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This study aims to investigate the performance analysis, science mapping and future direction of artificial intelligence (AI) technology, applications, tools and software used to preserve, curate and predict the historical value of cultural heritage.

This study uses the bibliometric research method and utilizes the Scopus database to gather data. The keywords used are “artificial intelligence” and “cultural heritage,” resulting in 718 data sets spanning from 2001 to 2023. The data is restricted to the years 2001−2023, is in English language and encompasses all types of documents, including conference papers, articles, book chapters, lecture notes, reviews and editorials.

The performance analysis of research on the use of AI to aid in the preservation of cultural heritage has been ongoing since 2001, and research in this area continues to grow. The countries contributing to this research include Italy, China, Greece, Spain and the UK, with Italy being the most prolific in terms of authored works. The research primarily falls under the disciplines of computer science, mathematics, engineering, social sciences and arts and humanities, respectively. Document types mainly consist of articles and proceedings. In the science mapping process, five clusters have been identified. These clusters are labeled according to the contributions of AI tools, software, apps and technology to cultural heritage preservation. The clusters include “conservation assessment,” “exhibition and visualization,” “software solutions,” “virtual exhibition” and “metadata and database.” The future direction of research lies in extended reality, which integrates virtual reality (VR), augmented reality (AR) and mixed reality (MR); virtual restoration and preservation; 3D printing; as well as the utilization of robotics, drones and the Internet of Things (IoT) for mapping, conserving and monitoring historical sites and cultural heritage sites.

The cultural heritage institution can use this result as a source to develop AI-based strategic planning for curating, preservation, preventing and presenting cultural heritages. Researchers and academicians will get insight and deeper understanding on the research trend and use the interdisciplinary of AI and cultural heritage for expanding collaboration.

This study will help to reveal the trend and evolution of AI and cultural heritage. The finding also will fill the knowledge gap on the research on AI and cultural heritage.

Some similar bibliometric studies have been conducted; however, there are still limited studies on contribution of AI to preserve cultural heritage in wider view. The value of this study is the cluster in which AI is used to preserve, curate, present and assess cultural heritages.

This study aims to explore Augmented Language Models (ALMs) for synthetic data generation in services marketing and research. It evaluates ALMs' potential in mirroring human responses and behaviors in service scenarios through comparative analysis with five empirical studies.

The study uses ALM-based agents to conduct a comparative analysis, leveraging SurveyLM (Bickley et al., 2023) to generate synthetic responses to the scenario-based experiment in Söderlund and Oikarinen (2018) and four more recent studies from the Journal of Services Marketing. The main focus was to assess the alignment of ALM responses with original study manipulations and hypotheses.

Overall, our comparative analysis reveals both strengths and limitations of using synthetic agents to mimic human-based participants in services research. Specifically, the model struggled with scenarios requiring high levels of visual context, such as those involving images or physical settings, as in the Dootson et al. (2023) and Srivastava et al. (2022) studies. Conversely, studies like Tariq et al. (2023) showed better alignment, highlighting the model's effectiveness in more textually driven scenarios.

To the best of the authors’ knowledge, this research is among the first to systematically use ALMs in services marketing, providing new methods and insights for using synthetic data in service research. It underscores the challenges and potential of interpreting ALM versus human responses, marking a significant step in exploring AI capabilities in empirical research.

Healthcare providers are under pressure due to increasing and more complex demands for services. Increased pressure on budgets and human resources adds to an ever-growing problem set. Competent leaders are in demand to ensure effective and well-performing healthcare organisations that deliver balanced results and high-quality services. Researchers have made significant efforts to identify and define determining competencies for healthcare leadership. Broad terms such as competence are, however, inherently at risk of becoming too generic to add analytical value. The purpose of this study is to suggest a holistic framework for understanding healthcare leadership competence, that can be crucial for operationalising important healthcare leadership competencies for researchers, decision-makers as well as practitioners.

In the present study, a critical interpretive synthesis (CIS) was conducted to analyse competency descriptions for healthcare leaders. The descriptions were retrieved from peer reviewed empirical studies published between 2010 and 2022 that aimed to identify healthcare services leadership competencies. Grounded theory was utilised to code the data and inductively develop new categories of healthcare leadership competencies. The categorisation was then analysed to suggest a holistic framework for healthcare leadership competence.

