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The primary objective of this paper is to show a systematic and methodological approach for the digitalization of critical clinical pathways (CPs) within the healthcare domain.

The methodology entails the integration of service design (SD) and action research (AR) methodologies, characterized by iterative phases that systematically alternate between action and reflective processes, fostering cycles of change and learning. Within this framework, stakeholders are engaged through semi-structured interviews, while the existing and envisioned processes are delineated and represented using BPMN 2.0. These methodological steps emphasize the development of an autonomous, patient-centric web application alongside the implementation of an adaptable and patient-oriented scheduling system. Also, business processes simulation is employed to measure key performance indicators of processes and test for potential improvements. This method is implemented in the context of the CP addressing transient loss of consciousness (TLOC), within a publicly funded hospital setting.

The methodology integrating SD and AR enables the detection of pivotal bottlenecks within diagnostic CPs and proposes optimal corrective measures to ensure uninterrupted patient care, all the while advancing the digitalization of diagnostic CP management. This study contributes to theoretical discussions by emphasizing the criticality of process optimization, the transformative potential of digitalization in healthcare and the paramount importance of user-centric design principles, and offers valuable insights into healthcare management implications.

The study’s relevance lies in its ability to enhance healthcare practices without necessitating disruptive and resource-intensive process overhauls. This pragmatic approach aligns with the imperative for healthcare organizations to improve their operations efficiently and cost-effectively, making the study’s findings relevant.

This chapter examines the Netherlands’ challenges in safeguarding its low-lying coastline against rising sea levels and the consequences of coastal defense strategies on marine life, particularly in relation to SDG14. Sea-level rise necessitates increased soft coastal defense strategies, affecting seafloor areas and marine biodiversity through sand extraction and sand nourishments. The use of hard structures for coastal defense contributes to the loss of natural coastal habitats, raising biodiversity concerns. The chapter explores the potential benefits of artificial hard surfaces as marine habitats, emphasising the need for careful design to prevent ecological problems caused by invasive species. Strategies for enhancing biodiversity on human-made hard substrate structures, including material variations, hole drilling, and adaptations, are discussed. The ecological impact of marine sand extraction is examined, detailing its effects on benthic fauna, sediment characteristics, primary production, and fish and shrimp populations. Solutions proposed include improved design for mining areas, ecosystem-based rules for extraction sites, and ecologically enriched extraction areas. The ecosystem effects of marine sand nourishments are also analysed, considering the impact on habitat suitability for various species. The chemical effects of anaerobic sediment and recovery challenges are addressed. Mitigation measures, such as strategic nourishment location and timing, adherence to local morphology, and technical solutions, are suggested. The chapter underscores the importance of education in Nature-based Solutions and announces the launch of a new BSc programme in Marine Sciences at Wageningen University & Research, integrating social and ecological knowledge to address challenges in seas, oceans, and coastal regions and support SDG14 goals.

Visual feedback control is a promising solution for robots work in unstructured environments, and this is accomplished by estimation of the time derivative relationship between the image features and the robot moving. While some of the drawbacks associated with most visual servoing (VS) approaches include the vision–motor mapping computation and the robots’ dynamic performance, the problem of designing optimal and more effective VS systems still remains challenging. Thus, the purpose of this paper is to propose and evaluate the VS method for robots in an unstructured environment.

This paper presents a new model-free VS control of a robotic manipulator, for which an adaptive estimator aid by network learning is proposed using online estimation of the vision–motor mapping relationship in an environment without the knowledge of statistical noise. Based on the adaptive estimator, a model-free VS schema was constructed by introducing an active disturbance rejection control (ADRC). In our schema, the VS system was designed independently of the robot kinematic model.

The various simulations and experiments were conducted to verify the proposed approach by using an eye-in-hand robot manipulator without calibration and vision depth information, which can improve the autonomous maneuverability of the robot and also allow the robot to adapt its motion according to the image feature changes in real time. In the current method, the image feature trajectory was stable in the camera field range, and the robot’s end motion trajectory did not exhibit shock retreat. The results showed that the steady-state errors of image features was within 19.74 pixels, the robot positioning was stable within 1.53 mm and 0.0373 rad and the convergence rate of the control system was less than 7.21 s in real grasping tasks.

Compared with traditional Kalman filtering for image-based VS and position-based VS methods, this paper adopts the model-free VS method based on the adaptive mapping estimator combination with the ADRC controller, which is effective for improving the dynamic performance of robot systems. The proposed model-free VS schema is suitable for robots’ grasping manipulation in unstructured environments.

In this paper we ask: “What are the opportunities and challenges that creative methods pose in terms of conducting research processes with indigenous peoples impacted by emergencies and disasters?” To do that, we critically examine the creative and collaborative methodological approaches applied in the research project, Moving with Risk: Forced Displacement and Vulnerability in Colombia. This project sought to understand the trajectories of risk of families who were forcibly displaced as a result of the armed conflict in Colombia and resettled in areas at risk of disaster.

