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Purpose This paper aims to propose a conceptual model for improving the organization of user needs information in the big data environment. Design/methodology/approach A conceptual model of the organization of user needs information based on Linked Data techniques is constructed. This model has three layers: the Data Layer, the Semantic Layer and the Application Layer. Findings Requirements for organizing user needs information in the big data environment are identified as follows: improving the intelligence level, establishing standards and guidelines for the description of user needs information, enabling the interconnection of user needs information and considering individual privacy in the organization and analysis of user needs. Practical implications This Web of Needs model could be used to improve knowledge services by matching user needs information with increasing semantic knowledge resources more effectively and efficiently in the big data environment. Originality/value This study proposes a conceptual model, the Web of Needs model, to organize and interconnect user needs. Compared with existing methods, the Web of Needs model satisfies the requirements for the organization of user needs information in the big data environment with regard to four aspects: providing the basis and conditions for intelligent processing of user needs information, using RDF as a description norm, enabling the interconnection of user needs information and setting various protocols to protect user privacy.

Information behavior and information practice, two major concepts denoting the general ways in which people deal with information, are analyzed. Because of their general nature, they may be conceived of as umbrella concepts drawing on \"umbrella discourses\" with similar names. Information behavior is currently the dominating umbrella concept, while information practice stands as a critical alternative. The discourses above appear to be quite fragmentary, and researchers on information seeking rarely reflect on the discursive nature of the umbrella concepts. The discourse on information behavior primarily draws on the cognitive viewpoint, while information practice is mainly inspired by the ideas of social constructionism. The comparative study of the above concepts and discourses serves the needs to generate a self-reflective attitude to familiar discursive formations, in particular among researchers of information seeking.

PurposeBuilding on the “needs–affordances–features” framework, the authors explored how users are motivated by their needs to actualize the feature-enabled affordances and engage in the metaverse.Design/methodology/approachThe data were collected through semi-structured and in-depth interviews with 35 participants. The authors applied thematic analysis to summarize the key features and affordances, supplemented by frequency analysis to explore the significance of the features. Sentiment analysis was employed to explicate the relationship between user affordance sentiments and engagement.FindingsThe key features of the metaverse portal components—hardware, software and content—afford user behaviors. The features of mechanics and physics engines are important for user engagement in the metaverse. The affordances are related to needs satisfaction and user engagement. Mental immersion was frequently mentioned by the participants, implying that it is significant to afford mental immersion in the metaverse.Practical implicationsThe findings of the study provide a rich understanding for practitioners in the metaverse on how to use the features to afford user behaviors and engage them. The authors identified the key elements of user engagement that can be used to guide metaverse game designers.Originality/valueThis study provides a rich and systematic understanding of features, affordances, needs satisfaction and engagement in the metaverse. Going beyond a fragmented view, the findings conclude a research framework that weaves features, affordances, needs and engagement together.

Purpose Qualitative researchers and information practitioners often investigate questions that probe the underlying mental models, nuanced perspectives, emotions and experiences of their target populations. The in-depth qualitative interview is a dominant method for such investigations and the purpose of this paper is to demonstrate how incorporating hybrid card-sorting activities into interviews can enable deeper participant reflections and generate rich data sets to increase understanding. Design/methodology/approach Following a review of relevant literature, the case illustration presented is a grounded theory study into the student-researcher information experience with personal academic information management. This study uses hybrid card sorting within in-depth, semi-structured interviews, a unique adaptation that extends multi-disciplinary awareness of the benefits of card-sort exercises for qualitative research. Findings Emerging from diverse fields, ranging from computer science, engineering, psychology and human–computer interaction, card sorting seeks to illuminate how participants understand and organise concepts. The case illustration draws largely on methods used in interaction design and information architecture. Using either open or fixed designs, or hybrid variations, card-sort activities can make abstract concepts more tangible for participants, offering investigators a new approach to interview questions with the aid of this interactive, object-based technique. Originality/value Opening with a comprehensive review of card-sort studies, the authors present an information experience case illustration that demonstrates the rich data generated by hybrid card sorting within qualitative interviews, or interactive interviews. This is followed by discussion of the types of research questions that may benefit from this original method.

Purpose The purpose of this paper is to discuss the emerging and innovative technologies which integrate together to form smart libraries. Smart libraries are the new generation libraries, which work with the amalgamation of smart technologies, smart users and smart services. Design/methodology/approach An extensive review of literature on “smart libraries” was carried to ascertain the emerging technologies in the smart library domain. Clarivate Analytic’s Web of Science and Sciverse Scopus were explored initially to ascertain the extent of literature published on Smart Libraries and their varied aspects. Literature was searched against various keywords like smart libraries, smart technologies, Internet of Things (IoT), Electronic resource management (ERM), Data mining, Artificial intelligence (AI), Ambient intelligence, Blockchain Technology and Augmented Reality. Later on, the works citing the literature on Smart Libraries were also explored to visualize a broad spectrum of emerging concepts about this growing trend in libraries. Findings The study confirms that smart libraries are becoming smarter with the emerging smart technologies, which enhances their working capabilities and satisfies the users associated with them. Implementing the smart technologies in the libraries has bridged the gap between the services offered by the libraries and the rapidly changing and competing needs of the humans. Practical implications The paper highlights the emerging smart technologies in smart libraries and how they influence the efficiency of libraries in terms of users, services and technological integration. Originality/value The paper tries to highlight the current technologies in the smart library set-ups for the efficient working of library set-ups.

