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The goal of this research is to look at how urban microfinance affects livelihood transformation in terms of poverty reduction, living standards, social well-being, empowerment and entrepreneurship.

This paper analyses the role of urban microfinance towards livelihood with special reference to Western Uttar Pradesh. Primary data were collected from 321 respondents who are users of a microfinance programme using a standardised questionnaire. The data were collected using a stratified random sampling technique, and the data were analysed using structural equation modelling.

Urban microfinance has a considerable impact on poverty reduction, the standard of living, social well-being, empowerment and entrepreneurship in the urban poor, according to the findings.

The fact that the majority of the borrowers were uneducated was the most significant barrier to them filling out the questionnaire. Their anxiety was the most significant psychological obstacle to successfully answering the questions, and it took time. As a result, it is urged that proper counselling be conducted before the poor borrowers fill out the questionnaire.

The current study highlights the factors that lead to the utilisation of microfinance services. This research will aid MFIs in selecting the appropriate products and services for the urban poor. The results of this study will aid them in understanding and meeting the expectations of microfinance CEOs.

This is a first study conducted in Northern zone of India measuring the roles urban microfinance institutions (MFIs) in uplifting the livelihood of urban poor.

The study aims to calculate the recall ratio of selected MSEs and provide a comprehensive ranking for MSEs using features, precision and recall analysis.

The study was divided into three consecutive sections: Keyword selections and checking demographic searchability; recall calculation among the MSEs and third calculating the Equal Weighted Score by allotting equal weight (0.25) to all MSEs to rank the MSEs based on the re-ranking aggregation approach.

The study clearly shows all the four MSEs considered—Dogpile, Metacrawler, DuckDuckGo and Startpage—Metacrawler (71%) ranked highest for recall, followed by DuckDuckGo (68%), Dogpile (63%) and Startpage (60%). The re-ranking aggregation approach results show DuckDuckGo (2) ranked 1st, followed by Startpage (2.5), Dogpile (2.75) and Metacrawler (2.75); lower scores indicate better performance. The findings indicate that DuckDuckGo is the best MSE regarding user experience (UX) and search quality.

The study used a re-ranking aggregation approach confined to past rankings and limited to four MSEs, limiting its generalizability.

The finding helps users and developers understand the strengths and weaknesses of the different MSEs, enabling more informed decision-making and enhancing UX.

The study selected a novel approach for assessing the MSEs, and no similar study conducted in the past used different performance metrics to rank the MSEs.

Adopting and implementing robotic technology applications in the library is a significant technological up-gradation today. The purpose of this study was to evaluate selected literature focused mainly on robotics technology applications in the field of libraries and to assess the online social attention to research publications.

The study employed Scientometric and altmetric tools to evaluate the research publications. The bibliographic data of research publications were downloaded from Scopus database and scrutinized one by one and 71 articles were selected which mainly focused on robotic technology in libraries. Altmetric data were collected from the Dimensions.ai database. The analysis was performed using MS Excel, Tableau, Biblioshiny, VOSviewer and SPSS software.

Research on robotic technology in the field of libraries has been experiencing a gradual increase, marked by an annual growth rate of 12.93%. The United States has prominently led the way as the most active participant and collaborator in this advancement. Among the various journals, Library Hi Tech has notably stood out as a significant contributor to this field. However, the research articles have garnered limited social attention and impact. Furthermore, the patterns of authorship collaboration have demonstrated relatively modest levels within the field, and a weak correlation has been observed between the social attention received and the Scopus citation metrics of the publications.

The research needs to be disseminated more through various social media platforms to increase its visibility. Sharing research information through social media can bridge the gap between academia and society. The findings of this study can serve as a valuable reference for researchers and policymakers.

This study presents a Scientometric analysis of the selected published literature on robotics technology applications in the field of libraries, highlighting the progress and development of worldwide research in this area.

The present study examines the features and services of four research data repositories (RDRs): Dataverse, Dryad, Zenodo and Figshare. The study explores whether these RDRs adhere to the FAIR principles and suggests the features and services that need to be added to enhance their functionality.

An online survey was conducted to identify the features of four popular RDRs. The study evaluates the features of four popular RDRs using the specially designed checklist method based on FAIR principles. The checklist is based on 11 construct progressions used to evaluate the features and services of four popular RDRs. The final checklist contains 11 constructs with 199 check spots.

