Clinging to Excel as a Security Blanket: Investigating Accountants' Resistance to Emerging Data Analytics Technology

Abstract

While many business publications stress the importance of data analytics skills and technology, The Wall Street Journal reports strong reactions of accountants faced with a move away from Excel. Accountants are traditionally the reliable source for business information, but that position may be eroded by the emerging profession of data analysts. Looking to the future, Big Four accounting firms stress the need for data analysis skills, and universities are launching degree programs. But what is the reaction of current working professionals? Are they heeding the call for data analytics? Evidence indicates there is some resistance to adoption of data analytics practices. This research proposes the use of Status Quo Bias Theory to investigate the reactions of accounting professionals across the spectrum of accounting sub-disciplines. The study seeks to understand factors that may influence practicing accountants' resistance to adopting new data analysis technologies.

The Data Analytics Journey: Interactions Among Auditors, Managers, Regulation, and Technology*

“…Brown‐Liburd et al (2015) acknowledge that it is necessary to use new tools to harness these large quantities of data, and Gray and Debreceny (2014) provide an example by explaining how data mining can facilitate auditors' work when clients have large quantities of electronic data (also see Pickard and Cokins 2015 and Perols et al 2017). Schmidt et al (2020) investigate the reluctance of some accountants to adopt data analytics in spite of these benefits. Other research focuses on the use of data visualization as a tool for auditors, finding benefits when used in certain combinations with traditional audit evidence (Rose et al 2017).…”

Auditors' Perception on the Impact of Artificial Intelligence on Professional Skepticism and Judgment in Oman

“…Another risk associated with AI is that the AI will not delete the irrelevant data and will have a dilution impact. Nevertheless AI will eventually learn and create patterns based on irrelevant data and thus will possess lack of prediction and concrete confirmation [29]. Further, such inclusion of irrelevant data can influence the audit process and audit quality [30] Professional skepticism and judgment will remain a crucial area for every audit irrespective of the use of any technology [20].…”

Unravelling the integrated information systems and management control paradox: enhancing dynamic capability through business intelligence

“…While organisations have increasingly implemented ERP systems that create large enterprise databases consisting of integrated transactional data, too often this data is not accessed and leveraged for operational decision‐making – the capability provided by BI. Operational managers often resort to their traditional spreadsheets using very limited data resources (Schmidt et al , 2019). Management needs to provide the encouragement and training necessary for operational managers to adapt to BI technologies and to assimilate such technologies into their decision‐making (Elbashir et al , 2011).…”