DPP Ph. D. Research

“Product Service System” Scopus – Bibliometrix

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Main Information

Overall Scope and Growth:

Document Characteristics and Impact:

* Document Average Age (7.28 years): An average age of ~7 years indicates the literature is relatively current. This suggests the field is actively developing. However, one should consider that specific area may have different average ages.
* Average Citations per Document (25.89): Approximately 26 citations per document suggests a reasonable level of impact within the field. This number should be interpreted carefully because citation counts can vary significantly across disciplines and over time. Newer publications may not have had sufficient time to accumulate citations. Consider normalizing citations by publication year to account for this.
* References (126147): A high number of references indicates that the documents within the collection are well-grounded in existing literature. The average references per document is around 32, which means the authors has done a solid work in literature review.
* Document Types: The distribution of document types provides insights into the nature of the research field.
* Articles (1568): Represents the most common type of document.
* Conference Papers (1750): A high number of conference papers suggests the field values conference presentations and proceedings as important avenues for disseminating research. This is important because the field value conference papers as means for rapid communication of results.
* Reviews (135): A moderate number of review articles suggests that there are efforts to synthesize and consolidate the existing research.
* Books and Book Chapters: The low number of books (21) and relatively low number of book chapters (288) compared to articles and conference papers might suggest that this field evolves too quickly to be captured effectively in book form.

Author Productivity and Collaboration:

Keywords:

Critical Discussion Points and Further Investigation:

In summary, this data suggests a growing, collaborative, and internationally connected research field with a reasonable level of impact. Further analysis, particularly focusing on citation patterns, journal distribution, and topic trends, will provide a more nuanced understanding of the field’s dynamics and key research areas. Remember to acknowledge the limitations of relying solely on bibliometric data.

Annual Scientific Production

Average Citations Per Year
Overall Structure

The plot visualizes how individual authors are connected to both specific cited references and relevant keywords. The lines show which authors cite which references and which keywords are associated with their work. Thicker bundles of lines generally indicate stronger or more frequent connections.

Interpretation by Field

* CR (Cited References – Left Field): This field lists the references cited in the publications. Each entry often represents a specific paper or book. The lines emanating from authors connect them to the works they have cited.
* Note that the reference “Morelli N. Developing new product service systems” is the most cited, according to the plot

Key Observations and Potential Insights

1. Product-Service Systems (PSS) as a Central Theme: The keyword “product-service systems” appears to be dominant (it’s at the top of the KW_Merged list), suggesting that this is a core concept within your dataset. Many authors and cited references are linked to this keyword.

2. Prominent Authors: Authors such as “Pezzotta G”, “Shimomura Y”, “Pirola F”, “Sakao” are most associated with Product-Service Systems,

3. Influential References: The references most frequently cited by authors in your dataset appear to be related to the concept and development of Product-Service Systems (PSS). Specifically, “Morelli N. Developing new product service systems” stands out.

4. Related Concepts: Besides PSS, the keywords reveal connections to other relevant concepts like:

* “Product Design”
* “Life Cycle”
* “Product-Service System”
* “Circular Economy”
* “Business Models”
* “Sustainability”

5. Specific Author-Reference-Keyword Clusters:
* Chowdhury S. is linked to the smart product-service reference and term.

* Morelli N. and Sakao T. are related to Morelli N. Developing new product service systems

* The authors at the bottom of the AU list relate to Keywords such as circular economy and Sustainability.

How to Use This Interpretation in Your Research

Critical Considerations

I hope this comprehensive interpretation is helpful. Let me know if you have any more specific questions or if you’d like me to focus on a particular aspect of the plot.

Three-Field Plot
Overall Structure

The plot visualizes how individual authors are connected to both specific cited references and relevant keywords. The lines show which authors cite which references and which keywords are associated with their work. Thicker bundles of lines generally indicate stronger or more frequent connections.

Interpretation by Field

* CR (Cited References – Left Field): This field lists the references cited in the publications. Each entry often represents a specific paper or book. The lines emanating from authors connect them to the works they have cited.
* Note that the reference “Morelli N. Developing new product service systems” is the most cited, according to the plot

Key Observations and Potential Insights

1. Product-Service Systems (PSS) as a Central Theme: The keyword “product-service systems” appears to be dominant (it’s at the top of the KW_Merged list), suggesting that this is a core concept within your dataset. Many authors and cited references are linked to this keyword.

