Main Information
Overall Impression: This appears to be a relatively small and recent collection of bibliographic data, spanning only from 2022 to 2025 and containing just 10 documents. However, the average citations per document is quite high, suggesting potentially impactful research despite the small size. The international collaboration rate is also significant. Let’s look at the specific metrics:
1. Scope and Timespan:
- Timespan: 2022:2025: This is a very short timespan. It indicates that the data collected is highly focused on recent publications. This is important to keep in mind when interpreting other metrics. Are we looking at an emerging field, or a field that is rapidly evolving?
- Sources (Journals, Books, etc): 8: The research is concentrated in a relatively small number of sources. This could suggest a narrow focus of the research area being analyzed, or it could indicate that the most important research in the field is published in a few key outlets. Consider *which* 8 sources these are – are they high-impact journals in the field? Knowing the specific journals would significantly improve the interpretation.
2. Productivity and Growth:
- Documents: 10: A very low number. It means that all interpretations need to be treated with caution, as statistical significance will be difficult to achieve. This small number could be due to a highly specific search strategy, a niche research area, or a limited timeframe.
- Annual Growth Rate %: -41.52: A negative growth rate is concerning. It suggests a decline in the number of publications within this collection over the short timeframe. This could be a real trend in the field (less research being conducted), or it could be an artifact of the data collection method (e.g., search terms became less relevant over time). It’s important to investigate *why* this growth rate is negative.
- Document Average Age: 2.2: This is very young, which is consistent with the 2022-2025 timespan. It means the analysis is heavily weighted towards recent publications.
3. Impact and Influence:
- Average citations per doc: 63.1: This is a surprisingly high number of citations *per document* for such a recent collection. This is a key finding! It could indicate that these documents are highly influential within their field, or that the field is one where citations accumulate very quickly. It’s essential to compare this citation rate to the average citation rate for similar documents (same document type, publication year, and field) within SCOPUS to understand its true significance. Without that comparison, it’s difficult to say how impressive this number really is.
4. Content and Focus:
- Keywords Plus (ID): 46; Author’s Keywords (DE): 38: The number of “Keywords Plus” is larger than the number of “Author’s Keywords”. Keywords Plus are terms automatically generated by the database, while Author’s Keywords are those chosen by the authors. The difference indicates that the database identifies more relevant terms than the authors provide and that the research area might be multidisciplinary and viewed from different perspectives.
- References: 777: Suggests a good level of scholarly grounding for the documents. A ratio of roughly 78 references per document seems typical for scientific publications.
5. Authors and Collaboration:
- Authors: 23: This indicates a moderate number of researchers contributing to these publications. Compared to the number of documents (10), this suggests some authors are involved in multiple publications within the collection.
- Authors of single-authored docs: 0; Single-authored docs: 0: This shows that all documents are the result of collaborative work.
- Co-Authors per Doc: 2.8: A co-author count near 3 suggests moderate collaboration intensity.
- International co-authorships %: 50: This is a very high percentage of international collaboration. It points towards a globalized research area, where researchers from different countries are actively working together.
6. Document Types:
- article: 5; conference review: 3; editorial: 2: The collection mainly comprises journal articles, with some conference reviews and editorials. This provides context for interpreting the citation metrics; different document types tend to have different citation patterns.
Critical Discussion Points and Further Investigation:
- Small Sample Size: The most critical point is the very small number of documents. Any conclusions drawn from these statistics should be treated as preliminary and require further investigation with a larger dataset.
- Citation Context: The high average citation rate is interesting, but needs to be benchmarked against the field. Are these citations concentrated in a few highly-cited papers, or are they distributed more evenly? A citation distribution analysis would be useful.
- Negative Growth Rate: Understanding the reason for the negative growth rate is crucial. Has the research focus shifted? Is there a decline in funding for this area? Is it an artefact of the data collection?
- Source Analysis: Identifying the specific sources (journals, books) would provide valuable context. Are they high-impact, specialized, or general-interest publications?
- Keyword Analysis: Examining the most frequent keywords (both Author’s Keywords and Keywords Plus) can reveal the main themes and research questions being addressed in this collection.
- Database Specifics: It is good to know that the data comes from SCOPUS. SCOPUS is a reputable database, but it’s important to be aware of its coverage and potential biases.
In summary, while the small sample size limits the generalizability of the findings, this preliminary analysis suggests a recent, globally collaborative, and potentially impactful research area experiencing a recent decline in publications. Further investigation is needed to confirm these trends and gain a deeper understanding of the field. You should expand the search, examine the leading sources and the key publications with more detailed metrics.
