Search results

This paper aims to critically review the intersection of searching and learning among children in the context of voice-based conversational agents (VCAs). This study presents the opportunities and challenges around reconfiguring current VCAs for children to facilitate human learning, generate diverse data to empower VCAs, and assess children’s learning from voice search interactions.

The scope of this paper includes children’s use of VCAs for learning purposes with an emphasis on conceptualizing their VCA use from search as learning perspectives. This study selects representative works from three areas of literature: children’s perceptions of digital devices, children’s learning and searching, and children’s search as learning. This study also includes conceptual papers and empirical studies focusing on children from 3 to 11 because this age spectrum covers a vital transitional phase in children’s ability to understand and use VCAs.

This study proposes the concept of child-centered voice search systems and provides design recommendations for imbuing contextual information, providing communication breakdown repair strategies, scaffolding information interactions, integrating emotional intelligence, and providing explicit feedback. This study presents future research directions for longitudinal and observational studies with more culturally diverse child participants.

This paper makes important contributions to the field of information and learning sciences and children’s searching as learning by proposing a new perspective where current VCAs are reconfigured as conversational voice search systems to enhance children’s learning.

The landscape of search engine usage has evolved since the last known data were used to calculate click-through rate (CTR) values. The objective was to provide a replicable method for accessing data from the Google search engine using programmatic access and calculating CTR values from the retrieved data to show how the CTRs have changed since the last studies were published.

In this study, the authors present the estimated CTR values in organic search results based on actual clicks and impressions data, and establish a protocol for collecting this data using Google programmatic access. For this study, the authors collected data on 416,386 clicks, 31,648,226 impressions and 8,861,416 daily queries.

The results show that CTRs have decreased from previously reported values in both academic research and industry benchmarks. The estimates indicate that the top-ranked result in Google's organic search results features a CTR of 9.28%, followed by 5.82 and 3.11% for positions two and three, respectively. The authors also demonstrate that CTRs vary across various types of devices. On desktop devices, the CTR decreases steadily with each lower ranking position. On smartphones, the CTR starts high but decreases rapidly, with an unprecedented increase from position 13 onwards. Tablets have the lowest and most variable CTR values.

The theoretical implications include the generation of a current dataset on search engine results and user behavior, made available to the research community, creation of a unique methodology for generating new datasets and presenting the updated information on CTR trends. The managerial implications include the establishment of the need for businesses to focus on optimizing other forms of Google search results in addition to organic text results, and the possibility of application of this study's methodology to determine CTRs for their own websites.

This study provides a novel method to access real CTR data and estimates current CTRs for top organic Google search results, categorized by device.

The purpose of this research is to provide insights into the daily search strategies of users, which can inform the enhancement of search experiences across multiple applications. By understanding how users navigate and interact with different apps during their search processes, the study seeks to contribute to the design of more intuitive and user-friendly app systems.

This study employs a mixed-methods approach to analyze users' daily search strategies in a natural cross-app interactive environment. Data collection was conducted using the Critical Incident Technique and the Micro-Moment Time Line, involving 204 participants to capture their real-time search experiences. Open coding techniques were utilized to categorize sequential search tactics, while the PrefixSpan algorithm was applied to identify patterns in frequently applied search strategies.

The study findings unveil a comprehensive framework that includes a variety of intra-app search tactics and inter-app switching tactics. Five predominant search strategies were identified: Iterative querying, Selective results adoption, Share-related, Recommended browsing, and Organizational results strategies. These strategies reflect the nuanced ways in which users engage with apps to fulfill their information needs.

This research represents a pioneering effort in systematically identifying and categorizing daily search strategies within a natural cross-app interaction context. It offers original contributions to the field by combining intra-app and inter-app tactics, providing a holistic view of user behavior. The implications of these findings are significant for app developers and designers, as they can leverage this knowledge to improve app functionality and user manuals, ultimately enhancing the overall search experience for users.

This study aims to explore the capabilities, limitations and potential of ChatGPT applicable to online reference services in academic libraries.

This study used the method of qualitative content analytics to assess the general capabilities of ChatGPT applicable in academic libraries. Two experienced academic librarians had face-to-face interactions with ChatGPT by asking ten most common questions often asked by faculty and students at the Georgia Southern University Libraries (https://library.georgiasouthern.edu/). To examine the ChatGPT’s applicability and capability, they also compared the ChatGPT with a popular online chat reference tool called LibChat, which is now widely used in academic libraries in 91 countries worldwide.

