Skip to main content
SpringerLink
Log in

Cognitive Chatbot for Personalised Contextual Customer Service: Behind the Scene and beyond the Hype

  • Published:
Information Systems Frontiers Aims and scope Submit manuscript

Abstract

With the proliferation of the use of chatbots across industries, business-to-business (B2B) businesses have started using cognitive chatbots for improved customer service which signifies our research. By extending the Technology Acceptance Model and Information Systems Success Model, this study examines personalised contextual customer service using cognitive chatbot. A quantitative research method is applied to the primary data collected from 300 respondents of B2B businesses. The study contributes to the limited research on chatbots and suggests improvement in customer service. The findings provide evidence of high value by customers, particularly while checking for real-time information on reliability and accessibility of products/services. The automated answers to repetitive questions on the recurrent issues create a seamless experience for the customers. This research makes significant theoretical contributions by integrating two models into a simplified model in chatbot literature and manifest that trust affects the willingness to use the cognitive chatbot which drives automation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  • Adam, M., Wessel, M., & Benlian, A. (2020). AI-based chatbots in customer service and their effects on user compliance. Electronic Markets, 1-19.

  • Aggelidis, V. P., & Chatzoglou, P. D. (2009). Using a modified technology acceptance model in hospitals. International Journal of Medical Informatics, 78(2), 115–126.

    Article  Google Scholar 

  • Ahmad, S., Bhatti, S. H., & Hwang, Y. (2019). E-service quality and actual use of e-banking: Explanation through the technology acceptance model. Information Development, 36(4), 503–519.

    Article  Google Scholar 

  • Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In: Action control (pp. 11–39). Springer.

  • Alalwan, A. A., Rana, N. P., Algharabat, R., & Tarhini, A. (2016). A systematic review of extant literature in social media in the marketing perspective. In: Conference on e-business, e-services and e-society (pp. 79–89). Springer.

  • Alalwan, A. A., Baabdullah, A. M., Rana, N. P., Tamilmani, K., & Dwivedi, Y. K. (2018). Examining adoption of mobile internet in Saudi Arabia: Extending TAM with perceived enjoyment, innovativeness and trust. Technology in Society, 55, 100–110.

    Article  Google Scholar 

  • Alam, M. Z., Hoque, M. R., Hu, W., & Barua, Z. (2020). Factors influencing the adoption of mHealth services in a developing country: A patient-centric study. International Journal of Information Management, 50, 128–143.

    Article  Google Scholar 

  • An, M., Lee, C., & Noh, Y. (2010). Risk factors at the travel destination: Their impact on air travel satisfaction and repurchase intention. Service Business, 4(2), 155–166.

    Article  Google Scholar 

  • Anderson, J. C., & Gerbing, D. W. (1984). The effect of sampling error on convergence, improper solutions, and goodness-of-fit indices for maximum likelihood confirmatory factor analysis. Psychometrika, 49(2), 155–173.

    Article  Google Scholar 

  • Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411–423.

    Article  Google Scholar 

  • Anderson, E. W., Fornell, C., & Lehmann, D. R. (1994). Customer satisfaction, market share, and profitability: Findings from Sweden. Journal of Marketing, 58(3), 53–66.

    Article  Google Scholar 

  • Apollo, B. (2019). Understanding B2B Buying Behaviour. Retrieved from https://www.inflexion-point.com/blog/understanding-b2b-buying-behaviour. Accessed 19 August 2020.

  • Ashfaq, M., Yun, J., Yu, S., & Maria, S. (2020). I, Chatbot: Modeling the determinants of users’ satisfaction and continuance intention of AI-powered service agents. Telematics and Informatics, 54, 101473.

    Article  Google Scholar 

  • Avlonitis, G. J., & Panagopoulos, N. G. (2005). Antecedents and consequences of CRM technology acceptance in the sales force. Industrial Marketing Management, 34(4), 355–368.

    Article  Google Scholar 

  • Baier, D., Rese, A., & Röglinger, M. (2018). Conversational user interfaces for online shops? A categorization of use cases. In: Completed research paper, 39th international conference on information systems (ICIS2018), San Francisco, USA. December 13–16, 2018.

  • Bakken, S., Grullon-Figueroa, L., Izquierdo, R., Lee, N. J., Morin, P., Palmas, W., Teresi, J., Weinstock, R., Shea, S., & Starren, J. (2006). Development, validation, and use of English and Spanish versions of the telemedicine satisfaction and usefulness questionnaire. Journal of the American Medical Informatics Association, 13(6), 660–667.

    Article  Google Scholar 

  • Bartlett, M. S. (1954). A note on the multiplying factors for various χ2 approximations. Journal of the Royal Statistical Society: Series B: Methodological, 16(2), 296–298.

    Google Scholar 

  • Bédard, J., & Gendron, Y. (2004). Qualitative research on accounting: Some thoughts on what occurs behind the scene. In: The real life guide to accounting research (pp. 191-206). Elsevier.

  • Berkley, B. J., & Gupta, A. (1994). Improving service quality with information technology. International Journal of Information Management, 14(2), 109–121.

    Article  Google Scholar 

  • Boerman, S. C., Kruikemeier, S., & Zuiderveen Borgesius, F. J. (2017). Online behavioral advertising: A literature review and research agenda. Journal of Advertising, 46(3), 363–376.

