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How to Recognize Empirical Journal Articles

Definition of an empirical study: An empirical research article reports the results of a study that uses data derived from actual observation or experimentation. Empirical research articles are examples of primary research.

Parts of a standard empirical research article: (articles will not necessary use the exact terms listed below.)

Abstract ... A paragraph length description of what the study includes.
Introduction ...Includes a statement of the hypotheses for the research and a review of other research on the topic.
Who are participants
Design of the study
What the participants did
What measures were used
Results ...Describes the outcomes of the measures of the study.
Discussion ...Contains the interpretations and implications of the study.
References ...Contains citation information on the material cited in the report. (also called bibliography or works cited)

Characteristics of an Empirical Article:

Empirical articles will include charts, graphs, or statistical analysis.
Empirical research articles are usually substantial, maybe from 8-30 pages long.
There is always a bibliography found at the end of the article.

Type of publications that publish empirical studies:

Empirical research articles are published in scholarly or academic journals
These journals are also called “peer-reviewed,” or “refereed” publications.

Examples of such publications include:

American Educational Research Journal
Computers & Education
Journal of Educational Psychology

Databases that contain empirical research: (selected list only)

List of other useful databases by subject area

This page is adapted from Eric Karkhoff's Sociology Research Guide: Identify Empirical Articles page (Cal State Fullerton Pollak Library).

Sample Empirical Articles

Roschelle, J., Feng, M., Murphy, R. F., & Mason, C. A. (2016). Online Mathematics Homework Increases Student Achievement. AERA Open . ( L INK TO ARTICLE )

Lester, J., Yamanaka, A., & Struthers, B. (2016). Gender microaggressions and learning environments: The role of physical space in teaching pedagogy and communication. Community College Journal of Research and Practice , 40(11), 909-926. ( LINK TO ARTICLE )

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Introduction to Empirical Research

Databases for finding empirical research, guided search, google scholar, examples of empirical research, sources and further reading.

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Introductory Video This video covers what empirical research is, what kinds of questions and methods empirical researchers use, and some tips for finding empirical research articles in your discipline.

Guided Search: Finding Empirical Research Articles This is a hands-on tutorial that will allow you to use your own search terms to find resources.

Study on radiation transfer in human skin for cosmetics
Long-Term Mobile Phone Use and the Risk of Vestibular Schwannoma: A Danish Nationwide Cohort Study
Emissions Impacts and Benefits of Plug-In Hybrid Electric Vehicles and Vehicle-to-Grid Services
Review of design considerations and technological challenges for successful development and deployment of plug-in hybrid electric vehicles
Endocrine disrupters and human health: could oestrogenic chemicals in body care cosmetics adversely affect breast cancer incidence in women?

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Penn State University Libraries

Empirical research in the social sciences and education.

What is Empirical Research and How to Read It
Finding Empirical Research in Library Databases
Designing Empirical Research
Ethics, Cultural Responsiveness, and Anti-Racism in Research
Citing, Writing, and Presenting Your Work

Contact the Librarian at your campus for more help!

Introduction: What is Empirical Research?

Empirical research is based on observed and measured phenomena and derives knowledge from actual experience rather than from theory or belief.

How do you know if a study is empirical? Read the subheadings within the article, book, or report and look for a description of the research "methodology." Ask yourself: Could I recreate this study and test these results?

Key characteristics to look for:

Specific research questions to be answered
Definition of the population, behavior, or phenomena being studied
Description of the process used to study this population or phenomena, including selection criteria, controls, and testing instruments (such as surveys)

Another hint: some scholarly journals use a specific layout, called the "IMRaD" format, to communicate empirical research findings. Such articles typically have 4 components:

Introduction : sometimes called "literature review" -- what is currently known about the topic -- usually includes a theoretical framework and/or discussion of previous studies
Methodology: sometimes called "research design" -- how to recreate the study -- usually describes the population, research process, and analytical tools used in the present study
Results : sometimes called "findings" -- what was learned through the study -- usually appears as statistical data or as substantial quotations from research participants
Discussion : sometimes called "conclusion" or "implications" -- why the study is important -- usually describes how the research results influence professional practices or future studies

Reading and Evaluating Scholarly Materials

Reading research can be a challenge. However, the tutorials and videos below can help. They explain what scholarly articles look like, how to read them, and how to evaluate them:

CRAAP Checklist A frequently-used checklist that helps you examine the currency, relevance, authority, accuracy, and purpose of an information source.
IF I APPLY A newer model of evaluating sources which encourages you to think about your own biases as a reader, as well as concerns about the item you are reading.
Credo Video: How to Read Scholarly Materials (4 min.)
Credo Tutorial: How to Read Scholarly Materials
Credo Tutorial: Evaluating Information
Credo Video: Evaluating Statistics (4 min.)
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Last Updated: Feb 18, 2024 8:33 PM
URL: https://guides.libraries.psu.edu/emp

University of La Verne
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Identify Empirical Research Articles

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The Literature Review:a few steps on conducting it Permission granted from Writing at the University of Toronto
Six steps for writing a literature review This blog, written by Tanya Golash-Bozal PhD an Associate Professor of Sociology at the University of California at Merced, offers a very nice and simple advice on how to write a literature review from the point of view of an experience professional.
The Writing Center, University of North Carolina at Chapel Hill Permission granted to use this guide.
Writing Center University of North Carolina
Literature Reviews Otago Polytechnic in New Zealand produced this guide and in my opinion, it is one of the best. NOTE: Except where otherwise noted, content on this site is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. However, all images and Otago Polytechnic videos are copyrighted.

What Are Empirical Articles?

As a student at the University of La Verne, faculty may instruct you to read and analyze empirical articles when writing a research paper, a senior or master's project, or a doctoral dissertation. How can you recognize an empirical article in an academic discipline? An empirical research article is an article which reports research based on actual observations or experiments. The research may use quantitative research methods, which generate numerical data and seek to establish causal relationships between two or more variables.(1) Empirical research articles may use qualitative research methods, which objectively and critically analyze behaviors, beliefs, feelings, or values with few or no numerical data available for analysis.(2)

How can I determine if I have found an empirical article?

When looking at an article or the abstract of an article, here are some guidelines to use to decide if an article is an empirical article.

Is the article published in an academic, scholarly, or professional journal? Popular magazines such as Business Week or Newsweek do not publish empirical research articles; academic journals such as Business Communication Quarterly or Journal of Psychology may publish empirical articles. Some professional journals, such as JAMA: Journal of the American Medical Association publish empirical research. Other professional journals, such as Coach & Athletic Director publish articles of professional interest, but they do not publish research articles.
Does the abstract of the article mention a study, an observation, an analysis or a number of participants or subjects? Was data collected, a survey or questionnaire administered, an assessment or measurement used, an interview conducted? All of these terms indicate possible methodologies used in empirical research.
Introduction -The introduction provides a very brief summary of the research.
Methodology -The method section describes how the research was conducted, including who the participants were, the design of the study, what the participants did, and what measures were used.
Results -The results section describes the outcomes of the measures of the study.
Discussion -The discussion section contains the interpretations and implications of the study.
Conclusion -
References -A reference section contains information about the articles and books cited in the report and should be substantial.
How long is the article? An empirical article is usually substantial; it is normally seven or more pages long.

When in doubt if an article is an empirical research article, share the article citation and abstract with your professor or a librarian so that we can help you become better at recognizing the differences between empirical research and other types of scholarly articles.

How can I search for empirical research articles using the electronic databases available through Wilson Library?

A quick and somewhat superficial way to look for empirical research is to type your search terms into the database's search boxes, then type STUDY OR STUDIES in the final search box to look for studies on your topic area. Be certain to use the ability to limit your search to scholarly/professional journals if that is available on the database. Evaluate the results of your search using the guidelines above to determine if any of the articles are empirical research articles.
In EbscoHost databases, such as Education Source , on the Advanced Search page you should see a PUBLICATION TYPE field; highlight the appropriate entry. Empirical research may not be the term used; look for a term that may be a synonym for empirical research. ERIC uses REPORTS-RESEARCH. Also find the field for INTENDED AUDIENCE and highlight RESEARCHER. PsycArticles and Psycinfo include a field for METHODOLOGY where you can highlight EMPIRICAL STUDY. National Criminal Justice Reference Service Abstracts has a field for DOCUMENT TYPE; highlight STUDIES/RESEARCH REPORTS. Then evaluate the articles you find using the guidelines above to determine if an article is empirical.
In ProQuest databases, such as ProQuest Psychology Journals , on the Advanced Search page look under MORE SEARCH OPTIONS and click on the pull down menu for DOCUMENT TYPE and highlight an appropriate type, such as REPORT or EVIDENCE BASED. Also look for the SOURCE TYPE field and highlight SCHOLARLY JOURNALS. Evaluate the search results using the guidelines to determine if an article is empirical.
Pub Med Central , Sage Premier , Science Direct , Wiley Interscience , and Wiley Interscience Humanities and Social Sciences consist of scholarly and professional journals which publish primarily empirical articles. After conducting a subject search in these databases, evaluate the items you find by using the guidelines above for deciding if an article is empirical.
"Quantitative research" A Dictionary of Nursing. Oxford University Press, 2008. Oxford Reference Online. Oxford University Press. University of La Verne. 25 August 2009
"Qualitative analysis" A Dictionary of Public Health. Ed. John M. Last, Oxford University Press, 2007. Oxford Reference Online . Oxford University Press. University of La Verne. 25 August 2009

Empirical Articles:Tips on Database Searching

Identifying Empirical Articles
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Last Updated: Mar 5, 2024 4:49 PM
URL: https://laverne.libguides.com/empirical-articles

Identifying Empirical Research Articles

Identifying empirical articles.

Searching for Empirical Research Articles

What is Empirical Research?

An empirical research article reports the results of a study that uses data derived from actual observation or experimentation. Empirical research articles are examples of primary research. To learn more about the differences between primary and secondary research, see our related guide:

Primary and Secondary Sources

By the end of this guide, you will be able to:

Identify common elements of an empirical article
Use a variety of search strategies to search for empirical articles within the library collection

Look for the IMRaD layout in the article to help identify empirical research. Sometimes the sections will be labeled differently, but the content will be similar.

I ntroduction: why the article was written, research question or questions, hypothesis, literature review
M ethods: the overall research design and implementation, description of sample, instruments used, how the authors measured their experiment
R esults: output of the author's measurements, usually includes statistics of the author's findings
D iscussion: the author's interpretation and conclusions about the results, limitations of study, suggestions for further research

Parts of an Empirical Research Article

Parts of an empirical article.

The screenshots below identify the basic IMRaD structure of an empirical research article.

Introduction

The introduction contains a literature review and the study's research hypothesis.

The method section outlines the research design, participants, and measures used.

Results

The results section contains statistical data (charts, graphs, tables, etc.) and research participant quotes.

The discussion section includes impacts, limitations, future considerations, and research.

Learn the IMRaD Layout: How to Identify an Empirical Article

This short video overviews the IMRaD method for identifying empirical research.

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Last Updated: Nov 16, 2023 8:24 AM

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Psychological Science

Prospective submitters of manuscripts are encouraged to read Editor-in-Chief Simine Vazire’s editorial , as well as the editorial by Tom Hardwicke, Senior Editor for Statistics, Transparency, & Rigor, and Simine Vazire.

Psychological Science , the flagship journal of the Association for Psychological Science, is the leading peer-reviewed journal publishing empirical research spanning the entire spectrum of the science of psychology. The journal publishes high quality research articles of general interest and on important topics spanning the entire spectrum of the science of psychology. Replication studies are welcome and evaluated on the same criteria as novel studies. Articles are published in OnlineFirst before they are assigned to an issue. This journal is a member of the Committee on Publication Ethics (COPE) .

Quick Facts

Read the February 2022 editorial by former Editor-in-Chief Patricia Bauer, “Psychological Science Stepping Up a Level.”

Read the January 2020 editorial by former Editor Patricia Bauer on her vision for the future of Psychological Science .

Read the December 2015 editorial on replication by former Editor Steve Lindsay, as well as his April 2017 editorial on sharing data and materials during the review process.

Watch Geoff Cumming’s video workshop on the new statistics.

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Featured research from psychological science, teens who view their homes as more chaotic than their siblings have poorer mental health in adulthood.

Many parents ponder why one of their children seems more emotionally troubled than the others. A new study in the United Kingdom reveals a possible basis for those differences.

Rewatching Videos of People Shifts How We Judge Them, Study Indicates

Rewatching recorded behavior, whether on a Tik-Tok video or police body-camera footage, makes even the most spontaneous actions seem more rehearsed or deliberate, new research shows.

Loneliness Bookends Adulthood, Study Shows

Loneliness in adulthood follows a U-shaped pattern: It’s higher in younger and older adulthood, and lowest during middle adulthood, according to new research that examined nine longitudinal studies from around the world.

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Empirical Research in the Social Sciences and Education

What is empirical research.

Finding Empirical Research
Designing Empirical Research
Ethics & Anti-Racism in Research
Citing, Writing, and Presenting Your Work

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Gratitude to Penn State

Thank you to librarians at Penn State for serving as the inspiration for this library guide

An empirical research article is a primary source where the authors reported on experiments or observations that they conducted. Their research includes their observed and measured data that they derived from an actual experiment rather than theory or belief.

How do you know if you are reading an empirical article? Ask yourself: "What did the authors actually do?" or "How could this study be re-created?"

Key characteristics to look for:

Specific research questions to be answered
Definition of the population, behavior, or phenomena being studied
Description of the process or methodology used to study this population or phenomena, including selection criteria, controls, and testing instruments (example: surveys, questionnaires, etc)
You can readily describe what the authors actually did

Layout of Empirical Articles

Scholarly journals sometimes use a specific layout for empirical articles, called the "IMRaD" format, to communicate empirical research findings. There are four main components:

Introduction : aka "literature review". This section summarizes what is known about the topic at the time of the article's publication. It brings the reader up-to-speed on the research and usually includes a theoretical framework
Methodology : aka "research design". This section describes exactly how the study was done. It describes the population, research process, and analytical tools
Results : aka "findings". This section describes what was learned in the study. It usually contains statistical data or substantial quotes from research participants
Discussion : aka "conclusion" or "implications". This section explains why the study is important, and also describes the limitations of the study. While research results can influence professional practices and future studies, it's important for the researchers to clarify if specific aspects of the study should limit its use. For example, a study using undergraduate students at a small, western, private college can not be extrapolated to include all undergraduates.
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Last Updated: May 8, 2024 3:28 PM
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Data and Statistical Sources: Empirical Articles: Finding Empirical Articles

About Empirical Articles
Finding Empirical Articles

Search strategies

This page primarily describes how to find empirical articles using the EBSCO databases that the library subscribes to. However, there are many databases at Cornell that are not presented in the EBSCO format. Find them listed by subject at this link . You can use similar strateg to find empirical articles.

You may also add specific statistical terms to your search, such as chi, t-test, p-value, or standard deviation. Try searching with terms used in the scientific method: method, results, discussion, or conclusion.

Empirical Articles in EBSCO

Cornell subscribes to scores of databases that provide full text journal articles. Many of the databases are purchased from EBSCO and can be searched using its interface.

Here is a sample search in the Business Source Complete database. Other EBSCO databases with empirical articles and a similar search interface are listed in the box called "EBSCO Databases.

Note that "Economics - Statistical Methods" is a subject term . It is combined with the keyword "labor economics." Instead of typing 'DE "ECONOMICS -- Statistical Methods'' in the search box, you can just type ECONOMICS -- Statistical Methods and select "Subject Term" from the drop down menu.

Sample search in Business Source Complete database

EBSCO Databases

Academic Search Premier This multi-disciplinary database provides full text for more than 8,500 journals, including full text for more than 4,600 peer-reviewed titles. PDF backfiles to 1975 or further are available for well over one hundred journals, and searchable cited references are provided for more than 1,000 titles.
Business Source Complete Business Source Complete provides full text for scholarly business journals and other sources, including full text for more than 1,800 peer-reviewed business publications. Coverage includes virtually all subject areas related to business. This database provides full text (PDF) for top scholarly journals, including the Harvard Business Review. It also includes industry and country reports from Euromonitor and company and industry reports from Datamonitor.
EconLit with Full Text Abstracts, indexing, and full-text articles in all fields of economics, including capital markets, country studies, econometrics, economic forecasting, environmental economics, government regulations, labor economics, monetary theory, and urban economics.
PsycINFO Contains citations and summaries of the international literature in psychology and related behavioral and social sciences, including psychiatry, sociology, anthropology, education, pharmacology, and linguistics. Includes applied psychology, communication systems, developmental psychology, educational psychology, experimental human and animal psychology, personality, physical and psychological disorders, physiological psychology and intervention, professional personnel and issues, psychometrics, social processes and issues, sports psychology and leisure, and treatment and prevention.
Sociology Source Ultimate An expanded version of SocINDEX, including greater coverage of peer-reviewed journals, international resources and open access titles. Provides citations and direct links to the texts of journal articles, book chapters and conference proceedings, some as far back as 1880. Comprehensive coverage encompassing sub-disciplines and related areas of the social sciences, including labor, crime, demography, economic sociology, immigration, ethnic, racial and gender studies, family, political sociology, religion, development, social psychology, social structure, social work, socio-cultural anthropology, social history, theory, methodology, and more.”
MEDLINE Compiled by the U.S. National Library of Medicine (NLM), MEDLINE is the world's most comprehensive source of life sciences and biomedical bibliographic information. It contains nearly eleven million records from over 7,300 different publications from 1965 to present.