It was found that as an artificial intelligence (AI)-powered real-time chatbot ChatGPT could effectively provide faculty and students with general guidance on locating the needed information resources and services in academic libraries, though its responses might not be accurate or truthful all the time. Embedded into the LibAnswers system of the Springshare’s products (www.springshare.com/libanswers/), LibChat serves as a real-time online chat tool used by academic libraries for reference services, but it is only available during the regular librarians' duty hours. This technical limitation does not meet the dynamic needs of faculty, students, staff, and local community users. Only well-optimized AI-driven chat products like ChatGPT could provide 24/7 online services to support uninterrupted academic library services in the future.

This study only examined the general capability and potential of ChatGPT3.5 in specific subject areas. Additional studies are needed to further explore how the latest capabilities of ChatGPT4.0 or newer version, such as its text-to-image, text-to-speech, text-to-text, text-to-video and Web search, could impact future reference services of academic libraries. ChatGPT’s primary optimization and upgrades in the future may also change and impact this study's findings. The comparison between ChatGPT and LibChat presents a significant breakthrough of the generative AI technology in academic libraries. This comparative study encourages more academic experts, faculty, librarians and scholars to track the advance of generative AI applications, including ChatGPT, adopted in academic learning environments. In addition, the ChatGPT's complete capability and potential enhanced and integrated in the future may go beyond what this study evaluated.

This study examined the strengths and weaknesses of ChatGPT applicable to reference services of academic libraries. Through a comparison between ChatGPT and LibChat, this study suggests that optimized AI online chatbots still have a long way to go to meet the dynamic needs of faculty and students in the ever-changing academic learning environments. To contribute to the existing research literature focusing on the rise of generative AI tools such as ChatGPT, this study provides a valuable reference for the applicability of generative AI applications in academic libraries to promote more library creation and innovation in the coming years of the 21st century.

This study aims to analyze the status and application/use of human–computer interaction (HCI) in libraries by conducting a systematic literature review (SLR).

A Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) approach was used to search Scopus, Web of Science and Google Scholar databases. The search criteria included research studies published in English language between 2010 and 2021, which were 4,167 citations. Out of 4,167 citations, a total of 50 studies were selected for the final analysis.

The results showed a positive attitude of librarians toward HCI applications in libraries worldwide. The results depict that one-third (30%) of the studies were conducted in the USA, followed by four (8%) studies in China. Out of 50 studies, a portion of 15 (30%) studies were based on digital libraries, followed by seven (14%) studies on academic libraries and five (10%) studies on libraries and their websites. HCI was used for searching and retrieving information, users’ interaction, authentication, online help/support, feedback, library web access, web OPAC, virtual access to resources, indigenous repository and virtual services. The most productive year was 2015, and journal of The Electronic Library had more articles on HCI than other journals.

The findings of this study could assist policymakers and library authorities in reconciling the HCI application in libraries for providing effective and efficient access and services to end-users.

This study is unique as no comprehensive study has been conducted on the use of HCI in librarianship using the SLR method.

Informational conflict and uncertainty are common features across a range of sources, topics and tasks. Search engines and their presentation of results via search engine results pages (SERPs) often underpinned by knowledge graphs (KGs) are commonly used across tasks. Yet, it is not clear how search does, or could, represent the informational conflict that exists across and within returned results. The purpose of this paper is to review KG and SERP designs for representation of uncertainty or disagreement.

The authors address the aim through a systematic analysis of material regarding uncertainty and disagreement in KG and SERP contexts. Specifically, the authors focus on the material representation – user interface design features – that have been developed in the context of uncertainty and disagreement representation for KGs and SERPs.

Searches identified n = 136 items as relevant, with n = 4 sets of visual materials identified from these for analysis of their design features. Design elements were extracted against sets of design principles, highlighting tensions in the design of such features.

The authors conclude by highlighting two key challenges for interface design and recommending six design principles in representing uncertainty and conflict in SERPs. Given the important role technologies play in mediating information access and learning, addressing the representation of uncertainty and disagreement in the representation of information is crucial.

This conceptual paper aims to demonstrate a holistic, multifaceted framework of interest development, information search, and knowledge construction (ISK) on children’s diverse information search behaviors.

By reviewing previous literature on children’s interest development, online information seeking and search, and knowledge construction, we propose the ISK framework. Then, we provide example case studies with pilot analyses using qualitative approaches (e.g. video-based interaction analysis, thematic analysis) showing how the framework can be applied to different types of authentic information-seeking situations for children.

