@article{MTMT:36873164,
	title = {Assessing EU Member States’ Circular Economy Transition},
	url = {https://m2.mtmt.hu/api/publication/36873164},
	author = {Alaraj, Ahmad and Dobos, Imre and Szabó, Mariann},
	journal-iso = {REG STAT},
	journal = {REGIONAL STATISTICS},
	volume = {16},
	unique-id = {36873164},
	issn = {2063-9538},
	year = {2026},
	eissn = {2064-8243},
	orcid-numbers = {Dobos, Imre/0000-0001-6248-2920; Szabó, Mariann/0000-0002-7501-949X}
}

@article{MTMT:36381748,
	title = {Unveiling indirect greenhouse gas emissions: an environmentally extended input-output analysis of the Visegrad countries' carbon footprint},
	url = {https://m2.mtmt.hu/api/publication/36381748},
	author = {Csutora, Mária and Dobos, Imre and Vetőné, Mózner Zsófia},
	journal-iso = {REG STAT},
	journal = {REGIONAL STATISTICS},
	volume = {16},
	unique-id = {36381748},
	issn = {2063-9538},
	year = {2026},
	eissn = {2064-8243},
	orcid-numbers = {Dobos, Imre/0000-0001-6248-2920}
}

@article{MTMT:36070433,
	title = {Statistics on the use of AI technologies in the member states of the EU},
	url = {https://m2.mtmt.hu/api/publication/36070433},
	author = {Lülök, Gergely and Dobos, Imre and Sebestyén, Zoltán},
	doi = {10.15196/RS160101},
	journal-iso = {REG STAT},
	journal = {REGIONAL STATISTICS},
	volume = {16},
	unique-id = {36070433},
	issn = {2063-9538},
	abstract = {This study provides a comprehensive statistical analysis of the uptake and application of artificial intelligence (AI) technologies in the European Union (EU) member states up to 2023. The research draws on data from 150,400 companies to examine the relationship between different AI technologies such as machine learning, process automation and text mining. Using correlation, factor and principal component analysis, the study explores the extent and effectiveness of the integration of technologies, providing a new scientific perspective on the industrial application and strategy of AI-based innovations. The analysis has revealed that countries with higher levels of digital skills and advanced technological infrastructure, such as Denmark and Finland, exhibit significantly higher AI adoption rates. Furthermore, the results highlight how closely certain technologies, such as machine learning and robotic process automation, are related. The results offer significant contributions to facilitate a more effective application of AI technologies in the European industrial environment and provide guidance for future development strategies.},
	keywords = {statistical analysis; European Union; ARTIFICIAL INTELLIGENCE (AI); Eurostat},
	year = {2026},
	eissn = {2064-8243},
	orcid-numbers = {Dobos, Imre/0000-0001-6248-2920; Sebestyén, Zoltán/0000-0002-2382-8797}
}

@{MTMT:35668914,
	title = {2020 Digital Maturity Survey for Small and Medium Enterprises in Hungary},
	url = {https://m2.mtmt.hu/api/publication/35668914},
	author = {Bánhidi, Zoltán and Dobos, Imre and Kulcsár, Imre Gábor and Nemeslaki, András},
	unique-id = {35668914},
	year = {2025},
	orcid-numbers = {Bánhidi, Zoltán/0000-0003-0262-5197; Dobos, Imre/0000-0001-6248-2920; Nemeslaki, András/0000-0002-4917-6782}
}

@article{MTMT:35665816,
	title = {Digital Country Rankings for the Visegrád Group Countries with DEA and TOPSIS},
	url = {https://m2.mtmt.hu/api/publication/35665816},
	author = {Bánhidi, Zoltán and Dobos, Imre and Kalló, Noémi and Tarjáni, Ariella Janka},
	doi = {10.54694/stat.2024.32},
	journal-iso = {STATISTIKA},
	journal = {STATISTIKA: STATISTICS AND ECONOMY JOURNAL},
	volume = {105},
	unique-id = {35665816},
	issn = {0322-788X},
	abstract = {Our paper is based on the five principal dimensions of the International Digital Economy and Society Index (I-DESI), but instead of using the original scoring model based on arbitrary pre-determined weights, we apply more objective ranking methods that use the statistical properties of the data series to determine where the Visegrad Group (V4) countries (Czechia, Hungary, Poland and Slovakia) stand in terms of digital development among the countries of the European Union and other developed countries in the data set. The ranking is performed using the DEA-CWA (Data Envelopment Analysis/Common Weights Analysis) method (with six models) and the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method. Although the resulting weight vectors differ significantly from the arbitrary weights set by the European Commission, the country rankings remain similar, displaying relatively little sensitivity to the weighting method chosen.},
	year = {2025},
	eissn = {1804-8765},
	pages = {87-101},
	orcid-numbers = {Bánhidi, Zoltán/0000-0003-0262-5197; Dobos, Imre/0000-0001-6248-2920; Kalló, Noémi/0000-0003-3193-081X}
}

