The effect of chronic caffeine supplementation on endurance performance has been studied extensively in different populations. However, concurrent research on the effects of acute supplementation of caffeine on cardiorespiratory responses during endurance exercise in hot and humid conditions is unavailable
Source: Ping, WC, Keong, CC & Bandyopadhyay, A 2010, ‘Effects of acute supplementation of caffeine on cardiorespiratory responses during endurance running in a hot and humid climate’, Indian Journal of Medical Research, vol. 132, pp. 36–41. Used under a CC-BY-NC-SA licence.
Step two of writing a literature review is synthesis.
Synthesis describes combining separate components or elements to form a connected whole.
You will use the results of your analysis to find themes to build your literature review around. Each of the themes identified will become a subheading within the body of your literature review.
A good place to start when identifying themes is with the dependent variables (results/findings) that were investigated in the research studies.
Because all of the research articles you are incorporating into your literature review are related to your topic, it is likely that they have similar study designs and have measured similar dependent variables. Review the ‘Results’ column of your analysis grid. You may like to collate the common themes in a synthesis grid (see, for example Table 7.4 ).
Step three of writing a literature review is evaluation, which can only be done after carefully analysing your research papers and synthesising the common themes (findings).
During the evaluation stage, you are making judgements on the themes presented in the research articles that you have read. This includes providing physiological explanations for the findings. It may be useful to refer to the discussion section of published original investigation research papers, or another literature review, where the authors may mention tested or hypothetical physiological mechanisms that may explain their findings.
When the findings of the investigations related to a particular theme are inconsistent (e.g., one study shows that caffeine effects performance and another study shows that caffeine had no effect on performance) you should attempt to provide explanations of why the results differ, including physiological explanations. A good place to start is by comparing the methodologies to determine if there are any differences that may explain the differences in the findings (see the ‘Experimental design’ column of your analysis grid). An example of evaluation is shown in the examples that follow in this section, under ‘Running performance’ and ‘RPE ratings’.
When the findings of the papers related to a particular theme are consistent (e.g., caffeine had no effect on oxygen uptake in both studies) an evaluation should include an explanation of why the results are similar. Once again, include physiological explanations. It is still a good idea to compare methodologies as a background to the evaluation. An example of evaluation is shown in the following under ‘Oxygen consumption’.
Once you have completed the analysis, and synthesis grids and written your evaluation of the research papers , you can combine synthesis and evaluation information to create a paragraph for a literature review ( Figure 7.4 ).
The following paragraphs are an example of combining the outcome of the synthesis and evaluation stages to produce a paragraph for a literature review.
Note that this is an example using only two papers – most literature reviews would be presenting information on many more papers than this ( (e.g., 106 papers in the review article by Bain and colleagues discussed later in this chapter). However, the same principle applies regardless of the number of papers reviewed.
The next part of this chapter looks at the each section of a literature review and explains how to write them by referring to a review article that was published in Frontiers in Physiology and shown in Figure 7.1. Each section from the published article is annotated to highlight important features of the format of the review article, and identifies the synthesis and evaluation information.
In the examination of each review article section we will point out examples of how the authors have presented certain information and where they display application of important cognitive processes; we will use the colour code shown below:
This should be one paragraph that accurately reflects the contents of the review article.
The introduction should establish the context and importance of the review
The reference section provides a list of the references that you cited in the body of your review article. The format will depend on the journal of publication as each journal has their own specific referencing format.
It is important to accurately cite references in research papers to acknowledge your sources and ensure credit is appropriately given to authors of work you have referred to. An accurate and comprehensive reference list also shows your readers that you are well-read in your topic area and are aware of the key papers that provide the context to your research.
It is important to keep track of your resources and to reference them consistently in the format required by the publication in which your work will appear. Most scientists will use reference management software to store details of all of the journal articles (and other sources) they use while writing their review article. This software also automates the process of adding in-text references and creating a reference list. In the review article by Bain et al. (2014) used as an example in this chapter, the reference list contains 106 items, so you can imagine how much help referencing software would be. Chapter 5 shows you how to use EndNote, one example of reference management software.
Click the drop down below to review the terms learned from this chapter.
Copyright note:
Bain, A.R., Morrison, S.A., & Ainslie, P.N. (2014). Cerebral oxygenation and hyperthermia. Frontiers in Physiology, 5 , 92.
Pautasso, M. (2013). Ten simple rules for writing a literature review. PLoS Computational Biology, 9 (7), e1003149.
How To Do Science Copyright © 2022 by University of Southern Queensland is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.
To answer this question, please read the following content excerpted from the UCLA Undergraduate Science Journal guidelines . Also, if you scroll down this page there is a link to a recorded webinar about science literature reviews. I have also included some links to books about engineering and STEM literature reviews at the bottom of this page.
Guide to Writing a Review Article: What is a Literature Review?
A literature review addresses a specific topic by evaluating research that others have done on it. As an author, you will weave your review article around a certain thesis or problem you wish to address, evaluate the quality and the meaning of the studies done before, and arrives at a conclusion about the problem based on the studies evaluated.
A literature review is not a summary and it is not a list. The author cannot simply cite the studies that have been done and the results that have been obtained. If you describe past research without evaluating it, then your “review” is little more than a book report. A literature review must be a synthesis of the results of your search, organized around your chosen theme.
The article should be your evaluation of the literature and of the issue at stake. This is a challenging piece of work. You must:
1. Organize information and relate it to your thesis or research question
2. Synthesize results into a summary of what is and isn’t known
3. Identify contradictions, inconsistencies, and gaps in the research
4. Identify and analyze controversy when it appears in the literature
5. Develop questions for further research
6. Draw conclusions based on your evaluation of the studies presented
Literature Review vs. Research Article
A literature review surveys research done by others in a particular area. You will read and evaluate studies done by others, instead of conducting a new study yourself. Research articles, on the other hand, present research that you have conducted yourself. A research article should contain enough background information and literature evaluation to shed light on your study, but the ultimate purpose of the paper is to report research done by you.
Photo by National Cancer Institute on Unsplash
Below is a recorded webinar led by the science and engineering librarians from Western Michigan State University on science literature reviews:
Here is another video on literature reviews by Associate Professor Cecile Badenhorst of Memorial University of Newfoundland. Dr. Badenhorst uses examples from research in the field of education, but the theoretical components she introduces are of interest to science students as well.
Manisha Bahl, A Step-by-Step Guide to Writing a Scientific Review Article, Journal of Breast Imaging , Volume 5, Issue 4, July/August 2023, Pages 480–485, https://doi.org/10.1093/jbi/wbad028
Scientific review articles are comprehensive, focused reviews of the scientific literature written by subject matter experts. The task of writing a scientific review article can seem overwhelming; however, it can be managed by using an organized approach and devoting sufficient time to the process. The process involves selecting a topic about which the authors are knowledgeable and enthusiastic, conducting a literature search and critical analysis of the literature, and writing the article, which is composed of an abstract, introduction, body, and conclusion, with accompanying tables and figures. This article, which focuses on the narrative or traditional literature review, is intended to serve as a guide with practical steps for new writers. Tips for success are also discussed, including selecting a focused topic, maintaining objectivity and balance while writing, avoiding tedious data presentation in a laundry list format, moving from descriptions of the literature to critical analysis, avoiding simplistic conclusions, and budgeting time for the overall process.
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A literature review is an integrated analysis -- not just a summary-- of scholarly writings and other relevant evidence related directly to your research question. That is, it represents a synthesis of the evidence that provides background information on your topic and shows a association between the evidence and your research question.
A literature review may be a stand alone work or the introduction to a larger research paper, depending on the assignment. Rely heavily on the guidelines your instructor has given you.
Why is it important?
A literature review is important because it:
APA Style Blog - for those harder to find answers
Your literature review should be guided by your central research question. The literature represents background and research developments related to a specific research question, interpreted and analyzed by you in a synthesized way.
How many studies do you need to look at? How comprehensive should it be? How many years should it cover?
Make a list of the databases you will search.
Where to find databases:
Some questions to help you analyze the research:
Tips:
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Email citation, add to collections.
Your saved search, create a file for external citation management software, your rss feed.
Affiliation.
Literature reviews are valuable resources for the scientific community. With research accelerating at an unprecedented speed in recent years and more and more original papers being published, review articles have become increasingly important as a means to keep up to date with developments in a particular area of research. A good review article provides readers with an in-depth understanding of a field and highlights key gaps and challenges to address with future research. Writing a review article also helps to expand the writer's knowledge of their specialist area and to develop their analytical and communication skills, amongst other benefits. Thus, the importance of building review-writing into a scientific career cannot be overstated. In this instalment of The FEBS Journal's Words of Advice series, I provide detailed guidance on planning and writing an informative and engaging literature review.
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Literature Review is a comprehensive survey of the works published in a particular field of study or line of research, usually over a specific period of time, in the form of an in-depth, critical bibliographic essay or annotated list in which attention is drawn to the most significant works.
Also, we can define a literature review as the collected body of scholarly works related to a topic:
The objective of a Literature Review is to find previous published scholarly works relevant to an specific topic
A literature review is important because it:
All content in this section is from Literature Review Research from Old Dominion University
Keep in mind the following, a literature review is NOT:
Not an essay
Not an annotated bibliography in which you summarize each article that you have reviewed. A literature review goes beyond basic summarizing to focus on the critical analysis of the reviewed works and their relationship to your research question.
Not a research paper where you select resources to support one side of an issue versus another. A lit review should explain and consider all sides of an argument in order to avoid bias, and areas of agreement and disagreement should be highlighted.
A literature review serves several purposes. For example, it
As Kennedy (2007) notes*, it is important to think of knowledge in a given field as consisting of three layers. First, there are the primary studies that researchers conduct and publish. Second are the reviews of those studies that summarize and offer new interpretations built from and often extending beyond the original studies. Third, there are the perceptions, conclusions, opinion, and interpretations that are shared informally that become part of the lore of field. In composing a literature review, it is important to note that it is often this third layer of knowledge that is cited as "true" even though it often has only a loose relationship to the primary studies and secondary literature reviews.
Given this, while literature reviews are designed to provide an overview and synthesis of pertinent sources you have explored, there are several approaches to how they can be done, depending upon the type of analysis underpinning your study. Listed below are definitions of types of literature reviews:
Argumentative Review This form examines literature selectively in order to support or refute an argument, deeply imbedded assumption, or philosophical problem already established in the literature. The purpose is to develop a body of literature that establishes a contrarian viewpoint. Given the value-laden nature of some social science research [e.g., educational reform; immigration control], argumentative approaches to analyzing the literature can be a legitimate and important form of discourse. However, note that they can also introduce problems of bias when they are used to to make summary claims of the sort found in systematic reviews.
