Despite the growing adoption of Business Process Management (BPM) in public administration, little is known about which factors drive BPM project success in countries like Brazil, where bureaucratic processes, heterogeneous legacy systems, and political instability create unique implementation challenges. Existing studies identify generic BPM and Information Technology (IT) success factors, yet no research has systematically examined success factors within public-sector BPM projects, leaving a critical contextual gap. This study addresses it through an action research project conducted at a large Brazilian national public institution. Across a ten-month Government-to-Government initiative, 22 semi-structured interviews were conducted and analyzed at each project cycle, allowing success factors to be identified, followed, updated, and validated by both the project team and the client. Results confirm several factors known in the literature while revealing new factors, including analysis of information systems and legacy mappings, project manager authority and influence, client systemic understanding, and an open-minded environment for change. A comparative analysis shows divergence in how stakeholders value these factors: the implementation team identifies more technical and managerial elements, while the client emphasizes prioritization and internal constraints. The study advances theory by contextualizing BPM success factors for the public sector. It offers practical guidance to strengthen governance, stakeholder engagement, and continuity of BPM initiatives in politically dynamic public organizations.

Key Accounts Managers are responsible in an organization for the management of large clients requiring Management Information Systems that provide them in real-time accurate and timely information for decision-making. This project arises, therefore, as a response to this need and its main objective is to provide the Key Accounts Managers of a fuel trading company with an effective management control tool, having been chosen the Tableau de Bord. As contributions of this project to the academic and practical community, we have the systematization of Key Performance Indicators for the Key Accounts Department using the OVAR (Objectifs-Variables d'Action-Responsables) method and their availability in dashboards that constitute the Tableau de Bord.

Decisions based on artificial intelligence can reproduce biases or prejudices present in biased historical data and poorly formulated systems, presenting serious social consequences for underrepresented groups of individuals. This paper presents a systematic literature review of technical, feasible, and practicable solutions to improve fairness in artificial intelligence classified according to different perspectives: fairness metrics, moment of intervention (pre-processing, processing, or post-processing), research area, datasets, and algorithms used in the research. The main contribution of this paper is to establish common ground regarding the techniques to be used to improve fairness in artificial intelligence, defined as the absence of bias or discrimination in the decisions made by artificial intelligence systems.

This study has significant implications for practice, research, and education by providing new insights into IT project success. It expands the body of knowledge on project management by reporting project success (and not exclusively project management success), grounded in several objective criteria such as deliverables usage by the client in the post-project stage, hiring of project-related support/maintenance services by the client, contracting of new projects by the client, and vendor recommendation by the client to potential clients. Researchers can find a set of criteria they can use when studying and reporting the success of IT projects, thus expanding the current perspective on evaluation and contributing to more accurate conclusions. For practitioners, this study provides a rich set of criteria that can be used for evaluating their projects, as well as strong evidence of the importance of considering not only project execution, but also post-project outcomes and impacts in the evaluation.

Over the years, medicine has continuously tried to provide the best treatment for patients. In cancer treatment, science has been at the service of medicine, to treat and discover new and better treatment approaches to “save lives” or increase longevity. Among the new disciplines that have been used to try to meet these challenges are Big Data, Bioinformatics, Machine Learning, Data Mining, Pharmacogenomics and Genomics. Whole genome sequencing has led medicine into several fields, such as broad knowledge of the human body, high accuracy in detecting a pathology or the most promising therapeutic line for a particular individual. The aim of the study is to understand and evaluate the main similarity metrics that could eventually be used in drug recommendation systems in cancer patients. Ten of the most popular similarity indices were used in the evaluation of gene expression of twenty samples from the Genomics of Drug Sensitivity in Cancer (GDSC) Project dataset, concerning breast and skin cancer pathology. The results obtained from the tested similarity indices are discussed, proposing some of them for the mentioned types of recommendation systems.

