The Role of Chatbots in Academic Libraries: An Experience-based Perspective

References

• Ali, M., Naeem, S., & Bhatti, R. (2020). Artificial intelligence tools and perspectives of university librarians: An overview. Business Information Review, 37(3), 116–124. https://doi.org/10.1177/0266382120952016
   Google Scholar
• André, E., & Rist, T. (2000). Adding life-like synthetic characters to the web. In M. Klusch & L. Kerschberg (Eds.), Cooperative information agents IV – The future of information agents in cyberspace. CIA 2000. Lecture notes in computer science. Vol. 1860, pp. 51–89 Springer.
   Google Scholar
• Araujo, T. (2018). Living up to the chatbot hype: The influence of anthropomorphic design cues and communicative agency framing on conversational agent and company perceptions. Computers in Human Behavior, 85, 183–189. https://doi.org/10.1016/j.chb.2018.03.051
   Web of Science ®Google Scholar
• Arlitsch, K., & Newell, B. (2017). Thriving in the age of accelerations: A brief look at the societal effects of artificial intelligence and the opportunities for libraries. Journal of Library Administration, 57(7), 789–798. https://doi.org/10.1080/01930826.2017.1362912
   Web of Science ®Google Scholar
• Battarbee, K. (2003). Defining co-experience. Proceedings of the 2003 International Conference on Designing Pleasurable Products and Interfaces – DPPI ‘03. doi:10.1145/782896.782923.
   Google Scholar
• Brekhus, W. H., Galliher, J. F., & Gubrium, J. F. (2005). The need for thin description. Qualitative Inquiry, 16(6), 1–19. https://doi.org/10.1177/1077800405280663
   Google Scholar
• Bryman, A. (2004). Encyclopedia of social science research methods. Encyclopedia of Social Science Research Methods, 1143, 1144. https://doi.org/10.4135/9781412950589
   Google Scholar
• Candello, H., Pinhanez, C., & Figueiredo, F. (2017). Typefaces and the perception of humanness in natural language chatbots. In C. Lampe, M. C. Schraefel, J. P. Hourcade, C. Appert, & D. Wigdor (Eds.), Proceedings of the 2017 CHI conference on human factors in computing systems (pp. 3476–3487). Association for Computing Machinery.
   Google Scholar
• Chen, R., & He, F. (2003). Examination of brand knowledge, perceived risk and consumers’ intention to adopt an online retailer. Total Quality Management & Business Excellence, 14(6), 677–693. https://doi.org/10.1080/1478336032000053825
   Web of Science ®Google Scholar
• Chopra, S., Gianforte, R., & Sholar, J. (2016). Meet Percy: The CS 221 teaching assistant chatbot. ACM Transactions on Graphics, 1(1), 1–8.
   Google Scholar
• Chowdhury, G. (2003). Natural language processing. Annual Review of Information Science and Technology, 37(1), 51–89. ISSN 0066-4200. https://doi.org/10.1002/aris.1440370103
   Google Scholar
• Cox, A. M., Pinfield, S., & Rutter, S. (2018). The intelligent library: Thought leaders’ views on the likely impact of artificial intelligence on academic libraries. Library Hi Tech, 37(3), 418–435. https://doi.org/10.1108/LHT-08-2018-0105
   Web of Science ®Google Scholar
• Cox, D. F., & Rich, S. U. (1964). Perceived risk and consumer decision-making – the case of telephone shopping. Journal of Marketing Research, 1(4), 32–39. https://doi.org/10.1177/002224376400100405
   Web of Science ®Google Scholar
• Denzin, N. K., & Lincoln, Y. S. (Eds.). (2003). The landscape of qualitative research: Theories and issues (2nd ed.). SAGE Publications, Inc.
