Managing Data Sovereignty: An Organizational Competence for Successful Open Value Creation
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Moschko, Lukas; Blazevic, Vera; Piller, Frank T. Article — Published Version Managing Data Sovereignty: An Organizational Competence for Successful Open Value Creation R&D Management Provided in Cooperation with: John Wiley & Sons Suggested Citation: Moschko, Lukas; Blazevic, Vera; Piller, Frank T. (2024) : Managing Data Sovereignty: An Organizational Competence for Successful Open Value Creation, R&D Management, ISSN 1467-9310, Wiley, Hoboken, NJ, Vol. 55, Iss. 4, pp. 1124-1137, https://doi.org/10.1111/radm.12740 This Version is available at: https://hdl.handle.net/10419/329796 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/
R&D Management, 2025; 55:1124–1137 https://doi.org/10.1111/radm.12740 1124 R&D Management RESEARCH ARTICLE OPEN ACCESS Big Data, Open Data, and Open Innovation Managing Data Sovereignty: An Organizational Competence for Successful Open Value Creation LukasMoschko1,2 | VeraBlazevic2,3 | FrankT.Piller2 1Robert Bosch GmbH, Gerlingen- Schillerhohe, Germany | 2School of Business and Economics, Institute for Technology and Innovation Management, RWTH Aachen University, Aachen, Germany | 3Institute for Management Research, Radboud University Nijmegen, Nijmegen, The Netherlands Correspondence: Frank T. Piller ([email protected]) Received: 2 November 2023 | Revised: 10 October 2024 | Accepted: 19 November 2024 Funding: This work was supported by Deutsche Forschungsgemeinschaft. Keywords: data driven value creation| data sovereignty| industrial incumbents| open value creation| strategyas- practice ABSTRACT Managing big data benefits organizations via efficient and effective datadriven decision making, productivity gains, customization potential, or innovation opportunities. Datadriven innovation opportunities are particularly valuable in open value creation (OVC), that is, the collaboration of independent stakeholders who open their boundaries to share data and knowledge to jointly create value they could not have achieved in isolation. At the same time, however, incumbents often struggle with the implementation of data sharing in the broader stakeholder ecosystem. In this exploratory study, based on an abductive qualitative study with 27 expert interviews and three industry roundtables with 19 experts, we explore how the concept of organizational data sovereignty could serve as an organizational competency to address the data challenges for OVC. Using the strategyas- practice framework, we define organizational data sovereignty as the selfdetermined and intentional exercise of control over an organization's data assets and show which data sovereignty practices, in line with practitioner and praxis conditions, support interorganizational sharing and management of big data in OVC. Specifically, we identify key practices for implementing data sovereignty, such as constituting the special value of data and establishing a consistent value assessment for data. When designed and applied in the right organizational framework that allows for openness, agility, and interdisciplinarity, they enable industrial enterprises to realize the potential of OVC. 1 | Introduction Many companies today are pursuing digitalization ambitions, such as complementing physical goods with digital offerings or using data streams from their processes for new types of innovation (Ceipek et al. 2021; Petruzzelli, Murgia, and Parmentola2022). These initiatives are fundamentally based on “big data,” that is, large and complex data sets, including structured and unstructured data, that are characterized by their large volume, great variety, high velocity of change, and demand for high veracity (Cappa etal. 2021). One strategy of companies to increase the value of big data is to pool and share large amounts of data when one company's data does not provide enough volume, variety, or veracity, as typical for training artificial intelligence (AI) or machine learning (ML) models (Dąbrowska etal.2022; van Dyck etal.2023; Rindfleisch, O'Hern, and Sachdev2017). Therefore, big data is not limited to an intraorganizational perspective, as it often requires extended interactions with external stakeholders such as suppliers, customers, or complementors, up to seizing opportunities for joint value creation and value capture based on data exchange (König2017; Minssen, Rajam, and Bogers 2020; Trabucchi, Buganza, and Pellizzoni 2017). In the context of open innovation, the idea of open value creation (OVC) involves opening the boundaries between different organizations to share information and knowledge This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2024 The Author(s). R&D Management published by RADMA and John Wiley & Sons Ltd.
