Research, projects

Head of the research group:

The group studies mathematical analysis and its applications. In particular, it is currently studying stochastic dynamical systems which lead to nonlinear extensions of major results of classical probability theory. This also gives tools to efficiently solve optimization problems on metric spaces, thus can be used to solve various applied mathematical problems. Other than these topics the group studies functional analysis and operator theoretical questions.

In recent years, enterprises and societies around the world are being heavily affected by the global virus pandemic (COVID-19). The crisis has already transformed into an economic and labour market crisis, affecting all enterprises. However, enterprises are coping and trying to respond to these challenges in different ways. Some have been heavily affected due to the closures and reduced working hours recommended to curb the spread of the virus. While others have heavily relayed on digital technologies, which enabled work from home and establishing new online sales and collaboration channels. 

The emergence of new digital technologies has brought the era of so-called digital transformation (DT). DT refers to a process of reshaping traditional business practices through the adoption and use of digital technologies (Rogers 2016). Enterprises are in different stages of DT (Probst et al. 2018). Still, many of them, mostly small and medium-sized enterprises (SMEs), are facing challenges of establishing digital capabilities and taking advantage of the opportunities of the latest technologies (Bouwman et al. 2018). Unlike large enterprises, SMEs have a lower level of available resources, digital skills, limited personnel, and budget and thus lagging behind in the digital transformation race (Pucihar et al. 2019). As SMEs are the backbone of the European economy (Bouwman et al. 2018), providing a potential source for jobs and economic growth they need to be more innovative and try to integrate digital technology in all their operational areas, including planning, finance, production, marketing, and human resource management (Kane et al. 2018). This will enable them to increase productivity, remain competitive in the market, and maintain profitable even in a global crisis like a COVID-19. 

Even though the advancements in digital technologies provide unprecedented opportunities for SMEs little is known how they adopt and leverage digital technologies to cope with the consequences of COVID-19 (Winarsih, Indriastuti, and Fuad 2020). Moreover, although European Commission tries to shape Europe’s digital future with European digital strategy where the common goals are provided there are diversities among the European Union (EU) countries, including policy, design, funding approach, financial, size, and implementation strategies. These differences also result in the different levels of digital competitiveness of EU countries (Probst et al. 2018). Thus, the comparison between two or more EU countries could provide more detailed insight on how SMEs in the different EU countries deal with the consequences of COVID-19 using digital technologies. 

To better understand the impact of COVID-19 on SMEs, in TET project we aim to investigate how different SMEs in EU countries (Slovenia and Hungary) responded to the crises with the use of digital technologies. To achieve this goal we will use a mixed-method research approach – qualitative (multiple case study) and further on a quantitative study (survey) (Venkatesh, Brown, and Bala 2013). We explore how SMEs were facing challenges of the COVID-19 situation and using digital technologies to overcome these challenges, what were the changes which reflected in their operations (business models), for instance, establishing remote work, webshop, electronic data interchange, web portals. Additionally, we investigate how these changes will be incorporated into their future strategies, for example increasing investment in digital technology, providing enhanced training to improve digital skills of employees, establishing new online channels for collaboration with suppliers, buyers, customers, designing new products and services. 

The research is performed in several phases: (1) developing of common case study protocol; (2) collecting and analysing data within a multiple case study; (3) developing questionnaire based on qualitative data analysis, conducting survey, and analysing data; (4) demonstrating practical implications – separately for each country and together; (5) providing theoretical implications, which may also serve for future research. 

The research results will provide deeper understandings of how SMEs, being in different stages of DT, in Slovenia and Hungary used digital technology to respond to the challenges that emerged with the pandemic situation. Moreover, we will compare results in both countries and assess similarities and differences between SMEs and provide recommendations for enterprises to progress their DT journey. 

References: 

Bouwman, Harry, Shahrokh Nikou, Francisco J Molina-Castillo, and Mark De Reuver. 2018. “The Impact of Digitalization on Business Models.” Digital Policy, Regulation and Governance. 

Kane, Gerald C, Doug Palmer, Anh Nguyen Phillips, David Kiron, and Natasha Buckley. 2018. “Coming of Age Digitally: Learning, Leadership, and Legacy.” MIT Sloan Management Review and Deloitte Insights, 5. 

Probst, Laurent, Virginie Lefebvre, Christian Martinez-Diaz, Nuray Unlu Bohn, PwC, Demetrius Klitou, Johannes Conrads, and CARSA. 2018. “Digital Transformation Scoreboard 2018: EU Businesses Go Digital: Opportunities, Outcomes and Uptake.” Luxembourg. 

Pucihar, Andreja, Gregor Lenart, Mirjana Kljajić Borštnar, Doroteja Vidmar, and Marjeta Marolt. 2019. “Drivers and Outcomes of Business Model Innovation—Micro, Small and Medium-Sized Enterprises Perspective.” Sustainability 11 (2): 344. 

