literature review on management

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

• How to Write a Literature Review | Guide, Examples, & Templates

How to Write a Literature Review | Guide, Examples, & Templates

Published on January 2, 2023 by Shona McCombes . Revised on September 11, 2023.

What is a literature review? A literature review is a survey of scholarly sources on a specific topic. It provides an overview of current knowledge, allowing you to identify relevant theories, methods, and gaps in the existing research that you can later apply to your paper, thesis, or dissertation topic .

There are five key steps to writing a literature review:

• Search for relevant literature
• Evaluate sources
• Identify themes, debates, and gaps
• Outline the structure
• Write your literature review

A good literature review doesn’t just summarize sources—it analyzes, synthesizes , and critically evaluates to give a clear picture of the state of knowledge on the subject.

Instantly correct all language mistakes in your text

Upload your document to correct all your mistakes in minutes

Table of contents

What is the purpose of a literature review, 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, free lecture slides, other interesting articles, frequently asked questions, introduction.

• Quick Run-through
• Step 1 & 2

When you write a thesis , dissertation , or research paper , you will likely have to conduct a literature review to situate your research within existing knowledge. The literature review gives you a chance to:

• Demonstrate your familiarity with the topic and its scholarly context
• Develop a theoretical framework and methodology for your research
• Position your work in relation to other researchers and theorists
• Show how your research addresses a gap or contributes to a debate
• Evaluate the current state of research and demonstrate your knowledge of the scholarly debates around your topic.

Writing literature reviews is a particularly important skill if you want to apply for graduate school or pursue a career in research. We’ve written a step-by-step guide that you can follow below.

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

• Academic style
• Vague sentences
• Style consistency

See an example

Writing literature reviews can be quite challenging! A good starting point could be to look at some examples, depending on what kind of literature review you’d like to write.

• Example literature review #1: “Why Do People Migrate? A Review of the Theoretical Literature” ( Theoretical literature review about the development of economic migration theory from the 1950s to today.)
• Example literature review #2: “Literature review as a research methodology: An overview and guidelines” ( Methodological literature review about interdisciplinary knowledge acquisition and production.)
• Example literature review #3: “The Use of Technology in English Language Learning: A Literature Review” ( Thematic literature review about the effects of technology on language acquisition.)
• Example literature review #4: “Learners’ Listening Comprehension Difficulties in English Language Learning: A Literature Review” ( Chronological literature review about how the concept of listening skills has changed over time.)

You can also check out our templates with literature review examples and sample outlines at the links below.

Download Word doc Download Google doc

Before you begin searching for literature, you need a clearly defined topic .

If you are writing the literature review section of a dissertation or research paper, you will search for literature related to your research problem and questions .

Make a list of keywords

Start by creating a list of keywords related to your research question. Include each of the key concepts or variables you’re interested in, and list any synonyms and related terms. You can add to this list as you discover new keywords in the process of your literature search.

• Social media, Facebook, Instagram, Twitter, Snapchat, TikTok
• Body image, self-perception, self-esteem, mental health
• Generation Z, teenagers, adolescents, youth

Search for relevant sources

Use your keywords to begin searching for sources. Some useful databases to search for journals and articles include:

• Your university’s library catalogue
• Google Scholar
• Project Muse (humanities and social sciences)
• Medline (life sciences and biomedicine)
• EconLit (economics)
• Inspec (physics, engineering and computer science)

You can also use boolean operators to help narrow down your search.

Make sure to read the abstract to find out whether an article is relevant to your question. When you find a useful book or article, you can check the bibliography to find other relevant sources.

You likely won’t be able to read absolutely everything that has been written on your topic, so it will be necessary to evaluate which sources are most relevant to your research question.

For each publication, ask yourself:

• What question or problem is the author addressing?
• What are the key concepts and how are they defined?
• What are the key theories, models, and methods?
• Does the research use established frameworks or take an innovative approach?
• What are the results and conclusions of the study?
• How does the publication relate to other literature in the field? Does it confirm, add to, or challenge established knowledge?
• What are the strengths and weaknesses of the research?

Make sure the sources you use are credible , and make sure you read any landmark studies and major theories in your field of research.

You can use our template to summarize and evaluate sources you’re thinking about using. Click on either button below to download.

Take notes and cite your sources

As you read, you should also begin the writing process. Take notes that you can later incorporate into the text of your literature review.

It is important to keep track of your sources with citations to avoid plagiarism . It can be helpful to make an annotated bibliography , where you compile full citation information and write a paragraph of summary and analysis for each source. This helps you remember what you read and saves time later in the process.

Don't submit your assignments before you do this

The academic proofreading tool has been trained on 1000s of academic texts. Making it the most accurate and reliable proofreading tool for students. Free citation check included.

Try for free

To begin organizing your literature review’s argument and structure, be sure you understand the connections and relationships between the sources you’ve read. Based on your reading and notes, you can look for:

• Trends and patterns (in theory, method or results): do certain approaches become more or less popular over time?
• Themes: what questions or concepts recur across the literature?
• Debates, conflicts and contradictions: where do sources disagree?
• Pivotal publications: are there any influential theories or studies that changed the direction of the field?
• Gaps: what is missing from the literature? Are there weaknesses that need to be addressed?

This step will help you work out the structure of your literature review and (if applicable) show how your own research will contribute to existing knowledge.

• Most research has focused on young women.
• There is an increasing interest in the visual aspects of social media.
• But there is still a lack of robust research on highly visual platforms like Instagram and Snapchat—this is a gap that you could address in your own research.

There are various approaches to organizing the body of a literature review. Depending on the length of your literature review, you can combine several of these strategies (for example, your overall structure might be thematic, but each theme is discussed chronologically).

Chronological

The simplest approach is to trace the development of the topic over time. However, if you choose this strategy, be careful to avoid simply listing and summarizing sources in order.

Try to analyze patterns, turning points and key debates that have shaped the direction of the field. Give your interpretation of how and why certain developments occurred.

If you have found some recurring central themes, you can organize your literature review into subsections that address different aspects of the topic.

For example, if you are reviewing literature about inequalities in migrant health outcomes, key themes might include healthcare policy, language barriers, cultural attitudes, legal status, and economic access.

Methodological

If you draw your sources from different disciplines or fields that use a variety of research methods , you might want to compare the results and conclusions that emerge from different approaches. For example:

• Look at what results have emerged in qualitative versus quantitative research
• Discuss how the topic has been approached by empirical versus theoretical scholarship
• Divide the literature into sociological, historical, and cultural sources

Theoretical

A literature review is often the foundation for a theoretical framework . You can use it to discuss various theories, models, and definitions of key concepts.

You might argue for the relevance of a specific theoretical approach, or combine various theoretical concepts to create a framework for your research.

Like any other academic text , your literature review should have an introduction , a main body, and a conclusion . What you include in each depends on the objective of your literature review.

The introduction should clearly establish the focus and purpose of the literature review.

Depending on the length of your literature review, you might want to divide the body into subsections. You can use a subheading for each theme, time period, or methodological approach.

As you write, you can follow these tips:

• Summarize and synthesize: give an overview of the main points of each source and combine them into a coherent whole
• Analyze and interpret: don’t just paraphrase other researchers — add your own interpretations where possible, discussing the significance of findings in relation to the literature as a whole
• Critically evaluate: mention the strengths and weaknesses of your sources
• Write in well-structured paragraphs: use transition words and topic sentences to draw connections, comparisons and contrasts

In the conclusion, you should summarize the key findings you have taken from the literature and emphasize their significance.

When you’ve finished writing and revising your literature review, don’t forget to proofread thoroughly before submitting. Not a language expert? Check out Scribbr’s professional proofreading services !

This article has been adapted into lecture slides that you can use to teach your students about writing a literature review.

Scribbr slides are free to use, customize, and distribute for educational purposes.

Open Google Slides Download PowerPoint

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

A literature review is a survey of scholarly sources (such as books, journal articles, and theses) related to a specific topic or research question .

It is often written as part of a thesis, dissertation , or research paper , in order to situate your work in relation to existing knowledge.

There are several reasons to conduct a literature review at the beginning of a research project:

• To familiarize yourself with the current state of knowledge on your topic
• To ensure that you’re not just repeating what others have already done
• To identify gaps in knowledge and unresolved problems that your research can address
• To develop your theoretical framework and methodology
• To provide an overview of the key findings and debates on the topic

Writing the literature review shows your reader how your work relates to existing research and what new insights it will contribute.

The literature review usually comes near the beginning of your thesis or dissertation . After the introduction , it grounds your research in a scholarly field and leads directly to your theoretical framework or methodology .

A literature review is a survey of credible sources on a topic, often used in dissertations , theses, and research papers . Literature reviews give an overview of knowledge on a subject, helping you identify relevant theories and methods, as well as gaps in existing research. Literature reviews are set up similarly to other  academic texts , with an introduction , a main body, and a conclusion .

An  annotated bibliography is a list of  source references that has a short description (called an annotation ) for each of the sources. It is often assigned as part of the research process for a  paper .  

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

McCombes, S. (2023, September 11). How to Write a Literature Review | Guide, Examples, & Templates. Scribbr. Retrieved August 12, 2024, from https://www.scribbr.com/dissertation/literature-review/

Is this article helpful?

Shona McCombes

Other students also liked, what is a theoretical framework | guide to organizing, what is a research methodology | steps & tips, how to write a research proposal | examples & templates, get unlimited documents corrected.

✔ Free APA citation check included ✔ Unlimited document corrections ✔ Specialized in correcting academic texts

JavaScript seems to be disabled in your browser. For the best experience on our site, be sure to turn on Javascript in your browser.

• Browse Series

Review Of Management Literature

Review of Management Literature is a multi-disciplinary series presenting unique and groundbreaking literature reviews and examinations of new and emerging trends in research across the management discipline and beyond.

Examining broad disciplinary areas including new research developments in fields including Marketing and HRM as well as well as more focussed studies on the big data and gamification in management literature the series also explores practical research guidance with titles examining methodologies for conducting literature reviews in management domain.

Supported by a highly reputable editorial board from across the globe the series will be an essential research resource to access and interpret cutting edge global developments from across the management discipline.

From £95.00

From £80.00

From £89.99

Information

• Author Services

Initiatives

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

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

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

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

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

Original Submission Date Received: .

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

Article Menu

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

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

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

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

A bi-objective model for the multi-period inventory-based reverse logistics network: a case study from an automobile component distribution network.

1. Introduction

• To optimize the transportation system in the ISACO company.
• To cut down transportation costs.
• To increase customer satisfaction by increasing the supply of customer demands.
• To allow the customers to return unused parts (which are not used by customers due to seasonal variations or environmental changes and market fluctuations.
• To collect and dispose or recycle the stock parts.

2. Literature Review

2.1. a review of the literature on distribution systems in supply chain management, 2.2. a review of the literature on green logistics in supply chain management, 3. materials and methods.

• Very high transportation costs induced by long round-trip distances.
• High costs imposed on the company as a result of vehicle breakdown.
• Frequent troubles related to timely goods delivery (e.g., the cities located far from Tehran, the chances are high that the goods do not reach on time).
• To benefit from the full capacity of cars, it is required that the amount of the ordered goods reach a certain quantity and then the goods be delivered to the representatives, which leads to dissatisfaction among the representatives and losing the competitive market.
• The lack of order and prioritization in the current system.
• Not considering different scenarios in decision making.
• Not being able to return unused or low-use parts by the representatives.
• The lack of an integrated system for receiving scrap parts.
• Not able to implement strategic planning.
• Some of the expected merits of the new system are the following:
• Reducing the costs resulting from redundant transportation.
• Increasing the representatives’ satisfaction level due to goods’ timely delivery and increasing the power to supply the demanded goods and the possibility of returning low-use parts to the representative.
• Systematizing transportation system which curbs other nuisances.
• Increasing the flexibility of the system.
• Decreasing the risks such as the sensitive parts becoming faulty during long transportation or the possibility of vehicle breakdowns that impose losses on the company.
• Building regional warehouses and reducing the heavy costs of the central warehouse.
• Controlling the system better and the potential to constantly improve.

5. Discussion and Conclusions

• Employing a multi-period model along with the power of inventory management so that it leads to reduced costs and increased revenue.
• With respect to the variety of available products, the number of product groups should be increased and included in the proposed model.
• Reducing the time of ordering periods to better use the multi-period model, supplying faster and more up-to-date customer demands in the year, and removing the barriers of the inventory cost increase through modeling and making decisions at the tactical and operational level.
• Raising the number of customers and applying the proposed model to the actual number of customers. It is worth mentioning that in this model, they were integrated into the provincial centers to facilitate the modeling of customer demand.
• Constructing regional warehouses in the locations suggested by the model outputs considering the construction cost and setting up and storing the goods in these warehouses.
• Launching the central warehouse number 2 when its effectiveness gets approved in all the models to properly benefit from it.
• Regularly controlling the proposed performance evaluation indices considering the possibility of changing the supply or demand pattern and making suitable decisions accordingly.
• Investigating the demand pattern in various time periods and the possibility of presenting a supplementary model for the probability mode of demand.
• Investigating the profit from waste recycling.
• Investigating the benefits of the brand’s mental image in terms of compliance with environmental issues.
• Considering production issues in the supply chain and distribution system.
• Including the demand of the different classes of customers in the distribution system and locating facilities; accordingly, in other words, assessing the effect of marketing decisions on the strategic macro-decisions of facility location.
• Considering other location benchmarks.
• Determining the order supply deadline for all sorts of goods orders and programming to supply them within the deadline and its effect on facility location problems.
• Considering other objective functions like social aspects, employment rates, and environmental impacts according to the priorities of managers and decision-makers.

Author Contributions

Data availability statement, conflicts of interest.

• Kamalahmadi, M.; Parast, M.M. A review of the literature on the principles of enterprise and supply chain resilience: Major findings and directions for future research. Int. J. Prod. Econ. 2016 , 171 , 116–133. [ Google Scholar ] [ CrossRef ]
• Wang, G.; Gunasekaran, A.; Ngai, E.W.T.; Papadopoulos, T. Big data analytics in logistics and supply chain management: Certain investigations for research and applications. Int. J. Prod. Econ. 2016 , 176 , 98–110. [ Google Scholar ] [ CrossRef ]
• Alshubiri, F. The Impact of Green Logistics-Based Activities on the Sustainable Monetary Expansion Indicators of Oman. J. Ind. Eng. Manag. 2017 , 10 , 389–405. [ Google Scholar ] [ CrossRef ]
• Sheikh Azadi, A.H.; Shamsi Nesary, V.; Kebriyaii, O.; Khalilzadeh, M.; Antucheviciene, J. Design of a Green Supply Chain Based on the Kano Model Considering Pricing. Sustainability 2023 , 15 , 13038. [ Google Scholar ] [ CrossRef ]
• Liou, J.J.H.; Tamošaitienė, J.; Zavadskas, E.K.; Tzeng, G.-H. New hybrid COPRAS-G MADM Model for improving and selecting suppliers in green supply chain management. Int. J. Prod. Res. 2015 , 54 , 114–134. [ Google Scholar ] [ CrossRef ]
• Scott, J.; Ho, W.; Dey, P.K.; Talluri, S. A decision support system for supplier selection and order allocation in stochastic, multi-stakeholder and multi-criteria environments. Int. J. Prod. Econ. 2015 , 166 , 226–237. [ Google Scholar ] [ CrossRef ]
• Dweiri, F.; Kumar, S.; Khan, S.A.; Jain, V. Designing an integrated AHP based decision support system for supplier selection in automotive industry. Expert Syst. Appl. 2016 , 62 , 273–283. [ Google Scholar ] [ CrossRef ]
• Mogeni, L.M.; Kiarie, D.M. Effect of Green Logistics Practices on Performance of Supply Chains in Multinational Organizations in Kenya. Ind. Eng. Lett. 2016 , 6 , 40–50. [ Google Scholar ]
• Muangpan, T.; Chaowarat, M.; Neamvonk, J. The key activities of green logistics management in the Thai automotive industry. J. Arts Sci. Commer. 2016 , 7 , 1–11. [ Google Scholar ] [ CrossRef ]
• Brusset, X.; Teller, C. Supply chain capabilities, risks, and resilience. Int. J. Prod. Econ. 2017 , 184 , 59–68. [ Google Scholar ] [ CrossRef ]
• Ribeiro, J.P.; Barbosa-Povoa, A. Supply Chain Resilience: Definitions and quantitative modelling approaches. Comput. Ind. Eng. 2018 , 115 , 109–122. [ Google Scholar ] [ CrossRef ]
• Heidari, A.; Imani, D.M.; Khalilzadeh, M. A hub location model in the sustainable supply chain considering customer segmentation. J. Eng. Des. Technol. 2021 , 19 , 1387–1420. [ Google Scholar ] [ CrossRef ]
• Eydi, A.; Fazayeli, S.; Ghafouri, H. Multi-period configuration of forward and reverse integrated supply chain networks through transport mode. Sci. Iran. 2020 , 27 , 935–955. [ Google Scholar ] [ CrossRef ]
• Antucheviciene, J.; Jafarnejad, A.; Amoozad Mahdiraji, H.; Razavi Hajiagha, S.H.; Kargar, A. Robust Multi-Objective Sustainable Reverse Supply Chain Planning: An Application in the Steel Industry. Symmetry 2020 , 12 , 594. [ Google Scholar ] [ CrossRef ]
• Gholamian, N.; Mahdavi, I.; Mahdavi-Amiri, N.; Tavakkoli-Moghaddam, R. Hybridization of an interactive fuzzy methodology with a lexicographic min-max approach for optimizing a multi-period multi-product multi-echelon sustainable closed-loop supply chain network. Comput. Ind. Eng. 2021 , 158 , 107282. [ Google Scholar ] [ CrossRef ]
• Alinezhad, M.; Mahdavi, I.; Hematian, M. A fuzzy multi-objective optimization model for sustainable closed-loop supply chain network design in food industries. Environ. Dev. Sustain. 2022 , 24 , 8779–8806. [ Google Scholar ] [ CrossRef ]
• Pazhani, S.; Mendoza, A.; Nambirajan, R.; Narendran, T.T.; Ganesh, K.; Olivares-Benitez, E. Multi-period multi-product closed loop supply chain network design: A relaxation approach. Comput. Ind. Eng. 2021 , 155 , 107191. [ Google Scholar ] [ CrossRef ]
• Nazari-Ghanbarloo, V.; Ghodratnama, A. Optimizing a robust tri-objective multi-period reliable supply chain network considering queuing system and operational and disruption risks. Oper. Res. 2021 , 21 , 1963–2020. [ Google Scholar ] [ CrossRef ]
• Rabe, M.; Gonzalez-Feliu, J.; Chicaiza-Vaca, J.; Tordecilla, R.D. Simulation-Optimization Approach for Multi-Period Facility Location Problems with Forecasted and Random Demands in a Last-Mile Logistics Application. Algorithms 2021 , 14 , 41. [ Google Scholar ] [ CrossRef ]
• Zarrat Dakhely Parast, Z.; Haleh, H.; Avakh Darestani, S. Green reverse supply chain network design considering location-routing-inventory decisions with simultaneous pickup and delivery. Environ. Sci. Pollut. Res. 2021 . [ Google Scholar ] [ CrossRef ]
• Abdolazimi, O.; Bahrami, F.; Shishebori, D. A multi-objective closed-loop supply chain network design problem under parameter uncertainty: Comparison of exact methods. Environ. Dev. Sustain. 2022 , 24 , 10768–10802. [ Google Scholar ] [ CrossRef ]
• Maliki, F.; Souier, M.; Dahane, M. A multi-objective optimization model for a multi-period mobile facility location problem with environmental and disruption considerations. Ann. Oper. Res. 2022 , 1–26. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Gupta, S.; Vijaygargy, L.; Sarkar, B. A bi-objective integrated transportation and inventory management under a supply chain network considering multiple distribution networks. RAIRO Oper. Res. 2022 , 56 , 3991–4022. [ Google Scholar ] [ CrossRef ]
• Bortolini, M.; Calabrese, F.; Gabriele Galizia, F.; Mora, C. A three-objective optimization model for mid-term sustainable supply chain network design. Comput. Ind. Eng. 2022 , 168 , 108131. [ Google Scholar ] [ CrossRef ]
• Mogale, D.G.; De, A.; Ghadge, A.; Aktas, E. Multi-objective modelling of sustainable closed-loop supply chain network with price-sensitive demand and consumer’s incentives. Comput. Ind. Eng. 2022 , 168 , 108105. [ Google Scholar ] [ CrossRef ]
• Al-Refaie, A.; Kokash, T. Optimization of sustainable reverse logistics network with multi-objectives under uncertainty. J. Remanuf. 2023 , 13 , 1–23. [ Google Scholar ] [ CrossRef ]
• Meidute-Kavaliauskiene, I.; Yıldırım, F.; Ghorbani, S.; Činčikaitė, R. The Design of a Multi-Period and Multi-Echelon Perishable Goods Supply Network under Uncertainty. Sustainability 2022 , 14 , 2472. [ Google Scholar ] [ CrossRef ]
• Allehashemi, T.; Hassanzadeh Amin, S.; Zolfaghari, S. A proposed multi-objective model for cellphone closed-loop supply chain optimization based on fuzzy QFD. Expert Syst. Appl. 2022 , 210 , 118577. [ Google Scholar ] [ CrossRef ]
• Abdi, F.; Farughi, H.; Sadeghi, H.; Arkat, J. Location-inventory-reliability optimisation problem in a multi-objective multi-period three-level supply chain network with stochastic demand. Eur. J. Ind. Eng. 2023 , 17 , 479–528. [ Google Scholar ] [ CrossRef ]
• Arab Momeni, M.; Jain, V.; Bagheri, M. A Multi-Objective Model for Designing a Sustainable Closed-Loop Supply Chain Logistics Network. Logistics 2024 , 8 , 29. [ Google Scholar ] [ CrossRef ]
• Varshney, N.; Gupta, S.; Ahmed, A. Addressing uncertainty in closed-loop supply chain networks: A multi-objective approach to integrated production and transportation problems. J. Model. Manag. 2024 , in press . [ Google Scholar ] [ CrossRef ]
• Xia, H.; Chen, Z.; Milisavljevic-Syed, J.; Salonitis, K. Uncertain programming model for designing multi-objective reverse logistics networks. Clean. Logist. Supply Chain 2024 , 11 , 100155. [ Google Scholar ] [ CrossRef ]