Search Terms

Some keywords for research studies:

Empirical Studies
Observations
Methodology
Correlation
Standard Deviation
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Last Updated: Feb 3, 2023 9:13 AM
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Finding Empirical Research Articles

Introduction
Methods or Methodology
Results or Findings

The method for finding empirical research articles varies depending upon the database* being used.

1. The PsycARTICLES and PsycInfo databases (both from the APA) includes a Methodology filter that can be used to identify empirical studies. Look for the filter on the Advanced Search screen. To see a list and description of all of the of methodology filter options in PsycARTICLES and PsycInfo visit the APA Databases Methodology Field Values page .

Methodology filter in PsychARTICLES database

2. When using databases that do not provide a methodology filter—including ProQuest Psychology Journals and Academic Search Complete—experiment with using keywords to retrieve articles on your topic that contain empirical research. For example:

empirical research
empirical study
quantitative study
qualitative study
longitudinal study
observation
questionnaire
methodology
participants

Qualitative research can be challenging to find as these methodologies are not always well-indexed in the databases. Here are some suggested keywords for retrieving articles that include qualitative research.

qualitative
ethnograph*
observation*
"case study”
"focus group"
"phenomenological research"
"conversation analysis"

*Recommended databases are listed on the Databases: Find Journal Articles page of this guide.

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Last Updated: May 9, 2024 9:22 AM
URL: https://libguides.bentley.edu/psychology

Open access

Published: 22 December 2022

A systematic review of high impact empirical studies in STEM education

Yeping Li 1 ,
Yu Xiao 1 ,
Ke Wang 2 ,
Nan Zhang 3 , 4 ,
Yali Pang 5 ,
Ruilin Wang 6 ,
Chunxia Qi 7 ,
Zhiqiang Yuan 8 ,
Jianxing Xu 9 ,
Sandra B. Nite 1 &
Jon R. Star 10

International Journal of STEM Education volume 9 , Article number: 72 ( 2022 ) Cite this article

11k Accesses

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The formation of an academic field is evidenced by many factors, including the growth of relevant research articles and the increasing impact of highly cited publications. Building upon recent scoping reviews of journal publications in STEM education, this study aimed to provide a systematic review of high impact empirical studies in STEM education to gain insights into the development of STEM education research paradigms. Through a search of the Web of Science core database, we identified the top 100 most-cited empirical studies focusing on STEM education that were published in journals from 2000 to 2021 and examined them in terms of various aspects, including the journals where they were published, disciplinary content coverage, research topics and methods, and authorship’s nationality/region and profession. The results show that STEM education continues to gain more exposure and varied disciplinary content with an increasing number of high impact empirical studies published in journals in various STEM disciplines. High impact research articles were mainly authored by researchers in the West, especially the United States, and indicate possible “hot” topics within the broader field of STEM education. Our analysis also revealed the increased participation and contributions from researchers in diverse fields who are working to formulate research agendas in STEM education and the nature of STEM education scholarship.

Introduction

Two recent reviews of research publications, the first examining articles in the International Journal of STEM Education (IJSTEM) and the second looking at an expanded scope of 36 journals, examined how scholarship in science, technology, engineering, and mathematics (STEM) education has developed over the years (Li et al., 2019 , 2020a ). Although these two reviews differed in multiple ways (e.g., the number of journals covered, the time period of article publications, and article selection), they shared the common purpose of providing an overview of the status and trends in STEM education research. The selection of journal publications in these two reviews thus emphasized the coverage and inclusion of all relevant publications but did not consider publication impact. Given that the development of a vibrant field depends not only on the number of research outputs and its growth over the years but also the existence and influence of some high impact research articles, here we aimed to identify and examine those high impact research publications in STEM education in this review.

Learning from existing reviews of STEM education research

Existing reviews of STEM education have provided valuable insights about STEM education scholarship development over the years. In addition to the two reviews mentioned above, there are many other research reviews on different aspects of STEM education. For example, Chomphuphra et al. ( 2019 ) reviewed 56 journal articles published from 2007 to 2017 covering three popular topics: innovation for STEM learning, professional development, and gender gap and career in STEM. They identified and selected these journal articles through searching the Scopus database and two additional journals in STEM education that were not indexed in Scopus at that time. Several other reviews have been conducted and published with a focus on specific topics, such as the assessment of the learning assistant model (Barrasso & Spilios, 2021 ), STEM education in early childhood (Wan et al., 2021 ), and research on individuals' STEM identity (Simpson & Bouhafa, 2020 ). All of these reviews helped in summarizing and synthesizing what we can learn from research on different topics related to STEM education.

Given the on-going rapid expansion of interest in STEM education, the number of research reviews in STEM education research has also been growing rapidly over the years. For example, there were only one or two research reviews published yearly in IJSTEM just a few years ago (Li, 2019 ). However, the situation started to change quickly over the past several years (Li & Xiao, 2022 ). Table 1 provides a summary list of research reviews published in IJSTEM in 2020 and 2021. The journal published a total of five research reviews in 2020 (8%, out of 59 publications), which then increased to seven in 2021 (12%, out of 59 publications).

Taking a closer look at these research reviews, we noticed that three reviews were conducted with a broad perspective to examine research and trends in STEM education (Li et al., 2020a , 2020b ) or STEAM (science, technology, engineering, arts, and mathematics) education (Marin-Marin et al., 2021 ). Relatively large numbers of publications/projects were reviewed in these studies to provide a general overview of research development and trends. The other nine reviews focused on research on specific topics or aspects in STEM education. These results suggest that, with the availability of a rapidly accumulating number of studies in STEM education, researchers have started to go beyond general research trends to examine and summarize research development on specific topics. Moreover, across these 12 reviews, researchers used many different approaches to search multiple data sources (often with specified search terms) to identify and select articles, including journal publications, research reports, conference papers, or dissertations. It appears that researchers have been creative in developing and using specific approaches to select and review publications that are pertinent to their topics. At the same time, however, none of these reviews were designed and conducted to identify and review high impact research articles that had notable influences on the development of STEM education scholarship.

The importance of examining high impact empirical research publications in STEM education

STEM education differs from many other fields, as STEM itself is not a discipline. There are diverse perspectives about the disciplinarity of STEM and STEM education (e.g., Erduran, 2020 ; Li et al., 2020a ; Takeuchi et al., 2020 ; Tytler, 2020 ). The complexity and ambiguity in viewing and examining STEM and STEM education presents challenges as well as opportunities for researchers to explore and specify what and how they do in ways different from and/or connected with traditional education in the individual disciplines of science, technology, engineering, and mathematics.

Although the field of STEM education is still in an early stage of its development, STEM education has experienced tremendous growth over the past decade. This field has evolved from traditional individual discipline-based education in STEM fields to multi- and interdisciplinary education in STEM. The development of STEM education has been supported by multiple factors, including research funding (Li et al., 2020b ) and the growth of research publications (Li et al., 2020a ). High impact publications play a very large role in the growth of the field, as they are read and cited frequently by others and serve to shape the development of scholarship in the field more than other publications.

Among high impact research publications, we can identify several different types of articles, including empirical studies, research reviews, and conceptual or theoretical papers. Research reviews and conceptual/theoretical papers are very valuable, as they synthesize existing research on a specific topic and/or provide new perspective(s) and direction(s), but they are typically not empirical studies. Review articles aim to provide a summary of the current state of the research in the field or on a particular topic, and they help readers to gain an overview about a topic, key issues and publications. Thus, they are more about what has been published in the literature about a topic and less about reporting new empirical evidence about a topic. Similarly, theoretical or conceptual papers tend to draw on existing research to advance theory or propose new perspectives. In contrast, empirical studies require the use and analysis of empirical data to provide empirical evidence. While reporting original research has been typical in empirical studies in education, these studies can also be secondary analyses of empirical data that test hypotheses not considered or addressed in previous studies. Empirical studies are generally published in academic, peer-reviewed journals and consist of distinct sections that reflect the stages in the research process. With the aim to gain insights about research development in STEM education, we thus decided to focus here on empirical studies in STEM education. Examining and reviewing high impact empirical research publications can help provide us a better understanding about emerging trends in STEM education in terms of research topics, methods, and possible directions in the future.

Considerations in identifying and selecting high impact empirical research publications

Publishing as a way of disseminating and sharing knowledge has many types of outlets, including journals, books, and conference proceedings. Different publishing outlets have different advantages in reaching out to readers. Researchers may search different data sources to identify and select publications to review, as indicated in Table 1 . At the same time, journal publications are commonly chosen and viewed as one of the most important outlets valued by the research community for knowledge dissemination and exchange. Specifically, there are two important advantages in terms of evaluating the quality and impact of journal publications over other formats. First, journal publications typically go through a rigorous peer-review process to ensure the quality of manuscripts for publication acceptance based on certain criteria. In educational research, some common criteria being used include “Standards for Reporting on Empirical Social Science Research in AERA Publications” (AERA, 2006 ), “Standards for Reporting on Humanities-Oriented Research in AERA Publications” (AERA, 2009 ), and “Scientific Research in Education” (NRC, 2002 ). Although the peer-review process is also employed in assessing and selecting proposals or papers for publication acceptance in other formats such as books and conference proceedings, the peer-review process employed by journals (esp. those reputable and top journals in a field) tends to be more rigorous and selective than other publication formats. Second, the impact of journals and their publications has frequently been evaluated by peers and different indexing services for inclusion, such as Clarivate’s Social Sciences Citation Index (SSCI) and Elsevier’s Scopus. The citation information collected and evaluated by indexing services provides another important measure about the quality and impact of selected journals and their publications. Based on these considerations, we decided to select and review those journal publications that can be identified as having high citations to gain an overview of their impact on the research development of STEM education.

Focusing on the selection and review of journal publications with high citations has also been used by many other scholars. For example, Martín‐Páez et al. ( 2019 ) conducted a literature review to examine how STEM education is conceptualized, used, and implemented in educational studies. To ensure the quality of published articles for review, they searched and selected journal articles published in the 2013–2018 period from the Web of Science (WoS) database only. Likewise, Akçayır and Akçayır ( 2017 ) conducted a systematic literature review on augmented reality used in educational settings. They used keywords to search all SSCI-indexed journals from WoS database to identify and select published articles, given that WoS provides easy access to search SSCI indexed articles. In addition to the method of searching the WoS database, some researchers used other approaches to identify and select published articles with high citations. For example, some researchers may search different databases to identify and select articles for reviews, such as Scopus (Chomphuphra et al., 2019 ) and Google (Godin et al., 2015 ). In comparison, however, the WoS core database is more selective than many others, including Scopus. The WoS is the world’s leading scientific citation search and analytical information platform (Li et al., 2018 ), and has its own independent and thorough editorial process to ensure journal quality together with the most comprehensive and complete citation network ( https://clarivate.com/webofsciencegroup/solutions/webofscience-ssci/ ). Its core database has been commonly used as a reliable indexing database with close attention to high standard research publications with a peer-review process and is thus used in many research review studies (e.g., Akçayır & Akçayır, 2017 ; Li et al., 2018 ; Marín-Marín et al., 2021 ; Martín‐Páez et al., 2019 ).

It should be noted that some researchers have used a different approach to identify and select high impact publications other than focusing on article citations. This alternative approach is to identify leading journals from specific fields first and then select relevant articles from these journals. For example, Brown ( 2012 ) identified and selected eight important journals in each STEM discipline after consulting with university faculty and K-12 teachers. Once these journals were selected, Brown then located 60 articles that authors self-identified as connected to STEM education from over 1100 articles published between January 1, 2007 and October 1, 2010. However, as there was no well-established journal in STEM education until just a few years ago (Li et al., 2020a ), the approach used by Brown may be less useful for identifying high impact publications in the field of STEM education. In fact, researchers in STEM education have been publishing their high-quality articles in many different journals, especially those well-established journals with an impact factor. Thus, this approach will not help ensure the selection of high impact articles in STEM education, even though they were selected from well-recognized journals rooted in each of STEM disciplines.

In summary, we searched the WoS core database to identify and select high impact empirical research articles in STEM education as those highly cited articles published in journals indexed and collected in the WoS.

Current review

Similar to previous research reviews (e.g., Li et al., 2020a ), we need to specify the scope of the current review with specific considerations of the following two issues:

What time period should be considered?

How should we identify and select highly cited research publications in STEM education?

Time period

As discussed in a previous review (Li et al., 2020a ), the acronym STEM did not exist until the early 2000s. The existence of the acronym has helped to focus attention on and efforts in STEM education. Thus, consistent with the determination of the time period used in the previous review on examining the status and trends in STEM education, we decided to select articles starting from the year 2000. At the same time, we can use the acronym of STEM as an identifier in locating journal articles in a way as done before (Li et al., 2020a ) and also by others (e.g., Brown, 2012 ; Mizell & Brown, 2016 ). We chose the end of 2021 as the end of the time period for publication search and inclusion.

Searching and identifying highly cited empirical research journal publications in STEM education

To identify and select journal articles in STEM education from the WoS core database, we decided to use the common approach of keyword searches as used in many other reviews (e.g., Gladstone & Cimpian, 2021 ; Winterer et al., 2020 ). Li et al. ( 2020a ) also noted the complexity and ambiguity of identifying publications in STEM education. Thus, we planned to identify and select publications in STEM education as those self-identified by authors. As mentioned above, we then used the acronym STEM (or STEAM) as key terms in our search for publications in STEM education.

Different from the previous review on research status and trends in STEM education (Li et al., 2020a ), the current review aimed to identify and select high impact journal articles but not coverage. Thus, we decided to define and limit the scope of high impact empirical research journal publications as the top 100 most-cited empirical research journal publications obtained from the WoS core database.

Research questions

Li et al. ( 2020a ) showed that STEM education articles have been published in many different journals, especially with the limited journal choices available in STEM education. Given a broader range of journals and a longer period of time to be covered in this review, we can thus gain some insights through examining multiple aspects of the top 100 most-cited empirical studies, including journals in which these empirical studies were published, publication years, disciplinary content coverage, research topics and methods. In addition, recent reviews suggested the value of examining possible trends in the authorship and school level focus (Li, 2022 ; Li & Xiao, 2022 ). Taken together, we are interested in addressing the following six research questions:

What are the top 100 most-cited empirical STEM education research journal publications?

What are the distributions and patterns of the top 100 most-cited empirical research publications in different journals?

What is the disciplinary content coverage of the top 100 most-cited empirical research journal publications and possible trends?

What are research topics and methods of the top 100 most-cited empirical research journal publications?

What are the corresponding authors’ nationalities/regions and professions?

What are school level foci of the top 100 most-cited empirical research journal publications over the years?

Based on the above discussion, we carried out the following steps for this systematic review to address these research questions.

Searching and identifying the top 100 most-cited empirical research journal publications in STEM education

Figure 1 provides a summary of the article search and selection process that was used for this review. The process started with a search of the WoS core database on September 12, 2022 under the field of “topic” (covering title, abstract, author keywords, and keywords plus), using the search terms: “STEM” OR “STEAM” OR “science, technology, engineering, and mathematics”. Because there are many different categories in the WoS database, we then specified the publication search using the four WoS categories listed under “education”: “Education Educational Research,” “Education Scientific Disciplines,” “Psychology Educational,” and “Education Special.” The time period of publication search was further specified as starting from 2000 to 2021.

Flowchart of publication search, identification, and selection process

The search returned 9275 publications under “Education Educational Research,” 2161 under “Education Scientific Disciplines,” 247 under “Psychology Educational,” and 15 under “Education Special.” The combined list of all publications was then placed in descending order in terms of citation counts up to the search date of Sept. 12, 2022, and each publication record was screened one-by-one by three researchers using the inclusion or exclusion criteria (see Table 2 ). At times when the publication record listed was not detailed enough, we searched and obtained the full article to screen and check to determine its eligibility. The process ended after identifying and selecting the top 100 most-cited empirical research journal publications.

