Wolverhampton Intellectual Repository and E-Theses
WIRE is the open access repository for research publications and outputs by researchers based at the University of Wolverhampton.
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Recent Submissions
Item Development and evaluation of an urban data governance reference model based on design science research(Elsevier, 2025-12-31)In the urban context, data governance has only recently gained attention, though the increased importance of data with the emergence of smart cities is unprecedented. Data governance helps ensure the efficient management, utilization, and protection of data, all essential for enhancing service delivery, refining decision-making processes, and fostering trust in data integrity. This study presents a data governance reference model adapted to urban requirements – the urban data governance reference model – developed following the design science research paradigm. We describe the steps of the reference model development, from establishing a scientific theory base to analysis of the problem environment in 27 EU cities to the development process of the artifact and evaluation through expert interviews in 10 EU cities. The findings reveal that no reference model for urban data governance exists in the scientific literature. In practice, cities face challenges such as data silos, lack of interoperability, and redundancies, as well as a lack of data culture. Support for creating data governance programs is also lacking. The urban data governance reference model harmonizes technology, organization, and culture through four foundation layers and four pillars. Experts’ evaluation of the reference model provides essential insights into its completeness, comprehensibility, applicability, and possible improvement measures for future research. It is highly adaptable and can serve as an orientation aid for cities implementing data governance.Item Brands and Psychological Influences on Consumer Behaviour(Springer Nature Switzerland, 2025-12-31)Consumers are influenced by a myriad of factors in their choices of brands which can be categorised differently. The impact of psychological and personal influences which comprise of factors like perception, motivation, learning and memory, and attitudes on brand consumption is considerable. This chapter features a meticulous discussion of these issues and their implications on brand consumption and branding strategies. These are underpinned by themes such as brand positioning/repositioning, the extended realities, webrooming, and showrooming and how they explain consumer day-to-day brand decisions. The understanding of consumer learning and memory are usually linked to brand awareness, brand association, brand recognition, brand recall, brand relearning and brand associative networks. These are carefully examined in this chapter. Moreover, the issues around the underpinning factors that motivate consumer brand choices, their attitude formation and change for brands are also examined with a robust discussion of their implication for strategic brand management.Item Written evidence submitted by Caroline Dixey; Suresh Renukappa; Subashini Suresh; and Redouane Sarrakh (DS0005)(UK Parliament, 2025-03-20)Item Unveiling the impact of socioeconomic and demographic factors on graduate salaries: a machine learning explanatory analytical approach using Higher Education Statistical Agency data(MDPI, 2025-03-11)Graduate salaries are a significant concern for graduates, employers, and policymakers, as various factors influence them. This study investigates determinants of graduate salaries in the UK, utilising survey data from HESA (Higher Education Statistical Agency) and integrating advanced machine learning (ML) explanatory techniques with statistical analytical methodologies. By employing multi-stage analyses alongside machine learning models such as decision trees, random forests and the explainability with SHAP stands for (Shapley Additive exPanations), this study investigates the influence of 21 socioeconomic and demographic variables on graduate salary outcomes. Key variables, including institutional reputation, age at graduation, socioeconomic classification, job qualification requirements, and domicile, emerged as critical determinants, with institutional reputation proving the most significant. Among ML methods, the decision tree achieved a standout with the highest accuracy through rigorous optimisation techniques, including oversampling and undersampling. SHAP highlighted the top 12 influential variables, providing actionable insights into the interplay between individual and systemic factors. Furthermore, the statistical analysis using ANOVA (Analysis of Variance) validated the significance of these variables, revealing intricate interactions that shape graduate salary dynamics. Additionally, domain experts’ opinions are also analysed to authenticate the findings. This research makes a unique contribution by combining qualitative contextual analysis with quantitative methodologies, machine learning explainability and domain experts’ views on addressing gaps in the existing identification of graduate salary predicting components. Additionally, the findings inform policy and educational interventions to reduce wage inequalities and promote equitable career opportunities. Despite limitations, such as the UK-specific dataset and the focus on socioeconomic and demographic variables, this study lays a robust foundation for future research in predictive modelling and graduate outcomes.Item Preventing dementia: areas of specific focus for India(Geriatric Care and Research Organisation (GeriCaRe), 2025-03-18)Dementia is a growing concern in India, requiring urgent attention due to the enormous need for care and support. There are various dementia risk factors that can be identified and dealt with effectively. Some of them have higher relevance in the Indian context, such as illiteracy, road traffic accidents associated with traumatic brain injury, pollution, metabolic disorders, and missed depression diagnoses. Increased awareness, appropriate strategies, and actions at various levels involving individuals, health professionals, and state authorities are required.
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