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To explore the project-specific information-seeking pathways of Chinese parents of children with autism spectrum disorder (ASD) and understand the information seeking in their daily lives.

This study utilized the interview, critical incident technique and information horizon mapping to gather data from 27 Chinese parents of children with ASD during project-specific information seeking. These data were then examined using content analysis and descriptive statistics methods.

First, the information-seeking pathways of parents of children with ASD show the significant importance of both interpersonal and online sources, characterized by the combined use of multiple types of information sources. Second, information-seeking pathways considerably vary across project contexts. For diagnosis and school selection, parents had fewer pathways and primarily relied on interpersonal sources. Meanwhile, they utilized interpersonal and online sources combined for selecting rehabilitation institutions and methods, with the most information-seeking pathways for rehabilitation methods.

This study presents novel insights into the information-seeking pathways of Chinese parents of children with ASD in seeking project-specific information. It provides valuable references for relevant governments and rehabilitation institutions in building information service platforms, strengthening information quality regulation and providing information resources for parents of children with ASD.

The purpose of this study is to examine the Google Scholar (GS) and Scopus citations profiles of library and information science (LIS) faculty members employed in central universities in India to determine their research online visibility.

The data was collected through manual searches conducted on GS and Scopus profiles by the end of August 2023, using the names of the faculty members along with their affiliations. The determination of the minimum sample size for each was calculated based on Cochran’s formula.

The study revealed that out of 104 LIS faculty members from 19 central universities, 78 (75.0%) faculty members have profiles on GS, while 61 (58.6%) of them are on Scopus. The study found that the faculty members have a substantial number of publications on GS, while their publication count on Scopus appears comparatively lower. The results suggest that certain faculty members have produced a modest number of publications but have received a substantial number of citations compared to their colleagues. Consequently, it can be inferred that there is no straightforward correlation between the volume of publications and citation metrics.

As the study exclusively focused on LIS faculty members working within central universities in India who have profiles on GS and Scopus, the researchers did not reach all LIS faculty members in India.

The significance of this research lies in its potential of insights into research productivity and its impact, which are crucial aspects of academia. The study provides valuable insights for individual researchers, LIS departments, institutes and universities in India and other countries to enhance their research performance and foster collaboration by establishing new research guidelines.

There have been no published research studies regarding the GS and Scopus citation metrics concerning LIS faculty members across all central universities in India.

The purpose of the paper is to explore the rapidly evolving landscape of artificial intelligence (AI) tools in academic research, highlighting their potential to transform various stages of the research process. AI tools are transforming academic research, offering numerous benefits and challenges.

Academic research is undergoing a significant transformation with the emergence of (AI) tools. These tools have the potential to revolutionize various aspects of research, from literature review to writing and proofreading. An overview of AI applications in literature review, data analysis, writing and proofreading, discussing their benefits and limitations is given. A comprehensive review of existing literature on AI applications in academic research was conducted, focusing on tools and platforms used in various stages of the research process. AI was used in some of the searches for AI applications in use.

The analysis reveals that AI tools can enhance research efficiency, accuracy and quality, but also raise important ethical and methodological considerations. AI tools have the potential to significantly enhance academic research, but their adoption requires careful consideration of methodological and ethical implications. The integration of AI tools also raises questions about authorship, accountability and the role of human researchers. The authors conclude by outlining future directions for AI integration in academic research and emphasizing the need for responsible adoption.

As AI continues to evolve, it is essential for researchers, institutions and policymakers to address the ethical and methodological implications of AI adoption, ensuring responsible integration and harnessing the full potential of AI tools to advance academic research. This is the contribution of the paper to knowledge.

Powder bed additive manufacturing has recently seen substantial growth, yet consistently producing high-quality parts remains challenging. Recoating streaking is a common anomaly that impairs print quality. Several data-driven models for automatically detecting this anomaly have been proposed, each with varying effectiveness. However, comprehensive comparisons among them are lacking. Additionally, these models are often tailored to specific data sets. This research addresses this gap by implementing and comparing these anomaly detection models for recoating streaking in a reproducible way. This study aims to offer a clearer, more objective evaluation of their performance, strengths and weaknesses. Furthermore, this study proposes an improvement to the Line Profiles detection model to broaden its applicability, and a novel preprocessing step was introduced to enhance the models’ performances.

All found anomaly detection models have been implemented along with several preprocessing steps. Additionally, a new universal benchmarking data set has been constructed. Finally, all implemented models have been evaluated on this benchmarking data set and the effect of the different preprocessing steps was studied.

