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 2026.
LLM-Powered Educational Conversational Agent for Open Educational Resources
Research Context: Educational repositories gather diverse Open Educational Resources (OER), yet sparse metadata and inconsistent terminology reduce findability. Large Language Models (LLMs) with retrieval-augmented generation (RAG) can bridge vocabulary gaps by capturing semantic similarity, thereby improving recall and user experience. Scientific and/or Practical Problem: The national OER repository (ProEdu) depends on a solely lexical engine. This dependence creates difficulties in handling synonyms, paraphrases, and domain shifts, resulting in suboptimal recall and inconsistent rankings. Proposed Solution and/or Analysis: We develop a prototype of an Educational Conversational Agent (ECA) that integrates retrieval and response generation. Three pipelines are evaluated: ProEdu, which employs a lexical approach; a field-weighted Elasticsearch (ES); and a semantic RAG system utilizing Sentence-Transformers (all-MiniLM-L6-v2) embeddings with a FAISS (Facebook AI Similarity Search) index and Llama for text generation, plus a lightweight reranking mechanism. Related IS Theory: We assert that AI-enhanced repositories diminish search obstacles and assist educators in effectively identifying suitable materials. Furthermore, the conversational interface alleviates the cognitive load by providing verified sources within context. Research Method: A comparative assessment involved 22 interdisciplinary prompts in ten domains. For each prompt, we established gold-standard datasets, formulated standardized queries, and calculated precision, recall, and F1-score. Summary of Results: The Llama/FAISS pipeline achieves the best coverage-relevance balance driven by high recall. ES attains a similar F1 through higher precision but lower recall. ProEdu performs poorly in F1. Error analysis shows semantic retrieval excels in cross-vocabulary matches and multi-facet intents. Contributions and Impact to IS area: We deliver a replicable benchmark for large-scale OER search (prompts, metrics, code) and a pragmatic architecture combining semantic RAG and ES to balance recall and precision on cost-efficient infrastructure. Prompt templates and evaluation scripts support adoption.
Benefits and Challenges in BPM Adoption in Organizations: An Analysis of Industry Professionals’ Perceptions
Research Context: Business Process Management (BPM) is as a key strategic approach to enhance organizational efficiency and align operations with digital transformation. In the face of constant technological evolution and market demands, organizations are challenged to integrate BPM within increasingly dynamic environments, where people, processes, and technologies must operate in synergy. Scientific and/or Practical Problem: It is crucial to understand how BPM is currently perceived, implemented, and evaluated in organizations, considering both the consolidated theoretical foundations of recent decades and the new practices that are emerging in organizational contexts. Proposed Solution and/or Analysis: This study investigates the critical success factors, main challenges, and perceived benefits of BPM adoption, based on the perspectives of professionals from both public and private organizations. The focus is on the organizational domain and the interplay between people, processes, and technologies. Related IS Theory: The research is grounded in the Socio-technical Systems Theory, which views organizations as interdependent systems composed of social and technical subsystems. This theory supports the analysis of how BPM effectiveness depends on the balanced integration of human, technological, and organizational elements. Research Method: A survey was conducted with 62 professionals using a structured questionnaire based on existing literature and adapted from a previous study to capture updated perceptions about BPM practices. Summary of Results: Findings show that while BPM is recognized for its strategic value (especially in improving communication, standardization, and organizational alignment)it still faces obstacles such as low maturity, cultural resistance, and lack of performance indicators. Leadership support, team training, and cross-level engagement are highlighted as key enablers of BPM success. Contributions and Impact to IS area: This study contributes to Information Systems research by emphasizing BPM as a socio-technical approach and offering theoretical and practical insights into its implementation in organizational settings undergoing digital transformation.
Scalper Major: A Computational Solution for Automated Trading in High Volatility Environments
Research Context: The foreign exchange (Forex) market is the largest and most liquid in the world, characterized by high volatility and continuous operation. In this environment, human decision-making is often compromised by emotional biases and the limited ability to process large amounts of information in real time. Scientific and/or Practical Problem: Traditional trading strategies have weaknesses, including delayed signals and a lack of robust risk metrics. Furthermore, many existing studies in the literature focus only on cumulative return, neglecting the risk-return trade-off and practical applicability. Proposed Solution and/or Analysis: This work presents Scalper Major, an automated trading system designed to operate consistently in the Forex market. Its modular architecture integrates technical and managerial heuristics, as well as strict risk and capital management mechanisms. Related IS Theory: The research is based on the principles of information systems, applied to automated decision-making, aligning technical indicators, computational heuristics, and financial metrics as reliable support tools for investors. Research Method: The system was implemented in MQL5 on the MetaTrader 5 platform. The evaluation was conducted through eight-year backtests on four major currency pairs, taking into account commissions, execution delays, and various market scenarios. Summary of Results: With an initial capital of $20,000.00, Scalper Major achieved significant results: a net profit of $751,533.23, a win rate of 82.54%, and a maximum drawdown of 9.43%. The Sharpe Ratio of 1.63 demonstrates superior risk-return efficiency compared to related studies. Contributions and Impact to IS area: The study, in addition to proposing an operational tool, presents a methodological advancement by creating a novel compilation of the main evaluation indicators for automated trading systems.
BACK2SERIES: Designing a Backtesting Framework for Time Series Forecasting into a RESTful API
Research Context: Time series forecasting plays a crucial role in economics, finance, and other domains where accurately anticipating future outcomes is essential for informed decision-making. However, evaluating time series regression models requires specific methods to preserve temporal dependencies and ensure realistic assessments. Scientific and/or Practical Problem: Common validation approaches, such as random cross-validation, ignore time order, generating look-ahead bias and unreliable results. Existing libraries like skforecast and sktime provide partial solutions but lack comprehensive backtesting features, standardized procedures, and user-friendly interfaces for non-programmers. Proposed Solution and/or Analysis: This study introduces BACK2SERIES, a modular RESTful API for time series regression backtesting. It supports multiple scikit-learn estimators, expanding and rolling windows, periodic hyperparameter tuning, and proper data standardization to prevent information leakage. Its API architecture allows integration with Large Language Models (LLMs) for automated workflows. Related IS Theory: This work is grounded in Socio-technical Theory, as it integrates technical automation with human interaction, balancing system efficiency and user accessibility. It also draws on Process Virtualization Theory, enabling remote, automated, and reproducible workflows that make time series forecasting more accessible to both technical and non-technical users. Research Method: Following an exploratory case study methodology, the system was implemented in Python using FastAPI, Pandas, and scikit-learn. A case study was conducted forecasting the USD/BRL exchange rate using other emerging market currencies to validate the framework’s reproducibility and automation capabilities. Summary of Results: The API produced unbiased evaluations, automated hyperparameter tuning, and consistent model comparisons. Tests of LLM interaction confirmed that structured prompts enable complete, valid API calls. Contributions and Impact to IS Area: BACK2SERIES advances the Information Systems field by bridging rigorous time series evaluation with accessible deployment. It enables reproducible, automated, and explainable forecasting workflows, supporting both technical and non-technical users in data-driven decision-making.
A Generic Extractive Multi-document Text Summarization Method Using Memetic Algorithm and Combinatorial Optimization
Research Context:Automatic text summarization remains a subject of considerable relevance across multiple domains. In particular, extractive multi-document generic summarization has garnered increased attention due to its capacity to mitigate information overload in a wide range of applications. Scientific and/or Practical Problem: The volume of unstructured text data produced on the internet has grown exponentially in recent years, driven by advances in information and communication technologies (ICTs). This massive generation of data makes it difficult for users to find relevant information. Proposed Solution and/or Analysis: This study introduces, implements, and applies the memetic algorithm known as Holistic Text Summarization with the Shuffled Frog-Leaping Algorithm (HSSFLA) to address the generic extractive multi-document multi-language text summarization problem using combinatorial optimization techniques. Related IS Theory: This research integrates swarm intelligence, memetic algorithms and combinatorial optimization. Research Method: An in vitro experiment was conducted to quantitatively compare the summary quality between the proposed method and similar methods in the literature. Summary of Results: Experiments were carried out on the DUC2001/2002 benchmark datasets, and performance was evaluated using the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metric. The results demonstrate that the proposed approach yielded an average improvement of 25.12% in ROUGE-1 and 34.91% in ROUGE-2 on the DUC 2001 dataset. On the DUC2002 dataset, the method achieved average gains of 35.42% in ROUGE-1 and 36.08% in ROUGE-2. Contributions and Impact to IS area: HSSFLA, a memetic algorithm based on swarm intelligence, was developed to solve this problem for the first time. It creates holistic summaries, in which it evaluates the quality of the summary as a whole, rather than focusing exhaustively on finding the best individual sentences. HSSFLA outperforms the results of the scientific literature in DUC2001 and DUC2002.
Improving the quality of information systems for diagnosis: an approach to detecting and correcting segmentation errors
Research Context: The development of automated Information Systems (IS) capable of segmenting the left ventricle (LV) in cardiac magnetic resonance imaging (MRI) and estimating clinically relevant biomarkers is fundamental to support diagnostic decision-making. Scientific and/or Practical Problem: Many automated IS based on deep learning (DL) are not fully reliable and lack dedicated modules for error detection, which makes them dependent on constant manual inspection and correction. Proposed Solution and/or Analysis: We propose a post-processing method for IS that automatically detects and corrects segmentation errors in LV cardiac MRI produced by DL systems. Detection is performed by combining metrics computed between consecutive time frames of MRI to identify inconsistent segmentations in the temporal dimension; the correction step reconstructs the problematic frame by interpolating nearby segmentations. Related IS Theory: This work is grounded in the perspectives of Information Processing Theory. Research Method: The method was validated on LV segmentations containing both artificially generated and real DL errors, using the LVQuan19 dataset with reference segmentations for all time instants. Summary of Results: The method achieved detection performance with F1-score values up to 0.99 on real data, particularly for severe errors. Regarding the correction step, the selected strategy effectively improved segmentation consistency, achieving Dice coefficient values close to 0.95, indicating excellent agreement with reference segmentations. Contributions and Impact to IS area: This study contributes to the Information Systems field by introducing a method that improves the reliability of automated computer-aided systems for diagnosis. Although validated in the context of LV segmentation, the proposed approach can be applied to other domains where sequential data consistency is critical.
Brazilian Smart Cities Through the Lens of Information Technology: A Systematic Mapping Study
Research Context: The densification of urban populations has posed significant challenges to the scientific community, public administrators, and the private sector, demanding innovative and sustainable solutions to enhance the quality of life in these environments. Smart cities utilise information technology resources to harness data, applications, and devices, thereby enabling intelligent and integrated services across diverse urban contexts. Scientific and/or Practical Problem: Despite extensive research work across various application domains, there is a lack of recent and comprehensive studies that systematically examine the application of information technology within the Brazilian smart city research community. Proposed Solution and/or Analysis: This paper aims to map and categorise studies conducted by Brazilian authors that explore the use of information technology to deliver solutions in smart city scenarios, based on the indicator dimensions defined by the ABNT NBR ISO 37122 standard. Related IS Theory: The study is grounded in General Systems Theory, as the categorisation of primary studies facilitates a broader and more structured understanding of this research domain. Research Method: A systematic mapping of the literature was undertaken using the ACM Digital Library, IEEE Xplore, ScienceDirect, Scopus, and Web of Science databases, encompassing papers published between 2014 and 2024. Summary of Results: From a total of 1,041 unique publications identified during the search phase, 642 were selected and categorised. The findings reveal the thematic and contextual concentration of studies, the temporal evolution of publications by type, the national institutions and researchers with the greatest influence, as well as the principal venues for disseminating research outcomes. Contributions and Impact to IS area: This work offers a comprehensive and up-to-date synthesis of smart city approaches from the standpoint of information technology, contributing to the advancement of knowledge within the Brazilian research landscape.
Leveraging Ontology-based Systems through Continuous Ontology Engineering
Research Context: Ontologies have been recognized as key enablers of digital transformation in industry. They are used to assign semantics to information items, supporting data and knowledge management based on a common and shared understanding and representation. They play a paramount role in the Semantic Web and the implementation of Linked Open Data (LOD). Scientific and/or Practical Problem: Developing and maintaining ontologies may be complex, time-consuming, and resource-intensive. Additionally, traditional sequential ontology engineering methods often fail to address industrial needs such as changing requirements and time constraints. Iterative approaches mitigate some challenges but are not always sufficient. Proposed Solution and/or Analysis: To align ontology development with information systems development, both processes must be integrated. An end-to-end flow that integrates and supports ontology engineering from conception to operation is required to enable a continuous and industry-aligned process. We propose the use of a Continuous Ontology Engineering (COE) approach. We define principles that characterize COE, report on its adoption in a multinational chemical company, and share lessons learned. Related IS Theory: This work relates to the organizational knowledge creation theory. Ontologies structure and organize knowledge, help capture tacit knowledge and transform it into explicit, enabling knowledge integration into information systems and supporting open data publication. Research Method: We conducted a participative case study to investigate the use of COE. Summary of Results: Findings suggest that COE contributes to aligning ontology and information system development. However, challenges remain, such as the lack of comprehensive and integrated tool support. Contributions and Impact to IS area: For researchers, this work advances the state of the art in a novel and relevant topic for developing knowledge-based systems, Semantic Web, and LOD solutions. For practitioners, it offers knowledge about COE and an example that may inspire organizations to implement COE practices to align ontology and information systems development.
