DPP Ph. D. Research

“Product Service System*” AND “Business Model*” WoS – Bibliometrix

Main Information

Overall Impression:

The bibliographic collection represents a moderately-sized body of research (484 documents) spanning the period 2015-2025. The average age of documents suggests a relatively recent focus, indicating the research area is likely active and evolving. The collection shows reasonable levels of collaboration and impact, especially considering the timeframe. The data was sourced from the Web of Science (WOS), a reputable database known for its high-quality indexing, lending credibility to the collection’s overall significance.

Detailed Interpretation:

1. Scope and Growth:

2. Productivity:

3. Impact and Citations:

4. Authors and Collaboration:

Critical Discussion Points & Further Investigation:

By addressing these points, you can develop a more nuanced and insightful interpretation of your bibliometric results. Remember to compare your findings to benchmarks and trends in related fields to provide context and assess the relative significance of your findings.

Annual Scientific Production

Average Citations Per Year

Three-Field Plot

Overall Structure and Purpose

This plot is a visual representation of the relationships between three key bibliographic elements:

The lines connecting these three fields show the co-occurrence or association between items. For example, a line connecting “Author A” to “Keyword B” implies that Author A has publications that use Keyword B. The thickness/intensity of the lines would ideally represent the strength or frequency of that connection.

Specific Interpretation of the Plot

1. Cited References (CR):

* The cited references field lists several publications, indicated by author names, years, and journal information. The presence of “clean prod” (clean production) in many of the cited references suggests that the research area has a strong connection to sustainability, environmental impact, or eco-friendly practices.
* Key cited works appear to be from authors like Bocken, Mont, Tukker, Osterwalder and Baines. These works are fundamental to the field.

2. Authors (AU):

* Parida V and Kohtamaki M appear to have many connections to the cited references and keywords.
* Baines T and Mont O also appear to be key actors.
* The presence of authors from diverse institutions and countries can provide insights into the global distribution of research within the field.

3. Keywords (KW\_Merged):

* “Servitization” and “Product-Service Systems” seem to be prevalent keywords, indicating a focus on business models that integrate products and services.
* Other important keywords include “innovation,” “business models,” “sustainability,” “circular economy,” and “design.” This reveals a multidisciplinary nature of the field, encompassing technological, economic, and environmental aspects.

Interconnections and Insights

Suggestions for Further Analysis and Discussion

Critical Considerations

Keyword Merging: How were the keywords merged (KW\_Merged)? Different merging strategies can affect the interpretation.

Data Source (WOS): The analysis is based on data from the Web of Science (WOS). WOS has its own biases (e.g., towards journals indexed in WOS), so the results might not be fully representative of the entire research landscape.

Most Relevant Sources

Most Local Cited Sources

Core Sources by Bradford’s Law

Sources’ Local Impact

Sources’ Production over Time

Most Relevant Authors

Most Local Cited Authors

Authors’ Production over Time

Overall Observations:

The plot visualizes the publishing trends and citation impact of leading authors within the dataset, likely focused on Product-Service Systems (PSS) and related fields like servitization and circular economy. The red lines represent the author’s active publishing timeline within the dataset’s scope. The bubbles indicate the number of articles published in a specific year (size) and the total citations received that year (color intensity).

Individual Author Analysis:

Potential Research Questions & Further Investigation:

Critical Considerations:

Normalization: Citation counts should ideally be normalized by year and field to account for differences in citation practices across disciplines and over time.

Citation Counts as a Metric: While citations are a common measure of impact, they are not perfect. Factors like self-citation, citation bias, and the “Matthew effect” (already well-known authors receive more citations) can influence citation counts.

Time Window: The analysis only considers publications within the dataset’s time frame. Authors may have significant publications outside this period.

Field Specificity: The results are specific to the scope of the search terms used to create the dataset. Different search terms would yield different results.

