From data to knowledge: how DPP startups span boundaries across stakeholder divides

The global economy is undergoing a significant transformation toward a circular economy, largely motivated by environmental concerns and regulatory action, particularly within the European Union. Central to this transition is the increasing demand for radical transparency across a product’s life cycle to allow stakeholders to track and verify sustainability practices. In response, organizations are adopting digital solutions to record and communicate product data across complex value chains, with Digital Product Passports (DPPs) emerging as a promising tool (Jensen et al., 2023). The EU’s Ecodesign for Sustainable Products Regulation (ESPR) requires extensive information sharing throughout supply chains, positioning DPPs as structured digital records that capture environmental, compositional and compliance data over a product’s entire lifecycle.

DPP development is currently being spearheaded by startups offering modular, often blockchain-based platforms designed for traceability and compliance. While blockchain provides valuable decentralized infrastructure, its technical robustness alone is insufficient for successful adoption (Vannoy and Palvia, 2010). Clear communication remains essential for translating technical capabilities into shared understanding among users, yet the path to widespread DPP adoption is impeded by several human-centered challenges including opaque language, inconsistent user onboarding experiences, and low technical literacy among stakeholders (Vannoy and Palvia, 2010; Zerfass et al., 2020).

Beyond technical feasibility, implementing circular economy infrastructures such as DPPs requires coordination among diverse actors including manufacturers, regulators, technology providers and consumers. Prior research on circular economy implementation highlights that many sustainability initiatives fail not because of technological limitations but because stakeholders struggle to interpret new systems, understand their roles, and align expectations across supply chains (Kirchherr et al., 2017; De Jesus and Mendonça, 2018). Kirchherr et al. (2017) identified cultural barriers and lack of consumer awareness as among the most critical impediments to circular economy transitions, while De Jesus and Mendonça (2018) demonstrated that informational and interpretive barriers frequently outweigh technological ones in eco-innovation adoption. These findings underscore that the challenge of circularity is fundamentally communicative: without shared understanding across supply chain actors, even technically sound systems remain underutilized.

In the case of DPPs, organizations must build data infrastructures and translate complex regulatory and technical concepts into formats that different stakeholders can understand and act upon. These interpretive challenges make communication a critical component of successful circular economy transitions. Communication is instrumental in building trust by clarifying complex processes and aligning stakeholder expectations, making it a strategic asset for DPP startups. However, many communication professionals lack the specific training needed to effectively translate emerging technologies (Zerfass et al., 2020). While blockchain’s inherent transparency can boost credibility, this potential is only realized if the transparency is communicated in ways that are accessible and meaningful to users.

DPPs thus represent a knowledge management challenge as much as a technical one. The core function of a DPP is not merely to store data but to make product-related knowledge accessible, interpretable, and actionable across actors with divergent expertise and institutional logics. A DPP that captures material composition, carbon footprint, and repair instructions does not create value by default. It does so only when manufacturers can fold this knowledge into production decisions, consumers can act on it when purchasing, and recyclers can apply it at end of life. This knowledge activation function connects DPPs directly to SDG 12 (Responsible Consumption and Production), particularly Target 12.8, which calls for ensuring that people have the relevant information and awareness for sustainable development and lifestyles. The DPP is the instrument; communication across knowledge boundaries is the mechanism through which that instrument delivers its intended outcomes: increased transparency and traceability of materials and environmental impacts, more sustainable decisions for businesses and consumers, facilitation of reuse, repair and recycling, and compliance with emerging European regulations. Yet the value of the DPP is not merely technical but depends substantially on how it is communicated to stakeholders, making the information understandable and actionable across the entire product lifecycle.

While studies have examined DPP architectures and data requirements (Jensen et al., 2023; Wicaksono et al., 2025; Wan and Jiang, 2025), the communicative mechanisms through which these systems are made legible to heterogeneous stakeholders remain largely unexplored. This gap is consequential because the ESPR implementation timeline is approaching, with battery passports mandated from February 2027 and textiles expected to follow, and early evidence suggests that stakeholder confusion, rather than technical incapacity, poses the primary barrier to adoption (Jansen et al., 2023). Existing literature treats “information” within DPP systems as a neutral object requiring transmission rather than a knowledge asset requiring translation. Consequently, little is known about how startups, the primary architects of DPP platforms, translate these systems across knowledge boundaries in emerging regulatory contexts.

This study applies Carlile’s (2004) boundary-spanning framework to examine how startups communicate the structure and value of DPPs across diverse stakeholder groups. Drawing on qualitative analysis of digital materials from 12 European startups and interviews with ten industry practitioners, the research investigates the following question:

The findings reveal layered communication strategies that include standardization, narrative framing, technical metaphors and ESG-aligned messaging, offering both theoretical insights into boundary-spanning and practical guidance for startups navigating DPP adoption in emerging regulatory contexts.

This study builds on five strands of literature: EU policy developments around DPPs, the technical architecture of DPPs, blockchain as an enabling infrastructure, communication challenges in technology adoption, and Carlile’s (2004) boundary-spanning theory. Each strand is reviewed below, with attention to the gaps that motivate this study’s research question.

The European Green Deal and Circular Economy Action Plan introduced DPPs as digital tools for improving product transparency and supporting circularity goals. The ESPR, adopted in 2024, requires lifecycle traceability and machine-readable formats for product information across a broad range of product categories. DPPs act as dynamic, updateable records linked to products via QR or NFC, hosting environmental and compliance data (Wicaksono et al., 2025; European Commission, 2022).

The regulatory trajectory is concrete and imminent. The EU Battery Regulation (2023 / 1542) represents the first sector-specific DPP mandate, requiring battery passports from February 2027 that include information on carbon footprint, recycled content and supply chain due diligence. Textiles, electronics and construction products are expected to follow under delegated acts being prepared under the ESPR framework. The policy rationale extends beyond environmental reporting: DPPs are envisioned as instruments for consumer empowerment, enabling informed purchasing decisions based on product durability, repairability and end-of-life recyclability (European Commission, 2022).

Importantly, DPPs differ from existing product information schemes such as Environmental Product Declarations (EPDs) or energy labels in several respects. Unlike static labels, DPPs are designed to be dynamic and updateable throughout the product lifecycle, accommodating information from multiple supply chain actors. They are intended to be machine-readable and interoperable, facilitating automated compliance verification. This dynamic, multi-stakeholder character introduces communicative complexity that static labeling schemes do not face, making the question of how DPPs are made understandable across diverse audiences a pressing research concern.

DPPs function as dynamic digital identities that integrate technical specifications, repair histories, and usage data across multi-tier supply chains to enable lifecycle-based sustainability governance (Jensen et al., 2023; Voulgaridis et al., 2024; Zhang and Seuring, 2024). To guarantee data integrity and traceability across these fragmented networks, DPP developers frequently leverage blockchain infrastructures (Baralla et al., 2019). Through decentralized ledgers and smart contracts, blockchain shifts the foundation of supply chain trust from central institutional authority to cryptographic proof (Treiblmaier, 2018; Saberi et al., 2019). However, this architectural framework introduces severe operational and legal tensions, particularly regarding data governance. The inherent immutability of blockchain directly clashes with the right to erasure under Article 17 of the EU’s General Data Protection Regulation (GDPR), forcing developers to construct complex workarounds such as off-chain data storage paired with immutable on-chain cryptographic hashes (Saberi et al., 2019).

