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This paper aims to address which resources provided by an entrepreneurial ecosystem (EE) are necessary for deep technology entrepreneurship.

The authors used a novel approach known as necessary condition analysis (NCA) to data on EEs and deep-tech startups from 132 countries, collected in a global innovation index and Crunchbase data sets. The NCA makes it possible to identify whether an EEs resource is a necessary condition that enables entrepreneurship.

Necessary conditions are related to political and business environment; education, research and development; general infrastructure; credit; trade; diversification and market size; and knowledge absorption capacity.

The results show that business and political environments are the most necessary conditions to drive deep-tech entrepreneurship.

Policymakers could prioritize conditions that maximize entrepreneurial output levels rather than focusing on less necessary elements.

Some resources require less performance than others. So, policymakers should consider allocating policy efforts to strengthen resources that maximize output levels.

Studies on deep-tech entrepreneurship are scarce. This study provides a bottleneck analysis that can guide the formulation of policies to support deep-tech entrepreneurship, as it allows to identify priority areas for resource allocation.

Understanding matching patterns and determinants of attracting quality talents is an under-researched area, especially from a firm perspective. Firm’s recruitment strategies have an impact on the sorting patterns in the labour market which remains undetermined. This paper aims to explore the drivers of attracting and recruiting quality talents. Also, the role of policies including the national labour laws, industry norms and localised firm policies have on hiring practices and drivers in a developing country.

This study is underpinned by network theory, equity theory, social exchange theory and resource-based theory. The authors leveraged on a mixed methodology that is a structured questionnaire administered to 200 firm representatives in Lagos and interviews with key informants from the demand side for labour.

The study revealed that firms can leverage on salary, brand name, referral, job security as core factors in attracting and recruiting quality talents. Also, digitisation is a key strategy leveraged on attracting and recruiting quality talents. Techniques such as the use of social media, traditional media, online interviews, physical interviews have proven to help in selecting quality talents.

Specifically, the paper throws light on how firms use different recruitment channels for hiring workers, and how the use of these channels affects the quality of matches. Furthermore, the role of social networks, wages and benefits for firm recruitment and matching efficiency was well highlighted.

Diagnosing brain tumors is a process that demands a significant amount of time and is heavily dependent on the proficiency and accumulated knowledge of radiologists. Over the traditional methods, deep learning approaches have gained popularity in automating the diagnosis of brain tumors, offering the potential for more accurate and efficient results. Notably, attention-based models have emerged as an advanced, dynamically refining and amplifying model feature to further elevate diagnostic capabilities. However, the specific impact of using channel, spatial or combined attention methods of the convolutional block attention module (CBAM) for brain tumor classification has not been fully investigated.

To selectively emphasize relevant features while suppressing noise, ResNet50 coupled with the CBAM (ResNet50-CBAM) was used for the classification of brain tumors in this research.

The ResNet50-CBAM outperformed existing deep learning classification methods like convolutional neural network (CNN), ResNet-CBAM achieved a superior performance of 99.43%, 99.01%, 98.7% and 99.25% in accuracy, recall, precision and AUC, respectively, when compared to the existing classification methods using the same dataset.

Since ResNet-CBAM fusion can capture the spatial context while enhancing feature representation, it can be integrated into the brain classification software platforms for physicians toward enhanced clinical decision-making and improved brain tumor classification.

This research has not been published anywhere else.