Annual Scientific Production
Average Citations Per Year
Three-Field Plot
Overall Structure and Key Observations:
- Central Node (AU – Authors): The authors are the central focus. The plot visualizes which authors are frequently cited alongside specific keywords and which references they cite.
- Left Node (CR – Cited References): This shows the foundational or influential works that the authors in the central node draw upon.
- Right Node (KW\_Merged – Keywords): This illustrates the main themes, concepts, and research areas associated with the authors in the center. Merged keywords indicate a combination of author-supplied keywords and/or index keywords.
Interpreting the Connections:
1. Author to Cited References (AU -> CR):
* The lines connecting authors to cited references reveal the intellectual roots of their work. A thick line suggests a strong reliance on a particular reference by that author or a group of authors.
* *Example:* If “parida v” has a strong connection to “chauhan c. parida v. dhir a. linking circular economy,” it indicates that parida v’s current work builds significantly on the prior “linking circular economy” research by Chauhan, Parida, and Dhir.
2. Author to Keywords (AU -> KW\_Merged):
* This shows the topical focus of each author’s work as reflected in the assigned keywords. This helps understand what themes the author is actively publishing on.
* *Example:* If “parida v” is strongly linked to “artificial intelligence,” it indicates that Parida’s research is focused on or related to artificial intelligence.
3. Overlap and Clustering:
* Look for authors who cite similar references or share similar keywords. This indicates potential collaborations, shared research interests, or schools of thought.
* *Example:* If several authors are linked to both the “circular economy” and specific cited references on business models, it indicates a community researching circular economy business models.
Specific elements in the graph:
- Parida V, a central figure in the graph, is linked to keywords like ‘artificial intelligence’ and references in the area of sustainable product service systems, which potentially underscores the connection between their work and topics related to AI.
- References to Circular economy and Product Service Systems (PSS) appear with certain authors, indicating those are significant topics.
- The presence of ‘big data’ and ‘internet of things’ as keywords reflects trends.
How to use this plot in your research:
- Identify Influential Authors: See who has many connections, suggesting broader impact in the field.
- Explore Research Areas: Identify clusters of authors and keywords to map out different sub-fields or research areas within your dataset.
- Trace Intellectual Lineages: Follow the links from authors to cited references to understand the evolution of ideas and the foundational works in the field.
- Find Potential Collaborators: Identify authors with shared interests (similar keywords, citing the same references).
- Spot Emerging Trends: Note frequently occurring keywords that might represent newer or rapidly developing areas of research.
- Evaluate Authors’ Contributions: Examine the spread of connections for each author. A wide range of connections indicates interdisciplinary work.
Critical Considerations:
- SCOPUS Bias: Remember this is based on SCOPUS data. Other databases may yield different results.
- Keyword Accuracy: Merged keywords are helpful but rely on the accuracy of indexing and author-provided terms.
- Citation Context: The plot doesn’t tell you *how* the references are being used (supportive, critical, etc.). Further investigation is needed.
- Data Cleaning: Ensure the data is clean (e.g., author name variations are resolved) for accurate results.
By systematically analyzing these connections, you can gain valuable insights into the structure, key themes, and intellectual landscape of the research area represented by your SCOPUS dataset. Good luck!
Most Relevant Sources
Core Sources by Bradford’s Law
Sources’ Local Impact
Sources’ Production over Time
Most Relevant Authors
Authors’ Production over Time
Overall Interpretation:
The plot visualizes the publication trends and impact of key authors in the field, sourced from the SCOPUS database. It displays their publishing activity and citation impact over time, allowing us to identify leaders, emerging researchers, and influential publications.
Key Elements to Consider:
- Author Timeline (Red Horizontal Line): Indicates the publishing career span of each author within the dataset.
- Bubble Size: Represents the number of articles published in a given year. Larger bubbles signify higher productivity in that specific year.
- Bubble Color Intensity: Represents the total citations per year (TC/year). Darker, more intense colors indicate a higher citation impact for publications in that year.
Individual Author Analysis:
- PARIDA V: This author shows a research timeline spanning from 2022 to 2024. The year 2022 seems to be their most productive and impactful year. This author has a highly cited paper with Chauhan C and Dhir A with TCpY of 112.8, this indicates a high impact in the scientific comunity.
- KOHTAMÄKI M: Similar to PARIDA V, this author also has a research timeline spanning from 2022 to 2024. It shows that 2025 also seems to be their most productive and impactful year.
- Other Authors: The remaining authors (ALP E, BLIUMSKA-DANKO K, BOUILLASS G, CHAUHAN C, DHIR A, HAN Y, HENNEBERG S, HERZOG M) have less data point, making it difficult to assess their trends.
