Applying Design Thinking to Research Political Science’s “Wicked Problems”: A Pragmatic and Abductive Approach ()
1. Introduction
Political science, the study of power, governance, and decision-making within and among societies, encompasses comparative social and public policy, health and wellbeing, environmental policy, democracy and representation, and many other areas central to societal functioning. The discipline relies on diverse methodologies, principally quantitative modeling, but also case studies, ethnographic research, and historical analysis, to explore and understand political phenomena (Easton, 1953; Almond & Powell, 1996). However, political science frequently confronts “wicked problems”—issues that are dynamic, multi-dimensional, and resistant to simple solutions (Rittel & Webber, 1973). Traditional research methodologies in political science, while valuable, often struggle to account for the complexity and unpredictability inherent in political systems. They may also fall short in engaging with the lived experiences of stakeholders affected by policies and governance structures. In this context, design thinking offers a fresh, human-centered, and iterative approach that aligns well with the discipline’s need for innovative, context-sensitive inquiry (Brown, 2009).
Design thinking as a research method prioritizes empathy, stakeholder engagement, and iterative problem-solving, making it particularly suitable for addressing the challenges of political science. By focusing on abductive reasoning—generating hypotheses in response to observed phenomena—and pragmatic inquiry, design thinking fosters actionable insights that bridge the gap between theory and practice (Dorst, 2011; Kolko, 2010). This paper makes a case for design thinking method, a human-centered, pragmatic, abductive qualitative methodology, being able to play an important role in addressing pressing societal “wicked problems” in contexts of high dynamics and uncertainty, where more classical research methods might not be sufficient. We venture beyond this purely conceptual claim and introduce a standardized application framework, a guiding boilerplate, of design thinking method, tailored to political science. By using it, scholars can ensure to comply with academia’s exigences of rigor all while aggregating relevant theoretical and practical contributions. This method has the potential to enrich the discipline’s methodological toolkit and enhance its capacity to address pressing societal and thereby political challenges.
2. Theoretical Underpinning
2.1. Political Science
Political science examines the structures, processes, and outcomes of political systems, focusing on the distribution and exercise of power. Core areas of inquiry include the study of political institutions, public policy, behavior, and international relations (Goodin & Klingemann, 1996). Researchers in political science aim to understand phenomena ranging from voter behavior and public sector management to global governance and environmental policy. The discipline’s reliance on diverse methodologies reflects its complexity. Quantitative methods like statistical modeling are common for exploring voting patterns or policy impacts, while qualitative approaches such as case studies and ethnography delve into political cultures and narratives (King, Keohane, & Verba, 1994). Despite these tools, political science often faces challenges in addressing emergent, cross-disciplinary issues that defy traditional frameworks.
2.2. Why Political Science Needs Design Thinking Method
Design thinking method (DTM) addresses two critical gaps in political science research.
2.2.1. Wicked Problems (Complex, Intertwined Value-Lade Challenges)
Political science often grapples with multifaceted issues involving multiple stakeholders with conflicting interests. Design thinking’s iterative, stakeholder-centered approach allows researchers to navigate this complexity and co-create solutions (Rittel & Webber, 1973; Cross, 2011). The term “wicked problems” was coined by Horst Rittel and Melvin Webber in their exploration of challenges in social policy and urban planning. According to their 1973 paper, wicked problems are issues that cannot be definitively formulated, have no clear solution, and are fundamentally shaped by the context in which they arise. Wicked problems differ from “tame” problems, which are well-structured and solvable through established methods, such as mathematical equations or engineering processes. The archetypal wicked problem is climate change, which exemplifies these characteristics. Climate change lacks a definitive problem statement, as it encompasses diverse issues such as greenhouse gas emissions, deforestation, and socio-economic inequality. Potential solutions, from carbon pricing to geoengineering, carry trade-offs and uncertainties. Moreover, the problem is deeply entangled with global politics, economic systems, and cultural values. In sum, wicked problems are extremely hard to frame with classical research approaches. Goodin & Klingemann (1996) recommend diversity in research approaches for deeper insights and provide a comprehensive overview of political science methodologies. King, Keohane, & Verba (1994) discuss the importance of rigorous inference in political research. Cross (2011) details how design thinking method fosters creativity and innovation in tackling complex problems. Brown (2009) emphasizes the iterative, human-centered nature of design thinking as a methodology of complex, socially-laden problems. We summarize the defining characteristics of wicked problems in the table below (Table 1).