Integrative Review Considered a form of research that reviews, critiques, and synthesizes representative literature on a topic in an integrated way such that new frameworks and perspectives on the topic are generated. The body of literature includes all studies that address related or identical hypotheses. A well-done integrative review meets the same standards as primary research in regard to clarity, rigor, and replication.
Historical Review Few things rest in isolation from historical precedent. Historical reviews are focused on examining research throughout a period of time, often starting with the first time an issue, concept, theory, phenomena emerged in the literature, then tracing its evolution within the scholarship of a discipline. The purpose is to place research in a historical context to show familiarity with state-of-the-art developments and to identify the likely directions for future research.
Methodological Review A review does not always focus on what someone said [content], but how they said it [method of analysis]. This approach provides a framework of understanding at different levels (i.e. those of theory, substantive fields, research approaches and data collection and analysis techniques), enables researchers to draw on a wide variety of knowledge ranging from the conceptual level to practical documents for use in fieldwork in the areas of ontological and epistemological consideration, quantitative and qualitative integration, sampling, interviewing, data collection and data analysis, and helps highlight many ethical issues which we should be aware of and consider as we go through our study.
Systematic Review This form consists of an overview of existing evidence pertinent to a clearly formulated research question, which uses pre-specified and standardized methods to identify and critically appraise relevant research, and to collect, report, and analyse data from the studies that are included in the review. Typically it focuses on a very specific empirical question, often posed in a cause-and-effect form, such as "To what extent does A contribute to B?"
Theoretical Review The purpose of this form is to concretely examine the corpus of theory that has accumulated in regard to an issue, concept, theory, phenomena. The theoretical literature review help establish what theories already exist, the relationships between them, to what degree the existing theories have been investigated, and to develop new hypotheses to be tested. Often this form is used to help establish a lack of appropriate theories or reveal that current theories are inadequate for explaining new or emerging research problems. The unit of analysis can focus on a theoretical concept or a whole theory or framework.
* Kennedy, Mary M. "Defining a Literature." Educational Researcher 36 (April 2007): 139-147.
All content in this section is from The Literature Review created by Dr. Robert Larabee USC
Robinson, P. and Lowe, J. (2015), Literature reviews vs systematic reviews. Australian and New Zealand Journal of Public Health, 39: 103-103. doi: 10.1111/1753-6405.12393
What's in the name? The difference between a Systematic Review and a Literature Review, and why it matters . By Lynn Kysh from University of Southern California
Systematic review or meta-analysis?
A systematic review answers a defined research question by collecting and summarizing all empirical evidence that fits pre-specified eligibility criteria.
A meta-analysis is the use of statistical methods to summarize the results of these studies.
Systematic reviews, just like other research articles, can be of varying quality. They are a significant piece of work (the Centre for Reviews and Dissemination at York estimates that a team will take 9-24 months), and to be useful to other researchers and practitioners they should have:
Not all systematic reviews contain meta-analysis.
Meta-analysis is the use of statistical methods to summarize the results of independent studies. By combining information from all relevant studies, meta-analysis can provide more precise estimates of the effects of health care than those derived from the individual studies included within a review. More information on meta-analyses can be found in Cochrane Handbook, Chapter 9 .
A meta-analysis goes beyond critique and integration and conducts secondary statistical analysis on the outcomes of similar studies. It is a systematic review that uses quantitative methods to synthesize and summarize the results.
An advantage of a meta-analysis is the ability to be completely objective in evaluating research findings. Not all topics, however, have sufficient research evidence to allow a meta-analysis to be conducted. In that case, an integrative review is an appropriate strategy.
Some of the content in this section is from Systematic reviews and meta-analyses: step by step guide created by Kate McAllister.
Nowadays, there is a huge demand for scientific literature reviews as they are especially appreciated by scholars or researchers when designing their research proposals. While finding information is less of a problem to them, discerning which paper or publication has enough quality has become one of the biggest issues. Literature reviews narrow the current knowledge on a certain field and examine the latest publications’ strengths and weaknesses. This way, they are priceless tools not only for those who are starting their research, but also for all those interested in recent publications. To be useful, literature reviews must be written in a professional way with a clear structure. The amount of work needed to write a scientific literature review must be considered before starting one since the tasks required can overwhelm many if the working method is not the best.
Writing a scientific review implies both researching for relevant academic content and writing , however, writing without having a clear objective is a common mistake. Sometimes, studying the situation and defining the work’s system is so important and takes equally as much time as that required in writing the final result. Therefore, we suggest that you divide your path into three steps.
Think about your target and narrow down your topic. If you don’t choose a well-defined topic, you can find yourself dealing with a wide subject and plenty of publications about it. Remember that researchers usually deal with really specific fields of study.
It is time to be a critic and locate only pertinent publications. While researching for content consider publications that were written 3 years ago at the most. Write notes and summarize the content of each paper as that will help you in the next step.
Check some literature review examples to decide how to start writing a good literature review . When your goals and structure are defined, begin writing without forgetting your target at any moment.
Related: Conducting a literature survey? Wish to learn more about scientific misconduct? Check out this resourceful infographic.
Here you have a to-do list to help you write your review :
Scholars and researchers are usually the best candidates to write scientific literature reviews, not only because they are experts in a certain field, but also because they know the exigencies and needs that researchers have while writing research proposals or looking for information among thousands of academic papers. Therefore, considering your experience as a researcher can help you understand how to write a scientific literature review.
Have you faced challenges while drafting your first literature review? How do you think can these tips help you in acing your next literature review? Let us know in the comments section below! You can also visit our Q&A forum for frequently asked questions related to copyrights answered by our team that comprises eminent researchers and publication experts.
Thank you for your information. It adds knowledge on critical review being a first time to do it, it helps a lot.
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Affiliations Centre for Functional and Evolutionary Ecology (CEFE), CNRS, Montpellier, France, Centre for Biodiversity Synthesis and Analysis (CESAB), FRB, Aix-en-Provence, France
Published: July 18, 2013
Citation: Pautasso M (2013) Ten Simple Rules for Writing a Literature Review. PLoS Comput Biol 9(7): e1003149. https://doi.org/10.1371/journal.pcbi.1003149
Editor: Philip E. Bourne, University of California San Diego, United States of America
Copyright: © 2013 Marco Pautasso. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was funded by the French Foundation for Research on Biodiversity (FRB) through its Centre for Synthesis and Analysis of Biodiversity data (CESAB), as part of the NETSEED research project. The funders had no role in the preparation of the manuscript.
Competing interests: The author has declared that no competing interests exist.
Literature reviews are in great demand in most scientific fields. Their need stems from the ever-increasing output of scientific publications [1] . For example, compared to 1991, in 2008 three, eight, and forty times more papers were indexed in Web of Science on malaria, obesity, and biodiversity, respectively [2] . Given such mountains of papers, scientists cannot be expected to examine in detail every single new paper relevant to their interests [3] . Thus, it is both advantageous and necessary to rely on regular summaries of the recent literature. Although recognition for scientists mainly comes from primary research, timely literature reviews can lead to new synthetic insights and are often widely read [4] . For such summaries to be useful, however, they need to be compiled in a professional way [5] .
When starting from scratch, reviewing the literature can require a titanic amount of work. That is why researchers who have spent their career working on a certain research issue are in a perfect position to review that literature. Some graduate schools are now offering courses in reviewing the literature, given that most research students start their project by producing an overview of what has already been done on their research issue [6] . However, it is likely that most scientists have not thought in detail about how to approach and carry out a literature review.
Reviewing the literature requires the ability to juggle multiple tasks, from finding and evaluating relevant material to synthesising information from various sources, from critical thinking to paraphrasing, evaluating, and citation skills [7] . In this contribution, I share ten simple rules I learned working on about 25 literature reviews as a PhD and postdoctoral student. Ideas and insights also come from discussions with coauthors and colleagues, as well as feedback from reviewers and editors.
How to choose which topic to review? There are so many issues in contemporary science that you could spend a lifetime of attending conferences and reading the literature just pondering what to review. On the one hand, if you take several years to choose, several other people may have had the same idea in the meantime. On the other hand, only a well-considered topic is likely to lead to a brilliant literature review [8] . The topic must at least be:
Ideas for potential reviews may come from papers providing lists of key research questions to be answered [9] , but also from serendipitous moments during desultory reading and discussions. In addition to choosing your topic, you should also select a target audience. In many cases, the topic (e.g., web services in computational biology) will automatically define an audience (e.g., computational biologists), but that same topic may also be of interest to neighbouring fields (e.g., computer science, biology, etc.).
After having chosen your topic and audience, start by checking the literature and downloading relevant papers. Five pieces of advice here:
The chances are high that someone will already have published a literature review ( Figure 1 ), if not exactly on the issue you are planning to tackle, at least on a related topic. If there are already a few or several reviews of the literature on your issue, my advice is not to give up, but to carry on with your own literature review,
The bottom-right situation (many literature reviews but few research papers) is not just a theoretical situation; it applies, for example, to the study of the impacts of climate change on plant diseases, where there appear to be more literature reviews than research studies [33] .
https://doi.org/10.1371/journal.pcbi.1003149.g001
When searching the literature for pertinent papers and reviews, the usual rules apply:
If you read the papers first, and only afterwards start writing the review, you will need a very good memory to remember who wrote what, and what your impressions and associations were while reading each single paper. My advice is, while reading, to start writing down interesting pieces of information, insights about how to organize the review, and thoughts on what to write. This way, by the time you have read the literature you selected, you will already have a rough draft of the review.
Of course, this draft will still need much rewriting, restructuring, and rethinking to obtain a text with a coherent argument [11] , but you will have avoided the danger posed by staring at a blank document. Be careful when taking notes to use quotation marks if you are provisionally copying verbatim from the literature. It is advisable then to reformulate such quotes with your own words in the final draft. It is important to be careful in noting the references already at this stage, so as to avoid misattributions. Using referencing software from the very beginning of your endeavour will save you time.
After having taken notes while reading the literature, you will have a rough idea of the amount of material available for the review. This is probably a good time to decide whether to go for a mini- or a full review. Some journals are now favouring the publication of rather short reviews focusing on the last few years, with a limit on the number of words and citations. A mini-review is not necessarily a minor review: it may well attract more attention from busy readers, although it will inevitably simplify some issues and leave out some relevant material due to space limitations. A full review will have the advantage of more freedom to cover in detail the complexities of a particular scientific development, but may then be left in the pile of the very important papers “to be read” by readers with little time to spare for major monographs.