This article is intentionally written from the perspective of the researchers’ positionality. In doing so, we embrace writing that is situated and embodied in the researcher’s experiences and positionalities. This reflexive writing allows us to question the methodological experience of the research project we are analysing and, at the same time, ourselves, “the researchers” be questioned by it.

In this paper, we show how creative methods and participatory research can foster awareness and become the basis for inclusive and reciprocal research processes with indigenous communities in disaster studies. Specifically, we show that the use of creative methods helped us recognise that agency needs to be framed in collective spaces with the indigenous women we were working with and in relation to their livelihoods needs. We argue that finding spaces to conduct collaborative research and recognize agency is inextricably related to how the researchers reflect on their positionality.

This article contributes to critical perspectives in Disaster Studies and to an overall understanding of the role that creative methodologies play in research processes with people affected by disasters. It provides a novel perspective on the opportunities and challenges of applying arts-based methods in disaster risk studies with indigenous communities in Latin America.

The evolution of digital construction within Industrial Revolution 5.0 emphasises the harmonious integration of humans and technology, value-driven technology implementation, adaptive leadership and user-centric design solutions. This shift necessitates architects to enhance their soft skills to navigate the transition effectively. Failure to demonstrate these skills may confine architects to passive roles as technology consumers rather than innovative leaders. While existing research predominantly focuses on the technical aspects of digital construction, this study investigates how architects effectively manifest their soft skills within such dynamic contexts, addressing a critical gap in the current literature.

Employing an interpretive phenomenological approach, the research engaged 14 individual architects practicing in architectural consultancy practices (ACPs) in Malaysia through semi-structured interviews and written exercises to unveil their experiences.

Thematic analysis revealed six primary themes: “analytical and critical thinking,” “digital communication,” “diversity in leadership styles,” “emotional intelligence,” “ethics and professionalism,” and “curiosity and agility.”

Furthermore, the developed thematic map serves as a foundational resource for human resource practices aiming to design upskilling and reskilling programs that focus on nurturing soft skills. Emphasising soft skills, these programs are designed to thrive in the digital construction era and beyond.

These shared experiences significantly contribute to the theoretical understanding of soft skills within the digital construction context, providing novel insights into their practical application when addressing real-world implications.

This paper aims to test the effectiveness of the Haryono Umar (HU)-model used in corruption prevention strategies through corruption detection as a tool for detecting corruption because the mode of corruption is increasingly dynamic and complex by focusing on the causes of corruption: pressure, opportunity, rationalization, capability and lack of integrity.

The research uses multiple regression methods, classification and regression trees and the HU-model application system developed by researchers. The research sample uses secondary data from financial reports on the Indonesia stock exchange according to organizational clustering (such as red, grey and green areas).

The research result showed that of the 470 sample companies, there were 445 companies, or 98.9%, in the red cluster (indicated corruption), 19 companies, or 4.04, in the green clusters or not indicated corruption and six companies, or 1.28%, were included in the grey cluster or potential corruption. By knowing the cluster of an organization, efforts to prevent corruption can be made effective and efficient. Implementing the HU-model proves that the amount of pressure, the abundance of opportunities, the ease of rationalization and the high level of position and authority strengthen the drive for corruption if there is a lack of integrity.

Each internal organization can use this model independently and find conditions related to corruption so that they can immediately take action to prevent it.

The application of the HU-model is a discovery in preventing corruption by focusing on the possibility of corruption occurring in each organization through organizational clustering.

There is growing recognition that transformation of healthcare systems towards health and well-being systems requires a continuous learning process. This explorative study aims to gain insight into the experiences with and investment in these learning processes within regional partnerships for health and in what they need to enhance their learning capacity to use the learning for transformation.

17 interviews were held with programme managers, data scientists, trusted advisors and a citizen representative, all involved in the learning process on a regional level in ten Dutch regional partnerships. The interviews were inductively and thematically analysed, focusing on the experiences and perceptions underlying the learning processes.

Regional partnerships invest in learning processes by organizing interactions between different groups of stakeholders and by reflecting on specific themes or on a region-wide level. Difficulty was found in region-wide reflection and in enhancing the learning capacity within the partnerships. Further enhancing the learning capacity required: (1) Investment in (the use of) expertise for translating learning outcomes into concrete action; (2) Leadership for change, underpinned by a shared sense of urgency to learn for transformation and (3) A facilitative environment for change which is both based on facilitative system structures and a basis of trust and commitment to learn and adapt.

The study highlighted the difficulty of learning on a region-wide level and the struggle to apply this learning for transformation. It provides insights into how learning processes and learning capacity can be further improved.

Research suggests the hotel industry has grown to integrate corporate social responsibility (CSR) programs towards addressing issues relating to the sustainable development goals (SDGs). Nevertheless, how the progression of research has influenced the field of CSR and the SDGs remains unexplored. This paper aims to address the knowledge gap.

The research is based on a review of 230 papers from Scopus Indexed Journals – both mainstream and specialty. A content analysis was conducted to identify the major themes and the evolution of CSR research in the hotel industry.

