Scientific Reports

scientific reports impact factor ranking

Subject Area and Category

  • Multidisciplinary

Nature Publishing Group

Publication type

Information.

How to publish in this journal

[email protected]

scientific reports impact factor ranking

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

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

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

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

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

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

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

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

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

Scimago Journal & Country Rank

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Articles Journal Citation Reports: Quartile rankings and other metrics

Journal citation reports: quartile rankings and other metrics, may 24, 2022 • knowledge, information.

Bob Wilson

Scientific Reports - WoS Journal Info

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  • Impact Factor & Ranking Results

Learn more about Sage journals’ research impact through the following areas: 

  • Impact Factor and rankings results
  • Article and journal level metrics

Sage and DORA

*From the Journal Citation Reports (Web of Science Group, 2023)

With 965 journals now ranked in the JCR, SAGE continues to experience consistent growth within the reports.  This year ESCI and AHCI indexed journals were given Impact Factors resulting in 293 new journals receiving rankings. 76 SAGE journals have received a top 10 category rank, with 1 journal receiving their first Impact Factor (IF) in the SSCI/SCIE index. 191 titles are now ranked in the top 30% of the JCR, and 54% of SAGE journals are ranked within the top half of their subject category. In the 2023 release SAGE publishes the market leading journal within 6 categories shown below.

A 8% increase in coverage in the SSCI over the last five years means that there are now 452 SAGE journals in the SSCI.  49% of the SSCI journals received an increased IF in the 2023 release. 120 journals are now placed in the top 25% of the JCR rankings for their category.

In the 2023 release SAGE publishes the market-leading journal within 6 SSCI categories: Family Studies; Geography; Psychology, Multidisciplinary; Psychology, Psychoanalysis; Psychology, Social; Social Work.

Over the last five years, SAGE has seen a 7% increase in the number of titles in the SCI index, primarily due to strategic acquisitions and organic growth across the medical, engineering and technology disciplines, with SAGE continuing to be one of the top five publishers of medical journals in the world. SAGE’s coverage within SCI has 273 journals now ranked and 37 journals in the top 25% of their SCI category.

In the 2023 release SAGE publishes the market-leading journal by 5 year Impact Factor in 14 categories: Audiology & Speech-Language Pathology; Education & Educational Research; Education, Special; Family Studies; Geography (SSCI); History & Philosophy Of Science (SCIE and SSCI); Psychology, Multidisciplinary; Psychology, Social; Rehabilitation (SSCI); Social Sciences, Interdisciplinary; Social Work; Sociology; Womens Studies.

Article and journal-level metrics

Article-level metrics, including downloads and citations, are available and offer valuable insights into a researcher’s contribution to the discipline and the wider community. Furthermore, a full range of journal-level metrics, including downloads and impact data from abstracting and indexing services such as Google Scholar, Journal Citation Reports, and Scopus (where available), can be explored for each journal.

The above features highlight our ongoing commitment to balanced, broad, and responsible research. We are pleased to share our support for the principles of the Declaration of Research Assessment (DORA), which encourages publishers to report on a range of metrics based on the scientific content of an article rather than publication metrics of the journal in which it was published. Learn more about the various ways we are taking action.

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Scientific Reports impact factor, indexing, ranking (2024)

Scientific

Aim and Scope

The Scientific Reports is a research journal that publishes research related to Multidisciplinary . This journal is published by the Nature Publishing Group. The ISSN of this journal is 20452322 . Based on the Scopus data, the SCImago Journal Rank (SJR) of Scientific Reports is 0.973 .

Scientific Reports Ranking

The Impact Factor of Scientific Reports is 4.996.

The impact factor (IF) is a measure of the frequency with which the average article in a journal has been cited in a particular year. It is used to measure the importance or rank of a journal by calculating the times its articles are cited.

The impact factor was devised by Eugene Garfield, the founder of the Institute for Scientific Information (ISI) in Philadelphia. Impact factors began to be calculated yearly starting from 1975 for journals listed in the Journal Citation Reports (JCR). ISI was acquired by Thomson Scientific & Healthcare in 1992, and became known as Thomson ISI. In 2018, Thomson-Reuters spun off and sold ISI to Onex Corporation and Baring Private Equity Asia. They founded a new corporation, Clarivate , which is now the publisher of the JCR.

Important Metrics

Scientific reports indexing.

The Scientific Reports is indexed in:

  • Web of Science (SCIE)

An indexed journal means that the journal has gone through and passed a review process of certain requirements done by a journal indexer.

The Web of Science Core Collection includes the Science Citation Index Expanded (SCIE), Social Sciences Citation Index (SSCI), Arts & Humanities Citation Index (AHCI), and Emerging Sources Citation Index (ESCI).

Scientific Reports Impact Factor 2024

The latest impact factor of Scientific Reports is 4.996 .

The impact factor (IF) is a measure of the frequency with which the average article in a journal has been cited in a particular year. It is used to measure the importance or rank of a journal by calculating the times it's articles are cited.

Note: Every year, The Clarivate releases the Journal Citation Report (JCR). The JCR provides information about academic journals including impact factor. The latest JCR was released in June, 2023. The JCR 2024 will be released in the June 2024.

Scientific Reports Quartile

The latest Quartile of Scientific Reports is Q1 .

Each subject category of journals is divided into four quartiles: Q1, Q2, Q3, Q4. Q1 is occupied by the top 25% of journals in the list; Q2 is occupied by journals in the 25 to 50% group; Q3 is occupied by journals in the 50 to 75% group and Q4 is occupied by journals in the 75 to 100% group.

Publication fee

According to journal website, the publication fee of Scientific Reports is around 2090 EUR; 2390 USD; 1890 GBP .

The Scientific Reports has also Journal waiver policy (for developing countries, authors etc).

An article processing charge (APC), also known as a publication fee, is a fee which is sometimes charged to authors. Most commonly, it is involved in making a work available as open access (OA), in either a full OA journal or in a hybrid journal.

Call for Papers

Visit to the official website of the journal/ conference to check the details about call for papers.

How to publish in Scientific Reports?

If your research is related to Multidisciplinary, then visit the official website of Scientific Reports and send your manuscript.

Tips for publishing in Scientific Reports:

  • Selection of research problem.
  • Presenting a solution.
  • Designing the paper.
  • Make your manuscript publication worthy.
  • Write an effective results section.
  • Mind your references.

Acceptance Rate

Journal publication time.

The Journal Publication Time means the average number of weeks between article submission and publication. According to the journal website, the Scientific Reports publishes research articles in 20 weeks on an average.

Final Summary

  • The impact factor of Scientific Reports is 4.996.
  • The Scientific Reports is a reputed research journal.
  • It is published by Nature Publishing Group .
  • The journal is indexed in UGC CARE, Scopus, SCIE, DOAJ, PubMed .
  • It is an open access journal .
  • The (SJR) SCImago Journal Rank is 0.973 .
  • The publication time (Average number of weeks between article submission and publication) of the journal is 20 weeks .
  • The Publication fee (APC) Scientific Reports 2090 EUR; 2390 USD; 1890 GBP .

