GUEST 2.0: empowering digital transformation strategy and innovation management

Digital transformation (DT), “a process where digital technologies create disruptions triggering strategic responses from organizations that seek to alter their value creation paths while managing the structural changes and organizational barriers that affect the positive and negative outcomes of this process” (Vial, 2021), has profoundly reshaped innovation processes and accelerated knowledge creation (Appio, Frattini, Petruzzelli, & Neirotti, 2021). Successful digital innovation extends beyond technology deployment; it hinges on strategic, organizational and contextual factors. Failing to form strategic implementation partnerships, for example, may result in unsuccessful innovation outcomes (Setzke, Riasanow, Böhm, & Krcmar, 2023). Furthermore, DT possibilities unfold progressively “like the peak of a mountain” (Baiyere, Salmela, Nieminen, & Kankainen, 2025), along increasing societal demands, environmental volatility, regulatory uncertainty and fuzziness. This creates conditions of deep uncertainty and reinforces the interdependence between strategic intent and implementation. Unlike traditional innovation focused primarily on economic growth, DT increasingly integrates socio-environmental objectives while maintaining competitiveness (Bai, Dallasega, Orzes, & Sarkis, 2020). As a result, organizations must develop new multi-stakeholder value propositions and adapt their organizational and business processes (Wessel, Baiyere, Ologeanu-Taddei, Cha, & Blegind-Jensen, 2021).

Innovation management (IM) is crucial to navigating DT, helping organizations harness opportunities while strengthening capabilities (Appio et al., 2021). Consequently, research connecting DT and IM is expanding, although the literature remains fragmented (Appio et al., 2021) and largely conceptual (Romme & Holmström, 2023), with only a few frameworks linking strategy articulation to implementation. One such framework is found in Appio et al. (2021). Although relevant, some aspects of that framework require further investigation. For instance, the macro-strategic layer focuses primarily on ecosystem innovation and competitive dynamics (relying on contributions from Hilbolling, Berends, Deken, and Tuertscher (2021) only), without systematically addressing uncertainty and fuzziness, or the tension between short-term economic objectives and long-term societal value in digital transformation.

The MIND framework of Baiyere et al. (2025) supports organizations in assessing digital capabilities and selecting contextually appropriate digital solutions. While linked to strategizing, the primary focus of MIND is capability assessment rather than structuring iterative strategy design and validation processes. Setzke et al. (2023) discuss constructs such as structural separation and centralized decision-making, while Mwesiumo, Vaagen, Pushparajah, and Norem (2025) examine structural separation in the adoption of advanced digital manufacturing technologies. All these studies provide useful conceptual insights but offer limited guidance on actionable strategic decision routines.

The integrative theoretical framework of Vial (2021) explains DT through mechanisms mediated by dynamic capabilities, such as sensing, seizing and transforming. It also highlights a limited understanding of how these capabilities are enacted through concrete routines and decision processes, particularly the microfoundations of dynamic capabilities, defined as the organizational skills, processes, structures and decision rules that enable strategic renewal (Teece, 2007). Thus, while dynamic capabilities theory explains adaptive advantage conceptually, the proceduralization of these capabilities into repeatable strategic routines remains insufficiently explored.

Implementation-oriented studies have largely been technology-focused. For instance, Dutta, Choi, Somani, and Butala (2020) discuss blockchain implementation challenges across industries, and Perboli et al. (2018a, b) describe a DT case for logistics innovation through blockchain, emphasizing integration and strategy articulation. However, moving from isolated cases toward mature, scalable DT requires stronger strategy articulation, explicitly linked to implementation processes. Despite the proliferation of DT research, structured methods for designing and validating transformation strategies remain limited.

Industry digital transformation playbooks provide additional guidance but operate at a different level. Frameworks such as the MIT CISR “Future Ready” pathways (Weill & Woerner, 2018) and the MIT/Capgemini digital transformation studies (Westerman et al., 2011, 2014) offer strategic orientation and maturity diagnostics. These approaches help organizations benchmark capabilities and identify transformation trajectories, but emphasize directional guidance and benchmarking rather than structured uncertainty assessment or iterative strategy validation.

At the execution level, frameworks such as OKRs (Doerr, 2018), SAFe (Leffingwell, 2016), and Lean Portfolio Management institutionalize performance alignment, funding governance and large-scale delivery coordination. Drawing on goal-setting theory (Locke & Latham, 2002), portfolio management research (Cooper, Edgett, & Kleinschmidt, 1999), ambidexterity theory (O'Reilly and Tushman, 2013) and agile scaling literature (Dikert, Paasivaara, & Lassenius, 2016), these approaches translate strategic directions into objectives, funded initiatives and coordinated development cycles. However, they largely assume that strategic options have already been articulated and do not explicitly structure uncertainty evaluation, stakeholder value negotiation or sustainability-oriented trade-off analysis.

Collectively, prior research provides valuable insights into the mechanisms, orientations and execution infrastructures of digital transformation, yet the strategic design layer linking these elements remains under-developed. Conceptual frameworks explain transformation dynamics, while playbooks and execution tools guide strategic direction and implementation coordination. However, the literature offers limited guidance on how organizations systematically generate, evaluate and iteratively refine digital transformation strategies under conditions of uncertainty, ecosystem interdependence and sustainability trade-offs. Addressing this gap requires methodologies that operationalize dynamic capabilities into structured, repeatable strategic decision routines.

GUEST 2.0 addresses this gap. Positioned between conceptual theory and execution infrastructures, with a design process grounded in Design Science Research, the proposed framework institutionalizes problem-solving processes aligned with dynamic capabilities (Teece, 2007) and research needs identified by Vial (2021), thereby enabling organizations to design and implement sustainability-aligned digital transformation strategies under uncertainty. The main contribution is a structured synthesis of the core elements and decision-making processes involved in strategizing digital transformation.

