Proposing a Perceived Expertise Tool in Business Data Analytics

Germanakos, Panagiotis, Lekkas, Zacharias, Amyrotos, Christos and Andreou, Panayiotis orcid iconORCID: 0000-0002-6369-1094 (2021) Proposing a Perceived Expertise Tool in Business Data Analytics. In: UMAP '21: 29th ACM Conference on User Modeling, Adaptation and Personalization, 21-25 June 2021, Utrecht, Netherlands.

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The business data analytics domain exhibits a particularly diversified and demanding field of interaction for the end-users. It entails complex tasks and actions expressed by multidimensional data visualization and exploration contents that users with different business roles, skills and experiences need to understand and make decisions so to meet their goals. Many times this engagement is proven to be overwhelming for professionals, highlighting the need for adaptive and personalized solutions that would consider their level of expertise towards an enhanced user experience and quality of outcomes. However, measuring adequately the perceived expertise of individuals using standardized means is still an open challenge in the community. As most of the current approaches employ participatory research design practices that are time consuming, costly, difficult to replicate or to produce comparable, unbiased, results for informed interpretations. Hence, this paper proposes a systematic alternative for capturing expertise through a Perceived Expertise Tool (PET) that is devised based on grounded theoretical perspectives and psychometric properties. Preliminary evaluation with 54 professionals in the data analytics domain showed the accepted internal consistency and validity of PET as well as its significant correlation with other affiliated theoretical and domain-specific concepts. Such findings may suggest a good basis for the standardized modeling of users’ perceived expertise that could lead to effective adaptation and personalization.

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