1125 enabled by data sharing (Cappa et al. 2019; Chesbrough, Lettl, and Ritter 2018). OVC involves collaborative efforts between independent stakeholders who rely on their capabilities for joint value creation and capture (Chesbrough, Lettl, and Ritter2018; Bharadwaj etal.2013). Its benefits have been demonstrated in different settings and for different types of stakeholders, including suppliers, customers, competitors, or research institutions (Alexy etal.2018; Barczak etal.2022; Minssen, Rajam, and Bogers2020; Petruzzelli, Murgia, and Parmentola2022). A recent example of such an open exchange of large amounts of industrial data is the emergence of “interconnected digital twins.” Digital twins are virtual representations of industrial assets and processes that are updated and synchronized with a certain specificity and frequency throughout their lifecycle (Fukawa and Rindfleisch 2023). Digital twins are increasingly interconnected, which means that they are shared between organizations to facilitate more effective decision making, enable more efficient processes, and develop new products and services (van Dyck etal.2023). Companies such as Siemens or Nivida use the term “industrial metaverse” for this OVC among a large number of industrial actors (Endres, Indulska, and Ghosh2024). These calls for open data sharing with external stakeholders contrast with the strategic behavior of many industrial incumbents, which tend to take a more restrictive approach (Smith and Beretta2021; Moschko, Blazevic, and Piller2023). Many firms view data, and the strategic advantages associated with it, as a proprietary resource. Openness is perceived as a threat to their competitive advantage (Alexy etal.2018). Moreover, data sharing can be an invasion of privacy and a loss of confidential or competitively valuable information (Betzing etal.2020; Hummel etal.2021; Trabucchi etal.2023). In addition, organizations must first establish an appropriate infrastructure for collecting, storing, sharing, and analyzing data. They also consider legal and insurance requirements to protect themselves from claims for damages because of data breaches or to protect intellectual property (Cappa etal.2021; Trabucchi, Buganza, and Pellizzoni 2017). Therefore, data governance is crucial to manage the opportunities of data openness as well as the fear of losing data control (Jarke, Otto, and Ram2019; Cappa etal.2022). In this context, we propose the concept of data sovereignty as a framework for managers to develop guidelines on how to benefit from big data for innovation and OVC while mitigating the risks of data use (e.g., loss of reputation because of data scandals or loss of competitive advantage because of data breaches). Data sovereignty can be understood as an organization's ability “of being entirely selfdetermined with regard to its data” (Otto etal.2017, 86). Data sovereignty has recently received much attention in technology policy discourses at the governmental level (Couture and Toupin2019; Hummel etal.2021), often addressing citizens' sovereignty over their personal data. Research in information systems has highlighted the importance of data sovereignty for the cocreation of value, for example, on industrial platforms or in the “Internet of Things” (Koren etal.2021; Otto2019). However, in (innovation) management research, the idea of data sovereignty as a framework for balancing control and openness in innovation ecosystems has not yet been explored (Rummel, Hüsig, and Steinhauser2022). In particular, we lack theory on how data sovereignty can enable OVC and datadriven innovation. Furthermore, we lack knowledge on how firms can build and maintain organizational data sovereignty. The motivation of our paper is to address these gaps. We consider data sovereignty as an organizational competency in the context of datadriven OVC, responding to calls in previous research to investigate our research question of how data sovereignty enables OVC, especially for industry incumbents (Hummel etal.2021), and how this competency can be built (Zrenner etal.2019). In doing so, we advance the theory of the openness versus control debate in interorganizational innovation management (Alexy etal.2018; Ritala and Stefan2021) through the concept of organizational data sovereignty. We provide a finegrained framework for data governance in innovation management, shedding light on how the mundane, everyday practices of innovation practitioners shape and refine organizational decisions about which data elements to open or protect. Our work explains how these microlevel practices, taken together, enable organizations to strategically calibrate their data boundaries to maximize value creation and capture potential. We also extend the current discourse on data sovereignty in the policy literature by introducing a managerial perspective. Furthermore, we contribute to previous research on essential competencies in open innovation and value creation in firms, especially in the context of digitization (Dąbrowska etal.2022; Chesbrough, Lettl, and Ritter2018). To answer our research question, we rely on an intensive abductive qualitative study with 27 indepth interviews and a post hoc data collection in the form of three industry roundtables with 19 experts from different industrial firms, enablers (such as software or IT providers), public institutions and associations involved in industrial digitization activities. Theoretically, we draw on the strategyas- practice (S- as- P) framework as our theoretical lens. S- as- P creates