PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

The enterprise management system : evaluating the use of information technology and information systems

Treść / Zawartość
Identyfikatory
Warianty tytułu
PL
System zarządzania przedsiębiorstwem : ocena wykorzystania technologii informacyjnej i systemów informacyjnych
Języki publikacji
EN
Abstrakty
EN
The purpose of this study is to complement the existing approaches towards the evaluation of the quality of ITS use in enterprise management. This article explores the point of using information technologies and systems in enterprise management and articulates an evaluation approach that can be applied to it. In this article, a Technology Acceptance Model is used to evaluate the use of information systems and technologies. According to this model, the use of any technology will be effective only if the Perceived Usefulness and the Perceived Ease of Use are high. The study surveys 120 industry experts. The survey sample is composed in accordance with the compatible criteria (competence, area of engagement, and experience of work in the leading companies). The Enterprise-Resource Planning, Customer Relationships Management, and Supplier Relationships Management systems have the highest scores, with the reliability coefficient of 0.89. A comparative assessment has been conducted on information technologies, which are used in operations management. The proposed approach can be used in any enterprise.
PL
Celem tego badania jest uzupełnienie istniejących podejść do oceny jakości wykorzystania ITS w zarządzaniu przedsiębiorstwem. W tym artykule bada się zastosowanie technologii i systemów informatycznych w zarządzaniu przedsiębiorstwem oraz przedstawia podejście ewaluacyjne, które można do niego zastosować. W tym artykule zastosowano model akceptacji technologii do oceny wykorzystania systemów i technologii informatycznych. Zgodnie z tym modelem korzystanie z dowolnej technologii będzie skuteczne tylko wtedy, gdy postrzegana użyteczność i odczuwalna łatwość użytkowania będą wysokie. W badaniu wzięło udział 120 ekspertów branżowych. Próbka ankiety składa się zgodnie z kompatybilnymi kryteriami (kompetencje, obszar zaangażowania i doświadczenie w pracy w wiodących firmach). Systemy planowania przedsiębiorstwa i zarządzania relacjami z klientami oraz zarządzania relacjami z dostawcami mają najwyższe wyniki, a współczynnik niezawodności wynosi 0,89. Przeprowadzono ocenę porównawczą technologii informatycznych wykorzystywanych w zarządzaniu operacjami. Proponowane podejście można zastosować w dowolnym przedsiębiorstwie.
Rocznik
Strony
103--118
Opis fizyczny
Bibliogr. 56 poz., tab.
Twórcy
  • Department of Organization management in mechanical engineering, State University of Management, Moscow, Russia, viktoria.borisova@yandex.ru
  • Department of Management organization in engineering, State University of Management, Moscow, Russia, demkina.o.v@gmail.com
  • Department of Sociology and human resource, North-Eastern University, Yakutsk, Russia, mikanya23@mail.ru
Bibliografia
  • 1. Abri, A.G., Mahmoudzadeh, M. (2014). Impact of Information Technology on Productivity and Efficiency in Iranian Manufacturing Industries. Journal of Industrial Engineering International, 11, 143-157.
  • 2. Afanasyev, M., Shash, N. (2018). Interrelation of Economic Growth and Levels of Public Expenditure in the Context of Wagners Law. Public administration issues, 6, 174-183.
  • 3. Agostinho, C., Ducq, Y., Zacharewicz, G., Sarraipa, J., Lampathaki, F., Poler, R. & Jardim-Goncalves, R. (2015). Towards a sustainable interoperability in networked enterprise information systems: Trends of knowledge and model-driven technology. Comput. Industry, 79, 64-76.
  • 4. Appiahene, P., Ussiph, N. & Missah, Y.M. (2018). Information Technology Impact on Productivity: A Systematic Review and Meta-Analysis of the Literature. International Journal of Information Communication Technologies and Human Development (IJICTHD), 10, 39-61.
  • 5. Bhattacharya, P. (2018). Aligning Enterprise Systems Capabilities with Business Strategy: An Extension of the Strategic Alignment Model (SAM) using Enterprise Architecture. Procedia Computer Science, 138, 655-662.
  • 6. Cavdar, S.C., Aydin, A.D. (2015). An empirical analysis about technological development and innovation indicators. Procedia-Social and Behavioral Sciences, 195, 1486-1495.
  • 7. Crocker, L., Algina, J. (2006). Introduction to Classical and Modern Test Theory (pp. 527). Cengage Learning.
  • 8. Danaei, A., Hosseini, A. (2013). Performance measurement using balanced scorecard: A case study of pipe industry. Management Science Letters, 3(5), 1433-1438.
  • 9. DeLone, W.H., McLean, E.R. (2016). Information Systems Success Measurement. Foundations and Trends® in Information Systems, 2(1), 1-116.
  • 10. DeStefano, T., Kneller, R. & Timmis, J. (2018). Broadband infrastructure, ICT use and firm performance: Evidence for UK firms. Journal of Economic Behavior and Organization, 155(C), 110-139.