    Article  Google Scholar 

  • Bone, S. A., Fombelle, P. W., Ray, K. R., & Lemon, K. N. (2015). How customer participation in B2B peer-to-peer problem-solving communities influences the need for traditional customer service. Journal of Service Research, 18(1), 23–38.

    Article  Google Scholar 

  • Butler, B., Sproull, L., Kiesler, S., & Kraut, R. (2002). Community effort in online groups: Who does the work and why. Leadership at a distance: Research in technologically supported work, 1, 171–194.

  • Cao, M., Zhang, Q., & Seydel, J. (2005). B2C e-commerce web site quality: An empirical examination. Industrial Management & Data Systems, 105(5), 645–661.

    Article  Google Scholar 

  • Carter, L., & Bélanger, F. (2005). The utilization of e-government services: Citizen trust, innovation, and acceptance factors. Information Systems Journal, 15(1), 5–25.

    Article  Google Scholar 

  • Chatterjee, S., Rana, N. P., & Dwivedi, Y. K. (2021). Assessing consumers’ co-production and future participation on value co-creation and business benefit: An FPCB model perspective. Information Systems Frontiers, 1–20.

  • Chau, P. Y., & Hu, P. J. H. (2002). Investigating healthcare professionals’ decisions to accept telemedicine technology: An empirical test of competing theories. Information & Management, 39(4), 297–311.

    Article  Google Scholar 

  • Chen, C. (2006). Identifying significant factors influencing consumer trust in an online travel site. Information Technology & Tourism, 8(3–4), 197–214.

    Article  Google Scholar 

  • Chen, S., Kang, J., Liu, S., & Sun, Y. (2019). Cognitive computing on unstructured data for customer co-innovation. European Journal of Marketing, 54(3), 570–593.

    Article  Google Scholar 

  • Chung, M., Ko, E., Joung, H., & Kim, S. J. (2018). Chatbot e-service and customer satisfaction regarding luxury brands. Journal of Business Research, 177, 587–595.

    Google Scholar 

  • Clikeman, P. M. (1999). Improving information quality. Internal Auditor, 56(3), 32–34.

    Google Scholar 

  • Colace, F., De Santo, M., Pascale, F., Lemma, S., & Lombardi, M. (2017). BotWheels: a Petri Net based Chatbot for Recommending Tires. In DATA, 350–358.

  • Cole, D. A. (1987). Utility of confirmatory factor analysis in test validation research. Journal of Consulting and Clinical Psychology, 55(4), 584–594.

    Article  Google Scholar 

  • Cui, L., Huang, S., Wei, F., Tan, C., Duan, C., & Zhou, M. (2017). Superagent: A customer service chatbot for e-commerce websites. In: Proceedings of ACL 2017, System Demonstrations (pp. 97-102).

  • Cunningham, L. F., Young, C. E., & Lee, M. (2002). Cross-cultural perspectives of service quality and risk in air transportation. Journal of Air Transportation, 7(1), 3–26.

    Google Scholar 

  • Dai, H., & Palvi, P. C. (2009). Mobile commerce adoption in China and the United States: A cross-cultural study. ACM SIGMIS Database: the DATABASE for Advances in Information Systems, 40(4), 43–61.

    Article  Google Scholar 

  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–339.

    Article  Google Scholar 

  • De Graaf, M. M., & Allouch, S. B. (2013). Exploring influencing variables for the acceptance of social robots. Robotics and Autonomous Systems, 61(12), 1476–1486.

    Article  Google Scholar 

  • De, P., Hu, Y. J., & Rahman, M. S. (2018). Avoid these five digital retailing mistakes. MIT Sloan Management Review, 59(3), 1–4.

    Google Scholar 

  • DeLone, W. H., & McLean, E. R. (1992). Information systems success: The quest for the dependent variable. Information Systems Research, 3(1), 60–95.

    Article  Google Scholar 

  • Delone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4), 9–30.

    Article  Google Scholar 

  • Dowling, G. R., & Staelin, R. (1994). A model of perceived risk and intended risk-handling activity. Journal of Consumer Research, 21(1), 119–134.

    Article  Google Scholar 

  • Dwivedi, Y. K., Ismagilova, E., Rana, N. P., & Raman, R. (2021). Social media adoption, usage and impact in business-to-business (B2B) context: A state-of-the-art literature review. Information Systems Frontiers, 1–23.

  • Eren, B. A. (2021). Determinants of customer satisfaction in chatbot use: Evidence from a banking application in Turkey. International Journal of Bank Marketing, 39(2), 294–311.

    Article  Google Scholar 

  • Eyssel, F., Kuchenbrandt, D., & Bobinger, S. (2011). Effects of anticipated human-robot interaction and predictability of robot behavior on perceptions of anthropomorphism. In: Proceedings of the 6th international conference on human-robot interaction (pp. 61-68).

  • Eyssel, F., De Ruiter, L., Kuchenbrandt, D., Bobinger, S., & Hegel, F. (2012). ‘If you sound like me, you must be more human’: On the interplay of robot and user features on human-robot acceptance and anthropomorphism. In: 2012 7th ACM/IEEE international conference on human-robot interaction (HRI) (pp. 125-126). IEEE.

  • Fan, H., & Poole, M. S. (2006). What is personalization? Perspectives on the design and implementation of personalization in information systems. Journal of Organizational Computing and Electronic Commerce, 16(3–4), 179–202.

