Browsing by Author "Hall, Paula."

Now showing 1 - 2 of 2

Enablers of knowledge sharing behaviour in female SMME networks.
(2019) Hall, Paula.; McArthur, Brian Walter.; Ellis, Deborah Ann.
Support for female entrepreneurs in South Africa has numerous economic and social benefits. Female SMME networks have arisen as an important support mechanism to increase female entrepreneurs’ social capital and enable knowledge sharing. The purpose of this study was to determine the nature, extent and enablers of knowledge sharing in female SMME networks. Using a quantitative questionnaire, this study statistically analysed female SMME networks members’ knowledge sharing behaviour (KSB) in relation to four key enablers derived from social capital theory. The study also analysed the type of knowledge shared and sought to determine if members’ KSB depended on demographic or business-related factors. Female SMME network members were found to display a high degree of KSB, particularly sharing of tacit knowledge. Two of the knowledge sharing enablers in the relational dimension of social capital, trust and social identity, were found to be highly correlated with members’ KSB, as did shared goals in the cognitive dimension. Surprisingly, social media usage, in the structural dimension, was found to have only a moderate correlation with KSB. This may be due to the members’ preference to share more tacit knowledge through socialisation than explicit knowledge through social media. Another unexpected finding was that KSB was found not to be dependent on member’s age, experience or education, nor on the number of employees or business sector of the SMME. This suggests that female SMME networks are conducive to knowledge sharing irrespective of the nature of the businesses or types of members. A multiple regression analysis between social capital, as a single aggregated construct made up of the four enablers, and KSB, found that social capital is a good predictor of KSB. However, these findings were limited to a small sample of members of SMME networks in KwaZulu-Natal - further studies are needed to establish more generalisable results. This research contributes to the study of KSB in inter-organisational networks, using a social capital framework, particularly in the context of female SMME networks. Given the importance of knowledge sharing for business success, female SMME networks should be supported in order to develop female entrepreneurs and their contribution to the South African economy.
Exploring the effects of women in Artificial Intelligence networks (WAINs) on women’s careers in Artificial Intelligence.
(2023) Hall, Paula.; Ellis, Deborah Ann.
The underrepresentation of women in the AI field is one of the causes of AI application biases needing resolution as part of the growing movement toward more ethical AI. One potential solution is to encourage more women to be involved in developing and deploying AI solutions, precipitating the growth of professional women in AI support networks. This research aimed to determine how women in AI networks contribute to women advancing and persisting in the AI workforce by developing and testing a model that links networking behaviour to women’s career persistence and advancement in AI. The study addressed the need for theoretical and empirical investigation into women in AI, networking behaviour, and the benefits networks offer to members, especially for their careers. Notably, there is a gap in the literature concerning formal women-only networks, with limited previous research on gender bias in the context of women in artificial intelligence networks (WAINs). The study followed a phased mixed method research process. First, a systematic review of AI gender bias was conducted, which supported the premise for increased gender diversity in AI. A conceptual model was then created from an intersection of multiple theories and models on gender in IT and networking, which a panel of women in AI experts reviewed, verified, and refined through in-depth interviews. The final phase tested the model propositions using structured equation modelling. The findings revealed that social support, opportunities and resources provided by professional WAINS contribute to the persistence and advancement of women in AI careers. This research provides an original contribution by suggesting a solution for improving gender diversity in AI development teams through the resources, opportunities and social support provided by WAINs. The research also contributes to a better understanding of women’s careers and networking behaviour specifically in the AI field. With the proliferation of AI-based solutions and the integration of AI into automated decision-making, reducing the gender gap in the AI workforce is more important than ever. Recommendations include active support for WAINs by businesses and policy bodies, while WAIN organisers and women in AI should co-create career enhancing resources and support in WAINs.