Across European higher education systems, calls to fundamentally rethink research funding architectures are gaining momentum. Competitive grant schemes, dominated by peer reviewed calls, remain the prevailing model but face growing criticism. Rising application volumes reduce success rates without reflecting research quality (Naddaf, 2025). Competitive funding is also inefficient (Sandström & Van den Besselaar, 2018), requiring substantial reviewer time while selection quality remains uncertain (Bendiscioli, 2019; Guthrie, Ghiga I, & Wooding, 2018; Jerrim, & Vries, 2020). Moreover, objective assessment of research proposals remains challenging (Bornmann, et al., 2014), leaving room for informal mechanisms such as nepotism and gender bias (Sandström & Hällsten, 2004; Wennerås & Wold, 1997). For researchers, this system often results in increased workload, demotivation and diminished career prospects (Kinman, 2015; Winefield et al., 2003).

In response, Ghent University reformed its internal research funding model by replacing competitive calls with freely deployable, non-competitive research funding for every research-active professor (Upton, 2023). This reform aims to stimulate collaboration (e.g., pooling resources for PhD funding), support innovative research beyond call-driven logics, reduce workload and strengthen academic autonomy. Over time, it seeks to reshape research culture. This raises a fundamental policy question: Does non-competitive research funding achieve its intended effects, and how does it influence collaboration, workload, autonomy, motivation, research conditions and different forms of research?

To address this, Ghent University is developing a transparent and methodologically robust monitoring system combining survey data with researcher-level and organizational-level administrative data, which together provide several advantages.

First, survey data examine mechanisms underlying differences in research conditions and outcomes - such as job demands, job resources, autonomy and wellbeing – grounded in the Job Demand–Control model (Karasek & Theorell, 1990) and the Job Demands–Resources model (Bakker & Demerouti, 2017). These frameworks explain why  effects occur, rather than simply indicating whether they occur.

Second, the survey measures intentions and expected behavioural changes using the Theory of Planned Behaviour (Ajzen, 2011). Because intentions are strong predictors of future behaviour, they provide early signals of how researchers expect to adjust their research agendas, collaborations and resource use. These insights emerge long before traditional output indicators—such as publications, citations or grant applications—become visible and are less sensitive to external influences such as publication cycles, review delays or disciplinary norms.

Third, integrating administrative data and survey data provides a holistic perspective of academic research while reducing researcher burden. Administrative data capture structural characteristics (e.g., personnel records, publication counts, potentially funding data), while survey data highlight experiential and contextual dimensions absent from institutional systems 

Finally, insights from policy enactment research (Ball et al., 2012) inform the monitoring design, recognising that funding reforms are interpreted and shaped differently across organisational contexts.

Together, these elements allow two key contributions: (1) it broadening research evaluation beyond narrow output metrics by incorporating mechanisms, experiences and intentions; and (2) enhancing policy relevance through indicators that support evidence‑informed decision‑making while remaning sensitive to disciplinary diversity and varied research practices.

In this Ignite Talk, we present: (a) the indicator set under development, and (b) the conceptual survey instrument. Participants are invited to provide feedback on the conceptual model, indicator set, survey design and broader European applicability.

In line with the ECER theme, we conceptualise monitoring as a participatory policy technology: a collaborative tool for collective understanding and democratic improvement rather than a top‑down control mechanism. This work contributes to future research governance by providing an evidence‑informed and participatory monitoring model for emerging funding systems, and to SIG 23 by framing monitoring as a political and organisational practice shaped by actors, institutional cultures and interpretations of funding regimes.

The monitoring framework follows a multi-method design combining researcher-level and organizational-level administrative data with a comprehensive survey instrument. Administrative data provide objective information about research-related structural characteristics, such as personnel records, publication output, project participation and—where available—funding-related metadata. Survey data complement these records by capturing experiences, mechanisms and perceptions that are not embedded in institutional systems. The monitoring system and its indicators are developed through an iterative co design process involving faculties, the Research Council, the data protection officer, data managers, early career researchers and individual academics. This participatory approach ensures institutional legitimacy and alignment with disciplinary diversity. The monitoring system and survey instrument are theoretically grounded in two complementary models of work experiences and conditions: the Job Demand–Control model (Karasek & Theorell, 1990) and the Job Demands–Resources model (Bakker & Demerouti, 2017). These frameworks help explain how work demands, autonomy and available resources influence motivation, wellbeing and research conditions. The survey thereby uncovers mechanisms that may shape differences in output, collaboration or research environments. In addition, the Theory of Planned Behaviour (Ajzen, 2011) provides a framework for assessing intentions, which serve as powerful predictors of future behaviour. By mapping intentions and behavioural expectations, the monitoring system identifies early signs of possible changes in research practices, thematic orientations, collaboration patterns and resource use. This is particularly important because intentions shift faster than traditional output indicators, and are less influenced by external factors such as publication cycles, disciplinary variation or timing of funding calls. Together, administrative and survey data allow for a holistic, multidimensional understanding of academic research. This integrated approach increases analytical depth while reducing survey burden, as much information is automatically retrieved from existing systems. Its primary purpose is to detect whether non-competitive research funding achieves its intended effects and to identify the mechanisms and early indicators through which these effects may emerge. Insights from policy enactment theory (Ball, Maguire & Braun, 2012) further inform the methodological design, ensuring that the monitoring system captures how non-competitive funding is interpreted, adapted and operationalised within different organisational and disciplinary settings. Ignite Talk focus: We present the indicator set and the conceptual survey instrument, and invite participants—particularly from different European contexts—to provide feedback on conceptual coherence, indicator relevance and methodological robustness.

This project develops a transparent, shared and widely supported monitoring system that conceptualises non-competitive research funding as a policy intervention with structural implications for academic research. By combining researcher-level and organizational-level administrative data with survey indicators on job demands, job resources, autonomy, motivation, wellbeing and intentions, the system transcends traditional performance measurement and provides a rich and multidimensional understanding of research conditions. The survey component offers a key advantage: it can detect early changes triggered by policy interventions. Intentions, expectations and perceptions evolve more quickly than traditional output indicators—such as publications or grant applications—while also being less sensitive to external influences. Drawing on the Theory of Planned Behaviour, the monitoring system thus delivers early insights into how researchers anticipate adapting their behaviours, collaborations and thematic orientations. The integration of administrative and survey data creates a holistic perspective on research work: administrative data capture objective structures, while survey data highlight experiential and mechanism based dimensions not found in existing databases. This approach also reduces survey burden and increases analytical accuracy. The multi-method design, grounded in established theoretical models (JDC, JD-R, TPB) and supported by participatory collaboration with diverse stakeholders, yields a robust, transparent and methodologically innovative instrument. Guided by insights from policy enactment, the system recognises that non-competitive funding is enacted differently across contexts and that monitoring must reflect these situated realities. As such, it contributes to trust, legitimacy and transparency in the university’s research funding policy. At the European level, this framework offers a transferable and scalable model for institutions exploring alternatives to competitive funding structures—and how to monitor them. It facilitates inter-institutional learning and contributes to broader debates on sustainable, equitable and human-centred research funding in European higher education.

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