An improved CoCoSo framework under hesitancy and inconsistency: application in influencer selection
DOI: https://doi.org/10.3846/jbem.2026.28478Abstract
Influencer marketing has become a core component of digital advertising, yet selecting the most suitable influencer remains a complex multi-criteria problem. Despite its growing importance, research on systematic influencer selection frameworks—particularly those addressing hesitancy and inconsistency in expert judgment—remains limited. This study proposes a decision-making framework integrating Dempster–Shafer theory with Multi-Criteria Decision-Making (MCDM) techniques to address hesitant evaluations. Moreover, Murphy’s combination rule replaces the traditional Dempster–Shafer rule to manage inconsistent assessments more effectively. Objective criteria weights are derived using the CRiteria Importance Through Intercriteria Correlation (CRITIC) and the Method based on the Removal Effects of Criteria (MEREC) methods, reducing subjectivity while accounting for information variability, inter-criteria correlation, and removal effects. The Borda rule is applied to aggregate the utility functions of the Combined Compromise Solution (CoCoSo) method, ensuring fairer ranking results. A case study conducted in a beauty technology company, based on data from experienced professionals, validates the proposed model. The results confirm that the Dempster–Shafer–based CRITIC–MEREC–CoCoSo framework provides a reliable, interpretable and data-driven tool for effective influencer selection.
Keywords:
influencer marketing, MCDM, Dempster–Shafer theory, CRITIC, MEREC, CoCoSoHow to Cite
Share
License
Copyright (c) 2026 The Author(s). Published by Vilnius Gediminas Technical University.
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
AlFarraj, O., Alalwan, A. A., Obeidat, Z. M., Baabdullah, A., Aldmour, R., & Al-Haddad, S. (2021). Examining the impact of influencers’ credibility dimensions: Attractiveness, trustworthiness and expertise on the purchase intention in the aesthetic dermatology industry. Review of International Business and Strategy, 31(3), 355–374. https://doi.org/10.1108/ribs-07-2020-0089
Aw, E. C.-X., & Agnihotri, R. (2024). Influencer marketing research: Review and future research agenda. Journal of Marketing Theory and Practice, 32(4), 435–448. https://doi.org/10.1080/10696679.2023.2235883
Bala, R., Kumar, V., & Sharma, R. (2024). Navigating consumer engagement: Unveiling the influence of social media influencers. In Global perspectives on social media influencers and strategic business communication (pp. 273–290). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-0912-4.ch014
Bulut, E., & Duru, O. (2018). Analytic hierarchy process (AHP) in maritime logistics: Theory, application and fuzzy set integration. In P. T.-W. Lee & Z. Yang (Eds.), Multi-criteria decision making in maritime studies and logistics: Applications and cases (pp. 31–78). Springer International Publishing. https://doi.org/10.1007/978-3-319-62338-2_3
Chakrabortty, R. K., Abdel-Basset, M., & Ali, A. M. (2023). A multi-criteria decision analysis model for selecting an optimum customer service chatbot under uncertainty. Decision Analytics Journal, 6, Article 100168. https://doi.org/10.1016/j.dajour.2023.100168
Chen, P. (2021). Effects of the entropy weight on TOPSIS. Expert Systems with Applications, 168, Article 114186. https://doi.org/10.1016/j.eswa.2020.114186
Chen, X., & Deng, Y. (2023). A novel combination rule for conflict management in data fusion. Soft Computing, 27(22), 16483–16492. https://doi.org/10.1007/s00500-023-09112-w
Chen, Y.-H., Lin, I.-K., Huang, C.-I., & Chen, H.-S. (2024). How key opinion leaders’ expertise and renown shape consumer behavior in social commerce: An analysis using a comprehensive model. Journal of Theoretical and Applied Electronic Commerce Research, 19(4), 3370–3385. https://doi.org/10.3390/jtaer19040163
Chiu, Y.-J., Hong, L.-S., Song, S.-R., & Cheng, Y.-C. (2024). Unveiling the dynamics of consumer attention: A two-stage hybrid MCDM analysis of key factors and interrelationships in influencer marketing. Mathematics, 12(7), Article 981. https://doi.org/10.3390/math12070981
Cornelis, C., Deschrijver, G., & Kerre, E. E. (2006). Advances and challenges in interval-valued fuzzy logic. Fuzzy Sets and Systems, 157(5), 622–627. https://doi.org/10.1016/j.fss.2005.10.007
Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., & Tesconi, M. (2015). Fame for sale: Efficient detection of fake Twitter followers. Decision Support Systems, 80, 56–71. https://doi.org/10.1016/j.dss.2015.09.003
Deng, Y., & Chan, F. T. S. (2011). A new fuzzy Dempster MCDM method and its application in supplier selection. Expert Systems with Applications, 38(8), 9854–9861. https://doi.org/10.1016/j.eswa.2011.02.017
Diakoulaki, D., Mavrotas, G., & Papayannakis, L. (1995). Determining objective weights in multiple criteria problems: The CRITIC method. Computers & Operations Research, 22(7), 763–770. https://doi.org/10.1016/0305-0548(94)00059-H
Fakhreddin, F., & Foroudi, P. (2022). Instagram influencers: The role of opinion leadership in consumers’ purchase behavior. Journal of Promotion Management, 28(6), 795–825. https://doi.org/10.1080/10496491.2021.2015515
Fan, F., Fu, L., & Jiang, Q. (2023). Virtual idols vs online influencers vs traditional celebrities: How young consumers respond to their endorsement advertising. Young Consumers: Insight and Ideas for Responsible Marketers, 25(3), 329–348. https://doi.org/10.1108/yc-08-2023-1811
Farivar, S., Wang, F., & Yuan, Y. (2021). Opinion leadership vs. para-social relationship: Key factors in influencer marketing. Journal of Retailing and Consumer Services, 59, Article 102371. https://doi.org/10.1016/j.jretconser.2020.102371
Fei, L., Deng, Y., & Hu, Y. (2019). DS-VIKOR: A new multi-criteria decision-making method for supplier selection. International Journal of Fuzzy Systems, 21(1), 157–175. https://doi.org/10.1007/s40815-018-0543-y
Gandhi, M., & Muruganantham, A. (2015). Potential influencers identification using multi-criteria decision making (MCDM) methods. Procedia Computer Science, 57, 1179–1188. https://doi.org/10.1016/j.procs.2015.07.411
Garcia-Garcia, G. (2022). Using multi-criteria decision-making to optimise solid waste management. Current Opinion in Green and Sustainable Chemistry, 37, Article 100650. https://doi.org/10.1016/j.cogsc.2022.100650
Gong, X., Ren, J., Zeng, L., & Xing, R. (2022). How KOLs influence consumer purchase intention in short video platforms: Mediating effects of emotional response and virtual touch. International Journal of Information Systems in the Service Sector (IJISSS), 14(1), 1–23.
Javed, D., Jhanjhi, N. Z., Khan, N. A., Ray, S. K., Al-Dhaqm, A., & Kebande, V. R. (2025). Identification of spambots and fake followers on social network via interpretable AI-based machine learning. IEEE Access, 13, 52246–52259. https://doi.org/10.1109/ACCESS.2025.3551993
Jin, S. V., Muqaddam, A., & Ryu, E. (2019). Instafamous and social media influencer marketing. Marketing Intelligence & Planning, 37(5), 567–579. https://doi.org/10.1108/MIP-09-2018-0375
Keshavarz-Ghorabaee, M., Amiri, M., Zavadskas, E. K., Turskis, Z., & Antucheviciene, J. (2021). Determination of objective weights using a new method based on the removal effects of criteria (MEREC). Symmetry, 13(4), Article 525. https://doi.org/10.3390/sym13040525
Komasi, H., Yazdi, A. K., Jamini, D., Tan, Y., & Ocampo, L. (2025). Sustainable industrial development potential of Iranian provinces using the integration of MEREC and TRUST methods. Journal of Management Analytics, 12(4), 849–881. https://doi.org/10.1080/23270012.2025.2506748
Lendvai, L., Jakab, S. K., & Singh, T. (2025). Optimal design of agro-residue filled poly(lactic acid) biocomposites using an integrated CRITIC-CoCoSo multi-criteria decision-making approach. Scientific Reports, 15(1), Article 11586. https://doi.org/10.1038/s41598-025-92724-z
Leung, F. F., Gu, F. F., Li, Y., Zhang, J. Z., & Palmatier, R. W. (2022). Influencer marketing effectiveness. Journal of Marketing, 86(6), 93–115. https://doi.org/10.1177/00222429221102889
Li, D.-F. (2005). Multiattribute decision making models and methods using intuitionistic fuzzy sets. Journal of Computer and System Sciences, 70(1), 73–85. https://doi.org/10.1016/j.jcss.2004.06.002
Li, J., Fang, H., & Song, W. (2019). Modified failure mode and effects analysis under uncertainty: A rough cloud theory-based approach. Applied Soft Computing, 78, 195–208. https://doi.org/10.1016/j.asoc.2019.02.029
Li, T., & Fei, L. (2025). Exploring obstacles to the use of unmanned aerial vehicles in emergency rescue: A BWM-DEMATEL approach. Technology in Society, 81, Article 102863. https://doi.org/10.1016/j.techsoc.2025.102863