   Google Scholar
• De Visser, E. J., Monfort, S. S., McKendrick, R., Smith, M. A., McKnight, P. E., Krueger, F., & Parasuraman, R. (2016). Almost human: Anthropomorphism increases trust resilience in cognitive agents. Journal of Experimental Psychology: Applied, 22(3), 331–349. https://doi.org/10.1037/xap0000092
   PubMed Web of Science ®Google Scholar
• Dreyer, T. (2016). Survey finds half of consumers would prefer to conduct all customer service via messaging. Retrieved April, 2022, from https://venturebeat.com/2016/12/12/new-survey-andinsights-about-what-consumers-want-in-a-chatbot/
   Google Scholar
• Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-services adoption: A perceived risk facets perspective. International Journal of Human-Computer Studies, 59(4), 451–474. https://doi.org/10.1016/S1071-5819(03)00111-3
   Web of Science ®Google Scholar
• Forsythe, S. M., & Shi, B. (2003). Consumer patronage and risk perceptions in internet shopping. Journal of Business Research, 56(11), 867–875. https://doi.org/10.1016/S0148-2963(01)00273-9
   Web of Science ®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
• Grönroos, C., & Voima, P. (2013). Critical service logic: Making sense of value creation and co-creation. Journal of the Academy of Marketing Science, 41(2), 133–150. https://doi.org/10.1007/s11747-012-0308-3
   Web of Science ®Google Scholar
• Hassenzahl, M. (2013). User experience and experience design. In The encyclopedia of human-computer interaction (Chap. 3, 2nd ed.). Interaction Design Foundation. https://www.interaction-design.org/literature/book/the-encyclopedia-of-human-computer-interaction-2nd-ed/user-experience-and-experience-design
   Google Scholar
• Hassenzahl, M., & Tractinsky, N. (2006). User experience-a research agenda. Behaviour & Information Technology, 25(2), 91–97. https://doi.org/10.1080/01449290500330331
   Web of Science ®Google Scholar
• Hervieux, S., & Wheatley, A. (2021). Perceptions of artificial intelligence: A survey of academic librarians in Canada and the United States. The Journal of Academic Librarianship, 47(1), 102270. https://doi.org/10.1016/j.acalib.2020.102270
   Web of Science ®Google Scholar
• Janssen, C. P., Donker, S. F., Brumby, D. P., & Kun, A. L. (2019). History and future of human-automation interaction. International Journal of Human-Computer Studies, 131, 99–107. https://doi.org/10.1016/j.ijhcs.2019.05.006
   Web of Science ®Google Scholar
• Kaushal, V., & Yadav, R. (2020). Understanding customer experience of culinary tourism through food tours of Delhi. International Journal of Tourism Cities, 7(3), 683–701. https://doi.org/10.1108/IJTC-08-2019-0135
   Web of Science ®Google Scholar
• Kaushal, V., & Yadav, R. (2021). Delivering Superior Customer Experience through New-Age Technologies. In Industry 4.0 Technologies for Business Excellence, (pp. 47–60). CRC Press.
   Google Scholar
• Kimani, E., Rowan, K., McDuff, D., Czerwinski, M., & Mark, G. (2019, September 3–6). A conversational agent in support of productivity and wellbeing at work. 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII) (pp. 1–7).
   Google Scholar
• Kothari, C. R. (2004). Research methodology: Methods and techniques (2nd ed.). New Age International.
