Oliver Gassmann

The goal of an ecosystem is the creation of a value proposition firms could not offer alone. But how are new value propositions being defined in emerging ecosystems? Our study with eight cases yields several implications along these lines: First, how to come up with an initial idea for a value proposition and convince partners to commit to it? And, second, how to setup the iterative process of value proposition adaption? And how to find a way to align both customer and partner interests along that way? Overall, this helps to understand how factors in the early stages of ecosystem development affect its value proposition.

Leading pharmaceutical companies have historically been engaged in all stages of the R&D pipeline, from target identification through to drug marketing, and thus have been termed fully integrated pharmaceutical companies (FIPCOs). In the past decade, externally invented assets gained through partnering or mergers and acquisitions (M&As) have become a prominent component of the pipelines of such companies. Given the long development cycles and the increasing level of collaboration between pharma companies and biotech companies, however, the precise origins of innovation are not obvious, in part as regulatory approvals for innovative drugs by agencies such as the FDA are granted to the company filing the applications, which is not necessarily the company that discovered the active pharmaceutical ingredient. Understanding the origins of innovation provides a deeper understanding of the pharma business model and could support strategic improvements. We therefore systematically assessed the origins of innovation for all new molecular entities (NMEs) and new therapeutic biologics (NTBs) approved between 2015 and 2021 by the Center of Drug Evaluation and Research (CDER) of the US Food and Drug Administration (FDA) for the top 20 biopharma companies globally by sales in 2020 (see Supplementary information for details).

R&D productivity continues to be the industry's grand challenge. We analyzed the R&D input, output, and outcome of 16 leading research-based pharmaceutical companies over 20 years (2001-2020). Our analysis shows that pharma companies increased their R&D spending at a compound annual growth rate of 6% (2001-2020) to an average R&D expenditure per company of $6.7 billion (2020). The companies in our investigation launched 251 new drugs representing 46% of all CDER-related FDA approvals in the past 20 years. The average R&D efficiency of big pharma was $6.16 billion total R&D expenditures per new drug. Almost half of the leading companies needed to compensate for their negative R&D productivity through mergers and acquisitions.

This paper depicts smart city initiatives as hybrid organizations that incorporate competing institutional logics. These logics are not equally pronounced across initiatives. The salience of the logics affects initiatives’ selective coupling with economic, social, and environmental sustainability and, hence, their triple-layered sustainability performance. We suggest that the performance-related selective coupling is driven by two mechanisms: leveraging experience and seeking legitimacy. An empirical analysis of 251 initiatives supports our arguments. In line with the theory of selective coupling, we further find that initiatives embedded in a market logic display better overall sustainability performance than those embedded in a social welfare logic.

Artificial Intelligence (AI) reshapes companies and how innovation management is organized. Consistent with rapid technological development and the replacement of human organization, AI may indeed compel management to rethink a company’s entire innovation process. In response, we review and explore the implications for future innovation management. Using ideas from the Carnegie School and the behavioral theory of the firm, we review the implications for innovation management of AI technologies and machine learning-based AI systems. We outline a framework showing the extent to which AI can replace humans and explain what is important to consider in making the transformation to the digital organization of innovation. We conclude our study by exploring directions for future research.