a shared understanding of the focal actors involved, the practices and routines that guide their actions, the actual practice including the context, and their interdependencies (Whittington2014) to explain how their interactions in everyday situations generate strategic implications for organizations (Arvidsson, Holmström, and Lyytinen2014; Mintzberg1973). S- as- P has already proven to be an appropriate metatheory for digital transformation research (Chanias, Myers, and Hess 2019; Pelletier and Raymond2020). We contribute to the existing literature at the intersection of data, openness, and innovation by focusing on the practices (i.e., actual actions) of innovation managers in managing the challenges and risks of interorganizational data sharing, such as regulatory compliance, data security risks, complexities of data localization and access, or organizational resistance to data sharing. Although the praxis of digital innovation activities in industrial incumbents (Moschko, Blazevic, and Piller2023) as well as the role of practitioners have already been considered in previous research (Kurzhals, Graf- Vlachy, and König2020; Lievens and Blazevic2021), we still consider these elements to contribute to our understanding of how the interaction of these elements
1126 R&D Management, 2025 in everyday situations generates strategic implications for managing the challenges and risks associated with data sovereignty and OVC. 2 | Theoretical Background Historically, the term sovereignty has its origins in constitutional politics and law. It is increasingly used in academic and policy publications on digital transformation or Industry 4.0, along with terms such as data, digital, or technology sovereignty (Couture and Toupin 2019; Dąbrowska et al. 2022; Hummel etal.2021). In general, sovereignty refers to “supreme and independent power or authority in government, as possessed or claimed by a state or community” (Dictionary2023). In the context of big data, data sovereignty is considered an active competency (Alboaie and Cosovan2017; Esposito etal.2018), as it requires engagement with relevant data of an organization and its ecosystem. Data sovereignty thus contrasts with related terms such as data privacy or data protection, which imply a passive role of the stakeholders involved (Jarke, Otto, and Ram2019). Data sovereignty requires companies to be aware of their data and its value to their business, but also to be willing to share data with other stakeholders in the context of OVC. Therefore, the term is associated with innovation (Hummel et al. 2021; König 2017) and the goal of “enabling data richness” (Jarke, Otto, and Ram2019, 550). Hence, we propose data sovereignty as a meaningful framework to study and manage the interconnected relationship between big data and open innovation, which is the focus of this special issue (Cappa etal.2022). However, there is a lack of clarity about how organizations can achieve a higher degree of data sovereignty (Zrenner etal.2019). First, in an industrial context, the meaning and significance of data depends on its context and use (Esposito etal.2018). Although data from one company's production environment may be of particular importance to that company, the same data may not be of additional value to another company. The technical design of IT systems also plays a role. For example, a centralized IT system with uniform data formats and storage locations facilitates discoverability and processability, but conflicts with stakeholders' desire for control and sovereignty over their data. Similarly, legal uncertainties often arise because geographic location plays an important role in data transfer and storage, creating ambiguity about the application of different (national) laws on data transfer and use (Trabucchi etal.2023). This diversity of data sovereignty challenges suggests an industrial practice through “a mix of technical approaches, methods, and governance rules” (Jarke2017, 12). This includes an awareness of the nature of data among the stakeholders involved (König 2017) and the consequences of its use. Similarly, the technical parameters of an IT infrastructure could enable data sharing via standardized architectures and interfaces without sacrificing ownership control (Otto 2019). Furthermore, (national) legal frameworks for companies and industryspecific jurisdictional requirements are pointed out as drivers of data sovereignty (Vaile2014), as well as individual training among the actors involved (Meyer2016) to fight organizational resistance. The various data sovereignty challenges and potential solutions ask for a strategic framework, in which we propose organizational data sovereignty to describe an organization's competency in the selfdetermined use and handling of data. Organizational data sovereignty requires not only a technological consideration in companies, but rather competencies and resources for firms' (digital) innovation and OVC management. Here, the S- as- P perspective offers the opportunity to understand how the mundane, everyday practices of data sharing for (open) value creation enacts a company's digital strategy (Arvidsson and Holmström 2017; Besson and Rowe 2012; Bharadwaj etal.2013). In this view, “strategy is conceptualized as a situated, socially accomplished activity, while strategizing comprises those actions, interactions and negotiations of multiple actors and the situated practices that they draw upon in accomplishing that activity” (Jarzabkowski, Balogun, and Seidl2007). OVC between interorganizational