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.
Purpose This study aims to conduct a bibliometric analysis to provide a comprehensive overview of the existing literature on investor sentiment and herding behavior in financial markets and to identify promising avenues for future research. Design/methodology/approach A total of 2,351 articles published between 1976 and 2024 were retrieved from the Scopus database. The analysis was carried out using the Bibliometrix R package and VOSviewer. A performance analysis was conducted to examine publication trends, leading authors, key journals and the most influential studies. Additionally, a science mapping approach, specifically bibliographic coupling and thematic evolution, was used to explore the intellectual structure of the field. Findings The findings indicate a significant growth in scholarly output over the past four decades, reflecting increasing academic interest in the behavioral aspects of financial decision-making. Bibliometric mapping identifies four major thematic clusters: drivers and global financial implications of investor sentiment, investor sentiment as a tool for market forecasting and volatility analysis, behavioral anomalies and collective behavior in financial decision-making and patterns and determinants of herding across global financial markets. The analysis also highlights the evolving nature of the field, with recent research increasingly integrating sentiment analytics and advanced computational techniques to understand investor behavior and financial market dynamics. Originality/value This study takes a pioneering approach by integrating bibliometric analysis of both investor sentiment and herding behavior within the context of financial markets. It maps the conceptual and intellectual structure of the field, highlights key thematic areas and offers future research directions, making it a valuable contribution to the advancement of the behavioral finance field.
Purpose This study aims to examine the knowledge structure, thematic evolution and methodological progression of corporate financial distress prediction (FDP) research through a comprehensive bibliometric and science mapping approach. Design/methodology/approach The study analyses 1,213 Scopus-indexed publications spanning 1981 to February 2026. It uses a combination of performance analysis and science mapping techniques, including co-citation analysis, bibliographic coupling and author keyword co-occurrence networks, to systematically map the intellectual, social and conceptual structure of FDP research. VOSviewer software is used to perform network visualisation and clustering analyses. Findings The findings reveal a progressive evolution of FDP research from traditional accounting-based and statistical models to advanced data-driven approaches using machine learning, artificial intelligence and textual analytics. Thematic clustering indicates an increase in interdisciplinarity, integrating financial, computational, governance and macroeconomic perspectives. Methodological advancement occurs through integration rather than replacement, with classical financial theories continuing to underpin contemporary predictive models. Research limitations/implications The study is limited to corporate FDP literature indexed in the Scopus database. Future studies could enhance depth and coverage by incorporating broader financial distress contexts, interdisciplinary perspectives and additional databases, as well as explainable and context-sensitive FDP models to improve practical applicability. Originality/value This study offers a theory-linked, knowledge-discovery-oriented synthesis of FDP research, moving beyond descriptive bibliometric reviews. It maps the field’s intellectual and thematic structure and demonstrates how interdisciplinary integration shapes the evolution of corporate FDP research.
