Abstract
The aim of this study was to create a new, comprehensive methodology for assessing the quality and performance of video broadcasts using virtual human technologies. The research methodology included analysing existing methodologies and adapting them to the specifics of virtual hosts. New evaluation tools were developed, considering parameters such as technical quality, emotional support, interactivity, social presence, streamer attractiveness and intention to continue watching. The main results of the study showed that technological aspects of video streaming have a significant impact on viewers’ perception of such videos. High video and audio quality, and broadcast stability increase audience satisfaction and engagement. In addition, the emotional interaction between the virtual host and the audience promotes a deeper understanding and increases trust. The interactivity and social presence of the virtual host create a sense of community and engagement, which positively affects the overall perception of the broadcast. Viewers’ self-efficacy, information overload and cognitive dissonance factors were also examined, which helps to better understand the psychological state of viewers. The findings suggest that in order to achieve a high level of authenticity and trust in virtual influencers, it is necessary to consider technological aspects, aesthetic aspects, the level of trust in the host, parameters of its audience (their motivations, cultural and personal characteristics that can affect the specifics of assessing the quality and effectiveness of the broadcast), parameters of the host itself (realism, emotional expressiveness, interactivity, presence, and absence of humour, and so on). The proposed methodology allows for a comprehensive assessment of all these parameters, contributing to the improvement of the quality and effectiveness of live broadcasts with virtual hosts
Keywords
virtual avatar; host; streaming; perception; metrics
References
[1] Akramullah, S. (2014). Digital video concepts, methods, and metrics: Quality, compression, performance, and power trade-off analysis. Berkeley: Springer. doi: 10.1007/978-1-4302-6713-3.
[2] Andersson, V., & Sobek, T. (2020). Virtual avatars, virtual influencers & authenticity. Retrieved from https:// gupea.ub.gu.se/handle/2077/64928.
[3] Bharadwaj, N., Ballings, M., Naik, P.A., Moore, M., & Arat, M.M. (2022). A new livestream retail analytics framework to assess the sales impact of emotional displays. Journal of Marketing, 86(1), 27-47. doi: 10.1177/ 00222429211013042.
[4] Brown, S.M. (2020). Let’s get virtual: Measuring virtual influencer’s endorser effectiveness. Retrieved from https:// oaktrust.library.tamu.edu/handle/1969.1/192198.
[5] Camilleri, M.A., & Falzon, L. (2021). Understanding motivations to use online streaming services: Integrating the technology acceptance model (TAM) and the uses and gratifications theory (UGT). Spanish Journal of Marketing, 25(2), 217-238. doi: 10.1108/SJME-04-2020-0074.
[6] Chen, C.Y., & Lin, S.S. (2018). What drives live-stream usage intention? The perspectives of flow, entertainment, social interaction, and endorsement. Telematics and Informatics, 35(1), 293-303. doi: 10.1016/j.tele.2017.12.003.
[7] Chen, H.-C., Wang, C.-C., Hung, J.C., & Hsueh, C.-Y. (2022). Employing eye tracking to study visual attention to live streaming: A case study of Facebook live. Sustainability, 14(12), article number 7494. doi: 10.3390/ su14127494.
[8] Chen, J., & Liao, J. (2022). Antecedents of viewers’ live streaming watching: A perspective of social presence theory. Frontiers in Psychology, 13, article number 839629. doi: 10.3389/fpsyg.2022.839629.
[9] Chiou, E.K., Schroeder, N.L., & Craig, S.D. (2020). How we trust, perceive, and learn from virtual humans: The influence of voice quality. Computers & Education, 146, article number 103756. doi: 10.1016/j.compedu.2019.103756.
[10] Gefen, D., & Straub, D.W. (2004). Consumer trust in B2C e-Commerce and the importance of social presence: Experiments in e-Products and e-Services. Omega, 32(6), 407-424. doi: 10.1016/j.omega.2004.01.006.
[11] Gerlich, M. (2023). The power of virtual influencers: Impact on consumer behaviour and attitudes in the age of AI. Administrative Sciences, 13(8), article number 178. doi: 10.3390/admsci13080178.
[12] Hofeditz, L., Nissen, A., Schütte, R., & Mirbabaie, M. (2022). Trust me, I’m an influencer! – A comparison of perceived trust in human and virtual influencers. Retrieved from https://aisel.aisnet.org/ecis2022_rip/27.
[13] Kim, D., & Jo, D. (2022). Effects on co-presence of a virtual human: A comparison of display and interaction types. Electronics, 11(3), article number 367. doi: 10.3390/electronics11030367.
[14] Laghari, A.A., Shahid, S., Yadav, R., Karim, S., Khan, A., Li, H., & Shoulin, Y. (2023). The state of art and review on video streaming. Journal of High-Speed Networks, 29(3), 211-236. doi: 10.3233/JHS-222087.
