Exp Q14 MBA 87 3.8276 1.03662 .11114

iMBA 84 4.2500 .92976 .10145Alumni Engage Exp Q15 MBA 87 3.9310 .98587 .10570

iMBA 84 4.2738 .98606 .10759Alumni Engage Exp Q16 MBA 88 3.9091 1.02426 .10919

iMBA 85 3.9176 1.00252 .10874Alumni Engage Exp Q17 MBA 87 3.4598 1.18914 .12749

iMBA 85 3.6706 1.14838 .12456Academic Exp Q18 MBA 91 4.3407 .88468 .09274

iMBA 86 4.5814 .62243 .06712Academic Exp Q19 MBA 92 4.1304 .89218 .09302

iMBA 86 4.4419 .72930 .07864Academic Exp Q20 MBA 93 4.2258 .91044 .09441

iMBA 86 4.4884 .77830 .08393Academic Exp Q21 MBA 93 4.4839 .78868 .08178

iMBA 85 4.6588 .60853 .06600Academic Exp Q22 MBA 93 4.5699 .63242 .06558

iMBA 66 4.6364 .69363 .08538Academic Exp Q23 MBA 93 4.5806 .71205 .07384

iMBA 82 4.7439 .56242 .06211Academic Exp Q24 MBA 92 4.2935 .97820 .10198

iMBA 85 4.7412 .51531 .05589Academic Exp Q25 MBA 81 4.2346 .89821 .09980

iMBA 79 4.6582 .63823 .07181Impact on Career Q26 MBA 92 3.8696 1.14082 .11894

iMBA 85 3.8235 1.13574 .12319Impact on Career Q27 MBA 92 4.0543 1.04160 .10859

iMBA 86 4.1512 .98837 .10658Impact on Career Q28 MBA 92 4.3804 .83656 .08722

iMBA 86 4.2907 .87939 .09483Impact on Career Q29 MBA 94 3.2447 1.24163 .12806

iMBA 86 3.4070 1.13114 .12197Impact on Career Q30 MBA 95 4.1263 .93675 .09611

iMBA 86 4.2326 1.02526 .11056Impact on Career Q31 MBA 95 4.0842 1.03824 .10652

iMBA 86 4.1977 1.04959 .11318

61  

Group N Mean Std. Deviation Std. Error MeanImpact on Relations Q32 MBA 95 3.0316 1.42510 .14621

iMBA 86 3.9186 1.15009 .12402Impact on Relations Q33 MBA 73 3.2329 1.37962 .16147

iMBA 42 3.2619 1.28897 .19889Impact on Relations Q34 MBA 81 3.5556 1.22474 .13608

iMBA 56 4.0179 .99984 .13361Impact on Relations Q35 MBA 94 4.1277 1.06996 .11036

iMBA 85 4.6118 .63797 .06920Impact on Relations Q36 MBA 94 4.1383 1.01177 .10436

iMBA 86 4.3953 .78634 .08479Impact on Relations Q37 MBA 91 3.3626 1.06996 .11216

iMBA 84 3.8095 1.04681 .11422Impact on Relations Q38 MBA 85 3.3294 1.13796 .12343

iMBA 77 4.1818 1.08491 .12364

Independent Sample T-test Results for Each Item

The T-test for equality of means was run to determine if there was a significant

difference in the means between the two groups for each question. The iMBA graduates

indicated higher levels of pride as a member of the alumni association, survey question

eight, (M= 4.12, SD = .96) compared to MBA graduate (M = 3.5, SD = 1.31) and this

difference was found to be significant t(141) = -3.332, p =.001. Membership into the

alumni association requires payment annually and graduates may or may not renew his or

her membership if he or she does not feel membership is providing an adequate return on

investment. The iMBA graduates are younger and may have a higher initial sense of

pride belonging to the alumni association that could wane over time if a graduate does

not feel the benefit of the membership.

Interesting results on the individual T-test were found in the student experience—

academic. Of the eight questions asked five were found to have a significant difference.

In each case the iMBA graduate indicated higher levels of preparedness and positive

62  

experience than the traditional MBA graduate. The iMBA graduates indicated on

question 18, coursework preparedness, higher levels (M= 4.58, SD = .62) compared to

MBA graduates (M = 4.34, SD = .88) and this difference was found to be significant

t(161) = -2.103, p =.008. iMBA graduates indicated higher levels of positive experiences

with regards to interaction with faculty and fellow students despite the fact that MBA

students would have more opportunity for personal interaction. The iMBA graduates

indicated on question 21, interaction with MBA faculty, a higher positive experience

(M=4.65, SD=.60) compared to MBA graduates (M = 4.48, SD = .78). This difference

was found to be significant t(171) = -1.665, p = .013.

The experience of interaction with fellow students, during class (question 23) and

outside of class (question 24), was also found to be higher among iMBA graduates than

MBA graduates. For question 23 the mean and standard deviation for each group was

iMBA graduates (M = 4.74, SD = .56) and MBA graduates (M = 4.58, SD = .71). This

difference was significant t(171) = -1.692, p = .005. For question 24 the mean score was

also higher among iMBA graduates. The means and standard deviation for iMBA (M =

4.74, SD = .51) and for MBA (M = 4.29, SD = .97). This difference was significant

t(140) = -3.850, p = .000. Finally, in the academic experience the iMBA graduates (M =

4.65, SD = .63) found interaction with faculty outside of the classroom, question 25, a

more positive experience than the MBA graduates (M = 4.23, SD = .89). Again, this

difference was significant t(144) = -3.446, p = .001.

It would be natural to assume that face-to-face interaction during class and during

classroom breaks would provide higher levels of interaction experiences. However,

perhaps the online experience is more focused, i.e., to reply to questions or inquiries

63  

online requires a higher level of attentiveness to the questions than during the course of a

conversation, i.e., listening to someone versus hearing someone. Perhaps individuals

conducting his or her studies online is more cautious in replying or participating because

online responses are recorded permanently in cyberspace, i.e., people give more

thoughtful answers. Thoughtful answers could lead higher interaction experience

because the individual feels the person providing the answer really cares to help.

Finally, an examination of student experience—impact on relationships questions

32, 34, and 35 all were found to have a difference that was significant. For each question

iMBA graduates indicated a higher level of positive experience than MBA graduates.

Question 32, providing new lifelong relationships, iMBA (M = 3.91, SD = 1.15) and

MBA (M = 3.03, SD = 1.42), significant t(176) = -4.627, p = .008; question 34, attending

outside university events, iMBA (M = 4.01, SD = .99), significant t(131) = -3.424, p =

.022 and MBA (M = 3.55, SD = 1.22); and question 35, impact relationship on own

student experience, iMBA (M = 4.61, SD = .63) and MBA (M = 4.12, SD = 1.06),

significant t(154) = -3.717, p = .000. It appears that online networking may encourage

long-term relationships because the structure for maintaining the relationship was

reinforced during the course, i.e., communicating via electronic means and not socially in

person. For question 34, attending events other than athletic events a positive experience

in building relationships, only 56 replied (a lower number of N/A would be expected

because online students are not located in proximity to campus to attend events);

however, of those that did attend replied they had a higher experience in building

relationships. Additionally, the experience of meeting individuals for the iMBA

graduate at events could be reinforcing the relationship that was already established

64  

through the online experience; therefore, the event provided the positive experience of

meeting someone the graduate “already knows” but not yet in person. The result of the

Independent Sample T-test for the select items discussed is shown in Table 4.9.

Table 4.9: Independent Sample T-test Results for Select Items.

Levene's Test for Equality of Variances

t-test for Equality of Means

F Sig. t df

Sig.

(2-

tailed)

Mean

Difference

Std. Error

Difference

95% Confidence

Interval of the

Difference

Lower Upper

Alumni Assoc.

Exp8Q8

Equal variances

assumed

10.648 .001 -3.306 150 .001 -.62162 .18804 -.99317 -.25007

Equal variances not

assumed

-3.332 141.113 .001 -.62162 .18654 -.99040 -.25284

Academic

Exp1Q18

Equal variances

assumed

7.187 .008 -2.083 175 .039 -.24074 .11558 -.46885 -.01262

Equal variances not

assumed

-2.103 161.932 .037 -.24074 .11448 -.46680 -.01467

Academic

Exp4Q21

Equal variances

assumed

6.311 .013 -1.646 176 .102 -.17495 .10631 -.38475 .03485

Equal variances not

assumed

-1.665 171.289 .098 -.17495 .10509 -.38240 .03250

Academic

Exp6Q23

Equal variances

assumed

7.954 .005 -1.667 173 .097 -.16326 .09791 -.35651 .02999

Equal variances not

assumed

-1.692 171.010 .092 -.16326 .09648 -.35371 .02720

Academic

Exp7Q24

Equal variances

assumed

32.923 .000 -3.764 175 .000 -.44770 .11894 -.68244 -.21295

Equal variances not

assumed

-3.850 140.176 .000 -.44770 .11630 -.67762 -.21778

Academic

Exp8Q25

Equal variances

assumed

10.788 .001 -3.432 158 .001 -.42366 .12346 -.66750 -.17982

Equal variances not

assumed

-3.446 144.538 .001 -.42366 .12295 -.66667 -.18065

65  

Levene's Test for Equality of Variances

t-test for Equality of Means

F Sig. t df

Sig.

(2-

tailed)

Mean

Difference

Std. Error

Difference

95% Confidence

Interval of the

Difference

Lower Upper

Impact on

Relationships1Q32

Equal variances

assumed

7.213 .008 -4.578 179 .000 -.88703 .19376 -1.26937 -.50468

Equal variances not

assumed

-4.627 176.742 .000 -.88703 .19172 -1.26539 -.50866

Impact on

Relationships3Q34

Equal variances

assumed

5.370 .022 -2.337 135 .021 -.46230 .19786 -.85360 -.07100

Equal variances not

assumed

-2.424 131.218 .017 -.46230 .19071 -.83956 -.08504

Impact on

Relationships4Q35

Equal variances

assumed

14.386 .000 -3.628 177 .000 -.48411 .13343 -.74742 -.22079

Equal variances not

assumed

-3.717 154.127 .000 -.48411 .13026 -.74143 -.22678

Descriptive Statistics for the Constructs

For further analysis, the mean score of all items underlying a construct was

calculated to represent each construct measure. An examination of the mean scores

demonstrated that a higher percentage of scores did fall on the right side of the

distribution for: Alumni engagement—engagement (M = 3.69, SD = .8600), alumni

experience—alumni association (M = 3.24, SD = .7870), student experience—impact on

career (M = 3.98, SD = .8034), student experience—impact on relationships (M = 3.71,

SD = .8842), and student experience—academic (M = 4.47, SD = .5541). This indicated

that graduates had positive alumni and student experiences as greater than 3 on the

Likert-scale indicated an affirmed reply to the question asked. The descriptive statistics

for each construct: Alumni experience—alumni association and engagement and student

66  

experience—impact on careers, impact on relationships, and academic are delineated in

Table 4.10.

Table 4.10: Descriptive Statistics for Constructs.

N Minimum Maximum Mean

Std.

Deviation Skewness Kurtosis

Std.

Error

Std.

