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Studying the gap between improvements in operational performance of a manufacturing organization does not necessarily represent the existent of safe and healthy work. The purpose of this paper is to fill this gap validating a scale about social practices.

The literature was studied; data analysis instrument and the scale validated by Q-sort. The reliability and validity of research instrument indicators were drawing from the analysis of judges. The data were assessed by convergence matrix.

It was validated five social practices factors. It was enabled the adequacy of the name of the constructs and establishment which indicators better convergence to the constructs.

The judge´s number that answered the research was low. The level of convergence related of two factors was above 50 percent.

It is possible to achieve better levels of performance through social practices. Organizations must rethink the management and the routine of the workers to implement the operational practices.

The practices need to have with well-defined rules, as well as action to drive compliance. This vision also needs to be expanded to suppliers, customers and society.

Highlight five points: technology is the main factor for analyzes and decisions; the search for quality leads organizations to seek practices that improve workers’ well-being, health and safety; the activities of the worker are carried out on the factory, or in the work environment; Should not to belittle the local community; culture is an essential factor to continuous improvement.

The establishment of partnerships between companies, government and universities aims to enhance innovation and the technological development of institutions. The biotechnology sector has grown in recent years mainly driven by its cooperative business model. Compared to other countries, this sector is slowly advancing in Brazil, with delays in science, technology and innovation, especially in the private sector. This paper aims to examine, through social network analysis, the collaborative networks between institutions that filed patents in biotechnology – medicinal preparations from plants – whose inventions had Brazil as the priority country.

The study of technological cooperation using patent documents is a reliable approach as they serve as good indicators of the interactions between organizations that focus on innovation and development of new product. Social network analysis of cooperation networks helps to understand the connections between patent assignees, and how they establish relationships.

Results show that public universities are the institutions that most deposit patents, as well as those that co-operate the most, especially Universidade of Campinas. The study also reveals the critical role of Research Support Agencies in stimulating research and technological development, which result in new technologies.

The study applied the social network analysis to provide an overview of the interactions among Brazilian institutions with the purpose of helping in decision-making and inciting public policies to leverage the biotechnology sector.

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.