Click here to enlarge figure

Share and Cite

Khalilzadeh, M.; Antucheviciene, J.; Božanić, D. A Bi-Objective Model for the Multi-Period Inventory-Based Reverse Logistics Network: A Case Study from an Automobile Component Distribution Network. Systems 2024 , 12 , 299. https://doi.org/10.3390/systems12080299

Khalilzadeh M, Antucheviciene J, Božanić D. A Bi-Objective Model for the Multi-Period Inventory-Based Reverse Logistics Network: A Case Study from an Automobile Component Distribution Network. Systems . 2024; 12(8):299. https://doi.org/10.3390/systems12080299

Khalilzadeh, Mohammad, Jurgita Antucheviciene, and Darko Božanić. 2024. "A Bi-Objective Model for the Multi-Period Inventory-Based Reverse Logistics Network: A Case Study from an Automobile Component Distribution Network" Systems 12, no. 8: 299. https://doi.org/10.3390/systems12080299

Article Metrics

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

Subscribe to receive issue release notifications and newsletters from MDPI journals

• Systematic review
• Open access
• Published: 07 August 2024

Models and frameworks for assessing the implementation of clinical practice guidelines: a systematic review

• Nicole Freitas de Mello   ORCID: orcid.org/0000-0002-5228-6691 1 , 2 ,
• Sarah Nascimento Silva   ORCID: orcid.org/0000-0002-1087-9819 3 ,
• Dalila Fernandes Gomes   ORCID: orcid.org/0000-0002-2864-0806 1 , 2 ,
• Juliana da Motta Girardi   ORCID: orcid.org/0000-0002-7547-7722 4 &
• Jorge Otávio Maia Barreto   ORCID: orcid.org/0000-0002-7648-0472 2 , 4  

Implementation Science volume  19 , Article number:  59 ( 2024 ) Cite this article

679 Accesses

6 Altmetric

Metrics details

The implementation of clinical practice guidelines (CPGs) is a cyclical process in which the evaluation stage can facilitate continuous improvement. Implementation science has utilized theoretical approaches, such as models and frameworks, to understand and address this process. This article aims to provide a comprehensive overview of the models and frameworks used to assess the implementation of CPGs.

A systematic review was conducted following the Cochrane methodology, with adaptations to the "selection process" due to the unique nature of this review. The findings were reported following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) reporting guidelines. Electronic databases were searched from their inception until May 15, 2023. A predetermined strategy and manual searches were conducted to identify relevant documents from health institutions worldwide. Eligible studies presented models and frameworks for assessing the implementation of CPGs. Information on the characteristics of the documents, the context in which the models were used (specific objectives, level of use, type of health service, target group), and the characteristics of each model or framework (name, domain evaluated, and model limitations) were extracted. The domains of the models were analyzed according to the key constructs: strategies, context, outcomes, fidelity, adaptation, sustainability, process, and intervention. A subgroup analysis was performed grouping models and frameworks according to their levels of use (clinical, organizational, and policy) and type of health service (community, ambulatorial, hospital, institutional). The JBI’s critical appraisal tools were utilized by two independent researchers to assess the trustworthiness, relevance, and results of the included studies.

Database searches yielded 14,395 studies, of which 80 full texts were reviewed. Eight studies were included in the data analysis and four methodological guidelines were additionally included from the manual search. The risk of bias in the studies was considered non-critical for the results of this systematic review. A total of ten models/frameworks for assessing the implementation of CPGs were found. The level of use was mainly policy, the most common type of health service was institutional, and the major target group was professionals directly involved in clinical practice. The evaluated domains differed between the models and there were also differences in their conceptualization. All the models addressed the domain "Context", especially at the micro level (8/12), followed by the multilevel (7/12). The domains "Outcome" (9/12), "Intervention" (8/12), "Strategies" (7/12), and "Process" (5/12) were frequently addressed, while "Sustainability" was found only in one study, and "Fidelity/Adaptation" was not observed.

Conclusions

The use of models and frameworks for assessing the implementation of CPGs is still incipient. This systematic review may help stakeholders choose or adapt the most appropriate model or framework to assess CPGs implementation based on their specific health context.

Trial registration

PROSPERO (International Prospective Register of Systematic Reviews) registration number: CRD42022335884. Registered on June 7, 2022.

Peer Review reports

Contributions to the literature

Although the number of theoretical approaches has grown in recent years, there are still important gaps to be explored in the use of models and frameworks to assess the implementation of clinical practice guidelines (CPGs). This systematic review aims to contribute knowledge to overcome these gaps.

Despite the great advances in implementation science, evaluating the implementation of CPGs remains a challenge, and models and frameworks could support improvements in this field.

This study demonstrates that the available models and frameworks do not cover all characteristics and domains necessary for a complete evaluation of CPGs implementation.

The presented findings contribute to the field of implementation science, encouraging debate on choices and adaptations of models and frameworks for implementation research and evaluation.

Substantial investments have been made in clinical research and development in recent decades, increasing the medical knowledge base and the availability of health technologies [ 1 ]. The use of clinical practice guidelines (CPGs) has increased worldwide to guide best health practices and to maximize healthcare investments. A CPG can be defined as "any formal statements systematically developed to assist practitioner and patient decisions about appropriate health care for specific clinical circumstances" [ 2 ] and has the potential to improve patient care by promoting interventions of proven benefit and discouraging ineffective interventions. Furthermore, they can promote efficiency in resource allocation and provide support for managers and health professionals in decision-making [ 3 , 4 ].

However, having a quality CPG does not guarantee that the expected health benefits will be obtained. In fact, putting these devices to use still presents a challenge for most health services across distinct levels of government. In addition to the development of guidelines with high methodological rigor, those recommendations need to be available to their users; these recommendations involve the diffusion and dissemination stages, and they need to be used in clinical practice (implemented), which usually requires behavioral changes and appropriate resources and infrastructure. All these stages involve an iterative and complex process called implementation, which is defined as the process of putting new practices within a setting into use [ 5 , 6 ].

Implementation is a cyclical process, and the evaluation is one of its key stages, which allows continuous improvement of CPGs development and implementation strategies. It consists of verifying whether clinical practice is being performed as recommended (process evaluation or formative evaluation) and whether the expected results and impact are being reached (summative evaluation) [ 7 , 8 , 9 ]. Although the importance of the implementation evaluation stage has been recognized, research on how these guidelines are implemented is scarce [ 10 ]. This paper focused on the process of assessing CPGs implementation.

To understand and improve this complex process, implementation science provides a systematic set of principles and methods to integrate research findings and other evidence-based practices into routine practice and improve the quality and effectiveness of health services and care [ 11 ]. The field of implementation science uses theoretical approaches that have varying degrees of specificity based on the current state of knowledge and are structured based on theories, models, and frameworks [ 5 , 12 , 13 ]. A "Model" is defined as "a simplified depiction of a more complex world with relatively precise assumptions about cause and effect", and a "framework" is defined as "a broad set of constructs that organize concepts and data descriptively without specifying causal relationships" [ 9 ]. Although these concepts are distinct, in this paper, their use will be interchangeable, as they are typically like checklists of factors relevant to various aspects of implementation.

There are a variety of theoretical approaches available in implementation science [ 5 , 14 ], which can make choosing the most appropriate challenging [ 5 ]. Some models and frameworks have been categorized as "evaluation models" by providing a structure for evaluating implementation endeavors [ 15 ], even though theoretical approaches from other categories can also be applied for evaluation purposes because they specify concepts and constructs that may be operationalized and measured [ 13 ]. Two frameworks that can specify implementation aspects that should be evaluated as part of intervention studies are RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) [ 16 ] and PRECEDE-PROCEED (Predisposing, Reinforcing and Enabling Constructs in Educational Diagnosis and Evaluation-Policy, Regulatory, and Organizational Constructs in Educational and Environmental Development) [ 17 ]. Although the number of theoretical approaches has grown in recent years, the use of models and frameworks to evaluate the implementation of guidelines still seems to be a challenge.

This article aims to provide a complete map of the models and frameworks applied to assess the implementation of CPGs. The aim is also to subside debate and choices on models and frameworks for the research and evaluation of the implementation processes of CPGs and thus to facilitate the continued development of the field of implementation as well as to contribute to healthcare policy and practice.

A systematic review was conducted following the Cochrane methodology [ 18 ], with adaptations to the "selection process" due to the unique nature of this review (details can be found in the respective section). The review protocol was registered in PROSPERO (registration number: CRD42022335884) on June 7, 2022. This report adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [ 19 ] and a completed checklist is provided in Additional File 1.

Eligibility criteria

The SDMO approach (Types of Studies, Types of Data, Types of Methods, Outcomes) [ 20 ] was utilized in this systematic review, outlined as follows:

Types of studies

All types of studies were considered for inclusion, as the assessment of CPG implementation can benefit from a diverse range of study designs, including randomized clinical trials/experimental studies, scale/tool development, systematic reviews, opinion pieces, qualitative studies, peer-reviewed articles, books, reports, and unpublished theses.

Studies were categorized based on their methodological designs, which guided the synthesis, risk of bias assessment, and presentation of results.

Study protocols and conference abstracts were excluded due to insufficient information for this review.

Types of data

Studies that evaluated the implementation of CPGs either independently or as part of a multifaceted intervention.

Guidelines for evaluating CPG implementation.

Inclusion of CPGs related to any context, clinical area, intervention, and patient characteristics.

No restrictions were placed on publication date or language.

Exclusion criteria

General guidelines were excluded, as this review focused on 'models for evaluating clinical practice guidelines implementation' rather than the guidelines themselves.

Studies that focused solely on implementation determinants as barriers and enablers were excluded, as this review aimed to explore comprehensive models/frameworks.

Studies evaluating programs and policies were excluded.

Studies that only assessed implementation strategies (isolated actions) rather than the implementation process itself were excluded.

Studies that focused solely on the impact or results of implementation (summative evaluation) were excluded.

Types of methods

Not applicable.

All potential models or frameworks for assessing the implementation of CPG (evaluation models/frameworks), as well as their characteristics: name; specific objectives; levels of use (clinical, organizational, and policy); health system (public, private, or both); type of health service (community, ambulatorial, hospital, institutional, homecare); domains or outcomes evaluated; type of recommendation evaluated; context; limitations of the model.

Model was defined as "a deliberated simplification of a phenomenon on a specific aspect" [ 21 ].

Framework was defined as "structure, overview outline, system, or plan consisting of various descriptive categories" [ 21 ].

Models or frameworks used solely for the CPG development, dissemination, or implementation phase.

Models/frameworks used solely for assessment processes other than implementation, such as for the development or dissemination phase.

Data sources and literature search

The systematic search was conducted on July 31, 2022 (and updated on May 15, 2023) in the following electronic databases: MEDLINE/PubMed, Centre for Reviews and Dissemination (CRD), the Cochrane Library, Cumulative Index to Nursing and Allied Health Literature (CINAHL), EMBASE, Epistemonikos, Global Health, Health Systems Evidence, PDQ-Evidence, PsycINFO, Rx for Change (Canadian Agency for Drugs and Technologies in Health, CADTH), Scopus, Web of Science and Virtual Health Library (VHL). The Google Scholar database was used for the manual selection of studies (first 10 pages).

Additionally, hand searches were performed on the lists of references included in the systematic reviews and citations of the included studies, as well as on the websites of institutions working on CPGs development and implementation: Guidelines International Networks (GIN), National Institute for Health and Care Excellence (NICE; United Kingdom), World Health Organization (WHO), Centers for Disease Control and Prevention (CDC; USA), Institute of Medicine (IOM; USA), Australian Department of Health and Aged Care (ADH), Healthcare Improvement Scotland (SIGN), National Health and Medical Research Council (NHMRC; Australia), Queensland Health, The Joanna Briggs Institute (JBI), Ministry of Health and Social Policy of Spain, Ministry of Health of Brazil and Capes Theses and Dissertations Catalog.

The search strategy combined terms related to "clinical practice guidelines" (practice guidelines, practice guidelines as topic, clinical protocols), "implementation", "assessment" (assessment, evaluation), and "models, framework". The free term "monitoring" was not used because it was regularly related to clinical monitoring and not to implementation monitoring. The search strategies adapted for the electronic databases are presented in an additional file (see Additional file 2).

Study selection process

The results of the literature search from scientific databases, excluding the CRD database, were imported into Mendeley Reference Management software to remove duplicates. They were then transferred to the Rayyan platform ( https://rayyan.qcri.org ) [ 22 ] for the screening process. Initially, studies related to the "assessment of implementation of the CPG" were selected. The titles were first screened independently by two pairs of reviewers (first selection: four reviewers, NM, JB, SS, and JG; update: a pair of reviewers, NM and DG). The title screening was broad, including all potentially relevant studies on CPG and the implementation process. Following that, the abstracts were independently screened by the same group of reviewers. The abstract screening was more focused, specifically selecting studies that addressed CPG and the evaluation of the implementation process. In the next step, full-text articles were reviewed independently by a pair of reviewers (NM, DG) to identify those that explicitly presented "models" or "frameworks" for assessing the implementation of the CPG. Disagreements regarding the eligibility of studies were resolved through discussion and consensus, and by a third reviewer (JB) when necessary. One reviewer (NM) conducted manual searches, and the inclusion of documents was discussed with the other reviewers.

Risk of bias assessment of studies

The selected studies were independently classified and evaluated according to their methodological designs by two investigators (NM and JG). This review employed JBI’s critical appraisal tools to assess the trustworthiness, relevance and results of the included studies [ 23 ] and these tools are presented in additional files (see Additional file 3 and Additional file 4). Disagreements were resolved by consensus or consultation with the other reviewers. Methodological guidelines and noncomparative and before–after studies were not evaluated because JBI does not have specific tools for assessing these types of documents. Although the studies were assessed for quality, they were not excluded on this basis.

Data extraction

The data was independently extracted by two reviewers (NM, DG) using a Microsoft Excel spreadsheet. Discrepancies were discussed and resolved by consensus. The following information was extracted:

Document characteristics : author; year of publication; title; study design; instrument of evaluation; country; guideline context;

Usage context of the models : specific objectives; level of use (clinical, organizational, and policy); type of health service (community, ambulatorial, hospital, institutional); target group (guideline developers, clinicians; health professionals; health-policy decision-makers; health-care organizations; service managers);

Model and framework characteristics : name, domain evaluated, and model limitations.

