In the evolving landscape of the post-pandemic labor market, university students face unprecedented challenges and opportunities in their employment choices. A groundbreaking study led by Cai, Chen, Wang, and colleagues delves deeply into the motivational and contextual factors influencing students’ decisions to pursue flexible employment arrangements. Employing a sophisticated combination of Partial Least Squares Structural Equation Modeling (PLS-SEM), Artificial Neural Networks (ANN), and Fuzzy-Set Qualitative Comparative Analysis (fsQCA), this research offers a multi-layered perspective on what drives flexible employment intentions among this demographic.
At the heart of the investigation lies the complex interplay between intrinsic and extrinsic motivation, psychological mediators, and demographic variables. The study reveals that intrinsic motivation—stemming from an individual’s internal desire for autonomy, competence, and relatedness—is not a straightforward predictor of job intention. Instead, it operates predominantly through indirect pathways involving crucial mediators such as work-life balance, job satisfaction, and self-development perception. This nuanced finding aligns with Self-Determination Theory (SDT), which posits that psychological needs must be met within supportive conditions to translate into behavioral intentions.
On the other hand, extrinsic motivation emerges as a more direct and multifaceted force exerting significant influence on students’ employment intentions. This form of motivation, often propelled by external rewards, social expectations, and practical considerations, is shown to act both directly and indirectly via the same mediators that channel intrinsic desires. The Theory of Planned Behavior (TPB) finds empirical grounding here, reinforcing the notion that external attitudes and social norms profoundly shape behavioral intentions, particularly in a labor market that increasingly values flexibility.
The multidimensional roles of mediators such as work-life balance and job satisfaction are highlighted repeatedly throughout the study. Notably, these factors surface as pivotal drivers in both PLS-SEM and ANN analyses. The Artificial Neural Network model assigns job satisfaction the highest relative importance, underscoring its critical position in influencing flexible employment choices. This dual-methodological approach transcends the traditional linear modeling of employment intentions, acknowledging the complexity and nonlinearity inherent in human decision-making processes.
Moreover, the research identifies significant demographic moderators with practical implications. Gender and household registration type—the latter distinguishing local from non-local students—demonstrate meaningful differences in how extrinsic motivation influences job intention through work-life balance and satisfaction. This finding cautions against one-size-fits-all motivational strategies and champions a more personalized approach to leadership and management in educational and organizational settings. Other demographic factors, such as work experience, volunteer experience, and career planning, while evaluated, showed less pronounced moderating effects.
The study’s integration of Leadership and Motivation Theory further illuminates the importance of tailoring engagement strategies across diverse student populations. Contemporary leadership frameworks emphasize the necessity of recognizing intrinsic and extrinsic motivational differences as critical levers for optimizing organizational and individual outcomes. By dissecting these relationships in a nuanced post-pandemic context, the researchers contribute a timely blueprint for educators and employers aiming to foster innovation and satisfaction among emerging workforce members.
Another groundbreaking facet of this research lies in its application of fuzzy-set qualitative comparative analysis (fsQCA). Departing from traditional variable-centric methods, fsQCA uncovers configurations—or combinations—of factors that collectively lead to high levels of employment intention. Work-life balance and extrinsic motivation, in particular, emerge as central or peripheral conditions within five distinct causal pathways. Configuration 1 stands out for its strong consistency and unique coverage, confirming the importance of multidimensional synergy in shaping students’ intentions toward flexible employment.
These fsQCA insights resonate strongly with Social Exchange Theory (SET), which highlights the reciprocal nature of benefits influencing decision-making processes. In this context, students weigh personal priorities like maintaining work-life balance against employer-offered extrinsic incentives, reflecting a dynamic exchange that shapes flexible employment preferences. This perspective enriches the theoretical discourse by framing employment intentions as outcomes of negotiated mutual benefits, particularly salient in atypical or evolving work arrangements post-pandemic.
