Search results

The paper deals with the child benefits system in the Czech Republic, Slovak Republic and Sweden.

The authors describe the systems as the key baseline for subsequent qualitative and quantitative comparison. An essential element is the quantitative comparison of child benefits using their statistically stationarised values.

The Czech and Slovak systems provide a comparable rate of coverage as the Swedish system regarding the payment of both types of benefits, i.e. child benefits and tax allowances, for the first and second child; however, from the third child, the individual differences are considerable. Albeit the concepts of Czech and Slovak systems are framed by the same historical origins and conceptual approach, they differ significantly, with Slovakia providing the lowest aggregate level of child benefits.

The paper provides insight into the child benefit systems in the respective countries. These systems are at the centre of attention of policymakers who are attempting to maintain birth rates and reduce child poverty. The Czech Republic has the lowest level of at-risk-of-poverty rates for persons under 16 years of age, while natality rates are comparable.

The behaviour of a library user is based on his ability to evaluate the consumption of library services based on alternative market services. The purpose of this paper is to study the usage behaviour in a public library building in the context of alternative costs instead of a narrow focus on book circulation data.

By library usage mining, using associative rules, the authors described the behaviour of library users and identified the typical behaviour during the visits. The authors analyse the results in the context of alternative costs assigned to the visits.

The results confirm that some underused services, such as digital services, deliver significantly greater benefit. The frequency of use, the duration of visit and the number of items used are associated with higher alternative costs. There were no significant differences in alternative cost within economic groups (excluding pensioners). This paper identified 41 frequent patterns with different alternative costs.

The limitation of this study is the fact that data for library usage mining were collected using a questionnaire survey.

This may be particularly important for both policy makers and library management. The first beneficiaries are donors and patrons, who can learn about the benefit that libraries bring to society. The proposed system for library usage mining also enables managers to promote specific (effective) services, take steps to avoid readers leaving, and improve the adoption of library services. It can also be used to adapt the location of library services. Librarians, especially those who engage in acquisitions, may also use this information in their evidence-based decisions about the design of services.

So far, there has been no relevant research on the economic aspects of extracted behaviour patterns. Therefore, this study revealed users’ economic preferences using a questionnaire survey that supplemented transaction data. The ability to describe users’ behaviour can provide library management with enough information to realise evidence-based decision making.

The study aims to analyse library user preferences in the willingness to read and pay for e-books, using a sample of both active readers (users of public library services) and non-users (the general population).

Two empirical surveys were conducted from August to November of 2019; the research sample consisted of 1,334 users from the Municipal Library of Prague and 1,101 non-users from the general Czech population. The research was focussed on e-book user preferences. The willingness to pay (WTP) for e-book services and the determinants that affect this willingness were also examined.

The results show the specific approach of Czech readers, whose main determinant of WTP is not the content, but the price and method of its payment (allocation). Some people prefer a cheaper annual lump sum, whereas others may prefer a charge of small regular fees. The decision to pay depends on their reading or payment habits.

This study also aims to clarify the demand for various types of digital media in Czech libraries and the preferred distribution models. Furthermore, the study determines the dependence of the preferences of library users in their WTP for e-books using different evaluation models. The originality of this study is in the evaluation of the determinants of WTP for e-books, which makes this study unique, and the findings should contribute to the expansion of existing knowledge in the field of information science.

This study aims to compare machine learning models, datasets and splitting training-testing using data mining methods to detect financial statement fraud.

This study uses a quantitative approach from secondary data on the financial reports of companies listed on the Indonesia Stock Exchange in the last ten years, from 2010 to 2019. Research variables use financial and non-financial variables. Indicators of financial statement fraud are determined based on notes or sanctions from regulators and financial statement restatements with special supervision.

The findings show that the Extremely Randomized Trees (ERT) model performs better than other machine learning models. The best original-sampling dataset compared to other dataset treatments. Training testing splitting 80:10 is the best compared to other training-testing splitting treatments. So the ERT model with an original-sampling dataset and 80:10 training-testing splitting are the most appropriate for detecting future financial statement fraud.

