Blog Details
- Member
- November 1, 2026
Interdisciplinarity as an Architecture for Impact
Research Management: Interdisciplinarity as an Architecture for Impact
A Srijan Sanchar Perspective
From disciplinary excellence to institutional value creation
The central challenge of research management today is not merely increasing research output. It is enabling universities to convert their distributed intellectual capabilities into research that creates measurable academic, technological, economic and societal impact.
Interdisciplinarity offers a pathway towards this transformation. However, conventional approaches often treat it as a matter of bringing researchers from different disciplines together. A more productive approach is to treat interdisciplinarity as an institutionally engineered value-creation system.
This perspective brings together three complementary propositions:
Interdepartmental collaboration as an internal value chain: Departments, faculty, students and research facilities become interconnected contributors and beneficiaries of research.
Output–Input analysis as a research management framework: Research capabilities, outputs and outcomes are mapped across departments to identify complementarities, gaps and opportunities.
Isomorphism-Enabled Interdisciplinarity: Srijan Sanchar's methodology identifies structural equivalences across disciplines, enabling researchers to collaborate without sacrificing their deep domain expertise.
The resulting proposition is that universities can move from managing research as a collection of disciplinary activities to managing it as an interconnected system of knowledge creation, translation and application.
1. Interdepartmental Collaboration as an Internal Research Value Chain
1.1 The university as an interconnected knowledge ecosystem
A university is a collection of specialised knowledge centres. Each department possesses intellectual capital, research capabilities, faculty expertise, laboratories, datasets and student talent.
Yet these capabilities frequently operate within departmental boundaries.
The opportunity is to recognise that one department's research output can become another department's research input.
This is analogous to industrial ecology, where the output or by-product of one production process becomes a resource for another. In a university, the equivalent resource is knowledge, data, methods, models, prototypes or research capabilities.
1.2 The internal customer–supplier model
The internal customer is not merely an administrative concept. It provides a way to identify the value that one academic unit creates for another.
Knowledge supplier | Research output | Internal customer | Value created |
|---|---|---|---|
Engineering | Prototype, instrument, computational model | Physics, Chemistry | Experimental capability |
Computer Science | Algorithms, simulation, AI tools | All research departments | Analytical capability |
Mathematics | Statistical models, optimisation | Economics, Biology, Engineering | Modelling and inference |
Social Sciences | Behavioural insights, field methods | Engineering, Management, Health | Contextual relevance |
Humanities | Historical, ethical and cultural analysis | Technology, Policy, Education | Meaning, legitimacy and interpretation |
Design and Visual Arts | Visualisation, interaction design | Science, Engineering, Education | Communication and usability |
Management | Business models, implementation pathways | Technology, Research Centres | Adoption and scalability |
These relationships are illustrative. A department can simultaneously be a supplier, customer and co-creator.
1.3 The interdepartmental research value chain
The university can organise collaboration as a sequence of value-creating exchanges.
The value chain should not be interpreted as a rigid linear process. Feedback, iteration and reciprocal learning are essential.
1.4 Internal customer value propositions
Each collaboration should identify what the receiving department gains.
Internal customer | Value proposition |
|---|---|
Faculty | Access to complementary expertise, methods and research opportunities |
Research scholars | Access to new analytical tools, datasets, facilities and interdisciplinary mentorship |
Students | Exposure to real-world problems and cross-domain learning |
Research centres | Greater capability to address complex problems |
University leadership | Better utilisation of existing intellectual and physical assets |
External partners | Integrated solutions rather than isolated disciplinary contributions |
1.5 The management implication
Universities could establish a Research Value Exchange mechanism that:
Maps the capabilities and research requirements of each department.
Identifies potential internal suppliers and customers.
Records research outputs that can be reused by other departments.
Facilitates faculty-to-faculty and scholar-to-scholar connections.
Recognises contributions to other departments as part of research performance.
Tracks the value created through these exchanges.
The key management shift is from asking:
How much research does each department produce?
to asking:
How much additional research value does each department enable across the university?
This is consistent with emerging research assessment thinking. The OECD's 2026 policy brief highlights the limitations of narrow publication-based metrics and the need to recognise collaboration, openness and societal impact.
