Search results

Although the challenges associated with big data are increasing, the question of the most suitable big data analytics (BDA) platform in libraries is always significant. The purpose of this study is to propose a solution to this problem.

The current study identifies relevant literature and provides a review of big data adoption in libraries. It also presents a step-by-step guide for the development of a BDA platform using the Apache Hadoop Ecosystem. To test the system, an analysis of library big data using Apache Pig, which is a tool from the Apache Hadoop Ecosystem, was performed. It establishes the effectiveness of Apache Hadoop Ecosystem as a powerful BDA solution in libraries.

It can be inferred from the literature that libraries and librarians have not taken the possibility of big data services in libraries very seriously. Also, the literature suggests that there is no significant effort made to establish any BDA architecture in libraries. This study establishes the Apache Hadoop Ecosystem as a possible solution for delivering BDA services in libraries.

The present work suggests adapting the idea of providing various big data services in a library by developing a BDA platform, for instance, providing assistance to the researchers in understanding the big data, cleaning and curation of big data by skilled and experienced data managers and providing the infrastructural support to store, process, manage, analyze and visualize the big data.

The study concludes that Apache Hadoops’ Hadoop Distributed File System and MapReduce components significantly reduce the complexities of big data storage and processing, respectively, and Apache Pig, using Pig Latin scripting language, is very efficient in processing big data and responding to queries with a quick response time.

According to the study, there are significantly fewer efforts made to analyze big data from libraries. Furthermore, it has been discovered that acceptance of the Apache Hadoop Ecosystem as a solution to big data problems in libraries are not widely discussed in the literature, although Apache Hadoop is regarded as one of the best frameworks for big data handling.

This paper aims to give an overview of the history and evolution of commercial search engines. It traces the development of search engines from their early days to their current form as complex technology-powered systems that offer a wide range of features and services.

In recent years, advancements in artificial intelligence (AI) technology have led to the development of AI-powered chat services. This study explores official announcements and releases of three major search engines, Google, Bing and Baidu, of AI-powered chat services.

Three major players in the search engine market, Google, Microsoft and Baidu started to integrate AI chat into their search results. Google has released Bard, later upgraded to Gemini, a LaMDA-powered conversational AI service. Microsoft has launched Bing Chat, renamed later to Copilot, a GPT-powered by OpenAI search engine. The largest search engine in China, Baidu, released a similar service called Ernie. There are also new AI-based search engines, which are briefly described.

This paper discusses the strengths and weaknesses of the traditional – algorithmic powered search engines and modern search with generative AI support, and the possibilities of merging them into one service. This study stresses the types of inquiries provided to search engines, users’ habits of using search engines and the technological advantage of search engine infrastructure.

The 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.

A 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.

The 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.

The 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.

Seekers who visit professional virtual communities (PVCs) are usually motivated by knowledge-seeking, which is a complex cognitive process. How do seekers search for knowledge, and how is their search linked to prior knowledge or PVC situation factors? From the cognitive process and interactional psychology perspectives, this study investigated the three-way interactions between seekers’ expertise, task complexity, and perceptions of PVC features (i.e. knowledge quality and system quality) on knowledge-seeking strategies and resultant outcomes.

A field experiment was conducted with 119 seekers in a PVC using a 2 × 2 factorial design of seekers’ expertise (i.e. expert versus novice) and task complexity (i.e. low versus high).

The study reveals three significant insights: (1) For a high-complexity task, experts adopt an ask-directed searching strategy compared to novices, whereas novices adopt a browsing strategy; (2) For a high-complexity task, experts who perceive a high system quality are more likely than novices to adopt an ask-directed searching strategy; and (3) Task completion time and task quality are associated with the adoption of ask-directed searching strategies, whereas knowledge seekers’ satisfaction is more associated with the adoption of browsing strategy.

We draw on the perspectives of cognitive process and interactional psychology to explore potential two- and three-way interactions of seekers’ expertise, task complexity, and PVC features on the adoption of knowledge-seeking strategies in a PVC context. Our findings provide deep insights into seekers’ behavior in a PVC, given the popularity of the search for knowledge in PVCs.

