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

Yogesh K. Dwivedi, Marijn Janssen, Emma L. Slade, Nripendra P. Rana, Vishanth Weerakkody, Jeremy Millard, Jan Hidders, Dhoya Snijders

Research output: Contribution to journalArticleScientificpeer-review

56 Citations (Scopus)
33 Downloads (Pure)

Abstract

Innovation is vital to find new solutions to problems, 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 interrelationships 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 identified 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, demonstrating 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 manage innovation through BOLD offers important theoretical and practical contributions.

Original languageEnglish
Pages (from-to)1-16
Number of pages16
JournalInformation Systems Frontiers: a journal of research and innovation
DOIs
Publication statusPublished - 2016

Keywords

  • Big data
  • Innovation
  • Interpretive structural modelling
  • Linked data
  • Open data

Fingerprint Dive into the research topics of 'Driving innovation through big open linked data (BOLD): Exploring antecedents using interpretive structural modelling'. Together they form a unique fingerprint.

Cite this