Anais do Simpósio Brasileiro de Sistemas de Informação (SBSI)
Os Anais do Simpósio Brasileiro de Sistemas de Informação trazem os trabalhos selecionados e apresentados na trilha principal de cada edição do evento.
Este documento traz o front matter dos Anais do SBSI 2025.
Social media user stance detection without stance text
Context: User stance detection systems are intended to determine whether an individual holds a stance for or against a given target topic. For instance, ‘Vaccines save lives’ is a stance in favour of vaccination. Problem: Most existing approaches to stance detection take as an input a piece of text assumed to convey a stance towards the intended target, and then predict its polarity. However, when stance text is unavailable (e.g., when the individual did not publish any opinion regarding that particular topic), models of this kind are unable to determine the user’s stance. Solution: In the case of social media, we may consider as a substitute for stance text the use of other (non-stance) publications made by the user, or even by their close contacts. Information Systems theory: The work was considering Social Network Theory and Social Information Processing Theory. Method:We built a number of stance detection models from stance text and from non-stance data authored by the target users and their close contacts using supervised BERT, prompt-based LLama, and stacking. Results: The non-stance publications made by an individual and, to a lesser extent, those made by his/her contacts, were found to be strong predictors of user stance and, in some cases, their combination was shown to outperform the actual use of text stance. Contributions: Using network-related information may be indeed a substitute for stance text, and may enable the development of systems for scenarios in which stance text is unavailable.
A BPM Maturity Model to guide the evolution of BPM Competences in a Public Institution
Context: Business Process Management (BPM) has proven to be an essential practice for optimizing operational efficiency, especially in public institutions that face complex challenges related to hierarchical rigidity and the implementation of organizational changes. Problem: The lack of BPM maturity models tailored to the public sector creates significant gaps in the ability of government organizations to assess and improve their BPM competencies. Without a clear framework, these institutions struggle to improve their processes and reach higher levels of maturity. Solution: This work proposes a BPM maturity model specifically developed for public institutions, aiming to support the gradual evolution of their process management competencies. The model includes six key factors and 51 capability areas, along with five maturity levels that provide clear guidelines for implementation. IS Theory: The research is based on the Design Science Research (DSR) approach, which guides the development and evaluation of practical artifacts and theoretical contributions. The proposed model aims to contribute to the BPM maturity theory, especially in the public context. Method: The study was conducted through a case study in a Brazilian public institution, where the maturity model was applied to assess the current BPM level. The evaluation was supported by a spreadsheet tool that automates the maturity level verification process. Results: The maturity model evaluation revealed that the public institution studied had not yet reached maturity level two, indicating the need for significant improvements in strategic areas. The assessments highlighted the model’s usefulness as a practical tool for identifying deficiencies and outlining clear action plans. Contributions and Impact: This work contributes to the field of Information Systems (IS) by offering a maturity model adapted to the public sector, providing a practical and prescriptive approach to improving business process management.
CR-ONTO: Ontology-Based Reasoning for Credit Default Classification
Context: Credit defaults are operations less explored by financial institutions compared to performing and non-performing loans. This is because recovering credit default is often ineffective and involves high collection costs. Problem: The literature presents studies that describe ontologies to represent knowledge about loans, but they do not address the conceptualization of default. Furthermore, the ontologies do not explore the possibility of recovering these credits in default condition. Solution: This article presents CR-Onto, an ontology designed for the classification of loans in general, with an emphasis on default. CR-Onto can represent the knowledge of the context involving the credit default and respond to questions suggesting whether a credit default can be settled. SI Theory: CR-Onto is the result of an interdisciplinary study that integrates concepts from finance, computing, and knowledge representation. By organizing data into an ontology, performing inferences, and executing queries, the data is transformed into information that can help credit advisors at a financial institution make better decisions. Method: The ontology has axioms and semantic rules used to provide queries and inferences about its instantiated base. The results were analyzed using a quantitative approach. Summary of results: A synthetic dataset based on a financial institution was constructed to simulate the lifecycle of 5 banking clients. This dataset populated 35 distinct instances of the CR-Onto ontology. This enabled the execution of DL Query to answer 6 competency questions. Contributions and Impact in the IS area: CR-Onto is capable of identifying which credit operations have a higher probability of being resolved. A specialist can utilize these classification results as part of an Information System within a decision support platform.
SolidarIta: a Fundraising System to Support Organized Civil Society Institutions
Context: In contexts of economic vulnerability, non-profit and solidarity organizations, such as Organized Civil Societies (OCSs) in Brazil, play a pivotal role in promoting equity and combating inequalities. With approximately 879,000 OCSs in 2023, these entities act where the State and the market fail, but face challenges in raising funds due to a still limited donation culture. Problem: Transparency and integrity in fundraising are essential, but money laundering and threats such as extortion and phishing compromise transaction security. The lack of clarity in operations can undermine donors’ trust, making fundraising even more difficult. Solution: This paper presents a solution using Information Technology through the SolidarIta system, which aims to simplify the donation process for needy entities. IS Theory: SolidarIta allows registered organizations to create wish lists, facilitating the digital connection between donors and suppliers, such as supermarkets. Donors can choose items and make payments, while partners care for delivery, ensuring transparency. Method: The investigative research used literature analysis to model the system, followed by data collection through questionnaires to assess its impact and usability. Summary of Results: The system has demonstrated its effectiveness, with 89.3% of participants expressing satisfaction and 93.6% finding it useful. These results instill confidence in the potential of the SolidarIta system. Contributions and Impact in the IS area: SolidarIta facilitates interaction between donors, organizations, and partners, promoting transparent and efficient donations, improving the user experience, and increasing engagement in solidarity actions.
A Systematic Mapping on Software Functionalities in Information Systems Used by Community Health Agents for Mouth Cancer Prevention
Context: The adoption of digital technologies in the health domain can optimize data collection and storage processes, especially in the context of fieldwork carried out by Community Health Agents (CHA). Among the pathologies monitored, mouth cancer is one of them that, if detected early, can improve the patient’s prognosis. Problem: When analyzing the situation in Brazil, we notice an information gap in the state of the art regarding Information Systems (IS) specifically designed for the use of CHAs for collecting data from patients at high risk for mouth cancer. Solution: To advance towards the engineering of an IS to bridge this gap, we can elicit requirements collected from other IS currently used by CHAs reported in the literature. IS Theory: This study is based on General Systems Theory, since we examine the functionalities of health IS reported in primary studies. Method: A systematic mapping was conducted to identify requirements for healthcare ISs. Summarization of Results: Out of a total of 146 primary studies, 22 were selected, analyzed, and their data extracted. The results show that data collection is present in approximately 82% of the existing tools and applications. In addition, 45% of the studies report that automation and agility would contribute to the use of these devices, and 80% of the included studies mention personal data and symptoms or diseases as the main information to be extracted during home visits. Contributions and Impact on the IS Area: The results can serve as a reference for researchers and professionals seeking to implement solutions related to the Health IS domain.
Machine learning approaches for efficient recognition of Brazilian Sign Language
Context: The Brazilian Sign Language (LIBRAS) is the primary language for the Brazilian Deaf community. However, the lack of LIBRAS interpreters presents challenges for the inclusion of individuals with hearing impairments in society. Additionally, learning a sign language can be as difficult for hearing individuals as learning a foreign language. Problem: There is no official translator between Brazilian Portuguese and LIBRAS, making communication between hearing and Deaf individuals challenging. Solution: This study presents machine learning algorithms that can recognize 44 LIBRAS signs and translate them in real time to Brazilian Portuguese. IS Theory: This work is grounded in the Diffusion of Innovations Theory, focusing on how new technologies are adopted within communities. The proposed LIBRAS recognition system serves as an innovative solution to enhance communication between hearing and Deaf individuals. Method: This research adopts an experimental approach with quantitative and descriptive analyses. Data were collected from three different datasets, representing a total of 44 distinct LIBRAS signs. MediaPipe Hands was used to extract the spatial coordinates of hand movements. Then, some machine learning models were employed to recognize the signs, comparing their performance based on accuracy, F1 Score, precision, and recall metrics. Summary of Results: The results indicated that the ExtraTree algorithm was the best choice for the 44 signs dataset, achieving an accuracy of 98.01%, with an F1 Score of 98.01%, precision of 98.02%, and recall of 98.01%. Contributions and Impacts in IS Area: This paper contributes a low-cost approach for sign language recognition that requires only a camera to detect hands and recognize signs.
Real-Time Heart Failure Prediction: An Approach for Ambient Assisted Living
Context: The integration of IoT in healthcare has enhanced the capability of Ambient Assisted Living by enabling reliable real-time health monitoring, particularly for heart failure identification. Predictive models play a crucial role in identifying potential heart failures, improving patient outcomes through continuous monitoring and analysis. Problem: Traditional predictive models rely on centralized servers, facing issues like network latency, disruptions, and data overload. These challenges hinder real-time health data processing, limiting their ability to provide timely heart failure risk predictions. Solution: This study proposes a framework that embeds machine learning models directly into mobile devices, leveraging edge computing for real-time heart failure risk assessment. By processing data locally, the solution reduces latency, enhances reliability, and ensures greater data privacy while maintaining predictive accuracy.IS Theory: The framework aligns with socio-technical IS theory by integrating technical innovations and user-centric needs. Embedding predictive models into mobile devices enhances real-time predictions and patient care, bridging technology and human interaction. Method: A heart failure prediction dataset was utilized, employing supervised classification algorithms—Random Forest, K-Nearest Neighbors (KNN), and Logistic Regression. Data preprocessing included handling missing values, feature scaling, and encoding. The trained models were deployed on mobile devices using ONNX Runtime for efficient real-time inference. Summary of Results: The proposed system successfully performed real-time heart failure risk prediction on mobile devices, achieving competitive accuracy. Random Forest outperformed other models, reaching an accuracy of 85.33%, demonstrating its effectiveness in edge computing environments. The approach significantly mitigates the connectivity and latency challenges of centralized systems while enhancing data security. Impact in the IS Area: This research highlights the potential of edge computing to enhance real-time healthcare applications by reducing reliance on cloud infrastructure. Future works includes conducting real-environment patient evaluations to validate the system’s clinical applicability, integrating biomedical sensors, improving predictive models using deep learning, and exploring federated learning for privacy-preserving model training.
Mapping and Redesign of Administrative Processes with BPM and User Journey: An Experience Report for Digital Transformation
Context: Companies’ Digital Transformation (DT) intensified during the Covid-19 pandemic, involving the digitization of processes and cultural adaptation for continuous innovation, increased productivity, and service quality. The interaction of people, procedures, and technologies is crucial in this process. Problem: Traditional organizations with rigid cultures rely on outdated procedures, hindering the adoption of more efficient work practices. Solution: This research explores the use of the Business Process Management (BPM) life cycle to improve three processes in a Brazilian company, integrating user journey to improve processes towards DT. IS Theory: This study is carried out under the Socio-Technical Theory, examining the interaction between people, processes, and technologies to achieve balanced and effective digital transformation. Method: This exploratory research carries a qualitative experience report of the mapping and redesign of business processes of a countryside unit from "Alpha", a Brazilian vocational education organization. Summary of Results: Three financial processes were mapped, analyzed, and redesigned with the use of BPM and user journey map. The improvement proposals were based on the problems and opportunities identified in the processes. Changes in systems and innovative actions were proposed within the company to enhance process efficiency and agility through digital transformation. Contributions and Impact in the IS area: This study reports one execution of a BPM process integrated with user experience techniques aimed at DT. Through the lens of the Socio-Technical Theory, the research presents steps taken into the mapping, analysis, and redesign of processes contributing in similar contexts for both practitioners and researchers in the Information Systems (IS) field.
Covering Problem Applied for Allocation of Storage Yards in Sustainable Forest Management (SFM)
Context: Providing resources for a growing population is a challenge. Sustainable forest management emerges as an alternative for resource extraction from native forests in a planned manner, ensuring balance in the activity. Problem: Although SFM brings benefits, companies often plan their logging operations empirically, failing to achieve the best balance between resource efficiency and forest preservation. The optimal location of wood storage yards influences the construction of roads and skid trails, offering both economic and environmental benefits by minimizing the hauling distance for timber. Solution: An adaptation of the covering problem is proposed to create an optimization model for the allocation of wood storage yards within the scope of SFM. IS Theory: The forest ecosystem can be seen as a complex system, where ecological, social, and economic variables are considered to sustainably optimize resources. Method: To evaluate the effectiveness of optimizing yard allocation, a quantitative study was conducted using a computational model applied to a dataset provided by Aguiar et al. (2020).A comparative analysis approach was used to compare the model’s results with the data from the aforementioned study. Summary of Results: The results demonstrate that the model was effective in determining optimal locations for the yards. Compared to the p-median problem, it offers the additional benefit of eliminating the need to specify the number of yards, allowing for generalization to other datasets. Contributions and Impact on IS: This work shows how intelligent systems can optimize complex processes, promoting sustainable natural resource management and technological innovation in the forestry sector. It serves as a foundation for future studies in optimization and environmental management.
Process Automation with BPM and Emerging Technologies for Service and Industrial Process Optimization: Systematic Mapping
Context: The adoption of emerging technologies such as Artificial Intelligence (AI), Machine Learning (ML), Robotic Process Automation (RPA), and Process Mining (PM) is transforming business process management (BPM) and mainly its automation process, enhancing operational efficiency in services and industrial processes, and promoting innovation. Problem: The integration of emerging technologies with BPM faces organizational and technical challenges, including workforce adaptation, resistance to change and high costs, limiting its potential to increase efficiency and productivity. Solution: This article aims to investigate and analyze the approaches and structures that enable the integration of emerging technologies with BPM, addressing key questions of interest regarding their impact, implementation, and effectiveness in organizational settings. Information Systems Theory: This research was based on the aegis of the Diffusion of Innovations Theory, exploring how emerging technologies drive innovations in BPM, transforming organizational processes, accelerating automation and promoting operational efficiency and workforce reorganization. Method: We conducted a systematic mapping of studies published between 2019 and 2024, with a quantitative analysis of the selected studies. Summary of Results: The analysis highlighted AI, RPA, and Process Mining as the most cited technologies in BPM automation. However, gaps were identified in the integration of emerging technologies, pointing out technical challenges and opportunities for improvement. Contributions and Impact: This article provides insights into the integration of emerging technologies with BPM, addressing workforce adaptation and resistance to change. Its findings foster artifacts for IT managers and BPM professionals, promoting efficiency, competitiveness, innovation and new academic research.
Designing a Training Journey for Privacy and Information Security Practitioners in the Federal Public Administration
Context: The Ministry of Management and Innovation in Public Services (MGI) leads the formulation and coordination of the Digital Government Strategy (EGD). DEPSI, under the Secretariat of Digital Government (SGD), is responsible for the Privacy and Information Security Program (PPSI), which aims at data privacy, compliance, and institutional resilience. Problem: The culture of privacy and information security in the Federal Public Administration faces development challenges. Despite the PPSI, there is a lack of awareness initiatives, training, a clear strategy, best practices, and performance indicators. Proposed Solution: The proposal aims to develop a training journey for Practitioners working in roles related to privacy and information security, with the goal of identifying and promoting the best practices, skills, and competencies required for these roles. IS Theory: This study aligns with Organizational Information Processing Theory, providing mechanisms to help organizations adapt to regulatory uncertainties in privacy and security. Method: We employed a mixed approach, combining document analysis, a literature review, and a survey. Guidelines and standards were analyzed to map competencies and responsibilities, while the survey gathered practitioners’ perceptions of the proposed training journey. Summary of Results: We identified the key profiles and their corresponding responsibilities, and proposed a personalized training journey. Survey results indicated that the journey meets Practitioners’ expectations, being well-evaluated in terms of criteria and assigned weights. Contributions and Impact in IS: This work contributes by presenting a proposal for a Training Journey to assess the knowledge of Federal Public Administration employees and guide them on the best paths for professional development.