This comparison shows that the improved Line Profiles model established it as the most efficient detection approach in this study’s benchmark data set. Furthermore, while most state-of-the-art neural networks perform very well off the shelf, this comparison shows that specialised detection models outperform all others with the correct preprocessing.

This comparison gives new insights into different recoater streaking (RCS) detection models, showcasing each one with its strengths and weaknesses. Furthermore, the improved Line Profiles model delivers compelling performance in detecting RCS.

Given the magnitude of the literature, a researcher must be selective of research papers and publications in general. In other words, only papers that meet strict standards of academic integrity and adhere to reliable and credible sources should be referenced. The purpose of this paper is to approach this issue from the prism of scientometrics according to the following research questions: Is it necessary to judge the quality of scientific production? How do we evaluate scientific production? What are the tools to be used in evaluation?

This paper presents a comparative study of scientometric evaluation practices and tools. A systematic literature review is conducted based on articles published in the field of scientometrics between 1951 and 2022. To analyze data, the authors performed three different aspects of analysis: usage analysis based on classification and comparison between the different scientific evaluation practices, type and level analysis based on classifying different scientometric indicators according to their types and application levels and similarity analysis based on studying the correlation between different quantitative metrics to identify similarity between them.

This comparative study leads to classify different scientific evaluation practices into externalist and internalist approaches. The authors categorized the different quantitative metrics according to their types (impact, production and composite indicators), their levels of application (micro, meso and macro) and their use (internalist and externalist). Moreover, the similarity analysis has revealed a high correlation between several scientometric indicators such as author h-index, author publications, citations and journal citations.

The interest in this study lies deeply in identifying the strengths and weaknesses of research groups and guides their actions. This evaluation contributes to the advancement of scientific research and to the motivation of researchers. Moreover, this paper can be applied as a complete in-depth guide to help new researchers select appropriate measurements to evaluate scientific production. The selection of evaluation measures is made according to their types, usage and levels of application. Furthermore, our analysis shows the similarity between the different indicators which can limit the overuse of similar measures.

The subject of this paper is the phenomenon of social media aesthetics, which can be perceived as a tool for promoting and building the image of libraries, especially in terms of merchandising. The aim of this paper is to analyse the potential of the dark academia social media trend in the promotion of academic libraries.

The article is based on a review of the social networking sites YouTube and Instagram and an analysis of network resources using the Brand24 tool.

Resources that are described by Internet users as “dark academia” are popular in social media. Dark academia as an aesthetic concept creates potential for the promotion of academic libraries, especially those that are more traditional in terms of their architecture, décor or how they offer their services.

The paper concerns a phenomenon which, although popular socially, has not yet been scientifically analysed in the literature on the subject. Since the topic is new and there is no scientific literature on it, the author had to base the paper on less standard sources of information (e.g. analysis of the content of social media). The article is a review, an introduction, as well as an invitation to further discussion. The author's aim is not to comprehensively cover this topic but only to draw attention to an interesting and rarely discussed issue that has great potential for practical activities.

The topic has great potential for the practical improvement of the promotional activities of libraries, especially older, more traditional libraries, to create a strong and positive image on the basis of characteristics often perceived as weaknesses.

Social media services are powerful social impact tools. Showing the potential role of social media aesthetics for cultural institutions could serve to make the public more aware of the role of the proper use of social media for promotion and image building.

The use of social media aesthetics is very rarely discussed in the subject literature.

The purpose of this paper is to provide an integrative picture of the state of the art of the literature on digitalisation of small and medium-sized enterprises (SMEs) as an enabler for their internationalisation process and as a comprehensive view of the specific domains impacted by digital technologies as well as their repercussions on the international outreach.

A systematic review which leverages a descriptive analysis of extant literature and an axial coding technique has been conducted to shed light on the current knowledge and to identify primary research areas and future research lines.

The research indicates that digitalisation impacts the internationalisation of SMEs in three specific domains: (1) internationalisation through the adoption of information and communication technologies (ICT) technologies and e-commerce platforms; (2) international expansion through the digitalisation of value chain activities and (3) international outreach through knowledge acquisition on digital platforms.

The value of this study is threefold. First, the authors attempt to systematically review the literature on SMEs digitalisation and internationalisation and provide a holistic perspective on the intertwining of these two research streams. Second, the authors propose a novel conceptualisation on the dimensions of SMEs digitalisation as enablers to internationalisation. Third, the authors put forward promising future lines of research.

1. Digitalisation represents a pivotal strategy that allows companies to build new strategic capabilities and is a propeller for SMEs internationalisation.

