Advancing Synthetic R&D Through Scenarios: Integrating Science, Technology, and Stakeholder Needs

Abstract

Bridging the gap between scientific knowledge and practical application remains a central challenge in research and development (R&D). Analytical R&D generates factual knowledge through established methodologies, whereas synthetic R&D integrates this knowledge with technological and practical insights to produce artifacts and solutions. Despite its importance, synthetic R&D has lacked a coherent methodological foundation, limiting its broader recognition and systematic practice. We develop and demonstrate a scenario-based methodology that provides a structured framework for synthetic R&D. A scenario is defined as a development pathway that systematically links scientific understanding to clearly defined application objectives. By defining internal and external scenario structures, the framework enables systematic planning, execution, and evaluation of projects. Scenarios also serve as boundary objects, facilitating collaboration and communication across different disciplines and organizations. The originality of this study lies in transforming previously implicit or fragmented practices into a systematic, reproducible, and evaluable process. The methodology is validated through empirical case studies: nanomaterials standardization, deployment of ear thermometers during public health emergencies, and geochemical mapping for environmental policy. These cases illustrate how structured scenarios support knowledge integration, enhance adaptability across technical domains, and generate outcomes with demonstrable industrial and societal value. These findings suggest that scenario-based approaches provide a robust, generalizable foundation for advancing synthetic R&D in parallel with analytical R&D.

1. Introduction

Transforming scientific knowledge into real-world innovations has long been recognized as a central challenge in research and development (R&D), particularly in the so-called “valley of death,” where promising findings frequently fail to translate into practical applications. This paper examines this issue from the perspective of synthetic R&D.

Traditionally, research has been classified into basic, applied, and developmental categories based on development stages. In contrast, this paper adopts a methodological perspective on R&D, distinguishing between analytical and synthetic R&D. These R&Ds are relevant across all traditional categories. For clarity, analytical R&D emphasizes analytical approaches, whereas synthetic R&D emphasizes synthetic approaches. This paper focuses on synthetic R&D due to its direct relevance to practical applications and innovation.

Analytical approaches dissect complex systems and phenomena to generate new factual knowledge, whereas synthetic approaches integrate such knowledge with technological insights to create new artifacts. While analytical approaches have long drawn on a well-established methodological tradition, synthetic approaches still lack a comparable conceptual foundation despite their critical role in bridging science and practice.

To address this gap, the paper proposes a scenario-based framework for synthetic R&D that integrates diverse knowledge inputs into actionable outcomes, enabling systematic planning, execution, and evaluation of R&D projects. Synthetic R&D is closely aligned with Mode 2 knowledge production, which emphasizes context-driven, problem-focused, and transdisciplinary knowledge creation [1,2]. Through the use of scenarios, the proposed framework operationalizes Mode 2 principles. In addition, Section 2.2 reviews related methodological work that supports innovation.

The remainder of the paper is structured as follows:

Section 1 and Section 2 (Introduction and Methodologies): Define analytical and synthetic R&D and derive their key characteristics to provide the conceptual foundation.

Section 3 (Scenario-Based Framework): Define a scenario in synthetic R&D and propose two hypotheses: (1) each scenario has an internal structure, and (2) scenarios collectively form an external structure that interrelates them.

Section 4 (Case Studies): Analyze existing projects to test whether these internal and external structures facilitate to effectively achieve project goals.

Section 5 (Discussions): Identify challenges in applying scenarios and propose measures, offering an open discussion for future research.

This study aims to provide teams, managers, and policymakers in synthetic R&D with practical guidance for improving planning, evaluation, and decision-making. Throughout the paper, the term “R&D professional” refers to individuals engaged in R&D activities, encompassing not only academic researchers but also practitioners and developers.