The set of information to be extracted, shown in the systematic review protocol, was adjusted to improve the organization of the analysis.

The "level of use" refers to the scope of the model used. "Clinical" was considered when the evaluation focused on individual practices, "organizational" when practices were within a health service institution, and "policy" when the evaluation was more systemic and covered different health services or institutions.

The "type of health service" indicated the category of health service where the model/framework was used (or can be used) to assess the implementation of the CPG, related to the complexity of healthcare. "Community" is related to primary health care; "ambulatorial" is related to secondary health care; "hospital" is related to tertiary health care; and "institutional" represented models/frameworks not specific to a particular type of health service.

The "target group" included stakeholders related to the use of the model/framework for evaluating the implementation of the CPG, such as clinicians, health professionals, guideline developers, health policy-makers, health organizations, and service managers.

The category "health system" (public, private, or both) mentioned in the systematic review protocol was not found in the literature obtained and was removed as an extraction variable. Similarly, the variables "type of recommendation evaluated" and "context" were grouped because the same information was included in the "guideline context" section of the study.

Some selected documents presented models or frameworks recognized by the scientific field, including some that were validated. However, some studies adapted the model to this context. Therefore, the domain analysis covered all models or frameworks domains evaluated by (or suggested for evaluation by) the document analyzed.

Data analysis and synthesis

The results were tabulated using narrative synthesis with an aggregative approach, without meta-analysis, aiming to summarize the documents descriptively for the organization, description, interpretation and explanation of the study findings [ 24 , 25 ].

The model/framework domains evaluated in each document were studied according to Nilsen et al.’s constructs: "strategies", "context", "outcomes", "fidelity", "adaptation" and "sustainability". For this study, "strategies" were described as structured and planned initiatives used to enhance the implementation of clinical practice [ 26 ].

The definition of "context" varies in the literature. Despite that, this review considered it as the set of circumstances or factors surrounding a particular implementation effort, such as organizational support, financial resources, social relations and support, leadership, and organizational culture [ 26 , 27 ]. The domain "context" was subdivided according to the level of health care into "micro" (individual perspective), "meso" (organizational perspective), "macro" (systemic perspective), and "multiple" (when there is an issue involving more than one level of health care).

The "outcomes" domain was related to the results of the implementation process (unlike clinical outcomes) and was stratified according to the following constructs: acceptability, appropriateness, feasibility, adoption, cost, and penetration. All these concepts align with the definitions of Proctor et al. (2011), although we decided to separate "fidelity" and "sustainability" as independent domains similar to Nilsen [ 26 , 28 ].

"Fidelity" and "adaptation" were considered the same domain, as they are complementary pieces of the same issue. In this study, implementation fidelity refers to how closely guidelines are followed as intended by their developers or designers. On the other hand, adaptation involves making changes to the content or delivery of a guideline to better fit the needs of a specific context. The "sustainability" domain was defined as evaluations about the continuation or permanence over time of the CPG implementation.

Additionally, the domain "process" was utilized to address issues related to the implementation process itself, rather than focusing solely on the outcomes of the implementation process, as done by Wang et al. [ 14 ]. Furthermore, the "intervention" domain was introduced to distinguish aspects related to the CPG characteristics that can impact its implementation, such as the complexity of the recommendation.

A subgroup analysis was performed with models and frameworks categorized based on their levels of use (clinical, organizational, and policy) and the type of health service (community, ambulatorial, hospital, institutional) associated with the CPG. The goal is to assist stakeholders (politicians, clinicians, researchers, or others) in selecting the most suitable model for evaluating CPG implementation based on their specific health context.

Search results

Database searches yielded 26,011 studies, of which 107 full texts were reviewed. During the full-text review, 99 articles were excluded: 41 studies did not mention a model or framework for assessing the implementation of the CPG, 31 studies evaluated only implementation strategies (isolated actions) rather than the implementation process itself, and 27 articles were not related to the implementation assessment. Therefore, eight studies were included in the data analysis. The updated search did not reveal additional relevant studies. The main reason for study exclusion was that they did not use models or frameworks to assess CPG implementation. Additionally, four methodological guidelines were included from the manual search (Fig.  1 ).

PRISMA diagram. Acronyms: ADH—Australian Department of Health, CINAHL—Cumulative Index to Nursing and Allied Health Literature, CDC—Centers for Disease Control and Prevention, CRD—Centre for Reviews and Dissemination, GIN—Guidelines International Networks, HSE—Health Systems Evidence, IOM—Institute of Medicine, JBI—The Joanna Briggs Institute, MHB—Ministry of Health of Brazil, NICE—National Institute for Health and Care Excellence, NHMRC—National Health and Medical Research Council, MSPS – Ministerio de Sanidad Y Política Social (Spain), SIGN—Scottish Intercollegiate Guidelines Network, VHL – Virtual Health Library, WHO—World Health Organization. Legend: Reason A –The study evaluated only implementation strategies (isolated actions) rather than the implementation process itself. Reason B – The study did not mention a model or framework for assessing the implementation of the intervention. Reason C – The study was not related to the implementation assessment. Adapted from Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71. https://doi.org/10.1136/bmj.n71 . For more information, visit:

According to the JBI’s critical appraisal tools, the overall assessment of the studies indicates their acceptance for the systematic review.

The cross-sectional studies lacked clear information regarding "confounding factors" or "strategies to address confounding factors". This was understandable given the nature of the study, where such details are not typically included. However, the reviewers did not find this lack of information to be critical, allowing the studies to be included in the review. The results of this methodological quality assessment can be found in an additional file (see Additional file 5).

In the qualitative studies, there was some ambiguity regarding the questions: "Is there a statement locating the researcher culturally or theoretically?" and "Is the influence of the researcher on the research, and vice versa, addressed?". However, the reviewers decided to include the studies and deemed the methodological quality sufficient for the analysis in this article, based on the other information analyzed. The results of this methodological quality assessment can be found in an additional file (see Additional file 6).

Documents characteristics (Table  1 )

The documents were directed to several continents: Australia/Oceania (4/12) [ 31 , 33 , 36 , 37 ], North America (4/12 [ 30 , 32 , 38 , 39 ], Europe (2/12 [ 29 , 35 ] and Asia (2/12) [ 34 , 40 ]. The types of documents were classified as cross-sectional studies (4/12) [ 29 , 32 , 34 , 38 ], methodological guidelines (4/12) [ 33 , 35 , 36 , 37 ], mixed methods studies (3/12) [ 30 , 31 , 39 ] or noncomparative studies (1/12) [ 40 ]. In terms of the instrument of evaluation, most of the documents used a survey/questionnaire (6/12) [ 29 , 30 , 31 , 32 , 34 , 38 ], while three (3/12) used qualitative instruments (interviews, group discussions) [ 30 , 31 , 39 ], one used a checklist [ 37 ], one used an audit [ 33 ] and three (3/12) did not define a specific instrument to measure [ 35 , 36 , 40 ].

Considering the clinical areas covered, most studies evaluated the implementation of nonspecific (general) clinical areas [ 29 , 33 , 35 , 36 , 37 , 40 ]. However, some studies focused on specific clinical contexts, such as mental health [ 32 , 38 ], oncology [ 39 ], fall prevention [ 31 ], spinal cord injury [ 30 ], and sexually transmitted infections [ 34 ].

Usage context of the models (Table  1 )

Specific objectives.

All the studies highlighted the purpose of guiding the process of evaluating the implementation of CPGs, even if they evaluated CPGs from generic or different clinical areas.

Levels of use

The most common level of use of the models/frameworks identified to assess the implementation of CPGs was policy (6/12) [ 33 , 35 , 36 , 37 , 39 , 40 ]. In this level, the model is used in a systematic way to evaluate all the processes involved in CPGs implementation and is primarily related to methodological guidelines. This was followed by the organizational level of use (5/12) [ 30 , 31 , 32 , 38 , 39 ], where the model is used to evaluate the implementation of CPGs in a specific institution, considering its specific environment. Finally, the clinical level of use (2/12) [ 29 , 34 ] focuses on individual practice and the factors that can influence the implementation of CPGs by professionals.

Type of health service

Institutional services were predominant (5/12) [ 33 , 35 , 36 , 37 , 40 ] and included methodological guidelines and a study of model development and validation. Hospitals were the second most common type of health service (4/12) [ 29 , 30 , 31 , 34 ], followed by ambulatorial (2/12) [ 32 , 34 ] and community health services (1/12) [ 32 ]. Two studies did not specify which type of health service the assessment addressed [ 38 , 39 ].

Target group

The focus of the target group was professionals directly involved in clinical practice (6/12) [ 29 , 31 , 32 , 34 , 38 , 40 ], namely, health professionals and clinicians. Other less related stakeholders included guideline developers (2/12) [ 39 , 40 ], health policy decision makers (1/12) [ 39 ], and healthcare organizations (1/12) [ 39 ]. The target group was not defined in the methodological guidelines, although all the mentioned stakeholders could be related to these documents.

Model and framework characteristics

Models and frameworks for assessing the implementation of cpgs.

The Consolidated Framework for Implementation Research (CFIR) [ 31 , 38 ] and the Promoting Action on Research Implementation in Health Systems (PARiHS) framework [ 29 , 30 ] were the most commonly employed frameworks within the selected documents. The other models mentioned were: Goal commitment and implementation of practice guidelines framework [ 32 ]; Guideline to identify key indicators [ 35 ]; Guideline implementation checklist [ 37 ]; Guideline implementation evaluation tool [ 40 ]; JBI Implementation Framework [ 33 ]; Reach, effectiveness, adoption, implementation and maintenance (RE-AIM) framework [ 34 ]; The Guideline Implementability Framework [ 39 ] and an unnamed model [ 36 ].

Domains evaluated

The number of domains evaluated (or suggested for evaluation) by the documents varied between three and five, with the majority focusing on three domains. All the models addressed the domain "context", with a particular emphasis on the micro level of the health care context (8/12) [ 29 , 31 , 34 , 35 , 36 , 37 , 38 , 39 ], followed by the multilevel (7/12) [ 29 , 31 , 32 , 33 , 38 , 39 , 40 ], meso level (4/12) [ 30 , 35 , 39 , 40 ] and macro level (2/12) [ 37 , 39 ]. The "Outcome" domain was evaluated in nine models. Within this domain, the most frequently evaluated subdomain was "adoption" (6/12) [ 29 , 32 , 34 , 35 , 36 , 37 ], followed by "acceptability" (4/12) [ 30 , 32 , 35 , 39 ], "appropriateness" (3/12) [ 32 , 34 , 36 ], "feasibility" (3/12) [ 29 , 32 , 36 ], "cost" (1/12) [ 35 ] and "penetration" (1/12) [ 34 ]. Regarding the other domains, "Intervention" (8/12) [ 29 , 31 , 34 , 35 , 36 , 38 , 39 , 40 ], "Strategies" (7/12) [ 29 , 30 , 33 , 35 , 36 , 37 , 40 ] and "Process" (5/12) [ 29 , 31 , 32 , 33 , 38 ] were frequently addressed in the models, while "Sustainability" (1/12) [ 34 ] was only found in one model, and "Fidelity/Adaptation" was not observed. The domains presented by the models and frameworks and evaluated in the documents are shown in Table  2 .

Limitations of the models

Only two documents mentioned limitations in the use of the model or frameworks. These two studies reported limitations in the use of CFIR: "is complex and cumbersome and requires tailoring of the key variables to the specific context", and "this framework should be supplemented with other important factors and local features to achieve a sound basis for the planning and realization of an ongoing project" [ 31 , 38 ]. Limitations in the use of other models or frameworks are not reported.

Subgroup analysis

Following the subgroup analysis (Table  3 ), five different models/frameworks were utilized at the policy level by institutional health services. These included the Guideline Implementation Evaluation Tool [ 40 ], the NHMRC tool (model name not defined) [ 36 ], the JBI Implementation Framework + GRiP [ 33 ], Guideline to identify key indicators [ 35 ], and the Guideline implementation checklist [ 37 ]. Additionally, the "Guideline Implementability Framework" [ 39 ] was implemented at the policy level without restrictions based on the type of health service. Regarding the organizational level, the models used varied depending on the type of service. The "Goal commitment and implementation of practice guidelines framework" [ 32 ] was applied in community and ambulatory health services, while "PARiHS" [ 29 , 30 ] and "CFIR" [ 31 , 38 ] were utilized in hospitals. In contexts where the type of health service was not defined, "CFIR" [ 31 , 38 ] and "The Guideline Implementability Framework" [ 39 ] were employed. Lastly, at the clinical level, "RE-AIM" [ 34 ] was utilized in ambulatory and hospital services, and PARiHS [ 29 , 30 ] was specifically used in hospital services.

Key findings

This systematic review identified 10 models/ frameworks used to assess the implementation of CPGs in various health system contexts. These documents shared similar objectives in utilizing models and frameworks for assessment. The primary level of use was policy, the most common type of health service was institutional, and the main target group of the documents was professionals directly involved in clinical practice. The models and frameworks presented varied analytical domains, with sometimes divergent concepts used in these domains. This study is innovative in its emphasis on the evaluation stage of CPG implementation and in summarizing aspects and domains aimed at the practical application of these models.

The small number of documents contrasts with studies that present an extensive range of models and frameworks available in implementation science. The findings suggest that the use of models and frameworks to evaluate the implementation of CPGs is still in its early stages. Among the selected documents, there was a predominance of cross-sectional studies and methodological guidelines, which strongly influenced how the implementation evaluation was conducted. This was primarily done through surveys/questionnaires, qualitative methods (interviews, group discussions), and non-specific measurement instruments. Regarding the subject areas evaluated, most studies focused on a general clinical area, while others explored different clinical areas. This suggests that the evaluation of CPG implementation has been carried out in various contexts.

The models were chosen independently of the categories proposed in the literature, with their usage categorized for purposes other than implementation evaluation, as is the case with CFIR and PARiHS. This practice was described by Nilsen et al. who suggested that models and frameworks from other categories can also be applied for evaluation purposes because they specify concepts and constructs that may be operationalized and measured [ 14 , 15 , 42 , 43 ].

The results highlight the increased use of models and frameworks in evaluation processes at the policy level and institutional environments, followed by the organizational level in hospital settings. This finding contradicts a review that reported the policy level as an area that was not as well studied [ 44 ]. The use of different models at the institutional level is also emphasized in the subgroup analysis. This may suggest that the greater the impact (social, financial/economic, and organizational) of implementing CPGs, the greater the interest and need to establish well-defined and robust processes. In this context, the evaluation stage stands out as crucial, and the investment of resources and efforts to structure this stage becomes even more advantageous [ 10 , 45 ]. Two studies (16,7%) evaluated the implementation of CPGs at the individual level (clinical level). These studies stand out for their potential to analyze variations in clinical practice in greater depth.

In contrast to the level of use and type of health service most strongly indicated in the documents, with systemic approaches, the target group most observed was professionals directly involved in clinical practice. This suggests an emphasis on evaluating individual behaviors. This same emphasis is observed in the analysis of the models, in which there is a predominance of evaluating the micro level of the health context and the "adoption" subdomain, in contrast with the sub-use of domains such as "cost" and "process". Cassetti et al. observed the same phenomenon in their review, in which studies evaluating the implementation of CPGs mainly adopted a behavioral change approach to tackle those issues, without considering the influence of wider social determinants of health [ 10 ]. However, the literature widely reiterates that multiple factors impact the implementation of CPGs, and different actions are required to make them effective [ 6 , 46 , 47 ]. As a result, there is enormous potential for the development and adaptation of models and frameworks aimed at more systemic evaluation processes that consider institutional and organizational aspects.

In analyzing the model domains, most models focused on evaluating only some aspects of implementation (three domains). All models evaluated the "context", highlighting its significant influence on implementation [ 9 , 26 ]. Context is an essential effect modifier for providing research evidence to guide decisions on implementation strategies [ 48 ]. Contextualizing a guideline involves integrating research or other evidence into a specific circumstance [ 49 ]. The analysis of this domain was adjusted to include all possible contextual aspects, even if they were initially allocated to other domains. Some contextual aspects presented by the models vary in comprehensiveness, such as the assessment of the "timing and nature of stakeholder engagement" [ 39 ], which includes individual engagement by healthcare professionals and organizational involvement in CPG implementation. While the importance of context is universally recognized, its conceptualization and interpretation differ across studies and models. This divergence is also evident in other domains, consistent with existing literature [ 14 ]. Efforts to address this conceptual divergence in implementation science are ongoing, but further research and development are needed in this field [ 26 ].

The main subdomain evaluated was "adoption" within the outcome domain. This may be attributed to the ease of accessing information on the adoption of the CPG, whether through computerized system records, patient records, or self-reports from healthcare professionals or patients themselves. The "acceptability" subdomain pertains to the perception among implementation stakeholders that a particular CPG is agreeable, palatable or satisfactory. On the other hand, "appropriateness" encompasses the perceived fit, relevance or compatibility of the CPG for a specific practice setting, provider, or consumer, or its perceived fit to address a particular issue or problem [ 26 ]. Both subdomains are subjective and rely on stakeholders' interpretations and perceptions of the issue being analyzed, making them susceptible to reporting biases. Moreover, obtaining this information requires direct consultation with stakeholders, which can be challenging for some evaluation processes, particularly in institutional contexts.

The evaluation of the subdomains "feasibility" (the extent to which a CPG can be successfully used or carried out within a given agency or setting), "cost" (the cost impact of an implementation effort), and "penetration" (the extent to which an intervention or treatment is integrated within a service setting and its subsystems) [ 26 ] was rarely observed in the documents. This may be related to the greater complexity of obtaining information on these aspects, as they involve cross-cutting and multifactorial issues. In other words, it would be difficult to gather this information during evaluations with health practitioners as the target group. This highlights the need for evaluation processes of CPGs implementation involving multiple stakeholders, even if the evaluation is adjusted for each of these groups.

Although the models do not establish the "intervention" domain, we thought it pertinent in this study to delimit the issues that are intrinsic to CPGs, such as methodological quality or clarity in establishing recommendations. These issues were quite common in the models evaluated but were considered in other domains (e.g., in "context"). Studies have reported the importance of evaluating these issues intrinsic to CPGs [ 47 , 50 ] and their influence on the implementation process [ 51 ].

