Ecosystem theory has emerged as a useful conceptualization for understanding the logic of business and innovation in complex business environments. The theory has especially helped researchers model the interrelations among social, environmental, and technological factors, which are all necessary conditions for innovation. However, several critics have called for more conceptual and methodological rigor in ecosystem research. The purpose of this article is to address the challenge that ecosystem theory lacks a congruent perspective of the conceptual structure of the theory. To that end, the article presents a formal concept analysis of 92 definitions of business, innovation, entrepreneurial and platform ecosystems. The analysis identifies and critically reviews the conceptual structure of ecosystem theory and concludes that the theory is largely congruent. Different perspectives are related to centralization and process- vs. structure- based views of ecosystems as well as to specific conceptualizations of ecosystem types. Two arguments are proposed based on the analysis. First, ecosystem theory serves the purpose of explaining activities and relations between actors that engage in interorganizational innovation if the theory is considered in terms of structure, beyond its metaphorical meaning. On the other hand, current ecosystem theory includes fewer substantive conceptualizations of assets, outcomes, and institutions. Second, the analysis shows that ecosystem theory operates at a high level of abstraction and remains vague in terms of what elements constitute an ecosystem; therefore, the theory would benefit from further theorization on microstructures. In addition, the article discusses potential research gaps and proposes research strategies for further ecosystem theorizing. 

There is a rapid growth of research on ecosystems, but the critics point out that ecosystem concepts are not rigorously defined and that there is no general agreement on the definitions. This has led to challenges in operationalizations of the ecosystem concepts. To address the gap in understanding ecosystem research methodology, we examine the choice of methods in 944 empirical ecosystem research articles and argue that Social Network Analysis (SNA) as a research method can improve ecosystem research, as it provides a rich set of methods and metrics for the study of ecosystems as structures. We find several issues related to operationalization of the ecosystem concepts in empirical research, discuss the appropriateness of method choices and point out that these issues lead to challenges in theorization based on empirical research. Also, we find that SNA has two potential methodological bridging positions in ecosystem research. First, SNA provides a natural methodological counterpart to the ecosystem conceptualizations that are focused on ecosystems as structures or complex systems. Second, SNA can be used creatively together with other qualitative and quantitative research methods. Further, we identify methodological research gaps in ecosystem research and propose a research agenda for SNA in ecosystem research. Therefore, this review extends the critical ecosystem research from the critique of ecosystem concepts to a less developed topic of methodological questions in ecosystems research. 

Understanding of business environments is a universal managerial problem, and the ecosystem theory has provided useful conceptualization for making sense of the logic of business and innovation in complex environments. Still, the use of the concept in empirical research has proven to be a methodological challenge, much due to the lack of methodological development, especially related to ecosystem microstructures, quantitative methods, and comparative research. Therefore, the purpose of this article is to address the shortcomings in the ecosystem methodology by developing a methodological framework for understanding ecosystems based on their microstructures. For this, we propose and operationalize a methodological framework that is based on Social Network Analysis and event data, such as financial transaction data. The central features of the framework are flows between actors that form actor-level -networks, and system-level network structures that emerge from these microfoundations. An argument is made that the network structures can be analyzed using Social Network Analysis methods and metrics. Based on an analysis of a network of 2 buyers and 412 suppliers, the operationalization illustrates how ecosystem-level properties, such as actor roles, interdependency, and business goals, emerge from bilateral ties between buyers and sellers. The benefits of using event data for ecosystem analysis are that data is comprehensive, and analysis methods are scalable, efficient, and possible to automate. Therefore, the methodology enables quantitative and comparative research, and efficient analysis of ecosystem microstructures. Such improvements in the cost–benefit ratio related to ecosystem analysis may have wide implications for research and practice in the future.

The concept of the innovation ecosystem has attracted much research in strategy and innovation fields. Recently, several authors have criticized the conceptual and methodological rigor in innovation ecosystem research and called for research focusing on the systemic nature of innovation ecosystems. This paper builds on these critical accounts by developing a methodological approach for innovation ecosystem research. The paper shows how a research project may progress from qualitative data collection to quantitative SNA analysis and presentation and demonstrates protocols to ensure rigor in each of the research project phases. The methodological approach allows for a multi-level, multi-layer innovation ecosystem research. Also, an argument is made that this methodological approach may pave the way towards conceptually and methodologically rigorous innovation ecosystem research, which is a necessary condition for ecosystem performance analysis or comparative ecosystem research. 

Purpose:The purpose of this article is to increase the knowledge of real estate firms’ capabilities to innovate and, consequently, their capacity to absorb new innovations and benefit from digital technologies in an ecosystem context.Design/methodology/approach:The results are based on 32 interviews with representatives of Swedish real estate owners, real estate owner industry associations and suppliers of digital technology to real estate owners. The data is interpreted using theories on absorptive capacity (i.e. the capacity to absorb new innovations), innovation capabilities, and innovation ecosystems.Findings:The real estate owners, technology suppliers, and real estate owner industry associations have expanded their innovation capabilities and reshaped their innovation ecosystem by initiating a number of different digitalization activities, for example, development of new IT systems, digital platforms, services and business models. The absorptive capacity has been improved as the organizations have changed routines and structures related to innovation and they have taken on new roles related to digitalization and innovation, making them better able to absorb new innovations. Also, this article identifies several drivers and obstacles to digitalization in the real estate sector.Research limitations/implications:The increased capabilities related to digitalization can lead to better absorptive capacity on an individual firm level, which can contribute to the overall development of these firms on a longer term. Also, new capabilities may lead to better absorptive capacity in the real estate sector at large, as firms may benefit from each other’s capabilities through collaboration. The limitations are that this study does not interview tenants or facility management firms, and that the findings represent the context of the Swedish real estate market.Originality/value:This article investigates innovation capabilities, absorptive capacity and innovation ecosystems of real estate owners, their technology suppliers and real estate owner industry associations on the organizational level and on the sector level, into which there is little previous research. Also, the article highlights the novelty of digitalization as a phenomenon in the sector. 

This paper presents a research agenda for Social Network Analysis (SNA) in Digitalization in Sustainable Built Environment research (DSBE). The paper contains a description of SNA and reviews the use of SNA in DSBE research. Also, the paper identifies future research challenges that SNA can help resolve. 

A literature review of DSBE finds that of 506 articles in 36 journals, only 1 article includes SNA. This is surprising, given the growing interest in SNA in digitally related fields. We review the 506 DSBE articles and analyze how SNA can contribute to the field. To understand how SNA can be used in DSBE research, we also analyze how SNA has been applied in Digitalization in Built Environment research that is not focused on sustainability. 

Findings are that SNA can be a useful and powerful tool because it can help in analysis of how multiple actors interact, which is a common theme in built environment research. An example is that architects, contractors, and developers are separate actor groups that can improve sustainable solutions through networked cooperation. Another area in which SNA can advance research is in the study of how actor networks of companies or people relate to networks of data or physical buildings or sensors. An example is that actors may change their behavior based on sensor data, and SNA can study sensor data networks and actor behavior networks as interconnected layers. 

Methodological advances are key to advancing research, and SNA can make it possible for the built environment area to advance sustainability and digitalization research, because SNA has a rich array of empirical and conceptual tools for the study of networks.