This study explores the influence of the power of family business successors on firm innovation under the theory of social embeddedness. Based on the 2000-2019 unbalanced panel data of listed Chinese family enterprises, this study empirically examines the differences in the influence of the implicit and explicit power of successors on incremental and radical innovation respectively. Our findings show that explicit power has a more positive impact on incremental innovation, while implicit power is more conducive to promoting radical innovation. In addition, the study finds that the reason why the explicit power of succession does not have a significant impact on radical innovation, that is, the reason why board dissent is not related to radical innovation, is that some of the major innovation decisions in the enterprise are not all made at formal meetings. The research conclusions not only extend the theoretical application of social embeddedness in family enterprises, but also provide certain practical guidance for promoting enterprise innovation.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9662740 | PMC |
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0275603 | PLOS |
Prediction-powered inference (PPI) [1] and its subsequent development called PPI++ [2] provide a novel approach to standard statistical estimation leveraging machine learning systems to enhance unlabeled data with predictions. We use this paradigm in clinical trials. The predictions are provided by disease progression models, providing prognostic scores for all the participants as a function of baseline covariates.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Institute of Theoretical & Applied Informatics, Polish Academy of Sciences (IITiS-PAN), 44-100 Gliwice, Poland.
Edge computing systems must offer low latency at low cost and low power consumption for sensors and other applications, including the IoT, smart vehicles, smart homes, and 6G. Thus, substantial research has been conducted to identify optimum task allocation schemes in this context using non-linear optimization, machine learning, and market-based algorithms. Prior work has mainly focused on two methodologies: (i) formulating non-linear optimizations that lead to NP-hard problems, which are processed via heuristics, and (ii) using AI-based formulations, such as reinforcement learning, that are then tested with simulations.
View Article and Find Full Text PDFPNAS Nexus
January 2025
Max Planck Institute for Intelligent Systems, Tübingen 72076, Germany.
Networked datasets can be enriched by different types of information about individual nodes or edges. However, most existing methods for analyzing such datasets struggle to handle the complexity of heterogeneous data, often requiring substantial model-specific analysis. In this article, we develop a probabilistic generative model to perform inference in multilayer networks with arbitrary types of information.
View Article and Find Full Text PDFPharm Stat
January 2025
Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA.
A recent study design for clinical trials with small sample sizes is the small n, sequential, multiple assignment, randomized trial (snSMART). An snSMART design has been previously proposed to compare the efficacy of two dose levels versus placebo. In such a trial, participants are initially randomized to receive either low dose, high dose or placebo in stage 1.
View Article and Find Full Text PDFJ Pathol Inform
January 2025
University of Michigan Medical School, Department of Pathology, 2800 Plymouth Road, Ann Arbor, MI 48109-2800, USA.
Digital pathology is a tool of rapidly evolving importance within the discipline of pathology. Whole slide imaging promises numerous advantages; however, adoption is limited by challenges in ease of use and speed of high-quality image rendering relative to the simplicity and visual quality of glass slides. Herein, we introduce Iris, a new high-performance digital pathology rendering system.
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