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The paper aims to assess the corporate social responsibility (CSR) domains in Nepalese companies and explain the active CSR activities related to concerned domain.

The study is descriptive and is based on a review of previous research findings and focus group discussion.

It has three major findings. First, there is a gradual shift from philanthropic domain of CSR to the economic domain. Second, a number of CSR activities have emerged from within the domains. Finally, some of the CSR intents are even linked to the Millennium Development Goals of the country.

This study is mainly based on qualitative analysis (focus group discussions) of the participants in three different discussion programs.

This paper is useful to academicians and companies seeking to understand what kind of CSR activities are undergoing in Nepalese companies in different domains.

This is perhaps the very first investigation of its kind in the Nepalese context.

A small wind turbine blade was designed and optimized in this research paper. The blade plays an important role, because it is the most important part of the energy absorption system. Consequently, the blade has to be designed carefully to enable to absorb energy with its greatest efficiency. The main objective of this paper is to optimized blade number and selection of tip speed ratio corresponding to the solidity. The power performance of small horizontal axis wind turbines was simulated in detail using blade element momentum methods (BEM). In this paper for wind blade design various factors such as tip loss, hub loss, drag coefficient, and wake were considered. The design process includes the selection of the wind turbine type and the determination of the blade airfoil, twist angle distribution along the radius, and chord length distribution along the radius. A parametric study that will determine if the optimized values of blade twist angle and chord length create the most efficient blade geometry. The 3-bladed, 5-bladed and 7-bladed rotor achieved maximum values of Cp 0.46, 0.5 and 0.48 at the tip speed ratio 7, 5 and 4 respectively. It was observed that using BEM theory, maximum Cp varied with strongly solidity and weakly with the blade number. The studies showed that the power coefficient increases upto blade number B = 5, while the blade number if increased above 5 then the power coefficient decreases at operating pitch angle equal to 3°. Highest Cp would have solidity between 4% to 6% for number of blade 3 and design point tip speed ratio of about "7". Highest Cp would have solidity ranging from 5% to 10% for number of blade 5 and 7 and design point tip speed ratio of about 5 and 4 respectively.

The study aims to investigate the consumers' behavioral intention toward green hotels. The tendency of individuals to afford green hotels is further escalating with progressing coronavirus disease-2019 (COVID-19) pandemic recurring waves. The increased worry of consumers toward health, hygiene and the climate is acquiring momentum and transforming how consumers traditionally perceive green hotels.

The study has recommended an integrated framework incorporating various research fields as attitude-behavior-context theory, theory of planned behavior (TPB) and moderating influences to study the associations among the antecedents of consumers' behavioral intention toward green hotels. The study comprised the participation of 536 respondents residing in the Delhi and National Capital Region (NCR) of India. The data analysis strategy involved the use of structural equation modeling (SEM) analysis to test the proposed research framework.

The results and findings of the study indicated a significant influence of fear and uncertainty of the COVID-19 pandemic and environmental concern on green trust. The results also revealed the considerable impact of green trust on willingness to pay premium, attitude and subjective norms, which significantly influenced behavioral intention. The analysis also revealed the moderating influence of environmental concern in the relationship of green trust and behavioral intention.

The study has recommended significant theoretical. The theorists may use this research framework to analyze better the transforming consumer behavior trends toward green hotels in the ongoing fearful and uncertain COVID-19 pandemic scenario.

The study has recommended significant managerial implications. The industry practitioners may also utilize the framework to sustain the hotel business and bring new strategic insights into practice to combat the impact of the pandemic and simultaneously win consumers' trust in green hotels.

Although the researchers have previously emphasized consumers' intention toward green practices embraced by hotels, the impact of the COVID-19 pandemic on the green hotel industry gained noticeable attention from researchers. Furthermore, there is a scarcity of literature providing insights on the behavioral dynamism of hotel customers' trust, attitude and willingness to pay for green hotels during the repetitive waves of the COVID-19 pandemic. The study will support the existing literature gap by enlightening the associations among the various antecedents of green hotels' behavioral intention, COVID-19 and environmental concern.

Artificial intelligence (AI) is increasingly applied by businesses to optimize their processes and decision-making, develop effective and efficient strategies, and positively influence customer behaviors. Businesses use AI to generate behaviors such as customer retention (CR). The existing literature on “AI and CR” is vastly scattered. The paper aims to review the present research on AI in CR systematically and suggest future research directions to further develop the field.

The Scopus database is used to collect the data for systematic review and bibliometric analysis using the VOSviewer tool. The paper performs the following analysis: (1) year-wise publications and citations, (2) co-authorship analysis of authors, countries, and affiliations, (3) citation analysis of articles and journals, (4) co-occurrence visualization of binding terms, and (5) bibliographic coupling of articles.

