# How teacher autonomy support and student creativity jointly contribute to self-regulated learning: a dynamic, person-environment fit perspective

Fernando Núñez-Regueiro, Nicolas B Verger, Pascal Bressoux, Anatolia Batruch, Marinette Bouet, Marco Bressan, Genavee Brown, Fabrizio Butera,

Anthony Cherbonnier, Céline Darnon, et al.

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## To cite this version:

Fernando Núñez-Regueiro, Nicolas B Verger, Pascal Bressoux, Anatolia Batruch, Marinette Bouet, et al.. How teacher autonomy support and student creativity jointly contribute to self-regulated learning: a dynamic, person-environment fit perspective. Thinking Skills and Creativity, 2025, 58, pp.101873. 10.1016/j.tsc.2025.101873. hal-05101299

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Submitted on 6 Jun 2025

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How teacher autonomy support and student creativity jointly
contribute to self-regulated learning: a dynamic,
person-environment fit perspective

Fernando Núñez-Regueiro<sup>a</sup>, Nicolas B. Verger<sup>a, *,</sup> Pascal Bressoux<sup>a</sup>,
Anatolia Batruch<sup>b</sup>, Marinette Bouet<sup>c</sup>, Marco Bressan<sup>d</sup>, Genavee Brown<sup>e, i</sup>,
Fabrizio Butera<sup>b</sup>, Anthony Cherbonnier<sup>e</sup>, Céline Darnon<sup>f</sup>, Marie Demolliens<sup>f</sup>, Anne-Laure de Place<sup>a, h</sup>, Olivier Desrichard<sup>g</sup>, Luc Goron<sup>e</sup>, Brivael Hémon<sup>e</sup>,
Pascal Huguet<sup>f</sup>, Eric Jamet<sup>e</sup>, Vincent Mazenod<sup>c</sup>, Nathalie Mella-Barraco<sup>g</sup>,
Estelle Michinov<sup>e</sup>, Nicolas Michinov<sup>e</sup>, Nana Ofosu<sup>g</sup>, Laurine Peter<sup>e</sup>, Céline Poletti<sup>d</sup>,
Isabelle Régner<sup>d</sup>, Mathilde Riant<sup>a</sup>, Anaïs Robert<sup>f</sup>, Ocyna Rudmann<sup>b</sup>,
Camille Sanrey<sup>a, j</sup>, Arnaud Stanczak<sup>f</sup>, Farouk Toumani<sup>c</sup>, Emilio Paolo Visintin<sup>b, k</sup>,
Pascal Pansu<sup>a</sup>

ARTICLE INFO

Keywords:

Teacher autonomy support

Creativity

Self-regulated learning

Person-environment fit

Random intercept cross-lagged panel model

Cubic response surface analysis

A B S T R A C T

According to emergent research, being creative at school could benefit self-regulated learning, but only within “creative learning” contexts that encourage student creativity. This study explored these relations among vocational students in high school (N = 8224). Theoretically, it merged creativity research and self-determination theory (Ryan & Deci, 2020) by operationalising creative-supportive contexts as teaching that supports the need for autonomous learning. Methodologically, the study investigated reciprocal influences between student perceptions of creativity (i.e., use of creative cognitive processes while studying), teacher autonomy support (i.e., teacher behaviours that foster autonomous learning behaviour) and self-regulation (i.e., regulation of resources, efforts, motivation in learning activities), while accounting for different temporalities of effects (i.e., lagged vs. contemporaneous) and for the possibility that self-regulation

<footnote><sup>a</sup> Laboratoire de Recherche sur les Apprentissages en Contexte (LaRAC), University Grenoble Alpes, Grenoble, France</footnote>

<footnote>b Laboratoire de Psychologie Sociale de l'Université de Lausanne (UNILaPS), University of Lausanne, Switzerland</footnote>

<footnote>c Laboratoire d'Informatique de Modélisation et d'Optimisation des Systèmes (LIMOS), UMR-6158, University Clermont Auvergne, Clermont-Ferrand, France</footnote>

<footnote>Laboratoire de Psychologie Cognitive (LPC), UMR7290, Aix-Marseille University, Marseille, France</footnote>

<footnote>Laboratoire de Psychologie: Cognition, Comportement, Communication (LP3C), EA 1285, University Rennes, Rennes, France</footnote>

<footnote>Laboratoire de Psychologie Sociale et Cognitive (LAPSCO), UMR-6024, University Clermont Auvergne, Clermont-Ferrand, France</footnote>

<footnote>Groupe de Recherche en Psychologie de la Santé (GREPS), University of Geneva, Geneva, Switzerland</footnote>

<footnote>Laboratoire Parisien de Psychologie Sociale (LAPPS), Université Paris 8 Vincennes-Saint-Denis, Saint-Denis, France</footnote>

<footnote>Department of Psychology, Northumbria University, Newcastle upon Tyne, UK</footnote>

<footnote>Laboratoire de Psychologie des Cognitions (LPC), University of Strasbourg, France</footnote>

<footnote>b Department of Humanities, University of Ferrara, Italy</footnote>

<footnote>* Corresponding author at: University Grenoble Alpes, Grenoble, France.</footnote>

<footnote>E-mail address: nicolas.verger@dcu.ie (N.B. Verger).</footnote>
responds to a process of fit between student creativity and teacher autonomy support. Overall, results from random-intercept cross-lagged panel models and cubic response surface analysis revealed complex patterns of relations. Whereas self-regulation predicted positive changes in creativity and autonomy support over time (lagged effects), the reciprocal effects on self-regulation appeared instead to be contemporaneous and to be optimal when both creativity and autonomy support were present (person-environment fit). Moreover, teacher autonomy support played a dual role, partly compensating for low creativity to maintain adequate self-regulation, and partly increasing creativity among students. Implications on reciprocal relations between creativity and self-regulation, on fostering creative learning contexts via teacher autonomy support, and on modelling strategies in creativity research are discussed.

# 1. Introduction

Creativity can be generally defined as the capacity to produce useful and original behaviours or cognitions, particularly to address and overcome complex problems or social situations requiring appropriate and unfamiliar action from the individual (Gláveanu, 2013; Runco & Jaeger, 2012; and see Verger, 2024; Verger et al., 2024 for alternative definitions embedded in care and ecological preservation). At school, student creativity—particularly creative cognitive processes such as idea generation and flexible thinking (Miller, 2009; Rogaten & Moneta, 2015)—may influence learning, notably by mobilising available cognitions in order to expand knowledge and understanding based on personal insights—a process called “creative learning” (Beghetto, 2016).

A systematic review concluded that school environments supporting creative learning enhanced student achievement, motivation, focus and engagement in school activities and creative skills (Davies et al., 2013). This review also concluded that teachers play a central role in supporting student creativity by adopting a teaching style that accounts for students’ needs and involve them in the learning process. More research is now needed to theorise further on the nature of this teaching style. The concept of teacher autonomy support, i.e., teaching that satisfies students’ need for autonomous learning (Ryan & Deci, 2017, 2020; Slemp et al., 2024), could provide a valuable yet overlooked framework for characterising creative learning processes at school.

School functioning is also strongly dependent upon students' self-regulation, that is, the process by which students set learning goals, monitor progress, and regulate their behaviour to reach these goals (Panadero, 2017; Pintrich, 2000; Pintrich & De Groot, 1990; Schunk, 2005). Current theoretical and empirical approaches underscore the importance of self-regulation abilities (e.g., metacognitive and resource management strategies to re-evaluate ideas, sustain efforts, etc.) in supporting creativity, and the capacity to translate ideas into actions in particular (Ivcevic & Nusbaum, 2017; Zielińska et al., 2022). Few studies have investigated the beneficial effects of creativity (Forgeard & Kaufman, 2016; Snyder et al., 2019) and, in particular, the contribution of student creativity (e.g., use of creative cognitions) to self-regulated learning.

Does the creative process inform self-regulated learning, for example by expanding self-regulation abilities, or by adapting self-regulation to new learning situations? According to theoretical rationales, this might depend on the degree of creativity afforded by the learning situation and, in particular, by the pedagogical style of the teacher (Kaufman & Beghetto, 2013). This hypothesis has been corroborated by preliminary findings (Gajda et al., 2017). In other words, creativity effects on self-regulation may depend on a process of fit with the learning environment created by the teacher, or person-environment fit (Choi, 2004; Hunt, 1975). To date, no study has examined how this person-environment fit process might relate to self-regulated learning.

The present study investigates how student creativity, teacher autonomy support, and self-regulated learning are linked, while accounting for the possibility that the effects of creativity on self-regulation may depend on the degree of teacher autonomy support (person-environment fit). We focus on the context of vocational education in France which is characterised by an overrepresentation of students with learning difficulties, absenteeism and dropout behaviour (Núñez-Regueiro, 2018; Núñez-Regueiro et al., 2022; Núñez-Regueiro & Bressoux, submitted; Lannegrand-Willems et al., 2012; Teese, 2011). Understanding how student creativity and teacher support may contribute to self-regulated learning can therefore provide insights into levers that can reinforce vocational students' adaptation and guide them toward graduation.

# 2. Self-regulation at school through the prism of creative learning and autonomy support

## 2.1. Mutual influences of creativity and self-regulation

The creative process is an undetermined phenomenon in which goals are often ill-defined and shifting (Ivcevic et al., 2024a). From having an idea to implementing it as a “creative product” (i.e., a novel cognition or behaviour; Ivcevic & Nusbaum, 2017), there is little possibility of knowing how this idea will be transformed, what it will manifest into, and how it will yield novel and meaningful insights, notably for students who engage in this process (Beghetto, 2021; Corazza, 2016; Gláveanu & Beghetto, 2021). As an undetermined process, creative products require the usage of complex and effortful strategies to calibrate one’s cognitions before, during, and after completing the creative task. Hence, creativity depends on self-regulation abilities (Ivcevic & Nusbaum, 2017).

