Experimental Governance: Insights into Its Application in Business Processes and Future Research Directions

Abstract

Experimental Governance (EG) has emerged as a strategic framework for managing complexity in high-uncertainty environments. However, its application in the private sector remains fragmented, often conflated with purely operational tools. This study addresses this gap by performing a conceptual transfer of EG principles into the domain of business processes. Through an expanded Systematic Literature Review (SLR) of 41 peer-reviewed articles (covering the period 2004–2026), we identify what we term the ‘Internalization Paradox’: while firms rapidly adopt experimental methodologies like Agile or Lean, they often fail to embed them into formal governance structures that ensure long-term accountability and institutional learning. This updated review incorporates cutting-edge discussions on Artificial Intelligence (AI) governance, experimentalist metagovernance, and the strategic regulation of uncertainty. Our findings suggest that organizational resilience is not merely a byproduct of technological readiness, but an emergence of ‘Institutionalized Experimentalism’. We propose a Conceptual Framework that operationalizes EG through iterative feedback loops, corporate sandboxes, and adaptive decision rights, providing a robust roadmap for future empirical research in management and organizational theory.

1. Introduction

In an era of unprecedented market volatility, private organizations have increasingly turned to agile methodologies and rapid prototyping to maintain competitive advantage. However, a critical disconnect persists: while experimental tools are advancing, organizational governance structures remain either overly rigid or dangerously informal. This gap characterizes the Internalization Paradox: the adoption of experimental processes without a corresponding governance architecture to ensure legitimacy and scalability. Recent literature emphasizes that experimental governance allows stakeholders to test possible futures and embrace creativity in the pursuit of sustainability goals (Eneqvist et al., 2022). As an analytical framework, it has been employed to study how actors formulate and adjust policies and manage organizations (Wang et al., 2022). However, the field still suffers from conceptual and methodological dispersion. While specific approaches have progressed, a comprehensive framework connecting experimental development to the corporate governance domain is still lacking. To overcome this gap, a more cohesive organization is imperative to guide the stages of design, implementation, and evaluation (Laakso et al., 2017).

Although Experimental Governance (EG) is well-established in public policy and urban sustainability as a way to navigate ambiguous goals in complex systems, its systematic application to business processes remains undertheorized. Current literature in climate and urban governance highlights EG’s success through iterative review cycles and multi-level coordination. Yet, how these principles translate into corporate hierarchies (where incentives and accountability differ significantly from the public sector) remains a prominent theoretical lacuna. Despite the evolution of the theme, a significant gap remains in the disconnect between strategic experimentation (the ‘what’ and ‘how’ of testing solutions) and institutionalized governance (the ‘who’ and ‘under what rules’). Without a cohesive organizational structure, experimental initiatives in firms often remain as ‘isolated pilots’ that fail to scale or provide legitimate data for board-level decision-making. This study contributes to the Management and Organizational Theory domain, specifically providing a framework where experimentation is not just a tactical tool, but a core governance process that defines how authority and resources are redistributed during uncertainty.

In this context, the study contributes to the literature not by claiming a mature field of experimental governance within firms, but by performing a conceptual transfer from public and urban contexts to business processes. It explicitly functions as a gap-mapping effort, identifying why corporate applications remain underdeveloped and providing a theoretical synthesis to operationalize these principles in private management. The study addresses the central research question: How are the concepts inherent to experimental governance being applied in business processes? The main objective is to analyze how companies utilize experimental governance by (i) presenting intrinsic aspects of the theme; (ii) identifying organizations employing these models; and (iii) demonstrating practical applications in business processes through an expanded Systematic Literature Review (SLR) of 41 peer-reviewed documents (2004–2026).

Current research highlights that experimental governance often involves collaborative triple-helix or quadruple-helix partnerships among the public sector, private sector, academia, and civil society (Eneqvist & Karvonen, 2021). These collaborations foster co-creation through distributed decision-making, though they raise complex questions about power dynamics and conflict resolution. Recent studies by Gartlinger and Gualini (2025) and Kuhlmann et al. (2019) suggest that governance is experimental when interventions are designed as dynamic and preliminary processes, aiming to create spaces for probing and learning rather than stipulating definitive, rigid goals. In practice, experimental governance has been applied across various fields, including healthcare, education, and climate change (Kampfmann et al., 2024). Radosevic and Zoretic (2024) emphasize the role of stakeholders in shaping innovation policies and the importance of accountability through feedback cycles. Furthermore, the establishment of urban laboratories and “experimental nations” illustrates the top-down appeal of testing technologies and participatory approaches on a small scale before broader implementation (Leino & Åkerman, 2022).

The application of this approach in business processes presents a distinct set of challenges. Primary concerns involve legitimacy and accountability, specifically the difficulties in defining the distribution of responsibilities and the flow of information among actors, which can compromise transparency (Radosevic & Zoretic, 2024). In regions with institutional fragility, governance may lack the stability required for effective experimental processes, while implementing experiments in high-stakes sectors like aviation or finance often faces significant operational failures (Wang et al., 2022). Furthermore, there is a persistent risk of depoliticization, where experimentation might lead to a release of corporate responsibility in addressing critical crises (Haderer, 2023).

Conversely, the benefits of Experimental Governance are rooted in enhanced innovation and flexibility, allowing actors to test strategies before committing to definitive decisions (Eneqvist et al., 2022). It favors collective action and sustainability by involving multiple stakeholders and promoting knowledge sharing (Lopez-Ortego et al., 2024). By creating spaces for probing, EG enables adaptive problem-solving for complex environmental and social issues, fostering institutional resilience through iterative learning (Kuhlmann et al., 2019). Challenging the common-sense view that corporate experimentation requires less rigor than public policy, this study argues that the lack of formal experimental governance is precisely what renders business innovation ephemeral. Consequently, we propose a framework to transform isolated experimental ‘pilots’ into a systemic organizational capability.

The unit of analysis for this study is explicitly defined at the organizational level. While EG is frequently discussed at the meso-level (multi-helix models), this research focuses on how these principles are translated into internal firm mechanisms. The need to transition from rigid control models to dynamic structures is amplified by recent technological transformations. As discussed by Ansell and Trondal (2025), the rise of Artificial Intelligence (AI) and radical uncertainty require organizations to abandon static rules in favor of “experimentalist metagovernance.” This perspective is reinforced by Sabel and Zeitlin (2025), who advocate for “Recursive Accountability” as the mechanism to bridge local experimentation with central strategic design. Furthermore, the role of Corporate Sandboxes (identified by Van der Heijden (2026) as fundamental instruments for the “regulation of uncertainty”) provides the institutional guardrails necessary for experimentation to generate legitimacy and resilience within a horizon extending to 2026.

To address this gap, this study employs a qualitative meta-synthesis grounded in an SLR. This choice is justified by the need to deconstruct existing governance mechanisms across diverse fields and reassemble them into a coherent framework applicable to business processes, providing a theoretically grounded response to the challenges of institutionalizing experimentation in private firms.

2. Materials and Methods

This study employs a qualitative approach through a Systematic Literature Review (SLR). The SLR is not merely a survey of existing literature but serves as the core theoretical-methodological framework for this research, enabling the identification of how experimental governance (a concept traditionally rooted in public policy) is being translated into business processes. The methodological rigor follows the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to ensure transparency, reproducibility, and validity (Page et al., 2021).

2.1. Search Strategy and Data Sources

The methodological flow of this Systematic Literature Review (SLR) followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol to ensure replicability and rigor. The process was conducted in two distinct phases: the original systematic search and a targeted update to incorporate the state-of-the-art literature from 2025 and 2026.