This paper aims to propose a robust method for non-rigid point set registration, using the Gaussian mixture model and accommodating non-rigid transformations. The posterior probabilities of the mixture model are determined through the proposed integrated feature divergence.

The method involves an alternating two-step framework, comprising correspondence estimation and subsequent transformation updating. For correspondence estimation, integrated feature divergences including both global and local features, are coupled with deterministic annealing to address the non-convexity problem of registration. For transformation updating, the expectation-maximization iteration scheme is introduced to iteratively refine correspondence and transformation estimation until convergence.

The experiments confirm that the proposed registration approach exhibits remarkable robustness on deformation, noise, outliers and occlusion for both 2D and 3D point clouds. Furthermore, the proposed method outperforms existing analogous algorithms in terms of time complexity. Application of stabilizing and securing intermodal containers loaded on ships is performed. The results demonstrate that the proposed registration framework exhibits excellent adaptability for real-scan point clouds, and achieves comparatively superior alignments in a shorter time.

The integrated feature divergence, involving both global and local information of points, is proven to be an effective indicator for measuring the reliability of point correspondences. This inclusion prevents premature convergence, resulting in more robust registration results for our proposed method. Simultaneously, the total operating time is reduced due to a lower number of iterations.

Social media has played a pivotal role in polarizing views on Russia–Ukraine conflict. The effects of polarization in online interactions have been extensively studied in many contexts. This research aims to examine how multiple social media sources may act as an integrator of information and act as a platform for depolarizing behaviors.

This study analyzes the communications of 6,662 tweets related to the sanctions imposed on Russia by using textual analytics and predictive modeling.

The research findings reveal that the tweeting behavior of netizens was depolarized because of information from multiple social media sources. However, the influx of information from non-organizational sources such as trending topics and discussions has a depolarizing impact on the user’s pre-established attitude.

For policymakers, conflict mediators and observers, and members of society in general, there is a need for (1) continuous and consistent communication throughout the crisis, (2) transparency in the information being communicated and (3) public awareness of the polarized and conflicting information being provided from multiple actors that may be biased in the claims being made about the conflict crisis.

While previous research has examined Russia–Ukraine conflict from a variety of perspectives, this is the first study to examine how social media might be used to reduce attitude polarization during times of conflict.

Focusing on the quality of teaching and learning, this study aims to explore the perceptions of accountancy students in two emerging UK Higher Education Institutions (HEIs) of the quality of their learning experiences and the impact of these experiences on generic skills development.

A questionnaire survey was used to collect the data. OLS regression was used to test the hypothesis regarding the impact of student learning experiences (lecturer ability, assessment and curriculum) on generic skills development.

Students value the lecturer as the most important determinant of the quality of their experience. They rated their assessment programme very positively, and the curriculum suggests that students tend to experience a deep blended approach to learning. They also felt that they acquired a wide range of soft competency skills such as those associated with research, critical thinking and time management. Multivariate findings indicate that lecturer ability and curriculum contribute significantly and positively to generic skills development.

The study provides a benchmark for international accounting and business educators in any efforts to assess the efficacy of HE delivery since the pandemic. By implication, it enables the identification of enhancements to the previous character of delivery and hence offers the means to direct improvements to the student experience. Such improvements can then be seen in the National Student Survey (NSS) scores, thereby positively contributing to the next Teaching Excellence Framework. Additionally, such tangible enhancements in NSS scores may be advantageous to HEIs, in the UK and other Western countries, in their efforts to recruit international students on whom they place great reliance for increased revenue, to their international business education programmes.

This study addresses the research gap surrounding the link between teaching and learning approaches in accounting and the development of generic skills. Furthermore, acknowledging that the COVID-19 pandemic with its imposed structural change in the HE teaching and learning environment ushered in a new model of curriculum delivery, this study reflects on the pre-COVID-19 scenario and gathers student perceptions of their teaching and learning experiences before the changes necessitated by lockdowns. It therefore brings the opportunity to anchor future research exploring the post-COVID-19 environment and secure comparative analyses.

This paper aims to study the after-effects of the crises hit by the Lebanese population and the solutions implemented to alleviate the ensuing problems on resource sharing in Lebanese libraries.

