Dr David Bozward

Tag: capability development

  • From Classroom to Capability: Rethinking Graduate Outcomes

    From Classroom to Capability: Rethinking Graduate Outcomes

    The modern university faces a credibility problem. Not because knowledge has lost value—but because the translation of that knowledge into real-world capability is increasingly uncertain. Across the UK and globally, graduate outcomes are under scrutiny. Employment rates fluctuate, underemployment persists, and employers continue to report skills gaps despite rising qualification levels.

    The issue is not that graduates lack intelligence or effort. It is that the system they emerge from is misaligned with how capability is actually developed, demonstrated, and deployed in the real world.

    This blog argues for a fundamental shift: from measuring what students know to understanding what they can do. From classroom achievement to capability formation.

    The Illusion of Outcomes

    Graduate outcomes are typically measured through metrics such as employment rates, salary levels, and progression into further study. In the UK, frameworks linked to the Office for Students (OfS), particularly B3 conditions, reinforce these indicators as proxies for institutional performance.

    But these metrics mask deeper structural problems.

    A graduate employed six months after finishing their degree may appear as a success in data terms. Yet this tells us little about:

    • The relevance of their role to their studies
    • Their long-term career trajectory
    • Their capacity to adapt, innovate, or lead
    • Their ability to create value—either for themselves or others

    The result is a system optimised for short-term outputs, not long-term capability.

    This is the graduate employability illusion: a belief that employment equals preparedness.

    What Employers Actually Value

    Employer surveys consistently highlight a mismatch between graduate skills and workplace expectations. While technical knowledge remains important, employers increasingly prioritise:

    • Problem-solving in ambiguous environments
    • Communication across disciplines and cultures
    • Initiative and self-direction
    • Commercial awareness
    • Adaptability and resilience

    These are not easily taught through lectures or assessed through exams. They are developed through experience, reflection, and application.

    In other words, capability is not a product of curriculum delivery alone. It is the result of a developmental process that integrates knowledge, action, and context.

    Capability vs Competence

    To rethink graduate outcomes, we must first distinguish between competence and capability.

    Competence refers to the ability to perform specific tasks to a defined standard. It is often narrow, measurable, and context-dependent.

    Capability, by contrast, is broader and more dynamic. It includes:

    • The ability to apply knowledge in unfamiliar situations
    • The capacity to learn, unlearn, and relearn
    • The confidence to act under uncertainty
    • The judgement to make decisions with incomplete information

    Universities have become highly effective at delivering competence. But the future economy demands capability.

    This distinction matters because competence can be assessed at a single point in time. Capability must be observed over time, across contexts.

    The Structural Problem: Education Without Application

    The dominant model of higher education remains rooted in knowledge transmission. Despite decades of reform, many programmes still follow a familiar pattern:

    1. Content delivery (lectures, readings)
    2. Knowledge testing (exams, essays)
    3. Qualification awarding (degree classification)

    What is often missing is systematic application.

    Students may complete a degree having:

    • Never worked on a real-world problem
    • Never engaged with an external stakeholder
    • Never experienced failure in a safe but meaningful environment
    • Never reflected deeply on their own development

    This is not a criticism of individual academics. It is a structural issue embedded in how programmes are designed, validated, and funded.

    From Curriculum to Capability Systems

    If we are serious about improving graduate outcomes, we need to move beyond curriculum design and towards capability systems.

    A capability system integrates five core elements:

    1. Knowledge Foundations

    Disciplinary knowledge remains essential. But it must be framed not as an endpoint, but as a tool.

    The question is not “What should students know?” but “What should they be able to do with what they know?”

    2. Applied Experience

    Capability develops through doing. This includes:

    • Live projects with industry
    • Simulations and experiential learning
    • Internships and placements
    • Entrepreneurial ventures

    These experiences must be embedded, not optional.

    Tools like SimVenture demonstrate how simulation-based learning can provide structured environments for decision-making under uncertainty—bridging the gap between theory and practice.

    3. Reflective Practice

    Experience alone is not enough. Without reflection, learning remains superficial.

    Students need structured opportunities to:

    • Analyse their decisions
    • Understand their strengths and weaknesses
    • Develop self-awareness
    • Build a narrative of their capability

    Reflection transforms activity into learning.

