research reports on computer science impact factor

• SpringerLink shop

Journal Metrics Reports 2023

• Computer Science

Announcement of the latest impact factors from the Journal Citation Reports

Researchers consider a number of factors in deciding where to publish their research, such as journal reputation, readership and community, speed of publication, and citations. See how we share a whole range of information to help the research community decide which journal is the best home for their research as well as what the metrics can tell you about the performance of a journal and its articles.

Explore journal impact metrics

computational complexity

Impact Factor 0.7

5 Year Impact Factor 1

Cite Score 1.5

Social Media Mentions 17

Downloads 10,377

Machine Vision and Applications

Impact Factor 2.4

5 Year Impact Factor 2.6

Cite Score 6.3

Submission to First Decision - Days (Median) 20

Social Media Mentions 286

Downloads 205,231

Journal of Cryptology

Impact Factor 2.3

Cite Score 7.1

Social Media Mentions 101

Downloads 261,178

AI & SOCIETY

Impact Factor 2.9

5 Year Impact Factor 2.9

Cite Score 8

Submission to First Decision - Days (Median) 9

Social Media Mentions 1,564

Downloads 1,550,634

Applicable Algebra in Engineering, Communication and Computing

Impact Factor 0.6

5 Year Impact Factor 0.7

Cite Score 2.9

Social Media Mentions 9

Downloads 58,449

Theory of Computing Systems

Cite Score 1.9

Submission to First Decision - Days (Median) 5

Social Media Mentions 35

Downloads 115,519

Acta Informatica

Impact Factor 0.4

5 Year Impact Factor 0.6

Cite Score 2.4

Submission to First Decision - Days (Median) 8

Social Media Mentions 34

Downloads 98,971

New Generation Computing

Impact Factor 2

5 Year Impact Factor 1.7

Cite Score 5.9

Submission to First Decision - Days (Median) 39

Social Media Mentions 21

Downloads 133,734

Engineering with Computers

Cite Score 16.5

Social Media Mentions 79

Downloads 356,953

The Visual Computer

Impact Factor 3

5 Year Impact Factor 3

Cite Score 5.8

Submission to First Decision - Days (Median) 2

Social Media Mentions 501

Downloads 566,870

Distributed Computing

Impact Factor 1.3

5 Year Impact Factor 1.6

Cite Score 3.2

Submission to First Decision - Days (Median) 7

Social Media Mentions 43

Downloads 71,300

Algorithmica

Impact Factor 0.9

Cite Score 2.8

Submission to First Decision - Days (Median) 14

Social Media Mentions 93

Downloads 266,700

Neural Computing and Applications

Impact Factor 4.5

5 Year Impact Factor 4.7

Cite Score 11.4

Submission to First Decision - Days (Median) 16

Social Media Mentions 795

Downloads 2,196,260

Multimedia Systems

Impact Factor 3.5

5 Year Impact Factor 3.1

Cite Score 5.4

Submission to First Decision - Days (Median) 21

Social Media Mentions 63

Downloads 237,050

Impact Factor 3.3

5 Year Impact Factor 2.8

Cite Score 8.2

Social Media Mentions 69

Downloads 280,296

Requirements Engineering

Impact Factor 2.1

5 Year Impact Factor 2.5

Submission to First Decision - Days (Median) 32

Social Media Mentions 26

Downloads 289,928

The VLDB Journal

Impact Factor 2.8

5 Year Impact Factor 3.6

Cite Score 12.3

Social Media Mentions 151

Downloads 299,055

Personal and Ubiquitous Computing

Cite Score 6.6

Social Media Mentions 825

Downloads 823,591

International Journal on Digital Libraries

Impact Factor 1.6

Cite Score 4.3

Submission to First Decision - Days (Median) 36

Social Media Mentions 235

Downloads 175,759

International Journal on Software Tools for Technology Transfer

Impact Factor 1.1

5 Year Impact Factor 1.1

Cite Score 4.5

Social Media Mentions 38

Downloads 144,279

Artificial Life and Robotics

Impact Factor 0.8

5 Year Impact Factor 0.8

Cite Score 2

Downloads 112,667

International Journal on Document Analysis and Recognition (IJDAR)

Impact Factor 1.8

5 Year Impact Factor 2.4

Cite Score 6.2

Submission to First Decision - Days (Median) 3

Social Media Mentions 102

Downloads 83,180

Pattern Analysis and Applications

Impact Factor 3.7

5 Year Impact Factor 2.7

Cite Score 7.4

Submission to First Decision - Days (Median) 6

Social Media Mentions 57

Downloads 159,095

Virtual Reality

Impact Factor 4.4

5 Year Impact Factor 5.4

Cite Score 8.3

Submission to First Decision - Days (Median) 11

Social Media Mentions 649

Downloads 637,114

Cognition, Technology & Work

Cite Score 6.9

Submission to First Decision - Days (Median) 25

Social Media Mentions 61

Downloads 275,638

Knowledge and Information Systems

Impact Factor 2.5

Cite Score 5.7

Submission to First Decision - Days (Median) 15

Social Media Mentions 368

Downloads 414,515

International Journal of Information Security

Social Media Mentions 109

Downloads 310,254

Universal Access in the Information Society

Cite Score 6.1

Submission to First Decision - Days (Median) 12

Social Media Mentions 271

Downloads 463,879

Software and Systems Modeling

5 Year Impact Factor 2.1

Cite Score 6

Social Media Mentions 288

Downloads 354,238

Autonomous Agents and Multi-Agent Systems

Submission to First Decision - Days (Median) 13

Downloads 192,107

Artificial Intelligence Review

Impact Factor 10.7

5 Year Impact Factor 11.7

Cite Score 22

Social Media Mentions 759

Downloads 1,537,425

Annals of Mathematics and Artificial Intelligence

Impact Factor 1.2

Cite Score 3

Social Media Mentions 27

Downloads 39,087

Applied Intelligence

Impact Factor 3.4

5 Year Impact Factor 3.9

Social Media Mentions 476

Downloads 1,294,189

Artificial Intelligence and Law

Impact Factor 3.1

Cite Score 9.5

Social Media Mentions 154

Downloads 276,305

Automated Software Engineering

5 Year Impact Factor 2.3

Cite Score 4.8

Social Media Mentions 14

Downloads 93,754

Cluster Computing

Impact Factor 3.6

5 Year Impact Factor 2.2

Cite Score 9.7

Social Media Mentions 266

Downloads 595,716

Constraints

Impact Factor 0.5

5 Year Impact Factor 1.8

Cite Score 2.2

Submission to First Decision - Days (Median) 27

Social Media Mentions 13

Downloads 48,072

Computer Supported Cooperative Work (CSCW)

