Search results

Biomimetics are expected to contribute to sustainable environmental management; however, there has been no exploration of industry perceptions by using empirical data. This study aims to identify the trends and perceptions of biomimetics. The industrial sectors in Japan and international patent application trends are analyzed.

An online survey to identify the perceptions of staff members in Japanese private companies (n = 276) was conducted. Japan is an emerging country in terms of the social implementation of biomimetics, and this paper can provide insights into other such countries.

It is identified that the strength of connections to biomimetics differs across industrial sectors. The respondents from companies that use nanoscale biomimetics tend to have the knowledge of, and experience in, biomimetics. Regarding the overall understanding of patent applications, Japanese private company employees require knowledge of patent application trends and country rankings as potential factors influencing the development of biomimetics.

Knowledge transfer and sharing of experience among engineers and researchers of nanoscale technologies and urban scales are necessary to facilitate biomimetic advancement.

The results of the first survey and an analysis of the perceptions of staff members in private companies in Japan are provided to show the challenges in the social implementation of biomimetics. The results can be referred to for the social implementation of biomimetics in emerging countries. The method of this study can be applied to an international comparative analysis in future research.

This paper aims to explore Web 2.0 technologies usage and trends in the top 100 US academic libraries as exemplified through the academic library websites.

The top 100 universities were selected from the US News & World Report's (USNWR) 2013 ranked list's top 100 of the best colleges in the USA. Content analysis was used in terms of quantitative approach. A checklist was developed as the main research instrument based on other checklists and questionnaires. Each of the selected university library websites was visited within a period of two weeks to explore implementation and usage of web tools, including SNS, blog, RSS, wikis, podcast/vodcast, and social bookmarking/tagging.

All 100 academic libraries had a social media presence on Facebook and Twitter, making SNS the most widely applied Web 2.0 tool. The wiki was the least applied Web 2.0 technology, with a 34 percent participation rate. Blog was the second most popular tool with a 99 percent participation rate, followed by RSS and IM/Chat with 97 percent and 91 percent respectively. The vodcast and podcast had 47 percent and 46 percent participation rates respectively, while social bookmarking/tagging were also used by 39 percent of the academic libraries.

This study is completely based on publicly available data regarding usage of Web 2.0 applications. Web 2.0 tools used on library intranets were not analyzed for this study. Outcomes suggest that academic libraries are increasingly using Web 2.0 applications to promote themselves, enhance library services, and highlight resources to patrons.

This is the first research that draws an overall picture of the usage and trends of Web 2.0 applications in the top 100 US academic libraries. This study demonstrates some noticeable patterns regarding usage, trends, and adoption which are relevant to how Web 2.0 applications are perceived and used within academic libraries. The study provides academic libraries with helpful information to better meet their user needs by effectively applying Web 2.0. Additionally, library managers, librarians and other libraries may also find this research beneficial as they plan to deploy Web 2.0.

The literature of lean six sigma (LSS) is flooded with frameworks. But to date, there has been no attempt to expose the inconsistencies in the existing frameworks and associated research trends. The purpose of this paper is to critically review 26 LSS frameworks and highlight inconsistencies. The study also helps in analysing research trends and constructs of LSS frameworks, and facilitates new applications.

It uses Scopus database and finalises 26 frameworks in the LSS domain considering a timeframe from 2000 to 2017, using a structured literature review approach and involving domain experts. It uses a battery of systematic requirements-based questions to understand trends and covers building blocks/constructs also.

It reveals inconsistencies at three different stages, namely, the framework stage, usage stage capturing existing research trends and construct stage. It also offers details at building block/construct level. Thus, it offers guidance in reducing inconsistencies and possible modifications in the existing frameworks. It not only exposes existing constructs and sub-constructs but also guides about the new upcoming changes in the business influencing the LSS domain. It also educates about the strengths and weaknesses of each framework.

This study tries to reduce inconsistencies, but does not offer new generalised or sector-specific framework.

This study will assist for modification/corrections in the existing frameworks by reducing inconsistencies and will encourage new frameworks to facilitate applications.

This is a unique study which offers frameworks-based review, portrays research trends and guides about constructs of a framework by exposing inconsistencies at each stage. It helps in reducing inconsistencies and offers guidance regarding modifications in the LSS framework considering upcoming challenges.