Authors’ Local Impact

Most Relevant Affiliations

Affiliations’ Production over Time

Corresponding Author’s Countries

Overall Productivity:

International Collaboration (MCP):

Balance Between Domestic and Global Research:

Points for Further Discussion/Investigation:

In conclusion:

This analysis highlights the varied approaches countries take towards research in this field. Some countries, like Sweden and the United Kingdom, combine strong domestic research with international collaboration. Others, like Finland, heavily rely on international partnerships. Germany appears to prioritize domestic research, while others, like Norway and USA, favour international collaboration. These differences can be attributed to a combination of factors, including funding policies, research priorities, and the specific nature of the research field. Further investigation is needed to fully understand the underlying drivers of these collaboration patterns. Remember to consider the potential biases of the WOS database when interpreting these results.

Countries’ Scientific Production

SWEDEN134
UK131
CHINA94
ITALY88
BRAZIL86
GERMANY86
FINLAND70
NETHERLANDS54
FRANCE45
SPAIN42
BELGIUM33
USA32
JAPAN31
AUSTRALIA26
NORWAY25
SOUTH KOREA24
DENMARK21
INDIA18
SWITZERLAND16
POLAND14
GREECE11
AUSTRIA10
MEXICO8
PORTUGAL8
CHILE7

Countries’ Production over Time

Most Cited Countries

Most Global Cited Documents

Most Local Cited Documents
Overall Observations:

Key Articles & Interpretations:

Let’s examine some articles based on different criteria:

* KOHTAMÄKI M, 2019, J BUS RES: LC 63, GC 539, NLC 7.35, NGC 6.56 – This article appears to be a cornerstone publication. It has strong local (LC and NLC) and global (GC and NGC) citation impact. This suggests the article is both highly relevant to your specific research area and broadly influential in the wider academic community. Further investigation into the content of this article is warranted to understand its core contribution.
* LINDER M, 2017, BUS STRATEG ENVIRON: LC 49, GC 569, NLC 3.32, NGC 4.18 – Similar to the previous one, this article demonstrates a significant global influence, and its local citations are also strong. The journal *Business Strategy and the Environment* is also a good outlet for reaching the broader academic community.
* KOHTAMÄKI M, 2020, TECHNOL FORECAST SOC: LC 31, GC 450, NLC 6.11, NGC 6.93 – Very similar to the first Kohtamäki’s article, this article has high local and global normalized citations. It shows the author’s high influence in the field.
* BAINES T, 2017, INT J OPER PROD MAN: LC 67, GC 478, NLC 4.55, NGC 3.51- This article has the highest number of local citations.

* VEZZOLI C, 2015, J CLEAN PROD: LC 54, GC 245, NLC 5.08, NGC 3.07 – This article has a strong local impact (LC and NLC are high) but its global citation counts are relatively lower. This suggests the article addresses a topic that is highly specific and relevant to the specific research field defined by your collection.
* YANG MY, 2019, J CLEAN PROD: LC 33, GC 97, NLC 3.85, NGC 1.18 – This article is interesting. The number of global citations is not so high, but the local relevance, demonstrated by LC and NLC, suggests a relevant contribution within the analyzed sample.

* Look for articles where the NLC is significantly higher than the NGC. This might indicate a paper that is highly impactful within your field but hasn’t yet achieved its full potential for broader recognition. No one seems to stand out particularly.

Recommendations for Further Investigation:

1. Content Analysis: Read the abstracts (and potentially the full text) of the highest-cited articles, especially the KOHTAMÄKI papers, to understand their core arguments, methodologies, and findings. This will provide valuable context for interpreting the bibliometric data.
2. Keyword Analysis: Analyze the keywords associated with these highly cited articles to identify key themes and concepts within your research area. This could be done in Biblioshiny as well.
3. Citation Network Analysis: Use Biblioshiny to visualize the citation network among these articles. This will reveal clusters of related research and identify influential papers that bridge different areas.
4. Author Analysis: Examine the publication records of the most prolific and highly cited authors in your collection (e.g., KOHTAMÄKI, YANG, ARMSTRONG). Understanding their research trajectories and collaborations can provide valuable insights.
5. Compare NLC/NGC Ratios: Calculate the ratio of NLC to NGC for each article. A high ratio suggests the article is more influential within your specific field than globally, indicating a highly specialized contribution.
6. Consider the Journals: Research the aims and scope of the journals where these articles are published. This will help you understand the types of research that are considered relevant and impactful within your field.