The paper is structured as follows. Section 2 provides a synthesis of the core elements of strategizing DT. Section 3 outlines the methodology, Section 4 presents the GUEST 2.0 framework and Section 5 demonstrates its practical application through a use case. We conclude in Section 6. A glossary of the key terms used throughout the paper is provided in Appendix C.

This paper presents a methodology that operationalizes dynamic capabilities through repeatable strategic decision routines; i.e. not a comprehensive enterprise architecture for DT. For this reason, in this section we synthesize the core requirements of strategic design and learning processes that enable transformation, as follow: DT strategy as a dynamic design problem rather than a static planning exercise, balancing exploration and exploitation, managing uncertainty through adaptive decision processes, creating value across ecosystems, embedding ethical and sustainability considerations and monitoring progress through integrated performance measures. These requirements define the design principles of GUEST 2.0 and are operationalized across the five GUEST 2.0 phases, as detailed in Section 4.

Managerial components, such as governance, organizational change management and alignment, and emerging forms of human–AI collaborations, are embedded within the framework and contextually discussed in the SINFONICA case in Section 5, rather than represented as standalone elements.

DT requires balancing exploration of emerging opportunities (innovation, sustainability, new value creation) with exploitation of existing capabilities (efficiency, performance, short-term returns). These competing objectives create structural and strategic tensions (O'Reilly & Tushman, 2004). Sustainability further intensifies these tensions by introducing long-term, system-level and often externalized value considerations that may conflict with immediate organizational objectives. DT strategies must therefore enable the simultaneous pursuit of short-term performance and long-term transformation, often across different organizational units and stakeholder groups (O'Reilly & Tushman, 2004). The concept of green ambidexterity captures this dual pursuit of exploratory and exploitative innovation in addressing environmental and societal challenges (Zhao, Zhang, Jiang, & Feng, 2021; Khan et al., 2021; Martínez-Falcó, Sánchez-García, Marco-Lajara, & Visser, 2024).

Dynamic capabilities provide the theoretical rationale for managing these tensions. By enabling organizations to sense, seize and reconfigure resources in response to changing conditions, they support both adaptation and strategic renewal in digital transformation (Teece, 2018). As emphasized by Warner and Wäger (2019) and Baiyere et al. (2025), digital transformation is an ongoing process requiring agility across business models, organizational structures and collaborative ecosystems, with dynamic capabilities as a key enabler of such renewal (Bocken and Geradts, 2020). Consequently, effective DT strategies must balance short-term performance with long-term transformation across multiple stakeholders and organizational levels.

DT unfolds under multiple forms of uncertainty (technological, market, regulatory and systemic interdependencies). Consequently, DT strategies must move beyond reactive planning toward proactive and flexible decision-making. Flexibility, as a key enabler of innovation in operations and supply chains (Simchi-Levi, 2010), requires preserving alternative courses of action rather than committing prematurely to a single solution, while decision processes must evolve as new information becomes available. Systemic risks such as vulnerability imposed by the combined impact of interconnectedness and uncertainty in DT projects further necessitates the transition toward proactivity and flexibility (Ransbotham, Fichman, Gopal, & Gupta, 2016).

This perspective aligns with calls for more systematic approaches to decision-making in DT, where uncertainty is treated not only as a source of risk but also as a source of opportunity, and challenges are presented with a systematic decision-making focus, rather than technology focus (Framinan, Perez-Gonzalez, & Fernandez-Viagas, 2023).

Further, flexibility most often involves excess use of resources by maintaining alternatives, which may conflict with environmental sustainability objectives aimed at minimizing resource use. Moreover, both flexibility and sustainability initiatives usually entail early-stage investments, accumulating early costs that may impose a high financial burden on organizations, with benefits that are uncertain, delayed or externalized. Consequently, creating business value through flexibility, while focusing on environmental and sustainability objectives, requires balancing priorities and strategies by clearly distinguishing between short-term operational improvements and long-term value. This loops back to the conflicting tensions driven by ambidexterity, as discussed in the previous section, and how actors work around them. Addressing these requires decision frameworks that handle apparent contradictions and allow internalizing externalities and long-term societal value.

In sum, effective DT strategies require adaptive decision processes that support continuous learning, reassessment of alternatives and timely adaptation as conditions change. In this sense, proactivity and flexibility become key capabilities for navigating uncertainty while sustaining long-term transformation efforts, with AI-driven advanced analytics as powerful tools for balancing these trade-offs; e.g. predictive analytics for decarbonization (George, Merrill, & Schillebeeckx, 2021) and AI-driven climate resilience models (Kochanski, Rolnick, Donti, & Kaack, 2019) for exploration.

DT increasingly occurs within multi-actor ecosystems, where value is co-created across firms, institutions and society. Strategies must therefore integrate heterogeneous stakeholders, leverage network effects, platform dynamics and scalability and address interdependencies and systemic risks. This extends beyond firm-centric optimization toward ecosystem-wide value creation.

Platform-based strategies that facilitate circular business models, such as scalable reproducibility and product-as-a-service offerings, support organizational ambidexterity by balancing exploitation and exploration. For terminology concerning networks, platforms and strategy, we refer to McIntyre and Srinivasan (2017), synthesizing perspectives from industrial economics, strategic management and technology management. The authors also propose a future research agenda to address the nature and relative strength of network effects and platform quality and the drivers of indirect network effects (as expanded upon in Zhao et al., 2021).

