# Building Trust in Business Ecosystems: The Interplay of Technology and Community in Governing Data Sharing

Ulriikka Järvihaavisto, Mikael Öhman

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Ulriikka Järvihaavisto, Mikael Öhman. Building Trust in Business Ecosystems: The Interplay of Technology and Community in Governing Data Sharing. IFIP International Conference on Advances in Production Management Systems (APMS), Sep 2022, Gyeongju, South Korea. pp.361-369, 10.1007/978-3-031-16407-1_43. hal-05386099

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# Building Trust in Business Ecosystems: The Interplay of Technology and Community in Governing Data Sharing

Ulriikka Järvihaavisto(✉) and Mikael Öhman

Department of Industrial Engineering and Management, School of Science, Aalto University,
P.O. Box 15500, 00076 Espoo, Aalto, Finland
{ulriikka.jarvihaavisto,mikael.ohman}@aalto.fi

**Abstract.** Data sharing remains one of the biggest challenges for emerging industrial ecosystems. Lack of trust, and perceived risks are holding companies back, as no one wants to go first and get it wrong. At the core of this challenge is a lack of understanding how data sharing could, and should be governed in business ecosystems. Despite growing scholarly interest in data sharing and data markets, research on the governance mechanisms of data sharing is scarce. In this paper, we analyze the governance of data sharing in two emerging business ecosystems. Based on our findings, we elaborate on the complementary role of community- and technology-based governance mechanisms. We discuss our findings in light of emerging industry 4.0 business ecosystems.

**Keywords:** Data sharing · Governance · Business ecosystems

## 1 Introduction

In recent years, ecosystems and digital platforms have received increasing attention from both management scholars and business practitioners. Adner [1] defines ecosystems as “the alignment structure of the multilateral set of partners that need to interact in order for a focal value proposition to materialize”. Digital platforms on the other hand are often seen as a core technology on top of which different ecosystems operate. Despite the increasing interest on the phenomena, we see surprisingly little research on ecosystems and platforms conducted in the industrial context.

The industrial context differs from purely digital contexts, as it typically entails existing system architectures that connect hardware and machines, legacy software, strong existing organizational identities [2] and different business models. Empirical evidence on data sharing in emerging industrial business ecosystems is scarce [3]. And can as such be considered as one of the biggest challenges in industrial business ecosystem emergence. An underlying reason for this challenge is a lack of understanding on how data sharing should be governed in business ecosystems [4].
Even though data sharing, and data markets have drawn attention from business scholars, governance mechanisms of data sharing have barely been discussed. Further, when discussed, data sharing is often discussed solely from a technology governance perspective, neglecting the impact of community governance mechanisms. In this study we seek to understand how technology- and community governance mechanisms complement each other in the governance of data sharing.

We approach this question through conducting a polar case study with two emerging business ecosystems, where one case starts with an existing community and develops the platform technology, and the other starts with the technology, and seeks to develop the community. We base our analysis on a recent systematic literature review on the governance of data sharing in business ecosystems [5], which identified three interrelated governance mechanisms: usability, transparency and exchangeability. Based on our findings, we discuss learnings for industrial business ecosystems that seek to redeem the promises of industry 4.0.

## 2 Data Sharing Governance in Business Ecosystems

Before reviewing prior work on the three governance mechanisms of data sharing [5], we consider two concepts that are directly linked to all of them: data ownership and sovereignty. Data ownership is often considered as a governance issue of its own [6]. Ownership can be defined as the legally enforceable right to determine the terms and conditions of data use [7–13]. When the data source is an individual, or the data shared is personal data, ownership itself is not questioned [8]. However, distinguishing between personal and non-personal data can be problematic [14, 15].

Data sovereignty is derived from the concept of data ownership [7, 10, 12, 13], and denotes that the data owner should be able to maintain control over the shared data [11]. If an intermediary, such as a disseminator or data broker is involved in data sharing, maintaining the owner’s data sovereignty becomes the responsibility of the intermediary [7]. Sovereignty is also determined by system architecture [13] and interaction topology [11].

### 2.1 Usability

Usability is about what is shared, to whom it's shared, and for how long it is shared. Usability has been discussed in the literature especially in the perspective of data source: how do you share enough data for it to be useful, but little enough to avoid unwanted purposes [16]. Ensuring privacy is a key part of usability [10, 17, 18]. This is discussed especially in the healthcare sector, where personal data is concerned [16, 19]. Data granularity is a key issue that supports privacy and affects the willingness to share data [17]. However, strict limitations on usability might stifle innovation [16]. Indeed, from the perspective of the data user, usability has attracted surprisingly little academic attention. In the platform ecosystem literature, data use case is seen as a governance factor, meaning that data users need to have a clear idea what data is important to them and why [6]. In community governance perspective, continuously maintaining definitions of data use
cases and related stakeholders are seen key aspects of data governance [6]. Further, intermediaries between data source and user can in some cases improve usability [14].