2. Methodologies of Synthetic R&D

Analytical approach is defined as a method that dissects complex systems and phenomena into their elements to uncover new factual knowledge, whereas synthetic approach is defined as a method that integrates such factual knowledge with technological insights to create new artifacts. Analytical R&D typically addresses questions such as “What is it?” and “Why is it so?”, with the aim of finding underlying truths. By contrast, synthetic R&D asks “What should be done?” and “How should it be done?”, focusing on developing methodological knowledge for intentional creation.

This section deductively derives the characteristics of synthetic R&D from its definition, in comparison with analytical R&D, and additionally reviews previous work on strategies for accelerating innovation.

2.1. Characteristics of Analytical and Synthetic R&D

The features of analytical and synthetic R&D are inferred from their definitions.

Table 1. Characteristics of Analytical and Synthetic R&D.

Characteristic	Analytical R&D	Synthetic R&D
Objective	Seeking the truth	Creating artifacts
Approach	Analysis	Synthesis
Action	Discovery	Invention
Motivation	Intellectual curiosity	Realizing practical value
Knowledge type	Factual knowledge (laws, data, formulas)	Methodological knowledge (methods, models)
Scenario	Peripheral	Central
Field(s) involved	Single field	Multiple fields
Solution uniqueness	One unique solution	Multiple acceptable solutions
Evaluation criterion	Consistency and coherence	Practical value
Originality	Novelty in factual findings	Novelty in artifacts and methods
Review	Peer review	Stakeholder review

summarizes these characteristics, highlighting their implications for knowledge generation, integration, and project design.

Analytical R&D is typically conducted in a single discipline. Research in this mode progresses toward one solution that ultimately gains consensus among researchers. By contrast, synthetic R&D frequently involves multiple fields and allows for several acceptable solutions, each adapted to evolving industrial or societal contexts.

A scenario, provided as a structured plan or process for guiding an R&D project, plays a central role in synthetic R&D, whereas in analytical R&D it remains peripheral, as the emphasis is more on uncovering new factual knowledge.

The criteria for evaluating outcomes are also different. Analytical R&D emphasizes the contribution of new factual knowledge and its coherency in the discipline, whereas synthetic R&D is assessed primarily in terms of its external value—its usefulness and impact on stakeholders. Originality in analytical R&D lies in the novelty of factual findings; in synthetic R&D, it resides in the creation of new artifacts or methods that provide values in real-world.

Finally, the evaluation mechanisms differ substantially. Analytical R&D is usually assessed through peer review by experts in the same field, whereas synthetic R&D is evaluated by stakeholders who prioritize practical utility and real-world impact.

It is understood from the characteristics in Table 1 that the methodological foundations for synthetic R&D are less developed than those for analytical R&D and often evolve independently in different technological domains, without a unified framework. This fragmentation may hinder innovation by limiting the integration of knowledge across scientific and technological fields and by reducing the efficiency of collaboration across organizational boundaries. These challenges largely arise from limited communication and understanding across disciplinary and organizational boundaries.

2.2. Previous Research on Innovation and Scenario Methods

Over the past two decades, a wide range of approaches has been proposed to accelerate innovation. These may be broadly distinguished into macro-level approaches, which focus on institutional and systemic structures, and micro-level approaches, which emphasize methodologies at the project level.

Macro-level approaches primarily concern institutional reforms aimed at strengthening linkages among key actors. Early contributions include the concept of open innovation [3], mechanisms of technology transfer [4], and university–industry–government collaboration frameworks [5]. More recent directions emphasize co-creation with stakeholders [6], convergence science that integrates knowledge across disciplines [7], and mission-oriented R&D targeting societal challenges [8]. Collectively, these approaches enhance knowledge flows and collaborative capacity across organizations and disciplines, thereby reducing institutional barriers that hinder the translation of research into application.

Micro-level approaches focus on methodologies that guide knowledge integration in individual projects. A substantial body of research has advanced the concept of boundary objects [9,10,11] as tools for facilitating communication across heterogeneous communities. In addition, a distinct line of studies has developed scenario-based Synthetic R&D [12,13,14], which directly addresses the structuring of R&D processes themselves. Related methodologies in design research—such as user-participatory design [15], co-creation in design [16], and design thinking [17]—have likewise provided frameworks for aligning expertise and stakeholder perspectives, though their primary emphasis remains on design practices rather than on R&D methodologies.

