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Presentation skills are one of the most important tools that are required on numerous occasions in education and business. In most of the business and economics colleagues, presentation skills are taught as part of the curriculum, of at least several courses. Therefore, it could be expected that presentation skills would be highly developed among business and economics students. However, in practice, people develop numerous fears and barriers to the presentation in public. On the other hand, students do not behave in the same manner taking into account their fear towards the presentation in public. The goal of the paper is to investigate if there are homogenous groups of students according to their attitude towards the presentation, both as sources of success and as a source of fear. Cluster analysis has been employed to fulfil the paper goal. Non-hierarchical k-means analysis has been conducted on data collected by the research instrument about the benefits and fears from the presentations on the sample of students enrolled in business and economics colleagues. Results indicate that there are homogenous groups of students according to presentation perceived benefits and fears, but the composition of these groups indicates that perceived benefits and fears of presentations are not always related in the same direction.

Background and Purpose: Data mining techniques are intensely used in various industries for the purpose of fraud prevention and detection. Research that focuses on the leasing industry is scarce, although frauds in the field of leasing occur rather often. First, we identify clusters of business clients in one leasing company by using the method of self-organising maps based on leasing contract attributes. Second, we compare clusters based on the presence of fraudulent clients, in order to develop fraudsters’ profiles.

This article aims to examine socioeconomic determinants of suicide rates across European countries. Using Bayesian Model Averaging and panel data specification with fixed effects on a sample of 25 European countries and two subsamples between 2001 and 2019, a data-driven model was estimated. Empirical findings for the whole sample suggested GDP per capita as a regressor with very strong posterior inclusion probabilities and negative effects of GDP per capita on suicide rates. Marriage rates appeared with positive inclusion probabilities and effects from marriage rates to suicide rates were negative. Share of population ages below fifteen years and divorce rates were identified as regressors with weak inclusion probabilities. The effects from a share of the population aged below fifteen years to suicide rates were negative while effects from divorce rates to suicide rates were positive. Empirical findings for a subsample of post-socialist countries revealed divorce rates as the most prominent regressor explaining suicide rates in post-socialist countries with very strong posterior inclusion probabilities. Female participation in the labour force and share of the urban population appeared as regressors with strong prior inclusion probabilities. The effects of female participation in the labour force on suicide rates were negative as well as the effects of urbanization on suicide rates. GDP per capita appeared as a regressor with positive inclusion probabilities and the effects of GDP per capita on suicide rates were negative. For a subsample of non-socialist countries, empirical findings suggested a share of the urban population and female participation in the labour force as regressors with very strong posterior inclusion probabilities. Unlike the estimates for the subsample of post-socialist countries, effects from participation in the labour force to suicide rates as well as from urbanization to higher suicide rates were positive. [ABSTRACT FROM AUTHOR]

Background: The students’ progression towards completing their higher education degrees possesses stochastic characteristics, and can therefore be modelled as an absorbing Markov chain. Such application would have a high practical value and offer great opportunities for implementation in practice.

Background: Scientific research publishing carries significant role in the development of the society. Apart from the dissemination of knowledge, there are also motives for publication of scientific research results at the level of individual researchers. Objectives: The goal of the paper is to propose simple, yet highly applicable advice when pursuing the publication of a paper in a scientific journal providing a closer look to economics, business and management journals that focus on Eastern European countries. Methods/Approach: The 4 Cs model of scientific writing and publication is presented, based on four questions: (1) How to pick a topic relevant for publication?; (2) How to select a journal for possible publication?; (3) How to structure the paper in accordance with the IMRAD format?, and (4) How to efficiently write the paper? Results: Step-by-step application of the 4C’s model is presented in the paper with an outlook to economics, business and management journals that focus on Eastern European countries. Conclusions: Publication in a scientific journal is an important venue for scientific researchers. In preparing the presentation of the scientific research results for the publication, number of issues relating content, style, composition and presentation should be taken into account.

The main aim of this study is to explore the mediating effect of innovation in the relationship between human resource management (HRM) practices and organizational performance. HRM practices are observed through selective hiring, training, participative decision-making, and rewarding. Innovation is conceptualized over behavioral, product, process, and market innovation. The study relies upon the principles of social exchange theory and resource-based theory. Data were collected from 408 managers in an emerging economy context. The proposed conceptual model is evaluated with structural equation modeling using Lisrel 8.8 and SPSS 22. Study findings suggest that innovation influences the relationship between selective hiring, training, and participative decision-making and organizational performance. However, no mediating effect of innovation was found in a relationship between rewarding and organizational business performances. Since human resources and innovation are among the leading sources for building competitive advantage for companies, the study findings contribute to HRM and innovation. [ABSTRACT FROM AUTHOR]

