In this study, the initial problem is the capacity of the human mind to set up a conceptual model. The novelty of this article is that we show that defined concepts created by the human mind can be passed on to an artificial intelligence-based expert system. The expert system helps the human mind to settle the logical connections between the defined terms and the conceptual model thus the created model will be better than what man could have set up without an expert system. The appearance and role of employee turnover has become a more and more important factor in the daily business of corporate life. Scientific journals have examined the positive and negative effects of it, which has also provided useful knowledge for practitioners. These articles have examined the impact of employee turnover in countless aspects, but one aspect was the same in all literature: the impact of turnover on a company is not negligible and cannot be ignored in terms of neither material nor moral aspects. As a starting point for our research, we systematically reviewed the literature on employee turnover and selected six concepts that are bilaterally related to our phenomenon. Based on the terms and the correlation of it, we created a conceptual model that was examined with the help of an artificial intelligence-based system. To select a system, we reviewed the classifications of the artificial intelligence-based systems which can model human decision making and can help our research. Relying on the processing of the literature review articles, we selected and briefly characterized a rule-based reasoning system, and investigated the rule constellations of it, which can model the turnover cases as the topic of our study. Based on our experience in observing, consulting, and working with decision-makers, we examined the aspects of employee turnover phenomenon in the analysis and we constructed a three-level model that found logical relationships between each subcategory and was able to realistically reflect certain behavioral patterns of the physical workforce of a manufacturing company. The analysis was performed using a rule-based system, which used logical rules and found classical “if-then” connections in the employee behavior cases. According to our examination, our outcomes can provide credible results for further research activities as well as for practitioners.

The environment of workplaces, the performance of the employee, and the different effects of the withdrawal behaviors are a common interesting area for many researchers. The scientific journals have published several aspects of the employee's behavior in the work environment to present the most im-portant effects on it. This study organically combines the literature-defined concepts of behavioral patterns with a case-based analysis. We connect performance and employee behavior to the cognition of human behavior, human mindset, and the process of decision-making. The focal point is the concep-tual triad of employee turnover, commitment, and satisfaction. It has been investigated from the trust approach because this is the environment in which we can set up a pattern of employee mindset at work that can help the company in the strategic decision-making process. 73 dismissal cases of manual workers from manufacturing companies served as the basis of inves-tigation. Instead of the common Likert scale in surveys, this study modelled the human mindset based on logical correlations with help of an expert system that can provide a deeper insight into the human decision logic. The nov-elty of the research method creates an opportunity to represent information more nuanced than statistical methods. The paper discusses the potential implications of the model in terms of employee turnover and human decision-making priority.

In this study, the initial problem is the capacity of the human mind to set up a conceptual model. The novelty of this article is that we show that defined concepts created by the human mind can be passed on to an artificial intelligence-based expert system. The expert system helps the human mind to settle the logical connections between the defined terms and the conceptual model thus the created model will be better than what man could have set up without an expert system. These articles have examined the impact of employee turnover in countless aspects as one of potential index of a corporation. As a starting point for our research, we systematically reviewed the literature on employee turnover and selected six concepts that are bilaterally related to our phenomenon. Based on the terms and the correlation of it, we created a conceptual model that was examined with the help of an artificial intelligence-based system. Based on our experience in observing, consulting, and working with decision-makers, we examined the aspects of employee turnover phenomenon in the analysis. This was performed using a rule-based system, which used logical rules and found classical 'if-then' connections in the employee behavior cases. According to our examination, our outcomes can provide credible results for further research activities as well as for practitioners.

Leaders and managers frequently face the need to make highly complex decisions with incomplete or fragmented information. Traditional decision support systems largely emphasize the visualization of data but often fall short in producing context-sensitive insights that can directly inform decision-making. This paper examines how narrative techniques combined with machine learning can strengthen communication across organizational hierarchies, particularly by improving the transfer of tacit expertise and contextual knowledge. To explore this, a transdisciplinary literature review was conducted using articles published within the last five years from databases such as Scopus, Web of Science, and ScienceDirect. The review highlights that narrative-driven reporting has been most commonly applied in fields such as accounting and sustainability, where expert interpretation replaces purely numerical summaries with more meaningful analytical explanations. Such approaches can also embed sentiment and personalization, commonly referred to as Narrative Disclosure Tone. Building on this foundation, the study investigates how Artificial Intelligence-driven decision support can formally integrate narrative elements to enhance report clarity, usability, and strategic relevance. Findings suggest that combining machine learning with expert-driven narrative reporting supports more innovative decision support systems and facilitates the alignment of tacit knowledge with data-driven insights.