CLASSIFICATION OF ENTREPRENEURIAL INTENTIONS BY NEURAL NETWORKS, DECISION TREES AND SUPPORT VECTOR MACHINES

Entrepreneurial intentions of students are important to recognize during the study in order to provide those students with educational background that will support such intentions and lead them to successful entrepreneurship after the study. The paper aims to develop a model that will classify students according to their entrepreneurial intentions by benchmarking three machine learning classifiers: neural networks, decision trees, and support vector machines. A survey was conducted at a Croatian university including a sample of students at the first year of study. Input variables described students’ demographics, importance of business objectives, perception of entrepreneurial carrier, and entrepreneurial predispositions. Due to a large dimension of input space, a feature selection method was used in the pre-processing stage. For comparison reasons, all tested models were validated on the same out-of-sample dataset, and a cross-validation procedure for testing generalization ability of the models was conducted. The models were compared according to its classification accuracy, as well according to input variable importance. The results show that although the best neural network model produced the highest average hit rate, the difference in performance is not statistically significant. All three models also extract similar set of features relevant for classifying students, which can be suggested to be taken into consideration by universities while designing their academic programs

A Comparison of Machine Learning Methods in a High-Dimensional Classification Problem

Background: Large-dimensional data modelling often relies on variable reduction methods in the pre-processing and in the post-processing stage. However, such a reduction usually provides less information and yields a lower accuracy of the model. Objectives: The aim of this paper is to assess the high-dimensional classification problem of recognizing entrepreneurial intentions of students by machine learning methods. Methods/Approach: Four methods were tested: artificial neural networks, CART classification trees, support vector machines, and k-nearest neighbour on the same dataset in order to compare their efficiency in the sense of classification accuracy. The performance of each method was compared on ten subsamples in a 10-fold cross-validation procedure in order to assess computing sensitivity and specificity of each model. Results: The artificial neural network model based on multilayer perceptron yielded a higher classification rate than the models produced by other methods. The pairwise t-test showed a statistical significance between the artificial neural network and the k-nearest neighbour model, while the difference among other methods was not statistically significant. Conclusions: Tested machine learning methods are able to learn fast and achieve high classification accuracy. However, further advancement can be assured by testing a few additional methodological refinements in machine learning methods

Two-stage DEA-Truncated Regression:Application in Banking Efficiency and Financial Development

This study evaluates the efficiency of peripheral European domestic banks and examines the effects of bank-risk determinants on their performance over 2007–2014. Data Envelopment Analysis is utilised on a Malmquist Productivity Index in order to calculate the bank efficiency scores. Next, a Double Bootstrapped Truncated Regression is applied to obtain bias-corrected scores and examine whether changes in the financial conditions affect differently banks’ efficiency levels. The analysis accounts for the sovereign debt crisis period and for different levels of financial development in the countries under study. Such an application in the respective European banking setting is unique. The proposed method also copes with common misspecification problems observed in regression models based on efficiency scores. The results have important policy implications for the Euro area, as they indicate the existence of a periphery efficiency meta-frontier. Liquidity and credit risk are found to negatively affect banks productivity, whereas capital and profit risk have a positive impact on their performance. The crisis period is found to augment these effects, while bank-risk variables affect more banks' efficiency when lower levels of financial development are observed

Measuring organisational performance using a mix of OR methods

Performance measurement has become an increasingly important issue in recent years. In spite of the remarkable progress that has been achieved in this area of research, many performance measurement initiatives fall short of their potential in supporting decision-making. This paper argues that adopting a multi-method approach to assessing performance has the potential to result in more comprehensive and effective performance measurement systems. To support this assertion, the paper discusses the development of a performance measurement system for a Business Tax Department, which combined the use of several operational research (OR) techniques including qualitative system dynamics, data envelopment analysis and multiple criteria decision analysis. The use of these OR techniques was influential in developing and implementing the performance measurement system and has the potential to be transferred to other contexts

Uncertain Multi-Criteria Optimization Problems

Most real-world search and optimization problems naturally involve multiple criteria as objectives. Generally, symmetry, asymmetry, and anti-symmetry are basic characteristics of binary relationships used when modeling optimization problems. Moreover, the notion of symmetry has appeared in many articles about uncertainty theories that are employed in multi-criteria problems. Different solutions may produce trade-offs (conflicting scenarios) among different objectives. A better solution with respect to one objective may compromise other objectives. There are various factors that need to be considered to address the problems in multidisciplinary research, which is critical for the overall sustainability of human development and activity. In this regard, in recent decades, decision-making theory has been the subject of intense research activities due to its wide applications in different areas. The decision-making theory approach has become an important means to provide real-time solutions to uncertainty problems. Theories such as probability theory, fuzzy set theory, type-2 fuzzy set theory, rough set, and uncertainty theory, available in the existing literature, deal with such uncertainties. Nevertheless, the uncertain multi-criteria characteristics in such problems have not yet been explored in depth, and there is much left to be achieved in this direction. Hence, different mathematical models of real-life multi-criteria optimization problems can be developed in various uncertain frameworks with special emphasis on optimization problems

Digital destination promotion: understanding and maximizing the use of digital and cultural assets to enhance tourists’ decision making and destination marketing strategies

With the overarching research question “how Information and Communication Technologies can be used to support a destination in improving tourists’ information search and decision making through the use of its digital and cultural assets” this thesis connects the three themes of eTourism, destination marketing and heritage tourism through a user-centric approach and the application of innovative technologies. The eight papers provided utilise and investigate the application of technology to improve the effectiveness and promotion of destination marketing and destination marketing organisations whilst, at the same time, improving user experiences. Interdisciplinary research focuses on the opportunities provided by digital and cultural assets of destinations to enhance destination marketing efforts. This research recognises and discusses the importance and challenges of the commodification process of tangible and intangible heritage as part of the marketing process. Methodologies appropriate to each of the research purposes were applied and data was triangulated to improve understanding. Quantitative data was collected through questionnaires, web crawlers and log files enabling the research to draw on analytical methods such as correspondence and cluster analysis, as well as data envelopment analysis (DEA). Qualitative methods such as workshop cycles, observations, and interviews were used to provide rich narratives analysed through content analysis. The results from the eight papers enhance destination marketing efforts by providing a better understanding of user behaviour and preferences based on travel personalities, travel and search pattern. They provide a clearer representation of the technologies, digital assets and e-Services available, discussing web site content and effectiveness. Strategies and innovative ideas to improve the current utilisation of digital technologies are provided based on the outcomes of the studies presented. Furthermore, a reflection on the use of intangible cultural heritage assets within destination marketing supported through the use of technologies is explored to enhance opportunities for destination marketing. V The research presents innovative and new ways to a destination to create new meanings and unique selling points (USPs) through cultural heritage assets and user-centric technologies. It introduces an interpretative strategy within destination marketing, and ideas to make the tourists’ holiday choice process more engaging. It enhances the understanding of on-line destination presentation, enabling comparisons between providers and improving their competitiveness. The main contribution of this work is new and enhanced insights how to improve on-line destination presentation by understanding its current representation and users’ search and behaviour patterns online and during travelling. It provides examples for the usefulness of ICT and cultural heritage in order to improve destinations’ marketing efforts. It also adds to the debate of the application of technologies for heritage interpretation and the commodification of (local) cultural heritage assets for destination marketing and tourism purposes

Operational Research: Methods and Applications

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes

Operational Research: Methods and Applications

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order