The Influence of Response Time and Feedback Type on the User Experience of Voice Interaction among Older Adults

References

• Barber, R. E., & Lucas, H. C. (1983). System response time operator productivity, and job satisfaction. Communications of the ACM, 26(11), 972–986. https://doi.org/10.1145/182.358464
   Web of Science ®Google Scholar
• Bhatti, N. T., Bouch, A., & Kuchinsky, A. (2000). Integrating user-perceived quality into web server design. Computer Networks, 33(1–6), 1–16. https://doi.org/10.1016/S1389-1286(00)00087-6
   Web of Science ®Google Scholar
• Block, R. A., Zakay, D., & Hancock, P. A. (1999). Developmental changes in human duration judgments: A meta-analytic review. Developmental Review, 19(1), 183–211. https://doi.org/10.1006/drev.1998.0475
   Web of Science ®Google Scholar
• Blut, M., Wang, C., Wünderlich, N. V., & Brock, C. (2021). Understanding anthropomorphism in service provision: A meta-analysis of physical robots, chatbots, and other AI. Journal of the Academy of Marketing Science, 49(4), 632–658. https://doi.org/10.1007/s11747-020-00762-y
   Web of Science ®Google Scholar
• Buraga, S. C., & Dospinescu, O. (2021). A knowledge-based pilot study on assessing the music influence. Computers, Materials & Continua, 66(3), 2857–2873. https://doi.org/10.32604/cmc.2021.014429
   Web of Science ®Google Scholar
• Carstensen, L. L. (1995). Evidence for a life-span theory of socioemotional selectivity. Current Directions in Psychological Science, 4(5), 151–156. https://doi.org/10.1111/1467-8721.ep11512261
   Web of Science ®Google Scholar
• Chattaraman, V., Kwon, W., Gilbert, J. E., & Ross, K. (2019). Should AI-based, conversational digital assistants employ social- or task-oriented interaction style? A task-competency and reciprocity perspective for older adults. Computers in Human Behavior, 90, 315–330. https://doi.org/10.1016/j.chb.2018.08.048
   Web of Science ®Google Scholar
• Chen, K., & Chan, A. H. S. (2014). Gerontechnology acceptance by elderly Hong Kong Chinese: A senior technology acceptance model (STAM). Ergonomics, 57(5), 635–652. https://doi.org/10.1080/00140139.2014.895855
   PubMed Web of Science ®Google Scholar
• Chen, X., Guan, D., Zhou, M., Wang, R., & Wei, H. (2019). Research on response time of intelligent product’s voice user interface. Chinese Journal of Ergonomics, 25(1), 1–5. https://doi.org/10.13837/j.issn.1006-8309.2019.01.0001
   Google Scholar
• Chen, X., Wang, R., Zou, L., & Xu, M. (2021). Research on wake-up rate experience of intelligent voice products. Chinese Journal of Ergonomics, 27(4), 33–37. https://doi.org/10.13837/j.issn.1006-8309.2021.04.0006
   Google Scholar
• Cheng, T., & Zhang, Z. (2017). Study on system response time in human–computer interaction. Chinese Journal of Ergonomics, 23(5), 69–74. https://doi.org/10.13837/j.issn.1006-8309.2017.05.0013
   Google Scholar
• Craik, F. I. M., & Hay, J. F. (1999). Aging and judgments of duration: Effects of task complexity and method of estimation. Perception & Psychophysics, 61(3), 549–560. https://doi.org/10.3758/BF03211972
   PubMed Web of Science ®Google Scholar
• Czaja, S. J., Charness, N., Fisk, A. D., Hertzog, C., Nair, S. N., Rogers, W. A., & Sharit, J. (2006). Factors predicting the use of technology: Findings from the center for research and education on aging and technology enhancement (CREATE). Psychology and Aging, 21(2), 333–352. https://doi.org/10.1037/0882-7974.21.2.333
   PubMed Web of Science ®Google Scholar
• Dabrowski, J., & Munson, E. V. (2011). 40 years of searching for the best computer system response time. Interacting with Computers, 23(5), 555–564. https://doi.org/10.1016/j.intcom.2011.05.008
   Web of Science ®Google Scholar
• de la Mora Velasco, E., & Hirumi, A. (2020). The effects of background music on learning: A systematic review of literature to guide future research and practice. Educational Technology Research and Development, 68(6), 2817–2837. https://doi.org/10.1007/s11423-020-09783-4
   Web of Science ®Google Scholar
