Artificial Intelligence Chatbot Adoption Framework for Real-Time Customer Care Support in Kenya

Authors

  • Geoffrey Nyongesa  Department of Information Technology, Masinde Muliro University of Science and Technology, Kakamega Kenya
  • Kelvin Omieno  Department of Information Technology, Masinde Muliro University of Science and Technology, Kakamega Kenya
  • Daniel Otanga  Department of Information Technology and Informatics, Kaimosi Friends University College, Kaimosi, Kenya

DOI:

https://doi.org/10.32628/CSEIT20667

Keywords:

Artificial Intelligence, Chatbot, Customer Experience.

Abstract

In today’s society, most if not all sectors digitize and automate in order to become more efficient. The increasing availability and sophistication of software technologies disrupt labor markets by making workers redundant. Within this context, a significant change is companies’ introduction of chatbots as a supplement to human customer support is vital. Chatbots are computer programs that interact with humans through natural language. The purpose of chatbots is to simulate a human conversation in response to natural language input through text or voice. There seems to be no proper guidelines for adoption of chatbots for provision of customer care support services in telecommunication industry in Kenya. The aim of this research was to develop a framework for adoption of artificially intelligent chatbot application in telecommunication industry. This was achieved through determination of the status of implementation of chatbots in Kenya and identification of key metrics that served as indicators for chatbot adoption. The metrics were identified through review of previous technology adoption frameworks and models. The study adopted mixed methods where qualitative and quantitative data was collected using interview schedules and questionnaires respectively. Content analysis was used in analyzing qualitative data. Quantitative data was analyzed descriptively and results were presented using tables. The target population included experts in the field of AI in two telecommunication firms in Kenya. The study sample was drawn, using Delphi technique, from the two telecommunication firms. The descriptive and principal component analysis were utilized. The results of this research study will be crucial to all telecommunication firms in guiding them on the most effective and efficient way of adopting AI chatbot application for customer support services.