2. Prominent Authors: Authors such as “Pezzotta G”, “Shimomura Y”, “Pirola F”, “Sakao” are most associated with Product-Service Systems,

3. Influential References: The references most frequently cited by authors in your dataset appear to be related to the concept and development of Product-Service Systems (PSS). Specifically, “Morelli N. Developing new product service systems” stands out.

4. Related Concepts: Besides PSS, the keywords reveal connections to other relevant concepts like:

* “Product Design”
* “Life Cycle”
* “Product-Service System”
* “Circular Economy”
* “Business Models”
* “Sustainability”

5. Specific Author-Reference-Keyword Clusters:
* Chowdhury S. is linked to the smart product-service reference and term.

* Morelli N. and Sakao T. are related to Morelli N. Developing new product service systems

* The authors at the bottom of the AU list relate to Keywords such as circular economy and Sustainability.

How to Use This Interpretation in Your Research

Critical Considerations

I hope this comprehensive interpretation is helpful. Let me know if you have any more specific questions or if you’d like me to focus on a particular aspect of the plot.

Most Relevant Sources

Core Sources by Bradford’s Law

Sources’ Local Impact

Sources’ Production over Time

Most Relevant Authors


Authors’ Production over Time

General Observations:

Individual Author Analysis:

Here’s a breakdown of each author, combining information from the plot and the provided list of top-cited articles:

Key Insights and Interpretations:

1. Emerging Trends: The field appears to be rapidly evolving, with a strong emphasis on digitalization, servitization, and circular economy, as highlighted by the highly cited works of Parida V. and the focus of Pezzotta and Pirola. This is also supported by the works of Bertoni M.
2. Methodological Approaches: Several authors (e.g., Ming X, Zheng P) focus on developing and applying specific methodologies for designing and evaluating PSS, indicating a need for structured approaches in this complex domain.
3. Industry 4.0 and Smart PSS: The prevalence of “smart” and “digital” in the titles of highly cited articles indicates a strong connection to Industry 4.0 and the integration of digital technologies into PSS.
4. Literature Reviews: The citation counts of literature reviews (e.g., Pezzotta G, Pirola F) suggest that these reviews serve as valuable resources for researchers entering the field, helping them understand the state-of-the-art and identify research gaps.
5. Evolution of Research Focus: Comparing earlier publications with recent ones for authors like Shimomura Y and Zhang Y reveals a shift towards more contemporary topics like digital twins, blockchain, and cloud computing within the PSS context.

Further Discussion Points for Researchers:

By combining the visual insights from the “Authors’ Production Over Time” plot with the information on highly cited articles, researchers can gain a comprehensive understanding of the key players, influential works, and emerging trends in the field of Product-Service Systems. Remember to critically evaluate these insights and consider the limitations of the data and metrics used.

Author Productivity through Lotka’s Law

Authors’ Local Impact

Most Relevant Affiliations

Affiliations’ Production over Time

Corresponding Author’s Countries

Overall Productivity:

Single vs. Multiple Country Publications (SCP vs. MCP):

International Collaboration (MCP Ratio):

Key Insights and Discussion Points:

* Geopolitical Considerations: The dominance of China in publication volume warrants further investigation. This could be attributed to factors such as government investment in research, a large research workforce, and strategic focus on specific research areas.
* Research Funding and Infrastructure: Countries with higher SCP ratios may have well-established domestic funding mechanisms and research infrastructure, enabling them to conduct research independently.
* Collaboration as a Necessity: Countries with smaller research communities or specialized expertise might rely more on international collaborations to access resources, knowledge, and diverse perspectives. The high MCP ratios of Switzerland, Singapore, and Belgium support this.
* Data Limitations and Context:
* This analysis is based on corresponding author affiliation. While useful, it doesn’t fully capture the extent of international collaboration, as authors from other countries might be involved without being the corresponding author.
* The specific time frame of the data collection is not mentioned. Publication trends can shift over time, so the results should be interpreted with this in mind.
* The subject area of the publications isn’t specified. Collaboration patterns can vary significantly across different fields of research.
* Strategic Implications: The MCP ratio can be a valuable indicator for policymakers to assess the internationalization of their research systems. Countries aiming to enhance their global research impact may consider strategies to promote international collaborations.
* SCOPUS Database: Keep in mind that the analysis is based on data from SCOPUS. While SCOPUS is a major bibliographic database, it doesn’t index all publications worldwide. Results may differ if using other databases like Web of Science.

