Performance-Oriented Digital Twin Technologies for Digital Characters and Avatars: A Bibliometric and Thematic Analysis
Abstract
This bibliometric study analyzes the evolution of research on digital actors from 1995 to 2023 using data from the Scopus and Web of Science databases. Through a systematic and rigorous selection process, the study identifies publication growth patterns, leading authors and journals, geographical contributions, and the thematic evolution of the field. Unlike previous bibliometric reviews focused broadly on digital twins or virtual avatars, this study uniquely examines the intersection between digital twin technologies and performance-oriented digital actors, integrating perspectives from engineering, artificial intelligence, and digital character creation. The findings delineate an emerging and previously underexplored research subfield and provide a structured framework that highlights conceptual trends, research gaps, and directions for future studies.
- Referencias
- Cómo citar
- Del mismo autor
- Métricas
Alexander, O., Rogers, M., Lambeth, W., Chiang, J. Y., Ma, W. C., Wang, C. C., & Debevec, P. (2010). The digital emily project: Achieving a photorealistic digital actor. IEEE Computer Graphics and Applications, 30(4), 20–31. https://doi.org/10.1109/MCG.2010.65
Bastos Porsani, G., Lersundi, K., Sanchez-Ostiz Gutierrez, A., & Fernandez Bandera, C. (2021). Interoperability between building information modelling (BIM) and building energy model (BEM). Applied Sciences, 11(5), 2167. https://doi.org/10.3390/app11052167
Bordegoni, M., & Ferrise, F. (2023). Exploring the intersection of metaverse, digital twins, and artificial intelligence in training and maintenance. Journal of Computing and Information Science in Engineering, 23(6), 060806. https://doi.org/10.1115/1.4062455
Boussetta, M., Ababou, M., Faquir, S., & Rabiai, S. (2024). Artificial intelligence and digital infrastructure for sustainable industry and enhanced corporate performance: A bibliometric analysis of smart optimization technologies. En International Conference on Electrical Systems and Smart Technologies (pp. 93–102). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-86705-7_9
Chaudhary, R., Aslam, A. M., Bhardwaj, A., Kumar, N., & Buyya, R. (2024). Digital twins-enabled game theoretical models and techniques for metaverse connected and autonomous vehicles: A survey. SSRN. https://doi.org/10.2139/ssrn.4713492
Chua, P. C., Moon, S. K., Ng, Y. T., & Ng, H. Y. (2022). A surrogate model to predict production performance in digital twin-based smart manufacturing. Journal of Computing and Information Science in Engineering, 22(3), 031007. https://doi.org/10.1115/1.4053038
Fera, M., Greco, A., Caterino, M., Gerbino, S., Caputo, F., Macchiaroli, R., & D’Amato, E. (2019). Towards digital twin implementation for assessing production line performance and balancing. Sensors, 20(1), 97. https://doi.org/10.3390/s20010097
Guo, X., Liu, L., Huang, J., Wang, H., Du, X., Shi, J., & Wang, Y. (2023). A digital twin modeling method for array antenna assembly performance real-time analysis. The International Journal of Advanced Manufacturing Technology, 126(7), 3765–3781. https://doi.org/10.1007/s00170-023-11324-1
He, W., Mao, J., Song, K., Li, Z., Su, Y., Wang, Y., & Pan, X. (2023). Structural performance prediction based on the digital twin model: A battery bracket example. Reliability Engineering & System Safety, 229, 108874. https://doi.org/10.1016/j.ress.2022.108874
He, X., Lai, X., Yang, L., Zhang, F., Zhou, D., Song, X., & Sun, W. (2022). M-LFM: A multi-level fusion modeling method for shape-performance integrated digital twin of complex structure. Frontiers of Mechanical Engineering, 17(4), 52. https://doi.org/10.1007/s11465-022-0708-0
He, Y., Aliyu, A., Evans, M., & Luo, C. (2021). Health care cybersecurity challenges and solutions under the climate of COVID-19: Scoping review. Journal of Medical Internet Research, 23(4), e21747. https://doi.org/10.2196/21747
Heim, M. (1995). The design of virtual reality. Body & Society, 1(3-4), 65–77. https://doi.org/10.1177/1357034X95001003004