2. Output–Input Analysis: A Framework for Research Resource Circulation
2.1 From research accounting to research ecology
Industrial ecology studies material and energy flows across interconnected production systems. The objective is to identify resource dependencies, waste, opportunities for reuse and systemic efficiency.
A university can apply a similar logic to intellectual and research resources.
The proposed Research Input–Output Exchange Matrix (RIOEM) treats research outputs as potential inputs into other research activities.
This is a proposed Srijan Sanchar-compatible management framework, not an established standard with a universally accepted formula.
2.2 The basic matrix
Rows represent departments supplying research outputs. Columns represent departments receiving or using those outputs.
Output supplier ↓ / Input receiver → | Engineering | Computer Science | Social Sciences | Management | Humanities |
|---|---|---|---|---|---|
Engineering | — | Prototypes, sensor data | Technology context | Product requirements | Design history |
Computer Science | Simulation tools | — | Behavioural data analysis | Decision models | Text analytics |
Social Sciences | User research | Behavioural datasets | — | Market and adoption insights | Cultural interpretation |
Management | Commercialisation pathways | AI adoption models | Organisational insights | — | Institutional context |
Humanities | Ethical and design principles | Language and narrative resources | Historical interpretation | Cultural intelligence | — |
The entries indicate possible exchanges, not verified existing relationships.
2.3 The extended matrix: Inputs, outputs and outcomes
The first matrix identifies exchanges. A second matrix assesses the research lifecycle.
Research stage | Inputs | Activities | Outputs | Outcomes |
|---|---|---|---|---|
Knowledge creation | Faculty expertise, funding, facilities | Investigation, experimentation | Publications, datasets, models | New knowledge |
Knowledge integration | Outputs from multiple departments | Synthesis, modelling | Joint frameworks, new propositions | New research capability |
Knowledge translation | Research findings, industry problems | Prototyping, validation | Technologies, methods, policy briefs | Adoption and implementation |
Knowledge diffusion | Research outputs, communication resources | Training, dissemination | Courses, repositories, open resources | Broader capability and reach |
Knowledge renewal | Feedback, impact evidence | Evaluation, redesign | Revised hypotheses, new projects | Sustained research ecosystem |
This lifecycle approach is supported by research impact literature that distinguishes inputs, activities, outputs, outcomes and longer-term impacts, while recommending continuous monitoring rather than relying only on final publications or patents.
2.4 A department-level research exchange scorecard
A university could measure the following dimensions:
Dimension | Proposed indicator | Management question |
|---|---|---|
Research input | Resources received from other departments | How dependent is the department on external academic capabilities? |
Research output | Outputs made available to other departments | What intellectual value does it contribute to the university? |
Reuse | Number of outputs reused by other research teams | Are research outputs being converted into new research inputs? |
Integration | Joint projects and co-developed propositions | Is collaboration producing new knowledge? |
Efficiency | Research outputs per unit of resource | Are shared capabilities improving resource utilisation? |
Outcomes | New methods, validated solutions, research capacity | What new capabilities have been created? |
Impact | Academic, economic, social and environmental effects | What difference has the research made? |
Renewal | New projects arising from previous outputs | Is the research ecosystem becoming self-reinforcing? |
2.5 A quantitative starting point
A simple measure of internal research output utilisation could be:
A complementary measure of cross-departmental input dependence:
These measures need careful interpretation. High utilisation is not automatically high impact, and high dependence is not necessarily inefficiency.
A more useful institutional measure would track additional value created through reuse:
Where:
\(i\) = supplying department
\(j\) = receiving department
\(\Delta O_{ij}\) = additional research output or capability enabled through the exchange
\(w_{ij}\) = an agreed weighting for the importance of the contribution
The weights should be transparent and discipline-sensitive. The objective is not to force all research into a single numerical ranking, but to make otherwise invisible contributions observable.