    4. Capability Signalling

    One of the biggest challenges graduates face is not capability itself, but the ability to demonstrate it.

    Traditional CVs and degree classifications are blunt instruments. They do not capture:

    • How a student solved a problem
    • How they worked in a team
    • How they responded to failure

    Emerging approaches—such as digital credentials and portfolio-based assessment—offer more nuanced ways of signalling capability.

    Platforms like AlmaBridge illustrate how verifiable, granular credentials can provide a richer picture of what graduates can actually do.

    5. Transition Support

    The final stage is the transition from education to employment—or entrepreneurship.

    This is where many systems fail.

    Careers services are often:

    • Reactive rather than proactive
    • Transactional rather than developmental
    • Focused on job search rather than capability building

    A capability-based model requires a reimagined transition system—one that supports:

    • Career exploration
    • Skill articulation
    • Network building
    • Opportunity creation

    The Role of Entrepreneurship

    Entrepreneurship provides a powerful lens through which to rethink graduate outcomes.

    Not because every student should start a business—but because entrepreneurial thinking develops core capabilities:

    • Opportunity recognition
    • Resourcefulness
    • Value creation
    • Risk management

    Your own 9 Stages of Entrepreneurial Lifecycle model offers a useful framework here. From discovery through to exit, each stage requires different capabilities—and exposes students to different forms of learning.

    Embedding entrepreneurial thinking across disciplines can shift education from passive consumption to active creation.

    Measurement: What Should We Actually Track?

    If we move towards capability, our metrics must evolve.

    Instead of focusing solely on employment outcomes, institutions should track:

    1. Capability Development

    • Evidence of applied learning
    • Progression in key capabilities (e.g. problem-solving, communication)
    • Student self-efficacy

    2. Engagement with Practice

    • Participation in live projects
    • Industry interaction
    • Experiential learning hours

    3. Value Creation

    • Contributions to organisations or communities
    • Entrepreneurial activity
    • Innovation outputs

    4. Longitudinal Outcomes

    • Career progression over 3–5 years
    • Role complexity and responsibility
    • Income growth relative to sector

    5. Adaptability

    • Career changes
    • Upskilling and reskilling activity
    • Ability to navigate transitions

    This requires better data systems, but more importantly, a shift in mindset.

    The Institutional Challenge

    Rethinking graduate outcomes is not a minor adjustment. It requires systemic change.

    1. Academic Culture

    Academics must be supported to integrate application and reflection into their teaching—not as an add-on, but as core practice.

    2. Programme Design

    Courses need to be structured around capability development pathways, not just content coverage.

    3. Partnerships

    Stronger relationships with industry, public sector organisations, and communities are essential to provide real-world contexts.

    4. Technology Infrastructure

    Platforms that track, verify, and showcase capability must be integrated into the student journey.

    5. Policy Alignment

    Regulators and funding bodies must move beyond narrow outcome metrics and support more holistic approaches.

    A Practical Model: The Capability Loop

    To make this operational, consider a simple but powerful model: the Capability Loop.

    1. Learn – Acquire knowledge and frameworks
    2. Apply – Use them in real or simulated contexts
    3. Reflect – Analyse performance and learning
    4. Signal – Capture and communicate capability
    5. Transition – Deploy capability in the real world

    This loop repeats throughout the student journey.

    The key is not any single element, but the integration of all five.

    Why This Matters Now

    Several forces are converging to make this shift urgent:

    1. Labour Market Volatility

    Careers are no longer linear. Graduates must be prepared for multiple transitions.

    2. Technological Change

    Automation and AI are reshaping roles. Knowledge alone is insufficient; adaptability is critical.

    3. Rising Student Expectations

    Students increasingly question the return on investment of higher education. They want outcomes that are tangible and meaningful.

    4. Policy Pressure

    Frameworks like those from the Office for Students are intensifying scrutiny on outcomes, creating both risk and opportunity for institutions.

    From System Efficiency to Human Capability

    At its core, this is a philosophical shift.

    Higher education has, in many ways, become a system optimised for efficiency:

    • Standardised curricula
    • Scalable delivery
    • Measurable outputs

    But capability is not easily standardised. It is:

    • Contextual
    • Developmental
    • Individual

    If universities are to remain relevant, they must rebalance—maintaining rigour while embracing complexity.