Rogers, David L. 2016. The Digital Transformation Playbook. Columbia University Press. 

Venkatesh, Viswanath, Susan A Brown, and Hillol Bala. 2013. “Bridging the Qualitative-Quantitative Divide: Guidelines for Conducting Mixed Methods Research in Information Systems.” MIS Quarterly 37 (1): 21–54. 

Winarsih, Maya Indriastuti, and Khoirul Fuad. 2020. “Impact of Covid-19 on Digital Transformation and Sustainability in Small and Medium Enterprises (Smes): A Conceptual Framework.” In Conference on Complex, Intelligent, and Software Intensive Systems, 1194 AISC:471–76. Springer. 

D1

Asemi, A., Asemi, A., & Ko, A. (2023). Adaptive neuro-fuzzy inference system for customizing investment type based on the potential investors’ demographics and feedback. Journal of Big Data, 10(1), 1-27. https://link.springer.com/content/pdf/10.1186/s40537-023-00784-7.pdf

Q1

Asadnia, A., CheshmehSohrabi, M., Shabani, A., Asemi, A., & Demneh, M. T. (2023). Future of information retrieval systems and the role of library and information science experts in their development. Journal of Librarianship and Information Science, 55(1), 177-190. https://journals.sagepub.com/doi/full/10.1177/09610006211067537

Sebrek, S. S., Garrido, B. P., & Michalkó, G. (2022). Why are Unfavorable Signs of Overtourism Ignored by Urban Politics? An Attention-based Explanation of No Intervention. Tourism Planning & Development, 1-9. https://www.tandfonline.com/doi/full/10.1080/21568316.2022.2151503

Wachs, J. (2023). Digital traces of brain drain: developers during the Russian invasion of Ukraine. EPJ Data Science, 12(1), 14. https://link.springer.com/content/pdf/10.1140/epjds/s13688-023-00389-3.pdf?pdf=button

Q2

Ebrahimi, F., Asemi, A., & Ko, A. (2023). Identifying effective criteria for author matching in bioinformatics. Informatics in Medicine Unlocked, 38, 101224. https://www.sciencedirect.com/science/article/pii/S2352914823000667

Asemi, A., Asemi, A., & Ko, A. (2023). Investment Recommender System Model Based on the Potential Investors’ Key Decision Factors. Big Data.

Asemi, A., Asemi, A., & Ko, A. (2023). Unveiling the impact of managerial traits on investor decision prediction: ANFIS approach. Soft Computing, 1-21. https://link.springer.com/content/pdf/10.1007/s00500-023-08102-2.pdf

Ferenci, T., Hári, P., Vájer, P., & Jánosi, A. (2023). External validation of the GRACE risk score in patients with myocardial infarction in Hungary. IJC Heart & Vasculature, 46, 101210. https://www.sciencedirect.com/science/article/pii/S2352906723000416

Blaskovics, B., Czifra, J., Klimkó, G., & Szontágh, P. (2023). Impact of the Applied Project Management Methodology on the Perceived Level of Creativity. Acta Polytechnica Hungarica, 20(3), 101-120. http://unipub.lib.uni-corvinus.hu/8247/1/Blaskovics_Czifra_Klimko_Szontagh_132.pdf

Stippinger, M., Hanák, D., Kurbucz, M. T., Hanczár, G., Törteli, O. M., & Somogyvári, Z. (2023). BiometricBlender: Ultra-high dimensional, multi-class synthetic data generator to imitate biometric feature space. SoftwareX, 22, 101366. https://www.sciencedirect.com/science/article/pii/S2352711023000626

Kurbucz, M. T. (2023). hdData360r: A high-dimensional panel data compiler for governance, trade, and competitiveness indicators of World Bank Group platforms. SoftwareX, 21, 101297. https://www.sciencedirect.com/science/article/pii/S2352711022002151

Boros, A., Lentner, C., Nagy, V., & Tőzsér, D. (2023). Perspectives by green financial instruments–a case study in the Hungarian banking sector during COVID-19. Banks and Bank Systems, 18(1), 116-126. http://unipub.lib.uni-corvinus.hu/8120/1/BBS_2023_01_Boros.pdf

Pérez Garrido, B., Semenova, V., & Sebrek, S. S. (2023). Exploring the profile of innovative enterprises in high-tech manufacturing sectors: The case of the regions of Madrid and Catalonia in 2016. Regional Statistics, 13(1), 119-148. http://unipub.lib.uni-corvinus.hu/8114/1/rs130106.pdf

Sebestyén, Z., & Tarcsay, Z. (2022). Extensions of positive symmetric operators and Krein’s uniqueness criteria. arXiv preprint arXiv:2209.00318. https://arxiv.org/pdf/2209.00318.pdf