Overall Scope and Collection Characteristics:
- Timespan (2021-2025): This indicates a relatively recent collection, spanning five years. This is crucial as interpretations need to consider the current state of research within this timeframe.
- Sources (12): The research draws from only 12 sources (Journals, books etc). The results of the analysis are based on a relatively small number of sources. Further, a deeper dive into the specific sources is needed. What are the 12 sources? Are these high-impact journals in the field? Are they representing a broad perspective, or a niche area? The limited number of sources suggests potential limitations in generalizability.
- Documents (13): A small number of documents (13) limits the statistical power of any conclusions you might draw. The small sample size means any patterns you observe might be more susceptible to being influenced by outliers or specific biases within those 13 publications.
- Annual Growth Rate (62.66%): A high annual growth rate suggests a rapidly evolving research area. It could reflect increasing interest, funding, or breakthroughs in the field during the period. However, with such a small initial number of documents, this percentage can be easily skewed by just a few additional publications each year. Investigate the cause: Is this growth due to a genuine surge in research, or simply because the research area is new to the database?
- Document Average Age (0.769 years): This very low average age confirms the recent nature of the research. It also suggests that the area is at the forefront of current scholarly activity.
- References (623): The number of references provides context regarding the knowledge base upon which these documents are built. The average references per document is 47.9, indicating that each document is fairly well grounded in existing literature.
Impact and Influence:
- Average Citations per Document (13.38): This is a key indicator of the impact of the documents within the collection. A value of 13.38 citations per document suggests that, on average, the publications in this collection are being noticed and used by other researchers. However, you need to consider the field. Some fields naturally have higher citation rates than others. Compare this number to the average citation rate in similar fields within SCOPUS to determine if 13.38 is high, average, or low. Citation counts take time to accumulate. Since the documents are relatively recent, this number might increase significantly over the next few years.
Content and Keywords:
- Keywords Plus (ID: 93): Keywords Plus are terms automatically generated by databases like SCOPUS based on the cited references. This gives you insight into the *broader* context and related topics associated with the research in your collection. A high number of Keywords Plus compared to Author’s Keywords suggests the research touches upon many areas outside of what the authors explicitly identify.
- Author’s Keywords (DE: 46): These are the terms authors themselves assign to their publications. This shows the *specific* focus of the research. Comparing the number of Author’s Keywords to Keywords Plus helps understand the focus of the study. Do the author keywords align with the automatically generated keywords? If there is a large difference, it might suggest that the authors’ framing of their work is different from how the database (and potentially other researchers) perceive it.
Authors and Collaboration:
- Authors (50): With only 13 documents and 50 authors, there’s an average of roughly 3.85 authors per document. This could suggest high degree of collaboration, but it could also suggest few prolific researchers.
- Authors of Single-Authored Docs (2): This indicates some individual research efforts within the collection.
- Single-Authored Docs (2): With only 2 single-authored documents out of 13, collaborative research seems to be the norm in this area.
- Co-Authors per Doc (3.85): Reinforces the collaborative nature suggested by the high number of authors.
- International Co-authorships % (23.08): This indicates a moderate level of international collaboration. It suggests the research field benefits from global perspectives and expertise, but it’s not overwhelmingly international.
Document Types:
- Article: 3, Book Chapter: 2, Conference Paper: 6, Review: 2: The distribution of document types provides insights into the communication channels prevalent in this field. The presence of conference papers suggests that conferences are important venues for disseminating research. The presence of review papers suggests a need for consolidating and synthesizing existing knowledge.
Critical Discussion Points & Next Steps:
1. Small Sample Size: The most significant limitation is the small number of documents. Any conclusions drawn from this analysis should be treated with caution and framed as preliminary observations. You should explicitly acknowledge the limitations of the sample size in any report or presentation.
2. Source Homogeneity: With only 12 sources, assess whether the collection provides a representative view of the field or is biased toward specific perspectives or journals.
3. Citation Analysis: Compare the average citation rate to the average citation rate for documents of similar age and document type (articles, reviews, etc.) within the same field in SCOPUS. This will give you a better sense of the relative impact.
4. Qualitative Review: Conduct a qualitative review of the 13 documents to understand the research questions, methodologies, and key findings. This will provide a deeper understanding of the research area and help you contextualize the quantitative findings.
5. Keyword Analysis: Perform a more detailed analysis of the author keywords and Keywords Plus. Identify the most frequent terms and explore the relationships between them. This can reveal the key themes and emerging trends in the field.
6. Collaboration Network: Visualize the co-authorship network to identify the most influential researchers and research groups.
7. Longitudinal Analysis: If possible, expand the timespan of the analysis to include earlier years. This would provide a longer-term perspective on the evolution of the research area.
By addressing these points, you can move beyond simple descriptive statistics and develop a more nuanced and insightful interpretation of your bibliometric data. Remember to always consider the context of the data and the limitations of the analysis. Good luck!
Annual Scientific Production
Average Citations Per Year
Three-Field Plot
Overall Structure and Purpose
This plot is designed to visualize the relationships between three key elements within your SCOPUS dataset:
- AU (Authors): This is your target field, placed centrally. It represents the authors who have published in the selected collection.
- CR (Cited References): Placed on the left, this shows the references that are cited by the publications in your dataset.