Further Research Directions:

By considering these points, you can develop a more nuanced and comprehensive understanding of the research landscape and the role of international collaboration in driving scientific progress. Remember to always acknowledge the limitations of the data and consider the broader context when interpreting bibliometric results.

Countries’ Scientific Production

GERMANY2389
CHINA2109
ITALY1096
UK1010
SWEDEN893
BRAZIL563
FRANCE528
JAPAN448
NETHERLANDS340
FINLAND300
SOUTH KOREA294
USA284
DENMARK225
SWITZERLAND175
SPAIN174
SINGAPORE169
AUSTRALIA158
BELGIUM154
INDIA146
GREECE137
INDONESIA123
PORTUGAL107
AUSTRIA92
NORWAY74
POLAND57
MALAYSIA55
CANADA45
IRELAND43
MEXICO42

Countries’ Production over Time

Most Cited Countries

Most Global Cited Documents
Tukker (2004); Mont (2002); Baines (2007); Tukker (2015); Neely (2008); Evans (2017); Linder (2017)

Most Local Cited Documents
Tukker (2004), Baines (2007); Mont (2002); Meier (2010); Tukker (2015)
Overall Observations:

Key Articles & Potential Interpretations:

Here’s a breakdown of some notable articles, categorized by their global and local influence:

* TUKKER A, 2004, BUS STRATEGY ENVIRON: LC 1003, GC 1931, NLC 6.22, NGC 5.79 – This article stands out with the highest LC in the dataset and a very high GC. This suggests it is a seminal work that has been highly influential both within this specific research focus and in the broader academic community. While NLC and NGC are not the highest, the raw citation counts are impressive.
* BAINES TS, 2007, PROC INST MECH ENG PART B J ENG MANUF: LC 755, GC 1629, NLC 17.98, NGC 17.61 – Similar to Tukker (2004), this article demonstrates strong local and global recognition, with a high NLC and NGC. This likely represents another foundational paper that shaped the field and had a broad impact.
* MONT OK, 2002, J CLEAN PROD: LC 550, GC 1743, NLC 3, NGC 2.77 – This article has a slightly lower NLC and NGC despite high LC and GC, indicating it may have been published at a time when the field was less developed or the journal had less reach.
* TUKKER A, 2015, J CLEAN PROD: LC 477, GC 1465, NLC 38.97, NGC 36.92 – This article has very high NLC and NGC scores, suggesting it has made a large impact relative to its publication year.
* NEELY A, 2008, OPER MANAGE RES: LC 176, GC 1070, NLC 17.82, NGC 20.26 – Although its LC is relatively low compared to others, its GC is substantial (over 1000), and its NGC is quite high. This implies that while the article is relevant to this specific research area, its impact extends beyond, influencing broader operation management research.

* MEIER H, 2010, CIRP ANN MANUF TECHNOL: LC 480, GC 820, NLC 35.85, NGC 26.15 – With a high LC and good GC, particularly its very high NLC and NGC, this article seems to be very important for the particular research collection being analyzed.
* Articles such as BEUREN FH, 2013, REIM W, 2015, CAVALIERI S, 2012, and ANNARELLI A, 2016 fall into this category. Their higher NLC relative to NGC indicates they may address specific problems or contexts highly relevant to this research area but less broadly applicable.

Interpreting the “Why”:

* Research Focus: Based on the journal titles and article titles (you might want to provide those for a more precise interpretation), the research area likely revolves around topics such as:
* Sustainable Business Models
* Product-Service Systems (PSS)
* Cleaner Production
* Manufacturing Technology and Management
* Design and Innovation
* Community Structure: The high concentration of articles in the *Journal of Cleaner Production* suggests a well-defined and active research community. Analyzing the authors of these frequently cited articles could reveal key players and research groups within the field.
* Knowledge Flow: Comparing the NLC and NGC values can provide insights into how knowledge is being created and disseminated. Are locally relevant findings also impacting the broader field, or is there a degree of specialization?
* Emerging Trends: Examining the most recent articles (e.g., ZHENG P, 2018) with high NLC and NGC could highlight emerging trends and research priorities within the field.

