In the rapidly evolving landscape of higher education, the intricate relationship between academic staff job performance and job burnout has garnered increasing attention. A groundbreaking study conducted in China spanning the years 2020 to 2023 delves deeply into this complex dynamic, employing advanced statistical modeling to illuminate how job performance impacts burnout levels among university faculty. This extensive research is particularly timely, given the global shock precipitated by the COVID-19 pandemic and its profound influence on psychological well-being and professional efficacy in academia.
At the heart of this study lies the utilization of panel data, a robust analytic approach that synthesizes the benefits of both cross-sectional and time-series data. Unlike traditional methods that might capture a snapshot or a simple trend, panel datasets track multiple observations of the same subjects over time. This richness in data allows for a more nuanced exploration of individual variations and temporal shifts, especially pertinent when studying phenomena like burnout that evolve gradually and are influenced by multifaceted factors. The researchers’ choice to rely on panel data is also instrumental in mitigating biases that often plague observational studies, such as unobservable individual differences that could skew results.
An innovative feature of this study involves the deployment of the interactive fixed effects model developed by Bai (2009), a sophisticated extension of classic fixed effects models. This approach takes into account not only individual and time effects but also multifarious dynamic shocks affecting all participants in the panel. Such multidimensional treatment is crucial amid a global pandemic context, where shared external shocks—like lockdowns, remote work transitions, and mental health challenges—introduce cross-sectional dependencies and complicate causal inference. By estimating underlying common factors and assigning individualized response patterns, this model adeptly controls for simultaneity bias, a persistent challenge when disentangling bidirectional influences between job performance and burnout.
The empirical foundation of this research is drawn from a meticulously curated panel dataset comprising 20,620 academic staff members from twelve Chinese universities, strategically sampled across eastern, central, and western regions. Leveraging China’s well-established regional classifications and the stratification of universities by national designations—namely 985 Project, 211 Project, regular, and vocational institutions—the sampling process ensured both representativeness and diversity. The inclusion criteria were stringent: participants had to be active employees with complete KPI (Key Performance Indicator) data from 2020 through 2023, enabling longitudinal insights and preserving data integrity.
Job performance in this study was evaluated via KPI systems standardized by the Chinese Ministry of Education. These indices encompass ethical conduct, quality of teaching, research output, and administrative responsibilities, offering a multifaceted measure of academic contributions. Performance levels were categorized into four tiers: non-performance, low, average, and high performance, corresponding respectively to unqualified, basically qualified, qualified, and excellent descriptors. Such granular classification facilitates detailed subgroup analyses to discern nuanced patterns in the burnout-performance relationship.
Burnout, a psychological syndrome characterized by emotional exhaustion, depersonalization, and reduced personal accomplishment, was quantified using a triangulated approach involving three widely validated instruments: the Oldenburg Burnout Inventory (OLBI), the Maslach Burnout Inventory (MBI), and the Copenhagen Burnout Inventory (CBI). Mental health centers within the sampled universities routinely assess faculty members through online group assessments supplemented by individual face-to-face evaluations. The resulting burnout levels are stratified into non-burnout, low, moderate, and high categories, allowing researchers to track severity and progression over time objectively.
The study conspicuously incorporates psychological counselling as a moderating variable, acknowledging its critical role in mitigating burnout. Rooted in mandates by the Chinese State Council and National Health Commission, university mental health centers provide counselling services at varying frequencies tailored to individual needs. The counselling engagement levels—ranging from no counselling to short-, medium-, and long-term interventions—offer a spectrum of support intensity. By analyzing these frequencies alongside performance and burnout metrics, the researchers explore how psychological counselling influences both the prevalence and intensity of burnout in an academic context.
A rigorous stratified random sampling method underpins participant selection, minimizing sampling bias and enhancing the generalizability of findings. The personnel departments of selected universities aided data collection by randomizing staff selection via office automation systems, ensuring impartial representation across performance groups. Furthermore, secondary data sources, including KPI results, psychological counselling logs, and medical burnout reports, were meticulously harmonized. This archival data usage effectively circumvents common method bias, which often distorts survey-based research, bolstering the credibility of the conclusions drawn.
Ethical considerations were paramount throughout the study. Institutional review board approval was secured prior to data collection, and participating universities agreed to anonymity and voluntary involvement stipulations. Additionally, sensitive data were encoded using university region codes combined with participant numbers to uphold confidentiality, preventing any identification of individual respondents. Researchers thus maintained strict adherence to privacy principles while having access to comprehensive longitudinal datasets.
Data analysis unfolded in a multistep process, combining multiple regression, group regression, and hierarchical linear regression techniques. Initial analyses tested the primary hypothesis linking job performance to burnout, revealing complex interactions that varied across performance levels. Subsequent moderated regression analyses unveiled the buffering effect of psychological counselling, elucidating how different counselling frequencies attenuate the adverse psychosocial impacts of burnout on faculty performance. Group regressions further substantiated these moderation effects within distinct performance strata, ensuring robustness against sample heterogeneity.
The findings highlight a dynamic and reciprocal relationship between job performance and burnout. Higher performance levels tend to correlate with lower burnout incidence, yet this pattern is substantially influenced by the presence and intensity of psychological counselling. In particular, individuals receiving medium- to long-term counselling exhibited marked resilience against burnout despite demanding performance expectations. This underscores the imperative of institutional mental health support systems as integral components of faculty development and retention strategies.
Moreover, the research elucidates subtle regional disparities in burnout trends linked to varying institutional pressures and resource availability. Eastern regions, often home to more prestigious universities with demanding performance criteria, displayed heightened sensitivity to burnout symptoms absent adequate counselling. Conversely, central and western regions showed more heterogeneous patterns, suggesting contextual factors such as infrastructure and administrative practices further modulate the burnout-performance interplay.
This study is pioneering in its methodological rigor and comprehensive scope, setting a new standard for burnout research in academia. By combining advanced panel data econometrics with rich, multifaceted data sources, it transcends previous limitations imposed by cross-sectional designs and simplistic models. The implications extend beyond China, offering valuable lessons for international higher education systems grappling with workforce well-being amid unprecedented global challenges.
Future research avenues emerging from this work include longitudinal tracking of post-pandemic recovery trajectories, exploration of digital counselling modalities, and deeper investigation into disciplinary and gender-specific burnout patterns. Additionally, integrating qualitative insights with quantitative findings could enrich understanding of individual lived experiences behind the data trends.
In conclusion, the synergy between academic job performance and burnout is a complex, evolving phenomenon demanding sophisticated analytical frameworks and holistic institutional interventions. This study’s innovative use of the interactive fixed effects model of panel data, combined with comprehensive secondary datasets and stratified sampling, provides compelling evidence that psychological counselling serves as a critical moderator. By fostering more resilient academic environments, universities can safeguard both their staff’s well-being and their institutional excellence in an increasingly competitive global knowledge economy.
Subject of Research: The influence of academic staff job performance on job burnout and the moderating effect of psychological counselling among university faculty in China.
Article Title: The influence of academic staff job performance on job burnout: the moderating effect of psychological counselling.
Article References:
Lei, M., Alam, G.M. & Bashir, K. The influence of academic staff job performance on job burnout: the moderating effect of psychological counselling.
Humanit Soc Sci Commun 12, 749 (2025). https://doi.org/10.1057/s41599-025-05043-z
Image Credits: AI Generated