In the school context, self-regulated learning describes the general process of monitoring and planning of behaviour towards learning goals (Cleary & Zimmerman, 2012; Panadero, 2017; Pintrich & De Groot, 1990). Although self-regulation theories differ with respect to the phases or areas of regulation involved (Panadero, 2017), most agree that self-regulation abilities include a broad repertoire of cognitive, metacognitive, and resource management strategies that allow students to calibrate their thoughts, emotions
and behaviours, in complex and effortful tasks (Pintrich & De Groot, 1990).

According to creativity theorists, similar self-regulation abilities intervene in individual creativity in a dynamic process called “self-regulation for creativity” (Ivcevic & Nusbaum, 2017) or “creative self-regulation” (Zielińska et al., 2022). Preliminary evidence has thus shown that the individuals who are most successful in implementing their ideas in various types of creative projects (interests/hobbies, school tasks, extracurricular activities) show greater self-regulation abilities, such as being able to forethink prior to the task and to manage their goals and resources by analysing what they have done well or poorly during the creative process (Zielińska et al., 2022). We posit that a similar process reinforces student creativity (notably creative cognitions) via self-regulated learning. For example, when confronted with success and failure, ups and downs, praise and criticism in learning activities, students may mobilise self-regulation abilities to manage more realistic goals and challenges in their creative process, eventually finding more effective approaches to incorporate creativity into the learning tasks.

Although current models support the hypothesis that self-regulation abilities contribute to improving students' creative processes (H1a; Ivcevic & Nusbaum, 2017; Zielińska et al., 2022), it might be equally important to understand the extent to which, conversely, creativity improves students' self-regulation abilities (H1b). Understanding this reverse causality can clarify the benefits of creative learning Verger et al., 2024a; Forgeard & Kaufman, 2016), including its role as a coping resource for students exposed to academic or other life stressors (e.g., difficult school tasks, conflicts with peers, or negative life events) that erode self-regulation capacities (Núñez-Regueiro, 2017; Verger, 2023; Dupéré et al., 2015; Skinner & Saxton, 2019). Being creative could expand the repertoire of self-regulation capacities, by facilitating the emergence of alternative metacognitive or resource management strategies (e.g., modifying goals before a task, reconsidering available options). In other words, creativity could sustain students' self-regulation through a process we might call “creativity for self-regulation”, by analogy with the reverse process of self-regulation for creativity (Ivcevic & Nusbaum, 2017). These processes would result in a bidirectional causality in which student creativity and self-regulation mutually reinforce each other over time (see Fig. 1, H1).

While there are many studies documenting the effects of self-regulation on creative processes (Ivcevic et al., 2024a; Ivcevic & Nusbaum, 2017; Zielińska et al., 2023; Zielińska, Lebuda, Czerwonka et al., 2024a, 2024b), studies on the effects of creativity on self-regulation are scarce. For example, a qualitative study showed that parents use creative activities as an educational tool to foster their children's overall self-regulation abilities (Verger et al., 2024b). Similarly, being creative has been shown to be negatively related to academic stress and positively related to positive achievement emotions among college students, suggesting it might constitute a coping resource to preserve self-regulated learning in the university context (Xu & Wang, 2022). More research is needed to explore the effects of creativity on self-regulation in the school context and understand how teachers can foster creative learning.

## 2.2. Creating contexts for creative learning using teachers' autonomy support

Creativity theorists have pointed to the need for identifying under which conditions student creativity can provide optimal or suboptimal support to learning processes in the school context (Davies et al., 2013; Kaufman & Beghetto, 2013). They have suggested that creative learning at school is optimal if and only if (a) it is provided in conjunction with self-awareness and control over the learning situation to assess whether creative behaviours (vs. conforming behaviours) are required from the learning tasks; and (b) if the student possesses enough creative cognitions, such as abilities to solve problems and to translate ideas into action.

So, how should teachers behave to support both conditions of optimal creative learning? According to some authors, a major provider of self-awareness and control (i.e., condition a) is the teacher's feedback throughout the learning process, which helps students assess the appropriateness of using their creative cognitions (Kaufman & Beghetto, 2013). Teachers can develop creative learning environments by providing pupils with “more control of their learning and opportunities to work at their own pace” during classroom activities (Davies et al., 2013, p. 89). A multimethod study (Gajda et al., 2017) combining quantitative indicators and microanalysis of classroom interactions provided evidence that student creativity was positively associated with achievement when the teacher encouraged and guided the exploration of novel ideas from the students. Conversely, when the teacher did not encourage or even mocked student ideas, the association between creativity and achievement became negative (Gajda et al., 2017).

In this study, we propose to characterize teachers' support for creative learning within the framework of Self-Determination Theory (Ryan & Deci, 2017, 2020), in terms of teacher autonomy support. Teacher autonomy support refers to behaviour that encourages and guides student's autonomous learning (e.g., offering students choices and options in class, making sure that the goals of the course are well understood), thereby developing the student's “autonomy” or sense of initiative and ownership of the learning activity (Núñez-Regueiro et al., 2025; Deci & Ryan, 2000; Ryan & Deci, 2020). This kind of support strongly aligns with a creative learning environment that encourage self-awareness and self-paced learning, as described earlier (Davies et al., 2013; Gajda et al., 2017; Kaufman & Beghetto, 2013).<fnref n="1" />

Previous research—including meta-analyses—has consistently shown that teacher autonomy support has positive effects on student well-being and achievement (Núñez-Regueiro et al., 2024; Ryan & Deci, 2020; Slemp et al., 2024; Ryan & Deci, 2020; Slemp et al., 2024). Preliminary findings among secondary school students have reported positive effects of various forms of teacher support

<footnote n="1">¹ We focus on teacher autonomy support for several reasons. This kind of support contributes to satisfy students' basic needs for autonomy, but also basic needs for relatedness and competence at school, makings it an essential aspect of needs-supportive teaching (Ryan & Deci, 2020; Slemp et al., 2024). Teacher autonomy support is also more impactful than other kinds of needs-supportive teaching (relatedness support, competence support) in fostering well-being, learning and, self-regulated learning (Núñez-Regueiro et al., 2025; Slemp et al., 2024). These other kinds of needs-supportive behaviours are less directly relevant to creative learning environments.</footnote>
<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Dynamic and person-environment fit processes linking student creativity, teacher autonomy support and self-regulation. Note. Dynamic bidirectional (H1) and mediation processes (H2) relate to longitudinal or “lagged” effects across time points, whereas person-environment fit processes (H3) relate to immediate or “contemporaneous” effects within time points. Person-environment fit processes are anticipated where both creativity and autonomy support are required for optimal self-regulation (H3a), where autonomy support may compensate for low creativity to maintain self-regulation (H3b), or where creativity may compensate for low autonomy support to maintain self-regulation (H3c).</figcaption></figure>

(emotional, academic, competence support) on creative self-efficacy (Liu et al., 2021), and of teacher autonomy support on self-regulation strategies (Martinek et al., 2016). Other studies have also found positive associations between students' feelings of autonomy in the classroom and their self-regulation (León et al., 2015) or their creativity (Baum & Baumann, 2018; Sheldon, 1995).

Yet, only one study investigated the causal ordering of these relations over time, finding that self-regulation strategies (e.g., metacognitive use) tended to reinforce teacher autonomy support, more than the reverse (unidirectional influence; Schuitema et al., 2016). This partly contradicted previous findings based on cross-sectional data (Martinek et al., 2016). It is therefore possible that relations among constructs differ according to the time interval considered, for example if they relate to cross-sectional (contemporaneous) or longitudinal (lagged) relations over time, as underscored by new methods for longitudinal data analysis (Marsh et al., 2024; Muthén & Asparouhov, 2024). Accordingly, two complementary perspectives, one longitudinal and one cross-sectional, need to be envisaged to investigate the interrelations occurring between teacher autonomy support, creativity, and self-regulation.

### 2.2.1. Longitudinal perspective on autonomy-supportive teaching (dynamic)

First, as a driver of autonomous learning, teacher autonomy support could contribute to the emergence of optimal creative learning by providing students with more opportunities to test and adapt creative cognitions in learning situations at school (e.g., by enhancing creative self-efficacy or creative cognitions; Baum & Baumann, 2018; Liu et al., 2021; Sheldon, 1995), which may explain its association with self-regulated learning (León et al., 2015; Martinek et al., 2016). This would point to a putative mediation effect by creativity linking teacher autonomy support and self-regulatory mechanisms (autonomy support > creativity > self-regulation; see Fig. 1, H2). Moreover, based on other findings (Schuitema et al., 2016), a retroaction from self-regulation to teacher autonomy support is also expected over time (self-regulation > autonomy support > creativity; see Fig. 1, H2). Because such relations depend on repeated experiences of creative learning (i.e., testing and adapting creating cognitions in context to support autonomous learning and self-regulation), they are expected to relate to processes of change over the school year, for example from one semester to another. In analytical terms (see Method), this corresponds to longitudinal or “lagged” effects across time intervals (Marsh et al., 2024; Muthén & Asparouhov, 2024; Núñez-Regueiro et al., submitted).