Lim, Y. R., Ariffin, A. S., Ali, M., & Chang, K.-L. (2021). A hybrid MCDM model for live-streamer selection via the fuzzy Delphi method, AHP, and TOPSIS. Applied Sciences, 11(19), Article 9322. https://doi.org/10.3390/app11199322
Lin, M., Huang, C., & Xu, Z. (2020). MULTIMOORA based MCDM model for site selection of car sharing station under picture fuzzy environment. Sustainable Cities and Society, 53, Article 101873. https://doi.org/10.1016/j.scs.2019.101873
Liu, J., Bao, X. & Chen, L. (2025) Artificial intelligence in educational technology and transformative approaches to English language using fuzzy framework with CRITIC-TOPSIS method. Scientific Reports, 15, Article 25542. https://doi.org/10.1038/s41598-025-09844-9
Lou, C., & Yuan, S. (2019). Influencer marketing: How message value and credibility affect consumer trust of branded content on social media. Journal of Interactive Advertising, 19(1), 58–73. https://doi.org/10.1080/15252019.2018.1533501
Mallipeddi, R. R., Kumar, S., Sriskandarajah, C., & Zhu, Y. (2022). A framework for analyzing influencer marketing in social networks: Selection and scheduling of influencers. Management Science, 68(1), 75–104. https://doi.org/10.1287/mnsc.2020.3899
Mendel, J. M. (2017). Type-2 fuzzy sets. In Uncertain rule-based fuzzy systems: Introduction and new directions (2nd ed., pp. 259–306). Springer International Publishing. https://doi.org/10.1007/978-3-319-51370-6_6
Migkos, S. P., Giannakopoulos, N. T., & Sakas, D. P. (2025). Impact of influencer marketing on consumer behavior and online shopping preferences. Journal of Theoretical and Applied Electronic Commerce Research, 20(2), Article 111. https://doi.org/10.3390/jtaer20020111
Mukhametzyanov, I. Z. (2023). Rank reversal in MCDM models: Contribution of the normalization. In Normalization of multidimensional data for multi-criteria decision making problems: Inversion, displacement, asymmetry (pp. 111–127). Springer International Publishing. https://doi.org/10.1007/978-3-031-33837-3_6
Murphy, C. K. (2000). Combining belief functions when evidence conflicts. Decision Support Systems, 29(1), 1–9. https://doi.org/10.1016/S0167-9236(99)00084-6
Nadeem, R., Singh, R., Patidar, A., Yusliza, M. Y., Ramayah, T., & Azmi, F. T. (2025). Prioritizing determinants of employees’ green behavior in the Indian hotel industry: An analytic hierarchy process (AHP) and fuzzy AHP approach. Journal of Hospitality and Tourism Insights, 8(8), 2900–2919. https://doi.org/10.1108/jhti-07-2024-0737
Pan, M., Blut, M., Ghiassaleh, A., & Lee, Z. W. Y. (2025). Influencer marketing effectiveness: A meta-analytic review. Journal of the Academy of Marketing Science, 53(1), 52–78. https://doi.org/10.1007/s11747-024-01052-7
Pan, Y.-R., Tan, G. W.-H., Aw, E. C.-X., & Ooi, K.-B. (2025). When things fall apart: Exploring brand hate in influencer endorsements. Technological Forecasting and Social Change, 220, Article 124321. https://doi.org/10.1016/j.techfore.2025.124321
Ping, Y., Hill, C., Zhu, Y., & Fresneda, J. (2023). Antecedents and consequences of the key opinion leader status: An econometric and machine learning approach. Electronic Commerce Research, 23(3), 1459–1484. https://doi.org/10.1007/s10660-022-09650-9
Qian, J., & Park, J.-S. (2021). Influencer-brand fit and brand dilution in China’s luxury market: The moderating role of self-concept clarity. Journal of Brand Management, 28(2), 199–220. https://doi.org/10.1057/s41262-020-00226-2
Qu, Z., Wan, C., Yang, Z., & Lee, P. T.-W. (2017). A discourse of multi-criteria decision making (MCDM) approaches. In Multi-criteria decision making in maritime studies and logistics: Applications and cases (pp. 7–29). Springer. https://doi.org/10.1007/978-3-319-62338-2_2
Ramya, G. R., & Bagavathi Sivakumar, P. (2025). Discovering social media influencers using a deep regression analysis. SN Computer Science, 6(4), Article 377. https://doi.org/10.1007/s42979-025-03916-3
Reinikainen, H., Munnukka, J., Maity, D., & Luoma-aho, V. (2020). ‘You really are a great big sister’ – parasocial relationships, credibility, and the moderating role of audience comments in influencer marketing. Journal of Marketing Management, 36(3–4), 279–298. https://doi.org/10.1080/0267257X.2019.1708781
Ross, C. (2025). Influencer marketing market size worldwide from 2015 to 2025. Statista. Retrieved October 5, 2025, from https://www.statista.com/statistics/1092819/global-influencer-market-size/
