Although various actors have examined the user acceptance of e-government developments, less attention has so far devoted to the relationship between attitudes of certain commuter groups against digital technologies and their intention to engage in productive time-use by mobile devices. This paper aims to fill this gap by establishing an overall framework which focuses on Hungarian commuters’ attitudes toward e-government applications as well as their possible demands of developing them. Relying on a representative questionnaire survey conducted in Hungary in March and April 2020, the data were examined by a machine learning and correlations to identify the factors, attitudes and demands that influence the use of mobile devices during frequent commuting. The paper argues that the regularity of commuting in rural areas, as well as the higher levels of qualification and employment status in cities show a more positive, technophile attitude to new ICT and mobile technologies that strengthen the demands for digital development, with special regard to optimising e-government applications for certain types of commuting groups. One of the main limitations of this study is that results suggest a picture of the commuters in a narrow timeframe. The findings suggest that developing e-government applications is necessary and desirable from both of the supply and demand sides. Based on prior scholarly knowledge, no research has ever analysed these correlations in Hungary where commuters are among the European citizens who spend extensive time with commuting.

Acock, A. C., & Stavig, G. R. (1979). A Measure of Association for Nonparametric Statistics. Social Forces, 57(4), 1381. https://doi.org/10.2307/2577276

Mujali Al-Rawahna, A. S., Chen, S.-C., & Hung, C.-W. (2018). The Barriers of E-Government Success : An Empirical Study from Jordan. International Journal of Managing Public Sector Information and Communication Technologies, 9(2), 01–18. https://doi.org/10.5121/ijmpict.2018.9201

Al-Shafi, S., Weerakkody, V., Janssen, M. (2009). Investigating the adoption of eGovernment services in Qatar using the UTAUT model. AMCIS 2009 Proceedings, 260.

Bednar, P. M., & Welch, C. (2019). Socio-Technical Perspectives on Smart Working: Creating Meaningful and Sustainable Systems. Information Systems Frontiers, 22(2), 281–298. https://doi.org/10.1007/s10796-019-09921-1

Bijker, W., Hughes, T., & Pinch, T. (editors). (1987). The Social Construction of Technological Systems. Cambridge, MA: MIT Press.

Breiman, L. (2001). Random forests. Machine learning, 45, 5-32. https://doi.org/10.1023/A:1010933404324

Chan, F., Thong, J., Venkatesh, V., et al. (2010). Modeling Citizen Satisfaction with Mandatory Adoption of an E-Government Technology. Journal of the Association for Information Systems, 11(10), 519–549. https://doi.org/10.17705/1jais.00239

Choi, J. (2016). Why do people use news differently on SNSs? An investigation of the role of motivations, media repertoires, and technology cluster on citizens’ news-related activities. Computers in Human Behavior, 54, 249–256. https://doi.org/10.1016/j.chb.2015.08.006

Clark, B., Chatterjee, K., Martin, A., et al. (2019). How commuting affects subjective wellbeing. Transportation, 47(6), 2777–2805. https://doi.org/10.1007/s11116-019-09983-9

Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319. https://doi.org/10.2307/249008

Dwivedi, Y. K., Rana, N. P., Janssen, M., et al. (2017). An empirical validation of a unified model of electronic government adoption (UMEGA). Government Information Quarterly, 34(2), 211–230. https://doi.org/10.1016/j.giq.2017.03.001

Elle, M., Dammann, S., Lentsch, J., et al. (2010). Learning from the social construction of environmental indicators: From the retrospective to the pro-active use of SCOT in technology development. Building and Environment, 45(1), 135–142. https://doi.org/10.1016/j.buildenv.2009.05.011

Ettema, D., Friman, M., Gärling, T., et al. (2012). How in-vehicle activities affect work commuters’ satisfaction with public transport. Journal of Transport Geography, 24, 215–222. https://doi.org/10.1016/j.jtrangeo.2012.02.007

European Commission. (2022). Digital Economy and Society Index (DESI) 2022. Hungary. European Commission.

Fási, Cs. (2019). Competitiveness Studies – The Digital Preparedness of the Hungarian Public Service. JURA, 2, 264-274.

Government of Hungary. (2021). Hungary. National Reform Programme of Hungary. Available online: https://commission.europa.eu/system/files/2021-11/nrp_2021_hungary_en.pdf (accessed on 1 June 2024).