Error

Engagement 169 1.00 5.00 3.6974 .86009 -.702 .187 .753 .371

AlumniAsso 87 1.20 4.90 3.2471 .78708 -.325 .258 -.214 .511

ImpactCareer 175 1.33 5.00 3.9886 .80341 -.928 .184 .474 .365

ImpactRelations 105 1.00 5.00 3.7184 .88429 -.890 .236 .520 .467

AcademicExp 142 2.25 5.00 4.4771 .55416 -1.271 .203 1.352 .404

Valid N

(listwise)

65

For questions 1 thru 38 respondents failed to provide an answer 49 times and

replies were coded as 99 and they were not used in the analysis. For questions 1 thru 38

respondents answered N/A for a combined total of 546 times. These replies were coded

as 0 and they were not used in the analysis. If a respondent’s answer was coded either 0

or 99 then that case was dropped from the analysis for the constructs if the case is

missing data required for the specific analysis as the exclude cases pairwise option was

utilized. Questionnaires, however, were included in any of the analysis for which there

was the necessary information. The alumni experience—alumni association experienced

a number of dropped questionnaires. N/A replies for the alumni experience—alumni

association totaled 302. The student experience—impact on relationships N/A replies

totaled 138.

67  

The outcome of these dropped cases as a result of the number of N/A impacted

alumni experience—alumni association and student experience—impact on relationships

the greatest. For example, on question 3, “I find the alumni association sponsored events

beneficial” 50 respondents answered N/A and on question 33, “I found attending athletic

events a positive experience in building relationships” respondents answered N/A 66

times. See table 4.11 for the number of cases for each of the constructs and by each

group. An examination of the data indicates there was a systematic pattern to missing

data. Many participants did not respond to The Pennsylvania State University alumni

experience perhaps they have never had an experience with the alumni association or its

events.

Table 4.11: Cases by Constructs and Group.

Group

Cases Valid Missing Total N Percent N Percent N Percent Engagement MBA 86 90.5% 9 9.5% 95 100.0%

iMBA 83 96.5% 3 3.5% 86 100.0%AlumniAsso MBA 44 46.3% 51 53.7% 95 100.0%

iMBA 43 50.0% 43 50.0% 86 100.0%ImpactCareer MBA 90 94.7% 5 5.3% 95 100.0%

iMBA 85 98.8% 1 1.2% 86 100.0%ImpactRelationship MBA 65 68.4% 30 31.6% 95 100.0%

iMBA 40 46.5% 46 53.5% 86 100.0%AcademicExp MBA 79 83.2% 16 16.8% 95 100.0%

iMBA 63 73.3% 23 26.7% 86 100.0%

The aforementioned results does provide some insight into the iMBA group and

MBA group, but to examine predictability of future donor behavior the next construct of

the instrument’s variables: alumni experience—alumni association, alumni experience—

68  

engagement, student experience—impact on career, student experience—relationships,

and student experience—academic was tested.

Hypotheses Testing

One-way ANOVA and logistic regression was conducted with alumni and student

experiences as independent variables and alumni donor behavior as a dependent variable.

One-way ANOVA is used to determine whether there are any significant differences

between the means of the MBA and iMBA groups. Logistic regression is used to predict

whether the alumnus will donate or not using alumni and student experiences as the

predicator. The study used the level of statistical significant at the standard p < .05 and p

< .01 levels (Field, 2009). The Cronbach’s alpha was calculated as a traditional method

of scale reliability (Bryman, 2008). Income, gender, age, ethnicity, and marital status

were controlled to help in determining the true influence of the independent variable on

the dependent variable (Creswell, 2009).

Because the data challenged the assumption of normality it was elected also to run

the Mann-Whitney test. The Mann-Whitney test is a non-parametric test that carries out

the data based on the ranks rather than the actual data (Field, 2009). The results of the

analysis for each of the seven hypotheses follows.

H1: The MBA graduate will demonstrate higher levels of student experience—

impact on relationships.

The first hypothesis, the MBA graduate will demonstrate higher levels of student

experience—impact on relationships, is based on the added in person interaction that a

69  

student of the traditional MBA program would have over the iMBA program.

Additionally, attending class on campus will provide additional opportunities for the

MBA student to be involved with activities that promote a positive student experience.

Byrman (2008) provides a guideline in evaluation of internal reliability,

“Cronbach’s alpha is a commonly used to test of internal reliability…the figure 0.80 is

typically employed as a rule of thumb to denote an acceptable level of internal

reliability.” (p. 151). For the questions that measure student experience on the impact on

relationships the Cronbach’s α = .870 (see Table 4.13) indicates good reliability.

Deleting any of the items would not increase the overall Cronbach’s α as they are all less

than .870 (see Table 4.12). This indicates that all items are positively contributing to the

overall reliability.

Table 4.12: Item-Total Statistics for Cronbach’s Alpha of Impact on Relationship.

Scale Mean

if Item Deleted

Scale Variance if

Item Deleted

Corrected Item-Total Correlation

Squared Multiple

Correlation

Cronbach's Alpha if

Item Deleted

Impact on Relationships1

22.5048 26.387 .674 .555 .850

Impact on Relationships2

22.8667 27.982 .622 .602 .856

Impact on Relationships3

22.4095 27.263 .757 .700 .836

Impact on Relationships4

21.7238 29.029 .768 .694 .839

Impact on Relationships5

21.7810 31.077 .561 .494 .863

Impact on Relationships6

22.4762 30.983 .523 .476 .867

Impact on Relationships7

22.4095 28.590 .667 .558 .849

70  

Scale Mean

if Item Deleted

Scale Variance if

Item Deleted

Corrected Item-Total Correlation

Squared Multiple

Correlation

Cronbach's Alpha if

Item Deleted

Impact on Relationships1

22.5048 26.387 .674 .555 .850

Impact on Relationships2

22.8667 27.982 .622 .602 .856

Impact on Relationships3

22.4095 27.263 .757 .700 .836

Impact on Relationships4

21.7238 29.029 .768 .694 .839

Impact on Relationships5

21.7810 31.077 .561 .494 .863

Impact on Relationships6

22.4762 30.983 .523 .476 .867

The value of Cronbach’s α demonstrated overall good reliability of .870 and is

shown in table 4.13.

Table 4.13: Cronbach’s Alpha for Items Underlying Impact on Relationship.

Cronbach's Alpha Cronbach's Alpha Based on

Standardized Items N of Items

.870 .874 7

One-way ANOVA was conducted to answer H1: the MBA group will

demonstrate higher levels of student experience—impact on relationships. Descriptive

statistics initially ran demonstrated a higher mean for the iMBA group (M = 4.07, SD =

.5885) than the MBA group (M = 3.50, SD = .9663).

71  

Table 4.14: Descriptive Statistics on Student Experience—Impact on Relationships.

N Mean Std.

DeviationStd.

Error

95% Confidence Interval for Mean

Minimum MaximumLower Bound

Upper Bound

MBA 65 3.5011 .96631 .11986 3.2617 3.7405 1.00 5.00iMBA 40 4.0714 .58857 .09306 3.8832 4.2597 2.29 5.00Total 105 3.7184 .88429 .08630 3.5472 3.8895 1.00 5.00

The results of One-way ANOVA test indicated that there was a significant mean

difference between the MBA and the iMBA graduate, F(1, 103) = 11.323, p < .05. The

effect size, r = .31, indicates a medium effect. Although there is a significant difference

between the MBA and iMBA, the iMBA group demonstrated higher levels of student

experience—impact on relationships than the MBA group: MBA (M = 3.50, SD = .9663)

and iMBA (M = 4.07, SD = .5885). The findings did not support H1, the MBA graduate

will demonstrate higher levels of student experience—impact on relationships.

Table 4.15: One-way ANOVA for Student Experience—Impact on Relationships.

Sum of Squares df Mean Square F Sig. Between Groups 8.054 1 8.054 11.323 .001 Within Groups 73.270 103 .711

Total 81.325 104

As a result of the distribution of the data that addressed above in this chapter, a

non-parametric test, Mann-Whitney, was run to analyze the data. The non-parametric

tests make fewer assumptions than parametric tests and analysis is based on the ranks of

the data rather than the actual data (Field, 2009). The non-parametric test confirmed the

One-way ANOVA result. The student experience—impact on relationships for MBA (M

72  

= 3.50) did differ significantly from iMBA (M = 4.07), U = 834.50, z = -3.080, p < .005, r

= .30. The effect size, r = .30 indicates a medium effect. Additionally, a Chi-square test

was performed and the findings, X2(5, N=181) = 16.90, p < .05 did support the findings

of the One-way ANOVA.

H2: The MBA graduate will demonstrate higher levels of student experience—

academic.

The same procedure to analyze H2, that the MBA graduate will demonstrate

higher levels of student experience—academic, was followed.

The overall α for testing H2 was .864 (see Table 4.17). Table 4.16, Cronbach’s

Alpha Item-Total Statistics, shows that only AcademicExp3 (questionnaire question 20)

would provide a .002 increase if deleted. All questions were kept and an α of .864

demonstrated good reliability.

73  

Table 4.16: Cronbach’s Alpha Item-Total Statistics of Academic Experience.

Scale Mean

if Item Deleted

Scale Variance if

Item Deleted

Corrected Item-Total Correlation

Squared Multiple

Correlation

Cronbach's Alpha if Item

Deleted Academic Exp1

31.3592 14.969 .649 .566 .843

Academic Exp2

31.5282 14.776 .675 .591 .840

Academic Exp3

31.4648 15.910 .457 .257 .866

Academic Exp4

31.2465 15.265 .678 .517 .840

Academic Exp5

31.2254 15.637 .667 .529 .842

Academic Exp6

31.1479 16.311 .557 .470 .853

Academic Exp7

31.3451 14.908 .623 .474 .846

Academic Exp8

31.4014 14.951 .628 .431 .845

The value of Cronbach’s α demonstrated overall good reliability of .864 and is shown in

Table 4.17.

Table 4.17: Cronbach’s Alpha for Items Underlying Academic Experience.

Cronbach's Alpha Cronbach's Alpha Based on

Standardized Items N of Items .864 .866 8

Descriptive statistics for student experience—academic showed iMBA (M = 4.61,

SD = .4192) with a higher mean score than MBA (M = 4.36, SD = .6233) and is shown in

Table 4.18.

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Table 4.18: Descriptive Statistics on Student Experience—Academic.

N Mean Std.

DeviationStd.

Error

95% Confidence Interval for Mean

Minimum MaximumLower Bound

Upper Bound

MBA 79 4.3687 .62336 .07013 4.2290 4.5083 2.25 5.00iMBA 63 4.6131 .41924 .05282 4.5075 4.7187 3.63 5.00Total 142 4.4771 .55416 .04650 4.3852 4.5690 2.25 5.00

The One-way ANOVA test found there was a significant difference between the

MBA and the iMBA groups, F (1, 140) = 7.114, p < .05. The effect size, r = .21,

indicated a small effect. Although there is a significant difference between the MBA and

the iMBA, the iMBA group (M = 4.61, SD = .4192) demonstrated higher levels of

student experience—academic than the MBA group (M = 4.36, SD = .6233). The

findings do not support H2 that the MBA group demonstrates higher level of student

experience—academic.

Table 4.19. ANOVA for Student Experience—Academic.

Sum of Squares df Mean Square F Sig.