Development of an Artificial Intelligence-Aided Software for Annotating Image Datasets
Context: Deep learning is a highly successful class of methods in the field of artificial intelligence (AI) that has a variety of applications. To perform well, deep learning models require a large amount of high-quality annotated data. Problem: Data annotation is a time-consuming and laborious task that requires a significant amount of human labor, which makes it expensive. Solution: This work aims to reduce the time required to annotate image datasets by building an easy-to-use software tool that has semi-automated annotation powered by an artificial intelligence model. IS Theory: The work is based on the Socio-technical theory because we developed and evaluated a tool to be acceptable and useful for the users. Method: We developed a web-based tool and employed HQ-SAM, a deep neural network for image segmentation based on Vision Transformers, to generate polygon annotations based on the user’s prompts. Although HQ-SAM has a good zero-shot generalizability, we fine-tuned it on the Bean Leaf Dataset to evaluate how well the network adapts to specific tasks. Summary of results: We observed an increase in accuracy of the fine-tuned model compared to the pre-trained one. We tested our tool with 20 participants, all of whom are from the computer vision and graphics fields. We asked them to annotate the same two images both manually and AI-aided, and recorded the annotation times. Lastly, we asked the participants to fill out a usability form about their user experience. In our evaluation, we registered a median speedup of 1.5× regarding the AI-aided annotation compared to manual annotation and overly positive answers regarding our tool’s ease of use and usefulness. Contribution: We expect the proposed system to significantly reduce the human effort required for image dataset annotation, leading to faster annotation times. This offers a valuable contribution to the computer vision and AI communities, speeding up the dataset creation process.
MAIA: Methodology for Assessing the Impacts of Threats to Information Systems
Context: Cyberattacks have increased in the last decades. The lack of security results in scenarios full of vulnerabilities in information systems. A pentest can be defined as a proactive attempt to assess the security of an information system. It is mandatory in specific organizational scenarios and must be performed by third-party companies, which can imply high costs for the organization. Problem: Some organizations are often unfamiliar with a pentest or cannot pay for it. Other solutions must be proposed for them. Solution: An Information System Threat Impact Assessment Methodology (MAIA) easy to follow was this work’s objective. The goals were the methodology itself and the index that could quantify the vulnerabilities’ impacts. IS Theory: This work followed the General Systems Theory, in particular with regard to systems security. Method: The research is prescriptive in nature, and its evaluation was carried out through a case study in a big company. The results are both quantitative and qualitative. Summary of Results: The practical results in a real organization show the vulnerabilities identified, and the final vulnerability index indicated a high risk to the company. It shows the MAIA applicability. Contributions and Impact on the IS area: The main contribution is a methodology that is an alternative approach to traditional pentests. The methodology may be conducted by multidisciplinary teams. Decisions regarding the correction of vulnerabilities can be taken based on the results of MAIA.
Smart Farming for Poultry: Leveraging Chicken Raising with Low-Cost IoT-based Information Systems
Context: Brazil is among the most influential global poultry meat production countries. Occupying second place among the world’s largest producers, Brazil is the first in the export ranking. Hence, to meet the high demand, the market depends on the high productivity and performance of the birds produced. Problem: As a tropical country, production faces difficulties due to the birds’ sensitivity to fluctuations in environmental factors, such as temperature and relative humidity. These and other factors can lead to loss of production and low profitability. Proposed Solution: This paper investigates the impact of a low-cost IoT-based Information System (IS) for autonomous environmental regulation in poultry farming. Information Systems Theory: We conduct this research under the positivism umbrella, while still conceiving the solution under the General Systems Theory. Method: We conducted a classical controlled experiment. We used free-range chicken breed Label Rouge and Gallus Domesticus, separating them into intervention environment (under climate monitoring and regulation) and control environment (without monitoring/regulation). We considered the following variables for data analysis: weight gain, feed consumption and mortality. Student’s t test was applied. Summary of Results: The results showed a significant gain in the Label Rouge free-range chickens breed in the fifth week of evaluation, with the monitored chickens obtaining an average weight of 25% greater than those not monitored. Contributions and Impact in the IS area: The contributions of this research can be considered twofold: prescriptive, with the proposal of a low-cost IoT-based IS architecture for autonomous environmental regulation in poultry farming; and descriptive, with an empirical study being conducted and the results being reported. We conclude that the system’s automation can significantly reduce the need for manual monitoring, offering substantial labor savings and operational efficiency, especially advantageous for small-scale poultry producers.
A Survey on Global Software Development: Current Practices and Challenges
Context: Global Software Development (GSD) is a growing reality in the software organizations, aiming to meet the needs for agility and competitiveness in the globalized market. GSD has been propelled during the COVID-19 pandemic, reaching new heights. Problem: One of the critical challenges in GSD is how the geographical distribution of development teams impacts people communication and collaboration, which can lead to inefficiencies in the information system projects. Proposed Solution: This study explores and summarizes the current practices and challenges in GSD by gathering the perceptions and experiences of Brazilian developers through a comprehensive survey. IS Theory: The research is grounded in the theory of socio-technical systems, particularly focusing on the interplay between organizational structures and the technologies that support distributed software teams. Method: A total of 44 Brazilian software developers answered the survey, providing valuable insights into their perceptions and experiences. The study provides a detailed overview of the complexities and opportunities inherent in GSD. Summary of Results: The findings emphasize the critical importance of effective communication in managing distributed teams and highlight challenges in remote collaboration, such as the need for robust digital infrastructure and enhanced information security. The main contribution of this work encompasses socio-technical issues regarding GSD, which emerged or remain after the COVID-19 pandemic, and that can be addressed by software organizations.
DevSecOps Practices for GDPR, HIPAA or LGPD Compliance in Software Development: A Systematic Review
Context: The current software development scenario requires integrating security and regulatory compliance practices, especially after implementing regulations such as LGPD, GDPR, and HIPAA. Problem: There is a lack of frameworks that effectively combine security, regulatory compliance, and continuous software delivery in DevSecOps environments. Solution: This study aims to identify and analyze approaches, methods, tools, and frameworks that promote regulatory compliance in DevSecOps practices through a systematic literature review. IS Theory: Sociotechnical Theory underpins the analysis, considering the interaction between technical (automated tools and processes) and social (organizational culture and collaborative practices) aspects necessary to effectively implement regulatory compliance. Method: A systematic review was carried out following the guidelines for performing systematic literature reviews in software engineering and analyzing 15 primary studies identified in five scientific databases (ACM Digital Library, IEEE Xplore Digital Library,Web of Science, Science Direct and Scopus). Summarization of Results: The need for automation of compliance checks, early integration of security practices, and establishing an organizational culture that prioritizes regulatory compliance was identified. Contributions and Impact on IS: The study provides an overview of existing practices and frameworks, highlighting the need for a sociotechnical approach that integrates technological and organizational aspects to ensure regulatory compliance in DevSecOps environments, contributing to the advancement of secure software development practices.
Predicting Chronic Phase Progression in Chikungunya Patients Using Machine Learning Models
Context: This research is set within the domain of neglected tropical diseases, specifically focusing on Chikungunya, a mosquito-borne viral disease. The study is motivated by the prevalence of Chikungunya in Brazil and the challenges associated with its chronic symptoms. Problem: The primary issue addressed is the difficulty in predicting which patients with acute Chikungunya will progress to the chronic phase. This progression leads to prolonged joint pain and other severe symptoms, affecting quality of life. Solution: We evaluate machine learning models, trained to predict the likelihood of acute Chikungunya cases progressing to the chronic phase. Theory of IS: The Information Processing Theory serves as the theoretical foundation, explaining how data is transformed into actionable insights. In this context, machine learning models act as information processors, learning from a real dataset how to produce predictions that can aid clinical decisions. Method: The methodology involves a quantitative analysis of different machine learning models, using documented data from Chikungunya diagnosed patients. The goal is to assess the models’ performance in predicting disease progression. Summary of Results: Preliminary results show that the XGBoost model demonstrates 79.31% of recall and 74.19% of f1-score in distinguishing patients likely to develop chronic conditions. Contributions and impact on the field of IS: This study contributes to both information systems and healthcare by enhancing the process of decision-making for health professionals. The implications are significant for academic research and practical application (including SUS applicability), providing information that facilitate earlier and more targeted treatments, focusing on patient quality of life.
A Study on Transparency in Software Development Projects From the Perspective of Project Managers
Context: Transparency is a fundamental concept for projects and their management, as this attribute can enhance trust, communication, stakeholder engagement, risk management, accountability, adaptability, and performance monitoring. Problem: The scarcity of studies on transparency in software projects hinders its implementation and limits the understanding of stakeholders’ needs, resulting in poor communication and a negative impact on project success. Solution: In order to improve the understanding of transparency in software development, the study aims to understand how project managers conceptualize and assess the relevance of transparency in software projects, as well as to identify the mechanisms used to promote it. Information Systems Theory: This work was developed based on Stakeholder Theory, which highlights the need for organizations to consider the expectations of all impacted groups. The study addresses how transparency influences these stakeholders and its relevance in group engagement. Method: This study adopts a qualitative and exploratory approach. A total of 12 interviews were conducted with guests holding management positions in the software field, which were analyzed using Bardin’s Thematic Analysis. Summary of Results: The interview codes were divided into four groups that highlight the study’s objectives: "Transparency Assessment," "Transparency Conceptualization," "Challenges Faced," and "Tools, Mechanisms, and Practices." Within these groups, there are 46 categories, which encompass a total of 399 codes. Contributions and Impact in the area of Information Systems: This article contributes to a better understanding of transparency in the context of software project management. Consequently, it is possible to explore better ways to implement it, thereby promoting trust and informed decision-making among stakeholders.
Planning for the Future: How ICT Professors Approach Retirement and Post-Career Life
Context: Professors’ attitudes toward retirement vary widely, ranging from enthusiasm to reluctance. The retirement process of university professors in the field of Information and Communication Technology (ICT) presents a critical characteristic: ICT educators often work at the cutting edge of innovation, which may instill a strong drive to remain professionally engaged even after reaching traditional retirement age. Problem: The research problem focuses on understanding the factors that impact the retirement process of ICT professors in higher education. Solution: This study aims to investigate the human, organizational, legal, and regulatory factors that may affect the retirement process of ICT professors. IS Theory: This research is based on Contingency Theory, which emphasizes the need for organizations to adapt their practices to specific environmental conditions. Method: We conducted a survey with 176 ICT professors from various universities across the country to gather data on their perceptions and preparations for retirement. Summary of Results: Our findings reveal that most professors begin to consider retirement early (31.3%) or mid-career (21.6%). Over 64% plan to engage in leisure activities and travel after retirement, while continuing to contribute to academic research. Financial reserves and post-career activity planning are key aspects of this process. However, most universities do not offer training courses to prepare them for retirement. Contributions and Impact in IS: This study contributes to the Information Systems (IS) field by applying Contingency Theory to the development of guidelines that address the complex retirement process for ICT professors. This research also opens avenues for further studies on how IS frameworks can be applied to address transitions in professional life cycles, particularly in knowledge-driven fields like academia.
Analysis of Ensembles Applied to Time Series Forecasting of Hydroelectric Dams
Context: Dams are essential for electricity generation in Brazil. Given the countrys vast territorial extent, it is crucial to optimize the use of its energy sources, particularly the hydrological potential of rivers. Problem: Flow forecasting in river basins is critical for the safety and efficiency of operations. Underestimation errors can increase the risk of dam overflow and failure, while overestimations compromise water storage, affecting supply and power generation during dry periods. Solution: This study proposes a dynamic selection method of machine learning models to achieve more accurate and stable flow forecasts. IS Theory: The research is based on Dynamic Capabilities Theory, emphasizing the ability for internal reconfiguration to address complex issues such as time series forecasting. Method: Data were initially normalized and temporally adjusted to ensure consistency. A diverse set of base models was selected based on their intrinsic characteristics, then integrated into a dynamic ensemble framework. Performance was evaluated using MAPE and RMSE metrics, allowing comparative analysis between the proposed dynamic ensemble and traditional deep learning models. Results: The dynamic selection approach showed significant improvements, achieving errors of 23.88 (MAPE) and 852.97 (RMSE), outperforming traditional and deep learning models. Contributions and Impact in the IS Field: This work demonstrates that dynamic model selection is a superior and promising approach compared to isolated model use, offering a significant advancement for flow forecasting in dam systems and contributing to more efficient water resource management in the Brazilian context.
University Outreach Management: Assessment and Perspectives from the Case Study of the University’s Academic Week
Context: The Brazilian National Council of Education’s Resolution Nº. 7/2018 mandates the integration of outreach activities into higher education curricula, effective from 2023. This requirement has created a need for efficient solutions to manage and support outreach activities within universities. Problem: Managing outreach activities manually is challenging, often leading to inefficiencies and missed opportunities for student engagement. The absence of a comprehensive system for managing these activities makes it difficult for coordinators to track and validate students’ participation effectively. Solution: This study presents the development and evaluation of a web-based IS designed to support the management of outreach programs and projects within academic institutions, particularly focusing on use during large-scale events like an Academic Week. Theory of IS: The research is based on Soft Systems Theory (SST), which helps address the complex social and organizational issues involved in managing university outreach activities. Method: A case study method was employed during our university’s AcademicWeek, involving over 300 users. Data were collected through direct observation and a structured survey, utilizing the AttrakDiff technique to evaluate user satisfaction with usability and functionality. Summary of Results: The Web-based IS demonstrated effective support for managing outreach activities and received positive feedback from users. Key insights were collected, including suggestions to improve navigability, performance optimization, and user support materials. Contributions and Impact in the IS Area: This research contributes a tailored management Web-based IS that addresses specific needs in academic outreach, potentially enhancing the efficiency and accessibility of outreach activities. The findings provide valuable insights for further development and set the groundwork for similar applications in educational institutions.
Turnover Management Method Based in Predictive Analysis
Context: IT companies struggle with the high statistics of job rotation every year. The high employee turnover in Brazilian software development companies hampers the execution of long-term strategic planning, as well as hinders new business opportunities and software projects due to the loss of intellectual capital. Problem: This article aimed to investigate the turnover data of an IT company in Brazil, and develop a data-driven method to predict voluntary turnover, to opportunize the company to act to minimize it. Solution: We propose a turnover management process, supported by a predictive model using artificial intelligence (AI). IS Theory: Contingency Theory, TAM(Technology Acceptance Model), Knowledge Management, and Organizational Justice are some of the most relevant theories in the relationship between Information Systems (IS) and turnover. Propper management policies can improve communication, engagement, and organizational justice — factors that have a direct impact on employee retention, as showed in this study. Method: The research is prescriptive in nature, and its evaluation was conducted through a case study. The results were analyzed using a qualitative approach. Summary of Results: the feasibility of applying the proposed process has been confirmed according to each stage of the turnover management process and the results obtained. Contributions and Impact on the IS Field: The primary contribution of this work is to assist companies in developing strategies for effective turnover management to minimize it, using a process based on a predictive model supported by AI.
