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Understanding the Impact of Immersion and Authenticity on Satisfaction Behavior in Learning Analytics Tasks

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  • Suning Zhu

    (Auburn University)

  • Ashish Gupta

    (Auburn University)

  • David Paradice

    (Auburn University)

  • Casey Cegielski

    (Auburn University)

Abstract

As business analytics (BA) applications permeate across various industry sectors, the workforce needs to be trained and upskilled to meet the challenges of understanding and implementing analytics methodologies. To achieve payoffs from the resource investments in BA training, it is critical for enterprises to understand an individual’s learning behavior along with the process and outcome-centric satisfaction associated with a collaborative analytics training task. This study focuses on identifying the factors that influence the process of learning during BA training to entry-level BA users. Drawing on the theories of situated cognition, goal setting, and flow, we propose a model that explains how trainees in a group learn through a process that is influenced by the characteristics of BA training context through context authenticity, the traits of trainees through task motivation and preference towards teamwork. Using an experimental design built on data collection and a unique task of real visits to a historic cemetery, we found that context authenticity and task motivation have significant impact on focused immersion, which in turn significantly impacts process and outcome satisfaction for learning an analytics task. Results of this study extend and validate the theories of situated cognition, goal setting, and flow within the context of business analytics training. Based on these findings, we provide recommendations for practitioners for designing effective analytics tasks for better training outcomes.

Suggested Citation

  • Suning Zhu & Ashish Gupta & David Paradice & Casey Cegielski, 2019. "Understanding the Impact of Immersion and Authenticity on Satisfaction Behavior in Learning Analytics Tasks," Information Systems Frontiers, Springer, vol. 21(4), pages 791-814, August.
    • Handle: RePEc:spr:infosf:v:21:y:2019:i:4:d:10.1007_s10796-018-9865-4
    DOI: 10.1007/s10796-018-9865-4
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    2. Sepasgozar, Samad M.E., 2022. "Immersive on-the-job training module development and modeling users’ behavior using parametric multi-group analysis: A modified educational technology acceptance model," Technology in Society, Elsevier, vol. 68(C).
    3. Raed S. Algharabat & Nripendra P. Rana, 2021. "Social Commerce in Emerging Markets and its Impact on Online Community Engagement," Information Systems Frontiers, Springer, vol. 23(6), pages 1499-1520, December.
    4. Luvai Motiwalla & Amit V. Deokar & Surendra Sarnikar & Angelika Dimoka, 2019. "Leveraging Data Analytics for Behavioral Research," Information Systems Frontiers, Springer, vol. 21(4), pages 735-742, August.

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