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Co-evolution of platform architecture, platform services, and platform governance: Expanding the platform value of industrial digital platforms (2102.04862v1)

Published 25 Jan 2021 in cs.SE

Abstract: Industrial manufacturers increasingly develop digital platforms in the business-to-business (B2B) context. This emergent form of digital platforms requires a profound yet little understood holistic perspective that encompasses the co-evolution of platform architecture, platform services, and platform governance. To address this research gap, our study examines multiple platform sponsors from an industrial manufacturing context. The study demarcates three platform archetypes: product platform, supply chain platform, and platform ecosystem. We argue that each platform archetype involves a gradual development of platform architecture, platform services, and platform governance, which mirror each other. We also find that each platform archetype is characterized by a specific innovation mechanism that contributes to the platform service discovery and expands the platform value. Our study extends the co-evolution perspective of platform ecosystem literature and digital servitization literature.

Citations (191)

Summary

  • The paper reveals a comprehensive framework by identifying three evolutionary phases—from data collection to AI enablement—that drive platform value.
  • It employs empirical analysis to demonstrate how the interplay of architecture, services, and governance fosters innovation in industrial digital ecosystems.
  • The findings underscore strategic recommendations for collaborative governance and open ecosystems to boost digital transformation in B2B contexts.

Co-evolution of Platform Architecture, Platform Services, and Platform Governance in Industrial Digital Platforms

The paper under review investigates the intricate dynamics underpinning industrial digital platforms, emphasizing their role within the business-to-business (B2B) setting. Focused on the co-evolution of platform architecture, platform services, and platform governance, this research presents a nuanced exploration of how these elements interrelate to enhance the overall platform value.

Overview of Industrial Digital Platforms

Industrial firms are increasingly relying on digital platforms to connect an array of IoT-enabled devices, promoting digital servitization as a pivotal transformation driver. This research identifies three primary archetypes of industrial platforms: product platforms, supply chain platforms, and platform ecosystems. Each archetype is characterized by specific architectural, service, and governance developments that advance the potential of digital transformation. As such, the paper extends our understanding of digital servitization literature by focusing on the holistic evolution of these platforms.

Evolutionary Phases

The development of industrial digital platforms unfolds across three distinct phases:

  1. Product Data Collection Phase: Here, firms focus on installing sensors and gathering data from various machines. This phase lays the groundwork for creating basic monitoring services characterized by machine-centric views and preliminary analysis.
  2. Analytics Utilization Phase: This phase involves leveraging advanced sensors and implementing cloud analytics to elevate data correlation and pattern recognition. It corresponds with optimization service development that integrates data across entire fleets rather than individual units, expanding the scope and depth of platform value.
  3. Artificial Intelligence Enablement Phase: Advanced AI techniques and open APIs define this phase, enhancing platform capabilities with autonomous solutions. The platform ecosystem architecture, coupled with governance that encourages third-party collaborative innovation, supports the creation of autonomous advisory services.

Numerical Findings and Implications

The research reveals distinct phases correlated with particular innovation mechanisms: search depth, search breadth, and recombination. For instance, monitoring services derived significant insights through the depth of data within specific machine datasets, whereas optimization services benefited from broader search strategies across diverse datasets. Autonomous services further extended platform value via recombination strategies, utilizing micro-services in novel configurations.

Theoretical and Practical Implications

Theoretically, the paper advances the literature on co-evolutionary processes and digital ecosystems by demonstrating how platform architecture, services, and governance need to mirror each other. It sheds light on the limitations of existing theories on platform competition, arguing for a cooperative model influenced by high switching costs and platform openness in the B2B context. Practically, it underscores the importance of strategic interactions between various stakeholders, indicating that platform sponsors need to encourage collaboration with both complementors and customers to maximize platform potential.

Future Directions

Given these insights, future research could elucidate the processes underlying the early phases of platform evolution, especially the transition from proprietary to open ecosystems. Furthermore, understanding how actor-specific data shapes platform governance and how industrial partnerships foster new governance frameworks offers fertile ground for further investigation. Additionally, more empirical studies could scrutinize the selective promotion of complementors and delve into the complexities of platform coopetition versus competition in industrial settings.

In conclusion, this paper contributes significantly to our comprehension of industrial digital platforms by articulating a comprehensive framework for their evolution. It uncovers the intertwined roles of technological architecture, governance structures, and service development in driving platform-based innovation, pointing towards strategic pathways for firms aiming to leverage digital transformation effectively.