Between Groups 2.094 1 2.094 7.114 .009 Within Groups 41.207 140 .294

Total 43.301 141

The Mann-Whitney test for H2 found MBA (M = 4.36) did differ significantly

from iMBA (M = 4.61), U = 1942.00, z = -2.265, p < .005, r = .19. The effect size of r =

.19 indicated a small effect. The non-parametric test confirmed the findings of the One-

75  

way ANOVA. Additionally, a Chi-square test was performed and the findings, X2(4,

N=181) = 11.857, p < .05 did support the findings of the One-way ANOVA.

H3: There is a difference between the iMBA group and the MBA group student

experience—impact on career.

H3 analyses test whether there is a difference in student experience—impact on

career between the iMBA and MBA group.

The overall Cronbach’s alpha for impact on career was .867 (Table 4.21). Table

2.20 shows that none of the questions if deleted would increase the overall α of .867

because all scores were less than .867.

Table 4.20: Item-Total Statistics for Cronbach’s Alpha of Impact on Career.

Scale Mean

if Item Deleted

Scale Variance if

Item Deleted

Corrected Item-Total Correlation

Squared Multiple

Correlation

Cronbach's Alpha if

Item DeletedImpact on Career1

20.0971 15.732 .690 .518 .839

Impact on Career2

19.8457 16.488 .693 .605 .839

Impact on Career3

19.5886 17.450 .721 .634 .838

Impact on Career4

20.6000 15.954 .616 .416 .855

Impact on Career5

19.7543 17.416 .588 .372 .857

Impact on Career6

19.7714 16.361 .711 .570 .836

The value of α = .867 demonstrated overall good reliability, see Table 4.23.

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Table 4.21: Cronbach’s Alpha for Items Underlying Impact on Career.

Cronbach's Alpha Cronbach's Alpha Based on

Standardized Items N of Items .867 .871 6

Descriptive statistics showed the mean for iMBA (M = 4.01, SD = .8037) was

greater than MBA (M = 3.96, SD = .0869) and are shown in Table 4.22.

Table 4.22: Descriptive Statistics for Student Experience—Impact on Career.

N Mean Std.

DeviationStd.

Error

95% Confidence Interval for Mean

Minimum MaximumLower Bound

Upper Bound

MBA 90 3.9667 .80696 .08506 3.7977 4.1357 1.50 5.00iMBA 85 4.0118 .80376 .08718 3.8384 4.1851 1.33 5.00Total 175 3.9886 .80341 .06073 3.8687 4.1084 1.33 5.00

The One-way ANOVA found that there was not a significant difference in student

experience—impact on career between the iMBA and MBA groups, F (1,173) = .137, p >

.05. The effect was negligible at r = .08. Because there was not a significant difference

between the iMBA group and the MBA group student experience—impact on career H3

is not supported.

Table 4.23: ANOVA for Student Experience—Impact on Career.

Sum of Squares df Mean Square F Sig.

Between Groups .089 1 .089 .137 .712 Within Groups 112.222 173 .649

Total 112.310 174

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The Mann-Whitney test for H3 found the student experience—impact on career

for MBA (M = 3.966) did not differ significantly from iMBA (M = 4.011), U = 3691.50,

z = -.400, p > .05, r = .03. The r score indicates the effect was negligible. Mann-

Whitney confirmed the findings of the One-way ANOVA. Additionally, a Chi-square

test was performed and the findings, X2(5, N=181) = 6.743, p > .05 did support the

findings of the One-way ANOVA.

H4: There is a difference in alumni experience—alumni association between iMBA

and MBA graduates.

H4 analyses tested if there is a difference in alumni experience—association

between iMBA and MBA graduates. The overall Cronbach’s α for alumni association

experience was .891, indicating good reliability (see Table 4.25). Table 4.24 shows that

none of the items deleted would increase the overall α.

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Table 4.24: Item-Total Statistics for Cronbach’s Alpha of Alumni Association.

Scale Mean if

Item Deleted

Scale

Variance if

Item Deleted

Corrected

Item-Total

Correlation

Squared

Multiple

Correlation

Cronbach's

Alpha if Item

Deleted

Alumni Assoc.

Exp1

29.1379 50.190 .808 .780 .871

Alumni Assoc.

Exp2

29.2069 51.538 .748 .759 .875

Alumni Assoc.

Exp3

29.1264 52.554 .626 .578 .882

Alumni Assoc.

Exp4

29.9310 51.460 .556 .386 .886

Alumni Assoc.

Exp5

28.5287 51.764 .572 .515 .885

Alumni Assoc.

Exp6

29.0345 51.917 .566 .649 .885

Alumni Assoc.

Exp7

28.9310 49.646 .709 .660 .875

Alumni Assoc.

Exp8

28.4368 51.691 .606 .470 .882

Alumni Assoc.

Exp9

30.1034 47.559 .620 .558 .884

Alumni Assoc.

Exp10

29.8046 49.531 .614 .490 .882

The value of Cronbach’s α demonstrated overall good reliability of .891, see

Table 4.25.

Table 4.25: Cronbach’s Alpha for Items Underlying Alumni Association.

Cronbach's Alpha Cronbach's Alpha Based on

Standardized Items N of Items .891 .898 10

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The mean score for iMBA (M = 3.29, SD = .7134) and for MBA (M = 3.20, SD =

.8586) is delineated in Table 4.26.

Table 4.26: Descriptive Statistics for Alumni Association.

N Mean Std.

DeviationStd.

Error

95% Confidence Interval for Mean

Minimum MaximumLower Bound

Upper Bound

MBA 44 3.2000 .85861 .12944 2.9390 3.4610 1.20 4.90iMBA 43 3.2953 .71346 .10880 3.0758 3.5149 1.50 4.50Total 87 3.2471 .78708 .08438 3.0794 3.4149 1.20 4.90

The One-way ANOVA test found there was not a significant difference in the

alumni association experience between the MBA group and the iMBA group, F(1, 86) =

.317, p > .05. Additionally, the effect was negligible at r = .06. The findings did not

support H4 that there is a difference in alumni association experience between MBA and

iMBA graduates.

Table 4.27: ANOVA for Alumni Association.

Sum of Squares df Mean Square F Sig.Between Groups .198 1 .198 .317 .575Within Groups 53.079 85 .624

Total 53.277 86

The results of the Mann-Whitney confirmed the alumni association experience for

MBA (M = 3.200) did not differ significantly from iMBA (M = 3.295), U = 880.50, z = -

.557, p > .05, r = .06. The r score indicates the effect was negligible. Additionally, a

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Chi-square test was performed and the findings, X2(5, N=181) = 3.825, p > .05 did

support the findings of the One-way ANOVA.

H5: There is a difference in alumni experience—engagement between iMBA and

MBA graduates

H5 analyses tested if there is a difference in alumni experience—engagement

between iMBA and MBA graduates. For the seven questions making the variable,

alumni experience—engagement, the overall Cronbach’s α was .891 (see Table 4.29).

All items remained in the analysis because the Cronbach’s α would not increase if an

item were to be deleted and is shown in table 4.28.

Table 4.28: Item-Total Statistics for Cronbach’s Alpha Alumni Engagement.

Scale Mean

if Item Deleted

Scale Variance if

Item Deleted

Corrected Item-Total Correlation

Squared Multiple

Correlation

Cronbach's Alpha if

Item Deleted

Alumni Engage Exp1

22.6509 25.812 .734 .724 .869

Alumni Engage Exp2

22.3846 26.464 .715 .727 .872

Alumni Engage Exp3

22.3314 25.973 .751 .637 .867

Alumni Engage Exp4

21.8580 27.777 .704 .582 .874

Alumni Engage Exp5

21.7811 27.351 .751 .641 .869

Alumni Engage Exp6

21.9704 28.838 .588 .499 .886

Alumni Engage Exp7

22.3136 27.586 .587 .377 .888

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Table 4.29: Cronbach’s Alpha for Items Underlying Alumni Engagement.

Cronbach's Alpha Cronbach's Alpha Based on

Standardized Items N of Items .891 .892 7

The mean score for the MBA group (M = 3.58, SD = .8500) was less than the

iMBA group mean score (M = 3.81, SD = .8589) as delineated in Table 4.30.

Table 4.30: Descriptive Statistics for Alumni Engagement.

N Mean Std.

DeviationStd.

Error

95% Confidence Interval for Mean

Minimum MaximumLower Bound

Upper Bound

MBA 86 3.5814 .85005 .09166 3.3991 3.7636 1.00 5.00iMBA 83 3.8176 .85897 .09428 3.6300 4.0051 1.00 5.00Total 169 3.6974 .86009 .06616 3.5668 3.8280 1.00 5.00

The results of the One-way ANOVA test indicated that the difference between the

mean scores was not significant, F(1, 168) = 3.227, p > .05, as delineated in Table 4.30.

The effect was r = .13, indicating a small effect. The findings do not support H5 that

there is a difference in alumni experience—engagement between the two groups.

Table 4.31: ANOVA for Alumni Engagement.

Sum of Squares df Mean Square F Sig. Between Groups 2.356 1 2.356 3.227 .074 Within Groups 121.923 167 .730

Total 124.278 168

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The results of the Mann-Whitney confirmed that the alumni engagement

experience for MBA (M = 3.581) did differ significantly from iMBA (M = 3.817), U =

2915.50, z = -2.059, p < .05, r = .15. However, the r score indicates a rather small effect.

The Mann-Whitney nonparametric test differed from the One-way ANOVA test results.

The One-way ANOVA test involves more assumptions than the Mann-Whitney test;

therefore, the result of the One-way ANOVA that the findings do not support H5 is

retained. Additionally, a Chi-square test was performed and the findings, X2(5, N=181) =

7.059,

p > .05 did support the findings of the One-way ANOVA.

H6: Student experience distinguishes alumni donors from non-donors.

H6, student experience distinguishes alumni donors from non-donors, seeks to

help determine predictors of alumni donor behavior. For fundraising operations at public

universities to utilize resources effectively acquiring a better understanding of what

impacts alumni to donate is warranted. Logistic regression was utilized to test H6.

Logistic regression was used because to donate or not is a dichotomous and categorical

dependent variable and was used to understand predictors of alumni donor behavior. The

test was conducted to determine the impact the factors, student experience—

relationships, academic, and impact on career presented simultaneously to predict alumni

donor behavior.

The initial -2 Log Likelihood of 127.496 is at step 0 when none of the variables

are in the model. The -2 Log Likelihood is a measure of how much unexplained

variability there is in the data. “The difference in the log-likelihood indicates how much

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new variances have been explained by the model” (Field, 2009, p. 308). The results are

delineated in Table. 4.32.

Table 4.32: Iteration History Student Experience of Donors and Non-Donors.

Iteration -2 Log likelihood Coefficients

Constant Step 0 1 127.496 .043

2 127.496 .043 a. Constant is included in the model. b. Initial -2 Log Likelihood: 127.496 c. Estimation terminated at iteration number 2 because parameter estimates changed by less than .001.

Block 0 is the results with only the constant included before the variable student

experience—relationships, academic, and impact on career are entered into the equation.

SPSS correctly classified cases as 51.1% would not be a donor, Table 4.33.

Table 4.33: Block Classification Student Experience of Donors and Non-Donors.

Observed

Predicted Philanthropy Percentage

Correct Non-Donor Donor Step 0 Philanthropy Non-Donor 0 45 .0

Donor 0 47 100.0 Overall Percentage 51.1

a. Constant is included in the model. b. The cut value is .500

The predictive model chi-square had 3 degrees of freedom, a value of 19.05, and a

probability of p < 0.000, indicating the model has poor fit prior to entering the predictors,

Table 4.34.

