Resumen en Inglés:

ABSTRACT Some participation and collaboration problems between local beneficiaries and foreign experts in international development projects (IDPs) are due to two groups using different types of knowledge: indigenous and global respectively. Study proposes creating locally low cost indigenous knowledge expert systems and startups incrementally to solve participation and collaboration problems that can be evolved and scaled up in a sustainable way. Method nexus for evolutionary prototyping product and startup creation is explored. Future IDPs will be supported by artificial intelligence enabled tools requiring some basic local AI understanding. Complementary indigenous knowledge expert systems can create synergy with IDPs artificial intelligence systems but will require local ES development capabilities to be built and evolved in advance.

Resumen en Portugués:

RESUMO Este estudo investiga a contribuição da linguagem Python na análise e otimização de dados para Search Engine Optimization (SEO), com foco na organização e recuperação da informação. Adota-se uma abordagem teórico-exploratória, fundamentada em levantamento bibliográfico em bases reconhecidas, como Scopus e Web of Science, além da análise de ferramentas especializadas em SEO. O estudo identificou que Python, por meio de bibliotecas como Pandas e NumPy, permite a automação de processos essenciais, como extração de dados, análise de palavras-chave e modelagem preditiva de padrões de indexação. Os resultados apontam que a aplicação dessas ferramentas melhora a eficiência das estratégias de Search Engine Optimization, tornando-as mais precisas e baseadas em dados. Além disso, destaca-se a interseção entre Ciência da Informação e Ciência da Computação, evidenciando como a programação pode contribuir para a estruturação semântica e organização dos conteúdos digitais. Conclui-se que Python oferece soluções eficazes para a automação e análise preditiva no Search Engine Optimization, potencializando a visibilidade e recuperação da informação nos motores de busca. Sugere-se a realização de estudos experimentais para validar a aplicação prática dessas técnicas em cenários reais.

Resumen en Inglés:

ABSTRACT This study investigates the contribution of the Python programming language to data analysis and optimization for Search Engine Optimization (SEO), with an emphasis on information organization and retrieval. A theoretical-exploratory approach was adopted, based on a bibliographic review in recognized databases such as Scopus and Web of Science, in addition to the analysis of specialized SEO tools. The study identified that Python, through libraries such as Pandas and NumPy, enables the automation of essential processes, including data extraction, keyword analysis, and predictive modeling of indexing patterns. The results indicate that applying these tools enhances the efficiency of SEO strategies, making them more precise and data-driven. Furthermore, the intersection between Information Science and Computer Science is highlighted, demonstrating how programming can contribute to the semantic structuring and organization of digital content. It is concluded that Python provides effective solutions for automation and predictive analysis in SEO, increasing visibility and information retrieval in search engines. Future research should focus on experimental studies to validate the practical application of these techniques in real-world scenarios.

Resumen en Inglés:

ABSTRACT This study analyzes how the Management and Information Systems literature has investigated the associations between Overload, Anxiety, Fatigue, Avoidance, and Literacy to develop a preliminary research model in Big Data environments to be tested in future studies. We identified 93 articles for analysis, and we found nine direct associations between these variables. These results served as a basis for us to appropriate their theoretical backgrounds and adapt them to develop a preliminary research model to investigate how Cognitive Overload, Anxiety, Cognitive Fatigue, Avoidance Behavior, and Data Literacy are associated in Big Data Environments.

Resumen en Inglés:

ABSTRACT Extended Reality (XR) - a combination of virtual, augmented, and mixed reality technologies- is emerging as an innovative tool in STEM education. XR technologies provide faculty and students with engaging and interactive teaching and learning experiences. However, the factor that is relatively less researched, but directly influences the successful integration of XR in higher education, i. e., faculty adoption, is overlooked. This study analyzes the factors influencing university faculty’s adoption and integration of XR in STEM education by focusing on four-dimensional factors. The contextual factors review institutional support and infrastructure of the academic institutions, individual factors examine digital literacy levels, and experience with XR for STEM faculty, social factors review the influence of peer collaboration and student feedback, and technological features examine the usability of XR tools and privacy concerns for the faculty and student data. Using a faculty-centric approach, a survey of 500 STEM faculty members was conducted, and rigorous statistical analyses were employed, including independent t-tests, ANOVA, Chi-square tests, and multiple regression, to evaluate the impact of these factors. Results show that student feedback is the major factor influencing XR adoption, followed by institutional support and ease of use. Contrarily, data privacy concerns and digital literacy gaps are identified as major barriers to XR integration in STEM education. Based on the findings of the survey, the study outlines (1) the need for XR-based faculty training; (2) the need for increased institutional support; and (3) the need for user-centric design of XR tools as the main considerations to enable effective use of XR in higher education. The results emphasize the need to factor both technical and social aspects, facilitating a successful XR integration; thereby creating a foundation for potentially new and more interactive forms of pedagogy.

Resumen en Inglés:

ABSTRACT The exploratory and descriptive research aimed to identify the areas of knowledge, authors, institutions, scientific production, co-authorship networks and themes that involve the Chinese social credit system. The methodology used was Bibliometrics, using Scopus and the Lattes Platform as sources of information, as well as digital tools for data collection, analysis, and visualization: Bibliometrix, scriptLattes and VOSviewer. The results revealed that this topic is relatively new and emerging. The area of Social Sciences stood out in terms of volume of scientific production. The converging themes between the samples of the two databases were ethics, governance, and big data.