Hosamo, H. H., Nielsen, H. K., Kraniotis, D., Svennevig, P. R., & Svidt, K. (2023). Digital twin framework for automated fault source detection and prediction for comfort performance evaluation of existing non-residential Norwegian buildings. Energy and Buildings, 281, 112732. https://doi.org/10.1016/j.enbuild.2022.112732
Jamshidi, M. B., Sargolzaei, S., Foorginezhad, S., & Moztarzadeh, O. (2023). Metaverse and microorganism digital twins: A deep transfer learning approach. Applied Soft Computing, 147, 110798. https://doi.org/10.1016/j.asoc.2023.110798
Lai, J. Y., & Chang, L. T. (2021). Impacts of augmented reality apps on first graders' motivation and performance in English vocabulary learning. Sage Open, 11(4). https://doi.org/10.1177/21582440211047549
Li, L., Lei, B., & Mao, C. (2022). Digital twin in smart manufacturing. Journal of Industrial Information Integration, 26, 100289. https://doi.org/10.1016/j.jii.2021.100289
Li, Y. T., & Li, T. Y. (2008). A unified approach to planning versatile motions for an autonomous digital actor. Journal of Advanced Computational Intelligence and Intelligent Informatics, 12(3), 277–283. https://doi.org/10.20965/jaciii.2008.p0277
Liu, M., Fang, S., Dong, H., & Xu, C. (2020). Review of digital twin about concepts, technologies, and industrial applications. Journal of Manufacturing Systems, 58, 346–361. https://doi.org/10.1016/j.jmsy.2020.06.011
Longo, F., Nicoletti, L., & Padovano, A. (2019). Ubiquitous knowledge empowers the smart factory: The impacts of a service-oriented digital twin on enterprises’ performance. Annual Reviews in Control, 47, 221–236. https://doi.org/10.1016/j.arcontrol.2019.01.001
Lu, X., Shi, Z., Zhang, X., Cao, L., & Liang, S. (2025). Exploring the field of virtual avatar: A bibliometric and content analysis. International Journal of Human–Computer Interaction, 41(12), 7825–7841. https://doi.org/10.1080/10447318.2024.2400405
Massafra, A., Costantino, C., Predari, G., & Gulli, R. (2023). Building information modeling and building performance simulation-based decision support systems for improved built heritage operation. Sustainability, 15(14), 11240. https://doi.org/10.3390/su151411240
Mogensen, R. S., Rodriguez, I., Schou, C., Mortensen, S., & Sørensen, M. S. (2021). Evaluation of the impact of wireless communication in production via factory digital twins. Manufacturing Letters, 28, 1–5. https://doi.org/10.1016/j.mfglet.2021.01.006
Ofosu, R., Hosseinian-Far, A., & Sarwar, D. (2022). Digital twin technologies, architecture, and applications: A comprehensive systematic review and bibliometric analysis. En Blockchain and Other Emerging Technologies for Digital Business Strategies (pp. 105-142). Springer. https://doi.org/10.1007/978-3-030-98225-6_5
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. International Journal of Surgery, 88, 105906. https://doi.org/10.1016/j.ijsu.2021.105906
Park, S. Y., Kim, S. J., Park, C. H., Kim, J., & Lee, D. Y. (2023). Data‐driven prediction models for forecasting multistep ahead profiles of mammalian cell culture toward bioprocess digital twins. Biotechnology and Bioengineering, 120(9), 2494–2508. https://doi.org/10.1002/bit.28405
Psarommatis, F. (2021). A generic methodology and a digital twin for zero defect manufacturing (ZDM) performance mapping towards design for ZDM. Journal of Manufacturing Systems, 59, 507–521. https://doi.org/10.1016/j.jmsy.2021.03.021
Qamsane, Y., Phillips, J. R., Savaglio, C., Warner, D., James, S. C., & Barton, K. (2022). Open process automation-and digital twin-based performance monitoring of a process manufacturing system. IEEE Access, 10, 60823–60835. https://doi.org/10.1109/ACCESS.2022.3179982
Qi, L., Xu, X., Wu, X., Ni, Q., Yuan, Y., & Zhang, X. (2023). Digital-twin-enabled 6G mobile network video streaming using mobile crowdsourcing. IEEE Journal on Selected Areas in Communications. https://doi.org/10.1109/JSAC.2023.3310077
Stary, C. (2023). Digital process twins as intelligent design technology for engineering metaverse/XR applications. Sustainability, 15(22), 16062. https://doi.org/10.3390/su152216062
