Managing Metaverse Marketing Challenges Considering Service Quality Model: Application of Intuitionistic Fuzzy Decision Support System

Fatemeh Ghasemzadeh; Babak Ghahremannasab; Dragan Pamucar

doi:10.31181/sdmap31202663

Authors

Fatemeh Ghasemzadeh Department of Management, University of Zanjan, Zanjan, Iran Author https://orcid.org/0000-0002-0269-2122
Babak Ghahremannasab Department of Management, University of Zanjan, Zanjan, Iran Author https://orcid.org/0009-0009-4201-325X
Dragan Pamucar 1) Department of Applied Mathematical Science, College of Science and Technology, Korea University, Sejong 30019, Republic of Korea; 2) UNEC Applied Artificial Intelligence Research Center, Azerbaijan State University of Economics (UNEC), Baku, Azerbaijan; 3) Faculty of Engineering, Dogus University, 34775 Umraniye, Istanbul, Turkiye Author https://orcid.org/0000-0001-8522-1942

DOI:

https://doi.org/10.31181/sdmap31202663

Keywords:

Marketing, Metaverse, SERVQUAL, FUCOM-ARAS, Decision-Making, Intuitionistic Triangular Fuzzy Number

Abstract

This study aims to explore the application of advanced decision-making frameworks for identifying and prioritizing marketing challenges in the Metaverse, with a focus on the real estate sector. It examines the integration of innovative methodologies to enhance service quality and customer experience within virtual environments. The research adopts a novel ARAS-FUCOM-SERVQUAL methodology to systematically evaluate and rank Metaverse marketing challenges. Data were collected from a cohort of international real estate agents using intuitionistic triangular fuzzy numbers to assess SERVQUAL (Service Quality) dimensions. The study employs multi-criteria decision-making (MCDM) techniques to analyze and validate the collected data, ensuring robust prioritization of the challenges. The findings reveal ten critical marketing challenges in the Metaverse, including interoperability, data privacy, customer engagement, and regulatory complexities. The results demonstrate the effectiveness of the ARAS-FUCOM-SERVQUAL framework in providing actionable insights for strategic planning and resource allocation. Key managerial recommendations are proposed to address these challenges and optimize marketing strategies in virtual environments. This research introduces an innovative combination of the Additive Ratio Assessment System (ARAS) and the Full Consistency Method (FUCOM) with SERVQUAL dimensions, tailored to the Metaverse context. It highlights the potential of these methodologies to bridge the gap between traditional service quality assessments and the evolving requirements of marketing in virtual environments. The study offers a comprehensive framework for addressing the complexities of Metaverse marketing, particularly in the dynamic real estate industry.

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References

Stephenson, N. (1992). Snow Crash. Bantam.

Economist, T. (2020, October 1). A novelist's vision of the virtual world has inspired an industry. The Economist. Retrieved January 4, 2023, from https://www.economist.com/technology-quarterly/2020/10/01/a-novelists-vision-of-the-virtualworld-has-inspired-an-industry;accessedon.

Hofman, H. (2022, November 29). 3 ways the metaverse will transform your supply chain. Maersk. Retrieved November 29, 2022, from https://www.maersk.com/insights/digitalisation/2022/11/29/how-the-metaverse-will-transform-supply-chain-management.

Lovich, D. (2022). What is the metaverse and why should you care. Forbes. Retrieved August 16, 2022.

Kim, J. (2021). Advertising in the metaverse: Research agenda. Journal of Interactive Advertising, 21(3), 141–144. https://doi.org/10.1080/15252019.2021.2001273

Rosário, A. T., Lopes, P. R., & Rosário, F. S. (2023). Metaverse in Marketing: Challenges and Opportunities. In Handbook of Research on AI-Based Technologies and Applications in the Era of the Metaverse (pp. 204–227).

Bushell, C. (2022). The Impact of Metaverse on Branding and Marketing. Available at SSRN 4144628. https://dx.doi.org/10.2139/ssrn.4144688

Yaqoob I, Salah K, Jayaraman R, Omar M (2023). Metaverse applications in smart cities: Enabling technologies, opportunities, challenges, and future directions. Internet of Things, 100884. https://doi.org/10.1016/j.iot.2023.100884

Koohang A, Nord JH, Ooi K-B, Tan GW-H, Al-Emran M, Aw EC-X, et al. (2023). Shaping the metaverse into reality: a holistic multidisciplinary understanding of opportunities, challenges, and avenues for future investigation. Journal of Computer Information Systems, 63(3), 735–765. https://doi.org/10.1080/08874417.2023.2165197

Crosby, P. B. (1979). Quality is Free. New American Library.