While existing literature extensively documents DPP architectures and lifecycle data requirements (Wan and Jiang, 2025), much of this work remains oriented toward information flows, data structures and technical transmission. This focus can overlook the interpretive work needed when heterogeneous actors, ranging from industrial manufacturers to everyday consumers, interact with the system. Although blockchain can provide decentralized mechanisms for data integrity and traceability, these backend verification mechanisms may remain difficult for nontechnical stakeholders to interpret. Building on Treiblmaier’s (2018) view of blockchain in supply chains as requiring managerial and organizational framing, cryptographic assurances must be translated into accessible narratives of transparency and accountability. Consequently, a DPP represents a knowledge integration challenge rather than a simple database transmission task; its viability depends not on backend data presence alone, but on communicative boundary-spanning mechanisms that make data comprehensible across organizational divides.

Technical solutions alone are insufficient for stakeholder adoption. Communication barriers frequently limit uptake of complex technological systems. Research on social influence in technology adoption has demonstrated that adoption is shaped by social influence processes including informational and normative pressures, while subsequent work by Magsamen-Conrad and Dillon (2020) demonstrated that perceived complexity varies significantly across stakeholder groups, mediated by prior technological experience and social context. In multi-stakeholder environments such as DPP ecosystems, shared understanding is essential, but often absent.

Three adjacent literatures illuminate why communication is not merely a supporting function but a constitutive element of technology adoption. First, framing theory from social movement studies offers a directly relevant lens. Three core framing tasks have been identified in social movement studies (Benford and Snow, 2000), diagnostic framing (identifying problems), prognostic framing (proposing solutions) and motivational framing (providing rationales for action), that collectively shape how actors perceive and respond to collective endeavors. These framing tasks map directly onto the communicative challenges DPP startups face: they must diagnose a transparency problem, position DPPs as the solution, and motivate stakeholder participation through appeals to regulatory compliance, competitive advantage or ethical responsibility.

Second, science and technology studies (STS) provide the concept of “translation” as a process through which actors construct networks of alignment around new technologies. The sociology of translation identifies four moments of translation (Callon, 1986), problematization, interessement, enrollment and mobilization, through which innovators define problems, attract allies, assign roles, and stabilize networks. This framework is particularly relevant to DPP startups, which must simultaneously problematize existing opacity in supply chains, interest diverse actors in the DPP solution, enroll them in specific roles (data providers, auditors, consumers), and mobilize ongoing participation. The translation lens foregrounds that technology adoption is not a passive diffusion process but an active construction of meaning and relationships.

Third, strategic communication scholarship has shown that organizations construct legitimacy through discursive practices. Sensegiving, the process of influencing how others construct meaning about organizational phenomena, is especially important during periods of institutional uncertainty (Cornelissen et al., 2015). For DPP startups operating in pre-regulatory environments, sensegiving becomes a primary organizational activity: they must help stakeholders make sense of systems, regulations and roles that are still evolving.

Despite this rich body of work on communication and technology adoption, existing DPP literature has not engaged substantively with these perspectives. The dominant focus remains on technical architecture and data requirements, leaving the communicative dimensions of DPP adoption theoretically underdeveloped. The present study addresses this gap by drawing on Carlile’s (2004) boundary-spanning framework, which integrates concerns about representational alignment and interpretive meaning-making within a single analytical lens.

Carlile’s (2004) framework offers an integrative theory for understanding how knowledge is managed across organizational boundaries. Developed through empirical study of product development in manufacturing firms, the framework identifies three progressively complex types of knowledge boundaries: syntactic, semantic and pragmatic.

At the syntactic level, the challenge is establishing a common lexicon or representational format. Knowledge can be transferred effectively once a shared vocabulary exists, but communication breaks down when actors lack common categories, data standards or interface conventions. At the semantic level, the challenge deepens: even when actors share a common language, they may interpret the same information differently due to divergent professional training, institutional logics or domain expertise. Here, knowledge must be translated rather than merely transferred. At the pragmatic level, the challenge becomes political: actors may understand one another perfectly but have conflicting interests that prevent knowledge integration. Pragmatic boundary-spanning requires transformation, actors must be willing to alter their own knowledge and practices to accommodate others (Carlile, 2004).

Carlile’s framework has been applied productively beyond its original manufacturing context. In software development, it has illuminated how distributed teams construct shared understanding across technical and organizational divides (Kotlarsky et al., 2015). In healthcare, it has explained how clinicians and administrators bridge professional boundaries around patient data systems (Oborn and Dawson, 2010). In innovation ecosystems, it has shown how intermediary organizations facilitate knowledge flows between universities and industry (Villani et al., 2017). These applications confirm the framework’s generalizability while also revealing that boundary-spanning dynamics vary significantly by context, particularly in the relative prominence of syntactic versus semantic challenges.

Central to Carlile’s framework is the concept of boundary objects, originally theorized by Star and Griesemer (1989) as artifacts that inhabit multiple social worlds simultaneously and satisfy the informational requirements of each. Boundary objects are flexible enough to be used differently across communities yet robust enough to maintain common identity. In the DPP context, web pages, whitepapers and product dashboards can be understood as prospective boundary objects: they are designed to be interpreted by multiple audiences, regulators, brand managers, consumers, logistics operators, while maintaining a coherent core representation of the DPP system.

This conceptualization of DPPs as boundary objects has begun to attract scholarly attention. Gieß and Möller (2025) employed Carlile’s framework to model the value ecosystem of DPPs through e³-value modeling, articulating how DPPs facilitate “a shared language (e.g. harmonizing data syntax and semantics)” across stakeholders such as manufacturers, suppliers, consumers, end-of-life actors and regulatory authorities. Their structural model identifies the actors and value flows that constitute the DPP ecosystem and demonstrates the framework’s relevance through the EU Battery Regulation case. However, while Gieß and Möller (2025) establish DPPs as boundary objects at the level of ecosystem architecture, they do not examine the communicative practices through which the harmonization of “syntax and semantics” is actually accomplished by the actors who design and deploy DPP systems. Their structural perspective leaves open the question of how startups, the principal architects of current DPP platforms, construct shared language and meaning across stakeholder groups in practice. The present study addresses this gap by complementing structural ecosystem modeling with empirical analysis of communicative strategies, treating the boundary-object function not as an attribute of the DPP itself but as an ongoing accomplishment of designed communication.

Recent scholarship has begun connecting DPPs explicitly to knowledge management. Becchi et al. (2026) examined DPPs as integrative tools embedding sustainability, open innovation, and knowledge management into organizational practice within the Italian fashion and cosmetics sectors. Drawing on consumer surveys, they found that DPPs function as capability-dependent knowledge systems. Their effectiveness hinges on whether consumers possess the absorptive capacity (Cohen and Levinthal, 1990) to recognize, assimilate and apply the information presented to them. This demand-side finding has a direct supply-side implication that remains unexplored: if stakeholder absorptive capacity determines whether DPP information becomes actionable knowledge, then the communicative strategies through which startups design and present DPP information are not merely marketing functions but knowledge-enabling mechanisms. The present study addresses this supply-side gap by examining how startups construct the communicative conditions under which DPP data can be recognized, interpreted and acted upon by stakeholders with varying absorptive capacities.

The preceding sections establish that DPP communication involves spanning knowledge boundaries across diverse stakeholders. To ground this challenge within knowledge management theory, this section draws on three KM constructs that clarify the mechanisms through which DPP information is transformed into actionable knowledge.

