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The purpose of the present study is to identify the active and dead links of uniform resource locators (URLs) associated with web references and to compare the effectiveness of Chrome, Google and WayBack Machine in retrieving the dead URLs.

The web references of the Library Hi Tech from 2004 to 2008 were selected for analysis to fulfill the set objectives. The URLs were extracted from the articles to verify their accessibility in terms of persistence and decay. The URLs were then executed directly in the internet browser (Chrome), search engine (Google) and Internet Archive (WayBack Machine). The collected data were recorded in an excel file and presented in tables/diagrams for further analysis.

From the total of 1,083 web references, a maximum number was retrieved by the WayBack Machine (786; 72.6 per cent) followed by Google (501; 46.3 per cent) and the lowest by Chrome (402; 37.1 per cent). The study concludes that the WayBack Machine is more efficient, retrieves a maximum number of missing web citations and fulfills the mission of preservation of web sources to a larger extent.

A good number of studies have been conducted to analyze the persistence and decay of web-references; however, the present study is unique as it compared the dead URL retrieval effectiveness of internet explorer (Chrome), search engine giant (Google) and WayBack Machine of the Internet Archive.

The web references of a single journal, namely, Library Hi Tech, were analyzed for 5 years only. A major study across disciplines and sources may yield better results.

URL decay is becoming a major problem in the preservation and citation of web resources. The study has some healthy recommendations for authors, editors, publishers, librarians and web designers to improve the persistence of web references.

As digital procurement continues to transform heavily as a value center and create new business models by linking businesses with a web of external partners, the full path to achieving such an all-encompassing thing is unknown. Thus, the study aims to explore the research gap through an exhaustive bibliometric and systematic literature review on the Digital procurement theme in the supply chain domain.

This study is a qualitative and quantitative analysis of this field, using performance analysis and science mapping to examine 583 articles published from 2002 to 2021.

A systematic literature review indicated core topics on “sustainable or green procurement” and “emerging landscape of technology” in the field of study.

Though the Scopus database used for the analysis is the largest, it may not have complete coverage of all published articles in the field of study; thus, this study is a representation of only a sample rather than its entire population.

Outcome is based on the review of the past 20 years’ contribution on the topic starting from 2002 to 2021.

A community demonstrates the unique qualities and relationships between its members that distinguish it from other communities within a network. Network analysis relies heavily on community detection. Despite the traditional spectral clustering and statistical inference methods, deep learning techniques for community detection have grown in popularity due to their ease of processing high-dimensional network data. Graph convolutional neural networks (GCNNs) have received much attention recently and have developed into a potential and ubiquitous method for directly detecting communities on graphs. Inspired by the promising results of graph convolutional networks (GCNs) in analyzing graph structure data, a novel community graph convolutional network (CommunityGCN) as a semi-supervised node classification model has been proposed and compared with recent baseline methods graph attention network (GAT), GCN-based technique for unsupervised community detection and Markov random fields combined with graph convolutional network (MRFasGCN).

This work presents the method for identifying communities that combines the notion of node classification via message passing with the architecture of a semi-supervised graph neural network. Six benchmark datasets, namely, Cora, CiteSeer, ACM, Karate, IMDB and Facebook, have been used in the experimentation.

In the first set of experiments, the scaled normalized average matrix of all neighbor's features including the node itself was obtained, followed by obtaining the weighted average matrix of low-dimensional nodes. In the second set of experiments, the average weighted matrix was forwarded to the GCN with two layers and the activation function for predicting the node class was applied. The results demonstrate that node classification with GCN can improve the performance of identifying communities on graph datasets.

The experiment reveals that the CommunityGCN approach has given better results with accuracy, normalized mutual information, F1 and modularity scores of 91.26, 79.9, 92.58 and 70.5 per cent, respectively, for detecting communities in the graph network, which is much greater than the range of 55.7–87.07 per cent reported in previous literature. Thus, it has been concluded that the GCN with node classification models has improved the accuracy.

This study aims to measure the global research landscape of the National Institute of Pharmaceutical Education and Research (NIPER) of India on a set of quantitative and qualitative metrics in terms of research output toward exploring research trends and give an overview of collaborative practices by researchers of NIPERs.

The present study has selected the Scopus database as a tool to retrieve potential publications of studied NIPERs during the last 12 years (2010–2021). NIPER-Mohali, NIPER-Hyderabad, NIPER-Ahmedabad, NIPER-Guwahati and NIPER-Kolkata have been selected for the study. The study has adopted a comprehensive search strategy to extract 3,926 publications data. VOS viewer 1.6.17, BibExcel and Microsoft Excel were used for data analysis and visualization.