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Table 4.34: Omnibus Tests for Model Coefficients Student Experience.

Chi-square df Sig.

Step 1 Step 19.005 3 .000 Block 19.005 3 .000 Model 19.005 3 .000

The Cox and Snell R Square and Nagelkerke R Squares (Table 4.35) provided an

indication of the amount of variation in the dependent variable (donor or non-donor)

explained by the model. The results indicate that between 18.7% and 24.9% of the

variability is explained by the set of variables. Compared to the model prior to the step

(the measure of the improvement in the predictive power of the model since the previous

step) the -2 Log Likelihood improved to 108.491 from 127.496, indicating an

improvement in the fit of the predictive model.

Table 4.35: Model Summary for Student Experience.

Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square 1 108.491a .187 .249 a. Estimation terminated at iteration number 4 because parameter estimates changed by

less than .001.

The results of the Hosmer and Lemeshow test in Table 4.36 shows the model is a

good fit as the significance of .868 is not statistically significant, p > .05.

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Table 4.36: Hosmer and Lemeshow Goodness of Fit Test for Student Experience.

Step Chi-square df Sig. 1 3.878 8 .868

The classification table provides an indication of how well the model is able to

predict the correct category (donor or non-donor) for each case in comparison with the

classification table shown in Table 4.37 to see how much improvement there is when the

predictor variables are included in the model. The model correctly classified 72.8% of

cases overall (Table 4.37), an improvement over the initial finding of 51.1%. The

predictive success was 69.9% for non-donor and 76.6% for donor.

Table 4.37: Classification of Donor and Non-Donor for Student Experience.

Observed

Predicted Philanthropy Percentage

Correct Non-Donor Donor Step 1 Philanthropy Non-Donor 31 14 68.9

Donor 11 36 76.6 Overall Percentage 72.8

The predictive model found two of the three factors, student experience—impact

on career and student experience—impact on relationships are significant (p < .05), p =

.017 and p = .011 respectively. These two factors contribute significantly to the

predictive ability of the model for alumni donors and non-donors.

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Table 4.38: Block 1 Method Variables in the Equation for Student Experience.

B S.E. Wald df Sig. Exp(B)

95% C.I.for EXP(B)

Lower UpperStep 1a

ImpactCareer 1.062 .443 5.744 1 .017 2.891 1.213 6.887ImpactRelationship .982 .386 6.473 1 .011 2.669 1.253 5.687AcademicExp -.793 .668 1.410 1 .235 .452 .122 1.675Constant -4.439 1.855 5.729 1 .017 .012

a. Variable(s) entered on step 1: ImpactCareer, ImpactRelationship, AcademicExp.

Logistic regression was used to predict to donate or not (0 = non-donor and 1 =

donor) for 92 alumni using student experience as the predictor. A test of the full model

against a constant only model was statistically significant, indicating predictors as a set

reliably distinguished between donor and non-donor (chi-square = 19.005, p < .000, df =

3).

Nagelkerke’s R2 of .249 indicated a small relationship between predication and

grouping. Prediction success overall was 72.8 percent (68.9 percent for non-donor and

76.6 percent for donor). The Wald criterion demonstrated that impact on career (p =

.017) and impact on relationship (p = .011) did make a significant contribution to

prediction. Academic experience was not a significant predictor. Based on the odds ratio

alumni with positive student experience—impact on career was 2.8 times higher to

donate than alumni that did not have a positive experience. Alumni with positive student

experience—impact on relationships was 2.6 times higher to donate than alumni that did

not. The findings support H6, that student experience distinguishes alumni donors from

non-donors.

87  

H7: Alumni experience distinguishes alumni donors from non-donors.

H7, alumni experience distinguishes alumni donors from non-donors, seeks to

help determine predictors of alumni donor behavior by examining alumni experiences a

graduate has with the university.

The initial -2 Log Likelihood of 119.175 is at step 0 when none of the

variables are in the model is delineated in Table 4.39.

Table 4.39: Iteration History Alumni Experience of Donors and Non-Donors.

Iteration -2 Log likelihood Coefficients

Constant Step 0 1 119.175 .047

2 119.175 .047 a. Constant is included in the model. b. Initial -2 Log Likelihood: 119.175 c. Estimation terminated at iteration number 2 because parameter estimates changed by less than .001.

Block 0 is the results with only the constant included before the variable alumni

experience—alumni association and engagement are entered into the equation. SPSS

correctly classified cases as 51.2 percent would not be a donor.

88  

Table 4.40: Block Classification Alumni Experience of Donors and Non-Donors.

Observed

Predicted Philanthropy Percentage

Correct Non-Donor Donor Step 0 Philanthropy Non-Donor 0 42 .0

Donor 0 44 100.0 Overall Percentage 51.2

a. Constant is included in the model. b. The cut value is .500

The predictive model chi-square has 2 degrees of freedom, a value of 22.865, and

a probability of p < 0.000, indicating the model has poor fit prior to entering the

predictors (Table 4.41).

Table 4.41: Omnibus Tests for Model Coefficients Alumni Experience.

Chi-square df Sig. Step 1 Step 22.865 2 .000

Block 22.865 2 .000 Model 22.865 2 .000

The Cox and Snell R Square and Nagelkerke R Squares (Table 4.42) provide an

indication of the amount of variation in the dependent variable (donor or non-donor)

explained by the model. The results indicate that between 23.3 percent and 31.1 percent

of the variability is explained by the set of variables. The model improved from a -2 Log

Likelihood of 119.175 to 93.310.

89  

Table 4.42: Model Summary for Student Experience.

Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square 1 96.310a .233 .311 a. Estimation terminated at iteration number 5 because parameter estimates changed by less than .001.

After entering the items, the results of the Hosmer and Lemeshow test (Table

4.43) shows the model is a good fit as the significance of .382 is not statistically

significant, p > .05.

Table 4.43: Hosmer and Lemeshow Goodness of Fit Test for Alumni Experience.

Step Chi-square df Sig. 1 7.468 7 .382

The classification table provides an indication of how well the model is able to

predict the correct category (donor and non-donor) for each case in comparison with the

classification table shown in Table 4.44 to see how much improvement there is when the

predictor variables are included in the model. The model correctly classified 69.8 percent

of case overall (Table 4.44), an improvement over the finding of 51.2 percent. The

predictive success was 71.4 percent for non-donor and 68.2 percent for donor.

90  

Table 4.44: Classification of Donor and Non-Donor for Alumni Experience.

Observed

Predicted Philanthropy Percentage

Correct Non-Donor Donor Step 1 Philanthropy Non-Donor 30 12 71.4

Donor 14 30 68.2 Overall Percentage 69.8

a. The cut value is .500

For the predictive model one of the two factors, alumni experience—association

was significant (p < .05), p = .000.

Table 4.45: Block 1 Method Variables in the Equation for Alumni Experience.

B S.E. Wald df Sig. Exp(B)

95% C.I.for EXP(B)

Lower UpperStep 1a

Engagement -.030 .388 .006 1 .938 .970 .453 2.077AlumniAsso 1.657 .472 12.350 1 .000 5.245 2.081 13.219Constant -5.260 1.549 11.534 1 .001 .005

a. Variable(s) entered on step 1: Engagement, AlumniAsso.

Logistic regression was used to predict to donate or not (0 = non-donor and 1 =

donor) for 86 alumni using student experience as the predictor. A test of the full model

against a constant only model was statistically significant, indicating that the predictors

as a set reliably distinguished between donor and non-donor (chi-square = 22.865, p <

.000, df = 2).

Nagelkerke’s R2 of .311 indicated a small relationship between predication and

grouping. Prediction success overall was 69.8 percent (71.4 percent for non-donor and

68.2 percent for donor). The Wald criterion demonstrated that engagement did not make

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a significant contribution to prediction. Alumni association was a significant predictor (p

=.000). Additionally, based on the odds ratio alumni with alumni association experience

are 5.24 times higher to donate than alumni that did not have an alumni association

experience. The findings support H7 that alumni experience distinguishes alumni donors

from non-donors.

The next chapter provides an overview of the findings, limitations of the study,

and suggestions for future research.

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CHAPTER 5

DISCUSSION AND ANALYSIS OF FINDINGS

Discussion

With the increased pressure from legislators to keep tuition costs down for

students while at the same time decreasing state appropriations to support public higher

education, public universities are experiencing tremendous economic stress. Private

philanthropy will continue to play an increasingly critical role in the quality of education

delivered by public institutions. State governments are facing enormous financial

pressures as result of pension benefits to be paid out, rising healthcare costs, rising

infrastructure costs, and competition from others states, as well as other countries,

seeking businesses to relocate. Institutions of higher education will likely continue to

experience cuts in state appropriations. Additionally, some of the government

appropriation funds are used for research purposes. The average taxpayer is more

concerned about the access and costs of public higher education for his or her

son/daughter than the research that is taking place at the university. As a result, public

universities must seek alternative sources of funding. This has put increased pressure on

the development operations of these institutions to undertake extremely ambitious

fundraising goals, regardless of the state of the economy.

Public higher education is finding itself in a position of selling itself more than

ever. Universities must sell themselves to state governments by showing that they are

worthy of the limited resources available. They must sell themselves to the taxpayer who

is frustrated by higher taxes and who does not value having a public university, perhaps

because the taxpayer is frustrated that his or her son or daughter was unable to secure

placement to said university. Institutions must also sell themselves to the potential

93  

student who sees costs of going to school rising only to find it difficult to secure

employment after graduation. Public universities need to review their business model—

which entails fundraising.

Public universities are facing competition from the private universities and for-

profit organizations. Public universities need to continue to sell themselves as a viable

and sustainable solution to the challenges facing the world to secure charitable giving. A

plethora of constituents must continue to have confidence in public universities. If public

universities are to thrive, the alumni of public universities need to embrace the

philanthropic culture that is found in private universities. For example, private

universities often solicit the parents of incoming freshman prior to the first day of class,

and they have very strong senior class-gift programs to indoctrinate students prior to

becoming a graduate.

The added challenge of creating this culture of philanthropy with traditional

students is the new type of student who has never set foot on campus—the online

graduate. It is evident that online programming can be a tremendous resource to an

institution, but if universities think beyond the student’s academic career then this group

represents a viable source of future philanthropic revenue.

Overview of Findings

The notion that personal face-to-face interaction in the traditional MBA would

enhance the student’s experience as it pertains to relationships and academic over the

iMBA graduate was not supported. The results of the study demonstrated there was a

significant difference between the MBA and iMBA groups in student experience—

impact on relationship and academic. However, the iMBA graduates demonstrated higher

94  

levels than the MBA graduates. Therefore, H1 and H2 were not supported. These

findings were somewhat surprising given existing literature on alumni giving.

It appears that iMBA graduates forged stronger and more lasting relationships

while students than traditional graduates. This may be attributed to the staying power via

the ease of communication through the Internet. Additionally, the fact that the iMBA

group does study in cohorts and does have a two week in-person workshop experience

could contribute to the outcome of the findings. Unlike the traditional system of delivery

where a relationship is forged through in person class time, the online experience has less

social boundaries, e.g., communication is ongoing and not primarily during class time.

