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The purpose of this paper is to show that third-party quality audits (TPQAs) facilitate performance improvement and give confidence to organisations concerning the process quality of services and products. However, because of inconsistencies and unethical practices often observed in the industry, organisations question the significance of TPQA. A perception exists that its initial purpose as an impartial tool ensuring quality of deliverables is no longer upheld. Hence, the need to determine and explain the influence of the ISO 19011 standard interpretation on the application of the audit guidelines in performing TPQA, to promote consistency in the audit process.

The study employed document analysis of the ISO 19011 standard, followed by semi-structured interviews with certification managers (CBs) to gain insight related to their interpretation and application of the ISO 19011 guidelines.

The CBs interpret the ISO 19011 guidelines differently; hence, their application of the standard to compile their audit documents differ. Adherence to the principles of auditing particularly, integrity and independence were found as the core of the audit process while their disregard reflects failure of the real intent of auditing. The inconsistencies in the audit procedures and documents developed for auditors are ascribed to some CBs’ personal interpretations.

The study explores how the different interpretations of the ISO 19011 standard prevail and are perceived by the CBs and auditors. The findings aim to support standardisation and reduce the variations across and amongst the different CBs and auditors.

In the rapidly evolving digital economy, businesses face formidable pressures to maintain their competitive standing, prompting a surge of interest in the intersection of artificial intelligence (AI) and digital transformation (DT). This study aims to assess the impact of AI technologies on corporate DT by scrutinizing 3,602 firm-year observations listed on the Shanghai and Shenzhen stock exchanges. The research delves into the extent to which investments in AI drive DT, while also investigating how this relationship varies based on firms' ownership structure.

To explore the influence of AI technologies on corporate DT, the research employs robust quantitative methodologies. Notably, the study employs multiple validation techniques, including two-stage least squares (2SLS), propensity score matching and an instrumental variable approach, to ensure the credibility of its primary findings.

The investigation provides clear evidence that AI technologies can accelerate the pace of corporate DT. Firms strategically investing in AI technologies experience faster DT enabled by the automation of operational processes and enhanced data-driven decision-making abilities conferred by AI. Our findings confirm that AI integration has a significant positive impact in propelling DT across the firms studied. Interestingly, the study uncovers a significant divergence in the impact of AI on DT, contingent upon firms' ownership structure. State-owned enterprises (SOEs) exhibit a lesser degree of DT following AI integration compared to privately owned non-SOEs.

This study contributes to the burgeoning literature at the nexus of AI and DT by offering empirical evidence of the nexus between AI technologies and corporate DT. The investigation’s examination of the nuanced relationship between AI implementation, ownership structure and DT outcomes provides novel insights into the implications of AI in the diverse business contexts. Moreover, the research underscores the policy significance of supporting SOEs in their DT endeavors to prevent their potential lag in the digital economy. Overall, this study accentuates the imperative for businesses to strategically embrace AI technologies as a means to bolster their competitive edge in the contemporary digital landscape.