References

  1. Huang, M. H., & Rust, R. T. (2013). IT-related service: A multidisciplinary perspective. Journal of Service Research, 16(3), 251-258.
  2. Rust, R. T., & Huang, M. H. (2014). The service revolution and the transformation of marketing science. Marketing Science, 33(2), 206-221.
  3. Hartmann, PM, Hartmann, PM, Zaki, M., Zaki, M., Feldmann, N., Feldmann, N., ... & Neely, A. (2016). Capturing value from big data-a taxonomy or data-driven business models used by start-up firms. International Journal of Operations & Production Management, 36 (10), 1382-1406.
  4. Kunz, W., Aksoy, L. Bart, Y., Heinonen, K., Kabadayı, S., Ordenes, FV, ... & Theodoulidis, B. (2017). Customer engagement in a big data world. Journal of Services Marketing, 31 (2).
  5. Amin, A., Anwar, S., Adnan, A., Nawaz, M., Alawfi, K., Hussain, A., & Huang, K. (2017). Customer churn prediction in the telecommunication sector using a rough set approach. Neurocomputing, 237, 242-254.
  6. Coussement, K., Lessmann, S., & Verstraeten, G. (2017). A comparative analysis of data preparation algorithms for customer churn prediction: A case study in the telecommunication industry. Decision Support Systems, 95, 27-36.
  7. Hou, R., Wu, J., & Du, H. S. (2017). Customer social network affects marketing strategy: A simulation analysis based on competitive diffusion model. Physica A: Statistical Mechanics and its Applications, 469, 644-653.
  8. Gustafsson, A., Högström, C., Radnor, Z., Friman, M., Heinonen, K., Jaakkola, E., & Mele, C. (2016). Developing service research paving the way to transdisciplinary research. Journal of Service Management, 27 (1), 9-20.
  9. Bijmolt, T. H., Leeflang, P. S., Block, F., Eisenbeiss, M., Hardie, B. G., Lemmens, A., & Saffert, P. (2010). Analytics for customer engagement. Journal of Service Research, 13(3), 341-356.
  10. Demirkan, H., & Parts, D. (2013). Leveraging the capabilities of service-oriented decision support systems: Putting analytics and big data in the cloud. Decision Support Systems, 55 (1), 412-421.
  11. Klaus, P., 2014. Measuring customer experience: How to develop and execute the most profitable customer experience strategies. Springer.
  12. IBM (2016). Using Watson to improve customer service. from: https://www.ibm.com/blogs/watson/2016/04/using-watson-improve-customer-service/
  13. Chakrabarti, C. and Luger, G.F., 2015. Artificial conversations for customer service chatter bots: Architecture, algorithms, and evaluation metrics. Expert Systems with Applications, 42(20), pp.6878-6897.
  14. Heinonen, K., Strandvik, T., Mickelsson, K-J., Edvardsson, B., Sundström, E., and Andersson, P. (2010): A Customer Dominant Logic of Service, Journal of Service Management, 21 (4) 531-548
  15. Ordenes, F., Theodoulidis, B., Burton, J., Gruber, T and Zaki, M. (2014). Analyzing Customer Experience Feedback Using Text Mining: A Linguistics-Based Approach. Journal of Service Research.
  16. Keiningham, T., Ball, J., Benoit, S., Bruce, H. L., Buoye, A., Dzenkovska, J., ... & Zaki, M. (2017). The interplay of customer experience and commitment. Journal of Services Marketing, 31(2).
  17. Walczuch, R., Lemmink, J., & Streukens, S. (2007). The effect of service employees’ technology readiness on technology acceptance. Information & Management, 44(2), 206-215.
  18. Carlson, J., & O'Cass, A. (2010). Exploring the relationships between e-service quality, satisfaction, attitudes and behaviors in content-driven e-service web sites. Journal of Services Marketing, 24 (2), 112-127.
  19. HOLBROOK, Morris, and HIRSCHMAN, Elizabeth. The Experiential Aspects of Consumption: Consumer Fantasies, Feelings, and Fun. Journal of Consumer Research, v. 9, n.2, pp. 132–140, 1982.
  20. MEYER, Christopher, and SCHWAGER, Andre. Understanding Customer Experience. Harvard Business Review, 2007. Available at: https://hbr.org/2007/02/understanding-customer-experience.
