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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/tjytozdoxngpdm7r2xx36dlknu" style="color: black;">Journal of Global Information Management</a>
From the perspective of collaborative innovation risk, the relationship between different risks and IP is explored based on the BPNN (Back Propagation Neural Network) model. Then, the SE (Synergy Effect) and the DC (Dynamic Capability) are introduced as intermediary and moderating variables. Following specific enterprise data input, the relationship between collaborative innovation risk and IP is analyzed based on deep learning and its endogenous mechanism. Conclusion: the analysis model of<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.4018/jgim.298673">doi:10.4018/jgim.298673</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ufc44hy2kjfkpesm2kxt5g7j24">fatcat:ufc44hy2kjfkpesm2kxt5g7j24</a> </span>
more »... rprise IP based on deep learning BPNN can well process enterprise data; different types of collaborative innovation risks in industrial parks have significantly negative effects on IP; the negative effect of organizational collaborative risk on IP is −0.268; apart from market risk factors, the other collaborative innovation methods further hinder the improvement of IP by inhibiting SE; apart from the risk of benefit distribution, the other collaborative innovation risks and IP are negatively regulated by DC, and the regulation effect on the risk of innovative factor input is the largest.
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