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Nanosensors have a wide range of applications because of their high sensitivity, selectivity and specificity. In the past decade, extensive and pervasive research related to nanosensors has led to significant progress in diverse fields, such as biomedicine, environmental monitoring and industrial process control. This led to better and more efficient detection and monitoring of physical and chemical properties at better resolution, opening new horizons in the development of novel technologies and applications for improved human health, environment protection, enhanced industrial processes, etc.

In this paper, the authors discuss the application of citation network analysis in the field of nanosensor research and development. Cluster analysis was carried out using papers published in the field of nanomaterial-based sensor research, and an in-depth analysis was carried out to identify significant clusters. The purpose of this study is to provide researchers to identify a pathway to the emerging areas in the field of nanosensor research. The authors have illustrated the knowledge base, knowledge domain and knowledge progression of nanosensor research using the citation analysis based on 3,636 Science Citation Index papers published during the period 2011 to 2021.

Among these papers, the bibliographic study identified 809 significant research publications, 11 clusters, 556 research sector keywords, 1,296 main authors, 139 referenced authors, 63 nations, 206 organizations and 42 journals. The authors have identified single quantum dot (QD)-based nanosensor for biological applications, carbon dot-based nanosensors, self-powered triboelectric nanogenerator-based nanosensor and genetically encoded nanosensor as the significant research hotspots that came to the fore in recent years. The future trend in nanosensor research might focus on the development of efficient and cost-effective designs for the detection of numerous environmental pollutants and biological molecules using mesostructured materials and QDs. It is also possible to optimize the detection methods using theoretical models, and generalized gradient approximation has great scope in sensor development.

The future trend in nanosensor research might focus on the development of efficient and cost-effective designs for the detection of numerous environmental pollutants and biological molecules using mesostructured materials and QDs. It is also possible to optimize the detection methods using theoretical models, and generalized gradient approximation has great scope in sensor development.

This is a novel bibliometric analysis in the area of “nanomaterial based sensor,” which is carried out in CiteSpace software.

The purpose of this study is to provide a systematic review of the existing literature on social media (SM) use and examine its relationship with various facets of social well-being (SWB).

The study identifies and selects relevant articles using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, wherein 273 articles were identified using the keyword search criteria from 5 databases namely Web of Science, Emerald, Pubmed, Google Scholar and EBSCOhost, and finally, 20 relevant studies were included for this systematic review. In order to provide directions for future research, a thorough profile with the key findings and knowledge gaps is presented.

The majority of the reviewed studies report an increase in the use of SM, especially amongst adolescents, and this suggests a seriously detrimental impact on their SWB in terms of cyberbullying, lifestyle comparison and impact on self-esteem, substance abuse, declined academic performance, fear of missing out (FoMo) and social overload. However, some of the studies reported life satisfaction, a reduction in loneliness and improved social support and belongingness, particularly those focussing on old age people who experience social isolation. The review also affirmed improved job performance and employees’ well-being. These findings vary across various demographic variables and various SM platforms namely Facebook, Twitter, Instagram, WhatsApp, WeChat, YouTube, etc.

The findings have significant implications for SM researchers, family members and educators concerning promoting appropriate SM use, especially in terms of their SWB. The study also provides various suggestions for future studies and the need to further explore the topic as the field of SM use and SWB is ever-growing.

Using a newly compiled corpus module consisting of utterances from Asian learners during L2 English interviews, this study examined how Asian EFL learners' L1s (Chinese, Indonesian, Japanese, Korean, Taiwanese and Thai), their L2 proficiency levels (A2, B1 low, B1 upper and B2+) and speech task types (picture descriptions, roleplays and QA-based conversations) affected four aspects of vocabulary usage (number of tokens, standardized type/token ratio, mean word length and mean sentence length).

Four aspects concern speech fluency, lexical richness, lexical complexity and structural complexity, respectively.

Subsequent corpus-based quantitative data analyses revealed that (1) learner/native speaker differences existed during the conversation and roleplay tasks in terms of the number of tokens, type/token ratio and sentence length; (2) an L1 group effect existed in all three task types in terms of the number of tokens and sentence length; (3) an L2 proficiency effect existed in all three task types in terms of the number of tokens, type-token ratio and sentence length; and (4) the usage of high-frequency vocabulary was influenced more strongly by the task type and it was classified into four types: Type A vocabulary for grammar control, Type B vocabulary for speech maintenance, Type C vocabulary for negotiation and persuasion and Type D vocabulary for novice learners.