There is probably a continuum between mini- and full reviews. The same point applies to the dichotomy of descriptive vs. integrative reviews. While descriptive reviews focus on the methodology, findings, and interpretation of each reviewed study, integrative reviews attempt to find common ideas and concepts from the reviewed material [12] . A similar distinction exists between narrative and systematic reviews: while narrative reviews are qualitative, systematic reviews attempt to test a hypothesis based on the published evidence, which is gathered using a predefined protocol to reduce bias [13] , [14] . When systematic reviews analyse quantitative results in a quantitative way, they become meta-analyses. The choice between different review types will have to be made on a case-by-case basis, depending not just on the nature of the material found and the preferences of the target journal(s), but also on the time available to write the review and the number of coauthors [15] .
Whether your plan is to write a mini- or a full review, it is good advice to keep it focused 16 , 17 . Including material just for the sake of it can easily lead to reviews that are trying to do too many things at once. The need to keep a review focused can be problematic for interdisciplinary reviews, where the aim is to bridge the gap between fields [18] . If you are writing a review on, for example, how epidemiological approaches are used in modelling the spread of ideas, you may be inclined to include material from both parent fields, epidemiology and the study of cultural diffusion. This may be necessary to some extent, but in this case a focused review would only deal in detail with those studies at the interface between epidemiology and the spread of ideas.
While focus is an important feature of a successful review, this requirement has to be balanced with the need to make the review relevant to a broad audience. This square may be circled by discussing the wider implications of the reviewed topic for other disciplines.
Reviewing the literature is not stamp collecting. A good review does not just summarize the literature, but discusses it critically, identifies methodological problems, and points out research gaps [19] . After having read a review of the literature, a reader should have a rough idea of:
It is challenging to achieve a successful review on all these fronts. A solution can be to involve a set of complementary coauthors: some people are excellent at mapping what has been achieved, some others are very good at identifying dark clouds on the horizon, and some have instead a knack at predicting where solutions are going to come from. If your journal club has exactly this sort of team, then you should definitely write a review of the literature! In addition to critical thinking, a literature review needs consistency, for example in the choice of passive vs. active voice and present vs. past tense.
Like a well-baked cake, a good review has a number of telling features: it is worth the reader's time, timely, systematic, well written, focused, and critical. It also needs a good structure. With reviews, the usual subdivision of research papers into introduction, methods, results, and discussion does not work or is rarely used. However, a general introduction of the context and, toward the end, a recapitulation of the main points covered and take-home messages make sense also in the case of reviews. For systematic reviews, there is a trend towards including information about how the literature was searched (database, keywords, time limits) [20] .
How can you organize the flow of the main body of the review so that the reader will be drawn into and guided through it? It is generally helpful to draw a conceptual scheme of the review, e.g., with mind-mapping techniques. Such diagrams can help recognize a logical way to order and link the various sections of a review [21] . This is the case not just at the writing stage, but also for readers if the diagram is included in the review as a figure. A careful selection of diagrams and figures relevant to the reviewed topic can be very helpful to structure the text too [22] .
Reviews of the literature are normally peer-reviewed in the same way as research papers, and rightly so [23] . As a rule, incorporating feedback from reviewers greatly helps improve a review draft. Having read the review with a fresh mind, reviewers may spot inaccuracies, inconsistencies, and ambiguities that had not been noticed by the writers due to rereading the typescript too many times. It is however advisable to reread the draft one more time before submission, as a last-minute correction of typos, leaps, and muddled sentences may enable the reviewers to focus on providing advice on the content rather than the form.
Feedback is vital to writing a good review, and should be sought from a variety of colleagues, so as to obtain a diversity of views on the draft. This may lead in some cases to conflicting views on the merits of the paper, and on how to improve it, but such a situation is better than the absence of feedback. A diversity of feedback perspectives on a literature review can help identify where the consensus view stands in the landscape of the current scientific understanding of an issue [24] .
In many cases, reviewers of the literature will have published studies relevant to the review they are writing. This could create a conflict of interest: how can reviewers report objectively on their own work [25] ? Some scientists may be overly enthusiastic about what they have published, and thus risk giving too much importance to their own findings in the review. However, bias could also occur in the other direction: some scientists may be unduly dismissive of their own achievements, so that they will tend to downplay their contribution (if any) to a field when reviewing it.
In general, a review of the literature should neither be a public relations brochure nor an exercise in competitive self-denial. If a reviewer is up to the job of producing a well-organized and methodical review, which flows well and provides a service to the readership, then it should be possible to be objective in reviewing one's own relevant findings. In reviews written by multiple authors, this may be achieved by assigning the review of the results of a coauthor to different coauthors.
Given the progressive acceleration in the publication of scientific papers, today's reviews of the literature need awareness not just of the overall direction and achievements of a field of inquiry, but also of the latest studies, so as not to become out-of-date before they have been published. Ideally, a literature review should not identify as a major research gap an issue that has just been addressed in a series of papers in press (the same applies, of course, to older, overlooked studies (“sleeping beauties” [26] )). This implies that literature reviewers would do well to keep an eye on electronic lists of papers in press, given that it can take months before these appear in scientific databases. Some reviews declare that they have scanned the literature up to a certain point in time, but given that peer review can be a rather lengthy process, a full search for newly appeared literature at the revision stage may be worthwhile. Assessing the contribution of papers that have just appeared is particularly challenging, because there is little perspective with which to gauge their significance and impact on further research and society.
Inevitably, new papers on the reviewed topic (including independently written literature reviews) will appear from all quarters after the review has been published, so that there may soon be the need for an updated review. But this is the nature of science [27] – [32] . I wish everybody good luck with writing a review of the literature.
Many thanks to M. Barbosa, K. Dehnen-Schmutz, T. Döring, D. Fontaneto, M. Garbelotto, O. Holdenrieder, M. Jeger, D. Lonsdale, A. MacLeod, P. Mills, M. Moslonka-Lefebvre, G. Stancanelli, P. Weisberg, and X. Xu for insights and discussions, and to P. Bourne, T. Matoni, and D. Smith for helpful comments on a previous draft.
A literature review is a body of text that aims to review the critical points of current knowledge on a particular topic. Most often associated with science-oriented literature, such as a thesis, the literature review usually proceeds a research proposal, methodology and results section. Its ultimate goals is to bring the reader up to date with current literature on a topic and forms that basis for another goal, such as the justification for future research in the area. (retrieved from http://en.wikipedia.org/wiki/Literature_review )
The literature review is the section of your paper in which you cite and briefly review the related research studies that have been conducted. In this space, you will describe the foundation on which your research will be/is built. You will:
The literature review should be selective and should group the cited studies in some logical fashion.
If you need some additional assistance writing your literature review, the Knight Institute for Writing in the Disciplines offers a Graduate Writing Service .
For more information, visit our guide devoted to " Demystifying the Literature Review " which includes:
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Our paper analyzes the current state of research on artificial intelligence (AI) adoption from a production perspective. We represent a holistic view on the topic which is necessary to get a first understanding of AI in a production-context and to build a comprehensive view on the different dimensions as well as factors influencing its adoption. We review the scientific literature published between 2010 and May 2024 to analyze the current state of research on AI in production. Following a systematic approach to select relevant studies, our literature review is based on a sample of articles that contribute to production-specific AI adoption. Our results reveal that the topic has been emerging within the last years and that AI adoption research in production is to date still in an early stage. We are able to systematize and explain 35 factors with a significant role for AI adoption in production and classify the results in a framework. Based on the factor analysis, we establish a future research agenda that serves as a basis for future research and addresses open questions. Our paper provides an overview of the current state of the research on the adoption of AI in a production-specific context, which forms a basis for further studies as well as a starting point for a better understanding of the implementation of AI in practice.
Avoid common mistakes on your manuscript.
The technological change resulting from deep digitisation and the increasing use of digital technologies has reached and transformed many sectors [ 1 ]. In manufacturing, the development of a new industrial age, characterized by extensive automation and digitisation of processes [ 2 ], is changing the sector’s ‘technological reality’ [ 3 ] by integrating a wide range of information and communication technologies (such as Industry 4.0-related technologies) into production processes [ 4 ].
Although the evolution of AI traces back to the year 1956 (as part of the Dartmouth Conference) [ 5 ], its development has progressed rapidly, especially since the 2010s [ 6 ]. Driven by improvements, such as the fast and low-cost development of smart hardware, the enhancement of algorithms as well as the capability to manage big data [ 7 ], there is an increasing number of AI applications available for implementation today [ 8 ]. The integration of AI into production processes promises to boost the productivity, efficiency as well as automation of processes [ 9 ], but is currently still in its infancy [ 10 ] and manufacturing firms seem to still be hesitant to adopt AI in a production-context. This appears to be driven by the high complexity of AI combined with the lack of practical knowledge about its implementation in production and several other influencing factors [ 11 , 12 ].
In the literature, many contributions analyze AI from a technological perspective, mainly addressing underlying models, algorithms, and developments of AI tools. Various authors characterise both machine learning and deep learning as key technologies of AI [ 8 , 13 ], which are often applied in combination with other AI technologies, such as natural language recognition. While promising areas for AI application already exist in various domains such as marketing [ 14 ], procurement [ 15 ], supply chain management [ 16 ] or innovation management [ 17 ], the integration of AI into production processes also provides significant performance potentials, particularly in the areas of maintenance [ 18 ], quality control [ 19 ] and production planning and management [ 20 ]. However, AI adoption requires important technological foundations, such as the provision of data and the necessary infrastructure, which must be ensured [ 11 , 12 , 21 ]. Although the state of the art literature provides important insights into possible fields of application of AI in production, the question remains: To what extent are these versatile applications already in use and what is required for their successful adoption?
Besides the technology perspective of AI, a more human-oriented field of discussion is debated in scientific literature [ 22 ]. While new technologies play an essential role in driving business growth in the digital transformation of the production industry, the increasing interaction between humans and intelligent machines (also referred to as ‘augmentation’) creates stress challenges [ 23 ] and impacts work [ 24 ], which thus creates managerial challenges in organizations [ 25 , 26 ]. One of the widely discussed topics in this context is the fear of AI threatening jobs (including production jobs), which was triggered by e.g. a study of Frey, Osborne [ 27 ]. Another issue associated to the fear of machines replacing humans is the lack of acceptance resulting from the mistrust of technologies [ 28 , 29 ]. This can also be linked to the various ethical challenges involved in working with AI [ 22 ]. This perspective, which focuses on the interplay between AI and humans [ 30 ], reveals the tension triggered by AI. Although this is discussed from different angles, the question remains how these aspects influence the adoption of AI in production.