General Observations and Potential Insights:
- Recency of Data: The data seems concentrated in the recent years (2022-2025), which might suggest a relatively new field or a recent surge in research activity within this specific area.
- Collaboration: The high citation count for a paper co-authored by multiple authors suggests that collaboration is important.
Critical Discussion Points:
- Database Limitations: Remember that this analysis is based solely on SCOPUS data. Results might differ if using other databases (Web of Science, Google Scholar).
- Citation Lag: Citation counts take time to accumulate. Publications from 2024/2025 might not yet have reached their full citation potential.
- Field-Specific Context: The interpretation should be contextualized within the specific research field. Citation patterns and publication rates vary across disciplines. Knowing the field (e.g., its size, the typical citation lifespan of articles) is crucial.
- Journal Impact: The impact of the journals in which the articles are published should also be considered. Publications in high-impact journals are more likely to be cited.
- Author Self-Citations: The number of citations might be inflated by author self-citations.
Next Steps for the Researcher:
1. Cross-validation: Compare these findings with data from other bibliographic databases.
2. Qualitative Review: Conduct a deeper qualitative analysis of the most highly cited articles to understand the reasons for their impact.
3. Network Analysis: Explore author co-citation networks to identify research clusters and influential groups.
4. Trend Analysis: Project future trends based on the observed patterns. Are certain authors continuing to rise in influence? Is the field becoming more collaborative?
By combining quantitative bibliometric data with qualitative insights and critical evaluation, you can develop a more comprehensive understanding of the research landscape and the contributions of key authors in your field.
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 References
Reference Spectroscopy
Overall Interpretation
The RPYS plot visualizes the historical roots of the research area under investigation. It shows which publications and years have had a disproportionately high impact on the current body of literature. The black line indicates the total number of citations to publications from a given year, while the red line highlights years where citation frequency significantly deviates from the median of the preceding five years, pinpointing landmark periods.
Key Observations and Possible Interpretations:
1. Early Foundational Works (1969-1981):
* The earliest peak in citations corresponds to works published between 1969 and 1981. This indicates foundational research that continues to be relevant.
* 1969 (Dalkey): “The Delphi Method” suggests the early importance of forecasting and expert opinion elicitation, possibly related to decision-making within organizations or technology development.
* 1973 (Small): The co-citation analysis paper signals the emergence of bibliometrics itself as a field and its application to understanding the structure of science.
* 1975 (Williamson): “Markets and Hierarchies” points to the enduring influence of transaction cost economics on organizational theory and strategic management.
* 1981 (Elphick and McCarthy): The inclusion of Beer’s work on the “Brain of the Firm” suggests an early interest in cybernetics and systems thinking for organizational control. McCarthy’s work on AI emphasizes the connection of philosophy with AI.
2. Mid-Period Influence (1988-1993):
* 1988 (Vandermerwe and Rada): “Servitization of Business” marks the rising importance of the service economy and the shift from product-centric to service-oriented business models. The repeated citation of the same work underscores its significance.
* 1993 (Moore): “Predators and Prey” indicates the influence of ecological thinking on competitive strategy and business ecosystems.
3. Modern Dominance (2010-2018):
* 2010 (Various authors): This year is a major turning point with a high frequency of citations. The high number of publications indicates a surge in interest in Business Model innovation. In particular, Osterwalder and Pigneur’s “Business Model Generation” is a dominant work in the field. In addition, publications by Teece and Casadesus-Masanell/Ricart highlight a focus on business models in the context of strategy and innovation. There is a focus on circular economies, business value, and network mapping.
* 2013 (Various authors): The high citation frequencies in 2013 demonstrate an established emphasis on Product-Service Systems and Sustainable Product-Service Systems. The inclusion of Langley et al.’s work on process studies highlights the importance of qualitative research methods in understanding organizational change.
* 2018 (Various authors): This year is a peak of citations indicating the confluence of several trends that have influenced this research area: Circular Economy, servitization, digitalization, Internet of Things (IoT), Industry 4.0, and Artificial Intelligence (AI). The citations span a wide range of topics. The quantity of research published shows the growing importance of digital economies.
4. Recent Trends (2021): Citations in the most recent years continue to emphasize servitization and sustainable products.
Areas for Critical Discussion and Further Research:
- The “Why” Behind the Peaks: Explore the specific events, technological advancements, or societal shifts that might explain the surge in interest and publications during the peak years. For example, what drove the focus on servitization in the late 1980s and early 1990s? Why the explosion of business model research in 2010?