This study can be used by regulators, investors, stakeholders and financial crime experts to add insight into better methods of detecting financial statement fraud.

This study proposes a machine learning model that has not been discussed in previous studies and performs comparisons to obtain the best financial statement fraud detection results. Practitioners and academics can use findings for further research development.

The purpose of this study is to introduce innovative ideas into the treatment of the radical and incremental innovations and to fill the research gap by using: (1) methods that can perform complicated tasks and solve complex problems leading in creation of radical and incremental innovation and (2) a broad sample of firms across countries. The authors’ ambition is to contribute to the scientific knowledge by producing evidence about the novel usage of artificial neural network techniques for measuring European firms' innovation activities appearing in black boxes of innovation processes.

In this study, the authors incorporate an international context into Chesbrough's open innovation (OI) theory and, on the one hand, support the hypothesis that European radical innovators benefit more from foreign cooperation than incremental innovators. On the other hand, the results of the analyses show that European incremental innovators rely on domestic cooperation supported by cooperation with foreign public research institutes. Moreover, the use of decision trees (DT) allows the authors to reveal specific patterns of successful innovators emerging within the hidden layers of neural networks.

The authors prove that radical European innovators using either internal or external R&D strategies, while the combinations of these strategies do not bring successful innovation outputs. In contrast, European incremental innovators benefit from various internal R&D processes in which engagement in design activities plays a crucial role.

The authors introduce innovative ideas into the treatment of hidden innovation processes and measuring the innovation performance (affected by domestic or international cooperation) of European firms. The approach places emphasis on the novelty of innovation and the issue of international cooperation in the era of OI by designing the framework using a combination of artificial neural networks and DT.

This study aims to examine the ability of big data analytics (BDA) to investigate financial reporting quality (FRQ), identify the knowledge base and conceptual structure of this research field and explore BDA techniques used over time.

This study uses a comprehensive bibliometric analysis approach (performance analysis and science mapping) using software packages, including Biblioshiny and VOSviewer. Multiple analyses are conducted, including authors, sources, keywords, co-citations, thematic evolution and trend topic analysis.

This study reveals that the intellectual structure of using BDA in investigating FRQ encompasses three clusters. These clusters include applying data mining to detect financial reporting fraud (FRF), using machine learning (ML) to examine FRQ and detecting earnings management as a measure of FRQ. Additionally, the results demonstrate that ML and DM algorithms are the most effective techniques for investigating FRQ by providing various prediction and detection models of FRF and EM. Moreover, BDA offers text mining techniques to detect managerial fraud in narrative reports. The findings indicate that artificial intelligence, deep learning and ML are currently trending methods and are expected to continue in the coming years.

To the best of the authors’ knowledge, this study is the first to provide a comprehensive analysis of the current state of the use of BDA in investigating FRQ.

This study aims to develop an evaluation index to evaluate the economic value among the values of the library and also attempts to measure the economic value of the library by performing a survey of the librarians and users at public libraries. The results of this research would likely encourage the librarians to feel increasingly confident about the library, while students and users, in general, would also likely be able to realize the economic value and presence of the library in more perceptive ways, thereby contributing to the activation of the library.

This study pertains to the development of an evaluation index for assessing the economic value of the library and, to evaluate the economic value of the library, has derived a preliminary evaluation index by collectively gathering and analyzing the domestic and foreign papers on the value of the library in its first phase. The preliminary evaluation index derived was verified by conducting three phases of Delphi survey by selecting ten experts. The survey questionnaire was developed to measure the economic value based on the final evaluation index derived from this study, and the economic value was measured against the perceptions of librarians and users of the public library.