    Conclusion: A New Contract with Students

    Rethinking graduate outcomes is not just about metrics or pedagogy. It is about redefining the implicit contract between universities and students.

    The old contract was:

    “We will provide knowledge, and you will translate it into success.”

    The new contract must be:

    “We will help you develop the capability to create value—in any context you choose.”

    This is a more demanding promise. It requires deeper engagement, more complex systems, and greater accountability.

    But it is also a more honest one.

    Because in the end, what matters is not what students know when they leave the classroom.

    It is what they can do when they enter the world.

  • Entrepreneurship Is Not Start-Up: A New Framework for Value Creation, Education, and Economic Growth

    Entrepreneurship Is Not Start-Up: A New Framework for Value Creation, Education, and Economic Growth

    Entrepreneurship has been reduced to a narrow and ultimately unhelpful idea: starting a business.

    Across universities, policy frameworks, and media narratives, entrepreneurship is framed through start-up activity—pitch decks, venture capital, and the pursuit of rapid scale. This interpretation is not simply incomplete; it is distorting how we educate students, design economic policy, and evaluate success.

    The consequence is a system that rewards activity over impact, formation over function, and visibility over value.

    If we are serious about improving productivity, employability, and long-term economic resilience, we need to move beyond the start-up myth and return to a more fundamental question:

    What is entrepreneurship actually for?

    The Problem: We Are Measuring the Wrong Thing

    Entrepreneurship policy and education are dominated by simplistic metrics:

    • Number of start-ups created
    • Amount of funding raised
    • Survival rates over three to five years

    These measures are easy to quantify, but they are poor proxies for what really matters: value creation.

    A business can be launched, funded, and sustained without creating meaningful economic or social value. Equally, significant value can be created within existing organisations, communities, or informal economies without ever appearing in start-up statistics.

    This misalignment has three critical consequences.

    First, it leads to policy inefficiency. Governments invest heavily in start-up ecosystems without understanding whether those ventures contribute to productivity, innovation, or regional development.

    Second, it creates educational distortion. Universities design entrepreneurship programmes around venture creation rather than capability development, leaving graduates underprepared for complex, non-linear careers.

    Third, it results in entrepreneurial failure. Founders are encouraged to pursue ideas without understanding the resources, processes, and conditions required to create sustainable value.

    In short, we are optimising for the wrong outcome.

    Reframing Entrepreneurship: From Activity to Value

    To correct this, entrepreneurship must be redefined.

    Entrepreneurship is not the act of starting a business. It is:

    The process of creating, capturing, and sustaining value through the effective orchestration of resources over time.

    This definition shifts the focus in three important ways.

    First, it places value at the centre, not activity. The purpose of entrepreneurship is not formation but transformation.

    Second, it emphasises process, recognising that entrepreneurship unfolds over time rather than occurring at a single moment of creation.

    Third, it highlights resource orchestration, acknowledging that entrepreneurs do not simply use resources—they combine, adapt, and transform them.

    This reframing aligns more closely with established economic theory. Joseph Schumpeter, for example, positioned the entrepreneur as an agent of “creative destruction,” reshaping markets through innovation rather than merely creating firms (Schumpeter, 1934). Similarly, Peter Drucker emphasised entrepreneurship as a systematic practice of innovation and value creation (Drucker, 1985).

    Yet despite this intellectual foundation, contemporary systems have drifted toward a far narrower interpretation.

    The Missing Mechanism: Understanding Entrepreneurial Capital

    If entrepreneurship is about value creation, the next question is straightforward:

    How is value actually created?

    The answer lies in capital—not just financial capital, but a broader set of resources that entrepreneurs draw upon and combine.

    The Eight Capitals Model provides a more complete view:

    • Financial Capital (money and funding)
    • Human/Experiential Capital (skills, knowledge, experience)
    • Social Capital (networks and relationships)
    • Intellectual Capital (ideas, IP, systems)
    • Cultural Capital (norms, behaviours, identity)
    • Natural Capital (environmental and physical resources)
    • Manufactured Capital (infrastructure, tools, technology)
    • Spiritual Capital (purpose, values, motivation)

    Traditional approaches overemphasise financial capital, yet evidence consistently shows that access to networks, knowledge, and institutional support often matters more in determining entrepreneurial outcomes (Acs et al., 2014).