- KW\_Merged (Merged Keywords): On the right, these are the keywords associated with the publications. This likely represents a combination of author-supplied keywords and indexer-assigned keywords.
The lines connecting the three fields illustrate the connections between authors, the references they cite, and the keywords that describe their work. The thicker the line, the stronger the connection between those elements.
Key Observations and Interpretation
1. Prominent Authors: The authors listed in the middle (AU field) are central figures in the research area captured by your dataset. The more connections an author has to both cited references and keywords, the more influential or central they are within this network.
2. Key Cited References: The CR field shows which publications are most frequently cited by the authors in your dataset. This indicates foundational works, influential studies, or significant contributions to the field. The connections to authors show who is actively building upon those prior works.
3. Dominant Keywords: The KW\_Merged field reveals the main themes, topics, and concepts within your dataset. The keywords with the most connections are the most prevalent in the research represented.
4. Topic Clusters and Research Areas: By examining the connections between authors, cited references, and keywords, you can identify clusters of research activity. For example:
* Authors citing specific foundational papers (CR) and using particular keywords (KW\_Merged) are likely working within the same research area.
* Groups of authors citing similar papers and using similar keywords might represent distinct schools of thought or approaches to a particular problem.
Specific Examples and Potential Insights (based on the visible portion of the plot):
- Digital Product Passport and Circular Economy: There’s a strong theme around “digital product passport” and “circular economy,” as evidenced by their presence in both the AU and KW\_Merged fields. Several authors are directly connected to these keywords, and certain cited references seem to be pivotal in this area.
- Identifying Influential References: References related to “digital product passport” such as “adisorn t. tholen I. gotz t. towards a digital product passport” or “plociennik c. et al. towards a digital lifecycle passport for the” appear to be influential and have many connections with the other fields.
- Author Specializations: You could look at individual authors (e.g., “duarte n”, “janßen j”) and see which cited references they draw upon and which keywords they use. This would reveal their specific focus within the broader field. For example, based on their connection to “digital product passport”, we could say that “duarte n” and “janßen j” specialize in that area.
- Emerging Trends: Look for keywords that are strongly connected to authors but have fewer connections to older cited references. This could indicate newly emerging areas of research or novel applications of existing concepts. The artificial intelligence keyword would be an example of an emerging trend.
Critical Discussion and Further Steps
- Data Cleaning: Consider the KW\_Merged field. Are there variations of the same keyword (e.g., “circular economy” vs. “circular economies”)? Cleaning and standardizing keywords can improve the accuracy of the visualization.
- Thresholds and Filtering: Experiment with different thresholds for the connections (lines) in the plot. You can filter out weaker connections to focus on the most significant relationships.
- Database Bias: Remember that this analysis is based on SCOPUS data. Results might differ if you used Web of Science or another database.
- Interpret with Domain Expertise: This bibliometric analysis provides a valuable overview, but it’s crucial to interpret the results in light of your own knowledge of the field. Are there any surprises? Do the connections make sense based on your understanding of the research landscape?
In summary, this Three-Field Plot offers a rich, interconnected view of the research landscape. By carefully analyzing the relationships between authors, cited references, and keywords, you can gain valuable insights into the key themes, influential works, and emerging trends within your area of study.
Most Relevant Sources
Sources’ Production over Time
*IFIP Advances in ICT
Most Relevant Authors
Authors’ Production over Time
Authors’ Local Impact
Most Relevant Affiliations
Affiliations’ Production over Time
Countries’ Scientific Production
Countries’ Production over Time
Most Cited Countries
Most Global Cited Documents
Most Local Cited Documents
Most Local Cited References
Reference Spectroscopy
Overall Interpretation:
The RPYS plot reveals the historical roots and evolution of the research area being analyzed. It highlights key publication years that have had a disproportionately large impact on subsequent research. The black line indicates the overall trend in cited references across time, while the red line pinpoints years where the citation frequency significantly deviated from the preceding 5-year median, suggesting seminal work or paradigm shifts.
Key Observations and Discussion Points:
- Early Foundations (Pre-1990): The plot indicates a relatively low but consistent citation rate from the 1970s through the late 1980s, with no major peaks. This could suggest the area was nascent during this period or that foundational works were established but not heavily cited until later developments occurred.
