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This research aimed to visualize and analyze the co-word network and thematic clusters of the intellectual structure in the field of linked data during 1900–2021.

This applied research employed a descriptive and analytical method, scientometric indicators, co-word techniques, and social network analysis. VOSviewer, SPSS, Python programming, and UCINet software were used for data analysis and network structure visualization.

The top ranks of the Web of Science (WOS) subject categorization belonged to various fields of computer science. Besides, the USA was the most prolific country. The keyword ontology had the highest frequency of co-occurrence. Ontology and semantic were the most frequent co-word pairs. In terms of the network structure, nine major topic clusters were identified based on co-occurrence, and 29 thematic clusters were identified based on hierarchical clustering. Comparisons between the two clustering techniques indicated that three clusters, namely semantic bioinformatics, knowledge representation, and semantic tools were in common. The most mature and mainstream thematic clusters were natural language processing techniques to boost modeling and visualization, context-aware knowledge discovery, probabilistic latent semantic analysis (PLSA), semantic tools, latent semantic indexing, web ontology language (OWL) syntax, and ontology-based deep learning.

This study adopted various techniques such as co-word analysis, social network analysis network structure visualization, and hierarchical clustering to represent a suitable, visual, methodical, and comprehensive perspective into linked data.

Advances in material agency driven by artificial intelligence (AI) have facilitated breakthroughs in material adaptivity enabling smart objects to autonomously provide individualized smart services, which makes smart objects act as social actors embedded in the real world. However, little is known about how material adaptivity fosters the infusion use of smart objects to maximize the value of smart services in customers' lives. This study examines the underlying mechanism of material adaptivity (task and social adaptivity) on AI infusion use, drawing on the theoretical lens of social embeddedness.

This study adopted partial least squares structural equation modeling (PLS-SEM), mediating tests, path comparison tests and polynomial modeling to analyze the proposed research model and hypotheses.

The results supported the proposed research model and hypotheses, except for the hypothesis of the comparative effects on infusion use. Besides, the results of mediating tests suggested the different roles of social embeddedness in the impacts of task and social adaptivity on infusion use. The post hoc analysis based on polynomial modeling provided a possible explanation for the unsupported hypothesis, suggesting the nonlinear differences in the underlying influencing mechanisms of instrumental and relational embeddedness on infusion use.

The formation mechanisms of AI infusion use based on material adaptivity and social embeddedness help to develop the business strategies that enable smart objects as social actors to exert a key role in users' daily lives, in turn realizing the social and economic value of AI.

This study advances the theoretical research on material adaptivity, updates the information system (IS) research on infusion use and identifies the bridging role of social embeddedness of smart objects as agentic social actors in the AI context.

The Operations and Maintenance (O&M) cost of a facility is typically 60–85% of the total life cycle cost of a building whereas its design and construction cost accounts for only 5–10%. Therefore, enhancing and optimizing the O&M of a facility is a crucial issue. In addition, with the increasing complexities in a building's operating systems, more technologically advanced solutions are required for proactively maintaining a facility. Thereby, a tool is needed which can optimize and reduce the cost of facility maintenance. One of the solutions is Augmented or Mixed Reality (AR/MR) technologies which can reduce repair time, training time and streamline inspections. Therefore, the purpose of this study is to establish contextual knowledge of AR/MR application in facilities operation and maintenance and present an implementation framework through the analysis and classification of articles published between 2015 and 2022.

To effectively understand all AR/MR applications in facilities management (FM), a systematic literature review is performed. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) protocol was followed for searching and describing the search strategies. Keywords were identified through the concept mapping technique. The Scopus database and Google Scholar were employed to find relevant articles, books and conference papers. A thorough bibliometric analysis was conducted using VOS Viewer and subsequently, a thematic analysis was performed for the selected publications.

The use of AR/MR within facilities O&M could be categorized into five different application areas: (1) visualization; (2) maintenance; (3) indoor localization and positioning; (4) information management and (5) indoor environment. After a thematic analysis of the literature, it was found that maintenance and indoor localization were the most frequently used research application domains. The chronological evolution of AR/MR in FM is also presented along with the origin of publications, which showed that the technology is out of its infancy stage and is ready for implementation. However, literature showed many challenges hindering this goal, that is (1) reluctance of the organizational leadership to bear the cost of hardware and trainings for the employees, (2) Lack of BIM use in FM and (3) system lagging, crashing and unable to register the real environment. A preliminary framework is presented to overcome these challenges.

This study accommodates a variety of application domains within facilities O&M. The publications were systematically selected from the existing literature and then reviewed to exhibit various AR/MR applications to support FM. There have been no literature reviews that focus on AR and/or MR in the FM and this paper fills the gap by not only presenting its applications but also developing an implementation framework.