• Ferreira, V. F., Paiva, G. P., Prando, N., Graça, C. R., & Kouyoumdjian, J. A. (2016). Time perception and age. Arquivos de Neuro-Psiquiatria, 74(4), 299–302. https://doi.org/10.1590/0004-282X20160025
   PubMed Web of Science ®Google Scholar
• Frohlich, P. (2005). Dealing with system response times in interactive speech applications. In CHI ’05 Extended Abstracts on Human Factors in Computing Systems – CHI '05: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 1379–1382). Association for Computing Machinery. https://doi.org/10.1145/1056808.1056921
   Google Scholar
• Go, E., & Sundar, S. S. (2019). Humanizing chatbots: The effects of visual, identity and conversational cues on humanness perceptions. Computers in Human Behavior, 97, 304–316. https://doi.org/10.1016/j.chb.2019.01.020
   Web of Science ®Google Scholar
• Gorn, G., Chattopadhyay, A., Sengupta, J., & Tripathi, S. (2004). Waiting for the web: How screen color affects time perception. Journal of Marketing Research, 41(2), 215–225. https://doi.org/10.1509/jmkr.41.2.215.28668
   Web of Science ®Google Scholar
• Hattie, J. A., & Timperley, H. S. (2007). The power of feedback. Review of Educational Research, 77(1), 81–112. https://doi.org/10.3102/003465430298487
   Web of Science ®Google Scholar
• Holtgraves, T. M., Ross, S. J., Weywadt, C. R., & Han, T. L. (2007). Perceiving artificial social agents. Computers in Human Behavior, 23(5), 2163–2174. https://doi.org/10.1016/j.chb.2006.02.017
   Web of Science ®Google Scholar
• Hoxmeier, J. A., & Dicesare, C. A. (2000). System response time and user satisfaction: An experimental study of browser-based applications. In AMCIS 2000 Proceedings (p. 347). AIS Electronic Library (AISeL). http://aisel.aisnet.org/amcis2000/347.
   Google Scholar
• Këpuska, V. Z., & Klein, T. B. (2009). A novel wake-up-word speech recognition system, wake-up-word recognition task, technology and evaluation. Nonlinear Analysis: Theory, Methods & Applications, 71(12), e2772–e2789. https://doi.org/10.1016/j.na.2009.06.089
   Web of Science ®Google Scholar
• Kim, S. (2021). Exploring how older adults use a smart speaker-based voice assistant in their first interactions: Qualitative study. JMIR mHealth and uHealth, 9(1), e20427. https://doi.org/10.2196/20427
   PubMed Web of Science ®Google Scholar
• Kim, S., & Choudhury, A. (2021). Exploring older adults’ perception and use of smart speaker-based voice assistants: A longitudinal study. Computers in Human Behavior, 124, 106914. https://doi.org/10.1016/j.chb.2021.106914
   Web of Science ®Google Scholar
• Kocaballi, A. B., Berkovsky, S., Quiroz, J. C., Laranjo, L., Tong, H. L., Rezazadegan, D., Briatore, A., & Coiera, E. (2019). The personalization of conversational agents in health care: Systematic review. Journal of Medical Internet Research, 21(11), e15360. https://doi.org/10.2196/15360
   PubMed Web of Science ®Google Scholar
• Konya-Baumbach, E., Biller, M., & von Janda, S. (2023). Someone out there? A study on the social presence of anthropomorphized chatbots. Computers in Human Behavior, 139, 107513. https://doi.org/10.1016/j.chb.2022.107513
   Web of Science ®Google Scholar
• Kortum, P. T., Peres, S. C., & Stallmann, K. (2011). Extensible auditory progress bar design: Performance and aesthetics. International Journal of Human–Computer Interaction, 27(9), 864–884. https://doi.org/10.1080/10447318.2011.555310
   Web of Science ®Google Scholar
• Kuzminykh, A., Sun, J., Govindaraju, N., Avery, J., & Lank, E. (2020). Genie in the bottle: Anthropomorphized perceptions of conversational agents. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1–13). Association for Computing Machinery. https://doi.org/10.1145/3313831.3376665
   Google Scholar
• Lamotte, M., & Droit-Volet, S. (2017). Aging and time perception for short and long durations: A question of attention? Timing & Time Perception, 5(2), 149–167. https://doi.org/10.1163/22134468-00002086
   Google Scholar