   Google Scholar
• Laforet, S., & Li, X. (2005). Consumers’ attitudes towards online and mobile banking in China. International Journal of Bank Marketing, 23(5), 362–380. https://doi.org/10.1108/02652320510629250
   Google Scholar
• Littler, D., & Melanthiou, D. (2006). Consumer perceptions of risk and uncertainty and the implications for behaviour towards innovative retail services: The case of internet banking. Journal of Retailing and Consumer Services, 13(6), 431–443. https://doi.org/10.1016/j.jretconser.2006.02.006
   Google Scholar
• Longoni, C., Bonezzi, A., & Morewedge, C. (2019). Resistance to medical artificial intelligence. Journal of Consumer Research, 46(4), 629–650. https://doi.org/10.1093/jcr/ucz013
   Web of Science ®Google Scholar
• Lopez-Nicolas, C., & Molina-Castillo, F. J. (2008). Customer knowledge management and E-commerce: The role of customer perceived risk. International Journal of Information Management, 28(2), 102–113. https://doi.org/10.1016/j.ijinfomgt.2007.09.001
   Web of Science ®Google Scholar
• Luckin, R. (2017). Towards artificial intelligence-based assessment systems. Nature Human Behaviour, 1(3), 0028. https://doi.org/10.1038/s41562-016-0028
   Web of Science ®Google Scholar
• Martins, C., Oliveira, T., & Popovič, A. (2014). Understanding the internet banking adoption: A unified theory of acceptance and use of technology and perceived risk application. International Journal of Information Management, 34(1), 1–13. https://doi.org/10.1016/j.ijinfomgt.2013.06.002
   Web of Science ®Google Scholar
• Mckie, I. A. S., & Narayan, B. (2019). Enhancing the academic library experience with chatbots: An exploration of research and implications for practice. Journal of the Australian Library and Information Association, 68(3), 268–277. https://doi.org/10.1080/24750158.2019.1611694
   Web of Science ®Google Scholar
• McNeal, M. L., & Newyear, D. (2013). Introducing chatbots in libraries. Library Technology Reports, 49(8), 5–10.
   Google Scholar
• Murray, J., Elms, J., & Curran, M. (2019). Examining empathy and responsiveness in a high-service context. International Journal of Retail & Distribution Management, 47(12), 1364–1378. https://doi.org/10.1108/IJRDM-01-2019-0016
   Web of Science ®Google Scholar
• Novak, T. P., Hoffman, D. L., & Yung, Y. (2000). Measuring the customer experience in online environments: A structural modeling approach. Marketing Science, 19(1), 22–42. https://doi.org/10.1287/mksc.19.1.22.15184
   Web of Science ®Google Scholar
• Powell, P. A., & Roberts, J. (2017). Situational determinants of cognitive, affective, and compassionate empathy in naturalistic digital interactions. Computers in Human Behavior, 68, 137–148. https://doi.org/10.1016/j.chb.2016.11.024
   Web of Science ®Google Scholar
• Qian, L., & Yin, J. (2017). Linking Chinese cultural values and the adoption of electric vehicles: The mediating role of ethical evaluation. Transportation Research Part D: Transport And Environment, 56, 175–188. https://doi.org/10.1016/j.trd.2017.07.029
   Web of Science ®Google Scholar
• QSR International. (2018a). About coding. WWW Document, QSR Int. http://help-nv11. qsrinternational.com/desktop/concepts/about_coding.html
   Google Scholar
• Reeves, B., & Nass, C. (1996). The media equation: How people treat computers, television, and new media like real people and places. Cambridge University Press.
   Google Scholar
• Reynolds, T. J., & Gutman, J. (1988). Laddering theory, method, analysis, and interpretation. Journal of Advertising Research, 28, 11–31.
   Google Scholar
• Rubin, V. L., Chen, Y., & Thorimbert, L. M. (2010). Artificially intelligent conversational agents in libraries. Library Hi Tech, 28, 11–31. https://doi.org/10.1108/07378831011096196
   Web of Science ®Google Scholar
• Saunders, B., Sim, J., Kingstone, T., Baker, S., Waterfield, J., Bartlam, B., & Jinks, C. (2018). Saturation in qualitative research: Exploring its conceptualization and operationalization. Quality & Quantity, 52(4), 1893–1907. https://doi.org/10.1007/s11135-017-0574-8
   PubMed Web of Science ®Google Scholar
• Shorey, S., Ang, E., Yap, J., Ng, E. D., Lau, S. T., & Chui, C. K. (2019). A virtual counseling application using artificial intelligence for communication skills training in nursing education: development study. Journal of medical Internet research, 21(10), e14658. https://doi.org/10.2196/14658
   PubMed Web of Science ®Google Scholar
• Singh, A., Ramasubramanian, K., & Shivam, S. (2019). Introduction to Microsoft Bot, RASA, and Google Dialogflow. In Building an enterprise chatbot (pp. 281–302). Apress.
