Organizational behaviour is one of the most important assets of an organization; it may increase business value and profitability. Although it comes to life itself with the sector’s contribution, leadership, environment, market conditions and competition, unemployment etc., there must be some ways to lead or direct an organizational behaviour. However, it is not practical to monitor and control organizational behaviour in detail with bare eyes. Technology-wise, we are not away from building a system to learn, understand and monitor the organizational behaviour and give alarms or signals to top management when the system perceives an extraordinary condition. This is doable with machine learning algorithms which is a model of Artificial Intelligence (AI). In the literature, there are plenty of machine learning algorithms that have been proved that they work or learn very well. Both supervised and unsupervised algorithms may be used to learn organizational behaviour and detect anomalies in the behaviour. In this paper, we propose a novel system to build a data warehouse with he data available in an organization and design it for learning the organizational behavior by machine, i.e. computers. The system we propose may be used to spot daily, weekly or monthly changes in organizational behaviour. These changes may have positive or negative effects on the performance of organizations, so this system may also be used as a decision support system for top management. When necessary feedback is given to the system, it may also develop or learn new features to interpret the effect of the change on the organization’s overall performance. The system may also be used to compare the changes in organizational behaviour every year. Nevertheless, it is a useful tool to track, learn and monitor the interactions of each employee in the system. In the study, human resources management has been used as a sample model to detail the proposed system.

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Silahtaroglu, G., & Vardarler, P. (2016). A novel data mining study to spot anomalies in organizations: A human resources management case. International Journal of Business and Administrative Studies, 2(4), 89-95.