First, absorptive capacity, originally defined by Cohen and Levinthal (1990) as the ability to recognize, assimilate and apply external knowledge, has been extended to interorganizational and ecosystem contexts. Zahra and George (2002) distinguished between potential absorptive capacity (acquisition and assimilation) and realized absorptive capacity (transformation and exploitation), arguing that the gap between the two depends on social integration processes. In the DPP context, this distinction is directly relevant: supply chain actors may acquire DPP data (potential capacity) without being able to transform it into operational decisions (realized capacity). The communication strategies identified in this study can be understood as social integration mechanisms designed to bridge this gap, helping stakeholders move from data acquisition to knowledge application. Marrucci et al. (2022) demonstrated this connection empirically within the Journal of Knowledge Management, showing that absorptive capacity mediates the relationship between circular economy practices and organizational performance, and arguing that organizations need structured knowledge processes to translate environmental information into actionable practice.

Second, knowledge integration theory (Grant, 1996) holds that organizational capability resides in the integration of specialized knowledge held by different actors not in what any single actor knows alone. DPP ecosystems exemplify this challenge: manufacturers hold material composition knowledge, logistics providers hold movement data, recyclers hold end-of-life processing knowledge, and regulators hold compliance requirements. No single actor possesses the full knowledge picture. The DPP itself functions as a knowledge integration device, but only if its information can be interpreted across these specialized domains. The boundary-spanning strategies identified in this study, particularly role definition structures, data schema structuring, and contextual use cases, can be understood as knowledge integration tools that enable cross-domain comprehension.

Third, knowledge governance (Foss, 2007) examines the organizational structures and mechanisms through which knowledge processes are coordinated. In multi-actor ecosystems, knowledge governance extends beyond individual firms to encompass interorganizational arrangements for creating, sharing and protecting knowledge. DPP systems raise fundamental knowledge governance questions: who controls data access, who validates sustainability claims, who bears responsibility for data quality, and how is proprietary knowledge protected within a transparency-mandated system. The privacy and control framing strategies identified in this study address these governance concerns communicatively, constructing narratives about data ownership and selective disclosure that manage the tension between mandated transparency and commercial confidentiality.

Together, these KM perspectives reframe the research question. DPP communication is a knowledge management challenge, not just a marketing or technology adoption problem involving the construction of absorptive capacity among diverse stakeholders, the integration of specialized knowledge across organizational boundaries, and the governance of knowledge flows within an emerging regulatory ecosystem. This reframing positions the present study at the intersection of boundary-spanning theory and knowledge management scholarship, complementing Becchi et al. (2026) demand-side analysis of consumer absorptive capacity with a supply-side analysis of the communicative mechanisms through which startups construct the conditions for knowledge integration.

Drawing on this framework, the present study conceptualizes DPP communication as a boundary-spanning challenge operating at two levels. At the syntactic level, the challenge involves establishing common representational formats, terminologies and data schemas that enable DPP information to be transferred across stakeholder groups. At the semantic level, the challenge involves translating the meaning and value of DPPs across divergent institutional logics, ensuring that what “traceability” means to a compliance officer aligns with what it means to a brand manager or a consumer. The study focuses on these two levels because its data sources, public-facing communication materials and interview narratives about communication strategies, capture representational and interpretive practices rather than interest-based negotiations. The pragmatic level, involving conflicting interests and political negotiations, is acknowledged as an important dimension but falls outside the scope of this study’s data and is identified as a direction for future research in Section 6.

This study applies Carlile’s (2004) boundary-spanning framework as the analytical lens to examine how startups communicate the structure and value of DPPs. The framework provides a structured approach focusing specifically on syntactic boundaries, where actors lack a shared representational format, and semantic boundaries, where actors differ in their interpretation of information. This diagnostic focus allows for a structured analysis of communicative practices, identifying syntactic boundary-spanning through the use of standardized terminology, data schemas and interface labels, and semantic boundary-spanning through narrative framing, metaphoric simplification, and onboarding flows that align complex systems with stakeholder logic.

A purposeful sampling strategy was deployed to identify information-rich cases relevant to the study’s research question (Patton, 2015). Given the nascent and rapidly evolving state of the DPP ecosystem, no centralized, comprehensive database of active ventures existed at the time of data collection. Consequently, candidate startups were identified between February and May 2025 through a multi-channel pragmatic search strategy, combining scans of EU project directories (e.g. CORDIS), circular economy industry event participant lists, and professional network referrals. These channels were supplemented by targeted web searches utilizing variations of keywords such as “digital product passport startup,” “DPP platform” and “product traceability.” All tracking was limited to ventures maintaining English-language public materials. This flexible, inductive approach is highly consistent with purposeful sampling protocols in emerging fields where population boundaries are not yet institutionally defined (Patton, 2015). This multisource qualitative design follows recent methodological precedents in DPP and digital sustainability research. Chaudhuri et al. (2025) used stakeholder interviews to examine DPP impact pathways in electronics supply chains, while Gieß and Möller (2025) analyzed the DPP value ecosystem through a design-science approach. The present study similarly combines practitioner accounts with designed communication artifacts, using triangulation to examine how DPP systems are made understandable across stakeholder groups.

The initial search process yielded a pool of candidate ventures that were filtered across progressive inclusion criteria. To be retained, startups had to be based or operating in the EU and feature explicit, English-language public communications regarding their DPP architectures. Collected artifacts (product web pages, whitepapers and technical documents) were screened for analytical relevance, prioritizing materials displaying communicative features relevant to Carlile’s (2004) syntactic or semantic boundary-spanning framework. Purely transactional text, administrative updates and redundant media were excluded. The multi-channel search process surfaced 21 candidate ventures before the inclusion criteria were applied. This screening process ultimately identified the 12 final startups used for the analysis (Table 1).

As Table 1 shows, the corpus is unevenly distributed across startups: seven of the 12 ventures supplemented their web pages with whitepapers or technical documents, while five (Accentis, FluxyOne, Lingon, Path.Era, UNDO) relied exclusively on web-based communication. This variation is itself analytically meaningful, suggesting differences in organizational maturity, target audience sophistication and communicative resourcing. Circularize contributed by far the largest volume of technical content (22,024 words), reflecting its enterprise-oriented positioning and detailed documentation practices. By contrast, startups with web-only footprints relied more heavily on semantic strategies such as narrative framing, metaphor and visual cues, although Table 3 confirms that syntactic strategies such as standardized terminology also appeared across their web pages. These ventures lacked the more elaborate syntactic documentation that whitepapers provide. This compositional asymmetry shaped the cross-case analysis presented in Section 4 and informed the discussion of how boundary-spanning capability scales with organizational resources (Section 5.4).

To prevent larger documents from dominating the analysis, coding was conducted at the startup level, recording whether each strategy was present or absent in each startup’s complete set of materials instead of counting individual occurrences. This means that Circularize’s 22,024 words of technical content carry the same analytical weight in Table 1 as Accentis’s 1,353 words. The relevant unit of analysis is therefore the startup’s communicative repertoire, not the volume of any individual document.

The study employed a multi-modal data collection strategy comprising publicly available digital artifacts and expert interviews to capture both designed communication strategies and the rationale behind those choices.

The analysis included public materials such as product web pages, company-authored whitepapers, technical documentation, and one investor deck related to DPP architecture. Artifacts were selected if they explicitly referenced DPP functionality and demonstrated communicative features relevant to Carlile’s framework, specifically syntactic strategies (e.g. standardized terminology) and semantic translation (e.g. analogies, narratives). Whitepapers were specifically analyzed as communicative artifacts and potential boundary objects that align ventures with regulatory narratives. Webpages were accessed between February 2025–November 2025.

Ten semi-structured interviews were conducted with six startup founders and four specialists working in DPP implementation, sustainability or product design roles. Participants represented diverse sectoral perspectives including fashion and textiles, consumer electronics, built environment, luxury and automotive, Web3 and cross-sectoral DPP consulting. Table 2 provides an overview of all participants, their roles, sector focus, and the distinctive perspective each brought to the analysis. Interviews were guided by a semi-structured protocol organized around five thematic areas:

1. the participant’s role and stakeholder context;

2. the challenges of communicating core technical concepts such as blockchain and traceability;

3. the materials and tools developed to support stakeholder explanation;

4. recurring misunderstandings and the tradeoff between accuracy and accessibility; and

5. reflections on trust-building and breakthrough moments.