    Article  Google Scholar 

  • Fan, A., Wu, L., Miao, L., & Mattila, A. S. (2020). When does technology anthropomorphism help alleviate customer dissatisfaction after a service failure?–the moderating role of consumer technology self-efficacy and interdependent self-construal. Journal of Hospitality Marketing & Management, 29(3), 269–290.

    Article  Google Scholar 

  • Ferrettini, G., Escriva, E., Aligon, J., Excoffier, J. B., & Soulé-Dupuy, C. (2021). Coalitional strategies for efficient individual prediction explanation. Information Systems Frontiers, 1–27.

  • Figalist, I., Elsner, C., Bosch, J., & Olsson, H. H. (2019). Customer churn prediction in B2B contexts. In: International conference on software business (pp. 378–386). Springer.

  • Finch, J. F., & West, S. G. (1997). The investigation of personality structure: Statistical models. Journal of Research in Personality, 31(4), 439–485.

    Article  Google Scholar 

  • Følstad, A., Nordheim, C. B., & Bjørkli, C. A. (2018). What makes users trust a chatbot for customer service? An exploratory interview study. In: International conference on internet science (pp. 194–208). Springer.

  • Fornell, C. G., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

    Article  Google Scholar 

  • Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144.

    Article  Google Scholar 

  • Gefen, D., & Straub, D. W. (2000). The relative importance of perceived ease of use in IS adoption: A study of e-commerce adoption. Journal of the Association for Information Systems, 1(1), 8.

    Article  Google Scholar 

  • Gefen, D., & Straub, D. W. (2004). Consumer trust in B2C e-commerce and the importance of social presence: Experiments in e-products and e-services. Omega, 32(6), 407–424.

    Article  Google Scholar 

  • Gharib, R. K., Philpott, E., & Duan, Y. (2017). Factors affecting active participation in B2B online communities: An empirical investigation. Information & Management, 54(4), 516–530.

    Article  Google Scholar 

  • Grant, R. A. (1991). Building and testing a causal model of an information technology's impact. Journal of Information Technology Management, 2(1), 11–23.

    Google Scholar 

  • Griva, A., Bardaki, C., Pramatari, K., & Doukidis, G. (2021). Factors affecting customer analytics: Evidence from three retail cases. Information Systems Frontiers, 1–24.

  • Hai, L. C., & Alam Kazmi, S. H. (2015). Dynamic support of government in online shopping. Asian Social Science, 11(22).

  • Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (1998). Multivariate data analysis, 5(3), 207–219. Prentice Hall.

  • Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (2006). Multivariate data analysis (6th ed.). Prentice Hall.

    Google Scholar 

  • Halvorsrud, R., Kvale, K., & Følstad, A. (2016). Improving service quality through customer journey analysis. Journal of Service Theory and Practice, 26(6), 840–867.

    Article  Google Scholar 

  • Hettel, T., Flender, C., & Barros, A. (2008). Scaling choreography modelling for B2B value-chain analysis. In: International conference on business process management (pp. 294–309). Springer.

  • Ho, R. C. (2021). Chatbot for online customer service: Customer engagement in the era of artificial intelligence. In: Impact of Globalization and Advanced Technologies on Online Business Models (pp. 16-31). IGI Global.

  • Hooper, D., Coughlan, J., & Mullen, M. R. (2008). Structural equation modelling: Guidelines for determining model fit. Electronic Journal of Business Research Methods, 6(1), 53–60.

    Google Scholar 

  • Hoxmeier, J. A., & DiCesare, C. (2000). System response time and user satisfaction: An experimental study of browser-based applications. AMCIS 2000 Proceedings, 347.

  • Hu, T., Xu, A., Liu, Z., You, Q., Guo, Y., Sinha, V., Luo, J., & Akkiraju, R. (2018). Touch your heart: A tone-aware chatbot for customer care on social media. In: Proceedings of the 2018 CHI conference on human factors in computing systems (pp. 1-12).

  • Hurwitz, J., Kaufman, M., Bowles, A., Nugent, A., Kobielus, J. G., & Kowolenko, M. D. (2015). Cognitive computing and big data analytics. Wiley.

    Google Scholar 

  • Iannacci, F., Fearon, C., & Pole, K. (2020). From acceptance to adaptive acceptance of social media policy change: A set-theoretic analysis of B2B SMEs. Information Systems Frontiers, 1–18.

  • Im, I., Kim, Y., & Han, H. J. (2008). The effects of perceived risk and technology type on users’ acceptance of technologies. Information & Management, 45(1), 1–9.

    Article  Google Scholar 

  • Irani, Z., Bukhari, S. M. F., Ghoneim, A., Dennis, C., & Jamjoom, B. (2013). The antecedents of travellers’ e-satisfaction and intention to buy airline tickets online. Journal of Enterprise Information Management, 26(6), 624–641.

    Article  Google Scholar 

  • ISO. 9241. (1998). Ergonomic requirements for office work with visual display terminals (VDTs): Part 11: Guidance on usability. International Organization for Standardization.

    Google Scholar 

  • Jaakkola, E., Helkkula, A., Aarikka-Stenroos, L., & Verleye, K. (2015). The co-creation experience from the customer perspective: Its measurement and determinants. Journal of Service Management, 26(2), 321–342.