Taken together, these two streams of research suggest that bridging the persistent gap between scientific knowledge and practical application requires both systemic reforms at the institutional level and methodological innovations at the project level. In this context, the present study stands in the lineage of Synthetic R&D [12,13,14] by offering a methodological perspective on how R&D processes can be structured, while at the same time complementing broader management- and design-oriented research [3,4,5,6,7,8,9,10,11,15,16,17].

In the field of technology, scenario methods are typically used to explore uncertain future environments, such as emerging market trends, technological breakthroughs, or shifts in societal needs [18,19]. With respect to these efforts, the scenarios presented in this study adopt a distinct perspective, with clearly defined R&D goals. They propose structured development pathways that systematically link scientific and technical knowledge with application goals in an R&D project, thereby providing a reproducible framework for guiding, evaluating, and sharing synthetic R&D efforts.

3. Scenario-Based Framework for Synthetic R&D

In Section 2, analytical and synthetic R&D were contrasted in terms of their objectives, approaches, and evaluation systems. A key distinction lies in the role of scenarios: they are peripheral in analytical R&D but central in synthetic R&D. Here, a scenario is defined as a structured development pathway that systematically connects scientific and technical knowledge with application goals in an R&D project. Scenarios therefore play a pivotal role in synthetic R&D, where a rigorous process is essential to achieving project objectives.

From this definition, the structures of scenarios are derived. Scenarios are categorized into two types: internal and external. The internal structure describes the constituent elements of a scenario and their interrelationships, while the external structure captures the connections among multiple interacting scenarios. This section provides a general overview of both types, aiming to help R&D professionals design robust scenarios for their projects and collaborate effectively with other teams.

3.1. Internal Structures

Synthetic R&D aims to create practical value by integrating scientific knowledge with technological insights. Structured scenarios guide this integration, functioning as testable hypotheses that combine diverse scientific and technical elements to achieve impactful outcomes. By serving as boundary objects, scenarios make knowledge tangible, shareable, and actionable across organizational and disciplinary boundaries.

Figure 1 presents the internal structure of a synthetic R&D scenario, highlighting the hierarchical integration from foundational inputs to R&D outcomes and target applications.

As illustrated in Figure 1, the process begins with two key types of inputs: scientific knowledge, such as theories, datasets, and predictive models, and technological insights, including design strategies, integration methods, and performance goals. These inputs are integrated into technical modules, which serve as functional building blocks evaluated against criteria such as performance, feasibility, and alignment with objectives. The modular structure allows flexible arrangement and recombination of modules, facilitating comparison of alternative approaches and adaptation of scenarios to evolving project requirements or new insights.

Scenarios also operationalize the distinctive methodological criteria of synthetic R&D:

• Iterative design: stepwise development and refinement of artifacts, enabling teams to track design decisions
• Adaptability: alternative pathways and contingencies, supporting flexible responses to emerging challenges
• Interdisciplinary integration: systematic combination of knowledge from different scientific and technological domains
• Stakeholder alignment: shared reference frameworks as boundary objectives across organizations

Evaluated modules are then integrated into tangible R&D outputs, such as prototypes, established methods, or validated models, which can be applied to industrial deployment, policy development, or further synthetic R&D. Well-structured scenarios incorporate clear objectives, coherent relationships among modules, and transparent evaluation logic. Multiple scenarios may run in parallel to explore strategic options and refine underlying hypotheses.

The impact and relevance of synthetic R&D are validated across sectors:

• Industry: product innovation, technology transfer, compliance and benchmarking, and IP protection
• Society: sustainability, public health, policymaking support, and open data for civic engagement
• Universities and public research institutes: publications, collaborations with industry and society, open-source releases, contributions to standards, and patents

This scenario-based modular framework fosters effective communication and collaboration across academic, industrial, and public innovation efforts.