Limited knowledge exists regarding the adverse effects of artificial intelligence adoption, including platforms like ChatGPT, on users’ mental well-being. The current research seeks to adopt the insight from the stressor-strain-outcome paradigm and a moderated mediation model to examine how technology anxiety moderates the direct and indirect relationships between compulsive use of ChatGPT, technostress, and life satisfaction.

Drawing data from a sample of 2,602 ChatGPT users in Vietnam, PROCESS macro was approached to test the moderated mediation model.

The findings indicate that compulsive use of ChatGPT exhibited a substantial and positive impact on technostress, while technostress was found to have a negative influence on life satisfaction. Moreover, although compulsive use of ChatGPT did not show a significant direct effect, it indirectly impacts life satisfaction via technostress. Remarkably, technology anxiety was found to significantly moderate both direct and indirect associations between compulsive use of ChatGPT, technostress, and life satisfaction.

Based on the findings of this research, some practical implications are provided.

The research offers a fresh perspective by applying the stressor-strain-outcome perspective to provide empirical evidence on the moderated mediation effects of technology anxiety and technostress on the relationship between compulsive use of ChatGPT and users’ life satisfaction. The research thus sheds new light on artificial intelligence adoption and its effects on users’ mental health.

The purpose of this systematic review is to critically analyze pharmaceutical industry case studies on the implementation of Lean 4.0 methodologies to synthesize key lessons, benefits and best practices. The goal is to inform decisions and guide investments in related technologies for enhancing quality, compliance, efficiency and responsiveness across production and supply chain processes.

The article utilized a systematic literature review (SLR) methodology following five phases: formulating research questions, locating relevant articles, selecting and evaluating articles, analyzing and synthesizing findings and reporting results. The SLR aimed to critically analyze pharmaceutical industry case studies on Lean 4.0 implementation to synthesize key lessons, benefits and best practices.

Key findings reveal recurrent efficiency gains, obstacles around legacy system integration and data governance as well as necessary operator training investments alongside technological upgrades. On average, quality assurance reliability improved by over 50%, while inventory waste declined by 57% based on quantified metrics across documented initiatives synthesizing robotics, sensors and analytics.

As a comprehensive literature review, findings depend on available documented implementations within the search period rather than direct case evaluations. Reporting bias may also skew toward more successful accounts.

Synthesized implementation patterns, performance outcomes and concealed pitfalls provide pharmaceutical leaders with an evidence-based reference guide aiding adoption strategy development, resource planning and workforce transitioning crucial for Lean 4.0 assimilation.

This systematic assessment of pharmaceutical Lean 4.0 adoption offers an unprecedented perspective into the real-world issues, dependencies and modifications necessary for successful integration, absent from conceptual projections or isolated case studies alone until now.

The purpose of this study was to analyze importance of artificial intelligence (AI) in education and its emphasis on assessment and adversity quotient (AQ).

The study utilizes a systematic literature review of over 141 journal papers and psychometric tests to evaluate AQ. Thematic analysis of quantitative and qualitative studies explores domains of AI in education.

Results suggest that assessing the AQ of students with the help of AI techniques is necessary. Education is a vital tool to develop and improve natural intelligence, and this survey presents the discourse use of AI techniques and behavioral strategies in the education sector of the recent era. The study proposes a conceptual framework of AQ with the help of assessment style for higher education undergraduates.

Research on AQ evaluation in the Indian context is still emerging, presenting a potential avenue for future research. Investigating the relationship between AQ and academic performance among Indian students is a crucial area of research. This can provide insights into the role of AQ in academic motivation, persistence and success in different academic disciplines and levels of education. AQ evaluation offers valuable insights into how individuals deal with and overcome challenges. The findings of this study have implications for higher education institutions to prepare for future challenges and better equip students with necessary skills for success. The papers reviewed related to AI for education opens research opportunities in the field of psychometrics, educational assessment and the evaluation of AQ.

In the modern digital realm, while artificial intelligence (AI) technologies pave the way for unprecedented opportunities, they also give rise to intricate cybersecurity issues, including threats like deepfakes and unanticipated AI-induced risks. This study aims to address the insufficient exploration of AI cybersecurity awareness in the current literature.

Using in-depth surveys across varied sectors (N = 150), the authors analyzed the correlation between the absence of AI risk content in organizational cybersecurity awareness programs and its impact on employee awareness.

A significant AI-risk knowledge void was observed among users: despite frequent interaction with AI tools, a majority remain unaware of specialized AI threats. A pronounced knowledge difference existed between those that are trained in AI risks and those who are not, more apparent among non-technical personnel and sectors managing sensitive information.

This study paves the way for thorough research, allowing for refinement of awareness initiatives tailored to distinct industries.

It is imperative for organizations to emphasize AI risk training, especially among non-technical staff. Industries handling sensitive data should be at the forefront.

