Abstract
Recognising the potential to enhance the television (TV) experience, the integration of proactive behaviours in an intelligent TV assistant is thought to reduce the user’s interaction effort and promote a more natural, friendly and empathetic use. However, existing research lacks a comprehensive understanding of the desirable dynamic and features of proactive behaviours in the TV context and their effects on perceived empathy and overall user experience (UX). This paper discusses the appropriateness and usefulness of proactive behaviours in an intelligent TV assistant prototype, seeking insights into their effects on perceived empathy and UX. To operationalise the study, the Wizard-of-OZ (WoZ) method was used to analyse users’ perceptions of the design of the proactive scenarios that compose the prototype. The results showed that the prototype provided a good UX and was perceived as being empathetic. However, although all proactive scenarios were seen as useful some issues related to their suitability to the user’s context and preferences were identified. The results of this study provide significant contributions to the TV domain, highlighting users’ propensity to adopt proactive TV assistants and emphasising the importance of personalisation for better UX and perceived empathy.
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The authors have no competing interests to declare that are relevant to the content of this article.
Notes
- 1.
#P.7 – represents participant #7.
References
Ammari, T., Kaye, J., Tsai, J.Y., Bentley, F.: Music, search, and IoT: How people (really) use voice assistants. ACM Trans. Comput.-Hum. Interact. 26(3), 28 (2019)
Reicherts, L, Zargham, N., Bonfert, M., Rogers, Y., Malaka, R.: May I Interrupt? Diverging opinions on proactive smart speakers. In Proceedings of the 3rd Conference on Conversational User Interfaces (CUI ‘21), pp. 10 ACM, New York (2021)
McLean, G., Osei-Frimpong, K.: Hey Alexa ... examine the variables influencing the use of artificial intelligent in-home voice assistants. Comput. Hum. Behav. 99, 28–37 (2019)
Fernandes, S., Abreu, J., Almeida, P., Santos, R.: A review of voice user interfaces for interactive TV. Commun. Comput. Infor. Sci. 1004, 115–128 (2019)
Statista. Anzahl der Nutzer Virtueller Digitaler ASsistenten Weltweit in den Jahren von 2015 Bis 2021. https://de.statista.com/statistik/daten/studie/620321/umfrage/nutzungv%on-virtuellen-digitalen-assistenten-weltweit/. Accessed 27 Dec 2023
Zargham, N., et al.: Understanding circumstances for desirable proactive behaviour of voice assistants: the proactivity dilemma. In: Proceedings of the 4th Conference on Conversational User Interfaces (CUI’22), Article 3, pp. 1–14. ACM, New York (2022)
Kraus, M., Wagner, N., Callejas, Z., Minker, W.: The role of trust in proactive conversational assistants. IEEE Access 9, 112821–112836 (2021)
Miksik, O., et al.: Building Proactive Voice Assistants: When and How (not) to Interact (2020). arXiv:2005.01322
Marques, T., Abreu, J., Santos, R.: Proactivity in the TV context: understanding the relevance and characteristics of proactive behaviours in voice assistants. In: Proceedings of the 2023 ACM International Conference on Interactive Media Experiences, pp. 314–319. ACM, New York (2023)
Schweitzer, N., Gollnhofer, J. F., Bellis, E.: Exploring the potential of proactive AI-enabled technologies. In: Proceedings of the Conference on AMA Summer Educators, IN18-IN19 (2018)
Silva, A.B., et al.: Intelligent personal assistants: a systematic literature review. Expert Syst. Appl. 147, 113193 (2020)
Kraus, M., et al.: “Was that successful?” On integrating proactive meta-dialogue in a DIY-Assistant using multimodal cues. In: Proceedings of the 2020 International Conference on Multimodal Interaction (ICMI ‘20), pp. 585–594. ACM, New York, NY (2020)
Campbell, I.C.: Amazon’s Alexa can now act on its own hunches to turn off lights and more. https://www.theverge.com/2021/1/25/22249044/amazon-alexa-update-proactive-hunches-guard-plus-subscription. Accessed 29 Dec 2023
Binay, D.: Stay on top of your day with proactive help from your Assistant. https://www.blog.google/products/assistant/stay-top-your-day-proactive-help-your-assistant/. Accessed 29 Dec 2023
Romero, J.: Google TV’s new Ambient Mode with “proactive personal results” rolling out for some. https://chromeunboxed.com/google-tv-ambient-mode-proactive?utm_content=cmp-true. Accessed 29 Jan 2024
Abreu, J., Santos, R., Silva, T., Marques, T., Cardoso, B.: Proactivity: the next step in voice assistants for the TV ecosystem. Commun. Comput. Inf. Sci. 1202, 103–116 (2020)
Sarikaya., R.: The technology behind personal digital assistants: an overview of the system architecture and key components. IEEE Sig. Process. Mag. 34(1), 67–81 (2017)