3.2. External Structures

Synthetic R&D is not confined to isolated, single-scale activities. Rather, it operates in a broader system of interrelated scenarios functioning at different scales and addressing various objectives. Figure 2 illustrates this external structure, where multiple synthetic R&D scenarios are hierarchically organized and interconnected, forming a modular and scalable architecture. This configuration enables the dynamic exchange and integration of outputs across projects with varying scopes and timeframes.

In this framework, smaller-scale scenarios—such as those addressing specific technical challenges (e.g., optimizing a manufacturing process, designing a novel material, or developing a measurement method)—produce validated outputs that serve as modules or foundational components for more complex, higher-level scenarios. These larger-scale scenarios often target broader societal or industrial goals, such as the implementation of low-carbon energy systems, the design of resilient public health infrastructures, or the advancement of circular manufacturing strategies. The reusability of these modules ensures methodological coherence, reduces duplication, and accelerates progress toward strategic objectives.

These scenarios and their modules function as boundary objects, providing shared references that are interpretable across different teams, disciplines, and organizational contexts. By acting as boundary objects, individual scenarios and their modules facilitate knowledge transfer, alignment of objectives, and integration of outputs, while allowing teams to maintain their own specialized perspectives. This enhances interdisciplinary collaboration, supports cumulative learning, and ensures that R&D outputs are applicable across multiple domains.

The hierarchical structure is fractal-like, meaning that similar patterns of integration, hypothesis refinement, and scenario validation recur at multiple levels of scale. Each scenario—regardless of its size—follows a common logic grounded in modular composition, iterative evaluation, and outcome-driven hypothesis testing. This recursive architecture supports vertical integration of R&D efforts—from foundational research to application—and preserves traceability of knowledge and results throughout the R&D pipeline.

Moreover, the external structure, strengthened by boundary objects, facilitates collaboration across disciplinary and institutional boundaries. Standardized scenario documentation, common evaluation frameworks, and agreed-upon performance metrics serve as tangible boundary objects, allowing different project teams to coordinate their efforts while maintaining independence within their respective domains. This interoperability strengthens interdisciplinary collaboration and fosters cumulative learning across projects.

In summary, the external structure of scenario-based synthetic R&D functions as a multi-level integration framework enhanced by boundary objects. It connects technical, organizational, and strategic dimensions of R&D efforts, enabling coordinated progress toward system-level innovation. This structural coherence is particularly essential for addressing complex societal challenges that require sustained collaboration across diverse knowledge domains and institutional contexts.

4. Representative Scenario Case Studies

Section 2 defines synthetic R&D and derives its defining characteristics in comparison with analytical R&D. Section 3 then defines scenarios and derives their internal and external structures, emphasizing these as essential features of synthetic R&D. In this section, the hypotheses thus derived are examined through three representative synthetic R&D projects in the fields of nanotechnology, metrology, and geology, as published in the Journal for Synthetic R&D [13]. Based on these case studies, the roles of scenarios are further explored.

4.1. Establishing Testing Standards for Nanomaterials

Standards are intellectual assets for industries, consumers, and regulators, and can be developed through synthetic R&D approaches that integrate diverse expertise across disciplinary and organizational boundaries. Nanomaterials, with their nanoscale dimensions, possess unique properties that enable diverse industrial applications. Reliable quality testing standards are essential to ensure consistent performance, foster trust, and enhance credibility in the global marketplace. While a large body of basic research has accumulated, translating these findings into practical applications remains a major challenge.

Since 2008, the International Organization for Standardization (ISO) has developed testing standards for various nanomaterials, exemplifying the practical application of synthetic R&D frameworks described in Section 2 and Section 3. In this framework, scenarios are organized hierarchically and modularly, enabling activities at different scales and objectives to be effectively coordinated. In the context of international standardization, which involves multiple countries and diverse stakeholders, a scenario functions as a boundary object. It provides shared reference points that facilitate communication, knowledge integration, and consensus-building across institutional and national boundaries.