  21. ZWILLING, Martin. Customer Experience’ Is Today’s Business Benchmark. Forbes, 2014. Available at: https://www.forbes.com/sites/martinzwilling/2014/03/10/customer-experience-is-todays-business-benchmark/#4a190d8e5011.
  22. ALLEN, James, REICHHELD, Frederick, and HAMILTON, Barney. The Three “Ds” of Customer Service. Harvard Business School, 2005. Available at: http://hbswk.hbs. edu/archive/5075.html.
  23. WANG, Yonggui, LO, Hing-Po, and YANG, Yongheng. An integrated framework for service quality, customer value, satisfaction: Evidence from China’s telecommunication industry. Information systems frontiers, v. 6, n. 4, pp. 325-340, 2004.
  24. KUO, Ying-Feng, WU, Chi-Ming, and DENG, Wei-Jaw. The relationships among service quality, perceived value, customer satisfaction, and post-purchase intention in mobile value-added services. Computers in Human Behaviour, v. 25, n. 4, pp. 887– 896, 2009.
  25. KIM, Moon-Koo, PARK, M Myeong-Cheol, and JEONG, Dong-Heon. The effects of customer satisfaction and switching barrier on customer loyalty in Korean mobile telecommunication services. Telecommunications policy, v. 28, n.2, pp. 145-159, 2004.
  26. MICIACK, Alan and DESMARAIS, Mike. Benchmarking service quality performance at business-to-business and business-to-consumer call centers. The Journal of Business & Industrial Marketing, v. 16, n. 5, pp. 340-53, 2001.
  27. SANTOURIDIS, Ilias, and TRIVELLAS, Panagiotis. Investigating the impact of service quality and customer satisfaction on customer loyalty in mobile telephony in Greece. The TQM Journal, v. 22, n. 3, pp. 330-343, 2010.
  28. Michael, J and Mitchell, T. (2015), “Machine learning: Trends, perspectives, and prospects.” Science 349(6245): 255.
  29. Alan Turing, "Computing Machinery and Intelligence".(Turing 1950) In 1956, at the original Dartmouth AI summer conference, Ray Solomonoff, "An Inductive Inference Machine".(Solomonoff 1956)
  30. Tom Mitchell: "A computer program is set to learn from an experience E with respect to some task T and some performance measure P if its performance on T as measured by P improves with experience E.
  31. Luger, George; Stubblefield, William (2004). Artificial Intelligence: Structures and Strategies for Complex Problem Solving (5th ed.). Benjamin/Cummings. ISBN 978-0-8053-4780-7.
  32. Jordan, M. I.; Mitchell, T. M. (16 July 2015). "Machine learning: Trends, perspectives, and prospects". Science. 349 (6245): 255–260. Bibcode:2015Sci...349255J. doi:10.1126/science.aaa8415. PMID 26185243.
  33. Russell, Stuart J.; Norvig, Peter (2003), Artificial Intelligence: A Modern Approach (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, ISBN 0-13-790395-2
  34. Poole, David; Mackworth, Alan; Goebel, Randy (1998). Computational Intelligence: A Logical Approach. New York: Oxford University Press. ISBN 978-0-19-510270-3.
  35. Mittal, "Versatile question answering systems: seeing in synthesis" Archived 1 February 2016 at the Wayback Machine, IJIIDS, 5(2), 119–142, 2011
  36. Russell & Norvig. Applications of natural language processing, including information retrieval (i.e. text mining) and machine translation: 2003, pp. 840–857, Luger & Stubblefield 2004, pp. 623–630
  37. Cambria, Erik; White, Bebo (May 2014). "Jumping NLP Curves: A Review of Natural Language Processing Research Review Article]". IEEE Computational Intelligence Magazine. 9 (2): 48–57. doi:10.1109/MCI.2014.2307227.
  38. John Dowding, Jean Mark Gawron, Doug Applet, John Bear, Lynn Cherney, Robert Moore, Douglas Moran "GEMINI: A NATURAL LANGUAGE SYSTEM FOR SPOKEN-LANGUAGE UNDERSTANDING", SRI International, 333 Ravenswood Avenue, Menlo Park, CA 94025.
  39. Computer Science and Technology Board ^ National Research Council (ed.): 1997, More Than Screen Deep: Toward Every-Citizen Interfaces to the Nation's Information Infrastructure, National Academy Press, Washington, D.C.