2.2.2. Theory-Practice Divide
The academics-practitioner gap represents a significant challenge in public policy and political science, where academic research often remains disconnected from the realities faced by policymakers and practitioners. This gap emerges because theoretical models and frameworks, while valuable for explaining political phenomena, are frequently too abstract or generalized to address the nuanced, context-specific challenges of public policy implementation. For instance, while a theoretical model may highlight the importance of fiscal discipline, it often fails to account for the political, cultural, and administrative constraints faced by policymakers in different contexts (Van de Ven & Johnson, 2006). This disconnection limits the ability of academic research to inform actionable policy solutions, creating frustration among practitioners who must navigate the complexities of governance without adequate guidance from academia.
Table 1. Characteristics of wicked problems.
Characteristic |
Description |
Example |
No Definitive Formulation |
Wicked problems lack a clear and agreed-upon problem statement; stakeholders define the problem differently (Rittel & Webber, 1973; Head, 2008). |
Climate change involves issues like emissions, deforestation, and inequality, each framed differently. |
No Stopping Rule |
There is no clear endpoint; solutions can always be improved or refined further (Conklin, 2006; Mason & Mitroff, 1981). |
Poverty alleviation requires continuous efforts as new challenges emerge. |
Solutions Are Better or Worse, Not Right or Wrong |
There are no objectively correct solutions; evaluations depend on values and priorities (Churchman, 1967; Brown et al., 2010). |
Urban planning decisions may prioritize economic growth, sustainability, or social equity. |
Unique Nature |
Every wicked problem is unique and context-specific, making solutions non-transferable (Weber & Khademian, 2008; Termeer et al., 2015). |
Addressing homelessness in one city may not apply to another due to cultural and systemic differences. |
Interconnectedness |
Wicked problems are symptoms of broader systemic issues and cannot be isolated (Rittel & Webber, 1973; Levin et al., 2012). |
Climate change is connected to energy systems, economic policies, and global governance. |
Stakeholder Conflict |
Multiple stakeholders have conflicting interests, values, and goals (Head & Alford, 2015; Van Bueren et al., 2003). |
Environmentalists, corporations, and governments often disagree on the best climate policies. |
Irreversible Consequences |
Poorly conceived solutions can have long-lasting, unintended impacts (APSC, 2007; Levin et al., 2012). |
Geoengineering attempts to combat climate change may create unforeseen environmental consequences. |
In public policy, the stakes of this divide are particularly high. Policies that are theoretically sound may falter during implementation due to unforeseen barriers, such as resistance from stakeholders, logistical constraints, or conflicting priorities among government agencies (Head, 2010). Conversely, practitioners may develop ad hoc solutions that address immediate challenges but fail to engage with the broader theoretical principles necessary for long-term success. This misalignment undermines the potential for mutual learning between theory and practice, resulting in policies that are either impractical or lack a robust conceptual foundation.
Design thinking method offers a pathway to bridge this divide by emphasizing co-creation and iterative problem-solving. By engaging stakeholders—academics, practitioners, and beneficiaries—throughout the research process, design thinking ensures that theoretical insights are grounded in practical realities. Iterative prototyping allows researchers and practitioners to test solutions in real-world conditions, refining both theoretical models and practical applications simultaneously. By integrating practical needs with theoretical frameworks, this approach embodies the principles of “pragmatic interdisciplinarity” (Ansell, 2011) and operationalizes the call for “engaged scholarship” (Van de Ven & Johnson, 2006), fostering a dynamic exchange between theoretical rigor and practical utility. This approach is particularly well-suited for public policy, where complex, context-sensitive problems require innovative, collaborative, and adaptive solutions.