2. Through e-commerce, SMEs could compete at the same level of multinational companies but enduring lower costs of expansion.

3. Digital platforms allow SMEs to enhance the learning processes about international markets through an immediate access to relevant information.

4. Digital entrepreneurship has enabled SMEs to develop new configurations of value chain activities, evolving their business model or reaching new markets.

5. SMEs are changing the “business as usual” paradigm offering digital tools to build modular architectures that are scalable and agile in their evolution ability.

Digitalisation represents a pivotal strategy that allows companies to build new strategic capabilities and is a propeller for SMEs internationalisation.

Through e-commerce, SMEs could compete at the same level of multinational companies but enduring lower costs of expansion.

Digital platforms allow SMEs to enhance the learning processes about international markets through an immediate access to relevant information.

Digital entrepreneurship has enabled SMEs to develop new configurations of value chain activities, evolving their business model or reaching new markets.

SMEs are changing the “business as usual” paradigm offering digital tools to build modular architectures that are scalable and agile in their evolution ability.

This study addresses how algorithmic recommendations and their affordances shape everyday information practices among young people.

Thematic interviews were conducted with 20 Finnish young people aged 15–16 years. The material was analysed using qualitative content analysis, with a focus on everyday information practices involving online platforms.

The key finding of the study is that the current affordances of algorithmic recommendations enable users to engage in more passive practices instead of active search and evaluation practices. Two major themes emerged from the analysis: enabling not searching, inviting high trust, which highlights the how the affordances of algorithmic recommendations enable the delegation of search to a recommender system and, at the same time, invite trust in the system, and constraining finding, discouraging diversity, which focuses on the constraining degree of affordances and breakdowns associated with algorithmic recommendations.

This study contributes new knowledge regarding the ways in which algorithmic recommendations shape the information practices in young people's everyday lives specifically addressing the constraining nature of affordances.

The purpose of this study is to conduct a comprehensive systematic literature review using bibliometric approach to investigate the academic structure of World Trade Organization Dispute Settlement research.

The study examines the bibliographic information for 1,858 articles from Scopus and the Australian Business Deans Council-indexed journals published between 1995 and 2024 using Dimensions.ai and Google Scholar search engines. Exploratory-cum-descriptive research design with bibliometric approach is used to answer the stated literature review research questions.

The data shows a gradual decline in WTO-Dispute Settlement System (WTO-DSS) research relative to the total international business area in the three decades. Developed countries appear as key contributors to the research, with the USA and the UK standing out as the most productive and influential research countries. The study shows a significant change in the focus of this research corpus from legalized to non-legalized approaches, with a greater emphasis on transparency and environmental sustainability. The research identifies global politics and international trade law as influential subjects in the discipline.

To the best of the authors’ knowledge, the study is a first of its kind where bibliometric approach has been used to study the evolution of WTO-DSS literature. The study adds to the understanding of WTO Dispute Settlement research patterns and recommends future research options.

This bibliometric study aims to analyze publication trends, active countries, collaborations, influential citations and thematic evolution in learning analytics (LA) research focused on higher education (HE) during and after the COVID-19 lockdown period.

From the Scopus database, this bibliometric analysis extracts and evaluates 609 scholarly articles on LA in HE starting in 2019. The multidimensional process identifies the scope impacts, advancing the understanding of LA in HE. An analysis of co-citation data uncovers the key influences that have shaped the literature. This study uses the stimulus-organism-response (SOR) theory to suggest future research directions and organizational adaptations to new LA technologies and learner responses to LA-enabled personalized interventions.

Learning analytics are becoming important in the HE environment during and after the COVID-19 lockout. Institutions have used LA to collect socio-technical data from digital platforms, giving them important insights into learning processes and systems. The data gathered through LA has assisted in identifying areas for development, opening the path for improved student success and academic performance evaluation and helping students transition to the workforce.

The study’s concentration on the post-COVID-19 timeframe may lead to paying attention to potential pandemic developments. Nonetheless, the findings provide a thorough picture of LA’s contributions to HE and valuable ideas for future study initiatives. Future research with the SOR framework suggests areas for additional study to maximize LA’s potential in diverse HE situations.

This study adds to the growing corpus of knowledge on learning analytics in HE, especially in light of the COVID-19 lockdown and its aftermath. By using bibliometric analysis, the study provides a complete and evidence-based understanding of how LA has been used to address challenges related to HE. This study uses bibliometric analysis and SOR theory to appraise and map HE learning analytics research. The selected study themes can help scholars, educators and institutions shape their future efforts to improve teaching, learning and support mechanisms through learning analytics.