Dynamic Complexity and Institutional Decision-Making in UK Higher Education: A Multimethod Systemic Assessment

UK higher education institutions operate under performance, resource, and stakeholder pressures whose effects are mediated by feedback, delays and competing constraints. This study examines how such dynamic complexity influences learning and teaching decision-making and evaluates a multimethod systems design combining a Systemigram, interview-informed causal loop diagram (CLD), Viable System Model (VSM) and Ideal Final Result (IFR). The CLD was developed from two rounds of semi-structured interviews comprising 24 interviews, analysed through thematic and open coding and subsequently validated with participants. The analysis identifies reinforcing mechanisms linking learning and teaching quality, National Student Survey (NSS)/Postgraduate Taught Experience Survey (PTES) outcomes, institutional attractiveness, enrolment, revenue and reinvestment, alongside balancing constraints through which enrolment increases staff–student ratios and marking pressure, reducing teaching and assessment/feedback quality. The VSM identifies coordination, resource control and intelligence requirements, while the IFR translates the identified tensions into design principles. The methods help explain why apparently rational local interventions can generate delayed or counterproductive effects and identify workload, staffing capacity, leading indicator and coordination interventions for UK Higher Education Institutions (HEIs).

Authors

Institutions

Publication Details

Journal
Systems
Published
2026-09-10
DOI
https://doi.org/10.3390/systems14091129
Primary Topic
Complex Systems and Decision Making
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Dynamic Complexity and Institutional Decision-Making in UK Higher Education: A Multimethod Systemic Assessment

Amin Hosseinian‐Far, Amanda Relph
Systems
Complex Systems and Decision Making
article

Dynamic Complexity and Institutional Decision-Making in UK Higher Education: A Multimethod Systemic Assessment

Amin Hosseinian‐Far, Amanda Relph
article en

Abstract

UK higher education institutions operate under performance, resource, and stakeholder pressures whose effects are mediated by feedback, delays and competing constraints. This study examines how such dynamic complexity influences learning and teaching decision-making and evaluates a multimethod systems design combining a Systemigram, interview-informed causal loop diagram (CLD), Viable System Model (VSM) and Ideal Final Result (IFR). The CLD was developed from two rounds of semi-structured interviews comprising 24 interviews, analysed through thematic and open coding and subsequently validated with participants. The analysis identifies reinforcing mechanisms linking learning and teaching quality, National Student Survey (NSS)/Postgraduate Taught Experience Survey (PTES) outcomes, institutional attractiveness, enrolment, revenue and reinvestment, alongside balancing constraints through which enrolment increases staff–student ratios and marking pressure, reducing teaching and assessment/feedback quality. The VSM identifies coordination, resource control and intelligence requirements, while the IFR translates the identified tensions into design principles. The methods help explain why apparently rational local interventions can generate delayed or counterproductive effects and identify workload, staffing capacity, leading indicator and coordination interventions for UK Higher Education Institutions (HEIs).

SystemsVol. 14(9)
University of Hertfordshire (GB)
Openalex Percentile: Top 6%
Complex Systems and Decision Making
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.