Purpose This study aims to develop a role-oriented competency framework for data visualization services (DVS) among academic librarians. It seeks to address the limitations of existing competency models, which are often static and insufficiently aligned with librarians’ actual service roles, by explicitly linking competencies to practical responsibilities in the context of research communication and user engagement. Design/methodology/approach A qualitative research design was adopted, using semi-structured interviews with 24 academic librarians from top-tier universities in China. The collected data were analyzed thematically to identify recurring patterns in roles, responsibilities and required competencies related to DVS. Findings The study proposes a competency framework structured around four key service roles: steward, trainer, collaborator and advocate. These roles encompass 11 core responsibilities, each associated with specific knowledge, skills and attributes. The findings highlight the multifaceted nature of DVS and demonstrate how competencies vary depending on librarians’ functional roles. Originality/value This research advances the theoretical understanding of data visualization competencies by introducing a dynamic, role-based framework that closely reflects professional practice. It offers practical value for academic libraries by informing targeted professional development, strategic staffing and the design of data-driven services in support of digital scholarship.
Purpose This study aims to enhance the user experience for patrons and improve library operation efficiency through implementation of an artificial intelligence (AI)-driven Q&A chatbot. Design/methodology/approach Using RAG technology, this study developed Nana, a multimodal intelligent library chatbot based on open-source pretrained language models. Following multiple rounds of iteration and debugging to optimize its local documents retrieval and summarization capabilities, Nana’s performance was compared with four other library chatbots via four key metrics. Findings The results demonstrate that the authors’ chatbot performs exceptionally well in evaluations, effectively addressing common library reference queries and optimizing librarians’ time by promptly answering frequently asked questions. In addition, the authors’ chatbot exhibits robust performance in understanding and responding to queries in atypical scenarios. As a multimodal chatbot, it is distinguished by its unique capability to process image data and user-uploaded documents, significantly enhancing its utility and user-friendliness. Practical implications This research offers a practical roadmap for libraries seeking to upgrade their intelligent services. The RAG-based multimodal chatbot can efficiently handle user inquiries by retrieving local resources accurately. By reducing the burden of routine Q&A on library staff and improving service accessibility, it serves as a scalable framework for digital libraries (DL) aiming to modernize the user experience. Originality/value This study advances smart digital libraries by developing a multimodal RAG-based Q&A chatbot, thereby addressing functional gaps in existing systems. Unlike conventional text-only chatbots, it features domain-adapted RAG optimization to boost the accuracy of retrieving library-specific local resources, plus integrated multimodal interaction (text, document, visual) for diverse user needs. Validated against four benchmarks, this study offers both a high-performance conversational solution and a replicable framework for domain-specific multimodal RAG chatbots in information services.
Purpose Based on the cognition-affect-conation (C-A-C) framework, this study aims to examine the dual effect of both enablers and inhibitors on user health information disclosure intention on AI-generated content (AIGC) platforms. Design/methodology/approach The authors adopted a mixed method of structural equation modeling and fuzzy-set qualitative comparative analysis to analyze data. Findings The results show that information quality, privacy assurance and perceived empathy affect perceived trust, which promotes disclosure intention. In contrast, information overload and low transparency lead to cognitive dissonance, which inhibits disclosure intention. Research limitations/implications The results imply that AIGC platforms need to build user trust and mitigate cognitive dissonance to promote user disclosure intention and ensure sustainable development. Originality/value Prior research has often used the privacy calculus theory to investigate user disclosure intention, emphasizing a rational cognitive perspective (the trade-off between benefits and risks) while neglecting the role of affective factors in behavioral decision. Drawing on the C-A-C framework, this study reveals the formation mechanism of AIGC user health information disclosure intention.
Purpose This paper aims to propose a method based on multi-source data and deep learning to early identify emerging technologies (ETs). On the one hand, this paper considers sufficient data sources related to the key characteristics of ETs, which can help enterprises and countries grasp technological innovation opportunities and market development trends in advance. On the other hand, this paper proposes a method based on deep learning to fit the complex and non-linear relationships more accurately between features, thereby achieving the early identification of ETs. Design/methodology/approach First, multi-source data sets are collected for emerging technology identification, such as papers, patents and industry reports, to cover multi-dimensional characteristics of ETs from different perspectives. Second, the corresponding features are designed and extracted from multi-source data, to expand and supplement current insufficient features. Finally, a time series forecasting model based on deep learning is designed to fuse multiple features, which can better fit complex and non-linear relationships between features. Findings The experiment on artificial intelligence (AI) demonstrates that integrating multi-source data can improve the performance of the model, with each individual data source contributing unique predictive value. Among them, patent data serves as the strongest individual predictor, while combining it with academic papers and industry reports yields the best performance by capturing diverse signals. In addition, long short-term memory networks (LSTM) achieve the strongest overall regression and ranking performance among the four compared models. Originality/value This work expands a single data source into multiple data sources, by fully considering knowledge impact, technology impact and market impact to comprehensively identify emerging technology. This study classifies multiple features into different categories based on the characteristics of ETs and design methods to extract corresponding features from multi-source data. This study designs a deep learning model to fuse multiple features for better fitting complex relationships among them. Compared with representative linear, tree-based and recurrent baseline models, LSTM achieves the strongest overall regression and ranking performance.
Purpose The rapid integration of artificial intelligence (AI) technologies into information services has made librarians’ competencies and acceptance levels toward these technologies an increasingly critical research concern. This study aims to examine the relationship between AI literacy levels and AI technology acceptance levels among librarians in Türkiye. Design/methodology/approach The technology acceptance model (TAM) scale and the AI literacy scale were used as measurement instruments in the study. Data were collected from 203 librarians working in different types of libraries through an online survey. Pearson correlation and multiple linear regression analyses revealed that AI literacy significantly predicted technology acceptance, with the evaluation dimension emerging as the dominant predictor of both perceived usefulness and perceived ease of use. Findings The finding that demographic variables largely showed limited effects suggests that AI literacy and acceptance are shaped by professional training and organizational conditions. Originality/value This study represents one of the first empirical investigations to examine the relationship between AI literacy and technology acceptance within an integrated model framework in the Türkiye library science context.