In addition to intermediaries, we can identify two technological approaches facilitating usability: confidentiality approaches and federated analysis. They ensure that the shared data contains only the necessary information, which is important especially when the purpose of data use is to derive aggregate insights from large datasets. In confidentiality approaches, data is generalized by reducing the level of detail in a dataset [10, 18], or by fragmentation of the database, all the while preserving aggregate usability [20]. Federated analysis is used for the purpose of training AI models [8]. It entails aggregating model parameters from locally stored data sets, meaning that the data is technically not shared, only the insight. Even though, it is yet rarely used in practice, it is seen to provide great opportunities in the future use.

## 2.2 Transparency

Transparency is about whether and to what extent there is a visibility into what data are used for, by whom data are used, and for how long [17]. In the perspective of the data source, it should be clear how the shared data is used [8, 12, 21], especially when sharing personal data [22]. In addition, the data user should have information of data provenance [6, 7, 23]. Thus, trust in the source of the data [11], in the history of the data [7, 8] and in the quality of data [24] is important both in the perspective of data source and user. Including metadata in data sharing facilitates transparency in transactions [6].

Furthermore, distributed ledger technologies (often in combination with federated architectures) also facilitate transparency through auditability in data sharing. Distributed ledger technologies can also be used for ensuring sovereignty and provenance [9, 23, 25]. The link between transparency and trust is also reflected in the technological discussion, where blockchain is referred to as a “trust technology” [9].

## 2.3 Exchangeability

Exchangeability defines the incentives to share data. Especially when the data user can be expected to profit from the data, data source is expected to require some kind of compensation from sharing the data [12]. Compensation for sharing personal data typically comes in the form of access to services [14], discounts [12], or some form of tokens [8]. However, also monetary compensation is often expected [22]. With more altruistic purposes for the use of data, data sharing can be governed by consent [9, 16], which may be extended to third parties [21]. However, questions concerning consent and compensation typically become harder to answer when re-using or aggregating data [16].

Blockchain-enabled smart contracts can be used to reduce transaction costs of data sharing by streamlining processes and removing the need for intermediaries [8, 23, 25], and ensuring transaction validity [26]. The caveat is that the data sharing agreement needs to be computationally representable [23] for it to be machine-readable and -executable [11].
# 3 Methodology and Empirical Case Studies

Industry 4.0 business ecosystems are still in their infancy, and currently organized mostly in a centralized manner. In order to understand how manufacturing industries can evolve towards more extensive data sharing and (de-centralized) ecosystemic cooperation, we study two non-manufacturing contexts where this is happening. We use a longitudinal, explorative polar case study as our research method [27]. For evaluating the generalizability of our findings, understanding and explicating the context embedded nature of knowledge is of key importance [28].

The studied empirical cases, the Streamr Community and Barcelona Green Shops, are part of the Atarca EU Horizon 2020 project that studies accounting technologies for anti-rival coordination and allocation. Through our involvement in the project, we have been able to study the evolution of the two de-centralized ecosystems that utilize distributed ledger technologies. From the perspective of ecosystem evolution, the cases have one fundamental difference (making them polar opposites in our study): Whereas Streamr Community builds the technology with the collaborative community emerging around it, Barcelona Green Shops starts with an existing community, building the collaborative technology for it.

Longitudinal (and still ongoing) data collection extends over the entire EU-project. Collected data includes 3 workshops, meeting notes from 5 project meetings. For this report we conducted two 1-h interviews with key case informants, focusing explicitly on governance aspects related to data sharing. Further, we included secondary data such as project reports, community websites and white papers in the analysis. Based on the framework by Järvihaavisto & Öhman [5], we performed both within case- and cross case analysis.

## Case: Streamr community

Streamr (https://streamr.network/) is developing core technology for decentralized real-time data sharing. Leveraging the core technology, the formation of decentralized platforms and services is a collective effort by the Streamr community. So far, community contributions have mainly been technical, ranging from providing a part of the technical infrastructure to taking part in software development. However, to some extent these contributions have also included various technology governance activities (decision making, decision support).

The challenge faced by the Streamr Community is to extend the realm of contributions beyond the traditional programming-related tasks. This is of special importance as dedicated non-programming contributions are increasingly important to the long-term viability of Web3 projects (DLT-based, including cryptocurrencies) in general. Streamr is pioneering new ways of sharing and giving acknowledgements using shareable, non-transferable non-fungible tokens.

## Case: Barcelona Green Shops

Barcelona Green Shops focus on creating a business platform that enhances networking and information sharing among “Green shops of Barcelona” community members. The community, formed around sustainable (and local) products, is managed by Rezero (https://rezero.cat/en/) and consists of shopkeepers, their clients, and suppliers. NGO Novact (https://novact.org/) facilitates the building of the platform in the ATARCA
project. Due to community's focus on sustainability and local producers, the ecosystem is geographically concentrated in the Barcelona region.

The challenge faced by Barcelona Green Shops is to incentivize the existing community stakeholders to participate in the platform that promotes coordination, information sharing, and community building among the shops. To achieve this, Barcelona Green Shops uses Barcelona's social currency REC. Moreover, the social currency facilitates the consumers' green and sustainable consumption, manifesting the Green Shops' sustainability impact, while building feedback channels (product demand and quality) among all members of the Barcelona Green Shop community.