Background: Our research assumes that the software quality affects the product validity. This assumption also refers to embedded software. Objectives: This paper analyses the Stackelberg equilibrium in which the consumer is the leader and the producer of embedded software is the follower. Methods/Approach: A comparative statics analysis of a producer's reaction is carried out and confirms our intuition that the product price is positively correlated to the number of employees and the software quality. Results: An increase in wage has an adverse effect on producer’s reaction. Derived results are illustrated numerically and Stackelberg and cooperative equilibrium are compared. It is shown that the welfare loss is smaller with higher quality software for any number of employees. Conclusions: Although the equilibrium involves less employed workers, the optimal software quality is higher. The optimal product price is lower, which puts the consumer and the producer in a better position

Notice of redundant publication and erratum on the paper published in Business Systems Research, Vol. 7 No. 1, 2016, pp. 104-118.

The goal of the paper is to present the framework for combining clustering and classification for churn management in telecommunications. Considering the value of market segmentation, we propose a three-stage approach to explain and predict the churn in telecommunications separately for different market segments using cluster analysis and decision trees. In the first stage, a case study churn dataset is prepared for the analysis, consisting of demographics, usage of telecom services, contracts and billing, monetary value, and churn. In the second stage, k-means cluster analysis is used to identify market segments for which chi-square analysis is applied to detect the clusters with the highest churn ratio. In the third stage, the chi-squared automatic interaction detector (CHAID) decision tree algorithm is used to develop classification models to identify churn determinants at the clusters with the highest churn level. The contribution of this paper resides in the development of the structured approach to churn management using clustering and classification, which was tested on the churn dataset with a rich variable structure. The proposed approach is continuous since the results of market segmentation and rules for churn prediction can be fed back to the customer database to improve the efficacy of churn management. [ABSTRACT FROM AUTHOR]

The business success of small- and medium-sized enterprises (SMEs) increasingly relies on the adoption of various technological innovations. For today's unpredictable business operations, business intelligence systems (BISs) represent one of the most prominent tools with a significant impact on business performance. However, different internal and external risks may influence BIS adoption. The goal of this paper is to investigate the risks that impact BIS adoption in SMEs, using the Technology, Organization, and Environment (TOE) framework. For that purpose, we develop the logistic regression model, using data collected by a questionnaire survey using a sample of 100 Croatian SMEs. The results indicate the applicability of the TOE theoretical framework for examining BIS adoption in SMEs. Given the results obtained, the sampled SMEs should take into account the internal risks related to the organizational dimension and external risks related to the environmental dimension. Our research did not reveal the significant impact of technological risks that encompass characteristics of considered technological innovation related to the technology dimension. [ABSTRACT FROM AUTHOR]

Data mining is an efficient methodology for uncovering and extracting information from large databases, which is widely used in different areas, e.g., customer relation management, financial fraud detection, healthcare management, and manufacturing. Data mining has been successfully used in various fraud detection and prevention areas, such as credit card fraud, taxation fraud, and fund transfer fraud. However, there are insufficient researches about the usage of data mining for fraud related to internal control. In order to increase awareness of data mining usefulness in internal control, we developed a case study in a project-based organization. We analyze the dataset about working-hour claims for projects, using two data mining techniques: chi-square automatic interaction detection (CHAID) decision tree and link analysis, in order to describe characteristics of fraudulent working-hour claims and to develop a model for automatic detection of potentially fraudulent ones. Results indicate that the following characteristics of the suspected working-hours claim were the most significant: sector of the customer, origin and level of expertise of the consultant, and cost of the consulting services. Our research contributes to the area of internal control supported by data mining, with the goal to prevent fraudulent working-hour claims in project-based organizations. [ABSTRACT FROM AUTHOR]

The term corporate sustainability represents a crucial part of the concept of sustainable development. The main goal of the companies, which adopted corporate sustainability practice is to fulfil a sustainable development agenda and brings a balanced approach to economic and social progress and environmental management. The purpose of the article is to highlight the importance and issues of corporate sustainability in the context of awareness and approaches to social responsibility in the Fourth Industrial Revolution. The paper aims to make order in the plurality of definitions of concept «corporate sustainability» and its practical understanding in terms of «corporate social responsibility». It does so through a literature review and the analysis of different attempts of definition. It identifies the main challenges these concepts bring with them. [ABSTRACT FROM AUTHOR]

The goal of this research is to investigate the impact of different algorithms for the feature selection for the purpose of credit risk scoring for the entrepreneurial funding by the Croatian financial institution. We use demographic and behavioral data, and apply various algorithms for the development of classification model. In addition, we evaluate several algorithms for the variable selection, which are additionally based on the classification accuracy. Sequential Minimal Optimization algorithm in combination with the Class CfcSubsetEval and ConsistencySubsetEval algorithms for variable selection was the most accurate in predicting credit default, and therefore the most useful for the credit risk scoring. [ABSTRACT FROM AUTHOR]