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The purpose of this study is to address the generalised lack of guidance on ethical treatment of corporate (e.g. non-research) data in higher education institutions, by focusing on the case of the University of Queensland (Brisbane, Australia). No actionable framework is currently available in the country to govern the ethical usage of corporate data. As such, this research takes a stakeholder-centred approach to data ethics; the lived experience of the stakeholders involved coupled with a theory-based ethical framework allowed the authors build to build a framework to guide ethical data practice.

Adopting a revised canonical action research approach focused on intervention on the context, the authors conducted a review of the literature on ethical usage of data in higher education institutions; administered one survey to university students (n = 168); and facilitated three workshops with professional staff (two) and students (one).

Collected data highlighted how, among other themes, the role and ethical importance of transparency was the dominant claim among all stakeholder groups. Findings helped the authors develop an Enhanced Enterprise Data Ethics Framework (EEDEF) emphasising transparency and stakeholder-centricity.

Legislation is the driver to regulate the use of corporate data in higher education; however, this can be problematic because legislation is retrospective, lacks normativity and offers scarce directions for cases that do not exactly follow within the legislative mandate. In light of these regulatory limitations, the authors’ EEDEF offers operators guidance on how to ethically manage corporate data in the higher education environment.

This study fills gaps in praxis and theory; that is the lack of literature and guiding ethical frameworks to inform data practice in higher education. This research fosters a more ethical data management by virtue of genuine and authentic engagement with stakeholders and emphasises the importance of strategic decision-making and maturity of data culture in the higher education sector.

The purpose of this study is to present a novel extended hybrid analytical method (HAM) that leverages a two-dimensional (2-D) coupling between the semi-analytical Maxwell–Fourier analysis and the finite element method (FEM) in Cartesian coordinates.

The proposed model is applied to flat permanent-magnet linear electrical machines with rotor-dual. The magnetic field solution across the entire machine is established by coupling an exact analytical model (AM), designed for regions with relative magnetic permeability equal to unity, with a FEM in ferromagnetic regions. The coupling between AM and FEM occurs bidirectionally (x, y) along the edges separating teeth regions and their adjacent regions through applied boundary conditions.

The developed HAM yields accurate results concerning the magnetic flux density distribution, cogging force and induced voltage under various operating conditions, including magnetic or geometric parameters. A comparison with hybrid finite-difference and hybrid reluctance network methods demonstrates very satisfactory agreement with 2-D FEM.

The original contribution of this paper lies in establishing a direct coupling between the semi-analytical Maxwell–Fourier analysis and the FEM, particularly at the interface between adjacent regions with differing magnetic parameters.

This research demonstrates the theoretical merit of a reference architecture-based approach to life cycle cost (LCC) analysis system provision in the built environment. LCC insight is considered fundamental to sustainable decision making by asset managers; however, the current capabilities in practice do not align with the political ambition and the scale of competencies required to realise sectoral emissions–reduction targets.

In pursuing practical outcomes, the study employs a custom design science research-inspired methodology. Domain requirements are gathered via literature research as an initial top-down software reference architecture which is refined, bottom-up, through testing and implementation in a representative case study. A prototype IT system and reference architecture artefact are developed and used to evaluate the concept qualitatively through broad practitioner focus groups.

Sentiment analysis of the expert opinions is broadly positive and helps to substantiate the proposal’s theoretical suitability in addressing the scalability challenge. Additionally, constructive feedback provides guidance towards this trajectory, highlighting the importance of aligning with existing communities and standards, broadening future research scope to consider further scenarios and prioritisation of efforts to build trust around contracts and data quality.

The novelty of the work is the provision of the reusable LCC reference architecture development methodology.

The concept has the potential to provide LCC capabilities to industry at scale while the artefacts developed herein can be appended to existing LCC standards as implementation guidance to support IT system developers. Furthermore, the developed methodology can be employed in harmonisation efforts between policy and practice.

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 examines how key actors in the UK food system (FS) understand the role of the local food sector in relation to FS resilience.

Discourse analysis was used to assess and compare the framings of the UK FS in 36 publications released during Covid-19 from alternative food networks (AFNs) actors and from other more mainstream FS actors, including the UK government.

The analysis shows that AFNs actors perceive the UK FS as not resilient and identify local FSs as a route towards greater resilience (“systemic” framing). In contrast, other food actors perceive the UK FS as already resilient, with the role of local food limited to specific functions within the existing system (“add-on” framing). The two groups converge on the importance of dynamic public procurement and local abattoir provision, but this convergence does not undermine the fundamental divergence in the understanding of the role of “the local” in resilient UK FSs. The local food sector’s messages appear to have gone largely unheard in mainstream policy.

The paper presents an analysis of public sector reports focused on the UK FS released during the Covid-19 pandemic years 2020–2021. The corpus inclusion criteria mean that publications during this period which focus on other food sector issues, such social injustices, climate change and health, were not included in the analysis, although they may have touched upon local food issues. The authors further recognise that Covid-19 had a longer lasting effect on FSs than the years 2020–2021, and that many other publications on FSs have been published since. The time span chosen targets the time at which FSs were most disrupted and therefore aims to capture emerging issues and solutions for the UK FS. The authors’ insights should be further validated through a more complete review of both public reports and academic papers covering a wider base of food-related issues and sectors as well as a broader timespan.