Quality Evaluation of Software Functional Requirements Generated by LLMs: A Systematic Mapping Study
Research Context: Large Language Models (LLMs) have been increasingly applied in software engineering, especially in the generation of functional requirements for information systems. Scientific and/or Practical Problem: However, the quality of these requirements still raises concerns, such as ambiguities, incompleteness, and dependency on prompts. Proposed Solution and/or Analysis: This study conducts a systematic mapping study to analyze how the literature has evaluated requirements generated by LLMs. Related IS Theory: The work is aligned with the sociotechnical perspective, considering information systems requirements as both technical and social artifacts. Research Method: A systematic mapping study was conducted, reviewing 1,875 studies and selecting 51 primary studies published between 2020 and 2025. Summary of Results: The findings indicate a diversity of evaluation practices, combining traditional criteria and NLP metrics, with advantages in automation and standardization, but limitations regarding the absence of benchmarks and validation in industrial contexts, especially in the development of information systems. Contributions and Impact to IS area: The study contributes to academia by consolidating evaluation approaches and identifying gaps, and to industry by supporting the understanding of risks and opportunities in the use of LLMs in the requirements engineering of information systems.
PEP Scan - A Tool for Assisting in the Identification of Potential Improper Benefits Receipt by PEPs in Brazil
Research Context: PEP Scan focuses on the use of technology to combat corruption in Brazil. Its primary objective is the monitoring of Politically Exposed Persons (PEP), aiming to prevent the misuse of public funds and strengthen transparency. Scientific and/or Practical Problem: There is a critical gap in public tools for integrating and analyzing heterogeneous government data. The core challenge lies in adapting data mining techniques to identify fraud, with a specific focus on uncovering complex connections. Proposed Solution and/or Analysis: The proposed solution is PEP Scan, a web-based tool that integrates public data. It employs a Breadth-First Search (BFS) algorithm and Visual Data Mining (VDM) techniques to map suspicious connections, enabling the exploration of patterns through an interactive graph interface. Related IS Theory: Pep Scan aligns with socio-technical IS theory by integrating technical innovations and user-centric needs. Research Method: The methodology involved the development of a prototype that integrated and processed public databases. The research core was the adaptation of the BFS algorithm to map connections. Two kinds of evaluation were performed: an analysis of the architectural quality and another related to usability, employing the heuristics proposed by Jakob Nielsen. Summary of Results: PEP Scan demonstrated its effectiveness by identifying PEPs who received benefits unduly, including over 400 city councilors. The tool also revealed indirect and complex connections, validating its capability to uncover non-trivial patterns. Contributions and Impact to IS area: This research provides a replicable model and a practical tool for combating corruption. The work contributes to the Information Systems field by demonstrating how technical rigor and a focus on usability can promote citizen science and strengthen public transparency.
Privacy Risks Associated with the Use of LLMs in Software Development
Research Context: Large Language Models (LLMs), e.g., GPT-2 and GPT-4, are increasingly embedded in software development to assist with code generation, testing, and documentation. Adoption promises productivity gains but also raises challenges for Information Systems (IS), particularly data privacy and regulatory compliance. Scientific and/or Practical Problem: Developers may inadvertently include personal or sensitive information in prompts. LLMs can temporarily retain and reproduce this data in later outputs, causing privacy breaches. This creates technical, ethical, and legal concerns under the Brazilian LGPD and the European GDPR. While recent studies survey developer perceptions, empirical evidence showing violations in practice remains scarce. Proposed Solution and/or Analysis: We present an experiment simulating a Membership Inference Attack (MIA) on a local GPT-2 instance. Using synthetic personal data with realistic identifiers, we evaluate how sensitive information can be recalled by the model and propose mitigation strategies aligned with regulatory and organizational frameworks. Related IS Theory: The study is grounded in the Socio-Technical Systems Theory and Information Systems models such as the People-Process-Technology (PPT) framework, emphasizing the alignment of human, organizational, and technological dimensions. It also connects to the GranDSI-BR 2016–2026 research challenges on privacy, ethics, and security in intelligent IS. Research Method: We adopted a mixed-methods approach, combining an experimental MIA simulation on GPT-2 with a literature review and regulatory analysis. Synthetic Brazilian personal data ensured realistic and ethically compliant experimentation. Summary of Results: The experiment showed that GPT-2 can reproduce sensitive identifiers from prior prompts, even without fine-tuning or persistent memory. Contributions and Impact to IS area: The study provides reproducible experimental evidence of privacy risks in LLMs and reinforces the urgency of embedding privacy-by-design principles into IS development workflows.
A Hybrid Approach for Named Entity Recognition in Clinical Case Reports
Context: Natural Language Processing (NLP) has shown significant impact in healthcare by enabling knowledge extraction from unstructured texts. However, in Brazilian Portuguese, there remains a gap in developing models capable of processing clinical case reports, rich sources of crucial information for diagnostics, treatments, and decision-making. Problem: While some models exist (e.g., BioBERTpt), they are scarce and deliver limited performance for Portuguese clinical texts. The lack of accurate entity recognition reduces the ability to transform raw data into structured information that supports clinical decision-making. Solution: This work presents ClinptHyb, a hybrid Named Entity Recognition (NER) model combining a spaCy-based pipeline (ClinptCy, trained from scratch on SemClinBr) with a Large Language Model (Llama-3.3-70b) as reviewer. The hybrid design enhances entity identification and helps detect inconsistencies or omissions in the corpus. IS Theory: Grounded in the Organizational Information Processing Theory (OIPT), the study emphasizes how structuring clinical narratives reduces information uncertainty and increases organizational capacity to process data, thereby improving decision-making in healthcare contexts. Method: Following Design Science Research, the study included corpus preprocessing, stratified dataset splitting, spaCy model training, and prompt-based LLM revision. Evaluation employed Precision, Recall, and F1-score against the SemClinBr gold standard. Summary of Results: ClinptHyb consistently improved over the baseline ClinptCy and outperformed BioBERTpt in 8 of the 10 evaluated categories. The Negation class reached an F1-score of 0.97, a critical result for safe clinical interpretation. The model also revealed annotation inconsistencies, highlighting its potential as a support tool for dataset refinement. Contributions and Impact on the IS Area: This research presents the first hybrid NER approach that combines spaCy with an LLM for Portuguese clinical texts, aligned with IS for data, information, and knowledge management. It contributes to decision support systems, clinical annotation workflows, and the advancement of NLP technologies adapted to the Brazilian healthcare context.
Optimized Proposed Location of Primary Healthcare Units: A Case Study in Alegre, Espírito Santo, Brazil
Research Context: This study proposed the optimization of the location of Primary Healthcare Units (UBS) in the municipality of Alegre-ES, using high-resolution population density data and real road network data. The research integrated Geographic Information Systems (GIS) and combinatorial optimization techniques to support local planning in public health. Scientific and/or Practical Problem: The current distribution of UBS shows inequalities in access, resulting in longer distances traveled by the population, which compromises coverage, equity, and efficiency of basic healthcare delivery. Proposed Solution and/or Analysis: The p-median model was applied, solved with CPLEX, to identify optimal locations for UBS, considering real urban road distances. Scenarios comparing the current situation and optimized relocations were evaluated using distance metrics. Related IS Theory: The theoretical foundation involves decision support systems in Information Systems, location models, and the integration of large geospatial datasets for public policy formulation. Research Method: The methodology included extraction and aggregation of population data from the Brazil: High Resolution Population Density — Meta dataset, calculation of distances via OpenStreetMap, modeling the pmedian problem with CPLEX, and statistical and cartographic analysis of the results. Summary of Results: The results demonstrated that the relocation of Primary Health Care Units would reduce the average distance traveled by the population in all scenarios. In the best-case scenario, the reduction would reach 40,02%, potentially increasing coverage and promoting greater equity in access to healthcare services. Contributions and Impact to IS area: The study contributed a reproducible pipeline integrating Big Geodata, OpenStreetMap, and exact optimization, strengthening the interface between Information Systems, public health, and urban planning, providing valuable tools for municipal managers.
Utilization of a Conceptual Model in Projects Observatories Development: A Case Study
Research Context: Observatories are information systems that support project monitoring, providing transparency and decision-making support through data collection, processing, storage, and dissemination. Despite their potential, the practical application of conceptual models for project observatories remains limited. Scientific and/or Practical Problem: The third version of the Model for Project Observatories (MPO) had not been assessed in real-world contexts, raising questions about its applicability, completeness, and usability. Proposed Solution and/or Analysis: This study evaluates the MPO through two multiple-case studies, examining the application of its concepts, identifying gaps, and suggesting improvements, with emphasis on usability aspects. Related IS Theory: The MPO builds on a sociotechnical perspective and relates to theories of conceptual modeling and project management support systems, highlighting transparency and governance in information systems. Research Method: A multiple-case study was conducted, following a qualitative approach. Data were collected through interviews, participant observation, document analysis, and source code review, allowing the assessment and refinement of the model. Summary of Results: The results show that most MPO concepts were successfully applied. Some concepts were only partially incorporated, and new elements were suggested to enhance usability. Cross-case analysis provided insights into strengths and limitations of the model. Contributions and Impact to IS area: This study empirically validates and refines the MPO, reinforcing its role as a conceptual foundation for project observatories. It contributes to the IS field by demonstrating the use of observatories as tools for transparency and project management, and by offering guidelines for the effective implementation of conceptual models.
Canopy-Guided Construction of ANN Search Graphs under Cosine Similarity
Research Context: Nearest Neighbor Search (NNS) is a fundamental problem in computing, with applications in recommendation systems, semantic search, information retrieval, and Retrieval-Augmented Generation (RAG) in Large Language Models (LLMs). High-dimensional datasets with millions of vectors make exact search computationally intractable, demanding efficient approximations. Practical Problem: Approximate Nearest Neighbor Search (ANNS) has become the standard approach, with graph-based methods standing out. However, structures such as Hierarchical Navigable Small World (HNSW) graphs impose high construction and update costs, which limits their scalability in real-world scenarios. Proposed Solution: This paper proposes the integration of Canopy Clustering as a divide-and-conquer heuristic to mitigate the computational burden of HNSW graph construction under cosine similarity, enabling more cost-effective ANNS pipelines in information retrieval systems. Related IS Theory: The research aligns with Information Systems theories on efficiency of knowledge retrieval, and the optimization of data-intensive processes in decision-support environments. It builds upon principles of data organization and access structures in IS, particularly the trade-offs between accuracy and computational feasibility in large-scale information retrieval. Research Method: Controlled experiments were conducted to evaluate the applicability of divide-and-conquer and Canopy Clustering prior to HNSW construction. Comparative analyses measured build time, query latency, and recall performance. Summary of Results: Preliminary findings suggest that Canopy Clustering can reduce construction costs while sustaining recall levels adequate for IS applications. Contributions and Impact to IS area: The work introduces a novel heuristic combination to enhance the scalability of graph-based similarity search indexing, providing methodological advances that may extend to other high-dimensional data processing tasks. It offers actionable insights for designing more efficient and viable intelligent information systems.
Evaluating Robustness and Detection of Adversarial Attacks in EEG-Based Brain-Computer Interfaces
Research Context: Brain-computer interfaces (BCI) are systems that capture brain signals through techniques such as electroencephalography (EEG), processing these signals for various applications, especially in the control of devices for people with motor limitations. Despite the benefits, there are security concerns, including adversarial and cybersecurity attacks. Due to the emergence of brain-computer interaction devices on the market, it is necessary to analyze the security of these devices. Scientific and/or Practical Problem: Machine learning classifiers used in BCIs are vulnerable to adversarial attacks, which can compromise accuracy, safety, and user privacy. The lack of systematic evaluation of these vulnerabilities represents a gap in current research. Proposed Solution and/or Analysis: This work aims to emulate and analyze adversarial attacks in classifiers of BCI devices. Our experiments used the Foolbox tool to evaluate different adversarial techniques, such as DeepFool, FGSM, PGD, and Carlini-Wagner. Our evaluation identifies the negative effects of adversarial attacks on data classification. Related IS Theory: Technology acceptance model; Information processing theory. Research Method: Experiments were conducted on the BCI Competition 2008 Graz dataset, with attacks emulated during inference. Detection mechanisms based on Random Forest, SVM, and KNN were trained and evaluated to assess the feasibility of automatic defense. Summary of Results: Classifier accuracy decreased sharply under attack, with success rates ranging from 75.2% to 100%. Detection models achieved 83% accuracy with Random Forest and SVM for FGSM attacks, but only 5% with KNN for DeepFool, highlighting the challenge of detecting subtle perturbations. Contributions and Impact to IS area: The work demonstrates the vulnerabilities of BCI classifiers, proposes an evaluation pipeline for adversarial robustness, and emphasizes the importance of integrating security assessment into BCI development. Results have direct implications for information systems dealing with sensitive biomedical data.
An IoT-Based Remote Monitoring Information System for Hydroponic Greenhouses in the Context of Smart Farming
Research Context: Climate change and population growth pose challenges to food security, requiring innovations in agriculture. Greenhouses emerge as an alternative for controlled cultivation, and hydroponics optimizes water and space usage while allowing precise nutrient management. Scientific and/or Practical Problem: However, hydroponic systems require investment, specialized technical knowledge, and ongoing monitoring. They are vulnerable to energy failures, nutrient imbalances, and environmental variations, leading to rapid production losses, particularly for small producers with limited resources. Proposed Solution and/or Analysis: This work proposes a low-cost IoT-based remote monitoring information system for hydroponic greenhouses. The system integrates sensors connected to Arduino, transmitting real-time data to the ThingSpeak cloud platform. The application enables the visualization of critical variables, provides automatic alerts in the event of deviations, and facilitates remote calibration of pH and electrical conductivity sensors. Related IS Theory: The solution aligns with the principles of Smart Farming and socio-technical IS theory. By supporting decision-making with real-time data and integrating technology with organizational and social dimensions, it reinforces the role of IS in promoting sustainability and efficiency in agriculture. Research Method: An experimental prototype was deployed in a real hydroponic greenhouse, monitoring two reservoirs and cultivation benches. Data from sensors were collected, transmitted via Wi-Fi, stored in ThingSpeak, and consumed by the mobile system for validation under production conditions. Summary of Results: The system successfully monitored variables such as temperature, pH, conductivity, solution volume, turbidity, and flow. Alerts enabled timely corrective actions, including adjustments to nutrient and management of excessive conductivity, while ThingSpeak dashboards supported historical analysis. Contributions and Impact to IS area: The research demonstrates a low-cost, modular IoT infrastructure integrated with a mobile interface for agriculture. It contributes to IS by bridging technology, organization, and social impact, offering accessible tools that support decision-making, resource efficiency, and preventive management in food production.
