Abstract
Over the past several decades, DSS has progressed toward becoming a solid academic field. Nevertheless, since the mid-1990s, the inability of DSS to fully satisfy a wide range of information needs of practitioners provided an impetus for a new breed of DSS called business intelligence systems (BIS). This paper examines the major differences among decision support systems (DSS), business intelligence (BI), and data analytics (DA). These three systems are different in terms of data, analytical environment, analytical tools, their focuses, and others. Next, the paper briefly describes the major characteristics of each. Third, A survey of DSS, BI, and DA is conducted covering the period of January 2016 through December 2019 to report publication trends of each system. The final section summarizes the findings and discusses their implications. This research provided a picture of what has happened over the past several years. The biggest accomplishment the DSS community achieved is that it provided the foundational concepts such as the DDM paradigm and the definitions of DSS. The DSS area has given the fruits of the DSS research to numerous other fields. Further, DSS and BI have a mild level of increased publications, while there is very sharp increase in DA (349%).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Eom, S.: Longitudinal author cocitation mapping: the changing structure of decision support systems research (1969–2012). Found. Trends® Inf. Syst. 1(4), 277–384 (2016)
Eom, S.B.: The Development of Decision Support Systems Research: A Bibliometrical Approach. The Edwin Mellen Press, Lewiston, NY (2007)
Zaman, M.: Business Intelligence: Its Ins and Outs (2005)
Sharda, R., et al.: Business Intelligence and Analytics: Systems for Decision Support, 10th edn. Pearson, Boston (2015)
Sprague, R.H., Carlson, E.D.: Building Effective Decision Support Systems. Prentice Hall, Englewood Cliffs (1982)
Keen, P.G.W., Scott Morton, M.S.: Decision Support Systems: An Organizational Perspective. Addison-Wesley, Reading (1978)
Eom, S.B.: Reference disciplines of decision support systems. In: Burstein, F., Holsapple, C.W. (eds.) Handbook on Decision Support Systems 1: Basic Themes, pp. 141–159. Springer, Heiderberg (2008)
Eom, H.B., Lee, S.M.: A survey of decision support system applications (1971-April 1988). Interfaces 20(3), 65–79 (1990)
Eom, S., Kim, E.: A survey of decision support system applications (1995-2001). J. Oper. Res. Soc. 57(11), 1264–1278 (2006)
Eom, S.B., et al.: A survey of decision support system applications (1988-1994). J. Oper. Res. Soc. 49(2), 109–120 (1998)
Eckerson, W.: Smart Comapnies in the 21st Century: The Secrets of Creating Successful Business Intelligence Solutions. The Data Warehousing Institute, Seattle (2003)
Eom, S.B.: Data warehousing. In: Zeleny, M. (ed.) The IEBM Handbook of Information Technology in Business, 1st edn, pp. 496–503, Thomson Learning, London (2000)
Eom, S.B.: Assessing the current state of intellectual relationships between the decision support systems area and academic disciplines. In: Kumar, K., DeGross, J.I. (eds.) Proceedings of The Eighteenth International Conference on Information Systems, pp. 167–182. International Conference on Information Systems, Atlanta (1997)
Eom, S.B.: The intellectual development and structure of decision support systems (19911995). Omega 26(5), 639–658 (1998)
Eom, S.B.: Intellectual relationships between information systems and psychology. In: Proceedings of Associations for Information Systems 8th Americas Conference on Information Systems, Dallas, TX (2002)
Eom, S.B.: Intellectual relationships between the decision support systems area and cognitive science. In: The Eighth International Confernce of the Association of Information Systems SIG/DSS and International Society of Decision Support Systems: Trends in DSS Research and Practice, Porto Alegre, Brazil (2005)
Bocewicz, G., et al.: Traffic flow routing and scheduling in a food supply network. Ind. Manag. Data Syst. 117(9), 1972–1994 (2017)
Mogre, R., et al.: A decision framework to mitigate supply chain risks: an application in the offshore-wind industry. IEEE Trans. Eng. Manag. 63(3), 316–325 (2016)
Backiel, A., et al.: Predicting time-to-churn of prepaid mobile telephone customers using social network analysis. J. Oper. Res. Soc. 67(9), 1135–1145 (2016)
Besikçi, E.B., et al.: An artificial neural network based decision support system for energy efficient ship operations. Comput. Oper. Res. 66, 393–403 (2016)
Bowen, Z., et al.: Group decision making with heterogeneous preference structures: an automatic mechanism to support consensus reaching. Group Decis. Negot. 28(3), 585–617 (2019)
Lima, A.S., et al.: A consensus-based multicriteria group decision model for information technology management committees. IEEE Trans. Eng. Manag. 65(2), 276–292 (2018)
Pyrko, I., et al.: Knowledge acquisition using group support systems. Group Decis. Negot. 28(2), 233–253 (2019)
