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Finetuning Analytics Information Systems for a Better Understanding of Users: Evidence of Personification Bias on Multiple Digital Channels

Author

Listed:
  • Bernard J. Jansen

    (Hamad Bin Khalifa University
    Education City)

  • Soon-gyo Jung

    (Hamad Bin Khalifa University)

  • Joni Salminen

    (University of Vaasa)

Abstract

Although the effect of hyperparameters on algorithmic outputs is well known in machine learning, the effects of hyperparameters on information systems that produce user or customer segments are relatively unexplored. This research investigates the effect of varying the number of user segments on the personification of user engagement data in a real analytics information system, employing the concept of persona. We increment the number of personas from 5 to 15 for a total of 330 personas and 33 persona generations. We then examine the effect of changing the hyperparameter on the gender, age, nationality, and combined gender-age-nationality representation of the user population. The results show that despite using the same data and algorithm, varying the number of personas strongly biases the information system’s personification of the user population. The hyperparameter selection for the 990 total personas results in an average deviation of 54.5% for gender, 42.9% for age, 28.9% for nationality, and 40.5% for gender-age-nationality. A repeated analysis of two other organizations shows similar results for all attributes. The deviation occurred for all organizations on all platforms for all attributes, as high as 90.9% in some cases. The results imply that decision makers using analytics information systems should be aware of the effect of hyperparameters on the set of user or customer segments they are exposed to. Organizations looking to effectively use persona analytics systems must be wary that altering the number of personas could substantially change the results, leading to drastically different interpretations about the actual user base.

Suggested Citation

  • Bernard J. Jansen & Soon-gyo Jung & Joni Salminen, 2024. "Finetuning Analytics Information Systems for a Better Understanding of Users: Evidence of Personification Bias on Multiple Digital Channels," Information Systems Frontiers, Springer, vol. 26(2), pages 775-798, April.
  • Handle: RePEc:spr:infosf:v:26:y:2024:i:2:d:10.1007_s10796-023-10395-5
    DOI: 10.1007/s10796-023-10395-5
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    References listed on IDEAS

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    1. Bernard J. Jansen & Mimi Zhang & Kate Sobel & Abdur Chowdury, 2009. "Twitter power: Tweets as electronic word of mouth," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(11), pages 2169-2188, November.
    2. Lin, Canchu & Kunnathur, Anand, 2019. "Strategic orientations, developmental culture, and big data capability," Journal of Business Research, Elsevier, vol. 105(C), pages 49-60.
    3. Daniel D. Lee & H. Sebastian Seung, 1999. "Learning the parts of objects by non-negative matrix factorization," Nature, Nature, vol. 401(6755), pages 788-791, October.
    4. Salminen, Joni & Yoganathan, Vignesh & Corporan, Juan & Jansen, Bernard J. & Jung, Soon-Gyo, 2019. "Machine learning approach to auto-tagging online content for content marketing efficiency: A comparative analysis between methods and content type," Journal of Business Research, Elsevier, vol. 101(C), pages 203-217.
    5. Shailaja Venkatsubramanyan & Timothy R. Hill, 2010. "An empirical investigation into the effects of web search characteristics on decisions associated with impression formation," Information Systems Frontiers, Springer, vol. 12(5), pages 579-593, November.
    6. Hossain, Md Afnan & Akter, Shahriar & Yanamandram, Venkata, 2020. "Revisiting customer analytics capability for data-driven retailing," Journal of Retailing and Consumer Services, Elsevier, vol. 56(C).
    7. Raghav Pavan Karumur & Tien T. Nguyen & Joseph A. Konstan, 2018. "Personality, User Preferences and Behavior in Recommender systems," Information Systems Frontiers, Springer, vol. 20(6), pages 1241-1265, December.
    8. Xu, Zhenning & Frankwick, Gary L. & Ramirez, Edward, 2016. "Effects of big data analytics and traditional marketing analytics on new product success: A knowledge fusion perspective," Journal of Business Research, Elsevier, vol. 69(5), pages 1562-1566.
    9. Lene Nielsen & Kira Storgaard Hansen & Jan Stage & Jane Billestrup, 2015. "A Template for Design Personas: Analysis of 47 Persona Descriptions from Danish Industries and Organizations," International Journal of Sociotechnology and Knowledge Development (IJSKD), IGI Global, vol. 7(1), pages 45-61, January.
    10. Roland T. Rust & Ming-Hui Huang, 2014. "The Service Revolution and the Transformation of Marketing Science," Marketing Science, INFORMS, vol. 33(2), pages 206-221, March.
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