Abstract
The present study advances our understanding of human-AI interactions, by identifying and analyzing chatbot affordances in prior research. The results of this review consolidate research findings on chatbots’ affordances, which must be taken into consideration when chatbot-based services are designed and deployed. Specifically, the review of state-of-the-art literature led to the identification of nine high level affordances: Human Like Conversing, Assistance Provision, Facilitation, Distilling Information, Enriching Information, Context Identification, Personalization, Fostering Familiarity and Ensuring Privacy. Our contribution is twofold. First, we map the chatbot affordances identified in prior research and group them in higher-level, overarching affordances through a thematic analysis. Furthermore, we identify areas for future research providing a foundation for researchers aiming to engage with the research area.
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References
Nuruzzaman, M., Hussain, O.K.: IntelliBot: a dialogue-based chatbot for the insurance industry. Knowl-Based Syst. 196, 105810 (2020)
Stoeckli, E., Dremel, C., Uebernickel, F., Brenner, W.: How affordances of chatbots cross the chasm between social and traditional enterprise systems. Electron. Markets 30(2), 369–403 (2020)
Mehr, H., Harvard Ash Center Technology & Democracy Fellow: Artificial Intelligence for Citizen Services and Government. Ash Center for Democratic Governance and Innovation, Harvard Kennedy School, August 2017, pp. 1–12 (2017)
Androutsopoulou, A., et al.: Transforming the communication between citizens and government through AI-guided chatbots. Gov. Inf. Q. 36(2), 358–367 (2019)
Vassilakopoulou, P., Pappas, I.O.: Streamlining chatbot – chat employee interaction: an exploratory study. In: Information and Communication Technologies in Organizations and Society (ICTO 2020). Springer (2020)
Poser, M., Singh, S., Bittner, E.: Hybrid Service Recovery: Design for Seamless Inquiry Handovers between Conversational Agents and Human Service Agents. ScholarSpace (2021)
Gatt, A., Krahmer, E.: Survey of the State of the Art in natural language generation: core tasks, applications and evaluation. J. Artif. Intell. Res. 61, 65–170 (2018)
Juniper Research: Chatbots: Vendor Opportunities & Market Forecasts 2020–2024 (2020)
Feine, J., Morana, S., Gnewuch, U.: Measuring Service Encounter Satisfaction with Customer Service Chatbots Using Sentiment Analysis. AIS eLibrary (2019)
National Association of State Chief Information Officers (NASCIO): Chat with us: How States are Using Chatbots to Respond to the Demands of COVID-19 (2020). https://www.nascio.org/wp-content/uploads/2020/06/NASCIO_ChatbotsRespondtoCOVID-19.pdf.
Amershi, S., et al.: Guidelines for human-AI interaction. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. ACM, New York (2019)
Følstad, A., Nordheim, C.B., Bjørkli, C.A.: What makes users trust a chatbot for customer service? An exploratory interview study. In: Bodrunova, S.S. (ed.) INSCI 2018. LNCS, vol. 11193, pp. 194–208. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01437-7_16
Diederich, S., et al.: Emulating empathetic behavior in online service encounters with sentiment-adaptive responses: insights from an experiment with a conversational agent (2019)
Rai, A., Constantinides, P., Sarker, S.: Editor’s comments: next-generation digital platforms: toward human–AI hybrids. MIS Q. 43(1), iii–x (2019)
Gibson, J.J.: The theory of affordances. In: Perceiving, Acting and Knowing, pp. 67–82. Lawrence Erlbaum Associates, Mahwah (1977)
Markus, M.L., Silver, M.S.: A foundation for the study of IT effects: a new look at DeSanctis and Poole’s concepts of structural features and spirit. J. Assoc. Inf. Syst. 9(10), 5 (2008)
Kitchenham, B.: Procedures for Performing Systematic Reviews. Keele University Technical Report, UK, 2004, TR/SE-0401, pp. 1–26 (2004)
Webster, J., Watson, R.T.: Analyzing the past to prepare for the future: writing a literature review. MIS Q. xiii–xxiii (2002)
Lunberry, D., Liebenau, J.: Human or machine? a study of anthropomorphism through an affordance lens. In: Metallo, C., Ferrara, M., Lazazzara, A., Za, S. (eds.) Digital Transformation and Human Behavior. LNISO, vol. 37, pp. 201–215. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-47539-0_15
Waizenegger, L., et al.: Conversational agents - exploring generative mechanisms and second-hand effects of actualized technology affordances. In: Hawaii International Conference on System Sciences (HICSS), p. 10 (2020)
Moussawi, S.: User experiences with personal intelligent agents: a sensory, physical, functional and cognitive affordances view. In: Proceedings of the 2018 ACM SIGMIS Conference on Computers and People Research, Buffalo-Niagara Falls, pp. 86–92. Association for Computing Machinery (2018)
Lippert, A., Gatewood, J., Cai, Z., Graesser, A.C.: Using an adaptive intelligent tutoring system to promote learning affordances for adults with low literacy skills. In: Sottilare, R.A., Schwarz, J. (eds.) HCII 2019. LNCS, vol. 11597, pp. 327–339. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-22341-0_26
Stoeckli, E., Uebernickel, F., Brenner, W.: Exploring affordances of slack integrations and their actualization within enterprises – towards an understanding of how chatbots create value. In: Hawaii International Conference on System Sciences (HICSS), p. 10 (2018)
Knote, R., et al.: Value co-creation in smart services: a functional affordances perspective on smart personal assistants. J. Assoc. Inf. Syst. 22, 78 (2020)
Meske, C., Amojo, I., Thapa, D.: Understanding the affordances of conversational agents in mental mobile health services. In: ICIS 2020 Proceedings (2020)
Barnett, A., et al.: Enacting ‘more-than-human’ care: clients’ and counsellors’ views on the multiple affordances of chatbots in alcohol and other drug counselling. Int. J. Drug Policy 102910 (2020)
Pappas, I.O., Mikalef, P., Giannakos, M.N., Krogstie, J., Lekakos, G.: Big data and business analytics ecosystems: paving the way towards digital transformation and sustainable societies. IseB 16(3), 479–491 (2018)
Pappas, I.O., et al.: Social innovation and social entrepreneurship through big data: developing a research agenda. In: 11th Mediterranean Conference on Information Systems (MCIS), Genoa, Italy (2017)
Vassilakopoulou, P., Hustad, E.: Bridging digital divides: a literature review and research agenda for information systems research. Inf. Syst. Front. (2021). https://doi.org/10.1007/s10796-020-10096-3
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Mygland, M.J., Schibbye, M., Pappas, I.O., Vassilakopoulou, P. (2021). Affordances in Human-Chatbot Interaction: A Review of the Literature. In: Dennehy, D., Griva, A., Pouloudi, N., Dwivedi, Y.K., Pappas, I., Mäntymäki, M. (eds) Responsible AI and Analytics for an Ethical and Inclusive Digitized Society. I3E 2021. Lecture Notes in Computer Science(), vol 12896. Springer, Cham. https://doi.org/10.1007/978-3-030-85447-8_1
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DOI: https://doi.org/10.1007/978-3-030-85447-8_1
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