2.3. Pragmatism and Abductive Reasoning
Pragmatism, rooted in the works of Dewey (1938) and James (1907), and further explored in the context of educational and communication research by Biesta and Burbules (2003), emphasizes the importance of inquiry as a dynamic, iterative process embedded in real-world contexts. Knowledge is not static but evolves through action and reflection. This aligns with political science’s need for adaptable methods that respond to changing socio-political environments.
Abductive reasoning, introduced by Peirce (1934), complements this by focusing on hypothesis generation in conditions of uncertainty. Rather than seeking definitive answers, abduction thrives in ambiguity, offering plausible explanations for observed phenomena. In political science, abduction can illuminate unexpected voter behaviors, policy outcomes, or governance failures (Kolko, 2010).
2.4. Key Applications of Design Thinking Method
Design thinking’s flexibility and stakeholder-centered approach make it well-suited for a variety of political science research areas (Hwei, 2022). Below is an expanded discussion of key applications, followed by a summarized table.
Comparative Social and Public Policy: Design thinking enables researchers to explore and compare the effectiveness of policies across different contexts. Empathy-driven approaches uncover stakeholder needs, while prototyping allows for testing policy innovations iteratively. An example is the prototyping universal healthcare models across diverse cultural and economic settings to refine equitable solutions.
Environmental Policy and Politics: Environmental governance requires collaborative, adaptive approaches to address issues like climate change and resource management. Design thinking facilitates co-creation with stakeholders and iterative testing of interventions. One application example is the iterative developing of carbon pricing models to balance economic and environmental goals.
Political Campaigns and Representation: Political campaigns benefit from testing and refining strategies through stakeholder feedback. Design thinking helps optimize voter outreach by iterating campaign messages and materials. Here, one could imagine testing voter engagement initiatives with underrepresented groups to increase turnout.
In a short table, we describe possible applications where an abductive qualitative method such as design thinking method could yield thick and salient results (Table 2).
Table 2. Examples of applications of design thinking method to political science.
Area of Application |
Design Thinking Application |
Example Use Case |
Comparative Social Policy |
Prototyping policy solutions for diverse social contexts. |
Testing universal basic income models in urban and rural settings. |
Environmental Policy |
Co-creating strategies with communities and stakeholders. |
Developing sustainable urban planning initiatives through participatory workshops. |
Political Campaigns |
Iteratively refining campaign strategies to improve voter outreach. |
Testing narratives to engage first-time voters. |
Immigration Policy |
Mapping immigrant journeys to identify systemic barriers. |
Designing refugee resettlement programs with feedback from affected communities. |
Evidence-Based Policymaking |
Testing pilot interventions to validate policy designs. |
Experimenting with digital governance tools in local government. |
2.5. Theoretical Alignment with Political Science
Design thinking aligns with foundational political science principles by addressing complexity, fostering stakeholder engagement, and operationalizing iterative learning. Its pragmatic and abductive nature resonates with the discipline’s emphasis on contextual, actionable insights (see Table 3 below).
Table 3. How design thinking method aligns theoretically with political science.
Theoretical Concept |
Design Thinking Alignment |
Relevance to Political Science |
Pragmatism |
Emphasizes iterative learning and context-sensitive inquiry. |
Aligns with the need for adaptive solutions in governance and
policy-making (Dewey, 1938). |
Abduction |
Facilitates hypothesis generation in uncertain contexts. |
Illuminates unexpected political behaviors and policy outcomes (Peirce, 1934; Kolko, 2010). |
Complexity and Wicked Problems |
Thrives in non-linear, multi-stakeholder environments. |
Tackles systemic issues like climate change and fiscal reform (Rittel & Webber, 1973). |
Evidence-Based Policymaking |
Integrates iterative testing and refinement. |
Enhances the validity and impact of policy interventions (Van de Ven & Johnson, 2006). |
2.6. Comparing Design Thinking Method, Grounded Theory and
Action Research
How does Design Thinking Method compare to other powerful qualitative research methods, such as Grounded Theory and Action Research? Design Thinking Method excels in contexts where traditional research approaches struggle to address dynamic, ambiguous, and value-laden issues. Its iterative, abductive process and human-centered philosophy make it particularly well-suited for tackling “wicked problems” (Rittel & Webber, 1973) characterized by conflicting stakeholder perspectives, incomplete information, and no definitive solutions. DTM thrives in environments requiring co-creation and innovation, such as public policy development, governance, and social impact initiatives, where bridging the gap between theoretical knowledge and actionable solutions is critical.
