Enhancing Business Administration Through Decision Support Systems
A Comprehensive Review
DOI:
https://doi.org/10.55537/spk.v4i2.1138Keywords:
business administration, crisis management, data-driven decision making, decision support systems (dss), operational efficiency, strategic managementAbstract
Decision Support Systems (DSS) are critical tools in modern business administration, aiding in data analysis, decision-making, and strategic planning, the evolution of DSS has been driven by advancements in technology, increasing the complexity and volume of data businesses handle, understanding the impact of DSS on business processes and outcomes is essential for leveraging their full potential. To review and synthesize existing research on the impact of Decision Support Systems on business administration, and to identify key benefits, challenges, and best practices associated with the implementation and use of DSS in business settings. Conducted a comprehensive literature review of academic journals, industry reports, and case studies on DSS in business administration, also analyzed data from studies focusing on different aspects of DSS, including implementation strategies, technological advancements, and their effects on decision-making processes. DSS significantly improve decision-making efficiency and accuracy by providing timely and relevant information, successful implementation of DSS is associated with enhanced strategic planning, better resource allocation, and improved overall business performance, common challenges include high implementation costs, complexity of integration with existing systems, and the need for ongoing user training and support. Decision Support Systems play a pivotal role in enhancing business administration by transforming data into actionable insights. Businesses that effectively implement and utilize DSS can achieve competitive advantages through improved decision-making capabilities. Future research should focus on addressing the challenges of DSS implementation and exploring emerging technologies that can further enhance their effectiveness
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References
R. H. Bonczek, C. W. Holsapple, and A. B. Whinston, Foundations of decision support systems. Academic Press, 2014. DOI: https://doi.org/10.1016/C2013-0-10396-0
A. Intezari and S. Gressel, “Information and reformation in KM systems: big data and strategic decision-making,” Journal of Knowledge Management, vol. 21, no. 1, pp. 71–91, 2017. DOI: https://doi.org/10.1108/JKM-07-2015-0293
V. L. Sauter, Decision support systems for business intelligence. John Wiley & Sons, 2014.
S. Akter, R. Bandara, U. Hani, S. F. Wamba, C. Foropon, and T. Papadopoulos, “Analytics-based decision-making for service systems: A qualitative study and agenda for future research,” Int J Inf Manage, vol. 48, pp. 85–95, 2019. DOI: https://doi.org/10.1016/j.ijinfomgt.2019.01.020
D. Arnott and G. Pervan, “A critical analysis of decision support systems research revisited: the rise of design science,” Journal of Information Technology, vol. 29, pp. 269–293, 2014. DOI: https://doi.org/10.1057/jit.2014.16
J. L. Kwan, L. Lo, J. Ferguson, H. Goldberg, J. P. Diaz-Martinez, G. Tomlinson, et al., “Computerised clinical decision support systems and absolute improvements in care: Meta-analysis of controlled clinical trials,” BMJ, vol. 370, p. m3216, 2020. DOI: https://doi.org/10.1136/bmj.m3216
A. A. M. Aina, W. Hu, and A.-N. N. A. M. Mohammed, “Use of management information systems impact on decision support capabilities: A conceptual model,” J. Int. Bus. Res. Mark, vol. 1, no. 4, pp. 27–31, 2016. DOI: https://doi.org/10.18775/jibrm.1849-8558.2015.14.3004
S. Rouhani, A. Ashrafi, A. Zare Ravasan, and S. Afshari, “The impact model of business intelligence on decision support and organizational benefits,” Journal of Enterprise Information Management, vol. 29, no. 1, pp. 19–50, 2016. DOI: https://doi.org/10.1108/JEIM-12-2014-0126
Y. Hasan, A. Shamsuddin, and N. Aziati, “The impact of management information systems adoption in managerial decision making: A review,” The International Scientific Journal of Management Information Systems, vol. 8, no. 4, pp. 10–17, 2013.
