Skip to main content
Book cover

International Workshop on Chatbot Research and Design

CONVERSATIONS 2022: Chatbot Research and Design pp 190–210Cite as

Designing Context-Aware Chatbots for Product Configuration

  • 246 Accesses

Part of the Lecture Notes in Computer Science book series (LNCS,volume 13815)

Abstract

Product configurators provide an interface for customizing complex products. However, large form-based configurators overwhelm many end users and are often considered expert tools. This paper therefore addresses the problem of the complexity of current product configurators. Since chatbots can respond flexibly to queries and offer a natural language interface, they have the potential to simplify the configuration process. In this paper, we present a chatbot for product configuration that we developed using the design science research approach and in collaboration with an industrial partner. We derive design principles for configurator chatbots from user interviews that relate in particular to the flexibility of the chatbot compared to a static process. These design principles were implemented in our chatbot artifact which was evaluated in an online experiment (N = 12) and compared to a baseline chatbot with an inflexible configuration process. Our results indicate that the proposed design increased dependability and configuration performance, and overall had positive effects on participants’ engagement. Thus, this study contributes prescriptive knowledge on the design of context-aware chatbots for product configuration and a novel artifact in the form of a context-aware configurator chatbot prototype.

Keywords

  • Chatbots
  • Product configuration
  • Context-awareness

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-031-25581-6_12
  • Chapter length: 21 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   49.99
Price excludes VAT (USA)
  • ISBN: 978-3-031-25581-6
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   64.99
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.

References

  1. Pine, B.J., II., Victor, B., Boynton, A.C.: Making mass customization work. Harv. Bus. Rev. 71, 108–111 (1993)

    Google Scholar 

  2. Huffman, C., Kahn, B.E.: Variety for sale: mass customization or mass confusion? J. Retail. 74, 491–513 (1998). https://doi.org/10.1016/S0022-4359(99)80105-5

    CrossRef  Google Scholar 

  3. Felfernig, A. (ed.): Knowledge-Based Configuration from Research to Business Cases. Morgan Kaufmann is an Imprint of Elsevier, Amsterdam (2014)

    Google Scholar 

  4. Li, F., Jagadish, H.V.: Constructing an interactive natural language interface for relational databases. Proc. VLDB Endow. 8, 73–84 (2014). https://doi.org/10.14778/2735461.2735468

  5. Sun, Y., Zhang, Y.: Conversational recommender system. In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. ACM, New York (2018)

    Google Scholar 

  6. Randall, T., Terwiesch, C., Ulrich, K.T.: Principles for user design of customized products. Calif. Manag. Rev. 47, 68–85 (2005). https://doi.org/10.2307/41166317

    CrossRef  Google Scholar 

  7. Sweller, J.: Cognitive load during problem solving: effects on learning. Cogn. Sci. 12, 257–285 (1988). https://doi.org/10.1016/0364-0213(88)90023-7

    CrossRef  Google Scholar 

  8. Blecker, T., Abdelkafi, N., Kreutler, G., et al.: Product configuration systems: state of the art, conceptualization and extensions (2004)

    Google Scholar 

  9. Dale, R.: The return of the chatbots. Nat. Lang. Eng. 22, 811–817 (2016). https://doi.org/10.1017/s1351324916000243

    CrossRef  Google Scholar 

  10. Gupta, S., Borkar, D., de Mello, C., et al.: An e-commerce website based chatbot. Int. J. Comput. Sci. Inf. Technol. 6, 1483–1485 (2015)

    Google Scholar 

  11. Cui, L., Huang, S., Wei, F., et al.: SuperAgent: a customer service chatbot for e-commerce websites. In: Proceedings of ACL 2017, System Demonstrations, Stroudsburg, PA, USA. Association for Computational Linguistics (2017)

    Google Scholar 

  12. Nica, I., Tazl, O.A., Wotawa, F.: Chatbot-based tourist recommendations using model-based reasoning. In: ConfWS, pp. 25–30 (2018)

    Google Scholar 

  13. Christakopoulou, K., Radlinski, F., Hofmann, K.: Towards conversational recommender systems. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York (2016)

    Google Scholar 

  14. Clark, L., Pantida, N., Cooney, O., et al.: What makes a good conversation? Challenges in designing truly conversational agents. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (2019)