Data analysis

To address research question 3, we categorized all 100 publications in terms of the number of STEM disciplines covered in a study. Two general categories were used for this review: publications within a single discipline of STEM vs. those with multi- or inter-disciplines of STEM. In contrast to the detailed classifications used in a previous review (Li et al., 2020a ), this simplified classification can help reveal overall trends in disciplinary content coverage and approach reflected in high impact empirical research in STEM education.

To examine research topics, we used the same list of topics from previous reviews (Li & Xiao, 2022 ; Li et al., 2020a ). The following list contains the seven topic categories (TCs) that were used to classify and examine all 100 publications identified and selected from the search in this study.

TC1: Teaching, teacher, and teacher education in STEM (including both pre-service and in-service teacher education) in K-12 education;

TC2: Teacher and teaching in STEM (including faculty development, etc.) at post-secondary level;

TC3: STEM learner, learning, and learning environment in K-12 education;

TC4: STEM learner, learning, and learning environments (excluding pre-service teacher education) at post-secondary level;

TC5: Policy, curriculum, evaluation, and assessment in STEM (including literature reviews about a field in general);

TC6: Culture, social, and gender issues in STEM education;

TC7: History, epistemology, and perspectives about STEM and STEM education.

To examine research methods, we coded all publications in terms of the following methodological categories: (1) qualitative methods, (2) quantitative methods, and (3) mixed methods. We assigned each publication to only one research topic and one method, following the process used in the previous reviews (Li et al., 2019 , 2020a ). When there was more than one topic or method that could have been used for a publication, a final decision was made in choosing and assigning the primary topic and/or method after discussion.

To address research question 5, we examined the corresponding author’s (or the first author, if no specific indication was given about the corresponding author) nationality/region and profession. Many publications in STEM education have joint authorship but may contain limited information about different co-authors. Focusing on the corresponding author’s nationality/region is a feasible approach as we learned from a previous research review (Li et al., 2020a ). For the corresponding author’s profession, we used the same two general categories from the recent reviews (Li, 2022 ; Li & Xiao, 2022 ): “education” and “STEM+” that differentiate a corresponding author’s profession in education/educational research vs. disciplines and fields other than education. If a publication’s corresponding author was listed as affiliated with multiple departments/institutions, the first department/institution affiliation was chosen and used to identify the author’s nationality/region and profession.

To answer research question 6, we adopted the three categories from recent research reviews: K-12, postsecondary, and general (Li, 2022 ; Li & Xiao, 2022 ). The use of these school level categories helped reveal the distribution of STEM education research interests and development over the school level span. While the first two categories are self-explanatory, the “general” category is for those empirical research publications on questions or issues either pertinent to all school levels or that cross the boundary of K-12 school and college.

Results and discussion

The following sections are structured to report findings as corresponding to each of the six research questions.

Distributions and patterns of highly cited publications across different journals

Forty-five journals were identified as publishing these top 100 most-cited articles. Table 4 shows that the majority (26) of these journals focus on general educational research or educational psychology, publishing 52 of the top 100 most-cited articles. Fourteen journals with titles specifying a single discipline of STEM published 38 of these top 100 articles, three journals with two specified STEM disciplines in their titles published seven of these articles, one journal with three specified STEM disciplines published one article, and one journal specifying all four STEM disciplines published two articles. Among these 45 journals, 36 journals are indexed in SSCI, with the remaining nine journals indexed in ESCI (Emerging Sources Citation Index). These are clearly all reputable and well-established journals, with 36 established before 2000 and 9 established in or after 2000. Only three journals in the list are Open Access (OA) journals, and they were all established after 2000. The results suggest that researchers have been publishing high impact STEM education research articles in a wide range of well-established traditional journals, with the majority in general educational research or educational psychology with a long publishing history. It further confirms that the importance of STEM education research has been well-recognized in educational research or educational psychology as noted above. At the same time, the results imply that the history of STEM education itself has been too brief to establish its own top journals and identity except only one in STEM education (IJSTEM) (Li et al., 2020a ).

Among these 45 journals listed in Table 4 , we classified them into two general categories: general education research journals (26, all without mention of a discipline of STEM in a journal’s title) and those (19) with one or more STEM disciplines specified in a journal’s title. Figure 3 presents the distributions of these top 100 articles in these two general categories over the years. Among 49 articles published before 2014, the majority (31, 63%) of these articles were published in journals on general educational research or educational psychology. However, starting in 2014, a new trend emerged with more of these highly cited articles (30 out of 51, 59%) published in journals with STEM discipline(s) specified. The result suggests a possible shift of developing and gaining disciplinary content consciousness in STEM education research publications.

Trend of the top 100 most-cited articles published in journals without vs. with subject discipline(s) of STEM specified. (Note: 0 = journals without STEM discipline specified, 1 = journals with STEM discipline(s) specified.)

As a further examination of the distribution of publications in journals specified with STEM discipline(s), Fig. 4 shows the distributions of these highly cited articles in different journal categories over the years. It is clear that these highly cited articles were typically published in journals on general educational research or educational psychology before 2014. However, things started to change since 2014, with these highly cited articles published in more diverse journals including those with STEM discipline(s) specified in the journal titles. The journals that include only a single discipline of STEM have been more popular than others among those journals that specify one or more STEM disciplines. The result is not surprising as journals specified with a single discipline of STEM are more common, often with a long publishing history and support from well-established professional societies of education on a single discipline of STEM. This trend suggests that the importance of STEM education has also gained increasing recognition from professional societies that used to focus on a single discipline of STEM.

Distribution of highly cited research articles across different journal categories over the years. (Note: 0 = journals without STEM discipline specified, 1 = journals with a single discipline of STEM specified, 2 = journals with two disciplines of STEM specified, 3/4 = journals with 3 or 4 disciplines of STEM specified.)

To glimpse into those recent changes, we took a closer look at the six articles published in 2018 and 2019 as examples (see Table 5 ). All of these articles have been highly cited in just 3 or 4 years, with an average of 102 citations (range, 75–144) per article. Across these six articles, the majority were published in journals whose titles specified one or more STEM disciplines: three in journals with a single discipline of STEM specified, one in a journal on STEM education, and two in journals on general educational research. At the same time, these recent publications are not specifically on any single discipline of STEM, but multi- and interdisciplinary STEM education.

Disciplinary content coverage

The search of STEM education publications from the WoS core database relied on several keywords that the authors used to self-identify their research on STEM education. After coding and categorizing all top 100 publications, 25 research publications were found as focusing on a single discipline of STEM and 75 publications on multi- and interdisciplinary STEM education. The majority of these 100 most-cited empirical studies, in their focus on multi- and interdisciplinary STEM education, reflects the overall focus in STEM education, a trend consistent with what was learned from a previous review of journal publications in STEM education (Li et al., 2020a ).

Among the 25 research articles on a single discipline of STEM, the majority of these articles (56%, 14 out of 25) focused on science, 5 articles on technology, 4 articles on mathematics, and 2 articles on engineering. The result suggests that of the four STEM disciplines, arguably “science” is the broadest category and so it is not surprising that the number of publications on science is the most prevalent. Indeed, the result is also consistent with what we can learn from Table 4 . Among the 14 journals specifying a single STEM discipline that published 38 of the top 100 articles, seven journals focus on “science” that published 27 of these 38 articles.

To examine possible trends over time, Fig. 5 shows the distribution of these 100 articles across these two disciplinary content coverage categories over the years. For each of the publishing years from 2005 to 2019, there were always more high citation empirical publications on multi- and interdisciplinary STEM education than high citation publications focusing on a single discipline of STEM. Moreover, there were no high citation publications on a single discipline of STEM before 2011 or after 2017 that made the cut for inclusion in the top 100 list. These results suggest an overall trend of on-going emphasis on multi- and interdisciplinary research in STEM education, which can be further verified by what we learned from the six recent publications in Table 5 .

Publication distribution by disciplinary content coverage over the years. (Note: S = single discipline of STEM, M = multiple disciplines of STEM.)

Research topics and methods

Table 6 presents the distribution of all 100 highly cited publications classified in terms of the seven topic categories (TCs) over the years. Overall, all seven TCs have publications that were on the top 100 high citation publication list. There were clearly the most publications on TC6 (culture, social, and gender issues in STEM education), followed by publications on TC4 (STEM learner, learning, and learning environments at post-secondary level). The large number of publications with high citations in these two categories suggest possible evolution of research interests and topics in the field of STEM education. Taking a closer look at the six recent publications in Table 5 , it is clear that culture, social, and gender issues were the focus in these recent publications, with the exception of one publication on assessment. This result presents a picture that appears somewhat different from what we learned from previous research reviews that did not focus exclusively on high impact publications from the WoS database (Li & Xiao, 2022 ; Li et al., 2020a ).

Looking at the distribution of these publications within each of the seven TCs, “culture, social, and gender issues in STEM education” (TC6) is a topic area that consistently has some highly cited research publications in almost each of the publishing years. “STEM learner, learning, and learning environments at post-secondary level” (TC4) also has some consistent and on-going research interest with highly cited publications making the list in most of these publishing years. In contrast, publication distributions in the rest of the TCs did not present clearly notable patterns over the years.

Figure 6 shows the number of publications distributed over the years by research methods in these empirical studies. The use of quantitative methods (71) is dominant overall and is especially prevalent among these most-cited publications in the years from 2005 to 2019, a result consistent with what we learned from a previous research review (Li et al., 2020a ). Across these three methodological classifications, qualitative methods were used in 20 empirical studies, and mixed methods were used in only 9 empirical studies. Comparatively, there were many more articles published between 2010 and 2016 that used quantitative methods than the other two methods. However, there were somehow less dramatic differences in method use among empirical studies published either before 2010 or after 2016. As the use of different methods can help reveal ways of collecting and analyzing data to provide empirical evidence, it would be interesting to learn more about possible development and use of research methods in STEM education in the future as a new empirical research paradigm.

Publication distribution in terms of research methods over the years. (Note: 1 = qualitative, 2 = quantitative, 3 = mixed.)

Corresponding author’s nationality/region and profession Footnote 1

Examining the corresponding author’s nationality/region helps reveal the international diversity in research engagement and scholarly contribution to STEM education. Figure 7 indicates 87 highly cited publications (87%, out of 100 publications) with the corresponding author from the United States, followed by 6 publications (6%) contributed by researchers in the U.K., and the remaining 7 publications with the corresponding author from seven other countries/regions (i.e., one publication for each country/region). The results show some international diversity in terms of the number of country/region represented, but with a clear dominance of research contributions from the West especially the United States. The result echoes what we learned above about the corresponding author’s nationality/region for the top ten most-cited articles (see Table 3 ).

Distribution of corresponding author’s nationality/region of the top 100 articles

Recent reviews of journal publications in IJSTEM suggest a trend of increasing diversity in research contributions from many more different countries/regions (Li, 2022 ; Li & Xiao, 2022 ). We would not be surprised if the list of top 100 most-cited empirical research publications contained more contributions from other countries/regions in the future.

After coding the corresponding author’s profession in these top 100 articles, we found that similar numbers of publications had corresponding authors who were researchers in education (49) and STEM+ (51). This result is consistent with what we learned from the corresponding authors’ profession distribution in recent publications in IJSTEM (Li, 2022 ). The diversity in contributing to STEM education scholarship from researchers with various disciplinary training is evident.

To examine possible trends in the corresponding authors’ profession over time, Fig. 8 shows the distributions of these publications in the two profession categories over the years. It is interesting to note that researchers in education typically served as the corresponding authors for more articles published before 2014: 31 articles by researchers in education and 18 articles by researchers in STEM+ for a total of 49 published before 2014. However, a new trend has emerged since 2014, with many more researchers in STEM+ serving as the corresponding authors for these highly cited research articles: 18 articles by researchers in education and 33 articles by researchers in STEM+ for a total of 51 published since 2014.

Distribution of publications by corresponding author’s profession over the years. (Note: 1 = education, 2 = STEM+)

This trend is consistent with what we learned above about the increased number of these publications in journals specified with STEM discipline(s) since 2014 (see Figs. 3 and 4 ). We see an increasing number of researchers in STEM+ fields contributing and publishing empirical research articles in many journals associated with STEM discipline(s) since 2014, resulted in an increase in citations from professional communities while furthering the development of STEM education scholarship. The result is also consistent with what we learned from the authorship development of publications in IJSTEM over the years (Li & Xiao, 2022 ), an increasing trend of having STEM education scholarship contributions from diverse STEM+ fields.

Publications by school level over the years

With an increasing trend of contributions from researchers in diverse STEM+ fields, the identification of school level can help reveal where these high impact research publications focus on issues in STEM education. The coding results show that the majority (63) of these 100 most-cited articles focused on issues at the postsecondary level, 30 articles on issues at the K-12 school level, and 7 articles in the category of “general.”

Figure 9 presents the distributions of these highly cited publications across these three school categories over the years. It is interesting to note that high impact publications on issues at the postsecondary level outnumbered those in other two categories in almost every of these publishing years. As educational issues in K-12 school level were typically attended to by researchers in education, the increasing number of contributions from researchers in diverse STEM+ fields likely pushed the number of citations on publications that fit their interests more at the postsecondary level. The result is consistent with a growing trend in IJSTEM publications on STEM education at the post-secondary level revealed in a recent review (Li & Xiao, 2022 ).

Distribution of highly cited publications by school level focus and year. (Note: 1 = K-12 school level, 2 = Post-secondary level, 3 = General.)

We also noticed that almost no articles in the category of “general” before 2011 and after 2015 made to the list of top 100 most-cited publications. This result suggests that high impact empirical research in STEM education was conducted more at the school level rather than on issues across the boundary of K-12 school and college. With an increasing number of publications in the “general” category noted in recent review of IJSTEM publications (Li & Xiao, 2022 ), it would be interesting to learn more about cross-school boundary development of STEM education scholarship in the future.

Concluding remarks

This systematic review of high impact empirical studies in STEM education explores the top 100 most-cited research articles from the WoS database as published in journals from 2000 to 2021. These articles were published in a wide range of 45 reputable and well-established journals, typically with a long publishing history. These publications present an overall emphasis more on multi- and interdisciplinary STEM education rather than a single discipline of STEM, with an increasing trend of publishing in journals whose title specified one or more STEM discipline(s). Before 2014, 37% (18 out of 49) of these most-cited articles were published in journals whose title specified with a STEM discipline(s). In contrast, 59% (30 out of 51) articles were published in such journals since 2014, and even more so with 67% of the six articles published in 2018 and 2019. This trend is further elevated with two of those high impact articles recently published in this journal, International Journal of STEM Education . There appears a growing sense of developing disciplinary content consciousness and identity in STEM education.

Consistent with our previous reviews (Li et al., 2019 , 2020a ), the vast majority of these highly cited STEM research publications were contributed by authors from the West, especially the United States where STEM and STEAM education originated. Although there were contributions from eight other countries/regions in these top 100 publications, the diversity of international engagement and contribution was limited. Our results also provide an explanation of what may become “hot” topics among these highly cited articles. In particular, the topic of “culture, social, and gender issues in STEM education” is quite prevalent among those highly cited research publications, followed by the topic area of “STEM learner, learning, and learning environments at post-secondary level.” In comparison, topics related to disciplinary content integration in STEM teaching and learning and STEM teacher training have not yet emerged as “hot” among these highly cited empirical studies. Given that an increasing trend of diversity was noted from a review of recent publications in IJSTEM (Li, 2022 ), we would not be surprised if there will be more high impact research publications contributed by researchers from many other countries/regions on diverse topics in the future.

As STEM education does not have a long history, there will be many challenges and opportunities for new development in STEM education. One important dimension is research method. Among the top 100 most-cited empirical studies, quantitative methods were used as the dominant approach, followed by qualitative methods and then mixed methods. This is not surprising as research in multidisciplinary STEM education may require the use and analysis of data across different disciplines, more frequently in large quantitative data than in other data formats. However, when research questions evolve in the future, it would be interesting to learn more about method development and use in STEM education as a new research paradigm.

We started this review with the intention of gaining insights into the development of STEM education scholarship beyond what we learned about publication growth in STEM education from prior reviews. Indeed, this systematic review provided us with the opportunity to learn about possible trends and gaps in different aspects as discussed above. At the same time, we can learn even more by making connections across these different aspects. One important question in STEM education is to understand the nature of STEM education scholarship and to find ways of developing STEM education scholarship. However, STEM is not a discipline by itself, which suggests possible fundamental differences between STEM education scholarship and scholarship typically defined and classified for a single discipline of STEM. With the increasing participation and contributions from researchers in diverse STEM+ fields as we learned from this review, there is a good possibility that the nature of STEM education scholarship will be collectively formulated with numerous contributions from diverse scholars. Continuing analyses of high impact publications is an important and interesting topic that can yield more insights in the years to come.