Critical Considerations:

By combining the bibliometric data with a deeper understanding of the research content, you can draw more meaningful and nuanced conclusions about the key trends, influential publications, and leading researchers in your field. Remember to use these insights to inform your own research and identify potential areas for future investigation.

Most Local Cited References

Reference Spectroscopy
Overall Interpretation

This RPYS plot visualizes the historical roots of your research area by showing when the references cited in your current collection of papers were originally published. The black line represents the overall citation activity for each year, while the red line highlights years where the citation frequency significantly exceeded the 5-year median. These peaks in the red line indicate “seminal” or foundational years for your field. The list of highly cited references for those peak years give a sense of what specific works were particularly influential.

Key Observations and Potential Insights

1. Emergence of the Field: The black line shows citations steadily increasing to the current date. The field seems relatively new and is in a growth phase.

2. Seminal Years (Red Line Peaks): The red line shows important year clusters. These periods represent when key ideas and concepts were introduced or consolidated within your field. We can break these down:

* 1979: The peak here, and the publications list suggests that the most influential publications in the field were focused on organizational studies and marketing research.
* 1985: The list of publications indicates key works related to competitive strategy, institutionalism, and marketing.
* 1988: Publications include work related to consumer research.
* 1995: Includes publications from the Academy of Management Review.
* 1999, 2002, 2006, 2010, 2013, 2017: These are clearly about circular economy and product service systems.

3. Reference Age: Notice how references prior to the 1970s are rare. This indicates a field that’s largely built upon relatively recent scholarship. While older works might exist, they don’t have the same level of continuing influence within your specific research domain, as defined by the publications included in your analysis.

4. Database Influence: The fact that your data is from Web of Science (WOS) is relevant. WOS has a specific coverage profile, which might influence the prominence of certain publication types or journals in your RPYS plot. It is important to consider the potential bias and coverage limitations of Web of Science when interpreting your results.

Critical Discussion Points and Further Investigation

1. Thematic Clusters: The seminal papers in the list suggest potential shifts in the field’s focus. Investigate whether these periods represent distinct sub-disciplines or schools of thought within the field. For example, is there a shift from general management and strategy topics towards sustainability related topics? Are there schools of thought centered around particular authors?

2. Methodological Shifts: Do the seminal papers point to the adoption of new methodologies or research approaches within your field? For example, the presence of “NATURALISTIC INQUIRY” (Lincoln & Guba, 1985) might indicate a growing interest in qualitative research methods.

3. Database Bias: Because you used WOS, it’s worth considering whether your results would differ if you used a different database like Scopus or Google Scholar. Each database indexes different journals and has different citation coverage, which can affect the prominence of specific publications and research areas in your RPYS plot.

4. Missing Pieces: Are there any periods in your field’s history that seem underrepresented in the RPYS plot? This could indicate areas where further research is needed, or it could reflect a limitation of the data source.

5. Compare with Other Datasets: To account for database bias, you might compare the results with RPYS plots generated from different databases.

In summary, your RPYS plot reveals a research field that has foundations from the 1970s-1980s. The seminal years reveal core concepts and prominent researchers within your domain. However, critical reflection on the analysis by acknowledging the limitations of the WOS database, the potential influence of specific schools of thought, and unexplored areas may further enhance the rigor of your interpretations. Remember to connect these observations back to the specific research questions that you are investigating. Good luck!

Most Frequent Words

WordCloud

TreeMap

Words’ Frequency over Time

Words’ Frequency over Time

Overall Interpretation:

The plot visualizes the evolution of research topics within your WOS dataset from 2016 to 2024, based on the keywords extracted from the `KW_Merged` field. Each line represents the lifespan of a specific term, showing how frequently it appeared in the literature over time. The size of the bubbles indicates the term’s prominence in a given year. The light blue line shows the interquartile range, giving an idea of the spread of the frequency of the term, while the central dot marks the median frequency. The plot specifically highlights the *k=3* most frequent terms for each year.

Key Observations and Discussion Points:

1. Emergence and Decay of Trends:

* Early Trends (2016-2018): “Pss design”, “integrated solutions”, “product service system (pss)” are the early terms, so the field of PSS appears to have been already consolidated at the beginning of the time frame analyzed.
* Mid-Period Trends (2020-2022): A cluster of terms related to business and service innovation including “business models”, “servitization”, “product-service systems”, “circular economy”, “innovation” emerge around 2020.
* Recent Trends (2024): “systematic literature review”, “experiences” and “digital servitization” appear as the most recent trend. This is likely reflecting a growing maturity and reflection on the field.