• Lang, A. (1995). Defining audio/video redundancy from a limited-capacity information processing perspective. Communication Research, 22(1), 86–115. https://doi.org/10.1177/009365095022001004
   Web of Science ®Google Scholar
• Lang, A. (2000). The limited capacity model of mediated message processing. Journal of Communication, 50(1), 46–70. https://doi.org/10.1111/j.1460-2466.2000.tb02833.x
   Web of Science ®Google Scholar
• Lehmann, J. A., & Seufert, T. (2017). The influence of background music on learning in the light of different theoretical perspectives and the role of working memory capacity. Frontiers in Psychology, 8, 1902. https://doi.org/10.3389/fpsyg.2017.01902
   PubMed Web of Science ®Google Scholar
• Li, J., Zou, L., Li, M., Fu, S., & Li, S. (2020). Research on response time of intelligent voice customer service. Chinese Journal of Ergonomics, 26(4), 29–34. https://doi.org/10.13837/j.issn.1006-8309.2020.04.0006
   Google Scholar
• Li, M., Guo, F., Wang, X., Chen, J., & Ham, J. (2022). Effects of robot gaze and voice human-likeness on users’ subjective perception, visual attention, and cerebral activity in voice conversations. Computers in Human Behavior, 141, 107645. https://doi.org/10.1016/j.chb.2022.107645
   Web of Science ®Google Scholar
• Li, W., Xu, J., & Zhang, P. (2016). Wake-up-word speech recognition system based on the posterior of states. In Editorial Office of Technical Acoustics (Eds.), Proceedings of the 2016 National Acoustics Symposium (pp. 599–602). Acoustical society of China.
   Google Scholar
• Li, X., & Sung, Y. (2021). Anthropomorphism brings us closer: The mediating role of psychological distance in user–AI assistant interactions. Computers in Human Behavior, 118, 106680. https://doi.org/10.1016/j.chb.2021.106680
   Web of Science ®Google Scholar
• Li, Y., Wang, J., & Wang, W. (2019). Optimization of VUI feedback mechanism based on the time perception. Decoration, (7), 100–103. https://doi.org/10.16272/j.cnki.cn11-1392/j.2019.07.023
   Google Scholar
• Lin, L., Huang, P., & Weng, L. (2017). Selecting path models in SEM: A comparison of model selection criteria. Structural Equation Modeling, 24(6), 855–869. https://doi.org/10.1080/10705511.2017.1363652
   Web of Science ®Google Scholar
• Liu, N., Pu, Q., Shi, Y., Zhang, S., & Qiu, L. (2022). Older adults’ interaction with intelligent virtual assistants: The role of information modality and feedback. International Journal of Human–Computer Interaction, 39(5), 1162–1183. https://doi.org/10.1080/10447318.2022.2074667
   Web of Science ®Google Scholar
• Lopatovska, I., & Oropeza, H. (2018). User interactions with “Alexa” in public academic space. Proceedings of the Association for Information Science and Technology, 55(1), 309–318. https://doi.org/10.1002/pra2.2018.14505501034
   Google Scholar
• Miller, R. B. (1968). Response time in man-computer conversational transactions. In Association for Computing Machinery, Fall Joint Computer Conference, Part Ion – AFIPS ’68 (Fall, Part I) (pp. 267–277). Association for Computing Machinery. https://doi.org/10.1145/1476589.1476628
   Google Scholar
• Mishra, A., Shukla, A., & Sharma, S. K. (2021). Psychological determinants of users’ adoption and word-of-mouth recommendations of smart voice assistants. International Journal of Information Management, 67, 102413. https://doi.org/10.1016/j.ijinfomgt.2021.102413
   Web of Science ®Google Scholar
• Nallam, P., Bhandari, S., Sanders, J., & Martin-Hammond, A. (2020). A question of access: Exploring the perceived benefits and barriers of intelligent voice assistants for improving access to consumer health resources among low-income older adults. Gerontology & Geriatric Medicine, 6, 2333721420985975. https://doi.org/10.1177/2333721420985975
   Web of Science ®Google Scholar
• Nass, C., & Moon, Y. (2000). Machines and mindlessness: Social responses to computers. Journal of Social Issues, 56(1), 81–103. https://doi.org/10.1111/0022-4537.00153
   Web of Science ®Google Scholar
• Ni, Y. (2019). Analysis on speech interaction design for elderly cognition. Industrial Design, (9), 37–38.
