DPP Ph. D. Research

Business Model Innovation” and “Ecosystem”

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Main Information


Overall Summary: This Scopus-derived collection, spanning 2009 to 2025, represents a growing and moderately impactful body of research with a strong collaborative element. The collection, comprising 374 documents sourced from 257 different sources, reflects a multi-faceted area of study with contributions from a relatively large author base.

Detailed Interpretation:

Further Questions and Considerations:

By considering these points, you can use the bibliometric statistics to gain a deeper understanding of the research landscape and formulate meaningful research questions for further exploration.

Annual Scientific Production


Average Citations Per Year


Three-Field Plot
Overall Structure and Purpose

The plot aims to show how authors (AU, the target field) are connected to both cited references (CR, left field) and keywords (KW_Merged, right field). The lines connecting the fields indicate the strength and direction of the relationships. Thicker lines suggest a stronger or more frequent association.

Analysis of Each Field and Their Connections

* Key Observation: The authors with the most prominent positions in the center field (e.g., ‘parida v’, ‘lindgren p’) appear to be central figures in the dataset. These may be authors with prolific publication records or authors who have been heavily cited.

* Key Observation: The links from CR to AU indicate which authors in the dataset are citing specific prior works. The references that are most frequently linked likely represent foundational or highly influential papers in the field. For example:

* “chesbrough h. business model innovation:…” is a frequently cited article. This suggest that a number of author cite this reference.
* “zott c. amit r. business model design: an activity system per” is a frequently cited article. This suggest that a number of author cite this reference.

* Key Observation: The links from AU to KW_Merged show which keywords are most strongly associated with the publications of particular authors. For example:

* “business model innovation” appears to be a prominent keyword. The authors connected to this keyword are likely contributing to research in this area.
* Other prominent keywords include “ecosystems”, “digitalization” and “business models”.

Interpreting the Connections

1. Key Authors and Foundational Literature: The plot helps identify key authors and the foundational literature that underpins their work.
2. Research Themes and Keyword Associations: The plot shows how authors are contributing to specific research themes as represented by the keywords. For instance, an author heavily linked to “business model innovation” is likely actively researching and publishing in that area.
3. Citation Patterns: The connections between CR and AU reveal citation patterns. If a particular cited reference has many connections to different authors, it suggests that this reference is highly influential across the field.
4. Bridging Concepts: The plot can reveal how authors bridge different concepts or disciplines. For instance, an author connected to both “digitalization” and “business models” keywords might be researching digital business models.
5. Knowledge Diffusion: By visualizing who is citing whom, the plot hints at knowledge diffusion patterns within the field.

Limitations and Considerations for Interpretation

Overall Summary

This three-field plot provides a valuable visual overview of the intellectual structure of the research field represented by the SCOPUS collection. It allows researchers to quickly identify key authors, influential publications, important research themes, and the connections between them. To get even more insights, it would be benificial to interact with the plot dynamically.

Most Relevant Sources


Core Sources by Bradford’s Law


Sources’ Local Impact

Sources’ Production over Time


Most Relevant Authors

Authors’ Production over Time

Overall Observations:

Individual Author Analysis:

Key Insights & Interpretations:

Critical Discussion Points:

Recommendations:

By combining the visual representation of the plot with the specific publication data, you can gain a richer understanding of the intellectual landscape and the key contributors in the field. Remember to critically evaluate the findings and consider the limitations of the bibliometric data.

Author Productivity through Lotka’s Law


Authors’ Local Impact


Most Relevant Affiliations


Affiliations’ Production over Time


Corresponding Author’s Countries


Overall Interpretation:

The plot illustrates the distribution of scientific publications across different countries, focusing on the *corresponding author’s* affiliation. It highlights both the overall productivity of each country (total articles) and the degree to which their research is conducted in collaboration with researchers from other nations (MCP vs. SCP). The MCP ratio provides a standardized way to compare the international engagement of each country.