Figshare has attained the highest features for findability, accessibility, interoperability and reusability. It is identified that Figshare, with 116 (58.3%) scored the highest points and ranked no 1. It has also been found that Figshare recorded the highest features in 6 constructs out of the 11. Dataverse, with 90 (45.2%) features, ranked 2nd; Zenodo, with 86 (43.2%), ranked 3rd. The lowest features are found in Dryad, with 85 (42.7%). Furthermore, the study found that all four popular RDRs have poor features relating to “research data access metrics” features 23.3%, “output, data license and other advanced features” 22.6%. The very less features recorded in the category “services in RDRs” are 15.9%. Therefore, the features of these three constructs framed under FAIR need to be upgraded to improve the functionalities of the four popular RDRs.

The findings of the study are useful for researchers in choosing the appropriate RDR for accessing and sharing data and can be used by data scientists, librarians and policymakers in starting the research data management services in academic and research institutions. Furthermore, the study can also help impart research data literacy instructions to researchers and faculty members.

This study has prepared a special checklist based on FAIR principles to evaluate the features and services of RDRs. No prior study has been conducted to explore the features of popular RDRs and their compliance with FAIR principles based on the checklist method.

This study aims to analyse the ethical implications associated with the development of artificial intelligence (AI) technologies and to examine the potential ethical ramifications of AI technologies.

This study undertakes a thorough examination of existing academic literature pertaining to the ethical considerations surrounding AI. Additionally, it conducts in-depth interviews with individuals to explore the potential benefits and drawbacks of AI technology operating as autonomous ethical agents. A total of 20 semi-structured interviews were conducted, and the data were transcribed using grounded theory methodology.

The study asserts the importance of fostering an ethical environment in the progress of AI and suggests potential avenues for further investigation in the field of AI ethics. The study finds privacy and security, bias and fairness, trust and reliability, transparency and human–AI interactions as major ethical concerns.

The implications of the study are far-reaching and span across various domains, including policy development, design of AI systems, establishment of trust, education and training, public awareness and further research. Notwithstanding the potential biases inherent in purposive sampling, the constantly evolving landscape of AI ethics and the challenge of extrapolating findings to all AI applications and contexts, limitations may still manifest.

The novelty of the study is attributed to its comprehensive methodology, which encompasses a wide range of stakeholder perspectives on the ethical implications of AI in the corporate sector. The ultimate goal is to promote the development of AI systems that exhibit responsibility, transparency and accountability.

Internet gaming disorder (IGD) has become a crucial concern due to its rising prevalence and potential mental health impacts, which can lead to significant psychological, social and functional impairments, affecting academic performance, relationships and overall well-being of an individual. This paper aims to evaluate the published literature on IGD, identifying its growth, prolific authors, journals, countries, collaboration patterns and emerging research hotspots.

The present study has used scientometrics and altmetrics approaches to evaluate the published IGD literature. The data was collected from the Scopus database using the keywords TITLE-ABS-KEY, “Internet Gaming Disorder” or “Gaming Addiction” by filtering the period of 2014–2023, “research articles” and “English” language. Dimensions.ai has been used to extract the altmetric attention score. The results showed a total of 1,454 documents. The analysis has been done using VoSviewer and MS Excel.

The results showed an upward trend in publications over time. Dr Mark Griffiths is the most productive author, and the Journal of Behavioral Addictions is prolific. China is the most productive country. The study found minimal collaboration between the authors. The results highlighted that IGD predominantly affects adolescent males, leading to anxiety and significant impacts on brain function.

To the best of the authors’ knowledge, studies examining IGD research using both scientometric and altmetric approaches within the specified time frame have yet to be identified.

The study aims to analyze research trends and hotspots in the field of food and nutrition literacy to inform evidence-based policymaking, and promote the health and well-being of the general population.

The Scopus database was used to retrieve relevant research articles using specific keywords related to food or nutritional literacy.

The analysis included 341 research articles, predominantly authored by scholars from the USA. Growth pattern of publications indicated recent evolution of the food and nutrition literacy concepts. The research hotspots identified included the development of tools to assess literacy, assessing food and nutritional literacy competencies among school children and adolescents and finally, cross-sectional survey studies on various international cultures to assess food and nutritional literacy. The top cited articles in the field focused on the operational definition and difference between nutrition literacy and food literacy.

The findings underscore the need for comparative studies across countries, and advocacy for policy change to advance food and nutrition literacy among school students and vulnerable populations.

Policymakers, public health officials and practitioners can use the research findings to inform the development of evidence-based policy frameworks and interventions aimed at addressing the gaps in food and nutrition literacy.

By promoting and advocating for policy reforms, and addressing gaps in food and nutrition literacy, the study contributes to fostering global future public health.

The study provides insights into the evolving research landscape on food and nutrition literacy, emphasizing the growing scholarly interest in understanding the concept and its public health impact.