A comparison of how different FS actors understand the importance of local food, especially in relation to resilience, has not been undertaken to date. The findings raise important questions about the disconnect between AFN actors and other actors in the framing of resilience. Considering the need to ensure resilience of the UK FS, this study's findings raise important insights for UK food policy about the “local food blindspot” and for food movement actors wishing to progress their vision of transformative change.

The purpose of this paper is to fill a gap in the literature regarding the open innovation management approaches that small and medium-sized enterprises (SMEs) can use to access digital technologies and incorporate them into their organizational processes. The research question is: What organizational and process-level managerial actions do SMEs take to successfully access and implement digital technologies within their organizational processes?

Using Guertler et al.'s (2020) Action Innovation Management Research (AIM-R) framework, this study examines the digital transformation experiences of 10 European SMEs to gain insights into the managerial actions that foster successful digital transformation.

The findings of the paper reveal two major contributions. First, a digital transformation roadmap for SMEs is proposed, with a focus on accessing external resources and reconfiguring internal ones to ease their digital transformation journey. Second, three distinct paradoxes that influence the digital transformation process in SMEs are highlighted, providing useful insights into the challenges and tensions SMEs face during this journey.

This paper provides a unique perspective on the digital transformation of SMEs by examining the managerial actions required for successful technology adoption and revealing the paradoxes that may emerge during this transformative process. The findings form the basis for future research, deepening our understanding of digital transformation in SMEs and providing actionable advice to managers and practitioners navigating this journey.

The presence of green spaces plays a vital role in promoting urban sustainability. Urban green parks (UGPs) help create sustainable cities while providing fundamental ecological functions. However, rapid urbanization has destroyed crucial green areas in Ranchi City, endangering inhabitants’ health. This study aims to locate current UGPs and predict future UGP sites in Ranchi City, Jharkhand.

It uses geographic information system (GIS) and analytical hierarchical process (AHP) to evaluate potential UGP sites. It involves the active participation of urban communities to ensure that the UGPs are designed to meet dweller’s needs. The site suitability assessment is based on several parameters, including the normalized difference vegetation index (NDVI), land use and land cover (LULC), population distribution, PM 2.5 levels and the Urban Heat Island (UHI) effect. The integration of these factors enables an evaluation of potential UGP’s sites.

The findings of this research reveal that 54.39% of the evaluated areas are unsuitable, 15.55% are less suitable, 12.76% are moderately suitable, 11.52% are highly suitable and 5.78% are very highly suitable for UGPs site selection. These results emphasize that the middle and outer regions of Ranchi City are the most favorable locations for establishing UGPs. The NDVI is the most important element in UGP site appropriateness, followed by LULC, population distribution, PM 2.5 levels and the UHI effect.

This study improves the process of integrating AHP and GIS, and UGPs site selection maps help urban planners and decision-makers make better choices for Ranchi City’s sustainability and greenness.

Circular supply chain management (CSCM) is considered a promising solution to attain sustainability in the current industrial system. Despite the exigency of this approach, its application in the food industry is a challenge because of the nature of the industry and CSCM being a novel approach. The purpose of this study is to develop an industry-based systematic analysis of CSCM by examining the challenges for its application, exploring the effects of recognised challenges on various food supply chain (FSC) stages and investigating the business processes as drivers.

Stakeholder theory guided the need to consider stakeholders’ views in this research and key stakeholders directly from the food circular supply chain were identified and interviewed (n = 36) following qualitative methods.

Overall, the study reveals that knowledge, perception towards environmental initiatives and economic viability are the major barriers to circular supply chain transition in the UK FSC.

This research provides a holistic perspective analysing the loopholes in different stages of the supply chain and investigating the way a particular circular supply chain stage is affected by recognised challenges through stakeholder theory, which will be a contribution to designing management-level strategies. Reconceptualising this practice would be beneficial in bringing three-tier (economic, environmental and social) benefits and will be supportive to engage stakeholders in the sustainability agenda.

This study identifies a methodology to explore the issues of enterprise innovation ecosystem health (EIEH).

First, this study constructs the indicator system of EIEH based on the research objective; second, the dynamic vertical projection method (DVPM) and entropy weight method are proposed to analyze the status and influencing factors of EIEH; finally, the future development of EIEH is analyzed using GM (1,1).

In terms of methodology, the DVPM can effectively analyze EIEH, which can not only analyze the development status and potential of EIEH every year but also analyze the comprehensive state of EIEH for many years. In terms of practice, the value and grade of EIEH in China have been gradually increasing from 2016 to 2020, but the overall development is unbalanced, and five key factors affecting EIEH have been identified. The EIEH in China is predicted to steadily grow from 2021 to 2025.

The analytical method employed in this study can effectively analyze EIEH, which provides a new analytical perspective for the evaluation of EIEH and enriches the research content of the enterprise innovation ecosystem (EIE). By analyzing the results, we can gain a comprehensive understanding of the state of different EIEs, enabling each EIE to design tailored remedial measures to enhance EIEH and achieve sustainable development.

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.