Challenges and Strategies in the Implementation of the International Patient Summary in Accordance with International Standards: A Systematic Review
Context: The growing international movement of people necessitates adopting interoperable health data solutions like the International Patient Summary (IPS). Diverse standards, terminologies, and specifications require significant efforts to achieve agility, interoperability, compliance, security, and privacy. Problem: Few studies discuss the challenges and strategies for implementing the IPS in compliance with international standards; additionally, many professionals are unaware of the benefits of compliance, hindering adoption. Solution: This study aims to identify challenges, barriers, strategies, and success factors in IPS implementation, emphasizing interoperability, technical specifications, and health standards. IS Theory: Anchored in Sociotechnical Theory, the study examines how the interaction between technical components (international standards, interoperability) and social components (regulations, collaboration, clinical routines) influences IPS implementation. Method: A systematic review following the PRISMA 2020 protocol identified 43 relevant studies, analyzed quantitatively and qualitatively to consolidate information on compliance standards, challenges, and strategies. Summary of Results: Key challenges include harmonizing international standards, ensuring patient data privacy and security, and interoperability among heterogeneous systems. Effective strategies involve developing harmonized implementation guides and collaboration among standardization organizations like ISO, HL7, and CEN. Contributions and Impacts in the IS area: This study underscores the importance of a sociotechnical approach in IPS implementation per international standards, contributing to public policies and the development of health information systems that integrate technological and organizational aspects, promoting interoperability, security, and privacy of health data.
Business Intelligence for Academic Management Systems: Results of a Study in a Brazilian Federal University
Context: Brazilian public universities require managing vast and diverse academic and administrative information. The government requires periodic reports about finances, education, research, and social impact to obtain enough economic resources for universities’ continuous operations. Additionally, since the approval of the National Law nº 12.527 (LAI - Lei de Acesso à Informação) of 2012, all public organizations must provide information as requested by any civilian, public, or private organization. Problem: Answering diverse information requests by public universities demands a lot of time and effort from public officials since, to produce a report, they must consult different systems and documents. These tasks increase the workload of public employees, which is repetitive and manual work. Solution: This work investigates the information necessities of public universities in Brazil to manage their academic sectors strategically, using Business Intelligence (BI) techniques. IS Theory: This work was developed using organizational information processing theory, which helps to understand how information is processed and exchanged within organizations and among their members. Method: Observations and semi-structured interviews were conducted with experts working in a public university’s academic sector. The interviews were transcripted, and topic analysis was conducted to identify necessary functional and non-functional requirements. Results: Requirements were specified using the Personas method, containing stakeholders’ needs, motivations, and goals for using a BI solution. Moreover, the required structured, semi-structured, and non-structured data to support operational, managerial, and strategic activities in the academic sector were identified. A preliminary conceptual model of the BI solution is also proposed. Additionally, important data integrations among university sectors were defined. Contributions: The results of this study are hoped to be used as an asset to define a BI solution to support academic management in Brazilian public universities. As a future work, it is intended to develop an initial BI tool and create a national reference architecture to guide the development of these solutions.
An AI Gen and LLM-Based Platform for Pedagogical Support and Sensory Accommodations for Neurodivergent Students
Context This research focuses on inclusive, personalized learning for neurodivergent students within educational contexts. The motivation arises from the need for sensory accommodations due to sensory processing challenges commonly experienced by individuals with conditions like ASD and ADHD. Problem Neurodivergent individuals often face sensory integration challenges that impact their school, family, and clinical environments. Without proper accommodations, these challenges hinder development and learning, placing additional demands on teachers and therapists. This problem spans technological, social, and educational dimensions, as inadequate support systems may compromise well-being and intervention effectiveness. Solution The proposed solution is a generative AI platform that uses Small and Large Language Models (SLMs and LLMs) to suggest personalized pedagogical activities and sensory accommodations. It supports teachers, families, and therapists by continuously adjusting approaches to meet students’ individual needs. IS Theory The research is grounded in Sociotechnical Systems Theory, examining the interplay between technology and human factors to ensure a balanced, effective solution within its usage environment. Method A Proof of Concept was employed with qualitative and quantitative methods, including expert interviews and platform testing to gather feedback on functionality and effectiveness. Results Summary Preliminary findings show high user acceptance and improved sensory adaptation for students. The platform demonstrates potential for dynamic, individualized sensory support. Contributions and Impact on IS This research expands generative AI applications in inclusive education, offering an innovative approach for personalized sensory accommodations. Insights from this study could guide academics and practitioners in developing scalable, human-centered support systems for educational and clinical settings.
Intelligent Methodologies for Preparing Reference Prices in Brazilian Public Biddings
Context: This research focuses on building reference prices in Brazilian public tenders, integrating statistical techniques and AI to improve automated decision accuracy and detect issues like overpricing and underpricing. Problem: Traditional methods for calculating reference prices, like arithmetic mean and lowest price, often cause distortions in public tenders. This research evaluates how AI can enhance accuracy and transparency in audits. Solution: The proposed solution is software that combines statistical algorithms with machine learning to calculate reference prices more robustly. The first version uses statistical methods; the second integrates TensorFlow, expanding irregularity detection. IS Theory: Based on Design Science Research, this study develops and evaluates artifacts to solve identified problems, applying scientific techniques to optimize reference price calculations. Method: A quantitative experiment using public tender data compares the software’s performance with traditional methods, focusing on average deviation and machine learning accuracy. Summary of Results: The first software version reduced average deviation to 2.53%, versus 15.58% for traditional methods. The machine learning version achieved 70% accuracy in detecting pricing irregularities, adding an extra verification layer. Contributions and Impacts in the IS area: This research contributes to Information Systems by proposing a system that combines statistics and AI, enhancing accuracy and transparency. The solution improves public bidding efficiency and is adaptable to other government and industry areas.
Organizational Collaborative Relationships in Agile Contexts: A Mapping Study
Context: Collaboration is used in intra- and inter-organizational relationships as a supportive practice to help organizations remain competitive. Agile methods and practices facilitate collaboration in relationships and interactions among software development team members, customers, and stakeholders. Problem: There are still gaps in understanding the effective use of agile methods in different organizational relationships (intra- and inter-organizational). Solution: This study aims to identify the state-of-the-art regarding organizational collaborative relationships supported by agile methods and practices. IS Theory: The study is analyzed from the perspective of General Systems Theory. Method: A systematic literature mapping was conducted. Summary of Results: From 37 studies, evidence was found that using agile methods and practices in different organizational collaborative relationships can positively impact aspects such as innovation, knowledge transfer, and management practices. Additionally, challenges were identified, especially regarding difficulties with collaboration, communication, and coordination in large-scale developments or those involving many actors. Contributions and Impact on the IS Field: The results enabled an understanding of the motivations behind agile adoption in different organizational collaborative relationships, identifying how these approaches influence collaborative interactions and relationships and presenting the managerial aspects involved. The findings are expected to help managers and organizations enhance collaborative practices by providing a comprehensive view of this topic’s motivations, benefits, and challenges.
Performance evaluation of LLMs in the Text-to-SQL task in Portuguese
Context: The rising need for consulting data in industry and academic contexts has fueled Text-to-SQL development, where natural language queries are translated into SQL, making data access easier. Problem: Most research focuses on English Text-to-SQL, leaving Portuguese—a language spoken by over 260 million people— underrepresented, creating challenges for organizations reliant on accurate data retrieval in Portuguese. Solution: This study evaluates the effectiveness of various Large Language Models (LLMs) on Portuguese Text-to-SQL tasks using a validated translation of the Spider benchmark. IS Theory: This research applies Task-Technology Fit (TTF) Theory to assess how well LLMs meet the needs of Portuguese Text-to-SQL tasks. TTF evaluates the match between LLM capabilities and task requirements, including language understanding, schema recognition, and SQL generation. Proper alignment is key for effective data retrieval, particularly for Portuguese-language applications in organizational decisionmaking. Method: A comparative analysis of seven LLMs—tested on both Portuguese and English Spider benchmarks—was performed. Exact Match (EM) and Execution Accuracy (EX) metrics measured performance, and a zero-shot prompting approach maintained consistency. Results Summary: Larger LLMs and specialized code models excelled, showing less performance variance between Portuguese and English tasks. Generalist models, however, produced verbose outputs, which may limit practical use in production systems. Contributions and Impact on IS: This research establishes baseline Portuguese Text-to-SQL metrics and insights into language adaptability in LLMs, offering guidance for organizations seeking Portuguese-language data solutions. By bridging language gaps, it advances data-driven practices and fosters growth in Portuguese-speaking regions.
Real-Time Monitoring System for Forest Fire Prevention and Combat: A Case Study in the Brazilian Semi-Arid Region
Context: Wildfires pose significant environmental, economic, and social challenges, particularly in dry tropical forest biomes, such as Caatinga in Brazil. Traditional firefighting approaches often lack real-time data, impacting the efficiency of firefighting teams. Problem: Current firefighting operations lack integrated technological tools that support decision-making with real-time data on fire outbreaks, weather, and regional geography. This gap in operational support can lead to delayed responses, increased resource consumption, and compromised safety. Proposed Solution: This study presents a comprehensive decision-support service that aggregates real-time data from IoT sensors, satellite imagery, and weather APIs into an integrated system. This service equips firefighting teams with advanced tools for tracking, weather updates, fire management, and strategic planning, centralizing actionable information to enhance response capabilities. IS Theory: The proposed system is based on General Systems Theory, which supports interconnected subsystems for data collection, analysis, and user interaction, collectively enhancing operational support and strategic planning in firefighting. Method: A case study approach was applied, implementing the service for a regional fire brigade. Data from various sources were collected, processed, and displayed through an integrated system, including both a mobile and web application. System performance was evaluated using quantitative and qualitative metrics. Results Summary: The decision-support service provides real-time fire monitoring and aids in efficient firefighting planning and operations. Positive feedback from users indicates improved situational awareness and resource allocation. Contributions and Impact in IS: This tool enhances Information Systems by enabling actionable insights and real-time operations. Its scalable architecture adapts to various biomes, showcasing the value of integrated technologies in managing complex challenges and transforming traditional disaster response practices.
Organizational Interoperability: A Systematic Mapping Study
Context: Organizational interoperability enables collaboration amomg organizations with different structures and processes, promoting efficiency and integration in sectors such as public administration, health, and education. Problem: Implementing organizational interoperability presents significant challenges, including the lack of commonly accepted processes, difficulties interpreting administrative procedures and legislation, and the absence of conceptual models to define inter-organizational relationships. There are few studies that report organizational interoperability, and there is no consensus on several concepts and structures. Solution: The study maps existing approaches, including models for assessing organizational interoperability maturity, and a conceptual model based on Systems of Information Systems (SoIS) to help create interoperability connections between organizations. Information systems theory: Organizational interoperability research is associated with organizational information processing theory. Method: A systematic literature mapping was conducted in databases such as IEEE Xplore, ACM Digital Library, ScienceDirect, and Scopus. Summary of Results: Ten main studies were identified. In general, organizational interoperability has been treated as the ability of organizations to align business processes and expectations for effective information exchange. Challenges include the need for standardized processes, difficulties with regulations, and the absence of conceptual models. Solutions involve process-oriented approaches, but adoption still needs to be improved. Contributions and Impact in the area of information systems: The study contributes by providing a comprehensive view of organizational interoperability, identifying gaps, challenges, and possible solutions. It reinforces the importance of interoperability for inter-organizational collaboration to promote a more efficient and collaborative environment.
Ana Patrícia Fontes Magalhães Mascarenhas
Strategies of Intelligent Tutoring Systems to Engage Students in Online Learning Before LLM Approaches
Context: Several studies have investigated the integration of Intelligent Tutoring Systems (ITS) in education. However, there is still a gap in understanding approaches that promote student engagement in online learning. Problem: The literature lacks specific analyses that clarify the role of ITS in supporting student engagement in online learning environments, particularly before the advent of Large Language Models (LLM). Solution: This study analyzes the (inter)national literature to identify indicators of ITS contributions to student engagement, focusing on solutions implemented prior to the adoption of LLM. ITS Theory: The research is grounded in ITS theory by addressing ITS as mediating tools in online learning interactions, emphasizing their potential for enhancement through novel technological approaches. Method: Relevant articles from the literature were selected and reviewed to map themes addressed by ITS, identify the main types of solutions, and evaluate their implications for future ITS designs. Summary of Results: The results highlight the themes explored by ITS, the primary solutions developed, and their implications. Among the 15 studies analyzed in the Brazilian context, they also emphasize the potential of combining earlier and current solutions while maintaining the crucial role of human tutors in the teaching-learning process. Contributions and Impact on ITS: This study advances the ITS field by offering theoretical and practical insights for designing ITS that integrate traditional and modern approaches. By focusing on the relationship between ITS and student engagement, the research contributes to the development of tools that enhance online learning effectiveness and foster better interactions between students, systems, and human tutors.
Teaching Assistant Based on a Brazilian Large Language Model
Context: The use of AI Teaching Assistants (AI-TAs) in educational contexts aims to support student learning in complex subjects, where there are challenges in assimilating interdisciplinary concepts and a need for quick and accurate responses. Problem: Students face barriers when studying subjects that require prior knowledge in multiple areas, leading to comprehension difficulties that negatively affect their academic progress. Proposed Solution: An AI-TA based on the Brazilian language model Sabiá 2.0 is proposed, incorporating mechanisms for Quick Verification, Expansion, and Error Correction to enhance the question-and-answer system. IS Theory: This work is grounded in Social Learning Theory, which explores how individuals acquire knowledge in technology-mediated interaction contexts. This theory is applicable to the use of AI-TAs, which act as learning mediators, promoting knowledge acquisition and facilitating understanding of complex concepts. Method: The methodology is Design Science Research, with a descriptive approach and quantitative analysis, including proof of concept and practical evaluation with users. The system was implemented in an educational environment and tested for performance and student satisfaction. Summary of Results: The system showed a high satisfaction rate regarding response quality; however, it revealed limitations in response time for model-based queries, suggesting improvements in API optimization. Contributions and Impact in IS Field: This study contributes to the IS field by demonstrating how AI models can enhance teaching and student support, proposing a robust and adaptable solution for learning environments.
Pedagogical and Accessibility Guidelines for Digital Educational Games
Context: The gaming industry in Brazil has grown progressively in recent years, accompanied by an increase in the demand for accessibility. Problem: However, the development of accessible products has not advanced at the same pace, resulting in a significant gap. Problems such as the lack of audio description and subtitles are critical barriers that affect the access, learning, and entertainment provided by digital games. These problems contribute to the social exclusion of people with disabilities. Solution: Given this scenario, this paper presents pedagogical and accessibility guidelines for online educational games. Information Systems Theory: Considering the BYU library, this work fits into the context of Equity Theory, whose concept is related to the willingness to equally recognize the rights of each individual. Method: These guidelines evolved into the design and implementation stages of a game, which was tested by a blind user. Results: The evaluation resulted in mostly positive feedback. Opportunities for improving the proposal were also identified, which will be considered in new phases of the research. Despite the challenges encountered in relation to the system, including hardware, software, and internet issues during the evaluation, the work was successful in adapting the game to improve accessibility, allowing the blind user to play and perform all the tasks. Contribution: Mainly, this result reinforces the importance of incorporating accessibility guidelines into digital products and contributes to progress towards the inclusion of all people in the context of digital games.
