Improving the Chances of Success of Resource Hungry Self-Service BI Systems in the Post Covid Context: With a Focus on ‘Satisfaction’

Abstract

As organizations invest trillions in Information Technology (IT) to cater to growing demands of customers, the complexity of Business Intelligence Systems expand, thereby enabling businesses to power efficient decisions driven by data. In a context, where considerable resources are invested by organizations to enable access to data, information & insights through the self-service route for their employees, there is a critical need to improve the chances of success of resource hungry self-service Business Intelligence (BI) systems (like PowerBI, Tableau etc.) to unlock value from the investments. Even though ‘Satisfaction’ is the most frequently used construct to explain IT/Information System (IS) Continuance Intention, in the post Covid context, our current understanding of post-adoption ‘Satisfaction’ of self-service BI systems in a bandwidth constrained environment (like home) is in-adequate to enable targeted interventions to drive usage. In this study, we review the diverse literature on Satisfaction to establish its central role in driving the success of self-service BI systems. This would inspire future research into the relative influence of System Quality measures on Satisfaction thereby providing actionable guidance to Human Computer Interaction (HCI) designers & product teams.