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Leveraging interdependencies among platform and complementors in innovation ecosystem.

ABSTRACT: In the platform-based innovation ecosystem, the symbiotic evolution and interaction between the participating entities have drawn extensive attention. However, there is a lack of understanding of the co-specialization and co-innovation among members of the innovation ecosystem. This paper addresses this gap. In this paper, the collaborative innovation between a platform firm and different kinds of complementors, and its effects on the performance of the innovation ecosystem are simulated through an improved NK model. The result shows that the co-innovation performance between generalist complementors and the platform firm is generally outperformed than that between the specialist complementors and the platform firm. As the interdependencies between the complementary components get stronger, the innovation performance of the innovation ecosystem influenced by the interaction between different types of complementors and platform firms tend to be an inverted U-shape.

SUBMITTER: Tang F

PROVIDER: S-EPMC7535022 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Leveraging interdependencies among platform and complementors in innovation ecosystem.

Tang Fangcheng F Qian Zeqiang Z

PloS one 20201005 10

In the platform-based innovation ecosystem, the symbiotic evolution and interaction between the participating entities have drawn extensive attention. However, there is a lack of understanding of the co-specialization and co-innovation among members of the innovation ecosystem. This paper addresses this gap. In this paper, the collaborative innovation between a platform firm and different kinds of complementors, and its effects on the performance of the innovation ecosystem are simulated through ...[more]

PMID: 33017407

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Project description:Open-source software tools are often used for analysis of scientific image data due to their flexibility and transparency in dealing with rapidly evolving imaging technologies. The complex nature of image analysis problems frequently requires many tools to be used in conjunction, including image processing and analysis, data processing, machine learning and deep learning, statistical analysis of the results, visualization, correlation to heterogeneous but related data, and more. However, the development, and therefore application, of these computational tools is impeded by a lack of integration across platforms. Integration of tools goes beyond convenience, as it is impractical for one tool to anticipate and accommodate the current and future needs of every user. This problem is emphasized in the field of bioimage analysis, where various rapidly emerging methods are quickly being adopted by researchers. ImageJ is a popular open-source image analysis platform, with contributions from a global community resulting in hundreds of specialized routines for a wide array of scientific tasks. ImageJ's strength lies in its accessibility and extensibility, allowing researchers to easily improve the software to solve their image analysis tasks. However, ImageJ is not designed for development of complex end-to-end image analysis workflows. Scientists are often forced to create highly specialized and hard-to-reproduce scripts to orchestrate individual software fragments and cover the entire life-cycle of an analysis of an image dataset. KNIME Analytics Platform, a user-friendly data integration, analysis, and exploration workflow system, was designed to handle huge amounts of heterogeneous data in a platform-agnostic, computing environment and has been successful in meeting complex end-to-end demands in several communities, such as cheminformatics and mass spectrometry. Similar needs within the bioimage analysis community led to the creation of the KNIME Image Processing extension which integrates ImageJ into KNIME Analytics Platform, enabling researchers to develop reproducible and scalable workflows, integrating a diverse range of analysis tools. Here we present how users and developers alike can leverage the ImageJ ecosystem via the KNIME Image Processing extension to provide robust and extensible image analysis within KNIME workflows. We illustrate the benefits of this integration with examples, as well as representative scientific use cases.

Project description:BackgroundThe development of Electronic Health Records (EHRs) in hospitals offers the ability to reuse data from patient care activities for clinical research. EHR4CR is a European public-private partnership aiming to develop a computerized platform that enables the re-use of data collected from EHRs over its network. However, the reproducibility of queries may depend on attributes of the local data. Our objective was 1/ to describe the different steps that were achieved in order to use the EHR4CR platform and 2/ to identify the specific issues that could impact the final performance of the platform.MethodsWe selected three institutional studies covering various medical domains. The studies included a total of 67 inclusion and exclusion criteria and ran in two University Hospitals. We described the steps required to use the EHR4CR platform for a feasibility study. We also defined metrics to assess each of the steps (including criteria complexity, normalization quality, and data completeness of EHRs).ResultsWe identified 114 distinct medical concepts from a total of 67 eligibility criteria Among the 114 concepts: 23 (20.2%) corresponded to non-structured data (i.e. for which transformation is needed before analysis), 92 (81%) could be mapped to terminologies used in EHR4CR, and 86 (75%) could be mapped to local terminologies. We identified 51 computable criteria following the normalization process. The normalization was considered by experts to be satisfactory or higher for 64.2% (43/67) of the computable criteria. All of the computable criteria could be expressed using the EHR4CR platform.ConclusionsWe identified a set of issues that could affect the future results of the platform: (a) the normalization of free-text criteria, (b) the translation into computer-friendly criteria and (c) issues related to the execution of the query to clinical data warehouses. We developed and evaluated metrics to better describe the platforms and their result. These metrics could be used for future reports of Clinical Trial Recruitment Support Systems assessment studies, and provide experts and readers with tools to insure the quality of constructed dataset.

Dataset Information

Leveraging interdependencies among platform and complementors in innovation ecosystem.

Publications

Leveraging interdependencies among platform and complementors in innovation ecosystem.

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