TalentJobRadar: Advanced Data-Driven Recommendations for In-Demand QA Soft Skills and Career Opportunities
Introduction: Software Quality Assurance (QA) is essential to ensure that software products meet predefined requirements. While QA tasks are technical, soft skills play a crucial role in project success, product quality, and the productivity of QA professionals. Objectives: The main objective of this work is to provide a job and skill recommendation tool focused on the Brazilian QA market. Methods: Data was extracted from 2,164 LinkedIn job postings using a data-driven, inductive approach, combining both manual and automated processes. Job descriptions and users’ skills were mapped into binary vectors for comparison. The tool displays job recommendations in a card format, showing company name, required skills, LinkedIn links, and the user’s suitability for the position. Additionally, it suggests skills for improvement and highlights the top three skills associated with the user’s current soft skills, presenting a radar chart that shows job availability by seniority level. Evaluation: We conducted a preliminary evaluation of the tool using 45 synthetic profiles representing varying skill levels to simulate diverse user scenarios, allowing us to assess the system’s adaptability and effectiveness. Job recommendations demonstrated notable precision, recall, and F1-score values, while skill recommendations showed positive results in terms of relative coverage, precision, and relevance. However, applying filters led to a decrease in overall metrics, highlighting limitations in filtered recommendations. Conclusion: The tool shows promise in helping QA professionals at various experience levels identify relevant job opportunities and areas for skill enhancement. Although filtered recommendations present limitations, the system effectively highlights suitable positions and skills for development, supporting employability in the QA field. Future improvements include refining filtered recommendation accuracy and expanding to technical skill recommendations.
Management framework for Big Data projects based on success factors and criteria
Context: The objective of this research is to examine success management in Big Data projects. Big Data informs strategic decisionmaking, but managing these projects effectively presents challenges to delivering value for organizations. Problem: The issue addressed is the lack of a structured management model focused on success in Big Data projects to reduce failure rates. Organizations face unique challenges with Big Data, often leading to project failures. Solution: Thiswork proposes a conceptual framework combining project management and success management for Big Data projects. It was developed through a literature review and interviews with experienced professionals in the field. IS Theory: This research is grounded in success management theory from project management, which is widely used in information systems projects. This study demonstrates its relevance to Big Data projects. Method: Design Science Research (DSR) methodology guided this study, including a literature review and expert interviews to refine a framework for Big Data project success management. Summary of Results: The framework addresses gaps found in the literature and interviews, covering project planning, execution, and closure, and incorporating the specific characteristics of Big Data and Success Management. Contributions and Impact on the IS Field: This research contributes to data science by applying success management principles, offering a practical and academic framework for Big Data project management. It provides strategies for effectively managing Big Data project success.
LAWS: Language Annotation Web System Using ELAN Format
Context: Linguists dedicate extensive time to annotating audiovisual media, where the efficiency of annotation tools significantly impacts their productivity. Existing tools, such as ELAN, are wellknown and widely used but present challenges due to their complex interfaces, especially for novice users. Solution: To overcome these challenges, we propose LAWS, a web-based linguistic annotation tool that emphasizes simplicity, intuitiveness, and a user-friendly interface to improve accessibility and usability for linguists. Methodology: The development of LAWS focused on streamlining access, handling, and documentation processes in linguistic annotation through a visually accessible web-based platform designed to reduce learning curves and enhance user experience. Results: LAWS offers a straightforward alternative to traditional tools, addressing usability gaps and providing linguists with an efficient, easy-to-navigate solution for their annotation tasks. Conclusions: By simplifying the linguistic annotation process, LAWS empowers linguists with an intuitive tool, promoting productivity and reducing the barriers associated with complex annotation software.
A Mapping Review to Understand Web and Mobile Apps Accessibility for Adults with Autism
Context: With the rise of the neurodiversity topic, there is a need to investigate the demands of autistic individuals for Information Systems, ensuring a more accessible and user-friendly experience. Problem: Despite existing guidelines for autistic individuals, there is a limited synthesis of findings to identify gaps and best practices, particularly for adults. Solution: This article reviews key guides, frameworks, evaluation tools, recommendations, and design standards that support the development and usability assessment of mobile and web applications for autistic users. Method: This research follows a mapping study approach with a narrative synthesis of 23 research articles. IS theory: This study applies Cognitive Load Theory to analyze usability challenges and evaluate the effectiveness of accessibility guidelines. Results: 23 papers were found within the Guideline for the Accessibility of Web Interfaces with a Focus on Autism (GAIA), covering visual/textual vocabulary, customization, redundant representations, participation, multimedia, and navigability. However, no studies were found in state visibility, recognition, predictability, response to actions, and touchscreen interactions. Barriers were analyzed across cognitive overload, processing, distinguishability, and breaking expectations.Contributions in the Field: We identified nine frameworks for designing and evaluating Information Systems for autistic users, improving usability and accessibility. However, further investigation is needed to assess the effectiveness of these guidelines, as some remain unvalidated.
Enhancing Alzheimer’s Disease Diagnosis: Insights from MLP and 1D CNN Models
Context: Alzheimer’s Disease (AD) is a complex neurodegenerative disorder that requires early diagnosis to improve patient outcomes. Recent advances in computational intelligence have sparked interest in leveraging machine learning to enhance diagnostic accuracy and efficiency. These innovations are crucial for transforming decision-making within Information Systems in clinical settings. Problem: Traditional methods like PET-scans and cerebrospinal fluid collection are highly accurate but costly and invasive, limiting accessibility. Developing data-driven, non-invasive solutions that retain diagnostic accuracy while handling complex biomedical data, such as plasma protein concentrations, remains a challenge. Solution: This study utilizes neural networks, specifically Multi-Layer Perceptron (MLP) and One-Dimensional Convolutional Neural Network (1D CNN). Preprocessing included Recursive Feature Elimination (RFE) for feature selection and Synthetic Minority Oversampling Technique (SMOTE) for data augmentation, addressing class imbalance. SI Theory: Grounded in Complexity Theory, the study examines how machine learning models can enhance data-driven medical systems by efficiently managing critical, highly sensitive datasets. Method: An experimental quantitative approach was used to evaluate binary and multiclass classifiers on a dataset with 120 protein features from 259 patients. Summary of Results: The MLP exhibited strong performance in specific subsets, achieving superior metrics in the binary classification after feature selection and data augmentation. Meanwhile, the 1D CNN excelled in multiclass classification, leveraging its convolutional layers to extract critical features from subtle protein variations, improving accuracy and robustness. Contributions and Impact on IS Field: This research enhances medical information systems by proposing machine learning models that can be integrated for accurate diagnostics, supporting clinical decision-making and advancing healthcare practices.
ECOS Modeling: A Modeling Tool, Repository for Models and Evolution Analysis of Software Ecosystems
Context: Software Ecosystems (SECO) are a set of actors and components that function as a unit, relating to each other based on a common interest in providing solutions or services to the software industry. Problem: For a better visualization and understanding of SECOs, recent studies propose modeling the network formed by them. However, there is still no formalized modeling standard for this area, and there is a lack of tools, approaches, and notations that assist modeling. Solution: In this context, this work aims to present an evolution of the SECO modeling tool, called ECOS Modeling, in its version 4.0. In this version, some new features were added, such as saving the registration and login in the tool, saving and sharing the model in the repository, generating an analysis report of the SECO evolution, viewing the numerical data of the model and making the tool available in other languages. IS Theory: General systems theory, specifically the interfaces between different parts of systems and solutions that communicate, exchanging information. These parts can be systems from different institutions that require integration, consisting of Information Systems. Method: The tool evaluation was planned based on the TAM (Technology Acceptance Model), widely used to measure technology acceptance. The process involved the participation of 49 experts with experience in the areas of software and systems engineering. The selection of experts sought to ensure diversity of profiles, including academics and industry professionals, to broaden the scope and relevance of the results. Summary of Results: The evaluation results demonstrated strong validation of the tool, with experts rating it as highly useful in terms of usability, functionality, and alignment with requirements. The majority (73%) found the tool very useful, 71% stated that it fully met the proposed requirements, and 69% reported ease in finding information and resources. The tool’s navigation was considered clear and easy by 55%, while 49% always knew their location and next steps within the tool. In addition, 55% of experts found the interface visually appealing and 45% reported low mental effort when using it. The study also revealed that 55% used modeling tools frequently and 63% worked with tools saving models in repositories, reinforcing the relevance and suitability of the tool for its target audience. Contributions and Impact in the IS area: As an emerging area, the results contribute to Information Systems by providing a practical tool for modeling, analyzing and evolving SECO. The main contribution is to fill the gap of a formal standard for SECO modeling, allowing the visualization and sharing of ecosystem dynamics, in addition to offering detailed reports that support the understanding of its evolution and identification of deficiencies.
Machine Learning-Assisted Tools for User eXperience Evaluation: A Systematic Mapping Study
Context: Information Systems (IS) have grown exponentially, significantly influencing professional and personal environments. Both scenarios require a distinguished User Experience (UX), which generates positive feelings such as loyalty, learning, and satisfaction from end users. Consequently, tools, software, and applications that integrate Machine Learning (ML) techniques with UX are necessary for enhancing the quality of IS and increasing the productivity of UX specialists. Problem: There is a continued need for more experimental evidence regarding the development, employability/ applicability, evaluation, and evolution of current technologies that automate manual tasks performed by experts. Specifically, such technologies aim to reduce workload, eliminate evaluation biases, and identify patterns that might go unnoticed during assessments. Method: This work aims to summarize and characterize, through a Systematic Mapping Study (SMS), the tools that employ ML techniques to assist in the UX evaluation process. To help us, we defined seven sub-questions that will be addressed based on the data collected from the selected studies. Contributions and Impact: Based on the selected studies, we analyzed and characterized the assessment tools to provide a comprehensive understanding for both the academic and professional communities. This work presents the current state of tools that integrate ML techniques for UX evaluation, offering valuable insights into their effectiveness and application within the IS domain.
Wearable IoT Devices: Feasibility and Effectiveness in Implementing Local Decision-Making for Elderly Fall Detection
Context: The growing use of IoT wearables in healthcare highlights their potential for elderly fall detection. However, wearable devices based on inertial sensors have processing and energy consumption limitations, making them unsuitable for complex algorithms. Traditional cloud systems face latency and bandwidth issues when processing inertial data, which may delay caregiver responses. Edge computing, with local decision-making, emerges as an efficient and energy-conscious alternative. Problem: The overload in transmitting inertial sensor data to remote servers can delay notifications, putting elderly individuals whose falls are undetected at risk. Rapid intervention is crucial to prevent severe consequences. Solution: This study implements a decision tree algorithm processed locally on the ESP32 microcontroller, enabling real-time fall detection while reducing energy consumption and response times. SI Theory: The study applies General Systems Theory by integrating inertial sensor data, software algorithms, and human behavior. This systemic approach optimizes device performance, promoting greater efficiency in real-time health monitoring and improving elderly safety. Method: This applied, quantitative research pre-processed accelerometer and gyroscope data into statistical metrics. A decision tree algorithm was developed and validated for local processing on the ESP32, using a dataset tailored for fall detection. Summary of Results: The proposed solution on the ESP32 detected falls with precision, without false positives, distinguishing daily activities in controlled tests. Additionally, it demonstrated high energy efficiency, validating its viability for IoT wearable devices. Contributions and Impact in the IS area: This study validates the integration of edge computing with wearable devices, balancing computational efficiency, energy consumption, and safety, offering effective solutions for health monitoring.
Conceptual Modeling of Algorithm Parameterization and Results Considering Volatile Data Requirements:A Case Study in the Context of Primary Healthcare
Context: Conceptual modeling in software projects is often affected by volatile data requirements, which influence design, logic, and performance. Flexible data modeling is essential to adapt to these changes, especially in healthcare applications where diverse, evolving data types are common. Ensuring adaptability in these settings supports operational efficiency and timely decision-making. Problem: Frequent updates in the requirements of Primary Healthcare (PHC) systems increase maintenance costs and cause operational issues, especially when systems need to meet new legislative or operational demands. These changes can disrupt database schemas and impact software development costs. Proposed Solution: This paper introduces six schema alternatives designed to model algorithm parameters and outputs flexibly, reducing the impact of changing requirements on system structure. These adaptable schemas aim to minimize the need for frequent reconfigurations. IS Theory: The research is based on the Theory of Information Systems Flexibility, which focuses on creating adaptable data structures to ensure long-term efficiency in dynamic settings. This theoretical foundation aligns well with PHC’s evolving data needs, requiring resilient and adaptable systems. Method: A prescriptive approach was used, combining theoretical evaluation of schema options with a case study in a real-world PHC project. This mixed-method approach provided insights into schema flexibility and usability. Summary of Results:We concluded that the proposed database schema is highly effective for adapting to new data requirements, allowing algorithm data and results to be stored as JSON attributes. This flexibility in data inputs and outputs supports evolving PHC requirements. Contributions and Impact on IS: This study introduces a database schema for managing volatile data requirements in healthcare IS and reducing IS development costs.
Fifteen years of process mining in Brazil: Current contributions, most used techniques, and challenges
Context: Process mining is a discipline that combines data science (data mining) and process science (Business Process Management - BPM), which has become crucial for large organizations as it enables the discovery, maintenance, and improvement of business processes. Problem: In the context of process mining in Brazil, there is a gap in the consolidated knowledge about the state of the art and the main topics being discussed. Solution: This study aims to understand what are the most frequent process mining techniques and challenges discussed within Brazilian research and the contributions to the advancement of the discipline in the country. IS Theory: This study was conceived under the aegis of Argumentation Theory, presenting information and evidence on selected studies and the results discussed. Method: A systematic literature review (SLR) from the last 15 years on process mining in Brazil was carried out, considering studies with Brazilian affiliation and using searches in scientific databases. Summary of Results: Through the analysis of 153 studies, we identified 9 different groups regarding Brazilian contributions to the advancement of the discipline in the country. By examining the results obtained, the methods applied, and the findings presented in the studies included on the SLR, we identified the most commonly used process mining technique in national research, as well as the main challenges cited by Brazilian authors, notably the need to address log quality. Contributions and Impact in the IS area: This study increases visibility of the current state of process mining research in Brazil in order to highlight new research opportunities within the Brazilian market.