Further Steps:

1. Topic Modeling/Keyword Analysis: Perform topic modeling or keyword analysis on the abstracts of these articles to identify the specific themes and research questions being addressed.
2. Co-citation Analysis: Explore which articles are frequently cited *together* to reveal intellectual connections and clusters of research.
3. Author Network Analysis: Visualize the collaboration network of authors to understand the structure and dynamics of the research community.
4. Content Analysis: Conduct a deeper qualitative analysis of the most influential articles to understand their key contributions and arguments.
5. Compare with broader Scopus data: check total number of articles in Scopus on the research topic.

By combining bibliometric data with qualitative analysis, you can gain a more comprehensive understanding of the research landscape, identify key trends, and position your own research within the broader context. Remember that bibliometric data is just one piece of the puzzle; expert knowledge and critical evaluation are essential for drawing meaningful conclusions.

Most Local Cited References
Mont (2002); Olivia (2003); Vandermerwe (1988)

Reference Spectroscopy
Understanding the Plot

Overall Interpretation

The plot strongly suggests that the research area has seen a significant surge in interest and activity in more recent years. The period from the late 1990s onward appears particularly crucial, with the red line indicating several peak years of influential publications.

Analysis of Peak Years & Key Publications

Here’s an interpretation of the provided peak years based on the most cited references:

Discussion Points & Further Research

In summary, this RPYS plot provides a valuable overview of the historical development and current trends in the research area. By examining the peak years and key publications, you can gain a deeper understanding of the field’s intellectual foundations and identify promising avenues for future research.

Most Frequent Words

WordCloud

TreeMap

Words’ Frequency over Time

Trend Topics

Overall Interpretation

The plot visualizes the evolution of research interests related to your topic over time (2001-2023). It shows which keywords, as reflected in the `KW_Merged` field of your SCOPUS collection, have gained or lost prominence. A clear trend of increased research interest in “product-service systems” and related concepts such as “industry 4.0” and “smart manufacturing” is observed towards the end of the period examined.

Key Observations and Potential Interpretations

1. Early Stage (2001-2011):

* A few keywords such as “environmental engineering” and “eco-costs” appear at the beginning, showing the early research trend to this area. Then, related concepts such as “services” and “industrial products” started showing up and had a stable frequency in the period.
* Overall, the term frequencies are lower, which might suggest a smaller research community or a less mature stage of development in your field during that period.

2. Growth Phase (2013-2019):

* The plot shows a clear acceleration in the emergence of new trending keywords.
* Keywords like “design”, “business modeling”, “product development”, and “productservice system (pss)” begin to gain traction, indicating a shift towards more integrated and service-oriented approaches.
* The increasing bubble sizes suggest a higher frequency of these terms in the literature, signifying growing research interest.

3. Peak of Interest (2019-2023):

* The most recent years show the highest frequency of terms.
* “Product-service system,” “manufacturing,” “sustainability,” and “decision making” dominate, indicating their importance.
* The emergence of terms like “Industry 4.0”, “smart manufacturing”, and “circular economy” highlights the integration of digital technologies and sustainability into the research landscape.

Specific Term Observations

How to Use This Interpretation

Critical Considerations

In summary, this trend topics plot provides a valuable overview of the evolution of your research field. By carefully analyzing the identified trends and considering the limitations of the data, you can gain insights that inform your research and contribute to the advancement of knowledge.

Clustering by Coupling

Co-occurrence Network
Overall Structure:

The network visualizes the relationships between keywords, where the size of a node (circle) represents the frequency of a keyword and the lines connecting nodes represent the strength of their co-occurrence (how often they appear together in the same articles). The thicker the line, the stronger the association. Given the parameters `normalize = association`, the edge weights are normalized to reflect the strength of the relationship, controlling for the frequency of the individual terms. The graph is divided into several clusters with the `walktrap` algorithm.

