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Open Access
Article
Publication date: 30 October 2023

Koraljka Golub, Xu Tan, Ying-Hsang Liu and Jukka Tyrkkö

This exploratory study aims to help contribute to the understanding of online information search behaviour of PhD students from different humanities fields, with a focus on…

Abstract

Purpose

This exploratory study aims to help contribute to the understanding of online information search behaviour of PhD students from different humanities fields, with a focus on subject searching.

Design/methodology/approach

The methodology is based on a semi-structured interview within which the participants are asked to conduct both a controlled search task and a free search task. The sample comprises eight PhD students in several humanities disciplines at Linnaeus University, a medium-sized Swedish university from 2020.

Findings

Most humanities PhD students in the study have received training in information searching, but it has been too basic. Most rely on web search engines like Google and Google Scholar for publications' search, and university's discovery system for known-item searching. As these systems do not rely on controlled vocabularies, the participants often struggle with too many retrieved documents that are not relevant. Most only rarely or never use disciplinary bibliographic databases. The controlled search task has shown some benefits of using controlled vocabularies in the disciplinary databases, but incomplete synonym or concept coverage as well as user unfriendly search interface present hindrances.

Originality/value

The paper illuminates an often-forgotten but pervasive challenge of subject searching, especially for humanities researchers. It demonstrates difficulties and shows how most PhD students have missed finding an important resource in their research. It calls for the need to reconsider training in information searching and the need to make use of controlled vocabularies implemented in various search systems with usable search and browse user interfaces.

Article
Publication date: 23 December 2022

Konstantinos Chytas, Anastasios Tsolakidis, Evangelia Triperina and Christos Skourlas

The purpose of this paper is to introduce an interactive system that relies on the educational data generated from the online Universities services to assess, correct and…

Abstract

Purpose

The purpose of this paper is to introduce an interactive system that relies on the educational data generated from the online Universities services to assess, correct and ameliorate the learning process for both students and faculty.

Design/methodology/approach

In the presented research, data from the online services, provided by a Greek University, prior, during and after the COVID-19 outbreak, are analyzed and utilized in order to ameliorate the offered learning process and provide better quality services to the students. Moreover, according to the learning paths, their presence online and their participation in the services of the University, insights can be derived for their performance, so as to better support and assist them.

Findings

The system can deduce the future learning progression of each student, according to the past and the current performance. As a direct consequence, the exploitation of the data can provide a road map for the strategic planning of universities, can indicate how the learning process can be updated and amended, both online and in person, as well as make the learning experience more essential, effective and efficient for the students and aiding the professors to provide a more meaningful and to-the-point learning experience.

Originality/value

Nowadays, educational activities in academia are strongly supported by online services, information systems and online educational materials. The learning design in the academic setting is primarily facilitated in the University premises. However, the exploitation of the contemporary technologies and supporting materials that are available online can enrich and transform the educational process and its results.

Details

Data Technologies and Applications, vol. 57 no. 3
Type: Research Article
ISSN: 2514-9288

Keywords

Article
Publication date: 15 March 2023

Qiao Li, Chunfeng Liu, Jingrui Hou and Ping Wang

As an emerging tool for data discovery, data retrieval systems fail to effectively support users' cognitive processes during data search and access. To uncover the relationship…

Abstract

Purpose

As an emerging tool for data discovery, data retrieval systems fail to effectively support users' cognitive processes during data search and access. To uncover the relationship between data search and access and the cognitive mechanisms underlying this relationship, this paper examines the associations between affective memories, perceived value, search effort and the intention to access data during users' interactions with data retrieval systems.

Design/methodology/approach

This study conducted a user experiment for which 48 doctoral students from different disciplines were recruited. The authors collected search logs, screen recordings, questionnaires and eye movement data during the interactive data search. Multiple linear regression was used to test the hypotheses.

Findings

The results indicate that positive affective memories positively affect perceived value, while the effects of negative affective memories on perceived value are nonsignificant. Utility value positively affects search effort, while attainment value negatively affects search effort. Moreover, search effort partially positively affects the intention to access data, and it serves a full mediating role in the effects of utility value and attainment value on the intention to access data.

Originality/value

Through the comparison between the findings of this study and relevant findings in information search studies, this paper reveals the specificity of behaviour and cognitive processes during data search and access and the special characteristics of data discovery tasks. It sheds light on the inhibiting effect of attainment value and the motivating effect of utility value on data search and the intention to access data. Moreover, this paper provides new insights into the role of memory bias in the relationships between affective memories and data searchers' perceived value.