Automating Business Process Modeling: An LLM-Based Approach with Situation-Based Modeling Notation
Research Context: Although business process modeling is beneficial for clarifying organizational processes, it remains a task with little automation and requires significant human effort. Scientific and/or Practical Problem: To reduce this effort, research has advanced toward automating the task, mainly focusing on extracting imperative models from textual descriptions. However, imperative models may lead to a single solution that does not necessarily represent the best fit. Proposed Solution and/or Analysis: The declarative Situation-Based Modeling Notation (SBMN) allows modelers to generate multiple modeling structure alternatives, enabling them to choose the one that best fits the context. This work investigates the use of Large Language Models (LLMs) to identify core business process entities and constraints represented in SBMN from textual descriptions. Related IS Theory: This research is grounded in the Task-Technology Fit (TTF) theory, applied to evaluate the adequacy of LLMs in supporting business process analysts during modeling activities. Research Method: A dataset of 133 textual descriptions paired with SBMN models was employed. Each description was processed by three medium-scale LLMs and three extra-large LLMs using zero-shot prompts to identify domain situations and Active Flow Objects. The outputs were semantically compared with the corresponding SBMN models to measure their similarity. Summary of Results: Large-scale LLMs (e.g., Claude Sonnet and Qwen3-80B) were able to identify flow objects and situations in SBMN with relevant accuracy, demonstrating the feasibility of their use to support the modeling task. Contributions and Impact to IS area: This research provides: (i) empirical evidence highlighting the strengths and limitations of current LLMs in the process modeling domain; (ii) a dataset containing aligned pairs of textual descriptions and SBMN models; and (iii) a semantic–structural similarity metric enabling the quantitative evaluation of SBMN component extraction accuracy from text.
Evaluating the Effectiveness and Impact of Machine Learning Models for Forecasting Meal Demand in University Restaurants
Research Context: University restaurants face the challenge of balancing meal supply and demand, where efficient food management is essential to reduce waste and optimize costs, both crucial for service sustainability. Scientific and/or Practical Problem: Forecasting meal demand is complex due to consumption variability, the influence of holidays, and frequent menu changes. Proposed Solution and/or Analysis: This study applies machine learning models to forecast daily meal demand, using historical consumption data, holiday information, and menu attributes. The research also investigates the impact of these variables on forecast quality and accuracy. Related IS Theory: The study is based on Information Systems theory, particularly in the use of data and analytical intelligence to support decision-making. In this context, machine learning models act as information processors, capable of transforming real data into useful predictions for operational management. Research Method: The research followed the CRISP-DM framework: definition of objectives and context (Business Understanding); data collection and exploration (Data Understanding); cleaning and transformation (Data Preparation); training and calibration of models (Decision Tree, XGBoost, LSTM) (Modeling); performance evaluation with R² and comparison to the restaurant’s forecasts (Evaluation); and preparation for practical application (Deployment). Summary of Results: The XGBoost model achieved the best performance, reaching R²=0.92, outperforming the restaurant’s original forecast (R²=0.83). This represents a significant improvement in forecast accuracy, contributing to waste reduction and better inventory management. The inclusion of menu information was also shown to enhance prediction quality. Contributions and Impact to IS area: The study demonstrates how machine learning can be applied in institutional foodservice, providing operational, economic, and environmental benefits. It reinforces the role of Information Systems in enabling more accurate and sustainable decision-making.
Bridging AI and Ethics: An LLM-Based Framework for Transparent and Inclusive Credit Decisions
1) Research Context: The integration of advanced Machine Learning (ML) models into Intelligent Information Systems (IS) has created highly accurate but opaque "black-box" systems, especially in sensitive domains like credit scoring. 2) Scientific and/or Practical Problem: This opacity undermines user trust, can perpetuate algorithmic bias, and challenges regulatory compliance (e.g., LGPD, GDPR). This creates a critical gap between AI’s technical power and the socio-technical need for accountability in IS. 3) Proposed Solution and/or Analysis: We propose and validate a two-layer framework that uses Large Language Models (LLMs) to translate technical outputs from Explainable AI (XAI) methods, like SHAP and LIME, into actionable, natural language narratives for non-expert users. 4) Related IS Theory: Grounded in Decision Support Systems (DSS) theory, this work extends the classical DSS goal. It enhances decision quality not just via predictive accuracy, but by improving the transparency, trustworthiness, and interpretability of the system’s reasoning for stakeholders. 5) Research Method: We conducted an applied, experimental study on a public retail credit dataset. The methodology involved data preprocessing, XGBoost predictive modeling, quantitative evaluation of explanation fidelity with the MEMC metric, and developing a functional web prototype. 6) Summary of Results: The framework effectively identified key credit denial factors with high fidelity, validated by the MEMC metric. The LLM-synthesis layer successfully transformed complex XAI data into clear, understandable, and practical explanations, enhancing the system’s clarity and actionability. 7) Contributions and Impact to IS area: This study contributes a validated framework for building more ethical, transparent, and socially inclusive intelligent systems. Its impact lies in bridging the gap between advanced AI and human-centric requirements, enabling responsible AI adoption and strengthening human-AI collaboration in decision-making.
Quality Assurance and Testing Strategies in a Digital Platform for Screening and Early Diagnosis of Oral Cancer
Research Context: Software testing in healthcare systems is critical, as failures can compromise diagnoses, professional trust and patient safety. In severe conditions, such as oral cancer, the absence of robust validation strategies may lead to delays or clinical errors with serious consequences. Scientific and/or Practical Problem: Despite advances in AI-based healthcare platforms, gaps remain in the adoption of integrated testing strategies that ensure quality, usability, and security in critical systems. These shortcomings can hinder the reliability of AI-driven tools in clinical environments. Proposed Solution and/or Analysis: This study reports the testing practices applied in the Sobre-Vidas Platform-“Oral Cancer”, designed to support early diagnosis through AI. A multidimensional testing strategy was implemented, encompassing back-end (unit, integration, performance, and security) and front-end testing (usability, responsiveness, accessibility and functional), supported by tools including Python, JUnit, SonarQube, Locust, and Cypress. Related IS Theory: The work is grounded in software quality models, particularly ISO/IEC 25010, and in socio-technical system theory, which emphasizes the interplay between technical reliability and user acceptance within digital health environments. Research Method: A qualitative case study approach was adopted, combining practices such as test driven development (TDD) and automated API testing with validation in real world healthcare contexts. Trials were conducted with physicians, dentists, and health secretaries in two municipalities in the state of Goiás, with feedback collected through observation, interviews, and usability sessions. Summary of Results: The need for adjustments in workflows, terminology, and interface design was revealed, highlighting that continuous and integrated testing processes are essential for ensuring clinical effectiveness, safety, and system adoption. Contributions and Impact to IS Area: This work demonstrates how integrated testing strategies, aligned with established quality frameworks, can enhance trust and adoption of AI-driven healthcare platforms. It broadens the discussion on software testing as a socio-technical enabler of safety and innovation in digital health.
Noise in Brazilian Clinical Anamnesis: An Empirical Study
Research Context: The lack of representative data can limit the development of robust clinical Natural Language Processing (NLP) models, as models trained on idealized data can perform poorly on noisy real-world Electronic Health Records (EHRs). Scientific and/or Practical Problem: A performance gap exists when these NLP models are deployed on noisy, real-world clinical text. This issue can be found in less-resourced languages, such as Brazilian Portuguese, where the scarcity of data can limit the development of effective clinical information systems. Proposed Solution and/or Analysis: This study addresses this challenge by presenting a systematic approach to identify and quantify textual noise patterns found in Brazilian Portuguese clinical narratives. Related IS Theory: Based in Task-Technology Fit (TTF) Theory, this study investigates the misalignment between the task of reliable information extraction from noisy EHRs and the technology of NLP models, which can presuppose clean data. Research Method: A multi-stage methodology was employed to identify textual noise. Starting with a classification stage to flag candidate tokens likely representing typos and abbreviations, followed by a lexicon-based validation executed to refine this selection, ensuring that only authentic noise instances were selected. Summary of Results: The analysis of a dataset of clinical anamneses revealed not only a high incidence of textual noise, but also a consistent recurrence of specific noisy tokens across the dataset, demonstrating the widespread nature of data quality issues in this domain. Contributions and Impact to IS area: A taxonomy of textual noise, complemented by two JSON files that structurally map the noisy tokens, establishing an empirical benchmark for Brazilian Portuguese clinical text and formalizing the data quality challenges that must be overcome for successful NLP implementation.
Organizational Interoperability: Strengthening Collaboration in Systems of Information Systems
Research Context: Collaborative System of Information Systems (SoIS) involve multiple independent organizations that must interact dynamically, without centralized control, to achieve both collective and individual objectives. Organizational interoperability is crucial in this context but still lacks adequate solutions when participants frequently join or leave the collaboration. Scientific and/or Practical Problem: Current BPM and SOA approaches were designed for static intra or inter-organizational contexts and do not handle the dynamic nature of SoIS. This limitation creates difficulties in maintaining process continuity when participants enter or exit, compromising global objectives. Proposed Solution and/or Analysis: This research proposes a solution for organizational interoperability in collaborative SoIS comprising a metamodel and a semi-automated simulation environment. The metamodel specifies key concepts, properties, and relationships for modeling collaborative SoIS, while the simulation environment enables the specification and enactment of model instances according to the metamodel. Related IS Theory: Research on organizational interoperability is associated with organizational information processing theory and the complexity of information systems. Research Method: The Design Science Research (DSR) approach was adopted, following the steps of investigation, design, validation, implementation and evaluation. Summary of Results: The metamodel was applied in an e-commerce scenario, showing it to be consistent, expressive, and able to represent domain concepts through models adhering to its rules and structure. The simulation environment executed these models, enabling analysis of participant entry and exit dynamics and demonstrating process continuity despite organizational changes. Contributions and Impact to IS area: The approach provide both a conceptual foundation and a means for analyzing and improving collaboration in dynamic interorganizational contexts. The impact lies in enabling more resilient and adaptive SoIS, capable of maintaining continuity and interoperability even under frequent organizational changes.
Ana Patrícia Fontes Magalhães Mascarenhas
Experimental Evaluation of Machine Learning Algorithms for Classifying Health-Related News with Indications of Irregularity
Research Context: The increasing production of unstructured data, particularly textual data, has driven the application of Natural Language Processing (NLP) techniques in public administration. The analysis of multiple information sources enables the identification of patterns and the development of predictive models to optimize strategies, improve service delivery, and ensure population monitoring and safety. Scientific and/or Practical Problem: The auditing process is characterized by high costs, long duration, and heavy reliance on human and material resources, necessitating solutions capable of automating the analysis of corruption reports. Proposed Solution and/or Analysis: Focusing on the preliminary identification of potential irregularities, the use of machine learning models is proposed to support the auditing process by identifying health-related news that may indicate irregularities. Related IS Theory: This study is grounded in Cognitive Load Theory, as it examines methods to reduce information overload. Research Method: A controlled in vitro experiment was conducted to scientifically evaluate 54 machine learning models and compare metrics including Accuracy, Precision, Recall, and F1-score. Additionally, an asymptotic complexity analysis of the algorithms was performed. Summary of Results: The Random Forest model stood out in terms of effectiveness, achieving an accuracy of 99.90%, a recall of 98.62%, and an F1-score of 99.28%, while Naive Bayes and Logistic Regression excelled in efficiency, with linear complexity 𝑂 (𝑛𝑑) for both training and prediction and low memory usage. Contributions and Impact to IS area: The results demonstrate the feasibility of using machine learning models to identify health-related news with potential irregularities. This approach enhances the information gathering and corruption evidence stage performed by AudSUS auditors for detecting potential irregularities, thereby contributing to the efficiency of public resource management.
Privacy-Enhancing Technologies in Digital Public Services: Bridging Legal Demands and Sociotechnical Design
Research Context: Digital government increasingly relies on large-scale data processing for public services. Ensuring citizens’ privacy while enabling data-driven value creation is a critical challenge for Information Systems (IS) in the public sector. Scientific and/or Practical Problem: Traditional safeguards (e.g., access control, anonymization) are insufficient against reidentification risks and inter-organizational data sharing demands. Public agencies lack actionable guidance to select and deploy Privacy-Enhancing Technologies (PETs) fitting legal, organizational, and technical constraints. Proposed Solution and/or Analysis: We synthesize categories and application patterns of PETs (Differential Privacy, Secure Multiparty Computation, Homomorphic Encryption, Federated Learning, Trusted Execution Environments, Synthetic Data) and analyze their suitability to government scenarios. We provide policy-to-PET mapping using recent Brazilian federal decrees. Related IS Theory: Grounded in Sociotechnical Systems (alignment of people, processes, and technologies), Privacy by Design as a strategy, and Information Governance for accountability, transparency, and risk management. Research Method: Concept-centric analysis of PETs and their governance implications; document analysis of 2025 executive decrees mentioning personal data; analytic generalization to derive PET selection rationales for public-sector IS. Summary of Results: We (i) clarify privacy vs personal data protection for design, (ii) categorize PETs by function and lifecycle stage (data in use, input/output privacy), (iii) derive PET recommendations for inter-agency collaboration, secure analytics, and transparency (e.g., MPC/HE for cross-entity processing; Differential Privacy for open statistics), and (iv) identify capability and governance gaps (skills, interoperability, stewardship). Contributions and Impact to IS area: We bridge PETs’ technical capabilities with IS governance needs in digital government, offering a rationale for PET selection under regulatory, organizational, and sociotechnical constraints. The study advances responsible innovation in IS, informs publicsector architectures, and aligns with Brazil’s GranDSI-BR (2016–2026) by addressing ethics, transparency, and societal impacts of intelligent IS.
