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Tytuł artykułu

Effective information system and organisational efficiency

Treść / Zawartość
Identyfikatory
Warianty tytułu
PL
Skuteczny system informacyjny i efektywność organizacyjna
Języki publikacji
EN
Abstrakty
EN
The present study aims to identify the effectiveness of information systems on organizational efficiency in technical and operational perspectives in the various public and private sectors. The novelty of the research focuses on data security practices, perceived severity, response efficacy, perceived usefulness and perceived behavioral control on organisational effectiveness. The survey data is gathered from 200 professionals representing public and private sectors in India and MENA countries who broadly support these outcomes. The analysis of data was generated by applying an equation model and various descriptive statistical tools using AMOS and SPSS. The study results reveal that an effective information system with comprehensive information management avoids potential cyber-attacks and enhances the organisation’s performance. The study identified that repeated cyberattacks threaten the reputation of the business and its organizational operations. Therefore, creating effective awareness about risks and challenges on business operation among the workforce enhances the performance and efficiency of the organization. This empirical research has contributed significantly for organizations to emphasize suitable measures to incorporate effective information and risk mitigation plan to protect data and efficiency. The research outcome emphasized that training and awareness are mediating variables to enhance performance.
PL
Niniejsze badanie ma na celu określenie skuteczności systemów informatycznych w zakresie sprawności organizacyjnej w perspektywach technicznych i operacyjnych w różnych sektorach publicznym i prywatnym. Nowość badania koncentruje się na praktykach w zakresie bezpieczeństwa danych, postrzeganej dotkliwości, skuteczności reakcji, postrzeganej użyteczności i postrzeganej kontroli behawioralnej nad efektywnością organizacji. Dane ankietowe zostały zebrane od 200 specjalistów reprezentujących sektor publiczny i prywatny w Indiach i krajach MENA, którzy szeroko popierają te wyniki. Analiza danych została wygenerowana przez zastosowanie modelu równania i różnych opisowych narzędzi statystycznych przy użyciu AMOS i SPSS. Wyniki badania pokazują, że skuteczny system informacyjny z kompleksowym zarządzaniem informacją pozwala uniknąć potencjalnych cyberataków i poprawia wydajność organizacji. Badanie wykazało, że powtarzające się cyberataki zagrażają reputacji firmy i jej działaniom organizacyjnym. Dlatego budowanie wśród pracowników skutecznej świadomości na temat zagrożeń i wyzwań związanych z działalnością biznesową zwiększa wydajność i efektywność organizacji. To badanie empiryczne w znacznym stopniu przyczyniło się do podkreślenia przez organizacje odpowiednich środków w celu włączenia skutecznych informacji i planu łagodzenia ryzyka w celu ochrony danych i wydajności. Wyniki badań podkreślają, że szkolenie i świadomość są zmiennymi pośredniczącymi w zwiększaniu wydajności.
Rocznik
Strony
398--413
Opis fizyczny
Bibliogr. 38 poz., rys., tab.
Twórcy
  • Kingdom University, Bahrain
autor
  • Kingdom University, Bahrain
  • Graphic Era (Deemed to be University), India
  • Kalasalingam Academy of Research and Education, India
Bibliografia
  • 1.Aghakhani, N., Roshani, R., Zarei, A., Delirrad, M., Rahbar, N. and Cheraghi, R., (2020). Analysis of occupational injuries in employees of forensic medicine organizations of West Azerbaijan province in 2016. Iran Occupational Health, 16(6), 16-26.
  • 2.Al-Gasawneh, J.A., Anuar, M.M., Dacko-Pikiewicz, Z., Saputra, J. (2021). The impact of customer relationship management dimensions on service quality. Polish Journal of Management Studies, 23 (2), 24-41.
  • 3.Anand, R., Medhavi, S., Soni, V., Malhotra, C. and Banwet, D. K., (2018). Transforming information security governance in India (A SAP-LAP based case study of security, IT policy and e-governance). Information and Computer Security, 26(1), 58-90.