Digital platforms also impose risks to be considered in the DT architecture. We highlight the above named vulnerability associated with digital interconnectedness, i.e. the possibility that disruptions in one part of the ecosystem cascade through the interconnected network (Ransbotham et al., 2016). Another challenge lies in maintaining trust and psychological safety, as essential for stakeholder engagement, data sharing and collaborative innovation. It is well known that virtual environments can hinder the development of trust and openness necessary for such interactions (Zhang, Fang, Wei, & Chen, 2010; Dutta et al., 2020). On the other side, fostering proactive, flexible data-driven strategies that replicate human-centered adaptability, can help overcome trust-related barriers. That needs a stronger integration of socio-behavioral and organizational dynamics into the design and governance of digital platforms and ecosystems. Human-centric considerations are often overlooked in the discourse around DT technologies.

DT enables innovation and operational efficiency, but it also places greater responsibility on organizations to ensure the ethical deployment of digital technologies (Vial, 2021). Moreover, increased regulatory scrutiny, evolving societal expectations and the growing use of AI and big data introduce ethical risks such as privacy violations, data misuse and algorithmic bias. Ethical considerations – including responsible data management, transparency, accountability and fairness – should be treated as an integral component of DT strategy rather than an afterthought and embedded in strategies and governance structures to balance technological innovation with social and moral responsibilities (Vial, 2021). Embedding ethical principles into DT initiatives also contributes to trust development within innovation ecosystems, thereby supporting long-term value creation.

The transformative role of digital technologies in shaping new user standards, multi-stakeholder benefits and broader environmental and societal impact is a critical but underrepresented research aspect in the DT literature (Perboli et al., 2018a, b; Bai et al., 2020; Dutta et al., 2020). The assessment of digital technologies beyond technical and economic feasibility to include environmental and social implications through the lens of ecosystem-wide value creation (Wessel et al., 2021) is, therefore, necessary for a DT strategy.

Each digital technology exerts a distinct influence on industry dynamics and sustainability dimensions. Hence, every technology must be carefully assessed in terms of its interplay with business objectives, digital capabilities, organizational processes and sustainability outcomes (Bai et al., 2020; Zanoni, Ashourpour, Bacchetti, Zanardini, & Perona, 2019). Moreover, technology-enabled configurations of sustainability-oriented business models should also explicitly account for industry-specific challenges and opportunities related to scaling. For an example, in creative consumer goods industries (Lerro, Schiuma, & Manfredi, 2022), advanced additive manufacturing technologies such as 3D knitting facilitate “local” co-creation with travel-experience-as-a-service offerings linked to the product as added value. Such hybrid innovations affect the triple bottom line of sustainability – environmental by circular and local production; social by competence building and job creation locally; and economic by value-added services without additional resource consumption – , but generate new challenges, such as bridging two traditionally very distinct sectors, i.e. manufacturing and tourism (Santarsiero, Carlucci, Lerro, & Schiuma, 2024).

Digital transformation unfolds under conditions of uncertainty and is often implemented through iterative, project-based initiatives involving multiple stakeholders (Ghassemi & Becerik-Gerber, 2011; Gonçalves, Penha, Silva, Martens, & Silva, 2023; Warner & Wäger, 2019). This challenges traditional approaches that separate strategy formulation from implementation. Instead, DT strategies should be developed and refined through iterative cycles of experimentation, validation, and learning, enabling organizations to evaluate alternatives, adapt to emerging information and balance innovation with operational objectives (Baiyere et al., 2025; Vaagen & Ballard, 2021).

This perspective aligns with Design Science Research (DSR), which emphasizes iterative build–evaluate cycles (Hevner, March, Park, & Ram, 2004), and supports the development of theoretically rigorous yet practically relevant solutions (Romme & Holmström, 2023). Consequently, effective DT strategies should be treated as evolving designs that are continuously tested, refined and adapted through both single-loop and double-loop learning (Argyris, 1993). DSR applicability spans multiple fields, including information systems (Peffers, Tuunanen, Rothenberger, & Chatterjee, 2007), operations research (Manson, 2006), operations management (Holmström, Ketokivi, & Hameri, 2009; Meredith, Raturi, Amoako-Gyampah, & Kaplan, 1989), construction management (Formoso, da Rocha, Tzortzopoulos-Fazenda, Koskela, & Tezel, 2012), and more recently, the development of new project management knowledge (Gregor & Zwikael, 2024; Vaagen & Ballard, 2021).

Of DSR approaches suited for digital transformation we highlight prototyping, as a learning-based approach facilitating early-stage innovation, testing, refinement and risk reduction (Baldassarre et al., 2020), future-oriented DSR with design-driven foresight emphasizing anticipation and preparedness (Simeone & D'Ippolito, 2022), and design-driven innovation which evaluates innovations through desirability, feasibility and viability across stakeholder ecosystems (Corà & Fazio, 2024).

To support digital transformation under uncertainty, with iterative decision processes, organizations require a combination of leading and lagging indicators. Leading indicators provide proactive guidance and early signals about the effectiveness of strategic decisions under uncertainty, adaptive capabilities, speed of learning, social capital and customer responsiveness, reflecting dynamic capabilities required for sensing and seizing opportunities (Teece, 2007; Vial, 2021; Simchi-Levi, 2010; Vaagen & Wallace, 2026). Examples include measures of responsiveness, flexibility and vulnerability to uncertainty, strategy cycle time, experimentation rate, capability readiness and social network metrics such as network density and structural hole. These correspond to the early stages of strategizing DT. Lagging indicators capture the realized outcomes of transformation initiatives, including operational, economic, social and environmental performance.

DT performance measures must also be addressed with respect to how they enable the dual value objectives of ambidexterity and long-term competitiveness. For example: How social capital, enabled by digital connectivity, affects innovation potential and decision-making capabilities? How supply chain integration through blockchain facilitates optimized supply chain planning? Is there a need for convergence among enabling technologies; if so, which technologies and how (Framinan et al., 2023)? Stochastic optimization, for example, is suited to assess adaptation and escalation measures in the face of uncertainty, accounting for the two-stage structure of costs and benefits of flexibility and environmental actions, but it comes short for large applications where convergence with advanced simulation and AI tools is needed.