Key Observations and Discussion Points:

1. Most Productive Countries:

* China leads in total publications (34 articles), but a significant portion (25) are Single Country Publications (SCP). This suggests a strong domestic research focus within this dataset, despite the growing internationalization of Chinese science.
* Sweden follows in the second place with 21 articles.
* Finland and Germany are tied in third place with 18 articles each.
* The United Kingdom and the USA have a relatively similar number of articles (17), indicating considerable contributions to this research area.

2. International Collaboration Levels (MCP Ratio):

* Austria shows the highest international collaboration rate (66.7%), with most of their publications involving multiple countries. This suggests that Austrian researchers in this field are highly integrated into international networks.
* Brazil follows Austria with a high MCP rate of 57.1%.
* Finland (55.6%) and Sweden (52.4%) also display a high proportion of international collaborations, indicating a strong emphasis on global partnerships in their research strategies.
* Countries like Switzerland and Romania have an MCP of 0%, indicating that all publications from these countries are Single Country Publications.
* Denmark (15.4%) and Indonesia (20%) have relatively low MCP ratios, suggesting a greater emphasis on domestic research efforts compared to other countries in this list.

3. Balance Between Domestic and Global Research:

* The data highlights a spectrum of approaches. Some countries, like China, prioritize domestic research output while still engaging in international collaborations to some extent. Others, like Austria, heavily rely on international partnerships to produce research.
* It’s important to consider the potential reasons for these differences. Factors might include:
* Funding policies: National research funding agencies might prioritize domestic collaborations in some countries.
* Research infrastructure: Countries with well-developed research infrastructure may be less reliant on international collaboration.
* Research culture: Different countries may have varying cultural norms and attitudes towards international collaboration.
* Specific research area: This observation only refers to the selected database and the specific query. The countries might be different if you change the database or the query.

4. Comparison of Productivity and Collaboration:

* While China is the most productive, its MCP ratio is lower than many European countries. This could be due to the large scale of its domestic research sector.
* Countries like Finland and Sweden demonstrate a strong combination of research productivity and international collaboration, suggesting a well-integrated and globally engaged research community.

Critical Discussion Points & Further Investigation:

In summary, this “Corresponding Author’s Country Collaboration Plot” provides a valuable snapshot of the geographic distribution of research activity and the extent of international collaboration in this particular SCOPUS dataset. By examining the balance between SCP and MCP publications, researchers can gain insights into the research strategies and priorities of different countries and identify potential areas for further investigation.

Countries’ Scientific Production


Countries’ Production over Time


Most Cited Countries


Most Global Cited Documents


Most Local Cited Documents

Overall Observations:

* Recent Publications Dominate Local Citations: Many of the highly locally cited articles are from 2018 onwards. This suggests a few possibilities:
* The research area represented by your dataset is relatively new or experiencing rapid growth, with recent publications building upon each other.
* The dataset might be biased towards more recent publications.
* Global Citation Counts Vary Widely: The range of Global Citations (GC) is substantial, indicating some articles have had a far greater overall impact than others. It’s important to note how NTC is impacted by the older publications and how that relates to the GC.
* Normalization Matters: NLC and NGC are crucial. A high LC but low NLC might mean an article is frequently cited within the dataset simply because citation practices are common in that area, not necessarily because of exceptional importance. Conversely, a relatively lower LC but a high NLC could indicate a very significant paper for the specific community. Similarly for GC and NGC.

Key Articles and Their Potential Significance:

Let’s analyze some specific articles based on the interplay between LC, GC, NLC, and NGC:

Questions for Further Investigation:

Next Steps:

1. Read the Abstracts (and ideally the full text) of the top articles: This will give you a much deeper understanding of their contribution.
2. Use Biblioshiny’s Network Analysis tools: Explore citation networks to visualize relationships between articles and identify key research clusters.
3. Consider Subgroup Analysis: If your dataset covers multiple sub-themes, analyze these metrics separately for each sub-theme to identify differences in citation patterns.

By combining these quantitative metrics with a qualitative understanding of the research content, you can develop a much richer and more nuanced interpretation of your bibliometric analysis. Let me know if you’d like me to elaborate on any of these points or suggest other analyses.

