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3.1
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Information
Special Issues
Information Sharing and Knowledge Management
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Information Sharing and Knowledge Management

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Processes".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 7317

Special Issue Editor

Dr. Muhammad Azeem Akbar

E-Mail Website
Guest Editor
Department of Software Engineering, LUT University, 53850 Lappeenranta, Finland
Interests: global software development; cloud-based outsourcing; quantum software; blockchain; IoT security; AI and IoT ethics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Global software development (GSD) has been widely adopted by the software industry to gain economic benefits. Organizations which engage in GSD face various challenges, the majority being associated with information sharing among overseas development teams. Effective information sharing standards are required to  share and understand knowledge required for developing quality software in the context of GSD. Real-world organizations are looking for appropriate practices and tools to address information sharing challenges in GSD. The aim of this Special Issue is to provide a platform for both practitioners and researcher to discuss the suitability of information sharing techniques, processes, and frameworks to fix information sharing problems in GSD and other software development environments, e.g., DevOps and Microservices. We welcome articles in which authors report research studies for the application of effective and sustainable information sharing and management methods for software development process improvements.

Dr. Muhammad Azeem Akbar
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • information sharing
  • communication strategies and tools
  • knowledge management
  • soft computing techniques for information management
  • software development process improvements
  • AI techniques for information management
  • fuzzy-based information sharing optimization
  • systematic literature review
  • information sharing ethics
  • other relevant work

Published Papers (2 papers)

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Research

18 pages, 837 KiB  
Article
A Semantic Similarity-Based Identification Method for Implicit Citation Functions and Sentiments Information
by Rami Malkawi, Mohammad Daradkeh, Ammar El-Hassan and Pavel Petrov
Information 2022, 13(11), 546; https://doi.org/10.3390/info13110546 - 17 Nov 2022
Cited by 2 | Viewed by 2042
Abstract
Automated citation analysis is becoming increasingly important in assessing the scientific quality of publications and identifying patterns of collaboration among researchers. However, little attention has been paid to analyzing the scientific content of the citation context. This study presents an unsupervised citation detection [...] Read more.
Automated citation analysis is becoming increasingly important in assessing the scientific quality of publications and identifying patterns of collaboration among researchers. However, little attention has been paid to analyzing the scientific content of the citation context. This study presents an unsupervised citation detection method that uses semantic similarities between citations and candidate sentences to identify implicit citations, determine their functions, and analyze their sentiments. We propose different document vector models based on TF-IDF weights and word vectors and compare them empirically to calculate their semantic similarity. To validate this model for identifying implicit citations, we used deep neural networks and LDA topic modeling on two citation datasets. The experimental results show that the F1 values for the implicit citation classification are 88.60% and 86.60% when the articles are presented in abstract and full-text form, respectively. Based on the citation function, the results show that implicit citations provide background information and a technical basis, while explicit citations emphasize research motivation and comparative results. Based on the citation sentiment, the results showed that implicit citations tended to describe the content objectively and were generally neutral, while explicit citations tended to describe the content positively. This study highlights the importance of identifying implicit citations for research evaluation and illustrates the difficulties researchers face when analyzing the citation context. Full article
(This article belongs to the Special Issue Information Sharing and Knowledge Management)
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23 pages, 621 KiB  
Article
Information Adoption Patterns and Online Knowledge Payment Behavior: The Moderating Role of Product Type
by Mohammad Daradkeh, Amjad Gawanmeh and Wathiq Mansoor
Information 2022, 13(9), 414; https://doi.org/10.3390/info13090414 - 31 Aug 2022
Cited by 7 | Viewed by 3672
Abstract
The development of online knowledge payment platforms in recent years has increased their respective market value by nurturing content resources and improving content ecology. Yet, the underlying factors of knowledge seekers’ payment behaviors and their information adoption mechanisms are poorly understood. Based on [...] Read more.
The development of online knowledge payment platforms in recent years has increased their respective market value by nurturing content resources and improving content ecology. Yet, the underlying factors of knowledge seekers’ payment behaviors and their information adoption mechanisms are poorly understood. Based on the information adoption model, this study develops a research model to examine the relationship between information adoption patterns and knowledge seekers’ payment behavior, and explore the moderating effect of product type on this relationship. To test the research model and hypotheses, we used a multi-analytic approach combining text and regression analysis on a sample of 4366 social Q&A data collected from Quora+ between August 2021 and August 2022. We further classified the product types into utilitarian and hedonic, and compared the differences in influence paths between product types. The results show that the completeness, vividness, and relevance of the product description have a significant positive impact on knowledge payment behavior. The reputation, experience, and integrity of the knowledge provider have a positive impact on knowledge payment behavior. Compared to utilitarian knowledge products, the payment behavior for hedonic products is more related to the reputation and experience of the knowledge provider. This study provides insights into the factors that influence online knowledge payment behavior and practical guidance for the development of online knowledge payment services and platforms. Full article
(This article belongs to the Special Issue Information Sharing and Knowledge Management)
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