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This study aims to investigate the role of information normalization in online healthcare consultation, a typical complex human-to-human communication requiring both effectiveness and efficiency. The globalization and digitization trend calls for high-quality information, and normalization is considered an effective method for improving information quality. Meanwhile, some researchers argued that excessive normalization (standardized answers) may be perceived as impersonal, repetitive, and cold. Thus, it is not appreciated for human-to-human communication, for instance, when patients are anxious about their health condition (e.g. with high-risk disease) in online healthcare consultation. Therefore, the role of information normalization in human communication is worthy to be explored.

Data were collected from one of the largest online healthcare consultation platforms (Dxy.com). This study expanded the existing information quality model by introducing information normalization as a new dimension. Information normalization was assessed using medical templates, extracted through natural language processing methods such as Bidirectional Encoder Representations from Transformers (BERT) and Latent Dirichlet Allocation (LDA). Patient decision-making behaviors, namely, consultant selection and satisfaction, were chosen to evaluate communication performance.

The results confirmed the positive impact of information normalization on communication performance. Additionally, a negative moderating effect of disease risk on the relationship between information normalization and patient decision-making was identified. Furthermore, the study demonstrated that information normalization can be enhanced through experiential learning.

These findings highlighted the significance of information normalization in online healthcare communication and extended the existing information quality model. It also facilitated patient decision-making on online healthcare platforms by providing a comprehensive information quality measurement. In addition, the moderating effects indicated the contradiction between informational support and emotional support, enriching the social support theory.

This paper reports on a workshop discussing the views of the augmentative and alternative communication (AAC) community on the opportunities and risks posed by the integration of artificial intelligence (AI) into voice output communication aid systems. The views of the community on whether a Code of Practice was needed for the use of this new technology were also sought.

This was an explorative, qualitative study in which members of the AAC community attending a session at a UK national conference were invited to discuss the topic, responding to structured questions from the research team. The use of AI for both novel language generation and rate enhancement was discussed within the session.

Many potential opportunities and benefits of AI to AAC users were discussed by the group. Risks associated with new and existing biases in AI language models were raised, as was the need to ensure that outputs generated by AI were authentically authored by users. Whilst there was broad support for the idea of a Code of Practice, questions were posed about how it would be designed and what it should contain.

This study presents a unique insight into the views of the AAC community on the benefits and risks of incorporating AI into AAC systems. The views of the community on the need for a Code of Practice may support how the field moves forward with this complex technology.

China’s healthcare system is being burdened by the increasing prevalence of chronic diseases. Therefore, this study investigated the health service requirements of mobile health applications (mHealth apps) users in Hangzhou, China. This study aimed to propose suggestions and theoretical references to improve mHealth apps and promote their development, thereby meeting public medical and health needs and creating an efficient medical service system.

We constructed a model of health service demands using a literature review, network survey, and semi-structured interviews. We analyzed the demand attributes using the Kano model and Better-Worse index and obtained the priority ranking of demands.

The results revealed 25 demand elements in four dimensions: must-be (M), one-dimensional (O), attractive (A), and indifferent (I) requirements. The findings suggest that mHealth app developers can optimize health services by categorizing and managing health services, focusing on middle-aged users, enhancing the professionalism of health service providers, and improving the feedback mechanism.

Studies on mHealth apps user demands, particularly on health service needs, remain scarce. This study employed a mixed-methods approach, integrating both qualitative and quantitative research techniques, to establish a priority ranking of user health service needs for mHealth apps. The study offers recommendations and theoretical references to optimize and improve mHealth app services.

1. Construct a better health service requirements model for mHealth app users.

2. Obtain the prioritization of demand elements in the model.

3. Propose some management suggestions to improve mHealth apps.

Construct a better health service requirements model for mHealth app users.

Obtain the prioritization of demand elements in the model.

Propose some management suggestions to improve mHealth apps.

Since the withdrawal of IFRIC 3 in 2005, there has been a regulatory freeze in accounting for emission rights that contrasts with the international momentum of climate-related financial disclosures. This paper explores how different narratives and institutional dynamics explain the failure to produce guidance on accounting for emission rights.

