Literaturnachweis - Detailanzeige
| Autor/in | Naccarato, Shawn L. |
|---|---|
| Titel | Predicting Alumni Giving at a Public Comprehensive Regional University: Predictive Multivariate Causal Models for Annual Giving, Significant Cumulative Giving, Major Giving, and Planned Giving |
| Quelle | (2019), (187 Seiten)
PDF als Volltext Ph.D. Dissertation, Saint Louis University |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Monographie |
| ISBN | 978-1-3922-8607-4 |
| Schlagwörter | Hochschulschrift; Dissertation; Predictor Variables; Alumni; Donors; Private Financial Support; Educational Finance; Universities; Causal Models; Prediction; Accuracy; Holistic Approach; Decision Making |
| Abstract | A historic period of state divestment in public higher education, exacerbated by the "Great Recession" and attendant financial repercussions, has significantly altered public higher education financing. The most significant impact has been cost shift from the state to students via increasing tuition rates. These changes threaten student access, quality public education, and the American Dream. Vitality of American public higher education will depend upon institutional capacity to increase revenue sources other than tuition. Increasing alumni donation could be a key strategy in increasing alternate revenue. A preponderance of existing literature on alumni donor characteristics has focused on traditional private non-profit institutions and larger research focused public institutions with limited focus on regional comprehensive public institutions. The purpose of this study was expansion of the understanding of alumni donative behavior with specific focus on a medium-sized comprehensive regional public university. The author first sought to develop a strong theoretical alumni-giving decision model and second the development and testing of four multivariate causal models useful in predicting the four primary gift types: (a) annual gift model, (b) significant cumulative giving model, (c) major giving model, and, (d) planned giving model.Utilizing discriminant function analysis (DFA), all predictive models were built and tested. All predictive models demonstrated at least 97% predictive accuracy with regard to non-givers in each giving type with mixed results in predicting givers in each category. The significant cumulative giving and major giving models showed relatively high predictive accuracy of givers, 58.7% and 54% respectively, while predictive accuracy of givers in the annual giving and planned giving models fell to 25% or less. The exceptionally strong predictive accuracy with regard to non-givers coupled with other statistically significant findings provide optimism for further refinement of the models and additional research. Based on this study, the researcher articulates three primary theories and implications for practice: (a) central importance of alumni experience and the role of alumni relations and programming; (b) the interconnectedness of giving types, need for holistic approach, and the foundational role of annual giving; and (c) the need for improved data-based decision making in the field. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.] (As Provided). |
| Anmerkungen | ProQuest LLC. 789 East Eisenhower Parkway, P.O. Box 1346, Ann Arbor, MI 48106. Tel: 800-521-0600; Web site: http://www.proquest.com/en-US/products/dissertations/individuals.shtml |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2024/1/01 |
Literaturbeschaffung und Bestandsnachweise in Bibliotheken prüfen
Permalink als QR-Code
Inhalt auf sozialen Plattformen teilen (nur vorhanden, wenn Javascript eingeschaltet ist)
Teile diese Seite:
