Data-driven culture and orchestrated data ecosystems: a conceptual model based on the resource-based view

With the advancement of digital technologies, organizations are accumulating increasing volumes of data, yet they still face challenges in utilizing them effectively. Hannila, Silvola, Harkonen, and Haapasalo (2022) highlight that while data hold significant potential, companies often lack the capability to analyze them properly, limiting their strategic use. In this context, a data-driven culture, characterized by a set of organizational behaviors and practices that guide decision-making based on data, positions data as strategic assets for organizations (Kremser & Brunauer, 2019).

In data ecosystems, defined as networks of organizations that use data from various sources to create value across different businesses (Gelhaar, Groß, & Otto, 2021), a data-driven culture becomes even more relevant, as it impacts the continuous exchange of information between organizations. Oliveira and Lóscio (2018) describe these ecosystems as complex networks that depend on data for their operations. In collaborative environments such as ecosystems, adopting a data-driven culture contributes to efficiency and value generation through data (Gelhaar et al., 2021). Thus, the interdependence between a data-driven culture and data ecosystems suggests that the development of this culture within an organization is directly related to the dynamics of the ecosystem in which it operates.

Guggenberger, Otto, and Gelhaar (2020) emphasize that collaboration among participants in a data ecosystem is necessary to ensure the quality and reliability of shared information, while data governance defines policies for access, security, and interoperability, all of which facilitate the adoption of data-driven practices (Gelhaar et al., 2021). Data literacy and the integration of metrics and indicators also support the consolidation of a data-driven culture (Anderson, 2015). Therefore, the relationship between data ecosystems and a data-driven culture extends beyond information exchange, involving a set of practices that structure data management and usage, influencing the development of competitive advantages.

From the perspective of the Resource-Based View (RBV), a data-driven culture can be regarded as a strategic resource for organizations. RBV posits that valuable, rare, inimitable, and well-organized (VRI + O) resources provide sustainable competitive advantages (Barney, 1991), suggesting that adopting and strengthening a data-driven culture can generate competitive advantages for organizations. This is particularly relevant in data ecosystems, where the ability to collaborate and make data-driven decisions is important for collective success (Chatterjee, Chaudhuri, & Vrontis, 2021).

This conceptual paper examines how orchestrated data ecosystems, guided by a central orchestrator, enable organizations to cultivate a data-driven culture as a strategic resource that meets the VRI + O criteria, using an integrative literature review (Webster & Watson, 2002) and a theory-building approach (Jaakkola, 2020). This study extends RBV to ecosystems by considering data-driven culture as an intangible resource that can contribute to sustainable competitive advantages.

A data-driven culture is a specific form of organizational culture in which data usage guides decision-making within an organization. Kremser and Brunauer (2019) define it as an environment where decisions are based on insights derived from well-governed data, ensuring continuous access to structured information and the dissemination of knowledge through data. Chatterjee et al. (2021) add that this culture manifests through beliefs and practices that value information as a strategic foundation, influencing organizational performance. Schlegel, Wallner, Monauni, and Kraus (2023) extend this perspective by situating it within the broader concept of data-drivenness, emphasizing that its implementation requires not only technology but also the acceptance and integration of data into the organizational structure. Thus, a data-driven culture is not limited to data usage but also involves organizational and behavioral changes regarding data utilization, impacting organizational outcomes.

From a market perspective, companies such as Lufthansa, operating in the aviation industry, argue that a data-driven culture contributes to the digital transformation process, increasing operational efficiency and improving decision-making in a dynamic market (Haude, Blohm, & Lagardère, 2024). Similarly, Forbes (2024) highlights that building a data-driven culture aligns organizations around data-related objectives, fostering innovation and supporting faster decision-making. Additionally, Cambridge Spark (2024) emphasizes that the strategic use of data helps replace intuition-based decisions with data-driven choices, reducing risks and enhancing efficiency.

Despite its relevance, developing a data-driven culture remains a challenge for many organizations. Reports such as those from MIT Sloan Management Review (2020) suggest that culture, rather than technology, is the main barrier to the success of data initiatives. KPMG (2021) reinforces that becoming a data-driven organization is a priority, but it requires significant changes, such as data literacy and motivation for data use at all organizational levels. Given this context, understanding the characteristics of a data-driven culture supports the identification of elements that facilitate its development within organizations.

A data-driven culture has five key characteristics: data access and sharing, data literacy, goals and metrics, an inquisitive and learning-oriented culture, and data-driven leadership (Anderson, 2015). Access and sharing ensure that data are available to support decision-making at various organizational levels, with data governance playing a structuring role in defining policies related to access, accountability, and confidentiality (Kremser & Brunauer, 2019). In this context, data democratization enables different organizational actors to use information consistently and transparently.

Data literacy refers to the ability of organizational members to interpret and effectively use analytical information. Anderson (2015) points out that not all employees need to be experts but should have a basic understanding of analytical patterns and tools, while Berndtsson, Forsberg, and Olsson (2018) emphasize the importance of analytical capabilities for data operations. Training initiatives aimed at developing analytical skills are thus relevant for consolidating a data-driven culture.

Anderson (2015) argues that using metrics fosters transparency and helps managers and employees align with strategic objectives, as goals and indicators quantify business objectives and guide organizational efforts. Agyei-Owusu, Acquah, Asante, and Andoh-Baidoo (2021) highlight that the relationship between data-driven culture and performance depends directly on well-defined metrics for measuring results, ensuring continuous evidence-based monitoring. This context reinforces an inquisitive and learning-oriented culture, characterized by structured questioning and ongoing review of decisions based on data and numerical evidence. Anderson (2015) suggests that in data-driven organizations, decision-making occurs through discussions grounded in verifiable information, while Kremser and Brunauer (2019) emphasize that this approach strengthens organizational culture by encouraging iterative analysis and continuous improvement.

Leadership is important to sustaining a data-driven culture as it influences practices from data collection to decision-making through a top-down approach (Anderson, 2015). Chatterjee et al. (2021) support this perspective by stating that leadership should emphasize the utility and benefits of a data-driven culture as a strategic element for organizational success. Thus, consolidating a data-driven culture depends on integrating these characteristics into data-driven practices, which translate cultural aspects into concrete actions focused on structured data use.

A data-driven culture materializes through organizational practices that integrate data into decision-making processes, fostering an analytical and structured approach. Anderson (2015) highlights that business intelligence and analytics tools enable the extraction of insights from large volumes of data, providing support for more informed decisions. Kremser and Brunauer (2019) add that data governance policies establish standards for quality, accessibility, and security, ensuring that information is used reliably and in alignment with organizational objectives. Additionally, Chatterjee et al. (2021) point out that automating analytical processes reduces reliance on manual analyses, increasing the speed and accuracy of data-driven decisions.

Adopting these practices typically requires organizational changes that enable the integration of data into decision-making structures. Schlegel et al. (2023) state that this transition involves adapting norms and routines and revising the criteria that guide information use within organizations. Kremser and Brunauer (2019) reinforce that implementing data-driven practices is not limited to technology but requires structural adjustments that encourage continuous data use. Berndtsson et al. (2018) highlight that training programs contribute significantly to this process by fostering analytical competencies and promoting a systematic approach to data use.

Regarding the dissemination of these practices, Acquah, Naude, and Soni (2021) argue that organizational culture directly influences collaboration and information sharing, impacting how data are used within organizations. Kremser and Brunauer (2019) add that data integration reduces information fragmentation and strengthens cross-sector cooperation, expanding the reach of a data-driven culture. Thus, data-driven practices translate a data-driven culture into concrete organizational processes, structuring workflows that promote systematic data use in decision-making. In data ecosystems, these practices become even more relevant, as collaboration among different actors requires governance mechanisms, interoperability, and structured information sharing, extending the impact of a data-driven culture beyond organizational boundaries.

