Towards A Big Data Theory Model

Big Data is an emerging research topic. The term remains fuzzy and is seen as an umbrella term. Origin, composition, possible strategies, and outcomes are uncertain. Thus, the positioning of publications addressing business administrated issues related to Big Data is impeded. From a practitioner's point of view, the ability to communicate a value proposition is impeded due to the difficulty in scoping the intended artifact and the interpretation of arisen company results. So, underlying relationships and concepts have to be described. The missing theoretical fundament of Big Data has been stated in literature. While some publications actually address this need, the majority of them remain methodically weak. In a previous study we deduced an initial qualitative Big Data theory model based on expert interviews and grounded theory. It is this paper's goal to verify the given model in a quantitative way and test it through structural equation modeling. Thereby, hypothesis are deduced and Big Data indicators presented. As a result, a Big Data theory model arises. All hypotheses of our research model are significant, and the study makes three principal contributions to the scientific discussion about Big Data. First, it unveils the underlying characteristics of Big Data. Second, we show the addressability of Big Data through strategies. Hereby, possible strategies to address Big Data are highlighted. Third, we found evidence that positive outcomes like return of investments through Big Data are possible. Thereby, the latter two aspects are of major interest for practice. The presented work contributes to the scientific discussion and supports a development of this domain.