Parasuraman, A., Zeithaml, V. A., & Berry, L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64(1), 12–40.

Dwivedi YK, Hughes L, Baabdullah AM, Ribeiro-Navarrete S, Giannakis M, Al-Debei MM, et al. (2022). Metaverse beyond the hype: Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 66, 102542. https://doi.org/10.1016/j.ijinfomgt.2022.102542

Tan, T. M., & Salo, J. (2023). Ethical marketing in the blockchain-based sharing economy: Theoretical integration and guiding insights. Journal of Business Ethics, 183(4), 1113–1140. https://doi.org/10.1007/s10551-021-05015-8

Aleksić A, Jokić Ž, Živković M, Radovanović M, Tošić S. (2025). Application of the DIBR II-fuzzy MABAC model for shooting stance selection. Journal of Decision Analytics and Intelligent Computing, 5(1), 202–218. https://doi.org/10.31181/jdaic10024112025a

Fujita, T., Mehmood, A., & Ghaib, A. A. (2025). Hyperfuzzy control system and superhyperfuzzy control system. Smart Multi-Criteria Analytics and Reasoning Technologies, 1(1), 1–21. https://doi.org/10.65069/smart1120252

Pamučar, D., Stević, Ž., & Sremac, S. (2018). A new model for determining weight coefficients of criteria in mcdm models: Full consistency method (fucom). Symmetry, 10(9), 393. https://doi.org/10.3390/sym10090393

Stević, Željko, Bašić, A., Moslem, S., & Zhong, K. (2023). An integrated ABC-FUCOM model for product classification. Spectrum of Engineering and Management Sciences, 1(1), 81–89. https://doi.org/10.31181/sems1120239k

Zavadskas, E. K., & Turskis, Z. (2010). A new additive ratio assessment (ARAS) method in multicriteria decision‐making. Technological and Economic Development of Economy, 16(2), 159–172. https://doi.org/10.3846/tede.2010.10

Seiler, V. L., & Reisenwitz, T. H. (n.d.). A Review of Service Quality Research in.

Rasheed, F., Kousar, S., Shabbir, J., Kausar, N., Pamucar, D., & Gaba, U. (2021). Use of intuitionistic fuzzy numbers in survey sampling analysis with application in electronic data interchange. Complexity, 2021, 1–12. https://doi.org/10.1155/2021/9989477

Liu, N., & Xu, Z. (2021). An overview of ARAS method: Theory development, application extension, and future challenge. International Journal of Intelligent Systems, 36(7), 3524–3565. https://doi.org/10.1002/int.22425

Nunić, Z. (2018). Evaluation and selection of Manufacturer PVC carpentry using FUCOM-MABAC model. Operational Research in Engineering Sciences: Theory and Applications, 1(1), 13–28. https://doi.org/10.31181/oresta19012010113n

Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49–57. https://doi.org/10.1016/j.omega.2014.11.009

Mkedder, N., & Das, M. (2024). Metaverse integration challenges: An in-depth ISM and MICMAC analysis. Journal of Retailing and Consumer Services, 77, 103684. https://doi.org/10.1016/j.jretconser.2023.103684

Jaimini, U., Zhang, T., Brikis, G,. & Sheth, A. (2022). iMetaverseKG: Industrial Metaverse Knowledge Graph to Promote Interoperability in Design and Engineering Applications. IEEE Internet Computing, 26(6), 59–67. https://doi.org/10.1109/MIC.2022.3212085

Klaus, P., & Manthiou, A. (2024). Metaverse retail: Pioneering research avenues for tomorrow's marketplace. Journal of Retailing and Consumer Services, 78, 103782. https://doi.org/10.1016/j.jretconser.2024.103782

Rajamanickam, T. (2022, November 23). The metaverse and communication service providers. Nokia. Retrieved November 23, 2022, from https://www.nokia.com/blog/the-metaverse-and-communication-service-providers/.

Tan, K.-L. (2022, June 10). DATA MANAGEMENT FOR THE METAVERSE. SIGMOD Blog. Retrieved June 10, 2022, from https://wp.sigmod.org/?p=3481.