    Article  Google Scholar 

  • Kaiser, H. F. (1970). A second generation little jiffy. Psychometrika, 35(4), 401–415.

    Article  Google Scholar 

  • Kamal, S. A., Shafiq, M., & Kakria, P. (2020). Investigating acceptance of telemedicine services through an extended technology acceptance model (TAM). Technology in Society, 60, 101212.

    Article  Google Scholar 

  • Kandeil, D. A., Saad, A. A., & Youssef, S. M. (2014). A two-phase clustering analysis for B2B customer segmentation. In: 2014 International Conference on Intelligent Networking and Collaborative systems (pp. 221-228). IEEE.

  • Kasilingam, D. L. (2020). Understanding the attitude and intention to use smartphone chatbots for shopping. Technology in Society, 62, 101280.

    Article  Google Scholar 

  • Kasinathan, V., Abd Wahab, M. H., Idrus, S. Z. S., Mustapha, A., & Yuen, K. Z. (2020). Aira chatbot for travel: Case study of AirAsia. Journal of Physics: Conference Series, 1529(2), 022101 IOP Publishing.

    Google Scholar 

  • Kawaf, F., & Tagg, S. (2017). The construction of online shopping experience: A repertory grid approach. Computers in Human Behavior, 72, 222–232.

    Article  Google Scholar 

  • Ke, C., Lou, V. W. Q., Tan, K. C. K., Wai, M. Y., & Chan, L. L. (2020). Changes in technology acceptance among older people with dementia: The role of social robot engagement. International Journal of Medical Informatics, 141, 104241.

    Article  Google Scholar 

  • Khalifa, M., & Shen, K. N. (2008). Explaining the adoption of transactional B2C mobile commerce. Journal of Enterprise Information Management, 21(2), 110–124.

    Article  Google Scholar 

  • Kim, D. J., Ferrin, D. L., & Rao, H. R. (2008). A trust-based consumer decision-making model in electronic commerce: The role of trust, perceived risk, and their antecedents. Decision Support Systems, 44(2), 544–564.

    Article  Google Scholar 

  • Kline, R. B. (2015). Principles and practice of structural equation modeling (4th ed.). Guilford Publications.

    Google Scholar 

  • Koponen, J. P., & Rytsy, S. (2020). Social presence and e-commerce B2B chat functions. European Journal of Marketing, 54(6), 1205–1224.

  • Koumaras, V., Foteas, A., Kapari, M., Sakkas, C., Koumaras, H. (2018). 5G performance testing of mobile chatbot applications. In: 2018 IEEE 23rd international workshop on computer aided modeling and Design of Communication Links and Networks (CAMAD), pp. 1–6.

  • Kranzbühler, A. M., Kleijnen, M. H., Morgan, R. E., & Teerling, M. (2018). The multilevel nature of customer experience research: An integrative review and research agenda. International Journal of Management Reviews, 20(2), 433–456.

    Article  Google Scholar 

  • Kumar, P., Dwivedi, Y. K., & Anand, A. (2021). Responsible artificial intelligence (AI) for value formation and market performance in healthcare: The mediating role of Patient’s cognitive engagement. Information Systems Frontiers, 1–24.

  • Kuo, B. C., Roldan-Bau, A., & Lowinger, R. (2015). Psychological help-seeking among Latin American immigrants in Canada: Testing a culturally-expanded model of the theory of reasoned action using path analysis. International Journal for the Advancement of Counselling, 37(2), 179–197.

    Article  Google Scholar 

  • Kwon, K., & Kim, C. (2012). How to design personalization in a context of customer retention: Who personalizes what and to what extent? Electronic Commerce Research and Applications, 11(2), 101–116.

    Article  Google Scholar 

  • Lee, S. M., & Lee, D. (2020). “Untact”: A new customer service strategy in the digital age. Service Business, 14(1), 1–22.

    Article  Google Scholar 

  • Lee, S. M., & Lim, S. (2018). Living innovation: From value creation to the greater good. Emerald Group Publishing.

    Book  Google Scholar 

  • Liebermann, Y., & Stashevsky, S. (2002). Perceived risks as barriers to internet and e-commerce usage. Qualitative Market Research: An International Journal, 5(4), 291–300.

    Article  Google Scholar 

  • Liu, X. (2020). Analyzing the impact of user-generated content on B2B Firms' stock performance: Big data analysis with machine learning methods. Industrial Marketing Management, 86, 30–39.

    Article  Google Scholar 

  • Liu, X., & Wang, Q. (2005). Study on application of a quantitative evaluation approach for software architecture adaptability. In: Fifth international conference on quality software (QSIC'05) (pp. 265-272). IEEE.

  • Liu, C., Jiang, J., Xiong, C., Yang, Y., & Ye, J. (2020). Towards building an intelligent Chatbot for customer service: Learning to respond at the appropriate time. In: Proceedings of the 26th ACM SIGKDD international conference on Knowledge Discovery & Data Mining (pp. 3377-3385).

  • Lomné, V., Prouff, E., & Roche, T. (2013). Behind the scene of side channel attacks. In: International conference on the theory and application of cryptology and information security (pp. 506–525). Springer.

  • Luhmann, N. (1988). Law as a social system. The Northwestern University Law Review, 83, 136.

    Google Scholar 

  • Luo, X., Tong, S., Fang, Z., & Qu, Z. (2019). Frontiers: Machines vs. humans: The impact of artificial intelligence chatbot disclosure on customer purchases. Marketing Science, 38(6), 937–947.