### 2.2.2. Cross-sectional perspective on autonomy-supportive teaching (person-environment fit)

Second, teacher autonomy support could play a more instantaneous role in everyday classroom activities, by providing the right amount of autonomous learning through which students' creativity can be explored in self-regulation processes. In this person-environment fit (P-E fit) perspective (see Fig. 1, H3; Núñez-Regueiro, 2017; Choi, 2004; Gilbreath et al., 2011; Hunt, 1975), the effects of student creativity on self-regulation would depend on the immediate availability of teacher support for autonomous learning (and conversely), as anticipated by creativity theorists (Davies et al., 2013; Kaufman & Beghetto, 2013) and as suggested by previous research (Gajda et al., 2017; Zielińska et al., 2022). Because these fit processes are specific to day-to-day learning situations, they are expected to lead to immediate changes in self-regulation—such as changes within a given trimester—, corresponding to “contemporaneous” effects (Marsh et al., 2024; Muthén & Asparouhov, 2024; Núñez-Regueiro et al., submitted).

From a technical standpoint, the person-environment fit perspective can be optimally explored by visualising how the combined levels of creativity and autonomy support relate to self-regulation on a given occasion, using polynomial regression and response surface analysis (e.g., Choi, 2004; Gilbreath et al., 2011; Núñez-Regueiro et al., 2025; Núñez-Regueiro et al., 2024). Three major hypotheses of interdependent or compensatory effects between creativity and autonomy support can be envisaged (see also
Núñez-Regueiro & Wang, 2024). The hypothesis of interdependent P-E fit (see Fig. 1, H3a) assumes that the effects of autonomy support and creativity on self-regulation are interdependent: low levels on one component (e.g., low creativity) will undermine the beneficial effect of the other component (e.g., high autonomy support; Kaufman & Beghetto, 2013). This would imply a quadratic or inverted-U shaped relation between P-E fit (i.e., teacher autonomy support vs. student creativity) and self-regulation, also known as a “congruence effect” in response surface analysis (Cable & Edwards, 2004; Choi, 2004; Gilbreath et al., 2011; Humberg et al., 2022; Núñez-Regueiro & Juhel, 2022, 2024a).

Alternatively, one might anticipate situations where one component compensates for the absence of the other, resulting in a cubic relation between P-E fit and self-regulation, or “asymmetric congruence effect” (Humberg et al., 2022; Núñez-Regueiro & Juhel, 2022, 2024a). According to the P-E fit compensated by autonomy support (Fig. 1, H3b), high levels of teacher autonomy support may suffice to maintain high levels of self-regulation even among students low in creativity, by fostering feelings of autonomy and intrinsic motivation in the classroom (León et al., 2015; Slemp et al., 2024). In this hypothesis, the positive effects of creativity may instead be offset by low levels of teacher autonomy support, notably due to the absence of opportunities for autonomous learning necessary for self-awareness in creative learning (Gajda et al., 2017).

Conversely, the hypothesis of P-E fit compensated by creativity (Fig. 1, H3c) assumes that low levels of teacher autonomy support can be compensated by high levels of creativity to maintain functional levels of self-regulation, but that high levels of teacher autonomy support may fail in maintaining self-regulation in the absence of student creativity. This hypothesis therefore underscores the role of student creativity as a prerequisite for bearing the fruits of autonomous learning (i.e., high teacher autonomy support), and as a coping resource capable of overcoming academic stress and negative emotions (see Xu & Wang, 2022), such as those generated by adverse teaching situations that frustrate the student's need for autonomous learning. Despite their fundamental differences, all interdependent and compensatory P-E fit mechanisms (H3a, H3b, H3c) predict that high levels of both autonomy support and creativity are necessary for optimal self-regulatory processes at school.

## 3. Present study

This study aimed to address the role played by student creativity (i.e., creative cognitions) and teacher autonomy support in fostering self-regulated learning at school. Two research questions were addressed. In terms of dynamics of change, the study explored how creativity, self-regulation, and teacher autonomy support mutually influenced each other over time (RQ1). Assuming that student creativity acts both as causes and effects of self-regulation, it was expected that positive changes in one construct (e.g., creativity) would lead to subsequent positive changes in the other construct (e.g., self-regulation), and reciprocally (H1). Moreover, based on theory pointing to the importance of autonomous learning in creativity, teacher autonomy support was expected to facilitate self-regulation by providing opportunities for the usage and emergence of creativity (mediation by creativity, H2). In terms of person-environment fit processes, the study also investigated how the degree of fit between teacher autonomy support and students' creativity mattered in explaining self-regulation levels (RQ2). Three P-E fit hypotheses were put forward. The first one highlighted the interdependency of effects (H3a), the second one a compensatory mechanism by autonomy support (H3b), and the third one a compensatory mechanism by creativity (H3c). The highest levels of self-regulation were expected among students experiencing both teacher autonomy support and creativity.

## 4. Method

### 4.1. Participants

Students were recruited as part of the ProFan project, a large-scale research initiative launched in 2017 that aimed to evaluate the effects of collaborative learning on various key outcomes. It included students from 109 vocational secondary schools across France, studying business, health services and electricity. Data were collected on three occasions (T1= beginning of grade 11, T2= end of grade 11, T3= beginning of grade 12). Although the target sample comprised 10,035 students, 6.6% of them had no administrative data (e. g., no class identifier), another 3.4% had non-admissible data (i.e., self-reported satisficing behaviour). The final sample therefore

<table><caption>**Table 1**

Means, standard deviations, range, and correlations of study variables.</caption><thead><tr><th></th><th>M</th><th>SD</th><th>Range</th><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th><th>6</th><th>7</th><th>8</th></tr></thead><tbody><tr><td>1. Creativity (T1)</td><td>3.772</td><td>0.939</td><td>[1, 6]</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>2. Creativity (T2)</td><td>3.773</td><td>0.996</td><td>[1, 6]</td><td>0.516*</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>3. Creativity (T3)</td><td>3.969</td><td>0.973</td><td>[1, 6]</td><td>0.437*</td><td>0.507*</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>4. Autonomy support (T1)</td><td>2.78</td><td>0.794</td><td>[1, 5]</td><td>0.261*</td><td>0.197*</td><td>0.149*</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>5. Autonomy support (T2)</td><td>2.786</td><td>0.820</td><td>[1, 5]</td><td>0.200*</td><td>0.273*</td><td>0.191*</td><td>0.472*</td><td></td><td></td><td></td><td></td></tr><tr><td>6. Autonomy support (T3)</td><td>2.938</td><td>0.806</td><td>[1, 5]</td><td>0.157*</td><td>0.171*</td><td>0.288*</td><td>0.385*</td><td>0.470*</td><td></td><td></td><td></td></tr><tr><td>7. Self-regulation (T1)</td><td>4.830</td><td>1.199</td><td>[1, 7]</td><td>0.442*</td><td>0.340*</td><td>0.290*</td><td>0.376*</td><td>0.288*</td><td>0.255*</td><td></td><td></td></tr><tr><td>8. Self-regulation (T2)</td><td>4.632</td><td>1.266</td><td>[1, 7]</td><td>0.320*</td><td>0.469*</td><td>0.344*</td><td>0.287*</td><td>0.396*</td><td>0.288*</td><td>0.581*</td><td></td></tr><tr><td>9. Self-regulation (T3)</td><td>4.895</td><td>1.225</td><td>[1, 7]</td><td>0.258*</td><td>0.336*</td><td>0.489*</td><td>0.206*</td><td>0.250*</td><td>0.392*</td><td>0.496*</td><td>0.589*</td></tr></tbody></table>

Note. N = 8224 high school students, 441 classes.

*p* < .05.
comprised 9357 students (8224 with non-missing data), including 45 % males aged between 15 and 17 years old.

## 4.2. Measures

Statistics for students' creative cognitions, teacher autonomy support, and self-regulation are reported in Table 1. All measures showed metric longitudinal invariance (see Supplementary Material A) and good to excellent reliability ($\bar{\omega}$ = [.75, 0.90]).

**Creativity.** Student creativity was measured in the form of creative cognitions, via the Use of Creative Cognition Scale (UCCS; Rogaten & Moneta, 2015). The UCCS comprises 5 items answered on a 6-point Likert scale (1=“never” to 6=“always”), asking each student to rate the frequency with which they use original and effective strategies to advance towards problem resolution and insights in learning activities at school. Sample items included “I find effective solutions by combining multiple ideas”, “While working on something, I try to generate as many ideas as possible”, or “If I get stuck on a problem, I try to take a different perspective of the situation”. The UCCS is similar in content to measures of creative ideation—that is, the perceived capacity to generate and explore ideas in general (e.g., the Ideational Behavior Scale (Runco et al., 2001). However, its focus on ideas specifically generated to resolve learning tasks brings it closer to a measure of creative product (like creative activities or performance), albeit in a less tangible form (Gajda et al., 2017; Zielińska et al., 2022). The UCCS may therefore be characterised as a measure of creative production in the form of novel ideas and ways of learning students generate in their studies (we return to this element in the Limitations Section). This scale has been shown to relate positively with correlates of the creative process such as student flow, intrinsic motivation and adaptive metacognitive traits (Rogaten & Moneta, 2015). The scale showed excellent reliability ($\bar{\omega}$= 0.86) and excellent fit to the data [$\chi^2(90)=971.4, p < .001$, CFI= 0.969, TLI= 0.964, RMSEA= 0.041, SRMR= 0.037].

