Purpose This study aims to examine how linguistic and structural features of research abstracts are associated with citation performance across six selected emerging scientific domains: Blockchain, Climate Science, Gene Editing, Nanomedicine, Quantum Computing and Space Mining. Design/methodology/approach A dataset of 39,350 journal articles (2015–2024) from selected domains was retrieved from the Scopus database and analysed using computational text mining techniques in Python. Six abstract-level features length, readability (Flesch Reading Ease), subjectivity, polarity, sentiment and keyword density were correlated with citation counts using Pearson’s correlation and regression modelling. Findings Abstracts of 150–350 words receive significantly higher citations, with a consistent negative correlation between length and citations (r = −0.13 to −0.18). Keyword density is the strongest bivariate predictor across domains (r = 0.17–0.23), followed by readability (r = 0.08–0.15); subjectivity shows mixed, weak effects, while polarity and sentiment show negligible associations (r = −0.05 to 0.06). Cross-validated multivariate and machine-learning models indicate negligible to negative out-of-sample predictive power for citation counts in every domain. These bivariate correlations should be read as weak, within-sample associations rather than validated predictors. Originality/value This study provides large-scale cross-domain evidence, linking linguistic and structural features of abstracts with citation performance across six emerging research domains, while cautioning, through rigorous cross-validation, that these features alone are weak and have limited generalisability as predictors of citation performance.
Purpose This study aims to investigate generative AI (GenAI) adoption in Indian Subcontinent higher education – a region marked by extreme socio-economic inequalities, infrastructural deficits and linguistic diversity – examining how perceived ease of use, usefulness, satisfaction, trust and security influence students’ continuance intention. Design/methodology/approach A sequential mixed-methods design was used. Quantitative data from an online survey of higher education students (n = 704) across seven South Asian countries were analyzed using partial least squares-structural equation modelling. Qualitative data were collected via semi-structured interviews with 14 professors from six countries within the Indian Subcontinent. The Technology Continuance Theory (TCT) served as the analytical framework. Findings Perceived ease of use, usefulness, satisfaction, trust and security significantly shape continuance intention. Satisfaction emerged as the strongest predictor of continuance intention (ß = 0.571, p < 0.001). Applications concentrate on content creation, administrative automation and performance enhancement. However, limited broadband access, chronic power instability, entrenched inequalities and underrepresentation of regional languages in large language models constrain equitable adoption. Research limitations/implications The cross-sectional design limits causal inferences. Self-reported data may introduce bias and the sample overrepresents urban, English-proficient populations. Future research should use longitudinal designs, include administrator perspectives, experimentally evaluate interventions for linguistic barriers and conduct comparative studies across Global South and North contexts. Practical implications South Asian higher education institutions must address infrastructure deficits (reliable electricity, affordable internet), integrate GenAI tools with clear ethical guidelines, advocate for regional-language large language models, negotiate subsidized licenses and provide faculty development programs to bridge the generational digital divide. Social implications Without inclusive digital policies and culturally attuned regulations, GenAI risks exacerbating divides between elite urban universities and underfunded rural colleges and between English speakers and regional language users. North–South partnerships are essential for equitable and sustainable higher education in the Global South. Originality/value To the best of the authors’ knowledge, this study is among the first to examine GenAI continuance intention across the entire Indian Subcontinent (seven countries). It extends TCT by integrating region-specific barriers – infrastructural instability, linguistic exclusion and socio-economic inequality – alongside AI-specific antecedents. The mixed-methods design provides robust, context-sensitive evidence from both students and professors.
Purpose This study aims to explore how students’ information retrieval (IR) skills influence their academic success and personal growth. It also measured students’ self-assessed IR skills, their knowledge of complex search techniques and scholarly information resources and the association between these skills and their academic and cognitive skill development. Design/methodology/approach This study used a quantitative research approach, collecting data from 374 undergraduate and graduate students at a public university in Bangladesh via a printed questionnaire survey and using a convenience sampling technique. Non-parametric tests and Spearman’s rank correlation analysis examined differences between groups and relationships among variables. Findings Results revealed that students generally lacked confidence in retrieving information with Boolean operators, truncation and metadata filtering, despite showing some proficiency in basic IR tasks like keyword searching and source identification. The most frequently used scholarly information resources were Google Scholar, ResearchGate and Academia.edu, while more specialized resources like Scopus, IEEE Xplore and Web of Science were less frequently used. Finally, students’ IR skills varied significantly based on their field of study, gender, education level, CGPA, research experience, internet skills, IR training and use of AI technologies and their academic achievement and cognitive skill development were strongly linked to their perceived IR skills. Research limitations/implications This study was conducted at a single public university in Bangladesh. Therefore, the findings should be interpreted within the context of the participating institution and warrant validation through multi-institutional studies. Originality/value To the best of the authors’ knowledge, this study is the first attempt in Bangladesh to identify the association of IR skills with students’ academic performance and cognitive skill development.