stakeholders necessitates strategic practices from innovation managers to navigate the diverse concerns and interests at play (Reypens, Lievens and Blazevic2021). Innovation managers must employ a repertoire of data sovereignty practices to facilitate secure data sharing that resonate with their own as well as stakeholders' concerns. To do so, the S- as- P discourse pays particular attention to three essential elements (Huang etal.2014; Jarzabkowski2004): – Practitioners are the key actors in initial and ongoing strategizing who are part of the focal organization or external, relevant stakeholders. – Their actions are directed by practices that can be characterized as established explicit or implicit processes or routines that can be traced back to previous norms and traditions within and outside the organization. – The praxis describes the set of de facto activities of creating and enacting strategy, which in reality may deviate from practices, for example, because of unpredicted incidents. In the next section, we use this differentiation as an analytical framework to analyze exploratory findings on how different firms built and managed data sovereignty when using big data for OVC (Chanias, Myers, and Hess 2019; Pelletier and Raymond2020). 3 | Research Method To explore from an (innovation) management perspective how data sovereignty can be defined and built in industrial companies to enable OVC, we rely on a qualitative, abductive design of expert interviews and roundtable (focus group) discussions (Edmondson and McManus2007; Sætre and Van de Ven2021). We engaged in an iterative process of moving back and forth between empirical observations and our evolving theoretical understanding of organizational data sovereignty. This abductive reasoning allowed us to continuously refine our theoretical conceptualization based on the insights and perspectives shared by the experts in the main study and the roundtable participants in the post hoc study. By adopting this adaptive approach, we were able to account for unexpected findings, incorporate contextual nuances, and develop a more comprehensive understanding of our phenomenon (Bamberger 2018). Our sample consists mainly of industrial incumbents, but also of socalled digitization
1127 enablers (e.g., software or network technology providers), public institutions and associations dealing with data sovereignty issues (e.g., research institutions or business associations). Prior to the data collection, all authors were already actively involved in the field of digital innovation and transformation. Although the first and third authors regularly attended practitioner meetings to discuss issues related to datadriven, open innovation, the second author was not as involved and therefore maintained a more distant, objective perspective. A total of 27 interviews were conducted. Table1 provides an overview of all interviews conducted, including what type of organizations interviewees worked at. The interviewees all held a senior or executive position related to digitization, and involved in the use of data to create value in an intra- or interorganizational environment. These employees put organizational data sovereignty into practice in their daily tasks and routines and hence have the agency to implement datadriven OVC projects. Although we followed an interview guide, the direction of the conversation was based on the experts' preferred topics (Spradley2016). More detailed questions were asked about data sharing, collaboration, the companies' competencies in data processing and analysis, as well as tensions and how to deal with them. Finally, we asked in a very open way what could be improved with regard to (the implementation of) digitization activities. We deliberately did not use terms such as data or digital sovereignty to avoid onedimensional or exclusively technologyoriented answers and socially desirable answers. The recorded interviews were transcribed verbatim. TABLE 1 | Expert interviews conducted. Expert # Size Category Position Duration 1Large Industry Senior Manager Innovation 51:37 2Large Industry Manager Digital Activation & Enablement 69:44 3Large Industry Head of Digitalization—Production & Assets 58:02 4Large Industry Head of Innovation Management 56:12 5Large Industry Business Operations Leader 70:32 6Large Industry Project Engineer 64:49 7SME Industry Head of IT 84:03 8Large Industry Senior IoT & Digital Innovation Manager 41:26 9Large Enabler Vice President External Cooperation 53:30 10 Large Industry Global Strategy & Business Dev. Manager 74:45 11 SME Misc. Innovation Manager 56:22 12 Large Industry Manager for Digitalization 66:53 13 Large Enabler Manager Digital SC & Industrie 4.0 43:54 14 Large Enabler Head of Logistics 70:10 15 Large Industry Innovation Culture & Open Innovation 52:55 16 Large Industry Digital Transformation Manager 100:19 17 SME Industry Managing Director 37:12 18 Large Industry Senior Digital Innovation Manager 62:33 19 Large Industry Head of Digital Lab 52:37 20 Large Industry Vice President Corporate Industry 4.0 68:23 21 SME Enabler Head of Product Management 55:58 22 Large Industry Digital Project Manager 53:02 23 SME Misc. Managing Director 36:08 24 SME Enabler Head of Digital Business Models 33:29 25 N/A Public Org. Head of Department Digitization 28:50 26 N/A Public Org. Executive Director of the institute 45:20 27 N/A Public Org. Software Developer 41:00 Abbreviations: Misc., miscellaneous companies; N/A, not applicable; Public Org., public organizations and associations.