The global scientific research output of NIPERs accrued 3,926 publications with an average of 327 publications per year. The retrieved publications fetched a total of 67,772 citations with an average citation impact of 17.26. There observed a steady growth of publications from 168 to 509 registered with an average growth rate of 18.44%. The mean relative growth rate and doubling time of research output are 0.26 and 2.94. The authorship patterns explore collaborative trends as most of the publications were published by multiple authors (99.39%). NIPERs have expanded their outreach to collaborate with the USA, Malaysia, Saudi Arabia, Australia and the UK to collaborate on research and regulatory reforms exhibits in the USA as a major contributor.

The present study is the first effort to evaluate the global research productivity of NIPERs and assess the current research trends on a set of quantitative and qualitative metrics to provide some insights into the complex dynamics of research productivity. The study’s outcome may help to identify the current research progress of NIPERs at the global level.

This study aims to investigate the research productivity in terms of publications count of the top four premiers Indian Institute of Management (IIM) institutions and to explore the current research trends.

Bibliometric techniques were employed to assess the performance in terms of research productivity of authors affiliated with IIMs. The Elsevier Scopus database was selected as a tool to extract the prospective publications data limiting the time frame for 2010–2021. The IIM-Ahmedabad, IIM-Bangalore, IIM-Calcutta and IIM-Lucknow have been selected for the study. The harvested data were analyzed by using the standard bibliometric indicators and scientometric parameters to measure the research landscape such as average growth rate, compound average growth rate, relative growth rate, doubling time, degree of collaboration, collaborative index, collaborative coefficient and modified collaborative coefficient. VOSviewer 1.6.17, BibExcel and Microsoft Excel were used for data analysis and visualization.

The research productivity of selected four IIMs has shown an upward trend during the study period from 2010–2021 and accrued 4,397 publications with an average of 366 publications per year. The authorship patterns demonstrate the collaborative trends as most of the publications were produced by the multiple-authors (81.03%). IIM-Ahmedabad has produced the maximum number of publications (32.20%). The research productivity of IIMs has come out in collaboration with the 125 nations across the world and the USA, the UK, Canada, Germany and China are the front runners with IIMs in the collaborative network. The high magnitude and density of collaboration are evident from the calculated mean values of the degree of collaboration (0.82). The mean values of the collaborative index (2.64), collaborative coefficient (0.51) and modified collaborative coefficient (0.51) demonstrated a positive trend, but indicate the fluctuation in the collaborative pattern as time proceeds.

The study is limited to the publications data indexed in the Scopus database, therefore the outcome may not be generalized across other databases available in the public domain like Web of Science (WoS), PubMed, Dimensions and Google Scholars.

The findings of the study may aid academics and library professionals in identifying research trends, collaboration networks and evaluating other academic and research institutions by using the current advancement in data analysis.

The present study is the first effort to evaluate the research productivity of IIMs. The expanding literature will make an important contribution to identifying patterns and evaluating current research trends on a worldwide scale.

Social media facilitates the communication and the relationship between healthcare professionals and patients. However, limited research has examined the role of social media in a physicians' online return. This study, therefore, investigates physicians' online economic and social capital return in relation to physicians' use of social media and consumer engagement.

Using ordinary least squares (OLS) regression with fixed effects (FE) and panel data collected from Sina Weibo and Sina Health, this study analyzes the impact of physicians' social media use and consumer engagement on physicians' online return and the moderation effect of professional seniority.

The results reveal that physicians' use of social media and consumer sharing behavior positively affect physicians' online economic return. In contrast, consumer engagement positively impacts physicians' online social capital return. While professional seniority enhances the effect of physicians' social media use on online economic return, professional seniority only enhances the relationship between consumers' sharing behavior to the posts and physicians' online social capital return when professional seniority comes to consumer engagement.

This study reveals the different roles of social media use and consumer engagement in physicians' online return. The results also extend and examine the social media affordances theory in online healthcare communities and social media platforms.

Whilst a modest number of investigations have been undertaken concerning nanofluids (NFs), the exploration of fluid flow under exponentially stretching velocities using NFs remains comparatively uncharted territory. This work presents a distinctive contribution through the comprehensive examination of heat and mass transfer phenomena in the NF ND–Cu/H₂O under the influence of an exponentially stretching velocity. Moreover, the investigation delves into the intriguing interplay of gyrotactic microorganisms and convective boundary conditions within the system.

Similarity transformations have been used on PDEs to convert them into dimensionless ODEs. The solution is derived by using the homotopy analysis method (HAM). The pictorial notations have been prepared for sundry flow parameters. Furthermore, some engineering quantities are calculated in terms of the density of motile microbes, Nusselt and Sherwood numbers and skin friction, which are presented in tabular form.