Cite Score 6.4

Social Media Mentions 71

Downloads 256,042

Data Mining and Knowledge Discovery

5 Year Impact Factor 5.3

Cite Score 10.4

Social Media Mentions 414

Downloads 453,666

Distributed and Parallel Databases

Impact Factor 1.5

5 Year Impact Factor 1.3

Cite Score 3.5

Social Media Mentions 32

Downloads 86,194

Designs, Codes and Cryptography

Impact Factor 1.4

5 Year Impact Factor 1.5

Downloads 200,003

Education and Information Technologies

Impact Factor 4.8

5 Year Impact Factor 4.8

Cite Score 10

Submission to First Decision - Days (Median) 10

Social Media Mentions 692

Downloads 3,239,628

Empirical Software Engineering

5 Year Impact Factor 4.5

Cite Score 8.5

Submission to First Decision - Days (Median) 18

Social Media Mentions 293

Downloads 651,301

Ethics and Information Technology

5 Year Impact Factor 4.2

Social Media Mentions 460

Downloads 636,106

Genetic Programming and Evolvable Machines

Impact Factor 1.7

Social Media Mentions 41

Downloads 142,609

Journal of Grid Computing

5 Year Impact Factor 3.5

Cite Score 8.7

Downloads 35,646

International Journal of Parallel Programming

5 Year Impact Factor 0.9

Cite Score 4.4

Social Media Mentions 51

Downloads 96,993

Journal of Automated Reasoning

5 Year Impact Factor 1.2

Cite Score 3.6

Downloads 128,382

Journal of Intelligent Information Systems

Cite Score 7.2

Social Media Mentions 67

Downloads 245,774

Journal of Mathematical Imaging and Vision

Social Media Mentions 103

Downloads 160,670

Journal of Network and Systems Management

Impact Factor 4.1

Cite Score 7.6

Social Media Mentions 45

Downloads 151,678

Machine Learning

Impact Factor 4.3

5 Year Impact Factor 5.8

Cite Score 11

Social Media Mentions 1,341

Downloads 1,349,126

Minds and Machines

Impact Factor 4.2

5 Year Impact Factor 7.5

Cite Score 12.6

Social Media Mentions 575

Downloads 571,386

Multimedia Tools and Applications

Submission to First Decision - Days (Median) 28

Social Media Mentions 2,088

Downloads 2,985,475

Natural Computing

Submission to First Decision - Days (Median) 19

Social Media Mentions 33

Downloads 137,033

Neural Processing Letters

Impact Factor 2.6

Cite Score 4.9

Social Media Mentions 240

Downloads 340,520

Numerical Algorithms

5 Year Impact Factor 1.9

Cite Score 4

Social Media Mentions 18

Downloads 217,913

Programming and Computer Software

Cite Score 1.6

Social Media Mentions 15

Downloads 35,556

Photonic Network Communications

Cite Score 4.1

Downloads 37,448

Scientometrics

5 Year Impact Factor 3.8

Submission to First Decision - Days (Median) 31

Social Media Mentions 2,710

Downloads 1,747,173

Software Quality Journal

Submission to First Decision - Days (Median) 17

Downloads 166,733

The Journal of Supercomputing

Submission to First Decision - Days (Median) 93

Social Media Mentions 163

Downloads 809,043

Real-Time Systems

Submission to First Decision - Days (Median) 22

Downloads 80,528

User Modeling and User-Adapted Interaction

5 Year Impact Factor 4.3

Cite Score 8.9

Social Media Mentions 62

Downloads 226,192

International Journal of Computer Vision

Impact Factor 11.6

5 Year Impact Factor 14.5

Cite Score 29.8

Submission to First Decision - Days (Median) 33

Social Media Mentions 1,280

Downloads 876,080

World Wide Web

Impact Factor 2.7

Cite Score 7.3

Social Media Mentions 188

Downloads 227,777

Innovations in Systems and Software Engineering

Cite Score 3.8

Social Media Mentions 12

Downloads 75,036

Journal of Computer Science and Technology

Social Media Mentions 110

Downloads 56,384

Journal of Computer Virology and Hacking Techniques

5 Year Impact Factor 2

Submission to First Decision - Days (Median) 46

Downloads 105,401

Science China Information Sciences

Impact Factor 7.3

Social Media Mentions 140

Downloads 183,758

Pattern Recognition and Image Analysis

Cite Score 1.8

Social Media Mentions 16

Downloads 31,640

Journal of Real-Time Image Processing

Cite Score 6.8

Submission to First Decision - Days (Median) 1

Social Media Mentions 53

Downloads 202,780

Machine Intelligence Research

Impact Factor 6.4

5 Year Impact Factor 6.4

Cite Score 6.7

Social Media Mentions 189

Downloads 130,260

Frontiers of Computer Science

Cite Score 8.6

Social Media Mentions 214

Downloads 36,637

Frontiers of Information Technology & Electronic Engineering

Social Media Mentions 30

Downloads 127,272

Swarm Intelligence

Social Media Mentions 50

Downloads 86,952

Signal, Image and Video Processing

Submission to First Decision - Days (Median) 4

Downloads 292,507

Service Oriented Computing and Applications

Cite Score 2.6

Social Media Mentions 5

Downloads 71,238

Automatic Control and Computer Sciences

Cite Score 1.7

Downloads 26,429

Automatic Documentation and Mathematical Linguistics

5 Year Impact Factor 0.4

Social Media Mentions 4

Downloads 7,393

Scientific and Technical Information Processing

Cite Score 1

Social Media Mentions 3

Downloads 47,264

Optical Memory and Neural Networks

Impact Factor 1

Downloads 15,126

Cryptography and Communications

Cite Score 2.5

Social Media Mentions 11

Downloads 73,678

Journal on Multimodal User Interfaces

Impact Factor 2.2

Submission to First Decision - Days (Median) 41

Social Media Mentions 10

Downloads 117,845

KI - Künstliche Intelligenz

Submission to First Decision - Days (Median) 50

Social Media Mentions 46

Downloads 228,093

Social Network Analysis and Mining

Social Media Mentions 533

Downloads 524,637

Journal of Cryptographic Engineering

Cite Score 4.7

Downloads 80,034

Journal of Cloud Computing

Social Media Mentions 49

Downloads 733,672

EPJ Data Science

5 Year Impact Factor 3.4

Social Media Mentions 824

Downloads 578,929

Network Modeling Analysis in Health Informatics and Bioinformatics

Downloads 105,906

International Journal of Multimedia Information Retrieval

5 Year Impact Factor 4.9

Cite Score 7.8

Downloads 146,121

Progress in Artificial Intelligence

Submission to First Decision - Days (Median) 74

Downloads 105,769

Health Information Science and Systems

Impact Factor 4.7

Cite Score 11.3

Social Media Mentions 54

Downloads 200,039

Journal of Big Data

Impact Factor 8.6

5 Year Impact Factor 12.4

Cite Score 17.8

Submission to First Decision - Days (Median) 56

Social Media Mentions 280

Downloads 2,559,548

International Journal of Artificial Intelligence in Education

Cite Score 11.1

Social Media Mentions 377

Downloads 551,057

Data Science and Engineering

Impact Factor 5.1

Social Media Mentions 20

Downloads 305,944

International Journal of Data Science and Analytics

Social Media Mentions 181

Downloads 373,346

Computational Visual Media

Impact Factor 17.3

Cite Score 16.9

Social Media Mentions 40

Downloads 128,873

Applied Network Science

Cite Score 4.6

Social Media Mentions 776

Downloads 581,134

International Journal of Educational Technology in Higher Education

5 Year Impact Factor 9.9

Cite Score 19.3

Social Media Mentions 1,356

Downloads 2,572,502

International Journal of Intelligent Robotics and Applications

Social Media Mentions 19

Downloads 90,120

Journal of Healthcare Informatics Research

Impact Factor 5.4

5 Year Impact Factor 4.6

Cite Score 13.6

Social Media Mentions 42

Downloads 107,968

Journal of Membrane Computing

Impact Factor 1.9

Cite Score 5.5

Downloads 33,967

Cybersecurity

Impact Factor 3.9

Downloads 408,523

CCF Transactions on Pervasive Computing and Interaction

Cite Score 5.1

Downloads 64,990

Visual Computing for Industry, Biomedicine, and Art

Impact Factor 3.2

Cite Score 5.6

Social Media Mentions 28

Downloads 294,735

CCF Transactions on High Performance Computing

Downloads 48,454

International Journal of Networked and Distributed Computing

Social Media Mentions 1

Downloads 9,876

Computational Science and Engineering

Recommended for you, learn more about journal metrics.

Read how to measure a journal’s impact.

Sign up for new issue alerts

Never miss an issue! Have free table of contents alerts for your journals of choice delivered directly to your inbox.

Journals metrics by subject

• Biomedical Sciences
• Business & Management
• Earth Sciences & Geography
• Education & Language
• Engineering
• Environmental Sciences
• Food Science & Nutrition
• Life Sciences
• Mathematics
• Public Health
• Social Sciences

Discover more than 1000 journals

University Library

Computer Science Research Resources: High-Impact Journals

• Find Articles & Papers
• High-Impact Journals
• Standards & Technical Reports
• Patents & Government Documents
• E-Books & Reference
• Dissertations & Theses
• Additional Resources

Engineering Easy Search

Top u of i computer science journals.

Top journals written in and cited by University of Illinois at Urbana-Champaign faculty.

• Journal of High Energy Physics
• Monthly Notices of the Royal Astronomical Society
• The European Physical Journal C - Particles and Fields
• Physical Review D - Particles, Fields, Gravitation, and Cosmology
• Physics Letters B - Nuclear Elementary Particle And High Energy Physics
• The Astrophysical Journal
• Physical Review Letters
• Astrophysical Journal Letters

Top Computer Science Journals

Top journals as determined by  Thomson Reuters  Journal Impact Factor 2021 Rankings.