1128 R&D Management, 2025 The data analysis followed an abductive approach to capture the perspectives of the informants. In a first step of analysis, we started with our broad theoretical understanding of data sovereignty to guide our analysis, but we maintained flexibility to identify novel, datadriven insights. As we iteratively analyzed the data, we refined our coding structure to incorporate unexpected themes and patterns that emerged. This resulted in a coding scheme that reflected both theoretical underpinnings and surprising, datadriven findings. These codes were then consolidated into categories, which are presented in Table2. We critically reviewed the results of this analysis, first individually and then collectively. In a second step of the analysis and coding, we applied S- as- P as an appropriate metatheoretical perspective for further interpretation of the findings. This helped to categorize our findings into an established framework and allowed us to consider interrelationships between different findings (Sætre and Van de Ven2021; Jarzabkowski, Seidl, and Balogun2022). Therefore, the three elements of the S- as- P literature (practices, practitioners, praxis) as well as their interactions were used to explain our findings. During the elaboration of our data analysis, we decided to focus on data sovereignty practices. Table2 provides an overview of the coding scheme and includes sample quotes. Subsequently, data collection was further enhanced by conducting a post hoc study via three industry roundtables with highlevel experts from industrial companies. These discussions helped to reflect on and validate the preliminary findings. The participants were purposefully selected with an international background, and held leadership positions in their respective organizations. During the recorded roundtables, we presented findings from an earlier version of this article for triangulation and to gather additional feedback. We also used openended questions from the interview guide to stimulate discussion among the participants and to ensure consistency in responses and empirical data. Table3 lists the practitioner roundtables and their participants. For the expert interviews, we focused on interviewees from industrial incumbents; for the roundtable discussions, we also recruited some startup or scaleup companies to obtain a more diverse view. The roundtable workshops lasted between 75 and 90 min and were conducted online. Prior to the discussion, the facilitator explained the focus of the study on data sovereignty, using the same material as in the introduction of the first study. Following introductions of all participants, the workshops covered the topics outlined in Table4. All workshops were recorded and transcribed for the purpose of evaluation. We then discussed the workshop results in our author team, contrasting the findings with the interview results. In all three workshops, participants largely confirmed the qualitative findings regarding the definition and elements of the S- as- P framework, the importance of practices for organizational data sovereignty, and enriched our perspective with numerous examples and illustrations, so that data saturation can be assumed (Aldiabat and Le Navenec2018). Various measures were considered to promote validity and reliability (Goffin etal.2019). Respondents were assured of confidentiality and anonymity regarding the collection, analysis, and use of data. This allowed them to speak openly, and we sought their validation by providing them with insight into the results of the analysis. This ensured the credibility and rigor of our findings (Gioia, Corley, and Hamilton2013). Finally, all authors thoroughly discussed the empirical findings through multiple rounds of abstraction to connect them to existing theoretical perspectives. The results of the post hoc data collection provided robust insights into the implications of our initial findings and guided the development of recommendations for managerial practice. Our abductive approach allowed us to integrate the triangulated findings from the initial and post hoc study into a unified conceptual framework of organizational data sovereignty. 4 | Results Our findings include not only aspects of all three elements of the S- as- P framework, but also interdependencies among them, as shown in Figure1. Based on our findings, we adapted the S- as- P framework to focus on the impact of organizational data sovereignty on OVC. In particular, the post hoc study reinforced that OVC requires multiple organizations to implement and align their respective data sovereignty practices to jointly create value. Generally, the interviewees and roundtable participants confirmed the importance of big data in industrial OVC, but also reported challenges, complications, and obstacles in this context. The following quote is a representative opinion we heard from many interviewees. “The whole factory floor is full of gold, because data is everywhere and is basically gold. So the new currency or the new commodity. But people don't know that. They don't realize that these data represent a value. But the value is only presented here