The mixed convection effect associated with the combined effect of the buoyancy ratio, bioconvection Rayleigh constant and the resistivity due to the magnetization property gives rise to attenuating the velocity distribution significantly in the case of hybrid nanoliquid. The parameters involved in the profile of motile microorganisms attenuate the profile significantly.

The current simulations have uncovered fascinating discoveries about how metallic NFs behave near a stretched surface. These insights give us valuable information about the characteristics of the boundary layer close to the surface under exponential stretching.

The novelty of the current investigation is the analysis of NF ND–Cu/H₂O along with an exponentially stretching velocity in a system with gyrotactic microorganisms. The investigation of fluid flow at an exponentially stretching velocity using NFs is still relatively unexplored.

The purpose of this paper is to analyze to what extent and in what ways signaling theory has been explored within the field of international marketing. This paper systematically reviews the use of signaling theory in the field of international marketing. Communication is a core aspect of the international marketing process. Research in this field has explored effective and unique ways of improving the communication flow to reduce the asymmetry of information between international consumers and the firm. This notion is adopted, enhanced and strengthened by signaling theory. Signaling theory has recently received the attention of international marketing scholars.

The systematic review methodology was applied for the purpose of identifying the relevant studies. We extracted academic articles over the last 23 years from the domain of international marketing that directly contribute to signaling theory based on 57 journal articles extracted through the systematic review process.

Based on systematic research the results reveal that the topic has grown and continues to expand within the broader international marketing field. We offer a theoretical conceptual framework to better understand signaling theory in the context of international marketing.

The authors map and critically evaluate the use of signaling theory in international marketing. Relevance of signaling theory in international marketing is growing and authors present an integrative framework that organizes the existing literature, and provides scholars to further expand on emerging themes of the domain. The paper offers some useful future research directions.

The outcomes of information behaviours have traditionally been conceptualised as use or effects. The adoption of a sociological stance, based on a practices approach, provides the opportunity to challenge these understandings. The non-Western setting further enhances the possibilities for conceptualising the outcomes of information practices as forms of capital.

This ethnographic study uses a Bourdieusian approach to investigate the information practices of diasporic devotees and monks of a Theravada Buddhist Temple in Sydney, Australia. The insider position of one researcher brought strong insights into the data, while the theoretical approach shared with the other researchers reinforced an outsider perspective.

The Temple’s online sources and personal communication with other devotees provide a diverse range of sources that devotees use in information-based cultural practices and everyday life information practices. These practices lead to outcomes that can be identified as economic, social and cultural capital. Pin or merit emerges as an important outcome of practices which is not easily accommodated by the concept of outcome, nor by Bourdieu’s categories of capital.

Adding to the small number of studies concerned with information practices in a spiritual context, this study shows the value of a Bourdieusian approach in identifying the outcomes of information practices as capital, but highlights the shortcomings of applying Western concepts in non-Western settings. It proposes the possibility of a new form of capital, which will need to be tested rigorously in studies in other spiritual settings.

A number of recent artificial intelligence (AI)-enabled technologies, including summarisers, paraphrasers and the cutting-edge chatbots not only have outstanding potentials in modern educational systems but also could lead to a dramatic paradigm shift in the whole education process. This study aims to explore the factors that shape the academic community’s desire and intention to use AI conversational chatbot technology, with a particular focus on the leading ChatGPT.

This study uses a mixed method approach to explore the educators’ adoption of chatbots through an empirically validated model. The model, known as the “Educators’ Adoption of ChatGPT”, was developed by integrating the theoretical foundations of both the Unified Theory of Acceptance and Use of Technology and Status Quo Bias (SQB) frameworks, as well as insights gathered from interviews. The relationships within this model were then tested using a quantitative approach. The partial least squares-structural equation modelling method was used to analyse 243 valid survey responses.

The outcomes of the analysis indicated that perceived educators’ effort expectancy, educators’ autonomous motivation, perceived learners’ AI competency, perceived educators’ competency, innovative behaviour towards technological agility and perceived students’ engagement are significant determinants of educators’ intention to use chatbots. In contrast, perceived unfair evaluation of students, perceived students’ overreliance and perceived bias/inaccuracies were shown to have significant impacts on the resistance to use the technology, which typically implies a negatively significant influence on the educators’ use intention. Interestingly, perceived fraudulent use of ChatGPT was proven insignificant on the resistance to use chatbots.

This study makes a significant contribution to the field of educational technology by filling the gap in research on the use and acceptance of AI-enabled assistants in education. It proposes an original, empirically validated model of educator adoption, which identifies the factors that influence educators’ willingness to use chatbots in higher education and offers valuable insights for practical implementation.