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February 16, 2024 by Gary Price

ADVERTISEMENT

Journal Citation Reports (JCR): Clarivate Announces Changes to Journal Impact Factor Category Rankings

From a Clarivate Blog Post:

Over the past few years, we have implemented a series of policy changes for the Journal Citation Reports (JCR)™ aimed at aligning coverage between the Web of Science Core Collection™ and the JCR, providing more transparency of the data underlying JCR metrics encouraging a more inclusive, more holistic way of comparing journals. [Clip] Recent changes to the JCR have included the addition of profile pages for journals indexed in the Arts and Humanities Citation Index (AHCI)™ and Emerging Sources Citation Index (EHCI)™ and the introduction of the  Journal Citation Indicator  (JCI) in 2021. The JCI is field-normalized to facilitate the comparison of journals across different disciplines, including the arts and humanities. We also extended the Journal Impact Factor (JIF)™ to AHCI and ESCI journals in 2023 so that it now encompasses all quality journals in the Web of Science Core Collection. [Clip] In making these changes, we have evolved the JIF from an indicator of scholarly impact (the numerical value of the JIF) in the sciences and social sciences to an indicator of both scholarly impact  and  trustworthiness (having a JIF – regardless of the number) across all disciplines at the journal level. In 2023, we also changed the way the JIF is displayed – transitioning from three decimal places to one. This is important as it created more ties in JIF rankings to encourage consideration of additional indicators and descriptive factors alongside the JIF when comparing journals. Our commitment to enhancing transparency and trust continues in the forthcoming JCR release in June 2024. Two notable changes,  which we announced last year , will be implemented in the JIF category rankings. We will move from edition-specific category JIF rankings to unified rankings for each of our 229 science and social science categories. We will no longer provide separate JIF rankings for the  nine subject categories that are indexed in multiple editions.  For example, the Psychiatry category is included in both Science Citation Index – Expanded (SCIE)™ and Social Sciences Citation Index (SSCI)™ and we currently publish a separate Psychiatry ranking for each edition. We will replace these separate rankings with a single, unified ranking. Additionally, the new unified rankings will include journals indexed in ESCI. Using Psychiatry once again as our example – we will display a single Psychiatry ranking that includes journals indexed in SCIE, SSCI and ESCI. [Clip] This is the first in a series of updates on the 2024 JCR.

Learn More, Read the Complete Blog Post

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About Gary Price

Gary Price ( [email protected] ) is a librarian, writer, consultant, and frequent conference speaker based in the Washington D.C. metro area. He earned his MLIS degree from Wayne State University in Detroit. Price has won several awards including the SLA Innovations in Technology Award and Alumnus of the Year from the Wayne St. University Library and Information Science Program. From 2006-2009 he was Director of Online Information Services at Ask.com.

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Scientific Reports - Impact Score, Ranking, SJR, h-index, Citescore, Rating, Publisher, ISSN, and Other Important Details

Published By: Nature Publishing Group

Abbreviation: Sci. Rep.

Impact Score The impact Score or journal impact score (JIS) is equivalent to Impact Factor. The impact factor (IF) or journal impact factor (JIF) of an academic journal is a scientometric index calculated by Clarivate that reflects the yearly mean number of citations of articles published in the last two years in a given journal, as indexed by Clarivate's Web of Science. On the other hand, Impact Score is based on Scopus data.

Important details, about scientific reports.

Scientific Reports is a journal published by Nature Publishing Group . This journal covers the area[s] related to Multidisciplinary, etc . The coverage history of this journal is as follows: 2011-2022. The rank of this journal is 4401 . This journal's impact score, h-index, and SJR are 4.44, 282, and 0.973, respectively. The ISSN of this journal is/are as follows: 20452322 . The best quartile of Scientific Reports is Q1 . This journal has received a total of 306481 citations during the last three years (Preceding 2022).

Scientific Reports Impact Score 2022-2023

The impact score (IS), also denoted as the Journal impact score (JIS), of an academic journal is a measure of the yearly average number of citations to recent articles published in that journal. It is based on Scopus data.

Prediction of Scientific Reports Impact Score 2023

Impact Score 2022 of Scientific Reports is 4.44 . If a similar downward trend continues, IS may decrease in 2023 as well.

Impact Score Graph

Check below the impact score trends of scientific reports. this is based on scopus data., scientific reports h-index.

The h-index of Scientific Reports is 282 . By definition of the h-index, this journal has at least 282 published articles with more than 282 citations.

What is h-index?

The h-index (also known as the Hirsch index or Hirsh index) is a scientometric parameter used to evaluate the scientific impact of the publications and journals. It is defined as the maximum value of h such that the given Journal has published at least h papers and each has at least h citations.

Scientific Reports ISSN

The International Standard Serial Number (ISSN) of Scientific Reports is/are as follows: 20452322 .

The ISSN is a unique 8-digit identifier for a specific publication like Magazine or Journal. The ISSN is used in the postal system and in the publishing world to identify the articles that are published in journals, magazines, newsletters, etc. This is the number assigned to your article by the publisher, and it is the one you will use to reference your article within the library catalogues.

ISSN code (also called as "ISSN structure" or "ISSN syntax") can be expressed as follows: NNNN-NNNC Here, N is in the set {0,1,2,3...,9}, a digit character, and C is in {0,1,2,3,...,9,X}

Table Setting

Scientific Reports Ranking and SCImago Journal Rank (SJR)

SCImago Journal Rank is an indicator, which measures the scientific influence of journals. It considers the number of citations received by a journal and the importance of the journals from where these citations come.

Scientific Reports Publisher

The publisher of Scientific Reports is Nature Publishing Group . The publishing house of this journal is located in the United Kingdom . Its coverage history is as follows: 2011-2022 .

Call For Papers (CFPs)

Please check the official website of this journal to find out the complete details and Call For Papers (CFPs).

Publication Fees

The highest fee charged by this journal is  1990 USD  as publication fees (article processing charges or APCs).

There is a waiver policy for these charges.

Review Speed

Expect on an average  20 weeks  from submission to publication in Scientific Reports Journal.

Abbreviation

The International Organization for Standardization 4 (ISO 4) abbreviation of Scientific Reports is Sci. Rep. . ISO 4 is an international standard which defines a uniform and consistent system for the abbreviation of serial publication titles, which are published regularly. The primary use of ISO 4 is to abbreviate or shorten the names of scientific journals using the technique of List of Title Word Abbreviations (LTWA).

As ISO 4 is an international standard, the abbreviation ('Sci. Rep.') can be used for citing, indexing, abstraction, and referencing purposes.

How to publish in Scientific Reports

If your area of research or discipline is related to Multidisciplinary, etc. , please check the journal's official website to understand the complete publication process.

Acceptance Rate

The journal has been described as a megajournal, conceptually similar to PLOS ONE, with a business model based on article processing charges.The journal's editorial board is extremely large, with several thousand listed members. The Guide to Referees states that to be published, "a paper must be scientifically valid and technically sound in methodology and analysis", and reviewers have to ensure manuscripts "are not assessed based on their perceived importance, significance or impact", but this procedure has been questioned. The acceptance rate for Scientific Reports was reported to be 48%, based on the published rate by the journal in 2019.
  • Interest/demand of researchers/scientists for publishing in a specific journal/conference.
  • The complexity of the peer review process and timeline.
  • Time taken from draft submission to final publication.
  • Number of submissions received and acceptance slots
  • And Many More.

The simplest way to find out the acceptance rate or rejection rate of a Journal/Conference is to check with the journal's/conference's editorial team through emails or through the official website.

Frequently Asked Questions (FAQ)

What is the impact score of scientific reports.