2. Popularity and Duration:

* The size of the bubbles allows you to easily identify the periods when a topic was most actively researched. For instance, “servitization” seems to have a large bubble in 2020-2022, suggesting a peak in interest during those years.
* The width of the line representing each term shows for how long that term has been relevant.

3. Thematic Clusters:

* Product-Service Systems (PSS) and Servitization: The prominence of terms like “product service system (pss),” “product-service systems,” “servitization,” and “business models” indicates a strong focus on the shift towards service-oriented business models and integrated product-service offerings.
* Sustainability: The emergence of “sustainable business models” suggests an increasing interest in incorporating sustainability considerations into business models.
* Innovation & Transformation: The presence of terms like “innovation,” “transformation,” and “circular economy” points towards research on broader strategic and transformative changes in businesses and industries.

4. Data-Driven Questions for Further Investigation:

* Why did certain trends peak and then decline? For example, why did “servitization” become so prominent in 2020-2022?
* What are the connections between these trends? Are “circular economy” and “sustainable business models” influencing each other? How do they relate to “PSS” and “servitization”?
* What methodologies or approaches are being used to study these trends? A deeper dive into the abstracts of the publications associated with these keywords could reveal common research methods.
* Are there any surprising absences? Are there expected keywords that *don’t* appear in the plot, and if so, why might that be? (e.g. emerging technologies not mentioned?)

Critical Considerations and Limitations:

Recommendations for your Research:

1. Cross-validate with qualitative analysis: Conduct a closer reading of highly cited papers associated with the emerging topics, to better understand the context and the core meaning.
2. Check the influence of COVID: the peak on the terms around 2020 could be caused by the global pandemic.

By considering these interpretations, questions, and limitations, you can use this trend topics plot as a valuable starting point for a deeper exploration of your research field. Remember to always critically evaluate the results and consider the context in which the data was generated. Let me know if you’d like to explore any of these areas in more detail!

Clustering by Coupling

Co-occurrence Network
Overall Structure and Parameters:

Communities (Clusters/Topics):

The Walktrap algorithm has identified two distinct communities, represented by red and blue nodes. Let’s interpret what these clusters likely represent:

* Red Cluster: This cluster appears to revolve around digital servitization and business model innovation. Key terms include:
* “Product-service systems”
* “Servitization”
* “Business models”
* “Innovation”
* “Digital servitization”
* “Industry 4.0”
* “Technology”
* “Transformation”
* “Digitalization”
* “Impact”
* “Smart”

*Interpretation:* This community probably reflects research focusing on the *digital transformation of product-service systems*, including the role of Industry 4.0 technologies and innovative business models to deliver these services. The focus is on the *impact* of digitalization and the adoption of “smart” technologies within this domain. It is likely focused on the strategic and managerial implications of adopting servitization.
* Blue Cluster: This cluster seems to be centered on circular economy and sustainable design of product-service systems. Key terms include:
* “Circular Economy”
* “Design”
* “Sustainability”
* “Management”
* “Product-service system”
* “Challenges”
* “Framework”
* “Opportunities”
* “Sharing Economy”
* “Circular Business Models”
* “Implementation”

*Interpretation:* This community reflects research related to the *sustainable design and implementation of product-service systems*, specifically focusing on principles of circular economy, sharing economy, and the challenges and opportunities associated with these approaches. It appears to be focused on the “nuts and bolts” of implementation and the concrete frameworks for achieving sustainability within this context.

Most Connected Terms and Their Relevance:

The size of the nodes corresponds to their degree centrality (number of connections). The larger nodes are the most connected terms, indicating they are central to the research field represented by this collection:

Interpretation and Discussion Points:

* Future Research Directions: The network suggests areas for future research:
* Integrating Sustainability and Digitalization: Exploring how digital technologies can be leveraged to create more sustainable and circular PSS solutions. The connection between the two clusters seems relatively weaker, suggesting opportunity to study these intersections more explicitly.
* Implementation Challenges: The presence of “Challenges” in the sustainability cluster indicates that practical implementation is a key concern. Research could focus on overcoming these barriers.
* Value Creation: The presence of “Value Creation” suggests research into how these create value for stakeholders.
* Management & Frameworks: The importance of management and frameworks in the Blue Cluster suggests that research on governance, organizational structures, and policy related to sustainability-oriented PSS is important.