Granted that the in-class experience does not preclude students from communicating

online, it typically does not happen unless the faculty member assigns class projects

forcing communication outside of class. Often online courses require students to provide

dialogue online and interact with their fellow classmates. It could also be that the online

class encourages students to utilize or establish class groups using social media sites such

as LinkedIn.

The graduates of the iMBA demonstrated higher levels of academic experience

(H2). This is encouraging for public universities seeking to increase their revenue stream

by expanding the online delivery option. The fact that the traditional delivery experience

would be an enhancement or advantage over the online experience was not supported.

Further research focused on comparing the online educational experience and the

traditional experience for students attending the same university and enrolled in the same

program or taking a class taught both online and in the traditional method is warranted.

95  

H3, H4, and H5 were not supported because a significant difference was not

found between the MBA and the iMBA graduates. The impact on career, alumni

experience—alumni association, and alumni experience—engagement are all after the

fact, i.e., experiences after the student graduated from the MBA program. Although a

significant difference was not found, it is encouraging that development professionals

working at public universities can view MBA graduates as viable source of philanthropic

dollars and influence these areas. The alumnus/alumna that is engaged and involved with

his or her alma mater is more likely to donate. Taking a proactive approach allows the

development and alumni offices to test programming, seek feedback, and then target

fundraising campaigns. This will allow universities to use resources more wisely. The

influence that the alumni and development offices can have on the student experiences is

limited.

The importance of having effective communication to target alumni is paramount.

For example, reaching out to alumni and asking them to participate in a 20 year reunion

to connect with former classmates to see how individuals have “changed” over time may

not be appropriate for those online alumni who have never personally met their

classmates. Perhaps alumni offices could explore the possibility of online reunions

targeted to those alumni that never set foot on campus.

The findings of the study support H6 and H7. This is in keeping with previous

studies. Further examination of alumni predictors of alumni donor behavior with greater

segmentation of the alumni body, e.g., online undergraduates who are members of the

Alumni Association and have volunteered after graduation, is warranted. Most

importantly, public universities need to improve their use of the alumni database and stop

96  

treating all alumni as having the same likelihood of making a donation to the university,

as the research has proven that not all alumni are likely to donate. In other words, mass

solicitations (mailing to the entire database) are not an effective use of resources. This

requires a change in the mindset of university fundraiser as note by Birkholz (2008) “The

concept of likelihood is rarely used to prequalify potential donors in advance of prospect

research or even roll-out to the frontline gift officers” (Birkholz, 2008, p. 4). This needs

to change. The initial investment for improved database mining and surveying alumni

will pay dividends for public universities for target solicitations.

The private for-profit education sector has done an excellent job is utilizing data

obtained to improve marketing using target marketing—reaching the correct population

who are most likely to response positively to the call to action. The online alumnus

provides an advantage to a university’s development target marketing plan. The

traditional graduate could have a plethora of experiences that resonates. It could be a

certain professor or class, the time studying in the library, participation in an on campus

club, or attendance at a football game, etc. Therefore, these students have many possible

affinity groups and possible connections that will be critical for that graduate to make his

or her first gift to the university.

The online student has limited connections or affinities. This will help the

university in their targeted fundraising appeals. The classroom experience is limited to

the material or topic under review and not the breaks taken during class, e.g., socializing

among students. Granted, an online student may have additional experiences with

university outside the classroom. For example, a student may take the courses online but

live near enough to attend football games. But the first targeted mass approach that

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aligns with the online experience to the online graduate will incur a higher associated

response than seeking to target some other associated, and unknown, affinity association.

In light of the vast amount of philanthropic dollars given every year in the U.S.

and the need for resources public universities’ fundraising operations must become more

sophisticated. It is equally important for researchers continue to work to fill the gaps that

exist in the literature surrounding public higher education fundraising. With performance

based funding and the financial challenges facing policy makers a greater understanding

and knowledge of this area of public higher education is warranted. This study does add

to the literature, but there are limitations.

Limitations of Study

Some of the questionnaires returned replied N/A on certain questions. This

limited the study as this answer disqualified the case from analysis in measuring the

variables. In reviewing the questionnaire it could have restricted certain questions such

that the scale was of importance and not actual action; for example, attending a university

sponsored event. Analysis of the data demonstrated there was a systematic pattern of no

answers for the alumni associated experiences related questions. Limiting the N/A option

would have provided more data to be used for analysis in the study.

The stratified sample used to capture appropriate representation from the

traditional MBA program and the iMBA program did not provide expected results. The

samples returned from the traditional MBA graduates was disappointing, 31 precent,

compared to the iMBA return of 45 precent. Although the overall response rate of 36

percent is acceptable it was disappointing that the return rates from the two groups were

not closer. The return discrepancy of the two groups was compounded by the high

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number of N/A responses for the alumni associated experiences related questions.

Future studies would benefit from having the resources to expand the number of

questionnaires mailed out, although that does not insure a different outcome. Further,

future studies may benefit from targeting alumni association members.

The data pushed the bounds regarding the assumption of normality, the data fell to

the right. This indicated a majority of the respondents had a positive experience at The

Pennsylvania State University. This begs the question if an individual who did not have a

good experience or was indifferent felt returning a questionnaire associated with The

Pennsylvania State University was not worth the effort. Additionally, because the

researcher was not involved at a senior management level with the university perhaps

individuals who had negative experiences felt that communicating via the questionnaire

would not reach the individuals who could act on the information he or she provided.

Another limitation of the study was the two groups were graduates of the MBA

program from the same university for their graduate degree, but it was unknown where

the respondents did their undergraduate degree. Therefore, some of the respondents

could have acquired their undergraduate degree from The Pennsylvania State University

as well and this could have influenced his or her response to the questionnaire.

The study was limited to a quantitative approach, and research results may have

benefited from conducting a qualitative study of random alumni in the population frame

to compare to the quantitative results obtained. Additional research could be undertaken

to expand the knowledge base for the fundraising professional, the field of public

administration and public policy concerning public higher education.

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Future Research

The study contributes to the literature gap that exists in predicting donor behavior

by examining the online alumnus and the traditional alumnus. Additionally, the study

examined alumni that graduated from the same university, The Pennsylvania State

University, with the same degree, but from a different system of delivery.

The literature would benefit by further examination into the online alumni pool to

build on the results acquired from this study and further support predictors of alumni

donor behavior. Future research should expand the study to other universities

(comparing traditional and online alumni) for comparison purposes.

The literature would also benefit from comparing the online alumni from two

different universities. This would provide insight on the educational delivery system and

provide enhanced understanding of the differences among online students. This segment

of the alumni population continues to grow and with improved technology (perhaps the

future will provide a version of the online program that involves holograms or video that

will mimic the traditional classroom experience virtually) is it likely to continue growing.

It would also be beneficial to have a longitudinal study done to understand donor

behavior over time. A study that would commence when the students enroll to acquire

student experiences combined with studies once the student becomes an alumnus/alumna

would help provide a better understanding of how alumni donor behavior changes over

time. The next chapter provides the conclusion of the study.

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CHAPTER 6

CONCLUSION

Introduction

Online education is here to stay and is growing rapidly (Harper, 2008; Mayadas,

Bourne, & Bacsich, 2009). Nearly 17 percent of all students are learning online and the

program offerings are diverse with 43 percent of colleges with traditional face-to-face

programs offer online business programs, liberal arts and sciences, and general studies;

40 percent offer online humanities, 35 percent offer online computer and information

sciences, and other online programs stand at 36 percent (Harper, 2008). The diversity of

these programs will continue to grow as universities see the aging population as a target

audience to deliver online course. Twenty percent of the U.S. population or 70 million

people will be aged 65 by 2030 and universities are targeting this group for academic

involvement. As reported by Harper, “Many universities have targeted the adult back-to-

school market with Internet-based programs. Over two-thirds of Public and Private

Institutions now report the online education is critical to their long-term strategy”

(Harper, 2008, p. 2694). The online option has broadened the notion of life-long learning

because of increased access.

The adult back-to-school online degree is seeing growth through the master’s

degree. The private for-profit universities are leading the charge, “Traditional colleges

still produce most the bachelor’s degrees in teaching—ASU topped the list with 979

bachelor’s degrees in 2011. But online schools such as Phoenix and Walden University

awarded thousands more master’s degrees than even the top traditional schools, all of

which are pushing to offer online coursework” (Toppo & Schnaars, 2012). Public

universities are taking note of this growth by the for-profit sector and recognize that

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business as usual is a death sentence. As noted by a team of researchers, “Of the public

institutions with large online enrollments are recognizing the strategic advantages of

online…our discussion with presidents with the presidents or chancellors of Pennsylvania

State University, University of Massachusetts…University of Illinios indicate clearly that

they are including online instruction as a strategic asset that is integral to the planning

activities of their institutions” (Mayadas, Bourne, & Bacsich, 2009, p. 88)

Online programs and courses are providing much needed revenues to universities.

Furthermore, after the initial start-up costs associated with launching an online course or

program the long-term net revenue for the university is higher than for the traditional

delivery model because of lowered expenses in maintaining a university campus. Of

course, with a university that has a serious deferred maintenance expense the net revenue

of the traditional revenue would be even more compromised. Data show that since the

2008 recession public universities have seen their deferred maintenance bill balloon with

some universities stating the figure is in the hundreds of millions of dollars. Some state

system institutions stating the figure is in the billions (Carlson, 2012).

Another interesting evolution of the online movement is the massive open online

courses or MOOC’s. These are online courses open on the web and are offered free

through organizations such as Coursera, EdX, and Udacity. An in-depth examination of

the MOOC’s is beyond the scope of this study, e.g. agreements with these MOOC’s

between professors and not necessarily between MOOC’s and the university, but it is

worth noting a recent development launched by Coursera. Coursera announced an

employee-matching service called Coursera Career Services (Young, 2012). The

relevance of this new service is that a student regardless of where they are conducting

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their studies may enroll in just one MOOC’s course and become eligible to be placed in

the employment pool that potential employers may review. If an employer is interested

in contacting the student the student may elect to have his or her contact information

forwarded to the potential employer. The company is charged for each introduction that

Coursera makes. This provides potential employers with a database of potential

candidates. In addition, this benefits student and parents who will see that “online

courses are undeniable chipping at the traditional boundaries of higher education. Until

now, most of the millions of students who register for them [MOOC course] could not

earn credit for their work. But that is changing…the three leading providers, Udacity,

EdX and Coursera, are all offering proctored exams, and in some cases, certification for

transfer credit through the American Council on Education” (Lewin, 2013).

Attending a MOOC allows students attending universities with a lesser reputation

to get noticed. This was the case of one online computer-science course that was offered;

“the online students took the same quizzes and tests as a group of students enrolled at

Stanford University at the same time. The top 411 students all from came the thousands

of students who took the course online, with the strongest-performing Stanford student

ranking 412th in the final standings…that Stanford student earned a 98 percent in the

course” (Young, 2012).

As confidence continues to grow among employers that online students can offer

the skill set they need, public universities must shape the argument that the traditional

student experience provides something, not just for the benefit of the student, but for the

potential employer. In other words, will students who restrict their educational

experience to the online delivery system appear to be more serious and focused than a

103  

student who is seeking the traditional education experience in the eyes of the potential

employers? As the employment market remains a challenge for recent graduates and the

global competition remains, students and their parents will be more and more focused on

the return on investment for a university degree and will be keen to understand how

companies will be hiring and from what student population—traditional or online.