The latest impact score of Scientific Reports is 4.44. It is computed in the year 2023.

What is the h-index of Scientific Reports?

The latest h-index of Scientific Reports is 282. It is evaluated in the year 2023.

What is the SCImago Journal Rank (SJR) of Scientific Reports?

The latest SCImago Journal Rank (SJR) of Scientific Reports is 0.973. It is calculated in the year 2023.

What is the ranking of Scientific Reports?

The latest ranking of Scientific Reports is 4401. This ranking is among 27955 Journals, Conferences, and Book Series. It is computed in the year 2023.

Who is the publisher of Scientific Reports?

Scientific Reports is published by Nature Publishing Group. The publication country of this journal is United Kingdom.

What is the abbreviation of Scientific Reports?

This standard abbreviation of Scientific Reports is Sci. Rep..

Is "Scientific Reports" a Journal, Conference or Book Series?

Scientific Reports is a journal published by Nature Publishing Group.

What is the scope of Scientific Reports?

  • Multidisciplinary

For detailed scope of Scientific Reports, check the official website of this journal.

What is the ISSN of Scientific Reports?

The International Standard Serial Number (ISSN) of Scientific Reports is/are as follows: 20452322.

What is the best quartile for Scientific Reports?

The best quartile for Scientific Reports is Q1.

What is the coverage history of Scientific Reports?

The coverage history of Scientific Reports is as follows 2011-2022.

Credits and Sources

  • Scientific Reports Official Website
  • Scientific Reports Wikipedia
  • Scimago Journal & Country Rank (SJR), https://www.scimagojr.com/
  • Journal Impact Factor, https://clarivate.com/
  • Issn.org, https://www.issn.org/
  • Scopus, https://www.scopus.com/
Note: The impact score shown here is equivalent to the average number of times documents published in a journal/conference in the past two years have been cited in the current year (i.e., Cites / Doc. (2 years)). It is based on Scopus data and can be a little higher or different compared to the impact factor (IF) produced by Journal Citation Report. Please refer to the Web of Science data source to check the exact journal impact factor ™ (Thomson Reuters) metric.

Impact Score, SJR, h-Index, and Other Important metrics of These Journals, Conferences, and Book Series

Check complete list

Scientific Reports Impact Score (IS) Trend

Top journals/conferences in multidisciplinary.

Predatory Reports

High impact factor journals and predatory journals list 2023.

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  • Ranking Factors

Last Year’s Google Ranking Factors Changes, Explained

Get a better understanding of Google's ranking systems with this excerpt from SEJ's Ranking Factors 2023 ebook. Look at the impact of query intent on ranking.

This is an excerpt from SEJ’s Ranking Factors 2023 ebook with changes and updates to bring it up to date. SEO changes quickly!

Ranking factors are getting more difficult to fully categorize.

Today, Google uses the terms “systems” and “signals” more than “ ranking factors .”

Google says, about how it ranks results :

“Google uses automated ranking systems that look at many factors and signals about hundreds of billions of web pages and other content in our Search index to present the most relevant, useful results, all in a fraction of a second.”

There are multiple ranking systems , and they all make use of different combinations of signals.

Google is (and has been for some time) shifting away from a model where a collection of quantitative factors determines ranking.

Instead, Google is building collections of qualitative signals that come together to approximate bigger – human – questions and decisions, such as:

  • What does authority look like , and how does the concept apply to this query?
  • How does the intent behind a query impact the effectiveness of different answers?
  • How directly helpful is this content , and how likely will a user be satisfied after reading it?
  • How do users experience this page ? Is the experience good or bad?

Many SEO professionals are numbers people. Researchers. Data divers. Google releases a little bit of information about its algorithms, and we cling like limpets.

For many years, some have even attempted to use clues from interpreting patents to try and decipher the algorithmic impact of everything from social media to co-citation.

But Google patents aren’t the Constitution.

No ultimate document holds the secrets to the ranking algorithms – though I’d love to see a heist movie about stealing it from Google HQ. (We all know Nicholas Cage would take part.)

Interpreting patents is a good skill and can provide important insights.

But you should weigh the business impact of obsessing over individual elements against leaning into understanding your audience.

As algorithms get more complex and AI becomes more advanced, it’s only going to become more difficult to pinpoint the exact sources of data they use to make decisions.

Ranking factors aren’t going away ; they’re evolving.

The cornerstones of ranking will always be there, but the more complexity gets added to the systems, the less it benefits us to interrogate every potential signal.

What The Heck Happened With “Page Experience” & What’s A Ranking System?

In April 2023, Google moved several entries from its “ranking systems” documentation and placed them elsewhere:

  • Page experience.
  • Mobile-friendliness.
  • Page speed.
  • Security and HTTPs.

Several SEO pros lost their collective cool over this change.

Google’s Search Liaison account on X (formerly Twitter) shared this statement :

“Our guidance on page experience is here, as we shared last week along with our blog post: https://developers.google.com/search/docs/appearance/page-experience It does *not* say page experience is somehow ‘retired’ or that people should ignore things like Core Web Vitals or being mobile-friendly. The opposite. It says if you want to be successful with the core ranking systems of Google Search, consider these and other aspects of page experience. We also made an update to our page on ranking systems last week. Ranking *systems* are different than ranking *signals* (systems typically make use of signals). We had some things listed on that page relating to page experience as “systems” that were actually signals. They shouldn’t have been on the page about systems. Taking them off didn’t mean we no longer consider aspects of page experience. It just meant these weren’t ranking *systems* but instead signals used by other systems. … The big takeaway? As our guidance on page experience says in the first sentence: ‘Google’s core ranking systems look to reward content that provides a good page experience.’ … ”

This seems to mean that the changes were a matter of organization and not any functional algorithm adjustment.

A ranking system is a broad application of signals that go toward a specific goal or evaluation.

Ranking systems can use ranking signals, but not necessarily all the time or for every query.

“Page experience” is not a ranking system.

However, it is a collection of ranking signals that multiple ranking systems can and do use to evaluate and reward pages with good user experience.

Click Data – The Antitrust Lawsuit & CTR As A Ranking Factor

A software engineer who left Google in November 2022 was called to give testimony during the antitrust suit against Google.

I started seeing chatter all over social media about his smoking gun statement on click data in ranking.

His testimony called attention to the probability that Google uses clicks and other data about interactions on SERPs in ranking algorithms and that Google is evasive about this fact to prevent SEO professionals from influencing the rankings.

This data may not be used for much longer, as Law360 reported: The former Googler’s testimony said the ‘situation is changing rapidly,’ and that Google now has systems that can be trained just as well without user data.

“Great,” I said to myself, “How many conclusions do I need to reassess?”

Thankfully, none so far. My first thought was CTR, but we’re still dubious about CTR as a ranking factor, even with the new information.

There’s a difference between live ranking signals and data used for analysis.

Ex-Google Search Quality team member Pedro Dias has a great take on this, saying in a LinkedIn post,

“There’s a difference between: directly using a signal in rankings; looking at the data and assess which parts could be useful for rankings” Screenshot from LinkedIn, October 2023

Using data to analyze results and train algorithms is much, much different from using it live in result delivery. These signals are more likely used for training and evaluation purposes than live results ordering.

Instead of focusing on click metrics just as a direct ranking signal, consider them as a measure of how your user interacts with your page –  because that is what matters. So either way, it can be considered important.

