Search results

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.

Vaccine hesitancy is a long-standing issue among both the general population and health communicators. This study aims to ascertain the inclination and the reasons for vaccine hesitancy by conducting content analysis and sentiment analysis of the perspectives expressed in comments on videos related to vaccine hesitancy uploaded from India on YouTube.

The assessment of the sentiments of the vaccine-hesitant population is done using Valence Aware Dictionary and sEntiment Reasoner sentiment analysis module implemented with Python’s NLTK library to automatically determine the sentiments of the comments. Manual content analysis was performed on 60.09% viewer comments randomly selected from the total comments in 238 videos on vaccine hesitancy originated from India and labelled each comment with labels “Anti”, “Pro”, “Confused”, “Not Applicable” and “Unrelated” labels.

The study found “Mistrust-Government policies”, “Fear-health related consequences”, “Mistrust-Scientific research”, “Vaccine effectiveness and efficacy” and “Misinformation/myths” as the top five determinants for vaccine hesitancy, whereas “Religious beliefs”, “Fear-Economic consequences”, “Side Effects- short-term” and “Fear-mode of administration” found to be the lesser cited reasons for vaccine hesitancy. However, the study also investigates changes in the inclination of Indian commenters towards vaccine hesitancy and revolving issues over time.

Public health policymakers and health communicators may find the study useful in determining vaccine hesitancy factors in India.

The originality of this study lies in its approach. To date, no sentiment analysis has been conducted on the content released on YouTube by Indian content creators regarding pro- and anti-vaccination videos. This inquiry seeks to fill this research gap.

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.

This bibliometric study aims to analyze publication trends, active countries, collaborations, influential citations and thematic evolution in learning analytics (LA) research focused on higher education (HE) during and after the COVID-19 lockdown period.

From the Scopus database, this bibliometric analysis extracts and evaluates 609 scholarly articles on LA in HE starting in 2019. The multidimensional process identifies the scope impacts, advancing the understanding of LA in HE. An analysis of co-citation data uncovers the key influences that have shaped the literature. This study uses the stimulus-organism-response (SOR) theory to suggest future research directions and organizational adaptations to new LA technologies and learner responses to LA-enabled personalized interventions.

Learning analytics are becoming important in the HE environment during and after the COVID-19 lockout. Institutions have used LA to collect socio-technical data from digital platforms, giving them important insights into learning processes and systems. The data gathered through LA has assisted in identifying areas for development, opening the path for improved student success and academic performance evaluation and helping students transition to the workforce.

The study’s concentration on the post-COVID-19 timeframe may lead to paying attention to potential pandemic developments. Nonetheless, the findings provide a thorough picture of LA’s contributions to HE and valuable ideas for future study initiatives. Future research with the SOR framework suggests areas for additional study to maximize LA’s potential in diverse HE situations.

This study adds to the growing corpus of knowledge on learning analytics in HE, especially in light of the COVID-19 lockdown and its aftermath. By using bibliometric analysis, the study provides a complete and evidence-based understanding of how LA has been used to address challenges related to HE. This study uses bibliometric analysis and SOR theory to appraise and map HE learning analytics research. The selected study themes can help scholars, educators and institutions shape their future efforts to improve teaching, learning and support mechanisms through learning analytics.

Sharing and obtaining information over social media has enabled people to express their opinions regarding any event. Since the tweets regarding the Russia-Ukraine war were extensively publicized on social media, this study aims to analyse the temporal sentiments people express through tweets related to the war.

Relevant hashtag related to the Russia-Ukraine war was identified, and tweets were downloaded using Twitter API, which were later migrated to Orange Data mining software. Pre-processing techniques like transformation, tokenization, and filtering were applied to the extracted tweets. VADER (Valence Aware Dictionary for Sentiment Reasoning) sentiment analysis module of Orange software was used to categorize tweets into positive, negative and neutral ones based on the tweet polarity. For ascertaining the key and co-occurring terms and phrases in tweets and also to visualize the keyword clusters, VOSviewer, a data visualization software, was made use of.