when you analyze it and turn it into added value and then sell it” (expert #9). In addition, internal data needs to be integrated with external data to achieve higher degrees of OVC: “I believe that there is also an advantage to using external data […], there is also a necessity to do so, because many of the innovative scenarios, whether in Industry 4.0 or in mobility or in healthcare, are actually based on the fact that I have my own data under control, that I combine it with data from […] business partners that I somehow know. But then I also enrich it with contextual information. And from this combination, actually something new emerges” (expert #26). Respondents also mentioned the importance of developing dedicated skills and competencies of data processing and analysis, confirming earlier research (Cappa et al. 2021). In this light, established project management methods did not always seem to be promising, as “digitization, simply because it is so interlinked, often involves more different people or more stakeholders […] to fully exploit a process that has now been digitized, for example, I have to involve all of a whole chain of people” (expert #6). Instead, agile development methods were suggested, which demand different skills: “The ‘agile’ in the methodology is always a tough one for people and I think it's important that people understand that that is the best way to develop right now and what that truly means” (expert #10). The implementation of organizational data sovereignty for OVC was often hindered by ambiguity about how to securely handle data to (co- )create and develop innovations. For expert #3 “it is
1129 TABLE 2 | Coding schemes for the first and second step of interview data analysis. Code Description Exemplary quote First step of analysis Problem Problems and challenges with regard to data and their use, showing necessity for data sovereignty I would first drop the data protection issue. That is a hurdle in many many areas. So maybe not completely abolish it, but significantly relax it so that we can also use Zoom now, for example. We're not allowed to use Zoom internally, not even with customers. But that's the tool that all customers can use, because it's just so easy. So I'm now on an tablet, I also have from work, but it's not in the company (#11) Definition Contribution to a definition of or understanding of data sovereignty So actually, it's very much about skills in crosscompany collaboration. There is actually even an ISO standard. 44,001 on how to collaborate across companies […], these are the technical topics for data sharing […]. We now assume that we have data in digital twins, then it often becomes exciting. And if there is data, then of course there are a lot of new technologies that facilitate that, e.g. cloud technologies (#13) Advantages Possible advantages of higher sovereignty with regard to data Through data collection and data exchange, of course, new business models are created or existing business processes can be accelerated (#5) Problem solving Problem solution through better data sovereignty Let's say, I would imagine, if Big Data approach to production, there would probably be some potential to tease out. But that's not being done at the moment, because we're not sure about our data (#6) Implementation Measure to achieve higher sovereignty with regard to data Just let us do it. It's really a culture clash that we have to some extent. These extreme concerns with all the changes. Yes, we have systems that work. They're running. Why would we change anything? We're safe. And […] I say “come on, we should just test it and see, does it work?” And then if it works, then think how do we fit that into our security concepts and all the IP protection and everything that we need (#12) Digital innovation Interrelationship between data sovereignty and digital innovation Trusting and honest cooperation across departments. I believe it is imperative for digital innovation projects that several people are always involved, and that they do not work in a classic, singlediscipline way, but rather in a way that brings together several areas (#2) Second step of analysis (1) Practitioners Reference to practitioners in the sense of S- as- P I would hire more people on this topic than we can. That's because of the market. There are simply not as many as we can hire (#9) (2) Practices Reference to practices in the sense of S- as- P Many companies want to use this agile method, but somehow this loop of one week and again and again doesn't quite fit with some industries (#2) (3) Praxis Reference to praxis in the sense of S- as- P Especially for our company […], which is somewhat more riskaverse due to its portfolio and history as […] a typical industrial company perhaps compared to a digital company (#1) (Continues)