• Nature Machine Intelligence 2021 Impact Factor: 25.898
• IEEE Transactions on Pattern Analysis and Machine Intelligence 2021 Impact Factor: 24.314
• IEEE Transactions on Cybernetics 2021 Impact Factor: 19.118
• Information Fusion 2021 Impact Factor: 17.564
• IEEE Transactions on Evolutionary Computation 2021 Impact Factor: 16.497
• IEEE Transactions on Neural Networks and Learning Systems 2021 Impact Factor: 14.225
• Artificial Intelligence 2021 Impact Factor: 14.050
• IEEE transactions on Affective Computing 2021 Impact Factor: 13.990
• Medical Image Analysis 2021 Impact Factor: 13.828
• International Journal of Computer Vision 2021 Impact Factor: 13.369

Prominent Science Journals

• Nature 2021 Impact Factor: 69.504
• Science 2021 Impact Factor: 63.832
• Proceedings of the National Academy of Sciences 2021 Impact Factor: 10.700

Subject Guide

Ask a Librarian

• << Previous: Find Articles & Papers
• Next: Standards & Technical Reports >>
• Last Updated: Jun 16, 2023 9:35 AM
• URL: https://guides.library.illinois.edu/cs

• Flashes Safe Seven
• FlashLine Login
• Faculty & Staff Phone Directory
• Emeriti or Retiree
• All Departments
• Maps & Directions

• Building Guide
• Departments
• Directions & Parking
• Faculty & Staff
• Give to University Libraries
• Library Instructional Spaces
• Mission & Vision
• Newsletters
• Circulation
• Course Reserves / Core Textbooks
• Equipment for Checkout
• Interlibrary Loan
• Library Instruction
• Library Tutorials
• My Library Account
• Open Access Kent State
• Research Support Services
• Statistical Consulting
• Student Multimedia Studio
• Citation Tools
• Databases A-to-Z
• Databases By Subject
• Digital Collections
• Discovery@Kent State
• Government Information
• Journal Finder
• Library Guides
• Connect from Off-Campus
• Library Workshops
• Subject Librarians Directory
• Suggestions/Feedback
• Writing Commons
• Academic Integrity
• Jobs for Students
• International Students
• Meet with a Librarian
• Study Spaces
• University Libraries Student Scholarship
• Affordable Course Materials
• Copyright Services
• Selection Manager
• Suggest a Purchase

Library Locations at the Kent Campus

• Architecture Library
• Fashion Library
• Map Library
• Performing Arts Library
• Special Collections and Archives

Regional Campus Libraries

• East Liverpool
• College of Podiatric Medicine

• Kent State University
• Computer Science
• Research Impact Metrics

Computer Science: Research Impact Metrics

• Technical Standards
• Technical Reports

Journal Level Metrics

Journal impact factors :.

Data only available through Journal Citation Reports (JCR) in Web of Science (subscription required). Journal Impact Factors should only be used to compare journals in the same discipline.

Below is the how the 2018 Journal Impact Factor of the New England Journal of Medicine   (NEJM) was calculated. An Impact Factor of 71 means that in 2018 on average an article published in 2016 or 2017 in NEJM was cited 71 times.

• Web of Science Core Collection This link opens in a new window Provides access to three multidisciplinary databases covering selected journal literature and conference proceedings in the areas of the arts and humanities, sciences, and social sciences.

Eigenfactor Score : 

The Eigenfactor Score of a journal is calculated using Web of Science citation data. It measures how many times on average an article published in a specific journal have been cited in the past five years. It eliminates journal self citations and gives citations from highly ranked journals more weight. The sum of Eigenfactor scores of all journals listed in Journal Citation Report is 100. Eigenfactor Scores are adjusted for differences across disciplines.

A journal's Eigenfactor score can be found in Journal Citation Report (in Web of Science, Subscription required) or at Eigenfactor's website (free).

• Eigenfactor

Scimago Journal Rank  (SJR): 

SJR is calculated with Elsevier's Scopus citation Data. It accounts for the prestige of journals where the citations come from when counting the number of citations received by a journal. It measures weighed average number of citations that an article in a journal received from previous three years. It is adjusted for differences across disciplines.

• Scimago Journal Rank (Elsevier)

Author Level Metrics

The h-index:.

The h-index was proposed in 2005 by Dr. Jorge E. Hirsch to quantify a researcher's scholarly output. The h-index is defined as : “ A scientist has index h if h of his or her Np papers have at least h citations each and the other (Np – h) papers have ≤h citations each.” The graph below marks where the number of citations meets the number of publications.

How to calculate a researcher's h-index (h-indexes for the same researcher calculated from different databases will be different):

Web of science author search:.

Enter a researcher's last name and first name initial in Author Index and add corresponding author name(s) in Author search box. 

Click on "Create Citation Report" on the up right corner of the search results page and the author's h-index will be calculated automatically.

Google Scholar:

A researcher's Google Scholar profile needs to be set up.

• Albert Einstein's Google Scholar profile and h-index
• Dr. Hirsch's article "An index to quantify an individual's scientific research output" in arXiv.org
• Image attribution: h-index difinition

Article Level Metrics

Article citation counts:.

Many bibliographic databases, such as Web of Science and  SciFinder-n, provide citation counts for articles indexed in them. Google Scholar provides citation counts for items in it, and links to citation counts in Web of Science (with a subscription).

Altmetrics: alternatives to traditional citation-based metrics. 

Altmetrics are metrics complementary to citation-based metrics. They can include "citations in public policy documents, discussions on research blogs, mainstream media coverage, bookmarks on reference managers like Mendeley, and mentions on social networks."

Databases from EBSCOhost, such as Academic Search Complete, provide PlumX metrics. Many journals and publishers, such as Science and PLOS One, provide article-level altmerics.

• What are Altmetrics?
• << Previous: Datasets
• Last Updated: Jul 12, 2024 1:09 PM
• URL: https://libguides.library.kent.edu/computer-science

Street Address

Mailing address, quick links.

• How Are We Doing?
• Student Jobs

Information

• Accessibility
• Emergency Information
• For Our Alumni
• For the Media
• Jobs & Employment
• Life at KSU
• Privacy Statement
• Technology Support
• Website Feedback

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals

Journal Metrics

This page provides information on peer review performance and citation metrics for the Nature Portfolio journals. Data are collected annually for full calendar years.

On this page

2023 peer review metrics, 2023 journal metrics, definitions, editorials and other content.

Submission to first editorial decision: the median time (in days) from when a submission is received to when a first editorial decision about whether the paper was sent out for formal review or not is sent to the authors.

Submission to Accept: the median time (in days) from the published submission date to the final editorial acceptance date.

Top of page ⤴

On this page you will find a suite of citation-based metrics for Nature Portfolio journals, produced by Clarivate Analytics. Brief definitions for each of the metrics used to measure the influence of our journals are included below the tables . Article-level metrics are also available on each article page, allowing readers to track the reach of individual papers.

Commentaries on Impact Factors and their use and misuse can be found in our editorials and other content, going back for many years, links to a sample of which are provided at the end of the page .

For recently launched journals, metrics are calculated from available citation data. If a metric uses multiple years of data, new journals may have partial metrics.

While the metrics presented here are not intended to be a definitive list, we hope that they will prove to be informative. The page is updated on an annual basis.

Journal Impact Factor:

The Journal Impact Factor is defined as all citations to the journal in the current JCR year to items published in the previous two years, divided by the total number of scholarly items (these comprise articles, reviews, and proceedings papers) published in the journal in the previous two years. Though not a strict mathematical average, the Journal Impact Factor of 1.0 mean that, on average, the articles published one or two years agao have been cited one time. A Journal Impact Factor of 2.5 means that, on average, the articles published one or two years ago have been cited two and a half times. The citing works may be articles published in the same journal. However, most citing works are from different journals, proceedings, or books indexed in Web of Science Core Collection. (Source:  Clarivate Analytics )

5-year Journal Impact Factor:

The 5-year journal Impact Factor, available from 2007 onward, is the average number of times articles from the journal published in the past five years have been cited in the JCR year. It is calculated by dividing the number of citations in the JCR year by the total number of articles published in the five previous years. (Source:  Clarivate Analytics )

Immediacy index:

The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. The aggregate Immediacy Index indicates how quickly articles in a subject category are cited. The Immediacy Index is calculated by dividing the number of citations on articles published in a given year by the number of articles published in that year. Because it is a per-article average, the Immediacy Index tends to discount the advantage of large journals over small ones. However, frequently issued journals may have an advantage because an article published early in the year has a better chance of being cited than one published later in the year. many publications that publish infrequently or late in the year have low Immediacy Indexes. For comparing journals specializing in cutting-edge research, the immediacy index can provide a useful perspective (Source:  Clarivate Analytics )

Eigenfactor® Score:

The Eigenfactor Score calculation is based on the number of times articles from the journal published in the past five years have been cited in the JCR year, but it also considers which journals have contributed these citations so that highly cited journals will influence the network more than lesser cited journals. References from one article in a journal to another article from the same journal are removed, so that Eigenfactor Scores are not influenced by journal self-citation. (Source:  Clarivate Analytics )

Article Influence Score:

The Article Influence Score determines the average influence of a journal's articles over the first five years after publication. It is calculated by multiplying the Eigenfactor Score by 0.01 and dividing by the number of articles in the journal, normalized as a fraction of all articles in all publications. This measure is roughly analogous to the 5-Year Journal Impact Factor in that it is a ratio of a journal's citation influence to the size of the journal's article contribution over a period of five years. (Source:  Clarivate Analytics )

• Nature and the Nature journals are diversifying their presentation of performance indicators. Nature . Time to remodel the journal impact factor , July 2016
• The journal impact factor is a much-criticized yet still-used number. As with any metric, it should not be used uncritically and without an understanding of what it measures. Nature Methods . On Impact , August 2015.
• Use these ten principles to guide research evaluation, urge Diana Hicks, Paul Wouters and colleagues. Nature . Bibliometrics: The Leiden Manifesto for research metrics , 22 April 2015.
• The San Francisco Declaration on Research Assessment (DORA), an initiative spearheaded by the American Society for Cell Biology, aims to reform research assessment. Nature Cell Biology . Ending the tyranny of the impact factor , January 2014.
• In deciding how to judge the impact of research, evaluators must take into account the effects of emphasizing particular measures — and be open about their methods. Nature . The maze of impact metrics , 17 October 2013.
• As the journal's first impact factor is released, it is time to reflect on journal metrics and how Nature Climate Change has been making its mark. Nature Climate Change . Having an impact , July 2013.
• Citation analyses can condense scholarly output into numbers, but they do not live up to peer review in the evaluation of scientists. Online usage statistics and commenting could soon enable a more refined assessment of scientific impact. Nature Materials . Measuring impact , July 2011.
• The classic impact factor is outmoded. Is there an alternative for assessing both a researcher's productivity and a journal's quality? Nature Immunology . Ball and chain , October 2010.
• Nature Metrics special , June 2010. The value of scientific output is often measured, to rank one nation against another, allocate funds between universities, or even grant or deny tenure. Scientometricians have devised a multitude of 'metrics' to help in these rankings. Do they work? Are they fair? Are they over-used? Nature investigates.
• Transparency, education and communication are key to ensuring that appropriate metrics are used to measure individual scientific achievement. Nature . Assessing Assessment , 17 June 2010.
• Research assessment rests too heavily on the inflated status of the impact factor. Nature . Not-so-deep impact , 23 June 2005.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Google Scholar Metrics

Google Scholar Metrics provide an easy way for authors to quickly gauge the visibility and influence of recent articles in scholarly publications. Scholar Metrics summarize recent citations to many publications, to help authors as they consider where to publish their new research.

To get started, you can browse the top 100 publications in several languages , ordered by their five-year h-index and h-median metrics. To see which articles in a publication were cited the most and who cited them, click on its h-index number to view the articles as well as the citations underlying the metrics.

You can also explore publications in research areas of your interest. To browse publications in a broad area of research, select one of the areas in the left column. For example: Engineering & Computer Science or Health & Medical Sciences .

To explore specific research areas, select one of the broad areas, click on the "Subcategories" link and then select one of the options. For example: Databases & Information Systems or Development Economics.

Browsing by research area is, as yet, available only for English publications. You can, of course, search for specific publications in all languages by words in their titles.

Scholar Metrics are currently based on our index as it was in July 2024 .

Available Metrics

The h-index of a publication is the largest number h such that at least h articles in that publication were cited at least h times each. For example, a publication with five articles cited by, respectively, 17, 9, 6, 3, and 2, has the h-index of 3.

The h-core of a publication is a set of top cited h articles from the publication. These are the articles that the h-index is based on. For example, the publication above has the h-core with three articles, those cited by 17, 9, and 6.

The h-median of a publication is the median of the citation counts in its h-core. For example, the h-median of the publication above is 9. The h-median is a measure of the distribution of citations to the articles in the h-core.

Finally, the h5-index , h5-core , and h5-median of a publication are, respectively, the h-index, h-core, and h-median of only those of its articles that were published in the last five complete calendar years.

We display the h5-index and the h5-median for each included publication. We also display an entire h5-core of its articles, along with their citation counts, so that you can see which articles contribute to the h5-index. And there's more! Click on the citation count for any article in the h5-core to see who cited it.

Coverage of Publications

Scholar Metrics currently cover articles published between 2019 and 2023 , both inclusive. The metrics are based on citations from all articles that were indexed in Google Scholar in July 2024 . This also includes citations from articles that are not themselves covered by Scholar Metrics.

Since Google Scholar indexes articles from a large number of websites, we can't always tell in which journal a particular article has been published. To avoid misidentification of publications, we have included only the following items:

• journal articles from websites that follow our inclusion guidelines ;
• selected conference articles in Engineering and Computer Science.

Furthermore, we have specifically excluded the following items:

• court opinions, patents, books, and dissertations;
• publications with fewer than 100 articles published between 2019 and 2023;
• publications that received no citations to articles published between 2019 and 2023.

Overall, Scholar Metrics cover a substantial fraction of scholarly articles published in the last five years. However, they don't currently cover a large number of articles from smaller publications.

Inclusion and Corrections

If you can't find the journal you're looking for, try searching by its abbreviated title or alternate title. There're sometimes several ways to refer to the same publication. (Fun fact: we've seen 959 ways to refer to PNAS.)

If you're wondering why your journal is not included, or why it has fewer citations than it surely deserves, that is often a matter of configuring your website for indexing in Google Scholar. Please refer to the inclusion manual . Also, keep in mind that Scholar Metrics only include publications with at least a hundred articles in the last five years.

• Privacy & Terms

• IEEE CS Standards
• Career Center
• Subscribe to Newsletter
• IEEE Standards

• For Industry Professionals
• For Students
• Launch a New Career
• Membership FAQ
• Membership FAQs
• Membership Grades
• Special Circumstances
• Discounts & Payments
• Distinguished Contributor Recognition
• Grant Programs
• Find a Local Chapter
• Find a Distinguished Visitor
• About Distinguished Visitors Program
• Find a Speaker on Early Career Topics
• Technical Communities
• Collabratec (Discussion Forum)
• My Subscriptions
• My Referrals
• Computer Magazine
• ComputingEdge Magazine
• Let us help make your event a success. EXPLORE PLANNING SERVICES
• Events Calendar
• Calls for Papers
• Conference Proceedings
• Conference Highlights
• Top 2024 Conferences
• Conference Sponsorship Options
• Conference Planning Services
• Conference Organizer Resources
• Virtual Conference Guide
• Get a Quote
• CPS Dashboard
• CPS Author FAQ
• CPS Organizer FAQ
• Find the latest in advanced computing research. VISIT THE DIGITAL LIBRARY
• Open Access
• Tech News Blog
• Author Guidelines
• Reviewer Information
• Guest Editor Information
• Editor Information
• Editor-in-Chief Information
• Volunteer Opportunities
• Video Library
• Member Benefits
• Institutional Library Subscriptions
• Advertising and Sponsorship
• Code of Ethics
• Educational Webinars
• Online Education
• Certifications
• Industry Webinars & Whitepapers
• Research Reports
• Bodies of Knowledge
• CS for Industry Professionals
• Resource Library
• Newsletters
• Women in Computing
• Digital Library Access
• Organize a Conference
• Run a Publication
• Become a Distinguished Speaker
• Participate in Standards Activities
• Peer Review Content
• Author Resources
• Publish Open Access
• Society Leadership
• Boards & Committees
• Local Chapters
• Governance Resources
• Conference Publishing Services
• Chapter Resources
• About the Board of Governors
• Board of Governors Members
• Diversity & Inclusion
• Open Volunteer Opportunities
• Award Recipients
• Student Scholarships & Awards
• Nominate an Election Candidate
• Nominate a Colleague
• Corporate Partnerships
• Conference Sponsorships & Exhibits
• Advertising
• Recruitment
• Publications
• Education & Career

IEEE CS Publications Achieve Significant Increases in Impact Factors

LOS ALAMITOS, Calif., 30 July 2020 – The IEEE Computer Society (IEEE CS)—the leading publisher of peer-reviewed magazines and journals covering all aspects of computer science, engineering, and technology—announced that its publications earned high 2019 impact factors, as reported by Clarivate Analytics Journal Citation Reports (JCR). Many of the impact factors increased compared to 2018.

