Advertisement

Customer Acceptance of Shopping-Assistant Chatbots

  • Tiago Araújo
  • Beatriz CasaisEmail author
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 167)

Abstract

Due to the increased use of chat applications, it is expected that consumer buying behavior will change. Chatbots have been identified by companies as being an excellent opportunity to better exploit social networks and also get competitive differentiation through relationship marketing in the digital environment. This paper discusses through the Technology Acceptance Model (TAM), the adoption of chatbots as shopping assistants in e-commerce. A sample of 237 Portuguese respondents between 18 and 54 years old that answered a questionnaire evidenced that the dimensions that most influence their acceptance of chatbots were the compatibility and attitude towards mobile advertising. These results emphasize that there is a need to introduce better digital solutions to boost the results of companies. The investment in chatbots is thus an important factor of differentiation.

Keywords

Digital marketing Digital consumer User experience Omnichannel Chatbots Shopping assistants 

References

  1. 1.
    Barwitz, N., Maas, P.: Understanding the omnichannel customer journey: determinants of interaction choice. J. Interact. Mark. 43(August), 116–133 (2018)CrossRefGoogle Scholar
  2. 2.
    Bogdanovych, A., Simoff, S.: Implicit training of virtual shopping assistants in 3D electronic institutions. In: E-Commerce 2005 IADIS International Conference Proceedings, January, pp. 50–57 (2005)Google Scholar
  3. 4.
    Chen, M.Y., Teng, C.I.: A comprehensive model of the effects of online store image on purchase intention in an e-commerce environment. Electron. Commerce Res. 13(1), 1–23 (2013)CrossRefGoogle Scholar
  4. 3.
    da Chen, L., Gillenson, M.L., Sherrell, D.L.: Enticing online consumers: an extended technology acceptance perspective. Inf. Manag. 39(8), 705–719 (2002)CrossRefGoogle Scholar
  5. 5.
    Dakouan, C., Benabdelouahed, R., Anabir, H.: Inbound marketing vs. outbound marketing: independent or complementary strategies. Expert J. Mark. 7(1), 1–6 (2019)Google Scholar
  6. 6.
    Davis, F.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13(3), 319–340 (1989)CrossRefGoogle Scholar
  7. 7.
    Elmorshidy, A.: Benefits analysis of live customer support chat in E-commerce websites: dimensions of a new success model for live customer support chat. In: 2011 10th International Conference on Machine Learning and Applications and Workshops, vol. 2, pp. 325–329. IEEE (2011)Google Scholar
  8. 8.
    Elmorshidy, A.: Applying the technology acceptance and service quality models to live customer support chat for e-commerce websites. J. Appl. Bus. Res. 29(2), 589–596 (2013)CrossRefGoogle Scholar
  9. 9.
    Elmorshidy, A., Mostafa, M., El-Moughrabi, I., Al-Mezen, H.: Factors influencing live customer support chat services: an empirical investigation in Kuwait. J. Theor. Appl. Electron. Commer. Res. 10(3), 63–76 (2015)CrossRefGoogle Scholar
  10. 10.
    Eroglu, S.A., Machleit, K.A., Davis, L.M.: Atmospheric qualities of online retailing: a conceptual model and implications. J. Bus. Res. 54, 177–184 (2001)CrossRefGoogle Scholar
  11. 11.
    Gupta, S., Borkar, D., Mello, C.De, Patil, S.: An E-commerce website based Chatbot. Int. J. Comput. Sci. Inf. Technol. 6(2), 1483–1485 (2015)Google Scholar
  12. 12.
    Gursoy, D., Chi, O.H., Lu, L., Nunkoo, R.: Consumers acceptance of artificially intelligent (AI) device use in service delivery. Int. J. Inf. Manag. 49, 157–169 (2019)CrossRefGoogle Scholar
  13. 13.
    Hassenzahl, M., Tractinsky, N.: User experience—a research agenda. Behav. Inf. Technol. 25(March–April), 91–97 (2006)Google Scholar
  14. 14.