- 1990: Economics of Natural Resources and the Environment: The first identified peak occurs in 1990. The cited reference, “Economics of Natural Resources and the Environment” by Pearce and Turner, suggests the integration of economic principles into environmental considerations was a significant influence. This aligns with the growing awareness of sustainability challenges and the need to incorporate economic valuation into environmental decision-making during that period. *Researchers should investigate how this work shaped subsequent policy analysis and economic modeling in the field.*
- 2000: Industrial Symbiosis and Material Flow Analysis: A double peak appears in 2000. The highlighted references, including Chertow’s work on industrial symbiosis and Schmidt-Bleek’s work, indicate a growing interest in industrial ecology, cleaner production, and resource efficiency. Industrial Symbiosis being a key piece of literature in that time that discusses the network of companies from different sectors exchanging materials, water, energy and by-products. *This suggests a shift towards a more systemic and circular view of industrial processes. Subsequent research should explore how these concepts were operationalized and the challenges faced in their implementation.*
- 2002: Biomimicry and Cradle-to-Cradle Design: The peaks in 2002, featuring works like Benyus’s “Biomimicry” and McDonough and Braungart’s “Cradle to Cradle,” signal a further evolution toward more holistic and regenerative approaches. Biomimicry emphasizes learning from nature’s designs, while Cradle to Cradle promotes closed-loop systems that eliminate waste. *Researchers might investigate how these design philosophies have influenced product development and innovation in the field, and how they connect with broader circular economy principles.*
- 2015: Emergence of the “Circular Economy” Concept: 2015 shows a huge peak with multiple references focused on the Circular Economy, including the first circular economy action plan by the EU and the Ellen MacArthur Foundation report. This signifies a pivotal year when the concept of a circular economy gained widespread attention and became a central framework for sustainable development. *It would be valuable to examine how the different papers from 2015 defined and conceptualized the circular economy, and how these definitions have evolved over time.* Furthermore, many documents in 2015 suggest that there was a wide variety of documents being created around the circular economy, therefore a possible conclusion is that this was a year of policy/strategy and literature emergence and definition.*
- 2020: Digitalization, Circular Economy and Industry 4.0 Converge: This peak strongly represents the convergence of digital technologies, the circular economy, and Industry 4.0 concepts. Documents such as the “A New Circular Economy Action Plan” showcase increased policy focus, while a plethora of publications show an explosion of research on related topics like digital twins, digital product passports, and digital-enabled circular strategies. *It would be worth investigating how these different technological developments were implemented in real-world circular economy initiatives, and what are the data governance challenges and opportunities.*
- 2022: Continuation of “Circular Economy” and Industry 4.0 Themes: The peak in 2022 indicates a continued emphasis on the circular economy, digital technologies, and new policy implementations across the field. It is expected that as the digital transformation continues, research will explore advanced uses cases of digital tools for sustainable practices.
Critical Discussion Points:
- Data Source Bias: The analysis relies on the SCOPUS database. Consider whether this database provides comprehensive coverage of all relevant literature, including publications in languages other than English or grey literature (reports, working papers, etc.). *Explore how results might differ with other databases (Web of Science, etc.) and how this might shape interpretation.*
- Citation Frequency vs. Actual Impact: While citation frequency provides a measure of influence, it doesn’t necessarily reflect the true impact or quality of a publication. Some highly cited works may be controversial or have been subject to criticism. *Researchers should critically evaluate the content of these highly cited works and consider alternative measures of impact, such as policy influence, practical applications, or qualitative assessments.*
- Field Specificity: This plot is specifically tied to the data input into it, therefore it is important to consider how the results and observations may differ if this was another, or sub, field of research. The keywords and parameters used to get the list of documents from SCOPUS should be provided, and one should be cautious about the inferences that may be performed from this RPYS plot.
- Temporal Dynamics: The RPYS plot shows how the field has evolved over time. It is important to consider the broader societal, economic, and technological contexts that may have influenced these trends. *Researchers might explore how policy changes, technological breakthroughs, or shifts in public opinion have affected the research agenda and the adoption of specific concepts or practices.*
- Future Research Directions: Based on the observed trends, what are the most promising avenues for future research? Are there emerging topics or areas where further investigation is needed? *Consider how the field can build upon its historical foundations to address current and future sustainability challenges.*
By considering these points, researchers can develop a more nuanced and insightful understanding of the historical development and current state of the field, and inform future research directions.
Most Frequent Words
WordCloud
TreeMap
Words’ Frequency over Time
Trend Topics
Overall Interpretation:
The plot shows the trend of four key topics (“digital product passport”, “digital products”, “circular economy”, and “business models”) based on their frequency in the SCOPUS database over time (from 2023 to 2025). The size of the bubbles indicates the prominence of each topic in a given year. The lines represent the interquartile range and show the spread of the frequency for each topic in a year, while the mid-point of that line shows the median frequency.
Specific Observations:
- “Digital product passport” and “digital products”: Both these topics appear to have gained significant traction in 2025. The large bubble size suggests a high frequency within the SCOPUS data, indicating increasing research or discussion around these themes.
- “Circular Economy”: This topic shows a presence in both 2023 and 2025, with the 2025 having lower frequency and a much lower spread.
- “Business Models”: This topic has a low median frequency in 2023, and it appears not relevant in 2025, suggesting that this keyword does not appear with the highest median frequency in that year.
Possible Research Implications and Discussion Points:
1. Emergence of “Digital Product Passport”:
* The strong emergence of “digital product passport” in 2025 suggests a growing interest in traceability, transparency, and data sharing related to products. This is potentially linked to:
* Increased regulatory pressure for product information (e.g., sustainability standards, safety regulations).
* Consumer demand for more information about product origin, composition, and environmental impact.
* Technological advancements enabling easier data capture and sharing throughout the product lifecycle.
* *Discussion Point:* Is the rise of “digital product passport” driven by policy, technology, or consumer behavior? What are the main research areas within this topic (e.g., data standards, security, implementation challenges)?
2. Continued Relevance of “Digital Products”:
* The persistence of “digital products” as a trend topic indicates the ongoing importance of research in this area. This could encompass a wide range of themes:
* Digital product design and development.
* Digital product marketing and sales.
* The economics of digital products.
* The impact of digital products on society.