Availability of data and materials

The data and materials used and analyzed for the report were obtained through searching the Web of Science database, and related journal information are available directly from these journals’ websites.

Our analysis found that the vast majority (94%) of these top 100 articles had the same researcher to serve as the first author and the corresponding author. There are 10 articles that had more than one corresponding authors, and we chose the first corresponding author as listed in our coding.

Abbreviations

Association for computing machinery AERA

American Educational Research Association

Cell biology education

Emerging Sources Citation Index

Institute of electrical and electronics engineers

International Journal of STEM Education

Kindergarten-Grade 12

National Research Council

Social Sciences Citation Index

Science, technology, engineering, and mathematics

Disciplines or fields other than education, including those commonly considered under the STEM umbrella plus some others

Science, technology, engineering, arts, and mathematics

Topic category

Web of Science

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The author would like to thank Marius Jung and the staff at SpringerOpen for their support in publishing this article.

This work was supported by National Social Science Foundation of China, BHA180134.

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YL conceived the study, helped with article search and screening, conducted data analyses, and drafted the manuscript. YX and KW contributed with article search, identification, selection and coding. NZ, YP, RW, CQ, ZY, and JX contributed with data coding. SBN and JRS reviewed drafts and contributed to manuscript revisions. All authors read and approved the final manuscript.

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Research Guides

Empirical Research: Defining, Identifying, & Finding

Searching for empirical research.

Defining Empirical Research
Introduction

Where Do I Find Empirical Research?

How do i find more empirical research in my search.

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Because empirical research refers to the method of investigation rather than a method of publication, it can be published in a number of places. In many disciplines empirical research is most commonly published in scholarly, peer-reviewed journals . Putting empirical research through the peer review process helps ensure that the research is high quality.

Finding Peer-Reviewed Articles

You can find peer-reviewed articles in a general web search along with a lot of other types of sources. However, these specialized tools are more likely to find peer-reviewed articles:

Library databases
Academic search engines such as Google Scholar

Common Types of Articles That Are Not Empirical

However, just finding an article in a peer-reviewed journal is not enough to say it is empirical, since not all the articles in a peer-reviewed journal will be empirical research or even peer reviewed. Knowing how to quickly identify some types non-empirical research articles in peer-reviewed journals can help speed up your search.

Peer-reviewed articles that systematically discuss and propose abstract concepts and methods for a field without primary data collection.
Example: Grosser, K. & Moon, J. (2019). CSR and feminist organization studies: Towards an integrated theorization for the analysis of gender issues .
Peer-reviewed articles that systematically describe, summarize, and often categorize and evaluate previous research on a topic without collecting new data.
Example: Heuer, S. & Willer, R. (2020). How is quality of life assessed in people with dementia? A systematic literature review and a primer for speech-language pathologists .
Note: empirical research articles will have a literature review section as part of the Introduction , but in an empirical research article the literature review exists to give context to the empirical research, which is the primary focus of the article. In a literature review article, the literature review is the focus.
While these articles are not empirical, they are often a great source of information on previous empirical research on a topic with citations to find that research.
Non-peer-reviewed articles where the authors discuss their thoughts on a particular topic without data collection and a systematic method. There are a few differences between these types of articles.
Written by the editors or guest editors of the journal.
Example: Naples, N. A., Mauldin, L., & Dillaway, H. (2018). From the guest editors: Gender, disability, and intersectionality .
Written by guest authors. The journal may have a non-peer-reviewed process for authors to submit these articles, and the editors of the journal may invite authors to write opinion articles.
Example: García, J. J.-L., & Sharif, M. Z. (2015). Black lives matter: A commentary on racism and public health .
Written by the readers of a journal, often in response to an article previously-published in the journal.
Example: Nathan, M. (2013). Letters: Perceived discrimination and racial/ethnic disparities in youth problem behaviors .
Non-peer-reviewed articles that describe and evaluate books, products, services, and other things the audience of the journal would be interested in.
Example: Robinson, R. & Green, J. M. (2020). Book review: Microaggressions and traumatic stress: Theory, research, and clinical treatment .

Even once you know how to recognize empirical research and where it is published, it would be nice to improve your search results so that more empirical research shows up for your topic.

There are two major ways to find the empirical research in a database search:

Use built-in database tools to limit results to empirical research.
Include search terms that help identify empirical research.
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Empirical Research: Definition, Methods, Types and Examples

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Empirical research: Definition

Empirical research: origin, quantitative research methods, qualitative research methods, steps for conducting empirical research, empirical research methodology cycle, advantages of empirical research, disadvantages of empirical research, why is there a need for empirical research.

Empirical research is defined as any research where conclusions of the study is strictly drawn from concretely empirical evidence, and therefore “verifiable” evidence.

This empirical evidence can be gathered using quantitative market research and qualitative market research methods.

For example: A research is being conducted to find out if listening to happy music in the workplace while working may promote creativity? An experiment is conducted by using a music website survey on a set of audience who are exposed to happy music and another set who are not listening to music at all, and the subjects are then observed. The results derived from such a research will give empirical evidence if it does promote creativity or not.

LEARN ABOUT: Behavioral Research

You must have heard the quote” I will not believe it unless I see it”. This came from the ancient empiricists, a fundamental understanding that powered the emergence of medieval science during the renaissance period and laid the foundation of modern science, as we know it today. The word itself has its roots in greek. It is derived from the greek word empeirikos which means “experienced”.

In today’s world, the word empirical refers to collection of data using evidence that is collected through observation or experience or by using calibrated scientific instruments. All of the above origins have one thing in common which is dependence of observation and experiments to collect data and test them to come up with conclusions.

LEARN ABOUT: Causal Research

Types and methodologies of empirical research

Empirical research can be conducted and analysed using qualitative or quantitative methods.

Quantitative research : Quantitative research methods are used to gather information through numerical data. It is used to quantify opinions, behaviors or other defined variables . These are predetermined and are in a more structured format. Some of the commonly used methods are survey, longitudinal studies, polls, etc
Qualitative research: Qualitative research methods are used to gather non numerical data. It is used to find meanings, opinions, or the underlying reasons from its subjects. These methods are unstructured or semi structured. The sample size for such a research is usually small and it is a conversational type of method to provide more insight or in-depth information about the problem Some of the most popular forms of methods are focus groups, experiments, interviews, etc.

Data collected from these will need to be analysed. Empirical evidence can also be analysed either quantitatively and qualitatively. Using this, the researcher can answer empirical questions which have to be clearly defined and answerable with the findings he has got. The type of research design used will vary depending on the field in which it is going to be used. Many of them might choose to do a collective research involving quantitative and qualitative method to better answer questions which cannot be studied in a laboratory setting.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

Quantitative research methods aid in analyzing the empirical evidence gathered. By using these a researcher can find out if his hypothesis is supported or not.

Survey research: Survey research generally involves a large audience to collect a large amount of data. This is a quantitative method having a predetermined set of closed questions which are pretty easy to answer. Because of the simplicity of such a method, high responses are achieved. It is one of the most commonly used methods for all kinds of research in today’s world.

Previously, surveys were taken face to face only with maybe a recorder. However, with advancement in technology and for ease, new mediums such as emails , or social media have emerged.

For example: Depletion of energy resources is a growing concern and hence there is a need for awareness about renewable energy. According to recent studies, fossil fuels still account for around 80% of energy consumption in the United States. Even though there is a rise in the use of green energy every year, there are certain parameters because of which the general population is still not opting for green energy. In order to understand why, a survey can be conducted to gather opinions of the general population about green energy and the factors that influence their choice of switching to renewable energy. Such a survey can help institutions or governing bodies to promote appropriate awareness and incentive schemes to push the use of greener energy.

Learn more: Renewable Energy Survey Template Descriptive Research vs Correlational Research

Experimental research: In experimental research , an experiment is set up and a hypothesis is tested by creating a situation in which one of the variable is manipulated. This is also used to check cause and effect. It is tested to see what happens to the independent variable if the other one is removed or altered. The process for such a method is usually proposing a hypothesis, experimenting on it, analyzing the findings and reporting the findings to understand if it supports the theory or not.

For example: A particular product company is trying to find what is the reason for them to not be able to capture the market. So the organisation makes changes in each one of the processes like manufacturing, marketing, sales and operations. Through the experiment they understand that sales training directly impacts the market coverage for their product. If the person is trained well, then the product will have better coverage.

Correlational research: Correlational research is used to find relation between two set of variables . Regression analysis is generally used to predict outcomes of such a method. It can be positive, negative or neutral correlation.

LEARN ABOUT: Level of Analysis

For example: Higher educated individuals will get higher paying jobs. This means higher education enables the individual to high paying job and less education will lead to lower paying jobs.

Longitudinal study: Longitudinal study is used to understand the traits or behavior of a subject under observation after repeatedly testing the subject over a period of time. Data collected from such a method can be qualitative or quantitative in nature.

For example: A research to find out benefits of exercise. The target is asked to exercise everyday for a particular period of time and the results show higher endurance, stamina, and muscle growth. This supports the fact that exercise benefits an individual body.

Cross sectional: Cross sectional study is an observational type of method, in which a set of audience is observed at a given point in time. In this type, the set of people are chosen in a fashion which depicts similarity in all the variables except the one which is being researched. This type does not enable the researcher to establish a cause and effect relationship as it is not observed for a continuous time period. It is majorly used by healthcare sector or the retail industry.

For example: A medical study to find the prevalence of under-nutrition disorders in kids of a given population. This will involve looking at a wide range of parameters like age, ethnicity, location, incomes and social backgrounds. If a significant number of kids coming from poor families show under-nutrition disorders, the researcher can further investigate into it. Usually a cross sectional study is followed by a longitudinal study to find out the exact reason.

Causal-Comparative research : This method is based on comparison. It is mainly used to find out cause-effect relationship between two variables or even multiple variables.

For example: A researcher measured the productivity of employees in a company which gave breaks to the employees during work and compared that to the employees of the company which did not give breaks at all.

LEARN ABOUT: Action Research

Some research questions need to be analysed qualitatively, as quantitative methods are not applicable there. In many cases, in-depth information is needed or a researcher may need to observe a target audience behavior, hence the results needed are in a descriptive analysis form. Qualitative research results will be descriptive rather than predictive. It enables the researcher to build or support theories for future potential quantitative research. In such a situation qualitative research methods are used to derive a conclusion to support the theory or hypothesis being studied.

LEARN ABOUT: Qualitative Interview

Case study: Case study method is used to find more information through carefully analyzing existing cases. It is very often used for business research or to gather empirical evidence for investigation purpose. It is a method to investigate a problem within its real life context through existing cases. The researcher has to carefully analyse making sure the parameter and variables in the existing case are the same as to the case that is being investigated. Using the findings from the case study, conclusions can be drawn regarding the topic that is being studied.

For example: A report mentioning the solution provided by a company to its client. The challenges they faced during initiation and deployment, the findings of the case and solutions they offered for the problems. Such case studies are used by most companies as it forms an empirical evidence for the company to promote in order to get more business.

Observational method: Observational method is a process to observe and gather data from its target. Since it is a qualitative method it is time consuming and very personal. It can be said that observational research method is a part of ethnographic research which is also used to gather empirical evidence. This is usually a qualitative form of research, however in some cases it can be quantitative as well depending on what is being studied.

For example: setting up a research to observe a particular animal in the rain-forests of amazon. Such a research usually take a lot of time as observation has to be done for a set amount of time to study patterns or behavior of the subject. Another example used widely nowadays is to observe people shopping in a mall to figure out buying behavior of consumers.

One-on-one interview: Such a method is purely qualitative and one of the most widely used. The reason being it enables a researcher get precise meaningful data if the right questions are asked. It is a conversational method where in-depth data can be gathered depending on where the conversation leads.

For example: A one-on-one interview with the finance minister to gather data on financial policies of the country and its implications on the public.

Focus groups: Focus groups are used when a researcher wants to find answers to why, what and how questions. A small group is generally chosen for such a method and it is not necessary to interact with the group in person. A moderator is generally needed in case the group is being addressed in person. This is widely used by product companies to collect data about their brands and the product.

For example: A mobile phone manufacturer wanting to have a feedback on the dimensions of one of their models which is yet to be launched. Such studies help the company meet the demand of the customer and position their model appropriately in the market.

Text analysis: Text analysis method is a little new compared to the other types. Such a method is used to analyse social life by going through images or words used by the individual. In today’s world, with social media playing a major part of everyone’s life, such a method enables the research to follow the pattern that relates to his study.

For example: A lot of companies ask for feedback from the customer in detail mentioning how satisfied are they with their customer support team. Such data enables the researcher to take appropriate decisions to make their support team better.

Sometimes a combination of the methods is also needed for some questions that cannot be answered using only one type of method especially when a researcher needs to gain a complete understanding of complex subject matter.

We recently published a blog that talks about examples of qualitative data in education ; why don’t you check it out for more ideas?

Since empirical research is based on observation and capturing experiences, it is important to plan the steps to conduct the experiment and how to analyse it. This will enable the researcher to resolve problems or obstacles which can occur during the experiment.

Step #1: Define the purpose of the research

This is the step where the researcher has to answer questions like what exactly do I want to find out? What is the problem statement? Are there any issues in terms of the availability of knowledge, data, time or resources. Will this research be more beneficial than what it will cost.

Before going ahead, a researcher has to clearly define his purpose for the research and set up a plan to carry out further tasks.

Step #2 : Supporting theories and relevant literature

The researcher needs to find out if there are theories which can be linked to his research problem . He has to figure out if any theory can help him support his findings. All kind of relevant literature will help the researcher to find if there are others who have researched this before, or what are the problems faced during this research. The researcher will also have to set up assumptions and also find out if there is any history regarding his research problem

Step #3: Creation of Hypothesis and measurement

Before beginning the actual research he needs to provide himself a working hypothesis or guess what will be the probable result. Researcher has to set up variables, decide the environment for the research and find out how can he relate between the variables.

Researcher will also need to define the units of measurements, tolerable degree for errors, and find out if the measurement chosen will be acceptable by others.

Step #4: Methodology, research design and data collection

In this step, the researcher has to define a strategy for conducting his research. He has to set up experiments to collect data which will enable him to propose the hypothesis. The researcher will decide whether he will need experimental or non experimental method for conducting the research. The type of research design will vary depending on the field in which the research is being conducted. Last but not the least, the researcher will have to find out parameters that will affect the validity of the research design. Data collection will need to be done by choosing appropriate samples depending on the research question. To carry out the research, he can use one of the many sampling techniques. Once data collection is complete, researcher will have empirical data which needs to be analysed.

LEARN ABOUT: Best Data Collection Tools

Step #5: Data Analysis and result

Data analysis can be done in two ways, qualitatively and quantitatively. Researcher will need to find out what qualitative method or quantitative method will be needed or will he need a combination of both. Depending on the unit of analysis of his data, he will know if his hypothesis is supported or rejected. Analyzing this data is the most important part to support his hypothesis.

Step #6: Conclusion

A report will need to be made with the findings of the research. The researcher can give the theories and literature that support his research. He can make suggestions or recommendations for further research on his topic.

A.D. de Groot, a famous dutch psychologist and a chess expert conducted some of the most notable experiments using chess in the 1940’s. During his study, he came up with a cycle which is consistent and now widely used to conduct empirical research. It consists of 5 phases with each phase being as important as the next one. The empirical cycle captures the process of coming up with hypothesis about how certain subjects work or behave and then testing these hypothesis against empirical data in a systematic and rigorous approach. It can be said that it characterizes the deductive approach to science. Following is the empirical cycle.

Observation: At this phase an idea is sparked for proposing a hypothesis. During this phase empirical data is gathered using observation. For example: a particular species of flower bloom in a different color only during a specific season.
Induction: Inductive reasoning is then carried out to form a general conclusion from the data gathered through observation. For example: As stated above it is observed that the species of flower blooms in a different color during a specific season. A researcher may ask a question “does the temperature in the season cause the color change in the flower?” He can assume that is the case, however it is a mere conjecture and hence an experiment needs to be set up to support this hypothesis. So he tags a few set of flowers kept at a different temperature and observes if they still change the color?
Deduction: This phase helps the researcher to deduce a conclusion out of his experiment. This has to be based on logic and rationality to come up with specific unbiased results.For example: In the experiment, if the tagged flowers in a different temperature environment do not change the color then it can be concluded that temperature plays a role in changing the color of the bloom.
Testing: This phase involves the researcher to return to empirical methods to put his hypothesis to the test. The researcher now needs to make sense of his data and hence needs to use statistical analysis plans to determine the temperature and bloom color relationship. If the researcher finds out that most flowers bloom a different color when exposed to the certain temperature and the others do not when the temperature is different, he has found support to his hypothesis. Please note this not proof but just a support to his hypothesis.
Evaluation: This phase is generally forgotten by most but is an important one to keep gaining knowledge. During this phase the researcher puts forth the data he has collected, the support argument and his conclusion. The researcher also states the limitations for the experiment and his hypothesis and suggests tips for others to pick it up and continue a more in-depth research for others in the future. LEARN MORE: Population vs Sample

LEARN MORE: Population vs Sample

There is a reason why empirical research is one of the most widely used method. There are a few advantages associated with it. Following are a few of them.

