EsACT - Artigos em Revistas Indexados à WoS/Scopus
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Percorrer EsACT - Artigos em Revistas Indexados à WoS/Scopus por Objetivos de Desenvolvimento Sustentável (ODS) "09:Indústria, Inovação e Infraestruturas"
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- Bridging innovation strategies and intellectual property: a systematic review-based conceptual framework and a roadmap for future research.Publication . Pires, Rui A.R.; Ferreira, João J.Understanding the connection between innovation strategies and intellectual property (IP) is crucial for academia, policymakers, and managers. This topic has gained interest from the scientific community. However, the literature is fragmented and dispersed due to increasing knowledge, and earlier literature reviews do not provide a holistic assessment of the existing research on how innovation strategies are connected to IP. This study aims to address this gap by answering the following questions: What is the status of the art on innovation strategies and IP regarding theoretical and methodological approaches, contextual elements, and key research themes? How can this research be advanced and developed further? To this purpose, we undertake a comprehensive review and synthesis of existing knowledge in this field based on the content analysis of 144 articles indexed in the Web of Science database. Findings indicate that (i) the knowledge-based and resource-based views are the main theoretical foundations, (ii) the empirical-quantitative approach is the main methodology, (iii) the Asian, European, and North American countries are the main geographical scopes explored, and (iv) there are six major research themes addressing innovation strategies, IP protection mechanisms, determinants of innovation strategies and IP, learning and knowledge, interorganizational collaboration, and value creation and appropriation outcomes. Therefore, this study contributes to the literature on innovation and IP strategies by systematically reviewing and analyzing the existing knowledge, proposing an integrative framework highlighting the interconnections among the major research themes, and setting a roadmap for future research.
- Business model innovations and their relationship with sustainable development in agribusiness: a bibliometric analysisPublication . Estácio, Rita; Campos, Susana; Correia, Ricardo Jorge; Teixeira, Mário SérgioThis article identifies the main links, commonalities, and future interdisciplinary topics between business model innovations and sustainable development in agribusiness. A systematic bibliometric analysis of the literature on business model innovations related to sustainable development in agribusiness was carried out using RStudio – Biblioshiny for Bibliometrix. Two hundred and fifty-one documents published in journals indexed in Scopus and Web of Science were analyzed between 2001 and 2023. Business model innovations and sustainable development in agribusiness are seen as ways of responding to social, economic, and environmental challenges. The research also reveals that when sustainable development is the topic, innovations in business models tend to incorporate practices that seek profitability and minimization of environmental and social impacts, promoting corporate social responsibility and long-term sustainability. There is a growing consensus that purely profit-focused business models have considerable limitations in neglecting social business activities and environmental impacts. About the question, “What are the innovations in business models that can improve sustainable development in agribusiness?”, the literature provides no practical answers. The next steps, therefore, are to identify and analyse successful case studies.
- Introducing a Chatbot to the Web Portal of a Higher Education Institution to Enhance Student InteractionPublication . Oliveira, Pedro Filipe; Matos, PauloThis paper introduces the implementation of a chatbot on the web portal of a higher education institution, aiming to enhance student interaction and provide seamless access to information and support services. With the increasing reliance on digital platforms for student engagement, a chatbot offers a user-friendly and efficient means of communication, catering to the diverse needs of students in a higher education setting. The chatbot developed utilizes natural language processing, machine learning, and artificial intelligence algorithms to engage in dynamic conversations with students. We use Large Language Models (LLMs), because these and vector databases are revolutionizing the way we handle and retrieve complex data structures. Their main objective is to provide instant responses, personalized guidance, and timely support for various aspects of student life within the institution, namely the following: Information Retrieval, where the chatbot acts as a virtual collaborator, offering quick and accurate responses to frequently asked questions regarding admissions, programs, course registration, financial aid, and campus facilities, reducing the need for manual information searches; Academic Support, where the chatbot assists students in academic matters, such as course selection, prerequisites, graduation requirements, and study resources. It can offer personalized recommendations based on a student’s academic profile and preferences; Campus Services, which provides information about campus services, extracurricular activities, events, and resources; and Appointment Scheduling, which facilitates appointment scheduling with academic advisors, and support staff, streamlining administrative processes and ensuring timely access to guidance and assistance. This development follows a user-centric approach, incorporating feedback from students, faculty, and administrators to ensure that the chatbot meets their specific needs and preferences. Rigorous testing and quality assurance measures are implemented to guarantee the accuracy, reliability, and security of the chatbot. In conclusion, we achieve a functional chatbot with a medium computational heaviness; in this way, it can be practical to use it in real time by the students on the institution’s web portal. The introduction of a chatbot on the web portal of a higher education institution represents a significant advancement in facilitating student interaction and support services. By providing instant and personalized responses, the chatbot streamlines communication, reduces response times, and empowers students to find information and resources efficiently. As chatbot technology continues to evolve, ongoing enhancements and refinements will ensure that it remains a valuable tool for enhancing student experiences, promoting engagement, and fostering a positive learning environment within the institution.
- PROMORE: a procedural modeler of virtual rural environments with artificial dataset generation capabilities for remote sensing contextsPublication . Adão, Telmo; Cerqueira, João; Adão, Miguel; Silva, Nuno; Pascoal, David; Magalhães, Luís G.; Barros, Tiago; Premebida, Cristiano; Nunes, Urbano J.; Peres, Emanuel; Morais, RaulRemote sensing (RS) is a rapidly evolving field that facilitates the study of phenomena on the Earth’s surface. Through various platforms, including satellites, manned aircraft, and remotely piloted aerial vehicles (RPAV), RS has been strategically applied to critical sectors like agriculture and forestry, which are essential for humanity’s sustenance. Key applications include crops classification, yield estimation and livestock monitoring and quantification. In the era of artificial intelligence (AI), the development of deep learning (DL) models for such applications often requires extensive field data collection and labor-intensive image labeling, which are both time-consuming and resource-intensive. To address these challenges, this paper presents Procedural Modeling of Rural Environments (PROMORE), a parameterizable, ontology driven system designed to generate 3D virtual environments encompassing forestry, farmland – mainly focused on vineyards – and village settings. This system also implements functionalities to automate the extraction of training data for deep learning applications in remote sensing, with the declared aim of providing complementary capabilities to data augmentation techniques, encompassing both traditional methods (e.g., flips, rotations, zooming) and advanced approaches such as generative adversarial networks (GANs). By simulating RPAV flights and managing virtual object visibility, PROMORE enables the automatic labeling, delineation, and highlighting of elements of interest (e.g., vine plants, trees, buildings), facilitating the generation of datasets tailored for tasks such as semantic segmentation, and object detection.