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This study aims to provide insight into Library and Information Science (LIS) research in India using scientometric approaches. Web of Science (WoS) and SCOPUS databases were used for data retrieval. The study examines productivity in terms of source types, gender distribution, document formats, authorship and other factors. In addition, this study sought to identify trends or patterns in the research preferences of LIS scientists through text analysis.

Data were downloaded from the WoS and Scopus databases over 22 years and analysed using VOSviewer, Orange, Biblioshiny and CRExplorer softwares.

The findings reveal that 5,692 out of the 9,384 documents in both databases underwent the final examination. In total, 466 different sources produced all of those papers. Author analysis revealed that 6,603 different authors authored 5,692 documents. There were 4,209 male and 1,063 female authors. Furthermore, India shares maximum collaborations with the USA and England. The spectrogram features nine significant peaks corresponding to Lotka’s, Bradford’s and similar laws. Text analysis revealed that Indian LIS researchers have consistently investigated open access and digital or open libraries.

The findings of this study will provide readers with a better understanding of India’s contribution to LIS. In addition, the study will help academics identify research gaps and undiscovered areas in the Indian context that require further investigation.

Not many studies highlight Indian research trends and international collaboration in LIS. This study highlights research trends, collaboration and gender productivity in LIS. The most cited references and trending topics were also identified using reference publication year spectroscopy and text analysis techniques.

Social media have been playing a critical role in seeking and sharing health related information and consequently shaping individuals’ health behaviors. This study investigates how information seeking about Covid-19 vaccine on social media is related to vaccine receiving intentions. The study furthers explores the association of trust in social media and uncertainty about Covid-19 with information seeking and the moderating role of prior social media experience on this association.

We developed a questionnaire and collected data from 525 educated social media users through “Google Forms.” Further, we applied ordinary least squares (OLS) regress to test the study hypothesis.

We noted that trust in social media and uncertainty about Covid-19 vaccine positively influenced information seeking which further positively affected vaccine receiving intentions. However, the moderating effect of prior social media experience was not only noted as weak but also found negatively affecting the associations of trust in social media and uncertainty about Covid-19 vaccine with information seeking.

The findings provide insights into understanding of public perceptions regarding Covid-19 vaccine in the cultural contexts of a developing country. Further, it informs about the public patterns of seeking information related to health issues on social media, an understanding which may likely benefit policymakers, health care providers and researchers to understand the antecedents and behavioral outcomes of seeking information through social media during health crisis. The study also elucidates the leveraging power of social media to motivate the public to accept the Covid-19 vaccines.

The study uniquely combines the antecedents and behavioral outcomes of information seeking through social media in the particular context of Covid-19. It further extends the literature by introducing the conditional role of prior social media experience.

Indian patients have different medicine systems available at the service that alter their healthseeking behaviour (HSB). This study aims to examine the beliefs and behaviour of patients in India towards the healthcare system and how it affects their intention to use healthcare chatbots.

A survey instrument was developed from standard scales and validated by experts. The data was collected from 397 respondents in an urban area and tested using a structural equation model in SAS JMP software.

The study found that awareness and perception of chatbots and distrust on doctors and health systems impact trust in a chatbot. The results show that trust in chatbots influences the intention to use chatbots. The belief in alternative medicine systems and HSB also influence the intention to use chatbots. The study findings also imply that health-care chatbots should cater to HSB and the belief in alternative medicine.

The study was conducted only among the urban population because services based on technology are more available in metro cities. Bengaluru is considered the representative population of urban India.

The level of disruption that chatbots can provide to the healthcare system makes this study significant. The study findings will help to manage the factors that can enable chatbot inclusivity, as the current system is inaccessible to many patients.

This paper addresses an identified need to study patients’ trust in the Indian healthcare system and their intention to use chatbots. The level of disruptions these chatbots can cause in the health-care system is undeniable and patients’ trust in these chatbots will eventually transform the health-care sector.