Most Local Cited References

Reference Spectroscopy

Overall Interpretation:

The RPYS plot visualizes the historical roots and intellectual development of the research area represented by your SCOPUS dataset. The black line showing the total number of cited references per year, provides a general overview of the historical scholarly activity influencing your research field. The red line, highlighting years where citation frequency significantly deviated from the 5-year median, identifies specific years which had a significant impact. The fact that you have so many of these peak years suggests that the field is building on a range of foundational ideas.

Key Observations and Interpretation:

1. Dominance of Recent References (Black Line): The black line (total cited references) shows a strong upward trend towards the later years. This indicates that research in this field is heavily reliant on more recent publications. This is a typical pattern in many scientific fields, as knowledge accumulates and the focus shifts to contemporary issues. It’s important to consider how much the field relies on new publications and if the newer contributions truly build upon existing and older knowledge, or simply ignore previous findings.

2. Significant Historical Reference Years (Red Line and Lists): The red line reveals specific “citation peaks” – years when publications from those years were exceptionally influential. The provided list gives us a deeper understanding of these peak years:

* 1967: The Dawn of Grounded Theory: The overwhelming presence of Glaser and Strauss’s “The Discovery of Grounded Theory” demonstrates the fundamental importance of grounded theory methodology in the intellectual history of this field. It suggests that a significant portion of the research relies on qualitative methodologies and inductive reasoning. Other references from this year (e.g., Thompson on organizations, Levi-Strauss on the savage mind, Nunnally on psychometric theory) suggest broader intellectual influences from organizational theory, anthropology, and psychometrics.

* 1985: Competitive Advantage and Network Effects: The appearance of Porter’s “Competitive Advantage” points to the influence of strategic management thinking. Katz and Shapiro’s work on network externalities suggests an interest in economics and the dynamics of network-based industries.

* 1990: Absorptive Capacity and Architectural Innovation: Cohen and Levinthal’s “Absorptive Capacity” is a key concept in innovation management. Henderson and Clark’s “Architectural Innovation” offers another prominent idea in the same field. Ostrom on Governing the Commons suggests interests related to institutional governance and collective action, as well as Corbin and Strauss on grounded theory, showing the continuing relevance of qualitative research.

* 1997: Dynamic Capabilities and Disruptive Innovation: The strong presence of Teece, Pisano, and Shuen’s “Dynamic Capabilities” and Christensen’s “The Innovator’s Dilemma” indicates a focus on how organizations adapt and compete in dynamic environments and the impact of disruptive technologies.

* 2001: E-Business Value Creation: The multiple citations to Amit and Zott’s “Value Creation in E-Business” show a strong interest in the rise of internet technologies and its impact on business strategy.

* 2004: Business Ecosystems and Service-Dominant Logic: Iansiti and Levien’s work on “The Keystone Advantage” and Vargo and Lusch’s “Evolving to a New Dominant Logic for Marketing” signify a shift towards viewing business in terms of ecosystems and evolving theoretical perspectives in marketing. Also Osterwalder’s work on business model ontology.

* 2007: Theory Building from Cases and Dynamic Capabilities (revisited): Eisenhardt and Graebner on theory building indicates continued interest in qualitative approaches. Teece’s clarification of “Dynamic Capabilities” signals ongoing refinement of this concept. Zott and Amit suggest continued interests in business model innovation.

* 2010: Business Model Innovation: Osterwalder and Pigneur’s book on “Business Model Generation” had a very big impact. Teece on business models is also cited. Chesbrough on business model innovation highlights a subfield of innovation studies.

* 2013: Qualitative Rigor and Sustainable Innovation: Gioia, Corley, and Hamilton highlight an emphasis on making qualitative research more rigorous, while Boons and Lüdeke-Freund show the importance of sustainable development.

* 2017: Business Model Innovation and Ecosystems: Foss and Saebi discuss business model innovation while Adner analyzes ecosystems. Massa, Tucci, and Afuah give a critical assessment of business model research.

Critical Discussion Points and Further Analysis:

In summary, this RPYS plot offers a valuable historical and intellectual map of your research area. By carefully examining the peaks and valleys, and by critically evaluating the key publications identified, you can gain a deeper understanding of the field’s past, present, and future trajectory. Use this analysis to inform your own research, to identify potential gaps in the literature, and to contribute to the ongoing evolution of the field.

