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With the arrival of the big data era, governments have appointed a chief data officer (CDO) to meet the opportunities and challenges brought by big data. The existing research on the CDOs is very limited, and what does exist focuses primarily on what are CDOs do. Little research has explored how CDOs do. To fill this gap, this study employed ambidexterity theory to investigate the ambidexterity of CDOs’ impact on data-driven innovation.

To empirically test the model, a survey study was conducted to empirically test the model. Data were collected from 261 CDOs in government and government employees in big data management centers or bureaus. The collected data were analyzed quantitatively to answer hypotheses using a structural equation model.

The findings suggest that data exploitation and data exploration significantly influence data-driven leadership, culture and value propositions. Data-driven leadership and value propositions significantly impact government performance.

This study is one of the first attempts to investigate how CDOs work, especially when promoting data-driven innovation. In addition, this study extends ambidexterity theory into the issue of the CDO in government.

COVID-19 has forced audit firms to change the way they operate. One change has been to rely more on information technology (IT) and IT human capital to overcome COVID-19-related challenges. We refer to audit firms’ use of these two resources as audit firm informatization (AFI). It is important to understand whether AFI helps audit firms address challenges created by the pandemic. Thus, this study examines the impact of AFI on audit quality during the COVID-19 pandemic in China with a focus on IT human capital.

We use a mixed-methods approach. First, we perform multivariate regression analyses on archival data. Specifically, we investigate the relationship between IT human capital and audit quality and the two mechanisms (i.e. improved efficiency and reduced audit risk) underlying the relationship. We also investigate how this relationship is moderated by features of clients, audit firms and individual auditors. Then we use interviews to corroborate the results of our regression analyses.

Our analyses of archival data show that IT human capital positively affects audit quality through improved efficiency and reduced audit risk and that this positive impact is more pronounced for clients in non-manufacturing industries, those with a more opaque information environment, audit firms with greater industry coverage and individual auditors with less experience. Our interview data indicate that audit firms with more advanced AFI and a higher level of IT human capital in particular are less disrupted by the pandemic and are better able to use IT to address challenges associated with COVID-19. Furthermore, the results confirm that improved efficiency and reduced audit risk are the mechanisms through which AFI enhances audit quality. Finally, we identify issues associated with the use of IT.

This study is the first to investigate how IT human capital (and by extension AFI) influences audit quality in the context of the COVID-19 pandemic. Our findings should be of interest to practitioners and setters of auditing standards.