The full interview guide is provided in  Appendix 1.

Participants were recruited through direct outreach via professional email and LinkedIn messages, targeting individuals identified through the startup sample as well as broader DPP-focused professional networks. All interviews were conducted via video call and recorded with participant consent, then transcribed verbatim to support line-by-line coding.

Interview duration ranged from 25 to 35 min, which is shorter than is typical in qualitative interview research. This reflects two characteristics of the participant pool. First, all interviewees were senior practitioners with limited availability who agreed to focused, time-bounded conversations rather than open-ended discussions. Second, and more significantly for data quality, participants consistently directed the researcher to materials, their own websites, whitepapers, blog posts and published reports, that elaborated on points raised during the interview. These referenced materials were subsequently incorporated into the digital artifact corpus described above, enabling triangulation between spoken accounts and documented communication strategies. In effect, each interview functioned as an interpretive entry point into a much larger body of self-produced communicative material, mitigating the limitations of the relatively short interview format. Where points required further clarification, brief follow-up exchanges were conducted via email.

An important scope clarification is warranted. This study examines the production side of DPP communication: the strategies startups design and deploy to make their systems understandable. It does not directly examine whether stakeholders actually understand, trust or adopt DPP systems as a result of these strategies. Claims about communicative effectiveness are therefore inferential not demonstrated. This distinction between intended communication and demonstrated impact is acknowledged throughout the findings and identified as a priority for future reception-side research in Section 6.

As shown in Table 2, the participant sample captures a range of organizational functions and sectoral orientations. The six founders brought perspectives on platform design, market positioning and stakeholder onboarding, while the four specialists contributed insights from implementation, sustainability management and supply chain consulting. This diversity of roles ensured that the analysis captured both the design intentions behind communication strategies (founders) and the practical reception and adaptation challenges encountered in deployment (specialists). Notably, participants’ perspectives clustered around two recurring tensions: the challenge of translating technical concepts for nontechnical audiences and the difficulty of positioning DPPs as value-creating tools rather than compliance burdens.

The data were analyzed using an inductive thematic analysis approach, guided by Carlile’s (2004) boundary-spanning framework as a sensitizing lens. The analysis was conducted in three iterative cycles to ensure a balance between theoretical grounding and empirical emergence. First, open coding involved a line-by-line review of all data sources to identify over 40 preliminary codes related to communication strategies and explanations. Second, axial clustering grouped these initial codes into broader categories corresponding to Carlile’s syntactic (e.g. standardization, structuring) and semantic (e.g. metaphor, narrative) boundary-spanning strategies, reducing the code set. Third, refinement led to the final 16 codes (5 syntactic and 11 semantic strategies) capturing the core communication practices. Of these, 14 appear in the web page corpus (Table 3); two codes, Intentional Simplification and Recurring Misunderstandings, emerged only from interview data. The complete codebook is provided in  Appendix 2. Constant comparison ensured conceptual clarity, and the findings were systematically triangulated across digital artifacts and interview narratives to identify consistent patterns and adaptive practices. Coding was performed manually using Microsoft Excel for code tracking and categorization and Microsoft Word for annotation of digital artifacts and interview transcripts. To strengthen analytical rigor, the coding process involved a second coder who independently reviewed the data and developed parallel codes during the open and axial cycles. Discrepancies between the two coders were resolved through discussion, with the third author serving as a third reviewer who examined contested codes and contributed to the refinement of the final code set. This collaborative coding process surfaced interpretive ambiguities early, supported convergence around stable code definitions, and reduced the risk of single-researcher bias. Thematic saturation was assessed within the boundaries of the selected corpus and the coding categories used. After the eighth interview and analysis of roughly three-quarters of the artifact corpus, no new codes emerged. Given the rapidly evolving DPP field and the purposeful sampling strategy employed, saturation across the broader startup ecosystem is not claimed.

The resulting data structure is presented in Table 3, which traces the analytical progression from first-order empirical observations through second-order themes to the two aggregate dimensions that organize the findings.

One analytical challenge that emerged during coding was the occasional blurriness between syntactic and semantic categories. Visual standardization, simplified narratives and contextual examples can simultaneously perform representational and interpretive functions. For instance, a color-coded compliance dashboard is syntactically standardized (consistent visual format) but also semantically translative (converting complex data into an intuitive red-yellow-green signal). In such cases, the coders applied the code corresponding to the primary function within the communicative context, with secondary functions noted in analytical memos. This boundary permeability between strategy types is itself theoretically significant and is discussed further in Section 5.1 as evidence for the bundled deployment pattern.

The study’s trustworthiness was pursued through several strategies aligned with Lincoln and Guba (1985) quality criteria for qualitative research. Credibility was supported through data triangulation across three source types (web pages, whitepapers, interviews), enabling cross-validation of observed patterns. Transferability was pursued through thick description of communicative strategies and contextual detail about the DPP ecosystem. Dependability was supported by maintaining a documented audit trail of coding decisions across the three analytical cycles. Confirmability was enhanced through systematic triangulation and the use of Carlile’s established theoretical framework as a sensitizing lens, reducing the risk of idiosyncratic interpretation.

Participants provided informed verbal consent at the start of each interview, after being briefed on the study’s aims, the use of recordings for transcription, and their right to withdraw at any time. The startups in the artifact corpus are referred to by their actual names because the analysis draws exclusively on publicly available communication materials produced for external audiences; no confidential or proprietary information is reported. Interview participants, by contrast, are referred to by role-based pseudonyms (e.g. Founder 1, Specialist 2) to protect individual identifiability while preserving the analytical relevance of their position.

The empirical findings on how DPP startups navigate syntactic and semantic knowledge boundaries through strategic communication design are presented in this chapter. The analysis of digital artifacts (44,195 words from websites and documents) and interviews (ten practitioners) revealed layered strategies for bridging knowledge gaps deployed across different media and stakeholder contexts. The findings are organized into three complementary analytical layers, each revealing distinct aspects of startup communication strategies. Web pages (Section 4.1) demonstrate how startups prioritize semantic strategies to capture broad stakeholder attention. Whitepapers and technical artifacts (Section 4.2) reveal emphasis on syntactic strategies designed for enterprise and regulatory audiences. Expert interviews (Section 4.3) uncover the strategic reasoning behind these public communications. The cross-case synthesis (Section 4.4) suggests that startups bundle syntactic and semantic strategies deliberately, extending linear accounts of technology communication in pre-regulatory contexts.

The analysis of publicly accessible web pages from twelve startups active in the DPP space revealed a deliberate and structured effort to span both syntactic and semantic knowledge boundaries, in line with Carlile’s (2004) boundary-spanning framework. These web pages function as marketing instruments and designed as prospective boundary objects (Star and Griesemer, 1989), translating complex technical infrastructures into comprehensible, actionable and trustworthy propositions for diverse stakeholders. Below, the observed strategies are examined through the dual lenses of syntactic and semantic boundary-spanning.

Syntactic boundary-spanning strategies focus on establishing common representational formats and terminologies that facilitate unambiguous knowledge transfer. Five distinct syntactic strategies were identified across the web page corpus.

