The daily dynamics of basic psychological need satisfaction at work, their determinants, and their implications: An application of Dynamic Structural Equation Modeling

ABSTRACT

Drawing on self-determination theory, this study focuses on the person- and occasion-specific components of the daily dynamics of employees’ global psychological need satisfaction at work. Predictors (job demands related to information and communication technologies, segmentation norms, and workload) and outcomes (perceived productivity, psychological detachment, work-family conflict, job satisfaction, and personal satisfaction) were also examined across both levels to better grasp the mechanisms underlying these short-term dynamics. A total of 129 French employees filled out questionnaire surveys at the end of each workday for five days (521 observations). Results from Dynamic Structural Equation Modeling (DSEM) showed clear associations between need satisfaction, the predictors, and the outcomes at the person-specific level. However, and although need satisfaction levels were found to fluctuate on a daily basis, they seemed immune to the effects of daily fluctuations in predictor levels, and unlikely to generate matching fluctuations in outcome levels. These results suggest strong homoeostatic processes protecting employees’ functioning against daily fluctuations, but that the accumulation of such fluctuations over the work week may jeopardize these processes.

KEYWORDS:

• Basic psychological need satisfaction
• job demands
• well-being
• self-determination theory
• dynamic structural equation modeling

Acknowledgements

We would like to thank C. Douillet, C. Delaunoy, L. Kerrouche, L. Massenet-Valac, and M. Pieltin for their invaluable help with this study’s data collection. We would also like to thank N. Stefaniak for his input during preliminary steps of data analysis.

Correspondence should be addressed to Tiphaine Huyghebaert-Zouaghi; Université de Reims Champagne Ardenne; UFR Lettres et Sciences Humaines; Laboratoire Cognition, Santé, Société; 57 rue Pierre Taittinger, Reims Cedex 51 571, France. Email: [email protected]

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data are available upon request from the authors.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/1359432X.2023.2276534.

Notes

1. We hereafter use the term “global BPNS” to reflect the global experience of need satisfaction anchored in all three needs (i.e., capturing the satisfaction of all three needs in a single factor). In research anchored in basic psychological needs theory (Vansteenkiste et al., 2020), this terminology (i.e., global) does not reflect a trait-like level of analysis such as that proposed in Vallerand’s (1997) hierarchical model of motivation.

2. By lagged predictions, we mean using predictors measured at Time t to predict outcomes (e.g., BPNS) measured at Time t + 1, while controlling for the autoregressive stability of the occasion-specific outcome levels (i.e., controlling for the effects of outcome levels measured at Time t-1 on outcome levels measured at Time t).

3. Preliminary analyses showed that lag 2 or 3 autoregressions or predictions did not add to the model.

4. These are factor scores saved from preliminary measurement models defined using the referent indicator approach (i.e., fixing the loading and intercept of a referent indicator to respectively 1 and 0). Although this approach maximizes the similarity between the original scaling of the measure and that of the latent factors, it never perfectly replicates it. The 4 to 8 range simply reflects the fact that no participant had very low levels of BPNS and indicates that the scaling of the factor scores was slightly higher than that of the original measure (1 to 7). The 4 to 8 scale is the one relevant for interpretations.

5. No evidence of a correlation was found between person-specific levels of BPNS and the random slope reflecting inter-individual differences in the size of the autoregressions (b = .016 [−.080 to .198]; β = .090).