Human Factor in Information Systems Development and Management

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: 31 July 2024 | Viewed by 11446

Special Issue Editors


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Guest Editor
Institute of Psychology, University of Wrocław, 50-137 Wrocław, Poland
Interests: research methodologies; ICT in transition economies; ICT for development; business statistics; business psychology

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Guest Editor
Department of Business Intelligence in Management, Wroclaw University of Economics and Business, 53-345 Wrocław, Poland
Interests: knowledge grid; knowledge validation and verification; knowledge management; expert system applications; artificial intelligence and database technology; distance and open learning

Special Issue Information

Dear Colleagues,

The significance of the “human factor” is beginning to dawn on software vendors, and though difficult and intangible to measure by nature, the human factor is starting to be seen for what it is: the core of an information system (IS). Yet, in many cases, the collaboration and communication with the users is still ineffective or simply neglected.

Nowadays, information systems are fuelled by large datasets and embedded with artificial intelligence (AI) capabilities driven by machine learning (ML) methods and techniques. The requirements formulated toward modern software systems have considerably changed, imposing novel challenges and vast opportunities. However, while organizations are rushing to deploy AI solutions, the voice of users often seems faint.

Moreover, with the proliferation of information systems (ISs) in both business and personal applications increasing, the growing research interest across diverse disciplines in the human factor is unsurprising. Although many studies have been devoted to addressing the role of the human factor in IS development and management, one can notice the vastly different circumstances due to the COVID-19 pandemic.

In the current situation, COVID-19 has appeared as a real challenge; however, it has also presented an opportunity to tackle a plethora of issues related to work processes, procedures and policies. Therefore, decision makers need to adapt and apply new way of managing communication and collaboration with IS users.

In summary, this Special Issue of the Big Data and Cognitive Computing journal aims to collect and disseminate actual state-of-the-art research regarding the role and impact of the human factor in information system development and management.

Dr. Paweł Weichbroth
Dr. Jolanta Kowal
Dr. Mieczysław Lech Owoc
Guest Editors

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. Big Data and Cognitive Computing 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 1800 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.

Published Papers (2 papers)

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Research

24 pages, 2389 KiB  
Article
The Extended Digital Maturity Model
by Tining Haryanti, Nur Aini Rakhmawati and Apol Pribadi Subriadi
Big Data Cogn. Comput. 2023, 7(1), 17; https://doi.org/10.3390/bdcc7010017 - 17 Jan 2023
Cited by 10 | Viewed by 8620
Abstract
The Digital Transformation (DX) potentially affects productivity and efficiency while offering high risks to organizations. Necessary frameworks and tools to help organizations navigate such radical changes are needed. An extended framework of DMM is presented through a comparative analysis of various digital maturity [...] Read more.
The Digital Transformation (DX) potentially affects productivity and efficiency while offering high risks to organizations. Necessary frameworks and tools to help organizations navigate such radical changes are needed. An extended framework of DMM is presented through a comparative analysis of various digital maturity models and qualitative approaches through expert feedback. The maturity level determination uses the Emprise test of the international standard ISO/IEC Assessment known as SPICE. This research reveals seven interrelated dimensions for supporting the success of DX as a form of development of an existing Maturity Model. The DX–Self Assessment Maturity Model (DX-SAMM) is built to guide organizations by providing a broad roadmap for improving digital maturity. This article presents a digital maturity model from a holistic point of view and meets the criteria for assessment maturity. The case study results show that DX-SAMM can identify DX maturity levels while providing roadmap recommendations for increasing maturity levels in every aspect of its dimensions. It offers practical implications for improving maturity levels and the ease of real-time monitoring and evaluating digital maturity. With the development of maturity measurement, DX-SAMM contributes to the sustainability of the organization by proposing DX strategies in the future based on the current maturity achievements. Full article
(This article belongs to the Special Issue Human Factor in Information Systems Development and Management)
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23 pages, 4084 KiB  
Article
Locating Source Code Bugs in Software Information Systems Using Information Retrieval Techniques
by Ali Alawneh, Iyad M. Alazzam and Khadijah Shatnawi
Big Data Cogn. Comput. 2022, 6(4), 156; https://doi.org/10.3390/bdcc6040156 - 13 Dec 2022
Cited by 1 | Viewed by 1819
Abstract
Bug localization is the process through which the buggy source code files are located regarding a certain bug report. Bug localization is an overwhelming and time-consuming process. Automating bug localization is the key to help developers and increase their productivities. Expanding bug reports [...] Read more.
Bug localization is the process through which the buggy source code files are located regarding a certain bug report. Bug localization is an overwhelming and time-consuming process. Automating bug localization is the key to help developers and increase their productivities. Expanding bug reports with more semantic and increasing software understanding using information retrieval and natural language techniques will be the way to locate the buggy source code file, in which the bug report works as a query and source code as search space. This research investigates the effect of segmenting open source files into executable code and comments, as they have a conflicting nature, seeks the effect of synonyms on the accuracy of bug localization, and examines the effect of “part-of-speech” techniques on reducing the manual inspection for appropriate synonyms. This research aims to approve that such methods improve the accuracy of bug localization tasks. The used approach was evaluated on three Java open source software, namely Eclipse 3.1, AspectJ 1.0, and SWT 3.1; we implement our dedicated Java tool to adopt our methodology and conduct several experiments on each software. The experimental results reveal a considerable improvement in recall and precision levels, and the developed methods display an accuracy improvement of 4–10% compared with the state-of-the-art approaches. Full article
(This article belongs to the Special Issue Human Factor in Information Systems Development and Management)
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