If you’re focusing on what matters – content, authority, user experience – then whether CTR and other user behavior is a ranking factor shouldn’t change your overall strategy.

You don’t have control over click data; you can only use it for measurement.

While there is increasing reason to believe that “click data” is used in search as a feedback mechanism, it’s not helpful for you to focus on it as a needle to move. Use it the way Google does: as an assessment tool.

User Signals In Search

The more we find out and with each new event, the more open to speculation the issue of user data seems to become.

  • Google still claims not to consider user behavior in ranking.
  • What are the implications of Google cutting ties with Appen , one of its major contractors, in training AI and search results?
  • What the heck is going on with search results quality right now?

When it comes to Appen, I can see arguments in both directions. It could be that Google plans to rely on automated algorithms and aggregate user data instead of human quality ratings.

Or this could simply speak to a cost-cutting decision in the midst of layoffs and unfavorable legal judgments .

As for the declining quality of search results, in my opinion, that’s an argument against the idea that user behavior data is a ranking factor.

People are unsatisfied with search results  and in quite large numbers.

This being the case, an algorithm that accounts for user behavior  should see this and adjust, right? This presents four alternative situations in my mind:

  • The algorithms are, to use a technical term, completely borked.
  • User behavior and click data are not direct ranking signals.
  • Both of the above.
  • The fourth situation requires reading into a recent Google announcement about the upcoming Gemini AI model and speculating about its meaning. At the end of this post, we find this:

“We’re already starting to experiment with Gemini in Search, where it’s making our Search Generative Experience (SGE) faster for users, with a 40% reduction in latency in English in the U.S., alongside improvements in quality.”

There are two things going on here:

  • “We’re already starting to experiment with Gemini in Search …”
  • “… making our Search Generative Experience (SGE) faster …”

Gemini is at least in Labs. Are some elements of it in live Search too?

Will a Gemini release herald an SGE release?

This is happening fast. Google could well have decided that the current algorithms aren’t capable of solving the current issues, and are, instead, moving ahead as quickly as possible with Gemini. This could change what we know about ranking signals and systems.

Will Google Use Click / Behavior Data As Ranking Signals In The Future?

There is still an argument supporting the fact that Google uses, or at least would like to use, behavioral data to rank content.

In fact, it’s objectively true that it already does this in YouTube search .

Engagement is one of the three pillars of YouTube search. On YouTube, user engagement signals, in aggregate, directly impact a video’s ranking on the platform.

In explaining how the YouTube search algorithm works, the documentation says:

“At YouTube Search, we prioritize three main elements to provide the best search results: relevance, engagement and quality. These three elements are given differing importance based on the type of search. To estimate relevance we look into many factors, such as how well the title, tags, description, and video content match your search query. Engagement signals are a valuable way to determine relevance. We incorporate aggregate engagement signals from users, i.e. we may look at the watch time of a particular video for a particular query to determine if the video is considered relevant to the query by other users. Finally, for quality, our systems are designed to identify signals that can help determine which channels demonstrate expertise, authoritativeness, and trustworthiness on a given topic.”

In its documentation for creators about how to grow a channel , YouTube says this:

“Insider tip: Our algorithm doesn’t pay attention to videos, it pays attention to viewers. So, rather than trying to make videos that’ll make an algorithm happy, focus on making videos that make your viewers happy.”

This is a pretty good indication that Google would absolutely use behavior and click signals in search  if it could do so reliably .

Therein lies the problem. On YouTube, all the data it needs is right there, contained inside the platform.

This isn’t the case for Google Search because not all websites use Google Analytics, and not all users use Chrome.

In addition, it’s much easier to interpret positive and negative engagement behaviors with videos than it is text.

I believe these two things to be true:

  • Google knows that direct user feedback is the best way to determine whether content is “good” and would implement this into live results ordering in Search if it could.
  • Currently, and previously, this was not achievable algorithmically.

Who knows, maybe further development of AI will present new solutions.

This is a very roundabout way of saying:

User behavior data is probably used in search to fine-tune and evaluate results, but probably not to make in-the-moment delivery decisions. Even if it was used this way, it shouldn’t matter to you all that much because you can only control engagement by making better content, which should be your goal anyway.

The more interesting question right now is how the heck do we, as SEO professionals, advise people to stand by content best practices while the search results seem to reward spam?

Still working on that one.

More resources:

  • History of Google Algorithm Updates
  • How Search Engines Work
  • Ranking Factors 2023: Systems, Signals, and Page Experience

Featured Image: Paulo Bobita/Search Engine Journal

SEO content writer and editor for 6+ years. Formerly a live theater professional. Bumbling through fatherhood. I’m fascinated by how ...

Google Ranking Factors 2023: Systems, Signals, and Page Experience

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  • Published: 17 February 2024

Improving the risk management process in quality management systems of higher education

  • Oleg Bazaluk 1 ,
  • Artem Pavlychenko 2 ,
  • Olena Yavorska 3 ,
  • Olha Nesterova 4 ,
  • Vitaliy Tsopa 5 ,
  • Serhii Cheberiachko 3 ,
  • Oleg Deryugin 6 &
  • Vasyl Lozynskyi 1 , 7  

Scientific Reports volume  14 , Article number:  3977 ( 2024 ) Cite this article

Metrics details

  • Human behaviour
  • Risk factors

The purpose of this paper is to improve the risk management process in the quality management system of higher education, taking into account the hazardous factors that increase the probability of occurrence and severity of consequences of undesirable events, as well as favorable factors that reduce the probability of occurrence and severity of consequences of hazardous events. The basis of risk management in the quality management systems of higher education institutions is the “Bowtie” method, which involves six main steps of identifying inconsistency, determining the impact of hazardous and favorable factors according to the impact group, ranking hazardous and favorable factors, calculating risk, substantiating precautionary measures and checking calculations. To rank hazardous and favorable factors, the authors used the “Decision Making Trial and Evaluation” method (hereinafter—DE-MATEL), which is based on paired comparison and decision-making tools based on graph theory. An improved process is proposed for risk assessment, which differs from the known ones by the presence of the identification of the cause-and-effect relationship “hazard (inconsistency)-hazardous event-consequences”, identification of hazardous and favorable factors of the internal and external environment that affect the probability and/or the degree of severity of a hazardous event—the appearance of an inconsistency, which is carried out after the inconsistency has been determined; determination of causal hazardous and favorable factors by an acceptable method. Registers of inconsistencies (hazards), hazardous and favorable factors have been developed and proposed based on the requirements for accreditation of educational programs and the international standard ISO 9001:2015, which will allow, based on a risk-oriented approach, to provide a basis for setting the goals of a higher education institution under martial law in order to guarantee effective implementation of the mission and strategy. They are proposed for decision-making in the quality management systems of educational organizations on the substantiation of precautionary or corrective measures based on ranking the risks from identified inconsistencies, which are determined taking into account the impact of the entire set of identified significant hazardous and favorable factors. The value of this paper is to improve the quality risk management process in educational organizations, taking into account the impact of hazardous and favorable factors, and to develop an appropriate step-by-step algorithm of actions and a risk assessment form.