Phase 1 Initial Systematic Search: the primary search was executed in the Scopus, Web of Science (WoS), Science Direct, and the EBSCOhost Portal. Using Boolean operators, the strings focused on “Experimental Governance” OR “Experimentalist Governance” cross-referenced with “Business” OR “Management” OR “Corporate.” The choice of the specific term ‘Experimental Governance’ was deliberate to capture studies explicitly aligned with the framework of Sabel and Zeitlin (2008). While related terms like ‘adaptive governance’ or ‘organizational experimentation’ exist, they often lack the institutionalized multi-level feedback structure central to our research question. This initial phase resulted in 38 peer-reviewed articles that met all inclusion criteria (English language, peer-reviewed, and theoretical-empirical relevance to the research questions).

Phase 2: Targeted 2025–2026 Update: to address the rapid evolution of the field and the emergence of Artificial Intelligence (AI) governance, a secondary targeted search was performed in early 2026. This stage aimed to identify “Early View” and “In Press” publications from 2025 and 2026. Three additional seminal works (D39, D40, and D41) were identified and integrated into the corpus. These articles provide critical insights into Experimentalist Metagovernance, Recursive Accountability, and Corporate Sandboxes, bringing the final corpus to 41 articles.

Final Synthesis and Coding: the final selection of 41 documents underwent a three-step analysis:

• Bibliometric Mapping: to identify temporal and geographical trends.
• Qualitative Content Analysis: using the coding schema through the Atlas.ti software (version 23) to extract governance mechanisms.
• Conceptual Transposition: where the 41 studies provided the foundation for the four Research Propositions (P1–P4) and the Final Conceptual Framework.

The inclusion of the most recent literature (2025/2026) ensured theoretical saturation, as the new data confirmed the foundational categories while providing the necessary technological context (AI and Radical Uncertainty) to bridge the “Corporate Gap” identified in previous versions of this study.

2.2. Eligibility and Selection Criteria

To ensure that the analysis corpus directly addressed the intersection of experimentation and organizational governance, specific criteria were established (

Table 1. Inclusion and Exclusion Criteria.

Types of Criteria	Description
Inclusion	Peer-reviewed journal articles; Open-access articles; Articles published exclusively in English; Articles addressing the theme of experimental governance in organizational and business contexts.
Exclusion	Works that are not scientific articles (non-systematic reviews, editorials, theses, books, chapters); Articles using the term “experimental” only in a methodological sense (laboratory studies); Articles with no clear relation to the central theme.

Created by the authors.

).

2.3. Selection Process and Sample Credibility

The selection process followed the four PRISMA phases: Identification, Screening, Eligibility, and Inclusion (

Table 2. PRISMA Flow Diagram.

Phase	Step	Number of Records	Decision or Exclusion Reason
I. Identification	Records identified in databases	N = 412	Results from the search using the term “experimental governance” in Scopus, Web of Science, Science Direct, and EBSCOhost Portal.
	Records by Database
	Scopus	N = 121
	Web of Science	N = 95
	Science Direct	N = 154
	EBSCOHost Portal	N = 42
	Additional records identified through other sources	3	Targeted manual search for “Early View” and “In Press” publications (2025–2026).
II. Screening	Records removed (duplicates)	n = 152	Estimate: 415 (total)—263 (for screening).
	Records for title and abstract screening	N = 263	Initial pool for title and abstract screening (260 original + 3 update records).
	Records excluded by title and abstract	n = 212	Excluded for not meeting criteria such as being an article, open access, or published in English.
III. Eligibility	Articles retrieved for full-text assessment	N = 51	Articles that passed the initial screening phase.
	Articles excluded after full-text review	n = 10	Excluded for lack of alignment with research questions or conceptual core.
IV. Inclusion	Studies included in the final analysis corpus	N = 41	Final corpus for content analysis and conceptual framework derivation (38 + 3 updated works).

Created by the authors based on PRISMA Flow Diagram from Page et al. (2021).

).

The search strategy is detailed in Table 2, which presents a summary of the results obtained from searches conducted in the scientific databases Scopus, Web of Science, Science Direct, and the EBSCOhost Portal—four of the world’s leading research databases. The search used the term “experimental governance” and originally covered papers published between 2004 and 2024. To ensure the inclusion of the state-of-the-art literature, a targeted update search was performed in early 2026 to identify ‘Early View’ and ‘In Press’ publications for the 2025–2026 horizon (n = 3), bringing the final longitudinal scope to 22 years (2004 to 2026). These selected journals and papers are directly related to the conceptual transfer proposed in this study. Additionally, the final distribution of documents published per year was identified, as shown in Figure 1.

The analysis of Figure 1 indicates that scientific production related to Experimental Governance has not only maintained a consistent frequency over the last decade but has shown a notable acceleration in the most recent period (2024–2026). With a minimum of three publications per year and a peak in the last biennium, this trend demonstrates the consolidation of the topic as a field of high strategic relevance. The recent surge in publications reflects the academic community’s urgent response to the governance of radical uncertainties and disruptive technologies, confirming that the framework proposed herein aligns with the current frontier of organizational theory.”

While the core search string used was ‘Experimental Governance’, the analysis phase (meta-synthesis) incorporated secondary terms to refine the qualitative coding. Terms such as ‘Agile Methods’, ‘Continuous Learning’, and ‘Iterative Prototyping’ were used as secondary filters during the screening and coding stages in Atlas.ti (version 23). This allowed the study to identify where experimental governance mechanisms overlap with established management methodologies, ensuring a broader understanding of the phenomenon beyond the specific nomenclature.

Addressing the “Corporate Gap” and Research Credibility: The final selection of 41 articles reflects the current state of the art, where experimental governance is still emerging within the private sector. Although the sample size is relatively small, the credibility and precision of the results are guaranteed by means:

• Theoretical Saturation (2004–2026): The qualitative analysis reached a point of saturation where additional studies (including the most recent contributions from Ansell and Trondal (2025), Sabel and Zeitlin (2025), and Van der Heijden (2026)) no longer sparked new theoretical insights regarding core mechanisms. Instead, these recent works reinforced and refined the existing categories of “Recursive Accountability” and “Corporate Metagovernance,” confirming the robustness of the identified framework.
• Multidimensional Thematic Coding: All 41 studies were subjected to rigorous content analysis (Hennink et al., 2020). This process specifically mapped public-sector experimental traits (such as flexibility, co-creation, and iterative feedback loops) onto business process frameworks. The inclusion of 2025/2026 literature allowed for a more precise coding of modern drivers, such as Artificial Intelligence (AI) governance and the regulation of radical uncertainty, ensuring the coding schema is future-proof.
• Reflexive Analysis of Field Incipiency: The relatively low proportion of legacy private-sector literature identified is a significant finding in itself. It documents the “incipient” nature of the field, which this study addresses by providing a foundational conceptual bridge. By incorporating “Early View” and 2026-horizon publications, this research demonstrates that while the field is nascent, it is accelerating toward a “Metagovernance” model that is critical for contemporary organizational resilience.

2.4. Data Analysis and Synthesis

The final corpus (

Table 3. Corpus of Analysis.

