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| Format: | Recurso digital |
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Zenodo
2025
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| Online Access: | https://doi.org/10.5281/zenodo.17920436 |
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Table of Contents:
- <p><span>Organizational behavior research clearly indicates that in remote work scenarios, individual task behavior, collaborative behavior, psychological safety perception, communication delays, and team dependency structures all significantly impact organizational performance. Incorporating these behavioral mechanisms into a mathematical description can form a new optimization dynamic model. This study constructs a novel swarm intelligence optimization algorithm based on key variables of remote work organizational behavior: Remote Collaborative Dynamics Optimization (RCDO). The algorithm introduces organizational behavioral variables such as remote attention drift factor, cross-time zone communication damping factor, collaborative potential field, psychological safety constraint potential, and social presence gradient. This paper proposes several novel mathematical mechanisms never before seen in optimization algorithms, including: a remote collaboration tensor update mechanism, an attention contraction equation, a virtual presence field gravity model, and a path integral adjustment mechanism for the social trust field. This paper also provides the complete mathematical derivation of the algorithm, the construction of the dynamic model, variable definitions, optimization rules, and iterative mechanisms, and reconstructs the entire computational logic using rigorous pure-text mathematical formulas.</span></p> <p> </p> <p><span>This algorithm provides a new paradigm for the migration of organizational behavior theory to computational intelligence, offers theoretical support for the quantitative understanding of remote organizational effectiveness, and provides a novel solution framework for complex optimization problems with characteristics of human organizational interaction.</span></p>