Next Generation Smart Sustainable Auditing Systems using Big Data Analytics: Understanding the interaction of critical barriers

Shukla, Manish; Mattar, Lana

Abstract

In the current scenario, sustainable auditing, for example roundtable of sustainable palm oil (RSPO), requires a huge amount of data to be manually collected and entered into paper forms by farmers. Such systems are inherently inefficient, time-consuming, and, prone to errors. Researchers have proposed Big Data Analytics (BDA) based framework for next-generation smart sustainable auditing systems. Though theoretically feasible, real-life implementation of such frameworks is extremely difficult. Thus, this paper aims to identify the critical barriers that hinder the application of BDA based smart sustainable auditing system. It also aims to explore the dynamic interrelations among the barriers. We applied Interpretive Structural Modelling (ISM) approach to develop the model that extrapolates BDA adoption barriers and their relationships. The proposed model illustrates how barriers are spread over various levels and how specific barriers impact other barriers through direct and/or transitive links. This study provides practitioners with a roadmap to prioritise the interventions to facilitate the adoption of BDA in the sustainable auditing systems. Insights of this study could be used by academics to enhance understanding of the barriers to BDA applications.

Citation

Shukla, M., & Mattar, L. (2019). Next Generation Smart Sustainable Auditing Systems using Big Data Analytics: Understanding the interaction of critical barriers. Computers and Industrial Engineering, 128, 1015-1026. https://doi.org/10.1016/j.cie.2018.04.055

• Journal Article Type: Article
• Acceptance Date: Apr 30, 2018
• Online Publication Date: May 4, 2018
• Publication Date: Feb 28, 2019
• Deposit Date: May 8, 2018
• Publicly Available Date: Nov 4, 2019
• Journal: Computers and Industrial Engineering
• Print ISSN: 0360-8352
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 128
• Pages: 1015-1026
• DOI: https://doi.org/10.1016/j.cie.2018.04.055
• Public URL: https://durham-repository.worktribe.com/output/1331852