Industry 5.0 provides significant social, environmental and economic benefits over previous industrial revolutions. However, research in the field of industry 5.0 is still at a very nascent stage. Little is known about research trends in the field and its applications in specific sectors as construction which have relatively low sustainability index. Hence, this study aims to explore research trends in industry 5.0 and its applications in the construction industry.

The study adopted a bibliometric analysis with the use of the vosviewer. The main search words used in the study were “Industry 5.0”, “Society 5.0” AND “Fifth Industrial Revolution”. Initial extracted papers with these keywords were 1036. After refining based on language, document type, key words and timeframe, a total of 188 papers were found suitable for the analysis.

The most impactful research on Industry 5.0 to date was from the United States, Australia and Sweden while the journal sustainability was found to be the highest document source on Industry 5.0. The application areas of industry 5.0 in the construction industry include health and safety, collaborative working, waste management, measurement of environmental features (indoor air quality, atmospheric pressure, noise, humidity and temperature) and forecasting of environmental, social or financial outcomes.

The study used review based papers which form the bulk of the existing research on industry 5.0. This limits understanding of the practical applications, benefits and challenges associated with the use of industry 5.0.

This work contributes to the theory and practice of industry 5.0 by providing baseline data for future research directions on industry 5.0. The work specifically highlights the application areas of industry 5.0 in the construction industry and also indicates the need for questionnaire, interviews or case study based research to enhance understanding of the practical applications of industry 5.0.

This paper aims to present a comprehensive overview of the patterns and trends of publications on artificial intelligence (AI) in personalised learning. It addresses the need to investigate the intellectual structure and development of this area in view of the growing amount of related research and practices.

A bibliometric analysis was conducted to cover publications on AI in personalised learning published from 2000 to 2022, including a total of 1,005 publications collected from the Web of Science and Scopus. The patterns and trends in terms of sources of publications, intellectual structure and major topics were analysed.

Research on AI in personalised learning has been widely published in various sources. The intellectual bases of related work were mostly on studies on the application of AI technologies in education and personalised learning. The relevant research covered mainly AI technologies and techniques, as well as the design and development of AI systems to support personalised learning. The emerging topics have addressed areas such as big data, learning analytics and deep learning.

This study depicted the research hotspots of personalisation in learning with the support of AI and illustrated the evolution and emerging trends in the field. The results highlight its latest developments and the need for future work on diverse means to support personalised learning with AI, the pedagogical issues, as well as teachers’ roles and teaching strategies.

The textile sector is one of the sectors where competition is intense and requires the production of high-value-added products. This study aims to conduct patent analysis to find the technology status, recent trends, applications and technological evaluations of protective textile technologies in practice.

More than 36,840 patent documents related to protective textile technologies are available for researchers, patent examiners and patent researchers. Patent analysis is conducted to report the technology status, recent trends and applications of protective textile technologies. This analysis provides insights into the possible future directions of protective textile technologies in practice. Additionally, association rule mining (ARM) is performed to find the hidden patterns among protective textile technologies.

The development of protective textile technologies is revealed by the technology evaluation in this study. In addition, the sub-technology classes affecting protective textile technologies are examined using the cooperative patent classification (CPC) codes of the patent documents. Technology status and recent trends of protective textile technologies are provided in detail. The results of this study show that (1) protective textile technologies are constantly being developed, (2) the working areas of medical protective textiles are increasing, (3) there are frequent studies on fabric structures for saving lives within the framework of human needs and (4) there are four technology classes, namely A41D, Y10T, B32B and A62B impacting the other technology classes related to textile technologies such as D10B, Y10T, F41H, A62D, D04H, Y10S and D10B.

To have a competitive advantage in the marketplace, evaluation of textile technologies is critical in developing “functionalized” and “technologized” textile products. In particular, evaluating technologies in developing protective textile products is extremely important to meet customer demands and present competitive products in the market. Examining these patents for technology developers, decision-makers and policymakers is an urgent and necessary job. However, studies examining the development of protective textile technologies with patent analysis are very limited in the literature. To fill this gap, technology status, recent trends and applications of protective textile technologies are reported based on patent analysis and ARM in this study.

Multi-criteria decision-making (MCDM) techniques are decision support systems that provide systematic approaches to solve hospitality and tourism (H&T) problems while minimizing the risk of failure. However, less is known about the application of MCDM techniques in H&T research. This study aims to systematically assess the use of MCDM techniques in H&T research to classify its current application and determine its application potential for H&T research.