Most Frequent Words


WordCloud


TreeMap

Words’ Frequency over Time


Trend Topics

Overall Observations:

Specific Topic Trends and Possible Interpretations:

1. Circular Economy, Entrepreneurship, Circular Business Models, Business Models, Sustainability, Ecosystem:
* Trend: These appear to be the most prominent topics in the 2024, showing high term frequency.
* Interpretation: This likely reflects the increasing importance of sustainable business practices and innovative business models that focus on resource efficiency and environmental responsibility. The high frequency of “circular economy,” “circular business models,” and “sustainability” underscores the growing emphasis on these concepts in the business and academic worlds. The presence of “ecosystem” highlights the system-level thinking often associated with sustainability and circularity, emphasizing interconnectedness and collaboration. “Entrepreneurship” indicates innovative and new companies are building on this new economy.

2. Business Model Innovation, Ecosystems, Innovation
* Trend: These terms experienced an increase in frequency and prominence around 2022.
* Interpretation: This may suggest a growing focus on disruptive business models, digital transformation, and the role of ecosystems in fostering innovation.

3. Artificial Intelligence, Internet of Things, Smart City, Information Systems
* Trend: Appear on the graph around 2020, and 2018
* Interpretation: This may suggest a growing focus on disruptive business models, digital transformation, and the role of ecosystems in fostering innovation.

4. Economics, Manufacture, Ecology
* Trend: The emergence of these terms occurs in 2016.
* Interpretation: They could be the starting focus of the research and have now evolved to the more complex and innovative themes shown in the later years.

Considerations for Critical Discussion:

Recommendations for Further Analysis:

By considering these points, you can provide a more nuanced and critical interpretation of the trend topics plot, leading to more meaningful insights for your research.

Clustering by Coupling


Co-occurrence Network

Overall Structure:

The network visually presents a landscape of interconnected keywords. Nodes (circles) represent keywords, and the lines connecting them represent the strength of their co-occurrence within the analyzed publications. The thicker the line, the more frequently those keywords appear together. Node size also reflects centrality/frequency of the keyword. The use of “association” normalization likely means the edge weights reflect how much more often two keywords co-occur than expected by chance.

Community Detection (Topics):

The Walktrap algorithm has identified distinct communities (clusters) within the network, visually represented by different colors (blue and red). This suggests that the publications in your dataset can be grouped based on shared thematic areas. Let’s interpret these clusters:

Key Terms and Their Relevance:

Interpretation and Discussion Points:

Based on this analysis, you could interpret your data as indicating a strong research trend focused on the following:

Critical Discussion:

In summary: This word co-occurrence network provides a valuable overview of the key themes and relationships within your SCOPUS dataset. It suggests a vibrant and evolving research landscape focused on the intersection of sustainability, business model innovation, and digital technologies. By considering the limitations of the analysis and exploring the data further, you can develop a more nuanced and insightful understanding of the research trends in this area.

Good luck with your research! Let me know if you want to explore any of these aspects in more detail.

Thematic Map

Overall Interpretation

This strategic map is a visual representation of the relationships between keywords in your Scopus dataset related to (though not explicit, I assume from the keywords) business, innovation, and technology. The map is divided into four quadrants based on two dimensions:

Based on these dimensions, the map identifies:

Cluster Analysis

Now let’s analyze the clusters individually, using the provided list of the top three most central articles (by PageRank) for each:

Motor Themes (Top Right)

