Proposition of a Unified Theory on Resistance to Data: Empirical Studies and Experimentations to Reduce Barriers to Data Usage in Decision Support Systems

Data is ubiquitous and constitutes a significant source of competitive advantages for private and public organizations that manage to turn it into proper support for decision-makers. This holds true as long as two postulates are verified; “using data boosts quality of decision output” and “people are willing to use data”. While support for the first one is significant, we find surprisingly too few pieces of evidence for the second one. The second postulate being unverified can however have some serious implications. If people do not want to use data but are actually constrained to do so for instance, they will face anxieties, poor understanding and reduced decision outcomes. If resistant people decide not to use data, they take the risk of missing important business opportunities. The alternative scenario, on which we focus in this project, is to understand this resistance and mitigate it to make resistant people more comfortable with data. There is currently no theory that offers a complete view on the resistance of people to data. Therefore, this project intends to propose a unified Resistance to Data Theory (RDT) with three main contributions: (1) RDT formation where we will design the theory by identifying existing factors that influence this resistance from the literature and uncovering new ones with qualitative studies, (2) RDT
validation where we will validate the theory and (3) RDT exploitation where new approaches to Decision Support Systems will be identified to decrease resistance to data. Together, these three contributions provide a comprehensive view on a new fundamental issue (resistance to provided data) with a potentially significant switch in the perception of how data is used within an organization, and how data should be introduced to decision-makers to exploit its full potential more systematically.