Questionnaire to academic libraries. Nine libraries in Lebanon offer resource sharing services; all nine are academic. A self-administered questionnaire was sent to these nine libraries; eight responded. A semi-quantitative approach was used to analyze the responses. The questionnaire was administered through Microsoft Forms. The form was designed to include dichotomous questions for the sake of quantitative analysis and a short answer format that allowed responders to provide additional details. American University of Beirut Data. The access services department at the American University of Beirut Jafet Library compiles on a daily basis interlibrary loan statistics. The compiled data were divided into two groups: duration of two fiscal years prior to June 2020; and from June 2020 to June 2022. June 2020 was used as a cutoff date after which the interlibrary loan initiatives in response to COVID-19 were implemented. The services that were analyzed were RSCVD, OCLC WorldShare ILL profiled groups, Emergency Temporary Access Service for HathiTrust members and Open Access Services from vendors.

Libraries in Lebanon were able to continue providing resource-sharing services with a minimal budget due to four major initiatives and through the help of the international resource-sharing community.

Limited publication about inter library loan in Lebanese Libraries. The pandemic hit economies worldwide; however, Lebanon got hit by three synergetic crises, and the initiatives were instrumental in drastically reducing resource sharing expenditure without affecting the interlibrary loan fill rate.

The purpose of this paper is to scrutinise the effectiveness of four derivative exchanges’ enforcement efforts since 2007. These exchanges include the Commodity Exchange Inc. and ICE Futures US from the United States and ICE Futures Europe and the London Metal Exchange from the UK.

The paper examines 799 enforcement notices published by four exchanges through a behavioural science lens: HUMANS conceived by Hunt (2023) in Humanizing Rules: Bringing Behavioural Science to Ethics and Compliance.

The paper finds the effectiveness of the exchanges’ enforcement efforts to be a mixed picture as financial markets transition from the digital to artificial intelligence era. Humans remain a key cog in the wheel of market participants’ trading operations, albeit their roles have changed. Despite this, some elements of exchanges’ enforcement regimes have not kept pace with the move from floor to remote trading. However, in other respects, their efforts are or should be, effective, at least in behavioural terms.

The paper’s findings are arguably limited to exchanges based in Anglophone jurisdictions. The information published by the exchanges is variable, making “like-for-like” comparisons difficult in some areas.

The paper makes several recommendations that, if adopted, could help exchanges to increase the potency of their enforcement programmes.

A key aim of the paper is to shift the lens through which the debate concerning the efficacy of exchange-level oversight is conducted. Hitherto, a legal lens has been used, whereas this paper uses a behavioural lens.

The purpose of this study is to explore the roles that sociocultural systems such as traditions, religious practices, and rituals play in upholding gender imbalance in Fiji.

This qualitative study relies on results from semi-structured interviews with auditors, educationalists, academics, partners from leading accounting/audit firms, company directors and high-ranked government officers. Thematic analysis was conducted both manually and using MAXQDA software, and the themes that emerged from both analyses are complementary. A few more complementary analyses were also conducted such as Word Cloud.

The results support the claim that the religious traditions and rituals are strongly linked to gender-inequitable beliefs and suggest sociocultural factors impose on women experiencing self-effacing emotions and passive acceptance of lower status, contributing to the persistence of gender inequality. It also emphasizes the need to challenge certain sociocultural practices to promote greater gender equality, which is the theme emerged from thematic analysis. Additionally, this paper proposes four distinct types of attitudes in this regard as self-effacing feminist, self-effacing traditional, self-effacing modesty and talented driven.

Respondents' openness authenticity may be limited by factors like selection bias, small sample size and other potential constraints in this study.

The findings might influence stakeholders to advocate for policy changes to promote women's representation in leadership positions. The results give voice to various segments of society who are advocating greater gender diversity on board representation in Fiji. The themes immerged and theories developed would make a substantial contribution to the existing literature.

The findings highlight the importance of addressing gender inequality in leadership positions to promote inclusive and sustainable growth.

This study sheds light on the less-explored domain of internal barriers to gender equality within Fiji. It adds a novel dimension to the understanding of how cultural norms intersect with individual perceptions to shape gender inequality.