**Teacher autonomy support.** Students reported their perceptions of teacher autonomy support via the Learning Climate Questionnaire (LCQ; Williams & Deci, 1996), one of the most commonly used scales to measure autonomy support in the classroom and in other organizational contexts (Slemp et al., 2024). Teacher autonomy support was measured by 4 items relating to teacher behaviours that foster autonomous learning behaviour in the classroom (sample items: “My teacher encourages me to ask questions”; “My teacher listens to how I would like to do things”; “My teacher tries to understand how I see things before suggesting a new way to do things.”). The items were answered on a 5-point Likert scale of agreement (1=“not true at all” to 5=“totally true”), and resulted in a measure with good reliability ($\bar{\omega}$= 0.75) and excellent fit to the data [$\chi^2(53)=361.3, p < .001$, CFI= 0.981, TLI= 0.976, RMSEA= 0.028, SRMR= 0.028].

**Self-regulation.** Self-regulated learning was measured in the form of self-regulation strategies based on 9 items freely adapted from the Motivated Strategies for Learning Questionnaire (MLSQ; Duncan & McKeachie, 2005; Pintrich et al., 1993). Rated on a 7-point Likert scale of agreement (1=“totally disagree” to 7=“totally agree”), the items focused on resource management regulation (e.g., “I organize myself to finish my homework on time”, “I prepare thoroughly before a test”, “I listen carefully to what teachers say”), and effort and motivation regulation (e.g., “I try hard to finish even difficult exercises”, “I avoid getting distracted by others”, “I am capable of motivating myself to do my homework”). The scale also showed excellent reliability ($\bar{\omega}$= 0.90) and an excellent fit to the data [$\chi^2(328)=3249.6, p < .001$, CFI= 0.951, TLI= 0.947, RMSEA= 0.038, SRMR= 0.041].

## 4.3. Analytic strategy

**Cross-lagged panel model.** Reciprocal relations among constructs were investigated using the cross-lagged panel model (CLPM) and the random intercept CLPM (RI-CLPM) (Hamaker et al., 2015; Marsh et al., 2024; Mulder & Hamaker, 2021; Núñez-Regueiro et al., 2022). The CLPM has been used in past research to analyse reciprocal influences, but has been criticised for aggregating stable interindividual variance (baseline or “trait” factors) and dynamic intraindividual variance (momentary deviations ordynamic “state” factors), which can create statistical artefacts whereby correlational evidence between trait factors appears in the guise of directional effects between state factors (Hamaker et al., 2015; Núñez-Regueiro et al., 2022). The RI-CLPM overcomes this problem by formally disaggregating trait and state factors, notably by introducing random intercepts representing individual differences in baseline levels (i.e., trait factors) of the dynamic constructs (Hamaker et al., 2015). In this approach, significant cross-lagged effects (CL) of one construct (e.g., creativity) onto another (e.g., self-regulation) can be interpreted as predictive causal effects (or Granger-causal effects; Granger, 1969), net of trait factors. This approach was implemented for each pair of variables (creativity and self-regulation, autonomy and creativity, autonomy and self-regulation), before being tested for all three variables simultaneously using trivariate CLPMs and RI-CLPMs. For clarity of exposition, the Results focus on the final trivariate RI-CLPMs, while complementary bivariate models (CLPMs, RI-CLPMs) are detailed in SM-B.

In building these models, goodness-of-fit was regarded as satisfactory and excellent for comparative fix index (CFI) values above 0.90 and 0.95, and for root-mean-square error of approximation (RMSEA) values below 0.08 and 0.06, respectively. Parametric constraints between nested models were retained when the variation in goodness-of-fit was higher than or equal to -0.01 for CFI (i.e., $\Delta CFI \ge -0.01$). Simulation studies have shown that these cutoff values are good at detecting nontrivial misspecifications (Cheung & Rensvold, 2002; Hu & Bentler, 1999), which is why they are preferred in tests of measurement invariance and lagged effects (Marsh et al., 2024; Muthén & Asparouhov, 2024; Núñez-Regueiro et al., 2021).

For better accuracy and interpretability, all models were based on latent factors by incorporating a measurement model of the dynamic constructs with scalar invariance (see SM-A), using the Mplus software (Muthén & Muthén, 2022). CL effects were estimated longitudinally between adjacent measurement occasions (“lag1” effects from occasion T to occasion T + 1), but also cross-sectionally within occasions by including contemporaneous effects (“lag0” effects from occasion T to occasion T; Marsh et al., 2024; Muthén & Asparouhov, 2024). Modelling both lagged and contemporaneous effects is subject to a number of restrictions (e.g., time-invariance of
CL effects, nonduality constraints) and may lead to convergence issues or negative R2s. Following recommendations (Muthén & Asparouhov, 2024), the decision was made to remove any CL effects estimated at zero. This approach was effective in obtaining well-behaved models in which the temporalities of effects could be established. As the CLPMs confounded trait and state factors (adding another layer of interpretability issue), contemporaneous effects were only investigated in the RI-CLPMs. Following Orth et al. (2024), lagged effects were interpreted as small ($\beta$= 0.03), medium ($\beta$= 0.07) and large ($\beta$= 0.12) from completely standardised coefficients. Effect size benchmarks for interpreting contemporaneous effects were not available at the time of this study.

**Response surface analysis.** Combinatory effects of autonomy support and creativity on self-regulation were identified using cubic response surface analysis, implemented in *RSAtools* (Núñez-Regueiro & Juhel, 2024c). Cubic RSA examines how two factors x and y (e. g., autonomy support and creativity) interact to influence an outcome z (e.g., self-regulation). The goal is to understand how the degree of congruence or “fit” between the factors (e.g., *autonomy support* = *creativity*), and their degree of incongruence or “misfit” (e.g., *autonomy support* = – *creativity*) relates to the outcome. This is enabled by modeling the interaction via polynomial regression and interpreting the nonlinearities occurring in the response surface. Cubic RSA (Humberg et al., 2022; Núñez-Regueiro & Juhel, 2022, 2024a) is particularly powerful, overcoming the limitations of simpler techniques such as moderation analyses or quadratic RSA (Humberg et al., 2019; Skinner et al., 2022), which both restrict the response surface to U or inverted-U patterns (quadratic nonlinearities) and often result in spurious findings when the response surface is S-shaped (cubic nonlinearities).

In order to identify fit and misfit processes, best-fitting response surfaces are explored and compared among 37 families of polynomials characterised by constraints applied to the parameters of the cubic model defined by $z = b_1x + b_2y + b_3x^2 + b_4xy + b_5y^2 + b_6x^3 + b_7x^2y + b_8xy^2 + b_9y^3$ (Núñez-Regueiro & Juhel, 2024a). Families obtain specific solutions (either linear, quadratic, or cubic) by modifying the parameters responsible, respectively, for trends ($b_1$ to $b_2$), curvatures ($b_3$ to $b_5$) and asymmetric curvatures ($b_6$ to $b_9$) in the response surface z. The best-fitting solutions are then selected based on tests of absolute fit against the saturated cubic model, on parsimony indices (AIC and BIC weights), on the significance of the family-specific parameters, and on evidence of good fit to the data (e.g., adjusted R², CFI ≥ 0.95, TLI ≥ 0.95, RMSEA ≤ 0.06, SRMR ≤ 0.08). In this study, this identification strategy was combined with parametric invariance testing to verify that the solutions across waves could be combined without loss of generality (i.e., ΔCFI ≥ -0.01), by comparing an integrative solution against a multigroup solution differentiating waves (see similar approach for cross-national RSA, Núñez-Regueiro, 2024).

To separate trait and state factors (as in the RI-CLPM), cubic RSA was applied to the dynamic states of each construct on each of the 3 measurement waves (T1, T2, T3). This was obtained by extracting the residuals from the trivariate RI-CLPM (defined above) as factor scores and using these factor scores as predictors and outcomes in the polynomial models for RSA. The trivariate RI-CLPM corresponded to the final model obtained (see Results, Fig. 2, and model “RI-CLPM-CONT”, Table 2), except that the contemporaneous effects of autonomy support and creativity on self-regulation were removed to be analysed via cubic RSA. This approach enabled modelling the polynomial effects on self-regulation of creativity-autonomy support fit using contemporaneous effects (lag0), while adjusting for endogeneity from trait factors and past dynamic states (see, for a similar approach, Humberg et al., 2024).

The best-fitting polynomial solution was then interpreted by probing the curvatures of the response surface over the line of congruence (LOC, where *autonomy support* = *creativity*) and the line of incongruence (LOIC, where *autonomy support* = – *creativity*), notably by identifying reversal points where the behaviour changes sign (Núñez-Regueiro & Juhel, 2022, 2024b). Only reversals over the LOIC were found. To gauge the impact of reversals in the present sample, tests were conducted to identify students situated beyond the reversal line and with self-regulation levels significantly contradicting what would be expected from the linear trend. Significant differences were established by observing that the 95 % confidence interval of the expected value of self-regulation did not contain the observed value of each student. Effect sizes for explained variance in polynomial regression were considered small (R²= 0.02), medium