Community Detection (Clusters/Topics):

The `walktrap` algorithm has identified distinct communities within the network, represented by node colors:

Key Terms and Their Relevance:

Interpretation & Discussion Points:

1. Dominant Themes: The network suggests that the dominant themes in PSS research, as reflected in these SCOPUS publications, are related to design, implementation, and sustainability. The strong connections between these clusters underscore the integrated nature of these considerations.

2. Emerging Trends: The presence of “Industry 4.0” and “smart products” indicate emerging trends focusing on leveraging advanced technologies to enhance PSS offerings. The isolated position of these keywords suggest that they are not yet fully integrated into the mainstream PSS research, so further investigations are needed to understand the dynamics behind it.

3. Theoretical vs. Practical Focus: The presence of keywords like “business models” and “economics” (in the sustainability cluster) alongside more practical terms like “manufacturing” and “product development” suggests a mix of theoretical and applied research in the field.

4. Gaps and Opportunities: Consider areas *not* well represented in the network. Are there specific technologies, application domains, or theoretical frameworks that are under-explored?

Further Analysis & Critical Evaluation:

By carefully considering these aspects, you can move beyond a descriptive interpretation and develop a more critical and nuanced understanding of the research landscape of Product-Service Systems. Good luck!

Thematic Map
Understanding the Strategic Map

The strategic map visualizes research themes based on their centrality and density.

The map is divided into four quadrants:

Cluster Descriptions and Centrality

Based on the information you provided and the image, here’s a breakdown of the clusters and their most central articles:

1. Product-Service System (Bottom Right – Basic Theme):
* Theme: This cluster focuses on the core concept of “product-service system.” This area, while central, shows lower density, implying that while foundational, it might need to mature further in specific directions.
* Central Articles:
* YAMADA S, 2018, ADV TRANSDISCIPL ENG (Pagerank 0.264)
* SCHEEPENS AE, 2016, J CLEAN PROD (Pagerank 0.257)
* HUANG PC-H, 2014, INNOV, COMMUN ENG – PROC INT CONF INNOV, COMMUN ENG, ICICE (Pagerank 0.234)
* Interpretation: These articles likely lay the groundwork for PSS research, defining key concepts, methodologies, or early case studies. The presence of “product design” hints at a strong engineering perspective within this core PSS theme.

2. Product-Service Systems (Center-Right):
* Theme: This cluster is very similar to the “Product-Service System” cluster but seems to represent a slightly more developed (denser) area.
* Central Articles:
* MARILUNGO E, 2016, PROCEDIA CIRP (Pagerank 0.279)
* SARANCIC D, 2022, SUSTAIN PROD CONSUM (Pagerank 0.272)
* SCHERER JO, 2016, PROCEDIA CIRP (Pagerank 0.263)
* Interpretation: The *Procedia CIRP* journal suggests a focus on manufacturing and production engineering perspectives. Articles in this cluster probably discuss methodologies, frameworks, or case studies related to PSS implementation and design.

3. Smart Products (Center):
* Theme: This cluster is focused on the use of smart products within product-service systems, potentially linking to Industry 4.0.
* Central Articles:
* SCHOLTYSIK M, 2021, PROC DES SOC (Pagerank 0.246)
* MOURTZIS D, 2022, PROCEDIA CIRP-a (Pagerank 0.243)
* RAPACCINI M, 2022, COMPUT IND (Pagerank 0.228)
* Interpretation: The presence of articles from *PROC DES SOC* and *Computers & Industrial Engineering* indicates research related to design, engineering, and the application of smart technologies in industrial contexts. These articles might cover topics like IoT-enabled services, data-driven optimization of PSS, or the role of AI in PSS.

4. Sustainability (Top Left – Niche Theme):
* Theme: This cluster focuses on the intersection of sustainability and product-service systems.
* Central Articles:
* WEVER R, 2015, HANDB OF ETHICS, VALUES, AND TECHNOLOGICAL DESIGN: SOURCES, THEORY, VALUES AND APPLICATION DOMAINS (Pagerank 0.213)
* XING K, 2013, INT J PROD RES (Pagerank 0.212)
* GUSTAFSSON KF, 2021, PROC DES SOC (Pagerank 0.208)
* Interpretation: The articles in this cluster explore the ethical dimensions, production research and design aspects of sustainability within PSS. This indicates that sustainability considerations are being integrated into PSS design and implementation but are still somewhat separate from the core PSS research. This could also indicate the presence of researchers in this field more focused on the niche of sustainability, and less on the broader field of product-service systems.

