Mario Jadrić – University of Split, Faculty of Economics, Business and Tourism, Cvite Fiskovica 5, 21000 Split, Croatia

 

DOI: https://doi.org/10.31410/ITEMA.S.P.2020.1

 

4th International Scientific Conference on Recent Advances in Information Technology, Tourism, Economics, Management and Agriculture – ITEMA 2020, Online/virtual, October 8, 2020, SELECTED PAPERS published by the Association of Economists and Managers of the Balkans, Belgrade; Printed by: SKRIPTA International, Belgrade, ISBN 978-86-80194-37-0, ISSN 2683-5991, DOI: https://doi.org/10.31410/ITEMA.S.P.2020

 

 

Abstract

Research in the smart city domain is characterised by distinct multidisciplinarity. The reason for this is the broadness of the domain, classified into six key categories: smart governance, smart people, smart living, smart mobility, smart economy, and smart environment, all focal points of research in separate scientific fields. Also, many researchers argue about the best approach and steps in the development of smart cities highlighting different technological, economic, or sociological aspects of research. This paper aims to explore and clarify the differences in smart city research from two different perspectives – information systems and management. Abstracts from almost 5.000 papers from the WoS database and more than 7.000 papers from the Scopus database were downloaded and analysed. Publications categorised into two perspectives were then analysed descriptively, including data about the number of papers, year of publication, and country of publishing. Furthermore, automated text mining procedure was performed for additional interpretation of attributes and occurrences from the two observed perspectives. The use of six smart city categories as keywords within each set was also analysed and visualised. The results indicate clear differences in both research approaches and research subjects between the two perspectives.

 

Keywords

Smart city, Information systems, Management, Text mining.


References

Aloqaily, M., Otoum, S., Ridhawi, I. A., & Jararweh, Y. (2019). An Intrusion Detection System for Connected Vehicles in Smart Cities. Ad Hoc Networks. doi:10.1016/j.adhoc.2019.02.001
Appio, F. P., Lima, M., & Paroutis, S. (2018). Understanding Smart Cities: Innovation ecosystems, technological advancements, and societal challenges. Technological Forecasting and Social Change. doi:10.1016/j.techfore.2018.12.018
Ardito, L., Ferraris, A., Messeni Petruzzelli, A., Bresciani, S., & Del Giudice, M. (2018). The role of universities in the knowledge management of smart city projects. Technological Forecasting and Social Change. doi:10.1016/j.techfore.2018.07.030
Caragliu, A., & Del Bo, C. F. (2018). Smart innovative cities: The impact of Smart City policies on urban innovation. Technological Forecasting and Social Change. doi:10.1016/j.techfore.2018.07.022
Centenaro, M., Vangelista, L., Zanella, A., & Zorzi, M. (2016). Long-range communications in unlicensed bands: the rising stars in the IoT and smart city scenarios, in IEEE Wireless Communications, vol. 23, no. 5, pp. 60-67, October 2016, DOI: 10.1109/MWC.2016.7721743.

