This research assessed the consistency of using demographic characteristics for the purpose of predicting behavior relationships for establishing market segmentation within volunteer recruitment. A study of ex post-facto design that examined the use of a predictive model that included several demographic factors that used logistic regression to determine the statistical significance of those characteristics on specific methods of recruitment. The study used secondary data from the American Society of Association Executives center for Association Leadership Survey Questionnaire: Decision to Volunteer Survey completed by 26,305 association members. A quantitative study was performed using the following: one categorical dichotomous dependent variable, method of recruitment and four categorical dichotomous predictor independent variables, gender, age, ethnicity and career situation. The study showed a significance for ethnicity as a predictor in the method of recruitment. However, the study did not find statistical significance for age, gender or career situation as predictors for volunteer recruitment. The study did not result in expected findings. In fact, only one of the hypotheses was supported in this study. The results suggest that there may be other factors at work that are influencing the prediction of the method of recruitment by consideration of demographic characteristics. Additional analysis using different demographic characteristics may also show more statistically significant results.
|Adviser||Linda J. Roseburr|
|Subjects||Marketing; Management; Demography|
About ProQuest Dissertations & Theses
With nearly 4 million records, the ProQuest Dissertations & Theses (PQDT) Global database is the most comprehensive collection of dissertations and theses in the world. It is the database of record for graduate research.
PQDT Global combines content from a range of the world's premier universities - from the Ivy League to the Russell Group. Of the nearly 4 million graduate works included in the database, ProQuest offers more than 2.5 million in full text formats. Of those, over 1.7 million are available in PDF format. More than 90,000 dissertations and theses are added to the database each year.