Baiyere et al. (2025) provide a supporting, not exhaustive, guide to DT performance measures. However, these are often poorly defined or weakly linked to strategic objectives (Mahboub, Sadok, Chehri, & Saadane, 2023; Baiyere et al., 2025), with overly focus on financial measures in the public sector and digital economy, rather than on integrated social, environmental and digital performance in private enterprises (Mahboub et al., 2023). This may be an outcome of poorly formulated DT goals (Baiyere et al., 2025), but also highlights the need for integrated measures that simultaneously capture digital and sustainability outcomes while balancing short- and long-term objectives (Adams, Jeanrenaud, Bessant, Denyer, & Overy, 2016).

Table 1 provides a comparison of established DT frameworks named in Section 1 (not as an exhaustive list but examples of relevant frameworks) and the GUEST 2.0 framework, in the view of their alignment with the design principles defined in this section.

This study adopts a DSR approach to develop the GUEST 2.0 framework. GUEST.2.0 expands upon its predecessor, the GUEST method (Perboli, 2016; The GUEST Initiative Team, 2017), by explicitly addressing the strategic and organizational challenges of DT in innovation processes and incorporating the core requirements of DT strategy identified in Section 2. The framework is the outcome of a multi-year development process following DSR principles as outlined by Manson (2006), adapted from Takeda, Veerkamp, and Yoshikawa (1990). This approach emphasizes iterative learning, experimentation and prototyping, where successive iterations generate improvement points that refine both the conceptual framework and its practical application. Each iteration refined the structure of the methodology, the relationships among its components and the artifacts supporting decision-making. The build–evaluate cycles generated higher-order double-loop learning with redefined values, objectives, strategies and rules, in response to emerging challenges on the intersection of DT–IM. Double-loop learning enables deeper adaptation beyond single-loop learning, which focuses only on optimizing strategies within existing goal structures, and is essential for navigating rapidly changing and uncertain environments (Argyris, 1993), such as DT.

In this sense, learning-based development is not only a central component of strategizing in DT, as discussed in Section 2, but also the methodological foundation underlying the development of GUEST 2.0. By applying DSR principles, the framework aims to bridge theoretical insights and managerial practice, contributing both to the academic understanding of DT strategy and to the development of actionable methods for its implementation.

Artifact development: The development of GUEST 2.0 followed an iterative design cycle consisting of problem identification, artifact design, prototyping and refinement. The initial problem framing emerged from the literature, which highlighted the absence of structured methodologies linking DT strategy articulation to implementation under uncertainty, and from GUEST pilots such as blockchain for supply chain and logistics innovation (Perboli et al., 2018a, b) and digital social innovation for inclusive, cooperative connected and automated mobility (the SINFONICA project described in Section 5). These also prompted the need to synthesize the core requirements of DT strategy (Section 2), forming the conceptual foundation for the framework.

Iterative learning and customization: Consistent with the DSR approach, the development of GUEST.2.0 relied on learning-based experimentation. Prototypes of the framework were applied in exploratory contexts to test their usefulness for articulating DT strategies and guiding implementation decisions. These applications provided insights into how organizations assess uncertainties, identify alternative solutions and enabling options, and coordinate transformation initiatives across organizational levels. Observations from these iterations informed subsequent refinements of the framework, improving both its conceptual coherence and practical applicability. Figure 1 is an illustration of the iterative development process, where the upper part shows the original GUEST development, the lower part focuses on GUEST.2.0 with refined problem awareness and problem definition for DT. A detailed account of the main GUEST iterations, including the application domain, the corresponding published reference and the specific methodological refinement each iteration introduced, is provided in Table 3 in Appendix A.

Evaluation: The practical relevance of GUEST 2.0 for digital innovation was examined through a case application presented in Section 5. The case demonstrates how GUEST 2.0 can support organizations in structuring strategy development and decision-making processes while coordinating DT initiatives. Rather than testing causal relationships, the evaluation focuses on assessing the framework's utility, coherence and applicability in addressing the strategic design challenges. This approach is consistent with the objectives of DSR, where the value of the artifact lies in its ability to provide actionable guidance while contributing to theoretical understanding.

The GUEST framework is designed to remain flexible and adaptable across sectors and topics while maintaining its core structure. Depending on the context or project, it can be applied fully or partially, focusing only on relevant phases and tools, and omitting or simplifying others. The iterative nature of the framework supports adaptation in response to unforeseen changes, such as scope widening after identifying unexpected opportunities or challenges, or shifts in value proposition after gaining deeper insight. A successful solution might also inspire further innovation, prompting a cycle of ideation and implementation for additional products or services. This iterative, flexible approach helps the framework remain robust across different environments, enabling continuous innovation. Table 3 in Appendix A lists of the main GUEST iterations with improvement points.

GUEST has been used to accelerate innovation in more than 50 industrial and research consortia, by more than 40 SMEs and small innovation groups, and by about 250 new users per year in Master's, PhD and Professional Education, to introduce Business Strategies and Business Development to non-business professionals.

GUEST 2.0 operationalizes the requirements identified in Section 2 through a structured, iterative decision process consisting of five interconnected phases: GO, UNIFORM, EVALUATE, SOLVE, TEST.