Hahn, G.J., Brandenburg, M.: A sustainable aggregate production planning model for the chemical process industry. Comput. Oper. Res. 94, 154–164 (2018)
Salam, M.A., Khan, S.A.: Simulation based decision support system for optimization. Ind. Manag. Data Syst. 116(2), 236–254 (2016)
Hernández, J.E., et al.: A DSS-based framework for enhancing collaborative web-based operations management in manufacturing SME supply chains. Group Decis. Negot. 25(6), 1237–1259 (2016)
Asgharizadeh, E., et al.: A combined approach of multiple attribute decision making for ranking and selection of project executive contractors in tenders. J. Econ. Manag. Perspect. 11(3), 647–655 (2017)
Ahmed, M., et al.: A cost-effective decision-making algorithm for an RFID-enabled HMSC network design: A multi-objective approach. Ind. Manag. Data Syst. 117(9), 1782–1799 (2017)
Thuan, N.H., et al.: A decision tool for business process crowdsourcing: ontology, design, and evaluation. Group Decis. Negot. 27(2), 285–312 (2018)
Martins, C.L., et al.: An MCDM project portfolio web-based DSS for sustainable strategic decision making in an electricity company. Ind. Manag. Data Syst. 117(7), 1362–1375 (2017)
Ivkovic, I., et al.: Prioritizing strategic goals in higher education organizations by using a SWOT-PROMETHEE/GAIA-GDSS Model. Group Decis. Negot. 26(4), 829–846 (2017)
Guido, R., Conforti, D.: A hybrid genetic approach for solving an integrated multi-objective operating room planning and scheduling problem. Comput. Oper. Res. 876, 270–280 (2017)
Nasir, M., et al.: A comparative data analytic approach to construct a risk trade-off for cardiac patients’ re-admissions. Ind. Manag. Data Syst. 119(1), 189–209 (2019)
Dellaert, N., et al.: A variable neighborhood search algorithm for the surgery tactical planning problem. Comput. Oper. Res. 84, 216–226 (2017)
Kaplan, A.: The Conduct of Inquiry: Methodology for Behavioral Science. Transaction Publishers, New Brunswick, NJ (1998)
Eom, H.B., Lee, S.M.: Decision support systems applications research: a bibliography (1971-1988). Eur. J. Oper. Res. 46(3), 333–342 (1990)
Yeoh, W., Popovic, A.: Extending the understanding of critical success factors for implementing business intelligence systems. J. Assoc. Inf. Sci. Technol. 67(1), 134–144 (2016)
Popovič, A., et al.: Justifying business intelligence systems adoption in SMEs. Ind. Manag. Data Syst. 119(1), 210–228 (2019)
Roberts, N., et al.: Using information systems to sense opportunities for innovation: integrating postadoptive use behaviors with the dynamic managerial capability perspective. J. Manag. Inf. Syst. 33(1), 45–55 (2016)
Kuntonbutr, C., Kulken, M.: The effect of business intelligence on business unit strategies, international operations and business growth. J. Econ. Manag. Perspect. 11(3), 1800–1807 (2017)
Kache, F., Seuring, S.: Challenges and opportunities of digital information at the intersection of Big Data Analytics and supply chain management. Int. J. Oper. Prod. Manag. 37(1), 10–36 (2017)
Mahesar, H.A., et al.: Integrating customer relationship management with big data analytics in retail stores: a case of hyper-star and metro. J. Bus. Strat. 11(2), 141–158 (2017)
Verma, N., Singh, J.: An intelligent approach to Big Data analytics for sustainable retail environment using Apriori-MapReduce framework. Ind. Manag. Data Syst. 117(7), 1503–1520 (2017)
Zheng, K., et al.: Big data-driven optimization for mobile networks toward 5G. IEEE Netw. 30(1), 44–51 (2016)
Eugster, P., et al.: Big data analytics beyond the single datacenter. Computer 50(6), 60–68 (2017)
Dolan-Canning, R.: Sensor-based and cognitive assistance systems in industry 4.0: big data analytics, smart production, and sustainable value creation. Econ. Manag. Finan. Mark. 14(3), 16–22 (2019)
Strang, K.D.: Beyond engagement analytics: which online mixed-data factors predict student learning outcomes? Educ. Inf. Technol. 22(3), 917–937 (2017)
Williams, P.: Assessing collaborative learning: big data, analytics and university futures. Assess. Eval. Higher Educ. 42(6), 978–989 (2017)
Winig, L.: A data-driven approach to customer relationships: a case study of nedbank’s data practices in South Africa. MIT Sloan Manag. Rev. 58(2), 3 (2017)
Khan, M.: Challenges with big data analytics in service supply chains in the UAE. Manag. Decis. 57(8), 2124–2147 (2019)
Fitzgerald, M.: Data-driven city management: a close look at amsterdam’s smart city initiative. MIT Sloan Manag. Rev. 57(4) (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Eom, S. (2020). DSS, BI, and Data Analytics Research: Current State and Emerging Trends (2015–2019). In: Moreno-Jiménez, J., Linden, I., Dargam, F., Jayawickrama, U. (eds) Decision Support Systems X: Cognitive Decision Support Systems and Technologies. ICDSST 2020. Lecture Notes in Business Information Processing, vol 384. Springer, Cham. https://doi.org/10.1007/978-3-030-46224-6_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-46224-6_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-46223-9
Online ISBN: 978-3-030-46224-6
eBook Packages: Computer ScienceComputer Science (R0)