Abstract

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
The rapid evolution of Artificial Intelligence (AI) is reshaping Human Resource Management (HRM), with growing interest in its role in talent identification. While AI has demonstrated effectiveness in analysing structured data, its limitations in assessing qualitative attributes such as creativity, adaptability, and emotional intelligence remain underexplored. This study addresses these gaps through an exploratory mixed-methods design, combining a global survey (n = 240) with semi-structured interviews of HR professionals. Quantitative analysis highlights patterns of association between key competencies, while qualitative findings provide contextual insights into perceptions of fairness, bias, and cultural resistance. The results suggest that AI can complement, but not replace, human judgement, supporting a Hybrid Evaluative Model that integrates algorithmic efficiency with human interpretation. The study contributes rare empirical evidence to a nascent field, highlights the ethical imperatives of bias mitigation and transparency, and underscores the importance of cultural context (collectivist versus individualist orientations) in shaping the acceptance and effectiveness of AI-enabled HR practices. These findings offer practical guidance for organisations and advance theory-building at the intersection of AI and HRM.

Abstract
In recent years, companies have faced increasing pressure from globalization, requiring them to adapt not only to survive but also to thrive in a highly competitive environment. This adaptation has been facilitated by the efficient integration of technology, achieved through digital processes and collaboration tools. Digital transformation has emerged as a critical element for maintaining competitiveness as economies become increasingly digital. To succeed in this ever-evolving environment, companies must balance leveraging existing strengths with seeking new organizational agility. Integrating advanced technologies like Artificial Intelligence (AI) and Web Technologies into education and professional training is a strategic response to the challenges posed by the current digital landscape. AI, with its adaptability and automation capabilities, offers benefits such as increased efficiency, personalized learning, and streamlined administrative processes. Continuous evaluation of teaching and learning, along with data extraction and predictive analysis, enhances e-learning quality and informs organizational decisions. This research aims to investigate how advanced technologies can predict and adapt organizational training needs to improve competency development and overall effectiveness. The research adopts a Design Science Research (DSR) methodology, focusing on the development and implementation of an AI-based framework for personalized training recommendations. Expected outcomes include integrating AI-driven predictive models with existing Human Resources Management Systems to identify and address training needs, fostering employee skill development, organizational agility, and competitiveness in a rapidly changing market. Additionally, addressing this issue promotes a more inclusive and empowering work environment, enabling employees to thrive in an increasingly digital world. © 2025 Elsevier B.V., All rights reserved.

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.