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Extensive research has focused on various dimensions of dropout reasons, yet it often overlooks the importance of considering different dropout directions in the analysis. Our cross-sectional study examines the association of factors of six dimensions of dropout reasons (learner, professional, company, school, activity, and context) with four directions of dropout intention (upwards, downwards, horizontal: occupational change, and horizontal: company change). Stepwise regression analyses using dropout intention as a prior inner condition (before actual dropout behavior) with data from 559 industrial and office management trainees in Germany are conducted. Results indicate that the largest association across different directions of dropout intentions is exerted by the trainees' occupational identity (professional dimension) and the year in training (context dimension). School factors like the use of outdated teaching equipment and learner factors like trainees' high professional commitment predict upward dropouts, while a low level of trainees' workplace social involvement (activity dimension) predicts downward dropouts. Company factors like unfavourable working regulations predict occupational change, and a low level of trainees' functional involvement (activity dimension) predicts company change. Additionally, we confirm the previously found significant associations of activity and professional factors, particularly the cooperation between learning venues (school and training company) and the alignment between training and the desired occupation. The desired occupation predicts dropout intentions in the upward and horizontal direction (occupational change), and a low level of learning venue cooperation predicts the upward, horizontal (company change), and downward directions. To mitigate dropout rates, we recommend that vocational education and training stakeholders adopt preventive strategies by targeting specific directions of dropout intentions and the factors associated with the six dimensions of dropout reasons.

After more than 50 years of research on drop-out from Vocational Education and Training (VET), databases provide vast amounts of drop-out studies from around the globe that entail hundreds of potential drop-out factors. However, many scholars tend to explore the topic without detailed and theoretically grounded foundation. Several diverging research foci exist within the research field of drop-out from VET: Studies differ with regard to the analysed phase of drop-out (development, decision-making, paths after dropping out), the applied sample perspective (learners, educators, further stakeholders), and the overall research perspective that is applied based on a specific theoretical approach. Given the range of drop-out factors, stakeholders, and possible theoretical approaches within studies, this article aims at organising the research on the multi-facetted phenomenon for future endeavours by presenting a framework model that comprises a three-step recommendation: First, the model illustrates different phases (Development-, Decision-, Adjustment-Phase) of drop-out research that should be differentiated. Second, it is argued that the chosen sample perspective is highly relevant for drop-out research and must be chosen carefully. Third, the overarching disciplinary research perspectives on drop-out from VET, which scholars may adopt within their research, entail particular relevant variables and focal points and should therefore be discussed. Four examples of research perspectives (economical, psychological, sociological, pedagogical) are briefly presented, structured on a micro-, meso- and macro-level, to exemplify different theoretical accesses to drop-out research. Throughout the article, the novel organisation framework is explained and exemplified by illustrating relevant aspects of each research area and exemplary literature. The presented framework will help organising the research field and enable scholars to delimit their work more precisely, leading to a more traceable structure of further research efforts and more significant contributions to the state of knowledge.

Despite high drop-out rates from vocational education and training (VET) throughout most countries and a long research tradition on potential drop-out reasons, little is known about the effects exerted on drop-out intentions by the quality of training. Furthermore, only rarely do scholars distinguish between different drop-out directions, and systematic insights on possibly differing causes are scarce. This study explores the factors influencing four directions of drop-out intention ('upwards', 'downwards', 'company change', 'occupation change'). Linear regression modelling is used to analyse survey data on the motivation, socio-demographic aspects and competency of 562 trainees as industrial management assistants in Germany and on how they perceived the training quality. The results show that different directions of drop-out intention stem from various factors, with training quality in general having the largest effect. Additionally, the findings indicate a two-tier-scheme of influence factors, 'core' and 'direction-typical' factors.

Abstract Despite high drop-out rates from vocational education and training (VET) throughout most countries and a long research tradition on potential drop-out reasons, little is known about the effects exerted on drop-out intentions by the quality of training. Furthermore, only rarely do scholars distinguish between different drop-out directions, and systematic insights on possibly differing causes are scarce. This study explores the factors influencing four directions of drop-out intention (‘upwards’, ‘downwards’, ‘company change’, ‘occupation change’). Linear regression modelling is used to analyse survey data on the motivation, socio-demographic aspects and competency of 562 trainees as industrial management assistants in Germany and on how they perceived the training quality. The results show that different directions of drop-out intention stem from various factors, with training quality in general having the largest effect. Additionally, the findings indicate a two-tier-scheme of influence factors, ‘core’ and ‘direction-typical’ factors.