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People with intellectual disabilities (IDs) typically present with co-occurring communication difficulties. Research indicates that cognitive-linguistic impairment is predictive of poor health literacy, which can preclude individuals from developing a comprehensive understanding of their health, care and treatment needs. People accessing forensic intellectual and developmental disability (FIDD) services are more likely to engage meaningfully in treatment and rehabilitation only if they feel empowered to play active roles in their own care. Delivering proposed treatment programmes in an accessible format, which meets their language needs, is likely to promote this.

This paper aims to describe the development and evaluation of an easy read version of the “Ten point treatment programme” within an inpatient FIDD service. An expert panel comprising speech and language therapists and education professionals utilised a quality improvement method to develop this. Patients and staff evaluated this resource and gave feedback through surveys or meetings. The resource was refined based on responses, and a final version produced.

The easy-read Ten-Point Treatment programme resource was considered valuable by patients and staff from this service.

Future research should seek to pilot this resource, to explore alternative multi-modal means of accessible information provision coproduced at every level and to examine ways in which this resource could be incorporated within health-care consultations. Another research direction will be to assess the impact of this resource on comprehension, drawing upon objective, previously validated measures.

Provision of an easy-read version of the Ten-Point Treatment Programme may support individuals with communication difficulties to understand health and care information relevant to their inpatient admission and may empower them to take more active roles in their treatment pathway. It is important to acknowledge, however, that easy read is not an accessible means of information provision for all individuals with ID and that the empirical evidence for its impact is limited.

This is the first published attempt to evaluate the acceptability of an easy-read version of the Ten-Point Treatment Programme within an inpatient FIDD service in the UK.

It is suggested that autism assessments are not sensitive to the female autism presentation. Autistic women are often misdiagnosed or delayed in receiving their diagnosis. More guidance for assessing clinicians is required to improve the validity and effectiveness of autism diagnostic assessments of women. This study aims to explore the experiences of autistic women during their diagnostic assessments and understand what they considered important for diagnostic assessments of females.

A focus group with four adult autistic women was conducted, exploring their experiences and recommendations. Thematic analysis was used to identify overarching themes.

The main themes were 1) experience of the assessment; 2) gender-sensitive assessment content; and 3) clinician characteristics and skills. Participants made specific recommendations within these themes for how diagnostic assessments of females should be conducted.

This study differs from previous research by focusing on the experiences of autistic women specifically during diagnostic assessments and what they recommend for future assessments.

Building on the forms of immersion in computer games, this chapter argues that games, whether played alone or with others, require a level of engagement equal to that of immersive artworks. Drawing on concepts from Ernest Adams, Staffan Björk and Jussi Holopainen, the chapter explores how games and digital worlds engage and immerse the viewer through sensory-motoric, cognitive, emotional and spatial elements. These digital spaces position the audience as both participants and co-creators, similar to immersive art forms dating from the 1950s and 1960s, where the shift from passive spectator to active participant, as seen in the works of Allan Kaprow and the Light and Space Movement, is mirrored in contemporary digital practices. This chapter examines how digital tools and new thinking enable unique opportunities for representation, display, engagement and interpretation, where the concept of an expanded narrative, incorporating active spaces and cross-disciplinary collaboration, is crucial for contemporary digital and new media practice. This chapter explores the intersection of art and VR gaming, using Half-Life: Alyx as a case study to demonstrate immersive engagement. Highlighting the impact of the COVID-19 pandemic, this chapter also discusses how cultural and heritage institutions have adopted technology to engage remote audiences. By linking research with practical examples, the chapter illustrates how VR and gaming push the boundaries of immersive experiences, creating novel opportunities for audience interaction and engagement.

This study aims to investigate the impact of artificial intelligence (AI) on customer loyalty in the luxury fashion market. It explores how AI-driven tools influence customer trust, satisfaction, commitment and engagement, which in turn affect loyalty. By examining these relationships, the study provides insights into the acceptance and effectiveness of AI technologies in enhancing customer loyalty within the luxury fashion sector.

This study employs structural equation modelling (SEM) to analyse data collected from 406 luxury consumers in Iran. The data was gathered using a targeted sampling procedure, leveraging DigiKala’s e-commerce platform. A comprehensive literature review informed the measurement items, and a seven-point Likert scale was used. The methodology includes confirmatory factor analysis (CFA) to assess the reliability and validity of the constructs, followed by hypothesis testing through SEM.

The study reveals that AI significantly enhances customer loyalty in the luxury fashion market by positively influencing trust, satisfaction, commitment and engagement. Satisfaction and engagement were found to be key mediators between AI and loyalty, while trust had no direct impact on loyalty. The results underscore the importance of AI-driven personalized experiences in fostering stronger customer relationships and loyalty.

This study is one of the first to explore the impact of AI on customer loyalty in the luxury fashion market, using a comprehensive model that includes trust, satisfaction, commitment and engagement as mediators. It extends the stimulus-organism-response (SOR) and technology acceptance model (TAM) frameworks, offering valuable insights for luxury brands on how AI can be leveraged to enhance customer relationships and loyalty.

Previous studies highlighted a dearth of scale to measure algorithm aversion among working professionals. Accordingly, this study tried to adopt and validate an algorithm aversion scale for employees.

The scale was developed and validated in three stages. In the first stage, a five-item xenophobic scale was adopted. Its items were rephrased to match the algorithm aversion-related workplace context. In the second stage, the sample was divided into two subsamples using random sampling. The exploratory factor analysis (EFA) applied on the first subsample. The confirmatory factor analysis (CFA) was conducted on the second sample to check the model’s fitness proposed by EFA. In the third stage, a sample of 378 respondents was collected. The Cronbach’s alpha (CA) and composite reliability (CR) values checked internal consistency reliability of the proposed algorithm aversion scale.

The EFA yielded a one-factor solution with acceptable factor loading (greater than 0.50) of all (five) statements. All values of model indices in CFA were in the acceptable range, which recommended the factorial structure of the scale. The acceptable (greater than 0.70) values of CA and CR concluded the internal consistency reliability. The convergent validity was suggested by appropriate average variance extracted (AVE) values (greater than 0.50). In addition, as expected, the proposed algorithm aversion scale reported a statistically significant positive correlation with neuroticism and a considerable negative relationship with trust. These findings concluded the criterion validity of the scale.

This study uses primary data to adopt and validate algorithm aversion scale.

Serving the public sector, emergency response organizations operate in uncertain environments. This study aims to understand the impact of effective coordination and collaboration of volunteers and career responders on response time performance considering the complexity of their task environments measured as population density. The operations strategy models designed for manufacturing and services in the private sector provide overarching theoretical insights to explore the role of organizational design characterized by the mix of volunteers and career responders in the public sector emergency services.

This study uses hierarchical regression analysis to substantiate the theoretical model using publicly available data extracted from the US National Fire Incident Reporting System. The data is analyzed using the US National Fire Protection Association’s standard to define fire department organizational types working in differing task environments categorized as rural, suburban and urban, using population density from the US Census Bureau.

This study upholds the framework of operations strategy model for public services and finds that fire department organizational design and task environment significantly influence operational performance measured by incident response time. While increasing the percentage of career firefighters can improve response time for volunteer fire departments, once a threshold is achieved, such an increase does not help to improve incident response time for combination and career fire departments.

This study takes a fresh look at public emergency services using operations strategy models and explores the operational impact of fire department organizational design using the mix of volunteer and career firefighters under varying environmental conditions. While the findings are specific to one public emergency service domain, they open avenues for future research to extend these models to other emergency service types and public services.