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This study analyzed the determinants that impact innovation on offshore wind energy (OSW) for a select group of countries, applying mixed-method approaches for a period between 2011 and 2021. The OSW sector witnessed cost reductions in recent years; therefore, this study analyzed how these factors impact technological innovation in the OSW sector. The fixed effects results proved trademark, carbon emissions, offshore wind capacity, and electricity from renewable energy, technical and scientific journals are significant and impact innovation regarding offshore wind energy. The maximum likelihood (MLE) coefficients are more robust than the restricted maximum likelihood (MREL) and better explained the significance of the variables in spurring OSW innovation. Ultimately, the interaction term "cross" came out significant in the analysis. It signified the importance of the interaction variables in scaling innovation. Similarly, the study forecasted OSW capacity additions to grow to more than 28GW by 2036, at a 48.8% growth rate, from the current over 55 G.W. capacity. Additionally, the infrastructure development of the OSW sector via a fitted line between total global offshore capacity and the development projects observed a negative relationship among these dual factors evaluated in the OSW, showing a decreasing trend of capacity additions among countries, as well as the fitted line relationship between total country OSW capacity and operational projects showed that China leads the globe with operational OSW projects. At the same time, Brazil is the new leader in the world regarding OSW capacity. The general analysis of the parameters of the OSW infrastructure plan showed the sector is declining regarding secured projects, development projects, operational projects, and capacities additions in some countries. However, global total capacity additions are on a steady path, declining a bit. This is attributable to the pandemic that slowed the global economy. This study will serve as a reference document for policy formulators regarding scaling up innovation for offshore wind energy.

Despite the vital role of women entrepreneurs in the economic development of their families and countries, it has been revealed that they have low business performance compared to their male counterparts. In this paper, an analysis of the business profile and entrepreneurial characteristics of women owned Small and Medium Enterprises in the Tamale Metropolis has been carried out. A cross-sectional design was used to collect quantitative and qualitative data from a variety of women entrepreneurs. Women entrepreneurs who have benefited from microcredit were selected using simple random sampling. They were then categorized based on their economic activities. The study found that, the level of education of the respondents was generally low; over 69% had no formal education, 20% had primary education, 9.5% had secondary and 1% had tertiary education. Furthermore, 54.2% of the enterprises were managed by the entrepreneurs’ themselves whiles 7.8% were run by the owner and other non-family employees. Also Only about 3% of the businesses surveyed were registered.

This paper examined the determinants of Small and Medium Enterprises (SMEs) growth in the Tamale Metropolis, Ghana using logistic regression. Growth was measured based on three key performance indicators of gross profits, employment and asset levels.Logistic regressions were estimated to identify the determinants of growth for each of the performance indicators. A survey was conducted on 199 women owned enterprises in the Tamale Metropolis of Ghana that had accessed micro credit from 2010 - 2014. The relationship between the performance indicators and a set of explanatory variables grouped into entrepreneur characteristics, business profile, loan and banking profile and enterprise size were considered. The results showed that entrepreneur characteristics and business profile significantly predicts the growth in gross profits of the SMEs. Specifically the person(s) put in charge of the day-to-day running of the business enterprise had a significant relationship with the growth in gross profit of the enterprise at the 1% significance level whilst household size of entrepreneur was significant at the 5% level. However, employment and asset growth were not significantly predicted by the explanatory variables at both the 1 and 5 % levels.