The models explicitly present the "strategies" domain, and its evaluation was usually included in the assessments. This is likely due to the expansion of scientific and practical studies in implementation science that involve theoretical approaches to the development and application of interventions to improve the implementation of evidence-based practices. However, these interventions themselves are not guaranteed to be effective, as reported in a previous review that showed unclear results indicating that the strategies had affected successful implementation [ 52 ]. Furthermore, model domains end up not covering all the complexity surrounding the strategies and their development and implementation process. For example, the ‘Guideline implementation evaluation tool’ evaluates whether guideline developers have designed and provided auxiliary tools to promote the implementation of guidelines [ 40 ], but this does not mean that these tools would work as expected.

The "process" domain was identified in the CFIR [ 31 , 38 ], JBI/GRiP [ 33 ], and PARiHS [ 29 ] frameworks. While it may be included in other domains of analysis, its distinct separation is crucial for defining operational issues when assessing the implementation process, such as determining if and how the use of the mentioned CPG was evaluated [ 3 ]. Despite its presence in multiple models, there is still limited detail in the evaluation guidelines, which makes it difficult to operationalize the concept. Further research is needed to better define the "process" domain and its connections and boundaries with other domains.

The domain of "sustainability" was only observed in the RE-AIM framework, which is categorized as an evaluation framework [ 34 ]. In its acronym, the letter M stands for "maintenance" and corresponds to the assessment of whether the user maintains use, typically longer than 6 months. The presence of this domain highlights the need for continuous evaluation of CPGs implementation in the short, medium, and long term. Although the RE-AIM framework includes this domain, it was not used in the questionnaire developed in the study. One probable reason is that the evaluation of CPGs implementation is still conducted on a one-off basis and not as a continuous improvement process. Considering that changes in clinical practices are inherent over time, evaluating and monitoring changes throughout the duration of the CPG could be an important strategy for ensuring its implementation. This is an emerging field that requires additional investment and research.

The "Fidelity/Adaptation" domain was not observed in the models. These emerging concepts involve the extent to which a CPG is being conducted exactly as planned or whether it is undergoing adjustments and adaptations. Whether or not there is fidelity or adaptation in the implementation of CPGs does not presuppose greater or lesser effectiveness; after all, some adaptations may be necessary to implement general CPGs in specific contexts. The absence of this domain in all the models and frameworks may suggest that they are not relevant aspects for evaluating implementation or that there is a lack of knowledge of these complex concepts. This may suggest difficulty in expressing concepts in specific evaluative questions. However, further studies are warranted to determine the comprehensiveness of these concepts.

It is important to note the customization of the domains of analysis, with some domains presented in the models not being evaluated in the studies, while others were complementarily included. This can be seen in Jeong et al. [ 34 ], where the "intervention" domain in the evaluation with the RE-AIM framework reinforced the aim of theoretical approaches such as guiding the process and not determining norms. Despite this, few limitations were reported for the models, suggesting that the use of models in these studies reflects the application of these models to defined contexts without a deep critical analysis of their domains.

Limitations

This review has several limitations. First, only a few studies and methodological guidelines that explicitly present models and frameworks for assessing the implementation of CPGs have been found. This means that few alternative models could be analyzed and presented in this review. Second, this review adopted multiple analytical categories (e.g., level of use, health service, target group, and domains evaluated), whose terminology has varied enormously in the studies and documents selected, especially for the "domains evaluated" category. This difficulty in harmonizing the taxonomy used in the area has already been reported [ 26 ] and has significant potential to confuse. For this reason, studies and initiatives are needed to align understandings between concepts and, as far as possible, standardize them. Third, in some studies/documents, the information extracted was not clear about the analytical category. This required an in-depth interpretative process of the studies, which was conducted in pairs to avoid inappropriate interpretations.

Implications

This study contributes to the literature and clinical practice management by describing models and frameworks specifically used to assess the implementation of CPGs based on their level of use, type of health service, target group related to the CPG, and the evaluated domains. While there are existing reviews on the theories, frameworks, and models used in implementation science, this review addresses aspects not previously covered in the literature. This valuable information can assist stakeholders (such as politicians, clinicians, researchers, etc.) in selecting or adapting the most appropriate model to assess CPG implementation based on their health context. Furthermore, this study is expected to guide future research on developing or adapting models to assess the implementation of CPGs in various contexts.

The use of models and frameworks to evaluate the implementation remains a challenge. Studies should clearly state the level of model use, the type of health service evaluated, and the target group. The domains evaluated in these models may need adaptation to specific contexts. Nevertheless, utilizing models to assess CPGs implementation is crucial as they can guide a more thorough and systematic evaluation process, aiding in the continuous improvement of CPGs implementation. The findings of this systematic review offer valuable insights for stakeholders in selecting or adjusting models and frameworks for CPGs evaluation, supporting future theoretical advancements and research.

Availability of data and materials

Abbreviations.

Australian Department of Health and Aged Care

Canadian Agency for Drugs and Technologies in Health

Centers for Disease Control and

Consolidated Framework for Implementation Research

Cumulative Index to Nursing and Allied Health Literature

Clinical practice guideline

Centre for Reviews and Dissemination

Guidelines International Networks

Getting Research into Practice

Health Systems Evidence

Institute of Medicine

The Joanna Briggs Institute

Ministry of Health of Brazil

Ministerio de Sanidad y Política Social

National Health and Medical Research Council

National Institute for Health and Care Excellence

Promoting action on research implementation in health systems framework

Predisposing, Reinforcing and Enabling Constructs in Educational Diagnosis and Evaluation-Policy, Regulatory, and Organizational Constructs in Educational and Environmental Development

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

International Prospective Register of Systematic Reviews

Reach, effectiveness, adoption, implementation, and maintenance framework

Healthcare Improvement Scotland

United States of America

Virtual Health Library

World Health Organization

Medicine I of. Crossing the Quality Chasm: A New Health System for the 21st Century. 2001. Available from: http://www.nap.edu/catalog/10027 . Cited 2022 Sep 29.

Field MJ, Lohr KN. Clinical Practice Guidelines: Directions for a New Program. Washington DC: National Academy Press. 1990. Available from: https://www.nap.edu/read/1626/chapter/8 Cited 2020 Sep 2.

Dawson A, Henriksen B, Cortvriend P. Guideline Implementation in Standardized Office Workflows and Exam Types. J Prim Care Community Heal. 2019;10. Available from: https://pubmed.ncbi.nlm.nih.gov/30900500/ . Cited 2020 Jul 15.

Unverzagt S, Oemler M, Braun K, Klement A. Strategies for guideline implementation in primary care focusing on patients with cardiovascular disease: a systematic review. Fam Pract. 2014;31(3):247–66. Available from: https://academic.oup.com/fampra/article/31/3/247/608680 . Cited 2020 Nov 5.

Article   PubMed   Google Scholar  

Nilsen P. Making sense of implementation theories, models and frameworks. Implement Sci. 2015;10(1):1–13. Available from: https://implementationscience.biomedcentral.com/articles/10.1186/s13012-015-0242-0 . Cited 2022 May 1.

Article   Google Scholar  

Mangana F, Massaquoi LD, Moudachirou R, Harrison R, Kaluangila T, Mucinya G, et al. Impact of the implementation of new guidelines on the management of patients with HIV infection at an advanced HIV clinic in Kinshasa, Democratic Republic of Congo (DRC). BMC Infect Dis. 2020;20(1):N.PAG-N.PAG. Available from: https://search.ebscohost.com/login.aspx?direct=true&db=c8h&AN=146325052&amp .

Browman GP, Levine MN, Mohide EA, Hayward RSA, Pritchard KI, Gafni A, et al. The practice guidelines development cycle: a conceptual tool for practice guidelines development and implementation. 2016;13(2):502–12. https://doi.org/10.1200/JCO.1995.13.2.502 .

Killeen SL, Donnellan N, O’Reilly SL, Hanson MA, Rosser ML, Medina VP, et al. Using FIGO Nutrition Checklist counselling in pregnancy: A review to support healthcare professionals. Int J Gynecol Obstet. 2023;160(S1):10–21. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146194829&doi=10.1002%2Fijgo.14539&partnerID=40&md5=d0f14e1f6d77d53e719986e6f434498f .

Bauer MS, Damschroder L, Hagedorn H, Smith J, Kilbourne AM. An introduction to implementation science for the non-specialist. BMC Psychol. 2015;3(1):1–12. Available from: https://bmcpsychology.biomedcentral.com/articles/10.1186/s40359-015-0089-9 . Cited 2020 Nov 5.

Cassetti V, M VLR, Pola-Garcia M, AM G, J JPC, L APDT, et al. An integrative review of the implementation of public health guidelines. Prev Med reports. 2022;29:101867. Available from: http://www.epistemonikos.org/documents/7ad499d8f0eecb964fc1e2c86b11450cbe792a39 .

Eccles MP, Mittman BS. Welcome to implementation science. Implementation Science BioMed Central. 2006. Available from: https://implementationscience.biomedcentral.com/articles/10.1186/1748-5908-1-1 .

Damschroder LJ. Clarity out of chaos: Use of theory in implementation research. Psychiatry Res. 2020;1(283):112461.

Handley MA, Gorukanti A, Cattamanchi A. Strategies for implementing implementation science: a methodological overview. Emerg Med J. 2016;33(9):660–4. Available from: https://pubmed.ncbi.nlm.nih.gov/26893401/ . Cited 2022 Mar 7.

Wang Y, Wong ELY, Nilsen P, Chung VC ho, Tian Y, Yeoh EK. A scoping review of implementation science theories, models, and frameworks — an appraisal of purpose, characteristics, usability, applicability, and testability. Implement Sci. 2023;18(1):1–15. Available from: https://implementationscience.biomedcentral.com/articles/10.1186/s13012-023-01296-x . Cited 2024 Jan 22.

Moullin JC, Dickson KS, Stadnick NA, Albers B, Nilsen P, Broder-Fingert S, et al. Ten recommendations for using implementation frameworks in research and practice. Implement Sci Commun. 2020;1(1):1–12. Available from: https://implementationsciencecomms.biomedcentral.com/articles/10.1186/s43058-020-00023-7 . Cited 2022 May 20.

Glasgow RE, Vogt TM, Boles SM. *Evaluating the public health impact of health promotion interventions: the RE-AIM framework. Am J Public Health. 1999;89(9):1322. Available from: /pmc/articles/PMC1508772/?report=abstract. Cited 2022 May 22.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Asada Y, Lin S, Siegel L, Kong A. Facilitators and Barriers to Implementation and Sustainability of Nutrition and Physical Activity Interventions in Early Childcare Settings: a Systematic Review. Prev Sci. 2023;24(1):64–83. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139519721&doi=10.1007%2Fs11121-022-01436-7&partnerID=40&md5=b3c395fdd2b8235182eee518542ebf2b .

Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, et al., editors. Cochrane Handbook for Systematic Reviews of Interventions. version 6. Cochrane; 2022. Available from: https://training.cochrane.org/handbook. Cited 2022 May 23.

Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372. Available from: https://www.bmj.com/content/372/bmj.n71 . Cited 2021 Nov 18.

M C, AD O, E P, JP H, S G. Appendix A: Guide to the contents of a Cochrane Methodology protocol and review. Higgins JP, Green S, eds Cochrane Handb Syst Rev Interv. 2011;Version 5.

Kislov R, Pope C, Martin GP, Wilson PM. Harnessing the power of theorising in implementation science. Implement Sci. 2019;14(1):1–8. Available from: https://implementationscience.biomedcentral.com/articles/10.1186/s13012-019-0957-4 . Cited 2024 Jan 22.

Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A. Rayyan-a web and mobile app for systematic reviews. Syst Rev. 2016;5(1):1–10. Available from: https://systematicreviewsjournal.biomedcentral.com/articles/10.1186/s13643-016-0384-4 . Cited 2022 May 20.

JBI. JBI’s Tools Assess Trust, Relevance & Results of Published Papers: Enhancing Evidence Synthesis. Available from: https://jbi.global/critical-appraisal-tools . Cited 2023 Jun 13.

Drisko JW. Qualitative research synthesis: An appreciative and critical introduction. Qual Soc Work. 2020;19(4):736–53.

Pope C, Mays N, Popay J. Synthesising qualitative and quantitative health evidence: A guide to methods. 2007. Available from: https://books.google.com.br/books?hl=pt-PT&lr=&id=L3fbE6oio8kC&oi=fnd&pg=PR6&dq=synthesizing+qualitative+and+quantitative+health+evidence&ots=sfELNUoZGq&sig=bQt5wt7sPKkf7hwKUvxq2Ek-p2Q#v=onepage&q=synthesizing=qualitative=and=quantitative=health=evidence& . Cited 2022 May 22.

Nilsen P, Birken SA, Edward Elgar Publishing. Handbook on implementation science. 542. Available from: https://www.e-elgar.com/shop/gbp/handbook-on-implementation-science-9781788975988.html . Cited 2023 Apr 15.

Damschroder LJ, Aron DC, Keith RE, Kirsh SR, Alexander JA, Lowery JC. Fostering implementation of health services research findings into practice: A consolidated framework for advancing implementation science. Implement Sci. 2009;4(1):1–15. Available from: https://implementationscience.biomedcentral.com/articles/10.1186/1748-5908-4-50 . Cited 2023 Jun 13.

Proctor E, Silmere H, Raghavan R, Hovmand P, Aarons G, Bunger A, et al. Outcomes for implementation research: conceptual distinctions, measurement challenges, and research agenda. Adm Policy Ment Health. 2011;38(2):65–76. Available from: https://pubmed.ncbi.nlm.nih.gov/20957426/ . Cited 2023 Jun 11.

Bahtsevani C, Willman A, Khalaf A, Östman M, Ostman M. Developing an instrument for evaluating implementation of clinical practice guidelines: a test-retest study. J Eval Clin Pract. 2008;14(5):839–46. Available from: https://search.ebscohost.com/login.aspx?direct=true&db=c8h&AN=105569473&amp . Cited 2023 Jan 18.

Balbale SN, Hill JN, Guihan M, Hogan TP, Cameron KA, Goldstein B, et al. Evaluating implementation of methicillin-resistant Staphylococcus aureus (MRSA) prevention guidelines in spinal cord injury centers using the PARIHS framework: a mixed methods study. Implement Sci. 2015;10(1):130. Available from: https://pubmed.ncbi.nlm.nih.gov/26353798/ . Cited 2023 Apr 3.

Article   PubMed   PubMed Central   Google Scholar  

Breimaier HE, Heckemann B, Halfens RJGG, Lohrmann C. The Consolidated Framework for Implementation Research (CFIR): a useful theoretical framework for guiding and evaluating a guideline implementation process in a hospital-based nursing practice. BMC Nurs. 2015;14(1):43. Available from: https://search.ebscohost.com/login.aspx?direct=true&db=c8h&AN=109221169&amp . Cited 2023 Apr 3.

Chou AF, Vaughn TE, McCoy KD, Doebbeling BN. Implementation of evidence-based practices: Applying a goal commitment framework. Health Care Manage Rev. 2011;36(1):4–17. Available from: https://pubmed.ncbi.nlm.nih.gov/21157225/ . Cited 2023 Apr 30.

Porritt K, McArthur A, Lockwood C, Munn Z. JBI Manual for Evidence Implementation. JBI Handbook for Evidence Implementation. JBI; 2020. Available from: https://jbi-global-wiki.refined.site/space/JHEI . Cited 2023 Apr 3.

Jeong HJJ, Jo HSS, Oh MKK, Oh HWW. Applying the RE-AIM Framework to Evaluate the Dissemination and Implementation of Clinical Practice Guidelines for Sexually Transmitted Infections. J Korean Med Sci. 2015;30(7):847–52. Available from: https://pubmed.ncbi.nlm.nih.gov/26130944/ . Cited 2023 Apr 3.

GPC G de trabajo sobre implementación de. Implementación de Guías de Práctica Clínica en el Sistema Nacional de Salud. Manual Metodológico. 2009. Available from: https://portal.guiasalud.es/wp-content/uploads/2019/01/manual_implementacion.pdf . Cited 2023 Apr 3.

Australia C of. A guide to the development, implementation and evaluation of clinical practice guidelines. National Health and Medical Research Council; 1998. Available from: https://www.health.qld.gov.au/__data/assets/pdf_file/0029/143696/nhmrc_clinprgde.pdf .

Health Q. Guideline implementation checklist Translating evidence into best clinical practice. 2022.

Google Scholar  

Quittner AL, Abbott J, Hussain S, Ong T, Uluer A, Hempstead S, et al. Integration of mental health screening and treatment into cystic fibrosis clinics: Evaluation of initial implementation in 84 programs across the United States. Pediatr Pulmonol. 2020;55(11):2995–3004. Available from: https://www.embase.com/search/results?subaction=viewrecord&id=L2005630887&from=export . Cited 2023 Apr 3.

Urquhart R, Woodside H, Kendell C, Porter GA. Examining the implementation of clinical practice guidelines for the management of adult cancers: A mixed methods study. J Eval Clin Pract. 2019;25(4):656–63. Available from: https://search.ebscohost.com/login.aspx?direct=true&db=c8h&AN=137375535&amp . Cited 2023 Apr 3.

Yinghui J, Zhihui Z, Canran H, Flute Y, Yunyun W, Siyu Y, et al. Development and validation for evaluation of an evaluation tool for guideline implementation. Chinese J Evidence-Based Med. 2022;22(1):111–9. Available from: https://www.embase.com/search/results?subaction=viewrecord&id=L2016924877&from=export .

Breimaier HE, Halfens RJG, Lohrmann C. Effectiveness of multifaceted and tailored strategies to implement a fall-prevention guideline into acute care nursing practice: a before-and-after, mixed-method study using a participatory action research approach. BMC Nurs. 2015;14(1):18. Available from: https://search.ebscohost.com/login.aspx?direct=true&db=c8h&AN=103220991&amp .

Lai J, Maher L, Li C, Zhou C, Alelayan H, Fu J, et al. Translation and cross-cultural adaptation of the National Health Service Sustainability Model to the Chinese healthcare context. BMC Nurs. 2023;22(1). Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153237164&doi=10.1186%2Fs12912-023-01293-x&partnerID=40&md5=0857c3163d25ce85e01363fc3a668654 .

Zhao J, Li X, Yan L, Yu Y, Hu J, Li SA, et al. The use of theories, frameworks, or models in knowledge translation studies in healthcare settings in China: a scoping review protocol. Syst Rev. 2021;10(1):13. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7792291 .