1130 R&D Management, 2025 simply uncertainty in the company in most cases. What I mean by that: If data is going to the cloud, it's said ‘Woah, cloud, quite dangerous’. The fact that we as a chemical company cannot manage certain data centers as well as Microsoft or Amazon because their data center is outside our plant fences is still beyond the imagination of many people today. So a data Code Description Exemplary quote Interrelation (1–2) Reference to an interrelationship btw. practitioners and practices And that's just by introducing these systems, it's not done, but then you also have to train people more and more and also accompany them through this introduction. And unfortunately, that is not happening either. […] You just introduce a new system and are happy that the new system is finally up and running. Unfortunately, the employees are left by the wayside and are confronted with a lot of extra work, because they have to understand and learn it, and the training is usually inadequate (#7) Interrelation (1–3) Reference to an interrelationship btw, practitioners and praxis Because they [digital innovation projects] are even more strongly determined by uncertainty and perhaps also change more strongly in the course of the project. In this respect, I would say we need an open mindset. In addition to these technological or technical skills (#1) Interrelation (2–3) Reference to an interrelationship btw. practices and praxis And that's also the big difference in digital projects than in quotes classic projects as we know them. You just have to get into the spirit of this agile method (#3) TABLE 2 | (Continued) TABLE 3 | Participants in the post hoc roundtable workshops. Group Expert # Size Category Position 11.1 SME Enabler Founder & Managing Director 11.2 Large Industry Chief Operating Officer 11.3 Large Industry Project Leader 11.4 Large Industry Principal Key Expert 22.1 Large Industry Vice President Process Engineering 22.2 Large Industry Head of Data Strategy & Science 22.3 Large Industry Program Director “Factory of the Future” 22.4 SME Enabler Vice President R&D 22.5 Large Industry Technology Executive 22.6 Large Enabler Global Product Owner OT Security Services 22.7 Large Industry Authorized Representative Production 33.1 SME Enabler General Manager 33.2 N/A Public Org. Professor for digital business 33.3 SME Enabler Research Manager 33.4 Large Industry Director Strategy Development 33.5 Large Industry Technical Director Tech Fund 33.6 Large Industry IT Manager 33.7 Large Enabler Chief Technology Officer 33.8 Large Industry Technology Scouting Innovation & Ventures Advisor Abbreviations: N/A, not applicable; Public Org., public organizations and associations.
1131 center inside the factory fence [is seen as] great, the data is secure. But data going into the cloud, ‘Oh my God, there are Google and Microsoft’. There are so much […] halftruths coming together that people are just insecure there.” Innovation managers also faced administrative and regulatory hurdles that prevented internal and external data exchange. “Even across departments, it is sometimes not easy to access the same data in terms of authorization. And if you then go external, it is even more difficult in terms of the process, because of internal guidelines, external systems, firewalls, all these things make it difficult at the moment, because the infrastructure in our company has not been set up in such a way that you can work with changing partners” (expert #2). In this context, expert #1 outlined considerable difficulties in the use of external data: “That starts with the description of the property rights on the date and then the application and the clarification of the rights of the result exploitation. […].” After these initial observations, we refer to the three elements of the S- as- P framework below to further elaborate our analysis. In particular, we focus on the practices that practitioners engage in when implementing data sovereignty within their organizations. of how data sovereignty is established and enacted in practice. 4.1 | Enabling Practices for Data Sovereignty to Enable OVC In the S- as- P literature, practices are understood as routines or processes that provide practitioners with a framework for action. However, a major challenge for companies was the lack of consistent practices for handling data, limiting sovereignty. Instead of standardized procedures, many companies still relied on caseby- case or onpremises solutions and individual agreements. TABLE 4 | Setup of post hoc roundtable workshops. Agenda item Content Part 1: Introduction and general opening discussion Open discussion about the participants' experiences with sharing of industrial data within and across organizations in a manufacturing setting Part 2: Presentation and results of initial findings • Presentation of our first study setup and the results • Introduction to the Practice- Praxis- Practitioners (S- as- P) framework • Presentation of our initial definition of organizational data sovereignty Part 3: Feedback on and discussion of results of the initial findings • Gathering of initial feedback of participants on our results (individual, using comment function) • Open discussion of the results and whether they seem plausible and adequate for the participants • Implications for manufacturing organizations from various perspectives • Informal discussion of industrial data sharing and open innovation in a manufacturing context in general FIGURE 1 | S- as- P elements to enable open value creation.