Data ecosystems have been widely discussed in the literature as a way to understand the complexity of interactions between organizations and their data. Otto et al. (2019) draw an analogy with biological ecosystems, emphasizing their dynamic and adaptive nature. In this context, data ecosystems function as environments where participants continuously interact to create, transform, and use data strategically. These interactions may involve both competition and collaboration, depending on the needs and objectives of the stakeholders involved (Wang, 2021). Gelhaar et al. (2021) describe data ecosystems as networks of organizations that use information from multiple sources to generate value across different domains. Oliveira and Lóscio (2018) highlight that these networks consist of autonomous actors who assume different roles, such as data producers, consumers, and providers. Rantanen, Hyrynsalmi, and Hyrynsalmi (2019) emphasize the relevance of data usage, reuse, and storage, indicating that the circulation of information is a central element in the ecosystem’s dynamics.

Beyond interactions among actors, another important aspect of data ecosystems is the need for governance to ensure security, quality, and accessibility. Otto et al. (2019) point out that governance directly influences the ecosystem’s reliability by establishing the rules that guide data access and sharing. Guggenberger et al. (2020) add that system interoperability contributes to the integration and coordinated use of information, allowing data to flow efficiently among participants. Geisler, Otto, and Gelhaar (2021) extend this understanding by stating that data ecosystems not only structure information flows but also facilitate learning and knowledge generation through participant interactions.

Among the different configurations of data ecosystems, orchestrated data ecosystems stand out due to the presence of a central actor who coordinates interactions and defines guidelines for information sharing. Gelhaar et al. (2021) argue that the presence of this orchestrator influences the ecosystem’s dynamics by controlling key resources and establishing rules that shape collaboration among participants, directly impacting how data are accessed, processed, and used in decision-making. According to Guggenberger et al. (2020), orchestration does not eliminate participant autonomy but establishes parameters that ensure alignment around a common objective, promoting greater predictability and encouraging structured sharing practices.

The presence of an orchestrating actor also facilitates the implementation of stricter governance policies, ensuring minimum standards for quality and security. Oliveira and Lóscio (2018) emphasize that structured governance enhances ecosystem reliability, making it easier for participants to adopt consistent practices. Additionally, interoperability is strengthened, as centralized guidelines enable data standardization and integration across different systems (Otto et al., 2019). Collaboration among actors is another relevant aspect, as the coordination promoted by the orchestrator fosters a structured exchange of information and the adoption of data-driven practices.

Examples of orchestrated data ecosystems can be observed in various institutional contexts. Salerno and Maçada (2024) analyzed the Brazilian National Council of Justice (Conselho Nacional de Justiça, CNJ) as an orchestrating actor that centralizes the issuance of guidelines for data governance within the judicial ecosystem. The CNJ’s role involves integrating data from different courts and establishing minimum standards for judicial information management. Another example is the role of the Brazilian Armed Forces, where orchestration is evident in the coordination of strategic projects focused on modernization and the development of new operational capabilities through data, contributing to innovation processes (Ramalho, Tarraco, Yokomizo, & Bernardes, 2019).

Regarding the development of a data-driven culture, the presence of a central actor can encourage the adoption of data-driven practices by fostering an environment for structured data analysis and sharing. Guggenberger et al. (2020) and Gelhaar et al. (2021) argue that orchestrated data ecosystems facilitate participant integration by providing clear guidelines for data usage, reducing fragmentation, and increasing interaction predictability. Thus, orchestration serves as a mechanism that enables the efficient coordination of data flows within ecosystems, ensuring that information circulates in a structured manner and is used in alignment with established objectives. From the perspective of the Resource-Based View (RBV), this dynamic can be analyzed in terms of developing competitive advantage through the promotion of data-driven practices within the ecosystem.

The Resource-Based View (RBV), originally proposed by Penrose (1959) and consolidated by Barney (1991), posits that an organization’s resources—including assets, capabilities, processes, and information—are fundamental in strategy formulation and execution. These resources are categorized into physical capital (infrastructure, technology, and location), human capital (competencies, training, and experience), and organizational capital (internal structure, planning, and external relations). Effective resource management can provide a competitive advantage, defined as the implementation of a value-creating strategy that is not simultaneously adopted by competitors (Barney, 1991). When this strategy resists imitation, it results in a sustainable competitive advantage, even though it remains subject to changes in the business environment (Penrose, 1959). Figure 1 illustrates the RBV concept in relation to competitive advantage.