Details

Journal of Documentation, vol. 79 no. 5
Type: Research Article
ISSN: 0022-0418

Keywords

Article
Publication date: 24 August 2023

Ya-Ning Chen

In this study, the distinctive functional features of linked data (LD) catalogues were investigated to contrast with existing online public access catalogues (OPACs) and discovery…

Abstract

Purpose

In this study, the distinctive functional features of linked data (LD) catalogues were investigated to contrast with existing online public access catalogues (OPACs) and discovery systems using a checklist approach. The checklist was derived from a literature review and is composed of 10 items as follows: self-descriptive and dynamic content for resource description, linkage to external LD sources and online services, aggregation of knowledge contexts into knowledge graphs (KGs), URI-based link discovery, representation and query of LD relationships, URI-based serendipitous discovery, keyword recommendation, faceted limitation and browsing, visualization and openness of data.

Design/methodology/approach

Ten functional features derived from the literature were checked against existing LD catalogues offered by libraries, archives and museums (LAMs). The LD catalogues were regarded as qualified subjects if they offered functional features that were distinct from current OPACs and discovery systems through URI-based enrichment and aggregation from various LD sources. In addition to individual organizations, LD union catalogues were also included. However, LD hubs, such as ISNI, OCLC WorldCat Entities, VIAF and Wikidata, were excluded. In total, six LD catalogues from LAMs were selected as subjects for examination.

Findings

First, LD catalogues provide similar KG information through URI combination, and KGs also facilitate information serendipity, including social-document, intellectual, conceptual, spatial and temporal contexts and networks of corporate bodies, persons and families (CPFs). Second, LD catalogues have transformed the “seek first and browse later” paradigm into a “seek or browse” paradigm by refreshing the browsing function of traditional card catalogues with preview and new options to facilitate LD identification and discovery. Third, LD catalogues have refined keyword recommendation with the addition of the following fields: person’s title, CPF relationships, entity type and LD source. Lastly, a virtual union LD catalogue is offered.

Research limitations/implications

The proposed checklist revealed the unique/improved functional features of LD catalogues, allowing further investigation and comparison. More cases from the fields of medicine, engineering science and so on will be required to make revisions to fine-tune the proposed checklist approach.

Originality/value

To the best of the author’s knowledge, this is the first study to propose a checklist of functional features for LD catalogues and examine what the results and features of LD catalogues have achieved and are supported by from ontologies across LAMs. The findings suggest that LD provides a viable alternative to catalogues. The proposed checklist and results pave the way for the future development of LD catalogues and next-generation catalogues and also provide a basis for the future study of LD catalogues from other fields to refine the proposed checklist.

Details

The Electronic Library , vol. 41 no. 5
Type: Research Article
ISSN: 0264-0473

Keywords

Article
Publication date: 3 February 2023

Huyen Nguyen, Haihua Chen, Jiangping Chen, Kate Kargozari and Junhua Ding

This study aims to evaluate a method of building a biomedical knowledge graph (KG).

Abstract

Purpose

This study aims to evaluate a method of building a biomedical knowledge graph (KG).

Design/methodology/approach

This research first constructs a COVID-19 KG on the COVID-19 Open Research Data Set, covering information over six categories (i.e. disease, drug, gene, species, therapy and symptom). The construction used open-source tools to extract entities, relations and triples. Then, the COVID-19 KG is evaluated on three data-quality dimensions: correctness, relatedness and comprehensiveness, using a semiautomatic approach. Finally, this study assesses the application of the KG by building a question answering (Q&A) system. Five queries regarding COVID-19 genomes, symptoms, transmissions and therapeutics were submitted to the system and the results were analyzed.

Findings

With current extraction tools, the quality of the KG is moderate and difficult to improve, unless more efforts are made to improve the tools for entity extraction, relation extraction and others. This study finds that comprehensiveness and relatedness positively correlate with the data size. Furthermore, the results indicate the performances of the Q&A systems built on the larger-scale KGs are better than the smaller ones for most queries, proving the importance of relatedness and comprehensiveness to ensure the usefulness of the KG.

Originality/value

The KG construction process, data-quality-based and application-based evaluations discussed in this paper provide valuable references for KG researchers and practitioners to build high-quality domain-specific knowledge discovery systems.

