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What is Journal Impact Factor?

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Daunted by the idea of choosing the right journal for your paper? Don’t be. Metrics have become an everyday word in scholarship, in general. Within its many fields of research – if not all of them – they provide important data about a journal’s impact and relevance among its readers. In an era of information proliferation, it has become increasingly important to know where to capture the most attention and interest of your target audience.

So, whenever you are in doubt about which journal suits you better, don’t forget to browse its metrics; they will certainly help you with the decision-making process. Start, for example, with the Journal Impact Factor.

Impact factor (IF) is a measure of the number of times an average paper in a journal is cited, during a year. Clarivate Analytics releases the Journal Impact Factors annually as part of the Web of Science Journal Citation Reports®. Only journals listed in the Science Citation Index Expanded® (SCIE) and Social Sciences Citation Index® (SSCI) receive an Impact Factor.

What is a good impact factor for a scientific journal?

Impact Factors are used to measure the importance of a journal by calculating the number of times selected articles are cited within a particular year. Hence, the higher the number of citations or articles coming from a particular journal, or impact factor, the higher it is ranked. IF is also a powerful tool if you want to compare journals in the subject category.

Measuring a Journal Impact Factor:

• CiteScore metrics – helps to measure journal citation impact. Free, comprehensive, transparent and current metrics calculated using data from Scopus®, the largest abstract and citation database of peer-reviewed literature.
• SJR – or SCImago Journal Rank, is based on the concept of a transfer of prestige between journals via their citation links.
• SNIP – or Source Normalized Impact per Paper, is a sophisticated metric that accounts for field-specific differences in citation practices.
• JIF – or Journal Impact Factor is calculated by Clarivate Analytics as the average of the sum of the citations received in a given year to a journal’s previous two years of publications, divided by the sum of “citable” publications in the previous two years.
• H-index – Although originally conceived as an author-level metric, the H -index has been being applied to higher-order aggregations of research publications, including journals.

Deciding the perfect journal for your paper is an important step. Metrics are excellent tools to guide you through the process. However, we also recommend you not neglect a perfectly written text, not only scientific and grammatically but also fitting the chosen journal’s requirements and scope. At Elsevier, we provide text-editing services that aim to amend and adjust your manuscript, to increase its chances of a successful acceptance by your target journal. Although each journal has its own editorial team, the overall quality, language and whether the article is innovative may also play a role.

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What is an impact factor?

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The Journal Impact Factor (sometimes abbreviated to JIF or IF) is a metric that has been in use for decades. Initially created as a tool to help librarians decide which journals to subscribe to, it has changed over the years to be used in a variety of ways.

While some scholars still use the JIF to drive publication decisions, the many limitations of this metric mean that it is important not to use it as a proxy for the quality of an individual research output or for an individual researcher's contributions.

Impact Factor

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Impact Factor: Your Questions Answered

What does the impact factor measure? The Journal Impact Factor measures the frequency with which the “average article” in a journal has been cited in the last two years. As such, it may help reflect the importance of a journal in its field.

How is the impact factor calculated? The Journal Impact Factor is the average number of times articles from the journal published in the past two years have been cited in the JCR year. The Impact Factor is calculated by dividing the number of citations in the JCR year by the total number of articles published in the two previous years. An Impact Factor of 1.0 means that, on average, the articles published one or two year ago have been cited one time. Citing articles may be from the same journal; most citing articles are from different journals. More details as well as graphical representations are available via Clarivate.

Who produces the impact factor? The Journal Impact Factor is calculated every year in the Journal Citation Reports (JCR) database . JCR has been releasing impact factor data annually since 1975. The criteria and calculations used to calculate the metric have changed over time. 

Do all journals have an impact factor? No. Journals indexed in the databases  Science Citation Index Expanded  or the  Social Sciences Citation Index  receive a Journal Impact Factor. Inclusion in these databases is competitive and require journals to meet certain criteria .

Just because a journal does not have an impact factor, does not mean that it is of low quality or low impact in its field.

Journals can be removed from the JCR database for a number of reasons, including unethical behaviour .

Is there more than one metric for measuring journal impact? As a new academic author, it is easy to become overwhelmed by the number of metrics promoted by various publishers! There are a number of other metrics that purport to measure the impact of journals; however, the Journal Impact Factor continues to be the most influential. Other journal impact metrics include:

• Cite Score : This metric is similar to the Journal Impact factor, but uses a slightly different calculation (e.g. a four year time period rather than two), and is produced by a different company. Can be found in the Scopus database.
• Eigenfactor : This metric is intended to give a measure of how likely a journal is to be used, and is thought to reflect how frequently an average researcher would access content from that journal.
• Source Normalized Impact Factor (SNIP) : This metric measures contextual citation impact by weighting citations based on the total number of citations in a subject field. The impact of a single citation is given higher value in subject areas where citations are less likely, and vice versa.  As such, SNIP is meant to correct for differences in citation practices between various fields, thereby allowing for more accurate between-field comparisons of citation impact. Also found in the Scopus database.

Is impact factor important in my discipline? Use of the impact factor to drive publication decisions is very field specific. Talk to your advisor, mentors, and peers to find out if this metric, or any others, are important in your discipline. At an institutional level, the University of Calgary signed on to the Declaration on Research Assessment (DORA) in 2021. DORA explicitly guides against using the impact factor in decisions relating to funding, appointment, and promotion . 

What is a "good" impact factor? This is a very difficult question to answer! The numerical value of impact factors varies greatly between disciplines and even sub-disciplines, due to different publishing and citing patterns. You can take a look at how journals are ranked by discipline by performing a subject search in Journal Citation Reports . This will help you understand what high impact journals in a particular field are.

What are some criticisms of the impact factor? There is a large body of research pointing to the flaws and inappropriate uses of the impact factor and other research metrics. Some key criticisms include:

• Citation distributions within journals are highly skewed: for example, one "blockbuster" paper or highly cited item such as a review can artificially inflate the metric.
• Journal Impact Factors can be manipulated (or “gamed”) by editorial policy. For example, editors may encourage prospective authors to cite other items published in the same journal.
• Data used to calculate the Journal Impact Factors are neither transparent nor openly available to the public.

Unless otherwise noted, content is this guide is licensed under a Creative Commons Attribution 4.0 International License . 

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Journal Impact Factor--What is it?

Journal Impact Factor

An offshoot of citation analysis is Journal Impact Factor (JIF) which is used to sort or rank journals by their relative importance. The underlying assumption behind Impact Factors (IF) is that journals with high IF publish articles that are cited more often than journals with lower IF.

Impact factors may be used by:

• Authors to decide where to submit an article for publication.
• Libraries to make collection development decisions
• Academic departments to assess academic productivity
• Academic departments to make decisions on promotion and tenure.

Where to find Journal Impact Factors?

The most notable source for journal impact factors is the annual publication called the Journal Citation Reports (JCR) published by Thomson Scientific.

How is the Journal Impact Factor Calculated?

Thomson defines impact factor as, “The journal Impact Factor is the average number of times articles from the journal published in the past two years have been cited in the JCR year. The Impact Factor is calculated by dividing the number of citations in the JCR year by the total number of articles published in the two previous years. An Impact Factor of 1.0 means that, on average, the articles published one or two year ago have been cited one time. An Impact Factor of 2.5 means that, on average, the articles published one or two year ago have been cited two and a half times. Citing articles may be from the same journal; most citing articles are from different journals.”

A journal's impact factor for 2008 would be calculated by taking the number of citations in 2008 to articles that were published in 2007 and 2006 and dividing that number by the total number of articles published in that same journal in 2007 and 2006.Below is how Thomson calculated the 2008 impact factor for the journal Academy of Management Review :

Thus, the Impact Factor of 6.125 for the journal, Academy of Management Review for 2008 indicates that on average, the articles published in this journal in the past two years have been cited about 6.125 times.

Factors to Consider While Consulting Impact Factors:

Publication Date : The impact factor is based on citation frequency of articles from a journal in their first few years of publication. This does not serve well the journals with articles that get cited over a longer period of time (let's say, 10 years) rather than immediately. In other words, journals in rapidly expanding fields such as cell biology and computing tend to have much higher immediate citation rates leading to higher IFs than journals in fields like Education or Economics.

Journal Impact Factor not Article Impact Factor: Citations to articles in a journal are not evenly distributed. In fact, some articles in a journal may not be cited at all but a few highly cited articles could lead to a high IF. Therefore, the IF does not accurately reflect the quality of individual articles published in a journal. Also, journals with more issues and articles can have higher Impact Factors which could be misleading as it does not really reflect the quality of articles.

Review Articles: Review articles (which tend to receive more citations), editorials, letters, and news items are not counted in article total but if cited are counted as citations for the journal. This leaves room for manipulation of ratio used to calculate impact factors leading to inflated impact factors in some cases.

Clinical Journals: Clinical journals usually have low citation counts. This puts such journals at a disadvantage with research journals in the field that have higher citation counts.

Uneven Coverage : The Journal Citation Reports focuses much more on disciplines where the primary means of publishing is through journal article. It provides less coverage to areas in Social Sciences and Humanities, where books and other publishing formats are more prevalent.

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What is a good impact factor?

What is an impact factor?

How is an impact factor calculated, how to find the impact factor of a journal, frequently asked questions about impact factors, related articles.

An impact factor measures the average number of a journal's citations in a two-year period. Ultimately, this measure calculates the rank of the journal in question. The more citations a journal has, the higher ranked it is. With higher ranking comes more popularity, and most importantly, credibility.

The calculation of the impact factor of a journal is quite easy. The number of citations of a journal is divided by the number of citable articles (from the same journal) from a two-year period.

X= the number of times articles published in 2018 and 2019 were cited by indexed journals during 2020

Y= the total number of published (citable) articles in 2018 and 2019

X/Y= 2020 impact factor of a journal

Usually, the impact factor of a journal is measured by different entities. You can find a journal's impact factor by referring to the Journal Citations Report (JCR), Scopus , or Resurchify . You only need to type in the title, publisher’s name, ISSN, or search by subject category.

It’s worth highlighting that the impact factor is used to compare journals from the same fields. A history journal cannot be compared to a science journal. Therefore, there is no set impact factor number considered to be ideal since each field has a different measurement. In general, an impact factor of 10 or higher is considered remarkable, while 3 is good, and the average score is less than 1.

The very prestigious journal Nature had an impact factor of 69.504 in the year 2021.

➡️ Learn more: What is a good h-index?

An impact factor measures the average number of a journal's citations, in a two-year period. Ultimately, this measure calculates the rank of the journal in question.

The number of citations of a journal is divided by the number of citable articles (from the same journal) from a two year period.

X= the number of cited articles from 2018 and 2019 in 2020

Y= the number of published articles in 2018 and 2019

You can find a journal's impact factor by referring to the Journal Citations Report (JCR) or Scopus .

In general, an impact factor of 10 or higher is considered remarkable, while 3 is good, and the average score is less than 1.

Eugene Garfield, the founder of the Institute for Scientific Information (ISI), invented the measurement known as impact factor. You can read more about this in Origins of the journal impact factor .

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Introduction to Impact Factor and Other Research Metrics

• Types of Metrics
• Impact Factor
• Identifying Journals
• More Resources

What are the different metrics?