This article aims to provide new insights on the subject by presenting the results of laboratory experiments carried out with code-based, low-code, and extreme low-code technologies to study differences in productivity. Low-code technologies have clearly shown higher levels of productivity, providing strong arguments for low-code to dominate the software development mainstream in the short/medium term. The article reports the procedure and protocols, results, limitations, and opportunities for future research.

The possibility of performing the sequencing of the human genome has revolutionized the field of biology and medicine itself, paving the way to personalized medicine. In the case of cancer, which is a disease caused by mutations in DNA, the study of genome brings some advantages, because through the study of the functioning of the human organism and the mutations caused by tumor, this makes it possible to explore the effectiveness of a significant number of treatments through medication for certain cell lines derived from various oncological problems. Bioinformatics has some powerful tools that help in the acquisition, storage, analysis, and visualization of genomic data. The research underlying this article was based on the analysis of biological data and established as its main objective the test of the premise that the most effective medications for a particular cell line are those that have been shown to be most effective in cell lines that are visually similar. A sample of 100 images of cell lines was used, and the medications used on each cell line were compared and the above premise was validated, concluding that the similarity in DNA between cell lines allows similar treatments to be recommend.

This article aims to provide new insights on the subject by presenting the results of laboratory experiments carried out with code-based, low-code, and extreme low-code technologies to study differences in productivity. Low-code technologies have clearly shown higher levels of productivity, providing strong arguments for low-code to dominate the software development mainstream in the short/medium term. The article reports the procedure and protocols, results, limitations, and opportunities for future research.

The forgery of academic certificates is a global concern, being one of the best-known examples the case of fake doctors. This counterfeiting is facilitated because the validation of these certificates by the employers is, in most cases, through visual inspection of the certificates which, by itself, does not guarantee their veracity. In this sense, this work proposes a new certification approach based on blockchain technology that allows anyone to validate an academic certificate by placing the hash or the pdf of the academic certificate in a web application that performs this validation in a public blockchain network, without the need to authenticate.

The study of Information Systems Project Management (ISPM) practice is fundamental for developing knowledge in this field. Over the past few years, several studies have been conducted in organizations by professionals and academics to identify approaches, processes, tools, and techniques, among other relevant aspects of project management practice. The use of these practices can be related to various factors, such as trends in the world of work or even the cultural context. In this way, an insight into the context of a given region can support actions to improve ISPM practice and raise success rates in information systems projects. This paper presents the results of a systematic literature review that seeks to synthesize how project management on information systems is practiced in Portugal and identify opportunities for developing the project management body of knowledge.

COVID-19, mobility, socio-social changes have transferred to the world of social media communication, purchasing activities, the use of services. Corporate social media has been created to support clients in using various services, give them the possibility of easy communication without time and local barriers. Unfortunately, they still very rarely take into account the security and privacy of customers. Considering that the purpose of this article is to investigate the impact of social media on the company's image, it should be remembered that this image also works for the security and privacy of customer data. Data leaks or their sale are not welcomed by customers. The results of empirical research show that the safety, simplicity and variety of services offered on social media have a significant impact on the perceived quality, which in turn positively affects the reputation. The authors proposed a methodology based on the Kano model and customer satisfaction in order to examine the declared needs and undefined desires and divide them into different groups with different impacts on consumer satisfaction. The interview participants were employees of 10 randomly selected companies using social media to conduct sales or service activities. 5,000 people from Poland, Portugal and Germany participated in the study. 4,894 correctly completed questionnaires were received.