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Diagram of final model of reciprocal relations among constructs. Note. N = 8224 high school students, 441 classes. AUTO= autonomy support; CRE= creativity; REG= self-regulation. XY-standardised effects are reported. The diagram corresponds to the model “RI-CLPM-CONT” in Table 2. †p < .06; *p ≤ 0.05; **p < .01; ***p < .001.</figcaption></figure>
<table><caption>**Table 2**
Model parameters of random intercept cross-lagged panel models.</caption><thead><tr><th colspan="8">Parameters</th></tr></thead><tbody><tr><td>RI-CLPM:</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Autoregressive effects</td><td>AUTO</td><td>CRE</td><td>REG</td><td></td><td></td><td></td><td></td></tr><tr><td>T1-T2</td><td>0.221***</td><td>0.136**</td><td>0.232***</td><td></td><td></td><td></td><td></td></tr><tr><td>T2-T3</td><td>0.244***</td><td>0.162**</td><td>0.263***</td><td></td><td></td><td></td><td></td></tr><tr><td>Cross-lagged effects</td><td>AUTO</td><td>CRE</td><td>AUTO</td><td>REG</td><td>CRE</td><td>REG</td><td></td></tr><tr><td>T1-T2</td><td>→CRE</td><td>→AUTO</td><td>→REG</td><td>→AUTO</td><td>→REG</td><td>→CRE</td><td></td></tr><tr><td>T2-T3</td><td>0.042ns</td><td>0.015ns</td><td>0.035ns</td><td>0.077†</td><td>0.065*</td><td>0.123**</td><td></td></tr><tr><td>Contemporaneous effects</td><td>AUTO</td><td>CRE</td><td>AUTO</td><td>REG</td><td>CRE</td><td>REG</td><td></td></tr><tr><td>T2</td><td>→CRE</td><td>→AUTO</td><td>→REG</td><td>→AUTO</td><td>→REG</td><td>→CRE</td><td></td></tr><tr><td>T3</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Random intercepts (RIs)</td><td>AUTO-CRE</td><td>AUTO-REG</td><td>CRE-REG</td><td></td><td></td><td></td><td></td></tr><tr><td>r(intercepts)</td><td>0.382***</td><td>0.578***</td><td>0.589***</td><td></td><td></td><td></td><td></td></tr><tr><td>RI-CLPM-CONT (FINAL MODEL):</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Autoregressive effects</td><td>AUTO</td><td>CRE</td><td>REG</td><td></td><td></td><td></td><td></td></tr><tr><td>T1-T2</td><td>0.198***</td><td>0.131***</td><td>0.147***</td><td></td><td></td><td></td><td></td></tr><tr><td>T2-T3</td><td>0.216***</td><td>0.158***</td><td>0.168***</td><td></td><td></td><td></td><td></td></tr><tr><td>Cross-lagged effects</td><td>AUTO</td><td>CRE</td><td>AUTO</td><td>REG</td><td>CRE</td><td>REG</td><td></td></tr><tr><td>T1-T2</td><td>→CRE</td><td>→AUTO</td><td>→REG</td><td>→AUTO</td><td>→REG</td><td>→CRE</td><td></td></tr><tr><td>T2-T3</td><td></td><td></td><td></td><td>0.116**</td><td></td><td>0.084**</td><td></td></tr><tr><td>Contemporaneous effects</td><td>AUTO</td><td>CRE</td><td>AUTO</td><td>REG</td><td>CRE</td><td>REG</td><td></td></tr><tr><td>T2</td><td>0.277***</td><td></td><td>0.300***</td><td>→AUTO</td><td>→REG</td><td>→CRE</td><td></td></tr><tr><td>T3</td><td>0.283***</td><td></td><td>0.308***</td><td></td><td></td><td>0.364***</td><td></td></tr><tr><td>Trait factors (RIs)</td><td>AUTO-CRE</td><td>AUTO-REG</td><td>CRE-REG</td><td></td><td></td><td></td><td></td></tr><tr><td>r(RIs)</td><td>0.373***</td><td>0.539***</td><td>0.593***</td><td></td><td></td><td></td><td></td></tr></tbody></table>

Note. N = 8224 students, 441 classes. CRE = creativity; REG = self-regulation; AUTO = autonomy support; RI-CLPM = random intercept cross-lagged-panel model; CONT = RI-CLPM with contemporaneous effects. Measurement models with correlated uniqueness are included. XY-standardised effects are reported. Complementary models (including CLPMs) are reported in SM-B. ns = not significant at 10%; †p < .06; *p ≤ 0.05; **p < .01; ***p < .001.

(R² = 0.13), or large (R² = 0.26) following guidelines in behavioural sciences (Cohen, 1988).

**Missing data treatment and data clustering correction.** Missing data was accounted for 88 % of the sample (i.e., n = 8224 students from 441 classes) using full information maximum likelihood (Graham, 2012). To limit parametric bias, all parameters were estimated with a data clustering correction accounting for class-level variance (sandwich estimator; Muthén & Satorra, 1995).

# 5. Results

## 5.1. Results on reciprocal relations among constructs (RI-CLPM)

The correlations between the random intercepts of self-regulation, creativity, and autonomy support were high ($r_{AUTO.CRE} > 0.37$, $p < .001$, $r_{AUTO.REG} > 0.53$, $p < .001$, $r_{CRE.REG} > 0.58$, $p < .001$), indicating that students who were generally more creative and more autonomy-supported while studying also showed higher self-regulated learning. These random intercepts related to baseline levels, that is, stable individual differences that did not change over time. On this basis, it was possible to investigate how momentary deviations from these baseline levels, reflecting dynamic states, related to each other across constructs.

We first investigated lagged relations between dynamic states, that is, lag1 effects occurring from one wave to another (from occasion T to occasion T + 1). In this model (Model “RI-CLPM”, Table 2), self-regulation and creativity appeared to reinforce each other over time: when one construct showed positive changes on one occasion, the other construct also showed positive changes on a subsequent occasion. However, the effect of self-regulation on creativity ($\beta$= [.123, 0.139], $p$ = .01) was twice larger than the reciprocal effect ($\beta$= [.065, 0.078], $p$ = .053), suggesting that the causal ordering was driven by dynamics of self-regulation. Increases in self-regulation also appeared to induce increases in teacher autonomy support ($\beta$= [.077, 0.086], $p$ = .056), whereas the reciprocal effect of autonomy support on self-regulation did not reach statistical significance ($\beta$= [.035, 0.039], $p$ = .310). None of the lagged effects between teacher autonomy support and creativity were significant either (see Table 2). Overall, this model showed excellent fit to the data ($\chi^2_{1359} = 7083.4$, CFI = 0.949, TLI = 0.946, RMSEA = 0.023, SRMR = 0.032, AIC = 876817.7).

We then added contemporaneous effects between dynamic states, that is, lag0 effects occurring within the same wave (from occasion T to occasion T). Preliminary analyses showed that several lagged effects (i.e., of creativity, of teacher autonomy support) were estimated at zero after including these contemporaneous effects, and were therefore removed for parsimony (for details, see SM-B). In the final model (Model “RI-CLPM-CONT”, Table 2), significant contemporaneous effects on self-regulation were found for both creativity ($\beta$= [.457, 0.448], $p$ < .001) and teacher autonomy support ($\beta$= [.300, 0.308], $p$ < .001). In other words, dynamics in creativity and teacher autonomy support predicted day-to-day (proximal) changes in self-regulation, as opposed to long-term (distal)
changes. By contrast, the lagged effects of self-regulation on creativity ($\beta$= [.084, 0.096], $p$ = .006) and teacher autonomy support ($\beta$= [.116, 0.130], $p$ < .001) were robust to including their contemporaneous counterparts, confirming that self-regulation induced long-term changes (vs. day-to-day) on these constructs. Overall, self-regulation and creativity showed reciprocal effects on each other (validation of H1), albeit according to different temporal scales (lagged vs. contemporaneous effects).

In addition, this model revealed that teacher autonomy support had a positive contemporaneous effect on creativity ($\beta$= [.277, 0.283], $p$ < .001), whereas the reciprocal effect of creativity on autonomy support was null. Partly aligning with H2, this indicated that teacher autonomy support had an effect on self-regulation that was mediated by the contemporaneous effect of creativity on self-regulation (indirect effect, $\beta$= [.101, 0.104, $p$ < .001). This mediation was partial because the direct effect of autonomy support on self-regulation remained substantial.

Reported in Fig. 2, this final model had similar fit to the data (excellent) as the RI-CLPM ($\chi^2_{T359}$ = 7085.2, CFI = 0.949, TLI = 0.947, RMSEA = 0.023, SRMR = 0.032), but it showed greater parsimony (i.e., lower AIC = 876811.4 vs. 876817.7).

## 5.2. Results on fit processes between autonomy support and creativity (response surface analysis)

**Model identification.** For each wave of measurement (T1, T2, T3) and for the integrative dataset combining waves (T1+T2+T3), 37 families of polynomial models were compared in which autonomy support and creativity predicted self-regulation levels according to alternative P-E fit processes. Based on goodness of fit, parsimony and explained variance indices (e.g., wBIC, adjusted R², CFI, RMSEA), one family systematically appeared as the single best or among the top 3 best-fitting families, namely the family of “level-dependent incongruence effect rotated by Y” (family 36; Table 3). This family is characterised by equality constraints on some third-order polynomials (i.e., $b_6 = -b_7 = b_8$), and by the free estimation of the remaining parameters (i.e., $b_1$ to $b_5$, and $b_9$). The estimates and the response surfaces for this family showed great consistency across waves (see Table 4 and Fig. 3A, B, C), explaining large amounts of variance in self-regulation levels (adjusted R²= [.295, 0.426]). Constraining the family parameters to be equal across waves in the integrative solution resulted in a model with excellent fit to the data [$\chi^2(2)$= 8.4, $p$ = .015, CFI= 0.998, TLI= 0.991, RMSEA= 0.021, adjusted R²= 0.359], which showed negligible differences in fit as compared to the multigroup solution allowing parameters to differ across waves (ΔCFI= -0.006 ≥ -0.01). The integrative solution was therefore retained as valid for interpretation.