This paper mobilises the notion of field-configuring events to examine a sequence of six events between 2003 and 2016, including four public consultations and two dialogues between standard setters. The paper presents a qualitative analysis of documents produced in this space that investigates how different practices and narratives configured the field's positions, agenda, and meaning systems.

Accounting for emission rights was gradually decoupled from climate change and carbon markets, relegated to the research pipeline, and forgotten. The obstacles that the IASB and EFRAG found in presenting themselves as central in the recurring events, the excess of representations, and the increasingly technical and abstract debates eroded the 2003 momentum for regulation, making the different initiatives to revitalise the project vulnerable and open to scrutiny. Lukes (2021) refers to nondecision-making to express that some issues are suffocated before they are expressed.

The regulation of accounting for emission rights, an area that has received scant attention in the literature, provides some insights into the different narrative mechanisms that, materialising in specific times and spaces, draw regulatory attention to particular accounting issues, which are problematised and, eventually, forgotten. This study also illustrates that identifying interests is problematic as actors shift from alternative positions over a long period. The case examined also raises some doubts about the previous effectiveness of international standard setters in dealing with matters of connectivity between the environment and finance, as is the case for accounting for emissions rights.

This study aims to adopt a systematic review approach to examine the existing literature on law and LLMs.It involves analyzing and synthesizing relevant research papers, reports and scholarly articles that discuss the use of LLMs in the legal domain. The review encompasses various aspects, including an analysis of LLMs, legal natural language processing (NLP), model tuning techniques, data processing strategies and frameworks for addressing the challenges associated with legal question-and-answer (Q&A) systems. Additionally, the study explores potential applications and services that can benefit from the integration of LLMs in the field of intelligent justice.

This paper surveys the state-of-the-art research on law LLMs and their application in the field of intelligent justice. The study aims to identify the challenges associated with developing Q&A systems based on LLMs and explores potential directions for future research and development. The ultimate goal is to contribute to the advancement of intelligent justice by effectively leveraging LLMs.

To effectively apply a law LLM, systematic research on LLM, legal NLP and model adjustment technology is required.

This study contributes to the field of intelligent justice by providing a comprehensive review of the current state of research on law LLMs.

Technology-enabled healthcare focuses on providing better information flow and coordination in healthcare operations. Technology-enabled health services enable hospitals to manage their resources effectively, maintain continuous patient engagement and provide seamless services without compromising their perceived quality.

This study investigates the role of technology-enabled health services in improving perceived healthcare quality among patients. Data are collected from the users (n = 418) of health platforms offered in multi-specialty hospitals. Multiple learners are employed to accurately represent the users' perceived quality regarding the perceived usefulness of the features provided via these digital health platforms.

The best-fitted model using a decision tree classifier (accuracy = 0.86) derives the accurate significance of features offered in the digital health platform in fostering perceived healthcare quality. Diet and lifestyle recommendations (30%) and chatting with health professionals (11%) are the top features offered in digital health platforms that primarily influence the perceived quality of healthcare among users.

The predictability of perceived quality with the individual features existing in the digital health platform, the significance of the features on the perceived healthcare quality and the prediction rules showing the combined effect of features on healthcare quality can help healthcare managers accelerate digital transformation in hospitals by improving their digital health platform, designing and offering new health packages while strengthening their e-infrastructure.

The study represents perceived healthcare quality with the features offered in digital health platforms using machine learners based on users' post-pandemic experience. By advancing digital platforms with more patient-centric features using emerging technologies, this model can further foresee its impact on the perceived quality of healthcare, offering valuable directions to healthcare service providers. The study is limited to focusing on digital health platforms that can deal with people's general healthcare needs.

This exploratory study illustrated an integrated academic library supply chain (IALSC) model to design the strategic planning management tool of the academic library. The supply chain (SC) model has been widely used in manufacturing industries and has also been applied in many service industries with the same objectives. However, very few studies for academic libraries, particularly the implementation of the integrated SC model, are being executed, although it has been proven that SC management in practice can enhance stakeholder satisfaction, increase revenues and decrease total costs. The academic library also needs to be successful in providing quality products, services and information to fulfil the library users’ needs within the library budget. This research aims to develop a verified model of the integrated SC for the academic library.