Evaluating Knowledge Gain in Search Environments: An Exploratory Study of Learning Measurement
Research Context: Searching as Learning (SaL) frames web search as a process where users construct and refine knowledge. However, measuring knowledge gain in natural search environments remains a methodological challenge. Scientific and/or Practical Problem: Traditional behavioral proxies (e.g., dwell time, clicks) scale well but fail to capture conceptual change, while pre/post-tests provide richer insights but are intrusive. This gap limits the development of search systems that can evaluate and promote learning. Proposed Solution and/or Analysis: This study advances a computational measure based on entropy reduction and semantic similarity, and novelly operationalizes it through a browser plug-in that enables real-time measurement in natural search environments, extending prior formalizations and prototype-based validations of the DKG metric. Related IS Theory: The study draws on Shannon’s Information Theory and Information Processing Theory in IS to conceptualize knowledge gain as uncertainty reduction supported by socio-technical processes. Research Method: An experiment combined three structured search tasks, pre/post-tests, and Concurrent Think-Aloud protocols. Quantitative measures (Transfer of Learning scores, also known as ToL, and values from the proposed metric) were triangulated with qualitative coding using OISS and ESKiP frameworks. Summary of Results: Statistical analysis showed a moderate positive correlation between ToL and the proposed metric (r = 0.62, p < 0.01). Bland–Altman analysis revealed systematic differences in scale, with ToL showing higher values, yet relative patterns were consistent. Transcripts emphasized how strategies such as query specialization, evaluation of sources, and persistence in reformulation aligned with higher values. Contributions and Impact to IS area: The study contributes a validated computational metric and artifacts for measuring knowledge gain in real search environments. It reinforces the sociotechnical view of IS by linking human strategies, processes, and technological advantages, and points to adaptive search systems that could measure and promote learning.
Investigating Professionals’ Perceptions on the Software Supply Network Model in a Proprietary Educational Software Ecosystem
Research Context: Modeling helps understand the structure, actors, and interactions in software ecosystems (SECO). The software supply network (SSN) is one of the best-known SECO modeling notations. In a previous study, we proposed an SSN model for a proprietary educational SECO, namely the SOLAR SECO, and identified the need to analyze this SSN model from the professionals’ perceptions. Scientific and/or Practical Problem: The practical use of SSN models is still poorly explored, and little is known about how professionals perceive and apply them in real SECO. Proposed Analysis: This work analyzes this SSN model based on professionals’ perceptions to identify issues and challenges. Related IS Theory: Socio-technical Theory, which explains the way the interaction between social and technical elements affects professionals’ perceptions and use of artifacts, such as the SSN model. Research Method: We applied a questionnaire and conducted semi-structured interviews, analyzing the responses qualitatively. Summary of Results: Professionals perceived the model as useful for visualizing actors and supporting communication, but not for identifying new functionalities. Contributions and Impact to IS area: This study offers empirical evidence and a methodological procedure to support modeling and analysis of other SECO cases.
Towards a Conceptual Model on Causal Loop Diagram
Context: Information systems are increasingly complex, shaped by interdependent technical, organizational, and human factors. Understanding this environment is crucial for developing and using information systems effectively. Systems Thinking (ST) offers tools that help in this matter. In ST, a system comprises elements and interconnections coherently organized to produce characteristic behaviors. ST tools aid in modeling complex systems and improving the understanding of their dynamics. Causal Loop Diagrams (CLDs), one of the most used ST modeling tools, represent cause-and-effect relationships and feedback structures, supporting the identification of problem causes, behavioral patterns, and enabling informed decision-making. Problem: Despite its potential, the lack of knowledge and supporting tools has limited CLD adoption. Solution: To structure knowledge about CLD, we developed a conceptual model representing its main elements, relationships, and constraints. IS Theory: This work relates to the organizational knowledge creation theory. A conceptual model provides an abstract representation of a domain, serving as a theoretical foundation that captures essential entities, their properties, and relationships. It enables knowledge formalization and supports communication among stakeholders. Method: To evaluate the model clarity and correctness, it was assessed by domain experts. To demonstrate its applicability, it was used to develop a supporting tool for CLD creation and interpretation. Results: Findings indicate that the conceptual model adequately captures essential concepts for understanding CLDs and provides structured knowledge useful for building them. The model can also serve as a reference model to aid tool development and has potential applications in semantic annotation and interoperability. Contributions and Impact to IS area: For researchers, this work advances knowledge about CLD, providing a formal representation of its conceptualization. For practitioners, it offers a model that aids in CLD understanding and communication, and in developing computational solutions. It also offers a tool to support CLD creation and interpretation, fostering its use in practice.
Automated Ableism: A Systematic Review on AI and Discrimination against People with Disabilities
Research Context: Artificial Intelligence (AI) systems have expanded in decision-making processes but raise concerns about algorithmic discrimination, with ableism remaining an underexplored dimension in Information Systems (IS). Scientific and/or Practical Problem: Despite advances in the literature on algorithmic bias, discrimination against people with disabilities (PwD) in AI systems remains underexplored. This gap limits the development of fairer and more inclusive AI systems. Proposed Solution and/or Analysis: This study presents a Systematic Literature Review (SLR) on how AI systems can reproduce, intensify, or mitigate ableist practices. Related IS Theory: It is grounded in algorithmic justice, fairness in machine learning, and socio-technical perspectives on inclusion. Research Method: Following PRISMA and PICOC, 26 articles published between 2020 and 2025 were analyzed and assessed against quality criteria. Summary of Results: Three main trends were identified: AI that reinforces ableism, mitigation initiatives, and ethical-regulatory debates. The problem has been discussed mainly by researchers from the United States and Europe, highlighting the need for studies that consider different languages as well as diverse social and cultural behaviors. Contributions and Impact to IS area: The study addresses a neglected dimension of algorithmic bias and provides insights for researchers, practitioners, and policymakers committed to developing inclusive AI.
Primary Healthcare Efficiency Indicators for Analysis and Decision-Making Support for Managers
Research Context: Efficient management of Brazil’s Primary Healthcare (PHC) is essential to ensure universal access and quality of care, yet challenges in resource allocation and workforce distribution demand innovative solutions for equitable and sustainable delivery. Practical Problem: A key challenge in PHC management is the lack of integrated efficiency metrics across financial, human, and service dimensions, as current static evaluations limit comparative analysis and hinder effective resource allocation. Proposed Solution: By combining financial, human resource, and service delivery data from official sources, we present a set of operational efficiency indicators designed for PHC in Brazil, that enable comparative and dynamic evaluations across units, supporting equitable and evidence-based healthcare management. Related IS Theory: The solution integrates heterogeneous health data into an information system that supports decision-making, aligning automated tools with managerial workflows to improve efficiency in PHC. Research Method: Following an analytic approach, the study evaluates the proposed indicators through a detailed comparison between the Brazilian cities. A quantitative analysis assesses their practical applicability, ensuring the indicators’ effectiveness in supporting decision-making in PHC. Summary of Results: Experiments demonstrate that the proposed solution provides reliable efficiency indicators for PHC units and municipalities. The results highlight disparities in performance and illustrate how these indicators can support data-driven decisions to optimize financial and human resource allocation. Contributions and Impact to IS area: The study contributes to the IS field by proposing efficiency indicators tailored to Brazil’s Primary Health Care and integrating them into a decision-support system, enhancing transparency, comparability, and equity in public sector governance.
Predictive Modeling of Rice Production in South America Using Machine Learning
Research Context: Rice is a staple food for nearly half of the global population and is highly vulnerable to climate variability. In South America, accurate forecasting models are critical to ensure food security and guide agricultural decision-making. Scientific and/or Practical Problem: Traditional methods often fail to capture the complexity of agricultural systems shaped by environmental and socioeconomic factors. The lack of predictive systems adapted to South America restricts the ability of governments and producers to anticipate risks. This research addresses both a scientific challenge, by advancing predictive modeling, and a practical challenge, by supporting sustainable agricultural planning. Proposed Solution and/or Analysis: The study integrates agricultural production data from FAOSTAT (2004–2023) with climate variables from NASA POWER (2011–2023). It compares Linear Regression, Support Vector Regression (SVR), and XGBoost Regressor to identify the most reliable configuration for rice production forecasting. Related IS Theory: The work aligns with decision support systems (DSS) and information systems for sustainability, contributing to the Brazilian Grand Challenges in Information Systems (GranDSI-BR 2016–2026). Research Method: The approach combined exploratory analysis, training with multiple time windows (5, 10, 15 years), and cross-validation, using Mean Absolute Percentage Error (MAPE) as the main evaluation metric. Summary of Results: Longer windows (10 years) improved stability. XGBoost achieved the lowest MAPE and greater robustness, while climate data integration reduced errors in atypical years. Contributions and Impact to IS area: The study demonstrates the potential of machine learning–based predictive systems as components of agricultural information systems, reinforcing their role in enhancing food security, supporting public policies, and promoting sustainable resource management in South America and beyond.
How Can Collaborative Work Shape the Evolution of a Project Management Teaching Tool?
Research Context: Project Management (PM) is essential in Software Engineering (SE) education, requiring technical and collaborative skills. Effective tools must incorporate modern PM methodologies to address evolving educational needs. Practical Problem: Existing tools lack features such as real-time collaboration, integration of updated PM principles, and adaptability, limiting their ability to develop comprehensive PM skills among students. Proposed Solution: This study enhances a collaborative web-based IS for PM education, integrating the 6th and 7th editions of the PMBOK®. New features, including a real-time collaborative editor and hybrid project management approaches, were implemented to improve learning outcomes. Related IS Theory: Using the 3C model (Communication, Coordination, Cooperation), the tool was designed to facilitate effective collaboration and align with established collaborative frameworks in IS research. Research Method: We employed an user-centered development, with surveys and iterative testing guiding enhancements. Modern technologies, such as React and Yjs, enabled real-time functionalities. The tool’s usability and functionality were evaluated through user feedback. Summary of Results: The enhanced tool supports real-time collaboration, advanced document editing, and hybrid PM practices, addressing previous limitations. It serves as a practical example of applying IS frameworks to PM education. Contributions and Impact to IS Area: This study demonstrates how IS can transform PM education through collaborative systems and agile methodologies, aligning tools with both academic and industry needs. Future work includes case studies to further validate the tool’s impact.
Challenges and Solutions of Risk Management in Software Ecosystems
Research Context: Software ecosystems (SECO) differ from other software development contexts and increase the complexity of risk management. Scientific and/or Practical Problem: It remains unclear the way risk management is conducted in SECO, which risks are specific to this context, and which solutions have been proposed. Proposed Analysis: This study investigates risk management in SECO by identifying SECO-specific risks, mapping solutions and their evaluations, and highlighting existing challenges. Related IS Theory. Socio-technical Theory provides a perspective by emphasizing the interdependence of technical and social factors in risk management in SECO. Research Method: We conducted a systematic mapping study following established guidelines, including study selection, data extraction, and qualitative coding. Summary of Results: We identified 24 SECO-specific risks, 21 solutions, of which 15 were evaluated, and 14 challenges related to risk management in SECO. Contributions and Impact to IS area: This study consolidates knowledge on risk management in SECO and provides insights to support researchers and practitioners in designing more effective mitigation strategies.
LLMs in Practice: Results from a Survey with Software Development Professionals in Brazil
Research Context: The emergence of Large Language Models (LLMs) has been transforming the development of information systems, directly impacting activities across the system life cycle. Understanding how these technologies affect work practices is essential for advancing the Information Systems (IS) field and for guiding their adoption by organizations. Scientific and/or Practical Problem: Despite the growing use of LLMs in information systems development, there is still limited knowledge on how professionals across different roles, levels of experience, and work modalities perceive the impact of generative AI on daily activities, decision-making, and team performance. Proposed Solution and/or Analysis: This study investigates the perceptions and experiences of Brazilian software developers regarding the integration of Generative AI (LLMs) into their professional practice. The analysis aims to identify which activities are most impacted by AI, the most frequently adopted tools, and the level of trust professionals place in AI-generated outputs. Related IS Theory: The study aligns with the Systemic and Socially Aware Perspective for Information Systems challenge of GranDSI-BR, by examining how emerging technologies reshape sociotechnical systems and the dynamics of professional work. Research Method: An online survey was conducted with 94 professionals in the field of information systems development, covering diverse profiles. Data were collected through customized Likert scales, multiple-choice checkboxes, and closed-ended questions. Summary of Results: Findings highlight that, although the adoption of LLMs significantly increases developers’ productivity, there is still a moderate level of trust in the outputs and the absence of consolidated governance policies. Contributions and Impact to IS area: The results provide both theoretical and practical insights into the adoption of generative AI in information systems development, offering valuable guidance for researchers, managers, and educators seeking to integrate these technologies into IS safely and ethically.