Scholars have combined standard research metrics, like scholarly output and citation counts, into formulas to measure and assess author and journal impact in new ways. Some of these metrics include:

• Journal Impact Factor
• Eigenfactor score
• Altmetrics (alternative metrics)

On this page you will learn what these metrics measure, how to calculate these metrics, and databases and resources to look up each metric in.

Calculating bibliometrics

Calculating metrics can sometimes be complicated and confusing. This table provides a brief introduction to each calculation and what it means.

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Journal impact factors

Announcing the latest impact factors

The journal impact factor (JIF), as calculated by Clarivate Analytics, is a measure of the average number of times articles from a two-year time frame have been cited in a given year, according to citations captured in the Web of Science database.

The 2022 JIF (released in 2023), for example, was calculated as follows:

A = the number of times articles which published in 2010–2021 were cited in indexed journals during 2022 B = the total number of research and review articles from the journal published in 2020–2021

2022 JIF = A/B

This listing includes only journals that have a 2022 JIF. For a full list of journals published by APA, please visit the Journals homepage to browse our portfolio by title or subject.

Note: Categories marked with an asterisk (*) are in the Science Citation Index Expanded (SCIE); categories marked with a dagger (†) are in the Arts and Humanities Citation Index (AHCI). All other categories are in the Social Science Citation Index (SSCI). Journal titles without a category are in the Emerging Sources Citation Index (ESCI).

Following the release of the 2022 Journal Citation Reports (JCR) from Clarivate Analytics, APA Publishing is pleased to report that Journal Impact Factors (JIFs) have been assigned to 89% (79) of our titles. Among our ranked journals, 21% are in the top 10 of their categories and 44% are in their category’s top quartile.

Notably, 16 APA-published journals indexed in Clarivate’s Emerging Sources Citation Index received their first-ever JIFs, and they will be included in rankings starting in 2024:

• Clinical Practice in Pediatric Psychology (1.1 JIF; published on behalf of APA Division 54: Society of Pediatric Psychology )
• Consulting Psychology Journal: Practice and Research (1.1 JIF; published on behalf of APA Division 13: Society of Consulting Psychology )
• Couple and Family Psychology: Research and Practice (1.7 JIF; published on behalf of APA Division 43: Society for Couple and Family Psychology )
• Decision (1.5 JIF)
• Evolutionary Behavioral Sciences (1.3 JIF; published on behalf of the NorthEastern Evolutionary Psychology Society )
• Journal of Psychotherapy Integration (3.5 JIF; published on behalf of the Society for the Exploration of Psychotherapy Integration )
• Journal of Theoretical and Philosophical Psychology (1.3 JIF; published on behalf of APA Division 24: Society for Theoretical and Philosophical Psychology )
• Peace and Conflict: Journal of Peace Psychology (1.2 JIF; published on behalf of APA Division 48: Society for the Study of Peace, Conflict, and Violence )
• Psychology of Leaders and Leadership , formerly The Psychologist-Manager Journal , listed under its previous title in the 2022 JCR (0.6 JIF; published on behalf of the Society of Psychologists in Leadership )
• Psychology of Consciousness: Theory, Research, and Practice (2.0 JIF; published on behalf of APA Division 30: Society of Psychological Hypnosis )
• Psychomusicology: Music, Mind, and Brain (1.3 JIF)
• Qualitative Psychology (8.5 JIF; published on behalf of The Society for Qualitative Inquiry in Psychology, a section of APA Division 5 )
• Spirituality in Clinical Practice (1.7 JIF)
• Stigma and Health (3.0 IF)
• Translational Issues in Psychological Science (1.8 JIF)
• Traumatology (3.2 JIF; published on behalf of the Green Cross Academy of Traumatology )

According to Dr. Nandita Quaderi of Clarivate , “by expanding the JIF to all journals that have passed the rigorous Web of Science quality criteria, this latest enhancement also helps level the playing field for all quality journals including recently-launched journals, open access journals, journals with a niche or regionally-focused scope and journals from the Global South” (para. 6).

In addition to assigning JIFs to Emerging Sources Citation Index titles for the first time, the latest JCR reflected a number of significant changes , such as including online-first content (i.e., articles published online ahead of being assigned to a journal issue) in JIF calculations and displaying JIFs to one rather than three decimal places, which affected journal rankings.

In Clarivate’s competitive Social Sciences Citation Index—the primary index for psychology content—six APA-published titles saw an increase in their JIF, and 19 journals rose in rank within their category. Some examples include the following:

• Canadian Psychology / Psychologie canadienne , published on behalf of the Canadian Psychological Association , saw a 75% increase in its JIF, rising from 2.621 to 4.6 and jumping in rank from 68th to 25th out of the 147 titles in the Psychology, Multidisciplinary category.
• Canadian Journal of Experimental Psychology / Revue canadienne de psychologie expérimentale , published on behalf of the Canadian Psychological Association, had a 48% increase in its JIF, rising from 0.881 to 1.3.
• Journal of Applied Research in Memory and Cognition , published on behalf of the Society for Applied Research in Memory and Cognition , rose in rank from 14th to 8th out of the 89 journals in the Psychology, Experimental category. This is the first time the journal has appeared in the category’s top 10.

Other portfolio highlights

• APA publishes six journals in the top quartile of the large and diverse Psychology, Multidisciplinary category: Psychological Bulletin (ranking 3rd out of 147 journals, 22.4 JIF), American Psychologist (ranking 5th, 16.4 JIF), Psychological Methods (ranking 13th, 7.0 JIF), Psychological Review (ranking 18th, 5.4 JIF), Canadian Psychology / Psychologie canadienne (ranking 25th, 4.6 JIF; published on behalf of the Canadian Psychological Association), and Psychology of Sexual Orientation and Gender Diversity (ranking 34th, 3.8 JIF; published on behalf of APA Division 44: Society for the Psychology of Sexual Orientation and Gender Diversity ).

Psychological Bulletin boasts the highest JIF in APA’s portfolio (22.4 JIF), in addition to ranking 3rd out of the 147 journals in the Psychology, Multidisciplinary category, also ranks 3rd out of the 81 journals in the Psychology category within Clarivate’s Science Citation Index Expanded.

• Two APA journals rank in the top 20 of the Psychology, Applied category: Journal of Applied Psychology (ranking 5th, 9.9 JIF) and Journal of Occupational Health Psychology (ranking 18th, 5.1 JIF). Journal of Applied Psychology earned the most all-time citations (57,881, or 12.6% of all citations) in this 83-journal category.
• Psychoanalytic Psychology (1.1 JIF), published on behalf of APA Division 39: Division of Psychoanalysis , ranks 3rd of the 13 titles in the Psychology, Psychoanalysis category, marking over a decade in the category’s top quartile. In addition, on the basis of its Journal Citation Indicator (JCI; 1.3), a new field-normalized measurement of journal citation impact, the journal is ranked at number 1.
• Four APA titles rank in the top 20 of the Psychology, Experimental category: Emotion and Journal of Applied Research in Memory and Cognition (tied at the ranking of 8 out of 89 journals, 4.2 JIF), Journal of Experimental Psychology: General (ranking 12th, 4.1 JIF), and Psychology of Aesthetics, Creativity, and the Arts (ranking 19th, 3.6 JIF; published on behalf of APA Division 10: Society for the Psychology of Aesthetics, Creativity, and the Arts ).

When ranked on the basis of their JCI, Psychology of Aesthetics, Creativity, and the Arts landed at number 2, followed by Journal of Experimental Psychology: General at 13, Emotion at 16, and Journal of Applied Research in Memory and Cognition at 18.

• Five APA titles rank in the top quartile of the Psychology, Clinical category: Psychological Trauma: Theory, Research, Practice, and Policy (ranking 12th out of 131, 6.3 JIF; published on behalf of APA Division 56: Trauma Psychology ), Journal of Consulting and Clinical Psychology (ranking 14th, 5.9 JIF), Clinical Psychology: Science and Practice (ranking 15th, 5.8 JIF; published on behalf of APA Division 12: Society of Clinical Psychology ), Journal of Psychopathology and Clinical Science  (formerly Journal of Abnormal Psychology , listed under its previous title in the 2022 JCR; ranking 25th, 4.6 JIF), and Health Psychology (ranking 30th, 4.2 JIF; published on behalf of APA Division 38: Society for Health Psychology ).

Journal of Consulting and Clinical Psychology ranks 3rd for all-time citations (22,573, or 4% of all citations) in this 131-journal category.

• Three APA titles rank in the top 25 of the Psychology, Educational category: Journal of Educational Psychology (ranking 6th out of 60, 4.9 JIF), Journal of Counseling Psychology (ranking 9th, 3.9 JIF), and School Psychology (ranking 21st, 3.0 JIF, the journal’s highest rank and JIF to date; published on behalf of APA Division 16: School Psychology ).

Journal of Educational Psychology received a total of 21,442 citations, more than double those of the category’s number 1 journal.

• Journal of Personality and Social Psychology (7.6 JIF) again earned the most all-time citations (90,177, or 20% of all citations) in the competitive Psychology, Social category and ranked 3rd of 65 journals, marking over a decade as one of the top 5 journals in this category.

Showcasing the breadth and depth of our program, journals published by APA are also highly ranked in 25 categories related to psychology found in the Arts and Humanities Citation Index, the Social Sciences Citation Index, and the Science Citation Index Expanded. Some examples include the following:

• Humanities, Multidisciplinary : Psychology of Aesthetics, Creativity, and the Arts (3.6 JIF), published on behalf of APA Division 10: Society for the Psychology of Aesthetics, Creativity, and the Arts, is ranked number 1 out of the 146 journals in the Humanities, Multidisciplinary category. The journal is also ranked number 1 in this category on the basis of its JCI.
• Ethnic Studies : Cultural Diversity & Ethnic Minority Psychology (3.3 JIF), published on behalf of APA Division 45: Society for the Psychological Study of Culture, Ethnicity, and Race , retained its number 1 ranking out of the 20 journals in the growing Ethnic Studies category. We are proud to see this journal as the established go-to source for research on critical societal issues.
• Religion : Psychology of Religion and Spirituality (2.4 JIF), published on behalf of APA Division 36: Society for the Psychology of Religion and Spirituality , is ranked 4th out of the 150 journals in Religion , a category in Clarivate’s Arts and Humanities Citation Index. The journal ranks 2nd in this category on the basis of its JCI.
• Social Work : American Journal of Orthopsychiatry (3.3 JIF), published on behalf of the Global Alliance for Behavioral Health and Social Justice , ranks 4th out of the 44 titles in the expanding Social Work category, the journal’s highest ranking to date (up from 6th in the previous year).
• Rehabilitation : Two APA journals indexed in the Rehabilitation category remain in the category’s top two quartiles: Rehabilitation Psychology (ranking 10th out of 73, 2.7 JIF) and Psychiatric Rehabilitation Journal (ranking 35th, 1.9 JIF).
• Communication : Psychology of Popular Media (3.0 JIF) jumped in rank from 41st to 34th of the 96 journals in the Communication category, the journal’s highest ranking and JIF yet. It also jumped in rank in the Psychology, Multidisciplinary category, rising from 62nd to 51st out of 147 journals.
• Criminology & Penology : Psychological Violence (2.8 JIF) remains in the top quartile of the Criminology & Penology category, ranking 14th out of 69.
• Management : Journal of Applied Psychology (9.9 JIF) remains in the top quartile of the competitive Management category, ranking 26th out of 227 journals. The journal ranks 6th on the basis of its JCI and has the most all-time citations in the category (57,881, or 4% of the category’s citations).