    Google Scholar 

  15. Jain, M., Kumar, P., Kota, R., et al.: Evaluating and informing the design of chatbots. In: Proceedings of the 2018 Designing Interactive Systems Conference. ACM, New York (2018)

    Google Scholar 

  16. Vlasov, V., Drissner-Schmid, A., Nichol, A.: Few-shot generalization across dialogue tasks (2018)

    Google Scholar 

  17. Hevner, A.R.: A three cycle view of design science research. Scand. J. Inf. Syst. 19, 4 (2007)

    Google Scholar 

  18. Kuechler, B., Vaishnavi, V.: On theory development in design science research: anatomy of a research project. Eur. J. Inf. Syst. 17, 489–504 (2008). https://doi.org/10.1057/ejis.2008.40

    CrossRef  Google Scholar 

  19. Barnum, C.M.: Usability Testing Essentials: Ready, Set… Test!, 2nd edn. Morgan Kaufmann, Amsterdam (2020)

    Google Scholar 

  20. Ardissono, L., Felfernig, A., Friedrich, G., et al.: A framework for the development of personalized, distributed web-based configuration systems. AI Mag. 24, 93 (2003). https://doi.org/10.1609/aimag.v24i3.1721

    CrossRef  Google Scholar 

  21. Drews, M.: Interaction Patterns für Produktkonfiguratoren. In: Mensch & Computer 2008, pp. 367–376. Oldenbourg Wissenschaftsverlag GmbH (2008)

    Google Scholar 

  22. Kahneman, D., Tversky, A.: Prospect theory: an analysis of decision under risk. In: Handbook of the Fundamentals of Financial Decision Making, pp. 99–127. World Scientific (2013)

    Google Scholar 

  23. Diederich, S., Brendel, A.B., Kolbe, L.M.: Designing anthropomorphic enterprise conversational agents. Bus. Inf. Syst. Eng. 62(3), 193–209 (2020). https://doi.org/10.1007/s12599-020-00639-y

    CrossRef  Google Scholar 

  24. Følstad, A., Brandtzæg, P.B.: Chatbots and the new world of HCI. Interactions 24, 38–42 (2017). https://doi.org/10.1145/3085558

    CrossRef  Google Scholar 

  25. Dzindolet, M.T., Peterson, S.A., Pomranky, R.A., et al.: The role of trust in automation reliance. Int. J. Hum. Comput. Stud. 58, 697–718 (2003). https://doi.org/10.1016/S1071-5819(03)00038-7

    CrossRef  Google Scholar 

  26. Luger, E., Sellen, A.: “Like having a really bad PA” the gulf between user expectation and experience of conversational agents. In: Proceedings of the 2016 CHI Conference on Human Factors in Computing System, pp. 5286–5297 (2016)

    Google Scholar 

  27. Jain, M., Kota, R., Kumar, P., et al.: Convey: exploring the use of a context view for chatbots. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, pp. 1–6 (2018)

    Google Scholar 

  28. Gnewuch, U., Morana, S., Maedche, A.: Towards designing cooperative and social conversational agents for customer service (2017)

    Google Scholar 

  29. Cöster, R., Gustavsson, A., Olsson, T., et al.: Enhancing web-based configuration with recommendations and cluster-based help. In: 2002 Workshop on Recommendation and Personalization in eCommerce (2002)

    Google Scholar 

  30. Tiihonen, J., Felfernig, A.: An introduction to personalization and mass customization. J. Intell. Inf. Syst. 49(1), 1–7 (2017). https://doi.org/10.1007/s10844-017-0465-4

    CrossRef  Google Scholar 

  31. Braun, D., Hernandez-Mendez, A., Matthes, F., et al.: Evaluating natural language understanding services for conversational question answering systems. In: Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue, Stroudsburg, PA, USA. Association for Computational Linguistics (2017)

    Google Scholar 

  32. Davis, F.D., Bagozzi, R.P., Warshaw, P.R.: User acceptance of computer technology: a comparison of two theoretical models. Manag. Sci. 35, 982–1003 (1989). https://doi.org/10.1287/mnsc.35.8.982