Another thematic stream of current literature can be observed in a series of contributions on the organizational aspects of the technology. In comparison to the two research areas discussed above, the number of publications in this area seems to be smaller. This perspective focuses on issues to implement AI, such as the importance of a profound management structure [ 31 , 32 ], leadership [ 33 ], implications on the organizational culture [ 34 ] as well as the need for digital capabilities and special organizational skills [ 33 ]. Although some studies on the general adoption of AI without a sectoral focus have already been conducted (such as by Chen, Tajdini [ 35 ] or Kinkel, Baumgartner, Cherubini [ 36 ]) and hence, some initial factors influencing the adoption of AI can be derived, the contributions from this perspective are still scarce, are usually not specifically analyzed in the context of production or lack a comprehensive view on the organization in AI adoption.
While non-industry specific AI issues have been researched in recent years, the current literature misses a production-specific analysis of AI adoption, providing an understanding of the possibilities and issues related to integrating AI into the production context. Moreover, the existing literature tells us little about relevant mechanisms and factors underlying the adoption of AI in production processes, which include both technical, human-centered as well as organizational issues. As organizational understanding of AI in a business context is currently still in its early stages, it is difficult to find an aggregate view on the factors that can support companies in implementing AI initiatives in production [ 37 , 38 ]. Addressing this gap, we aim to systematise the current scientific knowledge on AI adoption, with a focus on production. By drawing on a systematic literature review (SLR), we examine existing studies on AI adoption in production and explore the main issues regarding adoption that are covered in the analyzed articles. Building on these findings, we conduct a comprehensive analysis of the existing studies with the aim of systematically investigating the key factors influencing the adoption of AI in production. This systematic approach paves the way for the formulation of a future research agenda.
Our SLR addresses three research questions (RQs). RQ1: What are the statistical characteristics of existing research on AI adoption in production? To answer this RQ, we conduct descriptive statistics of the analyzed studies and provide information on time trends, methods used in the research, and country specifications. RQ2: What factors influence the adoption of AI in production? RQ2 specifies the adoption factors and forms the core component of our analysis. By adoption factors, we mean the factors that influence the use of AI in production (both positively and negatively) and that must therefore be analyzed and taken into account. RQ3: What research topics are of importance to advance the research field of AI adoption in production? We address this RQ by using the analyzed literature as well as the key factors of AI adoption as a starting point to derive RQs that are not addressed and thus provide an outlook on the topic.
In order to create a sound information base for both policy makers and practitioners on the topic of AI adoption in production, this paper follows the systematic approach of a SLR. For many fields, including management research, a SLR is an important tool to capture the diversity of existing knowledge on a specific topic for a scientific investigation [ 39 ]. The investigator often pursues multiple goals, such as capturing and assessing the existing environment and advancing the existing body of knowledge with a proprietary RQ [ 39 ] or identifying key research topics [ 40 ].
Our SLR aims to select, analyze, and synthesize findings from the existing literature on AI adoption in production over the past 24 years. In order to identify relevant data for our literature synthesis, we follow the systematic approach of the Preferred Reporting Items for Systematic reviews (PRISMA) [ 41 ]. In evaluating the findings, we draw on a mixed-methods approach, combining some quantitative analyses, especially on the descriptive aspects of the selected publications, as well as qualitative analyses aimed at evaluating and comparing the contents of the papers. Figure 1 graphically summarizes the methodological approach that guides the content of the following sub-chapters.
Methodical procedure of our SLR following PRISMA [ 41 ]
Following the development of the specific RQs, we searched for suitable publications. To locate relevant studies, we chose to conduct a publication analysis in the databases Scopus, Web of Science and ScienceDirect as these databases primarily contain international scientific articles and provide a broad overview of the interdisciplinary research field and its findings. To align the search with the RQs [ 42 ], we applied predefined key words to search the titles, abstracts, and keywords of Scopus, Web of Science and ScienceDirect articles. Our research team conducted several pre-tests to determine the final search commands for which the test results were on target and increased the efficiency of the search [ 42 ]. Using the combination of Boolean operators, we covered the three topics of AI, production, and adoption by searching combinations of ‘Artificial Intelligence’ AND ‘production or manufacturing’ AND ‘adopt*’ in the three scientific databases. Although ‘manufacturing’ tends to stand for the whole sector and ‘production’ refers to the process, the two terms are often used to describe the same context. We also follow the view of Burbidge, Falster, Riis, Svendsen [ 43 ] and use the terms synonymously in this paper and therefore also include both terms as keywords in the study location as well as in the analysis.
AI research has been credited with a resurgence since 2010 [ 6 ], which is the reason for our choice of time horizon. Due to the increase in publications within the last years, we selected articles published online from 2010 to May 8, 2024 for our analysis. As document types, we included conference papers, articles, reviews, book chapters, conference reviews as well as books, focusing exclusively on contributions in English in the final publication stage. The result of the study location is a list of 3,833 documents whose titles, abstracts, and keywords meet the search criteria and are therefore included in the next step of the analysis.
For these 3,833 documents, we then conducted an abstract analysis, ‘us[ing] a set of explicit selection criteria to assess the relevance of each study found to see if it actually does address the research question’ [ 42 ]. For this step, we again conducted double-blind screenings (including a minimum of two reviewers) as pilot searches so that all reviewers have the same understanding of the decision rules and make equal decisions regarding their inclusion for further analysis.
To ensure the paper’s focus on all three topics regarded in our research (AI, production, and adoption), we followed clearly defined rules of inclusion and exclusion that all reviewers had to follow in the review process. As a first requirement for inclusion, AI must be the technology in focus that is analysed in the publication. If AI was only mentioned and not further specified, we excluded the publication. With a second requirement, we checked the papers for the context of analysis, which in our case must be production. If the core focus is beyond production, the publication was also excluded from further analysis. The third prerequisite for further consideration of the publication is the analysis of the adoption of a technology in the paper. If technology adoption is not addressed or adoption factors are not considered, we excluded the paper. An article was only selected for full-text analysis if, after analyzing the titles, abstracts, and keywords, a clear focus on all three research areas was visible and the inclusion criteria were met for all three contexts.
By using this tripartite inclusion analysis, we were able to analyse the publications in a structured way and to reduce the 3,833 selected documents in our double-blind approach to 300 articles that were chosen for the full-text analysis. In the process of finding full versions of these publications, we had to exclude three papers as we could not access them. For the rest of the 297 articles we obtained full access and thus included them for further analysis. After a thorough examination of the full texts, we again had to exclude 249 publications because they did not meet our content-related inclusion criteria mentioned above, although the abstract analysis gave indications that they did. As a result, we finally obtained 47 selected papers on which we base the literature analysis and synthesis (see Fig. 1 ).
Figure 2 summarises the results of the descriptive analysis on the selected literature regarding AI adoption in production that we analyse in our SLR. From Fig. 2 a), which illustrates annual publication trends (2010–2024), the increase in publications on AI adoption in production over the past 5 years is evident, yet slightly declining after a peak in 2022. After a steady increase until 2022, in which 11 articles are included in the final analysis, 2023 features ten articles, followed by three articles for 2024 until the cut-off date in May 2024. Of the 47 papers identified through our search, the majority (n = 33) are peer-reviewed journal articles and the remaining thirteen contributions conference proceedings and one book chapter (see Fig. 2 b)).
Descriptive analyses of the selected articles addressing AI adoption in production
The identified contributions reveal some additional characteristics in terms of the authors country base (Fig. 2 c)) and research methods used (Fig. 2 d)). Almost four out of ten of the publications were written in collaboration with authors from several countries (n = 19). Six of the papers were published by authors from the United States, five from Germany and four from India. In terms of the applied research methods used by the researchers, a wide range of methods is used (see Fig. 2 c), with qualitative methods (n = 22) being the most frequently used.
In order to derive a comprehensive list of factors that influence the use of AI in production at different levels, we follow a qualitative content analysis. It is based on inductive category development, avoiding prefabricated categories in order to allow new categories to emerge based on the content at hand [ 44 , 45 ]. To do this, we first read the entire text to gain an understanding of the content and then derive codes [ 46 ] that seem to capture key ideas [ 45 ]. The codes are subsequently sorted into distinct categories, each of which is clearly defined and establishes meaningful connections between different codes. Based on an iterative process with feedback loops, the assigned categories are continuously reviewed and updated as revisions are made [ 44 ].
Various factors at different levels are of significance to AI and influence technology adoption [ 47 , 48 ]. To identify the specific factors that are of importance for AI adoption in production, we analyze the selected contributions in terms of the factors considered, compare them with each other and consequently obtain a list of factors through a bottom-up approach. While some of the factors are based on empirical findings, others are expected factors that result from the research findings of the respective studies. Through our analysis, a list of 35 factors emerges that influence AI adoption in production which occur with varying frequency in the studies analyzed by our SLR. Table 1 visualizes each factor in the respective contributions sorted by the frequency of occurrence.
The presence of skills is considered a particularly important factor in AI adoption in the studies analyzed (n = 35). The availability of data (n = 25) as well as the need for ethical guidelines (n = 24) are also seen as key drivers of AI adoption, as data is seen as the basis for the implementation of AI and ethical issues must be addressed in handling such an advanced technology. As such, these three factors make up the accelerants of AI adoption in production that are most frequently cited in the studies analyzed.
Also of importance are issues of managerial support (n = 22), as well as performance measures and IT infrastructure (n = 20). Some factors were also mentioned, but only addressed by one study at a time: government support, industrial sector, product complexity, batch size, and R&D Intensity. These factors are often used as quantitatively measurable adoption factors, especially in empirical surveys, such the study by Kinkel, Baumgartner, Cherubini [ 36 ].
The 35 factors presented characteristically in Sect. 2.4 serve as the basis for our in-depth analysis and for developing a framework of influences on AI adoption in production which are grouped into supercategories. A supercategory describes a cluster of topics to which various factors of AI adoption in production can be assigned. We were able to define seven categories that influence AI adoption in production: the internal influences of ‘business and structure’, ‘organizational effectiveness’, ‘technology and system’, ‘data management’ as well as the external influences of the ‘regulatory environment’, ‘business environment’ and ‘economic environment’ (see Fig. 3 ). The factors that were mentioned most frequently (occurrence in at least half of the papers analyzed) are marked accordingly (*) in Fig. 3 .