Introduction

Higher education has entered the era of digitization, in which the central concept is quality 1 , which is a rather complex category, as it determines the standard of living of both an individual and society as a whole. Therefore, each educational institution plays a special role in shaping the future development of the country 2 . This requires the developers of educational policy to constantly raise the requirements for the quality of higher education by organizing the effective work of the accreditation agency, ranking universities and their financial support 3 , 4 . Thus, the accreditation process serves as a road map for creating and maintaining a culture of high-quality training of students, through the assessment of various external and internal factors, such as leadership, management, resources, teaching methods, evaluation, etc. 5 . In addition, accreditation helps higher education institutions to unify their vision through the mission, goals and strategic direction of development, which is at the basis of effective management of the system of students training. However, the low level of corporate culture, uneven distribution of workload, outdated teaching methods (there are quite significant dynamic changes in the world), insufficient attention to innovation and transformation are constant challenges, both for the heads of higher education institutions and for a teacher, in terms of ensuring quality of educational program, which requires a responsible attitude to the distribution of financial and human re-sources. On the other hand, modern trends show that ensuring the appropriate quality training of students requires the university to agree on the mission, strategic goals, policy, culture and key performance indicators 6 , 7 , 8 , 9 . As a result, some higher education institutions in order to improve efficiency are implementing an education quality management (QM) system based on international standards (Table 1 ), which focuses on meeting the requirements of stakeholders, managing resources, the teaching and learning process, as well as supporting all areas of university life affecting the quality of providing educational services.

The basis of the mentioned standards is a risk-oriented approach requiring higher education institutions to plan and perform certain actions regarding the consideration of risks and opportunities, which is the “foundation” of the effectiveness of the quality management system, the achievement of improved results and the prevention of negative impacts 10 , 11 . Hence, there is a need to develop a clear, efficient and effective risk management process, which is an urgent task due to the multifaceted nature of the problems faced by universities and a significant number of approaches that can be applied to solve them. It should be noted, based on a number of scientific studies 12 , 13 , that the implementation of quality management system in a higher education institution based on 9001:2015 requirements leads to several unresolved issues: for example, there is no clear methodology of risk management specifically in educational organizations with the specificity of training applicants whose quality of skills cannot be simultaneously tested (for example, new knowledge are found, a different vision that does not correspond to existing scientific paradigms 14 ), or there are no recommendations regarding the level of organizational culture at which the requirements of the specified standard will be effective and will be implemented.

Analysis of scientific research over the past years has shown an increase in interest in ensuring the sustainability of educational institutions in emergency situations, which is connected with the global spread of the pandemic and the need to transfer education to a distance format 15 . The largest number of studies is devoted to the identification of risks during the provision and organization of distance learning. In particular, the authors 16 paid attention to the stability of communication, the adverse effect of technological zones near computers, the lack of physical contact between teachers and students, and the overloading of students with online tasks.

At the same time, in the analysed work, the conclusions drawn were based on an online questionnaire of the participants of the educational process, which contained a limited number of hazardous factors (hereinafter—HF), which does not allow us to talk about a comprehensive assessment of the problem. In other studies, attention was focused on the study of negative consequences when using information technologies in the management system of an educational institution 17 . The authors, using their own eight-step algorithm, analysed problem areas of information systems and developed recommendations for reducing the negative consequences of various failures. However, the paper considered only the area of management of the educational institution, while the educational process itself and its provision was omitted.

We note the fact that in the domestic space, a fairly significant number of publications are devoted to the risks and challenges of reforming both secondary and higher education, taking into account the need for digitalization of the educational process 17 , 18 , 19 . For example, in the papers 20 , 21 , the authors analysed the risk factors and identified the biggest of them: low-quality Internet and lack of information and communication tools for learning, which is the biggest threat to distance learning. Another paper 22 systematized and singled out the groups of external and internal risks of educational activity, comprehensively revealed the system of external risks and proposed possible mechanisms for responding to them, outlined the range of losses (economic, social, political and pedagogical) that are the result of the occurrence of risky events. And also the main conclusion drawn from the publication is the need for the effective implementation of educational activities in the field of higher education, for higher education institutions to timely identify and identify risks, which will help reduce the level of uncertainty in the activities of educational institutions, to determine the direction of movement on which it is necessary to focus managerial, personnel, methodological, research, financial, technological and organizational resources. The authors of the article 23 revealed the need to apply a risk-oriented approach to the implementation of the quality management system of higher education institutions, and also systematized groups of internal risks in the course of operational processes in the activities of higher education institutions and proposed possible mechanisms for responding to them.

The conducted analysis shows that it is possible to apply a risk-oriented approach to the management processes of educational institutions only from the position of making managerial decisions to achieve quality goals, when the manager determines opportunities and associated risks 24 . However, there are issues related to the limitation of resources, which requires the elimination of existing deviations or errors in risk assessments that will lead to unwanted financial losses. Moreover, in the processes themselves, risk is proposed to be determined in relation to potential events that are influenced by groups of different factors or their combination 25 , and therefore, in order for organizations to carry out a qualitative or quantitative assessment of risk and make appropriate management decisions, it is necessary to develop an appropriate management process risks. The authors of the publication 26 suggest using the improved AHP methodology to assess the risks and opportunities of the quality development of higher education from the point of view of ISO45001:2018 as the case study of higher education institutions in China.

Thus, the conducted research analysis shows that there are several directions of quality assurance in higher education institutions: curricula, criteria and rules for assessing students, quality of teachers and educational process, quality of material-technical and information resources, as well as collection, analysis and use of information 27 . At the same time, ensuring the quality provision of educational services involves systematically identifying and managing the various processes encountered in educational organizations 28 . This is mainly done by applying a management system based on the PDCA cycle with a general focus on risk-oriented thinking, the key element of which is the implementation of necessary actions to achieve the planned measures and results based on risk management, requiring the effective process development.

The aim of this paper is to improve the risk management process in the quality management system of higher education in accordance with the requirements of 9001:2015, ISO 21001:2018 and other standards, taking into account HFs, which increase the probability of occurrence and severity of consequences, as well as FFs, which reduce the probability of occurrence and severity of consequences from hazardous events.

Materials and methods

The most common approach to risk management in the quality systems of any organization, which is the basis for developing an action plan to achieve the desired result and identifying the resources required for the effective and efficient functioning of each process, is the “Bowtie” method 29 .

This is the most common model, the popularity of which is attributed to the convenience and simplicity of presenting the cause-and-effect relationship between hazard, hazardous event and consequences. The visualisation of this model helps to clearly demonstrate the risk management process by determining the number of barriers (protective or precautionary measures) that are placed on the path between a hazard and a hazardous event, as well as between a hazardous event and its consequences. The number of barriers makes it possible, on the one hand, to estimate protective or precautionary measures for occupational safety, and on the other hand, to influence the probability of the hazardous event occurring. The latter is determined by the “as low as reasonably practicable” (ALARP) principle, according to which the residual risk level should be reduced as much as possible 30 .

It makes it possible, in general, to understand the reasons for the occurrence of a hazardous event from the presence of certain inconsistencies in any process or threats (challenges) to the organization and to assess the consequences of their occurrence, which is the basis of risk calculation. This method also allows you to justify the financial costs of the risk management process based on the analysis of the efficiency of the created protective barriers that are placed on the path from inconsistency to a hazardous event, as well as between a hazardous event and its consequences (Fig.  1 ).

figure 1

Risk management procedure in educational quality management systems based on the “Bowtie” model.