2.6 Beyond input–output: three useful frameworks
Framework
What it contributes
Application to university research
Industrial ecology
Resource circulation, reuse, dependencies
Identify knowledge outputs that can become inputs elsewhere
Input–Output Economics
Interdependencies among production sectors
Model how research capabilities support one another
Knowledge Value Chain
Knowledge creation, integration, transfer and application
Track conversion of research into useful capabilities
Systems Thinking
Feedback, emergence, adaptation
Identify reinforcing research cycles
Theory of Change / Logic Model
Outputs, outcomes, impact pathways
Connect research activity to intended societal change
Network Analysis
Nodes, links, centrality, gaps
Identify underconnected departments and research brokers
Framework | What it contributes | Application to university research |
|---|---|---|
Industrial ecology | Resource circulation, reuse, dependencies | Identify knowledge outputs that can become inputs elsewhere |
Input–Output Economics | Interdependencies among production sectors | Model how research capabilities support one another |
Knowledge Value Chain | Knowledge creation, integration, transfer and application | Track conversion of research into useful capabilities |
Systems Thinking | Feedback, emergence, adaptation | Identify reinforcing research cycles |
Theory of Change / Logic Model | Outputs, outcomes, impact pathways | Connect research activity to intended societal change |
Network Analysis | Nodes, links, centrality, gaps | Identify underconnected departments and research brokers |
A useful combination would be:
Research Input–Output Matrix + Knowledge Value Chain + Network Analysis + Impact Logic Model
Together, these can provide a university with a dynamic picture of its research ecosystem.
3. Srijan Sanchar's Isomorphism-Enabled Interdisciplinarity
3.1 The foundational proposition
The most significant contribution of Srijan Sanchar's methodology is the proposition that interdisciplinary collaboration can be organised around the structural equivalence of reasoning across domains rather than the superficial similarity of subject matter.
Researchers may work on entirely different subjects while employing remarkably similar cognitive operations.
A physicist studying phase transitions and a sociologist studying collective behaviour may both investigate:
Thresholds and tipping points
Feedback mechanisms
Emergent behaviour
Network effects
Stability and instability
Propagation of signals
Transformation of systems
Their domain knowledge remains distinct. Their underlying reasoning structures may be equivalent.
This provides a foundation for interdisciplinary collaboration without requiring researchers to become generalists or acquire extensive expertise in another discipline.
3.2 The theoretical foundation: structural mapping
The proposition has an important connection to established cognitive science.
Dedre Gentner's Structure-Mapping Theory argues that analogy is primarily concerned with mapping relationships between domains rather than transferring superficial attributes. Systematic, interconnected relationships are particularly important in productive analogies.
Srijan Sanchar extends this principle towards an institutional research management proposition:
Established cognitive principle | Srijan Sanchar's institutional extension |
|---|---|
Analogies map relational structures | Research disciplines can be mapped through common reasoning structures |
Higher-order relationships are more informative | Interconnected cognitive operations can reveal deeper research complementarities |
Structural alignment enables inference | Structural alignment can generate interdisciplinary research propositions |
Analogical reasoning supports learning | Analogical mapping can support research discovery and collaboration |
Knowledge representations enable comparison | Research portfolios can be represented in a common structural language |
Important distinction: Isomorphism-Enabled Interdisciplinarity is Srijan Sanchar's proposed methodology. Its relationship to structure-mapping theory provides a theoretical foundation, but its institutional effectiveness requires empirical validation.
3.3 The three-layer architecture
CSIR-HRDG-EMR-II ASPIRE
Layer 1 — Isomorphic Research Atlas
Discover
Maps research portfolios through underlying cognitive operations rather than disciplinary classifications.
Structural categories might include:
Feedback and control
Thresholds and phase transitions
Networks and propagation
Pattern recognition
Optimisation and resource allocation
Signal extraction and noise reduction
Emergence and adaptation
Institutional output: A map of hidden research complementarities and potential interdisciplinary connections.
Layer 2 — Knowledge Key
Translate
Provides personalised structural translations between disciplines.
A researcher does not need to learn the terminology of another discipline. The Knowledge Key reveals the shared structure using familiar domain language.
Institutional output: A low-friction pathway for researchers to recognise relevant collaborators and understand the research potential of a connection.
Layer 3 — Insight Loop
Create
Converts structural equivalence into research propositions.
Identify a signal or unresolved problem.
Map its causal structure.
Construct a learner-generated story or representation.
Identify corresponding structures in another domain.
Generate a cross-domain synthesis.
Formulate a testable proposition.
Institutional output: New hypotheses, research projects, models, prototypes and publications.
3.4 An illustrative interdisciplinary mapping
Consider two research problems.
Domain A: Condensed Matter Physics
How does a system transition from one stable state to another when a critical threshold is crossed?