- Evolution of Concepts: Trace how key concepts (e.g., business models, servitization, circular economy) have evolved over time. How have the definitions, applications, and theoretical underpinnings changed based on the publications from different eras?
- Interdisciplinary Influences: Identify the disciplines that have influenced this research area. The presence of works on artificial intelligence, ecology, and systems theory suggests a multidisciplinary nature. How have these different perspectives shaped the field?
- Methodological Shifts: Examine whether there have been changes in research methodologies over time. Has there been a move towards more quantitative or qualitative approaches? Has the rise of big data and machine learning influenced research methods?
- Geographical Focus: Analyze if there is a geographical concentration of research. Are certain countries or regions leading the way in this field?
- Limitations of the Data: Acknowledge any limitations of the data source (Scopus). Are there biases in the database coverage that might skew the results? Is Scopus fully representative of the field, or are there important publications in other databases?
- Future Directions: Based on the historical trends, what are the promising avenues for future research? What are the emerging challenges and opportunities in the field?
Using the Top Cited References:
The list of most cited references for each peak year provides a valuable starting point for deeper investigation. Analyze these publications to understand their key contributions, methodologies, and influence on subsequent research. Consider the following questions:
- What were the key insights or findings of these publications?
- How did they shape the research agenda in the field?
- How have they been challenged, extended, or refined by later work?
By critically examining the RPYS plot and the associated publications, you can gain a rich understanding of the historical development and intellectual foundations of your research area. This will not only inform your current research but also help you identify potential gaps and opportunities for future exploration.
Most Frequent Words
WordCloud
TreeMap
Words’ Frequency over Time
Clustering by Coupling
Co-occurrence Network
Overall Structure:
The network is relatively sparse, suggesting a focused set of research areas within the Scopus collection you analyzed. The graph displays distinct clusters, which is good for identifying key themes. The node sizes reflect the frequency of keyword appearance, and the edge thickness represents the strength of co-occurrence between keywords.
Community Detection (Topics):
The `walktrap` algorithm identified at least two distinct communities (represented by the colors red and blue). This points to separate, though possibly related, areas of focus within the dataset. Let’s examine each community:
- Red Cluster (Artificial Intelligence Focused): This community is dominated by “artificial intelligence” which forms a dense cluster with “circular business model”, “circular economy”, and “internet of things”. This suggest that there is research that considers artificial intelligence within business innovation and circular economy principles.
- Blue Cluster (Product-Service System Focused): This community appears to focus on “product-service system,” “product-service system (pss),” “business model innovation,” and “product-service systems”. This indicates a research interest in how product and services are integrated.
Most Connected Terms and Their Relevance:
- Artificial Intelligence: As the largest node, “artificial intelligence” is a central theme in your dataset. Its strong connections with other terms suggest that much of the research within your Scopus collection explores AI-related applications, implications, or theoretical underpinnings. The co-occurrence with “circular business model” and “circular economy” suggests studies that considers the application of AI to support sustainability transitions.
- Product-Service System: As the second largest node, “product-service system” is also a central theme in your dataset. The fact that it is connected to terms such as “product-service systems” and “product-service system (pss)” indicates a key topic within the collection. The term “business model innovation” suggests research explores how these systems require new business models.
Interpretation and Potential Research Questions:
This network highlights two main research streams within the Scopus data you provided: one centering on Artificial Intelligence and another focusing on Product-Service Systems.
Here are some questions you might consider based on this analysis:
- Intersection of AI and PSS: Is there research bridging the gap between these two main clusters? For example, are there studies investigating the use of AI to optimize or manage product-service systems?
- Evolution of Themes: Knowing the time frame of your data (which isn’t in the provided information but is crucial context), you could investigate if one theme is more recent than the other, suggesting a shift in research focus over time.
- Specificity of AI Applications: What specific applications of AI are being studied in relation to “circular business model,” “circular economy,” and “internet of things”?
- Business Model Implications of PSS: How are product-service systems reshaping business models? What are the main challenges and opportunities identified in the literature?
Limitations and Considerations:
- Keyword Selection: The analysis is based solely on keywords. Results might differ if you analyzed abstracts or full texts.
- Data Source: The analysis is limited to SCOPUS. Different databases may yield different results.
- Parameter Choices: The `association` normalization method affects how connections are weighted. Different normalization methods could reveal different relationships. The parameter `label.n = 50` limits the number of labels displayed, hiding potentially relevant keywords that may not be the most frequent.
- Context is Key: Remember that this is just a starting point. You need to combine this analysis with your domain knowledge to develop deeper insights.
By critically examining these aspects, you can move beyond a descriptive overview and formulate meaningful research questions, identify potential gaps in the literature, and ultimately contribute to the advancement of knowledge in your field.