Ambidexterity is operationalized through GO and UNIFORM, which define long-term stakeholder value and organizational readiness, EVALUATE, which compares exploratory and exploitative alternatives, and TEST, which supports iterative learning from both innovation and efficiency outcomes. Uncertainty management is primarily embedded in EVALUATE through alternative generation, scenario assessment and performance predictors, while TEST enables iterative refinement as new information emerges. Ecosystem value creation is addressed in GO through stakeholder mapping and value definition, strengthened in UNIFORM through alignment and governance design, and leveraged in EVALUATE and SOLVE through ecosystem orchestration and collaborative solution design. Ethical considerations are incorporated from GO onward through stakeholder value definition, governance choices in UNIFORM, solution assessment in EVALUATE and validation activities in TEST. Performance measures provide the connective mechanism across all five phases: value metrics in GO, capability metrics in UNIFORM, leading indicators in EVALUATE, implementation metrics in SOLVE and outcome validation in TEST.

Rather than restating conceptual principles, this section demonstrates how these requirements are enacted in practice, also illustrated by an example.

Table 2 provides an overview of stages, tools, and outputs, while a workflow diagram (Figure 4) and a phase-by-phase practitioner checklist (Table 4) are reported in Appendix B to support direct application of the methodology by practitioners.

The GO stage establishes the strategic foundations of DT by defining transformation objectives, identifying value creation opportunities and mapping relevant stakeholders within the digital ecosystem. Because DT frequently spans multiple organizational and ecosystem actors, this stage emphasizes ecosystem-wide value creation and sustainability considerations alongside organizational goals. Analytical tools such as stakeholder analysis, ecosystem mapping, the value priority matrix and business model representations (e.g. Business Model Canvas) support the articulation of strategic intent.

To identify and engage stakeholders in the innovation process, the Quadruple Helix (QH) model of innovation is proposed as the guiding theoretical and methodological framework. QH integrates industry, academia, government and civil society to better reflect the socio-cultural and regulatory dimensions of innovation, in addition to the more traditional business and research collaboration dimensions. For example, Government actors may facilitate innovation through incentive-based regulation and funding; academic partners support knowledge development and transfer and can host hybrid organizations between industry and academia, such as incubators, civil society ensures legitimacy and alignment with broader public values.

The outcome of GO is a shared definition of transformation goals and stakeholder value priorities that frame subsequent decision-making.

The UNIFORM stage aligns organizational capabilities, governance structures and decision-making processes with the strategic objectives established in the GO stage, ensuring that all actors operate from a shared mental model and language. Issues related to data migration and data governance are also to be handled in this phase, including specifying what information should be embedded in digital tools (e.g. platforms, models, AI systems) and how this informs decisions throughout the innovation lifecycle. DT initiatives typically require coordination across business units, IT functions and external partners, making organizational and digital maturity alignment and psychological safety critical for engagement, data sharing and future adaptability of digital ecosystems. Methods such as capability and readiness assessments, governance mapping, Lean Solution Canvas and organizational network analysis (ONA) help identify capability gaps, clarify roles and establish a common operational language across stakeholders. The result is organizational readiness and capability alignment supporting the transformation strategy.

The UNIFORM phase results in a structured foundation for strategic development, with shared terminology and standardized documents, enabling a coherent transition to the next phase.

The EVALUATE stage focuses on identifying alternative DT solutions. This is where proactive approaches for flexibility and iterative learning-based development processes are enabled. Structured comparison of alternatives helps avoid premature commitment to specific solutions. Analytical methods such as ICE analysis (Identifying opportunities, risks, and Challenges; Control and handle them by defining solution alternatives; Evaluation by defining performance indicators and predictors to monitor the implementation of the activities), and more sophisticated AI-based and analytical decision frameworks that foster flexibility and iterative development (e.g. digital twins, stochastic optimization, decision trees) are proposed. Standard tools such as SWOT analysis and Balanced Scorecard perspectives are also useful.

EVALUATE also includes the strategic orchestration of the innovation ecosystem. Building on the stakeholder network from the GO phase, one potentially useful approach is to combine the Quadruple Helix model with ONA to leverage network effects and enhance social capital by identifying influence structures, communication gaps and latent innovation potential within and across stakeholder groups. This supports stakeholder alignment, effective knowledge exchange and early validation of exploratory initiatives, as well as identification of systemic leverage points and co-creation opportunities. Practical frameworks, such as the 3E model of innovation of Lee and Kjaer (2023), i.e. Explore, Engage, Exploit, underscore the role of social networks and ecosystem approaches in mobilizing innovation and leveraging social capital across stakeholders for green ambidexterity and digital innovation.

Performance is measured primarily by leading metrics, such as predictors of adaptive capabilities, and performance predictors that capture the two-stage cost-benefit structure of DT.

In sum, EVALUATE ensures that GUEST 2.0 projects move forward not only with not only technical and organizational feasibility but also a strategic roadmap for shared, measurable and resilient value in a dynamic world.

The SOLVE stage translates selected transformation initiatives into actionable implementation pathways for co-designed solutions. This involves defining technological architectures, organizational changes and governance arrangements required to execute the selected initiatives. Tools such as Solution Canvas, system architecture mapping, process redesign approaches and lean and agile project or portfolio planning techniques support the adaptive decision processes, with real-time data serving as both input for evaluation and feedback for adjustment. The outcome is a structured roadmap linking strategic initiatives with operational execution. The Solution Canvas is applied to synthesize the chosen solution, detailing decision-makers, constraints, users, goals, channels and costs.

By mapping interdependencies among users and decisions, SOLVE supports platform-based strategies and fosters proactive decision-making.

A defining feature of this phase is the alignment between strategy development and implementation actions, ensuring that sustainability and digital priorities are embedded into the solution's architecture from the start. Solutions are developed using multi-actor collaboration, often through design thinking workshops or participatory modeling sessions. This collaborative approach reinforces trust, transparency and psychological safety.