Framework of factors influencing AI adoption in production
The internal influences on AI adoption in production refer to factors that an organization carries internally and that thus also influence adoption from within. Such factors can usually be influenced and clearly controlled by the organization itself.
The supercategory ‘business and structure’ includes the various factors and characteristics that impact a company’s performance, operations, and strategic decision-making. By considering and analyzing these business variables when implementing AI in production processes, companies can develop effective strategies to optimize their performance, increase their competitiveness, and adapt to changes in the business environment.
To understand and grasp the benefits in the use of AI, quantitative performance measures for the current and potential use of AI in industrial production systems help to clarify the value and potential benefits of AI use [ 49 , 54 , 74 , 79 , 91 ]. Assessing possible risks [ 77 ] as well as the monetary expected benefits for AI (e.g. Return on Investment (ROI)) in production plays an important role for adoption decisions in market-oriented companies [ 57 , 58 , 63 , 65 , 78 ]. Due to financial constraints, managers behave cautiously in their investments [ 78 ], so they need to evaluate AI adoption as financially viable to want to make the investment [ 61 , 63 , 93 ] and also drive acceptance [ 60 ]. AI systems can significantly improve cost–benefit structures in manufacturing, thereby increasing the profitability of production systems [ 73 ] and making companies more resilient [ 75 ]. However, in most cases, the adoption of AI requires high investments and the allocation of resources (s.a. personnel or financial) for this purpose [ 50 , 51 , 57 , 80 , 94 ]. Consequently, a lack of budgets and high expected transition costs often hinder the implementation of smart concepts [ 56 , 62 , 67 , 82 , 84 , 92 ]. It is up to management to provide necessary funding for AI adoption [ 53 , 59 , 79 ], which is required, for example, for skill development of employees [ 59 , 61 , 63 ], IT adaptation [ 62 , 66 ], AI development [ 74 ] or hardware deployment [ 68 ]. In their empirical study, Kinkel, Baumgartner, Cherubini [ 36 ] confirm a positive correlation between company size and the intensity in the use of AI technologies. Large companies generally stand out with a higher propensity to adopt [ 53 ] as they have less difficulties in comparison to small firms regarding the availability of resources [ 69 ], such as know-how, budget [ 68 , 84 ] and general data organization [ 68 ]. Others argue that small companies tend to be more open to change and are characterized by faster decision-making processes [ 68 , 93 ]. Product complexity also influences a company’s propensity for AI. Companies that produce rather simple products are more likely to digitize, which in turn offers good starting points for AI adoption. On the other hand, complex product manufacturers (often characterized by small batch sizes) are often less able to standardize and automate [ 36 ]. The company’s produced batch size has a similar influence on AI adoption. Small and medium batch sizes in particular hinder the integration of intelligent technologies, as less automation often prevails here as well. Nevertheless, even small and medium lot sizes can benefit economically from AI [ 36 ]. Since a high R&D intensity indicates a high innovation capability of a company, it is assumed to have a positive influence on AI adoption, as companies with a high R&D intensity already invest heavily in and use new innovations. This in turn speaks for existing competencies, know how and structures [ 36 ].
This supercategory focuses on the broader aspects that contribute to the effectiveness, development, and success of an organization when implementing AI in a production context. As the factors are interconnected and influence each other, decision makers should consider them carefully.
Users´ trust in AI is an essential factor to enable successful AI adoption and use in production [ 52 , 68 , 78 , 79 , 88 , 90 ]. From the users´ perspective, AI often exhibits the characteristics of a black box because its inherent processes are not fully understood [ 50 , 90 ] which can lead individuals to develop a fear towards the unknown [ 71 ]. Because of this lack of understanding, successful interaction between humans and AI is not guaranteed [ 90 ], as trust is a foundation for decisions that machines are intended to make autonomously [ 52 , 91 ]. To strengthen faith in AI systems [ 76 , 80 ], AI users can be involved in AI design processes in order to understand appropriate tools [ 54 , 90 ]. In this context, trust is also discussed in close connection with transparency and regulation [ 79 ]. User resistance is considered a barrier to implementing new information technologies, as adoption requires change [ 53 , 62 , 92 ]. Ignorance, as a kind of resistance to change, is a main obstacle to successful digital transformation [ 51 , 56 , 65 ]. Some employees may resist the change brought about by AI because they fear losing their jobs [ 52 ] or have other concerns [ 78 ]. Overcoming resistance to technology adoption requires organizational change and is critical for the success of adoption [ 50 , 51 , 62 , 67 , 71 , 80 ]. Therefore, change management is important to create awareness of the importance of AI adoption and increase acceptance of the workforce [ 66 , 68 , 74 , 83 ]. Management commitment is seen as a significant driver of technology adoption [ 53 , 59 , 81 , 82 , 86 ] and a lack of commitment can negatively impact user adoption and workforce trust and lead to skepticism towards technology [ 86 ]. The top management’s understanding and support for the benefits of the adopted technology [ 53 , 56 , 67 , 78 , 93 , 94 ] enhances AI adoption, can prioritize its implementation and also affects the performance of the AI-enabled application [ 55 , 60 , 83 ]. Preparing, enabling, and thus empowering the workforce, are considered the management’s responsibility in the adoption of digital technologies [ 59 , 75 ]. This requires intelligent leadership [ 52 ] as decision makers need to integrate their workforce into decision-making processes [ 75 ]. Guidelines can support managers by providing access to best practices that help in the adoption of AI [ 50 ]. Critical measures to manage organizational change include the empowerment of visionaries or appointed AI champions leading the change and the collaborative development of digital roadmaps [ 54 , 62 ]. To demonstrate management commitment, managers can create such a dedicated role, consisting of an individual or a small group that is actively and enthusiastically committed to AI adoption in production. This body is considered the adoption manager, point of contact and internal driver of adoption [ 62 , 74 , 80 ]. AI initiatives in production do not necessarily have to be initiated by management. Although management support is essential for successful AI adoption, employees can also actively drive integration initially and thus realize pilot projects or initial trials [ 66 , 80 ]. The development of strategies as well as roadmaps is considered another enabling and necessary factor for the adoption of AI in production [ 50 , 53 , 54 , 62 , 71 , 93 ]. While many major AI strategies already exist at country level to further promote research and development of AI [ 87 ], strategy development is also important at the firm level [ 76 , 77 , 81 ]. In this context, strategies should not be delegated top-down, but be developed in a collaborative manner, i.e. by engaging the workforce [ 75 ] and be in alignment with clear visions [ 91 , 94 ]. Roadmaps are used to improve planning, support implementation, facilitate the adoption of smart technologies in manufacturing [ 93 ] and should be integrated into both business and IT strategy [ 62 , 66 ]. In practice, clear adoption roadmaps that provide approaches on how to effectively integrate AI into existing strategies and businesses are often lacking [ 56 , 87 ]. The need for AI-related skills in organizations is a widely discussed topic in AI adoption analyses [ 79 ]. In this context, the literature points both at the need for specific skills in the development and design of AI applications [ 57 , 71 , 72 , 73 , 76 , 93 ] as well as the skills in using the technology [ 53 , 65 , 73 , 74 , 75 , 84 , 93 ] which availability in the firm is not always given [ 49 ]. AI requires new digital skills [ 36 , 50 , 52 , 55 , 56 , 59 , 61 , 63 , 66 , 78 , 80 ], where e.g. advanced analytics [ 64 , 75 , 81 ], programming skills [ 68 ] and cybersecurity skills [ 78 , 93 ] gain importance. The lack of skills required for AI is seen as a major challenge of digital transformation, as a skilled workforce is considered a key resource for companies [ 51 , 54 , 56 , 60 , 62 , 67 , 69 , 70 , 82 , 93 ]. This lack of a necessary skillset hinders the adoption of AI tools in production systems [ 58 , 77 ]. Closely related to skills is the need for new training concepts, which organizations need to consider when integrating digital technologies [ 49 , 50 , 51 , 56 , 59 , 63 , 71 , 74 , 75 ]. Firms must invest in qualification in order to create necessary competences [ 73 , 78 , 80 , 81 , 92 ]. Additionally, education must target and further develop the skills required for effectively integrating intelligent technologies into manufacturing processes [ 54 , 61 , 62 , 83 ]. Regarding this issue, academic institutions must develop fitting curricula for data driven manufacturing engineering [ 64 ]. Another driving factor of AI adoption is the innovation culture of an organization, which is influenced by various drivers. For example, companies that operate in an environment with high innovation rates, facing intense competitive pressures are considered more likely to see smart technologies as a tool for strategic change [ 83 , 91 , 93 ]. These firms often invest in more expensive and advanced smart technologies as the pressure and resulting competition forces them to innovate [ 93 ]. Another way of approach this is that innovation capability can also be supported and complemented by AI, for example by intelligent systems supporting humans in innovation or even innovating on their own [ 52 ].The entrepreneurial orientation of a firm is characterized in particular by innovativeness [ 66 ], productivity [ 63 ], risk-taking [ 86 ] as well as continuous improvement [ 50 ]. Such characteristics of an innovating culture are considered essential for companies to recognise dynamic changes in the market and make adoption decisions [ 51 , 71 , 81 , 84 , 86 , 94 ]. The prevalence of a digital mindset in companies is important for technology adoption, as digital transformation affects the entire organizational culture and behavior [ 59 , 80 , 92 ] and a lack of a digital culture [ 50 , 65 ] as well as a ‘passive mindset’ [ 78 ] can hinder the digital transformation of firms. Organizations need to develop a corresponding culture [ 66 , 67 , 71 ], also referred to as ‘AI-ready-culture’ [ 54 ], that promotes development and encourages people and data through the incorporation of technology [ 71 , 75 ]. With the increasing adoption of smart technologies, a ‘new digital normal’ is emerging, characterized by hybrid work models, more human–machine interactions and an increased use of digital technologies [ 75 , 83 ].
The ‘technology and system’ supercategory focuses on the broader issues related to the technology and infrastructure that support organizational operations and provide the technical foundation for AI deployment.