  • 11. Dorokhov, O.V. (2010). Criteria and Methods for Assessing the Efficiency of Information Systems. Information Processing Systems, 1(82).
  • 12. Durodolu, O.O. (2016). Technology Acceptance Model as a predictor of using information system’ to acquire information literacy skills. Library Philosophy and Practice (e-journal), 1450.
  • 13. Eroğlu, Ş., Cakmak, T. (2016). Enterprise Information systems within the context of information security: a risk assessment for a health organization in Turkey. Procedia Computer Science, 100, 979-986.
  • 14. Eroshkin, S.Yu., Kameneva, N.A., Kovkov, D.V. & Sukhorukov, A.I. (2017). Conceptual system in the modern information management. Procedia Computer Science, 103, 609-612.
  • 15. Fox, J.-P. (2010). Bayesian Item Response Modeling: Theory and Applications (pp. 323). Springer.
  • 16. Gerow, J.E., Thatcher, J.B. & Grover, V. (2015). Six types of IT-business strategic alignment: an investigation of the constructs and their measurement. European Journal of Information Systems, 24(5), 465-491.
  • 17. Haddara, M., Moen, H. (2017). User resistance in ERP implementations: A literature review. Procedia Computer Science, 121, 859-865.
  • 18. Hinkelmann, K., Gerber, A., Karagiannis, D., Thoenssen, B., Van der Merwe, A. & Woitsch, R. (2016). A new paradigm for the continuous alignment of business and IT: Combining enterprise architecture modelling and enterprise ontology. Computers in Industry, 79, 77-86.
  • 19. Hou, A.C.Y., Chen, Y.-Ch. & Shang, R.-A. (2016). Mutual relations in ERP implementation: the impacts of work alienation and organizational support in state-owned enterprise. Procedia Computer Science, 100, 1289-1296.
  • 20. Chavarria-Barrientos, D., Chen, D., Funes, R., Molina, A. & Vernadat, F. (2017). An Enterprise Operating System for the Sensing, Smart, and Sustainable Enterprise. IFAC PapersOnLine, 50(1), 13052-13058.
  • 21. Chuttur, M.Y. (2009). Overview of the Technology Acceptance Model: Origins, Developments and Future Directions. Indiana University, USA. Sprouts: Working Papers on Information Systems, 9(37).
  • 22. Johnson, M.A. (2006). An Investigation of Stratification Exposure Control Procedures in CATs Using the Generalized Partial Credit Model (pp. 157). Austin: The University of Texas.
  • 23. Kadiri, S.El., Grabot, B., Thoben, K.-D., Hribernik, K., Emmanouilidis, Ch., von Cieminski, G. & Kiritsis, D. (2015). Current trends on ICT technologies for enterprise information systems. Computers In Industry, 79, 14-33.
  • 24. Kappelman, L., McLean, E., Johnson, V. & Gerhart, N. (2014). The 2014 SIM IT key issues and trends study. MIS Quarterly Executive, 13(4), 237-263.
  • 25. Kline, P. (1994). A Guide to Test Construction (pp. 274). Kiev.
  • 26. Koryagin, N.D., Sukhorukov, A.I. & Medvedev, A.V. (2015). Application of modern methodological approaches to operating information systems management (pp. 148). Moscow: RIO MGTU GA.
  • 27. Kouziokas, G.N. (2016). Technology-based management of environmental organizations using an Environmental Management Information System (EMIS): Design and development. Environmental Technology & Innovation, 5, 106-116.
  • 28. Kusek, J.Z., Rist, R.C. (2004). Ten steps to a results-based monitoring and evaluation system: a handbook for development practitioners (pp. 248). Washington, DC: The World Bank.
  • 29. Lance, D., Muretta, M.M. (2013). The Influence of Enterprise Systems on Business and Information Technology. Retrieved from http://digitalcommons.mtech.edu/business_info_tech/1/?utm_source=digitalcommons.mtech.edu%2Fbusiness_info_tech%2F1&utm_medium=PDF&utm_campaign=PDFCoverPages
  • 30. Le, D.T., Nguyen, H.P., Ho, V.N., Ho, T.P.Y., Nguyen, Q.T. & Le, N.N.A. (2018). Technology Acceptance and Future of Internet Banking in Vietnam. Foresight and STI Governance, 12(2), 36-48.
  • 31. Li, Y. (2012). Behavioral Research Data Analysis with R (pp. 256). Springer.
  • 32. Liu, S., Du, S., Xi, L. (2018). Transient analysis of quality performance in two-stage manufacturing systems with remote quality information feedback. Computers & Industrial Engineering, 45(5), 528-543.
  • 33. Luo, F., Wu, F., Cai, Y. (2018). Electric Company Management Information System based on Unified Message Center. Procedia Computer Science, 139, 287-292.