The TEST stage validates transformation initiatives through experimentation and iterative learning. Rather than assuming strategic choices are correct at the outset, digital initiatives are tested through prototyping, pilot projects and limited-scale implementations, supported by KPI monitoring, gap analysis and stakeholder feedback sessions. Insights generated during this stage inform adjustments to both implementation plans and strategic assumptions. This iterative validation closes the loop between strategy and execution.

TEST reinforces ambidextrous learning by supporting both exploratory feedback (e.g. user insights, unmet needs) and exploitative control (e.g. cost tracking, efficiency measures). Flexibility plays a key role, enabling the solution to be adapted and redefined based on new insights. This phase embodies the ethos of GUEST 2.0 as a dynamic, iterative innovation cycle rather than a one-off project implementation. It supports evidence-based decision-making and nurtures a digital culture of agility, resilience and sustainability.

This example shows how GUEST 2.0 structures decision-making under uncertainty while leveraging digital technologies for flexible, data-driven and multi-stakeholder DT strategies.

In engineer-to-order shipbuilding, scope and design uncertainties, which cause disruptions across engineering, procurement and delivery, are typically managed through planned and team flexibility (Vaagen & Wallace, 2026), supported by psychological safety. Digital twins act as a key DT enabler, integrating stakeholders and enabling co-development and real-time experimentation. For instance, a virtual cruise vessel accommodation system can simulate passenger experience while evaluating performance for both supplier and shipbuilder, supporting shared decision-making and new value creation.

Applied to planning flexibility (i.e. accommodating late design changes, as an important value driver), GUEST 2.0 structures decisions across five phases:

1. GO: Defines context, stakeholders, problem/opportunity to be addressed (as named above) and digital twin interoperability across systems used by strategic stakeholders, and aligns value (e.g. using Value Priority Matrix).

2. UNIFORM: Aligns digital twin capabilities and formalizes the problem and value proposition (e.g. reducing uncertainty via real-time simulation using digital twins).

3. EVALUATE: Explores alternatives using digital twins and advanced analytics (e.g. stochastic programming) to derive decision rules for flexibility. Digital twins enable rapid scenario testing but do not generate flexibility; robust, repeatable decision rules reduce bias under uncertainty. Decision rules for flexibility essentially differ from those devised for reactive planning; cf. Vaagen & Wallace (2026).

4. SOLVE & TEST: Translate (design) strategy into execution via a project execution strategy (with an adequate level of flexibility within accepted time, cost and sustainability targets) and pilot implementations.

Across phases, performance predictors for uncertainty assessment and responsiveness (e.g. time-to-adapt, financial impact during adaptation, rework, sustainability) support evaluation and validation within iterative learning-based design processes (e.g. to establish shared understanding on the systemic impact of late changes). Digital twins further enhance ecosystem coordination, shared understanding, trust and psychological safety. Scaling is supported through collaborative lean construction and agile practices.

Inspired by agile methodologies, GUEST is based on a recurring cycle of meetings in which the project progresses through interconnected and incremental steps. A typical implementation involves small groups of people with diverse backgrounds and varying levels of seniority who meet regularly. To achieve innovation within a reasonable timeframe, groups should ideally include no more than 5–10 participants, as this size makes it easier to reach consensus, while larger groups are generally less effective. The meetings follow a structured approach, addressing one of the five GUEST steps in each meeting. Every meeting starts with a brief recap of the methodology and the current step, then a review of the information gathered in the previous phase, which is necessary as a basis for applying the relevant tools. The following meeting is then planned. The original GUEST was applied to small and medium companies seeking to develop a new project or service. It can be employed with a fast cycle of meetings every fortnight. The outcome is generally a research or pre-industrial proof of concept (PoC). GUEST was later successfully extended to large enterprises aiming to innovate. Small cross-functional teams can meet regularly and develop a PoC that can be validated and adopted by the company. When collaboration involves both private and public organizations, mixed teams are created. These groups may require more time to reach alignment and often meet monthly, as public sector constraints can slow decision-making. The outcome is typically either an exploitation strategy or a PoC. For large projects involving multiple companies, representative groups work together to define a structured exploitation strategy based on project results. In this case, the process does not start from scratch but builds on existing industrial outcomes. The result is usually an exploitation plan that consolidates individual results into two or three possible exploitation paths.

GUEST can be applied across different types of organizations and adapted to specific needs. A minimum level of organizational structure is required, making it less suitable for startups. Many of the tools used in GUEST are already familiar to companies and are easy to apply even for non-experts, which helps reduce implementation time.

Building on this general implementation framework, the following section illustrates how GUEST is applied for the design of a digital social innovation use case, called SINFONICA (SINFONICA Project Consortium, 2023).

The aim of the digital innovation initiative is to foster inclusive, cooperative, connected and automated mobility by following a bottom-up approach that analyzes the mobility needs of European citizens. Attention is given to vulnerable and under-researched user groups, directly involving those segments of society in a participative process. The goal is to develop functional and innovative strategies that effectively involve and connect users, service providers and other stakeholders of Cooperative, Connected and Automated Mobility (CCAM). The innovation project brings together stakeholders from 18 partners in six European countries, within a recently concluded three-year project.

The initiative brings together all aspects of R&D related to the mobility of the future: connected, shared and autonomous. In recent years, it has been shown that the transport sector, particularly road transport, is undergoing profound changes and research promises to achieve goals that, until now, were unthinkable. Examples include the drastic reduction of emissions, the optimization of traffic flows and the reduction of road accidents. However, in order to achieve these goals, it is necessary that the solutions of the new mobility paradigm are as inclusive, resilient, sustainable, accessible and reliable as possible. For this reason, SINFONICA designs and develops new strategies, along with functional and efficient tools, enabling the actors involved to collect and fully understand the needs, expectations, concerns and ambitions related to autonomous, connected and shared mobility. Actors include users, suppliers, citizens (including vulnerable groups), transport operators, public administrations, service providers, researchers, vehicle manufacturers and technology providers. Particular attention is paid to the needs, expectations, concerns and desires of user categories “with mobility challenges” (i.e. user groups with cognitive and physical disabilities, digitally vulnerable people, people living in rural areas, migrants, low-income families) and also to people who are temporarily in a vulnerable situation (pedestrians and cyclists).