    Google Scholar 

  • Lytras, M., Visvizi, A., Zhang, X., & Aljohani, N. R. (2020). Cognitive computing, Big Data Analytics and data driven industrial marketing. Industrial Marketing Management, 90, 663–666.

  • MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1(2), 130–149.

    Article  Google Scholar 

  • Madanchian, M., Hussein, N., Noordin, F., & Taherdoost, H. (2018). The impact of ethical leadership on leadership effectiveness among SMEs in Malaysia. Procedia Manufacturing, 22, 968–974.

    Article  Google Scholar 

  • Malhotra, N. K., Kim, S. S., & Patil, A. (2006). Common method variance in IS research: A comparison of alternative approaches and a reanalysis of past research. Management Science, 52(12), 1865–1883.

    Article  Google Scholar 

  • Marisa, F., Ahmad, S. S. S., Yusof, Z. I. M., Hunaini, F., & Aziz, T. M. A. (2019). Segmentation model of customer lifetime value in small and medium enterprise (SMEs) using K-means clustering and LRFM model. International Journal of Integrated Engineering, 11(3).

  • Marsh, H. W., Balla, J. R., & McDonald, R. P. (1988). Goodness-of-fit indexes in confirmatory factor analysis: The effect of sample size. Psychological Bulletin, 103(3), 391–410.

    Article  Google Scholar 

  • Mathiowetz, N. (2014). Considerations and factors for consumer Mobile services adoption in populations with diverse ages (doctoral dissertation, the College of St. Scholastica).

  • McGrath, R. (2018). How to improve customer service with Chatbots. Retrieved from https://chatbotsmagazine.com/ill-never-buy-from-them-again-using-chatbots-to-avoid-bad-customer-service-e6a967360244. Accessed on August 2020.

  • McLean, G., & Wilson, A. (2016). Evolving the online customer experience… is there a role for online customer support? Computers in Human Behavior, 60, 602–610.

    Article  Google Scholar 

  • Michiels, E. (2017). Modelling Chatbots with a cognitive system allows for a differentiating user experience. In: PoEM doctoral consortium (pp. 70-78).

  • Mindbrowser, (2017). Chatbot Survey 2017. Retrieved from https://mindbowser.com/chatbot-market-survey-2017/. Accessed on August 2020.

  • Mitchell, V. W. (1998). A role for consumer risk perceptions in grocery retailing. British Food Journal, 100(4), 171–183.

    Article  Google Scholar 

  • Monalisa, S., & Kurnia, F. (2019). Analysis of DBSCAN and K-means algorithm for evaluating outlier on RFM model of customer behaviour. Telkomnika, 17(1), 110.

    Article  Google Scholar 

  • Mulaik, S. A., James, L. R., Van Alstine, J., Bennett, N., Lind, S., & Stilwell, C. D. (1989). Evaluation of goodness-of-fit indices for structural equation models. Psychological Bulletin, 105(3), 430–445.

    Article  Google Scholar 

  • Mun, Y. Y., Jackson, J. D., Park, J. S., & Probst, J. C. (2006). Understanding information technology acceptance by individual professionals: Toward an integrative view. Information & Management, 43(3), 350–363.

    Article  Google Scholar 

  • Nguyen, T. (2019). Potential effects of chatbot technology on customer support: A case study. Retrieved from https://aaltodoc.aalto.fi/handle/123456789/38921. Assessed on September 2020.

  • Nguyen, N. X., Nguyen, D. T., Suseno, Y., & Bui Quang, T. (2020). The flipped side of customer perceived value and digital technology in B2B professional service context. Journal of Strategic Marketing, 1–21.

  • Nordheim, C. B., Følstad, A., & Bjørkli, C. A. (2019). An initial model of trust in chatbots for customer service—Findings from a questionnaire study. Interacting with Computers, 31(3), 317–335.

    Article  Google Scholar 

  • Nuruzzaman, M., & Hussain, O. K. (2018). A survey on chatbot implementation in customer service industry through deep neural networks. In: 2018 IEEE 15th international conference on e-business engineering (ICEBE) (pp. 54-61). IEEE.

  • Nuruzzaman, M., & Hussain, O. K. (2020). IntelliBot: A Dialogue-based chatbot for the insurance industry. Knowledge-Based Systems, 196, 105810.

    Article  Google Scholar 

  • Oluoch, F. M. (2017). Factors affecting internet banking adoption in Kenya: Case study of National Bank of Kenya and equity Bank (Doctoral dissertation, United States International University-Africa).

  • Olver, I. N., & Selva-Nayagam, S. (2000). Evaluation of a telemedicine link between Darwin and Adelaide to facilitate cancer management. Telemedicine Journal, 6(2), 213–218.

    Article  Google Scholar 

  • Oostenbrink, J. (2015). Financial impact of downtime decrease and performance increase of IT services (Bachelor's thesis, University of Twente).

  • Palmisano, C., Tuzhilin, A., & Gorgoglione, M. (2008). Using context to improve predictive modeling of customers in personalization applications. IEEE Transactions on Knowledge and Data Engineering, 20(11), 1535–1549.

    Article  Google Scholar 

  • Pandey, N., Nayal, P., & Rathore, A. S. (2020). Digital marketing for B2B organizations: Structured literature review and future research directions. Journal of Business & Industrial Marketing, 35, 1191–1204.