Isbell, C.L., Pierce, J.S.: An IP continuum for adaptive interface design. In: Proceedings of HCI International (2005)
Lopez-Tovar, H., Charalambous, A., Dowell, J.: Managing smartphone interruptions through adaptive modes and modulation of notifications. In: Proceedings of the 20th International Conference of Intelligent User Interface, pp. 296–299 (2015)
Yorke-Smith, N., Saadati, S., Myers, K.L., Morley, D.N.: The design of a proactive personal agent for task management. Int. J. Artif. Intell. Tools 21(1), 30 (2012)
Amershi, S., et al.: Guidelines for Human AI Interaction. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, Article 3, pp. 1–13 (2019)
Cha, N., et al.: Hello there! Is now a good time to talk? Opportune moments for proactive interactions with smart speakers. In: Proceeding of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies, vol. 4, no. 3, Article 74, pp. 28 (2020)
Meurisch, C., et al.: Exploring user expectations of proactive AI systems. In: Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 4(4), Article 146, pp. 22 (2020)
Tan, H., et al.: Relationship between social robot proactive behavior and the human perception of anthropomorphic attributes. Adv. Robot. 34(20), 1324–1336 (2020)
Brave, S., Nass, C., Hutchinson, K.: Computers that care: investigating the effects of orientation of emotion exhibited by an embodied computer agent. Int. J. Hum. Comput. Stud. 62(2), 161–178 (2005)
Ochs, M., Pelachaud, C., Sadek, D.: An empathic virtual dialog agent to improve human-machine interaction. In: Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS ‘08), pp. 89–06. International Foundation for Autonomous Agents and Multiagent Systems, Richland (2008)
Martin, B., Hanington, B.: Universal Methods of Design, 1sr edn. Rockport Publishers, Beverly (2013)
Bickmore, T., Gruber, A., Picard, R.: Establishing the computer–patient working alliance in automated health behavior change interventions. Patient Educ. Couns. 59(1), 21–30 (2005)
Mahlke, S., Thuring, M.: Studying antecedents of emotional experiences in interactive contexts. In: Proceedings of CHI 2007 - Emotion & Empathy (2007)
Abreu, J., Camargo, J., Santos, R., Almeida, P., Beça, P., Silva, T.: UX evaluation methodology for iTV: assessing a natural language interaction system. Commun. Comput. Inf. Sci. 1433, 149–161 (2021)
Bradley, M.M., Lang, P.J.: Measuring emotion: the self-assessment manikin and the semantic differential. J. Behav. Ther. Exp. Psychiatry 25(1), 49–59 (1994)
Brooke, J.: SUS-A quick and dirty usability scale. In: P. W. Jordan, B. Thomas, I. L. McClelland, B. Weerdmeester (Eds.), Usability Evaluation in Industry, pp. 6 (1996)
Hassenzahl, M., Burmester, M., Koller, F.: AttrakDiff: Ein Fragebogen zur Messung wahrgenommener hedonischer und pragmatischer Qualität. In: Szwillus, G., Ziegler, J. (eds.) Mensch & Computer 2003. Berichte des German Chapter of the ACM, vol. 57, pp. 187–196. Verlag, Vieweg+Teubner (2003)
Charrier, L., Rieger, A., Galdeano, A., Cordier, A., Lefort, M., Hassas, S.: The RoPE scale: a measure of how empathic a robot is perceived. In: Proceedings of the 14th ACM/IEEE International Conference of Human-Robot Interaction (HRI’19), pp. 656–657. IEEE Press (2019)
Braun, V., Clarke, V.: Using thematic analysis in psychology. Qual. Res. Psychol. 3(2), 77–101 (2006)
Saldaña, J.: The Coding Manual for Qualitative Researchers. SAGE Publications, London, UK (2016)
Nothdurft, F., Ultes, S., Minker, W.: Finding appropriate interaction strategies for proactive dialogue systems—an open quest. In: Proceedings of the 2nd European 5th Nordic Symposium Multimodal Communication, pp. 73–80. Electronic Press, Estonia (2015)
Peng, Z., Kwon, Y., Lu, J., Wu, Z., Ma, X.: Design and evaluation of service robot’s proactivity in decision-making support process. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, paper 98, pp. 1–13. ACM, New York (2019)
Biermann, M., Schweiger, E., Jentsch, M.: Talking to stupid?!? Improving Voice User Interfaces. Mensch und Computer 2019 - Usability Professionals. Gesellschaft für Informatik (2019)
Acknowledgments
The study reported in this publication was supported by FCT– Foundation for Science and Technology nr. 2020.08009. BD and DigiMedia Research Centre, under the project UIDB/05460/2020.
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Marques, T., Ferraz de Abreu, J., Santos, R. (2024). Insights from User Perceptions Towards the Design of a Proactive Intelligent TV Assistant. In: Marcus, A., Rosenzweig, E., Soares, M.M. (eds) Design, User Experience, and Usability. HCII 2024. Lecture Notes in Computer Science, vol 14714. Springer, Cham. https://doi.org/10.1007/978-3-031-61356-2_19
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