These findings provide clues for better understanding L2 English vocabulary usage among Asian learners during speech.

In past decades, artificial neural network (ANN) models have revolutionised various stock market operations due to their superior ability to deal with nonlinear data and garnered considerable attention from researchers worldwide. The present study aims to synthesize the research field concerning ANN applications in the stock market to a) systematically map the research trends, key contributors, scientific collaborations, and knowledge structure, and b) uncover the challenges and future research areas in the field.

To provide a comprehensive appraisal of the extant literature, the study adopted the mixed approach of quantitative (bibliometric analysis) and qualitative (intensive review of influential articles) assessment to analyse 1,483 articles published in the Scopus and Web of Science indexed journals during 1992–2022. The bibliographic data was processed and analysed using VOSviewer and R software.

The results revealed the proliferation of articles since 2018, with China as the dominant country, Wang J as the most prolific author, “Expert Systems with Applications” as the leading journal, “computer science” as the dominant subject area, and “stock price forecasting” as the predominantly explored research theme in the field. Furthermore, “portfolio optimization”, “sentiment analysis”, “algorithmic trading”, and “crisis prediction” are found as recently emerged research areas.

To the best of the authors’ knowledge, the current study is a novel attempt that holistically assesses the existing literature on ANN applications throughout the entire domain of stock market. The main contribution of the current study lies in discussing the challenges along with the viable methodological solutions and providing application area-wise knowledge gaps for future studies.

The objectives of the study were to identify the effects of blockchain technology (BT) on the university librarians, the impact of BT on the university library services and to reveal the challenges to adopt BT in the university libraries.

A systematic literature review was applied to address the objectives of the study. Around 25 studies published in peer-reviewed journals were selected to conduct the study.

The findings of the study revealed that blockchain technology (BT) has positive effects on the university librarians as it assists them in digital resources management, provision of integrated library services, effective records management and continued professional development. The study also displayed that BT has a positive impact on the university libraries through effective information management, user privacy, collaboration, technological innovation and access control. Results also revealed that technical issues, financial constraints, security problems, skill issues and sociocultural issues created challenges to adopt BT in the university libraries.

The study has offered theoretical implications for future investigators through the provision of innovative literature on the prospectus and challenges associated with blockchain in the context of librarianship. The study has also provided practical implications for management bodies by offering recommendations for the successful adoption of blockchain in the university libraries. Additionally, a framework has been developed to adopt BT successfully in the university libraries for the delivery of smart library services to library patrons.

The present study aims to propose a framework elucidating the attributes of mobile augmented reality (AR) shopping apps (i.e., spatial presence, perceived personalization and perceived intrusiveness) and how they translate to downstream consumer-related outcomes (i.e., immersion, psychological ownership and stickiness to the retailer).

By conducting a questionnaire-based survey, 308 responses were collected, and the data were submitted to partial least square structural equation modeling (PLS-SEM) and artificial neural network (ANN) analyses.

A few important findings were generated from the present study. First, attributes of mobile augmented reality shopping apps (i.e., spatial presence, perceived personalization and perceived intrusiveness) influence stickiness to the retailer through immersion and consumer empowerment in serial. Second, immersion positively influences psychological ownership. Third, the optimum stimulation level moderates the relationship between spatial presence and immersion. Lastly, a post-hoc exploratory finding yielded by the multigroup analysis uncovered the moderating effect of gender.

This study offers a novel contribution to the smart retail literature by investigating the role of mobile AR shopping apps in predicting consumers' stickiness to the retailer. A holistic framework elucidating the serial mediating effect of immersion and consumer empowerment, and the moderating roles of optimum stimulation level and gender were validated.

The non-fungible token (NFT) market had undergone dramatic growth and a sudden decline during 2021–2022. The market experienced a surge in prices in late 2021 and early 2022, with NFTs being sold at inflated prices. Despite this, by April 2022, the market underwent a correction, and the prices of NFTs returned to more reasonable levels. This can be a result of imitating the actions or judgments of a larger group, which is not systematically proven yet. Therefore, this study systematically investigates the applicability of herding behavior in the NFT market.

This research employs cross-sectional absolute deviation (CSAD) of returns and ordinary least squares (OLS) to test herding behavior with moving time windows of 10, 20 and 30 days based on the sales data collected from public interface of OpenSea between July 1, 2021 and June 30, 2022. Additionally, NFT-related keyword usage analysis is done for the detected herding periods.