This approach is characterized by the ability to take into account the influence on a change (increase or decrease) in the probability of occurrence and the severity of the consequences of a hazardous event of various external and internal hazardous and favorable factors (Fig.  2 ), which are proposed to be divided into seven groups: human, technical, organizational, economic, social and pedagogical, martial law (Fig.  3 ). In this case, hazardous factors (HF) influence an increase in the probability of occurrence and/or severity of consequences of a hazardous event and, as a result, an increase in risk, which is a loss of effectiveness, especially in quality management systems. Favorable factors (FF) have an impact on reducing the probability of occurrence and/or severity of consequences of a hazardous event and, ultimately, on reducing the risk together with protective or precautionary actions (measures), which is the improvement of effectiveness, especially in the quality of providing the educational process.

figure 2

Hazardous and favorable factors that lead to an increase in the probability of a hazardous event occurring.

figure 3

An improved risk management process consisting of six main steps is proposed for risk assessment (Fig.  4 ). The main difference between the proposed process and the known ones 27 , 31 , 32 , 33 is identification of the cause-and-effect relationship “hazard (inconsistency)-hazardous event-consequences”, identification of hazardous and favorable factors of the internal and external environment, which affect the probability and/or degree the severity of the hazardous event—the appearance of an inconsistency, which is carried out after the inconsistency has been determined; determination of causal (ignoring consequential) HFs and FFs by an acceptable method (for example, DEMATEL or ANP).

figure 4

Risk management process.

The first step is devoted to determining the cause-and-effect relationship between “hazard (inconsistency)-hazardous event-consequences” in the quality system of higher education, which can lead to the occurrence of a hazardous event under certain conditions, for example, a low level of training of higher education students. The result of inconsistencies can be seen as the loss of the image of the university, the number of applicants, financial support, personnel potential, and others (Table 2 ). This step requires the creation of an appropriate catalogue of hazards (inconsistencies), which are violations of the requirements of legislation in the field of education (higher education standards). The basis of its creation is a survey of interested parties, who are invited to provide an answer that affects the process of quality training of students. For example, the Table 3 provides the list of inconsistencies (threats) compiled on the basis of the requirements for accreditation of educational programs.

In the second step, we determine the HFs and FFs, from which we also form the corresponding register (Tables 4 , 5 ). In the given example, HF hazards (inconsistencies) include: non-fulfilment of the requirements for accreditation of educational programs, non-fulfilment of the requirements for the evacuation of applicants to a bomb shelter during an air raid alert, non-fulfilment of the requirements for traineeships of students at production enterprises—low competence that affect the probability of occurrence and degree of severity losses from this hazard The list of HFs and FFs is determined based on the development of various studies on the problems of management of higher education institutions, surveys of experts in this field, interviews with the institution management and staff. Please note that the list of HFs and FFs in each institution of higher education may differ, based on the specific circumstances that have developed in it. In addition, such a determination of HFs and FFs must be made for each hazard (inconsistency).

In order to identify the impact of HFs and FFs on the probability and severity of consequences of a hazardous event occurring due to one or another inconsistency, a process of surveying of all interested parties (teachers, students, employers, management of a higher education institution, technical workers, etc.) is carried out. They are offered to choose one or another HF and FF in an online questionnaire (Fig.  5 ).

figure 5

Fragment of a form for determining the influence of hazardous and favorable factors that increase the probability of a hazardous event occurring from a specific hazard (inconsistency).

The above excerpts are from a survey conducted at Dnipro University of Technology, located in the city of Dnipro, Ukraine, in the period from February to June 2022. The survey involved 112 people (43 men and 69 women) aged between 18 and 43. The majority of participants (70%) are university lecturers, 22% are applicants and 8 are employees of various departments. In this case, the average work experience of teachers is 12.4 years. Their scientific field of activity is related to mining (33.2%), construction (26.8%), service sector (31.4%), economics (4.2%), ecology (14.6%).

The result of such a procedure is the formation of a register (Table 6 ) of the most influential HFs and FFs on the determined inconsistency, which are then ranked to identify the most important ones. This procedure will be conducted by several recognized experts representing the management of the institution, teachers and at least 5 applicants.

In the third step, we study the interaction of HFs and FFs with the identified hazards (inconsistencies) and use the “Decision Making Trial and Evaluation” (DEMATEL) method, which is based on paired comparison and decision-making tools of graph theory 34 , 35 , which will allow us to transform cause-and-effect relationship in structural-visual models and identify and understand the interdependencies between different HFs and FFs that will lead to negative consequences. Relationships between influencing factors are made on the basis of paired comparison.

This case study involved five experts certified as internal or external auditors of quality management systems in accordance with the requirements of the ISO 9001:2015 international standard and the ISO 19011:2018 provisions; work experience in an educational organization is for at least 5 years; degree of education—the level is not lower than Doctor of Philosophy; they also have relevant documents certifying their training and knowledge of standards: standards and recommendations of the European Higher Education Area ESG-2015; ISO 21001:2018 ”Educational organizations—Management systems for educational organizations—Requirements with guidance for use”; 31000:2018 “Risk management. Principles and guidance”, and knowledge of the expert activity specifics; criteria and indicators of the effectiveness of an expert on the quality of education in EU countries . They are experienced in conducting expert examinations and audits.

To determine the degree of impact between various negative (positive) factors, criteria with a five-level scale are used, consisting of verbal expressions and corresponding fuzzy numbers (Table 7 ).

The n  ×  n pair comparison matrix is formed according to experts using the paired comparison method, which is a natural way and is consistent with human intuition and general way of thinking 39 . In this case, a data consistency check is applied using the following formula:

where m is criteria number, k is number of experts, d is the average value of the direct influence of criterion i on j. If the average consensus gap coefficient value is less than 0.05, we consider the expert’s assessment as stable 40 .

Variable H stands for number of experts; n is the number of considered criteria. The comparison between two factors i and j by expert k is shown as \(b_{ij}^{k}\) . According to the fuzzy theory in Table 7 , the value of “zero impact” is 0, “very low impact” is 1, “low impact” is 2, “high impact” is 3, and “very high impact” is 4. As a result, with an independent assessment of each expert, the response matrix is formed by Eq. ( 1 ). Given that each component does not affect itself, the diameter components of each response matrix \(B_{{}}^{(k)}\) are equal to zero:

The average matrix \(A = \left[ {a_{ij}^{{}} } \right]_{n \times n}\) has a direct impact on the matrix and the average value of expert ratings, which can be calculated according to Eq. ( 2 ). This matrix represents the direct impact of each criterion on the other criteria:

Matrix D can be calculated using Eqs. ( 3 ) and ( 4 ):

The matrix T c can be calculated according to Eq. ( 5 ).

In this equation, I is the identity matrix. Each element of the matrix T c can be formed with respect to a fuzzy number \(\overline{\tau }_{ij} = \left( {l_{ij}^{t} ,m_{ij}^{t} ,u_{ij}^{t} } \right)\) . Given Eqs. ( 5 ), ( 6 ), ( 7 ) and ( 8 ).

D 1 , D m , D u are all matrices n  ×  n .

To determine the effect and relevance of the criteria, the matrix T c must be fuzzy first. Equation ( 9 ) will be used so that there are no fuzzy T c matrices.

The action and relevance of criteria are determined using r and c values. The values of Eqs. ( 10 ) and ( 11 ) can be calculated:

r is the sum of row i , and c is the sum of column j of the matrix T c . r shows the total effect of direct and indirect influence of i on other criteria, and c shows direct and indirect total influence of factor j on other factors.