Sithi, S. S., Ara, M. A., Dhrubo, A. T., Rony, A. H., & Shabur, M. A. (2025). Sustainable supplier selection in the textile industry using triple bottom line and SWARA-TOPSIS approaches. Discover Sustainability, 6(1), Article 344. https://doi.org/10.1007/s43621-025-01206-9
Smets, P., & Kennes, R. (1994). The transferable belief model. Artificial Intelligence, 66(2), 191–234. https://doi.org/10.1016/0004-3702(94)90026-4
Thuy, D. C., Ngoc Quang, N., Huong, L. T., & Phuong, N. T. M. (2024). The moderating effects of involvement on the relationships between key opinion leaders, customer’s attitude and purchase intention on social media. Cogent Business & Management, 11(1), Article 2400600. https://doi.org/10.1080/23311975.2024.2400600
Torra, V. (2010). Hesitant fuzzy sets. International Journal of Intelligent Systems, 25(6), 529–539. https://doi.org/10.1002/int.20418
Vrontis, D., Makrides, A., Christofi, M., & Thrassou, A. (2021). Social media influencer marketing: A systematic review, integrative framework and future research agenda. International Journal of Consumer Studies, 45(4), 617–644. https://doi.org/10.1111/ijcs.12647
Wan, G., Rong, Y., & Garg, H. (2023). An efficient spherical fuzzy MEREC–CoCoSo approach based on novel score function and aggregation operators for group decision making. Granular Computing, 8(6), 1481–1503. https://doi.org/10.1007/s41066-023-00381-2
Yager, R. R. (1987). On the Dempster-Shafer framework and new combination rules. Information Sciences, 41(2), 93–137. https://doi.org/10.1016/0020-0255(87)90007-7
Yang, J.-B., & Xu, D.-L. (2013). Evidential reasoning rule for evidence combination. Artificial Intelligence, 205, 1–29. https://doi.org/10.1016/j.artint.2013.09.003
Yazdani, M., Zarate, P., Zavadskas, E. K., & Turskis, Z. (2018). A combined compromise solution (CoCoSo) method for multi-criteria decision-making problems. Management Decision, 57(9), 2501–2519. https://doi.org/10.1108/md-05-2017-0458
Ye, F., Ji, L., Ning, Y., & Li, Y. (2024). Influencer selection and strategic analysis for live streaming selling. Journal of Retailing and Consumer Services, 77, Article 103673. https://doi.org/10.1016/j.jretconser.2023.103673
Yong, D., WenKang, S., ZhenFu, Z., & Qi, L. (2004). Combining belief functions based on distance of evidence. Decision Support Systems, 38(3), 489–493. https://doi.org/10.1016/j.dss.2004.04.015
Yürüyen, A. A., Ulutaş, A., Demirhan, A., & Ozsalman, E. (2025). Environmental considerations in the selection of transport vehicles: A fuzzy FUCOM method. Management of Environmental Quality: An International Journal, 36(6), 1650–1669. https://doi.org/10.1108/meq-10-2024-0437
Zadeh, L. A. (1979). On the validity of Dempster’s rule of combination of evidence (Memorandum No. UCB/ERL M79/24). University of California, Berkeley.
Zhang, P., Cheng, Y., Sun, Y., Lei, D., & Shen, Z.-J. M. (2024). Optimizing influencer marketing campaign: A joint approach to influencer selection and traffic promotion. SSRN. https://doi.org/10.2139/ssrn.5050147
Zhang, W., & Deng, Y. (2019). Combining conflicting evidence using the DEMATEL method. Soft Computing, 23(17), 8207–8216. https://doi.org/10.1007/s00500-018-3455-8
Zhang, Y., Zhu, J., Chen, H., & Jiang, Y. (2025). Enhancing trust and empathy in marketing: Strategic AI and human influencer selection for optimized content persuasion. Journal of Consumer Behaviour, 24(2), 866–885. https://doi.org/10.1002/cb.2423
Zhao, Q., Liu, F., & Qiao, W. (2024). Evaluating industrial heritage value using cloud theory and Dempster–Shafer theory. Journal of Cultural Heritage, 68, 364–374. https://doi.org/10.1016/j.culher.2024.07.002
Zhou, L., Jin, F., Wu, B., Chen, Z., & Wang, C. L. (2023). Do fake followers mitigate influencers’ perceived influencing power on social media platforms? The mere number effect and boundary conditions. Journal of Business Research, 158, Article 113589. https://doi.org/10.1016/j.jbusres.2022.113589
Zimmermann, H.-J. (2010). Fuzzy set theory. WIREs Computational Statistics, 2(3), 317–332. https://doi.org/10.1002/wics.82
View article in other formats
CrossMark check
Published
Issue
Section
Copyright
Copyright (c) 2026 The Author(s). Published by Vilnius Gediminas Technical University.
License
This work is licensed under a Creative Commons Attribution 4.0 International License.