It is used to authenticate traditional research through various experiments and observations.
This research methodology makes the research being conducted more competent and authentic.
It enables a researcher understand the dynamic changes that can happen and change his strategy accordingly.
The level of control in such a research is high so the researcher can control multiple variables.
It plays a vital role in increasing internal validity .

Even though empirical research makes the research more competent and authentic, it does have a few disadvantages. Following are a few of them.

Such a research needs patience as it can be very time consuming. The researcher has to collect data from multiple sources and the parameters involved are quite a few, which will lead to a time consuming research.
Most of the time, a researcher will need to conduct research at different locations or in different environments, this can lead to an expensive affair.
There are a few rules in which experiments can be performed and hence permissions are needed. Many a times, it is very difficult to get certain permissions to carry out different methods of this research.
Collection of data can be a problem sometimes, as it has to be collected from a variety of sources through different methods.

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Empirical research is important in today’s world because most people believe in something only that they can see, hear or experience. It is used to validate multiple hypothesis and increase human knowledge and continue doing it to keep advancing in various fields.

For example: Pharmaceutical companies use empirical research to try out a specific drug on controlled groups or random groups to study the effect and cause. This way, they prove certain theories they had proposed for the specific drug. Such research is very important as sometimes it can lead to finding a cure for a disease that has existed for many years. It is useful in science and many other fields like history, social sciences, business, etc.

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With the advancement in today’s world, empirical research has become critical and a norm in many fields to support their hypothesis and gain more knowledge. The methods mentioned above are very useful for carrying out such research. However, a number of new methods will keep coming up as the nature of new investigative questions keeps getting unique or changing.

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Ai: a global governance challenge, empirical perspectives, normative perspectives, acknowledgement, conflict of interest.

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The Global Governance of Artificial Intelligence: Next Steps for Empirical and Normative Research

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Jonas Tallberg, Eva Erman, Markus Furendal, Johannes Geith, Mark Klamberg, Magnus Lundgren, The Global Governance of Artificial Intelligence: Next Steps for Empirical and Normative Research, International Studies Review , Volume 25, Issue 3, September 2023, viad040, https://doi.org/10.1093/isr/viad040

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Artificial intelligence (AI) represents a technological upheaval with the potential to change human society. Because of its transformative potential, AI is increasingly becoming subject to regulatory initiatives at the global level. Yet, so far, scholarship in political science and international relations has focused more on AI applications than on the emerging architecture of global AI regulation. The purpose of this article is to outline an agenda for research into the global governance of AI. The article distinguishes between two broad perspectives: an empirical approach, aimed at mapping and explaining global AI governance; and a normative approach, aimed at developing and applying standards for appropriate global AI governance. The two approaches offer questions, concepts, and theories that are helpful in gaining an understanding of the emerging global governance of AI. Conversely, exploring AI as a regulatory issue offers a critical opportunity to refine existing general approaches to the study of global governance.

La inteligencia artificial (IA) representa una revolución tecnológica que tiene el potencial de poder cambiar la sociedad humana. Debido a este potencial transformador, la IA está cada vez más sujeta a iniciativas reguladoras a nivel global. Sin embargo, hasta ahora, el mundo académico en el área de las ciencias políticas y las relaciones internacionales se ha centrado más en las aplicaciones de la IA que en la arquitectura emergente de la regulación global en materia de IA. El propósito de este artículo es esbozar una agenda para la investigación sobre la gobernanza global en materia de IA. El artículo distingue entre dos amplias perspectivas: por un lado, un enfoque empírico, destinado a mapear y explicar la gobernanza global en materia de IA y, por otro lado, un enfoque normativo, destinado a desarrollar y a aplicar normas para una gobernanza global adecuada de la IA. Los dos enfoques ofrecen preguntas, conceptos y teorías que resultan útiles para comprender la gobernanza global emergente en materia de IA. Por el contrario, el hecho de estudiar la IA como si fuese una cuestión reguladora nos ofrece una oportunidad de gran relevancia para poder perfeccionar los enfoques generales existentes en el estudio de la gobernanza global.

L'intelligence artificielle (IA) constitue un bouleversement technologique qui pourrait bien changer la société humaine. À cause de son potentiel transformateur, l'IA fait de plus en plus l'objet d'initiatives réglementaires au niveau mondial. Pourtant, jusqu'ici, les chercheurs en sciences politiques et relations internationales se sont davantage concentrés sur les applications de l'IA que sur l’émergence de l'architecture de la réglementation mondiale de l'IA. Cet article vise à exposer les grandes lignes d'un programme de recherche sur la gouvernance mondiale de l'IA. Il fait la distinction entre deux perspectives larges : une approche empirique, qui vise à représenter et expliquer la gouvernance mondiale de l'IA; et une approche normative, qui vise à mettre au point et appliquer les normes d'une gouvernance mondiale de l'IA adéquate. Les deux approches proposent des questions, des concepts et des théories qui permettent de mieux comprendre l’émergence de la gouvernance mondiale de l'IA. À l'inverse, envisager l'IA telle une problématique réglementaire présente une opportunité critique d'affiner les approches générales existantes de l’étude de la gouvernance mondiale.

Artificial intelligence (AI) represents a technological upheaval with the potential to transform human society. It is increasingly viewed by states, non-state actors, and international organizations (IOs) as an area of strategic importance, economic competition, and risk management. While AI development is concentrated to a handful of corporations in the United States, China, and Europe, the long-term consequences of AI implementation will be global. Autonomous weapons will have consequences for armed conflicts and power balances; automation will drive changes in job markets and global supply chains; generative AI will affect content production and challenge copyright systems; and competition around the scarce hardware needed to train AI systems will shape relations among both states and businesses. While the technology is still only lightly regulated, state and non-state actors are beginning to negotiate global rules and norms to harness and spread AI’s benefits while limiting its negative consequences. For example, in the past few years, the United Nations Educational, Scientific and Cultural Organization (UNESCO) adopted recommendations on the ethics of AI, the European Union (EU) negotiated comprehensive AI legislation, and the Group of Seven (G7) called for developing global technical standards on AI.

Our purpose in this article is to outline an agenda for research into the global governance of AI. 1 Advancing research on the global regulation of AI is imperative. The rules and arrangements that are currently being developed to regulate AI will have a considerable impact on power differentials, the distribution of economic value, and the political legitimacy of AI governance for years to come. Yet there is currently little systematic knowledge on the nature of global AI regulation, the interests influential in this process, and the extent to which emerging arrangements can manage AI’s consequences in a just and democratic manner. While poised for rapid expansion, research on the global governance of AI remains in its early stages (but see Maas 2021 ; Schmitt 2021 ).

This article complements earlier calls for research on AI governance in general ( Dafoe 2018 ; Butcher and Beridze 2019 ; Taeihagh 2021 ; Büthe et al. 2022 ) by focusing specifically on the need for systematic research into the global governance of AI. It submits that global efforts to regulate AI have reached a stage where it is necessary to start asking fundamental questions about the characteristics, sources, and consequences of these governance arrangements.

We distinguish between two broad approaches for studying the global governance of AI: an empirical perspective, informed by a positive ambition to map and explain AI governance arrangements; and a normative perspective, informed by philosophical standards for evaluating the appropriateness of AI governance arrangements. Both perspectives build on established traditions of research in political science, international relations (IR), and political philosophy, and offer questions, concepts, and theories that are helpful as we try to better understand new types of governance in world politics.

We argue that empirical and normative perspectives together offer a comprehensive agenda of research on the global governance of AI. Pursuing this agenda will help us to better understand characteristics, sources, and consequences of the global regulation of AI, with potential implications for policymaking. Conversely, exploring AI as a regulatory issue offers a critical opportunity to further develop concepts and theories of global governance as they confront the particularities of regulatory dynamics in this important area.

We advance this argument in three steps. First, we argue that AI, because of its economic, political, and social consequences, presents a range of governance challenges. While these challenges initially were taken up mainly by national authorities, recent years have seen a dramatic increase in governance initiatives by IOs. These efforts to regulate AI at global and regional levels are likely driven by several considerations, among them AI applications creating cross-border externalities that demand international cooperation and AI development taking place through transnational processes requiring transboundary regulation. Yet, so far, existing scholarship on the global governance of AI has been mainly descriptive or policy-oriented, rather than focused on theory-driven positive and normative questions.

Second, we argue that an empirical perspective can help to shed light on key questions about characteristics and sources of the global governance of AI. Based on existing concepts, the emerging governance architecture for AI can be described as a regime complex—a structure of partially overlapping and diverse governance arrangements without a clearly defined central institution or hierarchy. IR theories are useful in directing our attention to the role of power, interests, ideas, and non-state actors in the construction of this regime complex. At the same time, the specific conditions of AI governance suggest ways in which global governance theories may be usefully developed.

Third, we argue that a normative perspective raises crucial questions regarding the nature and implications of global AI governance. These questions pertain both to procedure (the process for developing rules) and to outcome (the implications of those rules). A normative perspective suggests that procedures and outcomes in global AI governance need to be evaluated in terms of how they meet relevant normative ideals, such as democracy and justice. How could the global governance of AI be organized to live up to these ideals? To what extent are emerging arrangements minimally democratic and fair in their procedures and outcomes? Conversely, the global governance of AI raises novel questions for normative theorizing, for instance, by invoking aims for AI to be “trustworthy,” “value aligned,” and “human centered.”

Advancing this agenda of research is important for several reasons. First, making more systematic use of social science concepts and theories will help us to gain a better understanding of various dimensions of the global governance of AI. Second, as a novel case of governance involving unique features, AI raises questions that will require us to further refine existing concepts and theories of global governance. Third, findings from this research agenda will be of importance for policymakers, by providing them with evidence on international regulatory gaps, the interests that have influenced current arrangements, and the normative issues at stake when developing this regime complex going forward.

The remainder of this article is structured in three substantive sections. The first section explains why AI has become a concern of global governance. The second section suggests that an empirical perspective can help to shed light on the characteristics and drivers of the global governance of AI. The third section discusses the normative challenges posed by global AI governance, focusing specifically on concerns related to democracy and justice. The article ends with a conclusion that summarizes our proposed agenda for future research on the global governance of AI.

Why does AI pose a global governance challenge? In this section, we answer this question in three steps. We begin by briefly describing the spread of AI technology in society, then illustrate the attempts to regulate AI at various levels of governance, and finally explain why global regulatory initiatives are becoming increasingly common. We argue that the growth of global governance initiatives in this area stems from AI applications creating cross-border externalities that demand international cooperation and from AI development taking place through transnational processes requiring transboundary regulation.

Due to its amorphous nature, AI escapes easy definition. Instead, the definition of AI tends to depend on the purposes and audiences of the research ( Russell and Norvig 2020 ). In the most basic sense, machines are considered intelligent when they can perform tasks that would require intelligence if done by humans ( McCarthy et al. 1955 ). This could happen through the guiding hand of humans, in “expert systems” that follow complex decision trees. It could also happen through “machine learning,” where AI systems are trained to categorize texts, images, sounds, and other data, using such categorizations to make autonomous decisions when confronted with new data. More specific definitions require that machines display a level of autonomy and capacity for learning that enables rational action. For instance, the EU’s High-Level Expert Group on AI has defined AI as “systems that display intelligent behaviour by analysing their environment and taking actions—with some degree of autonomy—to achieve specific goals” (2019, 1). Yet, illustrating the potential for conceptual controversy, this definition has been criticized for denoting both too many and too few technologies as AI ( Heikkilä 2022a ).

AI technology is already implemented in a wide variety of areas in everyday life and the economy at large. For instance, the conversational chatbot ChatGPT is estimated to have reached 100 million users just two months after its launch at the end of 2022 ( Hu 2023 ). AI applications enable new automation technologies, with subsequent positive or negative effects on the demand for labor, employment, and economic equality ( Acemoglu and Restrepo 2020 ). Military AI is integral to lethal autonomous weapons systems (LAWS), whereby machines take autonomous decisions in warfare and battlefield targeting ( Rosert and Sauer 2018 ). Many governments and public agencies have already implemented AI in their daily operations in order to more efficiently evaluate welfare eligibility, flag potential fraud, profile suspects, make risk assessments, and engage in mass surveillance ( Saif et al. 2017 ; Powers and Ganascia 2020 ; Berk 2021 ; Misuraca and van Noordt 2022 , 38).

Societies face significant governance challenges in relation to the implementation of AI. One type of challenge arises when AI systems function poorly, such as when applications involving some degree of autonomous decision-making produce technical failures with real-world implications. The “Robodebt” scheme in Australia, for instance, was designed to detect mistaken social security payments, but the Australian government ultimately had to rescind 400,000 wrongfully issued welfare debts ( Henriques-Gomes 2020 ). Similarly, Dutch authorities recently implemented an algorithm that pushed tens of thousands of families into poverty after mistakenly requiring them to repay child benefits, ultimately forcing the government to resign ( Heikkilä 2022b ).

Another type of governance challenge arises when AI systems function as intended but produce impacts whose consequences may be regarded as problematic. For instance, the inherent opacity of AI decision-making challenges expectations on transparency and accountability in public decision-making in liberal democracies ( Burrell 2016 ; Erman and Furendal 2022a ). Autonomous weapons raise critical ethical and legal issues ( Rosert and Sauer 2019 ). AI applications for surveillance in law enforcement give rise to concerns of individual privacy and human rights ( Rademacher 2019 ). AI-driven automation involves changes in labor markets that are painful for parts of the population ( Acemoglu and Restrepo 2020 ). Generative AI upends conventional ways of producing creative content and raises new copyright and data security issues ( Metz 2022 ).

More broadly, AI presents a governance challenge due to its effects on economic competitiveness, military security, and personal integrity, with consequences for states and societies. In this respect, AI may not be radically different from earlier general-purpose technologies, such as the steam engine, electricity, nuclear power, and the internet ( Frey 2019 ). From this perspective, it is not the novelty of AI technology that makes it a pressing issue to regulate but rather the anticipation that AI will lead to large-scale changes and become a source of power for state and societal actors.

Challenges such as these have led to a rapid expansion in recent years of efforts to regulate AI at different levels of governance. The OECD AI Policy Observatory records more than 700 national AI policy initiatives from 60 countries and territories ( OECD 2021 ). Earlier research into the governance of AI has therefore naturally focused mostly on the national level ( Radu 2021 ; Roberts et al. 2021 ; Taeihagh 2021 ). However, a large number of governance initiatives have also been undertaken at the global level, and many more are underway. According to an ongoing inventory of AI regulatory initiatives by the Council of Europe, IOs overtook national authorities as the main source of such initiatives in 2020 ( Council of Europe 2023 ). Figure 1 visualizes this trend.

Origins of AI governance initiatives, 2015–2022. Source : Council of Europe (2023 ).

According to this source, national authorities launched 170 initiatives from 2015 to 2022, while IOs put in place 210 initiatives during the same period. Over time, the share of regulatory initiatives emanating from IOs has thus grown to surpass the share resulting from national authorities. Examples of the former include the OECD Principles on Artificial Intelligence agreed in 2019, the UNESCO Recommendation on Ethics of AI adopted in 2021, and the EU’s ongoing negotiations on the EU AI Act. In addition, several governance initiatives emanate from the private sector, civil society, and multistakeholder partnerships. In the next section, we will provide a more developed characterization of these global regulatory initiatives.

Two concerns likely explain why AI increasingly is becoming subject to governance at the global level. First, AI creates externalities that do not follow national borders and whose regulation requires international cooperation. China’s Artificial Intelligence Development Plan, for instance, clearly states that the country is using AI as a leapfrog technology in order to enhance national competitiveness ( Roberts et al. 2021 ). Since states with less regulation might gain a competitive edge when developing certain AI applications, there is a risk that such strategies create a regulatory race to the bottom. International cooperation that creates a level playing field could thus be said to be in the interest of all parties.