Impact factor is a measurement of how often a scholarly publication’s articles are cited and therefore is an indicator of that publication’s importance and influence within a scientific community. IEEE Transactions on Pattern Analysis and Machine Intelligence ( TPAMI ) earned a very high impact factor of 17.861—the second-highest impact factor of all IEEE publications.

“Bolstered by the explosive growth of the computer-vision and machine-learning research communities,  TPAMI  continues to be one of IEEE’s flagship journals and one of the premier journals across all of computer science,” said Sven Dickinson, TPAMI editor in chief.

The IEEE CS journals with the highest 2019 impact factors are:

• TPAMI – 17.861
• IEEE Transactions on Affective Computing (TAC) – 7.51
• IEEE Transactions on Dependable and Secure Computing (TDSC) – 6.864
• IEEE Transactions on Software Engineering (TSE) – 6.11
• IEEE Transactions on Emerging Topics in Computing (TETC) – 6.043

The IEEE CS magazines with the highest 2019 impact factors are:

• IEEE MultiMedia – 4.96
• Computer – 4.41
• IEEE Pervasive Computing – 4.41
• IEEE Internet Computing – 4.23

“The release of the 2019 impact factors has confirmed the leadership role in computing of the IEEE CS publication portfolio across all areas of technical coverage,” said Fabrizio Lombardi, IEEE CS Vice President for Publications. “This remarkable accomplishment is not limited to the impact factor, as it also encompasses all other publication metrics such as article influence and Eigenfactor scores. I would like to extend my sincere thanks to all our volunteer constituencies (reviewers, authors, editorial board members, and editors in chief) and the entire IEEE CS staff who have enabled this success and continued ascent in the publication echelons of computer engineering and science.”

Impact factor measures the frequency with which the average article in a publication has been cited in a particular year. The calculation is based on a two-year period and involves dividing the number of times articles were cited by the number of articles that are citable. (Source: https://researchguides.uic.edu/if/impact )

Visit https://www.computer.org/publications/ieee-computer-society-publications-by-topic to learn more about the IEEE CS’s portfolio of peer-reviewed magazines and journals.

About the IEEE Computer Society The IEEE Computer Society is the world’s home for computer science, engineering, and technology. A global leader in providing access to computer science research, analysis, and information, the IEEE Computer Society offers a comprehensive array of unmatched products, services, and opportunities for individuals at all stages of their professional career. Known as the premier organization that empowers the people who drive technology, the IEEE Computer Society offers international conferences, peer-reviewed publications, a unique digital library, and training programs. Visit  computer.org  for more information.

Recommended by IEEE Computer Society

Amara's Law and Its Place in the Future of Tech

Cybersecurity Concerns for Coding Professionals

Why Professional Integrity Matters in AI

AI Has Arrived and Is Vital to Business Scalability

Gain An Edge On The Competition With Edge Computing

What is Green Coding and its Impact on Ethical Web Development?

AI-Driven Data Security to Protect Against AI-Driven Data Leakage

Seeking 2026 Editors in Chief

• Current Issue
• Archived Issue
• Special Issue
• Networks & Communications
• Data Analytics
• Editorial Board
• Topical Board
• Reviewer Board

Aims & Scope

• Submission Guidelines
• Article Processing Charge
• Journal History

Research Reports on Computer Science mainly reports on innovative research results that cover novel theories, technologies and engineering applications in the fields of computer science and engineering. In conjunction with the rapid development of this field, this journal provides a platform for expeditious dissemination of recent advances.   

The journal publishes papers that reflect a wide variety of ongoing research. Topics include, but are not limited to:

• Computer architecture
• Software engineering
• Database technology
• Big data processing
• Quantum computer
• Artificial intelligence
• Artificial neural networks
• Virtual reality
• Machine learning and automated reasoning
• Computer networks and distributed processing
• Internet of Things and cloud computing
• Intelligent human-machine interface
• Soft computing
• Evolutionary computing
• Data analytics

The journal considers contributions in the form of original research papers, short communications and review articles on cross-cutting theories, technologies and real-world applications in line with the journal scope.

Further Information : Article Processing Charge | Open Access Policy | Privacy Policy | Terms and Conditions

Contact Email：[email protected]

Copyright 2022 Universal Wiser Publisher Pte.Ltd. All rights reserved

IEEE Account

• Change Username/Password
• Update Address

Purchase Details

• Payment Options
• Order History
• View Purchased Documents

Profile Information

• Communications Preferences
• Profession and Education
• Technical Interests
• US & Canada: +1 800 678 4333
• Worldwide: +1 732 981 0060
• Contact & Support
• About IEEE Xplore
• Accessibility
• Terms of Use
• Nondiscrimination Policy
• Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. © Copyright 2024 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

Subject Area and Category

• Computer Networks and Communications
• Human-Computer Interaction

Multidisciplinary Digital Publishing Institute (MDPI)

Publication type

Information.

How to publish in this journal

[email protected]

The set of journals have been ranked according to their SJR and divided into four equal groups, four quartiles. Q1 (green) comprises the quarter of the journals with the highest values, Q2 (yellow) the second highest values, Q3 (orange) the third highest values and Q4 (red) the lowest values.

The SJR is a size-independent prestige indicator that ranks journals by their 'average prestige per article'. It is based on the idea that 'all citations are not created equal'. SJR is a measure of scientific influence of journals that accounts for both the number of citations received by a journal and the importance or prestige of the journals where such citations come from It measures the scientific influence of the average article in a journal, it expresses how central to the global scientific discussion an average article of the journal is.

Evolution of the number of published documents. All types of documents are considered, including citable and non citable documents.

This indicator counts the number of citations received by documents from a journal and divides them by the total number of documents published in that journal. The chart shows the evolution of the average number of times documents published in a journal in the past two, three and four years have been cited in the current year. The two years line is equivalent to journal impact factor ™ (Thomson Reuters) metric.

Evolution of the total number of citations and journal's self-citations received by a journal's published documents during the three previous years. Journal Self-citation is defined as the number of citation from a journal citing article to articles published by the same journal.

Evolution of the number of total citation per document and external citation per document (i.e. journal self-citations removed) received by a journal's published documents during the three previous years. External citations are calculated by subtracting the number of self-citations from the total number of citations received by the journal’s documents.

International Collaboration accounts for the articles that have been produced by researchers from several countries. The chart shows the ratio of a journal's documents signed by researchers from more than one country; that is including more than one country address.

Not every article in a journal is considered primary research and therefore "citable", this chart shows the ratio of a journal's articles including substantial research (research articles, conference papers and reviews) in three year windows vs. those documents other than research articles, reviews and conference papers.

Ratio of a journal's items, grouped in three years windows, that have been cited at least once vs. those not cited during the following year.

Evolution of the percentage of female authors.

Evolution of the number of documents cited by public policy documents according to Overton database.

Evoution of the number of documents related to Sustainable Development Goals defined by United Nations. Available from 2018 onwards.

Leave a comment

Name * Required

Email (will not be published) * Required

* Required Cancel

The users of Scimago Journal & Country Rank have the possibility to dialogue through comments linked to a specific journal. The purpose is to have a forum in which general doubts about the processes of publication in the journal, experiences and other issues derived from the publication of papers are resolved. For topics on particular articles, maintain the dialogue through the usual channels with your editor.

Follow us on @ScimagoJR Scimago Lab , Copyright 2007-2024. Data Source: Scopus®

Cookie settings

Cookie Policy

Legal Notice

Privacy Policy

Information

• Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

• Active Journals
• Find a Journal
• Proceedings Series
• For Authors
• For Reviewers
• For Editors
• For Librarians
• For Publishers
• For Societies
• For Conference Organizers
• Open Access Policy
• Institutional Open Access Program
• Special Issues Guidelines
• Editorial Process
• Research and Publication Ethics
• Article Processing Charges
• Testimonials
• Preprints.org
• SciProfiles
• Encyclopedia

Article Menu

• Subscribe SciFeed
• Recommended Articles
• Author Biographies
• Google Scholar
• on Google Scholar
• Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

Artificial intelligence tool adoption in higher education: a structural equation modeling approach to understanding impact factors among economics students.

1. Introduction

• What are the underlying factors that influence students’ interaction with and perception of AI tools?
• How do these underlying factors interrelate and affect each other?
• What are the direct and indirect effects of these factors on the frequency of AI tool usage?