Recommendations for further exploration:

By considering these points, you can craft a more insightful and data-driven discussion of your bibliometric analysis. Good luck!

Thematic Map

Understanding Strategic Maps

Strategic maps are a visual representation of the intellectual structure of a field. They plot clusters of keywords/themes based on two key dimensions:

The map is typically divided into four quadrants:

Analysis of the Provided Strategic Map

Based on the image and data provided, here’s an interpretation of the map:

1. Cluster Structure: The map shows three distinct clusters: “Circular Economy,” “Digital Servitization,” and “Product-Service Systems”.

2. Quadrant Placement and Interpretation:

* Circular Economy (Upper Left – Niche Theme): The “Circular Economy” cluster is located in the top-left quadrant. This indicates that while this research area is highly developed (high density), it has relatively low centrality within the entire knowledge base represented by your dataset. This suggests that circular economy, while being a mature and well-defined area, might be somewhat disconnected from other prominent themes in your specific dataset. This could mean that research on circular economy is often conducted in relative isolation or focuses on specific aspects not heavily integrated with broader topics. The presence of “sustainability” and “barriers” within this cluster suggests a focus on the challenges and environmental aspects of circular economy adoption.

* Digital Servitization (Around the center): The “Digital Servitization” cluster is located in the center of the graph, with relevance degree and development degree not high. This suggests a good starting point to look into the theme.

* Product-Service Systems (Lower Right – Basic Theme): The “Product-Service Systems” cluster is located in the bottom-right quadrant. This suggests that it has high centrality (is a core theme in the field) but relatively low density (is not as well-developed as other areas). This is quite interesting. It implies that product-service systems are fundamental and connected to many areas within the field but could benefit from further research and exploration. The association with “servitization” and “innovation” reinforces this idea, suggesting that the potential of PSS for driving innovation is recognized, but the specific mechanisms and implementations require further investigation.

3. Central Articles Within Each Cluster:

* Circular Economy: The most central articles for this cluster are:

* ARIOLI V, 2025, COMPUT IND ENG, pagerank 0.182
* GHAFOOR S, 2024, J CLEAN PROD, pagerank 0.182
* RIZOS V, 2016, SUSTAINABILITY-BASEL, pagerank 0.179

These articles likely represent key publications in the area of circular economy, possibly focusing on computational and industrial engineering aspects (ARIOLI), clean production strategies (GHAFOOR), and broader sustainability considerations (RIZOS). The journals these articles appear in provide further context to the specific focus of the cluster.

* Digital Servitization: The most central articles are:

* BENEDETTINI O, 2025, COMPUT IND ENG, pagerank 0.221
* PAGOROPOULOS A, 2017, J CLEAN PROD, pagerank 0.204
* MASTROGIACOMO L, 2020, CIRP J MANUF SCI TEC, pagerank 0.199

These articles likely deal with the integration of digital technologies into servitization strategies. Again, the journals highlight the specific angles: Computational and Industrial Engineering (BENEDETTINI), Clean Production (PAGOROPOULOS), and Manufacturing Science and Technology (MASTROGIACOMO).

* Product-Service Systems: The most central articles are:

* GALERA-ZARCO C, 2021, SUSTAINABILITY-BASEL, pagerank 0.276
* VARGAS JP, 2022, SUSTAINABILITY-BASEL, pagerank 0.244
* YANG MY, 2018, PROD PLAN CONTROL, pagerank 0.241

These articles probably discuss the design, planning, and control of product-service systems, with an emphasis on sustainability (GALERA-ZARCO, VARGAS) and production planning (YANG).

