Examining Factors that Influence Intent to Adopt Data Science

ABSTRACT

Data science is a relatively new and emerging field with strong job growth projections. In this work, we develop a new theoretical model based on the theory of planned behavior and the IS Success Model in order to understand public perceptions about data science. Specifically, we aim to determine the potential impact and if the public views data science as beneficial to organizations and society and whether this in turn leads to an intent to use data science. In order to answer the aforementioned questions, we develop a definition of data science derived from current, state-of-the-art literature. Next, we test our theoretical model via a survey instrument that adapts relevant constructs from academic literature. Results indicate support for our model and subsequent hypotheses which show that information quality and system quality impact social norms and behavioral control which in turn influences perceived benefits of data science which influences the intent to use data science. Our model can be employed to advance the adoption of data science as a tool for business and data driven decision-making as well as position academia to train future generations of data scientists.

KEYWORDS:

• Behavioral intention
• data science
• IS success model
• net benefits
• theory of planned behavior