Tabak RG, Khoong EC, Chambers DA, Brownson RC. Bridging research and practice: models for dissemination and implementation research. Am J Prev Med. 2012;43(3):337–50. Available from: https://pubmed.ncbi.nlm.nih.gov/22898128/ . Cited 2023 Apr 4.

Phulkerd S, Lawrence M, Vandevijvere S, Sacks G, Worsley A, Tangcharoensathien V. A review of methods and tools to assess the implementation of government policies to create healthy food environments for preventing obesity and diet-related non-communicable diseases. Implement Sci. 2016;11(1):1–13. Available from: https://implementationscience.biomedcentral.com/articles/10.1186/s13012-016-0379-5 . Cited 2022 May 1.

Buss PM, Pellegrini FA. A Saúde e seus Determinantes Sociais. PHYSIS Rev Saúde Coletiva. 2007;17(1):77–93.

Pereira VC, Silva SN, Carvalho VKSS, Zanghelini F, Barreto JOMM. Strategies for the implementation of clinical practice guidelines in public health: an overview of systematic reviews. Heal Res Policy Syst. 2022;20(1):13. Available from: https://health-policy-systems.biomedcentral.com/articles/10.1186/s12961-022-00815-4 . Cited 2022 Feb 21.

Grimshaw J, Eccles M, Tetroe J. Implementing clinical guidelines: current evidence and future implications. J Contin Educ Health Prof. 2004;24 Suppl 1:S31-7. Available from: https://pubmed.ncbi.nlm.nih.gov/15712775/ . Cited 2021 Nov 9.

Lotfi T, Stevens A, Akl EA, Falavigna M, Kredo T, Mathew JL, et al. Getting trustworthy guidelines into the hands of decision-makers and supporting their consideration of contextual factors for implementation globally: recommendation mapping of COVID-19 guidelines. J Clin Epidemiol. 2021;135:182–6. Available from: https://pubmed.ncbi.nlm.nih.gov/33836255/ . Cited 2024 Jan 25.

Lenzer J. Why we can’t trust clinical guidelines. BMJ. 2013;346(7913). Available from: https://pubmed.ncbi.nlm.nih.gov/23771225/ . Cited 2024 Jan 25.

Molino C de GRC, Ribeiro E, Romano-Lieber NS, Stein AT, de Melo DO. Methodological quality and transparency of clinical practice guidelines for the pharmacological treatment of non-communicable diseases using the AGREE II instrument: A systematic review protocol. Syst Rev. 2017;6(1):1–6. Available from: https://systematicreviewsjournal.biomedcentral.com/articles/10.1186/s13643-017-0621-5 . Cited 2024 Jan 25.

Albers B, Mildon R, Lyon AR, Shlonsky A. Implementation frameworks in child, youth and family services – Results from a scoping review. Child Youth Serv Rev. 2017;1(81):101–16.

Download references

Acknowledgements

Not applicable

This study is supported by the Fundação de Apoio à Pesquisa do Distrito Federal (FAPDF). FAPDF Award Term (TOA) nº 44/2024—FAPDF/SUCTI/COOBE (SEI/GDF – Process 00193–00000404/2024–22). The content in this article is solely the responsibility of the authors and does not necessarily represent the official views of the FAPDF.

Author information

Authors and affiliations.

Department of Management and Incorporation of Health Technologies, Ministry of Health of Brazil, Brasília, Federal District, 70058-900, Brazil

Nicole Freitas de Mello & Dalila Fernandes Gomes

Postgraduate Program in Public Health, FS, University of Brasília (UnB), Brasília, Federal District, 70910-900, Brazil

Nicole Freitas de Mello, Dalila Fernandes Gomes & Jorge Otávio Maia Barreto

René Rachou Institute, Oswaldo Cruz Foundation, Belo Horizonte, Minas Gerais, 30190-002, Brazil

Sarah Nascimento Silva

Oswaldo Cruz Foundation - Brasília, Brasília, Federal District, 70904-130, Brazil

Juliana da Motta Girardi & Jorge Otávio Maia Barreto

You can also search for this author in PubMed   Google Scholar

Contributions

NFM and JOMB conceived the idea and the protocol for this study. NFM conducted the literature search. NFM, SNS, JMG and JOMB conducted the data collection with advice and consensus gathering from JOMB. The NFM and JMG assessed the quality of the studies. NFM and DFG conducted the data extraction. NFM performed the analysis and synthesis of the results with advice and consensus gathering from JOMB. NFM drafted the manuscript. JOMB critically revised the first version of the manuscript. All the authors revised and approved the submitted version.

Corresponding author

Correspondence to Nicole Freitas de Mello .

Ethics declarations

Ethics approval and consent to participate, consent for publication, competing interests.

The authors declare that they have no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

13012_2024_1389_moesm1_esm.docx.

Additional file 1: PRISMA checklist. Description of data: Completed PRISMA checklist used for reporting the results of this systematic review.

Additional file 2: Literature search. Description of data: The search strategies adapted for the electronic databases.

13012_2024_1389_moesm3_esm.doc.

Additional file 3: JBI’s critical appraisal tools for cross-sectional studies. Description of data: JBI’s critical appraisal tools to assess the trustworthiness, relevance, and results of the included studies. This is specific for cross-sectional studies.

13012_2024_1389_MOESM4_ESM.doc

Additional file 4: JBI’s critical appraisal tools for qualitative studies. Description of data: JBI’s critical appraisal tools to assess the trustworthiness, relevance, and results of the included studies. This is specific for qualitative studies.

13012_2024_1389_MOESM5_ESM.doc

Additional file 5: Methodological quality assessment results for cross-sectional studies. Description of data: Methodological quality assessment results for cross-sectional studies using JBI’s critical appraisal tools.

13012_2024_1389_MOESM6_ESM.doc

Additional file 6: Methodological quality assessment results for the qualitative studies. Description of data: Methodological quality assessment results for qualitative studies using JBI’s critical appraisal tools.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ .

Reprints and permissions

About this article

Cite this article.

Freitas de Mello, N., Nascimento Silva, S., Gomes, D.F. et al. Models and frameworks for assessing the implementation of clinical practice guidelines: a systematic review. Implementation Sci 19 , 59 (2024). https://doi.org/10.1186/s13012-024-01389-1

Download citation

Received : 06 February 2024

Accepted : 01 August 2024

Published : 07 August 2024

DOI : https://doi.org/10.1186/s13012-024-01389-1

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Implementation
• Practice guideline
• Evidence-Based Practice
• Implementation science

Implementation Science

ISSN: 1748-5908

• Submission enquiries: Access here and click Contact Us
• General enquiries: [email protected]

Sweetspire Literature Management

Hours updated 1 month ago

Photos & videos

See all 24 photos

Location & Hours

Suggest an edit

155 Willowbrook Blvd

Wayne, NJ 07470

Amenities and More

Ask the community.

Ask a question

Yelp users haven’t asked any questions yet about Sweetspire Literature Management .

People also searched for

advertising services

brand management

digital marketing

professional services

Recommended Reviews

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

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 10 August 2024

Mapping biomimicry research to sustainable development goals

• Raghu Raman 1 ,
• Aswathy Sreenivasan 2 ,
• M. Suresh 2 &
• Prema Nedungadi 3  

Scientific Reports volume  14 , Article number:  18613 ( 2024 ) Cite this article

229 Accesses

Metrics details

• Environmental sciences
• Environmental social sciences

This study systematically evaluates biomimicry research within the context of sustainable development goals (SDGs) to discern the interdisciplinary interplay between biomimicry and SDGs. The alignment of biomimicry with key SDGs showcases its interdisciplinary nature and potential to offer solutions across the health, sustainability, and energy sectors. This study identified two primary thematic clusters. The first thematic cluster focused on health, partnership, and life on land (SDGs 3, 17, and 15), highlighting biomimicry's role in healthcare innovations, sustainable collaboration, and land management. This cluster demonstrates the potential of biomimicry to contribute to medical technologies, emphasizing the need for cross-sectoral partnerships and ecosystem preservation. The second thematic cluster revolves around clean water, energy, infrastructure, and marine life (SDGs 6, 7, 9, and 14), showcasing nature-inspired solutions for sustainable development challenges, including energy generation and water purification. The prominence of SDG 7 within this cluster indicates that biomimicry significantly contributes to sustainable energy practices. The analysis of thematic clusters further revealed the broad applicability of biomimicry and its role in enhancing sustainable energy access and promoting ecosystem conservation. Emerging research topics, such as metaheuristics, nanogenerators, exosomes, and bioprinting, indicate a dynamic field poised for significant advancements. By mapping the connections between biomimicry and SDGs, this study provides a comprehensive overview of the field's trajectory, emphasizing its importance in advancing global sustainability efforts.

Similar content being viewed by others

Assessing the sustainability of the European Green Deal and its interlin kages with the SDGs

Greater gains for Australia by tackling all SDGs but the last steps will be the most challenging

Six Transformations to achieve the Sustainable Development Goals

Introduction.

Biomimicry, which combines 'bio' (life) and 'mimicry' (imitation), uses nature's patterns to solve human problems, aligning with the SDGs by fostering innovations 1 . This discipline studies natural processes to inspire sustainable designs and promote responsible consumption and production 2 . Biomimicry emphasizes sustainability, ideation, and education in reconnecting with nature to achieve the SDGs 3 . Collaboration among designers, technologists, and business experts is vital for translating natural mechanisms into commercial solutions 4 . Biomimetics, which aims for radical innovations by replicating living systems, strives for breakthroughs in economic growth 5 . By promoting systemic change through the emulation of nature's regenerative processes, biomimicry's alignment with the SDGs could enhance sustainability efforts. Merging biomimicry insights with SDGs could exceed sustainability benchmarks.

Integrating biomimicry with sustainable development goals (SDGs) is crucial for addressing global challenges. The SDGs offer a blueprint for global well-being and environmental stewardship by 2030 6 . They aim to protect the environment and foster social and economic development. Biomimicry provides innovative approaches to these objectives, drawing from natural strategies. While SDGs offer clear targets, biomimicry complements these by providing a unique lens for solutions 7 . The investigation of biomimicry in conjunction with the SDGs is based on the understanding that the development of biologically inspired materials, structures, and systems offers a novel and sustainable solution to design problems, particularly in the built environment 8 . By mimicking nature's answers to complicated challenges, biomimicry produces creative, clever, long-lasting, and environmentally responsible ideas.

The SDGs outline a comprehensive sustainability agenda targeting social equity, environmental conservation, and poverty alleviation 9 . The use of biomimicry in research can lead to the development of solutions that mimic natural efficiency 10 , revolutionizing industries with resource-efficient technologies and enhancing sustainability. This synergy could lead to environmentally friendly products, improved energy solutions, and effective waste management systems. Integrating biomimicry into industry and education promotes environmental stewardship and ecological appreciation 11 . Marrying biomimicry research with SDGs has accelerated progress toward sustainable development.

Biomimicry can provide insightful and useful solutions consistent with sustainability ideals by imitating the adaptability and efficiency observed in biological systems 12 . The built environment's use of biomimicry has a greater sustainable impact when circular design features are included 13 . Reusing materials, cutting waste, and designing systems that work with natural cycles are all stressed in a circular design. Combining biomimicry and circular design promotes social inclusion, environmental resilience, resourcefulness, and compassionate governance, all of which lead to peaceful coexistence with the environment. This all-encompassing strategy demonstrates a dedication to tackling the larger social and environmental concerns that the SDGs represent and design challenges 14 . Complementing these studies, Wamane 7 examined the intersection of biomimicry, the environmental, social, and governance (ESG) framework, and circular economy principles, advocating for an economic paradigm shift toward sustainability.

A key aspect of realizing the impact of biomimicry on SDGs is the successful translation and commercialization of biomimicry discoveries. This involves overcoming barriers such as skill gaps, the engineering mindset, commercial acumen, and funding. Insights from the "The State of Nature-Inspired-Innovation in the UK" report provide a comprehensive analysis of these challenges and potential strategies to address them, underscoring the importance of integrating commercial perspectives into biomimicry research.

This research employs bibliometric techniques to assess the integration and coherence within circular economy policy-making, emphasizing the potential for a synergistic relationship between environmental stewardship, economic growth, and social equity to foster a sustainable future.

In addressing the notable gap in comprehensive research concerning the contribution of biomimicry solutions to specific SDGs, this study offers significant insights into the interdisciplinary applications of biomimicry and its potential to advance global sustainability efforts. Our investigation aims to bridge this research gap through a systematic analysis, resulting in the formulation of the following research questions:

RQ1: How does an interdisciplinary analysis of biomimicry research align with and contribute to advancing specific SDGs?

RQ2: What emerging topics within biomimicry research are gaining prominence, and how do they relate to the SDGs?

RQ3 : What are the barriers to the translation and commercialization of biomimicry innovations, and how can these barriers be overcome to enhance their impact on SDGs?

RQ4: Based on the identified gaps in research and the potential for interdisciplinary collaboration, what innovative areas within biomimicry can be further explored to address underrepresented SDGs?

The remainder of this paper is arranged as follows. Section " Literature review " focuses on the literature background of biomimicry, followed by methods (section " Methods ") and results and discussion, including emerging research topics (section " Results and discussion "). Section " Conclusion " concludes with recommendations and limitations.

Literature review

The potential of biomimicry solutions for sustainability has long been recognized, yet there is a notable lack of comprehensive studies that explore how biomimicry can address specific sustainable development goals (SDGs) (Table 1 ). This research aims to fill this gap by investigating relevant themes and building upon the literature in this field.

Biomimicry, with its roots tracing back to approximately 500 BC, began with Greek philosophers who developed classical concepts of beauty and drew inspiration from natural organisms for balanced design 15 . This foundational idea of looking to nature for design principles continued through history, as exemplified by Leonardo Da Vinci's creation of a flying machine inspired by birds in 1482. This early instance of biomimicry influenced subsequent advancements, including the Wright brothers' development of the airplane in 1948 12 , 15 . The term "bionics," coined in 1958 to describe "the science of natural systems or their analogs," evolved into "biomimicry" by 1982. Janine Benyus's 1997 book, “Biomimicry: Innovation Inspired by Nature,” and the founding of the Biomimicry Institute (Biomimicry 16 ) were pivotal, positioning nature as a guide and model for sustainable design. Benyus’s work underscores the potential of biomimicry in tackling contemporary environmental challenges such as climate change and ecosystem degradation 12 , 17 .

In recent years, the call for more targeted research in biomimicry has grown, particularly in terms of architecture and energy use. Meena et al. 18 and Varshabi et al. 19 highlighted the need for biomimicry to address energy efficiency in building design, stressing the potential of nature-inspired solutions to reduce energy consumption and enhance sustainability. This perspective aligns with that of Perricone et al. 20 , who explored the differences between artificial and natural systems, noting that biomimetic designs, which mimic the principles of organism construction, can significantly improve resource utilization and ecosystem restoration. Aggarwal and Verma 21 contributed to this discourse by mapping the evolution and applications of biomimicry through scientometric analysis, revealing the growing significance of nature-inspired optimization methodologies, especially in clustering techniques. Their work suggested that these methodologies not only provide innovative solutions but also reflect a deeper integration of biomimetic principles in technological advancements. Building on this, Pinzón and Austin 22 emphasized the infancy of biomimicry in the context of renewable energy, advocating for more research to explore how nature can inspire new energy solutions. Their work connects with that of Carniel et al. 23 , who introduced a natural language processing (NLP) technique to identify research themes in biomimicry across disciplines, facilitating a holistic understanding of current trends and future directions.

To further illustrate the practical applications of biomimicry, Nasser et al. 24 presented the Harmony Search Algorithm (HSA), a nature-inspired optimization technique. Their bibliometric analysis demonstrated the algorithm's effectiveness in reducing energy and resource consumption, highlighting the practical benefits of biomimicry in technological innovation. Rusu et al. 25 expanded on these themes by documenting significant advancements in soft robotics, showing how biomimicry influences design principles and applications in this rapidly evolving field. Their findings underscore the diverse applications of biomimetic principles, from robotics to building design. Shashwat et al. 26 emphasized the role of bioinspired solutions in enhancing energy efficiency within the built environment, promoting the use of high solar reflectance surfaces that mimic natural materials. This perspective is in line with that of Pires et al. 27 , who evaluated the application of biomimicry in dental restorative materials and identified a need for more clinical studies to realize the full potential of biomimetic innovations in healthcare. Liu et al. 28 explored the application of nature-inspired design principles in software-defined networks, demonstrating how biomimetic algorithms can optimize resource and energy utilization in complex systems. This study builds on the broader narrative of biomimicry's potential to transform various sectors by offering efficient, sustainable solutions. Finally, Hinkelman et al. 29 synthesized these insights by discussing the transdisciplinary applications of ecosystem biomimicry, which supports sustainable development goals by integrating biomimetic principles across engineering and environmental disciplines. This comprehensive approach underscores the transformative potential of biomimicry, suggesting that continued interdisciplinary research and innovation are crucial for addressing global sustainability challenges effectively.

PRISMA framework

This study utilizes the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework to structure its analysis, following the established five-step protocol: formulating research questions, defining a search strategy, executing a literature search, screening identified literature, and analyzing the findings (Page et al., 2021). The application of the PRISMA guidelines across various research domains, including the SDGs, is well documented 30 .

To ensure a comprehensive search, we searched the Scopus database, a widely utilized resource for bibliometric studies 31 (Donthu et al. 82 ), which led to the discovery of 46,141 publications from 2013 to 2023. This period marked significant research activity following the introduction of the SDGs at the Rio + 20 summit in 2012. Publications were identified using the following terms in the title and abstract: “ (biomimic* OR biomimetic* OR bioinspired OR bioinsp* OR bionic* OR nature-inspired OR "biologically inspired" OR bioinspiration OR biomimesis OR biognosis).”