* Deep Learning/Digital/Article: This cluster seems to group topics around digital transformation.
* Key Articles:
* BROCK K, 2019, TECHNOL FORECAST SOC CHANGE (pagerank 0.186)
* VALTER P, 2020, WIRELESS PERS COMMUN (pagerank 0.162)
* LINDGREN P, 2021, WIRELESS PERS COMMUN (pagerank 0.155)
* Interpretation: Given the high pagerank of the articles in technology forecasting and wireless communications, this cluster seems to be very dense and represents the cutting edge of innovation in the field.
* Internet of Things/Commerce/Big Data: This cluster focuses on the application of IoT, commerce and big data.
* Key Articles:
* CHEN Y, 2011, PROC – ANNU SRII GLOBAL CONF, SRII (pagerank 0.222)
* KAZANTSEV N, 2023, TECHNOL FORECAST SOC CHANGE (pagerank 0.213)
* ZHOU J, 2025, PLOS ONE (pagerank 0.156)
* Interpretation: The focus on applications, coupled with the high centrality, suggests a well-established area with many research activities, probably involving also technology forecasting.
* Innovation/Sustainability/Business Model:
* Key Articles:
* SNIHUR Y, 2022, LONG RANGE PLANN (pagerank 0.242)
* MARCON A, 2024, TECHNOL SOC (pagerank 0.241)
* MAGNAGHI M, 2025, TECHNOL FORECAST SOC CHANGE (pagerank 0.239)
* Interpretation: Innovation and business model are linked to sustainability. The technological aspect is not the only consideration. The topic is central (high centrality).

Basic Themes (Bottom Right)

* Business Model Innovation/Ecosystems/Business Models: This is a core cluster focusing on the intersection of business model innovation and ecosystems.
* Key Articles:
* SJÖDIN D, 2023, TECHNOL FORECAST SOC CHANGE (pagerank 0.316)
* PALMIÉ M, 2022, TECHNOL FORECAST SOC CHANGE (pagerank 0.27)
* LI X, 2023, J ENG TECHNOL MANAGE JET M (pagerank 0.228)
* Interpretation: The presence of multiple articles from *Technological Forecasting and Social Change* indicates that this is a key journal for this cluster. The focus on business model innovation suggests a concern with how businesses adapt to changing environments and technologies.

* AI/Artificial Intelligence/Smart Cities/Economics: This cluster appears to be centered on applications of AI in urban environments and the economic implications.
* Key Articles:
* VALTER P, 2017, GLOBAL WIREL SUMMIT, GWS (pagerank 0.118)
* OGILVIE T, 2015, RES TECHNOL MANAGE (pagerank 0.146)
* LAMPOLTSHAMMER TJ, 2024, FROM ELECTRON TO MOB GOV (pagerank 0.082)
* Interpretation: The inclusion of “economics” alongside AI and smart cities suggests research exploring the economic impact, business models, or policy implications of AI-driven urban development.

Emerging or Declining Themes (Bottom Left)

* Innovation Ecosystems/Data-Driven Innovation/Bibliometrics/Case Study Research/Complementary Assets: This cluster seems to group topics related to studying and understanding innovation processes.
* Key Articles (for some of the sub-themes within the cluster):
* TESCH JF, 2017, INT J INNOV MANAGE (pagerank 0.09)
* TESCH JF, 2021, DIGIT DISRUPTIVE INNOV (pagerank 0.09)
* LI B, 2023, TECHNOL ECON DEVELOP ECON (pagerank 0.006)
* Interpretation: The presence of “bibliometrics” and “case study research” suggests that this cluster is focused on *how we study* innovation, rather than innovation itself. The lower centrality and density suggest that while these are important methodological considerations, they may not be the primary focus of the overall research field represented by your dataset.

Niche Themes (Top Left)

* Risk Management/Smart Products/Centralized Management: This cluster is less central and more developed and contains keywords such as risk management and smart products.
* Key Articles:
* ZHENG M, 2017, PROCEDIA CIRP (pagerank 0.156)
* ZHENG M, 2016, PROC NORDDESIGN, NORDDESIGN (pagerank 0.152)
* KOHTAMÄKI M, 2021, THE PALGRAVE HANDB OF SERVITIZATION (pagerank 0.015)
* Interpretation: This suggests a focus on managing risks associated with innovative products. The cluster is a niche.

