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User Journey Map as a Method to Extrapolate User Experience Knowledge from User Generated Reviews

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Perspectives in Business Informatics Research (BIR 2022)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 462))

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Abstract

User-generated product reviews are valuable information resources about what users like about the product, their pain points, and overall product use cases. This information is valuable for product developers and designers for future product improvements. This research paper discusses the user journey mapping approach for analyzing product reviews. It proposes a method for structuring large amounts of user reviews and putting them on the journey map, classifying touchpoints, pain points, and product advantages. Machine learning algorithms on Apple Earpods Max noise-canceling headphone reviews are used to classify user-generated product reviews and validate the journey map. Created journey map showed a positive potential for the given approach to make sense of large amounts of user-generated content and give quantifiable proof of a user journey map.

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References

  1. Endmann, A., Keßner, D.: User journey mapping – a method in user experience design. i-com 15, 105–110 (2016)

    Google Scholar 

  2. Rosenbaum, M.S., Otalora, M.L., Ramírez, G.C.: How to create a realistic customer journey map. Bus. Horiz. 60, 143–150 (2017)

    Article  Google Scholar 

  3. Xiong, Z.Y., Xiao, J.X.: Research of smart door lock design based on an extended Kano model and user journey map. In: Proceedings - 2021 2nd International Conference on Intelligent Design, ICID 2021. Institute of Electrical and Electronics Engineers Inc., pp. 255–259 (2021)

    Google Scholar 

  4. Väinämö, O.: Refining the user journey co-creation processes, Master’s Thesis, Laurea University of Applied Sciences (2016)

    Google Scholar 

  5. Tupikovskaja-Omovie, Z., Tyler, D.: Eye tracking technology to audit google analytics: analysing digital consumer shopping journey in fashion m-retail. Int. J. Inf. Manag. 59 (2021)

    Google Scholar 

  6. Alkhalisi, A.F.: Creating a qualitative typology of electric vehicle driving: EV journey-making mapped in a chronological framework. Transport. Res. F: Traffic Psychol. Behav. 69, 159–186 (2020)

    Article  Google Scholar 

  7. Kokins, G., Straujuma, A., Lapiņa, I.: The role of consumer and customer journeys in customer experience driven and open innovation. J. Open Innov. Technol. Mark. Complex. 7(3), 185 (2021)

    Article  Google Scholar 

  8. Vollero, A., Sardanelli, D., Siano, A.: Exploring the role of the Amazon effect on customer expectations: an analysis of user-generated content in consumer electronics retailing. J. Consum. Behav. (2021)

    Google Scholar 

  9. Ishak, A., Ginting, R., Suwandira, B., Fauzi Malik, A.: Integration of Kano model and quality function deployment (QFD) to improve product quality: a literature review. In: IOP Conference Series: Materials Science and Engineering. IOP Publishing Ltd., Bristol (2020)

    Google Scholar 

  10. Chizinsky, K.: Quantifying user experience in a sales journey (2018)

    Google Scholar 

  11. Yunus, N.H.M., Sampe, J., Yunas, J., Pawi, A., Jalil, M.I.A.: Performance comparison of micromachined antennas optimized at 5 GHZ for RF energy harvester. Indones J. Electr. Eng. Comput. Sci. 15(1), 258–265 (2019)

    Google Scholar 

  12. Baudisch, P., Beaudouin-Lafon, M., Mackay, W.E.: Extended Abstracts of the 31st Annual CHI Conference on Human Factors in Computing Systems (2013)

    Google Scholar 

  13. Sindhu, C., Mukherjee, D., Sonakshi: A Joint sentiment-topic model for product review analysis of electronic goods. In: Proceedings - 5th International Conference on Computing Methodologies and Communication, ICCMC 2021. Institute of Electrical and Electronics Engineers Inc., pp. 574–578 (2021)

    Google Scholar 

  14. Bhavani, A., Santhosh Kumar, B.: A Review of state art of text classification algorithms. In: Proceedings - 5th International Conference on Computing Methodologies and Communication, ICCMC 2021. Institute of Electrical and Electronics Engineers Inc., pp. 1484–1490 (2021)

    Google Scholar 

  15. Ayoub, J., Zhou, F., Xu, Q., Yang, J.: Analyzing customer needs of product ecosystems using online product reviews. In: Proceedings of the ASME Design Engineering Technical Conference. American Society of Mechanical Engineers (ASME) (2019)

    Google Scholar 

  16. Yang, B., Liu, Y., Liang, Y., Tang, M.: Exploiting user experience from online customer reviews for product design. Int. J. Inf. Manage. 46, 173–186 (2019)

    Article  Google Scholar 

  17. De Keyser, A.: Understanding and managing the customer experience. Doctoral dissertation, Ghent University (2015)

    Google Scholar 

  18. Epp, A.M., Price, L.L.: Designing solutions around customer network identity goals. J. Mark. 75(2), 36–54 (2011)

    Article  Google Scholar 

  19. Badran, O., Al-Haddad, S.: The impact of software user experience on customer satisfaction. J. Manag. Inf. Decis. Sci. 21(1), 1–20 (2018)

    Google Scholar 

  20. Ņikitina, T., et al.: Competences for strengthening entrepreneurial capabilities in Europe. J. Open Innov. Technol. Mark. Complex. 6(3), 62 (2020)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Management Information Technology, Riga Technical University, Riga, Latvia

    Alberts Pumpurs

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  1. Alberts Pumpurs
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Correspondence to Alberts Pumpurs .

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Editors and Affiliations

  1. Riga Technical University, Riga, Latvia

    Ērika Nazaruka

  2. Rostock University, Rostock, Germany

    Kurt Sandkuhl

  3. Jönköping University, Jönköping, Sweden

    Ulf Seigerroth

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Pumpurs, A. (2022). User Journey Map as a Method to Extrapolate User Experience Knowledge from User Generated Reviews. In: Nazaruka, Ē., Sandkuhl, K., Seigerroth, U. (eds) Perspectives in Business Informatics Research. BIR 2022. Lecture Notes in Business Information Processing, vol 462. Springer, Cham. https://doi.org/10.1007/978-3-031-16947-2_14

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  • DOI: https://doi.org/10.1007/978-3-031-16947-2_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-16946-5

  • Online ISBN: 978-3-031-16947-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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