Figure 3 presents the internal structure of a scenario for developing ISO testing standards on nanomaterial quality [20]. It outlines the key stages of the standardization process and provides a sequential framework. Knowledge derived from surveys is incorporated through technical modules—covering relevant materials and their characterization—to define the scope of the standard. Within this scope, specific items to be standardized are identified and evaluated against criteria aligned with the intended objectives of the standard.

Figure 4 depicts the external structure of scenarios, illustrating interactions with stakeholders and organizational frameworks, each with its own scenario. Inputs from manufacturing industries, user industries, and research institutions converge in the standardization expert group, whose proposals are coordinated through the national standards bodies of participating countries. These bodies determine whether to approve or disapprove the draft, which is subsequently reviewed under the ISO/IEC Directives. In this way, the scenarios operate as boundary objects, enabling communication, coordination, and decision-making across external stakeholders, while maintaining coherence with the internal process outlined in Figure 3.

The originality of this scenario lies in designing a coherent, feasible, and international process, technically through the internal structure and organizationally through the external structure, that enables the establishment of international standards for nanomaterials. This approach has led to the publication of more than 15 ISO testing standards for nanomaterials, including graphene materials [21], clay nanoplate materials [22], and nanoporous silica [23].

By structuring the standardization process according to the scenario framework, these standards provide clear guidance for manufacturers, users, and related stakeholders. The approach ensures reliable quality assessment and enhances trust in the global market, demonstrating the practical effectiveness of scenario-based synthetic R&D in an international coordination environment.

4.2. Deploying Reliable Ear Thermometers for Public Health Emergencies

Accurate measurement of body temperature is a critical factor for enabling rapid screening, particularly during infectious disease outbreaks. Infrared ear thermometers, which detect radiation from the eardrum, allow non-contact and rapid measurements, making them especially effective in high-traffic settings such as airports and train stations.

When ear thermometers were first introduced in the early 2000s, however, concerns arose regarding their accuracy in real-world uncontrolled environments. Addressing this challenge required not only technological innovation but also the structured and integrative approach of synthetic R&D, as outlined in Section 2 and Section 3.

In 2002, just before the SARS (Severe Acute Respiratory Syndrome) outbreak, a Japanese thermometer manufacturer and the National Metrology Institute of Japan (AIST) launched a joint R&D project [24]. The objective was to secure a measurement accuracy of ±0.2 °C—equivalent to contact-type thermometers—across diverse environments. This initiative exemplified the essence of synthetic R&D by integrating industrial expertise, metrological science, and public health requirements. It also demonstrated the hierarchical and modular structure of scenarios, in which laboratory research, industrial development, and public deployment modules were coordinated within a unified framework.

Figure 5 illustrates the internal structure of a scenario, clarifying the division of technical roles between industry and a public research institute. By combining metrological expertise with manufacturing capabilities, the project develops ear thermometers that are highly sensitive, stable, and traceable to national measurement standards, thereby ensuring reliability in public use. The outcomes include the establishment of the Japanese Industrial Standard (JIS T 4207) [25] and the development of a national traceability system for infrared thermometers. Together, these measures provide long-term quality assurance and enable timely deployment during public health emergencies.

Figure 6 illustrates the external structure of scenarios, highlighting interactions among stakeholders and their integration into national and international system, each with its own scenario. The scenario coordinates (i) the transfer of metrological expertise and calibration methods from national metrology institutes (NMIs) to regulatory and standardization bodies, (ii) communication and alignment among manufacturers, NMIs, healthcare institutions, and consumer organizations to ensure reliability and public confidence, and (iii) integration with national standards bodies and governmental regulators to strengthen preparedness for health emergencies. The external scenarios function as boundary objects that facilitate collaboration, consensus-building, and international coordination, while maintaining coherence with the internal scenario shown in Figure 5.