* Circular Business Model Innovation: This cluster is less central and more developed and contains keywords such as circular business model innovation, electric vehicles, battery, decarbonisation, etc.
* Key Articles:
* CHIRUMALLA K, 2024, TECHNOL FORECAST SOC CHANGE (pagerank 0.235)
* TOORAJIPOUR R, 2022, ADV TRANSDISCIPL ENG (pagerank 0.181)
* ISLAM MT, 2024, HIGHLIGHTS SUSTAIN (pagerank 0.093)
* Interpretation: This suggests a focus on circular economy business models. The cluster is a niche.

Important Considerations & Questions for Further Analysis:

Next Steps for the Researcher:

1. Validate the Clusters: Carefully examine the articles within each cluster to ensure they conceptually fit together.
2. Explore the Relationships Between Clusters: Consider how the clusters interact. For example, are there overlaps or connections between “Business Model Innovation” and “AI/Smart Cities”?
3. Investigate the Emerging Themes: Pay close attention to the “Emerging or Declining Themes” quadrant. These areas could represent opportunities for future research.
4. Consider the Limitations: Be aware of the limitations of bibliometric analysis. The map reflects keyword co-occurrence, but it doesn’t capture the full complexity of the research field.
5. Contextualize the Findings: Relate your bibliometric findings to your own research question and the broader literature.
6. Investigate the clusters’ labels: the labels assigned to the clusters are automatically generated, and sometime they are not very good. Take some time to read the keywords that constitute them, and, if necessary, adjust the labels manually to reflect the cluster contents.

By carefully interpreting the strategic map and considering these additional factors, you can gain valuable insights into the structure and dynamics of your research field. Remember that this is just a starting point for further investigation! Let me know if you have more specific questions or would like to explore any of these points in more detail.

Factorial Analysis
Overall Structure:

Cluster Identification and Interpretation:

Visually, we can discern some potential clusters. I will try to describe them based on the keywords that appear together:

1. Literature Review/Digitization Cluster (Left Side): The cluster on the left, characterized by terms like “literature reviews,” “literature review,” and “digitization.” This suggests a body of literature focused on *synthesizing existing knowledge* in the realm of digitization, and possibly on *methodological research itself.*

2. Sustainability/Business Model Cluster (Lower Left): Keywords like “sustainable business,” “sustainable business model,” “sustainable development,” and “electronic commerce” form a cluster. This suggests a research stream focused on the *application of sustainability principles to business models and development.* “Electronic Commerce” suggests a focus on the digital aspects of sustainability.

3. Innovation/Ecosystem Cluster (Center): A central cluster includes terms like “digital platforms,” “artificial intelligence,” “sustainability,” “entrepreneur,” “innovation,” “business models,” “technological development,” “business ecosystem,” “big data,” “electronic commerce,” “business ecosystems,” “business model innovation,” and “digital technologies”. This suggests a focus on how digital technologies like AI, Big Data, digital platforms and e-commerce are used to foster innovation, create new business models, and develop ecosystems, possibly with an element of sustainability.

4. Entrepreneurial Ecosystem/Value Creation Cluster (Upper Right): The presence of “entrepreneurial ecosystem,” “value creation,” “blockchain,” and “competitive advantage” suggests a cluster focused on research that explores the dynamics of entrepreneurial ecosystems, value creation strategies (potentially leveraging blockchain), and gaining a competitive edge in the market.

5. Ecology/Economics Cluster (Lower Right): A cluster with “manufacture,” “economics” and “ecology” seems present. This may suggest an area focused on sustainable production and green economy.

Most Contributing Terms and Their Relevance:

Based on the parameters, keywords with a `minDegree` of 6 were considered. This means these keywords co-occurred with at least 6 other keywords in your dataset. Their prominence in the map suggests these are central concepts within your research collection:

Actionable Interpretations and Questions for Further Exploration:

1. Axis Interpretation: What distinguishes the *Literature Review/Digitization Cluster* from the *Entrepreneurial Ecosystem/Value Creation Cluster*? One hypothesis might be that Dim 1 represents a shift from theory and methodology to practice and application. Further examination of the articles associated with keywords on either end of Dim 1 could confirm or refute this. What is the main difference between the left side and the right side?
2. Cluster Relationships: How do the *Sustainability/Business Model Cluster* and the *Innovation/Ecosystem Cluster* interact? Is sustainable innovation a major theme? Are the AI/Big Data innovations being applied to sustainability challenges?
3. Missing Pieces: Are there any relevant keywords *not* appearing on the map? This could indicate gaps in the research or areas less represented in your specific dataset. Consider if there are related terms with a degree less than 6 that might be relevant but were excluded.
4. Database Bias: Remember this analysis is based on Scopus data. Consider how the trends might differ if you used Web of Science or a different database.