  40. Helander, M. G., Landauer, T. K., and Prabhu, P. V. (Eds.): 1997, Handbook of Human-Computer Interaction, (Second, Completely Revised ed.), Elsevier Science Ltd., Amsterdam.
  41. Fischer, G.: 1993a, beyond human computer interaction: Designing useful and usable computational environments. In: People and Computers VIII: Proceedings of the HCI'93 Conference (Loughborough, England), Cambridge University Press, Cambridge, UK, pp. 17^31.
  42. Schermerhorn, R. Cantrell, M. Scheutz, and X. Wu (2010), Robust Spoken Instruction Understanding for HCI. Bloomington: Indiana University.
  43. R. Cole, L. Hirschman, et al. (1995), The Challenge of Spoken Language Systems: Research Directions for the Nineties. Oregon: Graduate Institute.
  44. Abu Shawar, B., & Atwell, E. (2007). Chatbots: Are they really useful? Journal for Language Technology and Computational Linguistics, 22(1), 29-49. Retrieved from http://www.jlcl.org/2007_Heft1/Bayan_Abu-Shawar_and_Eric_Atwell.pdf
  45. Brandtzaeg, P. B., & Følstad, A. (2017). Why people use chatbots. In I. Kompatsiaris, J. Cave, A. Satsiou, G. Carle, A. Passani, E. Kontopoulos, S. Diplaris, & D. McMillan (Eds.), Internet Science: 4th International Conference, INSCI 2017 (pp. 377-392). Cham: Springer
  46. Accenture. (2016). Chatbots in Customer Service Retrieved from https://www.accenture.com/t00010101T000000__w__/br-pt/_acnmedia/PDF-45/Accenture-Chatbots-Customer-Service.pdf
  47. 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. doi: 10.1016/j.chb.2006.02.017
  48. Crutzen, R., Peters, G. J., Portugal, S. D., Fisser, E. M., & Grolleman, J. J. (2011). An artificially intelligent chat agent that answers adolescents' questions related to sex, drugs, and alcohol: An exploratory study. Journal of Adolescent Health, 48(5), 514-519. doi: 10.1016/j.jadohealth.2010.09.002
  49. Brynjolfsson, E., & McAfee, A. (2017). Machine, Platform, Crowd. New York, USA: W.W. Norton & Company.
  50. Fryer, L., & Carpenter, R. (2006). Bots as language learning tools. Language Learning & Technology, 10(3), 8-14. doi:10125/44068.
  51. Monsen, P. A. M. (2018). Rekruttering med Chatbot. Retrieved from https://hrnorge.no/aktuelt/rekruttering-med-chatbot.
  52. Weizenbaum, J. (1966). ELIZA - A computer program for the study of natural language communication between man and machine. Communications of the ACM, 9(1), 36-45. doi:10.1145/365153.365168
  53. Brandtzaeg, P. B., & Følstad, A. (2017). Why people use chatbots. In I. Kompatsiaris, J. Cave, A. Satsiou, G. Carle, A. Passani, E. Kontopoulos, S. Diplaris, & D. McMillan (Eds.), Internet Science: 4th International Conference, INSCI 2017 (pp. 377-392). Cham: Springer.
  54. Zumstein, D., & Hundertmark, S. (2017). Chatbots - An interactive technology for personalized communication, transactions and services. IADIS International Journal on WWW/Internet, 15(1), 96-109. Retrieved from http://www.iadisportal.org/ijwi/papers/2017151107.pdf.
  55. Servion. (2017). AI will power 95% of customer interactions by 2025. Retrieved from http://servion.com/blog/ai-will-power-95-customer-interactions-2025/
  56. Simonite, T. (2017). Facebook’s perfect, impossible chatbot. from MIT Technology Review https://www.technologyreview.com/s/604117/facebooks-perfect-impossible-chatbot/
  57. Coniam, D. (2014). The linguistic accuracy of chatbots: Usability from an ESL Perspective. Text Talk, 34(5), 545-567. doi:10.1515/text-2014-0018.
  58. Atwell, E., & Shawar, B.A. (2007). Chatbots: Are they Really Useful? LDV Forum, 22, 29-49.
  59. Mott, B., Lester, J., & Branting, K. (2004). Conversational Agents. The Practical Handbook of Internet Computing. doi:10.1201/9780203507223.ch10
  60. Kerly, A., Hall, P., & Bull, S. (2007). Bringing Chatbots into education: Towards Natural Language Negotiation of Open Learner Models. Applications and Innovations in Intelligent Systems XIV, 179-192. doi:10.1007/978-1- 84628-666-7_14