Details

Information Discovery and Delivery, vol. 51 no. 4
Type: Research Article
ISSN: 2398-6247

Keywords

Article
Publication date: 26 January 2022

Deden Sumirat Hidayat, Winaring Suryo Satuti, Dana Indra Sensuse, Damayanti Elisabeth and Lintang Matahari Hasani

Fish quarantine is a measure to prevent the entry and spread of quarantine fish pests and diseases abroad and from one area to another within Indonesia's territory. Based on these…

247

Abstract

Purpose

Fish quarantine is a measure to prevent the entry and spread of quarantine fish pests and diseases abroad and from one area to another within Indonesia's territory. Based on these backgrounds, this study aims to identify the knowledge, knowledge management (KM) processes and knowledge management system (KMS) priority needs for quarantine fish and other fishery products measures (QMFFP) and then develop a classification model and web-based decision support system (DSS) for QMFFP decisions.

Design/methodology/approach

This research methodology uses combination approaches, namely, contingency factor analysis (CFA), the cross-industry standard process for data mining (CRISP-DM) and knowledge management system development life cycle (KMSDLC). The CFA for KM solution design is performed by identifying KM processes and KMS priorities. The CRISP-DM for decision classification model is done by using a decision tree algorithm. The KMSDLC is used to develop a web-based DSS.

Findings

The highest priority requirements of KM technology for QMFFP are data mining and DSS with predictive features. The main finding of this study is to show that web-based DSS (functions and outputs) can support and accelerate QMFFP decisions by regulations and field practice needs. The DSS was developed using the CTree algorithm model, which has six main attributes and eight rules.

Originality/value

This study proposes a novel comprehensive framework for developing DSS (combination of CFA, CRISP-DM and KMSDLC), a novel classification model resulting from comparing two decision tree algorithms and a novel web-based DSS for QMFFP.

Details

VINE Journal of Information and Knowledge Management Systems, vol. 54 no. 2
Type: Research Article
ISSN: 2059-5891

Keywords

Article
Publication date: 20 July 2023

Elaheh Hosseini, Kimiya Taghizadeh Milani and Mohammad Shaker Sabetnasab

This research aimed to visualize and analyze the co-word network and thematic clusters of the intellectual structure in the field of linked data during 1900–2021.

Abstract

Purpose

This research aimed to visualize and analyze the co-word network and thematic clusters of the intellectual structure in the field of linked data during 1900–2021.

Design/methodology/approach

This applied research employed a descriptive and analytical method, scientometric indicators, co-word techniques, and social network analysis. VOSviewer, SPSS, Python programming, and UCINet software were used for data analysis and network structure visualization.

Findings

The top ranks of the Web of Science (WOS) subject categorization belonged to various fields of computer science. Besides, the USA was the most prolific country. The keyword ontology had the highest frequency of co-occurrence. Ontology and semantic were the most frequent co-word pairs. In terms of the network structure, nine major topic clusters were identified based on co-occurrence, and 29 thematic clusters were identified based on hierarchical clustering. Comparisons between the two clustering techniques indicated that three clusters, namely semantic bioinformatics, knowledge representation, and semantic tools were in common. The most mature and mainstream thematic clusters were natural language processing techniques to boost modeling and visualization, context-aware knowledge discovery, probabilistic latent semantic analysis (PLSA), semantic tools, latent semantic indexing, web ontology language (OWL) syntax, and ontology-based deep learning.

Originality/value

This study adopted various techniques such as co-word analysis, social network analysis network structure visualization, and hierarchical clustering to represent a suitable, visual, methodical, and comprehensive perspective into linked data.

Details

Library Hi Tech, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0737-8831

Keywords

Article
Publication date: 15 February 2023

Zahra Sarmast, Sajjad Shokouhyar, Seyed Hamed Ghanadpour and Sina Shokoohyar

Warranty service plays a critical role in sustainability and service continuity and influences customer satisfaction. Considering the role of social networks in customer feedback…

Abstract

Purpose

Warranty service plays a critical role in sustainability and service continuity and influences customer satisfaction. Considering the role of social networks in customer feedback channels, one of the essential sources to examine the reflection of a product/service is social media mining. This paper aims to identify the frequent product failures through social network mining. Focusing on social media data as a comprehensive and online source to detect warranty issues reveals opportunities for improvement, such as user problems and necessities. This model will detect the causes of defects and prioritize improving components in a product-service system based on FMEA results.