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Knowing where to publish your research is an important consideration. CiteScore metrics, SCImago Journal Rank (SJR), Source-Normalized Impact per Paper (SNIP) and Journal Impact Factor are among the tools offered to help you assess the journals in your field.

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Journal Impact Factors

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According to Journal Citation Reports (JCR) , an  impact factor is a ratio focusing on original research. 

Impact factor = # of citations to all items published in that journal in the past two years (divided by) # of articles and reviews published over those past two years referencing those citations

For example, if a journal has an impact factor of 2.5, this means in the indexed year each article published was cited on average 2.5 times in the previous two years in that journal.

Impact factor is used for journals only.

JCR only includes  12,000 journals and conference proceedings from over 3,300 publishers.

• InCitesTM: Journal Citation Reports® This Web of Science hosted database is a citation-based research evaluation tool for journal performance metrics with the goal of offering a systematic and objective means to evaluate the journals based on citation data.
• Tips for Using JCR Tips for using the Web of Science InCitesTM Journal Citation Reports

Metrics on the Web

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Answered By: Laurissa Gann Last Updated: Jun 13, 2022     Views: 1097545

Impact Factors are used to measure the importance of a journal by calculating the number of times selected articles are cited within the last few years. The higher the impact factor, the more highly ranked the journal. It is one tool you can use to compare journals in a subject category.

During 2017, the Journal Citation Reports (JCR) database tracked all impact factors for 12,298 journals. The table below shows the number and percentage of journals that were assigned impact factors ranging from 0 to 10+. Of 12,298 journals, only 239 titles, or 1.9% of the journals tracked by JCR, have a 2017 impact factor of 10 or higher. The top 5% of journals have impact factors approximately equal to or greater than 6 (610 journals or 4.9% of the journals tracked by JCR). Approximately two-thirds of the journals tracked by JCR have a 2017 impact factor equal to or greater than 1.

Impact Factors are useful, but they should not be the only consideration when judging quality. Not all journals are tracked in the JCR database and, as a result, do not have impact factors. New journals must wait until they have a record of citations before even being considered for inclusion. The scientific worth of an individual article has nothing to do with the impact factor of a journal.

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Comments (2)

• In 2016, the NEJM says their impact factor is 59. Is this possible? Do journals have IF that are greater than 30 today? ... Response from the MD Anderson Librarian... Yes, the NEJM has a 2015 impact factor of 59.558. There are 25 journals tracked by Journal Citation Reports that have an impact factor of 30 or higher. by Yvette Schlussel on May 22, 2017
• There is a journal with IF 2.88. Is this good or bad journal? The higher the Impact Factor, the better the journal. The 2.88 means that on average, any article published in that journal will be cited 2.88 times. You would have to compare this journal to journals in the same field to determine how it compares. by darshil trivedi on Dec 26, 2017

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Measuring Your Impact: Impact Factor, Citation Analysis, and other Metrics: Citation Analysis

• Measuring Your Impact

Citation Analysis

Find your h-index.

• Other Metrics/ Altmetrics
• Journal Impact Factor (IF)
• Selecting Publication Venues

About Citation Analysis

What is Citation Analysis?

The process whereby the impact or "quality" of an article is assessed by counting the number of times other authors mention it in their work.

Citation analysis invovles counting the number of times an article is cited by other works to measure the impact of a publicaton or author.  The caviat however, there is no single citation analysis tools that collects all publications and their cited references.  For a thorough analysis of the impact of an author or a publication, one needs to look in multiple databases to find all possible cited references. A number of resources are available at UIC  that identify cited works including: Web of Science, Scopus, Google Scholar, and other databases with limited citation data.

Citation Analysis - Why use it?

To find out how much impact a particular article or author has had, by showing which other authors cited the work within their own papers.  The H-Index is one specific method utilizing citation analysis to determine an individuals impact.

Web of Science

Web of Science provides citation counts for articles indexed within it.  It i ndexes over 10,000 journals in the arts, humanities,  sciences, and social sciences.

• Enter the name of the author in the top search box (e.g. Smith JT).  
• Select Author from the drop-down menu on the right.
• To ensure accuracy for popular names, enter Univ Illinois in the middle search box, then select “Address” from the field drop down menu on the right.  (You might have to add the second search box by clicking "add another field" before you enter the address)
• Click on Search
• a list of publications by that author name will appear.   To the right of each citation, the number of times the article has been cited will appear.   Click the number next to "times cited" to view the articles that have cited your article

Scopus provide citation counts for articles indexed within it (limited to article written in 1996 and after).   It indexes o ver 15,000 journals from over 4,000 international publishers across the disciplines.

• Once in Scopus, click on the Author search tab.
• Enter the name of the author in the search box.  If you are using initials for the first and/or middle name, be sure to enter periods after the initials (e.g. Smith J.T.). 
• To ensure accuracy if it is a popular name, you may enter University of Illinois in the affiliation field.  
• If more than one profile appears, click on your profile (or the profile of the person you are examining). 
• Once you click on the author's profile, a list of the publications will appear and to the right of each ctation, the number of times the article has been cited will appear.  
• Click the number to view the articles that have cited your article

 Dimensions (UIC does not subscribe but parts are free to use)

• Indexes over 28000 journals
• Does not display h-index in Dimensions but can calculate or if faculty, look in MyActivities
• Includes Altmetrics score
• Google Scholar

Google Scholar provides citation counts for articles found within Google Scholar.  Depending on the discipline and cited article, it may find more cited references than Web of Science or Scopus because overall, Google Scholar is indexing more journals and more publication types than other databases. Google Scholar is not specific about what is included in its tool but information is available on how Google obtains its content .   Limiting searches to only publications by a specific author name is complicated in Google Scholar.  Using Google Scholar Citations and creating your own profile will make it easy for you to create a list of publications included in Google Scholar.   Using your Google Scholar Citations account, you can see the citation counts for your publications and have GS calculate your h-index.  (You can also search Google Scholar by author name and the title of an article to retrieve citation information for a specific article.)

• Using your google (gmail) account, create a profile of all your articles captured in Google Scholar.  Follow the prompt on the scrren to set up your profile.   Once complete, this will show all the times the articles have been cited by other documents in Google Scholar and your h-index will be provided.  Its your choice whether you make your profile public or private but if you make it public, you can link to it from your own webpages.

Try Harzing's Publish or Perish Tool in order to more selectively examine published works by a specific author.

Databases containing limited citation counts:

• PubMed Central
• Science Direct
• SciFinder Scholar

About the H-index

The h-index is an index to quantify an individual’s scientific research output ( J.E. Hirsch )   The h-index is an index that attempts to measure both the scientific productivity and the apparent scientific impact of a scientist. The index is based on the set of the researcher's most cited papers and the number of citations that they have received in other people's publications ( Wikipedia )  A scientist has index h if h of [his/her] Np papers have at least h citations each, and the other (Np − h) papers have at most h citations each.

Find your h-index at:

Below are instructions for obtaining your h-index from Web of Science, Scopus, and Google Scholar.

Web of Science provides citation counts for articles indexed within it.  It indexes over 12,000 journals in the arts, humanities,  sciences, and social sciences.  To find an author's h-index in WOS:

• To ensure accuracy for popular names, add an additional search box and enter "Univ Illinois" and then select “Address” from the field drop down menu on the right.
• Click on Citation Report on the right hand corner of the results page.  The H-index is on the right of the screen.
• If more than one profile appears, click on your profile (or the profile of the person you are examining).  Under the Research section, you will see the h-index listed.
• If you have worked at more than one place, your name may appear twice with 2 separate h-index ratings.  Select the check box next to each relevent profile, and click show documents.

  Google Scholar

• Using your google (gmail) account, create a profile of all your articles captured in Google Scholar.  Follow the prompt on the screen to set up your profile.   Once complete, this will show all the times the articles have been cited by other documents in Google Scholar and your h-index will be provided.  Its your choice whether you make your profile public or private but if you make it public, you can link to it from your own webpages.
• See  Albert Einstein's
• Harzing’s Publish or Perish (POP) 
• Publish or Perish Searches Google Scholar.  After searching by your name, deselect from the list of articles retrieved those that you did not author.  Your h-index will appear at the top of the tool.  Note:This tool must be downloaded to use
• << Previous: Measuring Your Impact
• Next: Find Your H-Index >>
• Last Updated: Jun 14, 2024 1:10 PM
• URL: https://researchguides.uic.edu/if

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What is the impact factor, pros and cons of citescore and impact factor, alternatives to citescore and the impact factor, journal metrics comparison chart, article-level metrics.

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CiteScore : Metric in Scopus most closely related to Impact Factor. Citations received by all articles published in the last 4 complete years are divided by the number of articles published in the last 4 years.

SCImago Journal Rank (SJR) : Measures the scholarly influence of a journal by accounting for the number of citations as well as the prestige of the citing journals. SJR is based on the  eigenvector centrality measure  used in network theory. It is a size-independent measure that ranks journals based on their average prestige per article. 

Source Normalized Impact per Paper (SNIP) : Measures the contextual citation impact of a journal by weighting the citations based on the total number of citations in a discipline. This method normalized for differences in citation practices between disciplines, so that a single citation is given greater value where citations are less frequent in that field. 

Scopus also provides metrics for number of citations, number of documents, percentage of documents cited, and CiteScore rank (how the CiteScore for the journal compares to other journals in the same field). Explore all the metrics by searching the Sources list in Scopus .

The Impact Factor  is a long-standing metric commonly used to evaluate journals . It is an equation calculating the average citation frequency for a given journal over a given period of time. It is a ratio of citations to citable items. Generally speaking, the higher the number, the higher the quality and prestige of the journal, although the impact factor is most useful when evaluating journals within the same discipline. 

A/B = Impact Factor A = cites by all indexed articles in a given year to articles published in a specific journal in the two preceding years. B = total number of articles published by that journal in that time period.

The journal Impact Factor was invented in the 1960s by Eugene Garfield and was intended as a tool to help librarians make selection decisions and authors identify publishing venues. Today, the Impact Factor is a proprietary calculation that is available only through Thompson Reuters Journal Citation Reports. 

• Vetted, established metrics for measuring journal impact within a discipline
• Designed to eliminate bias based on journal size and frequency
• Individual articles makes an uneven contribution to overall metric.
• These metrics do not account for certain things, things like context (positive or negative citation) and intentionality (self-citation).
• The metrics are proprietary to and bound by the contents of their respective databases: Scopus for CiteScore and the Thomson Reuters database for Impact Factor. 
• Citations, on which the Impact Factor is based, count for  < 1% of an article's overall use . 

Eigenfactor : A measure of a journal's overall importance to the scientific community based on the origin of incoming citations over a period of time; citations from highly ranked journals are weighed more heavily. (Hosted by the University of Washington; built on Thomson Reuters bibliographic data.)

• Eigenfactor Journal Ranking

Journal Metrics : Publicly accessible metrics for journal evaluation that offer three alternative views of true citation impact of a journal. (Provided by Elsevier; built on Scopus bibliographic data.)