The relationship between resources and competitive advantage depends on specific characteristics. Initially, Barney (1991) argued that resources capable of generating a sustainable advantage must be valuable (contributing to organizational efficiency), rare (difficult to obtain and combine), imperfectly imitable (protected by historical conditions, causal ambiguity, and social complexity), and without close substitutes (VRIS). However, Barney and Clark (2007) later revised this framework, replacing “without close substitutes” with “organization” (VRI + O), emphasizing that, beyond possessing strategic resources, the organization must be structured to effectively exploit them.

Given the constant evolution of markets, Chan, Shaffer, and Snape (2004) highlight that maintaining competitive advantage requires the renewal of resources, known as dynamic resources, since strategic resources can become obsolete without adaptation. In this regard, Mahoney and Pandian (1992) argue that RBV incorporates a dynamic perspective by recognizing that an organization’s competencies and capabilities must evolve in response to environmental conditions. Thus, sustaining competitive advantage depends not only on possessing strategic resources but also on managing them efficiently and reconfiguring them in response to market shifts (Barney & Hesterly, 2006). This understanding is particularly relevant in the field of Information Systems (IS), where RBV has been applied to examine how technological and intangible resources influence organizations.

RBV has been employed to analyze how IS resources influence organizational strategy and performance. Wade and Hulland (2004) highlight that this approach allows for the evaluation of both technological and intangible resources, such as organizational culture, which significantly influences strategy formulation and implementation. In this context, Cao and Duan (2014) argue that a data-driven culture enhances an organization’s analytical capabilities, becoming a strategic differentiator by meeting the criteria of value, rarity, and inimitability. This is particularly relevant in data ecosystems, where continuous interaction among agents reinforces the inimitability of this resource.

The relationship between RBV and organizational culture had already been explored by Barney (1986) and Barney and Clark (2007), who suggest that culture can constitute a sustainable competitive advantage if it remains difficult to imitate. More recent studies, such as Naor, Jones, Bernardes, Goldstein, and Schroeder (2014), indicate that different combinations of organizational culture affect operational effectiveness in distinct ways, making replication complex. Barney, Ketchen, and Wright (2011) argue that RBV’s future depends on its ability to incorporate new elements into the analysis of competitive advantage, emphasizing intangible factors such as culture as relevant for developing sustainable advantages. Chan et al. (2004) reinforce this view by demonstrating that organizational culture, when combined with human capital management, directly influences decision-making and indirectly shapes organizational routines, such as efficiency and communication, making it a strategic factor that is difficult to replicate.

In the context of data ecosystems, Vafaei-Zadeh, Hanifah, and Ramayah (2024) emphasize that a data-driven culture moderates the relationship between analytical capabilities and sustainable competitive advantage, enhancing the strategic use of information. By integrating data-driven practices such as business intelligence, data governance, and automated analytics, organizations not only optimize decision-making but also establish a sustainable foundation for competitive differentiation, reinforcing RBV as a relevant framework for understanding data-driven culture and the development of competitive advantages.

This conceptual paper follows a constructivist approach to knowledge generation, recognizing the intrinsic and extrinsic value of conceptual essays in advancing theoretical and practical knowledge (Lindebaum, 2022). Ontologically, it adopts a moderate realist perspective, recognizing that organizational phenomena, such as data-driven culture and data ecosystems, are socially constructed but can be analyzed in a structured manner based on established theoretical frameworks (Fleetwood, 2005). Epistemologically, the study follows the theory-building from literature approach, in which the formulation of the model emerges from the critical articulation of prior studies, enabling new conceptual perspectives (Webster and Watson, 2002).

