Mariangela Lazoi

Data‐intensive organisations increasingly depend on competences that allow data to be interpreted, shared and translated into organisational action. Research on data skills, analytics capabilities and digital transformation has expanded rapidly, yet the knowledge and process mechanisms through which data‐related competences become organisationally consequential remain dispersed across several literatures. This article addresses this issue through an integrated bibliometric and interpretive analysis of 1346 publications indexed in Scopus and Web of Science, complemented by a targeted full‐text examination. The study combines descriptive profiling, co‐citation analysis, factorial analysis, thematic mapping and keyword evolution to reconstruct the intellectual and conceptual structure of competence‐centred research in data‐intensive contexts. The findings reveal three connected conceptual areas concerning competence development, organisational mobilisation and organisational outcomes. Integrating these patterns with the theoretical and full‐text evidence, the article develops the Data‐Related Competence Mobilisation Framework around three core components, namely data‐related competence development, knowledge‐and‐process embedding and value realisation, while adaptive renewal operates as a feedback logic connecting organisational outcomes with subsequent changes in competences and embedding arrangements. The study contributes to knowledge and process management research by theorising knowledge‐and‐process embedding as the mechanism through which data‐related competences become organisationally usable, actionable and value‐relevant.

Online data are instrumental in knowledge generation and decision-making processes, particularly for companies aiming to glean insights for marketing strategies and service optimizations. Web scraping emerges as a pivotal technique for automatic data collection from the internet, crucial in today's competitive business landscape where online presence is paramount. Coupled with advanced artificial intelligence (AI) models, web scraping becomes even more powerful, enabling deeper analysis of unstructured data for enhanced decision-making. However, managing and analysing the diverse online information presents challenges. Through a bibliometric analysis conducted on 567 collected papers, this study aims to elucidate the evolving business landscape of integrating web scraping with AI-based models. In particular, it addresses key research questions regarding the evolution, business implications, and future trends of this integration. Results show a significant growth in research in this domain, with implications spanning sentiment analysis, natural language processing integration, data intelligence, and deep learning integration. The critical interpretation of the results has allowed for the definition of a future research agenda that proposes concrete directions for the development of more transparent, safe, sustainable and democratic technologies. The paper provides both theoretical and practical contributions. From an academic perspective, it offers insights into the effective utilization of AI-based models and web scraping integration for optimized scraping strategies in business, providing a clear overview of emerging research direction in this field. From a managerial perspective, this paper guides managers in adopting advanced AI-based models to positively impact their company's decision-making involving a strategic and interdisciplinary reflection on the responsible use of scraping and AI.

In the shift toward sustainable and resource-efficient manufacturing, Artificial Intelligence (AI) is playing a transformative role in overcoming the limitations of traditional production scheduling methods. This study, based on a Systematic Literature Review (SLR), explores how AI techniques enhance Advanced Planning and Scheduling (APS) systems, particularly under finite-capacity constraints. Traditional scheduling models often overlook real-time resource limitations, leading to inefficiencies in complex and dynamic production environments. AI, with its capabilities in data fusion, pattern recognition, and adaptive learning, enables the development of intelligent, flexible scheduling solutions. The integration of metaheuristic algorithms—especially Ant Colony Optimization (ACO) and hybrid models like GA-ACO—further improves optimization performance by offering high-quality, near-optimal solutions without requiring extensive structural modeling. These AI-powered APS systems enhance scheduling accuracy, reduce lead times, improve resource utilization, and enable the proactive identification of production bottlenecks. Especially relevant in high-variability sectors like fashion, these approaches support Industry 5.0 goals by enabling agile, sustainable, and human-centered manufacturing systems. The findings have been highlighted in a structured framework for AI-based APS systems supported by metaheuristics that compares the Industry 4.0 and Industry 5.0 perspectives. The study offers valuable implications for both academia and industry: academics can gain a synthesized understanding of emerging trends, while practitioners are provided with actionable insights for deploying intelligent planning systems that align with sustainability goals and operational efficiency in modern supply chains.

Digital transformation is reshaping business strategies and driving innovation across various industries including Cultural Heritage (CH) and tourism. Digital technologies, such as eXtended Reality (XR) and the Internet of Things (IoT), are increasingly being adopted to enhance visitors’ experiences, foster interactive engagement, and promote cultural knowledge. Despite the growing number of digital solutions proposed in the CH sector, several challenges remain in differentiating digital products and services, including matching industry needs and user expectations. This aspect is of particular interest when dealing with small and medium enterprises (SMEs), which often suffer from limited resources. Therefore, to design an effective digital solution, like a cloud-based platform for tourism and heritage applications, it is essential to first identify the key requirements, expectations, and preferences of SMEs and customers. This study presents the findings of a survey-based analysis conducted among 122 CH and tourism professionals, focusing on the most relevant features, services, and functionalities that such platforms should integrate. Results indicate a strong demand for cloud-based solutions that incorporate XR, IoT, sensors, and smart devices to collect context data and deliver personalized, immersive, and context-aware experiences. These insights suggest valuable practices for the development of digital tools that effectively support cultural organizations in engaging visitors.