ADVANCED COMPUTATIONAL SIGNAL- AND DATA-DRIVEN ANALYTICS FOR MODELING LEADERSHIP AND ORGANISATIONAL BEHAVIOR
DOI:
https://doi.org/10.29284/256erm77Keywords:
Signal-Based Analytics, Temporal Networks, Leadership Modeling, Organisational Behaviour; Case StudyAbstract
The recent advances in signal- and data-driven analytics have enabled systematic modeling of the complex human-centred systems on the basis of data on interaction. The paper proposed a computational model of the analysis of the leadership and organisational behaviour relying on the organisational interactions representation as a time-dependent signal and networks. The approach applied to test the validity of the framework was the case-study approach which applied the real interaction records of organisations. The temporal network analysis and centrality-based indicators of the dynamics of influence in the leadership role were used to study the behavioural evolution. Change-point detection methods were detecting statistically significant behavioural transitions that related to organisational restructuring phases. It was identified that stability of leadership patterns and network structures was stable within robustness and sensitivity analysis of different time aggregation and the modelling settings. The results indicated that the impact of leadership was dynamic and relied on interaction signaling as compared to the official role posture. The work used the concepts of signal-processing to organisations and behaviour, with the combination of signal representation and temporal network modelling, and the validity of the results. The findings suggest the topicality of the recent signal and data analytics to gain insights into the complex organisational systems and contribute to the methodological problems in the context of the International Journal of Advances in Signal and Image Sciences.
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This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