Forty-one papers were included in the review. Coding and analysing the competence descriptions resulted in 12 healthcare leadership competence categories: (1) character, (2) interpersonal relations, (3) leadership, (4) professionalism, (5) soft HRM, (6) management, (7) organisational knowledge, (8) technology, (9) knowledge of the healthcare environment, (10) change and innovation, (11) knowledge transformation and (12) boundary spanning. Based on this result, a holistic framework for understanding and analysing healthcare services leadership competencies was suggested. This framework suggests that the 12 categories of healthcare leadership competencies include a range of knowledge, skills and abilities that can be understood across the dimension personal – and technical, and organisational internal and – external competencies.

This literature review was conducted with the results of searching only two electronic databases. Because of this, there is a chance that there exist empirical studies that could have added to the development of the competence categories or could have contradicted some of the descriptions used in this analysis that were assessed as quite harmonised. A CIS also opens for a broader search, including the grey literature, books, policy documents and so on, but this study was limited to peer-reviewed empirical studies. This limitation could also have affected the result, as complex phenomenon such as competence might have been disclosed in greater details in, for example, books.

The holistic framework for healthcare leadership competences offers a common understanding of a “fuzzy” concept such as competence and can be used to identify specific competency needs in healthcare organisations, to develop strategic competency plans and educational programmes for healthcare leaders.

This study reveals a lack of consensus regarding the use and understanding of the concept of competence, and that key competencies addressed in the included papers are described vastly different in terms of what knowledge, skills and abilities they entail. This challenges the operationalisation of healthcare services leadership competencies. The proposed framework for healthcare services leadership competencies offers a common understanding of work-related competencies and a possibility to analyse key leadership competencies based on a holistic framework.

This paper focuses on image-to-text manuscript processing through Handwritten Text Recognition (HTR), a Machine Learning (ML) approach enabled by Artificial Intelligence (AI). With HTR now achieving high levels of accuracy, we consider its potential impact on our near-future information environment and knowledge of the past.

In undertaking a more constructivist analysis, we identified gaps in the current literature through a Grounded Theory Method (GTM). This guided an iterative process of concept mapping through writing sprints in workshop settings. We identified, explored and confirmed themes through group discussion and a further interrogation of relevant literature, until reaching saturation.

Catalogued as part of our GTM, 120 published texts underpin this paper. We found that HTR facilitates accurate transcription and dataset cleaning, while facilitating access to a variety of historical material. HTR contributes to a virtuous cycle of dataset production and can inform the development of online cataloguing. However, current limitations include dependency on digitisation pipelines, potential archival history omission and entrenchment of bias. We also cite near-future HTR considerations. These include encouraging open access, integrating advanced AI processes and metadata extraction; legal and moral issues surrounding copyright and data ethics; crediting individuals’ transcription contributions and HTR’s environmental costs.

Our research produces a set of best practice recommendations for researchers, data providers and memory institutions, surrounding HTR use. This forms an initial, though not comprehensive, blueprint for directing future HTR research. In pursuing this, the narrative that HTR’s speed and efficiency will simply transform scholarship in archives is deconstructed.

Social media platforms that disseminate scientific information to the public during the COVID-19 pandemic highlighted the importance of the topic of scientific communication. Content creators in the field, as well as researchers who study the impact of scientific information online, are interested in how people react to these information resources and how they judge them. This study aims to devise a framework for extracting large social media datasets and find specific feedback to content delivery, enabling scientific content creators to gain insights into how the public perceives scientific information.

To collect public reactions to scientific information, the study focused on Twitter users who are doctors, researchers, science communicators or representatives of research institutes, and processed their replies for two years from the start of the pandemic. The study aimed in developing a solution powered by topic modeling enhanced by manual validation and other machine learning techniques, such as word embeddings, that is capable of filtering massive social media datasets in search of documents related to reactions to scientific communication. The architecture developed in this paper can be replicated for finding any documents related to niche topics in social media data. As a final step of our framework, we also fine-tuned a large language model to be able to perform the classification task with even more accuracy, forgoing the need of more human validation after the first step.

We provided a framework capable of receiving a large document dataset, and, with the help of with a small degree of human validation at different stages, is able to filter out documents within the corpus that are relevant to a very underrepresented niche theme inside the database, with much higher precision than traditional state-of-the-art machine learning algorithms. Performance was improved even further by the fine-tuning of a large language model based on BERT, which would allow for the use of such model to classify even larger unseen datasets in search of reactions to scientific communication without the need for further manual validation or topic modeling.

