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This study explores previous research related to textile management technologies, detecting gaps and opportunities for textiles made by Ainu people. We also propose an approach to digitally read Ainu textile patterns.

We employed indigenous and pluriversal design frameworks to evaluate textile pattern technologies. They were operationalised as Indigenous/local researchers involved in the investigation, multi-sensoriality of analysed items, prosperity for everyone involved, communal property of data, biological awareness, social complexity awareness and sensitivity of the analysed method if the items change.

Textile pattern technology researchers have mostly used neural networks and clustering methods. They have addressed social aspects since the 2000s. Investigations into the generation of textiles based in Poland constantly reflected the most pluriversal characteristics. Regarding Ainu textile research, most investigations have cited Indigenous sources. Two gaps emerged: the concentration of research datasets and results in enterprises or scholars and the focus on the formal characteristics of Ainu patterns in technical papers and contextual characteristics in ethnographic papers.

Heritage management is increasingly employing technological tools that should consider the sustainability of handmade/artisanal goods. As most investigations on textile patterns are conducted by the industry, their benefits are limited for heritage conservation.

Therefore, we suggest digital experts work together with ethnography and Indigenous experts, proposing a method for digital reading of Ainu textile patterns that incorporates pluriversal aspects into heritage conservation.

Pluriversal design is a set of onthologies proposed with Indigenous, mestizo and minorities from the Americas that is currently being diffused in the rest of the world, highly compatible with the analysis of Indigenous heritage.

An understanding of the impact of construction workers informal safety communication (CWISC), a form of parallel safety communication between workers, on safety performance among construction workers is crucial in order to develop effective strategies for improving safety performance in the construction industry. However, research remains scant on the impact of CWISC on safety performance. This study empirically aims to test the relationship between these important constructs.

Statistical analysis was used to examine the relationship in a hypothetical model with two latent variables; the exogenous variables represented by two groups of informal safety communication: friends and crew members and the endogenous variables represented by two groups of Safety performance metrics: safety compliance and safety participation, was tested.

The emergent findings revealed that there is a significant relationship between informal safety communication among crew members and safety compliance, and also between informal safety communication among friends on construction sites and safety participation. These findings emphasize the importance of fostering effective safety communication and collaboration within construction crews, as well as recognizing the influence friendships on safety performance. Stakeholders can leverage on these findings to implement policies to improve safety performance.

The study presents insightful practical knowledge on how CWISC impacts safety performance on construction sites. Practical recommendations for organizations are also proposed, e.g., development of team-building activities, platforms for sharing safety-related information and experiences, mentorship programs and initiatives that encourage social interaction among workers.

The purpose of this study to identify and optimize the machining of polyvinyl butyral (PVB) material for industrial uses. The research is based on input machining parameters of rotary ultrasonic machining for better understand the output response surface roughness (SR) property of polyvinyl butyral (PVB) by using the Taguchi approach. The grey relational grade analysis (GRG) is also implemented to resolve the complex interrelationship of SR data for optimization and predicting and validate the results.

In experimental work, the input parameters, namely, concentration, abrasives, power rate, grit size, tool material and hydrofluoric (HF) acid has been selected. The experiment’s design was created using MINITAB Software; the L27 orthogonal array was selected for the experimentation. SR was examined with the GRG technique for process optimization. On the other hand, for single parameter optimization analysis of variance (ANOVA) has been used.

ANOVA optimization technique gives the best result on concentration (40%) of abrasive (Al2O3+SiC+B4C), power rate (40%), grit size (600), HF acid (1.5%) and tool material (D2 alloy) are the optimal parameters to provide the slightest degree of SR. GRG optimization of multi-response parameter setting: 40% concentration, SiC+B4C mixed abrasive slurry, 40% of power rating, 280 grit size, 0.5% HF acid and high-speed tool steel tool material gives better results. The SR of PVB glass material improved by 20% after grey relational analysis.

There are several practical applications in a variety of material processing sectors, including metallurgy, machinery, electronics and transportation. These real-world applications have produced substantial and discernible economic benefits.

The analytical and optimization results will be used in the various material processing sectors, including metallurgy, machinery, electronics and transportation.

The ANOVA and grey theory approaches offer the reader a primary picture of the machining research and process parameter optimization. Combined abrasive slurry of Al₂O3+SiC+B₄C with a high power-rating exhibits lower SR. Similarly, grit size is vital; larger grits produce better SR. Ra – 0. 611 m is the lowest SR value at the hole found in trial 25 after the experimentation.

