An Integrated Group Decision-Making Approach Considering Uncertainty Conditions




Group decision-making, uncertainty conditions, cost-benefit estimation, Wald’s, Laplace’s, Hurwitz’s, Savage’s criteria


The management of business information processes needs effective decision-making models. That means to involve different methods, techniques, and principles to improve competitiveness and to achieve the planned business results. In this context, the article deals with the problem of group decision-making under uncertain conditions. To cope with such problems some well-known optimization strategies of Wald, Laplace, Hurwitz, and Savage are modified to take into account the experts’ opinions with different importance when forming the final group decision. Numerical testing is based on a case study for CRM software selection. The results are discussed based on the proposed models under two different cases derived from the case study. The conducted numerical testing of the proposed models demonstrates their applicability to cope simultaneously with multiple experts’ evaluations and uncertainty conditions.


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V. Shalamanov, V. Sabinski, T. Georgiev, “Optimization of the chief information officer function in large organizations”, Information & Security, vol. 46, no. 1, pp. 13-26, 2020, doi:

D. Borissova, Z. Dimitrova, V. Dimitrov, “How to support teams to be remote and productive: Group decision-making for distance collaboration software tools”, Information & Security, vol. 46, no. 1, pp. 36-52, 2020, doi:

D. Borissova, “A group decision making model considering experts competency: An application in personnel selections”, Comptes rendus de l’Academie Bulgare des Sciences, vol. 71, no. 11, pp. 1520-1527, 2018.

P. Ekel, J.S.C. Martini, R.M. Palhares, “Multicriteria analysis in decision making under information uncertainty”, APPL MATH COMPUT, vol. 200, no. 2, pp. 501-516, 2008, doi:

C. Werner, T. Bedford, R.M. Cooke, A.M. Hanea, O. Morales-Napoles, “Expert judgement for dependence in probabilistic modelling: A systematic literature review and future research directions,” EUR J OPER RES, vol. 258, no. 3, pp. 801-819, 2017, doi:

C. Hrach, R. Alt, “Configuration approach for analytical service models – development and evaluation”, in: 2020 IEEE 22nd CONF BUS INFORM, Antwerp, Belgium, 2020, pp. 260-269.

M. Kamariotou, F. Kitsios, “Information Systems Planning and Success in SMEs: Strategizing for IS”, in: BIS 2019, LNBIP, vol. 353, 2019, pp. 397-406,

D. Borissova, P. Cvetkova, I. Garvanov, M. Garvanova, “A framework of business intelligence system for decision making in efficiency management”, in: CISIM’2020, LNCS, vol. 12133, 2020, pp. 111-121.

D. Borissova, N. Keremedchieva, D. Keremedchiev, “Business intelligence approach to support decision making in publishing sector”, MIPRO, pp. 1532-1537, 2020, doi:

D. Korsemov, D. Borissova, I. Mustakerov, “Group decision making for selection of supplier under public procurement”, in: ICT Innovations 2018, COMM COM INF SC, vol. 940, 2018, pp. 51-58.

D. Borissova, D. Korsemov, I. Mustakerov, “Multi-criteria decision making problem for doing business: Comparison between approaches of individual and group decision making”, in: CISIM’2019, LNCS, vol. 11703, 2019, pp. 385-396.

I. Stankov, G. Tsochev, “Vulnerability and protection of business management systems: Threats and challenges. Problems of Engineering Cybernetics and Robotics, vol. 72, pp. 29-40, 2020,

R. Ketipov, G. Kostadinov, P. Petrov, I. Zankinski, T. Balabanov, “Genetic algorithm based formula generation for curve fitting in time series forecasting implemented as mobile distributed computing”, in: ADV HIGH PERF COM 2019, STUD COMP INTELL, vol. 902, 2021, pp. 40-47.