The methodology follows the principles of conceptual papers, which aim to refine theoretical constructs and propose new interpretations based on existing literature (MacInnis, 2011). The strategy aligns with Jaakkola’s (2020) proposition that conceptual review and analysis help identify theoretical gaps, connect concepts, and develop a more comprehensive explanatory framework. To structure the conceptual model, an integrative literature review was conducted, identifying academic works that address the core concepts of this study to consolidate accumulated knowledge on data-driven culture, data ecosystems, and the RBV (Torraco, 2005). This approach allows for the examination of different theoretical perspectives and the establishment of connections between these topics, leading to a deeper understanding of their relationships.

Based on the literature review and conceptual analysis, a conceptual model was developed with theoretical propositions that articulate the relationship between orchestrated data ecosystems and the development of sustainable competitive advantages through data-driven practices. The model emphasizes the role of the orchestrating actor in promoting these practices and explores how this process contributes to generating sustainable competitive advantages from the RBV perspective.

This section integrates the concepts of data-driven culture and data ecosystems within the RBV, presenting the conceptual model and the propositions of this theoretical study.

Hannila et al. (2022) highlight that a data-driven culture is associated with improving data-based decision-making, influencing organizational value creation. This development occurs through efficient data management, the application of analytical models, and trust in information, as pointed out by Otto et al. (2019). These authors also emphasize that transparency and reliability in information exchange among ecosystem participants directly affect its consolidation. Sambhara (2020) discusses the impact of ethical challenges and data integrity in this context, noting that trust-building among actors can be undermined by failures in data sharing and governance.

The motivation of actors to use data in a structured manner also influences the strengthening of a data-driven culture (Rantanen et al., 2019). Andrade-Rojas, Kathuria, and Konsynski (2021) add that the behavior of one participant can encourage the adoption of data-driven practices by others, highlighting the interdependence between data-driven culture and data ecosystems. Thus, the dissemination of a data-driven culture does not occur in isolation but is shaped by interactions among actors and the way data are shared and utilized.

In orchestrated data ecosystems, the presence of a central actor plays a significant role in this process. Otto et al. (2019) indicate that this actor establishes standards for data sharing and use, promoting greater alignment among participants. Guggenberger et al. (2020) and Gelhaar et al. (2021) further argue that by coordinating information flow and defining interoperability guidelines, the orchestrating actor reduces fragmentation and increases the predictability of interactions. Additionally, Kremser and Brunauer (2019) and Chatterjee et al. (2021) highlight that promoting structured analytical practices and creating guidelines for strategic data use strengthen the adoption of a data-driven culture among organizations in ecosystems. Thus, the following proposition is presented:

As previously argued, from the RBV perspective, identifying organizational resources is necessary to assess their potential to generate sustainable competitive advantages (Wade & Hulland, 2004). The theory establishes four characteristics that resources must possess to create sustainable competitive advantages: value, rarity, imperfect imitability, and organization (VRI + O) (Barney & Clark, 2007). Thus, it is proposed that practices derived from a data-driven culture constitute an organizational resource within the RBV framework. However, a single data-driven practice alone may not generate a sustainable competitive advantage, as it can be easily imitated. Conversely, the combined set of data-driven practices is unique and may meet the necessary requirements to be considered a sustainable competitive advantage. This position aligns with foundational RBV literature, which argues that the combination of different resources can lead to sustainable competitive advantages (Barney & Clark, 2007).

It is therefore posited that the set of data-driven practices, including those fostered by the data ecosystem, can generate a sustainable competitive advantage under RBV criteria. When analyzed alongside Proposition 1, this proposition allows for an assessment of whether the data ecosystem, through the orchestrating actor, influences the competitive advantages of both the organization and the ecosystem itself. That is, whether an organization within a given data ecosystem can obtain competitive advantages by being part of this network. Table 1 synthesizes the RBV criteria for developing sustainable competitive advantages across the two levels of analysis: organization and orchestrated data ecosystem.

