Driving innovation through big open linked data (BOLD): Exploring antecedents using interpretive structural modelling

Abstract

Innovation is vital to find new solutions to prob- lems, increase quality, and improve profitability. Big open linked data (BOLD) is a fledgling and rapidly evolving field that creates new opportunities for innovation. However, none of the existing literature has yet considered the interrelation- ships between antecedents of innovation through BOLD. This research contributes to knowledge building through utilising interpretive structural modelling to organise nineteen factors linked to innovation using BOLD iden- tified by experts in the field. The findings show that almost all the variables fall within the linkage cluster, thus having high driving and dependence powers, dem- onstrating the volatility of the process. It was also found that technical infrastructure, data quality, and external pressure form the fundamental foundations for innovation through BOLD. Deriving a framework to encourage and man- age innovation through BOLD offers important theoretical and practical contributions.