Digital innovation activities are data-driven, and the process of organizational digital innovation is inevitably influenced by their key participants, employees, as well as changes in the social institutional environment. How government support and employee structure impact organisational digital innovation was examined in this study. Since digital innovation activities are data-driven, the mediating role of data flows within digital innovation ecosystems was explored. A quantitative research design was employed, and data were collected by a survey from 299 firms in China. Results of structural equation modelling using SPSS and AMOS reveal that government support for enterprises in terms of policies and services, as well as the employee structure within enterprises, have a direct impact on organisational digital innovation. Data flows within digital innovation ecosystems mediate the relationship between government support and organisational digital innovation activities. Our findings provided evidence for theories of digital innovation ecosystems and employee-driven digital innovation. The results and conclusions in this study can provide reference for enterprises to achieve digital innovation breakthroughs, and for policymakers to formulate digital-related policies and regulations.
This work is to investigate the diagnostic value of a deep learning-based magnetic resonance imaging (MRI) image segmentation (IS) technique for shoulder joint injuries (SJIs) in swimmers. A novel multi-scale feature fusion network (MSFFN) is developed by optimizing and integrating the AlexNet and U-Net algorithms for the segmentation of MRI images of the shoulder joint. The model is evaluated using metrics such as the Dice similarity coefficient (DSC), positive predictive value (PPV), and sensitivity (SE). A cohort of 52 swimmers with SJIs from Guangzhou Hospital serve as the subjects for this study, wherein the accuracy of the developed shoulder joint MRI IS model in diagnosing swimmers' SJIs is analyzed. The results reveal that the DSC for segmenting joint bones in MRI images based on the MSFFN algorithm is 92.65%, with PPV of 95.83% and SE of 96.30%. Similarly, the DSC for segmenting humerus bones in MRI images is 92.93%, with PPV of 95.56% and SE of 92.78%. The MRI IS algorithm exhibits an accuracy of 86.54% in diagnosing types of SJIs in swimmers, surpassing the conventional diagnostic accuracy of 71.15%. The consistency between the diagnostic results of complete tear, superior surface tear, inferior surface tear, and intratendinous tear of SJIs in swimmers and arthroscopic diagnostic results yield a Kappa value of 0.785 and an accuracy of 87.89%. These findings underscore the significant diagnostic value and potential of the MRI IS technique based on the MSFFN algorithm in diagnosing SJIs in swimmers.