M. Perkusich, L. Chaves e Silva, A. Costa, F. Ramos, R. Saraiva, A. Freire, E. Dilorenzo, E. Dantas, D. Santos, K. Gorgonio, H. Almeida, A. Perkusich, “Intelligent software engineering in the context of agile software development: A systematic literature review”, INFORM SOFTWARE TECH, vol. 119, no. 106241, 2020, doi:

K. Stoyanova, V. Guliashki, “Two-stage portfolio risk optimisation based on MVO model”, International Journal of Reasoning-based Intelligent Systems, vol. 12, no. 1, pp. 70-79, 2020, doi:

A. Vodyaho, R. Yoshinov, N. Zhukova, A.M. Thaw, A. Saddam Ahmed, “Fog oriented model for data collection in the networks of mobile devices”, in: 2020 IEEE 10th INT CONF INTELL SYST, Varna, Bulgaria, 2020, pp. 421-425.

A. Tarhan, O. Turetken, H.A. Reijers, “Business process maturity models: A systematic literature review”, INFORM SOFTWARE TECH, vol. 75, pp. 122-134, 2016, doi:

L. Liu, W. Li, N.R. Aljohani, M.D. Lytras, S.-Ul Hassan, R. Nawaz, “A framework to evaluate the interoperability of information systems – Measuring the maturity of the business process alignment”, INT J INFORM MANAGE, vol. 54, no. 102153, 2020, doi:

A.-L. Lamprecht, et al., “Towards FAIR principles for research software,” Data Science, vol. 3, no.1, pp. 37-59, 2020, doi:

V. Jafari-Sadeghi, A. Garcia-Perez, E. Candelo, J. Couturier, “Exploring the impact of digital transformation on technology entrepreneurship and technological market expansion: The role of technology readiness, exploration and exploitation”, J BUS RES, vol. 124, pp. 100-111, 2021, doi:

C.-L. Chen, Y.-C. Lin, W.-H. Chen, C.-F. Chao, H. Pandia, “Role of government to enhance digital transformation in small service business”, Sustainability, vol. 13, no. 3, 2021, doi:

A. Di Vaio, R. Palladino, A. Pezzi, D.E. Kalisz, “The role of digital innovation in knowledge management systems: A systematic literature review”, J BUS RES, vol. 123, pp. 220-231, 2021, doi:

I. Petrov, “Improving the methodology of market structures analysis with innovative concepts for phase-structure states and set concentration index”, Economic Alternatives, vol. 1, pp. 5-15, 2016.

I. Mustakerov, D. Borissova, “Investments attractiveness via combinatorial optimization ranking”, International Journal of Management Science and Engineering vol. 7, no. 10, pp. 230-235, 2013, doi:

A. Van Looy, “A quantitative and qualitative study of the link between business process management and digital innovation”, INFORM MANAGE, vol. 58, no. 2, 103413 (2021).

M. Camargo, M. Dumas, O. Gonzalez-Rojas, “Automated discovery of business process simulation models from event logs”, DECIS SUPPORT SYST, vol. 134, 113284, 2020, doi:

H. Kir, N. Erdogan, “A knowledge-intensive adaptive business process management framework”, Information Systems, vol. 95, 101639, 2021, doi:

F. Klapproth, “Time and decision making in humans”, Cognitive, Affective, & Behavioral Neuroscience, vol. 8, pp. 509-524, 2008, doi:

R. Moura, C. Morais, E. Patelli, J. Lewis, M. Beer, “Human factors influencing decision-making: tendencies from first-line management decisions and implications to reduce major accidents”, in: ESREL’2017, Proc. of International Conference on Engineering Sciences and Technologies, 2017, pp. 69-34. doi: 10.1201/9781315210469-34

A. Boardman, D. Greenberg, A. Vining, D. Weimer, “Cost benefit analysis: Concepts and practice”, The Pearson Series in Economics, 4th edn., Pearson, 2010.



How to Cite

Borissova, D., & Dimitrova, Z. (2021). An Integrated Group Decision-Making Approach Considering Uncertainty Conditions. Business Information Systems, 1, 307–316.

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