This paper is aimed at presenting the Python implementation of the Value-Based Data Envelopment Analysis (VBDEA) method, which was designed to evaluate the efficiency of decision-making units (DMUs). This methodological framework explores the links between data envelopment analysis (DEA) and multi-criteria decision analysis (MCDA) and proposes a new perspective on the use of the additive DEA model using concepts from the multi-attribute value theory (MAVT). One of the major strengths of VBDEA over typical DEA methodologies is that it offers information on the main reasons behind DMUs’ (in)efficiency. Additionally, this approach allows straightforwardly ranking of efficient and inefficient DMUs, since it relies on a super-efficiency model. Because of the use of value functions, besides allowing the incorporation of the decision-maker (DM)’s preferences, this methodology easily handles negative or null data. In this context, we illustrate the Python implementation of the method by reproducing the main results obtained by (Gouveia et al., Or Spectrum 38:743–767, 2016), when these authors evaluated the performance of 12 health units in a Portuguese region incorporating management preferences given by real DMs.

Portuguese programs aimed at fostering Energy Efficiency (EE) measures often rely on cost–benefit approaches only considering the use phase and neglecting other potential impacts generated. Therefore, this work suggests a novel methodological framework by combining Hybrid Input–Output Lifecycle Analysis (HIO-LCA) with the Portuguese seasonal method for computing the households’ energy needs. A holistic assessment of the energy, economic, environmental, and social impacts connected with the adoption of EE solutions is conducted aimed at supporting decision-makers (DMs) in the design of suitable funding policies. For this purpose, 109,553 EE packages have been created by combining distinct thermal insulation options for roofs and façades, with the replacement of windows, also considering the use of space heating and cooling and domestic heating water systems. The findings indicate that it is possible to confirm that various energy efficiency packages can be used to achieve the best performance for most of the impacts considered. Specifically, savings-to-investment ratio (SIR), Greenhouse gases (GHG), and energy payback times (GPBT and EPBT) present the best performances for packages that exclusively employ extruded polystyrene (XPS) for roof insulation (packages 151 and 265). However, considering the remaining impacts created by the investment in energy efficiency measures, their best performances are obtained when roof and façades insulation is combined with the use of space heating and cooling and DHW systems to replace the existing equipment. If biomass is assumed to be carbon–neutral, solution 18,254 yields the greatest reduction in GHG emissions. Given these trade-offs, it is evident that multiobjective optimization methods employing the impacts and benefits assessed are crucial for helping DMs design future EE programs following their preferences.

The European Regional Development Fund devoted around 66 billion Euros to the financial support of innovation and productivity in European enterprises over the 2014–2020 programming period. In this framework, we assessed the implementation of the Operational Programmes dedicated to fostering research and innovation, particularly in small and medium-sized enterprises. With this aim, we used a network slack-based data envelopment analysis model paired with cluster analysis that encompasses a multitude of performance framework indicators to assess 53 Operational Programmes from 19 countries. Our findings suggest that compared to transition and less developed regions, more developed regions present a higher room for improvement. Also, less developed regions present a better performance when they employ their funding against more developed regions, suggesting that further funding should be channelled for leveraging research and innovation in the former regions. Finally, Operational Programme managers should focus on solving the problems both inherent to the poor outcomes in terms of enhancing the number of researchers working in improved research infrastructures and promoting the technology transfer between research institutions and enterprises.

Information technology professionals often refer to DevOps as a cultural or professional movement that presents a new approach to software delivery through collaboration between the development (Dev) and the operations (Ops) teams. It is an expanding phenomenon, but its adoption in organizations is still at an embryonic stage, requiring research to clarify the benefits, challenges, and barriers to its adoption. With this objective in mind, it was carried out an in-depth study in a large telecommunications (Telco) company that decided to undertake a process of migration to DevOps. The case study involved several stakeholders and covers the DevOps' ex-ante, adoption, and ex-post. Several important facets of DevOps are addressed in this paper, including: 1) practices (continuous delivery and continuous integration were considered the most relevant in the company), 2) benefits (from benefits stand out the improvement in the software quality and faster delivery, with fewer production failures), 3) barriers (the most significant obstacle was the resistance to change, in several dimensions), 4) success factors (the main influencing factors were top management support, implementation process and applied technology); and 5) others aspects (e.g., motivations and tools). Results provide academics and professionals with an integrated view of the conditions of DevOps adoption and its outcomes within organizations.