    Article  Google Scholar 

  • Pappas, I. O. (2018). User experience in personalized online shopping: A fuzzy-set analysis. European Journal of Marketing, 52(7/8), 1679–1703.

    Article  Google Scholar 

  • Pappas, I. O., Kourouthanassis, P. E., Giannakos, M. N., & Chrissikopoulos, V. (2014). Shiny happy people buying: The role of emotions on personalized e-shopping. Electronic Markets, 24(3), 193–206.

    Article  Google Scholar 

  • Pappas, I. O., Kourouthanassis, P. E., Giannakos, M. N., & Chrissikopoulos, V. (2016). Explaining online shopping behavior with fsQCA: The role of cognitive and affective perceptions. Journal of Business Research, 69(2), 794–803.

    Article  Google Scholar 

  • Pappas, I. O., Kourouthanassis, P. E., Giannakos, M. N., & Chrissikopoulos, V. (2017). Sense and sensibility in personalized e-commerce: How emotions rebalance the purchase intentions of persuaded customers. Psychology & Marketing, 34(10), 972–986.

    Article  Google Scholar 

  • Park, J., Ahn, J., Thavisay, T., & Ren, T. (2019). Examining the role of anxiety and social influence in multi-benefits of mobile payment service. Journal of Retailing and Consumer Services, 47, 140–149.

    Article  Google Scholar 

  • Paschen, J., Kietzmann, J., & Kietzmann, T. C. (2019). Artificial intelligence (AI) and its implications for market knowledge in B2B marketing. Journal of Business & Industrial Marketing, 37(4), 1410–1419.

    Article  Google Scholar 

  • Patil, S. (2019). Top 5 Industries that Can Benefit from Chatbots. Retrieved from https://commversion.com/top-5-industries-that-can-benefit-from-chatbots/. Accessed on August 2020.

  • Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 101–134.

    Article  Google Scholar 

  • Petter, S., DeLone, W., & McLean, E. (2008). Measuring information systems success: Models, dimensions, measures, and interrelationships. European Journal of Information Systems, 17(3), 236–263.

    Article  Google Scholar 

  • Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903.

    Article  Google Scholar 

  • Prabu, M., Sai Tarun, T., Shereef Naina Mohamed, A., & Vijay, A. (2020). Enhancing customer service using Chatbot application through artificial intelligence. Journal of Computational and Theoretical Nanoscience, 17(4), 1633–1637.

    Article  Google Scholar 

  • Przegalinska, A., Ciechanowski, L., Stroz, A., Gloor, P., & Mazurek, G. (2019). In bot we trust: A new methodology of chatbot performance measures. Business Horizons, 62(6), 785–797.

    Article  Google Scholar 

  • Ramasamy, A. (2019). How businesses can begin using Chatbots the right way. Retrieved from https://www.forbes.com/sites/forbestechcouncil/2019/01/18/how-businesses-can-begin-using-chatbots-the-right-way/#3d8366b12183. Assessed on august 2020.

  • Rana, N. P., & Dwivedi, Y. K. (2016). Using clickers in a large business class: Examining use behavior and satisfaction. Journal of Marketing Education, 38(1), 47–64.

    Article  Google Scholar 

  • Rana, N. P., Williams, M. D., Dwivedi, Y. K., & Williams, J. (2012). Theories and theoretical models for examining the adoption of e-government services. e-Service. Journal: A Journal of Electronic Services in the Public and Private Sectors, 8(2), 26–56.

    Google Scholar 

  • Rana, N. P., Dwivedi, Y. K., & Williams, M. D. (2013). Evaluating the validity of IS success models for the electronic government research: An empirical test and integrated model. International Journal of Electronic Government Research, 9(3), 1–22.

    Article  Google Scholar 

  • Rana, N., Dwivedi, Y., Weerakkody, V., & Piercy, N. (2014). Examining adoption of electronic district (e-district) system in Indian context: A validation of extended technology acceptance model. In: Twentieth Americas Conference on Information Systems, Savannah.

  • Rana, N. P., Dwivedi, Y. K., Williams, M. D., & Lal, B. (2015a). Examining the success of the online public grievance redressal systems: An extension of the IS success model. Information Systems Management, 32(1), 39–59.

    Article  Google Scholar 

  • Rana, N. P., Dwivedi, Y. K., Williams, M. D., & Piercy, N. C. (2015b). An extended DeLone and McLean's information system model for examining success of online public grievance redressal system in Indian context. International Journal of Indian Culture and Business Management, 10(3), 267–290.

    Article  Google Scholar 

  • Rana, N. P., Dwivedi, Y. K., Williams, M. D., & Weerakkody, V. (2015c). Investigating success of an e-government initiative: Validation of an integrated IS success model. Information Systems Frontiers, 17(1), 127–142.

    Article  Google Scholar 

  • Ransbotham, S., Kiron, D., & Prentice, P. K. (2016). Beyond the hype: The hard work behind analytics success. MIT Sloan Management Review, 57(3).

  • Rese, A., Ganster, L., & Baier, D. (2020). Chatbots in retailers’ customer communication: How to measure their acceptance? Journal of Retailing and Consumer Services, 56, 102176.

