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Title: Developing a hierarchical model of personality and motivation to predict youth volunteerism : a cross-culture study
Authors: Ngo, Thuy Anh
Keywords: Social sciences::Psychology::Applied psychology
Social sciences::Psychology::Motivation
Issue Date: 2019
Source: Ngo, T. A. (2019). Developing a hierarchical model of personality and motivation to predict youth volunteerism : a cross-culture study. Doctoral thesis, Nanyang Technological University, Singapore.
Abstract: Despite a plethora of societal problems that are best solved by citizen participation, the problem of inaction exists whereby members of society fail to mobilize themselves to respond to pressing social issues. This research examines the personality and motivation predictors of volunteering to understand and solve the problem of inaction. Using the Meta-Theoretic Model of Motivation and Personality (3M Model; Mowen, 2000) as the theoretical framework, we developed a conceptual hierarchical model of personality that consists of personality traits and motives previously found to predict volunteerism. In three studies, we tested this model empirically in two different samples of youths (age 18 to 25) in Singapore and in the United States. Results revealed the key personality traits and motives, and their patterns of interaction, that predict youth volunteering in both samples. Cross-cultural differences in personality predictors also emerged. Theoretically, these findings support the development of an overarching theory to explain the interplay of traits and motives in predicting a specific behavior. Practically, they offer insights into key personality predictors of youth volunteering and possible culture-specific influences. As a contribution to solving the problem of inaction, this research offers practical recommendations for improving volunteer management, and for developing a giving culture in our societies.
DOI: 10.32657/10220/49161
Schools: School of Social Sciences 
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SSS Theses

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