Chang, V., Wang, Y., & Wills, G. (2018). Research investigations on the use or non-use of hearing aids in the smart cities. Technological Forecasting and Social Change. doi:10.1016/j.techfore.2018.03.002
Daenekindt, S., & Huisman, J. (2020). Mapping the scattered field of research on higher education: a correlated topic model of 17,000 articles, 1991–2018. Higher education, 1–17. https://doi.org/10.1007/s10734-020-00500-x
Ferraris, A., Erhardt, N., & Bresciani, S. (2017). Ambidextrous work in smart city project alliances: unpacking the role of human resource management systems. The International Journal of Human Resource Management, 1–22. doi:10.1080/09585192.2017.1291530
Gharaibeh, A., Salahuddin, M. A., Hussini, S. J., Khreishah, A., Khalil, I., Guizani, M., & Al-Fuqaha, A. (2017). Smart Cities: A Survey on Data Management, Security, and Enabling Technologies. IEEE
Hossain, M. S., Muhammad, G., & Alamri, A. (2017). Smart healthcare monitoring: a voice pathology detection paradigm for smart cities. Multimedia Systems. doi:10.1007/s00530-017-0561-x
Hrcka, L., Simoncicova, V., Tadanai, O., Tanuska, P., Vazan, P. (2017). Using Text Mining Methods for Analysis of Production Data in Automotive Industry. Artif. Intell. Trends Intell. Syst. Adv. Intell. Syst. Comput.
Hu, L., Liu, A., Xie, M., & Wang, T. (2019). UAVs joint vehicles as data mules for fast codes dissemination for edge networking in Smart City. Peer-to-Peer Networking and Applications. doi:10.1007/s12083-019-00752-0
Jadrić M., Mijač T., Ćukušić M. (2020) Text Mining the Variety of Trends in the Field of Simulation Modelling Research. In: Buchmann R.A., Polini A., Johansson B., Karagiannis D. (eds) Perspectives in Business Informatics Research. BIR 2020. Lecture Notes in Business Information Processing, vol 398. Springer, Cham. https://doi.org/10.1007/978-3-030-61140-8_10
Justicia De La Torre, C., Sánchez, D., Blanco, I., Martín-Bautista, M.J. (2018). Text mining: Techniques, applications, and challenges. Int. J. Unc., Fuzz. KB. Syst. 26(4), 553–582. https://doi.org/10.1142/S0218488518500265
Kaur, A., Chopra, D.; Comparison of Text Mining Tools. In 5th International Conference on Reliability, Infocom Technologies and Optimisation (ICRITO), 365–376, (2016).
Kumar, H., Singh, M. K., Gupta, M. P., & Madaan, J. (2018). Moving towards smart cities: Solutions that lead to the Smart City Transformation Framework. Technological Forecasting and Social Change. doi:10.1016/j.techfore.2018.04.024
Liu, Y., Yang, C., Jiang, L., Xie, S., & Zhang, Y. (2019). Intelligent Edge Computing for IoT-Based Energy Management in Smart Cities. IEEE Network, 33(2), 111–117.
Manville C, et al. (2014). Mapping Smart Cities in the EU doi:10.1017/CBO9781107415324.004.
Mohamed, N., Al-Jaroodi, J., Jawhar, I., Idries, A., & Mohammed, F. (2018). Unmanned aerial vehicles applications in future smart cities. Technological Forecasting and Social Change. doi:10.1016/j.techfore.2018.05.004
Mohammadi, M., Al-Fuqaha, A., Guizani, M., & Oh, J.-S. (2018). Semisupervised Deep Reinforcement Learning in Support of IoT and Smart City Services. IEEE Internet of Things Journal, 5(2), 624–635. doi:10.1109/jiot.2017.2712560
Mora, L., Deakin, M., & Reid, A. (2018). Strategic principles for smart city development: A multiple case study analysis of European best practices. Technological Forecasting and Social Change. doi:10.1016/j.techfore.2018.07.035
Ning, Z., Huang, J., & Wang, X. (2019). Vehicular Fog Computing: Enabling Real-Time Traffic Management for Smart Cities. IEEE Wireless Communications, 26(1), 87–93. doi:10.1109/mwc.2019.1700441

North, M., (2012). Data Mining for the Masses, Global Text Project
RapidMiner: RapidMiner. 2020. [Online]. Available: http://docs.rapidminer.com/.
Rathore, M. M., Ahmad, A., Paul, A., & Rho, S. (2016). Urban planning and building smart cities based on the Internet of Things using Big Data analytics. Computer Networks, 101, 63–80. doi:10.1016/j.comnet.2015.12.023
Sepasgozar, S. M. E., Hawken, S., Sargolzaei, S., & Foroozanfa, M. (2018). Implementing citizen centric technology in developing smart cities: A model for predicting the acceptance of urban technologies. Technological Forecasting and Social Change. doi:10.1016/j.techfore.2018.09.012
Teng, H., Liu, W., Wang, T., Liu, A., Liu, X., & Zhang, S. (2019). A Cost-Efficient Greedy Code Dissemination Scheme Through Vehicle to Sensing Devices (V2SD) Communication in Smart City.
Trencher, G. (2018). Towards the smart city 2.0: Empirical evidence of using smartness as a tool for tackling social challenges. Technological Forecasting and Social Change. doi:10.1016/j.techfore.2018.07.033

 

Download Full Paper

Association of Economists and Managers of the Balkans – UdEkoM Balkan
179 Ustanicka St, 11000 Belgrade, Republic of Serbia

ITEMA conference publications are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.