• SCImago Journal Rank (SJR) : Defined above.
• Source Normalized Impact per Paper (SNIP) : Defined above.
• Impact per Paper (IPP) : Measures the ratio of citations to citable items for a given journal over a given period of time. IPP is the most direct correlate to the Impact Factor, but it calculates this ration over three years rather than two and it includes only peer-reviewed scholarly papers in both the numerator and the denominator. IPP is the foundational metric for the SNIP. 

Article-level metrics include:

• Citation count (Available through many databases and often on the publisher website.)
• Field-weighted citation impact (Calculated ratio in Scopus of the article's citations compared to the average number of citations received by all similar articles over a three-year window. A value greater than 1 means the article is cited greater than average.)
• Citation percentile (Scopus percentile comparing an article's citation count to the number of citations received by documents of the same type, published around the same time, in the same field.)
• View and/or download count (Number of times an article has been viewed and/or downloaded. Available in Scopus and often on the publisher website.)
• Altmetric score (Compilation of alternative metrics such as media mentions and citations in policy documents. Available through Altmetric Explorer for Institutions and on publisher websites which use Altmetric badges.)
• PlumX metrics  (Compilation of alternative metrics such as social media mentions and Mendeley readers. Available in Scopus and on publisher websites which use PlumX.)
• Article-Level Metrics: A SPARC Primer Guide to understanding the basics of Article-Level Metrics that explores the definition, application, opportunities and challenges presented by ALMs.
• More information on altmetrics available on this resource guide
• << Previous: Home
• Next: Altmetrics >>
• Last Updated: Jun 26, 2024 2:52 PM
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What is the impact of a research publication?

An increasing number of metrics are used to measure the impact of research papers. Despite being the most commonly used, the 2-year impact factor is limited by a lack of generalisability and comparability, in part due to substantial variation within and between fields. Similar limitations apply to metrics such as citations per paper. New approaches compare a paper's citation count to others in the research area, while others measure social and traditional media impact. However, none of these measures take into account an individual author's contribution to the paper or the number of authors, which we argue are key limitations. The UK's 2014 Research Exercise Framework included a detailed bibliometric analysis comparing 15 selected metrics to a ‘gold standard’ evaluation of almost 150 000 papers by expert panels. We outline the main correlations between the most highly regarded papers by the expert panel in the Psychiatry, Clinical Psychology and Neurology unit and these metrics, most of which were weak to moderate. The strongest correlation was with the SCImago Journal Rank, a variant of the journal impact factor, while the amount of Twitter activity showed no correlation. We suggest that an aggregate measure combining journal metrics, field-standardised citation data and alternative metrics, including weighting or colour-coding of individual papers to account for author contribution, could provide more clarity.

A number of developments in the metrics field have occurred in recent years, and, in this perspective article, we discuss whether they can inform how judgements are made about the impact of research papers in psychiatry and beyond.

The best-known approach has been to rely on journal impact factors, the most common of which is a 2-year impact factor, which calculates the average number of citations from articles published in the past 2 years of a particular journal. 1 Many arguments against journal impact factors have been outlined, including the skewed nature of citations in most journals, the variation between and within fields (with basic science attracting more citations) and research designs (with systematic reviews being relatively highly cited) and the citation lag time in some research fields being longer than 2 years. 2 A widely used alternative is the number of citations per paper, which can be drawn from research tools such as Scopus ( http://www.scopus.com ) and Google Scholar ( scholar.google.com ), with the latter including a broader range of citable items such as online reports and theses. The problem with citation counts is that they vary considerably by research area, and there have been recent attempts to account for this. One of these is the new iCite tool ( icite.od.nih.gov ) that normalises the number of citations of a particular paper to the median annual number of citations that NIH-funded papers in the field have received. 3 Finally, alternative metrics have been increasingly used and include tools such as Altmetric ( http://www.altmetric.com ), which aims to capture the media and social media interest in a publication, 4 and provides an overall article score and rankings compared with others in the same journal and/or time period.

A key problem with these approaches is that they do not account for an individual author's contribution to a paper, and therefore, high citation rates, h-indexes (for individuals) and iCite scores can be achieved for researchers who have not made significant contributions to a research area. The best example of this is being included as a coauthor of a large treatment trial or genetic consortium, where a researcher's contribution may be mostly in relation to participant recruitment. The Psychiatric Genomics Consortium workgroup for schizophrenia now average over 280 authors, although the number of authors varies considerably and is occasionally placed in the appendix. This highlights another problem with relying on measures of citation in that they do not account for the total number of authors. Accordingly, h-indexes should be routinely provided for papers where the author is first, corresponding or last author (and second author in psychology).

Another limitation is that some of these metrics are subject to measurement error, and some can be gamed. To take an example of the latter, Altmetric scores can potentially be artificially increased by robots that repost press releases. At the same time, they may not pick up all media activity if the article is not cited accurately or embedded in a hyperlink. In addition, they are considerably higher in studies on exercise, diet and lifestyle, and in areas that attract controversy. 5 Although some research has shown some correlation between alternative metrics and citations scores, 6 particularly early on after publication, they do not account for the inherent problems outlined above about the extent of an individual's contribution, normalisation by field and measurement error.

An important natural experiment has been undertaken in the UK where a very large sample of papers (k=148 755) was investigated against a gold standard of peer review as part of the 2014 Research Exercise Framework (or REF 2014). The REF was a national exercise undertaken to assess research from 2008 to 2013 in higher education institutions in the UK, which succeeded an earlier process (called the Research Assessment Exercise in 2008). It determined the extent of central government basic research funding for these institutions until the time of the next evaluation (thought to be in 2021). Three factors were considered—outputs (which made up 65% of the overall quality profile), impact (20%) and environment (15%). A detailed bibliometric analysis of the output data was published and provides a breakdown by unit of assessment. 7 Each eligible academic typically submitted four outputs for the REF. Here, we will discuss the assessment block that most departments of psychiatry and psychology will have entered, namely Unit 4 (Psychiatry, Clinical Psychology and Neurology), which assessed 9086 journal articles. The analysis took 15 different metrics for each paper and analysed to what extent, they were correlated with the final view of the REF panel (which was made up of an expert committee of 39 researchers). The strongest bivariate correlations between these metrics and scoring the highest score per paper are presented in figure 1 . Each paper was measured against a standard of originality, significance and rigour and the best papers were scored a 4*, which represented ‘world leading’, whereas a 3* reflected ‘internationally excellent in terms of originality, significance and rigour but which falls short of the highest standards of excellence’. Lower scores of 2*, 1* and unclassified were also given.

Strongest bivariate correlations between paper metrics and the highest REF score. *Source-Normalised Impact per Paper; †field-weighted citation impact; ‡Google Scholar citations.

The strongest correlation was with the SCImago Journal Rank, which is a metric based on the notion that not all citations have equivalent weight, and categorises journals per field into four categories (from low to high rank). It assumes that the subject field, quality and reputation of the journal have a direct effect on the value of a citation. This was followed by the absolute number of Scopus citations and the percentile of highly cited publications. The Source-Normalised Impact per Paper attempts to relativise the citation impact by weighting citations based on the total number of citations in a subject field. Thus, the impact of a single citation is given more value in participants where citations are less likely.

Notably, there was no correlation between Twitter activity generated by an article and a top REF score (of ‘4*’), and associations were weak for full-article requests, downloads and reads on one platform (Mendeley, http://www.mendeley.com ). Interestingly, for the overall REF which included 36 units, the 3 strongest markers of quality were the SCImago Journal Rank, the Source-Normalised Impact per Paper and the percentile. Correlations tended to be stronger in the sciences than in the arts and humanities.

What this suggests is that the judgement of an expert panel that was constituted to examine a paper's impact, perhaps the closest to a gold standard that is possible, was most strongly correlated with the SCImago Journal Rank, which itself is based mostly on the journal impact factor. This is not surprising as many such journals have more stringent peer and statistical review, insist on adhering to research guidelines, benefit from professional editors, and articles in high-impact journals are often cited to add legitimacy to a particular field of study, and may be included in introductions. Further, the analysis of the REF 2014 suggests that new metrics appear unlikely to replace simpler ones such as journal impact factor and number of citations per year.

So where does this leave someone trying to assess the impact of a paper? As there are difficulties with relying on one metric, we suggest that a combination of metrics should be used. We recommend that those most correlated with expert judgement take priority but can see a role for Altmetrics, with the caveats noted above, as a measure of wider public engagement, impact and interest. In the future, a combined score that takes into account journal impact factor, number of citations, iCite, Altmetric scores and a different colour coding or weighting for those papers where authors have made a substantial contribution (eg, where an author has been first/last/corresponding) would assist in providing some clarity.

Acknowledgments

SF and AW are funded by the Wellcome Trust.

Twitter: Follow Seena Fazel @seenafazel

Competing interests: None declared.

Provenance and peer review: Not commissioned; internally peer reviewed.

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Where do I find the Impact Factor of a journal?

The Impact Factor is a measure of scientific influence of scholarly journals. It measures the average number of citations received in a particular year by papers published in the journal during the two preceding years and is produced by a publisher called Thomson Reuters. The Impact Factor can be found on the Journal home page of journals that have an Impact Factor. 

Please note: Not all journals have an Impact Factor.

Follow these steps to find the Impact Factor of a journal:

• Search for a journal using the  ‘Journal/book title’  field on the ScienceDirect homepage or browse journal titles by selecting ' Journals & Books ' in the top right corner.
• Click the journal title to navigate to the journal’s home page.
• The Impact Factor and Journal CiteScore are mentioned in the header on the right side of the page.

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Impact factor

What is an impact factor?

The impact factor (IF) of a journal is a description of the influence the journal has in academic or university research circles. It is is a measure of how often the average research article in a journal has been cited or used in other research in any particular year.  The IF is used to measure the importance or rank of a journal by calculating the times it’s articles are cited. The higher the IF the more influential the journal.

Nature is the highest rated journal with an IF of 38.12

The Harvard Business Review has the lowest IF at 0.72

See also:  Harvard Business Review Impact factor

The Big difference between The Oxford Review and The Harvard Business Review

Back to The Oxford Review Encyclopaedia of Terms

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The Crucial Role of Impact Factor in Choosing the Right Journal

In academic publishing, the impact factor of a journal serves as a crucial metric for authors, researchers, and institutions. This measure, often abbreviated as IF, is a reflection of a journal's influence and prestige within its field. Understanding what impact factor is, how it is calculated, and its significance can greatly aid in selecting the right journal for your manuscript.

What is Impact Factor?

Impact factor is a bibliometric indicator used to evaluate the importance and influence of a scientific journal. It is calculated based on the average number of citations received per paper published in that journal during the preceding two years. The formula for calculating the impact factor is:

For example, if Journal X published 50 articles in 2021 and 2022, and these articles received 200 citations in 2023, the impact factor for 2023 would be:

This means, on average, each article published in Journal X was cited four times.

Importance of Impact Factor

• Indicator of Quality and Prestige: High impact factor journals are often considered prestigious. They are usually more selective in their acceptance process, ensuring that only high-quality research is published. Publishing in such journals can enhance the reputation of the authors and increase the visibility of their work.
• Visibility and Reach: Journals with higher impact factors tend to have a broader readership. Articles published in these journals are more likely to be read and cited by other researchers, increasing the dissemination and impact of the published work.
• Academic and Professional Advancement: Publishing in high impact factor journals can be crucial for career advancement, particularly in academia. It is often a significant criterion in tenure decisions, grant applications, and professional evaluations.
• Reflects Research Influence: A higher impact factor indicates that the research published in the journal has been widely recognized and cited by the academic community, reflecting its influence and contribution to the field.