2. Literature Review

2.1. familiarity with ai tools and their utilization in higher education, 2.2. access and subscription to ai tools, 2.3. frequency and impact of ai tool usage, 2.4. training and support, 2.5. general attitudes, 2.6. concerns, 2.7. integration of ai tools, 3. materials and methods, 3.1. data collection and preprocessing, 3.2. survey instrument.

• Demographics: This section collected fundamental data on the respondents’ age, gender, education level, university affiliation, specialization, and domicile type. These variables were essential for contextualizing the respondents’ backgrounds and ensuring the representativeness of the sample.
• Familiarity with AI Tools: The respondents’ familiarity with AI tools was assessed using a 5-point Likert scale, ranging from “Not at all” to “Extremely”. This scale provided a nuanced understanding of the respondents’ exposure to AI technologies. The reliability of this scale was validated using Cronbach’s alpha, yielding a value of 0.85, indicating good internal consistency.
• Known and Used AI Tools: Multiple-response items were employed to capture the variety of AI tools known and utilized by the respondents. This section provided an insight into the specific AI applications that the students were aware of and actively using.
• Access to AI Tools: The questions in this section measured the respondents’ access to AI tools, again using a Likert scale to capture the extent of accessibility, ranging from “Not at all” to “Extremely”. The reliability of this scale was also validated with a Cronbach’s alpha value of 0.87, indicating good internal consistency.
• Subscription to AI Tools: This part of the survey inquired whether the respondents had access to AI tools through university-provided subscriptions or through personal subscriptions. This was critical for understanding the sources of access to AI resources.
• Frequency and Impact of AI Tool Usage: Various Likert scales were used to assess the frequency of AI tool usage, as well as the perceived impact on academic efficiency, quality of work, and ease of use. These scales helped in quantifying the tangible benefits perceived by the students.
• Training and Support: This section included questions about any formal training received on the use of AI tools and the perceived usefulness of such training. This was essential for understanding the level of institutional support provided to the students.
• General Attitudes and Concerns: The questions were designed to gauge the respondents’ overall attitudes towards AI tools, including concerns about inaccuracy, cheating, privacy, and the broader integration of AI into academic activities.
• Integration of AI Tools: This section assessed how AI tools were integrated into the curriculum and academic activities. The questions focused on the manner of AI tool usage in coursework, projects, and other academic tasks, providing a clearer picture of the practical application of AI tools in the educational environment.

3.3. Exploratory and Confirmatory Factor Analysis

• X is the matrix of observed variables;
• L is the factor loading matrix;
• F is the matrix of latent factors;
• E is the matrix of unique variances (errors).
• Λ is the factor loading matrix;
• ξ is the vector of latent variables;
• δ is the vector of measurement errors.

3.4. Structural Equation Modeling

• η is the vector of endogenous latent variables;
• B is the matrix of coefficients for the relationships among endogenous variables;
• Γ is the matrix of coefficients for the relationships between exogenous and endogenous variables;
• ξ is the vector of exogenous latent variables;
• ζ is the vector of disturbances (errors).

4.1. Exploratory Factor Analysis (EFA)

4.2. confirmatory factor analysis (cfa), 4.3. structural equation model (sem) results, 5. discussion.

• Ind 1. The influence of general awareness and familiarity with AI tools (MR1) on perceived usefulness and positive attitudes towards AI (MR4) through formal training and integration (MR2).
• Ind 2. The influence of general awareness and familiarity with AI tools (MR1) on perceived usefulness and positive attitudes towards AI (MR4) through concerns regarding AI (MR3).
• Ind 3. The influence of general awareness and familiarity with AI tools (MR1) on the frequency of AI tool usage through formal training and integration (MR2).
• Ind 4. The influence of general awareness and familiarity with AI tools (MR1) on the frequency of AI tool usage through concerns regarding AI (MR3).
• Ind 5. The influence of general awareness and familiarity with AI tools (MR1) on the frequency of AI tool usage through perceived usefulness and positive attitudes towards AI (MR4).
• Ind 6. The influence of formal training and integration (MR2) on the frequency of AI tool usage through perceived usefulness and positive attitudes towards AI (MR4).
• Ind 7. The influence of concerns regarding AI (MR3) on the frequency of AI tool usage through perceived usefulness and positive attitudes towards AI (MR4).

6. Conclusions

Author contributions, data availability statement, conflicts of interest, appendix a. the survey instrument.

Appendix B. Demographic Characteristics of the Study Participants

Appendix c. efa factor loadings.