This study aims to explore blockchain potential for digital marketing (BlkChn-Mk-KAP) by developing and validating a measurement model for assessing the constructs of knowledge, attitude and practice (KAP) related to blockchain technology in digital marketing.

A four-study process was used. The first study reviewed the literature to develop a pool of possible measurement items. Using exploratory factor analysis and reliability assessments, Study 2 (n = 162) investigated the dimensionality of the items developed in Study 1. The factorial structure from Study 2 was validated in Study 3 (n = 204), and the measurement model invariance was assessed using covariance-based structural equation modelling (CB-SEM). Finally, in Study 4 (n = 203), the predictive validity of the BlkChn-Mk-KAP was tested using a CB-SEM approach, testing its constructs correlations with the perceived usefulness of blockchain for digital marketing.

The findings indicate that the BlkChn-Mk-KAP measurement model comprises three-dimensional multi-item scales: knowledge, attitude and practice.

This study introduces a promising BlkChn-Mk-KAP model to examine blockchain’s role in digital marketing. The authors acknowledge the sampling limitation in this research. To enhance the generalisability of the findings, future research should expand to different groups, including generation, gender and age. In addition, further exploration of the explicit links between blockchain knowledge, attitudes and subsequent digital marketing performance is warranted.

Educating employees about blockchain technology’s unique features can shape favourable attitudes and stimulate the utilisation of blockchain-enabled technologies in digital marketing practice. BlkChn-Mk-KAP can offer a reliable and valid instrument to benchmark marketers’ KAP of blockchain-powered digital marketing as they implement blockchain technology to gain a competitive advantage.

This study helps to adopt sustainable practices ensuring the wellbeing of the key stakeholders.

This research introduces the first validated conceptualisation and measurement model, BlkChn-Mk-KAP, to evaluate blockchain KAPs among digital marketing professionals.

Artificial intelligence (AI) has been touted as one of the viable solutions to address urban mobility issues. Despite a growing body of research on AI across various sectors, its use in the mobility sector remains underexplored. This study addresses this limitation by investigating AI applications and identifying the AI material properties and use cases that offer mobility-specific affordances.

Although AI applications in mobility are growing, academic research on the subject has yet to catch up. Therefore, we follow a systematic review and analysis of practitioner literature. We conducted a comprehensive search for relevant documents through Advanced Google and OECD databases and identified 173 sources. We selected 40 sources published between 2015 and 2022 and analysed the corpus of evidence through abductive qualitative analysis technique.

The analysis reveals that mobility organisations are implementing various AI technologies and systems such as cameras, sensors, IoT, computer vision, natural language processing, robotic process automation, machine learning, deep learning and neural networks. These technologies offer material properties for sensing mobility objects and events, comprehending mobility data, automating mobility activities and learning from mobility data. By exploiting these material properties, mobility organisations are integrating urban mobility management, personalising and automating urban mobility, enabling the smartification of infrastructure and asset management, developing better urban transport planning and management, and enabling automatic driving.

The study contributes a mid-range theory of the affordances of AI for mobility (AI4M) at the infrastructure, operation and service levels. This contribution extends the existing understanding of AI and offers an interconnected perspective of AI affordances for further research. For practitioners, the study provides insights on how to explore AI in alignment with organisational goals to collectively transform urban mobility to be affordable, efficient and sustainable.

According to extant Supply Chain Risk Management (SCRM) literature, manufacturing firms must align their choice of SC bridging or buffering strategies with their operating environment to achieve high plant performance and minimize SC disruption impacts. However, very few empirical studies have examined the relative performance of these strategies in dynamic industry environments. This study aims to address this research gap. This study also seeks to supplement the limited empirical research that has examined the empirical relationships between a firm’s Supply Base Complexity (SBC), the likelihood of SC disruptions, and plant performance.

This study uses data from a cross-sectional survey of 202 manufacturing firms in India. The data is analyzed, and the study hypotheses are tested using PLS path modeling and SPSS PROCESS Macro.

The study shows that increased SBC leads to an increased frequency of SC disruptions with a negative impact on plant performance. The study also finds that the firm’s implementation of SC bridging or buffering strategies effectively moderates this performance impact. However, the study results do not support the hypothesis that industry dynamism moderates the relative effectiveness of SC bridging or buffering strategies in mitigating the negative impact of SC disruptions.

The study adds to the limited empirical research examining the SC disruption risk associated with SBC and the resulting performance impact. It addresses a gap in extant research by evaluating the efficacy of SC bridging and buffering strategies in mitigating this performance impact in dynamic industry environments.