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Nuclear energy is a contested topic, requiring trade-offs in energy independence, ethicality and uncertainty. Anthropogenic climate change complicates these decisions further, with nuclear energy competing with other low-carbon and sustainable energy sources. Decisions about nuclear energy’s role, as part of a sustainable energy system, must be made in cooperation with all stakeholders. However, it is unclear how the public is involved in these decisions in the UK. This study aims to address this gap, exploring the degree to which public participation has occurred in the UK.

This paper conducted a scoping review of public participation in UK nuclear energy decision-making in the context of sustainable energy transitions, where the government retains and promotes nuclear energy as part of a sustainable energy system. Following a systematic literary search, this paper reviewed 28 academic and grey literature documents.

Public participation has primarily been conducted as consultations rather than active participation. There is limited evidence that consultations have meaningfully contributed to politically and socially responsible (i.e. individuals and groups working together for community benefit) decision-making, with public opinion on nuclear energy’s role being divided and is influenced by how it is framed.

Social aspects of nuclear energy development have historically received less attention than environmental and economic elements; the role of engagement and participation is relatively rare. Modern literature reviews in this context are largely absent, a gap this paper originally contribute to. This paper suggest ways in which how effective, inclusive engagement process could contribute to a fairer, responsible decision-making process and energy system in the UK.

Industry 4.0 defines the application of digital technologies on business infrastructure and processes. With the increasing need to take into account the social and environmental impact of technologies, the concept of Society 5.0 has been proposed to restore the centrality of humans in the proper utilization of technology for the exploitation of innovation opportunities. Despite the identification of humans, resilience and sustainability as the key dimensions of Society 5.0, the definition of the key factors that can enable Innovation in the light of 5.0 principles has not been yet assessed.

An SLR, followed by a content analysis of results and a clustering of the main topics, is performed to (1) identify the key domains and dimensions of the Industry 5.0 paradigm; (2) understand their impact on Innovation 5.0; (3) discuss and reflect on the resulting implications for research, managerial practices and the policy-making process.

The findings allow the elaboration of a multileveled framework to redefine Innovation through the 5.0 paradigm by advancing the need to integrate ICT and technology (Industry 5.0) with the human-centric, social and knowledge-based dimensions (Society 5.0).

The study detects guidelines for managers, entrepreneurs and policy-makers in the adoption of effective strategies to promote human resources and knowledge management for the attainment of multiple innovation outcomes (from technological to data-driven and societal innovation).

The COVID-19 pandemic has had a devastating impact on all facets of education. This led to educational institutions deploying blended and online systems for teaching and learning. The purpose of this study was to assess the role of blended learning in promoting quality education during the COVID-19 pandemic.

The qualitative research design was deployed in this study and enabled the researcher to collect data via in-depth interviews. Twenty-five (25) tertiary institutions accredited by the Ghana Tertiary Education Commission (GTEC) were randomly selected to participate in this study. The registrars of the institutions were purposively selected and served as the participants for the study. Thematic content analysis was used to analyze the data collected via the interview. Ethical considerations were adhered to during the study.

The study established that COVID-19 had a devastating effect on tertiary institutions; multiple technological and open-sourced systems were deployed for teaching and learning; blended learning was adopted to augment the traditional face-to-face mode of teaching and learning due to its ease of use, usefulness and accessibility as it was used for quizzes and assignments, accessing lecture notes, among others. Despite these, the deployment of technological and blended systems was met with challenges that somehow affected effective teaching, learning.

The study was limited to 25 tertiary educational institutions in Ghana. It was again limited to the COVID-19 era.

This research aids in understanding the extent of the impact of COVID-19 on teaching and learning and how blended learning is currently deployed and used in tertiary institutions in Ghana. The findings are relevant to policymakers and management of educational institutions as it informs them of the right method and tools to deploy for teaching and learning during pandemics.

As educational institutions globally are dealing with the effect of the COVID-19 pandemic, it is prudent to look into how tertiary institutions in Ghana deploy blended learning to facilitate teaching and learning. Thus, this paper is original as it fills the relevant literature gap in terms of scope, setting, methodology and findings.

Through the review of several journal articles on the adoption of information and communication technologies (ICTs) and how it impacts students’ motivation to continue with their studies or to drop out of their academic program, this study aims to review the literature on the impact of ICTs on student motivation at a university.

This paper is based on a systematic literature review steered by the PRISMA guidelines. This paper uses both Durban University of Technology subscription-based and publicly available papers. The research articles examined were published between 2018 and 2023 in Scopus, Web of Science and ScienceDirect.

Reviewed literature bespeaks that ICTs can increase student motivation by enhancing interactive, engaging and individualized learning. Digital technologies that engage students and offer a more engaging learning environment include instructional apps, online simulations and multimedia content. Using ICTs may be useful in lowering university dropout rates.

The systematic review yielded valuable insights for both academic research and real-world applications in education regarding the Durban University of Technology. The study offers a comprehensive analysis of the nexus between ICTs and student motivation.