The originality of this scenario lies in designing a coherent and feasible process to establish a reliable, rapid medical monitoring system in public health emergencies, enabled by collaboration between manufacturers and the national metrology institute, each contributing distinct expertise.

During the 2002 SARS outbreak, calibration devices and protocols maintained by AIST were swiftly transferred to multiple national metrology institutes across East Asia, supporting consistent performance of ear thermometers in different countries. In this process, the scenario functioned as a boundary object, facilitating communication and coordination among manufacturers, public research institutions, and international partners.

Nearly two decades later, the adaptability and continued relevance of this synthetic R&D approach were demonstrated once again during the COVID-19 pandemic, when the same calibration techniques were applied to thermographic cameras for mass screening. Today, ear thermometers are trusted worldwide in both clinical and household contexts, and their relevance is expected to grow further with the advancement of smart health monitoring technologies.

This case study validates the scenario framework introduced in Section 2 and Section 3 and demonstrates the practical value of synthetic R&D in bridging technological, institutional, and international boundaries.

4.3. Publishing Nationwide Geochemical Maps for Environmental Monitoring

Understanding the distribution of chemical elements across the Earth’s surface is essential for assessing environmental conditions and associated risks. Geochemical maps visualize these distributions and thereby inform decision-making in public health, agriculture, and resource management.

Nationwide geochemical mapping is a complex, long-term endeavor that requires sustained interdisciplinary collaboration across geology, chemistry, environmental science, and data science. As discussed in Section 2 and Section 3, a scenario-based synthetic R&D approach—integrating diverse methods, knowledge, and expertise—is vital for setting priorities, ensuring data quality, and managing projects of this scale.

In Japan, geochemical mapping began with regional surveys and progressively expanded into a nationwide effort covering both terrestrial and coastal areas [26]. Sediment samples collected from river junctions, estuaries, and coastal sites captured the flux of chemical elements from land to sea, producing a comprehensive dataset for environmental monitoring.

As depicted in Figure 7, the project is structured into hierarchical, modular components encompassing survey design, sample collection, analytical procedures, data integration, and map publication. This framework enables interdisciplinary collaboration across geology, chemistry, environmental science, and data science, while optimizing survey design under practical constraints, including limited personnel and funding. A coarser sampling mesh is employed to balance spatial coverage with available resources. The figure illustrates how scenario-based synthetic R&D integrates scientific rigor with practical limitations to generate actionable environmental information.

Figure 8 illustrates the external structure of scenarios for developing a nationwide geochemical map, with each entity representing a distinct scenario and depicting interactions between the project and external stakeholders. The structure encompasses the following:

• Coordination with government agencies and local authorities to ensure regulatory alignment and support implementation.
• Engagement with construction firms and geological survey companies to provide business-relevant information.
• Collaboration with academia and scientific disciplines—including geology, analytical chemistry, environmental science, and data science—to ensure the integrity of the project.
• Sharing geochemical maps with community organizations and end-users to guide decision-making in resource management, infrastructure development, and environmental monitoring.

This external structure functions as a boundary object, fostering collaboration, knowledge exchange, and interdisciplinary integration. Its originality lies in the design and implementation of coherent, feasible, interdisciplinary, and cross-organizational processes for establishing a large-scale visual database for environmental monitoring.

The Geochemical Map of Sea and Land Japan continue to support environmental regulation, public health, resource management, and even forensic science [27]. The project exemplifies the enduring value of synthetic R&D achieved through structured scenarios in addressing complex, long-term environmental challenges. It also highlights the pivotal role of boundary objects in coordinating diverse experts and organizations across institutional and disciplinary boundaries.