Osiris: a platform for advertising agricultural machinery rentals
Context: Agribusiness is a key pillar of the Brazilian economy, significantly contributing to national growth and wealth generation. This sector heavily relies on agricultural machinery technologies to enhance productivity and operational efficiency. Problem: The high costs of acquiring and maintaining agricultural machinery restrict access for small-scale farmers. Furthermore, idle equipment generates significant passive costs, creating an additional barrier to its efficient use. Solution: This study proposes a tool for advertising agricultural machinery rentals, grounded in the principles of the sharing economy. The platform connects equipment owners with idle machinery to renters in need of such resources. IS Theory: The study is based on the DeLone and McLean Information Systems Success Model, which highlights system quality, information quality, and user satisfaction as critical factors for success. Method: A qualitative heuristic evaluation approach was used, focusing on experts’ perceptions of the system prototype to identify usability barriers and opportunities for improvement. Summary of Results: Most usability issues were classified as low or moderate severity, suggesting they can be resolved without significantly impacting the user experience. Violations were most frequent in consistency, system visibility, and error prevention, highlighting areas for prioritized improvement. Contributions and Impact on IS Field: This study advances the Information Systems field by demonstrating how sharing economy principles can inform digital solutions for agribusiness. It validates the application of the DeLone and McLean model in rural contexts and offers insights for addressing usability challenges, fostering innovation and accessibility in agricultural systems.
Enhancing NeRFs for High-Quality Indoor Video Generation: A Study on Parameterization and Recording Methods
Context: The generation of 3D representations with Neural Radiance Fields (NeRFs) has revolutionized areas like virtual reality and space visualization, but video capture for these models lacks a systematic approach. Problem: Video capture for NeRFs is still based on trial and error, with few well-defined parameters, leading to inefficiencies, rework, and increased training times. This process is particularly challenging in indoor environments, where variations such as lighting and camera angles significantly impact the final quality of the reconstructions. Solution: This paper proposes a systematic approach to optimize video capture, evaluating parameters such as lighting, camera zoom, camera path, and height while presenting metrics to reduce visual artifacts. Information Systems Theory: The research is based on the Task-Technology Fit (TTF) Theory, which explores how technology should be adjusted to the specific needs of tasks, aiming to optimize video capture to enhance the quality of the generated models. Method: The research follows an experimental approach, using the Nerfstudio tool to test various parameters in a dataset of 48 videos. The analysis is quantitative, evaluating reconstruction quality metrics. Summary of Results: Lighting significantly impacts the quality of reconstructions, while changes in capture angles adversely affect the results. Contributions and Impact in the IS field: The research contributes a methodology to optimize video capture in NeRFs, driving technological advancements.
AnyLattes: An Application for Continuous Assessment of Lattes Curriculum Information
Context: With the advancement of technology, process automation becomes increasingly necessary, especially for data analysis and organization of information for decision making. Problem: The Process of obtaining scores from conferences and journals within publications in Lattes Curriculum demands a lot of time and manual work, in addition to the need for analysis to be done jointly at universities due to the way in which capes evaluates postgraduate programs. Solution: AnyLattes – An application for automation, data extraction and organization of information from the Lattes Curriculum to assist in the evaluation of postgraduate programs, in addition to serving as a basis for filling out the Sucupira platform. SI Theory: This work is influenced by Organizational Information Processing Theory, where organizations need quality information to deal with environmental uncertainty and improve their decision making. Method: The research has a prescriptive nature along with the evaluation carried out through proof of concept, in addition to the qualitative approach for analyzing the results. Summary of results: In the experiments carried out, AnyLattes showed optimization in the data extraction routine and organization and grouping of information for analysis of postgraduate programs in comparison to commonly used means. Contributions and Impact in the IS area: The contribution is to enable an application focused on optimizing, organizing and grouping information extracted from Lattes Curriculum for postgraduate programs, have a view of the entire program and proceed with consistent data in their decision making.
Accessibility Assessment in Banking Chatbots using Texts Written by Deaf People who use Portuguese as a Second Language
Context: The study examines the ability of banking chatbots to understand and respond to interactions with deaf people who use Portuguese as a second language (L2), with a focus on digital accessibility in automated banking services. Problem: The hypothesis is that banking chatbots have difficulty interpreting requests in Portuguese as L2 and expressions based on the gloss of Brazilian Sign Language (Libras), resulting in digital exclusion and barriers in service. Solution: The proposal diagnoses the ability of chatbots, aiming at their understanding in the interactions of deaf users in order to improve accessibility. SI Theory: The research uses Inclusive Design and Task-Technology Fit (TTF), assessing whether chatbots are suitable for the needs of users who use Portuguese as L2. Method: A case study was carried out, with a qualitative approach. Ten banking activities were simulated in the chatbots of Banco do Brasil, Bradesco and Itaú, using inputs in formal Portuguese and as L2, with the help of ChatGPT for translations. Summary of Results: The results indicate error rates between 10% and 70%, with most errors in inputs that used Portuguese as L2 or colloquial language, evidencing the inadequacy of the chatbots for the deaf community. Contributions and Impact to the Field of IS: The study suggests the need for improvements in the language models of the chatbots to make them more inclusive for deaf users, generating impact in academia and in the technology industry.
Alternative Representations of Business Process Models for Enhanced Understanding by People: A Systematic Mapping Study
Context: Business process models are essential resources in business process management approaches that allow people to understand how an organization works. With business process models, organizations can reflect on process improvements and bring innovation to their services and products. In this context, it is fundamental for the people involved in the organization to understand how the process is performed to avoid misunderstandings and losses. Problem: Although there are several ways to represent a business process model, it is challenging to find alternative representations apart from traditional approaches (graphical and textual) and identify non-traditional approaches, goals, advantages, disadvantages, audiences, etc. Solution: Therefore, in this paper, we explored how alternative representations of business process models are used to enhance people’s understanding. Method: We planned, described, and performed a systematic mapping of the literature (SML) for that. Summary Results: In the search step, we found 322 primary studies, but after a careful evaluation criteria analysis by peer review, we accepted only 23. The results point to several alternative business process model representations, highlighting the potential for enhancing people’s understanding. Additionally, we presented the advantages and disadvantages of alternative model representations related to specified audiences. Contributions to IS: Thus, this study brings contributions to the IS field by identifying aspects of alternative representations of business process models, which bring new ideas and reflections to explore new technological approaches to make organizational contexts more understandable and accessible for people. In addition, this research is aligned with the GranDSI-BR 2016-2026, once they approach innovative themes related to IS in an open and connected world.
Hello, Freire! How Can You Help Me? A Smart Chatbot to Enhance Access to Academic Opportunities and Institutional Matters
Context: Universities offer various resources and services that students often fail to utilize due to difficulties in accessing relevant information. Chatbots can improve access and interaction between students and institutions. Problem: Students struggle to find information about academic opportunities and institutional matters due to fragmented data, limiting engagement. Solution: This paper introduces Freire Assistant (Freire), a chatbot system powered by a Large Language Model (LLM) and built using Retrieval Augmented Generation (RAG) pipelines. Freire aims to streamline access to academic information through a fast, intuitive communication channel. IS theory: Thiswork was developed under the aegis of Soft Systems Theory, as it addresses the complexity of accessing academic information as a sociotechnical problem. Method: To evaluate Freire’s effectiveness in providing accurate and relevant information about academic opportunities and institutional matters, we conducted a case study at a Brazilian university. We quantitatively evaluated the contextual quality of Freire’s responses using the DeepEval framework. Additionally, we qualitatively evaluated Freire’s usability with 45 students through the Chatbot Usability Questionnaire (CUQ) which measures aspects related to Chatbot Personality, Onboarding, User Experience, and Error Handling. Summary of Results: The findings demonstrated Freire’s efficiency in generating contextually relevant and accurate responses. Furthermore, usability scores were generally high due to their simple interface and conversation-driven functionality. Contributions and Impact in the IS area: This study highlights how smart chatbots can enhance user experience and access to information in universities, providing a replicable model for other institutions facing similar challenges.
IrrigaFlow: An IoT Architecture Integrated with Fuzzy Logic for Sustainable Agricultural Irrigation Optimization
Context: The growing demand for food security and the scarcity of natural resources pose global challenges, particularly in agriculture, which accounts for a significant portion of water consumption. In Brazil, agriculture holds substantial economic importance, but its intensive water use calls for more sustainable practices. Problem: Managing irrigation adaptively and efficiently is challenging due to the complexity of multiple environmental variables. This reduces water-use efficiency and negatively impacts agricultural productivity. Solution: IrrigaFlow is a modular architecture that automates irrigation using IoT, fuzzy logic, and distributed processing. It consists of three layers, namely IoT Module, Network Edge, and Cloud, enabling real-time monitoring and adjustments based on local environmental data. This approach optimizes water usage and improves responsiveness to climatic conditions. Information Systems Theory: Grounded in the Sociotechnical Systems Theory, this proposal balances advanced technology with human and organizational contexts, promoting efficiency and sustainability by dynamically adapting to environmental and operational conditions. Methodology: A qualitative interpretative approach was employed, combining case studies and simulations. Environmental data collection, fuzzy logic processing, and MQTT-based communication were validated to ensure the system’s effectiveness before practical implementation. Results: The system demonstrated efficiency in irrigation management by adjusting water volume and timing based on environmental variables, showcasing its potential to optimize water use in agriculture. Contributions and Impact on Information Systems: From an academic perspective, this work lays a foundation for research on distributed technologies applied to agriculture. For the industry, it offers a replicable model that enhances water efficiency and sustainability
Interpretability of Machine Learning Models for Cardiac Arrhythmia Classification
Context: Cardiovascular diseases, particularly cardiac arrhythmias, cause global mortality. Electrocardiograms (ECG) are essential for diagnoses. However, the automated analysis of ECG signals using machine learning faces interpretability challenges, which are crucial for accepting these models in medical practice. Problem: The classification of cardiac arrhythmias using machine learning faces resistance in the medical field due to the black-box nature of these models, which hinders the understanding of decisions and reduces professional trust. There is a need for interpretable models that reveal the critical factors in diagnosis, fostering the reliable use of artificial intelligence in healthcare. Solution: This work proposes an interpretable model for classifying cardiac arrhythmias, using machine learning to identify and visually explain the ECG features contributing to the diagnosis, with explanations at the model, class, and specific signal levels. IS Theory: This work is based on the Technology Acceptance Model (TAM), which suggests that "perceived usefulness" and "perceived ease of use" influence the intention to use a system. In healthcare, these perceptions relate to the model’s ability to provide clear and helpful explanations, facilitating medical professionals’ adoption of artificial intelligence (AI). Method: Instead of using raw biosignals, extracted ECG features are employed to enhance interpretability. This approach provides model-agnostic explanations at both local and global levels. Interpretability techniques are applied to clarify the contribution of each feature to the diagnosis. Summary of Results: Features such as the variability and median of RR and PR intervals and the signal-to-noise ratio of ECG signals are crucial for accurate arrhythmia classification. Contributions and Impact on IS: The approach allows for understanding the influence of specific ECG features on diagnosis, helping to identify patterns that support classification decisions and promoting the adoption of AI with trust and responsibility in medical practice.
Enhancing Text Recognition in OCR Systems Through Image Processing with BSRGAN
Context: Image enhancement is essential for advancing Optical Character Recognition (OCR), a technology widely applied across various Information Systems (IS) to enable accurate text extraction from scanned documents, IDs, invoices, and other document types. Problem: Despite OCR’s importance, challenges such as noise, variable illumination, and low-resolution scans often compromise recognition quality, leading to distorted and inaccurate results. These issues can impact the reliability and effectiveness of IS. Solution: This study presents a methodology to improve the quality of low-resolution images by combining image filtering techniques with OpenCV, super-resolution using the BSRGAN model, and EasyOCR for character extraction. IS Theory: The research is anchored in the Information Quality theory in IS, addressing the importance of improving input data to enhance system outputs and reliability. Method: The proposed methodology consists of two main stages. First, low-resolution images are processed using the BSRGAN super-resolution model, which enhances image quality for improved OCR performance. Then, the enhanced images are processed by an OCR system to extract and convert characters into text. Validation was conducted on three datasets: Brazilian Identity Document (BID), IIIT 5K-Word, and SVHN, simulating real-world application conditions. Summary of Results: The results demonstrate the proposed methodology’s effectiveness in enhancing OCR accuracy, significantly reducing error rates in various contexts. Contributions and Impact on IS: This work contributes to the IS field by providing a solution that enhances OCR input quality, benefiting academia through advanced image processing research and the industry by enabling more reliable text recognition in practical applications.
Design, Integration, and Evaluation of a Demand Forecasting Service in the Context of Primary Healthcare
Context: Efficient management of Primary Healthcare (PHC) within Brazil’s Unified Health System is essential for ensuring quality healthcare services, particularly in addressing challenges related to resource allocation and workforce planning. Ensuring an equitable and sustainable distribution of healthcare resources is crucial to meeting the diverse needs of Brazil’s population. Problem: A key challenge in PHC is accurately forecasting demand, which is vital for optimizing resource allocation and preventing facility overcrowding. In the absence of precise projections, healthcare services may experience overburdened facilities and prolonged wait times, ultimately compromising the quality of care. Proposed Solution: This study presents a demand forecasting service tailored to Brazil’s PHC. By leveraging regression techniques, the system facilitates data-driven decision-making in resource allocation and workforce planning. Predictive analytics enable public health managers to anticipate demand more effectively, ensuring a better-prepared healthcare system capable of delivering effective services. Information Systems (IS) Theory: This research is grounded in the Theory of Socio-Technical Systems, as the proposed solution integrates diverse datasets and predictive models into an information system that actively interacts with existing humans workflows in PHC. By influencing decision-making and resource allocation, the system fosters a seamless integration between technological automation and human expertise, enhancing the adaptability and efficiency of healthcare services. Method: Following a prescriptive approach, the study evaluates the forecasting service through a detailed case study. A quantitative analysis assesses its accuracy and practical applicability, ensuring its effectiveness in supporting decision-making in PHC. Summary of Results: Experimental results demonstrate that the proposed service accurately predicts PHC demand by leveraging real-world data, which can lead to more timely and efficient resource allocation. Contributions and Impact on IS: This research contributes to the Information Systems field by offering a scalable and adaptable tool for Brazil’s public healthcare context, enabling efficient healthcare planning and enhancing resource management.
Explorando os Limites da Reprodutibilidade na Tarefa de Detecção de Comunidades em Modelos de Redes
Context: Network model-based studies are applied across various fields, including social media. Problem: Data availability and reproducibility are challenging due to restrictive data policies and the significant computational resources required to process large and complex networks. In this context, sampling techniques offer a viable alternative by selecting representative sub-networks that preserve the essential structural properties of the original network. Despite their potential, there is a dearth of studies investigating how sampling methods can generate networks at different scales and quantify their limitations in detecting communities, especially in conjunction with backbone extraction, a crucial step that can significantly affect the network’s probabilistic properties. Solution: This paper evaluates the effectiveness of different sampling methods in improving the availability and reproducibility of network analysis, with a focus on community detection and backbone extraction. SI Theory: Our work is supported by Social Network Theory, which emphasizes relationships and connections among actors within a network over individual attributes. Method: In our research we quantify the limits, properties and scenarios in which smaller network versions can provide comparable communities structures to the original network. Summarization of Results: Our results show that certain sampling methods can effectively capture community structures even in reduced network representations. Contributions and Impact in the area of SI: This research facilitates the reproducibility and democratization of network studies and provides guidelines for creating networks at different scales that allow researchers to replicate certain studies.
“I Felt Pressured to Give 100% All the Time”: How Are Neurodivergent Professionals Being Included in Software Development Teams?