Work ID	Article Title	Author(s)
D01	Experimental Governance: the open method of coordination	(Szyszczak, 2006)
D02	NanoŠmano Lab in Ljubljana: Disruptive prototypes and experimental governance of nanotechnologies in the hackerspaces	(Kera, 2012)
D03	Chinese strategies of experimental governance. the underlying forces influencing urban restructuring in the Pearl River Delta	(Schoon, 2014)
D04	The changing contours of experimental governance in European health care	(Fierlbeck, 2014)
D05	Experimental governance for low-carbon buildings and cities: value and limits of local action networks	(Van der Heijden, 2016)
D06	Dynamics of experimental governance: a meta-study of functions and uses of climate governance experiments	(Laakso et al., 2017)
D07	Special Issue on experimentation for climate change solutions editorial: the search for climate change and sustainability solutions—the promise and the pitfalls of experimentation	(Hildén et al., 2017)
D08	Analyzing REDD+ as an experiment of transformative climate governance: Insights from Indonesia	(Korhonen-Kurki et al., 2017)
D09	A novel understanding of experimentation in governance: co-producing innovations between “lab” and “field”	(Voß & Simons, 2018)
D10	Experimental governance: the role of municipalities in urban living labs	(Kronsell & Mukhtar-Landgren, 2018)
D11	‘Politics done like science’: Critical perspectives on psychological governance and the experimental state	(Jones & Whitehead, 2018)
D12	An evolutionary perspective on experimental local governance arrangements with local governments and residents in Dutch rural areas of depopulation	(Ubels et al., 2019)
D13	Municipalities as enablers in urban experimentation	(Mukhtar-Landgren et al., 2019)
D14	The tentative governance of emerging science and technology—a conceptual introduction	(Kuhlmann et al., 2019)
D15	Experimental governance? The emergence of public sector innovation labs in Latin America	(Ferreira & Botero, 2020)
D16	Experimental governance and urban planning futures: five strategic functions for municipalities in local innovation	(Eneqvist & Karvonen, 2021)
D17	Representing ‘place’: city climate commissions and the institutionalisation of experimental governance in Edinburgh	(Creasy et al., 2021)
D18	Transition governance for just, sustainable urban mobility: an experimental approach from Rotterdam, the Netherlands	(Loorbach et al., 2021)
D19	The politics of making Finland an experimenting nation	(Leino & Åkerman, 2022)
D20	Experimental governance and urban climate action—a mainstreaming paradox?	(Grönholm, 2022)
D21	Legitimacy in municipal experimental governance: questioning the public good in urban innovation practices	(Eneqvist et al., 2022)
D22	The emergence of relationality in governance of climate change adaptation	(Sebastian & Jacobs, 2022)
D23	A refined experimentalist governance approach to incremental policy change: the case of process-tracing China’s central government infrastructure PPP policies between 1988 and 2017	(Wang et al., 2022)
D24	Experimental governance in China’s higher education: stakeholder’s interpretations, interactions and strategic actions	(Han, 2022)
D25	To see, or not to see, that is the question: studying Dutch experimentalist energy transition governance through an evolutionary lens	(Gerritsen et al., 2022)
D26	The experimentation-accountability trade-off in innovation and industrial policy: are learning networks the solution?	(Radosevic et al., 2023)
D27	Co-evolutionary dynamics of experimental governance: a longitudinal study of sustainable mobility services in Oslo	(Røste, 2023)
D28	Experimental governance in ‘trapped’ regions? What can and cannot be done in Europe’s periphery	(Marques et al., 2023)
D29	Rescuing the Paris agreement: improving the global experimentalist governance by reclassifying countries	(Qin & Yu, 2023)
D30	Experimental climate governance as organized irresponsibility? A case for revamping governing (also) through government	(Haderer, 2023)
D31	A tale of urban experimentation in three Swedish municipalities	(Grundel & Trygg, 2024)
D32	Urban labs beyond Europe: the formation and contextualization of experimental climate governance in five Latin American cities	(Roll et al., 2024)
D33	EU smart specialization policy between experimentation and accountability: dynamic policy cycle perspective	(Radosevic & Zoretic, 2024)
D34	Urban negotiations in experimental governance exercises for the right to the city: notes on the experience of the Expanded Architecture collective	(Lopez-Ortego et al., 2024)
D35	Explaining policy failure in China	(Yasuda, 2024)
D36	Towards experimental standardization for AI governance in the EU	(Prifti & Fosch-Villaronga, 2024)
D37	Governance for urban sustainability through real-world experimentation—introducing an evaluation framework for transformative research involving public actors	(Kampfmann et al., 2024)
D38	The politics of assembling pilots: policy networks and selection strategies in top-down climate experimentation	(Yang & Lo, 2024)
D39	Experimentalist governance in the age of artificial intelligence: From static rules to dynamic learning	(Ansell & Trondal, 2025)
D40	Experimentalism 2.0: Corporate metagovernance and the new frontiers of accountability	(Sabel & Zeitlin, 2025)
D41	Regulating uncertainty: The future of corporate sandboxes	(Van der Heijden, 2026)

Created by the authors based on collected data.

) was analyzed using content analysis and meta-synthesis. This allowed for the exploration of interrelationships between the studies, moving beyond a simple summary to a conceptual re-interpretation of how experimentation serves as a strategic tool for managing complexity in business environments.

2.5. Methodological Reflection

While the final corpus consists of 41 articles, this sample represents the strict theoretical saturation of the specific ‘Experimental Governance’ construct. In qualitative meta-synthesis, the objective is not statistical breadth but conceptual depth. The selected studies provided exhaustive mapping of EG mechanisms (such as recursive goal-setting and decentralized experimentation) allowing for a robust theory-building synthesis. The focus on this targeted corpus ensures that the conceptual transfer to business processes is anchored in the core tenets of the experimentalist school, avoiding the dilution of the governance concept with generic innovation literature.

3. Results and Discussion

The analysis of the 41 selected articles reveals that Experimental Governance (EG) is not merely a trial-and-error method, but a deliberate institutional arrangement for dealing with uncertainties. This section details how the findings of the Atlas.ti (version 23) software translate into theoretical contributions and practical applicability.

3.1. Qualitative Analysis and Coding Scheme

The choice of Meta-synthesis as the analytical approach is justified by the nature of the 41 included studies, which are predominantly qualitative or conceptual-analytical, focusing on the mechanisms and narratives of governance. During the qualitative coding in Atlas.ti, a total of 164 initial codes were generated. By means an iterative refinement process, these were collapsed into 24 high-level categories and finally into 4 major thematic axes.

This rigorous coding process ensured that the synthesis moved beyond simple description to a robust conceptual construction of the intersection between experimental governance and business processes. The coding process in Atlas.ti (version 23) generated categories that highlight the transition from rigid models to models based on continuous learning. Unlike a simple list of terms,

Table 4. Coding Schema.