4.4. Examination of Scenarios in Synthetic R&D Projects

Over seventy papers on synthetic R&D outcomes published in Synthesiology [13] are reviewed [28]. The review focused on key project elements, including research objectives, scenario building for goal achievement, integration of interdisciplinary knowledge, and pathways to societal implementation. Some papers explicitly describe scenario formulation and the rationale behind decision-making, thereby clarifying both the societal significance of the R&D and its potential for practical implementation. Others, however, emphasize research outcomes or technological results—similar to the conventional reporting style for synthetic R&D—while providing insufficient descriptions of methodological frameworks or processes of knowledge integration. This suggests that the use of scenarios remains at an early stage and requires further development.

Regarding scenario forms, Figure 3, Figure 4 and Figure 5 as well as Figure 1 and Figure 2 depict them primarily in linear form. Depending on the research context, however, scenarios may also take cyclical [29,30], layered [31], or narrative forms [32,33]. Cyclical structures effectively represent iterative processes between R&D professionals and artifact users. Layered formats integrate multiple levels of abstraction, such as scientific principles, technological platforms, and societal implications. Narrative formats are particularly effective for capturing interdisciplinary and cross-organizational collaboration.

4.5. Roles of Scenarios

The three case studies examined did not aim at the development of a single elemental technology (such as a material), but rather at the realization of complex systems composed of multiple interdependent components. These cases demonstrate that scenarios are indispensable in these contexts: without a comprehensive and coherent scenario, even the successful development of individual components cannot guarantee the integrity of the overall outcome (i.e., its completeness, coherence, and integration). In this sense, the case studies serve as empirical evidence that scenarios operate as the structural backbone of synthetic R&D.

Building on these cases, a more general theoretical perspective can be developed. Scenarios function as an integrative framework that coordinates diverse knowledge components toward a shared goal. Their diagnostic role lies in revealing gaps or inadequacies in existing configurations when new industrial or societal demands arise. When artifacts fall short of intended performance, a pre-formulated scenario serves as an indispensable tool for scrutinizing failures, pinpointing their causes, and refining future scenarios—thereby driving synthetic R&D forward.

Their generative role lies in guiding the design of alternative configurations, either by refining existing knowledge components or by identifying additional technological modules to be incorporated. Moreover, when such scenarios are published in scholarly journals, alternative strategies can be critically debated in relevant technical communities, accelerating further technological development.

5. Discussions

As Section 2, Section 3 and Section 4 have shown, scenarios constitute the essence of synthetic R&D, providing strategic frameworks that link scientific knowledge, technological insights, technical modules, and artifacts into coherent processes. Despite their central role, scenarios are rarely described in detail. Most synthetic R&D papers focus on artifacts and their performance, offering little insight into the strategies or methods guiding their creation. This underreporting limits opportunities for R&D professionals to learn from each other and to accelerate progress through shared practices.

This section examines challenges associated with scenarios and explores approaches to address them, focusing on documentation, evaluation, disclosure, and publication.

5.1. Documentation Formats for Synthetic R&D

Manuscript formats for analytical R&D are well established, typically following introduction, materials, methods, results, and discussion. In contrast, synthetic R&D formats, as seen in existing studies [13], vary widely and lack standardization, making systematic comparison and knowledge accumulation difficult.

A scenario is expected to present a structured sequence: foundational scientific knowledge and technological insights, integration into technical modules with defined criteria, and artifacts with intended performance. It should also highlight interdisciplinary and cross-organizational contributions.

Based on these considerations, two approaches are proposed: to establish standardized manuscript formats for synthetic R&D, following introduction, scenario, artifact, performance, and discussion, and to take flexible scenario presentation—linear, cyclical, layered, or narrative—depending on the R&D context.

5.2. Evaluation of Scenario Design in Synthetic R&D

Synthetic R&D outcomes encompass not only the artifacts produced but also the methods by which they are created. Traditional evaluations have often prioritized artifacts over methods, thereby overlooking the strategic role of scenarios. Yet in synthetic R&D, scenario design is not a peripheral aid but the very foundation of project success. Accordingly, rigorous evaluation of scenario design should be recognized as critical.