Overall Interpretation & Discussion Points

Next Steps for the Researcher

1. Deep Dive into Central Articles: Read the most central articles in each cluster to gain a deeper understanding of the key concepts, methodologies, and research questions being addressed.
2. Explore the “Niche Theme”: Investigate why sustainability is positioned as a niche theme. Are there specific barriers preventing its wider adoption in PSS research? Are there particular sub-fields within PSS that are more focused on sustainability?
3. Consider Alternative Data Sources: Explore other databases (e.g., Web of Science) or use different keyword combinations to see if the strategic map changes significantly.
4. Analyze the Evolution of Themes: Perform a dynamic analysis to track how the centrality and density of these clusters change over time.

By carefully considering these interpretations and suggestions, you can use this strategic map to identify research gaps, explore emerging trends, and develop a more nuanced understanding of the product-service system landscape.

Factorial Analysis

Overall Structure and Dimensions:

Potential Clusters and Thematic Areas:

Based on the visual proximity of the keywords, here’s a potential breakdown of clusters and their possible thematic focus:

1. “Smart” Cluster (Top-Left):
* Keywords: “smart product-service system”, “smart products”
* Interpretation: This cluster represents research focused on the integration of smart technologies into products and services. The distance from other clusters indicates this area is somewhat distinct.
2. “Industry 4.0 & Product-Service Systems” Cluster (Near Center-Top Left):
* Keywords: “industry 4.0”, “product service systems”, “product-service systems”, “product and service design”, “knowledge management”.
* Interpretation: This cluster appears to be about how Product service systems apply on industry 4.0 and requires management of knowledge in design.
3. “Mainstream Product-Service System (PSS) Research” Cluster (Center):
* Keywords: “business models”, “economics”, “product-service system (pss)”, “life cycle”, “product-service system”
* Interpretation: This suggests a core area of PSS research, dealing with fundamental issues like business models, life cycle considerations, and economic impacts.
4. “Sustainability & Circular Economy” Cluster (Bottom-Left):
* Keywords: “sustainability”, “circular economy”, “manufacturing”, “environmental impact”, “sustainable products”, “sustainable development”.
* Interpretation: This cluster highlights research focused on the environmental and societal implications of products and services, with a strong emphasis on sustainability and circular economy principles.
5. “Competition & Manufacturing Industries” Cluster (Right):
* Keywords: “competing and services”, “manufacturing industries”, “sales”, “business modeling”.
* Interpretation: This could reflect the competitive aspects of manufacturing and service industries, potentially including business models and sales strategies.

Interpretation & Discussion Points:

Next Steps for the Researcher:

1. Qualitative Review: Conduct a qualitative review of the most cited or highly relevant papers associated with each cluster to gain a deeper understanding of the specific research questions, methodologies, and findings.
2. Keyword Expansion: Consider expanding the keyword list with related terms to refine the clusters and explore potential sub-themes.
3. Database Exploration: Analyze the distribution of publications across different journals or research groups to identify key contributors and influential research streams.
4. Temporal Analysis: Perform a temporal analysis to examine how the prominence of different themes has evolved over time.

Important Considerations:

I hope this interpretation is helpful. Please let me know if you have any specific questions or would like me to elaborate on any of these points.

Co-citation Network
Overall Structure:

The network visualization displays clusters of co-cited references, meaning that these groups of publications are frequently cited together in the same papers. This indicates a shared intellectual foundation or common usage in specific research contexts. The ‘walktrap’ clustering algorithm has identified distinct communities within the network. The size of the nodes represents the number of times a reference has been cited and the thickness of the lines indicates how many times two references have been co-cited.

Community Identification and Interpretation:

Based on the color-coded clusters, here’s a breakdown of what each community might represent (remember this requires domain knowledge for precise interpretation):

Key Observations & Implications:

Next Steps for Interpretation & Critical Discussion:

1. Content Analysis: The most important next step is to *read the key publications* in each cluster. Understand their core arguments, methodologies, and findings. This will allow you to accurately label the themes represented by each cluster.