* *Discussion Point:* How has the focus of research on “digital products” evolved over this period? What are the emerging challenges and opportunities in the digital product space?
3. Circular Economy Transition:
* The presence of “circular economy” suggests a continued focus on sustainability.
* *Discussion Point:* How does the research around “circular economy” intersect with the topics of “digital product passport” and “digital products”? Is technology playing a key role in enabling circular economy models?
4. Database and Keyword Considerations:
* This analysis is based solely on the SCOPUS database. It’s important to remember that the trends might look different if a different database (e.g., Web of Science, Dimensions) were used. SCOPUS has a bias toward certain disciplines and publication types.
* The keyword selection (“KW_Merged”) is critical. The specific keywords used in your search will directly influence the topics that appear in the trend analysis. Consider the search string used to generate the KW_Merged field.
Critical Questions to Consider:
- Causation vs. Correlation: The plot shows trends, but it doesn’t establish causality. Increased frequency of a term doesn’t necessarily mean it’s *more important* in a real-world sense.
- Contextual Factors: What major events or developments in the relevant industries or academic fields might explain the observed trends?
- Limitations of Bibliometric Analysis: Bibliometric analysis provides a quantitative overview, but it needs to be complemented with qualitative analysis (e.g., reading key papers) to gain a deeper understanding of the topics.
By considering these points, you can move beyond a simple description of the trends to a more nuanced and insightful interpretation of your bibliometric analysis. Remember that this is just a starting point, and further investigation is needed to validate and expand upon these observations.
Clustering by Coupling
Co-occurrence Network
Overall Structure
The network appears to be relatively sparse, with a few key nodes and clusters rather than a densely interconnected web. This suggests that while there are connections between the terms, certain concepts are more central and frequently co-occur than others. The use of the Walktrap community detection algorithm has resulted in three distinct communities based on keyword co-occurrences:
- Community 1 (Red): Appears to be centered around “Digital Product Passport”, “Digital Products”, “Business Models”, “Digital Technologies”, and “Twin”. This community highlights the intersection of digital technologies, business models, and the emerging concept of digital product passports.
- Community 2 (Blue): Centered around “Circular Economy” and “Sustainability”. This clearly represents the sustainability and circularity domain within the dataset.
- Community 3 (Green): Contains “Supply Chains.” This likely indicates research focusing on the operational and logistical aspects of circular economy and sustainable practices.
Community-Specific Analysis
- Digital Product Passport & Business Models (Red): The dominance of “Digital Product Passport” and “Digital Products” within this cluster suggests a significant research focus on this relatively new concept. The strong association with “Business Models” and “Digital Technologies” implies that research is exploring how these passports can be implemented and create value within a digital context. The link to “Twin” might reflect the use of Digital Twin technology in managing product information and lifecycles. The connection to “Sustainable Development” further underscores the role of digital product passports in promoting sustainability and tracking the environmental footprint of products. This seems to be the most prominent and focused research area within the dataset.
- Circular Economy & Sustainability (Blue): The close connection between “Circular Economy” and “Sustainability” is expected, as circular economy is often viewed as a key strategy for achieving sustainability goals. This cluster represents the broader context within which digital product passports and related technologies are being developed and deployed. The relative size of “Circular Economy” suggests that this concept is more prominent in the dataset than “Sustainability” alone. The presence of “.gs” next to circular economy is probably a typo due to the OCR.
- Supply Chains (Green): The isolated nature of “Supply Chains” as a community might suggest that while supply chains are relevant to the other topics, they are often studied in a more separate context within this dataset. The link to “Circular Economy” would indicate studies that specifically examine the role of supply chain management in enabling circular economy principles.
Most Connected Terms
The node size is proportional to the frequency and strength of co-occurrence with other terms. The most connected terms appear to be:
1. Digital Product Passport: Appears to be the most central and frequently co-occurring term, indicating its importance in the research landscape captured by the dataset.
2. Digital Products: Is in second most position in the network, further underpinning the focus of the papers analysed.
3. Business Models: The strong connection of “Business Models” indicates that research is actively exploring the economic and organizational implications of digital product passports and circular economy.
4. Circular Economy: Represents a core concept that is linked to both the technology/business side (digital product passports) and the broader sustainability context.
Interpretation and Discussion Points
- Emerging Research Area: The prominence of “Digital Product Passport” indicates that this is a rapidly developing research area.
- Technology-Driven Sustainability: The network highlights the increasing importance of digital technologies in promoting sustainability and circular economy.
- Interdisciplinary Nature: The connections between the different communities demonstrate the interdisciplinary nature of the field, requiring expertise in technology, business, and environmental science.
- Potential Research Gaps: The relatively weak connections between “Supply Chains” and the other clusters might indicate a need for more research on how digital product passports can be integrated into supply chain management practices to improve traceability and circularity.
Critical Considerations
- Database Bias: Remember that this analysis is based on SCOPUS data. The results might be different if a different bibliographic database (e.g., Web of Science) were used.
- Keyword Selection: The analysis is limited to the keywords assigned to the publications. The choice of keywords can influence the co-occurrence patterns.
- Normalization Method: The use of “association” as the normalization method influences the results. Other normalization methods might highlight different relationships.
- Parameter Choice: The clustering algorithm and other parameters (e.g., `community.repulsion`) influence the detected community structure.