Tan, Y. R., Hofmeister, M., Phua, S. Z., Brownbridge, G., Rustagi, K., Akroyd, J., & Kraft, M. (2024). Beyond connected digital twins—From GIS to the World Avatar. SSRN. https://doi.org/10.2139/ssrn.5072721
Vanderhorst, H. R., Heesom, D., & Yenneti, K. (2024). Technological advancements and the vision of a meta smart twin city. Technology in Society, 79, 102731. https://doi.org/10.1016/j.techsoc.2024.102731
Wallace, S. (2023). Digital twin and metaverse technologies, geospatial simulation and sensor fusion tools, and object perception and motion control algorithms in immersive hyper-connected virtual spaces. Linguistic and Philosophical Investigations, 22, 264–280. https://doi.org/10.22381/lpi22202316
Wang, J., Li, X., Wang, P., & Liu, Q. (2024). Bibliometric analysis of digital twin literature: A review of influencing factors and conceptual structure. Technology Analysis & Strategic Management, 36(1), 166-180. https://doi.org/10.1080/09537325.2022.2026320
Wang, S., Lai, X., He, X., Qiu, Y., & Song, X. (2022). Building a trustworthy product-level shape-performance integrated digital twin with multifidelity surrogate model. Journal of Mechanical Design, 144(3), 031703. https://doi.org/10.1115/1.4052390
Yang, L., Ni, S. T., Wang, Y., Yu, A., Lee, J. A., & Hui, P. (2024). Interoperability of the metaverse: A digital ecosystem perspective review. arXiv. https://doi.org/10.48550/arXiv.2403.05205
Yang, X., Ran, Y., Zhang, G., Wang, H., Mu, Z., & Zhi, S. (2022). A digital twin-driven hybrid approach for the prediction of performance degradation in transmission unit of CNC machine tool. Robotics and Computer-Integrated Manufacturing, 73, 102230. https://doi.org/10.1016/j.rcim.2021.102230
Yi, L., Glatt, M., Ehmsen, S., Duan, W., & Aurich, J. C. (2021). Process monitoring of economic and environmental performance of a material extrusion printer using an augmented reality-based digital twin. Additive Manufacturing, 48, 102388. https://doi.org/10.1016/j.addma.2021.102388
Zhou, Y., Xing, T., Song, Y., Li, Y., Zhu, X., Li, G., & Ding, S. (2021). Digital-twin-driven geometric optimization of centrifugal impeller with free-form blades for five-axis flank milling. Journal of Manufacturing Systems, 58, 22–35. https://doi.org/10.1016/j.jmsy.2020.06.019
Culbert, J. H., Hobert, A., Jahn, N. et al. (2025). Reference coverage analysis of OpenAlex compared to Web of Science and Scopus. Scientometrics, 130, 2475–2492. https://doi.org/10.1007/s11192-025-05293-3
Bastos Porsani, G., Lersundi, K., Sanchez-Ostiz Gutierrez, A., & Fernandez Bandera, C. (2021). Interoperability between building information modelling (BIM) and building energy model (BEM). Applied Sciences, 11(5), 2167. https://doi.org/10.3390/app11052167
Bordegoni, M., & Ferrise, F. (2023). Exploring the intersection of metaverse, digital twins, and artificial intelligence in training and maintenance. Journal of Computing and Information Science in Engineering, 23(6), 060806. https://doi.org/10.1115/1.4062455
Boussetta, M., Ababou, M., Faquir, S., & Rabiai, S. (2024). Artificial intelligence and digital infrastructure for sustainable industry and enhanced corporate performance: A bibliometric analysis of smart optimization technologies. En International Conference on Electrical Systems and Smart Technologies (pp. 93–102). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-86705-7_9
Chaudhary, R., Aslam, A. M., Bhardwaj, A., Kumar, N., & Buyya, R. (2024). Digital twins-enabled game theoretical models and techniques for metaverse connected and autonomous vehicles: A survey. SSRN. https://doi.org/10.2139/ssrn.4713492
Chua, P. C., Moon, S. K., Ng, Y. T., & Ng, H. Y. (2022). A surrogate model to predict production performance in digital twin-based smart manufacturing. Journal of Computing and Information Science in Engineering, 22(3), 031007. https://doi.org/10.1115/1.4053038
Fera, M., Greco, A., Caterino, M., Gerbino, S., Caputo, F., Macchiaroli, R., & D’Amato, E. (2019). Towards digital twin implementation for assessing production line performance and balancing. Sensors, 20(1), 97. https://doi.org/10.3390/s20010097