Specializing Small Language Models into Business and Industry Idea Reviewer Experts with Supervised Fine-Tuning
Research Context: The application of Natural Language Models in industrial and business environments is rapidly expanding. While powerful, these models often require specialization to match the performance of human experts. Practical Problem: Large Language Models (LLMs) face two major barriers for enterprise adoption: 1) the lack of specific, private knowledge required for nuanced tasks, such as classifying internal company innovations, and 2) the operational costs are prohibitively high for long-term, large-scale use. Proposed Solution: We propose a cost-effective alternative by fine-tuning Small Language Models (SLMs) and encoder models (BERTs) in business ideas classification, transforming them into expert systems tailored to a company’s unique context. Related IS Theory: This research is grounded in Task-Technology Fit (TTF) theory, examining the alignment between the task’s characteristics (classifying specialized ideas) and the technology’s attributes (general-purpose LLMs vs. fine-tuned SLMs and BERTs) to determine the optimal fit. Research Method: The research involves developing and evaluating a training method for SLMs and BERTs, with real-world data augmented by an artificial dataset. Additionally, the artificial dataset creation pipeline is showcased by the research. The performance of the resulting SLMs and BERTs are then compared against that of larger, general-purpose LLMs. Results: The findings indicate that the fine-tuned SLMs and BERTs achieve superior performance on the specialized classification task compared to larger, non-fine-tuned LLMs, while significantly reducing operational costs. The results also highlight that augmenting scarce real-world data with diverse artificial data can lead to a more robust, generalizable and rich model. Contributions: This work contributes to a practical and economically viable method for specialized AI agents creation and augmentation of scarce real-world data through synthetically made datasets. Its impact lies in enabling businesses to deploy tailored, high-performing AI solutions for specific and knowledge-based tasks without the high costs of large-scale, general-purpose models.
Text Message Routing System for Chat-Based Applications
Research Context: The integration of Large Language Models (LLMs) into banking customer service promises enhanced engagement but faces a trilemma of prohibitive operational costs, stochastic inaccuracy (hallucinations), and strict safety requirements. In the Brazilian financial sector, where inaccuracies can lead to direct financial harm, relying solely on monolithic LLMs is economically unsustainable and operationally risky. Practical Problem: Financial institutions struggle with the ”inference cost trap,” where token-based pricing scales linearly with usage, and the risk of hallucinations—where models generate plausible but incorrect responses for unfamiliar queries. Proposed Solution: This study validates a semantic routing architecture that functions as an intelligent decision layer, classifying user intent into four categories (Relevant, Unrelated, Chitchat, Spam) to route queries to cost-effective handlers. Five classification paradigms were benchmarked: Zero-shot GPT, Few-shot GPT, QLoRA fine-tuned GPT, Embedding Similarity Search, and BERT-based neural models. Related IS Theory: The research builds upon Task-Technology Fit (TTF) theory and contributes to the Green IS agenda by validating energy-efficient architectures. Research Method: A balanced dataset of 6,760 messages, curated from SMS spam data, SQuAD questions, private chats, and banking FAQs, was used to evaluate accuracy, resource usage, and cost. Summary of Results: Embedding Similarity (98.52%) and BERT-based models (98.96%) achieved superior accuracy compared to Zero-shot GPT (53.78%) and matched the performance of Fine-tuned GPT (97.93%). Crucially, the embedding approach reduced operational costs by 96% (from US$18.10 to US$0.32 per million requests) and slashed memory consumption from 6.52 GB to 1.04 GB. Contributions and Impact to IS Area: The study contributes a validated pattern for Frugal AI in Portuguese, demonstrating that open-source embedding models can effectively govern LLMs. This approach mitigates hallucination risks, reduces dependency on foreign APIs, and aligns with sustainable computing principles.
Interactive Information Visualization as Support for Computer-Aided Diagnosis: Prototype and Qualitative Evaluation
Research Context: Medical Information Systems (MIS) have evolved significantly to achieve efficiency and scalability in processing large amounts of multimodal data. Computer-Aided Diagnosis (CAD) models offer a second opinion to help physicians compose a more precise diagnosis. Effective CAD models need to be built on diverse, multicentric and, in specific cases, multimodal databases. Scientific and/or Practical Problem: The lack of standardization and complex interoperability among different MIS are major challenges that hinder the exploration of the maximum potential of digital data in healthcare applications, including the development of CAD models. Proposed Solution and/or Analysis: A generic, flexible and reusable relational data model was applied, developed specifically to support the training of CAD models from multimodal data. Through a case study using Cardiac Magnetic Resonance exams, a prototype with interactive Information Visualization (IV) functionalities was developed and qualitatively evaluated. Related IS Theory: The evaluation was inspired by the Fit-Viability Theory, as the prototype was evaluated primarily with regard to its potential for effective integration into real-world clinical workflows. Research Method: The research included: literature review; modeling; prototyping; development; qualitative evaluation through interviews with health professionals; and analysis and summarization of results. Summary of Results: Evaluated IV tools demonstrated strong potential to enhance clinical decision-making for the interviewed health professionals. From a Fit-Viability perspective, the system showed a promising fit with the users’ analytical needs and clinical workflows, while also indicating viability in terms of technical feasibility and resource compatibility within the healthcare setting. Contributions and Impact to IS area: Results showed that the strategic combination of a flexible data model and customizable IV functionalities can provide valuable research tools and diagnostic aids to physicians. These insights offer practical guidance for enhancing MIS and CAD capabilities, particularly in supporting the effective analysis of medical databases.
Use of ChatGPT in Low-Code Development: An Exploratory Study in the Salesforce Platform
Research Context: Large Language Models (LLMs) have emerged as promising resources to support software development. Beyond code generation, these tools can serve as technical and educational support, assisting developers in learning and applying concepts in complex low-code development platforms (LCDP). Scientific and/or Practical Problem: While LCDPs lower entry barriers, organizations still struggle to adopt them. As a first step, we investigate whether LLMs can effectively assist LCDP’s newcomers in learning, producing and deploying quality enterprise applications. Proposed Solution and/or Analysis: This paper presents an exploratory study on the use of ChatGPT to support the development of a Salesforce application. We focus on a zero-shot learning approach to resemble the profile of newcomers. The study aims to identify the suitability of LLMs to assist beginner low-code developer. Related IS Theory: ChatGPT is seen as part of a socio-technical network that transforms low-code development. Information systems are complex and require interoperability; in this context, generative AI expands accessibility and innovation. Research Method: Development of a low-code application through prompts, using ChatGPT as support. Evaluation of the application based on quantitative and qualitative metrics. Summary of Results: Simple requirements were often solved with a single prompt, whereas the most complex required iterations and significant debugging, revealing the model’s limits. Expert reviewers found the AI-generated solutions correct and aligned with Salesforce best practices, but noted that the code lacked structural best practices. Contributions and Impact to IS area: Contributes to SI by demonstrating that ChatGPT is a co-creative, educational partner that can boost development productivity and democratize development by lowering technical barriers. However, human expertise remains critical. This work demonstrates how strategic AI integration can enhance system delivery while supporting developer learning.
Segmentation of River Networks from Multispectral Remote Sensing Data Using Deep Neural Networks
Research Context: Accurate hydrological mapping is essential for water resource management, flow modeling, and risk assessment of flooding and erosion. Scientific Problem: Conventional methods and RGB satellite imagery fail to map watercourses obscured by dense vegetation, resulting in fragmented, unreliable cartography that compromises territorial planning. Proposed Solution: We propose a comparative analysis of river network semantic segmentation, evaluating U-Net and DeepLabv3+ architectures with ImageNet pre-trained backbones (EfficientNet-B5, ResNet-152). The analysis utilizes multispectral data (RGB, NIR) and spectral indices (NDWI, NDVI, GNDVI) to enhance feature discrimination. Related IS Theory: This work aligns with TaskTechnology Fit by evaluating the suitability of deep learning for automated river network extraction. Research Method: We created a dataset from satellite imagery and official vector data of two Brazilian basins (Doce, Itapemirim). Models were trained in intra-dataset, combined, and cross-dataset scenarios using mean Intersection over Union (mIoU). Summary of Results: For intra-dataset evaluation, U-Net using EfficientNet-B5 yielded 84.86±0.39% mIoU, whereas DeepLabv3+ with ResNet-152 showed 86.73±0.21%. Combined datasets provided stable metrics (82.25±0.51% for U-Net). Cross-dataset tests reached 63.96% mIoU, indicating a performance decrease. Contributions/Impact: This study demonstrates that U-Net and DeepLabv3+ architectures using deep backbones accurately segment river networks in complex environments. The methodology provides a viable approach to automate drainage network extraction, supporting environmental management and hydrological modeling.
Simplifying Bibliometric Analysis with Python Streamlit
Research Context: Bibliometric analysis is a research methodology that has become increasingly popular in recent years, in different domains of science. Many tools have been developed to assist researchers in making sense of this data. Scientific and Practical Problem: The tools available to perform bibliometric analysis today are mostly integrated only with Web of Science (WoS) and SCOPUS, limiting researchers who wish to diversify their sources. They do not offer any support in data preprocessing. Many scientists choose to use more than one tool to adapt to their needs, as rarely is one tool complete. Proposed Analysis: After studying the available software, packages, and articles on bibliometric analysis, we propose a new tool that can be adapted to multiple science databases, performs bibliometric analysis, supports Excel to facilitate data preprocessing, and is designed to be both user-friendly and accessible to a broad audience. Related IS Theory: Related IS Theory fields are Organizational knowledge creation and Information processing theory. Diffusion of innovations theory is also relevant. Research Method: Design Science Research (DSR) was applied to identify the necessities for bibliometric analysis not covered by the available tools and identify how we can improve these gaps in an efficient manner. Summary of Results: The resulting artifact can be a tool for scholars of various fields. The direct integration with Excel facilitates the preprocessing and integration with many databases. It facilitates the extraction of missing metadata and features a simple, intuitive interface. Contributions and Impact to IS area: This study proposes a novel approach to performing bibliometric analysis, utilizing multiple science databases and integrating all necessary visualization tools.
Unraveling the Status Quo of Decentralized Autonomous Organizations
Research Context: Decentralized Autonomous Organizations (DAO) emerge as innovative structures operating on blockchain infrastructures, providing transparency, decentralization, and autonomy in digital governance and coordination processes. Scientific and/or Practical Problem: Despite their transformative potential, DAO face significant obstacles, such as scalability limitations, risks of power concentration, legal ambiguities, and complex incentive models. Proposed Solution and/or Analysis: This study investigates recent scientific literature on DAO, seeking to understand their current state, focusing on how they have been conceptualized, characterized, evaluated, and applied. Related IS Theory: The research draws on the wisdom of crowd theory, which explains how collective decision-making can overcome individual choices, and sociotechnical theory, which emphasizes the interaction between technological infrastructures and social structures. Together, these perspectives frame DAO as hybrid systems that integrate algorithmic processes with collective human governance. Research Method: A systematic mapping study was conducted, encompassing the collection, selection, and coding of 47 primary studies published in relevant scientific sources. Summary of Results: The results highlight the conceptual consensus on the decentralized and smart contract-based nature of DAO, propose categories of structural challenges, and map recurring application domains, such as decentralized finance (DeFi), open science, energy, and digital art. Furthermore, the results demonstrate the transition of DAO from algorithmic entities to sociotechnical hybrids, connected to emerging trends such as the metaverse, NFTs, and decentralized artificial intelligence. Contributions and Impact to IS area: This study provides an updated theoretical foundation for the IS area, mapping the state of the art of DAO, identifying structural challenges, and emphasizing their evolution toward hybrid models. Most importantly, it reveals the complexity and originality of uniting the three main pillars of information systems (code, governance, and automated processes), thus contributing to the design of more resilient, inclusive, and sustainable digital governance systems in emerging decentralized ecosystems.
HSAE: A Hybrid Unsupervised Autoencoder for Zero-Day Attack Detection in Realistic Environments
Research Context: Anomaly detection in computer networks is essential for cybersecurity, especially in complex environments like corporate and IoT networks that face emerging threats. Scientific and/or Practical Problem: Traditional Intrusion Detection Systems (IDS) struggle to identify zero-day attacks, creating significant security gaps. Signature-based approaches fail against unknown threats, while supervised models require large volumes of labeled data rarely available in practice. Unsupervised methods often produce high false positive rates (FPR), limiting their operational viability. Proposed Solution and/or Analysis: This work proposes HSAE (Hybrid Scoring Autoencoder), a lightweight hybrid deep learning model for network anomaly detection. The solution combines autoencoder reconstruction error with an auxiliary output trained using pseudo-labels, enabling the model to learn normality patterns exclusively from benign traffic. Related IS Theory: The research is grounded in anomaly detection theory via machine learning, integrating unsupervised approaches. The model uses Autoencoder (AE) architecture and its principles of data compression and reconstruction to identify behavioral deviations. Research Method: The study conducted quantitative and empirical evaluation, training and testing the model on public datasets CICIDS2017 and ToN_IoT. HSAE performance was compared to Variational Autoencoder (VAE), the state-of-the-art, using metrics including AUC, precision, recall, F1-Score, and FPR. Summary of Results: HSAE consistently outperformed VAE, achieving AUC of 0.94 in corporate scenarios and 0.86 in IoT ransomware detection. Results demonstrated the model’s utility as a practical tool for high-precision anomaly identification with significantly lower false positive rates. Contributions and Impact to IS area: This work contributes to Information Systems by presenting an anomaly detection model tailored for realistic scenarios, offering practical approaches to managing zero-day threats. Its computational efficiency enables implementation in resource-constrained environments like IoT and industrial systems where security is critical.