Context: As the demand for digital solutions adapted to different user profiles increases, creating more inclusive and diverse software development teams becomes an important initiative to improve software product accessibility. Problem: However, neurodivergent professionals are underrepresented in this area, encountering obstacles from difficulties in communication and collaboration to inadequate software tools, which directly impact their productivity and well-being. Solution: This study seeks to understand the work experiences of neurodivergent professionals acting in different software development roles. A better understanding of their challenges and strategies to deal with them can collaborate to create more inclusive software development teams. IS Theory: We applied the Sociotechnical Theory (STS) to investigate how the social structures of organizations and their respective work technologies influence the inclusion of these professionals. Method: To address this study, we conducted semi-structured interviews with nine neurodivergent professionals in the Software Engineering field and analyzed the results by applying a continuous comparison coding strategy. Results: The results highlighted issues faced by interviewees, the main ones related to difficulties in communication, social interactions, and prejudice related to their diagnosis. Additionally, excessive in work tools became a significant challenge, leading to constant distractions and cognitive overload. This scenario negatively impacts their concentration and overall performance. Contributions and Impact in the IS area: As a contribution, this study presents empirically based recommendations to overcome sociotechnical challenges faced by neurodivergent individuals working in software development teams.
Community Detection and Analysis of Political Alliances in the Brazilian Congress Voting Network
Context: Network analysis in legislative studies reveals hidden alliances and ideological shifts, helping to understand evolving party behavior and dynamics in democratic systems. Problem: Many voting network studies lack filtering for non-polarized propositions, weakening ideological clarity and modularity. Without edge pruning, irrelevant connections clutter networks, reducing accuracy in detecting true political alliances and dynamics. Solution: This research uses the Leiden algorithm on polarized propositions with edge pruning to detect cohesive communities in Brazil’s Congress, enhancing modularity and revealing true political dynamics. IS Theory: This study uses Social Network Theory to examine voting alliances and Institutional Theory to analyze how norms shape party behavior in Brazil’s Congress, offering insights into political behavior within complex systems. Method: This study uses a quantitative, descriptive approach with network analysis to examine Brazilian congressional voting patterns. Using public data and the Leiden algorithm, it identifies political communities, enhanced by filtering polarized votes and preprocessing to clarify network structure. Summary of Results: Our analysis of Brazilian congressional alliances showed a 10.95% modularity improvement using the Leiden algorithm with edge pruning, revealing clear ideological divides and a dynamic "swing" community influencing alliances in key years. Contributions and Impact on IS: This research advances IS by using network analysis on political voting to clarify alliances. Techniques like polarized data filtering and network optimization provide valuable tools for analyzing complex networks in IS.
Metrics for Quality Assessment in Blockchain-based Systems: A Systematic Mapping Study
Context: Blockchain technology has been widely adopted across various industries, such as finance, healthcare, and logistics, due to its ability to ensure transaction integrity and transparency. Problem: The growing adoption of blockchain systems demands rigorous quality assessment to ensure compliance with requirements. While software metrics for centralized systems are well-studied, a clear research gap remains in understanding their systematic adoption for evaluating blockchain-based systems. Solution: This study identifies, analyzes, and categorizes software metrics used in blockchain-based systems assessment grounded on the quality characteristics of the ISO/IEC 25010 standard. IS Theory: The study uses the ISO/IEC 25010 as a theoretical foundation for classifying and analyzing quality metrics. Method: A Systematic Mapping Study was conducted, identifying 128 primary studies extracted from different academic databases. Results: The findings highlight performance and security as fundamental to quality in blockchain systems, focusing on throughput and latency metrics. Additionally, Hyperledger Caliper is a frequent tool for evaluating blockchain network performance. Smart contracts, on the other hand, emerge as a promising area, given the lack of studies exploring their security and efficiency. Contributions: This work enhances the understanding of quality assessment in blockchain systems by mapping software metrics to the ISO/IEC 25010, covering predominant metrics, quality characteristics, and evaluation methods.
A Business Intelligence Platform to Support Legal Data Management at the TJMA
Context: Courts worldwide face an increasing case backlog, making it essential to develop technological solutions that improve efficiency in case management. Problem: Despite advances in Information and Communication Technologies and the adoption of electronic case management systems, handling large volumes of procedural data remains challenging, particularly in managing case processes and recurring cases of similar legal matters. Solution: This paper explores how a Business Intelligence tool can support judicial precedent and suspended case management by offering filtering, grouping, and duplicate case identification functionalities, integrated with the Case Management Center System. IS Theory: This study is based on the Organizational Information Processing Theory, emphasizing the need to align information processing capabilities with organizational information demands to manage complex data effectively. Method: Using Design Science Research, a technological artifact was developed in collaboration between the Court of Justice of the State of Maranhão and the State University of Maranhão, with a case study focused on the specific challenges of managing legal precedents and suspended cases. Summary of Results: The tool effectively centralizes and interactively visualizes procedural data, optimizing time for identifying duplicate cases and providing a solid foundation for more informed judicial decisions. The artifact has been approved by the Maranhão State Court and is now publicly available on the court’s website, enhancing accessibility and transparency. Contributions and Impact in IS Field: The tool enhances administrative efficiency and judicial productivity at the Maranhão State Court. This work contributes to academia by addressing gaps in BI application for judicial management and offers a practical solution for data-intensive environments in the industry.
Assessment of Competences for LGPD DPO through ANPD Standard and Information Systems Curriculum
Context: The General Data Protection Law (LGPD) in Brazil formalized the Data Protection Officer (DPO) role, demanding professionals to ensure compliance, governance, and data security. This emphasizes the alignment between legal, technical, and governance competences, creating a critical need for academic preparation. Problem: The absence of a defined standard for DPO training creates challenges in adequately preparing professionals. Specifically, there is a lack of integration between the competences outlined by the National Data Protection Authority (ANPD) and the Information Systems (IS) programs’ curricula. Solution: This study maps the competences required by the ANPD for DPOs and evaluates the adherence of a public university’s IS curriculum to these competences, grouping them into broader categories to enhance alignment. IS Theory: Grounded in the Competence-Based Theory, the study aligns professional skills with regulatory and organizational demands, through the CHA Theory. Method: Using a qualitative, descriptive approach, the research applied content analysis to IS curricular components and ANPD standards. Competences were categorized and their curriculum coverage assessed. Results: The study identified 12 competences, grouped into 5 categories, with strong emphasis on governance, security, and communication. Gaps in legal and contractual aspects were noted. Contributions to IS: The study offers a framework to align IS curricula with regulatory and professional DPO requirements, supporting academic institutions in integrating multidisciplinary content. Impact on IS: This research bridges the gap between academia and the market, fostering professionals equipped to address challenges in data governance, security, and compliance, strengthening IS programs’ relevance in data protection.
The Artificial Intelligence Integration in the Brazilian Legal Sector: A Systematic Review
Context: The National Council of Justice (CNJ) reported 140 Artificial Intelligence (AI) projects in the Brazilian Judiciary by 2023, distributed across 62 courts, representing an increase of 26.13% over the previous year. This growth reflects a digital transformation in the legal sector, focusing on increasing productivity and reducing costs and procedural times. Problem: Despite the progress, there is still a gap in the systematic understanding of the IA implementations in the legal sector due to the lack of detailed documentation that compromises transparency and hinders the overall evaluation of the efficiency of these technologies. Proposed Solution: This study conducts a systematic review to map the current panorama of AI use in the Brazilian legal sector, identifying the types of projects implemented and their scientific and technical production characteristics. IS Theory: Based on the Diffusion of Innovations Theory, which analyzes how new technologies spread in social systems. Methodology: Following Kitchenham’s systematic review methodology and the PRISMA guidelines, studies published between 2020 and 2024 were analyzed using academic databases and judicial platforms. Summarization of Results: 90 projects were identified in higher, regional, and state courts and other bodies. The main applications involve textual classification and task automation to optimize procedural efficiency. A lack of documentation and standardization was also identified, highlighting the need for more project transparency. Contributions and Impact on IS: The study maps the intersection between AI, Information Systems, and the legal sector in Brazil, highlighting topics that need more attention. It contributes to the advancement of adaptive IS architectures and provides a theoretical-empirical basis to drive the digital transformation of the judicial system.
Students’ Perceptions of Speculative Design with Generative AI in Creating Futuristic Narratives: An Interdisciplinary Study with Undergraduate Students from Diverse Fields
Context: This study explores the integration of speculative design (SD) and Generative Artificial Intelligence (GenAI) in educational and interdisciplinary contexts. The goal is to prepare students to face future technological complexities, emphasizing ethical and social considerations. Problem: The research addresses the need to equip students with critical skills to engage with emerging technologies. Challenges include the usability of GenAI tools, the lack of ethical frameworks for responsible innovation, and the need for creative approaches to envision future scenarios. These challenges have technological, social, and educational dimensions. Solution: A participatory workshop that integrates SD and GenAI to inspire creativity and ethical reflection, enabling participants to collaboratively explore future technological implications. Information Systems Theory: The research draws on Actor-Network Theory (ANT) and speculative realism to analyze the dynamic interactions between human and non-human actors in sociotechnical networks. Method: The research employed a participatory design methodology, conducting a workshop with 17 undergraduate students from diverse fields. A qualitative approach was used to examine data collected through focus groups. The analysis focused on identifying themes related to user experiences and ethical considerations. Summary of Results: Four main themes emerged: engagement with the process, the applicability of GenAI, personal impact and recommendations, and ethical and social reflections. Participants valued SD as a critical tool for analyzing ethical implications and the social effects of speculative technologies. Contributions and Impact on IS: The study offers an innovative framework that combines SD and GenAI, promoting ethical and inclusive educational practices and contributing to more responsible sociotechnical systems.
Detection and Mitigation of Attacks at the Edge of IoT Networks Using Deep Learning and P4
Context: The concern for security in IoT networks is becoming increasingly common, considering the accelerated use of these devices in recent years. Problem: Traditional methods for detecting attacks in IoT networks are ineffective due to device heterogeneity. While learning approaches show promise in threat detection, the computational limitations of IoT devices make it challenging to implement these solutions. Solution: This work proposes a deep learning solution for IoT attack detection, focusing on efficient threat mitigation using the Programming Protocol-independent Packet Processors (P4) language, adaptable feature extraction for algorithm inference, and execution on SBC devices. SI theory: This work is based on General Systems Theory and Machine Learning Theory, emphasizing integrating components in IoT networks and learning for attack detection. Methods: This study uses a case study to evaluate the effectiveness of deep learning algorithms in detecting attacks in IoT networks, adopting a descriptive and quantitative approach with metrics like accuracy and F1 score. Results: In our work, the Long Short Term Memory (LSTM), Convolutional Neural Network (CNN), and Gated Recurrent Unit (GRU) algorithms achieved accuracy rates of 96.9%, 95.6%, and 93.9%, respectively. However, the CNN model was selected for the final implementation due to its superior inference time and resource consumption performance. Contributions and impact in the IS area: This work contributes to Information Systems by integrating deep learning and P4 programming for attack detection and mitigation in IoT networks, enhancing security, and promoting further research in the field.
Evaluation of Software Requirement Patterns for Electronic Health Record Systems
Context: An Electronic Health Record System (EHR-S) captures, stores, presents, transmits, or prints health information that allows the individualization of a patient. An EHR-S can be classified into one or more categories, depending on its scope (e.g., an electronic prescription system). Problem: Despite existing several software requirements in common between these categories, much effort and time may be spent in the development of such complex systems, specially in requirements engineering. Solution: We developed a Software Requirement Patterns Catalogue (SRPC), which groups analogous behaviors and services from EHR-S categories into well-defined templates that can be reused in future specifications. IS Theory: We used Representation Theory to elaborate on software patterns representing EHR-S requirements. Method: Fifty-five undergraduate students produced EHR-S requirements specifications with and without SRPC support. Quantitatively, the completeness, scope, and consistency of these documents were evaluated, as well as the SRPC’s usefulness and ease of use. Positive and negative aspects of the SRPC were also qualitatively analyzed. Results: The SRPC helps build more complete and correct specifications and conveys a perception of shortened elicitation and specification times. Contributions: A quantity-quality evaluation of an SRPC for EHR-S indicates requirement patterns improve the quality of specifications and save effort and time.
A Tool for Understanding and Improving Social and Human Factors in Requirements Management in Software Ecosystems
Context: Requirements management in software ecosystems (SECO) requires cooperation among multiple actors (e.g., keystone, users, and external developers), and the consideration of social and human factors (SHF) is essential in its activities. Problem: Requirements professionals rarely consider SHF in requirements management due to the lack of practical solutions that explicitly help them address these factors. Solution: Based on a rapid review on SHF in requirements engineering, we developed and evaluated a web tool designed to assist professionals understanding and improving SHF while executing requirements management activities, using sentiment analysis as a central feature. IS theory: This study is motivated by socio-technical theory, as the tool addresses the technical and social aspects of requirements management in SECO. The tool was evaluated using the Technology Acceptance Model (TAM) to understand professionals’ perceptions regarding its usefulness and ease of use. Method: We implemented the software solution and conducted a qualitative feasibility study with four requirements professionals working in SECO to evaluate the tool’s effectiveness and usability. Results: The findings indicate that the tool provides professionals with a new perspective for better understanding the SHF that influence requirements management activities in SECO. Contributions and Impact in the IS area: This study contributes with a tool to support requirements management professionals in understanding and improving SHF in SECO. In academia, it contributes to the knowledge of SHF that affects requirements management in SECO. This study also contributes to the Grand Research Challenge “Strengthening the Socio-technical Approach in Information Systems Research”.
An Evaluation of the Impact of Code Generation Tools on Software Development
Context: The rise of AI-assisted tools like GitHub Copilot aims to improve productivity in software development, raising questions about their practical impact on developer performance and code quality. Problem: While AI-assisted tools are promoted for enhancing productivity, empirical evidence on their real-world impact remains limited. This study addresses whether such tools improve programming workflows, focusing on developers with varying experience levels and examining effects on coding quality and efficiency. Solution: An empirical study is conducted to assess Copilot’s effectiveness through experiments with student developers, focusing on task efficiency and code correctness, guided by established indicators identified in the literature. IS Theory: Grounded in the Task-Technology Fit (TTF) theory, this research explores the extent to which Copilot aligns with programming tasks and meets user needs, ensuring its relevance to practical development scenarios. Method: A literature review identified key indicators for evaluating AI-assisted code generation, focusing on task completion time and code correctness. Data collection involved task-based experiments with students, followed by quantitative analysis comparing performance with and without Copilot. Summary of Results: Findings indicate that Copilot can significantly reduce task completion time. However, no statistically significant differences were observed in code correctness, suggesting that while Copilot improves efficiency, its recommendations require careful review to ensure quality. Contributions and Impact on IS Field: This study contributes empirical insights into AI-assisted coding’s advantages and challenges. The results inform academia and industry about best practices for implementing such tools effectively, emphasizing their potential for accelerating development while maintaining the need for human oversight.