By critically evaluating these aspects, researchers can gain a deeper understanding of the research landscape and identify potential areas for future investigation. This analysis suggests a strong and growing interest in the application of digital technologies, particularly digital product passports, to enable more sustainable and circular business models.
Thematic Map
Understanding Strategic Maps
Strategic maps, in the context of bibliometric analysis, are visual representations of the relationships between different research areas (represented here by clusters of keywords). They are typically divided into four quadrants:
- Motor Themes (Upper Right): High centrality and high density. These themes are both well-developed and important to the field. They drive the research area.
- Niche Themes (Upper Left): Low centrality and high density. These themes are well-developed but less connected to the overall field. They might represent specialized or emerging areas.
- Emerging or Declining Themes (Lower Left): Low centrality and low density. These themes are underdeveloped and not central. They might be fading areas or newly emerging ones that haven’t yet gained traction.
- Basic Themes (Lower Right): High centrality and low density. These themes are fundamental to the field but may not be actively developing. They represent the foundational knowledge.
Analysis of the Provided Map
Based on the strategic map and the cluster information, here’s a breakdown:
1. Cluster Positions and General Interpretation:
* Digital Product Passport/Circular Economy/Digital Products (Lower Right – Basic Theme): This cluster sits in the “Basic Theme” quadrant. This suggests that “digital product passport,” “circular economy,” and “digital products” are central to the field of study (high relevance/centrality) but might not be rapidly evolving or densely interconnected with other areas (low development/density). This means they are a foundational area.
* Circular Ecosystem/Data Sharing (Upper Right – Motor Theme): Located in the “Motor Theme” quadrant, this cluster indicates a highly important and well-developed area. “Circular ecosystem” and “data sharing” are both central to the field and have strong connections within the research landscape. This implies active research and significant impact.
* Digital Twin/Sustainable Development (Upper Left – Niche Theme): Situated in the “Niche Theme” quadrant, this cluster suggests a well-developed but perhaps less central area. “Digital Twin” and “Sustainable Development” are densely interconnected but might not be as strongly linked to the broader research field represented in the analysis. It’s a focused area of research.
* Supply Chains (Lower Left – Emerging or Declining Theme): This cluster falls into the “Emerging or Declining Themes” quadrant. “Supply Chains” may be a less central and less developed area within the context of this specific keyword analysis. It could represent a declining area of interest or, potentially, a nascent area that hasn’t yet fully developed.
* Textile Industry (Lower Left – Emerging or Declining Theme): Similarly to “Supply Chains,” “Textile Industry” appears in the “Emerging or Declining Themes” quadrant, suggesting it’s not a major focus or highly developed area in this keyword network.
2. Detailed Cluster Analysis Based on Central Articles and Pagerank:
* Digital Product Passport/Circular Economy/Digital Products:
* The presence of three articles related to “Digital Product Passport” suggests this is the core concept driving this cluster. The articles by Walden (2021), Spiß (2024), and Ventura (2025) all focus on this topic, indicating a relatively recent and sustained interest.
* The pageranks (0.205, 0.17, 0.144) are comparatively higher than those in other clusters (except one), implying a stronger influence of these “Digital Product Passport” articles within the overall network. This high relevance corroborates its position as a Basic Theme.
* *Considerations:* Why is this a Basic Theme rather than a Motor Theme? Perhaps the *application* of digital product passports in specific contexts (like circular economy) is still developing, even though the concept itself is well-established.
* Circular Ecosystem/Data Sharing:
* The article by Stiksma (2025) on “circular ecosystem” in *Lecture Notes in Computer Science* suggests this cluster is related to the computational or information aspects of circular ecosystems.
* The lower pagerank (0.109) *relative to the Digital Product Passport cluster* might indicate that while this is a “Motor Theme,” its individual components have slightly less overall influence than the core DPP concept, or it represents a smaller number of documents focusing on this intersection.
* *Considerations:* Investigate the specific focus of the Stiksma paper. Is it about data sharing *within* circular ecosystems? How is data sharing enabling circularity?
* Digital Twin/Sustainable Development:
* The article by Werner (2025) in the *International Journal of Production Research* suggests a focus on the application of digital twins in production or manufacturing contexts related to sustainable development.
* The pagerank (0.144) indicates a moderate level of influence. Its position as a Niche Theme suggests a specialized application of these concepts.
* *Considerations:* Explore how digital twins are being used to *achieve* sustainable development. Are they primarily focused on resource optimization, process improvement, or something else?
* Supply Chains:
* The article by Steinwender (2024) in *IFAC-PapersOnline* indicates a focus on control, automation, or systems engineering aspects of supply chains.
* The low pagerank (0.095) confirms its lower overall relevance in the network.
* *Considerations:* Why is “supply chains” appearing as an emerging or declining theme? Is it because the analysis focuses on a specific niche where supply chain research isn’t central (e.g., circular economy applications)? It might be useful to compare this result to a broader bibliometric analysis of supply chain research.
* Textile Industry:
* *Considerations*: This is too general. More context is needed to know what this refers to. I would have expected this to have a higher centrality based on the other clusters.
3. Parameters and Data Source Considerations:
* SCOPUS: The fact that the data comes from SCOPUS is important. SCOPUS is a broad database, but it does have biases.
