Clustering technics, like k-means and its extended version, fuzzy c-means clustering (FCM) are useful tools for identifying typical behaviours based on various attitudes and responses to well-formulated questionnaires, such as among forensic populations. As more or less standard questionnaires for analyzing aggressive attitudes do exist in the literature, the application of these clustering methods seems to be rather straightforward. Especially, fuzzy clustering may lead to new recognitions, as human behaviour and communication are full of uncertainties, which often do not have a probabilistic nature. In this paper, the cluster analysis of a closed forensic (inmate) population will be presented. The goal of this study was by applying fuzzy c-means clustering to facilitate the wider possibilities of analysis of aggressive behaviour which is treated as a heterogeneous construct resulting in two main phenotypes, premeditated and impulsive aggression. Understanding motives of aggression helps reconstruct possible events, sequences of events and scenarios related to a certain crime, and ultimately, to prevent further crimes from happening.

Globalization and its challenges for organizations led to the understanding that employees can be a critical factor contributing to the organization’s performance. Therefore, various studies sought to understand employee’s behaviour that in itself encompasses various forms of engagement. One of the constructs defining engagement is citizenship behaviour (OCB) and counterproductive work behaviour (CWB). Based on previous researches, the study aims to contribute to the knowledge on the correlation between OCB and CWB considered as a behavioural engagement, from one side, and interplay of these constructs with the related constructs such as a trait engagement, perception of organization, state engagement, from another side. Since the empirical studies typically tend to concentrate on one or several factors separately, it is difficult to get a better understanding of relationship of all forms of engagement in corpore. To address this gap, we create a complex model of investigation developed to describe the linkage of the factors - OCB, CWB and related constructs under one umbrella and, by employing a combined statistical and Fuzzy Signature (FSig) model, we investigated the link with behavioural engagement. The present study covered one region of the northern part of Lithuania. It is based on 144 completed questionnaires from 35 companies. Findings support the assumption of the relationships of behavioural engagement (i.e. OCB and CWB) and the remaining multifaceted factors, and make a step forward by offering a new model for investigation the multifaceted phenomenon of employee engagement.

The evaluation of data obtained from responses given to questionnaires in humanities and social sciences, such as management, linguistics, etc. is a complex task with the necessity of dealing with the inherent subjectivity and vagueness in such data. In this paper, a method based on fuzzy signatures (FSigs), suitable for analyzing questionnaires with hierarchically connected (partially) vague responses is proposed, and its applicability will be demonstrated by a real life problem; the partial analysis of an ongoing research examining employee behavior in various companies. The linkage of the factors hidden in the data bases obtained from the answers to the questionnaires, containing various factors interconnected in a more or less tight way, are represented by a hierarchical FSig system, allowing further evaluation and the discovery of emerging connections and deeper patterns among the responses, thus extending the idea of the original FSig model towards a more general, fuzzy-fuzzy signature approach. The method proposed here is a combination of some statistical elements with the Fuzzy Signature model, and it also uses Kohonen-maps in order to discover deeper structural components in the data pool. As FSigs are suitable to express hierarchically structured connections among vague and imprecise features of the individual data, the statistical analysis helps reveal the degrees of redundancies and the closeness of connectedness of the individual elements within the responses, and thus enable the construction of a relevant FSig tree graph for the data on hand, while further expert domain knowledge helps with determining the proper fuzzy aggregations in the intermediate nodes of the FSigs. The case study presented is based on data obtained from North Lithuanian companies. The results of the case study focusing on the analysis of the connection between OCB and CWB, and other factors, disclose some interesting and, partly unexpected, results. They indicate a strong and unambiguous relationship between career satisfaction and OCB, which is not very surprising. However, it is found that there is no relationship with gender, age, and actual position in the company, which are generally supposed to be determining factors. These results may be further validated by expert knowledge, and thus the new combined method for evaluating structured multicomponent data and internal dependencies is adequate.

Stakeholder Relationship Management Systems (SRMS) describe the general behavior of stakeholder relations at organizations. Knowing the interactions between the drivers of the SRMS helps to improve the efficiency of the whole system. This paper investigates the applicability of Fuzzy Cognitive Maps (FCM) to simulate the system dynamics and the causal connections within it. Previous investigations showed that FCM is a proper tool to analyze these relations and with the help of that the business management process and decision making in projects can be supported. The aim of this paper is to present the results of current simulations made with the help of FCM at Lithuanian and Hungarian organizations and to explain the causes of the identified differences. Beside that the paper investigates the influences of the modification of the threshold function's parameter on the final factor states.