    Entrepreneurs do not simply deploy these capitals independently. They orchestrate them—combining different forms of capital to create new forms of value.

    A founder launching a digital platform, for example, may rely heavily on intellectual and social capital in early stages, while scaling requires increasing levels of financial and manufactured capital.

    Understanding this dynamic is critical. Without it, both education and policy remain fundamentally incomplete.

    The Process Layer: The 9 Stages of Enterprise Development

    While capital explains what resources are used, it does not explain how entrepreneurship unfolds.

    Entrepreneurship is not a single act but a staged process. The 9 Stages of Enterprise Development provide a structured way to understand this progression:

    1. Discovery
    2. Modeling
    3. Startup
    4. Existence
    5. Survival
    6. Success
    7. Adaptation
    8. Independence
    9. Exit

    Each stage represents a different configuration of challenges, decisions, and resource requirements.

    Crucially, value is created differently at each stage.

    • In Discovery, value lies in identifying opportunities
    • In Startup, it lies in mobilising resources
    • In Survival, it lies in achieving cash flow stability
    • In Adaptation, it lies in responding to environmental change

    This staged perspective aligns with broader economic development theories, such as Walt Rostow’s model of economic growth, which highlights the importance of sequential development phases (Rostow, 1960). However, unlike linear economic models, entrepreneurship is iterative and adaptive.

    The key insight is this:

    Entrepreneurship is the dynamic interaction between capital and stages, producing value over time.

    An Integrated Framework for Entrepreneurship

    To move beyond fragmented thinking, these elements must be brought together into a single model.

    Integrated Entrepreneurship Framework

    This framework is deliberately simple but conceptually powerful.

    • Capital represents the resources available
    • Stages represent the process through which entrepreneurship unfolds
    • Value represents the outcome
    • Context shapes and constrains the system

    Most existing approaches focus on only one of these elements. Effective entrepreneurship requires understanding all four—and, critically, how they interact.

    Implications for Universities: From Knowledge to Capability

    This framework exposes a fundamental weakness in higher education.

    Universities largely focus on knowledge transfer, while entrepreneurship requires capability development.

    Students are taught:

    • Business planning
    • Marketing theory
    • Financial modelling

    But they are rarely taught:

    • How to mobilise different forms of capital
    • How to navigate different stages of development
    • How to create and measure value in real contexts

    As a result, graduates leave with theoretical understanding but limited practical capability.

    To address this, universities must:

    1. Embed capital awareness into curricula
      Students should understand the different forms of capital and how to access them.
    2. Align learning with stages
      Programmes should simulate the progression from discovery to growth, not just start-up.
    3. Measure value creation capability
      Assessment should focus on outcomes, not outputs.

    This is not a marginal adjustment. It is a structural shift in how education is designed.

    Implications for Policy: From Start-Ups to Systems

    The same issue applies at the policy level.

    Entrepreneurship policy has become overly focused on:

    • Start-up grants
    • Incubators and accelerators
    • Venture capital ecosystems

    While these have value, they represent only a small part of the system.

    A more effective approach would focus on capital ecosystems:

    • Strengthening networks (social capital)
    • Investing in skills and education (human capital)
    • Supporting infrastructure (manufactured capital)
    • Enabling knowledge transfer (intellectual capital)

    This is particularly important in regional and rural contexts, where traditional start-up models often fail to translate.

    You cannot build entrepreneurial economies by funding businesses alone. You must build the systems that enable value creation.

    Implications for Entrepreneurs: Better Decisions, Better Outcomes

    For practitioners, this framework provides a more realistic lens.

    Instead of asking:

    • “Is this a good idea?”

    Entrepreneurs should ask:

    • “What value am I creating?”
    • “What capital do I need—and what am I missing?”
    • “What stage am I in—and what does that require?”

    This shift leads to better decision-making.

    It reduces overconfidence in early stages, improves resource allocation, and increases the likelihood of sustainable growth.

    Conclusion: A Necessary Shift

    Entrepreneurship matters—not because it creates businesses, but because it creates value.

    If we continue to define entrepreneurship as start-up activity, we will continue to miseducate students, misallocate resources, and misunderstand economic growth.