PurposeThe PICCH research project contributes to opening a dialogue between cultural heritage archives and users. Hence, the users are identified and their information needs, the search strategies they apply and the search challenges they experience are uncovered.Design/methodology/approachA combination of questionnaires and interviews is used for collection of data. Questionnaire data were collected from users of three different audiovisual archives. Semi-structured interviews were conducted with two user groups: (1) scholars searching information for research projects and (2) archivists who perform their own scholarly work and search information on behalf of others.FindingsThe questionnaire results show that the archive users mainly have an academic background. Hence, scholars and archivists constitute the target group for in-depth interviews. The interviews reveal that their information needs are multi-faceted and match the information need typology by Ingwersen. The scholars mainly apply collection-specific search strategies but have in common primarily doing keyword searching, which they typically plan in advance. The archivists do less planning owing to their knowledge of the collections. All interviewees demonstrate domain knowledge, archival intelligence and artefactual literacy in their use and mastering of the archives. The search challenges they experience can be characterised as search system complexity challenges, material challenges and metadata challenges.Originality/valueThe paper provides a rare insight into the complexity of the search situation of cultural heritage archives, and the users’ multi-facetted information needs and hence contributes to the dialogue between the archives and the users.

We are all faced with uncertainty about the future, but we can get the measure of some uncertainties in terms of probabilities. Probabilities are notoriously difficult to communicate effectively to lay audiences, and in this review we examine current practice for communicating uncertainties visually, using examples drawn from sport, weather, climate, health, economics, and politics. Despite the burgeoning interest in infographics, there is limited experimental evidence on how different types of visualizations are processed and understood, although the effectiveness of some graphics clearly depends on the relative numeracy of an audience. Fortunately, it is increasingly easy to present data in the form of interactive visualizations and in multiple types of representation that can be adjusted to user needs and capabilities. Nonetheless, communicating deeper uncertainties resulting from incomplete or disputed knowledge—or from essential indeterminacy about the future—remains a challenge.

Purpose – Many video sharing sites (e.g. YouTube, Vimeo, and Break) host user-generated video content in the hopes of attracting viewers and thus profits. Therefore, continuous use and video sharing behavior on the part of site users is critical to the continue enjoyment of other users and to the video service providers business. The purpose of this paper is to provide an improved understanding of what motivates internet users to share videos and spend more time on video sharing web sites. Design/methodology/approach – The authors propose a research model based on Uses and Gratification Theory and on Social Cognitive Theory, incorporating key determinants of web site stickiness. An online survey instrument was developed to gather data, and 265 questionnaires were used to test the relationships in the model. Findings – The causal model was validated using SmartPLS 2.0, and 14 out of 18 study hypotheses were supported. The results indicated that continuance motivation and sharing behavior were important antecedents of YouTube stickiness and mediated the influence of need, personal, and environmental factors. Practical implications – The proposed framework can be used by online video service providers to develop a platform that satisfies user needs and to enhance sharing intention. Originality/value – The study provides a comprehensive framework of the antecedents and effects of continuance motivation and sharing behavior on video sharing web sites.

This article examines the linguistic structure of folksonomy tags collected over a thirty-day period from the daily tag logs of Del.icio.us, Furl, and Technorati. The tags were evaluated against the National Information Standards Organization (NISO) guidelines for the construction of controlled vocabularies. The results indicate that the tags correspond closely to the NISO guidelines pertaining to types of concepts expressed, the predominance of single terms and nouns, and the use of recognized spelling. Problem areas pertain to the inconsistent use of count nouns and the incidence of ambiguous tags in the form of homographs, abbreviations, and acronyms. With the addition of guidelines to the construction of unambiguous tags and links to useful external reference sources, folksonomies could serve as a powerful, flexible tool for increasing the user-friendliness and interactivity of public library catalogs, and also may be useful for encouraging other activities, such as informal online communities of readers and user-driven readers’ advisory services.

Artificial intelligence (AI) and learning analytics (LA) tools are increasingly implemented as decision support for learners and professionals. However, their affordances for guidance purposes have yet to be examined. In this paper, we investigated advantages and challenges of AI-enhanced LA tool for supporting career decisions from the user perspective. Participants ( N  = 106) interacted with the AI-enhanced LA tool and responded to open-ended questionnaire questions. Content analysis was utilized for the data analysis applying two distinct and robust frameworks: technology acceptance model (TAM) and career decision-making model (CDM) as well as looking into user needs. Results indicate that the AI-enhanced LA tool provided five main benefits to the users: provision of career information, research and analysis of the information, diversification of ideas on possible career paths, providing direction and decision support, and self-reflection. The participants perceived the AI-enhanced LA tool as a supportive asset to be used in transitional life situations characterized with uncertainty. Considerable use difficulties were reported as well as need for further diversification of ideas on possible career paths, need for personalization and self-reflection support, and need for further information. Results regarding perceived support for making career decisions showed that CDM elements were unequally supported by the AI-enhanced LA tool. Most support was focused to investigate smaller number of provided options and make decisions, while contextual information was lacking. Implications for career decision making are discussed.