During the screening phase, publications lacking complete author details were reviewed, narrowing the field to 46,083 publications for further analysis. The eligibility phase utilized proprietary algorithms to map publications to the 17 SDGs, informed by initiatives such as the University of Auckland (Auckland’s SDG mapping 32 ) and Elsevier's SDG Mapping Initiatives (Elsevier's SDG Mapping 33 ). The selection of the Elsevier SDG Mapping Initiative for this study was based on its seamless integration with Scopus, facilitating the use of predefined search queries for each SDG and employing a machine learning model that has been refined through expert review. This approach has been utilized in various studies to analyze research trends within emerging fields. For example, the exploration of green hydrogen was detailed by Raman et al. 34 , while investigations into Fake News and the Dark Web were conducted by Raman et al. 35 , 36 , 37 and Rama et al. 38 , respectively. These examples demonstrate the efficacy of SDG mapping in elucidating how research outputs align with and contribute to sustainable development goals in these emerging domains. This phase identified 13,287 publications as mapped to SDGs. In the inclusion phase, stringent criteria further filtered the publications to English-language journals and review articles, culminating in 13,271 publications deemed suitable for in-depth analysis. This process ensures a comprehensive and high-quality dataset for the study, reflecting the robust and systematic approach afforded by the PRISMA framework in evaluating literature relevant to SDGs.

Our keyword search strategy, while comprehensive, may capture papers that do not genuinely contribute to the field. To mitigate this, we employed manual verification. After the automated search, the authors conducted a manual review of a subset of the final set of identified papers to assess their relevance and authenticity in the context of biomimicry. The subset was based on 20 highly cited papers from each year. We believe that papers that are frequently cited within the community are more likely to be accurately classified. The authors mainly reviewed the introduction, methodology, and results sections to confirm the relevance and authenticity of the papers. However, we acknowledge that these steps may not fully eliminate the inclusion of irrelevant papers, which could skew the results of our meta-analysis.

SDG framework

The examination of sustainable development goals (SDGs) reveals their interconnected nature, where the achievement of one goal often supports progress in others. Studies by Le Blanc (2015) and Allison et al. (2016) have mapped out the complex web of relationships among the SDGs, identifying both strong and subtle linkages across different objectives. To visualize these connections, we employed a cocitation mapping approach using VOSviewer 39 , which allows us to depict the semantic relationships between SDGs through their cocitation rates in scholarly works. This approach generates a visual map where each SDG is represented as a node, with the node size reflecting the goal's research prominence and the thickness of the lines between nodes indicating the frequency of cocitations among the goals. This visual representation reveals the SDGs as an intricate but unified framework, emphasizing the collaborative nature of global sustainability initiatives.

Topic prominence percentile

The Scopus prominence percentile is a crucial metric indicating the visibility and impact of emerging research topics within the scientific community. High-ranking topics in this percentile are rapidly gaining attention, highlighting emerging trends and areas poised for significant advancements. This tool enables researchers and policymakers to identify and focus on innovative topics, ensuring that their efforts align with the forefront of scientific development 35 , 36 , 37 . Topics above the 99.9th percentile were used in this study.

Results and discussion

Rq1: sdg framework and interdisciplinary research (rq4).

This study evaluates biomimicry research through the framework of SDGs. A cocitation SDG map shows two clusters and provides insights into the interplay between biomimicry themes and SDGs, highlighting the cross-disciplinary nature of this research (Fig.  1 ). The blue box hidden behind the “3 – Good Health and Well-being” and “7 – Affordable and Clean Energy” is “11 – Sustainable cities and Communities”. The blue box hidden behind “15 – Life on Land” is “16 – Peace, Justice and Strong institutions”.

Interdisciplinary SDG network of biomimicry research.

Cluster 1 (Red): Biomimetic innovations for health, partnership, and life on land

This cluster comprises a diverse array of research articles that explore the application of biomimicry across various SDGs 3 (health), 17 (partnership), and 15 (land). The papers in this cluster delve into innovative biomimetic ideas, each contributing uniquely to the intersection of sustainable development and biological inspiration. SDG 3, emphasizing good health and well-being for all, is significantly represented, indicating a global effort to leverage biomimicry for advancements in healthcare, such as new medication delivery systems and medical technologies. Similarly, the frequent citations of SDG 17 underscore the vital role of partnerships in achieving sustainable growth, especially where bioinspired solutions require interdisciplinary collaboration to address complex challenges. Finally, the prominence of 15 SDG citations reflects a commitment to preserving terrestrial ecosystems, where biomimicry is increasingly applied in land management, demonstrating nature's adaptability and resilience as a model for sustainable practices. Table 2 lists the top 5 relevant papers from Cluster 1, further illustrating the multifaceted application of biomimicry in addressing these SDGs.

A unique binary variant of the gray wolf optimization (GWO) technique, designed especially for feature selection in classification tasks, was presented by Emary et al. 40 . GWO is a method inspired by the social hierarchy and hunting behavior of gray wolves to find the best solutions to complex problems. This bioinspired optimization technique was used to optimize SDG15, which also highlights its ecological benefits. The results of the study highlight the effectiveness of binary gray wolf optimization in identifying the feature space for ideal pairings and promoting environmental sustainability and biodiversity. Lin et al. 41 focused on SDG 3 by examining catalytically active nanomaterials as potential candidates for artificial enzymes. While acknowledging the limits of naturally occurring enzymes, this study explores how nanobiotechnology can address problems in the food, pharmaceutical, and agrochemical sectors.

The investigation of enzymatic nanomaterials aligns with health-related objectives, highlighting the potential for major improvements in human health. Parodi et al. 42 used biomimetic leukocyte membranes to functionalize synthetic nanoparticles, extending biomimicry into the biomedical domain. To meet SDG 3, this research presents "leukolike vectors," which are nanoporous silicon particles that can communicate with cells, evade the immune system, and deliver specific payloads. In line with the SDGs about health, this study emphasizes the possible uses of biomimetic structures in cancer detection and treatments. A novel strategy for biological photothermal nanodot-based anticancer therapy utilizing peptide‒porphyrin conjugate self-assembly was presented by Zou et al. 43 . For therapeutic reasons, efficient light-to-heat conversion can be achieved by imitating the structure of biological structures. By providing a unique biomimetic approach to cancer treatment and demonstrating the potential of self-assembling biomaterials in biomedical applications, this research advances SDG 3. Finally, Wang et al. 44 presented Monarch butterfly optimization (MBO), which is a bioinspired algorithm that mimics the migration patterns of monarch butterflies to solve optimization problems effectively. This method presents a novel approach to optimization, mimicking the migration of monarch butterflies, aligning with SDG 9. Comparative analyses highlight MBO's exceptional performance and demonstrate its capacity to address intricate issues about business and innovation, supporting objectives for long-term collaboration and sector expansion.

The publications in Cluster 1 show a wide range of biomimetic developments, from ecological optimization to new optimization techniques and biomedical applications. These varied contributions highlight how biomimicry can advance sustainable development in health, symbiosis, and terrestrial life.

Cluster 2 (green): Nature-inspired solutions for clean water, energy, and infrastructure

Cluster 2, which focuses on the innovative application of biomimicry in sustainable development, represents a range of research that aligns with SDGs 6 (sanitation), 7 (energy), 9 (infrastructure), and 14 (water). This cluster is characterized by studies that draw inspiration from natural processes and structures to offer creative solutions to sustainability-related challenges. The papers in this cluster, detailed in Table 3 , demonstrate how biomimicry can address key global concerns in a varied and compelling manner.

Within this cluster, the high citation counts for SDG 7 underscore the significance of accessible clean energy, a domain where biomimicry contributes innovative energy generation and storage solutions inspired by natural processes. This aligns with the growing emphasis on sustainable energy practices. The prominence of SDG 9 citations further highlights the global focus on innovation and sustainable industry, where biomimicry's role in developing nature-inspired designs is crucial for building robust systems and resilient infrastructure. Furthermore, the substantial citations for SDG 6 reflect a dedicated effort toward ensuring access to clean water and sanitation for all. In this regard, biomimicry principles are being applied in water purification technologies, illustrating how sustainable solutions modeled after natural processes can effectively meet clean water objectives.

The study by Sydney Gladman et al. (2016), which presented the idea of shape-morphing systems inspired by nastic plant motions, is one notable addition to this cluster. This discovery creates new opportunities for tissue engineering, autonomous robotics, and smart textile applications by encoding composite hydrogel designs that exhibit anisotropic swelling behavior. The emphasis of SDG 9 on promoting industry, innovation, and infrastructure aligns with this biomimetic strategy. SDGs 7 and 13 are addressed in the study of Li et al. 45 , which is about engineering heterogeneous semiconductors for solar water splitting. This work contributes to the goals of inexpensive, clean energy and climate action by investigating methods such as band structure engineering and bionic engineering to increase the efficiency of solar water splitting. Li et al. 46 conducted a thorough study highlighting the importance of catalysts for the selective photoreduction of CO2 into solar fuels. This review offers valuable insights into the use of semiconductor catalysts for selective photocatalytic CO2 reduction. Our work advances sustainable energy solutions by investigating biomimetic, metal-based, and metal-free cocatalysts and contributes to SDGs 7 and 13. Wang et al. 47 address the critical problem of water pollution. Creating materials with superlyophilic and superlyophobic qualities offers a creative method for effectively separating water and oil. This contributes to the goals of clean water, industry, innovation, and life below the water. It also correlates with SDGs 6, 9, and 14. Singh et al. 48 also explored the 'green' synthesis of metals and their oxide nanoparticles for environmental remediation, which furthers SDG 9. This review demonstrates the environmentally benign and sustainable features of green synthesis and its potential to lessen the environmental impact of conventional synthesis methods.

Cluster 2 provides nature-inspired solutions for clean water, renewable energy, and sustainable infrastructure, demonstrating the scope and importance of biomimicry. The varied applications discussed in these papers help overcome difficult problems and advance sustainable development in line with several SDGs.

RQ2: Emerging research topics

Temporal evolution of emerging topics.

Figure  2 displays the publication counts for various emerging topics from 2013 to 2022, indicating growth trends over the years. For 'Metaheuristics', there is a notable increase in publications peaking in approximately 2020, suggesting a surge in interest. 'Strain sensor' research steadily increased, reaching its highest publication frequency toward the end of the period, which is indicative of growing relevance in the field. 'Bioprinting' sharply increased over the next decade, subsequently maintaining high interest, which highlights its sustained innovation. In contrast, 'Actuators' showed fluctuating publication counts, with a recent upward trend. 'Cancer' research, while historically a major topic, displayed a spike in publications in approximately 2018, possibly reflecting a breakthrough or increased research funding. 'Myeloperoxidase' has a smaller presence in the literature, with a modest peak in 2019. The number of 'Water '-related publications remains relatively low but shows a slight increase, suggesting a gradual but increasing recognition of its importance. Research on exosomes has significantly advanced, particularly since 2018, signifying a greater area of focus. 'Mechanical' topic publications have moderate fluctuations without a clear trend, indicating steady research interest. 'Micromotors' experienced an initial publication surge, followed by a decline and then a recent resurgence, possibly due to new technological applications. 'Nanogenerators' have shown a dramatic increase in interest, particularly in recent years, while 'Hydrogel' publications have varied, with a recent decline, which may point toward a shift in research focus or maturity of the topic.

Evolution of emerging topics according to publications (y-axis denotes the number of publications; x-axis denotes the year of publication).

Figure  3 presents the distribution of various research topics based on their prominence percentile and total number of publications. Topics above the 99.9th percentile and to the right of the vertical threshold line represent the most emergent and prolific topics of study. Next, we examine the topics within each of the four quadrants, focusing on how each topic has developed over the years in relation to SDGs and the key phrases associated with each topic.

Distribution of research topics based on prominence percentile and total number of publications.

Next, we examine each research topic in four quadrants, assessing their evolution concerning SDGs. We also analyze the keyphrase cloud to identify which keyphrases are most relevant (indicated by their font size) and whether they are growing or not. In the key phrase cloud, green indicates an increasing relevance of the key phrase, grey signifies that its relevance remains constant, and blue represents a declining relevance of the key phrase.

Niche biomimetic applications

These are topics with a lower number of publications and prominence percentiles, indicating specialized or emerging areas of research that are not yet widely recognized or pursued (Quadrant 1—bottom left).

Myeloperoxidase; colorimetric; chromogenic compounds

The inclusion of myeloperoxidase indicates that inflammation and the immune system are the main research topics. The focus on chromogenic and colorimetric molecules suggests a relationship to analytical techniques for identifying biological materials. The evolution of the research is depicted in Fig.  4 a shows an evolving emphasis on various sustainable development goals (SDGs) over time. The research trajectory, initially rooted in SDG 3 (Good Health and Well-being), has progressively branched out to encompass SDG 7 (Affordable and Clean Energy) and SDG 6 (Clean Water and Sanitation), reflecting an expanding scope of inquiry within the forestry sciences. More recently, the focus has transitioned toward SDG 15 (Life on Land), indicating an increased recognition of the interconnectedness between forest ecosystems and broader environmental and sustainability goals. This trend underscores the growing complexity and multidisciplinary nature of forestry research, highlighting the need to address comprehensive ecological concerns along with human well-being and sustainable development.

Evolution of research ( a ) and key phrases ( b ).

The word cloud in Fig.  4 b highlights key phrases such as 'Biocompatible', 'Actuator', and 'Self-healing Hydrogel', reflecting a focus on advanced materials, while terms such as 'Elastic Modulus' and 'Polymeric Networks' suggest an emphasis on the structural properties essential for creating innovative diagnostic and environmental sensing tools. Such developments are pertinent to health monitoring and water purification, resonating with SDG 3 (Good Health and Well-being) and SDG 6 (Clean Water and Sanitation). The prominence of 'Self-healing' and 'Bioinspired' indicates a shift toward materials that emulate natural processes for durability and longevity, supporting sustainable industry practices aligned with SDG 9 (Industry, Innovation, and Infrastructure) and SDG 12 (Responsible Consumption and Production), contributing to the overarching aim of sustainable development.

Next, we analyzed the top 3 cited publications. Catalytically active nanomaterials, or nanozymes, are exciting candidates for artificial enzymes, according to Lin et al. 41 . The authors explore the structural features and biomimetics applications of these enzymes, classifying them as metal-, carbon-, and metal oxide-based nanomaterials. This study emphasizes the benefits of enzymes over natural enzymes, including their high stability, variable catalytic activity, and controlled production. Wang et al. 49 developed biomimetic nanoflowers made from nanozymes to cause intracellular oxidative damage in hypoxic malignancies. Under both normoxic and hypoxic conditions, the nanoflowers demonstrated catalytic efficiency. By overcoming the constraints of existing systems that depend on oxygen availability or external stimuli, this novel technique represents a viable treatment option for malignant neoplasms. Gao et al. 50 investigated the use of a dual inorganic nanozyme-catalyzed cascade reaction as a biomimetic approach for nanocatalytic tumor therapy. This approach produces a high level of therapeutic efficacy by cascading catalytic events inside the tumor microenvironment. This study highlights the potential of inorganic nanozymes for achieving high therapeutic efficacy and outstanding biosafety, which adds to the growing interest in nanocatalytic tumor therapy.

Water; hydrophobicity; aerogels

With an emphasis on hydrophobicity, aerogel use, and water-related features, this topic relates to materials science and indicates interest in cutting-edge materials with unique qualities. From Fig.  5 a, we can see that, initially, the focus was directed toward SDG 6 (Clean Water and Sanitation), which is intrinsically related to the research theme, as biomimetic approaches are leveraged to develop innovative water purification and management solutions. As the research progressed, the scope expanded to intersect with SDG 14 (Life Below Water) and SDG 7 (Affordable and Clean Energy), signifying a broadened impact of biomimetic innovations in marine ecosystem conservation and energy-efficient materials. The gradual involvement with SDG 9 (industry, innovation, and infrastructure) and SDG 13 (climate action) indicates the interdisciplinary reach of this research, which aims to influence industrial practices and climate change mitigation strategies.

The word cloud in Fig.  5 b reinforces this narrative by showcasing key phrases such as 'Hydrophobic', 'Bioinspired', 'Emulsion', and 'Oil Pollution', which reflect the emphasis on developing materials and technologies that mimic natural water repellency and separation processes. 'Aerogel' and 'polydopamine', along with 'Underwater' and 'Biomimetic Cleaning', suggest a strong focus on creating lightweight, efficient materials capable of self-cleaning and oil spill remediation. These keywords encapsulate the essence of the research theme, demonstrating a clear alignment with the targeted SDGs and the overall aim of sustainable development through biomimicry.

Three highly referenced works that have made substantial contributions to the field of biomimetic materials for oil/water separation are included in the table. The development of superlyophilic and superlyophobic materials for effective oil/water separation was examined by Wang et al. 47 . This review highlights the applications of these materials in separating different oil-and-water combinations by classifying them according to their surface wettability qualities. The excellent efficiency, selectivity, and recyclability of the materials—which present a viable treatment option for industrial oily wastewater and oil spills—are highlighted in the paper. Su et al. 51 explored the evolution of super wettability systems. The studies included superhydrophobicity, superoleophobicity, and undersea counterparts, among other extreme wettabilities. The kinetics, material structures, and wetting conditions related to obtaining superwettability are covered in the article. This demonstrates the wide range of uses for these materials in chemistry and materials science, including self-cleaning fabrics and systems for separating oil and water. Zhang et al. 52 presented a bioinspired multifunctional foam with self-cleaning and oil/water separation capabilities. To construct a polyurethane foam with superhydrophobicity and superoleophobicity, this study used porous biomaterials and superhydrophobic self-cleaning lotus leaves. Foam works well for separating oil from water because of its slight weight and ability to float on water. It also shows exceptional resistance to corrosive liquids. According to the article, multifunctional foams for large-scale oil spill cleaning might be designed using a low-cost fabrication technology that could be widely adopted.

Growing interest in bioinspired healthcare

These topics have a higher prominence percentile but a lower number of publications, suggesting growing interest and importance in the field despite a smaller body of research (Quadrant 2—top left).

Exosomes; extracellular vesicles; MicroRNAs

Exosomes and extracellular vesicles are essential for intercellular communication, and reference to microRNAs implies a focus on genetic regulation. The evolution of this topic reflects an increasing alignment with specific sustainable development goals (SDGs) over the years. The initial research focused on SDG 3 (good health and well-being) has expanded to encompass SDG 9 (industry, innovation, and infrastructure) and SDG 6 (clean water and sanitation), showcasing the multifaceted impact of biomimetic research in healthcare (Fig.  6 a). The research trajectory into SDG 9 and SDG 6 suggests broader application of bioinspired technologies beyond healthcare, potentially influencing sustainable industrial processes and water treatment technologies, respectively.