    The alternative is clear.

    We must move toward a model that recognises:

    • The role of capital
    • The importance of process
    • The centrality of value
    • The influence of context

    This is not simply an academic exercise. It is a practical necessity.

    The future of entrepreneurship lies not in more businesses—but in better value creation.

    References (APA Style)

    Acs, Z. J., Autio, E., & Szerb, L. (2014). National systems of entrepreneurship: Measurement issues and policy implications. Research Policy, 43(3), 476–494.

    Drucker, P. F. (1985). Innovation and entrepreneurship: Practice and principles. Harper & Row.

    Schumpeter, J. A. (1934). The theory of economic development. Harvard University Press.

    Rostow, W. W. (1960). The stages of economic growth: A non-communist manifesto. Cambridge University Press.

    Neck, H. M., Greene, P. G., & Brush, C. G. (2014). Teaching entrepreneurship: A practice-based approach. Edward Elgar.

    World Bank. (2020). Doing business 2020: Comparing business regulation in 190 economies. World Bank Publications.

    OECD. (2021). Entrepreneurship at a glance 2021. OECD Publishing.

  • Why Employability Metrics Are Failing Universities

    Why Employability Metrics Are Failing Universities

    Universities are under increasing pressure to demonstrate that their graduates secure meaningful employment. In response, governments and regulators have embedded employability metrics into performance frameworks, funding models, and league tables. In the UK, for example, graduate outcomes (B3) data has become a central feature of regulatory oversight and institutional strategy.

    On the surface, this seems entirely reasonable. Students invest significant time and money into higher education, and they expect a return in the form of improved career prospects. Policymakers, in turn, want assurance that universities are delivering value.

    Yet, despite this growing emphasis, a fundamental problem persists:

    Employability metrics, as currently designed, are failing universities—and more importantly, they are failing students.

    The Illusion of Measurement

    At the heart of the issue lies a simple but powerful question: what exactly are we measuring?

    Most employability metrics rely on narrow indicators such as:

    • Graduate employment rates
    • Salaries after 15 months
    • Job classification (e.g. “professional” roles)(Don’t ask me about Models)

    While these measures provide a snapshot, they do not capture the complexity of graduate outcomes.

    Employment is not a binary state. Nor is it a static endpoint. Careers evolve over time, often through nonlinear and unpredictable pathways. By reducing employability to short-term outcomes, metrics create an illusion of precision while obscuring the reality of graduate transitions.

    The Timing Problem

    One of the most widely used measures in the UK is based on graduate destinations approximately 15 months after completion. This timeframe is deeply problematic.

    Many graduates:

    • Pursue further study
    • Start businesses (which at 15 months is traveling through the valley of death)
    • Take interim roles while exploring career options
    • Enter industries with longer entry pathways

    For these individuals, early outcomes may appear weak, even though their long-term trajectories are strong.

    The result is a systematic distortion: universities are judged on when outcomes occur, rather than how meaningful those outcomes ultimately become.

    Penalising the Wrong Institutions

    Employability metrics often fail to account for differences in student demographics and institutional missions.

    Universities that:

    • Serve widening participation students
    • Operate in economically disadvantaged regions
    • Recruit non-traditional learners

    are frequently penalised.

    These institutions play a critical role in social mobility, yet their graduates may face structural barriers in the labour market. Lower short-term employment outcomes do not necessarily reflect poor educational quality—they often reflect inequality in opportunity.

    By ignoring context, current metrics risk reinforcing the very inequalities they are meant to address.

    The Narrow Definition of Success

    Another major limitation is the narrow definition of what constitutes “success.”

    Metrics typically prioritise:

    • Full-time employment
    • High salaries
    • Traditional career pathways (Occupation codes last changed on 4 April 2024)

    However, this excludes a wide range of valuable outcomes, including:

    • Entrepreneurship and self-employment
    • Portfolio careers
    • Social impact work
    • Creative and cultural industries

    In an economy increasingly characterised by flexibility and diversity, these pathways are not marginal—they are central.

    Yet, because they do not fit neatly into existing metrics, they are often undervalued or ignored.

    Behavioural Distortions

    Perhaps the most concerning consequence of current employability metrics is how they shape institutional behaviour.

    When universities are measured on specific indicators, they naturally optimise for those indicators.