Standardized terminology forms the baseline across all twelve startups, utilizing regulatory anchors like “Digital Product Passport,” “traceability,” “compliance” and “ESPR” to establish a shared lexicon that reduces friction for technically diverse audiences. This vocabulary is visually supported by visual standardization; platforms like TrusTrace and EON deploy modular dashboards and consistent iconography to mirror enterprise software conventions, lowering the cognitive load required to process transparency indicators.

Furthermore, data schema structuring prominently organizes complex information into predefined compliance categories (e.g. “Scope 1, 2, 3 emissions,” “material origin,” and “repair history”), signaling cross-platform interoperability to enterprise buyers. Responsibility is clarified through role definition structures, as seen in FluxyOne, Traced Systems and Accentis, which deploy layout blocks or list formats to delineate workflows for producers, distributors and regulators. Finally, a subset of ventures (e.g. EON, Lingon) develop stakeholder-specific materials such as tailored content layers or customized onboarding flows. As shown in Table 3, this resource-intensive personalization is deployed less consistently due to early-stage capital constraints.

Collectively, these syntactic mechanisms establish the representational common ground necessary to push data across stakeholder boundaries. However, stabilized formats alone cannot resolve the interpretive discrepancies that occur when actors view the same data through different institutional logics, necessitating complementary semantic strategies.

Startups deploy semantic strategies to bridge the interpretive gaps that arise when stakeholders view data through distinct institutional logics. Eight specific semantic mechanisms were identified across the web footprint.

The most pervasive is narrative framing, used by ventures like CTAC and FluxyOne to reframe compliance as a strategic asset (e.g. “Simplify EU Compliance. Empower Your Brand”), which serves a prognostic framing function by positioning DPPs as market opportunities rather than administrative burdens (Benford and Snow, 2000). To anchor these novel configurations, technical metaphors are widely utilized as cognitive bridges; for instance, CTAC explicitly maps a metaphorical transition “from a Bordeaux red booklet to a QR code” to demystify decentralized architectures.

Operational integration is demonstrated via contextual use cases in specific sectors (e.g. Circularize, Traced Systems), while progressive disclosure manages cognitive load by staggering complexity behind initial, accessible calls-to-action. Startups systematically employ simplicity of language, utilizing direct instructions like “Scan and interact” (Lingon) to avoid technical jargon.

Anxieties surrounding data exposure are mitigated via privacy and control framing, with platforms like Path.Era emphasizing selective transparency parameters (e.g. “you control who sees what”). To compel ecosystem participation, motivational framing tasks are executed via emotional and ethical hooks linked to ESG parameters, such as “forced labor prevention” and “supply chain justice” (UNDO, TrusTrace) (Benford and Snow, 2000). Finally, a small number of firms bridge remaining interpretive boundaries through tangible proof of concept demonstrations, offering live scannable interfaces or downloadable playbooks.

By orchestrating these strategies, startups establish an interpretive scaffolding that transforms raw backend records into accessible narratives across divergent stakeholder logics.

Table 4 summarizes the observed frequency of boundary-spanning strategies deployed across the web pages of the twelve startups. Strategies are grouped by boundary type, syntactic or semantic, following Carlile’s (2004) framework. The most frequently applied approaches include standardized terminology (12 / 12), narrative framing (11 / 12), contextual use cases (10 / 12) and progressive disclosure (10 / 12). The pattern suggests a shared communicative logic among startups aiming to make complex regulatory technologies intelligible to diverse stakeholders. Conversely, more resource-intensive strategies such as proof of concept demonstrations (4 / 12) and stakeholder-specific materials (6 / 12) appear less consistently, likely reflecting the additional capability and infrastructure required for their deployment, a constraint particularly relevant for early-stage ventures. Frequency alone does not determine theoretical importance. Proof of concept demonstrations (4 / 12) may bridge semantic gaps more effectively than standardized terminology (12 / 12), which functions as a baseline communicative requirement rather than a strategic differentiator. These frequencies describe patterns within a purposefully selected corpus of DPP startups and should not be interpreted as prevalence estimates for the broader DPP startup population.

These frequencies are based on coded web page content. Many startups use overlapping strategies, reflecting intentional bundling of boundary-spanning practices to enhance clarity and meaning across knowledge domains.

The web page analysis revealed a deliberate orchestration of boundary-spanning strategies. By simultaneously stabilizing representation (syntactic clarity) and fostering interpretation (semantic translation), startups construct communicative scaffolding designed to support understanding, trust and adoption among stakeholders with divergent technical fluency and institutional logics. This dual strategy echoes Carlile’s core assertion: that successful innovation at knowledge boundaries requires not only data transmission, but meaning-making across interpretive divides.

This section analyzes how boundary-spanning strategies adapt in form and function when targeting compliance officers and enterprise clients who require technical depth rather than initial accessibility. The key analytical question is not which strategies appear, but how they shift across media channels. By moving from public web spaces to technical documents, the strategic focus transitions from broad semantic appeals to syntactic formalization, driving narratives from aspirational to evidential configurations, and shifting data schemas from illustrative models to prescriptive structural templates.

Syntactic strategies across technical documents are highly elaborate. Standardized terminology shifts beyond a shared vocabulary (e.g. Circularize, Circulor, Kezzler); whitepapers embed these terms within formal frameworks, explicitly citing ESPR articles or GS1 Digital Link standards to construct verifiable compliance language. Data schema structuring moves from visual configurations to prescriptive technical tables. Documents from EON, TrusTrace and Kezzler outline granular field-level definitions and separate item-, batch- and SKU-level data architectures required for integration. Concurrently, role definition structures provide a prescriptive delineation of actors (producers, recyclers, auditors), explicitly linking each role to system access layers, data validation requirements and interaction paths.

While whitepapers emphasize syntactic precision, they also deploy semantic strategies, though in forms adapted for enterprise and regulatory audiences rather than the broad audiences targeted by web pages.

Narrative framing in whitepapers takes a more evidence-based form. Rather than the aspirational slogans found on web pages (“Empower Your Brand”), whitepapers frame DPPs through regulatory necessity and competitive positioning. CTAC and Kezzler emphasize alignment with the European Green Deal, while Traced Systems presents DPPs as proactive instruments for brand trust, supported by case evidence rather than emotional appeal. This shift from aspirational to evidential framing reflects audience expectations: enterprise buyers require demonstrated value rather than inspirational vision.

Contextual use cases were nearly ubiquitous and more technically detailed than their web page counterparts. Circulor, TrusTrace and CTAC offer domain-specific implementations, textile tracing protocols, battery compliance workflows, fashion product digitization pipelines, that ground abstract DPP functionality in operational specifics. Privacy and control framing was also more technically articulated, with Circularize, Kezzler and Traced Systems detailing specific mechanisms such as encryption protocols, selective disclosure architectures, and zero-knowledge proofs.

The investor deck analyzed (from one startup) exhibited what appears to be a distinct communication logic from whitepapers, though this observation is indicative given the single-artifact basis and would require further cases to establish as a broader pattern. While whitepapers emphasize technical compliance and operational integration, the investor deck prioritized market sizing, competitive differentiation and return-on-investment narratives. This suggests that investor-facing materials constitute a separate communicative genre with its own boundary-spanning requirements, a finding consistent with the broader observation that DPP startups must maintain multiple parallel communication strategies for different institutional audiences.

Overall, whitepapers and technical documents show that the same boundary-spanning strategy can operate differently depending on the communicative context. Narrative framing shifts from aspirational to evidential; data schemas move from illustrative to prescriptive; privacy framing transitions from reassuring to technical. This adaptation reveals that boundary-spanning is not a fixed repertoire but a contextually modulated practice.

The interview data uncovers the operational adjustments, strategic logic and interpretive challenges that occur beneath polished public narratives, tracking how startups iteratively refine their boundaries in real-time.