4. Parameters of the Analysis:

* Data Source (WOS): The fact that the data comes from the Web of Science is important. It means the analysis is based on a selection of high-quality, peer-reviewed publications.
* Keywords (KW_Merged): The analysis uses merged keywords, which is a good approach for capturing a broader understanding of the topics.
* N, Minfreq, Ngrams, Stemming: These parameters control the keyword selection and processing. `n=250` means the top 250 keywords were considered. `minfreq=2` means keywords had to appear at least twice. `ngrams=1` means only single-word keywords were used. `stemming=FALSE` means keywords were not stemmed.
* Community Detection (walktrap): The `walktrap` algorithm was used for cluster detection. This algorithm is sensitive to the network structure and can identify communities based on random walks.

Discussion Points and Further Research:

In Summary:

This strategic map provides a valuable overview of the intellectual landscape of your research area. It highlights the key themes, their relative importance, and their level of development. By considering the quadrant placement of the clusters, the central articles within each cluster, and the parameters of the analysis, you can gain insights into potential research gaps, opportunities for integration, and future directions for the field. Remember that this map is just one tool for understanding the literature, and it should be used in conjunction with other methods, such as literature reviews and expert consultations.

Factorial Analysis
Overall Structure and Interpretation

Cluster Identification and Interpretation

Based on the visual arrangement of keywords, several potential clusters emerge. Let’s analyze them from left to right:

1. Cluster 1: Digitalization and Manufacturing Transformation (Left-Bottom Quadrant): This cluster includes terms such as “digital servitization,” “capabilities,” “transformation,” “manufacturing firms,” and possibly “Industry 4.0” and “dynamic capabilities.”
* Interpretation: This cluster seems to represent research focused on how digital technologies are changing the manufacturing landscape. “Digital servitization” (integrating services with products) and “capabilities” suggest an emphasis on developing the necessary organizational abilities to implement these changes. “Transformation” and “Manufacturing firms” clearly point to the context. “Industry 4.0” reinforces the technological aspect, and “dynamic capabilities” highlights the ability to adapt to change.

2. Cluster 2: Business Model Innovation and Smart Systems (Bottom-Center): Contains terms like “business model innovation” and “smart.”
* Interpretation: This cluster highlights the intersection of business model innovation with smart technologies, which probably refers to the application of technologies like AI, IoT, and data analytics to create innovative and sustainable business models.

3. Cluster 3: PSS and Servitization (Top-Left Quadrant): This cluster includes terms like “offerings”, “product-service systems (pss)”, “transition”, “firms”, “servitization”, and “impact.”
* Interpretation: This cluster appears to be related to the product-service system and servitization. It suggests an area of focus on how firms transition to PSS models, the impact of these models, and the overall servitization process.

4. Cluster 4: PSS and Business Model (Top-Right Quadrant): Includes terms such as “business models”, “product-service system (pss)”, “model”, “of-the-art”, “design”, “challenges”, and “product-service system strategies”.
* Interpretation: This cluster emphasizes the ‘state of the art’ design and strategies of product-service systems. Keywords like ‘business models’ and ‘challenges’ indicate that this area of research also tackles the business models involved and the implementation challenges.

5. Cluster 5: Sustainability and Circular Economy (Right-Bottom Quadrant): This includes terms like “implementation”, “sustainability”, “circular economy”, “systems”, and “consumption.”
* Interpretation: This cluster concentrates on the sustainability and circular economy aspects. The term “implementation” is distinct, but related to this cluster, as it points to the implementation of circular economy models.

Relevance of Contributing Terms

Suggestions for Further Investigation

In summary, your factorial map reveals several key themes in your WOS collection. By understanding the relationships between these themes, you can gain a better understanding of the research landscape and identify potential areas for future research. Remember to ground your interpretations in the context of your specific research questions and dataset.

Co-citation Network
Overall Structure:

The network clearly shows two distinct clusters or communities, visually separated by color (blue and red). This immediately suggests two major, but somewhat distinct, bodies of literature within your dataset. The density of connections within each cluster indicates strong internal relationships between the cited references. The connections between the two cluster are weaker in comparaison of the internal one.

Community Detection (Walktrap):

The “walktrap” community detection algorithm has identified these groupings. Walktrap works by simulating random walks on the network. The intuition is that random walkers will tend to stay within a community for a longer time than they would spend crossing between communities. The “community.repulsion = 0.05” parameter indicates a weak repelling force between communities, likely contributing to the relatively distinct separation we observe. A lower value means communities are more prone to fuse to each other, while a higher value means that the communities are more prone to separation.