Second, the development of AI technology is a cross-border process carried out by transnational actors—multinational firms in particular. Big tech corporations, such as Google, Meta, or the Chinese drone maker DJI, are investing vast sums into AI development. The innovations of hardware manufacturers like Nvidia enable breakthroughs but depend on complex global supply chains, and international research labs such as DeepMind regularly present cutting-edge AI applications. Since the private actors that develop AI can operate across multiple national jurisdictions, the efforts to regulate AI development and deployment also need to be transboundary. Only by introducing common rules can states ensure that AI businesses encounter similar regulatory environments, which both facilitates transboundary AI development and reduces incentives for companies to shift to countries with laxer regulation.

Successful global governance of AI could help realize many of the potential benefits of the technology while mitigating its negative consequences. For AI to contribute to increased economic productivity, for instance, there needs to be predictable and clear regulation as well as global coordination around standards that prevent competition between parallel technical systems. Conversely, a failure to provide suitable global governance could lead to substantial risks. The intentional misuse of AI technology may undermine trust in institutions, and if left unchecked, the positive and negative externalities created by automation technologies might fall unevenly across different groups. Race dynamics similar to those that arose around nuclear technology in the twentieth century—where technological leadership created large benefits—might lead international actors and private firms to overlook safety issues and create potentially dangerous AI applications ( Dafoe 2018 ; Future of Life Institute 2023 ). Hence, policymakers face the task of disentangling beneficial from malicious consequences and then foster the former while regulating the latter. Given the speed at which AI is developed and implemented, governance also risks constantly being one step behind the technological frontier.

A prime example of how AI presents a global governance challenge is the efforts to regulate military AI, in particular autonomous weapons capable of identifying and eliminating a target without the involvement of a remote human operator ( Hernandez 2021 ). Both the development and the deployment of military applications with autonomous capabilities transcend national borders. Multinational defense companies are at the forefront of developing autonomous weapons systems. Reports suggest that such autonomous weapons are now beginning to be used in armed conflicts ( Trager and Luca 2022 ). The development and deployment of autonomous weapons involve the types of competitive dynamics and transboundary consequences identified above. In addition, they raise specific concerns with respect to accountability and dehumanization ( Sparrow 2007 ; Stop Killer Robots 2023 ). For these reasons, states have begun to explore the potential for joint global regulation of autonomous weapons systems. The principal forum is the Group on Governmental Experts (GGE) within the Convention on Certain Conventional Weapons (CCW). Yet progress in these negotiations is slow as the major powers approach this issue with competing interests in mind, illustrating the challenges involved in developing joint global rules.

The example of autonomous weapons further illustrates how the global governance of AI raises urgent empirical and normative questions for research. On the empirical side, these developments invite researchers to map emerging regulatory initiatives, such as those within the CCW, and to explain why these particular frameworks become dominant. What are the principal characteristics of global regulatory initiatives in the area of autonomous weapons, and how do power differentials, interest constellations, and principled ideas influence those rules? On the normative side, these developments invite researchers to address key normative questions raised by the development and deployment of autonomous weapons. What are the key normative issues at stake in the regulation of autonomous weapons, both with respect to the process through which such rules are developed and with respect to the consequences of these frameworks? To what extent are existing normative ideals and frameworks, such as just war theory, applicable to the governing of military AI ( Roach and Eckert 2020 )? Despite the global governance challenge of AI development and use, research on this topic is still in its infancy (but see Maas 2021 ; Schmitt 2021 ). In the remainder of this article, we therefore present an agenda for research into the global governance of AI. We begin by outlining an agenda for positive empirical research on the global governance of AI and then suggest an agenda for normative philosophical research.

An empirical perspective on the global governance of AI suggests two main questions: How may we describe the emerging global governance of AI? And how may we explain the emerging global governance of AI? In this section, we argue that concepts and theories drawn from the general study of global governance will be helpful as we address these questions, but also that AI, conversely, raises novel issues that point to the need for new or refined theories. Specifically, we show how global AI governance may be mapped along several conceptual dimensions and submit that theories invoking power dynamics, interests, ideas, and non-state actors have explanatory promise.

Mapping AI Governance

A key priority for empirical research on the global governance of AI is descriptive: Where and how are new regulatory arrangements emerging at the global level? What features characterize the emergent regulatory landscape? In answering such questions, researchers can draw on scholarship on international law and IR, which have conceptualized mechanisms of regulatory change and drawn up analytical dimensions to map and categorize the resulting regulatory arrangements.

Any mapping exercise must consider the many different ways in global AI regulation may emerge and evolve. Previous research suggests that legal development may take place in at least three distinct ways. To begin with, existing rules could be reinterpreted to also cover AI ( Maas 2021 ). For example, the principles of distinction, proportionality, and precaution in international humanitarian law could be extended, via reinterpretation, to apply to LAWS, without changing the legal source. Another manner in which new AI regulation may appear is via “ add-ons ” to existing rules. For example, in the area of global regulation of autonomous vehicles, AI-related provisions were added to the 1968 Vienna Road Traffic Convention through an amendment in 2015 ( Kunz and Ó hÉigeartaigh 2020 ). Finally, AI regulation may appear as a completely new framework , either through new state behavior that results in customary international law or through a new legal act or treaty ( Maas 2021 , 96). Here, one example of regulating AI through a new framework is the aforementioned EU AI Act, which would take the form of a new EU regulation.

Once researchers have mapped emerging regulatory arrangements, a central task will be to categorize them. Prior scholarship suggests that regulatory arrangements may be fruitfully analyzed in terms of five key dimensions (cf. Koremenos et al. 2001 ; Wahlgren 2022 , 346–347). A first dimension is whether regulation is horizontal or vertical . A horizontal regulation covers several policy areas, whereas a vertical regulation is a delimited legal framework covering one specific policy area or application. In the field of AI, emergent governance appears to populate both ends of this spectrum. For example, the proposed EU AI Act (2021), the UNESCO Recommendations on the Ethics of AI (2021), and the OECD Principles on AI (2019), which are not specific to any particular AI application or field, would classify as attempts at horizontal regulation. When it comes to vertical regulation, there are fewer existing examples, but discussions on a new protocol on LAWS within the CCW signal that this type of regulation is likely to become more important in the future ( Maas 2019a ).

A second dimension runs from centralization to decentralization . Governance is centralized if there is a single, authoritative institution at the heart of a regime, such as in trade, where the World Trade Organization (WTO) fulfills this role. In contrast, decentralized arrangements are marked by parallel and partly overlapping institutions, such as in the governance of the environment, the internet, or genetic resources (cf. Raustiala and Victor 2004 ). While some IOs with universal membership, such as UNESCO, have taken initiatives relating to AI governance, no institution has assumed the role as the core regulatory body at the global level. Rather, the proliferation of parallel initiatives, across levels and regions, lends weight to the conclusion that contemporary arrangements for the global governance of AI are strongly decentralized ( Cihon et al. 2020a ).

A third dimension is the continuum from hard law to soft law . While domestic statutes and treaties may be described as hard law, soft law is associated with guidelines of conduct, recommendations, resolutions, standards, opinions, ethical principles, declarations, guidelines, board decisions, codes of conduct, negotiated agreements, and a large number of additional normative mechanisms ( Abbott and Snidal 2000 ; Wahlgren 2022 ). Even though such soft documents may initially have been drafted as non-legal texts, they may in actual practice acquire considerable strength in structuring international relations ( Orakhelashvili 2019 ). While some initiatives to regulate AI classify as hard law, including the EU’s AI Act, Burri (2017 ) suggests that AI governance is likely to be dominated by “supersoft law,” noting that there are currently numerous processes underway creating global standards outside traditional international law-making fora. In a phenomenon that might be described as “bottom-up law-making” ( Koven Levit 2017 ), states and IOs are bypassed, creating norms that defy traditional categories of international law ( Burri 2017 ).

A fourth dimension concerns private versus public regulation . The concept of private regulation overlaps partly with substance understood as soft law, to the extent that private actors develop non-binding guidelines ( Wahlgren 2022 ). Significant harmonization of standards may be developed by private standardization bodies, such as the IEEE ( Ebers 2022 ). Public authorities may regulate the responsibility of manufacturers through tort law and product liability law ( Greenstein 2022 ). Even though contracts are originally matters between private parties, some contractual matters may still be regulated and enforced by law ( Ubena 2022 ).

A fifth dimension relates to the division between military and non-military regulation . Several policymakers and scholars describe how military AI is about to escalate into a strategic arms race between major powers such as the United States and China, similar to the nuclear arms race during the Cold War (cf. Petman 2017 ; Thompson and Bremmer 2018 ; Maas 2019a ). The process in the CCW Group of Governmental Experts on the regulation of LAWS is probably the largest single negotiation on AI ( Maas 2019b ) next to the negotiations on the EU AI Act. The zero-sum logic that appears to exist between states in the area of national security, prompting a military AI arms race, may not be applicable to the same extent to non-military applications of AI, potentially enabling a clearer focus on realizing positive-sum gains through regulation.

These five dimensions can provide guidance as researchers take up the task of mapping and categorizing global AI regulation. While the evidence is preliminary, in its present form, the global governance of AI must be understood as combining horizontal and vertical elements, predominantly leaning toward soft law, being heavily decentralized, primarily public in nature, and mixing military and non-military regulation. This multi-faceted and non-hierarchical nature of global AI governance suggests that it is best characterized as a regime complex , or a “larger web of international rules and regimes” ( Alter and Meunier 2009 , 13; Keohane and Victor 2011 ) rather than as a single, discrete regime.

If global AI governance can be understood as a regime complex, which some researchers already claim ( Cihon et al. 2020a ), future scholarship should look for theoretical and methodological inspiration in research on regime complexity in other policy fields. This research has found that regime complexes are characterized by path dependence, as existing rules shape the formulation of new rules; venue shopping, as actors seek to steer regulatory efforts to the fora most advantageous to their interests; and legal inconsistencies, as rules emerge from fractious and overlapping negotiations in parallel processes ( Raustiala and Victor 2004 ). Scholars have also considered the design of regime complexes ( Eilstrup-Sangiovanni and Westerwinter 2021 ), institutional overlap among bodies in regime complexes ( Haftel and Lenz 2021 ), and actors’ forum-shopping within regime complexes ( Verdier 2022 ). Establishing whether these patterns and dynamics are key features also of the AI regime complex stands out as an important priority in future research.

Explaining AI Governance

As our understanding of the empirical patterns of global AI governance grows, a natural next step is to turn to explanatory questions. How may we explain the emerging global governance of AI? What accounts for variation in governance arrangements and how do they compare with those in other policy fields, such as environment, security, or trade? Political science and IR offer a plethora of useful theoretical tools that can provide insights into the global governance of AI. However, at the same time, the novelty of AI as a governance challenge raises new questions that may require novel or refined theories. Thus far, existing research on the global governance of AI has been primarily concerned with descriptive tasks and largely fallen short in engaging with explanatory questions.

We illustrate the potential of general theories to help explain global AI governance by pointing to three broad explanatory perspectives in IR ( Martin and Simmons 2012 )—power, interests, and ideas—which have served as primary sources of theorizing on global governance arrangements in other policy fields. These perspectives have conventionally been associated with the paradigmatic theories of realism, liberalism, and constructivism, respectively, but like much of the contemporary IR discipline, we prefer to formulate them as non-paradigmatic sources for mid-level theorizing of more specific phenomena (cf. Lake 2013 ). We focus our discussion on how accounts privileging power, interests, and ideas have explained the origins and designs of IOs and how they may help us explain wider patterns of global AI governance. We then discuss how theories of non-state actors and regime complexity, in particular, offer promising avenues for future research into the global governance of AI. Research fields like science and technology studies (e.g., Jasanoff 2016 ) or the political economy of international cooperation (e.g., Gilpin 1987 ) can provide additional theoretical insights, but these literatures are not discussed in detail here.

A first broad explanatory perspective is provided by power-centric theories, privileging the role of major states, capability differentials, and distributive concerns. While conventional realism emphasizes how states’ concern for relative gains impedes substantive international cooperation, viewing IOs as epiphenomenal reflections of underlying power relations ( Mearsheimer 1994 ), developed power-oriented theories have highlighted how powerful states seek to design regulatory contexts that favor their preferred outcomes ( Gruber 2000 ) or shape the direction of IOs using informal influence ( Stone 2011 ; Dreher et al. 2022 ).

In research on global AI governance, power-oriented perspectives are likely to prove particularly fruitful in investigating how great-power contestation shapes where and how the technology will be regulated. Focusing on the major AI powerhouses, scholars have started to analyze the contrasting regulatory strategies and policies of the United States, China, and the EU, often emphasizing issues of strategic competition, military balance, and rivalry ( Kania 2017 ; Horowitz et al. 2018 ; Payne 2018 , 2021 ; Johnson 2019 ; Jensen et al. 2020 ). Here, power-centric theories could help understand the apparent emphasis on military AI in both the United States and China, as witnessed by the recent establishment of a US National Security Commission on AI and China’s ambitious plans of integrating AI into its military forces ( Ding 2018 ). The EU, for its part, is negotiating the comprehensive AI Act, seeking to use its market power to set a European standard for AI that subsequently can become the global standard, as it previously did with its GDPR law on data protection and privacy ( Schmitt 2021 ). Given the primacy of these three actors in AI development, their preferences and outlook regarding regulatory solutions will remain a key research priority.

Power-based accounts are also likely to provide theoretical inspiration for research on AI governance in the domain of security and military competition. Some scholars are seeking to assess the implications of AI for strategic rivalries, and their possible regulation, by drawing on historical analogies ( Leung 2019 ; see also Drezner 2019 ). Observing that, from a strategic standpoint, military AI exhibits some similarities to the problems posed by nuclear weapons, researchers have examined whether lessons from nuclear arms control have applicability in the domain of AI governance. For example, Maas (2019a ) argues that historical experience suggests that the proliferation of military AI can potentially be slowed down via institutionalization, while Zaidi and Dafoe (2021 ), in a study of the Baruch Plan for Nuclear Weapons, contend that fundamental strategic obstacles—including mistrust and fear of exploitation by other states—need to be overcome to make regulation viable. This line of investigation can be extended by assessing other historical analogies, such as the negotiations that led to the Strategic Arms Limitation Talks (SALT) in 1972 or more recent efforts to contain the spread of nuclear weapons, where power-oriented factors have shown continued analytical relevance (e.g., Ruzicka 2018 ).

A second major explanatory approach is provided by the family of theoretical accounts that highlight how international cooperation is shaped by shared interests and functional needs ( Keohane 1984 ; Martin 1992 ). A key argument in rational functionalist scholarship is that states are likely to establish IOs to overcome barriers to cooperation—such as information asymmetries, commitment problems, and transaction costs—and that the design of these institutions will reflect the underlying problem structure, including the degree of uncertainty and the number of involved actors (e.g., Koremenos et al. 2001 ; Hawkins et al. 2006 ; Koremenos 2016 ).

Applied to the domain of AI, these approaches would bring attention to how the functional characteristics of AI as a governance problem shape the regulatory response. They would also emphasize the investigation of the distribution of interests and the possibility of efficiency gains from cooperation around AI governance. The contemporary proliferation of partnerships and initiatives on AI governance points to the suitability of this theoretical approach, and research has taken some preliminary steps, surveying state interests and their alignment (e.g., Campbell 2019 ; Radu 2021 ). However, a systematic assessment of how the distribution of interests would explain the nature of emerging governance arrangements, both in the aggregate and at the constituent level, has yet to be undertaken.

A third broad explanatory perspective is provided by theories emphasizing the role of history, norms, and ideas in shaping global governance arrangements. In contrast to accounts based on power and interests, this line of scholarship, often drawing on sociological assumptions and theory, focuses on how institutional arrangements are embedded in a wider ideational context, which itself is subject to change. This perspective has generated powerful analyses of how societal norms influence states’ international behavior (e.g., Acharya and Johnston 2007 ), how norm entrepreneurs play an active role in shaping the origins and diffusion of specific norms (e.g., Finnemore and Sikkink 1998 ), and how IOs socialize states and other actors into specific norms and behaviors (e.g., Checkel 2005 ).

Examining the extent to which domestic and societal norms shape discussions on global governance arrangements stands out as a particularly promising area of inquiry. Comparative research on national ethical standards for AI has already indicated significant cross-country convergence, indicating a cluster of normative principles that are likely to inspire governance frameworks in many parts of the world (e.g., Jobin et al. 2019 ). A closely related research agenda concerns norm entrepreneurship in AI governance. Here, preliminary findings suggest that civil society organizations have played a role in advocating norms relating to fundamental rights in the formulation of EU AI policy and other processes ( Ulnicane 2021 ). Finally, once AI governance structures have solidified further, scholars can begin to draw on norms-oriented scholarship to design strategies for the analysis of how those governance arrangements may play a role in socialization.

