Data mining usage in Italian SMEs: an integrated SEM-ANN approach

Bach, Mirjana Pejić; Topalović, Amir; Turulja, Lejla

doi:10.1007/s10100-022-00829-x

Cited by 3 publications

(1 citation statement)

References 48 publications

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“…Data mining has become omnipresent in various domains in the recent decade, but its usage in small and medium enterprises (SMEs) is still underrepresented. The authors (Pejič Bach et al 2023 ) investigate the determinants of data mining usage in SMEs using the TOE Framework (Technology-Organisation-Environment). Duality in the analysis of monopsony in the labor market is discussed in Krpan ( 2023 ).…”

Section: Recent Developments: Papers In This Issuementioning

confidence: 99%

60 years of OR in Slovenia: development from a first conference to a vibrant community

Povh

Stirn

Žerovnik

2023

Cent Eur J Oper Res

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The paper provides a short history of the operations research (OR) in Slovenia. Some major events and achievements are mentioned and briefly discussed. The period starts in the year 1964, with the first symposium on OR in Slovenia. In the following decades, there were several important milestones: (1) the start of master’s and Ph.D. studies in OR in 1974, (2) the establishment of SSI-SSOR in 1992 (SSI-SSOR stands for the Slovenian Section for Operational Research within the Slovenian Society of Informatics), and (3) the start of a series of symposia in operations research in 1993. All these activities resulted in an extensive list of publications, projects, and monographs and international connections, proving that OR is still a vibrant field, which facilitates knowledge transfer from pure research to business applications.

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Section: Recent Developments: Papers In This Issuementioning

confidence: 99%

60 years of OR in Slovenia: development from a first conference to a vibrant community

Povh

Stirn

Žerovnik

2023

Cent Eur J Oper Res

View full text Add to dashboard Cite

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The use of machine learning techniques for assessing the potential of organizational resilience

Ewertowski,

Güldoğuş,

Kuter

et al. 2023

Cent Eur J Oper Res

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Organizational resilience (OR) increases when the company has the ability to anticipate, plan, make decisions, and react quickly to changes and disruptions. Thus the company should focus on the creation and implementation of proactive and innovative solutions. Proactive processing of information requires modern technological solutions and new techniques used. The main focus of this study is to propose the best technique of Machine Learning (ML) in the context of accuracy for predicting the attributes of the organizational resilience potential. Based on the calculations, the research includes estimating them through the applications of regression and machine learning methods. The dataset is obtained from the results of the our survey based on the questionnaire consisting of 48 items mainly established on OR attributes formed on ISO 22316:2017 standard. Based on the outcomes of the study, it can be stated that the optimal technique in the context of accuracy for predicting the attributes of the organizational resilience potential is ensemble methods. The k-nearest neighbor (KNN) filtering-based data pre-processing technique for stacked ensemble classifier is used. The stacking is achieved with three base classifiers namely Random Forest (RF), Naive Bayes (NB), and Support Vector Machine (SVM). The chosen ensemble method should be implemented in an organization systemically according to the circle of innovation, and should support the quality of managerial decision-making process by increasing the accuracy of organizational resilience potential prediction, and indication of the importance of attributes and factors affecting the potential for organizational resilience.

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Using Data Mining to Improve Decision-Making: Case Study of A Recommendation System Development

Mydyti¹,

Kadriu

Bach

2023

Organizacija

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Background and purpose This study aims to provide a practical perspective on how data mining techniques are used in the home appliance after-sales services. Study investigates on how can a recommendation system help a customer service company that plans to use data mining to improve decision making during its digital transformation process. In addition, study provides a detailed outline on the process for developing and analyzing platforms to improve data analytics for such companies. Methodology Case study approach is used for evaluating the usability of recommendation systems based on data mining approach in the context of home appliance after-sales services. We selected the latest platforms based on their relevance to the recommender system and their applicability to the functionality of the data mining system as trends in the system design. Results Evaluation of the impact on decision making shows how the application of data mining techniques in organizations can increase efficiency. Evaluation of the time taken to resolve the complaint, as a key attribute of service quality that affects customer satisfaction, and the positive results achieved by the recommendation system are presented. Conclusion This paper increases the understanding of the benefits of the data mining approach in the context of recommender systems. The benefits of data mining, an important component of advanced analytics, lead to an increase in business productivity through predictive analytics. For future research, other attributes or factors useful for the recommender systems can be considered to improve the quality of the results.

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Data mining usage in Italian SMEs: an integrated SEM-ANN approach

Cited by 3 publications

References 48 publications

60 years of OR in Slovenia: development from a first conference to a vibrant community

60 years of OR in Slovenia: development from a first conference to a vibrant community

The use of machine learning techniques for assessing the potential of organizational resilience

Using Data Mining to Improve Decision-Making: Case Study of A Recommendation System Development

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