Cultural Diversity in Academic Motivation: Universality and Model Complexity

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

This contribution centres on two interconnected conjectures. The first posits that motivational—and the way we model what influences motivation— can be significantly enhanced by integrating the concept of growth orientation. This integration enables to connect growth mindset frameworks and motivational theories through straightforward antecedent-consequence models. The second conjecture is that simpler models tend to vary less and are more stable across different groups compared to more complex models. An example brings these two conjectures together by analysing cultural differences in academic motivation. It compares three models using data from international students studying mathematics and statistics: (1) a comprehensive growth orientation model incorporating both global and specific factors, estimated using Bifactor Exploratory Structural Equation Modelling (B-ESEM); (2) a simplified version of this model with growth orientation as the sole antecedent factor; and (3) a Structural Equation Model (SEM) using specific factors as antecedents. In the illustrative example, the degree of cultural diversity in motivational levels is found to be relatively small, comparable in magnitude to gender differences. However, more complex antecedent-consequence models can easily lead to the conclusion that significant diversity exists between cultural groups. Our findings challenge common assumptions about the cultural specificity of academic motivation models and highlight a promising yet often overlooked factor for building robust explanations of learning motivation: students’ growth orientation.
Original languageEnglish
Article number146
Pages (from-to)30-48
Number of pages19
Journal Cross-Cultural Education Studies
Volume1
Issue number1
DOIs
Publication statusPublished - 17 Sept 2026

Keywords

  • Academic Motivation
  • Cultural Diversity
  • Growth Orientation
  • Model Complexity
  • B-ESEM

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