* KW\_Merged: The analysis uses merged keywords. This is a good approach for capturing related concepts, but it also means that the clusters represent broader themes rather than very specific keywords.
* n=250, minfreq=2, ngrams=1, stemming=FALSE: These parameters control the keyword selection and analysis.
* *n=250* means the top 250 keywords were used.
* *minfreq=2* means keywords had to appear at least twice to be included.
* *ngrams=1* means only single words were considered as keywords (not phrases).
* *stemming=FALSE* means keywords were not stemmed (e.g., “running” and “run” would be treated as different keywords). This could impact the results, as related forms of the same word might be counted separately.
* community.repulsion=0, repel=FALSE: These parameters relate to the network layout. Setting *community.repulsion=0* means there’s no forced separation between communities/clusters, potentially leading to overlap in the visualization if the clusters are related. *repel=FALSE* turns off the general node repulsion algorithm, which could make the visualization more compact but potentially harder to read if nodes overlap.
* cluster=walktrap: The *walktrap* algorithm is a community detection method based on random walks. It’s a good general-purpose algorithm, but different algorithms could produce slightly different cluster results.
Overall Interpretation and Potential Research Directions:
This strategic map paints a picture of a research landscape where “digital product passports” are a foundational element, particularly in relation to the actively developing area of “circular ecosystems” and “data sharing.” “Digital twins” in the context of “sustainable development” represent a specialized area. “Supply chains” and “textile industry” may be less central to this specific keyword network.
Further Research:
- Explore the relationships between the clusters more deeply: How are “digital product passports” specifically used to enable “circular ecosystems”? What role does “data sharing” play?
- Investigate the “Supply Chains” result: Why is it an emerging or declining theme in this context? Compare this to broader analyses of supply chain research.
- Analyze the content of the central articles: Read the articles by Stiksma, Walden, Spiß, Ventura and Werner to gain a deeper understanding of the specific research questions and findings in each cluster.
- Consider alternative clustering algorithms: Experiment with different community detection methods to see if the cluster structure changes significantly.
- Examine the temporal evolution: Analyze how these themes have changed over time. Is the “Digital Product Passport” cluster becoming more or less central? Is the “Supply Chains” cluster truly declining, or is it simply evolving into a different area?
By considering these points, you can move beyond a descriptive interpretation of the strategic map and develop a more critical and insightful understanding of the research landscape. Remember that bibliometric analysis is just one tool, and it’s important to combine it with your own domain expertise and a careful reading of the literature.
Factorial Analysis
Overall Structure:
The map visualizes the relationships between keywords based on their co-occurrence in the documents. The position of each term reflects its association with the dimensions (Dim 1 and Dim 2). The closer two terms are on the map, the more frequently they appear together in the analyzed documents. Dimensions 1 and 2 explain 21.27% and 16.43% of the total variance respectively, meaning that the map captures about 38% of the variance in keyword co-occurrence patterns.
Key Observations and Potential Interpretations:
- Dimension Interpretation:
* Dimension 1 (21.27%) seems to differentiate between a cluster related to “analog-to-digital” conversion, and supply chains, vs concepts related to ‘systemic approach’.
* Dimension 2 (16.43%) seems to separate “systemic approach” and “digital twin” from concepts like “analysis” and “case-studies”, potentially indicating a distinction between methodological approaches and application areas.
- Clusters and Relationships:
* Cluster 1 (Top Left): A cluster is formed by “analysis”, “case-studies”, and “sustainability”. This suggests research focusing on analyzing case studies to evaluate sustainability issues.
* Cluster 2 (Around Origin): “Decoupling,” “circular economy,” “business models,” and “digital products” are located near the origin. Their proximity suggests interconnected research involving circular economy principles, new business models, and the development of digital products, possibly related to sustainability. The proximity to the origin might also indicate that these keywords are more generally distributed across the corpus and not strongly associated with any particular dimension.
* Outlier – “Analog-to-Digital”: Located far on the right, “analog-to-digital” seems to be a distinct area of research within the collection. This suggests that a segment of the literature is focused on the conversion from analog to digital technologies.
* Outlier – “Systemic Approach”: Located far on the bottom left, this term is clearly separated from other concepts.
- Relevance of Terms:
* The terms further away from the origin are considered the most relevant.
* The spread of points across the map indicates that there is no single dominant research theme, but rather several distinct, though potentially related, areas of investigation.
Further Interpretation and Critical Discussion:
1. Database and Search Terms: The interpretation needs to consider the original search terms used to create the SCOPUS collection. Were there specific keywords or subject areas that might have biased the results?
2. Contextual Knowledge: Use your knowledge of the research field to interpret the meaning of the dimensions and clusters. Do the relationships between the terms make sense in the context of existing literature?
3. Limitations of MCA: Be aware that MCA is a descriptive technique. It reveals associations between keywords but does not imply causality. The interpretation of the dimensions is subjective and relies on the researcher’s understanding of the data.
4. Stemming: The analysis was performed with stemming `FALSE`. Running the analysis again with stemming = TRUE could lead to a different result.
5. Further Analysis:
* Investigate the documents associated with each cluster to understand the specific research questions being addressed.
* Examine the co-occurrence of keywords in more detail using other bibliometric techniques (e.g., co-occurrence network analysis).
