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This study aims to conduct a systematic literature review and bibliometric analysis on the topic of digital labour. The study also identifies the future research directions for the topic.

In total, 118 research papers were identified and reviewed from 11 established research databases and A*, A and B category journals from the ABDC journal list. The papers covered a timespan between 2006 and 2023. Bibliometric analysis was conducted to identify key research hotspots.

The emergent themes and associated sub-themes related to digital labour were identified from the literature. The paper found three significant themes that include digital labour platform, gig economy and productivity. This study also acts as a platform to initiate further research in this field for academicians, scholars, industry practitioners and policymakers. The future research scope in the topic is also presented.

The present study is unique in its nature as it approaches the topic of digital labour from all relevant perspectives.

Big Data is one of the most promising area where it can be applied to make a change is health care. Healthcare analytics have the potential to reduce the treatment costs, forecast outbreaks of epidemics, avoid preventable diseases, and improve the quality of life. In general, the lifetime of human is increasing along world population, which poses new experiments to today’s treatment delivery methods. Health professionals are skillful of gathering enormous volumes of data and look for best approaches to use these numbers. Big data analytics has helped the healthcare area by providing personalized medicine and prescriptive analytics, medical risk interference and predictive analytics, computerized external and internal reporting of patient data, homogeneous medical terms and patient registries, and fragmented point solutions. The data generated level within healthcare systems is significant. This includes electronic health record data, imaging data, patient-generated data, etc. While widespread information in health care is now mostly electronic and fits under the big data as most is unstructured and difficult to use. The use of big data in health care has raised substantial ethical challenges ranging from risks for specific rights, privacy and autonomy, to transparency and trust.

Does gender stereotype endorsement play a role in the customer's cognitive evaluation of new ventures owned by women entrepreneurs? The authors’ cross-cultural study integrates literature on gender stereotype endorsement and cognitive legitimacy to address this research question.

The authors use a two-study experimental design and analyze our results by cultural context to test our hypotheses: one drawn from college students in Iran and one from working professionals in the United States.

The authors’ comparative results suggest that the evaluation of feminine versus masculine characteristics of women entrepreneurs varies depending on the evaluator's (in this case the customer's) endorsement of gender stereotypes and the cultural context. Specifically, the authors found that a new venture owned by a woman entrepreneur who displays feminine characteristics is perceived as more legitimate when the customer endorses feminine stereotypes, regardless of the country.

The authors’ research contributes to the literature on cognitive legitimacy and women's entrepreneurship by unveiling the cultural conditions and factors that allow women entrepreneurs to benefit from acting in a stereotypically feminine way. The authors use a binary approach to gender. Future research should extend our findings to also include a non-binary approach.

This study contributes to women's entrepreneurship research by unraveling the implications of gender stereotype endorsement, legitimacy and culture in customer evaluation of ventures owned by women.