As a result, r  +  c indicates the importance of criterion i in the system. r  −  c shows the effect of criterion i in the system. If r  −  c is positive, the action of criterion i belongs to the group of causes, and if r  −  c is negative, the effect of the criterion, which belongs to the group of “dependents”.

The next step of the procedure is a risk analysis, which involves determining the probability and degree of severity of a hazardous event from the action of the causative HF. For this purpose, appropriate scales have been determined based on the recommendations of the ISO 73:2018 standard, which are summarised in Tables 8 and 9 .

Next, the risk level is calculated for each HF and FF as the product of the probability of a hazardous event ( B ) and the severity of the consequences ( T ) from all predetermined most significant HFs and FFs:

The risk from hazardous factors is determined by the formula:

The risk from favorable factors is determined by the formula:

The total risk from exposure to HF and FF is determined from:

where P + i ; P − i are the risk from the i-th hazardous/favorable factor, respectively B + i ; B − i are the probability of occurrence/non-occurrence of a hazardous event from the i-th hazardous/favorable factor; T + i , T − i are the severity of consequences of the occurrence/non-occurrence of a hazardous event from the i-th hazardous/favorable factor; P i is the total risk from exposure to hazardous/favorable factor.

The risk assessment form in the quality system is given in Table 10 .

After determining the level of risk (Table 11 ), solutions are proposed for preventive actions to reduce it. According to the results of determining the risk level, it can be assigned to one of the risk groups:

I, it is necessary to stop the activity of determining and implementing preventive and protective measures to reduce the risk to an acceptable level;

II, it is necessary to define and implement preventive and protective measures to reduce the risk to an acceptable level in case of partial cessation of activity;

III, no risk reduction measures are required, but hazard control is required;

IV, no risk reduction measures are required and no hazard controls are required.

Very often, the protective measures taken reduce the probability of the risk, but do not eliminate the hazard. In these cases, the probability of risk decreases, but its severity remains unchanged. It is also necessary to consider preventive actions aimed at reducing the severity of the consequences. If the level of risk is totally unacceptable and unacceptable, we understand that it is forbidden to carry out work without changing the conditions and without developing and implementing measures to reduce risks. First of all, preventive and protective measures should be implemented to prevent the realization of the hazard in the HE and/or to reduce the consequences of the HE. Control over the prohibition of works is established. It should be noted that the risk acceptance criteria given in Table 11 are notional and need to be assessed in each organization that tries to implement the described approach, as “they are based on the internal and external context and objectives of the organization, and they may arise from laws, policies, standards and other demands 41 . The most common and flexible framework used for risk criteria divides risks into the three groups mentioned above: “unacceptable area”, “ALARP area” and “acceptable area” 42 .

Take, for example, an educational training program for second-level Master students, where only HF is considered as an example. But the same approach is applied to FF. The list of its inconsistencies is formed based on the analysis of the results of the internal audit, in accordance with the requirements of ISO 9001:2015 or external accreditation of the educational program by experts of the National Agency for Quality Assurance of Higher Education. The result of the work is a catalogue of inconsistencies (Table 12 ).

The next step is to introduce a procedure for surveying all interested parties to identify the most influential HFs that increase the probability of occurrence and the severity of the consequences of a hazardous event. For example, for the specified educational program, taking into account its implementation in the conditions of martial law, the experts chose from the list given in Table 3 twelve HFs, which are shown in Table 13 .

Next, they are ranked using the DEMATEL method, where experts make paired comparison of the identified HFs, filling in the corresponding table (Fig.  6 ) in the Excel program. The dimensions of the matrix are determined by the number of HF (A12), which affect the appearance of a specific hazardous situation. Experts compile a matrix based on a subjective assessment of the greatest interaction with other indicators. In this case, the judgment is usually based on the interdependence of HF and inconsistencies. It is estimated that all HFs from each group will lead to a hazardous event and, in general, will harm the educational process. In addition, when compiling the matrix, experts are invited to take into account the possibility of controlling the manifestation of HF.

figure 6

An example of a paired comparison matrix filled in by one of the experts.

Further, the obtained results are processed using the appropriate mathematical apparatus, which is also performed in the Excel program, which allows obtaining HF prioritization (Table 14 ).

As a result, on the basis of a detailed analysis of the impact of HF, we obtain their prioritization, based on establishing the degree of importance and the level of impact on the occurrence of a specific hazardous event. Thus, in the given example, considering the inconsistency of H 14 “Teachers do not have appropriate qualifications”, it was established that the greatest influence is exerted by HF under the number A 40 , A 43 , A 41 , A 44 , A 27 , A 1 . At the same time, a negative sign in the impact level column indicates that they will have a greater impact on the magnitude of the consequences of the occurrence of a hazardous event. In a similar way, the FF analysis is conducted.

The constructed prioritization of HF compatible with their ranking allows obtaining a map of connections between HF dimensions (Fig.  7 ) that visually shows their interdependence.

figure 7

An example of a map of the connection between HF measurements.

Determining the causal HFs and, as a consequence, FFs obtained during a similar procedure using the DAMTEL method allows moving to the step of calculating the risk from exposure to significant (causal) HFs (Table 15 ) and FFs (Table 16 ). As a result, we obtain risk values which, under certain conditions, that is, when hazardous and favorable factors coincide, can compensate each other, thereby reducing the risk level in quality management systems. The issue of interaction between various factors is not simple, as it requires careful verification and the experience of experts who will conduct the appropriate analysis. Of course, specific recommendations for compensating for negative factors with favorable ones can be made only in case of a thorough study of organizational culture, understanding of social-psychological phenomena occurring in the company: relationships, connections, beliefs, politics, activities and relationships of groups 43 .

Since the total negative risk j from all n of HFs is equal to 122 (see Table 15 ), and the total positive risk j from all n of FFs is equal to 27 (see Table 16 ), in this case, taking, as a rough approximation, a linear relationship between the impact of HFs and FFs, the total risk in the quality management system will be equal to:

Although the proposed approach is not perfect, it does provide an overall measure of risk based on an integrated process that reflects the constructive confrontation of opposing factors. As a result, a creative solution to reduce the risk level is generated in the form of, perhaps even a new idea, as it becomes possible to comprehensively analyze the cause-and-effect relationships between the inconsistency and specific causal hazardous and favorable factors. Moreover, the proposed approach makes it possible to determine the most influential factor and further work with it: in case of a negative impact—by determining precautionary or preventative actions, and in case of a favorable factor—ways to enhance its influence.

The basic requirement for applying this approach is the need to adhere to standard academic training protocols, such as compliance with all rules and assumptions made, a clear basis for all choices, etc. In addition, the educational environment should meet reliability and validity criteria. The reliability requirement here refers to the degree to which a risk assessment produces the same results when analyzed repeatedly, and the validity requirement refers to the degree to which a risk assessment describes the specific concepts that it attempts to describe. Having accepted these criteria, results (beliefs), the value of the calculated risk level can be considered to a certain extent as justified 44 .

In the described process of risk management, the most important component is the identification of inconsistencies (hazards) and HF, which involves a thorough investigation of the cause and sources of risk, as well as events and situations that can significantly affect the overall results in relation to the goals of the educational process.