The GUEST approach for digital social innovation (also called GUEST-SI) pays attention to the definition of the potential segments of end-users of the products and services developed and tested in the project, as well as potential future exploitation pathways. GUEST-SI is used to shape and direct SINFONICA's development, ensuring that innovations are grounded in real needs and driven by inclusive collaboration.

The next sections detail how the proposed GUEST 2.0 method is applied in practice and how the core elements of DT strategizing, as defined in Section 2, are addressed.

Every GUEST project starts with identifying the different actors involved in the process, identifying for each actor their jobs (what they are trying to achieve in their work), the gains (the concrete benefits that they are seeking) and the pains (problems connected with their work). Once the jobs, gains and pains for each actor have been collected, it is possible to prioritize them to highlight the more important or urgent ones and visualize them through the Value Ring, a graphical tool that quickly and clearly shows the real needs of the actors. In SINFONICA, a participatory and mixed-method research approach was employed to uncover mobility needs across Europe. An online survey collected more than 4,200 responses from a wide range of users. Recognizing that digital tools alone are insufficient to reach certain groups, such as people with cognitive impairments or digital vulnerabilities, the survey was complemented with face-to-face research in four countries. This included in-depth interviews, focus groups and workshops, enabling direct engagement with individuals often excluded from conventional consultation processes. In addition to the qualitative data gathered through interviews and workshops, the online survey, conducted in eight languages in the spring of 2024, provided broad quantitative insights. It enabled the exploration of not only the technical and functional requirements of users but also their willingness to adopt cooperative, connected and automated public transport systems.

The findings were integrated into the SINFONICA Knowledge Map Explorer, a decision-support tool developed to assist the equitable and seamless deployment of CCAM solutions (Antonakopoulou, Fokeas, Tsougiannis, Krikochoriti, & Amditis, 2025). This map not only organizes insights about user needs and expectations but also promotes collaboration among stakeholders by offering a shared reference framework, thereby promoting trust and psychological safety. It serves as a valuable resource to ensure inclusivity and to address concerns around trust, digital access and social equity, while also respecting privacy and security. The knowledge gathered through this process will be made available to municipalities, transport operators, mobility service providers, and vehicle manufacturers. It informs policy, business models, and future research, offering concrete recommendations grounded in both empirical data and the lived experiences of users. The overall goal is to create a robust foundation for socially inclusive CCAM ecosystems while addressing barriers such as resistance to change, lack of digital literacy and economic inequalities.

A concrete outcome of applying GUEST-SI can be observed in the evaluation framework developed for the SINFONICA Knowledge Map Explorer (KME). During the GO and UNIFORM phases, stakeholder engagement activities revealed the importance of digital inclusion, accessibility and clarity of information, particularly for vulnerable users. These priorities, together with the risk of widening social and digital inequalities identified during the EVALUATE phase, were translated into explicit objectives and performance indicators. As a result, the validation framework extended beyond conventional usability measures and incorporated dedicated accessibility-related KPIs, including accessibility compliance and accessibility satisfaction.

During the TEST phase, these indicators were applied through dedicated validation sessions involving users with visual, hearing and cognitive impairments. This enabled the KME to be assessed through the explicit lens of users with diverse sensory, cognitive and physical abilities, leading to iterative improvements in screen-reader compatibility, information clarity, transparency, navigation, text simplification and cognitive accessibility. The process demonstrates how GUEST-SI can transform stakeholder needs identified in the early phases into measurable evaluation criteria and actionable design improvements.

For future CCAM implementations, a similar approach could be adopted by defining KPIs that assess whether information and commands are consistently available, perceivable, understandable and usable across different cognitive, sensory and physical profiles. GUEST-SI builds on inductive data analysis, allowing patterns and themes to emerge from the ground up, i.e. allowing opportunities to surface. This analytical approach opens space to explore potential applications of CCAM that are still in the early stages of conceptualization or that do not yet exist in today's markets. One such example is a Mobility-as-a-Service (MaaS) digital platform designed to integrate multiple transport modes within a single, user-friendly system. This solution facilitates real-time, personalized and inclusive travel for a wide range of users.

The outcome of the GO phase is a Value Priority Matrix generated for each of the proposed solutions.

The matrix for the MaaS digital platform is represented by Figure 2.

This matrix illustrates how different solutions generate value for the involved stakeholder groups. The horizontal axis represents stakeholder categories, while the vertical axis reflects priority levels, with a color-coded system: red for high priority, yellow for medium and green for low. Each cell within the matrix explains the specific value created for a stakeholder group and describes how that value is delivered or perceived. For instance, the MaaS platform generates high-priority value for local governments by improving multimodal integration and promoting environmentally sustainable travel, contributing directly to climate and urban development goals. By enabling and integrating real-time data from multiple sources, the platform also creates medium-priority value for local governments, improving urban planning and enabling rapid responses to traffic disruptions.

In complex, multi-actor environments, this tool helps visualize and compare the diverse and potentially conflicting values of multiple stakeholders. By emphasizing these differences early on, it works as a strategic alignment tool, ensuring more inclusive, balanced and widely accepted solutions.