The challenges of scientific communication are even higher with the rampant increase of misinformation in social media, and the difficulty of competing in a saturated attention economy of the social media landscape. Our study aimed at creating a solution that could be used by scientific content creators to better locate and understand constructive feedback toward their content and how it is received, which can be hidden as a minor subject between hundreds of thousands of comments. By leveraging an ensemble of techniques ranging from heuristics to state-of-the-art machine learning algorithms, we created a framework that is able to detect texts related to very niche subjects in very large datasets, with just a small amount of examples of texts related to the subject being given as input.

With this tool, scientific content creators can sift through their social media following and quickly understand how to adapt their content to their current user’s needs and standards of content consumption.

This study aimed to find reactions to scientific communication in social media. We applied three methods with human intervention and compared their performance. This study shows for the first time, the topics of interest which were discussed in Brazil during the COVID-19 pandemic.

In a world with over 8 billion people, ensuring sustainable food sources is paramount. This chapter explores the pivotal role of aquaculture in addressing the challenges of marine conservation and sustainable resource use. Aligned with the United Nations’ Sustainable Development Goal 14, aquaculture emerges as a solution to relieve pressure on wild fish stocks and enhance food security. The chapter emphasises the rapid growth of this sector and underscores the importance of international cooperation and policies like the Global Ocean Treaty in ensuring marine biodiversity. While acknowledging the potential of aquaculture, the chapter delves into environmental concerns surrounding fishmeal and fish oil in feed. It advocates for innovative technologies and ingredients to establish a circular bioeconomy. The significance of higher education in advancing sustainable aquafeed technology, breeding, and genetics is highlighted, with a discussion on milestones achieved by experts like Dr John E. Halver and Professor Simon J. Davies. Examining technological advances, the chapter explores molecular genetics, transgenics, and gene editing, particularly CRISPR biosciences, as transformative tools for enhancing aquaculture productivity and sustainability. Environmental impacts are addressed, proposing solutions such as Recirculating Aquaculture Systems (RAS) and Multitrophic Aquaculture Systems (MTA) to minimise ecological footprints. Throughout, there is a strong emphasis on the integral role of research and education in fostering sustainable aquaculture practices. The chapter advocates for specialised courses and programs in higher education to prepare the next generation for the challenges and opportunities in aquaculture, ensuring its contribution to global food security and environmental stewardship.

Netflix's short-lived Gothic sci-fi psychological thriller 1899 tells of the discovery of a ghost ship by a steam-liner bound for America. All aboard are tested by unexplained deaths, visions of past traumas and impossible events. 1899 exemplifies what Mittell calls ‘drillable media’ (2009), contemporary elaborate and inventive serial TV narratives which encourage the viewer into active reception, rewarding forensic deconstruction and analysis.

The creators sprinkle references to Classical mythology and philosophy throughout 1899, inviting literate, active consumers to use them in forensic reception. Featuring an ensemble cast of 20 principal characters, 1899 offers a variety of ‘masculine’ representations, and all can be described as Gothic. These characters are understood and enhanced through consideration of the allusions to Classical mythology in the text. These echoes enhance and shape the Gothic masculine representations in 1899. Furthermore, the female protagonist is imbued with counter-typical hegemonic gender traits and her connections to mythic and literary characters encourage a questioning of her status as hero.

With reference to Classical reception and Gothic studies, textual analysis and online analytical discourses posted by fans of 1899, this chapter explores the interplay between Gothic male representations, ‘masculine’ traits and allusions to mythology, employing both to create a television series which is both timeless and contemporary.

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.

The 21st century is marked by significant transformative shifts and pivotal global events that have exerted a profound impact on the global economy. These changes in market perceptions have served as the bedrock for shaping marketing actions and formulating strategic processes. Since the onset of this century, the landscape of marketing has experienced an unceasing evolution, compelling enterprises to engage in competitive endeavours, vying to introduce cutting-edge strategies and customer acquisition methodologies.

The fundamental objective of the conducted research was to unearth pioneering solutions within the domain of online marketing and discern the opportunities that these innovations confer within the framework of attaining organizational objectives. In pursuit of this objective, an exhaustive inquiry was undertaken through in-depth interviews conducted with three small-size enterprises. The findings underscored the critical role that innovations on the Instagram platform play in charting a course towards the accomplishment of business objectives for these companies.

Given the dynamic alterations brought about by the COVID-19 pandemic, which include heightened consumer consciousness and the expeditious advancement of digitalisation, this subject assumes paramount importance. Empirical findings derived from the research unequivocally substantiate that the incorporation of marketing innovations on the Instagram platform stands as a pivotal facet of companies' overarching marketing strategies. This integrative approach translates to augmented sales outcomes, heightened viewership, cultivation of brand image and an enhanced competitive stance.