Thematic Map
Understanding Strategic Maps
A strategic map, in the context of bibliometric analysis, is a visual representation of the relationships between different research themes within a field. It typically plots themes based on two key metrics:
- Centrality (Relevance Degree): This indicates the importance of a theme to the overall research field. Themes with high centrality are highly connected and influential within the network of research.
- Density (Development Degree): This represents the development or maturity of a theme. High density suggests that a theme is well-researched, with many internal connections and a solid body of literature.
The map is typically divided into four quadrants, each with a distinct strategic implication:
- Motor Themes (Upper Right): High centrality and high density. These are the driving forces of the field, well-developed and highly influential.
- Basic Themes (Lower Right): High centrality but low density. These are fundamental but underdeveloped areas. They are important but may require further exploration.
- Niche Themes (Upper Left): Low centrality but high density. These are specialized areas that are well-developed but have limited connections to the broader field.
- Emerging or Declining Themes (Lower Left): Low centrality and low density. These are either new areas that are just starting to gain attention or established areas that are losing relevance.
Interpretation of Your Strategic Map
Based on the provided information, your strategic map shows two clusters: “Artificial Intelligence” and “Digital Servitization.” Let’s analyze each:
* Artificial Intelligence (AI)
* Position: Located in the “Motor Themes” quadrant (or potentially near it, pending the exact position of the axes).
* Interpretation: This suggests that AI is a highly central and well-developed theme within your SCOPUS dataset. It’s a major driver of research in this area.
* Supporting Evidence: The high PageRank scores of the listed articles (Chauhan 2022, Han 2023, Pourranjbar 2023) confirm the centrality of this theme. PageRank is a measure of influence within a network, so high scores indicate these articles are heavily cited and connected to other research.
* Further Considerations: The association with “product-service system” and “product-service systems” suggests that AI is strongly connected to the development of intelligent or AI-driven services and the evolution of traditional product-service offerings.
* Digital Servitization
* Position: Located in the “Emerging or Declining Themes” quadrant.
* Interpretation: This suggests that Digital Servitization has lower centrality and density compared to AI within your dataset. This may indicate that it is a less mature research area. It also may be a declining theme.
* Supporting Evidence: The relatively lower PageRank scores for the listed articles (Kohtamäki 2022, Kohtamäki 2024) when compared to the AI articles suggest that this theme has less overall influence within the network represented by your data.
* Further Considerations: Although it’s in the “Emerging or Declining” quadrant, note that the publications listed (Kohtamäki 2022, 2024) are relatively recent. This could mean that digital servitization is emerging but not yet fully established or it is declining in the field. Further investigation into the trends over time is needed to verify this.
Parameter Considerations:
It’s essential to consider the parameters used to generate the map when interpreting the results:
- `field: KW_Merged`: The analysis is based on merged keywords, meaning that the clustering is determined by the co-occurrence of keywords across the documents.
- `n: 250`: Only the top 250 keywords were used. This means that less frequent keywords are excluded.
- `minfreq: 2`: Keywords with a frequency of less than 2 were excluded. This helps to focus on more common themes.
- `cluster: walktrap`: The “walktrap” algorithm was used for community detection. This algorithm tries to find natural communities in the network based on random walks.
- `SCOPUS`: The data source is SCOPUS, so the map reflects the trends and themes captured within that database. Results might differ slightly if you used Web of Science or another database.
Critical Discussion & Further Research
1. Validate the Findings: Check the recent literature yourself. Does AI genuinely appear to be the dominant and most influential theme? Is digital servitization truly less developed or declining? Look for recent reviews or surveys of the field to corroborate these findings.
2. Investigate Digital Servitization Further: Given the recent publications, dig deeper into why it appears in the “Emerging/Declining” quadrant. Is it a niche area within the broader SCOPUS dataset? Is it more prominent in other databases or specific journals not heavily represented in your data? Are there specific sub-topics within digital servitization that are driving the most recent research?
3. Explore the AI-Servitization Link: The connection between AI and product-service systems is noteworthy. Consider focusing your research on the intersection of these two themes. What specific AI techniques are being applied to servitization? What are the benefits and challenges of using AI to enhance services?
4. Consider Different Parameters: Experiment with different values for the parameters (e.g., `minfreq`, different clustering algorithms) and see how the map changes. This can help you assess the robustness of your findings and identify potentially overlooked themes.
5. Look at the network graph directly: Consider inspecting the actual network graph. Examining the connections between keywords directly, rather than relying solely on the strategic map’s categorization, can provide nuanced insights.