Limitations and Criticisms

While impact factor is a useful metric, it has its limitations and has been subject to criticism:

• Discipline Variability: Different academic fields have varying citation practices. For instance, journals in medicine or life sciences often have higher impact factors compared to those in the humanities or social sciences. Comparing impact factors across different disciplines can be misleading.
• Citation Manipulation: Some journals may engage in practices aimed at artificially boosting their impact factor, such as encouraging excessive self-citations or preferentially publishing review articles, which tend to garner more citations.
• Short-Term Focus: The two-year citation window may not adequately capture the long-term impact of research. Some significant studies might take several years to gain recognition and be cited.

Choosing the Right Journal

When choosing a journal for your manuscript, consider the following:

• Relevance and Scope: Ensure that the journal's scope aligns with the topic and objectives of your research. A journal that regularly publishes articles in your specific area of study is more likely to attract interested readers and citations.
• Target Audience: Consider the primary audience of the journal. Publishing in a journal that reaches the intended readership will enhance the visibility and impact of your research.
• Impact Factor and Beyond: While the impact factor is important, also consider other metrics and qualitative factors such as the journal's editorial board, peer review process, acceptance rate, and publication speed.
• Open Access Options: Open access journals can increase the accessibility of your work, potentially leading to higher citation rates. Evaluate the open access policies and fees associated with your chosen journal.
• Journal Reputation: Investigate the reputation and standing of the journal within the academic community. Peer recommendations and the journal's historical influence can provide valuable insights.

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• Published: 02 May 2024

Effectiveness of social media-assisted course on learning self-efficacy

• Jiaying Hu 1 ,
• Yicheng Lai 2 &
• Xiuhua Yi 3  

Scientific Reports volume  14 , Article number:  10112 ( 2024 ) Cite this article

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• Human behaviour

The social media platform and the information dissemination revolution have changed the thinking, needs, and methods of students, bringing development opportunities and challenges to higher education. This paper introduces social media into the classroom and uses quantitative analysis to investigate the relation between design college students’ learning self-efficacy and social media for design students, aiming to determine the effectiveness of social media platforms on self-efficacy. This study is conducted on university students in design media courses and is quasi-experimental, using a randomized pre-test and post-test control group design. The study participants are 73 second-year design undergraduates. Independent samples t-tests showed that the network interaction factors of social media had a significant impact on college students learning self-efficacy. The use of social media has a significant positive predictive effect on all dimensions of learning self-efficacy. Our analysis suggests that using the advantages and value of online social platforms, weakening the disadvantages of the network, scientifically using online learning resources, and combining traditional classrooms with the Internet can improve students' learning self-efficacy.

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Introduction.

Social media is a way of sharing information, ideas, and opinions with others one. It can be used to create relationships between people and businesses. Social media has changed the communication way, it’s no longer just about talking face to face but also using a digital platform such as Facebook or Twitter. Today, social media is becoming increasingly popular in everyone's lives, including students and researchers 1 . Social media provides many opportunities for learners to publish their work globally, bringing many benefits to teaching and learning. The publication of students' work online has led to a more positive attitude towards learning and increased achievement and motivation. Other studies report that student online publications or work promote reflection on personal growth and development and provide opportunities for students to imagine more clearly the purpose of their work 2 . In addition, learning environments that include student publications allow students to examine issues differently, create new connections, and ultimately form new entities that can be shared globally 3 , 4 .

Learning self-efficacy is a belief that you can learn something new. It comes from the Latin word “self” and “efficax” which means efficient or effective. Self-efficacy is based on your beliefs about yourself, how capable you are to learn something new, and your ability to use what you have learned in real-life situations. This concept was first introduced by Bandura (1977), who studied the effects of social reinforcement on children’s learning behavior. He found that when children were rewarded for their efforts they would persist longer at tasks that they did not like or had low interest in doing. Social media, a ubiquitous force in today's digital age, has revolutionized the way people interact and share information. With the rise of social media platforms, individuals now have access to a wealth of online resources that can enhance their learning capabilities. This access to information and communication has also reshaped the way students approach their studies, potentially impacting their learning self-efficacy. Understanding the role of social media in shaping students' learning self-efficacy is crucial in providing effective educational strategies that promote healthy learning and development 5 . Unfortunately, the learning curve for the associated metadata base modeling methodologies and their corresponding computer-aided software engineering (CASE) tools have made it difficult for students to grasp. Addressing this learning issue examined the effect of this MLS on the self-efficacy of learning these topics 6 . Bates et al. 7 hypothesize a mediated model in which a set of antecedent variables influenced students’ online learning self-efficacy which, in turn, affected student outcome expectations, mastery perceptions, and the hours spent per week using online learning technology to complete learning assignments for university courses. Shen et al. 8 through exploratory factor analysis identifies five dimensions of online learning self-efficacy: (a) self-efficacy to complete an online course (b) self-efficacy to interact socially with classmates (c) self-efficacy to handle tools in a Course Management System (CMS) (d) self-efficacy to interact with instructors in an online course, and (e) self-efficacy to interact with classmates for academic purposes. Chiu 9 established a model for analyzing the mediating effect that learning self-efficacy and social self-efficacy have on the relationship between university students’ perceived life stress and smartphone addiction. Kim et al. 10 study was conducted to examine the influence of learning efficacy on nursing students' self-confidence. The objective of Paciello et al. 11 was to identify self-efficacy configurations in different domains (i.e., emotional, social, and self-regulated learning) in a sample of university students using a person-centered approach. The role of university students’ various conceptions of learning in their academic self-efficacy in the domain of physics is initially explored 12 . Kumar et al. 13 investigated factors predicting students’ behavioral intentions towards the continuous use of mobile learning. Other influential work includes 14 .

Many studies have focused on social networking tools such as Facebook and MySpace 15 , 16 . Teachers are concerned that the setup and use of social media apps take up too much of their time, may have plagiarism and privacy issues, and contribute little to actual student learning outcomes; they often consider them redundant or simply not conducive to better learning outcomes 17 . Cao et al. 18 proposed that the central questions in addressing the positive and negative pitfalls of social media on teaching and learning are whether the use of social media in teaching and learning enhances educational effectiveness, and what motivates university teachers to use social media in teaching and learning. Maloney et al. 3 argued that social media can further improve the higher education teaching and learning environment, where students no longer access social media to access course information. Many studies in the past have shown that the use of modern IT in the classroom has increased over the past few years; however, it is still limited mainly to content-driven use, such as accessing course materials, so with the emergence of social media in students’ everyday lives 2 , we need to focus on developing students’ learning self-efficacy so that they can This will enable students to 'turn the tables and learn to learn on their own. Learning self-efficacy is considered an important concept that has a powerful impact on learning outcomes 19 , 20 .

Self-efficacy for learning is vital in teaching students to learn and develop healthily and increasing students' beliefs in the learning process 21 . However, previous studies on social media platforms such as Twitter and Weibo as curriculum support tools have not been further substantiated or analyzed in detail. In addition, the relationship between social media, higher education, and learning self-efficacy has not yet been fully explored by researchers in China. Our research aims to fill this gap in the topic. Our study explored the impact of social media on the learning self-efficacy of Chinese college students. Therefore, it is essential to explore the impact of teachers' use of social media to support teaching and learning on students' learning self-efficacy. Based on educational theory and methodological practice, this study designed a teaching experiment using social media to promote learning self-efficacy by posting an assignment for post-course work on online media to explore the actual impact of social media on university students’ learning self-efficacy. This study examines the impact of a social media-assisted course on university students' learning self-efficacy to explore the positive impact of a social media-assisted course.

Theoretical background

• Social media

Social media has different definitions. Mayfield (2013) first introduced the concept of social media in his book-what is social media? The author summarized the six characteristics of social media: openness, participation, dialogue, communication, interaction, and communication. Mayfield 22 shows that social media is a kind of new media. Its uniqueness is that it can give users great space and freedom to participate in the communication process. Jen (2020) also suggested that the distinguishing feature of social media is that it is “aggregated”. Social media provides users with an interactive service to control their data and information and collaborate and share information 2 . Social media offers opportunities for students to build knowledge and helps them actively create and share information 23 . Millennial students are entering higher education institutions and are accustomed to accessing and using data from the Internet. These individuals go online daily for educational or recreational purposes. Social media is becoming increasingly popular in the lives of everyone, including students and researchers 1 . A previous study has shown that millennials use the Internet as their first source of information and Google as their first choice for finding educational and personal information 24 . Similarly, many institutions encourage teachers to adopt social media applications 25 . Faculty members have also embraced social media applications for personal, professional, and pedagogical purposes 17 .

Social networks allow one to create a personal profile and build various networks that connect him/her to family, friends, and other colleagues. Users use these sites to stay in touch with their friends, make plans, make new friends, or connect with someone online. Therefore, extending this concept, these sites can establish academic connections or promote cooperation and collaboration in higher education classrooms 2 . This study defines social media as an interactive community of users' information sharing and social activities built on the technology of the Internet. Because the concept of social media is broad, its connotations are consistent. Research shows that Meaning and Linking are the two key elements that make up social media existence. Users and individual media outlets generate social media content and use it as a platform to get it out there. Social media distribution is based on social relationships and has a better platform for personal information and relationship management systems. Examples of social media applications include Facebook, Twitter, MySpace, YouTube, Flickr, Skype, Wiki, blogs, Delicious, Second Life, open online course sites, SMS, online games, mobile applications, and more 18 . Ajjan and Hartshorne 2 investigated the intentions of 136 faculty members at a US university to adopt Web 2.0 technologies as tools in their courses. They found that integrating Web 2.0 technologies into the classroom learning environment effectively increased student satisfaction with the course and improved their learning and writing skills. His research focused on improving the perceived usefulness, ease of use, compatibility of Web 2.0 applications, and instructor self-efficacy. The social computing impact of formal education and training and informal learning communities suggested that learning web 2.0 helps users to acquire critical competencies, and promotes technological, pedagogical, and organizational innovation, arguing that social media has a variety of learning content 26 . Users can post digital content online, enabling learners to tap into tacit knowledge while supporting collaboration between learners and teachers. Cao and Hong 27 investigated the antecedents and consequences of social media use in teaching among 249 full-time and part-time faculty members, who reported that the factors for using social media in teaching included personal social media engagement and readiness, external pressures; expected benefits; and perceived risks. The types of Innovators, Early adopters, Early majority, Late majority, Laggards, and objectors. Cao et al. 18 studied the educational effectiveness of 168 teachers' use of social media in university teaching. Their findings suggest that social media use has a positive impact on student learning outcomes and satisfaction. Their research model provides educators with ideas on using social media in the education classroom to improve student performance. Maqableh et al. 28 investigated the use of social networking sites by 366 undergraduate students, and they found that weekly use of social networking sites had a significant impact on student's academic performance and that using social networking sites had a significant impact on improving students' effective time management, and awareness of multitasking. All of the above studies indicate the researcher’s research on social media aids in teaching and learning. All of these studies indicate the positive impact of social media on teaching and learning.