To support business model design in innovative and uncertain contexts, the traditional Business Model Canvas used in the original GUEST is replaced by Lean Solution Canvas. This format is better suited to exploring new ventures where market conditions and user behaviors are still evolving (i.e. uncertain and fuzzy). This analytical tool builds on the insights developed during the GO phase and translates them into a structured representation of the proposed solution. It is organized into nine sections: the Problem, clearly defining the key issues to be addressed and the context in which they arise; the Customer Segments, identifying the target users or stakeholders affected by the problem; the Unique Value Proposition, outlining the specific value delivered by the solution and how it addresses user needs; the Solution, describing the core features and functionalities of the solution; the Key Metrics, used to evaluate the effectiveness and impact of the solution; the Channels, through which the solution will be delivered and communicated to users; the Cost Structure, covering the resources required to develop, implement and maintain the solution; the Revenue Streams or Expected Benefits, representing the tangible or intangible returns generated; and the Unfair Advantage, highlighting elements that make the solution distinctive or difficult to replicate.

For the MaaS digital platform, the Lean Solution Canvas (Figure 3) highlights key aspects of the proposed solution. It identifies the core problems. such as fragmented transport systems, limited integration, and poor accessibility for vulnerable users, and contrasts them with a clear value proposition, as an integrated, inclusive and flexible mobility service offering real-time, personalized travel planning. These exploratory blocks are combined with exploitation-focused ones, like the revenue streams. In a MaaS platform, expected revenues come not only from ticket sales in different forms (subscription, single rides or multimodal tickets) but also from public subsidies and data monetization.

Through its frameworks, stakeholder engagement and knowledge tools, SINFONICA GUEST-SI ensures that the design of new CCAM services is inclusive and data-driven and contributes to building the trust and societal acceptance required for a widespread adoption of CCAM; hence, contributing to strategic integration of DT and sustainability.

One key result is a simulation framework that allows users to model and test different CCAM fleet deployment scenarios (SINFONICA Consortium, 2025). The tool has a high level of configurability and includes parameters to simulate different proportions of physically vulnerable users. This makes it possible to assess how services perform under varying demographic conditions, supporting planning choices that balance flexibility, efficiency and equity.

The project prioritizes accessibility not only in the services it helps design but also in the tools it develops, ensuring the active participation of non-experts in shaping future mobility. The Knowledge Map Explorer and the Simulation Framework are designed with simplicity and user-friendly interfaces in mind. Along with the vocabulary, the policy recommendations and the guidelines for participatory processes contribute to involving all stakeholders in evidence-based design and ensuring the integration of technological innovation and sustainability.

Moreover, through participatory processes SINFONICA identifies not only users' needs and expectations for future mobility services (exploration) but also detects shortcomings and areas of improvement in current systems (exploitation). This dual perspective of ambidexterity allows the refinement of existing services while ensuring that the design of new solutions does not replicate existing problems. For example, participants often highlight the need for real-time, reliable information on the accessibility of vehicles and infrastructure. Users with physical disabilities may arrive at the train station only to find that the lift is out of service, forcing them to improvise an alternative plan, wasting money, time and energy. If such information were available in advance, they could plan a different route and avoid disruption. Put differently, flexible, data-driven strategies not only help overcome the challenge of trust-related barriers in digital platforms, but they also facilitate trust development in that the solution users receive is reliable and aligned with their needs.

Stakeholder engagement and ecosystem collaboration is enabled by the participatory processes described above. These, while not explicitly following the guidelines of the Quadruple Helix model of innovation, connect industry, civil society, universities and regulatory bodies in a way that facilitates the exploration of future mobility services, while detecting current challenges.

To measure the sustainability and effectiveness of the new transport modes, SINFONICA focuses on four key indicators: availability, accessibility, affordability and acceptability (Renzi and Winder, 2023). Availability ensures that the transport is reachable for all users, for example, using redundant and fail-safe technology. Accessibility can improve social inclusion of fragile users with factors like inclusive design and effective communication systems. Affordability is a key element of equity on the demand side, while on the supply side it can rely on public investments to lower production costs. The last dimension, acceptability, is a crucial ethical aspect that includes trust and data privacy. These dimensions link technological innovation with social responsibility and guide the definition of KPIs for evaluating sustainable transport practices.

The aim of this paper was to move beyond the fragmented understanding of strategizing for DT and to offer a unified framework that bridges theory and practice. The result is GUEST 2.0, a synthesis and decision process that is replicable, controllable and comparable across business cases and projects, even when tools are replaced. The proposed process enables, in Vial's (2021) terms, “reliable, repeatable communication and coordination activity directed toward modifying products, business models, and capabilities”. Where Vial asks “How do actors design dynamic problem-solving processes that are repeatable?”, GUEST 2.0 provides a structured design-science cycle that institutionalizes those processes, fostering proactive, flexible, data-driven strategies that are repeatable across business cases and that also help overcome trust-related barriers for DT.

From a practical perspective, the methodology allows for application and adaptation in real organizational contexts, by providing organizations with actionable tools and processes to guide digital innovation in ways that are responsive to internal capabilities and external societal demands. Ultimately, the framework empowers practitioners to make context-sensitive, robust and future-oriented DT decisions rather than technology-focused ones. By linking strategy articulation with execution, and aligning DT with broader sustainability goals, GUEST 2.0 offers a path forward for organizations seeking to create both economic and societal value through digital innovation.

Theoretically, the framework aims to fill a gap in the literature on the intersection of DT and innovation management by providing an instrumental decision-oriented methodology that aligns strategic intent with implementation. GUEST 2.0 contributes to the advancement of this field by operationalizing the key requirements such as ambidexterity, flexibility, data-driven decision-making, ecosystem connectivity and sustainability alignment within a coherent and practitioner-friendly model.

First Author: Methodology, Writing - Review and Editing; Second Author: Conceptualization, Validation, Writing - Original draft preparation; Third Author: Conceptualization, Validation, Writing - Original draft preparation.

During the preparation of this work the authors used ChatGPT to improve language and readability. After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.

The supplementary material for this article can be found online.