By considering these points, you can move beyond a simple description of the strategic map and develop a more critical and insightful understanding of the research landscape. Remember, bibliometric analysis is a tool to guide your research, not a substitute for your own expert knowledge and judgment.
Factorial Analysis
Overall Structure and Dimensions:
* Dimensionality Reduction: The MCA has reduced the complexity of the keyword co-occurrence data into two dimensions. Dim 1 explains 38.03% of the variance, and Dim 2 explains 24.25%. This means that together, these two dimensions capture a substantial, but not complete, portion of the relationships between the keywords. Further dimensions might reveal additional nuances.
* Interpretation of Dimensions: Interpreting the dimensions is crucial. We will have to do it in relation with the content of each quadrant.
* It is worth noting that on the x axes the dimension one go from business oriented key words to ‘waste management’
* On the y axes, we see a difference between words at the top of the quadrant such as ‘big data’ ‘bussiness’ and the words at the bottom such as ‘competition’ and ‘digital servitization’
Clusters and Associations:
- Cluster 1: Emerging Technologies & Business: The upper-left quadrant is characterized by terms like “big data,” “business,” “block-chain” and “artificial intelligence.” This suggests a cluster focused on applications of emerging technologies (blockchain, AI, Big Data) *within* a business context. The proximity of these terms indicates that publications frequently use these keywords together.
- Cluster 2: Product-Service Systems (PSS): Located centrally (around the center of the x axis), we see the terms “product-service system (pss)” and “product-service systems”. This suggests a dedicated area of research focusing on these systems. The lighter color (less saturated red) suggests that these terms might appear less frequently in the dataset compared to the other clusters, or that their associations are less strong.
- Cluster 3: Servitization & Competition: In the bottom-left quadrant, we see “digital servitization” and “competition”. This cluster hints to a research area focusing on the transition from products to services (servitization) and it’s related to a competitive environment
- Outlier: Waste Management: The term “waste management” is positioned far on the right (high Dim 1). This indicates that “waste management” is relatively distinct from the other themes identified in the other clusters. It is also worth noting that this is quite far from the other terms.
Key Observations and Potential Interpretations:
- Research Focus: The analysis reveals a research landscape heavily focused on the intersection of technology (big data, AI, blockchain) and business. The presence of Product-Service Systems suggests an interest in innovative business models.
- Potential Gaps: Depending on the specific research question, the relative isolation of “waste management” might indicate a potential gap or area for further investigation. Is waste management being sufficiently addressed in the context of digital transformation and new business models?
- The role of product-service systems: The terms ‘product-service systems’ and ‘product-service system (pss)’ appear in the center, indicating they are related to the other key words in the factorial map, but less strong than the terms ‘business’ or ‘big data’.
Critical Considerations and Further Steps:
- Data Scope: Remember the data is from Scopus and represents the keyword choices of authors in that database.
- Parameter Sensitivity: The choice of ‘KW_Merged’, n-grams=1, minDegree=1 will have influenced the map. Experimenting with different settings may reveal different perspectives.
- Qualitative Analysis: To fully understand the themes, integrate this quantitative analysis with a qualitative review of the most relevant papers within each cluster. This would help to give better context to the topics discussed.
- Statistical Significance: While the map visually represents associations, remember that MCA is descriptive. Further statistical tests might be needed to confirm the significance of the clusters.
- Stemming: Given stemming was set to false, it may be worth to activate it and to re-run the analysis.
By combining the visual insights from the map with your understanding of the research domain, you can formulate more nuanced interpretations and research questions.
Co-citation Network
Overall Structure:
The network appears sparse and highly fragmented. We see two main clusters, one blue and one red, suggesting two distinct research areas or schools of thought within your dataset. The lack of strong connections *between* these clusters indicates limited cross-citation or integration of ideas across these areas.
Communities:
The Walktrap algorithm has identified these two communities. We need to look at the content of the papers represented in each community to understand what defines them.
- Blue Cluster: The blue cluster seems to be centered around publications by Geissdoerfer M. (2017), Thomas-Szabo D. (2016 and 2020) and Pieroni M.P.P. (2019).
- Red Cluster: The red cluster appears to revolve around Chauhan C. (2022) and Reim W. (2022).
Interpretation of Central Terms:
Let’s interpret what these central terms might represent based on the information available.
- Geissdoerfer M. (2017): Given its prominence and central position in the blue cluster, this publication likely presents foundational concepts, a widely adopted methodology, or a key literature review for that area. You’ll need to consult the actual publication title and abstract to understand its specific contribution (e.g., it could be related to sustainable innovation, circular economy, or a particular business model).