   Google Scholar
• Nielsen, J. (1993). Usability engineering. Elsevier Inc.
   Google Scholar
• O'Brien, K., Liggett, A., Ramirez-Zohfeld, V., Sunkara, P., & Lindquist, L. A. (2020). Voice-controlled intelligent personal assistants to support aging in place. Journal of the American Geriatrics Society, 68(1), 176–179. https://doi.org/10.1111/jgs.16217
   PubMed Web of Science ®Google Scholar
• Pal, D., Arpnikanondt, C., Funilkul, S., & Varadarajan, V. (2019). User experience with smart voice assistants: The accent perspective. In 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) (pp. 1–6), Kanpur, India. https://doi.org/10.1109/ICCCNT45670.2019.8944754
   Google Scholar
• Polkosky, M. D., & Lewis, J. R. (2002). Effect of ticking rate on user estimation of system response time. International Journal of Human–Computer Interaction, 14(3), 423–446. https://doi.org/10.1207/S15327590IJHC143&4_09
   Google Scholar
• Poushneh, A. (2021). Humanizing voice assistant: The impact of voice assistant personality on consumers’ attitudes and behaviors. Journal of Retailing and Consumer Services, 58, 102283. https://doi.org/10.1016/j.jretconser.2020.102283
   Web of Science ®Google Scholar
• Pradhan, A., Findlater, L., & Lazar, A. (2019). “Phantom friend” or “Just a box with information”: Personification and ontological categorization of smart speaker-based voice assistants by older adults. In Proceedings of the ACM on Human–Computer Interaction, 3(CSCW) (pp. 1–21). https://doi.org/10.1145/3359316
   Google Scholar
• Qin, J., & Wang, Y. (2021). Conversational voice interaction system in high/low-context. Packaging Engineering, 42(10), 85–91. https://doi.org/10.19554/j.cnki.1001-3563.2021.10.012
   Google Scholar
• Ramsay, J., Barbesi, A., & Preece, J. (1998). A psychological investigation of long retrieval times on the World Wide Web. Interacting with Computers, 10(1), 77–86. https://doi.org/10.1016/s0953-5438(97)00019-2
   Web of Science ®Google Scholar
• Ren, W., Ma, J., & Zhang, Z. (2019). Age-related changes in time perception and the underlying neurobiological mechanism. Progress in Biochemistry and Biophysics, 46(1), 63–72. https://doi.org/10.16476/j.pibb.2018.0129
   Web of Science ®Google Scholar
• Rhee, C. E., & Choi, J. H. (2020). Effects of personalization and social role in voice shopping: An experimental study on product recommendation by a conversational voice agent. Computers in Human Behavior, 109, 106359. https://doi.org/10.1016/j.chb.2020.106359
   Web of Science ®Google Scholar
• Rosales, A., & Fernández-Ardèvol, M. (2016). Smartphones, apps and older people’s interests: From a generational perspective. In Association for Computing Machinery, Proceedings of the 18th International Conference on Human–Computer Interaction with Mobile Devices and Services – MobileHCI ’16 (pp. 491–503). Association for Computing Machinery. https://doi.org/10.1145/2935334.2935363
   Google Scholar
• Salthouse, T. (2010). Selective review of cognitive aging. Journal of the International Neuropsychological Society, 16(5), 754–760. https://doi.org/10.1017/S1355617710000706
   PubMed Web of Science ®Google Scholar
• Samrose, S., Anbarasu, K., Joshi, A., & Mishra, T. (2020). Mitigating boredom using an empathetic conversational agent. In Association for Computing Machinery, Proceedings of the 20th ACM International Conference on Intelligent Virtual Agents (pp. 1–8). Association for Computing Machinery. https://doi.org/10.1145/3383652.3423905
   Google Scholar
• Sears, A., Jacko, J. A., & Borella, M. S. (1997). Internet delay effects: How users perceive quality, organization, and ease of use of information. In Association for Computing Machinery, CHI '97: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 353–354). Association for Computing Machinery. https://doi.org/10.1145/1120212.1120430
   Google Scholar
• Selvidge, P., Chaparro, B. S., & Bender, G. T. (2000). The world-wide wait: Effects of delays on user performance. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 44(4), 416–419. https://doi.org/10.1177/154193120004400404
   Google Scholar
• Shneiderman, B. (1984). Response time and display rate in human performance with computers. ACM Computing Surveys, 16(3), 265–285. https://doi.org/10.1145/2514.2517
   Google Scholar
• Shneiderman, B., & Plaisant, C. (2009). Designing the user interface (5th ed.). Addison Wesley.