**Interpretation of the response surface.** An inspection of the response surfaces for the integrative solution (T1+T2+T3) showed that self-regulation levels were highest when both autonomy support and creativity were equally provided for and in large amounts. In other words, person-environment fit between student creativity and teacher autonomy support was positively related to self-regulation. This positive relation (i.e., $u_1 = b_1 + b_2 = 0.681$, $p$ < .001) was constant across values of creativity and autonomy support, as indicated by the quasi-linear pattern along the LOC (see Fig. 3E; quadratic and cubic effects were significant but disproportionately small compared to the linear effect).

On the contrary, the relation of person-environment misfit to self-regulation varied depending on predictor values, producing a cubic pattern (see Fig. 3F). This cubic pattern was non-proportional, in the sense that reversal points “r” in the curvature of the surface (see red lines, Fig. 3D, F) occurred at more extreme values of negative misfit (i.e., $r_1 = -1.635 SD$) than positive misfit (i.e., $r_2 = 0.827 SD$). In other words, at low values of misfit situated within the values of reversal points, experiencing negative misfit (i.e., more creativity than autonomy, or *autonomy support* = —creativity < 0) was associated with higher self-regulation. The negative linear trend here was small but significant ($v_1 = b_1 - b_2 = -0.210$, $p$ < .001). At high values of negative misfit, this pattern reversed so that

<table><caption>**Table 3**

Fit indices of polynomials models of creativity-autonomy fit in self-regulation.</caption><thead><tr><th>Model</th><th>χ<sup>2</sup></th><th>DF</th><th>p</th><th>wAIC</th><th>wBIC</th><th>adjR<sup>2</sup></th><th>CFI</th><th>TLI</th><th>RMSEA</th></tr></thead><tbody><tr><td>T1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Cubic model (saturated model)</td><td>0.0</td><td>0</td><td>—</td><td>0.247</td><td>0.000</td><td>0.295</td><td>1.000</td><td>1.000</td><td>0.000</td></tr><tr><td>26. Parallel, asym. weak congruence and strong incongruence effects</td><td>3.8</td><td>3</td><td>0.287</td><td>0.753</td><td>1.000</td><td>0.295</td><td>1.000</td><td>0.999</td><td>0.006</td></tr><tr><td>30. Asymmetric congruence effect rotated by Y</td><td>22.6</td><td>2</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.294</td><td>0.993</td><td>0.968</td><td>0.035</td></tr><tr><td><strong>36. Level-dependent incongruence effect rotated by Y</strong></td><td><strong>24.0</strong></td><td><strong>2</strong></td><td><strong>0.000</strong></td><td><strong>0.000</strong></td><td><strong>0.000</strong></td><td><strong>0.294</strong></td><td><strong>0.992</strong></td><td><strong>0.966</strong></td><td><strong>0.037</strong></td></tr><tr><td>T2</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Cubic model (saturated model)</td><td>0.0</td><td>0</td><td>—</td><td>0.352</td><td>0.001</td><td>0.368</td><td>1.000</td><td>1.000</td><td>0.001</td></tr><tr><td><strong>36. Level-dependent incongruence effect rotated by Y</strong></td><td><strong>2.6</strong></td><td><strong>2</strong></td><td><strong>0.272</strong></td><td><strong>0.562</strong></td><td><strong>0.750</strong></td><td><strong>0.374</strong></td><td><strong>1.000</strong></td><td><strong>0.999</strong></td><td><strong>0.006</strong></td></tr><tr><td>31. Asymmetric congruence effect rotated by X</td><td>5.2</td><td>2</td><td>0.076</td><td>0.157</td><td>0.210</td><td>0.373</td><td>0.999</td><td>0.996</td><td>0.014</td></tr><tr><td>23. Level-dependent incongruence effect</td><td>17.6</td><td>3</td><td>0.001</td><td>0.001</td><td>0.038</td><td>0.373</td><td>0.996</td><td>0.989</td><td>0.024</td></tr><tr><td>T3</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Cubic model (saturated model)</td><td>0.0</td><td>0</td><td>—</td><td>1.000</td><td>0.736</td><td>0.427</td><td>1.000</td><td>1.000</td><td>0.000</td></tr><tr><td>23. Level-dependent incong. effect</td><td>30.3</td><td>3</td><td>0.000</td><td>0.000</td><td>0.144</td><td>0.425</td><td>0.994</td><td>0.982</td><td>0.033</td></tr><tr><td><strong>36. Level-dependent incongruence effect rotated by Y</strong></td><td><strong>23.3</strong></td><td><strong>2</strong></td><td><strong>0.000</strong></td><td><strong>0.000</strong></td><td><strong>0.052</strong></td><td><strong>0.426</strong></td><td><strong>0.995</strong></td><td><strong>0.979</strong></td><td><strong>0.036</strong></td></tr><tr><td>32. Asymmetric incongruence effect rotated by Y</td><td>24.2</td><td>2</td><td>0.000</td><td>0.000</td><td>0.033</td><td>0.426</td><td>0.995</td><td>0.978</td><td>0.037</td></tr><tr><td>T1+T2+T3</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Cubic model (saturated model)</td><td>0.0</td><td>0</td><td>—</td><td>1.000</td><td>0.982</td><td>0.360</td><td>1.000</td><td>1.000</td><td>0.000</td></tr><tr><td><strong>36. Level-dependent incongruence effect rotated by Y</strong></td><td><strong>8.4</strong></td><td><strong>2</strong></td><td><strong>0.015</strong></td><td><strong>0.000</strong></td><td><strong>0.013</strong></td><td><strong>0.359</strong></td><td><strong>0.998</strong></td><td><strong>0.991</strong></td><td><strong>0.021</strong></td></tr><tr><td>31. Asymmetric congruence effect rotated by X</td><td>10.6</td><td>2</td><td>0.005</td><td>0.000</td><td>0.005</td><td>0.359</td><td>0.998</td><td>0.990</td><td>0.023</td></tr><tr><td>26. Parallel, asym. weak cong. and strong incongruence effects</td><td>11.4</td><td>3</td><td>0.010</td><td>0.000</td><td>0.000</td><td>0.358</td><td>0.996</td><td>0.989</td><td>0.023</td></tr></tbody></table>

Note. X= autonomy support; Y= creativity. Numbered models correspond to the 3 best-fitting families among 37 families (for complete lists, see SM-C). Models in bold font correspond to the best-fitting family selected for analysis. Parameters and response surfaces are reported in Table 4 and Fig. 3.
<table><caption>**Table 4**
Polynomial models of creativity-autonomy fit in self-regulation.</caption><thead><tr><th></th><th>T1</th><th>T2</th><th>T3</th><th>T1+T2+T3</th></tr></thead><tbody><tr><td>AUTO (b<sub>1</sub>)</td><td>0.215***</td><td>0.231***</td><td>0.253***</td><td>0.235***</td></tr><tr><td>CRE (b<sub>2</sub>)</td><td>0.400***</td><td>0.461***</td><td>0.490***</td><td>0.445***</td></tr><tr><td>AUTO*2 (b<sub>3</sub>)</td><td>0.012</td><td>0.035***</td><td>0.030***</td><td>0.027***</td></tr><tr><td>AUTO*CRE (b<sub>4</sub>)</td><td>-0.030*</td><td>-0.037*</td><td>-0.069***</td><td>-0.045***</td></tr><tr><td>CRE (b<sub>5</sub>)</td><td>-0.007</td><td>-0.018<sup>†</sup></td><td>-0.002</td><td>-0.009<sup>†</sup></td></tr><tr><td>AUTO*3 (b<sub>6</sub>)</td><td>0.020***</td><td>0.016*</td><td>0.012***</td><td>0.015***</td></tr><tr><td>AUTO*2* CRE (b<sub>7</sub>)</td><td>-0.020***</td><td>-0.016*</td><td>-0.012***</td><td>-0.015***</td></tr><tr><td>AUTO* CRE*2 (b<sub>8</sub>)</td><td>0.020***</td><td>0.016*</td><td>0.012***</td><td>0.015***</td></tr><tr><td>CRE*3 (b<sub>9</sub>)</td><td>-0.006*</td><td>-0.008<sup>†</sup></td><td>-0.008***</td><td>-0.007**</td></tr><tr><td>Variance explained (adj. R<sup>2</sup>)</td><td>29.4 %</td><td>37.4 %</td><td>42.6 %</td><td>35.9 %</td></tr></tbody></table>

Note. N = 8224 high school students, 441 classes. AUTO= autonomy support; CRE= creativity. Parameters b₁ to b₉ describe the effects of autonomy support (x) and creativity (y) on self-regulation (z), according to the cubic polynomial model $z = b₁x + b₂y + b₃x² + b₄xy + b₅y² + b₆x³ + b₇x²y + b₈xy² + b₉y³$. Fit indices and response surfaces are reported in Table 3 and Fig. 3.

† *p* ≤ 0.07; * *p* < 0.05; ** *p* < 0.01; *** *p* < 0.001.

<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Response surfaces of self-regulation as a function of creativity-autonomy fit.</figcaption></figure>

Note. N = 8224 high school students, 441 classes. Response surfaces for processes occurring on wave T1 (A), T2 (B), and T3 (C). For the integrative solution (T1+T2+T3), the response is reported along the three dimensions (D), along the LOC (or line of person-environment fit, i.e., *autonomy support* = *creativity*; E), and along the LOIC (or line of person-environment misfit, i.e., *autonomy support* = −*creativity*; F). Lines in red intersect reversal points (“r”) on the LOIC. Percentages correspond to students significantly affected by reversals in terms of self-regulation.

very high creativity provided with very low autonomy support (i.e., *autonomy support* = −*creativity* < −1.635 *SD*) resulted in a drop in self-regulation levels, which was significant for 3 % of the students in the sample. Conversely, at high values of positive misfit, experiencing moderately high autonomy support and moderately low creativity (i.e., *autonomy support* = −*creativity* > 0.827 *SD*) was related to a rapid increase in self-regulation, significantly raising self-regulation levels for 7 % of the students in the sample. Overall, the interpretation of the response surface along the LOC (positive linear) and LOIC (positive cubic pattern) indicated a person-environment fit process in which teacher autonomy support played a compensatory role in the absence of student creativity (validation of H3b).