Unlike methodologies like Grounded Theory, which focus on generating theory from data, or Action Research, which prioritizes participatory problem-solving within specific contexts, DTM offers a unique advantage: it transforms stakeholder insights into implementable prototypes that can be tested and refined in real-world conditions. As Liedtka (2015) argues, DTM’s iterative nature enables researchers and practitioners to navigate uncertainty and foster creative problem-solving by engaging with diverse perspectives and experimenting with practical interventions.
For example, DTM has been particularly effective in policy design. Brown (2009) highlights its use in addressing complex societal challenges such as urban planning and healthcare accessibility, where iterative prototyping allows for adaptive, user-informed solutions. Similarly, Kolko (2010) underscores DTM’s strength in abductive reasoning, making it invaluable for generating hypotheses in unpredictable political contexts. Schön’s (1983) concept of reflective practice further reinforces DTM’s capacity to align theoretical rigor with pragmatic action, enabling researchers to respond dynamically to emergent issues while maintaining methodological integrity.
In sum, DTM is best suited for addressing wicked problems where stakeholder engagement, iterative experimentation, and actionable outcomes are paramount. Its application enhances the relevance and impact of research, making it a critical tool for political science and other disciplines dealing with complex, real-world challenges. For clarity, we have summarized this comparison in a table (Table 4).
3. Toward a Conceptual Framework for Design Thinking
Method in Political Science Research
This section introduces a structured six-phase framework tailored for researchers who wish to apply design thinking methodology to complex socio-political phenomena. The framework is designed to provide clear guidance while maintaining academic rigor, offering researchers a roadmap to navigate wicked problems and present findings with credibility. To enhance clarity, each phase is illustrated with a real-world example that demonstrates its application in a political science context. The goal is to provide both theoretical grounding and practical utility, ensuring the framework supports rigorous, impactful research. A table is provided for quick referencing during the research process (Table 5).
Table 4. Comparing DTM with grounded theory and action research.
Aspect |
Design Thinking |
Grounded Theory |
Action Research |
Primary Aim |
Solve complex problems through innovative solutions. |
Develop theory grounded in empirical data. |
Address real-world problems while generating knowledge. |
Stakeholder Involvement |
Extensive and continuous engagement. |
Limited to data collection. |
Active and collaborative participation. |
Methodological Basis |
Abductive and pragmatic. |
Inductive and theory-building. |
Participatory and iterative. |
Iterative Nature |
Iterative with prototyping and testing phases. |
Iterative data collection and analysis. |
Cyclical (plan-act-observe-reflect). |
End Goal |
Practical, context-sensitive solutions. |
New theoretical insights. |
Immediate action and broader knowledge. |
Strengths |
Focused on innovation and bridging theory-practice. |
Robust, systematic, and theory-driven. |
Combines action with research for tangible impact. |
Weaknesses |
Resource-intensive, less focus on theory. |
Limited stakeholder interaction, less practical. |
Subjective and context-bound results. |
Table 5. Conceptual framework of design thinking method.