The word cloud (Fig.  6 b) underscores the central role of 'Extracellular Vesicles' and 'Exosomes' as platforms for 'Targeted Drug Delivery' and 'Nanocarrier' systems, which are key innovations in medical biotechnology. The prominence of terms such as 'Bioinspired', 'Biomimetic', 'Liposome', and 'Gold Nanoparticle' illustrates the inspiration drawn from biological systems for developing advanced materials and delivery mechanisms. These key phrases indicate significant advancements in 'Controlled Drug Delivery Systems', 'Cancer Chemotherapy', and 'Molecular Imaging', which have contributed to improved diagnostics and treatment options, consistent with the objectives of SDG 3.

The work by Jang et al. 53 , which introduced bioinspired exosome-mimetic nanovesicles for improved drug delivery to tumor tissues, is one of the most cited articles. These nanovesicles, which resemble exosomes but have higher creation yields, target cells and slow the growth of tumors in a promising way. Yong et al.'s 54 work presented an effective drug carrier for targeted cancer chemotherapy, focusing on biocompatible tumor cell-exocytosed exosome-biomimetic porous silicon nanoparticles. A paper by Cheng et al. 55 discussed the difficulties in delivering proteins intracellularly. This study suggested a biomimetic nanoparticle platform that uses extracellular vesicle membranes and metal–organic frameworks. These highly cited studies highlight the importance of biomimetic techniques in improving drug delivery systems for improved therapeutic interventions.

Nanogenerators; piezoelectric; energy harvesting

This topic advises concentrating on technology for energy harvesting, especially for those that use piezoelectric materials and nanogenerators. We see a rising focus on medical applications of biomimetics, from diagnostics to energy harvesting mimicking biological systems.

The evolution of this research topic reflects a broader contribution to the SDGs by not only addressing healthcare needs but also by promoting sustainable energy practices and supporting resilient infrastructure through biomimetic innovation (Fig.  7 a). Initially, the emphasis on SDG 3 (Good Health and Well-being) suggested the early application of biomimetic principles in healthcare, particularly in medical devices and diagnostics leveraging piezoelectric effects. Over time, the transition toward SDG 7 (Affordable and Clean Energy) and SDG 9 (Industry, Innovation, and Infrastructure) indicates an expansion of bioinspired technologies into sustainable energy solutions and industrial applications. Nanogenerators and energy harvesting techniques draw inspiration from biological processes and structures, aiming to optimize energy efficiency and contribute to clean energy initiatives.

The word cloud in Fig.  7 b emphasizes key phrases such as 'Piezoelectric', 'Energy Harvesting', 'Tactile Sensor', 'Triboelectricity', and 'Nanogenerators', highlighting the core technologies that are being developed. These terms, along with 'Bioinspired', 'Wearable Electronic Devices', and 'Energy Conversion Efficiency', illustrate the convergence of natural principles with advanced material science to create innovative solutions for energy generation and sensor technology.

Yang et al.'s 56 study in Advanced Materials presented the first triboelectrification-based bionic membrane sensor. Wearable medical monitoring and biometric authentication systems will find new uses for this sensor since it allows self-powered physiological and behavioral measurements, such as noninvasive human health evaluation, anti-interference throat voice recording, and multimodal biometric authentication. A thorough analysis of the state-of-the-art in piezoelectric energy harvesting was presented by Sezer and Koç 57 . This article addresses the fundamentals, components, and uses of piezoelectric generators, highlighting their development, drawbacks, and prospects. It also predicts a time when piezoelectric technology will power many electronics. The 2021 paper by Zhao et al. 58 examines the use of cellulose-based materials in flexible electronics. This section describes the benefits of these materials and the latest developments in intelligent electronic device creation, including biomimetic electronic skins, optoelectronics, sensors, and optoelectronic devices. This review sheds light on the possible drawbacks and opportunities for wearable technology and bioelectronic systems based on cellulose.

Leading edge of biomimetic sensing and electronics

This quadrant represents topics with both a high number of publications and a prominence percentile, indicating well-established and influential research areas (Quadrant 3—top right).

Strain sensor; flexible electronics; sensor

Figure  8 a highlights the progress of research on bioinspired innovations, particularly in the development of strain sensors and flexible electronics for adaptive sensing technologies. Initially, concentrated on health applications aligned with SDG 3 (Good Health and Well-being), the focus has expanded. The integration of SDG 9 (Industry, Innovation, and Infrastructure) indicates a shift toward industrial applications, while the incorporation of SDG 7 (Affordable and Clean Energy) suggests a commitment to energy-efficient solutions. Additionally, the mention of SDG 11 (Sustainable Cities and Communities) and SDG 12 (Responsible Consumption and Production) reflects the broadening scope to include urban sustainability and eco-friendly manufacturing practices.

Figure  8 b provides insight into the key phrases associated with this research topic, highlighting terms such as 'Bioinspired', 'Self-healing', 'Wearable Electronic Devices', 'Flexible Electronics', and 'Pressure Sensor'. These key phrases speak to the innovative approaches for creating sensors and electronics that are not only inspired by biological systems but also capable of seamlessly integrating human activity and environmental needs. The mention of 'Wearable Sensors' and 'Tactile Sensor' indicates a focus on user interaction and sensitivity, which is crucial for medical applications and smart infrastructure.

The top three articles with the most citations represent the cutting edge of this topic’s study. Chortos et al. 59 investigated how skin characteristics can be replicated for medicinal and prosthetic uses. Kim et al. 60 focused on creating ultrathin silicon nanoribbon sensors for smart prosthetic skin, opening up new possibilities for bionic systems with many sensors. A bioinspired microhairy sensor for ultraconformability on nonflat surfaces was introduced in Pang et al.'s 61 article, which significantly improved signal-to-noise ratios for accurate physiological measurements.

Cancer; photoacoustics; theranostic nanomedicine

Modern technologies such as photoacoustics, theranostic nanomedicine, and cancer research suggest that novel cancer diagnosis and therapy methods are highly needed. Figure  9 a traces the research focus that has evolved across various SDGs over time, commencing with SDG 3 (Good Health and Well-being), which is indicative of the central role of health in biomimetic research. It then extends into SDG 9 (Industry, Innovation, and Infrastructure) and SDG 7 (Affordable and Clean Energy), illustrating the cross-disciplinary applications of biomimetic technologies from healthcare to the energy and industrial sectors.

Figure  9 b provides a snapshot of the prominent keywords within this research theme, featuring terms such as “photodynamic therapy”, “photothermal chemotherapy”, “nanocarrier”, and “controlled drug delivery”. These terms underscore the innovative therapeutic strategies that mimic biological mechanisms for targeted cancer treatment. 'Bioinspired' and 'Biomimetic Synthesis' reflect the approach of deriving design principles from natural systems for the development of advanced materials and medical devices. 'Theranostic nanomedicine' integrates diagnosis and therapy, demonstrating a trend toward personalized and precision medicine.

A study conducted by Yu et al. 62 presented a novel approach for synergistic chemiexcited photodynamic-starvation therapy against metastatic tumors: a biomimetic nanoreactor, or bio-NR. Bio-NRs use hollow mesoporous silica nanoparticles to catalyze the conversion of glucose to hydrogen peroxide for starvation therapy while also producing singlet oxygen for photodynamic therapy. Bio-NR is promising for treating cancer metastasis because its coating on cancer cells improves its biological qualities. Yang et al.'s 63 study focused on a biocompatible Gd-integrated CuS nanotheranostic agent created via a biomimetic approach. This drug has low systemic side effects and good photothermal conversion efficiency, making it suitable for skin cancer therapy. It also performs well in imaging. The ultrasmall copper sulfide nanoparticles generated within ferritin nanocages are described in Wang et al.’s 64 publication. This work highlights the possibility of photoacoustic imaging-guided photothermal therapy with improved therapeutic efficiency and biocompatibility. These highly referenced articles highlight the significance of biomimetic techniques in furthering nanotheranostics and cancer therapy.

Established biomimetic foundations

Here, there are topics with a greater number of publications but a lower prominence percentile, which may imply areas where there has been significant research but that may be waning in influence or undergoing a shift in focus (Quadrant 4—bottom right).

Metaheuristics; Fireflies; Chiroptera

This topic is a fascinating mix of subjects. Using Firefly and Chiroptera in metaheuristic optimization algorithms provides a bioinspired method for resolving challenging issues. The thematic progression of research papers suggests the maturation of biomimetic disciplines that resonate with several SDGs (Fig.  10 a). The shift from initially aligning with SDG 3 (Good Health and Well-being) extends to intersecting with goals such as SDG 9 (Industry, Innovation, and Infrastructure), SDG 7 (Affordable and Clean Energy), SDG 11 (Sustainable Cities and Communities), SDG 13 (Climate Action), and SDG 15 (Life on Land). This diversification reflects the expansive utility of biomimetic approaches, from health applications to broader environmental and societal challenges.

The top keyphrases, such as 'Swarm Intelligence', 'Global Optimization', 'Cuckoo Search Algorithm', and 'Particle Swarm Optimization', are shown in Fig.  10 b highlights the utilization of nature-inspired algorithms for solving complex optimization problems. These terms, along with the 'Firefly Algorithm' and 'Bat Algorithm', underscore the transition of natural phenomena into computational algorithms that mimic the behavioral patterns of biological organisms, offering robust solutions in various fields, including resource management, logistics, and engineering design.

The three highly referenced metaheuristic publications centered around the “Moth Flame Optimization (MFO),” Salp Swarm Algorithm (SSA),” and Whale Optimization Algorithm (WOA).” The WOA, authored by Mirjalili and Lewis 65 , is a competitive solution for mathematical optimization and structural design issues because it emulates the social behavior of humpback whales. Inspired by the swarming behavior of salps, Mirjalili et al. 66 introduced the SSA and multiobjective SSA. This shows how well they function in optimizing a variety of engineering design difficulties. Finally, Mirjalili 67 suggested the MFO algorithm, which is modeled after the navigational strategy of moths and exhibits competitive performance in resolving benchmark and real-world engineering issues.

Bioprinting; three-dimensional printing; tissue engineering

The emphasis on sophisticated manufacturing methods for biological applications in this field suggests a keen interest in the nexus of biology and technology, especially in tissue engineering. As shown in Fig.  11 a, the topic's evolution encompasses Sustainable Development Goals (SDGs) that have transitioned over the years, including SDG 3 (Good Health and Well-being), which is inherently connected to the advancement of medical technologies and tissue engineering for health applications. This research also touches upon SDG 6 (Clean Water and Sanitation) and SDG 7 (Affordable and Clean Energy), suggesting applications of bioprinting technologies in the environmental sustainability and energy sectors. The progression toward SDG 9 (Industry, Innovation, and Infrastructure) and SDG 15 (Life on Land) reflects a broader impact, where biomimetic principles are applied to foster innovation in industrial processes and contribute to the preservation of terrestrial ecosystems.

Key phrases emerging from the word cloud in Fig.  11 b, such as “Hydrogel”, “Biofabrication”, “Tissue Scaffold”, and “Regenerative Medicine”, highlight the specialized methodologies and materials that are inspired by natural processes and structures. Terms such as 'Three-Dimensional Printing' and 'Bioprinting' underscore the technological advancements in creating complex biological structures, aiming to revolutionize the field of tissue engineering and regenerative medicine.

Three widely referenced papers about advances in 3D printing—particularly in bioprinting, soft matter, and the incorporation of biological tissue with functional electronics—are described next. Truby and Lewis’s 68 review of light- and ink-based 3D printing techniques is ground-breaking. This highlights the technology's capacity to create soft matter with tunable properties and its potential applications in robotics, shape-morphing systems, biologically inspired composites, and soft sensors. Ozbolat, and Hospodiuk 69 provide a thorough analysis of “extrusion-based bioprinting (EBB).” The adaptability of EBB in printing different biologics is discussed in the paper, with a focus on its uses in pharmaceutics, primary research, and clinical contexts. Future directions and challenges in EBB technology are also discussed. Using 3D printing, Mannoor et al. 70 presented a novel method for fusing organic tissue with functioning electronics. In the proof-of-concept, a hydrogel matrix seeded with cells and an interwoven conductive polymer containing silver nanoparticles are 3D printed to create a bionic ear. The improved auditory sensing capabilities of the printed ear show how this novel technology allows biological and nanoelectronic features to work together harmoniously.

RQ3: Translation and commercialization

Biomimicry offers promising solutions for sustainability in commercial industries with environmentally sustainable product innovation and energy savings with reduced resource commitment 71 . However, translating biomimicry innovations from research to commercialization presents challenges, including product validation, regulatory hurdles, and the need for strategic investment, innovative financial models, and interdisciplinary collaboration 71 , 72 , 73 , 74 . Ethical considerations highlight the need for universally applicable ethical guidelines regarding the moral debates surrounding biomimicry, such as motivations for pursuing such approaches and the valuation of nature 75 .

Addressing these barriers requires interdisciplinary collaboration, targeted education, and training programs. Strategic investment in biomimicry research and development is also crucial. Encouraging an engineering mindset that integrates biomimicry principles into conventional practices and developing commercial acumen among researchers is essential for navigating the market landscape 76 . Securing sufficient funding is essential for the development, testing, and scaling of these innovations 76 .

Successful case studies illustrate that the strategic integration of biomimicry enhances corporate sustainability and innovation (Larson & Meier 2017). In biomedical research, biomimetic approaches such as novel scaffolds and artificial skins have made significant strides (Zhang 2012). Architecture benefits through energy-efficient building facades modeled after natural cooling systems (Webb et al. 2017). The textile industry uses biomimicry to create sustainable, high-performance fabrics 77 .

RQ4: Interdisciplinary collaboration

Agricultural innovations (sdgs 1—no poverty and 2—zero hunger).

Environmental degradation, biodiversity loss, poverty, and hunger highlight the need for sustainable agricultural methods to mimic natural ecosystems. This includes computational models for ecological interactions, field experiments for biomimetic techniques, and novel materials inspired by natural soil processes. Research can develop solutions such as artificial photosynthesis for energy capture, polyculture systems mimicking ecosystem diversity, and bioinspired materials for soil regeneration and water retention 28 . These innovations can improve sustainability and energy efficiency in agriculture, addressing poverty and hunger through sustainable farming practices.

Educational models (SDG 4—Quality education)

Integrating sustainability principles and biomimicry into educational curricula at all levels presents opportunities for innovation. Collaborations between educators, environmental scientists, and designers can create immersive learning experiences that promote sustainability. This includes interdisciplinary curricula with biomimicry case studies, digital tools, and simulations for exploring biomimetic designs, and participatory learning approaches for engaging students with natural environments. Designing biomimicry-based educational tools and programs can help students engage in hands-on, project-based learning 10 , fostering a deeper understanding of sustainable living and problem-solving.

Gender-inclusive design (SDG 5—Gender inequality)

Gender biases in design and innovation call for research into biomimetic designs and technologies that facilitate gender equality. This includes participatory design processes involving women as cocreators, studying natural systems for inclusive strategies, and applying biomimetic principles to develop technologies supporting gender equality. Bioinspired technologies can address women's specific needs, enhancing access to education, healthcare, and economic opportunities. Interdisciplinary approaches involving gender studies, engineering, and environmental science can uncover new pathways for inclusive innovation.

Inclusive urban solutions (SDG 11—Sustainable cities and communities)

Rapid urbanization challenges such as housing shortages, environmental degradation, and unsustainable transportation systems require innovative solutions. Methodologies include systems thinking in urban planning, simulation tools for modeling biomimetic solutions, and pilot projects testing bioinspired urban innovations. Research on biomimetic architecture for affordable housing, green infrastructure for climate resilience, and bioinspired transportation systems can offer solutions. Collaborative efforts among architects, urban planners, ecologists, and sociologists are essential 78 .

Peace and justice (SDG 16—Peace, justice and institutions)

Social conflicts and weak institutions necessitate innovative approaches that integrate political science, sociology, and biology. Methods involve case studies, theoretical modeling, and participatory action research to develop strategies for peacebuilding and institutional development.

This research provides a comprehensive exploration of the multifaceted dimensions of biomimicry, SDG alignment, and interdisciplinary topics, demonstrating a clear trajectory of growth and relevance. Interdisciplinary collaboration has emerged as a pivotal strategy for unlocking the full potential of biomimicry in addressing underexplored SDGs.

While answering RQ1, the interdisciplinary analysis underscores the significant alignment of biomimicry research with several SDGs. This reflects the interdisciplinary nature of biomimicry and its ability to generate solutions for societal challenges. The analysis of two thematic clusters revealed the broad applicability of biomimicry across various sustainable development goals (SDGs). The first cluster includes health, partnership, and life on land (SDGs 3, 17, and 15), highlighting biomimicry's potential in medical technologies, sustainability collaborations, and land management. The second cluster encompasses clean water, energy, infrastructure, and marine life (SDGs 6, 7, 9, and 14), demonstrating innovative approaches to clean energy generation, sustainable infrastructure, and water purification.

In response to RQ2, this study highlights emerging topics within biomimicry research, such as metaheuristics and nanogenerators, which reflect a dynamic and evolving field that is swiftly gaining attention. These topics, alongside sensors, flexible electronics, and strain sensors, denote evolving research objectives and societal demands, pointing to new areas of study and innovation. This focus on interdisciplinary topics within biomimicry underscores the field’s adaptability and responsiveness to the shifting landscapes of technological and societal challenges.

In addressing RQ3, biomimicry holds potential for sustainable innovation but faces challenges in commercialization. Biomimicry inspires diverse technological and product innovations, driving sustainable advancements (Lurie-Luke 84 ). Overcoming these barriers through strategic investment, training, interdisciplinary collaboration, and ethical guidelines is essential for unlocking their full potential.

For RQ4 , the recommendations are formulated based on underexplored SDGs like 1, 4, 5, and 10 where biomimicry could play a pivotal role.

Future research could apply generative AI models to this dataset to validate the findings and explore additional insights. While our current study did not explore this topic, we see significant potential for this approach. Generative AI models can process extensive datasets and reveal patterns, potentially offering insights into biomimetic research correlations. The interpretation required for context-specific analysis remains challenging for generative AI 36 , 37

Our study provides valuable insights, but some limitations are worth considering. The chosen database might limit the comprehensiveness of the research captured, potentially excluding relevant work from other sources. Additionally, while the combination of cocitation mapping and BERTopic modeling provides a powerful analysis, both methods have inherent limitations. They may oversimplify the complexities of the field or introduce bias during theme interpretation, even with advanced techniques. Furthermore, our use of citations to thematically clustered publications as a proxy for impact inherits the limitations of citation analysis, such as biases toward established ideas and potential misinterpretations 79 , 80 . Another limitation of our study is the potential for missing accurate SDG mappings, as multiple SDG mapping initiatives are available, and our reliance on a single, Scopus-integrated method may not capture all relevant associations. Consequently, this could have resulted in the exclusion of papers that were appropriately aligned with certain SDGs but were not identified by our chosen mapping approach. Given these limitations, this study provides a valuable snapshot for understanding biomimicry research.