- Thomas-Szabo D. (2016 and 2020): The presence of two publications here by the same author indicates a potentially significant contribution. They might be building upon previous work or presenting a longitudinal study within that research area.
- Pieroni M.P.P. (2019): Similar to Geissdoerfer, this publication is likely important to the field.
- Chauhan C. (2022) and Reim W. (2022): Because they are clustered, it suggests that these papers are highly co-cited, perhaps because they address similar issues, use the same methods, or build on each other’s findings. Being more recent papers (2022), they might represent emerging trends or a more up-to-date perspective within the field.
Further Steps & Critical Discussion Points:
1. Identify the Content: Crucially, you need to identify the actual titles and abstracts of these key publications. This is the *only* way to determine the subject matter and understand the thematic focus of each cluster.
2. Interpret Community Themes: Once you know the content of the key papers, you can start to interpret the overarching themes of each community. What common problems are they addressing? What theories are they using? What methodologies are prevalent?
3. Investigate the Relationship (or lack thereof): Why are there only few connections between the blue and red clusters? Are these truly separate fields, or are there potential synergies that are not being explored in the literature? This could point to gaps in the research landscape.
4. Consider the Database: The data comes from SCOPUS. Consider any biases in the database (e.g., language, journal coverage).
5. Evaluate the Parameters: Think critically about the parameters you used to generate the network. Did the `edges.min` value of 2 filter out potentially important, but less frequent, connections? Would a different clustering algorithm (other than Walktrap) reveal a different community structure?
6. Temporal Dynamics: Consider the publication years. Is one cluster significantly “older” than the other? This could indicate an evolution of the field over time.
7. Relevance to Your Research: Most importantly, how do these clusters and their central publications relate to *your* research question? Does your work bridge these communities? Does it challenge the assumptions of one cluster versus the other?
By addressing these points, you can move beyond a purely descriptive analysis of the network and develop a more insightful and critical understanding of the research landscape. Remember that this network is just one representation of the data, and its interpretation should be grounded in a thorough understanding of the underlying literature.
Collaboration Network
Overall Structure
The network displays a relatively fragmented collaboration landscape. We see several distinct clusters, suggesting that collaboration within the dataset is primarily happening in closed circles rather than a widespread, interconnected network. The distance between clusters indicates a limited number of collaborations between these different author groups.
Communities (Based on Walktrap Algorithm)
The Walktrap algorithm has identified distinct communities within the author network. The colors on the graph represent these communities. We can see at least four distinct communities:
- Red Cluster: This appears to be the largest and most densely connected cluster. Key authors include “parida v,” “kohtamäki m,” “naeem f,” “henneberg’s,” and “säain d.” This suggests that this group is actively collaborating, potentially within a specific subfield or research project.
- Blue Cluster: This cluster consists of “ranjbari m,” “bliumska-danko k,” “bouillass g,” and “han y.” It’s a smaller, tightly knit group.
- Green Cluster: This small community consists of “kuhlenkötter b” and “herzog m alp e.” This suggests these two authors have collaborated within the scope of your dataset.
- Purple Cluster: This is the smallest community consisting of “shokouhyar s” and “pourranjbar a”
Most Connected Authors and Relevance
The size of the nodes represents the author’s degree centrality (number of connections). As the label size is proportional to the number of connection, some names are highlighted. Based on the apparent node sizes and label prominence, the most connected authors are:
- “parida v” and “kohtamäki m” (Red Cluster): These appear to be the most central figures in the network. Their prominence suggests they are prolific collaborators within this dataset, potentially acting as bridges within the “red” community. Their research interests likely represent a significant area of focus in your dataset.
- Other authors in the red cluster like “naeem f”, “henneberg’s” and “säain d” have an important role in the red cluster as well.
- “ranjbari m” (Blue Cluster): Appears to be a central author of the blue community.
- “kuhlenkötter b” (Green Cluster): Appears to be a central author of the green community.
- “shokouhyar s” (Purple Cluster): Appears to be a central author of the purple community.
Interpretation and Potential Insights
1. Collaboration Patterns: The network structure points to the need to further investigate why collaboration is clustered. Possible reasons include:
* Specialized subfields: Each cluster might represent researchers working within a specific, relatively isolated subfield.
* Geographic location: Authors in the same cluster might be located at the same institution or region, fostering easier collaboration.
* Research project focus: Each cluster could be associated with a large, specific research project involving a fixed set of collaborators.
2. Impact of Central Authors: The most connected authors (“parida v,” “kohtamäki m”) likely play a significant role in disseminating knowledge and facilitating research within their respective communities. Investigating their publications and research areas will provide valuable insights into the core themes of your dataset.