By IT infrastructure we refer to issues regarding the foundational systems and IT needed for AI adoption in production. Industrial firms and their IT systems must achieve a mature technological readiness in order to enable successful AI adoption [ 51 , 60 , 67 , 69 , 83 ]. A lack of appropriate IT infrastructure [ 68 , 71 , 78 , 91 ] or small maturity of Internet of Things (IoT) technologies [ 70 ]) hinders the efficient use of data in production firms [ 56 ] which is why firms must update their foundational information systems for successful AI adoption [ 53 , 54 , 62 , 66 , 72 , 75 ]. IT and data security are fundamental for AI adoption and must be provided [ 50 , 51 , 68 , 82 ]. This requires necessary developments that can ensure security during AI implementation while complying with legal requirements [ 52 , 72 , 78 ]. Generally, security concerns are common when implementing AI innovations [ 72 , 79 , 91 , 94 ]. This fear of a lack of security can also prevent the release of (e.g. customer) data in a production environment [ 56 ]. Additionally, as industrial production systems are vulnerable to failures as well as cyberattacks, companies need to address security and cybersecurity measures [ 49 , 76 , 88 , 89 ]. Developing user-friendly AI solutions can facilitate the adoption of smart solutions by increasing user understanding and making systems easy to use by employees as well as quick to integrate [ 50 , 72 , 84 ]. When developing user-friendly solutions which satisfy user needs [ 76 ], it is particularly important to understand and integrate the user perspective in the development process [ 90 ]. If employees find technical solutions easy to use, they are more confident in its use and perceived usefulness increases [ 53 , 67 , 68 ]. The compatibility of AI with a firm and its existing systems, i.e., the extent to which AI matches existing processes, structures, and infrastructures [ 53 , 54 , 56 , 60 , 78 , 80 , 82 , 83 , 93 , 94 ], is considered an important requirement for the adoption of AI in IT systems [ 91 ]. Along with compatibility also comes connectivity, which is intended to ensure the links within the overall network and avoid silo thinking [ 59 ]. Connectivity and interoperability of AI-based processes within the company’s IT manufacturing systems must be ensured at different system levels and are considered key factors in the development of AI applications for production [ 50 , 72 , 89 ]. The design of modular AI solutions can increase system compatibility [ 84 ]. Firms deciding for AI adoption must address safety issues [ 51 , 54 , 59 , 72 , 73 , 78 ]. This includes both safety in the use and operation of AI [ 60 , 69 ]. In order to address safety concerns of integrating AI solutions in industrial systems [ 49 ], systems must secure high reliability [ 71 ]. AI can also be integrated as a safety enabler, for example, by providing technologies to monitor health and safety in the workplace to prevent fatigue and injury [ 75 ].
Since AI adoption in the organization is strongly data-driven, the ‘data management’ supercategory is dedicated to the comprehensive aspects related to the effective and responsible management of data within the organization.
Data privacy must be guaranteed when creating AI applications based on industrial production data [ 49 , 58 , 59 , 60 , 72 , 76 , 78 , 79 , 82 , 88 , 89 , 91 , 94 ] as ‘[M]anufacturing industries generate large volumes of unstructured and sensitive data during their daily operations’ [ 89 ]. Closely related to this is the need for anonymization and confidentiality of data [ 61 , 69 , 70 , 78 ]. The availability of large, heterogeneous data sets is essential for the digital transformation of organizations [ 52 , 59 , 78 , 80 , 88 , 89 ] and is considered one of the key drivers of AI innovation [ 62 , 68 , 72 , 86 ]. In production systems, lack of data availability is often a barrier to AI adoption [ 58 , 70 , 77 ]. In order to enable AI to establish relationships between data, the availability of large input data that is critical [ 62 , 76 , 81 ]. New AI models are trained with this data and can adapt as well as improve as they receive new data [ 59 , 62 ]. Big data can thus significantly improve the quality of AI applications [ 59 , 71 ]. As more and more data is generated in manufacturing [ 85 ], AI opens up new opportunities for companies to make use of it [ 62 ]. However, operational data are often unstructured, as they come from different sources and exist in diverse formats [ 85 , 87 ]. This challenges data processing, as data quality and origin are key factors in the management of data [ 78 , 79 , 80 , 88 , 89 , 91 ]. To make production data valuable and usable for AI, consistency of data and thus data integrity is required across manufacturing systems [ 50 , 62 , 77 , 84 ]. Another key prerequisites for AI adoption is data governance [ 56 , 59 , 67 , 68 , 71 , 78 , 88 ] which is an important asset to make use of data in production [ 50 ] and ensure the complex management of heterogenous data sets [ 89 ]. The interoperability of data and thus the foundation for the compatibility of AI with existing systems, i.e., the extent to which AI matches existing processes, structures, and infrastructures [ 53 , 56 , 84 , 93 ], is considered another important requirement for the adoption of AI in IT systems. Data interoperability in production systems can be hindered by missing data standards as different machines use different formats [ 87 ]. Data processing refers to techniques used to preparing data for analysis which is essential to obtain consistent results from data analytics in production [ 58 , 72 , 80 , 81 , 84 ]. In this process, the numerous, heterogeneous data from different sensors are processed in such a way that they can be used for further analyses [ 87 ]. The capability of production firms to process data and information is thus important to enable AI adoption [ 77 , 86 , 93 ]. With the increasing data generation in the smart and connected factory, the strategic relevance of data analytics is gaining importance [ 55 , 69 , 78 ], as it is essential for AI systems in performing advanced data analyses [ 49 , 67 , 72 , 86 , 88 ]. Using analytics, valuable insights can be gained from the production data obtained using AI systems [ 58 , 77 , 87 ]. In order to enable the processing of big data, a profound data infrastructure is necessary [ 65 , 75 , 87 ]. Facilities must be equipped with sensors, that collect data and model information, which requires investments from firms [ 72 ]. In addition, production firms must build the necessary skills, culture and capabilities for data analytics [ 54 , 75 , 87 , 93 ]. Data storage, one of the foundations and prerequisites for smart manufacturing [ 54 , 68 , 71 , 74 ], must be ensured in order to manage the larg amounts of data and thus realize the adoption of intelligent technologies in production [ 50 , 59 , 72 , 78 , 84 , 87 , 88 , 89 ].
The external drivers of AI adoption in production influence the organization through conditions and events from outside the firm and are therefore difficult to control by the organization itself.
This supercategory captures the broader concept of establishing rules, standards, and frameworks that guide the behavior, actions, and operations of individuals, organizations, and societies when implementing AI.
AI adoption in production faces many ethical challenges [ 70 , 72 , 79 ]. AI applications must be compliant with the requirements of organizational ethical standards and laws [ 49 , 50 , 59 , 60 , 62 , 75 ] which is why certain issues must be examined in AI adoption and AI design [ 62 , 73 , 82 , 91 ] so that fairness and justice are guaranteed [ 78 , 79 , 92 ]. Social rights, cultural values and norms must not be violated in the process [ 49 , 52 , 53 , 81 ]. In this context, the explainability and transparency of AI decisions also plays an important role [ 50 , 54 , 58 , 70 , 78 , 89 ] and can address the characteristic of AI of a black box [ 90 ]. In addition, AI applications must be compliant with legal and regulatory requirements [ 51 , 52 , 59 , 77 , 81 , 82 , 91 ] and be developed accordingly [ 49 , 76 ] in order to make organization processes using AI clear and effective [ 65 ]. At present, policies and regulation of AI are still in its infancy [ 49 ] and missing federal regulatory guidelines, standards as well as incentives hinder the adoption of AI [ 67 ] which should be expanded simultaneously to the expansion of AI technology [ 60 ]. This also includes regulations on the handling of data (e.g. anonymization of data) [ 61 , 72 ].
The factors in the ‘business environment’ supercategory refer to the external conditions and influences that affect the operations, decision making, and performance of the company seeking to implement AI in a production context.
Cooperation and collaboration can influence the success of digital technology adoption [ 52 , 53 , 59 , 72 ], which is why partnerships are important for adoption [ 53 , 59 ] and can positively influence its future success [ 52 , 67 ]. Both intraorganizational and interorganizational knowledge sharing can positively influence AI adoption [ 49 ]. In collaborations, companies can use a shared knowledge base where data and process sharing [ 51 , 59 , 94 ] as well as social support systems strengthen feedback loops between departments [ 79 , 80 ]. With regard to AI adoption in firms, vendors as well as service providers need to collaborate closely to improve the compatibility and operational capability of smart technologies across different industries [ 82 , 93 ]. Without external IT support, companies can rarely integrate AI into their production processes [ 66 ], which is why thorough support from vendors can significantly facilitate the integration of AI into existing manufacturing processes [ 80 , 91 ]. Public–private collaborations can also add value and governments can target AI dissemination [ 60 , 74 ]. The support of the government also positively influences AI adoption. This includes investing in research projects and policies, building a regulatory setting as well as creating a collaborative environment [ 60 ]. Production companies are constantly exposed to changing conditions, which is why the dynamics of the environment is another factor influencing the adoption of AI [ 52 , 63 , 72 , 86 ]. Environmental dynamics influence the operational performance of firms and can favor an entrepreneurial orientation of firms [ 86 ]. In order to respond to dynamics, companies need to develop certain capabilities and resources (i.e. dynamic capabilities) [ 86 ]. This requires the development of transparency, agility, as well as resilience to unpredictable changes, which was important in the case of the COVID-19 pandemic, for example, where companies had to adapt quickly to changing environments [ 75 ]. A firm’s environment (e.g. governments, partners or customers) can also pressure companies to adopt digital technologies [ 53 , 67 , 82 , 91 ]. Companies facing intense competition are considered more likely to invest in smart technologies, as rivalry pushes them to innovate and they hope to gain competitive advantages from adoption [ 36 , 66 , 82 , 93 ].
By considering both the industrial sector and country within the subcategory ‘economic environment’, production firms can analyze the interplay between the two and understand how drivers can influence the AI adoption process in their industrial sector’s performance within a particular country.
The industrial sector of a firm influences AI adoption in production from a structural perspective, as it indicates variations in product characteristics, governmental support, the general digitalization status, the production environment as well as the use of AI technologies within the sector [ 36 ]. Another factor that influences AI adoption is the country in which a company is located. This influences not only cultural aspects, the availability of know-how and technology orientation, but also regulations, laws, standards and subsidies [ 36 ]. From another perspective, AI can also contribute to the wider socio-economic growth of economies by making new opportunities easily available and thus equipping e.g. more rural areas with advanced capabilities [ 78 ].
The analysis of AI adoption in production requires a comprehensive analysis of the various factors that influence the introduction of the innovation. As discussed by Kinkel, Baumgartner, Cherubini [ 36 ], our research also concludes that organizational factors have a particularly important role to play. After evaluating the individual drivers of AI adoption in production in detail in this qualitative synthesis, we draw a conclusion from the results and derive a research agenda from the analysis to serve as a basis for future research. The RQs emerged from the analyzed factors and are presented in Table 2 . We developed the questions based on the literature review and identified research gaps for every factor that was most frequently mentioned. From the factors analyzed and RQs developed, the internal environment has a strong influence on AI adoption in production, and organizational factors play a major role here.
