Background: Artificial Intelligence has been an area of great interest and investment in the industrial sector, offering numerous possibilities to enhance efficiency and accuracy in production processes. In this regard, this study aimed to identify the adoption challenges of Artificial Intelligence and determine which of these challenges apply to the industrial context of an emerging economy, considering the aspects of Industry 4.0. Methods: To achieve this objective, a literature review was conducted, and a survey was carried out among professionals in the industrial field operating within the Brazilian context. The collected data were analyzed using a quantitative approach through Cronbach’s alpha and the Lawshe method. Results: The results indicate that to enhance the adoption of Artificial Intelligence in the industrial context of an emerging economy, taking into account the needs of Industry 4.0, it is important to prioritize overcoming challenges such as “Lack of clarity in return on investment,” “Organizational culture,” “Acceptance of AI by workers,” “Quantity and quality of data,” and “Data protection”. Conclusions: Therefore, based on the achieved results, it can be concluded that they contribute to the development of strategies and practical actions aimed at successfully driving the adoption of Artificial Intelligence in the industrial sector of developing countries, aligning with the principles and needs of Industry 4.0.

This research aimed to analyze the importance of artificial intelligence and sustainability in higher education according to the literature in the field and to present the relationships of this context with the United Nations Sustainable Development Goals (SDGs). The adopted research strategies included bibliometric analysis using VOSviewer software and literature review, considering the Web of Science scientific database. The bibliometric analysis resulted in the clustering of four groups. The blue cluster highlighted the emergence of interest in studies on AI and sustainability in higher education following the Covid-19 pandemic. The green cluster emphasized the importance of more efficient teaching methods adapted to the demands of higher education, as well as the need to empower teachers to use artificial intelligence in developing students' skills and competencies, emphasizing sustainability. The yellow cluster indicated the presence of artificial intelligence in higher education based on the triad of sustainable education and innovation, aiming to prepare students for future challenges. The red cluster emphasized the impact of artificial intelligence in higher education, focusing on student learning, efficiency, and sustainable performance. Finally, the literature analysis identified the main AI technologies in higher education and their relationship with the United Nations SDGs. The reflections presented here can contribute to expanding discussions on the relationship between artificial intelligence and sustainability in higher education. From a practical standpoint, it can serve as a foundation for university managers aiming to promote the integration of AI into their teaching processes, considering the context of sustainability.

The study has as purpose to identify, analyze, and validate challenge constructs of Industry 4.0, which can affect the promotion of sustainability within the industry. A systematic literature review was conducted to identify challenges to the promotion of Industry 4.0 sustainability. A set of seventy challenges were grouped into the following eight constructs: finances, technology, organizational, human resources, legislation, geopolitical and economic factors, and both internal and external factors. Subsequently, the same constructs were validated using a survey involving industry professionals. The data were analyzed using the Lawshe method. Five constructs within the eight constructs were considered relevant to industry sustainability according to the experts’ opinion. The validated set of constructs included: finance, technology, organizational, human resources, and internal factors. This study contributes to the literature in the field by addressing a research gap of constructs identification based on expert’s opinions that impact Industry 4.0 in promotion of sustainable development. This study delivers theoretical and practical implications. From a theoretical standpoint, it contributes to expanding knowledge by providing valuable insights into the adoption of Industry 4.0 and its specific challenges concerning the pursuit of more sustainable practices. These implications extend to diverse research areas given the multidisciplinary nature of Industry 4.0.

(1) Background: This study aimed to analyze and rank the benefits of adopting Artificial Intelligence (AI) in the logistics area, considering the opinions of professionals working in the Brazilian logistics sector through the implementation of ISO/IEC 42001. (2) Methods: The procedures adopted included a literature review to identify the benefits of the use and the application of a survey aimed at professionals in the logistics sector. The analysis used the TOPSIS method to identify and rank the most decisive benefits. (3) Results: Of the 15 benefits mapped in the literature, the professionals attributed the greatest importance to customer satisfaction, operational efficiency, incentives for innovation, and improving the company’s image and competitive advantage. The results indicate that adopting ISO/IEC 42001 not only promotes process optimization and cost reduction, but also encourages innovation and strengthens competitiveness in the market. (4) Conclusions: The implementation of this standard is vital for companies in the logistics sector, especially in a Brazilian context of major infrastructural and regulatory challenges. The findings highlight the need for a strategic approach to the adoption of AI, emphasizing the importance of efficient logistics management and ethical practices in the application of this technology.

The aim of this study was to identify in the literature the digital technologies and their methods of application relevant to the functioning of reverse logistics (RL) for municipal solid waste. The method adopted was a systematic literature review using the PRISMA method with five stages, conducted over 9 months (from April to December 2023) and consulting the Scopus and Web of Science databases. The database is composed of 58 articles valid for the research objective, 16 digital technologies, and 61 practical applications to the context of RL of municipal solid waste (MSW) presented under three classifications. It presents a compilation of digital technologies applicable to MSW management and their various applications in the execution of RL of MSW. Until now, the applications of these digital technologies in MSW RL only existed in a scattered manner in the literature. In this way, it contributes to theoretical advancement by providing a solid foundation for future research and developments in this area. Furthermore, we provide a classification for these technologies, according to the pattern identified in this systematic review: individualized use such as blockchain, AI, Big Data, and IoT stand out as the technologies with the most applications, single-use such as tracking sensor, GPS, and RFID, and combined use with other technologies such as BIM+IoT+IA+Big Data. The study provides digital technologies and their various applications so that stakeholders can select them to overcome challenges in the reverse chain of MSW.

Background: The post-COVID-19 scenario has demonstrated the increasing importance of marketing for organizations, as retailers and entrepreneurs have had to adapt to new ways of selling their products and services. In this regard, this research aimed to identify challenges for developing the marketing plan of startups and validate them from the perspective of managers in the field, considering the market characteristics inherent to the post-COVID-19 era; Methods: To achieve this, a literature review and a survey were conducted among professionals in the field. The collected data were analyzed using the quantitative Lawshe method. Results: The results indicate that, for the development of the marketing plan of startups considering the post-COVID-19 reality, it is important to prioritize overcoming the challenges of “Consumer behavior pattern change”, “Differentiation from the competition”, “Digital expansion”, “Innovation capacity of companies”, “Creation of transformative marketing”, and “Reevaluation of marketing channels in the post-pandemic period”; Conclusions: Therefore, it can be concluded that these challenges reflect the main concerns and obstacles faced by startups in building effective marketing strategies and striving for a competitive position in the market. By recognizing and understanding these challenges, startups will be better prepared to face adversity and seize opportunities in this new market context.