Lu, S., & Mintz, O. (2023). Marketing on the metaverse: Research opportunities and challenges. AMS Review, 13(1), 151–166. https://doi.org/10.1007/s13162-023-00255-5

Lee, J., & Kwon, K. H. (2022). Future value and direction of cosmetics in the era of metaverse. Journal of Cosmetic Dermatology, 21(10), 4176–4183. https://doi.org/10.1111/jocd.14794

Panagiotakopoulos, D., Marentakis, G., Metzitakos, R., Deliyannis, I., & Dedes, F. (2022). Digital scent technology: Toward the internet of senses and the metaverse. IT Professional, 24(3), 52–59. https://doi.org/10.1109/MITP.2022.3177292

Bardawil, N. (2023). No country has jurisdiction over the metaverse (yet). BSA Ahmad Bin Hezeem & Associates LLP. Retrieved 2023, from https://bsabh.com/knowledge-hub/news/no-country-has-jurisdiction-over-the-metaverse-yet.

Liang, L. J., & Guo, G. (2023, June 26). The carbon footprint of the metaverse can be reduced. UNESCO Courier. Retrieved June 26, 2023, from https://courier.unesco.org/en/articles/liu-jianya-and-guo-liang-carbon-footprint-metaverse-can-be-reduced.

Borg, A. (2022, February 16). Even though it's virtual, the metaverse does actually impact the environment. World Economic Forum. Retrieved February 16, 2022, from https://www.weforum.org/agenda/2022/02/how-metaverse-actually-impacts-the-environment/.

Zhu, T., & Liu, Y. (2023). Learning personalized preference: A segmentation strategy under consumer sparse data. Expert Systems with Applications, 215, 119333. https://doi.org/10.1016/j.eswa.2022.119333

Baker, A. (2022, April 26). Network Requirements for the Metaverse, Are We Ready? ICE Connect. Retrieved April 26, 2022, from https://www.iceconnect.com/blog/it-support/metaverse-network-requirements/.

Sternlicht, L. S. A. (2023). A New Age of Digital Addiction - What The Metaverse Means for Mental Health and Digital Addiction. Family Addiction Specialist. Retrieved 2023, from https://www.familyaddictionspecialist.com/blog/a-new-age-of-digital-addiction-what-the-metaverse-means-for-mental-health-and-digital-addiction.

Juegoadmin. (2022, November 17). Metaverse Security Threats in the Future of Internet. Juego Studio. Retrieved November 17, 2022, from https://www.juegostudio.com/blog/metaverse-security-threats-in-the-future-of-internet.

Eric Hazan, G. K., Khan, H., Spillecke, D., & Yee, L. (2022b). Marketing in the metaverse: An opportunity for innovation and experimentation. McKinsey & Company. Retrieved April 2, 2023, from https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/marketing-in-the-metaverse-an-opportunity-for-innovation-and-experimentation.

Damar, M. (2021). Metaverse shape of your life for future: A bibliometric snapshot. Journal of Metaverse, 1(1), 1–8.

Bizzaco, M. (2021, February 4). What does the metaverse mean for smart homes. Digital Trends. Retrieved February 4, 2023, from https://www.digitaltrends.com/home/willthe-metaverse-affect-smart-home/.

Mourtzis, D. (2023). Digital twin inception in the Era of industrial metaverse. Frontiers in Manufacturing Technology, 3, 1155735. https://doi.org/10.3389/fmtec.2023.1155735

Mahnken, D. (2022, November 21). Digital logistics: The Future of Logistics in the Metaverse. Saloodo!. Retrieved November 21, 2022, from https://www.saloodo.com/blog/digital-logistics-the-future-of-logistics-in-the-metaverse/.

Kumar, S. (2022, May 4). Virtual power plants: Metaverse for the power sector. Financial Express. Retrieved May 4, 2022, from https://www.financialexpress.com/opinion/virtual-power-plants-metaverse-for-the-power-sector/2511907/.

Kuo, S.-Y., Tseng, F.-H., & Chou, Y.-H. (2023). Metaverse intrusion detection of wormhole attacks based on a novel statistical mechanism. Future Generation Computer Systems, 143, 179–190. https://doi.org/10.1016/j.future.2023.01.017

Oleksy, T., Wnuk, A., & Piskorska, M. (2023). Migration to the metaverse and its predictors: Attachment to virtual places and metaverse-related threat. Computers in Human Behavior, 141, 107642. https://doi.org/10.1016/j.chb.2022.107642

Qamar, S., Anwar, Z., & Afzal, M. (2023). A systematic threat analysis and defense strategies for the metaverse and extended reality systems. Computers & Security, 103127. https://doi.org/10.1016/j.cose.2023.103127