    This can lead to:

    • Overemphasis on short-term job outcomes
    • Strategic steering of students towards “safe” careers
    • Reduced support for entrepreneurship or risk-taking
    • Gaming of data through selective reporting or classification

    In extreme cases, employability becomes less about empowering students and more about managing metrics.

    This is a classic example of Goodhart’s Law:
    When a measure becomes a target, it ceases to be a good measure.

    The Missing Middle: Capability Development

    One of the most significant gaps in current frameworks is the absence of capability-based measures.

    Employability is not just about outcomes; it is about:

    • Skills development
    • Confidence and agency
    • Networks and social capital
    • The ability to navigate uncertainty

    These capabilities are developed over time and are often invisible in traditional metrics.

    For example, a student who:

    • Builds strong professional networks
    • Develops entrepreneurial skills
    • Gains meaningful project experience

    may be highly employable, even if their first job is not immediately “high status.”

    By focusing only on outcomes, metrics ignore the underlying processes that drive long-term success.

    Regional and Structural Blind Spots

    Employability metrics also fail to account for regional economic conditions.

    Graduates in areas with:

    • Limited job opportunities
    • Lower average wages
    • Sectoral decline

    are inherently disadvantaged in outcome-based measures.

    Universities cannot control local labour markets, yet they are judged as if they can.

    This creates a disconnect between:

    • Institutional performance
    • Regional economic realities

    and further disadvantages institutions located outside major economic hubs.

    Data Without Insight

    Another challenge is the overreliance on quantitative data without sufficient qualitative insight.

    Large-scale surveys provide valuable information, but they often lack depth. They do not capture:

    • Graduate experiences
    • Career aspirations
    • Barriers faced
    • Non-linear pathways

    Without this context, data can be misleading.

    For example, a graduate in a “non-professional” role may be:

    • Building experience in a chosen field
    • Transitioning between careers
    • Prioritising personal circumstances

    Yet, the metric records this simply as a negative outcome.

    Towards Better Employability Measures

    If current metrics are failing, what should replace them?

    A more effective approach would involve a shift from outcomes-only measurement to a multi-dimensional framework.

    1. Longitudinal Tracking

    Instead of focusing on short-term outcomes, metrics should track graduates over time:

    • 3 years
    • 5 years
    • 10 years

    This would provide a more accurate picture of career development.

    2. Contextualisation

    Metrics must account for:

    • Student demographics
    • Regional economic conditions
    • Institutional mission

    This would create fairer comparisons and more meaningful insights.

    3. Inclusion of Diverse Pathways

    Entrepreneurship, self-employment, and portfolio careers should be fully recognised and valued.

    This requires:

    • New classification systems
    • Better data collection methods

    4. Capability-Based Indicators

    Universities should be assessed on their ability to develop:

    • Skills
    • Networks
    • Confidence
    • Career management capabilities

    These are the foundations of employability.

    5. Integration with Skills Frameworks

    Linking outcomes to frameworks such as ESCO (European Skills, Competences, Qualifications and Occupations) would enable:

    • Better alignment with labour market needs
    • More granular analysis of skills development

    Reframing the Purpose of Employability

    Ultimately, the issue is not just technical—it is philosophical.

    What is the purpose of higher education?

    If employability is reduced to:

    • Immediate job outcomes
    • Salary levels

    then universities become training providers for the labour market.

    But higher education has a broader role:

    • Developing critical thinkers
    • Enabling social mobility
    • Fostering innovation and entrepreneurship
    • Contributing to society

    Employability should be understood as the capacity to create value over a lifetime, not just secure a job in the short term.

    Conclusion

    Employability metrics were introduced with good intentions: to ensure accountability, improve outcomes, and provide transparency.

    However, in their current form, they fall short.

    They:

    • Oversimplify complex realities
    • Ignore context
    • Distort behaviour
    • Undervalue diverse pathways

    Most importantly, they fail to capture what truly matters: the long-term ability of graduates to navigate, contribute to, and shape an ever-changing world.

    If universities are to fulfil their role in society, we must move beyond narrow metrics and embrace a richer, more nuanced understanding of employability.

    Because the goal is not just to produce graduates who get jobs.

    It is to develop individuals who can build careers, create opportunities, and drive the future of our economies.