Critical Considerations:

By investigating these clusters, examining the contributing keywords, and addressing the questions above, you’ll gain a much richer understanding of the intellectual landscape represented in your bibliometric data. Remember that this is just a starting point for your exploration! Good luck!

Co-citation Network

Overall Structure:

Community Detection (Walktrap Algorithm):

Most Connected Terms and Relevance:

Interpretation Guidance & Critical Discussion Points:

1. Central Theme Identification: Based on the most connected terms, try to define the overarching research theme represented by the core cluster. What specific concepts, theories, or methodologies are these highly cited works associated with? *Consider the literature that cites these foundational works. What are they building upon?*

2. Community-Specific Themes: Analyze the publications within each of the other communities (identified by different colors). What are the specific themes or research questions being addressed within each community? *How do these themes relate to or diverge from the central theme? Are there any emerging trends or debates within these communities?*

3. Bridging Works: Examine the works that connect different communities (nodes with connections to multiple clusters). Are these works acting as “bridging” literature, integrating different perspectives or applying concepts from one area to another?

4. Temporal Trends: Consider the publication dates of the cited works. Is the field primarily based on older, established works, or are there newer publications gaining prominence? *The “2010” dates in many central nodes suggest a significant wave of research activity around that time.*

5. Database Bias: Remember this network is based on your Scopus collection. *Consider the scope of Scopus and whether it may have introduced biases in terms of journal coverage, language, or regional representation.*

6. Limitations of Co-citation Analysis: Co-citation measures the *relatedness* of documents based on citation patterns, but it doesn’t necessarily indicate conceptual similarity or agreement. Cited works might be related because they are contrasting viewpoints or because one work critiques another.

7. Missing Nodes: Consider why some prominent authors or publications in the field *aren’t* highly connected in this network. This could be due to various factors, such as:

* They are not frequently cited *together* with the core literature.
* They are more recent and haven’t had time to accumulate citations.
* They are published in venues not well-covered by Scopus.

By critically examining the network structure, community composition, and key publications, you can develop a nuanced understanding of the intellectual landscape within your research area and identify potential avenues for further investigation. Remember to go back to the actual publications cited to validate your interpretations and gain deeper insights!

Historiograph

Overall Observations:

Cluster-Specific Analysis & Interpretation:

Cluster 1 (Purple): Business Model Innovation and Ecosystems (Carayannis Focus)

Cluster 2 (Red): Analytics and Service Business Models (Snihur Focus)

Cluster 3 (Orange): Business Model Innovation for Sustainability and Resilience (Chen and Parida Focus)

Cluster 4 (Blue): Conference Proceedings and Digital Economy

Cluster 5 (Green): Innovation 2.0 and New Business Models

Pivotal Works:

Notable Temporal Trends:

Critical Discussion Points for the Researcher:

1. Cluster Isolation: Why is the green cluster isolated? Is there a different research stream?
2. Methodological Diversity: Is there a methodological homogeneity within each cluster, or are there diverse approaches being used (e.g., case studies, surveys, modeling)?
3. Geographical Focus: Does the research have a specific geographical focus, and how might that influence the findings?
4. Missing Links: Are there potential connections *between* clusters that are not captured by direct citations? Could a broader analysis (e.g., co-citation, bibliographic coupling) reveal hidden relationships?
5. Theoretical Underpinnings: What are the dominant theoretical frameworks being used in each cluster (e.g., resource-based view, dynamic capabilities, transaction cost economics)?
6. Limitations: What are the limitations of this historiograph? Does it capture the full breadth of research in this area, or are there important publications missing from SCOPUS?