  61. Bickmore, T. W., Schulman, D., & Sidner, C. (2013). Automated interventions for multiple health behaviors using conversational agents. Patient Education and counseling, 92(2), 142-148. doi:10.1016/j.pec2013. 05.011
  62. Haubl, G., & Trifts, V. (2000). Consumer Decision Making in Online Shopping Environments: The Effects of Interactive Decision Aids. Marketing Science, 19(1), 4-21. doi:10.1287/mksc.19.1.4.15178
  63. Rowley, J. (2000). Product searching with shopping bots. Internet Research, 10(3), 203-214. doi:10.1108/10662240010331957
  64. Sadeddin, K. W., Serenko, A., & Hayes, J. (2007). Online shopping bots for electronic commerce: the comparison of functionality and performance. IJEB, 5(6), 576. doi:10.1504/ijeb.2007.016472
  65. Mhatre, N., Motani, K., Shah, M., & Mali, S. (2016). Donna Interactive Chat-bot acting as a Personal Assistant. International Journal of Computer Applications, 140(10), 6-11. doi:10.5120/ijca2016909460.
  66. Griol, D., Carbó, J., & Molina, J. M. (2013). AN AUTOMATIC DIALOG SIMULATION TECHNIQUE TO DEVELOPAND EVALUATE INTERACTIVE CONVERSATIONAL AGENTS. Applied Artificial Intelligence, 27(9), 759-780.doi:10.1080/ 08839514.2013.835230
  67. Kuligowska, K. (2015). Commercial Chatbot: Performance Evaluation, Usability Metrics and Quality Standards of Embodied Conversational Agents. PCBR, 2(02), 1-16. doi:10.18483/pcbr.22.
  68. CB Insights. The race for AI: Google, Twitter, Intel, Apple in a rush to grab artificial intelligence startups Internet]. New York: CB Insights. 2016 Dec 6 cited 2016 Dec 10]. Available from: https://www.cbinsights.com/blog/topacquirers-ai-startups-ma-timeline/.
  69. Kepuska, Veton, and Gamal Bohouta. "Comparing speech recognition systems (Microsoft API, Google API and CMU Sphinx)." Int. J. Eng. Res. Appl 7.03 (2017): 20-24.
  70. Hebert, Martial H., Charles E. Thorpe, and Anthony Stentz, eds. Intelligent unmanned ground vehicles: autonomous navigation research at Carnegie Mellon. Vol. 388. Springer Science & Business Media, 2012.
  71. Rahim, Siti Rohaya Mat, et al. "Artificial intelligence, smart contract and islamic finance." Asian Social Science 14.2 (2018): 145.
  72. Ferrucci D, Levas A, Bagchi S, Gondek D, Mueller ET. Watson: beyond jeopardy! Artif Intell 2013;199–200:93–105.
  73. https://eastafricadigitalmarketers.com/chatbot-messenger-development/.
  74. https://medium.com/@RateMyService/chatbotcx-9182f43552f3.
  75. https://medium.com/@RateMyService/chatbotcx-9182f43552f3.
  76. Nwosu, John Nwachukwu. "An Investigation into the Extent of the Use of Artificial Intelligence in Nigeria Banks."
  77. J. Bizimungu, "Babyl’s chatbot to enhance digital healthcare platform", The New Times Rwanda, Jan 2018, online] Available: https://www.newtimes.co.rw/section/read/227369.
  78. https://medium.com/@RateMyService/chatbotcx-9182f43552f3.
  79. https://www.cio.co.ke/ai-chatbot-proves-to-be-a-useful-teacher-in-farming-and-agronomy/.
  80. https://technobraingroup.com/digital/artificial-intelligence-machine-learning/chatbots/.
  81. Saunders M, Lewis P, Thornhill A. Research methods for business students. Pearson education; 2009.
  82. Mugenda A. Social science research: Conception, methodology and analysis. Nairobi: Kenya Applied Research and Training Services. 2008.
  83. Porter S, Woodbine G. The effect of ethics courses on the ethical judgement‐making ability of Malaysian accounting students. Journal of Financial Reporting and Accounting. 2010 Oct 26.

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2020-12-30

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[1]
Geoffrey Nyongesa, Kelvin Omieno, Daniel Otanga, " Artificial Intelligence Chatbot Adoption Framework for Real-Time Customer Care Support in Kenya" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 6, Issue 6, pp.100-117, November-December-2020. Available at doi : https://doi.org/10.32628/CSEIT20667