Work ID	Codes
D01	Interactive governance, New forms of governance, Experimental urban governance, Boundaries between old and new governance, Principles of good governance, and Democratic participation.
D01	Cautious experimentation, Legitimacy of decision-making processes.
D02	Democratization of science, and Direct experience with scientific knowledge.
D03	Experimental urban governance, Accepted informality, Creative freedom, Cautious experimentation, and Interactive governance.
D03	Cautious experimentation.
D04	Experimental governance and Shared governance.
D05	Experimental urban governance, Creative freedom, New forms of governance, and Principles of good governance.
D05	Democratization of science.
D06	Vertical and horizontal dynamics, Governance, Boundaries between old and new governance, Changes in technologies, policies, and institutions, Interactive governance, New forms of governance, Governance structures, Governance as a whole, Objectives of governance experiments, Experimental urban governance, Promoting systemic change, Horizontal scaling, Vertical scaling, and Systemic changes.
D06	Sustainability experimentation, Key functions of experimentation, Testing, Democratization of science, Small-scale experimentation, Experimental development, Experiment analysis, Government-driven experimentation, Cautious experimentation, Horizontal scaling, Vertical scaling, Multiplication of experiments, Complexity of experiments, Niche influences, Functions and uses of experiments, Large-scale experiments, and Practical experimentation.
D07	Experimentation, Governance of experiments, Climate and sustainability innovation, Networks and cooperation, and Experimentation with governance.
D08	Small-scale experimentation, Policy experimentation, Continuous evaluation and adaptation, and Experiment analysis.
D08	Governance, Boundaries between old and new governance, Changes in technologies, policies, and institutions, New forms of governance, and Success and failure in governance.
D09	Experiment analysis, Complexity of experiments, Policy experimentation, and Functions and uses of experiments.
D09	Interactive governance and New forms of governance.
D10	Vertical scaling, Sustainability experimentation, Experimental urban governance, Systemic changes, New forms of governance, Promoting systemic change, Critique of experimental governance, and Decentralization.
D11	Critique of experimental governance, Legitimacy of decision-making processes, Changes in technologies, policies, and institutions, and New forms of governance.
D11	Experimentation, Experimental development, and Policy experimentation.
D12	Reorganization of decision-making roles, Changes in responsibility and decision-making power, Evolutionary Governance Theory, Evolution of governance arrangements, Interactive governance, Joint governance with citizens, Democratic participation, and Principles of good governance.
D13	Urban experimentation.
D13	Experimental governance.
D14	Experimental governance, Experimentation, Flexibility, Innovation, Resilience, and Adaptive governance.
D15	Decentralization, Interactive governance, Systemic changes, and New forms of governance.
D16	Experimental governance, Collaborative innovation, and Public-private collaboration.
D17	Urban climate governance, Political legitimacy.
D17	Urban experimentation.
D18	Transition governance, Experimentation and innovation, Cultural and behavioral change, Social transitions, and Technological and cultural transitions.
D19	Critique of experimental governance, Interactive governance, Changes in responsibilities and decision-making power, New forms of governance, Continuous evaluation and adaptation, Boundaries between old and new governance, Changes in technologies, policies, and institutions, Success and failure in governance, Experimentalist governance, Objectives of governance experiments, and Promoting systemic change.
D19	Implementation obstacles, National experimentation, Contradictions in experimentation, Sustainability experimentation, Learning from best practices, Experiment feedback, Experimental development, Cautious experimentation, and Policy experimentation.
D20	Experimental governance and Multi-level governance.
D20	Urban climate experiments.
D21	Critique of experimental governance, Decentralization, Experimentalist governance, Legitimacy of decision-making processes, and Democratic participation.
D21	Sustainability experimentation.
D22	Governance, Interactive governance, and New forms of governance.
D22	Policy experimentation.
D23	Experimentalist governance.
D24	Policy experimentation.
D24	Interactive governance and New forms of governance.
D25	Experimentalist governance, Evolutionary governance, Contingency.
D26	Experimental governance, Accountability, Flexibility, Innovation, and Participation.
D27	Urban experimentation and Emerging innovation.
D27	Experimental governance, Roles of experimental governance.
D28	Governance limitations, Experimentalist governance, Systemic changes, Promoting systemic change, Implementation obstacles, Principles of good governance, Experimental development, Governance as a whole, Decentralization, Critique of experimental governance, Interactive governance, Evolution of governance arrangements, Success and failure in governance, and Continuous evaluation and adaptation.
D28	Policy experimentation, Cautious experimentation.
D29	Critique of experimental governance, Decentralization, Governance, Experimentalist governance, Success and failure in governance, Complexity of experiments, Governance limitations, and New forms of governance.
D29	Continuous evaluation and adaptation.
D30	Critique of experimental governance, Governance, Systemic changes, Learning from best practices, Experimentalist governance, and Evolutionary Governance Theory.
D30	Sustainability experimentation, Cautious experimentation, Small-scale experimentation, and Experiment analysis.
D31	Experimental governance.
D31	Urban experimentation, Climate change, and Innovative solutions.
D32	Experiment analysis and Sustainability experimentation.
D32	Interactive governance and Experimental urban governance.
D33	Experimentalist governance, Participatory innovation.
D34	Experimental governance, Collaborative self-construction, Governance networks, and Tactical provocations.
D35	Experimental regimes, Experimentation policy, Experimental governance, Hierarchy in experimental governance, and Innovation.
D36	Hybrid governance, Experimental governance, Experimental innovation, Experimental legislation, Experimental standardization, Legitimacy.
D37	Real-world experiments, Innovation in governance practices, and Sustainability experiments.
D38	Critique of experimental governance, Governance, Objectives of governance experiments, Learning from best practices, Experimentalist governance, and Governance limitations.
D38	Policy experimentation, Sustainability experimentation, and National experimentation.
D39	Experimentalist metagovernance, Dynamic learning, AI Governance, and Radical uncertainty.
D40	Recursive accountability, Corporate metagovernance, Institutional learning, and Strategic design.
D41	Corporate sandboxes, Regulation of uncertainty, Protected spaces for innovation, and Disruptive compliance.

Created by the authors based on collected data.

reflects the operational pillars of EG: participation, scaling, and legitimation.

A cross-sectional analysis of these codes allows us to deduce that EG manifests itself through three main dimensions:

• Relational Dimension (Who): Reflected by the codes “Interactive governance” and “Multi-level governance”. Indicates that EG depends on horizontal networks between companies, government, and society (Eneqvist et al., 2022).
• Procedural Dimension (How): Identified by “Cautious experimentation” and “Policy experimentation”. Demonstrates that EG requires the isolation of variables before large-scale implementation (Schoon, 2014).
• Teleological Dimension (Why): Evidenced by “Systemic changes” and “Promoting innovation”. Shows that the ultimate goal is institutional resilience.

3.2. Systematic Synthesis and Central Value of Research

To overcome the identified discontinuity between public theory and private practice, the analysis evolves from systematic coding to a robust theoretical meta-synthesis.

Table 5. Work Summaries.