Key Nodes & Their Relevance:

The node sizes are proportional to their degree centrality (number of connections). Thus, the largest nodes are the most frequently co-cited references in your dataset. From the image, these appear to be:

Inferences and Interpretation:

1. Two Major Research Streams: The two communities likely represent distinct, though related, research streams within the overall topic of your Web of Science collection. They might represent:
* Different theoretical perspectives
* Different methodologies
* Different application areas
* Evolution of research over time (if one community is older)

2. Significance of Key Citations: The highly cited papers (Tukker, Reim, Oliva, Vandermerwe) are foundational or pivotal works within those streams. Understanding *what* these papers are about is crucial. For example, if Tukker’s work is about sustainable innovation and Reim’s is about business models, this could indicate a core theme in the “red” community. If Oliva’s work is about customer relations, then this means that this is a core theme for the ‘blue’ community.

3. Parameter Choices: Consider how the chosen parameters influence the visualization.
* `label.n = 50`: Only the 50 most connected nodes have labels. This ensures readability but may hide less central, yet potentially relevant, references.
* `edges.min = 2`: Only co-citations that appear at least twice are shown as edges. This simplifies the graph and highlights stronger relationships. A lower value would show more connections and might reveal bridging papers between the two clusters.

Next Steps for the Researcher:

1. Identify the Content: The most important step is to *read* the most highly cited papers (Tukker 2004/2015, Reim 2015, Oliva 2003, and Vandermerwe 1988). Understand their key contributions and research focus.

2. Interpret Community Themes: Based on the key papers, characterize the central themes of each community. *Why* are these references being co-cited? What research questions, theories, or methodologies do they share?

3. Examine Bridging Nodes: Identify papers that connect the two communities. These may represent attempts to synthesize different perspectives or apply concepts from one stream to another.

4. Consider Temporal Trends: If the date ranges of the papers in each cluster differ significantly, this could indicate an evolution of the field. Is one community “older” and representing foundational work, while the other is newer and building upon that foundation?

5. Critique the Analysis: Reflect on the parameter choices and their impact on the network structure. Would different community detection algorithms yield different, potentially more nuanced, groupings? Would a different minimum edge weight reveal additional relationships?

By combining this quantitative network analysis with a qualitative understanding of the cited references, you can gain valuable insights into the structure and evolution of research within your chosen domain.

Good luck with your research!

Historiograph
Overall Structure & Temporal Trends

The historiograph shows a clear temporal progression, with older articles (Armstrong et al.) at the top and newer ones (Kohtamaki et al., Sjodin et al.) at the bottom. This is expected in a citation network visualizing temporal development. The majority of the research activity appears to be concentrated between 2017 and 2020.

Cluster Analysis and Topic Evolution:

Based on the network and the article titles, we can identify some potential thematic clusters and their evolution:

* Located at the top of the graph.
* Topics: Sustainable Product-Service Systems (PSS) for clothing, consumer perceptions, use-oriented clothing economy.
* *Interpretation:* This cluster represents foundational work on the application of sustainable PSS concepts specifically within the clothing industry, focusing on consumer behavior and alternative consumption models. The color (reddish) may indicate that these articles are not directly citing (or being cited by) the more recent cluster focused on digitization and business models, suggesting a potential separation between the sustainability-focused and technology-focused streams.
*Temporal Evolution:* The relatively isolated position suggests that either research in this specific area branched off or was absorbed into the broader PSS and circular economy research.

* Dominates the middle and lower sections of the graph. This is the most active area of research based on the number of nodes.
* Topics: Servitization, digitization, business models, value co-creation, circular economy.
* Key Articles and themes:
* Baines t, 2017: Servitization: Revisiting The State-Of-The-Art And Research Priorities: Suggests a review or consolidation of the servitization field up to that point.
* Coreynen w, 2017: Boosting Servitization Through Digitization: Highlights the increasing importance of digitization as an enabler of servitization.
* Kohtamäki m, 2019, 2020: Focuses on digital servitization, ecosystems, and the relationship between digitization and servitization in capturing financial potential. This suggests a deepening understanding of the financial implications of integrating digital technologies into service-oriented business models.
* Yang my, 2018, 2019: Business model archetypes for PSS and Circular Economy, suggesting interest in formalizing patterns and practices in PSS.
* *Interpretation:* This cluster reveals a strong trend towards integrating digitization into servitization research. The focus shifts from general PSS and servitization concepts to the specifics of *digital* servitization, its impact on business models, and its role in creating value and financial returns. The “digital servitization” and “circular economy” appear as a prominent research avenue, suggesting a concern with technology-enabled sustainability.

