Comparative Analysis of Adult Learner Motivation Across Vocational Sectors

Thesis Abstract

This study investigates how adult learners’ motivation varies across vocational sectors, addressing the persistent gap in understanding sector-specific motivational drivers that influence participation, persistence, and completion in continuing education. The problem centers on heterogeneous motivational patterns among adult learners in trades, healthcare, information technology, and service industries, which are inadequately captured by generic adult education theories. The aim is to compare motivational determinants across four vocational sectors and to identify sector-specific profiles that predict engagement in formal training and credential attainment. Specific objectives are (1) to quantify intrinsic and extrinsic motivation dimensions among adult learners in each sector using a standardized instrument; (2) to examine differences in motivation across sectors with multivariate analysis; (3) to assess the relationships between motivation, prior training, perceived relevance, and learning persistence; (4) to evaluate how demographic factors (age, gender, educational background) interact with sector to influence motivation; and (5) to develop a sector-specific motivational framework for adult education programming. A mixed-methods design is employed, combining a cross-sectional survey with a qualitative component to enrich interpretation. The population comprises adult learners enrolled in formal upskilling programs across four sectors skilled trades, healthcare, information technology, and hospitality. A stratified random sample of 480 participants (120 per sector) will be drawn from 12 training centers. Quantitative data will be collected via a validated Motivation for Adult Learning Scale, the Perceived Relevance of Training scale, and a demographic/credential history questionnaire. Reliability will be assessed using Cronbach’s alpha, with target coefficients above 0.80. Quantitative analysis will involve multivariate analysis of variance (MANOVA) to compare motivation dimensions across sectors, followed by hierarchical multiple regression to identify predictors of learning persistence. Structural equation modeling (SEM) will test a proposed model linking sector, motivational constructs, perceived relevance, and credential attainment. The qualitative component will include semi-structured interviews with a purposive subsample of 40 participants (10 per sector) to explore contextual factors shaping motivation; thematic analysis will be conducted to identify sector-specific motifs and corroborate quantitative findings. Expected findings include statistically significant differences in intrinsic motivation (interest, autonomy, and mastery) and extrinsic motivation (career advancement, salary enhancement, and social recognition) across sectors, with trades and IT displaying higher instrumental motivation while healthcare shows elevated intrinsic motivation related to patient-care ideals. The analysis is anticipated to reveal that perceived relevance mediates the relationship between sector and motivation, and that prior training and age moderate these associations. The SEM is expected to indicate a good fit (CFI > 0.90, RMSEA < 0.08), confirming a pathway from sector through motivational constructs to credential attainment and program persistence. The study contributes to knowledge by developing a nuanced, sector-sensitive model of adult learner motivation that moves beyond one-size-fits-all frameworks. It extends theoretical understandings by integrating expectancy-value and Self-Determination Theory within a vocational education context and by empirically validating sector-specific motivational profiles. The findings will inform practitioners and policymakers about designing targeted interventions—such as sector-tailored counseling, relevance-enhanced curricula, and flexible pacing—to improve participation, persistence, and completion rates in adult education. The research also provides methodological guidance for cross-sector comparisons in adult learning research, including sampling strategies, instrument adaptation, and mixed-methods integration. Conclusions will emphasize the necessity of acknowledging sectoral contexts when addressing adult learner motivation and offer concrete recommendations (1) implement sector-specific motivation enhancement programs emphasizing practical relevance and autonomy-supportive learning environments; (2) tailor outreach and advisory services to demographic subgroups most susceptible to motivational barriers; (3) align credential pathways with employer-recognized outcomes to strengthen perceived relevance; and (4) invest in periodic, cross-sectoral evaluation of motivational drivers to continually refine adult education offerings.