A Computational Intelligence-Driven Reference Architecture for Anomaly Detection in Software-Defined Networking (SDN)
Research Context: The rise of Cloud, 5G, and IoT demands the continuous control of millions of networked devices. While Software-Defined Networking (SDN) simplifies network management, it struggles with security and the fine-grained analysis necessary for reliable operational problem detection. Scientific Problem: A critical architectural gap exists as there is no standardized, flexible framework to catalog diverse network scenarios and automatically associate them with the most effective Computational Intelligence (CI) techniques. This lack of a self-learning structure severely hinders the intelligence of the SDN control layer. Proposed Solution: We present a novel, technology-agnostic Reference Architecture for anomaly detection, applicable to both SDN and traditional networks. This highly resilient design leverages hexagonal microservices and rigorously adopts the TM Forum’s Open Digital Architecture (ODA) model (TAM and eTOM) to achieve crucial industry standardization and interoperability. Related IS Theory: Work Systems Theory (proposing a systemic framework for an organizational process) and Institutional Theory, highlighted by the strategic adoption of ODA/TM Forum standards to ensure industry legitimacy. Research Method: An experimental methodology utilizing a Proof-of-Concept (PoC) prototype validated the architecture. We trained and assessed seven distinct Machine Learning (ML) algorithms against two different public datasets, proving the architecture’s inherent versatility. Summary of Results: Results confirm the absolute need for a flexible architecture, as the solution model varies significantly across scenarios. The analysis showed that the choice of the most effective algorithm is strictly contingent upon the specific anomaly type and the chosen evaluation metric. Contributions and Impact to IS Area: The main contribution is a standardized Reference Architecture that standardizes the evaluation, promotion, and application of diverse computational intelligence techniques according to the network context. This provides a blueprint for next-generation, self-learning, and standards-compliant network operations, marking a significant step towards truly autonomous networks.
Comparative Analysis of LLMs for Detecting Racism, Sexism, and Homophobia on Social Media
Research Context: Social media expansion has increased digital interactions, including hate speech such as racism, sexism, and homophobia. This challenges society and platforms to develop strategies for identifying and moderating harmful content; Scientific and/or Practical Problem: Despite AI advances, automatic detection faces limitations due to linguistic nuances, cultural context, and implementation costs. Scientifically, the challenge is evaluating model effectiveness; practically, it is developing economical, reliable large-scale solutions; Proposed Solution and/or Analysis: We conducted a comparative analysis of LLMs (GPT-3.5-Turbo, GPT-4.0, DeepSeek-V3 and Gemini-2.0-Flash) in detecting offensive social media comments. Tests on raw and preprocessed data using standardized prompts measured precision, cost, and execution time; Related IS Theory: The study draws on Information Systems theories, emphasizing socio-technical, ethical, and cost–benefit aspects (Socio-technical Theory, Actor-Network Theory, Resource-Based View, Dynamic Capabilities); Research Method: Over 2,000 comments were analyzed by LLMs using precision, recall, F1-score, operational cost, and processing time metrics; Summary of Results: GPT-4.0 achieved the highest F1-score (94.19%) but at high cost (US$ 26.99). DeepSeek-V3 balanced performance and cost (F1-score 93.37%, US$ 0.66). Gemini-2.0-Flash was the cheapest (US$ 0.12) but showed inconsistent results; Contributions and Impact to IS area: This work offers a practical framework for selecting LLMs for hate-speech detection based on accuracy, cost, and performance. It advances IS research by evaluating state-of-the-art models in real scenarios and providing guidance for ethical and efficient content moderation.
Implementation and Validation of a Business Intelligence Dashboard for Business Process Monitoring
Research Context: Business Process Management (BPM) is fundamental for competitive advantage and governance, especially in the public sector, where continuous monitoring supports strategic decision-making and performance improvement. Business Intelligence (BI) tools play a central role by linking process execution with managerial analysis. Scientific and/or Practical Problem: The process dashboard of the Court of Auditors of Pernambuco (TCE-PE) faced performance limitations and data inconsistencies, resulting from dependence on multiple SQL Server views. These views were shared across departments, which applied different data treatments, compromising information consistency and accurate visualization of key performance indicators. Proposed Solution and/or Analysis: This study proposes and validates the implementation of a new Process Dashboard using Qlik Sense, based on an architectural intervention that eliminates dependency on multiple views by centralizing transformation logic within the BI tool itself. Related IS Theory: The new tool was evaluated according to the DeLone and McLean Information Systems Success Model, covering dimensions of Information Quality, System Quality, Service Quality, Use/Intention to Use, User Satisfaction, and Net Impacts. Research Method: The research followed the Design Science Research Methodology (DSRM), structured into six stages (Problem Identification, Objective Definition, Design and Development, Demonstration, Evaluation, and Communication), conducted incrementally with institutional stakeholders. Summary of Results: The new dashboard eliminated reliance on external views, resulting in greater data consistency, easier maintenance, and improved process visualization reliability. The empirical evaluation with key users revealed a positive perception across all dimensions, with the highest scores in Net Benefits and Service Quality. Contributions and Impact to IS area: This study presents a validated model for restructuring data flows in BI environments within the public sector, addressing root causes of data inconsistencies and information quality issues. By centralizing data transformation, it strengthens reliability, transparency, and monitoring capacity, offering practical and theoretical contributions to the Information Systems field.
FREEsum: A Conceptual Framework for Evaluating Text Summarization Approaches
Research Context: The increasing amount of digital information in various areas, combined with the use of large language models (LLMs) in intelligent systems, chatbots, and LLM-based agents, underscores the importance of automatic text summarization for enhancing the efficiency of information processing and dissemination. Scientific and/or Practical Problem: Although advances in summarization techniques have been made, practitioners face difficulties in systematically comparing strategies, as automatic evaluations rely on metrics and tools with limited scope and insufficient integration with reproducible experimental pipelines, which creates challenges for rigorous benchmarking and the transparent selection of the best solutions. Proposed Solution and/or Analysis: This work proposes FREEsum—a conceptual, reproducible framework that enables the creation of end-to-end pipelines for automatic summarization evaluation, covering all stages of the experiment from data ingestion to results analysis, through declarative workflows and traceable artifacts, thus enabling systematic experimentation and direct comparison of summarization strategies. Related IS Theory: The framework applies Design Science Research principles and open-science practices, ensuring transparency, traceability, and reproducibility in Information Systems experiments. Research Method: FREEsum was developed and validated by implementing a configurable, reproducible experimental pipeline, including a proof-of-concept that demonstrates its applicability in realistic evaluation scenarios. Summary of Results: Experiments show that FREEsum standardizes benchmarking, streamlines configuration, supports method-and-metric trade-off analysis, and facilitates auditing across all experimental stages. Contributions and Impact to IS area: This work contributes to the field of Information Systems by connecting AI techniques for summarization (LLMand NLP-based) with core IS concerns, such as transparency, governance, and technological and economic impacts. A standardized, auditable, and reusable infrastructure for reliable and extensible summarization evaluation across domains, addressing IS demands for empirical rigor and transparent experimentation and supporting evidence-based adoption of AI methods, particularly in large-scale and complex-text settings where summarization mitigates information overload and improves interpretability.
A System for Workforce Sizing Optimization to Enhance Service Capacity in Primary Healthcare Units
Research Context: The Brazilian Unified Health System faces overload due to high demand for emergency services. Primary Health Care (PHC) plays a central role in mitigating this issue but suffers from the uneven distribution of human resources. Scientific and/or Practical Problem: The lack of optimized strategies for professional allocation generates imbalances among PHC units, resulting in service deficits in some regions and underutilization in others. Proposed Solution and/or Analysis: This study proposes a decision support system that uses open health data and mathematical optimization models to reallocate professionals across primary healthcare units. The system is formulated as an Integer Linear Programming (ILP) problem, aiming to maximize the minimum Service Capacity Margin (MCA), which reflects the balance between supply and demand. Related IS Theory: The research draws on Information Systems theories focused on decision support, such as Decision Support Systems (DSS) Theory, and mathematical modeling approaches in organizational contexts. Research Method: An ILP model (MAX-MCA) was developed to allocate professionals by category in Primary Health Care units. Demand was estimated from sociodemographic data, distributed using Voronoi diagrams, and adjusted based on actual service production from public records. Supply was calculated from the weekly working hours of professionals and average productivity standards defined by the respective professional councils. Summary of Results: Experiments were conducted in the municipalities of Ribeirão Preto and Guarulhos. In both scenarios, the model eliminated deficits and increased MCA across all analyzed categories, demonstrating more balanced and efficient utilization of human resources. Contributions and Impact to IS area: This work illustrates how optimization models integrated with Information Systems can support decision-making, promoting greater equity and efficiency in resource allocation. The study contributes to advancing operational research applications in health management.
Hybrid Summarization for Brazilian Judicial Decisions
Research Context: Judicial decisions in Brazil have a complex textual structure, comprising reports, votes, and summaries, which hinders systematic analysis and affects information intelligibility. This also applies to many other countries in the Global South. This situation challenges not only legal professionals but also Information Systems (IS) that support the processing and organization of large volumes of documents. Scientific and/or Practical Problem: Many systems struggle with lengthy texts due to language model limitations and the need to preserve technical accuracy and cohesion. This restricts the development of reliable solutions for judicial activities and transparency. Proposed Solution and/or Analysis: This study proposes and evaluates a hybrid summarization pipeline for decisions from the Federal Supreme Court, integrating extractive and abstractive methods to handle long documents without information loss. Related IS Theory: The research is grounded in Task-Technology Fit (TTF), justifying the alignment between the pipeline and the legal summarization task to support information-processing objectives. Additionally, Design Science Research is employed as the methodology for artifact development and evaluation. Research Method: The pipeline was implemented and tested on the RulingBR corpus, which comprises Brazilian Supreme Court decisions. Five summarization methods were compared: TF-IDF (baseline), BumbaBERT, Gemini-2.5, and two hybrid variants, evaluated using standard metrics (ROUGE, BERTScore, and METEOR). Summary of Results: Hybrid approaches balanced technical accuracy and textual cohesion, while chunked processing expanded the applicability of specialized models to long documents. Results demonstrate practical impact through faster case review and improved jurisprudential research. Contributions and Impact on IS area: The study contributes to IS by proposing strategies that support information governance in organizational environments while considering processing limitations. Aligned with the Grand Challenge of IS in the Open World, it provides guidance for scalable, transparent, and interoperable decision-support systems, improving institutional transparency and supporting legal activities in complex digital ecosystems.
Simplifying Administrative Texts for Plain Language using LLM: a Model Comparative Analysis
Research Context: Plain language is a growing tendency to create more inclusive and readable documents. Open and closed LLMs can be used to simplify documents, with varying costs and tradeoffs. Scientific and/or Practical Problem: Currently, the UFC Inova institution manually writes simplified versions of their public notices, causing longer publication time and repetitive effort. Proposed Solution and/or Analysis: We design a LLM-based pipeline that generates simplified versions of public notices with zero-shot prompting following plain language directives. We investigate how open LLMs compare to proprietary models. Although proprietary solutions are the cutting-edge models, adoption of open LLMs lowers the cost of ownership and avoids vendor lock-in, making them a sustainable choice for public-sector universities. Related IS Theory: We frame this work as a Socio-Technical Systems intervention to enhance access to public information, grounding it in prior research on text complexity and plain-language communication. Research Method: We evaluated a text simplification pipeline applied against different LLMs. The original and AI-generated versions were compared using statistical readability indexes and morphosyntactic metrics. Afterwards, two pairs of documents were evaluated in a survey with readers’ representatives. Summary of Results: The quantitative evaluations indicate that Gemini Flash outperformed the Pro version on both set of metrics, and the Qwen2.5:14b open model was closest to both in the morphosyntactic aspect. Regarding the qualitative evaluation, we observed that automated simplification was well received, but it may better support readers when combined with summarization. Contributions and Impact to IS area: This work provides a process to foster the adoption of plain language in the public sector, along with empirical evidence on the effectiveness of open LLMs compared to proprietary models.
Smart OCR: A Vertical AI Agent for Financial Document Validation — An Exploratory Case Study
Research Context: The growing complexity of fiscal and financial documents increases the need for intelligent solutions that automate validation. Vertical AI agents are emerging to improve accuracy and efficiency in financial workflows. Scientific and/or Practical Problem: Despite their potential, many AI systems face adoption barriers due to usability issues, lack of training, and misalignment with established practices. Understanding user perceptions in early stages is key to sustainable integration. Proposed Solution and/or Analysis: This study examines Smart OCR, a vertical AI agent that automates manual validation of fiscal and financial documents and provides a web interface for reviewing discrepancies. We assess perceptions of usefulness, usability, value, satisfaction, and recommendation intent. Related IS Theory: The analysis is grounded in the Technology Acceptance Model (TAM) and human–computer interaction literature, which emphasize perceived usefulness and ease of use as determinants of adoption. Research Method: The study took place in the financial sector of a research and development institute. Over two weeks, analysts and coordinators used Smart OCR integrated into their workflow. Data were collected through a structured questionnaire combining Likert items and open-ended questions. Summary of Results: Findings show high perceived usefulness, expected impact, and value, with strong recommendation intent. Usability and initial satisfaction were moderate, highlighting the need for clearer guidance and interface improvements. Contributions and Impact to IS area: The study offers empirical evidence on the acceptance of vertical AI agents in financial contexts and provides insights for IS design, onboarding, and organizational support to foster AI adoption.