Across interviews, syntactic boundary-spanning strategies were acknowledged as critical to enabling understanding in multi-actor ecosystems. However, unlike the polished implementations visible in web pages and whitepapers, interview narratives revealed the iterative, often improvised nature of syntactic alignment.

Standardized terminology emerged as a foundational but contested element. Founder 1 explained, “We merge all of that together in what we call a universal compliance tool,” pointing to a deliberate effort to create recognizable linguistic anchors. Yet Specialist 3 cautioned that “terminology should match the user’s environment, not just ours,” highlighting the risk that startup-created vocabulary may not align with operational discourse in client organizations. This tension between creator-defined and user-defined terminology represents an underexplored dimension of syntactic boundary-spanning.

Data schema structuring was discussed not only as a technical requirement but as a communication strategy that must adapt to sectoral data cultures. Founder 3 illustrated this: “We had to build the infrastructure […] we built DPP on fashion and textile,” describing an evolving modular logic. Founder 4 described translating backend data into legible frontend insights: “Users upload data, our dashboard classifies red, green, yellow,” a visual triage system designed for nontechnical users. Specialist 1 described deploying Excel-based templates with pre-filled categories to reduce ambiguity and training costs for manufacturing partners, suggesting that familiar tools can serve as syntactic bridges.

Stakeholder-specific materials took diverse forms. Founder 1 described designing simplified interfaces for nontechnical logistics staff: “We use QR codes for everything […] so nobody has to manually input things.” Specialist 2 emphasized the challenge of addressing varying technical maturity: “When you talk about blockchain, they assume it to be bitcoin […] and they are concerned.” This revealing observation demonstrates how preexisting mental models, particularly around blockchain, create syntactic barriers that require active decoupling before DPP-specific communication can proceed.

While syntactic strategies establish representational clarity, semantic strategies aim to enable meaning-making across divergent frames of reference. The interview data revealed that these strategies are not static repertoires but adaptive practices refined through stakeholder interaction.

Narrative framing was the dominant semantic tool. Founders described repositioning DPPs from compliance instruments to strategic assets. Founder 4 explained: “We turn DPP from a mandatory compliance burden to a tool for business insight,” while Founder 3 emphasized, “You don’t talk about the language of coding […] you talk about value.” Specialist 3 confirmed that reframing the DPP as a “business opportunity” rather than an “admin task” drove engagement with initially skeptical partners. These accounts demonstrate Benford and Snow (2000) prognostic framing in action: startups are actively constructing solution frames that resonate with stakeholder priorities.

Technical metaphors served as cognitive anchors. Participants likened the DPP to a “digital passport” (Founder 1), a “LinkedIn profile for products” (Specialist 1 and 3), and “the next generation of the barcode” (Founder 6). Founder 1 used “stamps” and “travel history” to convey data traceability, while Specialist 2 employed “human passport” analogies for identity logic. These metaphors did not merely simplify; they selectively foregrounded certain DPP features (identity, history, verification) while backgrounding others (decentralization, immutability), revealing the strategic choices embedded in metaphor selection.

Privacy and control framing was essential for managing adoption anxieties. Founder 3 recalled, “They thought they could hide things […] transparency affects business.” Specialist 2 elaborated: “They have a lot of uncertainties […] they are not comfortable revealing some of the data.” Startups responded by emphasizing selective disclosure and modular access rights. Founder 1 stated, “You choose what you share,” while Specialist 2 reassured clients that “blockchain won’t expose confidential data.” These framings convert a potential adoption barrier, fear of transparency, into a governance feature of the system.

A distinctive contribution of the interview data was its revelation of the interpretive gaps and communicative failures that startups must navigate. These challenges were absent from the polished web pages and whitepapers analyzed in Sections 4.1 and 4.2 but proved central to understanding how boundary-spanning operates in practice.

Several founders reported persistent misunderstandings during stakeholder discovery. Founder 6 noted that “clients completely misunderstand DPP value,” often conflating DPPs with existing barcode systems or basic product labeling. This confusion reflects the semantic challenge of distinguishing a genuinely novel concept from superficially similar existing tools. Specialist 2’s observation that stakeholders “assume blockchain to be bitcoin” reveals a parallel challenge at the infrastructure level: preexisting associations with cryptocurrency speculation undermine the credibility of blockchain-based traceability.

Startups addressed these misunderstandings through progressive disclosure and pain-point-first engagement. Founder 6 described the approach: “Start with their pain points […] then reveal how DPP can solve them.” This strategy inverts the typical technology communication sequence, rather than explaining the system first and benefits second, startups begin with stakeholder problems and work backward to the DPP solution. This approach aligns with Callon’s (1986) concept of problematization: startups must first establish that a problem exists and that stakeholders need the proposed solution before technical explanation becomes meaningful.

Founder 6 also highlighted the current gap in communication infrastructure: “We need blog posts, use cases, storytelling […] we haven’t produced enough yet.” This self-assessment reveals that boundary-spanning is a design and resource challenge, early-stage startups may lack the capacity to produce the full range of prospective boundary objects needed for effective multi-stakeholder communication.

Proof of concept demonstrations emerged as particularly effective for overcoming interpretive resistance. Specialist 2 noted that “pilot testing with an Italian textile manufacturer helped sell the concept,” while Founder 6 described a “Connected Fashion” application that authenticated a physical jacket in the metaverse. These experiential encounters, where stakeholders interact with functioning DPP systems rather than reading about them, were described by participants as more effective for bridging semantic gaps than textual or visual communication alone.

When viewed collectively, the findings across web pages, whitepapers and interviews reveal a layered, intentional use of boundary-spanning strategies to facilitate DPP adoption. The synthesis identifies three principal patterns.

First, strategy emphasis varies systematically by medium and audience. Web pages favor concise, visually engaging, and metaphor-driven communication, prioritizing semantic strategies that capture broad attention and construct initial credibility. Whitepapers rely on syntactic elaboration, detailed terminology, data schemas and technical definitions, reflecting the requirements of enterprise and compliance audiences. Interviews reveal the adaptive, iterative practices that underpin both public-facing outputs, including the strategic reasoning behind framing choices and the improvizational responses to stakeholder misunderstandings.

Second, certain strategies are universal while others are contingent. Standardized terminology, narrative framing and contextual use cases were observed across all data types and nearly all startups. These appear to constitute a baseline communicative repertoire for DPP ventures. In contrast, resource-intensive strategies, stakeholder-specific materials, interactive demos and detailed role definitions, were deployed less consistently, appearing primarily in more mature or better-resourced startups. This variation suggests that boundary-spanning capability scales with organizational resources, a finding with implications for startup strategy and investor expectations.

Third, and most significantly, syntactic and semantic strategies are bundled rather than sequenced. The dominant pattern across the data is simultaneous deployment of both strategy types, even within a single communication artifact. A web page that uses standardized ESPR terminology (syntactic) alongside narrative framing about “empowering your brand” (semantic) is not transitioning between boundary types but addressing both simultaneously.

This bundled approach extends sequential accounts of boundary-spanning by showing how, in the pre-regulatory DPP context, syntactic and semantic boundaries are addressed simultaneously because both representational formats and interpretive frameworks remain under construction.

To demonstrate how syntactic and semantic strategies are bundled within specific communication artifacts, three illustrative cases are presented below.

