In an increasingly knowledge-based global economy, social capital and knowledge sharing, particularly amongst small business, have become critical sources of competitive advantage, but how can such knowledge sharing be enabled? The objective of this research was to explore the effect of knowledge sharing enablers on knowledge sharing in female small business networks. This research addresses the call for research into the antecedents of social capital. Specifically, Trust, Social Identity, Social Media Usage and Shared Goals were included in the conceptual model for the study. Together these social capital enablers were found to be significant predictors of knowledge sharing behaviour although unique contributions varied. The research contributes to the growing body of literature on the dimensions of social capital and how they affect knowledge management, and is of use to practitioners involved in supporting female entrepreneurial networks.

PurposeGender bias in artificial intelligence (AI) should be solved as a priority before AI algorithms become ubiquitous, perpetuating and accentuating the bias. While the problem has been identified as an established research and policy agenda, a cohesive review of existing research specifically addressing gender bias from a socio-technical viewpoint is lacking. Thus, the purpose of this study is to determine the social causes and consequences of, and proposed solutions to, gender bias in AI algorithms.Design/methodology/approachA comprehensive systematic review followed established protocols to ensure accurate and verifiable identification of suitable articles. The process revealed 177 articles in the socio-technical framework, with 64 articles selected for in-depth analysis.FindingsMost previous research has focused on technical rather than social causes, consequences and solutions to AI bias. From a social perspective, gender bias in AI algorithms can be attributed equally to algorithmic design and training datasets. Social consequences are wide-ranging, with amplification of existing bias the most common at 28%. Social solutions were concentrated on algorithmic design, specifically improving diversity in AI development teams (30%), increasing awareness (23%), human-in-the-loop (23%) and integrating ethics into the design process (21%).Originality/valueThis systematic review is the first of its kind to focus on gender bias in AI algorithms from a social perspective within a socio-technical framework. Identification of key causes and consequences of bias and the breakdown of potential solutions provides direction for future research and policy within the growing field of AI ethics.Peer reviewThe peer review history for this article is available at https://publons.com/publon/10.1108/OIR-08-2021-0452