Perspectives on Women in Computing: A Study on the TikTok Platform
Context: Recently, social media platforms such as TikTok have grown significantly as communication tools. TikTok, in particular, stands out for its short and dynamic video format, which rapidly disseminates information on various topics. However, representation and perception of these topics can vary, often reflecting and perpetuating gender stereotypes. Problem: The underrepresentation and lack of inclusive environments for women in computing Solution: Creating a more inclusive and representative environment on the platform could attract and retain more women interested in computer science. IS Theory: Feminist theory provides a critical framework for discussing the importance of promoting gender diversity in computing and how social media platforms can play an essential role in this process. Method: The method used will involve creating experimental accounts on TikTok to simulate profiles, followed by the search and analysis of content related to computing. Summarizing the results: Analysis of computing videos shows more varied content for women and focuses on practical skills for men, highlighting the need for an inclusive approach to promoting gender equity in IT. Contributions and Impact on the IS field: Academically, the findings provide insights to inspire initiatives for the inclusion of women in computing and research on the impact of social media on women’s career choices. In the industry, they highlight the importance of promoting gender diversity in technology.
Digital Prescription and Dispensation of Medications
Context: In Brazil, the prescription and dispensing of medications remain largely manual, relying on physical documents. This approach poses challenges for security, traceability, and regulatory compliance, especially for controlled substances. Problem: Manual systems are insufficient for tracking medication dispensing, preventing misuse, and ensuring interoperability between healthcare providers and pharmacies. Solution: This study proposes a system that integrates the FHIR interoperability standard, adapted to produce self-contained documents, with JAdES digital signatures for secure and authentic prescription records. Blockchain is used to enable traceability and control over medication dispensing through an immutable record of transactions. Method: The research employed a Proof of Concept (PoC) methodology to validate the proposed system, focusing on analyzing the current manual processes and proposing a digital solution to address identified gaps. This PoC was conducted in a controlled laboratory environment to simulate real-world scenarios and test the integration of FHIR, JAdES signatures, and Blockchain technologies to evaluate the system’s functionality and compliance with regulatory requirements. Results: The system successfully generated secure, self-contained digital prescriptions and used Blockchain to trace and control medication dispensing. It improved regulatory compliance and addressed interoperability issues by eliminating external dependencies. Contributions: This work advances healthcare information systems by combining interoperability standards, electronic signatures, and Blockchain to digitize and secure critical processes, addressing key challenges in medication management.
Evolutionary Analysis of the Co-occurrence between Code Smells and Community Smells in Code Samples
Context: Code samples are widely used to demonstrate best practices and facilitate framework adoption, promoting productivity and innovation. In open-source projects, their evolution often involves diverse contributions, introducing technical complexity and challenges in community collaboration. This scenario can leads to the co-occurrence of Code Smells and Community Smells, impacting software quality, collaboration, and the sustainability of developer communities. Problem: The simultaneous evolution of Code Smells and Community Smells degrades code quality, reduces developer cohesion, and lowers collaborative efficiency, increasing the likelihood of project failure and making projects less appealing to new contributors. Solution: This study investigates how the co-occurrence and evolution of Code Smells and Community Smells interact over time. It proposes strategies based on metrics and temporal analysis to mitigate these impacts, improve maintainability, and strengthen community dynamics. Information Systems Theory: Social Network Theory provides a foundation to analyze the influence of human interactions on project evolution and team cohesion. Method: We mined open-source repositories—using techniques such as commit history mining, bug log analysis, and code metric extraction—and temporal analysis to quantitatively track and correlate the evolution of Code Smells and Community Smells across project milestones. Results Summary: Local commit surges trigger transient complexity spikes; geographic dispersion reduces cohesion; and diverse teams correlate with controlled code growth. Overall, smells interact to degrade maintainability and collaboration. Contributions: Addressing the SBSI Grand Challenges (2016–2026), this study integrates technical and social perspectives, fostering sustainable practices in open-source projects and enhancing the resilience and effectiveness of developer communities.
Ethical Requirements in the Age of Artificial Intelligence: A Systematic Literature Review
Context: Software developers and users are growing concerned about the ethical use of software, especially with Artificial Intelligence (AI). In this context, we investigated how ethical requirements can be elicited and incorporated into software development. Problem: The challenge is identifying and defining effective methods for eliciting and managing ethical requirements in software development. Solution: We conducted a Systematic Literature Review (SLR) to identify techniques, methods, processes, frameworks, and tools for eliciting, analyzing, and specifying ethical requirements. IS Theory: We explore the application of theories related to requirements engineering, ethics in technology, and data governance. It focuses, in particular, on ensuring that information systems comply with ethical and legal principles from the beginning of the development cycle. Method: Following the Kitchenham and Charters protocol, we conducted an SLR with stages of planning, conducting, and reporting the results. Summarization of Results: We have identified 47 primary studies. These studies address different approaches to eliciting ethical requirements, including techniques based on user stories, analysis of ethical guidelines, specific frameworks such as ECCOLA, and methods such as interviews and modeling. Contributions and Impact on the IS area: The report contributes to the field by consolidating existing practices in the literature regarding ethical requirements. It provides a comprehensive overview of the techniques and tools available for integrating ethical considerations into software systems and identifies gaps and opportunities for future research. The study significantly impacts the IS field by providing practical and theoretical guidelines for eliciting ethical requirements in information systems.
A Survey on the State of Practice in the Adoption of Schema Matching in Brazilian Companies
Context: Data integration is a fundamental activity for achieving full interoperability between Information Systems (IS). One of the most critical steps in this process is schema matching, which involves identifying correspondences between schema elements to ensure that equivalent information is correctly associated. Problem: The schema matching activity can be extremely time-consuming, expensive, repetitive, labor-intensive, and error-prone, potentially leading to significant losses. In this context, companies can employ tools to support or partially/fully automate this activity. However, the current state of such practices in Brazil remains unclear. Solution: Understanding the state of practice in Brazil can help guide research and development efforts by revealing how data integration is currently conducted in companies. This provides evidence of practical approaches and informs the structuring of training programs and the development of technologies to support such initiatives. IS Theory: We rely on Socio-technical systems theory, since we consider social and technical aspects as interdependent parts of a complex system. Method: A survey research was carried out. Summarization of Results: Thirty-five data professionals from 12 states and the Federal District participated in the survey. The results show that, despite familiarity with the concept, practical adoption faces challenges such as schema complexity, a lack of tools, and the absence of specific training. Nevertheless, for those who apply the technique, the benefits are clear: (i) more efficient data integration, (ii) error reduction, and (iii) increased agility. Contributions and Impact on the IS Area: The results contribute by providing evidence that can help guide research efforts and technological advancements to support schema matching in Brazil. This aims to enable full interoperability among information systems in the future, addressing Brazil’s Grand Challenges in Information Systems, such as Systems-of-Systems, Smart Cities, and Full Interoperability, which serves as an enabler for the other two.
Usability in IPC Price Collection Application: Evaluation of Interface and User Experience
Context: Price collection is essential for economic analyses and inflation monitoring. Problem: The traditional price collection method requires significant effort from researchers and often results in inconsistencies and formatting issues, impacting the accuracy of generated inflation indexes and increasing the data correction time. Proposed Solution: To mitigate these issues, a mobile price collection application was developed to automate and standardize data entry. Additionally, a usability analysis of the application is presented. Information Systems Theory: This work is based on the Boundary Object Theory, considering the application as a connection point between different user profiles, and on the Behavioral Decision Theory, which guides the design to minimize cognitive biases in data collection. Method: Usability tests proposed by the authors were conducted, along with the System Usability Scale (SUS), a method from the literature, for quantitative and qualitative analyses of user experience evaluation and system accuracy. Results Summary: The results show high acceptance and accuracy in the use of the application, with reduced errors and time in data collection. Findings indicated reliable consistency in usability evaluations and pointed to areas for improvement. Contributions and Impact on the Information Systems Area: This research demonstrates the positive impact of automation in economic data collection and reinforces the application of IS theories in interface design. The work contributes to usability studies and offers a model for standardizing collection processes that is applicable to various economic monitoring contexts.
An Ontological Characterization for SBSI Research Topics
Context: The Brazilian Information Systems (IS) community has grown in complexity involving different research topics. Characterizing the IS community research topics is vital to bringing knowledge to advance IS research maturity. Problem: The complexity, diversity, and plurality of research topics in IS present a challenging task involving collecting adequate data, analyzing, and exploring different techniques to understand the evolution in the area. Solution: Using research data published by the Brazilian Symposium on Information Systems (Simpósio Brasileiro de Sistemas de Informação - SBSI), we provide the NetO+ ontology to enable automated reasoning about SBSI research topics characterization. IS Theory: This study uses the language action perspective, highlighting what people do while communicating and how they create scientific reality using language. We focus on the SBSI research topics with automatic characterization using the Large Language Model (LLM) and ontology techniques. Method: We analyzed the technological research topics employing semantic analysis using an LLM to identify research topics with keywords related to articles’ titles. Also, the ACM taxonomy CCS Concepts in the IS branch were allied to ontological inferences. Summarization of Results: The findings with LLM to extract keywords from articles, and the ontology inference allows us to find research topics. This approach provides an overview of SBSI research, highlighting the topics’ evolution and their interconnections with ACM taxonomy. Contributions and Impacts on the IS area: This work provides IS research topics to bring knowledge enabling decision-making for sustainable community growth based on the socio-technical approach concerning excellence in IS conception, development, and maintainability.
Methodological Advances in Expert Systems with Artificial Intelligence
Context: With the advancement of artificial intelligence (AI), expert systems (ESs) continue to evolve and are essential to support decisions in complex domains such as healthcare, industry, and automation. Problem: The growth of AI technologies has expanded the possibilities of Expert Systems. With advances in natural language processing, there is a need for an updated mapping of the applications and methodologies of ESs, highlighting the role of new AI techniques. Solution: Literature review is essential to identify trends, methodologies, challenges, and innovations in ESs, providing a comprehensive overview of the most recent trends and their implications. IS Theory: When examining how ESs can integrate and process complex and constantly evolving data, this study is guided by the complex systems theory, which aims to explore how parts of systems exchange information and adapt to new conditions. Method: The study employs a qualitative and interpretative review to investigate the state of the art in inference methodologies, knowledge acquisition, and performance in ESs. Summary of Results: Expert systems have evolved significantly with the use of AI, including deep learning, hybrid models, and natural language processing, improving accuracy, adaptability, and the ability to process large volumes of data. Contributions and Impact on the IS Field: This study highlights the advances that make expert systems more effective, highlighting how the integration of AI improves the ability to deal with uncertainty and complex decisions. The new methodologies show potential to optimize reliability and decision making in critical environments.
Guidelines for Adoption Micro-frontend Architecture
Context: The rapid evolution of software development has led to challenges when integrating new technologies into monolithic frontend architectures. While microservices advanced backend modularity, frontends often face issues like code redundancy, scalability, and lack of modularity. The micro-frontend architecture emerged as a modular approach to address these challenges. Problem: Current monolithic frontend structures hinder agility and scalability in corporate environments, affecting both technology performance and business adaptability. This study targets challenges such as redundancy and the complexities of team coordination when scaling frontend projects. Proposed Solution: We developed a Guide for Adoption of Micro-Frontends (GAM), providing structured guidelines to support the effective implementation of micro-frontends in corporate contexts. IS Theory: This research draws upon the Technology Acceptance Model (TAM), evaluating the factors influencing the adoption of micro-frontend architecture within development teams. Method: We conducted a systematic mapping study (SMS) to investigate the impacts and challenges associated with adopting micro-frontends in software development. Next, we proposed the GAM and carried out a case study at Serasa Experian to assess the feasibility of the proposed guide. Additionally, we conducted a survey with development practitioners to explore their perceptions of the micro-frontend architecture and the workflows suggested by the GAM. Results: Results indicate that GAM is viewed positively by practitioners, assisting in the structured adoption of micro-frontends. Our case study highlights critical areas impacted by this approach, including performance, scalability, and parallel development, as well as issues with module complexity and developer experience. Contributions and Impact on IS: This research contributes a practical framework for the industry, addressing current gaps in micro-frontend implementation and setting a basis for further study on modular frontend architectures in Information Systems (IS). GAM provides actionable insights for practitioners, enhancing frontend modernization efforts with measurable impacts in agility and maintenance.
A Process for Building Datasets that Enable the Application of Different Methods for Detecting Fake News and Social Bots
Context: The spread of fake news on social media is an imperative concern. The dissemination of such news by social bots has added complexity to disinformation detection applications. Machine learning methods have been applied to classify news as fake and not fake and accounts as bot and not bot, based on labeled datasets. Problem: There are no datasets that embed simultaneously news labeled as fake and not fake and accounts labeled as bot and not bot. This gap hinders the evaluation of classification methods that could benefit from such data embedding. Solution: A process for building datasets that contain pieces of news and accounts appropriately labeled, and enable development and comparison of fake news and social bots detection methods. IS Theory: General Systems Theory1 and social Network Theory2. Method: Data requirements from SOTA3 fake news and social bot detection methods guided the development of the process. This process collects data from social networks and fact-checking agencies. A case study generated a dataset, illustrating the viability of the process. Summary of Results: The dataset generated is public and contains 440 labeled pieces of news and 6,274 labeled accounts. Most fake news detection methods improved their performance when they considered the labels of the accounts. Contributions and Impact on the IS field: The process that builds datasets that integrate labeled news and labeled accounts, and the dataset generated by the case study. Both contributions are related to the Grand Challenges in IS Research and the Sociotechnical Vision of IS.
Investigating requirements change requests
Context: Software development is a highly dynamic, collaborative, and constantly changing process. Hence, the efficient management of requirements change requests (RCR) is essential to avoid negative impacts such as delays and quality compromise. Problem: The management of RCR in software development demands an understanding of associated motivator factors, solutions, and challenges. Solution: In this study, we aim to identify factors, solutions, and challenges in RCR management. IS Theory: This study is motivated by socio-technical theory, which addresses the technical and social aspects of requirements management. It highlights the interplay between stakeholder collaboration and technical processes in effectively managing RCR. Method: Employing the rapid review of the literature (RR) method, the research analyzes 37 selected studies from a traditional scientific database in the field of computing for this purpose—out of 1,061 retrieved results. Summary Results: The research reveals 19 factors (e.g., change in users/clients needs), 35 solutions (e.g., COSMIC Functional Size Measurement), and 12 challenges (e.g., time and resource consumption), providing a comprehensive overview of the RCR management landscape in software development. Contributions to IS: This study contributes to understanding factors, solutions, and challenges for managing RCR in software development. For academia, it contributes to disseminating knowledge about RCR, which contributes to advancing the state of the art of requirements change management. For the industry, it assists professionals in identifying, adopting, and implementing effective strategies to face the challenges in the area.
Providing Task Execution Capabilities in LLM-Based Conversational Assistants
Context: Conversational assistants (CAs) powered by Large Language Models (LLMs) excel in generating coherent responses, but struggle with task execution requiring long-term memory or temporal awareness. This research addresses such limitations, particularly in healthcare applications where task continuity is crucial. Problem: Existing CAs often fail to meet user expectations in scenarios requiring long-term task management, such as scheduling reminders or monitoring chronic health conditions. This lack of temporal reasoning and memory continuity undermines user trust and engagement. Proposed Solution: We propose a multi-agent system integrating LLMs with structured data processing, that enables CAs to recognize, manage, and execute user requests, such as scheduling reminders or tracking tasks over time. The solution bridges the gap between conversational fluency and actionable outcomes. IT Theory: The study draws on theories of Human-Computer Interaction (HCI) and Temporal Awareness in Information Technology for allowing structured task execution. Method: The research combines proof-of-concept implementation with healthcare case studies. Two experimental phases evaluated the solution: a pilot test with 35 users and an expanded trial with 437 participants focusing on chronic disease management. Summarization of Results: Results demonstrated an improvement in task fulfillment and user engagement. The assistant successfully addressed 82% of task requests in simulations, with reduced user frustration and longer interactions during real-world trials. Contributions and Impact on IT: The study enhances the capabilities of conversational assistants by introducing temporal reasoning and structured task execution, making them more effective in managing real-world tasks. These advancements improve the applicability of CAs, particularly in domains like healthcare, where accurate and timely task management is critical.