Lebni, JY., Toghroli, R., Abbas, J., NeJhaddadgar, N., Salahshoor, MR., Mansourian, M., et al. (2020). A study of internet addiction and its effects on mental health: A study based on Iranian University Students. Journal of Education and Health Promotion, 9. https://doi.org/10.4103/jehp.jehp_148_20

Liang, H., Li, J., Wang, Y., Pan, J., Zhang, Y., & Dong, X. (2023). Metaverse virtual social center for the elderly communication during the social distancing. Virtual Reality & Intelligent Hardware, 5(1), 68–80. https://doi.org/10.1016/j.vrih.2022.07.007

Shim, B. K., Seo, J. Y., Na, K. Y., Lee, D. Y., Moon, M. H., & Lim, Y. K. (2022). A Study on Software Proposals for Optimization of Augmented Reality Glasses. In International Conference on Human-Computer Interaction (pp. 73). Springer. https://doi.org/10.1007/978-3-031-19679-9_73

Jeon, Y. A. (2022). Reading social media marketing messages as simulated self within a metaverse: an analysis of gaze and social media engagement behaviors within a metaverse platform. In 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW) (pp. 68). IEEE. https://doi.org/10.1109/VRW55335.2022.00068

Singh, N., Gupta, M., & Singh, P. (2022). Gap analysis between the customer's expectations and satisfaction of real estate sector using the SERVQUAL model. International Journal of Indian Culture and Business Management, 25(1), 1–16. https://doi.org/10.1504/IJICBM.2022.120921

Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1985). A conceptual model of service quality and its implications for future research. Journal of Marketing, 49(4), 41–50. https://doi.org/10.1177/002224298504900403

Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1994). Reassessment of expectations as a comparison standard in measuring service quality: implications for further research. Journal of Marketing, 58(1), 111–124. https://doi.org/10.1177/002224299405800109

Zeithaml, V. A., Parasuraman, A., & Berry, L. L. (1990). Delivering quality service: Balancing customer perceptions and expectations. Simon and Schuster.

Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X

Gau, W.-L., & Buehrer, D. J. (1993). Vague sets. IEEE Transactions on Systems, Man, and Cybernetics, 23(2), 610–614. https://doi.org/10.1109/21.229476

Bustince, H., & Burillo, P. (1996). Vague sets are intuitionistic fuzzy sets. Fuzzy Sets and Systems, 79(3), 403–405. https://doi.org/10.1016/0165-0114(95)00154-9

Atanassov, K. T., & Stoeva, S. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20(1), 87–96. https://doi.org/10.1007/978-3-7908-1870-3_1

Atanassov, K. T. (1989). More on intuitionistic fuzzy sets. Fuzzy Sets and Systems, 33(1), 37–45. https://doi.org/10.1016/0165-0114(89)90215-7

Bustince, H., & Burillo, P. (1995). Correlation of interval-valued intuitionistic fuzzy sets. Fuzzy Sets and Systems, 74(2), 237–244. https://doi.org/10.1016/0165-0114(94)00343-6

Hung, W.-L., & Wu, J.-W. (2002). Correlation of intuitionistic fuzzy sets by centroid method. Information Sciences, 144(1-4), 219–225. https://doi.org/10.1016/0165-0114(94)00343-6

Deschrijver, G., & Kerre, E. E. (2003). On the relationship between some extensions of fuzzy set theory. Fuzzy Sets and Systems, 133(2), 227–235. https://doi.org/10.1016/S0165-0114(02)00127-6

Xu, Z. (2007). Methods for aggregating interval-valued intuitionistic fuzzy information and their application to decision making. Control and Decision, 22(2), 215–219.

Atanassov, K. T. (1994). New operations defined over the intuitionistic fuzzy sets. Fuzzy Sets and Systems, 61(2), 137–142.

Zhang, X., & Liu, P. (2010). Method for aggregating triangular fuzzy intuitionistic fuzzy information and its application to decision making. Technological and Economic Development of Economy, 16(2), 280–290. https://doi.org/10.3846/tede.2010.18

Pervez, A. K., Maniruzzaman, M., Shah, A. A., Nabi, N., & Ado, A. M. (2020). The meagerness of simple likert scale in assessing risk: How appropriate the fuzzy likert is? NUST Journal of Social Sciences and Humanities, 6(2), 138–150. https://doi.org/10.51732/njssh.v6i2.55

Chen, S.-H., & Wang, C.-C. (2001). Representation, ranking, distance, and similarity of fuzzy numbers with step form membership function using k-preference integration method. In Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569) (pp. 69). IEEE. https://doi.org/10.1109/NAFIPS.2001.944706