The study aims to analyze the synergy of artificial intelligence (AI), with scientometrics, webometrics and bibliometrics to unlock and to emphasize the potential of the applications and benefits of AI algorithms in these fields.

By conducting a systematic literature review, our aim is to explore the potential of AI in revolutionizing the methods used to measure and analyze scholarly communication, identify emerging research trends and evaluate the impact of scientific publications. To achieve this, we implemented a comprehensive search strategy across reputable databases such as ProQuest, IEEE Explore, EBSCO, Web of Science and Scopus. Our search encompassed articles published from January 1, 2000, to September 2022, resulting in a thorough review of 61 relevant articles.

(1) Regarding scientometrics, the application of AI yields various distinct advantages, such as conducting analyses of publications, citations, research impact prediction, collaboration, research trend analysis and knowledge mapping, in a more objective and reliable framework. (2) In terms of webometrics, AI algorithms are able to enhance web crawling and data collection, web link analysis, web content analysis, social media analysis, web impact analysis and recommender systems. (3) Moreover, automation of data collection, analysis of citations, disambiguation of authors, analysis of co-authorship networks, assessment of research impact, text mining and recommender systems are considered as the potential of AI integration in the field of bibliometrics.

This study covers the particularly new benefits and potential of AI-enhanced scientometrics, webometrics and bibliometrics to highlight the significant prospects of the synergy of this integration through AI.

This study explores how corporate social responsibility (CSR) and artificial intelligence (AI) can be combined in the healthcare industry during the post-COVID-19 recovery phase. The aim is to showcase how this fusion can help tackle healthcare inequalities, enhance accessibility and support long-term sustainability.

Adopting a viewpoint approach, the study leverages existing literature and case studies to analyze the intersection of CSR and AI. It investigates AI’s capabilities in predictive analytics, telemedicine and resource management within the framework of CSR principles.

Integrating AI and CSR can profoundly enhance healthcare delivery by ensuring equitable access, optimizing resource allocation and fostering trust through transparency and ethical standards. This synergy benefits public health and enhances the corporate image and long-term viability of healthcare organizations.

The study is conceptual and relies on existing literature and case studies. Future research should empirically test the proposed models and frameworks in diverse healthcare settings to validate and refine these insights.

The insights from this study can be directly applied by healthcare organizations to develop policies and practices that integrate AI and CSR. This integration can promote ethical standards, enhance operational efficiency and, most importantly, improve patient outcomes.

Integrating AI and CSR in the healthcare sector carries consequences. It plays a role in promoting fairness among patients, bridging gaps in healthcare services, and boosting trust and independence through the clear and responsible use of AI technologies. This highlights the groundbreaking impact of this research within the healthcare industry.

This paper offers a viewpoint perspective on the strategic alignment of AI and CSR, presenting a novel approach to creating resilient healthcare systems in the post-COVID-19 era. It provides healthcare managers and policymakers with valuable insights on leveraging AI within CSR frameworks to achieve sustainable healthcare solutions, thereby contributing significantly to the field.

This research aims to analyse the current state of research on the application of Artificial Intelligence (AI) in libraries by examining document type, publication year, keywords, country and research methods. The overarching aim is to enrich the existing knowledge of AI-powered libraries by identifying the prevailing research gaps, providing direction for future research and deepening the understanding needed for effective policy development.

This study used advanced tools such as bibliometric and network analysis, taking the existing literature from the SCOPUS database extending to the year 2022. This study analysed the application of AI in libraries by identifying and selecting relevant keywords, extracting the data from the database, processing the data using advanced bibliometric visualisation tools and presenting and discussing the results. For this comprehensive research, the search strategy was approved by a panel of computer scientists and librarians.

The majority of research concerning the application of AI in libraries has been conducted in the last three years, likely driven by the fourth industrial revolution. Results show that highly cited articles were published by Emerald Group Holdings Ltd. However, the application of AI in libraries is a developing field, and the study highlights the need for more research in areas such as Digital Humanities, Machine Learning, Robotics, Data Mining and Big Data in Academic Libraries.

This study has excluded papers written in languages other than English that address domains beyond libraries, such as medicine, health, education, science and technology.

This article offers insight for managers and policymakers looking to implement AI in libraries. By identifying clusters and themes, the article would empower managers to plan ahead, mitigate potential drawbacks and seize opportunities for sustainable growth.

Previous studies on the application of AI in libraries have taken a broad approach, but this study narrows its focus to research published explicitly in Library and Information Science (LIS) journals. This makes it unique compared to previous research in the field.