    Holzwarth, M., Janiszewski, C., Neumann, M.M.: The influence of avatars on online consumer shopping behavior. J. Mark. 70(4), 19–36 (2006)CrossRefGoogle Scholar
  15. 15.
    Kaasinen, E.: User acceptance of mobile services—value, ease of use, trust and ease of adoption, 566. VTT Publications (2005)Google Scholar
  16. 16.
    Leeflang, P.S.H., Verhoef, P.C., Dahlström, P., Freundt, T.: Challenges and solutions for marketing in a digital era. Eur. Manag. J. 32(1), 1–12 (2014)CrossRefGoogle Scholar
  17. 17.
    Legris, P., Ingham, J., Collerette, P.: Why do people use information technology? a critical review of the technology acceptance model. Inf. Manag. 40(3), 191–204 (2003)CrossRefGoogle Scholar
  18. 18.
    Lemon, K.N., Verhoef, P.C.: Understanding customer experience throughout the customer journey. J. Mark. 80(November), 69–96 (2016)CrossRefGoogle Scholar
  19. 19.
    Mctear, M.: Future and Emerging Trends in Language Technology. Machine Learning and Big Data, vol. 10341, pp. 38–49 (2017)Google Scholar
  20. 20.
    Moon, Y.: Intimate exchanges: using computers to elicit self-disclosure from consumers. J. Consumer Res. 26(4), 323–339 (2000)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Moore, G.C., Benbasat, I.: Development of an instrument to measure the perceptions of adopting an information technology innovation. Inf. Syst. Res. 2(3), 192–222 (1991)CrossRefGoogle Scholar
  22. 22.
    Patrutiu-Baltes, L.: Inbound marketing—the most important digital marketing strategy. Bull. Transilvania Univ. Brasov 9(2), 61–68 (2016)Google Scholar
  23. 23.
    Petrie, H., Bevan, N.: The evaluation of accessibility, usability, and user experience. In: The Universal Access Handbook, vol. 1, pp. 1–16 (2009)Google Scholar
  24. 24.
    Piotrowicz, W., Cuthbertson, R.: Introduction to the special issue information technology in retail: toward omnichannel retailing. Int. J. Electron. Commerce 18(4), 5–16 (2014)CrossRefGoogle Scholar
  25. 25.
    Redmond, W.H.: The potential impact of artificial shopping agents in E-commerce markets. J. Interact. Mark. 5(1), 56–66 (2002)CrossRefGoogle Scholar
  26. 26.
    Sotolongo, N., Copulsky, J.: Conversational marketing: creating compelling customer connections. Appl. Mark. Anal. 4(1), 6–21 (2018)Google Scholar
  27. 27.
    Van der Heijden, H., Verhagen, T.: Online store image: conceptual foundations and empirical measurement. Inf. Manag. 41(5), 609–617 (2004)CrossRefGoogle Scholar
  28. 28.
    Van Eeuwen, M.V.: Mobile conversational commerce: messenger chatbots as the next interface between businesses and consumers. Master’s thesis, University of Twente (2017)Google Scholar
  29. 29.
    Vargo, R.L.S.: Service-dominant logic—a guiding framework for inbound marketing. Mark. Rev. St. Gallen, 6–10 (2009)Google Scholar
  30. 30.
    Venkatesh, V.: Determinants of perceived ease of use: integrating control, intrinsic motivation, Acceptance Model. Inf. Syst. Res. 11(4), 342–365 (2000)CrossRefGoogle Scholar
  31. 31.
    Verhoef, P.C., Kannan, P.K., Inman, J.J.: From multi-channel retailing to omni-channel retailing. Introduction to the special issue on multi-channel retailing. J. Retai. 91(2), 174–181 (2015)CrossRefGoogle Scholar
  32. 32.
    Wirth, N.: Hello marketing, what can artificial intelligence help you with? Int. J. Mark. Res. 60(5), 435–438 (2018)CrossRefGoogle Scholar
  33. 33.
    Wu, J.H., Wang, S.C.: What drives mobile commerce? an empirical evaluation of the revised technology acceptance model. Inf. Manag. 42(5), 719–729 (2005)MathSciNetCrossRefGoogle Scholar
  34. 34.
    Zarmpou, T., Saprikis, V., Markos, A., Vlachopoulou, M.: Modeling users’ acceptance of mobile services. Electron. Commerce Res. 12(2), 225–248 (2012)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.School of Economics and ManagementUniverstity of MinhoBragaPortugal
  2. 2.IPAMPortoPortugal
  3. 3.CiTURLeiriaPortugal

Personalised recommendations