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Generation and government level differences in public employees' self-directed learning readiness
Dissertation Abstracts International Section A: Humanities and Social Sciences ; 83(7-A):No Pagination Specified, 2022.
Article in English | APA PsycInfo | ID: covidwho-1824267
ABSTRACT
Training and development professionals need to know to what extent different generations within a multigenerational workforce are ready to self-direct their learning. The purpose of this quantitative comparative study was to examine differences in U.S. government employees' self-directed learning readiness (SDLR) by generation (Baby Boomers born between 1946-1964, Generation X individuals born between 1965-1980, and Generation Y individuals born between 1981-1999) and by government level (local, state, and federal). Malcolm Knowles's definition of SDL provided the foundation for the study. The researcher collected primary data via an online survey from a convenience sample of 226 employees, using Guglielmino's (1978) self-directed learning readiness scale. Three research questions addressed the main and interactive effects of employee generation and government level on SDLR. The results of the two-way ANOVA found no statistically significant main or interactive effects. However, descriptive statistics indicated that the highest levels of SDLR shifted from older employees at the local government level to younger employees at the state level and the youngest employees at the federal level Baby Boomers in local government (M = 131.18);Generation X in state government (M = 134.75);and Generation Y in federal government (M = 134.48). If confirmed by future research, these descriptive findings suggest the need for differentiated training for self-directed learning. The findings of this study cannot be considered conclusive due to the convenience nature of the sample, data collection during the atypical COVID-19 period, and possible desirability bias in the survey participants' responses. (PsycInfo Database Record (c) 2022 APA, all rights reserved)
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Collection: Databases of international organizations Database: APA PsycInfo Language: English Journal: Dissertation Abstracts International Section A: Humanities and Social Sciences Year: 2022 Document Type: Article

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Collection: Databases of international organizations Database: APA PsycInfo Language: English Journal: Dissertation Abstracts International Section A: Humanities and Social Sciences Year: 2022 Document Type: Article