Looking at the supercategory ‘business and environment’, performance indicators and investments are considered drivers of AI adoption in production. Indicators to measure the performance of AI innovations are necessary here so that managers can perform cost–benefit analyses and make the right decision for their company. There is a need for research here to support possible calculations and show managers a comprehensive view of the costs and benefits of technology in production. In terms of budget, it should be noted that AI adoption involves a considerable financial outlay that must be carefully weighed and some capital must be available to carry out the necessary implementation efforts (e.g., staffing costs, machine retrofits, change management, and external IT service costs). Since AI adoption is a complex process and turnkey solutions can seldom be implemented easily and quickly, but require many changes (not only technologically but also on an organizational level), it is currently difficult to estimate the necessary budgets and thus make them available. Especially the factors of the supercategory ‘organizational effectiveness’ drive AI adoption in production. Trust of the workforce is considered an important driver, which must be created in order to successfully implement AI. This requires measures that can support management in building trust. Closely related to this are the necessary change management processes that must be initiated to accompany the changes in a targeted manner. Management itself must also play a clear role in the introduction of AI and communicate its support, as this also influences the adoption. The development of clear processes and measures can help here. Developing roadmaps for AI adoption can facilitate the adoption process and promote strategic integration with existing IT and business strategy. Here, best practice roadmaps and necessary action steps can be helpful for companies. Skills are considered the most important driver for AI adoption in manufacturing. Here, there is a lack of clear approaches that support companies in identifying the range of necessary skills and, associated with this, also opportunities to further develop these skills in the existing workforce. Also, building a culture of innovation requires closer research that can help companies foster a conducive environment for AI adoption and the integration of other smart technologies. Steps for developing a positive mindset require further research that can provide approaches for necessary action steps and measures in creating a positive digital culture. With regard to ‘technology and system’, the factors of IT infrastructure and security in particular are driving AI adoption in production. Existing IT systems must reach a certain maturity to enable AI adoption on a technical level. This calls for clear requirements that visualize for companies which systems and standards are in place and where developments are needed. Security must be continuously ensured, for which certain standards and action catalogs must be developed. With regard to the supercategory ‘data management’, the availability of data is considered the basis for successful AI adoption, as no AI can be successfully deployed without data. In the production context in particular, this requires developments that support companies in the provision of data, which usually arises from very heterogeneous sources and forms. Data analytics must also be closely examined, and production companies usually need external support in doing so. The multitude of data also requires big data storage capabilities. Here, groundwork is needed to show companies options about the possibilities of different storage options (e.g., on premis vs. cloud-based).
In the ‘regulatory environment’, ethics in particular is considered a driver of AI adoption in production. Here, fundamental ethical factors and frameworks need to be developed that companies can use as a guideline to ensure ethical standards throughout the process. Cooperations and environmental dynamism drive the supercategory ‘business environment’. Collaborations are necessary to successfully implement AI adoption and action is needed to create the necessary contact facilitation bodies. In a competitive environment, companies have to make quick decisions under strong pressure, which also affects AI adoption. Here, guidelines and also best practice approaches can help to simplify decisions and quickly demonstrate the advantage of the solutions. There is a need for research in this context.
The use of AI technologies in production continues to gain momentum as managers hope to increase efficiency, productivity and reduce costs [ 9 , 13 , 20 ]. Although the benefits of AI adoption speak for themselves, implementing AI is a complex decision that requires a lot of knowledge, capital and change [ 95 ] and is influenced by various internal and external factors. Therefore, managers are still cautious about implementing the technology in a production context. Our SLR seeks to examine the emergent phenomenon of AI in production with the precise aim of understanding the factors influencing AI adoption and the key topics discussed in the literature when analyzing AI in a production context. For this purpose, we use the current state of research and examine the existing studies based on the methodology of a systematic literature analysis and respond to three RQs.
We answer RQ1 by closely analyzing the literature selected in our SLR to identify trends in current research on AI adoption in production. In this process, it becomes clear that the topic is gaining importance and that research has increased over the last few years. In the field of production, AI is being examined from various angles and current research addresses aspects from a business, human and technical perspective. In our response to RQ2 we synthesized the existing literature to derive 35 factors that influence AI adoption in production at different levels from inside or outside the organization. In doing so, we find that AI adoption in production poses particularly significant challenges to organizational effectiveness compared to other digital technologies and that the relevance of data management takes on a new dimension. Production companies often operate more traditionally and are sometimes rigid when it comes to change [ 96 , 97 ], which can pose organizational challenges when adopting AI. In addition, the existing machines and systems are typically rather heterogeneous and are subject to different digitalization standards, which in turn can hinder the availability of the necessary data for AI implementation [ 98 , 99 ]. We address RQ3 by deriving a research agenda, which lays a foundation for further scientific research and deepening the understanding of AI adoption in production. The results of our analysis can further help managers to better understand AI adoption and to pay attention to the different factors that influence the adoption of this complex technology.
Our paper takes the first step towards analysing the current state of the research on AI adoption from a production perspective. We represent a holistic view on the topic, which is necessary to get a better understanding of AI in a production-context and build a comprehensive view on the different dimensions as well as factors influencing its adoption. To the best of our knowledge, this is the first contribution that systematises research about the adoption of AI in production. As such, it makes an important contribution to current AI and production research, which is threefold:
First, we highlight the characteristics of studies conducted in recent years on the topic of AI adoption in production, from which several features and developments can be deduced. Our results confirm the topicality of the issue and the increasing relevance of research in the field.
Having laid the foundations for understanding AI in production, we focused our research on the identification and systematization of the most relevant factors influencing AI adoption in production at different levels. This brings us to the second contribution, our comprehensive factor analysis of AI adoption in production provides a framework for further research as well as a potential basis for managers to draw upon when adopting AI. By systematizing the relevant factors influencing AI adoption in production, we derived a set of 35 researched factors associated with AI adoption in production. These factors can be clustered in two areas of analysis and seven respective supercategories. The internal environment area includes four levels of analysis: ‘business and structure’ (focusing on financial aspects and firm characteristics), ‘organizational effectiveness’ (focusing on human-centred factors), ‘technology and system’ (based on the IT infrastructure and systems) as well as ‘data management’ (including all data related factors). Three categories are assigned to the external environment: the ‘regulatory environment’ (such as ethics and the regulatory forms), the ‘business environment’ (focused on cooperation activities and dynamics in the firm environment) and the ‘economic environment’ (related to sectoral and country specifics).
Third, the developed research plan as outlined in Table 2 serves as an additional outcome of the SLR, identifying key RQs in the analyzed areas that can serve as a foundation for researchers to expand the research area of AI adoption in production. These RQs are related to the mostly cited factors analyzed in our SLR and aim to broaden the understanding on the emerging topic.
The resulting insights can serve as the basis for strategic decisions by production companies looking to integrate AI into their processes. Our findings on the factors influencing AI adoption as well as the developed research agenda enhance the practical understanding of a production-specific adoption. Hence, they can serve as the basis for strategic decisions for companies on the path to an effective AI adoption. Managers can, for example, analyse the individual factors in light of their company as well as take necessary steps to develop further aspects in a targeted manner. Researchers, on the other hand, can use the future research agenda in order to assess open RQs and can expand the state of research on AI adoption in production.
Since a literature review must be restricted in its scope in order to make the analyses feasible, our study provides a starting point for further research. Hence, there is a need for further qualitative and quantitative empirical research on the heterogeneous nature of how firms configure their AI adoption process. Along these lines, the following aspects would be of particular interest for future research to improve and further validate the analytical power of the proposed framework.
First, the lack of research on AI adoption in production leads to a limited number of papers included in this SLR. As visualized in Fig. 2 , the number of publications related to the adoption of AI in production has been increasing since 2018 but is, to date, still at an early stage. For this reason, only 47 papers published until May 2024 addressing the production-specific adoption of AI were identified and therefore included in our analysis for in-depth investigation. This rather small number of papers included in the full-text analysis gives a limited view on AI adoption in production but allows a more detailed analysis. As the number of publications in this research field increases, there seems to be a lot of research happening in this field which is why new findings might be constantly added and developed as relevant in the future [ 39 ]. Moreover, in order to research AI adoption from a more practical perspective and thus to build up a broader, continuously updated view on AI adoption in production, future literature analyses could include other publication formats, e.g. study reports of research institutions and companies, as well discussion papers.
Second, the scope of the application areas of AI in production has been increasing rapidly. Even though our overview of the three main areas covered in the recent literature serves as a good basis for identifying the most dominant fields for AI adoption in production, a more detailed analysis could provide a better overview of possibilities for manufacturing companies. Hence, a further systematisation as well as evaluation of application areas for AI in production can provide managers with the information needed to decide where AI applications might be of interest for the specific company needs.
Third, the systematisation of the 35 factors influencing AI adoption in production serve as a good ground for identifying relevant areas influenced by and in turn influencing the adoption of AI. Further analyses should be conducted in order to extend this view and extend the framework. For example, our review could be combined with explorative research methods (such as case studies in production firms) in order to add the practical insights from firms adopting AI. This integration of practical experiences can also help exploit and monitor more AI-specific factors by observing AI adoption processes. In enriching the factors through in-depth analyses, the results of the identified AI adoption factors could also be examined in light of theoretical contributions like the technology-organization-environment (TOE) framework [ 47 ] and other adoption theories.
Fourth, in order to examine the special relevance of identified factors for AI adoption process and thus to distinguish it from the common factors influencing the adoption of more general digital technologies, there is a further need for more in-depth (ethnographic) research into their impacts on the adoption processes, particularly in the production context. Similarly, further research could use the framework introduced in this paper as a basis to develop new indicators and measurement concepts as well as to examine their impacts on production performance using quantitative methods.
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Heimberger, H., Horvat, D. & Schultmann, F. Exploring the factors driving AI adoption in production: a systematic literature review and future research agenda. Inf Technol Manag (2024). https://doi.org/10.1007/s10799-024-00436-z
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National Toxicology Program. NTP Monograph on the State of the Science Concerning Fluoride Exposure and Neurodevelopment and Cognition: A Systematic Review: NTP Monograph 08 [Internet]. Research Triangle Park (NC): National Toxicology Program; 2024 Aug.
The National Toxicology Program (NTP) conducted a systematic review of the published scientific literature because of public concern regarding the potential association between fluoride exposure and adverse neurodevelopmental and cognitive health effects.