The ISK framework demonstrates the multifaceted interplays between children’s information behavior and their cognitive and affective development. This framework was supported using previous studies and pilot empirical applications. We also included potential research questions that could be addressed using the framework.

Our paper provides a conceptual grounding to an in-depth, multidimensional understanding of children’s information behavior, which have been limitedly addressed in previous studies. Considering that children begin to search from their early stage of development and their search behaviors are tightly associated with other developmental states, our paper highlights the importance of investigating children’s search and information seeking and provides theoretical and empirical implications.

A methodological approach is required that complements studies based on surveys, providing a perspective with greater truthfulness and coverage. The study aims to develop a methodology to validate psychological/managerial constructs using data from Google Trends, taking as a case study a critical thinking (CT) scale in organizational domains previously supported by survey data.

The developed methodology consists of eight stages, in which the following is integrated: (1) Internet search interest data (19 Spanish-speaking countries); (2) deductive research processes (e.g. theoretical model, linguistic manifestations, fieldwork, data matrix, analysis statistical, reporting); (3) psychometric properties (e.g. construct validity, criterion validity, reliability) and (4) objective data to examine criterion validity (e.g. unemployment rate).

The application of the methodology produces evidence that supports the reliability (Cronbach’s alpha, Guttman’s λ₄), construct validity (intra-correlations and correlations with reference variables: “entrepreneurship,” “critical thinking,” “soccer,” “beer,” “pornography”) and criterion validity (prediction of unemployment rate) of the CT scale.

The methodology makes it possible to support or invalidate the quality of construct measurement scales by planning, capturing and processing data available on the internet.

This manuscript is useful for research in business management (and related areas), which is intensive in the use of psychological/managerial constructs.

The methodology uses a new type of evidence; it is noninvasive, usually more truthful than responses to surveys, and has greater coverage of people participating indirectly in the study.

This study examines the temporal dynamics shaping our understanding of search in education and the role language plays in legitimising these dynamics. It critiques the way online search is discursively constructed using home-education as a case study, and problematises how particular discourses are privileged, whom this privileging serves, as well as the likely consequences.

The study employs Faircloughian Critical Discourse Analysis (CDA) as its methodological framework. Search and discursive practices were recorded during observations, search-tasks and interviews with five Australian home-educating families. Discursive features from the Google interface were also analysed.

A discursive privileging of hasty search practices was identified. This was found alongside largely ineffectual search, but participants continued to discursively represent search as fast and easy. The study highlights the complex co-option of discourses surrounding online search that privilege particular temporal and commercial landscapes.

This study contributes new knowledge regarding time as a context for understanding search behaviours, locating the perception of temporal scarcity in education within broader discursive and social structures. To date, no studies are found which investigate the temporal factors surrounding search in home-education. Increasing global reliance upon online search means the findings have broad significance, as does the proliferation of home-education induced by COVID-19. Additionally, while much work problematises the power search engines wield to privilege certain discourses, few investigate the day-to-day discursive practices of searchers affording Google and others this power.

Online reviews serve as valuable sources of information, reflecting tourists’ attentions, preferences and sentiments. However, although the existing research has demonstrated that incorporating online review data can enhance the performance of tourism demand forecasting models, the reliability of online review data and consumers’ decision-making process have not been given adequate attention. To address the aforementioned problem, the purpose of this study is to forecast tourism demand using online review data derived from the analysis of review helpfulness.

The authors propose a novel “identification-first, forecasting-second” framework. This framework prioritizes the identification of helpful reviews through a comprehensive analysis of review helpfulness, followed by the integration of helpful online review data into the forecasting system. Using the SARIMAX model with helpful online review data sourced from TripAdvisor, this study forecasts tourist arrivals in Hong Kong during the period from August 2012 to June 2019. The SNAÏVE/SARIMA model was used as the benchmark model. Additionally, artificial intelligence models including long short-term memory, back propagation neural network, extreme learning machine and random forest models were used to assess the robustness of the results.

The results demonstrate that online review data are subject to noise and bias, which can adversely affect the accuracy of predictions when used directly. However, by identifying helpful online reviews beforehand and incorporating them into the forecasting process, a notable enhancement in predictive performance can be realized.

First, to the best of the authors’ knowledge, this study is one of the first to focus on the data issue of online reviews on tourism arrivals forecasting. Second, this study pioneers the integration of the consumer decision-making process into the domain of tourism demand forecasting, marking one of the earliest endeavors in this area. Third, this study makes a novel attempt to identify helpful online reviews based on reviews helpfulness analysis.