2. Contextualization: Relate the clusters and influential papers to the broader research landscape in your field. Are there any surprising omissions? How do these clusters relate to current debates or research gaps?

3. Temporal Analysis (Optional): If possible, perform a temporal analysis to understand how these clusters have evolved over time. This can reveal emerging trends and shifts in research focus.

4. Limitations: Be aware of the limitations of co-citation analysis. It primarily reflects citation patterns and may not fully capture the intellectual influence of certain works. Also, the search query used in SCOPUS to download the dataset will affect the generated network.

By combining the information gleaned from the network visualization with a deeper understanding of the content of the key publications, you can develop a robust and insightful interpretation of your research domain. Let me know if you want to explore any of these steps further!

Historiograph
Overall Structure and Temporal Trends:

The graph spans from 2000 to 2018. There seems to be a central cluster of papers published between 2002 and 2010, suggesting a core period of development in the field. The nodes placed at the periphery seems to be more recent. The connections seem dense, which suggests that PSS is a fast developing research field.

Key Observations by Cluster & Time Period:

* roy r, 2000: “A New Business Model for Baby Prams Based on Leasing and Product Remanufacturing”: A foundational work proposing alternative business models like leasing and remanufacturing, which are core tenets of PSS.
* mont ok, 2002: “Sustainable Product-Service Systems”: This signals the early formalization of PSS as a sustainability strategy.
* manzini e, 2003: “Implementing Service-Based Chemical Procurement: Lessons And Results”: Focuses on practical implementation and results.
* tukker a, 2004: “The Virtual Eco-Costs ’99: A Single Lca-Based Indicator For Sustainability And The Eco-Costs – Value Ratio (Evr) Model For Economic Allocation: A New Lca-Based Calculation Model To Determine The Sustainability Of Products And Services”: Introduces LCA (Life Cycle Assessment) and eco-cost indicators, linking sustainability assessment to PSS.

* Temporal Evolution: This initial cluster focuses on defining PSS, linking it to sustainability, and exploring alternative business models. It lays the groundwork for later, more specialized research.

* aurich jc, 2006: “Allocation In Recycling Systems: An Integrated Model For The Analyses Of Environmental Impact And Market Value”: Deals with recycling and environmental impact.
* morelli n, 2006: “Eight Types of Product-Service System: Eight Ways to Sustainability? Experiences from Suspronet”: Identifies different types of PSS, suggesting a move towards classification and understanding the variety of approaches.
* tukker a, 2006: “Service Engineering To Intensify Service Contents In Product Life Cycles”: Focuses on service engineering as a means to integrate services into product lifecycles.
* baines ts, 2007: “Computer Aided Quality Assurance Systems (Caqas) Scope, Requirements And Trends.”: Focuses on quality assurance systems.
* neely a, 2008: “Developing New Product Service Systems (Pss): Methodologies And Operational Tools”: Signals a focus on methodologies and tools for developing PSS.
* meier h, 2010: “Product-Services As A Research Field: Past, Present And Future. Reflections From A Decade Of Research”: Reflects on the development of the field.
* martinez v, 2010: “Editorial For The Special Issue Of The Journal Of Cleaner Production On Product Service Systems”: Editorial note; an important signal for the community, indicating that the topic is worthy of a special journal issue.

* Temporal Evolution: This cluster represents a period of expansion and diversification. Research moves towards specific methods, tools, and types of PSS. The inclusion of a retrospective (“Meier 2010”) indicates the field is gaining maturity.

* cavalieri s, 2012: “Integration Of A Service Cad And A Life Cycle Simulator”: Discusses tools for PSS design and lifecycle management.
* vasantha gva, 2012: “Rethinking Product Design For Remanufacturing To Facilitate Integrated Product Service Offerings”: Focuses on design for remanufacturing.
* boehm m, 2013: “The Transfer And Application Of Product Service Systems: From Academia To Uk Manufacturing Firms”: Addresses the practical application of PSS in industry.
* beuren fh, 2013: “Sustainable Urban Infrastructure In China: Towards A Factor 10 Improvement In Resource Productivity Through Integrated Infrastructure Systems”: Focuses on application of PSS in the context of Urban infrastructure in China.
* tukker a, 2015: “Sustainable Product-Service-Systems: The Kathalys Method”: Continues development of PSS methods.
* reim w, 2015: “Common Representation Of Products And Services: A Necessity For Engineering Designers To Develop Product-Service Systems”: Highlights the need for common representation in engineering design.
* vezzoli c, 2015: “Design Of Sustainable Product Life Cycles”: Continues the theme of sustainable design.
* annarelli a, 2016: “Value Creation In Pss Design Through Product And Packaging Innovation Processes”: Focuses on value creation through design.