    CrossRef  Google Scholar 

  33. Aladwani, A.M.: The development of two tools for measuring the easiness and usefulness of transactional Web sites. Eur. J. Inf. Syst. 11, 223–234 (2002). https://doi.org/10.1057/palgrave.ejis.3000432

    CrossRef  Google Scholar 

  34. Freeman, L.A., Jessup, L.M.: The power and benefits of concept mapping: measuring use, usefulness, ease of use, and satisfaction. Int. J. Sci. Educ. 26, 151–169 (2004). https://doi.org/10.1080/0950069032000097361

    CrossRef  Google Scholar 

  35. Lund, A.: Measuring usability with the USE questionnaire. Usability Interface 8(2), 3–6 (2001)

    Google Scholar 

  36. Mlekus, L., Bentler, D., Paruzel, A., et al.: How to raise technology acceptance: user experience characteristics as technology-inherent determinants. Gr. Interakt. Org. 51, 273–283 (2020). https://doi.org/10.1007/s11612-020-00529-7

  37. Laugwitz, B., Held, T., Schrepp, M.: Construction and evaluation of a user experience questionnaire. In: Holzinger, A. (ed.) USAB 2008. LNCS, vol. 5298, pp. 63–76. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-89350-9_6

    CrossRef  Google Scholar 

  38. Schrepp, M., Hinderks, A., Thomaschewski, J.: Construction of a Benchmark for the User Experience Questionnaire (UEQ), vol. 4, p. 40 (2017) https://doi.org/10.9781/ijimai.2017.445

Download references

Author information

Authors and Affiliations

  1. Karlsruhe Institute of Technology, Kaiserstraße 12, 76131, Karlsruhe, Germany

    Tom Niederer & Daniel Schloss

  2. CAS Software AG, CAS-Weg 1 - 5, 76131, Karlsruhe, Germany

    Noemi Christensen

Authors
  1. Tom Niederer
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daniel Schloss
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Noemi Christensen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Daniel Schloss .

Editor information

Editors and Affiliations

  1. SINTEF, Oslo, Norway

    Asbjørn Følstad

  2. University of Amsterdam, Amsterdam, The Netherlands

    Theo Araujo

  3. CERTH-ITI, Thessaloniki, Greece

    Symeon Papadopoulos

  4. Durham University, Durham, UK

    Effie L.-C. Law

  5. University of Edinburgh, Edinburgh, UK

    Ewa Luger

  6. University of Agder, Grimstad, Norway

    Morten Goodwin

  7. University of Oslo, Oslo, Norway

    Petter Bae Brandtzaeg

Appendix

Appendix

1.1 Questions Asked in the Semi-structured Interviews

Status Quo

  • “How often is the product configurator used by which user groups?”

  • “What are the goals of a customer when configuring the product?”

  • “What proportion of the configurations that have been started will be completed?”

Problem identification

  • “For what reasons do users cancel a product configuration?”

  • “How is the feedback on the product configurator?”

  • “What complaints or negative feedback about the configurator are there?”

  • “What are the hurdles in the current configuration process?”

Requirements

Abstract requirements

  • “What are frequently expressed customer requirements?”

  • “What are the relevant properties for you that a configurator has to implement?”

  • “How can intuitive operation or a pleasant process flow be achieved?”

Concrete requirements for configuration chatbot

  • “In your opinion, how would a text-based chatbot have to proceed in order to to enable a pleasant configuration process?”

  • “What abilities of the chatbot would be desirable”

  • Opt.: negative questions in cases of insufficient feedback

(translated from German).

1.2 Final Evaluation

Table 3. Final experiment groups
Full size table
Table 4. User-related constructs
Full size table
Table 5. Performance related metrics
Full size table

Rights and permissions

Reprints and Permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Niederer, T., Schloss, D., Christensen, N. (2023). Designing Context-Aware Chatbots for Product Configuration. In: , et al. Chatbot Research and Design. CONVERSATIONS 2022. Lecture Notes in Computer Science, vol 13815. Springer, Cham. https://doi.org/10.1007/978-3-031-25581-6_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-25581-6_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-25580-9

  • Online ISBN: 978-3-031-25581-6

  • eBook Packages: Computer ScienceComputer Science (R0)