The research’s methodological innovations merit particular attention. Historically, studies of employment intention have relied heavily on linear, often simplifying statistical techniques which inadequately capture the nuanced realities of decision-making. By combining PLS-SEM with ANN, this study harnesses the strengths of each: PLS-SEM’s capacity for hypothesis testing and ANN’s prowess in modeling nonlinear and complex interactions. As the authors demonstrate, this blend reveals subtle distinctions in variable influence and relative importance, advocating for more integrative methodological designs in future labor market research.
Additionally, the inclusion of fsQCA adds another layer of sophistication by shifting analytical focus from isolated factors to configurational causality. This method uncovers the multiplicity of pathways that lead to similar employment intentions, challenging the notion of linear causality and embracing the complexity of human behavior shaped by diverse interacting elements. Together, these methodological advancements offer a replicable paradigm for examining other multifaceted social phenomena beyond employment choices.
Despite the richness of its contributions, the study acknowledges important limitations and avenues for future inquiry. The sample, predominantly composed of Chinese university graduates, raises questions about generalizability. The unique socio-cultural and economic environment in China may shape employment tendencies differently than in other global contexts. To deepen the validity and applicability of findings, future research would benefit from cross-cultural or cross-regional comparative studies, incorporating broader participant demographics including non-graduate and postgraduate students.
Moreover, the cross-sectional design of the study captures a snapshot during a transitional labor market but cannot fully address the dynamic evolution of employment intentions over time. Longitudinal research is encouraged to unpack temporal shifts, particularly as economic conditions fluctuate and labor market structures adapt further in a post-pandemic world. Tracking changes across semesters or years would illuminate trajectories and causal pathways that remain obscured in single-point data.
The reliance on self-report measures represents another limitation, introducing potential social desirability and reporting biases. Triangulating data using qualitative interviews, experimental interventions, or third-party employment statistics could strengthen future investigations. Employing mixed-methods designs would also allow researchers to explore the lived experiences and contextual nuances underpinning motivational mechanisms.
Furthermore, the study’s variable set primarily emphasizes subjective psychological constructs such as work-life balance and job satisfaction. Yet, other influential factors—social support networks, career development opportunities, labor market demands, and emerging digital skill requirements—remain underexplored. Broadening the variable scope in subsequent research could offer a more holistic and actionable understanding of flexible employment choices. Special emphasis on cultural variations in social support and career opportunity perceptions would be particularly enlightening.
In sum, this study propels employment intention research beyond conventional theoretical boundaries, combining novel conceptual frameworks with demanding analytical tools to map the terrain of flexible employment choices among university students. Its findings underscore the primacy of work-life balance and job satisfaction while parsing the nuanced roles of intrinsic and extrinsic motivation. By marrying classical theories like SDT, TPB, and SET with modern methodologies, Cai and colleagues deliver a compelling narrative about the future of work preferences in a changed world.
The implications extend beyond academia into practical domains of education and enterprise management. Universities can leverage these insights to craft career services and motivational programs responsive to diverse student backgrounds and evolving labor market expectations. Simultaneously, employers across sectors can design more flexible and supportive work environments that resonate with younger generations’ psychological needs and external considerations, fostering retention, innovation, and agility.
As the pandemic accelerates shifts toward flexible work modalities, understanding the multifaceted drivers of such employment choices becomes ever more critical. This study’s pioneering integration of structural, neural, and configurational analyses offers not only a theoretical map but also a strategic compass for navigating and shaping the future of work for emerging talents worldwide.
Subject of Research: University students’ flexible employment intentions in the post-pandemic labor market and the motivational, psychological, demographic, and contextual factors influencing these choices.
Article Title: Post-pandemic job market: an analysis of factors influencing university students’ willingness for flexible employment based on SEM-ANN-fsQCA.
Article References:
Cai, Q., Chen, W., Wang, M. et al. Post-pandemic job market: an analysis of factors influencing university students’ willingness for flexible employment based on SEM-ANN-fsQCA. Humanit Soc Sci Commun 12, 793 (2025). https://doi.org/10.1057/s41599-025-05117-y
Image Credits: AI Generated