    Article  Google Scholar 

  • Rialle, V., Duchene, F., Noury, N., Bajolle, L., & Demongeot, J. (2002). Health" smart" home: Information technology for patients at home. Telemedicine Journal and E-Health, 8(4), 395–409.

    Article  Google Scholar 

  • Rietz, T., Benke, I., & Maedche, A. (2019). The impact of anthropomorphic and functional chatbot design features in enterprise collaboration systems on user acceptance. In: Proceedings of the 14th International Conference on Wirtschaftsinformatik. Siegen, Germany, February 24-27.

  • Rodriguez, M., & Boyer, S. (2020). The impact of mobile customer relationship management (mCRM) on sales collaboration and sales performance. Journal of Marketing Analytics, 1–12.

  • Rogers, E. M. (1995). Diffusion of innovations (pp. 15–23). ACM The Free Press.

    Google Scholar 

  • Ruhl, K. (2004). Qualitative research practice. A guide for social sciencestudents and researchers. Historical Social Research, 29(4), 171–177.

  • Rygielski, C., Wang, J. C., & Yen, D. C. (2002). Data mining techniques for customer relationship management. Technology in Society, 24(4), 483–502.

    Article  Google Scholar 

  • Sarbabidya, S., & Saha, T. (2020). Role of Chatbot in customer service: A study from the perspectives of the banking industry of Bangladesh. International Review of Business Research Papers, 16(1).

  • Schmitt, C. R. M. (2020). Artificial intelligence in customer service: How chatbots reshape customer service strategies: A guidance for an AI-based chatbot integration (doctoral dissertation).

  • Schou, C. (1996). Information systems security organization (ISSO) glossary of INFOSEC and INFOSEC related terms.

  • Sequeiros, H., Oliveira, T., & Thomas, M. A. (2021). The impact of IoT smart home services on psychological well-being. Information Systems Frontiers, 1–18.

  • Setzke, D. S., Riasanow, T., Böhm, M., & Krcmar, H. (2021). Pathways to digital service innovation: The role of digital transformation strategies in established organizations. Information Systems Frontiers, 1–21.

  • Sharma, S. K., Al-Badi, A., Rana, N. P., & Al-Azizi, L. (2018). Mobile applications in government services (mG-app) from user's perspectives: A predictive modelling approach. Government Information Quarterly, 35(4), 557–568.

    Article  Google Scholar 

  • Shawar, B. A., & Atwell, E. (2007). Chatbots: are they really useful?. In Ldv forum, 22(1), 29–49.

  • Sheehan, B., Jin, H. S., & Gottlieb, U. (2020). Customer service chatbots: Anthropomorphism and adoption. Journal of Business Research, 115, 14–24.

    Article  Google Scholar 

  • Sheikh, A., Ghanbarpour, T., & Gholamiangonabadi, D. (2019). A preliminary study of fintech industry: A two-stage clustering analysis for customer segmentation in the B2B setting. Journal of Business-to-Business Marketing, 26(2), 197–207.

    Article  Google Scholar 

  • Shih, H. P. (2004). An empirical study on predicting user acceptance of e-shopping on the web. Information & Management, 41(3), 351–368.

    Article  Google Scholar 

  • Shuhaiber, A., & Mashal, I. (2019). Understanding users’ acceptance of smart homes. Technology in Society, 58, 101110.

    Article  Google Scholar 

  • Siemieniako, D. (2019). New perspectives on b2b marketing–connecting marketing and technology. Engineering Management in Production and Services, 11(3), 7–7.

    Article  Google Scholar 

  • Strycharz, J., van Noort, G., Helberger, N., & Smit, E. (2019). Contrasting perspectives–practitioner’s viewpoint on personalised marketing communication. European Journal of Marketing, 53(4), 635–660.

    Article  Google Scholar 

  • Swanson, E. B. (1997). Maintaining IS quality. Information and Software Technology, 39(12), 845–850.

    Article  Google Scholar 

  • Tabachnick, B. G., Fidell, L. S., & Ullman, J. B. (2007). Using multivariate statistics, 5, 481–498. Pearson.

    Google Scholar 

  • Taherdoost, H. (2016). Validity and reliability of the research instrument; how to test the validation of a questionnaire/survey in a research. International Journal of Academic Research in Management, 5(3), 28–36.

    Google Scholar 

  • Taherdoost, H. (2018). A review of technology acceptance and adoption models and theories. Procedia Manufacturing, 22, 960–967.

    Article  Google Scholar 

  • Taherdoost, H., Namayandeh, M., & Jalaliyoon, N. (2011a). Information security and ethics in educational context: Propose a conceptual framework to examine their impact. International Journal of Computer Science and Information Security, 9(1), 134–138.

    Google Scholar 

  • Taherdoost, H., Sahibuddin, S., & Jalaliyoon, N. (2011b). Smart card security; technology and adoption. International Journal of Security, 5(2), 74–84.

    Google Scholar 

  • Taherdoost, H., Sahibuddin, S., & Jalaliyoon, N. (2014). Exploratory factor analysis; concepts and theory. Advances in Applied and Pure Mathematics, 27, 375–382.

  • Tam, J. L. M. (2012). The moderating role of perceived risk in loyalty intentions: An investigation in a service context. Marketing Intelligence & Planning, 30(1), 33–52.

    Article  Google Scholar 

  • Teo, T. S., Srivastava, S. C., & Jiang, L. (2008). Trust and electronic government success: An empirical study. Journal of Management Information Systems, 25(3), 99–132.