  • 4.Armenia, S., Angelini, M., Nonino, F., Palombi, G. and Schlitzer, M. F., (2021). A dynamic simulation approach to support the evaluation of cyber risks and security investments in SMEs. Decision Support Systems, 147. Bin, J., Joint, J., Academy, E. D., & Emirates, U. A. (n.d.). New Security Dynamics in the Gulf and the Transformation of the GCC States ’ Security Agenda. 1-10.
  • 5.Bhatt, D., Danalakshmi, D., Hariharasudan, A., Lis, M. and Grabowska, M., (2021). Forecasting of energy demands for smart home applications. Energies, 14(4): 1045.
  • 6.Caldwell, B. S., Nyre-Yu, M. and Hill, J. R., (2019). Advances in human-automation collaboration, coordination and dynamic function allocation. Advances in Transdisciplinary Engineering, 10, 348-359.
  • 7.Cao, C.-P., Huang, H., Yu, Y.-H. and Huo, F., (2014). Practice and thinking of quality management of organ procurement organization. Chinese Journal of Tissue Engineering Research, 18(36), 5891-5895.
  • 8.Chigada, J., Daniels, N., (2021). Exploring information systems security implications posed by BYOD for a financial services firm. Business Information Review.
  • 9.Chittister, C. G., Haimes, Y. Y., (2020). The Role of Modeling in the Resilience of Cyberinfrastructure Systems and Preparedness for Cyber Intrusions. Journal of Homeland Security and Emergency Management, 8(1).
  • 10.Culot, G., Fattori, F., Podrecca, M. and Sartor, M., (2019). Addressing Industry 4.0 Cybersecurity Challenges. IEEE Engineering Management Review, 47(3), 79-86.
  • 11.Daengsi, T., Wuttidittachotti, P., Pornpongtechavanich, P. and Utakrit, N., (2021). A comparative study of cybersecurity awareness on phishing among employees from different departments in an organization. 2021 2nd International Conference on Smart Computing and Electronic Enterprise: Ubiquitous, Adaptive, and Sustainable Computing Solutions for New Normal, ICSCEE 2021, 102-106.
  • 12.Gontar, P., Homans, H., Rostalski, M., Behrend, J., Dehais, F. and Bengler, K., (2018). Are pilots prepared for a cyber-attack? A human factors approach to the experimental evaluation of pilots’ behavior. Journal of Air Transport Management, 69, 26-37.
  • 13.Hair Jr., J. F., Matthews, L. M., Matthews, R. L. and Sarstedt, M., (2017). PLS-SEM or CB-SEM: updated guidelines on which method to use. International Journal of Multivariate Data Analysis, 1(2), 107.
  • 14.Ingalagi, S. S., Nawaz, N., Rahiman, H. U., Hariharasudan, A. and Hundekar, V., (2021). Unveiling the crucial factors of women entrepreneurship in the 21st century. Social Sciences, 10(5), 153.
  • 15.Jibril, A.B., Kwarteng, M.A., Appiah-Nimo, C., Pilik, M. (2019). Association rule mining approach: Evaluating pre-purchase risk intentions in the online second-hand goods market. Oeconomia Copernicana, 10(4), 669-688.
  • 16.Kolenikov, S., (2009). Confirmatory factor analysis using confa. Stata Journal, 9(3), 329-373.
  • 17.Kour, R., Karim, R., (2020). Cybersecurity workforce in railway: its maturity and awareness. Journal of Quality in Maintenance Engineering, 27(3), 453-464.
  • 18.Kralj, D., (2010). The role of environmental indicators in environmental management. International Conference on Circuits, Systems, Signals, CSS, 139-145.
  • 19.Lazanyi, K., Lambovska, M., (2020). Readiness for Industry 4.0 Related Changes: a Case Study of the Visegrad Four. Ekonomicko-Manazerske Spektrum, 14(2), 100-113.
  • 20.Leszczyna, R., Wallis, T. and Wróbel, M. R., (2019). Developing novel solutions to realise the European Energy - Information Sharing & Analysis Centre. Decision Support Systems, 122.
  • 21.Litchfield, I. J., Bentham, L. M., Lilford, R. J., McManus, R. J., Hill, A. and Greenfield, S., (2017). Adaption, implementation and evaluation of collaborative service improvements in the testing and result communication process in primary care from patient and staff perspectives: A qualitative study. BMC Health Services Research, 17(1).