Domain B: Political Sociology
How does a society transition from relative stability to collective mobilisation?
The Knowledge Key could identify a common structure:
Structural operation | Physics | Political sociology |
|---|---|---|
System | Physical material | Social population |
State | Phase or configuration | Social equilibrium |
Perturbation | External energy or force | Economic, political or social event |
Interaction | Particle interactions | Social interactions |
Threshold | Critical point | Mobilisation threshold |
Feedback | Reinforcement of physical fluctuations | Reinforcement of collective behaviour |
Transition | Phase change | Collective mobilisation |
The structural mapping creates a research question:
Can models of critical transitions provide useful analytical approaches for understanding the conditions under which collective social mobilisation becomes more likely?
This does not mean that society behaves identically to a physical material. It means that a potentially productive correspondence has been identified.
The Insight Loop must then test the limits of the analogy, identify domain-specific differences and determine whether the mapping generates useful explanatory or predictive value.
4. Integrating the Three Frameworks into Research Management
The three propositions become particularly powerful when integrated.
The integrated institutional model
Framework
Primary function
Management question
Interdepartmental Value Chain
Organises research exchanges
Who can contribute to whom?
Research Input–Output Matrix
Maps capabilities and resource flows
What is available, required or underutilised?
Isomorphic Research Atlas
Identifies structural complementarities
Where could deep expertise connect productively?
Knowledge Key
Reduces translation costs
How can researchers understand one another?
Insight Loop
Converts connections into propositions
What new research can emerge?
Impact Assessment
Evaluates the results
What value has been created?
Framework | Primary function | Management question |
|---|---|---|
Interdepartmental Value Chain | Organises research exchanges | Who can contribute to whom? |
Research Input–Output Matrix | Maps capabilities and resource flows | What is available, required or underutilised? |
Isomorphic Research Atlas | Identifies structural complementarities | Where could deep expertise connect productively? |
Knowledge Key | Reduces translation costs | How can researchers understand one another? |
Insight Loop | Converts connections into propositions | What new research can emerge? |
Impact Assessment | Evaluates the results | What value has been created? |
This integrated model offers a potential answer to a persistent institutional challenge: universities may possess the necessary intellectual capabilities for addressing complex problems, but lack the mechanisms to discover and combine them.
5. Research Management: From Departments to a Living Knowledge Network
The proposed architecture could enable a university to create a dynamic research management system with five institutional functions.
Function 1 — Research capability discovery
Create a structured representation of:
Faculty expertise and research interests
Existing projects and publications
Research facilities and equipment
Data, models and methodologies
Student capabilities
Industry and societal problem statements
The objective is to reveal capabilities that may be invisible outside their home departments.
Function 2 — Research opportunity discovery
Identify three types of opportunity:
Opportunity type | Description |
|---|---|
Capability gap | A research team requires expertise or resources available elsewhere |
Structural complementarity | Two domains employ similar reasoning structures but have not collaborated |
Boundary problem | A complex challenge requires several disciplines to explain or address it |
The third category is particularly important. It moves research management beyond simply matching existing capabilities to existing projects.
Function 3 — Research collaboration facilitation
Instead of asking faculty to independently discover interdisciplinary partners, the institution can facilitate:
Structured research conversations
Knowledge translation sessions
Joint problem formulation
Collaborative research design
Shared mentorship
Interdepartmental research proposals
The objective is to reduce the transaction costs of collaboration while preserving intellectual autonomy.
Function 4 — Research portfolio development
The university can identify research opportunities at three levels:
Level | Research opportunity |
|---|---|
Individual | Extend an existing research programme through a new structural connection |
Departmental | Build a distinctive research capability through complementary partnerships |
Institutional | Develop signature research themes addressing complex societal or industrial challenges |
Function 5 — Research impact and renewal
Every collaborative project should generate both direct outputs and potential new research inputs.
A publication may become a new dataset. A prototype may generate new scientific questions. An industry intervention may reveal a policy challenge. A student project may become a doctoral research programme.
The university thereby becomes a self-renewing research ecosystem.
6. Research Assessment: Recognising the Value of Interdisciplinarity
Traditional research metrics remain important, but they are insufficient for evaluating the institutional value of interdisciplinary collaboration.
A recent OECD policy brief highlights the need for research assessment systems to recognise collaboration, openness, societal relevance and contributions that extend beyond conventional publication-based measures.