• Learning self-efficacy

For the definition of concepts related to learning self-efficacy, scholars have mainly drawn on the idea proposed by Bandura 29 that defines self-efficacy as “the degree to which people feel confident in their ability to use the skills they possess to perform a task”. Self-efficacy is an assessment of a learner’s confidence in his or her ability to use the skills he or she possesses to complete a learning task and is a subjective judgment and feeling about the individual’s ability to control his or her learning behavior and performance 30 . Liu 31 has defined self-efficacy as the belief’s individuals hold about their motivation to act, cognitive ability, and ability to perform to achieve their goals, showing the individual's evaluation and judgment of their abilities. Zhang (2015) showed that learning efficacy is regarded as the degree of belief and confidence that expresses the success of learning. Yan 32 showed the extent to which learning self-efficacy is viewed as an individual. Pan 33 suggested that learning self-efficacy in an online learning environment is a belief that reflects the learner's ability to succeed in the online learning process. Kang 34 believed that learning self-efficacy is the learner's confidence and belief in his or her ability to complete a learning task. Huang 35 considered self-efficacy as an individual’s self-assessment of his or her ability to complete a particular task or perform a specific behavior and the degree of confidence in one’s ability to achieve a specific goal. Kong 36 defined learning self-efficacy as an individual’s judgment of one’s ability to complete academic tasks.

Based on the above analysis, we found that scholars' focus on learning self-efficacy is on learning behavioral efficacy and learning ability efficacy, so this study divides learning self-efficacy into learning behavioral efficacy and learning ability efficacy for further analysis and research 37 , 38 . Search the CNKI database and ProQuest Dissertations for keywords such as “design students’ learning self-efficacy”, “design classroom self-efficacy”, “design learning self-efficacy”, and other keywords. There are few relevant pieces of literature about design majors. Qiu 39 showed that mobile learning-assisted classroom teaching can control the source of self-efficacy from many aspects, thereby improving students’ sense of learning efficacy and helping middle and lower-level students improve their sense of learning efficacy from all dimensions. Yin and Xu 40 argued that the three elements of the network environment—“learning content”, “learning support”, and “social structure of learning”—all have an impact on university students’ learning self-efficacy. Duo et al. 41 recommend that learning activities based on the mobile network learning community increase the trust between students and the sense of belonging in the learning community, promote mutual communication and collaboration between students, and encourage each other to stimulate their learning motivation. In the context of social media applications, self-efficacy refers to the level of confidence that teachers can successfully use social media applications in the classroom 18 . Researchers have found that self-efficacy is related to social media applications 42 . Students had positive experiences with social media applications through content enhancement, creativity experiences, connectivity enrichment, and collaborative engagement 26 . Students who wish to communicate with their tutors in real-time find social media tools such as web pages, blogs, and virtual interactions very satisfying 27 . Overall, students report their enjoyment of different learning processes through social media applications; simultaneously, they show satisfactory tangible achievement of tangible learning outcomes 18 . According to Bandura's 'triadic interaction theory’, Bian 43 and Shi 44 divided learning self-efficacy into two main elements, basic competence, and control, where basic competence includes the individual's sense of effort, competence, the individual sense of the environment, and the individual's sense of control over behavior. The primary sense of competence includes the individual's Sense of effort, competence, environment, and control over behavior. In this study, learning self-efficacy is divided into Learning behavioral efficacy and Learning ability efficacy. Learning behavioral efficacy includes individuals' sense of effort, environment, and control; learning ability efficacy includes individuals' sense of ability, belief, and interest.

In Fig.  1 , learning self-efficacy includes learning behavior efficacy and learning ability efficacy, in which the learning behavior efficacy is determined by the sense of effort, the sense of environment, the sense of control, and the learning ability efficacy is determined by the sense of ability, sense of belief, sense of interest. “Sense of effort” is the understanding of whether one can study hard. Self-efficacy includes the estimation of self-effort and the ability, adaptability, and creativity shown in a particular situation. One with a strong sense of learning self-efficacy thinks they can study hard and focus on tasks 44 . “Sense of environment” refers to the individual’s feeling of their learning environment and grasp of the environment. The individual is the creator of the environment. A person’s feeling and grasp of the environment reflect the strength of his sense of efficacy to some extent. A person with a shared sense of learning self-efficacy is often dissatisfied with his environment, but he cannot do anything about it. He thinks the environment can only dominate him. A person with a high sense of learning self-efficacy will be more satisfied with his school and think that his teachers like him and are willing to study in school 44 . “Sense of control” is an individual’s sense of control over learning activities and learning behavior. It includes the arrangement of individual learning time, whether they can control themselves from external interference, and so on. A person with a strong sense of self-efficacy will feel that he is the master of action and can control the behavior and results of learning. Such a person actively participates in various learning activities. When he encounters difficulties in learning, he thinks he can find a way to solve them, is not easy to be disturbed by the outside world, and can arrange his own learning time. The opposite is the sense of losing control of learning behavior 44 . “Sense of ability” includes an individual’s perception of their natural abilities, expectations of learning outcomes, and perception of achieving their learning goals. A person with a high sense of learning self-efficacy will believe that he or she is brighter and more capable in all areas of learning; that he or she is more confident in learning in all subjects. In contrast, people with low learning self-efficacy have a sense of powerlessness. They are self-doubters who often feel overwhelmed by their learning and are less confident that they can achieve the appropriate learning goals 44 . “Sense of belief” is when an individual knows why he or she is doing something, knows where he or she is going to learn, and does not think before he or she even does it: What if I fail? These are meaningless, useless questions. A person with a high sense of learning self-efficacy is more robust, less afraid of difficulties, and more likely to reach their learning goals. A person with a shared sense of learning self-efficacy, on the other hand, is always going with the flow and is uncertain about the outcome of their learning, causing them to fall behind. “Sense of interest” is a person's tendency to recognize and study the psychological characteristics of acquiring specific knowledge. It is an internal force that can promote people's knowledge and learning. It refers to a person's positive cognitive tendency and emotional state of learning. A person with a high sense of self-efficacy in learning will continue to concentrate on studying and studying, thereby improving learning. However, one with low learning self-efficacy will have psychology such as not being proactive about learning, lacking passion for learning, and being impatient with learning. The elements of learning self-efficacy can be quantified and detailed in the following Fig.  1 .

Learning self-efficacy research structure in this paper.

Research participants

All the procedures were conducted in adherence to the guidelines and regulations set by the institution. Prior to initiating the study, informed consent was obtained in writing from the participants, and the Institutional Review Board for Behavioral and Human Movement Sciences at Nanning Normal University granted approval for all protocols.

Two parallel classes are pre-selected as experimental subjects in our study, one as the experimental group and one as the control group. Social media assisted classroom teaching to intervene in the experimental group, while the control group did not intervene. When selecting the sample, it is essential to consider, as far as possible, the shortcomings of not using randomization to select or assign the study participants, resulting in unequal experimental and control groups. When selecting the experimental subjects, classes with no significant differences in initial status and external conditions, i.e. groups with homogeneity, should be selected. Our study finally decided to select a total of 44 students from Class 2021 Design 1 and a total of 29 students from Class 2021 Design 2, a total of 74 students from Nanning Normal University, as the experimental subjects. The former served as the experimental group, and the latter served as the control group. 73 questionnaires are distributed to measure before the experiment, and 68 are returned, with a return rate of 93.15%. According to the statistics, there were 8 male students and 34 female students in the experimental group, making a total of 44 students (mirrors the demographic trends within the humanities and arts disciplines from which our sample was drawn); there are 10 male students and 16 female students in the control group, making a total of 26 students, making a total of 68 students in both groups. The sample of those who took the course were mainly sophomores, with a small number of first-year students and juniors, which may be related to the nature of the subject of this course and the course system offered by the university. From the analysis of students' majors, liberal arts students in the experimental group accounted for the majority, science students and art students accounted for a small part. In contrast, the control group had more art students, and liberal arts students and science students were small. In the daily self-study time, the experimental and control groups are 2–3 h. The demographic information of research participants is shown in Table 1 .

Research procedure

Firstly, the ADDIE model is used for the innovative design of the teaching method of the course. The number of students in the experimental group was 44, 8 male and 35 females; the number of students in the control group was 29, 10 male and 19 females. Secondly, the classes are targeted at students and applied. Thirdly, the course for both the experimental and control classes is a convenient and practice-oriented course, with the course title “Graphic Design and Production”, which focuses on learning the graphic design software Photoshop. The course uses different cases to explain in detail the process and techniques used to produce these cases using Photoshop, and incorporates practical experience as well as relevant knowledge in the process, striving to achieve precise and accurate operational steps; at the end of the class, the teacher assigns online assignments to be completed on social media, allowing students to post their edited software tutorials online so that students can master the software functions. The teacher assigns online assignments to be completed on social media at the end of the lesson, allowing students to post their editing software tutorials online so that they can master the software functions and production skills, inspire design inspiration, develop design ideas and improve their design skills, and improve students' learning self-efficacy through group collaboration and online interaction. Fourthly, pre-tests and post-tests are conducted in the experimental and control classes before the experiment. Fifthly, experimental data are collected, analyzed, and summarized.

We use a questionnaire survey to collect data. Self-efficacy is a person’s subjective judgment on whether one can successfully perform a particular achievement. American psychologist Albert Bandura first proposed it. To understand the improvement effect of students’ self-efficacy after the experimental intervention, this work questionnaire was referenced by the author from “Self-efficacy” “General Perceived Self Efficacy Scale” (General Perceived Self Efficacy Scale) German psychologist Schwarzer and Jerusalem (1995) and “Academic Self-Efficacy Questionnaire”, a well-known Chinese scholar Liang 45 .  The questionnaire content is detailed in the supplementary information . A pre-survey of the questionnaire is conducted here. The second-year students of design majors collected 32 questionnaires, eliminated similar questions based on the data, and compiled them into a formal survey scale. The scale consists of 54 items, 4 questions about basic personal information, and 50 questions about learning self-efficacy. The Likert five-point scale is the questionnaire used in this study. The answers are divided into “completely inconsistent", “relatively inconsistent”, “unsure”, and “relatively consistent”. The five options of “Completely Meet” and “Compliant” will count as 1, 2, 3, 4, and 5 points, respectively. Divided into a sense of ability (Q5–Q14), a sense of effort (Q15–Q20), a sense of environment (Q21–Q28), a sense of control (Q29–Q36), a sense of Interest (Q37–Q45), a sense of belief (Q46–Q54). To demonstrate the scientific effectiveness of the experiment, and to further control the influence of confounding factors on the experimental intervention. This article thus sets up a control group as a reference. Through the pre-test and post-test in different periods, comparison of experimental data through pre-and post-tests to illustrate the effects of the intervention.

Reliability indicates the consistency of the results of a measurement scale (See Table 2 ). It consists of intrinsic and extrinsic reliability, of which intrinsic reliability is essential. Using an internal consistency reliability test scale, a Cronbach's alpha coefficient of reliability statistics greater than or equal to 0.9 indicates that the scale has good reliability, 0.8–0.9 indicates good reliability, 7–0.8 items are acceptable. Less than 0.7 means to discard some items in the scale 46 . This study conducted a reliability analysis on the effects of the related 6-dimensional pre-test survey to illustrate the reliability of the questionnaire.

From the Table 2 , the Cronbach alpha coefficients for the pre-test, sense of effort, sense of environment, sense of control, sense of interest, sense of belief, and the total questionnaire, were 0.919, 0.839, 0.848, 0.865, 0.852, 0.889 and 0.958 respectively. The post-test Cronbach alpha coefficients were 0.898, 0.888, 0.886, 0.889, 0.900, 0.893 and 0.970 respectively. The Cronbach alpha coefficients were all greater than 0.8, indicating a high degree of reliability of the measurement data.

