Abstract
This 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. © 2025 by the authors. Licensee ESJ, Italy.

Abstract
Data governance plays a crucial role for organizations aiming to improve data quality, security, and compliance, yet research reveals ongoing challenges in implementation, maturity, and the practical effectiveness of current frameworks. Despite the availability of numerous concepts, models, and assessments, their actual impact and relevance remain fragmented and insufficiently explored. This Systematic Literature Review (SLR) investigates how data governance frameworks influence maturity and identifies the factors that drive their effectiveness. Through the synthesis of existing research, the review aims to clarify the relationship between governance frameworks and maturity levels, highlight operational benefits, and examine implementation challenges, ultimately contributing to both academic understanding and practical advancements in data governance. Analyzing the most relevant studies, the review seeks to uncover the main governance mechanisms, frameworks, and trends shaping this field, with a central question in focus: How can a structured master data management framework improve data governance maturity?.

Abstract
This paper explores the potential of Large Language Models (LLM) to optimize various stages of the software development lifecycle, including requirements 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 Science, which follows a practical and iterative approach. Requirements, design suggestions, and code samples are analyzed before and after the application of language 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 environments could further enhance code quality, traceability, and explainability.

Abstract
Efficient 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.

Abstract
This study aims to develop a methodology to assist Small and Medium Enterprises (SMEs) in effectively adopting Business Process Automation (BPA). Despite its growing importance in streamlining routine tasks and enabling employees to focus on more creative activities, numerous organizations face challenges in implementing BPA due to unclear procedures, insufficient knowledge of eligible processes, and uncertainty regarding the necessary technology. In response to these challenges, we introduce the Methodology for Business Process Automation (M4BPA), an artifact designed to guide SMEs through a structured BPA implementation process. The research follows the Design Science Research Methodology (DSRM). The requirements for the artifact came from the results of a previous Systematic Literature Review (SLR). M4BPA was demonstrated within real SME environments, providing solid evidence of its efficacy. The findings suggest that M4BPA significantly enhances SMEs' ability to implement BPA efficiently, offering a practical toolkit that facilitates the process. The novelty of this work lies in the development of a BPA methodology specifically tailored for SMEs, addressing existing gaps in current frameworks and providing a best-practice model for similar organizations. This research contributes to the intermediate results of a doctoral project, offering valuable insights for both practitioners and researchers in the field of BPA. © 2025 by the authors.

Abstract
Numerous Robotic Process Automation (RPA) market solutions with wildly disparate capabilities and business models are being put forth. RPA is still in its infancy, and its technology framework is continually evolving. There are very few comparative studies of RPA systems, and they do not make it simple to tailor the solution to the needs of the business choosing it. Thus, the research question is that it feasible to design a procedure that enables the choice of the most appropriate RPA tool while accounting for a particular business domain, reality, and set of requirements? In order to accomplish this, this study builds an artifact that comprises a collection of indicators to enable the long-term selection of the best RPA solution for each organization and/or business process using the methodological approach of Design Science Research. The artifact offers a methodology to categorize the level of adaptability of each solution for automating business processes, performs a comparative analysis of existing RPA solutions using a particular framework, and provides an overview of the features of currently available solutions on the market. The viability of the artifact is demonstrated using a real-world case situation. This test demonstrated the artifact's capacity to meet the goals.