Proposed assessment matrix
Assessment dimension | Conventional indicator | Interdisciplinary indicator |
|---|---|---|
Research productivity | Publications, citations | Joint outputs and new research propositions |
Research capability | Faculty expertise, grants | Complementary capabilities integrated |
Collaboration | Co-authorship | Depth and quality of knowledge exchange |
Resource utilisation | Grants, equipment utilisation | Reuse of internal research outputs |
Research novelty | Originality of individual research | Novel structural connections and hypotheses |
Research development | Projects completed | New research programmes emerging from collaboration |
Academic development | Student publications | Cross-domain research capability and mentorship |
Societal impact | Patents, technology transfer | Validated contributions to complex societal problems |
Institutional value | Departmental performance | Additional value enabled across departments |
A critical principle
Interdisciplinary research should not be assessed simply by counting the number of departments involved.
Three departments working on a project may produce less integration than two researchers who have genuinely combined their methods, models and explanatory structures.
The assessment must distinguish:
Multidisciplinary participation
Interdisciplinary integration
Transdisciplinary engagement with external stakeholders
Genuine knowledge creation through integration
7. The Research Management Dashboard
A university could operationalise the integrated framework through a dashboard organised around four questions.
Research Ecosystem Dashboard
Connectivity
Are departments connected to relevant capabilities?
Exchange
Are outputs being reused as research inputs?
Discovery
Are structural connections generating new propositions?
Impact
Are research outputs creating measurable value?
Connectivity
Are departments connected to relevant capabilities?
Exchange
Are outputs being reused as research inputs?
Discovery
Are structural connections generating new propositions?
Impact
Are research outputs creating measurable value?
The dashboard should combine quantitative indicators with qualitative evidence, including researcher narratives, case studies, external stakeholder feedback and documented examples of knowledge reuse.
8. The Research Agenda for Srijan Sanchar
The methodology presents a promising institutional proposition, but its strongest contribution will emerge through systematic validation.
Research question | Proposed investigation |
|---|---|
Can structural mapping identify productive interdisciplinary connections? | Compare algorithmically or manually identified connections with expert judgement |
Does the Knowledge Key reduce translation costs? | Measure time, comprehension and collaboration quality before and after translation |
Does isomorphism predict research novelty? | Compare generated propositions with conventional collaboration approaches |
Does the Insight Loop improve research outcomes? | Track proposition quality, testability and subsequent research development |
Can institutional adoption improve research value circulation? | Conduct longitudinal studies of departmental outputs, reuse and impact |
Where does isomorphism fail? | Develop criteria for distinguishing productive equivalence from superficial analogy |
A pilot could begin with a small number of departments and research challenges, then expand as the methodology and its evaluation become more robust.
9. Strategic Implications for Universities
The central implication is that research management need not be limited to allocating funds, setting targets and evaluating departmental performance.
It can become an active function of discovering, connecting and amplifying institutional intellectual capital.
This changes the role of university leadership in several ways:
Conventional research management | Isomorphism-enabled research management |
|---|---|
Manages disciplinary research portfolios | Manages interconnected knowledge capabilities |
Encourages collaboration | Engineers opportunities for collaboration |
Allocates resources to departments | Facilitates resource and knowledge circulation |
Evaluates completed outputs | Tracks outputs, reuse, outcomes and impact |
Relies on researchers to discover partners | Provides structural discovery and translation mechanisms |
Organises research around established disciplines | Enables research around emerging problems and structural complementarities |
The university's competitive advantage may increasingly depend not only on the excellence of its individual departments, but also on its ability to convert their distributed expertise into distinctive research capabilities.
10. Concluding Proposition
SRIJAN SANCHAR — RESEARCH MANAGEMENT PROPOSITION
From Disciplinary Excellence to Systemic Research Impact
A university's research potential is not merely the sum of its departments' capabilities. It also resides in the unexplored relationships among those capabilities.
Interdepartmental value chains reveal how knowledge can circulate. Input–output analysis identifies dependencies, opportunities and underutilised resources. Isomorphism-Enabled Interdisciplinarity discovers structural equivalences that can connect deep expertise across disciplinary boundaries.
Together, these mechanisms offer a foundation for transforming research management from the administration of research activities into the systematic creation of research value.