Empirical Exploration of a Unified Model of Task-Specific Motivation

Kees de Brabander, R.L. Martens

    Research output: Contribution to journalArticleAcademicpeer-review


    The aim of this investigation was to explore the merits of the Unified Model of Task-specific Motivation (UMTM). As in this model task-specific components from several partly conflicting theories were integrated, this study constitutes an important contribution to the further development of consensus about motivation theory. Four relatively independent types of valences are the core of the UMTM. Affective and cognitive valences represent feelings while doing an activity and thoughts about the value of its consequences respectively; both types of valences can be positive and negative, hence calling for approach and avoidance motivation respectively. The interaction between these four categories of valences results in a valence expectation, which influences readiness for action. Valences in turn are influenced by four categories of task-specific antecedents, namely appraisals of autonomy, feasibility, and social relatedness, and subjective norm. The global question we tried to answer was to what extent motivational data on specific activities could be modeled in accordance with the UMTM and how different, specific activities affect the influence that the components of the model exert. Structural equation modeling of questionnaire responses of 335 teachers on all components of the model except negative valences with respect to three imaginary types of professional learning activities (formal training, personal study, and reflection on practice) revealed that characteristics of the task determine to what extent components of the model come into play. It is concluded that the model represents a first promising step towards reuniting conflicting theories of motivation
    Original languageEnglish
    Article number83622
    Pages (from-to)540-560
    Number of pages21
    JournalPsychology (Irvine)
    Issue number4
    Publication statusPublished - 2018




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