  • 22.M’manga, A., Faily, S., McAlaney, J., Williams, C., Kadobayashi, Y. and Miyamoto, D., (2019). A normative decision-making model for cyber security. Information and Computer Security, 26(5), 636-646.
  • 23.Mahadevan, V., Agbinya, J. and Braun, R., (2006). Analyzing usability alternatives in multi-criteria decision making during ERP training. 7th International Conference on Information Technology Based Higher Education and Training, ITHET, 296-309.
  • 24.Mantha, B. R. K., García de Soto, B., (2021). Assessment of the cybersecurity vulnerability of construction networks. Engineering, Construction and Architectural Management, 28(10), 3078-3105.
  • 25.Mathiesen, P., Marjanovic, O., Delavari, H. and Bandara, W., (2013). A critical analysis of business process management education and alignment with industry demand: An Australian perspective. Communications of the Association for Information Systems, 33(1), 463-484.
  • 26.Mittal, H., (2020). How Does the Institutional Context of an Emerging Economy Shape the Innovation Trajectory of Different Types of Companies? a Case Study of India. Ekonomicko-Manazerske Spektrum, 14(2), 36-51.
  • 27.Nam, T., (2019). Understanding the gap between perceived threats to and preparedness for cybersecurity. Technology in Society, 58.
  • 28.Nica, E., Potcovaru, A.-M. and Hurdubei Ionescu, R. E., (2019). Resilient cyber-physical systems and big data architectures in industry 4.0: Smart digital factories, automated production systems, and innovative sustainable business models. Economics, Management, and Financial Markets, 14(2), 46-51.
  • 29.Porcedda, M. G., (2018). Patching the patchwork: appraising the EU regulatory framework on cyber security breaches. Computer Law and Security Review, 34(5), 1077-1098.
  • 30.Rajan, R., Rana, N. P., Parameswar, N., Dhir, S., Sushil and Dwivedi, Y. K., (2021). Developing a modified total interpretive structural model (M-TISM) for organizational strategic cybersecurity management. Technological Forecasting and Social Change, 170.
  • 31.Reading, F., Aspects, M., (2008). Spearman Rank Correlation Coefficient. Concise Encycl. Stat, 502-505.
  • 32.Rowland, Z., Krulicky, T. and Oliinyk, O., (2020). Capital Cost Quantification Model in Business Activity Planning: the Evidence of the Middle Europe Countries. Ekonomicko-Manazerske Spektrum, 14(1), 30-42.
  • 33.Sabillon, R., Serra-Ruiz, J., Cavaller, V. and Cano, J. J. M., (2019). An effective cybersecurity training model to support an organizational awareness program: The Cybersecurity Awareness Training Model (CATRAM). A case study in Canada. Journal of Cases on Information Technology, 21(3), 26-39.
  • 34.Sarı, T., Güleş, H. K. and Yiğitol, B., (2020). Awareness and readiness of Industry 4.0: The case of Turkish manufacturing industry. Advances in Production Engineering And Management, 15(1), 57-68.
  • 35.Sekaran, U., Bougie, R., (2016). Research methods for business: A skill building approach. John Wiley & Sons.
  • 36.Sujith Kumar, M., Vishal Gupta, N., Balamuralidhara, V., Biswas, S., Pramod Kumar, T. M. and Naga Krishna Teja, I., (2011). Compilation of key GMP requirements in us and Japan for tablet manufacturing. International Journal of Drug Development and Research, 3(4), 45-54.
  • 37.Trim, P. R. J., Lee, Y.-I., (2019). The role of B2B marketers in increasing cyber security awareness and influencing behavioural change. Industrial Marketing Management, 83, 224-238.
  • 38.Zauskova, A., Lyakina, M., Tretyak, V. and Miklencicova, R., (2020). Application of Artificial Neural Networks To Cost Factors Stimulating Innovation - the Case of Slovakia. Ekonomicko-Manazerske Spektrum, 14(1), 97-105.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-2d40bd2a-b299-4b1b-baf4-d85802a583a9
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