• Centre for the Governance of AI. Computing Power and the Governance of AI. 2024. Available online: https://www.governance.ai/post/computing-power-and-the-governance-of-ai (accessed on 22 August 2024).
• Utilitynet. Outlook 2024: Nine Major Trends in Computing Power Development. 2024. Available online: https://medium.com/@utilitynet_community/outlook-2024-nine-major-trends-in-computing-power-development-52cf46df8f48 (accessed on 23 August 2024).
• Maphosa, V.; Maphosa, M. Artificial intelligence in higher education: A bibliometric analysis and topic modeling approach. Appl. Artif. Intell. 2023 , 37 , 2261730. [ Google Scholar ] [ CrossRef ]
• Crompton, H.; Burke, D. Artificial intelligence in higher education: The state of the field. Int. J. Educ. Technol. High Educ. 2023 , 20 , 22. [ Google Scholar ] [ CrossRef ]
• Zhang, Z.; Xu, L. Student engagement with automated feedback on academic writing: A study on Uyghur ethnic minority students in China. J. Multiling. Multicult. Dev. 2022 , 43 , 1–14. [ Google Scholar ] [ CrossRef ]
• Pisica, A.I.; Edu, T.; Zaharia, R.M.; Zaharia, R. Implementing Artificial Intelligence in Higher Education: Pros and Cons from the Perspectives of Academics. Societies 2023 , 13 , 118. [ Google Scholar ] [ CrossRef ]
• Bucea-Manea-Țoniş, R.; Kuleto, V.; Gudei, S.C.D.; Lianu, C.; Lianu, C.; Ilić, M.P.; Păun, D. Artificial Intelligence Potential in Higher Education Institutions Enhanced Learning Environment in Romania and Serbia. Sustainability 2022 , 14 , 5842. [ Google Scholar ] [ CrossRef ]
• Săseanu, A.S.; Gogonea, R.-M.; Ghiţă, S.I. The social impact of using artificial intelligence in education. Amfiteatru Econ. 2024 , 26 , 89–105. [ Google Scholar ] [ CrossRef ]
• Farahani, M.S.; Ghasemi, G. Artificial Intelligence and Inequality: Challenges and Opportunities. Qeios 2024 , 7HWUZ2 , 1–14. [ Google Scholar ] [ CrossRef ]
• Wang, Y.M.; Wei, C.L.; Lin, H.H.; Wang, S.C.; Wang, Y.S. What drives students’ AI learning behavior: A perspective of AI anxiety. Interact. Learn. Environ. 2022 , 2022 , 1–17. [ Google Scholar ] [ CrossRef ]
• Al-Sharafi, M.A.; Al-Emran, M.; Iranmanesh, M.; Al-Qaysi, N.; Iahad, N.A.; Arpaci, I. Understanding the impact of knowledge management factors on the sustainable use of AI-based chatbots for educational purposes using a hybrid SEM-ANN approach. Interact. Learn. Environ. 2023 , 31 , 7491–7510. [ Google Scholar ] [ CrossRef ]
• Hultberg, P.T.; Santandreu Calonge, D.; Kamalov, F.; Smail, L. Comparing and assessing four AI chatbots’ competence in economics. PLoS ONE 2024 , 19 , e0297804. [ Google Scholar ] [ CrossRef ]
• Strzelecki, A. To use or not to use ChatGPT in higher education? A study of students’ acceptance and use of technology. Interact. Learn. Environ. 2023 , 31 , 1–14. [ Google Scholar ] [ CrossRef ]
• Al-Abdullatif, A.M. Modeling Students’ Perceptions of Chatbots in Learning: Integrating Technology Acceptance with the Value-Based Adoption Model. Educ. Sci. 2023 , 13 , 1151. [ Google Scholar ] [ CrossRef ]
• Chan, C.K.Y.; Hu, W. Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. Int. J. Educ. Technol. High Educ. 2023 , 20 , 43. [ Google Scholar ] [ CrossRef ]
• Farrokhnia, M.; Banihashem, S.K.; Noroozi, O.; Wals, A. A SWOT analysis of ChatGPT: Implications for educational practice and research. Innov. Educ. Teach. Int. 2023 , 61 , 460–474. [ Google Scholar ] [ CrossRef ]
• Labadze, L.; Grigolia, M.; Machaidze, L. Role of AI chatbots in education: Systematic literature review. Int. J. Educ. Technol. High Educ. 2023 , 20 , 56. [ Google Scholar ] [ CrossRef ]
• Wang, T.; Lund, B.D.; Marengo, A.; Pagano, A.; Mannuru, N.R.; Teel, Z.A.; Pange, J. Exploring the Potential Impact of Artificial Intelligence (AI) on International Students in Higher Education: Generative AI, Chatbots, Analytics, and International Student Success. Appl. Sci. 2023 , 13 , 6716. [ Google Scholar ] [ CrossRef ]
• Hasanein, A.M.; Sobaih, A.E.E. Drivers and Consequences of ChatGPT Use in Higher Education: Key Stakeholder Perspectives. Eur. J. Investig. Health Psychol. Educ. 2023 , 13 , 2599–2614. [ Google Scholar ] [ CrossRef ]
• Lo, C.K. What Is the Impact of ChatGPT on Education? A Rapid Review of the Literature. Educ. Sci. 2023 , 13 , 410. [ Google Scholar ] [ CrossRef ]
• Kuleto, V.; Ilić, M.; Dumangiu, M.; Ranković, M.; Martins, O.M.D.; Păun, D.; Mihoreanu, L. Exploring Opportunities and Challenges of Artificial Intelligence and Machine Learning in Higher Education Institutions. Sustainability 2021 , 13 , 10424. [ Google Scholar ] [ CrossRef ]
• Kashive, N.; Powale, L.; Kashive, K. Understanding user perception toward artificial intelligence (AI) enabled e-learning. The Int. J. Inf. Learn. Technol. 2020 , 38 , 1–19. [ Google Scholar ] [ CrossRef ]
• Chen, L.; Chen, P.; Lin, Z. Artificial Intelligence in Education: A Review. IEEE Access 2020 , 8 , 75264–75278. [ Google Scholar ] [ CrossRef ]
• Ruiz-Real, J.L.; Uribe-Toril, J.; Torres, J.A.; De Pablo, J. Artificial intelligence in business and economics research: Trends and future. J. Bus. Econ. Manag. 2021 , 22 , 98–117. [ Google Scholar ] [ CrossRef ]
• Popenici, S.A.D.; Kerr, S. Exploring the impact of artificial intelligence on teaching and learning in higher education. Res. Pract. Technol. Enhanc. Learn. 2017 , 12 , 22. [ Google Scholar ] [ CrossRef ]
• Zawacki-Richter, O.; Marín, V.I.; Bond, M.; Gouverneur, F. Systematic review of research on artificial intelligence applications in higher education—Where are the educators? Int. J. Educ. Technol. High Educ. 2019 , 16 , 1–27. [ Google Scholar ] [ CrossRef ]
• Weizenbaum, J. ELIZA: A computer program for the study of natural language communication between man and machine. Commun. ACM 1966 , 9 , 36–45. [ Google Scholar ] [ CrossRef ]
• Colby, K.M. Modeling a paranoid mind. Behav. Brain Sci. 1981 , 4 , 515–534. [ Google Scholar ] [ CrossRef ]
• Denecke, K.; Glauser, R.; Reichenpfader, D. Assessing the Potential and Risks of AI-Based Tools in Higher Education: Results from an eSurvey and SWOT Analysis. Trends High. Educ. 2023 , 2 , 667–688. [ Google Scholar ] [ CrossRef ]
• Nguyen, N.D. Exploring the role of AI in education. Lond. J. Soc. Sci. 2023 , 6 , 84–95. [ Google Scholar ] [ CrossRef ]
• Salinas-Navarro, D.E.; Vilalta-Perdomo, E.; Michel-Villarreal, R.; Montesinos, L. Using Generative Artificial Intelligence Tools to Explain and Enhance Experiential Learning for Authentic Assessment. Educ. Sci. 2024 , 14 , 83. [ Google Scholar ] [ CrossRef ]
• Almaraz-López, C.; Almaraz-Menéndez, F.; López-Esteban, C. Comparative Study of the Attitudes and Perceptions of University Students in Business Administration and Management and in Education toward Artificial Intelligence. Educ. Sci. 2023 , 13 , 609. [ Google Scholar ] [ CrossRef ]
• Tominc, P.; Rožman, M. Artificial Intelligence and Business Studies: Study Cycle Differences Regarding the Perceptions of the Key Future Competences. Educ. Sci. 2023 , 13 , 580. [ Google Scholar ] [ CrossRef ]
• Jo, H.; Bang, Y. Analyzing ChatGPT adoption drivers with the TOEK framework. Sci. Rep. 2023 , 13 , 1–17. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Selwyn, N. The Digital Native—Myth and Reality. Aslib Proc. 2009 , 61 , 364–379. [ Google Scholar ] [ CrossRef ]
• Warschauer, M.; Matuchniak, T. New Technology and Digital Worlds: Analyzing Evidence of Equity in Access, Use, and Outcomes. Rev. Res. Educ. 2010 , 34 , 179–225. [ Google Scholar ] [ CrossRef ]
• Pew Research Center. Digital Divide Persists Even as Lower-Income Americans Make Gains in Tech Adoption. Available online: https://www.pewresearch.org/fact-tank/2019/05/07/digital-divide-persists-even-as-lower-income-americans-make-gains-in-tech-adoption/ (accessed on 12 July 2024).
• Bower, M.; Kenney, J.; Dalgarno, B.; Lee, M.J.W.; Kennedy, G.E. Patterns and Principles for Blended Synchronous Learning: Engaging Remote and Face-to-Face Learners in Rich-Media Real-Time Collaborative Learning Environments. Australas J. Educ. Technol. 2017 , 33 , 1–23. [ Google Scholar ] [ CrossRef ]
• Fakhri, M.M.; Ahmar, A.S.; Isma, A.; Fadhilatunisa, R.; Fadhilatunisa, D. Exploring Generative AI Tools Frequency: Impacts on Attitude, Satisfaction, and Competency in Achieving Higher Education Learning Goals. J. Educ. Learn. Innov. 2024 , 4 , 196–208. [ Google Scholar ] [ CrossRef ]
• Venkatesh, V.; Davis, F.D. A theoretical extension of the technology acceptance model: Four longitudinal field studies. Manag. Sci. 2000 , 46 , 186–204. [ Google Scholar ] [ CrossRef ]
• Teo, T. Factors influencing teachers’ intention to use technology: Model development and test. Comput. Educ. 2011 , 57 , 2432–2440. [ Google Scholar ] [ CrossRef ]