This study used bibliometric analysis to examine all published MCDM studies focused on H&T industries, since 1997. In addition, topic modelling was used to discover key concepts. Finally, top cited studies in terms of total citations per year and total citations were qualitatively reviewed for more insights.

The findings revealed an ongoing interest in applying MCDM techniques in H&T research. Specifically, the extension of fuzzy theory in MCDM techniques is burgeoning among H&T researchers. However, a certain number of MCDM techniques seem to be ignored in this field with a repetitive application of MCDM techniques in particular areas.

The data for the current research was solely retrieved from Scopus and other databases were not included. Therefore, future research is called for to re-examine the study by considering data from various databases.

This study contributes to extant H&T literature by identifying the most prolific and influential countries, journals, publications and trends by applying MCDM techniques in H&T research, and elucidating the implications and characteristics of MCDM techniques in H&T research.

The use of digital inspection or e-inspection of transportation projects has been proven as an efficient method over the last decade. A wide range of studies were dedicated to developing and applying e-inspection techniques and technologies. However, there is a lack of a comprehensive systematic review and content analysis of using e-inspection in highway construction and maintenance projects. The main objectives of this study were to explore the current trend and identify relevant inspection technologies and their applications for highway construction projects.

A systematic review of 172 articles from 16 high-ranked academic journals in construction engineering and management published during 2000–2021 was conducted. This process resulted in 67 relevant articles included in the detailed content analysis. The analysis involved synthesizing six main construction elements and work types, nine typical inspection activities, and 23 technologies.

The result of the analysis showed that among the six construction elements and work types, bridge and hot mix asphalt (HMA) recorded the largest share of e-inspection research. For the nine inspection activities, progress monitoring of construction operations was the highest focused area of e-inspection research. The most common e-inspection technologies are geospatial tools, 3D modeling, and unmanned aircraft systems (UASs). Camera-based inspection has existed for decades, however, has limited research development. The critical success factors in implementing e-inspection in highway projects are sharing data among different technologies, inspector training, and reducing the cost of technology purchase.

This study is one of the first attempts to conduct a content analysis of the e-inspection implementation for highway projects. The findings of this study expose knowledge gaps in contemporary research related to implementation barriers such as cost of purchase and operation of e-inspection technologies and transferring data between technologies.

Big Data is disrupting the fashion retail industry and revolutionising the traditional fashion business models. Nowadays, leading fashion brands and new start-ups are actively engaging with Big Data analytics to enhance their operations and maximise on profitability. In hope of motivating and providing direction to fashion retail managers, industry experts, and academics alike, the purpose of this paper is to consider the most recent and trending applications of Big Data in fashion retailing with the aim of concisely summarising the industry’s current position and status.

This conceptual paper provides a brief introduction to the emerging topic of Big Data in fashion retailing by briefly synthesising findings from industry, market and academic research.

Most existing fashion brands are yet to fully engage with Big Data. The authors find that the main reasons underlying the application of Big Data analytics in fashion are trend prediction, waste reduction, consumer experience, consumer engagement and marketing, better quality control, less counterfeits and shortening of supply chains. The authors also identify key challenges which must be overcome for the most fashionable industry to be able to capitalize on Big Data to understand and predict fashion consumer behaviour.

The brief synthesis provides a foundation for future investigations into the use of Big Data in fashion retailing.

This paper serves as an up-to-date introduction to how Big Data can transform fashion retailing and can act as a sound reference guide for fashion industry managers and professionals grappling with Big Data-related issues.

The purpose of this paper is to analyze the trends and potential research issues involved with library-supported mobile learning.

Publications in the Scopus database from 2007 to 2016 are analyzed on various aspects, such as the adopted research methods, technologies, participants, application domains and research issues.

In addition to the awareness of the increasing number of digital library-associated mobile learning in the past decade, there are several interesting findings in this paper, such as most research mainly focused on investigating the affective domain of participants and assessing the effects of mobile learning. Most of the library-supported mobile learning research was conducted at special libraries, followed by academic libraries.

Potential applications and research directions of library-supported mobile learning are suggested based on the findings of the present paper.

This paper provides insight in relation to library-associated mobile learning. The findings and suggestions can serve as a good reference for researchers and school teachers when conducting mobile learning studies and to develop quality learning activities with access to library facilities using mobile and wireless communication technologies.