3. Limited Inter-cluster Collaboration: The lack of connections *between* clusters could indicate missed opportunities for cross-disciplinary collaboration or knowledge transfer. Identifying potential areas of overlap between the research of different clusters could be fruitful for future research directions.
4. Network Analysis Parameters: The network was generated using the ‘association’ normalization method. This normalization method emphasizes terms that co-occur more frequently than expected by chance, highlighting stronger relationships within the data.
Critical Discussion Points:
- Data Scope: Remember that this network is only representative of the publications *within your SCOPUS dataset*. It may not reflect the entire collaboration landscape of the field. The relevance of the findings depend on the extend of the collection used for the analysis.
- Community Detection Algorithm: The Walktrap algorithm is just one method for community detection. Other algorithms might reveal slightly different community structures.
- Interpretation Bias: Be mindful of potential biases in interpreting the “importance” of authors based solely on degree centrality. Other factors, such as citation counts or the impact of their publications, should also be considered.
Recommendations for Further Analysis:
- Content Analysis: Examine the publications of the most connected authors and the different communities to identify the key research themes and topics.
- Keyword Analysis: Analyze the keywords associated with each cluster to understand the specific research areas they represent.
- Temporal Analysis: Investigate how collaboration patterns have evolved over time. Are the clusters becoming more or less interconnected?
- Expand the Dataset: Consider expanding your SCOPUS dataset to include more publications and see how the network structure changes.
By combining this network analysis with a deeper dive into the content of the publications, you can gain valuable insights into the structure of collaboration, the key research areas, and potential opportunities for future research in the field.
Countries’ Collaboration World Map
Overall Interpretation:
The map visualizes the global landscape of scientific collaboration based on co-authorship data from SCOPUS. The intensity of color represents the research output of a country (total number of articles), while the connections (though not visible in this image) would usually show the collaboration links between countries. From this map, we can identify prominent scientific hubs and infer patterns of international research partnerships.
Key Observations & Potential Implications:
1. Major Hubs of Scientific Production:
* United States: The US appears as a significant contributor, indicated by its color intensity. This suggests a large volume of research output and likely a high degree of international collaboration.
* China: The color intensity indicates a substantial contribution to the overall research output. This aligns with China’s increasing prominence in scientific research globally.
* Europe (Specifically, Scandinavia): A concentration of intense colors is noticeable in Finland and Scandinavia, suggesting that these countries are strong scientific contributors, and may be very collaborative. This could stem from specific research strengths, funding initiatives, or a strong emphasis on international partnerships.
* Other Significant Contributors: It is important to notice South Africa, which could be an emerging hub for collaboration, or simply a research powerhouse in Africa.
2. Global Patterns of Collaboration (Inferred):
* While direct collaborative links (lines) aren’t visible, the map allows for inferences. Based on general knowledge: We can assume strong collaborative relationships between the US and countries in Europe and Asia, given the prominent research output of each region.
Discussion Points & Further Investigation:
- Database Influence: The analysis is based on SCOPUS data. While SCOPUS is a comprehensive database, it has some biases in terms of journal coverage (e.g., favoring English-language publications). It’s important to acknowledge this potential bias when interpreting the results.
- Field Specificity: The map represents an aggregate of all scientific fields. Drilling down into specific disciplines might reveal different patterns of collaboration and regional strengths. For example, certain countries might excel in particular fields and have more focused collaborations in those areas.
- Limitations: The map visualizes total article count. It doesn’t account for the *quality* or *impact* of the research. A country with a high article count might not necessarily have the most impactful research.
- Missing Information: The absence of lines makes it impossible to know with whom the nations are collaborating.
- Consider Normalization: Raw counts can be misleading. Consider normalizing research output by population size or GDP to get a more accurate picture of scientific productivity and international engagement.
- Policy Implications: These insights can inform science policy decisions, such as identifying strategic partners for research funding, promoting international collaborations in specific areas, or addressing disparities in research output between regions.
To further enhance this analysis, consider the following:
- Visualize the actual collaboration links: Displaying the lines representing co-authorship between countries would provide a much clearer picture of collaborative relationships.
- Filter by research area: Analyze collaboration patterns within specific disciplines to identify areas of strength and potential for growth.
- Incorporate other metrics: Include metrics such as citation counts, h-index, or altmetrics to assess the impact of research output and international collaborations.
- Compare across time periods: Analyze changes in collaboration patterns over time to identify emerging trends and shifts in global research dynamics.
By considering these points and conducting further analysis, you can gain a deeper and more nuanced understanding of international scientific collaboration and its implications.
