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

Suning Zhu (), Ashish Gupta (), David Paradice () and Casey Cegielski ()
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Suning Zhu: Auburn University
Ashish Gupta: Auburn University
David Paradice: Auburn University
Casey Cegielski: Auburn University

Information Systems Frontiers, 2019, vol. 21, issue 4, No 5, 814 pages

Abstract: 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.

Keywords: Business analytics; Focused immersion; Context authenticity; Satisfaction; Behavior (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s10796-018-9865-4

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