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- Artificial intelligence in recruitment: a multivocal review of benefits, challenges, and strategiesPublication . Trovão, Hugo; São Mamede, Henrique; Trigo, Paulo; Santos, VitorThis study investigates the role of artificial intelligence (AI) in recruitment, with a specific emphasis on small and medium enterprises (SMEs) and cultural diversity, two dimensions frequently underrepresented in existing research. The objective is to evaluate the benefits, challenges, and strategies for the responsible adoption of AI in recruitment. To achieve this, a Multivocal Literature Review (MLR) was conducted, systematically synthesising peer-reviewed studies and grey literature published from 2018 onwards. Following Kitchenham’s systematic review guidelines and Garousi’s multivocal extensions, academic and practitioner perspectives were analysed to capture both theoretical insights and real-world practices. The findings indicate that AI can streamline recruitment processes, improve decision-making accuracy, and enhance candidate experience through tools such as résumé screening, predictive analytics, and generative AI applications. However, issues of algorithmic bias, limited transparency, data quality, regulatory compliance, and workforce scepticism persist, particularly in SMEs that face resource constraints. Although much of the available evidence reflects Western contexts, this review broadens the scope by integrating global perspectives and highlighting how cultural and regional factors influence AI acceptance. The novelty of this study lies in combining academic and industry evidence to propose actionable strategies—such as bias audits, explainable AI frameworks, and human-in-the-loop approaches—for more inclusive, sustainable, and globally relevant adoption of AI in recruitment.
- Applying large language models to software develop-ment: enhancing requirements, design and codePublication . Santos, Gonçalo; Silveira, Clara; Santos, Vitor; Santos, Arnaldo; São Mamede, HenriqueThis paper explores the potential of Large Language Models (LLM) to optimize various stages of the software development lifecycle, including require-ments elicitation, architecture design, diagram creation, and implementation. The study is grounded in a real-world case, where development time and result quality are compared with and without LLM assistance. This research underscores the possibility of applying prompt patterns in LLM to support and enhance software development activities, focusing on a B2C digital commerce platform centered on fashion retail, designated LUNA. The methodology adopted is Design Sci-ence, which follows a practical and iterative approach. Requirements, design sug-gestions, and code samples are analyzed before and after the application of lan-guage models. The results indicate substantial advantages in the development process, such as improved task efficiency, faster identification of requirement gaps, and enhanced code readability. Nevertheless, challenges were observed in interpreting complex business logic. Future work should explore the integration of LLM with domain-specific ontologies and business rule engines to improve contextual accuracy in code and model generation. Additionally, refining prompt engineering strategies and combining LLM with interactive development envi-ronments could further enhance code quality, traceability, and explainability.
- Rebooting procurement processes: leveraging the synergy of RPA and BPM for optimized efficiencyPublication . Santos, Simão; São Mamede, Henrique; Santos, Vitor; MDPIEfficient procurement processes are pivotal for strategic performance in digital organizations, requiring continuous refinement driven by automation, integration, and performance monitoring. This research investigates and demonstrates the potential for synergies between RPA and BPM in procurement processes. The primary objective is to analyze and evaluate a manual procurement-intensive process to enhance efficiency, reduce time consuming interventions, and ultimately diminish costs and cycle time. Employing Design Science Research Methodology, this research yields a practical artifact designed to streamline procurement processes. An artifact was created using BPM methods and RPA tools. The RPA was developed after applying BPM Redesign Heuristics to the current process. A mixed-methods approach was employed for its evaluation, combining quantitative analysis on cycle time reduction with a qualitative Confirmatory Focus Group of department experts. The analysis revealed that the synergy between BPM and RPAs can leverage procurement processes, decreasing cycle times and workload on intensive manual tasks and allowing employees time to focus on other functions. This research contributes valuable insights for organizations seeking to harness automation technologies for enhanced procurement operations, with the findings suggesting promising enduring benefits for both efficiency and accuracy in the procurement lifecycle.
- A new proposed model to assess the digital organizational readiness to maximize the results of the digital transformation in SMEsPublication . Silva, Rui; São Mamede, Henrique; Santos, VitorScientific research in digital transformation is expanding in scope, quantity, and relevance, bringing forth diverse perspectives on which factors and specific dimensions—such as organizational structure, culture, and technological readiness—affect the success of digital transformation initiatives. Numerous studies have proposed mechanisms to assess an organization’s maturity through digital transformation across various models. Some of these models focus on external influences, others on internal factors, or both. Although these assessments provide valuable insights into a company’s transformation state, they often lack consistency, and recent research highlights key gaps. Specifically, many models primarily reflect the views of senior management on the general progress of digital transformation rather than on measurable outcomes. Moreover, these models tend to target large enterprises, overlooking small and medium enterprises (SMEs), which are crucial to economic growth yet face unique challenges, such as limited resources and expertise. Our study addresses these gaps by concentrating on SMEs and introducing a novel approach to assessing digital transformation readiness—a metric that reflects how prepared an organization is to optimize transformation outcomes. Following design science research methodology, we develop a model that centers on the perspectives of general employees, offering companies an in-depth view of their readiness across 20 dimensions. Each dimension is evaluated through behaviors indicative of the highest level of digital transformation readiness, helping companies identify areas to maximize potential benefits. Our model focuses not on technological quality but on the degree to which behaviors essential for leveraging technology and innovative business models are integrated within the organization.