Therefore, a student can benefit both from receiving credit for taking a course offered by

a MOOC provider and be entered in a significant potential employment pool.

Summary

The reaction of public universities to decreases in state funding has resulted in

increases in tuition. These increases are forcing some potential qualified students out of

the market. As a matter of public policy this runs contrary to the intent of public higher

education that was established to provide access for the citizenry of a particular state.

Taxpayers have viewed public universities as helping to fulfill societal needs. At the

heart of the debate are control, access, and money. “The traditional contract between

universities and taxpayers provided financial support to universities in return for their

giving up the decision making regarding access criteria and tuition rates. As support

wanes, restrictions on access and tuition hamper universities’ ability to adapt to

threatening external forces” (Fethke & Policano, 2012, p. 216). This new paradigm is

shaping public universities.

The performance of an institution’s development and alumni relations division

hinges on the division building and sustaining alumni relationships. These relationships

are shaped in part by a student’s educational experience and his or her alumni experience.

Increasing our understanding of organizational identity between traditional and online

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graduates will assist the fundraising divisions in meeting their objectives. The projected

increase in the number of alumni graduating from online programs translates into

increased pressure to maintain acceptable levels of alumni participation rates.

Foundations base the decision to support a university partially on the participation rates

exhibited by alumni. The education sector should be concerned about the future

relationship of online graduates as it relates to philanthropy because the competition for

philanthropic dollars is stiff. In a report released by Giving USA and the Center on

Philanthropy at Indiana University, giving to education declined for the second straight

year, while giving to human services, health, international aid, and the environment

increased (Indiana University, 2010).

The findings of this study place a spot light on the potential long-term missed

philanthropic revenue opportunity universities will experience if the online alumni

segment is not properly targeted. Many universities are currently not differentiating

online alumni from traditional alumni. Although online students experienced higher

positive outcomes in the areas of student and alumni experiences they are not treated by

alumni operations as a specific affinity group, e.g., a fraternity or the baseball team, when

it is evident their university experience is unlike the traditional student experience.

Therefore, the mode of receiving their education is in itself a unique affinity category.

Today’s traditional student is exposed to the impact philanthropy has had on a

campus, e.g., having class in a lecture hall named after a university benefactor or

engaging with students between classes who are seeking participation in student lead

fundraising efforts such as The Pennsylvania State University’s student Dance-A-Thon

for cancer research. The donor plaque on the wall may actually be for the benefit of

105  

recognizing the donor and is a condition of the gift, but the residual benefit is the

promotion of philanthropy and creating awareness among today’s students—future

alumni and donors. For the online student universities must begin to incorporate

philanthropy into the communication that takes place between the student and the

university during the course of the student’s online educational experience. For example,

the university could secure a donor to sponsor a course such that every student signed up

for the course receives a discount off the tuition price of the course and this is

communicated throughout the student’s experience, e.g., this course is sponsored by Joe

Donor, alumnus of the class 1975; therefore, all students are receiving a $250 discount

for the semester.

Finally, this study contributes to the gap in the literature that exists in

understanding the online graduate and donor behavior. Additionally, this contributes to

the field of public administration research in the area of public higher education. The

field of public administration needs to do more in the study of public education, including

public higher education. “Despite the significance of public education to Americans in

terms of the size of expenditures and the number of government employee and

governments in this area the field of public administration has virtually ignored this

important area” (Raffel, 2007, p. 137). Raffel identifies the position with an examination

in several areas.

First, an index entries of public administration textbooks by substantive category

lists education number 12 out of 15; analysis of the five leading journals of public

administration (Public Administration Review, Administration and Society, Journal of

Public Administration Research and Theory, American Review of Public Administration,

106  

and Public Administration Quarterly) indicates few articles on public education are

published, with the exception of the Public Administration Review which published on

average two articles per year. The great books of the field compiled by Frank P.

Sherwood for the March/April 1990 Public Administration Review issue only had

Mosher’s Democracy and the Public Service that highlighted education and the need for

universities to educate future public administrators; and a curriculum search of the

National Association of Schools of Public Affairs and Administration education does not

receive the specialization like criminal justice, health or the environment (Raffel, 2007).

Public administration researchers should be reaching across traditional academic

lines to the educational and political science programs to enhance the significance of

research in higher education within the field of public administration. Public higher

education will continue to evolve and transform in the future and at a faster pace than

what the industry has experienced traditionally. This study contributes to the theoretical

literature in donor behavior and, more specifically, pertaining to the online alumni.

Online education has demonstrated its staying power and is a source of new

revenue opportunities. It provides a lower cost of delivery per student, helps to increase a

university’s brand, and is creating a new group of alumni. These alumni will need to be

targeted differently than the traditional alumni body for future giving. Their educational

experiences will be distinctly different. The relationships they form with the university

will be generated differently from the beginning, i.e., lack of in-person relationship

building. However, the technological advancement can aid in this regard with the feel

that video conferencing can bring to the online experience.

107  

The future online alumnus most likely will enter into the donor stream via giving

online. How will universities move these alumni through the donor cycle in hopes of

securing a major gift is still unknown. The online alumnus will not find the campus visit

to take a trip down memory lane necessary or find the need to meet face-to-face with

fundraising professionals to increase their giving. Universities will need to be sensitive

to the online student experience as they seek to understand the online student university

affinity much like traditional students who have an affinity for a particular experience

while attending university. Universities should be taking action today to develop their

online alumni engagement and development programs to build and strengthen these

relationships. The evidence is clear that there is a high level of satisfaction with their

student and alumni experiences and significant potential for philanthropic resources.

108  

References

Albert, S., Ashforth, B. E., & Dutton, J. E. (2000). Organizational Identification:

Charting New Water and Building New Bridges. Academy of Management

Review, 25(1), 13-17.

Allen, E. I., & Seaman, J. (2010). Class differences: Online education in the United

States, 2010. U.S.: Babson Survey Research Group.

Ashforth, B. E., & Mael, F. (1989). Social identity theory and the organization. Academy

of Management Review, 14(1), 20-39.

Baade, R. A., & Sundberg, J. O. (1996). What determines alumni generosity. Economics

of Education Review, 15(1), 75-81.

Baruch, Y., & Holtom, B. C. (2008). Survey response rate levels and trends in

organizational research. Human Relations, 61(8), 1139-1160.

Baruch, Y., & Sang, K. J. (2012). Predicting MBA graduates' donation behavior to their

alma mater. The Journal of Management Development, 31(8), 808-825.

Bennett, R., & Savani, S. (2003). Predicting the accuracy of public perceptions of charity

performance. Journal of Targeting, Measurement and Analysis for Marketing,

11(4), 326-342.

Birkholz, J. A. (2008). Fundraising analytics. Hoboken, New Jersy: John Wiley and

Sons.

Blau, P. M. (1964). Exchange and power in social life. New York: John Wiley and Sons,

Inc.

109  

Bristol Jr., R. (1990). The life cycle of alumni donations. The Review of Higher

Education, 13, 503-518.

Brooks, A. C. (2002). Can nonprofit management help answer public management's "Big

Questions"? Public Administration Review, 62(3), 259-266.

Brooks, A. C. (2006). Efficient nonprofits? Policy Studies Journal, 35(3), 303-312.

Bruce, G. A. (2007). Exploring the Likelihood and Reality of MBA Alumni Financial

Donations. McLean, VA: Graduate Management Admission Council.

Bryman, A. (2008). Social research methods (3rd ed.). Oxford, England: Oxford

University Press.

Buchen, I. H. (2005). The future of higher education and professional training. The

Journal of Future Studies, Strategic Thinking and Policy, 7(4), 13-21.

Carlson, S. (2012, May). Administration: How the Campus Crumbles. Retrieved from

The Chronicle of Higher Education: http://chronicle.com/article/article-

content/131920/

Chronicle of Higher Education. (2012, November). Capital Campaign Update. Retrieved

from Chronicle of Higher Education: http://chronicle.com/article/Stanford-Raises-

62-Billion/130698/?sid=at&utm_source=at&utm_medium=en

Chronicle of Higher Education. (2012, Feburary). Finance. Retrieved from Chronicle of

Higher Education: http://chronicle.com/article/Stanford-Raises-62-

Billion/130698/?sid=at&utm_source=at&utm_medium=en

110  

Clotfelter, C. (2001). Who are the alumni donors? Giving by two generations of alumni

from selective colleges. Nonprofit Management and Leadership, 119-138.

Clotfelter, C. (2003). Alumni giving to elite private colleges and universities. Economics

of Education Review, 22, 109-120.

College Board. (2012). Trends in Higher Education. Retrieved from College Board

Advocacy and Policy Center: http://trends.collegeboard.org/

Commonwealth of Pennsylvania. (2012, January). Governor's Budget. Retrieved from

PA Office of the Budget:

http://www.portal.state.pa.us/portal/server.pt?open=512&objID=4571&mode=2#

2011-12

Council for Aid to Education. (2011). 2010 voluntary support of education. New York,

New York: Council for Aid to Education.

Creswell, J. W. (2009). Research design: Qualitative, quantitative, and mixed methods

approaches (3rd ed.). Thousand Oaks, CA: Sage Publications.

Cutlip, S. M. (1965). Fund raising in the United States. New Brunswick, NJ: Rutgers

Univesity Press.

Delaney, J. A., & Doyle, W. R. (2011). State spending on higher education: testing the

balance wheel over time. The Journal of Education Finance, 36(4), 343-368.

Distance Education Report. (2002). Will distance alumni become donors? Distance

Education Report.

111  

Distance Education Report. (2009). Is distance ed shrinking the alumni donor pool?

Distance Education Report, 13(4), 1-8.

Drezner, N. D. (2009). Why give?: Exploring social exchange and organization theories

in the promotion of philanthropic behaviors of African-American millennials at

private-HBCUs. International Journal of Educational Advancement, 9(3), 147-

165.

Drezner, N. D. (2011). Philanthrophy and fundraising in American higher education.

Hoboken, NJ: Wiley Periodicals.

Dye, T. R. (2008). Understanding public policy. Upper Saddle Rive, NJ: Pearson

Education, Inc.

Fethke, G. C., & Policano, A. J. (2012). Public no more, a new path to excellence for

America's public universities. Stanford, CA: Stanford University Press.

Field, A. (2009). Discovering statistics using SPSS (3rd ed.). Thousand Oaks, CA: SAGE

Publications.

Frank, R. (1996). Motivation, Cognition, and Charitable Giving. In J. B. Schneewind,

Giving: western ideas of philanthropy (pp. 130-152). Bloomington: Indiana

University Press.

Frumkin, P. (2000). Philanthorpic leverage. Society, 40-46.

Frumkin, P., & Kim, M. T. (2001). Strategic positioning and the financing of nonprofit

organizations: Is efficiency rewarded in the contributions markplace? Public

Administration Review, 61(3), 266-275.

112  

Gaier, S. (2005). Alumni satisfaction with their undergraduate academic experience and

the impact on alumni giving and participation. International Journal of

Educational Advancement, 5(4), 279-288.

Harper, R. L. (2008). Online Education in the United States Higher Education System. In

C. Bonk, & L. Hyunkyung, Proceedings of World Conference on E-Leanering in

Corporate, Government, Healthcare, and Higher Education (pp. 2691-2708).

Cheseapeake, VA: AACE.