Software is becoming increasingly larger and complex, and companies should be aware of which technologies and platforms allow for higher productivity, that then translates into lower costs, shorter development times, and fewer required specialized resources. This article presents the results of an experiment carried out to compare the productivity of two low-code/code-based software development technologies. The results show that the development and maintenance of software with low-code technology is significantly faster, thus supporting the high potential of this technology. The key lesson is that low-code technology needs to be seriously taken into account by companies because of the potential productivity increase it represents for the development of management information systems.

One of the great challenges for football coaches is to choose the football line-up that gives more guarantees of success. Even though there are several dimensions to analyse the problem, such as the opposing team characteristics. The objective of this study is to identify, based on the players’ physiological variables collected using Global Positioning Systems (GPS), which players are the most suitable to be part of the starting team/line-up. The work was developed in two stages, first with the choice of the most important variables using the Recursive Feature Elimination algorithm, and then using logistic regression on these chosen variables. The logistic regression resulted in an index, called the line-up preparedness index, for the following player positions: Fullbacks, Central Midfielders and Wingers. For the other players’ positions, the model results were not satisfactory.

Purpose: Few studies in the literature address the success of enterprise Information Systems (IS) projects, namely focusing on how success is influenced by project management practices. This research studied the impact of ISO 21500/PMBOK processes on the success of IS projects, aiming to contribute to a better understanding of management practices importance in the context of this type of projects. Design/methodology/approach: An international survey was used to collect data, which was analysed using descriptive and inferential statistics. Findings: The results show higher levels of success than usually reported in the literature. Furthermore, this research shows that overall success is strongly influenced by ISO/PMBOK project management processes, thus reinforcing the relevance of competent project management to improve the results of IS projects. Originality: Focusing on the specific case of IS projects, this study shows that higher levels of success are achieved by organizations with higher project management maturity.

Funds from the European Union that are devoted to fostering a low-carbon economy are aimed at assisting Member States and regions in implementing the required investments in energy efficiency, renewable energy, and smart distribution electricity grids, and for research and innovation in these areas. In this context, we assessed the implementation of these funds in small and medium-sized enterprises across different beneficiary countries and regions of the European Union. Therefore, this study uses a non-radial slack-based data envelopment analysis model coupled with cluster analysis that covers multiple aspects of evaluation, including two inputs and two outputs, to assess 102 programs from 22 countries. Overall, we were able to ascertain that there are 25 efficient operational programs that remain robustly efficient, whereas 51 remain robustly inefficient for data perturbations of 5 and 10%. Under the current output level, there was almost no input surplus. Therefore, to promote a low-carbon economy, operational program managers should concentrate on solving the problems behind the poor results achieved, both in terms of greenhouse gas emissions reduction and the pace of the programs’ implementation.

This paper presents the ISRI (Information Systems Research Indicators) Web tool, publicly and freely available at isri.sciencesphere.org. Targeting Information Systems (IS) researchers, it compiles and organizes IS adoption and use theories/models, constructs, and indicators (measuring variables) available in the scientific literature. Aiming to support the IS theory development process, the purpose of ISRI is to gather and systematize information on research indicators to help researchers and practitioners’ work. The tool currently covers eleven theories/models: DeLone and McLean’s IS Success Model (D&M ISS); Diffusion of Innovations Theory (DOI); Motivational Model (MM); Social Cognitive Theory (SCT); Task-Technology Fit (TTF); Technology Acceptance Model (TAM); Technology-Organization-Environment Framework (TOE); Theory of Planned Behavior (TPB); Decomposed Theory of Planned Behavior (DTPB); Theory of Reasoned Action (TRA); and Unified Theory of Acceptance and Use of Technology (UTAUT). It also includes currently over 400 constructs, nearly 2,500 indicators, and about 60 application contexts related to the models. For the creation of the tool’s database, nearly 580 references were used.