The economic value of the library was divided into the four categories of the local economy’s value enhancement, namely, connection with the local community, human resources development, job creation and investment value enhancement for the librarians and users surveyed for assessment purposes. Consequently, the area of connection with the local community turned out to be the highest at 4.15, followed by 4.02 of the investment value improvement, 3.58 for the local economy’s value improvement and 3.50 for the human resources development and job creation, respectively. Furthermore, the respondents demonstrated the highest level of consensus on average on how the resource sharing by libraries has helped to reduce the economic burden for the residents as a matter of social value for the public library while believing that the libraries deliver a high level of return on social investments.

There are not that many studies conducted on the economic influence or the value of public libraries in Korea, and they are merely referenced in part if and when referenced to the overall value of the library. Given that, the research that focuses only on the economic value of the library must be carried out. In this respect, this research has been quite meaningful. The evaluation index developed in this research is likely to become a basic tool that can be applied to public libraries, as well as other types of libraries. Furthermore, the evaluation index developed through this research could be applied to nonprofit organizations, such as libraries, and would likely have a social ripple effect as a research that evaluates and presents the economic value of libraries. Accordingly, in this research, we have analyzed the list presented by the American Library Association and domestic research results, and have also structured the core details and derived the preliminary economic value index. Finally, 4 evaluation areas, 7 evaluation items and 22 evaluation indicators have been developed through the Delphi survey through three phases.

Financial statement fraud (FSF) committed by companies implies the current status of the companies may not be healthy. As such, it is important to detect FSF, since such companies tend to conceal bad information, which causes a great loss to various stakeholders. Thus, the objective of the paper is to propose a novel approach to building a classification model to identify FSF, which shows high classification performance and from which human-readable rules are extracted to explain why a company is likely to commit FSF.

Having prepared multiple sub-datasets to cope with class imbalance problem, we build a set of decision trees for each sub-dataset; select a subset of the set as a model for the sub-dataset by removing the tree, each of whose performance is less than the average accuracy of all trees in the set; and then select one such model which shows the best accuracy among the models. We call the resulting model MRF (Modified Random Forest). Given a new instance, we extract rules from the MRF model to explain whether the company corresponding to the new instance is likely to commit FSF or not.

Experimental results show that MRF classifier outperformed the benchmark models. The results also revealed that all the variables related to profit belong to the set of the most important indicators to FSF and that two new variables related to gross profit which were unapprised in previous studies on FSF were identified.

This study proposed a method of building a classification model which shows the outstanding performance and provides decision rules that can be used to explain the classification results. In addition, a new way to resolve the class imbalance problem was suggested in this paper.

This study aims to provide a comprehensive overview of social contract theory (SCT) utilization in cybersecurity literature, elucidating the current state of research, identifying major applications and themes and highlighting gaps, particularly in empirical studies, and the integration of emerging technologies. The study also maps the contractual parties and governance tools discussed in SCT and cyberspace interactions.

This study uses a systematic literature review to investigate the application of SCT within the cybersecurity domain. Using a mixed-methods approach that combines quantitative and qualitative content analysis with the Grounded Theory strategy, over 30,000 documents were initially screened. The final data set comprised 873 peer-reviewed papers from multiple databases. MAXQDA software facilitated coding and thematic analysis to identify key SCT applications, governance tools and research gaps.

The review revealed the following: emerging technologies such as artificial intelligence (AI) and blockchain are sparsely researched within the SCT-cyberspace intersection, yet they offer solutions to various SCT-related issues; empirical studies are underrepresented, with theoretical explorations dominating the discourse; there is a notable gap in integrating emerging technologies such as AI within SCT frameworks; governance tools discussed are varied, including economic incentives, regulatory measures and informational strategies.

This study synthesizes SCT applications in cybersecurity, highlighting the interdisciplinary nature and potential for richer theoretical integration. By systematically mapping the literature, it identifies crucial gaps and offers a foundation for future empirical and theoretical research. The findings emphasize the importance of considering traditional SCT themes and contemporary technological contexts, contributing to the development of more robust frameworks for cyberspace governance.