Design/methodology/approach

Ontology-based methods, text mining and sentiment analysis with machine learning methods are performed on social media data to investigate product defects, symptoms and the relationship between warranty plans and customer behaviour. Also, the authors have incorporated multi-source data collection to cover all the possibilities. Then the authors promote a decision support system to help the decision-makers using the FMEA process have a more comprehensive insight through customer feedback. Finally, to validate the accuracy and reliability of the results, the authors used the operational data of a LENOVO laptop from a warranty service centre and classifier performance metrics to compare the authors’ results.

Findings

This study confirms the validity of social media data in detecting customer sentiments and discovering the most defective components and failures of the products/services. In other words, the informative threads are derived through a data preparation process and then are based on analyzing the different features of a failure (issues, symptoms, causes, components, solutions). Using social media data helps gain more accurate online information due to the limitation of warranty periods. In other words, using social media data broadens the scope of data gathering and lets in all feedback from different sources to recognize improvement opportunities.

Originality/value

This work contributes a DSS model using multi-channel social media mining through supervised machine learning for warranty-service improvement based on defect-related discovery to unravel the potential aspects of social networks analysis to predict the most vulnerable components of a product and the main causes of failures that lead to the inputs for the FMEA process and then, a cost optimization. The authors have used social media channels like Twitter, Facebook, Reddit, LENOVO Forums, GitHub, Quora and XDA-Developers to gather data about the LENOVO laptop failures as a case study.

Details

Industrial Management & Data Systems, vol. 123 no. 5
Type: Research Article
ISSN: 0263-5577

Keywords

Open Access
Article
Publication date: 28 November 2023

Silvia Massa, Maria Carmela Annosi, Lucia Marchegiani and Antonio Messeni Petruzzelli

This study aims to focus on a key unanswered question about how digitalization and the knowledge processes it enables affect firms’ strategies in the international arena.

3028

Abstract

Purpose

This study aims to focus on a key unanswered question about how digitalization and the knowledge processes it enables affect firms’ strategies in the international arena.

Design/methodology/approach

The authors conduct a systematic literature review of relevant theoretical and empirical studies covering over 20 years of research (from 2000 to 2023) and including 73 journal papers.

Findings

This review allows us to highlight a relationship between firms’ international strategies and the knowledge processes enabled by applying digital technologies. Specifically, the authors discuss the characteristics of patterns of knowledge flows and knowledge processes (their origin, the type of knowledge they carry on and their directionality) as determinants for the emergence of diverse international strategies embraced by single firms or by populations of firms within ecosystems, networks, global value chains or alliances.

Originality/value

Despite digital technologies constituting important antecedents and critical factors for the internationalization process, and international businesses in general, and operating cross borders implies the enactment of highly knowledge-intensive processes, current literature still fails to provide a holistic picture of how firms strategically use what they know and seek out what they do not know in the international environment, using the affordances of digital technologies.

Details

Journal of Knowledge Management, vol. 27 no. 11
Type: Research Article
ISSN: 1367-3270

Keywords

Article
Publication date: 10 August 2023

Richard Gruss, David Goldberg, Nohel Zaman and Alan Abrahams

The widespread adoption of online purchasing has prompted increasing concerns about product safety, and regulators are beginning to hold e-commerce sites accountable for dangerous…

Abstract

Purpose

The widespread adoption of online purchasing has prompted increasing concerns about product safety, and regulators are beginning to hold e-commerce sites accountable for dangerous product defects. For online consumers, understanding the many inherent safety risks among the extensive array of products they browse is a formidable task. The authors attempt to address this problem via a client-side software artifact that warns shoppers about potential product safety hazards at the point of sale.

Design/methodology/approach

In this study, the authors built four candidate designs and assessed their effectiveness by means of a large randomized controlled experiment (n = 466). The authors define effectiveness as significant changes in dependent variables associated with health behaviors and technology adoption.

Findings

The authors find that all of the designs score high on adoption likelihood, that designs incorporating highlighting and scoring are better at increasing safety knowledge and that simpler designs are better at enhancing safety awareness.

Originality/value

These findings will inform the design of safety information dissemination systems and open new areas of safety awareness enhancement research. More generally, the authors introduce a novel method of testing text visualization variations and their impact on behavioral decisions.

Details

Internet Research, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1066-2243

Keywords

1 – 10 of over 4000