This study was conducted to review the overview of green growth and examine the role of financial inclusion as well as economic integration and other variables on green growth in Association of Southeast Asian Nations (ASEAN) countries.

Principal component analysis (PCA) was used to construct financial inclusion variables and panel data regression analysis to examine the effect of financial inclusion and economic integration on green growth in 10 ASEAN countries from 2010 to 2021.

The results showed that financial inclusion had played a role in supporting green growth in ASEAN. The rapid development of green finance and green bonds promoted the implementation of better green growth. The variables of export diversification and trade openness had a significant effect on green growth. Therefore, there is a need for appropriate policies to prevent negative effects on the environment and the behavior of ASEAN countries.

The findings of this study suggest that policymakers in ASEAN countries not only focus on gaining economic benefits from financial inclusion and economic integration activities but also pay attention to environmental impacts. Moreover, the ASEAN region is actively developing strategic steps in providing easy access to capital and finance as well as expanding international trade activities through ASEAN Free Trade Area (AFTA). Therefore, it is hoped that apart from being able to establish sustainable policies, this region will also encourage and optimize previous policies to make them more environmentally friendly.

This study used a green growth approach with the Index by the Global Green Growth Institute. This index considered aspects of green economic opportunities and social inclusion that have not been applied in previous studies. In addition, this study contributed to review the activities of economic integration and financial inclusion and the sustainability of green growth in ASEAN countries. Until now, there has been no research focused on ASEAN; even though ASEAN has long carried out economic integration and encouraged financial inclusion policies, this region is vulnerable to environmental degradation issues.

This study proposes a machine learning framework to predict customer complaints from production line tests in an automotive company's lot-release process, enhancing Quality 4.0. It aims to design and implement the framework, compare different machine learning (ML) models and evaluate a non-sampling threshold-moving approach for adjusting prediction capabilities based on product requirements.

This study applies the Cross-Industry Standard Process for Data Mining (CRISP-DM) and four ML models to predict customer complaints from automotive production tests. It employs cost-sensitive and threshold-moving techniques to address data imbalance, with the F1-Score and Matthews correlation coefficient assessing model performance.

The framework effectively predicts customer complaint-related tests. XGBoost outperformed the other models with an F1-Score of 72.4% and a Matthews correlation coefficient of 75%. It improves the lot-release process and cost efficiency over heuristic methods.

The framework has been tested on real-world data and shows promising results in improving lot-release decisions and reducing complaints and costs. It enables companies to adjust predictive models by changing only the threshold, eliminating the need for retraining.

To the best of our knowledge, there is limited literature on using ML to predict customer complaints for the lot-release process in an automotive company. Our proposed framework integrates ML with a non-sampling approach, demonstrating its effectiveness in predicting complaints and reducing costs, fostering Quality 4.0.

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 improving customer experience (CX) has always been one of the top priorities of business process management (BPM), the evidence on the actual contribution made by traditional BPM to improving CX and customer experience management (CXM) is mixed. Recently, new and enhanced capability areas have been added to the traditional BPM frameworks, yet it is unclear which of them contribute to CXM. Moreover, it is not known which of them are necessary and which are sufficient conditions. The aim of this research is to shed light on the research gap concerning which BPM capabilities, especially new and enhanced ones, are relevant to CXM.

Quantitative data from 268 medium and large companies in 3 EU countries were analysed using hierarchical linear regression analysis and necessary condition analysis.

The results show that traditional BPM capabilities are a necessary condition for CXM, but with minor significance. Most highly significant necessary conditions and also most highly or medium significant sufficient conditions belong to the People or Culture area. Agile Process Improvement is the only new or enhanced BPM capability area in the Methods/IT area that is a necessary and also a sufficient condition for CXM maturity. Advanced Process Digitalisation was identified as neither a significant necessary nor a sufficient condition for CXM.

This research contributes to better understanding of the role played by BPM for CXM, where previous research provides mixed results.

The organizational digital transformation (ODT) in companies presents small and medium-sized enterprises (SMEs) – who remain at the beginning of this transformation – with the challenge of offering digital services based on sensor technologies. Against this backdrop, the present paper identifies ways SMEs can enable digital servitization through sensor technology and defines the possible scope of the organizational transformation process.

Around 21 semi-structured interviews were conducted with experts from different hierarchical levels across the German manufacturing SME ecosystem. Using the Gioia methodology, fields of action were identified by focusing on influencing factors and opportunities for developing these digital services to offer them successfully in the future.

The complexity of existing sensor offerings must be mastered, and employees' (data) understanding of the technology has increased. Knowledge gaps, which mainly relate to technical and organizational capabilities, must be overcome. The potential of sensor technology was considered on an individual, technical and organizational level. To enable the successful implementation of service offerings based on sensor technology, all relevant stakeholders in the ecosystem must network to facilitate shared value creation. This requires standardized technical and procedural adaptations and is an essential prerequisite for data mining.

Based on this study, current problem areas were analyzed, and potentials that create opportunities for offering digital sensor services to manufacturing SMEs were identified. The identified influencing factors form a conceptual framework that supports SMEs' future development of such services in a structured manner.