Abstract
The utilization of management control systems in university management poses a considerable challenge because university’s strategic goals are not identical to those applied in profit-oriented management. A university’s management control system should take into account the processing of management information for management purposes, allowing for the relationships between different groups of stakeholders. The specificity of the university operation assumes conducting long-term scientific research and educational programmes. Therefore, the controlling approach to university management should considerat long-term performance measurement as well as management in key areas such as research, provision of education to students, and interaction with the tertiary institution’s socio-economic environment. This paper aims to develop a conceptual framework of the Intelligent Management Control System for Higher Education (IMCSHE) based on cognitive agents. The main findings are related to developing the assumption, model, and technological basis including the artificial intelligence method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Etzkowitz, E., Leydesdorff, L.: The dynamics of innovation: from national systems and “mode 2” to a triple helix of university–industry-government relations. Res. Policy 29, 109–123 (2000)
Carayannis, E.G., Campbell, D.F.: Open innovation diplomacy and a 21st century fractal research, education and innovation (FREIE) ecosystem: building on the quadruple and quintuple helix innovation concepts and the “mode 3” knowledge production system. J. Knowl. Econ. 2(3), 327–372 (2011)
Baid, D.: A study of management control systems (MCS) and school performance. Metamorphosis: J. Manag. Res. 16(2), 92–106 (2017). https://journals.sagepub.com/doi/full/10.1177/0972622517731407. Accessed 28 Apr 2020
Kaplan, R.S., Norton, D.P.: The balanced scorecard – measures that drive performance. Harvard Bus. Rev. 70(1), 71–79 (1992)
Al-Tarawneh, H.A.: Design and use of management accounting and control systems in the Jordanian Universities. Int. J. Account. Financ. Report. 1(1), 242–254 (2011)
Beuren, I.M., Teixeira, S.A.: Evaluation of management control systems in a higher education institution with the performance management and control. J. Inf. Syst. Technol. Manag. 11(1), 169–192 (2014)
Tanveer, M., Karim, A.M.: Higher education institutions and the performance. Library Philos. Pract. (2183) (2018). ISSN 1522-0222
Cantele, S., Martini, M., Campedelli, B.: The implementation of management control systems in Italian universities: moving towards New University Management? In: CMS7 – 7th Critical Management Studies Conference (2011)
Lábas, I., Bács, Z.: Management control system in the University of Debrecen. Process Econ. Finance 32, 408–415 (2015)
Alach, Z.: Performance measurement maturity in a national set of universities. Int. J. Prod. Perform. Manag. 66(2) (2017)
McCormack, J., Propper, C., Smith, S.: Herding cats? Management and university performance. Econ. J. 124(578), F534–F564 (2014)
Vaishnavi, V., Kuechler, W., Petter, S. (eds.): Design Science Research in Information Systems, 20 January 2004 (created in 2004 and updated until 2015 by Vaishnavi, V. and Kuechler, W.); last updated (by Vaishnavi, V. and Petter, S.), 30 June 2019. http://www.desrist.org/design-research-in-information-systems/
Franklin, S., et al.: A LIDA cognitive model tutorial. Biol. Inspired Cogn. Archit. 105–130 (2016). https://doi.org/10.1016/j.bica.2016.04.003
McCall, R., Franklin, S., Faghihi, U., Snaider, J., Kugele, S.: Artificial motivation for cognitive software agents. J. Artif. General Intell. 11(1), 38–69 (2020). https://doi.org/10.2478/jagi-2020-0002
Hernes, M.: Consensus theory for cognitive agents’ unstructured knowledge conflicts resolving in management information systems. In: Nguyen, N.T., Kowalczyk, R., Hernes, M. (eds.) Transactions on Computational Collective Intelligence XXXII. LNCS, vol. 11370, pp. 1–119. Springer, Heidelberg (2019). https://doi.org/10.1007/978-3-662-58611-2_1
Hernes, M., Bytniewski, A.: Knowledge representation of cognitive agents processing the economy events. In: Nguyen, N.T., Hoang, D.H., Hong, T.-P., Pham, H., Trawiński, B. (eds.) ACIIDS 2018. LNCS (LNAI), vol. 10751, pp. 392–401. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75417-8_37
Bush, P.: Tacit Knowledge in Organizational Knowledge. IGI Global, Hershey, New York (2008)
Nycz, M., Owoc, M.L., Pondel, M.: Business intelligence concepts for education quality management. Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu. Informatyka Ekonomiczna 2010 | 16 | nr 104 Data Mining and Business Intelligence 47–59 (2010)
Acknowledgement
The project is financed by the Ministry of Science and Higher Education in Poland under the programme “Regional Initiative of Excellence” 2019 - 2022 project number 015/RID/2018/19 total funding amount 10 721 040,00 PLN.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 IFIP International Federation for Information Processing
About this paper
Cite this paper
Dudycz, H. et al. (2021). A Conceptual Framework of Intelligent Management Control System for Higher Education. In: Mercier-Laurent, E., Kayalica, M.Ö., Owoc, M.L. (eds) Artificial Intelligence for Knowledge Management. AI4KM 2021. IFIP Advances in Information and Communication Technology, vol 614. Springer, Cham. https://doi.org/10.1007/978-3-030-80847-1_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-80847-1_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-80846-4
Online ISBN: 978-3-030-80847-1
eBook Packages: Computer ScienceComputer Science (R0)