NTP initially published a systematic review of the experimental animal literature in 2016 that was subsequently expanded to include human epidemiological studies, mechanistic studies, and newer experimental animal literature (see Appendix B , Table B-1 for document and review timeline). Because of the high public interest in fluoride’s benefits and potential risks, NTP asked the National Academies of Sciences, Engineering, and Medicine (NASEM) to conduct an independent evaluation of the draft NTP Monograph on Fluoride Exposure and Neurodevelopmental and Cognitive Health Effects (2019 draft monograph dated September 6, 2019) and the revised draft (2020 draft monograph dated September 16, 2020), which addressed the NASEM committee’s recommendations for improvement. The NASEM committee determined that, “Overall the revised monograph seems to include a wealth of evidence and a number of evaluations that support its main conclusion, but the monograph falls short of providing a clear and convincing argument that supports its assessments….” Thus, NTP has removed the hazard assessment step and retitled this systematic review of fluoride exposure and neurodevelopmental and cognitive health effects as a “state-of-the-science” document to indicate the change. This state-of-the-science document does not include the meta-analysis of epidemiological studies or hazard conclusions found in previous draft monographs; however, it provides a comprehensive and current assessment of the scientific literature on fluoride as an important resource to inform safe and appropriate use. The meta-analysis is a separate peer-reviewed journal publication ( DTT Meta-analysis, Taylor et al. 2024, in press ).
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Canberra health services: literature review of older people (>65 years) with obesity accessing bariatric surgery and their health outcomes.
Conduct a literature review to help inform ACT Health’s activities relating to older people with obesity accessing bariatric surgery.
Degree level.
The Canberra Health Services (CHS) Department of Bariatric Medicine (DBM) is a publicly-funded and medically-led multi-disciplinary program for adults with class III obesity (defined as a body mass index [BMI] of ≥40kg/m2) and at least one obesity-related comorbidity.
The OMS provides medical reviews, case management, group education and individualised allied health support. There is an initial focus on lifestyle interventions (individual dietitian and exercise physiology/physiotherapy appointments and group psychology sessions) with some patients receiving more intensive interventions such as anti-obesity medications, Very Low Energy Diets and/or bariatric surgery. Access to interventions is dependent on several factors including the patient’s medical status and psychosocial capacity to engage.
Project: Literature Review of Older People (>65 years) with Obesity Accessing Bariatric Surgery and their Health Outcomes
Internship Availability | Semester 1, 2025 |
Internship Discipline/s | Chronic disease/obesity management |
Internship Level | Postgraduate Coursework only |
Available to International Students | Yes |
Preferred Project Skills: | |
Clearances Required | |
Host Supervisor | Dr Louise Brightman, Department of Bariatric Medicine (DBM, formerly known as Obesity Management Service [OMS]) |
Location | Belconnen Community Health Centre, 56 Lathlain Street Belconnen, ACT 2617 |
Project Opportunities/Benefits for the Intern |
|
The intern will conduct a literature review covering local and international research, clinical guidelines, and organisational reports to help inform ACT Health’s activities relating to addressing obesity and the impact it has on health outcomes. This information may then be used to help inform and/or improve DBM processes and policies. It may also lead to formal research projects using DBM data in the future.
IMAGES
COMMENTS
Literature reviews are in great demand in most scientific fields. Their need stems from the ever-increasing output of scientific publications .For example, compared to 1991, in 2008 three, eight, and forty times more papers were indexed in Web of Science on malaria, obesity, and biodiversity, respectively .Given such mountains of papers, scientists cannot be expected to examine in detail every ...
Examples of literature reviews. Step 1 - Search for relevant literature. Step 2 - Evaluate and select sources. Step 3 - Identify themes, debates, and gaps. Step 4 - Outline your literature review's structure. Step 5 - Write your literature review.
A literature review is essential to any scientific research study, which entails an in-depth analysis and synthesis of the existing literature and studies related to the research topic. The ...
A literature review is a critical analysis and synthesis of existing research on a particular topic. It provides an overview of the current state of knowledge, identifies gaps, and highlights key findings in the literature. 1 The purpose of a literature review is to situate your own research within the context of existing scholarship ...
The best proposals are timely and clearly explain why readers should pay attention to the proposed topic. It is not enough for a review to be a summary of the latest growth in the literature: the ...
Writing a Literature Review. A literature review is a document or section of a document that collects key sources on a topic and discusses those sources in conversation with each other (also called synthesis ). The lit review is an important genre in many disciplines, not just literature (i.e., the study of works of literature such as novels ...
2. Benefits of Review Articles to the Author. Analysing literature gives an overview of the "WHs": WHat has been reported in a particular field or topic, WHo the key writers are, WHat are the prevailing theories and hypotheses, WHat questions are being asked (and answered), and WHat methods and methodologies are appropriate and useful [].For new or aspiring researchers in a particular ...
7 Writing a Literature Review . Hundreds of original investigation research articles on health science topics are published each year. It is becoming harder and harder to keep on top of all new findings in a topic area and - more importantly - to work out how they all fit together to determine our current understanding of a topic.
Example: Predictors and Outcomes of U.S. Quality Maternity Leave: A Review and Conceptual Framework: 10.1177/08948453211037398 ; Systematic review: "The authors of a systematic review use a specific procedure to search the research literature, select the studies to include in their review, and critically evaluate the studies they find." (p. 139).
A literature review addresses a specific topic by evaluating research that others have done on it. As an author, you will weave your review article around a certain thesis or problem you wish to address, evaluate the quality and the meaning of the studies done before, and arrives at a conclusion about the problem based on the studies evaluated ...
Abstract. Scientific review articles are comprehensive, focused reviews of the scientific literature written by subject matter experts. The task of writing a scientific review article can seem overwhelming; however, it can be managed by using an organized approach and devoting sufficient time to the process.
scientific or scholarly revolution singlehandedly is our chief gal, we are bound to fail. Better to pursue the goals of normal science: ... The literature review is an opportunity to discover and craft your scholarly identity through the kinds of questions you engage, the discussions you enter, the critiques you launch, and the ...
A literature review is an integrated analysis-- not just a summary-- of scholarly writings and other relevant evidence related directly to your research question.That is, it represents a synthesis of the evidence that provides background information on your topic and shows a association between the evidence and your research question.
Literature reviews are valuable resources for the scientific community. With research accelerating at an unprecedented speed in recent years and more and more original papers being published, review articles have become increasingly important as a means to keep up to date with developments in a particular area of research.
Literature Review is a comprehensive survey of the works published in a particular field of study or line of research, usually over a specific period of time, in the form of an in-depth, critical bibliographic essay or annotated list in which attention is drawn to the most significant works.. Also, we can define a literature review as the collected body of scholarly works related to a topic:
Maria Watson is a PhD candidate in the Urban and Regional Science program at Texas A&M University. Her research interests include disaster recovery, public policy, and economic development. Literature reviews establish the foundation of academic inquires. However, in the planning field, we lack rigorous systematic reviews.
Literature reviews play a critical role in scholarship because science remains, first and foremost, a cumulative endeavour (vom Brocke et al., 2009). As in any academic discipline, rigorous knowledge syntheses are becoming indispensable in keeping up with an exponentially growing eHealth literature, assisting practitioners, academics, and graduate students in finding, evaluating, and ...
A scientific literature review usually includes a title, abstract, index, introduction, corpus, bibliography, and appendices (if needed). Present the problem clearly. Mention the paper's methodology, research methods, analysis, instruments, etc. Present literature review examples that can help you express your ideas. Remember to cite accurately.
Literature reviews are in great demand in most scientific fields. Their need stems from the ever-increasing output of scientific publications .For example, compared to 1991, in 2008 three, eight, and forty times more papers were indexed in Web of Science on malaria, obesity, and biodiversity, respectively .Given such mountains of papers, scientists cannot be expected to examine in detail every ...
A literature review is a body of text that aims to review the critical points of current knowledge on a particular topic. Most often associated with science-oriented literature, such as a thesis, the literature review usually proceeds a research proposal, methodology and results section. Its ultimate goals is to bring the reader up to date with ...
Literature reviews are valuable resources for the scientific community. With research accelerating at an unprecedented speed in recent years and more and more original papers being published, review articles have become increasingly important as a means to keep up-to-date with developments in a particular area of research.
Scientific literature encompasses a vast body of academic papers that spans various disciplines within the natural and social sciences. ... This evaluation, known as peer review, ensures the quality, validity, and reliability of the research before it becomes part of the scientific literature. Peer-reviewed publications contribute significantly ...
Literature reviews are foundational to any study. They describe what is known about given topic and lead us to identify a knowledge gap to study. All reviews require authors to be able accurately summarize, synthesize, interpret and even critique the research literature. 1, 2 In fact, for this editorial we have had to review the literature on ...
We review the scientific literature published between 2010 and May 2024 to analyze the current state of research on AI in production. Following a systematic approach to select relevant studies, our literature review is based on a sample of articles that contribute to production-specific AI adoption. Our results reveal that the topic has been ...
The Systematic Literature Review (SLR) is using ROSES (Reporting Standards for Systematic Evidence Syntheses), a set of guidelines developed by Haddaway et al. (2018). Compared to other protocols, ROSES is the most appropriate review methodology to use, given that the topic of this study is approaches for managing plastic waste.
This article presents a literature review on working through interpreters in old age psychiatry. The purpose of this paper is to systematically appraise the effect of use of interpreters for mental health problems in old age. The primary objective of the review is to assess the impact of a language barrier for assessment and management in relation to mental health problems in the old age.
This study systematically reviewed the scientific literature on natural remedies for male sexual dysfunction (MSD), including conditions like erectile dysfunction, premature ejaculation, and reduced libido. Limited scientific evidence exists regarding the efficacy and safety of these natural products.
This comprehensive review integrates current scientific literature on gut microbiota modulation as a therapeutic avenue for COPD management. Through a thorough discussion of studies investigating probiotics, prebiotics, synbiotics, antibiotics, dietary fiber, and fecal microbiota transplantation, this review summarizes the influence of these ...
This state-of-the-science document does not include the meta-analysis of epidemiological studies or hazard conclusions found in previous draft monographs; however, it provides a comprehensive and current assessment of the scientific literature on fluoride as an important resource to inform safe and appropriate use.
The intern will conduct a literature review covering local and international research, clinical guidelines, and organisational reports to help inform ACT Health's activities relating to addressing obesity and the impact it has on health outcomes. This information may then be used to help inform and/or improve DBM processes and policies.