It is expected that risks will be studied and predicted at each stage of the educational process, based on key performance indicators established in the higher education quality assurance system, which is also emphasized in the research paper 27 , 45 . Such an approach requires systematic effort on identifying inconsistencies by constructing a monitoring system that assesses risks at specified intervals and facilitates the revision of precautionary measures depending on the current situation. A similar approach is implemented in Ref. 32 , but the difference of the proposed one is the involvement of a wide range of interested parties, which is one of the most important conditions for the formation of a high-quality educational process 46 , 47 , 48 , 49 . It is through the elaboration of both external and internal inconsistencies, and most importantly, proposals (opportunities), there are real ways to substantiate relevant managerial decisions based on the financial capabilities of educational institutions 27 , 50 .

Taken together, identifying inconsistencies of hazardous factors, opportunities, creating a register of them that will be constantly updated, identifying significant threats (for example, due to a pandemic, military operations, economic challenges, natural disasters) 51 , 52 , constructing cause-and-effect relationships between a threat (including in relation to the educational environment, teaching methods, etc.)—a hazardous event (for example, loss of accreditation, low-quality educational services, etc.) and consequences, taking into account all additional components (significant hazardous factors, opportunities), is the foundation for conducting the justification of effective precautionary and protective measures 53 , 54 , and most importantly, readiness for changes and upcoming challenges 54 , 55 , 56 . It is the construction of such a system that will make it possible, in the future, based on the construction of even the simplest models 57 , 58 , to predict the development of events and ensure readiness to respond to challenges 59 , 60 . The more detailed the current state that has developed at a specific point in time in the quality system of a conditional university 61 , 62 is analyzed, the more opportunities there are for adopting appropriate ways of developing the educational environment, meeting the needs of interested parties, and most importantly, creating a safe environment, the potential of which will reveal the opportunities of participants in the educational process (both teachers in terms of ensuring academic freedom, and applicants, if possible, to reveal the relevant abilities that will be in demand in the labor market).

Despite the significant proposed approach publicity, it has significant advantages over the existing ones, as it allows a more thorough study of the occurrence of a hazardous event from the combined action of several factors. In addition, when compiling appropriate registers, experts analyze all the causes of the hazardous event occurring, assess the effectiveness of existing monitoring tools, which is the basis for savings through the redistribution of already existing resources 13 .

The strengths of the above approach include an integrated approach to the implementation of corrective actions, which is expressed in comprehensive monitoring and assessment of results, partnership among all interested parties, taking into account the impact of negative and favorable factors on the risk level. In addition, this allows analyzing the processes at the university in terms of the effectiveness of their functioning, which helps to create conditions for systematic educational process management, as opposed to intuitive control (based on tradition, trial and error).

Given that the risk management process is defined as “a planned and structured process aimed at improving the effectiveness of decision-making to ensure the appropriate activity of higher education institutions at a certain time 63 , there is a need to understand their consequences, which can be done through the construction of cause-and-effect relationships between challenges and possible hazardous events. In this case, to determine the “probability” of the hazardous event occurring, it is important to use a multivariate analysis 33 , 64 . Currently, there are powerful quantitative methods for assessing risks, such as Bayesian networks, but they are much more complex than the Bowtie method, and on the other hand, they require appropriate additional research to identify the relationships between various hazardous factors. We believe that Bayesian models are the next step in improving quality systems, since artificial intelligence can be used to perform the appropriate analysis.

Despite the considerable proposed approach is quite cumbersome, it has significant advantages over the existing ones, as it allows for a more thorough investigation of the occurrence of a hazardous event 65 from the combined action of several HFs. In addition, during the compilation of relevant registers, experts analyse all the reasons for the occurrence of a hazardous event, evaluate the effectiveness of existing control measures, which is the basis of savings due to the redistribution of already existing resources.

It should be noted that most of the relevant HFs are interdependent, which is also taken into account during the implementation of the given approach. The more relationships are established, the higher the score will be compared to other HFs.

In general, referring to the requirements of the international standard ISO 9001:2015, there is a need to introduce a policy of the quality management system of higher education under martial law, which is based on the culture and traditions of the higher education institution, a set of beliefs and values that determine the behavior of the educational institution, which is aimed at minimizing risks 66 .

The significance of the presented research lies in its practical use in educational institutions facing the choice of the optimal methodology for applying a risk-oriented approach in education quality management systems. The shortcomings of the study include the influence of subjectivity at the first stages of the risk management process, which is characteristic of expert methods, when forming from the relevant registers, determining the dependence between inconsistency and HF. However, in the future, the application of a mathematical approach to risk ranking will allow us to identify inappropriate relationships and return to the revised previous steps of HF identification, which are associated with a specific inconsistency.

Conclusions

The improvement of the risk management process in the system of higher education of students under martial law is based on the sequence of six main steps and differs from known processes by the presence of the identification of the cause-and-effect relationship “hazard (inconsistency)-hazardous event-consequences”, identification of HFs and FFs (hazards-inconsistencies) of the internal and external environment, which affect the probability and/or degree of severity of a hazardous event—the occurrences of inconsistency, which is carried out after determining the inconsistency; determination of causal (ignoring consequential) HFs and FFs by an acceptable method (for example, DEMATEL or ANP).

The developed and described registers of inconsistencies (hazards), as well as HFs and FFs, based on the requirements for accreditation of educational programs and the international standard ISO 9001:2015, etc., using a risk-based approach, provide the basis for transforming the goals of a higher education institution in conditions of martial law in order to guarantee the effective implementation of the mission and strategy of the relevant requirements.

It is proposed to determine the criteria for risk analysis, which involves establishing the probability and degree of severity of a hazardous event from the action of the causal HFs and FFs impact, to use the cumulative effect of the causal HFs, as well as FFs, which is calculated as the sum of their effects.

Flexible forms that can be used by institutions of higher education are offered for the initial assessment of negative risks of HF hazards, prioritization of HFs and FFs based on the degree of importance and level of impact, analysis of inconsistencies as a result of the examination of a conditional educational program, a questionnaire to establish the impact of HFs and FFs on a specific inconsistency without changes or after adaptation in accordance with the specifics of the relevant higher education institution.

Data availability

All data generated or analyzed during this study are included in this published article.

Abbreviations

Decision making trial and evaluation method

Quality management

Hazardous factors

Analytic hierarchy process

Analytic network process

Group of factors

Hazardous event

Very high impact

High impact

Very low impact

Zero impact

Occupational risks

Favorable factor

Hazardous factor

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Acknowledgements

The authors would like to thank the anonymous reviewers and editor for their valuable comments and recommendations concerning the improvement of the paper.

This study was carried out as part of the project “Belt and Road Initiative Center for Chinese-European studies (BRICCES)” and was funded by the Guangdong University of Petrochemical Technology.

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Conceptualization, A.P. and O.Y.; methodology, O.N., S.C. and O.B.; software, S.C. and O.D.; validation, O.Y., S.C. and O.D.; formal analysis, V.T., S.C. and O.D.; investigation, A.P., O.N. and S.C.; resources, O.Y. and O.D.; data curation, S.C. and V.L.; writing-original draft preparation, A.P., O.N. and S.C.; writing-review and editing, V.T., S.C., O.D., and V.L.; visualization, O.D. and V.L.; supervision, A.P.; project administration, O.B.; funding acquisition, O.B. and V.L. All authors have read and agreed to the published version of the manuscript.

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