In summary, this factorial map provides a valuable overview of the intellectual structure of your SCOPUS collection. By carefully interpreting the dimensions, clusters, and key terms, you can gain insights into the main research themes and their interrelationships. Remember to consider the limitations of the method and to use your domain expertise to provide a nuanced interpretation. Let me know if you want me to elaborate on any specific aspect or perform further analyses!
Co-citation Network
Overall Network Structure
- Sparse Network: The network is relatively sparse, meaning there aren’t many connections between the cited references. This suggests that while there are common citations, the overall field might be diverse or the documents in your collection are not highly interlinked in their referencing behavior. This might also reflect a focused search strategy, pulling together a specific subset of literature.
- Limited Community Structure: The Walktrap algorithm identified a few communities, indicated by the different colors (blue, red, and green). However, these communities are not very dense, and the connections between them (especially through the central cluster) are visible. This points to some degree of integration between these research streams but indicates that each has an independent nature.
Community Analysis
- Red Cluster: This cluster seems to be the most central and densely connected. It includes the references “adisorn t. 2021-1”, “langley d.j. 2023”, “rumetshofer t. 2024”, “jensen”, and “walden j. 2021”. The prominence of “adisorn t. 2021-1” suggests that it could be a foundational or highly influential work within the dataset. Considering it is linked to “langley d.j. 2023”, “rumetshofer t. 2024” and “walden j. 2021” the central research is possibly trending and highly actual.
- Green Cluster: This cluster is composed of “chauhan c. 2022” and “kristoffersen e. 2020”. The presence of a single edge between this cluster and the red cluster suggests that these references are related, but they represent a distinct area of research.
- Blue Cluster: This small cluster contains “plociennik c. 2022” and “g m.r.”. Being completely isolated from other clusters, this indicates a separate theme or research area.
Relevance of Most Connected Terms
- Centrality and Influence: The size of the nodes corresponds to their degree centrality (number of connections). “adisorn t. 2021-1” has a clear prominence, suggesting that this paper is a central reference point in the dataset. The high connectivity of “adisorn t. 2021-1” suggests that it either introduces a key methodology, synthesizes existing knowledge, or presents significant empirical findings that are built upon by other studies.
- Recent Research: The inclusion of recent publications (2021-2024) suggests the network is capturing the state of the field’s development.
Interpretation and Further Investigation
Based on this analysis, here are some possible interpretations and avenues for further exploration:
1. Research Focus: The dominance of “adisorn t. 2021-1” and its connection to other recent publications suggests the research is on a recent topic or methodology. This provides a starting point for deeper analysis. *Action:* Read “adisorn t. 2021-1” to understand its central contribution.
2. Community Themes: Each cluster represents a distinct sub-theme. *Action:* Examine the abstracts and keywords of the papers in each cluster to discern the specific research areas.
3. Bridging Research: The links between clusters indicate relationships. *Action:* Examine the papers that cite publications from different clusters to understand how the different research themes intersect.
4. Impact Analysis: It is not evident from the graph, what impact publications of certain communities have. *Action:* Examine a time-line plot showing node centrality of the different nodes of the network.
Critical Discussion Points
- Data Scope: Remember that the network’s structure is influenced by the scope of your SCOPUS search.
- Normalization: Be mindful that citation counts can be influenced by publication age. Newer papers may not have had as much time to accrue citations.
- Interpretation Bias: Avoid over-interpreting the community assignments. The Walktrap algorithm provides a heuristic for grouping, but the actual research landscape might be more complex.
In summary, this co-citation network provides a snapshot of the intellectual structure of the field represented by your SCOPUS data. You can gain deeper insights by closely examining the content of the key papers and understanding how the different communities relate to each other. You can iterate on this analysis by modifying network parameters (e.g., edge weights, clustering algorithms) or by focusing on specific subsets of the data.
Historiograph
Overall Interpretation:
This historiograph represents a citation network focused on the topic of Digital Product Passports (DPPs) and their role in the Circular Economy (CE). The network is relatively small, spanning from 2021 to 2025, suggesting that this is a developing area of research. The central position of Walden J (2021) indicates it as a foundational paper in this domain, with subsequent works building upon it. The size of the nodes suggests that they have very few citations, with the exception of the work by Monteiro I (2025) and Walden J (2021). The future articles that are built upon the foundation article are all published in the same year, indicating a high concentration of work around the topic in the year 2025.
Key Observations and Interpretations:
1. Main Citation Path and Pivotal Work:
* The primary citation path flows from Walden J (2021) to the other papers in the network. This is evident in the direct links radiating from Walden J’s paper to all other papers
* Walden J (2021): “Digital Product Passports As Enabler Of The Circular Economy” This work appears to be the cornerstone of this research area. It’s the earliest publication in the network and directly cited by all the subsequent publications. It likely establishes the core concepts of using DPPs for enabling CE.
2. Temporal Trends and Knowledge Development:
* Initial Focus (2021-2024): The research starts with a general overview of DPPs as enablers of CE (Walden, 2021). The subsequent works then begin to explore specific aspects: the transition from analogue to digital in a particular industry (Langley, 2023), and the value assessment of DPPs using a case study approach (Domskienė, 2024). This suggests that the field is initially focusing on understanding and applying the general concept of DPPs in practice.