* Other Notable Articles:
* Vezzoli c, 2015: New Design Challenges To Widely Implement ‘Sustainable Product-Service Systems’: it indicates early stage of Sustainable Product-Service Systems.
* linder m, 2017: Circular Business Model Innovation: Inherent Uncertainties: Highlights the challenges and uncertainties associated with implementing circular business models, indicating a critical perspective on the topic.

Pivotal Works

Determining pivotal works requires considering node size (citation count within this network) and centrality (position within the network). Based on the image and information provided:

Knowledge Development

The historiograph suggests the following trajectory of knowledge development:

1. Foundation: Early research focused on sustainable PSS, particularly in the clothing industry, and explored consumer perceptions.
2. Expansion: The field broadened to encompass general servitization concepts and business models.
3. Digitization Focus: Digitization emerged as a key enabler of servitization, leading to research on digital servitization business models and their impact on value co-creation and financial performance.
4. Circular Economy Integration: Integration of circular economy principles into PSS and business models, with a focus on uncertainties and challenges in their implementation.

Critical Discussion Points & Further Research

Limitations

In conclusion, this historiograph provides a valuable overview of the evolution of research in servitization, digitization, and sustainable business models. It highlights the increasing importance of digitization and the emergence of digital servitization as a key research area. However, further research is needed to address the critical discussion points and validate the findings.

Collaboration Network
Overall Network Structure:

Community-Specific Insights:

Interpretation and Discussion Points:

1. Specialization: The distinct communities might indicate specialization within the research area. Each community may be focusing on a specific sub-topic or using different methodologies.
2. Interdisciplinary Research: The limited connections between communities could imply a lack of strong interdisciplinary collaboration within the field. However, the presence of a few connecting links suggests some level of interdisciplinary engagement.
3. Central Author’s Role: The central author ‘parida v’ could be a key figure bridging different communities. The analysis can be deepened to explore whether ‘parida v’ acts as a knowledge broker connecting otherwise disparate groups.
4. Data limitations: The fragmentation may be affected by database selection, keywords used, and the time frame for the data collection. For example, restricting the database to Web of Science might exclude relevant collaborations published in other databases.

Further Investigation:

By exploring these questions, researchers can gain a deeper understanding of the dynamics within their field and identify opportunities for future collaboration.

Countries’ Collaboration World Map
Overall Observations

The map visually represents the intensity of research output (color shading) and collaborative links (connecting lines) between countries. Darker shades indicate higher research output, and the lines represent co-authorship relationships.

Key Hubs of Scientific Production

Key International Partnerships

Global Patterns of Collaboration

Interpretation Considerations and Next Steps

Further Analysis and Discussion Points

1. Network Analysis: Consider using network analysis techniques to quantify the strength of ties between countries, identify central actors (using metrics like betweenness centrality), and detect potential research clusters.
2. Temporal Trends: Analyze how collaboration patterns have evolved over time by creating multiple maps for different time periods.
3. Specific Disciplines: How does this map change when you filter it by specific research areas (e.g., medicine, engineering, social sciences)?
4. Funding Landscape: Are there specific international funding programs that might be driving these collaborations?
5. Geopolitical Factors: How do political relationships or trade agreements influence scientific collaboration?

By considering these aspects, you can move beyond a descriptive overview to a more insightful interpretation of the global research collaboration landscape. Let me know if you want to explore any of these avenues in more detail!

FRANCEMEXICO1
GERMANYMEXICO1
ITALYMEXICO1
SPAINMEXICO2
SWEDENMEXICO2
UNITED KINGDOMMEXICO1
USAMEXICO1

Deja un comentario

Tu dirección de correo electrónico no será publicada. Los campos obligatorios están marcados con *