[15] Li, M., & Wenjie, S. (2021). Application of virtual reality technology and digital twin in digital media communication. Journal of Intelligent & Fuzzy Systems, 40(4), 6655-6667. doi: 10.3233/JIFS-189501.
[16] Looi, J., & Kahlor, L.A. (2024). Artificial intelligence in influencer marketing: A mixed-method comparison of human and virtual influencers on Instagram. Journal of Interactive Advertising, 24(2), 107-126. doi: 10.1080/15252019.2024.2313721.
[17] Lv, J., Cao, C., Xu, Q., Ni, L., Shao, X., & Shi, Y. (2022). How live streaming interactions and their visual stimuli affect users’ sustained engagement behaviour – a comparative experiment using live and virtual live streaming. Sustainability, 14(14), article number 8907. doi: 10.3390/su14148907.
[18] Min, X., Zhai, G., Zhou, J., Farias, M.C., & Bovik, A.C. (2020). Study of subjective and objective quality assessment of audio-visual signals. IEEE Transactions on Image Processing, 29, 6054-6068. doi: 10.1109/TIP.2020.2988148.
[19] Moustakas, E., Lamba, N., Mahmoud, D., & Ranganathan, C. (2020). Blurring lines between fiction and reality: Perspectives of experts on marketing effectiveness of virtual influencers. In International conference on cyber security and protection of digital services (cyber security) (pp. 1-6). Dublin: IEEE. doi: 10.1109/ CyberSecurity49315.2020.9138861.
[20] Nasir, M. (2015). The role of CRM in marketing. Retrieved from http://surl.li/dldvjd.
[21] Park, S., & Catrambone, R. (2021). Social responses to virtual humans: The effect of human-like characteristics. Applied Sciences, 11(16), article number 7214. doi: 10.3390/app11167214.
[22] Schuck, P., Altmeyer, M., Krüger, A., & Lessel, P. (2022). Viewer types in game live streams: Questionnaire development and validation. User Modeling and User-Adapted Interaction, 32, 417-467. doi: 10.1007/s11257022-09328-9.
[23] Sharma, U., Malviya, A., & Shree, R. (2024). Exploring the role of edge computing in optimizing broadcast technologies. In Proceedings of the 13th international conference on communication systems and network technologies (pp. 117-126). Jabalpur: IEEE. doi: 10.1109/CSNT60213.2024.10546077.
[24] Sjöblom, M., & Hamari, J. (2017). Why do people watch others play video games? An empirical study on the motivations of Twitch users. Computers in Human Behavior, 75, 985-996. doi: 10.1016/j.chb.2016.10.019.
[25] Song, S.W., & Shin, M. (2024). Uncanny valley effects on chatbot trust, purchase intention, and adoption intention in the context of e-commerce: The moderating role of avatar familiarity. International Journal of Human-Computer Interaction, 40(2), 441-456. doi: 10.1080/10447318.2022.2121038.
[26] Tu, Z., Wang, Y., Birkbeck, N., Adsumilli, B., & Bovik, A.C. (2021). UGC-VQA: Benchmarking blind video quality assessment for user generated content. IEEE Transactions on Image Processing, 30, 4449-4464. doi: 10.1109/ TIP.2021.3072221.
[27] Wu, H., Zhang, E., Liao, L., Chen, C., Hou, J., Wang, A., Sun, W., Yan, Q., & Lin, W. (2023). Exploring video quality assessment on user generated contents from aesthetic and technical perspectives. In Proceedings of the IEEE/CVF international conference on computer vision (ICCV) (pp. 20144-20154). London: IEEE. doi: 10.48550/ arXiv.2211.04894.
[28] Ye, C., Zheng, R., & Li, L. (2022). The effect of visual and interactive features of tourism live streaming on tourism consumers’ willingness to participate. Asia Pacific Journal of Tourism Research, 27(5), 506-525. doi: 10.1080/10941665.2022.2091940.
[29] Zhang, M., Sun, L., Qin, F., & Wang, G.A. (2021). E-service quality on live streaming platforms: Swift guanxi perspective. Journal of Services Marketing, 35(3), 312-324. doi: 10.1108/JSM-01-2020-0009.
[30] Zhang, S., & Pan, Y. (2023). Mind over matter: Examining the role of cognitive dissonance and self-efficacy in discontinuous usage intentions on pan-entertainment mobile live broadcast platforms. Behavioral Sciences, 13(3), article number 254. doi: 10.3390/bs13030254.
[31] Zhang, Y., Wang, X., & Zhao, X. (2023). Supervising or assisting? The influence of virtual anchor driven by AIhuman collaboration on customer engagement in live streaming e-commerce. Electronic Commerce Research. doi: 10.1007/s10660-023-09783-5.