Hashemi, R. R., Le Blanc, L. A., Bahrami, A. A., Bahar, M., & Traywick, B. (2009).

Association analysis of alumni giving: a formal concept analysis. International

Journal of Intelligent Information Technologies, 5(2), 17-32.

Hyndman, N. (1990). Charity accounting: An empirical study of the information needs of

contributors to UK fund raising charities. Financial Accountability and

Management, 6(4), 295-307.

Indiana University. (2010, June). Center on Philanthropy at Indiana University.

Retrieved from Center on Philanthropy at Indiana University:

http://www.philanthropy.iupui.edu/news/2010/06/pr-GUSA2010.aspx

Iwaarden, J. v., Wiele, T. v., William, R., & Moxham, C. (2009). Charities: How

important is performance to donors? International Journal of Quality and

Reliability Management, 26(1), 5-22.

Kanuka, H., & Conrad, D. (2003). The name of the game: Why distance education says it

all. The Quarterly Review of Distance Education, 4(4), 385-393.

113  

Lei, S. A., & Gupta, R. K. (2010). College distance education courses: Evaluating

benefits and costs from institutional, faculty and students' perspectives.

Education, 130(4), 616-631.

Lewin, T. (2013, April 30). Technology: Colleges Adapt Online Courses to Ease Burden.

Retrieved from The New York Times:

http://www.nytimes.com/2013/04/30/education/colleges-adapt-online-courses-to-

ease-burden.html?pagewanted=all&_r=0

Lindahl, W. E., & Conley, A. T. (2002). Literature review: Philanthropic Fundraising.

Nonprofit Managment and Leadership, 13(1), 91-112.

Liu, Y. (2006). Determinants of private giving to public colleges and universities.

International Journal of Educational Advancement, 6(2), 119-140.

Mael, F., & Ashforth, B. E. (1992). Alumni and their alma mater: A partial test of the

reformulated model of organizational identification. Journal of Organizational

Behavior, 13(2), 103-123.

Mann, T. (2007). College fund raising using theoretical perspectives to understand donor

motives. International Journal of Educational Advancement, 7(1), 35-45.

Mathur, A. (1996). Older adults' motivations for gift giving to charitable organizations:

An exchange theory perspective. Psychology and Marketing, 13(1), 107-123.

Mayadas, A. F., Bourne, J., & Bacsich, P. (2009). Online education today. Science,

323(2), 85-89.

114  

McDearmon, J. T., & Shirley, K. (2009). Characteristics and institutional factors related

to young alumni donors and non-donors. International Journal of Educational

Advancement, 9(2), 83-95.

McLendon, M. K., Deaton, S. B., & Hearn, J. C. (2007). The enactment of reforms in

state governance of higher education: Testing the political instability hypothesis.

The Journal of Higher Education, 78(6), 645-675.

Meer, J., & Rosen, H. S. (2010). Family bonding with universities. Research in Higher

Education, 51, 641-658.

Miller, M. T., & Casebeer, A. L. (1990). Donor characteristics of college of education

alumni: examining undergraduate involvement. Retrieved from ERIC database

(ED 323836).

Monk, J. (2003). Patterns of giving to one's alma mater amoung young graduates from

selective institutions. Economic of Education Review, 22(2), 121-130.

Newman, M. D. (2011). Does members matter? Examing the relationship between

Alumni Association membership and alumni giving. International Journal of

Educational Advancement, 10(4), 163-179.

Pennslyvania State System of Higher Education. (2012, July). Press Releases. Retrieved

from Pennslyvania State System of Higher Education:

http://www.passhe.edu/inside/ne/press/Lists/Press%20Releases/pressup.aspx?ID=

525&ContentTypeId=0x01006B3D98C5084ABB47927D422E92C00C3300058D

FAF00E84824A8F87467AD4FF8E26

115  

Pennsylvania State System of Higher Education. (2011, January). Press Releases.

Retrieved from Pennsylvania State System of Higher Education:

http://www.passhe.edu/inside/ne/press/Lists/Press%20Releases/pressup.aspx?ID=

461&ContentTypeId=0x01006B3D98C5084ABB47927D422E92C00C3300058D

FAF00E84824A8F87467AD4FF8E26

Pennsylvania State System of Higher Education. (2013, March 5). Board of Governors.

Retrieved from PASSHE: http://www.passhe.edu/inside/bog/Pages/BOG-

Home.aspx

Pennsylvania State University. (2010). Penn State Online. Retrieved from Worldcampus:

http://www.worldcampus.psu.edu/iMBA.shtml

Pennsylvania State University. (2013, March). Trustees. Retrieved from Pennsylvania

State University: http:/www.psu.edu/trustees/governance.html

Pfeffer, J., & Salancik, G. R. (1978). The external control of organizations: A resource

dependence perspective. New York, NY: Haper and Row Publishers.

Prince, R. A., & File, K. M. (1994). The seven faces of philanthropy. San Franciso:

Jossey Bass.

Raffel, J. A. (2007, January/February). Why has public admininstration ignored public

education, and does it matter? Public Administration Review, 135-151.

Rogelberg, S. C., & Stanton, J. M. (2007, April). Understanding and dealing with

organizational survey nonresponse. Organizational Research Methods, 10(2),

195-209.

116  

Rudolph, F. J. (1990). The American and college and university: A history. Athens, GA:

University of Georgia Press.

Sivo, S. A., Saunders, C., Chang, Q., & Jiang, J. (2006). How low should you go? Low

response rates and the validity of inference in IS questionnaire research. Journal

of the Association for Information Systems, 7(6), 351-414.

Slaughter, S., & Rhoades, G. (2004). Academic capitalism and the new economy:

Markets, states, and higher education. Baltimore: John Hopkins University Press.

Sloan Constortium. (2013, January 8). Survey Results. Retrieved from The Sloan

Constortium: http://sloanconsortium.org/news_press/january2013_new-study-

over-67-million-students-learning-online

Smith, S. R. (2008). The challenge of strenthening nonprofits and civil society. Public

Administration Review, S132-S145.

Smith, S. R. (2010). Nonprofits and public administration: Reconciling performance

management and citizen engagement. The American Review of Public

Administration, 129-152.

Sun, X., Hoffman, S. C., & Grady, M. L. (2007). A multivariate causal model of alumni

giving: Implications for alumni fundraisers. International Journal of Educational

Advancement, 7(4), 307-332.

Tabachnick, B. G., & Fidell, L. S. (2001). Using multivariate statistics (4th ed.). Boston:

Allyn & Bacon.

117  

Tom, G., & Elmer, L. (1994). Alumni willingness to give and contribution behavior. The

Journal of Services Marketing, 8(2), 57-62.

Toppo, G., & Schnaars, C. (2012, August 7). Online education degrees skyrocket.

Retrieved from USAToday:

http://usatoday30.usatoday.com/news/education/story/2012-08-07/online-

teaching-degrees/56849026/1

Tsao, J. T., & Coll, G. (2005). To give or not to give: factors determining alumni intent to

make donations as a PR outcome. Journalism and Mass Communication

Educator, 381-392.

Tsiotsou, R. (2007). An empirically based typology of intercollegiate athletic donors:

High and low motivation scenarios. Journal of Targeting, Measurement and

Analysis for Marketing, 15(2), 79-92.

Tsunoda, K. (2010). Asian American giving to US higher education. International

Journal of Education Advancement, 10(1), 2-23.

U.S. Census Bureau. (2011). The 2011 statistical abstract, the national data book.

Retrieved from U.S. Census Bureau:

http://www.census.gov/compendia/statab/cats/education.html

Urban Institute. (2013). Quick facts about nonprofits. Retrieved from National center for

charitable statistics: http://nccs.urban.org/statistics/quickfacts.cfm

118  

Visser, P. S., Krosnick, J. A., Marquette, J., & Curtin, M. (1996). Mail surveys for

evaluation forecasting? An evaluation of the Columbus Dispatch poll. Public

Opinion Quarterly, 60, 181-227.

Wastyn, M. L. (2009). Why alumni don't give: A qualitative study of what motivates

non-donors to higher education. International Journal of Educational

Advancement, 96-108.

Weerts, D. J., & Ronca, J. M. (2007). Profiles of supportive alumni: donors, volunteers,

and those who "do it all". International Journal of Educational Advancement,

7(1), 20-34.

WHYY. (2012, February). Radio Times with Marty Moss-Coane. Retrieved from

WHYY: http://whyy.org/cms/radiotimes/2012/02/16/higher-education-in-

pennsylvania-what-cuts-mean-for-students-educators-and-the-economy/

Williams, S. R. (2007). Donor preferences and charitable giving. International Journal of

Educational Advancement, 7(3), 176-189.

Young, J. R. (2012, December). Providers of Free MOOC's Now Charge Employers for

Access to Student Data-Technology. Retrieved from The Chronicle of Higher

Education: http://chronicle.com/article/Providers-of-Free-MOOCs-Now/136117/

 

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Appendix A: Cover Letter and Questionnaire

Dear Participant:

I am a Ph.D. student in the Public Administration program at Penn State University. I am a Penn State student researcher and I am conducting this study for research purposes. The attached survey is part of my dissertation for the completion of my Ph.D. in Public Administration from Penn State University. The purpose of my study is to attempt to understand student educational and alumni experiences between traditional and online MBA graduates. I hope you will take a few minutes to complete the survey, it would be greatly appreciated. Please do not write your name on the survey, all responses will be kept confidential. No personally identifying information will be recorded on the survey. In the event of a publication or presentation resulting from the research, there will be no personally identifiable information. Your participation in this study is voluntary and you can stop at any time. You may choose not to answer any questions you do not want to. I would greatly appreciate if you could spare the 12 minutes to complete the enclosed questionnaire and send it back to me in the enclosed postage-paid envelope. Without your help, this research could not be conducted. You may ask me any questions about this study. Please contact me at your convenience at jwk22@psu.edu or at 484-400-6005. If you have questions about your rights in participating in this study you may contact the Penn State University Institutional Review Board (IRB) at by mail at 330 Building, Suite 205, University Park, PA 16802, by phone at 814-865-1775, or by email at ORProtections@psu.edu. Thank you for your help! Sincerely, Jason W. Ketter Ph.D. candidate, Penn State University PS Thank you for returning the enclosed survey.

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Survey

Participant:

Thank you. Thank you for taking a few minutes to complete this questionnaire. The

questionnaire is designed so you need only check off your answers [X] or [√]. All

information is keep confidential and no individual will be identified. The information

you provide will be combined with many others for statistical analysis. Please check

your answer from disagree to agree, or no application (NA) if the question does apply to

you. Again, thank you for your help.