A scenario is a hypothesis formulated prior to action and critically examined after project completion. It defines the trajectory of the entire endeavor, including the integration of foundational knowledge, technical modules, targeted artifacts, and collaboration across interdisciplinary and cross-organizational boundaries. Scenario quality is assessed by the coherence and feasibility of these components in relation to the intended goal. Ultimately, the robustness of scenario design determines whether R&D efforts culminate in meaningful outcomes or dissipate without lasting impact.

Crucially, even when artifacts fall short of their intended performance, a well-formulated and rigorously evaluated scenario provides the indispensable framework for diagnosing failures, identifying underlying causes, and reconfiguring future directions—making scenario design the central engine propelling synthetic R&D forward.

When synthetic R&D outcomes are submitted to scholarly journals, it is recommended that reviewers assess scenario design using criteria explicitly tailored for synthetic R&D rather than those inherited from analytical R&D. Journals and authors can adopt approaches such as clearly defining scenario evaluation metrics, emphasizing coherence and feasibility of integrated components, and highlighting the role of scenarios in guiding R&D outcomes, thereby ensuring that the central contribution of scenario design to innovation is properly recognized.

5.3. Disclosure and Publication of Scenarios

In industrial settings, scenario development in synthetic R&D is closely linked to competitive strategy and intellectual property. Consequently, companies are cautious about publicly disclosing scenarios, particularly when they involve sensitive information regarding future products, technologies, or market positioning. Disclosure is generally limited to pre-competitive topics or occurs after commercialization [32,34].

By contrast, public research institutions and universities face fewer confidentiality constraints and are more willing to disclose scenarios, especially when doing so demonstrates their synthetic R&D capabilities and institutional contributions to innovation.

This asymmetry in disclosure practices hinders the broader dissemination and standardization of scenario-based methodologies and limits engagement with industry stakeholders. To address these challenges, organizations should develop disclosure frameworks that balance multiple objectives simultaneously, including maintaining openness while protecting confidentiality, enabling selective sharing of both pre- and post-competitive scenarios, and formally recognizing and rewarding industrial contributions to scenario transparency.

Such frameworks protect commercial interests while facilitating the creation of shared methodological repositories, thereby supporting cumulative learning and fostering cross-sector collaboration. Beyond structural and institutional measures, the sustainable advancement of scenario-based synthetic R&D depends on supportive institutional cultures and the integrative competencies of R&D professionals.

Crucially, transparent and strategic disclosure directly strengthens the development, refinement, and rigorous evaluation of scenario design. This ensures that synthetic R&D benefits from shared insights, iterative improvement, and broader validation, positioning scenario design as a central driver of R&D success rather than a peripheral component. Effective disclosure is thus integral to maximizing the overall impact of synthetic R&D.

6. Conclusions

This study advances the methodology of synthetic R&D, a field long overlooked despite its essential role in innovation. By defining synthetic R&D and deriving its key characteristics, we introduce the scenario concept as a structured framework for integrating diverse scientific knowledge and technological insights into practical outcomes for industry and society.

Building on this foundation, we formulate hypotheses in which scenarios embody both internal and external structures to guide the organization of synthetic R&D projects. These structures not only align knowledge with project objectives but also establish mechanisms for fostering interdisciplinary and cross-organizational collaboration, thereby strengthening the collective capacity to implement synthetic R&D.

The hypotheses were examined through empirical case studies in nanotechnology, healthcare, and environmental policy, which confirmed the role of internal and external structures in integrating knowledge and facilitating collaboration across disciplines and organizations.

The proposed scenario-based framework transforms synthetic R&D from a fragmented practice into a systematic and testable process, providing teams, managers, and policymakers with reproducible methods for planning, executing, and evaluating synthetic R&D. By demonstrating how structured scenarios facilitate innovation through knowledge integration, this study establishes synthetic R&D as an essential complement to analytical R&D and a key driver of progress in science, technology, and society. We conclude by discussing the challenges and potential measures for broader adoption of scenario-based approaches in R&D communities.