Open AccessPeer ReviewedDouble-Anonymous ReviewCC BY 4.0 ↗Continuous Publication
1.0 Aims and Scope
Applied Policy, Institutions & Public Management (APIPM) publishes theoretically informed and empirically grounded scholarship on public policy, public administration, institutional performance and management of public organisations and programmes.
The journal focuses on how policies and institutions function in practice: how programmes are designed, implemented, financed, managed, evaluated and adapted; how administrative systems influence outcomes; and how public organisations respond to changing social, economic and technological conditions.
APIPM welcomes scholarship on policy implementation, institutional reform, public finance, public-sector management, programme evaluation, health policy, education policy, social policy, decentralisation, local government, regulation, public-service delivery, administrative capacity, accountability, digital government and AI in public administration.
The journal seeks to bridge research and practice. It therefore values studies capable of identifying not merely whether an intervention or institution appears effective, but why outcomes differ across contexts, which implementation mechanisms matter and what limitations affect the transferability of findings.
1.1 Core Research Areas
Core areas include public administration; institutional design and reform; policy implementation; implementation science in public programmes; programme and policy evaluation; public financial management; budgeting; public-sector performance; public procurement; decentralisation; local governance; accountability; regulatory governance; civil-service reform; organisational capacity; public leadership; service delivery; public-sector innovation; digital government; artificial intelligence in public administration; health administration; education administration; and development policy.
1.2 Interdisciplinary and Emerging Areas
The journal welcomes work drawing on economics, political science, management, sociology, development studies, public health, education, data science and organisational studies where the principal contribution relates to policy, institutions or public management.
Emerging areas include algorithmic administration, AI-supported public decision-making, digital public services, adaptive governance, resilience of public institutions, evidence-use systems and data-driven public management.
2.0 Contributions We Welcome
APIPM welcomes Original Research, Reviews, Systematic Reviews where relevant, programme evaluations, comparative institutional studies, qualitative and mixed-methods research, Policy and Practice Articles, Perspectives, Case Studies and Editorials.
Submissions should state the policy or management problem clearly and distinguish empirical findings from normative recommendations.
2.1 Evidence and Methodological Expectations
Relevant approaches may include experimental and quasi-experimental evaluation, administrative-data analysis, surveys, interviews, case studies, comparative institutional analysis, process tracing, qualitative research, mixed methods, economic evaluation and implementation research.
Authors should explain institutional and policy context sufficiently for readers to determine whether findings are transferable to other settings.
3.0 Disciplinary Standards
Evaluation studies should specify the intervention, comparator where applicable, outcomes, implementation setting and analytical approach.
Claims of causality should match the research design. Observational association should not be presented as causal evidence without appropriate justification.
Studies using administrative records should address data provenance, completeness, confidentiality and governance.
3.1 Research Transparency
Authors should disclose relevant organisational, institutional, funding or political relationships where these could influence interpretation.
Qualitative and mixed-methods work should explain sampling, data collection, analytical approach and researcher positioning where appropriate.
4.0 Topics Outside Scope
APIPM does not normally consider partisan advocacy presented as scholarship; commentary unsupported by evidence; manuscripts concerned exclusively with electoral competition without a substantive public-policy or administrative question; purely descriptive institutional accounts without analytical contribution; or recommendations that substantially exceed the evidence presented.
From this journal
APIPM editorials
These four editorials develop the journal’s research priorities and the questions that define its field.
Evidence about what may work is indispensable to public policy, but evidence does not implement itself. Public programmes are translated into practice through organizations, budgets, administrative routines, intergovernmental relationships, frontline discretion, monitoring systems, and local interpretations of policy purpose. This editorial argues that the persistent gap between policy research and public management should be treated as an implementation problem rather than as a simple deficit of evidence. Implementation quality depends on both the credibility of the underlying evidence and the institutional capacity to interpret, resource, adapt, coordinate, and sustain action. The analysis connects policy-implementation research, street-level bureaucracy, evidence-informed policymaking, implementation science, organizational learning, and programme evaluation. Particular attention is given to fragmented responsibilities, resource constraints, implementation fidelity versus adaptation, frontline discretion, stakeholder resistance, and weak monitoring. Priorities include comparative implementation evaluation, administrative-data use, adaptive implementation, implementation-cost analysis, organizational learning, and systematic examination of why similar policies produce different outcomes across settings. Public-management scholarship should therefore evaluate policies as operating institutional systems, not merely as formally designed interventions.
Public institutions are increasingly required to maintain essential services through health emergencies, economic shocks, environmental hazards, infrastructure failures, migration pressures, technological disruption, and fiscal stress. Resilience, however, cannot be reduced to organizational survival or rapid restoration of pre-crisis routines. This editorial argues that effective public-sector resilience combines continuity, adaptation, learning, legitimacy, and accountability. Institutions that absorb disruption but cannot correct failed practices are brittle in a different form, while flexible emergency action without oversight can erode rights, trust, and procedural safeguards. The current evidence base connects crisis management, organizational resilience, policy capacity, interagency networks, adaptive governance, continuity planning, and public accountability. Major challenges include rigid procedures, coordination failures, information bottlenecks, uneven workforce capacity, emergency powers, infrastructure dependencies, and inequitable service disruption. Priorities include resilience measurement, scenario planning, institutional learning, workforce and digital resilience, intergovernmental coordination, emergency accountability, and long-term post-crisis evaluation. Resilient administration should ultimately be judged by whether institutions can preserve essential public value while adapting transparently and learning from disruption.
Digital platforms, cloud services, automation, analytics, electronic records, and artificial intelligence are becoming embedded in public administration. Yet acquisition of new technology does not by itself constitute digital transformation. This editorial argues that government digitalization is fundamentally an organizational and institutional transformation in which technical systems interact with administrative rules, data practices, professional roles, procurement, accountability, and service design. The contemporary landscape spans digital government, algorithmic decision support, generative AI, integrated administrative systems, robotic process automation, and data-intensive public services. Major constraints include legacy systems, weak interoperability, data quality, workforce skills, vendor dependence, cybersecurity, privacy, automation bias, digital exclusion, and limited evidence about realized productivity or service-quality gains. The research agenda should prioritize public-sector AI evaluation, administrative process redesign, interoperable data systems, workforce development, responsible procurement, algorithmic auditing, cybersecurity, human oversight, and longitudinal assessment of outcomes. Digital transformation should ultimately be judged by whether technology strengthens public capability and public value, not by the number of systems procured or automated.
Policy evaluation is central to accountable public management, but an average treatment effect is rarely sufficient for governing complex programmes. Decision makers need to know how an intervention produced outcomes, for whom, under which institutional conditions, at what cost, over what time period, and with what unintended consequences. This editorial argues for an evaluative model that combines causal inference with implementation analysis, theory, context, equity, cost, and organizational learning. Randomized and quasi-experimental designs remain powerful tools for estimating effects, while process evaluation, realist and theory-based approaches, qualitative inquiry, administrative-data analysis, and mixed methods can explain mechanisms and transferability. Key challenges include attribution, selection bias, implementation variation, weak outcome measures, short follow-up, missing data, publication incentives, and external validity. Priorities include linked administrative data, longer-term follow-up, replication across settings, mechanism analysis, distributional effects, cost-effectiveness, adaptive experimentation, mixed-method evaluation, and transparent reporting of null and adverse findings. Evaluation should support policy learning and adaptation rather than merely classify programmes as successful or unsuccessful.