The validity, also known as accuracy, reflects how close the measurement result is to the “true value”. Validity includes structure validity, content validity, convergent validity, and discriminative validity. Because the experiment is a small sample study, we cannot do any specific factorization. KMO and Bartlett sphericity test values are an important part of structural validity. Indicator, general validity evaluation (KMO value above 0.9, indicating very good validity; 0.8–0.9, indicating good validity; 0.7–0.8 validity is good; 0.6–0.7 validity is acceptable; 0.5–0.6 means poor validity; below 0.45 means that some items should be abandoned.

Table 3 shows that the KMO values of ability, effort, environment, control, interest, belief, and the total questionnaire are 0.911, 0.812, 0.778, 0.825, 0.779, 0.850, 0.613, and the KMO values of the post-test are respectively. The KMO values are 0.887, 0.775, 0.892, 0.868, 0.862, 0.883, 0.715. KMO values are basically above 0.8, and all are greater than 0.6. This result indicates that the validity is acceptable, the scale has a high degree of reasonableness, and the valid data.

In the graphic design and production (professional design course), we will learn the practical software with cases. After class, we will share knowledge on the self-media platform. We will give face-to-face computer instruction offline from 8:00 to 11:20 every Wednesday morning for 16 weeks. China's top online sharing platform (APP) is Tik Tok, micro-blog (Micro Blog) and Xiao hong shu. The experiment began on September 1, 2022, and conducted the pre-questionnaire survey simultaneously. At the end of the course, on January 6, 2023, the post questionnaire survey was conducted. A total of 74 questionnaires were distributed in this study, recovered 74 questionnaires. After excluding the invalid questionnaires with incomplete filling and wrong answers, 68 valid questionnaires were obtained, with an effective rate of 91%, meeting the test requirements. Then, use the social science analysis software SPSS Statistics 26 to analyze the data: (1) descriptive statistical analysis of the dimensions of learning self-efficacy; (2) Using correlation test to analyze the correlation between learning self-efficacy and the use of social media; (3) This study used a comparative analysis of group differences to detect the influence of learning self-efficacy on various dimensions of social media and design courses. For data processing and analysis, use the spss26 version software and frequency statistics to create statistics on the basic situation of the research object and the basic situation of the use of live broadcast. The reliability scale analysis (internal consistency test) and use Bartlett's sphericity test to illustrate the reliability and validity of the questionnaire and the individual differences between the control group and the experimental group in demographic variables (gender, grade, Major, self-study time per day) are explained by cross-analysis (chi-square test). In the experimental group and the control group, the pre-test, post-test, before-and-after test of the experimental group and the control group adopt independent sample T-test and paired sample T-test to illustrate the effect of the experimental intervention (The significance level of the test is 0.05 two-sided).

Results and discussion

Comparison of pre-test and post-test between groups.

To study whether the data of the experimental group and the control group are significantly different in the pre-test and post-test mean of sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief. The research for this situation uses an independent sample T-test and an independent sample. The test needs to meet some false parameters, such as normality requirements. Generally passing the normality test index requirements are relatively strict, so it can be relaxed to obey an approximately normal distribution. If there is serious skewness distribution, replace it with the nonparametric test. Variables are required to be continuous variables. The six variables in this study define continuous variables. The variable value information is independent of each other. Therefore, we use the independent sample T-test.

From the Table 4 , a pre-test found that there was no statistically significant difference between the experimental group and the control group at the 0.05 confidence level ( p  > 0.05) for perceptions of sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief. Before the experiment, the two groups of test groups have the same quality in measuring self-efficacy. The experimental class and the control class are homogeneous groups. Table 5 shows the independent samples t-test for the post-test, used to compare the experimental and control groups on six items, including the sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief.

The experimental and control groups have statistically significant scores ( p  < 0.05) for sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief, and the experimental and control groups have statistically significant scores (t = 3.177, p  = 0.002) for a sense of competence. (t = 3.177, p  = 0.002) at the 0.01 level, with the experimental group scoring significantly higher (3.91 ± 0.51) than the control group (3.43 ± 0.73). The experimental group and the control group showed significance for the perception of effort at the 0.01 confidence level (t = 2.911, p  = 0.005), with the experimental group scoring significantly higher (3.88 ± 0.66) than the control group scoring significantly higher (3.31 ± 0.94). The experimental and control groups show significance at the 0.05 level (t = 2.451, p  = 0.017) for the sense of environment, with the experimental group scoring significantly higher (3.95 ± 0.61) than the control group scoring significantly higher (3.58 ± 0.62). The experimental and control groups showed significance for sense of control at the 0.05 level of significance (t = 2.524, p  = 0.014), and the score for the experimental group (3.76 ± 0.67) would be significantly higher than the score for the control group (3.31 ± 0.78). The experimental and control groups showed significance at the 0.01 level for sense of interest (t = 2.842, p  = 0.006), and the experimental group's score (3.87 ± 0.61) would be significantly higher than the control group's score (3.39 ± 0.77). The experimental and control groups showed significance at the 0.01 level for the sense of belief (t = 3.377, p  = 0.001), and the experimental group would have scored significantly higher (4.04 ± 0.52) than the control group (3.56 ± 0.65). Therefore, we can conclude that the experimental group's post-test significantly affects the mean scores of sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief. A social media-assisted course has a positive impact on students' self-efficacy.

Comparison of pre-test and post-test of each group

The paired-sample T-test is an extension of the single-sample T-test. The purpose is to explore whether the means of related (paired) groups are significantly different. There are four standard paired designs: (1) Before and after treatment of the same subject Data, (2) Data from two different parts of the same subject, (3) Test results of the same sample with two methods or instruments, 4. Two matched subjects receive two treatments, respectively. This study belongs to the first type, the 6 learning self-efficacy dimensions of the experimental group and the control group is measured before and after different periods.

Paired t-tests is used to analyze whether there is a significant improvement in the learning self-efficacy dimension in the experimental group after the experimental social media-assisted course intervention. In Table 6 , we can see that the six paired data groups showed significant differences ( p  < 0.05) in the pre and post-tests of sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief. There is a level of significance of 0.01 (t = − 4.540, p  = 0.000 < 0.05) before and after the sense of ability, the score after the sense of ability (3.91 ± 0.51), and the score before the Sense of ability (3.41 ± 0.55). The level of significance between the pre-test and post-test of sense of effort is 0.01 (t = − 4.002, p  = 0.000). The score of the sense of effort post-test (3.88 ± 0.66) will be significantly higher than the average score of the sense of effort pre-test (3.31 ± 0.659). The significance level between the pre-test and post-test Sense of environment is 0.01 (t = − 3.897, p  = 0.000). The average score for post- Sense of environment (3.95 ± 0.61) will be significantly higher than that of sense of environment—the average score of the previous test (3.47 ± 0.44). The average value of a post- sense of control (3.76 ± 0.67) will be significantly higher than the average of the front side of the Sense of control value (3.27 ± 0.52). The sense of interest pre-test and post-test showed a significance level of 0.01 (− 4.765, p  = 0.000), and the average value of Sense of interest post-test was 3.87 ± 0.61. It would be significantly higher than the average value of the Sense of interest (3.25 ± 0.59), the significance between the pre-test and post-test of belief sensing is 0.01 level (t = − 3.939, p  = 0.000). Thus, the average value of a post-sense of belief (4.04 ± 0.52) will be significantly higher than that of a pre-sense of belief Average value (3.58 ± 0.58). After the experimental group’s post-test, the scores for the Sense of ability, effort, environment, control, interest, and belief before the comparison experiment increased significantly. This result has a significant improvement effect. Table 7 shows that the control group did not show any differences in the pre and post-tests using paired t-tests on the dimensions of learning self-efficacy such as sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief ( p  > 0.05). It shows no experimental intervention for the control group, and it does not produce a significant effect.

The purpose of this study aims to explore the impact of social media use on college students' learning self-efficacy, examine the changes in the elements of college students' learning self-efficacy before and after the experiment, and make an empirical study to enrich the theory. This study developed an innovative design for course teaching methods using the ADDIE model. The design process followed a series of model rules of analysis, design, development, implementation, and evaluation, as well as conducted a descriptive statistical analysis of the learning self-efficacy of design undergraduates. Using questionnaires and data analysis, the correlation between the various dimensions of learning self-efficacy is tested. We also examined the correlation between the two factors, and verifies whether there was a causal relationship between the two factors.

Based on prior research and the results of existing practice, a learning self-efficacy is developed for university students and tested its reliability and validity. The scale is used to pre-test the self-efficacy levels of the two subjects before the experiment, and a post-test of the self-efficacy of the two groups is conducted. By measuring and investigating the learning self-efficacy of the study participants before the experiment, this study determined that there was no significant difference between the experimental group and the control group in terms of sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief. Before the experiment, the two test groups had homogeneity in measuring the dimensionality of learning self-efficacy. During the experiment, this study intervened in social media assignments for the experimental group. The experiment used learning methods such as network assignments, mutual aid communication, mutual evaluation of assignments, and group discussions. After the experiment, the data analysis showed an increase in learning self-efficacy in the experimental group compared to the pre-test. With the test time increased, the learning self-efficacy level of the control group decreased slightly. It shows that social media can promote learning self-efficacy to a certain extent. This conclusion is similar to Cao et al. 18 , who suggested that social media would improve educational outcomes.

We have examined the differences between the experimental and control group post-tests on six items, including the sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief. This result proves that a social media-assisted course has a positive impact on students' learning self-efficacy. Compared with the control group, students in the experimental group had a higher interest in their major. They showed that they liked to share their learning experiences and solve difficulties in their studies after class. They had higher motivation and self-directed learning ability after class than students in the control group. In terms of a sense of environment, students in the experimental group were more willing to share their learning with others, speak boldly, and participate in the environment than students in the control group.

The experimental results of this study showed that the experimental group showed significant improvement in the learning self-efficacy dimensions after the experimental intervention in the social media-assisted classroom, with significant increases in the sense of ability, sense of effort, sense of environment, sense of control, sense of interest and sense of belief compared to the pre-experimental scores. This result had a significant improvement effect. Evidence that a social media-assisted course has a positive impact on students' learning self-efficacy. Most of the students recognized the impact of social media on their learning self-efficacy, such as encouragement from peers, help from teachers, attention from online friends, and recognition of their achievements, so that they can gain a sense of achievement that they do not have in the classroom, which stimulates their positive perception of learning and is more conducive to the awakening of positive effects. This phenomenon is in line with Ajjan and Hartshorne 2 . They argue that social media provides many opportunities for learners to publish their work globally, which brings many benefits to teaching and learning. The publication of students' works online led to similar positive attitudes towards learning and improved grades and motivation. This study also found that students in the experimental group in the post-test controlled their behavior, became more interested in learning, became more purposeful, had more faith in their learning abilities, and believed that their efforts would be rewarded. This result is also in line with Ajjan and Hartshorne's (2008) indication that integrating Web 2.0 technologies into classroom learning environments can effectively increase students' satisfaction with the course and improve their learning and writing skills.