Ad-hoc v.s LLM based System for Information Retrieval in Large Tabular Data: A Comparative Study in Public Medicine Procurement Audits
Background: Auditing is key when dealing with public expenses. Despite its importance, frequently auditing efforts must prioritize few targets due to a lack of human resources. However, leveraging the auditing process by developing a system that can automatically process large documents is a feasible task. Problem: The Information Retrieval (IR) problem considered in this work relies on two components: (i) the text to be searched and (ii) the data source where the required information is supposed to be. The first component is not standardized, presenting a challenge to an automated solution. The second component is structured; however, it is available in a large data source, which may consist of an obstacle for some automated IR methods. Specifically, given a drug specification, our system must find all available products that match this description in a large data source. Solution: This work investigates two different information retrieval solutions. The first approach basically relies on apriori knowledge of the problem for preprocessing the text and computing words similarity. The second approach leverages a powerful LLM to search in the same data source. IS Theory: Information Processing Theory Research Method: Proof of Concept Experimental Results: The results show that the proposed Ad-hoc method reaches accuracies from 72.4% up to 86.9% while the LLM based approach struggles to find satisfactory results mainly by its non-deterministic behavior and the hallucination problem. Contribution: With regard to the industry, the developed system has the potential to significantly improve the quality and scale of auditing processes. For the academy, the present work unveils limitations of using LLM based approaches for searching in large structured tabular data (± 25000 rows).
The Influence of Organizational Culture on Business Process Management: An Update of a Systematic Literature Review
Context: Organizational culture is a factor that influences Business Process Management (BPM) initiatives, impacting the effective implementation and adoption of continuous improvement, innovation, and strategic alignment practices, both in the organizational dimension and in human interactions between team members. Problem: The implementation of BPM is hampered by cultural barriers, such as resistance to change and risk aversion. These issues negatively affect operational efficiency and employee satisfaction. Solution: The proposed solution consists of identifying and analyzing the cultural aspects that influence the adoption of BPM. IS Theory: The study is based on the Innovation Adoption Theory, which explores how and why innovations are adopted in various organizational contexts. Method: The method adopted consisted of an update of the systematic literature review, with data from 2011 to 2024. A mapping of the previous study supported this study. Summary of results: The results indicate that companies with a culture focused on innovation and collaboration have a significantly higher BPM adoption rate, highlighting the importance of an adaptive cultural approach. Contributions and Impact on the IS area: The contributions include the identification of cultural practices that facilitate the adoption of BPM, and providing practical guidelines for academics and professionals. The impact on the area of Information Systems (IS) is that it highlights the potential of organizational environments that are conducive to innovation, improving the effectiveness and efficiency of IS.
Analyzing Performance of Geographic Databases to Enhance Social Analysis in Map Applications
Context: Social mapping applications are essential tools for understanding regional needs. In addition to physical geography, these maps integrate data on infrastructure, activities, and social demands— visualizing regions, points of interest, and relevant layers. This approach provides a clear and comprehensive view to support business, government, and social decisions. Problem: Managing social mapping data presents challenges, especially when storing sets of coordinates. This limits specialized database functions, impacting application performance in advanced geospatial functions. In particular, it is difficult to define which technologies are most appropriate to handle such data. Solution: We propose to use geographic databases such as PostgreSQL and MySQL Spatial. To do this, we propose to identify which functionalities are facilitated by these two technologies and compare their performance in executing these functionalities. IS Theory: Guided by the sociotechnical theory of Innovations Theory, this study examines how IS technologies interact in social contexts. Method: This study used a qualitative survey of professionals to propose functionalities considered useful for social analysis and an empirical experiment on the performance of the functionalities using PostGIS and MySQL Spatial databases. Results: Using a geographic database improved data processing, especially for large data. PostGIS showed better scalability and faster response times than MySQL Spatial as data volumes increased. Contributions and Impact on the IS field: This research improves the handling of geospatial data in IS, offering a practical solution to optimize queries, benefiting academia and industry.
Could you tell me the process ID? Structuring Text Documents from the Brazilian Electronic Information System Using a Named Entity Recognition Approach
Context: In the context of valuing open data, transparency, and efficiency in public services, there is a growing demand for studies to improve government systems. Some public agencies use the Brazilian Electronic Information System (Sistema Eletrônico de Informações - SEI), a procedural management system that centralizes electronic processes and promotes administrative efficiency. Problem: Although SEI has contributed to advancements in public administration, significant challenges remain in information search and retrieval due to the inefficient keyword-based approach currently available. These difficulties are enhanced by the high amount of documents generated daily, which are written in natural language and present variability in categories, writing styles, and structures. As a result, searching for relevant documents in SEI is time-consuming, leading users to create unnecessary processes and inconsistent resolutions when compared to previously completed processes. Solution: A Natural Language Processing (NLP) pipeline was proposed to extract information from SEI documents using Named Entity Recognition (NER) models. IS Theory: Organizational Information Processing. Method: This research adopts a descriptive approach. Public SEI documents were collected using a web crawler, and trained annotators built a corpus to enable the training of state-of-the-art NER models. The models’ performances were compared and quantitatively analyzed. Summary of Results: A Brazilian Portuguese labeled corpus of SEI for NER was curated and validated, leading to an NLP pipeline for information extraction. Contributions and Impact in the IS area: This research provides a baseline for structuring data from Electronic Information Systems, enabling more effective strategies for search and retrieval tasks.
MMAI-LGPD: A Maturity Model for Governance and Data Compliance in Information Systems Institutions
Context: The General Data Protection Law (LGPD) in Brazil demands organizations implement governance, compliance, and data security frameworks. This requirement is particularly significant for institutions managing Information Systems (IS), which face challenges in aligning technological innovation with regulatory demands. The MMAI-LGPD addresses this gap by integrating legal, organizational, and technological dimensions into a structured compliance framework tailored for IS institutions. Problem: Despite the urgency of LGPD compliance, there is no widely adopted maturity model specifically designed for IS institutions, leaving organizations struggling to balance governance, security, and operational efficiency while meeting legal and ethical requirements. Solution: This paper presents the MMAI-LGPD, a model that categorizes compliance into five maturity levels and defines 57 items across six dimensions, offering a pathway for improving governance, security, and data management practices. IS Theory: The research adopts a sociotechnical approach, building on maturity models in Information Systems to integrate governance, technology, and legal perspectives. Method: A qualitative, prescriptive approach was used, including content analysis of LGPD requirements and case studies with six IS institutions. Axial coding validated the model's applicability and identified organizational gaps in compliance practices. Results: The MMAI-LGPD provides a practical tool for assessing and improving compliance maturity. Validation results demonstrate its effectiveness in aligning governance, technology, and legal frameworks in IS institutions. Contributions and Impact on IS: This research bridges academia and practice, advancing maturity model studies in IS. The MMAI-LGPD enables IS institutions to meet regulatory demands while fostering ethical and sustainable practices in digital ecosystems.
Digital Technologies and Information Systems in Social Innovation Ecosystems
Context: The Global Digital Compact, signed in 2024, emphasizes cross-sector collaboration and recognizes science, technology, and innovation as pillars for accelerating the achievement of the Sustainable Development Goals (SDGs). Problem: Despite the growing recognition of digital technologies and information systems (IS) in advancing the SDGs, there is limited understanding of how these technologies are effectively employed in social innovation ecosystems (SIEs). This gap may hinder the articulation and sharing of innovation capabilities. Solution: This study aims to map how digital technologies and IS are employed in SIEs and their alignment with the eight priority technologies outlined by the United Nations (UN). IS Theories: Grounded in Dynamic Capabilities Theory and Social Innovation Ecosystems. Method: A systematic mapping study with an exploratory approach, following the SEGRESS framework. A total of 28 primary studies were analyzed from 176 selected across five digital libraries (Scopus, ScienceDirect, Compendex, SciELO, and SBC OpenLib), using qualitative data analysis. Summary of Results: The study identified 38 technologies used in SIEs, covering 13 different types of SIEs. However, only five of the eight priority technology categories defined by the UN were represented. Additionally, the findings suggest that all three dimensions of dynamic capabilities remain underdeveloped within the identified SIEs. Contributions and Impact in the IS Field: This study contributes to GranDSI Br 2016–2026, particularly in Methodologies and Technologies for Citizen Participation. It frames SIEs as key actors in addressing complex social challenges and provides a comprehensive overview of digital technology adoption, highlighting challenges and opportunities for strengthening technological capabilities to accelerate progress toward the SDGs across the dimensions of technology, organizations, and people.
Drift Detection Methods on Machine Learning Systems: a Discussion over Discrete Live Data
Context: Machine learning has become an essential tool for addressing complex problems in information systems, encompassing industrial, commercial, and residential applications. Problem: Machine learning systems without frequent retraining are prone to data and concept drift, compromising predictive accuracy. This issue is particularly critical in scenarios where retraining is infeasible due to high computational costs or data unavailability. Solution: This study evaluates the performance of drift detection methods in discrete time series with controlled changes in mean and standard deviation using synthetic Gaussian signals. IS Theory: The General Systems Theory underpins the study by emphasizing how the interplay between drift detection and adaptive systems contributes to maintaining stability and efficiency in dynamic environments. Method: Experiments were conducted with variations in mean, standard deviation, and both parameters simultaneously in order to obtain qualitative patterns of drift detectors behaviors. The detectors ADWIN, KSWIN, and Page-Hinkley were tested under this scenario. Summary of Results: The findings reveal that ADWIN and Page-Hinkley exhibited greater precision and robustness, while KSWIN showed excessive sensitivity, leading to a high number of false positives. Contributions to the IS Field: This research offers a comprehensive analysis of drift detectors’ performance, specifically in scenarios involving changes in mean and standard deviation, providing useful reference for designing resilient machine learning-based forecasting systems. Impacts on the IS Field: The study advances the development of information systems that can adapt to dynamic data environments characterized by shifts in mean and standard deviation, with direct applications in industrial contexts and energy management.
Beyond the user experience: Evaluating a Digital Serious Game in Project Management
Context: The project management classroom often relies on theoretical teaching methods. However, a gap exists between the demand for skilled project managers and the ability of traditional education to fully prepare students. Problem: A major challenge in education, including project management learning, is maintaining student engagement and knowledge retention. Traditional methods often struggle to replicate real-world complexities, resulting in a gap between theoretical knowledge and practical application. This study explores whether a serious game can effectively bridge this gap. Solution: Serious games emerge as a promising solution to enhance engagement and knowledge retention in various learning contexts. In this study, we evaluated a digital serious game to improve both the tool and the students' experience. IS Theory: The theory of organizational learning proposes that organizations can learn and adapt. Serious align with this theory by providing simulated environments for collaborative learning and practical experience. Method: The study utilizes a mixed-methods approach, combining quantitative and qualitative data collection techniques to evaluate a serious game by a group of project management students. Summarization of Results: The study found that the serious game was effective in engaging students and enhancing their understanding of project management concepts. Students reported positive experiences with the game, highlighting its ability to simulate real-world scenarios. The game also demonstrated potential for improving knowledge retention and practical skill development. Contributions and Impact on IS: Providing empirical evidence for the effectiveness of serious games in project management, the study can inform future research and practice in the field.
Alexandre José Henrique de Oliveira Luna
Digital Preservation through Interface Design: A Semiotic Analysis in File Saving Systems
Context: Digital preservation research seeks strategies to ensure the longevity and accessibility of digital information, mitigating risks of obsolescence and the loss of cultural, historical, and scientific heritage. Interface design plays a crucial role in shaping user behavior and influencing digital preservation practices. Problem: Widely used systems, such as word processors and operating systems, often lack explicit digital preservation strategies. The absence of features like metadata completion, open format adoption, and automated backups creates a gap between system capabilities and sustainable preservation practices, limiting users’ ability to ensure long-term access to their data. Solution: This research applies the Semiotic Inspection Method (MIS) to critically analyze the messages conveyed by save file interfaces in Microsoft Word and Windows 10. The study identifies gaps and opportunities to integrate design guidelines that encourage digital preservation, focusing on sustainable formats, metadata management, and user awareness. IS Theory: Grounded in Semiotic Engineering, the research interprets user-system interaction as a communicative process. By reconstructing designers’ meta-messages, it evaluates how interface design supports—or hinders—digital preservation. Method: The study employs a qualitative, exploratory approach using the Semiotic Inspection Method to analyze static, dynamic, and metalinguistic signs in Microsoft Word and Windows 10 interfaces. This method reconstructs implicit designer messages to assess their alignment with digital preservation needs. Results: The analysis reveals that current interfaces prioritize efficiency and usability but neglect explicit communication of digital preservation strategies. Users receive no direct encouragement to adopt open formats, complete metadata, or implement backup routines. The findings emphasize the need for educational and interactive features that guide users toward sustainable digital preservation practices. Contributions and Impact in the IS Field: This study contributes to Information Systems (IS) research by proposing interface design guidelines that align usability with digital preservation principles, enhancing informational sustainability. Academically, it expands the application of MIS to evaluate system design in the context of digital preservation.
Context: Innovation is essential for economic and technological development, with patents serving as a key metric to monitor and stimulate this innovation. However, patent information is often fragmented and distributed across isolated systems, hindering strategic and global analyses. Problem: The lack of integration between patent databases limits the ability to conduct comparative and evolutionary analyses, thereby restricting efforts to identify trends, prospect opportunities, and monitor international competitiveness. Solution: The MiraBR system was developed as an open-source web application that collects, integrates, and analyzes patent data from multiple national and international sources. Using a web crawler, data is extracted from databases such as INPI, Derwent, and Espacenet, processed, and integrated into a centralized database. The system supports multi-language search queries and enables analyses based on classifications, countries, inventors, and patent holders. It also provides intuitive visualizations that facilitate strategic and comparative analysis. IS Theory: This work is conceived under the Socio-technical theory, as the system integrates human and technical elements. Method: The research involved a proof of concept to validate the system’s core functionalities and a quasi-experiment to assess its usability and effectiveness in real-world scenarios. The quasi-experiment included specialists from the intellectual property field, who evaluated the system’s performance and relevance through practical application and structured questionnaires. The analysis approach was descriptive, aiming to present the system’s capabilities and its alignment with user needs, with a focus on quantitative metrics (e.g., precision and recall) and qualitative feedback (e.g., usability assessments). This mixed-method approach ensured a comprehensive evaluation of the system, addressing both technical performance and user satisfaction. Results: The system was evaluated by INPI experts and received high approval for its ease of use, rich visualizations, and quality of the analyses. Precision and recall metrics indicated satisfactory performance in information retrieval. Contributions: MiraBR contributes to innovation management by enabling integrated strategic analyses of patents, enhancing technological support for science and innovation in Brazil. It also paves the way for future research in data mining and visualization applied to the domain.