   Google Scholar
• Song, Y., Yang, Y., & Cheng, P. (2022). The investigation of adoption of voice-user interface (VUI) in smart home systems among Chinese older adults. Sensors, 22(4), 1614. https://doi.org/10.3390/s22041614
   PubMed Web of Science ®Google Scholar
• Tangmanee, C., & Nontasil, P. (2014). Perception of delay and attitude toward feedback display: An exploration into downloaders’ demographics. International Arab Journal of e-Technology, 3(4), 242–249. https://search.emarefa.net/detail/BIM-365835.
   Google Scholar
• Taylor, S. (1994). Waiting for service: The relationship between delays and evaluations of service. Journal of Marketing, 58(2), 56–69. https://doi.org/10.2307/1252269
   Web of Science ®Google Scholar
• Thadhani, A. J. (1981). Interactive user productivity. IBM Systems Journal, 20(4), 407–423. https://doi.org/10.1147/sj.204.0407
   Google Scholar
• United Nations. (2023). World Social Report 2023: Leaving no one behind in an ageing world. https://desapublications.un.org/publications/world-social-report-2023-leaving-no-one-behind-ageing-world
   Google Scholar
• Voorveld, H. A. M., & Araujo, T. B. (2020). How social cues in virtual assistants influence concerns and persuasion: The role of voice and a human name. Cyberpsychology, Behavior and Social Networking, 23(10), 689–696. https://doi.org/10.1089/cyber.2019.0205
   PubMed Web of Science ®Google Scholar
• Wang, J., Li, Y., Yang, S., Dong, S., & Li, J. (2023). Waiting experience: Optimization of feedback mechanism of voice user interfaces based on time perception. IEEE Access, 11, 21241–21251. https://doi.org/10.1109/ACCESS.2023.3250278
   Web of Science ®Google Scholar
• Xie, Y., Zhu, K., Zhou, P., & Liang, C. (2023). How does anthropomorphism improve human–AI interaction satisfaction: A dual-path model. Computers in Human Behavior, 148, 107878. https://doi.org/10.1016/j.chb.2023.107878
   Web of Science ®Google Scholar
• Yang, Y. (2020). Research on the influence of different feedback methods on voice interaction experience [Master’s thesis]. Tianjin Normal University. https://doi.org/10.27363/d.cnki.gtsfu.202<0.001699
   Google Scholar
• Yang, Y. (2021). Research on the application of linear regression analysis in R language. Journal of Fujian Computer, 37(8), 25–32. https://doi.org/10.16707/j.cnki.fjpc.2021.08.007
   Google Scholar
• Yap, Y. Y., Tan, S. H., & Choon, S. W. (2022). Elderly’s intention to use technologies: A systematic literature review. Heliyon, 8(1), e08765. https://doi.org/10.1016/j.heliyon.2022.e08765
   PubMed Web of Science ®Google Scholar
• Zhao, Y., Chen, Y., & Liu, Q. (2023). User study on intelligent voice assistants: Theoretical progress and practical implications. Digital Library Forum, 19(5), 26–34.
   Google Scholar
• Zhu, Y., Janssen, M., Wang, R., & Liu, Y. (2022). It is me, chatbot: Working to address the COVID-19 outbreak-related mental health issues in China. User experience, satisfaction, and influencing factors. International Journal of Human–Computer Interaction, 38(12), 1182–1194. https://doi.org/10.1080/10447318.2021.1988236
   Web of Science ®Google Scholar