We only selected students from one university to conduct a survey, and the survey subjects were self-selected. Therefore, the external validity and generalizability of our study may be limited. Despite the limitations, we believe this study has important implications for researchers and educators. The use of social media is the focus of many studies that aim to assess the impact and potential of social media in learning and teaching environments. We hope that this study will help lay the groundwork for future research on the outcomes of social media utilization. In addition, future research should further examine university support in encouraging teachers to begin using social media and university classrooms in supporting social media (supplementary file 1 ).

The present study has provided preliminary evidence on the positive association between social media integration in education and increased learning self-efficacy among college students. However, several avenues for future research can be identified to extend our understanding of this relationship.

Firstly, replication studies with larger and more diverse samples are needed to validate our findings across different educational contexts and cultural backgrounds. This would enhance the generalizability of our results and provide a more robust foundation for the use of social media in teaching. Secondly, longitudinal investigations should be conducted to explore the sustained effects of social media use on learning self-efficacy. Such studies would offer insights into how the observed benefits evolve over time and whether they lead to improved academic performance or other relevant outcomes. Furthermore, future research should consider the exploration of potential moderators such as individual differences in students' learning styles, prior social media experience, and psychological factors that may influence the effectiveness of social media in education. Additionally, as social media platforms continue to evolve rapidly, it is crucial to assess the impact of emerging features and trends on learning self-efficacy. This includes an examination of advanced tools like virtual reality, augmented reality, and artificial intelligence that are increasingly being integrated into social media environments. Lastly, there is a need for research exploring the development and evaluation of instructional models that effectively combine traditional teaching methods with innovative uses of social media. This could guide educators in designing courses that maximize the benefits of social media while minimizing potential drawbacks.

In conclusion, the current study marks an important step in recognizing the potential of social media as an educational tool. Through continued research, we can further unpack the mechanisms by which social media can enhance learning self-efficacy and inform the development of effective educational strategies in the digital age.

Data availability

The data that support the findings of this study are available from the corresponding authors upon reasonable request. The data are not publicly available due to privacy or ethical restrictions.

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Acknowledgements

This work is supported by the 2023 Guangxi University Young and middle-aged Teachers' Basic Research Ability Enhancement Project—“Research on Innovative Communication Strategies and Effects of Zhuang Traditional Crafts from the Perspective of the Metaverse” (Grant Nos. 2023KY0385), and the special project on innovation and entrepreneurship education in universities under the “14th Five-Year Plan” for Guangxi Education Science in 2023, titled “One Core, Two Directions, Three Integrations - Strategy and Practical Research on Innovation and Entrepreneurship Education in Local Universities” (Grant Nos. 2023ZJY1955), and the 2023 Guangxi Higher Education Undergraduate Teaching Reform General Project (Category B) “Research on the Construction and Development of PBL Teaching Model in Advertising” (Grant Nos.2023JGB294), and the 2022 Guangxi Higher Education Undergraduate Teaching Reform Project (General Category A) “Exploration and Practical Research on Public Art Design Courses in Colleges and Universities under Great Aesthetic Education” (Grant Nos. 2022JGA251), and the 2023 Guangxi Higher Education Undergraduate Teaching Reform Project Key Project “Research and Practice on the Training of Interdisciplinary Composite Talents in Design Majors Based on the Concept of Specialization and Integration—Taking Guangxi Institute of Traditional Crafts as an Example” (Grant Nos. 2023JGZ147), and the2024 Nanning Normal University Undergraduate Teaching Reform Project “Research and Practice on the Application of “Guangxi Intangible Cultural Heritage” in Packaging Design Courses from the Ideological and Political Perspective of the Curriculum” (Grant Nos. 2024JGX048),and the 2023 Hubei Normal University Teacher Teaching Reform Research Project (Key Project) -Curriculum Development for Improving Pre-service Music Teachers' Teaching Design Capabilities from the Perspective of OBE (Grant Nos. 2023014), and the 2023 Guangxi Education Science “14th Five-Year Plan” special project: “Specialized Integration” Model and Practice of Art and Design Majors in Colleges and Universities in Ethnic Areas Based on the OBE Concept (Grant Nos. 2023ZJY1805), and the 2024 Guangxi University Young and Middle-aged Teachers’ Scientific Research Basic Ability Improvement Project “Research on the Integration Path of University Entrepreneurship and Intangible Inheritance - Taking Liu Sanjie IP as an Example” (Grant Nos. 2024KY0374), and the 2022 Research Project on the Theory and Practice of Ideological and Political Education for College Students in Guangxi - “Party Building + Red”: Practice and Research on the Innovation of Education Model in College Student Dormitories (Grant Nos. 2022SZ028), and the 2021 Guangxi University Young and Middle-aged Teachers’ Scientific Research Basic Ability Improvement Project - "Research on the Application of Ethnic Elements in the Visual Design of Live Broadcast Delivery of Guangxi Local Products" (Grant Nos. 2021KY0891).

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The contribution of H. to this paper primarily lies in research design and experimental execution. H. was responsible for the overall framework design of the paper, setting research objectives and methods, and actively participating in data collection and analysis during the experimentation process. Furthermore, H. was also responsible for conducting literature reviews and played a crucial role in the writing and editing phases of the paper. L.'s contribution to this paper primarily manifests in theoretical derivation and the discussion section. Additionally, author L. also proposed future research directions and recommendations in the discussion section, aiming to facilitate further research explorations. Y.'s contribution to this paper is mainly reflected in data analysis and result interpretation. Y. was responsible for statistically analyzing the experimental data and employing relevant analytical tools and techniques to interpret and elucidate the data results.

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Hu, J., Lai, Y. & Yi, X. Effectiveness of social media-assisted course on learning self-efficacy. Sci Rep 14 , 10112 (2024). https://doi.org/10.1038/s41598-024-60724-0

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Seventeen journals lose impact factors for suspected citation manipulation

Clarivate, the company that calculates Journal Impact Factors based on citations to articles, didn’t publish the metric for 17 journals this year due to suspected citation manipulation. That’s a substantial increase from last year , when only four were excluded. 

The increase is, in part, case of rising tides lifting (sinking?) all boats: In its 2024 Journal Citation Reports , Clarivate included an additional 7,200 journals from the Emerging Sources Citation Index (ESCI) and the the Arts and Humanities Citation Index (AHCI), a spokesperson for the company said, resulting in a larger number of impact factor suppressions than in past years. 

Clarivate suppressed nearly twice as many journals in 2020 , when it penalized 33 for self-citation. The company suppressed 10 in 2021 , and three the following year . 

In an email to Retraction Watch, a spokesperson said journals are suppressed from the report due to “anomalous citation behavior, including where there is evidence of excessive journal self-citation and/or citation stacking (which involves two or more journals). We do not presume a motive or accuse these journals of wrongdoing.”

Though the metric is controversial , many institutions use impact factors as an indicator of journal quality to evaluate the work of researchers – meaning journal suppressions can have negative effects for authors as well as for journals, which may see decreased submissions. 

Robert Mendelsohn , the editor-in-chief of Climate Change Economics , said his journal was suppressed because of “too many citations” from Springer’s Environmental Science & Pollution Research , which also did not receive an impact factor:

Clarivate said this was unusual for an economics journal and therefore suspicious. We tried to explain that our journal was focused on climate change and that it was important to link the economic studies to natural science.  This explained why so many authors had science citations.  Clarivate apparently did not care and gave us a year to change our ways. Keeping climate change studies anchored in natural science, however, is important. So we are not changing our citation policies.

A spokesperson for Springer said the publisher was disappointed that Clarivate had chosen to suppress Environmental Science and Pollution Research from the Journal Citation Reports, and that they are investigating Clarivate’s concerns. 

Rostyslav Vlokh , editor-in-chief of Ukranian Journal for Physical Optics , told us the reason his journal was suppressed was because of the “abnormally large number of citations (46%) for our journal in 2023” that came from Optik journal. He denied that his journal had any control over the “editorial politics” of Optik.  

“The citation stacking is quite a thin issue in this case, i.e., we cannot unambiguously prove or deny it – many authors were involved in the citation,” Vlokh said, calling the Clarivate editorial team “a bit hasty” in their decision-making. 

Editor-in-chief of Activities, Adaptation & Aging , Lim Weng Marc , told us in an email, “We are indeed sadden [sic] by this outcome.” He attributes the suppression of his impact factor to “change in our journal’s focus in 2021, which created a niche, and possibly due to the fairly small number of articles we publish.” 

The journal published 17 articles in 2021, 18 in 2022, and 28 in 2023. This low number “could have magnified the supposed self-citation to the articles in the same issue which our editorial introduces,” Marc said. 

Catherine Liu, a publisher for Elsevier, commented on the suppression of Resources Policy :

As shown by the Clarivate analysis, the citations from Resources Policy to 2 other small journals indeed provided a substantial boost to the citations numbers for 2 other small journals, with respect to the body of outgoing citations from Resources Policy they appear to be a tiny fraction (0.6%). However, although Resources Policy is prestigious within the community, and the scope of the problem is limited, Clarivate still insist to suppress Resources Policy from receiving Journal Impact Factor this time.

Liu noted that Elsevier is investigating this issue and might retract flagged papers or “at minimum” re-review and correct them, based on Elsevier’s recently updated retractions policy, which seeks to address compromised peer review and systematic review or citation problems. 

Clarivate is verifying Cuadernos De Economía following a high number of citations in other journals, a spokesperson for Elsevier’s Cuadernos De Economía said.

Two of the suppressed journals, Granular Computing , a Springer title, and Elsevier’s Information Sciences , both have Witold Pedrycz as editor-in-chief. Pedrycz did not respond to our request for comment. 

Elsevier takes these claims “very seriously” and is reviewing the articles highlighted by Clarivate, a spokesperson for Elsevier said. Clarivate was concerned about citations in 12 articles published in Information Sciences in 2023 and elected to suppress the journal’s impact factor for one year, the spokesperson said.

The other suppressed journals which didn’t respond to our request for comment are as follows:

• Engineering, Technology & Applied Science Research
• Exploratory Animal and Medical Research
• Library Hi Tech (Emerald Insight) 
• Regional Statistics (Hungarian Central Statistical Office)
• SOCAR Proceedings (“OilGasScientificResearchProject” Institute of State Oil Company of Azerbaijan Republic (SOCAR))
• Panminerva Medica , Minerva Medica , and Gazzetta Medica Italiana Archivo Per Le Scienze Mediche (Edizioni Minerva Medica)  
• Annals Of Financial Economics (World Scientific Publishing)

“Suppressed journals remain in the Web of Science Core Collection – although they may be subject to re-evaluation and will be removed from coverage if they fail – and will be eligible for inclusion in the Journal Citation Reports again the following year,” a spokesperson for Clarivate said.

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6 thoughts on “seventeen journals lose impact factors for suspected citation manipulation”.

Is Cuadernos De Economía actually an Elsevier journal right now (it was in the recent past). See e.g. https://www.elsevier.es/es-revista-cuadernos-economia-329 which suggests it has departed Elsevier.

According to Sciencedirect it was transferred back to the society in 2018 already. The latest issue on Sciencedirect is from January-April 2018.

I don’t quite understand why the Resource Policy has been suppressed… is 0.6% big?

I see the self citation thing a lot. It looks good until you actually read the bibliography and realize some of the sources aren’t sources and some of them actually conflict with the publication.

Any journal with trace of plagiarism should be retracted.

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