Phase |
Nature of the Phase |
Purpose |
Example Use Case |
Template |
Academic Reference |
Empathize |
Understanding stakeholders and gathering qualitative data. |
Uncover stakeholder needs and problem nuances. |
Mapping barriers to voter participation in underserved communities. |
Stakeholder Persona Canvas. |
Brown (2009) |
Define |
Synthesizing insights to frame the problem. |
Articulate a focused research question or challenge. |
Defining the challenge of low voter trust and accessibility. |
Problem Statement Worksheet. |
Charmaz (2006) |
Ideate |
Generating hypotheses and brainstorming solutions. |
Produce a range of possible approaches to address the problem. |
Brainstorming voter outreach strategies with local stakeholders. |
Hypothesis Generation Matrix. |
Cross (2011) |
Prototype |
Developing tangible models or interventions. |
Create testable versions of solutions. |
Designing a mobile voting unit prototype. |
Policy Prototype Worksheet. |
Dorst (2011) |
Test |
Validating and refining solutions through iteration. |
Incorporate stakeholder feedback to improve ideas. |
Piloting mobile voting units in select districts. |
Iterative Feedback Log. |
Kolko (2010) |
Document |
Compiling findings into a rigorous manuscript. |
Communicate research with academic rigor. |
Publishing a study on the effectiveness of mobile voting units. |
Manuscript Checklist. |
Van de Ven & Johnson (2006) |
3.1. Phase 1: Empathize: Understanding Stakeholders and Context
Nature of the Phase: The Empathize phase focuses on deeply understanding the lived experiences, needs, and challenges of stakeholders involved in political systems or affected by public policies. This phase sets the foundation for the research by gathering rich qualitative data and insights directly from the people who experience the political or policy issue firsthand. Techniques such as interviews, focus groups, ethnographic observation, and journey mapping are essential in this phase.
Purpose: To uncover the nuances of the problem space, identify key stakeholder perspectives, and capture the emotional, cultural, and practical dimensions of the issue under investigation. This helps ensure that subsequent research and solutions are grounded in real-world experiences.
Example Use Case: In a study aimed at improving voter participation in underserved communities, researchers engage with local residents, election officials, and community leaders to understand barriers to voting, such as logistical challenges, mistrust in the electoral system, or lack of accessible polling locations.
Example Template: Stakeholder Persona Canvas.
This tool captures the key traits, needs, and pain points of stakeholders, including:
Demographics: Age, gender, socioeconomic status, and education.
Behavioral Traits: Typical actions, habits, or decisions in the context of the issue.
Goals: What stakeholders aim to achieve (e.g., fair representation, accessible voting).
Challenges: Barriers preventing them from achieving their goals.
Example of successful application: The Danish government’s innovation unit, MindLab, engaged with citizens and public servants to understand their experiences with public services. Through interviews and ethnographic studies, they identified pain points in service delivery, leading to more user-centered policy designs (Lykketoft, 2016).
3.2. Phase 2: Define: Framing the Problem
Nature of the Phase: The Define phase involves synthesizing the qualitative insights gathered during the Empathize phase to articulate the core problem and establish a clear research focus. This phase requires analyzing data to identify recurring themes, contradictions, and opportunities for intervention.
Purpose: To frame a precise research question or problem statement that aligns with stakeholders’ needs and priorities. A well-defined problem guides the development of hypotheses and ensures that the research remains focused and actionable.
Example Use Case: Building on the voter participation example, researchers use data from interviews and journey mapping to articulate the core challenge: “How can local governments increase trust and accessibility in voting processes for underserved communities?”
Example Template: Problem Statement Worksheet.
This worksheet helps researchers succinctly define the problem, including:
Context: Overview of the issue and its significance (e.g., low voter turnout in rural areas).
Stakeholder Needs: Insights from the Empathize phase (e.g., desire for transparent election systems).
Research Question: A focused question to guide subsequent phases (e.g., “What strategies can increase voter participation?”).
Success Metrics: Indicators of a successful outcome (e.g., increased voter turnout by 15% in target communities).
Examples of Successful Applications in the Field: The City of Calgary employed design thinking method to research its waste management system and design improvements. By synthesizing insights from stakeholder engagements, they redefined the problem as a need to increase public participation in recycling programs, shifting the focus from merely optimizing collection routes (Christensen, 2012).
The U.S. Department of Veterans Affairs utilized design thinking method to redefine the problem of veteran homelessness. By synthesizing insights from stakeholder engagements, they reframed the issue to focus on improving access to support services, rather than solely providing housing (Liedtka & Salzman, 2018).