Data availability

All data generated or analyzed during this study are included in this published article and its supplementary information files.

El-Zeiny, R. M. A. Biomimicry as a problem solving methodology in interior architecture. Procedia. Soc. 50 , 502–512 (2012).

Article   Google Scholar  

Othmani, N. I., Sahak, N. M. & Yunos, M. Y. M. Biomimicry in agrotechnology: Future solution of water problem for the agriculture industry?. IOP Conf. Ser.: Earth Environ. Sci. 756 (1), 012051. https://doi.org/10.1088/1755-1315/756/1/012051 (2021).

Bensaude-Vincent, B. Bioinformed emerging technologies and their relation to the sustainability aims of biomimicry. Environ. Values 28 (5), 551–571 (2019).

Chirazi, J. E. Commercialization journeys: Bringing biomimetic innovation to the market. In Biomim. Mat., Des. and Habit. (pp. 393–436). Elsevier (2022).

Vincent, J. F., Bogatyreva, O. A., Bogatyrev, N. R., Bowyer, A. & Pahl, A. K. Biomimetics: Its practice and theory. J. R. Soc. Interface 3 (9), 471–482 (2006).

Article   PubMed   PubMed Central   Google Scholar  

Pogge, T. & Sengupta, M. The sustainable development goals: A plan for building a better world?. J. Glob. Ethics 11 (1), 56–64 (2015).

Wamane, G. V. A “new deal” for a sustainable future: Enhancing circular economy by employing ESG principles and biomimicry for efficiency. Manag. Environ. Qual. https://doi.org/10.1108/MEQ-07-2022-0189 (2023).

Butt, A. N. & Dimitrijević, B. Multidisciplinary and transdisciplinary collaboration in nature-based design of sustainable architecture and urbanism. Sustainability 14 (16), 10339 (2022).

Gupta, J. & Vegelin, C. Sustainable development goals and inclusive development. INEA 16 , 433–448 (2016).

Google Scholar  

Hayes, S., Desha, C. & Baumeister, D. Learning from nature—Biomimicry innovation to support infrastructure sustainability and resilience. TFSC 161 , 120287 (2020).

Toner, J., Desha, C., Reis, K., Hes, D. & Hayes, S. Integrating ecological knowledge into regenerative design: A rapid practice review. Sustainability 15 (17), 13271 (2023).

Jamei, E. & Vrcelj, Z. Biomimicry and the built environment, learning from nature’s solutions. Appl. Sci. 11 (16), 7514 (2021).

Article   CAS   Google Scholar  

Blanco, E., Pedersen Zari, M., Raskin, K. & Clergeau, P. Urban ecosystem-level biomimicry and regenerative design: Linking ecosystem functioning and urban built environments. Sustainability 13 (1), 404 (2021).

Rebecca, C. Climate Action Through Biomimcry: Innovation for the SDGs. Sustainable Brands. https://sustainablebrands.com/read/product-service-design-innovation/climate-action-through-biomimicry-innovation-for-the-sdgs . (Accessed on January 9, 2024) (2020).

Vierra, S. Biomimicry: Designing to model nature. Whole Build. Des. Guide 1–10 (2011).

Biomimicry Institute. Case Studies of Commercialisation of Biomimicry Innovations (2023).

Fayemi, P. E., Wanieck, K., Zollfrank, C., Maranzana, N. & Aoussat, A. Biomimetics: Process, tools and practice. Bioinsp. Biomim. 12 (1), 011002. https://doi.org/10.1088/1748-3190/12/1/011002 (2017).

Article   PubMed   CAS   Google Scholar  

Meena, A. K., D’Costa, D., Bhavsar, S., Kshirsagar, M. & Kulkarni, S. Applications of biomimicry in construction and architecture: A bibliometric analysis. Lib. Phil. Prac. Lincoln, NE, USA , 1–17 (2021).

Varshabi, N., Arslan Selçuk, S. & Mutlu Avinç, G. Biomimicry for energy-efficient building design: A bibliometric analysis. Biomimetics 7 (1), 21 (2022).

Perricone, V., Langella, C. & Santulli, C. Sustainable Biomimetics: A Discussion on Differences in Scale, Complexity, and Organization Between the Natural and Artificial World. In Bion. Sustain. Design 171–193 (Springer Nature Singapore, 2022).

Aggarwal, S., & Verma, A. Scientometric analysis of research trends in clustering and nature inspired techniques. In AIP Conf. Proc. (Vol. 2576, No. 1). AIP Publishing (2022, December).

Pinzón, O. A. & Austin, M. C. Nature as an Inspiration to Exploit Renewable Energy Sources: A Review on Trends and Biological Strategies. In 2022 8th International Engineering, Sciences and Technology Conference (IESTEC) 463–468 (IEEE, 2022).

Carniel, T., Cazenille, L., Dalle, J. M. & Halloy, J. Using natural language processing to find research topics in Living Machines conferences and their intersections with Bioinspiration & Biomimetics publications. Bioinsp. & Biomim 17 (6), 065008 (2022).

Nasser, H. K. et al. Harmony search algorithm for solving combinatorial optimization problems: Bibliometric analysis. Math. Model. Eng. Prob. 10 (3), 906 (2023).

Rusu, D. M. et al. Soft robotics: A systematic review and bibliometric analysis. Micromachines 14 (2), 359. https://doi.org/10.3390/mi14020359 (2023).

Shashwat, S. et al. A review on bioinspired strategies for an energy-efficient built environment. Energy Build. 296 , 113382 (2023).

Pires, P. M. et al. Bioactive restorative materials applied over coronal dentine—A bibliometric and critical review. Bioengineering 10 (6), 731. https://doi.org/10.3390/bioengineering10060731 (2023).

Article   PubMed   PubMed Central   CAS   Google Scholar  

Liu, H., Liao, X. & Du, B. The applications of nature-inspired meta-heuristic algorithms for decreasing the energy consumption of software-defined networks: A comprehensive and systematic literature review. Sustain. Comput.: Inform. Syst. 39 , 100895. https://doi.org/10.1016/j.suscom.2023.100895 (2023).

Hinkelman, K., Yang, Y. & Zuo, W. Design methodologies and engineering applications for ecosystem biomimicry: An interdisciplinary review spanning cyber, physical, and cyber-physical systems. Bioinsp. Biomim. 18 (2), 021001 (2023).

Raman, R., Lathabhai, H., Mandal, S., Kumar, C. & Nedungadi, P. Contribution of business research to sustainable development goals: bibliometrics and science mapping analysis. Sustainability 15 (17), 12982 (2023).

Nedungadi, P. et al. Mapping autism’s research landscape: Trends in autism screening and its alignment with sustainable development goals. Front. Psychiatry 14 , 1294254 (2024).

Auckland's SDG Mapping. University of Auckland's SDG Mapping initiative from https://www.sdgmapping.auckland.ac.nz , accessed on 4th February 2024

Elsevier's SDG Mapping - Elsevier's SDG Mapping Initiative from https://www.elsevier.com/about/partnerships/sdg-research-mapping-initiative , accessed on 4th February 2024

Raman, R., Nair, V. K., Prakash, V., Patwardhan, A. & Nedungadi, P. Green-hydrogen research: What have we achieved, and where are we going? Bibliometrics analysis. Energy Rep. 8 , 9242–9260 (2022).

Raman, R. et al. Fake news research trends, linkages to generative artificial intelligence and sustainable development goals. Heliyon 10 (3), e24727 (2024).

Raman, R. et al. ChatGPT: Literate or intelligent about UN sustainable development goals?. Plos one 19 (4), e0297521 (2024).

Raman, R., Calyam, P. & Achuthan, K. ChatGPT or bard: Who is a better certified ethical hacker?. Comput. Secur. 140 , 103804 (2024).

Rama, R., Nair, V. K., Nedungadi, P., Ray, I. & Achuthan, K. Darkweb research: Past, present, and future trends and mapping to sustainable development goals. Heliyon 9 (11), e22269 (2023).

Van Eck, N. J. & Waltman, L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 84 , 523–538 (2010).

Article   PubMed   Google Scholar  

Emary, E., Zawbaa, H. M. & Hassanien, A. E. Binary gray wolf optimization approaches for feature selection. Neurocomputing 172 , 371–381 (2016).

Lin, Y., Ren, J. & Qu, X. Catalytically active nanomaterials: a promising candidate for artificial enzymes. Acc. Chem. Res. 47 (4), 1097–1105 (2014).

Parodi, A. et al. Synthetic nanoparticles functionalized with biomimetic leukocyte membranes possess cell-like functions. Nat. Nanotechnol. 8 (1), 61–68 (2013).

Article   ADS   PubMed   CAS   Google Scholar  

Zou, Q. et al. Biological photothermal nanodots based on self-assembly of peptide–porphyrin conjugates for antitumor therapy. J. Am. Chem. Soc. 139 (5), 1921–1927 (2017).

Wang, G. G., Deb, S. & Cui, Z. Monarch butterfly optimization. Neural Comput. Appl. 31 , 1995–2014 (2019).

Li, X. et al. Engineering heterogeneous semiconductors for solar water splitting. J. Mater. Chem. 3 (6), 2485–2534 (2015).

Li, X., Yu, J., Jaroniec, M. & Chen, X. Cocatalysts for selective photoreduction of CO 2 into solar fuels. Chem. Rev. 119 (6), 3962–4179 (2019).

Wang, B., Liang, W., Guo, Z. & Liu, W. Biomimetic superlyophobic and superlyophilic materials applied for oil/water separation: A new strategy beyond nature. Chem. Soc. Rev. 44 (1), 336–361 (2015).

Singh, J. et al. ‘Green’synthesis of metals and their oxide nanoparticles: Applications for environmental remediation. J. Nanobiotechnology 16 (1), 1–24 (2018).

Wang, Z. et al. Biomimetic nanoflowers by self-assembling nanozymes to induce intracellular oxidative damage against hypoxic tumors. Nat. Commun. 9 (1), 3334 (2018).

Article   ADS   PubMed   PubMed Central   Google Scholar  

Gao, S. et al. Nanocatalytic tumor therapy by biomimetic dual inorganic nanozyme-catalyzed cascade reaction. Adv. Sci. 6 (3), 1801733 (2019).

Su, B., Tian, Y. & Jiang, L. Bioinspired interfaces with superwettability: From materials to chemistry. J. Am. Chem. Soc. 138 (6), 1727–1748 (2016).

Zhang, X., Li, Z., Liu, K. & Jiang, L. Bioinspired multifunctional foam with self-cleaning and oil/water separation. Adv. Funct. Mater. 23 (22), 2881–2886 (2013).

Jang, S. C. et al. Bioinspired exosome-mimetic nanovesicles for targeted delivery of chemotherapeutics to malignant tumors. ACS Nano 7 (9), 7698–7710 (2013).

Yong, T. et al. Tumor exosome-based nanoparticles are efficient drug carriers for chemotherapy. Nat. Commun. 10 (1), 3838 (2019).

Cheng, G. et al. Self-assembly of extracellular vesicle-like metal–organic framework nanoparticles for protection and intracellular delivery of biofunctional proteins. J. Am. Chem. Soc. 140 (23), 7282–7291 (2018).

Yang, J. et al. Eardrum-inspired active sensors for self-powered cardiovascular system characterization and throat-attached anti-interference voice recognition. Adv. Mater. 27 (8), 1316–1326 (2015).

Sezer, N. & Koç, M. A comprehensive review on the state-of-the-art of piezoelectric energy harvesting. Nano Energy 80 , 105567 (2021).

Zhao, D. et al. Cellulose-based flexible functional materials for emerging intelligent electronics. Adv. Mater. 33 (28), 2000619 (2021).

Chortos, A., Liu, J. & Bao, Z. Pursuing prosthetic electronic skin. Nat. mater. 15 (9), 937–950 (2016).

Kim, J. et al. Stretchable silicon nanoribbon electronics for skin prosthesis. Nat. Commun. 5 (1), 5747 (2014).

Pang, C. et al. Highly skin-conformal microhairy sensor for pulse signal amplification. Adv. Mater. 27 (4), 634–640 (2015).

Yu, Z., Zhou, P., Pan, W., Li, N. & Tang, B. A biomimetic nanoreactor for synergistic chemiexcited photodynamic and starvation therapy against tumor metastasis. Nat. Commun. 9 (1), 5044 (2018).

Yang, W. et al. Albumin-bioinspired Gd: CuS nanotheranostic agent for in vivo photoacoustic/magnetic resonance imaging-guided tumor-targeted photothermal therapy. ACS Nano 10 (11), 10245–10257 (2016).

Wang, Z. et al. Biomineralization-inspired synthesis of copper sulfide–ferritin nanocages as cancer theranostics. ACS Nano 10 (3), 3453–3460 (2016).

Mirjalili, S. & Lewis, A. The whale optimization algorithm. Adv. Eng. Softw. 95 , 51–67 (2016).

Mirjalili, S. et al. Salp Swarm Algorithm: A bioinspired optimizer for engineering design problems. Adv. Eng. Softw. 114 , 163–191 (2017).

Mirjalili, S. Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowl. Based Syst. 89 , 228–249 (2015).

Truby, R. L. & Lewis, J. A. Printing soft matter in three dimensions. Nature 540 (7633), 371–378 (2016).

Ozbolat, I. T. & Hospodiuk, M. Current advances and future perspectives in extrusion-based bioprinting. Biomater. 76 , 321–343 (2016).

Mannoor, M. S. et al. 3D printed bionic ears. Nano Lett. 13 (6), 2634–2639 (2013).

Article   ADS   PubMed   PubMed Central   CAS   Google Scholar  

Kennedy, E. B. & Marting, T. A. Biomimicry: Streamlining the front end of innovation for environmentally sustainable products. Res. Technol. Manage. 59 (4), 40–48 (2016).

Chazhaev, M. Economic potential of biotechnologies: challenges and windows of opportunity. BIO Web Conf. 76 , 10002 (2023).

Kasatova, A. A., Vagizova, V. I. & Tufetulov, A. M. Stages of biotechnology commercialization in the system of interaction between financial and innovative industrial structures. Acad. Strateg. Manag. J. 15 , 191 (2016).

Rovalo, E., McCardle, J., Smith, E. & Hooker, G. Growing the practice of biomimicry: Opportunities for mission-based organisations based on a global survey of practitioners. Technol. Anal. Strateg. Manag. 32 (1), 71–87 (2020).

Broeckhoven, C. & Winters, S. Biomimethics: a critical perspective on the ethical implications of biomimetics in technological innovation. Bioinspir. Biomim. 18 (5), 053001 (2023).

Article   ADS   Google Scholar  

Biomimicry Innovation Lab. The State of Nature-Inspired Innovation in the UK. Retrieved from https://www.biomimicryinnovationlab.com/blog/the-state-of-nature-inspired-innovation-in-the-uk (2023).

Weerasinghe, D. U., Perera, S. & Dissanayake, D. Application of biomimicry for sustainable functionalization of textiles: Review of current status and prospectus. Textile Res. J. 89 (22), 4282–4294 (2019).

Madmar, S., Shah, M. Z., Matusin, A. M. R. A. & Ilhan, A. A. Applications of biomimicry to urban planning: interrogating the relevance of emerging approaches to design cities by inspiring from nature. IOP Conf. Ser.: Earth Environ. Sci. 1274 (1), 012015 (2023).

Letrud, K. & Hernes, S. Affirmative citation bias in scientific myth debunking: A three-in-one case study. PLoS One 14 (9), e0222213 (2019).

McCain, K. W. Obliteration by incorporation. Beyond bibliometrics: Harnessing multidimensional indicators of scholarly impact. J. Scientometr. Res. 4 (1), 129–149 (2014).

Benyus, J. M. (1997). Biomimicry: Innovation inspired by nature.

Donthu, N., Kumar, S., Pandey, N., Pandey, N., & Mishra, A. (2021). Mapping the electronic word-of-mouth (eWOM) research: A systematic review and bibliometric analysis. J. Bus. Res. , 135 , 758–773.

Sydney Gladman, A., Matsumoto, E. A., Nuzzo, R. G., Mahadevan, L., & Lewis, J. A. (2016). Biomimetic 4D printing. Nature materials , 15 (4), 413–418.

Lurie-Luke, E. Product and technology innovation: What can biomimicry inspire?. Biotechnology advances , 32 (8), 1494–1505 (2014).

Download references

This research received no specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and affiliations.

Amrita School of Business, Amritapuri, Amrita Vishwa Vidyapeetham, Kollam, Kerala, India

Raghu Raman

Amrita School of Business, Amrita Vishwa Vidyapeetham, Coimbatore, Tamil Nadu, India

Aswathy Sreenivasan & M. Suresh

Amrita School of Computing, Amritapuri, Amrita Vishwa Vidyapeetham, Kollam, Kerala, India

Prema Nedungadi

You can also search for this author in PubMed   Google Scholar

Contributions

R.R.—Conceptualization; supervision; methodology; data curation; visualization; writing—original draft; and writing—review and editing. A.S.—Data curation; Writing—original draft; and Writing—review and editing. M.S.—writing—original draft; and writing—review and editing. P.N.—Data curation; writing—original draft; and writing—review and editing.

Corresponding author

Correspondence to Raghu Raman .

Ethics declarations

Competing interests.

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ .

Reprints and permissions

About this article

Cite this article.

Raman, R., Sreenivasan, A., Suresh, M. et al. Mapping biomimicry research to sustainable development goals. Sci Rep 14 , 18613 (2024). https://doi.org/10.1038/s41598-024-69230-9

Download citation

Received : 28 April 2024

Accepted : 01 August 2024

Published : 10 August 2024

DOI : https://doi.org/10.1038/s41598-024-69230-9

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Sustainable development goal
• Network map
• Thematic areas

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

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

Sign up for the Nature Briefing: Anthropocene newsletter — what matters in anthropocene research, free to your inbox weekly.