By addressing these questions, the researcher can gain a deeper understanding of the evolution of knowledge in this area and identify potential avenues for future research. This analysis provides a strong foundation for a critical literature review.

Collaboration Network
Overall Structure:

The network is relatively sparse, indicating a moderate level of collaboration within the analyzed SCOPUS dataset. Instead of one giant component, we see a collection of smaller, distinct clusters of authors. This suggests that while there are collaborations, they are often confined to specific groups or research teams. The parameters used ensure that isolates (authors with no connections) were removed, so every node present in the graph has at least one collaborator.

Community Detection (Walktrap Algorithm):

The Walktrap algorithm has identified several distinct communities, visually represented by the different colors. This is a key finding. It strongly indicates that the authors within this dataset tend to work in specific sub-areas or on specific projects. Each color represents a distinct group of researchers who collaborate more frequently with each other than with those in other colored groups.

Notable Communities and Key Authors:

Relevance of Most Connected Terms (Labels):

The labels provided (limited to the top 50 most connected) highlight the key players in this collaboration network. The size of the labels reflects the degree of connection (centrality). Authors with larger labels (e.g., Parida V) are likely prolific collaborators and potentially key figures in their respective research areas.

Interpretation and Discussion Points:

1. Interdisciplinary Nature: The presence of distinct communities could suggest that the overall research area covered by the SCOPUS dataset is interdisciplinary, with researchers from different subfields collaborating on specific projects.

2. Research Group Dynamics: Each cluster likely represents a specific research group or team. Analyzing the publication records of these groups would reveal their specific research topics and how they relate to each other.

3. Impact of Central Authors: Authors like “Parida V” play a crucial role in knowledge dissemination and collaboration within the network. They may be acting as bridges between different communities.

4. Potential for Increased Collaboration: The relatively sparse nature of the network suggests that there is potential for increased collaboration between different research groups. Identifying common research interests and facilitating communication could lead to new insights and innovations.

5. Limitations: The network is based solely on author collaboration as indexed in SCOPUS. It does not account for other forms of collaboration (e.g., data sharing, joint grant proposals) or collaborations that are not reflected in publications indexed in SCOPUS. The parameters chosen for network generation (e.g., association normalization, the Walktrap algorithm’s community.repulsion parameter) influence the structure of the resulting network. Therefore, the conclusions drawn should be considered within the context of these limitations.

Further Steps:

In summary, this author collaboration network provides valuable insights into the structure of research collaborations within the analyzed SCOPUS dataset. The presence of distinct communities highlights the importance of research group dynamics and the potential for increased collaboration across different subfields. Focus on authors such as “Parida V” for a deeper understanding of the area. The parameters used to generate the network have influenced the structure of the network. Further investigation is needed to uncover the specific research topics and relationships between these communities.

Countries’ Collaboration World Map
Overall Observations:

Key Hubs of Scientific Production:

Key International Partnerships:

Global Patterns of Collaboration:

Interpretation and Potential Discussion Points:

* Data Bias: It’s crucial to acknowledge the potential bias introduced by using only SCOPUS data. SCOPUS may have a stronger representation of research from certain regions or disciplines, which could skew the results.
* Language Bias: Publications in languages other than English might be underrepresented in SCOPUS, potentially affecting the visibility of research from non-English-speaking countries.
* Collaboration Drivers: The strong collaborative links between specific countries likely reflect factors such as:
* Shared research interests
* Funding programs promoting international collaboration
* Historical ties and existing research networks
* Mobility of researchers (e.g., migration of scientists)
* Under-Representation: The relatively weak representation of some regions might highlight the need for:
* Increased investment in research infrastructure
* Strategies to promote international collaboration
* Greater inclusion of research from these regions in global databases.
* Field Specificity: This analysis captures overall scientific collaboration. Examining collaboration patterns within specific disciplines could reveal different dynamics and regional strengths.

Recommendations for Further Analysis:

By considering these points, you can move beyond a purely descriptive interpretation of the map and develop a more nuanced and critical understanding of global research collaboration patterns. Remember to always acknowledge the limitations of the data and consider the broader context when interpreting bibliometric results.

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