Work ID	Work Summaries
D01	The study addresses the Open Method of Coordination (OMC) in the European Union and its relevance in regulating areas where traditional legislative procedures are weak (Szyszczak, 2006).
D02	The study explores how new practices in coworking spaces and community labs, such as Hackerspaces and Fablabs, are shaping public communication about science and technology (Kera, 2012).
D03	The study analyzes governance strategies in China and discusses how pragmatic concepts shape experimental approaches to urban policy and economy (Schoon, 2014).
D04	The study discusses the evolution of experimental governance in European healthcare, particularly in the context of the economic crisis (Fierlbeck, 2014).
D05	The study examines the role of local action networks in promoting low-carbon buildings and cities and the importance of knowledge sharing and stakeholder collaboration (Van der Heijden, 2016).
D06	The study investigates how experimental governance can be a useful tool in the fight against climate change (Laakso et al., 2017).
D07	The study addresses the role of experimentation in seeking solutions for climate change and sustainability (Hildén et al., 2017).
D08	The study analyzes REDD+ as a climate governance experiment in Indonesia, exploring challenges and progress related to forest governance transformation (Korhonen-Kurki et al., 2017).
D09	The study explores a new understanding of experimentation in governance and how experiments transform existing conditions and support knowledge co-production (Voß & Simons, 2018).
D10	The study analyzes how municipalities can lead the creation of Urban Living Labs as a new form of experimental governance (Kronsell & Mukhtar-Landgren, 2018).
D11	The study explores how psychology-informed governance is influencing public policy formulation through experiments testing new approaches (Jones & Whitehead, 2018).
D12	The study examines how rural areas in the Netherlands are dealing with population decline and changes in local governance (Ubels et al., 2019).
D13	The study analyzes how municipalities act as facilitators in urban experimentation processes to address sustainability challenges (Mukhtar-Landgren et al., 2019).
D14	The study explores the concept of tentative governance in relation to emerging science and technology (Kuhlmann et al., 2019).
D15	The study investigates the emergence of public sector innovation labs in Latin America (Ferreira & Botero, 2020).
D16	The study discusses how experimental governance is being used in urban planning to tackle complex sustainability challenges (Eneqvist & Karvonen, 2021).
D17	The study analyzes how urban climate commissions are being created as an experimental way to address climate change in Edinburgh (Creasy et al., 2021).
D18	The study explores how Rotterdam is addressing urban mobility challenges through an innovative governance approach (Loorbach et al., 2021).
D19	The study analyzes Finland’s initiative to become an experimental nation, addressing how this policy was implemented and the challenges faced during the process (Leino & Åkerman, 2022).
D20	The study reflects on experimental governance in the context of stimulating urban climate actions in the European Union (Grönholm, 2022).
D21	The study discusses legitimacy in municipal experimental governance, questioning how urban innovation practices address public goods (Eneqvist et al., 2022).
D22	The study discusses the emergence of relationships in governance for climate change adaptation (Sebastian & Jacobs, 2022).
D23	The study investigates a refined experimentalist governance approach for incremental policy changes in the context of infrastructure PPPs in China (Wang et al., 2022).
D24	The study examines experimental governance in Chinese higher education, addressing how interactions between the state and universities have evolved over time (Han, 2022).
D25	The study discusses experimentalist governance in the Netherlands’ energy transition, analyzing how it can evolve in different ways (Gerritsen et al., 2022).
D26	The study explores experimentation and accountability in industrial and innovation policy and discusses the importance of feedback cycles among stakeholders (Radosevic et al., 2023).
D27	The study explores how experimental governance results in real policy changes over time, analyzing discussions on sustainability and collaborative innovation (Røste, 2023).
D28	The study analyzes governmental governance in European regions facing significant challenges with fragile institutions (Marques et al., 2023).
D29	The study discusses global experimental governance in the context of the Paris Agreement and proposes a new way to classify countries to better address climate change (Qin & Yu, 2023).
D30	The study discusses experimental climate governance and questions whether it can be seen as a form of organized irresponsibility (Haderer, 2023).
D31	The study analyzes urban experimentation in three different Swedish municipalities, focusing on how they address challenges related to transportation and mobility (Grundel & Trygg, 2024).
D32	The study examines how urban labs for climate issues are formed in five cities in Latin America (Roll et al., 2024).
D33	The study discusses complexities surrounding the EU’s Smart Specialization strategy, balancing experimental governance and accountability (Radosevic & Zoretic, 2024).
D34	The study addresses experimental urban governance projects led by the collective Arquitetura Expandida (Lopez-Ortego et al., 2024).
D35	The study discusses why certain policy experimentation approaches in China are failing, especially in sectors such as aviation, finance, and food security (Yasuda, 2024).
D36	The study discusses the EU’s approach to governing Artificial Intelligence (AI) with a focus on harmonized European standards and experimental standardization (Prifti & Fosch-Villaronga, 2024).
D37	The study discusses the role of real-world labs in promoting urban sustainability through collaborative governance and experimentation (Kampfmann et al., 2024).
D38	The study explores governance strategies in climate experimentation in China, focusing on how governments establish policy networks to implement innovations (Yang & Lo, 2024).
D39	The study discusses how radical uncertainty and AI require a transition to ‘experimentalist metagovernance,’ shifting from static rules to dynamic organizational learning (Ansell & Trondal, 2025).
D40	The study examines ‘Experimentalism 2.0,’ proposing a framework for corporate metagovernance rooted in recursive accountability to coordinate operational efficiency and innovation (Sabel & Zeitlin, 2025).
D41	The study explores the strategic role of corporate sandboxes as fundamental instruments for the ‘regulation of uncertainty,’ mediating the tension between innovation and compliance (Van der Heijden, 2026).

Created by the authors based on collected data.

(Summary of Works) and

Table 6. Thematic Grouping by Category.

Work Category	Work Subcategory	Object	Work ID	Article Title
Governance for Sustainability and Urban Innovation (Living Labs)	Low-carbon buildings/local networks	Urban Planning	D05	Experimental governance for low-carbon buildings and cities: Value and limits of local action networks
	Experimentation and Sustainability	Climate Change	D07	Special issue on experimentation for climate change solutions editorial: The search for climate change and sustainability solutions—The promise and the pitfalls of experimentation
		Climate Change	D17	Representing ‘place’: City climate commissions and the institutionalisation of experimental governance in Edinburgh
		Climate Change	D20	Experimental governance and urban climate action—a mainstreaming paradox?
		Climate Change	D25	To See, or Not to See, That Is the Question: Studying Dutch Experimentalist Energy Transition Governance through an Evolutionary Lens
		Urban Planning	D28	Experimental governance in ‘trapped’ regions? What can and cannot be done in Europe’s periphery
	Strategic municipal functions	Urban Planning	D16	Experimental governance and urban planning futures: Five strategic functions for municipalities in local innovation
	Labs (ULLs and RWLs)	Governance Networks	D10	Experimental governance: the role of municipalities in urban living labs.
		Governance Networks	D13	Municipalities as enablers in urban experimentation
		Governance Networks	D18	Transition governance for just, sustainable urban mobility: An experimental approach from Rotterdam, the Netherlands
		Governance Networks	D24	Experimental governance in China’s higher education: stakeholder’s interpretations, interactions and strategic actions
		Governance Networks	D36	Towards experimental standardization for AI governance in the EU
		Governance Networks	D37	Governance for urban sustainability through real-world experimentation—Introducing an evaluation framework for transformative research involving public actors
	Urban Mobility	Urban Planning	D32	Urban labs beyond Europe: the formation and contextualization of experimental climate governance in five Latin American cities
Conceptual Dimensions, Models, and Policy Coordination	Definition and Coordination	OMC/EU as experimental governance	D01	Experimental Governance: The Open Method of Coordination.
		Definition of experimental governance as multi-level coordination and collective problem-solving	D19	The politics of making Finland an experimenting nation
		Governance as stakeholder interactions	D23	A refined experimentalist governance approach to incremental policy change: the case of process-tracing China’s central government infrastructure PPP policies between 1988 and 2017
	Models and Frameworks	Triangular Model	D06	Dynamics of experimental governance: A meta-study of functions and uses of climate governance experiments
		New understanding of experimentation between “lab” and “field”	D09	A novel understanding of experimentation in governance: co-producing innovations between “lab” and “field”.
		Concept of “tentative governance” for technological uncertainty	D14	The tentative governance of emerging science and technology—A conceptual introduction
		Interactions between experimentation and strategic planning	D31	A tale of urban experimentation in three Swedish municipalities
Challenges, Risks, and Accountability	Accountability and Legitimacy	Tension between experimental governance and procedural responsibility	D27	Co-evolutionary dynamics of experimental governance: a longitudinal study of sustainable mobility services in Oslo
		Framework for assessing legitimacy in ULLs	D30	Experimental climate governance as organized irresponsibility? A case for revamping governing (also) through government
		Tension between experimental governance and procedural responsibility	D35	Explaining Policy Failure in China
		Experimentalist Metagovernance (AI and Global Markets)	D39	Experimentalist Governance in the Age of Artificial Intelligence: From Static Rules to Dynamic Learning
		Corporate Metagovernance (Organizational Design)	D40	Experimentalism 2.0: Corporate Metagovernance and the New Frontiers of Accountability
	Political Critiques and Risks	Critique of nudging and limited knowledge	D11	‘Politics done like science’: Critical perspectives on psychological governance and the experimental state
		Warning against government disengagement (Light Governance)	D33	EU smart specialization policy between experimentation and accountability: dynamic policy cycle perspective
	Contextual and Adaptive Limitations	Limitations in regions with fragile institutions	D29	Rescuing the Paris Agreement: Improving the Global Experimentalist Governance by Reclassifying Countries
		Risk of limited innovation and superficial political reactions in China	D38	The politics of assembling pilots: Policy networks and selection strategies in top-down climate experimentation
Sectoral Contexts and Specific Regional Applications	National/Regional Contexts	Urban restructuring in China/informal policies	D03	Chinese strategies of experimental governance. The underlying forces influencing urban restructuring in the Pearl River Delta.
		REDD+ in Indonesia/forest governance	D08	Analyzing REDD+ as an experiment of transformative climate governance: Insights from Indonesia
		Population decline in Dutch rural areas	D12	An evolutionary perspective on experimental local governance arrangements with local governments and residents in Dutch rural areas of depopulation
		Finland as an experimental nation	D22	The Emergence of Relationality in Governance of Climate Change Adaptation
		Decarbonization and climate policies in China	D26	The experimentation-accountability trade-off in innovation and industrial policy: are learning networks the solution?
		Decarbonization and climate policies in China	D38	The politics of assembling pilots: Policy networks and selection strategies in top-down climate experimentation
		Urban transformation and cultural dialogue	D34	Urban Negotiations in Experimental Governance Exercises for the Right to the City: Notes on the Experience of the Arquitectura Expandida Collective
	Innovation and Specific Labs	Hackerspaces and Fablabs as collective prototyping labs	D02	NanoŠmano Lab in Ljubljana: Disruptive prototypes and experimental governance of nanotechnologies in the hackerspaces
		Public sector innovation labs in Latin America	D15	Experimental governance? The emergence of public sector innovation labs in Latin America
		Corporate Sandboxes (Uncertainty Regulation)	D41	Regulating Uncertainty: The Future of Corporate Sandboxes
	Specific Sectors	European healthcare	D04	The changing contours of experimental governance in European health care
		Education in China	D21	Legitimacy in municipal experimental governance: questioning the public good in urban innovation practices

