Applying Affordance Theory to Big Data Analytics Adoption

Big data may be studied for three use cases-adoption by an organization, actual use by its employees and effect of adopting big data. Affordance theory has been adopted to study usage and effect of information technology. The theory has two basic constituents- an organization with a goal and the IT artifacts. The organization interacts with the IT artifacts to achieve the goal. In this work, we have applied affordance theory for the adoption phase. In case of big data adoption, the organization may not be able to decide a goal for big data. The organization may not have exposure to big data and may not have required skills to decide the goal. The proposed framework addresses the issues of possible inadequacy of the organization and its existing IT artifacts. We have introduced a separate phase for deciding a goal that precedes adoption. The organization may augment its skill set and its IT artifacts with the help of external agencies for deciding a goal. The IT artifacts have been replaced with data echo system to include the management component associated with IT artifacts. In the proposed framework, we have introduced team for facilitating interaction, perceiving emerged affordances and actualizing affordances. We have used the case study method to verify the efficacy of the adopted framework. The results clearly show that the framework is effective in studying the adoption of big data analytics.