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Due to globalization and digitalization, the world of work is undergoing comprehensive change. These trends are challenging management accounting (MA) and pressuring individuals and organizations to change. The literature postulates a replacement of traditional organizational careers by “new” career models characterized by dynamism and flexibility. However, the state of the art on careers in MA lacks empirical evidence and has disparate research interests.

In this study, the authors investigate the status quo of careers in MA, key influencing factors and assumed change in such careers. To do so, the authors conducted a quantitative empirical study, based primarily on the careers of 83 graduates of a department offering a MA major at a German-speaking university. Nine qualitative empirical interviews supplement the quantitative findings.

The authors’ findings indicate that while MA careers are changing, the characteristics of the profession are continuing to concur with the traditional organizational understanding of careers. Accumulated professional experience is the key factor to achieving a management position although management accountants tend to become more dynamic in terms of career paths and career understanding. Thus, employment in various functional areas opens new career paths in MA.

The methodology of analysing quantitative and empirical cross-sectional data and the resulting final sample size is too small to guarantee robust statistical inference. Moreover, further interviews would lead to greater data saturation.

The study sheds light on the under-researched question of how careers in MA proceed and develop. This could be of interest for practitioners working with management accountants such as personnel consultants.

This study contributes to the field through its comprehensive consideration of careers in MA in this changed context, thus providing new insights for academia and business practice.

The purpose of this study is to examine the state of research into adoption of machine learning systems within the health sector, to identify themes that have been studied and observe the important gaps in the literature that can inform a research agenda going forward.

A systematic literature strategy was utilized to identify and analyze scientific papers between 2012 and 2022. A total of 28 articles were identified and reviewed.

The outcomes reveal that while advances in machine learning have the potential to improve service access and delivery, there have been sporadic growth of literature in this area which is perhaps surprising given the immense potential of machine learning within the health sector. The findings further reveal that themes such as recordkeeping, drugs development and streamlining of treatment have primarily been focused on by the majority of authors in this area.

The search was limited to journal articles published in English, resulting in the exclusion of studies disseminated through alternative channels, such as conferences, and those published in languages other than English. Considering that scholars in developing nations may encounter less difficulty in disseminating their work through alternative channels and that numerous emerging nations employ languages other than English, it is plausible that certain research has been overlooked in the present investigation.

This review provides insights into future research avenues for theory, content and context on adoption of machine learning within the health sector.