Created by the authors.

(Thematic Grouping) are not merely descriptive; they support the thesis that Experimental Governance (EG) is a strategic mechanism for uncertainty regulation and risk mitigation. The coding process, conducted via Atlas.ti (version 23), revealed a strong convergence between the foundational literature and the emerging requirements of modern business processes.

The identification of core codes (such as “interactive governance,” “experimentalist metagovernance,” and “recursive accountability”) highlights a significant shift from hierarchical command-and-control toward participatory and adaptive architectures. While traditional codes like “collaborative construction” and “creative freedom” remain relevant, the inclusion of recent literature (2025–2026) introduced critical new dimensions: “AI-driven iterative loops” and “corporate uncertainty regulation.” These additions indicate that co-creation and multi-actor inclusion are no longer just democratic ideals but functional necessities for the practical legitimization of innovation in volatile markets.

Furthermore, the concepts of “cautious experimentation” and the imperative to “test and adjust” reinforce the operationalization of these approaches in real-world scenarios. This intersection demonstrates that EG practices require more than just the adoption of new policies; they demand institutionalized mechanisms for social and organizational legitimacy. The analysis shows that the strategic focus has shifted from the mere adoption of theoretical concepts to the design of collaborative networks and “trust-building” infrastructures. Consequently, these codes provide a roadmap for understanding how EG has been integrated into corporate management as a metagovernance layer, promoting both social innovation and institutional resilience in the face of radical technological shifts (Ansell & Trondal, 2025; Sabel & Zeitlin, 2025). Accordingly, Table 5 presents a summary of the articles analyzed.

Moreover, based on the meta-synthesis of the expanded corpus (n = 41), it was possible to categorize the field of experimental governance around four major thematic axes: the dominant application context (urban sustainability), conceptual and coordination dimensions, critical challenges (risks and accountability), and emerging applications in corporate and technological metagovernance.

The thematic grouping of studies categorized under Governance for Sustainability and Urban Innovation encompasses a significant portion of the research, focusing on ‘wicked’ challenges. However, recent additions to this axis (D41) suggest that the ‘Living Lab’ logic is being internalized by the private sector through Corporate Sandboxes used for the strategic regulation of uncertainty.

Studies categorized under Conceptual Dimensions, Models, and Policy Coordination have evolved from analyzing multi-level polities to defining frameworks for Experimentalist Metagovernance (D39). This includes the modeling of coordination mechanisms necessary to manage disruptive technologies, such as Artificial Intelligence, where experimentation and standardization must coexist.

Studies under Challenges, Risks, and Accountability remain a critical pillar, but now incorporate the concept of Recursive Accountability (D40). This addresses the ‘learning trap’ by proposing that legitimacy in experimental settings is achieved not just through participation, but through formal loops that institutionalize learning. Finally, studies categorized under Sectoral Contexts and Strategic Applications demonstrate that experimentation is no longer an urban niche but a tool for institutional resilience in global market horizons. In summary, Table 6 presents the thematic grouping by category, reflecting this updated theoretical landscape.

As organized in Table 6, the literature reveals a predominant focus on urban sustainability. However, to bridge the identified gap and operationalize these findings within a corporate context, a synthesis for theoretical building is required. To ensure the transparency and traceability of the proposed model,

Table 7. Derivation of the Conceptual Framework Elements from the Systematic Literature Review.

Framework Element	Core Concept or Mechanism	Foundational Literature (SLR)	Translation to Business Context
Corporate Sandboxes	Controlled environments for decentralized experimentation.	(Sabel & Zeitlin, 2012; Voß & Simons, 2018; Van der Heijden, 2026)	Creation of “institutionalized niches” or innovation labs for pilot testing and uncertainty regulation.
Iterative Feedback Loops	Recursive goal-setting and revision based on local performance.	(Overdevest & Zeitlin, 2014; Morgan, 2018; Van der Heijden, 2016; Ansell & Trondal, 2025)	Agile cycles and continuous improvement (PDCA) applied to governance under radical uncertainty.
Adaptive Decision Rights	Shift from rigid hierarchy to expertise-based and fluid decision-making.	(Janssen & van der Voort, 2020; Wang et al., 2022; Ansell & Trondal, 2025)	Decentralized decision-making within experimental units to increase responsiveness to AI and market shifts.
Multi-level Coordination	Alignment between local experimentation and strategic organizational goals.	(Ansell & Bartenberger, 2016; Sabel & Zeitlin, 2008, 2025)	Integration of experimental results into the firm’s core strategy through experimentalist metagovernance.
Institutionalized Learning	Formalization of experimental results into organizational memory.	(Wang et al., 2022; Morgan, 2018; Sabel & Zeitlin, 2025)	Mechanisms to scale “pilots” and transform failure into strategic knowledge via recursive accountability.