Case 1: CTAC’s web page. CTAC’s primary web page deploys syntactic strategies through consistent use of ESPR-aligned terminology (“Digital Product Passport,” “compliance,” “traceability”) and structured role definitions for producers and regulators. Simultaneously, the same page employs semantic strategies through the metaphorical transition “from a Bordeaux red booklet to a QR code,” which translates the abstract concept of regulatory digitization into a vivid, recognizable image. The tagline “Simplify EU Compliance. Empower Your Brand” reframes compliance as strategic opportunity. Within a single page, the user encounters standardized regulatory language (syntactic), narrative reframing (semantic) and metaphorical simplification (semantic), all addressing both representational clarity and interpretive alignment at once.

Case 2: Kezzler’s whitepaper. Kezzler’s technical documentation provides detailed data schema structuring at item, batch and SKU levels, aligned with GS1 Digital Link standards (syntactic). Role definitions specify distinct access rights and data responsibilities for producers, auditors and consumers (syntactic). Yet the same document frames DPPs as instruments for ESG compliance and ethical supply chain management, invoking prevention of forced labor and proof of recycled content as value propositions (semantic). Privacy and control framing reassures enterprise readers through references to encryption protocols and selective disclosure mechanisms (semantic). The whitepaper thus bundles technical specification with value-laden narrative within a single artifact designed for enterprise and regulatory audiences.

Case 3: Founder 6’s communication architecture. Interview data from Founder 6 revealed a multi-layered communication design that bundles strategies across touchpoints. At the syntactic level, the startup uses blockchain-based product authentication with standardized data fields. At the semantic level, Founder 6 described DPPs as “the next generation of the barcode” and emphasized that “one scan, countless impacts,” combining metaphorical simplification with progressive disclosure. Their “Connected Fashion” demo, which authenticated a physical jacket in the metaverse, functioned as an experiential translation device that simultaneously demonstrated technical capability (syntactic) and constructed meaning through tangible interaction (semantic). Notably, Founder 6 also described a deliberate pain-point-first engagement sequence: “start with their pain points, then reveal how DPP can solve them,” illustrating that the bundling is strategically sequenced within individual stakeholder encounters even as syntactic and semantic strategies co-occur.

These cases demonstrate that bundling is not accidental but a designed communication architecture. Startups combine regulatory vocabulary, data structures, metaphors, narratives and ethical appeals within the same artifacts, instead of presenting them separately. Figure 1 synthesizes these patterns into a conceptual model, mapping how syntactic and semantic strategies are bundled across communication media, stakeholder groups and adoption stages.

The central empirical finding of this study is that DPP startups deploy syntactic and semantic strategies simultaneously, not in isolation. This finding supports and contextually extends Carlile’s (2004) framework. The core insight of the boundary-spanning framework, that successful knowledge transfer requires both structural alignment and interpretive engagement, was confirmed across all data sources. Interviewees described cycles of explanation, simplification and reinterpretation as stakeholders moved from onboarding to adoption, supporting the proposition that boundary-spanning is recursive rather than linear.

The simultaneous deployment pattern observed here represents a contextual extension of the framework not a contradiction. In Carlile’s original manufacturing setting, knowledge boundaries exist between established communities of practice with stable expertise domains, and boundary-spanning can proceed in identifiable phases. In the pre-regulatory DPP context, the communities of practice themselves are still forming. There is no established “DPP professional community” with settled terminology and shared interpretive frames. Startups must therefore construct both the representational infrastructure (syntactic) and the interpretive framework (semantic) at the same time, because neither exists independently of the other. A standardized term like “Digital Product Passport” has no fixed syntactic meaning without the semantic narrative that explains its purpose and value.

From a knowledge management perspective, this bundling can be understood through the lens of absorptive capacity (Cohen and Levinthal, 1990; Zahra and George, 2002). Syntactic strategies like standardized terminology and data schemas build the representational foundation stakeholders need to acquire DPP knowledge (potential absorptive capacity). Semantic strategies like narrative framing and contextual use cases then support the transformation of that knowledge into operational understanding (realized absorptive capacity). The bundled deployment pattern reflects the simultaneous need for both capacity types in contexts where stakeholders lack prior related knowledge of the system being communicated.

This finding also validates technology adoption research. The finding that perceived complexity varies across stakeholder groups (Magsamen-Conrad and Dillon, 2020) was directly confirmed through interview data, where founders described calibrating communication not just to technical expertise but to institutional logics: regulators seek compliance verification while consumers want sustainability validation. Becchi et al. (2026) identification of DPPs as capability-dependent knowledge systems reinforces this point from the demand side, suggesting that the bundled strategies observed here represent the supply-side mechanisms through which startups attempt to construct the absorptive capacity their stakeholders require.

The findings reveal a recurrent an emergent conceptualization termed “trust translation”: the process by which startups convert the cryptographic guarantees embedded in blockchain infrastructure into accessible narratives about transparency, accountability and sustainability. Trust translation involves three simultaneous transformations: converting cryptographic trust into institutional compliance assurance; converting technical data into experiential understanding via visual interfaces; and repositioning mandatory compliance obligations as strategic value narratives.

Trust translation is theoretically distinct across three structural dimensions. First, it differs from purely discursive mechanisms like sensegiving (Gioia and Chittipeddi, 1991) and framing (Benford and Snow, 2000) because it requires a technical infrastructure (blockchain architectures and data schemas) as a material precondition, transforming the underlying nature of system trust rather than simply adjusting persuasive messaging. Second, unlike legitimacy work (Suchman, 1995) or boundary work (Gieryn, 1983), its core operational target is bridging cross-domain comprehension gaps rather than pursuing normative acceptance or defending domain boundaries. Third, it diverges from technology translation (Callon, 1986) and generic interorganizational trust-building because it specifically converts the material substrate of backend cryptographic verification into a legible baseline for institutional belief, rather than focusing on actor network enrollment.

Consequently, trust translation operates as a critical knowledge intermediation role that bridges the structural gap between potential and realized absorptive capacity (Zahra and George, 2002). It empowers heterogeneous supply chain actors to move from passive data access to operational realization. The ethical and emotional hooks observed in the corpus serve a vital knowledge governance function (Foss, 2007), anchoring the normative legitimacy required for transparent cross-boundary knowledge integration.

DPP communication is about governing knowledge flows across organizational boundaries, not just making systems understandable across organizational boundaries. DPP ecosystems raise fundamental knowledge governance questions (Foss, 2007): who controls data access, who validates sustainability claims, who bears responsibility for data quality, and how is proprietary knowledge protected within a transparency-mandated system.

The privacy and control framing strategies identified in this study address these governance concerns communicatively. Statements such as “you control who sees what” and emphasis on selective disclosure and zero-knowledge proofs construct narratives about data ownership that manage the tension between mandated transparency and commercial confidentiality. These are more than reassurance tactics. They represent the communicative construction of knowledge governance arrangements in an ecosystem where formal governance structures have not yet been established by regulators.

This observation connects to a deeper theoretical point. The identified strategy bundles suggest that unresolved regulatory tensions – such as data ownership conflicts, competitive fears and compliance cost asymmetries – are managed communicatively by startups, allowing semantic translation to function discursively as pragmatic boundary management. This suggests that the boundary between semantic and pragmatic levels in Carlile’s framework may be more permeable than the original formulation implies, particularly in pre-regulatory contexts where institutional rules are still under construction.

For knowledge management scholarship, this finding extends the understanding of how knowledge governance operates in emerging ecosystems. Unlike established interorganizational networks where governance mechanisms such as contracts, standards and auditing procedures formalize knowledge flows, DPP ecosystems rely on communicative governance: startups use narrative framing, privacy assurances and role definitions to establish provisional governance arrangements that precede formal regulation. This positions communication design as what might be understood as a form of proto-governance, potentially constructing the normative and procedural groundwork within which formal knowledge governance structures may eventually be embedded. This proposition requires further empirical investigation through longitudinal research that tracks whether communicative arrangements solidify into enforceable governance structures.