    Article  Google Scholar 

  • Trivedi, J. (2019). Examining the customer experience of using banking Chatbots and its impact on brand love: The moderating role of perceived risk. Journal of Internet Commerce, 18(1), 91–111.

    Article  Google Scholar 

  • Van Doorn, J., Mende, M., Noble, S., Hulland, J., Ostrom, A., 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.

    Article  Google Scholar 

  • Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342–365.

    Article  Google Scholar 

  • Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204.

    Article  Google Scholar 

  • Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27, 425–478.

    Article  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.

    Article  Google Scholar 

  • Visweswara, U. M., Gohad, A., Yadav, S. K., Karthik, N. V. S., & Babu, S. M. (2013). Smarter commerce: NLP SpokenWeb based B2B messaging. In: 2013 2nd International Conference on Advanced Computing, Networking and Security (pp. 12-17). IEEE.

  • Waghmare, C. (2019). Business benefits of using chatbots. In: Introducing Azure Bot service (pp. 147–165). Apress.

  • Whitten, P., Doolittle, G., & Mackert, M. (2005). Providers' acceptance of telehospice. Journal of Palliative Medicine, 8(4), 730–735.

    Article  Google Scholar 

  • Wu, J. J., & Chang, Y. S. (2005). Towards understanding members' interactivity, trust, and flow in online travel community. Industrial Management & Data Systems, 105(7), 937–954.

    Article  Google Scholar 

  • Wu, J. H., & Wang, S. C. (2005). What drives mobile commerce?: An empirical evaluation of the revised technology acceptance model. Information & Management, 42(5), 719–729.

    Article  Google Scholar 

  • Xu, A., Liu, Z., Guo, Y., Sinha, V., & Akkiraju, R. (2017). A new chatbot for customer service on social media. In: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (pp. 3506-3510).

  • Yousafzai, S. Y., Pallister, J. G., & Foxall, G. R. (2003). A proposed model of e-trust for electronic banking. Technovation, 23(11), 847–860.

    Article  Google Scholar 

  • Yu, C. E. (2020). Humanlike robots as employees in the hotel industry: Thematic content analysis of online reviews. Journal of Hospitality Marketing & Management, 29(1), 22–38.

    Article  Google Scholar 

  • Zaied, A. N. H. (2012). An integrated success model for evaluating information system in public sectors. Journal of Emerging Trends in Computing and Information Sciences, 3(6), 814–825.

    Google Scholar 

  • Zamora, J. (2017). I’m sorry, dave, i’m afraid i can’t do that: Chatbot perception and expectations. In Proceedings of the 5th international conference on human agent interaction, 253–260.

  • Zaykin, D. V., Zhivotovsky, L. A., Westfall, P. H., & Weir, B. S. (2002). Truncated product method for combining P-values. Genetic Epidemiology: The Official Publication of the International Genetic Epidemiology Society, 22(2), 170–185.

    Article  Google Scholar 

  • Zhang, X., He, S., Huang, Z., & Zhang, A. (2020). A survey on modularization of Chatbot conversational systems. In: International Conference on Database Systems for Advanced Applications (pp. 175–189). Springer.

  • Zheng, Y., Zhao, K., & Stylianou, A. (2013). The impacts of information quality and system quality on users' continuance intention in information-exchange virtual communities: An empirical investigation. Decision Support Systems, 56, 513–524.

    Article  Google Scholar 

Download references

Acknowledgements

The infrastructural support provided by KIIT, Bhubaneswar, IIM Ranchi, Ranchi and FORE School of Management, New Delhi in completing this paper is gratefully acknowledged. The authors like to acknowledge Prof. Debanjali Roy (debanjali.royksol@kiit.ac.in) and Prof. Hatice Kizgin (kizgin.hatice@googlemail.com) for their exceptional support in the proofread of the manuscript.

Author information

Authors and Affiliations

  1. Indian Institute of Management Ranchi, Suchana Bhawan, 5th Floor, Audrey House Campus, Meur’s Road, Ranchi, Jharkhand, 834008, India

    Rajat Kumar Behera & Pradip Kumar Bala

  2. School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT) Deemed to be University, KIIT Road, Patia, Bhubaneswar, Odisha, 751024, India

    Rajat Kumar Behera

  3. Department of Information Technology, FORE School of Management, New Delhi, Delhi, 110016, India

    Arghya Ray

Authors
  1. Rajat Kumar Behera
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pradip Kumar Bala
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Arghya Ray
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rajat Kumar Behera.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix 1

Appendix 1

Five-point Likert scale questions (1: Totally disagree, 2: Disagree, 3: Neutral, 4: Agree, 5: Totally agree) are captured in Table 7 and were asked to customer service leader who owns overall service (i.e., define key process indicators, develop programs and procedures to enhance productivity and performance, resolve conflicts of customers) and customer service manager who oversees the execution of customer service and coordinate the team through the shift. The objective is to check for the improved intention of the adoption of the cognitive chatbot

Table 7 Measurement items
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Behera, R.K., Bala, P.K. & Ray, A. Cognitive Chatbot for Personalised Contextual Customer Service: Behind the Scene and beyond the Hype. Inf Syst Front (2021). https://doi.org/10.1007/s10796-021-10168-y

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10796-021-10168-y

Keywords

Search

Navigation