In light of the particularities of AI and its political landscape, we expect that global governance scholars will be motivated to refine and adapt these broad theoretical perspectives to address new questions and conditions. For example, considering China’s AI sector-specific resources and expertise, power-oriented theories will need to grapple with questions of institutional creation and modification occurring under a distribution of power that differs significantly from the Western-centric processes that underpin most existing studies. Similarly, rational functionalist scholars will need to adapt their tools to address questions of how the highly asymmetric distribution of AI capabilities—in particular between producers, which are few, concentrated, and highly resourced, and users and subjects, which are many, dispersed, and less resourced—affects the formation of state interests and bargaining around institutional solutions. For their part, norm-oriented theories may need to be refined to capture the role of previously understudied sources of normative and ideational content, such as formal and informal networks of computer programmers, which, on account of their expertise, have been influential in setting the direction of norms surrounding several AI technologies.

We expect that these broad theoretical perspectives will continue to inspire research on the global governance of AI, in particular for tailored, mid-level theorizing in response to new questions. However, a fully developed research agenda will gain from complementing these theories, which emphasize particular independent variables (power, interests, and norms), with theories and approaches that focus on particular issues, actors, and phenomena. There is an abundance of theoretical perspectives that can be helpful in this regard, including research on the relationship between science and politics ( Haas 1992 ; Jasanoff 2016 ), the political economy of international cooperation ( Gilpin 1987 ; Frieden et al. 2017 ), the complexity of global governance ( Raustiala and Victor 2004 ; Eilstrup-Sangiovanni and Westerwinter 2021 ), and the role of non-state actors ( Risse 2012 ; Tallberg et al. 2013 ). We focus here on the latter two: theories of regime complexity, which have grown to become a mainstream approach in global governance scholarship, as well as theories of non-state actors, which provide powerful tools for understanding how private organizations influence regulatory processes. Both literatures hold considerable promise in advancing scholarship of AI global governance beyond its current state.

As concluded above, the current structure of global AI governance fits the description of a regime complex. Thus, approaching AI governance through this theoretical lens, understanding it as a larger web of rules and regulations, can open new avenues of research (see Maas 2021 for a pioneering effort). One priority is to analyze the AI regime complex in terms of core dimensions, such as scale, diversity, and density ( Eilstrup-Sangiovanni and Westerwinter 2021 ). Pointing to the density of this regime complex, existing studies have suggested that global AI governance is characterized by a high degree of fragmentation ( Schmitt 2021 ), which has motivated assessments of the possibility of greater centralization ( Cihon et al. 2020b ). Another area of research is to examine the emergence of legal inconsistencies and tensions, likely to emerge because of the diverging preferences of major AI players and the tendency of self-interest actors to forum-shop when engaging within a regime complex. Finally, given that the AI regime complex exists in a very early state, it provides researchers with an excellent opportunity to trace the origins and evolution of this form of governance structure from the outset, thus providing a good case for both theory development and novel empirical applications.

If theories of regime complexity can shine a light on macro-level properties of AI governance, other theoretical approaches can guide research into micro-level dynamics and influences. Recognizing that non-state actors are central in both AI development and its emergent regulation, researchers should find inspiration in theories and tools developed to study the role and influence of non-state actors in global governance (for overviews, see Risse 2012 ; Jönsson and Tallberg forthcoming ). Drawing on such work will enable researchers to assess to what extent non-state actor involvement in the AI regime complex differs from previous experiences in other international regimes. It is clear that large tech companies, like Google, Meta, and Microsoft, have formed regulatory preferences and that their monetary resources and technological expertise enable them to promote these interests in legislative and bureaucratic processes. For example, the Partnership on AI (PAI), a multistakeholder organization with more than 50 members, includes American tech companies at the forefront of AI development and fosters research on issues of AI ethics and governance ( Schmitt 2021 ). Other non-state actors, including civil society watchdog organizations, like the Civil Liberties Union for Europe, have been vocal in the negotiations of the EU AI Act, further underlining the relevance of this strand of research.

When investigating the role of non-state actors in the AI regime complex, research may be guided by four primary questions. A first question concerns the interests of non-state actors regarding alternative AI global governance architectures. Here, a survey by Chavannes et al. (2020 ) on possible regulatory approaches to LAWS suggests that private companies developing AI applications have interests that differ from those of civil society organizations. Others have pointed to the role of actors rooted in research and academia who have sought to influence the development of AI ethics guidelines ( Zhu 2022 ). A second question is to what extent the regulatory institutions and processes are accessible to the aforementioned non-state actors in the first place. Are non-state actors given formal or informal opportunities to be substantively involved in the development of new global AI rules? Research points to a broad and comprehensive opening up of IOs over the past two decades ( Tallberg et al. 2013 ) and, in the domain of AI governance, early indications are that non-state actors have been granted access to several multilateral processes, including in the OECD and the EU (cf. Niklas and Dencik 2021 ). A third question concerns actual participation: Are non-state actors really making use of the opportunities to participate, and what determines the patterns of participation? In this vein, previous research has suggested that the participation of non-state actors is largely dependent on their financial resources ( Uhre 2014 ) or the political regime of their home country ( Hanegraaff et al. 2015 ). In the context of AI governance, this raises questions about if and how the vast resource disparities and divergent interests between private tech corporations and civil society organizations may bias patterns of participation. There is, for instance, research suggesting that private companies are contributing to a practice of ethics washing by committing to nonbinding ethical guidelines while circumventing regulation ( Wagner 2018 ; Jobin et al. 2019 ; Rességuier and Rodrigues 2020 ). Finally, a fourth question is to what extent, and how, non-state actors exert influence on adopted AI rules. Existing scholarship suggests that non-state actors typically seek to shape the direction of international cooperation via lobbying ( Dellmuth and Tallberg 2017 ), while others have argued that non-state actors use participation in international processes largely to expand or sustain their own resources ( Hanegraaff et al. 2016 ).

The previous section suggested that emerging global initiatives to regulate AI amount to a regime complex and that an empirical approach could help to map and explain these regulatory developments. In this section, we move beyond positive empirical questions to consider the normative concerns at stake in the global governance of AI. We argue that normative theorizing is needed both for assessing how well existing arrangements live up to ideals such as democracy and justice and for evaluating how best to specify what these ideals entail for the global governance of AI.

Ethical values frequently highlighted in the context of AI governance include transparency, inclusion, accountability, participation, deliberation, fairness, and beneficence ( Floridi et al. 2018 ; Jobin et al. 2019 ). A normative perspective suggests several ways in which to theorize and analyze such values in relation to the global governance of AI. One type of normative analysis focuses on application, that is, on applying an existing normative theory to instances of AI governance, assessing how well such regulatory arrangements realize their principles (similar to how political theorists have evaluated whether global governance lives up to standards of deliberation; see Dryzek 2011 ; Steffek and Nanz 2008 ). Such an analysis could also be pursued more narrowly by using a certain normative theory to assess the implications of AI technologies, for instance, by approaching the problem of algorithmic bias based on notions of fairness or justice ( Vredenburgh 2022 ). Another type of normative analysis moves from application to justification, analyzing the structure of global AI governance with the aim of theory construction. In this type of analysis, the goal is to construe and evaluate candidate principles for these regulatory arrangements in order to arrive at the best possible (most justified) normative theory. In this case, the theorist starts out from a normative ideal broadly construed (concept) and arrives at specific principles (conception).

In the remainder of this section, we will point to the promises of analyzing global AI governance based on the second approach. We will focus specifically on the normative ideals of justice and democracy. While many normative ideals could serve as focal points for an analysis of the AI domain, democracy and justice appear particularly central for understanding the normative implications of the governance of AI. Previous efforts to deploy political philosophy to shed light on normative aspects of global governance point to the promise of this focus (e.g., Caney 2005 , 2014 ; Buchanan 2013 ). It is also natural to focus on justice and democracy given that many of the values emphasized in AI ethics and existing ethics guidelines are analytically close to justice and democracy. Our core argument will be that normative research needs to be attentive to how these ideals would be best specified in relation to both the procedures and outcomes of the global governance of AI.

AI Ethics and the Normative Analysis of Global AI Governance

Although there is a rich literature on moral or ethical aspects related to specific AI applications, investigations into normative aspects of global AI governance are surprisingly sparse (for exceptions, see Müller 2020 ; Erman and Furendal 2022a , 2022b ). Researchers have so far focused mostly on normative and ethical questions raised by AI considered as a tool, enabling, for example, autonomous weapons systems ( Sparrow 2007 ) and new forms of political manipulation ( Susser et al. 2019 ; Christiano 2021 ). Some have also considered AI as a moral agent of its own, focusing on how we could govern, or be governed by, a hypothetical future artificial general intelligence ( Schwitzgebel and Garza 2015 ; Livingston and Risse 2019 ; cf. Tasioulas 2019 ; Bostrom et al. 2020 ; Erman and Furendal 2022a ). Examples such as these illustrate that there is, by now, a vibrant field of “AI ethics” that aims to consider normative aspects of specific AI applications.

As we have shown above, however, initiatives to regulate AI beyond the nation-state have become increasingly common, and they are often led by IOs, multinational companies, private standardization bodies, and civil society organizations. These developments raise normative issues that require a shift from AI ethics in general to systematic analyses of the implications of global AI governance. It is crucial to explore these normative dimensions of how AI is governed, since how AI is governed invokes key normative questions pertaining to the ideals that ought to be met.

Apart from attempts to map or describe the central norms in the existing global governance of AI (cf. Jobin et al.), most normative analyses of the global governance of AI can be said to have proceeded in two different ways. The dominant approach is to employ an outcome-based focus ( Dafoe 2018 ; Winfield et al. 2019 ; Taeihagh 2021 ), which starts by identifying a potential problem or promise created by AI technology and then seeks to identify governance mechanisms or principles that can minimize risks or make a desired outcome more likely. This approach can be contrasted with a procedure-based focus, which attaches comparatively more weight to how governance processes happen in existing or hypothetical regulatory arrangements. It recognizes that there are certain procedural aspects that are important and might be overlooked by an analysis that primarily assesses outcomes.

The benefits of this distinction become apparent if we focus on the ideals of justice and democracy. Broadly construed, we understand justice as an ideal for how to distribute benefits and burdens—specifying principles that determine “who owes what to whom”—and democracy as an ideal for collective decision-making and the exercise of political power—specifying principles that determine “who has political power over whom” ( Barry 1991 ; Weale 1999 ; Buchanan and Keohane 2006 ; Christiano 2008 ; Valentini 2012 , 2013 ). These two ideals can be analyzed with a focus on procedure or outcome, producing four fruitful avenues of normative research into global AI governance. First, justice could be understood as a procedural value or as a distributive outcome. Second, and likewise, democracy could be a feature of governance processes or an outcome of those processes. Below, we discuss existing research from the standpoint of each of these four avenues. We conclude that there is great potential for novel insights if normative theorists consider the relatively overlooked issues of outcome aspects of justice and procedural aspects of democracy in the global governance of AI.

Procedural and Outcome Aspects of Justice

Discussions around the implications of AI applications on justice, or fairness, are predominantly concerned with procedural aspects of how AI systems operate. For instance, ever since the problem of algorithmic bias—i.e., the tendency that AI-based decision-making reflects and exacerbates existing biases toward certain groups—was brought to public attention, AI ethicists have offered suggestions of why this is wrong, and AI developers have sought to construct AI systems that treat people “fairly” and thus produce “justice.” In this context, fairness and justice are understood as procedural ideals, which AI decision-making frustrates when it fails to treat like cases alike, and instead systematically treats individuals from different groups differently ( Fazelpour and Danks 2021 ; Zimmermann and Lee-Stronach 2022 ). Paradigmatic examples include automated predictions about recidivism among prisoners that have impacted decisions about people’s parole and algorithms used in recruitment that have systematically favored men over women ( Angwin et al. 2016 ; O'Neil 2017 ).

However, the emerging global governance of AI also has implications for how the benefits and burdens of AI technology are distributed among groups and states—i.e., outcomes ( Gilpin 1987 ; Dreher and Lang 2019 ). Like the regulation of earlier technological innovations ( Krasner 1991 ; Drezner 2019 ), AI governance may not only produce collective benefits, but also favor certain actors at the expense of others ( Dafoe 2018 ; Horowitz 2018 ). For instance, the concern about AI-driven automation and its impact on employment is that those who lose their jobs because of AI might carry a disproportionately large share of the negative externalities of the technology without being compensated through access to its benefits (cf. Korinek and Stiglitz 2019 ; Erman and Furendal 2022a ). Merely focusing on justice as a procedural value would overlook such distributive effects created by the diffusion of AI technology.

Moreover, this example illustrates that since AI adoption may produce effects throughout the global economy, regulatory efforts will have to go beyond issues relating to the technology itself. Recognizing the role of outcomes of AI governance entails that a broad range of policies need to be pursued by existing and emerging governance regimes. The global trade regime, for instance, may need to be reconsidered in order for the distribution of positive and negative externalities of AI technology to be just. Suggestions include pursuing policies that can incentivize certain kinds of AI technology or enable the profits gained by AI developers to be shared more widely (cf. Floridi et al. 2018 ; Erman and Furendal 2022a ).

In sum, with regard to outcome aspects of justice, theories are needed to settle which benefits and burdens created by global AI adoption ought to be fairly distributed and why (i.e., what the “site” and “scope” of AI justice are) (cf. Gabriel 2022 ). Similarly, theories of procedural aspects should look beyond individual applications of AI technology and ask whether a fairer distribution of influence over AI governance may help produce more fair outcomes, and if so how. Extending existing theories of distributive justice to the realm of global AI governance may put many of their central assumptions in a new light.

Procedural and Outcome Aspects of Democracy

Normative research could also fruitfully shed light on how emerging AI governance should be analyzed in relation to the ideal of democracy, such as what principles or criteria of democratic legitimacy are most defensible. It could be argued, for instance, that the decision process must be open to democratic influence for global AI governance to be democratically legitimate ( Erman and Furendal 2022b ). Here, normative theory can explain why it matters from the standpoint of democracy whether the affected public has had a say—either directly through open consultation or indirectly through representation—in formulating the principles that guide AI governance. The nature of the emerging AI regime complex—where prominent roles are held by multinational companies and private standard-setting bodies—suggests that it is far from certain that the public will have this kind of influence.

Importantly, it is likely that democratic procedures will take on different shapes in global governance compared to domestic politics ( Dahl 1999 ; Scholte 2011 ). A viable democratic theory must therefore make sense of how the unique properties of global governance raise issues or require solutions that are distinct from those in the domestic context. For example, the prominent influence of non-state actors, including the large tech corporations developing cutting-edge AI technology, suggests that it is imperative to ask whether different kinds of decision-making may require different normative standards and whether different kinds of actors may have different normative status in such decision-making arrangements.

Initiatives from non-state actors, such as the tech company-led PAI discussed above, often develop their own non-coercive ethics guidelines. Such documents may seek effects similar to coercively upheld regulation, such as the GDPR or the EU AI Act. For example, both Google and the EU specify that AI should not reinforce biases ( High-Level Expert Group on Artificial Intelligence 2019 ; Google 2022 ). However, from the perspective of democratic legitimacy, it may matter extensively which type of entity adopts AI regulations and on what grounds those decision-making entities have the authority to issue AI regulations ( Erman and Furendal 2022b ).

Apart from procedural aspects, a satisfying democratic theory of global AI governance will also have to include a systematic analysis of outcome aspects. Important outcome aspects of democracy include accountability and responsiveness. Accountability may be improved, for example, by instituting mechanisms to prevent corruption among decision-makers and to secure public access to governing documents, and responsiveness may be improved by strengthening the discursive quality of global decision processes, for instance, by involving international NGOs and civil movements that give voice to marginalized groups in society. With regard to tracing citizens’ preferences, some have argued that democratic decision-making can be enhanced by AI technology that tracks what people want and consistently reach “better” decisions than human decision-makers (cf. König and Wenzelburger 2022 ). Apart from accountability and responsiveness, other relevant outcome aspects of democracy include, for example, the tendency to promote conflict resolution, improve the epistemic quality of decisions, and dignity and equality among citizens.

In addition, it is important to analyze how procedural and outcome concerns are related. This issue is often neglected, which again can be illustrated by the ethics guidelines from IOs, such as the OECD Principles on Artificial Intelligence and the UNESCO Recommendation on Ethics of AI. Such documents often stress the importance of democratic values and principles, such as transparency, accountability, participation, and deliberation. Yet they typically treat these values as discrete and rarely explain how they are interconnected ( Jobin et al. 2019 ; Schiff et al. 2020 ; Hagendorff 2020 , 103). Democratic theory can fruitfully step in to explain how the ideal of “the rule by the people” includes two sides that are intimately connected. First, there is an access side of political power, where those affected should have a say in the decision-making, which might require participation, deliberation, and political equality. Second, there is an exercise side of political power, where those very decisions should apply in appropriate ways, which in turn might require effectiveness, transparency, and accountability. In addition to efforts to map and explain norms and values in the global governance of AI, theories of democratic AI governance can hence help explain how these two aspects are connected (cf. Erman 2020 ).