Guo, X., Liu, L., Huang, J., Wang, H., Du, X., Shi, J., & Wang, Y. (2023). A digital twin modeling method for array antenna assembly performance real-time analysis. The International Journal of Advanced Manufacturing Technology, 126(7), 3765–3781. https://doi.org/10.1007/s00170-023-11324-1
He, W., Mao, J., Song, K., Li, Z., Su, Y., Wang, Y., & Pan, X. (2023). Structural performance prediction based on the digital twin model: A battery bracket example. Reliability Engineering & System Safety, 229, 108874. https://doi.org/10.1016/j.ress.2022.108874
He, X., Lai, X., Yang, L., Zhang, F., Zhou, D., Song, X., & Sun, W. (2022). M-LFM: A multi-level fusion modeling method for shape-performance integrated digital twin of complex structure. Frontiers of Mechanical Engineering, 17(4), 52. https://doi.org/10.1007/s11465-022-0708-0
He, Y., Aliyu, A., Evans, M., & Luo, C. (2021). Health care cybersecurity challenges and solutions under the climate of COVID-19: Scoping review. Journal of Medical Internet Research, 23(4), e21747. https://doi.org/10.2196/21747
Heim, M. (1995). The design of virtual reality. Body & Society, 1(3-4), 65–77. https://doi.org/10.1177/1357034X95001003004
Hosamo, H. H., Nielsen, H. K., Kraniotis, D., Svennevig, P. R., & Svidt, K. (2023). Digital twin framework for automated fault source detection and prediction for comfort performance evaluation of existing non-residential Norwegian buildings. Energy and Buildings, 281, 112732. https://doi.org/10.1016/j.enbuild.2022.112732
Jamshidi, M. B., Sargolzaei, S., Foorginezhad, S., & Moztarzadeh, O. (2023). Metaverse and microorganism digital twins: A deep transfer learning approach. Applied Soft Computing, 147, 110798. https://doi.org/10.1016/j.asoc.2023.110798
Lai, J. Y., & Chang, L. T. (2021). Impacts of augmented reality apps on first graders' motivation and performance in English vocabulary learning. Sage Open, 11(4). https://doi.org/10.1177/21582440211047549
Li, L., Lei, B., & Mao, C. (2022). Digital twin in smart manufacturing. Journal of Industrial Information Integration, 26, 100289. https://doi.org/10.1016/j.jii.2021.100289
Li, Y. T., & Li, T. Y. (2008). A unified approach to planning versatile motions for an autonomous digital actor. Journal of Advanced Computational Intelligence and Intelligent Informatics, 12(3), 277–283. https://doi.org/10.20965/jaciii.2008.p0277
Liu, M., Fang, S., Dong, H., & Xu, C. (2020). Review of digital twin about concepts, technologies, and industrial applications. Journal of Manufacturing Systems, 58, 346–361. https://doi.org/10.1016/j.jmsy.2020.06.011
Longo, F., Nicoletti, L., & Padovano, A. (2019). Ubiquitous knowledge empowers the smart factory: The impacts of a service-oriented digital twin on enterprises’ performance. Annual Reviews in Control, 47, 221–236. https://doi.org/10.1016/j.arcontrol.2019.01.001
Lu, X., Shi, Z., Zhang, X., Cao, L., & Liang, S. (2025). Exploring the field of virtual avatar: A bibliometric and content analysis. International Journal of Human–Computer Interaction, 41(12), 7825–7841. https://doi.org/10.1080/10447318.2024.2400405
Massafra, A., Costantino, C., Predari, G., & Gulli, R. (2023). Building information modeling and building performance simulation-based decision support systems for improved built heritage operation. Sustainability, 15(14), 11240. https://doi.org/10.3390/su151411240
Mogensen, R. S., Rodriguez, I., Schou, C., Mortensen, S., & Sørensen, M. S. (2021). Evaluation of the impact of wireless communication in production via factory digital twins. Manufacturing Letters, 28, 1–5. https://doi.org/10.1016/j.mfglet.2021.01.006
Ofosu, R., Hosseinian-Far, A., & Sarwar, D. (2022). Digital twin technologies, architecture, and applications: A comprehensive systematic review and bibliometric analysis. En Blockchain and Other Emerging Technologies for Digital Business Strategies (pp. 105-142). Springer. https://doi.org/10.1007/978-3-030-98225-6_5
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. International Journal of Surgery, 88, 105906. https://doi.org/10.1016/j.ijsu.2021.105906