3.3. Phase 3: Ideate: Generating Hypotheses and Solutions
Nature of the Phase: The Ideate phase emphasizes creative problem-solving and divergent thinking to explore a range of possible solutions to the defined problem. This phase leverages brainstorming sessions, co-creation workshops, and interdisciplinary collaboration to generate innovative ideas and hypotheses.
Purpose: To produce a variety of potential approaches for addressing the problem, considering feasibility, stakeholder impact, and alignment with the research question. This phase fosters creativity while ensuring ideas remain grounded in the insights from previous phases.
Example Use Case: In the voter participation example, researchers conduct brainstorming workshops with stakeholders—residents, election officials, and civic organizations—to explore solutions such as mobile voting units, online registration tools, or voter education campaigns.
Example Template: Hypothesis Generation Matrix.
A structured table that organizes potential solutions based on:
Hypothesis: A specific idea to address the problem (e.g., “Offering free transportation to polling stations will increase voter turnout.”).
Feasibility: The practicality of implementing the idea (e.g., high, medium, low).
Impact: The expected effect on the target population (e.g., high, medium, low).
Alignment with Stakeholder Needs: How well the idea meets the priorities identified in the Empathize phase.
Example of a Successful Application in the Field: The New Zealand government conducted ideation sessions with citizens and public servants to generate ideas for improving public services. This collaborative ideation led to the development of more user-friendly digital platforms for government services (Lewis et al., 2019).
3.4. Phase 4: Prototype: Making Ideas Tangible
Nature of the Phase: Develop tangible models/interventions that address the research problem.
Purpose: Transform hypotheses into testable solutions that stakeholders can evaluate.
Example Use Case: Designing a prototype for a policy simulation tool to test the impacts of fiscal reform on income inequality.
Example Template: Policy Prototype Worksheet.
Key components of the policy prototype.
Hypothesized outcomes and impacts.
Tools or platforms used (e.g., simulation software).
Example of a Successful Application: The Singapore Ministry of Manpower prototyped a new work pass application system by creating a mock-up of an online platform. This allowed them to test and refine the process before full-scale implementation (DEAC, 2020).
3.5. Phase 5: Test: Experiment with Users
Nature of the Phase: Iteratively refine prototypes through feedback and real-world applications.
Purpose: Validate the effectiveness of prototypes and adjust them based on stakeholder input.
Example Use Case: Piloting a participatory budgeting initiative in a local government context to refine public engagement strategies.
Example Template: Iterative Feedback Log.
Stakeholder feedback (e.g., community leaders, public officials).
Revisions made to the prototype.
Final insights and actionable recommendations.
Example of a Successful Application: The British Columbia Ministry of Transportation tested a new public engagement website with a small group of users. Feedback from this testing phase led to significant improvements in the system’s usability and accessibility (EDSC Innovation Lab., 2019).
3.6. Phase 6: Document: Compile Findings
Nature of the Phase: Compile research findings into a rigorous academic manuscript. This phase, while wrapping the five phases in the end as a kind of sixth phase, is in reality ongoing. Much of the write up happens iteratively as the research progresses. In the end the researcher will reflect on whether they will write the research up sequentially, or whether another format is more concise and clear.
Purpose: Communicate the research process, findings, and implications in a way that meets academic standards and that is comprehensible and clear to both scholars and practitioners.
Example Use Case: Writing a comparative study of environmental policy prototypes in urban governance contexts.
Example Template: Manuscript Checklist.
Adherence to qualitative research standards.
Reflexivity and bias mitigation.
Integration of visual aids (e.g., policy diagrams, user feedback).
Example of a Successful Application: The Design Singapore Council documented the redesign of public services, providing detailed case studies that outline the design thinking process, challenges faced, and outcomes achieved. These documents serve as valuable resources for other public sector organizations (Mariani et al., 2024).