This research used both qualitative and quantitative approaches to achieve its objectives. The proposed conceptual SC model, named as IALSC, for the academic library has been developed using the system thinking method; eventually, it has been validated through the fuzzy Delphi method, an expert judgement technique.

The research findings could contribute to academic library management in planning and formulating a roadmap for the library to increase its quality services for all stakeholders.

The conceptual model would have a high potential to be proposed as the strategic decision-making tool for an academic library, i.e. the flow of funds through the operations of the library, the library stakeholders’ satisfaction measurement, the decision process currently made by the library management team on the purchase of new library resources, the library resource suppliers, etc.

This research proposes to organise and distil this massive amount of data, making it easier to understand. Using data mining, machine learning techniques and visual approaches, researchers and managers can extract valuable insights (on guests' preferences) and convert them into strategic thinking based on exploration and predictive analysis. Consequently, this research aims to assist hotel managers in making informed decisions, thus improving the overall guest experience and increasing competitiveness.

This research employs natural language processing techniques, data visualisation proposals and machine learning methodologies to analyse unstructured guest service experience content. In particular, this research (1) applies data mining to evaluate the role and significance of critical terms and semantic structures in hotel assessments; (2) identifies salient tokens to depict guests' narratives based on term frequency and the information quantity they convey; and (3) tackles the challenge of managing extensive document repositories through automated identification of latent topics in reviews by using machine learning methods for semantic grouping and pattern visualisation.

This study’s findings (1) aim to identify critical features and topics that guests highlight during their hotel stays, (2) visually explore the relationships between these features and differences among diverse types of travellers through online hotel reviews and (3) determine predictive power. Their implications are crucial for the hospitality domain, as they provide real-time insights into guests' perceptions and business performance and are essential for making informed decisions and staying competitive.

This research seeks to minimise the cognitive processing costs of the enormous amount of content published by the user through a better organisation of hotel service reviews and their visualisation. Likewise, this research aims to propose a methodology and method available to tourism organisations to obtain truly useable knowledge in the design of the hotel offer and its value propositions.

Globally, contemporary school learning environments are being transformed to incorporate innovative designs and pedagogies, digital technology and new ergonomic furniture – often ‘un-classroom-like’ in their form but can more readily accommodate the needs of 21st century learners. In this concluding chapter, key strategies, themes and ideas presented by contributing authors are presented. Linking the discussion of the book to the changing context of education and design, this chapter provides insights and reflections for supporting educators and learners to more effectively use the learning environments as a pedagogical and learning tool.

The purpose of this paper is to examine patient engagement in remote consultation services, an increasingly important issue facing Healthcare Operations Management (HOM) given the significant expansion in this and other forms of telehealth worldwide over the last decade. We use our analysis of the literature to develop a comprehensive framework that incorporates the patient journey, multidimensionality, antecedents and consequences, interventions and improvement options, as well as the cyclic nature of patient engagement. We also propose measures suitable for empirical assessment of different aspects of our framework.

We undertook a comprehensive review of the extant literature using a systematic review approach. We identified and analysed 63 articles published in peer-reviewed scientific journals between 2003 and 2022.

We conceptualise patient engagement with remote consultation across three key aspects: dimensions, process, and the antecedents and consequences of engagement. We identify nine contextual categories that influence such engagement. We propose several possible metrics for measuring patient engagement during three stages (before service, at/during service and after service) of remote consultation, as well as interventions and possible options for improving patient engagement therein.

The primary contribution of our research is the development of a comprehensive framework for patient engagement in remote consultation that draws on insights from literature in several disciplines. In addition, we have linked the three dimensions of engagement with the clinical process to create a structure for future engagement assessment. Furthermore, we have identified impact factors and outcomes of engagement in remote consultation by understanding which can help to improve levels of adoption, application and satisfaction, and reduce healthcare inequality. Finally, we have adopted a “cyclic” perspective and identified potential interventions that can be combined to further improve patient engagement in remote consultation.