Work

High Resources don’t Always Buffer Stress: Optimal Margins in Demands-Resources fit and Student Engagement

Year: 2026

Type: article

Abstract: A long-standing assumption in educational psychology is that study resources buffer the negative impact of study demands, thereby sustaining student engagement under academic pressure. Yet current approaches offer conflicting interpretations, with some suggesting that engagement peaks when resources exceed demands, others suggesting it peaks at balanced levels, and still others pointing to situations in which even well-matched demands and resources become detrimental. These inconsistencies have received limited attention in the field and appear to be tied to analytical approaches that mask the full scope of demands-resources (D-R) configurations. In this reflection, we shed light on these limitations and introduce response surface analysis (RSA) as a flexible framework for conceptualizing and testing distinct D-R fit and misfit processes. To illustrate this approach, we analyze three educational datasets from France and China spanning middle school to higher education ( N = 423; 587; 734), modeling how key demands (study workload) and resources (social support, self-efficacy) interact to predict student engagement. Across contexts, the response surfaces consistently revealed “optimal margins of D-R fit”: Engagement tended to be higher when resources exceeded demands and was sustained at intermediate levels of D-R fit, but declined at both low- and high-intensity D-R fit. These patterns partly contradict the “buffer effect” narrative, calling for renewed theory on D-R interactions. By integrating theoretical perspectives and introducing a methodology to represent their predictions within a unified framework, this reflection provides a basis for advancing research on student stress and engagement across educational contexts.

Source: Educational Psychology Review

DOI: 10.1007/s10648-026-10229-1

Authors: Fernando Núñez-Regueiro, Aline Vansoeterstede, Emilie Boujut, Emilie Cappe, Jacques Juhel, Xin Tang