  • 34. Malyzhenkov, P., Ivanova, M. (2017). An Enterprise Architecture-Based Approach to the IT-Business Alignment: An Integration of SAM and TOGAF Framework, [In:] Workshop on Enterprise and Organizational Modeling and Simulation (pp. 159-173). Springer, Cham.
  • 35. Manna, A., Sengupta, A. & Mazumdar, Ch. (2016). A Survey of Trust Models for Enterprise Information Systems. Procedia Computer Science, 85, 527-534.
  • 36. Mislevy, R.J., Wilson, M.R., Ercikan, K. & Chudowsky, N. (2001). Psychometric Principles in Student Assessment. International Handbook of Educational Evaluation (pp. 52). Dordrecht, the Netherlands: Kluwer Academic Press.
  • 37. Nam, W.-H., Kim, T., Hong, E.-M., Choi, J.-Y. & Kim, J.-T. (2017). A Wireless Sensor Network (WSN) application for irrigation facilities management based on Information and Communication Technologies (ICTs). Computers and Electronics in Agriculture, 143, 185-192.
  • 38. Niemi, E., Pekkola, S. (2017). Using enterprise architecture artefacts in an organisation. Enterprise Information Systems, 11(3), 313-338.
  • 39. Norpadzlihatun, M. (2013). Risk-Based Decision Making Framework For The Integrated Environmental Management of Dredging Sediments. Centre for Environmental Policy, Faculty of Natural Sciences, Imperial College London.
  • 40. Oliva, F.L. (2016). A maturity model for enterprise risk management. International Journal of Production Economics, 173, 66-79.
  • 41. Ozusaglam, S., Kesidou, E. & Wong, C.Y. (2018). Performance effects of complementarity between environmental management systems and environmental technologies. International Journal of Production Economics, 197(C), 112-122.
  • 42. Panetto, H., Zdravkovic, M., Jardim-Goncalves, R., Romero, D., Cecil, J. & Mezgar, I. (2015). New perspectives for the future interoperable enterprise systems. Comput. Industry, 79, 47-63.
  • 43. Persse, J.R. (2001). Implementing the Capability Maturity Model. Wiley Computer Publishing.
  • 44. Pourmirza, S., Peters, S.P.F., Dijkman, R.M. & Grefen, P.W.P.J. (2017). A systematic literature review on the architecture of business process management systems. Information Systems, 66, 43-58.
  • 45. Pushkar, A.I., Garkin, V.V. (2014). Metrics and measures of quality assessment of enterprise information system. Mathematical Methods, Models and Information Technologies in Economics Reeve B (pp. 67). An Introduction to Modern Measurement Theory.
  • 46. Reeve, B.B. (2009). Why Teachers Adopt a Controlling Motivating Style Toward Students and How They Can Become More Autonomy Supportive. Educational Psychologist, 44(3), 159-175.
  • 47. Rezvani, A., Khosravi, P. (2017). Promoting the continuing usage of strategic information systems: The role of supervisory leadership in the successful implementation of enterprise systems. International Journal of Information Management, 37, 417-430.
  • 48. Romero, D., Vernadat, F. (2016). Enterprise information systems state of the art: Past, present and future trends. Comput. Industry, 79(C), 3-13.
  • 49. Saeidi, P., Saeidi, S.P., Sofian, S., Saeidi, S.P., Nilashi, M. & Mardani, A. (2019). The Impact of Enterprise Risk Management on Competitive Advantage by Moderating Role of Information Technology. Computer Standards & Interfaces, 63, 67-82.
  • 50. Saprykina, A.O. (2015). The technology acceptance model as a tool for evaluating the subjective effectiveness of e-portfolio technology. Education Theory and Practice in the Modern World: Materials of the VII International Scientific Conference (pp. 108-110). St. Petersburg: Svoye Izdatelstvo Publishing House.
  • 51. Singh, J. (2004). Tackling measurement problems with Item Response Theory: Principles, characteristics, and assessment, with an illustrative example. Journal of Business Research, 57(2), 184-208.
  • 52. Surendran, P. (2012). Technology Acceptance Model: A Survey of Literature. International Journal of Business and Social Research (IJBSR), 2(4).
  • 53. Sushil, S. (2018). Incorporating polarity of relationships in ISM and TISM for theory building in information and organization management. International Journal of Information Management, 43, 38-51.
  • 54. Taherdoost, H. (2018). A review of technology acceptance and adoption models and theories. Procedia Manufacturing, 22, 960-967.
  • 55. United Nations Development Programme, (2002). Handbook on monitoring and evaluating for results. Evaluation Office: New York (pp. 140).
  • 56. Wright, B., Stone, M. (1999). Measurement Essentials. 2nd edition (pp. 221). Wilmington, Delaware: Wide Range, Inc.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-972744ae-f581-4eb8-b332-41c4ca2cfc27
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.