Jang, J., & Ko, J. (2019). Factors associated with commuter satisfaction across travel time ranges. Transportation Research Part F: Traffic Psychology and Behaviour, 66, 393–405. https://doi.org/10.1016/j.trf.2019.09.019

Julsrud, T. E., & Denstadli, J. M. (2017). Smartphones, travel time-use, and attitudes to public transport services. Insights from an explorative study of urban dwellers in two Norwegian cities. International Journal of Sustainable Transportation, 11(8), 602–610. https://doi.org/10.1080/15568318.2017.1292373

Kaiser, T., & Gadár, L. (2023). Survey data on the attitudes towards digital technologies and the way of managing e-governmental tasks. Data in Brief, 46, 108871. https://doi.org/10.1016/j.dib.2022.108871

Ma, L., & Ye, R. (2019). Does daily commuting behavior matter to employee productivity? Journal of Transport Geography, 76, 130–141. https://doi.org/10.1016/j.jtrangeo.2019.03.008

Malokin, A., Circella, G., & Mokhtarian, P. L. (2019). How do activities conducted while commuting influence mode choice? Using revealed preference models to inform public transportation advantage and autonomous vehicle scenarios. Transportation Research Part A: Policy and Practice, 124, 82–114. https://doi.org/10.1016/j.tra.2018.12.015

Mensah, I. K. (2020). Impact of Government Capacity and E-Government Performance on the Adoption of E-Government Services. International Journal of Public Administration, 43(4), 303–311. https://doi.org/10.1080/01900692.2019.1628059

Mensah, I. K., Zeng, G., & Luo, C. (2020). E-Government Services Adoption: An Extension of the Unified Model of Electronic Government Adoption. SAGE Open, 10(2), 215824402093359. https://doi.org/10.1177/2158244020933593

Ministry of Innovation, Technology and Ministry of Interior. (2020). National Digitalization Strategy 2021-230 (Hungarian). Available online: https://digital-strategy.ec.europa.eu/en/policies/broadband-hungary (accessed on 1 June 2024).

Ministry of Interior. (2020). Statistics. Population Data Portal. Available online: https://nyilvantarto.hu/hu/statisztikak?stat=kozerdeku (accessed on 2 June 2024).

Pieterson, W. J., & Ebbers, W. E. (2020). Channel choice evolution: An empirical analysis of shifting channel behavior across demographics and tasks. Government Information Quarterly, 37(3), 101478. https://doi.org/10.1016/j.giq.2020.101478

Rogers, E. M. (2010). Diffusion of innovations, 4th ed. The Free Press.

Rosenbaum, J., Zepic, R., Schreieck, M., et al. (2018). Barriers to mobile government adoption: An exploratory case study of an information platform for refugees in Germany. Academic Conferences and Publishing International Limited.

Singleton, P. A. (2018). How Useful is Travel-Based Multitasking? Evidence from Commuters in Portland, Oregon. Transportation Research Record: Journal of the Transportation Research Board, 2672(50), 11–22. https://doi.org/10.1177/0361198118776151

Song, J. W., & Chung, K. C. (2010). Observational Studies: Cohort and Case-Control Studies. Plastic and Reconstructive Surgery, 126(6), 2234–2242. https://doi.org/10.1097/prs.0b013e3181f44abc

St-Louis, E., Manaugh, K., van Lierop, D., et al. (2014). The happy commuter: A comparison of commuter satisfaction across modes. Transportation Research Part F: Traffic Psychology and Behaviour, 26, 160–170. https://doi.org/10.1016/j.trf.2014.07.004

Taylor, S., & Todd, P. A. (1995). Understanding Information Technology Usage: A Test of Competing Models. Information Systems Research, 6(2), 144–176. https://doi.org/10.1287/isre.6.2.144

Van den Ban, A. W., Hawkins, S. S. (1988). Agricultural Extension, 2nd ed. Cambridge, Massachusetts: Blackwell Science Ltd.

Vargáné Dudás, P., & Dávid, L. D. (2024). Unlocking the potential: UTAUT2 framework for embracing self-driving tractors in modern agriculture. Journal of Infrastructure, Policy and Development, 8(6), 3442. https://doi.org/10.24294/jipd.v8i6.3442

Venkatesh, Morris, Davis, & Davis. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425. https://doi.org/10.2307/30036540

Venkatesh, V., Thong, J., & Xu, X. (2016). Unified Theory of Acceptance and Use of Technology: A Synthesis and the Road Ahead. Journal of the Association for Information Systems, 17(5), 328–376. https://doi.org/10.17705/1jais.00428

World Population Review. (2024): Budapest population 2024. Available online: https://worldpopulationreview.com/world-cities/budapest-population (accessed on 1 June 2024).

Ye, R., & Titheridge, H. (2017). Satisfaction with the commute: The role of travel mode choice, built environment and attitudes. Transportation Research Part D: Transport and Environment, 52, 535–547. https://doi.org/10.1016/j.trd.2016.06.011