## 6. Discussion

This study shows that student creativity in learning (i.e., creative cognitions while studying) plays a key role in supporting self-regulated learning among vocational students, especially when it is provided in conjunction with opportunities for autonomous learning via teachers’ behaviour. A comparison of alternative modelling strategies further demonstrated that the relations among these constructs are complex and may require differentiating lagged and contemporaneous effects in dynamics of change. Overall, these findings (for an overview, see Fig. 4) have strong implications for creativity theory and methodology, and for interventions to promote
the educational benefits of creative learning among at-risk students in vocational education.

## 6.1. Summary of main findings

### 6.1.1. Creativity enhances the emergence of day-to-day self-regulated learning

A major contribution of this study was to provide direct evidence that student creativity contributes to the emergence of self-regulation abilities, that is, evidence for a process of creativity for self-regulation (see Section 2.1; validation of H1b). This was demonstrated by using conventional (CLPM) as well as more advanced models (i.e., RI-CLPM) for predictive causality, which enabled the differentiation of individual traits from dynamic states of self-regulation and creativity. The comparison of alternative temporalities of effects (lag0 vs. lag1 effects) showed that the predictive effect of creativity on self-regulation was better described as immediate rather than deferred over time. In other words, variations in creativity on occasion T (e.g., on Semester 2) predicted contemporaneous or “day-to-day” (proximal) variations in self-regulation on the same occasion T, as opposed to variations in future or “over time” self-regulation (e.g., on Semester 3). Because the models controlled for major confounders of change (i.e., trait factors and past dynamic states), the observed predictive effects of state T creativity on state T self-regulation could reasonably be attributed to the pure (uncontaminated) within-person variance in creativity levels occurring on state T, thereby suggesting a causal effect (Hamaker et al., 2015; Núñez-Regueiro et al., 2022; but see Limitations). This finding aligns with past research reporting the beneficial effects of creative activities or creative cognition in reinforcing self-regulation or adaptation in educational contexts (Verger et al., 2024b; Xu & Wang, 2022). As discussed next, this contemporaneous effect partly relied on the perceived availability of teacher autonomy support (see Section 6.1.2).

### 6.1.2. High teacher autonomy support is optimal for day-to-day self-regulation and creativity

A second contribution of this study was to show that the beneficial effects of creativity on day-to-day (proximal) self-regulation processes were partly dependent on the amount of autonomy support perceived from teachers in classroom activities (validation of H3). Aligning with a process of person-environment fit compensated by teacher autonomy support (validation of H3b), self-regulation reached its highest level when high creativity was met with high autonomy support, but dropped when it was met with low autonomy support. Conversely, a compensatory process occurred for students with low creativity and high autonomy support from their teacher, which resulted in substantially increased self-regulation levels. Because this complex process concerned a nontrivial amount of students (10 % of the sample), replicated across measurement occasions and appeared as the best-fitting solution among 37 P-E fit alternatives, it is safe to say that it provided the most accurate representation of the present data (Núñez-Regueiro & Juhel, 2022). In addition, a complementary process of mediation was identified whereby teacher autonomy support contributed to self-regulation by sustaining creative cognitions (validation of H2). This mediation process was only partial, because the remaining effect of teacher autonomy support on self-regulation responded to the P-E fit with creativity. In other words, creative learning contexts in this study appeared to be driven both by a process of fit between person (student creativity) and environment (teacher autonomy support) characteristics (Gajda et al., 2017; Kaufman & Beghetto, 2013), and by the learning context created by the teacher in support of autonomous learning. This observation, along with the finding that teacher autonomy support also compensated for low creativity in P-E fit processes, highlights the educational benefits of fostering feelings of autonomy in classroom activities, as previous research had demonstrated in relation to self-regulation (León et al., 2015; Martinek et al., 2016), and to other youth outcomes as well (Núñez-Regueiro et al., 2024; Ryan & Deci, 2020; Slemp et al., 2024).

<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Synthesis of relations among constructs within and across occasions. Note. Dashed arrows denote immediate effects within an occasion (contemporaneous effects), whereas solid arrows denote short-term effects of one construct onto another across occasions (lagged effects). In the present study, occasions corresponded to semesters.</figcaption></figure>
### 6.1.3. Self-regulation dominates predictive relations to creativity and autonomy support

Finally, this research demonstrated that self-regulation played a dominant role in its relations to creativity and autonomy support, in the sense that it appeared to be a cause rather than an effect of these two constructs. Although creativity positively predicted changes in self-regulation over time (lagged effect), the reciprocal effect of self-regulation on creativity was nearly twice as large (i.e., $β = 0.14$ vs. $β = 0.08$), which can be interpreted as evidence for the causal dominance of self-regulation on creativity (Hamaker et al., 2015). This provides strong support for the model of self-regulation for creativity in which self-regulation abilities are a prerequisite for the creative process (validation of H1a; Ivcevic & Nusbaum, 2017), while also expanding this model with evidence for a reverse causal effect of creativity on self-regulation, albeit of a different, more immediate nature.

Moreover, it was found that positive changes in self-regulation on one occasion T (e.g., semester 1) were related to moderate, positive changes in teacher autonomy support on a subsequent occasion $T + 1$ (e.g., semester 2; $β = 0.08$). The reciprocal lagged effect of teacher autonomy support appeared much weaker and not statistically significant after controlling for the correlation between baseline levels (using the RI-CLPM). In other words, students reported higher levels of autonomy support after experiencing increased self-regulation abilities in a previous semester, but increases in autonomy support did not predict higher self-regulation abilities in a future semester (albeit it supported proximal or day-to-day self-regulation; see 6.1.2). This conclusion fully aligns with previous findings among Dutch secondary students (Schuitema et al., 2016) and suggests that teacher behaviour (e.g., autonomy support) is malleable to changes in student behaviour (e.g., self-regulation), although the effects of this malleability are delayed (e.g., from one semester to another).

## 6.2. Limitations

Some limitations need to be considered before drawing implications from this study. First of all, the study was conducted in the context of vocational education in France, the student population of which has learning and motivational difficulties that may not reflect the general population in France or in other national contexts. However, the alignment of the findings with previous research and theory suggests strong external validity.

Second, all data were self-reported by students and may have lacked accuracy due to potential biases (e.g., consistency, self-presentation biases; Paulhus & Vazire, 2007). Although student reports are the standard way of measuring teacher autonomy support (Aelterman et al., 2019; Slemp et al., 2024), observations of teaching behaviour reported by independent evaluators might provide a more direct measurement approach (Gajda et al., 2017). Recent methodological developments have enabled automatising the encoding of recorded teaching behaviour (e.g., teacher messages framed as gains or losses; Falcon et al., 2023), thus opening the possibility for large-scale open data encoding. This could be used to encode teacher autonomy support in student-teacher interactions in future studies.

Similarly, our measure of creativity (the UCCS) was self-reported and may therefore lack objectivity and reliability found in other types of measures. More specifically, the UCCS asks students to rate the frequency with which they use original or flexible strategies to solve learning tasks. As a latent construct, this is less tangible than creativity measures based on observable performance tasks (e.g., drawing, writing a story, or producing concrete creative products; see Gajda et al., 2017; Zielińska et al., 2022). At the same time, it could be argued that such performance tasks may not fully reflect the creative thinking most commonly required in school, where students are typically expected to generate effective and original ideas in response to academic challenges. By contrast, the UCCS's focus on creativity-in-action during learning may offer a level of contextual alignment that is well suited to the study of creative processes in educational settings. This distinction should be kept in mind when interpreting our findings, as the relations between creativity and self-regulation in this applied context (i.e., academic learning) may differ from those observed in more general or decontextualised creativity tasks.

Finally, the data collection was designed in such a way that measurement occasions corresponded to semesters. This resulted in time intervals spanning roughly 5 months between occasions (excluding summer vacations), which corresponds to intervals used in previous longitudinal data analysis on the topic (Schuitema et al., 2016), but is considerably larger than more fine-grained, micro-analyses of teaching and creativity behaviours occurring in 45-minute lessons (Gajda et al., 2017). In the present study, we adopted a balancing approach that approximated fine-grained time intervals (similar to Gajda et al., 2017) in the form of contemporaneous effects relating state changes within a semester, while also accounting for larger time intervals relating state changes across semesters (similar to Schuitema et al., 2016). This approach, defended elsewhere (Marsh et al., 2024), offers the advantage of integrating dynamics of change across multiple temporal scales.

Yet, this approach also has the disadvantage of challenging the notion that causal effects should be prospective in nature (i.e., from one measurement occasion to another), especially as it relates to contemporaneous effects (Muthén & Asparouhov, 2024). It should be recalled, here, that contemporaneous effects in this study were based on residuals that controlled for stable and dynamic sources of endogeneity in processes of change (i.e., stable trait factors and past dynamic states on T-1) and were therefore strongly exogenous in nature, which is an essential condition for causal inference (Imbens & Rubin, 2015). Nonetheless, future studies might consider using shorter time intervals for data collection (e.g., spanning a few weeks) and see whether this factor modifies the present results in a nontrivial way.

