Search results

This study aims to examine how women entrepreneurs in Kazakhstan's male-dominated sectors utilize the concept of positionality to navigate and redefine gender norms, focusing on their engagement with entrepreneurial masculinities and femininities. It explores the transformative potential of their strategic actions on gender dynamics within the entrepreneurial ecosystem.

Employing qualitative research through 27 in-depth interviews, this study adopts constructivist grounded theory to delve into how women entrepreneurs interact with gender norms within their entrepreneurial context. This approach highlights the dynamic interplay between gender norms and the strategies employed by women entrepreneurs to navigate these challenges.

The findings reveal that women entrepreneurs actively employ and navigate entrepreneurial masculinities and femininities as strategies to challenge traditional gender roles. Their approaches vary from conforming to, challenging and creatively redefining the gendered expectations encountered in their entrepreneurial journey. This demonstrates their agency in reshaping gender norms and contributing to the diversity of gender performances within the domain of entrepreneurship.

While focused on Kazakhstan, the study's findings suggest broader implications for understanding gender dynamics in entrepreneurship across different cultural contexts. Future research could extend this inquiry to varied sociocultural settings, employing post-structuralist and ethnographic methodologies to further explore the performance of gender roles and the negotiation of belonging in entrepreneurial contexts.

By foregrounding the concept of positionality, this study enriches the dialogue on gender dynamics within entrepreneurship, offering fresh perspectives on the agency of women entrepreneurs in male-dominated sectors. It illustrates how gender identities and performances are not fixed but are actively constructed and negotiated, contributing to the evolving landscape of entrepreneurial masculinities and femininities.

Demand forecasts are a key component of planning efforts and are crucial for managing core operations. This study aims to evaluate the use of several machine learning (ML) algorithms to forecast demand for residential construction in Jordan.

The identification and selection of variables and ML algorithms that are related to the demand for residential construction are indicated using a literature review. Feature selection was done by using a stepwise backward elimination. The developed algorithm’s accuracy has been demonstrated by comparing the ML predictions with real residual values and compared based on the coefficient of determination.

Nine economic indicators were selected to develop the demand models. Elastic-Net showed the highest accuracy of (0.838) versus artificial neural networkwith an accuracy of (0.727), followed by Eureqa with an accuracy of (0.715) and the Extra Trees with an accuracy of (0.703). According to the results of the best-performing model forecast, Jordan’s 2023 first-quarter demand for residential construction is anticipated to rise by 11.5% from the same quarter of the year 2022.

The results of this study extend to the existing body of knowledge through the identification of the most influential variables in the Jordanian residential construction industry. In addition, the models developed will enable users in the fields of construction engineering to make reliable demand forecasts while also assisting in effective financial decision-making.