Five research themes are identified, namely, (1) AI and customer churn prediction in CR, (2) AI and customer service experience in CR, (3) AI and customer sentiment analysis in CR, (4) AI and customer (big data) analytics in CR, and (5) AI privacy and ethical concerns in CR. Based on the research themes, fifteen future research objectives and a future research framework are suggested.

The paper has important implications for researchers and managers as it reveals vital insights into the latest trends and paths in AI-CR research and practices. It focuses on privacy and ethical issues of AI; hence, it will help the government develop policies for sustainable AI adoption for CR.

To the author's best knowledge, this paper is the first attempt to comprehensively review the existing research on “AI and CR” using bibliometric analysis.

With the time and increase in usage of information technology (IT), blockchain technology is gaining immense attention from researchers, academicians, and practitioners because of its distinctive features such as transference, security and data reliability. The purpose of this study is to endow with a systematic review of literature on blockchain in context to the government education sector in terms of its usage, benefits, obstacles and practical implementation in future areas in education.

The study adopted a bibliometric visualization tool to classify data in yearly publications, highly cited journals, prominent authors, leading publications in countries and institutions and highly cited papers – the data was extracted from the SCOPUS database by using relevant keywords. Thus, the following research questions were developed: How has blockchain technology been used in the government educational sector? What are the benefits examined in the field of education? What were the problems/obstacles faced using the technology in a government education structure?

The findings identify and provide a comprehensive review of the technique regarding the present research stream in terms of highest publication, author, journal, subject-wise and relevance of the technology in government education structure. Thus, the future research potential of the technology in the education sector is much more as it is in the initiation stage. A lot of opportunities and benefits need to be extracted at large.

The present findings of the study provide a base work for government education institutions, policy developers and researchers to investigate other areas where the technology can be implemented. Finally, more technology applications will develop strategies for proper data management and cost-effective decisions.

This study explains the relevance of technology in education through bibliometric visualization. The study adopted the review and significance of blockchain technology in the government education sector by identifying its benefits, current scenario, application and future research potential areas.

The purpose of the study is to evaluate certain characteristics of the videos of the software Koha and DSpace posted on YouTube. Since YouTube has the potential to provide the content creator to share their knowledge and experience through their content which has become much more beneficial to the information seeker. Nowadays, people search for queries or tutorial videos on YouTube very often to earn a better understanding of the term. Sentiment analysis of the viewers' opinion of the videos is another purpose of this study.

Dataset for evaluating the characteristic of the videos of Koha and DSpace was extracted by using Webometric Analyst by creating YouTube API. Once retrieval of data was completed, a manual verification was enhanced to filter out spam videos unrelated to the scope. After the confirmation of authentic relatable videos, seeking the video's id as query, the comments per video were extracted using Webometric Analyst. For opinion mining, the Parallel Dots API web service was used in Google Sheets as an addon function. The sentiment, multilingual sentiment, emotion, intention and word frequency of the viewers' opinion was examined with the help of certain default functionalities.

Webometric Analyst extracted a total of 461 and 397 videos of Koha and DSpace, respectively, uploaded on the YouTube platform. The findings of the study indicate that the growth rate of videos on Koha is decreasing, while the number of videos uploaded on DSpace is gradually increased in the last 10 years. The highest number of videos posted in 1–20 min duration category with mostly high definition (HD) with standard YouTube license and prominently in the English language. The sentiment analysis of the total extracted comments on Koha and DSpace videos found to be 2043 and 862 comments, respectively, among whom “Positive” comments are mostly found and with “Happy” emotion can be highly detected with most supportive “Feedback” intention on both Koha and DSpace videos. The top word frequency signifies that the users of both the software are using the comments section of the videos on YouTube to ask and provide troubleshooting help to each other.

The present study has some limitations too; the dataset for the study includes only those videos whose title, description or keywords sections had the query terms “Koha” or “DSpace” there are chances that some videos would have been left out from the dataset related to these software.

This is the first paper to evaluate the characteristics and sentiment of both the videos Koha and DSpace. Through this, the popularity, likeness and dislike and the impact of the contents of the videos uploaded will be disclosed, and creators can make an improvement by referring this, and the seekers will adapt to the use of correct and authentic information.

The emergence of cryptocurrencies has tremendously changed the way of financial transactions around the world which has led to form distinct discussions in the field regarding its reliability. This paper aims to evaluate the published literatures on cryptocurrency identifying its growth, citation, prolific authors, journals, countries, active funding agencies, collaboration pattern and emerging research hotspots in the area.