EMOB-UX: Methodological Adaptation and Systematization of Usability Patterns, Guidelines, and Heuristics in E-commerce for Urban Mobility Contexts —A Case Study in Road Toll Systems
Research Context: The digitalization of urban mobility services, especially in toll e-commerce systems, introduces complex rules, regulatory requirements, and sociotechnical diversity. This scenario demands solutions integrating technology, processes, and people, grounded in usability and user experience. Scientific and/or Practical Problem: Generic usability patterns, guidelines, and heuristics do not adequately address domain-specific aspects such as tariff variability, regulatory interoperability, and user heterogeneity, which compromises effectiveness and digital inclusion. Proposed Solution and/or Analysis: The study adapted and applied the EMOB-UX method to the e-commerce domain in urban mobility, with emphasis on toll roads. It developed, validated, and delivered a systematized set of usability patterns, guidelines, and heuristics, configuring a scientific artifact that supports both the design and evaluation of digital systems in this context. Related IS Theory: The work aligns with IS theories on acceptance and success of digital systems, such as the DeLone and McLean IS Success Model, the Unified Theory of Acceptance and Use of Technology (UTAUT), the Diffusion of Innovations, and Task–Technology Fit (TTF). Research Method: The study was empirical-applied, exploratory, and mixedmethod, with an experimental technical procedure. EMOB-UX was structured into eight stages, including scope review, heuristic evaluation, interviews, usability tests, and questionnaires, with triangulation of qualitative and quantitative data. Summary of Results: Seven patterns were consolidated by interface components, complemented by consolidated guidelines (organization, responsiveness, feedback, accessibility, security) and emergent ones (sustainability, AI, automation). Ten domain-specific heuristics were also proposed: five novel and five adapted from Nielsen. Prototype applications showed improvements in effectiveness, efficiency, and user satisfaction. Contributions and Impact to IS Area: The study delivers a validated and systematized artifact, associated with a replicable methodological process, providing a theoretical and practical basis for regulated contexts and contributing to IS Grand Challenges in digital inclusion and governance of digital services.
The Evolution of Cloud Ecosystems: How AWS, Azure, and GCP Transformed Between 2008 and 2025
Research Context: Cloud computing has become foundational in the digital world, with AWS, Azure, and GCP leading dynamic ecosystems that enable rapid adoption of technologies such as infrastructure as code, MLOps pipelines, and generative AI. Scientific and/or Practical Problem: Frequent changes in API, services, and compliance requirements demand constant adaptation from developers. However, there is limited understanding of how communities respond to these evolving platforms over time. Proposed Solution and/or Analysis: We analyze cloud-related discussions on Stack Overflow from 2008 to 2025 to uncover how developer concerns evolve across adoption stages. Related IS Theory: This study draws on the socio-technical theory as an inspiration for analyzing digital platforms as evolving systems of interdependent actors. From this perspective, developer communities act as co-creators of platform knowledge and adaptability, shaping both technological trajectories and governance mechanisms. Research Method: We apply a reproducible method combining large-scale Q&A mining, topic modeling, and contextual trend analysis across three time windows. Summary of Results: We identify successive thematic waves in discussions, reflecting stages from IaaS (Infrastructure as a Service) and PaaS (Platform as a Service) provisioning to containers, serverless, and, more recently, data engineering and AI. These transitions mirror both platform evolution and developer needs. Contributions and Impact to IS area: Our findings offer theoretical and practical insights on developer engagement, documentation strategies, and platform governance. We also contribute a replicable methodology for longitudinal analysis of evolving digital ecosystems.
A Retrieval-Augmented Generation Information System for the Oil and Gas Industry
Research Context: The oil and gas industry relies on extensive and complex technical documentation, making manual information retrieval slow and inefficient for engineers and technicians who require quick and reliable answers for decisions. Scientific and/or Practical Problem: Generalist Large Language Models (LLMs) often struggle with specialized domains, leading to inaccuracies, contextual errors (”hallucinations”), and an inability to handle specific technical jargon. Proposed Solution and/or Analysis: This paper presents ”RAG Petrolês”an information system assistant built using Retrieval-Augmented Generation (RAG) on ”Petrolês”, a collection of Portuguese theses and dissertations on the topic. It uses the IBM Granite model for embeddings, FAISS for similarity search, and a combination of the DeepSeek R1 and Mistral Small 3.2 LLMs to generate and refine answers. Related IS Theory: The work is grounded in the Information Processing theory where a Retrieval-Augmented Generation (RAG) system integrates external knowledge bases with LLMs to enhance accuracy, contextual relevance of their responses and reduce hallucinations. Research Method: A quantitative and qualitative evaluation was performed. The system’s performance was tested against a custom dataset of 1500 questions. The evaluation involved a manual analysis of 150 answers and a broader statistical analysis of all 1500 responses using an ”LLM-as-a-Judge”approach combined with Prediction-Powered Inference (PPI) to ensure robust results. Summary of Results: The proposed system achieved an accuracy of 88.88% in manual evaluation. The statistical analysis resulted in an accuracy range from 81.48% to 97.71% within a 95% confidence interval. Outperforming baseline models and demonstrating superior performance compared to a direct adaptation of a similar existing framework. Contributions and Impact to IS area: This research demonstrates the effectiveness of a specialized RAG system in a technical, non-English domain. It provides a viable architecture for creating highly accurate, specialized information systems assistants without resorting to retrain foundational models.
Moving From MVP Towards a Product for Environmental Management System: An Action Research
Research Context: Environmental management systems increasingly demand not only regulatory compliance but also technical robustness and scalability to support the daily operations of organizations. Moving beyond a Minimum Viable Product (MVP) towards a consolidated product requires both architectural evolution and systematic validation of new functionalities. Practical Problem: While MVPs provide early validation of market needs, they often lack essential modules and technical maturity. In the environmental domain, this limitation can compromise usability, reliability, and the integration of critical processes such as financial control, commercial proposals, and data management. Proposed Solution: This study reports the technical evolution and structured validation of Suassu, an environmental management platform. Improvements included refactoring the system’s architecture, enhancing scalability, and implementing three new modules: Commercial Management, Financial Management, and Document Management. Related IS Theory: Grounded in Design Science and Technical Action Research under a sociotechnical (People–Process–Technology) lens, which informs iterative artifact evolution and evaluation through established quality and usability constructs. Research Method: Following Action Research methodology, the project was conducted in iterative cycles of diagnosis, planning, action, and evaluation. Validation combined qualitative and quantitative approaches, including functional testing, usability assessment (SUS), Thinking Aloud sessions, and semi-structured interviews. Summary of Results: Results demonstrate improvements in usability, operational efficiency, and user confidence, while also identifying areas for refinement in system integration and consistency. The introduction of dashboards provided strategic value by enabling data-driven decision-making. Contributions and Impact to IS Area: The study contributes by illustrating how MVPs can be systematically evolved into mature, integrated products in regulated domains. It advances Information Systems research by combining technical refactoring, module expansion, and structured validation methodologies within an Action Research framework.
A Solution for Automatic Task Allocation in Software Development Teams based on Truck Factor and Implemented on GitHub and Trello
Context: Software development involves several steps and activities. One of these activities is task allocation. This activity is related to project management and is decisive for the success or failure of the project since it involves risks related to time and, consequently, costs. Practical Problem: Inefficient task allocation, combined with team member rotation and knowledge concentration among a few members, can negatively impact deadlines, costs, and even lead to project discontinuation. Proposed Solution: All knowledge related to the project must be distributed equally among all team members to mitigate the negative impacts of possible departures of team members. This work proposes a web solution for task allocation that considers the knowledge level of the team members on the project repository. IS Theory: The approach builds on theories of project management, knowledge distribution, and collaborative software development, emphasizing metrics for team knowledge assessment. Research Method: A web application implementing the proposed approach was developed and evaluated in the context of software development teams. Summary of Results: The solution enables balanced knowledge distribution among team members, mitigating risks associated with departures and knowledge concentration, and improves task allocation efficiency. Contributions: The proposed approach provides a practical tool for software project management, enhancing knowledge management and reducing project risks, contributing to both the research and practice of Information Systems.
An Analysis of Software Vulnerabilities and Weaknesses in Machine Learning Libraries
Research Context: This article investigates software vulnerabilities and weaknesses across 273 machine learning (ML) library repositories. Scientific and/or Practical Problem: The scientific problem lies in comprehensively mapping security issues within the rapidly expanding landscape of ML libraries. Practically, this research addresses the need for a scalable methodology to effectively track, collect, and correlate existing vulnerabilities and weaknesses in these libraries. Proposed Solution and/or Analysis: We propose a scalable methodology designed to track, collect, and correlate security issues by leveraging CVEs (Common Vulnerabilities and Exposures) and CWEs (Common Weakness Enumeration). Related IS Theory: This research draws upon Complexity Theory, recognizing the intricate interconnectedness of ML ecosystems, where a single change or vulnerability can trigger cascading and unpredictable effects throughout the entire system. Research Method: Our research employed an empirical software analysis approach, involving the mining of 273 machine learning library repositories. We utilized CodeQL and Dependabot as part of the methodology to systematically track, collect, and conduct an in-depth analysis of the interconnections between CWEs and CVEs related to identified vulnerabilities and weaknesses. Summary of Results: Our findings revealed various security flaws, including validation failures, access control issues, memory management errors, development flaws, path-traversal vulnerabilities, and cryptographic weaknesses. Notably, these included CWEs from the Top 25 Most Dangerous Software Weaknesses. These results underscore the critical need for proactive measures to enhance security and reliability within ML systems. Contributions and Impact to IS area: Contributions include an automated methodology for characterizing vulnerabilities and weaknesses in ML libraries; an analysis of vulnerabilities and weaknesses in 273 ML libraries, and a dataset of CVEs/CWEs interconnections for ML.
A Systematic Review on Language Model Compression: Perspectives for Efficiency and Sustainability in Information Systems
Research Context: The rapid adoption of Large Language Models (LLMs) offers unprecedented opportunities for Intelligent Information Systems. However, their high computational cost creates significant deployment barriers in resource-constrained environments. This limited access for smaller organizations and developing regions reinforces social and economic disparities in the adoption of those systems. Scientific and/or Practical Problem: LLMs demand excessive resources, which hinders their integration into sustainable INFORMATION SYSTEMS. Furthermore, there is a lack of systematic evidence on how compression affects robustness, factuality, and applicability in different contexts. Proposed Solution and/or Analysis: This work presents a systematic literature review analyzing 30 peer-reviewed studies on LLM compression methods. The review identifies trade-offs, evaluate empirical results, and highlight gaps for future research. Related IS Theory: We interpret computational efficiency and accessibility as strategic resources (Resource-Based View) that enable the development of sustainable, and equitable digital infrastructures. Research Method: This study follows PICO structure with inclusion/exclusion criteria to works published between 2018 and January 2025. It was centered around eight research questions addressing efficiency, robustness, hardware compatibility, knowledge preservation, hybridization, architectural adaptation, evaluation metrics, and industrial viability. Summary of Results: Our study shows that efficiency and system-focused methods are the most developed and investigated for LLM compression for training and inference efficiency. Among these techniques, quantization consistently outperforms pruning and error-based methods, balancing performance and design savings. However, research on robustness, generalization, and practical use remains limited. Recent advances in shared hardware designs show potential for scalability and lower power consumption. However, practical studies on the long-term sustainability, ethical issues, and social impacts of LLM compression are rare in current scientific literature. Contributions and Impact to IS area: This work contributes by mapping key challenges in LLM compression research, offering insights to design efficient, sustainable, and socially responsible intelligent systems that align AI’s technical progress with goals of inclusion and environmental responsibility.
A Survey on Technostress in the Brazilian Software Companies focusing on Impacts and Interventions
Research Context: Technostress has become increasingly prevalent in the Information Technology sector, manifesting as cognitive overload, fatigue, and anxiety resulting from the intensive use of Information and Communication Technologies (ICTs). In software development companies, where technological dependence is high and deadlines are strict, technostress has a direct impact on professionals’ well-being and productivity.Scientific and/or Practical Problem: One of the critical challenges is understanding how technostress, driven by intensive and constant use of ICT, impacts professionals’ well-being and performance, which can lead to reduced productivity and inefficiencies in software development processes. Proposed Solution and/or Analysis: This study explores and summarizes the origins, impacts, and interventions of technostress by applying the RED/ICT Scale, gathering the perceptions of Brazilian IT professionals through an exploratory survey complemented with semi-structured interviews. Related IS Theory: The study is grounded in Socio-Technical Theory, which emphasizes the interaction between technological and social factors in the workplace, supporting an understanding of how ICT-related demands shape stress experiences. Research Method: We surveyed 86 professionals from Brazilian software development companies and conducted six semi-structured interviews, providing valuable insights into their perceptions and experiences. Summary of Results: The findings revealed a moderate impact on disbelief (42%), with concerns about excessive reliance on technology. Fatigue had the highest impact (73%), reflecting exhaustion and an increased workload. Anxiety averaged 55%, strongly linked to a lack of internet access (65%), rapid technological changes (58%), and demand overload (54%). Ineffectiveness scored lower (39%), but still indicated distraction and adaptation difficulties (45%). Interviews reinforced these results, highlighting constant pressure to stay updated and limited organizational support. Contributions and Impact to IS area: The main contribution of this work is to understand technostress in the Brazilian IT context, which software organizations can mitigate through interventions such as training, work-life balance policies, and psychological support.
Blended SoS: A New Evolutionary Approach for Complex Information Systems
Research Context: This study investigates complex socio-technical Systems-of-Systems (SoS), focusing on environments where operational technology (OT) and information technology (IT) converge, such as in the domains of Agro 4.0 and eHealth. These domains are at the forefront of digital transformation, requiring innovative approaches to integrate distinct and emerging technologies effectively. Scientific and/or Practical Problem: Existing SoS classifications are insufficient to capture the hybrid and evolutionary nature of real-world systems. Newly designed, legacy, and proprietary systems often coexist and must interoperate within the same ecosystem, creating gaps in understanding emergent behaviors, stakeholder interactions, and technical interoperability. This represents a significant challenge for building resilient and adaptable systems. Proposed Solution and/or Analysis: We introduce the concept of Blended SoS, a novel category of SoS characterized by limited central control, heterogeneous constituents, and adaptive, mission-driven behaviors. We organized this concept within a framework that integrates principles from mission engineering, SoS engineering, and information systems (IS) to guide the design, operation, and evolution of complex systems across various domains. Related IS Theory: The research adopts principles from hermeneutics and grounded theory, providing an interpretive understanding and enabling the emergence of substantial theory within the IS field. Research Method: Qualitative data from interviews and surveys were systematically coded and synthesized into a theoretical framework, validated against existing literature. Summary of Results: Findings highlight the importance of adaptive architectures, stakeholder-centric governance, and technical interoperability to ensure resilience, continuous availability, and alignment with missions. Contributions and Impact to IS area: This study formalizes the Blended SoS category, providing a framework for managing adaptive, multi-domain systems. This framework advances sociotechnical understanding of SoS, guides interoperability and governance, and supports decision-making in complex, dynamic IS environments.