In sum, the global governance of AI raises a number of issues for normative research. We have identified four promising avenues, focused on procedural and outcome aspects of justice and democracy in the context of global AI governance. Research along these four avenues can help to shed light on the normative challenges facing the global governance of AI and the key values at stake, as well as provide the impetus for novel theories on democratic and just global AI governance.

This article has charted a new agenda for research into the global governance of AI. While existing scholarship has been primarily descriptive or policy-oriented, we propose an agenda organized around theory-driven positive and normative questions. To this end, we have outlined two broad analytical perspectives on the global governance of AI: an empirical approach, aimed at conceptualizing and explaining global AI governance; and a normative approach, aimed at developing and applying ideals for appropriate global AI governance. Pursuing these empirical and normative approaches can help to guide future scholarship on the global governance of AI toward critical questions, core concepts, and promising theories. At the same time, exploring AI as a regulatory issue provides an opportunity to further develop these general analytical approaches as they confront the particularities of this important area of governance.

We conclude this article by highlighting the key takeaways from this research agenda for future scholarship on empirical and normative dimensions of the global governance of AI. First, research is required to identify where and how AI is becoming globally governed . Mapping and conceptualizing the emerging global governance of AI is a first necessary step. We argue that research may benefit from considering the variety of ways in which new regulation may come about, from the reinterpretation of existing rules and the extension of prevailing sectoral governance to the negotiation of entirely new frameworks. In addition, we suggest that scholarship may benefit from considering how global AI governance may be conceptualized in terms of key analytical dimensions, such as horizontal–vertical, centralized–decentralized, and formal–informal.

Second, research is necessary to explain why AI is becoming globally governed in particular ways . Having mapped global AI governance, we need to account for the factors that drive and shape these regulatory processes and arrangements. We argue that political science and IR offer a variety of theoretical tools that can help to explain the global governance of AI. In particular, we highlight the promise of theories privileging the role of power, interests, ideas, regime complexes, and non-state actors, but also recognize that research fields such as science and technology studies and political economy can yield additional theoretical insights.

Third, research is needed to identify what normative ideals global AI governance ought to meet . Moving from positive to normative issues, a first critical question pertains to the ideals that should guide the design of appropriate global AI governance. We argue that normative theory provides the tools necessary to engage with this question. While normative theory can suggest several potential principles, we believe that it may be especially fruitful to start from the ideals of democracy and justice, which are foundational and recurrent concerns in discussions about political governing arrangements. In addition, we suggest that these two ideals are relevant both for the procedures by which AI regulation is adopted and for the outcomes of such regulation.

Fourth, research is required to evaluate how well global AI governance lives up to these normative ideals . Once appropriate normative ideals have been selected, we can assess to what extent and how existing arrangements conform to these principles. We argue that previous research on democracy and justice in global governance offers a model in this respect. A critical component of such research is the integration of normative and empirical research: normative research for elucidating how normative ideals would be expressed in practice, and empirical research for analyzing data on whether actual arrangements live up to those ideals.

In all, the research agenda that we outline should be of interest to multiple audiences. For students of political science and IR, it offers an opportunity to apply and refine concepts and theories in a novel area of global governance of extensive future importance. For scholars of AI, it provides an opportunity to understand how political actors and considerations shape the conditions under which AI applications may be developed and used. For policymakers, it presents an opportunity to learn about evolving regulatory practices and gaps, interests shaping emerging arrangements, and trade-offs to be confronted in future efforts to govern AI at the global level.

A previous version of this article was presented at the Global and Regional Governance workshop at Stockholm University. We are grateful to Tim Bartley, Niklas Bremberg, Lisa Dellmuth, Felicitas Fritzsche, Faradj Koliev, Rickard Söder, Carl Vikberg, Johanna von Bahr, and three anonymous reviewers for ISR for insightful comments and suggestions. The research for this article was funded by the WASP-HS program of the Marianne and Marcus Wallenberg Foundation (Grant no. MMW 2020.0044).

We use “global governance” to refer to regulatory processes beyond the nation-state, whether on a global or regional level. While states and IOs often are central to these regulatory processes, global governance also involves various types of non-state actors ( Rosenau 1999 ).

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Can green finance reduce carbon emission? A theoretical analysis and empirical evidence from China

Research Article
Published: 10 May 2024

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Peifeng Jiang 1 ,
Chaomin Xu 2 &
Yizhi Chen ORCID: orcid.org/0009-0007-7399-5378 3

As an important way for China to achieve its dual-carbon goal, green finance has become the foundation for promoting high-quality economic development in China. In order to clarify the mechanism of green finance on carbon emissions, this paper puts green finance into the economic model and deduces the relationship between green finance and carbon emission reduction. This paper is based on the panel data of 30 provinces in China (excluding Tibet, Hong Kong, Macao, and Taiwan) from 2008 to 2019, using the individual fixed effect model, dynamical model, mediator model, and SDM model to study the impact of green finance on carbon emissions and its impact path of upgrading of the industrial structure and the development of science and technology based on the measurement of the green finance development index of each province by the entropy method. The findings show that the development of green finance can reduce carbon emission significantly, which can be sustained until at least the third phase and generates spatial spillover effects; regional heterogeneity analysis finds that the development of green finance shows geographical discrepancies: compared with the eastern and western regions, the development of green finance in central region can reduce carbon emissions more significantly; not only can the development of green finance directly reduce carbon emission, but also through the upgrading of industrial structure and technological innovation. The research not only provides a new perspective and supplementary empirical evidence for understanding the carbon emission reduction effect of green finance, but also offers some useful references for green finance to contribute to carbon emission reduction.

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This paper is funded in accordance with the China Association for Science and Technology for the 2022 Graduate Science Popularization Ability Improvement Project (KXYJS2022064) Jiangsu Provincial Postgraduate Practice Innovation Program (KYCX23_1097).

Yizhi Chen thanks the China Association for Science and Technology for the 2022 Graduate Science Popularization Ability Improvement Project (KXYJS2022064) Jiangsu Provincial Postgraduate Practice Innovation Program (KYCX23_1097) for its support.

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Peifeng Jiang

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Contributions

Author 1 (first author): Peifeng Jiang. PF contributed to the conceptualization, methodology, design, theoretical model derivation, and data analysis of the study and was a major contributor in writing the manuscript.

Author 2: Chaomin Xu. CX contributed to the investigation, former analysis, data analysis, validation, and writing of the study.

Author 3 (corresponding author): Yizhi Chen. YC contributed to the project administration, supervision, revising, and editing of the study.

All authors read and approved the final manuscript.

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Correspondence to Yizhi Chen .

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Conceptualization

Under the background of the increasingly serious problem of carbon emissions, how China achieves “carbon peak” and “carbon neutrality” shows its pursuit of high-quality economic development and its responsibility of a major country. In addition, whether China can achieve “carbon peak” and “carbon neutrality” will significantly affect the global carbon reduction action. But there are few theoretical research on carbon emission and green finance. So this paper attempts to construct an economic model of green finance and carbon emission.

Methodology

This paper uses the individual fixed effect model, dynamical model, mediator model, and SDM model to study the impact of green finance on carbon emissions and its impact path of upgrading of the industrial structure and the development of science and technology.

The data processing, modeling analysis, and plotting in this paper were carried out using Excel and Stata.

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The manuscript describes original work and is not under consideration by any other journal. All authors have approved the manuscript that is enclosed.

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Jiang, P., Xu, C. & Chen, Y. Can green finance reduce carbon emission? A theoretical analysis and empirical evidence from China. Environ Sci Pollut Res (2024). https://doi.org/10.1007/s11356-024-33572-8

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Published : 10 May 2024

DOI : https://doi.org/10.1007/s11356-024-33572-8

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Empirical research is based on observed and measured phenomena and derives knowledge from actual experience rather than from theory or belief. How do you know if a study is empirical? Read the subheadings within the article, book, or report and look for a description of the research "methodology."
Subject Guides: Identify Empirical Research Articles: Home
An empirical research article is an article which reports research based on actual observations or experiments. The research may use quantitative research methods, which generate numerical data and seek to establish causal relationships between two or more variables. (1) Empirical research articles may use qualitative research methods, which ...
Identifying Empirical Articles
Identifying Empirical Research Articles. Look for the IMRaD layout in the article to help identify empirical research.Sometimes the sections will be labeled differently, but the content will be similar. Introduction: why the article was written, research question or questions, hypothesis, literature review; Methods: the overall research design and implementation, description of sample ...
Empirical Research: Defining, Identifying, & Finding
Once you know the characteristics of empirical research, the next question is how to find those characteristics when reading a scholarly, peer-reviewed journal article.Knowing the basic structure of an article will help you identify those characteristics quickly. The IMRaD Layout. Many scholarly, peer-reviewed journal articles, especially empirical articles, are structured according to the ...
Empirical Research
The term "empirical" entails gathered data based on experience, observations, or experimentation. In empirical research, knowledge is developed from factual experience as opposed to theoretical assumption and usually involved the use of data sources like datasets or fieldwork, but can also be based on observations within a laboratory setting.
The Psychology of Morality: A Review and Analysis of Empirical Studies
We review empirical research on (social) psychology of morality to identify which issues and relations are well documented by existing data and which areas of inquiry are in need of further empirical evidence. An electronic literature search yielded a total of 1,278 relevant research articles published from 1940 through 2017.
Full article: Research Problems and Hypotheses in Empirical Research
Research problems and hypotheses are important means for attaining valuable knowledge. They are pointers or guides to such knowledge, or as formulated by Kerlinger ( 1986, p. 19): " … they direct investigation.". There are many kinds of problems and hypotheses, and they may play various roles in knowledge construction.
Psychological Science
Psychological Science, the flagship journal of the Association for Psychological Science, is the leading peer-reviewed journal publishing empirical research spanning the entire spectrum of the science of psychology.The journal publishes high quality research articles of general interest and on important topics spanning the entire spectrum of the science of psychology.
Overview
An empirical research article reports research based on actual observation or experiment. The research may use quantitative or qualitative research methods. Qualitative Research objectively and critically analyzes behaviors, beliefs, feelings, or other values ("People suffering from Illness A tend to be more cautious.")
Empirical Research in the Social Sciences and Education
An empirical research article is a primary source where the authors reported on experiments or observations that they conducted. Their research includes their observed and measured data that they derived from an actual experiment rather than theory or belief. ... Scholarly journals sometimes use a specific layout for empirical articles, called ...
Finding Empirical Articles
You can use similar strateg to find empirical articles. You may also add specific statistical terms to your search, such as chi, t-test, p-value, or standard deviation. Try searching with terms used in the scientific method: method, results, discussion, or conclusion.
The Empirical Research Paper: A Guide
The abstract should be written after your have completed your research and written your article. The abstract is very concise and often does not exceed more than 120 words. It should contain a sentence addressing each section of the the article including: The research question; The important information pertaining to the participants (or subjects)
The Neuroscience of Growth Mindset and Intrinsic Motivation
Most empirical research on growth mindset and intrinsic motivation has focused on behavioral methods and self-reports of experiences. There is little information about the internal processes of motivation at a higher level of resolution. It is, therefore, relevant and timely to examine the existing literature and empirical research that is ...
LibGuides: Psychology: Find Empirical Research Articles
The method for finding empirical research articles varies depending upon the database* being used. 1. The PsycARTICLES and PsycInfo databases (both from the APA) includes a Methodology filter that can be used to identify empirical studies. Look for the filter on the Advanced Search screen. To see a list and description of all of the of ...
How do I know if a research article is empirical?
Empirical research draws from observed or measured phenomena and derives knowledge from actual experimentation or observation. Empirical research articles are considered original, primary research. In these types of articles, readers will generally find the following sections organized by IMRaD format (Introduction, Method, Results, and ...
Journal of Empirical Research on Human Research Ethics
The Journal of Empirical Research on Human Research Ethics (JERHRE) is the only journal in the field of human research ethics dedicated exclusively to empirical research.Empirical knowledge translates ethical principles into procedures appropriate to specific cultures, contexts, and research topics. The journal's distinguished editorial and advisory board brings a range of expertise and ...
A systematic review of high impact empirical studies in STEM education
Top 100 most-cited empirical research articles from 2000 to 2021. Figure 2 shows the distribution of the top 100 most-cited empirical research journal publications in STEM education over the years 2000-2021. As the majority of these publications (72 out of 100, 72%) were published between 2011 and 2016, the results suggest that publications ...
APA PsycArticles
The citation footprint of APA's journals (PDF, 91KB) is more than double our article output, demonstrating our commitment and focus on editorial excellence.Research published in APA PsycArticles provides global, diverse perspectives on the field of psychology. The database is updated bi-weekly, ensuring your patrons are connected to articles revealing the latest psychological findings.
Searching for Empirical Research
Note: empirical research articles will have a literature review section as part of the Introduction, but in an empirical research article the literature review exists to give context to the empirical research, which is the primary focus of the article. In a literature review article, the literature review is the focus.
Free APA Journal Articles
Recently published articles from subdisciplines of psychology covered by more than 90 APA Journals™ publications. For additional free resources (such as article summaries, podcasts, and more), please visit the Highlights in Psychological Research page. Browse and read free articles from APA Journals across the field of psychology, selected by ...
Effects of Mindfulness on Psychological Health: A Review of Empirical
Abstract. Within the past few decades, there has been a surge of interest in the investigation of mindfulness as a psychological construct and as a form of clinical intervention. This article reviews the empirical literature on the effects of mindfulness on psychological health. We begin with a discussion of the construct of mindfulness ...
Empirical research
A scientist gathering data for her research. Empirical research is research using empirical evidence. It is also a way of gaining knowledge by means of direct and indirect observation or experience. Empiricism values some research more than other kinds. Empirical evidence (the record of one's direct observations or experiences) can be analyzed ...
Empirical Research: Definition, Methods, Types and Examples
Empirical research is defined as any research where conclusions of the study is strictly drawn from concretely empirical evidence, and therefore "verifiable" evidence. This empirical evidence can be gathered using quantitative market research and qualitative market research methods. For example: A research is being conducted to find out if ...
Critical Science: A systematic literature review of empirical research
This systematic literature review examines empirical CSA research published between 1979 and 2022 to better understand what the CSA means for the profession. A total of 71 articles met the ...
Global Governance of Artificial Intelligence: Next Steps for Empirical
The purpose of this article is to outline an agenda for research into the global governance of AI. The article distinguishes between two broad perspectives: an empirical approach, aimed at mapping and explaining global AI governance; and a normative approach, aimed at developing and applying standards for appropriate global AI governance.
The top 10 journal articles
This review article in Technology, Mind, and Behavior (Vol. 2, No. 1) combines theory and prior research to derive four explanations for "Zoom fatigue," the feeling of exhaustion brought on by video calls: excessive close-up eye contact with speakers, constant self-evaluation of one's own image on the screen, remaining in a fixed position ...
Can green finance reduce carbon emission? A theoretical ...
Third, in terms of empirical research, due to the interactive relationship between green finance and carbon emissions, there may be serious endogeneity problems in the empirical model, which leads to errors of estimation results in the model (Guo 2022; Yan et al. 2016). In the existing literature, although there are some scholars who use TSLS ...
International tourism and economic growth: Empirical evidence from
The International Journal of Tourism Research (IJTR) is a travel research journal publishing current research developments in tourism and hospitality. Abstract This study evaluates the trend and growth pattern of international tourism and analyzes the impact of tourism on the economic growth of Kerala for the past four decades from 1980 to 2019

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A systematic review of high impact empirical studies in STEM education

Introduction

Learning from existing reviews of STEM education research

The importance of examining high impact empirical research publications in STEM education

Considerations in identifying and selecting high impact empirical research publications

Current review

Time period

Searching and identifying highly cited empirical research journal publications in STEM education

Research questions

Searching and identifying the top 100 most-cited empirical research journal publications in STEM education

Data analysis

Results and discussion

Top 100 most-cited empirical research articles from 2000 to 2021

Distributions and patterns of highly cited publications across different journals

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Research topics and methods

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Empirical Research: Defining, Identifying, & Finding

Where Do I Find Empirical Research?

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Common Types of Articles That Are Not Empirical

Empirical Research: Definition, Methods, Types and Examples

Empirical research: Definition