• Sánchez, R.A.; Hueros, A.D. Motivational Factors that Influence the Acceptance of Moodle Using TAM. Comput. Hum. Behav. 2010 , 26 , 1632–1640. [ Google Scholar ] [ CrossRef ]
• Abdullah, F.; Ward, R. Developing a General Extended Technology Acceptance Model for E-Learning (GETAMEL) by Analysing Commonly Used External Factors. Comput. Hum. Behav. 2016 , 56 , 238–256. [ Google Scholar ] [ CrossRef ]
• Cheng, Y. Effects of quality antecedents on e-learning acceptance. Internet Res. 2012 , 22 , 361–390. [ Google Scholar ] [ CrossRef ]
• Park, S.Y.; Nam, M.-W.; Cha, S.-B. University Students’ Behavioral Intention to Use Mobile Learning: Evaluating the Technology Acceptance Model. Br. J. Educ. Technol. 2012 , 43 , 592–605. [ Google Scholar ] [ CrossRef ]
• AACSB. Available online: https://www.aacsb.edu/insights/articles/2024/06/ai-upskilling-for-business-school-faculty (accessed on 5 June 2024).
• Ajzen, I. The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 1991 , 50 , 179–211. [ Google Scholar ] [ CrossRef ]
• Teo, T. Modelling technology acceptance in education: A study of pre-service teachers. Comput. Educ. 2009 , 52 , 302–312. [ Google Scholar ] [ CrossRef ]
• Davis, F.D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989 , 13 , 319–340. [ Google Scholar ] [ CrossRef ]
• Lee, M.K.; Cheung, C.M.; Chen, Z. Acceptance of Internet-based learning medium: The role of extrinsic and intrinsic motivation. Inf. Manag. 2005 , 42 , 1095–1104. [ Google Scholar ] [ CrossRef ]
• Yuen, A.H.K.; Ma, W.W.K. Exploring teacher acceptance of e-learning technology. Asia-Pac. J. Teach. Educ. 2008 , 36 , 229–243. [ Google Scholar ] [ CrossRef ]
• Kim, J.; Cho, Y.H. My teammate is AI: Understanding students’ perceptions of student-AI collaboration in drawing tasks. Asia Pac. J. Educ. 2023 , 43 , 1–15. [ Google Scholar ] [ CrossRef ]
• Holden, H.; Rada, R. Understanding the Influence of Perceived Usability and Technology Self-Efficacy on Teachers’ Technology Acceptance. J. Res. Technol. Educ. 2011 , 43 , 343–367. [ Google Scholar ] [ CrossRef ]
• Tarhini, A.; Hone, K.; Liu, X. Measuring the Moderating Effect of Gender and Age on E-Learning Acceptance in England: A Structural Equation Modeling Approach for An Extended Technology Acceptance Model. J. Educ. Comput. Res. 2014 , 51 , 163–184. [ Google Scholar ] [ CrossRef ]
• McIntire, A.; Calvert, I.; Ashcraft, J. Pressure to Plagiarize and the Choice to Cheat: Toward a Pragmatic Reframing of the Ethics of Academic Integrity. Educ. Sci. 2024 , 14 , 244. [ Google Scholar ] [ CrossRef ]
• Ertmer, P.A.; Ottenbreit-Leftwich, A.T. Teacher Technology Change: How Knowledge, Confidence, Beliefs, and Culture Intersect. J. Res. Technol. Educ. 2010 , 42 , 255–284. [ Google Scholar ] [ CrossRef ]
• Howard, S.K.; Ma, J.; Yang, J. Student rules: Exploring patterns of students’ computer-efficacy and engagement with digital technologies in learning. Comput. Educ. 2016 , 101 , 29–42. [ Google Scholar ] [ CrossRef ]
• Zhu, C.; Wang, D.; Cai, Y.; Engels, N. What core competencies are related to teachers’ innovative teaching? Asia-Pac. J. Teach. Educ. 2013 , 41 , 9–27. [ Google Scholar ] [ CrossRef ]
• Scherer, R.; Siddiq, F.; Tondeur, J. The Technology Acceptance Model (TAM): A Meta-Analytic Structural Equation Modeling Approach to Explaining Teachers’ Adoption of Digital Technology in Education. Comput. Educ. 2019 , 128 , 13–35. [ Google Scholar ] [ CrossRef ]
• Almasri, F. Exploring the Impact of Artificial Intelligence in Teaching and Learning of Science: A Systematic Review of Empirical Research. Res. Sci. Educ. 2024 , 54 , 977–997. [ Google Scholar ] [ CrossRef ]
• Davidescu, A.; Strat, V.A. An empirical analysis of regional poles from the perspective of Romanian sustainable development. Procedia Econ. Financ. 2014 , 10 , 114–124. [ Google Scholar ]
• Romanian Ministry of Education. 2023. Available online: https://www.edu.ro/sites/default/files/_fi%C8%99iere/Minister/2024/div/Raport_metaranking_2023.pdf (accessed on 15 May 2024).
• Babeș-Bolyai University. Available online: https://consortiul-universitaria.ubbcluj.ro/ (accessed on 13 July 2024).
• Bucharest University of Economic Studies. Available online: https://international.ase.ro/21/ (accessed on 5 July 2024).
• Bollen, K.A. Structural Equations with Latent Variables ; Wiley-Interscience: New York, NY, USA, 1989. [ Google Scholar ]
• Bentler, P.M. Comparative fit indexes in structural models. Psychol. Bull. 1990 , 107 , 238–246. [ Google Scholar ] [ CrossRef ]
• Kline, R.B. Principles and Practice of Structural Equation Modeling , 4th ed.; Guilford Press: New York, NY, USA, 2015. [ Google Scholar ]
• Hair, J.F.; Black, W.C.; Babin, B.J.; Anderson, R.E. Multivariate Data Analysis , 8th ed.; Cengage Learning: Boston, MA, USA, 2018. [ Google Scholar ]
• Byrne, B.M. Structural Equation Modeling with AMOS: Basic Concepts, Applications, and Programming , 3rd ed.; Routledge: London, UK, 2016. [ Google Scholar ]
• Hoyle, R.H. (Ed.) Handbook of Structural Equation Modeling ; Guilford Press: New York, NY, USA, 2012. [ Google Scholar ]
• Iacobucci, D. Structural equations modeling: Fit indices, sample size, and advanced topics. J. Consum. Psychol. 2010 , 20 , 90–98. [ Google Scholar ] [ CrossRef ]
• MacCallum, R.C.; Austin, J.T. Applications of structural equation modeling in psychological research. Annu. Rev. Psychol. 2000 , 51 , 201–226. [ Google Scholar ] [ CrossRef ]
• Harman, H.H. Modern Factor Analysis ; University of Chicago Press: Chicago, IL, USA, 1976. [ Google Scholar ]
• Fabrigar, L.R.; Wegener, D.T.; MacCallum, R.C.; Strahan, E.J. Evaluating the use of exploratory factor analysis in psychological research. Psychol. Methods 1999 , 4 , 272. [ Google Scholar ] [ CrossRef ]
• Kaiser, H.F. The varimax criterion for analytic rotation in factor analysis. Psychometrika 1958 , 23 , 187–200. [ Google Scholar ] [ CrossRef ]
• Tabachnick, B.G.; Fidell, L.S.; Ullman, J.B. Using Multivariate Statistics ; Pearson: Boston, MA, USA, 2013; Volume 6, pp. 497–516. [ Google Scholar ]
• Hu, L.; Bentler, P.M. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct. Equ. Model. A Multidiscip. J. 1999 , 6 , 1–55. [ Google Scholar ] [ CrossRef ]
• Crompton, H.; Edmett, A.; Ichaporia, N.; Burke, D. AI and English language teaching: Affordances and challenges. Br. J. Educ. Technol. 2024 , 1–27. [ Google Scholar ] [ CrossRef ]
• Bsharat, M.; Ibrahim, O. Quality of service acceptance in cloud service utilization: An empirical study in Palestinian higher education institutions. Educ. Inf. Technol. 2020 , 25 , 863–888. [ Google Scholar ] [ CrossRef ]
• Zhai, X.; Chu, X.; Chai, C.S.; Jong, M.S.Y.; Istenic, A.; Spector, M.; Liu, J.B.; Yuan, J.; Li, Y. A Review of Artificial Intelligence (AI) in Education from 2010 to 2020. Complexity 2021 , 1 , 8812542. [ Google Scholar ] [ CrossRef ]
• Venkatesh, V.; Bala, H. Technology Acceptance Model 3 and a Research Agenda on Interventions. Decis. Sci. 2008 , 39 , 273–315. [ Google Scholar ] [ CrossRef ]
• Kumar, V.R.; Raman, R. Student Perceptions on Artificial Intelligence (AI) in higher education. In Proceedings of the 2022 IEEE Integrated STEM Education Conference (ISEC), Princeton, NJ, USA, 26 March 2022; pp. 450–454. [ Google Scholar ] [ CrossRef ]
• Dahri, N.A.; Yahaya, N.; Al-Rahmi, W.M.; Vighio, M.S.; Alblehai, F.; Soomro, R.B.; Shutaleva, A. Investigating AI-based academic support acceptance and its impact on students’ performance in Malaysian and Pakistani higher education institutions. Educ. Inf. Technol. 2024 , 29 , 1–50. [ Google Scholar ] [ CrossRef ]

Click here to enlarge figure

Share and Cite

Sova, R.; Tudor, C.; Tartavulea, C.V.; Dieaconescu, R.I. Artificial Intelligence Tool Adoption in Higher Education: A Structural Equation Modeling Approach to Understanding Impact Factors among Economics Students. Electronics 2024 , 13 , 3632. https://doi.org/10.3390/electronics13183632

Sova R, Tudor C, Tartavulea CV, Dieaconescu RI. Artificial Intelligence Tool Adoption in Higher Education: A Structural Equation Modeling Approach to Understanding Impact Factors among Economics Students. Electronics . 2024; 13(18):3632. https://doi.org/10.3390/electronics13183632

Sova, Robert, Cristiana Tudor, Cristina Venera Tartavulea, and Ramona Iulia Dieaconescu. 2024. "Artificial Intelligence Tool Adoption in Higher Education: A Structural Equation Modeling Approach to Understanding Impact Factors among Economics Students" Electronics 13, no. 18: 3632. https://doi.org/10.3390/electronics13183632

Article Metrics

Article access statistics, further information, mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals