Ojaras Purvinis
6504223189
Publications - 7
Statistical and fuzzy signature-based analysis of the aggressive attitudes of a forensic population
Publication Name: Journal of Infrastructure Policy and Development
Publication Date: 2024-01-01
Volume: 8
Issue: 8
Page Range: Unknown
Description:
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.
Open Access: Yes
A New Similarity Measure of Fuzzy Signatures with a Case Study Based on the Statistical Evaluation of Questionnaires Comparing the Influential Factors of Hungarian and Lithuanian Employee Engagement
Publication Name: Mathematics
Publication Date: 2022-08-01
Volume: 10
Issue: 16
Page Range: Unknown
Description:
Similarity between two fuzzy values, sets, etc., may be defined in various ways. The authors here attempt introducing a general similarity measure based on the direct extension of the Boolean minimal form of equivalence operation. It is further extended to hierarchically structured multicomponent fuzzy signatures. Two versions of this measure, one based on the classic min–max operations and one based on the strictly monotonic algebraic norms, are proposed for practical application. A real example from management science is chosen, namely the comparison of employee attitudes in two different populations. This example has application possibilities in the evaluation and analysis of employee behaviour in companies as, due to the complex aspects in analysing multifaceted behavioural paradigms in organizational management, it is difficult for companies to make reliable decisions in creating processes for better social interactions between employees. In the paper, the authors go through the steps of building a model for exploring a set of different features, where a statistical pre-processing step enables the identification of the interdependency and thus the setup of the fuzzy signature structure suitable to describe the partially redundant answers given to a standard questionnaire and the comparison of them with help of the (pair of the) new similarity measures. As a side result in management science, by using an internationally applied standard questionnaire for exploring the factors of employee engagement and using a sample of data obtained from Hungarian and Lithuanian firms, it was found that responses in Hungary and Lithuania were partially different, and the employee attitude was thus in general different although in some questions an unambiguous similarity could be also discovered.
Open Access: Yes
DOI: 10.3390/math10162923
Modelling OCB and CWB by combined Fuzzy Signature model
Publication Name: Economic Research Ekonomska Istrazivanja
Publication Date: 2021-01-01
Volume: 34
Issue: 1
Page Range: 1546-1565
Description:
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.
Open Access: Yes
Analyzing employee behavior related questionnaires by combined fuzzy signature model
Publication Name: Fuzzy Sets and Systems
Publication Date: 2020-09-15
Volume: 395
Issue: Unknown
Page Range: 254-272
Description:
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.
Open Access: Yes
Evaluation of Questionnaires by Combining Fuzzy Signatures, Factor Analysis and Least Squares Method
Publication Name: Ines 2020 IEEE 24th International Conference on Intelligent Engineering Systems Proceedings
Publication Date: 2020-07-01
Volume: Unknown
Issue: Unknown
Page Range: 215-218
Description:
A survey based on a standard questionnaire on employee satisfaction was carried out in Hungary. The questionnaire was developed by international university research consortium. The qualitative data were collected from 1159 respondents. The subjective and therefore inexact answers represented in the Likert scale were mapped into fuzzy membership degrees. The article presents a method that consists of the combination of factor analysis and the least square method, applied for developing the fuzzy signature characterizing the employees' behavioural engagement.
Open Access: Yes
A combined fuzzy and least squares method approach for the evaluation of management questionnaires
Publication Name: Studies in Computational Intelligence
Publication Date: 2020-01-01
Volume: 819
Issue: Unknown
Page Range: 157-165
Description:
A set of answers to questions to employees of various companies in Lithuania may refer to various positive and negative aspects of the attitudes of employees. These are called Organizational Citizenship Behavior (positive) and Counterproductive Work Behavior (negative). The components in the answers may be grouped by expert knowledge, and by statistical analysis and, according to these approaches, based on expert domain knowledge by management specialists, fuzzy signature structures describing the mutual effects of single elements in the questionnaire may be created. There are some slight differences between the two results, that indicate that expert knowledge is sometimes not objective. An additional step applying hybrid Generalised Reduced Gradient algorithm and Genetic Evolutionary Algorithm for heuristic optimization of the aggregation parameters in the Fuzzy Signatures reveals a final model according to the responses. These latter results raise some new questions, including the idea of the use of undeterministic graphs, thus resulting in Fuzzy Fuzzy Signatures. The method could be applied to other similar multicomponent vague data pools.
Open Access: Yes
Some considerations on data mining from questionnaires by constructing fuzzy signatures based on factor analysis
Publication Name: Journal of Intelligent and Fuzzy Systems
Publication Date: 2019-01-01
Volume: 36
Issue: 4
Page Range: 3739-3749
Description:
To interpret and to process the answers to questionnaires with large amount of questions may be not easy task. They are multidimensional data, sometimes with high dimensionality (in the hundreds). Therefore, it is necessary that some data reduction approach should be employed. On the other hand, answers to specific questions in questionnaires are imprecise, and the type and degree of imprecision is determined by the kind of the questions. The authors of the paper consider the imprecise answers to management type questions using a numerical scale as fuzzy degrees, and based on the semantic connections among the individual questions, a hierarchical structure is assumed. The paper suggests the use of factor analysis in order to determine this hierarchical structure, and thus the construction of fuzzy signatures from the tree graph representing the connections among the questions and answers, and the values normalized into membership degrees are assigned to the leaves of this tree. An interesting issue is how to determine the aggregations at the intermediate nodes. This may happen based on management science domain expert knowledge, and validated by the obtained results. Kohonen maps are used to demonstrate the clusters emerging among the overall fuzzy degrees representing the Fuzzy Signatures. The evaluation brings some results that partly confirm soft science based assumptions about employee behavior in the literature, and partly bring some interesting novel recognitions that may be brought in feedback to the original management science related problem, where the new method is illustrated.
Open Access: Yes
DOI: 10.3233/JIFS-18548