   Google Scholar
• Smutny, P., & Schreiberova, P. (2020). Chatbots for learning: A review of educational chatbots for the Facebook messenger. Computers & Education, 151, 103862. https://doi.org/10.1016/j.compedu.2020.103862
   Web of Science ®Google Scholar
• Suresh, A. M., & Shashikala, R. (2011, August 19–21). Identifying factors of consumer perceived risk towards online shopping in India. 3rd International Conference on Information and Financial Engineering IPEDR (Vol. 12, pp. 336–341). Shanghai, China.
   Google Scholar
• Talley, N. B. (2016). Imagining the use of intelligent agents and artificial intelligence in Academic Law Libraries*. Law Library Journal, 108(3), 383. https://doi.org/10.7282/T3Z03C65
   Web of Science ®Google Scholar
• Tandon, U., Kiran, R., & Sah, A. N. (2016). Understanding online shopping adoption in India: Unified theory of acceptance and use of technology 2 (UTAUT2) with perceived risk application. Service Science, 8(4), 420–437. https://doi.org/10.1287/serv.2016.0154
   Web of Science ®Google Scholar
• Taylor, J. W. (1974). The role of risk in consumer behaviour: A comprehensive and operational theory of risk taking in consumer behaviour. Journal of Marketing, 38(2), 54–60. https://doi.org/10.1177/002224297403800211
   Web of Science ®Google Scholar
• Van Doorn, J., Mende, M., Noble, S. M., Hulland, J., Ostrom, A. L., Grewal, D., & Petersen, J. A. (2017). Domo arigato Mr. Roboto: Emergence of automated social presence in organizational frontlines and customers’ service experiences. Journal of Service Research, 20(1), 43–58. https://doi.org/10.1177/1094670516679272
   Web of Science ®Google Scholar
• Verhoef, P. C., Lemon, K. N., Parasuraman, A., Roggeveen, A., Tsiros, M., & Schlesinger, L. A. (2009). Customer experience creation: Determinants, dynamics and management strategies. Journal of Retailing, 85(1), 31–41. https://doi.org/10.1016/j.jretai.2008.11.001
   Web of Science ®Google Scholar
• Vincze, J. (2017). Virtual reference librarians (chatbots). Library Hi Tech News, 34(4), 5–8. https://doi.org/10.1108/LHTN-03-2017-0016
   Google Scholar
• Walter, S., & Lankes, D. R. (2015). The innovation agenda. College & Research Libraries, 76(7), 854–858. https://doi.org/10.5860/crl.76.7.854
   Web of Science ®Google Scholar
• Wang, Y., Gu, J., Wang, S., & Wang, J. (2019). Understanding consumers’ willingness to use ride-sharing services: The roles of perceived value and perceived risk. Transportation Research Part C: Emerging Technologies, 105, 504–519. https://doi.org/10.1016/j.trc.2019.05.044
   Web of Science ®Google Scholar
• Watson, H. J. (2017). Preparing for the cognitive generation of decision support. MIS Quarterly Executive, 16(3). https://aisel.aisnet.org/misqe/vol16/iss3/3
   Web of Science ®Google Scholar
• Wiak, S., & Kosiorowski, P. (2010, June 17–19). The use of psycholinguistics rules in case of creating an intelligent chatterbot. International Conference on Artificial Intelligence and Soft Computing, Poland, pp. 689–697. Springer, Berlin, Heidelberg.
   Google Scholar
• Wood, C. M., & Scheer, L. K. (1996). Incorporating perceived risk into models of consumer deal assessment and purchase intent. In K. P. Corfman & J. G. Lynch, Jr (Eds.), Advances in consumer research. Association for Consumer Research, Vol. 23, pp. 399–404.
   Google Scholar
• Wood, D. A., & Evans, D. J. (2018). Librarians’ perceptions of artificial intelligence and Its potential impact on the profession. Computers in Libraries, 38(1), 26–30.
   Google Scholar
• Złotowski, J., Yogeeswaran, K., & Bartneck, C. (2017). Can we control it? Autonomous robots threaten human identity, uniqueness, safety, and resources. International Journal of Human Computer Studies, 100, 48–54. https://doi.org/10.1016/j.ijhcs.2016.12.008
   Web of Science ®Google Scholar