* Expansion and Specialization (2025): In 2025, the research diversifies into several specialized areas:
* Singh (2025): Focuses on integrating DPPs into information systems for production planning and control, suggesting a move towards practical implementation within manufacturing.
* Ventura (2025): Examines the impact of CE policies and international standards, indicating a growing awareness of the regulatory and standardization landscape.
* Stiksma (2025): Explores the use of Digital Twins to facilitate circularity, indicating an interest in leveraging advanced technologies.
* Monteiro (2025): Investigates Industry 4.0 technologies for circularity in the footwear industry, pointing to sectoral applications.
* Overall Trend: The trend observed is a progression from foundational concepts to concrete applications, policy considerations, and integration with Industry 4.0 technologies. It suggests that the initial enthusiasm for DPPs is being tempered by the need to address practical challenges, regulatory issues, and technological integration.
3. Cluster Analysis & Topic Evolution:
* This network could be viewed as a single, tightly coupled cluster centered around the core concept of DPPs and the circular economy.
* The absence of distinct clusters means that the field hasn’t yet significantly diverged into separate sub-topics. However, the specializations emerging in 2025 (information systems, policy, digital twins, specific industries) might indicate the potential for future clustering.
Potential Research Questions & Directions (based on the analysis):
- Impact of Walden (2021): Conduct a deeper qualitative analysis of Walden (2021) to understand its specific contributions and how subsequent papers have built upon or challenged its findings.
- Barriers to Implementation: Given the focus on practical applications in 2025, investigate the barriers to implementing DPPs in different industries. Are there technological, economic, or organizational challenges that need to be addressed?
- Policy and Standardization: Explore the specific CE policies and international standards mentioned by Ventura (2025). How are these policies shaping the development and adoption of DPPs?
- Industry-Specific Applications: Investigate the nuances of applying DPPs in different industries (e.g., furniture, consumer electronics, footwear). What are the unique challenges and opportunities in each sector?
- Future Research: Based on the trends, what are the potential areas for future research? Could we expect to see more work on data security, interoperability, or the use of AI in managing DPP data?
Important Considerations:
- Database Scope: The analysis is limited to the SCOPUS database. Expanding the search to other databases (e.g., Web of Science, IEEE Xplore) might reveal additional relevant publications and a more comprehensive picture of the field.
- Citation Lag: Citation counts can lag behind publication dates. The 2025 publications may accumulate more citations over time, potentially changing the relative importance of different nodes.
- Co-citation Analysis: Consider conducting a co-citation analysis to identify clusters of papers that are frequently cited together, even if they don’t directly cite each other. This can reveal hidden relationships and intellectual communities within the field.
- Keyword Analysis: Perform a keyword analysis to identify the key concepts, themes, and research areas associated with DPPs.
By critically examining these aspects, you can develop a more nuanced understanding of the research landscape and identify potential areas for future investigation. Good luck!
Collaboration Network
Overall Structure and Interpretation:
The network visually represents collaboration patterns among authors within the Scopus collection. The graph is relatively sparse, suggesting that there isn’t a high degree of widespread collaboration across all authors in the dataset. Instead, we see distinct clusters or communities, indicating groups of authors who collaborate more frequently with each other than with authors outside of their group. The ‘normalize = association’ parameter emphasizes the strength of co-occurrence in publications, so links represent authors who frequently publish together. The Walktrap algorithm has identified these clusters (communities).
Community Detection (Clusters):
The Walktrap algorithm has identified several communities, each represented by a different color. Key observations:
- Distinct Communities: The visual separation between clusters suggests that these are relatively independent research groups or teams.
- Community Size: The different sizes of the clusters could reflect the size and activity of the research groups or the centrality of particular authors within the network. The size of nodes (authors) is determined by their betweenness centrality.
Key Authors and Potential Topics:
Focusing on the labelled authors within each community can provide insights into the research areas being addressed. Here is a breakdown based on observed author names:
- Cluster 1 (Brown): *Angelopoulos M* and *Hooijer C*. This cluster represents a single group of collaborators.
- Cluster 2 (Purple): *Janbenninger S.*.
- Cluster 3 (Teal): *Bachlechalund S*.
- Cluster 4 (Green): *Hammerschaper S*.
- Cluster 5 (Orange): *Rebelo R* and *Baptista AJ*.
- Cluster 6 (Blue): *Stiksma F* and *Moreira JLR*.
- Cluster 7 (Pink): *D’Adamo I*.
- Cluster 8 (Light Blue): *Marinkovic M* and *Walden J*.
- Cluster 9 (Salmon): *Silva B* and *Venturaarte N*.
- Cluster 10 (Gray): *Bressanelli G*.
- Cluster 11 (Red): *Domskiene J*.
In summary:
This network analysis provides a snapshot of the collaboration landscape within the Scopus collection. The distinct clusters highlight research communities working on related topics. To gain a deeper understanding, it would be beneficial to:
1. Investigate the publications of the most connected authors in each community. This would help to determine the specific research themes associated with each cluster.
2. Examine the relationships between communities (if any). Are there authors who act as bridges between different clusters?
3. Consider the time frame of the Scopus collection. Collaboration patterns may evolve over time.
4. Account for changes in community structure or emerging collaborations This type of diachronic analysis could reveal trends in research collaboration in the area.