For startups and regulatory technology ventures, communication design should be treated as strategic knowledge infrastructure not a secondary marketing function. The bundled deployment of syntactic and semantic strategies should be intentional, with specific combinations tailored to stakeholder absorptive capacity and adoption stage. Concretely, a bundled communication architecture should include:

• standardized regulatory glossaries and data schemas aligned with ESPR requirements as syntactic foundations;

• stakeholder-specific onboarding pathways that begin with sector-relevant pain points and progressively introduce technical complexity;

• interactive demonstrations and proof-of-concept pilots that convert potential absorptive capacity into realized absorptive capacity through experiential learning; and

• privacy governance narratives that preemptively address data ownership concerns.

For policymakers, the findings indicate that DPP adoption will depend partly on how effectively regulatory requirements are translated by intermediary actors. Policymakers should consider providing communication support tools alongside technical requirements: standardized DPP glossaries, sector-specific implementation guides, consumer-facing explanatory templates, and training modules for SMEs with limited absorptive capacity. The connection to SDG 12 (Responsible Consumption and Production) is direct: DPPs support SDG Target 12.8 by making product sustainability data accessible to consumers, but this accessibility depends on communicative design, not data availability alone. The gap between data provision and knowledge activation identified by Becchi et al. (2026) reinforces the need for policy attention to the communicative infrastructure surrounding DPP presupposes.

For supply chain managers evaluating DPP vendors, communication quality directly signals implementation readiness; vendors demonstrating bundled boundary-spanning are communicatively better equipped for multi-stakeholder deployment than those relying on technical sophistication alone. Furthermore, this communication varies systematically by stakeholder type: producer-facing communication relies more heavily on syntactic strategies: data schemas, role definitions, compliance terminology and structured dashboards that integrate with existing enterprise systems. Consumer-facing communication, by contrast, prioritizes semantic strategies: simplified language, emotional and ethical hooks, proof-of-concept demos and progressive disclosure that is designed to build understanding incrementally. This producer-consumer asymmetry mirrors the distinction between potential and realized absorptive capacity (Zahra and George, 2002): producers typically possess higher prior related knowledge and require representational alignment, while consumers require more extensive interpretive support to transform product data into meaningful purchasing decisions.

Several boundary limitations shape the interpretation of these findings and outline an agenda for future research. First, the qualitative sample is bounded by 12 early-stage European startups. Future work should compare these ventures with established incumbent firms and introduce cross-cultural designs to assess how geographic variations in regulatory landscapes affect communicative setups across EU, US and Asian contexts. Second, this cross-sectional study focuses exclusively on the production side of DPP communication, mapping designed strategies rather than stakeholder reception. Experimental or survey-based reception studies are required to determine whether strategies like progressive disclosure or technical metaphors successfully enhance stakeholder comprehension, trust and adoption intentions. Third, the 25–35 min interview duration provides targeted strategic insights but represents a snapshot of early ecosystem practices. Longitudinal research designs should track startups across shifting regulatory timelines to capture how communication mechanisms adjust as DPPs transition from anticipatory voluntary tools to mandated compliance protocols. Finally, this analysis isolates syntactic and semantic knowledge boundaries, leaving Carlile’s (2004) pragmatic, interest-based layer outside its empirical scope. While the observed privacy configurations discursively manage underlying industrial tensions regarding data exposure and compliance cost asymmetries, they do not resolve them structurally. Ethnographic or action-research methodologies investigating real-time supplier negotiations and power dynamics represent a vital frontier to integrate pragmatic transformations into the current framework, while large-scale quantitative surveys can test the explicit relationship between specific strategy bundles and real adoption rates.

This study investigated how early-stage DPP ventures bridge knowledge gaps to help diverse stakeholders understand and adopt their systems. Drawing on Carlile’s (2004) boundary-spanning theory and a multi-modal qualitative analysis of web pages, whitepapers and interviews across 12 European startups, the research revealed that boundary-spanning in the DPP context is a bundled, adaptive and strategically layered process rather than a sequential progression from structural to interpretive alignment.

The study’s primary theoretical contribution is the concept of bundled boundary-spanning: the simultaneous deployment of syntactic and semantic strategies in pre-regulatory contexts where both representational formats and interpretive frameworks must be constructed at once. This extends Carlile’s framework beyond established organizational settings to anticipatory regulatory environments. Complementing this, the concept of trust translation captures how startups convert cryptographic guarantees into accessible narratives, a communicative function essential for emerging technologies built on infrastructure (blockchain) that is technically robust but institutionally illegible.

For practice, the central implication is clear: communication design is not a secondary marketing function but strategic infrastructure for DPP adoption. Startups that treat boundary-spanning as a deliberate, multi-modal competence, rather than an afterthought to technical development, are better positioned to navigate the interpretive challenges of emerging regulatory ecosystems. As DPP presupposes move from policy aspiration to operational reality, the ability to make complex systems understandable across stakeholder divides will determine not only which platforms succeed commercially but whether the broader circular economy transition achieves the transparency and participation it requires.

The concept of trust translation may extend beyond DPPs to other technology domains where cryptographic or algorithmic guarantees must be converted into stakeholder understanding before adoption can proceed. Blockchain-based carbon credit systems, AI-driven medical diagnostics, and algorithmic credit scoring all involve technically robust systems whose trust properties remain opaque to end users. In each case, intermediary actors face the same challenge: converting technical assurance into institutional confidence through designed communication.

Serdar Temiz is based at the Department of Industrial Engineering and Management, Uppsala University, Uppsala, Sweden.

Ayse Ege Palaz is based at the Department of Business Studies, Uppsala University, Uppsala, Sweden.

Anders Brantnell is based at the Department of Industrial Engineering and Management, Uppsala University, Uppsala, Sweden, and Department of Women’s and Children’s Health, Uppsala University, Uppsala, Sweden.

This research was conducted in accordance with university’s guidelines for non-medical research involving professional participants acting in their organizational capacity. Because the study involved no vulnerable populations, sensitive personal data or experimental interventions, formal ethical review board approval was not required. All participants provided informed verbal consent prior to data collection.

The following semi-structured interview guide was used across all ten interviews. Questions were adapted to participant role (founder vs specialist) and follow-up probes were used as appropriate to the conversation flow.

Can you tell me briefly about your role and how you have been involved in DPP development or implementation?

Who are your typical stakeholders, clients, partners, or regulators, and how technical are they?

Do you find that some concepts (e.g. blockchain, traceability) are harder to explain than others?

Have you developed a “go-to” metaphor or analogy when talking about your product?

What materials or tools have you created to help explain your system (e.g. whitepapers, onboarding flows, videos, diagrams)?

Are these designed more for investors, customers, or regulators?

Can you share an example of something that really worked well in helping others understand?

Have you encountered recurring misunderstandings about your product or its capabilities?

Have you ever had to intentionally simplify or omit details to avoid confusion?

How do you balance being accurate versus being understandable?

Has your communication strategy changed as your product matured or the market evolved?

If someone misunderstands your tech but buys into it anyway, is that acceptable, or problematic?

What would you say is the most important thing to get across to gain trust and understanding?

Is there anything you wish your customers or the public could better understand about DPPs?

Can you share a moment when your team successfully made a breakthrough in explaining a complex idea?

The codebook below presents the final set of 16 codes derived from the three-cycle thematic analysis described in Section 3.3. Codes are grouped by boundary type (syntactic and semantic) and indicate the data source(s) in which each code was observed: “Materials” refers to digital artifacts (web pages, whitepapers, investor deck), “Interviews” refers to interview data, and “Both” indicates codes observed across both source types.