3.7. Recommendations for Evidencing Academic Rigor
To apply and evidence rigor is particularly crucial when employing qualitative abductive methods because these approaches challenge traditional research paradigms grounded in deductive or quantitative frameworks. Critics often perceive abductive methods as subjective or lacking structure, especially among scholars and reviewers unfamiliar with these approaches (Lincoln & Guba, 1985; Charmaz, 2006). To address this skepticism, authors must explicitly demonstrate rigor by documenting each phase of the research process transparently. This includes methodological justification, thick descriptions of data and context, and reflexivity, where researchers critically examine their role in shaping the research (Finlay, 2002). Also helpful is to provide rich, detailed narratives to contextualize findings and enhance credibility (Geertz, 1973). Triangulation—using multiple data sources or perspectives—further enhances credibility and validity. Additionally, iterative cycles of hypothesis refinement must be clearly illustrated, aligning with Charmaz’s (2006) emphasis on systematic theory construction. Reflexivity is also an excellent tool. Acknowledge the researcher’s role and potential biases, and demonstrate how they were addressed (Finlay, 2002). By evidencing rigor, authors not only validate their findings but also educate reviewers on the robustness and scholarly value of abductive qualitative inquiry.
4. Theoretical and Practical Contribution
In this paper we strive to advance the methodological discourse in political science by integrating design thinking as a pragmatic and abductive research approach. Our work provides a systematic framework and actionable templates for addressing the discipline’s most complex challenges, such as policy reform and governance. We purport that the inclusion of iterative, stakeholder-centered processes enriches the theoretical landscape by aligning with the dynamic and interdisciplinary nature of political phenomena. As emphasized by Schön (1983), reflective practices like those in design thinking foster deeper theoretical insights, particularly in emergent and uncertain contexts.
Practically, this framework empowers political scientists, policymakers, and public managers to co-create solutions that are both context-sensitive and action-oriented. By integrating empathy and iterative prototyping, the method bridges the gap between academic inquiry and policy implementation, fostering evidence-based and inclusive approaches. For example, the use of participatory workshops and iterative budgeting simulations enhances public sector accountability and responsiveness (Van de Ven & Johnson, 2006).
5. Conclusion, Limitations and Ideas for Future Research
Design thinking offers a transformative methodology for political science, emphasizing empathy, collaboration, and iterative learning. By operationalizing this method through a structured framework, this paper addresses the theory-practice gap and provides actionable pathways for tackling complex political challenges. The proposed framework not only enriches academic research but also equips policymakers and practitioners with tools to navigate uncertainty and drive systemic change.
Still, many limitations define this work in progress, for example in terms of i) contextual sensitivity: the proposed framework may require adaptation for different political or cultural contexts, potentially limiting generalizability; ii) empirical validation: while conceptually robust, the proposed framework requires empirical validation across diverse political systems; and iii) resource intensity: iterative prototyping and stakeholder engagement can be resource-intensive, posing challenges for large-scale implementation.
Ideas for Future Research definitely should include i) diverse empirical applications: one might conduct case studies using the framework in diverse political settings, such as municipal governance or international diplomacy, experimenting with the proposed templates and/or others taken from design thinking method and/or other pragmatic and abductive methods; ii) integration with quantitative methods: it might be exciting to explore how design thinking method can complement quantitative models in political science, for example sequentially either ahead of a quantitative study for a better and more holistic comprehension of the context at hand, or subsequent to a quantitative study, delving deeper in one of the identified phenomena for more thick data; iii) cross-disciplinary research: it could be worth while to investigate synergies between design thinking method and systems theory to address global challenges like climate change or migration; and iv) digital tools: design thinking method is often time-consuming and do develop and test digital platforms that support the application of design thinking method in policy-making contexts could be a way to render the methodology more efficient while maintaining its abductive, holistic qualities.
This conceptual paper is work in progress. In line with its content, it is iterative, hoping for feedback. Its authors believe in sharing reflections while they are still rough, fragile and messy and while research prototypes are running—for the pleasure of inquiry and debate, and for the advancement of pragmatic experimental research epistemologies. Objectivity does not repose on the lone shoulders of each academic author, but can—and does—emerge from interplay, and dialogue.