This study aims to develop an improved understanding of the formation of citizens' purchase intention to increase the adoption of prefabricated housing (PH).

An integrative model of the theory of planned behavior (TPB) and norm activation model (NAM) was proposed based on previous studies. To verify the conceptual model, an analysis was conducted after data collection from a questionnaire survey. Lastly, findings were presented by explaining the formation of purchase intention in the egoistic and altruistic contexts. Practical implications were likewise discussed.

Findings manifest that citizens' purchase intention is influenced by egoistic and altruistic cognitions. An effective strategy is to show citizens the pro-environmental features of PH to promote its adoption because they value the environmental performance of housing. Meanwhile, consumers' social fitness also plays an essential role in decision-making, and the dual contradiction in the PH market is revealed.

This study extends the knowledge of psychological decision-making theories in the field of purchase intention toward PH by proposing an integrative framework of TPB and NAM. Results indicate a systematic and comprehensive understanding of consumers' decision-making in the PH domain. Moreover, results of this research contribute to specifying and refining the applicable contexts of TPB and NAM by adding two antecedents: subjective knowledge and environmental concern. This research contributes to the literature by being one of the first to investigate purchase intention toward a high-cost product with invisible technological innovation.

This study seeks to explore the relationship between performance relative to aspiration and SMEs' internationalization speed, and moderating effects of top management's policy knowledge and institutional distance between the above relation.

This study tests the authors’ hypotheses using data on Chinese manufacturing SMEs over a 5-year period from 2013 to 2017. The authors leverage archival panel data on publicly listed companies on the SME Board, GEM and New OTC Market in the Shanghai and Shenzhen Stock Exchanges. The authors then collected data from the WIND and ZEPHYR databases.

The results confirm a U-shaped relation between performance relative to aspiration and SMEs' internationalization speed, and show that this relation is steepening by top management's policy knowledge in home country but flattening by institutional distance as environmental dynamics.

The study findings contribute to the international business field by exploring how a firm's risk situation in internationalization can change, thereby influencing SMEs' international expansion.

This study provides a comprehensive overview of the research landscape of Kansei engineering (KE) within the domain of emotional clothing design. It explores the pivotal technologies, challenges and potential future directions of KE, offering application methodologies and theoretical underpinnings to support emotional clothing design.

This study briefly introduces KE, outlining its overarching research methodologies and processes. This framework lays the groundwork for advancing research in clothing Kansei. Subsequently, by reviewing literature from both domestic and international sources, this research initially explores the application of KE in the design and evaluation of clothing products as well as the development of intelligent clothing design systems from the vantage point of designers. Second, it investigates the role of KE in the customization of online clothing recommendation systems and the optimization of retail environments, as perceived by consumers. Finally, with the research methodologies of KE as a focal point, this paper discusses the principal challenges and opportunities currently confronting the field of clothing Kansei research.

At present, studies in the domain of clothing KE have achieved partial progress, but there are still some challenges to be solved in the concept, technical methods and area of application. In the future, multimodal and multisensory user Kansei acquisition, multidimensional product deconstruction, artificial intelligence (AI) enabling KE research and clothing sales environment Kansei design will become new development trends.

This study provides significant directions and concepts in the technology, methods and application types of KE, which is helpful to better apply KE to emotional clothing design.

The existing literature on the effects of capital mobility and financial development on sustainable trade development in sub-Saharan African (SSA) countries has been centered on production-based carbon emissions without investigating consumption-based or trade-adjusted carbon emissions. The purpose of this paper is to examine the effects of capital mobility and financial development on sustainable trade development, specifically trade-adjusted carbon emissions in SSA economies.

The study employed the novel GMM-PVAR estimator and the Drisc-Kraay fixed effect panel corrected standard error (PCSE) dynamic ordinary least squares (DOLS) and the fully modified least squares (FMOLS) approaches on panel data from 46 sub-Saharan African (SSA) countries over the period 1992–2021.

The study established that capital mobility has a significant positive effect on sustainable trade development in SSA in the long run. Further, the empirical results reveal that the link between financial development and sustainable trade development is significantly positive in the long run. Moreover, the results suggest that capital mobility and financial development have predictive power on sustainable trade development.

The findings of the study imply that policymakers ought to pay equal attention to capital mobility and financial development when developing sustainable trade development policies.

The existing literature has been centered on production-based carbon emissions, without specifically considering sustainable trade development (consumption-based carbon emissions). To the best of our knowledge, this is the first study to examine the effect of capital mobility and financial development on sustainable trade development in SSA countries context.