## 6.3. Implications for creativity research and educational practice

The present findings consolidate and expand knowledge on creative processes at school and on their relation to self-regulated learning. On the one hand, the finding that self-regulation dominated the relation to creativity provides novel evidence, of a causal
nature (i.e., predictive or Granger-causality; Granger, 1969; Hamaker et al., 2015), that self-regulation abilities are key to the emergence of creativity in the school context, as theorised in the process of self-regulation for creativity (Ivcevic & Nusbaum, 2017). Previous studies have shown that self-regulation abilities occurring before, during and after the completion of a creative activity (e.g., writing a short story), correlated positively to obtaining the creative product (Ivcevic et al., 2024b; Zielińska et al., 2022).

Our study sheds new light on this process, showing that self-regulation abilities exogenous to creative processes (i.e., self-regulated learning) contribute uniquely to creativity development. This also aligns with Stephen King's advice on sustaining creative work (cited in Zielinska et al., 2022, p. 1): First, build a toolbox—e.g., self-regulation abilities—, and then use those tools to overcome obstacles during the creative process and complete one's creative products. In the present study, the creative products corresponded to creative cognitions (i.e., our measure of creativity) used by students to overcome obstacles by applying novel ideas or resolution strategies during learning tasks. Without self-regulation abilities (i.e., resource management, cognitive, and metacognitive strategies to monitor learning progress; Pintrich, 2000; Pintrich & De Groot, 1990), the original thoughts or actions envisioned by students in our study were insufficient to sustain creative cognitions over time.

On the other hand, the finding that creativity (creative cognitions) fostered the emergence of day-to-day self-regulation abilities opens new theoretical avenues, underscoring the existence of a process we theorised as creativity for self-regulation. This process complements the process of self-regulation for creativity where self-regulation abilities shape creativity (Ivcevic & Nusbaum, 2017), through a reverse causal pathway in which self-regulation abilities are shaped by creative cognitions. This points to the existence of a virtuous circle by which self-regulation and creative processes may reinforce each other at school, but also of a more vicious circle in which less creative students find themselves trapped in the incapacity to develop their self-regulated learning in day-to-day activities. To prevent such entrapment, teacher autonomy support could play a fundamental compensatory role by serving as a resource to foster creativity (as suggested by findings on reciprocal relations) and by encouraging less creative students to engage in self-regulated learning (as suggesting by findings on P-E fit processes). In the long run, this kind of "self-regulation prompts" (Zielińska, Lebuda, Czerwonka et al., 2024) from the teacher might induce a process of self-regulation for creativity, thereby liberating students from their low-creative entrapment.

This compensatory effect confirms teacher autonomy support as a key lever to foster educational contexts where youth can thrive and develop their full potential as human beings, as self-determination theory has consistently shown in decades of research (Ryan & Deci, 2017, 2020; Slemp et al., 2024). Future studies might investigate whether this improvement can be explained by the reinforcement of students' self-perceptions of being creative (i.e., creative self-concept) and their ability to overcome adversity (i.e., growth mindset), which are two major correlates of creative self-regulation at school (Zielińska et al., 2022). The fact that the present study was conducted among vocational students is very encouraging in this regard. Indeed, previous research has found that vocational students (in France or in similar systems, e.g., Flanders) lack basic academic skills and the motivational resources that allow them to persevere, be creative and find meaning in their school activities (Núnez-Requeiro, 2018; Van Houtte, 2016; Lannegrand-Willems et al., 2012; Van Houtte, 2016). The present findings suggest that developing teacher autonomy support might be an effective means to help low-creative and low-self-regulated students reconnect with their learning processes in vocational education. Numerous interventions exist to increase teacher autonomy support, with positive effects on both students' outcomes and teaching practice (Reeve & Cheon, 2021). Teacher autonomy support is also more likely to grow if teachers are themselves supported in their need for autonomy by the school system in which they evolve (Slemp et al., 2020).

At a methodological level, this study also underscores the importance of differentiating trait and state factors in modelling processes of change in creativity studies. When it comes to quantifying processes of change over time, quite distinct results may be found depending on whether stable (unvarying) differences between individuals are or are not conflated with dynamic states (Hamaker et al., 2015; Núnez-Requeiro et al., 2022). In this study, the CLPMs (conflated approach) suggested that teacher autonomy support, creativity, and self-regulation had mutual influences on each other that unfolded over several months, corresponding to the intervals between measurement points (see SM-B). By contrast, the RI-CLPMs (not conflated) revealed that many of these lagged influences were spurious in nature and were driven by the correlations between trait factors that, when ignored in the CLPM, appeared in the guise of prospective effects. Instead, the effects of creativity and autonomy support on self-regulation appeared to be immediate (vs. lagged; see SM-B and Results).

A takeaway message is that, when investigating reciprocal effects between dynamic constructs (e.g., between self-regulation and creativity), creativity researchers would do well to account for trait factors to minimise the risk of obtaining spurious effects, and consider both lagged and contemporaneous effects to shed light on antecedents and consequences of creativity. More generally, beyond issues of model identification, increasing precision in the conceptualization and empirical testing of change processes can enhance the validity of theoretical claims. This involves specifying the temporal dynamics through which creativity-related constructs unfold and interact over time. Such an approach aligns with recent calls for greater theoretical specificity in psychological research, particularly regarding the mechanisms and temporalities of change (Murayama & von Keyserlingk, 2025).

## 7. Conclusion

This study provided evidence that creativity and self-regulated learning work in tandem at school, especially in creative learning contexts. High self-regulation abilities applied to specific learning goals can contribute to effectively develop creativity of a general nature over time (i.e., creative cognitions). Conversely, creativity may foster day-to-day, specific self-regulation abilities for learning (i.e., metacognitive, cognitive, and resource management strategies), insofar as it is provided with sufficient autonomy support from the teacher. Within this bidirectional relationship between creativity and self-regulation processes at school, teacher autonomy support plays a dual role, both enhancing the benefits of creative learning and compensating for low creativity. Optimal synergies
between teacher autonomy support, student creativity and self-regulated learning are therefore crucial to improve the motivational and learning processes of at-risk students who need it the most.

## Funding

This research was supported financially by the French Ministry of National Education, Youth and Sports (MENJS); the Ministry of Higher Education, Research and Innovation (MESRI); "Mission Monteil pour le numérique éducatif ", and the "Programme d'investissements d'avenir, expérimentation ProFAN" (PIA).

## CRediT authorship contribution statement

**Fernando Núnez-Regueiro:** Writing – review & editing, Writing – original draft, Formal analysis, Data curation. **Nicolas B. Verger:** Writing – review & editing, Writing – original draft, Validation, Formal analysis, Data curation. **Pascal Bressoux:** Writing – review & editing, Validation, Supervision, Resources, Project administration, Methodology, Investigation. **Anatolia Batruch:** Project administration, Methodology, Investigation, Funding acquisition, Conceptualization. **Marinette Bouet:** Project administration, Methodology, Investigation, Funding acquisition, Conceptualization. **Marco Bressan:** Project administration, Methodology, Investigation, Funding acquisition, Conceptualization. **Genavee Brown:** Project administration, Methodology, Investigation, Funding acquisition, Conceptualization. **Fabrizio Butera:** Project administration, Methodology, Investigation, Funding acquisition, Conceptualization. **Anthony Cherbonnier:** Project administration, Methodology, Investigation, Funding acquisition, Conceptualization. **Céline Darnon:** Resources, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization. **Marie Demolliens:** Project administration, Methodology, Investigation, Funding acquisition, Conceptualization. **Anne-Laure de Place:** Writing – review & editing, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization. **Olivier Desrichard:** Project administration, Methodology, Investigation, Funding acquisition, Conceptualization. **Luc Goron:** Project administration, Methodology, Investigation, Funding acquisition, Conceptualization. **Brivael Hémon:** Project administration, Methodology, Investigation, Funding acquisition, Conceptualization. **Pascal Huguet:** Project administration, Methodology, Investigation, Funding acquisition, Conceptualization. **Eric Jamet:** Project administration, Methodology, Investigation, Funding acquisition, Conceptualization. **Vincent Mazenod:** Project administration, Methodology, Investigation, Funding acquisition, Conceptualization. **Nathalie Mella-Barraco:** Project administration, Methodology, Investigation, Funding acquisition, Conceptualization. **Estelle Michinov:** Project administration, Methodology, Investigation, Funding acquisition, Conceptualization. **Nicolas Michinov:** Project administration, Methodology, Investigation, Funding acquisition, Conceptualization. **Nana Ofosu:** Project administration, Methodology, Investigation, Funding acquisition, Conceptualization. **Laurine Peter:** Methodology, Investigation, Conceptualization. **Céline Poletti:** Methodology, Investigation, Conceptualization. **Isabelle Régner:** Methodology, Investigation, Conceptualization. **Mathilde Riant:** Writing – review & editing, Project administration, Methodology, Investigation, Conceptualization. **Anaïs Robert:** Project administration, Methodology, Investigation, Funding acquisition, Conceptualization. **Ocyna Rudmann:** Project administration, Methodology, Investigation, Funding acquisition, Conceptualization. **Camille Sanrey:** Project administration, Methodology, Investigation, Funding acquisition, Conceptualization. **Arnaud Stanczak:** Project administration, Methodology, Investigation, Conceptualization. **Farouk Toumani:** Project administration, Methodology, Investigation, Conceptualization. **Emilio Paolo Visintin:** Project administration, Methodology, Investigation, Conceptualization. **Pascal Pansu:** Writing – review & editing, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization.

## Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

## Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.tsc.2025.101873.

## Data availability

Data will be made available on request.

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