Scientometrics and Altmetrics parameters have been incorporated in the study. Literatures covered from the Scopus database searching within “Article Title, Abstract, Keywords” with keywords “cryptocurrency” OR “digital currency” OR “bitcoin” OR “Ethereum” by limiting the time range of 2013–2022, English language and journal articles only. Total 6,107 documents have been identified. The further analysis and visualisation is performed using MSExcel, VOSviewer, Biblioshiny and Tableau. Another tool, Dimension.ai is used to identify the Altmetric Attention Score.

The findings reveal that the growth of research and citation rate hiked from the year 2017 till now. Elie Bouri is the top contributor, IEEE Access is the most prolific journal, China being the prolific country. Topics like Blockchain, Bitcoin, Ethereum, smart contracts, financial markets are emerging researched hotspots. The reliability of crypto market is still not clear because of its high volatility. The findings of the study will be more useful in the academia, subject specialists, research institutions, funding agencies, publishing agencies in decision-making.

To the best of the authors’ knowledge, there is no such study found considering both Scientometrics and Altmetrics approaches on cryptocurrency research with the selected time bound.

Since the education of Master of Science students in the discipline of agriculture is catalectic without libraries, the present paper seeks to assess the information literacy competency (ILC) of post graduate (PG) students at Haryana Agricultural University, Hisar, India, along with the impact of instruction initiatives in this respect.

Based on the Association of College and Research Libraries (ACRL) Information Literacy Standards for Science and Engineering/Technology, 2006, an instrument was developed with 79 scoring items in the first part of the questionnaire along with non‐scoring items in the second part. Broad headings of the questions asked are shown in detail. Validity and reliability of the instrument are also reported.

Information literacy competency of PG students has been found satisfactory along with significant difference between the first and second year students. Results of the paper distinguish between the first and second year students in the light of five information literacy (IL) standards along with other considerations. Various instruction initiatives were found to have a positive impact upon the ILC of PG students.

Since the study has been able to establish a positive relationship between instruction initiatives and ILC, such initiatives may be introduced in other academic institutions.

None of the earlier reported research instruments had used science and engineering/technology IL standards, especially upon PG students in agriculture. This study further paved the way for the development of such an nstrument along with assessing the ILC of PG students and impact of instruction initiatives in an agricultural university.

This paper aims to provide a critical review of water generation from atmospheric air by using desiccant materials. Over the past few years, there has been very high stress on water scarcity, especially in Asian and African countries. Because of this insecurity, many countries are focusing on their research in the field of water technologies. Water generation from atmospheric air by using desiccant materials is one of the techniques among the air-to-water generators (AWGs).

A structured and systematic literature review has been presented to observe and understand the past trend/patterns in the field of water generation from atmospheric air by using desiccant materials. To understand the water generation technologies based on desiccant materials, the research papers from the years 1987 to 2022 have been studied and included.

The properties of the different and most probable desiccant materials in the field of AWGs have been discussed. A detailed review of testing reports of collected water samples has also been presented in tabular form. Finally, the economic analysis has been done and future prospects have been discussed. It is also found that the capacity of solid desiccant materials to adsorb the water is less as compared to liquid desiccant materials. But, the adsorption capacity can be improved by using composite desiccant materials.

The uniqueness of this manuscript lies in the compiling and examination of the existed published research papers, including variables such as author, year and geographical location, experimental/simulative, types of desiccant material, type of setup, desiccant material type and quantity and type of concentrator. This manuscript provides critique to the empirical and conceptual research in AWG technologies and also stimulates researchers to explore the topic very carefully.

The purpose of this paper is to predict the machining performance of electrical discharge machining of Ti-13Nb-13Zr (TNZ) alloy, a promising biomedical alloy, using artificial neural networks (ANN) models.

In the research, three major performance characteristics, i.e. the material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR), were chosen for the study. The input parameters for machining were the voltage, current, pulse-on time and pulse-off time. For the ANN model, a two-layer feedforward network with sigmoid hidden neurons and linear output neurons were chosen. Levenberg–Marquardt backpropagation algorithm was used to train the neural networks.

The optimal ANN structure comprises four neurons in input layer, ten neurons in hidden layer and one neuron in the output layer (4–10-1). In predicting MRR, the 60–20-20 data split provides the lowest MSE (0.0021179) and highest R-value for training (0.99976). On the contrary, the 70–15-15 data split results in the best performance in predicting both TWR and SR. The model achieves the lowest MSE and highest R-value for training in predicting TWR as 1.17E-06 and 0.84488, respectively. Increasing the number of hidden neurons of the network further deteriorates the performance. In predicting SR, the authors find the best MSE and R-value as 0.86748 and 0.94024, respectively.

This is a novel approach in performance prediction of electrical discharge machining in terms of new workpiece material (TNZ alloys).