Ensuring employees are aware of AI-related threats can lead to a safer digital environment for both organizations and society at large, given the pervasive nature of AI in everyday life.

Unlike most of the papers about AI risks, the authors do not trust subjective data from second hand papers, but use objective authentic data from the authors’ own up-to-date anonymous survey.

This study examines the effect of contextual perceived value activated by contextual marketing offers and information privacy concerns on consumer loyalty in mobile commerce.

The survey was conducted through 340 mobile users in Morocco and the collected data were analyzed using structural equation modeling.

This study's results show that contextual marketing and information privacy concerns are key determinants in improving customer loyalty in the m-commerce context. Perceived ubiquity has a positive impact on perceived trust, which also impacts consumer loyalty. Information privacy concerns also have a positive impact on customer satisfaction, yet it does not impact perceived trust, which is contrary to the results of other researchers. It can also be concluded that customer satisfaction and trust are important antecedents of consumer loyalty.

This research gives rise to some important managerial and strategic implications in order to integrate contextual marketing strategies, as well as theoretical implications that concern this field of study.

This research makes a significant contribution to knowledge by examining the role of contextual marketing and information privacy concerns in the m-commerce context. These results will be considered useful for marketers and for businesses in general who wish to integrate a marketing strategy that is based on a customer-centric approach. It also contributes to the related literature, as there are few studies focused on m-commerce and contextual marketing within the context of Morocco.

ChatGPT is a versatile technology with practical use cases spanning many professional disciplines including marketing. Being a recent innovation, however, there is a lack of academic insight into its tangible applications in the marketing realm. To address this gap, the current study explores ChatGPT’s application in marketing by mining social media data. Additionally, the study employs the stages-of- growth model to assess the current state of ChatGPT’s adoption in marketing organizations.

The study collected tweets related to ChatGPT and marketing using a web-scraping technique (N = 23,757). A topic model was trained on the tweet corpus using latent Dirichlet allocation to delineate ChatGPT’s major areas of applications in marketing.

The topic model produced seven latent topics that encapsulated ChatGPT’s major areas of applications in marketing including content marketing, digital marketing, search engine optimization, customer strategy, B2B marketing and prompt engineering. Further analyses reveal the popularity of and interest in these topics among marketing practitioners.

The findings contribute to the literature by offering empirical evidence of ChatGPT’s applications in marketing. They demonstrate the core use cases of ChatGPT in marketing. Further, the study applies the stages-of-growth model to situate ChatGPT’s current state of adoption in marketing organizations and anticipate its future trajectory.

The paper aims to provide an overview of the state-of-the-art of the event industry in the context of digitalization to understand how digital technologies change the event industry and what research topics are the most promising for further exploration.

A bibliometric analysis of the existing body of knowledge on the topic was conducted and the results were visualized using CiteSpace 5.8.R3. A total of 1999 articles and proceeding papers from the Web of Science Core Collection published between 2007 and 2022 were selected for our analysis. Based on the articles and proceeding papers in the Web of Science Core Collection database, we selected a set of publications for our analysis. The data were obtained through specific keywords related to our research topic. The method involves a process of three main stages: data collection, data processing and the bibliometric analysis.

Co-citation analysis indicated that issues of crowd management and tracking human mobility during mass events are important for the event industry and that technologies such as the Internet of Things, special-purpose mobile applications and systems make it easier for an event organizer to handle the issues. The findings demonstrated a weak scientific collaboration between countries in the topic studied and shift of research hotspots to study of satisfaction, motivation and behavioral patterns of events attendees. Based on this analysis, three directions for future research were revealed.

The results should be interpreted in light of our sample, because the analysis was conducted within our sample which has boundaries. We collected data from all categories in the Web of Science Core Collection database, but we considered only articles and proceeding papers as opposed to all possible types of scientific publications and other databases. In the study, we focused on detecting the state-of-the-art of the event industry in the context of digitalization overall. More specific topics that could be analyzed remain, for example, the dependency of digital technologies from the event type, etc.

This study reflects the state-of-the-art of the event industry in the context of digitalization. It provides researchers with key developmental trends in the event industry, which assists them in more deeply understanding the evolution of research hotspots in the field during last 15 years and defining future research agenda. The paper presents an overview of digital technologies used in various types of events and describes the issues and results related to the implementing digital technologies. The results obtained were extremely important, as they can be used by event managers and organizers to enhance customers’ experience during the events.

This study reflects the state-of-the-art of the event industry in the context of digitalization. This is the first attempt to make an overall analysis of scientific papers published in the Web of Science Core Collection on the topic studied without excluding any categories. The search procedure is transparent, and the results can be reproduced in other search fields using the same approach. Based on this analysis, three directions for future research were revealed including technological aspects of online event-based social networks, issues of crowd management and security at mass events and issues of attendees’ acceptance of novel digital technologies.