Park, S. Y., Kim, S. J., Park, C. H., Kim, J., & Lee, D. Y. (2023). Data‐driven prediction models for forecasting multistep ahead profiles of mammalian cell culture toward bioprocess digital twins. Biotechnology and Bioengineering, 120(9), 2494–2508. https://doi.org/10.1002/bit.28405
Psarommatis, F. (2021). A generic methodology and a digital twin for zero defect manufacturing (ZDM) performance mapping towards design for ZDM. Journal of Manufacturing Systems, 59, 507–521. https://doi.org/10.1016/j.jmsy.2021.03.021
Qamsane, Y., Phillips, J. R., Savaglio, C., Warner, D., James, S. C., & Barton, K. (2022). Open process automation-and digital twin-based performance monitoring of a process manufacturing system. IEEE Access, 10, 60823–60835. https://doi.org/10.1109/ACCESS.2022.3179982
Qi, L., Xu, X., Wu, X., Ni, Q., Yuan, Y., & Zhang, X. (2023). Digital-twin-enabled 6G mobile network video streaming using mobile crowdsourcing. IEEE Journal on Selected Areas in Communications. https://doi.org/10.1109/JSAC.2023.3310077
Stary, C. (2023). Digital process twins as intelligent design technology for engineering metaverse/XR applications. Sustainability, 15(22), 16062. https://doi.org/10.3390/su152216062
Tan, Y. R., Hofmeister, M., Phua, S. Z., Brownbridge, G., Rustagi, K., Akroyd, J., & Kraft, M. (2024). Beyond connected digital twins—From GIS to the World Avatar. SSRN. https://doi.org/10.2139/ssrn.5072721
Vanderhorst, H. R., Heesom, D., & Yenneti, K. (2024). Technological advancements and the vision of a meta smart twin city. Technology in Society, 79, 102731. https://doi.org/10.1016/j.techsoc.2024.102731
Wallace, S. (2023). Digital twin and metaverse technologies, geospatial simulation and sensor fusion tools, and object perception and motion control algorithms in immersive hyper-connected virtual spaces. Linguistic and Philosophical Investigations, 22, 264–280. https://doi.org/10.22381/lpi22202316
Wang, J., Li, X., Wang, P., & Liu, Q. (2024). Bibliometric analysis of digital twin literature: A review of influencing factors and conceptual structure. Technology Analysis & Strategic Management, 36(1), 166-180. https://doi.org/10.1080/09537325.2022.2026320
Wang, S., Lai, X., He, X., Qiu, Y., & Song, X. (2022). Building a trustworthy product-level shape-performance integrated digital twin with multifidelity surrogate model. Journal of Mechanical Design, 144(3), 031703. https://doi.org/10.1115/1.4052390
Yang, L., Ni, S. T., Wang, Y., Yu, A., Lee, J. A., & Hui, P. (2024). Interoperability of the metaverse: A digital ecosystem perspective review. arXiv. https://doi.org/10.48550/arXiv.2403.05205
Yang, X., Ran, Y., Zhang, G., Wang, H., Mu, Z., & Zhi, S. (2022). A digital twin-driven hybrid approach for the prediction of performance degradation in transmission unit of CNC machine tool. Robotics and Computer-Integrated Manufacturing, 73, 102230. https://doi.org/10.1016/j.rcim.2021.102230
Yi, L., Glatt, M., Ehmsen, S., Duan, W., & Aurich, J. C. (2021). Process monitoring of economic and environmental performance of a material extrusion printer using an augmented reality-based digital twin. Additive Manufacturing, 48, 102388. https://doi.org/10.1016/j.addma.2021.102388
Zhou, Y., Xing, T., Song, Y., Li, Y., Zhu, X., Li, G., & Ding, S. (2021). Digital-twin-driven geometric optimization of centrifugal impeller with free-form blades for five-axis flank milling. Journal of Manufacturing Systems, 58, 22–35. https://doi.org/10.1016/j.jmsy.2020.06.019
Culbert, J. H., Hobert, A., Jahn, N. et al. (2025). Reference coverage analysis of OpenAlex compared to Web of Science and Scopus. Scientometrics, 130, 2475–2492. https://doi.org/10.1007/s11192-025-05293-3
Hincapié Montoya, M., Guzmán-Ramírez, J. A., & Valencia-Arias, A. (2026). Performance-Oriented Digital Twin Technologies for Digital Characters and Avatars: A Bibliometric and Thematic Analysis. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 15, e33105. https://doi.org/10.14201/adcaij.33105
Downloads
Download data is not yet available.
+
−