As digital platforms are transforming almost every industry today, they are slowly finding their way into the mainstream information systems (ISs) literature. Digital platforms are a challenging research object because of their distributed nature and intertwinement with institutions, markets and technologies. New research challenges arise as a result of the exponentially growing scale of platform innovation, the increasing complexity of platform architectures and the spread of digital platforms to many different industries. This paper develops a research agenda for digital platforms research in IS. We recommend researchers seek to (1) advance conceptual clarity by providing clear definitions that specify the unit of analysis, degree of digitality and the sociotechnical nature of digital platforms; (2) define the proper scoping of digital platform concepts by studying platforms on different architectural levels and in different industry settings; and (3) advance methodological rigour by employing embedded case studies, longitudinal studies, design research, data-driven modelling and visualisation techniques. Considering current developments in the business domain, we suggest six questions for further research: (1) Are platforms here to stay? (2) How should platforms be designed? (3) How do digital platforms transform industries? (4) How can data-driven approaches inform digital platforms research? (5) How should researchers develop theory for digital platforms? and (6) How do digital platforms affect everyday life?

This paper explores how service value is created in a network context and how the structure and dynamics of the value network as well as customer expectations influence the complexity of the services ecosystem. The paper then discusses what transformative role information and communication technology (ICT) plays in coordinating and delivering value and managing this complexity. A conceptual model is developed for understanding and investigating the nature, delivery, and exchange of service value and assessing the complexity of a service value network. Three central arguments are presented. First, value in the services economy is driven and determined by the end consumer and delivered through a complex web of direct and indirect relationships between value network actors. Second, the complexity of service value networks not only depends on the number of actors but also on the conditional probabilities that these actors are involved in delivering the service to the consumer. Third, ICT plays a central role in reducing complexity for consumers by providing greater levels of value network integration, information visibility, and means to manage and anticipate change.

The mobile ecosystem is characterized by a large and complex network of companies interacting with each other, directly and indirectly, to provide a broad array of mobile products and services to end-customers. With the convergence of enabling technologies, the complexity of the mobile ecosystem is increasing multifold as new actors are emerging, new relations are formed, and the traditional distribution of power is shifted. Drawing on theories of network science, complex systems, interfirm relationships, and the creative art and science of visualization, this paper identifies key players and maps the complex structure and dynamics of nearly 7000 global companies and over 18,000 relationships in the converging mobile ecosystem. Our approach enables decision makers to (i) visually explore the complexity of interfirm relations in the mobile ecosystem, (ii) discover the relation between current and emerging segments, (iii) determine the impact of convergence on ecosystem structure, (iv) understand a firm's competitive position, and (v) identify interfirm relation patterns that may influence their choice of innovation strategy or business models.

Understanding and managing supply chain risks is a critical functional competency for today's global enterprises. A lack of this competency can have significant negative outcomes, including costly production and delivery delays, loss of future sales, and a tarnished corporate image. The ability to identify and mitigate risks, however, is complicated as supply chains are becoming increasingly global, complex, and interconnected. Drawing on the complex systems and epidemiology literature, and using a computational modeling and network analysis approach, we examine the impact of global supply network structure on risk diffusion and supply network health and demonstrate the importance of supply network visibility. Our results show a significant association between network structure and both risk diffusion and supply network health. In particular, our results indicate that small‐world supply network topologies consistently outperform supply networks with scale‐free characteristics. Theoretically, our study contributes to our understanding of risk management and supply networks as complex networked systems using a computational approach. Managerially, our study illustrates how decision makers can benefit from a network analytic approach to develop a more holistic understanding of system‐wide risk diffusion and to guide network governance policies for more favorable health level outcomes. The article concludes by highlighting the main findings and discussing possibilities of future research directions.

Supply chains are continuously evolving and adapting systems driven by complex sociotechnical interfirm interactions. Traditional engineering and operations management modeling approaches have primarily focused on technical issues and are not well suited to effectively capture the many complex structural and behavioral aspects of supply chain systems (SCSs). There is growing recognition by the supply chain community of the significant benefits a network analytic lens can provide to understand, design, and manage SCSs. We systematically review and analyze the relevant literature and, drawing on a multidisciplinary theoretical foundation, develop an integrative framework. Our framework identifies three distinct, but interdependent themes that characterize the study of SCSs: SCS network structure (i.e., system architecture), SCS network dynamics (i.e., system behavior), and SCS network strategy (i.e., system policy and control). We elaborate on these themes, review key findings, identify the current limitations and knowledge gaps, and discuss the fundamental benefits derived from adopting an integrated SCSs perspective. We conclude with future research directions for network analysis in SCS design and management, in particular, and complex enterprise systems, in general. and Sussman, 2011; Bartolomei et al., 2012], a comprehensive understanding of the performance and behavior of supply chain systems (SCSs) therefore requires consideration of both technical and social issues. Traditional engineering and operations management modeling approaches have primarily focused on technical issues [Min and Zhou, 2002]. These approaches, however, are not well suited to effectively capture and describe the structural and behavioral complexities inherent in SCSs.An emerging interdisciplinary lens promising to overcome this theoretical and methodological gap is the use of network analysis approaches [Basole et al., 2011]. Network analysis draws on theories from the social, organizational, and complexity sciences and leverages graph theoretic methods to model, analyze, and visualize the structure, dynamics, and strategies that shape SCSs. There has been a surge in scholarly studies modeling an SCS as a complex network of interactions between system entities since the seminal work by Choi and colleagues [Choi, Dooley, and Rungtusanatham, 2001;Choi and Hong, 2002] and more recently Borgatti and Li [2009]. However, there is no organizing framework to facilitate an understanding of the plethora of supply chain management (SCM) issues examined using network analysis. Moreover, previous work on the use of network analysis in systems engineering (SE) is quite sparse. Notable exceptions include the work by Batallas and Yassine [2006], Braha and Bar-Yam [2006], Collins, Yassine, and Borgatti [2009], and Bartolomei et al. [2012], where the focus is almost entirely on product development; there are no SE studies that have used network analysis for the study of SCSs. Consequently, there is a window of opportunity to review and illustrate the va...