PSU Alumni Experience – Alumni Association

1. I find the alumni association in general helpful.

[ ] disagree [ ] somewhat disagree [ ] neutral [ ] somewhat agree [ ] agree

[ ] NA

2. I find the alumni association website helpful.

[ ] disagree [ ] somewhat disagree [ ] neutral [ ] somewhat agree [ ] agree

[ ] NA

3. I find the alumni association sponsored events beneficial.

[ ] disagree [ ] somewhat disagree [ ] neutral [ ] somewhat agree [ ] agree

[ ] NA

4. The alumni association has been a helpful resource for my career.

[ ] disagree [ ] somewhat disagree [ ] neutral [ ] somewhat agree [ ] agree

[ ] NA

5. I find the alumni magazine of high quality.

[ ] disagree [ ] somewhat disagree [ ] neutral [ ] somewhat agree [ ] agree

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[ ] NA

6. I find the alumni association post office mailings of high quality.

[ ] disagree [ ] somewhat disagree [ ] neutral [ ] somewhat agree [ ] agree

[ ] NA

7. I find the alumni association electronic mailings of high quality.

[ ] disagree [ ] somewhat disagree [ ] neutral [ ] somewhat agree [ ] agree

[ ] NA

8. I am proud to be a member of the alumni association.

[ ] disagree [ ] somewhat disagree [ ] neutral [ ] somewhat agree [ ] agree

[ ] NA

9. I am an active member of the alumni association.

[ ] disagree [ ] somewhat disagree [ ] neutral [ ] somewhat agree [ ] agree

[ ] NA

10. I encourage my fellow alumni to be active members of the alumni association.

[ ] disagree [ ] somewhat disagree [ ] neutral [ ] somewhat agree [ ] agree

[ ] NA

PSU Alumni Experience—Engagement

11. I believe it is important for alumni to serve on various alumni volunteer boards or

committees.

[ ] disagree [ ] somewhat disagree [ ] neutral [ ] somewhat agree [ ] agree

[ ] NA

12. I believe it is important for alumni to volunteer to benefit the university.

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[ ] disagree [ ] somewhat disagree [ ] neutral [ ] somewhat agree [ ] agree

[ ] NA

13. I believe it is important for alumni to attend university events.

[ ] disagree [ ] somewhat disagree [ ] neutral [ ] somewhat agree [ ] agree

[ ] NA

14. I believe it is important for alumni to network with fellow alumni.

[ ] disagree [ ] somewhat disagree [ ] neutral [ ] somewhat agree [ ] agree

[ ] NA

15. I believe it is important for alumni to serve as mentors to current students.

[ ] disagree [ ] somewhat disagree [ ] neutral [ ] somewhat agree [ ] agree

[ ] NA

16. I believe it is important for alumni to speak in class to share their experiences.

[ ] disagree [ ] somewhat disagree [ ] neutral [ ] somewhat agree [ ] agree

[ ] NA

17. I believe it is important for alumni to help in the admission process to help recruit

students.

[ ] disagree [ ] somewhat disagree [ ] neutral [ ] somewhat agree [ ] agree

[ ] NA

PSU MBA Student Experience—Academic

18. My MBA academic coursework prepared me well for my career.

[ ] disagree [ ] somewhat disagree [ ] neutral [ ] somewhat agree [ ] agree

[ ] NA

19. I found the majority of my MBA coursework provided the skills I need for my career.

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[ ] disagree [ ] somewhat disagree [ ] neutral [ ] somewhat agree [ ] agree

[ ] NA

20. I found the majority of my MBA coursework challenging.

[ ] disagree [ ] somewhat disagree [ ] neutral [ ] somewhat agree [ ] agree

[ ] NA

21. I found my interaction with my MBA faculty during class a positive experience.

[ ] disagree [ ] somewhat disagree [ ] neutral [ ] somewhat agree [ ] agree

[ ] NA

22. I found the MBA classroom experience a positive experience.

[ ] disagree [ ] somewhat disagree [ ] neutral [ ] somewhat agree [ ] agree

[ ] NA

23. I found my interaction with my fellow MBA students during class a positive

experience.

[ ] disagree [ ] somewhat disagree [ ] neutral [ ] somewhat agree [ ] agree

[ ] NA

24. I found my interaction for MBA coursework purposes with my fellow MBA students

outside of class a positive experience.

[ ] disagree [ ] somewhat disagree [ ] neutral [ ] somewhat agree [ ] agree

[ ] NA

25. I found my interaction for MBA coursework purposes with my MBA faculty outside

of class a positive experience.

[ ] disagree [ ] somewhat disagree [ ] neutral [ ] somewhat agree [ ] agree

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[ ] NA

PSU MBA Student Experience—Impact on Career

26. I credit my MBA for my career advancement.

[ ] disagree [ ] somewhat disagree [ ] neutral [ ] somewhat agree [ ] agree

[ ] NA

27. I credit my MBA for my increase in earnings potential.

[ ] disagree [ ] somewhat disagree [ ] neutral [ ] somewhat agree [ ] agree

[ ] NA

28. I credit my MBA for my increased career options.

[ ] disagree [ ] somewhat disagree [ ] neutral [ ] somewhat agree [ ] agree

[ ] NA

29. I credit my MBA for my job security.

[ ] disagree [ ] somewhat disagree [ ] neutral [ ] somewhat agree [ ] agree

[ ] NA

30. I credit my MBA for giving me added confidence in my workplace.

[ ] disagree [ ] somewhat disagree [ ] neutral [ ] somewhat agree [ ] agree

[ ] NA

31. I credit my MBA for allowing me to seek new career opportunities that I otherwise

would be not qualified for.[ ] disagree [ ] somewhat disagree [ ] neutral [ ]

somewhat agree [ ] agree [ ] NA

PSU MBA Student Experience – Impact on Relationships

32. My MBA program provided me with new lifelong relationships.

[ ] disagree [ ] somewhat disagree [ ] neutral [ ] somewhat agree [ ] agree

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[ ] NA

33. I found attending athletic events a positive experience in building relationships.

[ ] disagree [ ] somewhat disagree [ ] neutral [ ] somewhat agree [ ] agree

[ ] NA

34. I found attending events other than athletic events a positive experience in building

relationships.

[ ] disagree [ ] somewhat disagree [ ] neutral [ ] somewhat agree [ ] agree

[ ] NA

35. I found my relationships with my fellow MBA students impacted positively on my

own student experience.

[ ] disagree [ ] somewhat disagree [ ] neutral [ ] somewhat agree [ ] agree

[ ] NA

36. I found my relationships with my MBA faculty impacted positively on my own

student experience.

[ ] disagree [ ] somewhat disagree [ ] neutral [ ] somewhat agree [ ] agree

[ ] NA

37. I found the registration process for enrolling into the MBA program positively

enhanced my relationship with the university.

[ ] disagree [ ] somewhat disagree [ ] neutral [ ] somewhat agree [ ] agree

[ ] NA

38. I found the graduation experience positively enhanced my relationship with the

university.

[ ] disagree [ ] somewhat disagree [ ] neutral [ ] somewhat agree [ ] agree

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[ ] NA

PSU Philanthropy Experience

39. Which of the following best describes your financial philanthropic support of the

university?

Select only one:

[ ] I philanthropically support the university and plan to continue at the same

level of support.

[ ] I philanthropically support the university and plan to increase my support in

the future.

[ ] I philanthropically support the university and plan to decrease my support in

the future.

[ ] I have never philanthropically supported the university and do not plan to in

the future.

[ ] I have never philanthropically supported the university but plan to in the

future.

[ ] I have philanthropically supported the university but do not plan to continue.

Demographic Information

40. Gender: [ ] Female [ ] Male

41. Please indicate your annual salary:

[ ] less than $50,000 [ ] $50,000 - $74,999[ ] $75,000 - $99,999

[ ] $100,000 - $149,999[ ] $150,000 or more

42. Please indicate your age:

[ ] 25 years of age or younger [ ] 26 – 35 years of age [ ] 36 – 45 years of age

[ ] 46 – 55 years of age [ ] 56 years of age and older

43. Ethnicity:

[ ] Asian or Pacific Islander [ ] Black/African American [ ] Hispanic

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[ ] Native American [ ] White/Caucasian [ ] Other

44. Marital Status: [ ] Single [ ] Married

45. PSU MBA graduation year: _________

FINISHED! 

Thank you for your help!   

Please fold this survey and return in the postage‐paid envelope 

provided. 

   

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Appendix B: Reminder Post Card

JasonW.KetterBriefCurriculumVitae

EDUCATION DoctorateofPhilosophy,PublicAdministration,ThePennsylvaniaStateUniversity,Harrisburg,PA,(2013).Researchinterests:Fundraising,nonprofitmanagementandleadership,andpublic‐privatepartnerships.

MasterofBusinessAdministration,KutztownUniversityofPennsylvania,Kutztown,PA,(2005)MasterofPublicAdministration,UniversityofWisconsinOshkosh,Oshkosh,WI,(1995)BachelorofArts,TheUniversityofIowa,IowaCity,IA,(1988).Major:Economics,Minor:BusinessAdministration

PROFESSIONALEXPERIENCEDirectorofAdvancementforFacultyofBusiness,Economics,andLaw,TheUniversityofQueensland,Brisbane,Australia,(3/2013–present)ExecutiveDirector,KutztownUniversityFoundation,Kutztown,PA,(2/2011–2/2013)DirectorofDevelopment,ThePennsylvaniaStateUniversity,Harrisburg,PA,(1/2008–1/2011)

VPforInstitutionalAdvancement,DelawareValleyCollege,Doylestown,PA,(3/2004–01/2008)

AssociateVicePresidentforDevelopment,AlbrightCollege,Reading,PA,(08/2001–03/2004)

Dir.ofDevelopment&Dir.ofMajorGiftsValleyForgeMilitaryAcademyandCollege,Wayne,PA,(01/2000–07/2001)IndependentConsultant,Muscat,SultanateofOman,(08/1999–11/1999)Lecturer,SultanQaboosUniversity,Muscat,SultanateofOman,(02/1997–08/1999)DirectorofMajorGiving,DelawareValleyCollege,Doylestown,PA,(09/1995–01/1997)DirectoroftheAnnualFund,WidenerUniversity,Chester,PA,(08/1994–08/1995)

DirectoroftheAnnualFund,UniversityofWisconsinOshkosh,Oshkosh,WI,(01/1990–08/1994)Manager,ReeseBrothers,IowaCity,IA,(09/1988–11/1989)ADDITIONALPROFESSIONALACTIVITIESANDCONFERENCESExecutiveVicePresidentandmemberoftheBoardofDirectors,PleinFoundation,Henderson,NV(11/2004–present)BoardMember,NortheastBerksChamberofCommerce,Kutztown,PA(11/2011–2/2013)VicePresident,DoctoralStudentOrganization,ThePennsylvaniaStateUniversity,Harrisburg,PA,(1/2009–10/2010)ConferencePresenter,InternationalSocietyforThirdSectorResearch,Istanbul,Turkey,(07/2010).WomenLeadersandManagementofPublicRelationsinNonprofitOrganizations.Consultant,CalcuttaHouse,Philadelphia,PA,(Summer2006)MemberandSpeaker,AssociationofFundraisingProfessionals.NationalPhilanthropyDayForumSpeaker,Fogelsville,PA,(November2004and2005).NortheastWisconsinChapter,WI:MembershipCommitteeViceChairman(1994),BoardofDirectors(1993‐1994),PhilanthropyDayCommittee(1993),ProgramSpeaker(January1992),ProfessionalDevelopmentCommittee(1992‐1994)MemberandSpeaker,CouncilofAdvancementandSupportofEducation.DistrictTwoAnnualConference,Philadelphia,PA,(February2004),ProgramSpeaker;ConferenceonMakingTheMajorGiftAsk,SanAntonio,TX,(2002);DistrictTwoAnnualConference,Philadelphia,PA,(1996);DistrictFiveAnnualConference,Chicago,IL,(1992);ConferenceonAnnualGiving,Montreal,Canada,(1990)