A. Merendino et al., “Big data, big decisions: The impact of big data on board level decision-making,” J Bus Res, vol. 93, pp. 67–78, 2018. DOI: https://doi.org/10.1016/j.jbusres.2018.08.029
M. J. Al Shobaki and S. S. A. Naser, “Decision support systems and its role in developing the universities strategic management: Islamic University in Gaza as a case study,” International Journal of Advanced Research and Development, vol. 1, no. 10, pp. 33–47, 2016
P. B. Keenan and P. Jankowski, “Spatial decision support systems: Three decades on,” Decis Support Syst, vol. 116, pp. 64–76, 2019. DOI: https://doi.org/10.1016/j.dss.2018.10.010
M. Marabelli, S. Newell, and V. Handunge, “The lifecycle of algorithmic decision-making systems: Organizational choices and ethical challenges,” The Journal of Strategic Information Systems, vol. 30, no. 3, p. 101683, 2021. DOI: https://doi.org/10.1016/j.jsis.2021.101683
B. Vasey, M. Nagendran, B. Campbell, D. A. Clifton, G. S. Collins, S. Denaxas, et al., “Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI,” BMJ, vol. 377, p. e070904, 2022. DOI: https://doi.org/10.1136/bmj-2022-070904
B. Wieder and M.-L. Ossimitz, “The impact of Business Intelligence on the quality of decision making–a mediation model,” Procedia Comput Sci, vol. 64, pp. 1163–1171, 2015. DOI: https://doi.org/10.1016/j.procs.2015.08.599
D. Schneider and U. Seelmeyer, “Challenges in using big data to develop decision support systems for social work in Germany,” Journal of Technology in Human Services, vol. 37, no. 2–3, pp. 113–128, 2019. DOI: https://doi.org/10.1080/15228835.2019.1614513
S. Teerasoponpong and A. Sopadang, “Decision support system for adaptive sourcing and inventory management in small-and medium-sized enterprises,” Robot Comput Integr Manuf, vol. 73, p. 102226, 2022. DOI: https://doi.org/10.1016/j.rcim.2021.102226
L. Wei, X. Li, M. Zhang, Q. Ni, and X. Zhou, “A decision support system for urban infrastructure inter-asset management employing domain ontologies and qualitative uncertainty-based reasoning,” Expert Systems with Applications, vol. 158, p. 113461, Nov. 2020. DOI: https://doi.org/10.1016/j.eswa.2020.113461
V. Rossi, F. Salinari, S. Poni, T. Caffi, and T. Bettati, “Addressing the implementation problem in agricultural decision support systems: the example of vite. net®,” Comput Electron Agric, vol. 100, pp. 88–99, 2014. DOI: https://doi.org/10.1016/j.compag.2013.10.011
Y. Wang, “When artificial intelligence meets educational leaders’ data-informed decision-making: A cautionary tale,” Studies in Educational Evaluation, vol. 69, p. 100872, 2021. DOI: https://doi.org/10.1016/j.stueduc.2020.100872
S. Chatterjee, R. Chaudhuri, S. Gupta, U. Sivarajah, and S. Bag, “Assessing the impact of big data analytics on decision-making processes, forecasting, and performance of a firm,” Technol Forecast Soc Change, vol. 196, p. 122824, 2023. DOI: https://doi.org/10.1016/j.techfore.2023.122824
E. S. Berner and T. J. La Lande, “Overview of clinical decision support systems,” in Clinical Decision Support Systems, E. S. Berner, Ed., Health Informatics. Cham: Springer, 2016, pp. 1–17. DOI: https://doi.org/10.1007/978-3-319-31913-1_1
F. Ahmed, Y. Qin and M. Aduamoah, "Employee readiness for acceptance of decision support systems as a new technology in E-business environments; A proposed research agenda," 2018 7th International Conference on Industrial Technology and Management (ICITM), Oxford, UK, 2018, pp. 209-212. DOI: https://doi.org/10.1109/ICITM.2018.8333948
J. E. Frisk and F. Bannister, “Improving the use of analytics and big data by changing the decision-making culture: A design approach,” Management Decision, vol. 55, no. 10, pp. 2074–2088, 2017. DOI: https://doi.org/10.1108/MD-07-2016-0460
Y. Niu, L. Ying, J. Yang, M. Bao, and C. B. Sivaparthipan, “Organizational business intelligence and decision making using big data analytics,” Inf Process Manag, vol. 58, no. 6, p. 102725, 2021. DOI: https://doi.org/10.1016/j.ipm.2021.102725
G. Van Valkenhoef, T. Tervonen, T. Zwinkels, B. De Brock, and H. Hillege, “ADDIS: a decision support system for evidence-based medicine,” Decis Support Syst, vol. 55, no. 2, pp. 459–475, 2013. DOI: https://doi.org/10.1016/j.dss.2012.10.005
M. Kukar, P. Vračar, D. Košir, D. Pevec, and Z. Bosnić, “AgroDSS: A decision support system for agriculture and farming,” Comput Electron Agric, vol. 161, pp. 260–271, 2019. DOI: https://doi.org/10.1016/j.compag.2018.04.001
M. Kunath and H. Winkler, “Integrating the Digital Twin of the manufacturing system into a decision support system for improving the order management process,” Procedia CIRP, vol. 72, pp. 225–231, 2018. DOI: https://doi.org/10.1016/j.procir.2018.03.192
D. Fogli and G. Guida, “Knowledge-centered design of decision support systems for emergency management,” Decis Support Syst, vol. 55, no. 1, pp. 336–347, 2013. DOI: https://doi.org/10.1016/j.dss.2013.01.022
P. S. Roshanov, N. Fernandes, J. M. Wilczynski, B. J. Hemens, J. J. You, S. M. Handler, et al., “Features of effective computerised clinical decision support systems: meta-regression of 162 randomised trials,” BMJ, vol. 346, p. f657, 2013. DOI: https://doi.org/10.1136/bmj.f657
S. Khairat, D. Marc, W. Crosby, and A. Al Sanousi, “Reasons for physicians not adopting clinical decision support systems: critical analysis,” JMIR Med Inform, vol. 6, no. 2, p. e8912, 2018. DOI: https://doi.org/10.2196/medinform.8912
Ag. Polyakova, Mp. Loginov, Ev. Strelnikov, and Nv. Usova, “Managerial decision support algorithm based on network analysis and big data,” International Journal of Civil Engineering and Technology, vol. 10, no. 2, pp. 291–300, 2019.
H. Zhang, Z. Zang, H. Zhu, M. I. Uddin, and M. A. Amin, “Big data-assisted social media analytics for business model for business decision making system competitive analysis,” Inf Process Manag, vol. 59, no. 1, p. 102762, 2022. DOI: https://doi.org/10.1016/j.ipm.2021.102762
N. R. Palakurti, “Next-Generation Decision Support: Harnessing AI and ML within BRMS Frameworks,” International Journal of Creative Research In Computer Technology and Design, vol. 5, no. 5, pp. 1–10, 2023,
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