An increase in the number of tweets is witnessed in the initial days, while a decline is observed over time. Most tweets are negative in nature, followed by positive and neutral ones. It is also ascertained that tweets from verified accounts are more impactful than unverified ones. russiaukrainewar, ukraine, russia, false, war, nato, zelensky and stoprussia are the dominant co-occurring keywords. Ukraine, Russia and Putin are the top hashtags for sentiment representation. India, the USA and the UK contribute the highest tweets.

The study tries to explore the public sentiments expressed over Twitter related to Russia-Ukraine war.

The process of redesigning is one of the essential steps in upcycling, which comprises ideation, reconstruction and fitting. This paper aims to study the best practice of upcycling in the clothing industry. This study is an attempt to standardise upcycling/redesign process.

An exploratory approach was adopted to perform the research. This study draws on the multiple organisations involved in the upcycling of clothes. The organisations chosen for this study are located in Sweden and Romania using the snowball technique. Semi-structured interviews, direct and participatory observation approaches were used to collect information. The collected data are systematically analysed using NVivo 10 software.

This paper provides empirical insights into the diverse practices of upcycling. Process, product and demand-based were three fundamental approaches to performing the redesigning process. The fabric quality and durability, variations in size, colour and pattern, skills and efforts required in the extraction of parts and environmental consciousness and awareness were the main factors influencing upcycling process.

The use of the European case may miss best practices from the other region. This study may help scholars to understand the method of upcycling. A practitioner of upcycling can use the findings to improve and standardise the existing process. This research is beneficial for society, as this leads to the reduction of textile wastage.

This paper conceptualises some of the best practices of clothes redesign. This provides a good insight for the organisation for the improvement in the redesign business.

The purpose of this study was to perform sentiment analysis and analyze the growth and popularity of Drupal, Joomla and WordPress on YouTube over a four-year period. This included identifying the most liked and commented videos for each content management system (CMS), ranking the CMSs based on the number of positive comments they received, and using natural language processing techniques to identify the top ten most frequently appearing words in videos about the CMSs.

The data for assessing the features of the videos of Drupal, WordPress and Joomla was extracted using Webometric Analyst version 4.4. with the help of the YouTube application programming interface key for videos on the selected CMSs uploaded from 2019 to 2022. The extraction of comments and sentiment analysis for the relevant videos was done using Mozdeh.

This study scrutinized 371, 234 and 313 videos of WordPress, Joomla and Drupal on YouTube. The findings reveal that there is a chronological growth of videos of the three CMSs in four years and till the present time, WordPress has the highest number of videos followed by Drupal and then Joomla. Regarding the ranking of highly liked videos, WordPress again wins the list with the highest number of likes in its videos followed by Drupal and then Joomla. For analyzing sentiments of the total comments extracted 123,409 for WordPress, 1,790 for Joomla and 1,783 for Drupal, respectively, WordPress receives the highest average positive comments followed by Drupal then Joomla. In top word frequency, the word “thank” highly occurs and viewers are asking for more tutorial videos.

To the best of the authors’ knowledge, this study is the first attempt for analyzing the sentiments of WordPress, Drupal and Joomla using Mozdeh software within the concerning period.

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.

This research aims to investigate the showrooming phenomenon in the context of the evolving omnichannel shopping landscape, which seamlessly integrates both physical and online retail channels. Showrooming, wherein customers browse products in physical stores but ultimately purchase from online competitors, poses a potential threat to the job security and job satisfaction of sales staff in brick-and-mortar (B&M) stores. To address this issue, this study explores the relationship between showrooming, self-efficacy, sales performance, job insecurity and job satisfaction of sales staff, using the job demands-resources (JDR-R) model as a theoretical framework.

This research employs quantitative research methods and gathers data from 219 sales staff working in Indian retail stores. Structural equation modeling is used to test the proposed hypotheses.

The results indicate that showrooming is associated with a decrease in the self-efficacy, sales performance and job satisfaction of sales staff. Furthermore, the result indicates that showrooming is positively associated with increased job insecurity among the sales staff.

This study offers valuable contributions to existing literature and offers insights for both retailers and salespeople regarding the potential repercussions of showrooming. It also suggests coping strategies to address the challenges posed by showrooming and the behavior of showroomers.

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.