As per the results of the data analyzed, herding behavior was evidenced using 10-, 20- and 30-day time windows from July 1, 2021 to June 30, 2022because of media movement. The findings revealed that this behavior was present and aligned with the overall behavior of the market.

This study introduces CSAD to examine herding behavior patterns within the NFT market. Complementing this method, keyword count-based analysis is employed to identify the underlying causes of herding behavior. Through this comprehensive approach, this study not only uncovers the roots of herding behavior but also offers an assessment of the time windows during which it occurs, considering the plausible socioeconomic contexts that influence these trends.

This study aims to explore the consumers’ choices of mobile payments (m-payments) using a comprehensive unified model. The financial technology for digital m-payment has been increasingly introduced in the market, yet their acceptance has remained low.

This study uses the unified theory of acceptance and use of technology (UTAUT) with additional constructs of social influence, trust, anxiety, personal innovativeness and grievance redressal (GR). Structural equation modeling is used to evaluate the predictive model of attitudes toward m-payment. Individuals’ responses to questions regarding their attitude and intention to accept m-payment were gathered and examined through the lens of extended UTAUT model.

While the model supports TAM classical role, empirical examination of the model revealed that users’ attitudes and intentions are influenced by trust, personal innovativeness and social influence. Moreover, intention to use and GR are significant positive predictors of m-payment usage behavior.

M-payment provides customers with new digital payment platforms while providing businesses and marketing agents with more alternatives for online marketing. However, there is not much reported about m-payment adoption in Pakistan. This research introduces and evaluates new constructs that were not included in the original model. In Pakistan, to the best of the authors’ knowledge, this is a first of its kind of research which is purely based on the customers’ perspective of m-payment adoption.

This paper aimed to explore the presence of altmetric data across the sub-categories of the medical science discipline and also explore whether the openness of articles results in (dis)advantage for altmetrics mentions.

The research implies data carpentry methods for gathering bibliographic data related to narrow fields of medical science discipline from the Scopus database with at least one Indian author affiliation during 2012–2021. The corresponding data were also collected from three different sources: Altmetric.com, Mendeley.com and Unpaywall.org, using OpenRefine and REST/API calls. Further, the authors observed open access altmetric advantages (OAAA) and categorical OAAA (COAAA) across seven altmetric platforms for all articles as well as discipline-wise.

The result shows that the overall coverage of altmetric events is still low, but it shows an increasing trend from the past. Mendeley has the largest coverage; almost 97.12% of publications are covered. The health policy discipline has extensive coverage across altmetric platforms (nearly 57.40% of publications in altmetrics and 99.23% in Mendeley), whereas the drug guides has the lowest (almost 0.92% in Altmetrics and 77.05% in Mendeley). Moreover, the OA articles have been highly covered in altmetrics than those of non-OA articles, and bronze OA articles covered mostly compared to others. News registered with the significant OA altmetric advantages across disciplines. Categorically, bronze and hybrid OA have the largest altmetric advantages.

This research is a unique attempt to apply OAAA and COAAA to explore OA altmetric advantages of narrow subject categories of medical science disciplines.

Since the introduction of balanced scorecard by Kaplan and Norton in 1992, it garnered considerable research and practice attention across disciplines. Using bibliometric analysis, this study examines trends in balanced scorecard research in last 20 years and identifies future areas of research.

The Web of Science database was used to extract research papers from the 2003 to 2023 period with “Balanced Scorecard” as topic. The final sample consisted of 445 articles. Trends and patterns were analyzed using bibliometric analysis through research profiling and thematic analysis.

The findings reveal that BSC, spanning across disciplines, including business and operations, has enriched the theory and practice of BSC research. Analytical and survey methods were more prevalent than primary studies. Scholars from the USA and the UK have made noteworthy contributions to balanced scorecard research. Emerging themes include integrating human resources, sustainability, subjectivity in performance evaluation and non-financial performance indicators in BSC for better strategic decision-making.

The study would inspire researchers to generate new research questions and hypotheses and help in identifying gaps in the current knowledge base and areas where further investigation is needed. Managers would gain useful insights into performance management by studying the BSC research evolution to find a fit for modern-day industry needs.

The authors’ contribution fills the void by providing useful account of extent research over last 20 years using bibliometric analysis and motivate future research directions.