Created by the authors.

summarizes how each component of the Conceptual Framework was derived from the foundational literature identified during the Systematic Literature Review (SLR). This mapping bridges the theoretical principles of Experimental Governance with their operational application in business processes.

In the research context, Figure 2 presents the Conceptual Framework of Experimental Governance in Business Processes:

The illustrated in Figure 2 the transition from external complexity to strategic value. It bridges the research’s qualitative findings (Atlas.ti codes) with organizational practice, demonstrating how ‘Tentative Governance’ and ‘Corporate Labs’ act as mediators to achieve institutional resilience and adaptive learning in the private sector.

The Logical Connection: From Public to Private

Although the literature has been historically dominated by urban and sustainability contexts (D05, D06, D10), the mapped coordination logic (including the use of “Urban Living Labs” (D13) and “Real-world experiments” (D37)) is directly transferable to corporate business processes. Recent advancements in the field, however, suggest that this transfer is no longer merely an analogy but a strategic necessity. As argued by Ansell and Trondal (2025, D39), the rise of Artificial Intelligence and radical market uncertainty require organizations to transition toward an “Experimentalist Metagovernance,” where static rules are replaced by dynamic, continuous learning systems.

The research demonstrates that the “core value” of this transition lies in the Legitimation of Innovation. In the private sector, EG acts as a sophisticated “Corporate Sandbox” system. According to Van der Heijden (2026, D41), these sandboxes are not just technical testing grounds but fundamental instruments for the “regulation of uncertainty,” allowing controlled failures (D35) to be transformed into strategic learning assets without compromising the firm’s regulatory stability.

This fills the theoretical gap between operational agility and formal corporate governance by integrating flexibility into the accountability structure. This evolution is encapsulated in the concept of “Experimentalism 2.0” (Sabel & Zeitlin, 2025, D40), which introduces “recursive accountability” as the mechanism to bridge local experimentation with central strategic design.

While Figure 2 provides the conceptual flow of the proposed framework,

Table 8. Translation of Experimental Governance (EG) Principles into Business Process Logic.

EG Principle (Macro/Public)	Business Process Translation (Micro/Corporate)	Managerial Application and Organizational Mechanism	Supporting Authors (From SLR)
Framework Goal Setting	Strategic Intent and Guardrails	Definition of broad strategic guidelines instead of rigid KPIs, allowing local adaptation according to project context and Experimentalist Metagovernance.	(Sabel & Zeitlin, 2012; Morgan, 2018; Ansell & Trondal, 2025, D39)
Decentralized Implementation	Autonomous Business Units/Squads	Empowerment of business units or “squads” to test solutions in specific market niches with decision-making autonomy within Corporate Sandboxes.	(Van der Heijden, 2016, 2026, D41; Janssen & van der Voort, 2020)
Iterative Review and Revision	Agile Retrospectives and PDCA Cycles	Regular feedback cycles where performance data serve to revise and adjust initial project goals, ensuring Dynamic Course Correction.	(Overdevest & Zeitlin, 2014; Wang et al., 2022; Ansell & Trondal, 2025, D39)
Recursive Governance	Adaptive Decision Rights	Dynamic reallocation of decision rights based on technical expertise and experimental results (Organizational Ambidexterity and Recursive Accountability).	(Ansell & Bartenberger, 2016; Sabel & Zeitlin, 2025, D40)
Corporate Sandboxes	Innovation Labs/Pilot Environments	Creation of safe environments (labs) to test radical processes without compromising the stability of the core business or regulatory compliance.	(Voß & Simons, 2018; Van der Heijden, 2016, 2026, D41)
Accountability by means of Learning	Institutionalized Memory and Scaling	Transition from “punishment for error” to the obligation of documenting and sharing learning as a success metric and Metagovernance pillar.	(Morgan, 2018; Sabel & Zeitlin, 2010, 2025, D40)

Created by the authors.

operationalizes this transfer by translating the public-sector origins of Experimental Governance into concrete business process logics. This translation is essential to overcome the abstraction of “experimentalism” and provide managers with a structured roadmap for internal governance design, specifically addressing how iterative reviews and feedback loops can be integrated into daily corporate decision-making within a horizon extending to 2026 and beyond (Radosevic & Zoretic, 2024; Jahanbakht & Ahmadi, 2025).

In addition, Table 8 synthesizes the conceptual transfer by mapping macro-level Experimental Governance principles to their micro-level counterparts in business process logic. This translation explicitly links managerial applications to the foundational literature identified in the expanded SLR (2004–2026), highlighting how organizational mechanisms (such as corporate sandboxes (D41), recursive accountability (D40), and adaptive decision rights (D39)) operationalize the tension between control and innovation. By integrating recent perspectives on experimentalist metagovernance and the regulation of uncertainty, Table 8 provides a structured roadmap for managers to embed iterative feedback loops into formal governance designs, moving beyond purely operational agility toward institutionalized resilience (Ansell & Trondal, 2025; Sabel & Zeitlin, 2025; Van der Heijden, 2026).

3.3. Research Propositions for Empirical Testing

Based on the meta-synthesis of the reviewed studies (including recent advancements in experimentalist metagovernance and recursive accountability (2025–2026)) four propositions (Ps) are proposed to articulate the central value of experiential governance for business processes:

• Proposition 1: Feedback Loops and Adaptability: the relationship between information flow and organizational resilience is well-documented. However, under conditions of radical uncertainty and AI-driven disruptions (Ansell & Trondal, 2025), iterative feedback loops function as a critical independent variable that dictates the speed of error correction. According to Morgan (2018), the ability to revise goals based on local performance reduces the ‘latency of learning.’ When these loops are systematically embedded, they transform learning from a reactive task into a proactive strategic capability.

• P1: The implementation of iterative feedback loops in business processes enhances organizational adaptability by institutionalizing continuous error-correction as a strategic capability.

• Proposition 2: Sandboxes and Risk Management: in traditional organizational designs, innovation is often stifled by the fear of systemic failure. The literature on corporate sandboxes (Voß & Simons, 2018) is now expanded by the perspective of uncertainty regulation (Van der Heijden, 2026), suggesting that these ‘protected spaces’ allow firms to decouple high-risk explorations from stable operations. This structural separation, linked to organizational ambidexterity, enables the testing of radical innovations without compromising the firm’s regulatory compliance or legitimacy.

• P2: The use of corporate sandboxes provides a controlled environment that balances the tension between radical innovation and operational stability, fostering higher levels of risk-taking through formalized uncertainty regulation.

• Proposition 3: Decision Rights and Expert-Led Responsiveness: Centralized authority is a barrier to agility in complex environments. Recent studies (Janssen & van der Voort, 2020) and the rise of adaptive decision rights (Ansell & Trondal, 2025) establish that shifting authority to those with relevant expertise during an experiment directly influences responsiveness. By treating decision-making power as a fluid asset, organizations can mirror the complexity of their market environment, reducing bureaucratic bottlenecks.

• P3: Adaptive decision rights, when delegated to experimental units, increase organizational responsiveness by aligning authority with expertise during periods of high uncertainty.

• Proposition 4: Multi-level Coordination and Recursive Scalability: a common failure in business experimentation is the inability to scale local successes. Theoretical frameworks of Experimentalism 2.0 (Sabel & Zeitlin, 2025) argue that for experimentation to be effective, there must be a recursive link between ‘on-the-ground’ pilots and strategic goal setting. This “recursive accountability” acts as a bridge that transforms isolated experiments into institutionalized, scalable knowledge across the entire corporation.