* Temporal Evolution: This cluster shows refinement and practical application of PSS concepts. There is a focus on design for specific contexts (e.g., remanufacturing, urban infrastructure) and the development of methodologies and tools.

* zheng p, 2018: “A New Method For Monitoring Industrial Product-Service Systems Based On Bsc And Ahp”: Focuses on monitoring and evaluation of PSS, which is important for practical implementation.

* Temporal Evolution: A single node in 2018 suggests ongoing research in monitoring and evaluation.

Pivotal Works and Citation Paths:

Suggestions for Further Exploration:

This analysis provides a good starting point for understanding the evolution of PSS research. By further investigating the relationships between these papers and considering the broader context, you can gain a deeper understanding of the field.

Collaboration Network
Overall Structure:

The network visually suggests a few distinct clusters or communities. These clusters are linked, but not strongly interconnected. This means there are groups of researchers who collaborate frequently amongst themselves, but less so with researchers in other groups. The positioning of nodes is driven by the “association” normalization, meaning authors closer together have a stronger co-authorship relationship. The edges (lines) represent co-authorship, and the dashed lines indicates weaker collaboration compared to solid ones.

Community Detection (Walktrap Algorithm):

The “walktrap” algorithm was used for community detection. This algorithm tries to find communities based on random walks on the network, assuming that short random walks tend to stay within the same community. The different colors represent the distinct communities identified by this algorithm. We have at least 6 distinct communities shown in the graph.

Most Connected Authors and Relevance:

Interpretation and Discussion Points:

1. Interdisciplinary Nature: The existence of distinct clusters might suggest different sub-disciplines or research focuses within the overall scope of the SCOPUS dataset. Researchers should investigate the topics each cluster is working on to see if there are clearly defined areas of research.

2. Key Collaborators: The most connected authors represent important nodes for knowledge dissemination and collaboration. Consider examining the publications of these authors to understand their specific contributions and how they bridge different research areas.

3. Potential for Collaboration: The links (edges) *between* clusters are important. If there are only a few weak links (dashed lines) between two clusters, it might suggest an opportunity to foster more collaboration between these areas. Are there shared research questions or methodologies that could benefit from a more integrated approach?

4. Database Scope: Remember that this network is based on *your specific SCOPUS dataset*. The network structure might look different if you expanded the search terms or time frame.

5. Normalization Effects: The “association” normalization emphasizes relationships between authors who appear together more often than expected by chance. This is useful for highlighting strong collaborative links.

6. Community Repulsion: The `community.repulsion = 0.05` parameter prevents communities from overlapping too much, making the visual separation clearer. A higher value would push communities further apart.

Critical Evaluation and Further Steps:

By carefully examining the structure of the network, the composition of the communities, and the roles of key authors, you can gain valuable insights into the dynamics of collaboration within your research area. Remember to consider the parameters used to generate the network and the scope of your data when interpreting the results.

Countries’ Collaboration World Map

1. Major Hubs of Scientific Production:

2. Key International Partnerships:

3. Global Patterns of Collaboration:

Important Considerations and Critical Questions:

Further Analysis:

To delve deeper, I recommend the following:

1. Filter by Research Area: Analyze collaboration networks for specific research areas within your dataset to uncover field-specific patterns.
2. Temporal Analysis: Examine how collaboration networks have changed over time to identify emerging partnerships and shifting research priorities.
3. Collaboration Strength Metrics: Calculate metrics such as the number of co-authored publications, the citation impact of collaborative papers, or the relative contribution of different countries to collaborative projects.
4. Compare with National R&D Investments: Compare this with the overall national investment in Research and Development and see if there’s a corelation.

By considering these factors and conducting further analysis, you can gain a more nuanced understanding of the dynamics of international scientific collaboration within your chosen research area. Let me know if you want to explore any of these aspects in more detail!

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