On the evaluation of algorithm fairness strategies: a use case on gender bias
Research Context: The growing use of Machine Learning (ML) techniques in the development of predictive models to support decision-making and the development of information systems, while fostering advancements, has introduced significant ethical challenges, notably the emergence of biases that may lead to unfair decisions. Scientific and/or Practical Problem: The main scientific and practical challenge addressed is the identification and mitigation of algorithmic biases that may reproduce or exacerbate discrimination against minority groups, with a specific focus on the sensitive attribute gender. Proposed Solution and/or Analysis: This study presents an experimental evaluation of different bias mitigation strategies, including the use of the EqOddsPostprocessing and Reweighing methods from AI Fairness 360 toolkit, the application of weights, and the randomization of sensitive attribute values. Fairness performance was assessed using the Equal Opportunity and Demographic Parity metrics. Related IS Theory: This research is grounded in the theory of Algorithmic Fairness, aimed at ensuring impartiality, and in the concept of SocioTechnical Bias, which acknowledges that socially embedded prejudices are reflected in the outcomes produced by ML algorithms. Research Method: An experimental evaluation was conducted on a binary classification problem using the Portuguese SATDAP dataset. The baseline model was built with the Decision Tree algorithm, chosen for interpretability. Methodology comprised five experimental scenarios designed to test mitigation strategies and assess fairness through cross-validation. Summary of Results: Findings showed that Reweighing method fostered fairer predictions according to fairness metrics, in addition to yielding a slight but notable improvement in performance metrics such as accuracy, precision, recall, and f1-score. Contributions and Impact to IS area: This research reinforces the importance of integrating ethical guidelines and bias mitigation methodologies in developing ML systems, contributing to the construction of solutions fostering predictive fairness and countering discrimination without negatively impacting predictive performance.
A Web-Based Information System for Ornamental Rock Classification and Similarity Search Using Siamese Networks
Research Context: The time-consuming, subjective, and error-prone process for identification of ornamental rocks, which is traditionally based on visual analysis and expert knowledge. This task is vital for competitiveness in construction, mining, and geology. Advances in computer vision create opportunities to automate such tasks and provide aid for professionals. Scientific/Practical Problem: The ornamental rock industry faces challenges in accurately and efficiently classifying rocks and retrieving similar units for high-demand users. The current manual effort is imprecise and slow, highlighting the need for AI solutions to improve the process. Proposed Solution and/or Analysis: This work proposes a web-based information system that uses Siamese Networks to aid analysts and general users in finding the class of a given ornamental rock, and its most similar pair of images in a dataset. Related IS Theory: Task-Technology Fit (TTF) guides our work. The proposed system is designed to allow smoother and quicker task completion, reducing time and effort. In addition, users can more easily and effectively achieve their desired outcomes. The smoother task execution can lead to lower costs associated with task performance. Research Method: A system is proposed using Siamese Networks to perform classification and similarity recognition of ornamental rocks. Summary of Results: The system successfully finds the class of the given image, as well as the image pair that most resembles it. The Siamese network comparison technique also allows for the correct identification of classes not used in training, but existing in the user’s database. Contributions and Impact to IS area: This work bridges advanced vision models and decision support in ornamental rocks identification, transforming a neural network model into a reliable, evidence-based system and task-aligned evaluation.
Privacy, Data Protection, Risk, and Compliance in the Age of Generative AI Systems: A Systematic Mapping Study
Research Context: Generative Artificial Intelligence (GenAI), particularly Large Language Models (LLMs), is advancing rapidly, raising concerns about privacy, data protection, risk management, and regulatory compliance. Despite transformative potential, adoption remains immature and constrained by ethical, technical, and legal limits. Scientific and/or Practical Problem: Organizations, developers, and end users face risks of privacy violations, re-identification, model inversion, and opacity. Current frameworks such as GDPR and Brazil’s LGPD struggle to address GenAI’s complexity, leaving gaps between technical safeguards and legal obligations. Proposed Solution and/or Analysis: We performed a systematic mapping focused on privacy-preserving mechanisms, risk management frameworks, and compliance models applicable to GenAI. We synthesize state-of-the-art approaches, their strengths and limitations, and assess how they enable trustworthy adoption. Related IS Theory: The study is grounded in information systems governance, privacy by design, and responsible AI, using sociotechnical lenses integrating technological, organizational, and regulatory perspectives. Research Method: Following a protocol, we searched four major digital libraries (ACM DL, IEEE Xplore, ScienceDirect, SpringerLink), applied inclusion and exclusion criteria, and conducted quality assessment. From 1,138 initial studies, 44 were analyzed in depth, and 15 met all thresholds. Summary of Results: We identify four principal categories of privacy techniques (differential privacy, federated learning, cryptographic approaches, synthetic data), five risk management frameworks (e.g., NIST AI RMF, MITRE ATLAS/AI Security), and compliance instruments (DPIA, conformity assessment, FRIA). Comparative analyses reveal trade-offs among robustness, scalability, and regulatory alignment. Contributions and Impact to the IS area: This study consolidates mechanisms for addressing privacy, risk, and compliance in GenAI, highlights gaps between technical safeguards and legal requirements, and distills design implications for trustworthy systems. It supports scholars and practitioners in engineering responsible AI and informs IS research agendas, organizational policies, and regulatory strategies for intelligent information systems.
Wildfire Monitor: Real-Time Prediction and Interpretable Alerts from Fused Satellite and Meteorological Data
Research Context: Wildfires cause significant environmental, economic, and social damage, requiring intelligent systems capable of supporting prevention and mitigation actions. Scientific and/or Practical Problem: The increasing frequency of wildfires in Brazil and worldwide demands innovative technological approaches for real-time monitoring and accurate prediction of fire outbreaks using reliable and official data sources. Proposed Solution and/or Analysis: This paper presents the Cariri Wildfire Monitor, a system for mapping and monitoring wildfires through a web portal that integrates Artificial Intelligence (AI) and official datasets. The proposal includes a technological solution for continuous monitoring and a predictive module for fire hotspots. Related IS Theory: The system design is grounded in Information Systems (IS) theories related to data-driven decision support, predictive analytics, and socio-environmental information integration for sustainable management. Research Method: The system performs data fusion from INPE and INMET sources, applies regression models to predict wildfire hotspots, and integrates a decision layer with Large Language Models (LLMs) to produce auditable alerts and qualitative insights. Summary of Results: Among the results, the ExtraTrees regressor achieved R2=0,9843. In the stratified audit of the LLM module with live threshold 0,758 (proxy 0,500; Npop=1000; Maudit used = 12), we obtained Accuracy (w) 0,7973±0,1165, and p95 latencies between 4,75s and 5,95s. Contributions and Impact to IS area: The system demonstrates the potential of predictive computational models and LLMs in environmental monitoring, contributing to the development of intelligent, auditable, and sustainable Information Systems solutions for wildfire management.
CSIS Ecosystem: Impacts of Pragmatic Detection on a Campus Surveillance Case Study
Research Context: In modern universities, where there is a constant flow of people, it is essential to implement computer vision surveillance systems to detect incidents and alert security personnel, in order to ensure the safety of all members of the academic community. Practical Problem: Universities are faced with numerous security issues, including limited resources, a diverse range of technical infrastructure, and a mix of old and new systems. Besides, traditional security methods are reactive, slow to notice incidents, which in turn makes quick response in emergencies very hard. Proposed Solution: We present the solution in the form of the Campus Surveillance Interoperability System (CSIS), which is an open-source, modular, and interoperable architecture that we put forth for the specific surveillance needs of universities. CSIS utilizes a variety of computer vision models from the YOLOv11x set, specialized in recognizing weapons, fires, floods, graffiti, suspicious behavior, and license plates. Related IS Theory: Our paper adopts a Socio-technical theory, which recognizes that the effectiveness of information systems depends on the interaction between social and technical components rather than on technology alone. Research Method: In a real-world university setting, we put our proof of concept to the test. We had real-time video stream input, ensemble-based inference, and a centralized alert management system. We assessed the accuracy of the models. Summary of Results: We achieved an average accuracy of 83% for license plate recognition, weapon detection, graffiti, and smoke detections. Although we achieved moderate accuracy in fire detection and lower performance in flood detection. Considering sociotechnical concerns, we evaluate CSIS’s ability to automate surveillance tasks. Contributions and Impact to IS Area: CSIS architecture introduces an interoperable architecture for intelligent surveillance, demonstrating a model of interoperability in real time;
Iala Patrícia de Jesus Monteiro de Jesus
Assuring Trustworthy Data: A Dual-Criteria Analysis of Anonymization and System Reliability in Digital Health (A Systematic Review)
Research Context: The Digital Health sector faces the critical challenge of reconciling clinical data privacy with system reliability and utility. The risk in Brazil is high; the sector is the most targeted by ransomware, facing catastrophic financial losses. Scientific and/or Practical Problem: A persistent gap exists in the literature concerning the systematic evaluation of how anonymization techniques impact integrity and the lack of consensus on the dual criteria (technical and sociotechnical) necessary to assess effectiveness in Health Information Systems (HIS). Proposed Solution and/or Analysis: A Systematic Literature Review (SLR) mapped anonymization techniques (RQ1), analyzed their reliability impact (RQ1.1), and identified formal criteria for user trust (RQ2), addressing the core privacy-utility dilemma. Related IS Theory: The study is based on the Sociotechnical Systems Theory (SST), recognizing that anonymization and reliability are outcomes of integrating technology with critical social factors, including governance and user trust. The research aligns with GranDSI-BR Challenges 2 and 4. Research Method: The SLR followed Kitchenham and PRISMA guidelines, utilizing the PICOC model. The search across four major databases yielded 20 high-quality articles published between 2020 and 2025. Summary of Results: Dominant techniques are Blockchain and Federated Learning (FL), substantially enhancing data integrity and the privacy-utility balance. Reliability is dually assessed by Technical criteria (e.g., Re-identification Risk) and Sociotechnical criteria (e.g., Governance, Public Perception). Contributions and Impact to IS area: The study consolidates a theoretical and empirical framework on anonymization’s influence on reliability, meeting GranDSI-BR Challenges 2 and 4. It offers practical subsidies for managers and regulators in designing verifiable and trustworthy digital health systems.
A Heuristics-Based Framework for User Satisfaction Evaluation Across the Lifecycle of Information Systems Projects
Research Context: User satisfaction is recognized as a key success criterion in Information Systems (IS) projects. In the IS project context, continuously monitoring this criterion throughout the project lifecycle can reduce training and rework costs, enhance user experience, and prevent client loss. Scientific and/or Practical Problem: Despite its relevance, there is a lack of methods in the IS project management literature to evaluate user satisfaction during the development cycle. Existing approaches focus mainly on end-stage evaluations, limiting timely adjustments and evidence-based decision-making during project execution. Proposed Solution and/or Analysis: This paper proposes a framework for assessing user satisfaction in IS projects through three stages: expectation elicitation, expert inspection, and post-use verification. All stages are supported by questionnaires derived from usability heuristics to guide structured evaluation and comparison. Related IS Theory: The study is grounded in the Information Systems Success Model, which identifies user satisfaction as an essential dimension of project success, and integrates principles from Human–Computer Interaction (HCI). Research Method: An exploratory review of relevant literature was conducted, followed by the design of the proposed framework and its validation through three instrumental case studies across different organizational domains. Summary of Results: The results indicate that the proposed framework enables the evaluation of user satisfaction during system development, allowing early identification of usability issues and improvement opportunities. Contributions and Impact to the IS Area: This work contributes to IS project success management by operationalizing user satisfaction as a continuous and measurable criterion, ensuring its systematic verification throughout the project lifecycle and providing managers with a practical tool for evidence-based decision support.
Intelligent Information System for Analyzing Success Factors in Public Policies: A Case Study of the Pé-de-Meia Program
Research Context: Public policies such as the 'Pé-de-Meia' program are crucial for reducing school dropout in Brazil, but their evaluation is complex due to vast amounts of data and inherent selection bias. Scientific and/or Practical Problem: There is a need for an approach that moves beyond simple causal inference to identify the contextual factors that enhance the program's success, providing actionable intelligence for public managers. Proposed Solution and/or Analysis: We developed an intelligent information system that integrates public data and uses a Machine Learning model (LightGBM) to identify the main predictors of success in student retention, complemented by a quasi-experimental analysis (PSM) to estimate impact. Related IS Theory: This work is grounded in the principles of Decision Support Systems (DSS), applying computational intelligence to transform raw governmental data into strategic insights for policy management. Research Method: The methodology involved a four-stage data processing pipeline: data collection (Censo Escolar, Portal da Transparência), feature engineering (creating a proxy for school enrollment dynamics), predictive modeling with LightGBM to rank success factors, and impact estimation using Propensity Score Matching. Summary of Results: The predictive analysis identified that, beyond the natural inertia of previous performance, the number of teachers and program intensity are the most significant actionable predictors of success. To ensure robustness, these factors were cross-validated using a Random Forest model. The quasi-experimental analysis, refined with strict common support and caliper matching, estimated a positive and statistically significant impact (ATT = +0.19 p.p., p-value < 0.001). Contributions and Impact to IS area: This study contributes a novel, rigorously validated method for public policy evaluation within the IS field. It demonstrates how a predictive system can provide immediate strategic insights, while advanced quasi-experimental techniques can detect causal effects even in early-stage implementations, offering a tangible artifact for evidence-based management.