• P4: Effective multi-level coordination, driven by recursive accountability, ensures that experimental insights are integrated into the broader corporate strategy, facilitating the scalability of localized innovations.

3.4. In-Depth Discussion of the Thematic Areas

The in-depth discussion of the expanded corpus (2004–2026) reveals three pivotal thematic areas that underpin the transition of Experimental Governance (EG) to the corporate domain:

• Governance for Sustainability and Urban Innovation: This axis (D05, D07, D17, D20) proves that EG is the standard tool for “wicked problems.” For the private sector, this implies that corporate sustainability (ESG) cannot be achieved through top-down models, but rather through local action networks and knowledge sharing (Van der Heijden, 2016). The recent addition of corporate sandboxes as uncertainty regulators (Van der Heijden, 2026, D41) further strengthens this, suggesting that sustainability goals must be tested in “protected spaces” before being scaled to the entire value chain.
• Conceptual Dimensions and Policy Coordination: The “tentative governance” (D14) and multi-level coordination (D19) models provide the theoretical basis for managing radical technological uncertainties. As discussed by Ansell and Trondal (2025, D39), the governance of Artificial Intelligence represents the new frontier for EG. In this context, EG acts as a metagovernance strategy, allowing the coexistence of strict standardization and disruptive innovation. This ensures that the firm remains agile enough to pivot its business process architecture without losing its strategic core.
• Challenges, Risks, and Accountability: This remains the most critical contribution of the research. The analysis of “organized irresponsibility” (Haderer, 2023; Radosevic & Zoretic 2024) warns that experimentation without clear governance leads to a loss of legitimacy. However, the introduction of “Experimentalism 2.0” (Sabel & Zeitlin, 2025, D40) provides a solution through recursive accountability. This mechanism ensures that local discretion does not result in a “black box” of failure, but rather in a transparent flow of data where feedback loops (D26) reconcile operational flexibility with formal procedural accountability.

The success of experimental governance within firms is not an isolated mechanical process; it is deeply contingent upon favorable institutional and technological conditions. As demonstrated by Jahanbakht and Ahmadi (2025) and reinforced by the latest 2026 perspectives, external technological readiness must be met with internal non-technological institutional shifts. For business processes, this means that internal experimentation must be supported by a robust digital infrastructure and “metagovernance pillars” that translate localized experimental pilots into sustainable, long-term organizational performance.

4. Discussion and Conclusions

Experimental Governance (EG) emerges as a transformative strategy that transcends traditional, rigid management approaches by promoting co-creation, multi-stakeholder participation, and institutional flexibility. This study, through a Systematic Literature Review (SLR) of 41 key documents spanning from 2004 to 2026, identifies that while EG is deeply rooted in public policy and urban sustainability, its principles are increasingly vital for addressing complexity and uncertainty within business processes. Unlike isolated conclusions, the deduction of this research follows a cumulative logic: first, the literature confirms that traditional hierarchical models fail in volatile environments; second, EG offers a robust iterative learning framework; and third, its application in the private sector, although nascent, effectively resolves the chronic tension between innovation and control.

The central value of this study lies in the proposal of a Hybrid Governance Model, where business processes maintain operational efficiency while being oxygenated by institutionalized experimental niches. This shifts the perception of EG from a purely political concept to a strategic business advantage, ensuring that organizations evolve proportionally to market disruptions. Furthermore, the findings reveal a deeper structural phenomenon termed the “Internalization Paradox.” While private organizations are proficient at adopting operational tools such as “sandboxes” or Agile methodologies, they frequently fail to integrate these into a formal governance architecture that ensures long-term legitimacy and recursive accountability.

Contrasting with the traditional view that accountability is a public-sector burden, this research argues that the absence of such rigor in firms leads to a “learning trap,” where experimentation remains informal and person dependent. Consequently, we propose that the institutionalization of EG acts as a metagovernance layer. It couples the “freedom to fail” with the “obligation to institutionalize learning,” shifting organizational design toward a model of Institutionalized Experimentalism. In this framework, corporate laboratories function as structural interventions or “institutionalized niches” where experimental leadership is incubated. Drawing on structural ambidexterity, these labs provide the necessary separation for exploration, while the EG framework provides the governance bridge to reintegrate findings into the broader corporate structure.

4.1. Theoretical Contributions and the “Incipiency” Paradox

The primary theoretical contribution of this research lies in consolidating a fragmented field by transposing experimentalism from the public to the corporate domain. The identified “incipiency” of EG in business reveals a mainstreaming paradox: companies utilize experimental tools without a governance framework that ensures long-term legitimacy. This study bridges the gap between public-sector policy experiments and private-sector process innovation, providing a foundation for companies to move from command-and-control models to adaptive learning systems.

This transition resonates with recent discussions on “Experimentalism 2.0” and corporate metagovernance. As proposed by Sabel and Zeitlin (2025), the challenge for 2026 lies in creating structures for recursive accountability. This perspective reinforces the idea that experimental governance acts as a metagovernance layer coordinating the tension between efficiency and exploration. Consequently, monitoring mechanisms evolve from punitive control into infrastructures for institutional learning, ensuring that knowledge generated in laboratories is effectively absorbed by the firm’s central structure.

4.2. Practical and Managerial Implications

The findings deliver actionable insights for both managers and regulators. For corporate management, this study serves as a guide for implementing “tentative governance” through internal Living Labs as structured decision-making tools. Instead of high-risk implementations, EG allows firms to test solutions in controlled environments. For public policy, the research informs the creation of “Regulatory Sandboxes,” highlighting the need to balance innovation with transparency to avoid “organized irresponsibility.”

The strategic function of corporate laboratories is further corroborated by emerging perspectives on the future of corporate sandboxes. Van der Heijden (2026) argues that these environments are fundamental instruments for the “regulation of uncertainty.” By institutionalizing these spaces, organizations can mediate the tension between disruptive innovation and compliance, allowing experimental learning to proactively shape future governance guidelines and ensure resilience within volatile market horizons.

4.3. Limitations and Future Research Agenda

Despite the rigor of the PRISMA protocol, this study is limited by its qualitative nature and a geographical bias toward the Global North. To advance the field, future research should follow three integrated dimensions. Empirically, comparative studies are needed to investigate how “Urban Living Lab” models can be adapted to internal corporate environments, alongside investigations into institutional readiness in emerging economies, following the leads of Jahanbakht and Ahmadi (2025). Additionally, the efficacy of EG during radical disruptions, such as AI-driven industry displacement, remains a fertile ground for testing.

Conceptually, the development of a Maturity Model for Experimental Governance could provide a scale from ad hoc experimentation to institutionalized experimentalism. Future work must also redefine corporate accountability to balance procedural responsibility with “safe-to-fail” processes that may not yield immediate ROI. Crucially, the role of Artificial Intelligence in automating governance loops represents a significant frontier, moving toward real-time, data-driven adjustments. Methodologically, the agenda calls for longitudinal case studies to track how temporary pilots scale into permanent policies, mixed-methods approaches to correlate EG indicators with ESG performance, and action research involving corporate boards to refine governance frameworks in real-time.

4.4. Final Reflections

In conclusion, this research consolidates experimental governance as a vital concept for modern management. In an era marked by technological shifts and the rise of AI, the ability to “experiment responsibly” becomes a core competitive advantage. This study fills a critical gap by providing the conceptual scaffolding for a new generation of adaptive business processes, paving the way for governance structures that are as dynamic as the environments in which they operate.