From a theoretical perspective, conceptualizing data-driven practices as an organizational resource provides an extended approach within RBV. Furthermore, analyzing the collective set of these practices as a single resource expands theoretical discussions, aligning with Barney and Clark’s (2007) view on resource complementarity. Given the dynamic nature of ecosystems, this perspective allows for an exploration of how the combination of data-driven practices can meet the VRI + O criteria and generate sustainable competitive advantages. Clarifying this relationship is relevant for understanding value creation in data ecosystems, where interoperability and actor interactions directly influence organizational competitiveness. Thus, the following proposition is presented:

Analyzing the set of data-driven practices as a strategic resource from the RBV perspective suggests that these practices generate value for both organizations and the data ecosystem. This effect is directly linked to the cooperation and interoperability that characterize data ecosystems (Curry & Ojo, 2020; Otto et al., 2019). When an actor adopts specific data-driven practices, their impact extends to other participants in the ecosystem, fostering the diffusion of a data-driven culture. One example is compliance with general data protection laws, where the implementation of governance standards by one organization can establish new benchmarks for secure information sharing, encouraging other organizations to adopt similar practices (Kira, Sinha, & Srinivasan, 2021). In the financial sector, the National Central Bank plays this role by setting guidelines that directly influence the data-driven practices of the banking ecosystem (Al Wahshi, Foster, & Abbott, 2022). Similarly, major retailers such as Amazon define analytical standards that shape the practices adopted by suppliers and partners (Bagnoli, Meggiorin, & Lucchese, 2022).

The literature reinforces the argument that data-driven practices enhance decision-making within organizations. Anderson (2015) emphasizes that structured data access reduces uncertainty and supports more informed decisions, while Chatterjee et al. (2021) highlight that automating analytical processes makes decisions faster and more consistent. Kremser and Brunauer (2019) argue that governance policies establish an environment in which information circulates reliably. When combined, practices such as business intelligence dashboards, analytics, data science, big data, modeling, and forecasting form a cohesive set that, under the RBV framework, can be classified as a strategic resource capable of meeting the criteria for sustainable competitive advantage.

In orchestrated data ecosystems, the influence of the central actor further strengthens this process. Guggenberger et al. (2020) argue that orchestration creates an environment where interaction coordination and structured guidelines facilitate data sharing. In this context, the set of data-driven practices not only enhances information usage but also strengthens collaboration and alignment among participants. The standardization promoted by the orchestrating actor facilitates data generation and quality assurance, making their use more efficient and enabling organizations to improve their analytical capabilities (Al Wahshi et al., 2022). The effect of this structuring is reflected in the creation of a more cohesive ecosystem oriented toward the strategic use of data.

Additionally, the role of the orchestrating actor is important in reducing barriers to the adoption of data-driven practices. According to KPMG (2021), the lack of clear guidelines and limited data access are challenges to the widespread adoption of this approach. By establishing policies and promoting interoperability, the orchestrating actor enhances the ecosystem’s ability to structure data in an accessible and reliable manner. Anderson (2015) reinforces that data democratization is essential for consolidating a data-driven culture, and in this regard, the orchestrating actor facilitates the process by creating conditions for data to be strategically utilized. Thus, the coordination established within the ecosystem strengthens the data-driven culture and enables the development of sustainable competitive advantages for both organizations and the ecosystem as a whole. Based on these arguments, the following propositions are presented:

The conceptual model developed in this theoretical study proposes that, in orchestrated data ecosystems, the orchestrating actor influences the adoption of data-driven practices by participating organizations, fostering the development of a data-driven culture and consequently contributing to the achievement of sustainable competitive advantages. From the RBV perspective, it is argued that the collective set of these practices can be understood as a strategic resource that meets the VRI + O criteria, enabling the attainment of sustainable competitive advantages for both organizations and the data ecosystem. Thus, the model advances RBV theory by contextualizing data-driven culture and practices within the logic of data ecosystems, aligning with Barney et al. (2011) perspective on the role of intangible resources, such as culture, in refining the theory. Figure 2 illustrates the conceptual model.

