Abstract

Abstract
In an era of data-driven organisational transformation, ensuring high data quality is critical to sustaining service excellence and effective decision-making. In the banking sector, where data integrity underpins trust, regulatory compliance, and operational performance, data quality is experienced not only as a technical attribute but also as a socio-organisational phenomenon shaped by user perception and governance structures. Addressing limitations in generic data quality frameworks, this study develops and analytically grounds a user-focused, context-specific data quality framework tailored to regulated banking environments. Guided by the Design Science Research methodology, the study integrates a systematic literature review with empirical insights from a structured survey of employees at a Eurozone bank. The results provide an interpretive understanding of how user perceptions of data quality influence day-to-day banking operations, revealing governance and accessibility-related patterns that affect service processes and decision-making. By explicitly positioning user perception as a central explanatory mechanism linking data systems, governance arrangements, and business processes, the proposed framework extends user-perceived data quality theory to regulated financial contexts. The study contributes to data quality and service research by demonstrating how perception-informed assessment can support continuous improvement, organisational learning, and more resilient data management practices in banking institutions.

Abstract
This chapter provides an overview of the development Phases involved in transforming a technology originated in research into a medical product for commercialization. It first describes the four main Phases, from the emergence of the need for the product to its post-marketing obligations. It is intended to help the interested reader understand the stages, documents, guidelines, and regulations that a medical device must go through in order to be marketed. Special highlight is given to the necessary topics that must be addressed in order for the device to be certified. Every product that goes to market must be certified by some regulatory body in order to ensure that it will not cause any negative impact on its users. Further, for medical devices, these requirements are heightened, as they may come in contact with the user, potentially causing a direct risk to them. Thus, reading this chapter will provide the reader with an understanding of these Phases within the industrial environment as well as the aspects that must be taken into account before placing a medical device on the market. © Springer Nature Switzerland AG 2026.

Abstract

Abstract
Wheeled mobile robots are increasingly deployed in harsh environments where dense obstacles, traps, variable terrain, soil effects, tight energy budgets, and sensor noise often deem classical navigation stacks insufficient. This paper presents a PRISMA-guided systematic review of recent work on Deep Reinforcement Learning (DRL) for wheeled ground-robot navigation in harsh environments and organizes the field via a practical six-dimensional taxonomy: environmental challenges, navigation architecture, observation modality, action strategy, action space, and learning algorithm. The taxonomy is refined through an iterative, evidence-grounded coding process on the included studies, and applied under a transparent coding protocol to support reproducible categorization. Across the literature, DRL appears both as a planner module as well as end-to-end policy (behavior) implementer tool. Regarding observation, mapless navigation based on LiDAR or cameras are prevalent. Actions are predicted mostly one time step ahead and are continuous. Actor-critic methods are prevalent, notably PPO and SAC are the common DRL methods used. As for the evaluation methodology, it remains largely simulation-based, with only limited sim-to-real protocols. Building on these findings, we use the previously mentioned taxonomy to identify common design choices for navigation in harsh terrains, propose minimum reporting practices to enable reproducible comparison, and propose research directions including energy-aware learning, improved robustness to sensor degradation, all weather soil-vehicle interaction modeling, short-horizon look-ahead for stability and smoothness, standardized tasks and metrics. The proposed taxonomy and guidelines, as well as identified trends, intend to help researchers and practitioners select methods that best suits their own objectives and constraints, thus hopefully accelerating progress from promising simulation results to dependable, field-ready autonomy.

Abstract
This study aimed to identify how service operations managers perceive the effects of task duration variability and activity pooling on key performance indicators such as flow time, queue length, perceived service quality, and customer satisfaction. A scenario-based experiment was conducted with 229 professionals working in service operations in Brazil and Portugal. Participants evaluated fictional processes with varying levels of variability (low vs. high) and task allocation formats (specialized vs. pooled). All scenarios were validated through computer simulations prior to the experiment. The results reveal a gap between analytical models in the literature and managerial perceptions. While queuing theory associates increased variability with performance deterioration, respondents frequently attributed positive effects to higher variability and activity pooling, especially in relation to perceived quality. The study contributes by uncovering managerial interpretations that diverge from established operations management principles, suggesting the need for greater integration between analytical approaches and service-oriented perspectives. From a practical standpoint, the findings underscore the importance of strengthening managerial training in process analysis and promoting the use of computational tools as support for decision-making in complex service operations.