Recent CSR research has tended to shift towards issues such as CSR communication and reporting from its earlier focus on the perceptions and practices of CSR. The SDGs, in general, and environmental sustainability, in particular, remain on the periphery of hotel industry’s CSR initiatives. This raises concern about the ability of CSR programs to facilitate the SDGs by 2030. This study highlights that both stakeholders (e.g. consumers, employees) and business forces (e.g. financial bottom line) can shape the hotel industry’s CSR strategies in contributing to the SDGs and greater sustainability.

The research advances the CSR and sustainability literature by undertaking a comprehensive review of business research on CSR in the hotel industry focusing on the SDGs, and advancing and facilitating discussion on existing knowledge in this field and how the field can be further developed.

In the context of the journey toward digital transformation and the realization of a fully connected factory, concepts such as data science, artificial intelligence (AI), machine learning (ML) and even predictive models emerge as indispensable pillars. Given the relevance of these topics, the present study focused on the analysis of customer complaint data, employing ML techniques to anticipate complaint accountability. The primary objective was to enhance data accessibility, harnessing the potential of ML models to optimize the complaint handling process and thereby positively contribute to data-driven decision-making. This approach aimed not only to reduce the number of units to be analyzed and customer response time but also to underscore the pressing need for a paradigm shift in quality management. The application of AI techniques sought to enhance not only the efficiency of the complaint handling process and data accessibility but also to demonstrate how the integration of these innovative approaches could profoundly transform the way quality is conceived and managed within organizations.

To conduct this study, real customer complaint data from an automotive company was utilized. Our main objective was to highlight the importance of artificial intelligence (AI) techniques in the context of quality. To achieve this, we adopted a methodology consisting of 10 distinct phases: business analysis and understanding; project plan definition; sample definition; data exploration; data processing and pre-processing; feature selection; acquisition of predictive models; evaluation of the models; presentation of the results; and implementation. This methodology was adapted from data mining methodologies referenced in the literature, taking into account the specific reality of the company under study. This ensured that the obtained results were applicable and replicable across different fields, thereby strengthening the relevance and generalizability of our research findings.

The achieved results not only demonstrated the ability of ML models to predict complaint accountability with an accuracy of 64%, but also underscored the significance of the adopted approach within the context of Quality 4.0 (Q4.0). This study served as a proof of concept in complaint analysis, enabling process automation and the development of a guide applicable across various areas of the company. The successful integration of AI techniques and Q4.0 principles highlighted the pressing need to apply concepts of digitization and artificial intelligence in quality management. Furthermore, it emphasized the critical importance of data, its organization, analysis and availability in driving digital transformation and enhancing operational efficiency across all company domains. In summary, this work not only showcased the advancements achieved through ML application but also emphasized the pivotal role of data and digitization in the ongoing evolution of Quality 4.0.

This study presents a significant contribution by exploring complaint data within the organization, an area lacking investigation in real-world contexts, particularly focusing on practical applications. The development of standardized processes for data handling and the application of predictions for classification models not only demonstrated the viability of this approach but also provided a valuable proof of concept for the company. Most importantly, this work was designed to be replicable in other areas of the factory, serving as a fundamental basis for the company’s data scientists. Until then, limited data access and lack of automation in its treatment and analysis represented significant challenges. In the context of Quality 4.0, this study highlights not only the immediate advantages for decision-making and predicting complaint outcomes but also the long-term benefits, including clearer and standardized processes, data-driven decision-making and improved analysis time. Thus, this study not only underscores the importance of data and the application of AI techniques in the era of quality but also fills a knowledge gap by providing an innovative and replicable approach to complaint analysis within the organization. In terms of originality, this article stands out for addressing an underexplored area and providing a tangible and applicable solution for the company, highlighting the intrinsic value of aligning quality with AI and digitization.

Although intellectual capital (IC) and human dynamic capabilities (HDCs) play a significant role in decarbonization processes, their measurement and reporting is under-researched. Hence, this study aims to identify the link between HDCs, carbon accounting and integrated reporting (IR) in the transition processes, investigating IC and HDCs in decarbonization processes to achieve net-zero business models (n-ZBMs).

A systematic literature review with a concise bibliometric analysis is conducted on 229 articles, published from 1990 to 2023 in Scopus database and Google Scholar. Reviewing data on publications, journals, authors and citations and analysing the article content, this study identifies the main search trends, providing a new conceptual model and future research propositions.

The results reveal that the literature has rarely focussed on carbon accounting in terms of IC and HDCs. Additionally, firms face pressure from institutions and stakeholders regarding legitimacy and transparency, necessitating a response considering IR and requiring n-ZBMs to be developed through IC and HDCs to meet social and environmental requirements.

Not only does this study link IC with HDCs to address carbon emissions through decarbonization practices, which has never been addressed in the literature to date, but also provides novel recommendations and propositions through which firms can sustainably transition to being net-zero emission firms, thereby gaining competitive advantage and contributing to the nation’s sustainability goals.