The implications of the model reinforce the role of data-driven practices and the orchestrating actor in structuring data-driven culture as a strategic resource within the RBV framework. Chatterjee et al. (2021) and Kremser and Brunauer (2019) highlight that practices such as data access and sharing, data literacy, and strategic indicator definition contribute to transforming organizational culture into a valuable, rare, and inimitable asset. Gelhaar et al. (2021) complement this view by arguing that governance and interoperability promoted by the orchestrating actor create an environment conducive to the adoption of data-driven practices, strengthening collaboration and integration within the ecosystem. By fostering a data-driven culture, the orchestrating actor enhances both the analytical capacity of organizations and the data ecosystem in which they operate.

Additionally, the proposed conceptual model offers a theoretical structure for understanding how a data-driven culture emerges in orchestrated data ecosystems. Organizational culture is frequently cited as one of the main challenges to the success of data-driven initiatives (MIT Sloan Management Review, 2020), and the model demonstrates how the central actor in the ecosystem can encourage data-driven practices aligned with RBV’s value, rarity, inimitability, and organization (VRI + O) criteria (Barney & Clark, 2007). By linking data-driven practices to sustainable competitive advantage, the model provides a theoretical foundation for understanding their impact on organizational innovation and efficiency, as highlighted in reports on data-driven strategy and performance (Forbes, 2024; KPMG, 2021).

Beyond its theoretical foundation, the proposed conceptual model opens new possibilities for the practical application of data-driven culture in orchestrated data ecosystems. Haude et al. (2024) emphasize that while technological infrastructure facilitates implementation, the true competitive differentiator lies in an organization’s ability to integrate data-driven practices into its strategy, strengthening governance and promoting the effective use of data. In this context, the orchestrating actor serves as a catalyst for this process, encouraging the structured adoption of data-driven practices and fostering alignment among ecosystem participants (Chatterjee et al., 2021). Thus, the conceptual model contributes both to the advancement of RBV theory and to enhancing competitiveness in orchestrated data ecosystems.

This conceptual paper examined the relationship between data-driven culture, orchestrated data ecosystems, and sustainable competitive advantage, using the Resource-Based View (RBV) as a theoretical framework. Grounded in an integrative literature review and the theory-building from literature approach (Webster & Watson, 2002; Jaakkola, 2020), the study proposed a conceptual model that articulates the influence of the orchestrating actor on the adoption of data-driven practices and its relevance for the creation of sustainable competitive advantage.

It was argued that the set of data-driven practices can be understood as a strategic resource that meets RBV’s VRI + O criteria, constituting a competitive differentiator for both individual organizations and the data ecosystem in which they operate (Barney & Clark, 2007). In orchestrated data ecosystems, the orchestrating actor is fundamental to coordinating information flows, establishing guidelines that structure data use, and encouraging the adoption of these practices among participants, fostering the development of a data-driven culture (Guggenberger et al., 2020; Gelhaar et al., 2021). By integrating RBV concepts into data ecosystems, this study contributes to the literature by demonstrating how a data-driven culture can be analyzed as an intangible resource that supports the development of sustainable competitive advantages (Barney & Clark, 2007).

Beyond its theoretical contribution, the proposed conceptual model advances the understanding of a data-driven culture as a strategic resource, whose adoption, facilitated by the orchestrating actor, can generate sustainable competitive advantages for both organizations and the ecosystem. By integrating RBV into the analysis of data ecosystems, the study reinforces the importance of orchestration in structuring participant interactions and coordinating information flows, ensuring that data-driven practices are implemented in a structured manner aligned with ecosystem objectives.

This study relied on theoretical construction to develop a conceptual model that structures the relationship between data-driven culture, orchestrated data ecosystems and sustainable competitive advantage. However, as a conceptual paper, it presents inherent limitations, particularly the lack of empirical validation. Future research should empirically test the proposed model across different organizational contexts, assessing its applicability and identifying which data-driven practices contribute most significantly to the development of sustainable competitive advantages. Additionally, further studies could examine how the orchestrator’s characteristics influence the diffusion of data-driven practices within the ecosystem.

This research was supported by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Brazil.