## 4 Findings

We use the abbreviations BGS for Barcelona Green Shops and SC for Streamr Community. We highlight the key distinction between the cases, i.e. the different starting points of the cases; with BGS starting from the community and building the technology, and with SC starting with the technology, and building the community.

As both of the ecosystems are in their early stages of their emergence, the governance is centralized. Rezero/Novact and Streamr company decides the rules who get to participate and how the data can be shared in the platforms. However, in both cases, the governance is expected to be de-centralized in the future, as the members take ownership in the web3 platforms. According to our interviewees, de-centralizing the current central governance is a key future challenge.

### Usability

Starting from the purpose of the shared data, we find that the primary purpose of BGS is to enable sharing of data on suppliers amongst shops. SC on the other hand does not hold any preconceived ideas about the purpose of the shared data. Going one step further, SC even distances itself from any responsibility for what data is shared on the platform. Elaborating on the purpose in the BGS case, we note that the purpose of data sharing is derived from the overarching purpose of the community. In order to promote sustainable and local production, which reaches consumers through community-centred small shops, the BGS network has to be able to compete in economic terms against major retail players. This is achieved through sharing information of suppliers, and enabling coordination in procurement. Conversely, while SC does not promote any specific purpose for data sharing, the technological focus is clearly on real-time data. Based on this we present our first finding:

> In a community-based ecosystem, the type of shared data is derived from community purpose, whereas in a technology-based ecosystem, the type of shared data is derived from the focus of technology.

### Transparency

In BGS the parties sharing data include shops, suppliers and customers. Despite sharing the overarching purpose (as mentioned above), the different parties mostly do not have an a priori relationship with each other, so trust is an issue. The shared identity promoted
by Rezero, however, acts as a common anchor for initial trust, and as a mediator, on which more in-depth relationships can be built. In SC data is produced by sharers and used by subscribers. The anonymity, or pseudonymity offered by the network means that trust cannot be based on social relations. In practice, the technology in SC offers the tools for data provenance and privacy, which is then implemented in apps that facilitate data sharing. Based on this we present our second finding:

> In a community-based ecosystem, with a trusted mediator, data sovereignty can be based on relational trust, whereas in a technology-based ecosystem, data sovereignty must be explicit and hard-wired.

## Exchangeability

In BGS, the shop keepers have joint incentive to share data: in collaboration they can join their market forces in order to compete with large global merchandise stores and promote sustainable and local production within their area. Data sharing in the platform is not monetized, but incentivized by offering “merit” tokens to regular contributors as a sign of appreciation. In SC, the company offers their DLT technology for users with different motivations to share and consume data, and transactions can be monetized in the platform. In SC, the incentives for sharing data are not known, however, SC encourages members to use and develop their platform by rewarding active contributors through appreciation by offering tokens and immaterial demonstrations of appreciation. Based on this we conclude that:

> If there is a common, social goal among the community members in the ecosystem, data sharing doesn't necessarily need to be monetized. On the other hand, monetization of data sharing can be used for financing the platform maintenance and development.

# 5 Discussion

There are no major technological barriers for data sharing in industrial context. Different DLT technology platforms, such as Streamr, enable ecosystems to share large data sets, even real-time data that suits industrial needs. However, in order for the data to flow in the ecosystem, there needs to be incentives for members to share. With a community-based purpose for data sharing, as in the Barcelona Green Shops case, the motivation for data sharing is tangible even though the data transaction itself is not monetized. Similar shared purpose can be seen in healthcare [16, 19], where patient data is readily shared (based on consent) for the purpose of medical research. However, the challenge in this type of community-based governance is how to maintain and finance common infrastructure and development of data sharing tools/platforms.

In technology-based ecosystems, transactions can only be incentivized through monetization, which does not further community building, and by extension industrial ecosystem emergence. Tokenization can be used, but the (relational or social) value of tokens is reliant on community building efforts. However, due to business-criticality, competitive advantage, and context specificity (potentially combined with risk aversiveness),
reaching an agreement on the exchange value of industrial data is challenging. Thus, fostering community-based data sharing in industrial ecosystems is of key importance. The challenge is that communities take time and effort to build and sustain. Our argument in this paper is that when starting with technology-based governance perspective, community building will be the challenge for achieving sustainable business ecosystem. However, when starting with community-based governance perspective, financing and maintaining common infrastructure will be the key challenge for sustainable data sharing ecosystems, see Fig. 1.

<figure><img src="image_3.png" /><figcaption>Fig. 1. Data sharing through community- and technology-based governance</figcaption></figure>

In industry 4.0 context, sustainability, quality, and standardization could be community purposes (along with their respective non-profit industry organizations) that incentivize industrial organizations to share their data within the business ecosystem, without the need for monetization of sharing. Also, increasing market power to counter dominant players could also be a unifying purpose for smaller industrial players (as in the BGS case). Through our case-study, we advance theoretical understanding of data sharing governance in business ecosystems. However, the generalizability of the results are limited [27], warranting further research in different contexts where data is shared in business ecosystems.

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