Purpose The rapid emergence of artificial intelligence (AI) chatbot applications has transformed digital information access, yet the theoretical understanding of how users evaluate new AI tools during early adoption remains limited. Grounded in the Technology Acceptance Model and Expectation Confirmation Theory, the study aims to address a conceptual gap regarding mixed sentiment and explore how demographic factors, such as gender, shape user experience during early adoption. Design/methodology/approach A mixed-methods approach was used to analyze 1,000 Google Play Store reviews. Reviews were categorized by sentiment: positive, negative, mixed (both positive and negative statements) or neutral. Gender predictions were also made from usernames to explore demographic differences in sentiment. Findings Of the 1,000 reviews, 487 were mixed reviews (48.7%), 373 were negative reviews (37.3%), 139 were positive reviews (13.9%) and one was neutral (0.1%), indicating that mixed and negative sentiment predominated. Users were mostly satisfied with DeepSeek’s usability and output quality, but criticized its performance and stability issues. Female users gave more negative reviews than male users (p < 0.05). Research limitations/implications The analysis was conducted on a stratified sample of 1,000 “most relevant” Google Play reviews from a short early adoption period, which may not accurately represent later user experiences or experiences on other platforms. Only reviews from phone users were included, and the neutral sentiment category contained too few cases for comparison. Gender inference was based on usernames and reflects predicted rather than self-identified gender. Therefore, the results should be interpreted as indicative of early user perceptions rather than long-term attitudes. Practical implications The findings indicate that infrastructure reliability is the primary determinant of early user satisfaction. Transparent communication about service limitations is recommended prior to large-scale deployment. For policymakers, the equity implications of performance-unstable free AI services warrant attention, particularly for users with limited access to alternatives. Originality/value The mixed-methods analysis of early DeepSeek user reviews, which distinguishes between mixed sentiment and positive and negative feedback, and incorporates a gender-based comparison, provides timely insight into user expectations and challenges during early adoption, supporting the development of more user-centered and stable AI systems.
Purpose The purpose of this study is to examine the use of YouTube as an academic learning resource among undergraduate students and to assess their perceptions, usage behavior and the challenges they face while using the platform for academic purposes. Design/methodology/approach The study used a quantitative survey with a structured questionnaire among undergraduate students of Ananda Chandra College, Jalpaiguri, West Bengal, India. A total of 526 valid responses, determined via the Krejcie and Morgan table (95% confidence, ± 5% margin), were analyzed using SPSS 25. Descriptive statistics (mean, standard deviation) summarized responses, while inferential analyses included chi-square tests, independent t-tests, repeated measures Analysis of Variance (ANOVA) and univariate factorial ANOVA to examine demographic effects and interactions on perceptions of YouTube’s academic utility. Findings The study revealed that all respondents used YouTube daily, with the largest proportion (42.6%) spending 2–3 h on the platform. Entertainment-related videos were the most viewed, followed closely by academic content. Students aged 19–21 years reported higher levels of YouTube usage than other age groups, demonstrating a significant association between age and daily usage patterns. A large majority of respondents subscribed to educational channels, with Khan Academy/Khan Sir (general education and competitive examination preparation) and Drishti IAS (civil services examination preparation) being the most popular. Students perceived YouTube as particularly valuable for accessing current information, exploratory learning and supplementary academic support. Significant differences were observed across content dimensions, with currency and exploratory learning receiving higher ratings than credibility and comparative effectiveness. Major challenges included advertisements, distracting recommendations and concerns regarding content organization and reliability. Practical implications Based on these findings, we recommend strengthening students’ digital and information literacy skills to enable them to critically evaluate educational content available on YouTube. Faculty members and academic libraries can support learning through curated educational resources and guided use of YouTube, while institutional administrators should promote digital learning support initiatives. Educational content creators may enhance learning outcomes by improving content credibility, organization and curriculum alignment. Originality/value This study contributes empirical evidence on how undergraduate students in a semi-rural Indian context use YouTube for academic learning, highlighting usage patterns, educational channel preferences, perceived learning value and challenges associated with the platform.