@article{MTMT:36115722,
	title = {An entropy-based digital maturity index for small and medium enterprises in Hungary},
	url = {https://m2.mtmt.hu/api/publication/36115722},
	author = {Bánhidi, Zoltán and Dobos, Imre},
	doi = {10.1007/s10100-025-00985-w},
	journal-iso = {CEJOR},
	journal = {CENTRAL EUROPEAN JOURNAL OF OPERATIONS RESEARCH},
	unique-id = {36115722},
	issn = {1435-246X},
	abstract = {This paper introduces a firm-level digital maturity index for small and medium enterprises (SME-DMI), created using an entropy-based objective weighting method. The indicator was developed through a representative survey of Hungarian firms and its main objective is to evaluate the digital applications, tools and skills used by companies. It encompasses digital tools and infrastructure access, digital application usage and related skill levels. In addition to the main dimensions and their associated weights, this indicator also analyses the relationships between digital dimensions and firm size, using ANOVA. The results demonstrate that the impact of firm size is significant for both digital skills and knowledge and for business applications. However, connectivity and access digital public services is no longer a distinguishing factor between small and large firms. © The Author(s) 2025.},
	keywords = {Hungary; Hungary; ENTROPY; SME; SME; ANOVA; Firm size; Digital tools; Entropy-based; digital skills; maturity indices; digital maturity; digital maturity; digital applications; Small-and-medium enterprise},
	year = {2025},
	eissn = {1613-9178},
	orcid-numbers = {Bánhidi, Zoltán/0000-0003-0262-5197; Dobos, Imre/0000-0001-6248-2920}
}

@CONFERENCE{MTMT:36187761,
	title = {Az EU tagállamainak rangsorolása a mesterséges intelligencia iparági alkalmazása alapján},
	url = {https://m2.mtmt.hu/api/publication/36187761},
	author = {Bánhidi, Zoltán and Dobos, Imre and Lülök, Gergely},
	booktitle = {XXXVI. Magyar Operációkutatási Konferencia},
	unique-id = {36187761},
	year = {2025},
	pages = {85-85},
	orcid-numbers = {Bánhidi, Zoltán/0000-0003-0262-5197; Dobos, Imre/0000-0001-6248-2920}
}

@misc{MTMT:36882779,
	title = {Az EU tagállamainak rangsorolása a mesterséges intelligencia vállalati alkalmazása alapján},
	url = {https://m2.mtmt.hu/api/publication/36882779},
	author = {Dobos, Imre and Lülök, Gergely},
	unique-id = {36882779},
	year = {2025},
	orcid-numbers = {Dobos, Imre/0000-0001-6248-2920}
}

@article{MTMT:35466374,
	title = {Where Central and Eastern European countries stand in terms of digital readiness},
	url = {https://m2.mtmt.hu/api/publication/35466374},
	author = {Dobos, Imre and Bánhidi, Zoltán},
	doi = {10.1556/204.2024.00012},
	journal-iso = {SOC ECON},
	journal = {SOCIETY AND ECONOMY},
	volume = {47},
	unique-id = {35466374},
	issn = {1588-9726},
	abstract = {The paper employs a cross-sectional data set comprising the main dimensions of the European Union's International Digital Economy and Society Index (I-DESI) and utilises grouping methods based on objective weights to evaluate the relative digital readiness of Hungary and other Central and Eastern European (CEE) member states of the EU. The objective was not to establish a total ordering (ranking) of the countries in the data set, but rather to identify the most appropriate means of grouping the CEE countries into homogeneous units, utilising multivariate statistical and decision-theoretical techniques (tiered DEA, partially ordered sets and clustering). Despite the disparate methodologies employed, the findings are consistent in that the CEE countries (including Hungary) exhibit a general resemblance to one another and demonstrate comparatively lower levels of digital readiness than Northern and Western European countries. The notable exception is Estonia, which exhibits a distinctive level of digital advancement.},
	year = {2025},
	eissn = {1588-970X},
	pages = {66-84},
	orcid-numbers = {Dobos, Imre/0000-0001-6248-2920; Bánhidi, Zoltán/0000-0003-0262-5197}
}

@article{MTMT:35665754,
	title = {Calculation of ecological land-footprint - based on the input-output model and focusing on the imported commodities},
	url = {https://m2.mtmt.hu/api/publication/35665754},
	author = {Dobos, Imre and Tóth-Bozó, Brigitta},
	doi = {10.17535/crorr.2025.0006},
	journal-iso = {CROAT OPER RES REV},
	journal = {CROATIAN OPERATIONAL RESEARCH REVIEW},
	volume = {16},
	unique-id = {35665754},
	issn = {1848-0225},
	abstract = {The ecological footprint has been a crucial ecological indicator for more than two decades, and the methodology for calculating it has developed significantly over the years. However, some issues and shortcomings still need to be addressed and specified further. This paper focuses on the embedded land requirements of imported commodities in input-output modelling approaches. We propose a refined model to overcome the shortcomings of two former models. Our model quantifies the embedded ecological land-footprint of imported commodities and their allocation between direct final consumption and production. In addition, we allocate the latter again among final consumption and exports using the framework of linear algebra and matrix arithmetic. We also propose ways of extending the model to overcome the general but misleading assumption in the literature that imported commodities have an equal per unit ecological footprint to domestic products, an approach that is based on the idea that trading partners have the same technological background.},
	year = {2025},
	eissn = {1848-9931},
	pages = {65-72},
	orcid-numbers = {Dobos, Imre/0000-0001-6248-2920; Tóth-Bozó, Brigitta/0000-0002-0428-6072}
}